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Supply-chains (SC) present performance bottlenecks that contribute to a high level of costs, infiltration of product quality, and impact productivity. Examples of such inhibitors include the bullwhip effect, new product lines, high inventory, and restrictive data flows. These bottlenecks can force manufacturers to source more raw materials and increase production significantly. Also, restrictive data flow in a complex global SC network generally slows down the movement of goods and services. The use of Distributed Ledger Technologies (DLT) in supply chain management (SCM) demonstrates the potentials to to reduce these bottlenecks through transparency, decentralization, and optimizations in data management. These technologies promise to enhance the trustworthiness of entities within the supply chain, ensure the accuracy of data-driven operations, and enable existing SCM processes to migrate from a linear to a fully circular economy. This paper presents a comprehensive review of 111 articles published in the public domain in the use and efficacy of DLT in SC. It acts as a roadmap for current and future researchers who focus on SC Security Management to better understand the integration of digital technologies such as DLT. We clustered these articles using standard descriptors linked to trustworthiness, namely, immutability, transparency, traceability, and integrity.

Many small and medium enterprises (SMEs) engage in dyadic information integration partnerships or partial integration with their direct suppliers and customers. They often utilize e-commerce or cloud computing technology platforms hosted by third-party providers to leverage such partnerships. However, information security breaches and disruptions caused by cyberattacks are commonplace in the information technology industry. The effects of said disruptions and breaches on e-commerce businesses under varied disruption conditions are still uncertain. Furthermore, the effect of security breaches on nonparticipating members of the supply chain is poorly understood, especially under various disruption profiles. Using discrete event modeling, in this article, we explore the impact of disruption caused by information security breaches on supply chain performance and the externality effect of partial integration on nonparticipants. We also examine the impact of breach disruption frequency and remediation length on supply chain performance with varying levels of information sharing. These impacts were studied under two typical inventory replenishment policies for SMEs. It was determined that remediation length should be a prioritized factor in impact management and that flexibility in the inventory replenishment policy can help mitigate the impact of information disruption on the inventory performance of businesses, especially that of nonparticipants, in information-sharing partnerships.

5G and subsequent cellular network generations aim to extend ubiquitous connectivity of billions of Internet-of-Things (IoT) for their consumers. Security is a prime concern in this context as adversaries have evolved to become smart and often employ new attack strategies. Network defenses can be enhanced against attacks by employing behavior models for devices to detect misbehavior. One example is Abusive Modeling (AM) that is inspired by financial technologies to defend adversaries operating with unlimited resources who have no intention of self-profit apart from harming the system. This article investigates behavior modeling against abusive adversaries in the context of 5G and beyond security functions for IoT. Security threats and countermeasures are discussed to understand AM. A complexitysecurity trade-off enables a better understanding of the limitations of state-based behavior modeling and paves the way as a future direction for developing more robust solutions against AM. Postprint

The emerging 4D printing techniques open new horizons for fabricating self-actuated deformable objects by combing strength of 3D printing and stimuli-responsive shape memory materials. This work focuses on designing self-actuated deformable solids for 4D printing such that a solid can be programmed into a temporary shape and later recovers to its original shape after heating. To avoid a high material cost, we choose a dual-material strategy that mixes an expensive thermo-responsive shape memory polymer (SMP) material with a common elastic material, % for fabricating objects, which however leads to undesired deformation at the shape programming stage. We model this shape programming process as two elastic models with different parameters linked by a median shape based on customizing a constitutive model of thermo-responsive SMPs. Taking this material modeling as a foundation, we formulate our design problem as a nonconvex optimization to find the distribution of SMP materials over the whole object as well as the median shape, and develop an efficient and parallelizable method to solve it. We show that our proposed approach is able to design self-actuated deformable objects that cannot be achieved by state of the art approaches, and demonstrate their usefulness with three example applications.

This paper proposes a new calibration method, called the mixed-binning (MB) method, to pursue high-linearity time-to-digital converters (TDCs) for light detection and ranging (LiDAR) applications. The proposed TDCs were developed using tapped delay-line (TDL) cells in field-programmable gate arrays (FPGAs). With the MB method, we implemented a resolution-adjustable TDC showing excellent linearity in Xilinx UltraScale FPGAs. We demonstrate a 128-channel TDC to show that the proposed method is cost-effective in logic resources. We also developed a software tool to predict the performances of TDL-based TDCs robustly. Results from both software analysis and hardware implementations are in a good agreement and show that the proposed design has great potential for multichannel applications; the averaged DNL_(pk-pk) and INL_(pk-pk) are close to or even less than 0.05 LSB in multichannel designs.

Funding: This work is partially supported by the EU H2020 project DARE, No. 777413; and by Google Cloud Platform research credits program. This work presents three new adaptive optimization techniques to maximize the performance of dispel4py workflows. dispel4py is a parallel Python-based stream-oriented dataflow framework that acts as a bridge to existing parallel programming frameworks like MPI or Python multiprocessing. When a user runs a dispel4py workflow, the original framework performs a fixed workload distribution among the processes available for the run. This allocation does not take into account the features of the workflows, which can cause scalability issues, especially for data-intensive scientific workflows. Our aim, therefore, is to improve the performance of dispel4py workflows by testing different workload strategies that automatically adapt to workflows at runtime. For achieving this objective, we have implemented three new techniques, called Naive Assignment, Staging and Dynamic Scheduling. We have evaluated our proposal with several workflows from different domains and across different computing resources. The results show that our proposed techniques have significantly (up to 10X) improved the performance of the original dispel4py framework. Postprint

Chatbots are increasingly becoming important gateways to digital services and information—taken up within domains such as customer service, health, education, and work support. However, there is only limited knowledge concerning the impact of chatbots at the individual, group, and societal level. Furthermore, a number of challenges remain to be resolved before the potential of chatbots can be fully realized. In response, chatbots have emerged as a substantial research area in recent years. To help advance knowledge in this emerging research area, we propose a research agenda in the form of future directions and challenges to be addressed by chatbot research. This proposal consolidates years of discussions at the CONVERSATIONS workshop series on chatbot research. Following a deliberative research analysis process among the workshop participants, we explore future directions within six topics of interest: (a) users and implications, (b) user experience and design, (c) frameworks and platforms, (d) chatbots for collaboration, (e) democratizing chatbots, and (f) ethics and privacy. For each of these topics, we provide a brief overview of the state of the art, discuss key research challenges, and suggest promising directions for future research. The six topics are detailed with a 5-year perspective in mind and are to be considered items of an interdisciplinary research agenda produced collaboratively by avid researchers in the field.

