Search results for: teaching learning

Authors: Babak Bahri, Fatemeh Yassaee Meybodi, Changiz Eslahchi

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In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores.

Keywords: drug synergy, clustering, prediction, machine learning., deep learning

Procedia PDF Downloads 59

Authors: William C. Dean

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In the pedagogical sequence of the four- and five-year architectural programs at Alfred State, the fourth-year Urban Design Studio constitutes the first course where students directly explore design issues in the urban context. It is the first large-scale, community-based service learning project for most of the participating students. The students learn key lessons that include the benefits of working both individually and in groups of different sizes toward a common goal, accepting - and responding creatively too - criticism from stakeholders at different points in the project, and recognizing the role that local politics and activism can play in planning for community development. Above all, students are exposed to the importance of good planning in relation to preservation and community revitalization. The purpose of this paper is to discuss the use of community-based service-learning projects in undergraduate architectural education to promote student civic engagement as a means of helping communities visualize potential solutions for revitalizing their neighborhoods and business districts. A series of case studies will be presented in terms of challenges that were encountered, opportunities for student engagement and leadership, and the feasibility of sustainable community development resulting from those projects. The reader will be encouraged to consider how they can recognize needs within their own communities that could benefit from the assistance of architecture students and faculty.

Keywords: urban design, service-learning, civic engagement, community revitalization

Procedia PDF Downloads 75

Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

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Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

Procedia PDF Downloads 399

Authors: Tholokuhle T. Ntshakala, Seraphin D. Eyono Obono

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Nations are still finding it quite difficult to win mega sport competitions despite the major contribution of sport to society in terms of social and economic development, personal health, and in education. Even though the world of sports has been transformed into a huge global economy, it is important to note that the first step of sport is usually its introduction to children at school through physical education or PE. In other words, nations who do not win mega sport competitions also suffer from a weak and neglected PE system. This problem of the neglect of PE systems is the main motivation of this research aimed at examining the factors affecting the perceived awareness of physical education teachers on the ICT's that are adoptable for the teaching and learning of physical education. Two types of research objectives will materialize this aim: relevant theories will be identified in relation to the analysis of the perceived ICT awareness of PE teachers and subsequent models will be compiled and designed from existing literature; the empirical testing of such theories and models will also be achieved through the survey of PE teachers from the Camperdown magisterial district of the KwaZulu-Natal province of South Africa. The main hypothesis at the heart of this study is the relationship between the demographics of PE teachers, their behavior both as individuals and as social entities, and their perceived awareness of the ICTs that are adoptable for PE, as postulated by existing literature; except that this study categorizes human behavior under performance expectancy, computer attitude, and social influence. This hypothesis was partially confirmed by the survey conducted by this research in the sense that performance expectancy and teachers’ age, gender, computer usage, and class size were found to be the only factors affecting their awareness of ICT's for physical education.

Keywords: human behavior, ICT Awareness, physical education, teachers

Procedia PDF Downloads 249

Authors: Linda Dusman, Linda Baker

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The Bearman Study of Audience Engagement examined the potential for real-time music education during online symphony orchestra concerts. It follows on the promising results of a preliminary study of STEAM (Science, Technology, Engineering, Arts, and Mathematics) education during live concerts, funded by the National Science Foundation with the Baltimore Symphony Orchestra. For the Bearman Study, audience groups were recruited to attend two previously recorded concerts of the National Orchestral Institute (NOI) in 2020 or the Utah Symphony in 2021. They used a smartphone app called EnCue to present real-time program notes about the music being performed. Short notes along with visual information (photos and score fragments) were designed to provide historical, cultural, biographical, and theoretical information at specific moments in the music where that information would be most pertinent, generally spaced 2-3 minutes apart to avoid distraction. The music performed included Dvorak Symphony No. 8 and Mahler Symphony No. 5 at NOI, and Mendelssohn Scottish Symphony and Richard Strauss Metamorphosen with the Utah Symphony, all standard repertoire for symphony orchestras. During each phase of the study (2020 and 2021), participants were randomly assigned to use the app to view program notes during the first concert or to use the app during the second concert. A total of 139 participants (67 in 2020 and 72 in 2021) completed three online questionnaires, one before attending the first concert, one immediately after the concert, and the third immediately after the second concert. Questionnaires assessed demographic background, expertise in music, engagement during the concert, learning of content about the composers and the symphonies, and interest in the future use of the app. In both phases of the study, participants demonstrated that they learned content presented on the app, evidenced by the fact that their multiple-choice test scores were significantly higher when they used the app than when they did not. In addition, most participants indicated that using the app enriched their experience of the concert. Overall, they were very positive about their experience using the app for real-time learning and they expressed interest in using it in the future at both live and streaming concerts. Results confirmed that informal real-time learning during concerts is possible and can generate enhanced engagement and interest in classical music.

