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International Workshop on Quality of Service

作者： Xi Tao Weipeng Cao Yinghui Pan Ye Liu Zhong Ming Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

In cloud computing, tenants tend to select predefined flavors with substantial resources that go beyond their needs, in order to avoid resource starvation. As a result, cloud service providers experience a high allocation ratio with a low utilization ratio of resources. To improve the resource utilization, cloud service providers offer lower-priced resources by building an overcommitted environment to reuse resources. However, the colocation interference of workloads in the overcommitted environment may cause performance anomalies and degrade quality of service (QoS). In this paper, we propose a real-time mechanism to pin a group of workloads to a certain subset of CPUs to increase resource utilization and minimize interference. To be specific, we leverage deep reinforcement learning (DRL) to automatically adjust the CPU pinning configuration in the overcommitted environment according to the performance of workloads.

关键词： Cloud computing Computational modeling Buildings Quality of service Interference Deep reinforcement learning Real-time systems

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Numerical Optimal Control Strategy for Series Tuned IPT Systems

Numerical Optimal Control Strategy for Series Tuned IPT Syst...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Weihao Dong Udaya Madawala Craig Baguley Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand Department of Engineering Computer & Mathematical Sciences Auckland University of Technology Auckland New Zealand

Compared to traditional strategies using single, two or three control variables, an optimal control strategy using four control variables can achieve optimal performance when mutual inductance (M) and output power $(P_{out})$ vary largely. For Series-Series (SS) compensated IPT systems, the optimum values for four control variables can be determined analytically using the optimal conditions, related to load impedance matching, minimizing secondary reactance, and operation of both the primary and secondary side converters with soft switching. Due to its highly non-linear and complex nature, the analytical approach cannot be used for all different types of compensation networks. This paper, therefore, proposes a numerical technique that can be used to determine the optimal conditions and subsequent optimal parameter values for any type of compensation network. Results of a SS compensated IPT system are presented in comparison to the traditional analytical approach to demonstrate the efficacy of the proposed numerical method.

关键词： Inductance Impedance matching Soft switching Optimal control Zero voltage switching Power electronics Power generation

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Effects of Cross-Coupling on EV Position Detection of Dynamic Wireless Charging Systems

Effects of Cross-Coupling on EV Position Detection of Dynami...

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International Conference on Electrical, Control and Instrumentation engineering (ICECIE)

作者： Zhongzheng Lin Udaya K. Madawala Craig Baguley Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Department of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9781665480772

Wireless charging while in motion through dynamic inductive power transfer (DIPT) provides multiple benefits for electric vehicle (EV) users. A DIPT system consists of transmitting (TX) coils on the ground and a receiving (RX) coil mounted at the bottom of an EV. Accurate position detection and synchronisation between TX and RX coils are essential to enable DIPT. However, for three-phase DIPT systems, the TX coil structure can result in cross-coupling effects that impact the accuracy of the position detection schemes. Therefore, this paper discusses the coil structure types that result in cross-coupling effects and investigates the impact of cross-coupling on position detection accuracy. The investigation is undertaken through a proposed mathematical model that is based on symmetrical components for distributed coil structures. Using the proposed model, it is shown how cross-coupling affects the position detection schemes based on reflected impedance measurements. The results are presented for a three-phase DIPT with distributed coil structure, and it is shown that conventional position detection schemes based on reflected impedance cannot be used. Therefore, new approaches are required.

关键词： Coils Impedance measurement Instruments Inductive charging Electric vehicles Mathematical models Real-time systems

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Implementing an NFT by Developing a Smart Contract in OpenSea Marketplace 6th

Implementing an NFT by Developing a Smart Contract in OpenSe...

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6th International Conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Shanmugam, Udhayakumar Krisha, Kotte Mohan Nancy John Mari Vienni, Silviya Nandhini, D. Uma Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Chennai India Software Analyst Virtusa Corporation Chennai India School of Information and Communications Technology Saskatchewan Polytechnic College SaskatoonSK Canada Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9783031840586

Blockchain-based security applications have tremendously increased in the recent past with a decentralized network being the best way to implement a secure environment. Blockchain is one such implementation methodology that is considered the biggest disruptive technology. Smart contract-based transactions and assets have a high value and work because of a consensus mechanism without centralized trusted authority. Developing a smart contract for non-fungible tokens has various scopes and our proposed work uses various tools and libraries to achieve it. Brownie is used to implement the smart contract and Infura is used for the Ethereum node. Opensea marketplace is used for rendering the transaction. An Interplanetary distributed file system is used to store the NFTs. The work proposes the coordination of multiple tools to create a blockchain that is reliable and efficient. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart contract

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Optimizing Weighted Ensemble ML IDS Using Parallelization and Batch Learning in Healthcare Networks