There exists widespread misunderstanding about the nature of computers and how and why they are liable to fail. The present approach to the disclosure or discovery and evaluation of evidence produced by computers in legal proceedings is unsatisfactory. The central problem is the evidential presumption that computers are reliable. This presumption is not warranted. To this end, recommendations are proposed to rectify this problem with the aim of increasing the probability of a fair trial. Index words: electronic evidence; computer systems; disclosure; discovery; recommendations for judges; fairness of legal proceedings

In this paper, we present a genetic algorithm (GA) based band power feature sparse learning (SL) approach for classification of electroencephalogram (EEG) (GABSLEEG) in motor imagery (MI) based brain-computer interfacing (BCI). The band power in the alpha and beta bands was extracted from the EEG segments and used as features to construct the SL dictionary, in which the GA was employed for channel selection. The GABSLEEG system was tested in three functional areas: i) classification of MI data and idle state data; ii) performance with decreased training data size; and iii) computational efficiency. The system was evaluated by dividing the data into training, validation, and testing sets. The proposed GABSLEEG model is found to significantly outperform conventional classifiers, including the support vector machine (SVM) classifier in (i-iii), and the random forest (RF) and the k-nearest neighbour (k-NN) classifiers in (i-ii). The GABSLEEG system consistently had a higher classification accuracy, sensitivity, and specificity. The average accuracy of the proposed system was 99.65%, on BCI Competition IV dataset 1 and 96.08% for BCI Competition III dataset IVa with the idle state included as a class, which was on a par with state-of-the-art SL and even deep learning approaches.

Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the difficulty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspecified underwater environment. Therefore, we propose the Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) to enable energy-efficient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby the Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neuro-fuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg–Nash equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a significant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay.

PurposeEven though there is a noticeable market value in the mobile gaming apps industry, there has been limited research examining experiential value of gamers with respect to in-game ads in gaming apps. This study fills the void in the literature by examining factors associated with “experiential value of gamers through ads in gaming apps” as well as investigating its antecedents (cognitive and affective involvement) and consequences (positive word of mouth and intention to continue playing the mobile game).Design/methodology/approachA total of 600 valid responses from gamers was used to test the model fit, measurement and structural models, conditional probabilistic queries, and nonlinearity.FindingsThis study found that experiential value of gamers through ads in gaming apps is a second-order factor of four constructs: escapism, enjoyment, social affiliation and entertainment. Most of the structural paths between cognitive/affective involvement and dimensions of experiential value are supported. Surprisingly, only social affiliation and entertainment values predict positive word of mouth and intention to continue playing the mobile game, in a nonlinear way.Originality/valueThis study is the first to introduce “experiential value of gamers through ads in gaming apps”. The findings have important implications for companies to develop brand and communication strategies by leveraging specific advertisement formats and present their ads to the right audience in the right gaming apps and at the right time.

The outbreak of the COVID-19 virus has caused widespread panic and global initiatives are geared towards treat-ment and limiting its spread. With technological advancements, several mechanisms and mobile applications have been developed that attempt to trace the physical contact made by a person with someone who has been tested COVID-19 positive. While designing these apps, user's privacy has been an afterthought and has resulted in mass violations of privacy of the public and the patients. A total of 32 countries have designed apps and rely on them as a strategy to flatten the pandemic curve. Along with lack of privacy, these methodologies are centralized, where they are fully controlled by the government and the healthcare providers. Owing to these and many other concerns, people are hesitant in the adoption of these technologies. This paper presents a detailed analysis of user tracking apps belonging to 32 countries, thus demonstrating that they collect personal data and are a gross violation of user privacy. This paper presents a novel architecture for the efficient, effective and privacy-preserving contact tracing of COVID-19 patients using blockchain. The proposed architecture preserves the privacy of individuals and their contact history by encrypting all the data specific to an individual using a privacy-preserving Homomorphic encryption scheme and storing it on a permissioned blockchain network. The contacts made with a COVID-19 positive patient are identified by performing search queries directly over the Homomorphic encrypted data stored in the blocks. Therefore, only those contacts that are suspected to be COVID-19 positive may be decrypted by the healthcare professional or government for further contact tracing/ diagnosis and COVID-19 testing;thereby leading to enhanced privacy.

The role of software in society has changed drastically since the start of the 21st century. Software can now partially or fully facilitate anything from diagnosis to treatment of a disease, regardless of whether it is psychological or pathological, with the consequence of software being comparable to any other type of medical equipment, and this makes discovering when software must comply with such rules vital to both manufacturers and regulators. In lieu of the Medical Device Regulation we expand on the idea of intention, and identify the criteria software must fulfil to be considered medical devices within EU-law., Comment: 13 pages, 2 figures

Fieldwork was funded by an Arcus Foundation grant to STT and a Wildlife Acoustics grant to JVB. ID is supported in part by funding from the National Research Foundation of South Africa (Grant ID 90782, 105782). ED is supported by a postdoctoral fellowship from the African Institute for Mathematical Sciences South Africa, Stellenbosch University and the Next Einstein Initiative. This work was carried out with the aid of a grant from the International Development Research Centre, Ottawa, Canada (www.idrc.ca), and with financial support from the Government of Canada, provided through Global Affairs Canada (GAC; www.international.gc.ca). Extracting species calls from passive acoustic recordings is a common preliminary step to ecological analysis. For many species, particularly those occupying noisy, acoustically variable habitats, the call extraction process continues to be largely manual, a time-consuming and increasingly unsustainable process. Deep neural networks have been shown to offer excellent performance across a range of acoustic classification applications, but are relatively underused in ecology. We describe the steps involved in developing an automated classifier for a passive acoustic monitoring project, using the identification of calls of the Hainan gibbon Nomascus hainanus, one of the world's rarest mammal species, as a case study. This includes preprocessing-selecting a temporal resolution, windowing and annotation; data augmentation; processing-choosing and fitting appropriate neural network models; and post-processing-linking model predictions to replace, or more likely facilitate, manual labelling. Our best model converted acoustic recordings into spectrogram images on the mel frequency scale, using these to train a convolutional neural network. Model predictions were highly accurate, with per-second false positive and false negative rates of 1.5% and 22.3%. Nearly all false negatives were at the fringes of calls, adjacent to segments where the call was correctly identified, so that very few calls were missed altogether. A post-processing step identifying intervals of repeated calling reduced an 8-h recording to, on average, 22 min for manual processing, and did not miss any calling bouts over 72 h of test recordings. Gibbon calling bouts were detected regularly in multi-month recordings from all selected survey points within Bawangling National Nature Reserve, Hainan. We demonstrate that passive acoustic monitoring incorporating an automated classifier represents an effective tool for remote detection of one of the world's rarest and most threatened species. Our study highlights the viability of using neural networks to automate or greatly assist the manual labelling of data collected by passive acoustic monitoring projects. We emphasize that model development and implementation be informed and guided by ecological objectives, and increase accessibility of these tools with a series of notebooks that allow users to build and deploy their own acoustic classifiers. Publisher PDF

