Reviewerly/exHarmony huggingface dataset:
Investigating experiences of Afghan female university students towards online harassment

The principal aim of this study was investigating experiences of Afghan female university students towards online harassment, its effects and coping strategies. Also, it aimed to study the differences of participants’ experiences by age, class, and first language. The data were gathered from 150 undergraduate female students of Takhar University randomly via a Likert scale questionnaire containing 36 statements. SPSS version 25.0 was used for analyzing the data. Descriptive statistics were employed to determine the frequency and percentage. Inferential statistics one way-ANOVA test was also used to determine the difference in the experience of participants by age, class (year of study), and first language toward the issue. The outcome of the study revealed that 54–81 percent of the respondents that forms the majority have not experienced online harassment. Also the finding denoted that the effects of online harassment on the victims are considerably low because the majority of them have not experienced online harassment. Besides, the result indicated that forty five percent of the partakers agreed to use some coping strategies against online harassment such as: talking face to face with the harasser, talking online or via phone, reporting the case to the administrator of university, searching Internet for advice, asking their teachers to include online harassment preventive matters in their teachings’ programs, cleaning the received online messages, ignoring and keeping silence, deleting from their contacts individuals that they do not like to keep contact with them, and sharing the cases of harassment with their friends or family members. Moreover, the outcome represented that there is not any considerable distinction in the experience of participants by age, class, and first language concerning online harassment.
Open-source software for repositories

The modern science depends in many ways on efficient provision of fast, quality access to published research findings. The universities have been developing their institutional repositories that are mostly based on open-source software used in over 66% repositories, OpenDOAR reports. For their digital archives, these organizations choose between various possibilities: purchasing licenses, developing their own or using the open-source software. The author analyzes the use of software in institutional repositories of the world leading universities and compares these trends to that of foreign and national institutions. She focuses on the open-source software as that standing up the transition to the open science concept. The structure of 210 university repositories was reviewed. It was found that the open-source software was the most popular with Russian universities while their platforms selection was rather limited as compared to that of foreign universities. Further, unlike foreign universities, Russian higher educational institutions almost never develop their own repositories though have strong potential to do so.
Criteria for Assessing the Effectiveness of Blended Learning Technologies Used at the University

Introduction. The active use of blended learning technologies in universities has highlighted the problem of their effectiveness. The solution to this problem envisages further clarification of the distinctive features of blended learning technology and selection of performance indicators. The purpose of research paper is to justify the selection and define assessment criteria for effectiveness of blended learning technology, and to develop a multi-level system of indicators of their manifestation in university educational practice. Materials and Methods. Methods of systematic analysis of scholarly literature, grouping scientific information, modeling, point-rating scaling, comparison and methods of mathematical statistics. It also includes the results of an online survey conducted among students and teachers at Russian universities and the results of a content analysis of the open-ended question “Blended learning technology is effective if ...” of the “Unfinished sentences” methodology. Results. Within the framework of the complex approach, the selection of effective, didactic and resource criteria for assessing the effectiveness of blended learning technology have been substantiated. It was revealed that students prefer the didactic, and then the resource criterion in assessing the effectiveness of blended learning technologies, and teachers consider the resource criterion to be a priority, followed by the didactic one. Discussion and Conclusion. The research results contribute to the development of scientific ideas about the system of criteria for assessing the effectiveness of blended learning technology and increasing the level of reasonability of managerial decisions on their application in the higher education system. The article materials will be useful for university teaching staff and managers, as well as researchers concerned with analysis and assessment of blended learning and modern educational technologies’ effectiveness.
Vernadsky and Rubakin: Identity of bibliocultural interests and mentality

