Reviewerly/exHarmony huggingface dataset:
ODREĐIVANjE MERODAVNOG PRAVA ZA VANUGOVORNU ODGOVORNOST ZA UPOTREBU SISTEMA VEŠTAČKE INTELIGENCIJE – NEKOLIKO NAPOMENA IZ UGLA MEĐUNARODNOG PRIVATNOG PRAVA EVROPSKE UNIJE

In this paper author analyzes the application of conflict-of-law rules of Regulation No 864/2007 on the law applicable to non-contractual obligations (Rome II Regulation) on non-contractual obligations arising out of torts caused by operation of artificial intelligence (AI) systems. The attention is given to general conflict-of-law rules on torts (Art. 4 Rome II Regulation), special conflict-of-law rules on product liability (Art. 5 Rome II Regulation) and infringement of intellectual property rights (Art. 8 Rome II Regulation), as well as to provisions on parties’ freedom of choice (Art. 14 Rome II Regulation), whereas the focus is put on problems in their application which can arise with regard to specific characteristics of AI systems. In addition, the application of provisions of the HCCH 1971 Traffic Accident Convention and the HCCH 1973 Product Liability Convention on cases involving AI systems are shortly analyzed, since these conventions take precedence over Rome II Regulation in certain EU Member States. Finally, some remarks are given on the unilateral conflict-of-law rule contained in Proposal for a Regulation of the Parliament and of the Council on Liability for the Operation of Artificial Intelligence of 20 October 2020.
Extended phraseological units and literary genres

The present paper is based on the assumption that the language of the novel as well as that of its various subgenres is characterized by a statistically relevant overrepresentation of certain linguistic phenomena (e.g., lexemes, key words, collocations and colligations, Siepmann, 2015, 2016). Applying state-of-the-art lexicometric methods to extract recurring polylexical units in two large corpora of contemporary French and English novels, we explore the role of phraseological motifs in distinguishing literary subgenres. Unlike traditional corpus-stylistic analyses, which frequently focus on the style of a single author, our corpus-driven approach identifies features of literary (prose) genres on the basis of automatically extracted lexico-syntactic constructions (LSCs) that are statistically specific to a certain subgenre.
A Survey on Campus Companion

Abstract: Chatbots are expert systems designed to understand and respond to user queries in their own language, engaging in conversations similar to human interactions. This chatbot offers two modes of interaction, text and audio, enhancing user experience, particularly in answering through voice messages. College bot project addresses the challenges faced during the academic admission procedure at our institute, especially for parents residing in different locations. The system aims to provide a platform for students and parents to ask questions and resolve doubts using simple English text messages or audio commands, reducing the need for physical visits to the inquiry window.
Cognitive Diversity on Team Innovation During the Covid-19 Pandemic: Do We Need An Intrinsic Motivation as A Mediator?

This research aims to analyze the impact of cognitive diversity on intrinsic motivation, cognitive diversity on team innovation, and the impact of intrinsic motivation, as a mediator, on cognitive diversity and team innovation. This research applied an explanatory approach to hypothesis testing. We conducted a survey in which 107 questionnaires were distributed to 33 work teams in the furniture industry in West Sumatra, Indonesia. Findings indicate that cognitive diversity had a significant positive impact on team innovation. Contrastively, cognitive diversity did not have any effect on intrinsic motivation. Besides, we also figured out that intrinsic motivation was not a mediator between cognitive diversity and team innovation. Findings indicate that cognitive diversity had a significant positive impact on team innovation. Contrastively, cognitive diversity did not have any effect on intrinsic motivation. Besides, we also figured out that intrinsic motivation was not a mediator between cognitive diversity and team innovation. The study provides a comprehensive understanding of knowledge sharing and team climate on the relationship between cognitive diversity and team innovation, missing in previous empirical studies. Then, the study is relevant because of inconclusive findings from past studies examining the relationship between cognitive diversity and team innovation.
Syncronize Data Using MapReduceModel Programming

Research in the processing of the data shows that the larger data increasingly requires a longer time. Processing huge amounts of data on a single computer has limitations that can be overcome by parallel processing. This study utilized the MapReduce programming model data synchronization by duplicating the data from database client to database server. MapReduce is a programming model that was developed to speed up the processing of large data. MapReduce model application on the training process performed on data sharing that is adapted to number of sub-process (thread) and data entry to database server and displays data from data synchronization. The experiments were performed using data of 1,000, 10,000, 100,000 and 1,000,000 of data, and use the thread as much as 1, 5, 10, 15, 20 and 25 threads. The results showed that the use of MapReduce programming model can result in a faster time, but time to create many thread that many require a longer time. The results of the use of MapReduce programming model can provide time efficiency in synchronizing data both on a single database or a distributed database.
Implementation of Multi-Criteria Decision Making (MCDM) for new student majors recommendations

