Reviewerly/exHarmony huggingface dataset:
An Efficient and Privacy-Preserving Information Reporting Framework for Traffic Monitoring in Vehicular Networks

Traffic monitoring services show great potential for improving traffic efficiency in vehicular networks. With the assistance of 5G innovation, vehicles can report information such as driving misbehavior and traffic status to the traffic monitoring server which then returns feedback to the network to enhance the efficiency of traffic management. However, some challenging issues in information reporting need to be addressed first. Trivial reported information significantly increases the workload of the server. Moreover, a server is difficult to distinguish the truth of reported messages. As a result, the server is incapable of returning immediate feedback to the network, thereby reducing the quality of traffic services. Meanwhile, uploading location-based information to the server exposes vehicles' privacy. Although some existing techniques such as trust management benefit information reporting, a complete framework is still missing. In this paper, we propose an information reporting framework for solving both efficiency and privacy issues. Specifically, messages are probabilistically reported to the server depending on their importance. Furthermore, the DAG-based blockchain ensures that messages reported for a specific event are all gathered in the same ledger. We also present a scheme based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">geo-indistinguishability</i> to protect the location privacy of vehicles. Extensive experiments demonstrate that the proposed framework preserves the location privacy of vehicles and achieves efficiency with acceptable overheads.
A novel design of learnable pooling algorithm

Pooling is an important part of modern convolutional neural networks, which can expand the perception field, reduce the parameter matrix, and avoid overfitting. Currently used maximum pooling, average pooling and various subsequent improved pooling algorithms cannot take into account the contour and background information of the feature map at the same time. In addition, the performance of different pooling algorithms varies greatly on different models and datasets. In this paper, we propose a learnable pooling algorithm. The introduction of learnable parameters allows the pooling layer to adaptively optimize the selection of key feature information that is beneficial to improve model performance during model training. It is experimentally verified that the pooling algorithm has superior performance over the existing maximum pooling and average pooling on several classical models and public datasets for image classification and text classification. The pooling algorithm with the introduction of learnable parameters can better prevent overfitting, steadily improve the accuracy of the model, and is highly generalizable.
Research on handwriting recognition method based on machine learning

Handwritten digit recognition is a process of identifying zero to nine ten digits handwritten by human hands, and its related research has always been a hot topic in the field of machine learning classification. In order to explore the accuracy of the classification recognition of handwriting bodies by K nearest neighbor classifier and MLP multilayer perceptron, this paper first introduces the relevant algorithm principle and its research progress, and then experiments on K nearest neighbor classifier and MLP multilayer perceptron and summarizes the relevant experimental data. Experiments show that in the K nearest neighbor algorithm, the classification accuracy is the highest when the number of neighbors K=3; for the MLP multilayer perceptron algorithm, the classification rate is higher when the number of neurons is larger, the number of iterations is 1000, and the learning rate is smaller.
Location-based activity behavior deviation detection for nursing home using IoT devices

With the advancement of the Internet of Things(IoT) and pervasive computing applications, it provides a better opportunity to understand the behavior of the aging population. However, in a nursing home scenario, common sensors and techniques used to track an elderly living alone are not suitable. In this paper, we design a location-based tracking system for a four-story nursing home - The Salvation Army, Peacehaven Nursing Home in Singapore. The main challenge here is to identify the group activity among the nursing home’s residents and to detect if they have any deviated activity behavior. We propose a location-based deviated activity behavior detection system to detect deviated activity behavior by leveraging data fusion technique. In order to compute the features for data fusion, an adaptive method is applied for extracting the group and individual activity time and generate daily hybrid norm for each of the residents. Next, deviated activity behavior detection is executed by considering the difference between daily norm patterns and daily input data for each resident. Lastly, the deviated activity behavior among the residents are classified using a rule-based classification approach. Through the implementation, there are 44.4% of the residents do not have deviated activity behavior, while 37% residents involved in one deviated activity behavior and 18.6% residents have two or more deviated activity behaviors.
Modernized YOLOv4 with large kernels and AVOD2K dataset

