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22nd International Conference on Intelligent data Engineering and Automated Learning, IDEAL 2021

作者： Salamon, Vilmos Tibor Tashu, Tsegaye Misikir Horváth, Tomáš ELTE - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Telekom Innovation Laboratories Pázmány Péter sétány 1/C Budapest1117 Hungary Kombolcha Institute of Technology College of Informatics Wollo University Kombolcha208 Ethiopia Faculty of Science Institute of Computer Science Pavol Jozef Šafárik University Jesenná 5 Košice040 01 Slovakia

ISBN: (纸本)9783030916077

In this paper, we proposed Linear Concept Approximation, a novel multilingual document representation approach for the task of multilingual document representation and recommendation. The main idea is in creating representations by using mappings to align monolingual representation spaces using linear concept approximation, that in turn will enhance the quality of content-based Multilingual Document Recommendation Systems. The experimental results on JRC-Acquis have shown that our proposed approach outperformed traditional methods on the task of multilingual document recommendation. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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Stochastic models for generating synthetic HTTP source traffic

Stochastic models for generating synthetic HTTP source traff...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Jin Cao W.S. Cleveland Yuan Gao K. Jeffay F.D. Smith M. Weigle Department of Bell Laboratories Statistics and Data Mining Research Murray Hill NJ USA Dspartment of Statistics and the Department of Computer Science Purdue University West Lafayette IN USA Department of Computer Science North Carolina State University Chapel Hill NC USA

New source-level models for aggregated HTTP traffic and a design for their integration with the TCP transport layer are built and validated using two large-scale collections of TCP/IP packet header traces. An implementation of the models and the design in the ns network simulator can be used to generate web traffic in network simulations

关键词： Stochastic processes Traffic control Telecommunication traffic Peer to peer computing Statistics Network servers Hardware Internet Protocols computer science

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Providing end-to-end statistical delay guarantees with earliest deadline first scheduling and per-hop traffic shaping

Providing end-to-end statistical delay guarantees with earli...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： V. Sivaraman F. Chiussi Department of Computer Science University of California Los Angeles Los Angeles CA USA Data Networking Systems Research Bell Laboratories Lucent Technologies Inc. Holmdel NJ USA

This paper develops a framework for statistically guaranteeing end-to-end delay bounds to leaky-bucket-constrained flows transporting real-time traffic in a network of switches using earliest deadline first (EDF) packet scheduling and per-hop traffic shaping. We first analyze the delay-bound violation probabilities at an isolated EDF scheduler fed by fluid source processes generating extremal dual-leaky-bucket-regulated traffic. We compute a close upper bound by applying the Benes approach to an equivalent hypothetical system derived from the real one. We then extend the analysis to the end-to-end scenario in the presence of traffic re-shaping at each node in the network. We compare the analytical results with simulations, and show that the match is very close. We also investigate the advantages of smoothing the traffic at the ingress to the network, and propose a simple choice of smoothing parameters, which perform very well. Using realistic traffic parameters, we compare the schedulable region of our statistical framework with that of the corresponding deterministic framework, and demonstrate that the statistical framework allows tremendous improvements in network utilization, even for very low delay-violation probabilities. The framework developed in this paper is therefore highly useful in practical packet networks to provide quality of service to real-time applications in the form of statistical, rather than deterministic, end-to-end delay bounds.

关键词： Delay Telecommunication traffic Traffic control Probability Smoothing methods Packet switching Switches Scheduling algorithm Processor scheduling Upper bound

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Basic methods of change-point detection of financial fluctuations

Basic methods of change-point detection of financial fluctua...

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International Conference on Noise and Fluctuations (ICNF)

作者： Hideki Takayasu Graduate School of Advanced Mathematical Science MIMS Meiji Univ. Tokyo Japan Center for TDB-Advanced Data Analysis and Modeling Tokyo Institute of Technology Yokohama Japan Sony Computer Science Laboratories Tokyo Japan

Financial market time series are usually approximated by random walks, however, we can easily find significant deviation from a simple random walk by analyzing high frequency market data. It is important to detect change-points of potential statistical properties automatically from given time series. We apply Fisher's exact test for detection of trends in time series and show that the method works well for various types of temporal fluctuations. We show an example of application of this method for a foreign exchange market time series.

