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Reducing idleness in financial cloud services via multi-objective evolutionary reinforcement learning based load balancer

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science China(Information sciences) 2024年第2期67卷 20-40页

作者： Peng YANG Laoming ZHANG Haifeng LIU Guiying LI Department of Statistics and Data Science Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and Technology Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Guangdong OPPO Mobile Telecommunications Corp. Ltd Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology

In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services.

关键词： evolutionary reinforcement learning evolutionary multi-objective optimization load balance cloud computing

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Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6500-6508页

作者： Chen, Luefeng Cao, Wei Zheng, Biao Wu, Min Pedrycz, Witold Hirota, Kaoru China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Macau University of Science and Technology Institute of Systems Engineering Taipa999078 China Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

In the human pose estimation task, on the one hand, 3-D pose always has difficulty in dividing different 2-D poses if the view is limited;on the other hand, it is hard to reduce the lifting ambiguity because of the lack of depth information, it is an important and challenging problem. Therefore, two-stage representation refinement based on the convex combination for 3-D human pose estimation is proposed, in which the two-stage method includes a dense-spatial-temporal convolutional network and a local-to-refine network. The former is applied to determine the features between each video frame;the latter is used to get the different scales of pose details. It aims to address the difficulty of estimating 3-D human pose from 2-D image sequences. In such a way, it can better use the relations between every frame in the sequence of the pose video to produce more accurate results. Finally, we combine the above network with a block called convex combination to help refine the 3-D pose location. We test the proposed approach on both Human3.6m and MPII datasets. The result confirms that our method can achieve better performance than improved CNN supervision, a simple yet effective baseline, and coarse-to-fine volumetric prediction. Besides, a robustness test experiment is carried out for the proposed method while the input is interrupted. The result verifies that our method shows better robustness. © 2024 IEEE.

关键词： Convolution

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From Pixels to Components: Eigenvector Masking for Visual Representation Learning

arXiv

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arXiv 2025年

作者： Bizeul, Alice Sutter, Thomas M. Ryser, Alain Schölkopf, Bernhard von Kügelgen, Julius Vogt, Julia E. Department of Computer Science ETH Zürich Switzerland MPI for Intelligent Systems Tübingen Germany Seminar for Statistics ETH Zürich Switzerland

Predicting masked from visible parts of an image is a powerful self-supervised approach for visual representation learning. However, the common practice of masking random patches of pixels exhibits certain failure modes, which can prevent learning meaningful high-level features, as required for downstream tasks. We propose an alternative masking strategy that operates on a suitable transformation of the data rather than on the raw pixels. Specifically, we perform principal component analysis and then randomly mask a subset of components, which accounts for a fixed ratio of the data variance. The learning task then amounts to reconstructing the masked components from the visible ones. Compared to local patches of pixels, the principal components of images carry more global information. We thus posit that predicting masked from visible components involves more high-level features, allowing our masking strategy to extract more useful representations. This is corroborated by our empirical findings which demonstrate improved image classification performance for component over pixel masking. Our method thus constitutes a simple and robust data-driven alternative to traditional masked image modeling approaches† © 2025, CC BY.

关键词： Self-supervised learning

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Confidentiality. Integrity, and Availability (CIA) Approach for Private Dataset Management System Design

Confidentiality. Integrity, and Availability (CIA) Approach ...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Mahmud Isnan Gregorius Natanael Elwirehardja Dedy Ariansyah Reza Rahutomo Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Dataset management systems are essential for assisting research and development (R&D) organizations in applying data governance protocols, especially in managing the utilization of datasets. In R&D, datasets are vital sources for producing analysis, machine learning prediction, and supporting decision-making. As such, it is important to deploy dataset management systems with considerations regarding the aspects of Confidentiality, Integrity, and Availability (CIA) which is a design principle that emphasized information protection and collaboration facilitation among dataset users. The study proposed an analysis and a design for a dataset management system that takes CIA aspects into account. In the future, the proposed design will be utilized as a benchmark for building a dataset management system. In this study, the Waterfall method was adopted. A use case diagram, an ERD (Entity Relationship Diagram), and an activity diagram were constructed in the design stage. In the project implementation stage, the Laravel framework including Laravel Jetstream was considered to be used.

