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Robust optimization for quantum reinforcement learning control using partial observations

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Physical Review A 2022年第6期105卷 062443-062443页

作者： Chen Jiang Yu Pan Zheng-Guang Wu Qing Gao Daoyi Dong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou 310027 China The School of Automation Science and Electrical Engineering and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China The School of Engineering and Information Technology University of New South Wales Canberra Australian Capital Territory 2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of the quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of the quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and the quantum approximate optimization algorithm (QAOA). It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for a QAOA with a noisy control Hamiltonian. This robust control optimization model can be trained to compensate for the uncertainties in practical quantum computing.

关键词： Machine learning Quantum control Quantum engineering

Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining

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Journal of Computer Science & Technology 2020年第2期35卷 305-319页

作者： Fu-Zhen Zhuang Ying-Min Zhou Hao-Chao Ying Fu-Zheng Zhang Xiang Ao Xing Xie Qing He Hui Xiong Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Public Health Zhejiang University School of MedicineHangzhou 310027China Meituan-Dianping Group Beijing 100102China Microsoft Research Asia Beijing 100080China Department of Management Science and Information Systems Rutgers UniversityNew Jersey 07102U.S.A.

Transfer learning has attracted a large amount of interest and research in last decades, and some effort has been made to build more precise recommendation systems. Most previous transfer recommendation systems assume that the target domain shares the same/similar rating patterns with the auxiliary source domain, which is used to improve the recommendation performance. However, almost all existing transfer learning work does not consider the characteristics of sequential data. In this paper, we study the new cross-domain recommendation scenario by mining novelty-seeking trait. Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior, which has a profound business impact on online recommendation. Previous work performed on only one single target domain may not fully characterize users' novelty-seeking trait well due to the data scarcity and sparsity, leading to the poor recommendation performance. Along this line, we propose a new cross-domain novelty-seeking trait mining model (CDNST for short) to improve the sequential recommendation performance by transferring the knowledge from auxiliary source domain. We conduct systematic experiments on three domain datasets crawled from Douban to demonstrate the effectiveness of our proposed model. Moreover, we analyze the directed influence of the temporal property at the source and target domains in detail.

关键词： sequential recommendation novelty-seeking trait transfer learning

Securing the Future Internet of Things with Post-Quantum Cryptography

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arXiv 2022年

作者： Kumar, Adarsh Ottaviani, Carlo Gill, Sukhpal Singh Buyya, Rajkumar Department of Systemics School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science York Centre for Quantum Technologies University of York United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

Traditional and lightweight cryptography primitives and protocols are insecure against quantum attacks. Thus, a real-time application using traditional or lightweight cryptography primitives and protocols does not ensure full-proof security. Post-quantum Cryptography is important for the Internet of Things (IoT) due to its security against Quantum attacks. This paper offers a broad literature analysis of post-quantum cryptography for IoT networks, including the challenges and research directions to adopt in real-time applications. The work draws focus towards post-quantum cryptosystems that are useful for resource-constraint devices. Further, those quantum attacks are surveyed, which may occur over traditional and lightweight cryptographic primitives. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

HUNTER: AI based holistic resource management for sustainable cloud computing

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arXiv 2021年

作者： Tuli, Shreshth Gill, Sukhpal Singh Xu, Minxian Garraghan, Peter Bahsoon, Rami Prof, Scharam Dustdar Sakellariou, Rizos Rana, Omer Buyya, Rajkumar Casale, Giuliano Jennings, Nicholas R. Department of Computing Imperial College London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China School of Computing and Communications Lancaster University United Kingdom School of Computer Science University of Birmingham United Kingdom Distributed Systems Group Vienna University of Technology Austria Department of Computer Science University of Manchester United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energy-efficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multi-objective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the cloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively. © 2021, CC BY.

关键词： Energy efficiency

Architecture and Key Technologies of Parallel Dispatching System for Railway Technical Operation Stations

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Architecture and Key Technologies of Parallel Dispatching Sy...

Complex systems and Intelligent Science (CSIS-IAC), International Annual Conference on

作者： Gang Xiong Donghu Yang Wei Xu Runmei Li Shichao Chen Bing Song Xisong Dong Fenghua Zhu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Signal and Communication Research Institute China Academy of Railway Science Corporation Limited Beijing China The School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Beijing Huairou Academy of Parallel Sensing Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

A railroad technical operation station is an extensive and complex operation system and a variety of random disturbing factors further increase the complexity of its command and dispatching. To enhance the railroad technical operation stations' intelligence, and integration level, and enhance its dispatching management ability of complex operation plans, based on the ACP approach, this paper designs the parallel dispatching system (PDS) architecture, and describes its key technologies including the artificial dispatching system (ADS), computing experiments, parallel execution, etc. On the basis of the artificial dispatching system, computational experiments can optimize various types of technical operations and the overall operational process, to achieve parallel execution between the actual dispatching system and ADS. PDS can provide more efficient and intelligent command and dispatching solutions for railroad technical work stations, and promote the development of the railroad transportation industry in the direction of higher quality and efficiency.

