Reviewerly/exHarmony huggingface dataset:
Blockchain and smart contract enabled smart and secure electronic voting system

Building an electronic voting system is a prime requirement of a government election system to avoid the manipulation in the voting machines and make them more secure and efficient. There is a need of cost-efficient, time-saving, and trusted voting system. Blockchain technology is a revolutionary method that provides decentralised, distributed, and immutable ledgers features. The proposed work utilised this recent technology along with smart contract and keccak256 encryption algorithm to implement vote casting. The performance of the system is measured based on the execution time of the smart contract, average voting time per user, and the hidden and visible gas cost for smart contract deployment for voting. The results obtained come to be promising with an average execution time of 6ms per vote and the percentage of visible cost out of voting and contract deployment is 15.42% and hidden costs out of voting and contract deployment is 84.58%.
Making AI Understandable by Making it Tangible: Exploring the Design Space with Ten Concept Cards

The embodiment of Artificial Intelligence (AI) in everyday use products is raising challenges and opportunities for HCI and design research, such as human understandings of AI’s functions and states, passing back and forth of control, ethics, transparency and user experience. To respond to these challenges, HCI and design researchers have been working on research areas such as Explainable AI (XAI); Fairness, Accountability, and Transparency (FAccT); Human-Centered AI, as well as guidelines for Human-AI interaction design. Consequently, the interest in studying interaction modalities and their contributions to understandable and transparent AI has been growing. However, the tangible and embodied modality of interaction and more broadly studies of the forms of such everyday use products are relatively under-explored. This paper builds upon a larger project on designing graspable AI and it introduces a series of concept cards that aim to aid design researchers’ creative and critical exploration of tangible and understandable AI space. We conducted a user study in two parts of online sessions and semi-structured interviews and found out that to envision physicality and tangible interaction with AI felt challenging and “too abstract”. Even so, the act of creative exploration of that space not only supported the participants to gain new design perspectives, but also supported them to go beyond anthropomorphic forms of AI.
Chatbots: A game changer in mHealth

&lt;p&gt;This is a review of chatbots in the field of mHealth&lt;/p&gt;
Fuzzy Fault-Tolerant Control Applied on Two Inverted Pendulums with Nonaffine Nonlinear Actuator Failures

This paper deals with the problem of fault-tolerant control for a class of perturbed nonlinear systems with nonlinear non-affine actuator faults. Fuzzy systems are integrated into the design of the control law to get rid of the system nonlinearities and the considered actuator faults. Two adaptive controllers are proposed in order to reach the control objective and ensure stability. The first term is an adaptive controller involved to mollify the system uncertainties and the considered actuator faults. Therefore, the second term is known as a robust controller introduced for the purpose of dealing with approximation errors and exogenous disturbances. In general, the designed controller allows to deal automatically with the exogenous disturbances and actuator faults with the help of an online adaption protocol. A Butterworth low-pass filter is utilized to avoid the algebraic loop issue and allows a reliable approximation of the ideal control law. A stability study is performed based on Lyapunov's theory. Two inverted pendulum example is carried out to prove the accuracy of the controller.
Effect of Display Methods on Intentions to Use Virtual Reality in Museum Tourism

The application of virtual reality (VR) promises unique visitor experiences in museum tourism; however, the underlying characteristics of the experiences still need to be well-researched. This research used world-famous paintings (Vincent van Gogh’ s The Starry Night and Claude Monet’s Water Lilies) as examples, combining flow theory and Stimulus-Organism-Response framework and using the experimental research method. The findings were: (1) the display methods of museum exhibits affected visitors' flow experience and intention to use VR; (2) flow experience had a significant mediating effect in the main effect, and (3) the familiarity with VR had a moderating effect on flow experience’s mediating effect. The findings enriched the research literature on VR usage and display methods in museums, providing a theoretical reference and strategic suggestions for enterprises developing museum exhibits and applying VR technology.
Finding Minimum and Maximum Values of Variables in Mathematical Equations by Applying Firefly and PSO Algorithm

