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IEEE 14th International symposium and Workshops on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

作者： Hoque, Mohammad Ashraful Siekkinen, Matti Nurminen, Jukka K. Aalto, Mika Tarkoma, Sasu Aalto Univ Sch Sci Espoo Finland Nokia Solut & Networks Espoo Finland Univ Helsinki FIN-00014 Helsinki Finland

Multimedia streaming to mobile devices is challenging for two reasons. First, the way content is delivered to a client must ensure that the user does not experience a long initial playback delay or a distorted playback in the middle of a streaming session. Second, multimedia streaming applications are among the most energy hungry applications in smartphones. the energy consumption mostly depends on the delivery techniques and on the power management techniques of wireless access technologies (Wi-Fi, 3G, and 4G). In order to provide insights on what kind of streaming techniques exist, how they work on different mobile platforms, their efforts in providing smooth quality of experience, and their impact on energy consumption of mobile phones, we did a large set of active measurements with several smartphones having both Wi-Fi and cellular network access. Our analysis reveals five different techniques to deliver the content to the video players. the selection of a technique depends on the mobile platform, device, player, quality, and service. the results from our traffic and power measurements allow us to conclude that none of the identified techniques is optimal because they take none of the following facts into account: access technology used, user behavior, and user preferences concerning data waste. We point out the technique with the best playback buffer configuration, which provides the most attractive trade-offs in particular situations. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Performance analysis Measurement Power consumption Wireless multimedia Quality of experience (QoE)

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An efficient query mechanism of data provenance based on dependency view

An efficient query mechanism of data provenance based on dep...

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International symposium on Communications and Information Technologies (ISCIT)

作者： Xin Gao Xuan Sun The National Computer Network Emergency Response Technical Team/Coordination Center of China Peking China School of Information Management Beijing Information Science & Technology University Peking China

ISBN: (纸本)9781479944156

data provenance is useful for data analysis, including auditing, debugging, evaluating the trust and quality, access control and so on. For the scale of data keep increasing, data provenance also become large and constantly growing, and it brings challenges to the efficiency of provenance tracking which is the important base of data analysis. this paper proposes a kind of dependency view based on OPM to extract a global data provenance description for data process instance with more correlation information among the elements of data provenance, and then provides an efficient query mechanism based on dependency view of data provenance to support provenance tracking by constructing a set of query operations for both forward and backward provenance tracking.

关键词： Collaboration Big data Context Handheld computers Runtime Distributed databases data mining

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Misconfiguration detection for cloud datacenters using decision tree analysis

Misconfiguration detection for cloud datacenters using decis...

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Asia-Pacific Network Operations and Management symposium (APNOMS)

作者： Tetsuya Uchiumi Shinji Kikuchi Yasuhide Matsumoto Cloud Computing Research Center Fujitsu Laboratories Limited Kawasaki Kanagawa Japan

Since many components comprising large scale cloud datacenters have a great number of configuration parameters (e.g. hostnames, languages, and time zones), it is difficult to keep consistencies in the configuration parameters. In such cases, misconfigured parameters can cause service failures. For this reason, we propose a misconfiguration detection method for large-scale cloud datacenters, which can automatically determine possible misconfigurations by identifying the relations existing among majority of the parameters using statistical decision tree analysis. We have also developed a pattern modification method to improve the accuracy of the decision tree approach. We evaluated the misconfiguration detection performance of the proposed method by using both artificial data and actual data. the results show that we can achieve higher accuracy (78.6% in the actual data) in misconfiguration detection by using the pattern modification.

关键词： Servers Decision trees Cloud computing Accuracy Monitoring IP networks Algorithm design and analysis

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Sensitivity of shoreline-recession forecasts to sediment budget uncertainties

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JOURNAL OF COASTAL RESEARCH 2016年第75期000卷 947-951页

作者： de Figueiredo, Salette A. Cowell, Peter J. Univ Fed Rio Grande Inst Oceanog Rio Grande Brazil Univ Sydney Coastal Studies Unit Sydney NSW 2006 Australia

Intrinsic uncertainty associated with the stochastic nature of coastal processes and how they affect sediment budget estimates under present conditions already make predicting coastal change difficult. the possibility that under climate change conditions alteration of wave climate will have direct implications for alongshore transport in future, may complicate even more future forecasts of coastal change in response to sea-level rise. this study exploits the variability in sediment budgets along the Rio Grande do Sul coastal plain, in southern Brazil, to provide an experimental framework to evaluate the significance of such uncertainties in coastal response under the rising sea levels rates predicted for the next century. the analysis involved quantifying the variation in coastal recession sensitivity between different sectors along the coast (coastal cells), through the application of RanSTM (Random Shoreface Translation Model) using two different scenarios for the sediment-budget parameter. Sediment budget parameter was estimated based on historical shoreline changes and Holocene barrier stratigraphic data. the results show that forecasts for lower-magnitude coastal recession associated with more likely impacts (e.g. 90% risk) are more susceptible to uncertainties in littoral sediment budgets than high-magnitude less likely impacts (e.g. 10% risk). this is especially the case for coastal cells with low dune dimensions because in general such dunes have less capacity to buffer coastal-erosion effects. the reduced effect on high-end forecasts with respect to sediment budget uncertainties therefore favors better confidence in the forecasts.

