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In today’s pulp and paper industry the Kraft process is the most common method for pulp production. This method uses sodium based chemicals (white liquor) in the cooking process to remove lignin from the wood chips and create pulp. The remains from this process is called black liquor and is being sent to a recycling system for the purpose of recovering the cooking chemicals. Evaporation of black liquor is a big part of this recycling, and the evaporation plant consists of many different tanks that stores black liquor. At Smurfit Kappa Piteå a model has previously been created for the purpose of production planning. In this work the opportunity to add a part that simulates how the liquor stock in the chemical recovery system will change based on the planned production was investigated. This was done by estimating the amount of dry black liquor in the tanks through inflows and outflows. A formula for the produced black liquor was also developed. The results showed that simulating tank levels separately was difficult as data was lacking in some key areas. The final model is therefore a simplified version that estimates the total amount of dry black liquor in the evaporation plant. It simulates the black liquor buffer based on the planed production and how it will change over five days. This could be done with an error smaller than 6%, compared to measurements from sensors in the black liquor tanks. Attempts were also made to create similar models for the rest of the chemical recovery system. It was concluded that information about the inflow of green and white liquor has to be further investigated in order to implement these in the production planning model.

This is a multidisciplinary project, at the intersection between interaction design, software development and media art. Human-Powered Interactions (HPI) is a field of Human-Computer Interactions (HCI) interested in self-sustainable systems in which power generation and the user interaction are simultaneous, unlike, for example, dynamo torches which have rechargeable batteries. Commonly found on electronic readers, Electronic Paper (e-paper) screens are ultra-low powered and do not require electricity for images to stay displayed, only to refresh them. This project explores these technologies through the prism of art, by designing and building The Human-Powered Generative Art Machine, an interactive hand-crank-powered generative art frame with an e-paper display. Through an interview with a researcher working in the field, a series of hands-on workshops and the construction of the machine, I delve into several questions around HPI, e-paper and generative art. The research uncovers new opportunities around programming and creative practices, to understand the constraints of these technologies and how to work around them. Det här är ett tvärvetenskapligt projekt i skärningspunkten mellan interaktionsdesign, mjukvaruutveckling och mediekonst. Människodrivna Interaktioner är ett område inom Människa–DatorInteraktion som intresserar sig för självförsörjande system där kraftgenerering och användarinteraktion sker samtidigt, till skillnad från t.ex. dynamoficklampor som har uppladdningsbara batterier. Elektroniska pappersskärmar, som är vanliga i elektroniska läsplattor, har extremt låg energiförbrukning och kräver inte elektricitet för att bilderna ska visas, utan endast för att uppdatera dem. I detta projekt undersöktes dessa tekniker genom konstens prisma genom att utforma och bygga The Human-Powered Generative Art Machine, en interaktiv, handkrankdriven, generativ konstram med en e-papper-skärm. Genom en intervju med en forskare som arbetar på området, en serie praktiska workshops och en materialutforskning fördjupar jag mig i flera frågor kring Människodrivna Interaktioner, e-papper och generativ konst. Forskningen avslöjar nya möjligheter i fråga om programmering och kreativa metoder för att förstå begränsningarna i denna teknik och hur man kan arbeta runt dem.

Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not seem to be adequately reflected in many medical publications. This problem of knowledge transfer from statistical research to application was identified by some medical journals, which have published series of statistical tutorials and (shorter) papers mainly addressing medical researchers. The aim of this review was to assess the current level of knowledge with regard to regression modeling contained in such statistical papers. We searched for target series by a request to international statistical experts. We identified 23 series including 57 topic-relevant articles. Within each article, two independent raters analyzed the content by investigating 44 predefined aspects on regression modeling. We assessed to what extent the aspects were explained and if examples, software advices, and recommendations for or against specific methods were given. Most series (21/23) included at least one article on multivariable regression. Logistic regression was the most frequently described regression type (19/23), followed by linear regression (18/23), Cox regression and survival models (12/23) and Poisson regression (3/23). Most general aspects on regression modeling, e.g. model assumptions, reporting and interpretation of regression results, were covered. We did not find many misconceptions or misleading recommendations, but we identified relevant gaps, in particular with respect to addressing nonlinear effects of continuous predictors, model specification and variable selection. Specific recommendations on software were rarely given. Statistical guidance should be developed for nonlinear effects, model specification and variable selection to better support medical researchers who perform or interpret regression analyses.

This research paper is designed as a position paper with empirical elements to analyze the role of AI in the field of art. This work is separated in different parts of analysis, starting by the philosophy of art and its possible meaning in our society, human mind and its correlation with art and Artificial Intelligence. Then, the question of usage of AI in art will be risen. The discussion about AI in art can be separated into 2 parts: AI as a tool for art and AI as an artist. In order to see how those positions in the artistic field are possible, a theoretical research will be made. Moreover, an interview and the ”Turing” test will be launched with people who are related to the field of art, AI or none of them. This will be made in order to reflect on the matter from a practical point of view. The practical research and the theoretical one will be analyzed together. Denna forskningsartikel är utformad som en positionsuppsats med empiriska inslag för att analysera AI:s roll inom konstområdet. Detta arbete är separerat i olika delar av analysen, med början i konstens filosofi och dess möjliga betydelse i vårt samhälle, mänskliga sinne och dess samband med konst och artificiell intelligens. Då kommer frågan om användningen av AI i konsten att väckas. Diskussionen om AI i konst kan delas upp i 2 delar: Ai som verktyg för konst och AI som konstnär. För att se hur dessa positioner inom det konstnärliga området är möjliga kommer den teoretiska forskningen att göras, liksom intervjun och ”turing”-testet kommer att lanseras med personer som är relaterade till konstområdet, AI eller ingen av dem kommer att göra det. göras för att reflektera över saken från den praktiska synpunkten. Den praktiska forskningen och den teoretiska kommer att analyseras tillsammans.

Context: The retraction of research papers, for whatever reason, is a growing phenomenon. However, although retracted paper information is publicly available via publishers, it is somewhat distributed and inconsistent. Objective: The aim is to assess: (i) the extent and nature of retracted research in Computer Science (CS) (ii) the post-retraction citation behaviour of retracted works and (iii) the potential impact on systematic reviews and mapping studies. Method: We analyse the Retraction Watch database and take citation information from the Web of Science and Google scholar. Results: We find that of the 33,955 entries in the Retraction watch database (16 May 2022), 2,816 are classified as CS, i.e., approximately 8.3%. For CS, 56% of retracted papers, provide little or no information as to the reasons. This contrasts with 26% for other disciplines. There is also a remarkable disparity between different publishers, a tendency for multiple versions of a retracted paper over and above the Version of Record (VoR), and for new citations long after a paper is officially retracted. Conclusions: Unfortunately retraction seems to be a sufficiently common outcome for a scientific paper that we as a research community need to take it more seriously, e.g., standardising procedures and taxonomies across publishers and the provision of appropriate research tools. Finally, we recommend particular caution when undertaking secondary analyses and meta-analyses which are at risk of becoming contaminated by these problem primary studies., Comment: This is a preprint of the paper submitted to PLOS ONE

storage devices in micro sizes are receiving more and more attention. One of them, on-paper Microsupercapacitors (MSCs), has become a key energy storage device because of its good mechanical flexibility and high power density. In this project, a triphase system with electrochemically exfoliated graphene and graphene quantum dots to synergistically stabilize PEDOT:PSS in ethylene glycol/water solvent was developed for scalable and reliable inkjet printing. Without any post-treatment, the printed patterns with a large thickness (up to 9 μm, about 0.4 μm per layer) attain a sheet resistance of as low as 4 Ω2−1 and high resolution at a small drop spacing of 10 μm. Thanks to these feature, the areal capacitance of the on-paper MSCs can reach >2 mF cm−2 at a high scan rate of 1000 mV s−1. The device also exhibits excellent mechanical flexibility, long cycle life (>95% capacitance retention after 10000 cycles CV test) and long service time (retain 84% capacitance after 4 months in air without any encapsulation). Moreover, we can directly print the interconnect to connect 4 devices on paper substrate in series or in parallel and thus get rid of metal current collector. Med den snabba utvecklingen av flexibel och bärbar elektronik får energilagringsenheter i mikrostorlek allt mer uppmärksamhet. En av dem, mikrosuperkondensatorer (MSC) på papper, har blivit en viktig energilagringsenhet på grund av sin goda mekaniska flexibilitet och höga effekttäthet. I det här projektet utvecklades ett trefasigt system med elektrokemiskt exfolierad grafen och grafenkvantprickar för att synergistiskt stabilisera PEDOT:PSS i etylenglykol/vattenlösningsmedel för skalbar och tillförlitlig bläckstråleutskrift. Utan någon efterbehandling uppnår de tryckta mönstren med stor tjocklek (upp till 9 μm, ca 0,4 μmper lager) ett arkmotstånd på så lågt som 4 Ω2−1 och hög upplösning vid ett litet droppavstånd på 10 μm. Tack vare dessa egenskaper kan den ytliga kapacitansen hos MSC på papper nå >2 mF cm−2 vid en hög skanningshastighet på 1000 mV s−1. Anordningen uppvisar också utmärkt mekanisk flexibilitet, lång livslängd (>95% kapacitansbehållning efter 10000 cykler i CV-test) och lång livslängd (behåller 84% kapacitans efter 4 månader i luft utan inkapsling). Dessutom kan vi direkt skriva ut kopplingen för att ansluta fyra enheter på papperssubstratet i serie eller parallellt och på så sätt bli av med metallströmkollektorn.

Emergency management exercises could be carried out as simulations or serious games, as they allow for infinite replications in which participants can make mistakes without negative consequences. Alternate reality games (ARGs) offer a model for interactive networked narratives based on realistic and immersive emergency scenarios, and possibilities for collaborative problem solving with peer-to-peer communication. The aim of the paper is to analyze and discuss students’ attitudes towards the implementation of an emergency management exercise as a game inspired by ARGs, comprising a virtual crisis management exercise system. The study on which the paper is based was conducted using a qualitative approach together with a literature review and a thematic analysis of university students’ answers to an evaluation questionnaire, following their participation in a virtual exercise with an ARG design. The findings suggest that ARG concepts have potential in emergency management training and could be tested with rescue services staff, also across national borders. Realism should however be increased and additional ARG concepts added.

