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Frontiers of Computer Science 2017年第4期11卷 702-716页

作者： Wendi JI Xiaoling WANG Feida ZHU Shanghai Key Laboratory of Trustworthy Computing Institute for Data Science and Engineering East China Normal University Shanghai 200062 China School of Information Systems Singapore Management University Singapore 188065 Singapore

The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The ＂conversion＂ here refers to actu- ally a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product＇s ac- tual conversion period and the application＇s target conversion period has been the subtle culprit compromising many exist- ing recommendation algorithms. The challenging question： what products should be recom- mended for a given time period to maximize conversion--is what has motivated us in this paper to propose a rank-based time-aware conversion prediction model （rTCP）, which con- siders both recommendation relevance and conversion time. We adopt lifetime models in survival analysis to model the conversion time and personalize the temporal prediction by incorporating context information such as user preference. A novel mixture lifetime model is proposed to further accom- modate the complexity of conversion intervals. Experimental results on two real-world data sets illustrate the high good- ness of fit of our proposed model rTCP and demonstrate its effectiveness in time-aware conversion rate prediction for ad- vertising and product recommendation.

关键词： conversion time survival analysis product rec-ommendation advertising

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Resident participating GIS-based tsunami disaster control systems for local communities

Resident participating GIS-based tsunami disaster control sy...

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International Symposium on Communications and information Technologies

作者： Muramoto, Takashi Nakayama, Ken Kobayashi, Yoshitake Maekawa, Mamoru Faculty of Business Hachinohe University Aomori Japan Department of Information Systems Design Graduate School of Information Systems University of Electro-Communications Tokyo Japan Grid Computing and Distributed Systems (GRIDS) Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia

ISBN: (纸本)9780780397408

We propose and experiment a Social Networking Service (SNS) for local communities for tsunami disaster control. It is an easy-to-use GIS-based system with powerful GIS analysis capabilities. One of the features of the system is resident participation. The GIS layer structure proposed in this paper nicely supports this user participation. The system architecture and the use of the system for tsunami disaster control are discussed. We report ongoing developments in Hachinohe-City, Japan.

关键词： GIS tsunami disaster control internet application system design

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Hybrid phase-locked loop with fast locking time and low spur in a 0.18-μm CMOS process

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Chinese Physics B 2014年第7期23卷 748-753页

作者：朱思衡司黎明郭超史君宇朱卫仁 Beijing Key Laboratory of Millimeter Wave and Terahertz Technology Department of Electronic Engineering School of Information and ElectronicsBeijing Institute of Technology Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems EngineeringMonash University

We propose a novel hybrid phase-locked loop （PLL） architecture for overcoming the trade-off between fast locking time and low spur. To reduce the settling time and meanwhile suppress the reference spurs, we employ a wide-band single-path PLL and a narrow-band dual-path PLL in a transient state and a steady state, respectively, by changing the loop bandwidth according to the gain of voltage controlled oscillator （VCO） and the resister of the loop filter. The hybrid PLL is implemented in a 0.18-μm complementary metal oxide semiconductor （CMOS） process with a total die area of 1.4×0.46 mm2. The measured results exhibit a reference spur level of lower than -73 dB with a reference frequency of 10 MHz and a settling time of 20 μs with 40 MHz frequency jump at 2 GHz. The total power consumption of the hybrid PLL is less than 27 mW with a supply voltage of 1.8 V.

关键词： phase-locked loop （PLL） fast locking time low spur complementary metal oxide semiconductor（CMOS）

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Deep reinforcement learning-based algorithms selectors for the resource scheduling in hierarchical cloud computing

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Journal of Network and Computer Applications 2022年第0期208卷

