Reviewerly/exHarmony huggingface dataset:
Co-Investment Network of ERC-20 Tokens: Network Structure Versus Market Performance

Cryptocurrencies have attracted extensive attention from individual and institutional investors in recent years. In this emerging and inefficient capital market, the roles that institutional investors play can have a remarkable impact on the market. This paper investigates the ERC-20 token investment market from a network perspective. Using a dataset containing 317 ERC-20 tokens and their institutional investors at the end of June 2020, we construct a co-investment network of tokens connected by the sharing of institutional investors. Specifically, we examine whether the tokens’ market embeddedness, measured by their network structural properties, can influence their market performance, as well as whether the tokens’ structural similarity in the co-investment network can influence similarity of their market performance. Our results indicate that strength centrality, closeness centrality, betweenness centrality, and clustering coefficient have a significant impact on trading volume and liquidity of the market. And there is a significantly positive correlation between the Jaccard similarity index and tokens’ market performance similarity. This work demonstrates the non-negligible influence of the institutional investors and the diffusion of such influence through co-investment relationships in the cryptocurrency market. We expect the analysis could further enhance the understanding of the inefficiency and vulnerability of this emerging market.
Combined Engineering—Statistical Method for Assessing Solar Photovoltaic Potential on Residential Rooftops: Case of Laghouat in Central Southern Algeria

Solar energy planning becomes crucial to develop adaptive policies ensuring both energy efficiency and climate change mitigation. Cities, particularly building’s rooftops, constitute a promising infrastructure for enabling the use of locale solar resources. This study proposes a combined engineering–statistical methodology to assess the photovoltaic potential of residential rooftops. Using validated algorithms for solar simulation and geographical information system (GIS) for spatial dissemination, the proposed methodology deals with the lack of data and allows an accurate investigation of the geographical and technical potential. Applied to the municipality of Laghouat, the results reveal that suitable rooftops areas for PV installations in the examined typologies were approximately between 18 and 35%. Moreover, the deployment of distributed PV systems on residential rooftops provides significant technical potential, which could cover up to 55% of the annual electricity needs. These original findings offer a realistic assessment of the usable solar potential within municipalities, which helps decision-makers establish energy efficiency strategies by reducing energy consumption and increasing the share of renewable electricity production. Additionally, the discussion offers valuable insight into energy management and investigates eventual energy sharing among residential buildings to achieve a net-zero energy balance at the municipal level.
Hybrid Feature Selection for Amharic News Document Classification

Today, the amount of Amharic digital documents has grown rapidly. Because of this, automatic text classification is extremely important. Proper selection of features has a crucial role in the accuracy of classification and computational time. When the initial feature set is considerably larger, it is important to pick the right features. In this paper, we present a hybrid feature selection method, called IGCHIDF, which consists of information gain (IG), chi-square (CHI), and document frequency (DF) features’ selection methods. We evaluate the proposed feature selection method on two datasets: dataset 1 containing 9 news categories and dataset 2 containing 13 news categories. Our experimental results showed that the proposed method performs better than other methods on both datasets 1and 2. The IGCHIDF method’s classification accuracy is up to 3.96% higher than the IG method, up to 11.16% higher than CHI, and 7.3% higher than DF on dataset 2, respectively.
Assistant Training System of Teenagers’ Physical Ability Based on Artificial Intelligence

The rapid development of artificial intelligence technology makes it widely used in various fields. In order to more scientifically assist teenagers in physical training, this paper develops a set of teenagers’ physical training system based on artificial intelligence technology. Firstly, the experimental platform is built, and the sensor nodes are connected with the test host through the serial port to collect data to the experimental platform. The system consists of target detection module, data analysis module, and human posture estimation module. The background modeling method based on vibe model is used to form the target detection module, and the canny edge detection algorithm is used to form the data analysis module. Finally, the posture auxiliary index is established to estimate the human posture. This paper makes a systematic application test on a youth sports team. The experimental group was trained with artificial intelligence-based physical training system, while the control group was trained with traditional training methods. Before the experiment, the physical fitness of the two groups of subjects were evaluated, including standing long jump, 50 meters sprint, 30 s single swing rope skipping, pull-up, and squat 1RM. After 3 and 6 weeks of training, the physical fitness was evaluated again. The experimental results show that the intelligent assistant system established in this paper can accurately show that the physiological load of the athlete is in line with the law of physiological function change. After six weeks of training, the standing long jump of the experimental group has been improved by 20.97 cm, the 50 meters dash has been accelerated by 1.21 s, the 30 second single swing rope has been increased by 13.76, the pull-up has been increased by 1.41, and the squat 1RM has been increased by 15.16. This shows that the auxiliary training system based on artificial intelligence can help young athletes improve their physical quality and enhance their sports skills.
Lessons learned from a project examining the learning outcomes and experiences in 360o videos

