Search results for: Data integration

Authors: Mohd Hasmadi Ismail, Kamaruzaman Jusoff

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In order to develop forest management strategies in tropical forest in Malaysia, surveying the forest resources and monitoring the forest area affected by logging activities is essential. There are tremendous effort has been done in classification of land cover related to forest resource management in this country as it is a priority in all aspects of forest mapping using remote sensing and related technology such as GIS. In fact classification process is a compulsory step in any remote sensing research. Therefore, the main objective of this paper is to assess classification accuracy of classified forest map on Landsat TM data from difference number of reference data (200 and 388 reference data). This comparison was made through observation (200 reference data), and interpretation and observation approaches (388 reference data). Five land cover classes namely primary forest, logged over forest, water bodies, bare land and agricultural crop/mixed horticultural can be identified by the differences in spectral wavelength. Result showed that an overall accuracy from 200 reference data was 83.5 % (kappa value 0.7502459; kappa variance 0.002871), which was considered acceptable or good for optical data. However, when 200 reference data was increased to 388 in the confusion matrix, the accuracy slightly improved from 83.5% to 89.17%, with Kappa statistic increased from 0.7502459 to 0.8026135, respectively. The accuracy in this classification suggested that this strategy for the selection of training area, interpretation approaches and number of reference data used were importance to perform better classification result.

Keywords: Image Classification, Reference Data, Accuracy Assessment, Kappa Statistic, Forest Land Cover

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Authors: Harriet Koshie Lamptey, Richard Boateng

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Mobile learning (m-learning) is a novel approach to knowledge acquisition and dissemination and is gaining global attention. Steady progress in wireless technologies and the portability of communication devices continue to broaden the scope and use of mobiles. With the convergence of Web functionality onto mobile platforms and the affordability and availability of mobile technology, m-learning has the potential of being the next prevalent channel of education in both formal and informal settings. There is substantive literature on developed countries but the state in developing countries (DCs) however appears vague. This paper is a synthesis of extant literature on mobile learning in DCs. The research interest is based on the fact that in DCs, mobile communication and internet connectivity are popular. However, its use in education is under explored. There are some reviews on the state, conceptualizations, trends and teacher education, but to the authors’ knowledge, no study has focused on mobile learning adoption and integration issues. This study examines issues and gaps associated with its adoption and integration in DCs higher education institutions. A qualitative build-up of literature was conducted using articles pooled from electronic databases (Google Scholar and ERIC). To enable criteria for inclusion and incorporate diverse study perspectives, search terms used were m-learning, DCs, higher education institutions, challenges, benefits, impact, gaps and issues. The synthesis revealed that though mobile technology has diffused globally, its pedagogical pursuit in DCs remains quite low. The absence of a mobile Web and the difficulty of resource conversion into mobile format due to lack of funding and technical competence is a stumbling block. Again, the lack of established design and implementation rules to guide the development of m-learning platforms in DCs is a hindrance. The absence of access restrictions on devices poses security threats to institutional systems. Negative perceptions that devices are taking over faculty roles lead to resistance in some situations. Resistance to change can be a hindrance to the acceptance and success of new systems. Lack of interest for m-learning is also attributed to lower technological literacy levels of the underprivileged masses. Scholarly works on m-learning in DCs is yet to mature. Most technological innovations are handed down from developed countries, and this constantly creates a lag for DCs. Lack of theoretical grounding was also identified which reduces the objectivity of study reports. The socio-cultural terrain of DCs results in societies with different views and needs that have been identified as a hindrance to research. Institutional commitment decisions, adequate funding for the necessary infrastructural development as well as multiple stakeholder participation is important for project success. Evidence suggests that while adoption decisions are readily made, successful integration of the concept for its full benefits to be realized is often neglected. Recommendations to findings were made to provide possible remedies to identified issues.

Keywords: Developing countries, higher education institutions, mobile learning, literature review.

