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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Tang, Binhui Leng, Peiyun Shen, Xuxiang Wei, Yuhang Sichuan University Chengdu School of Computer and Software Chengdu Jincheng College School of Cyber Science and Engineering China Chengdu Jincheng College School of Computer and Software Chengdu China

ISBN: (纸本)9798350325485

Malware has become one of the most severe security threats in cyber security, among which APT malware attacks are more threatening than advanced sustainable threat attacks. In this paper, we perform APT malware and variant detection based on deep learning and analyze the results by detecting and attribution. The variant detection of APT malware can inhibit the spread of malicious code, which is important for network security detection and defense. This paper proposes a detection and classification method for APT malware and its variants based on deep learning. Firstly, the feature representation method of APT malware based on RGB images is proposed to solve the gradient explosion and gradient disappearance of grayscale map feature extraction and generate images with rich texture information, which can mine deeper features of APT malware attacks. Secondly, this paper also improves the convolutional neural network model by combining the Self-Attention mechanism with the spatial pyramid pool SPP-net to solve the problem of image input of different sizes, as the accuracy is above 92.14%. Then, the experiment results prove the predictive analysis of the model to understand the organization to which the APT attack belongs and the way of its specific attack, providing the possibility of tracing the source. Finally, the visual analysis of APT threat characteristics is presented. © 2023 IEEE.

关键词： Convolutional neural networks

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No-reference Image Contrast Quality Assessment based on the Degree of Uniformity in Probability Distribution

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IEIE Transactions on Smart Processing and Computing 2022年第2期11卷 85-91页

作者： Kim, Wonvin Yim, Changhoon Department of Computer Software Korean Bible University Seoul Korea Republic of Department of Computer Science and Engineering Konkuk University Seoul Korea Republic of

We propose a novel contrast-based no-reference image quality assessment (NR-IQA) method. The proposed method quantifies the degree of uniformity in the probability distribution of the pixel values of an image to measure contrast quality without the need for a reference image. To quantify the degree of symmetric uniformity in the distribution of pixel values for image contrast assessment, we divide the intensity levels into left and right ranges recursively at each depth as a binary tree. This symmetric hierarchical recursive calculation of the degree of uniformity for contrast measurement provides a good metric for NR-IQA. Experimental results show that the proposed method is more consistent with the levels of contrast decrement and is superior in assessing contrast-enhanced and dehazed images compared with state-of-the-art and classic NR-IQA methods. The proposed method is more suited to real-time applications due to its lower computational complexity compared with previous methods. © 2022 The Institute of Electronics and Information Engineers.

关键词： Probability distributions

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FogEye: Towards Performance Efficient Fog Networking Telemetry

FogEye: Towards Performance Efficient Fog Networking Telemet...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Chen, Simian He, Dongbiao Ming, Zhongxing Cui, Laizhong College of Computer Science and Software Engineering Shenzhen University China Sangfor Technologies Inc China China

ISBN: (纸本)9798350370218

The concept of fog networking has been introduced to provide Cloud-based services to Internet of Things (IoT) devices. One of the crucial tasks in this paradigm is network telemetry, which plays a vital role in ensuring comprehensive monitoring and management. It helps in coordinating the fog resources between the cloud services and the IoT devices, thus ensuring efficient and effective operations of the entire system. However, the existing network measurement still exhibits some drawbacks, such as a lack of measurement granularities and excessive bandwidth overhead. In the fog network, it remains undefined how to achieve network-wide performance between any links. In this work, We propose FogEye, a network-wide performance telemetry system applied in the fog network. Additionally, we propose a Hierholzer-based algorithm for graph coverage. It effectively reduces the telemetry overhead while ensuring accurate measurement. We conduct experiments in an actual enterprise cloud environment. The result shows that our algorithm can reduce at least 20% of probing packets. FogEye allows operators to query network performance for any link, achieving more cost-efficient traffic monitoring and management under centralized network control. © 2023 IEEE.

