Search results for: learning of categories

Authors: Apostolos Kalatzis, Ashish Teotia, Vishnunarayan Girishan Prabhu, Laura Stanley

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Emotions are functional behaviors influenced by thoughts, stimuli, and other factors that induce neurophysiological changes in the human body. Understanding and classifying emotions are challenging as individuals have varying perceptions of their environments. Therefore, it is crucial that there are publicly available databases and virtual reality (VR) based environments that have been scientifically validated for assessing emotional classification. This study utilized two commercially available VR applications (Guided Meditation VR™ and Richie’s Plank Experience™) to induce acute stress and calm state among participants. Subjective and objective measures were collected to create a validated multimodal dataset and classification scheme for affective state classification. Participants’ subjective measures included the use of the Self-Assessment Manikin, emotional cards and 9 point Visual Analogue Scale for perceived stress, collected using a Virtual Reality Assessment Tool developed by our team. Participants’ objective measures included Electrocardiogram and Respiration data that were collected from 25 participants (15 M, 10 F, Mean = 22.28  4.92). The features extracted from these data included heart rate variability components and respiration rate, both of which were used to train two machine learning models. Subjective responses validated the efficacy of the VR applications in eliciting the two desired affective states; for classifying the affective states, a logistic regression (LR) and a support vector machine (SVM) with a linear kernel algorithm were developed. The LR outperformed the SVM and achieved 93.8%, 96.2%, 93.8% leave one subject out cross-validation accuracy, precision and recall, respectively. The VR assessment tool and data collected in this study are publicly available for other researchers.

Keywords: affective computing, biosignals, machine learning, stress database

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Authors: Alaina Caulkett, Audra Selkowitz, Lauren Harter, Aimee DeFoe

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Educational robotics is an effective tool for teaching and learning STEM curricula. Yet, most traditional professional development programs do not cover engineering, coding, or robotics. This paper will give an overview of how and why the VEX Professional Development Plus Introductory Training courses were developed to provide guided, simple professional development in the area of robotics and computer science instruction. These training courses guide educators through learning the basics of VEX robotics platforms, including VEX 123, GO, IQ, and EXP. Because many educators do not have experience teaching robotics or computer science, this course is meant to simulate one on one training or tutoring through video-based instruction. These videos, led by education professionals, can be watched at any time, which allows educators to watch at their own pace and create their own personalized professional development timeline. This personalization expands beyond the course itself into an online community where educators at different points in the self-paced course can converse with one another or with instructors from the videos and learn from a growing community of practice. By the end of each course, educators are armed with the skills to introduce robotics or computer science in their classroom or educational setting. The design of the course was guided by a variation of the Understanding by Design (UbD) framework and included hands-on activities and challenges to keep educators engaged and excited about robotics. Some of the concepts covered include, but are not limited to, following build instructions, building a robot, updating firmware, coding the robot to drive and turn autonomously, coding a robot using multiple methods, and considerations for teaching robotics and computer science in the classroom, and more. A secondary goal of this research is to discuss how this professional development approach can serve as an example in the larger educational community and explore ways that it could be further researched or used in the future.

Keywords: computer science education, online professional development, professional development, robotics education, video-based instruction

Procedia PDF Downloads 89

Authors: Silvia C. Ambag, Racidon P. Bernarte, Jacquelyn B. Buccahi, Jessica R. Lacaron, Charlyn L. Mangulabnan

