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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

IEEE Transactions on Network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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A Transformer-based Time Series Prediction Model with Contrastive Learning for Nuclear Power System State Monitoring 15

A Transformer-based Time Series Prediction Model with Contra...

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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Gou, Pengfei Zheng, Daiwei Tang, Lei Liu, Mengjuan School of Information and Software Engineering University of Electronic Science and Technology of China UESTC Chengdu China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China NPIC Chengdu China

ISBN: (纸本)9798350354010

Prognostics and Health Management (PHM) is currently the mainstream solution for the condition monitoring and maintenance of industrial systems. In PHM, the system condition is monitored by monitoring the operational status parameters of the system to diagnose equipment faults, predict the remaining useful life, etc. Utilizing sequential operational status parameters to complete prediction and classification tasks is usually modeled as a time series processing task. Therefore, a high-performance time series prediction model is crucial for PHM. This paper focuses on the use of time series prediction models to achieve the prediction of key state parameters of the nuclear power system. However, there are still some challenges when directly applying existing time series prediction models to industrial systems. First, obtaining operational data on the nuclear power system is relatively difficult, resulting in a small amount of training data, which causes many models to face the risk of insufficient training or overfitting. Second, most of the existing time series prediction models are composed of high- and low-frequency decoupling modules, while the operational data of nuclear power systems often have fewer high-frequency components, making it difficult for many existing SOTA models to achieve good results. In order to address these challenges, we have developed a Transformer-based time series prediction model with contrastive learning for monitoring the status of nuclear power systems. We evaluated the new model on three nuclear power simulation datasets and four power transformer datasets. The experimental results show that the new model's performance is superior to baseline models. We also proved the effectiveness of our improvement through ablation experiments. © 2024 IEEE.

关键词： Prediction models

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Surtify: A Smart Surface Identification System Based on Multi-dimensional Acoustic Dispersion

Surtify: A Smart Surface Identification System Based on Mult...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Wang, Yunshu Yuan, Baojie Dong, Feihang Hong, Shicong Zou, Yongpan College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798331507886

With the continuous advancement of the smart home market, household items have become more intelligent. By identifying the different material attributes of various household tabletops, we can obtain contextual information and engage in further interactions. This paper explores a phenomenon, acoustic dispersion and applies it to surface identification. Integrating mobile deep learning, we propose a method to recognize solid surface materials through tapping and utilize tapping events as triggers for interaction by identifying surface material categories. To achieve the goal of zero training effort and tiny hardware cost for unseen users, we analyze the propagation patterns of different frequency components in tapping sound signals from both temporal and spatial dimensions. Based on this, we propose a time-frequency data augmentation strategy that combines multiple event points to improve the recognition accuracy. The results show that, using the leave-one-user-out cross validation for 24 volunteers trained with simple guidance on tapping intensity and position, our system achieves a recognition accuracy of 98.6% for 11 common material types. For 11 newly recruited volunteers without specific control over force and position, the accuracy also remains at 93.4%. We also conduct a series of cross-environment experiments and user study, all of which indicate the high potential of our system. © 2024 IEEE.

关键词： Smart homes

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Design of high output DC/DC boost circuit without boost transformer 8

Design of high output DC/DC boost circuit without boost tran...

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8th International Conference on Communication and Information Systems, ICCIS 2024

作者： Hong, Qin Zhou, Liang Jia, Zhongning Liu, Chun Liu, Jianbo Software Engineering Institute of Guangzhou Department of Computer Science Guangzhou China

