Reviewerly/exHarmony huggingface dataset:
A Deep Reinforcement Learning Approach for UAV Path Planning Incorporating Vehicle Dynamics with Acceleration Control

Unmanned aerial vehicles (UAVs) are experiencing a rapid expansion in their applications across various domains, including goods delivery, video capturing, and traffic control. The crucial aspect for UAVs to execute successful target tracking and obstacle avoidance maneuvers lies in the accuracy of their path planning operations. This research paper aims to contribute to the existing body of knowledge by presenting a novel model that incorporates acceleration control, accounting for changing variables such as UAV velocity and altitude, while also incorporating vehicle dynamics. To enhance the realism of the model, we include drag force as a factor. In this study, we focus on exploring the potential of deep reinforcement learning (DRL), specifically the deep deterministic policy gradient (DDPG) algorithm, for modeling a 3D continuous environment with a continuous set of actions. In order to improve the UAV’s performance in executing target tracking and obstacle avoidance maneuvers, we propose an innovative reward function based on the inner product. The training results show that the UAV successfully learns to perform the aforementioned tasks. Also, simulation results demonstrate the superior performance of the proposed UAV modeling and reward function compared to existing works.
Visible property enhancement techniques of IoT cameras using machine learning techniques

Perceiving a sight in low light is challenging due to low SNR and photon counts. Deeper learning is a kind of machine learning that is revolutionising picture identification and computer perception. In this study, deeper learning will be used to enhance low-light picture filtering. To do this, a literature review will be performed to gather inspiration for methods and features that may be applied to the final networks. A fully functioning deeper learning picture filtering system will then be created, allowing networks to be trained using guided learning and the filtered resulting images to be recorded to files. With its output pictures plainly showing it was filtering low-light shots, the network functioned effectively. To maximise the network's potential, it must be run for a longer length of time.
STUDYING COMPARATIVE CLOUD COMPUTING TECHNOLOGY

In order to provide quicker innovation, adaptable resources, and scale economies, cloud computing is the supply of computing services, including servers, storage, databases, networking, software, analytics, and intelligence, over the Internet. As technology advanced in recent years, cloud technologies were increasingly utilized. Cloud technologies are appealing because data may be stored, transmitted to numerous locations at once, and accessed at any time. It is a system that enables data storage on the Internet, releasing users from the constraints of physical settings and storage devices and enabling access from any location at any time.
Positive Effects of SIBI Alphabet Cards on Increasing Vocabulary Acquisition in Children with Special Needs Deaf

Children with Special Needs with hearing impairment have hearing loss, classified as deaf and hard of hearing. Hearing loss in children with special needs is associated with long-term academic and communication difficulties, so it will hinder the learning process and potentially experience obstacles in developing students' thinking or cognitive quality. This study aims to analyze the effect of Indonesian language learning media using the SIBI Alphabet Card on increasing vocabulary acquisition in children with hearing impairment. This research is true experimental research with the randomized three-group pretest-posttest only design. A total of 27 children with hearing impairment, categories slight, moderate, profound, male and female, aged 8-22 years. Subjects were randomly divided into three study groups, namely G1 (n = 10; Slight Deaf), G2 (n = 8; Moderate Deaf), G3 (n = 9; Profound Deaf). The Indonesian-language learning media intervention using the SIBI Alphabet Card was carried out 2x/week for four weeks. Data were collected before and after the intervention using a written test instrument. The data analysis technique used the Paired Samples T-Test test with the Packet for Social Science (SPSS) Statistics Software version 21. Based on the results of the study showed that there was an increase in vocabulary acquisition in the three groups before and after the intervention (p0.001). In general, concluded that the provision of Indonesian-language learning media using the SIBI Alphabet Card which is carried out two times/week for four weeks is effective in increasing vocabulary acquisition in deaf children with special needs.
Bilingual Teacher Talk Pada Pengajaran TEYL di Sepama Kamboja

This study focuses on translation analysis of the use of bilingual teacher talk in teaching TEYL (Teaching English for Young Learners) in Sepama Cambodian classrooms. The method used is qualitative descriptive. The results showed that the students as teachers in the Sepama class used 8 translation techniques based on theories from Molina and Albirr. The results of the analysis were obtained based on the processing of 35 data obtained from online learning videos. The literal translation technique was applied to 9 data or 25.71%. Amplification technique as much as 2 data or 5.71%. Variation technique 1 data (2.85%), Typical matching technique 2 data or 5.71%. While the reduction technique has 9 data (25.71%). Transposition and particulate techniques each have 3 data or 8.57%. and finally Modulation technique as much as 4 data or 11.43%. The reduction technique is widely used because the structure of BSa is simpler than BSu. While literal techniques are also often used because teachers must immediately convey BSa and BSu immediately respectively.
Variasi Sistem Sapaan di Instagram

