Search results for: digital learning environment

Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: social media, text mining, knowledge discovery, predictive analysis, machine learning

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Authors: Samhita Mummadi, Sree Divya Nagalli, Harshini Vemuri, Saketh Charan Nakka, Sumesh K. J.

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One of the most significant challenges faced by people in today’s digital era is an alarming increase in fraudulent activities on online platforms. The fascination with online shopping to avoid long queues in shopping malls, the availability of a variety of products, and home delivery of goods have paved the way for a rapid increase in vast online shopping platforms. This has had a major impact on increasing fraudulent activities as well. This loop of online shopping and transactions has paved the way for fraudulent users to commit fraud. For instance, consider a store that orders thousands of products all at once, but what’s fishy about this is the massive number of items purchased and their transactions turning out to be fraud, leading to a huge loss for the seller. Considering scenarios like these underscores the urgent need to introduce machine learning approaches to combat fraud in online shopping. By leveraging robust algorithms, namely KNN, Decision Trees, and Random Forest, which are highly effective in generating accurate results, this research endeavors to discern patterns indicative of fraudulent behavior within transactional data. Introducing a comprehensive solution to this problem in order to empower e-commerce administrators in timely fraud detection and prevention is the primary motive and the main focus. In addition to that, sentiment analysis is harnessed in the model so that the e-commerce admin can tailor to the customer’s and consumer’s concerns, feedback, and comments, allowing the admin to improve the user’s experience. The ultimate objective of this study is to ramp up online shopping platforms against fraud and ensure a safer shopping experience. This paper underscores a model accuracy of 84%. All the findings and observations that were noted during our work lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as technologies continue to evolve.

Keywords: behavior analysis, feature selection, Fraudulent pattern recognition, imbalanced classification, transactional anomalies

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Authors: Vikas Baliram Kalyankar

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Study of ecology is important as it plays role in development of environment engineering. With the advent of technologies the study of ecosystem structure and changes in it are remaining unnoticed. The ecological succession is the sequential replacement of plant species following changes in the environment. The present study depicts the primary ecological succession in an area leveled up to the height of five feet with no signs of plant life on it. The five quadrates of 1 meter square size were observed during the study period of six months. Rain water being the only source of water in the area increased its ecological importance. The primary successor was non- leguminous plant Balonites roxburgii during the peak drought periods in the region of the summer 2013-14. The increased nitrogen content of soil after the plant implied its role in atmospheric nitrogen fixation.

Keywords: succession, Balonites roxburgii, non-leguminous plant, ecology

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Authors: Yazmin Pineda Zapata

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Leading a complete school and culture transformation can be a daunting task for any administrator. This is especially true when change agents are advocating for inclusive reform in their schools. As leaders embark on this journey, they must ascertain that an inclusive environment is not a place, a classroom, or a resource setting; it is a place of acceptance nurtured by supportive and meaningful learning opportunities where all students can thrive. A qualitative approach, phenomenology, was used to investigate principals’ actions and behaviors that supported inclusive schooling for students with disabilities. Specifically, this study sought to answer the following research question: How do leaders develop and maintain inclusive education? Fourteen K-12 principals purposefully selected from various sources (e.g., School Wide Integrated Framework for Transformation (SWIFT), The Maryland Coalition for Inclusive Education (MCIE), The Arc of Texas Inclusion Works organization, The Association for Persons with Severe Handicaps (TASH), the CAL State Summer Institute in San Marcos, and the PEAK Parent Center and/or other recognitions were interviewed individually using a semi-structured protocol. Upon completion of data collection, all interviews were transcribed and marked using A priori coding to analyze the responses and establish a correlation among Villa and Thousand’s five organizational supports to achieve inclusive educational reform: Vision, Skills, Incentives, Resources, and Action Plan. The findings of this study reveal the insights of principals who met specific criteria and whose schools had been highlighted as exemplary inclusive schools. Results show that by implementing the five organizational supports, principals were able to develop and sustain successful inclusive environments where both teachers and students were motivated, made capable, and supported through the redefinition and restructuring of systems within the school. Various key details of the five variables for change depict essential components within these systems, which include quality professional development, coaching and modeling of co-teaching strategies, collaborative co-planning, teacher leadership, and continuous stakeholder (e.g., teachers, students, support staff, and parents) involvement. The administrators in this study proved the valuable benefits of inclusive education for students with disabilities and their typically developing peers. Together, along with their teaching and school community, school leaders became capable stakeholders that promoted the vision of inclusion, planned a structured approach, and took action to make it a reality.

