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

Leveraging Reinforcement Learning to Allocate Bandwidth in t...

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big data intelligence and computing (datacom), international conference on

作者： Mu-Cheng Wang Paul C. Hershey Theodore E Ioakimidis Raytheon Space and Airborne Systems Marlborough MA USA Raytheon Intelligence Information and Services Dulles VA USA

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

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).

关键词： Mission critical systems Decentralized control Reinforcement learning Computer architecture Bandwidth big data Real-time systems

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A Realtime Crypto computing and Block-Dependability

A Realtime Crypto Computing and Block-Dependability

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big data intelligence and computing (datacom), international conference on

作者： Kancharla Abhilash Nohpill Park Computer Science Oklahoma State University Stillwater OK USA

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

This paper proposes an analytical approach how to design and realize a crypto computing (Ethereum blockchain-based) under stringent real-time requirement. In order to evaluate the efficacy of the approach, a new analytical metric is defined and developed to estimate the dependability, referred to as the block-dependability. The proposed block-dependability precisely models the probability for the pending transactions to be posted within the current, in other words, within the target block delay, namely, within the deadline required if their expected execution times are within the temporal range of the deadline. Various methods how to prioritize and select transactions in the pending transaction pool in order to facilitate those transactions to be executed within their deadline requirements, such as the normal, random, sorted, and stratified, are proposed and simulated. A set of performance variables, or parameters, such as the number of pending transactions in the pool, the average speed of the transactions, gas fees, deadlines as well as the number of miners, are identified and taken into the block-dependability in order to reveal the influence of each variable on the block-dependability, versus each of those proposed prioritization and selection methods. Extensive parametric simulations are conducted and results are observed and discussed in the cases of the random transaction selection method and the sorted.

关键词： Measurement Computational modeling Smart contracts big data Real-time systems Delays Cryptocurrency

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Representing cloud security and privacy policies and detecting potential problems

Representing cloud security and privacy policies and detecti...

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big data intelligence and computing (datacom), international conference on

作者： Anthony Opara Youngsang Song Seong-Je Cho Lawrence Chung University of Texas at Dallas Dankook University

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

Cloud computing involves multiple stakeholders, each with its own security, privacy and trust requirements, where policy enforcement is shared between the various stakeholders, in contrast to on-premise computing, where an organizations policies are enforced using internal resources. To effectively manage and integrate security requirements in the cloud, the provider and customer needs to negotiate and agree upon the applied security policies. However, there is no standard way of expressing cloud security policies in a vendor-agnostic manner. Cloud security and privacy policies are often stated in an informal and ad-hoc manner, which leads to policy conflicts, incompleteness, ambiguity and inconsistencies. In this paper, we present CERBERUS, a framework for representing cloud security and privacy policies and detecting potential problems in the policies. CERBERUS adopts an object-oriented approach and consists of an ontology and notation, policies, guidelines and rules, and a tool for capturing and detecting policy errors. Using CERBERUS, policies can be analyzed for potential problems, including policy conflicts, inconsistencies, ambiguities, and incompleteness. An application of CERBERUS shows that it indeed helps to discover policy errors, which otherwise would go undetected or in many cases would be detected a posteriori at runtime.

关键词： Privacy data privacy Runtime Cloud computing security Standards organizations Organizations Ontologies

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Serverless Skeletons for Elastic Parallel Processing

Serverless Skeletons for Elastic Parallel Processing

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big data intelligence and computing (datacom), international conference on

作者： Stefan Kehrer Jochen Scheffold Wolfgang Blochinger Parallel and Distributed Computing Group Reutlingen University Germany

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

Serverless computing is an emerging cloud computing paradigm with the goal of freeing developers from resource management issues. As of today, serverless computing platforms are mainly used to process computations triggered by events or user requests that can be executed independently of each other. These workloads benefit from on-demand and elastic compute resources as well as per-function billing. However, it is still an open research question to which extent parallel applications, which comprise most often complex coordination and communication patterns, can benefit from serverless *** this paper, we introduce serverless skeletons for parallel cloud programming to free developers from both parallelism and resource management issues. In particular, we investigate on the well-known and widely used farm skeleton, which supports the implementation of a wide range of applications. To evaluate our concepts, we present a prototypical development and runtime framework and implement two applications based on our frame-work: Numerical integration and hyperparameter optimization - a commonly applied technique in machine learning. We report on performance measurements for both applications and discuss the usefulness of our approach.

关键词： Measurement Runtime Serverless computing Parallel processing Programming Hyperparameter optimization Skeleton

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SSN: A Spatial Semantic Network for High-accuracy, Stable and Generalized Intent Detection

SSN: A Spatial Semantic Network for High-accuracy, Stable an...

