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Over the years the number of research publications per year is growing exponentially. Finding research papers of quality from the massive literature of relevant articles is a challenging and time-consuming task. The approaches in the latest literature address citation recommendation by utilizing large bibliographic information and use machine learning and deep learning methods for the task. These techniques clearly require a large amount of training data as well as machines with high processing power. To overcome these issues, we propose a novel method by modifying the popular hyperlink induced topic search (HITS), a web page ranking algorithm, as citation recommendation using hyperlink induced topic search (CR-HITS) that works on a directed and weighted heterogeneous bibliographic network containing diverse types of nodes and edges. We define effective scoring schemes for nodes and edges based on basic bibliographic information like citations of papers, number of publications of an author, etc. Given a few seed papers, the citation recommendation algorithm CR-HITS is run on small neighborhoods of the seed papers and hence the time taken by the execution is very small to yield the final recommendations. To the best of our knowledge, HITS has been used for the first time for the citation recommendation problem. We perform extensive experimentation on DBLP (version-11) and ACM (version-9) datasets and compare the results with many baseline methods in terms of MAP, MRR, and recall@N measures. The performance of the proposed algorithms is superior with respect to the MAP metric and matches the second best for the other two metrics. Since the top two algorithms use deep learning methods and use much larger bibliographic information including abstracts of the papers, we claim that our approach utilizes very low resources, yet yields recommendations that are very close to the top recommendations. [ABSTRACT FROM AUTHOR]

In recent years, Sine Cosine Algorithm (SCA) is a kind of meta-heuristic optimization algorithm with simple structure, simple parameters and trigonometric function principle. It has been proved that it has good competitiveness among the existing optimization algorithms. However, the single mechanism of SCA leads to its insufficient utilization of the information of the whole population, insufficient ability to jump out of local optima and poor performance at solving complex objective function. Therefore, this paper introduces social learning strategy (SL) and elite opposition-based learning (EOBL) strategy to improve SCA, and proposes novel algorithm: enhancing Sine Cosine Algorithm based on elite opposition-based learning and social learning (ESLSCA). Social learning strategy takes full advantage of information from the entire population. The elite opposition-based learning strategy provides a possibility for the algorithm to jump out of local optima and increases the diversity of the population. To demonstrate the performance of ESLSCA, this paper uses 22 well-known benchmark test functions and CEC2019 test function set to evaluate ESLSCA. The comparisons show that the proposed ESLSCA has better performance than the standard SCA and it is very competitive among other excellent optimization algorithms. [ABSTRACT FROM AUTHOR]

The issue of traffic mismanagement is getting bad to worse as the trend of urbanization is increasing and posing significant threats to the urban areas. This paper addresses this issue of traffic mismanagement by proposing a Fog-Cloud centric Internet of Things (IoT)-based collaborative framework that enables machine learning-based situation-aware traffic management. This framework monitors the traffic dynamics at the fog layer for providing real-time analytics to the cloud and enabling the traffic situation-aware routing of the vehicles. Whereas the framework at the cloud layer determines the situation-aware analytics by predicting Waiting Time (WaT), Waiting Queue Length (WQueL) and next green phase duration (G) for each Traffic Movement Signal Point (TMSP) based on the real-time data and analytics provided by the fog layer. The road-infrastructure hosted fog layer classifies the In-schedule Reachability Status (IRS) that determines the ability of the vehicle to reach the respective TMSP down the lane before the start of the next green phase. After classifying all such vehicles on a lane by the fog layer, the cloud layer predicts the optimal duration of the next green phase at that respective TMSP to reduce the traffic congestion on the respective junction point and subsequently provides these area-wide analytics to the vehicle hosted fog nodes and traffic controllers on the respective junction points. The vehicles use these analytics for enabling situation-aware vehicle routing functionality by selecting a time optimized path and enabling balanced traffic load on all possible paths. The framework employs Logistic Regression (LR) for IRS classification at the fog layer and Artificial Neural Network (ANN) for traffic prediction at the respective TMSP at the cloud layer. The result depiction acknowledges LR's efficiency compared to other employed classifiers in terms of various statistical parameters. The optimal performance of ANN at the learning rate (LeR) of 0.1, momentum rate (MoR) of 0.95, and 500 epochs depict the prediction efficiency of ANN as compared to other used prediction approach. The contribution of this paper is two functionalities-based working of fog computing for providing real-time and situation-aware traffic analytics, adaptive traffic movement phase planning, time optimized navigation, and optimal traffic load balancing. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) often operate in hostile environments and are subject to frequent failures. Failure of multiple sensor nodes causes the network to split into disjoint segments, which leads to network partitioning. Federating these disjoint segments is necessary to prevent detrimental effects on WSN applications. This paper investigates a recovery strategy using mobile relay nodes (MD-carrier) for restoring network connectivity. The proposed MD-carrier Tour Planning (MDTP) approach restores network connectivity of partitioned WSNs with reduced tour length and latency. For this reason, failure nodes are identified, and disjoint segments are formed with the k-means algorithm. Then, the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are used for the election of an AGgregator Node (AGN) for each segment. Furthermore, an algorithm for identifying sojourn locations is proposed, which coordinates the maximum number of AGNs. Choosing the sojourn locations is a challenging task in WSN since the incorrect selection of the sojourn locations would degrade its data collection process. This paper uses the nature-inspired meta-heuristic Donkey And Smuggler Optimization (DASO) algorithm to compute the optimal touring path. MDTP reduces tour length and latency by an average of 30.28% & 24.56% compared to existing approaches. [ABSTRACT FROM AUTHOR]

To ensure the reliability and operational efficiency of the grid system, this paper proposes an unmanned aerial vehicle (UAV)-assisted Power Internet of Things (PIoT), which obtains real-time grid data through PIoT devices to support the management optimization of the grid system. Compared with traditional UAV-assisted communication networks, this paper enables data collection and energy transmission services for PIoT devices through UAVs. Firstly, the flight-hover-communication protocol is used. When the UAVs approach the target devices, they stop flying and remain hovering to provide services. The UAV selects full duplex mode in the hovering state, i.e., within the coverage area of the UAV, it can collect data from the target device while providing charging for other devices. Secondly, the UAVs can provide services to the required devices in sequence. Considering the priorities of the devices, both the data queue state and the energy pair state of network devices are considered comprehensively. Therefore, the optimization problem is constructed as a multi-objective optimization problem. First, the multi-objective optimization problem is transformed into a Markov process. Then, a multi-objective dynamic resource allocation algorithm based on reinforcement learning is proposed for solving the multi-objective optimization problem. The simulation results show that the proposed resource allocation scheme can effectively achieve a reasonable allocation of UAV resources, joint multi-objective optimization, and improved system performance. [ABSTRACT FROM AUTHOR]

