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ECOP: Energy Conserving Postboxes in Postbox Delay Tolerant Networks

Procedia computer science 2015年 57卷 952-959页

作者： Priyanka Das Tanmay De Department of Computer Science and Engineering NSHM Knowledge Campus Durgapur-713212 India Department of Computer Science and Engineering National Institute of Technology Durgapur-713209 India

The Delay Tolerant Network (DTN) came as a saviour for heterogeneous networks marked by intermittent connectivity and low energy levels. By the introduction of the BUNDLE layer in addition to the TCP/IP layers, the DTN tries to achieve what the TCP/IP could not i.e. allow communication in challenged networks. The Postbox DTN is a type of DTN which has a persistent node known as Postbox which is always ON. All nodes have a direct connection to it and no other connection between nodes exist. Hence all communication is made via and by the Postbox just like the real world Postbox scenario. Apart from the novel concept of a 1 hop routing strategy, it is very easy to deploy. However its ease of use comes at a huge cost of energy usage by its always ON Postboxes. This work proposes a modified Postbox model which conserves energy without depreciating its performance. Simulation results confirm our hypothesis.

关键词： Delay Tolerant Network (DTN) Postbox Energy Conservation Scheduled Power ON/OFF Time Sharing.

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Introduction to the soft computing and intelligent data analysis minitrack

Proceedings of the Annual Hawaii International Conference on...

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Proceedings of the Annual Hawaii International Conference on System sciences 2016年 2016-March卷 1710-1710页

作者： Kruse, Rudolf Pasi, Gabriella Alonso, Jose Maria Otto von Guericke University Magdeburg Germany Faculty of Computer Science Department of Knowledge Processing and Language Engineering Germany Universita degli Studi di Milano Bicocca Italy Dipartimento di Informatica Laboratorio di Information Retrieval Italy European Centre for Soft Computing Mieres Spain Cognitive Computing with Perceptions Spain

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Non-transferable Unidirectional Proxy Re-Encryption Scheme for Secure Social Cloud Storage Sharing

Non-transferable Unidirectional Proxy Re-Encryption Scheme f...

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International Conference on Intelligent Networking and Collaborative Systems (INCOS)

作者： Xu An Wang Fatos Xhafa Wei Hao Wei He Engineering University of Chinese Armed Police Force P. R. China School of Cyber Engineering Xidian University P. R. China Department of Computer Science Technical University of Catalonia Spain E-knowledge station of Chinese Armed Police Force P. R. China

Proxy re-encryption (PRE), introduced by Blaze et al. in 1998, allows a semi-trusted proxy with the re-encryption key to translatea ciphertext under the delegator into another ciphertext, which can be decrypted by the delegatee. In this process, the proxy is required to know nothing about the plaintext. Many PRE schemes have been proposed so far, however until now almost all the unidirectional PRE schemes suffer from the transferable property. That is, if the proxy and a set of delegatees collude, they can re-delegate the delegator's decryption rights to the other ones, while the delegator has no agreement on this. Thus designing non-transferable unidirectional PRE scheme is an important open research problem in the field. In this paper, we tackle this open problem by using the composite order bilinear pairing. Concretely, we design a non-transferable unidirectional PRE scheme based on Hohenberger et al.'s unidirectional PRE scheme. Furthermore, we discuss our scheme's application to secure cloud storage, especially for sharing private multimedia content for social cloud storage users.

