Search results for: Language learning tasks.

Authors: Isao Taguchi, Yasuo Sugai

Abstract:

This paper proposes an efficient learning method for the layered neural networks based on the selection of training data and input characteristics of an output layer unit. Comparing to recent neural networks; pulse neural networks, quantum neuro computation, etc, the multilayer network is widely used due to its simple structure. When learning objects are complicated, the problems, such as unsuccessful learning or a significant time required in learning, remain unsolved. Focusing on the input data during the learning stage, we undertook an experiment to identify the data that makes large errors and interferes with the learning process. Our method devides the learning process into several stages. In general, input characteristics to an output layer unit show oscillation during learning process for complicated problems. The multi-stage learning method proposes by the authors for the function approximation problems of classifying learning data in a phased manner, focusing on their learnabilities prior to learning in the multi layered neural network, and demonstrates validity of the multi-stage learning method. Specifically, this paper verifies by computer experiments that both of learning accuracy and learning time are improved of the BP method as a learning rule of the multi-stage learning method. In learning, oscillatory phenomena of a learning curve serve an important role in learning performance. The authors also discuss the occurrence mechanisms of oscillatory phenomena in learning. Furthermore, the authors discuss the reasons that errors of some data remain large value even after learning, observing behaviors during learning.

Keywords: data selection, function approximation problem, multistage leaning, neural network, voluntary oscillation.

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Authors: M. Ovinis, D. Kerr, K. Bouazza-Marouf, M. Vloeberghs

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In this paper, the feasibility of using machine vision to assess task completion in a surgical intervention is investigated, with the aim of incorporating vision based inspection in robotic surgery systems. The visually rich operative field presents a good environment for the development of automated visual inspection techniques in these systems, for a more comprehensive approach when performing a surgical task. As a proof of concept, machine vision techniques were used to distinguish the two possible outcomes i.e. satisfactory or unsatisfactory, of three primary surgical tasks involved in creating a burr hole in the skull, namely incision, retraction, and drilling. Encouraging results were obtained for the three tasks under consideration, which has been demonstrated by experiments on cadaveric pig heads. These findings are suggestive for the potential use of machine vision to validate successful task completion in robotic surgery systems. Finally, the potential of using machine vision in the operating theatre, and the challenges that must be addressed, are identified and discussed.

Keywords: Machine vision, robotic surgery, visual inspection.

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Authors: Adnan Z. Mkhelif

Abstract:

Grammatical collocations (GCs) are word combinations containing a preposition or a grammatical structure, such as an infinitive (e.g. smile at, interested in, easy to learn, etc.). Such collocations tend to be difficult for Iraqi EFL university students (IUS) to master. To help address this problem, it is important to identify the factors causing it. This study aims at investigating the effects of L2 proficiency, frequency of GCs and their transparency on IUSs’ productive knowledge of GCs. The study involves 112 undergraduate participants with different proficiency levels, learning English in formal contexts in Iraq. The data collection instruments include (but not limited to) a productive knowledge test (designed by the researcher using the British National Corpus (BNC)), as well as the grammar part of the Oxford Placement Test (OPT). The study findings have shown that all the above-mentioned factors have significant effects on IUSs’ productive knowledge of GCs. In addition to establishing evidence of which factors of L2 learning might be relevant to learning GCs, it is hoped that the findings of the present study will contribute to more effective methods of teaching that can better address and help overcome the problems IUSs encounter in learning GCs. The study is thus hoped to have significant theoretical and pedagogical implications for researchers, syllabus designers as well as teachers of English as a foreign/second language.

Keywords: Corpus linguistics, frequency, grammatical collocations, L2 vocabulary learning, productive knowledge, proficiency, transparency.

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Authors: Meltem Serhatli, Ferda N. Alpaslan

Abstract:

Processing the data by computers and performing reasoning tasks is an important aim in Computer Science. Semantic Web is one step towards it. The use of ontologies to enhance the information by semantically is the current trend. Huge amount of domain specific, unstructured on-line data needs to be expressed in machine understandable and semantically searchable format. Currently users are often forced to search manually in the results returned by the keyword-based search services. They also want to use their native languages to express what they search. In this paper, an ontology-based automated question answering system on software test documents domain is presented. The system allows users to enter a question about the domain by means of natural language and returns exact answer of the questions. Conversion of the natural language question into the ontology based query is the challenging part of the system. To be able to achieve this, a new algorithm regarding free text to ontology based search engine query conversion is proposed. The algorithm is based on investigation of suitable question type and parsing the words of the question sentence.