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This study aimed to answer the query, “What is the impact of Students Online Evaluation on PUP Faculty’s Performance?” The problem of the study was resolve through the objective of knowing the perceived impact of students’ online evaluation on PUP faculty’s performance. The objectives were carried through the application of quantitative research design and by conducting survey research method. The researchers utilized primary and secondary data. Primary data was gathered from the self-administered survey and secondary data was collected from the books, articles on both print-out and online materials and also other theses related study. Findings revealed that PUP faculty in general stated that students’ online evaluation made a highly positive impact on their performance based on their ‘Knowledge of Subject’ and ‘Teaching for Independent Learning’, giving a highest mean of 3.62 and 3.60 respectively., followed by the faculty’s performance which gained an overall means of 3.55 and 3.53 are based on their ‘Commitment’ and ‘Management of Learning’. From the findings, the researchers concluded that Students’ online evaluation made a ‘Highly Positive’ impact on PUP faculty’s performance based on all Four (4) areas. Furthermore, the study’s findings reveal that PUP faculty encountered many problems regarding the students’ online evaluation; the impact of the Students’ Online Evaluation is significant when it comes to the employment status of the faculty; and most of the PUP faculty recommends reviewing the PUP Online Survey for Faculty Evaluation for improvement. Hence, the researchers recommend the PUP Administration to revisit and revise the PUP Online Survey for Faculty Evaluation, specifically review the questions and make a set of questions that will be appropriate to the discipline or field of the faculty. Also, the administration should fully orient the students about the importance, purpose and impact of online faculty evaluation. And lastly, the researchers suggest the PUP Faculty to continue their positive performance and continue on being cooperative with the administrations’ purpose of addressing the students’ concerns and for the students, the researchers urged them to take the online faculty evaluation honestly and objectively.

Keywords: on-line Evaluation, faculty, performance, Polytechnic University of the Philippines (PUP)

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Authors: Khalid El Asri

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Culture influences lexicalization of salient concepts in a society. Hence, languages often have different degrees of equivalence regarding lexical items of different fields. The present study focuses on the field of emotions in English and Moroccan Arabic. Findings of a comparative study that involved fifty English emotions revealed that Moroccan Arabic has equivalence of some English emotion terms, partial equivalence of some emotion terms, and no equivalence for some other terms. It is hypothesized then that emotion terms that have near equivalence in Moroccan Arabic will be easier to acquire for EFL learners, while partially equivalent terms will be difficult to acquire, and those that have no equivalence will be even more difficult to acquire. In order to test these hypotheses, the participants (104 advanced Moroccan EFL learners and 104 native speakers of English) were given two tests: the first is a receptive one in which the participants were asked to choose, among four emotion terms, the term that is appropriate to fill in the blanks for a given situation indicating certain kind of feelings. The second test is a productive one in which the participants were asked to give the emotion term that best described the feelings of the people in the situations given. The results showed that conceptually equivalent terms do not pose any problems for Moroccan EFL learners since they can link the concept to an already existing linguistic category; whereas the results concerning the acquisition of partially equivalent terms indicated that this type of emotion terms were difficult for Moroccan EFL learners to acquire, because they need to restructure the boundaries of the target linguistic categories by expanding them when the term includes other range of meanings that are not subsumed in the L1 term. Surprisingly however, the results concerning the case of non-equivalence revealed that Moroccan EFL learners could internalize the target L2 concepts that have no equivalence in their L1. Thus, it is the category of emotion terms that have partial equivalence in the learners’ L1 that pose problems for them.

Keywords: acquisition, culture, emotion terms, lexical equivalence

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Authors: Prashanth Pillai, Purnaprajna Mangsuli

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Oil exploration and production companies invest considerable time and efforts to extract essential well attributes (like well status, surface, and target coordinates, wellbore depths, event timelines, etc.) from unstructured data sources like technical reports, which are often non-standardized, multimodal, and highly domain-specific by nature. It is also important to consider the context when extracting attribute values from reports that contain information on multiple wells/wellbores. Moreover, semantically similar information may often be depicted in different data syntax representations across multiple pages and document sources. We propose a hierarchical multi-source fact extraction workflow based on a deep learning framework to extract essential well attributes at scale. An information retrieval module based on the transformer architecture was used to rank relevant pages in a document source utilizing the page image embeddings and semantic text embeddings. A question answering framework utilizingLayoutLM transformer was used to extract attribute-value pairs incorporating the text semantics and layout information from top relevant pages in a document. To better handle context while dealing with multi-well reports, we incorporate a dynamic query generation module to resolve ambiguities. The extracted attribute information from various pages and documents are standardized to a common representation using a parser module to facilitate information comparison and aggregation. Finally, we use a probabilistic approach to fuse information extracted from multiple sources into a coherent well record. The applicability of the proposed approach and related performance was studied on several real-life well technical reports.