The prominent Russian scientists, Vladimir I. Vernadsky (1863–1945), whose 160-th anniversary we celebrate this year, and Nikolay A. (Nikolas) Rubakin (1862–1946), whose 160-th anniversary we celebrated last year, were both library workers and bibliographers. They both entered St. Petersburg Imperial University, the department for natural sciences of physics and mathemetics faculty, communicated closely when students, and maintained friends and scientific ties up to 1936. The cosmological and cosmographic ideas of Dmitry I. Mendeleev, their teacher, influenced them both. Like N. Rubakin, V. I. Vernadsky saw selfless service to the humankind as his supreme goal. In his studiesw of psychology, Rubakin came to the conclusion that human life passes at three levels, namely biological, social, and cosmical level. In his turn, Vladimir Vernadsky is among acknowledged adept of Russian cosmism and father of the teaching of noosphere. The humanitarian trend in Venradsky’s world outlook manifested as early in his life as did his interest in the natural sciences. He developed his strong humanist, social ethical ideals in his early years as the scientist and thinker. These two persons were both engaged in bibliographic work, synchronically for some period. Vladimir Vernadsky was among the founders of Moscow Commission for Home Reading of the Educational Department of the Society to Promote Technical Knowledge (1893); Nikolas Rubakin was a founder of the Department to Support Self-education at Petersburg Pedagogical Museum of Military Educational Institutions (1894). They both took the most active part in writing bibliographies of general and supplementary literature within the curricula of the two institutions. Vernadsky and Rubakin had their original attitude towards library work in the country. V. Vernadsky sent regularly scientific books and journals to Rubakin in Switzerland. Rubakin came into contact with Georgy, V. Vernadsky’s son, who was living in the U.S.Almost Vernadsky’s every scientific achievement and social merits were acknowledged officially, unlike N. Rubakin who had no academic honors at all. Nevertheless, their contribution to the Russian and world culture is of the same value
The concept of new information system of SB RAS State Public Scientific and Technological Library based on IRBIS64+

The authors review the principles and technologies of information retrieval systems implemented in Russia and abroad, with special attention to the function of e-library and interactive selective information dissemination. The main project aim is to build single comfortable information retrieval space integrating full-text abstracts, bibliographic resources legally acquired, structured and offered by SB RAS State Public Scientific and Technological Library.Based on the analysis of user search behavior of the Library’s information retrieval system based on IRBIS WEB-module and of the functional distinctions between IRBIS64+ and IRBIS WEB-module, the authors review the demands to contemporary e-catalogs and e-libraries, parameters of new information retrieval system, and stages of its implementation. The new information retrieval system based on IRBIS64+ WEB-module will provide access to all resources comprising literature in-flow to the Library with significant search potential, and guarantee their comfortable use by various user groups. IRBIS64+ WEB-module in the Library’s information retrieval system will provide searching in full-texts, and enable to make queries using Boolean logics, to setup distance between words, to rank search results, and to specify them with faceted navigation. The users will be also offered to use thematic search by navigators (GRNTI, thematic navigators of databases). The e-library is to provide open access to information resources of RAS SB SPSTL, eLIBRARY, and other open resources. The digital collections are to preserve printed original copies, which is important for old and rare publications.
Neural Style Transfer with Content Feature Segmentation

In our work we propose a new model by segmenting content features (i.e., segregating the feature map into several parts) of the content image in style transfer. We demonstrate that our novel way for style transfer with segmentation ties together the style of both global effect and local appearance that are used in majority of parametric and non-parametric techniques respectively. Our new model can successfully figure out the difficult problems with the current transfer techniques. For one thing, the new model in our research is able to avoid the distortion of local style maps, at the same time it also allows transfer in the level of semanteme compared to parametric means. For another thing, in contrast to other nonparametric approaches, it keeps the style image's overall appearance consistent and avoids washing-out images. We also present a feature optimization strategy based on this. Our trials have proven that our approach is suitable to a wide range of style genes and simultaneously yields higher-quality results over earlier papers.
Deep Learning Based Text Classification Methods

Text classification tasks are indispensable in natural language processing. With the development of Internet technology, the way people transmit information has changed from letters to the Internet. With the increase in the amount of information, manual data annotation is inefficient. After 2010, the emergence of deep learning methods has brought text classification into an epoch-making stage. ReNN-&gt; MLP-&gt; RNN-&gt; CNN-&gt; Attention -&gt; Transformer-&gt; GNN and other text classification methods are gradually being developed and known by everyone, which also shows that text classification is developing towards a text feature that does not rely on manually acquired text features and is directly learning from the text content and modelling. This paper will start with basic knowledge, first let everyone understand the nature, application and historical background of text classification, will briefly introduce shallow learning, then enter deep learning, Select the classic model from the six classification methods from ReNN to Transformer for brief analysis, briefly analyse the model from the principle of the model, what problems it is good at solving, problems or shortcomings that it is not good at solving. Finally, the paper describes the performance of these models on the dataset.
Reinforcement Learning for Improving Flappy Bird Game

