Reviewerly/exHarmony huggingface dataset:
IoMT-Based Smart Healthcare of Elderly People Using Deep Extreme Learning Machine

The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. There is a growing interest in providing solutions for elderly people living assistance in a world where the population is rising rapidly. The IoMT is a novel reality transforming our daily lives. It can renovate modern healthcare by delivering a more personalized, protective, and collaborative approach to care. However, the current healthcare system for outdoor senior citizens faces new challenges. Traditional healthcare systems are inefficient and lack user-friendly technologies and interfaces appropriate for elderly people in an outdoor environment. Hence, in this research work, a IoMT based Smart Healthcare of Elderly people using Deep Extreme Learning Machine (SH-EDELM) is proposed to monitor the senior citizens’ healthcare. The performance of the proposed SH-EDELM technique gives better results in terms of 0.9301 accuracy and 0.0699 miss rate, respectively.
Application of Blockchain Sharding Technology in Chinese Medicine Traceability System

Traditional Chinese Medicine (TCM) is one of the most promising programs for disease prevention and treatment. Meanwhile, the quality of TCM has garnered much attention. To ensure the quality of TCM, many works are based on the blockchain scheme to design the traceability scheme of TCM to trace its origin. Although these schemes can ensure the integrity, sharability, credibility, and immutability of TCM more effectively, many problems are exposed with the rapid growth of TCM data in blockchains, such as expensive overhead, performance bottlenecks, and the traditional blockchain architecture is unsuitable for TCM data with dynamic growth. Motivated by the aforementioned problems, we propose a novel and lightweight TCM traceability architecture based on the blockchain using sharding (LBS-TCM). Compared to the existing blockchain-based TCM traceability system, our architecture utilizes sharding to develop a novel traceability mechanism that supports more convenient traceability operations for TCM requirements such as uploading, querying, and downloading. Specifically, our architecture consists of a leader shard blockchain layer as its main component, which employs a sharding mechanism to conveniently TCM tracing. Empirical evaluations demonstrated that our architecture showed better performance in many aspects compared to traditional blockchain architectures, such as TCM transaction processing, TCM transaction querying, TCM uploading, etc. In our architecture, tracing TCM has become a very efficient operation, which ensures the quality of TCM and provides great convenience for subsequent TCM analysis and retrospective research.
Can multipartite entanglement be characterized by two-point connected correlation functions?

Abstract We discuss under which conditions multipartite entanglement in mixed quantum states can be characterized only in terms of two-point connected correlation functions, as it is the case for pure states. In turn, the latter correlations are defined via a suitable combination of (disconnected) one- and two-point correlation functions. In contrast to the case of pure states, conditions to be satisfied turn out to be rather severe. However, we were able to identify some interesting cases, as when the point-independence is valid of the one-point correlations in each possible decomposition of the density matrix, or when the operators that enter in the correlations are (semi-)positive/negative defined.
Photovoltaic Panel Deployment of Large-Scale Photovoltaic Power Plant Based on Meteorological and Topographical Factors

Developing and utilizing photovoltaic (PV) resources in accordance with local conditions is one of the effective methods to help power system reduce carbon. The paper discloses a PV panel deployment optimization method in large-scale PV power plants (PVPPs), including dividing the PVPP into multiple regions according to the topographical characteristics; based on the LSTM model with attention mechanism, forecast the PV power generation; eventually, the PV panels in the PVPP are deployed. When dividing the different regions in the PVPP, a variety of topographical features to reasonably divide each block into several regions. Then the effect of meteorological factors on the PV power generation in each region is quantified, to obtain a more precise value of PV power generation. The deployment of PV panels maximizes the use of existing PV resources, and increases effectiveness of PV output.
Competency approach in the extracurricular work of future language teachers

The article is devoted to topical issues of the organization of extracurricular work, which is one of the important factors in the successful implementation of educational and professional programs for the training of specialists in the field of language and literature education. The purpose of the research is to generalize the experience of extracurricular work at the Faculty of Ukrainian Philology and Journalism of the State Institution “Luhansk Taras Shevchenko National University”, specifically the experience of language and literature weeks. The types of activities provided by the analyzed forms of extracurricular work for higher education students are considered in the context of the competence approach to training future Ukrainian language and literature teachers. The article clarifies the competence potential of the forms of extracurricular work implemented within the framework of the programs of the “Ukrainian Language Week” and “Shevchenko Week” events. Considered various types of activities in which higher education students are involved both as participants and as organizers during the preparation and realization of creative competitions, scientific and practical conferences, literary and musical compositions, etc. The importance of these types of activities for the formation of language-communicative, subject-methodical, informational-digital, evaluative-analytical, projective, psychological, organizational and other professional competencies of the teacher of Ukrainian language and literature is outlined.
Attack Graphs &amp; Subset Sabotage Games