Keywords: Inclusive education, leaders, principals, shared-decision making, shared leadership, special education, sustainable change

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Authors: Menuka Maharjan

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In the name of food security, agriculture intensification through conventional farming is being implemented in Nepal. Government focus on increasing agriculture production completely ignores soil as well human health. This leads to create serious soil degradation, i.e., reduction of soil fertility and microbial activity and health hazard in the country. On this note, organic farming is sustainable agriculture approach which can address challenge of sustaining food security while protecting the environment. This creates a win-win situation both for people and the environment. However, people have limited knowledge on significance of organic farming for environment conservation and food security especially developing countries like Nepal. Thus, the objective of the study was to assess the impacts of organic farming on soil fertility and microbial activity compared to conventional farming and forest in Chitwan, Nepal. Total soil organic carbon (C) was highest in organic farming (24 mg C g⁻¹ soil) followed by conventional farming (15 mg C g⁻¹ soil) and forest (9 mg C g⁻¹ soil) in the topsoil layer (0-10 cm depth). A similar trend was found for total nitrogen (N) content in all three land uses with organic farming soil possessing the highest total N content in both 0-10 cm and 10-20 cm depth. Microbial biomass C and N were also highest under organic farming, especially in the topsoil layer (350 and 46 mg g⁻¹ soil, respectively). Similarly, microbial biomass phosphorus (P) was higher (3.6 and 1.0 mg P kg⁻¹ at 0-10 and 10-20 cm depth, respectively) in organic farming compared to conventional farming and forest at both depths. However, conventional farming and forest soils had similar microbial biomass (C, N, and P) content. After conversion of forest, the P stock significantly increased by 373% and 170% in soil under organic farming at 0-10 and 10-20 cm depth, respectively. In conventional farming, the P stock increased by 64% and 36% at 0-10 cm and 10-20 cm depth, respectively, compared to forest. Overall, organic farming practices, i.e., crop rotation, residue input and farmyard manure application, significantly alters soil fertility and microbial activity. Organic farming system is emerging as a sustainable land use system which can address the issues of food security and environment conservation by increasing sustainable agriculture production and carbon sequestration, respectively, supporting to achieve goals of sustainable development.

Keywords: organic farming, soil fertility, micobial biomas, food security

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Authors: Beryl Apondi Obola

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Introduction: Mosquitoes are known to transmit various diseases such as malaria, dengue fever, and Zika virus. Mosquito repellents are commonly used to prevent mosquito bites. However, some of these repellents contain chemicals that can be harmful to human health and the environment. Therefore, there is a need to find alternative mosquito repellents that are safe and effective. Objective: The objective of this research is to investigate the effectiveness of banana leaves as an alternative mosquito repellent on Plasmodium falciparum and Plasmodium vivax. Methodology: The research will be conducted in two phases. In the first phase, the repellent properties of banana leaves will be tested in a laboratory setting. The leaves will be crushed and mixed with water to extract the active ingredients. The extract will be tested against mosquitoes in a controlled environment. The number of mosquitoes that are repelled by the extract will be recorded. In the second phase, the effectiveness of the banana leaf extract will be tested in the field. The extract will be applied to the skin of human volunteers, and the number of mosquito bites will be recorded. The results will be compared to a commercially available mosquito repellent. Expected Outcomes: The expected outcome of this research is to determine whether banana leaves can be used as an effective mosquito repellent. If the results are positive, banana leaves could be used as an alternative to chemical-based mosquito repellents. Conclusion: Banana leaves have been used for various purposes in traditional medicine. This research aims to investigate the potential of banana leaves as an alternative mosquito repellent. The results of this research could have significant implications for public health and the environment

Keywords: banana leaf extract, mosquito repellant, plasmodium falciparum, public health

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Authors: Boudemagh Naime

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Automatic radar detection requires some methods of adapting to variations in the background clutter in order to control their false alarm rate. The problem becomes more complicated in non-Gaussian environment. In fact, the conventional approach in real time applications requires a complex statistical modeling and much computational operations. To overcome these constraints, we propose another approach based on artificial neural network (ANN-CMLD-CFAR) using a Back Propagation (BP) training algorithm. The considered environment follows a log-normal distribution in the presence of multiple Rayleigh-targets. To evaluate the performances of the considered detector, several situations, such as scale parameter and the number of interferes targets, have been investigated. The simulation results show that the ANN-CMLD-CFAR processor outperforms the conventional statistical one.

