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An Ontology-driven MapReduce Framework for Association Rules Mining in Massive data 22

An Ontology-driven MapReduce Framework for Association Rules...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Gahar, Rania Mkhinini Arfaoui, Olfa Sassi Hidri, Minyar Ben Hadj-Alouane, Nejib Univ Tunis El Manar Natl Engn Sch Tunis BP 37 Tunis 1002 Tunisia Imam Abdulrahman Bin Faisal Univ Preparatory Year & Supporting Studies Dammam Saudi Arabia UR OASIS Oasis Tunisia LR SITI Tunis Tunisia

To be competitive, companies need to be able to take advantage of the huge amounts of data, called also Big data deluge, to predict what might happen in the future. In this way, predictive analytics play an important role for extracting useful information which may extend the business strategy and so gain competitive advantages. Predictive analytics involve data mining algorithms to discover knowledge from huge volumes of data. In this context, Association Rules (ARs) mining is considered as one of the most wide-spread data mining techniques. It is especially based on frequent itemsets mining process. However, when it comes to Big data, ARs mining algorithms produce a huge amount of ARs, many of which are redundant and unuseful. To overcome this drawback, we propose a ontology-driven Map-Reduce Framework for ARs mining in massive data. Ontologies allow to filter the generated ARs and keep only useful ones. The filtering process is assured by a semantic pruning phase introduced in the MapReduce jobs in order to eliminate unuseful candidates from the computing of the Maximal Frequent Itemsets (MFI). This may allow a quantitative and especially qualitative reduction of the number of MFI and subsequently of the ARs. Extensive experiments on several datasets demonstrate the ability to handle massive data for mining ARs. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Ontology Semantic Pruning Big data Maximal Frequent Itemsets Association Rules MapReduce

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Towards an adaptable task model within an evolutionary environment 22

Towards an adaptable task model within an evolutionary envir...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Jarraya, Mouna Moussa, Faouzi Univ Tunis El Manar Fac Sci Tunis LIPAH Tunis 2092 Tunisia

Task models are powerful artifacts as they are the result of requirement analysis at the design and development stages. Unfortunately, their uses are limited at gathering requirements and specification for systems and they are almost not employed anymore after design step despite their cost in terms of time and effort. In fact, task models bring many benefits at operation time, when the system has been already deployed as: it represents user documentation;it is used for training operators, etc. Moreover, human performance depends on cognitive mechanisms. It evolves simultaneously with personal, environmental and social factor which defines the context. Any changes within the context can affect the reliability of human performance by committing errors or deviations which can cause serious accidents especially for life-critical systems. These deviations are not taken into consideration within task model as it is produced at design time and remains unchanged even when it is reused at operation time. The work reported in this paper aims at proposing an approach to create and generate at real time a deduced task model from the original one corresponding to a certain situation at a specified context. The goal is also to ensure that user system interaction in a critical situation is performed as planned and there is no detected deviation. Machine learning techniques are used to predict what tasks user should perform and what ones he/she should not. They enable to learn about human computer interaction variations in the future in order to mend task model. This approach will be evaluated through experiments in simulated scenarios about on-board car system. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Task model Human computer interaction Machine learning life-critical system

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Some Psychological Responses Measured by a Commercial Electrooculography Sensor and Its Applicability 22

Some Psychological Responses Measured by a Commercial Electr...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Kanematsu, Hideyuki Barry, Dana M. Ogawa, Nobuyuki Nakahira, Katsuko T. Yoshitake, Michiko Shirai, Tatsuya Kawaguchi, Masashi Kobayashi, Toshiro Yajima, Kuniaki Suzuka Coll Natl Inst Technol Suzuka Mie 5100294 Japan Clarkson Univ Potsdam NY 13699 USA Gifu Coll Natl Inst Technol Gifu 5010495 Japan Nagaoka Univ Technol Nagaoka Niigata 9402188 Japan Natl Inst Mat Sci Tsukuba Ibaraki 3050047 Japan Tsuyama Coll Natl Inst Technol Tsuyama Okayama 7088509 Japan Sendai Coll Natl Inst Technol Sendai Miyagi 9893128 Japan

Students' psychological responses were evaluated by a commercial electrooculography (EOG) sensor. As psychological responses, we picked up two factors concentration power and the impression about the simplicity for problem solving. A glasstype EOG sensor detected the electrooculogram and calculated the blink rate (focus), the blink strength (calmness) and the head posture (posture), constituting the final parameter called concentration power. In this study, we investigated the correlation between the two psychological responses and EOG parameters. We could confirm the qualitative correlation between the EOG parameter corresponding to concentration power and the psychological response. At this point, we presume that it could be applied to e-learning in virtual classrooms soon. As for the impression about the simplicity to solve problems, we could not found the reasonable correlation. However, the detailed analyses for all of the parameters and their time-dependent behaviors might make it clearer and also other relations with more psychological response. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： electrooculography EOG concentration power blink rate blink strength head posture e-learning virtual classrooms