Purpose The purpose of this paper is to identify whether there is a lower willingness to report a crime if a victim must hand in their mobile phone as evidence. If that is the case, the research seeks to examine whether privacy concerns and lower willingness correlate with one another and thereby investigate whether privacy concerns could lead to fewer crimes being reported and resolved. Design/methodology/approach A mobile phone survey was distributed to 400 Swedish adults to identify their hypothetical willingness to report certain crimes with and without handing in their mobile phones as evidence. The results were then analysed using inferential statistics. Findings The result suggests that there is no meaningful correlation between privacy attitudes and willingness to report crime when the handover of a mobile phone is necessary. The results of this study however show a significant lower willingness to report crimes when the mobile phone must be handed in. Research limitations/implications Because the chosen target group were Swedish adults, the research results may lack generalisability for other demographics. Therefore, researchers are encouraged to test other demographics. Originality/value This paper’s contribution is the novel exploration of attitudes and behaviours regarding the combination of privacy, digital forensics, mobile phones and crime reportage. This research effort examined the problematic situation that can arise for victims of crime, the invasion of privacy when providing evidence by handing in a mobile phone to the police’s forensic unit for examination.

This scoping review focuses on therapeutic interventions, which involve the creation of artworks in virtual reality. The purpose of this research is to survey possible directions that traditional practices of art therapy and therapeutic artmaking could take in the age of new media, with emphasis on fully immersive virtual reality. After the collection of papers from online databases, data from the included papers were extracted and analyzed using thematic analysis. The results reveal that virtual reality introduces novel opportunities for artistic expression, self-improvement, and motivation for psychotherapy and neurorehabilitation. Evidence that artmaking in virtual reality could be highly beneficial in therapeutic settings can be found in many aspects of virtual reality, such as its virtuality, ludicity, telepresence capacity, controlled environments, utility of user data, and popularity with digital natives. However, deficiencies in digital literacy, technical limitations of the current virtual reality devices, the lack of tactility in virtual environments, difficulties in the maintenance of the technology, interdisciplinary concerns, as well as aspects of inclusivity should be taken into consideration by therapy practitioners, researchers, and software developers alike. Finally, the reported results reveal implications for future practice.

This paper explores the significance of special teams, particularlypowerplay, in ice hockey. Despite the commonly held perception oftheir importance, little research has examined the impact of powerplayand penalty kill performance on overall team success. The paper usesseveral seasons of NHL data to characterize goal-scoring and manpoweropportunities, and perform analysis from several perspectives. The resultsindicate that individual even strength goals and powerplay goalshave similar value, but the larger share of even strength goals scoredover a season makes even strength play a more important contributorto team success. The paper also finds a high correlation between teamsthat perform above/below average during even strength and powerplay.This study provides valuable insights into the dynamics of ice hockeygameplay and the role of special teams in determining team success.

Purpose This paper aims to evaluate the Digital Government Collaborative Platform (DGCP), which facilitates collaborations between the citizens and the government to address environmental issues in Sri Lanka. The DGCP is an artifact developed by the value-sensitive design approach. Design/methodology/approach The DGCP is evaluated following the Framework for Evaluation in Design Science Research (FEDS). In total, 224 citizens participated in the survey based on the User Experience Questionnaire (UEQ) and open questions about human values embedded in the design. Fifteen government officers were interviewed to enhance the evaluation. Findings The DGCP received positive evaluations from the citizens and government officers. The platform is attractive, novel and pragmatic, also generating hedonic experiences for the citizens. The users believed that human values are reflected in the DGCP. Furthermore, they shared a few suggestions to improve it. Originality/value The paper contributes knowledge to evaluating digital government systems, especially in developing countries. The human-value-centered DGCP was evaluated using multiple methods of quantitative (i.e. UEQ Survey) and qualitative (i.e. qualitative interviews with stakeholders) techniques. Furthermore, the systematic process of DGCP evaluation produces a case-based guideline for evaluating related and similar digital government systems using FEDS.

An essential part of combating climate change is to reduce energy consumption. As the industrial sector was accountable for approximately a third of the total global carbon dioxide equivalents in 2019, it is crucial for industries to manage their energy usage. Research within the field of eco-feedback shows that visualising energy data can inspire energy reductions and promote knowledge. The thesis investigates how a dashboard for energy data visualisations for the industrial sector can be designed to be usable and to inspire behavioural change for its end users. Usability is researched in terms of initial learnability and subjective satisfaction. Perceived behavioural change is studied through changes in users' awareness and attitude towards energy management. A prototype of a dashboard for energy data visualisations is developed through three iterations of the HCD process. During the first iteration, a paper prototype is developed, and evaluated through a workshop. During the second iteration, the paper prototype is translated into a digital prototype, which is then translated into a software prototype. The software prototype is evaluated through end-user tests at the end of the second iteration. During the third iteration, the software prototype is improved, based on the input from the previous evaluation, and then evaluated through end-user tests. The usability of the software prototype is evaluated through the think-aloud method and a SUS questionnaire. Perceived behavioural change is evaluated by interviewing the test users about their change in attitude and awareness after using the dashboard. The thesis finds that the dashboard prototype was easy to use and interpret. The following design choices were concluded to contribute to the usability in terms of initial learnability and subjective satisfaction: visually separating information, information adjacency, adding explanatory texts, using colour as a visual encoding, using a colour scheme that provides contrast and consistency, filtering data through multi-select, providing different options for visualisation, using interactivity for further data exploration, giving the user response to actions, considering chart junk and the data-ink ratio, and using unambiguous icons. In addition, the thesis concludes that the dashboard inspired changes in both the user's awareness and attitude towards energy management. The design choices that were proven to affect the user's awareness and attitude were: letting the user choose their preferred unit to represent the data, presenting the user with tips, providing internal and external data comparisons, visualising multiple energy-related key figures, allowing for historic comparison, reminding the user of upcoming activities, presenting a list of scheduled measures and providing feedback of a prognosticated proposed measure in terms of its intended effects.

Implementation and use of an IT standard in software involves legal, technical and societal challenges. This paper addresses how an organisation can, and should, determine the conditions for implementation and use of the HEVC standard in software. The investigation considers the availability of the standard’s complete technical specification and the extent to which an organisation can access the information necessary to assess the licence conditions for standard essential patents impinging on the standard. Through an action case study approach the investigation analyses declarations in patent databases relevant to the standard and seeks to obtain patent licences from each declarant permitting implementation of the standard in software, where that software is to be provided under one (or several) of three specific open source software licences, and alternatively to be provided as an online service. Our analysis of legal and licensing conditions for use of the standard shows significant obstacles. We find that it is impossible to obtain licences from patent holders that would allow for implementation and use of the standard in open source software. The paper illuminates significant challenges related to conditions for use of the standard under (F)RAND terms and identifies that references to the standard in public procurement projects lead to anti-competitive effects. CC BY 4.0Published: 3-2-2023Corresponding author: bjorn.lundell@his.seThis research has been financially supported by the Swedish Knowledge Foundation (KK-stiftelsen) and participating partner organisations in the SUDO project.

We provide an overview of recent developments and future directions in the RowHammer vulnerability that plagues modern DRAM (Dynamic Random Memory Access) chips, which are used in almost all computing systems as main memory. RowHammer is the phenomenon in which repeatedly accessing a row in a real DRAM chip causes bitflips (i.e., data corruption) in physically nearby rows. This phenomenon leads to a serious and widespread system security vulnerability, as many works since the original RowHammer paper in 2014 have shown. Recent analysis of the RowHammer phenomenon reveals that the problem is getting much worse as DRAM technology scaling continues: newer DRAM chips are fundamentally more vulnerable to RowHammer at the device and circuit levels. Deeper analysis of RowHammer shows that there are many dimensions to the problem as the vulnerability is sensitive to many variables, including environmental conditions (temperature \& voltage), process variation, stored data patterns, as well as memory access patterns and memory control policies. As such, it has proven difficult to devise fully-secure and very efficient (i.e., low-overhead in performance, energy, area) protection mechanisms against RowHammer and attempts made by DRAM manufacturers have been shown to lack security guarantees. After reviewing various recent developments in exploiting, understanding, and mitigating RowHammer, we discuss future directions that we believe are critical for solving the RowHammer problem. We argue for two major directions to amplify research and development efforts in: 1) building a much deeper understanding of the problem and its many dimensions, in both cutting-edge DRAM chips and computing systems deployed in the field, and 2) the design and development of extremely efficient and fully-secure solutions via system-memory cooperation., Invited paper to appear in ASPDAC 2023

In this full research paper we examine questionable collaboration from a student perspective. Collaborating while solving computer lab assignments is often considered an important part when learning computer science, as it allows students to discuss their work, while also practicing working together. However, it also introduces risks, such as students collaborating in ways negatively impacting their learning outcomes and leading to inaccurate grading. Hence it is important to work towards reducing the use of these poor collaborative practices. In order to ameliorate the problem with academic misconduct, we need to understand students’ justifications for deviating from acceptable practices. In this paper we therefore investigate how students justify their collaborative practices during computer lab assignments in situations they experience as questionable. The justifications were collected through 15 semi-structured interviews with students experienced in pair programming, majoring in computer science and other technical fields from two large well-known European universities.The justifications from the interviews were analysed using phenomenography resulting in seven categories: external pressure, lack of interest, spending time on the assignment, understanding the end product, contributing to the process, learning from the assignment and reflecting on the purpose of the learning. These describe in which situations students might deviate from the rules and can be used by institutions to prevent such behavior.

Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review. Studies on preprints within bioRxiv have been largely focused on article metadata and how often these preprints are downloaded, cited, published, and discussed online. A missing element that has yet to be examined is the language contained within the bioRxiv preprint repository. We sought to compare and contrast linguistic features within bioRxiv preprints to published biomedical text as a whole as this is an excellent opportunity to examine how peer review changes these documents. The most prevalent features that changed appear to be associated with typesetting and mentions of supporting information sections or additional files. In addition to text comparison, we created document embeddings derived from a preprint-trained word2vec model. We found that these embeddings are able to parse out different scientific approaches and concepts, link unannotated preprint–peer-reviewed article pairs, and identify journals that publish linguistically similar papers to a given preprint. We also used these embeddings to examine factors associated with the time elapsed between the posting of a first preprint and the appearance of a peer-reviewed publication. We found that preprints with more versions posted and more textual changes took longer to publish. Lastly, we constructed a web application (https://greenelab.github.io/preprint-similarity-search/) that allows users to identify which journals and articles that are most linguistically similar to a bioRxiv or medRxiv preprint as well as observe where the preprint would be positioned within a published article landscape., Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review This study analyzes the full text content of the bioRxiv preprint repository, identifying field-specific patterns and changes that occur during publication, and providing a search tool that can identify the published papers that are most similar to a given bioRxiv or medRxiv preprint.

Background Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. Objective The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on Machine Learning (ML) and Deep Learning (DL) models. Method We carried out a Systematic Literature Review (SLR) in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and seven from a single backward snowballing procedure), only 15 relevant papers were identified. Results Results show that current research is focused on the binary classification of Dengue, primarily using tree-based ML algorithms. Only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its variants) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. Conclusions The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient’s quality of life., Author summary Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms and, sometimes, inaccurate test results. In this paper, we present the state of the art of studies investigating the automatic classification of arboviral diseases based on Machine Learning (ML) and Deep Learning (DL) models. Results show that current research is focused on the classification of Dengue, primarily using tree-based ML algorithms. The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient’s quality of life.

Stock market analysis is a hot-button topic, especially with the growth of online communities surrounding trading and investment. The goal of this paper is to examine the sentiment of r/wallstreetbets and the Google Trends score for a number of stocks – and then understanding whether the herding nature of investors on r/wallstreetbets is better at predicting the movement of the stock market than the WOC nature of Google Trends scores. Some combination of the herding and WOC values will also be used in predicting stock market fluctuations. Analysis will be done through the machine learning algorithms RFC and MLP. Through the mean and median precisions presented by the different machine learning algorithms the effectiveness of the predictor can be understood. This paper finds no real connection between either r/wallstreetbets sentiment or Google Trends data regarding predicting stock value fluctuations – with r/wallstreetbets yielding approximately 51%-52% mean precision depending on the machine learning algorithm used, and Google Trends precisions sitting at around 51%. The combination of r/wallstreetbets data and Google Trends data did not produce any significantly higher precision either, being between 51%-52%.

Modern crowd simulation models can display a range of different properties, such as realism, efficiency, or emergent self-organizing patterns. These properties often vary by the specific situation that the model is used in. A model can be realistic when used in one scenario and break down completely in another scenario. Knowing the limits of a model is essential to know when it can and cannot be used. Furthermore, describing these limits, and explaining their underlying causes, can allow improving the model, or creating a new model, to surpass the previous model in terms of flexibility of use. Lastly, testing potential changes to a model is important for finding the right way to improve that model in the future. In this study, a synthetic vision-based model by Ondřej et al. is tested. The model attempts to recreate human perception and decision-making for a more realistic navigation algorithm. In the paper where it was proposed, the model showed great efficiency in complex scenarios with a high agent density and high number of agents. This was due to highly efficient self-organizing emergent patterns that allowed the individual agents to reach their goals quickly compared to existing models, such as the RVO model. Ondřej et al. did not test their model in simpler scenarios. We aim to help further inform about which use cases the synthetic vision model is appropriate for, and to lay the groundwork for future efficiency improvements of the synthetic vision model in low-density scenarios. This paper reimplements and tests the synthetic vision-based model by Ondřej et al. in simpler scenarios by comparing its efficiency to the RVO model. We also investigate potential improvement of the synthetic vision model by varying a model parameter. The results show that the model completely fails in one simple test case, and that it is clearly outperformed by RVO in another. One problem revealed by the results is that the model has difficulty with adapting its path far enough ahead of a future collision, and that this problem can be at least somewhat mitigated by varying a model parameter. Two other problems were also revealed. The first is that the agents adjust their paths very weakly towards their goal. The second is that the agents sometimes unnecessarily slow their speed while close to another agent. We describe the underlying cause of each of these three problems based on the mathematical definition of the synthetic vision crowd simulation model. Our contribution should aid others in choosing to use this synthetic vision model in appropriate scenarios, and should be a valuable foundation for improving this model in the future. Moderna modeller för simulering av folkmassor kan uppvisa en rad olika egenskaper, t.ex. realism, effektivitet eller framväxande självorganiserande mönster. Dessa egenskaper varierar ofta beroende på den specifika situation som modellen används i. En modell kan vara realistisk när den används i ett scenario och bryta ihop helt och hållet i ett annat scenario. Det är viktigt att känna till en modells gränser för att veta när den kan användas och när den inte kan användas. Genom att beskriva dessa gränser och förklara de bakomliggande orsakerna kan man dessutom förbättra modellen eller skapa en ny modell som överträffar den tidigare modellen när det gäller flexibilitet i användningen. Slutligen är det viktigt att utvärdera potentiella förbättringar av en modell för att hitta rätt sätt att förbättra modellen i framtiden. I den här studien testas en syntetisk synbaserad modell av Ondřej et al. Modellen försöker återskapa mänsklig perception och beslutsfattande för en mer realistisk navigeringsalgoritm. I artikeln där den föreslogs visade modellen stor effektivitet i komplexa scenarier med hög agenttäthet och stort antal agenter. Detta berodde på de mycket effektiva självorganiserande framväxande mönster som gjorde att de enskilda agenterna kunde nå sina mål snabbt jämfört med befintliga modeller, t.ex. RVO-modellen. Ondřej et al. testade inte sin modell i enklare scenarier. Vi vill bidra till att ytterligare informera om vilka användningsfall som modellen för syntetisk syn är lämplig för samt lägga grunden för framtida effektivitetsförbättringar av modellen för syntetisk syn i scenarier med låg densitet. I det här dokumentet återimplementeras och testas den syntetisk synbaserade modellen av Ondřej et al. i enklare scenarier genom att dess effektivitet jämförs med RVO-modellen. Vi undersöker också potentiella förbättringar av den syntetiska synmodellen genom att variera en modellparameter. Resultaten visar att modellen helt misslyckas i ett enkelt testfall och att den är klart överträffad av RVO i ett annat. Ett problem som resultaten avslöjar är att modellen har svårt att anpassa sin väg tillräckligt långt före en framtida kollision, och att detta problem åtminstone till viss del kan mildras genom att variera en parameter i modellen. Två andra problem framkom också. Det första är att agenterna anpassar sina banor mycket svagt mot sitt mål. Det andra är att agenterna ibland sänker sin hastighet i onödan när de befinner sig nära en annan agent. Vi beskriver den bakomliggande orsaken till vart och ett av dessa tre problem utifrån den matematiska definitionen av simuleringsmodellen. Vårt bidrag bör hjälpa andra att välja att använda denna modell för syntetisk vision i lämpliga scenarier och bör utgöra en värdefull grund för att förbättra modellen i framtiden.

The Linz Center of Mechatronics’ SymSpace platform is designed to provide intelligent design and training for the traditional engineer training and industrial design approach in the field of motor design, which relies on the engineer’s own experience and manual work. This paper first analyzes and explores the usage patterns and possible improvement perspectives of motor design components using SymSpace user data. Then an attempt is made to summarize the motor design manual provided by LCM using a text summary model and use it for training engineers. Next, a question-and-answer system model was used to try to provide an aid system for engineers in design. The evaluation of text summaries and question and answer systems is difficult in the motor design domain because the amount of redundant textual information in this domain is small and key information is often presented in detail rather than in the main stem of the sentence. In this case, instead of evaluating the model using traditional machine scores, this paper refers to the feedback from LCM experts as future users. The final results show that, despite the problems of difficulty in explaining the reasons; the possibility of being misleading; and the loss of information details, both attempts are generally positive and the exploration in this direction is worthwhile. Symspace från Linz Center of Mechatronics är utformad för att tillhandahålla intelligent design och utbildning för den traditionella ingenjörsutbildningen och den industriella designmetoden inom motorkonstruktion, som bygger på ingenjörens egen erfarenhet och manuellt arbete. I den här artikeln analyseras och utforskas först användningsmönster och möjliga förbättringsperspektiv för komponenter för motorkonstruktion med hjälp av användaruppgifter från Symspace. Därefter görs ett försök att sammanfatta den motorkonstruktionshandbok som tillhandahålls av LCM med hjälp av en modell för textsammanfattningar och använda den för att utbilda ingenjörer. Därefter användes en modell för ett system med frågor och svar för att försöka tillhandahålla ett hjälpsystem för ingenjörer vid konstruktion. Utvärderingen av textsammanfattningar och fråga-och-svar-system är svår inom motorkonstruktionsområdet eftersom mängden överflödig textinformation inom detta område är liten och nyckelinformation ofta presenteras i detalj snarare än i huvudstammen av meningen. I det här fallet hänvisar den här artikeln i stället för att utvärdera modellen med hjälp av traditionella maskinpoäng till feedback från LCM-experter som framtida användare. De slutliga resultaten visar att trots problemen med svårigheten att förklara orsakerna, möjligheten att vara vilseledande och förlusten av informationsdetaljer är båda försöken generellt sett positiva och att utforskningen i denna riktning är värd att fortsätta.

A server is a specialized computer for running utility software. If we want to protect our data while using Internet, we use a proxy server. In this paper we will talking about proxy server, types of proxy servers and at the end about Apache http serves as forward proxy server. Apache can be also used as reverse proxy server, but that will be discussed in some next paper.

