Search results for: online teaching and learning

Authors: Caiwei Chen, Jianhao Li, Jiasong Zhu

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The reform and opening-up stimulated economic and technological take-off in China while resulting in massive urbanization and motorization. Wujiaochang area was set as a secondary business district in Shanghai to meet the growing demand, with the reconstruction of Wujiaochang Plaza in 2005 being a milestone of this intended urban renewal. Wujiaochang is now an economically dynamic area providing much larger traffic and transit capacity transportation-wise. However, this rebuilding has completely changed the face of the district. It is, therefore, appropriate to evaluate its impact on neighborhoods and communities while assessing the overall sustainability of such an operation. In this study, via an online questionnaire survey among local residents and daily visitors, we assess the perceptions and the estimated impact of Wujiaochang Plaza's reconstruction. We then confront these results to the 62 answers from local residents to a questionnaire collected on paper. The analysis of our data, along with observation and other forms of information -such as maps analysis or online applications (Dianping)- demonstrate major improvement in economic sustainability but also significant losses in environmental sustainability, especially in terms of active transportation. As for the social viewpoint, local residents' opinions tend to be rather positive, especially regarding traffic safety and access to consumption, despite the lack of connectivity and radical changes induced by Wujiaochang massive transformations. In general, our investigation exposes the overall positive outcomes of Wujiaochang Plaza reconstruction but also unveils major drawbacks, especially in terms of soft mobility and traffic fluidity. We gather that our approach could be of tremendous help for future major urban interventions, as such approaches in municipal regeneration are widely implemented in Chinese cities and yet still need to be thoroughly assessed in terms of sustainability.

Keywords: China's reform and opening-up, economical revitalization, neighborhood identity, sustainability assessment, urban renewal

Procedia PDF Downloads 233

Authors: Rikson Gultom

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Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

Procedia PDF Downloads 125

Authors: Sumayya S.

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Making cities adaptive and inclusive is one among the inherent goal and challenge for contemporary cities. This is a serious concern when the urban transformations occur in varying magnitude due to visible and invisible factors. One type of visibly invisible factor is ecommerce and its expanding operation that is understood to cause changes to the conventional spatial structure positively and negatively. With the continued growth in e-commerce activities and its future potential, market analysts, media, and even retailers have questioned the importance of a future presence of traditional Brick-and-mortar stores in cities as a critical element, with some even referring to the repeated announcement of the closure of some store chains as the end of the online shopping era. Essentially this raises the question of how adaptive and inclusive the cities are to the dynamics of transformative changes that are often unseen. People have become more comfortable with seating inside and door delivery systems, and this increased change in usage of public spaces, especially the commercial corridors. Through this research helped in presetting a new approach for planning and designing commercial activities centers and also presents the impact of ecommerce on the urban fabric, such as division and fragmentation of space, showroom syndrome, reconceptualization of space, etc., in a critical way. The changes are understood by analyzing the e-commerce logistic process. Based on the inferences reach at the conclusion for the need of an integrated approach in the field of planning and designing of public spaces for the sustainable omnichannel retailing. This study was carried out with the following objectives Monitoring the impact of e commerce on the traditional shopping space. Explore the new challenges and opportunities faced by the urban form. Explore how adaptive and inclusive our cities are to the dynamics of transformative changes caused by ecommerce.

Keywords: E-commerce, shopping streets, online environment, offline environment, shopping factors

Procedia PDF Downloads 81

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

Procedia PDF Downloads 253

Authors: Sapana Purandare

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The landscape of school leadership is evolving with the changing world of the 21st century. In this era, it is crucial to adapt our approaches to school leadership, with the school leader playing an important role in shaping the educational system. During the implementation of the LEAD project, the volume of 67 practices was impractical for any school leader to effectively incorporate. Consequently, this study aims to address this issue by administering a questionnaire to school leaders, including those from Kotak Education Foundation partner schools and others operating within similar contexts. The goal is to pinpoint the practices that can enhance school leadership and Student Learning Outcomes (SLO) both presently and in the near future. Utilizing the Qualtrics tool, a survey was conducted to identify the top 15 practices that respondents believe will be crucial for improving SLO over the next 10-15 years. Additionally, focus group discussions (FGDs) and interviews were conducted to elucidate the challenges hindering the implementation of these practices within schools. The recommendations derived from the identified top 15 practices will be instrumental in devising scalable models for LEAD and advocating for their adoption at the state level. Practices with higher standard deviations and average scores hold particular significance for future development. Furthermore, demographic factors such as age, gender, and years of service influence individuals' perceptions of these practices and thus warrant consideration in our analysis.