This qualitative research found the type of Instagram greeting and the factors that influence the selection of greetings using data from greeting words in Instagram users' captions from accounts that appeared randomly on the researcher's home page. The methods used are observation, documentation, and content analysis, with researchers using a print screen or munch screen features on computers or mobile phones to document data. The study has identified around 106 greetings in 9 different types, with the greeting to mention loyal customers having the highest number and the kinship greeting being the second. Many bussiness, health, parenting, and daily activities accounts choose kinship greetings to foster closeness or familiarity with their potential targets. This study shows that Instagram is a potential social media to influence customer decisions with information about their business. While there are still opportunities for more specific research, this study contributes to understanding the variety of greetings on Instagram in general.
Menggali Kreativitas Sastra Melalui Pendekatan Teori Sastra Modern: Implikasi bagi Pengajaran Sastra di Era Digital

Modern literary theory and technology approaches can be applied in literature teaching to increase students' creativity in understanding and enjoying literary works in the digital era. The use of technology can help students to broaden their horizons about literature and enrich their learning experience, but keep in mind that the use of technology also has some disadvantages. In this study, the author discusses the implications of using modern literary theory approaches and technology in literary teaching, as well as strategies that can be used to utilize technology wisely and effectively in the context of literary teaching. Through this approach, it is hoped that students can develop their creativity in understanding and enjoying literary works better in the digital era.
A Comparative Study on Government-sponsored International Scholarship Programs of Korea and the United States

[Purpose]The purpose of this study was to address implications and strategies for further development of the Global Korea Scholarship (GKS), a government-sponsored international scholarship program in Korea by investigating management styles and trends through a comparative analysis with the Fulbright Program.&#x0D; [Methods]This study selected cross-national study and literature review as research methods. The comprehensive literature review was conducted to produce criteria for comparison. After modifying and supplementing criteria suggested by former research, management, selection, and influence were adopted as criteria.&#x0D; [Results]As the main result of the research, firstly, the purpose of the GKS Program of Korea and the Fulbright Program of the United States are similar as they both emphasize public interest and diplomatic aspect. However, they show a substantial gap in the size of the budget and the composition of target nations. Next, while the GKS Program evaluated academic excellence based on quantitative and standardized indicators. the Fulbright Program affirmed that students may be selected based on multifaceted indicators. The third result is that both programs highlighted and utilized the network among scholars for the soft power that can be exercised after returning to their own countries.&#x0D; [Conclusion]Korea needs to promote change that can lead GKS to keep pace with global society at the government level by focusing on making it more sustainable. This change would be possible by expanding the range of target nations and diversifying the selection criteria, which will ultimately lead to attracting more quality students with diversity. Furthermore, the GKS Program should expand its influence in the global society by utilizing the existing network platform to encourage international cooperation.
Extracting relations from texts using vector language models and a neural network classifier

The article investigates the possibility of identifying the presence of SKOS (Simple Knowledge Organization System) relations between concepts represented by terms on the base of their vector representation in general natural language models. Several language models of the Word2Vec and GloVe families are considered, on the basis of which an artificial neural network (ANN) classifier of SKOS relations is formed. To train and test the efficiency of the classifier, datasets formed on the basis of the DBPedia and EuroVoc thesauri are used. The experiments performed have shown the high efficiency of the classifier trained using GloVe family models, while training it with use of Word2Vec models looks impossible in the bounds of considered ANN-based classifier architecture. Based on the results, a conclusion is made about the key role of taking into account the global context of the use of terms in the text for the possibility of identifying SKOS relations.
Differentiated Security in the Age of Cognitive Internet of Things (CIoT)

Our IoT systems are transforming from “perceptive” to “cognitive,” where connected IoT devices behave as agents and interact with each other in a context-aware manner with minimum human intervention, learning from both the physical environment and experience. New security challenges surface as IoT applications are empowered with more cognitive capability. The vast amount of ever-growing data needed to equip our devices with artificial intelligence puts enormous pressure on the protection of sensitive data flow and the efficiency of network resource utilization. In order to address the potential security challenges for cognitive IoT systems, we first drafted a survey to study the security requirements and challenges of recent IoT proposals for future smart cities and then proposed a differentiated security architecture that enhances security and efficiency for data communication. In the future, we aim to build an incentive mechanism to encourage secure peer-to-peer data sharing using our proposed differentiated security architecture and deep learning techniques.
The impact of regulatory mechanisms on vulnerability disclosure behavior during crowdsourcing cybersecurity testing