Keywords: radat detection, ANN-CMLD-CFAR, log-normal clutter, statistical modelling

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Authors: Won-Ho Kim

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In this paper, a design of H.263 based wireless video transceiver is presented for wireless camera system. It uses standard WIFI transceiver and the covering area is up to 100m. Furthermore the standard H.263 video encoding technique is used for video compression since wireless video transmitter is unable to transmit high capacity raw data in real time and the implemented system is capable of streaming at speed of less than 1Mbps using NTSC 720x480 video.

Keywords: wireless video transceiver, video surveillance camera, H.263 video encoding digital signal processing

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Authors: H. Zouar, I. Kassous, F. Benzert

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The relentless pace of technology development in the last two decades has pervaded much of the recent educational discourse on a nation-wide scale. The rippling echoes of the buzz that account for the myriad of advantages the new technologies bring to the pedagogical activity has inevitably transcended from the western world to the Algerian educational contexts. Attempts have been made by Algerian practitioners to heed this digital advancement and push their instructional practices forward. However, due to the still largely existing first-order barriers as exemplified in the forms of deficient institutional infrastructure and unavailability of sufficient digital materials, the results of those attempts have polarised the views of Algerian academics regarding technology integration within higher education context. Hence, this study aims at measuring the possibility of integrating technology in our classrooms in a way that conforms to the philosophy of hybrid education. It also attempts to re-consider teachers’ understanding of technology integration in our context. Furthermore, the purpose of this research is also to reveal the level of teachers’ awareness regarding the distinction between technology integration and instrumental use. In view of the nature of these aims, a mixed-methods mode of investigation has been adopted to collect both qualitative and quantitative data from different perspectives. The data collection tools comprise of an observation as well as students’ and teachers’ questionnaires. The findings show that despite the fact that the examined context is not without its technological limitations, technology integration can be successfully incorporated contingent on teachers' level of knowledge and agency. Technology integration in Algerian universities does not proceed as the bedrock theory of it entails due to issues within teachers' general understanding of utilizing technology in class. It seems that technology is a means to an end, depending on the teachers who make use of it in order to deliver lessons (PowerPoint presentation) and issue commands (Facebook posting). Teachers' ability to clearly discern between integrating technology in their practices versus employing it as an instrument of instruction needs further consideration in order to establish a solid understanding of technology integration within higher education context.

Keywords: technology integration, hybrid education, teachers' understanding, teachers' awareness, instrumental use

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Authors: Priya Sharma

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As one think of fashion, the only things that come to mind are feminine activities such as acquiring high-end bags, clothing, and shoes. A person's personal style is defined by their clothing. Fashion been more feminine over the centuries, but the masculine identity has also dwindled. Fashion has an impact on social status, trends, and the socio-economic and political environment. The major focus of this study is on how the most prominent fast fashion businesses establish their gender identities in order to achieve industry legitimacy. A questionnaire survey was conducted to understand the people prospection. It also helps in understanding the different driving factors which contribute collectively from the Doman from social and economic norms across the different reign in India. A conceptual module was made which help to understand the future scope of fashion with respect to gender identity in India. The ways there feel to create their own personal style and their feelings and how fashion can make more confident and authentic in their minds.

Keywords: fashion, gender, identity, feminism, environment

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Authors: A. Kebbeb, M. Mostefai, F. Benmerzoug, Y. Chahir

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The multimodal biometric identification is the combination of several biometric systems. The challenge of this combination is to reduce some limitations of systems based on a single modality while significantly improving performance. In this paper, we propose a new approach to the construction and the protection of a multimodal biometric database dedicated to an identification system. We use a topological watermarking to hide the relation between face image and the registered descriptors extracted from other modalities of the same person for more secure user identification.