We conduct research on a practical task called Armored Vehicle Object Detection (AVOD), which is designed to real-time locate and identify armored vehicles in the armored cluster. Considering the scarcity of available datasets under the background of AVOD, we carefully collect a new dataset named AVOD2K, containing 2,000 pictures which consist of nine types of armored vehicles in various environments. AVOD2K complements the missing armored vehicle dataset, and it could drive special vehicle object detection in complex scenarios. In addition, we use YOLOv4 as the baseline of the task and propose LK-CSPDarkNet to modernize YOLOv4 by combining depthwise separable convolutions and large kernels. As the parameters decreased by 7% and FLOPs a little drop, the modernized YOLOv4 outperforms the baseline in 2.4 AP.
Aesthetics and comprehension of curved 3D graphs in Virtual Reality

Studies and evaluations of 3D graph aesthetics beyond the desktop currently remain sparse, despite recent advancements and applications with the emergence of Immersive Analytics. This paper explores curvature aesthetics for the edges within a 3D graph visualisation and presents preliminary implementations to a Lombardi-inspired rendering for an edge. Additionally, we evaluate three approaches to curvature through two studies to compare against straight-lines as a baseline. The two studies focused on observing user preference and performance when navigating a curved graph without and with topological-based tasks in Virtual Reality (VR). We found that curved edges without specified orientations tended to perform worse in performance and preference compared to Lombardi-inspired implementations of edges. Pupillary response and the participant’s approaches to navigation were also observed to be indicative of the edge’s respective performance for topological-based tasks.
Towards an Automated, Vigilant, and Strategic HRM Function in Industry 5.0

The chapter references technological applications' advancements in the HRM function and suggests the revised HRM function in Industry 5.0. In the first part, a brief background has been given on the technological advancements in the function followed by an extensive literature review of the ongoing and probable application areas of technologies in HRM function. Subsequently, the author develops certain models of operations that the HRs will have to follow, which will help in imbibing the technological practices of machine learning, artificial intelligence, blockchain while adhering to the data privacy practices. Thus, this approach makes the HRM function more automated but vigilant and strategic simultaneously so that HRs can monitor and control the cobots in Industry 5.0.
Learning broad learning system with controllable sparsity through L0 regularization

As a novel neural network with efficient learning capacity, broad learning system (BLS) has achieved remarkable success in various regression and classification problems. Due to the broad expansion of nodes, however, BLS is known to have many redundant parameters and nodes, which will increase the memory and computation cost and is adverse to its deployment on equipment with limited resources. To optimize the number of neurons and parameters of BLS and then find the optimal sparse model under a given resource budget, in this paper, we introduce to train BLS through L0 regularization. The regularization constraint term of the BLS objective function is replaced by the L0 regularization method, and the normalized hard threshold iterative method is used to optimize the output weight. More concretely, the size of the model is fixed by controlling the number of output weights under given the resource size, and then parameters and nodes in the network are evaluated and selected from the node set in the training to obtain a BLS with controllable sparsity (CSBLS). Experiments on various data sets demonstrate the effectiveness of our proposed method.
Based on single image defogging algorithm

The atmosphere contains many tiny, suspended particles, and due to the scattering and absorption of these particles, images can show reduced visibility, distorted colors’, blurred details and other situations. Many computer vision applications are unable to accept these degraded images and therefore require high quality input images to ensure accurate work, provided by a defogging method. Single image deblurring utilizes physical models where transmission estimation is an important parameter in obtaining a fog-free image. The fog image is analyzed and pre-processed to highlight details and make it more suitable for human and machine recognition. The analysis of different deblurring methods divides them mainly into methods based on image a priori recovery, image enhancement and deep learning. The content of defogging-related algorithms is described, and future directions are analyzed.
Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions

Software testing identifies defects in software products with varying multiplying effects based on their severity levels and sequel to instant rectifications, hence the rate of a research study in the software engineering domain. In this paper, a systematic literature review (SLR) on machine learning-based software defect severity prediction was conducted in the last decade. The SLR was aimed at detecting germane areas central to efficient predictive analytics, which are seldom captured in existing software defect severity prediction reviews. The germane areas include the analysis of techniques or approaches which have a significant influence on the threats to the validity of proposed models, and the bias-variance tradeoff considerations techniques in data science-based approaches. A population, intervention, and outcome model is adopted for better search terms during the literature selection process, and subsequent quality assurance scrutiny yielded fifty-two primary studies. A subsequent thoroughbred systematic review was conducted on the final selected studies to answer eleven main research questions, which uncovers approaches that speak to the aforementioned germane areas of interest. The results indicate that while the machine learning approach is ubiquitous for predicting software defect severity, germane techniques central to better predictive analytics are infrequent in literature. This study is concluded by summarizing prominent study trends in a mind map to stimulate future research in the software engineering industry.
Phishing Scam Detection on Ethereum via Mining Trading Information

