Search results for: repetitive labor-intensive tasks

Authors: Pitigalage Chamath Chandira Peiris

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A widely researched domain in the field of image processing in recent times has been single image super-resolution, which tries to restore a high-resolution image from a single low-resolution image. Many more single image super-resolution efforts have been completed utilizing equally traditional and deep learning methodologies, as well as a variety of other methodologies. Deep learning-based super-resolution methods, in particular, have received significant interest. As of now, the most advanced image restoration approaches are based on convolutional neural networks; nevertheless, only a few efforts have been performed using Transformers, which have demonstrated excellent performance on high-level vision tasks. The effectiveness of CNN-based algorithms in image super-resolution has been impressive. However, these methods cannot completely capture the non-local features of the data. Enhancer is a simple yet powerful Transformer-based approach for enhancing the resolution of images. A method for single image super-resolution was developed in this study, which utilized an efficient and effective transformer design. This proposed architecture makes use of a locally enhanced window transformer block to alleviate the enormous computational load associated with non-overlapping window-based self-attention. Additionally, it incorporates depth-wise convolution in the feed-forward network to enhance its ability to capture local context. This study is assessed by comparing the results obtained for popular datasets to those obtained by other techniques in the domain.

Keywords: single image super resolution, computer vision, vision transformers, image restoration

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Authors: Chadia Nasri

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Researches in the field of foreign language learning have indicated the importance of the mastery of the four language skills; speaking, listening, writing and reading. As far as writing is concerned, recent studies have shown that this skill is unavoidable for learning a second language successfully. Writing is characterized as a complex system not easy to achieve. Writing has been proved to be affected by a variety of factors, particularly psychological ones; anxiety, intrinsic motivation, aptitude, etc. Intrinsic motivation is said to be the most influential factors in the foreign language learning process and is considered as the key factor for success. To investigate these two aspects; writing and intrinsic motivation, and the positive correlation between them, our hypothesis is designed on the basis that the degree of learners’ intrinsic motivation helps in facilitating their engagement in the writing tasks. Two questionnaires, one for teachers and the other for students, have been carried out to check the validity of the research hypothesis. As for the teachers’ questionnaire, the results have indicated their awareness of the importance of intrinsic motivation in the learning process and the role it plays in the mastery of their students’ writing skill. In addition, teachers have mentioned various procedures aiming at raising their students’ intrinsic motivation to write. The students’ questionnaire, on the other hand, has investigated students’ reasons for learning a foreign language with regard to their attitudes towards writing as an important skill that they need to master. Their answers to the questionnaire together with the marks they got in the second term test they have had in the writing module have been compared to see whether students’ writing proficiency can be determined by the degree of their intrinsic motivation. The comparison of the collected data has shown the positive correlation between both aspects.

Keywords: foreign language learning, intrinsic motivation, motivation, writing proficiency

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Authors: Beier Zhu, Rui Zhang, Qi Song

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Text contained within fixed-layout documents can be of great semantic value and so requires a high localization accuracy, such as ID cards, invoices, cheques, and passports. Recently, algorithms based on deep convolutional networks achieve high performance on text detection tasks. However, for text localization in fixed-layout documents, such algorithms detect word bounding boxes individually, which ignores the layout information. This paper presents a novel architecture built on convolutional neural networks (CNNs). A global text localization network and a regional bounding-box regression network are introduced to tackle the problem in a coarse-to-fine manner. The text localization network simultaneously locates word bounding points, which takes the layout information into account. The bounding-box regression network inputs the features pooled from arbitrarily sized RoIs and refine the localizations. These two networks share their convolutional features and are trained jointly. A typical type of fixed-layout documents: ID cards, is selected to evaluate the effectiveness of the proposed system. These networks are trained on data cropped from nature scene images, and synthetic data produced by a synthetic text generation engine. Experiments show that our approach locates high accuracy word bounding boxes and achieves state-of-the-art performance.

Keywords: bounding box regression, convolutional networks, fixed-layout documents, text localization

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Authors: Natalia Selezneva

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The level of crime depends on a number of factors, such as political and economic instability, social inequality and ineffective legislation. A special place in the overall level of crime takes juvenile delinquency. United Nations Standard Minimum developed rules for the administration of juvenile justice (The Beijing Rules), in order to ensure the rights of juvenile offenders under the various legal systems. Most countries support these recommendations, and Russia is no exception. Russia's criminal code establishes the minimum age of criminal liability; types of crimes for which the possible involvement of minors to justice; punishment; sentencing and execution of punishment for minors. However, these provisions cause heated debates in the scientific literature. The high level of juvenile crime indicates the ineffectiveness of legal regulation of criminal liability of minors. In order to ensure compliance with international standards require new and modern approaches to improve national legislation and practice of its application. Achieving this goal will be achieved through the following tasks: 1. Create sub-branches of law regulating the legal status of minors; 2. Improving the types of penalties; 3. The possibility of using alternative measures; 4. The introduction of the procedure of extrajudicial settlement of the conflict. The criminal law of each country depends on the historical, national and cultural characteristics. The development of the Russian legislation taking into account international experience is extremely essential and will be a new stage in the formation of a legal state, especially in the sphere of protection of the rights of juvenile offenders.