To better prevent the potential risks in Internet-based Supply Chain Financing (SCF) products, this paper optimizes and evaluates the Internet-based SCF-oriented Credit Risk Evaluation (CRE) method. Firstly, this paper summarizes 12 risk factors of SCF business, establishes a Risk Assessment Index System (RAIS) with good consistency and stability; then, the principles of Backpropagation (BP) Neural Network (NN) is expounded together with Support Vector Machines (SVM) and Genetic Algorithm (GA) model. Consequently, a CRE model is implemented by the NN tools in MATLAB based on the collection of multiple groups of SCF-oriented risk assessment samples. Subsequently, the assessment samples are trained and tested. Finally, the SCF-oriented CRE model is proposed and verified. The results show that the BP-GA model has presented high prediction consistency with the actual classification. According to the comparison of classification results of SVM, BP model, and BP-GA model, the classification accuracy of test samples of the proposed Internet-based SCF-oriented CRE system using BP-GA model reaches 97.19%; the Type I and Type II error rate of the CRE system based on BP-GA model is 7.2% and 14.21%, respectively. Therefore, a suitable SCF-oriented CRE method is put forward for China’s commercial banks along with scientific and feasible suggestions to manage SCF-oriented credit risks more reasonably and effectively.

Generative machine learning models are capable of generating remarkably realistic samples. Some models generate images that look entirely natural, and others generate text that reads as if a human wrote it. However, judging the quality of these models is a major challenge. Today, the most convincing method is to use humans to evaluate the quality of generated samples. However, humans are biased, costly, and inefficient. Therefore, there is a great need for automatic methods. MAUVE is a recent advancement in the evaluation of generative text models. It compares generated data with real data and returns a score that quantifies their similarity. This is accomplished with the help of a neural network, which provides the understanding of text required to evaluate its quality. MAUVE is motivated by its correspondence with human judgment, and this is shown in multiple experiments. This thesis contributes in two significant ways: First, we complement experiments and discussions made in the original paper. Importantly, we demonstrate that MAUVE sometimes fails to recognize quality differences between generative models. This failure is due to the choice of neural network. Later, we demonstrate that MAUVE can be used for more than just text evaluation. Specifically, we show that it can be applied to images. This is accomplished by using a neural network specialized in image recognition. However, the steps can be repeated for any data type, meaning that MAUVE can potentially become a more generalized measurement than suggested in the original paper. Our second contribution is an extension toMAUVEcalled Sequence-MAUVE (S-MAUVE). The score MAUVE produces can be seen as an average of the overall quality of generated text. However, some generative models initially produce excellent text, but see drops in quality as the sequences grow longer. Therefore, a single score that represents entire sequences is likely to omit important details. Instead, S-MAUVE evaluates generated text at the smallest possible level. The result is a sequence of scores, which give users more detailed feedback about the behavior of a generative model. Generativa maskininlärningsmodeller kan generera data av enastående kvalitet. Vissa modeller genererar bilder av ansikten som ser helt realistiska ut, och andra genererar text som verkar varit skriven av en människa. Trots detta så är det inte klart hur dessa modeller ska evalueras. Idag så är den främsta metoden mänsklig evaluering: En person får utgöra huruvida generade data verkar realistisk eller inte. Mänsklig evaluering har flera nackdelar. Människor är partiska, dyra och långsamma. Därför behövs det automatiska evalueringsverktyg. MAUVE är ett ny metod för att evaluera generative textmodeller som jämför hur lik genererad data är med äkta data. Detta åstadkoms med hjälp av ett neuralt nätverk, som bidrar med den förståelse av text som krävs för att evaluera den. MAUVE är motiverat av att dess omdömen överensstämmer med mänsklig evaluering. Den här uppsatsen bidrar på två sätt. Till att börja med komplementerar vi experiment och diskussioner gjorda i den ursprungliga rapporten o m MAUVE. Till exempel så visar vi att MAUVE ibland inte lyckas känna av kvalitetsskillnader mellan olika generativa modeller. Detta på grund av val av neuralt nätverk. Efteråt så demonstrerar vi att MAUVE kan appliceras på andra typer av data än text. Mer specifikt så applicerar vi MAUVE på bilder. Detta åstadkoms genom att använda ett neuralt nätverk specialiserat på bildigenkänning, istället för text. Stegen vi följer kan upprepas för vilken typ av data som helst, vilket innebär att MAUVE kan användas som ett mer generellt mått än vad den ursprungliga artikeln ger sken för. Vårt andra bidrag är att utveckla MAUVE till det vi kallar för S-MAUVE. MAUVE använder bara sammanfattningar av hela texter som bas för sina jämförelser. En konsekvens av det är att den endast gör påståenden om textdatas genomsnittliga kvalitet. Men, det är välkänt att kvaliteten hos genererad textdata kan variera beroende på var i texten man befinner sig. Många generativa textmodeller producerar sekvenser som är verklighetstrogna i början, men blir sämre och repetitiva senare. Till skillnad från MAUVE så evaluerar S-MAUVE genererad text på minsta möjliga detaljnivå. Resultaten är en sekvens av poäng, som ger användare mer information om egenskaperna hos den studerade generativa modellen.

This paper reviews the Open Charge Point Protocol (OCPP) and implements a Charging Station Management System (CSMS) targeting heavy-duty trucks. The new technique proposed in this paper is designed to maximize Electric vehicle (EV) owner benefits by charging at a low cost, and also the electric utility benefits (operating the system within the acceptable limits) by proper choice of electricity tariff structure. EV owners can be motivated to charge at off-peak hours which have low electricity prices and stop charging at peak hours which have high electricity prices. Detta dokument granskar Open Charge Point Protocol (OCPP) och implementerar en Charging Station Management System (CSMS) inriktat på tunga lastbilar. Den nya tekniken som föreslås i detta dokument är utformad för att maximera fördelarna för ägare av elfordon (EV) genom att ladda till en låg kostnad, och även fördelarna med elnätet (drift av systemet inom acceptabla gränser) genom korrekt val av elprisstruktur. Elbilsägare kan motiveras att ladda under lågtrafik som har låga elpriser och sluta ladda under rusningstid som har höga elpriser.

In this paper we explore the field of reinforcement learning which has proven to be successful at solving problems of random nature. Such problems can be video games, for example the classical game of Snake. The main focus of the paper is to analyze the speed, measured in Q-table updates, at which an agent can learn to play Snake by using Q-learning, specifically with a Q-table approach. This is done by changing a set of hyperparameters, one at the time, and recording the effects on training. From this, we were able to train the agent with only 225 000 Q-table updates, which took 7 seconds on a regular laptop processor, and achieve a high score of 52 (34 % cover of the grid). We were able to train the agent with only 100 000 updates but it came with reliability issues such as in some training sessions it did not perform well at all. I denna rapport utforskar vi ämnet reinforcement learning som har visat sig vara en framgångsrik metod för att lösa problem vars problemformulering inte är specifikt detaljerad. Sådana problem kan vara datorspel, till exempel det klassiska spelet Snake. Huvudfokuset i rapporten ligger i att analysera hur snabbt en agent kan lära sig spela spelet Snake. Algoritmen som analyseras är Q-learning med ett Q-table där snabbhet mäts i antalet Q-table uppdateringar. Detta görs genom att ändra en mängd hyperparametrar, en åt gången, och notera dess effekt på träningen. Utifrån detta kunde vi träna agenten med enbart 225 000 Q-table uppdateringar, vilket i realtid tog 7 sekunder på en vanlig bärbar dator, som högst uppnådde 52 poäng (34 % täckning av spelplanen). Vi kunde träna på betydlig mindre uppdateringar, närmare 100 000, men med mindre chans att lyckas bra. Ibland kunde en träningssession resultera i mycket dåliga resultat.

Defects during production may lead to material waste, which is a significant challenge for many companies as it reduces revenue and negatively impacts sustainability and the environment. An essential reason for material waste is a low degree of automation, especially in industries that currently have a low degree of digitalization, such as steel forging. Those industries typically rely on heavy and old machinery such as large induction ovens that are mostly controlled manually or using well-known recipes created by experts. However, standard recipes may fail when unforeseen events happen, such as an unplanned stop in production, which may lead to overheating and thus material degradation during the forging process. In this paper, we develop a digital twin-based optimization strategy for the heating process for a forging line to automate the development of an optimal control policy that adjusts the power for the heating coils in an induction oven based on temperature data observed from pyrometers. We design a digital twin-based deep reinforcement learning (DTRL) framework and train two different deep reinforcement learning (DRL) models for the heating phase using a digital twin of the forging line. The twin is based on a simulator that contains a heating transfer and movement model, which is used as an environment for the DRL training. Our evaluation shows that both models significantly reduce the temperature unevenness and can help to automate the traditional heating process., Comment: 12 pages, 5 figures, this paper will be published in the 10th Swedish Production Symposium 2022 (SPS2022)

As our society becomes increasingly digitalized, there is an ever-increasing need for forensic tools to become faster and faster. This paper was made to help the Police and other digital forensic investigators choose the fastest disk imaging tool while still maintaining the integrity of the imaged disk. To answer this, an experiment including 162 disk imaging tests was done, with an active imaging and verification time of over 160 hours. The results were analyzed with the help of a scoring system and statistical significance tests. The paper also aimed to show if there is any difference when making images of disks that are filled to 100% compared to disks filled to 50%, and which of the disk imaging tools that handles it best. The results of the experiment showed that Guymager was the fastest disk imaging tool among the tested alternatives. It also illustrated that the speed was affected by the disks being filled to 50% as opposed to 100%. Guymager showed the best performance improvement using the EWF_E01 format, and OSForensics showed the biggest improvement when imaging using the DD format.

