Reviewerly/exHarmony huggingface dataset:
A Study on Different Sign Language Recognition Techniques

Language is means of communicating and expressing by using sound wherein, Sign language is communicating through hand gesture, face expression, fingers movement. This sign language is for deaf and dump people. It is visual method to communicate. Very less work has been done on identification of sign language. This paper mainly focuses on comparative study of Sign Language techniques. It reviews different techniques for feature extraction and classification of Sign Language from video or images. Recognition of sign language from video is one of the challenging tasks as video has to be converted to frames and that frames can be used for identification. In future, we will propose a system which will identify gesture from Indian sign language.
Self-Mining Blockchain Mobile Unified Payment Interface

In the last decade, the blockchain industry has solidified itself as one of the most secure forms of data storage. The emergence of extremely secure cryptocurrencies that have a plethora of advantages over regular internet banking has brought about a revolutionary change in the banking industry. The mobile payment users have skyrocketed with an estimated proximity mobile payment transaction user count of 1.31 billion in 2023. Therefore, there is a need for a cryptocurrency based unified payment interface (UPI) that would grant additional security and improve the transaction process drastically over the existing mobile payment system. We worked out on this aspect and proposed a scheme that would allow mobile devices to mine blocks themselves and generate their own transactions rather than depending on third-party services or bank servers.
Do Students Learn More from Erroneous Code? Exploring Student Performance and Satisfaction in an Error-Free Versus an Error-full SAS® Programming Environment

Teaching students statistical programming languages while simultaneously teaching them how to debug erroneous code is challenging. The traditional programming course focuses on error-free learning in class while students’ experiences outside of class typically involve error-full learning. While error-free teaching consists of focused lectures emphasizing correct coding, error-full teaching would follow such lectures with debugging sessions. We aimed to explore these two approaches by conducting a pilot study of 18 graduate students who voluntarily attended a SAS programming seminar held weekly from September 2018 through November 2018. Each seminar had a 10-min error-free lecture, 15-min programming assignment, 5-min break, 10-min error-full lecture, and 15-min programming assignment. We examined student performance and preference. While four students successfully completed both assignments and ten students did not successfully complete either assignment, one student successfully completed only the first assignment that directly followed the error-free lecture and three students successfully completed only the second assignment that directly followed the error-full lecture. Of the 15 students who responded, twelve (80%) preferred error-full to error-free learning. We will evaluate error-full learning on a larger scale in an introductory SAS course. Supplemental files are available online for this article.
A Meta Reinforcement Learning-based Approach for Self-Adaptive System

A self-learning adaptive system (SLAS) uses machine learning to enable and enhance its adaptability. Such systems are expected to perform well in dynamic environments. For learning high-performance adaptation policy, some assumptions must be made on the environment-system dynamics when information about the real situation is incomplete. However, these assumptions cannot be expected to be always correct, and yet it is difficult to enumerate all possible assumptions. This leads to the problem of incomplete-information learning. We consider this problem as multiple model problem in terms of finding the adaptation policy that can cope with multiple models of environment-system dynamics. This paper proposes a novel approach to engineering the online adaptation of SLAS. It separates three concerns that are related to the adaptation policy and presents the modeling and synthesis process, with the goal of achieving higher model construction efficiency. In addition, it designs a meta-reinforcement learning algorithm for learning the meta policy over the multiple models, so that the meta policy can quickly adapt to the real environment-system dynamics. At last, it reports the case study on a robotic system to evaluate the adaptability of the approach.
Evolutionary Algorithms for Multi-Objective optimization of Drone Controller Parameters

Drones are effective for reducing human activity and interactions by performing tasks such as exploring and inspecting new environments, monitoring resources and delivering packages. Drones need a controller to maintain stability and to reach their goal. The most well-known drone controllers are proportional-integral-derivative (PID) and proportional derivative (PD) controllers. However, the controller parameters need to be tuned and optimized. In this paper, we introduce the use of two evolutionary algorithms, biogeography-based optimization (BBO) and particle swarm optimization (PSO), for multi-objective optimization (MOO) to tune the parameters of the PD controller of a drone. The combination of MOO, BBO, and PSO results in various methods for optimization: vector evaluated BBO and PSO, denoted as VEBBO and VEPSO; and non-dominated sorting BBO and PSO, denoted as NSBBO and NSPSO. The multi-objective cost function is based on tracking errors for the four states of the system. Two criteria for evaluating the Pareto fronts of the optimization methods, normalized hyper-volume and relative coverage, are used to compare performance. Results show that NSBBO generally performs better than the other methods.
A Decision Model for Ranking Asian Higher Education Institutes Using an NLP-Based Text Analysis Approach

