Reviewerly/exHarmony huggingface dataset:
Few-Shot Image Semantic Segmentation based on Contextual Information Encoding Strategy

<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> In recent years, remarkable advancements have been achieved in the field of image semantic segmentation through the utilization of deep convolutional neural networks (CNNs). Nevertheless, the development of semantic segmentation is hindered by the requirement of a substantial amount of intensively labeled training samples in conventional deep learning networks. The objective of few-shot semantic segmentation is to accurately segment objects within a target class using a limited number of annotated images for learning. Currently, most methods in the few-shot segmentation field are highly sensitive to target categories, meaning that they have weak generalization ability and that segmentation results can vary greatly across different categories. Furthermore, a common drawback among many of these methods is the underutilization of the semantic information available in the support set, leading to suboptimal segmentation performance. To overcome these challenges, we introduce a novel strategy for encoding contextual information in our paper, specifically designed for few-shot image semantic segmentation. First, we propose a metric network to obtain prototype representations of feature classes from the supporting images. Furthermore, we introduce a novel Contextual Advanced Semantic Extraction (CASE) module to learn the trade-off between depth, width, and resolution. To mitigate the detrimental effects of foreground-background class imbalance, we also put forth a hybrid loss strategy as an additional contribution.
A Study on the Acquisition of ‘안’ and ‘못’ Negation Expressions among Chinese Korean Beginners: Focused on Error Analysis of University Students in Jilin Province of China

Negation is one of the most fundamental and crucial aspects in Korean language learning. However, Chinese learners often make many errors when learning Korean negation. Due to Chinese and Korean are two totally different languages, Korean is an agglutinative language while Chinese is an isolating language, Chinese learners face difficulties in learning Korean negation. Representative negations in Korean include ‘안’ and ‘못’, while in Chinese, they are represented by ‘不’ and ‘没’. However, the negations in both languages do not correspond completely. Although there are similarities, there are also many differences. Therefore, Chinese learners often make errors in using Korean negations by the influence of their native language.&#x0D; This study aims to examine the acquisition and error patterns of negations when Chinese beginners use the negations ‘안’ and ‘못’. Firstly, learners were given tasks at the sentence and discourse levels. Through the analysis of the results, the types of errors made by the learners were examined. According to the results, there are three types of errors: substitution errors between ‘안’ and ‘못’, errors in the use of ‘안’ as a single negation form, and errors related to past tense. The causes of the errors were analyzed, and suggestions for resolving the errors were provided. I hope that this study will contribute to future education on Korean negations.
Testing and validation of AI and blockchain-based models for application in telehealth systems

In the context of telehealth systems, this work addresses the capabilities of artificial intelligence (AI) and blockchain technology. Computer systems can behave intelligently thanks to AI, carrying out operations that would otherwise require human intellect. Blockchain is a decentralized digital ledger technology that securely logs network-wide transactional activity. The study aims to test and validate blockchain- and AI-based models for telehealth systems, spanning steps like data collection, training, blockchain validation, security assessment, legal compliance, in-app testing, and documentation. This chapter outlines the roles, difficulties, and blockchain-based methods in telehealth. It provides an up-to-date review and a helpful manual for researchers interested in conducting additional studies in this area. Despite the telehealth systems&apos; infancy, the study outlines established standards for AI and blockchain models. There may be privacy and security problems with these models. Test scenarios scripted in PYTHON are used to gauge the model&apos;s performance and correctness. With this research, AI-inspired algorithms have been employed to predict chronic kidney diseases for their early detection and prevention. Classifiers such as Random Forest and eXtreme Gradient Boosting (XGBOOST) are utilized to carry out the forecast with the accuracy of such schemes ranging from 97.50% to 96.50%, respectively. The models&apos; consistency and dependability are ensured through stress testing that involves high user loads, unpredictable data input, and network outages. By locating potential flaws and enhancing model performance, this technique increases resilience.
Advancements in Cloud Security: An Enhanced Auth Privacy Chain-Based Hybrid Encryption Technique for Scalability