We consider an extended version of sabotage games played over Attack Graphs. Such games are two-player zero-sum reachability games between an Attacker and a Defender. This latter player can erase particular subsets of edges of the Attack Graph. To reason about such games we introduce a variant of Sabotage Modal Logic (that we call Subset Sabotage Modal Logic) in which one modality quantifies over non-empty subset of edges. We show that we can characterize the existence of winning Attacker strategies by formulas of Subset Sabotage Modal Logic.
Speech Recognition System of Specific Vocabulary

End-to-end speech recognition system can realize the conversion from input audio to character text without complicated data processing, and it is widely used in speech recognition field. In this paper, the speech recognition system of DeepSpeech2 is built by combining CNN network which is good at capturing sequence spatial features and RNN network which is good at capturing time sequence information. The system is trained based on the THCHS-30 Dataset, and achieves more accurate speech prediction by simplifying the process and enhancing the use of context information. Experiments based on this dataset show that the word error rate of this model is only 0.133, and the effect is very ideal.
INTEGRATION OF RESEARCH INFORMED COMPONENT IN THE TRAINING PROCESS OF FUTURE SPECIALISTS IN HIGHER EDUCATION ESTABLISHMENTS

The processes of world and European integration enter an increasingly large sphere of higher education in Ukraine. Our country has clearly defined the benchmark for entering the world and European educational environment, modernizing educational activities by the requirements set. This leads to scientifically based approaches to the training of specialists of the new generation. In conditions of Ukraine’s integration into the world and European educational space, it is expedient to study and reflect to the foreign experience of forming research and development skills of students of institutions of higher education. This aspect became the goal of our research. Applied research methods: analysis and synthesis of psychological and pedagogical literature (to clarify the specifics of research and developnet training); theoretical generalization (to formulate author’s conclusions). Currently, one of the leading global trends in university education is the integration of the research and developmetn component into the process of training of future specialists. The introduction of a research component to university curricula and programs has differences in different institutions of higher education. This component can be integrated into educational courses or can be provided by courses on the methodology of scientific research taught. However, researchers of the issue prefer a consistent holistic approach to the formation of research skills of undergraduate and graduate students. Therefore, the inclusion of opportunities for the development of research skills in undergraduate and graduate curricula should be encouraged. The pespectives for the further research we see in the study of foreign experience in the organization of research training of future specialists in the social sphere.
The specifics of the methodology of teaching contemporary art to art teachers in Russian and Chinese universities

The subject of the study is the peculiarities of modern art and pedagogical education in Russia and China. The aim is to determine the specifics of the methodology of teaching contemporary art to Chinese and Russian students – future art teachers in Russian and Chinese universities. The article discusses the features of teaching this discipline in the largest art and pedagogical universities of Russia and China at the beginning of the XXI century. The research methodology is based on the provisions of pedagogy and psychology, the specifics, theory and methodology of art and pedagogical education. To achieve this goal, methods such as theoretical analysis of sources related to the teaching of contemporary art in universities in Russia and China, comparison of curricula and programs in them, pedagogical observation of students' activities were used. The scientific novelty of the study lies in the fact that it highlights and summarizes specific features in the teaching of contemporary art in Chinese and Russian art and pedagogical institutions of higher education based on the analysis of curricula and projects of the Herzen Russian State Pedagogical University and Moscow State Pedagogical University, as well as Beijing Pedagogical University and Peking University. As a result, the process of integrating contemporary art into the practice of Chinese and Russian pedagogical universities is shown; the features of interaction of students in educational activities with current artistic practices as a way of mastering the world art experience and its reflection. The conclusions of the author of the publication are that in order to optimize the process of acquaintance of students with contemporary art, both Russian and Chinese art and pedagogical universities offer them to conduct creative experiments with new materials, forms and means of expression as part of their independent work, as well as interact in specially organized spaces with the work of current artists.
Analysis of the Quality and Educational Content of Parotidectomy Videos on YouTube Using the IVORY-Grading-System

Objective YouTube is one of the most frequently used video platforms for demonstration of surgical cases. The aim of this study was to evaluate the quality and the educational content of YouTube videos showing parotidectomy.
RTANet: Recommendation Target-Aware Network Embedding