Keywords: criminal law, juvenile offender, punishment, the age of criminal responsibility

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Authors: Esther Chiramal, Kelvin Soh Boon Kai

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Explainable AI is a strong strategy implemented to understand complex black-box model predictions in a human-interpretable language. It provides the evidence required to execute the use of trustworthy and reliable AI systems. On the other hand, however, it also opens the door to locating possible vulnerabilities in an AI model. Traditional adversarial text attack uses word substitution, data augmentation techniques, and gradient-based attacks on powerful pre-trained Bidirectional Encoder Representations from Transformers (BERT) variants to generate adversarial sentences. These attacks are generally white-box in nature and not practical as they can be easily detected by humans e.g., Changing the word from “Poor” to “Rich”. We proposed a simple yet effective Grey-box cum Black-box approach that does not require the knowledge of the model while using a set of surrogate Transformer/BERT models to perform the attack using Explainable AI techniques. As Transformers are the current state-of-the-art models for almost all Natural Language Processing (NLP) tasks, an attack generated from BERT1 is transferable to BERT2. This transferability is made possible due to the attention mechanism in the transformer that allows the model to capture long-range dependencies in a sequence. Using the power of BERT generalisation via attention, we attempt to exploit how transformers learn by attacking a few surrogate transformer variants which are all based on a different architecture. We demonstrate that this approach is highly effective to generate semantically good sentences by changing as little as one word that is not detectable by humans while still fooling other BERT models.

Keywords: BERT, explainable AI, Grey-box text attack, transformer

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Authors: Farzad Jafarpour Taher, Maghsud Solimanpur

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Production planning is one of the complex tasks in multi-product firms that produce a wide range of products. Since resources in mass production companies are limited and different products use common resources, there must be a careful plan so that firms can respond to customer needs efficiently. Azar-battery Company is a firm that provides twenty types of products for its customers. Therefore, careful planning must be performed in this company. In this research, the current conditions of Azar-battery Company were investigated to provide a mathematical programming model to determine the optimum production rate of the products in this company. The production system of this company is multi-stage, multi-product and multi-period. This system is studied in terms of a one-year planning horizon regarding the capacity of machines and warehouse space limitation. The problem has been modeled as a linear programming model with deterministic demand in which shortage is not allowed. The objective function of this model is to minimize costs (including raw materials, assembly stage, energy costs, packaging, and holding). Finally, this model has been solved by Lingo software using the branch and bound approach. Since the computation time was very long, the solver interrupted, and the obtained feasible solution was used for comparison. The proposed model's solution costs have been compared to the company’s real data. This non-optimal solution reduces the total production costs of the company by about %35.

Keywords: multi-period, multi-product production, multi-stage, production planning

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Authors: G. Erkomaishvili, M. Kobalava, T. Lazariashvili, N. Damenia

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Current state of foreign direct investment (FDI) in Georgia is analyzed and evaluated in the paper, the existing legislative background for regulating investments and stimulating policies to attract investments are shown. It is noted that in developing countries encouragement of investment activity, support and implementation are of the most important tasks, implying a consistent investment policy, investor-friendly tax regime and the legal system, reducing administrative barriers and restrictions, fare competitive conditions and business development infrastructure. The work deals with the determining factor of FDIs and the main directions of stimulation, as well as prospective industries where new investments are needed. Contributing and hindering factors and stimulating measures are analyzed. As a result of the research, the direct and indirect factors attracting FDI have been identified. Facilitating factors to FDI inflow are as follows: simplicity of starting business, geopolitical location, low taxes, access to credit, ease of ownership registration, natural resources, low burden of regulations, low level of corruption and low crime rates. Hindering factors to FDI inflow are as follows: small market, lack of policy for attracting investments, low qualification of the workforce (despite the large number of unemployed people it is difficult to find workers with necessary special skills and qualifications), high interest rates, instability of national currency exchange rate, presence of conflict zones within the country and so forth.

Keywords: foreign direct investment, investor, investment attracting marketing policies, reinvestment

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Authors: Catalina Sau Man Ng

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Workplace bullying is generally defined as a form of direct and indirect maltreatment at work including harassing, offending, socially isolating someone or negatively affecting someone’s work tasks. Workplace bullying is unfortunately commonplace around the world, which makes it a social phenomenon worth researching. However, the measurements and estimation methods of workplace bullying seem to be diverse in different studies, leading to dubious results. Hence, this paper attempts to examine the prevalence of workplace bullying in Hong Kong using the latent class analysis approach. It is often argued that the traditional classification of workplace bullying into the dichotomous 'victims' and 'non-victims' may not be able to fully represent the complex phenomenon of bullying. By treating workplace bullying as one latent variable and examining the potential categorical distribution within the latent variable, a more thorough understanding of workplace bullying in real-life situations may hence be provided. As a result, this study adopts a latent class analysis method, which was tested to demonstrate higher construct and higher predictive validity previously. In the present study, a representative sample of 2814 employees (Male: 54.7%, Female: 45.3%) in Hong Kong was recruited. The participants were asked to fill in a self-reported questionnaire which included measurements such as Chinese Workplace Bullying Scale (CWBS) and Chinese Version of Depression Anxiety Stress Scale (DASS). It is estimated that four latent classes will emerge: 'non-victims', 'seldom bullied', 'sometimes bullied', and 'victims'. The results of each latent class and implications of the study will also be discussed in this working paper.