Optimizing Weighted Ensemble ML IDS Using Parallelization an...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Blake Ward Daniel Gonzalez Jake Alt Dongfeng Fang Shengjie Xu Department of Computer Science and Software Engineering College of Engineering California Polytechnic State University San Luis Obispo CA USA Department of Cyber Intelligence and Information Operations College of Applied Science and Technology University of Arizona Tucson AZ USA

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

The increasing adoption of loT devices in healthcare necessitates the development of lightweight and fast intrusion de-tection systems (IDS). This paper proposes a weighted ensemble ML IDS with parallelized batch learning to optimize runtime while maintaining ML performance. The proposed method is compared to other solutions, evaluating ML performance and runtime across three different datasets. Experimental results demonstrate that although batch learning introduces feedback delay that slightly degrades ML performance, parallel computing significantly reduces runtime.

关键词： Performance evaluation Degradation Runtime Accuracy Wireless personal area networks Medical services Machine learning Parallel processing Delays

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OFFLINE MODEL-BASED OPTIMIZATION BY LEARNING TO RANK

arXiv

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arXiv 2024年

作者： Tan, Rong-Xi Xue, Ke Lyu, Shen-Huan Shang, Haopu Wang, Yao Wang, Yaoyuan Fu, Sheng Qian, Chao National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China Huawei Technologies Ltd. China

Offline model-based optimization (MBO) aims to identify a design that maximizes a black-box function using only a fixed, pre-collected dataset of designs and their corresponding scores. This problem has garnered significant attention from both scientific and industrial domains. A common approach in offline MBO is to train a regression-based surrogate model by minimizing mean squared error (MSE) and then find the best design within this surrogate model by different optimizers (e.g., gradient ascent). However, a critical challenge is the risk of out-of-distribution errors, i.e., the surrogate model may typically overestimate the scores and mislead the optimizers into suboptimal regions. Prior works have attempted to address this issue in various ways, such as using regularization techniques and ensemble learning to enhance the robustness of the model, but it still remains. In this paper, we argue that regression models trained with MSE are not well-aligned with the primary goal of offline MBO, which is to select promising designs rather than to predict their scores precisely. Notably, if a surrogate model can maintain the order of candidate designs based on their relative score relationships, it can produce the best designs even without precise predictions. To validate it, we conduct experiments to compare the relationship between the quality of the final designs and MSE, finding that the correlation is really very weak. In contrast, a metric that measures order-maintaining quality shows a significantly stronger correlation. Based on this observation, we propose learning a ranking-based model that leverages learning to rank techniques to prioritize promising designs based on their relative scores. We show that the generalization error on ranking loss can be well bounded. Empirical results across diverse tasks demonstrate the superior performance of our proposed ranking-based models than twenty existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Semi-supervised Balanced Distribution Adaptation Model for Intrusion Detection

Semi-supervised Balanced Distribution Adaptation Model for I...

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computer, Vision and Intelligent technology (ICCVIT), International Conference on

作者： Jianying Wang Rongrong Chen Yong Zhang Huzhou University Library Huzhou University Huzhou China School of Software Engineering Dalian University Dalian China School of Information Engineering Huzhou University Huzhou China School of Computer and Information Technology Liaoning Normal University Dalian China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

Intrusion Detection Systems (IDS) are essential for safeguarding modern network infrastructures against a wide range of cyber threats. However, the development of highly effective IDS models is often constrained by the limited availability of labeled data for training, as well as the issue of distribution shift when applying models across different network environments. To overcome these challenges, we present a semi-supervised balanced distribution adaptation (SSBDA) transfer learning method for detecting and preventing network intrusion, which learns from limited labeled data and predicts unlabeled data with different distributions. The proposed method first employs the maximum mean discrepancy measure the differences between the marginal and conditional distributions of both the source and target domains. The Hilbert-Schmidt independence criterion is then utilized to estimate the dependence between labels and features within the source domain. To further enhance the model’s generalization ability, manifold regularization is applied to preserve the data’s locality structure. Finally, the processed data is used to train a tri-training classifier. The SSBDA is compared with several well-known transfer learning-based models using the KDDCup99 and NSL-KDD data sets. The experimental results demonstrate that SSBDA outperforms the comparison models in terms of accuracy and F-score, highlighting its superior performance in intrusion detection tasks.