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Authors: Sara Jelodarian

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Organic agriculture is a kind of living and dynamic agriculture that was introduced in the early 20^th century. The fundamental basis for organic agriculture is in harmony with nature. This version of farming emphasizes removing growth hormones, chemical fertilizers, toxins, radiation, genetic manipulation and instead, integration of modern scientific techniques (such as biologic and microbial control) that leads to the production of healthy food and the preservation of the environment and use of agricultural products such as forage and manure. Supports from governments for the markets producing organic products and taking advantage of the experiences from other successful societies in this field can help progress the positive and effective aspects of this technology, especially in developing countries. This research proves that till 2030, 25% of the global agricultural lands would be covered by organic farming. Consequently Iran, due to its rich genetic resources and various climates, can be a pioneer in promoting organic products. In addition, for sustainable farming, blend of organic and other innovative systems is needed. Important limitations exist to accept these systems, also a diversity of policy instruments will be required to comfort their development and implementation. The paper was conducted to results of compilation of reports, issues, books, articles related to the subject with library studies and research. Likewise we combined experimental and survey to get data.

Keywords: Development, production markets, progress, strategic role, technology.

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Authors: Victor Maldonado

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Recent concerns of the growing impact of aviation on climate change has prompted the emergence of a field referred to as Sustainable or “Green” Aviation dedicated to mitigating the harmful impact of aviation related CO2 emissions and noise pollution on the environment. In the current paper, a unique “green” business jet aircraft called the TransAtlantic was designed (using analytical formulation common in conceptual design) in order to show the feasibility for transatlantic passenger air travel with an aircraft weighing less than 10,000 pounds takeoff weight. Such an advance in fuel efficiency will require development and integration of advanced and emerging aerospace technologies. The TransAtlantic design is intended to serve as a research platform for the development of technologies such as active flow control. Recent advances in the field of active flow control and how this technology can be integrated on a sub-scale flight demonstrator are discussed in this paper. Flow control is a technique to modify the behavior of coherent structures in wall-bounded flows (over aerodynamic surfaces such as wings and turbine nozzles) resulting in improved aerodynamic cruise and flight control efficiency. One of the key challenges to application in manned aircraft is development of a robust high-momentum actuator that can penetrate the boundary layer flowing over aerodynamic surfaces. These deficiencies may be overcome in the current development and testing of a novel electromagnetic synthetic jet actuator which replaces piezoelectric materials as the driving diaphragm. One of the overarching goals of the TranAtlantic research platform include fostering national and international collaboration to demonstrate (in numerical and experimental models) reduced CO2/ noise pollution via development and integration of technologies and methodologies in design optimization, fluid dynamics, structures/ composites, propulsion, and controls.

Keywords: Aircraft Design, Sustainable “Green” Aviation, Active Flow Control, Aerodynamics.

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Authors: Alan Walsh, Peter Walker

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Differences in commercial, professional and personal cultural traditions between western consultants and project sponsors in the Gulf Cooperation Council (GCC) region are potentially significant in the workplace, and this can impact on project outcomes. These cultural differences can, for example, result in conflict amongst senior managers, which can negatively impact the megaproject. New entrants to the GCC often experience ‘culture shock’ as they attempt to integrate into their unfamiliar environments. Megaprojects are unique ventures with individual project characteristics, which need to be considered when managing their associated risks. Megaproject research to date has mostly ignored the significance of the absence of cultural congruence in the GCC, which is surprising considering that there are large volumes of megaprojects in various stages of construction in the GCC. An initial step to dealing with cultural issues is to acknowledge culture as a significant risk factor (SRF). This paper seeks to understand the criticality for western consultants to address these risks. It considers the cultural barriers that exist between GCC sponsors and western consultants and examines the cultural distance between the key actors. Initial findings suggest the presence to a certain extent of ethnocentricity. Other cultural clashes arise out of a lack of appreciation of the customs, practices and traditions of ‘the Other’, such as the need for avoiding public humiliation and the hierarchal significance rankings. The concept and significance of cultural shock as part of the integration process for new arrivals are considered. Culture shock describes the state of anxiety and frustration resulting from the immersion in a culture distinctly different from one's own. There are potentially substantial project risks associated with underestimating the process of cultural integration. This paper examines two distinct but intertwined issues: the societal and professional culture differences associated with expatriate assignments. A case study examines the cultural congruences between GCC sponsors and American, British and German consultants, over a ten-year cycle. This provides indicators as to which nationalities encountered the most profound cultural issues and the nature of these. GCC megaprojects are typically intensive fast track demanding ventures, where consultant turnover is high. The study finds that building trust-filled relationships is key to successful project team integration and therefore, to successful megaproject execution. Findings indicate that both professional and social inclusion processes have steep learning curves. Traditional risk management practice is to approach any uncertainty in a structured way to mitigate the potential impact on project outcomes. This research highlights cultural risk as a significant factor in the management of GCC megaprojects. These risks arising from high staff turnover typically include loss of project knowledge, delays to the project, cost and disruption in replacing staff. This paper calls for cultural risk to be recognised as an SRF, as the first step to developing risk management strategies, and to reduce staff turnover for western consultants in GCC megaprojects.