Computers are often used when problems are big or hard to solve. However, traditional ways to find solutions are not enough when problems are very serious. Hence, turning to nature may be the answer to find solutions for these problems. Artificial intelligence tries to simulate creatures and activities in nature turning their techniques to find solutions for a given problem into an algorithm. Although there are many algorithms have been developed, whose works are inspired by nature, there has been continuous researches aimed at finding better and faster algorithms. Many mathematical optimization problems can be solved by Swarm intelligence algorithms. The aim of this algorithm is to get the optimum solution by repeated searches whose main concern is to discover the area related to solution. In this paper, Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) algorithm are executed. The two algorithms are implemented to find the minimum and maximum values of the mathematical equations. Users of the proposed system are able to read the equation directly through the execution time, by displaying and analyzing the obtained results which show that the FA algorithm has a better performance than PSO algorithm. In addition, the program is executed by MATLAB.
The Metaverse in Foreign Language Learning: A Theoretical Framework

Inevitably, recent ideas and developments in the metaverse that are based on social interactions can profoundly influence the foreign language learning process since the core of the mentioned process is communication and interaction. However, whether the metaverse can be used in foreign language learning remains a crucial question that is unanswered within a theoretical framework. This paper aims to provide a theoretical basis regarding the use of metaverse in foreign language learning. For this purpose, it first defines and introduces the metaverse, gives a brief history of the concept, and introduces Metaversal games. Then, it focuses on the relationship between the use of the metaverse and the theoretical points of view. It concludes that the metaverse platform can be used as an effective language learning environment in light of the current theories, approaches, and models. The paper also discusses certain problems regarding the use of the metaverse as a language learning platform.
Decision Tree, Naïve Bayes and Support Vector Machine Applying on Social Media Usage in NYC / Comparative Analysis

Data mining and classification are most research idea that used in many topics by researchers. This study presents the comparison of three algorithms for classifications such as (Decision Tree, Naïve Bayes and Support Vector Machine), applying for social media usage dataset by NYC, to get the best result of the classification algorithm that can classify the instances according to the platforms. The final result of this research refer to the Support Vector Machine returned the best result among these techniques.
A federated learning approach for smart healthcare systems

With periodic technology advancements and pandemic-like situations, remote patient health monitoring has increased significantly. The Internet of Things (IoT) devices, including wearables, sensors, and actuators deployed on the human body, detect and regulate physiological data. These systems can establish a trigger mechanism in the event of a possible health incident. Health monitoring using IoT devices generates a large amount of data. Several Machine Learning (ML) strategies have been utilized to analyze the collected data and derive precise predictions. The confidentiality of patient data is one of the essential requirements of these systems. It has been discovered that malicious coordination of ML algorithms might result in a massive attack surface, providing cyber- criminals with an accessible platform. Considering these requirements for using IoT systems for health monitoring, Federated Learning (FL) can solve data privacy challenges by training ML models locally on IoT de- vices without any data transfer to the cloud. FL facilitates information sharing among all IoT devices installed in hospitals through collabora- tive ML model training. This article will examine the significance of em- bedding FL into IoT-enabled smart hospitals and future guidelines for accomplishing this. This article discusses identifying rare diseases and critical care, resolving the problem of insufficient patient health data to train ML models, and preserving patient privacy.
Application of Blockchain in Supply Chain Management

In the rapidly evolving environment of the international supply chain, the traditional network of manufacturers and suppliers has grown into a vast ecosystem made of various products that move through multiple parties and require cooperation among stakeholders. Additionally, the demand for improved product visibility and source-to-store traceability has never been higher. However, traditional data sharing procedures in today’s supply chain are inefficient, costly, and unadaptable as compared to new and innovative technology. Blockchain technology has shown promising results for improving supply chain networks in recent applications and has already impacted our society and lifestyle by reshaping many business and industry processes. In an effort to understand the integration of blockchain technology in the supply chain, the proposed work consists of novel approach to improve the traceability in supply chain through the blockchain technology.
ABILITY, MOTIVATION, OPPORTUNITY-ENHANCING HRM PRACTICES AND CORPORATE ENVIRONMENTAL CITIZENSHIP: REVISITING THE MODERATING ROLE OF ORGANISATIONAL LEARNING CAPABILITY IN MALAYSIAN CONSTRUCTION COMPANIES