Influence Maximization (IM) is an important topic in the field of social network analysis, and is widely used in viral marketing, recommendation systems, rumor prevention and other fields. The meta-heuristic method has excellent performance because of high scalability and low complexity, however, the objective function of existing meta-heuristic methods can only be applied to propagation models with small probabilities. In addition, there is room for further improvement in performance of meta-heuristic methods. In order to solve the above problems, this paper transforms the influence maximization problem into an optimization problem and designs a novel objective function based on the six degrees of separation theory of social networks. Then, with the designed objective function, this paper discretizes the harris hawks optimization (HHO) algorithm by redefining the energy and location representation rules for influence maximization problem. Experimental results on eight real datasets demonstrate that the proposed objective function exhibits high accuracy and generality, suitable for various probability propagation models. With the exploration and exploitation process in steps, dynamically using different strategies various situations, the proposed DHHO algorithm exhibits better performance in dealing with influence maximization problem, outperforming the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Service composition is widely employed in cloud manufacturing. Due to the abundance of similar cloud manufacturing services, the search space for optimizing service composition tends to be expansive. Existing optimization models primarily focus on QoS (quality of service) while often neglecting QoC (quality of collaboration). Furthermore, there remains scope for improving the quality and stability of service composition optimization. Therefore, this paper proposes a two-phase method for optimizing service composition in cloud manufacturing. In the first phase, we introduce a service cluster-oriented service response framework, efficiently generating the candidate response service set to reduce solution search space. In the second phase, we construct an optimization model that integrates QoS and QoC. Subsequently, we devise an artificial bee colony (ABC) algorithm incorporating a multi-search strategy island model to optimize cloud manufacturing service composition. Experimental results demonstrate that the introduction of service clusters enhances search efficiency, with the proposed method outperforming compared ABC algorithms and other swarm intelligence algorithms in optimization quality and stability. [ABSTRACT FROM AUTHOR]

This paper proposes a Robust Spatial-Temporal Graph Neural Network (RSTGNN), which overcomes the limitations faced by graph-based models against dynamic graph perturbations using robust spatial-temporal self-attentions to learn dynamic graph embeddings. In the RSTGNN model training, a selective spatial self-attention technique is employed to aggregate neighboring information based on projected node similarity, which reduces attention weights of edges with less similarity, enabling better information aggregation and preventing the model from ignoring spatial-temporal information. The temporal self-attention layer in the RSTGNN model intensifies temporal patterns using time-span-limited temporal attention weights. Additionally, the model uses a spatial-temporal loss function that penalizes nodes and edges most likely perturbed to alleviate the influence of dynamic graph perturbation. Specifically, the spatial loss focuses on attention weights associated with high-degree and potentially-attacked nodes, while the temporal loss targets attention weights of high centrality-varied nodes to prevent nodes from experiencing excessive centrality changes. To verify the effectiveness of our approach, we evaluate RSTGNN compared with other graph-based models under different node-based or edge-based perturbation rates. Results demonstrate that RSTGNN maintains high effectiveness in dynamic node classification and link prediction for five real dynamic graph datasets. [ABSTRACT FROM AUTHOR]

Swarm intelligence (SI) algorithms represent a class of Artificial Intelligence (AI) optimization metaheuristics used for solving complex optimization problems. However, a key challenge in solving complex problems is maintaining the balance between exploration and exploitation to find the optimal global solution and avoid local minima. This paper proposes an innovative Swarm Intelligence (SI) algorithm called the Four Vector Intelligent Metaheuristic (FVIM) to address the aforementioned problem. FVIM's search strategy is guided by four top-performing leaders within a swarm, ensuring a balanced exploration-exploitation trade-off in the search space, avoiding local minima, and mitigating low convergence issues. The efficacy of FVIM is evaluated through extensive experiments conducted over two datasets, incorporating both qualitative and quantitative statistical measurements. One dataset contains twenty-three well-known single-objective optimization functions, such as fixed-dimensional and multi-modal functions, while the other dataset comprises the CEC2017 functions. Additionally, the Wilcoxon test was computed to validate the result's significance. The results illustrate FVIM's effectiveness in addressing diverse optimization challenges. Moreover, FVIM has been successfully applied to tackle engineering design problems, such as weld beam and truss engineering design. [ABSTRACT FROM AUTHOR]

The smart healthcare system advancements have introduced the Internet of Things, enabling technologies to improve the quality of medical services. The main idea of these healthcare systems is to provide data security, interaction between entities, efficient data transfer, and sustainability. However, privacy concerning patient information is a fundamental problem in smart healthcare systems. Many authentications and critical management protocols exist in the literature for healthcare systems, but ensuring security still needs to be improved. Even if security is achieved, it still requires fast communication and computations. In this paper, we have introduced a new secure privacy-enhanced fast authentication key management scheme that effectively applies to lightweight resource-constrained devices in healthcare systems to overcome the issue. The proposed framework is applicable for quick authentication, efficient key management between the entities, and minimising computation and communication overheads. We verified our proposed framework with formal and informal verification using BAN logic, Scyther simulation, and the Drozer tool. The simulation and tool verification shows that the proposed system is free from well-known attacks, reducing communication and computation costs compared to the existing healthcare systems. [ABSTRACT FROM AUTHOR]

The optimal foraging algorithm (OFA) is a swarm-based algorithm motivated by animal behavioral ecology theory. When solving complex optimization problems characterized by multiple peaks, OFA is easy to get trapped in local minima and encounters slow convergence. Therefore, this paper presents an improved optimal foraging algorithm with social behavior based on quasi-opposition (QOS-OFA) to address these problems. First, quasi-opposition-based learning (QOBL) is introduced to improve the overall quality of the population in the initialization phase. Second, an efficient cosine-based scale factor is designed to accelerate the exploration of the search space. Third, a new search strategy with social behavior is designed to enhance local exploitation. The cosine-based scale factor is used as a regulator to achieve a balance between global exploration and local exploitation. The proposed QOS-OFA is compared with seven meta-heuristic algorithms on a CEC benchmark test suite and three real-world optimization problems. The experimental results show that QOS-OFA is better than other competitors on most of the test problems. [ABSTRACT FROM AUTHOR]

Heterogeneous Information Networks (HINs) consist of multiple categories of nodes and edges and encompass rich semantic information. Representing HINs in a low-dimensional feature space is challenging due to its complex structure and rich semantics. In this paper, we focus on link prediction and node classification by learning efficient low-dimensional feature representations of HINs. Metapath-guided walkers have been extensively studied in the literature for learning feature representations. However, the metapath walker does not control the length of random walks, resulting in weak structural and semantic information embeddings. In this work, we present an influence propagation controlled metapath-guided random walk model (called IPCMetapath2Vec) for representation learning in HINs. The model works in three phases: first, we perform node transition to generate a metapath-guided random walk, which is conditioned on two factors: (i) type mapping of the next node according to the metapath, and (ii) compute influence propagation score for each node and detect potential influencers on the walk by a threshold based filter. Next, we provide the collected random walks as input to the skip-gram model to learn each node's feature representation. Lastly, we employ an attention mechanism that aggregates the learned feature representations of each node from various semantic metapath-guided walks, preserving the importance of different semantics. We use these network representation features to address link prediction and multi-label node classification tasks. Experimental results on two public HIN datasets, namely DBLP and IMDB, show that our model outperforms the state-of-the-art representation learning models such as DeepWalk, Node2vec, Metapath2Vec, and HIN2Vec by 4.5% to 17.2% in terms of micro-F1 score for multi-label node classification and 4% to 14.50% in terms of AUC-ROC score for link prediction. [ABSTRACT FROM AUTHOR]