Identification of the best institute for higher education has become one of the most challenging issues in the present education system. It has become more complicated as more institutes exist with extraordinary infrastructural facilities. Therefore, a decision model is required to identify the best institute for higher education based on multiple criteria. This article proposes a Natural Language Processing (NLP) -based decision model for the identification of the best higher education institute using MCDM methods. The existing decision models for the selection of the best higher education institutions consider a limited number of criteria for decision-making. In this proposed model, 17 criteria and 15 institute datasets have been identified for the development of the decision model through extensive research and experts opinion. The NLP-based text analysis approach is applied to extract the relevant information and convert it to a suitable format. As the relative importance of the criteria plays a crucial role in decision-making, CRITIC and Rank centroid methods are applied for the calculation of relative weights of criteria. TOPSIS method is used to generate the ranking grades of alternatives for each criterion. An objective function is defined to calculate the evaluation scores and select the best institute for higher education. It has been observed that the ranks obtained from the developed model match pretty well with the ranks obtained from other MCDM methods and the experts.
A Blockchain-Based Framework for Developing Traceability Applications towards Sustainable Agriculture in Vietnam

Recently, many governments in the world have been focusing on building sustainable agriculture to improve the life quality of farmers and significantly increase their income. In Vietnam, however, the farmers still face the problems of “good harvest–low prices, and vice versa” and lack capital for scaling or transforming the production model. One of the main reasons for this phenomenon is that the price of agricultural products does not depend on farmers’ efforts but is based on the purchase price of the trader or the market price. Besides, the farmers also maintain farming habits based on regional culture or follow trendy and profitable agricultural products. Those production strategies make this type of product oversupplied, leading to a down in price shortly, so the farmers’ income will decrease. The above problems stem from the lack of information and communication tools between actors in the agricultural value chain, especially between cooperatives, farmers, and consumers. This paper presents a Blockchain-based framework for developing a traceability solution as an effective method of communication between actors in the agricultural value chain toward a sustainable agricultural model. The proposed approach helps to fully convey the production and distribution of agricultural products and the ability to verify traceability information, thereby helping to increase prices and protect the brand of agricultural products.
Decision Tree Algorithm for Visual Art Design in a Psychotherapy System for College Students

With the development of society, psychological health becomes a basic standard for a college student to grow into a qualified person. This study is aimed at using data mining principles and methods to excavate the factors that lead to psychological problems of college students, to purposefully carry out psychological interventions, use visual art design methods to promote college students' psychological health treatment, and build a perfect system of college students' psychological treatment. Based on the theories of data mining, we built a data analysis model, elaborated the data preprocessing method, and applied the Apriori algorithm to analyze the data of obsessive-compulsive symptoms and interpersonal sensitivity symptoms, and various psychological problem attributes extracted the strong association rules and analyzed the results. Take advantage of the corresponding unique school environment and educational advantages, to build a set of mental health education methods suitable for modern college students, and truly enable them to obtain satisfactory psychological interventions in a reasonable art design treatment phase. Based on the above association pattern mining results, a series of preprocessing operations were performed on the data, and then, the Apriori algorithm was applied to discover the potential association relationships among 9 psychological dimension factors of college students, and then, the ID3 decision tree algorithm was used to construct a decision tree and pruning process, from which the classification rules of students' psychological problems were analyzed and discovered. These studies provide some practical reference basis for school counseling work.
Survey of Techniques on Data Leakage Protection and Methods to address the Insider threat

Abstract Data leakage is a problem that companies and organizations face every day around the world. Mainly the data leak caused by the internal threat posed by authorized personnel to manipulate confidential information. The main objective of this work is to survey the literature to detect the existing techniques to protect against data leakage and to identify the methods used to address the insider threat. For this, a literature review of scientific databases was carried out in the period from 2011 to 2022, which resulted in 42 relevant papers. It was obtained that from 2017 to date, 60% of the studies found are concentrated and that 90% come from conferences and publications in journals. Significant advances were detected in protection systems against data leakage with the incorporation of new techniques and technologies, such as machine learning, blockchain, and digital rights management policies. In 40% of the relevant studies, significant interest was shown in avoiding internal threats. The most used techniques in the analyzed DLP tools were encryption and machine learning.
Digital Competence of Special Education Teachers: Impact, Challenges and Opportunities

