Reviewerly/exHarmony huggingface dataset:
Views and Needs of Students, Parents, and Teachers on Closed-Circuit Television, Proximity Trackers, and Access Cards to Facilitate COVID-19 Contact Tracing in Schools: Thematic Analysis of Focus Groups and Interviews

Background Contact tracing is considered a key measure in preventing the spread of infectious diseases. Governments around the world adopted contact tracing to limit the spread of COVID-19 in schools. Contact tracing tools utilizing digital technology (eg, GPS chips, Bluetooth radios) can increase efficiency compared to manual methods. However, these technologies can introduce certain privacy challenges in relation to retention, tracking, and the using and sharing of personal data, and little is known about their applicability in schools. Objective This is the second of two studies exploring the potential of digital tools and systems to help schools deal with the practical challenges of preventing and coping with an outbreak of COVID-19. The aim was to explore the views, needs, and concerns among secondary school stakeholders (parents, teachers, pupils) regarding the implementation of three digital tools for contact tracing: access cards, proximity tracking, and closed-circuit television (CCTV). Methods Focus groups and interviews were conducted with secondary school students, parents, and teachers. The topic guide was informed by the Unified Theory of Technology and Acceptance. Data-driven and theory-driven approaches were combined to identify themes and subthemes. Results We recruited 22 participants. Findings showed that there is no single solution that is suitable for all schools, with each technology option having advantages and limitations. Existing school infrastructure (eg, CCTV and smart/access cards technology) and the geography of each school would determine which tools would be optimal for a particular school. Concerns regarding the cost of installing and maintaining equipment were prominent among all groups. Parents and teachers worried about how the application of these solutions will affect students’ right to privacy. Parents also appeared not to have adequate knowledge of the surveillance technologies already available in schools (eg, CCTV). Students, who were mostly aware of the presence of surveillance technologies, were less concerned about any potential threats to their privacy, while they wanted reassurances that any solutions would be used for their intended purposes. Conclusions Findings revealed that there is not one tool that would be suitable for every school and the context will determine which tool would be appropriate. This study highlights important ethical issues such as privacy concerns, balancing invasions of privacy against potential benefits, transparency of communication around surveillance technology and data use, and processes of consent. These issues need to be carefully considered when implementing contact tracing technologies in school settings. Communication, transparency, and consent within the school community could lead to acceptance and engagement with the new tools.
Review on Three-Factor Authorization based on different IoT Devices

Some of the most fundamental aspects of cybersecurity include authentication, confidentiality, integrity, and security. In this review paper, we provide a review of different existing Internet of things (IoT) devices like smart home devices, smart buildings, smart factories, etc. based on three factors of authentication. The solution of IoT applications anticipated by wireless networks to legitimate clients and users. For making secure IoT solutions for users, numerous mechanisms and protocols and cryptographic techniques have been studied which are proposed by some researchers. Things need a proper authentication system. Different research papers are reviewed that are based on Physically Secure lightweight protocol, multi-Gateway secure IoT Environments protocol, Tele biometric objects (TAO) with biometric sensors and telecommunication networks, and Multi-Factor Authentication that prevent for the users to avoid threats and man-in-the- middle attacks, etc. The paper concludes with some services that can benefit from security techniques anticipated from future technology.
Automated Creation of Mappings Between Data Specifications Through Linguistic and Structural Techniques

The ability to perform automated conversions between different data formats is key to achieve interoperability between heterogeneous systems. Conversions require the definition of mappings between concepts of separate data specifications, which is typically a difficult and time-consuming task. In this article, we present a technique that exploits, in part, semantic web technologies, to automatically suggest mappings to users, based on both linguistic and structural similarities between terms of different data specifications. In addition, we show how a machine-learned linguistic model created by gathering data from domain-specific sources can help increase the accuracy of the suggested mappings. The approach has been implemented in our prototype tool, SMART (SPRINT Mapping & Annotation Recommendation Tool), and it has been validated through tests carried out using specifications from the transportation domain.
Beyond Fads and Magic Bullets: The Promise of Behavioural Approaches in Development Economics