To manage the IoT resources to process the massive amount of collected data in complex systems, Allocation of tasks to nodes to increase user satisfaction has been noticed by researchers. In allocating tasks, the necessity of a powerful auxiliary platform is felt. Cloud and fog networks are two suitable options for performing and completing tasks on the Internet of Things. In the cloud network, due to offloading tasks to remote computing centers, problems such as increasing complete time, increasing traffic in the network, and high energy consumption for the Internet of Things network occur. The fog network solves the problem of completing tasks due to reducing the geographical distance between Internet of Things devices and processor nodes. The devices in the fog network cooperate with the Internet of Things network's diverse and variable nodes with time computing, storage, and processing resources. In this heterogeneous environment, the non-cooperation and dishonesty of malicious fog nodes through misrepresentation of shared resources and non-completion of tasks can disrupt the performance of the Internet of Things network. As a result, two fundamental challenges in the heterogeneous fog network include providing resources to allocate tasks and recognizing valid fog nodes. This paper focuses on creating a resource provider network to allocate tasks to authentic fog nodes with appropriate resources. When an Internet of Things node faces a shortage of resources and link prediction, it can form a resource supply network with neighboring nodes to solve the two stated challenges. For this purpose, in the first stage, efforts are made to select the most appropriate options among the neighboring nodes based on specific criteria. In the second step, reliable nodes are identified based on a trust mechanism, including resource trust and entity trust. In the last step, tasks are divided among reliable devices with sufficient resources. The evaluation of the proposed method in the OMNET++ simulator shows that the trust mechanism can lead to the identification of reliable sources and, as a result, reduce the time to complete tasks. [ABSTRACT FROM AUTHOR]

Cloud data centers face various challenges, such as high energy consumption, environmental impact, and quality of service (QoS) requirements. Dynamic virtual machine (VM) consolidation is an effective approach to address these challenges, but it is a complex optimization problem that involves trade-offs between energy efficiency and QoS satisfaction. Moreover, the workload patterns in cloud data centers are often non-stationary and unpredictable, which makes it difficult to model them. In this paper, we propose a new method for dynamic VM consolidation that optimizes both energy efficiency and QoS objectives. Our approach is based on Markov chains and the artificial feeding birds (AFB) algorithm. Markov chains are used to model the resource utilization of each individual VM and PM based on the changes that happen in workload data. AFB algorithm is a metaheuristic optimization technique that mimics the behavior of birds in nature. We modify the AFB algorithm to suit the characteristics of the VM placement problem and to provide QoS-aware and energy-efficient solutions. Our approach also employs an online step detection method to capture variations in workload patterns. Furthermore, we introduce a new policy for VM selection from overloaded hosts, which considers the abrupt changes in the utilization processes of the VMs. The proposed algorithms are evaluated extensively using the CloudSim Toolkit with real workload data. The proposed system outperforms evaluation policies in multiple metrics, including energy consumption, SLA violations, and other essential metrics. [ABSTRACT FROM AUTHOR]

The exposure of zero trust security in the Industrial Internet of Things (IIoT) increased in importance in the era where there is a huge risk of injection of malicious entities and owning the device by an unauthorized user. The gap in the existing approach of zero trust security is that continuous verification of devices is a time-consuming process and adversely affects the promising nature of the zero-trust model. Every time the node enters, even if the node is a member of the network, authorization of the node is necessary to ensure authenticity. This verification section of zero trust hinders the seamless working of the IIoT infrastructure. Therefore, the main objective of this paper is to propose the solution for the above-mentioned problem by enabling "device profiling" via deep reinforcement learning so that the same device can be identified and permitted access without hindering the working of Industrial Internet of Things infrastructure. The overall proposed approach works in different phases including the compression function for ensuring data confidentiality and integrity, then the device profiling is performed based on the features a device possesses, and lastly, deep reinforcement learning for anomaly detection. To test and validate the proposed approach, extensive experimentations were performed using measures such as false positive rate, data confidentiality rate, data integrity rate, and network access time, and results showed that the proposed technique titled "MMODPAD-DRL" outperforms the existing approaches in false positive rate by 27%, data confidentiality rate by 4% and data integrity rate by 3%, in addition, lessen the network access time by 20%. [ABSTRACT FROM AUTHOR]

The proliferation of available Web services presents a big challenge in selecting suitable services. Various methods have been devised to predict Quality of Service (QoS) values, aiming to address the service selection problem. However, these methods encounter numerous limitations that hinder their prediction accuracy. A key issue stems from the dynamic nature of the service environment, leading to fluctuations in QoS values due to factors like network load and hardware issues. To mitigate these challenges, QoS selection methods have leveraged contextual information from the surrounding environments, such as service invocation time, user, and service locations. Among these methods, Collaborative Filtering (CF) has gained notable importance. In recent years, several CF methods have incorporated service invocation time into their prediction processes, giving rise to what is commonly known as time-aware CF methods. Despite the increasing adoption of time-aware CF methods, there remains a notable absence of a dedicated and comprehensive literature review on this topic. Addressing this gap, this paper conducts an analysis of the literature, reviewing the forty (40) most prominent studies in this domain. It offers a thematic categorization of these studies along with an insightful analysis outlining their objectives, advantages, and limitations. The review also identifies key research gaps and proposes potential directions for future investigations. Overall, this literature review serves as an up-to-date resource for researchers engaged in service-oriented computing research. [ABSTRACT FROM AUTHOR]

The motivation of task scheduling in heterogeneous computing systems is the optimal management of heterogeneous distributed resources as well as the exploitation of system capabilities. Energy consumption is one of the most important issues in dealing with task scheduling in heterogeneous distributed systems. In addition to energy, the task completion time and the task cost have also been added to the concerns of the users. Since the nature of computing systems is heterogeneous and dynamic, task scheduling with traditional methods is inefficient. Meta-heuristic approaches for task scheduling in heterogeneous distributed systems are open problems that have attracted the attention of researchers. So far, many meta-heuristic approaches have addressed the task scheduling problem. However, most of these algorithms are developed for homogeneous systems and optimize only one of the quality-of-service parameters. With this motivation, this paper presents an optimization for energy-aware design of task scheduling in heterogeneous distributed systems using meta-heuristic approaches. We simultaneously consider several parameters such as energy, task completion time and task execution cost for task scheduling. The Harris Hawk Optimization (HHO) algorithm is considered for the optimization task due to its adaptability to large search spaces. We combine HHO with a greedy algorithm to avoid local optima and early convergence. The evaluation of the proposed method has been done through numerical simulations. Experimental results show promising performance of the proposed method in terms of energy consumption. [ABSTRACT FROM AUTHOR]

