Search results for: Face datasets

Authors: Melek Oktay, Ayşe Betül Gülbağcı, Mustafa Sarıöz

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Enterprise applications are complex systems that are hard to develop and deploy in organizations. Although software application development tools, frameworks, methodologies and patterns are rapidly developing; many projects fail by causing big costs. There are challenging issues that programmers and designers face with while working on enterprise applications. In this paper, we present the three of the significant issues: Architectural, technological and performance. The important subjects in each issue are pointed out and recommendations are given. In architectural issues the lifecycle, meta-architecture, guidelines are pointed out. .NET and Java EE platforms are presented in technological issues. The importance of performance, measuring performance and profilers are explained in performance issues.

Keywords: Enterprise Applications, Architecture, Technology, Performance.

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Authors: Farzaneh Noori

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Entrepreneurs face different sort of difficulties especially with customers, organizations and employees. Emotional intelligence which is the ability to understand and control the emotions is an important factor to help entrepreneurs end up challenges to the result they prefer. So it is assumed that entrepreneurs especially those who have passed the first challenging years of starting a new business, have high emotional intelligence. In this study the Iranian established entrepreneurs have been surveyed. According to Iran Gem 2014 report the percentage of established entrepreneur in Iran is 10.92%. So by using Cochran sample formula (1%) 96 Iranian established entrepreneurs have been selected and Emotional intelligence appraisal questionnaire distributed to them. The SPSS19 result shows high emotional intelligence in Iranian established entrepreneurs.

Keywords: Emotional intelligence, Emotional intelligence appraisal questionnaire, Entrepreneurs, Iran.

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Authors: Javad Rahimipour Anaraki, Saeed Samet, Mahdi Eftekhari, Chang Wook Ahn

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Feature selection and attribute reduction are crucial problems, and widely used techniques in the field of machine learning, data mining and pattern recognition to overcome the well-known phenomenon of the Curse of Dimensionality. This paper presents a feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset. It consists of two components: 1) a measure for feature subset evaluation, and 2) a search strategy. For the evaluation measure, we have employed the fuzzy-rough dependency degree (FRFDD) of the lower approximation-based fuzzy-rough feature selection (L-FRFS) due to its effectiveness in feature selection. As for the search strategy, a modified version of a binary shuffled frog leaping algorithm is proposed (B-SFLA). The proposed feature selection method is obtained by hybridizing the B-SFLA with the FRDD. Nine classifiers have been employed to compare the proposed approach with several existing methods over twenty two datasets, including nine high dimensional and large ones, from the UCI repository. The experimental results demonstrate that the B-SFLA approach significantly outperforms other metaheuristic methods in terms of the number of selected features and the classification accuracy.

Keywords: Binary shuffled frog leaping algorithm, feature selection, fuzzy-rough set, minimal reduct.

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Authors: Daniel Chen, George Mamic, Clinton Fookes, Sridha Sridharan

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An automatic method for the extraction of feature points for face based applications is proposed. The system is based upon volumetric feature descriptors, which in this paper has been extended to incorporate scale space. The method is robust to noise and has the ability to extract local and holistic features simultaneously from faces stored in a database. Extracted features are stable over a range of faces, with results indicating that in terms of intra-ID variability, the technique has the ability to outperform manual landmarking.

Keywords: Scale space volume descriptor, feature extraction, 3D facial landmarking

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Authors: Osamah A. Alsayegh

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Kuwait-s electric power system is vertically integrated organization owned and operated by the government. For more than five decades, the government of Kuwait has provided relatively reliable electric services to consumers with subsidized electric service fees. Given the country-s rapid socio-economical development and consequently the increase of electricity demand, a question that inflicts itself: Is it necessary to reform the power system to face the fast growing demand? This paper recommends that the government should consider the private sector as a partner in operating the power system. Therefore, power system restructuring is needed to allow such partnership. There are challenges that prevent such restructuring. Abstract recommendations toward resolving these challenges are proposed.

Keywords: Deregulation, electricity market, ISO, private sector.

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Authors: Chizoba Chinweze, Gwen Abiola-Oloke, Chike Jideani

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There is an urgent need to conserve the biological diversity of the Nigerian Environment for the future and present generation in the face of current energy resources development. This paper gives an in-depth analysis of the impact of oil and gas activities on the biological diversity of the Nigerian Niger Delta area and its consequences on the sustainable development of the host communities as it relates to their social, economic and environmental issues, particularly on the womenfolk who are the key managers of environmental resources. Also reviewed is the frustration of these communities that is reflected in unending conflicts.

