Search results for: enhancing learning experience

Authors: Abdel Ilah Alshabtat, Liang Dong

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In this paper, we proposed a new routing protocol for Unmanned Aerial Vehicles (UAVs) that equipped with directional antenna. We named this protocol Directional Optimized Link State Routing Protocol (DOLSR). This protocol is based on the well known protocol that is called Optimized Link State Routing Protocol (OLSR). We focused in our protocol on the multipoint relay (MPR) concept which is the most important feature of this protocol. We developed a heuristic that allows DOLSR protocol to minimize the number of the multipoint relays. With this new protocol the number of overhead packets will be reduced and the End-to-End delay of the network will also be minimized. We showed through simulation that our protocol outperformed Optimized Link State Routing Protocol, Dynamic Source Routing (DSR) protocol and Ad- Hoc On demand Distance Vector (AODV) routing protocol in reducing the End-to-End delay and enhancing the overall throughput. Our evaluation of the previous protocols was based on the OPNET network simulation tool.

Keywords: Mobile Ad-Hoc Networks, Ad-Hoc RoutingProtocols, Optimized link State Routing Protocol, Unmanned AerialVehicles, Directional Antenna.

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Authors: Anupama Pande, Ashok Kumar Thakur, Swapnoneel Roy

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A complex valued neural network is a neural network which consists of complex valued input and/or weights and/or thresholds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in signal processing. In Neural networks, generalized mean neuron model (GMN) is often discussed and studied. The GMN includes a new aggregation function based on the concept of generalized mean of all the inputs to the neuron. This paper aims to present exhaustive results of using Generalized Mean Neuron model in a complex-valued neural network model that uses the back-propagation algorithm (called -Complex-BP-) for learning. Our experiments results demonstrate the effectiveness of a Generalized Mean Neuron Model in a complex plane for signal processing over a real valued neural network. We have studied and stated various observations like effect of learning rates, ranges of the initial weights randomly selected, error functions used and number of iterations for the convergence of error required on a Generalized Mean neural network model. Some inherent properties of this complex back propagation algorithm are also studied and discussed.

Keywords: Complex valued neural network, Generalized Meanneuron model, Signal processing.

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Authors: Khondokar Farid Ahmmed, Robin Bown

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It is widely recognized that the formal financing of Small and Medium Size Enterprises (SMEs) by Private Commercial Banks (PCBs) is restricted. Due to changing financial market competition, SMEs are now important customers to PCBs in the member countries of the Asian Development Bank (ADB). Various initiatives in enhancing the efficiency of risk assessment of PCBs have failed in increasing financing accessibility in the traditional financing system where information asymmetry is a key constraint. In this circumstance, PCBs need to undertake a holistic approach. Holistic approach refers to methods that attempt to fundamentally change established traditions. To undertake holistic approach, this study intends to find the entire established financing culture between PCBs and SMEs in a new lens beyond the tradition on the basis of two basic questions: “What is the traditional lending culture between PCBs and SMEs” and “What could be potential role of PCBs to develop that culture where focusing on SME financing to PCBs". This study considered formal SME financing in Bangladesh by focusing on SMEs applying for their first loan. Bangladesh is a member country of ADB. The data collection method is semi-structured and we utilized face-to-face interviews with in-depth branch managers, higher officials and owner-managers of SME customers of PCBs and higher officials of SME Foundation and the Bangladesh central bank. Discourse analysis method was used for data analysis on the frame of thematic discussion fully based on participants’ views. The research found that branch managers and loan officers have a high level of power in assessing and financing decision-making. There is a changing attitude in PCB sector in requiring flexible collateral assets. Branch managers (Loan Officers) consider value of business prospect of owner-mangers as complementary of collateral assets. However, the study found the assessment process of business prospect is entirely unstructured and linked with socio-cultural settings that does not support PCBs’ changing manner in terms of collateral requirement. The study redefined and classified collateral assets to include all financing constructs in a structure. The degree of value of the collateral assets determines the degree of business prospects. This study suggested applying an outside classroom-learning paradigm such as “knowledge tour” to enhance the value of the kinds of collateral assets. This is the scope of PCBs in increasing SMEs’ financing eligibility in win-win basis. The findings and proposition could be effective in other ADB member countries and audiences in the field.