Keywords: natural language processing, deep learning, transformers, information retrieval

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Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

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With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

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Authors: Levent Dumenci, Laura A. Siminoff

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Health literacy has been shown to predict a variety of health outcomes. Reliable identification of persons with limited cancer health literacy (LCHL) has been proved questionable with existing instruments using an arbitrary cut point along a continuum. The CHLT-6, however, uses a latent mixture modeling approach to identify persons with LCHL. The purpose of this study was to estimate two-week and six-month stability of identifying persons with LCHL using the CHLT-6 with a discrete latent variable approach as the underlying measurement structure. Using a test-retest design, the CHLT-6 was administered to cancer patients with two-week (N=98) and six-month (N=51) intervals. The two-week and six-month latent test-retest agreements were 89% and 88%, respectively. The chance-corrected latent agreements estimated from Dumenci’s latent kappa were 0.62 (95% CI: 0.41 – 0.82) and .47 (95% CI: 0.14 – 0.80) for the two-week and six-month intervals, respectively. High levels of latent test-retest agreement between limited and adequate categories of cancer health literacy construct, coupled with moderate to good levels of change-corrected latent agreements indicated that the CHLT-6 classification of limited versus adequate cancer health literacy is relatively stable over time. In conclusion, the measurement structure underlying the instrument allows for estimating classification errors circumventing limitations due to arbitrary approaches adopted by all other instruments. The CHLT-6 can be used to identify persons with LCHL in oncology clinics and intervention studies to accurately estimate treatment effectiveness.

Keywords: limited cancer health literacy, the CHLT-6, discrete latent variable modeling, latent agreement

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Authors: Yen Chia-Ju, Cheng Ding-Ruei

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This study took case office building as subject and explored the responsive work order repair request of facilities and equipment in offices and public areas by gray theory, with the purpose of providing for future related office building owners, executive managers, property management companies, mechanical and electrical companies as reference for deciding and assessing forecast model. Important conclusions of this study are summarized as follows according to the study findings: 1. Grey Relational Analysis discusses the importance of facilities repair number of six categories, namely, power systems, building systems, water systems, air conditioning systems, fire systems and manpower dispatch in order. In terms of facilities maintenance importance are power systems, building systems, water systems, air conditioning systems, manpower dispatch and fire systems in order. 2. GM (1,N) and regression method took maintenance hours as dependent variables and repair number, leased area and tenants number as independent variables and conducted single month forecast based on 12 data from January to December 2011. The mean absolute error and average accuracy of GM (1,N) from verification results were 6.41% and 93.59%; the mean absolute error and average accuracy of regression model were 4.66% and 95.34%, indicating that they have highly accurate forecast capability.

Keywords: rey theory, forecast model, Taipei 101, office buildings, property management, facilities, equipment

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Authors: David A. Harness

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Natural evolution of ATP cognitive systems is to meet AI peer review standards. ATP process of axiom selection from Mizar to prove a conjecture would be further refined, as in all human and machine learning, by solving the real world problem of the proposed AI peer review challenge: Determine which conjecture forms the higher confidence level constructive proof between Standard Model of Physics SU(n) lattice gauge group operation vs. present non-standard 4D GEM EOS SU(n) lattice gauge group spatially extended operation in which the photon and electron are the first two trace angular momentum invariants of a gravitoelectromagnetic (GEM) energy momentum density tensor wavetrain integration spin-stress pressure-volume equation of state (EOS), initiated via 32 lines of Mathematica code. Resulting gravitoelectromagnetic spectrum ranges from compressive through rarefactive of the central cosmological constant vacuum energy density in units of pascals. Said self-adjoint group operation exclusively operates on the stress energy momentum tensor of the Einstein field equations, introducing quantization directly on the 4D spacetime level, essentially reformulating the Yang-Mills virtual superpositioned particle compounded lattice gauge groups quantization of the vacuum—into a single hyper-complex multi-valued GEM U(1) × SU(1,3) lattice gauge group Planck spacetime mesh quantization of the vacuum. Thus the Mizar corpus already contains all of the axioms required for relevant DeepMath premise selection and unambiguous formal natural language parsing in context deep learning.

