Reviewerly/exHarmony huggingface dataset:
PERFORMANCE COMPARISON OF SVM, CNN, HMM AND NEURO-FUZZY APPROACH FOR INDIAN SIGN LANGUAGE RECOGNITION

Hearing Impaired or mute peoples are uses sign language to express their thoughts in front of each other as well as normal people.This research paper describes proposed methodology for Indian Sign language recognition where images of alphabets signs are used for recognition.Various image processing techniques have been applied to smooth and filter the images.Similarity index values of testing data and training data have been found as a feature using correlation-coefficient algorithm.This paper consists of a comparison of classification algorithms: Support Vector Machine (SVM), Convolutional Neural Network (CNN), Hidden Markov Model (HMM), and Neuro-Fuzzy(NF) approach.Comparison performs by performance evaluation on MATLAB.Total 200 images of alphabets A to J are tested where 100 images are positive and remaining 100 images are negative.Testing results of positive images consists of accuracy, 94% for SVM, 70% for HMM, 95% for CNN and 97% for NF.Comparison of NF with SVM, HMM, CNN is also describing for the different parameters.Performance is calculated by the confusion matrix where NF approach consists of 96% accuracy.
Multi-qubit quantum computing using discrete-time quantum walks on closed graphs

Abstract Universal quantum computation can be realised using both continuous-time and discrete-time quantum walks. We present a version based on single particle discrete-time quantum walk to realize multi-qubit computation tasks. The scalability of the scheme is demonstrated by using a set of walk operations on a closed lattice form to implement the universal set of quantum gates on multi-qubit system. We also present a set of experimentally realizable walk operations that can implement Grover’s algorithm, quantum Fourier transformation and quantum phase estimation algorithms. An elementary implementation of error detection and correction is also presented. Analysis of space and time complexity of the scheme highlights the advantages of quantum walk based model for quantum computation on systems where implementation of quantum walk evolution operations is an inherent feature of the system.
Zephyrus: An Information Hiding Mechanism Leveraging Ethereum Data Fields

Permanent availability makes blockchain technologies a suitable alternative for building a covert channel.Previous works have analysed its feasibility in a particular blockchain technology called Bitcoin.However, Ethereum cryptocurrency is gaining momentum as a means to build distributed apps.The novelty of this paper relies on the use of Ethereum to establish a covert channel considering all transaction fields and smart contracts.No previous work has explored this issue.Thus, a mechanism called Zephyrus, an information hiding mechanism based on steganography, is developed.Moreover, its capacity, cost and stealthiness are assessed both theoretically, and empirically through a prototype implementation that is publicly released.Disregarding the time taken to send the transaction to the blockchain, its retrieval and the mining time, experimental results show that, in the best case, 40 Kbits can be embedded in 0.57 s. for US$ 1.64, and retrieved in 2.8 s.
Opinions of Secondary School Students on the Use of Mobile Augmented Reality Technology in Science Teaching

Mobile augmented reality (MAR) draws attention in terms of providing a flexible learning process and environment. It is thought that learning environments can be more effective using MAR technology. The research aimed to determine the secondary school students' views on the use of MAR technology in science teaching. The research was conducted with 143 students studying in the sixth grade at two different secondary schools in Turkey. In the research, convergent parallel patterns from mixed-method patterns were used. For eight weeks, the research was carried out to cover the systems unit in our body, situated in the 6th-grade science course curriculum. During the process, the Anatomy 4D application was used in the experimental group during the procedure, and the science course curriculum was used in the control group. In this paper, a part of the mixed research, the qualitative data collected from the experimental group's semi-structured interview form and diaries were analyzed. Descriptive analysis was used to analyze the data. As a result of the research, although the students do not have a clear idea about AR and MAR technologies at the beginning of the application, it is observed that their thoughts change over time, and MAR applications are effective on the success of the course.
Developing a Measure of Social, Ethical, and Legal Content for Intelligent Cognitive Assistants

We address the issue of consumer privacy against the backdrop of the national priority of maintaining global leadership in artificial intelligence, the ongoing research in Artificial Cognitive Assistants, and the explosive growth in the development and application of Voice Activated Personal Assistants (VAPAs) such as Alexa and Siri, spurred on by the needs and opportunities arising out of the COVID-19 global pandemic. We first review the growth and associated legal issues of the of VAPAs in private homes, banks, healthcare, and education. We then summarize the policy guidelines for the development of VAPAs. Then, we classify these into five major categories with associated traits. We follow by developing a relative importance weight for each of the traits and categories; and suggest the establishment of a rating system related to the legal, ethical, functional, and social content policy guidelines established by these organizations. We suggest the establishment of an agency that will use the proposed rating system to inform customers of the implications of adopting a particular VAPA in their sphere.
Interactive Methods of Teaching the Humanities in Higher Education Institutions