Keywords: CCA, financing, information asymmetry, PCA, PCB, financing.

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Authors: Yanwei Pang, Lei Zhang, Zhengkai Liu

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Studies in neuroscience suggest that both global and local feature information are crucial for perception and recognition of faces. It is widely believed that local feature is less sensitive to variations caused by illumination, expression and illumination. In this paper, we target at designing and learning local features for face recognition. We designed three types of local features. They are semi-global feature, local patch feature and tangent shape feature. The designing of semi-global feature aims at taking advantage of global-like feature and meanwhile avoiding suppressing AdaBoost algorithm in boosting weak classifies established from small local patches. The designing of local patch feature targets at automatically selecting discriminative features, and is thus different with traditional ways, in which local patches are usually selected manually to cover the salient facial components. Also, shape feature is considered in this paper for frontal view face recognition. These features are selected and combined under the framework of boosting algorithm and cascade structure. The experimental results demonstrate that the proposed approach outperforms the standard eigenface method and Bayesian method. Moreover, the selected local features and observations in the experiments are enlightening to researches in local feature design in face recognition.

Keywords: Face recognition, local feature, AdaBoost, subspace analysis.

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Authors: G. Settanni, A. Panarese, R. Vaira, A. Galiano

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Nowadays, artificial intelligence is used successfully in the field of e-commerce for its ability to learn from a large amount of data. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them the most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Also, Long Short-Term Memory algorithms have been implemented and trained on historical data in order to predict customer scores of the different items. Items with the highest scores are recommended to customers.

Keywords: Deep Learning, Long Short-Term Memory, Machine Learning, Recommender Systems, Support Vector Machine.

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Authors: Samal Abzhanova, Saule Mussabekova

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Today, a new system of education is being created in Kazakhstan in order to develop the system of education and to satisfy the world class standards. For this purpose, there have been established new requirements and responsibilities to the instructors. Students should not be limited with providing only theoretical knowledge. Also, they should be encouraged to be competitive, to think creatively and critically. Moreover, students should be able to implement these skills into practice. These issues could be resolved through the permanent improvement of teaching methods. Therefore, a specialist who teaches the languages should use up-to-date methods and introduce new technologies. The result of the investigation suggests that an interactive teaching method is one of the new technologies in this field. This paper aims to provide information about implementing new technologies in the process of teaching language. The paper will discuss about necessity of introducing innovative technologies and the techniques of organizing interactive lessons. At the same time, the structure of the interactive lesson, conditions, principles, discussions, small group works and role-playing games will be considered. Interactive methods are carried out with the help of several types of activities, such as working in a team (with two or more group of people), playing situational or role-playing games, working with different sources of information, discussions, presentations, creative works and learning through solving situational tasks and etc.

Keywords: Games, interactive learning, Kazakh language, teaching methods.

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Authors: Marko Spasojevic, Ying Hei Chui, Yuxiang Chen

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Increasing the level of exterior insulation in residential buildings is a popular way for improving the thermal characteristic of building enclosure and reducing heat loss. However, the layout and properties of materials composing the wall have a great effect on moisture accumulation within the wall cavity, long-term durability of a wall as well as the structural performance. A one-dimensional hygrothermal modeling has been performed to investigate moisture condensation risks and the drying capacity of standard 2×4 and 2×6 light wood-frame wall assemblies including exterior low-permeance extruded polystyrene (XPS) insulation. The analysis considered two different wall configurations whereby the rigid insulation board was placed either between Oriented Strand Board (OSB) sheathing and the stud or outboard to the structural sheathing. The thickness of the insulation varied between 0 mm and 50 mm and the analysis has been conducted for eight different locations in Canada, covering climate zone 4 through zone 8. Results show that the wall configuration with low-permeance insulation inserted between the stud and OSB sheathing accumulates more moisture within the stud cavity, compared to the assembly with the same insulation placed exterior to the sheathing. On the other hand, OSB moisture contents of the latter configuration were markedly higher. Consequently, the analysis of hygrothermal performance investigated and compared moisture accumulation in both the OSB and stud cavity. To investigate the structural performance of the wall and the effect of soft insulation layer inserted between the sheathing and framing, forty nail connection specimens were tested. Results have shown that both the connection strength and stiffness experience a significant reduction as the insulation thickness increases. These results will be compared with results from a full-scale shear wall tests in order to investigate if the capacity of shear walls with insulated sheathing would experience a similar reduction in structural capacities.