Keywords: latent class analysis, prevalence, survey, workplace bullying

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Authors: Eid Abo Hamza, Ahmed Helal

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Purpose: The objective is to measure the capacity of the working memory, ie. the maximum number of elements that can be retrieved and processed, by measuring the basic functions of working memory (inhibition/transfer/update), and also to investigate its relationship to life stress and depressive thoughts. Methods: The study sample consisted of 50 students from Egypt. A cognitive task was designed to measure the working memory capacity based on the determinants found in previous research, which showed that cognitive tasks are the best measurements of the functions and capacity of working memory. Results: The results indicated that there were statistically significant differences in the level of life stress events (high/low) on the task of measuring the working memory capacity. The results also showed that there were no statistically significant differences between males and females or between academic major on the task of measuring the working memory capacity. Furthermore, the results reported that there was no statistically significant effect of the interaction of the level of life stress (high/low) and gender (male/female) on the task of measuring working memory capacity. Finally, the results showed that there were significant differences in the level of depressive thoughts (high/low) on the task of measuring working memory. Conclusions: The current research concludes that neither the interaction of stressful life events, gender, and academic major, nor the interaction of depressive thoughts, gender, and academic major, influence on working memory capacity.

Keywords: working memory, depression, stress, life event

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Authors: Evangelia-Chrysanthi Kouklari, Stella Tsermentseli, Claire P. Monks

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Executive function (EF) refers to a set of future-oriented and goal-directed cognitive skills that are crucial for problem solving and social behaviour, as well as the ability to organise oneself. It has been suggested that EF could be conceptualised as two distinct but interrelated constructs, one emotional (hot) and one cognitive (cool), as it facilitates both affective and cognitive regulation. Cool EF has been found to be strongly related to Theory of Mind (ToM) that is the ability to infer mental states, but research has not taken into account the association between hot EF and ToM in Autism Spectrum Disorder (ASD) to date. The present study investigates the associations between both hot and cool EF and ToM in school-aged children with ASD. This cross-sectional study assesses 79 school-aged children with ASD (7-15 years) and 91 controls matched for age and IQ, on tasks tapping cool EF (working memory, inhibition, planning), hot EF (effective decision making, delay discounting), and ToM (emotional understanding and false/no false belief). Significant group differences in each EF measure support a global executive dysfunction in ASD. Strong associations between hot EF and ToM in ASD are reported for the first time (i.e. ToM emotional understanding and delay discounting). These findings highlight that hot EF also makes a unique contribution to the developmental profile of ASD. Considering the role of both hot and cool EF in association with ToM in individuals with ASD may aid in gaining a greater understanding not just of how these complex multifaceted cognitive abilities relate to one another, but their joint role in the distinct developmental pathway followed in ASD.

Keywords: ASD, executive function, school age, theory of mind

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Authors: Tukundane Hillary

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This research paper analytically evaluates the project control practice levels used by the building construction companies within Kampala, Uganda. The research also assesses the outcome of project control practices on the productivity of the companies. The research was performed to ascertain the current control practices among 160 respondents from various construction companies registered with the Uganda Registration Services Bureau. This research used amalgamation from multiple literature to obtain the variables. The research adopts 34 standard control practices from four vital project control duties: planning, monitoring, analyzing, and reporting. These project control tasks were organized using mean response ratings grounded on their relevance to the construction companies. Results showed that evaluating performance with the use of curves (4.32), timely access to information and encouragement (4.55), report representation using quantitative tools 4.75, and cost value comparison application during analysis (4.76) were rated least among the control practices. On the other hand, the top project control practices included formulation of the project schedule (8.88), Project feasibility validation (8.86), Budgeting for each activity (8.84), Key project route definition (8.81), Team awareness of the budget (8.77), Setting realistic targets for projects (8.50) and Consultation from subcontractors (8.74). From the results obtained by the sample respondents specified, it can be concluded that planning is the most vital project control task practiced in the building construction industry in Uganda. In addition, this research ascertained a substantial relationship between project control practices and the performance of building construction companies. Accordingly, this research recommends that project control practices be effectively observed by both contracting and consulting companies to enhance their overall performance and governance.

Keywords: cost value, project control, cost control, time control, project performance, control practices

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Authors: Mohamed Lotfy, Ghada Soliman

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Recognizing Arabic dot-matrix digits is a challenging problem due to the unique characteristics of dot-matrix fonts, such as irregular dot spacing and varying dot sizes. This paper presents an approach for recognizing Arabic digits printed in dot matrix format. The proposed model is based on Convolutional Neural Networks (CNN) that take the dot matrix as input and generate embeddings that are rounded to generate binary representations of the digits. The binary embeddings are then used to perform Optical Character Recognition (OCR) on the digit images. To overcome the challenge of the limited availability of dotted Arabic expiration date images, we developed a True Type Font (TTF) for generating synthetic images of Arabic dot-matrix characters. The model was trained on a synthetic dataset of 3287 images and 658 synthetic images for testing, representing realistic expiration dates from 2019 to 2027 in the format of yyyy/mm/dd. Our model achieved an accuracy of 98.94% on the expiry date recognition with Arabic dot matrix format using fewer parameters and less computational resources than traditional CNN-based models. By investigating and presenting our findings comprehensively, we aim to contribute substantially to the field of OCR and pave the way for advancements in Arabic dot-matrix character recognition. Our proposed approach is not limited to Arabic dot matrix digit recognition but can also be extended to text recognition tasks, such as text classification and sentiment analysis.