Advances in technologies associated with virtual reality provide interesting tools for e-learning. One such is 360° videos. Although their educational potential is supported by a number of researchers, there is limited empirical evidence backing such a claim, given that they have recently become popular. The study at hand presents the results of a project in which 360° videos were used by primary school students. Eighty-four students, aged ten to eleven, participated in the experiment. The results demonstrated that 360° videos helped them to acquire more knowledge compared to printed material. Then again, no statistically significant differences were noted when comparing 360° and regular videos. 360° videos provided a more immersive, motivational, and enjoyable learning experience. However, the low-cost HMDs used for viewing 360° videos and the applications in which they were embedded, were considered the least easy to use. Moreover, participants expressed the view that all tools fostered their learning. Overall, while the results give support to the hypothesis that 360o videos provide positive educational experiences, their actual impact on learning has to be further explored.
Similarities and Differences Between Eye and Mouse Dynamics During Web Pages Exploration

The study of eye movements is a common way to non-invasively understand and analyze human behavior. However, eye-tracking techniques are very hard to scale, and require expensive equipment and extensive expertise. In the context of web browsing, these issues could be overcome by studying the link between the eye and the computer mouse. Here, we propose new analysis methods, and a more advanced characterization of this link. To this end, we recorded the eye, mouse, and scroll movements of 151 participants exploring 18 dynamic web pages while performing free viewing and visual search tasks for 20 s. The data revealed significant differences of eye, mouse, and scroll parameters over time which stabilize at the end of exploration. This suggests the existence of a task-independent relationship between eye, mouse, and scroll parameters, which are characterized by two distinct patterns: one common pattern for movement parameters and a second for dwelling/fixation parameters. Within these patterns, mouse and eye movements remained consistent with each other, while the scrolling behaved the opposite way.
Automated Teaching System “Sets” (Research for Organizing the 1st Part of the Project)

The issues of building an automated learning system “Sets” which will allow students to master one of the important topics of the discipline “Discrete Mathematics” and to develop logical and mathematical thinking in this direction are studied. The corresponding topic of the 1st part of the project includes materials related to the concept of a set, operations on sets, algebra of sets, proofs of statements for sets, and the derivation of formulas for the number of set elements. The system is based on a construction of the statements proof editor for a set and of the formulas derivation editor for the number of set elements, both editors are to be used for teaching. The first of these allows students to split the original statement into a number of simpler statements, taken together equivalent to the original statement, to choose a method of proving each simple statement and to conduct their step-by-step proof. The second editor allows (using the inclusion-exclusion principle and the formula of the number of complement elements) to derive a step-by-step formula for the number of set elements through the specified numbers of elements for sets from which the resulting set is constructed. An important part of the system is to monitor the correctness of all actions of students, and on this basis the entire learning system is developed. The logical supervision over the correctness of the selected action in the first editor is performed by a Boolean function created by the system and corresponding to this action and by checking it for identical truth. In the second editor, invariants such as characteristic strings of the set and of its number of elements are used for verification. The rest of the system is related to learning of set algebra and to preparation to editors usage. The main focus here is on the learning strategy in which testing the understanding of the learned material is rather rigorous and eliminating the random choice of answers. The division of the material into sections with verification of the success of teaching not only by tests, but also by exercises and tasks, allows students to master the complex logical and mathematical techniques of proving statements for sets and derivation of formulas for the number of set elements.
Differentially private binary- and matrix-valued data query

Differential privacy has been widely adopted to release continuous- and scalar-valued information on a database without compromising the privacy of individual data records in it. The problem of querying binary- and matrix-valued information on a database in a differentially private manner has rarely been studied. However, binary- and matrix-valued data are ubiquitous in real-world applications, whose privacy concerns may arise under a variety of circumstances. In this paper, we devise an exclusive or (XOR) mechanism that perturbs binary- and matrix-valued query result by conducting an XOR operation on the query result with calibrated noises attributed to a matrix-valued Bernoulli distribution. We first rigorously analyze the privacy and utility guarantee of the proposed XOR mechanism. Then, to generate the parameters in the matrix-valued Bernoulli distribution, we develop a heuristic approach to minimize the expected square query error rate under ϵ -differential privacy constraint. Additionally, to address the intractability of calculating the probability density function (PDF) of this distribution and efficiently generate samples from it, we adapt an Exact Hamiltonian Monte Carlo based sampling scheme. Finally, we experimentally demonstrate the efficacy of the XOR mechanism by considering binary data classification and social network analysis, all in a differentially private manner. Experiment results show that the XOR mechanism notably outperforms other state-of-the-art differentially private methods in terms of utility (such as classification accuracy and F 1 score), and even achieves comparable utility to the non-private mechanisms.
Maintenance Action Recommendation Using Collaborative Filtering