Keywords: automated theorem proving, constructive quantum field theory, information theory, neural networks

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Authors: A. Martins, I. Martins, O. Pereira

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The purpose of this research is to focus on the importance of distributed leadership in universities and Higher Education Institutions (HEIs). The research question is whether there a significant finding in self-reported ratings of leadership styles of those respondents that are studying management. The study aims to further discover whether students are encouraged to become responsible and proactive citizens, to develop their skills set, specifically shared leadership and higher-level skills to inspire creation knowledge, sharing and distribution thereof. Contemporary organizations need active and responsible individuals who are capable to make decisions swiftly and responsibly. Leadership influences innovative results and education play a dynamic role in preparing graduates. Critical reflection of extant literature indicates a need for a culture of leadership and innovation to promote organizational sustainability in the globalised world. This study debates the need for HEIs to prepare the graduate for both organizations and society as a whole. This active collaboration should be the very essence of both universities and the industry in order for these to achieve responsible sustainability. Learning and innovation further depend on leadership efficacy. This study follows the pragmatic paradigm methodology. Primary data collection is currently being gathered via the web-based questionnaire link which was made available on the UKZN notice system. The questionnaire has 35 items with a Likert scale of five response options. The purposeful sample method was used, and the population entails the undergraduate and postgraduate students in the College of Law and Business, University of KwaZulu-Natal, South Africa. Limitations include the design of the study and the reliance on the quantitative data as the only method of primary data collection. This study is of added value for scholars and organizations in the innovation economy.

Keywords: knowledge creation, learning, performance, sustainability

Procedia PDF Downloads 279

Authors: Maria Lita Sudo

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The debate is an effective tool in cultivating critical thinking skills in English classes. It involves writing evidence-based arguments about a resolution in a form of constructive speech and oral discussion using constructive speech, which will then be attacked and defended. In the process of writing, EFL learners may experience anxiety, an emotional problem that affects writing achievement and cognitive processing. Thus, this study explored the sources and effect of English writing anxiety in the context of debate writing with a view to providing EFL teachers pedagogical suggestions in alleviating English writing anxiety in debate writing. The participants of this study are 95 Japanese senior high school EFL learners and 3 Japanese senior high school English teachers. In selecting the participants, opportunity sampling was employed and consent from Japanese English teachers was sought. Data were collected thru (1) observation (2) open-ended questionnaire and (3) semi-structured interview. This study revealed that not all teachers of English in the context of this study recognize the existence of English writing anxiety among their students and that the very nature of the debate, in general, may also be a source of English writing anxiety in the context of debate writing. The interview revealed that English writing anxiety affects students’ ability to retrieve L2 vocabulary. Further, this study revealed different sources of writing anxiety in debate writing, which can be categorized into four main categories: (1) L2 linguistic ability-related factors (2) instructional –related factors, (3) interpersonal-related factors, and (4) debate- related factors. Based on the findings, recommendations for EFL teachers and EFL learners in managing writing anxiety in debate writing are provided.

