Search results for: Organizational learning

Authors: Abdelouahab Errida, Bouchra Lotfi, Elalami Semma

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In this paper, we try to address the topic of multiple change management by adopting an engineered research methodology, conducted within a Moroccan company during its implementation of several change projects that aim at improving its supply chain management performance. Firstly, we present the key concepts related to our research, namely change management, multiproject management and supply chain management. Then, we try to assess how the change management and multi-project management are applied in this company. Finally, we try to propose an approach that will help managers in dealing with multiple change projects. This approach proposes to integrate change management, project management and multi-project management for managing change projects according to three organizational levels: executive level, project portfolio level and change project level.

Keywords: Change management, multi-project management, project management, change portfolio, supply chain management.

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Authors: Pelin Yildiz

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The term interactive education indicates the meaning related with multidisciplinary aspects of distance education following contemporary means around a common basis with different functional requirements. The aim of this paper is to reflect the new techniques in education with the new methods and inventions. These methods are better supplied by interactivity. The integration of interactive facilities in the discipline of education with distance learning is not a new concept but in addition the usage of these methods on design issue is newly being adapted to design education. In this paper the general approach of this method and after the analysis of different samples, the advantages and disadvantages of these approaches are being identified. The method of this paper is to evaluate the related samples and then analyzing the main hypothesis. The main focus is to mention the formation processes of this education. Technological developments in education should be filtered around the necessities of the design education and the structure of the system could then be formed or renewed. The conclusion indicates that interactive methods of education in design issue is a meaning capturing not only technical and computational intelligence aspects but also aesthetical and artistic approaches coming together around the same purpose.

Keywords: Interactive education, distance learning, designeducation, computational intelligence.

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Authors: C. N. Vanitha, M. Usha

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In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: Neural networks, pattern learning, security, wireless sensor networks.

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Authors: Abeer Aljohani

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The COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred as corona virus which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as Omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. Numerous COVID-19 cases have produced a huge burden on hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease based on the symptoms and medical history of the patient. As machine learning is a widely accepted area and gives promising results for healthcare, this research presents an architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard University of California Irvine (UCI) dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques on the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and Principal Component Analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, Receiver Operating Characteristic (ROC) and Area under Curve (AUC). The results depict that Decision tree, Random Forest and neural networks outperform all other state-of-the-art ML techniques. This result can be used to effectively identify COVID-19 infection cases.

Keywords: Supervised machine learning, COVID-19 prediction, healthcare analytics, Random Forest, Neural Network.

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Authors: F. Demir

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Strategic Management is highly critical for all types of organizations. This paper examines maturity level of strategic management practices of public and private sector organizations in Turkey, and presents a conceptual model for assessing the maturity of strategic management in any organization. This research focuses on R&D intensive organizations (RDO) because it is claimed that such organizations are more innovative and innovation is a critical part of the model. The Strategic management maturity model (S-3M) is basically composed of six maturity levels with five different dimensions. Based on 63 organizations, the findings reveal that the average maturity of all organizations in the sample group is three out of five. It corresponds to the stage of ‘performed’. Results simply show that the majority of organizations from various industries and sectors implement strategic management activities; however, they experience multiple challenges to optimize strategic management processes and integrate organizational components with business strategies. Briefly, they struggle to become an innovative organization.

Keywords: Strategic management, innovation, developing countries, research and development.

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Authors: Sobhani U. Pilapitiya, Tharanga Peiris

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Software engineering curriculums generally consist of industry-based practices such as project-based learning (PBL) which mainly focuses on efficient and innovative product development. These approaches can be tailored and used in project-based modules in software engineering curriculums. However, there are very limited attempts in the area especially related to Sri Lankan context. This paper describes a tailored pedagogical approach and its results of using design sprint which can be used for project-based modules in software engineering (SE) curriculums. A controlled group of second year software engineering students was selected for the study. The study results indicate that all of the students agreed that the design sprint approach is effective in group-based projects and 83% of students stated that it minimized the re-work compared to traditional project approaches. The tailored process was effective, easy to implement and produced desired results at the end of the session while providing students an enjoyable experience.

