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Modelling can be applied to all aspects of healthcare systems but one area in particular, clinical pathways, are of great interest currently due to many flow-oriented issues that are well-documented in the media. These pathways typically describe sequences of sorting and treatment activities such as surgical procedures or the care process for managing injuries, for example bone fractures. Previous efforts in using modelling languages have not been promoted as being highly generalizable nor have emphasised the inclusion of constraints defining rules for particular treatment activities. In this paper, we propose a workflow for building flexible models for healthcare systems by exploiting the combination of UML, OCL, and SMT solving. This paper serves as an exposition of an idea that can be developed into a more complete framework that could be used to create workflow models for improving the efficiency and safety of more complex clinical activities. A good application would be to tackle the prevalent problems of emergency departments and some of the challenges in this respect are discussed.

Cardiovascular conditions are significant causes of mortality and morbidity leading to substantial disability. The aim of the paper is to improve Physical and Rehabilitation Medicine (PRM) physicians' professional practice for people with cardiovascular conditions in order to promote their functioning and to reduce activity limitations and/or participation restrictions. A systematic review of the literature and a Consensus procedure by means of a Delphi process have been performed involving the delegates of all European countries represented in the UEMS PRM Section. The systematic literature review is reported together with thirty recommendations resulting from the Delphi procedure. The professional role of PRM physicians having expertise in the rehabilitation of cardiovascular conditions is to lead cardiac rehabilitation programs in multiprofessional teams, working in collaboration with other disciplines in a variety of settings to improve functioning of people with cardiovascular conditions. This evidence-based position paper represents the official position of the European Union through the UEMS PRM Section and designates the professional role of PRM physicians in persons with cardiovascular conditions.

Background: In recognition of the ongoing discussion on euthanasia and physician-assisted suicide, the Board of Directors of the European Association for Palliative Care commissioned this white paper from the palliative care perspective. Aim: This white paper aims to provide an ethical framework for palliative care professionals on euthanasia and physician-assisted suicide. It also aims to provide an overview on the available evidence as well as a discourse of ethical principles related to these issues. Design: Starting from a 2003 European Association for Palliative Care position paper, 21 statements were drafted and submitted to a five-round Delphi process Participants: A panel with 17 experts commented on the paper in round 1. Board members of national palliative care or hospice associations that are collective members of European Association for Palliative Care were invited to an online survey in rounds 2 and 3. The expert panel and the European Association for Palliative Care board members participated in rounds 4 and 5. This final version was adopted as an official position paper of the European Association for Palliative Care in April 2015. Results: Main topics of the white paper are concepts and definitions of palliative care, its values and philosophy, euthanasia and physician-assisted suicide, key issues on the patient and the organizational level. The consensus process confirmed the 2003 European Association for Palliative Care white paper and its position on the relationship between palliative care and euthanasia and physician-assisted suicide. Conclusion: The European Association for Palliative Care feels that it is important to contribute to informed public debates on these issues. Complete consensus seems to be unachievable due to incompatible normative frameworks that clash.

The present article aims at presenting several points-commonly not realized by potential TOEFL test takers-that may be utilized to take the paper-and-pencil TOEFL. To these ends, several topics will be presented covering mainly the test format and the scoring scheme. It is under these topics that several technical strategies are discussed with the perspectives on successful paper-based TOEFL test taking.

Dyslexia is a type of Specific Learning Disability (SLD) that affects the educational development of a student, as it is the reason of difficulties not only in writing or reading but also in other disciplines like Mathematics. Research has proven that Information and Communication Technologies (ICT) can have a positive effect to the educational development of a dyslexic student. In this paper, twenty 6th-grade primary-school students were observed while using computers during exams - ten with Dyslexia and ten with no SLDs. All students were called to solve a digital test and a paper test that were including some basic mathematic operations. The researcher recorded and examined the differences between the results from the two tests. Descriptive and deductive statistical analysis showed that students had better results when examined via a computer compared to a traditional written examination.