Keywords: Hygrothermal analysis, insulated sheathing, moisture performance, nail joints, wood shear wall.

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Authors: Kai-Chieh Lin, Chih-Fu Wu, Hsiang Ling Hsu, Yung-Hsiang Tu, Chia-Chen Wu

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With the rapid development of computer technology, the design of computers and keyboards moves towards a trend of slimness. The change of mobile input devices directly influences users’ behavior. Although multi-touch applications allow entering texts through a virtual keyboard, the performance, feedback, and comfortableness of the technology is inferior to traditional keyboard, and while manufacturers launch mobile touch keyboards and projection keyboards, the performance has not been satisfying. Therefore, this study discussed the design factors of slim pressure-sensitive keyboards. The factors were evaluated with an objective (accuracy and speed) and a subjective evaluation (operability, recognition, feedback, and difficulty) depending on the shape (circle, rectangle, and L-shaped), thickness (flat, 3mm, and 6mm), and force (35±10g, 60±10g, and 85±10g) of the keyboard. Moreover, MANOVA and Taguchi methods (regarding signal-to-noise ratios) were conducted to find the optimal level of each design factor. The research participants, by their typing speed (30 words/ minute), were divided in two groups. Considering the multitude of variables and levels, the experiments were implemented using the fractional factorial design. A representative model of the research samples were established for input task testing. The findings of this study showed that participants with low typing speed primarily relied on vision to recognize the keys, and those with high typing speed relied on tactile feedback that was affected by the thickness and force of the keys. In the objective and subjective evaluation, a combination of keyboard design factors that might result in higher performance and satisfaction was identified (L-shaped, 3mm, and 60±10g) as the optimal combination. The learning curve was analyzed to make a comparison with a traditional standard keyboard to investigate the influence of user experience on keyboard operation. The research results indicated the optimal combination provided input performance to inferior to a standard keyboard. The results could serve as a reference for the development of related products in industry and for applying comprehensively to touch devices and input interfaces which are interacted with people.

Keywords: Input performance, mobile device, slim keyboard, tactile feedback.

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Authors: Munir Majdalawieh, Adam Marks

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In today’s very competitive higher education industry (HEI), HEIs are faced with the primary concern of developing, deploying, and sustaining high quality academic programs. Today, the HEI has well-established accreditation systems endorsed by a country’s legislation and institutions. The accreditation system is an educational pathway focused on the criteria and processes for evaluating educational programs. Although many aspects of the accreditation process highlight both the past and the present (prove), the “program review” assessment is "forward-looking assessment" (improve) and thus transforms the process into a continuing assessment activity rather than a periodic event. The purpose of this study is to propose a conceptual measurement framework for program review to be used by HEIs to undertake a robust and targeted approach to proactively and continuously review their academic programs to evaluate its practicality and effectiveness as well as to improve the education of the students. The proposed framework consists of two main components: program review principles and the program review measurement matrix.

Keywords: Academic program, program review principles, curriculum development, accreditation, evaluation, assessment, review measurement matrix, program review process, information technologies supporting learning, learning/teaching methodologies and assessment.

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Authors: Ahmet Denker

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The trend in the world of Information Technology (IT) is getting increasingly large and difficult projects rather than smaller and easier. However, the data on large-scale IT project success rates provide cause for concern. This paper seeks to answer why large-scale IT projects are different from and more difficult than other typical engineering projects. Drawing on the industrial experience, a compilation of the conditions that influence failure is presented. With a view to improve success rates solutions are suggested.

Keywords: Software engineering, software economics, project management, large-scale projects.