Industrial cyber–physical systems (ICPSs) are widely deployed in monitoring and control of the nation's critical industrial processes, such as water distribution networks and power grids. ICPSs are the tight integration of cyber (software) and physical entities connected via communication networks. Communication networks are typically realized via wireless channels to reduce the cost of wires and installation. However, they are also inherently unreliable, easy to disrupt, and subvert, which makes them a potential target for cyberattacks. The failure of communication can cause data loss or delays, which can compromise system functionality and have catastrophic consequences due to the strict real-time requirements of ICPSs. Current run-time security monitors protect ICPSs either at communication level (through network intrusion monitors) or at application level (through threat detection monitors). Such monitors are layer-specific and, thus, fail to detect advanced threats arising from the multilayer disruption. In this article, we present a multilayer run-time security monitor that can detect discrepancies caused by interdependent application and communication layer attacks and prevent their propagation into the system's control loops. We demonstrate the effectiveness of the approach via an example of the ICPS used for control and monitoring of a water distribution network.

Purpose The purpose of the study is to optimise the cross-sectional shape of passively cooled horizontally mounted pin-fin heat sink for higher cooling performance and lower material usage. Design/methodology/approach Multi-objective shape optimisation technique is used to design the heat sink fins. Non-dominated sorting genetic algorithm (NSGA-II) is combined with a geometric module to develop the shape optimiser. High-fidelity computational fluid dynamics (CFD) is used to evaluate the design objectives. Separate optimisations are carried out to design the shape of bottom row fins and middle row fins of a pin-fin heat sink. Finally, a computational validation was conducted by generating a three-dimensional pin-fin heat sink using optimised fin cross sections and comparing its performance against the circular pin-fin heat sink with the same inter-fin spacing value. Findings Heat sink with optimised fin cross sections has 1.6% higher cooling effectiveness than circular pin-fin heat sink of same material volume, and has 10.3% higher cooling effectiveness than the pin-fin heat sink of same characteristics fin dimension. The special geometric features of optimised fins that resulted in superior performance are highlighted. Further, Pareto-optimal fronts for this multi-objective optimisation problem are obtained for different fin design scenarios. Originality/value For the first time, passively cooled heat sink’s cross-sectional shapes are optimised for different spatial arrangements, using NSGA-II-based shape optimiser, which makes use of CFD solver to evaluate the design objectives. The optimised, high-performance shapes will find direct application to cool power electronic equipment.

As an effective tool, roadside digital billboard advertising is widely used to attract potential customers (e.g., drivers and passengers passing by the billboards) to obtain commercial profit for the advertiser, i.e., the attracted customers' payment. The commercial profit depends on the number of attracted customers, hence the advertiser needs to adopt an effective advertising strategy to determine the advertisement switching policy for each digital billboard to attract as many potential customers as possible. Whether a customer could be attracted is influenced by numerous factors, such as the probability that the customer could see the billboard and the degree of his/her interests in the advertisement. Besides, cooperation and competition among all digital billboards will also affect the commercial profit. Taking the above factors into consideration, we formulate the dynamic advertising problem to maximize the commercial profit for the advertiser. To address the problem, we first extract potential customers' implicit information by using the vehicular data collected by Mobile CrowdSensing (MCS), such as their vehicular trajectories and their preferences. With this information, we then propose an advertising strategy based on multi-agent deep reinforcement learning. By using the proposed advertising strategy, the advertiser could determine the advertising policy for each digital billboard and maximize the commercial profit. Extensive experiments on three real-world datasets have been conducted to verify that our proposed advertising strategy could achieve the superior commercial profit compared with the state-of-the-art strategies.

IoT devices often collect data and store the data in the cloud for sharing and further processing. A natural solution for secure access is directly using the device owner?s identity as the private key to generate a signature for data authentication. However this will simultaneously expose this identity. Attribute based signature (ABS), which takes the signer?s attributes instead of his/her identity as the private key, can realize data authentication while preserving the signer?s identity privacy. In ABS, there are multiple authorities that issue different private keys for signers based on their various attributes, and a central authority is usually established to manage all these attribute authorities. However, one security concern is that if the central authority is compromised, the whole system will be broken. In this paper, we present an outsourced decentralized multi-authority attribute based signature (ODMA-ABS) scheme. The proposed ODMAABS achieves attribute privacy and stronger authority-corruption resistance than existing multi-authority attribute based signature schemes. In addition, the overhead to generate a signature is further reduced by outsourcing expensive computation to a signing cloud server. We provide extensive security analysis and experimental simulation of the proposed scheme. We also propose an access control scheme that is based on ODMA-ABS.

Background The use of Renin-Angiotensin system inhibitors (RASi) in patients with coronavirus disease 2019 (COVID-19) has been questioned because both share a target receptor site. Methods HOPE-COVID-19 (NCT04334291) is an international investigator-initiated registry. Patients are eligible when discharged after an in-hospital stay with COVID-19, dead or alive. Here, we analyze the impact of previous and continued in-hospital treatment with RASi in all-cause mortality and the development of in-stay complications. Results We included 6503 patients, over 18 years, from Spain and Italy with data on their RASi status. Of those, 36.8% were receiving any RASi before admission. RASi patients were older, more frequently male, with more comorbidities and frailer. Their probability of death and ICU admission was higher. However, after adjustment, these differences disappeared. Regarding RASi in-hospital use, those who continued the treatment were younger, with balanced comorbidities but with less severe COVID19. Raw mortality and secondary events were less frequent in RASi. After adjustment, patients receiving RASi still presented significantly better outcomes, with less mortality, ICU admissions, respiratory insufficiency, need for mechanical ventilation or prone, sepsis, SIRS and renal failure (p

In recent years, the number of objects connected to the internet have significantly increased. Increasing the number of connected devices to the internet is transforming today’s Internet of Things (IoT) into massive IoT of the future. It is predicted that, in a few years, a high communication and computation capacity will be required to meet the demands of massive IoT devices and applications requiring data sharing and processing. 5G and beyond mobile networks are expected to fulfill a part of these requirements by providing a data rate of up to terabits per second. It will be a key enabler to support massive IoT and emerging mission critical applications with strict delay constraints. On the other hand, the next generation of software-defined networking (SDN) with emerging cloudrelated technologies (e.g., fog and edge computing) can play an important role in supporting and implementing the above-mentioned applications. This paper sets out the potential opportunities and important challenges that must be addressed in considering options for using SDN in hybrid cloud-fog systems to support 5G and beyond-enabled applications.