Keywords: Description Logics, ontology, question answering, reasoning.

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Authors: Zenaide Carvalho da Silva, Andrey Ricardo Pimentel, Leandro Rodrigues Ferreira

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Learning styles (LSs) correspond to the individual preferences of a person regarding the modes and forms in which he/she prefers to learn throughout the teaching/learning process. The content presentation of learning objects (LOs) using knowledge about the students’ LSs offers them digital educational resources tailored to their individual learning preferences. In this context, the most relevant characteristics of the LSs along with the most appropriate forms of LOs' content presentation were mapped and associated. Such was performed in order to define the composition of an adaptive model of LO's content presentation considering the LSs, which was called Adaptation of Content Presentation of Learning Objects Considering Learning Styles (ACPLOLS). LO prototypes were created with interfaces that were adapted to students' LSs. These prototypes were based on a model created for validation of the approaches that were used, which were established through experiments with the students. The results of subjective measures of students' emotional responses demonstrated that the ACPLOLS has reached the desired results in relation to the adequacy of the LOs interface, in accordance with the Felder-Silverman LSs Model.

Keywords: Adaptation, interface, learning styles, learning objects, students.

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Authors: S.R.Kamat, A.Yoxall, C.Craig , M.J.Carré, J.Rowson

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The hand is one of the essential parts of the body for carrying out Activities of Daily Living (ADLs). Individuals use their hands and fingers in everyday activities in the both the workplace and home. Hand-intensive tasks require diverse and sometimes extreme levels of exertion, depending on the action, movement or manipulation involved. The authors have undertaken several studies looking at grip choice and comfort. It is hoped that in providing improved understanding of discomfort during ADLs this will aid in the design of consumer products. Previous work by the authors outlined a methodology for calculating pain frequency and pain level for a range of tasks. From an online survey undertaken by the authors with regards manipulating objects during everyday tasks, tasks involving gripping were seen to produce the highest levels of pain and discomfort. Questioning of the participants showed that cleaning tasks were seen to be ADL's that produced the highest levels of discomfort, with women feeling higher levels of discomfort than men. This paper looks at the methodology for calculating pain frequency and pain level with particular regards to gripping activities. This methodology shows that activities such as mopping, sweeping and hoovering shows the highest numbers of pain frequency and pain level at 3112.5 frequency per month while the pain level per person doing this action was 0.78.The study then uses thin-film force sensors to analyze the force distribution in the hand whilst hoovering and compares this for differing grip styles and genders. Women were seen to have more of their hand under a higher pressure than men when undertaking hoovering. This suggests that women may feel greater discomfort than men since their hand is at a higher pressure more of the time.

Keywords: hovering, grip, pain

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Authors: S. Yordchim, G. Anugkakul, T. Gibbs

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The English competence of Thai people was examined in the context of knowledge of English in everyday life for Small and Medium Entrepreneurs (SMEs), and also integrated with Second language acquisition (SLA) students’ classroom. Second language acquisition was applied to the results of the questionnaires and interview forms. Levels of the need on English used for SME entrepreneurs in Thailand, satisfaction on joining the street classroom project were shown to be significantly high for some certain language functions and satisfaction. Finding suggests that the language functions on etiquette for professional use is essential and useful because lesson learned can be used in the real situation for their career. Implications for the climate of the street classroom are discussed.

Keywords: English classroom, second language acquisition, Small and Medium Entrepreneurs, Thai students.

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Authors: Zhibo Xiao, Tharini Nayanika de Silva, Kezhi Mao

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In this paper, we present an evolving knowledge extraction system named AKEOS (Automatic Knowledge Extraction from Online Sources). AKEOS consists of two modules, including a one-time learning module and an evolving learning module. The one-time learning module takes in user input query, and automatically harvests knowledge from online unstructured resources in an unsupervised way. The output of the one-time learning is a structured vector representing the harvested knowledge. The evolving learning module automatically schedules and performs repeated one-time learning to extract the newest information and track the development of an event. In addition, the evolving learning module summarizes the knowledge learned at different time points to produce a final knowledge vector about the event. With the evolving learning, we are able to visualize the key information of the event, discover the trends, and track the development of an event.

Keywords: Evolving learning, knowledge extraction, knowledge graph, text mining.