Statins are one of the most commonly prescribed drugs in clinical practice. They are usually well tolerated and effectively prevent cardiovascular events. Most adverse effects associated with statin therapy are muscle-related. The recent statement of the European Atherosclerosis Society (EAS) has focused on statin associated muscle symptoms (SAMS), and avoided the use of the term 'statin intolerance'. Although muscle syndromes are the most common adverse effects observed after statin therapy, excluding other side effects might underestimate the number of patients with statin intolerance, which might be observed in 10-15% of patients. In clinical practice, statin intolerance limits effective treatment of patients at risk of, or with, cardiovascular disease. Knowledge of the most common adverse effects of statin therapy that might cause statin intolerance and the clear definition of this phenomenon is crucial to effectively treat patients with lipid disorders. Therefore, the aim of this position paper was to suggest a unified definition of statin intolerance, and to complement the recent EAS statement on SAMS, where the pathophysiology, diagnosis and the management were comprehensively presented.

Currently, readers often prefer to search for their interested papers based on a set of typed query keywords. As the keywords of a paper is often limited, paper recommender systems often need to recommend a set of papers which collectively satisfy the readers’ keyword query. However, the topics of recommended papers are probably not correlated with each other, which fail to meet the readers’ requirements on in-depth and continuous academic research. Furthermore, although existing paper citation graphs can model the papers’ correlations, they often face the data sparse problem which blocks accurate paper recommendations. To address these issues, we propose a keywords-driven and weight-aware paper recommendation approach, named LP-PRk+w (link prediction-paper recommendation), based on a weighted paper correlation graph. Concretely, we firstly optimize the existing paper citation graph modes by introducing a weighted similarity, after which we obtain a weighted paper correlation graph. Then we recommend a set of correlated papers based on the weighted paper correlation graph and the query keywords from readers. At last, we conduct large-scale experiments on a real-world Hep-Th dataset. Experimental results demonstrate that our proposal can improve the paper recommendation performances considerably, compared to other related solutions.

We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task -- the accuracy of the reconstructed camera pose -- as our primary metric. Our pipeline's modular structure allows easy integration, configuration, and combination of different methods and heuristics. This is demonstrated by embedding dozens of popular algorithms and evaluating them, from seminal works to the cutting edge of machine learning research. We show that with proper settings, classical solutions may still outperform the perceived state of the art. Besides establishing the actual state of the art, the conducted experiments reveal unexpected properties of Structure from Motion (SfM) pipelines that can help improve their performance, for both algorithmic and learned methods. Data and code are online https://github.com/vcg-uvic/image-matching-benchmark, providing an easy-to-use and flexible framework for the benchmarking of local features and robust estimation methods, both alongside and against top-performing methods. This work provides a basis for the Image Matching Challenge https://vision.uvic.ca/image-matching-challenge., Comment: Added: KeyNet-SOSNet, AffNet-HardNet, TFeat, MKD from kornia

This paper will discuss the integration of electronic Case Report Forms (e-CRFs) into an already existing Android-based Audio Computer-Assisted Self-Interview (ACASI) software solution that was developed for a public health project in Kampala, Uganda, the technical outcome results, and lessons learned that may be useful to other projects requiring or considering such a technology solution. The developed product can function without a connection to the Internet and allows for synchronizing collected data once connectivity is possible. Previously, only paper-based CRFs were utilized at the Uganda project site. A subset or select group of CRFs were targeted for integration with ACASI in order to test feasibility and success. Survey volume, error rate, and acceptance of the system, as well as the operational and technical design of the solution, will be discussed.

Purpose of the study: This study aimed to utilize the usage of App in Public Speaking course to ease and engage students of Office Management & Technology (OMT) in their learning experience by providing the simplified version of speech draft development. Methodology: The development of the App divided into two divisions, (a) the App content and, (b) the App design and concept. The content of the App was from the main textbook, simplified to serve the needs of OMT students, whereas the Apps’ design and concept were based on interactive design and waterfall models. Analysis, Design, Development, Implementation, and Evaluation (ADDIE) and Focus Group were also included. Main Findings: The Infusion Speech Skeleton App is projected to be a vital platform for students to improve their understanding of the course. The App is also expected to improve students’ performance particularly those who have trouble comprehending speech draft development. Applications of this study: Because the App is mainly developed with OMT students in the loop, most of the settings will be in OMT students’ favors. However, the Apps also can be beneficial to those taking literature, and other communications studies for basic reference. Novelty/Originality of this study: The App usage in learning was introduced in many courses, however, to our best knowledge this is the first attempt of App development and implementation in the course of public speaking, focusing on the need for non-communication students, OMT students.