关键词： Training Manifolds Dimensionality reduction Adaptation models computer vision Accuracy Transfer learning Intrusion detection NSL-KDD Network intrusion

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IQ-VFI: Implicit Quadratic Motion Estimation for Video Frame Interpolation

IQ-VFI: Implicit Quadratic Motion Estimation for Video Frame...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Mengshun Hu Kui Jiang Zhihang Zhong Zheng Wang Yinqiang Zheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Harbin Institute of Technology Shanghai Artificial Intelligence Laboratory The University of Tokyo

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Advanced video frame interpolation (VFI) algorithms approximate intermediate motions between two input frames to synthesize intermediate frame. However, they struggle to handle complex scenarios with curvilinear motions since they overlook the latent acceleration information between the input frames. Moreover, the supervision of predicted motions is tricky because ground-truth motions are not available. To this end, we propose a novel frame-work for implicit quadratic video frame interpolation (IQ-VFI), which explores latent acceleration information and accurate intermediate motions via knowledge distillation. Specifically, the proposed IQ-VFI consists of an implicit acceleration estimation network (IANet) and a VFI back-bone, the former fully leverages spatio-temporal information to explore latent acceleration priors between two input frames, which is then used to progressively modulate linear motions from the latter into quadratic motions in coarse-to-fine manner. Furthermore, to encourage both components to distill more acceleration and motion cues oriented towards VFI, we propose a knowledge distillation strategy in which implicit acceleration distillation loss and implicit motion distillation loss are employed to adaptively guide latent acceleration priors and intermediate motions learning, respectively. Extensive experiments show that our proposed IQ-VFI can achieve state-of-the-art performances on various benchmark datasets.

关键词： Interpolation computer vision Accuracy Motion estimation Computational modeling Estimation Benchmark testing

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Detection of Myocardial Infarction using Spectral Textures through ECG 3

Detection of Myocardial Infarction using Spectral Textures t...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Aziz, Sumair Khan, Muhammad Umar Iqtidar, Khushbakht Sharma, Siddhant Montes, Gabriel Axel Gareeballah, Awadia University of Engineering & Technology Taxila Taxila Pakistan University of Canberra Canberra Australia National University of Sciences and Technology Dept. of Computer Software Engg. Islamabad Pakistan Wright State University FairbornOH United States Neural Axis MiamiFL United States University of Newcastle CallaghanNSW Australia Taibah University College of Applied Medical Sciences Saudi Arabia

ISBN: (纸本)9798350332391

Myocardial infarction (MI), commonly known as a heart attack, can cause nonrecoverable impairment to the cardiac muscles, which may even result in fatality. Therefore, it is crucial to diagnose MI accurately and reliably. This article proposes a novel framework for detecting MI using a 12-channel Electrocardiogram (ECG) signal. The method filters the signal to eliminate motion artifacts and high-frequency noise, followed by the extraction of novel spectral texture patterns and Mel frequency cepstral coefficients (MFCCs) from all ECG signal channels. These features are combined to form a comprehensive feature representation. ANOVA is then applied to reduce feature dimensions, and various state-of-the-art classification methods such as support vector machines (SVM), Artificial Neural Networks (ANN), AdaBoost (AB), and K-nearest neighbors (KNN) are utilized. The AB classifier achieved the highest performance with 98.7% accuracy and 98.4% recall using 10-fold cross-validation. The proposed method is reliable and holds the potential for clinical adoption in hospitals. © 2023 IEEE.

关键词： Support vector machines

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The Art of Deception: Black-box Attack Against Text-to-Image Diffusion Model

The Art of Deception: Black-box Attack Against Text-to-Image...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yuetong Lu Jingyao Xu Yandong Li Siyang Lu Xiang Wei Wei Lu School of Software Engineering Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Computer Science University of Central Florida Orlando FL US

With the rise of Foundation models, Text-to-Image models, as one of its important branches, have been increasingly applied. While focusing on the impressive generation capabilities of these models, it is also crucial to pay attention to the robustness of the models against attacks. In this paper, we shift our focus towards studying the vulnerability of Text-to-Image (T2I) models. To this end, we propose a black-box attack method and demonstrate that T2I models are susceptible to adversarial text attacks. Specifically, this method can disrupt T2I models by making subtle modifications to the model’s input (i.e., prompt) without accessing the model parameters, resulting in the generation of incorrect images. It is worth mentioning that we discovered the ability to switch different types of tokenizers within this black-box framework to handle text, furthermore, this attack framework can be applied to target different versions of T2I models. The experiments indicate that the images generated through adversarial text exhibit noticeable errors. We also employ CLIP score, a metric used to evaluate the similarity between images and image descriptions, to assess the results. The findings demonstrate a significant decrease in the visual-textual similarity after the model is subjected to attacks. Additionally, we have identified a specific type of error that T2I models tend to make when facing attacks – when confronted with unrecognizable text, the model often interprets it as human-related content. This paper not only highlights the vulnerability of T2I models to adversarial text attacks but also further discusses potential methods that could enhance the robustness of these attack techniques. This provides a valuable reference for future research directions in this field.

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