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Orthogonal Nonnegative Locally Linear Embedding

Orthogonal Nonnegative Locally Linear Embedding

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Lei Wei Naiyang Guan Xiang Zhang Zhigang Luo Dacheng Tao National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 P.R. China These authors contributed equally to this paper National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 P.R. China Centre for Quantum Computation and Intelligence Systems and Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia

ISBN: (纸本)9781479906505

Nonnegative matrix factorization (NMF) decomposes a nonnegative dataset X into two low-rank nonnegative factor matrices, i.e., W and H, by minimizing either Kullback-Leibler (KL) divergence or Euclidean distance between X and WH. NMF has been widely used in pattern recognition, data mining and computer vision because the non-negativity constraints on both W and H usually yield intuitive parts-based representation. However, NMF suffers from two problems: 1) it ignores geometric structure of dataset, and 2) it does not explicitly guarantee partsbased representation on any datasets. In this paper, we propose an orthogonal nonnegative locally linear embedding (ONLLE) method to overcome aforementioned problems. ONLLE assumes that each example embeds in its nearest neighbors and keeps such relationship in the learned subspace to preserve geometric structure of a dataset. For the purpose of learning parts-based representation, ONLLE explicitly incorporates an orthogonality constraint on the learned basis to keep its spatial locality. To optimize ONLLE, we applied an efficient fast gradient descent (FGD) method on Stiefel manifold which accelerates the popular multiplicative update rule (MUR). The experimental results on real-world datasets show that FGD converges much faster than MUR. To evaluate the effectiveness of ONLLE, we conduct both face recognition and image clustering on real-world datasets by comparing with the representative NMF methods.

关键词： Nonnegative matrix factorization Locally linear embedding Fast gradient descent Stiefel manifold Dataset Nonnegative matrix decomposition geometrical construction NMF protocol images embedding Flue gas desulfurization Nearest neighbor Orthogonal

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An Adaptive Multi-objective Salp Swarm Algorithm for Efficient Demand Side Management

An Adaptive Multi-objective Salp Swarm Algorithm for Efficie...

引用

IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Zezheng Zhao Chunqiu Xia Lian Chi Xiaomin Chang Wei Li Ting Yang Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Business School Nanjing University of Information Science and Technology China Key Laboratory of Smart Grid of Ministry of Education School of Electrical and Information Engineering Tianjin University China

ISBN: (数字)9781728198668

ISBN: (纸本)9781728198675

With the continuous growth in population and energy demands more attention has been paid to energy consumption issues in residential environments. At the user-end, the home energy management system (HEMS) has been proposed as a cost-effective solution to reduce the electricity cost in households, while maintaining users' comfort and reducing the pressure on energy providers. However, it is a challenge to design a cost-effective scheduling strategies for HEMS which takes many objectives into consideration while potentially benefiting both users and providers. In our work, we propose a new approach named adaptive multi-objective salp swarm algorithm (AMSSA) based on traditional multi-objective salp swarm algorithm (MSSA) to realise a multi-objective optimisation approach for the power scheduling problem. AMSSA not only fulfils the trade-off among users' comfort, electricity cost and peak to average ratio (PAR), but also enhances the convergence speed for the overall optimisation process. Moreover, we also set up a testbed by using smart appliances and implemented our design on an edge-based energy management system. The experiment results demonstrated a reduction in both electricity cost (47.55%) and PAR (45.73%), compared with the case without a scheduling scheme.

关键词： Sociology Peak to average power ratio Sensor systems Scheduling Energy management systems Statistics Optimization

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Online Demand Peak Shaving with Machine-Learned Advice in Cyber-Physical Energy Systems

Online Demand Peak Shaving with Machine-Learned Advice in Cy...

引用

IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Minxi Fengl Wei Li Boyu Qin Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Electrical Engineering State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University China

With the deep integration of cyber tools, control algorithms are increasingly employed in cyber-physical energy systems to enhance management, cost efficiency, and robustness. Effective demand load management is crucial in cyber-physical energy systems as it directly impacts operational costs. Failure to adequately manage spiky or seasonal demand loads can lead to significant expenses on monthly utility bills. In this study, we propose AMPAMOD, a randomized online algorithm with machine-learned advice, to optimize battery operations and mitigate highly dynamic peak loads. AMPAMOD utilizes limited advice from machine learning algorithms to guide our online algorithm and ensure cost-effective peak load management. The theoretical analysis shows that our solution has minimal advice complexity, a linear computational cost, and an improved competitive ratio. Finally, we conduct extensive trace-driven experiments on real-world datasets. AMPAMOD achieves a peak shaving success rate of over 90%, outperforming baselines by at least 50%. These experimental results confirm theoretical findings and demonstrate the potential of AMPAMOD.