&lt;abstract&gt;&lt;p&gt;There are various regulatory mechanisms to coordinate vulnerability disclosure behaviors during crowdsourcing cybersecurity testing. However, in the case of unclear regulatory effectiveness, enterprises cannot obtain sufficient vulnerability information, third-party crowdsourcing cybersecurity testing platforms fail to provide trusted services, and the government lacks strong credibility. We have constructed a tripartite evolutionary game model to analyze the evolutionary process of the equilibrium of {legal disclosure, active operation, strict regulation}, and the paper reveals the impact of three regulatory mechanisms. We find that these participants' positive behaviors are in a stable state. Higher initial willingness accelerates the speed of reaching the evolutionary stability of the system, and this equilibrium is satisfied only if the governmental regulatory benefits are sufficiently high. Regarding the punishment mechanism, increased punishment for enterprises causes them to adopt positive behaviors faster, while the opposite occurs for platforms; increased punishment for platforms drives both participants to adopt positive behaviors faster. Concerning the subsidy mechanism, increased subsidy to enterprises causes them to adopt legal disclosure behaviors faster, while platforms remain unresponsive; increased subsidy to platforms motivates both players to choose their own positive behaviors. In terms of the collaborative disclosure mechanism, excessive collaborative costs reduce the platforms' willingness to operate actively, which decreases the enterprises' incentives to disclose vulnerability legally. These findings guide the government to establish suitable mechanisms to regulate the participants' behavior and promote the healthy development of the cybersecurity crowdsourcing industry.&lt;/p&gt;&lt;/abstract&gt;
PRACTICE-ORIENTED LEARNING AS A PREREQUISITE FOR MODERN TRAINING OF STUDENTS

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
Cooperative Virtual Machine Placement

Server virtualisation has played a preponderant role in cloud computing success todate. It controls hardware resource access and management for computing, storage and networking in cloud environments. There have been several approaches for virtual machine placement based on reinforcement learning, bin packing, game theory, multi-objective nonlinear optimisation and other heuristics. This paper proposes a cooperative virtual machine (VM) placement approach based on commitments made in a prior coalition formation phase. Based on these commitments and the availability of resources, we use a heuristic to place new VMs. Using the coalition structure, we narrow the space for candidates during a placement, reducing the computation cost of a VM placement. We evaluated our approach and compared it to existing methods.
Leap Motion Gesture Information Collection and Gesture Interaction System Construction

Gesture interaction has very important and far-reaching research significance. It completes the judgment and recognition of gestures through data collection and feature extraction of the original gestures, so that the computer can understand human gesture behavior and finally achieve natural virtual interaction. As an important means of human–computer interaction, the improvement of gesture recognition rate is an important research direction at present. In this paper, a method for correcting the interaction level of Leap Motion (LM) gestures is proposed. This method analyzes the recognition errors of LM by comparing the thresholds in real time, and corrects the hand position by using a hierarchical correction algorithm to solve the unstable recognition phenomenon in the process of human-hand interaction. Through the analysis of the experiment, compared with the traditional CNN model algorithm, the improved CNN has obvious advantages in the later stage of operation, the error is reduced by 36.55%, and the interactive content recognition accuracy is over 90%, which fully proves that this method can improve the recognition accuracy of LM and enhance the user experience. The results show that the proposed method has a high recognition rate. The method is applied to the gesture interaction system to realize the interaction between natural gestures and virtual objects, which increases the interactive fun and improves the interactive experience.
Temporal Image Forensics: What do we actually learn in data driven approaches ?

The assessment of the age of a given digital image, assuming that the EXIF data is not trustworthy, has been traditionally done by analyzing sensor defects which occur and accumulate over time. In recent work, deep-learning based technology has been increasingly used for the age assessment task, which raises the question, which image features are actually used by such techniques. Moreover, (public) datasets used in corresponding experimentation might be prone to content bias, such that instead of age groups, actually content groups are created. We will report on recent results on this topic also showing the importance of XAI techniques in this field.
DAT-MT Accelerated Graph Fusion Dependency Parsing Model for Small Samples in Professional Fields

