Search

Scientific retrieval systems need to be given domain search terms for searching publications, however, as natural language, search terms provided by users are often fuzzy and limited and some relevant terms are always overlooked in searching. Meanwhile, users always desire to be given domain related keywords to enlighten themselves what other terms can be used for their searching. This paper presents a concepts recommendation model in scientific paper retrieval, in which concepts are extracted from keyword in scientific papers, and some data mining algorithms are used to calculate the similarity between search terms and concepts and do recommendation for users. This model is simple and can be used with small dataset, in which all training data used is from meta data of papers that is easy to acquired. Experimental result hold good precision, which shows that this research not only simplifies searching step and improves the searching quality for users, but also lays the foundation for semantic search. [ABSTRACT FROM AUTHOR]

Edge computing enables portable devices to provide smart applications, and the indoor positioning technique offers accurate location-based indoor navigation and personalized smart services. To achieve the high positioning accuracy, an indoor positioning algorithm based on particle filter requires a large number of sample particles to approximate the probability density function, which leads to the additional computational cost and high fusion delay. Focusing on real-time and accurate positioning, an edge computing-enabled green multi-source fusion indoor positioning algorithm called APFP is proposed based on adaptive particle filter in this paper. APFP considers both pedestrian dead reckoning (PDR) signals in mobile terminals and the received signal strength indication (RSSI) of Bluetooth, and effectively merges the error-free accumulation of trilateral positioning and the accurate short-range positioning of PDR, which enables mobile terminals adaptively perform particle filter to reduce the computing time and power consumption while ensuring positioning accuracy simultaneously. Detailed experimental results show that, compared with the traditional particle filter algorithm and the map-constrained algorithm, the proposed APFP reduces fusion computing cost by 59.89% and 54.37%, respectively. [ABSTRACT FROM AUTHOR]

The digitalisation of healthcare has increased the risk of cyberattacks in this sector, targeting sensitive personal information. In this paper, we conduct a systematic review of existing solutions for data breach mitigation in healthcare, analysing 99 research papers. There is a growing trend in research emphasising the security of electronic health records, data storage, access control, and personal health records. The analysis identified the adoption of advanced technologies, including Blockchain and Artificial Intelligence, alongside encryption in developing resilient solutions. These technologies lay the foundations for addressing the prevailing cybersecurity threats, with a particular focus on hacking or malicious attacks, followed by unauthorised access. The research highlights the development of strategies to mitigate data breaches and stresses the importance of technological progress in strengthening data security. The paper outlines future directions, highlighting the need for continuous technological progress and identifying the gaps in the attack mitigations. [ABSTRACT FROM AUTHOR]

Recently, convergence has been presented as one of the technical innovations in many industrial sectors and diffusion has been discussed as providing the main influence on convergence. The present study used the VOSviewer to analyze convergence trends and the current state of joint research, complemented by quantitative analysis using information from scientific papers published in 2015 obtained through the Scopus database. The results of this study illustrate that convergence of research occurring in Korea is evident in a variety of sectors, e.g. chemistry, material science, mechanics and the electrical and electronics sectors. We discovered that a sector performing research characterized by convergence also was actively involved in joint research. We also discovered that institutions conducting many studies were doing so as partners within joint research with other institutions. There are two important applications: The present study identified important information that can be used to monitor convergence and diffusion appears to provide the most influence on convergence. [ABSTRACT FROM AUTHOR]

A Cloud Access Security Broker (CASB) is a security enforcement point or cloud-based software that is placed between cloud service users and cloud applications of cloud computing (CC) which is used to run the dimensionality, heterogeneity, and ambiguity correlated with cloud services. They permit the organization to amplify the reach of their security approaches past their claim framework to third-party computer programs and storage. In contrast to other systematic literature reviews (SLR), this one is directed at the client setting. To identify and evaluate methods to understand CASB, the SLR discusses the literature, citing a comprehension of the state-of-the-art and innovative characterization to describe. An SLR was performed to compile CASB related experiments and analyze how CASBs are designed and formed. These studies are then analyzed from different contexts, like motivation, usefulness, building approach, and decision method. The SLR has discussed the contrasts present between the studies and implementations, with planning accomplishments conducted with combinations of market-based courses of action, simulation tools, middleware's, etc. Search words with the keywords, which were extracted from the Research Questions (RQs), were utilized to recognize the essential consideration from the journal papers, conference papers, workshops, and symposiums. This SLR has distinguished 20 particular studies distributed from 2011 to 2021. Chosen studies were evaluated concurring to the defined RQs for their eminence and scope to particular CASB in this way recognizing a few gaps within the literature. Unlike other studies, this one concentrates on the customer's viewpoint. The survey uses a systematic analysis of the literature to discover and classify techniques for realizing CASB, resulting in a comprehensive grasp of the state-of-the-art and a novel taxonomy to describe CASBs. To assemble studies relating to CASB and investigate how CASB are engineered, a systematic literature review was done. These investigations are then evaluated from a variety of angles, including motivation, functionality, engineering approach, and methodology. Engineering efforts were directed at a combination of "market-based solutions", "middlewares", "toolkits", "algorithms", "semantic frameworks", and "conceptual frameworks", according to the study, which noted disparities in the studies' implementations. For further understanding, the different independent parameters influencing the CASB are studied using PCA (Principal Component Analysis). The outcome of their analysis was the identification of five parameters influencing the PCA analysis. The experimental results were used as input for Research Surface Methodology (RSM) to obtain an empirical model. For this, five-level coding was employed for developing the model and considered three dependent parameters and four center values. For more understanding of these independent variables' influence, on the CASB study, RSM analysis was employed. It was observed from the CCD (Central Composite Design) model that the actual values show significant influence with R2 = 0.90. This wide investigation reveals that CASB is still in a formative state. Even though vital advancement has been carried out in this zone, obvious challenges stay to be tended to, which have been highlighted in this paper. [ABSTRACT FROM AUTHOR]

Federated Learning has received much attention due to its data privacy benefits, but most existing approaches assume that client classes are fixed. Clients may remove old classes and add new ones, leading to catastrophic forgetting of the model. Existing methods have limitations, such as requiring additional client storage and distillation methods becoming less effective as new classes increase. For this reason, this paper proposes the Data-Model Anti-Forgetting (DMAF) framework. Specifically, in the proposed framework, an auxiliary client and group aggregation method to mitigate catastrophic forgetting at the data level has been proposed, which does not require clients to allocate additional storage space to store synthetic data and can balance class distributions. A multi-teacher integrated knowledge distillation method was adopted to retain old class knowledge by distilling multiple teacher models and design task fusion for further tuning of the global model. Finally, this paper conducts extensive experiments on public datasets to validate the effectiveness of DMAF. [ABSTRACT FROM AUTHOR]

This paper tries to improve systematicness of the test module's construction of the track and field (T&F) web course in physical education schools. Methods: literature study, expert interview, system design method, software programming method, questionnaire survey. Results: first, the test interface has the controllable multi-functional keys, the object and navigation path is easy to use and the links are smooth. Second, the question quality module of the question library is featured in its abundant test questions, various question types, reasonable evaluation standards and proper question quantity. Third, the diversity of the question combination helps inspire students and their creative thinking. Fourth, the effect of test administration is excellent, which can form a test paper quickly and easily, and possess functions of marking prompts and statistics. Conclusions: this research is applicable to the web-based test pattern of track and field teaching for physical education majors (selective course, required course, minor course), the operation guidance form for the test shall be strengthened; the editing and update of test papers shall be adjusted and arranged timely according to the teaching practice problems. [ABSTRACT FROM AUTHOR]