Keywords: Biodiversity, energy resources, sustainable development, and women issues.

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Authors: Paúl I. Cornejo

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The elastic energy released through Strombolian explosions has been quite studied, detailing various processes, sources, and precursory events at several volcanoes. We realized an analysis based on the relative partitioning of the elastic energy radiated into the atmosphere and ground by Strombolian-type explosions recorded at El Reventador volcano, using infrasound and seismic signals at high and moderate seismicity episodes during intense eruptive stages of explosive and effusive activity. Our results show that considerable values of Volcano Acoustic-Seismic Ratio (VASR or η) are obtained at high seismicity stages. VASR is a physical diagnostic of explosive degassing that we used to compare eruption mechanisms at El Reventador volcano for two datasets of explosions recorded at a Broad-Band BB seismic and infrasonic station located at ~5 kilometers from the vent. We conclude that the acoustic energy EA released during explosive activity (VASR η = 0.47, standard deviation σ = 0.8) is higher than the EA released during effusive activity; therefore, producing the highest values of η. Furthermore, we realized the analysis and characterization of the eruptive intensity for two episodes at high seismicity, calculating a η three-time higher for an episode of effusive activity with an occasional explosive component (η = 0.32, and σ = 0.42), than a η for an episode of only effusive activity (η = 0.11, and σ = 0.18), but more energetic.

Keywords: Effusive, explosion quakes, explosive, strombolian, VASR.

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Authors: Yukiyo Yamamoto, Jun Furuya, Shintaro Kobayashi

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Using 1km grid datasets representing monthly mean precipitation, monthly mean temperature, and dry matter production (DMP), we considered the regional plant production ability in Southeast and South Asia, and also employed pixel-by-pixel correlation analysis to assess the intensity of relation between climate factors and plant production. While annual DMP in South Asia was approximately less than 2,000kg, the one in most part of Southeast Asia exceeded 2,500 - 3,000kg. It suggested that plant production in Southeast Asia was superior to South Asia, however, Rain-Use Efficiency (RUE) representing dry matter production per 1mm precipitation showed that inland of Indochina Peninsula and India were higher than islands in Southeast Asia. By the results of correlation analysis between climate factors and DMP, while the area in most parts of Indochina Peninsula indicated negative correlation coefficients between DMP and precipitation or temperature, the area in Malay Peninsula and islands showed negative correlation to precipitation and positive one to temperature, and most part of India dominating South Asia showed positive to precipitation and negative to temperature. In addition, the areas where the correlation coefficients exceeded |0.8| were regarded as “susceptible" to climate factors, and the areas smaller than |0.2| were “insusceptible". By following the discrimination, the map implying expected impacts by climate change was provided.

Keywords: Asia, correlation analysis, plant production, precipitation, temperature.

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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: Bruce X. B. Yu, Yan Liu, Keith C. C. Chan

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We propose to record Activities of Daily Living (ADLs) of elderly people using a vision-based system so as to provide better assistive and personalization technologies. Current ADL-related research is based on data collected with help from non-elderly subjects in laboratory environments and the activities performed are predetermined for the sole purpose of data collection. To obtain more realistic datasets for the application, we recorded ADLs for the elderly with data collected from real-world environment involving real elderly subjects. Motivated by the need to collect data for more effective research related to elderly care, we chose to collect data in the room of an elderly person. Specifically, we installed Kinect, a vision-based sensor on the ceiling, to capture the activities that the elderly subject performs in the morning every day. Based on the data, we identified 12 morning activities that the elderly person performs daily. To recognize these activities, we created a HARELCARE framework to investigate into the effectiveness of existing Human Activity Recognition (HAR) algorithms and propose the use of a transfer learning algorithm for HAR. We compared the performance, in terms of accuracy, and training progress. Although the collected dataset is relatively small, the proposed algorithm has a good potential to be applied to all daily routine activities for healthcare purposes such as evidence-based diagnosis and treatment.

Keywords: Daily activity recognition, healthcare, IoT sensors, transfer learning.

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: Machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation.

