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MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics 40

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficien...

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40th IEEE international conference on Data Engineering, ICDE 2024

作者： Chen, Yu Zhang, Sheng Zhou, Ziying Wang, Xiaokun Liang, Yu Chen, Ning Yan, Yuting Xiao, Mingjun Wu, Jie Qian, Zhuzhong Xu, Harry Nanjing University State Key Lab. for Novel Software Technology China School of Systems Science Beijing Normal University China School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China China Temple University Center for Networked Computing United States Samueli School of Engineering University of California Computer Science Department Los Angeles United States

ISBN: (纸本)9798350317152

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy;meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches. © 2024 IEEE.

关键词： Profitability

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Algorithm for EEG Signal Recognition Based on Unsupervised Pretraining and Transformer Attention Network 13

Algorithm for EEG Signal Recognition Based on Unsupervised P...

引用

13th international conference of Information and Communication technology, ICTech 2024

作者： Zhao, Kui Li, Zhen Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350376258

This article proposes a Vision Transformer network based on pre-trained ResNet, a convolutional neural network, for automatic recognition of epilepsy EEG signals. An autoencoder model is constructed for unsupervised pre-training, obtaining weights on an unlabeled dataset. This approach differs from the lengthy and complex training of recurrent neural networks and multilayer perceptrons. ResNet, as a classic convolutional neural network, possesses strong feature learning capabilities for input data like images. Through unsupervised pre-training, ResNet can extract informative representations from large-scale unlabeled data, which helps improve the performance of subsequent classification tasks. EEG signals are time series data that require more modeling of temporal correlations compared to fixed-size inputs like images. EEG signals typically exhibit long-term temporal dependencies, and Transformers, through self-attention mechanisms, can capture long-range dependencies in time. Compared to recurrent neural networks and multilayer perceptrons, Transformer-based networks can better model global relationships in sequential data, thereby enhancing classification performance. Unsupervised pre-training eliminates the need for data annotation, greatly reducing manual labeling efforts. After the pre-training phase, the pretrained weights are saved as the initial weights for the subsequent network. A neural network based on Vision Transformer attention is constructed to enhance the network's feature extraction and learning capabilities. The pre-trained weights are iteratively fine-tuned, combining both steps. This approach accelerates the training and convergence speed of the Vision Transformer network, reduces computational costs, and improves the accuracy of the final classification results. Compared to other models such as LSTM, CNN, and SVM, various performance metrics show significant improvements. ©2024 IEEE.

关键词： Recurrent neural networks

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An adaptive Medical Cyber-Physical System for post diagnosis patient care using cloud computing and machine learning approach 3

An adaptive Medical Cyber-Physical System for post diagnosis...

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3rd international conference for Emerging technology, INCET 2022

作者： Miah, M. Saef Ullah Sarwar, Talha Bin Islam, Saima Sharleen Haque, Md. Samiul Masuduzzaman, Md. Bhowmik, Abhijit Universiti Malaysia Pahang Faculty of Computing Pekan Malaysia Department of Computer Science Dhaka Bangladesh University of Dhaka Institute of Information Technology Dhaka Bangladesh Kumoh National Institute of Technology Department of It Convergence Engineering Gumi Korea Republic of

ISBN: (纸本)9781665494991

Medical care is one of the most basic human needs. Due to the global shortage of doctors, nurses, and other healthcare personnel, medical cyber-physical systems are quickly becoming a viable option. Post-diagnosis surveillance is an essential application of these systems, which can be performed more successfully using various monitoring devices rather than active observation by nurses in their physical presence. However, most existing solutions for this application are rigid and do not consider current difficulties. Intelligent and adaptive systems can overcome the challenges because of the advances in relevant technology, especially healthcare 4.0. Therefore, this work presents an adaptive system based on cloud and edge computing architecture and machine learning approaches to perform post-diagnosis medical tasks on patients, thus reducing the need for nurses, especially in the post-diagnosis phase. © 2022 IEEE.

关键词： Cyber Physical System

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Prevention of DDoS Attacks Targeting Financial Services using Supervised Machine Learning and Stacked LSTM 7

Prevention of DDoS Attacks Targeting Financial Services usin...

