Search results for: linked data

Authors: Shrey Sharma, Gunjan Kumar Verma

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High density electrical prospecting has been widely used in groundwater investigation, civil engineering and environmental survey. For efficient inversion, the forward modeling routine, sensitivity calculation, and inversion algorithm must be efficient. This paper attempts to provide a brief summary of the past and ongoing developments of the method. It includes reviews of the procedures used for data acquisition, processing and inversion of electrical resistivity data based on compilation of academic literature. In recent times there had been a significant evolution in field survey designs and data inversion techniques for the resistivity method. In general 2-D inversion for resistivity data is carried out using the linearized least-square method with the local optimization technique .Multi-electrode and multi-channel systems have made it possible to conduct large 2-D, 3-D and even 4-D surveys efficiently to resolve complex geological structures that were not possible with traditional 1-D surveys. 3-D surveys play an increasingly important role in very complex areas where 2-D models suffer from artifacts due to off-line structures. Continued developments in computation technology, as well as fast data inversion techniques and software, have made it possible to use optimization techniques to obtain model parameters to a higher accuracy. A brief discussion on the limitations of the electrical resistivity method has also been presented.

Keywords: Resistivity, inversion, optimization.

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Authors: Chris Ewe, Naoum Jamous, Holger Schrödl

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Like most entrepreneurs, data center operators pursue goals such as profit-maximization, improvement of the company’s reputation or basically to exist on the market. Part of those aims is to guarantee a given quality of service. Quality characteristics are specified in a contract called the service level agreement. Central part of this agreement is non-functional properties of an IT service. The system availability is one of the most important properties as it will be shown in this paper. To comply with availability requirements, data center operators can use virtualization technologies. A clear model to assess the effect of virtualization functions on the parts of a data center in relation to the system availability is still missing. This paper aims to introduce a basic model that shows these connections, and consider if the identified effects are positive or negative. Thus, this work also points out possible disadvantages of the technology. In consequence, the paper shows opportunities as well as risks of data center virtualization in relation to system availability.

Keywords: Availability, cloud computing IT service, quality of service, service level agreement, virtualization.

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Authors: Ahmad Habibizad Navin, Mehdi Naghian Fesharaki, Mirkamal Mirnia, Mohamad Teshnelab, Ehsan Shahamatnia

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In this paper we introduce new data oriented modeling of uniform random variable well-matched with computing systems. Due to this conformity with current computers structure, this modeling will be efficiently used in statistical inference.

Keywords: Uniform random variable, Data oriented modeling, Statistical inference, Prodigraph, Statistically complete tree, Uniformdigital probability digraph, Uniform n-complete probability tree.

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Authors: Andreas Ahrens, Ojaras Purvinis Jelena Zāšcerinska

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A successful shopping experience without overcrowded shops and long waiting times undoubtedly leads to the release of happiness hormones and is generally considered as the goal of any optimization. Factors influencing the shopping experience can be divided into internal and external ones. External factors are related e. g. to the arrival of the customers to the shop whereas internal factors are linked with the service process itself when checking out (waiting in the queue to the cash register and the scanning of the goods as well as the payment process itself) or any other non-expected delay when changing the status from a visitor to a buyer by choosing goods or items. This paper divides the customer-server interaction in five phases starting with the customer arrival at the shop, the selection of goods, the buyer waiting in the queue to the cash register, the payment process and ending with the customer or buyer departure. Our simulation results show how five phases are intertwined and influence the overall shopping experience. Parameters for measuring the shopping experience based on a burstiness level in each of the five phases of the customer-server interaction are estimated.

Keywords: Customers’ burstiness, cash register, customers’ waiting time, gap distribution function.

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Authors: Phumelele P. Kubheka, Pius A. Owolawi, Gbolahan Aiyetoro

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Twitter is one of the most popular social media platforms where users share their opinions on different subjects. Twitter can be considered a great source for mining text due to the high volumes of data generated through the platform daily. Many industries such as telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model in this experiment. A higher topic coherence score indicates better performance of the model.

Keywords: Big data, latent Dirichlet allocation, latent semantic indexing, Telco, topic modeling, Twitter.