Keywords: debate, EFL learners, English writing anxiety, sources

Procedia PDF Downloads 132

Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

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Authors: Ahmed Inal, Samir Rouidi, Samir Bachouche

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The present study is focused on the distribution and composition of seafloor marine litter associated to trawlable fishing areas along Algerian coast. The sampling was done with a GOC73 bottom trawl during four (04) demersal resource assessment cruises, respectively, in 2016, 2019, 2021 and 2022, carried out on board BELKACEM GRINE R/V. A total of 254 fishing hauls were sampled for the assessment of marine litter. Hauls were performed between 22 and 600 m of depth, the duration was between 30 and 60 min. All sampling was conducted during daylight. After the haul, marine litter was sorted and split from the catch. Then, according to the basis of the MEDITS protocol, litters were sorted into six different categories (plastic, rubber, metal, wood, glass and natural fiber). Thereafter, all marine litter were counted and weighed separately to the nearest 0.5 g. The results shows that the maximums of marine litter densities in the seafloor of the trawling fishing areas along Algerian coast are, respectively, 1996 item/km2 in 2016, 5164 item/km2 in 2019, 2173 item/km2 in 2021 and 7319 item/km2 in 2022. Thus, the plastic is the most abundant litter, it represent, respectively, 46% of marine litter in 2016, 67% in 2019, 69% in 2021 and 74% in 2022. Regarding the weight of the marine litter, it varies between 0.00 and 103 kg in 2016, between 0.04 and 81 kg in 2019, between 0.00 and 68 Kg in 2021 and between 0.00 and 318 kg in 2022. Thus, the maximum rate of marine litter compared to the total catch approximate, respectively, 66% in 2016, 90% in 2019, 65% in 2021 and 91% in 2022. In fact, the average loss in catch is estimated, respectively, at 7.4% in 2016, 8.4% in 2019, 5.7% in 2021 and 6.4% in 2022. However, the bathymetric and geographical variability had a significant impact on both density and weight of marine litter. Marine litter monitoring program is necessary for offering more solution proposals.

Keywords: composition, distribution, seabed, marine litter, algerian coast

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Authors: Lynn Mcquarrie, Charlotte Enns

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The goal of this project was to develop a visually-based, signed language phonological awareness training program and to pilot the intervention with signing deaf children (ages 6 -10 years/ grades 1 - 4) who were beginning readers to assess the effects of systematic explicit American Sign Language (ASL) phonological instruction on both ASL vocabulary and English print vocabulary learning. Growing evidence that signing learners utilize visually-based signed language phonological knowledge (homologous to the sound-based phonological level of spoken language processing) when reading underscore the critical need for further research on the innovation of reading instructional practices for visual language learners. Multiple single-case studies using a multiple probe design across content (i.e., sign and print targets incorporating specific ASL phonological parameters – handshapes) was implemented to examine if a functional relationship existed between instruction and acquisition of these skills. The results indicated that for all cases, representing a variety of language abilities, the visually-based phonological teaching approach was exceptionally powerful in helping children to build their sign and print vocabularies. Although intervention/teaching studies have been essential in testing hypotheses about spoken language phonological processes supporting non-deaf children’s reading development, there are no parallel intervention/teaching studies exploring hypotheses about signed language phonological processes in supporting deaf children’s reading development. This study begins to provide the needed evidence to pursue innovative teaching strategies that incorporate the strengths of visual learners.

Keywords: American sign language phonological awareness, dual language strategies, vocabulary learning, word reading