Selection of the right major and according to ability is one way for students to be able to complete their studies at college. Error choosing majors is a problem that is often experienced by students. Many students are confused and careless in choosing a major, there are also those who choose a major based on the wishes of their parents or following a friend, so that during the lecture process there are many obstacles to achieving a bachelor's degree, even having to take education until the end of the study period, Multi Criteria Decision Making is one way that can be used to help recommend prospective students based on their abilities. The program is made specifically for Android-based Smartphones, so that prospective students can try to choose based on values ​​that match the criteria determined by the desired major without having to feel afraid or embarrassed to know whether to be accepted into the department or not. So with the creation of this system, it is expected to be able to provide recommendations to prospective students in choosing majors that are in accordance with their academic abilities and effective study time.
Data Collection and Feedback Preparation in Virtual Reality Training Systems

Nowadays virtual reality (VR) technologies are considered an increasingly important part of the educational process, especially in areas where practical work in laboratories is essential. It even helps students test situations that are impossible to perform in a real laboratory. Such training environments save costs, improve results and reduce training time. Thanks to visualisation technologies and special effects, it is possible to depict phenomena, like magnetic fields, radio waves etc. to better understand a topic. To achieve meaningful results with the use of virtual reality technologies, it is important that the designed training environment is capable of generating tasks and evaluating results instead of just instructing the participant to follow predefined operations based on a tutorial. This paper describes a training system that provides evaluation based on LEAN principles, so the gathered data and generated feedback at the and of a training session provide information in various levels of detail, including correct/incorrect operations, sequence, time spent, distance walked, score and suggestions. With this approach, the instructor can more accurately evaluate the trainee's knowledge level, and the trainee is more engaged and takes greater responsibility for the subject due to the active learning experience, as opposed to step-by-step training.
Planejamento e Seguimento de Caminhos com Árvores de Exploração Rápida e Controle Preditivo Não-Linear

Navigation by autonomous robots is an application of robotics that can revolutionize society. In situations where the map of the space to be navigated is known, some probabilistic and iterative algorithms can be used to generate paths. Rapidly-exploring random tree algorithms are effective even for robots with multiple degrees of freedom. This article uses and compares two rapidly-exploring random tree algorithms for path generation. Following these paths is performed by a non-linear predictive controller. The results indicate that it is possible to plan and execute a route, using the proposed methods, that takes an autonomous vehicle from the initial to the final position, avoiding any obstacles along the way.
Design and Implementation of Nusa Medis for Cloud Computing-Based Electronic Medical Record Systems

&#x0D; &#x0D; &#x0D; &#x0D; The background of this research is the importance of using information technology in health services to increase the efficiency and effectiveness of health services. However, there are still many challenges in implementing electronic medical record systems, such as difficulties in integrating data from various sources, data security issues, and limitations in data accessibility. The purpose of this research is to design and implement a cloud computing-based electronic medical record system that can meet the special needs of various healthcare providers and improve the accessibility of patient data safely and efficiently. The method used in this study is a system development approach based on the software development life cycle by combining user-based design principles and feedback from healthcare providers. The result of this research is a web-based electronic medical record system that can be accessed from anywhere with an internet connection, with features such as medical history tracking, appointment scheduling, prescription management, and billing management. This system also has a high level of security to protect patient privacy and prevent unauthorized access.&#x0D; &#x0D; &#x0D; &#x0D;
Authentication Authorization and Security Issues in Cloud Computing

Abstract: Cloud Computing is basically a technology used to store and access data and Cloud Computing is a technology that is growing day by day. One of the reasons for the growth of this technology today is that it can be accessed from anywhere with just a computer device and the internet. But here lies the security risk of user data. so, it is necessary to ensure the security of information for the user. user data security can be ensured through the authentication and authorization process. Allows the user to access the cloud platform after verifying the user's identity through an authentication and authorization process. This authentication and authorization method can reduce the data risk of cloud computing users. In this paper, we have tried to show the data risk aspects of cloud computing users and the possible solutions. We will also try to describe the data protection and security aspects of cloud computing.
E-POSYANDU: WEB-BASED MOTHER AND CHILD POSYANDU APPLICATION USING THE CODEIGNITER FRAMEWORK