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7th IEEE international conference for convergence in technology, I2CT 2022

作者： Kathirkamanathan, Nisanthan Thevarasa, Bahinthan Mahadevan, Gukajan Skandhakumar, Nimalaprakasan Kuruwitaarachchi, Nuwan University of Sri Jayewardenepura Faculty of Technology Department of Ict Colombo Sri Lanka

ISBN: (纸本)9781665421683

Distributed Denial of Service (DDoS) attacks are the most frequent and serious security threats to financial services. It is an attack-type that can be carried out fairly easily with the intention of making the resources unavailable to the users at the required time, but it is very difficult and costly to track and mitigate those attacks. In the recent past, many ISPs have adapted technologies to prevent DDoS at the network level, but they are not able to fully detect and mitigate many of the DDoS attacks as they originate from different types of devices and from several countries. There have also been several research strategies targeting mitigation of DDoS attacks in cloud computing environments, but none of them have tried to specifically target financial services transactions. Financial services are one of the most affected services among all the services in cloud computing environments. This research was conducted with the objective to mitigate DDoS attacks that target the financial services transactional endpoints of web applications, using stacked long short-term memory. This study mainly focused on the usage of deep neural networks to classify and mitigate DDoS attacks that are happening in the HTTP layer of a server, because deep learning neural networks have powerful performance when compared with other machine learning algorithms and intrusion detection techniques. This research study focused on three specific types of deep neural networks called long short-memory, gated recurrent unit, and simple artificial neural network. Keras and TensorFlow libraries were used to construct and train a stacked long short-term memory to discover the existence and DDoS types of attacks on a server. Moreover, during the research study, a high accuracy deep learning model was developed to predict and mitigate DDoS attacks in the application layer of transaction endpoints of financial services organizations. The main goal of the model produced in this research is to create

关键词： Denial-of-service attack

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Reasoning on Efficient Knowledge Paths: Knowledge Graph Guides Large Language Model for Domain Question Answering 15

Reasoning on Efficient Knowledge Paths: Knowledge Graph Guid...

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15th IEEE international conference on Knowledge Graph, ICKG 2024

作者： Jiang, Boran Wang, Yuqi Luo, Yi He, Dawei Cheng, Peng Gao, Liangcai Peking University The Institute of Computer Science and Technology Beijing China Organization State Key Laboratory of Media Convergence Production Technology and Systems Beijing China

ISBN: (纸本)9798331508821

Large language models (LLMs), such as GPT3.5, GPT4 and LLAMA2 perform surprisingly well and outperform human experts on many tasks. However, in many domain-specific evaluations, these LLMs often suffer from hallucination problems due to insufficient training of relevant corpus. Furthermore, fine-tuning large models may face problems such as the LLMs are not open source or the construction of high-quality domain instruction is difficult. Therefore, structured knowledge databases such as knowledge graph can better provide domain background knowledge for LLMs and make full use of the reasoning and analysis capabilities of LLMs. In some previous works, LLM was called multiple times to determine whether the current triplet was suitable for inclusion in the subgraph when retrieving subgraphs through a question. Especially for the question that require a multi-hop reasoning path, frequent calls to LLM will consume a lot of computing power. Moreover, when choosing the reasoning path, LLM will be called once for each step, and if one of the steps is selected incorrectly, it will lead to the accumulation of errors in the following steps. In this paper, we proposed an method of reasoning on efficient knowledge paths (RoK). This method can integrated and optimized the pipeline for selecting reasoning paths from KG based on LLM, which can reduce the dependency on LLM. In addition, we propose a simple and effective subgraph retrieval method based on chain of thought (CoT) and page rank which can returns the paths most likely to contain the answer. We conduct experiments on three datasets: GenMedGPT-5k [14], WebQuestions [2], and CMCQA [21]. Finally, RoK can demonstrate that using fewer LLM calls can achieve the same results as previous SOTAs models. © 2024 IEEE.

关键词： Knowledge graph

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Positive Concave Deep Equilibrium Models 41

Positive Concave Deep Equilibrium Models

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41st international conference on Machine Learning, ICML 2024

作者： Gabor, Mateusz Piotrowski, Tomasz Cavalcante, Renato L.G. Faculty of Electronics Photonics and Microsystems Wroclaw University of Science and Technology Wroclaw Poland Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Toruń Poland Fraunhofer Heinrich Hertz Institute Berlin Germany

Deep equilibrium (DEQ) models are widely recognized as a memory efficient alternative to standard neural networks, achieving state-of-the-art performance in language modeling and computer vision tasks. These models solve a fixed point equation instead of explicitly computing the output, which sets them apart from standard neural networks. However, existing DEQ models often lack formal guarantees of the existence and uniqueness of the fixed point, and the convergence of the numerical scheme used for computing the fixed point is not formally established. As a result, DEQ models are potentially unstable in practice. To address these drawbacks, we introduce a novel class of DEQ models called positive concave deep equilibrium (pcDEQ) models. Our approach, which is based on nonlinear Perron-Frobenius theory, enforces nonnegative weights and activation functions that are concave on the positive orthant. By imposing these constraints, we can easily ensure the existence and uniqueness of the fixed point without relying on additional complex assumptions commonly found in the DEQ literature, such as those based on monotone operator theory in convex analysis. Furthermore, the fixed point can be computed with the standard fixed point algorithm, and we provide theoretical guarantees of its geometric convergence, which, in particular, simplifies the training process. Experiments demonstrate the competitiveness of our pcDEQ models against other implicit models. Copyright 2024 by the author(s)

关键词： Fixed point arithmetic

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Comparartive Study and performance analysis of Householder Transform-Based RLS algorithm

Comparartive Study and performance analysis of Householder T...