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A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

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computer Modeling in engineering & sciences 2024年第7期140卷 907-927页

作者： Yong Ma Han Zhao Kunyin Guo Yunni Xia Xu Wang Xianhua Niu Dongge Zhu Yumin Dong School of Computer Information Engineering Jiangxi Normal UniversityNanchang330000China School of Digital Industry Jiangxi Normal UniversityShangrao334000China The College of Computer Science Chongqing UniversityChongqing400044China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Computer and Software Engineering Xihua UniversityChengdu610039China Electric Power Research Institute of State Grid Ningxia Electric Power Company Ltd. Yinchuan750002China College of Computer and Information Science Chongqing Normal UniversityChongqing401331China

Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to *** the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user ***,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource *** the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant *** response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance *** approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached ***,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and *** results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.

关键词： Mobile edge networks mobility fault tolerance cooperative caching multi-agent deep reinforcement learning content prediction

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Accelerating the cryo-EM structure determination in RELION on GPU cluster

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Frontiers of computer science 2022年第3期16卷 21-39页

作者： Xin YOU Hailong YANG Zhongzhi LUAN Depei QIAN Sino-German Joint Software Institute School of Computer Science and EngineeringBeihang UniversityBeijing 100191China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125

The cryo-electron microscopy(cryo-EM)is one of the most powerful technologies available today for structural *** RELION(Regularized Likelihood Optimization)implements a Bayesian algorithm for cryo-EM structure determination,which is one of the most widely used software in this *** researchers have devoted effort to improve the performance of RELION to satisfy the analysis for the ever-increasing volume of *** this paper,we focus on performance analysis of the most time-consuming computation steps in RELION and identify their performance bottlenecks for specific *** propose several performance optimization strategies to improve the overall performance of RELION,including optimization of expectation step,parallelization of maximization step,accelerating the computation of symmetries,and memory affinity *** experiment results show that our proposed optimizations achieve significant speedups of RELION across representative *** addition,we perform roofline model analysis to understand the effectiveness of our optimizations.

关键词： cryo-EM structure determination performance optimization GPU acceleration RELION

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A Comparative Analysis and Prediction of Diabetes Using Machine Learning Approaches 5th

A Comparative Analysis and Prediction of Diabetes Using Mach...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Ansari, Gufran Ahmad Bhat, Salliah Shafi Bhoite, Sachin Department of Computer Science and Engineering and Software Engineering University of Hail Hail Saudi Arabia Computer Applications B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai600048 India Pune411 038 India

ISBN: (纸本)9789819763177

Diabetes is a leading cause of death. People with type 1 diabetes (T1D) can face a number of consequences from their illness. Hyperglycemia is a symptom of T1D disease. Hyperglycemia can be symptoms that can be used for predicting diabetes in patients using the machine learning approach (MLA). Learning algorithms play a role in disease detection and prediction of decision-making. The leading cause of diabetes is really a high blood sugar level or a lack of insulin. As a consequence, it is critical to develop a reliable method to predict diabetes. Once it becomes a serious health concern, it will damage different organs such as the lungs, brain, nerves, blood vessels and heart as a result. Several approaches for detecting diabetes have been used in recent decades. Early diabetic diagnosis is currently very important to help the patients’ health, and this can be done by utilizing a variety of procedures. Tenfold cross-validation technique was used to train dataset for classification. In this paper, authors use different classifications of algorithms such as random forest, decision tree, logistic regression and multilayer perceptron for finding T1D patients. The accuracy of MLP is 98.077% which is the highest among other algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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CAPS: An Efficient Whole-Program Critical Paths Search Framework for Large-Scale software 35

CAPS: An Efficient Whole-Program Critical Paths Search Frame...

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35th International Conference on software engineering and Knowledge engineering, SEKE 2023

作者： Li, Peiyang Liu, Zixin Su, Yuening Wang, Hao Jiang, Bo State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University China

Tracking the flow of external inputs in a program with taint-analysis techniques can help developers better identify potential security vulnerabilities in the software. However, directly using the static taint analysis provided by Clang Static Analyzer is inefficient for large-scale software due to the huge but redundant ExplodedGraph generated. Therefore, we propose an efficient Whole-Program Critical Paths Search (CAPS) framework. It first performs a set of optimizations to reduce the ExplodedGraph of each function. Then, it constructs a global exploded graph by inserting call edges among the reduced ExplodedGraphs for each function within the Neo4j graph database. Finally, it proposes loop removal and graph segmentation to optimize the search process for critical paths on the global exploded graph. Our experiments on 3 large-scale software show that CAPS can significantly improve the efficiency of critical path search for large-scale software. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Static analysis