Keywords: audience engagement, informal education, music technology, real-time learning

Procedia PDF Downloads 191

Authors: Federico Pittino, Thomas Arnold

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The shredding of waste materials is a key step in the recycling process towards the circular economy. Industrial shredders for waste processing operate in very harsh operating conditions, leading to the need for frequent maintenance of critical components. Maintenance optimization is particularly important also to increase the machine’s efficiency, thereby reducing the operational costs. In this work, a monitoring system has been developed and deployed on an industrial shredder located at a waste recycling plant in Austria. The machine has been monitored for one year, and methods for predictive maintenance have been developed for two key components: the cutting knives and the drive belt. The large amount of collected data is leveraged by statistical machine learning techniques, thereby not requiring very detailed knowledge of the machine or its live operating conditions. The results show that, despite the wide range of operating conditions, a reliable estimate of the optimal time for maintenance can be derived. Moreover, the trade-off between the cost of maintenance and the increase in power consumption due to the wear state of the monitored components of the machine is investigated. This work proves the benefits of real-time monitoring system for the efficient operation of industrial shredders.

Keywords: predictive maintenance, circular economy, industrial shredder, cost optimization, statistical machine learning

Procedia PDF Downloads 107

Authors: Ann Dashwood

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Using standard Australian English with confidence is a cultural expectation of parents of primary school aged children who want to engage effectively with their children’s teachers and school administration. That confidence in support of their children’s learning at school is seldom experienced by parents whose first language is not English. Sharing language with competence in an intercultural environment is the common denominator for meaningful communication and engagement to occur in a school community. Experience in relevant interactive sessions is known to enhance engagement and participation. The purpose of this paper is to identify interactional settings for which parents who are isolated from the daily use of functional Australian cultural language learned to engage more effectively in their children’s learning at school. The outcomes measured parents’ intercultural engagement with classroom teachers and attention to the school’s administrative procedures. The study used quantitative and qualitative methods. The principles of communicative task-based language learning combined with intercultural communication principles provided the theoretical base for intensive English task-based learning and engagement. The quantitative analysis examined data samples collected by classroom teachers and administrators and parents’ writing samples. Interviews and observations qualitatively informed the study. Currently significant numbers of projects are active in community centres and schools to enhance English language knowledge of parents from Language Backgrounds Other Than English (LBOTE). The study was significant to explore the effects of conducting intensive English with parents of varied English language backgrounds by targeting language use for social interactions in the community, specific engagement in school activities, cultural interaction with teachers and responsiveness to complying with school procedures.

Keywords: engagement, intercultural communication, LBOTE, school community

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Authors: Emmanuel Ademola Anigilaje, Ibraheem Ishola

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Background: Childhood steroid-sensitive idiopathic nephrotic syndrome (SSINS) is plagued with relapses that contribute to its morbidity and the cost of treatment. Materials and Methods: This is a retrospective review of relapses among children with SSINS at the University of Abuja Teaching Hospital from January 2016 to July 2020. Triggers related to relapse incidents were noted. Chi-square test was deployed for predictors (factors at the first clinical presentations that associate with subsequent relapses) of relapses. Predictors with p-values of less than 0.05 were considered significant and 95% confidence intervals (CI) and odd ratio (OR) were described. Results: Sixty SSINS comprising 52 males (86.7%), aged 23 months to 18 years, with a mean age of 7.04±4.16 years were studied. Thirty-eight (63.3%) subjects had 126 relapses including infrequent relapses in 30 (78.9%) and frequent relapses in 8 (21.1%). The commonest triggers were acute upper respiratory tract infections (68, 53.9%) and urinary tract infections (UTIs) in 25 (19.8%) relapses. In 4 (3.2%) relapses, no trigger was identified. The time-to-first relapse ranged 14 days to 365 days with a median time of 60 days. The significant predictors were hypertension (OR=3.4, 95% CI; 1.04-11.09, p=0.038), UTIs (OR=9.9, 95% CI; 1.16-80.71, p= 0.014), malaria fever (OR=8.0, 95% CI; 2.45-26.38, p˂0.001), micro-haematuria (OR=4.9, 95% CI; 11.58-15.16, p=0.004), elevated serum creatinine (OR=12.3, 95%CI; 1.48-101.20, p=0.005) and hypercholesterolaemia (OR=4.1, 95%CI; 1.35-12.63, p=0.011). Conclusion: While the pathogenesis of relapses remains unknown, it is prudent to consider relapse-specific preventive strategies against triggers and predictors of relapses in our setting.

