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With the advancement of society and the improvement of living standards, the significance of students' physical health has become increasingly prominent. However, currently, the assessment and analysis of students' physical health heavily depend on conventional statistical methods. Even with the application of data mining-related methodologies for analysis and evaluation, the exploitation and utilization of physical health big data remain relatively restricted. In this paper, an improved Canopy-K-means algorithm based on principal component analysis (PCA) is used to construct and analyze a portrait of students' physical fitness and health. The method combines data dimensionality reduction techniques and cluster analysis techniques, and its combined performance is the best compared to other algorithms in the ablation experiments for both male and female student data groups. In this paper, the algorithm was used to process the grouping of physical fitness test data of male and female students, realize the construction and analysis of students' physical fitness and health portrait, and give the exercise prescription for different categories of students. In this paper, the physical health test data of students of Yunnan Agricultural University in 2020–2022 were collected to carry out experiments, and the results found that there are differences in physical health status among students of different genders, grades, and majors in this university, and the physical health status of the students of Classes 2018 and 2019 is generally deficient; on different majors, the students of the Faculty of Agricultural Sciences are slightly superior to the Faculty of Science and Technology, and the students of the Faculty of Science and Technology are slightly superior to the students of the Faculty of Humanities and Social Sciences. Our study offers novel methods and ideas for the assessment and analysis of students' physical health, holding significant implications for schools and related departments in formulating scientific and effective physical education policies and health promotion strategies. [ABSTRACT FROM AUTHOR]

The intrusion detection system is responsible for revealing different intrusion activities, including the denial of service, man-in-middle, Mirai, Scan, and other types of intrusion activities. It is used in many applications, including the smart home Internet of Things networks, where security risks threaten the privacy of individuals. In this context, many works were proposed for detecting and classifying the different types of attacks. However, many challenges are identified for this type of problem, such as the large amount of data available, the imbalanced nature of the data, and the quality of detection and classification outcomes. This paper aims to address these challenges by proposing an approach that considers a metaheuristic-based random weight neural network to detect intrusion activities and classify the different types and subtypes of activities. The following points summarize the contribution of this paper. First, the automatic tuning of the neural network parameters where the weights, biases, regularization value, the number of neurons, and the type of activation function are optimized by different metaheuristic algorithms to produce high-quality results. Second, the proposed approach adopts a clustering with reduction technique to tackle the challenge of processing large volumes of data. Third, oversampling the dataset is also embedded in the proposed approach to avoid a biased classification of the majority class. The experiments are conducted based on a large dataset with more than half a million instances. The results show that the proposed approach outperforms the other classification approaches in geometric mean (G-Mean) and has promising results. [ABSTRACT FROM AUTHOR]

In a permissionless social network environment, it is difficult for a user to assess the trustworthiness of other users. Thus, trust management becomes a crucial issue for securing social activities. The conventional method for assessing trust involves an authority to identify fakes. However, generative AI is gradually reducing the ability to distinguish by producing vivid news or images. Additionally, modern social networks are becoming flatter and more diverse, making conventional trust management methods unfeasible. There is an urgent need to design a suitable blockchain consensus for decentralized trust management. In this paper, we propose a novel three-layer autonomous trust management model for online social users based on blockchain technology and group decision making. Instead of solving a hash puzzle, which consumes too much computational resources for social network users, this paper proposes a lightweight and verifiable blockchain consensus proof of trust for decentralized trust management. Furthermore, performance analysis and simulation results show that our model is valid and flexible for both synthetic networks and real social networks. [ABSTRACT FROM AUTHOR]

Traditional point cloud simplification methods are slow to process large point clouds and prone to losing small features, which leads to a large loss of point cloud accuracy. In this paper, a new point cloud simplification method using a three-step strategy is proposed, which realizes efficient reduction of large point clouds while preserving fine features through point cloud down-sampling, normal vector calibration, and feature extraction based on the proposed feature descriptors and neighborhood subdivision strategy. In this paper, we validate the method using measured point clouds of large co-bottomed component surfaces, visualize the errors, and compare it with other methods. The results demonstrate that this method is well-suited for efficiently reducing large point clouds, even those on the order of ten million points, while maintaining high accuracy in feature retention, refinement precision, efficiency, and robustness to noise. [ABSTRACT FROM AUTHOR]

This paper proposes a parallel algorithm exploiting heterogeneous computing and edge computing for anomaly detection (AD) in remotely sensed multispectral images. These images present high spatial resolution and are captured onboard unmanned aerial vehicles. AD is applied to identify patterns within an image that do not conform to the expected behavior. In this paper, the anomalies correspond to human-made constructions that trigger alarms related to the integrity of fluvial ecosystems. An algorithm based on extracting spatial information by using extinction profiles (EPs) and detecting anomalies by using the Reed–Xiaoli (RX) technique is proposed. The parallel algorithm presented in this paper is designed to be executed on multi-node heterogeneous computing platforms that include nodes with multi-core central processing units (CPUs) and graphics processing units (GPUs) and on a mobile embedded system consisting of a multi-core CPU and a GPU. The experiments are carried out on nodes of the FinisTerrae III supercomputer and, with the objective of analyzing its efficiency under different energy consumption scenarios, on a Jetson AGX Orin. [ABSTRACT FROM AUTHOR]

The software-defined network paradigm, ensembled with a wireless sensor network, has emerged as a promising phenomenon to enable multi-tasking, re-configuration, and scalability. Termed the software-defined wireless sensor network (SDWSN), it divides the network into two planes: data and control. The data plane consists of software-defined sensor nodes (SDSN) that sense monitoring activity and generate data. On the other hand, the control plane has controller/control Nodes (CN) which collect data from SDSN, perform data aggregation, and then transmit toward the sink node. These CNs consume more amount of energy as compared to SDSNs as they perform multiple tasks. Following this scenario, this paper proposes an energy-efficient multi-objective optimization approach to solve the CNs placement problem through a meta-heuristic algorithm by considering the nodes' location, energy, and load distribution. This paper presents a particle swarm optimization-based controller placement and re-placement (PSO-CPR) algorithm for SDWSN. The PSO-CPR elects SDSNs to become CN based on their distance, residual energy, and capacity in the network. Moreover, the placement of a CN rotates within the cluster to avoid its failure and balance energy consumption. The simulation results show improved CN placement with respect to the state-of-the-art algorithms in terms of average delay by 23.5–37.4%, energy consumption by 18.6–32.6%, and probabilistic load distribution by 17.7–54.1%. Moreover, the comparative study also indicates that PSO-CPR achieve promising result by reducing packet loss by 14.4–27.5% and network re-clustering period by 32.3–68.3% and enhance the network lifetime by 22.6–42.5%. [ABSTRACT FROM AUTHOR]