Security remains a top priority in the ever-changing landscape of cloud computing. This in-depth examination examines the most recent breakthroughs in cloud security, with a particular emphasis on an emerging paradigm An Enhanced AuthPrivacyChain-Based Hybrid Encryption Technique. This study seeks to provide a detailed knowledge and critical analysis of the current status of safe cloud computing, tackling the critical challenge of scalability by drawing insights from an extensive collection of over 50 relevant research papers.&#x0D; By introducing a novel hybrid encryption model, the suggested technique extends the AuthPrivacyChain framework, a blockchain-based access control system. This paradigm intentionally blends the features of symmetric and asymmetric encryption, leveraging their distinct characteristics to strengthen cloud data security. The key goals are to achieve fine-grained access control, prevent illegal access, and ensure that encrypted data is handled efficiently in cloud environments.&#x0D; The review highlights the importance of attribute-based encryption, homomorphic encryption, and multi-party computation in reinforcing the security of cloud-stored data based on an evaluation of the selected references. The AuthPrivacyChain framework introduces an additional layer of security through blockchain, ensuring robust privacy protection and accountability in cloud transactions. Moreover, the hybrid encryption model effectively addresses the limitations associated with traditional encryption schemes, offering a judicious balance between security and performance.&#x0D; A central theme throughout the reviewed literature is the emphasis on scalability enhancement. The proposed technique demonstrates promising capabilities to meet the escalating demands of cloud computing environments, accommodating the burgeoning volume and intricacy of data while upholding stringent security measures.&#x0D; This review paper serves as an invaluable resource for researchers, industry practitioners, and policymakers immersed in the realm of cloud security. By synthesizing insights from a diverse array of sources, it not only contributes to the ongoing discourse on securing cloud data but also lays the groundwork for future advancements in the field, fostering innovation and resilience in the face of evolving cybersecurity challenges.
The Impact of User-Generated Content, Social Interactions and virtual Economies on Metaverse Environments

This study examines how user-generated content, social interactions, and virtual economies impact the Metaverse's changing environment. It offers crucial knowledge for policymakers, stakeholders, and developers of the Metaverse in building interesting and long-lasting virtual worlds. The study surveyed 385 respondents from the South Asian demographic using a Likert rating scale through an online survey. Demographic variables were analyzed using SPSS version 29, focusing on frequency and percentage distribution. Means and standard deviations were calculated for each variable and survey item. The main analysis included multiple regression to evaluate hypotheses regarding the interaction between user-generated content, social interaction, and the virtual economy. User-generated content significantly improves Metaverse environments (= 0.22, p&lt;0.01**), highlighting its crucial part in creating immersive virtual spaces. Metaverse environments bene t from social interactions (=0.16, p&lt;0.01**), fostering involvement and community. In the growth of the Metaverse, virtual economies are extremely important (=0.501, p&lt;0.001***), encouraging user participation and innovation. This research, which uses rigorous statistical analysis and focuses on South Asian demographics, offers novel insights into the metaverse dynamics. It serves as a crucial resource for the Metaverse's future growth and development by guiding developers, stakeholders, and policymakers as they navigate the Metaverse's seemingly limitless potential.
PERAMALAN HARGA CRUDE OIL MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM) DALAM RECURRENT NEURAL NETWORK (RNN)

Crude oil or petroleum is a very important requirement in meeting world energy consumption. Every country definitely needs a supply of petroleum to fulfill their needs. Fluctuations in oil prices are always considered as a barometer of the economy throughout the world, so any change in oil prices is always an interesting topic to be discussed in the economic environment in every country. Therefore it is necessary to predict the price of petroleum, while the method used to predict oil prices is the Long Short Term Memory method, this study aims to predict future crude oil prices based on historical data using the Long Short Term Memory method, knowing the accuracy Forecasting crude oil prices and increasing market efficiency to be more efficient in allocating resources and in this study resulted in an RMSE accuracy of 2,665 and 2.7% Mape for data starting in 2018-2023, while for data for 2020-2023 it produces an RMSE accuracy of 2,630 and MAPE is 2.9%.
A Shoe Shopping System based on Convolutional Neural Network Image Recognition

In this article, we present an innovative approach to enhance the online shoe shopping experience. The convolutional neural network (CNN) image recognition technology was used to enhance shoe classification and recommendations. By training the CNN model on an extensive dataset, unique shoe features and styles were learned. Integrated into a user-friendly online platform, the system offers real-time image recognition, allowing users to snap a photo of a desired shoe for instant identification, including brand, price, and availability details. Moreover, the CNN-based recommendation engine provides personalized suggestions based on style, color, and customer preferences, enriching the shopping experience. Evaluation results confirmed the system's feasibility, and user feedback highlighted its effectiveness in simplifying the shopping process and enhancing satisfaction. This innovative system presents a significant leap in merging AI and e-commerce and shows the potential of image recognition to transform online marketplaces, benefiting consumers, offering valuable insights for retailers, and ultimately reshaping the future of online shoe shopping.
Object Detection Algorithm Based on Improved YOLOv7 for UAV Images