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big data intelligence and computing (datacom), international conference on

作者： Zeng Zhi Chi-Yin Chow Jia-Dong Zhang Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

Nowadays, deep learning-based chatbot technologies have attracted the attention of many industries. Natural language understanding which consists of intent detection and slot filling is the first step in building a chatbot. Intent detection alone can be used to build a chatbot to answer frequently asked questions. However, there are still three major challenges in intent detection, namely, accuracy, stability and generalization ability. To overcome these three challenges, this paper proposes a new spatial semantic network (SSN) for intent detection method based on sentence embedding and support vector machine. Our SSN method consists of a frontend mapping a sentence to a dense vector in high dimensional space and a backend classifying the points into different intents. We conducted experiments on two popular real-world datasets. Experimental results show that our SSN method outperforms the state-of-the-art methods in terms of accuracy, stability as well as the ability to generalize on small training set.

关键词： Training Support vector machines Industries Accuracy Semantics Buildings big data

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Blockchain Synchronisation Performance Enhancement Using Forwarder Assignment in an Overlay Network

Blockchain Synchronisation Performance Enhancement Using For...

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big data intelligence and computing (datacom), international conference on

作者： Yean-Fu Wen Ping-Ju Wu Graduate Institute of Information Management National Taipei University New Taipei City Taiwan R.O.C

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

Broadcasting the block or transactions to all miners on the Internet is a major blockchain consensus feature. However, the current version of the Internet does not support broadcast/multicast functions. The miner who wins the billing right must send the block to all the miners through unicast. This operation requires a large amount of network resource to duplicate the large size of block transmission. Therefore, we attempted to improve the forward strategy in this study. An overlay network was used to construct a broadcast tree in an application, which is then mapped to the physical network. The problem is formulated as a forwarder assignment planning problem. Centralised broadcast schemes were designed to ensure forward miner selection, link construction, and the large size of block process and transmission on an overlay network. The assignment lists were included along the sending miners to avoid duplicate transmission in physical networks. An alternative IP-based method was proposed to reduce the collected bias with decentralised schemes. Finally, the experiment results demonstrate how the proposed method exhibited low delay.

关键词： Overlay networks Unicast Broadcasting big data Internet Blockchains Planning

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TF-RNN: A Method of Rumor Detection on Social Media

TF-RNN: A Method of Rumor Detection on Social Media

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big data intelligence and computing (datacom), international conference on

作者： Peng Xu Yonghao Zhu Kai Shuang Yulu Cao Yiming Liu Shu Wang State Key Lab of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Science and Technology on Communication Networks Laboratory Shijiazhuang China

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

Rumor can cause great harm to individuals and society and identifying such misinformation on social media is a challenging task. Unlike previous studies which are only based on text information or propagation structures of rumors, we attempt to address the problem of indentifying rumors out of microblogs by combining both of them and user-based features. Thus we propose Triple-feature Recursive Neural Networks (TF-RNN). To begin with, we model the dissemination of microblogs as tree-structured RNN. We then adopt Wide and Deep model to extract features, including the content-based and user-based features, of each node in the propagation tree as input to the tree-structured RNN. The classification result on datasets of Sina Weibo and Twitter demonstrates that the model is 2% to 3% more accurate than the baseline model respectively.

关键词： Accuracy Social networking (online) Computational modeling Blogs Neural networks big data Feature extraction

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Robust Classification of Step data of Exercise

Robust Classification of Step Data of Exercise

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big data intelligence and computing (datacom), international conference on

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

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

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.

关键词： Training Analytical models Costs Time series analysis big data Mobile handsets Computational complexity

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

Computational modelling of speech data integration to assess...

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big data intelligence and computing (datacom), international conference on

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

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

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.

关键词： Measurement Computational modeling data integration Oral communication Machine learning Companies Jitter

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An Analysis of the Number of Passengers Collected with A Practical Management Support System for Regional Public Transportation Service

An Analysis of the Number of Passengers Collected with A Pra...

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big data intelligence and computing (datacom), international conference on

作者： Chinasa Sueyoshi Hideya Takagi Kentaro Inenaga Department of Information Science Faculty of Science and Engineering Kyushu Sangyo University Fukuoka Japan Department of Information Science Graduate School of Information Science Kyushu Sangyo University Fukuoka Japan

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

The purpose of this study was to verify the effect of various public data made available on the Internet by route searching among regional public transportation services provided by municipalities. Our laboratory has been building fundamental regional public transportation data for Japan using the General Transit Feed Specification (GTFS), a transit data standard that was developed by a public agency in the U.S. in conjunction with Google. The dataset built in Japan (GTFS-JP) was formulated by the Ministry of Land, Infrastructure, Transport, and Tourism. Transit data used to build it gets displayed as route-search results on Google Maps or other domestic route-search content providers. Also, we have been developing a practical service-management support system for regional public transportation provided by municipalities. This system has two applications for tablets named ASHIYA and SHINGU. ASHIYA can be used for conducting simple questionnaire surveys for passengers inside community buses, whereas SHINGU records the number of ingress-egress passengers. We analyzed the effects on regional public transportation based on data measured and developed by using SHINGU and various data obtained for the community bus "Marinkusu" in Shingu Town, Kasuya Country, Fukuoka Prefecture.

关键词： Surveys Photography Web services Publishing Buildings big data Satellite images

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