Embedding and classifying test execution traces using neural networks by Foivos Tsimpourlas, Gwenyth Rooijackers, Ajitha Rajan, Miltiadis Allamanis

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Auto-Probabilistic Mining Method for Siamese Neural Network Training by Arseniy Mokin, Alexander Sheshkus, Vladimir L. Arlazarov

“…These findings highlight the potential effectiveness of the developed loss function and mining method in addressing a wide range of pattern recognition challenges.…”
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Mitigating the Concurrent Interference of Electrode Shift and Loosening in Myoelectric Pattern Recognition Using Siamese Autoencoder Network by Ge Gao, Xu Zhang, Xiang Chen, Zhang Chen

“…A Siamese auto-encoder network (SAEN) was established to learn robust feature representations against random occurrences of both electrode shift and loosening. …”
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Machine Learning-Based Network Anomaly Detection: Design, Implementation, and Evaluation by Pilar Schummer, Alberto del Rio, Javier Serrano, David Jimenez, Guillermo Sánchez, Álvaro Llorente

“…<b>Background:</b> In the last decade, numerous methods have been proposed to define and detect outliers, particularly in complex environments like networks, where anomalies significantly deviate from normal patterns. …”
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Study on Satisfaction Evaluation of Ecotourism Through Advanced Network Text Mining by Jia Gao, Leong Mow Gooi, Kim Mee Chong, Xiang Lyu, Jin Zhang

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Analysis of the Construction and Evolution of Ecological Security Patterns in Karst Areas by FU Li, PENG Shuangyun, GONG Luping, HUANG Bangmei, MA Dongling, ZHU Ziyi

“…［Methods］ Based on the evaluation of ecosystem service importance and ecological sensitivity, in combination with methods such as MSPA, MCR, and the gravity model, the ecological security pattern of the Pearl River source region from 1990 to 2020 was constructed, and its spatial-temporal evolution characteristics were analyzed. …”
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Mapping the scientific structure of organization and management of enterprises using complex networks by Alicia Olivares-Gil, Adrián Arnaiz-Rodríguez, José Miguel Ramírez-Sanz, José Luis Garrido-Labrador, Virginia Ahedo, César García-Osorio, José Ignacio Santos, José Manuel Galán

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Identifying Barong Tagalog Textile Using Convolutional Neural Network and Support Vector Machine with Structural Pattern Segmentation by Jeff B. Totesora, Edward C. Torralba, Cyrel O. Manlises

“…To identify structural patterns in textile classification, machine learning was used. …”
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Deep Time Series Intelligent Framework for Power Data Asset Evaluation by Lihong Ge, Xin Li, Li Wang, Jian Wei, Bo Huang

“…These data have both long-term and short-term patterns, and traditional evaluation methods such as autoregressive models or Gaussian processes may be difficult to fully capture their characteristics, resulting in evaluation bias. …”
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Inter-city movement pattern of notifiable infectious diseases in China: a social network analysisResearch in context by Lin-Jie Yu, Peng-Sheng Ji, Xiang Ren, Yan-He Wang, Chen-Long Lv, Meng-Jie Geng, Jin-Jin Chen, Tian Tang, Chun-Xi Shan, Sheng-Hong Lin, Qiang Xu, Guo-Lin Wang, Li-Ping Wang, Simon I. Hay, Wei Liu, Yang Yang, Li-Qun Fang

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The Patterns and Environmental Factors of Diversity, Co-Occurrence Networks, and Assembly Processes of Protistan Communities in Bulk Soils of Forests by Bing Yang, Lin Wu, Zhisong Yang, Zhihe Zhang, Wanju Feng, Weichao Zheng, Chi Xu

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Recognizing Mixing Patterns of Urban Agglomeration Based on Complex Network Assortativity Coefficient: A Case Study in China by Kaiqi Zhang, Lujin Jia, Sheng Xu

“…Based on multi-source data (Baidu index data, investment data of listed companies, high-speed rail operation data, and highway network data) from 2017 to 2019 across seven national-level urban agglomerations, this study introduces complex network assortativity coefficients to analyze the mechanisms of urban relationship formation from two dimensions, structural features and socioeconomic attributes, to evaluate how these features shape urban agglomeration networks and reveal the distribution of network assortativity coefficients across urban agglomerations to classify diverse developmental patterns. …”
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Local Binary Pattern–Cycle Generative Adversarial Network Transfer: Transforming Image Style from Day to Night by Abeer Almohamade, Salma Kammoun, Fawaz Alsolami