As cryptographic tokens and altcoins are increasingly being built to serve as utility tokens, the notion of useful work consensus protocols is becoming ever more important. With useful work consensus protocols, users get rewards after they have carried out some specific tasks useful for the network. While in some cases the proof of some utility or service can be provided, the majority of tasks are impossible to verify reliably. To deal with such cases, we design “Proof-of-Prestige” (PoP)—a reward system that can run directly on Proof-of-Stake (PoS) blockchains or as a smart contract on top of Proof-of-Work (PoW) blockchains. PoP introduces “prestige,” which is a volatile resource that, in contrast to coins, regenerates over time. Prestige can be gained by performing useful work, spent when benefiting from services, and directly translates to users minting power. Our scheme allows us to reliably reward decentralized workers while keeping the system free for the end-users. PoP is resistant against Sybil and collusion attacks and can be used with a vast range of unverifiable tasks. We build a simulator to assess the cryptoeconomic behavior of the system and deploy a full prototype of a content dissemination platform rewarding its participants. We implement the blockchain component on both Ethereum (PoW) and Cosmos (PoS), provide a mobile application, and connect it with our scheme with a negligible memory footprint. Finally, we adapt a fair exchange protocol allowing us to atomically exchange files for rewards also in scenarios where not all the parties have Internet connectivity. Our evaluation shows that even for large Ethereum traces, PoP introduces sub-millisecond computational overhead for miners in Cosmos and less than 0.013$ smart contract invocation cost for users in Ethereum.

This contribution describes the organization and the outcomes of the User Experience panel at the ASSETS 2020 virtual conference. The panel was the first one in the history of ASSETS to be conducted as a virtual event, which required a number of changes, both in terms of panellist and attendee involvement. Adapting to this new situation required new solutions that we believe can be useful for other future events - be they virtual, physical or hybrid. We believe the experiences reported also provide helpful insights for reflection on future work in the domain of accessible computing.

Configuration errors are among the dominant causes of system faults for the industrial automation and control systems (IACS). It is difficult to detect and correct such errors of IACS as there are various kinds of systems and devices with miscellaneous configuration specifications. In this paper, we first propose a streaming algorithm to keep all the configuration changes in the limited memory space. And, when making a new configuration change, another novel streaming algorithm is proposed to search and return all the similar historical changes which can be used to validate this new one. So far, we are the first to model the configuration changes of IACS as a data stream and apply the streaming similarity search in correcting configuration errors while overcoming the inherent unbounded-memory bottleneck. The theoretical correctness and complexity analyses are presented. Experiments with real and synthetic datasets confirm the theoretical analyses and demonstrate the effectiveness of the proposed method in preventing misconfigurations of IACS.

There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centred in aims, such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g. blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF., Graphical Abstract

In this article, we propose an approach to improve quality in crowdsourcing (CS) tasks using Task Completion Time (TCT) as a source of information about the reliability of workers in a game-theoretical competitive scenario. Our approach is based on the hypothesis that some workers are more risk-inclined and tend to gamble with their use of time when put to compete with other workers. This hypothesis is supported by our previous simulation study. We test our approach with 35 topics from experiments on the TREC-8 collection being assessed as relevant or non-relevant by crowdsourced workers both in a competitive (referred to as “Game”) and non-competitive (referred to as “Base”) scenario. We find that competition changes the distributions of TCT, making them sensitive to the quality (i.e., wrong or right) and outcome (i.e., relevant or non-relevant) of the assessments. We also test an optimal function of TCT as weights in a weighted majority voting scheme. From probabilistic considerations, we derive a theoretical upper bound for the weighted majority performance of cohorts of 2, 3, 4, and 5 workers, which we use as a criterion to evaluate the performance of our weighting scheme. We find our approach achieves a remarkable performance, significantly closing the gap between the accuracy of the obtained relevance judgements and the upper bound. Since our approach takes advantage of TCT, which is an available quantity in any CS tasks, we believe it is cost-effective and, therefore, can be applied for quality assurance in crowdsourcing for micro-tasks.

Funding: CSC-University of St Andrews Joint Scholarship (to X.Q.); International Postdoctoral Exchange Fellowship Program (Talent-Introduction Program) from China Postdoc Council (to X.Q.); National Institute of General Medical Sciences (NIGMS) of the National Institute of Health (grant R35GM142783 to C.W.K.C.). Part of the computation for this work is supported by USC’s Center for Advanced Research Computing (https://carc.usc.edu). Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describe them is principal component analysis (PCA) or the supervised linear discriminant analysis of principal components (DAPC). However, genetic features produced from both approaches could fail to correctly characterize population structure for complex scenarios involving admixture. In this study, we introduce Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC), a supervised non-linear approach for inferring individual geographic genetic structure that could rectify the limitations of these approaches by preserving the multimodal space of samples. We tested the power of KLFDAPC to infer population structure and to predict individual geographic origin using neural networks. Simulation results showed that KLFDAPC has higher discriminatory power than PCA and DAPC. The application of our method to empirical European and East Asian genome-wide genetic datasets indicated that the first two reduced features of KLFDAPC correctly recapitulated the geography of individuals and significantly improved the accuracy of predicting individual geographic origin when compared to PCA and DAPC. Therefore, KLFDAPC can be useful for geographic ancestry inference, design of genome scans and correction for spatial stratification in GWAS that link genes to adaptation or disease susceptibility. Publisher PDF

Internet of medical things (IoMT) is an emerging technology aiming to improve the patient's quality of life by enabling personalized e‐health services without limitations on time and location. Nevertheless, IoMT devices (eg, medical sensors) that constitute the key underlying elements of the IoMT edge network are vulnerable to various types of security threats and thus, they pose a significant risk to patient's privacy and safety. Based on that and the fact that the security is a critical factor for the successful integration of IoMT technology into pervasive healthcare systems, there is an urgent need for novel security mechanisms to preserve the security of the IoMT edge network. Toward this direction, the first step is the comprehensive understanding of existing and potential threats to the IoMT edge network environment. Thus, in this article, we provide a categorization of security threats to the edge network environment based on the major security objectives that they target. Moreover, we present a categorization of security countermeasures, derived from the literature, against threats to IoMT edge networks. The authors' intent is to provide a foundation for organizing research efforts toward the development of proper security countermeasures for protecting IoMT edge networks against internal and external threats.