Network embedding is a process of encoding nodes into latent vectors by preserving network structure and content information. It is used in various applications, especially in recommender systems. In a social network setting, when recommending new friends to a user, the similarity between the user's embedding and the target friend will be examined. Traditional methods generate user node embedding without considering the recommendation target. No matter which target is to be recommended, the same embedding vector is generated for that particular user. This approach has its limitations. For example, a user can be both a computer scientist and a musician. When recommending music friends with potentially the same taste to him, we are interested in getting his representation that is useful in recommending music friends rather than computer scientists. His corresponding embedding should consider the user's musical features rather than those associated with computer science with the awareness that the recommendation targets are music friends. In order to address this issue, we propose a new framework which we name it as Recommendation Target-Aware Network embedding method (RTANet). Herein, the embedding of each user is no longer fixed to a constant vector, but it can vary according to their specific recommendation target. Concretely, RTANet assigns different attention weights to each neighbour node, allowing us to obtain the user's context information aggregated from its neighbours before transforming this context into its embedding. Different from other graph attention approaches, the attention weights in our work measure the similarity between each user's neighbour node and the target node, which in return generates the target-aware embedding. To demonstrate the effectiveness of our method, we compared RTANet with several state-of-the-art network embedding methods on four real-world datasets and showed that RTANet outperforms other comparative methods in the recommendation tasks.
The CLARIN infrastructure as an interoperable language technology platform for SSH and beyond

Abstract CLARIN is a European Research Infrastructure Consortium developing and providing a federated and interoperable platform to support scientists in the field of the Social Sciences and Humanities in carrying-out language-related research. This contribution provides an overview of the entire infrastructure with a particular focus on tool interoperability, ease of access to research data, tools and services, the importance of sharing knowledge within and across (national) communities, and community building. By taking into account FAIR principles from the very beginning, CLARIN succeeded in becoming a successful example of a research infrastructure that is actively used by its members. The benefits CLARIN members reap from their infrastructure secure a future for their common good that is both sustainable and attractive to partners beyond the original target groups.
IMPLEMENTATION OF DISCOVERY LEARNING USING WEBBED AND SHARED TYPES IN IMPROVING STUDENT LEARNING OUTCOMES

This study aims to determine how effective the learning process in elementary schools using discovery learning models using learning with webbed and shared types on 3rd grade 4th theme to improve student learning outcomes. This research method uses a quasi-experimental method, where the sample data used are not selected at pure randomness, but are selected by random class. In this research apply The Matching – only Pretest-Postest Control Group Design. This research was conducted at one of SDN which is located in Kapur IX District, Lima Puluh Kota Regency. The results showed that the disco very learning learning model accompanied by the webbed and shared integration types was able to improve student learning outcomes, so that in practice the two types of integration did not experience significant differences, as evidenced by the results of the t-test which showed the two types of integration did not show any significant difference. significant difference when applied in the learning process in two different class groups.
DyViR: dynamic virtual reality dataset for aerial threat object detection

Unmanned combat aerial vehicles (i.e., drones), are changing the modern geopolitical stage’s surveillance, security, and conflict landscape. Various technologies and solutions can help track drones; each technology has different advantages and limitations concerning drone size and detection range. Machine learning (ML) can automatically detect and track drones in real-time while superseding human-level accuracy and providing enhanced situational awareness. Unfortunately, ML’s power depends on the data’s quality and quantity. In the drone detection task scenario, limited datasets provide limited environmental variation, view angle, view distance, and drone type. We developed a customizable software tool called DyViR that generates large synthetic video datasets for training machine learning algorithms in aerial threat object detection. These datasets contain video and audio renderings of aerial objects within user-specified dynamic simulated biomes (i.e., arctic, desert, and forest). Users can alter the environment on a timeline allowing changes to behaviors such as drone flight patterns and weather conditions across a synthetically generated dataset. DyViR supports additional controls such as motion blur, anti-aliasing, and fully dynamic moving cameras to produce imagery across multiple viewing angles. Each aerial object’s classification (drone or airplane) and bounding box data automatically exports to a comma-separated-value (CSV) file and a video to form a synthetic dataset. We demonstrate the value of DyViR by training a real-time YOLOv7-tiny model on these synthetic datasets. The performance of the object detection model improved by 60.4% over its counterpart not using DyViR. This result suggests a use-case of synthetic datasets to surmount the lack of real-world training data for aerial threat object detection.
Improving bit generation rates for quantum random number generators

Quantum Random Number Generators (QRNG) are quantum cryptographic protocols that distill secure random bit strings from quantum sources. One of the main research challenges in this area is to improve their random bit generation rates. Here we investigate several possible post processing strategies for QRNG protocols, showing when they help and when they hinder. We also look at the trade-offs to using these methods as some require a larger amount of initial randomness. Finally, we comment on some interesting future problems that remain open.
The Effect of Using Chat-bot Production Program in Nonviolent Communication(NVC) Classes for Upper Elementary Students