Influence maximization (IM) problem for messages propagation is an important topic in mobile social networks. The success of the spreading process depends on the mechanism for selection of the influential user. Beside selection of influential users, the computation and running time should be considered in this mechanism to ensure the accurecy and efficient. In this paper, considering that the overhead of exact computation varies nonlinearly with fluctuations in data size, random algorithm with smoother complexity change was designed to solve the IM problem in combination with greedy algorithm. Firstly, we proposed a method named two-hop neighbor network influence estimator to evaluate the influence of all nodes in the two-hop neighbor network. Then, we developed a novel greedy algorithm, the random walk probability cost-effective with lazy-forward (RWP-CELF) algorithm by modifying cost-effective with lazy-forward (CELF) with random algorithm, which uses 25–50 orders of magnitude less time than the state-of-the-art algorithms. We compared the influence spread effect of RWP-CELF on real datasets with a theoretically proven algorithm that is guaranteed to be approximately optimal. Experiments show that the spread effect of RWP-CELF is comparable to this algorithm, and the running time is much lower than this algorithm. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet of Things (IoT) networks provide benefits to humans in numerous domains by empowering the projects of smart cities, healthcare, industrial enhancement and so forth. The IoT networks include nodes, which deliver the data to the destination. However, the network nodes' connectivity is affected by the nodes' removal caused due to the malicious attacks. The ideal plan is to construct a topology that maintains nodes' connectivity after the attacks and subsequently increases the network robustness. Therefore, for constructing a robust scale-free network, two different mechanisms are adopted in this paper. First, a Multi-Population Genetic Algorithm (MPGA) is used to deal with premature convergence in GA. Then, an entropy based mechanism is used, which replaces the worst solution of high entropy population with the best solution of low entropy population to improve the network robustness. Second, two types of Edge Swap Mechanisms (ESMs) are proposed. The Efficiency based Edge Swap Mechanism (EESM) selects the pair of edges with high efficiency. While the second ESM named as EESM-Assortativity, transforms the network topology into an onion-like structure to achieve maximum connectivity between similar degree network nodes. Further, Hill Climbing (HC) and Simulated Annealing (SA) methods are used for optimizing the network robustness. The simulation results show that the proposed MPGA Entropy has 9% better network robustness as compared to MPGA. Moreover, both the proposed ESMs effectively increase the network robustness with an average of 15% better robustness as compared to HC and SA. Furthermore, they increase the graph density as well as network's connectivity. [ABSTRACT FROM AUTHOR]

Multi-workflows are commonly deployed on cloud platforms to achieve efficient computational power. Diverse task configuration requirements, the heterogeneous nature and dynamic electricity price of cloud servers impose significant challenges for economically scheduling multi-workflows. In this paper, we propose a Heuristic Electricity-cost-aware Multi-workflow Scheduling algorithm (HEMS) to search for an optimal scheduling plan which determines the optimal scheduling scheme for each task in each workflow, specifying the server to perform the task with determined resources in specific time. The objective is to minimize the total electricity cost of all servers while satisfying the deadline constraints of all workflows. The HEMS algorithm consists of five components: Workflow Scheduling Sequence Generation, Task Scheduling Sequence Initialization for each workflow, Optimal Scheduling Scheme Determination for each task, initial Task Scheduling Sequence Optimization, and Optimal Scheduling Plan Optimization. Experimental results demonstrate that HEMS consistently achieves the optimal scheduling plan with the lower total electricity cost (saving 54.5–69.1% on average) within slightly longer CPU time for various multi-workflows compared to existing three scheduling approaches. [ABSTRACT FROM AUTHOR]

Deep learning has shown superior performance in various applications. The emergence of graph convolution neural networks (GCNs) enables deep learning to learn the latent representation from graph-structured data with rich attributes. To be specific, the message passing mechanism of GCNs can aggregate and update messages through the topological relationship between nodes in a graph. The graph attention network (GAT) introduces the attention mechanism into GCNs when aggregating messages and achieves significant performance on the node classification task. However, focusing on each node in the neighborhood, GAT becomes extremely complex. In addition, although stacking network layers could obtain a wider receptive field, it also brings high time cost and leads to the difficulty of training. To handle this problem, this paper only divides the messages into two types, i.e. self message and neighborhood message. The neighborhood message comes from the neighborhood with(out) self-loop while the self message comes from the node itself. Then, we design a light attention mechanism that only focuses on two weights, one for the self message, and the other for the neighborhood message, to adaptively reveal the different contributions of messages from a node as well as its neighborhood. In addition, we also adopt linear propagation, a shallow and efficient method, to aggregate messages from distant neighbors and thus obtain a wider neighborhood receiving field. To verify the effectiveness of our proposed approach, extensive experiments have been conducted on the semi-supervised node classification task. Results show that our proposed approach achieves comparable or even better performance than the baseline methods with complicated GCN structures on the benchmark datasets. Specifically, the proposed light attention mechanism focusing on message passing exhibits a great efficiency improvement with the training time cost less than half of GAT. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) aka V2X is a growing area of research that aims at information exchange between vehicles and all other related objects to develop intelligent transportation systems. IoVs are characterized by high mobility, high-speed internet, varying node density, and dynamic topology and aim to minimize and communicate situations like traffic congestion, accidents, etc. Discovering a routing path in a highly unstable network environment to ensure the successful delivery of packets with minimal overheads. Finding reliable routing links as against shortest path routing is a necessity in IoV networks. In this paper, a routing protocol coined as the dynamic opportunistic routing protocol for IoV (DORP-IoV) is presented. DORP-IoV is an on-demand position-based protocol that seeks the advantage of wireless broadcast advantage to select a hop close to the virtual line of sight between source and destination for communicating the information to the destination. Vehicle movement direction and vehicle density around the ideal hop location are also considered while selecting the next hop for information forwarding. Communicating the information through an optimal number of intermediate nodes is the novelty of the work which ensures better packet delivery with minimized delay and routing overheads. The performance of DORP-IoV is evaluated and results are compared with the performance of Weighted- Greedy Perimeter Coordinator Routing (W-GPCR), Greedy Perimeter Coordinator Routing, and Greedy Perimeter Stateless Routing for varying node density and network connections for various metrics. DORP-IoV shows an improved performance in the range of 8–12% for packet delivery with similar performance for average end-to-end delay compared to W-GPCR. The optimal hop selection mechanism in DORP-IoV reduces the number of hops by 10–30% compared to W-GPCR. [ABSTRACT FROM AUTHOR]

Virtual Machine Placement (VMP) plays a significant role in improving efficiency of Cloud Data Center (CDC). With the dramatic increase in the use of cloud computing, it seems necessary to apply effective algorithms to reduce the power consumption of CDC. VMP is known as a NP-Hard problem that cannot be solved by deterministic algorithms in polynomial time. In this paper, an algorithm named Combinated Random Best First Fit (CRBFF) is proposed with the aim of increasing the Quality of Service (QoS), in which Virtual Machines (VMs) are optimally placed on heterogeneous Physical Machines (PMs). The effectiveness of CRBFF is evaluated by different metrics on Google Compute Engine (GCE), Amazon Web Service Elastic Compute Cloud (AWS EC2) and Microsoft Azure scenarios and the results show that CRBFF performs better than other common algorithms. [ABSTRACT FROM AUTHOR]