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Authors: Walaa Khalaf, Calogero Pace, Manlio Gaudioso

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We present an Electronic Nose (ENose), which is aimed at identifying the presence of one out of two gases, possibly detecting the presence of a mixture of the two. Estimation of the concentrations of the components is also performed for a volatile organic compound (VOC) constituted by methanol and acetone, for the ranges 40-400 and 22-220 ppm (parts-per-million), respectively. Our system contains 8 sensors, 5 of them being gas sensors (of the class TGS from FIGARO USA, INC., whose sensing element is a tin dioxide (SnO2) semiconductor), the remaining being a temperature sensor (LM35 from National Semiconductor Corporation), a humidity sensor (HIH–3610 from Honeywell), and a pressure sensor (XFAM from Fujikura Ltd.). Our integrated hardware–software system uses some machine learning principles and least square regression principle to identify at first a new gas sample, or a mixture, and then to estimate the concentrations. In particular we adopt a training model using the Support Vector Machine (SVM) approach with linear kernel to teach the system how discriminate among different gases. Then we apply another training model using the least square regression, to predict the concentrations. The experimental results demonstrate that the proposed multiclassification and regression scheme is effective in the identification of the tested VOCs of methanol and acetone with 96.61% correctness. The concentration prediction is obtained with 0.979 and 0.964 correlation coefficient for the predicted versus real concentrations of methanol and acetone, respectively.

Keywords: Electronic nose, Least square regression, Mixture ofgases, Support Vector Machine.

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Authors: Chang, Wei-Chen

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The purpose of this study is to research on thoughts transmitted from virtual fitting-room and to deduce discussion in an auxiliary narrative way. The research structure is based on 3D virtual fitting-room as the research subject. Initially, we will discuss the principles of narrate study, User Demand and so on by using a narrative design pattern to transmit their objective indications of “people-situation-reason-object", etc, and then to analyze the virtual fitting-room examples that are able to provide a new thinking for designers who engaged in clothing related industry – which comes in “story telling" and “user-centered design" forms. Clothing designs are not just to cover up the body to keep warm but to draw closer to people-s demand physiologically and psychologically through interactive designs so as to achieve cognition between people and environment. In the “outside" goal of clothing-s functional designs, we use tribal group-s behavior characteristics to “transform" the existing personal cultural stories, and “reform" them to design appropriate interactive products. Synthesizing the above matters, apart from being able to regard “narrate" as a kind of functional thinking process, we are also able to regard it as a kind of choice, arrangement and an activity of story expression, allowing interactive design-s spirit, product characteristics and experience ideas be transmitted to target tribal group in a visual image performance method. It is a far more confident and innovative attempt, and meanwhile, able to achieve entertainment, joyful and so forth fundamental interactive transmissions. Therefore, this study takes “user-centered design" thinking as a basis to establish a set of clothing designs with interactive experience patterns and to assist designers to examine the five sensual feeling of interactive demands in order to initiate a new value in textile industry.

Keywords: Virtual Fitting-room, Interactive Design, User Demand Evaluation, Intelligent Systems.

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Authors: Chiang-Ho Cheng, An-Shik Yang, Hong-Yih Cheng

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This paper applies the MEMS technology to design and fabricate a micro-bubble generator by a piezoelectric actuator. Coupled with a nickel nozzle plate, an annular piezoelectric ceramic was utilized as the primary structure of the generator. In operations, the piezoelectric element deforms transversely under an electric field applied across the thickness of the generator. The surface of the nozzle plate can expand or contract because of the induction of radial strain, resulting in the whole structure to bend, and successively transport oxygen micro-bubbles into the blood flow for enhancing the oxygen content in blood. In the tests, a high magnification microscope and a high speed CCD camera were employed to photograph the time evolution of meniscus shape of gaseous bubbles dispensed from the micro-bubble generator for flow visualization. This investigation thus explored the bubble formation process including the influences of inlet gas pressure along with driving voltage and resonance frequency on the formed bubble extent.

Keywords: Micro-bubble, nozzle, oxygenator, piezoelectric.

