Reviewerly/exHarmony huggingface dataset:
Analisis Keberhasilan Sistem Informasi Akademik Universitas Baturaja Menggunakan Human Organization Technology Fit Model

Academic Information System (SIAKAD) is a system implemented by Baturaja University as a support for lecture activities. This study aims to determine what factors influence the success of SIAKAD using the Human Organization Technology Fit Model by applying 8 variables, namely system quality, information quality, service quality, system use, user satisfaction, structure, Environment and net benefit. Data collection was carried out by distributing questionnaires to students and lecturers of Baturaja University. Sampling using Probability Sampling with Proportionate Stratified Random Sampling technique. The data analysis used is PLS-SEM using SmartPLS 3.0 software. Based on the results of hypothesis testing of all variables, it was found that the factors that influence the success of SIAKAD are service quality 7.808, user satisfaction 8.389, have an influence on system use. Information quality 3.046 service quality 7.326 has an influence on user satisfaction. Information quality 2,605 service quality 3,671 has an influence on structure. System use 2.98 structure 4.223 environment 7.875 has an influence on net benefits and structure 9.627 has an influence on the environment. And factors that do not affect the success of SIAKAD, namely system quality 1.103 has no effect on system use, system quality 1.167 has no effect on user satisfaction, system quality 1.304 has no effect on structure, information quality 0.394 has no effect on system use and user satisfaction 0.787 has no effect on the net benefit.
SMART LIBRARY ASSISTANT ROBOTIC SYSTEM

The goal of this project is to fully automate library operations through the use of robotics, the Internet of Things (IOT), RFID technology, and artificial intelligence (AI). A pick-and-place robot, a book repository, and a book withdrawal area make up the system. The robot is programmed to issue the book in the user's name in accordance with his needs. It will proceed to the section where the book is located, scan the RFID using a scanner, and select the book if the RFID matches. The user is required to return the book within the allotted time by placing it in the withdrawal box, where the book's RFID is scanned and the book is taken out of the user's possession.
Angry Friends, Happy Crowd: How the Emotionally Charged Content Influences Online Behavior

Social media are becoming new venues for more and more people to get information, express opinions, and connect with others. Various online communication behaviors carry and, meanwhile, are influenced by certain emotions. This study adopted a mixed method of survey and in-depth interviews to examine how the emotionally loaded content influenced people's online engagement. The targeted population focused on those active users on Chinese social media. The results revealed a unique phenomenon on social media in post-Covid China that both joy and anger attracted greater attention and more interaction. Also, the emotion of shock turned out to be unexpectedly influential on people's sharing behavior. It ranked ahead of anger and was second only to joy in getting people's attention. Furthermore, this study found that the content virality depended on the intended audience groupthat people tended to forward the angry messages to friends in private and retweet the joyful content publicly. These findings added nuances to previous literature concerning the role of emotions on social media. It reflected a relatively new reality of the post-Covid online phenomena in China.
Asymmetric cost aggregation network for efficient stereo matching

Abstract Cost aggregation is crucial to the accuracy of stereo matching. A reasonable cost aggregation algorithm should aggregate costs within homogeneous regions where pixels have the same or similar disparities. Otherwise, the estimated disparity map is prone to the well‐known edge‐fattening issue and the problem of losing fine structures. However, current state‐of‐the‐art convolutional neural networks (CNNs) mainly do cost aggregation with square‐kernel convolutional layers that learn to adjust their kernel elements to make the actual receptive fields of the aggregated costs adapt to homogeneous regions with various shapes. This is a mechanism that easily leads to the above issues due to the translation equivalence and content‐insensitivity properties of CNNs. To tackle these problems, a novel densely connected asymmetric convolution block (Dense‐ACB) based on asymmetric convolutions is proposed to explicitly construct receptive fields with various shapes, which effectively alleviates the issues caused by mismatching shapes of receptive fields and homogeneous regions. The proposed Dense‐ACB brings new insight to CNN‐based stereo matching networks. Based on the proposed cost aggregation method, an efficient and effective stereo matching network is built, which not only achieves competitive overall accuracy compared with state‐of‐the‐art models but also effectively alleviates the edge‐fattening problem and preserves fine structures.
Secure Electronic Medical Records Using Lattice Cryptography and Fog Computing Techniques

The usage of Information Technologies (IT), as well as data has increased over the years to improve businesses and human life generally thereby increasing productivity. In the health sector, Electronic Health Records (EHR) has helped in collecting demographic medical data which helps healthcare practitioners to provide quality health care. The EHR generates lots of medical data and are analyzed and processed to form big health data and sent to Agencies, Government to influence decisions in improving quality of life and to predict or prevent premature deaths and disease development. In this research work, we propose a model using lattice-based cryptography to encrypt health big data and deploy a decoy model which will be coined in fog computing facility to serve as honeypot or trap machine to attract attackers.
A Clinical Information Extractor Integrated with Global Semantic Features via Dynamic Attention Mechanism