The GISAID database has swiftly become the world’s largest genome sequence database. As of May 2022, there is currently over 10 million genome sequence submissions to GISAID compared to 25 thousand in early 2020. This paper has the scientific goal in examining how integral the GISAID database has been in the COVID-19 response. This goal was achieved by analysing the citation rate of GISAID in the highest cited COVID-19 papers according to Google Scholar. As well as performing interview studies with relevant people in the field and doing a literature study of GISAID. The thesis that GISAID has been key in battling the pandemic could be verified in this project as the citation rate of GISAID increased during the pandemic in COVID-19 papers. Moreover, the database has been cited as crucial in the development of vaccines and in the extensive monitoring of the virus. The vast amount of data is however a double-edged sword as there are reports of problems regarding the data quality. This is due to the current format of inserting data, the lack of educational material for the use of the database and potential patient confidentiality related issues. Other problems reported are the fact that there is a big gap between countries on a global scale regarding both the collect to submission time as well as the number of genomes logged to the database. Overall, these issues should be looked at and adjusted through the combined efforts of GISAID, the worlds various governments and the World Health Organization. Despite these issues regarding GISAID, we concluded that it has, and will continue to play a critical role in the pandemic response. Databasen GISAID har snabbt vuxit till världens största databas för genomdata. I maj 2022 fanns det över 10 miljoner genomsekvenser uppladdade till databasen jämfört med de 25 tusen under början av 2020. Detta projekt har det vetenskapliga målet att undersöka hur väsentlig GISAID databasen har varit under kampen mot COVID-19. Detta mål uppnåddes genom att analysera citeringsfrekvensen av GISAID i de mest citerade coronartiklarna enligt sökmotorn Google Scholar. Utöver detta gjordes även en intervjustudie fokuserad på intervjupersoner med relevant expertis, samt en litteraturstudie av GISIAD-databasen. Tesen att GISAID har varit en nyckelspelare i åtgärderna mot COVID-19 kunde verifieras med hjälp av dessa tre studier då citeringsfrekvensen av databasen har stigit under pandemin. Databasen har även citerats i litteraturen som oumbärlig i utvecklingen av vaccin, diagnostisering, och smittspårning av viruset. Den enorma mängden data kommer dock även med nackdelar då det finns negativa rapporter angående kvalitén på data från GISAID. Detta beror på formatet för insättning av data, avsaknaden av instruktionsmanualler och problem relaterade till patientsekretess. Andra rapporterade problem är gällande de stora skillnaderna mellan länders tid för sekvensering av data till uppladdning, och totalt antal genomsekvenser inskickade. Dessa problem bör vidareundersökas och åtgärdas i samarbete mellan GISAID, världens regeringar och Världshälsoorganisationen WHO. Trots problemen med GISAID kan vi dra slutsatsen att databasen har, och kommer fortsätta spela en avgörande roll i kampen mot coronapandemin.

The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained may lead to performance degradation during the system’s life cycle. Recent advances that study non-stationary environments have mainly focused on identifying and addressing such changes caused by a phenomenon called concept drift. Different terms have been used in the literature to refer to the same type of concept drift and the same term for various types. This lack of unified terminology is set out to create confusion on distinguishing between different concept drift variants. In this paper, we start by grouping concept drift types by their mathematical definitions and survey the different terms used in the literature to build a consolidated taxonomy of the field. We also review and classify performance-based concept drift detection methods proposed in the last decade. These methods utilize the predictive model’s performance degradation to signal substantial changes in the systems. The classification is outlined in a hierarchical diagram to provide an orderly navigation between the methods. We present a comprehensive analysis of the main attributes and strategies for tracking and evaluating the model’s performance in the predictive system. The paper concludes by discussing open research challenges and possible research directions. AIDA

Blockchain is an emerging technology that has been gaining popularity in many sectors over recent years. Being a disruptive and innovative technology, it has the potential to revolutionize the supply chain sector due to the many benefits it brings, such as security and automation. This paper sets out to look into what value blockchain can bring to the supply chain. A literature review was conducted on existing research based on blockchain uses in the supply chain. This was done to better understand the literature gap on the subject. This led to an interview study where blockchain experts from different areas in the supply chain sector were contacted to get a better understanding of what values can be created and what benefits blockchain can bring. The findings demonstrate that blockchain has great potential in the supply chain sector and has already shown its benefits in somecases. The maincontributions blockchain brings to supply chains are the value of creating trust in data and providing automation, which leads to many major benefits throughout supply chains. Blockchain brings an increase in trust of data as it creates one source of information for everyone involved in the supply chain. Automation of the supply chain means many manual processes of the supply chain can be automated. In this paper we look at real-life cases where blockchain has been implemented in the supply chain and what values it creates. Nevertheless, it seems that there is a lack of knowledge about the benefits and the potential uses of the technology. Blockchain är en ny teknik som har blivit allt populärare inom många sektorer under de senaste åren. Eftersom det är en störande och innovativ teknik har den potential att revolutionera försörjningskedjesektorn på grund av de många fördelar den medför, som säkerhet och automatisering. I den här artikeln vill vi undersöka vilket värde blockkedjan kan tillföra försörjningskedjan. En litteraturöversikt genomfördes om befintlig forskning baserad på blockkedjeanvändning i försörjningskedjan. Detta gjordes för att bättre förstå litteraturbristen i ämnet. Detta ledde till en intervjustudie där blockkedjeexperter från olika områden inom försörjningskedjesektorn kontaktades för att få en bättre förståelse för vilka värden som kan skapas och vilka fördelar blockkedjan kan medföra. Resultaten visar att blockchain har stor potential inom försörjningskedjesektorn och har redan visat sina fördelar i vissa fall. De viktigaste bidragen som blockkedjan ger till försörjningskedjor är värdet av att skapa förtroende för data och tillhandahålla automatisering, vilket leder till många stora fördelar i hela försörjningskedjan. Blockkedjan medför en ökning av förtroendet för data eftersom den skapar en enda informationskälla för alla som är involverade i försörjningskedjan. Automatisering av försörjningskedjan innebär att många manuella processer i försörjningskedjan kan automatiseras. I den här artikeln tittar vi på verkliga fall där blockchain har implementerats i försörjningskedjan och vilka värden det skapar. Trots detta verkar det som om det finns en brist på kunskap om fördelarna och de potentiella användningsområdena för tekniken.

Machine learning models are employed in several aspects of society, ranging from autonomous cars to justice systems. They affect your everyday life, for instance through recommendations on your streaming service and by informing decisions in healthcare, and are expected to have even more influence in society in the future. Among these machine learning models, we find neural networks which have had a wave of success within a wide range of fields in recent years. The success of neural networks are partly attributed to the very flexible model structure and, what it seems, endless possibilities in terms of extensions. While neural networks come with great flexibility, they are so called black-box models and therefore offer little in terms of interpretability. In other words, it is seldom possible to explain or even understand why a neural network makes a certain decision. On top of this, these models are known to be overconfident, which means that they attribute low uncertainty to their predictions, even when uncertainty is, in reality, high. Previous work has demonstrated how this issue can be alleviated with the help of ensembles, i.e. by weighing the opinion of multiple models in prediction. In Paper I, we investigate this possibility further by creating a general framework for ensemble distribution distillation, developed for the purpose of preserving the performance benefits of ensembles while reducing computational costs. Specifically, we extend ensemble distribution distillation to make it applicable to tasks beyond classification and demonstrate the usefulness of the framework in, for example, out-of-distribution detection. Another obstacle in the use of neural networks, especially deep neural networks, is that supervised training of these models can require a large amount of labelled data. The process of annotating a large amount of data is costly, time-consuming and also prone to errors. Specifically, there is a risk of incorporating label noise in the data. In Paper II, we investigate the effect of label noise on model performance. In particular, under an input-dependent noise model, we analyse the properties of the asymptotic risk minimisers of strictly proper and a set of previously proposed, robust loss functions. The results demonstrate that reliability, in terms of a model’s uncertainty estimates, is an important aspect to consider also in weak supervision and, particularly, when developing noise-robust training algorithms. Related to annotation costs in supervised learning, is the use of active learning to optimise model performance under budget constraints. The goal of active learning, in this context, is to identify and annotate the observations that are most useful for the model’s performance. In Paper III, we propose an approach for taking advantage of intentionally weak annotations in active learning. What is proposed, more specifically, is to incorporate the possibility to collect cheaper, but noisy, annotations in the active learning algorithm. Thus, the same annotation budget is enough to annotate more data points for training. In turn, the model gets to explore a larger part of the input space. We demonstrate empirically how this can lead to gains in model performance. Maskininlärningsmodeller används i flera delar av samhället, från autonoma fordon till rättssystem. De påverkar redan nu din vardag, exempelvis via personliga rekommendationer i din direktuppspelningstjänst (”streaming service”) och genom att agera beslutsstöd i vården, och förväntas ha än mer påverkan i samhället i framtiden. Bland dessa maskininlärningsmodeller, finner vi neurala nätverk som har haft stor framgång inom flera fält under det senaste årtiondet. Framgången beror delvis på neurala nätverks flexibla modellstruktur och, vad det verkar, oändliga utvecklingsmöjligheter. Neurala nätverk erbjuder stor flexibilitet, men har en nackdel i att de är så kallade black-box-modeller. Detta innebär att det sällan går att förklara eller ens förstå varför ett neuralt nätverk tar ett visst beslut. Dessutom, så har den här typen av modeller en tendens att vara överdrivet självsäkra, vilket betyder att de rapporterar låg osäkerhet i sina beslut, även när osäkerheten i själva verket är hög. För ett självkörande fordon skulle detta till exempel kunna innebära att fordonet bedömer en vänstersväng som mycket säker, när sikten över det mötande körfältet är skymd och ett mötande fordon mycket väl kan finnas just bakom krönet. Tidigare forskning har demonstrerat hur denna typ av problem kan avhjälpas genom att använda flera neurala nätverk som samspelar för att prediktera eller ta ett beslut. På detta sätt fås en modell som är mer korrekt och som också är mer pålitlig när det kommer till att ge en uppskattning av den egna osäkerheten. I denna avhandling undersöker vi vidare hur vi kan lära ett enskilt neuralt nätverk att efterlikna flera samspelande modeller, för att minska de kostnader som kommer med att ha flera samspelande modeller i bruk. En annan begränsande faktor när det kommer till neurala nätverk är att de kan behöva en stor mängd insamlad data med tillhörande etiketter för att lära sig den uppgift som de är ämnade för. Att införskaffa etiketter för en stor mängd datapunkter är både kostsamt och tidskrävande och det finns en risk att det blir fel i annoteringsprocessen. Mer specifikt så kan felaktiga etiketter, så kallat etikettbrus, inkluderas i datan. Detta i sin tur kan skada modellens förmåga att ta korrekta beslut. Vi undersöker hur denna effekt tar sig form och finner att etikettbrus inte bara kan ha en negativ effekt på nämnda förmåga att ta korrekta beslut, utan även på förmågan att skatta den egna osäkerheten. Relaterat till annoteringskostnader, föreslår vi till sist ett tillvägagångssätt för att utnyttja brusiga etiketter i aktiv inlärning. Målet med aktiv inlärning, i denna kontext, är att identifiera och annotera de observationer som kommer att vara mest hjälpsamma i modellens inlärningprocess. Förslaget är att, i aktiv inlärning, ge möjligheten att samla in billigare, men brusiga, etiketter. På så sätt kan en begränsad annoteringsbudget räcka till att annotera fler datapunkter, vilket i sin tur kan leda till en bättre modell. Det senare är något som påvisas experimentellt. Funding agencies: This research was financially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.

