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arXiv 2023年

作者： Wen, Haomin Lin, Youfang Wu, Lixia Mao, Xiaowei Cai, Tianyue Hou, Yunfeng Guo, Shengnan Liang, Yuxuan Jin, Guangyin Zhao, Yiji Zimmermann, Roger Ye, Jieping Wan, Huaiyu The Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC Beijing101318 China The School of Computing National University of Singapore Singapore College of Systems Engineering National University of Defense Technology Changsha China The School of Information Science and Engineering Yunnan University Kunming650091 China The Cainiao Network Hangzhou China The Alibaba Group Hangzhou China INTR Thrust and DSA Thrust The Hong Kong University of Science and Technology Guangzhou China

Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route&time prediction;(ii) model architecture, which encompasses sequence-based and graph-based models;and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field. © 2023, CC BY.

关键词： Taxonomies

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Integrated Parallel System for Audio Conferencing Voice Transcription and Speaker Identification

Integrated Parallel System for Audio Conferencing Voice Tran...

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2020 International Conference on High Performance Big Data and Intelligent systems, HPBD and IS 2020

作者： Miao, Ke Biermann, Oloff Miao, Zhen Leung, Simon Wang, Jianhong Gai, Keke Systems Group Mitacs Inc. Technology Department-Information Vancouver Canada University of British Columbia Computer Science Department Vancouver Canada University College London Electronic and Electrical Engineering Department London United Kingdom Cae Inc. Systems Development Department Vancouver Canada Nanjing Xinda Institute of Meteorological Science Technology China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728165110

In response to the request from a well-known international financial corporation, an integrated system prototype was architected and implemented to automatically record corporate audio conferencing, transcribe the recordings to text while identifying speakers, and compile the transcription and identification results into text-based meeting minutes, which then gets sent as meeting summary email attachments as well as saved into a meeting management database. Three technology focuses of this integrated system are discussed in this paper 1) Selection of a 3rd-party audio transcription and identification API (Audio API) through prototyping, factor comparison, and considering the existing technology environment at the corporation. 2) Optimize the adoption of the selected Audio API based on knowledge of Natural Language Process (NLP) methods. 3) Support asynchronous scheduling and processing of concurrent meetings using parallel computing architecture methods. The completed system was evaluated and shown to have met all the requirements from the corporation, perform well in audio language intelligent processing and multi-Threaded parallel execution. With further enhancements, we foresee this system solution has good commercial values and has potential to be adopted widely among other businesses. © 2020 IEEE.

关键词： computer architecture

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A multiagent framework for coordinating industrial symbiotic networks 21

A multiagent framework for coordinating industrial symbiotic...

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21st Workshop "From Objects to Agents", WOA 2020

作者： Yazdanpanah, Vahid Yazan, Devrim M. Zijm, Henk M. Research Group School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom Department Industrial Engineering and Business Information Systems Faculty of Behavioural Management and Social Sciences University of Twente Enschede7500 AE Netherlands

We present a formal multiagent framework for coordinating a class of collaborative industrial practices called "Industrial Symbiotic Networks (ISNs)" as cooperative games. The game-theoretic formulation of ISNs enables systematic reasoning about what we call the ISN implementation problem. Specifically, the characteristics of ISNs may lead to the inapplicability of standard fair and stable benefit allocation methods. Inspired by realistic ISN scenarios and following the literature on normative multiagent systems, we consider regulations and normative socio-economic policies as coordination instruments that in combination with ISN games resolve the situation. In this multiagent system, employing Marginal Contribution Nets (MC-Nets) as rule-based cooperative game representations foster the combination of regulations and ISN games with no loss in expressiveness. We develop algorithmic methods for generating regulations that ensure the implementability of ISNs and as a policy support, present the policy requirements that guarantee the implementability of desired ISNs in a balanced-budget way. © 2020 Copyright for this paper by its authors.

关键词： Multi agent systems

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EXPLAINABLE GRAPH PYRAMID AUTOFORMER FOR LONG-TERM TRAFFIC FORECASTING

arXiv

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arXiv 2022年

作者： Zhong, Weiheng Mallick, Tanwi Meidani, Hadi Macfarlane, Jane Balaprakash, Prasanna University of Illinois at Urbana-Champaign ChampaignIL United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Department of Civil and Environmental Engineering University of Illinois at Urbana-Champaign ChampaignIL United States Sustainable Energy Systems Group Lawrence Berkeley National Laboratory BerkeleyCA United States Mathematics and Computer Science Division Argonne Leadership Computing Facility Argonne National Laboratory LemontIL United States

Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic forecasting of up to 1 hour, long-term traffic forecasting that spans multiple hours remains a major challenge. Moreover, most of the existing deep learning traffic forecasting models are black box, presenting additional challenges related to explainability and interpretability. We develop Graph Pyramid Autoformer (X-GPA), an explainable attention-based spatial-temporal graph neural network that uses a novel pyramid autocorrelation attention mechanism. It enables learning from long temporal sequences on graphs and improves long-term traffic forecasting accuracy. Our model can achieve up to 35% better long-term traffic forecast accuracy than that of several state-of-the-art methods. The attention-based scores from the X-GPA model provide spatial and temporal explanations based on the traffic dynamics, which change for normal vs. peak-hour traffic and weekday vs. weekend traffic. © 2022, CC BY.