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Authors: Yogesh Aggarwal, Paratibha Aggarwal

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The paper presents a comparative performance of the models developed to predict 28 days compressive strengths using neural network techniques for data taken from literature (ANN-I) and data developed experimentally for SCC containing bottom ash as partial replacement of fine aggregates (ANN-II). The data used in the models are arranged in the format of six and eight input parameters that cover the contents of cement, sand, coarse aggregate, fly ash as partial replacement of cement, bottom ash as partial replacement of sand, water and water/powder ratio, superplasticizer dosage and an output parameter that is 28-days compressive strength and compressive strengths at 7 days, 28 days, 90 days and 365 days, respectively for ANN-I and ANN-II. The importance of different input parameters is also given for predicting the strengths at various ages using neural network. The model developed from literature data could be easily extended to the experimental data, with bottom ash as partial replacement of sand with some modifications.

Keywords: Self compacting concrete, bottom ash, strength, prediction, neural network, importance factor.

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Authors: Gry M, Bergström J, Lengquist J, Lindberg J, Drobin K, Schwenk J, Nilsson P, Schuppe-Koistinen I.

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Sensitive and predictive DILI (Drug Induced Liver Injury) biomarkers are needed in drug R&D to improve early detection of hepatotoxicity. The discovery of DILI biomarkers that demonstrate the predictive power to identify individuals at risk to DILI would represent a major advance in the development of personalized healthcare approaches. In this healthy volunteer acetaminophen study (4g/day for 7 days, with 3 monitored nontreatment days before and 4 after), 450 serum samples from 32 subjects were analyzed using protein profiling by antibody suspension bead arrays. Multiparallel protein profiles were generated using a DILI target protein array with 300 antibodies, where the antibodies were selected based on previous literature findings of putative DILI biomarkers and a screening process using pre dose samples from the same cohort. Of the 32 subjects, 16 were found to develop an elevated ALT value (2Xbaseline, responders). Using the plasma profiling approach together with multivariate statistical analysis some novel findings linked to lipid metabolism were found and more important, endogenous protein profiles in baseline samples (prior to treatment) with predictive power for ALT elevations were identified.

Keywords: DILI, Plasma profiling, PLSDA, Randomforest.

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Authors: Ebrahim Safarian, Kadir Bilen, Akif Ceviz

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The turbocharger and turbocharging have been the inherent component of diesel engines, so that critical parameters of such engines, as BSFC (Brake Specific Fuel Consumption) or thermal efficiency, fuel consumption, BMEP (Brake Mean Effective Pressure), the power density output and emission level have been improved extensively. In general, the turbocharger can be considered as the most complex component of diesel engines, because it has closely interrelated turbomachinery concepts of the turbines and the compressors to thermodynamic fundamentals of internal combustion engines and stress analysis of all components. In this paper, a waste gate for a conventional single stage radial turbine is investigated by consideration of turbochargers operation constrains and engine operation conditions, without any detail designs in the turbine and the compressor. Amount of opening waste gate which extended between the ranges of full opened and closed valve, is demonstrated by limiting compressor boost pressure ratio. Obtaining of an optimum point by regard above mentioned items is surveyed by three linked meanline modeling programs together which consist of Turbomatch®, Compal®, Rital® madules in concepts NREC® respectively.

Keywords: Turbocharger, Wastegate, diesel engine, CONCEPT NREC programs.

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Authors: S. Sarumathi, N. Shanthi, M. Sharmila

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Over the past epoch a rampant amount of work has been done in the data clustering research under the unsupervised learning technique in Data mining. Furthermore several algorithms and methods have been proposed focusing on clustering different data types, representation of cluster models, and accuracy rates of the clusters. However no single clustering algorithm proves to be the most efficient in providing best results. Accordingly in order to find the solution to this issue a new technique, called Cluster ensemble method was bloomed. This cluster ensemble is a good alternative approach for facing the cluster analysis problem. The main hope of the cluster ensemble is to merge different clustering solutions in such a way to achieve accuracy and to improve the quality of individual data clustering. Due to the substantial and unremitting development of new methods in the sphere of data mining and also the incessant interest in inventing new algorithms, makes obligatory to scrutinize a critical analysis of the existing techniques and the future novelty. This paper exposes the comparative study of different cluster ensemble methods along with their features, systematic working process and the average accuracy and error rates of each ensemble methods. Consequently this speculative and comprehensive analysis will be very useful for the community of clustering practitioners and also helps in deciding the most suitable one to rectify the problem in hand.