作者： Zhou, Guangyao Wen, Ruiming Tian, Wenhong Buyya, Rajkumar School of Information and Software Engineering University of Electronic Science and Technology of China China Cloud Computing and Distributed Systems Lab School of Computing and Information Systems The University of Melbourne Australia

cloud computing environment is becoming increasingly complex due to its large-scale information growth and increasing heterogeneity of computing resources. Hierarchical cloud computing dividing the system into multi-levels with multiple subsystems to support the adaptability to abundant requests from users has been widely applied and brings great challenges to resource scheduling. It is critical to find an effective way to address the complex scheduling problems in hierarchical cloud computing, whose scenarios and optimization objectives often change with the types of subsystems. In this paper, we propose a scheduling framework to select the scheduling algorithms (SFSSA) for different scheduling scenarios considering no algorithm well suitable to all scenarios. To concretize SFSSA, we propose deep learning-based algorithms selectors (DLS) trained by labeled data and deep reinforcement learning-based algorithms selectors (DRLS) trained by feedback from dynamic scenarios to complete the algorithms selection regarding the scheduling algorithms as selectable tools. Then, we apply strategies including pre-trained model, long experience reply and joint training to improve the performance of DRLS. To enable the quantitative comparison of selectors, we introduce a weighted cost model for the trade-off between solution and complexity. Through multiple sets of experiments in hierarchical cloud computing with multi subsystems for five types of scheduling problems and varying weights of cost, we demonstrate DLS and DRLS outperform baseline strategies. Compared with random selector, greedy selector, round-robin selector, single best selector, virtual best selector and single fast selector, DLS reduces the cost by 47.4%, 46.1%, 33.9%, 47.9%, 19.3%, 18.8% under stable parameter ranges, and DRLS reduces the cost by 41.1%, 40.6%, 11.7%, 42.3%, 11.5%, 12.5% in dynamic scenarios respectively. In experiments, we also validate DRLS has stronger adaptability than DLS in dynamic schedulin

关键词： Reinforcement learning

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Parameter-efficient fine-tuning of Whisper for low-resource speech recognition 5

Parameter-efficient fine-tuning of Whisper for low-resource ...

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5th International Seminar on Artificial Intelligence, Networking and information Technology, AINIT 2024

作者： Liu, Yunpeng Qu, Dan School of Information Systems Engineering University of Information Engineering Laboratory For Advanced Computing and Intelligence Engineering Department of Artificial Intelligence Henan China Systems Engineering University of Information Engineering Laboratory For Advanced Computing and Intelligence Engineering Henan China

ISBN: (纸本)9798350385557

Limited data availability remains a significant challenge for Whisper's low-resource speech recognition performance, falling short of practical application requirements. While previous studies have successfully reduced the recognition error rates of target language speech through fine-tuning, a comprehensive exploration and analysis of Whisper's parameter-efficient fine-tuning capabilities, the advantages and disadvantages are still lacking. This paper aims to fill this gap by conducting comprehensive experimental exploration for Whisper's low-resource speech recognition performance using two parameter-efficient fine-tuning strategies with limited supervised data from multiple low-resource languages. The results and analysis demonstrate that all fine-tuning strategies explored in this paper significantly enhance Whisper's performance. However, different strategies vary in their suitability and practical effectiveness, highlighting the need for careful selection based on specific use cases and resources available. © 2024 IEEE.

关键词： Speech recognition

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Meta-Adapter for Self-Supervised Speech Models: A Solution to Low-Resource Speech Recognition Challenges 30

Meta-Adapter for Self-Supervised Speech Models: A Solution t...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Chen, Yaqi Zhang, Hao Yang, Xukui Zhang, Wenlin Qu, Dan School of Information Systems Engineering Information Engineering University China Laboratory for Advanced Computing and Intelligence Engineering China

ISBN: (纸本)9782493814104

Self-supervised models have demonstrated remarkable performance in speech processing by learning latent representations from large amounts of unlabeled data. Although these models yield promising results on low-resource languages, the computational expense of fine-tuning all model parameters is prohibitively high. Adapters offer a solution by incorporating lightweight bottleneck structures into pre-trained models, enabling efficient parameter adaptation for downstream tasks. However, randomly initialized adapters often underperform in low-resource scenarios, limiting their applicability in low-resource languages. To address this issue, we develop the Meta-Adapter for self-supervised models to obtain meta-initialized parameters that facilitate quick adaptation to low-resource languages. Extensive experiments on the Common Voice and FLEURS datasets demonstrate the superior performance of Meta-Adapters on 12 low-resource languages spanning four different language families. Moreover, Meta-adapters show better generalization and extensibility than traditional pretraining methods. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Speech processing

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Artificial Intelligence (AI)-Centric Management of Resources in Modern distributed computing systems

Artificial Intelligence (AI)-Centric Management of Resources...