关键词： Graph neural networks

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A Reconfigurable Quantum Local Area Network Over Deployed Fiber

A Reconfigurable Quantum Local Area Network Over Deployed Fi...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Muneer Alshowkan Brian P. Williams Philip G. Evans Nageswara S. V. Rao Emma M. Simmerman Navin B. Lingaraju Hsuan-Hao Lu Andrew M. Weiner Nicholas A. Peters Joseph M. Lukens Quantum Information Science Group Oak Ridge National Laboratory Oak Ridge Tennessee USA Autonomous and Complex Systems Group Oak Ridge National Laboratory Oak Ridge Tennessee USA Stanford University Stanford California USA School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

We demonstrate a three-node telecom quantum local area network over deployed fiber. It has eight independent entanglement channels which are dynamically reconfigurable. We successfully show entanglement demand balanci... 详细信息

ISBN: (纸本)9781943580910

关键词： Lasers and electrooptics Optical fiber LAN Telecommunications Electrooptical waveguides Local area networks Fiber lasers

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Hierarchic Single Cluster Graph Partitioning: A Sequential Place Recognition Method

Hierarchic Single Cluster Graph Partitioning: A Sequential P...

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IEEE International Conference on systems, Man and Cybernetics

作者： Aaronkumar Ehambram Hanno Homann Sebastian P. Kleinschmidt Tobias Ritter Nicolas Fischer Bernardo Wagner Real Time Systems Group (RTS) Institute for Systems Engineering Leibniz Unversität Hannover Hannover Germany Connected Mobility and Computer Vision Systems Bosch Corporate Research Hildesheim Germany

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Localization and mapping are essential tasks in mobile robotics. A purely odometry-based pose estimation accumulates errors caused by factors such as unequal wheel-diameters or wheel-slippage and is, therefore, inaccurate. The resulting error can be corrected by recognizing previously observed places and constraining the robots' relative poses. A common challenge of such place recognition methods arises from the incorrect association of different places, which can corrupt the resulting pose correction and, consequently, any map update. Therefore, we propose a novel place recognition method that enables the selection of a correct place recognition hypothesis from a set of possible *** this work, we make use of the fact that multiple robots' trajectories run parallel in many scenarios, like in warehouse corridors or on public roads. In such situations, place recognition can be regarded as a sequential task along the robots' trajectories. We study the problem of repetitive, periodic and indistinguishable landmarks for place recognition on regularly-spaced guideposts on German rural roads. By interpreting place recognition hypotheses as nodes of a k-partite graph, we introduce our novel selection method, the Hierarchic Single Cluster Graph Partitioning, that enables the robust selection of the correct hypotheses by finding the optimal path within the graph. The selected place recognition information is used to build a map incorporating multiple observations acquired from multiple vehicles.

关键词： Roads Conferences Pose estimation Trajectory Mobile robots Task analysis Cybernetics

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Multi-Resolution Localization of Individual Logs in Wooden Piles Utilizing YOLO with Tiling on Client/Server Architectures 33

Multi-Resolution Localization of Individual Logs in Wooden P...

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33rd European Modeling and Simulation Symposium, EMSS 2021

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg4785 Austria Faculty of Computer Science and Mathematics University of Passau Innstraße 43 Passau94032 Germany

In industrial domains with time and cost intensive manual or semi-automated inspection the demand for automation is high. Utilizing state of the art deep learning models for localization in vision-based domains such as wood log analysis, the precision can be increased while reducing the demand for manual inspection. In this paper a YOLO network is trained on wood log images to allow for detection of single wood piles in images with hundreds and thousands instances. Due to the high variability in scale and large amount of wood logs within the images, common YOLO architectures are not applicable. Thus, tiling is necessitated to implicitly form a multi-resolution image pyramid. Due to lack in training data, besides common data augmentation modelling of different seasonal and weather conditions is applied. The wood log detection process can be run on a client/server architecture to allow for both, preview and refined results. Evaluation on real-world data sets shows an log detection accuracy of 82,9% utilizing a tiny YOLO model and 94,1% with a fully connected YOLO model, respectively. © 2021 The Authors.