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Authors: I. Benning

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Using technology to expand the learning space is critical for a productive mathematical discourse. This is a case study of two teachers who developed and enacted GeoGebra-based mathematics lessons following their engagement in a two-year professional development. The didactical and semiotic affordance of GeoGebra in widening the learning space for a productive mathematical discourse was explored. The approach of thematic analysis was used for lesson artefact, lesson observation, and interview data. The results indicated that constructing tools in GeoGebra provided a didactical milieu where students used them to explore mathematical concepts with little or no support from their teacher. The prompt feedback from the GeoGebra motivated students to practice mathematical concepts repeatedly in which they privately rethink their solutions before comparing their answers with that of their colleagues. The constructing tools enhanced self-discovery, team spirit, and dialogue among students. With regards to the semiotic construct, the tools widened the physical and psychological atmosphere of the classroom by providing animations that served as virtual concrete to enhance the recording, manipulation, testing of a mathematical idea, construction, and interpretation of geometric objects. These findings advance the discussion of widening the classroom for a productive mathematical discourse within the context of the mathematics curriculum of Ghana and similar sub-Saharan African countries.

Keywords: GeoGebra, theory of didactical situation, semiotic mediation, mathematics laboratory, mathematical discussion.

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Authors: Niall J. English, Sylvia Leatham, Maria Isabel Meza Silva, Denis P. Dowling

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The training and education of under- and post-graduate students can be promoted by more active learning especially in engineering, overcoming more passive and vicarious experiences and approaches in their documented effectiveness. However, the possibility of outreach to young pupils and school-children in primary and secondary schools is a lesser explored area in terms of Education and Public Engagement (EPE) efforts – as relates to feedback and influence on shaping 3rd-level engineering training and education. Therefore, the outreach and school-visit agenda constitutes an interesting avenue to observe how active learning, careers stimulus and EPE efforts for young children and teenagers can teach the university sector, to improve future engineering-teaching standards and enhance both quality and capabilities of practice. This intervention involved careers-outreach efforts to lead to statistical determinations of motivations towards engineering, manufacturing and training. The aim was to gauge to what extent this intervention would lead to an increased careers awareness in engineering, using the method of the schools-visits programme as the means for so doing. It was found that this led to an increase in engagement by school pupils with engineering as a career option and a greater awareness of the importance of manufacturing. 

Keywords: outreach, education and public engagement, careers, peer interactions

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Authors: Terna T. Paul, Agbara D. Onwoke

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The antifungal potential of ethanolic leaf extracts of Vernonia amygdalina in the biological control of some common tomato wilt fungi was investigated. The experiment was set up in Completely Randomized Design (CRD) with eight treatments and three replicates. 5 mm diameter agar discs of 7 days old cultures of Fusarium oxysporum and Sclerotium rolfsii were obtained using a sterile 5 mm diameter cork borer and cultured on Potato Dextrose Agar (PDA) inoculated with 5 ml of various concentrations of V. amygdalina ethanolic leaf extracts in petri dishes, and incubated for 10 days at 28 ⁰C. The highest radial growth inhibitions of F. oxysporum (34.98%) and S. rolfsii (31.05%) were recorded 48 hours post-inoculation, both at 75% extract concentration. The leaf extracts of V. amygdalina used in the study exhibited significant inhibition of radial growth of the test organisms (P ≤ 0.05) and could be applied in the biological control of fungal wilt pathogens of tomato as a means of enhancing tomato yield and productivity.

Keywords: Biological control, fungi, leaf extracts, tomato wilt, V. amygdalina.

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Authors: Anahita Dalmia

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There has been a recent rise in ‘audience-centric’ and immersive storytelling. This indicates audiences are gaining interest in experiencing real adventure with everything that encompasses the struggle, the new friendships, skill development, and growth. This paper examines two themed alternate reality games created by a group of students at the University of Southern California as an experiment in how to design an adventure and to evaluate its impact on participants. The experiences combined immersive improvisational theatre and live-action roleplaying to create socially aware experiences within the timespan of four hours, using Harry Potter and mythology as themes. In each experiment, over 500 players simultaneously embarked on quests -a series of challenges including puzzle-solving, scavenger-hunting, and character interactions- to join a narrative faction. While playing, the participants were asked to choose faction alignments based on the characters they interacted with, as well as their own backgrounds and moral values. During the narrative finale, the impact of their individual choices on the larger story and game were revealed. After the conclusion of each experience, participants filled out questionnaires and were interviewed. Through this, it was discovered that participants developed transferable problem-solving, team-work, and persuasion skills. They also learned about the theme of the experience and reflected on their own moral values and judgment-making abilities after they realized the consequences of their actions in the game-world, inspiring some participants to make changes outside of it. This reveals that alternative reality games can lead to socialization, educational development, and real-world change in a variety of contexts when implemented correctly. This experiment has begun to discover the value of alternate reality games in a real-world context and to develop a reproducible format to continue to create such an impact.