초등학교는 처음 속하는 작은 사회로, 학생은 그 속에서 자연스럽게 타인과의 갈등을 경험한다. 신체적·정서적 변화와 혼란스러움을 겪는 초등학교 고학년 학생들은 종종 학습한 갈등 해결법을 실천하지 못하는 모습을 보인다. 특히 최근 미디어를 통한 무분별한 콘텐츠 노출, 팬데믹으로 인한 거리두기 등의 영향으로 스스로 감정을 조절하고 갈등을 평화롭게 해결하는 일에 어려움을 느끼기도 한다. 이에 본 연구에서 실천으로 이어지는 비폭력대화 교육에 목적을 둔 프로젝트 수업을 진행하고 그 효과성을 검증하였다. 프로젝트 수업은 4단계 과정의 순차적 진행을 강조하는 비폭력대화법을 다루기 위해 절차적 사고를 바탕으로 한 챗봇 제작 프로그램(에이아이런)을 활용했다. 연구 결과 챗봇 제작 프로그램을 활용한 비폭력대화 교육은 학생의 실천 효과에 있어 유의한 결과가 나타났다. 또 학생에게 인간다운 삶과 기술을 연결 지어 고민하는 기회를 제공하며, 챗봇의 교육적 활용 범위를 확대한다는 점에서 의의가 있다.Elementary school is a small society to which students naturally experience conflicts with others. Upper elementary students who undergo physical and emotional changes and confusion often fail to practice the conflict resolution they have learned. In particular, it is difficult to control emotions and resolve conflicts peacefully due to the recent indiscriminate exposure of content through the media, and social distancing caused by the pandemic. Therefore, in this study, a project class aimed at nonviolent communication(NVC) education leading to practice was conducted and its effectiveness was verified. The project class used a chat-bot production program (AILearn) based on procedural thinking to deal with NVC that emphasize the sequential progression of the four-step process. As a result of the study, NVC education using chat-bot production programs showed significant results in the student's practical effect. It is also meaningful in that it provides students with the opportunity to think about human life and technology, and expands the scope of educational use of chat-bot.
Leveraging motion saliency via frame differencing for enhanced object detection in videos

Video sensors are ubiquitous in the realm of security and defense. Successive image data from those sensors can serve as an integral part of early-warning systems by drawing attention to suspicious anomalies. Using object detection, computer vision, and machine learning to automate some of those detection and classification tasks aids in maintaining a consistent level of situational awareness in environments with ever-present threats. Specifically, the ability to detect small objects in video feeds would help people and systems to protect themselves against far away or small hazards. This work proposes a way to accentuate features in video stills by subtracting pixels from surrounding frames to extract motion information. Features extracted from a sequence of frames can be used either alone, or that signal can be concatenated onto the original image to highlight a moving object of interest. Using a two-stage object detector, we explore the impacts of frame differencing on Drone vs. Bird videos from both stationary cameras as well as cameras that pan and zoom. Our experiments demonstrate that this algorithm is capable of detecting objects that move in a scene regardless of the state of the camera.
Research Challenges in Extreme Multi-label Classification

In high dimensional data, one of the challenges is to tackle high dimensional feature and label space. Currently, research is going on Extreme Multi-Label Classification, which is a supervised machine learning algorithm focusing on high dimensional data. “Extreme Multi-Label Classification” is the extension of Multi-Label Classification with high dimensional label space. One of the objectives is to extract important labels from high dimensional label space where traditional Multi-Label Classification approaches are failed. It is used in many classification applications such as Wikipedia Categorization, Product Recommendations, Tagging Applications, Search Query Recommendations, Word Recommendations etc. This paper covers challenges and approaches to handling the high dimensional Classification problem followed by potential directions for the improvements of the classifier.
MULTIVARIATE STATISTICAL METHODS IN DETERMINING THE SPATIAL DISTRIBUTION OF CHEMICAL ELEMENTS IN SOILS (KUMANOVO REGION, NORTH MACEDONIA)

The results of the distribution of various chemical elements in soil samples from the region of Kumanovo, North Macedonia, are presented. In order to determine the occurrence of chemical elements, soil samples were collected from a total of 51 locations. At each location, soil samples were collected from two layers, topsoil (0–5 cm) and subsoil (20–30 cm), in an area of 5×5 km2. The soil samples were analyzed for 5 elements using two instru-mental methods: inductively coupled plasma – atomic emission spectrometry (ICP-AES) for 17 macroelements (Al, B, Ba, Ca, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, P, Pb, V, and Zn) and inductively coupled plasma – mass spectrometry (ICP-MS) for 38 trace elements (Ag, As, Be, Bi, Br, Cd, Ce, Co, Cs, Dy, Er, Eu, Ga, Gd, Ge, Hf, Ho, In, La, Lu, Mo, Nb, Nd, Pr, Rb, Sb, Sc, Sm, Sn, Sr, Ta, Tb, Ti, Tm, W, Y, Yb, and Zr). Factor analysis was applied to analyze the factors affecting the linear combination variables grouped under the same factor. Spatial distribution maps for each factor as well as distribution maps for the analyzed elements were obtained by universal kriging interpolation. It was found that the distribution of most elements follows the lithology of the studied area, except for some elements (As, Pb, and Zn) whose higher contents were found in some specific parts of sub-areas, which is due to the current and past mining activities in the region.