Information studies have identified numerous needs and barriers to the integration of asylum seekers and refugees; however, little emphasis has been placed thus far on their need to keep their own culture, values, and traditions alive. In this work we use ethnographic constructivist grounded theory to explore the place of heritage in the information experience of people who have sought asylum in the United Kingdom. Based on our findings, we propose to conceptualise heritage as an affective and meaningful information literacy practice. Such conceptualisation fosters integration by allowing people to simultaneously maintain their own ways of knowing and adapt to local ones. Our research approach provides scholars with a conceptual tool to holistically explore affective, meaningful, and cultural information practices. This study also reveals implications for policymakers, third sector organisations, and cultural institutions working towards the more sustainable integration of asylum seekers and refugees.

Face views are particularly important in person-to-person communication. Differenes between the camera location and the face orientation can result in undesirable facial appearances of the participants during video conferencing. This phenomenon is particularly noticeable when using devices where the front-facing camera is placed in unconventional locations such as below the display or within the keyboard. In this paper, we take a video stream from a single RGB camera as input, and generate a video stream that emulates the view from a virtual camera at a designated location. The most challenging issue in this problem is that the corrected view often needs out-of-plane head rotations. To address this challenge, we reconstruct the 3D face shape and re-render it into synthesized frames according to the virtual camera location. To output the corrected video stream with natural appearance in real time, we propose several novel techniques including accurate eyebrow reconstruction, high-quality blending between the corrected face image and background, and template-based 3D reconstruction of glasses. Our system works well for different lighting conditions and skin tones, and can handle users wearing glasses. Extensive experiments and user studies demonstrate that our method provides high-quality results.

This paper presents work surrounding INJECT, a newsroom innovation offering digital tools to support journalists. Research showing increasing time and resource pressure on journalists has led to concerns about the demise of investigative reporting and the ability of today’s journalists to interrogate information adequately. Some digital innovations (e.g. tools facilitating robot journalism) have been viewed with suspicion by newsrooms. This paper reports on a research project that seeks to create an innovative tool to support the creative capabilities of time and resource poor journalists. The INJECT project used the advanced information discovery capabilities of digitisation to help journalists find new angles on stories and this paper analyses the extent to which such initiatives might harness digital innovation to benefit both the quality and range of reporting and thereby enhance creativity. It examines the potential of an information processing model of creativity derived from the INJECT tool to assist and support journalists, exploring the theoretical impact as well as the practical implications reported from the newsroom.

Cognitive modeling tools have been widely used by researchers and practitioners to help design, evaluate, and study computer user interfaces (UIs). Despite their usefulness, large-scale modeling tasks can still be very challenging due to the amount of manual work needed. To address this scalability challenge, we propose CogTool+, a new cognitive modeling software framework developed on top of the well-known software tool CogTool. CogTool+ addresses the scalability problem by supporting the following key features: (1) a higher level of parameterization and automation; (2) algorithmic components; (3) interfaces for using external data; and (4) a clear separation of tasks, which allows programmers and psychologists to define reusable components (e.g., algorithmic modules and behavioral templates) that can be used by UI/UX researchers and designers without the need to understand the low-level implementation details of such components. CogTool+ also supports mixed cognitive models required for many large-scale modeling tasks and provides an offline analyzer of simulation results. In order to show how CogTool+ can reduce the human effort required for large-scale modeling, we illustrate how it works using a pedagogical example, and demonstrate its actual performance by applying it to large-scale modeling tasks of two real-world user-authentication systems.

Epidemic diseases (EDs) present a significant but challenging risk endangering public health, evidenced by the outbreak of COVID‐19. Compared to other risks affecting public health such as flooding, EDs attract little attention in terms of risk assessment in the current literature. It does not well respond to the high practical demand for advanced techniques capable of tackling ED risks. To bridge this gap, an adapted fuzzy evidence reasoning method is proposed to realize the quantitative analysis of ED outbreak risk assessment (EDRA) with high uncertainty in risk data. The novelty of this article lies in (1) taking the lead to establish the outbreak risk evaluation system of epidemics covering the whole epidemic developing process, (2) combining quantitative and qualitative analysis in the fields of epidemic risk evaluation, (3) collecting substantial first‐hand data by reviewing transaction data and interviewing the frontier experts and policymakers from Chinese Centers for Disease Control and Chinese National Medical Products Administration. This work provides useful insights for the regulatory bodies to (1) understand the risk levels of different EDs in a quantitative manner and (2) the sensitivity of different EDs to the identified risk factors for their effective control. For instance, in the case study, we use real data to disclose that influenza has the highest breakout risk level in Beijing. The proposed method also provides a potential tool for evaluating the outbreak risk of COVID‐19.

Fog-assisted Internet-of-Things (Fog-IoT) systems are deployed in remote and unprotected environments, making them vulnerable to security, privacy, and trust challenges. Existing studies propose security schemes and trust models for these systems. However, mitigation of insider attacks, namely, blackhole, sinkhole, sybil, collusion, self-promotion, and privilege escalation, has always been a challenge and mostly carried out by the legitimate nodes. Compared to other studies, this article proposes a framework featuring attribute-based access control and trust-based behavioral monitoring to address the challenges mentioned above. The proposed framework consists of two components, the security component (SC) and the trust management component (TMC). SC ensures data confidentiality, integrity, authentication, and authorization. TMC evaluates Fog-IoT entities’ performance using a trust model based on a set of Quality of Service (QoS) and network communication features. Subsequently, trust is embedded as an attribute within SC’s access control policies, ensuring that only trusted entities are granted access to fog resources. Several attacking scenarios, namely, Denial of Service (DoS), Distributed DoS, probing, and data theft are designed to elaborate on how the change in trust triggers the change in access rights and, therefore, validates the proposed integrated framework’s design principles. The framework is evaluated on a Raspberry Pi 3 Model B+ to benchmark its performance in terms of time and memory complexity. Our results show that both SC and TMC are lightweight and suitable for resource-constrained devices.