Keywords: Adventure, alternate reality games, education, immersive entertainment, interactive entertainment.

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Authors: Anatoly Krivinsh

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The results of dissertation research "Preventing and  combating corruption in public procurement" are presented in this  publication. The study was conducted 2011 till 2013 in a Member  State of the European Union– in the Republic of Latvia.  Goal of the thesis is to explore corruption prevention and  combating issues in public procurement sphere, to identify the  prevalence rates, determinants and contributing factors and  prevention opportunities in Latvia.  In the first chapter the author analyzes theoretical aspects of  understanding corruption in public procurement, with particular  emphasis on corruption definition problem, its nature, causes and  consequences. A separate section is dedicated to the public  procurement concept, mechanism and legal framework. In the first  part of this work the author presents cognitive methodology of  corruption in public procurement field, based on which the author has  carried out an analysis of corruption situation in public procurement  in Republic of Latvia.  In the second chapter of the thesis, the author analyzes the  problem of corruption in public procurement, including its historical  aspects, typology and classification of corruption subjects involved,  corruption risk elements in public procurement and their  identification. During the development of the second chapter author's  practical experience in public procurements was widely used.  The third and fourth chapter deals with issues related to the  prevention and combating corruption in public procurement, namely  the operation of the concept, principles, methods and techniques,  subjects in Republic of Latvia, as well as an analysis of foreign  experience in preventing and combating corruption. The fifth chapter  is devoted to the corruption prevention and combating perspectives  and their assessment. In this chapter the author has made the  evaluation of corruption prevention and combating measures  efficiency in Republic of Latvia, assessment of anti-corruption  legislation development stage in public procurement field in Latvia. 

Keywords: Prevention of corruption, public purchases.

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Authors: Sanae Attioui, Said Najah

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The Earth's surface is constantly changing through forces of nature and human activities. Reliable, accurate, and timely change detection is critical to environmental monitoring, resource management, and planning activities. Recently, interest in deep learning algorithms, especially convolutional neural networks, has increased in the field of image change detection due to their powerful ability to extract multi-level image features automatically. However, these networks are prone to drawbacks that limit their applications, which reside in their inability to capture spatial relationships between image instances, as this necessitates a large amount of training data. As an alternative, Capsule Network has been proposed to overcome these shortcomings. Although its effectiveness in remote sensing image analysis has been experimentally verified, its application in change detection tasks remains very sparse. Motivated by its greater robustness towards improved hierarchical object representation, this study aims to apply a capsule network for PolSAR image Change Detection. The experimental results demonstrate that the proposed change detection method can yield a significantly higher detection rate compared to methods based on convolutional neural networks.

Keywords: Change detection, capsule network, deep network, Convolutional Neural Networks, polarimetric synthetic aperture radar images, PolSAR images.

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Authors: Lewis E. Hibell, Honghai Liu, David J. Brown

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This paper presents the use of a newly created network structure known as a Self-Delaying Dynamic Network (SDN) to create a high resolution image from a set of time stepped input frames. These SDNs are non-recurrent temporal neural networks which can process time sampled data. SDNs can store input data for a lifecycle and feature dynamic logic based connections between layers. Several low resolution images and one high resolution image of a scene were presented to the SDN during training by a Genetic Algorithm. The SDN was trained to process the input frames in order to recreate the high resolution image. The trained SDN was then used to enhance a number of unseen noisy image sets. The quality of high resolution images produced by the SDN is compared to that of high resolution images generated using Bi-Cubic interpolation. The SDN produced images are superior in several ways to the images produced using Bi-Cubic interpolation.

Keywords: Image Enhancement, Neural Networks, Multi-Frame.