关键词： Cloud computing Encryption Secure storage Proposals Public key Generators

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Largest source subset selection for instance transfer 7

Largest source subset selection for instance transfer

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7th Asian Conference on Machine Learning, ACML 2015

作者： Zhou, Shuang Schoenmakers, Gijs Smirnov, Evgueni Peeters, Ralf Driessens, Kurt Chen, Siqi Department of Knowledge Engineering Maastricht University P.O. Box 616 Maastricht6200 MD Netherlands School of Computer and Information Science Southwest University Chongqing China

Instance-transfer learning has emerged as a promising learning framework to boost performance of prediction models on newly-arrived tasks. The success of the framework depends on the relevance of the source data to the target data. This paper proposes a new approach to source data selection for instance-transfer learning. The approach is capable of selecting the largest subset S∗ of the source data which relevance to the target data is statistically guaranteed to be the highest among any superset of S∗. The approach is formally described and theoretically justified. Experimental results on real-world data sets demonstrate that the approach outperforms existing instance selection methods. © 2015 S. Zhou, G. Schoenmakers, E. Smirnov, R. Peeters, K. Driessens & S. Chen.

关键词： Artificial intelligence

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Pathological Brain Detection Based on Online Sequential Extreme Learning Machine

Pathological Brain Detection Based on Online Sequential Extr...

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University State Key Lab of CAD & CG Zhejiang University Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Key Laboratory of Statistical information technology and data mining State Statistics Bureau Department of Electrical Engineering The City College of New YorkCUNY

Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies(WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine(OS-ELM) was trained to differentiate pathological brains from the healthy *** experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%,which suggested that our method is effective.

关键词： magnetic resonance imaging wavelet entropy online sequential extreme learning machine Classification pattern recognition

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A managing QoE approach for provisioning user experience aware services using SDN 11

A managing QoE approach for provisioning user experience awa...

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11th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Q2SWinet 2015

作者： Da Silva, Madalena P. Dantas, M.A.R. Gonçalves, Alexandre L. Pinto, A.R. Department of Engineering and Knowledge Management Federal University of Santa Catarina Brazil Computer Science Federal University of Santa Catarina Brazil Control and Automation Federal University of Santa Catarina Brazil

ISBN: (纸本)9781450337571

Provision and delivery of services with quality is a classic research problem, however the computational resources available in the network infrastructure of providers are, usually, managed with conventional Quality of Service (QoS) parameters. This paper presents an approach of Quality of Experience (QoE) management for providing services aware of the user experience. QoE modeling and architecture are proposed, with a semantic engine able to learn the user's experience during the use of a service, detecting violations of QoS metrics and providing information, allowing the controller to perform actions in the elements of the Software Defined Networking. The experimental results demonstrate that the proposal is feasible and functional and that the time spent between QoE detection and adaptation of policies in network resources do not influence the quality perceived by the user. © 2015 ACM.

关键词： Quality of service

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Learning platform: Emotional learning 10

Learning platform: Emotional learning

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10th Iberian Conference on Information Systems and Technologies, CISTI 2015

作者： Faria, Ana Raquel Almeida, Ana Martins, Constantino Gonçalves, Ramiro Computer Science Department GECAD-Knowledge Engineering and Decision Support Group Institute of Engineering Polytechnic of Porto Porto Portugal Universidade de Trás-os-Montes e Alto Douro Vila Real Portugal

ISBN: (纸本)9789899843455

The purpose of this paper is to propose new model for emotional interaction that uses learning styles and student emotional state to adapt the user interface and learning path. This aims to reduce the difficulty and emotional stain that students encounter while interacting with learning platforms. To this end will be used techniques of Affective computer that can capture the student emotional state and base on that change the course parameters (flow, organization or difficulty) or even an emotional interaction in order to recapture the student attention. © 2015 AISTI.

关键词： Students

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“When 'bad' is 'good'”: Identifying personal communication and sentiment in drug-related tweets

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JMIR Public Health and Surveillance 2016年第2期2卷 e162页

作者： Daniulaityte, Raminta Chen, Lu Lamy, Francois R. Carlson, Robert G. Thirunarayan, Krishnaprasad Sheth, Amit Center for Interventions Treatment and Addictions Research Department of Population and Public Health Sciences Boonshoft School of Medicine Wright State University Kettering OH United States Ohio Center of Excellence in Knowledge-enabled Computing Department of Computer Science and Engineering Wright State University Dayton OH United States

