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Advancements in Computing (ICAC), International Conference on

作者： Lasal Sandeepa Hettiarachchi Senura Vihan Jayadeva Rusiru Abhisheak Vikum Bandara Dilmi Palliyaguruge Udara Srimath S.Samaratunge Arachchillage Dharshana Kasthurirathna Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398106

The importance of orchestration tools such as Kubernetes has become paramount with the popularity of software architectural styles such as microservices. Furthermore, advancements in containerization technologies such as Docker has also played a vital role when it comes to advancements in the field of DevOps, enabling developers and system engineers to deploy are manage applications much more effectively. However, infrastructure configuration and management of resources are still challenging due to the disjointed nature of the infrastructure and resource management tools’ failure to comprehend the deployed applications and create a holistic view of the services. This is partly due to the extensive knowledge required to operate these tools or due to the inability to perform specific tasks. As a result, multiple tools and platforms need to conFigure together to automate the deployment, monitoring and management processes to provide the optimal deployment strategy for the applications. In response to this issue, this research proposes an expert system that creates a centralized approach to cluster autoscaling and resource management, which also provides an automated low-latency container management system and resiliency evaluation for dynamic systems. Furthermore, the time series load prediction is done using a BiLSTM and periodically creates an optimized autoscaling policy for cluster performance, thus creating a seamless pipeline from deployment, monitoring scaling, and troubleshooting of distributed applications based on Kubernetes.

关键词： Time series analysis Pipelines Microservice architectures software Resource management Expert systems Task analysis

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Enhancing Content-Based Image Retrieval for Lung Cancer Diagnosis: Leveraging Texture Analysis and Ensemble Models 7

Enhancing Content-Based Image Retrieval for Lung Cancer Diag...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Jayabalaji, K.A. Laxmaiah, Bagam Thatipudi, Jayendra Gopal Chandraprabha, K. Badhe, Pravin Balaram, Allam Sri Krishna Arts And Science College Department of Software Systems Tamilnadu Coimbatore India Cmr Technical Campus Department of Cse Telangana Kandlakoya India University of North Texas Department of Computer Science DentonTX76203 United States Bannari Amman Institute of Technology Department of Information Technology Tamilnadu Sathyamangalam India Sinhgad College of Pharmacy Swalife Biotech Department of Pharmacology Pune India Mlr Institute of Technology Department of Computer Science And Engineering Telangana Hyderabad500043 India

ISBN: (纸本)9798350371406

In image retrieval systems, many search engines rely on text-based methods, extracting information from descriptions and tags to retrieve relevant images. However, this conventional approach often overlooks the optical or semantic information present in photographs, leading to errors in the retrieved results. To address this issue, it is essential to consider content-based information such as intensities, edges, and pixel values when designing image retrieval systems, especially in domains like medicine. This study focuses on developing a Content-Based Image Retrieval (CBIR) system for the domain of medicine, with a particular emphasis on lung cancer detection. Early identification and treatment of lung cancer are critical for patient outcomes, making suitable computer-Aided Diagnosis (CAD) systems essential for assisting clinicians. The proposed ensemble model incorporates various feature extraction methods, including image processing techniques and wavelet transform, to harness the valuable information from images. These features are then fed into a deep learning model to assess their effectiveness in image retrieval tasks. By combining machine learning and image processing techniques, this work presents a comprehensive CBIR system capable of efficiently retrieving relevant medical images. Such advancements in image retrieval technology hold significant promise for aiding medical professionals in diagnosing and treating diseases like lung cancer promptly. © 2023 IEEE.