With the development of science and technology, vehicles have gradually become smart network vehicles. However, applications on vehicles require great network and computing performance. It poses a great challenge to the existing Internet of Vehicles (IoV). Cloud computing can relieve the pressure of data processing, but the response time could take a long time. In the edge computing, edge server processing requests can effectively reduce response time. For example, during rush hour, in order to respond quickly, a large number of requests can be sent from the vehicles layer to the edge server. However, due to the limited bandwidth resources of edge servers, it is necessary to develop an effective bandwidth allocation model. The research in this paper consists of two main parts. First, this paper proposes a multi-objective bandwidth allocation optimization model for addressing this issue. The goal is to minimize average response time and maximize bandwidth utilization. Second, a multi-objective sparrow search algorithm (KMSSA) based on K-means algorithm is proposed to solve multi-objective bandwidth allocation. This paper compares KMSSA with particle swarm optimization (PSO) and sparrow search algorithm (SSA) on iFogSim platform. The results show that KMSSA has good optimization performance. The algorithm can be successfully applied to solve global multi-objective optimization problems. To compare with PSO algorithm, the performance of KMSSA is improved by 10%. To compare with SSA, the performance of KMSSA is improved by 5%. Also, KMSSA has superiority in solving practical problems. Its global optimal solution has superior performance in terms of average response time, bandwidth utilization, energy consumption, and cost. [ABSTRACT FROM AUTHOR]

In recent years, action recognition has been an essential branch of video understanding and a hot research direction. Among them, the graph convolutional network (GCN) is widely used in skeleton-based action recognition and has achieved remarkable performance. However, in practical situations, recognizing human action often depends on the movement of a part of the joints. In the existing GCN-based methods, the size of a single frame of the skeleton graph is fixed, and all joints of the human body will participate in the whole operation process, so the critical joints in the moving process cannot be flexibly selected. Therefore, this paper takes the adaptive graph convolutional network (AGCN) as the baseline and uses the graph-pooling method to select the critical joints in the human moving process. We design two new networks: Pooling-AGCN and U-AGCN and use them to form the multi-stream P&U AGCNs for action recognition. Extensive experiments show the complementarity between the two networks and that the method proposed in this paper outperforms the recent work on the three large-scale public datasets (NTU-RGB+D 60, NTU-RGB+D 120, Kinetics-Skeleton). [ABSTRACT FROM AUTHOR]

Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications. [ABSTRACT FROM AUTHOR]

Large number factorization is not only the most critical entry point for Rivest–Shamir–Adleman (RSA) security analysis, but also the most direct means of attacking the asymmetric encryption algorithm RSA. In this paper, the factorization methods of large numbers are summarized and analysed: classical integer factoring algorithms, Shor's circuit model algorithm, quantum adiabatic methods (integer factorization based on a quantum nuclear magnetic resonance (NMR) platform and D-Wave quantum annealing), and hybrid quantum-classical computing. Finally, the feasibility of integer factorization based on quantum adiabatics is discussed. In this paper, quantum annealing is regarded as a quantum attack method that is completely different from the famous Shor algorithm, and the potential of D-Wave factorization of large numbers to crack RSA cryptography is verified, which provides a new idea for a quantum attack on RSA public key cryptography. [ABSTRACT FROM AUTHOR]

3D integration is one of the scalable multiprocessor design solutions. The main challenge in 3D design is temperature traps, especially in the upper layers and hot spots. The current studies present solutions for solving temperature and hotspot problems in topology, routing, and mapping levels. Accordingly, the characteristics of temperature-aware core mapping are beneficial for dealing with the challenges of 3D chips. In this paper, the core temperature is modeled based on processing, communication power, neighbor node temperature, distance from the heat sink, and hotspot node. To find the optimal solution, this paper proposes thermal-aware application mapping techniques using genetic algorithms and fuzzy logic to minimize peak temperatures in 3D network-on-chip (NoC) architectures. Fitness functions include the above parameters with the same weight (GSM) and the fitness function with fuzzy logic by considering the effect of distance from heat sink (GFM1) and heat sink distance and the number of neighbor's core (GFM2). Specifically, the fuzzy logic-based GFM1 algorithm demonstrates superior performance over the genetic algorithm-driven GSM approach in reducing power consumption and energy costs associated with inter-core communication traffic. Across the different traffic patterns tested, GFM1 consistently achieves lower energy expenditure, with reductions upward of 50% compared to GSM. This underlines the strengths of the fuzzy technique in enabling thermal-aware mapping to minimize chip temperatures in the presence of intensive workloads and communication. In essence, the key takeaway is that GFM1 outperforms GSM in the context of power and energy metrics linked to handling on-chip traffic between processing cores. [ABSTRACT FROM AUTHOR]

Research on resource management focuses on optimizing system performance and energy efficiency by distributing shared resources like processor cores, caches, and main memory among competing applications. This research spans a wide range of applications, including those from high-performance computing, machine learning, and mobile computing. Existing research frameworks often simplify research by concentrating on specific characteristics, such as the architecture of the computing nodes, resource monitoring, and representative workloads. For instance, this is typically the case with cloud systems, which introduce additional complexity regarding hardware and software requirements. To avoid this complexity during research, experimental frameworks are being developed. Nevertheless, proposed frameworks often fail regarding the types of nodes included, virtualization support, and management of critical shared resources. This paper presents Stratus, an experimental framework that overcomes these limitations. Stratus includes different types of nodes, a comprehensive virtualization stack, and the ability to partition the major shared resources of the system. Even though Stratus was originally conceived to perform cloud research, its modular design allows Stratus to be extended, broadening its research use on different computing domains and platforms, matching the complexity of modern cloud environments, as shown in the case studies presented in this paper. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a collaborative artificial intelligence (AI) approach that enables distributed training of AI models without data sharing, thereby promoting privacy by design. However, it is essential to acknowledge that FL only offers a partial solution to safeguard the confidentiality of AI and machine learning (ML) models. Unfortunately, many studies fail to report the results of privacy measurement when applying FL, mainly due to assumptions that privacy is implicitly achieved as FL is a privacy-by-design approach. This trend can also be attributed to the complexity of understanding privacy measurement metrics and methods. This paper presents a survey of privacy measurement in FL, aimed at evaluating its effectiveness in protecting the privacy of sensitive data during the training of AI and ML models. While FL is a promising approach for preserving privacy during model training, ensuring privacy is genuinely achieved in practice is crucial. By evaluating privacy measurement metrics and methods in FL, we can identify the gaps in existing approaches and propose new techniques to enhance FL's privacy. A comprehensive study investigating "privacy measurement and metrics" in FL is therefore required to support the field's growth. Our survey provides a critical analysis of the current state of privacy measurement in FL, identifies gaps in existing research, and offers insights into potential research directions. Moreover, this paper presents a case study that evaluates the effectiveness of various privacy techniques in a specific FL scenario. This case study serves as tangible evidence of the real-world implications of privacy measurements, providing insightful and practical guidelines for researchers and practitioners to optimize privacy preservation while balancing other crucial factors such as communication overhead and accuracy. Finally, our paper outlines a future roadmap for advancing privacy in FL, combining traditional techniques with innovative technologies such as quantum computing and Trusted Execution Environments to fortify data protection. [ABSTRACT FROM AUTHOR]