Recent studies into streaming media delivery suggest that performance gains from cache hierarchies such as Information-Centric Networks (ICNs) may be negated by Dynamic Adaptive Streaming (DAS), the de facto method for retrieving multimedia content. The bitrate adaptation mechanisms that drive video streaming clash with caching hierarchies in ways that affect users' Quality of Experience (QoE). Cache performance also diminishes as consumers dynamically select content encoded at different bitrates. In this paper we use the evidence to draw a novel insight: in a cache hierarchy for adaptive streaming content, bitrates should be prioritized over or alongside popularity and hit rates. We build on this insight to propose RippleCache as a family of cache placement schemes that safeguard high-bitrate content at the edge and push low-bitrate content into the network core. Doing so reduces contention of cache resources, as well as congestion in the network. To validate RippleCache claims we construct two separate implementations. We design RippleClassic as a benchmark solution that optimizes content placement by maximizing a measure for cache hierarchies shown to have high correlation with QoE. In addition, our lighter-weight RippleFinder is then re-designed with distributed execution for application in large-scale systems. RippleCache performance gains are reinforced by evaluations in NS-3 against state-of-the-art baseline approaches, using standard measures of QoE as defined by the DASH Industry Forum. Measurements show that RippleClassic and RippleFinder deliver content that suffers less oscillation and rebuffering, as well as the highest levels of video quality, indicating overall improvements to QoE. Postprint

Anonymous credentials are a solid foundation for privacy-preserving Single Sign-On (SSO). They enable unlinkable authentication across domains and allow users to prove their identity without revealing more than necessary. Unfortunately, anonymous credentials schemes remain difficult to use and complex to deploy. They require installation and use of complex software at the user side, suffer from poor performance, and do not support security features that are now common, such as two-factor authentication, secret recovery, or support for multiple devices. In contrast, Open ID Connect (OIDC), the de facto standard for SSO is widely deployed and used despite its lack of concern for users’ privacy. We present EL PASSO, a privacy-preserving SSO system based on anonymous credentials that does not trade security for usability, and can be incrementally deployed at scale alongside Open ID Connect with no significant changes to end-user operations. EL PASSO client-side operations leverage a WebAssembly module that can be downloaded on the fly and cached by users’ browsers, requiring no prior software installation or specific hardware. We develop automated procedures for managing cryptographic material, supporting multi-device support, secret recovery, and privacy-preserving two-factor authentication using only the built-in features of common Web browsers. Our implementation using PS Signatures achieves 39x to 180x lower computational cost than previous anonymous credentials schemes, similar or lower sign-on latency than Open ID Connect and is amenable for use on mobile devices.

Continual learning is an emerging research challenge in human activity recognition (HAR). As an increasing number of HAR applications are deployed in real-world environments, it is important and essential to extend the activity model to adapt to the change in people’s activity routine. Otherwise, HAR applications can become obsolete and fail to deliver activity-aware services. The existing research in HAR has focused on detecting abnormal sensor events or new activities, however, extending the activity model is currently under-explored. To directly tackle this challenge, we build on the recent advance in the area of lifelong machine learning and design a continual activity recognition system, called HAR-GAN , to grow the activity model over time. HAR-GAN does not require a prior knowledge on what new activity classes might be and it does not require to store historical data by leveraging the use of Generative Adversarial Networks (GAN) to generate sensor data on the previously learned activities. We have evaluated HAR-GAN on four third-party, public datasets collected on binary sensors and accelerometers. Our extensive empirical results demonstrate the effectiveness of HAR-GAN in continual activity recognition and shed insight on the future challenges.

Structural health assessments are essential for infrastructure. By using an autonomous panorama vision‐based inspection system, the limitations of the human cost and safety factors of previously time‐consuming tasks have been overcome. The main damage detection challenges to panorama images are (1) the lack of annotated panorama defect image data, (2) detection in high‐resolution images, and (3) the inherent distortion disturbance for panorama images. In this paper, a new PAnoramic surface damage DEtection Network (PADENet) is presented to solve the challenges by (a) using an unmanned aerial vehicle to capture panoramic images and a distorted panoramic augmentation method to expand the panoramic dataset, (b) employing the proposed multiple projection methods to process high‐resolution images, and (c) modifying the faster region‐based convolutional neural network and training via transfer learning on VGG‐16, which improves the precision for detecting multiple types of damage in distortion. The results show that the proposed method is optimal for surface damage detection.

With the significant increase in energy demands in the last decade, the issues of unnecessary energy usage have increased rapidly. Therefore, there is an immediate need to provide a cheap and easily accessible monitoring tool for the energy consumed by an appliance used in homes and industries. Instead of monitoring the total power consumption of the houses and/or industries, it is useful to monitor the power consumption of the individual appliance, which in turn, helps in saving the overall energy usage and thereby makes it cost-effective. This paper presents a cost-efficient design and implementation of a monitoring system that can precisely measure the current and voltage of each appliance. The design provides tracking of device activity in a real-time environment for the industries and helps in adopting to the green initiative. The design comprises of Arduino based micro-controller and Raspberry Pi, that performs precise measurements of current and voltage of the device, followed by measuring the power consumed by the device. This paper presents two different system designs, one for the single-phase measurements and the other for the DC measurements. The single-phase measurement device comprises of 10-bit ADC whereas, the 24 V DC measurement device comprises of a 12-bit ADC, which provides higher measurement accuracy compared to other systems available in the market. The implemented design uses the EmonCMS web application to accumulate and envision the monitored data. It provides a flexible and user-friendly solution to monitor the measured data easily on any android or iOS devices.

Achieving secure communication between an Implantable Medical Device (IMD) inside the body and a gateway outside the body has showed its criticality with recent reports of hackings. The use of asymmetric cryptography is not a practical solution for IMDs due to the scarce computational and power resources, symmetric key cryptography is preferred. One of the factors in security of a symmetric cryptographic system is to use a strong key for encryption. A solution without using extensive resources in an IMD, is to extract it from the body physiological signals. To have a strong enough key, the physiological signal must be a strong source of randomness and InterPulse Interval (IPI) has been advised to be such that. A strong randomness source should have five conditions: Universality, Liveness, Robustness Permanence and Uniqueness. Nevertheless, for current proposed random extraction methods from IPI these conditions (mainly last three conditions) were not examined. In this study, firstly, we proposed a methodology to measure the last three conditions. Then, using a huge dataset of IPI values, we showed that IPI does not have conditions of Robustness and Permanence. Thus, extraction of a strong uniform random number from IPI value, mathematically, is impossible. Thirdly, rather than using the value of IPI, we proposed the trend of IPI as a source for a new randomness extraction method named as Martingale Randomness Extraction from IPI (MRE-IPI). MRE-IPI satisfies the Robustness condition completely and Permanence to some level. We, also, used randomness test suites and showed that MRE-IPI is able to outperform all recent randomness extraction methods from IPIs and its quality is half of the AES random number. To the best of our knowledge, this is the first work in this area which uses such a comprehensive method and large dataset to examine the randomness of a physiological signal., 17 pages, 18 figures