Keywords: Clustering, Cluster Ensemble methods, Co-association matrix, Consensus function, Median partition.

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Authors: Chuen Tse Kuah, Kuan Yew Wong, Farzad Behrouzi

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This paper is aimed to give an illustration on the application of Data Envelopment Analysis (DEA) as a tool to assess Quality Management (QM) efficiency. A variant of DEA, slack based measure (SBM) is used for this purpose. From this study, it is found that DEA is suitable to measure QM efficiency and give improvement suggestions to the inefficient QM.

Keywords: Quality Management, Data Envelopment Analysis, Slack Based Measure, Efficiency Measurement.

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Authors: Wang Yang

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Shenzhen is a modern city of China's reform and opening-up policy, the development of urban morphology has been established on the administration of the Chinese government. This city`s planning paradigm is primarily affected by the spatial structure and human behavior. The subjective urban agglomeration center is divided into several groups and centers. In comparisons of this effect, the city development law has better to be neglected. With the continuous development of the internet, extensive data technology has been introduced in China. Data mining and data analysis has become important tools in municipal research. Data mining has been utilized to improve data cleaning such as receiving business data, traffic data and population data. Prior to data mining, government data were collected by traditional means, then were analyzed using city-relationship research, delaying the timeliness of urban development, especially for the contemporary city. Data update speed is very fast and based on the Internet. The city's point of interest (POI) in the excavation serves as data source affecting the city design, while satellite remote sensing is used as a reference object, city analysis is conducted in both directions, the administrative paradigm of government is broken and urban research is restored. Therefore, the use of data mining in urban analysis is very important. The satellite remote sensing data of the Shenzhen city in July 2018 were measured by the satellite Modis sensor and can be utilized to perform land surface temperature inversion, and analyze city heat island distribution of Shenzhen. This article acquired and classified the data from Shenzhen by using Data crawler technology. Data of Shenzhen heat island and interest points were simulated and analyzed in the GIS platform to discover the main features of functional equivalent distribution influence. Shenzhen is located in the east-west area of China. The city’s main streets are also determined according to the direction of city development. Therefore, it is determined that the functional area of the city is also distributed in the east-west direction. The urban heat island can express the heat map according to the functional urban area. Regional POI has correspondence. The research result clearly explains that the distribution of the urban heat island and the distribution of urban POIs are one-to-one correspondence. Urban heat island is primarily influenced by the properties of the underlying surface, avoiding the impact of urban climate. Using urban POIs as analysis object, the distribution of municipal POIs and population aggregation are closely connected, so that the distribution of the population corresponded with the distribution of the urban heat island.

Keywords: POI, satellite remote sensing, the population distribution, urban heat island thermal map.

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Authors: Pallabi Kalita, Hui Tag, H. N. Sarma, A. K. Das.

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Wild edible food plants contain a number of organic phytochemical that have been linked to the promotion of good health. These plants used by the local people of Arunachal Pradesh (Northeast India) are found to have high nutritional potential to maintain general balance diet. A study was conducted to evaluate the nutritional potential of five commonly found, unexplored wild food plants namely, Piper pedicellatum C. DC (leaves), Gonostegia hirta (Blume ex Hassk.) Miq. (leaves), Mussaenda roxburghii Hook.f (leaves), Solanum spirale Roxb. (leaves and fruits) and Cyathea spinulosa Wall. ex Hook. (pith portion and tender rachis) from East Siang District of Arunachal Pradesh Northeast (India) for ascertaining their suitability for utilization as supplementary food. Results of study revealed that P. pedicellatum, C. spinulosa, and S. spirale (leaves) are the most promising species which have high nutritional content out of the five wild food plants investigated which is required for the normal growth and development of human.

Keywords: Wild edible plants, Gross energy, Gonostegia hirta, Cyathea spinulosa,

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Authors: Christian Mosch, Reiner Anderl, Antonio Álvaro de Assis Moura, Klaus Schützer

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Due to short product life cycles, increasing variety of products and short cycles of leap innovations manufacturing companies have to increase the flexibility of factory structures. Flexibility of factory structures is based on defined factory planning processes in which product, process and resource data of various partial domains have to be considered. Thus factory planning processes can be characterized as iterative, interdisciplinary and participative processes [1]. To support interdisciplinary and participative character of planning processes, a federative factory data management (FFDM) as a holistic solution will be described. FFDM is already implemented in form of a prototype. The interim results of the development of FFDM will be shown in this paper. The principles are the extracting of product, process and resource data from documents of various partial domains providing as web services on a server. The described data can be requested by the factory planner by using a FFDM-browser.

