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International Conference on Soft Computing and Measurements (SCM)

作者： Ivan I. Kholod Pavel V. Korytov Maria V. Sorochina Department of Computer Science and Engineering Saint-Petersburg Electrotechnical University “LETI” Saint-Petersburg Russia Department of Software Engineering and Computer Applications Saint-Petersburg Electrotechnical University “LETI” Saint-Petersburg Russia

The paper considers application of natural language processing methods (NLP) to solve the task of identifying the core skills acquired by students in the process of education. An ensemble solution, consisting of models WikiNEuRAL, KBIR-Inspec, ruGTP3Large, word2vec, and other NLP methods, is used to extract and process Russian language keywords. The described method was developed to automate the compilation of a student’s CV in the information system called “Partner’s Account” of the LETI” university.

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Numerical electronic device modeling for circuit simulators

Numerical electronic device modeling for circuit simulators

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： M. Yanilmaz V.W. Eveleigh Department of Electrical Engineering and Computer Science Technological Institute Northwestern University Evanston IL USA CASE (Computer Applications in Software Engineering) Center Syracuse University Syracuse NY USA

Static and dynamic numerical device modeling based on dot product evaluation of multidimensional Bernstein polynomial interpolants to evaluate device operating characteristics is presented as an alternative to analytical modeling and table look-up modeling schemes that have been proposed. The model formulation satisfies the requirements imposed by the algebraic-differential equation solution algorithms of most simulators, namely continuity of the output variables and preservation of the monotonicity and concavity or convexity of the data. It is to be noted, however, that those are necessary but not sufficient conditions. Simulator convergence is improved by treating the intrinsic node charges as state variables and using their variations over iteration steps as an iteration criterion along with the node voltage variation checks. MOSFET is taken as a benchmark device. Experimental results to illustrate the effect of the basic premises of the numerical model on simulation performance are presented.< >

关键词： Numerical models Circuit simulation Multidimensional systems Polynomials Analytical models Equations Sufficient conditions Voltage MOSFET circuits Numerical simulation

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White Box Testing Module for Evaluation of Transformers-Based DNNs

White Box Testing Module for Evaluation of Transformers-Base...

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Innovations in Intelligent Systems and applications Conference

作者： Gokhan Cetiner Cem Baglum Ugur Yayan Ahmet Yazici Computer Engineering Department Center of Intelligent Systems Applications Research (CISAR) Eskisehir Osmangazi University Eskisehir Turkiye Software Engineering Department Center of Intelligent Systems Applications Research (CISAR) Eskisehir Osmangazi University Eskisehir Turkiye

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

Artificial intelligence is actively becoming widespread in every field. The complex structure of deep neural network models, especially those used in real-world applications, may contain faults that lead to unexpected model behavior, even well-trained AI models. To detect these types of faults and model behaviors, it is necessary to perform a white box test of the model's reliability and adequacy by mutating its layers, activation functions, and similar model structures and parameters. White box testing is a method that tests the logical flow of software to detect possible faults and deficiencies in the code. The proposed module performs model-based testing using this testing technique. The module was developed as an extension of the traditional testing tool IM-FIT. It was inspired by IM-FIT's advanced fault detection capacity in traditional software testing, autonomous or customized testing options, and detailed report generation capabilities. The module carries out an advanced scanning and mutation process using its own unique fault library for deep neural networks. The module successfully performed the test by autonomously generating mutants and classifying these mutants. Tests were performed on a Transformer model using the TensorFlow library during the testing process. As a result of the tests performed with 5, 10, and 20 epoch values, a total of 606 mutants were produced. While 414 of these mutants were classified as killed, 192 were classified as survived. This study presents it to the literature as an effective mutation testing tool focusing on deep neural networks.