Keywords: biometric databases, multimodal biometrics, security authentication, digital watermarking

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Authors: Jarek Krajewski, Andre Wittenborn, Martin Sauerland

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This abstract describes a promising approach for estimating post-traumatic stress disorder (PTSD) based on prosodic speech characteristics. It illustrates the validity of this method by briefly discussing results from an Arabic refugee sample (N= 47, 32 m, 15 f). A well-established standardized self-report scale “Reaction of Adolescents to Traumatic Stress” (RATS) was used to determine the ground truth level of PTSD. The speech material was prompted by telling about autobiographical related sadness inducing experiences (sampling rate 16 kHz, 8 bit resolution). In order to investigate PTSD-induced speech changes, a self-developed set of 136 prosodic speech features was extracted from the .wav files. This set was adapted to capture traumatization related speech phenomena. An artificial neural network (ANN) machine learning model was applied to determine the PTSD level and reached a correlation of r = .37. These results indicate that our classifiers can achieve similar results to those seen in speech-based stress research.

Keywords: speech prosody, PTSD, machine learning, feature extraction

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

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In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

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Authors: M. Debieche, F. Kaoua

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This paper deals with the use two locals materials abundant in our country, with the view to use a mixture in the waterproofing the landfills. Our interest comes from the necessity to the environment protection, which has recently considerably grown. The site's waterproofing technique, in the landfills sites, is nowadays a very necessary condition to protect the environment, which requires the use of appropriate materials. To this end, an optimal mixture ensuring good performance in terms of hydraulic conductivity, durability and shear strength, mixtures based of sand at different concentrations of sodium bentonite, at compact state are prepared and studied. This study showed that a low permeability of mixture (sand / bentonite) can be achieved 6% of sodium bentonite. This mixture confers also good mechanical behavior, expressed by the recorded, reduction of friction (φ) and the increase of the cohesion (C). Thus, the selected formulation represents an optimal mixture for waterproofing systems. It guarantees an economical and ecological advantages.

Keywords: hydraulic conductivity, sand, sodium bentonite, sustainability

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Authors: Y. Y. Babanyara, B. A. Gana, T. Garba, M. A. Batari

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Proper management of dental waste is a crucial issue for maintaining human health and the environment. The waste generated in the dental clinics has the potential for spreading infections and causing diseases, so improper disposal of these dental wastes can cause harm to the dentist, the people in immediate vicinity of the dentist, waste handlers, general public and the environment through production of toxins or as by-products of the destruction process. Staff that provide dental healthcare ought to be aware of the proper handling and the system of management of dental waste used by different dental hospitals. The method of investigation adopted in the paper involved a desk study in which documents and records relating to dental waste handling were studied to obtain background information on existing dental waste management in Nigeria other countries of the world are also mentioned as examples. Additionally, information on generation, handling, segregation, risk associated during handling and treatment of dental medical waste were sought in order to determine the best method for safe disposal. This article provides dentists with the information they need to properly dispose of mercury and amalgam waste, and provides suggestions for managing the other wastes that result from the day-to-day activities of a dental office such as: used X-ray fixers and developers; cleaners for X-ray developer systems; lead foils, shields and aprons; chemiclave/chemical sterilant solutions; disinfectants, cleaners, and other chemicals; and, general office waste. Additionally, this study may be beneficial for authorities and researchers of developing countries to work towards improving their present dental waste management system.

Keywords: clinic, dental, disposal, environment, waste management

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Authors: L. S. Chathurika

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Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

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Authors: S. Chee Choy, Pauline Swee Choo Goh, Yow Lin Liew

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The purpose of this study is to evaluate the English version and a Malay translation of the 21-item Learner Awareness Questionnaire for its application to assess student learning in higher education. The Learner Awareness Questionnaire, originally written in English, is a quantitative measure of how and why students learn. The questionnaire gives an indication of the process and motives to learn using four scales: survival, establishing stability, approval, and loving to learn. Data in the present study came from 680 university students enrolled in various programs in Malaysia. The Malay version of the questionnaire supported a similar four-factor structure and internal consistency to the English version. The four factors of the Malay version also showed moderate to strong correlations with those of the English versions. The results suggest that the Malay version of the questionnaire is similar to the English version. However, further refinement for the questions is needed to strengthen the correlations between the two questionnaires.