Abstract The digital training of teachers in general, and especially special education teachers, has become a key axis for transforming the education system in favour of quality education, equality and equity. This study provides a systematic review of the literature in four databases (Scopus, ERIC, Dialnet and Web of Science) on the level of digital competence of special education teachers during the period 2010–2021. A total of 25 studies were analysed. The results were organised according to the description of the studies (year of publication, country of production, methodological approach) and the conceptual analysis of the network, which allowed us to assess the impact, challenges and opportunities of the digital competence of special education teachers. The main results of the review show digital competence as a pending subject for special education teachers. In this sense, this review includes suggestions that can be carried out when developing training actions to improve the level of digital competence of special education teachers. This training is crucial for the learning and academic success of students with special educational needs.
Hybrid regularization model combining overlapping group sparse second-order total variation and nonconvex total variation

Restoring the original image from an observed noisy or blurred version is a very important issue in image processing. The variational regularization methods obtained by functional minimizing are popular and widely used for image restoration with promising accuracy, but they may suffer from limited performances due to the presence of the conflict between noise-and-blur-removing and detail-and-texture-preserving. To solve this problem, a hybrid variational regularization model is proposed to effectively reconstruct image textures and details while eliminating noise and blur. The proposed model uses an overlapping group of sparse second-order total variation (TV) as the regularizer to restore image textures and details, whereas the nonconvex TV serves as the other regularizer to preserve image edges and contours. An alternating directional multiplier method algorithm is developed to numerically solve the proposed model, where the majorization–minimization and the iterative reweighted l1 (IRL1) algorithm are used to solve the overlapping group and the nonconvex minimization subproblem, respectively. The numerical experiments validate the proposed model and algorithm. In addition, compared with several state-of-the-art image restoration models, the proposed model shows the competitive performance in terms of PSNR, SSIM, and NIQE values.
Fusing machine learning with place-based survey methods: revisiting questions surrounding perceptual regions

This article explores questions on perceptions of the location of the 'Midwest', a contested vernacular region of the United States. We created a custom online survey with R's web framework Shiny, in which participants were presented with a blank web map and asked to 'draw' their definition of the Midwest. Instead of simply describing the aggregated results, we employ machine learning algorithms – Naive Bayes, Random Forest and Categorical Boosting – in an attempt to classify users into groups, with a focus on the features that most effectively separate responses. We also demonstrate a way to engineer features from a single spatial response question and provide an implementation through a small R package. Furthermore, we discuss misclassified observations and suggest some driving factors in the construction of regional perception. This research is important not only for its contribution to perceptual regions but also for the approach, which could be applied to place-based survey analysis more broadly.
Design of Data Sharing Platform Based on Blockchain and IPFS Technology

With the continuous development of the information age, data sharing and exchange are gradually increasing. The Internet and big data technology provide a guarantee for data sharing and transmission. At present, as the amount of data increases rapidly, how to realize data sharing has become a huge challenge. To solve this problem, this paper proposes a data sharing platform based on the combination of blockchain and interplanetary file system (IPFS) technology to solve the data sharing and storage. Firstly, by constructing the alliance blockchain, the consensus mechanism of computing power competition is used to maintain the data written into the blockchain, and the IPFS data storage system is established to store data using distributed storage, file splitting, and splicing technologies. Secondly, a data sharing platform composed of blockchain module, IPFS module, encryption and decryption module, and fast retrieval module is built. Data encryption is processed by encryption and decryption module, and the processed data is uploaded to THE IPFS module; the abstract and other information are finally written into the blockchain through the blockchain module. The fast retrieval module can quickly locate the required data according to the retrieval conditions in the mass blockchain data; finally, the security and storage of data sharing platform are guaranteed through security and performance evaluation. The research results solve the problem of large amount of data sharing, realize the data decentralization, and ensure the data storage security.
From community networks to shared networks: the paths of Latin-Centric Indigenous networks to a pluriversal internet