The identification of vital spreaders in complex networks has been one of the most interesting topics in network science. Several methods were proposed to deal with this challenge, but there still exist deficiencies in previous methods, such as excessive time complexity, inadequate accuracy of recognition results after dividing the topological structure, and the ignorance of neighbors' attribute information in the links' significance model. To address these issues and promote identifying ability more effectively, the proposed extended centrality upon hybrid information, named EISMC, introduces the interpretative structure model (ISM) and improves hierarchical weight results after the division in hierarchies. Based on the hierarchical structure of Improved Kshell decomposition (IKs), the weight value of each layer is updated, and meanwhile the local centrality under link significance (LinkC) is created to supplement local features in this method. In this paper, six real-world networks and nine comparison methods are applied to conduct a series of simulations and tests. Results demonstrate that the proposed method outperforms state-of-the-art algorithms in the identifying effects for good spreading influence. [ABSTRACT FROM AUTHOR]

Set computation methods have been widely used to compute reachable sets, design invariant sets and estimate system state for dynamic systems. The wrapping effect of such set computation methods plays an essential role in the accuracy of their solutions. This paper studies the wrapping effect of existing interval, zonotopic and polytopic set computation methods and proposes novel approaches to reduce the wrapping effect for these set computation methods based on the task of computing the dynamic evolution of a nonlinear uncertain discrete-time system with a set as the initial state. The proposed novel approaches include the partition of a polytopic set via Delaunay triangulation and also the representation of a polytopic set by the union of small zonotopes for the following set propagation. The proposed novel approaches with the reduced wrapping effect has been further applied to state estimation of a nonlinear uncertain discrete-time system with improved accuracy. Similar to bisection for interval and zonotopic sets, Delaunay triangulation has been introduced as a set partition tool for polytopic sets, which has opened new research directions in terms of novel set partition, set representation and set propagation for reducing the wrapping effect of set computation. [ABSTRACT FROM AUTHOR]

In this paper, we develop computing machinery within the framework of the String Multiset Rewriting calculus (SMSR), as defined by Barbuti et al. [4], to solve the SAT problem in linear time regarding the number of variables of a given conjunctive normal form. This shows that SMSR can be considered a computational model capable of significantly reducing the time requirement of classical decision problems. [ABSTRACT FROM AUTHOR]

The unknown behavior of GPUs and the differing characteristics among their generations present a serious challenge in the analysis and optimization of programs in these processors. As a result, performance models have been developed to better analyze and describe the behavior of these processors. These models help programmers to configure applications and developers to improve the performance of these devices. This paper introduces an analytical model, called Many-BSP, to predict the execution time of a CUDA kernel. This model has high portability and can easily be used on various devices. There are many GPU features and behaviors that affect performance and will be discussed, including multi-threading, coalesced access to global memory, shared memory bank conflict, dual-issue instructions, limitation of functional units, parallelism in instruction, thread and warp levels, the instruction pipeline, branch divergence, and intra-block and inter-block overlapping between communications and computations. This model also employs the tree hierarchy and parameters of the Multi-BSP model to estimate the communication latency with memory. In Many-BSP, the execution time of a kernel is predicted by static analysis of CUDA and PTX codes. The performance of the model is tested on three devices of different generations and three real-world benchmarks. The results show that the execution time of a CUDA kernel can be predicted with a maximum error of 12.33%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is constantly evolving. The variety of IoT applications has caused new demands to emerge on users' part and competition between computing service providers. On the one hand, an IoT application may exhibit several important criteria, such as deadline and runtime simultaneously, and it is confronted with resource limitations and high energy consumption on the other hand. This has turned to adopting a computing environment and scheduling as a fundamental challenge. To resolve the issue, IoT applications are considered in this paper as a workflow composed of a series of interdependent tasks. The tasks in the same workflow (at the same level) are subject to priorities and deadlines for execution, making the problem far more complex and closer to the real world. In this paper, a hybrid Particle Swarm Optimization and Simulated Annealing algorithm (PSO–SA) is used for prioritizing tasks and improving fitness function. Our proposed method managed the task allocation and optimized energy consumption and makespan at the fog-cloud environment nodes. The simulation results indicated that the PSO–SA enhanced energy and makespan by 5% and 9% respectively on average compared with the baseline algorithm (IKH-EFT). [ABSTRACT FROM AUTHOR]

With the development of big data, the problem of imbalanced data sets is becoming more and more serious. When dealing with high-dimensional imbalanced datasets, traditional classification algorithms usually tend to favor the majority class and ignore the minority class, which results in poor classification performance. In this paper, we study the issue of high-dimensional imbalanced dataset classification and propose a feature selection algorithm based on density clustering and importance measure (DBIM). DBIM firstly constructs multiple balanced subsets by randomly under-sampling the majority classes with the same number of samples as the minority classes and uses DBSCAN as the base classifier. This process quickly discovers feature distribution features based on density and generates the initial feature subspace. To select features with a strong classification of class labels, we propose to rank and select the generated initial feature subspace according to their importance. To avoid the redundancy between features and generate high-quality feature subsets, we further propose to design a new class distribution-based weight index combined with the redundancy evaluation index in the DBIM algorithm to calculate between features. Experimental results on eight publicly available datasets show that the DBIM algorithm proposed in this paper can generate feature subsets with high relevance and low redundancy, and can effectively reduce the dimensionality of high-dimensional imbalanced datasets and improve the classification performance. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is crucial for deploying a novel Artificial Intelligence (AI) model for both network and application management. However, using classical centralized learning algorithms in the IoT environment is challenging, given massively distributed private datasets. Advancements in AI have helped us solve various use cases, but it operates under two significant challenges. Firstly, the data exists in separate clusters, and secondly, the current AI has limited data privacy and security. Federated learning (FL) aims to preserve data privacy through distributed learning methods that keep the data in storage silos. Likewise, differential privacy improves data privacy by measuring the privacy loss in communication among the elements of FL. The paper proposes two adaptive approaches for making model training differentially private in a vertical federated environment. The first one uses random feature selection to train different machine learning models, and performance improvement is also proposed. The second approach uses a tree structure, i.e., Classification and Regression Trees, using some defined constraints. Further, we created a scheme to help identify malicious users/devices in a federated network cluster using parity checks for every FL iteration. [ABSTRACT FROM AUTHOR]

This paper presents an approach based on deep learning for accurate Electrocardiogram signal classification. The electrocardiogram is a significant signal in the realm of medical affairs, which gives vital information about the cardiovascular status of patients to heart specialists. Manually meticulous analysis of signals needs high and specific skills, and it is a time-consuming job too. The existence of noise, the inflexibility of signals, and the irregularity of heartbeats keep heart specialists in trouble. Cardiovascular diseases (CVDs) are the most important factor of fatality globally, which annually caused the deaths of 17.9 million people. Totally 31% of all death in the world are related to CVDs, which the age of 1/3 of patients that died because of CVDs is below 70 Because of the high percentage of mortality in cardiovascular patients, accurate diagnosis of this disease is an important matter. We present an approach to the analysis of electrocardiogram signals based on the convolutional neural network, discrete wavelet transformation with db2 mother wavelet, and synthetic minority over-sampling technique (SMOTE) on the MIT-BIH dataset according to the association for the advancement of medical instrumentation (AAMI) standards to increase the accuracy in electrocardiogram signal classifications. The evaluation results show this approach with 50 epoch training that the time of each epoch is 39 s, achieved 99.71% accuracy for category F, 98.69% accuracy for category N, 99.45% accuracy for category S, 99.33% accuracy for category V and 99.82% accuracy for category Q. It is worth mentioning that it can potentially be used as a clinical auxiliary diagnostic tool. The source code is available at https://gitlab.com/arminshoughi/ecg-classification-cnn. [ABSTRACT FROM AUTHOR]