The article analyzes modern methods of interactive teaching of humanities in higher education institutions. A characteristic feature of interactive learning is the constant, active interaction of all participants in the educational process (teacher - student, teacher - students, students - students). It focuses on the fact that student-centered learning requires teachers to use the latest methods, providing the maximum connection of theoretical information with its practical application, the development of professional skills by directly solving cases and problematic situations. Characteristic of modern higher education is the development of critical thinking of students using such methods as "Fish bone", Bloom's daisy, Bloom's cube, Brainstorming, six hats of de Bono thinking and more. Interactive learning is based on a personal-activity approach that focuses on the use of both situational and nonsituational learning methods. Extremely important for professional activity and team work is the ability to express their own ideas and prove their viability. For this purpose, teachers of higher education institutions use such methods as debate, aquarium, PRES method, discussions, "Choose/change position", etc. The creative component of education is an integral part of professional development. With this in mind, the article analyzes such forms of student work as writing essays, syncveins, diamonds, portfolios, etc. Interactive learning methods motivate participants in the learning process to learn, stimulate their independent thinking and decision-making, develop imagination and cognitive interest. These methods can create an educational environment in which theory and practice are acquired simultaneously, contributing to critical and logical thinking, the formation of personality.
ARMA-Based Segmentation of Human Limb Motion Sequences

With the development of human motion capture (MoCap) equipment and motion analysis technologies, MoCap systems have been widely applied in many fields, including biomedicine, computer vision, virtual reality, etc. With the rapid increase in MoCap data collection in different scenarios and applications, effective segmentation of MoCap data is becoming a crucial issue for further human motion posture and behavior analysis, which requires both robustness and computation efficiency in the algorithm design. In this paper, we propose an unsupervised segmentation algorithm based on limb-bone partition angle body structural representation and autoregressive moving average (ARMA) model fitting. The collected MoCap data were converted into the angle sequence formed by the human limb-bone partition segment and the central spine segment. The limb angle sequences are matched by the ARMA model, and the segmentation points of the limb angle sequences are distinguished by analyzing the good of fitness of the ARMA model. A medial filtering algorithm is proposed to ensemble the segmentation results from individual limb motion sequences. A set of MoCap measurements were also conducted to evaluate the algorithm including typical body motions collected from subjects of different heights, and were labeled by manual segmentation. The proposed algorithm is compared with the principle component analysis (PCA), K-means clustering algorithm (K-means), and back propagation (BP) neural-network-based segmentation algorithms, which shows higher segmentation accuracy due to a more semantic description of human motions by limb-bone partition angles. The results highlight the efficiency and performance of the proposed algorithm, and reveals the potentials of this segmentation model on analyzing inter- and intra-motion sequence distinguishing.
Reservoir Computing Based on Mutually Injected Phase Modulated Semiconductor Lasers as a Monolithic Integrated Hardware Accelerator

In this paper we propose and numerically study a neuromorphic computing scheme that applies delay-based reservoir computing in a laser system consisting of two mutually coupled phase modulated lasers. The scheme can be monolithic integrated in a straightforward manner and alleviates the need for external optical injection, as the data can be directly applied on the on-chip phase modulator placed between the two lasers. The scheme also offers the benefit of increasing the nodes compared to a reservoir computing system using either one laser under feedback or laser under feedback and optical injection. Numerical simulations assess the performance of the integrated reservoir computing system in dispersion compensation tasks in short-reach optical communication systems. We numerically demonstrate that the proposed platform can recover severely distorted 25 Gbaud PAM-4 signals for transmission distances exceeding 50 km and outperform other competing delay-based reservoir computing systems relying on optical feedback. The proposed scheme, thanks to its compactness and simplicity, can play the role of a monolithic integrated hardware accelerator in a wide range of application requiring high-speed real-time processing.
Assessment of the effect on the human body of the flicker of OLED displays of smartphones

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Research on the Application of Intelligent Evaluation and Teacher-student Cooperation Assessment System in Teaching English Writing

