On modeling random data to evaluate the performance of statistical tests in cryptography by U. Y. Palukha, M. A. Prokharchyk, Yu. S. Kharin

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A Deep Neural Network-Based Approach to Media Hotspot Discovery by Pan Luo

“…Finally, the text feature representation method based on graph convolutional neural network is combined with the clustering algorithm based on the moving range density maximum selection method to build a deep learning-based media hotspot discovery framework. …”
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Harnessing dual variational autoencoders to decode microbe roles in diseases for traditional medicine discovery by Qing Ye, Yaxin Sun, Yaxin Sun, Yaxin Sun

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GENERATION OF TEST BASES OF RULES FOR THE ANALYSIS OF PRODUCTIVITY OF LOGICAL INFERENCE ENGINE by Svitlana Shapovalova

“…The following methods are used: methods of comparison with the sample, graph theory, logical programming. The following results were obtained: the method provides opportunities: creation of conditions of rules that complicate the data flow network of the Rete-algorithm as much as possible; formation of test bases of rules for derivation both on logic of the first order, and on offer logic; simply increase the number of knowledge base rules while maintaining the output logic. …”
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Unreliable communication in high-performance distributed multi-agent systems: A ingenious scheme in high computing by Ali Mustafa, Muhammad Najam ul Islam, Salman Ahmed, Muhammad Ahsan Tufail

“…A dynamically changing network topology is considered in this research with unreliable communication links, and four different scenarios are established to be analyzed for the proposed consensus-based distributed estimation algorithm. …”
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Revisiting a Cutting-Plane Method for Perfect Matchings by Chen, Amber Q., Cheung, Kevin K. H., Kielstra, P. Michael, Winn, Avery D.

“…In 2016, Chandrasekaran, Végh, and Vempala (Mathematics of Operations Research, 41(1):23–48) published a method to solve the minimum-cost perfect matching problem on an arbitrary graph by solving a strictly polynomial number of linear programs. …”
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Dynamic Route Network Planning Problem for Emergency Evacuation in Restricted-Space Scenarios by Yi Hong, Deying Li, Qiang Wu, Hua Xu

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Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning by Ilya Larin, Alexander Karabelsky

“…Graph-based segmentation has a long history, e.g., the normalized cuts algorithm treated segmentation as a graph partitioning problem—but only recently have such ideas merged with deep learning in an unsupervised manner. …”
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Efficient and safe software defined network topology discovery protocol by Dong LI, Junqing YU, Yongpu GU, Pengcheng ZHAO

“…The network topology discovery in OpenFlow-based software-defined networks is mainly achieved by utilizing the OpenFlow discovery protocol (OFDP).However, it has been observed in existing research that OFDP exhibits low updating efficiency and is susceptible to network topology pollution attacks.To address the efficiency and safety concerns of the network topology discovery protocol, an in-depth investigation was conducted on the mechanism and safety of OFDP network topology discovery.The characteristics of network topology establishment and updating in OFDP were analyzed, and an improved protocol named Im-OFDP (improved OpenFlow discovery protocol) based on the minimum vertex covering problem in graph theory was proposed.In Im-OFDP, the switch port table and network link table were initially established using prior information obtained from OFDP network topology discovery.Subsequently, a graph model of the network topology was constructed, and the minimum vertex covering algorithm in graph theory was employed to select specific switches for the reception and forwarding of topology discovery link layer discovery protocol (LLDP) packets.Multi-level flow tables were designed based on the network topology structure, and these flow entries were installed on the selected switches by the controller to process LLDP packets.To address security issues, dynamic check code verification in LLDP packets was employed to ensure the safety of network links.Additionally, a network equipment information maintenance mechanism was established based on known network topologies to ensure the safety of the network equipment.Experimental results demonstrate a significant reduction in the number of network topology discovery messages, bandwidth overhead, and CPU overhead through the deployment of Im-OFDP.Moreover, the response time for node failures and link recovery time after mode failure is substantially reduced.Im-OFDP also effectively mitigates various network topology pollution attacks, such as link fabrication and switch forgery attacks.Overall, Im-OFDP has the capability to enhance the efficiency and safety of SDN topology discovery.…”
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Network latency clustering for detector placement on macroscopical prewarning by HE Hui, HU Ming-zeng, YUN Xiao-chun, ZHANG Hong-li

“…Research on the network-based and distributed intrusion detection was aroused by the burst-outs of large-scale security events.How to place detection instruments was the key to the detections.The problem of detector placement was turned to that of the clustering of topology graph.A novel bidirectional hierarchical clustering algorithm was put forward,which decreased the amount of result clusters by integration of initial marker selection method based on node out-degree.The simulation results demonstrates that our clustering approaches effectively identify clusters and been employed in the measured real network of the backbone.…”
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Similar Instances Reuse Based Numerical Control Process Decision Method for Prismatic Parts by Changhong XU, Shusheng ZHANG, Jiachen LIANG, Rui HUANG, Rong BIAN