Federated learning is a distributed machine learning approach that enables a large number of edge/end devices to perform on-device training for a single machine learning model, without having to share their own raw data. We consider in this paper a federated learning scenario wherein the local training is carried out on IoT devices and the global aggregation is done at the level of an edge server. One essential challenge in this emerging approach is IoT devices selection (also called scheduling), i.e., how to select the IoT devices to participate in the distributed training process. The existing approaches suggest to base the scheduling decision on the resource characteristics of the devices to guarantee that the selected devices would have enough resources to carry out the training. In this work, we argue that trust should be an integral part of the decision-making process and therefore design a trust establishment mechanism between the edge server and IoT devices. The trust mechanism aims to detect those IoT devices that over-utilize or under-utilize their resources during the local training. Thereafter, we introduce DDQN-Trust, a double deep Q learning-based selection algorithm that takes into account the trust scores and energy levels of the IoT devices to make appropriate scheduling decisions. Finally, we integrate our solution into four federated learning aggregation approaches, namely, FedAvg, FedProx, FedShare and FedSGD. Experiments conducted using a real-world dataset show that our DDQN-Trust solution always achieves better performance compared to two main benchmarks: the DQN and random scheduling algorithms. The results also reveal that FedProx outperforms the competitor aggregation models in terms of accuracy when integrated into our DDQN-Trust solution. [ABSTRACT FROM AUTHOR]

To bring more intelligence to edge systems, Federated Learning (FL) is proposed to provide a privacy-preserving mechanism to train a globally shared model by utilizing a massive amount of user-generated data on devices. FL enables multiple clients collaboratively train a machine learning model while keeping the raw training data local. When the dataset is horizontally partitioned, existing FL algorithms can aggregate CNN models received from decentralized clients. But, it cannot be applied to the scenario where the dataset is vertically partitioned. This manuscript showcases the task of image classification in the vertical FL settings in which participants hold incomplete image pieces of all samples, individually. To this end, the paper discusses AdptVFedConv to tackle this issue and achieves the CNN models' aim for training without revealing raw data. Unlike conventional FL algorithms for sharing model parameters in every communication iteration, AdptVFedConv enables hidden feature representations. Each client fine-tunes a local feature extractor and transmits the extracted feature representations to the backend machine. A classifier model is trained with concatenated feature representations as input and ground truth labels as output at the server-side. Furthermore, we put forward the model transfer method and replication padding tricks to improve final performance. Extensive experiments demonstrate that the accuracy of AdptVFedConv is close to the centralized model. [ABSTRACT FROM AUTHOR]

Smart objects in the Internet of Things (IoT) communicate with one another, gather information, and respond to users requests. In these systems, wireless sensors are used to collect data and monitor the environment at the lowest level. In IoT applications, wireless sensor networks play a pivotal role. Since IoT devices often use batteries, efficiency is important to them such that IoT-related standards and research efforts focus more on energy saving or conservation. In this paper, we have used two meta-heuristics algorithm for clustering and routing in IoT. We cluster the network using a clustering method called WOA-clustering based on the meta-heuristic Whale Optimization Algorithm (WOA) and select the optimal cluster heads. We then use a routing method called HHO-Routing based on the Harris Hawks Optimization (HHO) algorithm, a novel meta-heuristic algorithm, to route the cluster heads to BS. The use of the above methods results in reduced power consumption for reaching the base station (BS). Also, to prove the optimal performance of the proposed methods, these methods were simulated and compared with five different methods in a similar context. It was observed that the consumed energy, the number of survival cycles for the death of the first node, and the data transmission rate were improved. The proposed method is simulated in cooja simulator, and for a more accurate evaluation, we compare it with UCCGRA, PSO-SD, PUDCRP, EECRA, EEMRP algorithms. We see that the proposed method performs better than other methods in terms of energy consumption and network lifespan. [ABSTRACT FROM AUTHOR]

With the popularity of the Internet of Things (IoT), massive amounts of data are generated every second. By analyzing this data, attackers can launch kinds of attacks for their own profits, such as data tampering, malicious data injection, identity deception etc. To solve these problems, in this paper, we propose a Trusted Execution Environment-and-Blockchain-based data protection architecture (TEDA). In TEDA, edge devices in different IoTs maintain a consortium blockchain to achieve the secure read/write operations and verification of data together with cloud. Besides, to secure the local data processing in edge devices and manage internal light-weight devices, an Intel SGX-based module is designed. Furthermore, a new transaction structure is introduced to protect user's access pattern. The experimental results show that the space occupancy rates of write and read of TEDA with SGX are 0.84 × and 1.07 × than that of TEDA without SGX, and the time occupancy rates of write and read of TEDA with SGX are 0.94 × and 0.90 × than that of TEDA without SGX, which indicate TEDA has a good performance. [ABSTRACT FROM AUTHOR]

Smartphones have become highly personalized interfaces between people and the technology ecosystem around them. In this sense, they play a key role for a technology shift from the current Internet of Things to a future human-centric paradigm of an Internet of People, automatically adapting smart things and services to the preferences and context of their users. In this paper, we propose the use of Complex Event Processing (CEP) engines deployed in the users' smartphones granting them context-awareness capabilities in order to react to external stimulus, and enabling them to interact both with smart things and services in the surroundings of the users. With that purpose, we have designed a communication architecture that interconnects CEP engines running on smartphones, providing a framework for building applications for Mobile-based Collaborative Social Computing (MCSC). For that, we make use of previous works of the authors with Digital Avatars, a framework which promotes the use of smartphones for inferring and sharing a unique digital avatar or virtual profile of each user. The resulting framework, which we have called Collaborative CEP, allows to implement complex interactions among users, and between them and the IoT, a common need in Collaborative Social Computing applications. We provide a proof of concept based on the implementation of a Cops and Robbers game to test the expressiveness and correct functioning of the framework, and we evaluate its performance and efficiency. [ABSTRACT FROM AUTHOR]

The K-means algorithm has been successfully applied to many complex network analysis problems. However, this method is sensitive to how the first cluster centers are chosen. It is possible to minimize superfluous runs by choosing the first cluster center in advance because each run produces a unique set of results. To overcome this issue, an algorithm for Community Detection based on Transitive Closure (CoDTC) has been introduced. In this algorithm, the initial cluster center is provided by degree centrality and T-transitive closure. The algorithm initializes with the calculation of the similarity relation matrix. Then, to avoid the limitation of sparse problems in complex network analysis, we offer the idea of transitive closure on weighted networks to solve the sparsity issue. This notion is based on imposing a t-norm inequality on the connection weights and providing a method to compute them. Finally, based on T-transitive closure, new cluster centers are calculated iteratively to avoid random selection of cluster centers. In this paper, we demonstrate the efficacy of the CoDTC approach on a diverse range of real and artificial networks, encompassing both big and small communities. [ABSTRACT FROM AUTHOR]