关键词： computer aided diagnosis

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Stability evaluation for steep bank slope with microseismic monitoring in Three Gorges Reservoir area

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Journal of Mountain Science 2022年第6期19卷 1588-1601页

作者： XIE Qing-ming LONG Kui LI Zi-mu CHEN Li-chuan LIU Hong XU Hong HE Fei LIANG Dan XIANG Xue-kun MA Qiang Chongqing College of Electronic Engineering Chongqing 401331China Technology Innovation Center of Geohazards Automatic Monitoring Ministry of Natural ResourcesChongqing Institute of Geology and Mineral ResourcesChongqing 401120China College of Computer and Information Science College of Software Southwest UniversityChongqing 400715China Chongqing Rail Transit Design and Research Institute Chongqing 401122China

Rock slide is one of the common geohazard in the Three Gorges Reservoir area, and it affects the shipping of the Yangtze River and the safety of people living on the banks. In order to investigate the internal fracturing mechanism of rock mass, distributed microseismic monitoring network was arranged with 15 three component geophones(3C geophones), deployed at borehole and out of the sliding mass in the unstable Dulong slope. Stein Unbiased Risk Estimation(SURE) method was used to noise suppression for the microseismic record, and decomposition parameters of the Continuous Wavelet Transform(CWT) were determined with maximum energy of correlation coefficient(MECC) method. The signal-to-noise ratio was tripled after the process, and source parameters are obtained with full waveform inversion. The rupture volume model was counted by the irregular grid statistics with the events’ density. It shows that the rock slide is of a small scale and composed of a single block. Moreover, the relationship among microseismicity, displacement and rainfall were discussed in the paper. The deformation rate was dramatically changed in the period of intensive events. There is a good consistency especially in the rainfall period. Although there is a time delay, continuous rainfall is more likely to cause the increase of microseismic events. The results show that the Dulong slope is a shallow rock slide in the state of creep deformation, and the rupture mechanism of the rock mass is left-lateral normal fault with shear failure. The research provides more key information for the early warning and prevention of rock slides and helps to reduce the risk of geohazards.

关键词： Stability evaluation Microseismic monitoring Rock slide Rainfall Displacement

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Experiment Teaching Case Design of computer I/O Channel Virtual Simulation 18th

Experiment Teaching Case Design of Computer I/O Channel Vir...

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18th International Conference on computer Science and Education, ICCSE 2023

作者： Zhang, Dongdong Cheng, Xinyuan Gao, Zhen Qin, Guofeng Huang, Jie Wang, Lisheng College of Electronics and Information Engineering Tongji University Shanghai China National Computer and Information Technology Experiment Teaching Demonstration Center Tongji University Shanghai China College of Software Engineering Tongji University Shanghai China

ISBN: (纸本)9789819707904

The principle of computer composition is an important basic course for undergraduates majoring in computer science, in which the I/O channel control method in the input/output system is mainly applied to mainframes. However, mainframes that support I/O channel design experiments are expensive, complex and costly to operate and maintain, which makes it impossible to carry out hands-on experiments for large-scale students. In this paper, to break the situation that this part of content is mainly taught through textbook in traditional teaching, we design a virtual simulation experiment of computer I/O channel, which is an effective supplement to the practical teaching of computer composition principle. The experimental case has been applied in computer science and technology, information security and software engineering in Tongji University and other colleges and universities, and has been awarded Chinese national first-class course (Virtual Simulation Class). The experimental system has been opened and shared on Chinese national virtual simulation experiment teaching project sharing service platform (***). The experiment website is http://***/details/2020?id=7256. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer software

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SBiNE: Signed Bipartite Network Embedding 16th

SBiNE: Signed Bipartite Network Embedding

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Zhang, Youwen Li, Wei Yan, Dengcheng Zhang, Yiwen He, Qiang School of Computer Science and Engineering Anhui University Heifei230601 China School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC3122 Australia

ISBN: (纸本)9783030675363

This work develops a representation learning method for signed bipartite networks. Recent years, embedding nodes of a given network into a low dimensional space has attracted much interest due to it can be widely applied in link prediction, clustering, and anomalous detection. Most existing network embedding methods mainly focus on homogeneous networks with only positive edges and single node type. However, negative edges are more valuable than positive edges in certain analysis tasks. Even though the work on signed network representation learning distinguishes between positive and negative edges, it does not consider the difference in node types. Moreover, bipartite network representation learning which considers two types of vertices do not tell link signs. In order to solve this problem, we further consider the link sign on the basis of the bipartite network to conduct signed bipartite network analysis. In this paper, we propose a simple deep learning framework SBiNE, short for signed bipartite network embedding, which both preserves the first-order (i.e., observed links) and second-order proximity (i.e., unobserved links but have similar sign context), and then by optimizing the objective function, experiments on three datasets show that our proposed framework SBiNE is competitive in link sign prediction task. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Network embeddings

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A Novel Approach Detection for False Data Injection, and Man in the Middle Attacks in IoT and IIoT

A Novel Approach Detection for False Data Injection, and Man...