关键词： software testing Technological innovation Fault detection Focusing Artificial neural networks Transformers Libraries software reliability Intelligent systems Glass box

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A Maple Exploration of Problem 6 of the IMO 88 4th

A Maple Exploration of Problem 6 of the IMO 88

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4th Maple Conference, MC 2020

作者： Zeng, Zhenbing Sun, Xiang Huang, Yong Xu, Yaochen Chen, Xiaoru Yang, Lu Department of Mathematics Shanghai University Shanghai200444 China South China Institute of Software Engineering Guangzhou University Guangzhou510990 China Institute of Computing Sciences and Technology Guangzhou University Guangzhou510006 China Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu610041 China

ISBN: (纸本)9783030816971

In this paper, we show that by using Maple software, some direct searching computation could derive a solution to Problem 6 of the 1988 International Mathematics Olympiad, which asks to prove that if a and b are integers such that ab+ 1 divides a2+ b2, then (a2+ b2) / (ab+ 1 ) is the square of an integer. © 2021, Springer Nature Switzerland AG.

关键词： Integer International Mathematics Olympiad Maple software Square

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Intrusion Detection Mechanism Using Deep Learning

Intrusion Detection Mechanism Using Deep Learning

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： P. Ananthi K. Nirmaladevi Naveen Kumar S Department of Computer Technology-PG Kongu Engineering College Erode Tamilnadu Department of Computer Applications Madanapalle Institute of Technology & Science M.Sc Software Systems Kongu Engineering College Erode India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

In today's connected and data-driven world, networks and digital systems need to be protected from malicious attacks. The effectiveness of conventional Intrusion Detection Systems (IDS) in recognizing and impeding novel threats and intricate methodologies has its limits. This study introduces a new method for improving network security by using deep learning techniques in the design and development of an intrusion detection system (IDS). The suggested Intrusion Detection System uses deep learning techniques, specifically Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs), to identify and react to known and new security threats. Data can be taught to be encoded into a lower-dimensional representation and then decoded back into its original form using autoencoders (AEs). Autoencoders are a cutting-edge intrusion detection system that can lower false positives and raise overall detection accuracy because of their capacity to automatically learn from and adjust to the ever-changing threat landscape. They enhance computer networks capacity to spot irregularities and possible security threats. With the deep learning model obtaining a high accuracy of 99% in real-world threat detection, the results show that deep learning is effective in developing intrusion detection systems.

关键词： Deep learning Accuracy Autoencoders Intrusion detection Network security Threat assessment Real-time systems computer security Optimization Long short term memory

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Video Surveillance-Based Intrusion Detection System in Edge Cloud Environment 3rd

Video Surveillance-Based Intrusion Detection System in Edge ...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Sharma, Annu Devasenapathy, Deepa Raja, M. Shadrach, Finney Daniel Shirgire, Anil Arun, R. Yau, Thomas Moh Shan Department of Computer Science and Applications Rajarajeshwari College of Engineering Karnataka Bengaluru560074 India Instructor-II/Computing and Software Engineering U.A. Whitaker College of Engineering Florida Gulf Coast University Fort Myers United States Department of Computer Science and Engineering CMR Institute of Technology Karnataka Bengaluru560037 India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Civil Engineering Dr. D Y Patil Institute of Technology Maharashtra Pimpri Pune411018 India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Director of Centre of University Courses and Innovative Learning University of Technology Sarawak Jalan Universiti Sibu Sarawak96000 Malaysia

ISBN: (纸本)9789819986453

Video surveillance is mostly used for monitoring distant videos and the cloud memory in the system provides more secure to the data. But, in the existing methodologies poor intrusion detection accuracy as well as poor performance rate is considered as the main demerits. So, to overcome the above-mentioned demerits, a novel video surveillance-based Intrusion Detection System (IDS) (VS-IDS) in the edge cloud environment via deep learning method has been developed in this research. Different algorithms are used for object detection and intrusion detection. Subsequently, the proposed model can be implemented and verified in NVIDIA Jetson TX2 platform. The proposed model attains better rate of performance than the existing models. The Performance of the proposed model can be validated with accuracy, precision, recall, MSE and F-measure. Higher performance rate indicates that the system can detect intrusion more accurately than the existing mechanisms. 99% of detection accuracy can be attained through the proposed model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Context-aware and class imbalance invariant threat severity assessment for heterogeneous IoT