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Authors: Dhongkyu Yoon

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Environmental statistics reveals that the pollution of acid rain in South Korea is a serious issue. Yet the awareness of people is low. Even after a gradual decrease of pollutant emission in Korea, the acidity has not been reduced. There no boundaries in the atmosphere are set and the influence of the neighboring countries such as China is apparent. Governmental efforts among China, Japan and Korea have been made on this issue. However, not much progress has been observed. Along with the governmental activities, therefore, an active monitoring of the pollution among the countries and the promotion of environmental awareness at the civil level including especially the middle and high schools are highly recommended. It will be this young generation who will face damaged country as inheritance not the current generation.

Keywords: acid rain, international collaboration, pollution, South Korea

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Authors: M. Sharanreddy, P. K. Kulkarni

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Brain tumor is inherently serious and life-threatening disease. Brain tumor builds the intracranial pressure in the brain, by shifting the brain or pushing against the skull, and also damaging nerves and healthy brain tissues. This intracranial pressure affects and interferes with normal brain functionality, which results in generation of abnormal electrical activities from brain. With recent development in the medical engineering and instruments, EEG instruments are able to record the brain electric activities with high accuracy, which establishes EEG as a primary tool for diagnosing the brain abnormalities. Research scholars and general physicians, often face difficulty in understanding EEG patterns. This paper presents the EEG patterns associated with brain tumor by combing medicine theory and neurologist experience. Paper also explains the pros-cons of the EEG based brain tumor identification.

Keywords: Brain tumor, Electroencephalogram (EEG).

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Authors: F. Hadjaj, A. Belghachi, A. Halmaoui, M. Belhadj, H. Mazouz

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A laser is essentially an optical oscillator consisting of a resonant cavity, an amplifying medium and a pumping source. In semiconductor diode lasers, the cavity is created by the boundary between the cleaved face of the semiconductor crystal and air, and has reflective properties as a result of the differing refractive indices of the two media. For a GaAs-air interface a reflectance of 0.3 is typical and therefore the length of the semiconductor junction forms the resonant cavity. To prevent light being emitted in unwanted directions from the junction, sides perpendicular to the required direction are roughened. The objective of this work is to simulate the optical resonator Fabry-Perot and explore its main characteristics, such as FSR, finesse, linewidth, transmission and so on, that describe the performance of resonator.

Keywords: Fabry-Perot Resonator, laser diode.

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Authors: Yucel Ugurlu

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This paper explains a novel approach to human interactive e-learning systems using head posture images. Students- face and hair information are used to identify a human presence and estimate the gaze direction. We then define the human-computer interaction level and test the definition using ten students and seventy different posture images. The experimental results show that head posture images provide adequate information for increasing human-computer interaction in e-learning systems.

Keywords: E-learning, image segmentation, human-presence, gaze-direction, human-computer interaction, LabVIEW

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Authors: Hassan A. Sharafuddin, Chekra A. Allani

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Academicians at the Arab Open University have always voiced their concern about the efficacy of the blended learning process. Based on 75% independent study and 25% face-toface tutorial, it poses the challenge of the predisposition to adjustment. Being used to the psychology of traditional educational systems, AOU students cannot be easily weaned from being spoonfed. Hence they lack the motivation to plunge into self-study. For better involvement of AOU students into the learning practices, it is imperative to diagnose the factors that impede or increase their motivation. This is conducted through an empirical study grounded upon observations and tested hypothesis and aimed at monitoring and optimizing the students’ learning outcome. Recommendations of the research will follow the findings.

Keywords: Academic performance, blended learning, educational psychology, independent study, pedagogy.

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Authors: Christina Van Wingerden

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This paper focuses on a sense of belonging, isolation, and the use of a learning management system as a “third space” for connection and community. Given student use of learning management systems (LMS) for courses on campuses, moderate to high use of social media and hand-held devices, the author explores the possibilities of LMS as a third space. The COVID-19 pandemic has exacerbated student experiences of isolation, and research indicates that students who experience a sense of belonging have a greater likelihood for academic retention and success. The impacts on students of an LMS designed for student employee orientation and training were examined through a mixed methods approach, including a survey, individual interviews, and focus groups. The sample involved 250-450 undergraduate student employees at a US northwestern university. The goal of the study was to find out the efficiency and effectiveness of the orientation information for a wide range of student employees from multiple student affairs departments. And unexpected finding emerged within the study in 2015 and was noted again as a finding in the 2017 study. Students reported feeling like they individually connected to the department, and further to the university because of the LMS orientation. They stated they could see themselves as part of the university community and like they belonged. The orientation, through the LMS, was designed for and occurred online (asynchronous), prior to students traveling and beginning university life for the academic year. The students indicated connection and belonging resulting from some of the design features. With the onset of COVID-19 and prolonged sheltering in place in North America, as well as other parts of the world, students have been precluded from physically gathering to educate and learn. COVID-19 essentially paused face-to-face education in 2020. Media, governments, and higher education outlets have been reporting on widespread college student stress, isolation, loneliness, and sadness. In this context, the author conducted a current mixed methods study (online survey, online interviews) of students in advanced degree programs, like Ph.D. and Ed.D. specifically investigating isolation and sense of belonging. As a part of the study a prototype of a Canvas site was experienced by student interviewees for their reaction of this Canvas site prototype as a “third” space. Some preliminary findings of this study are presented. Doctoral students in the study affirmed the potential of LMS as a third space for community and social academic connection.