This comprehensive review paper examines the challenges faced by blockchain technology in terms of scalability and proposes potential solutions and future research directions. Scalability poses a significant hurdle for Bitcoin and Ethereum, manifesting as low throughput, extended transaction delays, and excessive energy consumption, thereby compromising efficiency. The current state of blockchain scalability is analyzed, encompassing the limitations of existing solutions such as Sharding and off-chain scaling. Various proposed remedies, including layer 2 scaling solutions, consensus mechanisms, and alternative approaches, are investigated. The paper also explores the impact of scalability on diverse blockchain applications and identifies potential future research directions by integrating data science techniques with blockchain technology. Notably, nearly 110 primary research papers from reputable scientific databases like Scopus, IEEE Explore, Science Direct, and Web of Science were reviewed, demonstrating scalability in blockchain comprising several elements. Transaction throughput and network latency emerge as the most prominent concerns. Consequently, this review offers future research avenues to address scalability challenges by leveraging data science techniques like distributed computing and parallel processing to divide and process vast datasets across multiple machines. The synergy between data science and blockchain holds promise as an optimal solution. Overall, this up-to-date understanding of blockchain scalability is invaluable to researchers, practitioners, and policy makers engaged in this domain. [ABSTRACT FROM AUTHOR]

In recent years, fog computing has become a promising technology that supports computationally intensive and time-sensitive applications, especially when dealing with Internet of Things (IoT) devices with limited processing capability. In this context, offloading can push resource-intensive tasks closer to the end devices at the network edge. This allows user equipment to profit from the fog computing environment by offloading their tasks to fog resources. Thus, computation offloading mechanisms can overcome the resource constraints of devices and enhance the system's performance by minimizing delay and extending the battery lifetime of devices. In this regard, designing an algorithm to decide which tasks to offload and where to execute them is crucial. Recently, there has been a growing interest in utilizing Reinforcement Learning (RL) and deep reinforcement learning (DRL), to address computation offloading mechanisms in the context of fog computing. This paper reviews the research conducted on Reinforcement learning (RL) and Deep Reinforcement Learning (DRL) based computation offloading mechanisms for IoT applications in the fog environment. We provide a comprehensive and detailed survey, analyzing and classifying the research paper in terms of RL techniques, objectives, architecture, and use cases. Then, in particular, we identify the advantages and weaknesses of each paper. After that, We systematically elaborate on open issues and future research directions that are crucial for the next decade. [ABSTRACT FROM AUTHOR]

Fog and cloud computing are emerging paradigms that enable distributed and scalable data processing and analysis. However, these paradigms also pose significant challenges for workflow scheduling and assigning related tasks or jobs to available resources. Resources in fog and cloud environments are heterogeneous, dynamic, and uncertain, requiring efficient scheduling algorithms to optimize costs and latency and to handle faults for better performance. This paper aims to comprehensively survey existing workflow scheduling techniques for fog and cloud environments and their essential challenges. We analyzed 82 related papers published recently in reputable journals. We propose a subjective taxonomy that categorizes the critical difficulties in existing work to achieve this goal. Then, we present a systematic overview of existing workflow scheduling techniques for fog and cloud environments, along with their benefits and drawbacks. We also analyze different workflow scheduling techniques for various criteria, such as performance, costs, reliability, scalability, and security. The outcomes reveal that 25% of the scheduling algorithms use heuristic-based mechanisms, and 75% use different Artificial Intelligence (AI) based and parametric modelling methods. Makespan is the most significant parameter addressed in most articles. This survey article highlights potentials and limitations that can pave the way for further processing or enhancing existing techniques for interested researchers. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative method to improve loop closure detection within the domain of Simultaneous Localization And Mapping (SLAM) by integrating a Multi-Scale Attention and Semantic Guidance (MSA-SG) framework. In SLAM systems, accurate loop closure detection is essential for minimizing localization errors over time and ensuring the reliability of the constructed maps in robotics navigation through uncharted environments. Our proposed method utilizes EfficientNet-EA for robust feature extraction and introduces MSA-SG, a novel mechanism that synergizes multiscale attention with semantic guidance to focus on critical semantic features essential for loop closure detection. This approach ensures the prioritization of static environmental landmarks over transient and irrelevant objects, significantly enhancing the accuracy and efficiency of loop closure detection in complex and dynamic settings. Experimental validations on recognized datasets underscore the superiority of our approach, demonstrating marked improvements in precision, recall, and overall SLAM performance. This research highlights the significant benefits of leveraging semantic insights and attentional focus in advancing the capabilities of loop closure detection for SLAM applications. [ABSTRACT FROM AUTHOR]

Code smells violate software development principles that make the software more prone to errors and changes. Researchers have developed code smell detectors using manual and semi-automatic methods to identify these issues. However, three key challenges have limited the practical use of these detectors: developers' subjective perceptions of code smells, lack of consensus in the detection process, and difficulty in setting appropriate detection thresholds. While code smell detection using machine learning has progressed significantly, there still appears to be a gap in understanding the effective utilization of deep learning (DL) approaches. This paper aims to review and identify current methods for code smell detection using DL techniques. A systematic literature review is conducted on 35 primary studies from a collection of 8739 publications between 2013 and the present. The analysis reveals that common code smells detected include Feature Envy, God Classes, Long Methods, Complex Classes, and Large Classes. The most popular DL algorithms used are Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), often combined with other techniques for better results. Algorithms that train models on large datasets with fewer independent variables demonstrate exemplary performance. The paper also highlights open issues and provides guidelines for future metric identification and selection research. [ABSTRACT FROM AUTHOR]

Traditional research on vehicular edge computing often ignores the challenges brought by the rapid movement of vehicles and the dynamic characteristics of the environment, and often ignores that different vehicles on the same path may generate the same computing tasks, resulting in a large number of repeated calculations. Therefore, this paper proposes a vehicular edge computing content caching solution using content prediction and D4PG. Considering the complexity of the vehicular edge environment, this study proposes a digital twin-assisted method to digitally simulate the vehicular edge environment to assist in the decision-making process related to traffic prediction and content caching strategy. In response to the problems of high-speed vehicle movement and dynamic environmental changes, this paper proposes an informer-based traffic prediction model, which uses the informer prediction model to predict the environment and provide information for vehicle task content caching. At the same time, considering the problem that different vehicles on the same path may generate the same computing tasks, this paper proposes a content caching model based on distributed deterministic policy gradient (D4PG), and uses the D4PG content caching model to determine the content caching strategy. Experimental results show that this scheme can effectively reduce the vehicle task processing delay. [ABSTRACT FROM AUTHOR]