Keywords: Patterns, triggers, predictors, steroid-sensitive idiopathic nephrotic syndrome, relapses, Nigeria

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Authors: L. Bih Ni, D. Norizah Ag Kiflee, T. Choon Keong, R. Talip, S. Singh Bikar Singh, M. Noor Mad Japuni, R. Talin

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The purpose of this study is to determine the effectiveness of video clips to enhance students' achievement and motivation towards learning and facilitating of history. We use narrative literature studies to illustrate the current state of the two art and science in focused areas of inquiry. We used experimental method. The experimental method is a systematic scientific research method in which the researchers manipulate one or more variables to control and measure any changes in other variables. For this purpose, two experimental groups have been designed: one experimental and one groups consisting of 30 lower secondary students. The session is given to the first batch using a computer presentation program that uses video clips to be considered as experimental group, while the second group is assigned as the same class using traditional methods using dialogue and discussion techniques that are considered a control group. Both groups are subject to pre and post-trial in matters that are handled by the class. The findings show that the results of the pre-test analysis did not show statistically significant differences, which in turn proved the equality of the two groups. Meanwhile, post-test analysis results show that there was a statistically significant difference between the experimental group and the control group at an importance level of 0.05 for the benefit of the experimental group.

Keywords: Video clips, Learning and Facilitation, Achievement, Motivation

Procedia PDF Downloads 136

Authors: Shereen Kamel

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This study explores the recent application of bilingual education in Egyptian public schools. It aims to provide an overall picture of bilingual education programs globally and examine its adequacy to the Egyptian social and cultural context. The study also assesses the current application process of teaching English as a Second Language in public schools from the early childhood education stage and onwards, instead of starting it from middle school; as a strategy that promotes English language proficiency and equity among students. The theoretical framework is based on Jim Cummins’ bilingual education theories and on recent trends adopting different developmental theories and perspectives, like Stephen Crashen’s theory of Second Language Acquisition that calls for communicative and meaningful interaction rather than memorization of grammatical rules. The question posed here is whether bilingual education, with its peculiar nature, could be a good chance to reach out to all Egyptian students and prepare them to become global citizens. In addition to this, a more specific question is related to the extent to which social and cultural variables can affect the young learners’ second language acquisition. This exploratory analytical study uses mixed-methods research design to examine the application of bilingual education in Egyptian public schools. The study uses a cluster sample of schools in Egypt from different social and cultural backgrounds to assess the determining variables. The qualitative emphasis is on interviewing teachers and reviewing students’ achievement documents. The quantitative aspect is based on observations of in-class activities through tally sheets and checklists. Having access to schools and documents is authorized by governmental and institutional research bodies. Data sources will comprise achievement records, students’ portfolios, parents’ feedback and teachers’ viewpoints. Triangulation and SPSS will be used for analysis. Based on the gathered data, new curricula have been assigned for elementary grades and teachers have been required to teach the newly developed materials all of a sudden without any prior training. Due to shortage in the teaching force, many assigned teachers have not been proficient in the English language. Hence, teachers’ incompetency and unpreparedness to teach this grade specific curriculum constitute a great challenge in the implementation phase. Nevertheless, the young learners themselves as well as their parents seem to be enthusiastic about the idea itself. According to the findings of this research study, teaching English as a Second Language to children in public schools can be applicable and is culturally relevant to the Egyptian context. However, there might be some social and cultural differences and constraints when it comes to application in addition to various aspects regarding teacher preparation. Therefore, a new mechanism should be incorporated to overcome these challenges for better results. Moreover, a new paradigm shift in these teacher development programs is direly needed. Furthermore, ongoing support and follow up are crucial to help both teachers and students realize the desired outcomes.

