Reviewerly/exHarmony huggingface dataset:
Psychological and pedagogical features of distance learning at a university

The article discusses psychological and pedagogical features of distance learning at a university, as well as trends and problems of organizing the educational process in the context of counteracting coronavirus infection in Russian universities. The urgency of the problem is argued by the fact that at present distance learning has become the most demanded and promising form of education. Special attention is paid to the introduction of distance learning technologies, where online learning is considered as an integral part of a harmonious education system, characterized by a number of features. As part of the research the following tasks have been solved: the problems caused by the distance learning mode have been set; data have been analyzed and an algorithm for solving problems have been defined, and recommendations aimed at creating psychologically comfortable conditions for students in the framework of distance learning have been developed; an algorithm aimed at identifying and studying the psychological difficulties arising in the process of distance learning has been formed. The research methodology was based on the use of a questionnaire, the purpose of which was to identify and study psychological problems arising in the process of distance learning. It has been concluded that the majority of students wish to continue their education in the traditional familiar form, which is more effective in perceiving information, mastering the material and developing the necessary skills and abilities. Also, the authors have proposed recommendations for teachers and students aimed at reducing discomfort during classes in an online format.
AI Horizons: Unveiling the Future of Generative Intelligence

Generative AI is basically a subfield of artificial intelligence. It mainly focuses on developing systems that can generate creative outputs such as images, music, text, and more. By deep learning techniques, Generative models are capable of independent producing content that look like human-generated creations. The key characteristic of Generative AI is its capacity to learn from huge datasets, catch patterns, and generate new content that show similar characteristics. In recent years, Generative AI models such as generative adversarial networks (GANs) and variational autoencoders (VAEs). GANs consist of two components: a generator network and a discriminator network those engaged in a competitive process of generating and evaluating content. VAEs employ an encoder-decoder architecture to learn and generate new samples. This paper discusses the key areas where Generative AI is expected to make significant contributions in the future. These areas include: Healthcare, Art and Entertainment, Ethical and Societal Considerations, Autonomous Systems, Content Creation etc.
Blockchain Applications Presentation Commentry

&nbsp;The 2021 ConV2X Annual Symposium&nbsp; featured a scientific program of academic/research presentations in addition to business and industry talks. The research track&nbsp; focused on exploring and sharing developments in blockchain and emerging technologies in health and clinical medicine. Submissions were based on original research, conceptual frameworks, proposed applications, position papers, case studies, and real-world implementation. Selection was based on a peer-review process. Faculty, students, and industry researchers were encouraged to submit abstracts to present&nbsp; ideas before an informed and knowledgeable audience of industry leaders, policy makers, funders, and researchers. All presentations were reviewed by a sub-group of the scientific reveiw committee. This video presentation is an example of the discussions that transpired for each category of submissions, specifically, blockchain applications. Submission Review Committee Dave Kochalko, CEO of ARTiFACTS Anjum Khurshid, UT Austin Carlos Caldas, UT Engineering Gil Alterovitz, Harvard Medical School Kayo Fujimoto, UT Health Houston Lei Zhang, University of Glasglow Sean Manion, CSciO of ConsenSys Health Vijayakuman Varadarajan, University of South Wales Vikram Dhillon, Wayne State University Yuichi Ikeda, Kyoto University
Perubahan Nilai Debris Indeks melalui Penyuluhan Media Leaflet dan Fliphart Siswa Kelas 5 SDN I Perumnas Way Halim Bandar Lampung

Development in the field of dental and oral health is one part of overall health development which has a great responsibility for increasing the value of dental and oral health in the community. Efforts to overcome dental and oral health problems must be based on a good understanding of the knowledge that affects the dental and oral health of the community, both health workers as service providers and the community as users of available services. Print media or Leaflets and Flipcharts are media used in counseling&#x0D; The purpose of this study is to determine the description of the success of counseling using leaflet and flipchart media on the value of debris index for students in grades 5A and 5B at SDN I Perumnas Way Halim Bandar Lampung. This type of research is descriptive and correlational research. The research was conducted at SDN I Perumnas Way Halim, Bandar Lampung. The population in this study were students in grades 5A and 5B at SDN I Perumnas Way Halim Bandar Lampung, totaling 80 people. with a population of 80 people as a sample. The variables used in this research are Leaflet and Flipchart media. Based on the research that has been done, it was found that the DI criteria before counseling was carried out using Leafet 1.12 (medium) and Flipchart 1.19 (moderate) media. while the DI criteria after counseling using Leaflet media is 0.03 (Good) and Flipchart is 0.02 (Good) then there is a decrease in DI value before and after counseling using Leaflet and Flipchart media
Pemanfaatan Algoritma K-Means untuk Membuktikan Implementasi Undang-Undang Pelanggaran Hukum Korupsi di Pengadilan Negeri Banjarmasin

