Reviewerly/exHarmony huggingface dataset:
Multi-level Contrastive Learning Framework for Sequential Recommendation

Sequential recommendation (SR) aims to predict the subsequent behaviors of users by understanding their successive historical behaviors. Recently, some methods for SR are devoted to alleviating the data sparsity problem (i.e., limited supervised signals for training), which take account of contrastive learning to incorporate self-supervised signals into SR. Despite their achievements, it is far from enough to learn informative user/item embeddings due to the inadequacy modeling of complex collaborative information and co-action information, such as user-item relation, user-user relation, and item-item relation. In this paper, we study the problem of SR and propose a novel multi-level contrastive learning framework for sequential recommendation, named MCLSR. Different from the previous contrastive learning-based methods for SR, MCLSR learns the representations of users and items through a cross-view contrastive learning paradigm from four specific views at two different levels (i.e., interest- and feature-level). Specifically, the interest-level contrastive mechanism jointly learns the collaborative information with the sequential transition patterns, and the feature-level contrastive mechanism re-observes the relation between users and items via capturing the co-action information (i.e., co-occurrence). Extensive experiments on four real-world datasets show that the proposed MCLSR outperforms the state-of-the-art methods consistently.
Applying digital technologies for work management of young scientists’ councils

The publication explores the features of the digital technologies’ usage to organize the work of the Young Scientists’ Councils and describes the best practices. The digital transformation of society and the quarantine restrictions caused by the COVID-19 pandemic have forced the use of various digital technologies for scientific communication, the organization of work for youth associations, and the training of students and Ph.D. students. An important role in increasing the prestige of scientific activity and encouraging talented young people to participate in scientific projects belongs to the Young Scientists’ Councils, which are created at scientific institutions and higher education institutions. It is determined that the peculiarities of the work of Young Scientists’ Councils are in providing conditions for further staff development of the institution in which they operate; contribution to the social, psychological and material support of young scientists and Ph.D. students; creating an environment for teamwork and collaborative partnership; development of leadership and organizational qualities; contribution to the development of digital competence. The advantages of using electronic social networks in higher education and research institutions are analyzed, namely: general popularity and free of charge; prompt exchange of messages and multimedia data; user-friendly interface; availability of event planning functions, sending invitations, setting reminders; support of synchronous and asynchronous communication between network participants; possibility of access from various devices; a powerful tool for organizing the learning process; possibility of organization and work of closed and open groups; advertising of various events, etc. Peculiarities of managing the activity of the Young Scientists’ Council with the use of digital technologies are determined. The Young Scientists’ Council is a social system, and therefore the management of this system refers to social management. The effectiveness of the digital technologies’ usage to manage the activities of the Young Scientists’ Council depends on the intensity and need for their use to implement organizational, presentation functions and to ensure constant communication. The areas to apply digital technologies for the work managing of Young Scientists’ Councils are sorted as the presentation of activity; distribution of various information for young scientists; conducting questionnaires, surveys; organization and holding of scientific mass events; managing of thematic workgroups, holding of work meetings. It is generalized and described the experience of electronic social networks usage for organizing and conducting of scientific mass events.
The use of cloud computing technology in professional training of future programmers

The article provides a brief analysis of the current state of the study of cloud technologies by future software engineers at foreign and Ukrainian universities. The author experience in the application of cloud technologies in the training of future software engineers in Ukraine is presented. The application of cloud business automation systems, online services to monitor the implementation of the software projects, Google services for collaboration, planning and productivity while studying professional disciplines and carrying out diploma projects is described. Based on the survey conducted at Stackoverflow, the state of application of cloud technologies by software engineers around the world has been analyzed. The cloud technologies that are not studied at the analyzed universities of Ukraine and those that are not popular with software developers in the world, but studied at Ukrainian universities by future software engineers are outlined. Conclusions are made on the modernization of training programs for future software engineers. Topics for the study of cloud technologies by future software engineers in the content of professional disciplines are proposed.
Normalization for fitch-style modal calculi

