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2024 International Conference on Thermofluids and Manufacturing science, ICTMS 2024

作者： Reshmi, V.P. Nagaraja, K.V. Shankara Department of Computer Science &engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Computational Science Lab Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Civil Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

Efforts are focused on optimizing thermal energy control and utilization within the system. The analysis is done on heat transmission characteristics of a TiO2.H2O nanofluid flowing over a stretching sheet with an integrated heat source/sink. Sensitivity analysis is performed using Response Surface Methodology (RSM) to understand heat transfer behaviour effectively. Governing equations are transformed into ordinary differential equations using suitable similarity variables. Mathematical formulation for steady two-dimensional flow of nanofluid in a porous medium along with a stretching sheet is established, and numerical outcomes are optimized using RSM. Influence of porous medium (A), volume fraction (φ), and heat source/sink (Hs) on Nusselt number (Nu) and skin friction factor (Cf) is analyzed through normal, response surface, and contour plots and it is found that for increasing Hs decreases Nu for higher φ values. © Published under licence by IOP Publishing Ltd.

关键词： Porous materials

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Spectroscopy and molecule opacity investigation on excited states of SiS

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Chinese Physics B 2024年第5期33卷 381-387页

作者：李瑞吕浩男桑纪群刘晓华梁桂颖吴勇 College of Teacher Education Qiqihar UniversityQiqihar 161006China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Department of Physics College of ScienceQiqihar UniversityQiqihar 161006China School of Data Science and Artificial Intelligence Jilin Engineering Normal UniversityChangchun 130052China HEDPS Center for Applied Physics and TechnologyPeking UniversityBeijing 100084China

The SiS molecule,which plays a significant role in space,has attracted a great deal of attention for many *** to complex interactions among its low-lying electronic states,precise information regarding the molecular structure of SiS is *** obtain accurate information about the structure of its excited states,the high-precision multireference configuration interaction(MRCI)method has been *** method is used to calculate the potential energy curves(PECs)of the 18Λ–S states corresponding to the lowest dissociation limit of *** core–valence correlation effect,Davidson’s correction and the scalar relativistic effect are also included to guarantee the precision of the MRCI *** on the calculated PECs,the spectroscopic constants of quasi-bound and bound electronic states are calculated and they are in accordance with previous experimental *** transition dipole moments(TDMs)and dipole moments(DMs)are determined by the MRCI *** addition,the abrupt variations of the DMs for the 1^(5)Σ^(+)and 2^(5)Σ^(+)states at the avoided crossing point are attributed to the variation of the electronic *** opacity of SiS at a pressure of 100 atms is presented across a series of *** increasing temperature,the expanding population of excited states blurs the band boundaries.

关键词： SiS opacity excited state spectroscopic constant configuration interaction

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Deep Learning Based Intrusion Detection in Cloud Services for Resilience Management

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Computers, Materials & Continua 2022年第6期71卷 5117-5133页

作者： S.Sreenivasa Chakravarthi R.Jagadeesh Kannan V.Anantha Natarajan Xiao-Zhi Gao CSE Center for Intelligent ComputingSree Vidyanikethan Engineering College&School of Computer Science and EngineeringVITChennaiTamilnadu600127India School of Computer Science and Engineering VITChennaiTamilnadu600127India CSE Center for Intelligent ComputingSree Vidyanikethan Engineering CollegeTirupati517102India School of Computing University of Eastern FinlandKuopio02130EspooFinland

In the global scenario one of the important goals for sustainable development in industrial field is innovate new technology,and invest in building *** the developed and developing countries focus on building resilient infrastructure and promote sustainable developments by fostering *** this juncture the cloud computing has become an important information and communication technologies model influencing sustainable development of the industries in the developing *** part of the innovations happening in the industrial sector,a new concept termed as‘smart manufacturing’has emerged,which employs the benefits of emerging technologies like internet of things and cloud *** services deliver an on-demand access to computing,storage,and infrastructural platforms for the industrial users through *** the recent era of information technology the number of business and individual users of cloud services have been increased and larger volumes of data is being processed and stored in *** a consequence,the data breaches in the cloud services are also increasing day by *** to various security vulnerabilities in the cloud architecture;as a result the cloud environment has become *** restore the normal behavior of the cloud,detect the deviations,and achieve higher resilience,anomaly detection becomes *** deep learning architectures-based anomaly detection mechanisms uses various monitoring metrics characterize the normal behavior of cloud services and identify the abnormal *** paper focuses on designing an intelligent deep learning based approach for detecting cloud anomalies in real time to make it more *** deep learning models are trained using features extracted from the system level and network level performance metrics observed in the Transfer Control Protocol(TCP)traces of the *** experimental results of the proposed approach demonstrate a superior performance in terms

