Reviewerly/exHarmony huggingface dataset:
Towards an adaptable framework for mobility assistive technologies

This study approaches adaptability for mobility assistive technologies by proposing an adaptable framework for mobility assistive technologies, inspired by ACT-R, the desiderata for developing cognitive architectures as highlighted by R. Sun and Vernon et al. and human behaviour. This study proposes an adaptable framework for shared control and autonomous mobility as the first step towards the formation of a comprehensive adaptive framework. This study proposes a minimalist four-module adaptive framework consisting of the perception/motor module, the cognitive module, the memory module, and the action module. The minimum requirements for the implementation of this framework on an intelligent wheelchair for shared/collaborative control were highlighted. Although an overview of the proposed adaptive framework for mobility assistive technologies was presented, there are still numerous concerns to address. One of the main issues remains the most suitable connectionist and symbolic process to be used in the hybrid framework, which will be investigated in future work. In addition, it is still essential to resolve details of the adaptation/learning process in the framework, so it will be necessary to develop learning mechanisms in cooperation with reward systems as well as observation/inference systems for the framework.
A Novel Binary Black Hole Algorithm for Feature Selection of Scientific and Technical Text

Selecting effective features from the raw attributes is an important step in scientific and technical text classification. Feature selection aims to pick the best subset of candidate attributes to achieve superior performance of the learning algorithm. Recently, many meta-heuristic algorithms were widely developed for feature selection in classification problems. Black Hole Algorithm (BHA) is an emerging meta-heuristic algorithm, inspired by the mechanism of star motion around black holes, which has achieved excellent performance on a variety of optimization problems. A novel Binary Black Hole Algorithm (BinBHA) was proposed in this work, to enhance the efficiency of searching optimal features from the high-dimensional attributes of scientific and technical texts. In BinBHA, all operators are binary encoded without continuous-binary transformation, which makes it better for exploring the solution space of discrete problems and finding globally optimal solutions. The proposed algorithm has been compared with several alternative state-of-art methods for feature selection, and the effectiveness of the approach was evaluated on several benchmark datasets. In this paper, BinBHA was successfully implemented on an actual scientific and technical text dataset, and achieved the best performance results among other algorithms. The experimental results demonstrated that the proposed binary encoding method can improve the prediction accuracy significantly while finding the optimal solution effectively.
New Areas of Blockchain Technology Applications During the COVID-19 Pandemic

The article is devoted to the analysis of new directions of application of blockchain technology that have emerged in the conditions of the Covid-19 pandemic, generalization of existing experience in using blockchain and definition of tasks for blockchain technology development. The paper analyzes the properties of blockchain technology, manifested in the created systems, which can solve several management problems in the process of combating the Covid-19 pandemic. The article describes the Covid-19 pandemic fight and their possible actions. The paper provides analysis and systematization of new areas of blockchain application, such as tracking contact with infected people, verification of vaccine authenticity, maintaining of the clinical trial data integrity, identifying counterfeit medicines and personal protective equipment, identifying foci of infection. The authors describe examples of specific solutions, showed that the blockchain technology can play an innovative role in solving a few problems that appeared during the pandemic. The existing limitations are revealed both in the blockchain technology itself and in the conditions of its application, which do not allow to fully realize the full potential of the technology. The description of new areas of application of blockchain technology is important both for the organizers of the COVID-19 pandemic response, and for researchers and developers of systems using blockchain technology.
Encrypting and Predicting Medical Diagnosis Data

With the rapid development in technology across various branches, data security is the rising concern. Currently, in the healthcare sector, users can contact doctors/hospitals, study or analyze symptoms using wide range of applications and information available on internet, and also occasionally predict diseases. Individuals can easily drop by an appropriate portal and enter their symptoms which then subsequently applies machine learning algorithm to the mentioned data and finally calculate chances of disease incidence. By such a process one may encounter several problems., as these hardly employ any successful data protection services. Therefore, there is a need to maintain the privacy of the individual. Here we focus on designing a CatBoost model with homomorphic encryption for protecting the patient data to make it more secure. Thus finally providing encrypted prediction of diagnosis which is efficient and effective.
Low Resolution Hand Gestures Recognition of Bengali Sign Alphabet by Using a Convolutional Neural Network