Under the impetus of mobile edge computing, mobile access networks and the Internet are deeply integrated providing low-latency, high-reliability computing power for service innovation. Due to the limitation of computing power resources, it is necessary for mobile smart terminals to offload essential service requests to edge servers to effectively enhance service quality. With the profound effect of selfishness, edge devices always make decisions to maintain their own performance regardless of the overall situation of the edge network. To this end, this paper proposes a willingness-aware collaborative offloading method in a mobile edge environment. It aims to efficiently offload services according to the offloading willingness of mobile smart terminals and the service willingness of edge servers. This approach seeks to resolve the selfishness in edge service offloading while reducing offloading costs and balancing the load on edge servers. At first, This method conducts the edge service offloading willingness model in a mobile edge environment. Subsequently, it models the willingness-aware service offloading problem in a mobile edge environment and transforms it into a multi-objective optimization problem with multiple constraints. Finally, a Markov decision process is constructed by utilizing DDPG, and a low-cost, high-quality, and load-balanced offloading scheme is ultimately achieved through iterative optimization. Experimental results on the Shanghai (Beijing) Telecom mobile communication base station dataset and the Shanghai (Beijing) taxi trajectory dataset indicate that the method proposed in this paper can effectively obtain willingness-aware service offloading schemes in mobile edge environments, and it outperforms typical baselines such as Random, GA, IA, DQN in overall performance. [ABSTRACT FROM AUTHOR]

In this paper, aiming at the problems of large randomness, low convergence accuracy, and easy falling into local optimum in the application of sparrow search algorithm to UAV three-dimensional path planning, a dynamic step opposition-based learning sparrow search algorithm is proposed. The algorithm first uses a good point set in the population initialization phase to improve the quality of the initial solution; secondly, the piecewise dynamic step size is used to optimize the update formula of the discoverer, and the extensive search is carried out in the early stage of the iteration. In the later stage, the known area is mined as much as possible to improve the search accuracy and convergence speed of the algorithm. Then, the crazy operator is integrated to optimize the predator update formula and improve the local search ability. Finally, t-distribution opposition-based learning is used to prevent the algorithm from falling into the local optimum. In this paper, the effectiveness of the improved algorithm is verified by six test functions and applied to the three-dimensional path planning of UAVs. The experimental results show that the proposed algorithm has a faster convergence speed than the traditional algorithm, and the planned path is shorter. [ABSTRACT FROM AUTHOR]

Securing image transmissions has become critical as the demand for image sharing and storage grows. In some commercial uses, in order to achieve a balance between encryption security and efficiency, some researchers have tried to encrypt only the region of interest of the user in the image. In order to accurately encrypt region of interest images, this paper proposed a region of interest image encryption algorithm based on lifting scheme and object detection. The algorithm automatically identifies the region of interest in the image and encrypts it securely and effectively. Firstly, the region of interest in the image is detected using the object detection model, and the non-maximum suppression algorithm is modified to solve the problem that the detection box outputted by the object detection model does not completely contain the region of interest. Then the existing pixel extraction method for region of interest images is improved to extract the pixels from the region of interest images at a faster speed, which improves the overall efficiency of the algorithm. Finally, based on the thought of wavelet lifting transform, combined with chaotic system, a two-layer hybrid lifting scheme encryption algorithm is proposed to encrypt the extracted pixels. Experimental results and security analysis show that the algorithm proposed in this paper can effectively protect all objects at once with high security. [ABSTRACT FROM AUTHOR]

To address the problem of the low success rate caused by the lack of verification mechanism of hashed time-locked algorithm in processing blockchain cross-chain transactions, this paper proposes blockchain cross-chain protocol based on improved hashed time-locked contract. Firstly, in order to improve the success rate of cross-chain transactions, this paper introduces the virtual account verification mechanism to verify the legitimacy of the transaction before processing. Secondly, this paper also proposes the parallel processing strategy based on the virtual trading pool, which makes unqualified-transactions in virtual trading pool process in parallel, and avoids to retrieve the account information which is obtained by crossing multi-chains. Finally, the proposed algorithm is compared with hashed time-locked algorithm (HTLC) and time-locked algorithm under different chain number. The success rate of the proposed algorithm is increased to more than 90%. For large-scale transactions, compared with HTLC algorithm, time-locked algorithm, the throughput of the proposed algorithm is increased by 72 and 44 percentage points respectively. Under different transaction distribution, the proposed algorithm also outperforms the two algorithms in terms of transaction delay. The experimental results show that this algorithm can effectively improve the success rate of cross-chain transaction and shorten the delay of cross-chain transaction. [ABSTRACT FROM AUTHOR]

With the increasing volume of data on the web, distributed file systems such as HDFS are widely used to provide efficient, fast and reliable access to the ever-growing volumes of data. Parameter tuning, a core performance management task, directly affects the performance of storage systems. However, large, non-linear, discrete and non-digital storage system configuration parameters significantly increase the storage system parameter tuning burden. Traditional manual tuning methods rely on a lot of a priori knowledge and have high tuning costs. In this paper, we propose a Smart gambler (SG) algorithm based on a simulated environment and Hill Climbing to reduce the probability of falling into a local optimum. In comparative experiments on publicly available datasets, SG achieves an average improvement of 3.14 % and 5.02 % in the highest tuning value over search algorithms such as Genetic Algorithm and an average time saving of 32.1 % and 50.6 % in tuning time for both workloads, respectively. [ABSTRACT FROM AUTHOR]

These days, cloud computing usage has been increasing with the rapid growth of Internet coverage all over the world to serve as a pay-per-use model using shared computing resources. Internet of Things (IoT) is a growing technology which is used in different applications and it needs cloud computing however the distance between cloud computing resources and the end system in IoT can cause a delay which is intolerable for delay-sensitive applications. Fog computing is a computing resource between cloud computing and end system to reduce the delay for the delay-sensitive applications in IoT. Therefore, load balancing functionality is a significant role to provide the required quality of service (QoS), quality of experience (QoE), and performance. Load balancing can be done based on response time, throughput, energy consumption, and utilization metrics. In this paper, the papers published in Elsevier, ACM, IEEE, Springer and Wiley between 2018 and 2022 have been examined to extract the load-balancing algorithms, system architecture, tools and applications, advantages and disadvantages. This review is useful for those working on load-balancing performance improvement. [ABSTRACT FROM AUTHOR]

In cloud-based recommendation system, the feature selection is implemented to reduce the large dimension of the cloud data. The feature selection increases the performance of the recommendation system without affecting the accuracy of the system. In this paper two filter model based algorithms SFS and MSFS are proposed to extract the necessary features for the recommendation system. The state of the art Naive bayes classification algorithm is used to evaluate the performance of the feature selection algorithm. The bench mark datasets Newsgroups, WebKB and Book Crossing are used for performance evaluation. The experimental results show that the proposed algorithm is superior to the existing feature selection algorithms T-Score, Information Gain and Chi squared. [ABSTRACT FROM AUTHOR]