On 27 September 2018, SSM has introduce a new submission platform known as Malaysian Business Reporting System (MBRS) that allow company in Malaysia to submit annual submission filing in Extensible Business Reporting Language (XBRL) format. Currently, submission filing using MBRS platform is only mandatory for unaudited companies’ and Certificate for Exempt Private Company (EPC) but it is expected that the mandatory submission shall be enforced to other type of companies soon. By applying Theory Acceptance Model (TAM), this study proposes a conceptual model to examine the behavioral intention to use MBRS by accounting practitioners in Malaysia. The resulting model will guide the development of strategies, especially for the Companies Commission of Malaysia (SSM) in promoting MBRS adoption in the future reporting.

We randomly selected 100 journal articles published in five proteomics journals in 2019 and manually examined each of them against a set of 13 criteria concerning the statistical analyses used, all of which were based on items mentioned in the journals' instructions to authors. This included questions such as whether a pilot study was conducted and whether false discovery rate calculation was employed at either the quantitation or identification stage. These data were then transformed to binary inputs, analyzed via machine learning algorithms, and classified accordingly, with the aim of determining if clusters of data existed for specific journals or if certain statistical measures correlated with each other. We applied a variety of classification methods including principal component analysis decomposition, agglomerative clustering, and multinomial and Bernoulli naïve Bayes classification and found that none of these could readily determine journal identity given extracted statistical features. Logistic regression was useful in determining high correlative potential between statistical features such as false discovery rate criteria and multiple testing corrections methods, but was similarly ineffective at determining correlations between statistical features and specific journals. This meta-analysis highlights that there is a very wide variety of approaches being used in statistical analysis of proteomics data, many of which do not conform to published journal guidelines, and that contrary to implicit assumptions in the field there are no clear correlations between statistical methods and specific journals.

2010년대 한국사회에서 혐오표현이 본격적으로 부상하며 언론학 또한 혐오 문제에 각별한 관심을 기울여 왔다. 본 논문은 언론학 분과에서 연구된 지난 10년의 혐오 관련 논문을 메타 분석하여 한국 사회에서 어떻게 혐오가 문제시되었고 현재에 이르렀는지를 입체적으로 살펴볼 목적으로 기술되었다. 분석논문은 한국연구재단에 등재된 사회과학 신문방송학 분야 학술지로 한정되었고 정량적 분석과 정성적 분석을 시행하였다. 정량적 분석을 통해 혐오표현, 표현의 자유, 여성혐오가 주된 연구키워드였으며 여성 연구자의 학술 개입이 두드려졌음을 확인하였다. 정성적 분석은 지난 10년 동안 여성혐오뿐만 아니라 레드콤플렉스, 지역, 이주 노동자와 외국인, 정치혐오와 같이 사실상 전방위적으로 한국 사회의 혐오 대상이 폭증하였음을 보여준다. 이는 신자유주의가 초래한 사회 갈등 심화와 더불어 모바일 미디어로의 매체 전경 변화, 언론과 미디어 플랫폼의 취약한 자율규제가 복합적으로 영향을 미친 탓이다. 이와 같은 상황 속에서 언론학은 표현의 자유를 적극적으로 해석하여 2010년대 후반에는 사회적 소수자의 표현의 자유를 보호하기 위한 혐오표현 규제 논의를 펼쳐내기 시작하였다.

