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arXiv 2020年

作者： Yu, Zhiyong Han, Lei Chen, Chao Guo, Wenzhong Yu, Zhiwen College of Mathematics and Computer Sciences Fuzhou University Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China School of Computer Science Chongqing University Chongqing400044 China

Tracking a suspicious car or a person in a city efficiently is crucial in urban safety management. But how can we complete the task with the minimal number of spatiotemporal searches when massive camera records are involved? To this end, this study proposes a strategy named intermediate searching at heuristic moments (IHMs). At each step, we determine which moment is the best one for the search based on a heuristic indicator. Then, at that moment, locations are searched one by one in descending order of predicted appearing probabilities until a search hit is obtained. We iterate this step until we derive the object's current location. Five searching strategies are compared via experiments. Among these strategies, the IHMs strategy is validated as the most efficient. IHMs can save up to 1/3 of the total cost. This result provides evidence that "searching at intermediate moments can save cost. Copyright © 2020, The Authors. All rights reserved.

关键词： Heuristic algorithms

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A Model to Enhance Governance Issues through Opinion Extraction

A Model to Enhance Governance Issues through Opinion Extract...

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International Conference and Workshop on computing and Communication (IEMCON)

作者： Kamran Shaukat Talha Mahboob Alam Muhammad Ahmed Suhuai Luo Ibrahim A. Hameed Muhammad Shahid Iqbal Jiaming Li Muhammad Atif Iqbal School of Electrical Engineering and Computing The University of Newcastle Newcastle Australia University of Engineering and Technology Lahore Pakistan Norwegian University of Science and Technology Norway Air University Islamabad Pakistan Data61 Commonwealth Scientific and Industrial Research Organization Australia

ISBN: (数字)9781728184166

ISBN: (纸本)9781728184173

We live in a world where data is expanding exponentially. Most of the data is unstructured when obtained through the web. Many organizations, institutes, and governments worldwide gather public views regarding their products, services, or policies. With thousands of reviews about some product, service, or policy, it is impossible to conclude some kind of final thought from it. To handle this, there is a desperate need for a model that can extract meaningful information from data to make correct and timely decisions for the efficient growth of business and smooth running of an organization or government. Otherwise, the practice of collecting and storing data will be ineffective. In this study, we focused on conducting an extensive public survey on issues of Southern Punjab, carry out appropriate processing on collected data and predict trends in public opinion for decision-making. Natural Language Processing (NLP) and Machine Learning (ML) have dealt with this problem. Different data preprocessing techniques have been utilized to remove the noise from data. Our experiments stated that unemployment, poverty, education, and corruption are the major issues of the targeted region. This study will help government officials and non-governmental organizations to be focused on the extracted issues in the specific region.

关键词： Terrorism Government Sociology Education data mining Unemployment Statistics

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Nuclear Medicine Artificial Intelligence in Action: The Bethesda Report (AI Summit 2024)

arXiv

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arXiv 2024年

作者： Rahmim, Arman Bradshaw, Tyler J. Davidzon, Guido Dutta, Joyita El Fakhri, Georges Ghesani, Munir Karakatsanis, Nicolas A. Li, Quanzheng Liu, Chi Roncali, Emilie Saboury, Babak Yusufaly, Tahir Jha, Abhinav K. Departments of Radiology and Physics University of British Columbia Canada Department of Radiology University of Wisconsin United States Department of Radiology Division of Nuclear Medicine & Molecular Imaging Stanford University United States Department of Biomedical Engineering University of Massachusetts Amherst United States PET Center Departments of Radiology & Biomedical Engineering and Bioinformatics & Data Science Yale University United States United Theranostics Department of Radiology Weill Cornell Medical College Cornell University New York United States Center for Advanced Medical Computing and Analysis Department of Radiology Massachusetts General Hospital Harvard Medical School United States Department of Radiology and Biomedical Imaging Yale University United States Departments of Biomedical Engineering and Radiology University of California Davis United States Department of Radiology and Imaging Sciences Clinical Center National Institutes of Health Russell H. Morgan Department of Radiology and Radiological Sciences Johns Hopkins School of Medicine United States Department of Biomedical Engineering Mallinckrodt Institute of Radiology Washington University St. Louis United States

The 2nd SNMMI Artificial Intelligence (AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD, on February 29 - March 1, 2024. Bringing together various community members and stakeholders, and following up on a prior successful 2022 AI Summit, the summit theme was "AI in Action". Six key topics included (i) an overview of prior and ongoing efforts by the AI task force, (ii) emerging needs and tools for computational nuclear oncology, (iii) new frontiers in large language and generative models, (iv) defining the value proposition for the use of AI in nuclear medicine, (v) open science including efforts for data and model repositories, and (vi) issues of reimbursement and funding. The primary efforts, findings, challenges, and next steps are summarized in this manuscript. © 2024, CC BY.

