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Cross-cluster asymmetric group key agreement for wireless sensor networks

science China(Information sciences) 2018年第4期61卷 226-228页

作者： Jiamin ZHENG Yu'an TAN Qikun ZHANG Xiaosong ZHANG Liehuang ZHU Quanxin ZHANG School of Computer Science and Technology Beijing Institute of Technology Research Center of Massive Language Information Processing and Cloud Computing Application Institute of Computer and Communication Engineering Zhengzhou University of Light Industry Department of Computer Science and Technology Tangshan University

Wireless sensor networks have some obvious negative characteristics,such as limited communication range,energy constraints,and vulnerability.A group key agreement in this environment requires lightweight cross-cluster... 详细信息

关键词： Cross-cluster asymmetric group key agreement for wireless sensor networks

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A privacy-preserving traffic monitoring scheme via vehicular crowdsourcing

arXiv

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arXiv 2019年

作者： Zhang, Chuan Zhu, Liehuang Xu, Chang Du, Xiaojiang Guizani, Mohsen Beijing Eng. Res. Center of Massive Language Information Processing and Cloud Computing Application School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Computer and Information Sciences Temple University Philadelphia United States College of Engineering Qatar University

The explosive growth of vehicle amount has given rise to a series of traffic problems, such as traffic congestion, road safety, and fuel waste. Collecting vehicles' speed information is an effective way to monitor the traffic condition and avoid vehicles being congested, which however may bring threats to vehicles' location and trajectory privacy. Motivated by the fact that traffic monitoring does not need to know each individual vehicle's speed and the average speed would be sufficient, we propose a privacy-preserving traffic monitoring (PPTM) scheme to aggregate vehicles' speeds at different locations. In PPTM, the roadside unit (RSU) collects vehicles' speed information at multiple road segments, and further cooperates with a service provider to calculate the average speed information for every road segment. To preserve vehicles' privacy, both homomorphic Paillier cryptosystem and super-increasing sequence are adopted. A comprehensive security analysis indicates that the proposed PPTM can preserve vehicles' identities, speeds, locations, and trajectories privacy from being disclosed. In addition, extensive simulations are conducted to validate the effectiveness and efficiency of the proposed PPTM scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Speed

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Task-oriented word embedding for text classification 27

Task-oriented word embedding for text classification

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27th International Conference on Computational Linguistics, COLING 2018

作者： Liu, Qian Huang, Heyan Gao, Yang Wei, Xiaochi Tian, Yuxin Liu, Luyang Department of Computer Science Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Centre for Artificial Intelligence University of Technology Sydney Australia

ISBN: (纸本)9781948087506

Distributed word representation plays a pivotal role in various natural language processing tasks. In spite of its success, most existing methods only consider contextual information, which is suboptimal when used in various tasks due to a lack of task-specific features. The rational word embeddings should have the ability to capture both the semantic features and task-specific features of words. In this paper, we propose a task-oriented word embedding method and apply it to the text classification task. With the function-aware component, our method regularizes the distribution of words to enable the embedding space to have a clear classification boundary. We evaluate our method using five text classification datasets. The experiment results show that our method significantly outperforms the state-of-the-art methods. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Embeddings

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What does explainable AI really mean? A new conceptualization of perspectives 1

What does explainable AI really mean? A new conceptualizatio...

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1st International Workshop on Comprehensibility and Explanation in AI and ML, CEX 2017

作者： Doran, Derek Schulz, Sarah Besold, Tarek R. Dept. of Computer Science and Engineering Kno.e.sis Research Center Wright State University DaytonOH United States Institute for Natural Language Processing University of Stuttgart Stuttgart Germany Department of Computer Science City University of London London United Kingdom

We characterize three notions of explainable AI that cut across research fields: opaque systems that offer no insight into its algorithmic mechanisms;interpretable systems where users can mathematically analyze its algorithmic mechanisms;and comprehensible systems that emit symbols enabling user-driven explanations of how a conclusion is reached. The paper is motivated by a corpus analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences in how work on explainable AI is positioned in various fields. We close by introducing a fourth notion: truly explainable systems, where automated reasoning is central to output crafted explanations without requiring human post processing as final step of the generative process. Copyright © 2018 for this paper by its authors. Copying permitted for private and academic purposes.

关键词： Artificial intelligence

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Achieving trust-based and privacy-preserving customer selection in ubiquitous computing

arXiv

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arXiv 2019年

作者： Zhang, Chuan Zhu, Liehuang Xu, Chang Sharif, Kashif Liu, Ximeng Du, Xiaojiang Guizani, Mohsen Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Application School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Information Systems Singapore Management University College of Mathematics and Computer Science Fuzhou University Department of Computer and Information Sciences Temple University Philadelphia United States Department of Electrical and Computer Engineering University of Idaho MoscowID United States

