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International Conference on computer Supported Cooperative Work in Design

作者： Huan Li Xiangjie Kong Guojiang Shen Xiaoran Yan Yao Yang Mario Collotta School of Computer Science and Technology Zhejiang University of Technology Hangzhou China Zhejiang Lab Hangzhou China Faculty of Engineering and Architecture Kore University of Enna Enna Italy

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Multivariate time series anomaly detection plays a crucial role in industrial production. However, the inherent complexity and randomness of time series pose significant challenges. Furthermore, existing detection methods struggle to provide reliable explanations for outliers. To address these issues, this paper presents an unsupervised multivariate time series anomaly detection model named Point-Correlate Adversarial Transformer (PCAT). In this work, we leverage Transformer networks to capture the underlying correlations between different points in a time series and reconstruct the original sequence. By analyzing the correlation differences and reconstruction errors, we identify anomalies at the point level. Our model incorporates an adversarial structure, enabling unsupervised learning and enhancing the learning capability and robustness of the detection network. Experimental evaluations on four real-world datasets demonstrate the superiority of our approach over other state-of-the-art models in terms of detection delay and accuracy.

关键词： Training Correlation Accuracy Federated learning Time series analysis Production Transformers

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Dynamic and Preemptive Task Offloading in Edge-cloud Computing Systems

Dynamic and Preemptive Task Offloading in Edge-cloud Computi...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Ding, Kexin Zhu, Jie Fan, Wei Nanjing University of Posts & Telecommunications Nanjing210003 China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing100864 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The edge-cloud computing systems are widely used to support various computation services. In this paper, we consider a dynamic task offloading problem in the edge-cloud computing system with multiple independent and stochastic arriving tasks. The system periodically schedules and offloads tasks to heterogenous resources in consideration of the required transmission delays and computation times. Our goal is to minimize the sum of weighted response times of all the tasks. A greedy local search based online offloading framework is proposed for the problem under study, which dynamically assigns tasks to the appropriate destination (edge servers or cloud servers) and preemptively allocates computing resources to each task according to its latency-sensitivity. Evaluation experiments are delicately designed on a number of testing instances with various parameter settings. Experimental results indicate that the proposal algorithm is more effective than the compared algorithms. © 2022 IEEE.

关键词： computation offloading

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Design of Optimal Service Scheduling based Task Allocation for Improving CRM in Cloud Computing 6

Design of Optimal Service Scheduling based Task Allocation f...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Suvarna Kumar, G. Priyadarshini, R. Parmenas, Naik Henokh Tannady, Hendy Rabbi, Fazle Andiyan, Andiyan Cse Mvgr College of Engg India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kalbis Institute Department of Management Indonesia Australian Computer Society Australia Universitas Faletehan Department of Architecture Indonesia

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

Cloud computing is a service level computing that provide various service to the customers in order to establish an effective customer Relationship management (CRM). This offers various services like virtual machine, self-service provisioning, elasticity computing and storage (pay-as-you-go). Cloud computing provides shared services by enhancing resource management scalability, interoperability and prediction resources as a key to realize the resource utilization with high-performance management metrics. However, when the number of users increase, the process of task scheduling and service allocation using a traditional computing environment will degrade the CRM. In order to address this challenge, this research study proposes an Optimal Service Level Scheduling (OSLS) based task allocation design for improving CRM in cloud computing environment. The Adaptive Service Level Scheduling Algorithm (ASLSA) and the Support Level Load Balancer (SLLB) will reduce the workload in cloud computing environment in order to improve the Quality of Service (QoS) of CRM. This process will optimize the resource utilization in cloud platform based on the service requirement. It provides optimal scheduling features to CRM in order to improve the service optimality based on task and enhance the computational processes such as service load management, heterogeneous service delivery, pricing, resource pools and elasticity. The proposed system leverages high performance when compared to the existing models. © 2022 IEEE.