This study examines the connection between ability, motivation, opportunity-enhancing HRM practices and corporate environmental citizenship. Organisational learning capability is also introduced as a moderator between the connections of AMO-enhancing HRM practices and corporate environmental citizenship. Systematic sampling technique was utilized to determine the study sample with a total response of 150. The study found that ability and opportunity-enhancing HRM practices are related to corporate environmental citizenship except for motivation-enhancing HRM practices. Similarly, organisational learning capability has no moderating effect on such relationships. In addition to that, this study has provided a comprehensive understanding of corporate environmental citizenship in the construction industry in Malaysia. This substantially adds a new dimension to the literature.
The COVID-19 pandemic and Bitcoin: Perspective from investor attention

The response of the Bitcoin market to the novel coronavirus (COVID-19) pandemic is an example of how a global public health crisis can cause drastic market adjustments or even a market crash. Investor attention on the COVID-19 pandemic is likely to play an important role in this response. Focusing on the Bitcoin futures market, this paper aims to investigate whether pandemic attention can explain and forecast the returns and volatility of Bitcoin futures. Using the daily Google search volume index for the "coronavirus" keyword from January 2020 to February 2022 to represent pandemic attention, this paper implements the Granger causality test, Vector Autoregression (VAR) analysis, and several linear effects analyses. The findings suggest that pandemic attention is a granger cause of Bitcoin returns and volatility. It appears that an increase in pandemic attention results in lower returns and excessive volatility in the Bitcoin futures market, even after taking into account the interactive effects and the influence of controlling other financial markets. In addition, this paper carries out the out-of-sample forecasts and finds that the predictive models with pandemic attention do improve the out-of-sample forecast performance, which is enhanced in the prediction of Bitcoin returns while diminished in the prediction of Bitcoin volatility as the forecast horizon is extended. Finally, the predictive models including pandemic attention can generate significant economic benefits by constructing portfolios among Bitcoin futures and risk-free assets. All the results demonstrate that pandemic attention plays an important and non-negligible role in the Bitcoin futures market. This paper can provide enlightens for subsequent research on Bitcoin based on investor attention sparked by public emergencies.
Big data in contemporary electron microscopy: challenges and opportunities in data transfer, compute and management

The second decade of the twenty-first century witnessed a new challenge in the handling of microscopy data. Big data, data deluge, large data, data compliance, data analytics, data integrity, data interoperability, data retention and data lifecycle are terms that have introduced themselves to the electron microscopy sciences. This is largely attributed to the booming development of new microscopy hardware tools. As a result, large digital image files with an average size of one terabyte within one single acquisition session is not uncommon nowadays, especially in the field of cryogenic electron microscopy. This brings along numerous challenges in data transfer, compute and management. In this review, we will discuss in detail the current state of international knowledge on big data in contemporary electron microscopy and how big data can be transferred, computed and managed efficiently and sustainably. Workflows, solutions, approaches and suggestions will be provided, with the example of the latest experiences in Australia. Finally, important principles such as data integrity, data lifetime and the FAIR and CARE principles will be considered.
Importance sampling for stochastic quantum simulations

Simulating many-body quantum systems is a promising task for quantum computers. However, the depth of most algorithms, such as product formulas, scales with the number of terms in the Hamiltonian, and can therefore be challenging to implement on near-term, as well as early fault-tolerant quantum devices. An efficient solution is given by the stochastic compilation protocol known as qDrift, which builds random product formulas by sampling from the Hamiltonian according to the coefficients. In this work, we unify the qDrift protocol with importance sampling, allowing us to sample from arbitrary probability distributions, while controlling both the bias, as well as the statistical fluctuations. We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage. Moreover, we incorporate recent work on composite channel and compute rigorous bounds on the bias and variance, showing how to choose the number of samples, experiments, and time steps for a given target accuracy. These results lead to a more efficient implementation of the qDrift protocol, both with and without the use of composite channels. Theoretical results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
Squeezed superradiance enables robust entanglement-enhanced metrology even with highly imperfect readout