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Authors: Zhe JIN, Motomichi TOYAMA

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PPX(Pretty Printer for XML) is a query language that offers a concise description method of formatting the XML data into HTML. In this paper, we propose a simple specification of formatting method that is a combination description of automatic layout operators and variables in the layout expression of the GENERATE clause of PPX. This method can automatically format irregular XML data included in a part of XML with layout decision rule that is referred to DTD. In the experiment, a quick comparison shows that PPX requires far less description compared to XSLT or XQuery programs doing same tasks.

Keywords: PPX, Irregular XML data, Layout decision rule, HTML.

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Authors: Munazza A. Mirza, Khawar Khurshid

Abstract:

Individuals are generally associated with different learning styles, which have been explored extensively in recent past. The learning styles refer to the potential of an individual by which s/he can easily comprehend and retain information. Among various learning style models, VARK is the most accepted model which categorizes the learners with respect to their sensory characteristics. Based on the number of preferred learning modes, the learners can be categorized as uni-modal, bi-modal, tri-modal, or quad/multi-modal. Although there is a prevalent belief in the learning styles, however, the model is not being frequently and effectively utilized in the higher education. This research describes the identification model to validate teacher’s didactic practice and student’s performance linkage with the learning styles. The identification model is recommended to check the effective application and evaluation of the various learning styles. The proposed model is a guideline to effectively implement learning styles inventory in order to ensure that it will validate performance linkage with learning styles. If performance is linked with learning styles, this may help eradicate the distrust on learning style theory. For this purpose, a comprehensive study was conducted to compare and understand how VARK inventory model is being used to identify learning preferences and their correlation with learner’s performance. A comparative analysis of the findings of these studies is presented to understand the learning styles of tertiary students in various disciplines. It is concluded with confidence that the learning styles of students cannot be associated with any specific discipline. Furthermore, there is not enough empirical proof to link performance with learning styles.

Keywords: Learning style, VARK, sensory preferences, identification model, didactic practices.

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Authors: Siyao Zhu, Yifang Xu

Abstract:

After natural and man-made disasters, robots can bypass the danger, expedite the search, and acquire unprecedented situational awareness to design rescue plans. Brain-computer interface is a promising option to overcome the limitations of tedious manual control and operation of robots in the urgent search-and-rescue tasks. This study aims to test the feasibility of using electroencephalography (EEG) signals to decode human intentions and detect the level of consensus on robot-provided information. EEG signals were classified using machine-learning and deep-learning methods to discriminate search intentions and agreement perceptions. The results show that the average classification accuracy for intention recognition and consensus assessment is 67% and 72%, respectively, proving the potential of incorporating recognizable users’ bioelectrical responses into advanced robot-assisted systems for emergency response.

Keywords: Consensus assessment, electroencephalogram, EEG, emergency response, human-robot collaboration, intention recognition, search and rescue.

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Authors: G. Cossu, A. Esposito, G. Picco, C. Scrizzi, A. Tartaglia, E. Tresso

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A collaboration among the Hospital S. Giovanni Battista of Turin, the Politecnico of Turin, and the MUST company is described. The content of the collaboration has been and is the use of ICT-s, e-learning, and blended learning for the internal professional education, training, and keeping up to date of the personnel of the hospital. A platform for the delivery of the teaching materials has been built, including an evaluation and self-evaluation tool. The first on line courses have been developed and delivered and many more are in preparation. The first results of the monitoring of the efficacy of the online education have been positive.

Keywords: E-learning, blended learning, on line education, ICT.

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Authors: Rishi Ruttun

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One of the major features of hypermedia learning is its non-linear structure, allowing learners, the opportunity of flexible navigation to accommodate their own needs. Nevertheless, such flexibility can also cause problems such as insufficient navigation and disorientation for some learners, especially those with Field Dependent cognitive styles. As a result students learning performance can be deteriorated and in turn, they can have negative attitudes with hypermedia learning systems. It was suggested that visual elements can be used to compensate dilemmas. However, it is unclear whether these visual elements improve their learning or whether problems still exist. The aim of this study is to investigate the effect of students cognitive styles and visual elements on students learning performance and attitudes in hypermedia learning environment. Cognitive Style Analysis (CSA), Learning outcome in terms of pre and post-test, practical task, and Attitude Questionnaire (AQ) were administered to a sample of 60 university students. The findings revealed that FD students preformed equally to those of FI. Also, FD students experienced more disorientation in the hypermedia learning system where they depend a lot on the visual elements for navigation and orientation purposes. Furthermore, they had more positive attitudes towards the visual elements which escape them from experiencing navigation and disorientation dilemmas. In contrast, FI students were more comfortable, did not get disturbed or did not need some of the visual elements in the hypermedia learning system.

