Search results for: Traditional learning

Authors: Al Mutawa A. Amir, Liaqat Ali, Faisal Ali

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After the World War II, the world economy was disrupted and changed due to oil and its prices. The research in this paper presents the basic statistical features and economic characteristics of the Gulf economy. The main features of the Gulf economies and its heavy dependence on oil exports, its dualism between modern and traditional sectors and its rapidly increasing affluences are particularly emphasized.  In this context, the research in this paper discussed the problems of growth versus development and has attempted to draw the implications for the future economic development of this area.

Keywords: Oil prices, Gulf Cooperation Council, economic growth, Gulf oil.

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Authors: Li Feng

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In IEEE 802.11 networks, it is well known that the traditional time-domain contention often leads to low channel utilization. The first frequency-domain contention scheme, the time to frequency (T2F), has recently been proposed to improve the channel utilization and has attracted a great deal of attention. In this paper, we present the latest research progress on the weighed frequency-domain contention. We compare the basic ideas, work principles of these related schemes and point out their differences. This paper is very useful for further study on frequency-domain contention.

Keywords: 802.11, wireless LANs, frequency-domain contention, T2F.

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Authors: Sharifah Mousli, Sona Taheri, Jiayuan He

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Autism spectrum disorder (ASD) is a complex developmental condition involving persistent difficulties with social communication, restricted interests, and repetitive behavior. The challenges associated with ASD can interfere with an affected individual’s ability to function in social, academic, and employment settings. Although there is no effective medication known to treat ASD, to our best knowledge, early intervention can significantly improve an affected individual’s overall development. Hence, an accurate diagnosis of ASD at an early phase is essential. The use of machine learning approaches improves and speeds up the diagnosis of ASD. In this paper, we focus on the application of unsupervised clustering methods in ASD, as a large volume of ASD data generated through hospitals, therapy centers, and mobile applications has no pre-existing labels. We conduct a comparative analysis using seven clustering approaches, such as K-means, agglomerative hierarchical, model-based, fuzzy-C-means, affinity propagation, self organizing maps, linear vector quantisation – as well as the recently developed optimization-based clustering (COMSEP-Clust) approach. We evaluate the performances of the clustering methods extensively on real-world ASD datasets encompassing different age groups: toddlers, children, adolescents, and adults. Our experimental results suggest that the COMSEP-Clust approach outperforms the other seven methods in recognizing ASD with well-separated clusters.

Keywords: Autism spectrum disorder, clustering, optimization, unsupervised machine learning.

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Authors: Sana Hamdi, Emna Bouazizi, Sami Faiz

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In recent years, real-time spatial applications, like location-aware services and traffic monitoring, have become more and more important. Such applications result dynamic environments where data as well as queries are continuously moving. As a result, there is a tremendous amount of real-time spatial data generated every day. The growth of the data volume seems to outspeed the advance of our computing infrastructure. For instance, in real-time spatial Big Data, users expect to receive the results of each query within a short time period without holding in account the load of the system. But with a huge amount of real-time spatial data generated, the system performance degrades rapidly especially in overload situations. To solve this problem, we propose the use of data partitioning as an optimization technique. Traditional horizontal and vertical partitioning can increase the performance of the system and simplify data management. But they remain insufficient for real-time spatial Big data; they can’t deal with real-time and stream queries efficiently. Thus, in this paper, we propose a novel data partitioning approach for real-time spatial Big data named VPA-RTSBD (Vertical Partitioning Approach for Real-Time Spatial Big data). This contribution is an implementation of the Matching algorithm for traditional vertical partitioning. We find, firstly, the optimal attribute sequence by the use of Matching algorithm. Then, we propose a new cost model used for database partitioning, for keeping the data amount of each partition more balanced limit and for providing a parallel execution guarantees for the most frequent queries. VPA-RTSBD aims to obtain a real-time partitioning scheme and deals with stream data. It improves the performance of query execution by maximizing the degree of parallel execution. This affects QoS (Quality Of Service) improvement in real-time spatial Big Data especially with a huge volume of stream data. The performance of our contribution is evaluated via simulation experiments. The results show that the proposed algorithm is both efficient and scalable, and that it outperforms comparable algorithms.

Keywords: Real-Time Spatial Big Data, Quality Of Service, Vertical partitioning, Horizontal partitioning, Matching algorithm, Hamming distance, Stream query.