关键词： Nuclear medicine

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Semi-supervisedly Co-embedding attributed networks

arXiv

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arXiv 2019年

作者： Meng, Zaiqiao Liang, Shangsong Fang, Jinyuan Xiao, Teng Department of Computing Science Sun Yat-sen University University of Glasgow School of Data and Computer Science Sun Yat-sen University

Deep generative models (DGMs) have achieved remarkable advances. Semi-supervised variational auto-encoders (SVAE) as a classical DGM offer a principled framework to effectively generalize from small labelled data to large unlabelled ones, but it is difficult to incorporate rich unstructured relationships within the multiple heterogeneous entities. In this paper, to deal with the problem, we present a semi-supervised co-embedding model for attributed networks (SCAN) based on the generalized SVAE for heterogeneous data, which collaboratively learns low-dimensional vector representations of both nodes and attributes for partially labelled attributed networks semi-supervisedly. The node and attribute embeddings obtained in a unified manner by our SCAN can benefit for capturing not only the proximities between nodes but also the affinities between nodes and attributes. Moreover, our model also trains a discriminative network to learn the label predictive distribution of nodes. Experimental results on real-world networks demonstrate that our model yields excellent performance in a number of applications such as attribute inference, user profiling and node classification compared to the state-of-the-art baselines. Copyright © 2019, The Authors. All rights reserved.

关键词： User profile

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A Knowledge Graph-based Sensitive Feature Selection for Android Malware Classification

A Knowledge Graph-based Sensitive Feature Selection for Andr...

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Asia-Pacific Conference on Software Engineering

作者： Duoyuan Ma Yude Bai Zhenchang Xing Lintan Sun Xiaohong Li Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin University Tianjin China Research School of Computer Science Australian National University Data61 CSIRO Australia State Grid Customer Service Center Tianjin China

ISBN: (数字)9781728195537

ISBN: (纸本)9781728195544

The rapid increase in Android malware has brought great challenges to malware analysis. To deal with such a severe situation, it has been proposed an effective way which groups malware with common behaviors into the same malware family. Although there are many methods for malware family classification, the most critical and primary step is always the definition of sensitive behavior in an application, which will be beneficial for the later classification task. Much existing literature has manually selected sensitive features, such as permission, or even designed graph-based features via the control flow graph. They heavily depend on expert knowledge and time-consuming malware application analysis, which means it has to focus on the mal ware itself to dig out valuable security knowledge at first. However, the zooming malware overwhelms such expensive feature definition methods. To overcome such a problem, we adopt a knowledge graph-based sensitive feature selection method for Android mal ware classification. Based on the Android Developer documentation, an Android API knowledge graph is constructed at first. We can obtain not only permission but also related critical API from this graph. Note that both hyperlink relation and similarity relation are used to find out the critical API. With the knowledge graph-based sensitive features, we represent each Android malware as a boolean feature vector and send it in to a machine learning classifier for malware classification. We evaluate our proposed methods on three well-known Android malware datasets, such as Genome, Drebin, and AMD. The experimental results show that: 1) our proposed sensitive API is advantageous for malware detection; 2) API chosen by similarity relation can marginally improve performance; 3) different permission groups also make an influence for classification.