The rapid development of information technology has made the amount of information in massive texts far exceed human intuitive cognition, and dependency parsing can effectively deal with information overload. In the background of domain specialization, the migration and application of syntactic treebanks and the speed improvement in syntactic analysis models become the key to the efficiency of syntactic analysis. To realize domain migration of syntactic tree library and improve the speed of text parsing, this paper proposes a novel approach—the Double-Array Trie and Multi-threading (DAT-MT) accelerated graph fusion dependency parsing model. It effectively combines the specialized syntactic features from small-scale professional field corpus with the generalized syntactic features from large-scale news corpus, which improves the accuracy of syntactic relation recognition. Aiming at the problem of high space and time complexity brought by the graph fusion model, the DAT-MT method is proposed. It realizes the rapid mapping of massive Chinese character features to the model’s prior parameters and the parallel processing of calculation, thereby improving the parsing speed. The experimental results show that the unlabeled attachment score (UAS) and the labeled attachment score (LAS) of the model are improved by 13.34% and 14.82% compared with the model with only the professional field corpus and improved by 3.14% and 3.40% compared with the model only with news corpus; both indicators are better than DDParser and LTP 4 methods based on deep learning. Additionally, the method in this paper achieves a speedup of about 3.7 times compared to the method with a red-black tree index and a single thread. Efficient and accurate syntactic analysis methods will benefit the real-time processing of massive texts in professional fields, such as multi-dimensional semantic correlation, professional feature extraction, and domain knowledge graph construction.
Analysis of Insurance Anti-fraud Game Based on Random Forest and XGBoost Model

With the rapid development of China's health insurance industry, cases of health insurance fraud are also on the rise year by year, which has had a negative impact on the development of the health insurance industry and social stability. This article analyzes the insurance anti fraud game based on the random forest and XGBoost model. The purpose of game analysis is to use game theory to analyze the occurrence process of health insurance fraud behavior, and then identify the main factors that affect moral hazard and insurance agency expenditure costs. And an insurance anti fraud game model was constructed in the article. Game theory can be used to analyze the process of health insurance fraud and identify the main factors that affect moral hazard. The most common type of health insurance fraud in China is doctor-patient collusion fraud. We will only discuss this type of fraud and not analyze other types. And through research, it has been shown that the XUBoost model has significant advantages in accuracy and recall, while the random forest has a higher accuracy. Obviously, the XUBoost model has good performance.
USE OF DAILY COMMUNICATION STRATEGIES FOR BASIC-LEVEL JAPANESE LEARNERS

Communication strategy is very important in interacting and conversing in a foreign language with native speakers. This research discusses the communication strategies used by a beginner Japanese learner from Indonesia who directly lives in Japan to communicate with Japanese people in everyday life. The data was taken from recording conversations with native Japanese speakers for 3 months in daily conversations. The result is that by using these communication strategies, a Japanese language learner can carry out daily activities by minimizing misunderstandings. The use of fillers or aizuchi is the most used strategy in conversations with Japanese people.&#x0D; Keywords: Communication strategy, Japanese, basic level
Review of machine learning algorithms for breast and lung cancer detection

In the innovative field of medicine, malignant growth has attracted significant attention from the research community due to the fact that genuine treatment of such diseases is currently unavailable. In fact, diseases of such types are so severe that the patient&apos;s life can be saved only when the disease is identified in the early stage, i.e., stages I and II. To accomplish this early-stage disease identification, machine learning (ML) and data mining systems are immensely useful. Specifically, using the large available data existing over the web-based repositories, ML techniques and data mining can be implemented to gather valuable information in view of cancer identification or classification. This chapter is oriented towards the aforementioned with an aim to conduct a point-by-point study of the most recent research on various ML techniques such as Artificial Neural Networks (ANNs), k-Nearest Neighbours (KNN), Support Vector Machines (SVMs), and Deep Neural Networks (DNNs). The main contribution of the chapter is the review followed by the decision on the &apos;best&apos; calculation for an a priori finding of breast and lung malignancy. The crude information from the mammogram or tomography images or the datasets which have been obtained are utilized as the information. The pre-processing of the data and related processes are conducted following which the best prediction model is obtained. Also, the processing time for testing, training, and compliance of all the cases is determined. The results of this study will aid in determining the most appropriate ML technique for the detection of tumours in breast and lung cancer.
Deep learning method on X-ray image super-resolution based on residual mode encoder-decoder network

Deep learning aims to improve the resolution of bicubically damaged images. These existing approaches do not work well for actual single super-resolution. To encode incredibly effective features and to regenerate high-quality images, we introduce an encoder-decoder residual network (EDRN) for a real single image super-resolution (SISR).