Creative AI in the music field has in recent years begun stepping out of the confines of academia and seen increased adoption among musicians thanks to developers launching consumer products powered by AI. These new tools are opening up new possibilities in music-making, but their increased use and development prompts inquiry regarding their sustainability. While studies have been conducted on the sustainability of training AI models, the sustainability of the usage of Creative AI remains largely unexplored. To amend this, this paper studies the energy consumption of using four music-related browser-based Creative AI tools. The four tools are Tone Transfer, Piano Scribe, MidiMe and Performance RNN, all developed by Google Magenta. The energy consumption of the tools was found by measuring the power provided to the computer. This was done by connecting a smart plug between the computer’s power cord and the wall socket. We found that Tone Transfer consumed the most energy per use with an average energy consumption of 392 J. MidiMe consumed the least energy per use with 138 J. All the tools consumed less energy per use than leaving the computer running in steady-state for 70 seconds. With this study, we have shown that the usage of music-related Creative AI tools does not represent a threat to sustainability goals. Our findings indicate that the tools studied in this paper manage to be efficient, while being both powerful and useful. This disputes the notion that there is a trade-off between performance and efficiency in the design of AI tools. We postulate that when developing tools for local use by consumers, developers are bound by limitations that force them to design efficient tools. Kreativ AI inom musikområdet har under de senaste åren börjat ta sig ut ur den akademiska världens ramar och anammats i högre grad bland musiker. Detta tack vare att utvecklare börjat lanserat konsumentprodukter som drivs av AI. Dessa nya verktyg öppnar upp för nya möjligheter inom musikskapande, men deras ökade användning och utveckling föranleder undersökningar om deras hållbarhet. Även om studier har gjorts gällande hållbarheten av att träna AI-modeller, är hållbarheten av användningen av Kreativ AI fortfarande till stor del outforskat. För att ändra detta studerar vi i denna artikel energiförbrukningen av att använda fyra musikrelaterade webbläsarbaserade Kreativa AI-verktyg. De fyra verktygen är Tone Transfer, Piano Scribe, MidiMe och Performance RNN, alla utvecklade av Google Magenta. Verktygens energiförbrukning hittades genom att mäta effekten till datorn. Detta gjordes genom att ansluta en smart kontakt mellan datorns nätsladd och vägguttaget. Vi fann att Tone Transfer förbrukade mest energi per användning med en genomsnittlig energiförbrukning på 392 J. MidiMe förbrukade minst energi per användning med 138 J. Alla verktyg förbrukade mindre energi per användning än vad som konsumeras av att låta datorn vara igång i steady-state i 70 sekunder. Med denna studie har vi visat att användningen av musikrelaterade Kreativa AI-verktyg inte utgör ett hot mot hållbarhetsmål. Våra resultat tyder på att verktygen som studerats i denna artikel lyckas vara effektiva, samtidigt som de är både kraftfulla och användbara. Detta ifrågasätter uppfattningen om att det finns en avvägning mellan prestanda och effektivitet i utformningen av AI-verktyg. Vi anser att när utvecklare utvecklar verktyg för lokal användning av konsumenter är utvecklare bundna av begränsningar som tvingar dem att designa effektiva verktyg.

PurposeThe purpose of this paper is to investigate practices of argumentation on Twitter discussions about climate change.Design/methodology/approachConversational threads were collected from the Twitter API. Fundamental concepts from argumentation theory and linking practices were operationalised through a coding schema for content analysis. Tweets were analysed in the context of the discussions and coded according to their argumentative approach, interaction type and argumentation stage. Linked and embedded sources were analysed in order to find how they were used in arguments, the plausibility and soundness of the message, the consistency and trustworthiness of the linked source and its adequacy with the target audience.FindingsAmong the interactions between arguers, this study found five typical practices and several patterns involving the dynamics of the conversations, the strategy of the argumentation and the linking practices. Although the rhetorical approach was prominent, the agreement was rarely achieved. The arguers used a variety of sources to justify or support their positions, often embedding non-textual content. These linking practices, together with the strategy adopted and the topics discussed, suggest the involvement of a multiple audience engaged in discussing ad lib scientific artefacts, topics and outputs.Originality/valueWhile Twitter has been the focus for many research papers, the conversational threads have been given little attention so far. With the Twitter API making conversations more accessible for research, this paper does not only give insight into multiple audience group argumentation dynamics but also provides a method to study the conversations from an argumentation theory perspective.

Modern telecommunication systems have seen an increased demand on delivering new types of digital media such as extended reality. The currently rolled out fifth generation mobile networks are expected to support virtual reality, augmented reality, and other immersive media. On the other hand, securing communication to prevent access from adversaries and hiding secret information have become an integral part of digital communications. In particular, steganography is a class of data hiding that provides covert communication between two parties such that the information exchange cannot be observed by an attacker. Because the human auditory system and human visual system are relatively insensitive to small changes in digital media, hiding secret information in digital media has increasingly been used. This thesis focuses on data hiding in 360o videos which offer large resolutions that can be used for hiding secret data. To keep the computational load of data hiding low, least significant bit (LSB) data hiding methods are considered and their performance is assessed in terms of capacity and imperceptibility. The proposed LSB data hiding methods account for the human viewing behavior of watching 360o videos on head-mounted displays (HMDs). This thesis is divided into an introduction part and a research part based on four peer-reviewed publications. The introduction provides fundamentals of data hiding, terminologies used in data hiding, LSB data hiding concepts, and performance measures used to assess data hiding methods. The first paper in the research part provides a survey on LSB data hiding in digital audio, images, videos, and three-dimensional (3D) media. The survey shows the tremendous potential of LSB data hiding in digital media and may assist in developing novel applications based on suitable performance trade-offs between data hiding attributes. It also reveals that LSB data hiding in 3D media such as 360o videos is not as developed as for conventional digital media. The second paper proposes an LSB data hiding method for 360o videos which takes into account that humans pay more attention to the equator region compared to the poles when viewing 360o videos on an HMD. The third paper presents a novel viewing direction based LSB data hiding method for 360o videos. The distributions of viewing direction frequency for latitude and longitude are used to control the amount of secret data to be hidden at the latitude, longitude, or both latitude and longitude of 360o videos. Analytical expressions for the capacity offered by this method are derived and imperceptibility is assessed through fidelity and quality metrics. The fourth paper proposes a viewing direction weighted bit plane LSB data hiding method for 360o videos that uses normalized Gaussian mixture models to control the amount of secret data and the number of bit planes used for data hiding in the latitude and longitude.

Users face many risks while being online, including ones posed by their own actions, such as keeping the same passwords on multiple services. This makes them potential victims of account takeovers, in which a hacker uses stolen credentials to acquire and use accounts for their own benefit. However, few papers have researched how users experience these incidents, and how this affects their trust in the service. To fill this research gap, 4 users that were part of the same account takeover incident were interviewed. Storytelling was used to present and analyse the interviews. One participant experienced the takeover as it happened, while the other three learned about it after it was mitigated. The results show that the role the user took, and how they valued the platform, affected their trust. This paper contributes to research by giving in-depth insights into account takeovers, and what challenges and opportunities the systems face. Användare står inför många risker när de är online, inklusive de som orsakas av deras egna handlingar, som att behålla samma lösenord på flera tjänster. Detta gör dem till potentiella offer för kontoövertaganden, där en hackare använder stulna inloggningsuppgifter för att ta kontroll över och använda konton till sin egen fördel. Men få studier har undersökt hur användare upplever dessa incidenter och hur detta påverkar deras förtroende för tjänsten. För att bidra med kunskap inom detta område intervjuades 4 användare som ingick i samma incident med kontoövertagelser. Storytelling användes för att presentera och analysera intervjuerna. En deltagare upplevde övertagandet medan det hände, medan de andra tre f ick veta om det efter att det hanterats. Resultaten visar att den roll användaren tog, och hur de värderade plattformen, påverkade deras förtroende. Denna artikel bidrar till forskningen genom att ge ingående insikter om övertaganden och vilka utmaningar och möjligheter systemen står inför.