The problem we were trying to solve in 2013 PHM Society Conference Data Challenge competition 1 is closely related to remote monitoring and diagnostics in industrial applications. This data was generated from an industrial piece of equipment with a sensor network to measure several parameters and an onboard condition monitoring system. The measured data goes through a control logic in order to monitor the equipment’s operating regime. At any time instant when some of these parameters meet a specific condition, the control system generates an unique event id/code. Each case is described by a set of event codes which characterize the atypical operating condition of the equipment. Some of these cases with specific event code combinations may be operationally significant and could be indicative of “Problem Types”, some of which are assumed to be known to the subject matter experts. As a response to these problems, domain experts recommend appropriate diagnostic measures (or maintenance actions) depending on the problem types. The goal of this data competition is to build an automated system that can recommend particular maintenance action(s) to mitigate these problem(s).
Agent-based automated persuasion with adaptive concessions tuned by emotions

Human-to-agent automated negotiation has many potentials in a variety of applications. How to design an agent with equivalent persuasion capabilities with its human rivals is the key to the success of such systems but the research on this problem is still at its early stage. With the aim of improving agents’ persuasion ability, this paper proposes to construct emotional agents and emotion-dependent persuasion actions in automated negotiation with multiple issues. First, a multi-issue evaluation function adjusted by the rival’s reputation is constructed to determine whether emotional persuasion is needed. Then, by applying the Weber-Fechner Law, this paper proposes a method to measure an agent’s emotion generated by evaluating the rival’s proposal. Persuasion is categorized into four types and an emotion-based method is proposed for an agent to select a persuasion type. The selected persuasion type is further related to updating concessions, so that an agent can make concessions adaptive to both the rival’s proposal and the focal agent’s emotional state. Moreover, a series of numerical experiments on bilateral negotiation between agents are conducted to illustrate the proposed model and validate its effectiveness in improving negotiation efficiency. Theoretical and practical implications as well as limitations are discussed in the end.
MODELLING THE SUCCESSFUL INTEGRATION OF MOBILE AUGMENTED REALITY TECHNOLOGY (MART) AMONG MALAYSIAN PRE-SERVICE TEACHERS

The integration of mobile augmented reality technology (MART) into the education system since the last two decades have brought to the topic of discussion among many scholars. Although numerous studies have highlighted its huge potentials and benefits toward both teacher's and students' teaching and learning process, several researchers reported that teachers’ readiness to integrate mobile augmented reality technology is still uncertain. Given the importance and consideration of research that has been undertaken, the present study was set to examine the Malaysian pre-service teachers’ readiness toward integrating MART into their learning and future teaching process. The Technology Acceptance Model (TAM) was adapted as the theoretical grounding of the study in explaining and predicting pre-service teachers’ decision to integrate mobile augmented reality technology. Data of the study was gathered from 303 Malaysian pre-service teachers and were analysed using the descriptive analysis of SPSS and Partial Least Square (PLS) of Structural Equation Modelling (SEM). Findings from the study demonstrate that pre-service teachers’ readiness to integrate MART is at a moderate level. The study also suggested that pre-service teachers’ perceived usefulness has the most significant direct effect on their decision to integrate MART. On the basis of these findings a purpose model is developed, and implications of theory development, practices, and policymaking are discussed in this paper.
Energy Reduction Through Memory Aware Real-Time Scheduling on Virtual Machine in Multi-Cores Server

