Search results for: learning methods

Authors: Sheelagh Heugh, Michael Upton, Kriya Kalidas, Stephen Breen

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This article aims to create awareness of the opportunities and issues the artificial intelligence (AI) tool GPT-3 (Generative Pre-trained Transformer-3) brings to higher education. Technological disruptors have featured in higher education (HE) since Konrad Klaus developed the first functional programmable automatic digital computer. The flurry of technological advances, such as personal computers, smartphones, the world wide web, search engines, and artificial intelligence (AI), have regularly caused disruption and discourse across the educational landscape around harnessing the change for the good. Accepting AI influences are inevitable; we took mixed methods through participatory action research and evaluation approach. Joining HE communities, reviewing the literature, and conducting our own research around Chat GPT-3, we reviewed our institutional approach to changing our current practices and developing policy linked to assessments and the use of Chat GPT-3. We review the impact of GPT-3, a high-powered natural language processing (NLP) system first seen in 2020 on HE. Historically HE has flexed and adapted with each technological advancement, and the latest debates for educationalists are focusing on the issues around this version of AI which creates natural human language text from prompts and other forms that can generate code and images. This paper explores how Chat GPT-3 affects the current educational landscape: we debate current views around plagiarism, research misconduct, and the credibility of assessment and determine the tool's value in developing skills for the workplace and enhancing critical analysis skills. These questions led us to review our institutional policy and explore the effects on our current assessments and the development of new assessments. Conclusions: After exploring the pros and cons of Chat GTP-3, it is evident that this form of AI cannot be un-invented. Technology needs to be harnessed for positive outcomes in higher education. We have observed that materials developed through AI and potential effects on our development of future assessments and teaching methods. Materials developed through Chat GPT-3 can still aid student learning but lead to redeveloping our institutional policy around plagiarism and academic integrity.

Keywords: artificial intelligence, Chat GPT-3, intellectual property, plagiarism, research misconduct

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Authors: Ms Azam Najafkouchak, David Todem, Dorothy Pathak, Pramod Pathak, Joseph Gardiner

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Propensity score (PS) methods have recently become the standard analysis as a tool for the causal inference in the observational studies where exposure is not randomly assigned, thus, confounding can impact the estimation of treatment effect on the outcome. For the binary outcome, the effect of treatment on the outcome can be estimated by odds ratios, relative risks, and risk differences. However, using the different PS methods may give you a different estimation of the treatment effect on the outcome. Several methods of PS analyses have been used mainly, include matching, inverse probability of weighting, stratification, and covariate adjusted on PS. Due to the dangers of discretizing continuous variables (exposure, covariates), the focus of this paper will be on how the variation in cut-points or boundaries will affect the average treatment effect (ATE) utilizing the stratification of PS method. Therefore, we are trying to avoid choosing arbitrary cut-points, instead, we continuously discretize the PS and accumulate information across all cut-points for inferences. We will use Monte Carlo simulation to evaluate ATE, focusing on two PS methods, stratification and covariate adjusted on PS. We will then show how this can be observed based on the analyses of the data from a case-control study of breast cancer, the Polish Women’s Health Study.

Keywords: average treatment effect, propensity score, stratification, covariate adjusted, monte Calro estimation, breast cancer, case_control study

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Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Hoa Pham

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The literature on language teaching and second language acquisition has been largely driven by monolingual ideology with a common assumption that a second language (L2) is best taught and learned in the L2 only. The current study challenges this assumption by reporting learners' positive perceptions of tertiary level teachers' code switching practices in Vietnam. The findings of this study contribute to our understanding of code switching practices in language classrooms from a learners' perspective. Data were collected from student participants who were working towards a Bachelor degree in English within the English for Business Communication stream through the use of focus group interviews. The literature has documented that this method of interviewing has a number of distinct advantages over individual student interviews. For instance, group interactions generated by focus groups create a more natural environment than that of an individual interview because they include a range of communicative processes in which each individual may influence or be influenced by others - as they are in their real life. The process of interaction provides the opportunity to obtain the meanings and answers to a problem that are "socially constructed rather than individually created" leading to the capture of real-life data. The distinct feature of group interaction offered by this technique makes it a powerful means of obtaining deeper and richer data than those from individual interviews. The data generated through this study were analysed using a constant comparative approach. Overall, the students expressed positive views of this practice indicating that it is a useful teaching strategy. Teacher code switching was seen as a learning resource and a source supporting language output. This practice was perceived to promote student comprehension and to aid the learning of content and target language knowledge. This practice was also believed to scaffold the students' language production in different contexts. However, the students indicated their preference for teacher code switching to be constrained, as extensive use was believed to negatively impact on their L2 learning and trigger cognitive reliance on the L1 for L2 learning. The students also perceived that when the L1 was used to a great extent, their ability to develop as autonomous learners was negatively impacted. This study found that teacher code switching was supported in certain contexts by learners, thus suggesting that there is a need for the widespread assumption about the monolingual teaching approach to be re-considered.