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IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia)

作者： Serkan Gönen Mehmet Ali Barişkan Derya Yıltaş Kaplan Ercan Nurcan Yilmaz Aydın Çetin Department of Computer & Software Engineering Istanbul Gelişim University Istanbul Türkiye Department of Computer Engineering Istanbul University-Cerrahpasa Istanbul Türkiye Faculty of Technology Gazi University Ankara Türkiye

Human beings have gone through stages that have made significant contributions to their lives with technological developments. One of the most important of these close to the present day is the introduction of IoTs, and IIoTs into our lives with the industry 4.0 process. From smart homes to smart grids, we are faced with a world that is getting smarter in every aspect of our lives. In this cyber environment where our data, including our personal data, is transferred to the virtual environment, the biggest threat is the attacks that can be made on this existing environment. The biggest methods used by attackers are to exploit the vulnerabilities that the existing system brings in addition to the old vulnerabilities. With the IoT process, there have been very important developments, and all operations can be carried out remotely via mobile phone or computer, such as checking the missing needs in the refrigerator, washing, drying clothes, and even in the most advanced dimension, reducing the temperature if it is too high, or increasing it if it is low, without people coming to their homes, and with these renovations, new types of threads become part of our lives. With this study we focus on one of more dangerous part of these attacks, False Data Injection (FDI), and Men in the Middle (MitM) attacks. With this study we detected MitM, and FDI attacks with success rate of %95 percent.

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iCTS: Iterative and Hierarchical Clock Tree Synthesis With Skew-Latency-Load Tree

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Li, Weiguo Huang, Zhipeng Yu, Bei Zhu, Wenxing Chen, Jian He, Zhixue Li, Xingquan South China University of Technology School of Software Engineering Guangzhou China Pengcheng Laboratory Shenzhen China Beijing Institute of Open Source Chip Beijing China Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong Fuzhou University Center for Discrete Mathematics and Theoretical Computer Science Fuzhou China

The advancement of modern clock tree synthesis (CTS) encounters a bottleneck, primarily due to the difficulty in achieving multi-objective co-optimization among complex design processes. To concurrently optimize skew, latency, and load capacitance, we propose an iterative and hierarchical CTS framework, which is composed of clustering, topology generation and routing, buffering, and optimization. First, we introduce a capacitance-based metric to achieve adaptive balanced clustering and optimize the cluster results through simulated annealing. Second, to construct a clock tree with lower latency, load capacitance, and skew, we introduce the skew-latency-load tree (SLLT), which combines the advantages of bound skew tree and Steiner shallow-light tree, and we propose an effective SLLT construction algorithm. Third, to further optimize CTS result by buffering, we introduce the critical wirelength evaluation (CWE) to evaluate the capability of each buffer, and propose the insertion delay estimation (IDE) to reduce the evaluation bias during buffering, then design the iterative skew convergence algorithm (ISCA) to achieve complete convergence of skew. We validate our solution using 28nm process technology. Compared to our method, the commercial tool increases skew, latency, and clock capacitance by 39.5%, 13.0%, and 18.5%, respectively, while the OpenROAD by 101.6%, 50.7%, and 25.5%, respectively. © 1982-2012 IEEE.

关键词： Simulated annealing

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Graph-Augmented Contrastive Clustering for Time Series Data

SSRN

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SSRN 2024年

作者： Zhang, Qin Liang, Zhuoluo Ngueilbaye, Alladoumbaye Zhang, Peng Chen, Junyang Chen, Xiaojun Huang, Joshua Zhexue College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China