Keywords: BRAGECRIM, Factory Planning Process, FactoryData Management, Web Services

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Authors: Shaban Pazi, Chris Chatwin, Rupert Young, Philip Birch

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Internet infrastructures in most places of the world have been supported by the advancement of optical fiber technology, most notably wavelength division multiplexing (WDM) system. Optical technology by means of WDM system has revolutionized long distance data transport and has resulted in high data capacity, cost reductions, extremely low bit error rate, and operational simplification of the overall Internet infrastructure. This paper analyses and compares the system impairments, which occur at data transmission rates of 2.5Gb/s and 10 Gb/s per wavelength channel in our proposed optical WDM system for Internet infrastructure in Tanzania. The results show that the data transmission rate of 2.5 Gb/s has minimum system impairments compared with a rate of 10 Gb/s per wavelength channel, and achieves a sufficient system performance to provide a good Internet access service.

Keywords: Internet infrastructure, WDM system, standard single mode fibers, system impairments.

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Authors: M. Uysal, M. Yilmaz, I. Tiryakioğlu

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Digital Terrain Model (DTM) is a digital numerical representation of the Earth's surface. DTMs have been applied to a diverse field of tasks, such as urban planning, military, glacier mapping, disaster management. In the expression of the Earth' surface as a mathematical model, an infinite number of point measurements are needed. Because of the impossibility of this case, the points at regular intervals are measured to characterize the Earth's surface and DTM of the Earth is generated. Hitherto, the classical measurement techniques and photogrammetry method have widespread use in the construction of DTM. At present, RADAR, LiDAR, and stereo satellite images are also used for the construction of DTM. In recent years, especially because of its superiorities, Airborne Light Detection and Ranging (LiDAR) has an increased use in DTM applications. A 3D point cloud is created with LiDAR technology by obtaining numerous point data. However recently, by the development in image mapping methods, the use of unmanned aerial vehicles (UAV) for photogrammetric data acquisition has increased DTM generation from image-based point cloud. The accuracy of the DTM depends on various factors such as data collection method, the distribution of elevation points, the point density, properties of the surface and interpolation methods. In this study, the random data reduction method is compared for DTMs generated from image based point cloud data. The original image based point cloud data set (100%) is reduced to a series of subsets by using random algorithm, representing the 75, 50, 25 and 5% of the original image based point cloud data set. Over the ANS campus of Afyon Kocatepe University as the test area, DTM constructed from the original image based point cloud data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that the random data reduction method can be used to reduce the image based point cloud datasets to 50% density level while still maintaining the quality of DTM.

Keywords: DTM, unmanned aerial vehicle, UAV, random, Kriging.

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Authors: D. M. Onyango, P. O. Angienda

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The purpose of the present study was to evaluate the epidemiology of waterborne diarrhoeal among children aged 6-36 months old in Busia town, western Kenya. The study was carried out between Feb. 2008 and Feb. 2010. Cases of diarrhoea reported in 385 households were linked to household water handling practices. A mother with a child of 6-36 months old was also included in the study. Diarrhoea prevalence among children 6-36 months was 16.7% in Busia town, Bwamani (19.6%) and Mayenje (10.6%) clustered in Mayenje sub-location reported the highest and the lowest prevalence of diarrhoea. There was a positive correlation between the prevalence of diarrhoea in children and the level of the mother-s education, 29.9% (n= 100). Diarrhoea cases decreased in range from 35.5% (n =102) to 4.8% (n= 16), corresponding to increase in age from 6-35 months on average. In conclusion, prevalence of diarrhoea in children of 6-36 months old was 16.7% in Busia town. This was higher in children whose mother-s age was below 18 years and with low level of education, the rate decreased with increase in age of children. Prevalence of diarrhoea in children aged 6-36months in households was higher in children aged 6-17 and 36 months and whose mothers were less educated and fell between the ages of 18-24 years. The Influence of human activities at the main source of drinking water on the prevalence of diarrhoea in these children was insignificant.

Keywords: Diarrhoea, Children, Mortality, Waterborne disease,

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Authors: Usman Qamar, Younus Javed

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Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and roughsets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%.