Keywords: Hypermedia learning, cognitive styles, visual elements, support, learning performance, attitudes and perceptions

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Authors: Katsumi Wasaki, Naoki Iwasaki

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Model-checking tools such as Symbolic Model Verifier (SMV) and NuSMV are available for checking hardware designs. These tools can automatically check the formal legitimacy of a design. However, NuSMV is too low level for describing a complete hardware design. It is therefore necessary to translate the system definition, as designed in a language such as Verilog or VHDL, into a language such as NuSMV for validation. In this paper, we present a meta hardware description language, Melasy, that contains a code generator for existing hardware description languages (HDLs) and languages for model checking that solve this problem.

Keywords: meta description language, software/hardware codesign, co-verification, formal verification, hardware compiler, modelchecking.

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Authors: Mahdi Hamzeh, Sied Mehdi Fakhraie, Caro Lucas

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All practical real-time scheduling algorithms in multiprocessor systems present a trade-off between their computational complexity and performance. In real-time systems, tasks have to be performed correctly and timely. Finding minimal schedule in multiprocessor systems with real-time constraints is shown to be NP-hard. Although some optimal algorithms have been employed in uni-processor systems, they fail when they are applied in multiprocessor systems. The practical scheduling algorithms in real-time systems have not deterministic response time. Deterministic timing behavior is an important parameter for system robustness analysis. The intrinsic uncertainty in dynamic real-time systems increases the difficulties of scheduling problem. To alleviate these difficulties, we have proposed a fuzzy scheduling approach to arrange real-time periodic and non-periodic tasks in multiprocessor systems. Static and dynamic optimal scheduling algorithms fail with non-critical overload. In contrast, our approach balances task loads of the processors successfully while consider starvation prevention and fairness which cause higher priority tasks have higher running probability. A simulation is conducted to evaluate the performance of the proposed approach. Experimental results have shown that the proposed fuzzy scheduler creates feasible schedules for homogeneous and heterogeneous tasks. It also and considers tasks priorities which cause higher system utilization and lowers deadline miss time. According to the results, it performs very close to optimal schedule of uni-processor systems.

Keywords: Computational complexity, Deadline, Feasible scheduling, Fuzzy scheduling, Priority, Real-time multiprocessor systems, Robustness, System utilization.

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Authors: Yi-Chun Chang, Jian-Wei Li

Abstract:

Web-based cooperative learning focuses on (1) the interaction and the collaboration of community members, and (2) the sharing and the distribution of knowledge and expertise by network technology to enhance learning performance. Numerous research literatures related to web-based cooperative learning have demonstrated that cooperative scripts have a positive impact to specify, sequence, and assign cooperative learning activities. Besides, literatures have indicated that role-play in web-based cooperative learning environments enhances two or more students to work together toward the completion of a common goal. Since students generally do not know each other and they lack the face-to-face contact that is necessary for the negotiation of assigning group roles in web-based cooperative learning environments, this paper intends to further extend the application of genetic algorithm (GA) and propose a GA-based algorithm to tackle the problem of role assignment in web-based cooperative learning environments, which not only saves communication costs but also reduces conflict between group members in negotiating role assignments.

Keywords: genetic algorithm (GA), role assignment, role-play; web-based cooperative learning.

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Authors: Ken Stevens

Abstract:

Networked schools have become a feature of education systems in countries that seek to provide learning opportunities in schools located beyond major centres of population. The internet and e-learning have facilitated the development of virtual educational structures that complement traditional schools, encouraging collaborative teaching and learning to proceed. In rural New Zealand and in the Atlantic Canadian province of Newfoundland and Labrador, e-learning is able to provide new ways of organizing teaching, learning and the management of educational opportunities. However, the future of e-teaching and e-learning in networked schools depends on the development of professional education programs that prepare teachers for collaborative teaching and learning environments in which both virtual and traditional face to face instruction co-exist.

Keywords: Advanced Placement, Cybercells, Extranet, Intranet.

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Authors: Mesfer Al Duhayyim, Paul Newbury

Abstract:

This paper proposes an effective adaptive e-learning system that uses a coloured concept map to show the learner's knowledge level for each concept in the chosen subject area. A Fuzzy logic system is used to evaluate the learner's knowledge level for each concept in the domain, and produce a ranked concept list of learning materials to address weaknesses in the learner’s understanding. This system obtains information on the learner's understanding of concepts by an initial pre-test before the system is used for learning and a post-test after using the learning system. A Fuzzy logic system is used to produce a weighted concept map during the learning process. The aim of this research is to prove that such a proposed novel adapted e-learning system will enhance learner's performance and understanding. In addition, this research aims to increase participants' overall understanding of their learning level by providing a coloured concept map of understanding followed by a ranked concepts list of learning materials.