Keywords: COVID-19, learning management systems, sense of belonging, third space.

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Authors: G. Sakar, F. Ç. Bolat

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In this study free vibration analysis of aluminum honeycomb sandwich structures were carried out experimentally and numerically. The natural frequencies and mode shapes of sandwich structures fabricated with different configurations for clamped-free boundary condition were determined. The effects of lower and upper face sheet thickness, the core material thickness, cell diameter, cell angle and foil thickness on the vibration characteristics were examined. The numerical studies were performed with ANSYS package. While the sandwich structures were modeled in ANSYS the continuum model was used. Later, the numerical results were compared with the experimental findings.

Keywords: Sandwich structure, free vibration, numeric analysis, 3D model, continuum model.

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Authors: Huang, Wei-Hsiu Chang, Pei-Chann, Wang, Lien-Chun

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The problems with high complexity had been the challenge in combinatorial problems. Due to the none-determined and polynomial characteristics, these problems usually face to unreasonable searching budget. Hence combinatorial optimizations attracted numerous researchers to develop better algorithms. In recent academic researches, most focus on developing to enhance the conventional evolutional algorithms and facilitate the local heuristics, such as VNS, 2-opt and 3-opt. Despite the performances of the introduction of the local strategies are significant, however, these improvement cannot improve the performance for solving the different problems. Therefore, this research proposes a meta-heuristic evolutional algorithm which can be applied to solve several types of problems. The performance validates BBEA has the ability to solve the problems even without the design of local strategies.

Keywords: Combinatorial problems, Artificial Chromosomes, Blocks Mining, Block Recombination

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Authors: Zhipeng Chen, Peng Zhang, Qingyun Liu, Li Guo

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With the Internet becoming the dominant channel for business and life, many IPs are increasingly masked using web proxies for illegal purposes such as propagating malware, impersonate phishing pages to steal sensitive data or redirect victims to other malicious targets. Moreover, as Internet traffic continues to grow in size and complexity, it has become an increasingly challenging task to detect the proxy service due to their dynamic update and high anonymity. In this paper, we present an approach based on behavioral graph analysis to study the behavior similarity of web proxy users. Specifically, we use bipartite graphs to model host communications from network traffic and build one-mode projections of bipartite graphs for discovering social-behavior similarity of web proxy users. Based on the similarity matrices of end-users from the derived one-mode projection graphs, we apply a simple yet effective spectral clustering algorithm to discover the inherent web proxy users behavior clusters. The web proxy URL may vary from time to time. Still, the inherent interest would not. So, based on the intuition, by dint of our private tools implemented by WebDriver, we examine whether the top URLs visited by the web proxy users are web proxies. Our experiment results based on real datasets show that the behavior clusters not only reduce the number of URLs analysis but also provide an effective way to detect the web proxies, especially for the unknown web proxies.

Keywords: Bipartite graph, clustering, one-mode projection, web proxy detection.