In order to meet ever-increasing demands for reliable parallel and distributed systems, it is crucial to evaluate the reliability and fault tolerance of their underlying interconnection networks. Such an interconnection network is usually modeled as a connected graph G, where the vertex set and edge set represent the processors and links between processors in the network, respectively. In this paper, we combine Fàbrega's idea about h-extra edge-connectivity and Sampathkumar's concept about r-component edge-connectivity to introduce a more refined parameter for characterizing fault tolerance of interconnection networks, named as h-extra r-component edge-connectivity. Given a connected graph G, for two integers h ≥ 1 and r ≥ 2 , the h-extra r-component edge-connectivity of G, denoted as c λ r h (G) , is the minimum cardinality among all edge subsets F ⊂ E (G) , if any, such that G - F has at least r components, and each component has at least h vertices. As an enhancement on hypercube, the n-dimensional augmented cube AQ n , introduced by Choudum and Sunitha in 2002, reserves several excellent topological properties. As | V (AQ n) | = 2 n , the h-extra three-component edge-connectivity of AQ n is well-defined for each integer h with 1 ≤ h ≤ ⌊ 2 n / 3 ⌋ . In this paper, a generalization of Xu et al.'s conclusion is obtained that finds an upper bound for the exact value of general h-extra three-component edge-connectivity of AQ n and shows that it is sharp for 1 ≤ h ≤ 2 n 2 - 1 and h = 2 c where 1 ≤ c ≤ n - 2 . Let h = ∑ i = 0 s 2 t i be a positive integer with t 0 > t 1 > ⋯ > t s ≥ 0 . Let δ = 0 if h is even and δ = 1 if h is odd. Specifically, c λ 3 h (AQ n) = (4 n - 4) h - 2 ∑ i = 0 s (2 t i - 1) 2 t i - 2 ∑ i = 0 s 4 i · 2 t i - δ for n ≥ 4 , h ≤ 2 n 2 - 1 , and c λ 3 2 c (AQ n) = (2 n - 2 c - 1) 2 c + 1 for n ≥ 4 and 1 ≤ c ≤ n - 2 . [ABSTRACT FROM AUTHOR]

Forest fire is natural disasters that are sudden, destructive and difficult to handle and rescue, with millions of hectares of forests burned every year all over the world, causing serious ecological damage, loss of life and property. Therefore, timely detection and treatment of early fire is of positive and important significance for forest fire early control. The fire detection method based on image processing is one of the most important means of preventing the occurrence of large-scale forest fires at present by extracting the flame and smoke features in the image and quickly determining the location of the fire. The current deep learning-based forest fire early detection methods have problems such as high false alarm rate due to small detection targets and complex environmental background, and large number of detection model parameters. In view of this, this paper proposes a lightweight early forest fire and smoke detection method based on GS-YOLOv5. Firstly, this paper proposes a novel Super-SPPF structure to replace the SPPF structure in YOLOv5, by which the output of the feature extraction network is used as input, and the input is divided into two branches to retain more semantic information, and the GhostConv operation is performed separately to reduce the number of model parameters. The Super-SPPF structure performs the serial MaxPooling operation on one of the branches, which improves the computation speed by choosing a smaller pooling kernel, and then fuses the outputs of the two branches to reduce the false alarm rate of the detection model. Secondly, C3Ghost is utilized instead of the C3 module in YOLOv5 to further reduce the number of detection model parameters. Finally, the coordinate attention (CA) module is introduced in backbone of YOLOv5 to obtain the relationship between channels and space, which enables the network to obtain the location information of interest more accurately and further improves the detection accuracy of early fires. In this paper, a self-constructed DL-Fire dataset is used to verify the performance of the GS-YOLOv5 detection model by collecting environmental interference samples and combining them with the D-Fire dataset. The experimental results show that the detection accuracy of GS-YOLOv5 is 95.9%, and the model size is 10.58 mb. Compared with YOLOv5, false alarm rate is reduced from 12% to 6% and computational complexity is reduced from 16.0 GFlops to 12.8 GFlops by GS-YOLOv5. [ABSTRACT FROM AUTHOR]

Reconfigurable hardware, extensively employed in mission-critical digital applications like space and military electronics due to its adaptability, encounters the issue of soft errors, especially in control path elements, which could result in functional failure. Various system-level fault tolerance methodologies exist, and this paper implements a bio-inspired fault tolerance technique called evolvable hardware (EHW). The preferred implementation of the EHW system involves hosting the evolutionary algorithm on the processor alongside the reconfigurable hardware. However, this approach encounters delays in the intercommunication of the evolved circuit between the reconfigurable hardware and the processor. To address this issue, the paper proposes a two-tier architecture to achieve absolute fault mitigation in the controller. In this architecture, Tier-1 involves the digital implementation of the genetic algorithm on the reconfigurable hardware to mitigate errors in the controller, while Tier-2 focuses on mitigating errors occurring in Tier-1. The aim is to establish an absolute and self-resilient controller hardware to mitigate faults. The study simulates faults at the target circuit and genetic module as a proof of concept. The proposed two-tier single event upset (SEU) mitigation is deployed on Microsemi's ProAsic3e FPGA (Field Programmable Gate Array), achieving an average efficiency of 91%. This efficiency is accompanied by ten times lesser resource utilization compared to traditional methodologies and a 30% accelerated speed when compared to hybrid evolvable systems. [ABSTRACT FROM AUTHOR]

To investigate how the petroleum industry evaluates and selects powerful cloud service providers, first, an evaluation index system including 25 indices such as scalability and private data protection is built. This index system can systematically examine the comprehensive strengths of cloud service providers. Aiming to solve the problems that the traditional expert evaluation method has high requirements on expert experience and is easily affected by subjective factors, a novel artificial intelligence evaluation model named TentISSA-BPNN is proposed. The objective evaluation ability of this model can be effectively used in evaluation research on cloud service providers for petroleum enterprises. In this model, the SSA algorithm is optimized by Tent chaotic mapping and adaptive inertia weight; an algorithm, TentISSA, that has good stability and fast convergence speed is designed and proposed; and the BPNN is improved with TentISSA to obtain more accurate evaluation results. To evaluate the performance of the TentISSA algorithm, nine unimodal and multimodal functions are selected in this paper to test the convergence accuracy. Then, seven models are selected as the control groups to validate the effectiveness and performance of the TentISSA-BPNN evaluation model proposed in this paper. Finally, the preprocessed data of the candidate cloud service providers are input into the trained neural network model proposed in this paper for evaluation. Based on the ranking of the evaluation scores, the comprehensive strengths of the cloud service providers are obtained to provide a decision-making reference for managers of petroleum enterprises in the process of choosing cloud service providers. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are mostly used for monitoring the environment; however, they are usually powered by non-rechargeable batteries with limited energy. Solar energy harvesting is an attractive solution to the limit by charging the sensor nodes; however, the harvested solar energy is easily affected by weather conditions. Based on the characteristics of uncertainty and intermittency of solar energy, this paper proposes a plus-profile solar energy prediction algorithm. This algorithm makes the prediction of future available solar energy by finding the data in the dataset that is most similar to the data of the day and combining it with recent weather trend. According to the predicted result, the paper further proposes an adaptive energy management scheme to suit the harvested energy. In the scheme, sensor nodes can adaptively adjust task scheduling to achieve energy neutrality. The simulation results show that compared with other algorithms, the prediction accuracy of the proposed prediction algorithm is improved by 17.7 and 22.4%, respectively, and the proposed energy management scheme reduced energy loss by 6.2 and 46.8%, respectively. [ABSTRACT FROM AUTHOR]

