Reviewerly/exHarmony huggingface dataset:
Social Media-Based Collaborative Learning Effects on Student Performance/Learner Performance With Moderating Role of Academic Self-Efficacy

Social media has always been described as the channel through which knowledge is transmitted between communities, students, and learners. This social media has been utilized by university students in a way to encourage collaborative learning and social interaction. This study explores the use of social media in the process of collaborative learning among university students in China using a survey method, a total of 583 students from different universities were surveyed in this study. Through this investigation, different factors enhancing collaborative learning among university students in the context of using social media are going to be examined. Structural equation modeling (SEM) and hierarchical regression were used to analyze the suggested hypothesis. Results show that perceived benefit, active learning, and interaction with students are significantly related to social media collaboration. However, perceived ease of use and perceived usefulness have an insignificant effect on social media collaborative learning. Additionally, students' academic self-efficacy significantly moderates the relationship between social media collaboration and learning performance. The implication and limitations of the study are also discussed in the last section.
Normalized Mutual Information-based equilibrium optimizer with chaotic maps for wrapper-filter feature selection

The problem of feature selection involves selecting the most informative subset of features from a data item which have the most impact in the context of classification. It is an optimization problem that requires discarding irrelevant and redundant data without compromising the classification accuracy rate of the learning algorithm. Due to the complexity of the problem, various stochastic methods have been employed to generate promising results. In this work, we propose Normalized Mutual Information-based equilibrium optimizer (NMIEO), a novel variant of the classical Equilibrium Optimizer as a wrapper-filter framework for feature selection. NMIEO incorporates a novel local search strategy based on Normalized Mutual Information to enhance the exploitation capability. Additionally, for improving the diversity of the solutions, Chaotic maps are utilized for better population initialization. The proposed method is evaluated on 14 challenging high-dimensional datasets selected from diverse domains. The results are tallied against eight well-known metaheuristic-based algorithms proposed recently in the literature for solving the feature selection problem. The experimental results and further analysis illustrate the superior performance of the proposed NMIEO relative to other wrapper-based algorithms that are currently in use. Furthermore, the Wilcoxon's Signed Rank Test demonstrates the statistical superiority of NMIEO over other competitive methods across all selected metrics, confirming the success of the proposed method.
Locality guided cross-modal feature aggregation and pixel-level fusion for multispectral pedestrian detection

Multispectral pedestrian detection has received much attention in recent years due to its superiority in detecting targets under adverse lighting/weather conditions. In this paper, we aim to generate highly discriminative multi-modal features by aggregating the human-related clues based on all available samples presented in multispectral images. To this end, we present a novel multispectral pedestrian detector performing locality guided cross-modal feature aggregation and pixel-level detection fusion. Given a number of single bounding boxes covering pedestrians in both modalities, we deploy two segmentation sub-branches to predict the existence of pedestrians on visible and thermal channels. By referring to the important locality information in the reference modality, we perform locality guided cross-modal feature aggregation to learn highly discriminative human-related features in the complementary modality by exploring the clues of all available pedestrians. Moreover, we utilize the obtained spatial locality maps to provide prediction confidence scores in visible and thermal channels and conduct pixel-wise adaptive fusion of detection results in complementary modalities. Extensive experiments demonstrate the effectiveness of our proposed method, outperforming the current state-of-the-art detectors on both KAIST and CVC-14 multispectral pedestrian detection datasets.
Integrating Edge Intelligence and Blockchain: What, Why, and How

Driven by an unprecedented boom in artificial intelligence (AI) and Internet of Things (IoT), edge intelligence (EI) pushes the frontier of AI from cloud to network edge, serving as a remarkable solution that unlocks the full potential of AI services. It is yet facing critical challenges in its decentralized management and security, limiting its capabilities to support services with numerous requirements. In this context, blockchain (BC) has been seen as a promising solution to tackle the above issues, and further support EI. Based on the number of citations or the relevance of emerging methods, this paper presents the results of a literature survey on the integration of EI and BC. Accordingly, we summarize the recent research efforts reported in the existing works on EI and BC. We then paint a comprehensive picture of the limitations of EI and why BC could benefit from EI. From there, we explore how BC benefits EI in terms of computing power management, data administration, and model optimization. In order to narrow the gap between immature BC and EI-amicable BC, we also probe into how to tailor BC to EI from four perspectives, including flexible consensus protocol, effective incentive, intellectuality smart contract, and scalability. Finally, some research challenges and future directions are presented. Different from existing surveys, our work focuses on the integration of EI and BC, develops some general models to help the reader build relevant optimization models in the integrated system, as well as provides detailed tutorials on implementation. We anticipate that this survey will motivate further discussions on the synergy of EI and BC, and offer some guidance in EI, BC, future networks, and other areas.
Real-Time Prediction of Simulator Sickness in Virtual Reality Games

