Reviewerly/exHarmony huggingface dataset:
Cognitive load considerations for Augmented Reality in network security training

This paper presents an Augmented Reality (AR)-based network cabling tutoring system that trains users how to interconnect cables within a Virtual Local Area Network (VLAN) on a physical switching rack. AR arrows combined with text-based instruction and a checklist provided assistance during practical learning. When learners made a mistake, an Intelligent Tutoring System (ITS) identified the source of the mistake and provided real-time feedback using text, AR and text-to-speech mechanisms. The design was motivated by the human-cognitive architecture and its five evolutionary principles (proposed by Sweller and Sweller (2006)). Users performed four consecutive network cabling training tasks with assistance from our ITS. We found that users made fewer errors when the AR cues, text-based instruction and checklist solutions were replaced with summarised information and then removed completely in the final task compared to those who used an identical system with fixed instruction. Cognitive Load Theory (CLT) explains our results by suggesting that the instructional mechanisms become redundant as knowledge increases. Implications of the study are discussed as well as how AR can help facilitate knowledge transfer in the network cabling domain.
Quantum Lego: Building Quantum Error Correction Codes from Tensor Networks

We introduce a flexible and graphically intuitive framework that constructs complex quantum error correction codes from simple codes or states, generalizing code concatenation. More specifically, we represent the complex code constructions as tensor networks built from the tensors of simple codes or states in a modular fashion. Using a set of local moves known as operator pushing, one can derive properties of the more complex codes, such as transversal non-Clifford gates, by tracing the flow of operators in the network. The framework endows a network geometry to any code it builds and is valid for constructing stabilizer codes as well as nonstabilizer codes over qubits and qudits. For a contractible tensor network, the sequence of contractions also constructs a decoding or encoding circuit. To highlight the framework’s range of capabilities and to provide a tutorial, we lay out some examples where we glue together simple stabilizer codes to construct nontrivial codes. These examples include the toric code and its variants, a holographic code with transversal non-Clifford operators, a three-dimensional stabilizer code, and other stabilizer codes with interesting properties. Surprisingly, we find that the surface code is equivalent to the two-dimensional Bacon-Shor code after “dualizing” its tensor-network encoding map.20 MoreReceived 27 December 2021Revised 4 April 2022Accepted 15 April 2022DOI:https://doi.org/10.1103/PRXQuantum.3.020332Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum error correctionTechniquesTensor network methodsQuantum InformationInterdisciplinary PhysicsNetworksCondensed Matter, Materials & Applied Physics
Audio-Visual Grounding Referring Expression for Robotic Manipulation

Referring expressions are commonly used when referring to a specific target in people's daily dialogue. In this paper, we develop a novel task of audio-visual grounding referring expression for robotic manipulation. The robot leverages both the audio and visual information to understand the referring expression in the given manipulation instruction and the corresponding manipulations are implemented. To solve the proposed task, an audio-visual framework is proposed for visual localization and sound recognition. We have also established a dataset which contains visual data, auditory data and manipulation instructions for evaluation. Finally, extensive experiments are conducted both offline and online to verify the effectiveness of the proposed audio-visual framework. And it is demonstrated that the robot performs better with the audio-visual data than with only the visual data.
Mechanism Design of Health Care Blockchain System Token Economy: Development Study Based on Simulated Real-World Scenarios

Despite the fact that the adoption rate of electronic health records has increased dramatically among high-income nations, it is still difficult to properly disseminate personal health records. Token economy, through blockchain smart contracts, can better distribute personal health records by providing incentives to patients. However, there have been very few studies regarding the particular factors that should be considered when designing incentive mechanisms in blockchain.The aim of this paper is to provide 2 new mathematical models of token economy in real-world scenarios on health care blockchain platforms.First, roles were set for the health care blockchain platform and its token flow. Second, 2 scenarios were introduced: collecting life-log data for an incentive program at a life insurance company to motivate customers to exercise more and recruiting participants for clinical trials of anticancer drugs. In our 2 scenarios, we assumed that there were 3 stakeholders: participants, data recipients (companies), and data providers (health care organizations). We also assumed that the incentives are initially paid out to participants by data recipients, who are focused on minimizing economic and time costs by adapting mechanism design. This concept can be seen as a part of game theory, since the willingness-to-pay of data recipients is important in maintaining the blockchain token economy. In both scenarios, the recruiting company can change the expected recruitment time and number of participants. Suppose a company considers the recruitment time to be more important than the number of participants and rewards. In that case, the company can increase the time weight and adjust cost. When the reward parameter is fixed, the corresponding expected recruitment time can be obtained. Among the reward and time pairs, the pair that minimizes the company's cost was chosen. Finally, the optimized results were compared with the simulations and analyzed accordingly.To minimize the company's costs, reward-time pairs were first collected. It was observed that the expected recruitment time decreased as rewards grew, while the rewards decreased as time cost grew. Therefore, the cost was represented by a convex curve, which made it possible to obtain a minimum-an optimal point-for both scenarios. Through sensitivity analysis, we observed that, as the time weight increased, the optimized reward increased, while the optimized time decreased. Moreover, as the number of participants increased, the optimization reward and time also increased.In this study, we were able to model the incentive mechanism of blockchain based on a mechanism design that recruits participants through a health care blockchain platform. This study presents a basic approach to incentive modeling in personal health records, demonstrating how health care organizations and funding companies can motivate one another to join the platform.
Infusing Model Predictive Control Into Meta-Reinforcement Learning for Mobile Robots in Dynamic Environments