Keywords: Western consultants in megaprojects, national culture impacts on GCC Megaprojects, significant risk factors in megaprojects, professional culture in megaprojects.

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Authors: S. Sarumathi, N. Shanthi, P. Ranjetha

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Data mining is the procedure of determining interesting patterns from the huge amount of data. With the intention of accessing the data faster the most supporting processes needed is clustering. Clustering is the process of identifying similarity between data according to the individuality present in the data and grouping associated data objects into clusters. Cluster ensemble is the technique to combine various runs of different clustering algorithms to obtain a general partition of the original dataset, aiming for consolidation of outcomes from a collection of individual clustering outcomes. The performances of clustering ensembles are mainly affecting by two principal factors such as diversity and quality. This paper presents the overview about the different cluster ensemble algorithm along with their methods used in cluster ensemble to improve the diversity and quality in the several cluster ensemble related papers and shows the comparative analysis of different cluster ensemble also summarize various cluster ensemble methods. Henceforth this clear analysis will be very useful for the world of clustering experts and also helps in deciding the most appropriate one to determine the problem in hand.

Keywords: Cluster Ensemble, Consensus Function, CSPA, Diversity, HGPA, MCLA.

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Authors: S. Kannimuthu, K. Premalatha

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This paper investigates a new data mining capability that entails mining of High Utility Itemsets (HUI) in a distributed environment. Existing research in data mining deals with only presence or absence of an items and do not consider the semantic measures like weight or cost of the items. Thus, HUI mining algorithm has evolved. HUI mining is the one kind of utility mining concept, aims to identify itemsets whose utility satisfies a given threshold. Although, the approach of mining HUIs in a distributed environment and mining of the same from XML data have not explored yet. In this work, a novel approach is proposed to mine HUIs from the XML based data in a distributed environment. This work utilizes Service Oriented Computing (SOC) paradigm which provides Knowledge as a Service (KaaS). The interesting patterns are provided via the web services with the help of knowledge server to answer the queries of the consumers. The performance of the approach is evaluated on various databases using execution time and memory consumption.

Keywords: Data mining, Knowledge as a Service, service oriented computing, utility mining.

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Authors: Gamze Uslu, Sebnem Baydere, Alper K. Demir

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In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces  high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before.

Keywords: Activity recognition, support vector machines, acceleration sensor, wireless sensor networks, packet loss.

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Authors: H. S. Huang, J. C. Jao, K. L. Yen, C. T. Tsai

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The purpose of this article applies the monthly final energy yield and failure data of 202 PV systems installed in Taiwan to analyze the PV operational performance and system availability. This data is collected by Industrial Technology Research Institute through manual records. Bad data detection and failure data estimation approaches are proposed to guarantee the quality of the received information. The performance ratio value and system availability are then calculated and compared with those of other countries. It is indicated that the average performance ratio of Taiwan-s PV systems is 0.74 and the availability is 95.7%. These results are similar with those of Germany, Switzerland, Italy and Japan.

Keywords: availability, performance ratio, PV system, Taiwan

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Authors: N. Veerasamy, C. J. Cheyne

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Users of computer systems may often require the private transfer of messages/communications between parties across a network. Information warfare and the protection and dominance of information in the military context is a prime example of an application area in which the confidentiality of data needs to be maintained. The safe transportation of critical data is therefore often a vital requirement for many private communications. However, unwanted interception/sniffing of communications is also a possibility. An elementary stealthy transfer scheme is therefore proposed by the authors. This scheme makes use of encoding, splitting of a message and the use of a hashing algorithm to verify the correctness of the reconstructed message. For this proof-of-concept purpose, the authors have experimented with the random sending of encoded parts of a message and the construction thereof to demonstrate how data can stealthily be transferred across a network so as to prevent the obvious retrieval of data.