关键词： Anomaly detection network flow data deep learning migration auto-encoder support vector machine

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A Composite Ansatz for Calculation of Dynamical Structure Factor

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Chinese Physics Letters 2024年第1期41卷 92-103页

作者：张玉佩莫崇杰张平康炜 HEDPS Center for Applied Physics and Technologyand School of PhysicsPeking UniversityBeijing 100871China Beijing Computational Science Research Center Beijing 100193China Institute of Applied Physics and Computational Mathematics Beijing 100088China HEDPS Center for Applied Physics and Technologyand College of EngineeringPeking UniversityBeijing 100871China

We propose an ansatz without adjustable parameters for the calculation of a dynamical structure *** ansatz combines the quasi-particle Green’s function,especially the contribution from the renormalization factor,and the exchange-correlation kernel from time-dependent density functional theory together,verified for typical metals and semiconductors from a plasmon excitation regime to the Compton scattering *** has the capability to reconcile both small-angle and large-angle inelastic x-ray scattering(IXS)signals with muchimproved accuracy,which can be used as the theoretical base model,in inversely inferring electronic structures of condensed matter from IXS experimental signals *** may also be used to diagnose thermal parameters,such as temperature and density,of dense plasmas in x-ray Thomson scattering experiments.

关键词： excitation inverse verified

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Unpaired 3D Shape-to-Shape Translation via Gradient-Guided Triplane Diffusion

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IEEE Transactions on Visualization and Computer Graphics 2025年第5期31卷 3376-3386页

作者： Zhang, Wenxiao Rahmani, Hossein Liu, Jun Hohai University College of Computer Science and Software Engineering Nanjing211100 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom

Unpaired shape-to-shape translation refers to the task of transforming the geometry and semantics of an input shape into a new shape domain without paired training data. Previous methods utilize GAN-based architectures to perform shape translation, employing adversarial training to transform the source shape encoding into the target domain in the low-dimensional latent feature space. However, these methods encounter difficulties in generating diverse and high-quality results, as they often suffer from issues such as "mode collapse". This leads to limited generation diversity and makes it challenging to find an accurate latent code that adequately represents the input shape. In this paper, we achieve unpaired shape-to-shape translation via a triplane diffusion model, in which we factorize 3D objects into triplane representations and conduct a diffusion process on these representations to accomplish shape domain transformation. We observe that by adding an appropriate amount of noise to an input object during the forward diffusion process, domain-specific shape structures are smoothed out while the overall structure is still preserved. Subsequently, we progressively remove the noise via an unconditional diffusion model trained on the target shape domain in the reverse diffusion process. This allows us to obtain a denoised output that retains the structural similarities of the source input while aligning with the distribution of the target shape domain. During this process, we propose two gradient-based guidance mechanisms to guide the translation process to guarantee more faithful results during the denoising process. We conduct extensive experiments on different shape domains, and the experimental results demonstrate that our method achieves superior shape fidelity with high quality compared to current state-of-the-art baselines. © 1995-2012 IEEE.

关键词： Signal encoding

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LiveDataLab: A Cloud-based Open Lab for Integrating Big Data Research, Education, and Applications

LiveDataLab: A Cloud-based Open Lab for Integrating Big Data...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Zhai, ChengXiang University of Illinois at Urbana-Champaign Siebel School of Computing and Data Science Grainger College of Engineering United States

ISBN: (纸本)9798350362480

We present the vision of LiveDataLab and discuss the new research directions and application opportunities it opens up. LiveDataLab is envisioned to be a cloud-based open lab infrastructure where research, education, and application development in big data can be integrated in one unified platform, thus accelerating research, technology transfer, and workforce development in big data. © 2024 IEEE.

关键词： Cloud platforms

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VisPhis: Automated Phishing Detection Bot for Open Source VoIP Applications 5

VisPhis: Automated Phishing Detection Bot for Open Source Vo...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Shaik, Nasir Hussain Kumar, Neelesh Menon, Rudraksh Sujatha, G. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur India

ISBN: (纸本)9798331518097

Mobile and Commercial devices are at high risk when navigating through websites and clicking on various links. Due to lack of knowledge, Individuals click on various links showing up on legitimate chats leading to clickjacking and phishing attacks. Every time a link shows up, Users cannot go to a site like virustotal and scan and wait for them to detect. This project aims to implement a structured script design and an automated program that scans and flags links as malicious and prevents certain cyberattacks. © 2024 IEEE.