POSYANDU (Integrated Service Post) is a monthly activity post to monitor the health status of pregnant women and toddlers (children under the age of five). This activity is carried out to monitor the growth and nutritional status of children under five on a regular basis and to understand the situation of pregnant women who are at risk so that preventive measures can be taken to prevent the death of pregnant women and malnourished children. However, in practice there are some difficulties such as searching for data and recording the same name in different formats. This study uses a qualitative method with a cross-sectional approach, where the aim of this research is to design and build a system that can manage Posyandu data in an integrated and well-computerized manner based on WEB cloud computing. The result of this study is a posyandu application consisting of immune data, vaccine data, child data, mother data, officer data, and posyandu data. The accuracy of the application implementation based on the blackbox method by posyandu officers who focus on the functionality side, especially on the input and output of the application by the officers, results of the application running according to the design requirements.&#x0D;
Evaluation of the Program for Giving Iron Tablets to Young Women

Background: Anemia due to iron deficiency is a prevalent health issue that affects many people, particularly women, from early childhood through adulthood. Bleeding can be a problem for pregnant women who have iron deficiency anemia, among other things, during pregnancy, labor, and the puerperium. Low birth weight, early birth, and problems of growth and development that result in stunting are some of the effects that anemic mothers experience while carrying their unborn children. Adolescent anemia continues up until the point at which the adolescent becomes pregnant. To treat iron-deficient anemia, the government is marketing blood replacement tablets. Pupose of study is to determine the effectiveness of iron tablets, the authors worked with the Klaten District Health Office to compare Hb and ferritin levels in pupils who had received blood tablets for 9 months versus kids who had not had blood tablets. Methods: This study used a cross-sectional research approach and is a case analysis study. The SMP Negeri I Jogonalan Klaten is the research location. Study do in June to September 2022. Respondents are 60 pupils from Klaten Public Middle School I, ages 11 to 14, participated in the survey. Of them, 30 female respondents had taken iron supplements for 9 months (40 weeks), while the remaining 30 served as controls and had not received blood supplements. After being told of the advantages of the study, participants who signed a written informed permission form and agreed to take part in it had their ferritin and hemoglobin levels checked. Results: When compared to the control group's ferritin value of 16.13% and Hb value of 6.45%, the treatment group's ferritin value was low (24.14%) and the Hb value was 10.34%. Conclusion: 20% of young women are still found to have anemia. Young women should be prevent anemia.
Leveraging Bluetooth low-energy technology to improve contact tracing among healthcare personnel in hospital setting during the coronavirus disease 2019 (COVID-19) pandemic

Abstract To improve contact tracing for healthcare workers, we built and configured a Bluetooth low-energy system. We predicted close contacts with great accuracy and provided an additional contact yield of 14.8%. This system would decrease the effective reproduction number by 56% and would unnecessarily quarantine 0.74% of employees weekly.
Exploring the Knowledge Distillation

Knowledge distillation is a well-known method of model compressing but it is largely under-used. Knowledge distillation has the ability to reduce the size of a model by transferring the knowledge from a large (pre-trained or not) model into a smaller one with minimum loss to the accuracy, for a better fit in edge devices and the devices with low computational power. Furthermore, it can be used also as a performance enhancer for the student model as we’ll see in the experiments since it doesn’t need any additional data to be effective. This paper argues that the knowledge distillation method or one of its variations should always be used on deep learning models. In our experiments we trained and tested our models on some well-known datasets (MNIST, CIFAR10 and CIFAR100) to prove that the knowledge distillation and two of its variations give positive results when applied on over-fitted or under-fitted teacher models.
Percolation Theories for Quantum Networks

Quantum networks have experienced rapid advancements in both theoretical and experimental domains over the last decade, making it increasingly important to understand their large-scale features from the viewpoint of statistical physics. This review paper discusses a fundamental question: how can entanglement be effectively and indirectly (e.g., through intermediate nodes) distributed between distant nodes in an imperfect quantum network, where the connections are only partially entangled and subject to quantum noise? We survey recent studies addressing this issue by drawing exact or approximate mappings to percolation theory, a branch of statistical physics centered on network connectivity. Notably, we show that the classical percolation frameworks do not uniquely define the network’s indirect connectivity. This realization leads to the emergence of an alternative theory called “concurrence percolation”, which uncovers a previously unrecognized quantum advantage that emerges at large scales, suggesting that quantum networks are more resilient than initially assumed within classical percolation contexts, offering refreshing insights into future quantum network design.
A Method for Image Anomaly Detection Based on Distillation and Reconstruction