Keywords: codeswitching, L1 use, L2 teaching, learners’ perception

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Authors: Warda Khan, Remo Lachmann, Adarsh S. Garakahally

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The increasing complexity of software and its applicability in a wide range of industries, e.g., automotive, call for enhanced quality assurance techniques. Test automation is one option to tackle the prevailing challenges by supporting test engineers with fast, parallel, and repetitive test executions. A high degree of test automation allows for a shift from mundane (manual) testing tasks to a more analytical assessment of the software under test. However, a high initial investment of test resources is required to establish test automation, which is, in most cases, a limitation to the time constraints provided for quality assurance of complex software systems. Hence, a computer-aided creation of automated test cases is crucial to increase the benefit of test automation. This paper proposes the application of machine learning for the generation of automated test cases. It is based on supervised learning to analyze test specifications and existing test implementations. The analysis facilitates the identification of patterns between test steps and their implementation with test automation components. For the test case generation, this approach exploits historical data of test automation projects. The identified patterns are the foundation to predict the implementation of unknown test case specifications. Based on this support, a test engineer solely has to review and parameterize the test automation components instead of writing them manually, resulting in a significant time reduction for establishing test automation. Compared to other generation approaches, this ML-based solution can handle different writing styles, authors, application domains, and even languages. Furthermore, test automation tools require expert knowledge by means of programming skills, whereas this approach only requires historical data to generate test cases. The proposed solution is evaluated using various multi-label evaluation criteria (EC) and two small-sized real-world systems. The most prominent EC is ‘Subset Accuracy’. The promising results show an accuracy of at least 86% for test cases, where a 1:1 relationship (Multi-Class) between test step specification and test automation component exists. For complex multi-label problems, i.e., one test step can be implemented by several components, the prediction accuracy is still at 60%. It is better than the current state-of-the-art results. It is expected the prediction quality to increase for larger systems with respective historical data. Consequently, this technique facilitates the time reduction for establishing test automation and is thereby independent of the application domain and project. As a work in progress, the next steps are to investigate incremental and active learning as additions to increase the usability of this approach, e.g., in case labelled historical data is scarce.

Keywords: machine learning, multi-class, multi-label, supervised learning, test automation

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Authors: Haixu Yu, Wenhui Cao

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Tailoring Large Language Models (LLMs) to perform legal reasoning has been a popular trend in the study of AI and law. Researchers have mainly employed two methods to unlock the potential of LLMs, namely by finetuning the LLMs to expand their knowledge of law and by restructuring the prompts (In-Context Learning) to optimize the LLMs’ understanding of the legal questions. Although claiming the finetuning and renovated prompting can make LLMs more competent in legal reasoning, most state-of-the-art studies show quite limited improvements of practicability. In this paper, drawing on the study of the complexity and low interpretability of legal texts, we propose a prompting strategy based on the Chain of Thought (CoT) method. Instead of merely instructing the LLM to reason “step by step”, the prompting strategy requires the tested LLM to identify the ambiguity in the questions as the first step and then allows the LLM to generate corresponding answers in line with different understandings of the identified terms as the following step. The proposed prompting strategy attempts to encourage LLMs to "interpret" the given text from various aspects. Experiments that require the LLMs to answer “case analysis” questions of bar examination with general LLMs such as GPT 4 and legal LLMs such as LawGPT show that the prompting strategy can improve LLMs’ ability to better understand complex legal texts.

Keywords: ambiguity-identification, prompt, large language model, legal text understanding

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Authors: Mohammad Rostami Mehr, Seyed Arya Mir Rashed, Arndt Lueder, Magdalena Missler-Behr

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This paper represents the conception that complex problems do not necessarily need a similar complex solution in order to cope with the complexity. Furthermore, a simple solution based on established methods can provide a sufficient way to deal with the complexity. To verify this conception, the presented paper focuses on the field of change management as a part of the new product development process in the automotive sector. In this field, dealing with increasing complexity is essential, while only non-flexible rigid processes that are not designed to handle complexity are available. The basic methodology of this paper can be divided into four main sections: 1) analyzing the complexity of the change management, 2) literature review in order to identify potential solutions and methods, 3) capturing and implementing expertise of experts from the change management field of an automobile manufacturing company and 4) systematical comparison of the identified methods from literature and connecting these with defined requirements of the complexity of the change management in order to develop a solution. As a practical outcome, this paper provides a method to capture the complexity of engineering changes (EC) and includes it within the EC evaluation process, following case-related process guidance to cope with the complexity. Furthermore, this approach supports the conception that dealing with complexity is possible while utilizing rather simple and established methods by combining them into a powerful tool.

Keywords: complexity management, new product development, engineering change management, flexibility

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Authors: Intan Azura Mokhtar, Yaacob Ibrahim

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In recent years, learning about sustainable development and sustainability has become an increasingly significant component in universities’ degree programmes and curricula. As the world comes together and races to fulfil the 17 United Nations’ sustainable development goals (SDGs) by the year 2030, our educational curricula and landscapes simultaneously evolve to integrate lessons and opportunities for sustainable development and sustainability to redefine our university education and set the trajectory for our young people to take the lead in co-creating solutions for a better world. In this paper, initiatives and projects that revolved around themes of sustainable development and sustainability in a young university in Singapore are discussed. These initiatives and projects were curated by a new centre in the university that focuses on community leadership, social innovation, and service learning and was led by the university’s academic staff. The university’s undergraduate students were also involved in these initiatives and projects and played an active role in reaching out to and engaging members of different segments of the community – to better understand their needs and concerns and to co-create with them relevant and sustainable solutions that generate positive social impact.