The history of development economics has been portrayed as a succession of fads and magic bullets. This study inquires whether behavioural approaches to economic development are destined to become such a fad or whether they have long-lasting contributions to offer. To be considered a successful program, behavioural approaches should advance our theoretical understanding of development processes and improve the quality and effectiveness of policy recommendations. I argue that behavioural development economics is not yet a cohesive field with a consensual definition and that different directions may be taken. Whether behavioural development economics will be able to deliver on the goals depends on the direction it takes. I distinguish three different views of behavioural development economics - the scarcity view, the non-optimal behaviour view, and the psycho-social-cultural-view - and argue that to be successful, the field should embrace the psycho-social-cultural view more enthusiastically, at the expense of the pure non-optimal behaviour view. I use the behavioural literature on under-saving in the Global South - one of the most-studied domains of behaviour in the field - to substantiate these claims.
Adversarial Attacks on Deep Learning-Based Methods for Network Traffic Classification

The network traffic data is easily monitored and obtained by attackers. Attacks against different network traffic threaten the environment of the intranet. Deep learning methods have been widely used to classify network traffic for their high classification performance. The application of adversarial samples in computer vision confirms that deep learning methods are flawed, allowing existing methods to generate incorrect results with high confidence. In this paper, the adversarial samples are used on the network traffic classification model, causing the CNN model to produce incorrect classification results for network traffic. By training the classification model adversarially, we validate the training effect and improve the classification accuracy by means of the FGSM attack method. By using the adversarial samples to the network traffic data, our approach enables proactive defence against intranet eavesdropping before the attack occurs by influencing the attacker’s classification model to misclassify.
Reducing Gas Consumption of Tornado Cash and Other Smart Contracts in Ethereum

Ethereum, the largest blockchain for running smart contracts, has been widely used, especially in financial and cryptocurrency exchange applications. Among them, Tornado Cash is a typical financial application that protects the privacy of users with anonymous transactions. However, users need to pay prohibitively high gas (transaction fees for smart contract calls) for anonymous transactions, which hinders Tornado Cash from wide applications. To address this issue, we introduced a new approach that shifts the high gas-consuming operations on smart contracts to local users. Furthermore, we use zero-knowledge proofs to ensure the operations are properly executed. The smart contract only needs to verify and update the results, which significantly reduces the gas fees of Tornado Cash. To validate our approach, we implemented a prototype and showed that our proposed method could save more than 61% of gas consumption of current operations while maintaining the privacy feature of Tornado Cash. Finally, we discussed further applications and open problems of our approach.
Trusted Hart for Mobile RISC-V Security

The majority of mobile devices today are based on Arm architecture that supports the hosting of trusted applications in Trusted Execution Environment (TEE). RISC-V is a relatively new open-source instruction set architecture that was engineered to fit many uses. In one potential RISC-V usage scenario, mobile devices could be based on RISC-V hardware. We consider the implications of porting the mobile security stack on top of a RISC-V system on a chip, identify the gaps in the open-source Keystone framework for building custom TEEs, and propose a security architecture that, among other things, supports the GlobalPlatform TEE API specification for trusted applications. In addition to Keystone enclaves the architecture includes a Trusted Hart – a normal core that runs a trusted operating system and is dedicated for security functions, like control of the device’s keystore and the management of secure peripherals. The proposed security architecture for RISC-V platform is verified experimentally using the HiFive Unleashed RISC-V development board.
The Effects of Using Virtual Reality on Thai Word Order Learning

Thai has its own unique spelling system and grammatical rules. Its word order is quite different from that of Mandarin and English, thus making it more difficult for students in Taiwan to learn. Past studies also point out that learning word order is one of the most difficult aspects when learning foreign languages. As science and technology advance, emerging technologies have been widely applied in foreign language learning. This research aims to explore the effect of using a multi-language VR learning assessment system on assisting Thai learners to learn grammatical word order, and to investigate the correlates between Thai self-efficacy, Thai language anxiety, word order learning retention, and task value of VR learning. In order to accomplish this purpose, we invited Thai learners who took Thai courses in the continuing education division of a national university in northern Taiwan to participate in a 5-week teaching experiment, during which the participants were asked to practice Thai word order for 20 min. They were administered a questionnaire to fill out after five weeks of practice and were tested for retention one month after the experiment. A total of 84 valid questionnaires were collected, with an effective return rate of 93.3%. Of the respondents, 30 were male (35.7%), and 54 were female (64.3%). The data were subjected to item analysis, reliability and validity analysis, and then underwent PLS-SEM for research model validation. The results revealed that: (1) Thai language self-efficacy was positively related to learning retention and task value; (2) Thai language anxiety was negatively related to learning retention and task value; (3) Learning retention was positively related to the task value of learning and continuous usage intention.
Fuzzy Object Ambiguity Determination and Human Attention Assessment for Domestic Service Robots