ISBN: (纸本)9798331531171

The development of IOT smart devices has necessitated increasingly stringent requirements for DC power sources in terms of output voltage, size, and cost. For instance, Avalanche Photodiodes (APDs), utilized as photodetectors, require a significantly high reverse bias voltage during the detection of feeble optical signals to achieve optimal gain and high signal-to-noise ratio, thereby ensuring exceptional sensitivity. To address the diverse demands of various smart devices for DC power, a high-output DC/DC boost circuit without a step-up transformer has been innovatively designed. This circuit is comprised of four fundamental components: an oscillator circuit, a comparator circuit, a voltage multiplier circuit, and a constant-current and voltage-stabilizing circuit. The paper conducts a comprehensive theoretical analysis of the circuit's topology, parameter selection, and operational process, which is further validated through Multisim simulation. Additionally, a PCB circuit was designed, fabricated, and underwent rigorous practical testing. The test results conclusively demonstrate that the designed boost circuit proficiently provides APDs with the appropriate bias voltage, eliminating the need for boost transformer. It boasts high output voltage, a simplistic structure, small size, and low cost, enabling it to seamlessly share the same input power source with the APD detection circuit and be integrated into the same PCB layout. © 2024 IEEE.

关键词： avalanche photodiode boost circuit direct current stabilized power supply oscillator voltage multiplier circuit

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A Semi-Supervised image classification algorithm inspired by the Primacy Effect 4

A Semi-Supervised image classification algorithm inspired by...

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4th International Conference on Digital Signal and Computer Communications, DSCC 2024

作者： Zhao, Dianqing Li, Chaofan Zhu, Anmin College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781510681538

Semi-supervised-Learning(SSL) providing a solution to leverage vast amounts of unlabeled data. In cognitive psychology, the Primacy-effect refers to the phenomenon where the initial information encountered tends to leave a deeper impression in human cognitive processes, serving as the basis for subsequent judgments. Inspired by the Primacy-effect, this paper proposes a novel semi-supervised image classification algorithm. The core idea of this algorithm is to mimic the beneficial effects of the Primacy effect on human cognitive processes, simulate similar phenomenon on artificial neural networks. In this paper, the initially labeled data is referred to as exemplar. The algorithm includes an exemplar prediction module, whose main function is to accurately identify examples, ensuring that the model forms a "deep impression" of them. We found that due to the scarcity of examples, it is easy to cause model overfitting. Therefore, we proposes the Weighted-Gradient-Chain technique. Additionally, Pseudo-labeling technique was employed, but during model training, we found that generated erroneous Pseudo-labels could introduce errors. To enhance the quality of Pseudo-label generation, this paper proposes a Pre-Pseudo-labeling method. A series of experiments were conducted on multiple datasets. The results indicate that the proposed model performed well. © 2024 SPIE.

关键词： Image classification

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SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction 38

SMART: Towards Pre-trained Missing-Aware Model for Patient H...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yu, Zhihao Chu, Xu Jin, Yujie Wang, Yasha Zhao, Junfeng School of Computer Science Peking University China Center on Frontiers of Computing Studies Peking University Beijing China National Research and Engineering Center of Software Engineering Peking University China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China China Nanhu Laboratory Jiaxing China

Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status. However, the prevalence of missing data in EHR poses significant challenges to existing methods, leading to spurious correlations and suboptimal predictions. While various imputation techniques have been developed to address this issue, they often obsess difficult-to-interpolate details and may introduce additional noise when making clinical predictions. To tackle this problem, we propose SMART, a Self-Supervised Missing-Aware RepresenTation Learning approach for patient health status prediction, which encodes missing information via missing-aware temporal and variable attentions and learns to impute missing values through a novel self-supervised pre-training approach which reconstructs missing data representations in the latent space rather than in input space as usual. By adopting elaborated attentions and focusing on learning higher-order representations, SMART promotes better generalization and robustness to missing data. We validate the effectiveness of SMART through extensive experiments on six EHR tasks, demonstrating its superiority over state-of-the-art methods. Our code is available at https://***/yzhHoward/SMART. © 2024 Neural information processing systems foundation. All rights reserved.