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Authors: Mona Soliman Habib

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This paper explores the scalability issues associated with solving the Named Entity Recognition (NER) problem using Support Vector Machines (SVM) and high-dimensional features. The performance results of a set of experiments conducted using binary and multi-class SVM with increasing training data sizes are examined. The NER domain chosen for these experiments is the biomedical publications domain, especially selected due to its importance and inherent challenges. A simple machine learning approach is used that eliminates prior language knowledge such as part-of-speech or noun phrase tagging thereby allowing for its applicability across languages. No domain-specific knowledge is included. The accuracy measures achieved are comparable to those obtained using more complex approaches, which constitutes a motivation to investigate ways to improve the scalability of multiclass SVM in order to make the solution more practical and useable. Improving training time of multi-class SVM would make support vector machines a more viable and practical machine learning solution for real-world problems with large datasets. An initial prototype results in great improvement of the training time at the expense of memory requirements.

Keywords: Named entity recognition, support vector machines, language independence, bioinformatics.

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Authors: Ashanie Guanathillake, Kithsiri Samarasinghe

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Wireless Sensor Networks consist of inexpensive, low power sensor nodes deployed to monitor the environment and collect data. Gathering information in an energy efficient manner is a critical aspect to prolong the network lifetime. Clustering  algorithms have an advantage of enhancing the network lifetime. Current clustering algorithms usually focus on global re-clustering and local re-clustering separately. This paper, proposed a combination of those two reclustering methods to reduce the energy consumption of the network. Furthermore, the proposed algorithm can apply to homogeneous as well as heterogeneous wireless sensor networks. In addition, the cluster head rotation happens, only when its energy drops below a dynamic threshold value computed by the algorithm. The simulation result shows that the proposed algorithm prolong the network lifetime compared to existing algorithms.

Keywords: Energy efficient, Global re-clustering, Local re-clustering, Wireless sensor networks.

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Authors: Amirhossein Safi

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Internet of things (IOT) is a kind of advanced information technology which has drawn societies’ attention. Sensors and stimulators are usually recognized as smart devices of our environment. Simultaneously, IOT security brings up new issues. Internet connection and possibility of interaction with smart devices cause those devices to involve more in human life. Therefore, safety is a fundamental requirement in designing IOT. IOT has three remarkable features: overall perception, reliable transmission, and intelligent processing. Because of IOT span, security of conveying data is an essential factor for system security. Hybrid encryption technique is a new model that can be used in IOT. This type of encryption generates strong security and low computation. In this paper, we have proposed a hybrid encryption algorithm which has been conducted in order to reduce safety risks and enhancing encryption's speed and less computational complexity. The purpose of this hybrid algorithm is information integrity, confidentiality, non-repudiation in data exchange for IOT. Eventually, the suggested encryption algorithm has been simulated by MATLAB software, and its speed and safety efficiency were evaluated in comparison with conventional encryption algorithm.

Keywords: Internet of things, security, hybrid algorithm, privacy.

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Authors: N. N. Mosola, K. F. Moeketsi, R. Sehobai, N. Pule

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The Internet brings increasing use of Information and Communications Technology (ICT) services and facilities. Consequently, new computing paradigms emerge to provide services over the Internet. Although there are several benefits stemming from these services, they pose several risks inherited from the Internet. For example, cybercrime, identity theft, malware etc. To thwart these risks, this paper proposes a holistic approach. This approach involves multidisciplinary interactions. The paper proposes a top-down and bottom-up approach to deal with cyber security concerns in developing countries. These concerns range from regulatory and legislative areas, cyber awareness, research and development, technical dimensions etc. The main focus areas are highlighted and a cybersecurity model solution is proposed. The paper concludes by combining all relevant solutions into a proposed cybersecurity model to assist developing countries in enhancing a cyber-safe environment to instill and promote a culture of cybersecurity.

Keywords: Cybercrime, cybersecurity, computer emergency response team, computer security incident response team.