Keywords: bilingual education, communicative approach, early childhood education, language and culture, second language acquisition

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Authors: Fatma Hsain Ali Suliman

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Personality plays an important role in acquiring a second language. For second language learners to make maximum progress with their own learning styles, their individual differences must be recognized and attended to. Personality is considered to be a pattern of unique characteristics that give a person’s behavior a kind of consistency and individuality. Therefore, the enclosed study, which is entitled “The Role of Personality in Second language Acquisition: Extroversion and Introversion”, tends to shed light on the relationship between learners’ personalities and second language acquisition process. In other words, it aims at drawing attention to how individual differences of students as being extroverts or introverts could affect the language acquisition process. As a literature review, this paper discusses the results of some studies concerning this issue as well as the point views of researchers and scholars who have focused on the effect of extrovert and introvert personality on acquiring a second language. To accomplish the goals of this study, which is divided into 5 chapters including introduction, review of related literature, research method and design, results and discussions and conclusions and recommendations, 20 students of English Department, Faculty of Arts, Misurata University, Libya were handed out a questionnaire to figure out the effect of their personalities on the learning process. Finally, to be more sure about the role of personality in a second language acquisition process, the same students who were given the questionnaire were observed in their ESL classes.

Keywords: second language acquisition, personality, extroversion, introversion, individual differences, language learning strategy, personality factors, psycho linguistics

Procedia PDF Downloads 630

Authors: Silvia Franco, Marcos Sarasola

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The Faculty of Business Administration at the Catholic University of Uruguay is performing an important educational innovation, unique in the country. In preparing future professionals in companies, teamwork competence is very important. However, there is no often a systematic and specific training in the acquisition of this competence in undergraduate students. For this reason, we have designed and implemented an educational innovation through an intervention method to improve teamwork competence for undergraduate students of business studies. Students’ teams are integrated according to the complementary roles of Belbin; changes in teamwork competence during training period are measured with CCSAC tool; classroom methodology in the prio-border teamwork by Team-Based Learning. Methodology also integrates coaching and support team performance during the first two semesters.

Keywords: business students, teamwork, learning, competences

Procedia PDF Downloads 349

Authors: Somayeh Sharifi

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The purpose of this study is to investigate the effects of collaborative reflection and class observation on improving the quality of teacher training courses and the students reading comprehension skills. 13 inexperienced English teachers teaching elementary courses that were at the same level of proficiency were chosen. Thirteen participants were allocated in two groups, with 7 teachers in the experimental group and the other 6 teachers in the control group. Since two groups were not selected randomly, this study is a form of quasi-experimental research. In addition to a 3-day teacher training course for both groups, teachers in experimental group recorded and observed 20 sessions of their own classes and 30 sessions of experienced teachers’ class and participated in 12 meetings -3 month once a week- in which teachers shared any event that they found interesting during observations and their own teaching and compare it with strategies that they learned in teacher training courses. In contrast, the control group did not engage in any process of observation and collaboration. In order to test students' performance in English before and after the treatment, a Key English Test (KET) was employed to check students' reading skill. The result of the test shows that there is not a significant difference in mean of scores in KET pretest in and, since they are close to each other. However by considering mean and median of posttest in both classes we perceive that although both control and experimental group students' proficiency in English enhanced, there was a significant difference in experimental group students' final scores before and after treatment.

Keywords: collaborative reflection, reading comprehension, teacher training courses, key English test (KET)

Procedia PDF Downloads 341

Authors: P. Kooche Baghy, S. Eskandari, E.javanmard

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Neural network has been used as a predictive means of cold rolling force in this dissertation. Thus, imposed average force on rollers as a mere input and five pertaining parameters to its as a outputs are regarded. According to our study, feed-forward multilayer perceptron network has been selected. Besides, Bayesian algorithm based on the feed-forward back propagation method has been selected due to noisy data. Further, 470 out of 585 all tests were used for network learning and others (115 tests) were considered as assessment criteria. Eventually, by 30 times running the MATLAB software, mean error was obtained 3.84 percent as a criteria of network learning. As a consequence, this the mentioned error on par with other approaches such as numerical and empirical methods is acceptable admittedly.