Keywords: design sprint, project-based learning, software engineering, curriculum

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Authors: Vasudev Das

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The specific problem is that private and public organizational leaders often do not understand the importance of sonic therapeutic intervention in preventing financial fraud. The study aimed to explore sonic therapeutic intervention practitioners' lived experiences regarding the value of sonic therapeutic intervention in preventing financial fraud. The data collection methods were semi-structured interviews of purposeful samples and documentary reviews, which were analyzed thematically. Four themes emerged from the analysis of interview transcription data: Sonic therapeutic intervention enabled self-control, pro-spiritual values, consequentiality mindset, and post-conventional consciousness. The itemized four themes helped non-engagement in financial fraud. Implications for positive social change include enhanced financial fraud management, more significant financial leadership, and result-oriented decision-taking in the financial market. Also, the study results can improve the increased de-escalation of anxiety/stress associated with defrauding.

Keywords: consciousness, consequentiality, rehabilitation, reintegration

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Authors: Ahlam Elkilani, Bayan Elsheikh Ali, Rasha Abu Romman, Amjed Al-mousa, Belal Sababha

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The amount of energy the world uses doubles every 20 years. Green homes play an important role in reducing the residential energy demand. This paper presents a platform that is intended to learn the behavior of home residents and build a profile about their habits and actions. The proposed resident aware home controller intervenes in the operation of home appliances in order to save energy without compromising the convenience of the residents. The presented platform can be used to simulate the actions and movements happening inside a home. The paper includes several optimization techniques that are meant to save energy in the home. In addition, several test scenarios are presented that show how the controller works. Moreover, this paper shows the computed actual savings when each of the presented techniques is implemented in a typical home. The test scenarios have validated that the techniques developed are capable of effectively saving energy at homes.

Keywords: Green Home, Resident Aware, Resident Profile, Activity Learning, Machine Learning.

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Authors: F. May Liou, Y. C. Edwin Tang

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This paper aims to provide a conceptual framework to examine competitive disadvantage of banks that suffer from poor performance. Banks generate revenues mainly from the interest rate spread on taking deposits and making loans while collecting fees in the process. To maximize firm value, banks seek loan growth and expense control while managing risk associated with loans with respect to non-performing borrowers or narrowing interest spread between assets and liabilities. Competitive disadvantage refers to the failure to access imitable resources and to build managing capabilities to gain sustainable return given appropriate risk management. This paper proposes a four-quadrant framework of organizational typology is subsequently proposed to examine the features of competitive disadvantage in the banking sector. A resource configuration model, which is extracted from CAMEL indicators to examine the underlying features of bank failures.

Keywords: Bank failure, CAMEL, competitive disadvantage, resource configuration.

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Authors: Klaus Stranacher, Vesna Krnjic, Thomas Zefferer

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The public sector holds large amounts of data of various areas such as social affairs, economy, or tourism. Various initiatives such as Open Government Data or the EU Directive on public sector information aim to make these data available for public and private service providers. Requirements for the provision of public sector data are defined by legal and organizational frameworks. Surprisingly, the defined requirements hardly cover security aspects such as integrity or authenticity. In this paper we discuss the importance of these missing requirements and present a concept to assure the integrity and authenticity of provided data based on electronic signatures. We show that our concept is perfectly suitable for the provisioning of unaltered data. We also show that our concept can also be extended to data that needs to be anonymized before provisioning by incorporating redactable signatures. Our proposed concept enhances trust and reliability of provided public sector data.

Keywords: Trusted Public Sector Data, Integrity, Authenticity, Reliability, Redactable Signatures.

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Authors: Shima Nikanjam, Badiossadat Hassanpour, Adi Irfan Che Ani

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The design process in architecture education is based upon the Learning-by-Doing method, which leads students to understand how to design by practicing rather than studying. First-year design studios, as starting educational stage, provide integrated knowledge and skills of design for newly jointed architecture students. Within the basic design studio environment, students are guided to transfer their abstract thoughts into visual concrete decisions under the supervision of design educators for the first time. Therefore, introductory design studios have predominant impacts on students’ operational thinking and designing. Architectural design thinking is quite different from students’ educational backgrounds and learning habits. This educational challenge at basic design studios creates a severe need to study the reality of design education at foundation year and define appropriate educational methods with convenient project types with the intention of enhancing architecture education quality. Material for this study has been gathered through long-term direct observation at a first year second semester design studio at the faculty of architecture at EMU (known as FARC 102), fall and spring academic semester 2014-15. Distribution of a questionnaire among case study students and interviews with third and fourth design studio students who passed through the same methods of education in the past 2 years and conducting interviews with instructors are other methodologies used in this research. The results of this study reveal a risk of a mismatch between the implemented teaching method, project type and scale in this particular level and students’ learning styles. Although the existence of such risk due to varieties in students’ profiles could be expected to some extent, recommendations can support educators to reach maximum compatibility.