关键词： Feature extraction Malware Security Task analysis Hypertext systems Smart phones Software engineering

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Collaborative Speculations on Future Themes for Participatory Design in Germany

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i-com 2022年第2期21卷 283-298页

作者： Mucha, Henrik Correia De Barros, Ana Benjamin, Jesse Josua Benzmüller, Christoph Bischof, Andreas Buchmüller, Sandra De Carvalho, Alexandra Dhungel, Anna-Katharina Draude, Claude Fleck, Marc-Julian Jarke, Juliane Klein, Stefanie Kortekaas, Caroline Kurze, Albrecht Linke, Diane Maas, Franzisca Marsden, Nicola Melo, Ricardo Michel, Susanne Müller-Birn, Claudia Pröbster, Monika Rießenberger, Katja Antonia Schäfer, Mirko Tobias Sörries, Peter Stilke, Julia Volkmann, Torben Weibert, Anne Weinhold, Wilhelm Wolf, Sara Zorn, Isabel Heidt, Michael Berger, Arne Fraunhofer-Institut für Optronik Systemtechnik und Bildauswertung IOSB Karlsruhe Germany Fraunhofer Center for Assistive Information and Communication Solutions-AICOS Porto Portugal University of Twente Enschede Netherlands Freie Universität Berlin Fachbereich Mathematik und Informatik Berlin Germany Chemnitz University of Technology Computer Science Chemnitz Germany TU Braunschweig University Institute of Flight Guidance Braunschweig Germany Universität Witten Herdecke Fakultät für Gesundheit Witten Germany Universität zu Lübeck Institut für Multimediale und Interaktive Systeme Lubeck Germany Fachbereich Elektrotechnik/Informatik Kassel Germany Hochschule Heilbronn Fakultät Informatik Heilbronn Germany Bremen Germany Katholische Hochschule Nordrhein Westfalen Forschung und Innovation in der Sozialen Arbeit Koln Germany Fakultät Angewandte Sozialwissenschaften Koln Germany Freie Universität Berlin Institut für Informatik Human-Centered Computing Research Group Berlin Germany Julius-Maximilians-Universität Würzburg Lehrstuhl für Psychologische Ergonomie Würzburg Germany Hochschule Heilbronn Fakultät Informatik Forschungsprofessur Sozioinformatik Heilbronn Germany Charité Universitätsmedizin Berlin Institute of the History of Medicine and Ethics in Medicine Berlin Germany Freie Universität Berlin Human-Centered Computing Berlin Germany OST-Ostschweizer Fachhochschule Institut für Altersforschung St Gallen Switzerland Utrecht University Utrecht Data School Governing the Digital Society Utrecht Netherlands Universität Siegen Lehrstuhl für Wirtschaftsinformatik und Neue Medien Siegen Germany Universität Würzburg Philosophie und Öffentliches Recht Wurzburg Germany Julius-Maximilians-Universität Würzburg Institut Mensch-Computer-Medien Würzburg Germany TH Köln Institut für Medienforschung und Medienpädagogik Leiterin des Forschungsschwerpunkts Digitale Technologien und Soziale Dienste Koln Germany Hochschule Anhalt Computer Science and Languages

Participatory Design means recognizing that those who will be affected by a future technology should have an active say in its creation. Yet, despite continuous interest in involving people as future users and consumers into designing novel and innovative future technology, participatory approaches in technology design remain relatively underdeveloped in the German HCI community. This article brings together the diversity of voices, domains, perspectives, approaches, and methods that collectively shape Participatory Design in Germany. In the following, we (1) outline our understanding of participatory practice and how it is different from mere user involvement;(2) reflect current issues of participatory and fair technology design within the German Participatory Design community;and (3) discuss tensions relevant to the field, that we expect to arise in the future, and which we derived from our 2021 workshop through a speculative method. We contribute an introduction and an overview of current themes and a speculative outlook on future issues of Participatory Design in Germany. It is meant to inform, provoke, inspire and, ultimately, invite participation within the wider Computer science community. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Human computer interaction

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Formation-containment control for general linear multi-agent systems with time-varying delays and switching topologies

Formation-containment control for general linear multi-agent...

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International Conference on Control and Automation (ICCA)

作者： Shiyu Zhou Yongzhao Hua Xiwang Dong Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing P.R. China University of Bristol Bristol United Kingdom Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing P.R. China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

Formation-containment control problems for general linear multi-agent systems with time-varying delays and switching topologies are studied. The leaders are required to accomplish a given time-varying formation and the followers are allowed to enter the convex envelope spanned by those of the leaders simultaneously. Firstly, formation-containment protocols based on distributed state observer with switching interaction topologies and time-varying delays are presented for leaders and followers respectively, where an edge-based state observer is presented for each follower to estimate the whole states of all the leaders under the influences of switching interaction topologies and time-varying delays. Then, formation- containment problems are transformed into asymptotic stability problems. Furthermore, an algorithm to determine the gain matrices in the protocols is given based on linear matrix inequality technique and common Lyapunov-Krasovskii stability theory. Sufficient conditions for multi-agent systems to achieve formation-containment under the designed protocol are proposed. Finally, numerical simulations are provided to demonstrate theoretical results.