关键词： Protocols Data security Decision making Organizations Machine learning Encryption Protection Information technology Research and development System analysis and design

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Enhanced Real-Time Camera Pose Estimation Using ArUco Markers in ROS-Based Robotic systems

Enhanced Real-Time Camera Pose Estimation Using ArUco Marker...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Syma Afsha Mir Mohibullah Sazid Md Shahedul Islam Kamruddin Nur EMJM in Intelligent Field Robotic Systems (IFRoS) Universitat de Girona Girona Spain Department of Computer Science and Engineering Stamford University Bangladesh Dhaka Bangladesh Department of Computer Science American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Accurate camera pose estimation is crucial for various applications in robotics and computer vision, enabling precise navigation and interaction with the environment. This paper presents a straightforward approach to camera pose estimation and calibration using ArUco markers. Different Aruco’s is used for getting the camera pose and calibration is performed with a single ArUco marker to determine the camera's intrinsic parameters. To enhance the accuracy of pose estimates, bundle adjustment is applied, minimizing re-projection errors through optimization. Additionally, Networkx is used to build a graph, visualizing the spatial relationships between detected markers. The entire system was tested in the Stonefish ROS simulator and Real Environment, validating the calibration and pose estimation processes. The results demonstrate the effectiveness of our methods, offering a reliable framework for applications that require precise camera pose estimation in robotic systems.

关键词： Visualization Accuracy Navigation Robot vision systems Pose estimation Cameras Real-time systems Calibration Reliability Optimization

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Scalability in Autoencoder-based OFDM Communication System

Scalability in Autoencoder-based OFDM Communication System

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Seizan Tsugawa Takao Toma Shiho Oshiro Tomohisa Wada Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Dept. of Engineering Area of Computer Science and Intelligent Systems University of the Ryukyus Okinawa Japan

This paper proposed Scalability in Autoencoder-based Orthogonal Frequency Division Multiplexing(OFDM) communication system. In the previous research, only the comparison between IEEE802.11a and Autoencoder by the conventional OFDM communication system was performed, and it was proved that the communication system created by Autoencoder exceeded the performance of the conventional system. Therefore, in this paper, it uses IEEE802.11n and compare whether it can be improved by expanding the bandwidth and using ***802.11n standard has an FFT length of 128, a subcarrier number of 114 (108 for data), and modulation schemes of Quadrature Phase Shift Keying(QPSK), 16 Quadrature Amplitude Modulation (16QAM). The GI length is 800ns and the symbol length is 4000ns. In the simulation, a computer simulation was performed using a conventional OFDM communication system and a communication system generated by Autoencoder. Assuming that the simulation environment had an Signal-to-Noise Ratio (SNR) of 0 to 30 and an amplitude r of 0.0 to 1.0, the Symbol Error Rate (SER) status for each SNR was output. As a result of computer simulation, QPSK converged at SNR 27 ㏈ at IEEE 802.11a, but was able to reduce SER overwhelmingly to SNR 12 ㏈ at IEEE 802.11n. Also, in 16QAM the convergence at r=0.0 is the same as for SNR22 ㏈, but in IEEE 802.11a it does not converge after r=0.6, but in IEEE 802.11n it does not converge only at r=0.9 and r=1.0 . As a future task, it will use IEEE802.11ac, which enables communication speeds several times faster than IEEE802.11n, examine whether it is possible to further improve accuracy. And it will continue our research to correspond MIMO communication.

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Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detection 2

Applying Pre-trained Multilingual BERT in Embeddings for Imp...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Rahman, Md Abdur Shahriar, Hossain Wu, Fan Cuzzocrea, Alfredo University of West Florida Dept. of Intelligent Systems and Robotics PensacolaFL United States University of West Florida Center for Cybersecurity PensacolaFL United States Tuskegee University Department of Computer Science TuskegeeAL United States University of Calabria Idea Lab Rende Italy

ISBN: (纸本)9798331529529

Large language models (LLMs) are renowned for their exceptional capabilities, and applying to a wide range of applications. However, this widespread use brings significant vulnerabilities. Also, it is well observed that there are huge gap which lies in the need for effective detection and mitigation strategies against malicious prompt injection attacks in large language models, as current approaches may not adequately address the complexity and evolving nature of these vulnerabilities in real-world applications. Therefore, this work focuses the impact of malicious prompt injection attacks which is one of most dangerous vulnerability on real LLMs applications. It examines to apply various BERT (Bidirectional Encoder Representations from Transformers) like multilingual BERT, DistilBert for classifying malicious prompts from legitimate prompts. Also, we observed how tokenizing the prompt texts and generating embeddings using multilingual BERT contributes to improve the performance of various machine learning methods: Gaussian Naive Bayes, Random Forest, Support Vector Machine, and Logistic Regression. The performance of each model is rigorously analyzed with various parameters to improve the binary classification to discover malicious prompts. Multilingual BERT approach to embed the prompts significantly improved and outperformed the existing works and achieves an outstanding accuracy of 96.55% by Logistic regression. Additionally, we investigated the incorrect predictions of the model to gain insights into its limitations. The findings can guide researchers in tuning various BERT for finding the most suitable model for diverse LLMs vulnerabilities. © 2024 IEEE.

关键词： Embeddings

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intelligent Constellation Generation based on Autoencoder Communication System

Intelligent Constellation Generation based on Autoencoder Co...