The JEL classification system is a standard way of assigning key topics to economic articles to make them more easily retrievable in the bulk of nowadays massive literature. Usually the JEL (Journal of Economic Literature) is picked by the author(s) bearing the risk of suboptimal assignment. Using the database of the Collaborative Research Center from Humboldt-Universität zu Berlin we employ a new adaptive clustering technique to identify interpretable JEL (sub)clusters. The proposed Adaptive Weights Clustering (AWC) is available on http://www.quantlet.de/ and is based on the idea of locally weighting each point (document, abstract) in terms of cluster membership. Comparison with $$k$$ k -means or CLUTO reveals excellent performance of AWC.

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

How best to estimate the accuracy of a predictive rule has been a longstanding question in statistics. Approaches to this task range from simple methods like Mallow’s Cp to algorithmic techniques l...

Die Biointelligenz zählt derzeit zu den am meisten beachteten Innovationspfaden in Deutschland und Europa. Maßgeblich für die Entwicklung der Vision der Biointelligenz sind die jüngsten Erfolge der Bio- und Informationstechnologie. So ist es nicht verwunderlich, dass die wissenschaftliche Gemeinschaft, politische Entscheidungsträger wie auch Vertreter der Industrie dem Thema Biointelligenz geradezu enthusiastisch gegenüberstehen. In der Region Stuttgart-Tübingen haben sich beispielsweise über 40 Wissenschaftler im Kompetenzzentrum Biointelligenz zusammengeschlossen, um das Thema gemeinsam, interdisziplinär voranzubringen. Nicht nur die Bundesregierung, sondern auch verschiedene Landesregierungen, planen umfangreiche Fördermaßnahmen. Auf Ebene der EU-Kommission wird hinter vorgehaltener Hand teilweise sogar der Begriff Industrie 5.0 verwendet. Fakt ist, dass die Biointelligenz enormes Potenzial für Sprunginnovationen und Standortvorteile in nahezu allen industriellen Branchen eröffnet. Vielfach wird der Begriff jedoch missverstanden, was in letzter Konsequenz dazu führt, dass die Bedeutung ausgehöhlt und damit das Potenzial für den Wirtschaftsstandort Deutschland unterminiert wird. Für Wissenschaft, Industrie und Politik ist es entscheidend einen Konsens zu finden. Dazu soll dieser Artikel einen Beitrag leisten, indem er die Begriffsherkunft zusammenfasst, einen detaillierten Definitionsvorschlag präsentiert und eine Kategorisierungssystematik erarbeitet. Biointelligence is currently one of the most noted innovation paths in Germany and Europe. Crucial to the development of the Biointelligence vision are recent biotech and information technology breakthroughs. It is therefore not surprising that the scientific community, political decision-makers as well as industry representatives are enthusiastic about the subject of biointelligence. In the Stuttgart-Tübingen region, for example, more than 40 scientists have joined forces in the Competence Center Biointelligence to promote the topic in an interdisciplinary fashion. Not only the federal government, but also various state governments are planning extensive support measures. At the level of the European Commission, the term Industry 5.0 is sometimes even used behind closed doors. The fact is that biointelligence opens up enormous potential for break through innovations, leap frogging and competitive advantages in almost all industrial sectors. In many cases, however, the term is misunderstood, which ultimately leads to undermining the meaning and thus undermining the potential for Germany as a business location. For science, industry and politics it is crucial to find a consensus. This article thus aims to contribute by summarizing the term background, presenting a detailed definition and developing a categorization system.

Sensitivity analysis (SA) has been used to evaluate the behavior and quality of environmental models by estimating the contributions of potential uncertainty sources to quantities of interest (QoI) in the model output. Although there is an increasing literature on applying SA in environmental modeling, a pragmatic and specific framework for spatially distributed environmental models (SD-EMs) is lacking and remains a challenge. This article reviews the SA literature for the purposes of providing a step-by-step pragmatic framework to guide SA, with an emphasis on addressing potential uncertainty sources related to spatial datasets and the consequent impact on model predictive uncertainty in SD-EMs. The framework includes: identifying potential uncertainty sources; selecting appropriate SA methods and QoI in prediction according to SA purposes and SD-EM properties; propagating perturbations of the selected potential uncertainty sources by considering the spatial structure; and verifying the SA measures based on post-processing. The proposed framework was applied to a SWAT (Soil and Water Assessment Tool) application to demonstrate the sensitivities of the selected QoI to spatial inputs, including both raster and vector datasets - for example, DEM and meteorological information - and SWAT (sub)model parameters. The framework should benefit SA users not only in environmental modeling areas but in other modeling domains such as those embraced by geographical information system communities.