Procedia PDF Downloads 323

Authors: Donald Wright

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The ultimate goal of this study is to reinvigorate the philosophical underpinnings the study of urbanization with scientific data with the goal of circumventing what seems an inevitable future clash between rural and urban populations. To that end urban infrastructure must be sustainable economically, politically and ecologically over the course of several generations as cities continue to grow with the incorporation of climate refugees. Our research will provide data concerning the projected increase in population over the coming two decades in Morocco, and the population will shift from rural areas to urban centers during that period of time. As a result, urban infrastructure will need to be adapted, developed or built to fit the demand of future internal migrations from rural to urban centers in Morocco. This paper will also examine how past experiences of internally displaced people give insight into the challenges faced by future migrants and, beyond the gathering of data, how people react to internal migration. This study employs four different sets of research tools. First, a large part of this study is archival, which involves compiling the relevant literature on the topic and its complex history. This step also includes gathering data bout migrations in Morocco from public data sources. Once the datasets are collected, the next part of the project involves populating the attribute fields and preprocessing the data to make it understandable and usable by machine learning algorithms. In tandem with the mathematical interpretation of data and projected migrations, this study benefits from a theoretical understanding of the critical apparatus existing around urban development of the 20th and 21st centuries that give us insight into past infrastructure development and the rationale behind it. Once the data is ready to be analyzed, different machine learning algorithms will be experimented (k-clustering, support vector regression, random forest analysis) and the results compared for visualization of the data. The final computational part of this study involves analyzing the data and determining what we can learn from it. This paper helps us to understand future trends of population movements within and between regions of North Africa, which will have an impact on various sectors such as urban development, food distribution and water purification, not to mention the creation of public policy in the countries of this region. One of the strengths of this project is the multi-pronged and cross-disciplinary methodology to the research question, which enables an interchange of knowledge and experiences to facilitate innovative solutions to this complex problem. Multiple and diverse intersecting viewpoints allow an exchange of methodological models that provide fresh and informed interpretations of otherwise objective data.

Keywords: climate change, machine learning, migration, Morocco, urban development

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Authors: Hozaifa Zaki, Ghada Soliman

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In this paper, we introduced an approach for Automated Dotted Arabic Expiration Date Extraction using Optimized Convolutional Autoencoder (ARABEX) with bidirectional LSTM. This approach is used for translating the Arabic dot-matrix expiration dates into their corresponding filled-in dates. A custom lightweight Convolutional Recurrent Neural Network (CRNN) model is then employed to extract the expiration dates. Due to the lack of available dataset images for the Arabic dot-matrix expiration date, we generated synthetic images by creating an Arabic dot-matrix True Type Font (TTF) matrix to address this limitation. Our model was trained on a realistic synthetic dataset of 3287 images, covering the period from 2019 to 2027, represented in the format of yyyy/mm/dd. We then trained our custom CRNN model using the generated synthetic images to assess the performance of our model (ARABEX) by extracting expiration dates from the translated images. Our proposed approach achieved an accuracy of 99.4% on the test dataset of 658 images, while also achieving a Structural Similarity Index (SSIM) of 0.46 for image translation on our dataset. The ARABEX approach demonstrates its ability to be applied to various downstream learning tasks, including image translation and reconstruction. Moreover, this pipeline (ARABEX+CRNN) can be seamlessly integrated into automated sorting systems to extract expiry dates and sort products accordingly during the manufacturing stage. By eliminating the need for manual entry of expiration dates, which can be time-consuming and inefficient for merchants, our approach offers significant results in terms of efficiency and accuracy for Arabic dot-matrix expiration date recognition.

Keywords: computer vision, deep learning, image processing, character recognition

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Authors: Shao-Chi Chien, Ying-Ju Chen, Chiao-Yu Tseng, Wan-Ting Lee, Yi-Wen Cheng

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Dr. George Leslie Mackay arrived at Takoukang (now Port of Kaohsiung) in Taiwan on December 30, 1871. When Dr. Mackay dedicated at Taiwan for 30 years, he has been an important factor in such areas as preaching, medical and engaged in education. Many researchers have thoroughly studied Dr. Mackay's travels to understand his impact on the state of education, medicine and religion in Taiwan. In the 30-year period of hard work, Dr. Mackay's made outstanding influence on the church in Taiwan. Therefore, the present study will be the mission of the establishment of hospitals, schools, churches which preaching, education, and medicine whether there are related the number of comparisons to explore. According to The Diaries of George Leslie Mackay, our research uses the Geographic Information System (GIS) to map the location of Dr. Mackay's travel in Taiwan and compares it with today's local churches, hospitals, and schools whether there are related the number of comparisons to explore. Therefore, our research focuses on the whole of Taiwan, divided into missionary, medical and education as the main content of the three major parts. Additionally, use of point layer, the surface layer of the property table to establish, in-depth mission of Dr. Mackay's development in Taiwan and Today's comparison. The results will be based on the classification of three different colors pictures that the distance of Mackay's contribution of preaching, medicine, and education. Our research will be compared with the current churches, hospitals, schools and the past churches, hospitals, schools. The results of the present study will provide a reference for future research.