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Authors: R. N. Begum, Ambalika Sharma, G. K. Singh

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Identity theft has serious ramifications beyond data and personal information loss. This necessitates the implementation of robust and efficient user identification systems. Therefore, automatic biometric recognition systems are the need of the hour, and electrocardiogram (ECG)-based systems are unquestionably the best choice due to their appealing inherent characteristics. The Convolutional Neural Networks (CNNs) are the recent state-of-the-art techniques for ECG-based user identification systems. However, the results obtained are significantly below standards, and the situation worsens as the number of users and types of heartbeats in the dataset grows. As a result, this study proposes a highly accurate and resilient ECG-based person identification system using CNN's dense learning framework. The proposed research explores explicitly the caliber of dense CNNs in the field of ECG-based human recognition. The study tests four different configurations of dense CNN which are trained on a dataset of recordings collected from eight popular ECG databases. With the highest False Acceptance Rate (FAR)  of 0.04% and the highest False Rejection Rate (FRR)  of 5%, the best performing network achieved an identification accuracy of 99.94%. The best network is also tested with various train/test split ratios. The findings show that DenseNets are not only extremely reliable, but also highly efficient. Thus, they might also be implemented in real-time ECG-based human recognition systems.

Keywords: Biometrics, dense networks, identification rate, train/test split ratio.

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Authors: Shaza Melies, Saadeya Salem, Seham Kareh

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Cartoon films are multisemiotic as various modes integrate in the production of meaning. This study aims to examine the cultural and linguistic specific references in the Egyptian dubbed cartoon film Mulan. The study examines the translation strategies implemented in the Egyptian dubbed version of Mulan to meet the cultural preferences of the audience. The study reached the following findings: Using the traditional translation strategies does not deliver the intended meaning of the source text and causes loss in the intended humor. As a result, the findings showed that in the dubbed version, translators tend to omit, change, or add information to the target text to be accepted by the audience. The contrastive analysis of the Mulan (English and dubbed versions) proves the connotations that the dubbing has taken to be accepted by the target audience. Cartoon films are multisemiotic as various modes integrate in the production of meaning. This study aims to examine the cultural and linguistic specific references in the Egyptian dubbed cartoon film Mulan. The study examines the translation strategies implemented in the Egyptian dubbed version of Mulan to meet the cultural preferences of the audience. The study reached the following findings: Using the traditional translation strategies does not deliver the intended meaning of the source text and causes loss in the intended humor. As a result, the findings showed that in the dubbed version, translators tend to omit, change, or add information to the target text to be accepted by the audience. The contrastive analysis of the Mulan (English and dubbed versions) proves the connotations that the dubbing has taken to be accepted by the target audience.

Keywords: Domestication, dubbing, Mulan, translation theories.

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Authors: Aaron A. R. Nwabude

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The research investigates the “impact of VLE on mathematical concepts acquisition of the special education needs (SENs) students at KS4 secondary education sector" in England. The overall aim of the study is to establish possible areas of difficulties to approach for above or below knowledge standard requirements for KS4 students in the acquisition and validation of basic mathematical concepts. A teaching period, in which virtual learning environment (Fronter) was used to emphasise different mathematical perception and symbolic representation was carried out and task based survey conducted to 20 special education needs students [14 actually took part]. The result shows that students were able to process information and consider images, objects and numbers within the VLE at early stages of acquisition process. They were also able to carry out perceptual tasks but with limiting process of different quotient, thus they need teacher-s guidance to connect them to symbolic representations and sometimes coach them through. The pilot study further indicates that VLE curriculum approaches for students were minutely aligned with mathematics teaching which does not emphasise the integration of VLE into the existing curriculum and current teaching practice. There was also poor alignment of vision regarding the use of VLE in realisation of the objectives of teaching mathematics by the management. On the part of teacher training, not much was done to develop teacher-s skills in the technical and pedagogical aspects of VLE that is in-use at the school. The classroom observation confirmed teaching practice will find a reliance on VLE as an enhancer of mathematical skills, providing interaction and personalisation of learning to SEN students.

Keywords: VLE, Mathematical Concepts Acquisition, PilotStudy, SENs, KS4, Education, Teacher

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Authors: Anupama Pande, Vishik Goel

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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 image and vision processing. In Neural networks, radial basis functions are often used for interpolation in multidimensional space. A Radial Basis function is a function, which has built into it a distance criterion with respect to a centre. Radial basis functions have often been applied in the area of neural networks where they may be used as a replacement for the sigmoid hidden layer transfer characteristic in multi-layer perceptron. This paper aims to present exhaustive results of using RBF units 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 Radial basis function in a complex valued neural network in image recognition 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 on a neural network model with RBF units. Some inherent properties of this complex back propagation algorithm are also studied and discussed.