In order to explore the application of neural networks in the intelligent protection of digital music copyrights, this paper initially investigates the key aspects of digital music-related supply chain management (SCM). Subsequently, this paper introduces a fuzzy, comprehensive evaluation-based method for authenticating digital music infringement. Furthermore, it employs a convolutional neural network to analyze digital music's source components. Within this context, an attention mechanism and an adaptive gate model are meticulously designed. Moreover, a selective adaptive cascade is implemented to optimize the structure of the multi-resolution coder–decoder. Through fuzzy authentication, it becomes feasible to accurately and reliably track and verify copyright information pertaining to digital music, thus providing an effective protective mechanism for the digital music industry. Experimental results reveal that in comparison with the SHN-4 series models, the network model embedded with the channel space (CS) attention module achieves improvements of up to 0.04 dB in terms of performance metrics for human voice and accompaniment sources. Furthermore, it outperforms these models by up to 0.19 dB in music source separation metrics. The incorporation of the adaptive attention mechanism profoundly enhances the model's performance in audio source separation tasks, benefiting both vocal and accompaniment sources. This improvement is evident in the form of a 0.44 SDR enhancement for vocal sources and a 0.18 SDR enhancement for accompaniment sources. Additionally, the proposed two-stage music separation-gate self-attention model surpasses the two-stage music separation-gate model in metrics associated with human voice and accompaniment sources. This outcome convincingly underscores the enhanced effectiveness achieved by integrating CS, self-attention, and adaptive connection in network performance, as well as the adaptive gate structure for music copyright protection. This approach heightens the model's autonomous selectivity, minimizes outliers, stabilizes waveform characteristics, and significantly contributes to the safeguarding of digital content. [ABSTRACT FROM AUTHOR]

In order to solve the problems of slow convergence and low accuracy of the traditional ant colony algorithm in solving the traveling salesman problem (TSP), this paper proposes a heterogeneous ant colony algorithm based on the selective evolution mechanism and game strategy (SGHACA). First, we propose a game mechanism. In each iteration, the best population and the worst population will play a game. At the end of the game, pheromone gains will be given to the participants according to the weight coefficients to motivate the populations to participate in the next round of the game, in order to promote cooperation among different populations and thus improve the diversity of the algorithm. Second, we introduce a selective evolution mechanism. The optimal paths of each iteration are compared with the historical optimal paths to evolve relatively better path fragments, thus improving the convergence speed of the algorithm. Finally, when the algorithm stagnates, we introduce a dynamic shaking mechanism, which is able to dynamically dither the pheromone on the current optimal path according to the number of iterations, increasing the probability that the algorithm chooses other paths, thus helping to jump out of the locally optimal solution. In order to verify the effectiveness of the improved algorithm in this paper, we experimentally verify through a large number of TSP instances that SGHACA is able to improve the convergence of the algorithm and the accuracy of the solution to a certain extent when solving large-scale TSP. Meanwhile, compared with the latest improved algorithms, the accuracy of the solution of SGHACA is better than most of the latest improved algorithms, which indicates that the improved algorithm of this paper has some competitiveness. [ABSTRACT FROM AUTHOR]

While traveling, people need to utilize smart tourism service systems based on the Internet of Things (IoT) to enhance their journeys' convenience. However, existing IoT-based smart tourism service systems still possess certain shortcomings. In light of this, this paper delves into the integration of IoT and machine learning (ML) technologies to improve the design of smart tourism service systems and establish a platform for intelligent tourism services. Simulated datasets are employed to evaluate the performance and functionality of the smart tourism service system. The primary contributions, as inferred from the test results, are as follows: In area 1, the model achieves a congestion recognition rate of 92.6% with an error rate of only 1.5% in congestion judgment. In area 2, congestion is accurately identified 93.5% of the time, with a misjudgment rate of just 1.2%. In area 3, the model achieves a congestion identification rate of 94.2% along with a 1.3% misjudgment rate, effectively identifying congestion situations in scenic spots. When comparing the research and design system with the system based on the support vector machine (SVM) algorithm, the results demonstrate that the smart tourism service system designed in this paper, leveraging IoT technology and ML algorithms, achieves higher prediction accuracy than the SVM-based system. These findings can serve as a valuable guide for designing future smart tourism service systems. [ABSTRACT FROM AUTHOR]

In the stochastic and dynamic edge-cloud collaborative environment, the computing resources of the host are limited, and the resource requirements of computing tasks are random and changeable. Therefore, how to efficiently schedule dynamic tasks and improve system performance becomes challenging. The scheduling algorithm based on deep reinforcement learning optimizes the delay and energy consumption of the system by dynamically interacting with the environment, which can solve the problem of dynamic and changeable environment to a certain extent, but there are still problems such as poor model adaptability, low training efficiency, and unbalanced system load. In this paper, aiming at optimizing the average response time of task scheduling and the average energy consumption of the system, a multi-objective task scheduling model is designed, and a task scheduling policy optimization algorithm based on improved asynchronous advantage actor-critic (A3C) is proposed. The residual convolutional neural network (RCNN) improves the network structure of A3C, using asynchronous multi-threaded training methods to interact with the edge-cloud collaborative environment and capturing the random dynamic characteristics of resources required for computing tasks and heterogeneous edge-cloud hosts resource change characteristics to better adapt to random dynamic environments, and it can quickly updating network parameters, improving training speed, and make full use of host resources to solve system load imbalance problems. Simulation results show that the scheduling algorithm proposed in this paper can effectively reduce task response time and system energy consumption in an edge-cloud collaborative environment. [ABSTRACT FROM AUTHOR]

Existing biohashing authentication schemes usually store bioinformation in the cloud, whose storage method can face the problems of bioinformation leakage and tampering, and single point of failure. To address the above problems, a ciphertext speech biohashing authentication scheme based on chaotic system and improved public chain is proposed. First, the fusion features are extracted and generated with the inner product of 3D chaotic system to generate the biohashing sequence. Second, the original speech is encrypted and uploaded to the InterPlanetary file system and then, uploaded to the blockchain after the data are encrypted. Finally, the server matches the hash sequence queried on the chain with the to-be-authenticated hash at the user's end using the Hamming distance. The experimental results show that the biohashing algorithm in this paper has better distinguishability, robustness, security and time efficiency. The key space of the speech encryption algorithm is 2 256 , which can well resist the exhaustive attack and ensure the security during speech authentication. In this paper, the combination of on-chain and off-chain storage of speech information can prevent the problem of bioinformation leakage and tampering, as well as avoiding the problem of single point of failure. [ABSTRACT FROM AUTHOR]

The reliable prediction of solar energy production and surplus is crucial for the stability of the electricity grid and effective energy distribution, particularly during peak consumption periods. Encouraging solar adoption by customers is also important despite fluctuations in solar energy generation. This paper addresses the need for an intelligent surplus solar energy prediction model, referred to as the Green Model, which aims to accurately predict surplus electrical energy that can be returned to the distribution grid. The Green Model utilizes a combination of a developed long short-term memory (LSTM) neural network and a novel optimization technique called deterministic selection network by gain sharing knowledge (DSN-GSK). The DSN-GSK optimizes the structure of LSTM by determining the optimal number of hidden layers, nodes in each layer, biases, weights, and activation functions based on the most important features that represent the relationship between generated solar energy and weather factors. The Green Model was evaluated and analysed, demonstrating high accuracy in the short-term prediction of surplus electrical energy with a minimal percentage of error. The model shows promising prospects for the development of returned electrical energy prediction. The main novelty of this paper lies in the development of the Green Model, which combines LSTM and DSN-GSK to enhance the accuracy of surplus solar energy prediction. The benefits of the Green Model include improved stability and efficiency of the electricity grid, effective utilization of renewable energy sources, and the reduction of negative environmental impacts. The contributions of this paper include advancing the field of intelligent energy prediction, optimizing the structure of LSTM, and providing valuable insights for the integration of renewable energy into the existing energy infrastructure. [ABSTRACT FROM AUTHOR]

