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检索条件"任意字段=International Conference on Big Data Intelligence and Computing, DataCom 2022"

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Leveraging Reinforcement Learning to Allocate Bandwidth in the Agent-based Resource Management System 5

Leveraging Reinforcement Learning to Allocate Bandwidth in t...

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Wang, Mu-Cheng Hershey, Paul C. Ioakimidis, Theodore E Raytheon Space and Airborne Systems MarlboroughMA United States Raytheon Intelligence Information and Services DullesVA United States

ISBN: (纸本)9781728141176

In this research, we utilize an agent-based resource management technique, namely the real-time Network Communication Resource Manager (NCRM) to manage battlefield network resources. The NCRM addresses the allocation problem by providing a process, an architecture, and an algorithm to allocate network resources to a mission based on its requirements and currently available resources. This approach enables resilient centralized command (i.e., mission analyses and resource allocation), distributed control, and decentralized execution (i.e., resource reservation and reallocation). Our solution focuses on maximizing the effectiveness of available (remaining) network assets to maintain integrity and efficiency of mission critical assets. NCRM can: (A) through intelligent path selection, deliver mission traffic over the optimal performing paths based on its characteristics or requirements and real-time path status;and (B) effectively converge to a new optimum that dynamically adapts itself to the latest network condition (e.g., newly introduced or lost assets/links). © 2019 IEEE.

关键词： Reinforcement learning

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SoLar: sinkhorn label refinery for imbalanced partial-label learning 22

SoLar: sinkhorn label refinery for imbalanced partial-label ...

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Proceedings of the 36th international conference on Neural Information Processing Systems

作者： Haobo Wang Mingxuan Xia Yixuan Li Yuren Mao Lei Feng Gang Chen Junbo Zhao Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University School of Software Technology Zhejiang University Department of Computer Sciences University of Wisconsin-Madison College of Computer Science Chongqing University and Center for Advanced Intelligence Project RIKEN

ISBN: (纸本)9781713871088

Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth. While a variety of label disambiguation methods have been proposed in this domain, they normally assume a class-balanced scenario that may not hold in many real-world applications. Empirically, we observe degenerated performance of the prior methods when facing the combinatorial challenge from the long-tailed distribution and partial-labeling. In this work, we first identify the major reasons that the prior work failed. We subsequently propose SoLar, a novel Optimal Transport-based framework that allows to refine the disambiguated labels towards matching the marginal class prior distribution. SoLar additionally incorporates a new and systematic mechanism for estimating the long-tailed class prior distribution under the PLL setup. Through extensive experiments, SoLar exhibits substantially superior results on standardized benchmarks compared to the previous state-of-the-art PLL methods.

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Architecture to Speed up Redundant data Processing in a MapReduce Cloud 5

Architecture to Speed up Redundant Data Processing in a MapR...

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Huang, Tzu-Chi Chu, Kuo-Chih Zeng, Xue-Yan Jou, Fu-Lin Tsai, Ming-Fong Shieh, Ce-Kuen Lunghwa University of Science and Technology Department of Electronic Engineering Taoyuan Taiwan National United University Department of Electronic Engineering Miaoli Taiwan National Cheng Kung University Department of Electrical Engineering Tainan Taiwan

ISBN: (纸本)9781728141176

A cloud owns many resources usable to applications, but economically utilizing or saving the resources always is an important issue on cloud computing nowadays. A cloud probably wastes resources to do extra unnecessary computations due to processing redundant data such as MapReduce applications calculating the page ranking of hot news or rendering map zooms of a geographic location. Now, a cloud can utilize the Architecture to Speed up Redundant data Processing (ASRDP) proposed in this paper in order to speed up the processing of redundant data in MapReduce. With the help of ASRDP, A cloud can greatly save the execution time of an application that processes redundant input data, especially when the application has high complexity in its Map function. According to the prototype implementation and related experiments with several popular applications in this paper, a cloud utilizes ASRDP to not only greatly improve performance when processing redundant input data but also intelligently avoid overheads of ASRDP when non-redundant input data. © 2019 IEEE.

关键词： MapReduce

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Computational modelling of speech data integration to assess interactions in B2B sales calls 5

Computational modelling of speech data integration to assess...