Keywords: Complex valued neural network, Radial BasisFunction, Image recognition.

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Authors: Aimen Mohamed Almaloul

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The main focus of this study is investigating the interference of Arabic in the use of English prepositions by Libyan university students. Prepositions in the tests used in the study were categorized, according to their relation to Arabic, into similar Arabic and English prepositions (SAEP), dissimilar Arabic and English prepositions (DAEP), Arabic prepositions with no English counterparts (APEC), and English prepositions with no Arabic counterparts (EPAC).

The subjects of the study were the first year university students of the English department, Sabrata Faculty of Arts, Azzawia University; both males and females, and they were 100 students. The basic tool for data collection was a test of English prepositions; students are instructed to fill in the blanks with the correct prepositions and to put a zero (0) if no preposition was needed. The test was then handed to the subjects of the study.

The test was then scored and quantitative as well as qualitative results were obtained. Quantitative results indicated the number, percentages and rank order of errors in each of the categories and qualitative results indicated the nature and significance of those errors and their possible sources. Based on the obtained results the researcher could detect that students made more errors in the EPAC category than the other three categories and these errors could be attributed to the lack of knowledge of the different meanings of English prepositions. This lack of knowledge forced the students to adopt what is called the strategy of transfer.

Keywords: Foreign language acquisition, foreign language learning, interference system, interlanguage system, mother tongue interference.

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Authors: Justinian Anatory, Ekael Stephen Manase

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In recent years, the adoption of mobile phones has been exceptionally rapid in many parts of the world, and Tanzania is not exceptional. We are witnessing a number of new mobile network operators being licensed from time to time by Tanzania Communications Regulatory Authority (TCRA). This makes competition in the telecommunications market very stiff. All mobile phone companies are struggling to earn more new customers into their networks. This trend courses a stiff competition. The various measures are being taken by different companies including, lowering tariff, and introducing free short messages within and out of their networks, and free calls during off-peak periods. This paper is aimed at investigating the influence of tariffs on students’ mobile customers in selecting their mobile network operators. About seventy seven students from high learning institutions in Dodoma Municipality, Tanzania, participated in responding to the prepared questionnaires. The sought information was aimed at determining if tariffs influenced students into selection of their current mobile operators. The results indicate that tariffs were the major driving factor in selection of mobile operators. However, female mobile customers were found to be more easily attracted into subscribing to a mobile operator due to low tariffs, a bigger number of free short messages or discounted call charges than their fellow male customers.

Keywords: Consumer Buying, mobile operators, tariff.

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Authors: Mohammad Ehson Rangiha, Bill Karakostas

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Social Business Process Management (SBPM) promises to overcome limitations of traditional BPM by allowing flexible process design and enactment through the involvement of users from a social community. This paper proposes a meta-model and architecture for socially driven business process management systems. It discusses the main facets of the architecture such as goalbased role assignment that combines social recommendations with user profile, and process recommendation, through a real example of a charity organization.

Keywords: Business Process Management, Goal-Based Modelling, Process Recommendation Social Collaboration, Social BPM.

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Authors: Rajinder Singh, Ram Valluru

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Chain Ladder (CL) method, Expected Loss Ratio (ELR) method and Bornhuetter-Ferguson (BF) method, in addition to more complex transition-rate modeling, are commonly used actuarial reserving methods in general insurance. There is limited published research about their relative performance in the context of Mortgage Insurance (MI). In our experience, these traditional techniques pose unique challenges and do not provide stable claim estimates for medium to longer term liabilities. The relative strengths and weaknesses among various alternative approaches revolve around: stability in the recent loss development pattern, sufficiency and reliability of loss development data, and agreement/disagreement between reported losses to date and ultimate loss estimate. CL method results in volatile reserve estimates, especially for accident periods with little development experience. The ELR method breaks down especially when ultimate loss ratios are not stable and predictable. While the BF method provides a good tradeoff between the loss development approach (CL) and ELR, the approach generates claim development and ultimate reserves that are disconnected from the ever-to-date (ETD) development experience for some accident years that have more development experience. Further, BF is based on subjective a priori assumption. The fundamental shortcoming of these methods is their inability to model exogenous factors, like the economy, which impact various cohorts at the same chronological time but at staggered points along their life-time development. This paper proposes an alternative approach of parametrizing the loss development curve and using logistic regression to generate the ultimate loss estimate for each homogeneous group (accident year or delinquency period). The methodology was tested on an actual MI claim development dataset where various cohorts followed a sigmoidal trend, but levels varied substantially depending upon the economic and operational conditions during the development period spanning over many years. The proposed approach provides the ability to indirectly incorporate such exogenous factors and produce more stable loss forecasts for reserving purposes as compared to the traditional CL and BF methods.