Domestic service robots have the promising potential of bringing significant services to the general population, and more importantly, successful applications of universal domestic service robots can potentially help mitigate critical societal issues such as senior care. In order to do so, domestic service robots need to integrate seamlessly into home environments. However, home environments are dynamic, complex and filled with personal items. Therefore, ambiguity can quickly arise for robots operating in such rich environments. In this paper, we propose an object ambiguity determination system that can determine the level of ambiguity in robot object selection tasks with fuzzy logic data integration. Additionally, we propose a functional human attention assessment system with fuzzy logic that enables the robot to determine user attention before committing to general disambiguation processes. Our preliminary results show that the proposed fuzzy logic inference systems can reliably estimate the robot object selection task ambiguity from object confidence level and the number of potential target objects that satisfy the user's command. Furthermore, fuzzy inference is applied to decide human eye gaze direction robustly. These subsystems can be utilized in the context of human-robot interaction to guide the robot when to seek feedback from a human partner in order to disambiguate reference in domestic service tasks. The source code of all proposed systems is available publicly on GitHub. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Efficient numerical approach to high-fidelity phase-modulated gates in long chains of trapped ions

Almost every quantum circuit is built with two-qubit gates in the current stage, which are crucial to the quantum computing in any platform. The entangling gates based on Mølmer-Sørensen schemes are widely exploited in the trapped-ion system, with the utilization of the collective motional modes of ions and two laser-controlled internal states, which are served as qubits. The key to realize high-fidelity and robust gates is the minimization of the entanglement between the qubits and the motional modes under various sources of errors after the gate operation. In this work, we propose an efficient numerical method to search high-quality solutions for phase-modulated pulses. Instead of directly optimizing a cost function, which contains the fidelity and the robustness of the gates, we convert the problem to the combination of linear algebra and the solution to quadratic equations. Once a solution with the gate fidelity of 1 is found, the laser power can be further reduced while searching on the manifold where the fidelity remains 1. Our method largely overcomes the problem of the convergence and is shown to be effective up to 60 ions, which suffices the need of the gate design in current trapped-ion experiments.
Power-Efficient Secured Hardware Design of AES Algorithm on High Performance FPGA

With the expansion and growth of industries, the two major issues exist that affect both civilization and the environment. Technology development has made it more difficult to communicate and transmit data over secure channels. The use of Green Communication (GC) technology and energy-efficient parts can lessen the lack of electricity and energy. The usage of various technologies under one framework is the main topic of this study. In this article, a hardware implementation of the Advanced Encryption Standard (AES) algorithm is shown. Field Programmable Gate Array (FPGA) devices are taken into consideration for hardware implementations. The power calculation of the AES algorithm is computed for several clock speeds of the Spartan-7 FPGA, and the results are examined using VIVADO tool.
Increasing Accuracy in Predicting Student Test Scores with Neural Networks using Domain Reduction Technique of Principal Component Analysis

This research uses Principal Component Analysis (PCA) in conjunction with a Neural Network to increase the accuracy of predicting student test scores. Much research has been conducted attempting to predict student test scores using a standard, well-known dataset. The dataset includes student demographic and educational data and test scores for Mathematics and Language. Multiple predictive algorithms have been used with a Neural Network being the most common.In this research PCA was used to reduce the domain space size using varying sizes. This began with just 1 attribute and increased to the full size of the original set’s domain values. The reduced domain values and the original domain values were independently used to train a Neural Network and the Mean Absolute errors were compared. Because results may vary depending upon which records in the dataset are training versus testing, 50 trials were conducted for each reduction size. Results were average and statistical tests were applied. Results show that using PCA prior to training the Neural Network can decrease the mean absolute error by up to 15%.
UArch: A Super-Resolution Processor With Heterogeneous Triple-Core Architecture for Workloads of U-Net Networks

High-resolution medical images are of critical significance to improve disease diagnosis. Limited by the camera and power of medical devices, medical images often have very low resolution. For example, wireless capsule endoscopes, often used to diagnose diseases of the small bowel, can only capture low-resolution endoscopic images. The existing super-resolution (SR) networks perform exceptionally well in recovering high-resolution images, but they are computationally expensive and require high bandwidth, which can result in unacceptable latency and bandwidth requirements for embedded medical devices. In this paper, we propose a U-Net-based SR (USR) network structure and an SR processor named UArch. The USR-s, which is the lightweight version of USR, has an SR performance of 42.68 dB for ×2 scale SR. The USR-s has 0.3 dB higher PSNR (peak signal-to-noise ratio) than the SR algorithm, which is often used in recent SR hardware. Based on well-designed strategies, including heterogeneous triple-core architecture, fine-grained on-chip memory allocation, out-of-order execution, and sub-tensor-based processing flow, the UArch, designed for U-Net networks, can fulfill ×2, ×3, and ×4 scale SR by deploying USR-s, achieving high throughput of 60 fps and low latency of 25 ms for ×2 scale 1920 × 1080 output image SR at 156 MHz. The UArch achieves high energy efficiency which is 2264.5 GOPS/W when synthesized and evaluated under the TSMC 28 nm process and which is 199.3 GOPS/W when implemented on Xilinx ZCU111. Our SR processor is capable of reconstructing high-quality endoscopic images and is more efficient than the previous state-of-the-art SR processors.
Adaptive Team Behavior Planning Using Human Coach Commands