关键词： Sea level rise large scale processes coastal response

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large-scale computational experiments for complex enterprise systems behaviour prediction

Large-scale computational experiments for complex enterprise...

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14th IFAC symposium on Information Control Problems in Manufacturing, INCOM'12

作者： Babiceanu, Radu F. Department of Systems Engineering University of Arkansas at Little Rock Little Rock AR 72204 United States

ISBN: (纸本)9783902661982

this work comes as a contribution to the efforts that are undergoing within engineering systems community to account for the increased complexity of today's manufacturing or service systems. these systems are becoming more and more complicated due to the increase in the number of elements, interconnections within the system, and necessary integration with other systems. Moreover, through the emphasis on self-organization and considering the multi-stakeholders context and objectives, these systems are crossing the line towards complexity. there is a need for developing a framework to be used in modeling, analysis, and integration of systems that operate in uncertain environments, in which characteristics such as adaptation, self-organization and evolution, or in other words behavior prediction, need to be addressed. the proposed complex enterprise systems framework combines knowledge coming from complex systems science and systems engineering domains, and uses computational intelligence and agent-based systems simulation methodologies. the approach requires computational experience in manipulating large amounts of data and building large-scale simulation models. A significant result to be made possible by this research is that systems may no longer have a fixed, life-cycle long, design based on identified requirements;systems will be engineered to evolve and adapt as needed during the operational phase, while respecting their operational environment constraints. © 2012 IFAC.

关键词： Life cycle

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FOG-Engine: Towards Big data Analytics in the Fog

FOG-Engine: Towards Big Data Analytics in the Fog

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IEEE International symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Farahd Mehdipour Bahman Javadi Aniket Mahanti Tech Futures Lab & Unitec Institute of Technology Auckland New Zealand Western Sydney University Sydney Australia University of Auckland Auckland New Zealand

Existing platforms fall short in providing effective solutions for big data analytics while the demands for processing large quantities of data in real-time are increasing. Moving data analytics towards where the data is generated and stored could be a solution for addressing this issue. In this paper, we propose a solution referred as FOG-engine, which is integrated into IoTs near the ground and facilitates data analytics before offloading large amounts of data to a central location. In this work, we introduce a model for data analytic using FOG-engines and discuss our plan for evaluating its efficacy in terms of several performance metrics such as processing speed, network bandwidth, and data transfer size.

关键词： fogs fog Performance metrics central location data analysis NEBULISED Solutions big data network bandwidth UNLOADING data transmission PROCESSING SPEED ground large QUANTITIES

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ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-Order Optimization 38

ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum an...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Jiang, Shuoran Chen, Qingcai Pan, Youcheng Xiang, Yang Lin, Yukang Wu, Xiangping Liu, Chuanyi Song, Xiaobao Harbin Inst Technol Shenzhen Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Harbin Inst Technol Shenzhen Inst Data Secur Shenzhen Peoples R China

ISBN: (纸本)1577358872

Lowering the memory requirement in full-parameter training on large models has become a hot research area. MeZO fine-tunes the large language models (LLMs) by just forward passes in a zeroth-order SGD optimizer (ZO-SGD), demonstrating excellent performance with the same GPU memory usage as inference. However, the simulated perturbation stochastic approximation for gradient estimate in MeZO leads to severe oscillations and incurs a substantial time overhead. Moreover, without momentum regularization, MeZO shows severe over-fitting problems. Lastly, the perturbation-irrelevant momentum on ZO-SGD does not improve the convergence rate. this study proposes ZO-AdaMU to resolve the above problems by adapting the simulated perturbation with momentum in its stochastic approximation. Unlike existing adaptive momentum methods, we relocate momentum on simulated perturbation in stochastic gradient approximation. Our convergence analysis and experiments prove this is a better way to improve convergence stability and rate in ZO-SGD. Extensive experiments demonstrate that ZO-AdaMU yields better generalization for LLMs fine-tuning across various NLP tasks than MeZO and its momentum variants.

关键词： Momentum

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Efficient visualization of encoded Fourier transform infrared microscopic data of osteoporotic bone

Efficient visualization of encoded Fourier transform infrare...