Currently, Artificial Intelligence becomes popularity among the human daily life, like games, Internet and so on. Authors has shown that in the game filed the Artificial Intelligence always have better performance than human beings, so in this article, the author wants to use AI to carry out on an old- fashion fame called Flappy Bird. This study aims to determine the specific method why AI has better performance than human beings. In this context, the author based on the process of experiments: It mainly used reinforcement learning model (Acting based on feedback from the environment, through continuous interaction with the environment, trial, and error, to ultimately accomplish a specific purpose or to maximize the overall benefits of the action) and supervised learning model (the process of making the machine learn a large amount of sample data with labels, training a model and making the model get the corresponding output according to the input) to improve the Flappy Bird and both two method are belonging to the machine learning. In addition, this study alters the layout of the game, including pipe, appearance of agent, and background of the game in order to make a more fashionable game. Furthermore, this study increases the number of agents, which makes it easier for agent to achieve higher score. Last but not the least, author establish a archive point, which means if the player face operation mistake and lead to game over, they bird will relive before passing the last pipe.
MBTI Personality Prediction Based on BERT Classification

Young people today tend to express their feelings and socialize on the internet instead of in real life, which makes social media practical in defining one's personality since their expressions usually exhibit their personalities. Predicting people's personalities based on their posts is a relatively challenging task requiring large quantities of processing data and modeling. This paper uses two word-embedding methods, BERT classification and TF-IDF Vectorizer, and three models, including Logistic Regression, K-Nearest Neighbors, and Random Forest Classifier, to find this task's state of the art method. In this case, with BERT classification, the state-of-the-art method for most of the Natural Language Processing(NLP) tasks, Logistic Regression is the best-performing model with an average accuracy of 87 percent.
Encouraging prosocial behavior from older adults through robot teleoperation: A feasibility study

Introduction In Japan, the social climate surrounding older adults has gotten worse as a result of the spread of COVID-19 and the growing isolation of older adults who are increasingly unable to engage in prosocial behavior through work and volunteering. This is detrimental to the physical and mental well-being of older adults. The purpose of this study is to look into robot teleoperation for older adults as a viable way to deal with these issues and overcome the barriers preventing older adults from engaging in prosocial behavior. Materials and methods We designed and tested a remote-control approach for dialogue agents that is appropriate for older adults as well as evaluating their impressions in a real-world setting. Twelve older adults participated in experiments in two separate locations, a children's center and the city ward office, where they could remotely teleoperate a robot and have conversations with the visitors. In the city ward office, the older adults had a conversation with the visitors and gave them information and trivia quizzes about the city. In the children's center, older adults had conversations with children regarding their age, family, their likes, and dislikes. A questionnaire and interview were set up after the experiments to understand their impressions of the system and to clarify how older adults feel about certain issues regarding remote-controlled work, starting a new job, social interaction, to what extent have older adults been affected by the pandemic, how and in what ways has it affected their involvement in society, and whether teleoperating a robot can be a suitable approach to encourage prosocial behavior from them through volunteer work and social engagement. Results The results show that older adults have a strong desire to engage in volunteer work, but are hampered mainly by physical isolation resulting from COVID-19 restrictions and their declining physical and mental health. Their impressions of the teleoperation system were highly positive, as they enjoyed having conversations with children through the robot. With this teleoperation system, older adults were able to remote control a robot by themselves without major issues. It made interaction simpler as conversing with children through a robot added a layer of anonymity that allowed older adults to express themselves freely without worrying about how they are perceived by others in public. Discussion Older adults were able to successfully engage in prosocial behavior through remote-controlling a robot. The system seems to be effective at easing the physical barriers preventing older adults from engaging in volunteer work, which have worsened since the spread of COVID-19.
Email Spam Filtering Methods: Comparison and Analysis

Email is a common way of communication due to its cheap cost, efficacy, and efficiency. With the emerging of deep learning and machine learning methods, spam filter classification achieves boosting performance with fast inference speed. However, individuals and email servers are affected by spam, which causes issues with wasted time and computer storage space, as well as adverse effects on bandwidth. Even worse, email users are susceptible to scams and fraud that may result in financial loss. Therefore, it is essential to discover an efficient approach to filter spam from the entire number of emails. The purpose of this study is to evaluate and contrast the five most popular machine learning based spam filtering techniques, including Naive Bayes, Supported Vector Machine K-Nearest Neighbor, and XGBoost. We evaluate them based on their performance and efficacy. We hope this paper will help to conclude the current condition and help the researchers to improve better algorithms with higher accuracy.
Comparison of Performances for Quantum and Conventional Algorithms: Shor’s algorithm and Boson Sampling