The recent proliferation of smart devices has given rise to ubiquitous computing, an emerging computing paradigm which allows anytime & anywhere computing possible. In such a ubiquitous computing environment, customers release different computing or sensing tasks, and people, also known as data processors, participate in these tasks and get paid for providing their idle computing and communication resources. Thus, how to select an appropriate and reliable customer while not disclosing processors' privacy has become an interesting problem. In this article, we present a trust-based and privacy-preserving customer selection scheme in ubiquitous computing, called TPCS, to enable potential processors select the customers with good reputation. The basic concept of TPCS is that each data processor holds a trust value, and the reputation score of the customer is calculated based on processors' trust values and feedbacks via a truth discovery process. To preserve processors' privacy, pseudonyms and Paillier cryptosystem are applied to conceal each processor's real identity. In addition, three authentication protocols are designed to ensure that only the valid data processors (i.e., the processors registering in the system, holding the truthful trust values, and joining the computing tasks) can pass the authentication. A comprehensive security analysis is conducted to prove that our proposed TPCS scheme is secure and can defend against several sophisticated attacks. Moreover, extensive simulations are conducted to demonstrate the correctness and effectiveness of the proposed scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Ubiquitous computing

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Gab: The alt-right social media platform

Gab: The alt-right social media platform

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2018 International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, BRiMS 2018

作者： Zhou, Yuchen Dredze, Mark Broniatowski, David A. Adler, William D. Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States Department of Engineering Management and Systems Engineering George Washington University WashingtonDC20052 United States Department of Political Science Northeastern Illinois University ChicagoIL60625 United States

This study proposes the use of Gab as a vehicle for politicalscience research regarding modern American politics and the Alt-Rightpopulation. We collect several million Gab messages posted on Gab website from August 2016 to February 2018. We conduct a preliminary analysis of Gab platform related to site use, growth and topics, which showsthat Gab is a reasonable resource for Alt-Right study. © 2018 Curran Associates Inc.. All rights reserved.

关键词： Alt-Right Gab politics topic model

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ByoVoz Automatic Voice Condition Analysis System for the 2018 FEMH Challenge

ByoVoz Automatic Voice Condition Analysis System for the 201...

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2018 IEEE International Conference on Big Data, Big Data 2018

作者： Arias-Londoño, Julián David Gómez-García, Jorge Andrés Moro-Velazquez, Láureano Godino-Llorente, Juan Ignacio Dept. of Systems Engineering and Computer Science Universidad de Antioquia Medellín Colombia Dept. of Teoría de la Señal y Comunicaciones Universidad Politécnica de Madrid Madrid Spain Center for Language and Speech Processing Johns Hopkins University Baltimore United States

ISBN: (纸本)9781538650356

This paper presents the methods and results used by the ByoVoz team for the design of an automatic voice condition analysis system, which was submitted to the 2018 Far East Memorial Hospital voice data challenge. The proposed methodology is based on a cascading scheme that firstly discriminates between pathological and normophonic voices, and then identifies the type of disorder. By using diverse feature selection techniques, a subset of complexity, spectral/cepstral and perturbation characteristics were identified for the proposed tasks. Then, several generative classification methodologies based on Gaussian Mixture Models and Gradient Boosting were employed to provide decisions about the input voices in the binary classification, and using one-vs-one classification systems based on Random Forests for the categorization according to the type of disorder. By using a 4-folds cross-validation approach on the training partition a sensitivity=0.93 and specificity=0.74 was obtained. Similarly, an unweighted average recall of 0.63 and an accuracy of 66% was obtained for the identification task. Using the scoring metric proposed in the challenge the final resulting score is of 0.77. When the testing partition was considered, a sensitivity of 0.92, specificity of 0.54, unweighted average recall of 0.61 and a score of 0.72 were obtained. This was the fifth best result in the competition. © 2018 IEEE.

关键词： speech recognition

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Rich morphological tagger for english: Exploring the cross-linguistic tradeoff between morphology and syntax 15

Rich morphological tagger for english: Exploring the cross-l...

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15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017

作者： Kirov, Christo Sylak-Glassman, John Knowles, Rebecca Cotterell, Ryan Post, Matt Center for Language and Speech Processing United States Department of Computer Science United States Human Language Technology Center of Excellence Johns Hopkins University United States

ISBN: (纸本)9781510838604

A traditional claim in linguistics is that all human languages are equally expressive- able to convey the same wide range of meanings. Morphologically rich languages, such as Czech, rely on overt inflectional and derivational morphology to convey many semantic distinctions. languages with comparatively limited morphology, such as English, should be able to accomplish the same using a combination of syntactic and contextual cues. We capitalize on this idea by training a tagger for English that uses syntactic features obtained by automatic parsing to recover complex morphological tags projected from Czech. The high accuracy of the resulting model provides quantitative confirmation of the underlying linguistic hypothesis of equal expressivity, and bodes well for future improvements in downstream HLT tasks including machine translation. © 2017 Association for Computational Linguistics.

关键词： Morphology

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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Database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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Systematically adapting machine translation for grammatical error correction 12

Systematically adapting machine translation for grammatical ...

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12th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2017, held in conjunction with EMNLP 2017

作者： Napoles, Courtney Callison-Burch, Chris Center for Language and Speech Processing Johns Hopkins University United States Computer and Information Science Department University of Pennsylvania United States

ISBN: (纸本)9781945626852

In this work we adapt machine translation (MT) to grammatical error correction, identifying how components of the statistical MT pipeline can be modified for this task and analyzing how each modification impacts system performance. We evaluate the contribution of each of these components with standard evaluation metrics and automatically characterize the morphological and lexical transformations made in system output. Our model rivals the current state of the art using a fraction of the training data. © EMNLP 2017 - 12th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2017 - Proceedings of the Workshop. All rights reserved.

关键词： Error correction

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