Keywords: singapore, sustainable development, sustainability, undergraduate education

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Authors: H. J. Wattimanela, U. S. Passaribu, A. N. T. Puspito, S. W. Indratno

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Molluca Collision Zone is located at the junction of the Eurasian plate, Australian, Pacific, and the Philippines. Between the Sangihe arc, west of the collision zone, and to the east of Halmahera arc is active collision and convex toward the Molluca Sea. This research will analyze the behavior of earthquake occurrence in Molluca Collision Zone related to the distributions of an earthquake in each partition regions, determining the type of distribution of a occurrence earthquake of partition regions, and the mean occurrence of earthquakes each partition regions, and the correlation between the partitions region. We calculate number of earthquakes using partition method and its behavioral using conventional statistical methods. The data used is the data type of shallow earthquakes with magnitudes ≥ 4 SR for the period 1964-2013 in the Molluca Collision Zone. From the results, we can classify partitioned regions based on the correlation into two classes: strong and very strong. This classification can be used for early warning system in disaster management.

Keywords: molluca collision zone, partition regions, conventional statistical methods, earthquakes, classifications, disaster management

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Authors: Alireza Moghtaderi, Negin Khakpour

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Disc Herniation is a common complication in the society and it is one of the main reasons for referring to physical medicine and rehabilitation clinics. Despite of various methods proposed for treatingthis disease, still there is disagreement on success of these methods especially in non-surgical methods, and thus current study aims at determining effect of laser beam and magnet on treatment of Intervertebral Disc Herniation. During a clinical trial study, 80 patients with Intervertebral Disc Herniation underwent a combined package of treatment including magnet, laser beam, PRP and Prolotherapy during 6 months. Average age of patients was 51.25 ± 10.7 with range of 25 – 71 years. 30 men (37.5%) and 50 women (62.5%) took part in the study. average weight of patients was 64.3 ± 7.2 with range of 49 – 79 kg. highest level of Disc Herniation was L5 – S1 with frequency of 17 cases (21.3%). Disc Herniation was severe in 30 cases before treatment, but it reduced to 3 casesafter treatment. This study indicates effect of combined treatment using non-invasive laser beam and magnet therapy on disco genic diseases and mechanical pains of spine is highly effective.

Keywords: hallux, valgus, botulinum toxin a, pain

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Authors: Nguyen Tien Thanh, Günter Meon

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Precipitation plays a key role in water cycle, defining the local climatic conditions and in ecosystem. It is also an important input parameter for water resources management and hydrologic models. With spatial continuous data, a certainty of discharge predictions or other environmental factors is unquestionably better than without. This is, however, not always willingly available to acquire for a small basin, especially for coastal region in Vietnam due to a low network of meteorological stations (30 stations) on long coast of 3260 km2. Furthermore, available gridded precipitation datasets are not fine enough when applying to hydrologic models. Under conditions of global warming, an application of spatial interpolation methods is a crucial for the climate change impact studies to obtain the spatial continuous data. In recent research projects, although some methods can perform better than others do, no methods draw the best results for all cases. The objective of this paper therefore, is to investigate different spatial interpolation methods for daily precipitation over a small basin (approximately 400 km2) located in coastal region, Southern Vietnam and find out the most efficient interpolation method on this catchment. The five different interpolation methods consisting of cressman, ordinary kriging, regression kriging, dual kriging and inverse distance weighting have been applied to identify the best method for the area of study on the spatio-temporal scale (daily, 10 km x 10 km). A 30-year precipitation database was created and merged into available gridded datasets. Finally, observed changes in constructed precipitation were performed. The results demonstrate that the method of ordinary kriging interpolation is an effective approach to analyze the daily precipitation. The mixed trends of increasing and decreasing monthly, seasonal and annual precipitation have documented at significant levels.

Keywords: interpolation, precipitation, trend, vietnam

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Authors: Slava Kalyuga, Sujanya Sombatteera

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In multimedia learning, written text is often transformed into spoken (narrated) text. This transient information may overwhelm limited processing capacity of working memory and inhibit learning instead of improving it. The paper reviews recent empirical studies in modality and verbal redundancy effects within a cognitive load framework and outlines conditions under which negative effects of transiency may occur. According to the modality effect, textual information accompanying pictures should be presented in an auditory rather than visual form in order to engage two available channels of working memory – auditory and visual - instead of only one of them. However, some studies failed to replicate the modality effect and found differences opposite to those expected. Also, according to the multimedia redundancy effect, the same information should not be presented simultaneously in different modalities to avoid unnecessary cognitive load imposed by the integration of redundant sources of information. However, a few studies failed to replicate the multimedia redundancy effect too. Transiency of information is used to explain these controversial results.