Electronic medical records have been rolled out in the past decades to facilitate the medical exports daily routine. However, the number of electronic medical records increases dramatically, which also causes huge workloads for the front-line clinical workers when they face the writing-up work. In this sense, researchers in the artificial intelligence domain wish to automate this process by constructing a natural language processing system, and medical information extraction is one of the key steps amongst the entire work. In this paper, we focus on medical information extraction from doctor-patient dialogues, and propose a novel encoder-decoder model which incorporates global information into the dialogue windows. The experiment on the MIE dataset suggests our model outperforms the compared baseline models, and achieves the state-of-the-art results, which proves our models effectiveness.
Game Theory Classification in Cybersecurity: A Survey.

Cyber security is a field designed to protect computers connected to the internet from attacks, and hence prevent unauthorized users from accessing the sensitive data present in it. Lately, it has witnessed intensified research from both academia and the industry. However, traditional cyber security technologies still face inadequacies in tackling the ever-dynamic frontier of cyber-attacks as a result of inability to incorporate behavioral tendencies of adaptive and intelligent adversaries in their security models. Game theory is often the first choice as a mathematical tool among researchers and industry practitioners for modeling conflict and cooperation among intelligent and rational actors. With its rich modeling and analytical techniques, and ability to forecast cyber-attacks and optimal defense strategies accurately, cyber security attacks are much easier to mitigate than traditional approaches. This paper reviews several game theoretic concepts and applications in the never-ending dynamic contradiction between the defender and the attacker. These concepts are classified according to applications and scenarios. Present research challenges are discussed and future directions for the need to incorporate attackers risk attitude as a determinant factor of the Nash Equilibrium is emphasized in the malicious insider threat scenario.
Face Recognition Analysis Based on the YOLO Algorithm

Algorithm YOLO consists of a discrete CNN method to achieve end-to-end detection of the target. The detected target is acquired by processing network prediction results that match up with the R-CNN algorithm; it is an integrated system with faster speed and an end-to-end mechanism for training. The first kind of method is slower but more accurate, and the second kind of algorithm is faster but less accurate. This paper introduces the Yolo algorithm. Face recognition analysis is an important task, basically summarizing the properties of this algorithm: Firstly, You Only Look Once which depicts that a single CNN operation is necessary, and Unified portrays that it is a combined system that gives end-to-end results of the prediction, while "real-time" says that the Yolo algorithm is quick. The YOLO 4 algorithm is slower than the SSD algorithm, but Yolo has continuously evolved, resulted in YOLO 9000. This paper basically portrays the principle of the Yolo3 algorithm, mainly the details related to the training, and detection, and finally gives how to use TensorFlow to realize the Yolo algorithm.
Research on SGD Algorithm Using Momentum Strategy

With the continuous development of stochastic gradient descent algorithms, many efficient momentum algorithms have appeared. Stochastic gradient descent(SGD) is one of the classic algorithms in optimization. Its accelerated version, the SGD algorithm with momentum strategy, has been a hot research topic in recent years. Therefore, this paper will analyze and summarize these series of algorithms, starting with the classical momentum algorithm, and introduce some improved versions of the momentum algorithm. Numerical experiments on real problems will also be done to evaluate the performance of these algorithms. It is proved that the addition of momentum and adaptive learning rate effectively improve the performance of these algorithms. In future research, some cutting-edge momentum algorithms and other basic network should be analyzed.
Integrated Knowledge Distillation for Efficient One-stage Object Detection Network

Due to the low latency requirements in object detection, numbers of one-stage methods like YOLO and SSD adopt a shared head for both classification and localisation tasks. While the decoupled head used to decouple the subtasks into different heads are getting more popular in one-stage detection because they improve accuracy. In contrast, the computational complexity caused by the decoupled head cant be ignored. To solve these problems, we propose an integrated knowledge distillation framework for transferring the representation ability of the decoupled head to the original coupled head and contributing to efficient one-stage object detection. It solves the problem that the coupled head is insufficient in handling the conflict of subtasks and avoids the time delay introduced by the coupling head and the increase of network parameters.
Improving Performance Parameters of Clusters Using Density-Based Algorithm