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Logical entity level sentiment analysis 22nd

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22nd international conference on Formal Grammar, FG 2017

作者： Petersen, Niklas Christoffer Villadsen, Jørgen Department of Management Engineering Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby2800 Denmark

ISBN: (纸本)9783662563427

We present a formal logical approach using a combinatory categorial grammar for entity level sentiment analysis that utilizes machine learning techniques for efficient syntactical tagging and performs a deep structural analysis of the syntactical properties of texts in order to yield precise results. The method should be seen as an alternative to pure machine learning methods for sentiment analysis, which are argued to have high difficulties in capturing long distance dependencies, and can be dependent on significant amount of domain specific training data. The results show that the method yields high correctness, but further investment is needed in order to improve its robustness. © Springer-Verlag GmbH Germany 2018.

关键词： Sentiment analysis

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Emotion classification with data augmentation using generative adversarial networks 22nd

Emotion classification with data augmentation using generati...

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22nd Pacific-Asia conference on Advances in Knowledge Discovery and data Mining, PAKDD 2018

作者： Zhu, Xinyue Liu, Yifan Li, Jiahong Wan, Tao Qin, Zengchang Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing China School of Electronic Engineering Bejing University of Posts and Telecommunications Beijing China Beijing San Kuai Yun Technology Co. Ltd. Beijing China School of Biological Science and Medical Engineering Beijing Advanced Innovation Centre for Biomedical Engineering Beihang University Beijing China

ISBN: (纸本)9783319930398

It is a difficult task to classify images with multiple class labels using only a small number of labeled examples, especially when the label (class) distribution is imbalanced. Emotion classification is such an example of imbalanced label distribution, because some classes of emotions like disgusted are relatively rare comparing to other labels like happy or sad. In this paper, we propose a data augmentation method using generative adversarial networks (GAN). It can complement and complete the data manifold and find better margins between neighboring classes. Specifically, we design a framework using a CNN model as the classifier and a cycle-consistent adversarial networks (CycleGAN) as the generator. In order to avoid gradient vanishing problem, we employ the least-squared loss as adversarial loss. We also propose several evaluation methods on three benchmark datasets to validate GAN’s performance. Empirical results show that we can obtain 5%–10% increase in the classification accuracy after employing the GAN-based data augmentation techniques. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Generative adversarial networks

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Malicious Nodes Detection based on Artificial Neural Network in IoT Environments

Malicious Nodes Detection based on Artificial Neural Network...

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international conference on Computer and Information Technology (ICCIT)

作者： Mirza Akhi Khatun Niaz Chowdhury Mohammed Nasir Uddin Computer Science & Engineering Jagannath University Dhaka Bangladesh Knowledge Media Institute The Open University Milton Keynes United Kingdom

ISBN: (数字)9781728158426

ISBN: (纸本)9781728158433

The central promise of the Internet of Things (IoT) is to accelerate the interaction with surroundings. Appliances such as smartwatches, smart bulbs, thermostats, fitness trackers, next-generation vehicles, and so on have gained the ability to communicate and accept instructions from outside with the help of various embedded devices. While doing so, they almost always operate with little to no human interaction. These embedded devices, alongside a lot of benefits, also bring forth several security challenges. From the manufacturers' point of view, performance and production remain the top priority leaving security a less addressed problem. This practice has manifested itself in the form of various attacks including the Distributed Denial of Service (DDoS) where malware prepared with the aid of malicious nodes in IoT devices are used to carry out the attack. It is, therefore, essential to comprehend the nature of these attacks and swiftly identify infected devices to combat this situation. Machine learning, or more particularly a branch of this technique commonly known as Deep learning, has already demonstrated its outstanding potentials while administering with the heterogeneous data of diverse sizes. Using the Artificial Neural Network (ANN), this work suggests a method of detecting malicious nodes in IoT environments. The contribution of the paper is in two-fold. First, it classifies the usual and malicious patterns of IoT devices in the network, and second, describes a scheme for successfully detecting malicious nodes with 77.51% accuracy.

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Control and Optimization of Distributed Solar Collector Fields

Control and Optimization of Distributed Solar Collector Fiel...

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IEEE international conference on intelligent engineering Systems (INES)

作者： Ryszard Klempous Manuel Berenguel Wroclaw University of Technology Institute of Engineering Cybernetics Wroclaw Poland Departamento de Informática University of Almería Almería Spain

This brief presents an overview of the main achievements of Prof. R. Klempous in his collaboration with the Automatic Control, Robotics an Mechatronics research group of CIESOL Center at Universidad de Almeria and the... 详细信息

关键词： Optimization data mining Distributed databases data analysis conferences Europe Monitoring

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The Future of Broadband Access Network Architecture and intelligent Operations

The Future of Broadband Access Network Architecture and Inte...