Keywords: cognitive load, transient information, modality effect, verbal redundancy effect

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Authors: Hamad Alhamad, Catriona Khamisha, Emma Green, Yvonne Robb

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Introduction: Practice education is a key component of occupational therapy education. This critical review aimed to explore students’ and clinical supervisors’ experiences of practice education, and to make recommendations for research. Method: The literature was systematically searched using five databases. Qualitative, quantitative and mixed methods studies were included. Critical Appraisal Skills Programme checklist for qualitative studies and Mixed Methods Assessment Tool for quantitative and mixed methods studies were used to assess study quality. Findings: Twenty-two studies with high quality scores were included: 16 qualitative, 3 quantitative and 3 mixed methods. Studies were conducted in Australia, Canada, USA and UK. During practice education, students learned professional skills, practical skills, clinical skills and problem-solving skills, and improved confidence and creativity. Supervisors had an opportunity to reflect on their practice and get experience of supervising students. However, clear objectives and expectations for students, and sufficient theoretical knowledge, preparation and resources for supervisors were required. Conclusion: Practice education provides different skills and experiences, necessary to become competent professionals; but some areas of practice education need to improve. Studies in non-western countries are needed to explore the perspectives of students and clinical supervisors in different cultures, to ensure the practice education models adopted are relevant.

Keywords: occupational therapy, practice education, fieldwork, students, clinical supervisors

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Authors: Dimitra Alexiou

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During the last 10 years, in spite of the economic crisis, the number of tourists arriving in Greece has increased, particularly during the tourist season from April to October. In this paper, the number of annual tourist arrivals is studied to explore their preferences with regard to the month of travel, the selected destinations, as well the amount of money spent. The collected data are processed with statistical methods, yielding numerical and graphical results. From the computation of statistical parameters and the forecasting with exponential smoothing, useful conclusions are arrived at that can be used by the Greek tourism authorities, as well as by tourist organizations, for planning purposes for the coming years. The results of this paper and the computed forecast can also be used for decision making by private tourist enterprises that are investing in Greece. With regard to the statistical methods, the method of Simple Exponential Smoothing of time series of data is employed. The search for a best forecast for 2017 and 2018 provides the value of the smoothing coefficient. For all statistical computations and graphics Microsoft Excel is used.

Keywords: tourism, statistical methods, exponential smoothing, land spatial planning, economy

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Authors: Patsara Sirikamonsin, Sathapath Kilaso

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This research aims to study the media and information demand for public relations in Salaya, Nakhonpathom. The research objectives are: 1. to research on conflicts of communication and seeking solutions and improvements of media information in Salaya, Nakhonpathom; 2. to study about opinions and demand for media information to reach out the improvements of people communications among Salaya, Nakhonpathom; 3. to explore the factors related to relationship and behaviors on obtaining media information for public relations among Salaya, Nakhonpathom. The research is conducted by questionnaire which is interpreted by statistical analysis concluding with analysis, frequency, percentage, average and standard deviations. The research results demonstrate: 1. The conflicts of communications among Salaya, Nakhonpathom are lacking equipment and technological knowledge and public relations. 2. Most people have demand on media improvements for vastly broadcasting public relations in order to nourish the social values. This research intentionally is to create the infographic media which are easily accessible, uncomplicated and popular, in the present.

Keywords: media and information, the public relation printed media, local learning resource

Procedia PDF Downloads 151

Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

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Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

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Authors: Ben Clapperton, Daniel Stahl, Kimberley Goldsmith, Trudie Chalder

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Chronic fatigue syndrome (CFS) is a condition characterised by chronic disabling fatigue and other symptoms that currently can't be explained by any underlying medical condition. Although clinical trials support the effectiveness of cognitive behaviour therapy (CBT), the success rate for individual patients is modest. Patients vary in their response and little is known which factors predict or moderate treatment outcomes. The aim of the project is to develop a prediction model from baseline characteristics of patients, such as demographics, clinical and psychological variables, which may predict likely treatment outcome and provide guidance for clinical decision making and help clinicians to recommend the best treatment. The project is aimed at identifying subgroups of patients with similar baseline characteristics that are predictive of treatment effects using modern cluster analyses and data mining machine learning algorithms. The characteristics of these groups will then be used to inform the types of individuals who benefit from a specific treatment. In addition, results will provide a better understanding of for whom the treatment works. The suitability of different clustering methods to identify subgroups and their response to different treatments of CFS patients is compared.

Keywords: chronic fatigue syndrome, latent class analysis, prediction modelling, self-organizing maps

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Authors: Imene Medfouni

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In the context of postcolonial Algeria, language policy, language planning as well as language attitudes have recently stirred up contested debates in higher education system. This linguistic and politically-oriented conflict have constantly created a complex environment for learning. In the light of this observation, English language situates itself at the core of this debate with respects to its international status and potential influences. This presentation is based on ongoing research that aims to gain a better understanding of the introduction of English as a medium of instruction (EMI) in a postcolonial context, marked by multilingualism and language conflict. This research offers interesting insights to critically explore EMI from different perspectives: policy, practices, and attitudes. By means of methodological triangulation, this research integrates a mixed approach, whereby the sources of data triangulation will be elicited from the following methods: classroom observations, document analysis, focus groups, questionnaires and interviews. Preliminary findings suggest that English language might not replace French status in Algerian universities because of the latter strong presence and diffusion within Algerian linguistic landscape.