“…The ant colony and simulated annealing algorithms are combined to solve the problem due to their strong ability for graph searching and global convergence, respectively. …”
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Extraction of exact symbolic stationary probability formulas for Markov chains in finite space with application to production lines. part II: unveiling accurate formulas for very s... by Konstantinos S. Boulas, Georgios D. Dounias, Chrissoleon T. Papadopoulos

“…That is because even small systems are characterized by the well-known state explosion problem.MethodsIn short serial production lines, the underlying Markov chain is depicted as a graph of the transition diagram, which is constructed by implementing an algorithm. …”
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Personalized Influential Community Search in Large Networks: A K-ECC-Based Model by Shi Meng, Hao Yang, Xijuan Liu, Zhenyue Chen, Jingwen Xuan, Yanping Wu

“…Graphs have been widely used to model the complex relationships among entities. …”
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scGRN-Entropy: Inferring cell differentiation trajectories using single-cell data and gene regulation network-based transfer entropy. by Rui Sun, Wenjie Cao, ShengXuan Li, Jian Jiang, Yazhou Shi, Bengong Zhang

“…Research on cell differentiation facilitates a deeper understanding of the fundamental processes of life, elucidates the intrinsic mechanisms underlying diseases such as cancer, and advances the development of therapeutics and precision medicine. …”
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Bytecode-based approach for Ethereum smart contract classification by Dan LIN, Kaixin LIN, Jiajing WU, Zibin ZHENG

“…In recent years, blockchain technology has been widely used and concerned in many fields, including finance, medical care and government affairs.However, due to the immutability of smart contracts and the particularity of the operating environment, various security issues occur frequently.On the one hand, the code security problems of contract developers when writing contracts, on the other hand, there are many high-risk smart contracts in Ethereum, and ordinary users are easily attracted by the high returns provided by high-risk contracts, but they have no way to know the risks of the contracts.However, the research on smart contract security mainly focuses on code security, and there is relatively little research on the identification of contract functions.If the smart contract function can be accurately classified, it will help people better understand the behavior of smart contracts, while ensuring the ecological security of smart contracts and reducing or recovering user losses.Existing smart contract classification methods often rely on the analysis of the source code of smart contracts, but contracts released on Ethereum only mandate the deployment of bytecode, and only a very small number of contracts publish their source code.Therefore, an Ethereum smart contract classification method based on bytecode was proposed.Collect the Ethereum smart contract bytecode and the corresponding category label, and then extract the opcode frequency characteristics and control flow graph characteristics.The characteristic importance is analyzed experimentally to obtain the appropriate graph vector dimension and optimal classification model, and finally the multi-classification task of smart contract in five categories of exchange, finance, gambling, game and high risk is experimentally verified, and the F1 score of the XGBoost classifier reaches 0.913 8.Experimental results show that the algorithm can better complete the classification task of Ethereum smart contracts, and can be applied to the prediction of smart contract categories in reality.…”
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Geometry and Topology Correction of 3D Building Models with Fragmented and Disconnected Components by Ahyun Lee

“…The proposed two-stage approach combines geometric refinement via duplicate vertex removal with topological refinement using a novel spatial partitioning-based Depth-First Search (DFS) algorithm for connected mesh clustering. This spatial partitioning-based DFS significantly improves upon traditional graph traversal methods like standard DFS, breadth-first search (BFS), and Union-Find for connectivity analysis. …”
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Local Outlier Detection Method Based on Improved K-means by Yu ZHOU, Hao XIA, Xuezhen YUE, Peichong WANG

“…They are often regarded as noise points due to their deviation from normal data points and are considered points of research value, occupying a small proportion of the dataset. …”
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Secure cloud computing: leveraging GNN and leader K-means for intrusion detection optimization by Raman Dugyala, Premkumar Chithaluru, M. Ramchander, Sunil Kumar, Arvind Yadav, N. Sudhakar Yadav, Diaa Salama Abd Elminaam, Deema Mohammed Alsekait

“…To address these limitations, this research proposes an optimized Intrusion Detection System (IDS) that leverages Graph Neural Networks and the Leader K-means clustering algorithm. …”
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UAV-Enabled Inspection System With No-Fly Zones: DRL-Based Joint Mobile Nest Scheduling and UAV Trajectory Design by Jin Dai, Yunfei Gao, Chao Cai, Wei Xiong, Manjia Liu

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Mapping Knowledge Domain to Analyze the Building Information Modeling on Building Energy Saving Based on Visualized Analysis Method by Gen Li, Haining Tian

“…In order to analyze the research status of BIM in the field of energy-saving transformation, find its research hotspots, and reveal its future development trend to guide the practice and application of building information model better, the mapping knowledge domain is constructed via the literature visualization analysis method based on the theory of cocitation analysis and the pathfinder algorithm This is to analyze the hot spots and reveal the frontier of building information modeling on building energy saving. …”
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