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An Energy-Efficient Scheduling Algorithm for Sporadic Real-Time Tasks in Multiprocessor Systems

An Energy-Efficient Scheduling Algorithm for Sporadic Real-T...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Dong-Song Zhang Fang-Yuan Chen Hong-Hua Li Shi-Yao Jin De-Ke Guo National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China School of Computing Science Simon Fraser University Burnaby BC Canada National Laboratory for Information System Engineering School of Information Systems and Management National University of Defense Technology Changsha China

As the energy consumption of embedded multiprocessor systems becomes increasingly prominent, the real-time energy-efficient scheduling in multiprocessor systems becomes an urgent problem to reduce the system energy consumption while meeting real-time constraints. For a multiprocessor with independent DVFS and DPM at each processor, this paper proposes an energy-efficient real-time scheduling algorithm named LRE-DVFS-EACH, based on LRE-TL which is an optimal real-time scheduling algorithm for sporadic tasks. LRE-DVFS-EACH utilizes the concept of TL plane and the idea of fluid scheduling to dynamically scale the voltage and frequency of processors at the initial time of each TL plane as well as the release time of a sporadic task in each TL plane. Consequently, LRE-DVFS-EACH can obtain a reasonable tradeoff between the real-time constraints and the energy saving. LRE-DVFS-EACH is also adaptive to the change of workload caused by the dynamic release of sporadic tasks, which can obtain more energy savings. The experimental results show that compared with existing algorithms, LRE-DVFS-EACH can not only guarantee the optimal feasibility of sporadic tasks, but also achieve more energy savings in all cases, especially in the case of high workloads.

关键词： Real time systems Scheduling algorithm Time frequency analysis Scheduling Heuristic algorithms Power demand

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Scientific Workflow Makespan Reduction through Cloud Augmented Desktop Grids

Scientific Workflow Makespan Reduction through Cloud Augment...

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IEEE International Conference on Cloud computing Technology and science (CloudCom)

作者： Christopher J. Reynolds Stephen Winter Gabor Z. Terstyanszky Tamas Kiss Pamela Greenwell Sandor Acs Peter Kacsuk Centre for Parallel Computing School of Electronics and Computer Science University of Westminster London UK Department of Biomedical Sciences University of Westminster London UK MTA SZTAKI Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Scientific workflows are common in biomedical research, particularly for molecular docking simulations such as those used in drug discovery. Such workflows typically involve data distribution between computationally demanding stages which are usually mapped onto large scale compute resources. Volunteer or Desktop Grid (DG) computing can provide such infrastructure but has limitations resulting from the heterogeneous nature of the compute nodes. These constraints mean that reducing the make span of a given workflow stage submitted to a DG becomes problematic. Late jobs can significantly affect the make span, often completing long after the bulk of the computation has finished. In this paper we present a system capable of significantly reducing the make span of a scientific workflow. Our system comprises a DG which is dynamically augmented with an infrastructure as a service (IaaS) Cloud. Using this solution, the Cloud resources are used to process replicated late jobs. Our system comprises a core component termed the scheduler, which implements an algorithm to perform late job detection, Cloud resource management (instantiation and reuse), and job monitoring. We offer a formal definition of this algorithm, whilst we also provide an evaluation of our prototype using a production scientific workflow.

关键词： Bridges Portals Servers Monitoring Cloud computing computer architecture

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CLAWER: Context-aware Cloud-Fog based Workflow Management Framework for Health Emergency Services

CLAWER: Context-aware Cloud-Fog based Workflow Management Fr...

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IEEE/ACM International Symposium on Cluster computing and the Grid (CCGRID)

作者： Shreya Ghosh Jaydeep Das Soumya K Ghosh Rajkumar Buyya Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9781728160955

ISBN: (纸本)9781728196497

With the major development of sensor technologies and advancements of communication network infrastructures, there is a growing interest to add more intelligence in the e-health monitoring for facilitating an effective healthcare system. While IoT devices are capable of continuous health-parameter sensing and providing notifications to the user, an effective business process management (BPM) facilitates effective system integration and data processing workflow. This paper proposes an efficient framework for managing emergency situations (specifically, health-related) through the analysis of heterogeneous data sources. The proposed framework, named CLAWER (CLoud-Fog bAsed Workflow for Emergency seRvice) aims to bridge the gap between process management and data analytics by providing an automated workflow for personalized health-monitoring and efficient recommendation system. Here, the IoT devices are used for collecting the movement and health data. The smart phone can act as an edge device to acquire data with user movement information. The accumulated data is initially processed inside the fog device, and finally the analysis and recommendations are generated by the cloud. In this paper the indoor health-status of the users are analysed in small cell cloud enhanced eNode B, which is used as fog device. The generated recommendations are stored in the fog device to provide the recommendations to the users with low latency and in timely manner. The experimental analysis of CLAWER yields better precision and recall values than the existing methods.