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international conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Charlie Chen-Yui Yang Guangzhi Li Xiang Liu Zonghuan Wu Kaiyu Zhang Futurewei 400 Crossing Blvd 2nd Fl. Bridgewater NJ USA

This paper presents an overview of the evolution towards a broadband optical access network. Network operations transformation with emphasis on automated broadband service monitoring, fault detection, fault recovery, and fault prediction is also discussed. However, in order to support broadband access network evolution and to accelerate telco operations transformation, traditional network operations, administration and maintenance (OAM) methods will not be sufficient. Therefore, we describe our key research contributions regarding new approaches to support closed-loop broadband network OAM that will eliminate human touches and automate non-physical fault recovery for daily routine tasks. We have designed and implemented a network knowledge engine (NKE) centered software framework to enable data collection, correlation, and analysis for troubleshooting complex issues in the broadband access network. Our solutions will address the following network management domains, including (a) knowledge management, (b) surveillance management, (c) incident management, and (d) problem management. In this paper, we focus on auto proactive and predictive trouble management which can verify and confirm a passive device problem location by NKE. We will describe a step-by-step methodology to control data process flow, optical fiber path discovery management, and auto-detection of failed passive devices by using the following techniques: (a) big data analytics, (b) knowledge graph, (c) machine learning (ML), and (d) artificial intelligence (AI) prediction rule and learning policy involving the spatial-temporal algorithm. With combinations of these technique, our preliminary study indicates that the accuracy of auto fault detection is 93%, while the false positive rate is ~0.01%. As results, we demonstrated the promise of this novel technology framework in supporting transformation from expert's domain knowledge into machine knowledge.

关键词： Broadband communication Bandwidth Knowledge engineering Coaxial cables Wireless communication Telephone sets Cable TV

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An Effective Model for Edge-Side Collaborative Storage in data-Intensive Edge Computing 22

An Effective Model for Edge-Side Collaborative Storage in Da...

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22nd IEEE international conference on Computer Supported Cooperative Work in Design, CSCWD 2018

作者： Li, Yunhao Luo, Junzhou Jin, Jiahui Xiong, Runqun Dong, Fang School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781538614822

Edge Computing is a new computing paradigm that performs data processing at the edge of the network (i.e., edge servers) to lower data processing latency. Existing research works have paid lots of attention to how to offload computation tasks from terminals to edge servers, but most of them ignored how to store tasks' necessary data like pretrained models or databases on edge servers. Recently, the data-intensive tasks like deep learning and augmented reality are becoming common, which need large data storages and powerful computation resources. This leads to a cumbersome challenge, since many lightweight edge servers have limited resources. If an edge server does not have a task's necessary data, it needs to offload the task to cloud data centers or download the necessary data from the cloud. Both cases could increase the data processing latency. To address this problem, this paper proposes an edge-side collaborative storage framework (ECS). In ECS, the edge servers collaboratively store and process data-intensive tasks' necessary data. Particularly, if an edge server does not have the necessary data, it will forward the task to the nearest servers that contain the data. An effective iterative data placement algorithm is also proposed to improve ECS's performance. The experimental results show that ECS is 2 x better than the traditional non-shared storage framework in terms of the cache hit rate. © 2018 IEEE.

关键词： computation offloading

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Non-Rectangular RoI Extraction and Machine learning Based Multiple Object Recognition Used for Time-Series Areal Images Obtained Using MAV 22

Non-Rectangular RoI Extraction and Machine Learning Based Mu...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Madokoro, Hirokazu Kainuma, Asahi Sato, Kazuhito Akita Prefectural Univ Fac Syst Sci & Technol Dept Machine Intelligence & Syst Engn 84-4 Aza Ebinoguchi Yurihonjo City Akita 0150055 Japan

This paper presents a novel method to recognize multiple objects from aerial scene images obtained using a micro air vehicle (MAV). As a robot vision extended platform, MAVs measure objects in time-series images obtained from various angles and altitudes with advanced outstanding active vision characteristics. The proposed method consists of four major steps: region of interest (RoI) extraction using binarized normed gradients (BING), feature point detection and description using accelerated-KAZE (AKAZE), generation of codebook as histogram features quantized using self-organizing map (SOMs), and semantic recognition of multiple objects using category maps created using counter propagation networks (CPNs). We obtained five datasets of timeseries aerial images at an atrium while flying a MAV manually. The original video images were downsampled from 30 fps to 1 fps in consideration of calculation cost and appearance changes between frames. We annotated ground truth (GT) labels to all images used for teaching signals for learning and validation signals for testing. Experimentally obtained results with leave-one-out cross-validation (LOOCV) revealed the mean recognition accuracy for all datasets as 71.96%. For each dataset, the maximum and minimum recognition accuracies were, respectively, 77.26% in dataset 3 and 65.25% in dataset 2. (C) 2018 The Authors. Published by Elseiver Ltd.

关键词： object recognition micro air vehicle (MAV) region of interest (RoI) self-organizing map (SOMs) counter propagation networks (CPNs)

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