Keywords: Adaptive e-learning system, coloured concept map, fuzzy logic, ranked concept list.

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Authors: Khaled M. Alhawiti

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This paper aims to analyze the role of natural language processing (NLP). The paper will discuss the role in the context of automated data retrieval, automated question answer, and text structuring. NLP techniques are gaining wider acceptance in real life applications and industrial concerns. There are various complexities involved in processing the text of natural language that could satisfy the need of decision makers. This paper begins with the description of the qualities of NLP practices. The paper then focuses on the challenges in natural language processing. The paper also discusses major techniques of NLP. The last section describes opportunities and challenges for future research.

Keywords: Data Retrieval, Information retrieval, Natural Language Processing, Text Structuring.

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Authors: Bekim Fetaji, Majlinda Fetaji

Abstract:

The development and use of mobile devices as well as its integration within education systems to deliver electronic contents and to support real-time communications was the focus of this research. In order to investigate the software engineering issues in using mobile devices a research on electronic content was initiated. The Developed MP3 mobile software solution was developed as a prototype for testing and developing a strategy for designing a usable m-learning environment. The mobile software solution was evaluated using mobile device using the link: http://projects.seeu.edu.mk/mlearn. The investigation also tested the correlation between the two mobile learning indicators: electronic content and attention, based on the Task Based learning instructional method. The mobile software solution ''M-Learn“ was developed as a prototype for testing the approach and developing a strategy for designing usable m-learning environment. The proposed methodology is about what learning modeling approach is more appropriate to use when developing mobile learning software.

Keywords: M-learning, mobile software development, mobiledevices, learning instructions, task based learning.

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Authors: David D. Hampton

Abstract:

Lesson study is used as an instructional technique to promote both student and faculty learning. However, little is known about the usefulness of learning communities in supporting results of lesson study on the self-efficacy and development for tenure-track faculty. This study investigated the impact of participation in a lesson study learning community on 34 new faculty members at a mid-size Midwestern University, specifically regarding implementing lesson study evaluations by new faculty on their reported self-efficacy. Results indicate that participation in a lesson study learning community significantly increased faculty members’ lesson study self-efficacy as well as grant and manuscript production over one academic year. Suggestions for future lesson study around faculty learning communities are discussed.

Keywords: Lesson study, learning community, lesson study self-efficacy, new faculty.

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Authors: Sumitra Nuanmeesri

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The research focus on study, analyze and design the model of the infrastructure and computer networks for distance education, online learning via new media, e-learning and blended learning. The collected information from study and analyze process that information was evaluated by the index of item objective congruence (IOC) by 9 specialists to design model. The results of evaluate the model with the mean and standard deviation by the sample of 9 specialists value is 3.85. The results showed that the infrastructure and computer networks are designed to be appropriate to a great extent appropriate to a great extent.

Keywords: Blended Learning, New Media, Infrastructure and Computer Network, Tele-Education, Online Learning.

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Authors: Yasser F. Hassan

Abstract:

Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.

Keywords: Cellular automata, neural cellular automata, deep learning, classification.

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Authors: Yasmeen Bassas, Sandra Kuebler, Allen Riddell

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Native Language Identification is one of the growing subfields in Natural Language Processing (NLP). The task of Native Language Identification (NLI) is mainly concerned with predicting the native language of an author’s writing in a second language. In this paper, we investigate the performance of two types of features; content-based features vs. content independent features when they are evaluated on a different corpus (using social media data “Reddit”). In this NLI task, the predefined models are trained on one corpus (TOEFL) and then the trained models are evaluated on a different data using an external corpus (Reddit). Three classifiers are used in this task; the baseline, linear SVM, and Logistic Regression. Results show that content-based features are more accurate and robust than content independent ones when tested within corpus and across corpus.

Keywords: NLI, NLP, content-based features, content independent features, social media corpus, ML.