Keywords: computer vision, pattern recognition, optical character recognition, deep learning

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Authors: Lethamaga Tladi, Ray Kekwaletswe

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This study sought to conceptualise a multi-level framework for the effective use of stock ordering system in small enterprises in a rural area context. The interpretive research methodology has been used to enable the researcher to analyse, in-depth, and the subjective meanings of small enterprises’ employees in using the stock ordering system. The empirical data was collected from 13 small enterprises’ employees as participants through semi-structured interviews and observations. Interpretive Phenomenological Analysis (IPA) approach was used to analyse the small enterprises’ employee’s own account of lived experiences in relations to stock ordering system use in terms of their relatedness to, and cognitive engagement with. A case study of Kgautswane, a rural area in Limpopo Province, South Africa, served as a social context where the phenomenon manifested. Technology-Organisation-Environment Theory (TOE), Technology-to-Performance Chain Model (TPC), and Representation Theory (RT) underpinned this study. In this multi-level study, the findings revealed that; At the organisational level, the effective use of stock ordering system was found to be associated with the organisational performance gains such as efficiency, productivity, quality, competitiveness, and market share. Equally so, at the individual level, the effective use of stock ordering system minimised the end-user’s efforts and time to accomplish their tasks, which yields improved individual performance. The Multi-level framework for effective use of stock ordering system was presented.

Keywords: effective use, multi-dimensions of use, multi-level of use, multi-level research, small enterprises, stock ordering system

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Authors: Zanele D. Ngwenya, Mustapha Mohammed, Felix D. Dakora

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Legumes are a major source of biological nitrogen, and therefore play a crucial role in maintaining soil productivity in smallholder agriculture in southern Africa. Through their ability to fix atmospheric nitrogen in root nodules, legumes are a better option for sustainable nitrogen supply in cropping systems than chemical fertilisers. For decades, farmers have been highly receptive to the use of rhizobial inoculants as a source of nitrogen due mainly to the availability of elite rhizobial strains at a much lower compared to chemical fertilisers. To improve the efficiency of the legume-rhizobia symbiosis in African soils would require the use of highly effective rhizobia capable of nodulating a wide range of host plants. This study assessed the morphogenetic diversity, photosynthetic functioning and relative symbiotic effectiveness (RSE) of groundnut, jack bean and soybean microsymbionts in Eswatini soils as a first step to identifying superior isolates for inoculant production. According to the manufacturer's instructions, rhizobial isolates were cultured in yeast-mannitol (YM) broth until the late log phase and the bacterial genomic DNA was extracted using GenElute bacterial genomic DNA kit. The extracted DNA was subjected to enterobacterial repetitive intergenic consensus-PCR (ERIC-PCR) and a dendrogram constructed from the band patterns to assess rhizobial diversity. To assess the N2-fixing efficiency of the authenticated rhizobia, photosynthetic rates (A), stomatal conductance (gs), and transpiration rates (E) were measured at flowering for plants inoculated with the test isolates. The plants were then harvested for nodulation assessment and measurement of plant growth as shoot biomass. The results of ERIC-PCR fingerprinting revealed the presence of high genetic diversity among the microsymbionts nodulating each of the three test legumes, with many of them showing less than 70% ERIC-PCR relatedness. The dendrogram generated from ERIC-PCR profiles grouped the groundnut isolates into 5 major clusters, while the jack bean and soybean isolates were grouped into 6 and 7 major clusters, respectively. Furthermore, the isolates also elicited variable nodule number per plant, nodule dry matter, shoot biomass and photosynthetic rates in their respective host plants under glasshouse conditions. Of the groundnut isolates tested, 38% recorded high relative symbiotic effectiveness (RSE >80), while 55% of the jack bean isolates and 93% of the soybean isolates recorded high RSE (>80) compared to the commercial Bradyrhizobium strains. About 13%, 27% and 83% of the top N₂-fixing groundnut, jack bean and soybean isolates, respectively, elicited much higher relative symbiotic efficiency (RSE) than the commercial strain, suggesting their potential for use in inoculant production after field testing. There was a tendency for both low and high N₂-fixing isolates to group together in the dendrogram from ERIC-PCR profiles, which suggests that RSE can differ significantly among closely related microsymbionts.