With the advancement in technology, data generated by non-stationary in day-to-day life is massive, continuous and rapid. Many applications such as IoT, transaction systems, network sensors, video surveillance systems, and network intrusion detection systems generate a massive amount of real-time data. The data used in traditional data mining is static in nature, and it can be revised for processing and Analysis. While data in data stream mining is dynamic in nature and it never stops. Besides, the data generated may have a change imbibed in its characteristics over a long/short period of time which is called concept drift. So, analysing such data has huge inbuilt challenges that deal with the dynamism of the characteristics of data itself. This dynamic nature is because of fast and continuous changing data and its enormity. To overcome this limitation, we can use modified clustering techniques that could help us in proper data analysis. Clustering is an effective method used in data mining; but clustering data streams may add some additional challenges such as storage capacity, limited time, one pass and rate of arrival. Furthermore, data streams are fickle in nature and because of this behaviour it needs to be processed as and when it arrives. In addition to that, knowledge about the number of clusters like in K-means clustering is unknown. In view of these characteristics of the data stream, the information or the data generated in the data stream are non-deterministic. Such non-deterministic information contains noise points or outliers, so developing an effective clustering algorithm in a data stream is a crucial task. These methods can work with labelled data in data stream clustering, which has the potential to identify clusters of any shape and noise. The motivation for this research work is using the said algorithm to address and overcome the constraints of the data stream and to dig out the best knowledge from it.
Rain Removal Algorithm Based on Retinex in Low Light Automatic Driving Scenario

In the field of automatic driving, clear image is one of the important prerequisites for the decision-making system relying on computer vision to make correct decisions. Rainy days and low illumination environment are two common scenes that seriously affect image quality. Researchers have proposed many effective methods for these two research fields. However, few applications have been studied in the field of automatic driving comprehensively. To solve the problem, a low illumination image enhancement method in rainy days is proposed, which combines the two scenes and can be applied to the field of automatic driving. This algorithm takes good advantage of the fact that raindrops cannot appear in the same frame at different times to eliminate the noise of raindrops in the image .And its main process is to fuse continuous image frames by maximum method through multi-scale Gaussian filtering. Thus, the fused image is used as the illumination map and the original image to be denoised is used as the input of Multi-Scale Retinex(MSR) method.Finally, the reflection image which can be directly applied in automatic driving is obtained.
THE ROLE OF USING VIRTUAL LABORATORIES IN TEACHING CHEMISTRY IN PEDAGOGICAL EDUCATIONAL INSTITUTIONS

The organization and improvement of virtual laboratories in the teaching of chemistry in the higher education system will be highly effective. Experiments that can be performed in the virtual laboratories ensure that they are performed in the same way as in a chemistry laboratory training room. The sequence of exercises is based on strict rules and serves to strengthen theoretical knowledge. And experiments also in the virtual laboratories encourages a clear understanding of the essence of the training.
THE MAIN CONCEPTUAL APPROACHES TO THE CREATION OF A MODERN COMPUTER SCIENCE CURRICULUM IN A SECONDARY SCHOOL

This article discusses the issues of developing the concept of a computer science course and the use of ICT tools in solving educational tasks, determining some of the main components of this process: goals, objectives, patterns, principles, conditions, content, development of the intended result, criteria and indicators.
Stratified Small Class Teaching and Educational Equity

This paper focuses on the relationship between performance-based classroom teaching and educational equity, with experimental data collected primarily through questionnaires, as it has been and continues to be a popular teaching method in China today. Through surveys and summaries, the stratified small class teaching does undermine educational equity, so in future we should avoid harm and be more rigorous in our use of stratified small class teaching.
Relationship Between Bilingual Verbal Working Memory and Language Dominance

The purpose of the study is to explore how language dominance impacts bilingual individuals verbal working memory. The study recruited 36 participants who are bilingual in both English and Chinese Mandarin age between 18 to 25 years old. In 90-minute experimental sessions, participants verbal working memory capacities both English and Chinese were measured by Following Instruction. And their language proficiencies in both English and Chinese were measured by Elicited Imitation Test. The result shows that Chinese dominant bilingual participants verbal working memory capacity were significantly impacted by their language dominance, but English bilingual participants verbal working memory capacity were not significantly impacted by their language dominance. Future research should consider to study whether Following Instructions is a reliable measure for bilingual populations verbal working memory.
Furnitureal-Home Furniture Décor Application using AR

Abstract: If you change the manner in which you check outthings, the things you take a gander at will change. If illustrations or advancements in technology help to improve the development of portable gadgets and fantasy looks of the products for product experience, they will help to improve how people view these items. As a rule, purchasing items without contact and feeling inconveniences the actual business and prompts time and exertion for working on the clients. The improvement and the advancement in innovation will help to improve how people view these items with Augmented Reality.
Yoga Posture Classification using Deep Learning