Persuasive Technology(PT) was pioneered by BJ Fogg. The theory has been developed in several aspects and applied in education, health, entertainment, and online shopping. Short-form video application TikTok first launched in the media market in 2016. During the global COVID-19 pandemic period, the growing popularity of TikTok helped it reach over 800 million users. As the new form of social media, it is persuasively shaping people’s life. The paper aims to investigate the relationship between behavior change and TikTok usage. Quantitative and qualitative research methods were used to investigate 178 participants. The research mainly focused on Chinese university students whose ages are around 18 to 25 as the target group. The result shows that 57.3% of participants feel the behavior change and 15.7% of them are not sure about that. In the qualitative research, from the in-depth interview, participants who are not sure about the effect also make using TikTok a habit without perception. The findings of this paper provide evidence that TikTok as a persuasive system has effects on users’ behavior and even changed their attitudes. The ethical concerns and sustainability are also discussed in the following parts. Persuasive Technology(PT) var banbrytande av BJ Fogg. Teorin har utvecklats i flera aspekter och tillämpats inom utbildning, hälsa, underhållning och online shopping. Kortformad videoapplikation TikTok lanserades först på mediemarknaden 2016. Under den globala covid-19-pandemiperioden hjälpte TikToks växande popularitet det att nå över 800 miljoner användare. Som den nya formen av sociala medier formar det på ett övertygande sätt människors liv. Uppsatsen syftar till att undersöka sambandet mellan beteendeförändring och TikTok-användning. Kvantitativa och kvalitativa forskningsmetoder användes för att undersöka 178 deltagare. Forskningen fokuserade främst på kinesiska universitetsstudenter vars ålder är runt 18 till 25 som målgrupp. Resultatet visar att 57,3 % av deltagarna känner att beteendet förändras och 15,7 % av dem är inte säkra på det. I den kvalitativa forskningen, från djupintervjun, gör deltagare som inte är säkra på effekten också att använda TikTok till en vana utan uppfattning. Resultaten av detta dokument ger bevis på att TikTok som ett övertygande system har effekter på användarnas beteende och till och med förändrat deras attityder. De etiska aspekterna och hållbarhet diskuteras också i följande delar.

Transformer models, delivering big improvement in AI text-models (NLP), are now being applied in Knowledge Tracing to track the knowledge of students over time. One of the first, SAINT, showed quite some improvement over the then SOTA results on the public EdNet dataset and caused an increase in research based on transformer-based models. In this paper, we firstly aim to reproduce the SAINT results on the EdNet dataset but are unable to report a similar performance as the original paper. This might be due to implementation details, which we were not able to completely reconstruct. We hope to pave the road for further reproducibility, as an increasingly important part of AI research. Furthermore, we apply the model to a company dataset much larger than any public dataset (more interactions, more exercises and more skills). Such a dataset is on the one hand more challenging (more skills mixed), and on the other hand, provides much more data (which should help our models). We compare the SAINT model and the seminal IRT model, and find that the SAINT model performance is 4% better in AUC but 1.7% worse in RMSE. Our experiments on window size suggest that transformer models still struggle with modelling beyond recent performance, and do not yet deliver the step-change observed in NLP. Transformermodeller, som ger stora förbättringar av AI-textmodeller (NLP), används nu i Knowledge Tracing för att spåra elevernas kunskaper över tid. En av de första, SAINT, visade en hel del förbättring jämfört med de dåvarande SOTA-resultaten på den offentliga EdNet-datauppsättningen och orsakade en ökning av forskning baserad på transformerbaserade modeller. I denna artikeln siktar vi först efter att återskapa SAINT-resultaten på EdNet-datauppsättningen, men vi kan inte rapportera liknande prestanda som den ursprungliga uppsatsen. Detta kan bero på implementeringsdetaljer som vi inte kunde rekonstruera helt. Vi hoppas kunna bana väg för ytterligare reproduktioner, som en allt viktigare del av AI-forskningen. Dessutom tillämpar vi modellen på en företagsdatauppsättning som är mycket större än någon offentlig datauppsättning (fler interaktioner, fler övningar och fler färdigheter). En sådan datauppsättning är å ena sidan mer utmanande (mer blandad kompetens), men å andra sidan ger den mycket mer data (vilket borde hjälpa våra modeller). Vi jämför SAINT-modellen och den framträdande IRT-modellen och finner att SAINT-modellens prestanda är 4% bättre i AUC men 1,7% sämre i RMSE. Våra experiment på fönsterstorlek tyder på att transformermodeller fortfarande kämpar med modellering utöver de senaste prestanda och ännu inte levererar den stegförändring som observerats i NLP.

In recent years the number of electronic healthcare records (EHRs)has increased rapidly. EHR represents a systematized collection of patient health information in a digital format. EHR systems maintain diagnoses, medications, procedures, and lab tests associated with the patients at each time they visit the hospital or care center. Since the information is available into multiple visits to hospitals or care centers, the EHR can be used to increasing quality care. This is especially useful when working with chronic diseases because they tend to evolve. There have been many deep learning methods that make use of these EHRs to solve different prediction tasks. Transformers have shown impressive results in many sequence-to-sequence tasks within natural language processing. This paper will mainly focus on using transformers, explicitly using a sequence of visits to do prediction tasks. The model presented in this paper is called CondBEHRT. Compared to previous state-of-art models, CondBEHRT will focus on using as much available data as possible to understand the patient’s trajectory. Based on all patients, the model will learn the medical ontology between diagnoses, medications, and procedures. The results show that the inferred medical ontology that has been learned can simulate reality quite well. Having the medical ontology also gives insights about the explainability of model decisions. We also compare the proposed model with the state-of-the-art methods using two different use cases; predicting the given codes in the next visit and predicting if the patient will be readmitted within 30 days.

Flying can constitute as much as a third of an academic institution’s total emissions and there’s a growing concern among researchers around their academic travel contributing to global warming. This paper is part of KTH FLIGHT, a research project for decreased CO2-emissions in flight-intensive organizations through creation and testing of practical tools. It is a Research through Design work that provides new knowledge about a previously largely unexplored implementation area of emission trading systems like Cap and Trade. These have previously helped reduce emissions at lower costs than tax based systems in various settings but little research exists in terms of design and important factors for a university to promote more sustainable travel patterns. This paper reveals challenges and opportunities regarding implementation and shows current feasibility in implementing in a Swedish university (KTH), and provides suggestions for choosing suitable users and direction of future research. Flygresor kan utgöra upp till en tredjedel av en akademisk institutions totala utsläpp och det finns en växande oro bland forskare kring att deras resande bidrar till den globala uppvärmningen. Denna rapport är en del av KTH FLIGHT, ett forskningsprojekt med syfte att minska CO2 -utsläpp i flygintensiva organisationer genom att skapa och testa praktiska verktyg. Detta är ett Research through Design-arbete som bidrar med ny kunskap om ett tidigare till stor del outforskat område för implementeringar av utsläppshandelssystem såsom Cap and Trade. Dessa har i olika sammanhang tidigare bidragit till minskade utsläpp med lägre kostnader än skattebaserade system. Men i dagsläget finns det lite forskning gällande dess design och viktiga faktorer för att främja mer hållbara resvanor på ett universitet. Denna uppsats visar på utmaningar och möjligheter vid en implementering och visar aktuell genomförbarhet i ett svenskt universitet (KTH), samt ger förslag på val av lämpliga användare samt inriktning för framtida forskning.

Background Ward-Based Outreach Teams’ (WBOTs) use of mobile technologies can promote better quality and improved health services for populations in low- and middle-income countries. However, the implementation of such systems is fraught with threats to sustainability. Purpose The purpose of this study was to gain a deeper understanding of users’ views and experiences of the implementation of mHealth in a selected sub-district in North West Province, South Africa. Methods The study was qualitative, exploratory and descriptive. Data were collected from community health workers (CHWs) (n = 24) and outreach team leaders (OTLs) (n = 4) who used mobile devices loaded with the Mobenzi application through semi-structured focus group interviews, using an interview guide. Creswell’s stages of thematic analysis were used for data analysis, and codes, categories and themes were validated with the participants. Results Three themes emerged from the findings, namely the transition from a paper-based system to an electronic system; the use of the application; and reverting to paper-based documentation. The findings revealed that WBOTs considered the mHealth application as useful, easy to use, and supportive to their workflow. They experienced some technical challenges and lamented the inaccessibility of technicians. The mobile initiative’s termination caused distress among the participants, especially CHWs who had to revert to paper-based documentation to capture community health data. OTLs were concerned about the quality of health data being captured in the absence of the application. Conclusions The study concluded that community-based health care programmes that use WBOTs and mHealth technologies are essential in strengthening the health care system. WBOTs are facilitators for improving access to health care. Policy-makers and district managers will gain useful insights if they engage application users in discussions regarding future mobile health interventions for household and community-based care.

Digital audio is the by far most common way to listen to music today. It is therefore important to store audio files as efficiently as possible. A common way of doing this is by using compression, often with mp3 files. As with all non-lossless compression, some data is lost. The purpose of this literature review is to examine at what point the compression is indiscernible. Four research papers were studied and their results compared and compiled to draw a more broad-based conclusion to the research question. The papers all used A/B tests to test at what bit rate subjects could not tell the difference between a compressed and an uncompressed file. The results indicate that most people with consumer-grade equipment are incapable of discerning between mp3 files encoded at 128 kbit/s mp3 and uncompressed or lossless audio. With trained ears and high-end equipment, the cutoff bit rate is instead in the 256 kbit/s range. Nästan all musik som lyssnas på idag är digital. Därför är det viktigt att kunna lagra ljudfiler så effektivt som möjligt. Ett vanligt sätt att göra detta är genom att använda komprimering, ofta med mp3-filer. Mp3 och många andra effektiva komprimeringsmetoder sker dock med viss degredering av datan. Syftet med denna literaturstudie är att undersöka när komprimeringen av mp3-filer blir omärkbar. Fyra forskningsartiklar valdes ut och studerades. Deras resultat jämfördes och samlades för att kunna dra en mer slutsats med bredare bas. Artiklarna använde alla A/B-tester för att undersöka vid exakt vilken bitrate kvalitéskillnaderna mellan en komprimerad mp3-fil och okomprimerad ljudfiler slutade vara märkbara. Resultaten tyder på att de flesta med konsumentutrustning inte hör någon skillnad på 128 kbit/s mp3-filer och förlustfria ljudfiler. Med träning och professionell utrustning är det dock möjligt att uppfatta skillnader på upp till 256 kbit/s.