Keywords: student learning, learner awareness, questionnaire development, instrument validation

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Authors: Taksina Bunbut

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Personality and grooming are vital for creating professionalism and safety image for all staffs in the airline industry. Airline Business Program also has an aim to educate students through the subject Personality Development and Grooming in order to elevate the quality of students to meet standard requirements of the airline industry. However, students agree that there are many difficulties that cause unsuccessful learning experience in this subject. The research is to study problems that can afflict students from getting good results in the classroom. Furthermore, exploring possible solutions to overcome challenges are also included in this study. The research sample consists of 140 students who attended the class of Personality Development and Grooming. The employed research instrument is a questionnaire. Statistic for data analysis is t-test and Multiple Regression Analysis. The result found that although students are satisfied with teaching and learning of this subject, they considered that teaching in English and teaching topics in social etiquette in different cultures are difficult for them to understand.

Keywords: personality development, grooming, Airline Business Program, soft skill

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Authors: Subhajit Das, Nirjhar Dhang

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Structural damage detection is a challenging work in the field of structural health monitoring (SHM). The damage detection methods mainly focused on the determination of the location and severity of the damage. Model updating is a well known method to locate and quantify the damage. In this method, an error function is defined in terms of difference between the signal measured from ‘experiment’ and signal obtained from undamaged finite element model. This error function is minimised with a proper algorithm, and the finite element model is updated accordingly to match the measured response. Thus, the damage location and severity can be identified from the updated model. In this paper, an error function is defined in terms of modal data viz. frequencies and modal assurance criteria (MAC). MAC is derived from Eigen vectors. This error function is minimized by teaching-learning-based optimization (TLBO) algorithm, and the finite element model is updated accordingly to locate and quantify the damage. Damage is introduced in the model by reduction of stiffness of the structural member. The ‘experimental’ data is simulated by the finite element modelling. The error due to experimental measurement is introduced in the synthetic ‘experimental’ data by adding random noise, which follows Gaussian distribution. The efficiency and robustness of this method are explained through three examples e.g., one truss, one beam and one frame problem. The result shows that TLBO algorithm is efficient to detect the damage location as well as the severity of damage using modal data.

Keywords: damage detection, finite element model updating, modal assurance criteria, structural health monitoring, teaching learning based optimization

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Authors: Justin D. Olmanson, Fitsum Abebe, Valerie Jones, Eric Kyle, Xianquan Liu, Katherine Robbins, Guieswende Rouamba

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The history of education technology--and designing, adapting, and adopting technologies for use in educational spaces--is nuanced, complex, and dynamic. Yet, despite a range of continually emerging technologies, the design and development process often yields results that appear quite similar in terms of affordances and interactions. Through this study we (1) verify the extent to which designs have been constrained, (2) consider what might account for it, and (3) offer a way forward in terms of how we might identify and strategically sidestep these influences--thereby increasing the diversity of our designs with a given technology or within a particular learning domain. We begin our inquiry from the perspective that a host of co-influencing elements, fields, and meta narratives converge on the education technology design process to exert a tangible, often homogenizing effect on the resultant designs. We identify several elements that influence design in often implicit or unquestioned ways (e.g. curriculum, learning theory, economics, learning context, pedagogy), we describe our methodology for identifying the elemental positionality embedded in a design, we direct our analysis to a particular subset of technologies in the field of literacy, and unpack our findings. Our early analysis suggests that the majority of education technologies designed for use/used in US public schools are heavily influenced by a handful of mainstream theories and meta narratives. These findings have implications for how we approach the education technology design process--which we use to suggest alternative methods for designing/ developing with emerging technologies. Our analytical process and re conceptualized design process hold the potential to diversify the ways emerging and established technologies get incorporated into our designs.