Keywords: Actuarial loss reserving techniques, logistic regression, parametric function, volatility.

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Authors: Willem Waegeman, Luc Boullart

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Instead of traditional (nominal) classification we investigate the subject of ordinal classification or ranking. An enhanced method based on an ensemble of Support Vector Machines (SVM-s) is proposed. Each binary classifier is trained with specific weights for each object in the training data set. Experiments on benchmark datasets and synthetic data indicate that the performance of our approach is comparable to state of the art kernel methods for ordinal regression. The ensemble method, which is straightforward to implement, provides a very good sensitivity-specificity trade-off for the highest and lowest rank.

Keywords: Ordinal regression, support vector machines, ensemblelearning.

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Authors: Han-yuh Liu, Chia-yen Wu

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Truly successful bloggers, navigating the public to know them, often use their blogs as a way to better communicate with customers. Integrating with marketing tools, storytelling can be regarded as one of the most effective ways that businesses can follow to gain competitive edge. Even though the literature on marketing contains much discussion of traditional vehicles, the issue of business blogs applying storytelling has, as yet, received little attention. In the exploration stage, this paper identifies four storytelling disciplines and then presents a road map to business blogging. This paper also provides a two-path framework for blog storytelling and initiates an issue for further study.

Keywords: Storytelling, business blog, blog content, blog position, blog navigation.

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Authors: Mr. Khalid I. Saad, Ms. Nusrat Afrin, Mr. Rajib Mikail

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The stop watch is used to measure the time required for a certain event. This is different from normal clocks in many ways, one of which is the accuracy of time. The stop watch requires much more accuracy than the normal clocks. In this paper, an ATmega8535 microcontroller was used to control the stop watch, by which perfect accuracy can be ensured. For compiling the C code and for loading the compiled .hex file into the microcontroller, AVR studio and PonyProg were used respectively. The stop watch is also different from traditional stop watches, as it contains two different timing modes namely 'Split timing' and 'Lap timing'.

Keywords: Stop Watch, Microcontroller, Split timing, Laptiming, LCD.

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Authors: Souâad Boudebza, Omar Nouali, Faiçal Azouaou

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Social networks have recently gained a growing interest on the web. Traditional formalisms for representing social networks are static and suffer from the lack of semantics. In this paper, we will show how semantic web technologies can be used to model social data. The SemTemp ontology aligns and extends existing ontologies such as FOAF, SIOC, SKOS and OWL-Time to provide a temporal and semantically rich description of social data. We also present a modeling scenario to illustrate how our ontology can be used to model social networks.

Keywords: Ontology, semantic web, social network, temporal modeling.

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Authors: Kemal Çomakli, Uğur Çakir

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This research evaluated and compared the thermodynamic performance of heat pump systems which can be run under two different modes as air to air and air to water by using only one compressor. To achieve this comparison an experimental performance study was made on a traditional vapor compressed heat pump system that can be run air to air mode and air to water mode by help of a valve. The experiments made under different thermal conditions. Thermodynamic performance of the systems are presented and compared with each other for different working conditions.

Keywords: Air source heat pump, Energy Analysis, Heat Pump

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Authors: Mahdi Yaghobi, Mohammad Haeri

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In this paper, we show that the stability can not be achieved with current stabilizing MPC methods for some unstable processes. Hence we present a new method for stabilizing these processes. The main idea is to use a new time varying weighted cost function for traditional GPC. This stabilizes the closed loop system without adding soft or hard constraint in optimization problem. By studying different examples it is shown that using the proposed method, the closed-loop stability of unstable nonminimum phase process is achieved.

Keywords: GPC, Stability, Varying Weighting Coefficients.

