Reviewerly/exHarmony huggingface dataset:
Evaluation of Student Performance Using Fuzzy Logic

This paper presents a new approach based on fuzzy reasoning; logic in order to have a better evaluation of the student's school or college. The ability to predict the performance of the students is an important issue for both the students and the university. A more appropriate and accurate assessment, it helps to have a better understanding of the necessary education and training to the students which helps the students to get a better performance, not only academic, but also in other areas of life. In this system, fuzzy logic, this means that the student takes the assessment; the performance should be a reasonable and acceptable performance in front of him. The previous methods that can be used to estimate only the most recent data available, that is, the prediction was not accurate, it is compared with the fuzzy logic approach. In the present system, the fuzzy logic is good enough to be dominated by the old ways. This paper describes a new approach for the prediction of the normal learning process and the learner's behavior, which can be easily accessible and easy to interpret.
Secure Shell Remote Access for Virtualized Computing Environment

Information processing in the era of big data is inseparable from the effective support of scientific computing. Complex scientific computing requires cloud computing to provide computing resources. One of the core foundations of Cloud computing is secure and reliable remote access technology. Users often log in to the remote server for scientific calculation. However, when users log in with public key, the steps are cumbersome. Therefore, this project develops a secure shell remote access information system for virtualized computing environment, which is called SSHRA for short. The system enables users to log in to the remote server more conveniently. The system can generate the corresponding certificate according to the public key provided by the user. Users use certificates for remote login. Users can obtain certificates through web or email. In addition, this system also designs an intelligent connection between multi hop servers. The system improves the security of remote login by limiting the IP, validity and available commands of the certificate. After users log in to the remote server with the certificate provided by the system, they can use commands to perform related operations. The system is developed based on open source software, so it has good scalability.
Attentive Feature Focusing for Person Search by Natural Language

Cross-modal Retrieval has made great improvement since social media and artificial intelligence is gradually changing our life style. Human interaction is no longer limited to a single mode. Cross-modal retrieval brings greater portability to users. Extracting more sufficient feature from multimedia data and better fusing the feature have become the focus of academic research. In response to these two issues, this paper applies deep learning as the foundation for person search by natural language, combining CNN and Transformer. Meanwhile, we utilize BERT, the most effective feature extraction network in Natural Language Processing and proposed a network architecture using attention mechanism. The framework is divided into a global branch and a fine-grained branch. At the same time, a human parsing network is designed to classify different parts of the characters to obtain feature representation in attribute level. In the visual-textual alignment, the k-reciprocal sampling algorithm is used to construct a more comprehensive sample pair. The proposed framework has achieved state-of-the-art performance in CUHK-PEDES, which proves its great capacity and prospect.
Competitive positions of universities in the regional higher education market

The current stage of higher education development is featured by a complex and dynamic environment, new threats associated with the coronavirus pandemic. These challenges dictate the need for universities to maintain firm competitive positions. Taking into account the peculiarities of the higher education and the subjective assessment of a number of factors, it is necessary to adapt the models of assessing the competitiveness of educational organizations to the specifics of the market. The purpose of the article is to modernize and test Porter's model of Five Competitive Forces for the education market system. The object of the study is the educational services market of the Sverdlovsk region. The most significant attention in the proposed model is paid to identifying and describing the features and structure of the five forces of competition. The authors propose to consider the power of internal and external consumers of university educational services as the power of customers, and the threat of online education and the creation of corporate universities of large companies as a threat from substitutes. The main attention of the authors is paid to an empirical study of the competitiveness of brands of universities in the Sverdlovsk region using the results of a survey of 177 graduates of the 11th grade of Yekaterinburg, conducted in 2020. The analysis of the applicants' motivation showed that the most important motives in choosing a university are the possibility of free education and the qualification of the teaching staff, further material benefits, such as fair wages and favorable employment. The most popular sources of information for applicants are the official website of the high school, as well as relatives, friends and acquaintances. As a result of the analysis, it is concluded that it is necessary to develop a marketing strategy to increase the competitive position, awareness and the level of public confidence of the university. The authors suggest universities to personalize relationships with students in order to increase loyalty to the university, use information technology to implement flexible programs of interaction with consumers of educational services.
Embodying an artificial hand increases blood flow to the investigated limb