Keywords: Construction, encode, interception, stealthy.

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Authors: Sarah O. Alhumoud, Mawaheb I. Altuwaijri, Tarfa M. Albuhairi, Wejdan M. Alohaideb

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Large-scale data stream analysis has become one of the important business and research priorities lately. Social networks like Twitter and other micro-blogging platforms hold an enormous amount of data that is large in volume, velocity and variety. Extracting valuable information and trends out of these data would aid in a better understanding and decision-making. Multiple analysis techniques are deployed for English content. Moreover, one of the languages that produce a large amount of data over social networks and is least analyzed is the Arabic language. The proposed paper is a survey on the research efforts to analyze the Arabic content in Twitter focusing on the tools and methods used to extract the sentiments for the Arabic content on Twitter.

Keywords: Big Data, Social Networks, Sentiment Analysis.

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Authors: Snezhana G. Gocheva-Ilieva, Ivan H. Feschiev

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In this study integral form and new recursive formulas for Favard constants and some connected with them numeric and Fourier series are obtained. The method is based on preliminary integration of Fourier series which allows for establishing finite recursive representations for the summation. It is shown that the derived recursive representations are numerically more effective than known representations of the considered objects.

Keywords: Effective summation of series, Favard constants, finite recursive representations, Fourier series

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Authors: Nikolaos Vassilas, Theocharis Tsenoglou, Djamchid Ghazanfarpour

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In this work, we explore the capability of the mean shift algorithm as a powerful preprocessing tool for improving the quality of spatial data, acquired from airborne scanners, from densely built urban areas. On one hand, high resolution image data corrupted by noise caused by lossy compression techniques are appropriately smoothed while at the same time preserving the optical edges and, on the other, low resolution LiDAR data in the form of normalized Digital Surface Map (nDSM) is upsampled through the joint mean shift algorithm. Experiments on both the edge-preserving smoothing and upsampling capabilities using synthetic RGB-z data show that the mean shift algorithm is superior to bilateral filtering as well as to other classical smoothing and upsampling algorithms. Application of the proposed methodology for 3D reconstruction of buildings of a pilot region of Athens, Greece results in a significant visual improvement of the 3D building block model.

Keywords: 3D buildings reconstruction, data fusion, data upsampling, mean shift.

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Authors: Nesli Aydin

Abstract:

Waste management decision making in developing countries has moved towards being more pragmatic, transparent, sustainable and comprehensive. Turkey is required to make its waste related legislation compatible with European Legislation as it is a candidate country of the European Union. Improper Turkish practices such as open burning and open dumping practices must be abandoned urgently, and robust waste management systems have to be structured. The determination of an optimum waste management system in any region requires a comprehensive analysis in which many criteria are taken into account by stakeholders. In conducting this sort of analysis, there are two main criteria which are evaluated by waste management analysts; economic viability and environmentally friendliness. From an analytical point of view, a central characteristic of sustainable development is an economic-ecological integration. It is predicted that building a robust waste management system will need significant effort and cooperation between the stakeholders in developing countries such as Turkey. In this regard, this study aims to provide data regarding the cost and environmental burdens of waste treatment technologies such as an incinerator, an autoclave (with different capacities), a hydroclave and a microwave coupled with updated information on calculation methods, and a framework for comparing any proposed scenario performances on a cost and environmental basis.

Keywords: Decision making, economic viability, environmentally friendliness, stakeholder, waste management systems.

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Authors: Claudineia Brazil, Elison Eduardo Jardim Bierhals, Luciane Teresa Salvi, Rafael Haag

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Renewable energy sources are dependent on climatic variability, so for adequate energy planning, observations of the meteorological variables are required, preferably representing long-period series. Despite the scientific and technological advances that meteorological measurement systems have undergone in the last decades, there is still a considerable lack of meteorological observations that form series of long periods. The reanalysis is a system of assimilation of data prepared using general atmospheric circulation models, based on the combination of data collected at surface stations, ocean buoys, satellites and radiosondes, allowing the production of long period data, for a wide gamma. The third generation of reanalysis data emerged in 2010, among them is the Climate Forecast System Reanalysis (CFSR) developed by the National Centers for Environmental Prediction (NCEP), these data have a spatial resolution of 0.50 x 0.50. In order to overcome these difficulties, it aims to evaluate the performance of solar radiation estimation through alternative data bases, such as data from Reanalysis and from meteorological satellites that satisfactorily meet the absence of observations of solar radiation at global and/or regional level. The results of the analysis of the solar radiation data indicated that the reanalysis data of the CFSR model presented a good performance in relation to the observed data, with determination coefficient around 0.90. Therefore, it is concluded that these data have the potential to be used as an alternative source in locations with no seasons or long series of solar radiation, important for the evaluation of solar energy potential.