The incompleteness of the knowledge graph limits its applications to various downstream tasks. To this end, numerous influential knowledge graph embedding models have been presented and have made great achievements in the domain of knowledge graph completion. However, most of these models only pay attention to the extraction of latent knowledge or translational features, and cannot comprehensively capture the surface semantics, latent interactions, and translational characteristics of triples. In this paper, a novel multi-channel convolutional model, MConvKGC, is presented for knowledge graph completion, which has three feature extraction channels and employs them to simultaneously extract shallow semantics, latent interactions, and translational characteristics, respectively. In addition, MConvKGC adopts an asymmetric convolutional block to comprehensively extract the latent interactions from triples, and process the generated feature maps with various attention mechanisms to further learn local dependencies between entities and relations. The results of the conducted link prediction experiments on FB15k-237, WN18RR, and UMLS indicate that our proposed MConvKGC shows excellent performance and outperforms previous state-of-the-art KGE models in the majority of cases. [ABSTRACT FROM AUTHOR]

Heterogeneous graph representation learning has strong expressiveness when dealing with large-scale relational graph data, and its purpose is to effectively represent the semantic information and heterogeneous structure information of nodes in the graph. Current methods typically use shallow models to embed semantic information on low-order neighbor nodes in the graph, which prevents the complete retention of higher-order semantic feature information. To address this issue, this paper proposes a heterogeneous graph network for higher-order semantic enhancement called HHSE. Specifically, our model uses the identity mapping mechanism of residual attention at the node feature level to enhance the information representation of nodes in the hidden layer, and then utilizes two aggregation strategies to improve the retention of high-order semantic information. The semantic feature level aims to learn the semantic information of nodes in various meta path subgraphs. Extensive experiments on node classification and node clustering on three real-existing datasets show that the proposed approach makes practical improvements compared to the state-of-the-art methods. Besides, our method is applicable to large-scale heterogeneous graph representation learning. [ABSTRACT FROM AUTHOR]

Current real-time systems applications are mostly large-scale, where tasks are intrinsically time-constrained and meaningfully dependent. Nevertheless, while the initial partitioning solution has a huge impact on scheduling results, most tasks-allocation problems are NP-complete and lack binding semantics. The scheduling analysis strategies are inevitably required to validate the system scheduling before its actual deployment. However, once the proposed scheduling is invalid, no possible corrections are provided, and a costing NP-complete partitioning regeneration is provoked as the only possible correction action. Whereas, sometimes nearby task re-allocations guarantee the scheduling correction. This paper proposes an approach to efficiently correct initial dependent task partitioning solutions while considering their allocation semantics. The ultimate objective is to conduct a correct schedule for multiprocessor real-time systems by means of a driven task re clustering and/or re-allocation without the need for extra complex partitioning regeneration. Simulation results show that our proposed approach is faster than classic partitioning regeneration, ameliorates the partitioning semantic results, and gives efficient scheduling results. [ABSTRACT FROM AUTHOR]

At present, large-scale sky surveys have obtained a large volume of stellar spectra. An efficient classification algorithm is of great importance to the practice of astronomical research. In this paper, we propose a novel parallel optimization algorithm based on a lock-free and shared-memory environment to solve the model for astronomical spectra class. Firstly, the SMOTE-TOMEK and RobustScaler are introduced to use for class balancing and data normalization. Secondly, 1-Dimensional Convolutional Neural Networks (1-D CNN) with L2-norm loss function is utilized as a classifier. Finally, LFA-SGD, LFA-Adagrad, LFA-RMSprop and LFA-Adam algorithms are proposed and applied to the classifier solution. The Lock-Free and shared-memory parallel Asynchronous environment (LFA) relies on GPU multiprocessing, allowing the algorithm to fully utilize the multi-core resources of the computer. Due to its sparsity, the convergence speed is significantly faster. The experimental results show that LFA-SGD algorithm and its variants achieved state-of-the-art accuracy and efficiency for astronomical spectra class. [ABSTRACT FROM AUTHOR]

Blockchain facilitates the education sector from its unique bundles of characteristics, like decentralization, immutability, reliability, transparency, traceability, security, and integrity. These characteristics improve the ecosystem's accountability, collaboration, credibility, identification, transparency, and trustability. Despite these attractive features and benefits, blockchain technology in education still needs to overcome many challenges that require consistent effort. This paper systematically studies leading blockchain proposals in the education sector. Our study mainly focuses on four categories: (i) Blockchain-based initiatives, (ii) Existing blockchain-based products/solutions, (iii) Applications/Use cases, benefits and challenges in the adoption of blockchain, and (iv) Future research direction in the education sector. We categorize the distinct challenges based on the Technology-Organization-Environment (TOE) framework. This article covers research articles published in eight well-known scientific databases between 2017 and the first quarter of 2023. We screen the articles with the help of the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) approach. This article also explores other educational areas that could benefit from blockchain due to its potential and attractive features. [ABSTRACT FROM AUTHOR]

Due to the evolving nature of complex networks, link prediction plays a crucial role in exploring likelihood of potential relationships among nodes. There exist a great number of strategies to apply the similarity-based metrics for estimating proximity of nodes in complex networks. In this paper, we propose three new variants – CCPAL3, LPCPA, and GPCPA – for the well-known Common Neighbor and Centrality-based Parameterized Algorithm (CCPA) taking into account 3-hop path, quasi-local path, and global path, respectively. In addition, four novel link prediction strategies based on community detection information, CCPA_CD, CCPAL3_CD, LPCPA_CD and GPCPA_CD, are proposed. Meanwhile, the Jaccard index is extended to three new metrics, i.e., Jaccard_L3, Jaccard_QuasiLoc and Jaccard_Global. Extensive experiments are conducted on thirteen real-world networks. The experimental results indicate that the proposed metrics improve the prediction accuracy measured by AUC and are more competitive on Precision compared to the state-of-the-art link prediction methods. [ABSTRACT FROM AUTHOR]