Virtual reality (VR) technology has progressed rapidly and is used in various domains, particularly games. Simulator sickness (SS) still represents a significant problem for its wider adoption. The most common way to detect SS is using the simulator sickness questionnaire (SSQ). SSQ is a subjective measurement and is inadequate for real-time applications such as VR games. This research aims to develop a model to predict SS in real time using in-game characters’ movement and users’ eye motion data during gameplay in VR games. To achieve this, we designed an experiment to collect such data with three types of games. We trained a long short-term memory neural network with the eye-tracking and character movement data to predict SS. Our model can predict SS in real time with an accuracy of 83.4% for players who suffer from severe sensitivity to SS. Our results indicate that, in VR games, our model is an accurate and efficient method to predict SS in real time.
Toward Secure and Privacy-Preserving Cloud Data Sharing: Online/Offline Multiauthority CP-ABE With Hidden Policy

The continuous development of cloud storage service technology, secure access control, and privacy issues have attracted more and more attention. The previous ciphertext policy attribute-based encryption (CP-ABE) schemes with the function of hidden policy are only suitable for a single authority, and the existing multiauthority CP-ABE schemes do not realize the hidden policy. In addition, a large number of schemes utilize AND gate access policies so that expressiveness is weak. In this article, a scheme of online/offline multiauthority CP-ABE supporting the policy hiding function is proposed. The proposed scheme uses a combination of multiple attribute authorities (AAs) and one central authority (CA). Each AA, respectively, controls different attribute sets and distributes attribute private keys to users. Moreover, the AA can also relieve the computation overhead of the CA. In order to enhance the expressiveness than that of the existing schemes, we adopt the access policy of the linear secret sharing scheme. In the previous schemes, the access policy is used as the ciphertext component and uploaded directly to the cloud server. Especially, in the scenario of medical cloud data sharing, access policy may contain sensitive information. Therefore, the proposed scheme preserves privacy information by realizing the technology of the hidden policy. To improve the performance, our scheme utilizes the online/offline encryption to achieve a low computation cost in the online phase. Additionally, we also proved that the proposed scheme is secure based on the standard model.
An Analysis of Document Summarization for Educational Data Classification Using NLP with Machine Learning Techniques

A document summarization network is a process to reduce the content of the papers without reducing its meaning, that are connected by links. Document summarization networks have become ubiquitous because of the widespread usage of online databases with considerable information. Because of its superior efficacy, topic modeling has become a popular method for document management. However, only a few subject models may be used to rank the importance of documents on different themes. Information retrieval relies heavily on document summaries. According to the existing text summarizing methods, the arrangement (indexing) is achieved on the premise of the phrases in the document and contains an array of posting lists. Words are the given arrangement (indexing) weight based on a document characteristic such as term frequency and text length. The topic level ranking of documents is calculated using topical rank, which specifies the value of documents on various themes. This survey can analyze the text rank algorithms of files to improve topic modeling. Furthermore, this survey can implement a topic discovery model for many educational databases.
Blockchain for V2X: Applications and Architectures

Modern vehicles rely on data from a vast array of sensors such as radar and GPS equipment that can be shared with surrounding vehicles and other interested parties. Vehicle-to-Everything (V2X) is the collection of systems that enable such communication. Although this data sharing has the potential to improve both the safety and efficiency of vehicles, ensuring that what is shared has not been altered, deleted, forged, leaked, or otherwise tampered with remains a challenging problem. Today, blockchain technology allows a system's participants to come to an agreement (consensus) on the state of the system and its data in a decentralized, trustless manner. This new technology may be capable of securing V2X data, as well as enabling other useful V2X services such as payments. However, the V2X ecosystem poses several unique challenges that complicate the application of blockchain technology, not least of which is the vast number of communications that any proposed blockchain network will need to support. This paper gives an overview of V2X and blockchain technology, explores potential applications of blockchain within the V2X domain and justifies its importance. It also reviews, analyzes, and discusses various blockchain architectures that could support V2X applications.
Cloud Computing Based Workload Prediction Using Cluster Machine Learning Approach