This research aims to demonstrate the implementation of the Anti-Corruption Law in the Banjarmasin District Court by utilizing the K-Means algorithm. Corruption, which persists in Indonesia over a prolonged period, has reached a critical level, making it crucial to enforce the law fairly and firmly. In this study, the panel of judges in the Banjarmasin District Court was analyzed using the K-Means Clustering method and silhouette coefficient to decide corruption cases that result in state losses. The research findings indicate that the optimal number of clusters is 3, with a value of 0.686. However, there is also a lowest value among the 4 clusters, which is 0.454. These clusters are then divided into three categories of enforcement, namely cases that have been executed (108 cases), cases that will be executed (26 cases), and cases that have not been executed (2 cases). All clusters have a silhouette score of 0.742, indicating successful enforcement. This research provides concrete evidence that the panel of judges in the Banjarmasin District Court has implemented the Anti-Corruption Law while considering state losses. By utilizing the K-Means algorithm, this study also contributes to a better understanding of enforcement practices in the court. It is expected that the results of this research will support efforts to enhance the implementation of the Anti-Corruption Law in Indonesia, particularly in the Banjarmasin District Court
How Moral Code Influences General Behaviors in a Community

Moral code is perceived as ethics which leads individuals or groups to make choices and behave. A written, formal and consistent set of rules prescribing righteous behaviors, accepted and followed by a person or groups of people as defined. General behaviors mean how a community usually behaves and responds to a specific thing (e.g., involving morality). A community is a group of people living together like a tribe, neighbourhood or society. The formation of a moral code is intriguing and thought-provoking. For instance, the objectiveness and subjectiveness of a moral code are constantly debated, as the exactitude of measurement throughout periods (e.g., is the established moral code still moral after a hundred years?). How could we prevent the moral code from becoming the tool of guilt tripping; why would crowds follow the same moral code in one distinct? To figure out these problems, we propose some hypotheses (1) The moral code in a distinct could lower the crime rate and reduce deviance. (2) The moral code could form general behaviors shared between people in a distinct and the general behaviors could be a legacy product. (3) The moral code formed by groups of people is inherent as the intrinsic ethical traits. (4) The basis of general behaviors influenced by the moral code is due to conformity. (5) In some distinct, the moral code could lead to inappropriate or even abnormally stubborn behaviors. In the following essay, we will further elaborate on how moral code influences general community behaviours.
A local optical flow eye-tracking method for depression detection

Aiming at the problem that depression detection is easily affected by subjective factors such as patient willingness, self-awareness, and doctor’s level, as well as inconvenient detection and high cost, a universal detection method with low cost and convenient deployment is proposed. A local optical flow tracking method is adopted, which is realized by eye region detection and optical flow estimation. Then, three experiments are designed to verify the accuracy of the method and the effectiveness of eye movement tracking. The formula for calculating the depression state is proposed, and the eye movement of one normal participant’s elliptical trajectory of the left and right pupils in the horizontal and vertical directions is 44.4805, 42.7013, 29.3571, and 31.1364 mm, respectively. To verify the effectiveness of the proposed method, three example points are selected. Among them, the fixation duration at P1 is 1.87 s, the eye movement direction at P2 and P3 is 1.168°, counterclockwise around the X axis, and the number of fixation times is 46. Finally, 18 normal participants and four depression-positive participants are tested by the exploratory eye movement paradigm. The results show that the accuracy rate of identifying depression patients using the NEF metric method can reach ∼81.8%, which can help quantify the detection of depression and provide the possibility for professionals to remotely diagnose individuals with depression.
Driving Decision Making of Autonomous Vehicle According to Queensland Overtaking Traffic Rules

Abstract Improving the safety of autonomous vehicles (AVs) by making driving decisions in accordance with traffic rules is a complex task. Traffic rules are often expressed in a way that allows for interpretation and exceptions, making it difficult for AVs to follow them. This paper proposes a novel methodology for driving decision making in AVs based on defeasible deontic logic (DDL). We use DDL to formalize traffic rules and facilitate automated reasoning, allowing for the effective handling of rule exceptions and the resolution of vague terms in rules. To supplement the information provided by traffic rules, we incorporate an ontology for AV driving behaviour and environment information. By applying automated reasoning to formalized traffic rules and ontology-based AV driving information, our methodology enables AVs to make driving decisions in accordance with traffic rules. We present a case study focussing on the overtaking traffic rule to illustrate the usefulness of our methodology. Our evaluation demonstrates the effectiveness of the proposed driving decision-making methodology, highlighting its potential to improve the safety of AVs on the road.
University Students’ Readiness towards Mobile Learning: The Stages of Change Model