Keywords: curriculum, design, innovation, meta narratives

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Authors: Abd Aziz Bin Abd Shukor, Eftah Binti Moh Hj Abdullah

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This case study aims to identify teachers’ perception as regards integrity in School-Ba sed Assessment (PBS) practice. This descriptive study involved 9 teachers from 4 secondary schools in 3 districts in the state of Perak. The respondents had undergone an integrity in PBS Practice interview using a focused group discussion method. The overall findings showed that the teachers believed that integrity in PBS practice could be achieved by adjusting the teaching methods align with learning objectives and the students’ characteristics. Many teachers, parents and student did not understand the best practice of PBS. This would affect the integrity in PBS practice. Teachers did not emphasis the principles and ethics. Their integrity as an innovative public servant may also be affected with the frequently changing assessment system, lack of training and no prior action research. The analysis of findings showed that the teachers viewed that organizational integrity involving the integrity of PBS was difficult to be implemented based on the expectations determined by Malaysia Ministry of Education (KPM). A few elements which assisted in the achievement of PBS integrity were the training, students’ understanding, the parents’ understanding of PBS, environment (involving human resources such as support and appreciation and non-human resources such as technology infrastructure readiness and media). The implications of this study show that teachers, as the PBS implementers, have a strong influence on the integrity of PBS. However, the transformation of behavior involving PBS integrity among teachers requires the stabilisation of support and infrastructure in order to enable the teachers to implement PBS in an ethical manner.

Keywords: assessment integrity, integrity, perception, school-based assessment

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Authors: Thelma Mort

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This empirical, qualitative research uses interviews of four intermediate phase English language student teachers at one university in South Africa and is an exploration of student teacher learning on their teaching practicum in their penultimate year of the initial teacher education course. The country’s post-apartheid language in education policy provides a context to this study in that children move from mother tongue language of instruction in foundation phase to English as a language of instruction in Intermediate phase. There is another layer of context informing this study which is the school context; the student teachers’ reflections are from their teaching practicum in resource constrained schools, which make up more than 75% of schools in South Africa. The findings were that in these schools, deep biases existed to local languages, that language was being used as a proxy for social class, and that conditions necessary for language acquisition were absent. The student teachers’ attitudes were in contrast to those found in the schools, namely that they had various pragmatic approaches to overcoming obstacles and that they saw language as enabling interdisciplinary work. This study describes language issues, tensions created by policy in South African schools and also supplies a regional account of learning to teach in resource constrained schools in Cape Town, where such language tensions are more inflated. The central findings in this research illuminate attitudes to language and language education in these teaching practicum schools and the complexity of learning to be a language teacher in these contexts. This study is one of the few local empirical studies regarding language teaching in the classroom and language teacher education; as such it offers some background to the country’s poor performance in both international and national literacy assessments.

Keywords: language teaching, narrative, post apartheid, South Africa, student teacher

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Authors: Meriam Khelifa

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In recent years, many studies have focused on the harvesting of the vibrations energy to produce electrical energy using contact separation (CS) triboelectric nanogenerators (TENG). The simplest design for a TENG consists of a capacitor comprising a single moving electrode. The conversion efficiency of vibration energy into electrical energy can, in principle, reach 100%. But to actually achieve this objective, it is necessary to optimize the parameters of the TENG, such as the dielectric constant and the thickness of the insulator, the load resistance, etc. In particular, the use of a switch which is actioned at optimal times within the TENG cycle is essential. Using numerical modeling and experimental design, we applied a methodology to find the TENG parameters which optimize the energy transfer efficiency (ETE) to almost 100% for any vibration frequency and amplitude. The rather simple design of a TENG is promising as an environment friendly device. It opens the doors for harvesting acoustic vibrations from the environment and to design effective protection against environmental noise.

Keywords: vibrations, CS TENG, efficiency, design of experiments

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Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

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Authors: Anđelka Stojanović, Ivan Mihajlović, Ivica Nikolić

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In the past years, due to the financial crisis and the tightening of conditions on the labor market, young people are facing great challenges in achieving financial independence and finding their place in society. Higher unemployment rates, poorer living conditions, separation from the labor market, and longer school-to-work transitions particularly affect rural youth and make significant differences between youth groups in rural and urban areas. Improving employability skills and development of instruments for further learning among young people “Not in Education, Employment, or Training” (NEET) should not be only the concerns of these people, already adequately be directed and supported by the institutions. According to the World Bank data, the share of youth not in education, employment or training (NEET), in the European Union in the past few years decreases but still shows a significant share in the total percentage of the youth population. In 2017 rate was 10.96% while in 2018 that value was 10.38%. When observing individual countries in Europe, especially those with lower incomes, this rate is much higher. It was concluded that this topic was not sufficiently elaborated and presented in the social and scientific environment. Therefore, the aim of this paper is to identify and systematize the problems of school-to-work transition among young NEETs living in rural areas as well as the initiatives for addressing their problems.