The successful operation of mobile robots requires them to adapt rapidly to environmental changes. To develop an adaptive decision-making tool for mobile robots, we propose a novel algorithm that combines meta-reinforcement learning (meta-RL) with model predictive control (MPC). Our method employs an off-policy meta-RL algorithm as a baseline to train a policy using transition samples generated by MPC when the robot detects certain events that can be effectively handled by MPC, with its explicit use of robot dynamics. The key idea of our method is to switch between the meta-learned policy and the MPC controller in a randomized and event-triggered fashion to make up for suboptimal MPC actions caused by the limited prediction horizon. During meta-testing, the MPC module is deactivated to significantly reduce computation time in motion control. We further propose an online adaptation scheme that enables the robot to infer and adapt to a new task within a single trajectory. The performance of our method has been demonstrated through simulations using a nonlinear car-like vehicle model with (i) synthetic movements of obstacles, and (ii) real-world pedestrian motion data. The simulation results indicate that our method outperforms other algorithms in terms of learning efficiency and navigation quality.
A Systematic Review of Data Models for the Big Data Problem

Nowadays, data are generated in a continuous streaming manner as the inputs of various applications.The sources of such generated data can be wired or wireless sensor networks commonly used in various fields of geographical, traffic, Internet of Things (IoT), financial tickers, Web2 and Web3, ecommerce, social networks, and online communities.The high volume, high variety, and high velocity of data have recently posed the challenge of 3Vs to this field, also known as the Big Data Problem.The 3Vs dimensions of complexities for the big data entails high-speed storage, scalability of database systems, suitable data models, real-time responsiveness and so on.Data model, as the representation schema of data is an essential issue since many others (e.g., DBMS systems' design, DB languages, etc.) rely on.So, the study of data models is a key and fundamental aspect in structuring, organizing, storing, and manipulating big data.It is also the essence in various areas of cloud migration, web-scale, and so forth.In this paper, we have systematically reviewed different types of data models, the rationale behind them, their applications and support capabilities, and the technologies to switch from one model to another.To address the user needs in various fields, a systematic review method is adopted to classify and present different types and characteristics of data models.
Underwater Animal Detection Using YOLOV4

Underwater computer vision system has been widely used for many underwater applications such as ocean exploration, biological research and monitoring underwater life sustainability. However, in counterpart of the underwater environment, there are several challenges arise such as water murkiness, dynamic background, low light and low visibility which limits the ability to explore this area. To overcome these challenges, there is a crucial to improve underwater vision system that able to efficiently adapt with varying environments. Therefore, it is great of significance to propose an efficient and precise underwater detection by using YOLOv4 based on deep learning algorithm. In the research, an open-source underwater dataset was used to investigate YOLOv4 performance based on metrics evaluation of precision and processing speed (FPS). The result shows that YOLOv4 able to achieve a remarkable of 97.96% for mean average precision with frame per second of 46.6. This study shows that YOLOv4 model is highly significant to be implemented in underwater vision system as it possesses ability to accurately detect underwater objects with haze and low-light environments.
Geothermal energy development roadmap of Taiwan by play fairway analysis