<ns4:p><ns4:bold>Background: </ns4:bold>The autonomic nervous system is the main determinant of the blood flow directed towards a body part, and it is tightly connected to the representation of the body in the brain; would the experimental modulation of the sense of ownership of the limb affect its blood perfusion?</ns4:p><ns4:p> <ns4:bold>Methods: </ns4:bold>In healthy participants, we employed the rubber hand illusion paradigm to modulate limb ownership while we monitored the brachial artery blood flow and resistance of the investigated limb.</ns4:p><ns4:p> <ns4:bold>Results: </ns4:bold>In all conditions with brush-stroking, we found an initial drop in the blood flow due to tactile stimulation. Subsequently, in the illusion condition where both the rubber and real hand experience synchronous brush-stroking, the blood flow rose significantly faster and reached significantly higher values. Moreover, the increase in blood flow correlated to the embodiment level measured by questionnaires and, negatively, to the change of peripherical vascular resistance.</ns4:p><ns4:p> <ns4:bold>Conclusions: </ns4:bold>These findings demonstrate that modulating the representation of a body part impacts its blood perfusion.</ns4:p>
Universal Watermark Attack in Image Classification

Recent researches have shown it is possible to fool a deep neural network classifier by adding watermark perturbations. The watermark can be used to protect copyright and has practical meaning, but currently, there is no method that can generate universal watermark perturbation. The main reason is that the fooling rate is no differentiable for the perturbations. In this paper, we adopt the loss function trick and mask function trick to generate universal watermark perturbation then evaluate its fooling rate with different watermark shapes, locations, numbers and deep neural network architectures. The results show the universal watermark perturbations achieve good performance and can be successfully used to generate universal watermark adversarial examples.
Empirical Analysis of Sentencing for Crimes of Infringing Citizens' Personal Information Based on SPSS

Normative, equal and reasonable sentencing for crimes of infringing on citizens' personal information is essential for protecting citizens' personal information rights and interests. An empirical study by randomly selecting sentencing judgments for 205 Defendants of crimes of infringing on citizens' personal information in basic-level courts across the country in 2021 found that in the context of increasingly strict personal information protection, there are large differences in terms of fixed-term imprisonment in judicial practice. Probation applies strictly. Other characteristics include that the types and amounts of personal information, type of criminal behavior, amount of illegal income and so on are inconsistent in the degree of sentencing. This article considers the influence of factors influencing the application of fixed-term imprisonment and probation, and establishes regression model, in order to provide some guidance for the people's courts on the sentencing of such crimes.
Advancing digital technologies in healthcare

The COVID-19 pandemic has catalyzed the pace and scope of digital technology (DIT) use in healthcare. It has facilitated health promotion, disease prevention, diagnosis and treatment of secondary health concerns, patient engagement, monitoring treatment adherence, and surveillance. COVID-19-related large databases and medical and public health research have been shared freely and rapidly. Continued adoption of technology can lead to better and faster diagnosis of health conditions and accelerate the attainment of sustainable development goals (SDGs). However, many barriers still remain. Key issues are high costs, lack of interoperability of technology, frequent need for software updates, training and development, concerns about privacy, technological disruption, and network coverage issues. Greater involvement of end users in the development and rollout of new digital technologies is needed to ensure faster and deeper implementation of technology in healthcare as uneven implementation can exacerbate the divide between the haves and the have-nots.
A Hybrid Recommender System based on DeepCF and Wide Linear Model for K12 Online Education

Compared to traditional classroom education, online education has the advantage of personalized teaching and is able to significantly improve learning efficiency and outcomes. K12 online education refers to online education serving students across 12 grade levels, from primary school to high school. As there are a plethora of courses offered on online education platforms, how to recommend proper courses for students is a serious challenge. In order to resolve this problem, this study proposes a hybrid recommendation model that combines the deep collaborative filtering (DeepCF) model and the wide linear model. Together, in the hybrid model, these models can integrate course features, student features, and side information. The DeepCF model learns low-dimension latent representations for both courses and students and integrates them into matrix factorization to predict ratings. The wide linear model uses a factorization machine to design and select features automatically. The hybrid model can achieve good performance and alleviate the problem of sparse features and cold start. Experimental results demonstrate that compared with the collaborative filter model, the hybrid model achieved a significant improvement with a 12.7% relative increase in AUC metric.
Exploiting Knowledge from Code to Guide Program Search