Sign language is an essential tool for the deaf and the hard of hearing community of approximately 1.33 billion people. Due to this fact, researches have been conducted for decades for near-accurate recognition of sign characters. Sensor-based approaches and vision-based approaches have been adapted so far for tackling this dilemma. Sensor-based approaches can obtain high performance but it is costly and demands physical contact to sensors. On the other hand, vision-based approaches are not costly, need no contact but have not yet been able to produce a high accuracy like sensor-based approaches. The dilemma of sign characters recognition gets more problematic for Bengali sign language as not many datasets regarding Bengali sign language are available. Moreover, not many significant contributions can be found in this domain like other popular languages such as English, Turkish, Japanese, and Indian sign language. Furthermore, one of the most popular Bengali sign language datasets, Ishara-Lipi, consists of a few low-resolution samples. This study is focused on recognizing the low-resolution hand gestures of Bengali sign language. In this research, a convolutional neural network has been proposed which is suitable for the recognition of low-resolution sign gestures. Experimental results showed that the proposed approach achieved 99.08%, 99.38%, and 99.07% overall accuracy for digits, characters, and both digits and characters of the Ishara-Lipi dataset respectively.
Evaluating the Spatial Continuity Effects of Augmented Reality System on Learning Performance and Psychological Factors

According to the cognitive theory of multimedia learning, the spatial continuity principle can reduce extraneous cognitive load. It can also help to enhance learning. This study performed comparative analyses on two mobile augmented reality learning systems based on the learning performance, cognitive load, and technology acceptance using the spatial continuity principle. A pretest, posttest, delayed test, cognitive load questionnaire, and technology acceptance questionnaire were employed for the evaluation. The learning performance results employed for the evaluation that the learners of the AR learning system designed using the spatial continuity principle showed greater improvement in the meaning of compound verbs and knowledge retention. This supports the spatial continuity principle. The questionnaire’s data demonstrated a high acceptance of the mobile AR language learning system by the learners. Additionally, in this study, the cognitive load questionnaire’s statistical analysis results revealed that the adoption of the spatial continuity principle did not reduce extraneous cognitive load.
Experience Report on Improving Software Engineering Education

Improving and adapting strategies to teach Software Engineering (SE) insights is one of the primary goals of SE schools. Every year, we come up with plenty of studies that provide suggestions and guidelines on improving teaching methods, problem-solving strategies, and curriculum design. On the other hand, we do not know how the world&#x2019;s top-notch universities focus on the process and academic improvement. This study shares our experience while investigating the Tsinghua University (QS World ranked 17)<sup>1</sup>, School of Software process and providing an initial guideline for further improvement. In this study, we, first, aim to gather suggestions and guidelines given in the literature. Secondly, we observe and analyze the feedback given by external reviewers and, as an example, map those feedback-augmented findings and suggestions onto school processes. Lastly, we suggest how the software school can further enhance the teaching strategies, curriculum design, and problem solving skills.
ISPREC: Integrated Scientific Paper Recommendation using heterogeneous information network

Due to the rapid expansion of online scientific articles, researchers have got into trouble finding reliable articles that are relevant to their research interests. Recently, a group of scientific paper recommendation algorithms has been proposed to solve this issue. But, they have two main shortcomings. First, they can only recommend papers to experienced researchers who have published some papers and not amateur ones. Second, they ignore some valuable sources of information in scientific article libraries. This paper presents a novel Integrated Scientific Paper RECommendation approach, called ISPREC, which integrates different pieces of information as a novel heterogeneous network structure, called SPIN. Thereafter, exploits a limited random-walk algorithm for a Top-N recommendation. Extensive experiments on a real-world dataset demonstrate a significant improvement of the proposed framework of ISPREC compared to the state-of-the-art scientific paper recommendation algorithms.
Understanding the Outside World: Customers and the Surrounding Environment

An idea for a start-up or a new product development is only a good idea if it meets the needs of the target customer. The starting point should therefore always be the consumer. It is important to focus on concrete target groups as there is no “one size fits all”. Knowing and describing these target groups appropriately requires a lot of research. We therefore explain the persona concept for illustrating target groups in this chapter. We also present techniques on how to research consumers’ underlying needs. Furthermore, it is important to analyse the consumers’ and the company’s environment. Based on established frameworks in research and practice, we will discuss the relevance of various forces in the micro- and macroenvironment that tentatively affect the success of a business model.
Creative Cyber Risk Management

The main aim of this paper is to analyse creative cyber risk management phenomena and identify the most frequently used techniques by management in the private and public sector. The used creative techniques and motivation of managers was identified through in-depth interviews with security experts responsible for identifying and analysing cyber risks in many organizations. The main motive for the using such techniques of creative cyber risk management by managers is the deliberate reduction of the value of risk to an acceptable level.
Automatic Exam Answer Checker using Optical Character Recognition and Sentence Embedding

