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Multimodal learning analytics 13

Multimodal learning analytics

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3rd international conference on learning Analytics and Knowledge, LAK 2013

作者： Blikstein, Paulo Stanford University Graduate School of Education and Computer Science 520 Galvez Mall CERAS 232 Stanford CA 94305 United States

ISBN: (纸本)9781450317856

New high-frequency data collection technologies and machine learning analysis techniques could offer new insights into learning, especially in tasks in which students have ample space to generate unique, personalized artifacts, such as a computer program, a robot, or a solution to an engineering challenge. To date most of the work on learning analytics and educational data mining has focused on online courses or cognitive tutors, in which the tasks are more structured and the entirety of interaction happens in front of a computer. In this paper, I argue that multimodal learning analytics could offer new insights into students' learning trajectories, and present several examples of this work and its educational application. © 2013 ACM.

关键词： data mining

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An Ubiquitous Domain Driven data mining Approach For Performance Monitoring in Virtual Organizations Using 360 Degree data mining & Opinion mining

An Ubiquitous Domain Driven Data Mining Approach For Perform...

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international conference on pattern recognition, Informatics and Mobile Engineering (PRIME)

作者： Suriyakumari, V. Kathiravan, A. Vijaya Periyar Univ Salem India Govt Arts Coll Autonomous Salem India

ISBN: (纸本)9781467358453;9781467358439

Performance evaluation in virtual organizations is one of the most important issues that have been considered due to the transition from industrial age to knowledge era. Virtual organizations, as one of the challenges of third millennium, which came to existence for enhancing organization's performance through outsourcing, are not excluding. A virtual organization and its smaller variant, the virtual team, is an organizational network that is structured and managed to function as an identifiable and complete organization. Determining what meanings virtual team members attach to performance evaluation system in IT Companies is a vital precursor to understand the effectiveness of the management practice, rendering this study a preliminary investigation. The literature confirms that perceptions of management practices in IT Industries can influence employee loyalty and role-related behaviors. Perceptions of unfairness can be more detrimental for geographically distributed workers in MNCs than for collocated teams. Although businesses continue to drive demands for virtual organizations, most contemporary studies of performance evaluation system are limited to traditional organizational settings. An interpretive, phenomenological domain Driven data mining (D3M) approach utilizing 360 Degree data mining for objective measurement and opinion mining for subjective measurement enabled a hermeneutic analysis process. The main objective of this research is to investigate the main factors that affect the performance of employees in virtual organization especially IT Companies and to show how these factors can be used for performance evaluation in virtual organization. Based on the review of literature, this study provides a unified domain Driven data mining (d3m) approach for evaluating data intelligence, domain intelligence, human intelligence, network intelligence, social intelligence, and meta synthesis of ubiquitous intelligence for performance appraisal in virtual organiza

关键词： Performance evaluation/appraisal Virtual organization Domain Driven data mining(D3M) data intelligence domain intelligence human intelligence network intelligence social intelligence ubiquitous intelligence 360 degree assessmen Opinion mining machine learning Support Vector machine (SVM)

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ZhiWo: Activity tagging and recognition system for personal lifelogs 13

ZhiWo: Activity tagging and recognition system for personal ...

引用

3rd ACM international conference on Multimedia Retrieval, ICMR 2013

作者： Zhou, Lijuan Marissa Gurrin, Cathal Qiu, Zhengwei CLARITY School of Computing Dublin City University Collins Avenue Glasnevin Dublin 9 Dublin Ireland

ISBN: (纸本)9781450320337

With the increasing use of mobile devices as personal recording, communication and sensing tools, extracting the semantics of life activities through sensed data (photos, accelerometer, GPS etc.) is gaining widespread public awareness. A person who engages in long-term personal sensing is engaging in a process of lifelogging. Lifelogging typically involves using a range of (wearable) sensors to capture raw data, to segment into discrete activities, to annotate and subsequently to make accessible by search or browsing tools. In this paper, we present an intuitive lifelog activity recording and management system called ZhiWo. By using a supervised machine learning approach, sensed data collected by mobile devices are automatically classified into different types of daily human activities and these activities are interpreted as life activity retrieval units for personal archives. © 2013 Authors.

关键词： Supervised learning

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Interpreting data mining results with linked data for learning analytics: Motivation, case study and directions 13

Interpreting data mining results with linked data for learni...