Information seeking modes are changing rapidly due to advancements in portable devices such as smartphones. The functionality of smartphones is helping individuals to enhance their online learning experiences. The study was conducted to assess the readiness level among students towards the adoption of mobile learning at The Islamia University of Bahawalpur. A survey method of research was used to collect data for this study. A self-administered questionnaire was distributed through convenience sampling among the enrolled students of The Islamia University of Bahawalpur. Of the 183 respondents, the majority of 118 (64.5%) were female. Overwhelming, 170 (92.9%) respondents' usage frequency was 'daily' for accessing information through mobile. Stages of change model indicated that most of the respondents were at the 'maintenance stage' indicating that they were regularly using mobile for learning. The main barriers to mobile learning included: 'mobile learning causes sore eyes', and 'mobile screen size makes it difficult to concentrate on contents. The study concluded that a high level of readiness exists among students towards adopting mobile learning as they are regularly using smartphones to access the information from online resources to fulfill the requirements of formal learning. A statistically significant difference was found in the readiness level as female students are more likely to adopt m-learning than male students. The study's findings are useful for librarians, faculty members, IT experts, and policymakers of the participating university in designing mobile-related services for students to enhance learning communications and collaborations.
Business Entities

If the economy is the backbone of a country, business entities define and affect its movements.Those entities produce business that, with the provision of goods, services, and jobs, is confirmed as one of the key pillars of the nation. Business, among the other things, defines the direction in which the nation is moving toward the world economy. The technological progress of recent years led to an innovation race to which every company (corporation) that intended to grow or, actually, to survive has an obligation to participate. Participating, however, requires the company (corporation) to marry the digital transformation process.
Recognition of Nastaliq Urdu Text using Multi-SVM

Optical Character Recognition has emerged as an attractive research field nowadays. Lot of work has been done in Urdu script based on various approaches and diverse methodologies have been put forward based on Nastaliq font style. Urdu is written diagonally from top to bottom, the style known as Nastaliq. This feature of Nastaliq makes Urdu highly cursive and more sensitive leading to a difficult recognition problem. Due to the peculiarities of Nastaliq Style of writing, we have chosen ligature as a basic unit of recognition in order to reduce the complexity of system. The accuracy rate of recognizing ligature in Urdu text corresponds to the efficiency with which the ligatures are segmented. In addition to extracting connected components, the ligature segmentation takes into consideration various factors like baseline information, height, width, and centroid. In this paper ligature Recognition is performed by using multi-SVM (Sup-port Vector Machine) approach which gives an accuracy of 97% when 903 text images are fed to it.
The Impact of the Social Sciences and Humanities in Europe and Beyond

Abstract: What is the role of the Social Sciences and Humanities (known collectively as SSH) in the journey to the Fourth Industrial Revolution? What is the impact of these disciplines for the challenges the world faces, supposedly defined by a highly dynamic phase of industrial and social restructuring, where the adaptive capacity of societies needs to be enhanced by specific skills and techno-social dependencies? What is the role of SSH in building cognitive competences, and new professional paths? This paper, part of the special focus of the Annual Review of New Directions in the Humanities, seeks to unveil the importance of SSH disciplines to the major STEM disciplines (science, technology, engineering, and mathematics). Although disciplines and research in Social Sciences and Humanities play a fundamental role in the production of knowledge relevant to society, the last decade of the 20th century has seen a continuous trend towards their devaluation. After a relatively glorious period during the first half of the century, when SSH disciplines still held a relative social influence, the arrival of digital technologies in 1990s, accelerated their decline. This article traces a brief overview of this decline and explores some of the causes as well as the challenges in valuing SSH. Focusing mainly on Europe, the paper presents the attempts, on behalf of the European Commission, to correct this decline. It also outlines some new ideas that could help a true integration and transfer of knowledge across STEM AND STEAM disciplines, such as the creation of specific 'missions'. Precisely, one of the diagnoses of the Horizon 2020 experience in Europe has been that investment in research is not effectively linked to the solution of specific problems. Thus, it is necessary to put in motion co-creation mechanisms among research disciplines and advance in the establishment of intersectoral bridges, bringing SSH research closer to the industry and other social sectors in order to solve the pressing challenges we face (climate change, mass migration, economic crisis, etc.)
Sentiment Analysis of Impact of Social Platforms on the Market Share of a Company