Keywords: genetic diversity, relative symbiotic effectiveness, inoculant, N₂-fixing

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Authors: Rafat Alwazna

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Translation teaching is a particular activity that includes translators and interpreters training either inside or outside institutionalised settings, such as universities. It can also serve as a means of teaching other fields, such as foreign languages. Translation teaching began in the twentieth century. Teachers of translation hold the responsibilities of educating students, developing their translation competence and training them to be professional translators. The activity of translation teaching involves various tasks, including curriculum design, course delivery, material writing as well as application and implementation. The present paper addresses translation teaching during COVID-19 outbreak, seeking to find out the challenges encountered by translation teachers in online translation teaching and the discoveries/solutions arrived at to resolve them. The paper makes use of a comprehensive questionnaire, containing closed-ended and open-ended questions to elicit both quantitative as well as qualitative data from about sixty translation teachers who have been teaching translation at BA and MA levels during COVID-19 outbreak. The data shows that about 40% of the participants evaluate their online translation teaching experience during COVID-19 outbreak as enjoyable and exhilarating. On the contrary, no participant has evaluated his/her online translation teaching experience as being not good, nor has any participant evaluated his/her online translation teaching experience as being terrible. The data also presents that about 23.33% of the participants evaluate their online translation teaching experience as very good, and the same percentage applies to those who evaluate their online translation teaching experience as good to some extent. Moreover, the data indicates that around 13.33% of the participants evaluate their online translation teaching experience as good. The data also demonstrates that the majority of the participants have encountered obstacles in online translation teaching and have concurrently proposed solutions to resolve them.

Keywords: online translation teaching, electronic learning platform, COVID-19 outbreak, challenges, solutions

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Authors: Alireza Nikravanshallmani, Jamshid Dehmeshki, Mojtaba Ahmadi

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Modeling of business processes, based on BPMN (Business Process Modeling Notation), helps analysts and managers to understand business processes, and, identify their shortages. These models provide a context to make rational decision of organizing business processes activities in an understandable manner. The purpose of this paper is to provide a framework for better understanding of business processes and their problems by reducing the cognitive load of displayed information for their audience at different managerial levels while keeping the essential information which are needed by them. For this reason, we integrate business process diagrams across the different managerial levels to develop a framework to improve the performance of business process management (BPM) projects. The proposed framework is entitled ‘Business process improvement framework based on managerial levels (BPIML)’. This framework, determine a certain type of business process diagrams (BPD) based on BPMN with respect to the objectives and tasks of the various managerial levels of organizations and their roles in BPM projects. This framework will make us able to provide the necessary support for making decisions about business processes. The framework is evaluated with a case study in a real business process improvement project, to demonstrate its superiority over the conventional method. A questionnaire consisted of 10 questions using Likert scale was designed and given to the participants (managers of Bank Refah Kargaran three managerial levels). By examining the results of the questionnaire, it can be said that the proposed framework provide support for correct and timely decisions by increasing the clarity and transparency of the business processes which led to success in BPM projects.

Keywords: business process management (BPM), business process modeling, business process reengineering (BPR), business process optimizing, BPMN

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Authors: Paulina Pietrzyk-Kowalec

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This paper presents the phenomenon of multilingualism becoming the norm rather than the exception in the European Union. It also seeks to describe the correlation between the command of foreign languages and employability. It is evident that the challenges of today's societies when it comes to employability and to the reality of the current labor market are more and more diversified. Thus, it is one of the crucial tasks of higher education to prepare its students to face this kind of complexity, understand its nuances, and have the capacity to adapt effectively to situations that are common in corporations based in the countries belonging to the EU. From this point of view, the assessment of the impact that the command of foreign languages of European university students could have on the numerous business sectors becomes vital. It also involves raising awareness of future professionals to make them understand the importance of mastering communicative skills in foreign languages that will meet the requirements of students' prospective employers. The direct connection between higher education institutions and the world of business also allows companies to realize that they should rethink their recruitment and human resources procedures in order to take into account the importance of foreign languages. This article focuses on the objective of the multilingualism policy developed by the European Commission, which is to enable young people to master at least two foreign languages, which is crucial in their future careers. The article puts emphasis on the existence of a crucial connection between the research conducted in higher education institutions and the business sector in order to reduce current qualification gaps.

Keywords: cross-cultural communication, employability, human resources, language attitudes, multilingualism

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Authors: Dainius Genys, Vylius Leonavicius, Ricardas Krikstolaitis

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Energy security problems in Lithuania are analyzed on a regular basis; however, there is no comprehensive research on the very issue of the concept of public energy security. There is a lack of attention not only to social determinants of perception of energy security, but also a lack of a deeper analysis of the public opinion. This article aims to research the Lithuanian public perception of energy security. Complex tasks were set during the sociological study. Survey questionnaire consisted of different sets of questions: view of energy security (risk perception, political orientation, and energy security; comprehensiveness and energy security); view of energy risks and threats (perception of energy safety factors; individual dependence and burden; disobedience and risk); view of the activity of responsible institutions (energy policy assessment; confidence in institutions and energy security), demographic issues. In this article, we will focus on two aspects: a) We will analyze public opinion on the most important aspects of energy security and social factors influencing them; The hypothesis is made that public perception of energy security is related to value orientations: b) We will analyze how public opinion on energy policy executed by the government and confidence in the government are intertwined with the concept of energy security. Data of the survey, conducted on May 10-19 and June 7-17, 2013, when Seimas and the government consisted of the coalition dominated by Social Democrats with Labor, Order and Justice Parties and the Electoral Action of Poles, were used in this article. It is important to note that the survey was conducted prior to Russia’s occupation of the Crimea.