关键词： Switches Delays Control systems Topology Protocols Symmetric matrices Time-varying systems

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Collusion-Proof Result Inference in Crowdsourcing

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Journal of Computer science & Technology 2018年第2期33卷 351-365页

作者： Peng-Peng Chen Hai-Long Sun Yi-Li Fang Jin-Peng Huai State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing 100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing 100191 China

In traditional crowdsourcing, workers are expected to provide independent answers to tasks so as to ensure the diversity of answers. However, recent studies show that the crowd is not a collection of independent workers, but instead that workers communicate and collaborate with each other. To pursue more rewards with little effort, some workers may collude to provide repeated answers, which will damage the quality of the aggregated results. Nonetheless, there are few efforts considering the negative impact of collusion on result inference in crowdsourcing. In this paper, we are specially concerned with the Collusion-Proof result inference problem for general crowdsourcing tasks in public platforms. To that end, we design a metric, the worker performance change rate, to identify the colluded answers by computing the difference of the mean worker performance before and after removing the repeated answers. Then we incorporate the collusion detection result into existing result inference methods to guarantee the quality of the aggregated results even with the occurrence of collusion behaviors. With real-world and synthetic datasets, we conducted an extensive set of evaluations of our approach. The experimental results demonstrate the superiority of our approach in comparison with the state-of-the-art methods.

关键词： crowdsourcing quality control collusion collaborative crowdsourcing result inference

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Learning vertex representations for bipartite networks

arXiv

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arXiv 2019年

作者： Gao, Ming He, Xiangnan Chen, Leihui Zhou, Aoying School of Data Science and Engineering East China Normal University Shanghai200062 China School of Computing National University of Singapore Singapore

—Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types. There has been relatively little research dedicated to NRL for bipartite networks. Arguably, generic network embedding methods like node2vec and LINE can also be applied to learn vertex embeddings for bipartite networks by ignoring the vertex type information. However, these methods are suboptimal in doing so, since real-world bipartite networks concern the relationship between two types of entities, which usually exhibit different properties and patterns from other types of network data. For example, E-Commerce recommender systems need to capture the collaborative filtering patterns between customers and products, and search engines need to consider the matching signals between queries and webpages. This work addresses the research gap of learning vertex representations for bipartite networks. We present a new solution BiNE, short for Bipartite Network Embedding, which accounts for two special properties of bipartite networks: long-tail distribution of vertex degrees and implicit connectivity relations between vertices of the same type. Technically speaking, we make three contributions: (1) We design a biased random walk generator to generate vertex sequences that preserve the long-tail distribution of vertices;(2) We propose a new optimization framework by simultaneously modeling the explicit relations (i.e., observed links) and implicit relations (i.e., unobserved but transitive links);(3) We explore the theoretical foundations of BiNE to shed light on how it works, proving that BiNE can be interpreted as factorizing multiple matrices. We perform extensive experiments on five real datasets covering the tasks of link prediction (cl

关键词： Collaborative filtering

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A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problem

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 9253-9290页

作者： Peipei Tang Chengjing Wang Defeng Sun Kim-Chuan Toh School of Computer and Computing Science Zhejiang University City College Hangzhou China School of Mathematics and National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Mathematics and Institute of Operations Research and Analytics National University of Singapore Singapore

In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for solving these problems. Our key idea for making the proposed PMM to be efficient is to develop a sparse semismooth Newton method to solve the corresponding subproblems. By using the Kurdyka-Łojasiewicz property exhibited in the underlining problems, we prove that the PMM algorithm converges to a d-stationary point. We also analyze the oracle property of the initial subproblem used in our algorithm. Extensive numerical experiments are presented to demonstrate the high efficiency of the proposed PMM algorithm.

关键词： nonconvex square-root regression problems proximal majorization-minimization semismooth Newton method

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