Low efficiency in teaching and time-consuming in writing evaluation are two big problems for college English teachers. Therefore, it is necessary to create a new teaching model to solve these problems existing in traditional classroom-based teaching. This research adopts the research methods of test comparison before and after the students’ composition experiment, questionnaire and semi-open interviews. Empirical research on a new teaching model that integrates the intelligent composition review and reform system represented by Piangai.com and the collaborative evaluation of teachers and students is conducted. The research results show that the new writing teaching model improves the quality of students’ writing, promotes students’ learning initiative, and enhances students’ writing self-efficacy. This writing teaching model provides ideas for solving the problem of time-consuming and inefficient English writing teaching in large classes.
The Digital Educational Environment: The Problem of Its Accessibility for Visually Impaired Students

The changes taking place in recent decades in the humanitarian sphere of the life of Russian society are manifested in the increased attention to problems of social integration of people with disabilities. The social state policy concerning such people is increasingly focused on creating equal opportunities so as to participate in the life of society and get involved in socially useful activities. One of the most important aspects of equal opportunities for the visually impaired is ensuring the access to information and opportunity to participate in public information exchange. The process of society informatization makes the solution of these problems even more urgent. In this regard, the wide distribution of digital electronic resources is of particular importance, taking into account the requirements for the visually impaired and computer technology. In the given research there substantiated possibilities of using digital educational resources, computer typhlotechnologies when interacting with the information public. Such results can be implemented in the organization of both academic and corporate education. The developed approaches can be used by various groups of developers to create their own digital educational environment
The Impact of Feedback on Students’ Autonomous ESP Learning Outcomes

The present paper provides the results of the feedback influence on students’ autonomous ESP learning. It is aimed to study the impact of feedback on autonomous learning outcomes of first-year students, who are studying a two-year university course of “English for Specific Purposes (ESP)”, the significant part of which is dedicated to self-regulated learning. The aim of the course is to improve the students’ proficiency in professionally oriented English communication to the level of B2 according to the Common European Framework of Reference for Languages (CEFR). The investigation has proved the importance of teachers’ support in students’ autonomous ESP learning. The outcomes of those students who received the feedback regularly signify that even being self-sufficient, students strive for teachers’ or peer observation and feedback in the educational process. In the article, the interdependence between the level of students’ autonomous ESP learning competence and students’ feedback literacy is presented. The results of the study suggest that students’ autonomous ESP learning outcomes are considerably influenced by supportive external written feedback if it is sought, and their feedback literacy level is at least moderate or higher. A higher level of students’ feedback literacy is observed among students with a more advanced level of autonomous ESP learning competence and who demonstrate better academic achievements in professionally oriented English communication. The coherence of the elaborated levels of Ukrainian students’ feedback literacy and the ways of its enhancing can be significant for educators in other countries.
A Statutory Article Retrieval Dataset in French

Statutory article retrieval is the task of automatically retrieving law articles relevant to a legal question.While recent advances in natural language processing have sparked considerable interest in many legal tasks, statutory article retrieval remains primarily untouched due to the scarcity of large-scale and high-quality annotated datasets.To address this bottleneck, we introduce the Belgian Statutory Article Retrieval Dataset (BSARD), which consists of 1,100+ French native legal questions labeled by experienced jurists with relevant articles from a corpus of 22,600+ Belgian law articles.Using BSARD, we benchmark several state-of-theart retrieval approaches, including lexical and dense architectures, both in zero-shot and supervised setups.We find that fine-tuned dense retrieval models significantly outperform other systems.Our best performing baseline achieves 74.8% R@100, which is promising for the feasibility of the task and indicates there is still room for improvement.By the specificity of the domain and addressed task, BSARD presents a unique challenge problem for future research on legal information retrieval.Our dataset and source code are publicly available."Should physicians, surgeons, health officers, pharmacists, midwives, and all others who, through their status or profession, be in possession of information confided to them reveal such secrets, they shall be punished with imprisonment of one to three years and a fine of 100 to 1000 euros or one of these penalties only -unless called to testify as a witness in a court of law (or before a parliamentary commission of inquiry) or compelled by a decree or order to divulge the secret.
The Case for Banning (and Mandating) Ransomware Insurance

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An improved NFC device authentication protocol