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Predicting correlation relationships of entities between attack patterns and techniques based on word embedding and graph convolutional network by Weicheng QIU, Xiuzhen CHEN, Yinghua MA, Jin MA, Zhihong ZHOU

“…Threat analysis relies on knowledge bases that contain a large number of security entities.The scope and impact of security threats and risks are evaluated by modeling threat sources, attack capabilities, attack motivations, and threat paths, taking into consideration the vulnerability of assets in the system and the security measures implemented.However, the lack of entity relations between these knowledge bases hinders the security event tracking and attack path generation.To complement entity relations between CAPEC and ATT＆amp;CK techniques and enrich threat paths, an entity correlation prediction method called WGS was proposed, in which entity descriptions were analyzed based on word embedding and a graph convolution network.A Word2Vec model was trained in the proposed method for security domain to extract domain-specific semantic features and a GCN model to capture the co-occurrence between words and sentences in entity descriptions.The relationship between entities was predicted by a Siamese network that combines these two features.The inclusion of external semantic information helped address the few-shot learning problem caused by limited entity relations in the existing knowledge base.Additionally, dynamic negative sampling and regularization was applied in model training.Experiments conducted on CAPEC and ATT＆amp;CK database provided by MITRE demonstrate that WGS effectively separates related entity pairs from irrelevant ones in the sample space and accurately predicts new entity relations.The proposed method achieves higher prediction accuracy in few-shot learning and requires shorter training time and less computing resources compared to the Bert-based text similarity prediction models.It proves that word embedding and graph convolutional network based entity relation prediction method can extract new entity correlation relationships between attack patterns and techniques.This helps to abstract attack techniques and tactics from low-level vulnerabilities and weaknesses in security threat analysis.…”
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Array inspired wideband and high gain antenna with enhanced pattern diversity for 5G mm wave networks by Nawaf A. Alqwaifly, Wahaj Abbas Awan, Nabeel Alsaab, Fahad Nasser Alsunaydih, Khaled Alhassoon

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Network analysis of cognitive function, glycemic–lipid profiles, and hepatic–renal function in individuals with diverse drinking patterns by Shuqi Xu, Shuqi Xu, Shuqi Xu, Shuqi Xu, Ranran Zhao, Ranran Zhao, Ranran Zhao, Jincheng Wang, Jincheng Wang, Jincheng Wang, Xue Yang, Xue Yang, Xue Yang, Lan Wang, Lan Wang, Lan Wang, Cuixia An, Cuixia An, Cuixia An, Xueyi Wang, Xueyi Wang, Xueyi Wang, Ran Wang, Ran Wang, Ran Wang

“…The node DSCT ranked highly in terms of betweenness centrality.ConclusionCorrelations may exist among cognitive function, glycemic and lipid profiles, and hepatic–renal function in individuals with varying alcohol consumption patterns. Lipid and liver function indicators were identified as the most central factors in the network model. …”
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Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network by Kai Bai, Siyi Jin, Zhaoshuo Zhang, Shengsheng Dai

“…To address these issues, this study proposes an improved LSTM neural network model for ROP prediction (CBT-LSTM). This model integrates the capability of a two-dimensional convolutional neural network (2D-CNN) for multi-feature extraction, the advantages of bidirectional long short-term memory networks (BiLSTM) for processing bidirectional temporal information, and the dynamic weight adjustment of the time pattern attention mechanism (TPA) for extracting crucial information in BiLSTM, effectively capturing key features in temporal data. …”
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Evaluation of Land Desertification Sensitivity and Ecological Spatial Network Optimization in Gansu Province by Qiu Shi, Yu Qiang, Yang Linzhe, Ma Jun, Wang Ge, Yue Depeng

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A Research on Black-box Evaluation Methods for Quantum Key Distribution Networks by DAI Hua, SUN Xin, MAO Yining, LI Yixuan, LÜ Yuxiang, WANG Hongyan, YU Xiaosong

“…【Conclusion】The paper identifies potential network security threats by setting dynamic thresholds and evaluates network security from multiple dimensions. …”
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Longitudinal evaluation of common and unique brain-networks in variants of primary progressive aphasia by Rajan Kashyap, Rose Dawn Bharath, Changsong Zhou, Kyrana Tsapkini, John E. Desmond, SH Annabel Chen, Kaviraja Udupa, PT Sivakumar, Sagarika Bhattacharjee

“…Since the subset of these subjects was scanned at the 6th and 12th months, the longitudinal changes in the rsfMRI networks were evaluated at each interval. Network features were correlated with clinical behaviours, and the longitudinal impact of the changes in these networks on behaviours was evaluated over the 12-month period. …”
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