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3rd international conference on learning Analytics and Knowledge, LAK 2013

作者： D'Aquin, Mathieu Jay, Nicolas Knowledge Media Institute Open University Walton Hall Milton Keynes MK7 6AA United Kingdom Université de Lorraine LORIA UMR 7503 Vandœvre-lès-Nancy F-54506 France

ISBN: (纸本)9781450317856

learning Analytics by nature relies on computational information processing activities intended to extract from raw data some interesting aspects that can be used to obtain insights into the behaviours of learners, the design of learning experiences, etc. There is a large variety of computational techniques that can be employed, all with interesting properties, but it is the interpretation of their results that really forms the core of the analytics process. In this paper, we look at a specific data mining method, namely sequential pattern extraction, and we demonstrate an approach that exploits available linked open data for this interpretation task. Indeed, we show through a case study relying on data about students' enrolment in course modules how linked data can be used to provide a variety of additional dimensions through which the results of the data mining method can be explored, providing, at interpretation time, new input into the analytics process. © 2013 ACM.

关键词： Linked data

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A Push Strategy for delivering of learning Objects using meta data based association analysis (FP-Tree)

A Push Strategy for delivering of Learning Objects using met...

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3rd international conference on Computer Communication and Informatics (ICCCI)

作者： Sabitha, A. Sai Mehrotra, Deepti Amity Univ Dept CS ASET Noida Uttar Pradesh India ASCS Noida Uttar Pradesh India

ISBN: (纸本)9781467329071;9781467329064

learning Materials are structured as learning Objects and are available in learning Object Repository(LOR) which are used in various courses of an Elearning environment. learning Management System aggregates these objects found in LOR, provides an infrastructure and platform through which learning content is delivered and managed. Adaptation, personalization, usage statistics are some of the LMS functionality. But due to the exponential availability of learning Objects, it leads to increase in difficulty to find the right resource to the user based on the context of learning or his/her preferences. When we search through keywords it results in huge quantity of information being displayed. In this paper we are considering the Search patterns of the users stored in search logs and based on it association rules are generated using Frequent pattern Tree. We can generate a list of Frequent learning objects using frequent item set mining approach FP-Tree, so that a reduced, appropriate and relevant objects can be delivered to the users.

关键词： learning Objects Frequent patternTree Apriori FP Growth Recommender Systems

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Proceedings - 2013 3rd international conference on Advanced Computing and Communication Technologies, ACCT 2013

Proceedings - 2013 3rd International Conference on Advanced ...

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2013 3rd international conference on Advanced Computing and Communication Technologies, ACCT 2013

ISBN: (纸本)9780769549415

The proceedings contain 68 papers. The topics discussed include: component criticality approach towards minimizing the risks of system failure;analysis of the techniques for software cost estimation;estimating of software quality with clustering techniques;methodology of the heuristic based hybrid clustering technique for pattern classification and recognition;intellectual climate system for monitoring industrial environment;a modified approach to text steganography using hypertext markup language;automatic speech reading by oral motion tracking for user authentication system;formal modeling of a tele-surgery domain as a multi-agent planning problem;analysis of multispectral image using discrete wavelet transform;review on mosaicing techniques in image processing;image texture analysis - survey;clustering technique on search engine dataset using data mining tool;and an efficient algorithm for mining association rules using confident frequent itemsets.

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A New SAFR Tool for Face recognition Using EGV-LBP-CMI-LDA Wrapped with Secured DWT Based Steganography

A New SAFR Tool for Face Recognition Using EGV-LBP-CMI-LDA W...

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3rd IEEE international Advance Computing conference (IACC)

作者： Jhodge, Shami Chiddarwar, Girija Shinde, Gitanjali Sinhgad Coll Management Dept MCA Pune Maharashtra India Sinhgad Coll Engn Dept Comp Engn Pune Maharashtra India SKN Coll Engn Dept Comp Engn Pune Maharashtra India

ISBN: (纸本)9781467345286

Face recognition technique nowadays is emerging as the most significant and challenging aspects in terms of security for identification of images in various fields viz. banking, police records, biometric etc. other than an individual's thumb and documented identification proofs. Till date for efficient net banking to be initiated, one has to provide the appropriate user name and password for purpose of authentication. This project introduces a vehicle to take a step forward in easy and more reliable authentication of an individual by providing Face Image along with User Name and Password to the system. In this an individual's face is identified by biometric authentication support with which, only a person whose account is, can access it. However while transferring this sensitive data of user image, from client machine to bank server it has to be protected from hackers and intruders from manhandling it, hence it is transferred using covert communication called Wavelet Decomposition based steganography. As face images are affected by different expressions, poses, occlusions, illuminations and aging over a period of time and it differs from the same person than those from different ones is the main difficult task in face recognition. Whenever image information is jointly co-ordinated in three aspects viz. image space, scale and orientation domains they carry much higher clues than seen in each domain individually. In the proposed method combination of Local Binary pattern (LBP) and Gabor features are used to increase the face recognition performance significantly to compare individual's face presentations.