Sentimental analysis is also known as opinion mining or emotion AI. It refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify, extract, and study affective states and subjective information. In this paper, Amazon reviews and blogs are analyzed to detect the sentiment using linguistic feature utility. Evaluation of the usefulness of existing lexical resources as well as capturing information about the informal and creative language used in online service platform is done. The goal of this research is to show the impact on the market-share of Vivo in comparison with that of Oppo and highlight the reason for the impact
An Enhancement of Grami Based on Threshold Policy for Pattern Big Graphs

Pattern Mining is the key mechanism to manage large scale data element. Frequent subgraph mining (FSM) considers isomorphism which is a subprocess of pattern mining is a well-studied problem in the data mining. Graphs are considered as a standard structure in many domains such as protein-protein interaction network in biological networks, wired or wireless interconnection networks, web data, etc. FSM is the task of finding all frequent subgraphs from a given database i.e. a single big graph or database of many graphs, whose support is greater than the given threshold value. Many databases consider small graphs for solving complex problems. The classification of graph depends upon the application requirement. A good mining architecture may prevent a lot of memory and time. This paper follows the Grami structure for the analysis of frequent subgraph mining and also introduces the 20% threshold policy for the enhancement of the directed pattern graphs. The constraint satisfaction problem (CSP) has been discussed and analyzed using the Grami approach. The proposed model is compared to Grami on twitter dataset based on the evaluation of time and memory consumed. The proposed algorithm shows an improvement of 3-4 % for both the parameters. The results show that the performance of Grami approach has been improved which shows a 6.6% reduction in time and 21% improvement in memory consumption using the proposed approach.
SemicNet: a semicircular network for the segmentation of the liver and its lesions

The traditional neural network used for medical image segmentation was not clear on the network depth, the importance of different depths and the rationality of jump connection. In view of these problems, we propose a convenient and efficient liver and lesion segmentation system, which uses a double-layer codec semi-circular network to combine the deep and shallow semantic information through dense jump connection, which is easier to be processed by the optimiser; The transition zone between liver and lesion segmentation is designed so that the result of liver segmentation can be effectively transmitted to lesion segmentation; We believe that the selection of complementary loss function combination for in-depth supervision can effectively receive the anti-propagation gradient signal and obtain more regularisation effects. Finally, in terms of liver segmentation, in addition to the model with lower accuracy than multiple loss functions for joint decision-making, all other evaluation indexes, including lesions, exceeded the fusion results of multiple models.
Framework for Enhancing the Performance of Classification by RCOS and HiForest

This framework includes two novel approaches to choose the outlier from various datasets. First one being Relative Cosine-based Outlier Score (RCOS).It's proposed to measure the deviation score of the objects in which each single attribute deviation is calculated and multiplied to get the entire object deviation. Initially we set the threshold. If the calculated score is greater than the threshold, then the instance is considered as an outlier. These are identified and removed since outliers are not required for classification. Now, the remaining normal objects are subjected to different methods of classification. The second method is Hybrid Isolation Forest (HiForest). It is an enhanced version of isolation forest. Similar to method outliers are identified and removed. An experimental analysis is performed on synthetic real time data sets considered from weka and UCI repository. Classification models are built and the generated results are tabulated and accuracy is recorded. The results obtained by the above methods are compared and graphs are plotted for visualization.
Mineralogy of Oxides from the Ground Mass of Kimberlites of Jakutija and Northern European Part of the USSR

The complex approach towards kimberlite studies taking in¬ to account their geo1ogico-structure1 position in the combina¬ tion with the detailed mineralogical investigations is thought
P.534 Psychotic depression as the initial presentation of lung cancer: Challenges in diagnosis and treatment of psychiatric manifestations of paraneoplastic disorders

Wireless Sensor Nodes play a prominent role in many military and civilian applications. They are used to collect data for various disciplines from diverse environments. The main drawback of this technology is the decrease in energy of the wireless sensor nodes as time progresses. A significant amount of works has been done and justified in using an autonomous bot to charge the wireless sensor nodes. There are a lot of difficulties to overcome to implement the concept of using an autonomous bot to charge the independent wireless sensor nodes. One such cumbersome issue is the scheming of an optimal path for the autonomous bot to charge these wireless sensor nodes efficiently. In this paper, we present a comparative study of various methods Ant Colony Optimization algorithm, Simulated Annealing and Tabu search to compute the tour of the Autonomous bot to recharge the nodes, considering the tour length and the time taken to compute the tour length. The result of this study gives which algorithm is best suited for a given network with a given number of nodes with respect to tour length and time taken to compute the tour.