Keywords: artificial neural network, Bayesian, cold rolling, force evaluation

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Authors: Qisheng Hu, Jianglei Han, Yue Yang, My Hoa Ha

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When a company is considering purchasing software for commercial use, contract risk assessment is critical to identify risks to mitigate the potential adverse business impact, e.g., security, financial and regulatory risks. Contract risk assessment requires reviewers with specialized knowledge and time to evaluate the legal documents manually. Specifically, validating contracts for a software vendor requires the following steps: manual screening, interpreting legal documents, and extracting risk-prone segments. To automate the process, we proposed a framework to assist legal contract document risk identification, leveraging pre-trained deep learning models and natural language processing techniques. Given a set of pre-defined risk evaluation problems, our framework utilizes the pre-trained transformer-based models for question-answering to identify risk-prone sections in a contract. Furthermore, the question-answering model encodes the concatenated question-contract text and predicts the start and end position for clause extraction. Due to the limited labelled dataset for training, we leveraged transfer learning by fine-tuning the models with the CUAD dataset to enhance the model. On a dataset comprising 287 contract documents and 2000 labelled samples, our best model achieved an F1 score of 0.687.

Keywords: contract risk assessment, NLP, transfer learning, question answering

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Authors: Motonobu Hattori

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In neural networks, when new patters are learned by a network, they radically interfere with previously stored patterns. This drawback is called catastrophic forgetting. We have already proposed a biologically inspired dual-network memory model which can much reduce this forgetting for static patterns. In this model, information is first stored in the hippocampal network, and thereafter, it is transferred to the neocortical network using pseudo patterns. Because, temporal sequence learning is more important than static pattern learning in the real world, in this study, we improve our conventional dual-network memory model so that it can deal with temporal sequences without catastrophic forgetting. The computer simulation results show the effectiveness of the proposed dual-network memory model.

Keywords: catastrophic forgetting, dual-network, temporal sequences, hippocampal

Procedia PDF Downloads 251

Authors: Afrin Jaman Bonny, Mehrin Jahan, Zannatul Ferdhoush, Mumenunnessa Keya, Md. Shihab Mahmud, Sharun Akter Khushbu, Sheak Rashed Haider Noori, Sheikh Abujar

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In 2020, the SARS-CoV-2 pandemic had led all the educational institutions to move to online learning platforms to ensure safety as well as the continuation of learning without any disruption to students’ academic life. But after the reopening of those educational institutions suddenly in Bangladesh, it became a vital demand to observe students take on this decision and how much they are comfortable with the new habits. When all educational institutions were ordered to re-open after more than a year, data was collected from students of all educational levels. A Google Form was used to conduct this online survey, and a total of 565 students participated without being pressured. The survey reveals the students' preferences for online and offline education systems, as well as their mental health at the time including their behavior to get back to offline classes depending on getting vaccinated or not. After evaluating the findings, it is clear that respondents' choices vary depending on gender and educational level, with female and male participants experiencing various mental health difficulties and attitudes toward returning to offline classes. As a result of this study, the student’s overall perspective on the sudden reopening of their educational institutions has been analyzed.

Keywords: covid-19 epidemic, educational proceeding, university students, school/college students, physical activity, online platforms, mental health, psychological distress

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Authors: Khoi A. Nguyen, Rodney A. Stewart, Hong Zhang

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‘Water-related energy’ is energy use which is directly or indirectly influenced by changes to water use. Informatics applying a range of mathematical, statistical and rule-based approaches can be used to reveal important information on demand from the available data provided at second, minute or hourly intervals. This study aims to combine these two concepts to improve the current water end use disaggregation problem through applying a wide range of most advanced pattern recognition techniques to analyse the concurrent high-resolution water-energy consumption data. The obtained results have shown that recognition accuracies of all end-uses have significantly increased, especially for mechanised categories, including clothes washer, dishwasher and evaporative air cooler where over 95% of events were correctly classified.

Keywords: deep learning network, smart metering, water end use, water-energy data

Procedia PDF Downloads 287

Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Amrane Meriem, Oukid Salyha

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The presence of defects or bugs in software can lead to costly setbacks, operational inefficiencies, and compromised user experiences. The integration of Machine Learning(ML) techniques has emerged to predict and preemptively address software defects. ML represents a proactive strategy aimed at identifying potential anomalies, errors, or vulnerabilities within code before they manifest as operational issues. By analyzing historical data, such as code changes, feature im- plementations, and defect occurrences. This en- ables development teams to anticipate and mitigate these issues, thus enhancing software quality, reducing maintenance costs, and ensuring smoother user interactions. In this work, we used a recommendation system to improve the performance of ML models in terms of predicting the code severity and effort estimation.