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Annual IEEE symposium on Computer-Based Medical Systems

作者： G. Zamora S. Yang S. Mitra M. Peterson E.P. Paschalis Department of Electrical and Computer Engineering Texas Tech University Lubbock Texas USA The Hospital for Special Surgery New York NY USA Hospital for Special Surgery New York NY USA

Fourier transform IR microspectroscopy (FTIRM) of bone slices has become a major research tool to analyze the chemical constitution of calcified structures. Qualitative as well quantitative measures of bone components using this technique have been shown to be useful in the characterization of normal bone in both animal and human specimens. Furthermore, the technique can be used to characterize bone diseases like osteoporosis and the effects of different chemical constitutions on bone structures. However, the ever-increasing number of FTIRM studies is yielding an amount of data that is becoming unmanageable in terms of size for the purpose of storage. We address this problem by applying two different lossless coding techniques to the images generated by the studies. Compression ratios as high as 100:1 are obtained by coding the lower bit levels of the image while retaining the information that is used by the specialists.

关键词： visualization Fourier transforms Image coding Chemical analysis Constitution Animal structures Humans Bone diseases Osteoporosis Image generation

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Addressing Digital and AI Skills Gaps in European Living Areas: A Comparative analysis of Small and large Communities 38

Addressing Digital and AI Skills Gaps in European Living Are...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Pham, Long O'Sullivan, Barry Scantamburlo, Teresa Mai, Tai Univ Coll Cork Sch Comp Sci & IT Insight SFI Res Ctr Data Analyt Cork Ireland Ca Foscari Univ Dept Environm Sci Informat & Stat Venice Italy Dublin City Univ ADAPT Res Ctr Sch Comp Dublin Ireland

ISBN: (纸本)1577358872

As Artificial Intelligence (AI) continues to permeate various aspects of societies, understanding the disparities in AI knowledge and skills across different living areas becomes imperative. Small living areas have emerged as significant contributors to Europe's economy, offering an alternative to the bustling environment of larger cities for those seeking an improved quality of life. Nonetheless, they often encounter challenges related to digital infrastructure, access to financial resources, and digital skills gaps, limiting their economic and social growth prospects. this study investigates the digital and AI skills gaps in the context of small and large European living areas, shedding light on the potential hindrances to unleashing the full economic and social potentials of these regions in an AI-enabled economy. Drawing from a comprehensive dataset encompassing 4,006 respondents across eight EU countries, this research examines the current perceptions and understandings of AI and digital skills within two distinct population groups: residents of smaller living areas and their counterparts in larger communities. through bivariate analysis, notable insights are revealed concerning trust in AI solutions and entities, self-assessed digital skills, AI Awareness, AI Attitudes and demography variables in both population groups. these insights may refer to the significance of addressing digital and AI skills gaps in fostering growth and preparedness for the AI-driven future. As AI becomes increasingly integral to various aspects of society, targeted interventions and policies are essential to bridge these gaps and enable individuals and communities to harness the transformative potential of AI-enabled economies.

关键词： Population statistics

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A Comprehensive analysis of the Effectiveness of large Language Models as Automatic Dialogue Evaluators 38

A Comprehensive Analysis of the Effectiveness of Large Langu...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Zhang, Chen D'Haro, Luis Fernando Chen, Yiming Zhang, Malu Li, Haizhou Natl Univ Singapore Singapore Singapore Univ Politecn Madrid Speech Technol Grp Madrid Spain Univ Elect Sci & Technol China Chengdu Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Peoples R China

ISBN: (纸本)1577358872

Automatic evaluation is an integral aspect of dialogue system research. the traditional reference-based NLG metrics are generally found to be unsuitable for dialogue assessment. Consequently, recent studies have suggested various unique, reference-free neural metrics that better align with human evaluations. Notably among them, large language models (LLMs), particularly the instruction -tuned variants like ChatGPT, are shown to be promising substitutes for human judges. Yet, existing works on utilizing LLMs for automatic dialogue evaluation are limited in their scope in terms of the number of meta-evaluation datasets, mode of evaluation, coverage of LLMs, etc. Hence, it remains inconclusive how effective these LLMs are. To this end, we conduct a comprehensive study on the application of LLMs for automatic dialogue evaluation. Specifically, we analyze the multi-dimensional evaluation capability of 30 recently emerged LLMs at both turn and dialogue levels, using a comprehensive set of 12 meta-evaluation datasets. Additionally, we probe the robustness of the LLMs in handling various adversarial perturbations at both turn and dialogue levels. Finally, we explore how model -level and dimension -level ensembles impact the evaluation performance. All resources are available at https: //***/e0397123/comp- analysis

关键词： Speech processing

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