In current history, cloud computing has dramatically changed the media. Despite the many advantages, specific problems often arise. Dynamic resource scaling and power usage are the major problems in cloud computing. These considerations contribute to an unreliable and expensive cloud framework. The working load forecast is one factor that enhances cloud reliability and operating costs. Exactness is the main element in predicting workload and current methods lag in achieving 100% reliable outcomes. Scientists are still continuously striving to better it. Throughout this article, we introduce a framework of workload prediction utilizing a cluster-based approach to machine learning. Based on the forecast workload, we consider predicting that same amount of time even before the expected time to allow enough time for the job schedule. We add a clustering-based prediction approach to increase the prediction accuracy further. Initially, we group all activities in many classes and afterward train a prediction model for every class.
The Use of Thermal Cameras for Pedestrian Detection

Visible-range camera sensors have been widely used for pedestrian detection. However, most of the methods, which employ visible-range color cameras, do not perform well under low-light and no-light conditions, e.g. during night time. Since the working principle of thermal camera sensors is mainly based on temperature and not light, they have been employed for person detection to overcome the drawbacks of visible-range sensors under these conditions. Every object gives off thermal energy, which is captured by a thermal camera sensor. When an object becomes hotter, it emits more thermal energy, and is therefore captured as much brighter or vice versa. Yet, compared to visible-range cameras, there are many additional challenges that need to be addressed when detecting pedestrians from thermal camera images. These challenges include bright hot objects close to humans, similar pixel values in an image due to weather conditions, or objects that block thermal cameras such as concrete or glass. Glass acts like a mirror for infrared radiation and reflects whatever is in front of the camera. Thus, novel methods are still required to accomplish pedestrian detection task from thermal camera images. To contribute to these efforts, we propose a new method and a modified object detection network incorporating saliency maps of thermal camera images. The features obtained from thermal images and their corresponding saliency maps are combined to obtain richer representations of pedestrian regions, and better detection performance. We perform extensive evaluations on five different datasets to compare the performance of the proposed approach with two baselines. Moreover, we evaluate and compare the transferability of these approaches by doing leave-one-out cross validation across different datasets. The results show that the proposed approach outperforms the baselines, and has better transferability properties across different thermal image datasets.
Real-time occlusion handling for augmented reality assistance assembly systems with monocular images

The application of Augmented Reality (AR) can effectively reduce the cognitive load of operators by presenting complex assembly work instruction with virtual augmented contents. If the virtual augmented contents superimposed on physical product without handling occlusion, the distortion may appear in AR guidance video. This paper proposes a monocular image based real-time occlusion handling method for virtual part model and physical product. In AR assistance assembly process, the virtual part model can be occluded by assembly scene or assembly objects. First, the monocular SLAM is used to reconstruct the assembly scene, and the reconstructed sparse 3D points are converted into depth points in the depth map. We use the color information of the assembly scene to control the propagation of parse depth points to the remaining pixels. With GPU acceleration calculation, a densification depth map of the assembly scene can be obtained. Then, the 3D models of the assembly objects are rendered as depth map according to the registration coordinates in AR assistance assembly system. The depth map of the assembly scene and assembly objects are synthesized as the final depth map for AR occlusion handling. Finally, the depth relationship between real assembly scenes, assembly objects, and virtual augmented contents are compared to determine the display pixels for AR effect. The results show that the proposed occlusion handling method shows high accuracy and fast speed in comparison with conventional methods, which demonstrates that our method can effectively solve the problem of occlusion handing for AR assistance assembly systems.
A Blockchain-Based Multi-CA Cross-Domain Authentication Scheme in Decentralized Autonomous Network

The continuous development of network technology has driven the emergence of smart devices, and the demand for smart devices interconnection has increased sharply, which requires the identity of devices to be authenticated to carry out secure communication. The traditional certificate-based identity authentication scheme can no longer meet the authentication requirements of massive devices. As an authority that issues and manages certificates, Certificate Authority (CA) creates data islands of intra-domain certificates, increasing the complexity of cross-domain authentication. In order to improve the efficiency of cross-domain authentication, this paper introduces blockchain technology, which can establish trust in an untrusted environment. We propose a multi-CA-based authentication architecture to establish distributed trust and share cross-domain certificate information among multiple domains. On this basis, we design a simplified identity authentication scheme to quickly complete cross-domain identity authentication and reduce authentication overhead. To further improve the efficiency of cross-domain authentication, a cross-domain certificate revocation mechanism is designed. The scheme has passed the formal security analysis, and the simulation results show that the cross-domain authentication scheme is efficient.
Privacy-Preserving Blockchain-Based Healthcare System for IoT Devices Using zk-SNARK