Keywords: Rough-sets, Classification, Feature Selection, Entropy, Outliers, Frequent itemset mining.

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Authors: Ilma Robo, Saimir Heta, Nedja Hysi, Vera Ostreni

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Introduction: Marginal gingivitis is a disease with considerable frequency among patients who present routinely for periodontal control and treatment. In fact, this disease may not have alarming symptoms in patients and may go unnoticed by themselves when personal hygiene conditions are optimal. The aim of this study was to collect retrograde data on the prevalence of marginal gingiva in the respective group of patients, evaluated according to specific periodontal diagnostic tools. Materials and methods: The study was conducted in two patient groups. The first group was with 34 patients, during December 2019-January 2020, and the second group was with 64 patients during 2010-2018 (each year in the mentioned monthly period). Bacterial plaque index, hemorrhage index, amount of gingival fluid, presence of xerostomia and candidiasis were recorded in patients. Results: Analysis of the collected data showed that susceptibility to marginal gingivitis shows higher values according to retrograde data, compared to cross-sectional ones. Susceptibility to candidiasis and the occurrence of xerostomia, even in the combination of both pathologies, as risk factors for the occurrence of marginal gingivitis, show higher values ​​according to retrograde data. The female are presented with a reduced bacterial plaque index than the males, but more importantly, this index in the females is also associated with a reduced index of gingival hemorrhage, in contrast to the males. Conclusions: Cross-sectional data show that the prevalence of marginal gingivitis is more reduced, compared to retrograde data, based on the hemorrhage index and the bacterial plaque index together. Changes in production in the amount of gingival fluid show a higher prevalence of marginal gingivitis in cross-sectional data than in retrograde data; this is based on the sophistication of the way data are recorded, which evolves over time and also based on professional sensitivity to this phenomenon.

Keywords: Marginal gingivitis, cross-sectional, retrograde, prevalence.

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Authors: Cheng-Hong Yang, Tsung-Mu Shih, Li-Yeh Chuang

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Serial Analysis of Gene Expression is a powerful quantification technique for generating cell or tissue gene expression data. The profile of the gene expression of cell or tissue in several different states is difficult for biologists to analyze because of the large number of genes typically involved. However, feature selection in machine learning can successfully reduce this problem. The method allows reducing the features (genes) in specific SAGE data, and determines only relevant genes. In this study, we used a genetic algorithm to implement feature selection, and evaluate the classification accuracy of the selected features with the K-nearest neighbor method. In order to validate the proposed method, we used two SAGE data sets for testing. The results of this study conclusively prove that the number of features of the original SAGE data set can be significantly reduced and higher classification accuracy can be achieved.

Keywords: Serial Analysis of Gene Expression, Feature selection, Genetic Algorithm, K-nearest neighbor method.

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Authors: Sumith Matharage, Damminda Alahakoon

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Textual data plays an important role in the modern world. The possibilities of applying data mining techniques to uncover hidden information present in large volumes of text collections is immense. The Growing Self Organizing Map (GSOM) is a highly successful member of the Self Organising Map family and has been used as a clustering and visualisation tool across wide range of disciplines to discover hidden patterns present in the data. A comprehensive analysis of the GSOM’s capabilities as a text clustering and visualisation tool has so far not been published. These functionalities, namely map visualisation capabilities, automatic cluster identification and hierarchical clustering capabilities are presented in this paper and are further demonstrated with experiments on a benchmark text corpus.

Keywords: Text Clustering, Growing Self Organizing Map, Automatic Cluster Identification, Hierarchical Clustering.

