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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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CitiSafe: Adaptive Spatial pattern Knowledge Using Fp-growth Algorithm for Crime Situation recognition

CitiSafe: Adaptive Spatial Pattern Knowledge Using Fp-growth...

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IEEE 10th international conference on Ubiquitous Intelligence and computing (UIC) / IEEE 10th international conference on Autonomic and Trusted computing (ATC)

作者： Isafiadel, Omowunmi E. Bagula, Antoine B. Univ Cape Town Dept Comp Sci ISAT Lab ZA-7925 Cape Town South Africa

ISBN: (纸本)9781479924813

Crime prevention and control are issues of great concern to the government and public safety agencies. If these issues are not well controlled and managed, they could drastically affect the economy of a country negatively over time, since more emigration is naturally induced. With the existence of crime databases in the society, data mining techniques are viable tools for crime situation recognition and control. However, most existing techniques often fall short to consider salient features during the mining process. In this paper, we experimented on various crime categories across 32 locations within the western cape province of the republic of South Africa. Our research focus is to create a flexible and effective, yet simple, solution to crime situation recognition. A successful mining mechanism, adopting a batch-merge paradigm, that builds upon the Fp-Growth algorithm, referred to as CitiSafe algorithm is presented, followed by concluding remarks. the visualization techniques presenting the crime patterns, through our algorithm, would assist law enforcement agencies and public safety organisations to channelize their resources accordingly to achieve a focused and effective crime prevention strategy.

关键词： Public safety Data Mining Crime Situation recognition and Intelligent Platforms

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An Effective Message Forwarding Algorithm for Delay Tolerant Network with Cyclic Probabilistic Influences

An Effective Message Forwarding Algorithm for Delay Tolerant...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Yitao Li Ling Ding Jie Sheng Cong Liu Ankur M. Teredesai Institute of Technology University of Washington Tacoma School of Information Science and Technology Sun Yat-sen (Zhongshan) University

In this paper, we present a disruption-tolerant message forwarding algorithm for optimizing the message delivery ratio of an ad-hoc network in presence of multiple unknown probabilistic influences that are cyclic in nature. Our model assumes each node moves independently from other nodes in the network, and each node's mobility behavior, although not deterministic, can be considered as random variables associated with an unknown sequence of probability distributions that follows a cyclic pattern over time. Based on those assumptions, our algorithm enables any node in the network to estimate the cycle lengths of hidden events that cause variations in each node's mobility behavior using its known mobility history as well as the history information from all peer nodes at its current landmark. Subsequently using such estimates, each node can predict other nodes' most likely trajectories in future and make optimized routing decisions based on these predictions. In this paper, we also report results from experiments that measure the effectiveness of our proposed algorithm by comparing its message delivery ratio with those of two baselines on networks of different sizes.

关键词： Prediction algorithms Routing Entropy Peer-to-peer computing History Mobile communication Ad hoc networks

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A Facial Expression Based Continuous Emotional State Monitoring System with GPU Acceleration

A Facial Expression Based Continuous Emotional State Monitor...

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10th IEEE international conference and Workshops on Automatic Face and Gesture recognition (FG)

作者： Cheng, Jinkuang Deng, Yangdong Meng, Hongying Wang, Zhihua Tsinghua Univ Inst Microelect Beijing Peoples R China Brunel Univ Sch Engn & Design Dept Elect & Comp Engn London England

ISBN: (纸本)9781467355445;9781467355452

Automatic detection and analysis of the emotional states provide important clues for predicting personal behaviors. the techniques are essential for monitoring and protecting such vulnerable people as patients with mental health conditions, persons under heavy stress, and young kids with neurodevelopmental disorders. However, few automatic emotional state recognition systems have been put into real-world applications. Among various reasons leading the lack of success, the overwhelming computing complexity is a major hurdle. In this paper, a continuously emotional state monitoring system that is capable of recognizing naturalistic facial expression in realtime is designed and implemented on a desktop PC with GPU as an accelerator. It has been tested in real scenarios and the experimental results prove promising.

关键词： affective computing dimension model of emotion facial expression pattern recognition GPU

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BodyGuards: A Clairvoyant Location Predictor using Frequent Neighbors and Markov Model

BodyGuards: A Clairvoyant Location Predictor using Frequent ...