Privacy is playing a crucial role in the smart health industry, where health service providers and their customers use the internet of things (IoT) to provide and consume health services. Preserving privacy for legitimate users and preventing illegitimate users from accessing services are difficult to implement simultaneously. In this study, we addressed this issue by proposing a new healthcare system for IoT based on the blockchain and zero-knowledge succinct noninteractive argument of knowledge (zk-SNARK). We employ the anonymity property of the public blockchain to protect users&#x2019; privacy. The zk-SNARK scheme works as an anonymous authenticator to prevent unauthorized users from using services. We also analyze the security of the proposed system by showing that it can resist various types of attacks, such as impersonation, collusion, and man-in-the-middle attacks. Finally, we evaluate the performance of the zk-SNARK scheme with respect to computational costs and the interactions with the Ethereum blockchain smart contract with respect to transaction fees.
FLY-SMOTE: Re-Balancing the Non-IID IoT Edge Devices Data in Federated Learning System

In recent years, the data available from IoT devices have increased rapidly. Using a machine learning solution to detect faults in these devices requires the release of device data to a central server. However, these data typically contain sensitive information, leading to the need for privacy-preserving distributed machine learning solutions, such as federated learning, where a model is trained locally on the edge device, and only the trained model weights are shared with a central server. Device failure data are typically imbalanced, i.e., the number of failures is minimal compared to the number of normal samples. Therefore, re-balancing techniques are needed to improve the performance of a machine learning model. In this paper, we present FL-M-SMOTE, a new approach to re-balance the data in different non-IID scenarios by generating synthetic data for the minority class in supervised learning tasks using a modified SMOTE method. Our approach takes <i>k</i> samples from the minority class and generates <i>M</i> new synthetic samples based on one of the nearest neighbors of each <i>k</i> sample. An experimental campaign on a real IoT dataset and three well-known public datasets show that the proposed solution improves the balance accuracy without compromising the model&#x2019;s accuracy.
Short Pairing-Free Blind Signatures with Exponential Security

This paper proposes the first practical pairing-free three-move blind signature schemes that (1) are concurrently secure, (2) produce short signatures (i.e., three or four group elements/scalars), and (3) are provably secure either in the generic group model (GGM) or the algebraic group model (AGM) under the (plain or one-more) discrete logarithm assumption (beyond additionally assuming random oracles). We also propose a partially blind version of one of our schemes. Our schemes do not rely on the hardness of the ROS problem (which can be broken in polynomial time) or of the mROS problem (which admits sub-exponential attacks). The only prior work with these properties is Abe's signature scheme (EUROCRYPT '02), which was recently proved to be secure in the AGM by Kastner et al. (PKC '22), but which also produces signatures twice as long as those from our scheme. The core of our proofs of security is a new problem, called weighted fractional ROS (WFROS), for which we prove (unconditional) exponential lower bounds.
Breast Cancer Detection Using Particle Swarm Optimization and Decision Tree Machine Learning Technique

Cancer of the breast is one of the deadliest diseases encountered by women and requires early diagnosis. Although more time is required for the traditional diagnostic process, a realistic and straightforward approach may be achieved through machine learning approaches to detect ailment. Advances in technology, however, generate different kinds of high-dimensioned data, majorly cancer or medical data. Staggeringly high data makes it harder for them to obtain insight knowledge; unrepresentative knowledge can contribute to skewed outcomes in classification. The feature selection process may be used to enhance classification performance to solve any of these challenges. In this article, Particle Swarm Optimization (PSO) is suggested to optimize the efficiency of the classification with the Decision Tree algorithm on a Wisconsin Breast Cancer dataset. The findings reveal that the performance of the system was 92.26% accuracy likened to the state-of-the-art. In conclusion, the system will help minimize the existence of breast cancer disease by creating an early detection system for diagnosis based on a machine learning approach, and it will help health practitioners in decision-making.