Background: To harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content. Objectives: The objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid-related tweets. Methods: Tweets were collected using Twitter streaming Application Programming Interface and filtered through the eDrugTrends platform using keywords related to cannabis, marijuana edibles, marijuana concentrates, and synthetic cannabinoids. After creating coding rules and assessing intercoder reliability, a manually labeled data set (N=4000) was developed by coding several batches of randomly selected subsets of tweets extracted from the pool of 15,623,869 collected by eDrugTrends (May-November 2015). Out of 4000 tweets, 25% (1000/4000) were used to build source classifiers and 75% (3000/4000) were used for sentiment classifiers. Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machines (SVM) were used to train the classifiers. Source classification (n=1000) tested Approach 1 that used short URLs, and Approach 2 where URLs were expanded and included into the bag-of-words analysis. For sentiment classification, Approach 1 used all tweets, regardless of their source/type (n=3000), while Approach 2 applied sentiment classification to personal communication tweets only (2633/3000, 88%). Multiclass and binary classification tasks were examined, and machine-learning sentiment classifier performance was compared with Valence Aware Dictionary for sEntiment Reasoning (VADER), a lexicon and rule-based method. The performance of each classifier was assessed using 5-fold cross validation that calculated average F-s

关键词： Cannabis EDrugTrends Machine learning Sentiment analysis Social media Synthetic cannabinoids Twitter

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A Method for QRS Delineation Based on STFT Using Adaptive Threshold

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Procedia computer science 2015年 54卷 646-653页

作者： Basheeruddin Shah Shaik G.V.S.S.K.R. Naganjaneyulu T. Chandrasheker A.V. Narasimhadhan Department of Computational Engineering Rajiv Gandhi University of Knowledge Technologies Krishna India Department of Electronics and Communication Engineering Rajiv Gandhi University of Knowledge Technologies Krishna India Department of Computer Science and Engineering Rajiv Gandhi University of Knowledge Technologies Krishna India Department of Electronics and Communication Engineering National Institute of Technology Surathkal India

Electrocardiogram (ECG) is the electrical manifestation of the contractile activity of the heart. In this work, it is proposed to utilize an adaptive threshold technique on spectrogram computed using Short Time Fourier Transform (STFT) for QRS complex detection in electrocardiogram (ECG) signal. The algorithm consists of preprocessing the raw ECG signal to remove the power-line interference, computing the STFT, applying adaptive thresholding technique and followed by identifying QRS peaks. Sensitivity, Specificity and Detection error rate are calculated on MIT-BIH database using the proposed method, which yields a competitive results when compared with the state of art in QRS detection.

关键词： Adaptive threshold Electrocardiogram QRS detection Short time fourier transform Time-frequency analysis.

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TKLBLIIR: Detecting Twitter Paraphrases with TweetingJay 9

TKLBLIIR: Detecting Twitter Paraphrases with TweetingJay

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9th International Workshop on Semantic Evaluation, SemEval 2015

作者： Karan, Mladen Glavaš, Goran Šnajder, Jan Bašić, Bojana Dalbelo Vulić, Ivan Moens, Marie-Francine University of Zagreb Faculty of Electrical Engineering and Computing Text Analysis and Knowledge Engineering Lab Unska 3 Zagreb10000 Croatia KU Leuven Department of Computer Science Language Intelligence Information Retrieval Group Celestijnenlaan 200A Leuven Belgium

ISBN: (纸本)9781941643402

When tweeting on a topic, Twitter users often post messages that convey the same or similar meaning. We describe TweetingJay, a system for detecting paraphrases and semantic similarity of tweets, with which we participated in Task 1 of SemEval 2015. TweetingJay uses a supervised model that combines semantic overlap and word alignment features, previously shown to be effective for detecting semantic textual similarity. TweetingJay reaches 65.9% F1-score and ranked fourth among the 18 participating systems. We additionally provide an analysis of the dataset and point to some peculiarities of the evaluation setup. © 2015 Association for Computational Linguistics

关键词： Semantics

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