关键词： Quality of service

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Velocity Potential Field Modulation for Dense Coordination of Polytopic Swarms and Its Application to Assistive Robotic Furniture

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IEEE Robotics and Automation Letters 2025年第7期10卷 7452-7459页

作者： Tang, Lixuan Ruegg, David Zhang, Runze Bolotnikova, Anastasia Rabaey, Jan Ijspeert, Auke Swiss Federal Institute of Technology in Lausanne - EPFL Biorobotics Laboratory Switzerland Tongji University Department of Computer Science and Technology Shanghai China Laboratory for Analysis and Architecture of Systems (LAAS-CNRS) Robotics and InteractionS Team France IMEC System-Technology Co-Optimization (STCO) Belgium

We explore the use of a mobile furniture swarm that are intended to assist users with limited mobility in their daily indoor activities. We focus on the multi-robot coordination problem when a dense target pose configuration is required, such as in an apartment setting. In those cases, the convergence of one robot to the target can be significantly affected by neighboring robots with specific shapes. In this letter, we propose a solution, named Velocity Potential Field Modulation (VPFM), to deal with the dense coordination problem of a polytopic swarm in a decentralized manner. We adapt our method to assistive applications, such as room reconfigurations and facilitating indoor movement of wheelchair users. We evaluate the performance of our method in simulations and on real-world mobile furniture hardware, demonstrating its effectiveness and real-time performance. © 2016 IEEE.

关键词： Collision Avoidance Path Planning for Multiple Mobile Robots or Agents Swarm Robotics

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An Improved Task Duplication based Clustering Algorithm for DAG Task Scheduling in Heterogenous and Distributed Systems

An Improved Task Duplication based Clustering Algorithm for ...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Fan, Wei Zhu, Jie Ding, Kexin Nanjing University of Posts & Telecommunications Nanjing210003 China Institute of Computing Technology Academy of Sciences State Key Laboratory Chinese of Computer Architecture Beijing100864 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Task scheduling in heterogenous and distributed systems for the directed acyclic graph (DAG) based applications has been widely studied. In DAG task scheduling problems, a set of distributed tasks with dependencies are dispatched to appropriate computing instances. The objective is to obtain the feasible schedule with the minimal schedule length, i.e., makespan. In this paper, we propose a task duplication based clustering framework for the problem under study. We employ the task duplication scheme in the framework which allows task clusters contain duplicated tasks in order to reduce the communication cost. A selection matrix is introduced to record the candidate tasks to generate clusters. Multiple feasible task clustering solutions are obtained based on the selection matrix and among which the best one with the minimal makespan is output. Experimental results indicate that the proposal outperforms compared algorithms on both effectiveness and robustness. © 2022 IEEE.

关键词： Clustering algorithms

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On the Design of a New Stochastic Meta-Heuristic for Derivative-Free Optimization 22nd

On the Design of a New Stochastic Meta-Heuristic for Der...

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22nd International Conference on Computational Science and Its Applications , ICCSA 2022

作者： Cruz, N.C. Redondo, Juana L. Ortigosa, E.M. Ortigosa, P.M. Department of Computer Architecture and Technology University of Granada Granada Spain Department of Informatics University of Almería ceiA3 Excellence Agri-food Campus Almería Spain

ISBN: (纸本)9783031105616

Optimization problems are frequent in several fields, such as the different branches of Engineering. In some cases, the objective function exposes mathematically exploitable properties to find exact solutions. However, when it is not the case, heuristics are appreciated. This situation occurs when the objective function involves numerical simulations and sophisticated models of reality. Then, population-based meta-heuristics, such as genetic algorithms, are widely used because of being independent of the objective function. Unfortunately, they have multiple parameters and generally require numerous function evaluations to find competitive solutions stably. An attractive alternative is DIRECT, which handles the objective function as a black box like the previous meta-heuristics but is almost parameter-free and deterministic. Unfortunately, its rectangle division behavior is rigid, and it may require many function evaluations for degenerate cases. This work presents an optimizer that combines the lack of parameters and stochasticity for high exploration capabilities. This method, called Tangram, defines a self-adapted set of division rules for the search space yet relies on a stochastic hill-climber to perform local searches. This optimizer is expected to be effective for low-dimensional problems (less than 20 variables) and few function evaluations. According to the results achieved, Tangram outperforms Teaching-Learning-Based Optimization (TLBO), a widespread population-based method, and a plain multi-start configuration of the stochastic hill-climber used. © 2022, The Author(s).

关键词： Function evaluation

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Leveraging Multimodal Shapley Values to Address Multimodal Collapse and Improve Fine-Grained E-Commerce Product Classification

Leveraging Multimodal Shapley Values to Address Multimodal C...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Ajibola Obayemi Khuong Nguyen School of Architecture Technology and Engineering University of Brighton Brighton UK Department of Computer Science Royal Holloway University of London London UK

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Multimodal models can experience multimodal collapse, leading to sub-optimal performance on tasks like fine-grained e-commerce product classification. To address this, we introduce an approach that leverages multimodal Shapley values (MM-SHAP) to quantify the individual contributions of each modality to the model's predictions. By employing weighted stacked ensembles of unimodal and multimodal models, with weights derived from these Shapley values (MM-SHAP), we enhance the overall performance and mitigate the effects of multimodal collapse. Using this approach we improve previous results(f1-score) from 0.67 to 0.79.