TechRxiv

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TechRxiv 2021年

作者： Nitish, A. Hanumanthappa, J. Prakash, S.P. Shiva Krinkin, Kirill The Department of Studies in Computer Science University of Mysore India The Department of Information Science & Engineering JSS Science and Technology University Mysuru India The Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University "LETI" Russia

Due to demand for information ubiquity and large-scale automation, proliferating Internet-connected heterogeneous devices exhibit significant variations in data processing capacities, purposes, operating principles, underlying protocols, and dynamic contexts. As a result, adversarial entities exploit the increasing heterogeneous network (HetIoT) vulnerabilities, leading to frequent high-impact attacks due to anomalous device interactions and scarce knowledgebase. This paper presents a two-fold solution to the problem through a network intrusion detection and prevention framework for HetIoT, called HetIoT-NIDPS. Firstly, we assign fault scores to the Expert-curated Knowledgebase (EK) framework, correlating with low-level alerts to assess threat severity and achieve context-awareness. Secondly, the proposed Beta distribution-based HetIoT traffic behavior approximation facilitates class imbalance invariance and improves classifier performance. Additionally, the HetIoT-NIDPS can detect zero-day attacks by identifying known attack variations upon encountering unseen traffic instances. Furthermore, the dynamic HetIoT contexts necessitate real-time threat assessment through online training—performed by analyzing small batches of network traffic samples. We propound the CorrELM classifier based on the extreme learning machine algorithm and test the hypotheses on the Bot-IoT dataset. Finally, we prioritize the correlated alerts based on their severity, determined from root cause analysis and threat severity assessment tables. The results obtained prove that the proposed HetIoT-NIDPS framework is context-aware—producing reduced false alerts, class imbalance invariant—facilitating near real-time threat assessment with unbiased classifier performance, and generalizable—applicable to many NID datasets, which the existing techniques lack when combined. © 2021, CC BY-NC-SA.

关键词： Heterogeneous networks

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Finding Time and Buffer Preserving Routes in Gaussian Intermittent Networks

Finding Time and Buffer Preserving Routes in Gaussian Interm...

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International Conference on Information Technology: New Generations (ITNG)

作者： Omar Y. Tahboub Javed I. Khan Software Engineering Applications PayPal San Jose California Media Communications and Networking Department of Math & Computer Science Kent State University Kent OH

Intermittent networks (IntNET) are networks where all the links in a path may not be available concurrently. Finding routes on such a network is hard. No trivial extension of classical routing protocols is known to provide a correct solution. In this paper, we present a constraint resource planning (CRP) based advanced route computation and scheduling algorithm for IntNET. The algorithm computes routes achieving near minimum delay and also achieve the bound on minimum intermediate buffer requirements. It was difficult previously to deploy such advanced routing. However, this design is suitable for the new IETF traffic engineering extensions that now enable such advanced externally computed route to be injected into a MPLS infrastructure.

关键词： Routing Delay Routing protocols Schedules Network topology Topology Bandwidth

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Everything you ever wanted to know about software engineering education, but were afraid to ask 98

Everything you ever wanted to know about software engineerin...

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3rd Annual Conference on Integrating Technology into computer Science Education, ITiCSE 1998

作者： Mead, Nancy R. Hilburn, Thomas B. Bagert, Donald J. Ryan, Michael Benediktsson, Oddur Van Scoy, Frances Software Engineering Institute Carnegie Mellon University 5000 Forbes Ave. PittsburghPA15213 United States Department of Computer Science Embry-Riddle Aeronautical University United States Department of Computer Science Texas Tech University LubbockTX79409-3104 United States School of Computer Applications Dublin City University Glasnevin Dublin 9 Ireland University of Iceland Dunhaga 5 Reykjavik Iceland Computer Science West Virginia University MorgantownWV26506-0209 United States

ISBN: (纸本)1581130007

In this panel, we will address questions on software engineering education that are voted on by the conference attendees in advance of the session. A sample set of questions will be posted. Conference attendees who will then vote on the questions to be addressed by the panel will augment these. After the panellists provide answers to a given question, the discussion will be opened up for follow-up questions or comments by the conference attendees. © 1998 ACM.

关键词： software engineering

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