Fitch-style modal lambda calculi enable programming with necessity modalities in a typed lambda calculus by extending the typing context with a delimiting operator that is denoted by a lock. The addition of locks simplifies the formulation of typing rules for calculi that incorporate different modal axioms, but each variant demands different, tedious and seemingly ad hoc syntactic lemmas to prove normalization. In this work, we take a semantic approach to normalization, called normalization by evaluation (NbE), by leveraging the possible-world semantics of Fitch-style calculi to yield a more modular approach to normalization. We show that NbE models can be constructed for calculi that incorporate the K, T and 4 axioms of modal logic, as suitable instantiations of the possible-world semantics. In addition to existing results that handle 𝛽-equivalence, our normalization result also considers 𝜂-equivalence for these calculi. Our key results have been mechanized in the proof assistant Agda. Finally, we showcase several consequences of normalization for proving meta-theoretic properties of Fitch-style calculi as well as programming-language applications based on different interpretations of the necessity modality.
Asynchronous Manual Work in Mixed Reality Remote Collaboration

Research in Collaborative Virtual Environments (CVEs) is becoming more and more significant with increasing accessibility of Virtual Reality (VR) and Augmented Reality (AR) technology, additionally reinforced by the increasing demand for remote collaboration groupware. While the research is focusing on methods for synchronous remote collaboration, asynchronous remote collaboration remains a niche. Nevertheless, future CVEs should support both paradigms of collaborative work, since asynchronous collaboration has as well its benefits, for instance a more flexible time-coordination. In this paper we present a concept of recording and later playback of highly interactive collaborative tasks in Mixed Reality (MR). Furthermore, we apply the concept in an assembly training scenario from the manufacturing industry and test it during pilot user experiments. The pilot study compared two modalities, the first one with a manufacturing manual, and another using our concept and featuring a ghost avatar. First results revealed no significant differences between both modalities in terms of time completion, hand movements, cognitive workload and usability. Some differences were not expected, however, these results and the feedback brought by the participants provide insights to further develop our concept.
Blockchain solution benefits for controlling pandemics: Bottom-up decentralization, automation with real-time update, and immutability with privacy preservation

The current COVID-19 pandemic has created turmoil around the world. To fight this ongoing global crisis and future ones, all stakeholders must collaborate and share timely and truthful information. This paper proposes a blockchain solution based on its inherent technological advantages. We posit that benefits can be derived from three unique blockchain features: bottom-up decentralization, automation with real-time update, and immutability with privacy preservation. A decentralized common platform provides easy access and increases participation in disease surveillance, which reduces the estimation errors of the compartmental model parameters. Automation with real-time update facilitates prompt detection and diagnosis, accurate contact tracing, and targeted mitigation and containment, achieving faster recovery and slower transmission. Being immutable while preserving privacy, the blockchain solution enhances respondents’ willingness to truthfully report their contact history, avoiding false and erroneous data that will cause wrong estimates on pandemic transmission and recovery. Thus, the blockchain solution mitigates three types of risks: sample variance, delay, and bias. Through simulation, we quantify the value of the blockchain solution in these three aspects. Accordingly, we provide specific action plans based on our research findings: before building blockchain solutions for controlling COVID-19, governments and organizations can calculate the blockchain benefits and decide whether or not they should invest in such blockchain solutions by conducting a cost-benefit analysis.
Gaze-Vergence-Controlled See-Through Vision in Augmented Reality

Augmented Reality (AR) see-through vision is an interesting research topic since it enables users to see through a wall and see the occluded objects. Most existing research focuses on the visual effects of see-through vision, while the interaction method is less studied. However, we argue that using common interaction modalities, e.g., midair click and speech, may not be the optimal way to control see-through vision. This is because when we want to see through something, it is physically related to our gaze depth/vergence and thus should be naturally controlled by the eyes. Following this idea, this paper proposes a novel gaze-vergence-controlled (GVC) see-through vision technique in AR. Since gaze depth is needed, we build a gaze tracking module with two infrared cameras and the corresponding algorithm and assemble it into the Microsoft HoloLens 2 to achieve gaze depth estimation. We then propose two different GVC modes for see-through vision to fit different scenarios. Extensive experimental results demonstrate that our gaze depth estimation is efficient and accurate. By comparing with conventional interaction modalities, our GVC techniques are also shown to be superior in terms of efficiency and more preferred by users. Finally, we present four example applications of gaze-vergence-controlled see-through vision.
Sign Language Recognition Method Based on Palm Definition Model and Multiple Classification