Oxidative stress induced by excessive levels of reactive oxygen species (ROS) underlies several diseases. Therapeutic strategies to combat oxidative damage are, therefore, a subject of intense scientific investigation to prevent and treat such diseases, with the use of phytochemical antioxidants, especially polyphenols, being a major part. Polyphenols, however, exhibit structural diversity that determines different mechanisms of antioxidant action, such as hydrogen atom transfer (HAT) and single-electron transfer (SET). They also suffer from inadequate in vivo bioavailability, with their antioxidant bioactivity governed by permeability, gut-wall and first-pass metabolism, and HAT-based ROS trapping. Unfortunately, no current antioxidant assay captures these multiple dimensions to be sufficiently “biorelevant,” because the assays tend to be unidimensional, whereas biorelevance requires integration of several inputs. Finding a method to reliably evaluate the antioxidant capacity of these phytochemicals, therefore, remains an unmet need. To address this deficiency, we propose using artificial intelligence (AI)-based machine learning (ML) to relate a polyphenol’s antioxidant action as the output variable to molecular descriptors (factors governing in vivo antioxidant activity) as input variables, in the context of a biomarker selectively produced by lipid peroxidation (a consequence of oxidative stress), for example F2-isoprostanes. Support vector machines, artificial neural networks, and Bayesian probabilistic learning are some key algorithms that could be deployed. Such a model will represent a robust predictive tool in assessing biorelevant antioxidant capacity of polyphenols, and thus facilitate the identification or design of antioxidant molecules. The approach will also help to fulfill the principles of the 3Rs (replacement, reduction, and refinement) in using animals in biomedical research.

Emotion recognition through facial expression and non-verbal speech represents an important area in affective computing. They have been extensively studied from classical feature extraction techniques to more recent deep learning approaches. However, most of these approaches face two major challenges: (1) robustness—in the face of degradation such as noise, can a model still make correct predictions? and (2) cross-dataset generalisation—when a model is trained on one dataset, can it be used to make inference on another dataset?. To directly address these challenges, we first propose the application of a spiking neural network (SNN) in predicting emotional states based on facial expression and speech data, then investigate, and compare their accuracy when facing data degradation or unseen new input. We evaluate our approach on third-party, publicly available datasets and compare to the state-of-the-art techniques. Our approach demonstrates robustness to noise, where it achieves an accuracy of 56.2% for facial expression recognition (FER) compared to 22.64% and 14.10% for CNN and SVM, respectively, when input images are degraded with the noise intensity of 0.5, and the highest accuracy of 74.3% for speech emotion recognition (SER) compared to 21.95% of CNN and 14.75% for SVM when audio white noise is applied. For generalisation, our approach achieves consistently high accuracy of 89% for FER and 70% for SER in cross-dataset evaluation and suggests that it can learn more effective feature representations, which lead to good generalisation of facial features and vocal characteristics across subjects.

Most of the vehicle manufacturers aim to deploy level-5 fully autonomous ground vehicles (FAGVs) on city roads in 2021 by leveraging extensive existing knowledge about sensors, actuators, telematics and Artificial Intelligence (AI) gained from the level-3 and level-4 autonomy. FAGVs by executing non-trivial sequences of events with decimetre-level accuracy live in Smart City (SC) and their integration with all the SC components and domains using real-time data analytics is urgent to establish better swarm intelligent systems and a safer and optimised harmonious smart environment enabling cooperative FAGVs-SC automation systems. The challenges of urbanisation, if unmet urgently, would entail severe economic and environmental impacts. The integration of FAGVs with SC helps improve the sustainability of a city and the functional and efficient deployment of hand over wheels on robotized city roads with behaviour coordination. SC can enable the exploitation of the full potential of FAGVs with embedded centralised systems within SC with highly distributed systems in a concept of Automation of Everything (AoE). This article proposes a synergistic integrated FAGV-SC holistic framework - FAGVinSCF in which all the components of SC and FAGVs involving recent and impending technological advancements are moulded to make the transformation from today's driving society to future's next-generation driverless society smoother and truly make self-driving technology a harmonious part of our cities with sustainable urban development. Based on FAGVinSCF, a simulation platform is built both to model the varying penetration levels of FAGV into mixed traffic and to perform the optimal self-driving behaviours of FAGV swarms. The results show that FAGVinSCF improves the urban traffic flow significantly without huge changes to the traffic infrastructure. With this framework, the concept of Cooperative Intelligent Transportation Systems (C-ITS) is transformed into the concept of Automated ITS (A-ITS). Cities currently designed for cars can turn into cities developed for citizens using FAGVinSCF enabling more sustainable cities.

An increasing number of cloud providers now offer mobile edge computing (MEC) services for their customers to support task offloading. This is undertaken to reduce latency associated with forwarding data from IoT devices owned by customers to cloud platforms. However, two challenges remain in existing MEC scenarios: (i) the coverage of MEC services is limited; (ii) there is limited ability to develop an audit trail about which MEC service providers have processed a user's data. A new architecture for automatically offloading user tasks in MEC scenarios is proposed that addresses the two challenges above. The architecture makes use of drones to dynamically cache data generated from IoT devices and forward this data to MEC servers that participate in a private blockchain network. Our simulated experiments demonstrate the flexibility of the task offloading process through the proposed architecture, which can provide greater visibility of MEC service providers involved in processing users' data.

Software-defined networks (SDNs) offer robust network architectures for current and future Internet of Things (IoT) applications. At the same time, SDNs constitute an attractive target for cyber attackers due to their global network view and programmability. One of the major vulnerabilities of typical SDN architectures is their susceptibility to Distributed Denial of Service (DDoS) flooding attacks. DDoS flooding attacks can render SDN controllers unavailable to their underlying infrastructure, causing service disruption or a complete outage in many cases. In this paper, machine learning-based detection and classification of DDoS flooding attacks on SDNs is investigated using popular machine learning (ML) algorithms. The ML algorithms, classifiers and methods investigated are quadratic discriminant analysis (QDA), Gaussian Naïve Bayes (GNB), $k$ -nearest neighbor (k-NN), and classification and regression tree (CART). The general principle is illustrated through a case study, in which, experimental data (i.e. jitter, throughput, and response time metrics) from a representative SDN architecture suitable for typical mid-sized enterprise-wide networks is used to build classification models that accurately identify and classify DDoS flooding attacks. The SDN model used was emulated in Mininet and the DDoS flooding attacks (i.e. hypertext transfer protocol (HTTP), transmission control protocol (TCP), and user datagram protocol (UDP) attacks) have been launched on the SDN model using low orbit ion cannon (LOIC). Although all the ML methods investigated show very good efficacy in detecting and classifying DDoS flooding attacks, CART demonstrated the best performance on average in terms of prediction accuracy (98%), prediction speed ( $5.3\,\,{\times }\,\,10^{5}$ observations per second), training time (12.4 ms), and robustness.