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Deep learning based water leakage detection for shield tunnel lining

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 887-898页

作者： Shichang LIU Xu XU Gwanggil JEON Junxin CHEN Ben-Guo HE College of Computer Science Sichuan UniversityChengdu 610065China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea School of Software Dalian University of TechnologyDalian 116621China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关键词： water leakage detection deep learning deconvolutional-feature pyramid spatial attention

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Detecting and Mitigating the Weakest Cybersecurity Link in an Information System 23

Detecting and Mitigating the Weakest Cybersecurity Link in a...

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23rd International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2024

作者： Sadeghian, Ali Mejri, Mohamed Department of Computer Science and Software Engineering Laval University Quebec Canada

ISBN: (纸本)9781643685380

In today's digital landscape, the prevention of cyber attacks has become exceptionally crucial. This is especially true for safety-critical systems, where safeguarding against these threats is of paramount importance. To address this concern, the MITRE Corporation has developed ATT&CK, an extensive framework comprising data matrices. This framework serves the purpose of assessing a company's security preparedness and pinpointing vulnerabilities that may exist within its infrastructure. By leveraging the capabilities of MITRE ATT&CK, including its tactics and techniques, in conjunction with the LDA4CPS tool, we have devised a novel approach to identify the most critical vulnerabilities in a susceptible system. Furthermore, incorporating MITRE ATT&CK mitigations tailored to the discovered vulnerability empowers the blue team (defensive side) with tangible, practical measures to fortify their security posture. This approach enhances their capability to effectively counter cyber threats, bolstering their overall defensive capabilities. © 2024 IOS Press. All rights reserved.

关键词： Cyber attacks

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Towards High-Performance Graph Processing: From a Hardware/software Co-Design Perspective

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Journal of Computer science & Technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem

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A Faster Algorithm for the 4-Coloring Problem 32

A Faster Algorithm for the 4-Coloring Problem

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32nd Annual European Symposium on Algorithms, ESA 2024

作者： Wu, Pu Gu, Huanyu Jiang, Huiqin Shao, Zehui Xu, Jin School of Computer Science Peking University Beijing China Institute of Computing Science and Technology Guangzhou University China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education Beijing China

ISBN: (纸本)9783959773386

We explore the 4-coloring problem, a fundamental combinatorial NP-hard problem. Given a graph G, the 4-coloring problem asks whether there exists a function f from the vertex set of G to {1, 2, 3, 4} such that f(u) ≠ f(v) for each edge uv of G. Such function f is referred to as a 4-coloring of G. The fastest known algorithm for the 4-coloring problem, introduced by Fomin, Gaspers, and Saurabh (COCOON 2007), exhibits a time complexity of O(1.7272n) and exponential space. In this paper, we propose an enhanced algorithm for the 4-coloring problem with a time complexity of O(1.7159n) and polynomial space. Our algorithm is deterministic and built upon a novel method. Specifically, inspired by previous algorithmic approaches for the 4-coloring problem, such as the aforementioned O(1.7272n) time algorithm, we consider the instance (G, I, S), where G is a graph and I, S are subsets of its vertex set representing vertices colored with 1 and vertices unable to be colored with 1, respectively. For a given instance (G, I, S), we aim to determine the existence of a 4-coloring f of G such that f(v) = 1 for v ∈ I and f(v) ≠ 1 for v ∈ S. Our key innovation lies in recognizing that, leveraging certain combinatorial properties, the instance (G, I, S) can be efficiently solved when G − I − S is a union of K3's and K4's (where K3 and K4 denote complete graphs with 3 and 4 vertices, respectively). The ability to efficiently solve instances (G, I, S), where G − I − S is comprised solely of K3's and K4's, enables us to devise a branching algorithm capable of efficiently addressing instances (G, I, S), where G − I − S is not a union of K3's and K4's (the other case). Based on this innovative method, we derive our final enhanced algorithm. © Pu Wu, Huanyu Gu, Huiqin Jiang, Zehui Shao, and Jin Xu;licensed under Creative Commons License CC-BY 4.0.

关键词： Graph algorithms

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