The recent emergence of time series contrastive clustering methods can be categorized into two classes. The first class uses contrastive learning for universal representations, which can be effective in various downstream tasks, but overlooks important categorical information and clustering objectives, leading to unsuitable models. The second class incorporates contrastive learning into clustering objectives but fails to fully explore potential connections and structures between time series data. To this end, we propose the "Time Series Graph-augmented Contrastive Clustering (TSGCC) method", a graph-augmented contrastive learning framework aiming to create cluster-friendly features and assignments by iteratively optimizing contrastive loss. The method uses a weighted KNN graph to build positive and negative sample pairs, projecting the instances into a feature space with the dimensionality number of clusters. Experimental results demonstrate that TSGCC outperforms 18 advanced time-series clustering methods on the 36 representatives out of the 128 UCR benchmarks, achieving the best clustering results on 12 datasets, with the highest average rank (3.22) of RI and the second-highest average rank (3.89) of NMI among 18 clustering methods. We further compared our method with recent approaches on the entire set of 128 UCR datasets, demonstrating our method's broad applicability and effectiveness. © 2024, The Authors. All rights reserved.

关键词： Time series

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Limited quantum advantage for stellar interferometry via continuous-variable teleportation

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Physical Review A 2024年第5期109卷 052434-052434页

作者： Zixin Huang Ben Q. Baragiola Nicolas C. Menicucci Mark M. Wilde School of Mathematical and Physical Sciences Macquarie University NSW 2109 Australia Centre for Quantum Software and Information Faculty of Engineering and IT University of Technology Sydney NSW 2007 Australia Centre for Quantum Computation and Communication Technology School of Science RMIT University Melbourne VIC 3000 Australia School of Electrical and Computer Engineering Cornell University Ithaca New York 14850 USA

We consider stellar interferometry in the continuous-variable (CV) quantum information formalism and use the quantum Fisher information (QFI) to characterize the performance of three key strategies: direct interferometry (DI), local heterodyne measurement, and a CV teleportation-based strategy. In the lossless regime, we show that a squeezing parameter of r≈2 (18 dB) is required to reach ∼95% of the QFI achievable with DI; such a squeezing level is beyond what has been achieved experimentally. In the low-loss regime, the CV teleportation strategy becomes inferior to DI, and the performance gap widens as loss increases. Curiously, in the high-loss regime, a small region of loss exists where the CV teleportation strategy slightly outperforms both DI and local heterodyne, representing a transition in the optimal strategy. We describe this advantage as limited because it occurs for a small region of loss, and the magnitude of the advantage is also small. We argue that practical difficulties further impede achieving any quantum advantage, limiting the merits of a CV teleportation-based strategy for stellar interferometry.

关键词： Quantum metrology Quantum parameter estimation Quantum teleportation

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Multi-Granularity Cross Transformer Network for Person Re-Identification

SSRN

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SSRN 2023年

作者： Li, Yanping Miao, Duoqian Zhang, Hongyun Zhou, Jie Zhao, Cairong Department of Computer Science and Technology Tongji University Shanghai200092 China College of Computer Science and Software Engineering Shenzhen University Nanshan District Guangdong Province Shenzhen City China

Person re-identification (Re-ID) aims to retrieve the same person in the gallery. Great efforts have been made to learn salient feature representations from global structure patterns. Transformer has been introduced to the Re-ID task due to its strong long-range dependency modeling ability. However, using a plain Transformer structure to extract global features will ignore discriminative semantic information implied in various local structures in the global feature maps of pedestrian images. To address this issue, we present a Multi-granularity Cross Transformer Network (MCTN) that progressively learns salient features of different local structures in a global context. Specifically, the network mainly consists of two new designs, i.e., a Multi-granularity Convolutional Layer (MCL) and a Pyramidal Cross Transformer learning layer (PCT). The MCL is intended to simulate human vision to investigate salient pedestrian features at various granularities. The PCT is designed to mine local information in the global structure from a coarse-to-fine perspective. Furthermore, considering that deep layers pay attention to more semantic information, no more fine-grained attention learning is required to avoid overfitting. The shallow layers, on the other hand, focus on details but also have a lot of semantic information that hasn't been mined yet. Consequently, a Hierarchical Aggregation Strategy (HAS) is introduced to fuse features learned by cross attention learning at different stages. Pedestrian features learned in shallow layers will serve as global priors for semantics learning in deep layers. We evaluate our method on four large-scale Re-ID datasets, and the experimental results reveal that the proposed method outperforms the state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Large dataset

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