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Silber-Varod, Vered Lerner, Anat Carmi, Nehoray Amit, Daphna Guttel, Yonathan Orlob, Chris Allouche, Omri The Open University of Israel Open Media and Information Lab Ra'anana Israel The Open University of Israel Mathematics and Computer Science Department Ra'anana Israel Gong.io Herzliya Israel Gong.io San Fransico United States

ISBN: (纸本)9781728141176

The business sector now recognizes the value of Conversation intelligence in understanding patterns, structures and insights of authentic conversation. Using machine learning methods, companies process massive amount of data about conversation content, vocal features and even speaker body gestures of spoken conversations. This study is a Work-in-Progress (WIP), aimed to modeling the dynamics between sales representatives and customers in business-to-business (B2B) sales calls, by relying solely on the acoustic signal. To this end, we analyze 358 sales calls at the Discovery phase. To model the conversations, we compute a basic set of acoustic features: Talk proportions, F0, intensity, harmonics-to-noise ratio (HNR), jitter, and shimmer. The plots of each acoustic feature reveal the interactions and common behavior across calls, on one hand, and within calls, on the other. The study demonstrates that using delta metrics to assess the interactions leads to new insights. © 2019 IEEE.

关键词： acoustic features Conversation intelligence conversation modelling sales calls speech data

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Stream Processing of Software-Defined Video Analytics on a Smart Campus 5

Stream Processing of Software-Defined Video Analytics on a S...

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Hsieh, Chia-Ying Li, Yen-Chun Hsu, Chia-Hsin Kuo, Yung-Ching Chen, Chung-Chiu Hsu, Cheng-Hsin Sheu, Jang-Ping National Tsing Hua University Department of Computer Science Hsin-Chu Taiwan

ISBN: (纸本)9781728141176

The demand for real-time video analytics is increasing due to the prosperity of surveillance cameras in smart spaces. In this extended abstract, we present our stream processing platform to dynamically deploy, manage, and upgrade software-defined video analytics. Our key innovation is to divide a smart space into multiple Internet-of-Things (IoT) zones. Each zone consists of a set of geo-collocated IoT devices, heterogeneous networks, and edge servers. By creating a hierarchical architecture of IoT zones, our distributed platform achieves better scalability and flexibility. We implement our stream processing platform using a set of opensource projects. We deploy it on a real testbed of eight smart street lamps on our campus, in which four surveillance cameras stream live videos to an edge server. The edge server runs multiple video analytics following the user demands, and the analytics results are stored in a time-series database for future usage. In this demo, we will demonstrate two sample video analytics: illegal parking detection and people counting. The conference attendees will get to navigate through our Web user interface for various operations, e.g., selecting surveillance cameras, rewinding to earlier date/time, and visualizing analytics results in plots. We believe our demo will stimulate constructive discussion at the conference venue in this emerging research area. © 2019 IEEE.

关键词： Fog computing

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Fuzzy-based approach for identifying epistasis in large-scale data 5

Fuzzy-based approach for identifying epistasis in large-scal...

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Yang, Cheng-Hong Moi, Sin-Hua Chuang, Li-Yeh Lin, Yu-Da National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung Taiwan I-Shou University Department of Chemical Engineering Institute of Biotechnology and Chemical Engineering Kaohsiung Taiwan

ISBN: (纸本)9781728141176

Identifying epistasis is critical for determining susceptibility to human genetic diseases. Multifactor dimensionality reduction (MDR) have enabled efficient calculation for obtaining superior detection. However, a vital issue that has not been studied extensively is the binary classification of multifactor genotypes into high-risk (H) and low-risk (L) teams, especially for large-scale data. In this study, the membership degree of a fuzzy logic system was introduced to overcome the limitations of binary classification. A fuzzy logic system combined with multiobjective MDR (FMOMDR) can enhance flexibility in multifactor genotypes at similar frequencies. Based on multiobjective MDR, FMOMDR can obtain higher detection rates than alternative MDR-based measurements when applied to large-scale simulated data. The results demonstrated that fuzzy classification of multifactor genotypes can provide insight into the uncertainty of H/L classification in large-scale datasets. © 2019 IEEE.

关键词： Dimensionality reduction

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Robust Classification of Step data of Exercise 5

Robust Classification of Step Data of Exercise

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Fu, Haoqi Shi, Keqin Sun, Weiqiang Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China

ISBN: (纸本)9781728141176

The step data of exercise(SDE) is a type of daily life data that is generated by humans and recorded by mobile devices. Collection of such data has become more and more popular, as it provides users with information regarding his/her level of activity, and a stimulus for persisting in an active lifestyle. When SDE becomes more widely available, it also provides health professionals with a new tool to interact with their clients. Most existing methods for classifying exercise modes rely on training models that are manually labeled, or micro-pattern analysis using manually selected lengths and micro-pattern quantities. However, SDE datasets are highly personalized, inaccurate and the cost of these methods is high. So we propose a new method to classify the SDE by utilizing its inherent characteristics. With the understanding that SDE reflects daily lifestyle, is personalized and stable over at least a period of time, this paper takes the understanding of users' exercise preference, obtained from dataset of respective users, as prior knowledge. Then, instead of using raw step data, we quantize the raw data into different levels of exercise and takes into account the moment of exercise occurrence. Results show that our method provides an easy to interpret and robust characterization of user's exercise habit. © 2019 IEEE.