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IEEE 10th international conference on Ubiquitous Intelligence and computing (UIC) / IEEE 10th international conference on Autonomic and Trusted computing (ATC)

作者： Assam, Roland Seidl, thomas Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9781479924813

the prediction of future locations is of enormous research interest, partly due to the fast growing number of users of pervasive devices, as well as the tons of spatio-temporal data generated by such devices. In this paper, we propose a novel enhanced Next Location prediction technique which utilizes a trajectory model called Time Mobility Context Correlation pattern (TMC-pattern) and sequence alignment to mine correlations between an object and its nearest neighbors. Hidden mobility patterns drawn from such correlations are utilized in the synthesis of weighted trees called TMC-Footprint trees. the weighted TMC-Footprint trees are used together with a Markov model to predict the next location of an object with an elevated accuracy of 14,3% when compared to a state-of-the-art work. Furthermore, prediction accuracy of existing next location predictors plummets rapidly if an object suddenly takes a new next location which is absent from its trajectory history. Our technique harnesses this problem using a novel notion of Surprise Path.

关键词： Prediction Location Based Services

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Proceedings - IEEE 10th international conference on Ubiquitous Intelligence and computing, UIC 2013 and IEEE 10th international conference on Autonomic and Trusted computing, ATC 2013

Proceedings - IEEE 10th International Conference on Ubiquito...

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10th IEEE international conference on Ubiquitous Intelligence and computing, UIC 2013 and 10th IEEE international conference on Autonomic and Trusted computing, ATC 2013

ISBN: (纸本)9781479924813

the proceedings contain 99 papers. the topics discussed include: hybrid localization for ubiquitous services;DE2: localization with the rotating RSS using a single beacon;user travelling pattern prediction via indistinct cellular data mining;BodyGuards: a clairvoyant location predictor using frequent neighbors and markov model;privacy preserving path recommendation for moving user on location based service;constructing adaptive indoor radio maps for dynamic wireless environments;exploring a new dimension in code mobility for ubiquitous embedded systems;PathSim: a tool for finding minimal energy device operation sequence for reaching a target context in a smart-home;using virtualization and guacamole/VNC to provide adaptive user interfaces to disabled people in cloud computing;towards physical activity recognition using smartphone sensors;and uOS: a resource rerouting middleware for ubiquitous games.

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Enhanced Tissue-Specific Molecular pattern Extraction Via Recursive Maximum Margin Criterion

Enhanced Tissue-Specific Molecular Pattern Extraction Via Re...

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3rd international conference on Civil and Architecture Engineering (ICCAE’2014);3rd international conference on Automation, Robotics and Mechanical Engineering (ICAMR’2014);3rd international conference on Machine Learning and Computer Science (IMLCS’2014);3rd international conference on Management, Humanity and Economics (ICMHE’2014);3rd international conference on Power, Control and VLSI Engineering (ICPCVE’2014)

作者： Kirill A.Veselkov Reza Mirnezami Zoltan Takats the Section of Computational and Systems Medicine at Imperial College London Sir Alexander Fleming Building the section of Biosurgery and Surgical Technology at Imperial College London 10 th Floor QEQM BuildingSt Mary's Hospital

Mass Spectrometry Imaging (MSI) is a rapidly advancing bioanalyitical approach that enables the simultaneous measurement of thousands of molecular species from intact tissue sections in a spatially resolved manner. It provides the opportunity to develop a fully automated chemical histology approach for accurate tissue classification. Such "machine-learned" tissue identification platforms require efficient and robust extraction of tissue-specific discriminating molecular ion patterns. Here, we present a comparative analysis of several supervised feature extraction methods and propose the use of a Recursive Maximum Margin Criterion (RMMC) method for enhanced extraction of tissue-specific discriminating molecular patterns.

关键词： Biomedical computing, dimensionality reduction, imaging mass spectrometry, pattern recognition, supervised feature extraction.

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Understand Group Travel Behaviors in An Urban Area Using Mobility pattern Mining

Understand Group Travel Behaviors in An Urban Area Using Mob...

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IEEE 10th international conference on Ubiquitous Intelligence and computing (UIC) / IEEE 10th international conference on Autonomic and Trusted computing (ATC)

作者： Du, Bowen Yang, Yang Lv, Weifeng Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China

ISBN: (纸本)9781479924813

With the development of cities, especially in developing countries, public transport is a major choice for millions city dwellers, which can ease the traffic pressure, such as crowdedness. However, for cities, in particular cities of developing countries, the continuous development of urban construction leads to the function of regions are changed, and then the collective that travel to specific locations in the city are redistributed, such as a new shopping mall in operation or a new-built community comes into service to meet travel demands of citizens. Currently, Automated Fare Collection Systems (AFCS) are widely used in cities around the world and large amounts of data from AFCS have been acquired. In this paper, we present a new framework to use the data through AFCS to discovering regions with high passenger gathering intensity and classify points in these regions with similar passenger gathering feature varying with time in dynamic way, which is called spark region. Furthermore, the novel definition group mobility pattern (GMP) is proposed to mine the regular group behavior among these spark regions. A series of analysis is employed by using large-scale and real-world data, which consists of nearly 17million people's daily public transit records, bus trajectories generated by over 14,854 buses organizations in Beijing at 20seconds interval. the actual application indicates group mobility pattern is helpful for diagnosis and understanding residence of each region with their demand for public transportation in a significant way.