The early and effective diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) has received increasing attention in recent years. However, currently available deep learning methods often ignore the contextual spatial information contained in structural MRI images used for early diagnosis and classification of Alzheimer's disease. This may lead us to miss important structural details by failing to adequately capture the potential connections between each slice and its neighboring slices. This lack of contextual information may cause the accuracy of the network model to suffer, which in turn affects its generalization ability and application in real-life scenarios. To explore deeper the connection between spatial context slices, this research is designed to develop a new network model to effectively detect or predict AD by digging into the deeper spatial contextual structural information. In this paper, we design a spatial context network based on 3D convolutional neural network to learn the multi-level structural features of brain MRI images for AD classification. The experimental results show that the model has good stability, accuracy and generalization ability. Our experimental method had a classification accuracy of 92.6% in the AD/CN comparison, 74.9% in the AD/MCI comparison, and 76.3% in the MCI/CN comparison. In addition, this paper demonstrates the effectiveness of the proposed network model through ablation experiments. [ABSTRACT FROM AUTHOR]

This paper solves the task of unsupervised video object segmentation (UVOS) that segments the objects of interest through the entire videos without any annotation. In recent years, many unsupervised video object segmentation (UVOS) methods have been proposed. Although these methods perform well, they rely on networks with heavy weights, often leading to large model size. In order to reduce the model size while keeping a competitive performance, we propose a saliency-based dual-attention (SDA) method for UVOS in this paper. In our method, we take optical flow and video frames as inputs and extract the appearance information and motion information from optical flow and video frames. We design a two-branch network with appearance information and motion information. The information from these two branches is fused via a saliency-based dual-attention module to segment the primary object in one path. The saliency-based dual-attention module is composed of saliency attention and saliency-based reverse attention. To demonstrate the effectiveness of our network, we tested it on the DAVIS-2016 and SegtrackV2 datasets. Experimental results demonstrate that our method can achieve competitive results in terms of accuracy and model size. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITS) are a promising research area that offers a variety of applications. The objective of these applications is to enhance road safety, to optimize traffic efficiency, and to provide a better driving experience. Yet, the efficiency of ITS applications, such as safety and driver assistance applications, relies essentially on the exchanged data between different entities of the network. Accordingly, trust management models are used to guarantee the quality of the data and to eliminate malicious and selfish nodes to secure vehicular communications. In this paper, we pay a special attention to the requirements of trust management models used in the context of ITS applications. We also dissected the trust model to extract the mechanisms used in the literature to fulfil the identified requirements. Furthermore, we present the most known simulators and evaluation metrics that are used to validate the proposed models. The aim of this study is to provide a global overview of the mechanisms that may be used to fulfil the crucial requirements of trust management models. For this purpose, we employed a systematic mapping study, through which we carefully analysed 60 selected articles. Through our analysis, five main requirements were identified: scalability, accuracy, robustness, privacy preservation, appropriate response time. Different mechanisms and techniques were applied to meet with the identified requirements. Two main findings are reported: (1) The accuracy and robustness requirements are the most considered requirements. On the other hand, the privacy requirement is the least covered by the publications, (2) the majority of the reviewed papers focus on addressing two or three requirements at most. A little number of publications covered all the requirements. Based on the identified research gaps, we highlight some future directions that may be investigated. We provide general recommendations that may serve as a guideline for researchers who want to design trust models that fulfil certain requirements. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an important virtual network that allows remote users to access linked multimedia devices. The development of IoT and its ubiquitous application across various domains of everyday life has led to continuous research efforts. Security is a perceptual concern for researchers involved in IoT as it is a key factor in the acceptance of any innovative technology. Numerous research studies have been conducted concentrating on the level of IoT security on a particular mechanism, on specific applications, or on categorizing vulnerabilities, in order to address a defined situation of securing an IoT network. This present paper aims to comprehensively review potential solutions for securing IoT, between emerging and traditional mechanisms, such as blockchain, machine learning, cryptography, and quantum computing. This study provides a comparative analysis of related papers with their characteristics, pros and cons. Accordingly, it taxonomizes relevant solutions based on their achieved security requirements. Furthermore, the potential benefits and challenges of each of the four mechanisms are discussed. [ABSTRACT FROM AUTHOR]

To improve the quality and efficiency of machine translation (MT) for low-resource languages (LRLs), this paper analyzes the application of genetic algorithm (GA) under the background of cloud computing (CC). By using GA to optimize MT for LRL, this study aims to address the limitations and challenges of traditional methods on LRL. Firstly, GA under the background of the CC platform is analyzed in detail. The CC platform offers powerful computing and storage capabilities that provide the basis for large-scale optimization using GA. GA is an optimization algorithm based on the principle of biological evolution, which seeks the optimal solution by simulating genes' genetic and evolutionary processes. In MT of LRL, GA can be used to optimize model parameters, select translation rules, and improve translation quality, thereby improving translation results. Secondly, the present situation of MT for LRL is described. Finally, the traditional LRL-oriented MT and the GA-optimized LRL-oriented MT are compared and analyzed. The results reveal that the traditional LRL-oriented MT has low accuracy and low customer satisfaction (CS). Considering the translation time, accuracy, and resource utilization rate (RUR), the highest rate is only about 47%. In contrast, GA-optimized LRL-oriented MT significantly improves translation accuracy and CS, reaching approximately 94%. The final evaluation results indicate that the accuracy of the model is usually above 70%, and up to 94%. Therefore, GA plays an important role in optimization for LRL-oriented MT. This paper provides technical support for optimizing LRL-oriented MT and contributes to the further development of MT technology. By applying GA, the limitations of traditional methods in LRL can be overcome and the quality and efficiency of translation can be improved. It is of great significance to improve the development of translation technology of LRL, enhance cross-language communication, and promote global cultural exchange. [ABSTRACT FROM AUTHOR]

Location-based skyline queries in road networks can quickly return the desired data to the user from a large amount of data according to the user's needs and location. However, there is no effective mechanism for calculating the road network distance and judging the dominant relationship between data points in the existing skyline queries algorithms in road networks. In this paper, we propose improved dynamic skyline queries in road networks, which can quickly compute all skyline points in road networks. Firstly, this paper proposes a GD-tree index, which efficiently stores the location information of the target tuple in the road network and speeds up the calculation of the road network distance. In addition, the concept of the optimal scan end point is proposed to reduce the calculation cost. The histogram of frequency statistical is proposed for the non-spatial dimension, and the nearest and farthest distance is calculated for the spatial dimension to select the optimal scan end point. Finally, extensive experiments in real road network datasets demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