Not only weighty energy usage pose issues for the environment, but it also raises server maintenance costs in data centers. The massive task with the various power control functions in computer components was made to minimize energy consumption. Increasing consumption of energy in data server environments means that data centers will have high maintenance costs. Various geo-distributed data centers are starting to grow in an age of data proliferation and information growth. Energy management for servers is now demanded for technological, environmental, and economic reasons. In this environment, the main memory is a major energy consumer, not less than the processor. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping Virtual Machine (VM) resources to the Main Memory (MM) demands to achieve good performance by minimizing the energy consumption within a certain limit is a huge challenge. This paper simulates energy-efficient task scheduling algorithms in a heterogeneous virtualized environment using real-time virtual machine scheduling to resolve the issue of energy consumption. Using a simulator Real-Time system SIMulator (RTSIM), several hardware-based scheduling algorithms are implemented to observe VM memory scheduling efficiency to save memory energy. The simulation results show that, compared to current energy-efficient scheduling methods Rate Monotonic (RM), Earliest-Deadline-First (EDF), and Least-Laxity-First (LLF), helps to reduce energy consumption and improve performance. It is also observed that memory-aware energy management architecture reduces energy and memory consumption efficiently by using EDF scheduling algorithms. In particular, EDF saves approximately 58.3 percent of memory energy than conventional systems that cannot benefit from memory-aware energy management algorithms. The energy efficiency of the algorithms continues to improve as the level of server consolidation rises. We also implemented the EDF scheduling algorithm in Xen’s Credit scheduler to see if the simulation outcomes can be simulated on physical systems. Results of simulation and deployment are equated, and comparable outcomes are achieved. We also identified that shared memory between virtual machines deliberately affects memory’s energy consumption based on the implementation.
Using E-Learning in English Language Teaching: A Systematic Review

The growth of education in Malaysia should be in line with the current development of Information and Communication Technology (ICT), as teaching methods have shifted from traditional teacher-centered to student-centered, as well as the skills required for a quality teacher are altering from time to time.However, English language teachers are not driven which will undesirably affect students' performance despite all the efforts that have been made to improve the development of education.Therefore, the paper aims to present a systematic review of appropriate published studies to determine ESL teachers' attitudes towards the use of elearning and identify issues faced by ESL teachers in the use of e-learning.This review synthesized findings from 22 journal articles published from the year 2016-2020 from Google Scholar and Eric accordingly.Overall, the results showed that there is positive reinforcement with the proper usage and incorporation of e-learning in the teaching of the English language as that will aid the teachers to perform better and create the quality level of students.
Heatwave distortion correction using an improved reference sample compensation method and multispectral digital image correlation

Heatwave distortions, caused by unevenly distributed temperature and refractive index in the optical path, unavoidably occur in high-temperature digital image correlation (DIC) measurement. To eliminate these distortions, a multispectral DIC-aided reference sample compensation method is proposed. The proposed method first adheres a correcting transparent glass (decorated with fluorescent speckle patterns) onto the test specimen (sprayed with red speckle patterns). Then, by illuminating the specimen with ultraviolet- and red-light sources, the blue light excited from the correcting glass and the red light reflected from the specimen surface can be captured by a 3CCD camera, forming a color image. After separating the recorded color images into red and blue subimages, the original and the correcting displacement fields can be calculated from these two sets of subimages using the subset-based local 2D-DIC algorithm. By point-to-point subtracting the correcting displacement fields from the original ones, the heatwave distortions can be eliminated, and the corrected real displacement fields can be obtained. For validation, static heatwave experiments show the feasibility and accuracy of the proposed method in correcting heatwave distortions. A uniaxial tensile test of an aluminum specimen with a central hole was also performed, further confirming the practicality of the proposed method in correcting heatwave distortions and revealing heatwave-hidden deformation.
A systematic variational approach to band theory in a quantum computer

Quantum computers promise to revolutionize our ability to simulate molecules, and cloud-based hardware is becoming increasingly accessible to a wide body of researchers. Algorithms such as Quantum Phase Estimation and the Variational Quantum Eigensolver are being actively developed and demonstrated in small systems. However, extremely limited qubit count and low fidelity seriously limit useful applications, especially in the crystalline phase, where compact orbital bases are difficult to develop. To address this difficulty, we present a hybrid quantum-classical algorithm to solve the band structure of any periodic system described by an adequate tight-binding model. We showcase our algorithm by computing the band structure of a simple-cubic crystal with one s and three p orbitals per site (a simple model for polonium) using simulators with increasingly realistic levels of noise and culminating with calculations on IBM quantum computers. Our results show that the algorithm is reliable in a low-noise device, functional with low precision on present-day noisy quantum computers, and displays a complexity that scales as Ω(M3) with the number M of tight-binding orbitals per unit-cell, similarly to its classical counterparts. Our simulations offer a new insight into the "quantum" mindset and demonstrate how the algorithms under active development today can be optimized in special cases, such as band structure calculations.