Keywords: English as a lingua franca, English as a medium of instruction, language policy and planning, multilingualism, postcolonial contexts, World Englishes

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Authors: Yan Torres, Fernanda Simoes, Francisco Petrucci-Fonseca, Freddie-Jeanne Richard

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The non-invasive samples are an alternative of collecting genetic samples directly. Non-invasive samples are collected without the manipulation of the animal (e.g., scats, feathers and hairs). Nevertheless, the use of non-invasive samples has some limitations. The main issue is degraded DNA, leading to poorer extraction efficiency and genotyping. Those errors delayed for some years a widespread use of non-invasive genetic information. Possibilities to limit genotyping errors can be done using analysis methods that can assimilate the errors and singularities of non-invasive samples. Genotype matching and population estimation algorithms can be highlighted as important analysis tools that have been adapted to deal with those errors. Although, this recent development of analysis methods there is still a lack of empirical performance comparison of them. A comparison of methods with dataset different in size and structure can be useful for future studies since non-invasive samples are a powerful tool for getting information specially for endangered and rare populations. To compare the analysis methods, four different datasets used were obtained from the Dryad digital repository were used. Three different matching algorithms (Cervus, Colony and Error Tolerant Likelihood Matching - ETLM) are used for matching genotypes and two different ones for population estimation (Capwire and BayesN). The three matching algorithms showed different patterns of results. The ETLM produced less number of unique individuals and recaptures. A similarity in the matched genotypes between Colony and Cervus was observed. That is not a surprise since the similarity between those methods on the likelihood pairwise and clustering algorithms. The matching of ETLM showed almost no similarity with the genotypes that were matched with the other methods. The different cluster algorithm system and error model of ETLM seems to lead to a more criterious selection, although the processing time and interface friendly of ETLM were the worst between the compared methods. The population estimators performed differently regarding the datasets. There was a consensus between the different estimators only for the one dataset. The BayesN showed higher and lower estimations when compared with Capwire. The BayesN does not consider the total number of recaptures like Capwire only the recapture events. So, this makes the estimator sensitive to data heterogeneity. Heterogeneity in the sense means different capture rates between individuals. In those examples, the tolerance for homogeneity seems to be crucial for BayesN work properly. Both methods are user-friendly and have reasonable processing time. An amplified analysis with simulated genotype data can clarify the sensibility of the algorithms. The present comparison of the matching methods indicates that Colony seems to be more appropriated for general use considering a time/interface/robustness balance. The heterogeneity of the recaptures affected strongly the BayesN estimations, leading to over and underestimations population numbers. Capwire is then advisable to general use since it performs better in a wide range of situations.

Keywords: algorithms, genetics, matching, population

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Authors: Prachi Choudhary, Jai Gopal Sharma

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Microbial cultures determine the type of organism, its abundance in the tested sample, or both. It is one of the primary diagnostic methods of microbiology. It is used to determine the cause of infectious disease by letting the agent multiply in a predetermined medium. Different bacteria produce colonies that may be very distinct from the bacterial species that produced them. To culture any pathogen or microorganism, we should first know about the types of media used in microbiology for culturing. Sometimes sub culturing is also done in various microorganisms if some mixed growth is seen in culture. Nearly 3 types of culture media based on consistency – solid, semi-solid, and liquid (broth) media; are further explained in the report. Then, The Five I's approach is a method for locating, growing, observing, and characterizing microorganisms, including inoculation and incubation. Isolation, inspection, and identification. For identification of bacteria, we have to culture the sample like urine, sputum, blood, etc., on suitable media; there are different methods of culturing the bacteria or microbe like pour plate method, streak plate method, swabbing by needle, pipetting, inoculation by loop, spreading by spreader, etc. After this, we see the bacterial growth after incubation of 24 hours, then according to the growth of bacteria antibiotics susceptibility test is conducted; this is done for sensitive antibiotics or resistance to that bacteria, and also for knowing the name of bacteria. Various methods like the dilution method, disk diffusion method, E test, etc., do antibiotics susceptibility tests. After that, various medicines are provided to the patients according to antibiotic sensitivity and resistance.