关键词： Machine learning Predictive models Electronic commerce

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Identification and Visualization of Zombie Enterprise Portraits-Mining Short-time Series Features from the Perspective of Image 4

Identification and Visualization of Zombie Enterprise Portra...

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4th IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2021

作者： Huang, Zhidong Guo, Yuxiang Cao, Di Hu, Chenrui Ding, Chenjun School of Computer Science and Technology Zhejiang University of Technology Hangzhou China School of Design and Architecture Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9781665441247

Governance of zombie enterprises is an important means to ensure the healthy, sustained development of the economy. Traditional methods such as identifying zombie enterprises based on expert knowledge suffer from incomplete expert database and increasingly complex economic environment. Thus the proposed data-driven system is implemented to not only identify zombie enterprises, but also visually present the enterprise portraits. In this paper, the three-year data of 50000 enterprise is transformed into N×N×3 image-format-matrix (N×N are the number of features). Afterward, Convolutional Neural Network, namely CNN is applied and result is got in one stage instead of fitting the data of each year and voting. It is also proved that CNN can effectively mine the short-time series features of enterprises by reconstruct the data into image-format-matrix. Considering the imbalance of data, Focal-Loss is implemented as the loss function when applying CNN model to the data. Grad-CAM, a model interpretive method in the image domain, is used to explain the CNN network after the fitting is completed. It is found that the model pays too much attention to salient features. Thus Mutual Channel Loss is further implemented to make the model pay attention to those indistinguishable features. At the same time, CBAM attention module is added to pay selective attention to different characteristics of enterprises in different years. The three-year information of 15050 enterprises collected from the State Administration for Industry and Commerce of China is used as the source data. The results show that comparing with other models, our CNN model reached the state of art in the rate of misjudgment and missed judgment. © 2021 IEEE.

关键词： Time series

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Building Hierarchical Tree Representations Using Homological-Based Tools 19th

Building Hierarchical Tree Representations Using Homological...

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19th International Conference on computer Analysis of Images and Patterns, CAIP 2021

作者： Díaz-del-Río, Fernando Sanchez-Cuevas, Pablo Molina-Abril, Helena Real, Pedro Moron-Fernández, María José Department of Computer Architecture and Technology University of Seville Seville Spain Seville Spain Seville Spain

ISBN: (纸本)9783030891305

A new algorithm for computing the α -tree hierarchical representation of a grey-scale digital image is presented here. The technique is based on an efficient simplified version of the Homological Spanning Forest (HSF) for encoding homological and homotopy-based information of binary digital images. We create one Adjacency Tree (AdjT) for each intensity contrast in a fully parallel manner. These trees, which define a Contrast Adjacency Forest (CAdjF), are in turn transversely interconnected by another couple of trees: the classical α -tree, and a new one complementing it, called here the α∗ -tree. They convey the information of the contours and the flat regions of the original color image, plus the relations between them. Using both the α and α∗ -trees, this new topological representation prevents some classical drawbacks that appear when working with a single tree. An implementation in OCTAVE/MATLAB validates the correctness of our algorithm. © 2021, Springer Nature Switzerland AG.

关键词： Forestry

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A New Methodology for Software-Simulation of Membrane Systems Using a Multi-Thread Programming Model

SSRN

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SSRN 2024年

作者： Cascado-Caballero, Daniel Diaz-Del-Rio, Fernando Cagigas-Muñiz, Daniel Orellana-Martín, David Pérez-Hurtado, Ignacio Department of Computer Architecture and Technology Universidad de Sevilla Seville Spain Research Group on Natural Computing Department of Computer Science and Artificial Intelligence Universidad de Sevilla Seville Spain

The evolution of simulation and implementation of P systems has been intense since the theoretical model of computation was created. In the field of software simulation of P systems, the proposals made so far have taken advantage mainly of the parallelism of GPUs, but not of the parallelism of existing multi-core processors. This paper proposes a methodology for simulating P systems using a multi-threaded methodological approach in a multi-core processor. This proposal has been implemented and tested using a simulator programmed in C#, and its correct operation has been tested for confluent and non-confluent systems. The experimental results confirm that the simulator scales well with the number of hardware threads of a mul-tiprocessor. The results obtained suggest that the methodology is valid and that it is worth testing it with more complex systems to find the limits of the methodology. © 2024, The Authors. All rights reserved.

关键词： Program processors

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