Quantum metrology protocols using entangled states of large spin ensembles attempt to achieve measurement sensitivities surpassing the standard quantum limit (SQL), but in many cases they are severely limited by even small amounts of technical noise associated with imperfect sensor readout. Amplification strategies based on time-reversed coherent spin-squeezing dynamics have been devised to mitigate this issue, but are unfortunately very sensitive to dissipation, requiring a large single-spin cooperativity to be effective. Here, we propose a new dissipative protocol that combines amplification and squeezed fluctuations. It enables the use of entangled spin states for sensing well beyond the SQL even in the presence of significant readout noise. Further, it has a strong resilience against undesired single-spin dissipation, requiring only a large collective cooperativity to be effective.
Gradient-Descent Quantum Process Tomography by Learning Kraus Operators

We perform quantum process tomography (QPT) for both discrete- and continuous-variable quantum systems by learning a process representation using Kraus operators. The Kraus form ensures that the reconstructed process is completely positive. To make the process trace preserving, we use a constrained gradient-descent (GD) approach on the so-called Stiefel manifold during optimization to obtain the Kraus operators. Our ansatz uses a few Kraus operators to avoid direct estimation of large process matrices, e.g., the Choi matrix, for low-rank quantum processes. The GD-QPT matches the performance of both compressed-sensing (CS) and projected least-squares (PLS) QPT in benchmarks with two-qubit random processes, but shines by combining the best features of these two methods. Similar to CS (but unlike PLS), GD-QPT can reconstruct a process from just a small number of random measurements, and similar to PLS (but unlike CS) it also works for larger system sizes, up to at least five qubits. We envisage that the data-driven approach of GD-QPT can become a practical tool that greatly reduces the cost and computational effort for QPT in intermediate-scale quantum systems.
Analysis of Movement and Activities of Handball Players Using Deep Neural Networks

This paper focuses on image and video content analysis of handball scenes and applying deep learning methods for detecting and tracking the players and recognizing their activities. Handball is a team sport of two teams played indoors with the ball with well-defined goals and rules. The game is dynamic, with fourteen players moving quickly throughout the field in different directions, changing positions and roles from defensive to offensive, and performing different techniques and actions. Such dynamic team sports present challenging and demanding scenarios for both the object detector and the tracking algorithms and other computer vision tasks, such as action recognition and localization, with much room for improvement of existing algorithms. The aim of the paper is to explore the computer vision-based solutions for recognizing player actions that can be applied in unconstrained handball scenes with no additional sensors and with modest requirements, allowing a broader adoption of computer vision applications in both professional and amateur settings. This paper presents semi-manual creation of custom handball action dataset based on automatic player detection and tracking, and models for handball action recognition and localization using Inflated 3D Networks (I3D). For the task of player and ball detection, different configurations of You Only Look Once (YOLO) and Mask Region-Based Convolutional Neural Network (Mask R-CNN) models fine-tuned on custom handball datasets are compared to original YOLOv7 model to select the best detector that will be used for tracking-by-detection algorithms. For the player tracking, DeepSORT and Bag of tricks for SORT (BoT SORT) algorithms with Mask R-CNN and YOLO detectors were tested and compared. For the task of action recognition, I3D multi-class model and ensemble of binary I3D models are trained with different input frame lengths and frame selection strategies, and the best solution is proposed for handball action recognition. The obtained action recognition models perform well on the test set with nine handball action classes, with average F1 measures of 0.69 and 0.75 for ensemble and multi-class classifiers, respectively. They can be used to index handball videos to facilitate retrieval automatically. Finally, some open issues, challenges in applying deep learning methods in such a dynamic sports environment, and direction for future development will be discussed.
Fake accounts on social media, epistemic uncertainty and the need for an independent auditing of accounts

This article seeks to understand why so little is known about the nature and extent of ‘fake or spam accounts’ across the leading social media companies, why this lack of knowledge is even greater outside the companies themselves and the implications of this epistemic uncertainty. It concludes that authorities, investors and the public should not be solely reliant on the companies for user account figures, and that there is a need for regular, independent, external audits of inauthentic accounts on social media.
NFT Become a Copyright Solution

NFT can help art creators to easily register their works for sale using a smart contract system where if the ownership of the work changes hands, the work will become the property of the new owner with proof of a digital certificate. With the steps of making the work so that it can be published in the NFT marketplace media platform and NFT offline gallery. In addition, NFT also has advantages and disadvantages. With that, NFT might be a solution for copyright of a work in the future. However, there are still many questions because NFT is still relatively new and not everyone knows this technology.