Keywords: Climate, reanalysis, renewable energy, solar radiation.

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Authors: Patrick Wessa, Bart Baesens

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This paper discusses the use of explorative data mining tools that allow the educator to explore new relationships between reported learning experiences and actual activities, even if there are multiple dimensions with a large number of measured items. The underlying technology is based on the so-called Compendium Platform for Reproducible Computing (http://www.freestatistics.org) which was built on top the computational R Framework (http://www.wessa.net).

Keywords: Reproducible computing, data mining, explorative data analysis, compendium technology, computer assisted education

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Authors: Temitope O. Tokosi

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Healthcare is a human right. The sensitivity of health issues has necessitated the introduction of Enterprise Content Management (ECM) at district hospitals in the Western Cape Province of South Africa. The objective is understanding clinicians’ perception of ECM at their workplace. It is a descriptive case study design of constructivist paradigm. It employed a phenomenological data analysis method using a pattern matching deductive based analytical procedure. Purposive and s4nowball sampling techniques were applied in selecting participants. Clinicians expressed concerns and frustrations using ECM such as, non-integration with other hospital systems. Inadequate access points to ECM. Incorrect labelling of notes and bar-coding causes more time wasted in finding information. System features and/or functions (such as search and edit) are not possible. Hospital management and clinicians are not constantly interacting and discussing. Information turnaround time is unacceptably lengthy. Resolving these problems would involve a positive working relationship between hospital management and clinicians. In addition, prioritising the problems faced by clinicians in relation to relevance can ensure problem-solving in order to meet clinicians’ expectations and hospitals’ objective. Clinicians’ perception should invoke attention from hospital management with regards technology use. The study’s results can be generalised across clinician groupings exposed to ECM at various district hospitals because of professional and hospital homogeneity.

Keywords: Clinician, electronic content management, hospital, perception, technology.

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Authors: Adekunle O. Afolabi, Pekka Toivanen

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The world is expected to experience growth in the number of ageing population, and this will bring about high cost of providing care for these valuable citizens. In addition, many of these live with chronic diseases that come with old age. Providing adequate care in the face of rising costs and dwindling personnel can be challenging. However, advances in technologies and emergence of the Internet of Things are providing a way to address these challenges while improving care giving. This study proposes the integration of recommendation systems into homecare to provide real-time recommendations for effective management of people receiving care at home and those living with chronic diseases. Using the simplified Training Logic Concept, stakeholders and requirements were identified. Specific requirements were gathered from people living with cancer. The solution designed has two components namely home and community, to enhance recommendations sharing for effective care giving. The community component of the design was implemented with the development of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People (ReSCAP). This component has illustrated the possibility of real-time recommendations, improved recommendations sharing among care receivers and between a physician and care receivers. Full implementation will increase access to health data for better care decision making.

Keywords: Recommendation systems, healthcare, internet of things, real-time, homecare.

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Authors: Shigeaki Sakurai, Ryohei Orihara

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This paper proposes a new method for analyzing textual data. The method deals with items of textual data, where each item is described based on various viewpoints. The method acquires 2- class classification models of the viewpoints by applying an inductive learning method to items with multiple viewpoints. The method infers whether the viewpoints are assigned to the new items or not by using the models. The method extracts expressions from the new items classified into the viewpoints and extracts characteristic expressions corresponding to the viewpoints by comparing the frequency of expressions among the viewpoints. This paper also applies the method to questionnaire data given by guests at a hotel and verifies its effect through numerical experiments.