As a typical representative of web 2.0, Ethereum has significantly boosted the development of blockchain finance.However, due to the anonymity and financial attributes of Ethereum, the number of phishing scams is increasing rapidly and causing massive losses, which poses a serious threat to blockchain financial security.Phishing scam address identification enables to detect phishing scam addresses and alerts users to reduce losses.However, there are three primary challenges in phishing scam address recognition task: 1) the lack of publicly available large datasets of phishing scam address transactions; 2) the use of multi-order transaction information requires a large number of queries and computations; and 3) the extraction of phishing scam address features relies on machine learning methods excessively, which leads to the loss of practical meaning and is harmful to the research of phishing scam addresses.This paper proposes a systematic phishing scam address recognition scheme, to simultaneously overcome the three challenges in phishing scam address recognition.In this paper, a systematic phishing scam address recognition scheme is proposed to addresses these issues.Specifically, due to the insufficient number of address tagged in the existing publicly available Ethereum phishing scam address transaction dataset, we first construct a transaction dataset involving over 10000 tagged addresses.To the best of our knowledge, this is the largest dataset of tagged addresses for Ethereum phishing scam detection.Then, we design a new heuristic rule to implement feature extraction of address nodes by analysing the traditional financial involved accounts combined with information specific to Ethernet transactions.After that, a novel adaptive feature importance filtering method is designed to adaptively adjust the filtering threshold based on the final classification results, which reduce the feature dimensionality while ensuring a certain detection performance.Finally, random forest is used to classify whether the addresses is a phishing scam address or not.Extensive experiments on real Ethereum datasets show that our approach (98.89%Precision, 98.35% Recall, 98.62% F1) achieves state-of-the-art performance.
Can the Electronic Health Record Predict Risk of Falls in Hospitalized Patients by Using Artificial Intelligence? A Meta-analysis

Because of an aging population worldwide, the increasing prevalence of falls and their consequent injuries are becoming a safety, health, and social-care issue among elderly people. We conducted a meta-analysis to investigate the benchmark of prediction power when using the EHR with artificial intelligence to predict risk of falls in hospitalized patients. The CHARMS guideline was used in this meta-analysis. We searched PubMed, Cochrane, and EMBASE. The pooled sensitivity and specificity were calculated, and the summary receiver operating curve was formed to investigate the predictive power of artificial intelligence models. The PROBAST table was used to assess the quality of the selected studies. A total of 132 846 patients were included in this meta-analysis. The pooled area under the curve of the collected research was estimated to be 0.78. The pooled sensitivity was 0.63 (95% confidence interval, 0.52-0.72), whereas the pooled specificity was 0.82 (95% confidence interval, 0.73-0.88). The quality of our selected studies was high, with most of them being evaluated with low risk of bias and low concern for applicability. Our study demonstrates that using the EHR with artificial intelligence to predict the risk of falls among hospitalized patients is feasible. Future clinical applications are anticipated.
Automated extraction and checking of property models from source code for robot swarms

As robots become a common presence in our everyday lives, ensuring the security and safety of robotic systems becomes an increasingly important and urgent challenge. Multi-robot systems, in particular, have the potential to revolutionize multiple industries---such as transportation and home care---where safety guarantees are a primary requirement. A known challenge for swarms and multi-robot systems is the gap between requirements and design, due to the need to translate swarm-level objectives into robot-level behaviors. In this paper, we focus on a less studied problem---the gap between requirements and implementation. As a case study, we use Buzz, that is a dynamic programming language designed for swarm robotics applications. Similarly to Python, Lua, and JavaScript, Buzz does not natively offer formal guarantees of correctness or safety. We propose an approach to automatically extract "as-implemented" models from Buzz programs, whose properties can then be formally analyzed and verified. Results obtained from the experiments performed on two medium-size open-source production-level systems for robotics research have also been reported. Our results show that the approach is feasible and is scalable to larger systems.
Nurse Information Security Policy Compliance, Information Competence, and Information Security Attitudes Predict Information Security Behavior