关键词： classification exercise habit prior knowledge SDE smart device

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Combining Multiple Ranking Systems on the Generalized Permutation Rank Space 5

Combining Multiple Ranking Systems on the Generalized Permut...

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Zhong, X. Hurley, L. Sirimulla, S. Schweikert, C. Hsu, D.F. Fordham University Laboratory of Informatics and Data Mining Department of Computer and Information Sciences New YorkNY United States University of Texas at El Paso School of Pharmacy Department of Pharmaceutical Sciences El PasoTX United States St John's University Division of Computer Science Mathematics and Science QueensNY United States

ISBN: (纸本)9781728141176

Given t ranking systems of n objects, consensus ranking (CR) aims to derive a ranking which best represents the consensus goal of these ranking systems. Since a ranking system of n objects is equivalent to a permutation of the n natural numbers [1, n] from 1 to n, the CR problem has been studied on the bubble-sort graph rank space Bn which consists of the set of all permutations of order n. However, it remains a challenging issue when a combination of ranking systems (consensus ranking) has ties. But Bn does not include permutation (or rank systems) with ties. In this paper, we propose a multi-layer combinatorial fusion algorithm for combining multiple ranking systems on the generalized permutation rank space where ties are allowed. Using two simulated data sets and an empirical data set in the molecular docking domain, we demonstrate the robustness of our approach. This study also provides a viable approach to data analytics, machine learning, and combinatorial fusion in the non-parametric rank space. © 2019 IEEE.

关键词： data Analytics

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Sun, Tingting Mo, Chunyang Zhao, Liang Chen, Zhikui Dalian Natural Resources Affairs Service Center Dalian116000 China Dalian University of Technology School of Software Technology Dalian116600 China Dalian University of Technology Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian116600 China

ISBN: (纸本)9781728141176

In the real world, data instances are usually represented from various perspectives, thus multi-view feature learning and clustering have attracted more and more attention. However, most existing works assume that the datasets are pure to learn the common features in the subspace, which ignore the noises and uncorrelated information. Therefore, the performance of them may be seriously degraded. To tackle it, in this paper, we develop a new subspace fusion model for multi-view data clustering. Specifically, we learn the correlated and uncorrelated features simultaneously for multi-view data in the subspace. Thus, the uncorrelated items can be separated from the correlated features and promising latent common features can be obtained. Moreover, the geometric structures of both data and feature manifold are constructed to preserve the distribution of data points in the common subspace. A new objective function is designed to solve the model and the optimization processes are presented. Experiments on real-world datasets show that the proposed method outperforms the compared state-of-the-art methods. © 2019 IEEE.

关键词： Contrastive Learning

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Rethinking Software Defined Cloud Storage for Disaggregation 5

Rethinking Software Defined Cloud Storage for Disaggregation

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5th IEEE international conference on big data intelligence and computing, datacom 2019

作者： Zou, Yi Raghunath, Arun Chagam, Anjaneya Sen, Sujoy Gohad, Tushar Intel Corp. Research Labs HillsboroOR United States Intel Corp. Data Center Group ChandlerAZ United States Intel Corp. Data Center Group HillsboroOR United States

ISBN: (纸本)9781728141176

Software Defined Storage (SDS) architectures abstract away underlying physical hardware and provide durability, availability and reliability guarantees by transparently creating replicas, erasure coding, and performing periodic background data checks. However, current SDS architectures are at odds with the evolving trend of data Centers towards physically separating server resources like storage, into distinct sleds connected via high speed interconnects such as NVMe over Fabric (NVMe-oF), for finer grained control and flexibility. The assumption of locally attached storage and a monolithic server design exacerbate the overheads of the internal operations performed within the SDS framework in a disaggregated data Center. We propose redefining the boundaries of separation within SDS architectures to address disaggregation overheads. Specifically, we decouple control and data plane operations and transfer block ownership to execute on remote storage targets. This redistribution of responsibilities provides a 40% reduction in bandwidth overheads and a 2X end-to-end latency reduction associated with internal SDS data replication traffic. Using Ceph as an example, we provide detailed analysis on the potential reduction in bandwidth and latency on the proposed architecture. We demonstrate decoupling the Ceph BlueStore functionality and integrating with NVMe-oF target layer. We further present preliminary results collected on a testing PoC Ceph cluster currently being developed. © 2019 IEEE.

关键词： Cloud storage

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