Keywords: energy security, public opinion, risk, energy threat, energy security policy

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Authors: Hesham Abd Elkhalek, Sherif M. Hafez, Yasser M. El Fahham

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Allocating limited budget to maintain bridge networks and selecting effective maintenance strategies for each bridge represent challenging tasks for maintenance managers and decision makers. In Egypt, bridges are continuously deteriorating. In many cases, maintenance works are performed due to user complaints. The objective of this paper is to develop a practical and reliable framework to manage the maintenance, repair, and rehabilitation (MR&R) activities of Bridges network considering performance and budget limits. The model solves an optimization problem that maximizes the average condition of the entire network given the limited available budget using Genetic Algorithm (GA). The framework contains bridge inventory, condition assessment, repair cost calculation, deterioration prediction, and maintenance optimization. The developed model takes into account multiple parameters including serviceability requirements, budget allocation, element importance on structural safety and serviceability, bridge impact on network, and traffic. A questionnaire is conducted to complete the research scope. The proposed model is implemented in software, which provides a friendly user interface. The framework provides a multi-year maintenance plan for the entire network for up to five years. A case study of ten bridges is presented to validate and test the proposed model with data collected from Transportation Authorities in Egypt. Different scenarios are presented. The results are reasonable, feasible and within acceptable domain.

Keywords: bridge management systems (BMS), cost optimization condition assessment, fund allocation, Markov chain

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Authors: Vanessa Do, Lauren Smith, Leslie Carver

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It is widely known that individuals with autism spectrum disorder (ASD) may exhibit sensitivity to stimuli. Even at a young age, they tend to display stimuli-related discomfort in their behavior during play. Play serves a crucial role in a child’s early years as it helps support healthy brain development, socio-emotional skills, and adaptation to their environment There is research dedicated to studying infant preferences for toys, especially in regard to: gender preferences, the advantages of promoting play, and the caregiver’s role in their child’s play routines. However, there is a disproportionate amount of literature examining how play patterns may differ in children with sensory sensitivity, such as children diagnosed with ASD. Prior literature has studied and found supporting evidence that individuals with ASD have deficits in social communication and have increased presence of repetitive behaviors and/or restricted interests, which also display in early childhood play patterns. This study aims to examine potential differences in toy preference between infants with (FH+) and without (FH-) a familial history of ASD ages 6. 9, and 12 months old. More specifically, this study will address the question, “do FH+ infants tend to play more with toys that require less social engagement compared to FH- infants?” Infants and their caregivers were recruited and asked to engage in a free-play session in their homes that lasted approximately 5 minutes. The sessions were recorded and later coded offline for engagement behaviors categorized by toy; each toy that the infants interacted with was coded as belonging to one of 6 categories: sensory (designed to stimulate one or more senses such as light-up toys or musical toys) , construction (e.g., building blocks, rubber suction cups), vehicles (e.g., toy cars), instructional (require steps to accomplish a goal such as flip phones or books), imaginative (e.g., dolls, stuffed animals), and miscellaneous (toys that do not fit into these categories). Toy engagement was defined as the infant looking and touching the toy (ILT) or looking at the toy while their caregiver was holding it (IL-CT). Results reported include/will include the proportion of time the infant was actively engaged with the toy out of the total usable video time per subject — distractions observed during the session were excluded from analysis. Data collection is still ongoing; however, the prediction is that FH+ infants will have higher engagement with sensory and construction toys as they require the least amount of social effort. Furthermore, FH+ infants will have the least engagement with the imaginative toys as prior literature has supported the claim that individuals with ASD have a decreased likelihood to engage in play that requires pretend play and other social skills. Looking at what toys are more or less engaging to FH+ infants is important as it provides significant contributions to their healthy cognitive, social, and emotional development. As play is one of the first ways for a child to understand the complexities of the larger world, the findings of this study may help guide further research into encouraging play with toys that are more engaging and sensory-sensitive for children with ASD.

Keywords: autism engagement, children’s play, early development, free-play, infants, toy

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Authors: Min-Hua Wu

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The untranslatability of an established poet’s tour de force is thoroughly explored by Matthew Arnold (1822-1888). In his On Translating Homer (1861), Arnold lists the four most striking poetic qualities of Homer, namely his rapidity, plainness and directness of style and diction, plainness and directness of ideas, and nobleness. He concludes that such celebrated English translators as Cowper, Pope, Chapman, and Mr. Newman are all doomed, due to their respective failure in rendering the totality of the four Homeric poetic qualities. Why poetic translation always amounts to being proven such a mission impossible for the translator? According to Arnold, it is because there constantly exists a mist interposed between the translator’s own literary self-obsession and the objective artistic qualities that reside in the work of the original author. Foregrounding such a seemingly empowering yet actually detrimental poetic mist, he explains why the aforementioned translators fail in their attempts to bring the Homeric charm to the British reader. Drawing on Arnold’s analytical study on Homeric translation, the research attempts to bring Yu Kwang-chung the poet vis-à-vis Yu Kwang-chung the translator, with an aim not so much to find any similar mist as revealed by Arnold between his Chinese poetry and English translation as to probe into a latent and veiled literary and lingual mist interposed between Chinese and English, if not between Chinese and English literatures. The major work studied and analyzed for this study is Yu’s own Chinese poetry and his own English translation collected in The Night Watchman: Yu Kwang-chung 1958-2004. The research argues that the following critical elements that characterizes Yu’s poetics are to a certain extent 'transformed,' if not 'lost,' in his English translation: a. the Chinese pictographic and ideographic unit terms which so unfailingly characterize the poet’s incredible creativity, allowing him to habitually and conveniently coin concrete textual images or word-scapes almost at his own will; b. the subtle wordplay and punning which appear at a reasonable frequency; c. the parallel contrastive repetitive syntactic structure within a single poetic line; d. the ambiguous and highly associative diction in the adjective and noun categories; e. the literary allusion that harks back to the old times of Chinese literature; f. the alliteration that adds rhythm and smoothness to the lines; g. the rhyming patterns that bring about impressive sonority and lingering echo to the ears of the reader; h. the grandeur-imposing and sublimity-arousing word-scaping which hinges on the employment of verbs; i. the meandering cultural heritage that embraces such elements as Chinese medicine and kung fu; and j. other features of the like. Once we appeal to the Arnoldian tribunal and resort to the strict standards of such a Victorian cultural and literary critic who insists 'to see the object as in itself it really is,' we may serve as a potential judge for the tug of war between Yu Kwang-chung the poet and Yu Kwang-chung the translator, a tug of war that will not merely broaden our understating of Chinese poetics but deepen our apprehension of Chinese-English translatology.