During the execution of tasks in a distributed computing system, the master needs to assign new workers to replace lagging workers that usually occur due to various issues such as network problems, hardware failures, software bugs, or high cluster load. If the completed part of tasks by lagging workers is not cached timely, new workers would need to recalculate it, leading to further delays. In the Spark platform, checkpointing allows for the preservation of intermediate results computed by workers in resilient distributed datasets (RDDs) by storing them in Hadoop distributed file system (HDFS). However, storing and retrieving data from HDFS still remains time-consuming. Traditional distributed cache strategies neglect to utilize both coding techniques and different lag probabilities of workers to reduce overhead of caching data. This paper introduces a distributed cache strategy based on Luby transform (LT) codes to enhance system's effectiveness and robustness. An optimization method is designed within prescribed cache limits to guide the selection of RDDs for caching. Additionally, a novel algorithm is proposed for determining the participation of specific partitions in encoding caching under consideration of worker failure probabilities and overhead of encoding and decoding. In the event of worker failure, intermediate data can be swiftly recovered through decoding, and then the standbys can rapidly take over stragglers' tasks, hence markedly mitigating overall task latency. Theoretical analysis and experimental results demonstrate that the LT code-based caching strategy exhibits more efficiency and lower latency compared to recomputation, HDFS-based and weight-based caching methods, and it saves much more memory overhead compared to Redis. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimization method is proposed based on the sparrow search algorithm, namely, multi-strategy-guided sparrow search algorithm (MGSSA). It is well-known that the basic SSA has limitations such as the slow convergence speed and vulnerability to local optimality. Surrounding these two issues, some strategies are presented in the MGSSA. Firstly, the newly introduced ring topology search strategy not only maintains the diversity of the entire population but also enhances the exploration ability of the SSA. Secondly, the proposed leader-based search strategy can improve exploitation ability of the SSA to prevent falling into local optimum as much as possible. Moreover, the coordinated learning strategy is put forward to better balance between the exploration and exploitation abilities. Finally, the MGSSA is compared with seventeen advanced algorithms on two well-known benchmark suites (i.e., CEC-2017 and CEC-2020). Meanwhile, the MGSSA-based forecasting approach is applied to predict the remaining useful life for lithium-ion batteries. The statistical results indicate that the MGSSA is a high-performance optimizer, which can not only solve the defects of the original SSA, but also obtain satisfactory solutions in both complex numerical optimization and real-world application problems. [ABSTRACT FROM AUTHOR]

Child Drawing Development Optimization is a recently developed metaheuristic algorithm that has been demonstrated to perform well on multiple benchmark tests. In this paper, a binary Child Drawing Development Optimization (BCDDO) is proposed for wrapper feature selection. The proposed BCDDO is utilized to choose a subset of important features to reach the highest classification accuracy. Harris Hawk optimization, salp swarm algorithm, gray wolf optimization, and whale optimization algorithm are utilized to evaluate the effectiveness and efficiency of the suggested feature selection method. In the field of feature selection to improve classification accuracy, the proposed method has gained a considerable classification accuracy advantage over previously mentioned methods. Four datasets are used in this research work; breast cancer, moderate COVID, big COVID, and Iris using XGBoost classifier and the classification accuracies were (98.83%, 98.75%, 99.36%, and 96%), respectively, for the four mentioned datasets. [ABSTRACT FROM AUTHOR]

Bilateral filtering is an image processing technique commonly adopted as intermediate step of several computer vision tasks. Opposite to the conventional image filtering, which is based on convolving the input pixels with a static kernel, the bilateral filtering computes its weights on the fly according to the current pixel values and some tuning parameters. Such additional elaborations involve nonlinear weighted averaging operations, which make difficult the deployment of bilateral filtering within existing vision technologies based on real-time and low-energy hardware architectures. This paper presents a new approximation strategy that aims to improve the energy efficiency of circuits implementing the bilateral filtering function, while preserving their real-time performances and elaboration accuracy. In contrast to the state-of-the-art, the proposed technique allows the filtering action to be on the fly adapted to both the current pixel values and to the tuning parameters, thus avoiding any architectural modification or tables update. When hardware implemented within the Xilinx Zynq XC7Z020 FPGA device, a 5 × 5 filter based on the proposed method processes 237.6 Mega pixels per second and consumes just 0.92 nJ per pixel, thus improving the energy efficiency by up to 2.8 times over the competitors. The impact of the proposed approximation on three different imaging applications has been also evaluated. Experiments demonstrate reasonable accuracy penalties over the accurate counterparts. [ABSTRACT FROM AUTHOR]

The emergence of a new generation of applications such as the Internet of Things, Artificial Intelligence, and 5G has brought fog computing as cloud development toward the edge network. It is done by adding an extra layer of resources to meet low latency, high performance, mobility, and interoperability challenges. The development of fog markets and new beneficial business paradigms such as fog services considerably affect the future of fogonomics (which is defined as the economics of fog computing). Thus, it is a challenging issue to design a market-oriented model to deal with the economics-based resource management in fog computing, which adjusts the resource supply of Fog Resource Providers (FRPs) and the demands of User Services (USs) based on users' requirements while satisfying all parties' utilities. This paper proposes the Fog-Marketing scheme consisting of a Resource Propagation Component (RPC) and a Contract-aware Market Selection Component (CMSC) for resource provisioning in multi-tier fog computing. The RPC is deployed in each fog node to perform hierarchical resource propagation of FRPs to auction-based Fog Decentralized Markets (FDMs) close to USs to increase revenue opportunities, and embedded CMSC in the user agent applies a matching strategy to assign USs and FDMs aims to promote USs' utility. The simulation results under extensive experiments confirmed that the proposed scheme succeeded in reducing the Round-Trip-Time, increasing the revenue of FRPs, and improving the utility of USs compared to existing models. [ABSTRACT FROM AUTHOR]

With the continuous expansion of network scale, fault tolerance and security in network operation have become increasingly important. The high vertex connectivity of the interconnection network indicates excellent fault tolerance. In order to address the drawbacks of traditional connectivity, the g-extra connectivity has been proposed to better reflect the fault tolerance of the network. RRect is a newly proposed server-centered data center network with good interconnection structure, and it has excellent flexibility and energy efficiency. In this paper, we focus on the logical structure of RRect, denoted by R R n , m , k . Firstly, we investigate the extra connectivity of R R n , m , k , and, in detail, we prove that for n ≥ 4 , m ≥ 1 , and k ≥ 4 , the { 1 , 2 , 3 } -extra connectivity of R R n , m , k is 2 k m (n - 1) + m n - 2 , 3 k m (n - 1) + m n - 3 , and 4 k m (n - 1) - 4 m , respectively, which are about 2, 3, and 4 times of its traditional connectivity. Secondly, we conduct numerical simulation experiments on the extra connectivity of R R n , m , k and evaluate the experimental performance. The result can provide a more comprehensive measurement of its reliability. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging is applied in the medical field for automated diagnosis of diseases, especially cancer. Among the various classification algorithms, the most suitable ones are machine and deep learning techniques. In particular, Vision Transformers represent an innovative deep architecture to classify skin cancers through hyperspectral images. However, such methodologies are computationally intensive, requiring parallel solutions to ensure fast classification. In this paper, a parallel Vision Transformer is evaluated exploiting technologies in the context of Edge and Cloud Computing, envisioning portable instruments' development through the analysis of significant parameters, like processing times, power consumption and communication latency, where applicable. A low-power GPU, different models of desktop GPUs and a GPU for scientific computing were used. Cloud solutions show lower processing times, while Edge boards based on GPU feature the lowest energy consumption, thus resulting as the optimal choice regarding portable instrumentation with no compelling time constraints. [ABSTRACT FROM AUTHOR]