Following the 2015 Paris Agreement dealing with the mitigation of global warming, the government and people of Taiwan have expressed their environmental awareness by accelerating the ongoing effort of substituting fossil-fuel power with green energy forms. With the deadline for promised energy policies approaching, more action is required of the geothermal option despite its high exploration cost and uncertainty issues. The Play Fairway Analysis (PFA) is an excellent low-cost decision-making tool with a quantitative approach and the capability to combine multiple factors from limited data sources and provide stakeholders a probability assessment in the early stage of the geothermal exploration. It can construct a prudent development roadmap combining geological elements alongside socioeconomic data in place. Considering the local aspects of the geological setting in metamorphic rocks and an active orogenic mountain belt uplift environment, the study adapts the PFA methodology using relevant data in geochemistry, geophysics, and geographies to depict the key geothermal factors in heat sources and fractural pathways. Existing elements available for analysis include (1) temperature of hot springs, silica geothermometry, and surface uplift rates for heat sources, and (2) active fault lines, shallow micro-earthquakes, dilatation strain rates, and elevation variation for fractural pathway. For pragmatic consideration, we choose population density and national parks to represent added human factors in this study to cover constrains such as operating cost, supply needs, regulations, and the like. Through normalizing and weighting processes from the ArcGIS platform, the resulting composite map, confirmed with previous research reports, displays the calculated values on each grid cell over the research area in an easy-to-read format. We present three suggested prospects labelled in this realistic map of potential geothermal distribution across Taiwan as candidates for the next phase of geothermal development projects as part of the long push to achieve our nation's green energy goals.
Commercial Entry Control Using Robotic Mechanism and Mobile Application for COVID-19 Pandemic

The control of entry to commercial premises has always existed, but with the pandemic, this has been adapted to include biosecurity measures that guarantee the safety of customers. Technology makes it possible to automate these activities, among the most popular tools are mobile applications that allow easy implementation and good reception from users. This work presents the entrance control to a commercial premise using a robotic mechanism and a mobile application connected through a cloud database. The system is implemented using accessible and low-cost components, among the tools are 3D printing, the ESP32 board, servo motors, proximity sensors, temperature, and heart rate. The mobile application is developed in App Inventor and Firebase is used for the remote database. The system automates the provision of hand sanitizer at the entrance and records these vital signs of the customers, allowing to generate a report of the attendees. The results show the readings carried out by validating the implemented system and a measure of acceptance of this technology is applied with a score of 73%, evidencing the deficiencies of the proposal from the users' perspective.
Enhancing Interactions for In-Car Voice User Interface with Gestural Input on the Steering Wheel

Voice user interfaces (VUI) are becoming indispensable in car for offering drivers the opportunity to make distraction-free inputs and conduct complex tasks. However, the usability and control efficiency of today's VUI remain to be enhanced due to its sequential nature. In this work, we explored gestural input on the steering wheel to improve the interaction efficiency of VUI. Based on limitations in VUI, we designed novel gestural commands on the steering wheel to augment them. We also elicited corresponding user-defined gestures by exploring drivers' touch behavior. Then, we implemented a prototype attached to the steering wheel for recognizing gestures. Finally, we evaluated our system's usability regarding driving performance, interaction efficiency, cognitive workload and user feedback. Results revealed that our system improved the control efficiency of VUI and reduced workload without a significant reduction in driving distraction than just using VUI.
Spatiotemporal Correlation Feature Spaces to Support Anomaly Detection in Water Distribution Networks

Monitoring disruptions to water distribution dynamics are essential to detect leakages, signal fraudlent and deviant consumptions, amongst other events of interest. State-of-the-art methods to detect anomalous behavior from flowarate and pressure signal show limited degrees of success as they generally neglect the simultaneously rich spatial and temporal content of signals produced by the multiple sensors placed at different locations of a water distribution network (WDN). This work shows that it is possible to (1) describe the dynamics of a WDN through spatiotemporal correlation analysis of pressure and volumetric flowrate sensors, and (2) analyze disruptions on the expected correlation to detect burst leakage dynamics and additional deviant phenomena. Results gathered from Portuguese WDNs reveal that the proposed shift from raw signal views into correlation-based views offers a simplistic and more robust means to handle the irregularity of consumption patterns and the heterogeneity of leakage profiles (both in terms of burst volume and location). We further show that the disruption caused by leakages can be detected shortly after the burst, highlighting the actionability of the proposed correlation-based principles for anomaly detection in heterogeneous and georeferenced time series. The computational approach is provided as an open-source tool available at GitHub.
Adaptive Interval Type-2 Fuzzy Tracking Control of PV Grid-Connected Inverters