Due to the increasing size of s (s), their design and optimization phases (i.e., ) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some trade-off between design quality and computational overhead. ML can build powerful models that have successfully been employed in related domains. In this survey, we categorize how () may be used and is used for design-time and run-time optimization and exploration strategies of s. A meta-study of published techniques unveils areas in that are well-explored and underexplored with, as well as trends in the employed algorithms. We present a comprehensive categorization and summary of the state of the art on for. Finally, we summarize remaining challenges and promising open research directions.

Conventional research trend detection methods require a large number of papers to perform analysis, while the trends detected are usually retrospective and cannot represent the emerging topic which is in embryonic stage, because detection method based on word features or citation relationships have to wait for the trend to come into form and become popular to detect it. Besides, the acquisition of paper full text is usually costly. We employ the paper metadata, which is relatively easy to acquire and process, to detect potentially valuable papers in specific field once the papers are published or their metadata become accessible and extract emerging cutting-edge trend from them. By comparing the result with conventional methods, we prove the effectiveness of the method.

This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR 2020. We provide an artifact that allows the replication of the experiments using a Python implementation. The artifact is easy to deploy with simple installation, training and evaluation. We reproduce the experiments conducted in the original paper and obtain similar performance as previously reported. The replication results of the experiments support the main claims in the original paper.

The entire scientific and academic community has been mobilized to gain a better understanding of the COVID-19 disease and its impact on humanity. Most research related to COVID-19 needs to analyze large amounts of data in very little time. This urgency has made Big Data Analysis, and related questions around the privacy and security of the data, an extremely important part of research in the COVID-19 era. The White House OSTP has, for example, released a large dataset of papers related to COVID research from which the research community can extract knowledge and information. We show an example system with a machine learning-based knowledge extractor which draws out key medical information from COVID-19 related academic research papers. We represent this knowledge in a Knowledge Graph that uses the Unified Medical Language System (UMLS). However, publicly available studies rely on dataset that might have sensitive data. Extracting information from academic papers can potentially leak sensitive data, and protecting the security and privacy of this data is equally important. In this paper, we address the key challenges around the privacy and security of such information extraction and analysis systems. Policy regulations like HIPAA have updated the guidelines to access data, specifically, data related to COVID-19, securely. In the US, healthcare providers must also comply with the Office of Civil Rights (OCR) rules to protect data integrity in matters like plasma donation, media access to health care data, telehealth communications, etc. Privacy policies are typically short and unstructured HTML or PDF documents. We have created a framework to extract relevant knowledge from the health centers’ policy documents and also represent these as a knowledge graph. Our framework helps to understand the extent to which individual provider policies comply with regulations and define access control policies that enforce the regulation rules on data in the knowledge graph extracted from COVID-related papers. Along with being compliant, privacy policies must also be transparent and easily understood by the clients. We analyze the relative readability of healthcare privacy policies and discuss the impact. In this paper, we develop a framework for access control decisions that uses policy compliance information to securely retrieve COVID data. We show how policy compliance information can be used to restrict access to COVID-19 data and information extracted from research papers.

In this paper, we work on analyzing old paper advertisement (Ad). An Ad usually contains various types of textual and non-textual objects, which may also be in different orientations. We attempt to detect such objects from an early Indian-print paper Ad database comprising 1500 Ad images. The past major object detectors did not perform well on this database. We propose a deep reinforcement learning-based orientation-aware object detector. Our system learns by itself where to look and what to look of an Ad image. Therefore, it can bypass the impeding zone due to degraded image quality. To find the looking spot, we come up with a foveal transformation. In reinforcement learning, we present a scheme for shaping an internal reward with a top-up. For oriented object detection, we also propose a generic loss function. Our system obtained encouraging results from the experiments performed on the Ad database.