Keywords: Architecture education, basic design studio, educational method, forms creation skill.

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Authors: Piedad Garrido, Fernando Repulles, Andy Bloor, Julio A. Sanguesa, Jesus Gallardo, Vicente Torres, Jesus Tramullas

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With the emergence and development of Information and Communications Technologies (ICTs), Higher Education is experiencing rapid changes, not only in its teaching strategies but also in student’s learning skills. However, we have noticed that students often have difficulty when seeking innovative, useful, and interesting learning resources for their work. This is due to the lack of supervision in the selection of good query tools. This paper presents AINA, an Information Retrieval (IR) computer system aimed at providing motivating and stimulating content to both students and teachers working on different areas and at different educational levels. In particular, our proposal consists of an open virtual resource environment oriented to the vast universe of Disney comics and cartoons. Our test suite includes Disney’s long and shorts films, and we have performed some activities based on the Just In Time Teaching (JiTT) methodology. More specifically, it has been tested by groups of university and secondary school students.

Keywords: Information retrieval, animation, educational resources, JiTT.

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Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: Artificial Neural Networks, ANNs, classifier algorithms, credit risk assessment, logistic regression, machine learning, support vector machines.

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Authors: Abdel Aziz, Hadia H.

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Since the 1980s, banks and financial service institutions have been running in an endless race of innovation to cope with the advancing technology, the fierce competition, and the more sophisticated and demanding customers. In order to guide their innovation efforts, several researches were conducted to identify the success and failure factors of new financial services. These mainly included organizational factors, marketplace factors and new service development process factors. They almost all emphasized the importance of customer and market orientation as a response to the highly perceptual and intangible characteristics of financial services. However, they deemphasized the critical characteristics of high involvement of risk and close correlation with the economic conditions, a factor that heavily contributed to the Global financial Crisis of 2008. This paper reviews the success and failure factors of new financial services. It then adds new perspectives emerging from the analysis of the role of innovation in the global financial crisis.

Keywords: Financial innovation, global financial crisis, lessons learned from global financial crisis, success factors in financial innovation.

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Authors: Václav Maněna, Roman Dostál, Štěpán Hubálovský

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Social networks play an important role in the lives of children and young people. Along with the high penetration of mobile technologies such as smartphones and tablets among the younger generation, there is an increasing use of social networks already in elementary school. The paper presents the results of research, which was realized at schools in the Hradec Králové region. In this research, the authors focused on issues related to communications on social networks for children, teenagers and young people in the Czech Republic. This research was conducted at selected elementary, secondary and high schools using anonymous questionnaires. The results are evaluated and compared with the results of the research, which has been realized in 2008. The authors focused on the possibilities of using social networks in education. The paper presents the possibility of using the most popular social networks in education, with emphasis on increasing motivation for learning. The paper presents comparative analysis of social networks, with regard to the possibility of using in education as well.

Keywords: Social networks, motivation, e-learning, mobile technology.

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Authors: Efat Lali Dastjerdi, Hassan Sadeghi Naeini, Hadi Sanjari

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One of the important steps in a safety and risk management system is the economical evaluation of occupational accident and diseases costs in order to decrease accidents from reoccurring in the workplace. This study proposed a plausible method for calculating occupational accident costs and illnesses in work place. This method design for cost estimation takes into account both the personnel, organizational level as well as the community level especially intended for an Iranian work place. The research indicates that a using systematic method for calculating costs which also provides risk evaluation can help managers to plan correctly the investment in health and safety measures. Using this method is that not only is it comprehensive, easy and practical and could be applied in practice by a manager within a short period of time but it also shows the importance of accident costs as well as calculates the real cost of an accident and illnesses.