关键词： Noise Fluctuations Conferences

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Peak-Based Philips Fingerprint Robust To Pitch-Shift For Massive Audio Retrieval

Peak-Based Philips Fingerprint Robust To Pitch-Shift For Mas...

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IEEE International Conference on Multimedia Big data (BigMM)

作者： Renjie Chu Baoning Niu Shanshan Yao Jianquan Liu School of Information and Computer Taiyuan University of Technology Taiyuan China Institute of Big Data Science and Industry Shanxi University Taiyuan China Biometrics Research Laboratories NEC Corporation Tokyo Japan

An ideal audio retrieval system identifies a short query snippet from a massive audio database with both robustness and efficiency. Unfortunately, none of the existing systems could robustly handle all distortions while keeping efficient. An efficient audio retrieval method of the systems must match the features of the fingerprint. Enhanced Sampling and Counting method (eSC), the state-of-the-art audio retrieval method, proposed for Philips-like fingerprints, has achieved both high efficiency and strong robustness, featuring time-stretch resistance. We argue that Philips fingerprint, robust to many types of distortions except speed-change which includes time-stretch and pitch-shift, combined with eSC is promising towards an ideal audio retrieval system, if we could make it robust to pitch-shift. To achieve the goal, this paper proposes a peak-point based energy bands computation method (PPEB) to enhance Philips fingerprint (PF) with resistance to pitch-shift, and the resulting fingerprint is called Peak-point based Philips fingerprint (PPF). Experimental results show that PPF can resist pitch-shift ranging from 70% to 130%, while retaining the robustness of PF to various noise distortions.

关键词： Fingerprint recognition Distortion Robustness Resists Spectrogram Filtering GSM

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End-to-end statistical delay guarantees using earliest deadline first (EDF) packet scheduling

End-to-end statistical delay guarantees using earliest deadl...

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IEEE Conference and Exhibition on Global Telecommunications (GLOBECOM)

This paper develops a framework for statistically guaranteeing end-to-end delay bounds to envelope-constrained connections transporting real-time traffic in a network of switches employing the earliest deadline first (EDF) packet scheduling scheme in conjunction with per-hop traffic shaping. The performance of an isolated EDF scheduler fed by Markovian sources has been studied earlier in the literature; we extend the analysis to the multi-node setting in the presence of per-node traffic shaping. By making conservative approximations, we show that the analysis is tractable, and offers large performance gains over frameworks which offer deterministic services (the difference is as large as 25% for reasonably realistic traffic mixes). We also investigate the advantages of smoothing the traffic at the ingress to the network. Our framework, we believe, could be of great use in networks which statistically guarantee end-to-end delay bounds to real-time applications.

关键词： Delay Scheduling algorithm Telecommunication traffic Traffic control Switches Quality of service Packet switching Global Positioning System Performance analysis Smoothing methods

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Self-Grouping Multi-network Clustering

Self-Grouping Multi-network Clustering

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IEEE International Conference on data Mining (ICDM)

作者： Jingchao Ni Wei Cheng Wei Fan Xiang Zhang Department of Electrical Engineering and Computer Science Case Western Reserve University NEC Laboratories America Baidu Research Big Data Lab College of Information Sciences and Technology Pennsylvania State University

ISBN: (纸本)9781509054749

Joint clustering of multiple networks has been shown to be more accurate than performing clustering on individual networks separately. Many multi-view and multi-domain network clustering methods have been developed for joint multi-network clustering. These methods typically assume there is a common clustering structure shared by all networks, and different networks can provide complementary information on this underlying clustering structure. However, this assumption is too strict to hold in many emerging real-life applications, where multiple networks have diverse data distributions. More popularly, the networks in consideration belong to different underlying groups. Only networks in the same underlying group share similar clustering structures. Better clustering performance can be achieved by considering such groups differently. As a result, an ideal method should be able to automatically detect network groups so that networks in the same group share a common clustering structure. To address this problem, we propose a novel method, ComClus, to simultaneously group and cluster multiple networks. ComClus treats node clusters as features of networks and uses them to differentiate different network groups. Network grouping and clustering are coupled and mutually enhanced during the learning process. Extensive experimental evaluation on a variety of synthetic and real datasets demonstrates the effectiveness of our method.