Cloud computing is a new paradigm. In recent years, cloud computing has been a popular model for hosting and providing services, as it has gained great popularity in providing multiple services such as storage and cloud hosting. Moreover, the cloud provides a powerful computing platform that enables many individuals and organizations to perform many tasks such as: use of online storage space, approval on business applications, developing custom computer software, and creating a virtual network environment. In previous years, the number of people using cloud services increased dramatically and a lot of data was stored in cloud computing environments. Meanwhile, data breaches of cloud services are also increasing every year due to hackers who are always trying to exploit security holes in the cloud architecture, so the security problems in these systems are horrific because they store a lot of private and confidential data. In this paper, the authors review security and privacy issues, bottlenecks associated with cloud computing, and the specific security issues in cloud computing layers and vulnerabilities, and how to avoid and control them. This paper helps the reader understand the problems related to cloud computing.

Our eventual goal regarding this study is to support students with developing paper-writing skill. In order to achieve this goal, we have been trying to find characteristic features of good papers through analyzing educational and other kinds of data. We take conference papers as target data and suppose full/regular papers are good because they are chosen as reviewers evaluate them more valuable to be presented in the conference than other papers. In our series of study, we have been surveying the differences of full and short papers. In this paper, we aim to investigate further differences of them by taking different analysis method. We have been using the numbers of occurrences of each word in full/short papers as the data for analysis. In this paper we use the numbers of full/short papers that contain the word instead of the total numbers of occurrences of words. We define a new index of a word which shows how likely it is used in full or short papers. We discuss its effectiveness by applying it to an experiment of distinguishing full and short papers.

Paper microfluidic or lateral flow devices have found many applications, especially in medical diagnostics. Their low cost and ease of use makes them particularly valuable in resource-limited and point-of-care applications. However, the process of developing new paper microfluidic devices is slowed by the need to find optimal values for their various design parameters, which determine the overall size and fluid volume requirements of the device. Often, researchers must design and test several different versions of a device to find a combination of parameters that functions as intended. To accelerate the development of new paper microfluidics, we developed a software framework that automatically designs custom paper microfluidic devices for a given application. Once the user specifies the desired device parameters, the software generates printable image files of the resulting devices, ready to output to a conventional wax ink color printer and test. As a proof-of-concept, we used our software to automatically design 51 different paper microfluidic devices we needed to create a functional lateral flow assay that detects protein and glucose in urine. These designs took only a few seconds to generate and were used in 120 lab experiments we performed in 16 h in the lab. Thus, with the help of our software framework, we went from an idea to a functional device in just two work days. By accelerating device development and enabling researchers without microfluidics experience to create custom devices, our software can help spread paper microfluidic technology to important new application areas.

Currently, the amount of information available to Web users is very large, and this situation is similar for scientific communities when searching for papers for their research. Recommender Systems (RSs) can help in this task because they combine computational techniques to select personalized items based on the users’ interests and according to the context in which users are inserted. The increase in the impact and scope of recommendations in the users’ lives, leads to the result on the ethical issues involved in the generation of recommendations and indicators for visualizing the results of the algorithms found. This paper presents a Recommender System for the Human-Computer Interaction (HCI) community, indicating papers from the Brazilian Symposium on Human Factors in Computing Systems related to the users’ profile applied to a post-processing strategy focused on fairness to balance the users’ interests. After the development of the RS and the Web environment, the results were obtained on the impact that the tool had on the community and demonstrated through the evaluation of the system.

Video cameras are becoming ubiquitous in our daily lives. With the recent advancement of Artificial Intelligence (AI), live video analytics are enabling various useful services, including traffic monitoring and campus surveillance. However, current video analytics systems are highly limited in leveraging the enormous opportunities of the deployed cameras due to (i) centralized processing architecture (i.e., cameras are treated as dumb streaming-only sensors), (ii) hard-coded analytics capabilities from tightly coupled hardware and software, (iii) isolated and fragmented camera deployment from different service providers, and (iv) independent processing of camera streams without any collaboration. In this paper, we envision a full-fledged system for software-defined video analytics with cross-camera collaboration that overcomes the aforementioned limitations. We illustrate its detailed system architecture, carefully analyze the key system requirements with representative app scenarios, and derive potential research issues along with a summary of the status quo of existing works.