Keywords: ost calculating model, Economics of health and Safety.

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Authors: Dewan Md. Farid, Jerome Darmont, Nouria Harbi, Nguyen Huu Hoa, Mohammad Zahidur Rahman

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In this paper, a new learning approach for network intrusion detection using naïve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datasets are dynamic, complex and contain large number of attributes. Some of the attributes may be redundant or contribute little for detection making. It has been successfully tested that significant attribute selection is important to design a real world intrusion detection systems (IDS). The purpose of this study is to identify effective attributes from the training dataset to build a classifier for network intrusion detection using data mining algorithms. The experimental results on KDD99 benchmark intrusion detection dataset demonstrate that this new approach achieves high classification rates and reduce false positives using limited computational resources.

Keywords: Attributes selection, Conditional probabilities, information gain, network intrusion detection.

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Authors: Dewan Md. Farid, Nouria Harbi, Emna Bahri, Mohammad Zahidur Rahman, Chowdhury Mofizur Rahman

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Recently, information security has become a key issue in information technology as the number of computer security breaches are exposed to an increasing number of security threats. A variety of intrusion detection systems (IDS) have been employed for protecting computers and networks from malicious network-based or host-based attacks by using traditional statistical methods to new data mining approaches in last decades. However, today's commercially available intrusion detection systems are signature-based that are not capable of detecting unknown attacks. In this paper, we present a new learning algorithm for anomaly based network intrusion detection system using decision tree algorithm that distinguishes attacks from normal behaviors and identifies different types of intrusions. Experimental results on the KDD99 benchmark network intrusion detection dataset demonstrate that the proposed learning algorithm achieved 98% detection rate (DR) in comparison with other existing methods.

Keywords: Detection rate, decision tree, intrusion detectionsystem, network security.

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.

Keywords: Lèvy flight, situation awareness, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence.

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Authors: Andreas Jede, Frank Teuteberg

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Cloud computing (CC) has already gained overall appreciation in research and practice. Whereas the willingness to integrate cloud services in various IT environments is still unbroken, the previous CC procurement processes run mostly in an unorganized and non-standardized way. In practice, a sufficiently specific, yet applicable business process for the important acquisition phase is often lacking. And research does not appropriately remedy this deficiency yet. Therefore, this paper introduces a field-tested approach for CC procurement. Based on an extensive literature review and augmented by expert interviews, we designed a model that is validated and further refined through an in-depth real-life case study. For the detailed process description, we apply the event-driven process chain notation (EPC). The gained valuable insights into the case study may help CC research to shift to a more socio-technical area. For practice, next to giving useful organizational instructions we will provide extended checklists and lessons learned.

Keywords: Cloud Procurement Process, IT-Organization, Event-driven Process Chain, In-depth Case Study.

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Authors: Sara Blanc, José V. Benlloch-Dualde, Laura Grindei, Ana C. Torres, Angélica Monteiro

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This paper presents a study that leads an experience by introducing digital learning applied to a case study focused on primary and secondary school garden-based education. The approach represents an example for interaction among different education and research agents at different countries and levels, such as universities, public and private researches and schools, to get involved in the implementation of education for sustainable development that will make students become more sensible to natural environment, more responsible for their consumption, more aware about waste reduction and recycling, more conscious of the sustainable use of natural resources and, at the same time, more ‘digitally competent’. The experience was designed attending to the European digital education context and OECD (Organization for Economic Co-operation and Development) directives in transversal skills education. The paper presents the methodology carried out in the study as well as outcomes obtained from the experience.

Keywords: School gardens, primary education, secondary education, science technology and innovation in education, digital learning, sustainable development goals, university, knowledge transference.