Aimed at the security authentication problem between Near Field Communication (NFC) devices, this paper uses the technology of asymmetric encryption algorithm, symmetric encryption algorithm, hash function, timestamp and survival period to improve the confidentiality, performance and security of the protocol. The symmetric encryption algorithm encrypts the transmission content, while the asymmetric encryption algorithm encrypts the shared key. The whole authentication process is secure, and the key distribution is secure. The improved NFC device authentication protocol can effectively resist the brute force attack, man-in-the-middle attack and replay attack in the authentication process, it can reduce the number of message transmission in the authentication process, improve the transmission efficiency, enhance the confidentiality, integrity, non-repudiation and improve the security of NFC device authentication.
Penerapan Algoritma K-Means Clustering Untuk Pengelompokan Penyebaran Covid-19 di Provinsi Jawa Barat

Covid-19 is a new category of disease that has spread all over the world, including in Indonesia. Covid-19 is caused by a virus that causes disturbances in the respiratory system, from mild symptoms such as flu, to lung infections, such as pneumonia. The spread of covid-19 is so fast and massive, in Indonesia the total spread of covid-19 has reached 1,066,313 people and West Java province occupies the second highest position for the spread of covid-19. Therefore, it is necessary to group cities/districts in the province of West Java to create a strategy for handling the spread of COVID-19. The clustering of the spread of COVID-19 is carried out using the K-Means clustering algorithm which groups data into several clusters based on the similarity of the data and is implemented in R language application tools and by using the CRISP-DM method so that each phase in data mining is more structured and clearly defined. Based on the results of the study, there were 3 clusters, namely cluster 1 as the cluster with the highest number of Covid-19 spreads, obtained by 2 districts/cities. For cluster 2 as a cluster with a medium number of Covid-19 spreads, 5 districts/cities are obtained. And the remaining 20 districts/cities are included in cluster 3 with a smaller number of Covid-19 spreads. The results of this study are expected to support the West Java provincial government to make strategic decisions in each cluster to reduce the spread of COVID-19 in West Java.
A Brief Study of Deep Reinforcement Learning with Epsilon-Greedy Exploration

This paper analyses a simple epsilon-greedy exploration approach to train models with Deep Q-Learning algorithm to involve randomness that helps prevail the agent over conforming to a single solution.This allows the agent to explore different solutions for a problem even after finding a solution.This helps the agent find the global optimum solution without being stuck in a local optimum.A simple block environment is built and used to assess the agent's ability to reach the destination, block A to reach block B. The model is trained repeatedly by feeding the game image and rewarding it based on the decisions made.The weights of the Neural Network of the Reinforcement Learning model are then adjusted by training the model after every iteration to improve the result.Furthermore, two different environments from the Gym library in Python is used to corroborate the results obtained.Here we have used TensorFlow to build and implement the model on the GPU for better and accelerated computation.
An Intelligent Web Service Composition and Resource-Optimization Method Using K-Means Clustering and Knapsack Algorithms

Service-oriented architecture (SOA) has emerged as a flexible software design style. SOA focuses on the development, use, and reuse of small, self-contained, independent blocks of code called web services that communicate over the network to perform a certain set of simple tasks. Web services are integrated as composite services to offer complex tasks and to provide the expected services and behavior in addition to fulfilling the clients’ requests according to the service-level agreement (SLA). Web service selection and composition problems have been a significant area of research to provide the expected quality of service (QoS) and to meet the clients’ expectations. This research paper presents a hybrid web service composition model to solve web service selection and composition problems and to optimize web services’ resource utilization using k-means clustering and knapsack algorithms. The proposed model aims to maximize the service compositions’ QoS and minimize the number of web services integrated within the service composition using the knapsack algorithm. Additionally, this paper aims to track the service compositions’ QoS attributes by evaluating and tracking the web services’ QoS using the reward function and, accordingly, use the k-means algorithm to decide to which cluster the web service belongs. The experimental results on a real dataset show the superiority and effectiveness of the proposed algorithm in comparison with the results of the state–action–reward–state–action (SARSA) and multistage forward search (MFS) algorithms. The experimental results show that the proposed model reduces the average time of the web service selection and composition processes to 37.02 s in comparison to 47.03 s for the SARSA algorithm and 42.72 s for the MFS algorithm. Furthermore, the average of web services’ resource utilization results increased by 4.68% using the proposed model in comparison to the resource utilization by the SARSA and MFS algorithms. In addition, the experimental results showed that the average number of service compositions using the proposed model improved by 26.04% compared with the SARSA and MFS algorithms.