Reproducibility is a major principle underpinning the scientific method. Especially, deep database environment systems variability represents an opportunity for reproducibility of database performance testing where metrics defining performance are intricately woven from the entirety of the testing environment-including data sets, schema design, queries, hardware configurations, DBMS parameters, versions, code, etc. Nevertheless, despite the availability of data and code, numerous studies have highlighted a disconcerting trend: analyzing the same dataset using different software can yield divergent results. This variance stems from the nuanced interplay between explicit and implicit parameters within multiple layers, spanning the hardware platform, database model, deployment intricacies, and physical structures, showcasing the complexity inherent in performance model generation and its sensitivity to intricate interactions. Moreover, many authors of database papers do not provide ways to easily reproduce their experiments. In this paper, we propose a framework called ReFleT (REproductibility-aware Framework for LEveraging database Testing) that aims at modeling the manifold variations within the database testing environment, thereby fortifying the reproducibility of database performance models. This comprehensive framework, ReFleT, stands on three different components: (i) a reproduction design specific language, (ii) a model repository for persisting the database environment testing, and (iii) ReFleT capabilities—an interface custom-crafted for seamless navigation, provision, exploration, and reproduction of database performance testing. To substantiate the efficacy of our framework, we present a compelling proof-of-concept through its application in a meticulously designed case study. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the AAPF* algorithm, an innovative approach that synergistically integrates the A-star search algorithm (A*) with the artificial potential field (APF) method. This algorithm is designed to enhance safety and ensure smoother global path planning for autonomous vehicles, particularly addressing vehicle cornering constraints. Initially, for augmenting the safety of autonomous vehicle, we implement an obstacle expansion strategy with a factor of 2 units, enhancing environmental adaptability. The study then delves into the classical A* algorithm, examining its core principles and characteristics, leading to the development of novel heuristic functions and search strategies that address the limitations inherent in the classic A* algorithm. Subsequently, we explore the APF algorithm, recognized for its excellence in obstacle avoidance in path planning. The paper culminates in the amalgamation of the APF's repulsive field concept with the improved A* algorithm, crafting a comprehensive global planning algorithm tailored for autonomous vehicle path planning schemes. Experiments conducted in a simulated environment model validate the AAPF* algorithm's efficacy in improving both the safety and smoothness, demonstrating its potential for real-world applications. [ABSTRACT FROM AUTHOR]

Many sensitive applications require that data remain confidential and undisclosed, even for intrusion detection objectives. For this purpose, the detection of anomalies in encrypted data has become increasingly vital. Deep learning models are becoming good tools to detect anomalies in encrypted data without the need to pass through data decryption. This paper presents a systematic review focusing on the advancements made in deep learning models for intrusion detection over encrypted data with privacy preservation. This study aims to guide researchers on how to select the right tools to set up an intrusion detection system over encrypted data with privacy preservation. The study presented the context and challenges of intrusion detection on encrypted data and how machine learning-based solutions can circumvent these challenges. The paper looks at recently proposed solutions, examines metrics for assessing model performance, and evaluates frequently used reference datasets. Deep learning models are also evaluated with statistics on the most frequent models, datasets, and encryption tools. The performance metrics of the studied solutions are investigated as a function of the encryption tools, the deployed deep learning models, the privacy preservation tools, the deployed datasets, and the eventual additional tools and algorithms. Our recommendations help researchers evaluate their proposals for preserving privacy and detecting intrusions on encrypted data using deep learning techniques. [ABSTRACT FROM AUTHOR]

The purpose of this study is to identify and classify studies published on software testing techniques applied to blockchain systems. Previously published reviews in related areas have a narrow focus and/or do not follow a systematic review protocol. We conducted a systematic mapping based on an initial selection of 1025 studies. A rigorous selection process resulted in a final pool of 17 primary studies. These studies are categorized with respect to the employed testing methods, considered quality attributes, and functionality. We observe that most of the publications focus on testing functional correctness or security, whereas the testing of runtime performance attracts less attention. Existing approaches mostly employ fuzz testing or mutation testing. Search-based testing is usually combined with these techniques. The application of model-based testing is rare. The adaptability of fuzz testing and model-based testing techniques to changing blockchain platforms and languages remains a concern. On the other hand, performance and scalability issues are noted for search-based techniques and mutation testing. The use and integration of multiple testing techniques also stand out as a viable research direction. [ABSTRACT FROM AUTHOR]

The present methods of data preservation and representation for barrel finishing processes which include paper and electronic documents have several disadvantages such as restrictions in size and complexity, and limitations on query and updation speed. Aiming at these disadvantages, a new database platform for barrel finishing data has been constructed by using database technology and case-based reasoning. The design procedure of the database platform is expounded in detail, covering analysis of database platform requirements, establishment for conceptual model of database data structure, designs for logical model of database data structure, determination for physical model of database data structure, choice for network structure of database platform, data management and storage method. The application results demonstrate that the database platform can ensure the safe and convenient storage as well as the sharing of experimental data of the barrel finishing process. It can also provide guidance and technical information for scientific researchers, experts, technicians, and production site operators to choose the processing technology and the processing parameters reasonably. [ABSTRACT FROM AUTHOR]

In the world of the growing internet of things (IoT), multi-access edge computing (MEC) is an important innovation. It strategically places servers in decentralized areas to reduce bandwidth usage and transmission latency. This paper focuses on the essential initial phase of MEC deployment, focusing on the major challenge of optimizing the placement and allocation of edge servers. Such optimization is central to increasing the benefits of edge computing systems while reducing costs and energy consumption and improving quality of service (QoS). Traditional optimization methods often treat variable categories homogeneously and neglect directional search considerations, thus compromising search efficiency for large-scale problems. To address these issues, our study develops a complex set of methods that combine decision variable classification and elitism to improve the optimization path and lead to a satisfactory solution. This innovative approach pursues two objectives: First, it articulates an objective to minimize energy consumption, incorporating considerations of proportional load balancing, latency reduction, and fair distribution of users across edge servers. Second, this objective is combined with the latency reduction objective, resulting in a dual-topic optimization task. We compare the proposed method with well-known approaches such as non-dominated sorting genetic algorithm II (NSGA-II), non-dominated sorting genetic algorithm III (NSGA-III), and multi-objective particle swarm optimization (MOPSO). Our findings demonstrate a minimum 3.2% reduction in latency in homogeneous server environments and a remarkable 5.02% reduction in heterogeneous server environments. Energy consumption also drops significantly compared to similar benchmarks. Energy consumption has also fallen significantly compared to comparative benchmarks. The application of the non-parametric Friedman test demonstrates the superiority of the proposed method, which ranks highest among state-of-the-art methods and emphasizes statistical precision. Through a detailed technical discussion, the paper substantiates the feasibility and superiority of the proposed optimization method in addressing the complex requirements of edge server placement and allocation within the MEC paradigm. [ABSTRACT FROM AUTHOR]

Nowadays, edge computing has been applied for providing various intelligent services, because it can provide ultra-low data transmission delay between user devices and service platforms, by placing several edge servers near users. Edge server placement (ESP) is one of the most critical problems, which greatly influences resource efficiency and service quality by deciding positions where each edge server is placed. As ESP problem is generally a multi-modal and discrete optimization problem with high dimension, this paper proposes a novel niching genetic algorithm (NichingGA) to provide ESP solutions with optimized average response time. The main idea of NichingGA is first dividing the population into several niches and establishing an elite niche. Then, inspired by heterosis in biology, NichingGA produces more diverse offspring by crossing individuals across niches, for improving the global search capability. NichingGA is evaluated by extensive experiments and proven to have better performance than 11 up-to-date approaches in improving the overall request response time. [ABSTRACT FROM AUTHOR]

Features within the dataset carry a significant role; however, resource utilization, prediction-time, and model weight are increased by utilizing high-dimensional data in intrusion-detection paradigm. This paper aims to design a novel lightweight intrusion detection system in two phases utilizing a swarm intelligence-based technique. In 1st-phase, essential features are selected using particle swarm optimization algorithm by considering imbalanced dataset. Ant colony optimization algorithm is utilized in 2nd-phase for extracting information-rich and uncorrelated features. Additionally, genetic algorithm is employed for fine-tuning each detection model. Proposed model's performance is evaluated on different base and ensemble classifiers, and it is observed that xgboost achieves best accuracy with 90.38%, 92.63%, and 97.87% on NSL-KDD, UNSW-NB15, and CSE-CIC-IDS2018 datasets, respectively. The proposed model also outperforms other traditional dimensionality reduction and state-of-the-art approaches with statistical validation. This paper also analyses objective function of each metaheuristic algorithm used in this paper, applying convergence graphs, box, and swarm plots. [ABSTRACT FROM AUTHOR]