Quantum computing has been extensive popularity discussed recently. The foundation of quantum study is the investigation of how quantum computing shows its superiority compared with conventional algorithms. On this basis, this paper focuses on the comparison of the performances of quantum and conventional algorithms about the same relevant area. Two classic problems, factorization of large integer and boson sampling, are chosen to show the specific difference of related quantum and conventional algorithms towards the same problem. This paper specifically compares the performance of Shor's algorithm and other advanced traditional integer factorization algorithms for prime factorization problems and compares the processing capabilities of traditional algorithms and Gaussian Boson sampling for sampling problems. According to the analysis, for the same research problem, quantum algorithms show strong advantages over traditional algorithms in many performances, e.g., time efficiency, algorithm adaptability. It leads to a deeper understanding of quantum superiority and a further explanation of the significance of the development of quantum computing.
Comparisons of Conventional Computing and Quantum Computing Approaches

Quantum computers are capable of ultra fast computation in the fields where classical computers fail. Even though quantum computers are nowhere near commercialization, many researchers have developed quantum algorithms in fields such as modern encryption and molecular simulation, which, in theory, are exponentially faster than their classical counterparts. In this case, this paper will discuss the advantages of quantum computers over classical computers in those fields by examining and analyzing the various quantum algorithms. To be specific, the develop routine as well as detail examples will be exhibited to illustrate the differences and preferences. In addition, this study will also fully aware of the challenges that quantum computing researchers are facing. On this basis, possible limitations of quantum computers are also presented. The aim is to promote interest in quantum computing by introducing their supremacy in modern encryption and biological science. These results shed light on guiding further exploration of quantum computing algorithms.
Analysis and Recognition of Human Gait Activity Based on Multimodal Sensors

Remote health monitoring plays a significant role in research areas related to medicine, neurology, rehabilitation, and robotic systems. These applications include Human Activity Recognition (HAR) using wearable sensors, signal processing, mathematical methods, and machine learning to improve the accuracy of remote health monitoring systems. To improve the detection and accuracy of human activity recognition, we create a novel method to reduce the complexities of extracting features using the HuGaDB dataset. Our model extracts power spectra; due to the high dimensionality of features, sliding windows techniques are used to determine frequency bandwidth automatically, where an improved QRS algorithm selects the first dominant spectrum amplitude. In addition, the bandwidth algorithm has been used to reduce the dimensionality of data, remove redundant dimensions, and improve feature extraction. In this work, we have considered widely used machine learning classifiers. Our proposed method was evaluated using the accelerometer angles spectrum installed in six parts of the body and then reducing the bandwidth to know the evolution. Our approach attains an accuracy rate of 95.1% in the HuGaDB dataset with 70% of bandwidth, outperforming others in the human activity recognition accuracy.
Clean-label poisoning attack with perturbation causing dominant features

Numerous studies have proved that deep learning models are sensitive to attacks from adversarial examples. However, due to the high computationally demanding and limiting explanation for deep neural networks (DNNs), research on poisoning attacks against deep models is insufficient. In this work, considering the interpretation that DNNs are feature extractors, we propose a clean-label poisoning attack against DNNs by dominant image features added to the original data. Each category of clean data is augmented with imperceptible perturbations which we call “dominant image features” of another category. The dominant image features determine which class the input belongs to, while the clean input behaves like noise. Well-trained DNNs on these poisoning data will get extremely low accuracy on clean test data. The attack can make personal data unlearnable for DNNs, which preserves an individual's privacy. Besides, our data poisoning attack can be expanded into a powerful clean-label backdoor attack in that the poisoned samples and their labels are consistent for humans. Experimental results show that our proposed attack can exceed the previous advanced poisoning attacks which demonstrate the effectiveness of our presented attack. Additionally, our work provides a new perspective on the interpretation of DNNs as feature extractors.
HUBO formulations for solving the eigenvalue problem

Solving the eigenvalue problem is particularly important in almost all fields of science and engineering. With the development of quantum computers, multiple algorithms have been proposed for this purpose. However, such methods are usually only applicable to matrices of specific types, such as unitary or Hermitian matrices. The quantum annealer of the D-Wave, a quantum computer, returns the minimum value of the quadratic unconstrained binary optimization (QUBO) model. Thus, quantum annealers can be leveraged to solve arbitrary eigenvalue problems by formulating corresponding QUBO models. In this paper, we propose two higher-order unconstrained optimization (HUBO) formulations to solve eigenvalue problems involving n×n general matrices. In addition, we use a formula to reduce the order and convert the HUBO model into a QUBO model. Further, by using a quantum approximate optimization algorithm, this method can be extended to a gate-model quantum computer.