Keywords: George Leslie Mackay, geographic information system, spatial distribution, color categories analysis

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Authors: Noga Bregman

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Accurate and efficient earthquake detection and phase picking are crucial for seismic hazard assessment and emergency response. This study introduces EQMamba, a deep-learning method that combines the strengths of the Earthquake Transformer and the Mamba model for simultaneous earthquake detection and phase picking. EQMamba leverages the computational efficiency of Mamba layers to process longer seismic sequences while maintaining a manageable model size. The proposed architecture integrates convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and Mamba blocks. The model employs an encoder composed of convolutional layers and max pooling operations, followed by residual CNN blocks for feature extraction. Mamba blocks are applied to the outputs of BiLSTM blocks, efficiently capturing long-range dependencies in seismic data. Separate decoders are used for earthquake detection, P-wave picking, and S-wave picking. We trained and evaluated EQMamba using a subset of the STEAD dataset, a comprehensive collection of labeled seismic waveforms. The model was trained using a weighted combination of binary cross-entropy loss functions for each task, with the Adam optimizer and a scheduled learning rate. Data augmentation techniques were employed to enhance the model's robustness. Performance comparisons were conducted between EQMamba and the EQTransformer over 20 epochs on this modest-sized STEAD subset. Results demonstrate that EQMamba achieves superior performance, with higher F1 scores and faster convergence compared to EQTransformer. EQMamba reached F1 scores of 0.8 by epoch 5 and maintained higher scores throughout training. The model also exhibited more stable validation performance, indicating good generalization capabilities. While both models showed lower accuracy in phase-picking tasks compared to detection, EQMamba's overall performance suggests significant potential for improving seismic data analysis. The rapid convergence and superior F1 scores of EQMamba, even on a modest-sized dataset, indicate promising scalability for larger datasets. This study contributes to the field of earthquake engineering by presenting a computationally efficient and accurate method for simultaneous earthquake detection and phase picking. Future work will focus on incorporating Mamba layers into the P and S pickers and further optimizing the architecture for seismic data specifics. The EQMamba method holds the potential for enhancing real-time earthquake monitoring systems and improving our understanding of seismic events.

Keywords: earthquake, detection, phase picking, s waves, p waves, transformer, deep learning, seismic waves

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Authors: Anna-Maria Saarela

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At the Savonia University of Applied Sciences (UAS), curriculum and studies have been improved by applying an Open Innovation Space approach (OIS). It is based on multidisciplinary action learning. The key elements of OIS-ideology are work-life orientation, and student-centric communal learning. In this approach, every participant can learn from each other and innovations will be created. In this social innovation educational approach, all practices are carried out in close collaboration with enterprises in real-life settings, not in classrooms. As an example, in this paper, Savonia UAS’s Future Food RDI hub (FF) shows how OIS practices are implemented by providing food product development and consumer research services for enterprises in close collaboration with academicians, students and consumers. In particular one example of OIS experimentation in the field is provided by a consumer research carried out utilizing verbal analysis protocol combined with audio-visual observation (VAP-WAVO). In this case, all co-learners were acting together in supermarket settings to collect the relevant data for a product development and the marketing department of a company. The company benefitted from the results obtained, students were more satisfied with their studies, educators and academicians were able to obtain good evidence for further collaboration as well as renewing curriculum contents based on the requirements of working life. In addition, society will benefit over time as young university adults find careers more easily through their OIS related food science studies. Also this knowledge interaction model re-news education practices and brings working-life closer to educational research institutes.