The term electronic commerce (e-commerce) refers to a business model that allows companies and individuals to buy and sell goods and services over the internet. The focus of this thesis is on online pharmacies, a segment of the ecommerce market. Even though internet pharmacies are still subject to the same stringent rules imposed on pharmacies that limit the scope for their market growth, it has shown a notable increase in the past decades. The main goal of this thesis is to develop a data-driven marketing strategy based on a Swedish based online pharmacy’s daily sales data. The methodology of the data analysis includes exploratory data analysis (EDA) and market basket analysis (MBA) using the Apriori algorithm and the application of marketing frameworks and theories from a data-driven standpoint. In addition to the data analysis, this paper proposes a conceptual framework of a digital marketing strategy based on the RACE framework (reach, act, convert, and engage). The result of the analysis has led to the following data-driven marketing strategy: Special attention should be paid to association rules with a high lift ration value; high gross profit margin percentile (GPMP) products should have a volume-based marketing strategy that focuses on lower prices on subsequent items; and price bundling is the best marketing strategy for low GPMP products. Some of the practical ideas mentioned in this thesis paper include optimizing keyword search for a high GPMP product type and sending reminder emails and push alerts to avoid cart abandonment. The findings and recommendations presented in this thesis can be used by online pharmacies to extract knowledge that may support several decisions ranging from raising overall order size, marketing campaigns, to increasing the sales of products with a high gross profit margin.

This paper presents methodologies for creating profiles of typical customers for certain types of services and products in user-oriented businesses. Because of the specific characteristics of each of the domains where these methodologies would be applied creating one solution for all purposes does not solve the problem. The emphasis of this paper is on experimenting with real data from telecommunication sector and on the construction of clustering system for user profiling. The approach for solving this problem involves analysing the characteristics of the observed data and adjusting the process of intelligent data analysis according to its specifications.

PurposeThe aim of the paper is to identify management practices that are characteristic for SMEs that achieve market success measured by their business performance in last their years of their operation analyze the relationships between management practices applied in small and medium-sized enterprises and their success measured by their business performance drawing on the data from 2710 SMEs operating on the Polish market.Approach/Methodology/DesignA cluster analysis was used to distinguish homogenous SME groups in view of their management practices. We examined differences between groups in terms of their business performance. The HINoV algorithm allowed six variables to be selected out of 32 management practices chosen initially for testing, with these variables providing the basis for grouping. Modal values and medians were calculated for 17 business performance measures in the three clusters produced. The subsequent analysis of those findings was focused on capturing significant differences.FindingsIn the group of 2710 Polish SMEs, it was possible to verify that there existed an association between management practices in the field of modern HRM, computer systems supporting management and the company’s economic performance, as measured by an increase in net revenue and number of customers over the last three years. In clusters where the above mentioned practices were appreciated, modal and median values of the increase reported in net revenue and number of customers were significantly higher.Practical implicationsThe research has shown that at a time marked by a shortage of highly skilled personnel one should pay particular attention to building an integrated and committed team of workers and to employee empowerment. The research has also shown that SME managers monitor only a fraction of basic business performance measures, which may prove to be a major risk to SMEs.Originality/ValuePrevious studies have been largely conducted in a fragmentary manner, i.e. they were concerned with the relationships between the application of some practices (strategic management, BPR, entrepreneurial orientation, monitoring, etc.) and selected business effectiveness measures. In this paper, the research covered SME management practices from a variety of areas which were then compared with the entrepreneurs’ assessment as to whether the company’s economic condition changed over the last three years. It is also the first attempt in post-socialist economies to identify those SME management practices that are related to better economic results.

The analysis of brain-imaging data requires complex and often non-linear transformations to support findings on brain function or pathologies. And yet, recent work has shown that variability in the choices that one makes when analyzing data can lead to quantitatively and qualitatively different results, endangering the trust in conclusions1–3. Even within a given method or analytical technique, numerical instabilities could compromise findings4–7. We instrumented a structural-connectome estimation pipeline with Monte Carlo Arithmetic8, 9, a technique to introduce random noise in floating-point computations, and evaluated the stability of the derived connectomes, their features10, 11, and the impact on a downstream analysis12, 13. The stability of results was found to be highly dependent upon which features of the connectomes were evaluated, and ranged from perfectly stable (i.e. no observed variability across executions) to highly unstable (i.e. the results contained no trustworthy significant information). While the extreme range and variability in results presented here could severely hamper our understanding of brain organization in brain-imaging studies, it also leads to an increase in the reliability of datasets. This paper highlights the potential of leveraging the induced variance in estimates of brain connectivity to reduce the bias in networks alongside increasing the robustness of their applications in the detection or classification of individual differences. This paper demonstrates that stability evaluations are necessary for understanding error and bias inherent to scientific computing, and that they should be a component of typical analytical workflows.

Distracted driving behavior is one of the main factors of road accidents. Accurately predicting the risk of driving behavior is of great significance to the active safety of road transportation. The large amount of information collected by the sensors installed on the vehicle can be identified by the algorithm to obtain the distracted driving behavior data, which can be used to predict the driving behavior risk of the vehicle and the area. In this paper, a new neural network named Driving Behavior Risk Prediction Neural Network (DBRPNN) is developed for prediction based on the distracted driving behavior data. The network consists of three modules: the Feature Processing Module, the Memory Module, and the Prediction Module. In this process, attribute data (time in a day, daily driving time, and daily driving mileage) that can reflect external factors and driver statuses, are added to the network to increase the accuracy of the model. We predicted the driving behavior risk of different objects (Vehicle and Area). For the applicability improvement of the model, we further classify the distracted driving behavior categories, and DBRPNN can provide more accurate risk prediction. The results show that compared with traditional models (Classification and Regression Tree, Support Vector Machines, Recurrent Neural Network, and Long Short-Term Memory), DBRPNN has better prediction performance. The method proposed in this paper has been fully verified and may be transplanted into active safety early warning system for more accurate and flexible application.

In their Commentary paper, Villaverde and Massonis (On testing structural identifiability by a simple scaling method: relying on scaling symmetries can be misleading) have commented on our paper in which we proposed a simple scaling method to test structural identifiability. Our scaling invariance method (SIM) tests for scaling symmetries only, and Villaverde and Massonis correctly show the SIM may fail to detect identifiability problems when a model has other types of symmetries. We agree with the limitations raised by these authors but, also, we emphasize that the method is still valuable for its applicability to a wide variety of models, its simplicity, and even as a tool to introduce the problem of identifiability to investigators with little training in mathematics.

This deliverable provides an extensive analysis of the dissemination activities and workshops organisation of the EXCELSIOR H2020 Teaming Project. The analysis starts with the report on our participation in conferences (11) and how the project was promoted through it. Then, we explain about the participation of our team members in talks (17), workshops (7) and seminars (12) as invited speakers. The deliverable continues with a thorough presentation of the lectures by invited speakers (8), the webinar (1) and the workshops (2) organized by our team. Additionally, we document about our participation in other events (i.e., European Researcher’s Night 2021 and SpaceUPCyprus 2021 Live). The last chapter provides the publications, journal papers, conference papers, and book sections for the reporting time period. The deliverable concludes by providing information on the outcome of the reported activities and how they have contributed to the progress of the EXCELSIOR H2020 Teaming Project. It is concluded that there is a strong need to establish links in the EMMENA region and connect with them. This has not been achieved yet, but a strategy was prepared to raise awareness about the EXCELSIOR Project in the EMMENA region and establish partnerships, starting with targeted stakeholders’ workshop in autumn 2021, where selected stakeholders from the region will be invited to be informed them about the project and provide them the space to discuss their needs and identify common scientific interests and ways of collaboration.

When keystroke dynamics are used for authentication, users tend to get different levels of security due to differences in the quality of their templates. This paper addresses this issue by proposing a metric to quantify the quality of keystroke dynamic templates. That is, in behavioral biometric verification, the user’s templates are generally constructed using multiple enrolled samples to capture intra-user variation. This variation is then used to normalize the distance between a set of enrolled samples and a test sample. Then a normalized distance is compared against a predefined threshold value to derive a verification decision. As a result, the coverage area for accepted samples in the original space of vector representation is discrete. Therefore, users with the higher intra-user variation suffer higher false acceptance rates (FAR). This paper proposes a metric that can be used to reflect the verification performance of individual keystroke dynamic templates in terms of FAR. Specifically, the metric is derived from statistical information of user-specific feature variations, and it has a non-decreasing property when a new feature is added to a template. The experiments are performed based on two public keystroke dynamic datasets comprising of two main types of keystroke dynamics: constrained-text and free-text, namely the CMU keystroke dynamics dataset and the Web-Based Benchmark for keystroke dynamics dataset. Experimental results based on multiple classifiers demonstrate that the proposed metric can be a good indicator of the template’s false acceptance rate. Thus, it can be used to enhance the security of the user authentication system based on keystroke dynamics.

Introduction Degenerative Cervical Myelopathy (DCM) is a common and disabling condition, with a relatively modest research capacity. In order to accelerate knowledge discovery, the AO Spine RECODE-DCM project has recently established the top priorities for DCM research. Uptake of these priorities within the research community will require their effective dissemination, which can be supported by identifying key opinion leaders (KOLs). In this paper, we aim to identify KOLs using artificial intelligence. We produce and explore a DCM co-authorship network, to characterise researchers’ impact within the research field. Methods Through a bibliometric analysis of 1674 scientific papers in the DCM field, a co-authorship network was created. For each author, statistics about their connections to the co-authorship network (and so the nature of their collaboration) were generated. Using these connectedness statistics, a neural network was used to predict H-Index for each author (as a proxy for research impact). The neural network was retrospectively validated on an unseen author set. Results DCM research is regionally clustered, with strong collaboration across some international borders (e.g., North America) but not others (e.g., Western Europe). In retrospective validation, the neural network achieves a correlation coefficient of 0.86 (p Discussion Analysis of the neural network shows that the nature of collaboration strongly impacts an author’s research visibility, and therefore suitability as a KOL. This also suggests greater collaboration within the DCM field could help to improve both individual research visibility and global synergy.