In its operating life, an agent that needs to act in real environments is required to deal with rules and constraints that humans ask to satisfy. The set of rules specified by the human might influence the role of the agent without changing its goal or its current task. To this end, classical planning methodologies can be enriched with temporal goals and constraints that enforce non-Markovian properties on past traces. This work aims at exploring the application of real-time dynamic generation of policies whose possible trajectories are compliant with a set of Pure-Past Linear Time Logic rules, introducing novel human-robot interaction modalities for the high-level control of strategies for multiple agents. For proving the effectiveness of the proposed approach, we have carried out an evaluation on a partially observable, unpredictable, and dynamic scenario: the RoboCup soccer competition. In particular, we exploit human indications to condition the robot's behavior before or during the time of the match, as happens during human soccer matches.
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications

This work takes a critical look at the application of conventional machine learning methods to wireless communication problems through the lens of reliability and robustness. Deep learning techniques adopt a frequentist framework, and are known to provide poorly calibrated decisions that do not reproduce the true uncertainty caused by limitations in the size of the training data. Bayesian learning, while in principle capable of addressing this shortcoming, is in practice impaired by model misspecification and by the presence of outliers. Both problems are pervasive in wireless communication settings, in which the capacity of machine learning models is subject to resource constraints and training data is affected by noise and interference. In this context, we explore the application of the framework of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">robust</i> Bayesian learning. After a tutorial-style introduction to robust Bayesian learning, we showcase the merits of robust Bayesian learning on several important wireless communication problems in terms of accuracy, calibration, and robustness to outliers and misspecification.
Innovations in Blockchain Using Artificial Intelligence

This cutting-edge technological era is assisted by many innovative projects like the Internet of Things, Cloud Computing, Artificial Intelligence, Augmented reality, Metaverse, and many more. Together, they resulted in a digital revolution called as the fourth industrial revolution, symbolised as 4IR or Industry 4.0. To increase people’s control over their personal data, these technologies must adhere to privacy regulations. Before initialising the interaction, every system authenticates users by means of a private/public key pair, a hardware token, third-party authentication software, or a username and password combination. Thus, the services accessed by users own their digital identity and are no longer in the control of users. Traditional identity management solutions rely on centralised certification authority (CA) to handle public key management and confirm the association of individuals with their individual keys. Consequently, they raise a number of security issues including the CA being the system’s single point of failure. In addition, with the current increase in users and the dispersed systems they utilise, maintaining public keys by a centralised CA is getting expensive. Additionally, centralised identity management solutions are incompatible and raise privacy issues. Self-Sovereign Identity (SSI) systems aim to address these difficulties by offering decentralised identity ecosystems that permit the registration and interchange of identity attributes and the propagation of trust across participating entities. Blockchain technology improves SSI security by allowing control of credential storage and disclosure. They increase data integrity, privacy, and interoperability. The study analyses how a blockchain-based decentralised identity management system can use SSI to deliver high-level security and transparency in Industry 4.0 environments. The effectiveness of Artificial Intelligence (AI) has also been witnessed in securing Identity and Access Management (I&AM) procedures.
Towards a Context-Aware Framework for Internet of Things and Smart Everything

New applications and new needs to be dealt with arise every day in nowadays society of the Internet of Things (IoT) and smart everything. Even though there are many applications for these domains, most of them do not facilitate context integration in their data processing. There are applications that present solutions to this problem, however, they are either too domain-specific or too general, and therefore cannot be easily reused in other software products. To fill this gap, we propose a context-aware framework that makes use of an ontology reusable for multiple IoT and smart domains. The framework will also provide an integration mechanism and an event pre-processing system, which will facilitate the work of developers. The latter will make use of the ontology by facilitating the definition of contextual events and their integration into decision-making systems. All this, together with our complex-event processing decision-making system will finally make possible to offer intelligent context-aware services and applications.