The old-fashioned manual system of answer script correction is tiresome and time-consuming. Human resources are consumed and wasted. The automatic exam answer checker evaluates the answers and efficiently allocates the marks. Exam management authorities have access to post questions and provide an answer key based on which the system evaluates solutions. The system uses several technologies such as Cloud computing, Optical Character Recognition, and Web Development and Hosting. The handwritten answers are uploaded, or typed solutions are sent to the system. The responses are evaluated according to the answer key provided by the exam authorities. Solutions are uploaded on a webpage where students can login using unique credentials and view their results. The system is user friendly and efficient
Robust Adversarial Classification via Abstaining

In this work, we consider a binary classification problem and cast it into a binary hypothesis testing framework, where the observations can be perturbed by an adversary. To improve the adversarial robustness of a classifier, we include an abstain option, where the classifier abstains from making a decision when it has low confidence about the prediction. We propose metrics to quantify the nominal performance of a classifier with an abstain option and its robustness against adversarial perturbations. We show that there exist a tradeoff between the two metrics regardless of what method is used to choose the abstain region. Our results imply that the robustness of a classifier with an abstain option can only be improved at the expense of its nominal performance. Further, we provide necessary conditions to design the abstain region for a 1-dimensional binary classification problem. We validate our theoretical results on the MNIST dataset, where we numerically show that the tradeoff between performance and robustness also exist for the general multi-class classification problems.
Optimism of characters in Andrea Hirata's Novel Entitled “Orang-Orang Biasa”: An Overview of Literature Psychology

Optimism is an attitude or pattern of positive thinking towards a problem or problem with strong self-defense so that self-confidence grows. This optimistic mindset tends to be less owned by someone for several reasons: lack of human resources, which makes a person not confident in making a decision. This study describes the aspects and functions of the character's optimism in Andrea Hirata's Orang-Orang Biasa novel. The approach used in this analysis uses literature psychology theory, which focuses on the optimism aspect proposed by Goleman. The type of research used is qualitative research with the data source is the novel Orang-Ordinary by Andrea Hirata. The data collection method used a documentation study, namely by reading extensively and repeatedly to get the content by the desired study. The analysis technique is by reducing the data or selecting the data, presenting the data in quotations, and drawing conclusions as the final stage of the research. Based on the results of the study, it shows that the optimism aspect in Andrea Hirata's Orang-Orang Biasa novel is shown in various values, namely (1) having high expectations, (2) not being easily discouraged, (3) being able to motivate oneself, high self-confidence and ( 4) not being resigned while the function of optimism in the novel is (1) as a means of motivation to strengthen oneself, (2) as part of a form of self-defense to overcome life's problems and (3) optimism as a support system.
ADVANCED EDU-AR-VIZ: a Framework for Selecting Appropriate Visual Augmentations in STEM Education

In today’s world, there is a growing demand for professionals with skills in Science, Technology, Engineering and Mathematics. At the same time, teaching STEM subjects can be a challenging task, as students struggle to understand abstract concepts and complex phenomena in lectures and textbooks. This has given rise to a new approach to learning called active learning, with the use of interactive simulations and three-dimensional visualisations to improve conceptual understanding. In this paper, we focus on the use of mobile AR as a tool to facilitate active learning for a new generation of learners. We introduce a framework for choosing appropriate visual augmentations for educational purposes. As a practical use case, we describe the design, development and evaluation of a mobile AR Android app for learning about magnetism. Our findings indicate that the use of AR on mobile phones is indeed interesting to students, even with prior knowledge of magnetism.
Membership, syndromic and causality graphs to represent the emergency physician’s reasoning

The reasoning of an emergency doctor is very particular because he often has no prior knowledge (he generally knows nothing about the patient), he has little time to understand the situation, and he must, by means of questioning, quickly make a decision based on diagnostic hypotheses and an estimate of the seriousness of the patient’s condition. In a previous paper we presented the 10 step reasoning model of an emergency physician used by the symptom checker MedVIR. This model is represented by a membership graph which describes the clinical information that helps physicians in their diagnosis by allowing them to ask the essential questions.This paper extends membership graphs to syndromic vision as well as to causality graphs. Syndromic graphs enable the simplification of the memorization of a disease whereas causality graphs make it possible to define the first cause of a disease, the chain of pathological processes, the syndromes which constitute it. These graphs enable the clinician to have a global view of diseases belonging to different and unrelated specialities and they will be able to contribute to the training of doctors and health professionals.
A Denoising Method for Hyperspectral Image Based on Group Sparse Representation