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Authors: Vesile Evrim, Aliyu Awwal

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Today, there is a large number of political transcripts available on the Web to be mined and used for statistical analysis, and product recommendations. As the online political resources are used for various purposes, automatically determining the political orientation on these transcripts becomes crucial. The methodologies used by machine learning algorithms to do an automatic classification are based on different features that are classified under categories such as Linguistic, Personality etc. Considering the ideological differences between Liberals and Conservatives, in this paper, the effect of Personality traits on political orientation classification is studied. The experiments in this study were based on the correlation between LIWC features and the BIG Five Personality traits. Several experiments were conducted using Convote U.S. Congressional- Speech dataset with seven benchmark classification algorithms. The different methodologies were applied on several LIWC feature sets that constituted by 8 to 64 varying number of features that are correlated to five personality traits. As results of experiments, Neuroticism trait was obtained to be the most differentiating personality trait for classification of political orientation. At the same time, it was observed that the personality trait based classification methodology gives better and comparable results with the related work.

Keywords: Politics, personality traits, LIWC, machine learning.

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Authors: Jocelyn Sackey, Priscilla Boahemaa, Mohammed A. Sanda

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This study explored the relationship between occupational stress and the perceived effectiveness of men and women managers in Ghanaian organizations. The exploration is underlined by attempt to understand the degree to which male and female managers in Ghanaian organizations experience occupational stress at the workplace. The purpose is to examine the sources and extents of occupational stress experienced by male and female managers in Ghana. Data was collected using questionnaires and analyzed using both descriptive statistics and correlation analysis. The results showed that female managers in Ghana are more likely to report of more stress experiences in the workplace than their male counterparts. The female managers are more likely to perceive role conflict and alienation as job stressors while the male managers perceived blocked career as a major source of workplace stress. It is concluded that despite the female managers experiencing enormous level of occupational stress, there was no significant differences between their managerial effectiveness and that of the male.

Keywords: Gender, job stress, managerial effectiveness, organizational environment

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Authors: I. Derek Kaka’an, P. Smolianov, S. Dion, C. Schoen, J. Norberg, C. G. Iortimah

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This study examines the current state of football in Nigeria to identify the country's practices, which could be useful internationally, and to determine areas for improvement. Over 200 sources of literature on sport delivery systems in successful sports nations were analyzed to construct a globally applicable model of elite football integrated with mass participation, comprising of the following three levels: macro (socio-economic, cultural, legislative, and organizational), meso (infrastructures, personnel, and services enabling sports programs) and micro level (operations, processes, and methodologies for the development of individual athletes). The model has received scholarly validation and has shown to be a framework for program analysis that is not culturally bound. It has recently been utilized for further understanding such sports systems as US rugby, tennis, soccer, swimming, and volleyball, as well as Dutch and Russian swimming. A questionnaire was developed using the above-mentioned model. Survey questions were validated by 12 experts including academicians, executives from sports governing bodies, football coaches, and administrators. To identify best practices and determine areas for improvement of football in Nigeria, 116 coaches completed the questionnaire. Useful exemplars and possible improvements were further identified through semi-structured discussions with 10 Nigerian football administrators and experts. Finally, a content analysis of the Nigeria Football Federation's website and organizational documentation was conducted. This paper focuses on the micro level of Nigerian football delivery, particularly talent search and development as well as advanced athlete preparation and support. Results suggested that Nigeria could share such progressive practices as the provision of football programs in all schools and full-time coaches paid by governments based on the level of coach education. Nigerian football administrators and coaches could provide better football services affordable for all, where success in mass and elite sports is guided by science focused on athletes' needs. Better implemented could be international best practices such as lifelong guidelines for health and excellence of everyone and integration of fitness tests into player development and ranking as done in best Dutch, English, French, Russian, Spanish, and other European clubs; integration of educational and competitive events for elite and developing athletes as well as fans as done at the 2018 World Cup Russia; and academies with multi-stage athlete nurturing as done by Ajax in Africa as well as Barcelona FC and other top clubs expanding across the world. The methodical integration of these practices into the balanced development of mass and elite football will help contribute to international sports success as well as national health, education, crime control, and social harmony in Nigeria.

Keywords: Football, high performance, mass participation, Nigeria, sport development.