The topological relationship of spatial data is essential to GIS data processing and spatial analysis such as in analysis of pipe explosion in gas pipeline network. The existing browser-side JavaScript topology check library is inefficient and even crashes when checking the pipe network topology relationships for large amounts of data. In this paper, we present a topology checking and optimization method for pipeline vector data in browser-side using quadtree. Firstly, an algorithm mechanism that conforms to GIS data is designed based on JavaScript shared memory mechanism, topological check algorithm characteristics, and spatial data high-precision characteristics. Then using a fast rejection experiment and straddle test to realize the browser-side topology checking algorithm, through tolerance setting, improve the inspection efficiency and accuracy, which solves the problem that Turf and Jsts libraries cannot set tolerance. Based on the concept of quadtree spatial index, an optimization method of browser-side quadtree topology checking algorithm(BQTCA) is proposed. Without setting tolerance, the topology check of 114 point data and 1881 line data takes 487 milliseconds, and the efficiency of BQTCA is about 12 times and 39 times higher than that of the well-known public libraries Turf and Jsts, respectively. When the data volume increases to 912 point data and 15048 line data, BQTCA takes 6970 ms, which is about 65 times and 190 times more efficient than Turf and Jsts, respectively. The larger the data volume is, the more pronounced the efficiency improvement of BQTCA. Even when the data volume is so large that Turf and Jsts can- not calculate even crash, BQTCA can still complete the checking calculation. Through experiments, BQTCA can significantly improve the efficiency of browser-side vector pipeline topology relationship inspection under a large amount of data, and meet the commercial application requirements. [ABSTRACT FROM AUTHOR]

With recent developments in cloud computing, massive unplanned traffic loads are submitted to cloud platforms. High traffic load variations lead to uncertainty in resource utilization. Therefore, efficient data-driven mechanisms for automatic resource management become crucial. These mechanisms enable complex and distributed systems to anticipate and efficiently react to workload fluctuations. They rely on accurate resource utilization prediction techniques to satisfy the resource needs, in order to fulfill the service level objective for cloud applications and infrastructures. In this paper, we propose a deep learning model to predict the resource consumption (e.g., CPU, memory) in network function virtualization infrastructures. We model an augmented graphical neural network (GNN) that exploit neighbouring relationships between virtual network functions (VNF) composing various service function chains (SFC), and use an augmented feature vector allowing to capture the consumption evolution of a VNF. The model enables to predict the resource needs of VNFs by identifying the multidimensional dependencies according to the graph structure of an SFC. The proposed GNN model has been compared with MLP, LSTM, hybrid LSTM and CNN models to evaluate its accuracy and efficiency. Real word datasets have been used to evaluate the proposed model using five performance metrics. The performance analysis reveals that our graph-features based GNN model outperforms the other models for SFCs with high traffic load variation. [ABSTRACT FROM AUTHOR]

Corporate social responsibility (CSR) has gained a great deal of interest in recent years due to the need for information that can help many stakeholders (e.g., governments, investors, professional organizations, researchers, etc.) understand companies' contributions to the environment and society. CSR disclosure (CSRD) is now the key source of such information when analyzing, for example, an institution's future performance. In the current body of CSRD literature, the majority of quantitative CSRD studies have relied on traditional statistical approaches for the correlation analysis of CSRD influencing factors. In this paper, we intend to quantitatively analyze firms' characteristics related to CSRD in Saudi Arabia, understand CSRD and its influencing factors, and predict CSRD patterns. This study lays the groundwork to help companies make informed decisions. It also helps many other stakeholders better understand CSRD's impacts. To achieve this, we propose a deep learning framework based on long short-term memory (LSTM) for identifying and predicting CSRD patterns. Moreover, a correlation-based technique is also used to visualize the relationships between variables and identify the significant features. The dataset used in this study was collected from annual reports, CSR reports, and firms' websites between 2015 and 2018. It contains a variety of variables to explain the CSR behaviour of 117 companies. The proposed framework is evaluated with several approaches, including logistic regression (LR), K-nearest neighbours (KNN), support vector machines (SVM), random forests (RF), and decision trees (DT). Compared to other machine learning models, experiment results show that LSTM achieved acceptable results with the highest accuracy of 88 % . [ABSTRACT FROM AUTHOR]

Driven by the demand for ubiquitous connection in the Internet of everything era, this paper introduces a new low earth orbit (LEO) satellite-based multi-access edge computing fusion architecture. The architecture regards LEO satellites deployed with high-performance edge modules as superior nodes, which can provide onboard processing mode edge task offloading service for users in complex regions. At the same time, the superior node also has the offloading decision and can offload some edge user tasks to the edge service center in the ground network. Based on differential game theory, we propose a two-stage computing resource purchase strategy and task-offloading resource pricing strategy to ensure the minimum cost of edge computing services for edge users and the maximum benefit for edge service providers and prove the existence of a unique Nash equilibrium solution using Piccard theorem. An algorithm based on the Runge–Kutta method is designed to solve Nash equilibrium, and the simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Classification is a widely used supervised learning technique that enables models to discover the relationship between a set of features and a specified label using available data. Its applications span various fields such as engineering, telecommunication, astronomy, and medicine. In this paper, we propose a novel rule-based classifier called RUCIB (RUle-based Classifier Inspired by BRADO), which draws inspiration from the socio-inspired swarm intelligence algorithm known as BRADO. RUCIB introduces two key aspects: the ability to accommodate multiple values for features within a rule and the capability to explore all data features simultaneously. To evaluate the performance of RUCIB, we conducted experiments using ten databases sourced from the UCI machine learning database repository. In terms of classification accuracy, we compared RUCIB to ten well-known classifiers. Our results demonstrate that, on average, RUCIB outperforms Naive Bayes, SVM, PART, Hoeffding Tree, C4.5, ID3, Random Forest, CORER, CN2, and RACER by 9.32%, 8.97%, 7.58%, 7.4%, 7.34%, 7.34%, 7.22%, 5.06%, 5.01%, and 1.92%, respectively. [ABSTRACT FROM AUTHOR]

Community detection is used to determine the network structure and node relationships. However, it raises privacy concerns when locating and disclosing the members' personal or community information. Community deception is a method of hiding a target community from community detection algorithms. It is accomplished by minimally rewiring the edges of the community in the network. In this paper, we propose PERMDEC, a novel community deception algorithm that operates on a weighted undirected network. PERMDEC determines which edges of a given community should be modified based on the parameter permanence loss and updates the network to hide a specific community. We tested PERMDEC on five community detection algorithms on eight real datasets with varying budget values. The performance is compared to the baseline method SECRETORUM using the deception score and NMI. In general, PERMDEC outperforms the existing method of deception for weighted networks. [ABSTRACT FROM AUTHOR]

Today, cloud computing technology has attracted the attention of many researchers. According to the needs of users to quickly execute requests and provide quality services, optimal allocation of resources and timing of task execution between virtual machines in cloud computing are of great importance. One of the important challenges that cloud service providers face is the effective management of resources by physical infrastructure. Therefore, in this paper, an autonomous system based on the Clipped Double Deep Q-Learning (CDDQL) Algorithm and the meta-heuristic Particle Swarm Optimization (PSO) for resource allocation is proposed in the Fog-cloud computing infrastructure. The PSO algorithm is used to prioritize the tasks and CDDQL is used as the core of the autonomous system (Auto-CDDQL) to allocate the desired VM resources to the tasks. The proposed Auto-CDDQL is implemented in the Fog and performs this process autonomously. By evaluating the results, it was observed that the amount of Make Span, response time, task completion, resource utilization, and energy consumption rate in the proposed AutoCDDQL on the c-hilo dataset, compared to the FCFS, RR, and PBTS methods, are significantly improved. [ABSTRACT FROM AUTHOR]