关键词： Deep learning

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Route Coverage Testing for Autonomous Vehicles via Map Modeling

Route Coverage Testing for Autonomous Vehicles via Map Model...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yun Tang Yuan Zhou Fenghua Wu Yang Liu Jun Sun Wuling Huang Gang Wang Alibaba-NTU Joint Research Institute Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Alibaba Group Alibaba DAMO Academy China

Autonomous vehicles (AVs) play an important role in transforming our transportation systems and relieving traffic congestion. To guarantee their safety, AVs must be sufficiently tested before they are deployed to public roads. Existing testing often focuses on AVs’ collision avoidance on a given route. There is little work on the systematic testing for AVs’ route planning and tracking on a map. In this paper, we propose CROUTE, a novel testing method based on a new AV testing criterion called route coverage. First, the map is modeled as a labeled Petri net, where roads, junctions, and traffic signs are modeled as places, transitions, and labels, respectively. Second, based on the Petri net, we define junctions’ topology features and route features for junction classification. The topology feature describes the topology of roads forming the junction, and the route feature identifies the actions that a vehicle can take to follow a route. They can characterize route types on a map. Hence, route coverage measures how many route types are covered. We then propose a systematic method that aims to cover all route types for a well-designed AV system with a small number of test cases. We implement and evaluate CROUTE on Baidu Apollo running with the LGSVL simulator. We carry out testing on the map from a section of San Francisco and find six different types of issues in Apollo. The experiment results show the validity of route coverage and the efficiency of CROUTE.

关键词： Systematics Roads Petri nets Web and internet services Transportation Topology Planning

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Revealing the Landscape of Privacy-Enhancing Technologies in the Context of Data Markets for the IoT: A Systematic Literature Review

arXiv

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arXiv 2021年

作者： Garrido, Gonzalo Munilla Sedlmeir, Johannes Uludag, Ömer Alaoui, Ilias Soto Luckow, Andre Matthes, Florian Technical University of Munich Department of Informatics Munich Germany Project Group Business & Information Systems Engineering of the Fraunhofer FIT Bayreuth Germany Ludwig Maximilian University of Munich Department of Computer Science Munich Germany Boltzmannstrasse 3 Garching85748 Germany

IoT data markets in public and private institutions have become increasingly relevant in recent years because of their potential to improve data availability and unlock new business models. However, exchanging data in markets bears considerable challenges related to disclosing sensitive information. Despite considerable research focused on different aspects of privacy-enhancing data markets for the IoT, none of the solutions proposed so far seems to find a practical adoption. Thus, this study aims to organize the state-of-the-art solutions, analyze and scope the technologies that have been suggested in this context, and structure the remaining challenges to determine areas where future research is required. To accomplish this goal, we conducted a systematic literature review on privacy enhancement in data markets for the IoT, covering 50 publications dated up to July 2020, and provided updates with 24 publications dated up to May 2022. Our results indicate that most research in this area has emerged only recently, and no IoT data market architecture has established itself as canonical. Existing solutions frequently lack the required combination of anonymization and secure computation technologies. Furthermore, there is no consensus on the appropriate use of blockchain technology for IoT data markets and a low degree of leveraging existing libraries or reusing generic data market architectures. We also identified significant challenges remaining, such as the copy problem and the recursive enforcement problem that - while solutions have been suggested to some extent - are often not sufficiently addressed in proposed designs. We conclude that privacy-enhancing technologies need further improvements to positively impact data markets so that, ultimately, the value of data is preserved through data scarcity and users' privacy and businesses-critical information are protected. Copyright © 2021, The Authors. All rights reserved.

关键词： Electronic data interchange

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Predicting the Intention to Use Social Media Sites: A Hybrid SEM - Machine Learning Approach 6th

Predicting the Intention to Use Social Media Sites: A Hybrid...

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6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Salloum, Said A. AlAhbabi, Nafla Mahdi Nasser Habes, Mohammed Aburayya, Ahmad Akour, Iman School of Science Engineering and Environment University of Salford Salford United Kingdom Machine Learning and NLP Research Group Department of Computer Science University of Sharjah Sharjah United Arab Emirates Ministry of Education Dubai United Arab Emirates Faculty of Mass Communication - Department of Radio and Television Yarmouk University Irbid Jordan Quality and Corporate Development Office Dubai Health Authority Dubai United Arab Emirates Information Systems Department University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9783030697167

The study conducted aims to form a conceptual model to calculate the pupils’ acceptance of social media in education and its factors. Although the amount of research done on the acceptance of social media applications has amplified, the factors affecting its acceptance for learning are not recognized. The study is carried out by extending the Technology Acceptance Model (TAM) using perceived playfulness and social influence. Alongside this, the collected data is evaluated through Machine Learning (ML) approaches and the partial least squares-structural equation modeling (PLS-SEM). A total of 369 students enrolled at highly regarded universities in the United Arab Emirates (UAE) filled out questionnaire surveys, then analyzed, and results are stated. This research suggests that students’ intention to adopt social media networks in learning is due to significant factors such as perceived playfulness, social influence, perceived usefulness, and perceived ease of use. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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