Phishing attacks are the biggest cybersecurity threats in the digital world. Attackers exploit users by impersonating real, authentic websites to obtain sensitive information such as passwords and bank statements. One common technique in these attacks is using malicious URLs. These malicious URLs mimic legitimate URLs, misleading users into interacting with malicious websites. This practice, URL phishing, presents a big threat to internet security, emphasizing the need for advanced detection methods. So we aim to enhance phishing URL detection by using machine learning and deep learning models, leveraging a set of low-level URL features derived from n-gram analysis. In this paper, we present a method for detecting malicious URLs using statistical features extracted from n-grams. These n-grams are extracted from the hexadecimal representation of URLs. We employed 4 experiments in our paper. The first 3 experiments used machine learning with the statistical features extracted from these n-grams, and the fourth experiment used these grams directly with deep learning models to evaluate their effectiveness. Also, we used Explainable AI (XAI) to explore the extracted features and evaluate their importance and role in phishing detection. A key advantage of our method is its ability to reduce the number of features required and reduce the training time by using fewer features after applying XAI techniques. This stands in contrast to the previous study, which relies on high-level URL features and needs pre-processing and a high number of features (87 high-level URL-based features). So our technique only uses statistical features extracted from n-grams and the n-gram itself, without the need for any high-level features. Our method is evaluated across different n-gram lengths (2, 4, 6, and 8), aiming to optimize detection accuracy. We conducted four experiments in our study. In the first experiment, we focused on extracting and using 12 common statistical features like mean, median, etc. In the first experiment, the XGBoost model achieved the highest accuracy using 8-gram features with 82.41%. In the second experiment, we expanded the feature set and extracted an additional 13 features, so our feature count became 25. XGBoost in the second experiment achieved the highest accuracy with 86.40%. Accuracy improvement continued in the third experiment, we extracted an additional 16 features (character count features), and these features increased XGBoost accuracy to 88.15% in the third experiment. In the fourth experiment, we directly fed n-gram representations into deep learning models. The Convolutional Neural Network (CNN) model achieved the highest accuracy of 94.09% in experiment four. Also, we applied XAI techniques, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). Through the explanation provided by XAI methods, we were able to determine the most important features in our feature set, enabling a reduction in feature count. Using fewer features (4, 7, 10, 13, 15), we got good accuracy compared to the 41 features used in experiment three and reduced the models' training times and complexity. This research aimed to enhance phishing URL detection by using machine learning and deep learning models, leveraging a set of low-level URL features derived from n-gram analysis. Our findings show the importance of using minimal statistical features to identify malicious URLs. Notably, the use of CNN had a great advancement, achieving an accuracy rate of 94.09% with using n-grams of URLs, surpassing traditional machine learning models. This achievement not only validates the efficacy of deep learning models in complex pattern recognition tasks but also highlights the efficiency of our feature selection approach, which relies on a lower number of features and is less complex compared to existing high-level feature-based studies. The research outcomes demonstrate a promising pathway toward developing more robust, efficient, and scalable phishing detection systems. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the large language model (LLM) ChatGPT-3.5 to automatically and intelligently generate a new metaheuristic algorithm (MA) according to the standard prompt engineering framework CRISPE (i.e., Capacity and Role, Insight, Statement, Personality, and Experiment). The novel animal-inspired MA named Zoological Search Optimization (ZSO) draws inspiration from the collective behaviors of animals for solving continuous optimization problems. Specifically, the basic ZSO algorithm involves two search operators: the prey-predator interaction operator and the social flocking operator to balance exploration and exploitation well. Furthermore, we designed four variants of the ZSO algorithm with slight human-interacted adjustment. In numerical experiments, we comprehensively investigate the performance of ZSO-derived algorithms on CEC2014 benchmark functions, CEC2022 benchmark functions, and six engineering optimization problems. 20 popular and state-of-the-art MAs are employed as competitors. The experimental results and statistical analysis confirm the efficiency and effectiveness of ZSO-derived algorithms. At the end of this paper, we explore the prospects for the development of the metaheuristics community under the LLM era. [ABSTRACT FROM AUTHOR]

In the modern, ever-shifting global agri-food environment, the topmost concern revolves around securing the safety, quality, and authenticity of agri-food products. Blockchain technology is being seen as a revolutionary solution for dealing with these issues, providing a decentralized and transparent ledger for the tracking of agri-food products. By incorporating global positioning system and navigation systems within blockchain-based traceability solutions amplifies the capabilities for real-time monitoring, security, and trust. This paper proposes a layered architecture for an efficient agri-food traceability system. The data layer, manages interactions between various entities in the supply chain management and generates agri-food product-related data. The blockchain layer manages data via transactions and smart contracts, using the interplanetary file system for secure, decentralized storage. The navigation layer, combines navigation with Indian constellation and global positioning system to offer precise real-time positioning and timing services, enhancing product tracking. This integrated approach not only improves food safety but also supports sustainability efforts by reducing food waste and bolstering consumer trust in the agri-food industry. We implement the proposed system using Remix IDE, MetaMask wallet, and the Sepolia test network, summarizing the deployment analysis. Performance evaluation is conducted using the JMeter simulation toolkit.The proposed framework achieves an average throughput of 329.26 transactions per second, latency of 49.3 ms, and response time of 87.9 ms. We conduct a comparative evaluation of the proposed system with related studies. From this comparative analysis, we observed that our proposed framework has better features than other related works. [ABSTRACT FROM AUTHOR]

This paper is inspired by traditional rural fishing methods and proposes a new metaheuristic optimization algorithm based on human behavior: Catch Fish Optimization Algorithm (CFOA). This algorithm simulates the process of rural fishermen fishing in ponds, which is mainly divided into two phases: the exploration phase and the exploitation phase. In the exploration phase, there are two stages to search: first, the individual capture stage based on personal experience and intuition, and second, the group capture stage based on human proficiency in using tools and collaboration. Transition from independent search to group capture during the exploration phase. Exploitation phase: All fishermen will surround the shoal of fish and work together to salvage the remaining fish, a collective capture strategy. CFOA model is based on these two phases. This paper tested the optimization performance of CFOA using IEEE CEC 2014 and IEEE CEC 2020 test functions, and compared it with 11 other optimization algorithms. We employed the IEEE CEC2017 function to evaluate the overall performance of CFOA. The experimental results indicate that CFOA exhibits excellent and stable optimization capabilities overall. Additionally, we applied CFOA to data clustering problems, and the final results demonstrate that CFOA's overall error rate in processing clustering problems is less than 20%, resulting in a better clustering effect. The comprehensive experimental results show that CFOA exhibits excellent optimization effects when facing different optimization problems. CFOA code is open at https://github.com/Meky-1210/CFOA.git. [ABSTRACT FROM AUTHOR]