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Authors: Nada Azhar

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This paper is drawn from a wider study of the management of gender, age and disability diversity in the banking sector in the Kingdom of Saudi Arabia (KSA), which aims to develop a framework for diversity management (DM) in this sector. The paper focuses on the management of disability diversity. The purpose of the paper is to assist in understanding disability DM in the banking sector in KSA and to make suggestions for its enhancement. Hence, it contributes to filling a research gap, as there is a dearth of literature on disability DM, in KSA in general, and in the banking sector specifically. Discrimination against people with disabilities is a social issue that has not been entirely overcome in any society. However, in KSA, Islam informs almost every aspect of daily life including work, and Islam is against discrimination. Hence, in KSA, there are regulations to accommodate people with disabilities; however, employers are still free not to hire job applicants with disabilities specifically because of their condition. Indeed, disabled people are almost entirely absent from the labour market. There are 12 Saudi-owned or part-Saudi-owned banks in KSA and two managers from each of these were interviewed, making a total of 24. The interviews aimed to investigate empirically the understanding of managers in the banking sector in KSA of diversity management, including disability DM, in the banking sector. The interview data were analysed using thematic analysis. Two interviewees stated that banks used the employment of people with disabilities to enhance their corporate image, while five expressed the opinion that disabled employees could contribute to the bank provided they did not have to deal with customers face-to-face. Nine of the interviewees perceived that disabled employees could be of value to the bank for their own sake, not only in ‘behind the scenes’ roles. Another two interviewees mentioned that employing disabled people could be part of the bank’s community service programme and one thought it would be part of the bank’s Saudisation efforts. The remaining five interviewees did not know how disabled people could contribute to the bank. The findings show that disability DM in the banking sector in KSA is a relatively new concept, and is not yet well understood. In the light of the findings, in order to achieve the purpose of the paper, the following suggestions were made for the enhancement of disability DM in the banking sector in KSA. A change in attitudes towards disabled people is necessary. Such a change in the workplace can only be achieved if a top-down approach is taken to the integration of disabled people. Hence, it is suggested that management and employees follow a course in disability awareness. Further, a diversity officer in the HR department could enhance the integration of disabled people into the banking workforce. It is also suggested that greater government support is required through closely monitored and enforced anti-discrimination legislation. Moreover, flexible working arrangements such as part-time work would facilitate the employment of disabled people and benefit other groups of employees.

Keywords: Banking, disability, diversity management, Kingdom of Saudi Arabia.

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Authors: Mohammad Javad Mollakazemi, Farhad Asadi

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In this paper a real-time obstacle avoidance approach for both autonomous and non-autonomous dynamical systems (DS) is presented. In this approach the original dynamics of the controller which allow us to determine safety margin can be modulated. Different common types of DS increase the robot’s reactiveness in the face of uncertainty in the localization of the obstacle especially when robot moves very fast in changeable complex environments. The method is validated by simulation and influence of different autonomous and non-autonomous DS such as important characteristics of limit cycles and unstable DS. Furthermore, the position of different obstacles in complex environment is explained. Finally, the verification of avoidance trajectories is described through different parameters such as safety factor.

Keywords: Limit cycles, Nonlinear dynamical system, Real time obstacle avoidance.

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Authors: Al-Amin Bhuiyan, Chang Hong Liu

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This paper addresses the development of an intelligent vision system for human-robot interaction. The two novel contributions of this paper are 1) Detection of human faces and 2) Localizing the eye. The method is based on visual attributes of human skin colors and geometrical analysis of face skeleton. This paper introduces a spatial domain filtering method named ?Fuzzily skewed filter' which incorporates Fuzzy rules for deciding the gray level of pixels in the image in their neighborhoods and takes advantages of both the median and averaging filters. The effectiveness of the method has been justified over implementing the eye tracking commands to an entertainment robot, named ''AIBO''.

Keywords: Fuzzily skewed filter, human-robot interface, rmscontrast, skin color segmentation.

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Authors: Malte Brettel, Niklas Friederichsen, Michael Keller, Marius Rosenberg

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The German manufacturing industry has to withstand an increasing global competition on product quality and production costs. As labor costs are high, several industries have suffered severely under the relocation of production facilities towards aspiring countries, which have managed to close the productivity and quality gap substantially. Established manufacturing companies have recognized that customers are not willing to pay large price premiums for incremental quality improvements. As a consequence, many companies from the German manufacturing industry adjust their production focusing on customized products and fast time to market. Leveraging the advantages of novel production strategies such as Agile Manufacturing and Mass Customization, manufacturing companies transform into integrated networks, in which companies unite their core competencies. Hereby, virtualization of the process- and supply-chain ensures smooth inter-company operations providing real-time access to relevant product and production information for all participating entities. Boundaries of companies deteriorate, as autonomous systems exchange data, gained by embedded systems throughout the entire value chain. By including Cyber-Physical-Systems, advanced communication between machines is tantamount to their dialogue with humans. The increasing utilization of information and communication technology allows digital engineering of products and production processes alike. Modular simulation and modeling techniques allow decentralized units to flexibly alter products and thereby enable rapid product innovation. The present article describes the developments of Industry 4.0 within the literature and reviews the associated research streams. Hereby, we analyze eight scientific journals with regards to the following research fields: Individualized production, end-to-end engineering in a virtual process chain and production networks. We employ cluster analysis to assign sub-topics into the respective research field. To assess the practical implications, we conducted face-to-face interviews with managers from the industry as well as from the consulting business using a structured interview guideline. The results reveal reasons for the adaption and refusal of Industry 4.0 practices from a managerial point of view. Our findings contribute to the upcoming research stream of Industry 4.0 and support decision-makers to assess their need for transformation towards Industry 4.0 practices. 

