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Cybersecurity Risk Management through Behavior-Based Contextual Analysis of Online Logs

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Journal of Software Engineering and Applications 2024年第6期17卷 487-507页

作者： Irawati Edlabadkar Vijay K. Madisetti School of Cybersecurity & Privacy Georgia Institute of Technology Atlanta GA USA

This paper studies cyber risk management by integrating contextual log analysis with User and Entity Behavior analytics (UEBA). Leveraging Python scripting and PostgreSQL database management, the solution enriches log data with contextual and behavioral information from Linux system logs and semantic datasets. By incorporating Common Vulnerability Scoring System (CVSS) metrics and customized risk scoring algorithms, the system calculates Insider Threat scores to identify potential security breaches. The integration of contextual log analysis and UEBA [1] offers a proactive defense against insider threats, reducing false positives and prioritizing high-risk alerts.

关键词： Cyber Risk UEBA CVSS

Process Characterization of the Transesterification of Rapeseed Oil to Biodiesel Using Design of Experiments and Infrared Spectroscopy

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Journal of Renewable Materials 2023年第4期11卷 1643-1660页

作者： Tobias Drieschner Andreas Kandelbauer Bernd Hitzmann Karsten Rebner Lehr-und Forschungszentrum Process Analysis and Technology(PA&T)der Hochschule Reutlingen Reutlingen72762Germany Department of Process analytics and Cereal Science Institute of Food Science and BiotechnologyUniversität HohenheimStuttgart70599Germany

For optimization of production processes and product quality,often knowledge of the factors influencing the process outcome is ***,process analytical technology(PAT)that allows deeper insight into the process and results in a mathematical description of the process behavior as a simple function based on the most important process factors can help to achieve higher production efficiency and *** present study aims at characterizing a well-known industrial process,the transesterification reaction of rapeseed oil with methanol to produce fatty acid methyl esters(FAME)for usage as biodiesel in a continuous micro reactor *** this end,a design of experiment approach is applied,where the effects of two process factors,the molar ratio and the total flow rate of the reactants,are *** optimized process target response is the FAME mass fraction in the purified nonpolar phase of the product as a measure of reaction *** quantification is performed using attenuated total reflection infrared spectroscopy in combination with partial least squares *** data retrieved during the conduction of the DoE experimental plan were used for statistical analysis.A non-linear model indicating a synergistic interaction between the studied factors describes the reactor behavior with a high coefficient of determination(R^(2))of ***,we applied a PAT approach to generate further insight into this established industrial process.

关键词： Process analytical technology transesterification design of experiment attenuated total reflection infrared spectroscopy partial least square regression

Coupled Attribute Similarity Learning on Categorical Data for Multi-Label Classification

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Journal of Beijing institute of Technology 2017年第3期26卷 404-410页

作者： Zhenwu Wang Longbing Cao Department of Computer Science and Technology China University of Mining and Technology(Beijing)Beijing 100083China the advanced analytics institute University of Technology Sydney 2007Australia

In this paper a novel coupled attribute similarity learning method is proposed with the basis on the multi-label categorical data（CASonMLCD）.The CASonMLCD method not only computes the correlations between different attributes and multi-label sets using information gain,which can be regarded as the important degree of each attribute in the attribute learning method,but also further analyzes the intra-coupled and inter-coupled interactions between an attribute value pair for different attributes and multiple *** paper compared the CASonMLCD method with the OF distance and Jaccard similarity,which is based on the MLKNN algorithm according to 5common evaluation *** experiment results demonstrated that the CASonMLCD method can mine the similarity relationship more accurately and comprehensively,it can obtain better performance than compared methods.

关键词： coupled similarity multi-label categorical data correlations

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Neural Machine Translation Models with Attention-Based Dropout Layer

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Computers, Materials & Continua 2023年第5期75卷 2981-3009页

作者： Huma Israr Safdar Abbas Khan Muhammad Ali Tahir Muhammad Khuram Shahzad Muneer Ahmad Jasni Mohamad Zain School of Electrical Engineering and Computer Science(SEECS) National University of Science and TechnologyIslamabadPakistan institute for Big Data analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA40450Shah AlamSelangorMalaysia

In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of *** model has obtained state-of-the-art performance for several language ***,there has been little work exploring useful architectures for Urdu-to-English machine *** conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and *** results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen *** trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,*** a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more *** attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear ***,we considered refining the attention-based models by defining an additional attention-based dropout *** dropout fixes alignment errors and minimizes translation errors at the word *** empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation *** ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as *** empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.