Accurate classification of gene expression data is crucial for disease diagnosis and drug discovery. However, gene expression data usually has a large number of features, which poses a challenge for accurate classification. In this paper, a novel feature selection method based on minimal redundancy maximal relevance (mRMR) and aquila optimizer is proposed, which introduces the mRMR method in the initialization stage of the population to generate excellent initial populations, effectively improve the quality of the population, and then, the using random opposition-based learning strategy to improve the diversity of aquila population and accelerate the convergence speed of the algorithm, and finally, introducing inertia weight in the position update formula in the late iteration of the aquila optimizer to avoid the algorithm falling into the local optimum and improve the algorithm's capability to find the optimum. In order to verify the effectiveness of the proposed method, ten real gene expression datasets are selected in this paper and compared with several meta-heuristic algorithms. Experimental results show that the proposed method is significantly superior to other meta-heuristic algorithms in terms of fitness value, classification accuracy and the number of selected features. Compared with the original aquila optimizer, the average classification accuracy of the proposed method on KNN and SVM classifiers is improved by 3.48–12.41% and 0.53–18.63% respectively. The proposed method significantly reduces the feature dimension of gene expression data, retains important features, and obtains higher classification accuracy, providing a new method and idea for feature selection of gene expression data. [ABSTRACT FROM AUTHOR]

Cloud computing has revolutionized the way a variety of ubiquitous computing resources are provided to users with ease and on a pay-per-usage basis. Task scheduling problem is an important challenge, which involves assigning resources to users' Bag-of-Tasks applications in a way that maximizes either system provider or user performance or both. With the increase in system size and the number of applications, the Bag-of-Tasks scheduling (BoTS) problem becomes more complex due to the expansion of search space. Such a problem falls in the category of NP-hard optimization challenges, which are often effectively tackled by metaheuristics. However, standalone metaheuristics generally suffer from certain deficiencies which affect their searching efficiency resulting in deteriorated final performance. This paper aims to introduce an optimal hybrid metaheuristic algorithm by leveraging the strengths of both the Artificial Gorilla Troops Optimizer (GTO) and the Honey Badger Algorithm (HBA) to find an approximate scheduling solution for the BoTS problem. While the original GTO has demonstrated effectiveness since its inception, it possesses limitations, particularly in addressing composite and high-dimensional problems. To address these limitations, this paper proposes a novel approach by introducing a new updating equation inspired by the HBA, specifically designed to enhance the exploitation phase of the algorithm. Through this integration, the goal is to overcome the drawbacks of the GTO and improve its performance in solving complex optimization problems. The initial performance of the GTOHBA algorithm tested on standard CEC2017 and CEC2022 benchmarks shows significant performance improvement over the baseline metaheuristics. Later on, we applied the proposed GTOHBA on the BoTS problem using standard parallel workloads (CEA-Curie and HPC2N) to optimize makespan and energy objectives. The obtained outcomes of the proposed GTOHBA are compared to the scheduling techniques based on well-known metaheuristics under the same experimental conditions using standard statistical measures and box plots. In the case of CEA-Curie workloads, the GTOHBA produced makespan and energy consumption reduction in the range of 8.12–22.76% and 6.2–18.00%, respectively over the compared metaheuristics. Whereas for the HPC2N workloads, GTOHBA achieved 8.46–30.97% makespan reduction and 8.51–33.41% energy consumption reduction against the tested metaheuristics. In conclusion, the proposed hybrid metaheuristic algorithm provides a promising solution to the BoTS problem, that can enhance the performance and efficiency of cloud computing systems. [ABSTRACT FROM AUTHOR]

In an era dominated by network connectivity, the reliance on robust and secure networks has become paramount. With the advent of 5G and the Internet of Things, networks are expanding in both scale and complexity, rendering them susceptible to a myriad of cyber threats. This escalating risk encompasses potential breaches of user privacy, unauthorized access to transmitted data, and targeted attacks on the underlying network infrastructure. To safeguard the integrity and security of modern networked societies, the deployment of Network Intrusion Detection Systems is imperative. This paper presents a novel lightweight detection model that seamlessly integrates Spiking Neural Networks and Convolutional Neural Networks with advanced algorithmic frameworks. Leveraging this hybrid approach, the proposed model achieves superior detection accuracy while maintaining efficiency in terms of power consumption and computational resources. This paper presents a new style recognition model that seamlessly integrates spiking neural networks and convolutional neural networks with advanced algorithmic frameworks. We call this combined method Spiking-HCCN. Using this hybrid approach, Spiking-HCCN achieves superior detection accuracy while maintaining efficiency in terms of power consumption and computational resources. Comparative evaluations against state-of-the-art models, including Spiking GCN and Spike-DHS, demonstrate significant performance advantages. Spiking-HCCN outperforms these benchmarks by 24% in detection accuracy, 21% in delay, and 29% in energy efficiency, underscoring its efficacy in fortifying network security in the face of evolving cyber threats. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as an efficient distribution platform in modern distributed computing offering scalability and flexibility. Task scheduling is considered as one of the main crucial aspects of cloud computing. The primary purpose of the task scheduling mechanism is to reduce the cost and makespan and determine which virtual machine (VM) needs to be selected to execute the task. It is widely acknowledged as a nondeterministic polynomial-time complete problem, necessitating the development of an efficient solution. This paper presents an innovative approach to task scheduling and allocation within cloud computing systems. Our focus lies on improving both the efficiency and cost-effectiveness of task execution, with a specific emphasis on optimizing makespan and resource utilization. This is achieved through the introduction of an Advanced Max–Min Algorithm, which builds upon traditional methodologies to significantly enhance performance metrics such as makespan, waiting time, and resource utilization. The selection of the Max–Min algorithm is rooted in its ability to strike a balance between task execution time and resource utilization, making it a suitable candidate for addressing the challenges of cloud task scheduling. Furthermore, a key contribution of this work is the integration of a cost-aware algorithm into the scheduling framework. This algorithm enables the effective management of task execution costs, ensuring alignment with user requirements while operating within the constraints of cloud service providers. The proposed method adjusts task allocation based on cost considerations dynamically. Additionally, the presented approach enhances the overall economic efficiency of cloud computing deployments. The findings demonstrate that the proposed Advanced Max–Min Algorithm outperforms the traditional Max–Min, Min–Min, and SJF algorithms with respect to makespan, waiting time, and resource utilization. [ABSTRACT FROM AUTHOR]

Some particular shaped datasets, such as manifold datasets, have restrictions on density peak clustering (DPC) performance. The main reason of variations in sample densities between clusters of data and uneven densities is not taken into consideration by the DPC algorithm, which could result in the wrong clustering center selection. Additionally, the use of single assignment method is leads to the domino effect of assignment errors. To address these problems, this paper creates a new, improved density peaks clustering method use the similarity assignment strategy with K nearest Neighbors (IDPC-SKNN). Firstly, a new method for defining local density is proposed. Local density is comprehensively consider in the proportion of the average density inside the region, which realize the precise location of low-density clusters. Then, using the samples' K-nearest neighbors information, a new similarity allocation method is proposed. Allocation strategy successfully address assignment cascading mistakes and improves algorithms robustness. Finally, based on four evaluation indicators, our algorithm outperforms all the comparative clustering algorithm, according to experiments conducted on synthetic, real world and the Olivetti Faces datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a new intelligent optimization algorithm named Black Eagle Optimizer (BEO) based on the biological behaviour of the black eagle. The BEO algorithm combines the biological laws of the black eagle and mathematical transformations to guide the search behaviour of the particles. The highly adaptive BEO algorithm has strong optimisation capabilities due to its unique algorithmic structure and novel iterative approach. In the performance testing experiments of the BEO algorithm, this paper firstly conducts the parametric analysis experiments of the BEO algorithm, then analyses the complexity of the BEO algorithm, and finally conducts a comprehensive testing of the performance of the BEO algorithm on 30 CEC2017 test functions with the widest variety of functions and 12 newest CEC2022 test functions, and its performance is compared with the seven state-of-the-art optimization algorithms. The test results show that the convergence accuracy of the BEO algorithm reaches the theoretical value in 100% of unimodal functions, the convergence accuracy is higher than the comparison algorithm in 78.95% of complex functions, and the standard deviation ranks in the top three in 90.48% of functions, which demonstrates the outstanding local optimisation ability, global optimisation ability and stability of BEO algorithm. Meanwhile, the BEO algorithm also maintains a fast convergence speed. However, the complexity analysis shows that the BEO algorithm has the disadvantage of slightly higher complexity. In order to verify the optimisation ability of the BEO algorithm in real engineering problems, we used the BEO algorithm to deal with four complex engineering design problems. The experimental results show that the BEO algorithm has excellent convergence accuracy and stability when dealing with real engineering problems, but the real-time performance is slightly below average. Therefore, the BEO algorithm is optimal for handling non-real-time engineering optimisation problems. The source code of the BEO algorithm is available at https://github.com/haobinzhang123/A-metaheuristic-algorithm. [ABSTRACT FROM AUTHOR]