Technologies for pattern recognition are used in various fields. One of the most relevant and important directions is the use of pattern recognition technology, such as gesture recognition, in socially significant tasks, to develop automatic sign language interpretation systems in real time. More than 5% of the world's population-about 430 million people, including 34 million children-are deaf-mute and not always able to use the services of a living sign language interpreter. Almost 80% of people with a disabling hearing loss live in low- and middle-income countries. The development of low-cost systems of automatic sign language interpretation, without the use of expensive sensors and unique cameras, would improve the lives of people with disabilities, contributing to their unhindered integration into society. To this end, in order to find an optimal solution to the problem, this article analyzes suitable methods of gesture recognition in the context of their use in automatic gesture recognition systems, to further determine the most optimal methods. From the analysis, an algorithm based on the palm definition model and linear models for recognizing the shapes of numbers and letters of the Kazakh sign language are proposed. The advantage of the proposed algorithm is that it fully recognizes 41 letters of the 42 in the Kazakh sign alphabet. Until this time, only Russian letters in the Kazakh alphabet have been recognized. In addition, a unified function has been integrated into our system to configure the frame depth map mode, which has improved recognition performance and can be used to create a multimodal database of video data of gesture words for the gesture recognition system.
Technology of forming future journalists’ social information competence in Iraq based on the use of a dynamic pedagogical site

The article reveals scientific approaches to substantiating and developing technology to form social information competence of future Iraqi journalists based on using a dynamic pedagogical site. After pre-interviewing students of the Journalism Faculty at Al-Imam Al-Kadhim University College for Islamic Sciences in Baghdad, the authors came to the conclusion there are issues on defining the essence of social information competences. It is established that the majority of respondents do not feel satisfied with the conditions for forming these competences in the education institutions. At the same time, there were also positive trends as most future journalists recognized the importance of these professional competences for their professional development and had a desire to attend additional courses, including distance learning ones. Subsequently, the authors focused on social information competence of future journalists, which is a key issue according to European requirements. The authors describe the essence of this competence as an integrative quality of personality, which characterizes an ability to select, transform information and allows to organize effective professional communication on the basis of the use of modern communicative technologies in the process of individual or team work. Based on the analysis of literary sources, its components are determined: motivational, cognitive, operational and personal. The researchers came to the conclusion that it is necessary to develop a technology for forming social information competence of future journalists based on the use of modern information technologies. The necessity of technology implementation through the preparatory, motivational, operational and diagnostic correction stages was substantiated and its model was developed. The authors found that the main means of technology implementation should be a dynamic pedagogical site, which, unlike static, allows to expand technical possibilities by using such applications as photo galleries, RSS modules, forums, etc. Technically, it can be created using Site builder. Further research will be aimed at improving the structure of the dynamic pedagogical site of the developed technology.
SoK: Fully Homomorphic Encryption over the [Discretized] Torus

First posed as a challenge in 1978 by Rivest et al., fully homomorphic encryption—the ability to evaluate any function over encrypted data—was only solved in 2009 in a breakthrough result by Gentry (Commun. ACM, 2010). After a decade of intense research, practical solutions have emerged and are being pushed for standardization.This paper explains the inner-workings of TFHE, a torus-based fully homomorphic encryption scheme. More exactly, it describes its implementation on a discretized version of the torus. It also explains in detail the technique of the programmable bootstrapping. Numerous examples are provided to illustrate the various concepts and definitions.
The usage of digital technologies in the university training of future bachelors (having been based on the data of mathematical subjects)

This article demonstrates that mathematics in the system of higher education has outgrown the status of the general education subject and should become an integral part of the professional training of future bachelors, including economists, on the basis of intersubject connection with special subjects. Such aspects as the importance of improving the scientific and methodological support of mathematical training of students by means of digital technologies are revealed. It is specified that in order to implement the task of qualified training of students learning econometrics and economic and mathematical modeling, it is necessary to use digital technologies in two directions: for the organization of electronic educational space and in the process of solving applied problems at the junction of the branches of economics and mathematics. The advantages of using e-learning courses in the educational process are presented (such as providing individualization of the educational process in accordance with the needs, characteristics and capabilities of students; improving the quality and efficiency of the educational process; ensuring systematic monitoring of the educational quality). The unified structures of “Econometrics”, “Economic and mathematical modeling” based on the Moodle platform are the following ones. The article presents the results of the pedagogical experiment on the attitude of students to the use of e-learning course (ELC) in the educational process of Borys Grinchenko Kyiv University and Alfred Nobel University (Dnipro city). We found that the following metrics need improvement: availability of time-appropriate mathematical materials; individual approach in training; students’ self-expression and the development of their creativity in the e-learning process. The following opportunities are brought to light the possibilities of digital technologies for the construction and research of econometric models (based on the problem of dependence of the level of the Ukrainian population employment). Various stages of building and testing of the econometric model are characterized: identification of variables, specification of the model, parameterization and verification of the statistical significance of the obtained results.
Decomposition of Matrix Product States into Shallow Quantum Circuits