Keywords: Industry 4.0., Mass Customization, Production networks, Virtual Process-Chain.

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Authors: T. Taufik, R. Prasetyo, D. Dolan, D. Garinto

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Today-s Voltage Regulator Modules (VRMs) face increasing design challenges as the number of transistors in microprocessors increases per Moore-s Law. These challenges have recently become even more demanding as microprocessors operate at sub voltage range at significantly high current. This paper presents a new multiphase topology with cell configuration for improved performance in low voltage and high current applications. A lab scale hardware prototype of the new topology was design and constructed. Laboratory tests were performed on the proposed converter and compared with a commercially available VRM. Results from the proposed topology exhibit improved performance compared to the commercially available counterpart.

Keywords: Voltage Regulator Modules, dc-dc converters, powerelectronics.

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Authors: H. O. Barbosa, A. C. R. da Silva, C. M. de Almeida, E. M. dos Santos, F. O. de Sousa, F. B. da S. Souza, F. B. da S. Souza, F. de O. Lima, L. H. Albuquerque, R. F. do Valle Cunha

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Software development is intrinsic human-based knowledge-intensive. Due to globalization, software development has become a complex challenge and we usually face barriers related to knowledge management, team building, costly testing processes, especially in distributed settings. In this paper, we present the use of experimental studies to improve our knowledge management process using the Wiki system. According to the results, it was possible to identify learning preferences from our software projects leader team, organize and improve the learning experience of our Wiki, and facilitate collaboration by newcomers to improve Wiki with new contents available in the Wiki.

Keywords: Mobile products, knowledge management process, Wiki system, Global Software Development.

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Authors: F. Di Pillo, G. Capece, L. Cricelli, N. Levialdi

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The liberalization and privatization processes have forced public utility companies to face new competitive challenges, implementing strategies to gain market share and, at the same time, keep the old customers. To this end, many companies have carried out mergers, acquisitions and conglomerations in order to diversify their business. This paper focuses on companies operating in the free energy market in Italy. In the last decade, this sector has undergone profound changes that have radically changed the competitive scenario and have led companies to implement diversification strategies of the business. Our work aims to evaluate the economic and financial performances obtained by energy companies, following the beginning of the liberalization process, verifying the possible relationship with the implemented diversification strategies.

Keywords: Business diversification strategies, M&A, the Italian energy market liberalization, economic and financial performances.

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Authors: Norihiro Nishio, Yuki Deguchi, Takahiro Sugiyama, Yoichi Takebayashi

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We developed a multi-camera control system that a (one) cameraman can operate several cameras at a compact studio. we analyzed a workflow of a cameraman of some program shootings with two cameras and clarified their heavy tasks. The system based on a dynamic workflow which adapts a program progressing and recommends of cameraman. we perform the automation of multicamera controls by modeling of studio environment and perform automatic camera adjustment for suitable angle of view with face detection. Our experiment at a real program shooting showed that one cameraman can carry out the task of shooting sufficiently.

Keywords: Camera work, compact studio, dynamic workflow, shooting support.

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Authors: Sophie Sturup

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Mega urban transport projects (MUTPs) are increasingly being used in urban environments to ameliorate the problem of congestion. However, a number of problems with regard to mega projects have been identified. In particular the seemingly institutionalised over estimation of economic benefits and persistent cost over runs, could mean that the wrong projects are selected, and that the projects that are selected cost more than they should. Studies to date have produced a number of solutions to these problems, perhaps most notably, the various methods for the inclusion of the private sector in project provision. However the problems have shown significant intractability in the face of these solutions. This paper provides a detailed examination of some of the problems facing mega projects and then examines Foucault-s theory of 'governmentality' as a possible frame of analysis which might shed light on the intractability of the problems that have been identified, through an identification of the art of government in which MUTPs occur.

Keywords: Michel Foucault, Governmentality, Mega projects, Transport.

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

Abstract:

Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: Computer vision, deep learning, object detection, semiconductor.

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