The technology of unmanned aerial vehicles (UAVs) has matured and widened the range of applications in engineering, agriculture, transportation, surveillance, and so on. Deep learning techniques for object detection assist operators in identifying targets and enhancing efficiency. Thus, it is important to improve the accuracy of object detection. However, due to the limited resources of the UAV platform and the necessity for real-time recognition, striking a balance between accuracy enhancement and decreasing computation is pivotal for development. Thus, we proposed an algorithm based on a YOLOv7 single-stage object detector with a detection head to improve the detection effect of small objects and using a modified feature layer attention module (M-FLAM) at the high-level feature layer to enhance the attention on small objects. The algorithm employed modified efficient layer aggregation network (M-ELAN) to reduce the number of parameters without significant loss in accuracy. Experiments were conducted on the VisDrone dataset. YOLOv7 achieved mean average precision (mAP) @ 0.5 of 46.6%, mAP @ 0.5:0.95 of 26.2%, and the number of parameters was 37.2 million. In comparison to YOLOv7, the developed algorithm demonstrated a 2.3% increase in mAP @ 0.5 and a 2.3% increase in mAP @ 0.5:0.95, with a notable 4.8% reduction in the number of parameters.
Methodology of Students’ Self-Educational Activity at a Technical University

There is considered the methodology of self-educational activity of students at technical universities. It is noted that in the context of transition to student-centered learning, the role of independent work of students is significantly increased. Various levels of independent work are described. The key role of the teacher in the successful organization of students’ self-educational activities is emphasized. The reasons for the insufficient effectiveness of independent work at technical universities are considered. Organizational and methodological recommendations for strengthening self-educational activity are given.
Force Feedback Glove Design for Virtual Reality Applications

Immersion is the most important feature of virtual reality (VR) interaction. To achieve an immersive experience in a virtual environment, visual-auditory information reception and haptic interactive feedback are required. As the demand for human-computer interaction continues to increase, natural gesture interaction and haptic feedback become increasingly important. To improve the immersive experience of virtual environments, we proposed force-tactile feedback and hand posture acquisition for measurement design. A simple structured, lightweight posture acquisition and force-tactile feedback device were designed in this study. The device consisted of five potentiometers on the fingers and five servos on the back of the hand plus a central processor connected to a computer device. The device was easily integrated with existing VR positioners. To evaluate the feasibility and usability of the interactive device, we performed common operations in the virtual experimental scenario, including hand pose replication, grasping cubes and spheres, and picking up and releasing bows and arrows. The results showed that force feedback improved the user’s experience during grasping in the virtual experimental scenario. All operations took significantly less time to complete and the user interaction was more accurate.
Privacy-Preserving Swarm Learning Based on Lightweight Homomorphic Encryption and Blockchain Technology

A decentralized solution called swarm learning integrates blockchain into machine learning with distributed data sources and computing power. The security of blockchain is inherited and the security and privacy of data is enhanced surpassing federated learning. Previous studies have explored the application of homomorphic encryption to privacy protection in distributed machine learning. However, existing homomorphic encryption algorithms require a large amount of computation, making them unsuitable for swarm learning. To solve this problem, we improved a privacy-preserving swarm learning framework based on an improved lightweight Paillier homomorphic encryption algorithm. We evaluated the performance of the improved algorithm under the federated learning environment and the security and the swarm environment. Experiment results showed that under the MNIST data set, the encryption time of the classical homomorphic encryption algorithm was 2.9 times longer than that of the improved algorithm. The improved algorithm improved the encryption efficiency of the homomorphic encryption algorithm and protected the privacy of swarm learning data.
Ising Machines Using Parallel Spin Updating Algorithms for Solving Traveling Salesman Problems

As a type of physics-inspired computation, Ising computing has raised an ever-growing interest in solving problems in non-deterministic polynomial (NP) complexity classes with polynomial time. As an NP-hard problem, the traveling salesman problem (TSP) plays an important role in various routing and scheduling applications. However, the execution speed and solution quality significantly deteriorate using a solver with simulated annealing (SA) due to the quadratically increasing number of spins and strong constraints placed on the spins. In this chapter, we present two improved algorithms based on momentum annealing and simulated bifurcation for fully connected Ising machines, which can realize parallel updating of spin states. Due to the massive parallel processing capacity, the algorithms can solve the TSP with an improvement in the solution quality with shorter runtime than using SA methods.
Machine learning for anomaly detection approaches, challenges, and applications

Machine learning (ML) has revolutionized anomaly detection, offering powerful tools to identify deviations from established patterns in diverse domains. This review paper comprehensively explores the landscape of ML-based anomaly detection approaches, delving into their strengths, limitations, and suitability for various applications. We begin by outlining the fundamental concepts of anomaly detection and its significance in real-world scenarios. Subsequently, we delve into the major categories of ML approaches employed for anomaly detection, including supervised, unsupervised, and semi-supervised techniques. Each category is explored in detail, highlighting its underlying principles, representative algorithms, and practical considerations. We then delve into the critical challenges that impede the efficacy of anomaly detection systems, encompassing data quality issues, imbalanced class distributions, concept drift, and the curse of dimensionality. To bridge the gap between theory and practice, we showcase the diverse applications of ML-powered anomaly detection across various sectors, including fraud prevention, cybersecurity, network intrusion detection, healthcare diagnostics, and industrial predictive maintenance. Finally, we conclude by discussing emerging trends and future directions in the field, emphasizing the potential of novel techniques like deep learning and reinforcement learning to further enhance anomaly detection capabilities.
Deep learning for image recognition: A survey of architectures and techniques