Keywords: inoculation, incubation, isolation, antibiotics suspectibility test, characterizing

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Authors: Mohsen Bahrami, Banafsheh Nikmehr, Yueqiang Song, Anuradha Koduru, Ayse K. Vuruskan, Hongkun Lu, Tamer M. Yalcinkaya

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In recent years, we have witnessed an explosion of studies aimed at using a combination of artificial intelligence (AI) and time-lapse imaging data on embryos to improve IVF outcomes. However, despite promising results, no study has used a matched cohort of transferred embryos which only differ in pregnancy outcome, i.e., embryos from a single clinic which are similar in parameters, such as: morphokinetic condition, patient age, and overall clinic and lab performance. Here, we used time-lapse data on embryos with known pregnancy outcomes to see if the rich spatiotemporal information embedded in this data would allow the prediction of the pregnancy outcome regardless of such critical parameters. Methodology—We did a retrospective analysis of time-lapse data from our IVF clinic utilizing Embryoscope 100% of the time for embryo culture to blastocyst stage with known clinical outcomes, including live birth vs nonpregnant (embryos with spontaneous abortion outcomes were excluded). We used time-lapse data from 200 elective single transfer embryos randomly selected from January 2019 to June 2021. Our sample included 100 embryos in each group with no significant difference in patient age (P=0.9550) and morphokinetic scores (P=0.4032). Data from all patients were combined to make a 4th order tensor, and feature extraction were subsequently carried out by a tensor decomposition methodology. The features were then used in a machine learning classifier to classify the two groups. Major Findings—The performance of the model was evaluated using 100 random subsampling cross validation (train (80%) - test (20%)). The prediction accuracy, averaged across 100 permutations, exceeded 80%. We also did a random grouping analysis, in which labels (live birth, nonpregnant) were randomly assigned to embryos, which yielded 50% accuracy. Conclusion—The high accuracy in the main analysis and the low accuracy in random grouping analysis suggest a consistent spatiotemporal pattern which is associated with pregnancy outcomes, regardless of patient age and embryo morphokinetic condition, and beyond already known parameters, such as: early cleavage or early blastulation. Despite small samples size, this ongoing analysis is the first to show the potential of AI methods in capturing the complex morphokinetic changes embedded in embryo time-lapse data, which contribute to successful pregnancy outcomes, regardless of already known parameters. The results on a larger sample size with complementary analysis on prediction of other key outcomes, such as: euploidy and aneuploidy of embryos will be presented at the meeting.

Keywords: IVF, embryo, machine learning, time-lapse imaging data

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Authors: Martynova Alina, Lyubov Buzoleva

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Streptococcus pneumoniae is the causative agent of pneumonias and meningitids throught all the world. Having high genetic diversity, this microorganism can cause different clinical forms of pneumococcal infections and microbiologically it is really difficult diagnosed by routine methods. Also, epidemiological surveillance requires more developed methods of molecular typing because the recent method of serotyping doesn't allow to distinguish invasive and non-invasive isolates properly. Non-coding genome of bacteria seems to be the interesting source for seeking of highly distinguishable markers to discriminate the subspecies of such a variable bacteria as Streptococcus pneumoniae. Technically, we proposed scheme of discrimination of S.pneumoniae strains with amplification of non-coding region (SP_1932) with the following restriction with 2 types of enzymes of Alu1 and Mn1. Aim: This research aimed to compare different methods of typing and their application for molecular epidemiology purposes. Methods: we analyzed population of 100 strains of S.pneumoniae isolated from different patients by different molecular epidemiology methods such as pulse-field gel electophoresis (PFGE), restriction polymorphism analysis (RFLP) and multilolocus sequence typing (MLST), and all of them were compared with classic typing method as serotyping. The discriminative power was estimated with Simpson Index (SI). Results: We revealed that the most discriminative typing method is RFLP (SI=0,97, there were distinguished 42 genotypes).PFGE was slightly less discriminative (SI=0,95, we identified 35 genotypes). MLST is still the best reference method (SI=1.0). Classic method of serotyping showed quite weak discriminative power (SI=0,93, 24 genotypes). In addition, sensivity of RFLP was 100%, specificity was 97,09%. Conclusion: the most appropriate method for routine epidemiology surveillance is RFLP with non-coding region of Streptococcsu pneumoniae, then PFGE, though in some cases these results should be obligatory confirmed by MLST.

Keywords: molecular epidemiology typing, non-coding genome, Streptococcus pneumoniae, MLST

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Authors: Mercedes Rodriguez Sanchez, Laura Luceño Casals

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With the rise of social media, creative people and brands everywhere are constantly generating content. The students with Bachelor's Degrees in Fashion Design use platforms such as Instagram or TikTok to look for inspiration and entertainment, as well as a way to develop their own ideas and share them with a wide audience. Information and Communications Technologies (ICT) have become a central aspect of higher education, virtually affecting every aspect of the student experience. Following the current trend, during the first semester of the second year, a collaborative project across two subjects –Design Management and History of Fashion Design– was implemented. After an introductory class focused on the relationship between fashion and cinema, as well as a brief history of 20th-century fashion, the students freely chose a work team and an iconic look from a movie costume. They researched the selected movie and its sociocultural context, analyzed the costume and the work of the designer, and studied the style, fashion magazines and most popular films of the time. Students then redesigned and recreated the costume, for which they were compelled to recycle the materials they had available at home as an unavoidable requirement of the activity. Once completed the garment, students delivered in-class, team-based presentations supported by the final design, a project summary poster and a making-of video, which served as a documentation tool of the costume design process. The methodologies used include Challenge-Based Learning (CBL), debates, Internet research, application of Information and Communications Technologies, and viewing clips of classic films, among others. After finishing the projects, students were asked to complete two electronic surveys to measure the acquisition of transversal and specific competencies of each subject. Results reveal that this activity helped the students' knowledge acquisition, a deeper understanding of both subjects and their skills development. The classroom dynamic changed. The multidisciplinary approach encouraged students to collaborate with their peers, while educators were better able to keep students' interest and promote an engaging learning process. As a result, the activity discussed in this paper confirmed the research hypothesis: it is positive to propose innovative teaching projects that combine academic research with playful learning environments.