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Effectiveness of Deep Learning Technologies to Assist Educational Inclusion for Children with Down Syndrome 5

Effectiveness of Deep Learning Technologies to Assist Educat...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Silva, Shehan Jayasinghe, Anjula Dilshan, Rishitha Dissanayake, Chandur Vidhanaarachchi, Samitha De Silva, Dilshan Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Down Syndrome (DS) is a genetic disorder causing intellectual disability and developmental delays. Despite instances of discrimination, several individuals with DS have achieved success through proper education and community support. This research addresses the challenges faced by children with DS in terms of motor skill development, cognitive impairments, and speech difficulties with the use of Deep Learning Technologies such as LSTM, CNN machine learning models. To bridge the educational gap and improve speech intelligibility, this study focuses on oral motor exercises that enhance articulation skills. Cognitive development in individuals with DS is also a concern, prompting the proposal of a self-learning system to foster cognitive growth. Fine motor skills improvement is explored through yoga exercises, and interventions like interactive technologies are considered to enhance learning experiences. This paper discusses the development of a user-friendly mobile application for early intervention in DS, integrating interactive activities using advanced technologies and a parent therapy dashboard. The system's potential for improving developmental skills and future expansion is emphasized. © 2023 IEEE.

关键词： Speech intelligibility

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Breaking the Cycle: Exploring the Advantages of Novel Evolutionary Cycles

Breaking the Cycle: Exploring the Advantages of Novel Evolut...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Tisdale, Braden N. Tauritz, Daniel R. Auburn University Bonsai Lab Department of Computer Science and Software Engineering AuburnAL United States

ISBN: (纸本)9781665430654

There have been many different forms of evolution-ary algorithms (EAs) designed by humans over the past 50 years, with many variants optimized for specific classes of problems. As computational resources grow, the automated design of EAs has become an increasingly viable method for improving performance. However, many components of EAs have been treated as largely immutable, for both human and automated designers, dramatically constraining design space. In particular, the evolutionary cycle (the repeating pattern of reproduction and survival) has little or no differences between most popular forms of EA. In a previous paper, we proposed a technique for automatically designing evolutionary cycles using directed graphs, greatly increasing the explorable design space. In this paper, we showcase an improved representation and evolutionary process, provide preliminary experiments demonstrating that EAs produced by this process can outperform those with a traditional cycle, and explore the phenotype landscape to show that the new space explored by our technique may contain better EAs than traditional cycles allow. © 2023 IEEE.

关键词： automated design evolutionary algorithms hyperheuristics

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Synthetic Aircraft Trajectory Generation Using Time-Based VQ-VAE

Synthetic Aircraft Trajectory Generation Using Time-Based VQ...

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2025 Integrated Communications, Navigation and Surveillance Conference, ICNS 2025

作者： Murad, Abdulmajid Ruocco, Massimiliano SINTEF Digital Department of Software Engineering Safety and Security Trondheim Norway Norwegian University of Science and Technology Department of Computer Science Trondheim Norway

ISBN: (纸本)9798331534738

In modern air traffic management, generating synthetic flight trajectories has emerged as a promising solution for addressing data scarcity, protecting sensitive information, and supporting large-scale analyses. In this paper, we propose a novel method for trajectory synthesis by adapting the Time-Based Vector Quantized Variational Autoencoder (TimeVQVAE). Our approach leverages time-frequency domain processing, vector quantization, and transformer-based priors to capture both global and local dynamics in flight data. By discretizing the latent space and integrating transformer priors, the model learns long-range spatiotemporal dependencies and preserves coherence across entire flight paths. We evaluate the adapted TimeVQVAE using an extensive suite of quality, statistical, and distributional metrics, as well as a flyability assessment conducted in an open-source air traffic simulator. Results indicate that TimeVQVAE outperforms a temporal convolutional VAE baseline, generating synthetic trajectories that mirror real flight data in terms of spatial accuracy, temporal consistency, and statistical properties. Furthermore, the simulator-based assessment shows that most generated trajectories maintain operational feasibility, although occasional outliers underscore the potential need for additional domain-specific constraints. Overall, our findings underscore the importance of multi-scale representation learning for capturing complex flight behaviors and demonstrate the promise of TimeVQVAE in producing representative synthetic trajectories for downstream tasks such as model training, airspace design, and air traffic forecasting. © 2025 IEEE.

关键词： Aerobraking

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