The extensive deployment of large cloud data centers has led to substantial energy consumption. Energy conservation is a critical concern for cloud service providers seeking to lower their operating costs within their data centers. To address this energy consumption challenge, effective approaches such as VM consolidation and VM migration are essential. These approaches must carefully balance the trade-off between energy consumption and Service Level Agreement Violations (SLAV). In this paper, we propose an energy-efficient VM selection algorithm for VM consolidation and call it the Simultaneous CPU–Ram Utilization Balancer (SCRUB) policy. This algorithm takes into account CPU and RAM utilization while trying to maintain a balance between energy consumption and SLAV. To evaluate the performance of our proposed method, we implemented it using the Cloudsim simulation toolkit and conducted simulations using real-world workload traces from PlanetLab and Google over three different days. The results show that the SCRUB VM selection policy has led to improvements in various metrics, including reduced energy consumption and a decreased number of VM migrations compared to existing VM selection policies. Specifically, it achieved a 16.98% decrease in energy consumption and a 46.42% reduction in the number of migrations for the PlanetLab dataset, and a 10.95% decrease in energy consumption and a 43.96% decline in the number of migrations for the Google dataset compared to the baseline algorithm MMT. [ABSTRACT FROM AUTHOR]

Optimizing computational offloading in Mobile Edge Computing (MEC) environments presents a multifaceted challenge requiring innovative solutions. Soft computing, recognized for its ability to manage uncertainty and complexity, emerges as a promising approach for addressing the dynamic multi-objective evaluation inherent in computational offloading scenarios. This paper conducts a comprehensive review and analysis of soft computing approaches for Dynamic Multi-Objective Evaluation of Computational Offloading (DMOECO), aiming to identify trends, analyze existing literature, and offer insights for future research directions. Employing a systematic literature review (SLR) methodology, we meticulously scrutinize 50 research articles and scholarly publications spanning from 2016 to November 2023. Our review synthesizes advancements in soft computing techniques, including fuzzy logic, neural networks, evolutionary algorithms, and probabilistic reasoning, as applied to computational offloading optimization within MEC environments. Within this comprehensive review, existing approaches are categorized and analyzed into distinct research lines based on methodologies, objectives, evaluation metrics, and application domains. The evolution of soft computing-based DMOECO strategies is emphasized, showcasing their effectiveness in dynamically balancing various computational objectives, including energy consumption, latency, throughput, user experience, and other pertinent factors in computational offloading scenarios. Key challenges, including scalability issues, lack of real-world deployment validation, and the need for standardized evaluation benchmarks, are identified. Insights and recommendations are provided to enhance computational offloading optimization. Furthermore, collaborative efforts between academia and industry are advocated to bridge the theoretical developments with practical implementations. This study pioneers the use of SLR methodology, offering valuable perspectives on soft computing in DMOECO and synthesizing state-of-the-art approaches. It serves as a crucial resource for researchers, practitioners, and stakeholders in the MEC domain, illuminating trends and fostering continued innovation in computational offloading strategies. [ABSTRACT FROM AUTHOR]

The blockchain-based Electronic Medical Record (EMR) data storage system encounters challenges which are data leakage, insufficient access control, and low retrieval efficiency. In response to these issues, this paper proposes a lightweight searchable encryption and access control system for sharing EMR data securely. The lightweight searchable encryption scheme which is achieved by integrating the trapdoor verification phase with the keyword retrieval phase is constructed using BGG13 + and MP12. User access control is implemented through a Boolean circuit to replace arithmetic operations and thus improve arithmetic efficiency. Since logic operations can reduce the computation time, this paper uses Boolean circuits for user rights verification implementation. In addition, this paper adopts the Bloom filter as the system index to enhance the efficiency of block-chain data retrieval. According to the simulation results, there is a performance advantage of this system over similar systems. [ABSTRACT FROM AUTHOR]

As a cornerstone of democratic governance, elections hold unparalleled significance, shaping a nation's trajectory. However, the prevailing ballot-paper based voting systems continue to face trust issues among significant populations. As a result, e-Voting has emerged as an appealing alternative, with numerous countries opting for its implementation globally. While e-Voting systems offer several advantages, they also come with their own set of challenges. Even a minor vulnerability can lead to massive manipulations in voting results. In recent years, there have been efforts to revolutionize the e-Voting paradigm by harnessing the potential of emerging technologies such as biometrics and blockchain. This paper proposes a Internet-based voting that adopts blockchain technology and biometric identification techniques. We use biometric modalities, such as fingerprint and facial recognition, for voter authentication while leveraging Hyperledger Fabric framework as blockchain network and ensuring a secure, transparent, and tamper-evident voting record. We demonstrate the proposed system with 100 participants in a preset environment where we collect the biometrics data. The results indicate that 87% of participants successfully registered with biometrics, while 88% cast their votes with a combination of either voter ID and fingerprint or voter ID with facial recognition. Our findings suggest that the proposed system allows voters to access the system seamlessly and automate identity verification procedures while ensuring a secure, decentralized, and distributed database network that maintains transparency. Future research shall be carried out in collaboration with election officials and voters to improve the system in real-world scenarios. [ABSTRACT FROM AUTHOR]

Massive amounts of real-time streaming network data are generated quickly because of the exponential growth of applications. Analyzing patterns in generated flow traffic streaming offers benefits in reducing traffic congestion, enhancing network management, and improving the quality of service management. Processing massive volumes of generated traffic poses more challenges when data traffic encryption is raised. Classifying encrypted network traffic in real-time with deep learning networks has received attention because of their excellent performance. The substantial volume of incoming packets, characterized by high speed and wide variety, puts real-time traffic classification within the domain of big data problems. Classifying traffic with high speed and accuracy is a significant challenge in the era of big data. The real-time nature of traffic intensifies deep learning networks, necessitating a considerable number of parameters, layers, and resources for optimal network training. Until now, various datasets have been employed to evaluate the effectiveness of previous methods for classifying encrypted traffic. The primary objective has been to enhance accuracy, precision, and F1-measure. Presently, encrypted traffic classification performance depends on pre-existing datasets. The learning and testing phases are done offline, and more research is needed to investigate the feasibility of these methods in real-world scenarios. This paper examines the possibility of a tradeoff between evaluating the model's effectiveness, execution time, and utilization of processing resources when processing stream-based input data for traffic classification. We aim to explore the feasibility of establishing a tradeoff between these factors and determining optimal parameter settings. This paper used the ISCX VPN-Non VPN 2016 public dataset to evaluate the proposed method. All packets from the dataset were streamed continuously through Apache Kafka to the classification framework. Numerous experiments have been designed to demonstrate the efficacy of the proposed method. The experimental results show that the proposed method outperforms the baseline methods by 11% in the F1-measure when the number of workers is two and by 25% when the number of workers is equal to 32. [ABSTRACT FROM AUTHOR]