Fuzzy logic systems with approximation capabilities provide effective control for nonlinear and uncertain systems.Due to the characteristics of photovoltaic (PV) and the PWM method, a grid-connected PV system is a considerably nonlinear system with unpredictable parameters.In this study, a new adaptive interval type-2 fuzzy approximation-based controller (AIT2FAC) was developed to control a three-phase grid-connected PV system.The proposed controller can be implemented without any prior knowledge of the system mathematical model.In the presence of both parametric and modeling uncertainty, the developed controller can achieve the control objectives.The proposed controller utilizes the principle of input-output feedback linearization and the approximation capability of fuzzy systems to the control inverter current components to track prescribed reference values.The proposed AIT2FAC controller is capable of handling system uncertainties due to the interval type-2 fuzzy logic system capability to cope with a high level of uncertainty.Lyapunov analysis is used to determine the closed-loop system stability and the updating laws of the proposed controller parameters.The effectiveness of the designed controller to achieve the required tracking is validated for different operating cases, including system disturbances, modelling, and parameter uncertainties.For evaluation, the proposed type-2 fuzzy controller is compared to a type-1 fuzzy controller in terms of some performance measures.The comparison results demonstrate that the proposed type-2 fuzzy controller has better tracking performance than the type-1 fuzzy controller in terms of the settling time, the maximum overshoot, the integral absolute error (IAE) and the integral time of absolute error (ITAE).
The innovative use of smartphone for sound STEM practical kit: a pilot implementation for secondary classroom

Background Recent studies have highlighted the application of STEM (Science, Technology, Engineering, and Mathematics) knowledge into daily life via hands-on practical works. The limitations of the existing conventional laboratory work can be potentially overcome by the implementation of the innovative use of smartphones as an experimental tool due to its effectiveness and convenience to apply anywhere and anytime.Purpose The purpose of the study was to develop and evaluate the effectiveness of a sound STEM practical kit using free mobile apps for secondary school students. This study also examined student perceptions of the implementation of the developed sound practical kit.Sample Two intact Form 2 classes of secondary school students from a mainstream school in the state of Perak, Malaysia, were chosen using the cluster sampling method as research sample. One of the classes was selected as an experimental group (n = 36 students) with the implementation of developed sound practical kit, while the other class was a comparison group (n = 30 students) employing a conventional teaching method.Methods A developmental research design according to the ASSURE (Analyse learners; State objectives; Select media and materials; Utilise media and materials; Require learner participation; and Evaluate) instructional design model and quasi-experimental were employed in this study. A set of achievement test and open-ended questions were employed as research instruments. The data were analysed using a non-parametric test that included Mann-Whitney test and Wilcoxon signed-rank test, while the students’ feedbacks and comments were analysed using Qualitative Data Analysis (QDA) software.Results The findings demonstrated a significant difference between the posttest of the comparison group and the experimental group students.Conclusions In summary, the developed sound practical kit improved students’ understanding on the topic of sound. In implication, this study indicates that the implementation of STEM practical kit using free mobile apps promotes the interactive STEM teaching and learning process in secondary education.
Toward Understanding Acceleration-based Activity Recognition Neural Networks with Activation Maximization

Although deep learning-based activity recognition using wearable sensors has been actively studied to implement smart applications such as supporting elderly care, healthcare, and home automation, techniques for understanding the inside of activity recognition networks have not yet been investigated thoroughly. In the computer vision research field, activation maximization (AM) was proposed to visualize the internal functions of networks. However, when conventional AM techniques, which are tailored to image-based recognition, are directly applied to acceleration-based activity recognition networks, meaningless and noisy signals are generated because of the difficulties in regularizing AM by directly using the values of the acceleration signals that are generated. This study proposes novel regularization techniques for AM using activity recognition networks that leverage activation values used in AM to indirectly control the acceleration signals that are generated. We evaluated the proposed method quantitatively using publicly available datasets and confirmed the effectiveness of the proposed techniques.
Local and Global Feature Based Explainable Feature Envy Detection

Code smell detection can help developers identify position of code smell in projects and enhance the quality of software system. Usually codes with similar semantic relationships have greater code dependencies, and most code smell detection methods ignore dependencies relationships within the source code. Thus, their detection results may be heavily influenced by inadequate code feature, which can lead to some code smell not being detected. In addition, existing methods cannot explain the correlation between detection results and code information. However, an explainable result can help developers make better judgments on code smell reconstruction. Accordingly, in this paper, we propose a local and global feature based explainable approach to detecting feature envy, one of the most common code smells. For the model to make the most of code information, we design different representation models for global code and local code respectively to extract different feature envy features, and automatically combine these features that are beneficial in terms of detection accuracy. We further design a code semantic dependency (CSD) to make the detection result easy to explain. The evaluation results of seven manual building code smell projects and three real projects show that the proposed approach improves on the state-of-the-art in detecting feature envy and boosting the explainability of results.