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SD-seq2seq : A Deep Learning Model for Bus Bunching Prediction Based on Smart Card Data

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SD-seq2seq : A Deep Learning Model for Bus Bunching Predicti...

International Conference on computer Communications and Networks (ICCCN)

作者： Zengyang Gong Bo Du Zhidan Liu Wei Zeng Pascal Perez Kaishun Wu College of Computer Science and Software Engineering Shenzhen University P. R. China SMART Infrastructure Facility University of Wollongong Australia School of Civil Mining and Environmental Engineering University of Wollongong Australia Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences P. R. China

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166087

Bus bunching, a phenomenon due to the failure of headway or timetable adherence, often causes low level of public transit service with poor bus on-time performance and excessive passenger waiting time. To mitigate bus bunching, an accurate and real-time prediction method plays an important role. In this paper, we propose a supply-demand seq2seq model called SD-seq2seq to predict bus bunching using smart card data. Features from both supply and demand sides of bus service are taken into account, like bus stop type, dwelling time, passenger demand and type, and so on. Extensive experiments on multiple bus routes in real world demonstrate that our method outperforms other baseline methods. The proposed method is expected to provide useful online information of bus operation to both bus operators and passengers.

关键词： Predictive models Trajectory Smart cards Data models Data mining Feature extraction Machine learning

Dynamic Facial Models for Video-Based Dimensional Affect Estimation

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Dynamic Facial Models for Video-Based Dimensional Affect Est...

International Conference on computer Vision Workshops (ICCV Workshops)

作者： Siyang Song Enrique Sánchez-Lozano Mani Kumar Tellamekala Linlin Shen Alan Johnston Michel Valstar University of Nottingham UK Samsung AI Centre Cambridge UK College of Computer Science and Software Engineering Shenzhen University China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Dimensional affect estimation from a face video is a challenging task, mainly due to the large number of possible facial displays made up of a set of behaviour primitives including facial muscle actions. The displays vary not only in composition but also in temporal evolution, with each display composed of behaviour primitives with varying in their short and long-term characteristics. Most existing work models affect relies on complex hierarchical recurrent models unable to capture short-term dynamics well. In this paper, we propose to encode these short-term facial shape and appearance dynamics in an image, where only the semantic meaningful information is encoded into the dynamic face images. We also propose binary dynamic facial masks to remove 'stable pixels' from the dynamic images. This process allows filtering of non-dynamic information, i.e. only pixels that have changed in the sequence are retained. Then, the final proposed Dynamic Facial Model (DFM) encodes both filtered facial appearance and shape dynamics of a image sequence preceding to the given frame into a three-channel raster image. A CNN-RNN architecture is tasked with modelling primarily the long-term changes. Experiments show that our dynamic face images achieved superior performance over the standard RGB face images on dimensional affect prediction task.

关键词： Face Heuristic algorithms Shape Task analysis Image sequences Estimation Standards

Cross-Layer Scheduling and Beamforming in Smart-Grid Powered Cellular Networks With Heterogeneous Energy Coordination

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arXiv 2020年

作者： Dong, Yanjie Hossain, Md Jahangir Cheng, Julian Leung, Victor C.M. Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada School of Engineering University of British Columbia KelownaBCV1V 1V7 Canada College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

—User scheduling, beamforming and energy coordination are investigated in smart-grid powered cellular networks (SGPCNs), where the base stations are powered by a smart grid and natural renewable energy sources. Heterogeneous energy coordination is considered in SGPCNs, namely energy merchandizing with the smart grid and energy exchanging among the base stations. A long-term grid-energy expenditure minimization problem with proportional-rate constraints is formulated for SGPCNs. Since user scheduling is coupled with the beamforming vectors, the formulated problem is challenging to handle via standard convex optimization methods. In practice, the beamforming vectors need to be updated over each slot according to the channel variations. User scheduling needs to be updated over several slots (frame) since the frequent scheduling of user equipment can cause reliability issues. Therefore, the Lyapunov optimization method is used to decouple the problem. A practical two-scale algorithm is proposed to schedule users at each frame, and obtain the beamforming vectors and amount of exchanged natural renewable energy at each slot. We prove that the proposed two-scale algorithm can asymptotically achieve the optimal solutions via tuning a control parameter. Numerical results verify the performance of the proposed two-scale algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Wireless networks

Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery

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arXiv 2022年

作者： Ventura, Jonathan Pawlak, Camille Honsberger, Milo Gonsalves, Cameron Rice, Julian Love, Natalie L.R. Han, Skyler Nguyen, Viet Sugano, Keilana Doremus, Jacqueline Andrew Fricker, G. Yost, Jenn Ritter, Matt Department of Computer Science & Software Engineering California Polytechnic State University 1 Grand Ave San Luis ObispoCA93107-0354 United States Department of Biological Sciences California Polytechnic State University 1 Grand Ave San Luis ObispoCA93107-0401 United States Department of Social Sciences California Polytechnic State University 1 Grand Ave San Luis ObispoCA93107-0329 United States Department of Political Science California Polytechnic State University 1 Grand Ave San Luis ObispoCA93107-0329 United States Orfalea College of Business California Polytechnic State University 1 Grand Ave San Luis ObispoCA93107 United States

Systematic maps of urban forests are useful for regional planners and ecologists to understand the spatial distribution of trees in cities. However, manually-created urban forest inventories are expensive and time-consuming to create and typically don’t provide coverage of private land. Toward the goal of automating urban forest inventory through machine learning techniques, we performed a comparative study of methods for automatically detecting and localizing trees in multispectral aerial imagery of urban environments, and introduce a novel method based on convolutional neural network regression. Our evaluation is supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. Our method outperforms previous methods, achieving 73.6% precision and 73.3% recall when trained and tested in Southern California, and 76.5% precision 72.0% recall when trained and tested across the entire state. To demonstrate the scalability of the technique, we produced the first map of trees across the entire urban forest of California. The map we produced provides important data for the planning and management of California’s urban forest, and establishes a proven methodology for potentially producing similar maps nationally and globally in the future. © 2022, CC BY.

关键词： Aerial photography

Text Cover Steganography Using Font Color of the Invisible Characters and Optimized Primary Color-intensities

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Text Cover Steganography Using Font Color of the Invisible C...

International Conference on Communication Technology (ICCT)

作者： Feno Heriniaina Rabevohitra Yantao Li School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China

Text cover steganography is concerned with using text support for hiding information. Despite its long and old history, the art of cover writing is still attracting the attention of researchers in the domain of information hiding. Unfortunately, only a few related works exist in terms of text-covered writing. We present a visual attack on the previous related work. Then, we use invisible characters to hide secret data within the text support. We further use a combination and variation of the three primary colors intensities to vehiculate the message when painting non-printing characters. The proposed algorithm makes use of the low-value color spaces to decrease the color variations of the embedded secret message. It also further strengthens the hiddenness of the non-printing characters even when replaced by visible characters.

关键词： Color Image color analysis Painting software Text processing Visualization Writing

Energy-Efficient resource allocation for NOMA enabled MEC networks with imperfect CSI

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arXiv 2020年

作者： Fang, Fang Wang, Kaidi Ding, Zhiguo Leung, Victor C.M. Department of Engineering Durham University DurhamDH1 3LE United Kingdom Department of Electrical and Electronic Engineering University of Manchester M13 9PL United Kingdom College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

The combination of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) can significantly improve the spectrum efficiency beyond the fifth-generation network. In this paper, we mainly focus on energy-efficient resource allocation for a multi-user, multi-BS NOMA assisted MEC network with imperfect channel state information (CSI), in which each user can upload its tasks to multiple base stations (BSs) for remote executions. To minimize the energy consumption, we consider jointly optimizing the task assignment, power allocation and user association. As the main contribution, with imperfect CSI, the optimal closed-form expressions of task assignment and power allocation are analytically derived for the two-BS case. Specifically, the original formulated problem is nonconvex. We first transform the probabilistic problem into a non-probabilistic one. Subsequently, a bilevel programming method is proposed to derive the optimal solution. In addition, by incorporating the matching algorithm with the optimal task and power allocation, we propose a low complexity algorithm to efficiently optimize user association for the multi-user and multi-BS case. Simulations demonstrate that the proposed algorithm can yield much better performance than the conventional OMA scheme but also the identical results with lower complexity from the exhaustive search with the small number of BSs. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing

STR: Secure computation on additive shares using the share-transform-reveal strategy

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arXiv 2020年

作者： Xia, Zhihua Gu, Qi Zhou, Wenhao Xiong, Lizhi Weng, Jian Xiong, Naixue N. College of Cyber Security Jinan University Guangzhou510632 China Engineering Research Center of Digital Forensics Ministry of Education School of Computer and Software Jiangsu Engineering Center of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science & Technology Nanjing210044 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China The College of Intelligence and Computing Tianjin University Tianjin300350 China

The rapid development of cloud computing probably benefits many of us while the privacy risks brought by semi-honest cloud servers have aroused the attention of more and more people and legislatures. In the last two decades, plenty of works seek to outsource various specific tasks to servers while ensuring the security of private data. The tasks to be outsourced are countless;however, the computations involved are similar. In this paper, we construct a series of novel protocols that support the secure computation of various functions on numbers (e.g., the basic elementary functions) and matrices (e.g., the calculation of eigenvectors and eigenvalues) on arbitrary n ≥ 2 servers. All protocols only require constant rounds of interactions and achieve low computation complexity. Moreover, the proposed n-party protocols ensure the security of private data even though n − 1 servers collude. The convolutional neural network models are utilized as the case studies to verify the protocols. The theoretical analysis and experimental results demonstrate the correctness, efficiency, and security of the proposed protocols. Copyright © 2020, The Authors. All rights reserved.

关键词： Additives

An Attack Vector Evaluation Method for Smart City Security Protection

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An Attack Vector Evaluation Method for Smart City Security P...

IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Mingyong Yin Qixu Wang Mingsheng Cao College of Computer Science Sichuan University Chengdu China College of Cybersecurity Sichuan University Chengdu China College of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781728133171

In the network security risk assessment on critical information infrastructure of smart city, to describe attack vectors for predicting possible initial access is a challenging task. In this paper, an attack vector evaluation model based on weakness, path and action is proposed, and the formal representation and quantitative evaluation method are given. This method can support the assessment of attack vectors based on known and unknown weakness through combination of depend conditions. In addition, defense factors are also introduced, an attack vector evaluation model of integrated defense is proposed, and an application example of the model is given. The research work in this paper can provide a reference for the vulnerability assessment of attack vector.

关键词： Security Smart cities Analytical models software Conferences Big Data Edge computing

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2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Zhou, Lei Feng, Liang Zhong, Jinghui Zhu, Zexuan Da, Bingshui Wu, Zhou College of Computer Science Chongqing Univ China College of Computer Science South China Univ. of Technology China College of Computer Science and Software Engineering Shenzhen Univ China School of Computer Engineering Nanyang Technological Univ Singapore Singapore School of Automation Chongqing Univ China

ISBN: (纸本)9781450357647

In contrast to the traditional single-task evolutionary algorithms, multi-factorial evolutionary algorithm (MFEA) has been proposed recently to conduct evolutionary search on multiple tasks simultaneously. It aims to improve convergence characteristics of the tasks to be tackled by seamlessly transferring knowledge among them. Towards superior multitasking performance, the evaluation of task relationship plays an important role for grouping the related tasks, and solve them at the same time. However, in the literature, only a little work has been conducted to provide deeper insights in the measure of task relationship in MFEA. In this paper, we thus present a study of similarity measure between tasks for MFEA from three different perspectives. 21 multitasking problem sets are developed to investigate and analyze the effectiveness of the three similarity measures with MFEA for evolutionary multitasking. © 2018 Copyright held by the owner/author(s).

关键词： Evolutionary algorithms