Keywords: cinema, cooperative learning, fashion design, higher education, upcycling

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Authors: Maja S. Todorovic

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This paper aims to promote entrepreneurial storytelling by exploring new ideas and learning practices. An entrepreneur needs to be a ‘storyteller’, an ‘epic hero’, capable of offering an emotional connection to his audience, a character with whom audience can identify with, rejoice, suffer, celebrate, fail – simply experience everything. In other words, a successful entrepreneur is giving tangible experience through his business story and that’s what makes his story and business alive. Use of mythology, eulogy, metaphor, epic, fairytales and cartoons, permeated with humor and sudden twists is a winning recipe for a business story that captures attention. In the business case of the Greenesignit! Card game, (educational and brainstorming tool for development of sustainable products) we will demonstrate how an entrepreneur successfully used visual narrative to communicate his story and at the same time as a vehicle to transmute his message in learning tool and product development.

Keywords: animating narrative, entrepreneur, Greeneisgnit! card game, visual storytelling

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Authors: Jianrong Wu, Xuan Fu, Akihiro Higashi, Zhiming Tan

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CMOS image sensors with a rolling shutter are used broadly in the digital cameras embedded in mobile devices. The rolling shutter suffers the flicker artifacts from the fluorescent lamp, and it could be observed easily. In this paper, the characteristics of illumination flicker in motion case were analyzed, and two efficient detection methods based on matching fragment selection were proposed. According to the experimental results, our methods could achieve as high as 100% accuracy in static scene, and at least 97% in motion scene.

Keywords: illumination flicker, embedded camera, rolling shutter, detection

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Authors: YU Kaijun, Liang Dong, Zhang Yarong, Jin Zhenzhou, Yang Zhaobao

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For the impact monitoring of distributed structures, the traditional positioning methods are based on the time difference, which includes the four-point arc positioning method and the triangulation positioning method. But in the actual operation, these two methods have errors. In this paper, the Multi-Agent Blackboard Coordination Principle is used to combine the two methods. Fusion steps: (1) The four-point arc locating agent calculates the initial point and records it to the Blackboard Module.(2) The triangulation agent gets its initial parameters by accessing the initial point.(3) The triangulation agent constantly accesses the blackboard module to update its initial parameters, and it also logs its calculated point into the blackboard.(4) When the subsequent calculation point and the initial calculation point are within the allowable error, the whole coordination fusion process is finished. This paper presents a Multi-Agent collaboration method whose agent framework is JADE. The JADE platform consists of several agent containers, with the agent running in each container. Because of the perfect management and debugging tools of the JADE, it is very convenient to deal with complex data in a large structure. Finally, based on the data in Jade, the results show that the impact location method based on Multi-Agent coordination fusion can reduce the error of the two methods.

Keywords: impact monitoring, structural health monitoring(SHM), multi-agent system(MAS), black-board coordination, JADE

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Authors: Muhammad Atif, Cang Yan

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The enhancement of low-light images is a significant area of study aimed at enhancing the quality of captured images in challenging lighting environments. Recently, methods based on convolutional neural networks (CNN) have gained prominence as they offer state-of-the-art performance. However, many approaches based on CNN rely on increasing the size and complexity of the neural network. In this study, we propose an alternative method for improving low-light images using an autoencoder-based multiscale knowledge transfer model. Our method leverages the power of three autoencoders, where the encoders of the first two autoencoders are directly connected to the decoder of the third autoencoder. Additionally, the decoder of the first two autoencoders is connected to the encoder of the third autoencoder. This architecture enables effective knowledge transfer, allowing the third autoencoder to learn and benefit from the enhanced knowledge extracted by the first two autoencoders. We further integrate the proposed model into the PIX to PIX GAN framework. By integrating our proposed model as the generator in the GAN framework, we aim to produce enhanced images that not only exhibit improved visual quality but also possess a more authentic and realistic appearance. These experimental results, both qualitative and quantitative, show that our method is better than the state-of-the-art methodologies.