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Authors: S. M. Biondi, V. Catania, R. Di Natale, A. R. Intilisano, D. Panno

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One of the major goals of Spoken Dialog Systems (SDS) is to understand what the user utters. In the SDS domain, the Spoken Language Understanding (SLU) Module classifies user utterances by means of a pre-definite conceptual knowledge. The SLU module is able to recognize only the meaning previously included in its knowledge base. Due the vastity of that knowledge, the information storing is a very expensive process. Updating and managing the knowledge base are time-consuming and error-prone processes because of the rapidly growing number of entities like proper nouns and domain-specific nouns. This paper proposes a solution to the problem of Name Entity Recognition (NER) applied to a SDS domain. The proposed solution attempts to automatically recognize the meaning associated with an utterance by using the PANKOW (Pattern based Annotation through Knowledge On the Web) method at runtime. The method being proposed extracts information from the Web to increase the SLU knowledge module and reduces the development effort. In particular, the Google Search Engine is used to extract information from the Facebook social network.

Keywords: Spoken Dialog System, Spoken Language Understanding, Web Semantic, Name Entity Recognition.

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Authors: Pooja Mishra, Chris Pollett

Abstract:

This paper explores efficient ways to implement various media-updating features like news aggregation, video conversion, and bulk email handling. All of these jobs share the property that they are periodic in nature, and they all benefit from being handled in a distributed fashion. The data for these jobs also often comes from a social or collaborative source. We isolate the class of periodic, one round map reduce jobs as a useful setting to describe and handle media updating tasks. As such tasks are simpler than general map reduce jobs, programming them in a general map reduce platform could easily become tedious. This paper presents a MediaUpdater module of the Yioop Open Source Search Engine Web Portal designed to handle such jobs via an extension of a PHP class. We describe how to implement various media-updating tasks in our system as well as experiments carried out using these implementations on an Amazon Web Services cluster.

Keywords: Distributed jobs framework, news aggregation, video conversion, email.

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Authors: [email protected]

Abstract:

Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data need a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM), ensemble learning with hyper parameters optimization, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: Machine learning, Deep learning, cancer prediction, breast cancer, LSTM, Score-Level Fusion.

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Authors: Richard Pircher, Lukas Zenk, Hanna Risku

Abstract:

Using activity theory, organisational theory and didactics as theoretical foundations, a comprehensive model of the organisational dimensions relevant for learning and knowledge transfer will be developed. In a second step, a Learning Assessment Guideline will be elaborated. This guideline will be designed to permit a targeted analysis of organisations to identify the status quo in those areas crucial to the implementation of learning and knowledge transfer. In addition, this self-analysis tool will enable learning managers to select adequate didactic models for e- and blended learning. As part of the European Integrated Project "Process-oriented Learning and Information Exchange" (PROLIX), this model of organisational prerequisites for learning and knowledge transfer will be empirically tested in four profit and non-profit organisations in Great Britain, Germany and France (to be finalized in autumn 2006). The findings concern not only the capability of the model of organisational dimensions, but also the predominant perceptions of and obstacles to learning in organisations.

Keywords: Activity theory, knowledge management organisational theory, "Process-oriented Learning and Information Exchange" (PROLIX).

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Authors: Mimi N. A. Mohamed

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The complexity of teaching English in higher institutions by non-native speakers within a second/foreign language setting has created continuous discussions and research about teaching approaches and teaching practises, professional identities and challenges. In addition, there is a growing awareness that teaching English within discipline-specific contexts adds up to the existing complexity. This awareness leads to reassessments, discussions and suggestions on course design and content and teaching approaches and techniques. In meeting expectations teaching at a university specified in a particular discipline such as engineering, English language educators are not only required to teach students to be able to communicate in English effectively but also to teach soft skills such as problem solving skills. This paper is part of a research conducted to investigate how English language educators negotiate with the complexities of teaching problem solving skills through English language teaching at a technical university. This paper reports the way an English language educator identified himself and the way he approached his teaching in this institutional context.

Keywords: English Language Teaching, Teacher Agency, Problem Solving Skills, Professional Identities.

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Authors: Saipunidzam Mahamad, Mohammad Noor Ibrahim, Mohamad Izzriq Ab Malek Foad, ShakirahMohd Taib

Abstract:

With the proliferation of the mobile device technologies, mobile learning can be used to complement and improve traditional learning problems. Both students and teachers need a proper and handy system to monitor and keep track the performance of the students. This paper presents an implementation of M-learning for primary school in Malaysia by using an open source technology. It focuses on learning mathematics using handheld devices for primary schools- students aged 11 and 12 years old. Main users for this system include students, teachers and the administrator. This application suggests a new mobile learning environment with mobile graph for tracking the students- progress and performance. The purpose of this system is not to replace traditional classroom but to complement the learning process. In a testing conducted, students who used this system performed better in their examination.

Keywords: M-Learning, Automated Mobile Graph.

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