Keywords: Text mining, Multiple viewpoints, Differential analysis, Questionnaire data

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Authors: Wang Lin, Li Zhiqiang

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The purpose of this paper is to analyze the cooperative learning behavior pattern based on the data of students' movement. The study firstly reviewed the cooperative learning theory and its research status, and briefly introduced the k-means clustering algorithm. Then, it used clustering algorithm and mathematical statistics theory to analyze the activity rhythm of individual student and groups in different functional areas, according to the movement data provided by 10 first-year graduate students. It also focused on the analysis of students' behavior in the learning area and explored the law of cooperative learning behavior. The research result showed that the cooperative learning behavior analysis method based on movement data proposed in this paper is feasible. From the results of data analysis, the characteristics of behavior of students and their cooperative learning behavior patterns could be found.

Keywords: Behavior pattern, cooperative learning, data analyze, K-means clustering algorithm.

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Authors: Ming Lun Wang, Yan Wang, Ning Ruo Sun

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With the development of cloud computing, more and more users start to utilize the cloud storage service. However, there exist some issues: 1) cloud server steals the shared data, 2) sharers collude with the cloud server to steal the shared data, 3) cloud server tampers the shared data, 4) sharers and key generation center (KGC) conspire to steal the shared data. In this paper, we use advanced encryption standard (AES), hash algorithms, and accountable key-policy attribute-based encryption without key escrow (WOKE-AKP-ABE) to build a security cloud storage scheme. Moreover, the data are encrypted to protect the privacy. We use hash algorithms to prevent the cloud server from tampering the data uploaded to the cloud. Analysis results show that this scheme can resist conspired attacks.

Keywords: Cloud storage security, sharing storage, attributes, Hash algorithm.

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Authors: Saad Al-Gahtani, Ghassan H Al Shatter

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Argument over the use of particular method in interlanguage pragmatics has increased recently. Researchers argued the advantages and disadvantages of each method either natural or elicited. Findings of different studies indicated that the use of one method may not provide enough data to answer all its questions. The current study investigated the validity of using multimethod approach in interlanguage pragmatics to understand the development of requests in Arabic as a second language (Arabic L2). To this end, the study adopted two methods belong to two types of data sources: the institutional discourse (natural data), and the role play (elicited data). Participants were 117 learners of Arabic L2 at the university level, representing four levels (beginners, low-intermediate, highintermediate, and advanced). Results showed that using two or more methods in interlanguage pragmatics affect the size and nature of data.

Keywords: Arabic L2, Development of requests, Interlanguage Pragmatics, Multimethod approach.

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Authors: Kohei Inoue, Kenji Hara, Kiichi Urahama

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We describe a relationship between integral images and differential images. First, we derive a simple difference filter from conventional integral image. In the derivation, we show that an integral image and the corresponding differential image are related to each other by simultaneous linear equations, where the numbers of unknowns and equations are the same, and therefore, we can execute the integration and differentiation by solving the simultaneous equations. We applied the relationship to an image fusion problem, and experimentally verified the effectiveness of the proposed method.

Keywords: Integral images, differential images, differential filters, image fusion.

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Authors: Ali Anaissi, Paul J. Kennedy, Madhu Goyal

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Analysis and visualization of microarraydata is veryassistantfor biologists and clinicians in the field of diagnosis and treatment of patients. It allows Clinicians to better understand the structure of microarray and facilitates understanding gene expression in cells. However, microarray dataset is a complex data set and has thousands of features and a very small number of observations. This very high dimensional data set often contains some noise, non-useful information and a small number of relevant features for disease or genotype. This paper proposes a non-linear dimensionality reduction algorithm Local Principal Component (LPC) which aims to maps high dimensional data to a lower dimensional space. The reduced data represents the most important variables underlying the original data. Experimental results and comparisons are presented to show the quality of the proposed algorithm. Moreover, experiments also show how this algorithm reduces high dimensional data whilst preserving the neighbourhoods of the points in the low dimensional space as in the high dimensional space.

Keywords: Linear Dimension Reduction, Non-Linear Dimension Reduction, Principal Component Analysis, Biologists.

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Authors: Alexander B. Bichkov, Valery V. Smirnov

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Total cross section of helium atom photo-ionization by weak short pulse is calculated using the variant of trajectory method, developed in our earlier work. The method enables simple estimation of total ionization probability (or cross section) without integration of differential one.