Human code is different from code generated by program search. We investigate if properties from human-generated code can guide program search to improve the qualities of the generated programs, e.g., readability and performance. Here we focus on program search with grammatical evolution, which produces code that has different structure compared to human-generated code, e.g., loops and conditions are hardly used. We use a large code-corpus that was mined from the open software repository service GitHub and measure software metrics and properties describing the code-base. We use this knowledge to guide the search by incorporating a new selection scheme. Our new selection scheme favors programs that are structurally similar to the programs in the GitHub code-base. We find noticeable evidence that software metrics can help in guiding evolutionary search.
A RNN-Based Hyper-heuristic for Combinatorial Problems

Designing efficient heuristics is a laborious and tedious task that generally requires a full understanding and knowledge of a given optimization problem. Hyper-heuristics have been mainly introduced to tackle this issue and are mostly relying on Genetic Programming and its variants. Many attempts in the literature have shown that an automatic training mechanism for heuristic learning is possible and can challenge human-based heuristics in terms of gap to optimality. In this work, we introduce a novel approach based on a recent work on Deep Symbolic Regression. We demonstrate that scoring functions can be trained using Recurrent Neural Networks to tackle a well-know combinatorial problem, i.e., the Multi-dimensional Knapsack. Experiments have been conducted on instances from the OR-Library and results show that the proposed modus operandi is an alternative and promising approach to human-based heuristics and classical heuristic generation approaches.
Decentralisation

A system such as bitcoin operates using a Proof of Work based consensus mechanism that many individuals have falsely claimed limits the ability for the system to scale. As a result, alternative proposals based on Proof of Stake are misleadingly being promoted as scaling solutions. However, it will be demonstrated that proof of stake is not a scaling solution but is rather a means of reintroducing ownership and equity control through a back door creation of a digital bearer share. In this, an equity system based on anonymous holdings is constructed that allows for digital control of a corporate entity outside of the provisions of regulatory controls.
Wasserstein Adversarially Regularized Graph Autoencoder

This paper introduces Wasserstein Adversarially Regularized Graph Autoencoder (WARGA), an implicit generative algorithm that directly regularizes the latent distribution of node embedding to a target distribution via the Wasserstein metric. To ensure the Lipschitz continuity, we propose two approaches: WARGA-WC that uses weight clipping method and WARGA-GP that uses gradient penalty method. The proposed models have been validated by link prediction and node clustering on real-world graphs with visualizations of node embeddings, in which WARGA generally outperforms other state-of-the-art models based on Kullback–Leibler (KL) divergence and typical adversarial framework.
An Automated Recognition System of Sign Languages Using Deep Learning Approach

Speech-impaired persons frequently use hand-based gestures and movements to communicate. Regrettably, many people cannot understand the semantics of these signs. So, the communication between the hearing people and the deaf community is very challenging. Since the deaf community is generally less skilled while writing a spoken language, so the alternative of written communication is another challenge. Also, the face to face communication is very slow between the hearing and deaf people. Therefore, to compensate for this type of problem, we proposed an automated real-time Sign Language recognition system. The major objective of the given paper is to contribute and additionally advance to the field of automated Sign Language recognition systems. The main focus of our work is to recognize the signs or gestures. The proposed model was successful in surpassing state of the art testing. We can achieve the highest generalized testing accuracy of 98.56% on the validation data and 99.91% on the test data. We aspire to add Natural Language Processing so the model can make words and sentences out of the letters it recognizes, which will be more practical to use.
A Comparison of Input Devices for Precise Interaction Tasks in VR-based Surgical Planning and Training

We present a comparison of input devices for common interaction tasks in medical VR training and planning based on two relevant applications. The chosen devices, VR controllers, VR Ink, data gloves, and a real medical instrument, differ in their degree of spe-cialization and their grip. The conducted user study shows that the controllers and VR Ink performed significantly better than the other devices regarding precision. Concerning questionnaire results, no device stands out but most participants preferred the VR Ink for both applications. These results can serve as a guide to identify an appropriate device for future medical VR applications.
Teaching Addition and Subtraction in Preschool Using Mobile Learning

Learning to solve problems is of utmost importance in the development of human beings. Therefore, teaching problem-solving in preschool education is highly relevant because children's informal knowledge begins formalization here. This paper presents the results of mobile learning as a teaching strategy during the time of Covid-19 social confinements. The aim was to promote the knowledge of addition and subtraction problem-solving in a preschool group in Tuxpan, Veracruz, Mexico. The qualitative research used an active research design applied to a sample of 19 children in collaboration with their parents. They worked with different educational applications using smartphones. During two interventions, the preschoolers formulated their answers to mathematical addition and subtraction problems, improved their counting skills, and demonstrated knowledge of cardinal value, stable order, and uniqueness.
Online Malayalam Script Assortment and Preprocessing for Building Recommender Systems