Keywords: software engineering, machine learning, bugs detection, effort estimation

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Authors: Mohammad Arabi

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The development of advanced technologies in the field of signal processing and vibration analysis has enabled more accurate analysis and fault detection in electrical systems. This research investigates the application of temporal and frequency features in detecting faults in electric motor bearings, aiming to enhance fault detection accuracy and prevent unexpected failures. The use of methods such as deep learning algorithms and neural networks in this process can yield better results. The main objective of this research is to evaluate the efficiency and accuracy of methods based on temporal and frequency features in identifying faults in electric motor bearings to prevent sudden breakdowns and operational issues. Additionally, the feasibility of using techniques such as machine learning and optimization algorithms to improve the fault detection process is also considered. This research employed an experimental method and random sampling. Vibration signals were collected from electric motors under normal and faulty conditions. After standardizing the data, temporal and frequency features were extracted. These features were then analyzed using statistical methods such as analysis of variance (ANOVA) and t-tests, as well as machine learning algorithms like artificial neural networks and support vector machines (SVM). The results showed that using temporal and frequency features significantly improves the accuracy of fault detection in electric motor bearings. ANOVA indicated significant differences between normal and faulty signals. Additionally, t-tests confirmed statistically significant differences between the features extracted from normal and faulty signals. Machine learning algorithms such as neural networks and SVM also significantly increased detection accuracy, demonstrating high effectiveness in timely and accurate fault detection. This study demonstrates that using temporal and frequency features combined with machine learning algorithms can serve as an effective tool for detecting faults in electric motor bearings. This approach not only enhances fault detection accuracy but also simplifies and streamlines the detection process. However, challenges such as data standardization and the cost of implementing advanced monitoring systems must also be considered. Utilizing temporal and frequency features in fault detection of electric motor bearings, along with advanced machine learning methods, offers an effective solution for preventing failures and ensuring the operational health of electric motors. Given the promising results of this research, it is recommended that this technology be more widely adopted in industrial maintenance processes.

Keywords: electric motor, fault detection, frequency features, temporal features

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Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Margit Kastner

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To overcome the negative consequences associated with large class sizes and to support students in developing the necessary competences (e.g., critical thinking, problem-solving, or team-work skills) a marketing course has been redesigned by implementing project-based learning with peer assessment (PBL&PA). This means that students can voluntarily take advantage of this supplementary offer and explore -in addition to attending the lecture where clicker questions are asked- a real-world problem, find a solution, and assess the results of peers while working in small collaborative groups. In order to handle this with little further effort, the process is technically supported by the university’s e-learning system in such a way that students upload their solution in form of an assignment which is then automatically distributed to peer groups who have to assess the work of three other groups. Finally, students’ work is graded automatically considering both, students’ contribution to the project and the conformity of the peer assessment. The purpose of this study is to evaluate students’ perception of PBL&PA using an online-questionnaire to collect the data. More specifically, it aims to discover students’ motivations for (not) working on a project and the benefits and problems students encounter. In addition to the survey, students’ performance was analyzed by comparing the final grades of those who participated in PBL&PA with those who did not participate. Among the 260 students who filled out the questionnaire, 47% participated in PBL&PA. Besides extrinsic motivations (bonus credits), students’ participation was often motivated by learning and social benefits. Reasons for not working on a project were connected to students’ organization and management of their studies (e.g., time constraints, no/wrong information) and teamwork concerns (e.g., missing engagement of peers, prior negative experiences). In addition, high workload and insufficient extrinsic motivation (bonus credits) were mentioned. With regards to benefits and problems students encountered during the project, students provided more positive than negative comments. Positive aspects most often stated were learning and social benefits while negative ones were mainly attached to the technical implementation. Interestingly, bonus credits were hardly named as a positive aspect meaning that intrinsic motivations have become more important when working on the project. Team aspects generated mixed feelings. In addition, students who voluntarily participated in PBL&PA were, in general, more active and utilized further course offers such as clicker questions. Examining students’ performance at the final exam revealed that students without participating in any of the offered active learning tasks performed poorest in the exam while students who used all activities were best. In conclusion, the goals of the implementation were met in terms of students’ perceived benefits and the positive impact on students’ exam performance. Since the comparison of the automatic grading with faculty grading showed valid results, it is possible to rely only on automatic grading in the future. That way, the additional workload for faculty will be within limits. Thus, the implementation of project-based learning with peer assessment can be recommended for large classes.