Keywords: exploring top sustainable practices, practice implementation, school leadership, student learning outcomes

Procedia PDF Downloads 38

Authors: Lavinia Flueckiger, Roselind Lieb, Andrea H. Meyer, Cornelia Witthauer, Jutta Mata

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Objective: Health behaviors tend to show a high variability over time within the same person. However, most existing research can only assess a snapshot of a person’s behavior and not capture this natural daily variability. Two intensive longitudinal studies examine the variability in health behavior over one academic year and their implications for other aspects of daily life such as affect and academic performance. Can already a single day of increased physical activity, snacking, or improved sleep have beneficial effects? Methods: In two intensive longitudinal studies with up to 65 assessment days over an entire academic year, university students (Study 1: N = 292; Study 2: N = 304) reported sleep quality, physical activity, snacking, positive and negative affect, and learning goal achievement. Results: Multilevel structural equation models showed that on days on which participants reported better sleep quality or more physical activity than usual, they also reported increased positive affect, decreased negative affect, and better learning goal achievement. Higher day-to-day snacking was only associated with increased positive affect. Both, increased day-to-day sleep quality and physical activity were indirectly associated with better learning goal achievement through changes in positive and negative affect; results for snacking were mixed. Importantly, day-to-day sleep quality was a stronger predictor for affect and learning goal achievement than physical activity or snacking. Conclusion: One day of better sleep or more physical activity than usual is associated with improved affect and academic performance. These findings have important implications for low-threshold interventions targeting the improvement of daily functioning.

Keywords: sleep quality, physical activity, snacking, affect, academic performance, multilevel structural equation model

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Authors: Ashmita Bhattacharya

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This study aims to investigate whether the English immersion experience offered by Kolkata’s high-cost private English-medium schools lead to additive or subtractive language learning outcomes for students. In India, English has increasingly become associated with power, social status, and socio-economic mobility. As a result, a proliferation of English-medium schools has emerged across Kolkata and the wider Indian context. While in some contexts, English language learning can be an additive experience, in others, it can be subtractive where proficiency in English is developed at the expense of students’ native language proficiency development. Subtractive educational experiences can potentially have severe implications, including heritage language loss, detachment from cultural roots, and a diminished sense of national identity. Thus, with the use of semi-structured interviews, the language practices and lived experiences of 12 former students who attended high-cost private English-medium schools in Kolkata were thoroughly explored. The data collected was thematically coded and analysis was conducted using the Thematic Analysis approach. The findings indicate that the English immersion experience at Kolkata’s high-cost private English-medium schools provide a subtractive language learning experience to students. Additionally, this study suggests that robust home-based support for native languages might be crucial for mitigating the effects of subtractive English education. Furthermore, the study underscores the importance of integrating opportunities within schools that promote Indian languages and cultures as it can create a more positive, inclusive, and culturally responsive environment. Finally, although subject to further evaluation, the study recommends the implementation of bilingual and multilingual educational systems and provides suggestions for future research in this area.

Keywords: bilingual education, English immersion, language loss, multilingual education, subtractive language learning

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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 70

Authors: Anna Citterbergova

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This study explores the safeguards and guarantees to children’s personal data protection under the current EU and US legislative framework, namely the GDPR (2018) and COPPA (2000). Considering that children are online for the majority of their free time, one cannot overlook the negative side effects that may be associated with online participation, which may put children’s wellbeing and their fundamental rights at risk. The question of whether the current relevant legislative framework in relation to the responsibilities of the internet service providers (ISPs) are adequate safeguards and guarantees to children’s personal data protection has been an evolving debate both in the US and in the EU. From a children’s rights perspective, processors of personal data have certain obligations that must meet the international human rights principles (e. g. the CRC, ECHR), which require taking into account the best interest of the child. Accordingly, the need to protect children’s privacy online remains strong and relevant with the expansion of the number and importance of social media platforms to human life. At the same time, the landscape of the internet is rapidly evolving, and commercial interests are taking a more targeted approach in seeking children’s data. Therefore, it is essential to constantly evaluate the ongoing and evolving newly adopted market policies of ISPs that may misuse the gap in the current letter of the law. Previous studies in the field have already pointed out that both GDPR and COPPA may theoretically not be sufficient in protecting children’s personal data. With the focus on social media platforms, this study uses the doctrinal-descriptive method to identifiy the mechanisms enshrined in the GDPR and COPPA designed to protect children’s personal data. In its second part, the study includes a data gathering phase by the national data protection authorities responsible for monitoring and supervision of the GDPR in relation to children’s personal data protection who monitor the enforcement of the data protection rules throughout the European Union an contribute to their consistent application. These gathered primary source of data will later be used to outline the series of benefits and challenges to children’s persona lata protection faced by these institutes and the analysis that aims to suggest if and/or how to hold ISPs accountable while striking a fair balance between the commercial rights and the right to protection of the personal data of children. The preliminary results can be divided into two categories. First, conclusions in the doctrinal-descriptive part of the study. Second, specific cases and situations from the practice of national data protection authorities. While for the first part, concrete conclusions can already be presented, the second part is currently still in the data gathering phase. The result of this research is a comprehensive analysis on the safeguards and guarantees to children’s personal data protection under the current EU and US legislative framework, based on doctrinal-descriptive approach and original empirical data.