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Kaisei Matsumoto Takao Toma Shiho Oshiro Tomohisa Wada Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Dept. of Engineering Area of Computer Science and Intelligent Systems University of the Ryukyus Okinawa Japan

This paper discusses intelligent constellation generation based on autoencoder communication system. In previous studies, the amplitude was set to fluctuate between r=0.0 and 1.0. However, when checking the generated constellation, distortion was confirmed instead of the conventional symbol arrangement. Therefore, in this paper, it compares the case where the amplitude is constant, the case where the average amplitude within a Minibatch is 1, and the case where the average amplitude is 1 for Interval time. The communication standard used in this research is IEEE 802.11a, assuming wireless Local Area Network (LAN) specifications. The IEEE 802.11a standard has an Fast Fourier Transform (FFT) length of 64, a subcarrier number of 52, and Quadrature Phase Shift Keying (QPSK) and 16 Quadrature Amplitude Modulation (QAM), modulation methods. A guard interval of 800 ns is added and the symbol length is 4000 ns. First, a simulation was performed under the condition that the amplitude was kept constant. QPSK with 4 symbols, constant amplitude model is rounded more than previous research result. 16QAM with 16 symbols is arranged regularly like lined up on a line. Second, the simulation was performed under the condition that the average amplitude within the minibatch was set to 1. QPSK with 4 symbols, appears to rotate clockwise. 16QAM with 16 symbols has a more uniform symbol placement than previous research result. Third, a simulation was performed under the condition that the average amplitude within Interval time was set to 1. QPSK with 4 symbols, is the closest to square among QPSK output results so far. The direction is slightly tilted, but if it can be rotated a little more, it may be possible to reproduce the same symbol arrangement as before. 16QAM with 16 symbols, the symbol arrangement is biased as a whole. However, it can be seen that are arranged in line on the line, perhaps due to regularity. As future work, in addition to the conditions set this time, it will exa

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Camouflaged Object Segmentation with Transformer 6th

Camouflaged Object Segmentation with Transformer

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6th International Conference on Cognitive systems and Signal Processing, ICCSIP 2021

作者： Wang, Haiwen Wang, Xinzhou Sun, Fuchun Song, Yixu State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China School of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9789811692468

The Vision Transformer (ViT) [6] directly applies a Transformer architecture to image classification and achieves an impressive result compared with convolutional networks. This paper presents a new ViT-base camouflaged object segmentation method, called COS Transformer, which aims to identify and segment objects concealed in a complex environment. The high intrinsic similarities between object and surrounding makes the task challenging than salient object detection. Most recent camouflaged object segmentation methods(e.g., EGNet [29], PraNet [10] and SINet [9]) adopt convolutional network with an encoder-decoder architecture and focused on increasing the receptive field, which is limited by the depth of the network. In camouflaged object segmentation (COS) task, the camouflage is mainly relied on contrast of the whole surrounding instead of the local information. We introduce transformer with global context awareness in this paper, for self-attention allowing COS Transformer to aggregate features globally even in the lowest layers. Specifically, the architecture is composed of a transformer-based encoder and a multi-layers feature aggregation refinement module. After training on the COD10K [9] dataset, COS Transformer attains excellent results compared to state-of-the-art convolutional networks, e.g. 11.7% improvement of E [8] on the COD10K contrasted to SINet. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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PDDepth: Pose Decoupled Monocular Depth Estimation for Roadside Perception System

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Huanan Zhang, Xinyu Chen, Zhengxian Jun, Li Liu, Huaping School of Vehicle and Mobility Tsinghua University Beijing100084 China State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China

Accurate depth information is crucial for roadside perception in Cooperative Vehicle Infrastructure systems. Beyond existing radar and LiDAR solutions, monocular depth estimation using surveillance cameras is emerging as a superior approach due to its cost-effectiveness and dense depth output. Unlike onboard cameras, roadside cameras are relatively fixed in position. Many existing monocular depth estimation methods, which do not independently model camera pose, tend to overfit to training data and produce suboptimal results when confronted with slight variations in camera poses, which may be caused by external forces within the same camera or across different cameras. To address this issue, a pose decoupled monocular depth estimation method specifically designed for roadside perception systems is proposed. This method separates depth estimation into two components: a pose-dependent modeling portion that recovers ground depth based on the current camera pose, and a pose-agnostic portion that estimates pixel height relative to the ground plane. Additionally, a knowledge distillation framework is introduced to improve the robustness of the proposed method against variations in roadside cameras. To validate the method, we propose the first open source dataset for roadside monocular depth estimation, DAIR-MDE, and a roadside instance segmentation dataset, DAIR-Ins, both derived from the DAIR dataset. The proposed method demonstrates significant advances over the state-of-the-art methods on DAIR-MDE. The proposed dataset and source code are publicly available at https://***/441599828/PDDepth. ©1991-2012 IEEE.

关键词： Roadsides

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