关键词： Bot (Internet)

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POLYIE: A Dataset of Information Extraction from Polymer Material Scientific Literature

POLYIE: A Dataset of Information Extraction from Polymer Mat...

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2024 Conference of the North American Chapter of the Association for computational Linguistics: Human Language Technologies, NAACL 2024

作者： Cheung, Jerry Junyang Zhuang, Yuchen Li, Yinghao Shetty, Pranav Zhao, Wantian Grampurohit, Sanjeev Ramprasad, Rampi Zhang, Chao College of Computing United States School of Materials Science and Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798891761148

Scientific information extraction (SciIE), which aims to automatically extract information from scientific literature, is becoming more important than ever. However, there are no existing SciIE datasets for polymer materials, which is an important class of materials used ubiquitously in our daily lives. To bridge this gap, we introduce POLYIE, a new SciIE dataset for polymer materials. POLYIE is curated from 146 full-length polymer scholarly articles, which are annotated with different named entities (i.e., materials, properties, values, conditions) as well as their N-ary relations by domain experts. POLYIE presents several unique challenges due to diverse lexical formats of entities, ambiguity between entities, and variable-length relations. We evaluate state-of-the-art named entity extraction and relation extraction models on POLYIE, analyze their strengths and weaknesses, and highlight some difficult cases for these models. To the best of our knowledge, POLYIE is the first SciIE benchmark for polymer materials, and we hope it will lead to more research efforts from the community on this challenging task. Our code and data are available on: https://***/jerry3027/PolyIE. © 2024 Association for computational Linguistics.

关键词： Information retrieval

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AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1626-1638页

作者： Haboub, Amine Baali, Hamza Bouzerdoum, Abdesselam Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Ar Rayyan Qatar University of Adelaide School of Electrical and Mechanical Engineering Australia Hamad Bin Khalifa University Ar Rayyan Qatar

This paper proposes a new deep learning framework for time series classification in the discrete cosine transform (DCT) domain with spectral enhancement and self-attention mechanisms. The time series signal is first partitioned into discrete segments. Each segment is rearranged into a matrix using a sliding window. The signal matrix is then transformed to spectral coefficients using a two-dimensional (2D) DCT. This is followed by logarithmic contrast enhancement and spectral normalization to enhance the DCT coefficients. The resulting enhanced coefficient matrix serves as input to a deep neural network architecture comprising a self-attention layer, a multi-layer convolutional neural network (CNN), and a fully connected multilayer perceptron (MLP) for classification. The AttDCT CNN model is evaluated and benchmarked on thirteen different time series classification problems. The experimental results show that the proposed model outperforms state-of-the-art deep learning methods by an average of 2.1% in classification accuracy. It achieves higher classification accuracy on ten of the problems and similar results on the remaining three. © 2020 IEEE.

关键词： Convolutional neural networks

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Hybrid CNN-BiLSTM Model for Power Quality Disturbance Classification 1

Hybrid CNN-BiLSTM Model for Power Quality Disturbance Classi...

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1st IEEE International Conference on Advanced Power engineering and Energy, APEE 2024

作者： Lin, Jiajian Tavalaei, Jalal Yeo, Lit Yen Zhou, Yutong Afrouzi, Hadi Nabipour Ektesabi, Mehran Motamed Swinburne University of Technology Faculty of Engineering Computing and Science Kuching Malaysia College of Mechancial Engineering Taishan University Taian China School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC Australia

ISBN: (纸本)9798350360936

With the evolution of intelligent grid tech, the need for monitoring power quality has markedly risen, essential for the continuous functioning of the power grid. Addressing this matter, this research proposes a combined approach combining Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks. The model effectively catches spatial or temporary aspects of power quality disruptions using CNN and employs BiLSTM to document long-term or temporal aspects, thus creating a profound correlation between input and output sequences. By substantially decreasing reliance on conventional physical attributes, this technique markedly enhances the precision in detecting power quality anomalies. Crucially, this technique, independent of particular original samples and existing information, holds widespread applicability and is applicable in identifying patterns in various other intricate systems. Research on experiments indicates that, as opposed to conventional machine learning frameworks, deep learning approaches are more effective in detecting disruptions in power quality. Our hybrid model excels in identifying power quality disturbance (PQD) and produces highly accurate recognition outcomes by autonomously analyzing central features of the disturbance indications. © 2024 IEEE.

关键词： Convolutional neural networks

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