Keywords: automated grading, large classes, peer assessment, project-based learning

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Authors: Raquel Mateo Mendaza, Carmen Novo Urraca

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The current paper presents the elaboration of a corpus database that aligns two different corpora in order to simplify the search of information both for researchers and students of Old English. This database comprises the information contained in two main reference corpora, namely the Dictionary of Old English Corpus (DOEC), compiled at the University of Toronto, and the York-Toronto-Helsinki Parsed Corpus of Old English (YCOE). The first one provides information on all surviving texts written in the Old English language. The latter offers the syntactical and morphological annotation of several texts included in the DOEC. Although both corpora are closely related, as the YCOE includes the DOE source text identifier, the main problem detected is that there is not an alignment of texts that allows for the search of whole fragments to be further analysed in terms of morphology and syntax. The database proposed in this paper gathers all this information and presents it in a simple, more accessible, visual, and educational way. The alignment of fragments has been done in an automatized way. However, some problems have emerged during the creating process particularly related to the lack of correspondence in the division of fragments. For this reason, it has been necessary to revise the whole entries manually to obtain a truthful high-quality product and to carefully indicate the gaps encountered in these corpora. All in all, this database contains more than 60,000 entries corresponding with the DOE fragments annotated by the YCOE. The main strength of the resulting product is its research and teaching implications in the study of Old English. The use of this database will help researchers and students in the study of different aspects of the language, such as inflectional morphology, syntactic behaviour of given words, or translation studies, among others. By means of the search of words or fragments, the annotated information on morphology and syntax will be automatically displayed, automatizing, and speeding up the search of data.

Keywords: alignment, corpus database, morphosyntactic analysis, Old English

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Authors: Eva Laryea, Clement Yeboah Authors

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A pretest-posttest within subjects, experimental design was employed to examine the effects of a computerized basic statistics learning game on achievement and statistics-related anxiety of students enrolled in introductory graduate statistics course. Participants (N = 34) were graduate students in a variety of programs at state-funded research university in the Southeast United States. We analyzed pre-test posttest differences using paired samples t-tests for achievement and for statistics anxiety. The results of the t-test for knowledge in statistics were found to be statistically significant indicating significant mean gains for statistical knowledge as a function of the game-based intervention. Likewise, the results of the t-test for statistics-related anxiety were also statistically significant indicating a decrease in anxiety from pretest to posttest. The implications of the present study are significant for both teachers and students. For teachers, using computer games developed by the researchers can help to create a more dynamic and engaging classroom environment, as well as improve student learning outcomes. For students, playing these educational games can help to develop important skills such as problem solving, critical thinking, and collaboration. Students can develop interest in the subject matter and spend quality time to learn the course as they play the game without knowing that they are even learning the presupposed hard course. The future directions of the present study are promising, as technology continues to advance and become more widely available. Some potential future developments include the integration of virtual and augmented reality into educational games, the use of machine learning and artificial intelligence to create personalized learning experiences, and the development of new and innovative game-based assessment tools. It is also important to consider the ethical implications of computer game-based pedagogy, such as the potential for games to perpetuate harmful stereotypes and biases. As the field continues to evolve, it will be crucial to address these issues and work towards creating inclusive and equitable learning experiences for all students. This study has the potential to revolutionize the way basic statistics graduate students learn and offers exciting opportunities for future development and research. It is an important area of inquiry for educators, researchers, and policymakers, and will continue to be a dynamic and rapidly evolving field for years to come.

Keywords: pretest-posttest within subjects, experimental design, achievement, statistics-related anxiety

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Authors: Nhac Thanh Huong

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New teaching methods have led to the changes in the teachers’ roles in an English class, in which teachers’ error correction is an integral part. Language error and corrective feedback have been the interest of many researchers in foreign language teaching. However, the techniques and the effectiveness of teachers’ feedback have been a question of much controversy. This present case study has been carried out with a view to finding out the patterns of teachers’ corrective feedback and their impact on students’ uptake in English speaking lessons of legal English major students at Hanoi Law University. In order to achieve those aims, the study makes use of classroom observations as the main method of data collection to seeks answers to the two following questions: 1. What patterns of corrective feedback occur in English speaking lessons for second- year legal English major students in Hanoi Law University?; 2. To what extent does that corrective feedback lead to students’ uptake? The study provided some important findings, among which was a close relationship between corrective feedback and uptake. In particular, recast was the most commonly used feedback type, yet it was the least effective in terms of students’ uptake and repair, while the most successful feedback, namely meta-linguistic feedback, clarification requests and elicitation, which led to students’ generated repair, was used at a much lower rate by teachers. Furthermore, it revealed that different types of errors needed different types of feedback. Also, the use of feedback depended on the students’ English proficiency level. In the light of findings, a number of pedagogical implications have been drawn in the hope of enhancing the effectiveness of teachers’ corrective feedback to students’ uptake in foreign language acquisition process.