An immense amount of research is happening in natural language processing across the globe. Availability of data is one of the major issues faced by researchers working in different languages worldwide. Huge volumes of data are being produced every second that is available on various servers and social networking sites. Though there are several methods to access data from these online repositories, the accessibility is limited and predefined, which may not be sufficient for the researchers and academia. This paper focuses on discussing methodologies to develop a customized data scraping algorithm in travel-related data from Facebook.com written in Malayalam Language, one of the prominent Dravidian languages in Indian state Kerala. The paper also focuses on preprocessing of data that is being scraped with the help of NLP techniques. Multiple requests have been raised to the server to fetch scattered and independent raw data from Facebook, and the algorithm succeeded in scraping sufficient information about posts details and user details.
Authentication of Mobile IoT Devices using HyperLedger Fabric Blockchain

The Internet of Things is a rapidly evolving field which gives rise to new needs related to devices management and security. In addition to being intelligent, devices become more and more mobile, and mobility could lead to security and performance issues if not correctly managed. In this paper, we propose an IoT authentication system considering mobile devices and using the Hyperledger Fabric Blockchain as a broker. Our system is scalable, and proproses two ways of managing device mobility. To demonstrate the reliability of our approachs, we provide a proof-of-concept based on a scenario example and an evaluation of the performance obtained.
Design of Computer Intelligent Evaluation System for Spoken English Based on Cloud Computing

The design and teaching of spoken English computer intelligent evaluation system should start from the language social environment and language professional needs, learn from the traditional classroom teaching, give full play to the characteristics and advantages of micro courses, design micro objectives, micro teaching plans, micro courseware, micro exercises, micro reflection and other links, and pay attention to the cultivation of students' initiative and autonomous learning ability. Oral computer intelligent test can be divided into structuralist oral test and communicative oral test. Structuralist oral test mainly tests speaking skills and sub skills related to speaking. In ordinary teaching, for the evaluation of students' oral English, many teachers often pursue the accuracy of pronunciation too much, so that the final evaluation result is only a score or a ranking, which can not reflect the development of students' oral ability and promote oral English teaching. This paper designs a computer intelligent evaluation system of spoken English based on cloud computing. Users can realize the functions of learning spoken English knowledge, simulating spoken English test and establishing an exclusive dictionary through mobile terminals.
Deep Learning in Mobile Devices and the Blockchain Era: An Overview

Developments in deep learning and artificial intelligence are becoming important technologies for automated computing and mobile device innovation. It has given modern mobile devices the ability to conduct activities such as object detection and recognition, speech to text, and so on. This paper examines existing research on deep learning, machine learning and artificial intelligence to assess the level of deep learning in the mobile device environment. Furthermore, we examined the benefits of blockchain and smart contract in the mobile device environment and finally, the obstacles associated with the integration of artificial intelligence in the mobile device environment were presented, emphasizing the importance of addressing these issues in the future in order to establish a more safe and resilient framework.
Boosting Segmentation Performance across Datasets using Histogram Specification with Application to Pelvic Bone Segmentation

Accurate segmentation of the pelvic computerized tomography (CT) imaging is crucial for the clinical diagnosis of pelvic bone diseases and for planning patient-specific hip surgeries. Several methodologies have been proposed for such segmentation tasks with the emergence and advancements of deep learning for digital healthcare. But in a low data scenario, the lack of abundant data needed to train a Deep Neural Network is a significant bottleneck. In this work, we propose a methodology based on modulation of image tonal distributions and deep learning to boost the performance of networks trained on limited data. The strategy involves pre-processing of test data through histogram specification. This simple yet effective approach can be viewed as a style transfer methodology. The segmentation task uses a U-Net configuration with an EfficientNet-B0 backbone, optimized using an augmented BCE-IoU loss function. This configuration is validated on a total of 284 images taken from two publicly available CT datasets, The Cancer Imaging Archive (TCIA) and the Visible Human Project (VHBD). The average performance measures for the Dice coefficient and Intersection over Union are 95.7% and 91.9%, respectively, which give strong evidence for the effectiveness of the approach, which is highly competitive with state-of-the-art methodologies.