关键词： Natural language processing neural machine translation word embedding attention perplexity selective dropout regularization Urdu Persian Arabic BLEU

A Novel Modified Tree-Seed Algorithm for High-Dimensional Optimization Problems

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Chinese Journal of Electronics 2020年第2期29卷 337-343页

作者： ZHAO Shijie GAO Leifu TU Jun YU Dongmei institute of Optimization and Decision College of Science Liaoning Technical University institute for Optimization and Decision analytics Liaoning Technical University

To efficiently handle high-dimensional continuous optimization problems, a Modified tree-seed algorithm(MTSA) is proposed by coupling a newly introduced control parameter named as Seed domain shrinkable coefficient(SDSC) and Local reinforcement strategy(LRS) based on gradient information of adjacentgeneration best trees. SDSC is dynamically decreased with iterations to adjust the produced domain of offspring seeds,for achieving the tradeoff between the global exploration and local exploitation. LRS strategy is to execute local exploitation process by employing gradient information of two best trees, for enhancing convergence efficiency and local optima avoidance with probabilities. The compared experimental results show the different effects of differenttype SDSC on MTSA, the faster convergence efficiency and the stronger robustness of the proposed MTSA.

关键词： Tree-seed algorithm Seed domain shrinkage Local reinforcement High-dimension optimization

A Novel Quantum Entanglement-Inspired Meta-heuristic Framework for Solving Multimodal Optimization Problems

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Chinese Journal of Electronics 2021年第1期30卷 145-152页

作者： ZHAO Shijie MA Shilin GAO Leifu YU Dongmei institute of Optimization and Decision College of Science Liaoning Technical University institute for Optimization and Decision analytics Liaoning Technical University

To solve Multimodal optimization problems(MOPs), a Novel Quantum entanglement-inspired meta-heuristic framework(NMF-QE) is proposed. Its main inspirations are two concepts of quantum physics:quantum entanglement and quantum superposition. When given Proto-born particles(PBPs) of a population, these two concepts are mathematically developed to generate twin-born and combination-born particles, *** if any elite-born particles would be created by a local re-searching strategy. These three or four groups of particles come together as a whole search population of NMF-QE to realize exploration and exploitation of algorithms. To guarantee dynamical optimization capability of NMF-QE, the individual evolutionary mechanism of some existing meta-heuristics will be adopted to iteratively create PBPs. A selected meta-heuristic is coupled with NMF-QE to present its improved variant. Numerical results show that the proposed NMF-QE can effectively improve optimization performance of meta-heuristics on MOPs.

关键词： Meta-heuristic optimization Quantum entanglement Long-distance effect Combination superposition Multimodal optimization

A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

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Computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separable convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-Excitation Networks(SENet)attention model is fused into the algorithm framework and a new detection scale suitable for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line YOLOv5 multi-scale integration SENet

众脑之力:通过开放神经影像数据和大规模协作催化神经精神疾病的创新发现

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Science Bulletin 2024年第10期69卷 1536-1555页

作者：鲁彬陈骁 Francisco Xavier Castellanos Paul M.Thompson 左西年臧玉峰严超赣 CAS Key Laboratory of Behavioral Science Institute of PsychologyBeijing 100101China Department of Psychology University of Chinese Academy of SciencesBeijing 100101China Department of Child and Adolescent Psychiatry NYU Grossman School of MedicineNew York 10016USA d Nathan Kline institute for Psychiatric Research Orangeburg 10962USA Imaging Genetics Center Mark&Mary Stevens Institute for Neuroimaging&InformaticsKeck School of MedicineUniversity of Southern CaliforniaLos Angeles 90033USA Developmental Population Neuroscience Research Center IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijing 100875China National Basic Science Data Center Beijing 100190China Centre for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal UniversityHangzhou 310004China institute of Psychological Science Hangzhou Normal UniversityHangzhou 310030China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment Hangzhou 311121China International Big-Data Center for Depression Research Institute of PsychologyChinese Academy of SciencesBeijing 100101China Magnetic Resonance Imaging Research Center Institute of PsychologyChinese Academy of SciencesBeijing 100101China

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric *** pooling images from various cohorts,statistical power has increased,enabling the detection of subtle abnormalities and robust associations,and fostering new research *** collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted ***-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric *** also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric ***,challenges such as data harmonization across different sites,privacy protection,and effective data sharing must be *** proper governance and open science practices,we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis,treatment selection,and outcome prediction,contributing to optimal brain health.

关键词： Magnetic resonance imaging Neuropsychiatric disorders Big data Open science Artificial intelligence

Toward Data-Driven Digital Therapeutics analytics:Literature Review and Research Directions

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 42-66页

作者： Uichin Lee Gyuwon Jung Eun-Yeol Ma Jin San Kim Heepyung Kim Jumabek Alikhanov Youngtae Noh Heeyoung Kim Korea advanced institute of Science and Technology Daejeon 34141Republic of Korea Inha University Incheon 402-751Republic of Korea KENTECH Naju-siRepublic of Korea

With the advent of digital therapeutics(DTx),the development of software as a medical device(SaMD)for mobile and wearable devices has gained significant attention in recent *** DTx evaluations,such as randomized clinical trials,mostly focus on verifying the effectiveness of DTx *** acquire a deeper understanding of DTx engagement and behavioral adherence,beyond efficacy,a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for *** this work,the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets,to investigate contextual patterns associated with DTx usage,and to establish the(causal)relationship between DTx engagement and behavioral *** review of the key components of datadriven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets,which helps to iteratively improve the receptivity of existing DTx.

关键词： Causal inference data-driven analytics framework digital therapeutics(DTx) mobile and wearable data technical and behavioral engagement

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