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Application management in Fog computing environments: A taxonomy, review and future directions

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arXiv 2020年

作者： Mahmud, Redowan Ramamohanarao, Kotagiri Buyya, Rajkumar Laboratory School of Computing and Information Systems Parkville Campus MelbourneVIC3010 Australia

The Internet of Things (IoT) paradigm is being rapidly adopted for the creation of smart environments in various domains. The IoT-enabled Cyber-Physical systems (CPSs) associated with smart city, healthcare, Industry 4.0 and Agtech handle a huge volume of data and require data processing services from different types of applications in real-time. The cloud-centric execution of IoT applications barely meets such requirements as the cloud datacentres reside at a multi-hop distance from the IoT devices. Fog computing, an extension of cloud at the edge network, can execute these applications closer to data sources. Thus, Fog computing can improve application service delivery time and resist network congestion. However, the Fog nodes are highly distributed, heterogeneous and most of them are constrained in resources and spatial sharing. Therefore, efficient management of applications is necessary to fully exploit the capabilities of Fog nodes. In this work, we investigate the existing application management strategies in Fog computing and review them in terms of architecture, placement and maintenance. Additionally, we propose a comprehensive taxonomy and highlight the research gaps in Fog-based application management. We also discuss a perspective model and provide future research directions for further improvement of application management in Fog computing. Copyright © 2020, The Authors. All rights reserved.

关键词： Fog computing

Thermal prediction for efficient energy management of clouds using machine learning

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arXiv 2020年

作者： Ilager, Shashikant Ramamohanarao, Kotagiri Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne MelbourneVIC3010 Australia

Thermal management in the hyper-scale cloud data centers is a critical problem. Increased host temperature creates hotspots which significantly increases cooling cost and affects reliability. Accurate prediction of host temperature is crucial for managing the resources effectively. Temperature estimation is a non-trivial problem due to thermal variations in the data center. Existing solutions for temperature estimation are inefficient due to their computational complexity and lack of accurate prediction. However, data-driven machine learning methods for temperature prediction is a promising approach. In this regard, we collect and study data from a private cloud and show the presence of thermal variations. We investigate several machine learning models to accurately predict the host temperature. Specifically, we propose a gradient boosting machine learning model for temperature prediction. The experiment results show that our model accurately predicts the temperature with the average RMSE value of 0.05 or an average prediction error of 2.38 °C, which is 6 °C less as compared to an existing theoretical model. In addition, we propose a dynamic scheduling algorithm to minimize the peak temperature of hosts. The results show that our algorithm reduces the peak temperature by 6.5 °C and consumes 34.5% less energy as compared to the baseline algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

THE DATABASE AND BENCHMARK FOR THE SOURCE SPEAKER TRACING CHALLENGE 2024

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arXiv 2024年

作者： Li, Ze Lin, Yuke Yao, Tian Suo, Hongbin Zhang, Pengyuan Ren, Yanzhen Cai, Zexin Nishizaki, Hiromitsu Li, Ming School of Computer Science Wuhan University Wuhan China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Duke Kunshan University Kunshan China AI Center OPPO Beijing China Key Laboratory of Speech Acoustics and Content Understanding Institute of Acoustics CAS China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Center for Language and Speech Processing Johns Hopkins University United States Integrated Graduate School of Medicine Engineering and Agricultural Sciences University of Yamanashi 4-4-37 Takeda Yamanashi Kofu400-8510 Japan

Voice conversion (VC) systems can transform audio to mimic another speaker’s voice, thereby attacking speaker verification (SV) systems. However, ongoing studies on source speaker verification (SSV) are hindered by limited data availability and methodological constraints. This paper presents the Source Speaker Tracking Challenge (SSTC) on STL 2024, which aims to fill the gap in the database and benchmark for the SSV task. In this study, we generate a large-scale converted speech database with 16 common VC methods and train a batch of baseline systems based on the MFA-Conformer architecture. In addition, we introduced a related task called conversion method recognition, with the aim of assisting the SSV task. We expect SSTC to be a platform for advancing the development of the SSV task and provide further insights into the performance and limitations of current SV systems against VC attacks. Further details about SSTC can be found here 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Database systems

A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects

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arXiv 2023年

作者： Wen, Haomin Lin, Youfang Wu, Lixia Mao, Xiaowei Cai, Tianyue Hou, Yunfeng Guo, Shengnan Liang, Yuxuan Jin, Guangyin Zhao, Yiji Zimmermann, Roger Ye, Jieping Wan, Huaiyu The Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC Beijing101318 China The School of Computing National University of Singapore Singapore College of Systems Engineering National University of Defense Technology Changsha China The School of Information Science and Engineering Yunnan University Kunming650091 China The Cainiao Network Hangzhou China The Alibaba Group Hangzhou China INTR Thrust and DSA Thrust The Hong Kong University of Science and Technology Guangzhou China

Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route&time prediction;(ii) model architecture, which encompasses sequence-based and graph-based models;and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field. © 2023, CC BY.

关键词： Taxonomies