Automatic train operation (ATO) is a critical component of automatic train control (ATC) systems. The ATO automatically adjusts the speed of trains, ensuring the safety of trains and increasing the passenger capacity of urban railway networks. The traditional ATO models employ a linear approximation approach to fit the nonlinear control model of the train, such as proportional–integral–derivative (PID) or model predictive control (MPC). However, due to the complexity of actual train operations, these methods fail to accurately fit train models. Thus, they are unable to shorten the operational intervals of trains and meet the demand for improving system efficiency of ATC. In recent years, machine learning-based methods have been employed for deriving more accurate train control models. However, these methods require trains to offload massive amounts of data to central servers for centralized training, which is challenging in CBTC systems due to limited wireless bandwidth. In this paper, we propose using federated learning (FL)-based edge computing to train the ATO model without transmitting a large amount of data. The onboard computing devices are used to perform local federated learning when the train is idle. Additionally, we present a Tab-Transformer-based machine learning model for ATO policy to improve the prediction accuracy of federally trained models. Our extensive simulation results demonstrate that the proposed federated learning scheme improves the efficiency of model training, and the Tab-Transformer-based ATO model achieves better driving performance. [ABSTRACT FROM AUTHOR]

In peer-to-peer (P2P) energy trading, members locally trade energy. Blockchain-based systems are employed for the above trading. These systems are limited in speed because of the time required in the consensus process to audit and verify transactions. Further energy is traded using auction, which also consumes time. In this paper, we propose a blockchain and machine learning-based system to speed up trading. This system uses Byzantine Fault Tolerance method to reduce consensus time and machine learning to reduce auction time. In addition, the proposed system provides loans to cash-deficient buyers thereby enabling them to purchase energy. Machine learning is used to arrive at optimal loan rates for buyers. All these claims have been verified by the simulation results. Simulation results show that the new mechanism finds the optimal value of the trader's quotes prices quickly by altering the value of the profit margin, and the new loan system increases the number of transactions in this blockchain-based system. Hence, the proposed system will promote P2P trading by making it faster and enabling greater participation of cash-deficient buyers in the trading. [ABSTRACT FROM AUTHOR]

Machine learning has been widely used to build intrusion detection models in detecting unknown attack traffic. How to train a model properly in order to attain the desired intrusion detection is an important topic. In contrast to offline learning, online learning proves more practical as it can update models simultaneously in the detecting process to comply with real network traffic. Active learning is an effective way to realize online learning. Among existing active learning mechanisms proposed to perform intrusion detection, most fail to meet the real online environment or to run persistently. This paper presents a new active online learning mechanism to secure better intrusion detection performance. The new mechanism advances related works in bringing the lifelong learning practice to fit in the online environment. It uses the efficient random forest (RF) as the detection model to train samples and adds a new tree to train a new batch of data when updating the model at each online stage, to pursue lifelong learning. By training a new batch of data only, it can keep the previously trained weights from being updated so as to preserve the past knowledge. Our mechanism is experimentally proved to yield better overall results than existing mechanisms: It produces superior training efficiency and detection performance—with the least training time, best training data quality and much reduced training data quantity. [ABSTRACT FROM AUTHOR]

UAVs in most real-world deployments communicate with a ground control station either in a one-hop manner, which has a limited working range, or relies on infrastructure support such as satellite communication, which is expensive. Although multi-hop transmission is more economic, it is lossy and generally incurs large delays. In this paper, we consider a mixed method, where a few UAVs are equipped with satellite modules, and aggregate and forward traffic of nearby UAVs. The surrounding UAVs and the forwarding UAV form a star topology. Based on this model, we design an UAV formation strategy to cover a given region to minimize both the total number of UAVs and the number of UAVs with satellite modules. Since these two goals may be competing, we propose seven strategies and analyze them theoretically. Simulations show that a good tradeoff between the two goals can be obtained when each star network consists of four UAVs. [ABSTRACT FROM AUTHOR]

Leukemia is a severe blood disorder that poses a threat to the life and well-being of patients. Accurate diagnosis of leukemia typing is crucial for treatment and prognosis. However, manual diagnosis requires significant resources and is subject to observer variation. Computer-aided diagnostic (CAD) technologies have emerged as a promising approach to complement manual diagnosis and alleviate the workload of specialized physicians. With the recent advances in deep learning, the performance of CAD has been substantially enhanced. In this paper, we propose a novel classification model, AMLNet, for acute myeloid leukemia (AML) cells. AMLNet is a lightweight model based on the ShuffleNet V2 architecture. We first validate the performance of the convolutional neural network and Transformer architecture models on the largest AML cell dataset to date. Then, we enhance the data augmentation approach and model structure of the ShuffleNet V2 model to develop AMLNet. The AUC, sensitivity, and accuracy of AMLNet are up to 0.9935%, 63.91%, and 96.43%, respectively, which achieves the optimal classification performance. Finally, the classification performance of AMLNet is verified on other public datasets, which is superior to other methods. AMLNet presents a promising solution for practical diagnosis scenarios since the end devices of assisted diagnosis often have limited computational power. The proposed AMLNet model effectively improves the classification performance of ShuffleNet V2 while offering the advantages of high performance and low computational consumption. Overall, our study demonstrates the potential of lightweight deep learning models in improving the accuracy of leukemia typing and reducing the workload of specialized physicians. [ABSTRACT FROM AUTHOR]

One of the main sources of information dissemination is social networks. Changes in social and Internet of Things (IoT) relationships can affect the quality of service (QoS) of device-to-device (D2D) communication in fifth-generation (5G) networks. Integration of social networks and IoT networks will allow users to improve their communication abilities with proximity users. The most challenging issues are interference and channel uncertainty due to massive connectivity and random movement of users in an urban dynamic environment. In this paper, D2D-based social Internet of Things (D2D–social IoT) in a 5G network is modeled for urban regions. To address these issues, we formulate a throughput maximization problem under the maximum power and QoS constraints. The problem is divided into multiple subproblems to reduce interference and enhance throughput. First, we leverage the interference channel link for intelligent resource sharing with better QoS by the spatiotemporal method. The second one involves bisection-based power search optimization by utilizing the Mellin transformation method for latency and power minimization. Simulation results demonstrate that the proposed method significantly improves the network performance in terms of throughput, sum rate, and reduction in network latency by up to 19.91%, 26.24%, and 31.57%, respectively. It is expected that an inclusive implementation of the said method can enable resource allocation in 5G-enabled social IoT networks for the development of smart city. [ABSTRACT FROM AUTHOR]