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Authors: Nagore Guerra Bilbao, Clemente Lobato Fraile

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This paper outlines the design and development of the MENDEPRO questionnaire, designed to analyze mentoring performance within a professional development process carried out with professors at the University of the Basque Country, Spain. The study took into account the international research carried out over the past two decades into teachers' professional development, and was also based on a thorough review of the most common instruments used to identify and analyze mentoring styles, many of which fail to provide sufficient psychometric guarantees. The present study aimed to gather empirical data in order to verify the metric quality of the questionnaire developed. To this end, the process followed to validate the theoretical construct was as follows: The formulation of the items and indicators in accordance with the study variables; the analysis of the validity and reliability of the initial questionnaire; the review of the second version of the questionnaire and the definitive measurement instrument. Content was validated through the formal agreement and consensus of 12 university professor training experts. A reduced sample of professors who had participated in a lifelong learning program was then selected for a trial evaluation of the instrument developed. After the trial, 18 items were removed from the initial questionnaire. The final version of the instrument, comprising 33 items, was then administered to a sample group of 99 participants. The results revealed a five-dimensional structure matching theoretical expectations. Also, the reliability data for both the instrument as a whole (.98) and its various dimensions (between .91 and .97) were very high. The questionnaire was thus found to have satisfactory psychometric properties and can therefore be considered apt for studying the performance of mentoring in both induction programs for young professors and lifelong learning programs for senior faculty members.

Keywords: Higher education, mentoring, professional development, university teachers.

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Authors: L. Lindsay, S. A. Coleman, D. Kerr, B. J. Taylor, A. Moorhead

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Cognitive decline and frailty is apparent in older adults leading to an increased likelihood of the risk of falling. Currently health care professionals have to make professional decisions regarding such risks, and hence make difficult decisions regarding the future welfare of the ageing population. This study uses health data from The Irish Longitudinal Study on Ageing (TILDA), focusing on adults over the age of 50 years, in order to analyse health risk factors and predict the likelihood of falls. This prediction is based on the use of machine learning algorithms whereby health risk factors are used as inputs to predict the likelihood of falling. Initial results show that health risk factors such as long-term health issues contribute to the number of falls. The identification of such health risk factors has the potential to inform health and social care professionals, older people and their family members in order to mitigate daily living risks.

Keywords: Classification, falls, health risk factors, machine learning, older adults.

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Authors: G. Türkmen, Ö. Tekin, K. Kurtuluş, Y. Y. Yurtseven, M. Baran

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A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: Convolutional Neural Networks, deep learning, image classification, scanning electron microscope.

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Authors: Dim En Nyaung, Thin Lai Lai Thein

Abstract:

Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: Opinion Mining, Opinion Summarization, Sentiment Analysis, Text Mining.

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Authors: M. M. Mashhadi, K. Mohajeri, M. D. Nayeri

Abstract:

Positioning the organization in the strategic environment of its industry is one of the first and most important phases of the organizational strategic planning and in today knowledge-based economy has its importance been duplicated for higher education institutes as the centers of education, knowledge creation and knowledge worker training. Up to now, various models with diverse approaches have been applied to investigate organizations- strategic position in different industries. Regarding the essential importance and strategic role of quality in higher education institutes, in this study, a quality-oriented approach has been suggested to positioning them in their strategic environment. Then the European Foundation of Quality Management (EFQM) model has been adopted to position the top Iranian business schools in their strategic environment. The result of this study can be used in strategic planning of these institutes as well as the other Iranian business schools.

Keywords: Strategic planning, Strategic positioning, Quality, EFQM model, Higher education institutions.

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Authors: L. N. A. Chandana Jayawardena, Ales Gregar

Abstract:

Occupational Self Efficacy (OSE) reflects the conviction of a person’s ability to fulfill his job related behavior at a perfectly acceptable level to the employer. Transformational leadership improves followers’ commitment by influencing their needs, values, and self-esteem. Employees also develop a dyadic relationship with their immediate superiors. Study was conducted amongst one hundred and twenty two (122) bank managers in Sri Lanka. They were selected based on multi-stage (seniority in the hierarchy, gender, department-wise etc.) stratified random sampling. Major objectives of this study were to analyze the impact of Transformational leadership style, and OSE along with Sociodemographic factors, and Career, Job and Organizational experience, to the Career satisfaction of managers. SPSS software was used for parametric and non-parametric statistical analyses. Career satisfaction had positive impacts with their Transformational leadership style, and their relationships with the immediate superior. Impact of sociodemographic factors, and career exposure to career satisfaction was assessed.

Keywords: Career success, Relationship with immediate superior, Transformational leadership.

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