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2022 IEEE international conference on Electronics, computing and Communication Technologies, CONECCT 2022

作者： Roopa, K.M. Jalaja, S. Vidya, S.S. Bangalore Institute of Technology Department of Mathematics Bangalore India Dept. of Electronics and Communication Bangalore Institute of Technology Bangalore India Dept. of Eie Bangalore Institute of Technology Bangalore India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

This paper presents a method to find the eigenvalues of the symmetric matrix. The proposed method is a combination of Householder's, Gerscgorin, Strum sequence and Chebyshev methods, which is called HGSCM. In this paper, the HGSCM gives eigenvalues with less number of computations compared with existing method which is a combination of Givens, Gerschgorin, Strum sequence and Secant (Newton Raphson) methods are discussed analytically. Further proposed modified Householder Transform is implemented on RLS algorithm. As a result it shows less computation, faster convergence rate and smaller error. © 2022 IEEE.

关键词： Adaptive filters

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First-order numerical method for the singularly perturbed nonlinear Fredholm integro-differential equation with integral boundary condition 2

First-order numerical method for the singularly perturbed no...

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2nd international Workshop on Mathematical Modeling and Scientific computing: Focus on Complex Processes and Systems, MMSC 2022

作者： Amirali, I. Durmaz, M.E. Acar, H. Amiraliyev, G.M. Department of Mathematics Faculty of Art and Science Düzce University Düzce81620 Turkey Department of Information Technology Kirklareli University Kirklareli39100 Turkey Paşa Neighborhood Millet Street 8/9 Şişli District Istanbul34377 Turkey

In this work, we consider first-order singularly perturbed quasilinear Fredholm integro-differential equation with integral boundary condition. Building a numerical strategy with uniform ϵ-parameter convergence is our goal. With the use of exponential basis functions, quadrature interpolation rules and the method of integral identities, a fitted difference scheme is constructed and examined. The weight and remainder term are both expressed in integral form. It is shown that the method exhibits uniform first-order convergence of the perturbation parameter. Error estimates for the approximation solution are established and a numerical example is given to validate the theoretical findings. © Published under licence by IOP Publishing Ltd.

关键词： Numerical methods

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Multi-strategy Sparrow Search Algorithm based on Differential Evolution 4

Multi-strategy Sparrow Search Algorithm based on Differentia...

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4th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2022

作者： Gao, Yongjun Chen, Hanning Tiangong University School of Computer Science and Technology Tianjin China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

For the purpose of improving the sparrow search algorithm, there are some disadvantages of low convergence accuracy, slow convergence speed and prone to get bogged down local optimization, a multi-strategy sparrow search algorithm is proposed in this paper. Firstly, Initializing the sparrow population using the chaotic opposition-based learning strategy, which raise the randomness of sparrow individuals and make the initial solution more uniformly distributed in the solution space. Secondly, the location updating formula of discoverer is improved, in which nonlinear weight factor and Cauchy operator are adopted to balance the global exploration and local exploitation ability of this algorithm. Finally, according to the idea of grey wolf optimizer and differential evolution algorithm, the individuals of the population are disturbed in stages to further improve the diversity of the population and reduce the possibility of the algorithm getting caught in the local extremum. Through experiments on 13 selected test functions, the simulation results prove that the proposed algorithm has better convergence speed and optimization accuracy than the original algorithm. © 2022 IEEE.

关键词： Chaotic systems

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AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation 42

AOCC-FL: Federated Learning with Aligned Overlapping via Cal...

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42nd IEEE international conference on Computer Communications, INFOCOM 2023

作者： Wang, Haozhao Xu, Wenchao Fan, Yunfeng Li, Ruixuan Zhou, Pan Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350334142

Federated Learning enables collaboratively model training among a number of distributed devices with the coordination of a centralized server, where each device alternatively performs local gradient computation and communication to the server. FL suffers from significant performance degradation due to the excessive communication delay between the server and devices, especially when the network bandwidth of these devices is limited, which is common in edge environments. Existing methods overlap the gradient computation and communication to hide the communication latency to accelerate the FL training. However, the overlapping can also lead to an inevitable gap between the local model in each device and the global model in the server that seriously restricts the convergence rate of learning process. To address this problem, we propose a new overlapping method for FL, AOCC-FL, which aligns the local model with the global model via calibrated compensation such that the communication delay can be hidden without deteriorating the convergence performance. Theoretically, we prove that AOCC-FL admits the same convergence rate as the non-overlapping method. On both simulated and testbed experiments, we show that AOCC-FL achieves a comparable convergence rate relative to the non-overlapping method while outperforming the state-of-the-art overlapping methods. © 2023 IEEE.

关键词： Learning systems

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