Human Posture detection using Deep learning techniques are widely useful in many areas like face recognition, video games, virtual reality, animations and many varieties of physical training. In this work, we are applying deep learning techniques like convolution neural networks (CNN) in the field of Yoga. It is a form of physical exercise which helps to improve both the physical and mental state of humans. There are different styles of yoga that combine physical poses, breathing techniques, and meditation or relaxation to promote mental and physical well-being. We have used a dataset which consists of five different yoga poses for training the model and evaluating the prediction accuracy of the model. The system recognizes a yoga pose from a given image using a CNN model and three CNN architectures such as VGG16, VGG19 and MobileNet. In comparison, among the four, VGG19 model achieved an accuracy of 99.49% which is higher than the other models.
HIT: Learning a Hierarchical Tree-Based Model with Variable-Length Layers for Recommendation Systems

Large-scale industrial recommendation systems (RS) usually confront computational problems due to the enormous corpus size. Hence, an efficient indexing structure is a practical solution to retrieve and recommend the most relevant items within a limited response time. The existing approaches that adopted embedding or tree-based index structures cannot handle the long-tail phenomenon. To address this issue, we propose a HIerarchical Tree-based model with variable-length layers (HIT) for recommendation systems. HIT consists of a hierarchical tree index structure and a user preference prediction model. It can fully exploit all the training data by dynamically adjusting the lengths of layers in its tree index structure, which can effectively alleviate the long-tail problem. To assess the models' resistance against the long-tail problem, we further define two types of equilibrium under our index structure. To satisfy the equilibrium, we propose a corresponding hierarchical tree learning algorithm. Furthermore, for those items with a rare appearance in the training data, on which the learning algorithm would fail, we design a dedicated bandit layer to solve them. Extensive experiments on three large-scale real-world datasets show that HIT can significantly outperform the existing methods in terms of efficient recommendations on items with different frequencies.
Universal Speech Translator

In summary, Meta explains that this all-in-one translator is “pushed via way of means of the purpose of breaking down language boundaries on an international scale” the use of system gaining knowledge of technology. Since a mechanical perception, "This is a provisional computational prototype based on the Thinly Gated Mixture of Experts, qualified on data gained using novel and effective data mining methods personalized to low-resource languages. It is a model that forms the basis of universal translation. In the system, BLEU (short for Bilingual Evaluation Understudy).
Disentangling Exploration and Exploitation in Deep Reinforcement Learning Using Contingency Awareness

This article investigates the efficiency of modelling contingency awareness in sparse reward environments for better exploration. We investigate this hypothesis on hard exploration games from the Atari 2600 platform through The Arcade Learning Environment. We develop a neural network architecture that separately models the extrinsic and intrinsic rewards, showing that it leads to more stable learning. Separately modelling the rewards leads to results that are comparable to the current state of the art approaches in a number of training steps that is a degree of magnitude lower. Our experiments empirically confirm that modelling contingency awareness using separate models for the extrinsic and intrinsic rewards leads to better exploration.
Improving Hyper-relational Knowledge Graph Representation with Multi-grained Encoding

Learning hyper-relational knowledge graph (HKG) representation has attracted growing interest from research communities recently. HKGs are typically organized as structured triples associating with additional qualifiers (i.e., key-value pairs) to provide unambiguous hyper-relational facts. The main challenge of HKG is to disambiguate representations of entities and relations by addressing different qualifiers. However, most current models for HKG representation are trained based on single-grained encoders, usually with fine-grained entities, relations, keys, and values in qualifiers, which makes it hard to learn the precise impact of the qualifiers on the whole triple. To overcome this problem, we propose a novel model HEAT for HKG representation by exploring multi-grained encoding, including coarse-grained and fine-grained encoding. Specifically, HEAT performs a graph coarsening method to treat each triple as an integrated coarse-grained node, which satisfies the correlation constraint between the triple and its corresponding qualifiers. Then HEAT leverages a two-stage graph encoder to encode the fine-grained element nodes and coarse-grained triple nodes. The experimental results on three datasets demonstrate that the proposed HEAT consistently outperforms several state-of-the-art baselines.
An Information Theoretic Perspective for Heterogeneous Subgraph Federated Learning

Mining graph data has gained wide attention in modern applications. With the explosive growth of graph data, it is common to see many of them collected and stored in different distinction systems. These local graphs can not be directly shared due to privacy and bandwidth concerns. Thus, Federated Learning approach needs to be considered to collaboratively train a powerful generalizable model. However, these local subgraphs are usually heterogeneously distributed. Such heterogeneity brings challenges for subgraph federated learning. In this work, we analyze subgraph federated learning and find that sub-optimal objectives under the FedAVG training setting influence the performance of GNN. To this end, we propose InfoFedSage, a federated subgraph learning framework guided by Information bottleneck to alleviate the non-iid issue. Experiments on public datasets demonstrate the effectiveness of InfoFedSage against heterogeneous subgraph federated learning.