Keywords: NEET youth, risks and initiatives, rural youth, school-to-work transition

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Authors: Utkarsh A. Mishra, Ankit Bansal

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At high temperature, radiative heat transfer is the dominant mode of heat transfer. It is governed by various phenomena such as photon emission, absorption, and scattering. The solution of the governing integrodifferential equation of radiative transfer is a complex process, more when the effect of participating medium and wavelength properties are taken into consideration. Although a generic formulation of such radiative transport problem can be modeled for a wide variety of problems with non-gray, non-diffusive surfaces, there is always a trade-off between simplicity and accuracy of the problem. Recently, solutions of complicated mathematical problems with statistical methods based on randomization of naturally occurring phenomena have gained significant importance. Photon bundles with discrete energy can be replicated with random numbers describing the emission, absorption, and scattering processes. Photon Monte Carlo (PMC) is a simple, yet powerful technique, to solve radiative transfer problems in complicated geometries with arbitrary participating medium. The method, on the one hand, increases the accuracy of estimation, and on the other hand, increases the computational cost. The participating media -generally a gas, such as CO₂, CO, and H₂O- present complex emission and absorption spectra. To model the emission/absorption accurately with random numbers requires a weighted sampling as different sections of the spectrum carries different importance. Importance sampling (IS) was implemented to sample random photon of arbitrary wavelength, and the sampled data provided unbiased training of MC estimators for better results. A better replacement to uniform random numbers is using deterministic, quasi-random sequences. Halton, Sobol, and Faure Low-Discrepancy Sequences are used in this study. They possess better space-filling performance than the uniform random number generator and gives rise to a low variance, stable Quasi-Monte Carlo (QMC) estimators with faster convergence. An optimal supervised learning scheme was further considered to reduce the computation costs of the PMC simulation. A one-dimensional plane-parallel slab problem with participating media was formulated. The history of some randomly sampled photon bundles is recorded to train an Artificial Neural Network (ANN), back-propagation model. The flux was calculated using the standard quasi PMC and was considered to be the training target. Results obtained with the proposed model for the one-dimensional problem are compared with the exact analytical and PMC model with the Line by Line (LBL) spectral model. The approximate variance obtained was around 3.14%. Results were analyzed with respect to time and the total flux in both cases. A significant reduction in variance as well a faster rate of convergence was observed in the case of the QMC method over the standard PMC method. However, the results obtained with the ANN method resulted in greater variance (around 25-28%) as compared to the other cases. There is a great scope of machine learning models to help in further reduction of computation cost once trained successfully. Multiple ways of selecting the input data as well as various architectures will be tried such that the concerned environment can be fully addressed to the ANN model. Better results can be achieved in this unexplored domain.

Keywords: radiative heat transfer, Monte Carlo Method, pseudo-random numbers, low discrepancy sequences, artificial neural networks

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Authors: Shanorfizah Mohd Safar, Shaizatulaqma Kamalul Ariffin

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Internationalization of halal small and medium-sized enterprises (SMEs) is necessary for SMEs to become more involved in regional trade and business cooperation. By internationalization, SMEs' profit can increase, and market expansion of SMEs is basic for rising economies of countries to contend all around in the halal industry globally. There are several modes of internationalization; exporting is one of the first steps for internationalization with less capital needed. The study examines the influential factors of export performance amongst halal SMEs in Malaysia. Specifically, this study examines the positive and significant relationships amongst human capital, managerial capability, Halal Assurance Management System (HAMS), digital transformation, government support, and networking capability on halal SMEs' export performance toward SMEs' competitive advantage. In addition, this study will examine innovation capabilities as a moderator in the relationship between independence variables and competitive advantage. Competitive advantage is the most compelling perspective that drives the export performance of halal SMEs in Malaysia. A quantitative method will be used: an online questionnaire survey distributed through emails and face-to-face toward selected halal-certificated SMEs registered in JAKIM, MATRADE website and SME Corp Malaysia website. Nevertheless, whether the halal SMEs practice global business, they will still be the potential respondents. The data were examined and obtained using the statistical software Smart PLS. The analysis used is reliability, correlation, and regression analysis to meet the research objectives. This study contributes significantly to the theory by integrating Resource Based View (RBV) theory, Technology–Organization–Environment (TOE) framework and Networking theory. In addition, this research extends the RBV by extending a variable, the Halal Assurance Management System. This study also examines a moderating role of innovation capabilities in the framework and competitive advantage as a mediator. This research aims to analyze the factors that will impact the internationalization of halal SMEs.