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IEEE cloud Summit

作者： Shashikant Ilager Rajeev Muralidhar Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia Amazon Web Services (AWS) Australia

ISBN: (数字)9781728182667

ISBN: (纸本)9781728182674

Contemporary distributed computing systems (DCS) such as cloud Data Centers are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries. On the other hand, the Internet of Things (IoT)-driven applications are producing a huge amount of data that requires real-time processing and fast response. Managing these resources efficiently to provide reliable services to end-users or applications is a challenging task. The existing Resource Management systems (RMS) rely on either static or heuristic solutions inadequate for such composite and dynamic systems. The advent of Artificial Intelligence (AI) due to data availability and processing capabilities manifested into possibilities of exploring data-driven solutions in RMS tasks that are adaptive, accurate, and efficient. In this regard, this paper aims to draw the motivations and necessities for data-driven solutions in resource management. It identifies the challenges associated with it and outlines the potential future research directions detailing where and how to apply the data-driven techniques in the different RMS tasks. Finally, it provides a conceptual data-driven RMS model for DCS and presents the two real-time use cases (GPU frequency scaling and data centre resource management from Google cloud and Microsoft Azure) demonstrating AI-centric approaches' feasibility.

关键词： Data centers cloud computing Computational modeling Real-time systems Resource management Artificial intelligence Task analysis

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Innovations in Computer Science and Engineering 1

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丛书名： Lecture Notes in Networks and systems

1000年

作者： H. S. Saini Rishi Sayal A. Govardhan Rajkumar Buyya

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SFedXL: Semi-Synchronous Federated Learning With Cross-Sharpness and Layer-Freezing

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IEEE Internet of Things Journal 2025年

作者： Zhao, Mingxiong Zhao, Shihao Feng, Chenyuan Yang, Howard H. Niyato, Dusit Quek, Tony Q. S. Yunnan University National Pilot School of Software Kunming650500 China Ministry of Education Engineering Research Center of Integration and Application of Digital Learning Technology Beijing100039 China Ministry of Education Engineering Research Center of Cyberspace Kunming650504 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China EURECOM Sophia Antipolis06410 France Zhejiang University University of Illinois Urbana-Champaign Institute Zhejiang University Haining314400 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore

Federated learning (FL) emerges as a potential solution for enabling multiple terminal devices to collaboratively accomplish computational tasks within an Unmanned Aerial Vehicle (UAV) swarm. However, traditional FL approaches, predicated on synchronous data aggregation, are not feasible for a UAV swarm owing to the inherently variable and dynamic nature of their communication networks compared with terrestrial systems. Furthermore, the data procured by UAVs is often highly heterogeneous, attributable to disparities in deployment environments and device attributes. Considering the distinct flight paths and unique operational conditions encountered by different UAVs, a considerable amount of data remains unlabeled. To tackle the challenges associated with asynchronous operations and the prevalence of unlabeled data, we introduce a novel framework termed Semi-synchronous FL with Cross-Sharpness and Layer-Freezing (SFedXL), tailored for a UAV swarm. In particular, we devise a cross-sharpness model training strategy aimed at optimizing the utilization of both labeled and unlabeled datasets. Additionally, we propose an innovative semi-synchronous model aggregation protocol, complemented by client-specific layer-freezing and client cluster scheduling, designed to expedite the training process. Our simulation results indicate that the proposed algorithm surpasses current FL methods in terms of object recognition accuracy and communication efficiency, albeit with a trade-off of increased local computation latency. © 2014 IEEE.

关键词： Federated learning

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Time Series Forecasting of COVID-19 Infections in United Arab Emirates using ARIMA

Time Series Forecasting of COVID-19 Infections in United Ara...

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2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Ismail, Leila Alhmoudi, Shaikhah Alkatheri, Sumyah United Arab Emirates University Distributed Computing and Systems Research Laboratory College of Information Technology Abu Dhabi15551 United Arab Emirates

ISBN: (纸本)9781728176246

Machine learning time series models have been used to predict COVID-19 pandemic infections. Based on the public dataset from Johns Hopkins, we present a novel framework for forecasting COVID-19 infections. We implement our framework for the United Arab Emirates (UAE) and develop autoregressive integrated moving average (ARIMA) time series forecast model. To the best of our knowledge, this is the only study to forecast the infections in UAE using the time series model. © 2020 IEEE.

关键词： COVID-19

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