The rapid pace of recent advancements in numerical computation, notably the rise of GPU and TPU hardware accelerators, have allowed tensor network (TN) algorithms to scale to even larger quantum simulation problems, and to be employed more broadly for solving machine learning tasks. The "quantum-inspired" nature of TNs permits them to be mapped to parametrized quantum circuits (PQCs), a fact which has inspired recent proposals for enhancing the performance of TN algorithms using near-term quantum devices, as well as enabling joint quantum-classical training frameworks which benefit from the distinct strengths of TN and PQC models. However, the success of any such methods depends on efficient and accurate methods for approximating TN states using realistic quantum circuits, something which remains an unresolved question. In this work, we compare a range of novel and previously-developed algorithmic protocols for decomposing matrix product states (MPS) of arbitrary bond dimensions into low-depth quantum circuits consisting of stacked linear layers of two-qubit unitaries. These protocols are formed from different combinations of a preexisting analytical decomposition scheme with constrained optimization of circuit unitaries, and all possess efficient classical runtimes. Our experimental results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods, with even greater benefits seen in the setting of limited computational resources. Given these promising results, we expect our proposed decomposition protocol to form a useful ingredient within any joint application of TNs and PQCs, in turn further unlocking the rich and complementary benefits of classical and quantum computation.
WEB PAGE RECOMMENDATION SYSTEM BY INTEGRATING ONTOLOGY AND STEMMING ALGORITHM

In this research, we offer a customized-recommendation system that uses item representations and user profiles based on the ontologies that provide personalized services to semantic applications. To develop and implement the personalized-recommendation system, a system that uses the representations of the items and the user profiles based on the ontologies to provide the semantic applications with personalized services. Recommendation systems can use semantic reasoning capabilities to overcome present system limits and increase the quality of recommendations. The recommender makes use of domain ontologies to improve personalization: on the one hand, a domain-based inference method is used to model user interests more effectively and accurately; on the other hand, a semantic similarity method is used to improve the stemmer algorithm, which is used by our content-based filtering approach, which provides a measure of the affinity between an item and the user. In recommender systems and web personalization, Web Usage Mining is crucial. This study presents an effective recommender system based on ontology and web usage mining. The approach's first step is to extract features from online documents and build on related ideas. Then, they create an ontology for the website using the concepts and relevant terms retrieved from the records. The semantic similarity of web documents is used to group them into multiple semantic themes, each with its own set of preferences. The suggested solution incorporates ontology and semantic knowledge into Web Usage Mining and personalization procedures, as well as a stemming algorithm, and gets an overall accuracy of 90%.
Autonomous Obstacle Avoidance Path Planning for Grasping Manipulator Based on Elite Smoothing Ant Colony Algorithm

Assembly robots have become the core equipment of high-precision flexible automatic assembly systems with a small working range. Among different fields of robot technology, path planning is one of the most important branches. In the present study, an elite smoothing ant colony algorithm (ESACO) is proposed for spatial obstacle avoidance path planning of the grasping manipulator. In this regard, the state transition probability and pheromone update strategies are improved to enhance the search capability of path planning symmetry and the convergence of the algorithm. Then a segmented B-spline curve is presented to eliminate path folding points and generate a smooth path. Finally, a manipulator control system based on the Arduino Uno microcontroller is designed to drive the manipulator according to the planned trajectory. The experimental results show that the performance of the ESACO algorithm in different scenarios has symmetry advantages, and the manipulator can efficiently complete the simulation trajectory with high accuracy. The proposed algorithm provides a feasible scheme for the efficient planning of manipulators in equipment manufacturing workshops.