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Authors: Orly Redlich

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Since the establishment of the state of Israel, and as a result of various events that led to it, Jewish citizens and Arab citizens of the state have been in constant conflict, which finds its expression in most levels of life. Therefore, the attitude of one group member to the other group members is mostly tense, loaded, and saturated with mutual suspicion. Within this reality, in many higher education institutions in Israel, Jews and Arabs meet with each other intensively and for several years. For some students, this is their first opportunity for a meaningful cross-cultural encounter. These intercultural encounters, which allow positive interactions between members of different cultural groups, may contribute to the formation of "intercultural competence" which means long-term change in knowledge, attitudes, and behavior towards 'the other culture'. The current study examined the concept of the ‘other’ among Jewish and Arab students studying together and their "intercultural competence". The study also examined whether there is a difference in the perception of the ‘other’ between students studying in different academic programs, and between students taking academic courses on multiculturalism. This quantitative study was conducted among 274 Arab and Jewish students studying together, for bachelors or master's degree, in various academic programs at the Israel Academic College of Ramat-Gan. The background data of the participants are varied, in terms of religion, origin, religiosity, employment status, living area, and marital status. The main hypothesis is that academic, social, and intercultural encounters between Jewish and Arab students, who attend college together, will be a significant factor in building "intercultural competence". Additionally, the existence of "intercultural competence" has been linked to demographic characteristics of the students, as well as the nature of intercultural encounters between Jews and Arabs in a higher education institution. The dependent variables were measured by a self-report questionnaire, using the components of '"intercultural competence"' among students, which are: 1. Cognitive knowledge of the ‘others’, 2. Feelings towards the ‘others’, 3. Change in attitudes towards the 'others', and 4. Change in behavior towards the ‘others’. The findings indicate a higher "intercultural competence" among Arab students than Jews; it was also found higher level of "intercultural competence" among Educational Counseling students than the other respondents. The importance of this research lies in finding the means to develop "intercultural competence" among Jewish and Arab students, which may reduce prejudice and stereotypes towards the other culture and may even prevent occurrences of alienation and violence in cross-cultural encounters in Israel.

Keywords: Cross-cultural learning, "intercultural competence", Jewish and Arab students, multiculturalism.

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Authors: M. G. Albanesi, R. Amadeo

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In this paper, a new approach for quality assessment tasks in lossy compressed digital video is proposed. The research activity is based on the visual fixation data recorded by an eye tracker. The method involved both a new paradigm for subjective quality evaluation and the subsequent statistical analysis to match subjective scores provided by the observer to the data obtained from the eye tracker experiments. The study brings improvements to the state of the art, as it solves some problems highlighted in literature. The experiments prove that data obtained from an eye tracker can be used to classify videos according to the level of impairment due to compression. The paper presents the methodology, the experimental results and their interpretation. Conclusions suggest that the eye tracker can be useful in quality assessment, if data are collected and analyzed in a proper way.

Keywords: eye tracker, video compression, video qualityassessment, visual attention

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Authors: Shivangi Agrawal, Hsiu-I Ting

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Entrepreneurship is an essential source of economic growth and a prominent factor influencing socio-economic development. Entrepreneurship education educates and enhances entrepreneurial activity. This study aims to understand current trends in entrepreneurship education and evaluate the effectiveness of diverse entrepreneurship education programs. An increasing number of universities offer entrepreneurship education courses to create and successfully continue entrepreneurial ventures. Despite the prevalence of entrepreneurship education, research studies lack inconsistency about the effectiveness of entrepreneurship education to promote and develop entrepreneurship. Strategies to develop entrepreneurial attitudes and intentions among individuals are hindered by a lack of understanding of entrepreneurs' educational purposes, components, methodology, and resources required. Lack of adequate entrepreneurship education has been linked with low self-efficacy and lack of entrepreneurial intent. Moreover, in the age of digitisation and during the COVID-19 pandemic, digital learning platforms (e.g. online entrepreneurship education courses and programs) and other digital tools (e.g. digital game-based entrepreneurship education) have become more relevant to entrepreneurship education. This paper contributes to the continuation of academic literature in entrepreneurship education by evaluating and assessing current trends in entrepreneurship education programs, leading to better understanding to reduce gaps between entrepreneurial development requirements and higher education institutions.

Keywords: entrepreneurship education, digital technologies, academic entrepreneurship, COVID-19

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Authors: S. H. Lee, M. J. Park, O. M. Kwon

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In this paper, reliable consensus of multi-agent systems with sampled-data is investigated. By using a suitable Lyapunov-Krasovskii functional and some techniques such as Wirtinger Inequality, Schur Complement and Kronecker Product, the results of such system are obtained by solving a set of Linear Matrix Inequalities (LMIs). One numerical example is included to show the effectiveness of the proposed criteria.

Keywords: Multi-agent, Linear Matrix Inequalities (LMIs), Kronecker Product, Sampled-Data, Lyapunov method.

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Authors: Nouha Khediri, Mohammed Ben Ammar, Monji Kherallah

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Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. However, accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER benefiting from deep learning, especially CNN and VGG16. First, the data are pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work.