关键词： DWT (Discrete Wavelet Transformation) E-GV-LBP (Effective Gabor Volume Linear Binary pattern) CMI (Conditional Mutual Information)

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learning model rules from high-speed data streams 3

Learning model rules from high-speed data streams

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3rd Workshop on Ubiquitous data mining, UDM 2013 - Co-located with the 23rd international Joint conference on Artificial Intelligence, IJCAI 2013

作者： Almeida, Ezilda Ferreira, Carlos Gama, João LIAAD INESC Porto L.A. Portugal

Decision rules are one of the most expressive languages for machine learning. In this paper we present Adaptive Model Rules (AMRules), the first streaming rule learning algorithm for regression problems. In AMRules the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of attribute values. Each rule in AMRules uses a Page-Hinkley test to detect changes in the process generating data and react to changes by pruning the rule set. In the experimental section we report the results of AMRules on benchmark regression problems, and compare the performance of our algorithm with other streaming regression algorithms. © 2013 IJCAI.

关键词： Regression analysis

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Evaluating the Effects of Textual Features on Authorship Attribution Accuracy

Evaluating the Effects of Textual Features on Authorship Att...

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3rd international conference on Computer and Knowledge Engineering (ICCKE)

作者： Ramezani, Reza Sheydaei, Navid Kahani, Mohsen Ferdowsi Univ Mashhad Dept Comp Engn DDEmS Lab Mashhad Iran Isfahan Univ Technol DM Lab Esfahan Iran

ISBN: (纸本)9781479920938

Authorship attribution (AA) or author identification refers to the problem of identifying the author of an unseen text. From the machine learning point of view, AA can be viewed as a multiclass, single-label text-categorization task. This task is based on this assumption that the author of an unseen text can be discriminated by comparing some textual features extracted from that unseen text with those of texts with known authors. In this paper the effects of 29 different textual features on the accuracy of author identification on Persian corpora in 30 different scenarios are evaluated. Several classification algorithms have been used on corpora with 2, 5, to, 20 and 40 different authors and a comparison is performed. The evaluation results show that the information about the used words and verbs are the most reliable criteria for AA tasks and also NLP based features are more reliable than BOW based features.

关键词： Authorship Attribution Author Identification Textual Features Persian Corpus data mining Classification

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Network traffic anomaly detection based on growing hierarchical SOM

Network traffic anomaly detection based on growing hierarchi...

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43rd Annual IEEE/IFIP international conference on Dependable Systems and Networks (DSN)

作者： Huang, Shin-Ying Huang, Yen-Nun Acad Sinica Res Ctr Informat Technol Innovat Taipei 115 Taiwan

ISBN: (纸本)9781479901814;9781467364713

Network anomaly detection aims to detect patterns in a given network traffic data that do not conform to an established normal behavior. Distinguishing different anomaly patterns from large amount of data can be a challenge, let alone visualizing them in a comparative perspective. Recently, the unsupervised learning method such as the K-means [3], self-organizing map (SOM) [2], and growing hierarchical selforganizing map (GHSOM) [1] have been shown to be able to facilitate network anomaly detection [4][5]. However, there is no study addressing both mining and detecting task. This study leverages the advantage of GHSOM to analyze the network traffic data and visualize the distribution of attack patterns with hierarchical relationship. In the mining stage, the geometric distances between each pattern and its descriptive information are revealed in the topological space. The density and the sample size of each node can help to detect anomalous network traffic. In the detecting stage, this study extends the traditional GHSOM and uses the support vector machine (SVM) [6] to classify network traffic data into the predefined categories. The proposed approach achieves (1) help understand the behaviors of anomalous network traffic data (2) provide effective classification rule to facilitate network anomaly detection and (3) accumulate network anomaly detection knowledge for both mining and detecting purpose. The public dataset and the private dataset are used to evaluate the proposed approach. The expected result is to confirm that the proposed approach can help understand network traffic data, and the detecting mechanism is effective for identifying anomalous behavior.

关键词： Network anomaly detection Neural networks data Clustering data Classification Visualization

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