This article examines, with ethnographic lenses, the emergence of shared networks in the Tseltal and Zapoteco communities in Chiapas and Oaxaca (Mexico). ‘Shared networks’ are first-mile signal-sharing practices that articulate interconnection infrastructure and values of coexistence to, in the cases studied, extend the internet to areas where the services of existing larger internet service providers are unsatisfactory or unavailable. It argues that by infrastructuring their own local networks and interconnecting to the global internet, Tseltal and Zapoteco people are effectively internet codesigners, building Latin-Centric Indigenous networks and shaping internet governance from below. When comunalidad values, supported by unlicensed frequencies of the electromagnetic spectrum, towers, radio antennas, houses’ rooftops, routers, and cables, intersect with the values of the internet service providers and their policies, hybrids emerge. Shared networks are a result of what these hybrids enact and constrain, as well as evidence of the vivid struggles for a more inclusive and pluriversal internet.
Customizable Encryption Algorithms to Manage Data Assets Based on Blockchain Technology in Smart City

The adoption of blockchain technology can provide data asset management’s high security, privacy, and traceability. After a comprehensive investigation of the present blockchain-based data asset management mechanism, it was determined that it is only relevant to a portion of the blockchain system design. To address this issue, a new model of data asset management based on blockchain technology is being developed, which incorporates applications at all levels of the blockchain system. This model implements a network layer node authority control mechanism, a consensus layer consensus mechanism with customizable attributes, improved data query efficiency at the data layer by optimizing the structure and building indexes, intelligent data management, and smart contract layer management. Furthermore, at the transaction layer, sharing information encryption using customizable encryption algorithms is introduced. The experimental results reveal that, when compared to the traditional paradigm, the new blockchain-based data asset management strategy enhances the efficiency of on-chain data queries by 2.33 times.
Automatic Text Summarization for Public Health WeChat Official Accounts Platform Base on Improved TextRank

With the rapid development of we-media information dissemination, WeChat official accounts platform has become an important way for people to obtain health related knowledge. However, the platform information is redundant, miscellaneous, and overloaded. In order to meet the increasingly accurate and efficient knowledge service needs of users, reorganizing and aggregating document knowledge resources is effective. If we use the way of artificial recognition to filter information, it will inevitably cause huge labor and time cost, and the effect is very little in front of massive articles. This paper proposes a text summarization method for the WeChat platform based on improved TextRank that takes into account both user demands and sentence features during the summarization process. The data source crawled from the Sogou WeChat platform. The results show that the TextRank algorithm has obvious hints on the accuracy of text summarization extraction after fusing the Word2vec model. The improved TextRank method, integrating user demands and sentence features into the model, makes the results of text summarization closer to the theme of the article and more able to meet the user demand. According to the complexity of the algorithm, this method is not suitable for the automatic summarization of long or multiple documents.
Cryptanalysis of a Certificateless Hybrid Signcryption Scheme and a Certificateless Encryption Scheme for Internet of Things

In wireless and mobile networks with limited storage and computing resources, certificateless cryptography has more advantages because of its low dependence on infrastructure and short security parameters. Recently, Gong et al. and Karati et al., respectively, proposed a new certificateless scheme in the Internet of Things environment, one of which is a certificateless hybrid signcryption scheme, and the other’s basis is a certificateless encryption scheme. Gong et al. and Karati et al. gave the formal security proof for their schemes, respectively. In this article, the attack algorithms against these two schemes are presented separately, thus proving that their schemes are insecure and not suitable for the Internet of Things environment.
LEGAN: A Light and Effective Generative Adversarial Network for medical image synthesis

Medical image synthesis plays an important role in clinical diagnosis by providing auxiliary pathological information. However, previous methods usually utilize the one-step strategy designed for wild image synthesis, which are not sensitive to local details of tissues within medical images. In addition, these methods consume a great number of computing resources in generating medical images, which seriously limits their applicability in clinical diagnosis. To address the above issues, a Light and Effective Generative Adversarial Network (LEGAN) is proposed to generate high-fidelity medical images in a lightweight manner. In particular, a coarse-to-fine paradigm is designed to imitate the painting process of humans for medical image synthesis within a two-stage generative adversarial network, which guarantees the sensitivity to local information of medical images. Furthermore, a low-rank convolutional layer is introduced to construct LEGAN for lightweight medical image synthesis, which utilizes principal components of full-rank convolutional kernels to reduce model redundancy. Additionally, a multi-stage mutual information distillation is devised to maximize dependencies of distributions between generated and real medical images in model training. Finally, extensive experiments are conducted in two typical tasks, i.e., retinal fundus image synthesis and proton density weighted MR image synthesis. The results demonstrate that LEGAN outperforms the comparison methods by a significant margin in terms of Fréchet inception distance (FID) and Number of parameters (NoP).