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Authors: Abolfazl Jalilvand, Mohammad Reza Feyzi, Sohrab Khanmohammad, Mohammad Bagher Bana Sharifian, Ali Sajjadi

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Nowadays the control of stator voltage at a constant frequency is one of the traditional and low expense methods in order to control the speed of induction motors near its nominal speed. The torque of induction motor is a nonlinear function of the firing angle, phase angle and speed. In this paper the speed control of induction motor regarding various load torque and under different conditions will be investigated based on a fuzzy controller with inverse training.

Keywords: Three phase induction motor, AC converter, speedcontrol, fuzzy control.

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Authors: Seema Chopra, R. Mitra, Vijay Kumar

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A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.

Keywords: Fuzzy control, neuro-fuzzy techniques, fuzzy subtractive clustering, extraction of rules, and optimization of membership functions.

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Authors: Kai Häussermann, Christoph Hubig, Paul Levi, Frank Leymann, Oliver Siemoneit, Matthias Wieland, Oliver Zweigle

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Using spatial models as a shared common basis of information about the environment for different kinds of contextaware systems has been a heavily researched topic in the last years. Thereby the research focused on how to create, to update, and to merge spatial models so as to enable highly dynamic, consistent and coherent spatial models at large scale. In this paper however, we want to concentrate on how context-aware applications could use this information so as to adapt their behavior according to the situation they are in. The main idea is to provide the spatial model infrastructure with a situation recognition component based on generic situation templates. A situation template is – as part of a much larger situation template library – an abstract, machinereadable description of a certain basic situation type, which could be used by different applications to evaluate their situation. In this paper, different theoretical and practical issues – technical, ethical and philosophical ones – are discussed important for understanding and developing situation dependent systems based on situation templates. A basic system design is presented which allows for the reasoning with uncertain data using an improved version of a learning algorithm for the automatic adaption of situation templates. Finally, for supporting the development of adaptive applications, we present a new situation-aware adaptation concept based on workflows.

Keywords: context-awareness, ethics, facilitation of system use through workflows, situation recognition and learning based on situation templates and situation ontology's, theory of situationaware systems

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Authors: Viacheslav Shkuratskyy, Aminu Bello Usman, Michael O’Dea, Mujeeb Ur Rehman, Saifur Rahman Sabuj

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This paper examines relationships between solar activity and earthquakes, it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity, and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to effect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth

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Keywords: K-Nearest Neighbour, Support Vector Regression, Random Forest Regression, Long Short-Term Memory Network, earthquakes, solar activity, sunspot number, solar wind, solar flares.

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Authors: Karen Armstrong

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This paper offers suggestions for educators at all levels about how to better prepare our students for the future, by building on the past. The discussion begins with a summary of changes in the World Wide Web, especially as the term Web 3.0 is being heard. The bulk of the discussion is retrospective and concerned with an overview of traditional teaching and research approaches as they evolved during the 20th century beginning with those grounded in the Cartesian reality of IA Richards- (1929) Practical Criticism. The paper concludes with a proposal of five strategies which incorporate timeless elements from the past as well as cutting-edge elements from today, in order to better prepare our students for the future.

Keywords: Web 3.0, Web 2.0 IA Richards, literacy education, new literacies, technology, paradigm shifts.

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Authors: Tatiani D. Mousoura

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Specific learning disability (SLD) in higher education has been partially explored in Greece so far. Moreover, opinions on professional perspectives for university students with SLD, is scarcely encountered in Greek research. The perceptions of the hidden character of SLD along with the university policy towards it and professional perspectives that result from this policy have been examined in the present research. This study has applied the paradigm of a Greek Tertiary Pedagogical Education Department (Early Childhood Education). Via mixed methods, data have been collected from different groups of people in the Pedagogical Department: students with SLD and without SLD, academic staff and administration staff, all of which offer the opportunity for triangulation of the findings. Qualitative methods include ten interviews with students with SLD and 15 interviews with academic staff and 60 hours of observation of the students with SLD. Quantitative methods include 165 questionnaires completed by third and fourth-year students and five questionnaires completed by the administration staff. Thematic analyses of the interviews’ data and descriptive statistics on the questionnaires’ data have been applied for the processing of the results. The use of medical terms to define and understand SLD was common in the student cohort, regardless of them having an SLD diagnosis. However, this medical model approach is far more dominant in the group of students without SLD who, by majority, hold misconceptions on a definitional level. The academic staff group seems to be leaning towards a social approach concerning SLD. According to them, diagnoses may lead to social exclusion. The Pedagogical Department generally endorses the principles of inclusion and complies with the provision of oral exams for students with SLD. Nevertheless, in practice, there seems to be a lack of regular academic support for these students. When such support does exist, it is only through individual initiatives. With regards to their prospective profession, students with SLD can utilize their personal experience, as well as their empathy; these appear to be unique weapons in their hands –in comparison with other educators− when it comes to teaching students in the future. In the Department of Pedagogy, provision towards SLD results sporadic, however the vision of an inclusive department does exist. Based on their studies and their experience, pedagogy students with SLD claim that they have an experiential internalized advantage for their future career as educators.