Keywords: Yu Kwang-chung, The Night Watchman, poetry translation, Chinese-English translation, translation studies, Matthew Arnold

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Authors: Mohammad Nehal Hasnine, Md Kamrul Hasan Chayon, Md Mobasswer Rahman

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Organizational tendencies towards computer-based information processing have been observed noticeably in the third-world countries. Many enterprises are taking major initiatives towards computerized working environment because of massive benefits of computer-based information processing. However, designing and developing information resource management software for small and mid-size enterprises under budget costs and strict deadline is always challenging for software engineers. Therefore, we introduced an approach to design mid-size enterprise software by using the Waterfall model, which is one of the SDLC (Software Development Life Cycles), in a cost effective way. To fulfill research objectives, in this study, we developed mid-sized enterprise software named “BSK Management System” that assists enterprise software clients with information resource management and perform complex organizational tasks. Waterfall model phases have been applied to ensure that all functions, user requirements, strategic goals, and objectives are met. In addition, Rich Picture, Structured English, and Data Dictionary have been implemented and investigated properly in engineering manner. Furthermore, an assessment survey with 20 participants has been conducted to investigate the usability and performance of the proposed software. The survey results indicated that our system featured simple interfaces, easy operation and maintenance, quick processing, and reliable and accurate transactions.

Keywords: end-user application development, enterprise software design, information resource management, usability

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Authors: Hichem Sassi

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Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

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Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

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Authors: Soundarya Lahari Vedula

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Dalit women make up a significant portion of the Indian population. However, they are victims of age old discrimination. This paper presents a brief background of the Indian caste system which is a hierarchical division placing Dalits at the lowest rank. Dalits are forced to perform menial and harsh tasks. They often face social ostracism. The situation of Dalit women is of unique significance as they face triple discrimination due to their caste, gender, and class. Dalit women are strictly withheld by the rigid boundaries of the caste system. They are discriminated at every stage of their life and are denied access to public places, education and healthcare facilities among others. They face the worst forms of sexual violence. In spite of legislations and international conventions in place, their plight is not adequately addressed. This paper discusses, in brief, the legal mechanism in place to prohibit untouchability. Furthermore, this paper details on the specific human rights violations faced by Dalit women in the social, economic and political spheres. The violations range from discrimination in public places, denial of education and health services, sexual exploitation and barriers to political representation. Finally, this paper identifies certain lacunae in the existing Indian statutes and broadens on the measures to be taken to improve the situation of Dalit women. This paper offers some recommendations to address the plight of Dalit women such as amendments to the existing statutes, effective implementation of legal mechanisms and a more meaningful interpretation of the international conventions.

Keywords: Dalit, caste, class, discrimination, equality

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Authors: Ralph Adaimy, Wassim El-Hajj, Ghassen Ben Brahim, Hazem Hajj, Haidar Safa

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Huge amounts of data and personal information are being sent to and retrieved from web applications on daily basis. Every application has its own confidentiality and integrity policies. Violating these policies can have broad negative impact on the involved company’s financial status, while enforcing them is very hard even for the developers with good security background. In this paper, we propose a framework that enforces security-by-construction in web applications. Minimal developer effort is required, in a sense that the developer only needs to annotate database attributes by a security class. The web application code is then converted into an intermediary representation, called Extended Program Dependence Graph (EPDG). Using the EPDG, the provided annotations are propagated to the application code and run against generic security enforcement rules that were carefully designed to detect insecure information flows as early as they occur. As a result, any violation in the data’s confidentiality or integrity policies is reported. As a proof of concept, two PHP web applications, Hotel Reservation and Auction, were used for testing and validation. The proposed system was able to catch all the existing insecure information flows at their source. Moreover and to highlight the simplicity of the suggested approaches vs. existing approaches, two professional web developers assessed the annotation tasks needed in the presented case studies and provided a very positive feedback on the simplicity of the annotation task.