Storm and flood disasters such as typhoons and torrential rains are becoming more intense and frequent. The national government and municipalities must respond to such natural disasters as soon as possible. When the scale of damage is large; however, it takes much time to investigate the severity of damage, and the initial response can be delayed. If we could precisely and rapidly estimate the severity of damage for each city at an early stage, the national government would be able to better support the municipalities, and consequently respond quickly to help citizens. In this paper, we propose a novel approach to estimate the severity of disaster damage within a short time period after a disaster occurs by exploiting real-time population data generated from cellular networks. First, we investigate the relationship between real-time population data and the severity of damage. Then, we design a Graph Convolutional Networks for Disaster Damage Estimation, called D2E-GCN, which fully exploits the directed and weighted characteristics of human mobility graph. We conduct an offline evaluation on real-world datasets including two typhoons that hit Japan. The evaluation results show that the proposed method outperforms baseline methods which do not consider the graph structure of cities, and the proposed method can estimate the severity of damage approximately 48 hours after typhoons passed. Moreover, we find the experimental insight that the estimation performance can be significantly affected by the graph construction method for GCN models.

In federated learning (FL), model updates from mutually distrusting parties are aggregated in a centralized fusion server. The concentration of model updates simplifies FL's model building process, but might lead to unforeseeable information leakage. This problem has become acute due to recent FL attacks that can reconstruct large fractions of training data from ostensibly "sanitized" model updates. In this paper, we re-examine the current design of FL systems under the new security model of reconstruction attacks. To break down information concentration, we build TRUDA, a new cross-silo FL system, employing a trustworthy and decentralized aggregation architecture. Based on the unique computational properties of model-fusion algorithms, we disassemble all exchanged model updates at the parameter-granularity and re-stitch them to form random partitions designated for multiple hardware-protected aggregators. Thus, each aggregator only has a fragmentary and shuffled view of model updates and is oblivious to the model architecture. The deployed security mechanisms in TRUDA can effectively mitigate training data reconstruction attacks, while still preserving the accuracy of trained models and keeping performance overheads low.

Blind natural video quality assessment (BVQA), also known as no-reference video quality assessment, is a highly active research topic. In our recent contribution titled "Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features" published in ACM Multimedia 2020, we proposed a two-level video quality model employing statistical temporal features and spatial features extracted by a deep convolutional neural network (CNN) for this purpose. At the time of publishing, the proposed model (CNN-TLVQM) achieved state-of-the-art results in BVQA. In this paper, we describe the process of reproducing the published results by using CNN-TLVQM on two publicly available natural video quality datasets.

Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. The PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary resulting in less accurate results. Recently, machine learning (ML) assisted models have been used in image analysis. We evaluated a combinations of four ML models - logistic regression, support vector machine, random forest, and artificial neural network, and three image color spaces - RGB, HSV, and LAB for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras, and users for food color and enzyme inhibition assays to create training and test datasets. Prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML model and colorspace combinations. All models better predicted coarse level classification than fine grained concentration labels. ML models using sample color along with a reference color increased the models’ ability in predicting the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration label prediction accuracy obtained for food color was 0.966 when using ANN model and LAB colorspace. The accuracy for enzyme inhibition assay was 0.908 when using SVM model and LAB colorspace. Appropriate model and colorspace combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool.

The pervasive use of software systems and current threat environment demand that software systems not only survive cyberattacks, but also bounce back better, stronger, and faster. However, what constitutes a modern software system? Where should the security and resilience mechanisms be-in the application software or in the cloud environment where it runs? In this concept paper, we set up a context to pose these questions and present a roadmap to answer them. We describe challenges to achieving resilience and beyond, and outline potential research directions to stimulate discussion in the workshop.