Keywords: low light image enhancement, deep learning, convolutional neural network, image processing

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Authors: Linda Blümel, Bettina Bert, Jan Brosda, Heidrun Fink, Melanie Hamann

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Age-related cognitive decline can eventually lead to dementia, the most common mental illness in elderly people and an immense challenge for patients, their families and caregivers. Cholinesterase inhibitors constitute the most commonly used antidementia prescription medication. The standardized Ginkgo biloba leaf extract EGb 761® is approved for treating age-associated cognitive impairment and has been shown to improve the quality of life in patients suffering from mild dementia. A clinical trial with 96 Alzheimer´s disease patients indicated that the combined treatment with donepezil and EGb 761® had fewer side effects than donepezil alone. In an animal model of cognitive aging, we compared the effect of combined treatment with EGb 761® or donepezil monotherapy and vehicle. We compared the effect of chronic treatment (15 days of pretreatment) with donepezil (1.5 mg/kg p. o.), EGb 761® (100 mg/kg p. o.), or the combination of the two drugs, or vehicle in 18 – 20 month old male OFA rats. Learning and memory performance were assessed by Morris water maze testing, motor behavior in an open field paradigm. In addition to chronic treatment, the substances were administered orally 30 minutes before testing. Compared to the first day and to the control group, only the combination group showed a significant reduction in latency to reach the hidden platform on the second day of testing. Moreover, from the second day of testing onwards, the donepezil, the EGb 761® and the combination group required less time to reach the hidden platform compared to the first day. The control group did not reach the same latency reduction until day three. There were no effects on motor behavior. These results suggest a superiority of the combined treatment of donepezil with EGb 761® compared to monotherapy.

Keywords: age-related cognitive decline, dementia, ginkgo biloba leaf extract EGb 761®, learning and memory, old rats

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Authors: Regis Pochon, Nicolas Stefaniak, Veronique Baltazart, Pamela Gobin

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Anxiety is known to have an impact on working memory. In reasoning or memory tasks, individuals with anxiety tend to show longer response times and poorer performance. Furthermore, there is a memory bias for negative information in anxiety. Given the crucial role of working memory in lexical learning, anxious students may encounter more difficulties in learning to read and spell. Anxiety could even affect an earlier learning, that is the activation of phonological representations, which are decisive for the learning of reading and writing. The aim of this study is to compare the access to phonological representations of beginning readers and writers according to their level of anxiety, using an auditory lexical decision task. Eighty students of 6- to 9-years-old completed the French version of the Revised Children's Manifest Anxiety Scale and were then divided into four anxiety groups according to their total score (Low, Median-Low, Median-High and High). Two set of eighty-one stimuli (words and non-words) have been auditory presented to these students by means of a laptop computer. Stimuli words were selected according to their emotional valence (positive, negative, neutral). Students had to decide as quickly and accurately as possible whether the presented stimulus was a real word or not (lexical decision). Response times and accuracy were recorded automatically on each trial. It was anticipated a) longer response times for the Median-High and High anxiety groups in comparison with the two others groups, b) faster response times for negative-valence words in comparison with positive and neutral-valence words only for the Median-High and High anxiety groups, c) lower response accuracy for Median-High and High anxiety groups in comparison with the two others groups, d) better response accuracy for negative-valence words in comparison with positive and neutral-valence words only for the Median-High and High anxiety groups. Concerning the response times, our results showed no difference between the four groups. Furthermore, inside each group, the average response times was very close regardless the emotional valence. Otherwise, group differences appear when considering the error rates. Median-High and High anxiety groups made significantly more errors in lexical decision than Median-Low and Low groups. Better response accuracy, however, is not found for negative-valence words in comparison with positive and neutral-valence words in the Median-High and High anxiety groups. Thus, these results showed a lower response accuracy for above-median anxiety groups than below-median groups but without specificity for the negative-valence words. This study suggests that anxiety can negatively impact the lexical processing in young students. Although the lexical processing speed seems preserved, the accuracy of this processing may be altered in students with moderate or high level of anxiety. This finding has important implication for the prevention of reading and spelling difficulties. Indeed, during these learnings, if anxiety affects the access to phonological representations, anxious students could be disturbed when they have to match phonological representations with new orthographic representations, because of less efficient lexical representations. This study should be continued in order to precise the impact of anxiety on basic school learning.

Keywords: anxiety, emotional valence, childhood, lexical access

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Authors: Seong Jun Yoon, Jasurbek Doliev, Sang Min Oh, Rodi Hartono, Kyoojae Shin

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Electric power steering (EPS), which is commonly used in electric vehicles recently, is an electric-driven steering device for vehicles. Compared to hydraulic systems, EPS offers advantages such as simple system components, easy maintenance, and improved steering performance. However, because the EPS system is a nonlinear model, difficult problems arise in controller design. To address these, various machine learning and artificial intelligence approaches, notably artificial neural networks (ANN), have been applied. ANN can effectively determine relationships between inputs and outputs in a data-driven manner. This research explores two main areas: designing an EPS identifier using an ANN-based backpropagation (BP) algorithm and enhancing the EPS system controller with an ANN-based Levenberg-Marquardt (LM) algorithm. The proposed ANN-based BP algorithm shows superior performance and accuracy compared to linear transfer function estimators, while the LM algorithm offers better input angle reference tracking and faster response times than traditional PID controllers. Overall, the proposed ANN methods demonstrate significant promise in improving EPS system performance.

Keywords: ANN backpropagation modelling, electric power steering, transfer function estimator, electrical vehicle driving system

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