In recent years, deep learning has emerged as a transformative force in the field of image recognition, revolutionizing the way machines perceive and understand visual information. This paper presents a thorough survey of the diverse architectures and techniques employed in deep learning for image recognition, shedding light on the remarkable progress and challenges within this dynamic domain. The survey begins by providing a comprehensive overview of the fundamental principles that underlie deep learning for image recognition. It delves into the evolution of neural network architectures, starting from early convolutional neural networks (CNNs) to more sophisticated models such as residual networks (ResNets), inception networks, and attention mechanisms. Each architecture is dissected to reveal its strengths, weaknesses, and the specific image recognition tasks for which it excels. The exploration extends beyond architecture, addressing the myriad techniques that enhance the performance and robustness of deep learning models in image recognition. Transfer learning, data augmentation, and regularization methods are scrutinized for their pivotal roles in training models with limited labeled data, while adversarial training is examined for its ability to fortify models against malicious attacks. The survey also highlights the significance of pre-processing and normalization techniques in optimizing input data for diverse neural network structures. Furthermore, the paper investigates the impact of deep learning in specialized domains of image recognition, including object detection, image segmentation, and facial recognition. It elucidates the distinctive challenges and tailored solutions associated with each subfield, emphasizing the versatility of deep learning architectures in addressing complex visual recognition tasks. A critical aspect of this survey involves the examination of challenges and potential future directions in deep learning for image recognition. The issues of interpretability, ethical considerations, and the demand for explainable AI are discussed, alongside the exploration of emerging technologies such as unsupervised learning and meta-learning.
Ambiguity Detection and Improvement for Malay Requirements Specification: A Systematic Review

Malaysian public sectors have invested billions in digitizing systems. Electronic government efforts created much software. Our informal interview taught us that many software projects encountered delays, and several failed. One of the main contributions of software failure is ambiguity in requirements specification (RS). Ambiguity is a familiar requirement smell that causes misinterpretation. Thus, we seek to devise a technique for detecting and improving ambiguous RS in the Malaysian public sector. One of our challenges is that the Malaysian public sector RS is developed in Malay, and most available techniques support English and other major languages. Hence, this paper investigates the automated and semi-automated techniques to detect and improve ambiguous RS. Following the standard guidelines for systematic mapping, review, snowballing, and quality assessment, we studied works from 2010 to 2022 on ambiguity detection and improvement techniques. We chose 42 articles as primary studies from 2,549. As a result, Natural Language Processing (NLP) and machine learning (ML) are the most promising techniques for automated and semi-automated ambiguous detection models. Furthermore, the ambiguous improvement technique began using deep learning (DL) in 2019. However, most proposed tools are still in the validation phase and are not widely employed, implying that tool development and validation research are progressing slowly. Apart from the generic linguistic context of RS, some research focuses on industrial domain-based RS. Our study shows that additional strategies have been developed to overcome RS-related issues.
Spatiotemporal Analysis for Rainfall Prediction Using Extreme Learning Machine Cluster

Rainfall prediction is an essential study as a guideline for water resources management to manage disasters. Still, earlier research cares much about temporal information, only considering a single spatial location. The earthâ€™s land surface has a large area of spatial location, so to manage spatial information simultaneously as temporal, we use spatiotemporal data to analyze rainfall prediction more accurately. This study uses the spatiotemporal Extreme Learning Machines (ELM) Cluster to forecast rainfall using CHIPRS data from satellites and stations. Data consists of spatial two dimensions and temporal data from 1981 to 2020. The dataset for the experiment contains 480 months. We use focal operation for data preprocessing to the nearest neighbor value. Moreover, the ELM cluster can manage every spatial location by sharing the output weight of ELM, so there is no spatial information left behind. Then, comparing the spatiotemporal Extreme Learning Machines Cluster among SVR, Linear Regression, Gaussian, Ridge, and Lasso are used to predict the data on those timescales. The results indicate that spatiotemporal ELM-Cluster can accurately forecast rainfall. Using ELM-Cluster in hydrological rainfall forecasting is encouraging, and the model can practically be used. Evaluation using MAE with a score of 66.77 and RMSE, 83.77, getting the fastest training with only 28.9 seconds compared to the other methods due to the ELM Cluster does not have backpropagation with spatial improvement.