With the increasing complexity of autonomous driving tasks, the computational demands on single vehicular computing units have escalated, more and more tasks need to be offloaded to the edge. These tasks vary in latency sensitivity: real-time tasks, critical for passenger safety, require strict deadline adherence, whereas the latency of standard tasks mainly affects the user experience and has more flexible constraints. Addressing the challenge of selecting suitable edge computing nodes to enhance the offloading success rate of real-time tasks amidst a vast and heterogeneous cluster becomes crucial. This paper introduces the adaptive priority-based hierarchical task offloading (APHTO) algorithm, which optimizes task offloading strategies by accounting for the diverse latency constraints of different task types. Experiments demonstrate that under optimal performance conditions, APHTO significantly outperforms existing algorithms such as Min–Min, Max–Min, CUS, and FMS in reducing task latency by 20.31%, increasing offloading success rates by 35.83%, and improving resource utilization by 30.21%, marking a substantial advancement in task offloading strategies for autonomous driving integrated with MEC. [ABSTRACT FROM AUTHOR]

The ability to uniquely identify all nodes in a network based on network distances has proven to be highly beneficial despite the computational challenges involved in discovering minimal resolving sets within an interconnection network. A subset R of vertices of a graph G is referred to as a resolving set of the graph if each node can be uniquely identified by its distance code with respect to R, with its minimal cardinality defining the metric dimension of G. Similarly, a resolving set F ⊆ V is designated as a fault-tolerant resolving set if F \ { s } serves as a resolving set for each s ∈ F . The minimum cardinality of F represents the fault-tolerant metric dimension of G. Although determining the precise metric dimension of a given graph remains challenging, various effective techniques and meaningful constraints have been developed for different graph families. However, no notable technique has been developed to find fault-tolerant metric dimension of a given graph. Recently, Prabhu et al. have shown that each twin vertex of G belongs to every fault-tolerant resolving set of G. Consequently, the fault-tolerant metric dimension is equal to the order of the graph G if and only if each vertex of G is a twin vertex, a characterization proved in Appl Math Comput 420:126897, 2022, corrects a wrong characterization in the literature. It is also interesting to note from the above literature correction that the twin vertices are necessary to form the fault-tolerant resolving set, but determining whether they are sufficient is challenging. Evidence of this context is also discussed in this paper through the amalgamation of perfect binary trees. This article focuses on determining the exact value of the fault-tolerant metric dimension of generalized fat trees. For the amalgamation of perfect binary trees, both the metric dimension and fault-tolerant metric dimension were precisely found. [ABSTRACT FROM AUTHOR]

This paper presents a new mesh data layout for parallel I/O of linear unstructured polyhedral meshes. The new mesh representation infers coherence across entities of different topological dimensions, i.e., grid cells, faces, and points. The coherence due to cell-to-face and face-to-point connectivities of the mesh is formulated as a tree data structure distributed across processors. The mesh distribution across processors creates consecutive and contiguous slices that render an optimized data access pattern for parallel I/O. A file format using the coherent mesh representation, developed and tested with OpenFOAM, enables the usability of the software at unprecedented scales. Further implications of the coherent and sliceable mesh representation arise due to simplifications in partitioning and diminished pre- and post-processing overheads. [ABSTRACT FROM AUTHOR]

Compared to traditional traffic signal control methods, the method driven by Deep Reinforcement Learning (DRL) has shown better performance. But the problem of low sample utilization in reinforcement learning also arises. To deal with the problem, this paper presents a novel Twin Delayed Deep Deterministic Policy Gradient with Dual Buffer (TD3_DB) for traffic signal control. In the proposed framework, two experience buffers are used to store important samples and normal samples, separately, and the proportion of the two buffers is adjusted adaptively. In addition, lane pressure, describing the dynamic feature of lane traffic flow, is used for the state design of the TD3 agent, which enhances the perception of the agent toward intersections. Comprehensive experiments on different traffic flow modes has shown, the dual experience replay scheme can improve the sample utilization, and the proposed TD3_DB performs better than other methods such as original TD3, Proximal Policy Optimization (PPO), etc., effectively reducing vehicle queue length and waiting time. [ABSTRACT FROM AUTHOR]

The prediction of power equipment activity plays a vital role in optimizing power resource dispatch, ensuring supply and demand balance, and guiding network planning and management. However, due to the complex nonlinear, multi-scale, and multivariate characteristics of power grid communication networks service activity (PCNSA) data, it is often challenging to capture its intrinsic patterns and dynamic changes through a simple model. To address this issue, this paper proposes a power grid communication network service activity prediction method based on convolutional neural network, bidirectional gated recurrent unit, and attention mechanism, referred to as CBGA. CNN is used to extract features from time-series data, BiGRU is used to capture long-term feature changes in the data, and the attention mechanism is used to enhance the extraction of important information. To validate the performance of the proposed method, experiments and comparisons were conducted on three real power grid communication network datasets, demonstrating that our method shows better performance and robustness. [ABSTRACT FROM AUTHOR]

Evolutionary multitask optimization (EMTO) has developed fast recently, and many algorithms have emerged that solve several different problems simultaneously through knowledge transfer. Most algorithms use a single search operator in their processing. However, different tasks have distinct characteristics, and a single operator is often inadequate to adapt to different stages of the same task. In this paper, we propose a multiple search operator selection strategy by adaptive probability allocation, named adaptive multi-operator selection (AMOS) to address EMTO that features rapid convergence of populations. It can automatically select the best multiple search operators based on the characteristics of specific tasks and different stages of evolution. The primary contributions of the proposed algorithm are as follows: (1) It combines the basic concepts of multi-operator integration and adaptive search operator selection to select the best multiple search operators for each task at different evolutionary stages; (2) It facilitates the knowledge transfer through different solving operators between tasks; (3) It can be flexibly embedded into various frameworks of general EMTO algorithms with good results. In the experiments, we validate the performance of AMOS on CEC2017 benchmark suite, CMTOPs benchmark suite, and real-world EMTO problems, and experimental results demonstrate the effectiveness and generality of the proposed strategy. [ABSTRACT FROM AUTHOR]

The layout optimization of wind turbines seeks to improve wind power conversion efficiency by minimizing the wake effect in wind farm. However, the existing optimization methods cannot provide a layout with high output power due to their inability to weaken the energy losses caused by the wake effect between turbines. In this paper, an equilibrium optimizer with local perturbation and adaptive escape strategies, termed adaptive equilibrium optimizer (AEO), is proposed to tackle the wind farm layout optimization problem (WFLOP). To be specific, the worst turbines in the layout are re-located to better locations to reduce wind losses by enhancing the exploitation capacity of AEO with a local perturbation strategy. Then, an adaptive escape strategy supports the particles to get out of the local optimum and to search toward more promising areas, which facilitates the adjustment of the layout to reduce the wake effect. Besides, an adaptive repair operator adaptively reconfigures infeasible layouts to augment the proportion of feasible solutions in the population; thus, AEO can sufficiently explore the layout space to optimize the output power of wind farms. Twelve prohibited area constraints, three turbine scales, and three wind farm sizes are utilized to evaluate the performance of AEO in two cases with various wind speeds and directions. The experimental results demonstrate that the AEO performs excellently in terms of efficiency, robustness, and generalization for the layout optimization of turbines with different constraints, scales, and wind farm sizes in comparison with the state-of-the-art metaheuristics and other methods reported in the literature. [ABSTRACT FROM AUTHOR]