Keywords: web applications security, secure information flow, program dependence graph, database annotation

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Authors: Nicole Brown

Abstract:

This study considers the illness experience of fibromyalgia patients by using identity boxes. The results improve health care professionals' understanding of patient experiences. Additionally, the concept of the identity boxes may offer a practical solution for helping patients accept the diagnosis of fibromyalgia. Fibromyalgia research traditionally refers to pain experiences and relies on questionnaires, surveys, interviews and some narrative analysis. However, due to the variability in symptoms, symptom levels, and locations, these methods may not be best suited to provide an insight into the patient experience. On the other hand, lengthy interview processes are not easily accessible for sufferers of fibromyalgia. In addition to timelines and diary extracts, this study uses identity boxes as its main data collection method. Participants are asked to find items in response to specific questions and to arrange them in their box. The objects represent the patients' experiences holistically. Participants provide photographs of their identity box at each stage of the process and explain their chosen items. The photographs of the identity boxes and the patients' explanations of their objects and their boxes are subjected to interpretative phenomenological analysis. Despite the unique forms of the completed boxes, common experiences are described: the need for comfort, the role of spirituality and the impact of fibromyalgia on everyday life, that it plays a significant role but those patients are determined not to let it rule their lives. The work with the identity boxes has shown beneficial impact due to the reflective nature involved in the tasks. Further investigations will be needed to identify the long-term impact of identity work using such boxes.

Keywords: biographical disruption, fibromyalgia, illness experience, illness narrative

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Authors: Surichai Butcha, Dararat Khampusaen

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As the Internet is recognized as a high potential and powerful educational tool to access sources of knowledge, plagiarism is an increasing unethical issue found in students’ writing. This paper is deriving from the 1st phase of an on-going study investigating the effects of the supplementary on citing sources on undergraduate students’ writing. The 40 participants were divided into 1 experimental group and 1 control group. Both groups were administered with a questionnaire on knowledge and an interview on attitude related to using sources in writing. Only the experimental group undertook the 4 lessons focusing on using outside sources and citing the original work (quoting, synthesizing, summarizing and paraphrasing) were delivered to them via e-learning tools throughout a semester. Participants were required to produce 4 writing tasks after each lesson. The results were concerned with types and factors on using outside sources in writing of Thai undergraduate EFL students from the survey. The interview results supported and clarified the survey result. In addition, the writing rubrics confirmed the types of plagiarism frequently occurred in students’ writing. The results revealed the types and factors on plagiarism including their perceptions on using the outside sources in their writing from the interview. The discussion shed the lights on cultural dimensions of plagiarism in student writing, roles of teachers, library, and university policy on the rate of plagiarism. Also, the findings promoted the awareness on ethics in writing and prevented the rate of potential unintentional plagiarism. Additionally, the results of this phase of study could lead to the appropriate contents to be considered for inclusion in the supplementary on using sources for writing for future research.

Keywords: citing source, EFL writing, e-learning, Internet, plagiarism

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Authors: Simao E. Luis

Abstract:

From the 1960s to the 1990s, the social-psychological framework of language attitudes that emerged from the Canadian research tradition was very influential. Integrativeness was one of the main variables in Gardner’s theory because refugees and immigrants were motivated to learn English and French to integrate into the Canadian community. Second language (L2) scholars have expressed concerns over integrativeness because it cannot explain the motivation of L2 learners in global contexts. This study aims to investigate student motivation to learn English as a foreign language in Mozambique, and to contribute to the ongoing validation of the L2 Motivational Self System theory in an under-researched country. One hundred thirty-seven (N=137) university students completed a well-established motivation questionnaire. The data were analyzed with SPSS, and descriptive statistics, correlations, multiple regressions, and MANOVA were conducted. Results show that many variables contribute to motivated learning behavior, particularly the L2 learning experience and attitudes towards the English language. Statistically significant differences were found between males and females, with males expressing more motivation to learn the English language for personal interests. Statistically significant differences were found between older and younger students, with older students reporting more vivid images of themselves as future English language users. These findings have pedagogical implications because motivational strategies are positively correlated with student motivated learning behavior. Therefore, teachers should design L2 tasks that can help students to develop their future L2 selves.

Keywords: English as a foreign language, L2 motivational self system, Mozambique, university students

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Authors: Ashok V. Sutagundar, Sunilkumar S. Manvi

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Energy, delay and bandwidth are the prime issues of wireless sensor network (WSN). Energy usage optimization and efficient bandwidth utilization are important issues in WSN. Event triggered data aggregation facilitates such optimal tasks for event affected area in WSN. Reliable delivery of the critical information to sink node is also a major challenge of WSN. To tackle these issues, we propose an event driven dynamic clustering and data aggregation scheme for WSN that enhances the life time of the network by minimizing redundant data transmission. The proposed scheme operates as follows: (1) Whenever the event is triggered, event triggered node selects the cluster head. (2) Cluster head gathers data from sensor nodes within the cluster. (3) Cluster head node identifies and classifies the events out of the collected data using Bayesian classifier. (4) Aggregation of data is done using statistical method. (5) Cluster head discovers the paths to the sink node using residual energy, path distance and bandwidth. (6) If the aggregated data is critical, cluster head sends the aggregated data over the multipath for reliable data communication. (7) Otherwise aggregated data is transmitted towards sink node over the single path which is having the more bandwidth and residual energy. The performance of the scheme is validated for various WSN scenarios to evaluate the effectiveness of the proposed approach in terms of aggregation time, cluster formation time and energy consumed for aggregation.

Keywords: wireless sensor network, dynamic clustering, data aggregation, wireless communication

Procedia PDF Downloads 431