Keywords: CNN, deep-learning, facial emotion recognition, machine learning.

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Authors: Sabah S. Al-Fedaghi

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The technique of k-anonymization has been proposed to obfuscate private data through associating it with at least k identities. This paper investigates the basic tabular structures that underline the notion of k-anonymization using cell suppression. These structures are studied under idealized conditions to identify the essential features of the k-anonymization notion. We optimize data kanonymization through requiring a minimum number of anonymized values that are balanced over all columns and rows. We study the relationship between the sizes of the anonymized tables, the value k, and the number of attributes. This study has a theoretical value through contributing to develop a mathematical foundation of the kanonymization concept. Its practical significance is still to be investigated.

Keywords: Balanced tables, k-anonymization, private data

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Authors: Chen Chou, Feng-Tyan Lin

Abstract:

Big Data has been attracted a lot of attentions in many fields for analyzing research issues based on a large number of maternal data. Electronic Toll Collection (ETC) is one of Intelligent Transportation System (ITS) applications in Taiwan, used to record starting point, end point, distance and travel time of vehicle on the national freeway. This study, taking advantage of ETC big data, combined with urban planning theory, attempts to explore various phenomena of inter-city transportation activities. ETC, one of government's open data, is numerous, complete and quick-update. One may recall that living area has been delimited with location, population, area and subjective consciousness. However, these factors cannot appropriately reflect what people’s movement path is in daily life. In this study, the concept of "Living Area" is replaced by "Influence Range" to show dynamic and variation with time and purposes of activities. This study uses data mining with Python and Excel, and visualizes the number of trips with GIS to explore influence range of Tainan city and the purpose of trips, and discuss living area delimited in current. It dialogues between the concepts of "Central Place Theory" and "Living Area", presents the new point of view, integrates the application of big data, urban planning and transportation. The finding will be valuable for resource allocation and land apportionment of spatial planning.

Keywords: Big Data, ITS, influence range, living area, central place theory, visualization.

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Authors: Jorge Estudillo Ramirez, Saul E. Pomares Hernandez

Abstract:

Multimedia distributed systems deal with heterogeneous data, such as texts, images, graphics, video and audio. The specification of temporal relations among different data types and distributed sources is an open research area. This paper proposes a fully distributed synchronization model to be used in multimedia systems. One original aspect of the model is that it avoids the use of a common reference (e.g. wall clock and shared memory). To achieve this, all possible multimedia temporal relations are specified according to their causal dependencies.

Keywords: Multimedia, Distributed Systems, Partial Ordering, Temporal Synchronization

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Authors: Yusuf Ziya Ayık, Ferhat Kahveci

Abstract:

Currently, database management systems have various tools such as backup and maintenance, and also provide statistical information such as resource usage and security. In terms of query performance, this paper covers query optimization, views, indexed tables, pre-computation materialized view, query performance analysis in which query plan alternatives can be created and the least costly one selected to optimize a query. Indexes and views can be created for related table columns. The literature review of this study showed that, in the course of time, despite the growing capabilities of the database management system, only database administrators are aware of the need for dealing with archival and transactional data types differently. These data may be constantly changing data used in everyday life, and also may be from the completed questionnaire whose data input was completed. For both types of data, the database uses its capabilities; but as shown in the findings section, instead of repeating similar heavy calculations which are carrying out same results with the same query over a survey results, using materialized view results can be in a more simple way. In this study, this performance difference was observed quantitatively considering the cost of the query.

Keywords: Materialized view, pre-computation, query cost, query performance.

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Authors: Paul Dallyn, Ashraf El-Hamalawi, Alessandro Palmeri, Bob Knight

Abstract:

In order to better understand the long term implications of the grout wear failure mode in large-diameter plainsided grouted connections, a numerical model has been developed and calibrated that can take advantage of existing operational plant data to predict the wear accumulation for the actual load conditions experienced over a given period, thus limiting the requirement for expensive monitoring systems. This model has been derived and calibrated based on site structural condition monitoring (SCM) data and supervisory control and data acquisition systems (SCADA) data for two operational wind turbine generator substructures afflicted with this challenge, along with experimentally derived wear rates.

Keywords: Grouted Connection, Numerical Model, Offshore Structure, Wear, Wind Energy.

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