Keywords: internationalization, halal SMEs, competitive advantage, export performance

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Authors: Sakshi V. Tantak, Vishap K. Malik, Neelanjney Pilarisetty

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A patent is an exclusive right granted for an invention. It can be a product or a process that provides an innovative method of doing something, or offers a new technical perspective or solution to a problem. A patent can be obtained by making the technical information and details about the invention publicly available. The patent owner has exclusive rights to prevent or stop anyone from using the patented invention for commercial uses. Any commercial usage, distribution, import or export of a patented invention or product requires the patent owner’s consent. It has been observed that the central and important parts of patents are scripted in idiosyncratic and complex linguistic structures that can be difficult to read, comprehend or interpret for the masses. The abstracts of these patents tend to obfuscate the precise nature of the patent instead of clarifying it via direct and simple linguistic constructs. This makes it necessary to have an efficient access to this knowledge via concise and transparent summaries. However, as mentioned above, due to complex and repetitive linguistic constructs and extremely long sentences, common extraction-oriented automatic text summarization methods should not be expected to show a remarkable performance when applied to patent documents. Other, more content-oriented or abstractive summarization techniques are able to perform much better and generate more concise summaries. This paper proposes an efficient summarization system for patents using artificial intelligence, natural language processing and deep learning techniques to condense the knowledge and essential information from a patent document into a single summary that is easier to understand without any redundant formatting and difficult jargon.

Keywords: abstractive summarization, deep learning, natural language Processing, patent document

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Authors: Konysbek Aksaule

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The purpose of this work is to explore new and effective ways of teaching English to students who are studying a foreign language since the timeliness of the problem disclosed in this article is due to the high level of English proficiency that potential specialists must have due to high competition in the context of global globalization. The article presents an analysis of the feasibility and effectiveness of using an authentic feature film in teaching English to students. The methodological basis of the study includes an assessment of the level of students' proficiency in a foreign language, the stage of evaluating the film, and the method of selecting the film for certain categories of students. The study also contains a list of practical tasks that can be applied in the process of viewing and perception of an original feature film in a foreign language, and which are aimed at developing language skills such as speaking and listening. The results of this study proved that teaching English to students through watching an original film is one of the most effective methods because it improves speech perception, speech reproduction ability, and also expands the vocabulary of students and makes their speech fluent. In addition, learning English through watching foreign films has a huge impact on the cultural views and knowledge of students about the country of the language being studied and the world in general. Thus, this study demonstrates the high potential of using authentic feature film in English lessons for pedagogical science and methods of teaching English in general.

Keywords: university, education, students, foreign language, feature film

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Authors: Jacopo Baboni Schilingi

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We here describe the theoretical and philosophical understanding of a long term use and development of algorithmic computer-based tools applied to music composition. The findings of our research lead us to interrogate some specific processes and systems of communication engaged in the discovery of specific cultural artworks: artistic creation in the sono-musical domain. Our hypothesis is that the patterns of auditory learning cannot be only understood in terms of social transmission but would gain to be questioned in the way they rely on various ranges of acoustic stimuli modes of consciousness and how the different types of memories engaged in the percept-action expressive systems of our cultural communities also relies on these shadowy conscious entities we named “Reduced Descriptive Structures”.

Keywords: algorithmic sonic computation, corrected and self-correcting learning patterns in acoustic perception, morphological derivations in sensorial patterns, social unconscious modes of communication

Procedia PDF Downloads 144