Hyperspectral remote sensing is developing rapidly and widely used in various areas. However, hyperspectral images are always disturbed by noise, and the hot topic about how to denoise the hyperspectral data cube arises. This paper takes the challenge and proposes a denoising method based on group sparse representation by analyzing the characteristics of hyperspectral data. The proposal takes into account the correlation of different spectral bands and denoise the hyperspectral data effectively by grouping, training adaptive dictionary and sparse coding. In this paper, a fast clustering method is researched and a group sparse coding method based on improved Orthogonal Matching Pursuit is proposed. Experiments prove that the proposed algorithm suppresses the noise in the hyperspectral image and achieves high peak signal to noise ratio with high speed.
Conflict Potential in Higher Education: Factors of Emergence and Growth

In this article, the author discusses the problems of conflicts in the field of higher education the analysis of the factors of their emergence and growth. The definition of social conflict potential and its difference from conflict are given. The content of modern Russian scientific literature devoted to this issue is analysed, making it possible to determine the most significant factors in the emergence of conflict potential. The most important factor is called the socio-political and economic upheavals of Russian society caused by perestroika, the collapse of the USSR, the breakdown of all established systems of society, and numerous educational reforms. There is a high degree of psychological tension in the teaching environment and discomfort caused by a high degree of responsibility, increasing loads of classroom and research work assigned to teachers, emotional burnout of teachers, which creates conditions for a high degree of readiness for deconstructive behaviour. The author states the polarisation of the value-semantic guidelines of the participants in the educational process, the commercialisation of education, its positioning as a service, the increasing social stratification, the tendency towards an increase in the elitisation of higher education, which not only generate conflict factors, but also determine the differentiation of demand for education. It is concluded that the factors of conflict can develop into social conflicts if the accumulated contradictions are not resolved in the future.
Multi-color Space Medical Endoscope Image Enhancement Method

Aiming at the problems of low contrast between the vascular part and the tissue part of the medical endoscope, and the vessels and lesions are not obvious enough, this paper proposes an improved method of medical endoscopic image enhancement based on the characteristics of medical images. First, we convert the image from RGB space to HSV space, split the V channel into a low frequency part and a high frequency part, use gamma correction for the low frequency part to adjust the brightness, denoise the high frequency part, and then do saturation correction on the S channel. Then, the image is converted from the HSV space to the YCrCb space, and the Y channel is weighted CLAHE to enhance the contrast. Next, we convert the image from YCrCb space to RGB color space. Finally, the Sobel operator edge detection is performed on the image, and the detected edge is enhanced with a mask to highlight the details. We use multiple sets of pictures to evaluate images from a subjective and objective perspective. The experimental results show that the algorithm proposed in this paper not only enhances the blood vessel, but also retains the authenticity of the image, and the effect is satisfactory.
A Privacy-Preserving Protocol for Proximity-Based Services in Social Networks

A number of real-life social networks provide the users with proximity-based services. This feature exposes to seri-ous privacy threats, because it could allow massive tracking from an honest-but-curious provider. Whilst proximity-based services have been deeply studied in the literature in a general setting, no solution (to the best of our knowledge) has been provided to the problem of delivering privacy-preserving proximity-based services entirely within existing social networks. The problem is not trivial, because a social network provider can play as a global passive adversary, monitoring the flow of all the messages exchanged in the network. Therefore, to allow proximity testing between Alice and Bob not requiring that they reveal their position to the social network provider is not enough. Indeed, even the fact that proximity testing is performed between Alice and Bob (independently of the result) is a serious privacy leakage. In this paper, we provide a solution preventing also this privacy leak, giving thus a concrete way to implement privacy-preserving proximity-based services in social networks.
Deep Learning Methodologies For Human Activity Recognition

Human activity recognition (HAR) is a field that has shown great attention in recent years. The main reasons are the high demand in several application domains, and the HAR process makes use of the time-series sensor data to deduce activities. In this paper, three main deep learning methodologies are proposed based on RNN architecture. The three methodologies are based on the long-term short memory (LSTM), Bi-directional long short-term memory (Bi-LSTM), and gated recurrent unit (GRU). The proposed methodologies are capable of classifying six main movements with acceptable performance. Data were collected from 30 subjects with 6 main activities obtained from them. Five main classifiers are applied to test the performance of these methodologies, and these classifiers are the random trees, random forests, k-nearest neighbor, artificial neural network (ANN), and support vector machine (SVM). The highest accuracy was achieved using BiLSTM based on ANN classifier reaching an accuracy of 95.2155%. Several performance measurements are provided to test the methodologies' recognition capability. A comparison with other related works is done to exploit how the proposed methods are capable of providing a reasonable accuracy for HAR.