Detection of the low-altitude and slow-speed small (LSS) targets is one of the most popular research topics in remote sensing. Despite of a few existing approaches, there is still an accuracy gap for satisfying the practical needs. As the LSS targets are too small to extract useful features, deep learning based algorithms can hardly be used. To this end, we propose in this article an effective strategy for determining the region of interest, using a multiscale layered image fusion method to extract the most representative information for LSS-target detection. In addition, an improved self-balanced sensitivity segment model is proposed to detect the fused LSS target, which can further improve both the detection accuracy and the computational efficiency. We conduct extensive ablation studies to validate the efficacy of the proposed LSS-target detection method on three public datasets and three self-collected datasets. The superior performance over the state of the arts has fully demonstrated the efficacy of the proposed approach.

In a rapidly changing IT environment, access to the resources involved in various projects might change randomly based on the role-based access control (RBAC) system. Hence, the security administrator needs to dynamically maintain the role assignments to users for optimizing user-role assignments. The manual updation of user-role assignments is prone to error and increases administrative workload. Therefore, a role recommendation model is introduced for the RBAC system to optimize user-role assignments based on user behaviour patterns. It is shown that the model automatically revokes and refurbishes the user-role assignments by observing user access behaviour. This model is used in the cloud for providing Role-Assignment-as-a-Service to optimize the cost of built-in roles. Several experiments are conducted to verify the proposed model using the Amazon access sample dataset. The experimental results show that the efficiency of the proposed model is 50% higher than the state-of-the-art.

Sentiment analysis is an emerging research field that can be integrated with other domains, including data mining, natural language processing and machine learning. In political articles, it is difficult to understand and summarise the state or overall views due to the diversity and size of social media information. A number of studies were conducted in the area of sentiment analysis, especially using English texts, while Arabic language received less attention in the literature. In this study, we propose a detection model for political orientation articles in the Arabic language. We introduce the key assumptions of the model, present and discuss the obtained results, and highlight the issues that still need to be explored to further our understanding of subjective sentences. The main purpose of applying this new approach based on Rough Set (RS) theory is to increase the accuracy of the models in recognizing the orientation of the articles. We present extensive simulation results, which demonstrate the superiority of the proposed model over other algorithms. It is shown that the performance of the proposed approach significantly improves by adding discriminating features. To summarize, the proposed approach demonstrates an accuracy of 85.483%, when evaluating the orientation of political Arabic datasets, compared to 72.58% and 64.516% for the Support Vector Machines and Naïve Bayes methods, respectively.

Purpose: The primary aim of the study is to identify the existence of post-traumatic stress disorder (PTSD) in an individual and to detect the dominance level of each affected brain region in PTSD using rs-fMRI data. This will assist the psychiatrists and neurologists to distinguish impartially between PTSD individuals and healthy controls for the brain-based treatment of PTSD. Methods: Twenty-eight individuals (14 with PTSD, 14 healthy controls) were assessed to obtain rs-fMRI data of their six brain regions-of-interest. The rs-fMRI data analyzed by the Artificial Neural Network (ANN), adopting the training-validation-testing approach to classify PTSD and to identify the most affected brain region due to PTSD. The classification accuracy is justified by a variety of different methods and metrics. Results: Three ANN models were established to attain the study’s purpose using the susceptible regions in the right, left, and both hemispheres and the classification accuracy of ANN models achieved 79%, 93.5%, and 94.5%, respectively. The prediction accuracy even increased in the independent holdout sample using trained models. The developed models are reliable, intellectually attractive, and generalize. Additionally, the most dominant region in the PTSD individuals was the left hippocampus and the least was the right hippocampus. Conclusion: The present investigation achieved high classification accuracy and identified the brain regions that highly contributed to differentiating PTSD individuals from healthy controls. The results indicated that the left hippocampus is the most affected brain region in PTSD individuals. Therefore, our findings are helpful for practitioners for diagnostic, medication, and therapy of the affected brain regions by knowing the strength of infected regions.

Mindfulness-based group therapy is a rapidly growing psychological approach that can potentially help people adjust to chronic illness and manage unpleasant symptoms. Emerging evidence suggests that mindfulness-based interventions may benefit people with Parkinson’s. The objective of the paper is to examine the appropriateness, feasibility, and potential cost-effectiveness of an online mindfulness intervention, designed to reduce anxiety and depression for people with Parkinson’s. We conducted a feasibility randomized control trial and qualitative interviews. Anxiety, depression, pain, insomnia, fatigue, impact on daily activities and health-related quality of life were measured at baseline, 4, 8, and 20 weeks. Semi-structured interviews were conducted at the end of the intervention. Participants were randomized to the Skype delivered mindfulness group (n = 30) or wait-list (n = 30). Participants in the mindfulness group were also given a mindfulness manual and a CD with mindfulness meditations. The intervention did not show any significant effects in the primary or secondary outcome measures. However, there was a significant increase in the quality of life measure. The incremental cost-effectiveness ratio was estimated to be £27,107 per Quality-Adjusted Life Year gained. Also, the qualitative study showed that mindfulness is a suitable and acceptable intervention. It appears feasible to run a trial delivering mindfulness through Skype, and people with Parkinson’s found the sessions acceptable and helpful.

Central Serous Retinopathy (CSR) or Central Serous Chorioretinopathy (CSC) is a significant disease that causes blindness and vision loss among millions of people worldwide. It transpires as a result of accumulation of watery fluids behind the retina. Therefore, detection of CSR at early stages allows preventive measures to avert any impairment to the human eye. Traditionally, several manual methods for detecting CSR have been developed in the past; however, they have shown to be imprecise and unreliable. Consequently, Artificial Intelligence (AI) services in the medical field, including automated CSR detection, are now possible to detect and cure this disease. This review assessed a variety of innovative technologies and researches that contribute to the automatic detection of CSR. In this review, various CSR disease detection techniques, broadly classified into two categories: a) CSR detection based on classical imaging technologies, and b) CSR detection based on Machine/Deep Learning methods, have been reviewed after an elaborated evaluation of 29 different relevant articles. Additionally, it also goes over the advantages, drawbacks and limitations of a variety of traditional imaging techniques, such as Optical Coherence Tomography Angiography (OCTA), Fundus Imaging and more recent approaches that utilize Artificial Intelligence techniques. Finally, it is concluded that the most recent Deep Learning (DL) classifiers deliver accurate, fast, and reliable CSR detection. However, more research needs to be conducted on publicly available datasets to improve computation complexity for the reliable detection and diagnosis of CSR disease., Comment: 18 pages, 11 figures, 2 tables