Keywords: Specific learning disability, dyslexia, pedagogy department, inclusion, professional role of SLDed educators, higher education, university policy.

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Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

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Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: Time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition.

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Authors: A. Hadi-Vencheh, N. Shayesteh Moghadam

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Data envelopment analysis (DEA) has gained great popularity in environmental performance measurement because it can provide a synthetic standardized environmental performance index when pollutants are suitably incorporated into the traditional DEA framework. Since some of the environmental performance indicators cannot be controlled by companies managers, it is necessary to develop the model in a way that it could be applied when discretionary and/or non-discretionary factors were involved. In this paper, we present a semi-radial DEA approach to measuring environmental performance, which consists of non-discretionary factors. The model, then, has been applied on a real case.

Keywords: Environmental performance, efficiency, non-discretionary variables, data envelopment analysis.

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Authors: Muhammad Raza Ali, Tim Morris

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Human computer interaction has progressed considerably from the traditional modes of interaction. Vision based interfaces are a revolutionary technology, allowing interaction through human actions, gestures. Researchers have developed numerous accurate techniques, however, with an exception to few these techniques are not evaluated using standard HCI techniques. In this paper we present a comprehensive framework to address this issue. Our evaluation of a computer vision application shows that in addition to the accuracy, it is vital to address human factors

Keywords: Usability evaluation, cognitive walkthrough, think aloud, gesture recognition.

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Authors: Jack R. McKenzie, Peter A. Appleby, Thomas House, Neil Walton

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Cold-start is a notoriously difficult problem which can occur in recommendation systems, and arises when there is insufficient information to draw inferences for users or items. To address this challenge, a contextual bandit algorithm – the Fast Approximate Bayesian Contextual Cold Start Learning algorithm (FAB-COST) – is proposed, which is designed to provide improved accuracy compared to the traditionally used Laplace approximation in the logistic contextual bandit, while controlling both algorithmic complexity and computational cost. To this end, FAB-COST uses a combination of two moment projection variational methods: Expectation Propagation (EP), which performs well at the cold start, but becomes slow as the amount of data increases; and Assumed Density Filtering (ADF), which has slower growth of computational cost with data size but requires more data to obtain an acceptable level of accuracy. By switching from EP to ADF when the dataset becomes large, it is able to exploit their complementary strengths. The empirical justification for FAB-COST is presented, and systematically compared to other approaches on simulated data. In a benchmark against the Laplace approximation on real data consisting of over 670, 000 impressions from autotrader.co.uk, FAB-COST demonstrates at one point increase of over 16% in user clicks. On the basis of these results, it is argued that FAB-COST is likely to be an attractive approach to cold-start recommendation systems in a variety of contexts.

Keywords: Cold-start, expectation propagation, multi-armed bandits, Thompson sampling, variational inference.

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Authors: Triyanto, Rima Vien Permata Hartanto

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Indonesia is a legal state. The consequence of this status is the recognition and protection of the existence of indigenous peoples. This paper aims to describe the dynamics of legal recognition and protection for indigenous peoples within the framework of Indonesian law. This paper is library research based on literature. The result states that although the constitution has normatively recognized the existence of indigenous peoples and their traditional rights, in reality, not all rights were recognized and protected. The protection and recognition for indigenous people need to be strengthened.

Keywords: Indigenous peoples, customary law, state law, state of law.

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Authors: Kyrre M Begnum, Matthew Disney

Abstract:

Virtualization and high performance computing have been discussed from a performance perspective in recent publications. We present and discuss a flexible and efficient approach to the management of virtual clusters. A virtual machine management tool is extended to function as a fabric for cluster deployment and management. We show how features such as saving the state of a running cluster can be used to avoid disruption. We also compare our approach to the traditional methods of cluster deployment and present benchmarks which illustrate the efficiency of our approach.

Keywords: Cluster management, clusters, high-performance, virtual machines, Xen

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