Keywords: corrective feedback, error, uptake, speaking English lesson

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Authors: Isabel Moscol, Carlos J. Díaz, Ciro Rodríguez

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Hip replacement becomes necessary when a person suffers severe pain or considerable functional limitations and the best option to enhance their quality of life is through the replacement of the damaged joint. One of the main components in femoral prostheses is the stem which distributes the loads from the joint to the proximal femur. To preserve more bone stock and avoid weakening of the diaphysis, a short starting stem was selected, generated from the intramedullary morphology of the patient's femur. It ensures the implantability of the design and leads to geometric delimitation for personalized optimization with machine learning (ML) and metaheuristic algorithms. The present study attempts to design a cementless short stem to make the strain deviation before and after implantation close to zero, promoting its fixation and durability. Regression models developed to estimate the percentage change of maximum principal stresses were used as objective optimization functions by the metaheuristic algorithm. The latter evaluated different geometries of the short stem with the modification of certain parameters in oblique sections from the osteotomy plane. The optimized geometry reached a global stress shielding (SS) of 18.37% with a determination factor (R²) of 0.667. The predicted results favour implantability integration in the short stem optimization to effectively reduce SS in the proximal femur.

Keywords: machine learning techniques, metaheuristic algorithms, short-stem design, stress shielding, hip replacement

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Authors: Benjamin Beltzung, Marie Pelé, Julien P. Renoult, Cédric Sueur

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To this day, it is not known if drawing is specifically human behavior or if this behavior finds its origins in ancestor species. An interesting window to enlighten this question is to analyze the drawing behavior in genetically close to human species, such as non-human primate species. A good candidate for this approach is the orangutan, who shares 97% of our genes and exhibits multiple human-like behaviors. Focusing on figurative aspects may not be suitable for orangutans’ drawings, which may appear as scribbles but may have meaning. A manual feature selection would lead to an anthropocentric bias, as the features selected by humans may not match with those relevant for orangutans. In the present study, we used deep learning to analyze the drawings of a female orangutan named Molly († in 2011), who has produced 1,299 drawings in her last five years as part of a behavioral enrichment program at the Tama Zoo in Japan. We investigate multiple ways to decipher Molly’s drawings. First, we demonstrate the existence of differences between seasons by training a deep learning model to classify Molly’s drawings according to the seasons. Then, to understand and interpret these seasonal differences, we analyze how the information spreads within the network, from shallow to deep layers, where early layers encode simple local features and deep layers encode more complex and global information. More precisely, we investigate the impact of feature complexity on classification accuracy through features extraction fed to a Support Vector Machine. Last, we leverage style transfer to dissociate features associated with drawing style from those describing the representational content and analyze the relative importance of these two types of features in explaining seasonal variation. Content features were relevant for the classification, showing the presence of meaning in these non-figurative drawings and the ability of deep learning to decipher these differences. The style of the drawings was also relevant, as style features encoded enough information to have a classification better than random. The accuracy of style features was higher for deeper layers, demonstrating and highlighting the variation of style between seasons in Molly’s drawings. Through this study, we demonstrate how deep learning can help at finding meanings in non-figurative drawings and interpret these differences.

Keywords: cognition, deep learning, drawing behavior, interpretability

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Authors: Hsin-Jen Chen

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Primary schooling in Taiwan is in a process of radical restructuring during the decade. At the center of these restructuring is the position of the principal and questions to do with how principals, as school leaders, respond to radical change. Adopting a case-study approach, the study chose a middle Taiwanese primary school to investigate how the principal learned to be political. Using micropolitical leadership, the principal at the researched site successfully coped with internal change and external demands. On the whole, judging from the principal’s leadership style on the mediation between parents and teachers, as well as school-based curriculum development, it could be argued that the principal was on the stance of being a leader of the cultural transformation instead of cultural reproduction. In doing so, the qualitative evidence has indicated that the principal seemed to be successful in coping with the demands of rapid change. Continuing learning for leadership is the core of working as a principal.

Keywords: micropolitics, leadership, micropolitical leadership, learning for leadership

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Authors: Nidhal K. Azawi, John M. Gauch

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Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images.

Keywords: colonoscopy classification, feature extraction, image alignment, machine learning

Procedia PDF Downloads 239