Nurses' attitudes toward information security can influence the hospital's information resources management and development. This study investigated the relationships between nurses' information security policy compliance, information competence, and information security attitudes, which are factors that influence information security behavior. Data were collected during September 2020. The participants were 200 clinical nurses from a general hospital in Korea. The self-reported questionnaire included questions on nurses' general characteristics, information security policy compliance, information competence, and information security attitudes. Information security policy compliance ( r = 0.554, P < .001) and information competence ( r = 0.614, P < .001) were positively associated with information security attitudes. Predictors of nurses' information security attitudes were information competence ( β = .439), information security policy compliance ( β = .343), prior information security-related education ( β = .113), and job position (nurse manager; β = .101). Implications for practice include the need for strategies to develop information security policy compliance and information competence to improve information security behavior, including different approaches tailored to nurses' job positions and previous information security education.
Online E-learning Mobile Application for Self-learning

This research work is focused on the accessible Mobile user application developments to facilitate student and faculty communication through native android applications. Covid’19 and this pandemic brings E-learning systems as majority education levels. Mobile technology has efficient learning systems in many countries like the United States where the students use google paths such as the classroom to extend learning effectively. Limitations in existing apps are that students are not appreciated and monitored for self learning, schedule sharing is only at the end of the course and also knowing the mapping concepts of teaching pedagogy is also less approachable. To overcome these problems mobile technology support is proposed in this work with these three modules such as i) Authentication self learning and performance (ASLP) - Authentication for right users along with improvement of monitoring self learning to analyze performance ii) Syllable Schedule (SS) - prior scheduling on syllable and organization of time table matching based on outcome iii) Authorized facilitator (AF) - Set permission based on designation such that facilitator communicates based on needs. To achieve the above highlighted terms Google API is applied by peer reviews and interactions enabled such that efficient mobile applications development is proved. Also the abstraction hierarchy is improved when setting the interaction module implies less complexity, less storage. Thus the scope of research work is to use classroom overall performance, interaction, development process to upgrade the results of students. Education level is also enhanced through online E-learning mobile technology (OELMT) that has native applications to develop students’ knowledge in a better way.
Towards a Dependable Energy Market: Proof of Authority in a Blockchain-based Peer-to-Peer Microgrid

For reasons of energy security, affordability and environment, the foundations of our energy markets are in flux. A promising approach for addressing some of the challenges faced comes in the form of blockchain-based peer-to-peer (P2P) microgrids, which we argue are a potential solution to many of the pitfalls of existing grid architectures. More specifically, this paper analyses consensus mechanisms that could be employed in blockchain-based microgrids, demonstrating that proof of authority (PoA) is a promising direction when seeking to improve the dependability of the energy market. We go on to specify a viable architecture for a PoA-based microgrid and provide experimental results to demonstrate that PoA is superior to proof of work (PoW) in this context.
Interdisciplinary Cyber Training

Research and practices about cybersecurity require an interdisciplinary view, necessity for more cybersecurity professionals as well as workforce with interdisciplinary knowledge. Courses with interdisciplinary themes should be developed in education institutions and interdisciplinary cybersecurity training should be organized in companies in order to avoid cyber-attacks. An interdisciplinary approach in education and training assures insight from many areas and provides a real basis for understanding cybersecurity. One important issue is the development of critical thinking skills to face the global challenges of this century, to take right decisions in everyday work and life. This paper describes shortly the importance of interdisciplinary view of cyber security and of critical thinking in education and training and gives as example the project InCyT. The team project develops and tests a digitally supported interdisciplinary training program to develop also critical thinking, a collaborative digital learning platform for small and medium sized companies (SMEs) and an adapted version for VET. SMEs in particular are objectives for criminal activities also due less knowledge about cybersecurity measures and about connections with other disciplines. SMEs resources are limited, so they need help.
ARventure: Edutainment Meets Science Mobile Application

Despite continuous growth in STEM-associated industries, the number of students pursuing Science, Technology, Engineering, and Mathematics (STEM) related subjects is declining. The implementation of Augmented Reality (AR) in education has the potential to improve not just the students’ conceptual comprehension and knowledge but also critical abilities like problem-solving, cooperation, and communication. This study intends to demonstrate how a mobile application embedded with AR that uses an educational scrapbook as its AR marker platform can improve the learning experience of secondary Malaysian secondary school students studying Science. 30 students across Malaysia were recruited. Their responses were analysed to determine whether the notion of employing a mobile application and scrapbook was feasible. Overall, the System Usability Scale (SUS) results were encouraging (mean=69.83, SD=13.36, n=30), suggesting the possibility of integrating AR as part of the learning medium that could improve the learning experience in Science subjects.