Keywords: personal data of children, personal data protection, GDPR, COPPA, ISPs, social media

Procedia PDF Downloads 92

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 89

Authors: Troy M. Houser

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The successor representation balances flexible and efficient reinforcement learning by learning to predict the future, given the present. As such, the successor representation models stimuli as what future states they lead to. Therefore, two stimuli that are perceptually dissimilar but lead to the same future state will come to be represented more similarly. This is very similar to an older behavioral paradigm -the acquired equivalence paradigm, which measures the generalization of learned associations. Here, we test via computational modeling the plausibility that the successor representation is the mechanism by which people generalize knowledge learned in the acquired equivalence paradigm. Computational evidence suggests that this is a plausible mechanism for acquired equivalence and thus can guide future empirical work on individual differences in associative-based generalization.

Keywords: acquired equivalence, successor representation, generalization, decision-making

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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 413

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 118

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

Procedia PDF Downloads 129

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

Procedia PDF Downloads 103

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 146

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 649

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 360

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

Procedia PDF Downloads 434

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

Procedia PDF Downloads 128

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 263

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 301

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

Procedia PDF Downloads 115

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

Procedia PDF Downloads 80

Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan

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Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of Big Data Technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centers or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through Vader and Roberta model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and TFIDF Vectorization, and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.

Keywords: counter vectorization, convolutional neural network, crawler, data technology, long short-term memory, web scraping, sentiment analysis

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Authors: Abdullah Alshakhi

Abstract:

This study aims to explore the assessment practices of transnational western teachers in Saudi EFL writing classrooms. The study adopts a critical ethnographic approach to understand the views and the experiences of four transnational TESOL professionals about how they navigate and negotiate their writing assessment practices in the Saudi EFL context. The qualitative data were collected through classroom observations and video recordings of the classroom teaching, which were followed by semi-structured interviews with the four TESOL teachers from Australia, England, USA, and Ireland. The data were analyzed from three perspectives of these transnational TESOL teachers in the Saudi EFL context: as a transnational teacher in monolingual context, as a transitional teacher abides by the prescribed curriculum and assessment instructions, and as a transnational teacher’s vision for monolingual students. The results of the study revealed that owing to the transnational teachers’ lack of understanding of the Saudi monolingual culture, bureaucratic structures, and top-down assessment policies in the institute where they work, their teaching and assessment of writing and other language skills are negatively affected and consequently had to be modified. Also, the Saudi learners’ lack of interest and their lower level of English proficiency pose serious challenges to those transnational teachers’ writing assessment practices. More often, the teachers find the prescribed writing curriculum and assessment tools ineffective in the Saudi EFL context. Because of these experiences, the transnational teachers in this study have exhibited their awareness of their monolingual/monoculture background, Saudi’s cultural and religious values, and institutional structures, which have helped them customize or supplement the writing assessment practices accordingly.

Keywords: critical ethnography, Saudi EFL context, TESOL professionals, transnationalism, writing assessment

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Authors: Mohammad Arabi

Abstract:

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: S. Nakayama

Abstract:

For non-native English speakers, using English modal auxiliary verbs appropriately can be among the most challenging tasks. This research sought to identify differences in modal verb usage between Japanese non-native English speakers (JNNS) and native speakers (NS) from two different perspectives: frequency of use and distribution of verb phrase structures (VPS) where modal verbs occur. This study can contribute to the identification of JNNSs' interlanguage with regard to modal verbs; the main aim is to make a suggestion for the improvement of teaching materials as well as to help language teachers to be able to teach modal verbs in a way that is helpful for learners. To address the primary question in this study, usage of nine central modals (‘can’, ‘could’, ‘may’, ‘might’, ‘shall’, ‘should’, ‘will’, ‘would’, and ‘must’) by JNNS was compared with that by NSs in the International Corpus Network of Asian Learners of English (ICNALE). This corpus is one of the largest freely-available corpora focusing on Asian English learners’ language use. The ICNALE corpus consists of four modules: ‘Spoken Monologue’, ‘Spoken Dialogue’, ‘Written Essays’, and ‘Edited Essays’. Among these, this research adopted the ‘Written Essays’ module only, which is the set of 200-300 word essays and contains approximately 1.3 million words in total. Frequency analysis revealed gaps as well as similarities in frequency order. Specifically, both JNNSs and NSs used ‘can’ with the most frequency, followed by ‘should’ and ‘will’; however, usage of all the other modals except for ‘shall’ was not identical to each other. A log-likelihood test uncovered JNNSs’ overuse of ‘can’ and ‘must’ as well as their underuse of ‘will’ and ‘would’. VPS analysis revealed that JNNSs used modal verbs in a relatively narrow range of VPSs as compared to NSs. Results showed that JNNSs used most of the modals with bare infinitives or the passive voice only whereas NSs used the modals in a wide range of VPSs including the progressive construction and the perfect aspect, both of which were the structures where JNNSs rarely used the modals. Results of frequency analysis suggest that language teachers or teaching materials should explain other modality items so that learners can avoid relying heavily on certain modals and have a wide range of lexical items to reflect their feelings more accurately. Besides, the underused modals should be more stressed in the classroom because they are members of epistemic modals, which allow us to not only interject our views into propositions but also build a relationship with readers. As for VPSs, teaching materials should present more examples of the modals occurring in a wide range of VPSs to help learners to be able to express their opinions from a variety of viewpoints.

Keywords: corpus linguistics, Japanese learners of English, modal auxiliary verbs, International Corpus Network of Asian Learners of English

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Authors: Mary Isobelle Mullaney

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The maxim "an unexamined life is not worth living," attributed to Socrates, prompts a contemplative reflection spanning over 32 years as a teacher educator in the Republic of Ireland. Taking time to contemplate the changes that have occurred and the current landscape provides valuable insights into the dynamic terrain of teacher preparation. The reflective journey traverses the impacts of global and societal shifts, responding to challenges, embracing advancements, and navigating the delicate balance between responsiveness to the world and the active shaping of it. The transformative events of the COVID-19 pandemic spotlighted the indispensable role of teachers in Ireland, reinforcing the critical nature of education for the well-being of pupils. Research solidifies the understanding that teachers matter and so it is worth exploring the pivotal role of the teacher educator. This reflective piece examines the changes in teacher education and explores the juxtapositions that have emerged in response to three decades of profound change. The attractiveness of teaching as a career is juxtaposed against the reality of the demands of the job, with conditions for public servants in Ireland undergoing a shift. High-level strategic discussions about increasing teacher numbers now contrast with a previous oversupply. The delicate balance between the imperative to increase enrolment (getting "bums on seats") and the gatekeeper role of teacher educators is explored, raising questions about maintaining high standards amid changing student profiles. Another poignant dichotomy involves the high demand for teachers versus the hurdles candidates face in becoming teachers. The rising cost and duration of teacher education courses raise concerns about attracting quality candidates. The perceived attractiveness of teaching as a career contends with the reality of increased demands on educators. One notable juxtaposition centres around the rapid evolution of Irish initial teacher education versus the potential risk of change overload. The Teaching Council of Ireland has spearheaded considerable changes, raising questions about the timing and evaluation of these changes. This reflection contemplates the vision of a professional teaching council versus its evolving reality and the challenges posed by the value placed on school placement in teacher preparation. The juxtapositions extend to the classroom, where theory may not seamlessly align with the lived experience. Inconsistencies between college expectations and the classroom reality prompt reflection on the effectiveness of teacher preparation programs. Addressing the changing demographic landscape of society and schools, there is a persistent incongruity between the diversity of Irish society and the profile of second-level teachers. As education undergoes a digital revolution, the enduring philosophies of education confront technological advances. This reflection highlights the tension between established practices and contemporary demands, acknowledging the irreplaceable value of face-to-face interaction while integrating technology into teacher training programs. In conclusion, this reflective journey encapsulates the intricate web of juxtapositions in Irish Initial Teacher Education. It emphasises the enduring commitment to fostering education, recognising the profound influence educators wield, and acknowledging the challenges and gratifications inherent in shaping the minds and futures of generations to come.

Keywords: Irish post primary teaching, juxtapositions, reflection, teacher education

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

Abstract:

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

Procedia PDF Downloads 78