Electronic medical records have the advantages of large storage capacity, convenience, improved efficiency, etc. However, since electronic medical records contain a large amount of patient's private information, if they are stored directly in the cloud server, patients may face the risk of privacy data leakage and electronic medical record data tampering. To solve the above problems, this paper proposes a secure sharing scheme of electronic medical records combining quantum key and blockchain. Based on the true randomness of quantum keys, the multi-user secure forwarding mechanism of quantum keys is designed using proxy re-encryption technology and blockchain. A fine-grained access control scheme for electronic medical records by users with different roles was proposed using data desensitization technology. To facilitate secure retrieval from the cloud server, searchable encryption technology is used. This paper analyzes the comprehensive performance level of the proposed scheme through blockchain performance testing and system efficiency testing. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time industrial defect detection method based on context enhancement and attention to address the problem that current general-purpose target detectors can hardly achieve high detection accuracy and fast detection speed simultaneously. First, a modified MonileNetV3 is used as the backbone network to reduce the number of parameters and improve the model detection speed. A lightweight TRANS module is proposed at the end of the backbone network to combine more layers of features provided by global contextual information for complex background small target detection. Secondly, a cross-layer multi-scale feature fusion network is designed to fully fuse the fine-grained and semantic feature information extracted by the backbone and enhance the spatial location information between neighboring feature layers. Finally, a cascaded Two-channel Efficient Space attention module is used to fully extract texture and semantic features from the defective regions, allowing the model to focus more on the wrong locations and improve the feature representation capability of the network. The NEU-DET steel and PCB datasets are used to test the effectiveness of the proposed model. The experimental results show that compared to the original YOLOv5s algorithm, the mAP metrics are improved by 5.9% and 0.6%, F1 is improved by 4.82% and 0.93%, respectively, and the parameters are reduced by 33.77 M, enabling fast detection of industrial surface defects and meeting the needs of the entire industry. [ABSTRACT FROM AUTHOR]

The study was to improve the teaching management mode of the track and field (T&F) web course in college. Methods: literature study, interview, system design method, software programming method, questionnaire survey. Results: the course teaching management modules are easy to operate with the multifunction and the smooth links; the design includes resource information, course view and course property, which make the content diverse and rich and easily adjustable; the question types of the assignments, test papers and course forum management are various, the scores are reasonable and the question bank can be recycled and edited; the course questionnaire includes question management, questionnaire management and voting which can be processed by automatic statistics and assessment. Conclusion: this study is applicable to the web-based teaching management of track and field for physical education majors (selective course, required course, minor course), the operation guidance form for the teaching management shall be strengthened; the editing and update of teaching management shall be adjusted and arranged timely according to the teaching practice problems. [ABSTRACT FROM AUTHOR]

Technological advancement has led to a dynamic change in how people interact. The interaction can be classified as—social networks (virtual) and real-life (physical) interaction. The relationships among nodes of social networks depend on communication among nodes and have been established by transmitting messages publicly or privately, depending on the trust among nodes. However, it poses three main challenges: (i) nodes can reveal their private message, (ii) private conversation among nodes can be hacked, and (iii) stored messages can be sent to another node, therefore, compromising privacy. The paper presents a privacy-preserving model using two major techniques, Binomial Distribution and Fuzzy Logic. This process has been followed by building predictive models using machine learning to provide potential directions to mitigate security threats posed to social networking platforms. The paper also provides a wholesome discussion of model design for predictive modelling, whereas most state-of-the-art techniques were primarily concerned about delivering potential directions for predictive modelling. The employed statistical and predictive modelling techniques suggest that friendship relationships in real life among people would transcend to ensure a secure and privacy-preserving relationship among them on social networks. Empirical results indicated that XG Boost supersedes the state-of-the-art models by achieving a predictive accuracy of 92.035%. In contrast, introducing new statistical features improves the performance of all our predictive models. [ABSTRACT FROM AUTHOR]

Land subsidence has become a widespread engineering geological problem, which can quickly induce many derived disasters. Over-exploitation of groundwater is one of the main factors of urban land subsidence. There is severe land subsidence in Jining, mainly induced by groundwater over-extraction. Therefore, the numerical simulation method is used in this paper to analyze and predict the law of land subsidence in Jining. Combined with the engineering geological and hydrogeological conditions of Jining City, a three-dimensional fluid–solid coupling model of land subsidence was established by using COMSOL software. The numerical results were verified using site monitoring data. The article predicts the land subsidence in the study area in 2030. In 2030, the maximum land subsidence is 224 mm. And this paper analyzes the land subsidence pattern under different groundwater extraction amounts. The results show that the land subsidence in the study area is effectively alleviated under the condition of reducing water extraction by 30%. It provides a basis for preventing and controlling land subsidence in Jining city. It is proved that land subsidence caused by groundwater extraction positively correlates with the pumping time and amount of water pumped. [ABSTRACT FROM AUTHOR]

The increasing amount of data produced by Internet of Things (IoT) devices imposes significant limitations on the resources available in conventional cloud data centers, undermining their capacity to accommodate time-sensitive IoT applications. Cloud-fog computing has emerged as a promising paradigm that extends cloud services to the network edge. However, the distribution of tasks in a cloud-fog environment presents new challenges. Our research paper introduces a semi-dynamic real-time task scheduling system designed explicitly for the cloud-fog environment. This algorithm effectively assigns jobs while minimizing energy consumption, cost, and makespan. An adapted version of the grey wolf optimizer is introduced to optimize task scheduling by considering various criteria such as task duration, resource requirements, and execution time. Our approach outperforms existing methods, such as genetic algorithm, particle swarm optimization, and artificial bee colony algorithm, in terms of makespan, total execution time, cost, and energy consumption. [ABSTRACT FROM AUTHOR]

There have been some works proposing meta-heuristic-based algorithms for the task offloading problem in Device–Edge–Cloud Collaborative Computing (DE3C) systems, due to their better performance than heuristic-based approaches. But these works don't fully exploit the complementarity of multiple meta-heuristic algorithms. In this paper, we combine the benefits of both swarm intelligence and evolutionary algorithms, for designing a high-efficient task offloading strategy. To be specific, our proposed algorithm uses the iterative optimization framework of Particle Swarm Optimization (PSO) to exploit the cognitions of swarm intelligence and applies the evolutionary strategy of Genetic Algorithm (GA) to preserve the diversity. In addition, to make full use of abundant cloud resources, we increase the probability of the cloud that a task is offloaded to, during each individual evolution. And to improve the overall acceptance ratio, we propose to reschedule tasks with deadline violations from their original assigned computing nodes to another ones with available resources for a task offloading solution. Extensive experiment results show that our proposed algorithm has better acceptance ratio and resource utilization than 13 classical and up-to-date methods, and verify the effectiveness and efficiency of our integration strategy and rescheduling scheme. [ABSTRACT FROM AUTHOR]