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Authors: A.A Issahanova, S. M. Jakupov, Z. B. Madalieva, A.A. Tolegenova

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In the article the experience of principle new technology development of ethnopsychological experiment on the basis of using other virtual independent experimental variables is presented. It is shown that ethnic prejudices are the result of forming and development of specific semantic barriers, arising up in the conditions of interethnic co-operation and people-s communication. Their overcoming is more successful in the conditions of the special organized process of teaching in a polyethnic environment, characteristic for the modern institute

Keywords: virtual monoethnic group, semantic barrier, general fund of semantic notions, polyethnic environment, ethnic prejudices

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Authors: Tahseen A. Jilani, Huda Yasin, Madiha Yasin, C. Ardil

Abstract:

In this paper we use data mining techniques to investigate factors that contribute significantly to enhancing the risk of acute coronary syndrome. We assume that the dependent variable is diagnosis – with dichotomous values showing presence or  absence of disease. We have applied binary regression to the factors affecting the dependent variable. The data set has been taken from two different cardiac hospitals of Karachi, Pakistan. We have total sixteen variables out of which one is assumed dependent and other 15 are independent variables. For better performance of the regression model in predicting acute coronary syndrome, data reduction techniques like principle component analysis is applied. Based on results of data reduction, we have considered only 14 out of sixteen factors.

Keywords: Acute coronary syndrome (ACS), binary logistic regression analyses, myocardial ischemia (MI), principle component analysis, unstable angina (U.A.).

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Authors: Suzanne Yaganeh, Janni Nielsen, Leif Bloch Rasmussen

Abstract:

This paper invites to dialogue and reflections on innovation and entrepreneurship by presenting concepts of innovation leading to the introduction of a complex theoretical framework; Cooperative Innovation (CO-IN). CO-IN is a didactic model enhancing and scaffolding processes of cooperation creating innovation drawing on a Scandinavian tradition. CO-IN is based on a cross-sectorial and multidisciplinary approach. We introduce the concept of complementarity to help capture the validity of diversity and we suggest the concept of “the space in between" to understand the creation of identity as a collective mind. We see dialogue and the use of multi modal techniques as essential tools for conceptualizations giving possibility for clarification of the complexity and diversity leading to decision making based on knowledge as commons. We introduce the didactic design and present our empirical findings from an innovation workshop in Argentina. In a final paragraph we reflect on the design as a support of the development of common ground, collective mind and collective action and the creation of knowledge as commons to facilitate innovation and entrepreneurship.

Keywords: CO-operative Innovation, didactic design, dialogue and ICT.

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Authors: Su-Hyun Jung, Chang-Bin Ha, Hyoung-Kyu Song

Abstract:

Recently, increasing the quality of experience (QoE) is an important issue. Since performance degradation at cell edge extremely reduces the QoE, several techniques are defined at LTE/LTE-A standard to remove inter-cell interference (ICI). However, the conventional techniques have disadvantage because there is a trade-off between resource allocation and reliable communication. The proposed scheme reduces the ICI more efficiently by using channel state information (CSI) smartly. It is shown that the proposed scheme can reduce the ICI with fewer resources.

Keywords: Adaptive beam forming, CoMP, LTE-A, ICI reduction.

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Authors: K. Durairaj, I. N. Umar

Abstract:

The asynchronous discussion forum is one of the most widely used activities in learning management system environment. Online forum allows participants to interact, construct knowledge, and can be used to complement face to face sessions in blended learning courses. However, to what extent do the students perceive the benefits or advantages of forum remain to be seen. Through content and social network analyses, instructors will be able to gauge the students’ engagement and knowledge construction level. Thus, this study aims to analyze the students’ level of knowledge construction and their participation level that occur through online discussion. It also attempts to investigate the relationship between the level of knowledge construction and their social interaction patterns. The sample involves 23 students undertaking a master course in one public university in Malaysia. The asynchronous discussion forum was conducted for three weeks as part of the course requirement. The finding indicates that the level of knowledge construction is quite low. Also, the density value of 0.11 indicating the overall communication among the participants in the forum is low. This study reveals that strong and significant correlations between SNA measures (in-degree centrality, out-degree centrality) and level of knowledge construction. Thus, allocating these active students in different group aids the interactive discussion takes place. Finally, based upon the findings, some recommendations to increase students’ level of knowledge construction and also for further research are proposed.

Keywords: Asynchronous Discussion Forums, Content Analysis, Knowledge Construction, Social Network Analysis.

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

Abstract:

Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: Anomaly detection, autoencoder, data centers, deep learning.

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