关键词： Cloud computing Roads Medical services Markov processes Monitoring Delays Biomedical monitoring

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TRANSDUCTIVE NONNEGATIVE MATRIX FACTORIZATION FOR SEMI-SUPERVISED HIGH-PERFORMANCE SPEECH SEPARATION

TRANSDUCTIVE NONNEGATIVE MATRIX FACTORIZATION FOR SEMI-SUPER...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Naiyang Guan Long Lan Dacheng Tao Zhigang Luo Xuejun Yang Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Changsha Hunan 410073 China Centre for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney Sydney NSW 2007 Australia State Key Laboratory of High Performance Computing National University of Defense Technology Changsha Hunan 410073 China

ISBN: (纸本)9781479928941

Regarding the non-negativity property of the magnitude spectrogram of speech signals, nonnegative matrix factorization (NMF) has obtained promising performance for speech separation by independently learning a dictionary on the speech signals of each known speaker. However, traditional NM-F fails to represent the mixture signals accurately because the dictionaries for speakers are learned in the absence of mixture signals. In this paper, we propose a new transductive NMF algorithm (TNMF) to jointly learn a dictionary on both speech signals of each speaker and the mixture signals to be separated. Since TNMF learns a more descriptive dictionary by encoding the mixture signals than that learned by NMF, it significantly boosts the separation performance. Experiments results on a popular TIMIT dataset show that the proposed TNMF-based methods outperform traditional NMF-based methods for separating the monophonic mixtures of speech signals of known speakers.

关键词： Speech Dictionaries as Topic Loudspeakers NMF protocol signals mixtures factorization HIGH-PERFORMANCE

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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International Conference on Data Engineering

作者： Junhui Gao Qianru Wang Xin Zhang Juan Shi Xiang Zhao Qingye Han Yan Pan School of Computer Science Northwestern Polytechnical University School of Computer Science and Technology Xidian University National Key Laboratory of Information Systems Engineering National University of Defense Technology Air Force Engineering University Laboratory for Big Data and Decision National University of Defense Technology School of Management Science and Real Estate Chongqing University National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery.

关键词： Autonomous aerial vehicles Data engineering Air to ground communication Trajectory Batteries Time factors Task analysis

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An Energy-Efficient Kernel Framework for Large-Scale Data Modeling and Classification

An Energy-Efficient Kernel Framework for Large-Scale Data Mo...

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IEEE International Symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Paul D. Yoo Jason W.P. Ng Albert Y. Zomaya Department Computer Engineering Etisalat British Telecom (BT) Innovation Centre Khalifa University of Science Technology and Research Abu Dhabi UAE Centre of Distributed and High Performance Computing School of Information Technologies (J12) University of Sydney NSW Australia

Energy-efficient computing has now become a key challenge not only for data-center operations, but also for many other energy-driven systems, with the focus on reducing of all energy-related costs, and operational expenses, as well as its corresponding and environmental impacts. Intelligent machine-learning systems are typically performance driven. For instance, most non-parametric model-free approaches are often known to require high computational cost in order to find the global optima. Designing more accurate machine-learning systems to satisfy the market needs will hence lead to a higher likelihood of energy waste due to the increased computational cost. This paper thus introduces an energy-efficient framework for large-scale data modeling and classification. It can achieve a test error comparable to or better than the state-of-the-art machine-learning models, while at the same time, maintaining a low computational cost when dealing with large-scale data. The effectiveness of the proposed approaches has been demonstrated by our experiments with two large-scale KDD datasets: Mtv-1 and Mtv-2.

关键词： Electromyography Machine learning Energy efficiency distributed processing

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Enhancing 2D Representation via Adjacent Views for 3D Shape Retrieval

Enhancing 2D Representation via Adjacent Views for 3D Shape ...

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International Conference on computer Vision (ICCV)

作者： Cheng Xu Zhaoqun Li Qiang Qiu Biao Leng Jingfei Jiang School of Computer Science & Engineering Beihang University Duke University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University National Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through projection. In this paper, we propose a convolutional neural network based method, CenterNet, to enhance each individual 2D view using its neighboring ones. By exploiting cross-view correlations, CenterNet learns how adjacent views can be maximally incorporated for an enhanced 2D representation to effectively describe shapes. We observe that a very small amount of, e.g., six, enhanced 2D views, are already sufficient for a panoramic shape description. Thus, by simply aggregating features from six enhanced 2D views, we arrive at a highly compact yet discriminative shape descriptor. The proposed shape descriptor significantly outperforms state-of-the-art 3D shape retrieval methods on the ModelNet and ShapeNetCore55 benchmarks, and also exhibits robustness against object occlusion.

关键词： Shape Three-dimensional displays Correlation Two dimensional displays Feature extraction Solid modeling Computational modeling

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