关键词： Clustering methods Tensile stress Measurement Symmetric matrices Conferences data mining Electrical engineering

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Augmenting Cybersecurity in WSN: AI-Based Clone Attacks Recognition Framework 1

Augmenting Cybersecurity in WSN: AI-Based Clone Attacks Reco...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Bhaskar, Seelam Ch Vijaya Kalyani, Gedela Al-Fatlawy, Ramy Alsalamy, Fatima Kurdi, Waleed Hadi Madhloom Al-Aboudy, H.M. Sujatha, G. MVSR Engineering College Department of IT Telangana Hyderabad India Karpaga Vinayaga College of Engineering and Technology Big Data Analytics Tamil Nadu Chengalpattu India The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq Altoosi University College Department of Computer Science Iraq Mazaya University College Department of Computer Techniques Engineering DhiQar Iraq Arunai Engineering College Department of ECE Tamil Nadu Tiruvannamalai India

ISBN: (纸本)9798350367003

Applications such as industrial automation, healthcare, and environmental monitoring need the use of wireless sensor networks (WSNs). However, due to their dispersed organizational makeup, they have become vulnerable to security risks, particularly clone assaults. To protect confidentiality, availability, and confidentiality, several attacks must be recognized and prevented. This project aims to offer an effective method for identifying and averting clone assaults. To identify cloned both nationally and internationally use a low-cost verification process. In this study, we offer a new adaptive sea-horse optimized light gradient boosting machine (ASHO-LGBM) technique for protecting the network against node identity duplicates. The ASHO approach is used in the ASHO-LGBM framework to improve the recognition accuracy of the light gradient boosting machine (LGBM) characteristics. The replications with the nodes intrusion detection (ID) are used to choose a most trustworthy communication mode. The procedure is intended to be implemented and used for gathering data through an internet component. Using a Python tool, the suggested technique is simulated and its delay, packet delivery ratio, packet drop, and energy are evaluated. When compared to other approaches, the study's results show that the ASHO-LGBM strategy's performance analysis achieves the highest accuracy rate. © 2024 IEEE.

关键词： Sensor nodes

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Stimulated emission from superradiant atoms in waveguide QED

Stimulated emission from superradiant atoms in waveguide QED

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Conference on Lasers and Electro-Optics (CLEO)

作者： Rui Asaoka Julio Gea-Banacloche Yuuki Tokunaga Kazuki Koshino Computer and Data Science Laboratories NTT Corporation Musashino Japan Department of Physics University of Arkansas Fayetteville Arkansas USA College of Liberal Arts and Sciences Tokyo Medical and Dental University Ichikawa Japan

We investigate the stimulated emission from superradiant atoms coupled to a waveguide induced by a coherent-state pulse. We provide the dynamical properties of this system and also evaluate the system as a amplifier.

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Stimulated emission Atom lasers Waveguide lasers Lasers and electrooptics Electro-optical waveguides

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PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization

arXiv

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arXiv 2024年

作者： Ye, Yuyang Tang, Lu-An Wang, Haoyu Yu, Runlong Yu, Wenchao He, Erhu Chen, Haifeng Xiong, Hui Department of Management Science and Information Systems Rutgers Business School Rutgers University NewarkNJ United States Department of Data Science and System Security NEC Laboratories PrincetonNJ United States Department of Computer Science University of Pittsburgh PittsburghPA United States Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou China

Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep Reinforcement Learning (DRL) based methods offer promising enhancements for sequential optimization processes and can be used for reducing carbon emissions. However, existing DRL methods need a pre-defined reward function to assess the impact of each action on the final sustainable development goals (SDG). In many real applications, such a reward function cannot be given in advance. To address the problem, this study proposes a Performance based Adversarial Imitation Learning (PAIL) engine. It is a novel method to acquire optimal operational policies for carbon neutrality without any pre-defined action rewards. Specifically, PAIL employs a Transformer-based policy generator to encode historical information and predict following actions within a multi-dimensional space. The entire action sequence will be iteratively updated by an environmental simulator. Then PAIL uses a discriminator to minimize the discrepancy between generated sequences and real-world samples of high SDG. In parallel, a Q-learning framework based performance estimator is designed to estimate the impact of each action on SDG. Based on these estimations, PAIL refines generated policies with the rewards from both discriminator and performance estimator. PAIL is evaluated on multiple real-world application cases and datasets. The experiment results demonstrate the effectiveness of PAIL comparing to other state-of-the-art baselines. In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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