关键词： smart card passenger volume spark region group mobility pattern

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Social Risk and Depression: Evidence from Manual and Automatic Facial Expression Analysis

Social Risk and Depression: Evidence from Manual and Automat...

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10th IEEE international conference and Workshops on Automatic Face and Gesture recognition (FG)

作者： Girard, Jeffrey M. Cohn, Jeffrey F. Mahoor, Mohammad H. Mavadati, Seyedmohammad Rosenwald, Dean P. Univ Pittsburgh Dept Psychol Pittsburgh PA 15260 USA Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Univ Denver Dept Elect & Comp Engn Denver CO 80208 USA

ISBN: (纸本)9781467355445;9781467355452

Investigated the relationship between change over time in severity of depression symptoms and facial expression. Depressed participants were followed over the course of treatment and video recorded during a series of clinical interviews. Facial expressions were analyzed from the video using both manual and automatic systems. Automatic and manual coding were highly consistent for FACS action units, and showed similar effects for change over time in depression severity. For both systems, when symptom severity was high, participants made more facial expressions associated with contempt, smiled less, and those smiles that occurred were more likely to be accompanied by facial actions associated with contempt. these results are consistent with the "social risk hypothesis" of depression. According to this hypothesis, when symptoms are severe, depressed participants withdraw from other people in order to protect themselves from anticipated rejection, scorn, and social exclusion. As their symptoms fade, participants send more signals indicating a willingness to affiliate. the finding that automatic facial expression analysis was both consistent with manual coding and produced the same pattern of depression effects suggests that automatic facial expression analysis may be ready for use in behavioral and clinical science.

关键词： behavioural sciences computing emotion recognition face recognition video signal processing

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User travelling pattern prediction via indistinct cellular data mining

User travelling pattern prediction via indistinct cellular d...

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10th IEEE international conference on Ubiquitous Intelligence and computing, UIC 2013 and 10th IEEE international conference on Autonomic and Trusted computing, ATC 2013

作者： Wang, Jingwei Yen, Neil Y. Guo, Bin Huang, Runhe Ma, Jianhua Ban, Tao Zhao, Hong Graduate School of Computer and Information Sciences Hosei University Japan School of Computer Science and Engineering University of Aizu Japan School of Computer Science Northwestern Polytechnical University China Faculty of Computer and Information Sciences Hosei University Japan Network Security Research Institute NICT Japan School of Software Engineering Beijing Jiaotong University China

ISBN: (纸本)9781479924813

Smart devices, i.e., smartphone, have come into our daily lives, which become obviously inseparable. Although a variety of functions (e.g., gaming, networking, etc.) are provided, making calls remain the major task. this phenomenon implies the possibility of understanding human behaviors, especially the action contexts (e.g., moving preference, regularity, sociability, etc.), can be expected. In addition, precise services become applicable to be provided through mining, analysis, and prediction of such information. In this study, we investigate the travelling pattern, focusing especially on routine (say excluding the events in holidays), of mobile users via real calling histories. A general model, Travelling pattern Model, was developed, primarily dealing with the contexts of calling and correlated geographical information. this model not only enables high prevision prediction of users but also benefits business models through the detail understanding of user behaviors. © 2013 IEEE.

关键词： Forecasting

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Imposing cognitive load to unmask prepared lies

Imposing cognitive load to unmask prepared lies

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2013 10th IEEE international conference and Workshops on Automatic Face and Gesture recognition, FG 2013

作者： Zurloni, Valentino Diana, Barbara Elia, Massimiliano Human Sciences for Education Department 'Riccardo Massa' University of Milano-Bicocca Milan Italy

ISBN: (纸本)9781467355452

the aim of our study is to examine whether the overall organization of behavior differs when people report truthful vs. deceptive messages within the framework of the T-pattern model. We tested the hypothesis that the differences between liars and truth tellers will be greater under high cognitive load conditions. We argue that recalling stories in reverse order will produce cognitive overloading in subjects, because their cognitive resources are already partially spent on the lying task;this should emphasize nonverbal differences between liars and truth tellers. Results support the hypothesis that discriminating behavioral patterns between truth and lie could be easier under high cognitive load condition. Moreover, they suggest that future research on deception detection may focus more on patterns of behavior rather than on individual cues. © 2013 IEEE.

关键词： behavioural sciences computing cognition

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