Keywords: Evaluation of Photo-Ionization, Helium, Trajectory Method

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Authors: Siyuan Jing, Kun She

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Neighborhood Rough Sets (NRS) has been proven to be an efficient tool for heterogeneous attribute reduction. However, most of researches are focused on dealing with complete and noiseless data. Factually, most of the information systems are noisy, namely, filled with incomplete data and inconsistent data. In this paper, we introduce a generalized neighborhood rough sets model, called VPTNRS, to deal with the problem of heterogeneous attribute reduction in noisy system. We generalize classical NRS model with tolerance neighborhood relation and the probabilistic theory. Furthermore, we use the neighborhood dependency to evaluate the significance of a subset of heterogeneous attributes and construct a forward greedy algorithm for attribute reduction based on it. Experimental results show that the model is efficient to deal with noisy data.

Keywords: attribute reduction, incomplete data, inconsistent data, tolerance neighborhood relation, rough sets

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Authors: Zhen Peng, Baifeng Wu

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Modern computing technology enters the era of parallel computing with the trend of sustainable and scalable parallelism. Single Instruction Multiple Data (SIMD) is an important way to go along with the trend. It is able to gather more and more computing ability by increasing the number of processor cores without the need of modifying the program. Meanwhile, in the field of scientific computing and engineering design, many computation intensive applications are facing the challenge of increasingly large amount of data. Data parallel computing will be an important way to further improve the performance of these applications. In this paper, we take the accurate collision detection in building information modeling as an example. We demonstrate a model for constructing a data parallel algorithm. According to the model, a complex object is decomposed into the sets of simple objects; collision detection among complex objects is converted into those among simple objects. The resulting algorithm is a typical SIMD algorithm, and its advantages in parallelism and scalability is unparalleled in respect to the traditional algorithms.

Keywords: Data parallelism, collision detection, single instruction multiple data, building information modeling, continuous scalability.

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Authors: C. Xavier Mendieta, J. J McArthur

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Governments are playing an increasingly active role in reducing carbon emissions, and a key strategy has been the introduction of mandatory energy disclosure policies. These policies have resulted in a significant amount of publicly available data, providing researchers with a unique opportunity to develop location-specific energy and carbon emission benchmarks from this data set, which can then be used to develop building archetypes and used to inform urban energy models. This study presents the development of such a benchmark using the public reporting data. The data from Ontario’s Ministry of Energy for Post-Secondary Educational Institutions are being used to develop a series of building archetype dynamic building loads and energy benchmarks to fill a gap in the currently available building database. This paper presents the development of a benchmark for college and university residences within ASHRAE climate zone 6 areas in Ontario using the mandatory disclosure energy and greenhouse gas emissions data. The methodology presented includes data cleaning, statistical analysis, and benchmark development, and lessons learned from this investigation are presented and discussed to inform the development of future energy benchmarks from this larger data set. The key findings from this initial benchmarking study are: (1) the importance of careful data screening and outlier identification to develop a valid dataset; (2) the key features used to develop a model of the data are building age, size, and occupancy schedules and these can be used to estimate energy consumption; and (3) policy changes affecting the primary energy generation significantly affected greenhouse gas emissions, and consideration of these factors was critical to evaluate the validity of the reported data.

Keywords: Building archetypes, data analysis, energy benchmarks, GHG emissions.

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Authors: Roelien Goede

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Data Structures and Algorithms is a module in most Computer Science or Information Technology curricula. It is one of the modules most students identify as being difficult. This paper demonstrates how programming a solution for Sudoku can make abstract concepts more concrete. The paper relates concepts of a typical Data Structures and Algorithms module to a step by step solution for Sudoku in a human type as opposed to a computer oriented solution.

Keywords: Data Structures, Algorithms, Sudoku, ObjectOriented Programming, Programming Teaching, Education.

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Authors: Kunyanuth Kularbphettong, Cholticha Tongsiri

Abstract:

The purpose of this research aims to discover the knowledge for analysis student motivation behavior on e-Learning based on Data Mining Techniques, in case of the Information Technology for Communication and Learning Course at Suan Sunandha Rajabhat University. The data mining techniques was applied in this research including association rules, classification techniques. The results showed that using data mining technique can indicate the important variables that influence the student motivation behavior on e-Learning.

Keywords: association rule mining, classification techniques, e- Learning, Moodle log Motivation Behavior

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