Image anomaly detection is a trending research topic in computer vision. The objective is to build models using available normal samples to detect various abnormal images without depending on real abnormal samples. It has high research significance and value for applications in the detection of defects in product appearance, medical image analysis, hyperspectral image processing, and other fields. This paper proposes an image anomaly detection algorithm based on feature distillation and an autoencoder structure, which uses the feature distillation structure of a dual-teacher network to train the encoder, thus suppressing the reconstruction of abnormal regions. This system also introduces an attention mechanism to highlight the detection objects, achieving effective detection of different defects in product appearance. In addition, this paper proposes a method for anomaly evaluation based on patch similarity that calculates the difference between the reconstructed image and the input image according to different regions of the image, thus improving the sensitivity and accuracy of the anomaly score. This paper conducts experiments on several datasets, and the results show that the proposed algorithm has superior performance in image anomaly detection. It achieves 98.8% average AUC on the SMDC-DET dataset and 98.9% average AUC on the MVTec-AD dataset.
ADN-BERT: Attention-Based Deep Network Model Using BERT for Sarcasm Classification

Sarcasm classification has gained popularity among researchers due to its complexity and implicit form of context representation. The most challenging aspect of sarcasm identification is to understand the exact context behind the statement. Therefore, a context-based model helps solve this critical task in the research domain. The importance of sarcasm detection and classification is primarily helpful for society to avoid misinterpreting statements or reviews that could affect one’s mental condition and perception. Unfortunately, to increase the retention level of audience toward media news, often the media incorporate sarcasm in their news headlines. However, people find it difficult to detect sarcasm in news headlines, resulting in them having a false impression of the news and spreading it to their surroundings. Therefore, it has become a significant concern among the society to develop a sense of understanding of the hidden context behind the statement before making any type of impression and judgment. In this paper, we have focused on this problem statement and developed a novel technique called an Attention-based deep network model using Bidirectional Encoder Representations from Transformers (BERT) i.e., ADN-BERT to classify news headlines into sarcastic and non-sarcastic categories. The News Headlines dataset is collected from Kaggle, and we have incorporated text augmentation to increase the size of the dataset to yield better results and accuracy. The evaluation metrics used in our study include accuracy, recall, precision, and F1-score. Our proposed model (ADN-BERT) has outperformed the recent state-of-the-art techniques with 94.1% accuracy, 95.2% recall, 94.5% precision, and 94.8% F1-score.Please check and approve the edits made in the chapter title and running title.Edited. ApprovedPlease check and confirm if the author names and initials are correct.Yes, correct.
Revisiting Deep Generalized Canonical Correlation Analysis

Canonical correlation analysis (CCA) is a classic statistical method for discovering latent co-variation that underpins two or more observed random vectors. Several extensions and variations of CCA have been proposed that have strengthened our capabilities in terms of revealing common random factors from multiview datasets. In this work, we first revisit the most recent deterministic extensions of deep CCA and highlight the strengths and limitations of these state-of-the-art methods. Some methods allow trivial solutions, while others can miss weak common factors. Others overload the problem by also seeking to reveal what is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not common</i> among the views – i.e., the private components that are needed to fully reconstruct each view. The latter tends to overload the problem and its computational and sample complexities. Aiming to improve upon these limitations, we design a novel and efficient formulation that alleviates some of the current restrictions. The main idea is to model the private components as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">conditionally</i> independent given the common ones, which enables the proposed compact formulation. In addition, we also provide a sufficient condition for identifying the common random factors. Judicious experiments with synthetic and real datasets showcase the validity of our claims and the effectiveness of the proposed approach.
Artikel Review : Pengelolaan Standar Pendidikan Berbasis Teknologi Informasi dan Komunikasi sebagai Upaya Peningkatan Mutu Pendidikan di Indonesia

The rapid development of information technology in the current era of globalization cannot be avoided anymore its influence on the world of education. The development of technology helps learners create learning communities by using digital communication channels. In addition, the use of information and communication technology is able to accelerate effective and efficient management and education systems. The purpose of writing a review article is to describe the utilization of managing educational standards based on information and communication technology in the world of education. The method used in this study is library research. The data collection method uses the help of the internet to browse various references to books and previous research journals that are in accordance with the Management of Information and Communication Technology-Based Education Standards in relation to improving the quality of national education. The results of this study showed that the development of information and communication technology had a rapid impact on the world of education, especially in learning activities, educational administration and also on the management of educational standards in the 21st century.