Keywords: collaboration, education, food science, industry, knowledge transfer, RDI, student

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Authors: Marzieh Imani

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Antoni Gaudi and Santiago Calatrava have reputation for designing bio-inspired creative and technical buildings. Even though they have followed different independent approaches towards design, the source of bio-inspiration seems to be common. Taking a closer look at their projects reveals that Calatrava has been influenced by Gaudi in terms of interpreting nature and applying natural principles into the design process. This research firstly discusses the dialogue between Biomimicry and architecture. This review also explores human/nature discourse during the history by focusing on how nature revealed itself to the fine arts. This is explained by introducing naturalism and romantic style in architecture as the outcome of designers’ inclination towards nature. Reviewing the literature, theoretical background and practical illustration of nature have been included. The most dominant practical aspects of imitating nature are form and function. Nature has been reflected in architectural science resulted in shaping different architectural styles such as organic, green, sustainable, bionic, and biomorphic. By defining a set of common aspects of Gaudi and Calatrava‘s design approach and by considering biomimetic design categories (organism, ecosystem, and behaviour as the main division and form, function, process, material, and construction as subdivisions), Gaudi’s and Calatrava’s project have been analysed. This analysis explores if their design approaches are equivalent or different. Based on this analysis, Gaudi’s architecture can be recognised as biomorphic while Calatrava’s projects are literally biomimetic. Referring to these architects, this review suggests a new set of principles by which a bio-inspired project can be determined either biomorphic or biomimetic.

Keywords: biomimicry, Calatrava, Gaudi, nature

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Authors: Adam Abdullahi Mohammed

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Performance appraisal is an assessment made to ensure the level of a worker’s productivity in a given period of time. The appraisal system is divided into two categories that are traditional methods and modern methods, with emphasis based on the evaluation of work results. In the traditional approach of staff appraisal, which puts more emphasis on individual traits, supervisors are required to measure employees through interactions based on what they achieved with reference to job descriptions, as well as rating them based on questionnaires without staff interaction. These methods are not effective because staff may give biased information. The study will attempt to assess the effect of performance appraisal on employee productivity at Yobe State University, Damaturu. It is aimed at assessing the process, methods, and objectives of performance appraisal and its feedback to know how they affect the success of the appraisal, its results, and employee productivity. In this study, a quantitative research method is adopted in collecting and analyzing data, and a questionnaire will be used as data collecting instrument. As it is a case study, the target population is the staff of the department of Islamic Studies. The research will employ a census sampling technique where all the subjects in the target populations are given a chance to participate in the study. This sampling method was considered because the entire target population is considered researchable. The expected findings are that staff performance appraisal in the department of Islamic Studies has effects on employee productivity; this is to say if it is given due consideration and the needful being done will improve employee productivity.

Keywords: performance appraisal, employee productivity, Yobe state University, appraisal feedback

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Authors: Palak Sadhwani, Susie Pryor

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This paper investigates interprofessional collaboration (IPC) in the context of entrepreneurship education. Collaboration has been found to enhance problem solving, leverage expertise, improve resource allocation, and create organizational efficiencies. However, research suggests that successful collaboration is hampered by individual and organizational characteristics. IPC occurs when two or more professionals work together to solve a problem or achieve a common objective. The necessity for this form of collaboration is particularly prevalent in cross-disciplinary fields. In this study, we utilize social exchange theory (SET) to examine IPC in the context of an entrepreneurship living learning community (LLC) at a large university in the Western United States. Specifically, we explore these research questions: How are rules or norms established that govern the collaboration process? How are resources valued and distributed? How are relationships developed and managed among and between parties? LLCs are defined as groups of students who live together in on-campus housing and share similar academic or special interests. In 2007, the Association of American Colleges and Universities named living communities a high impact practice (HIP) because of their capacity to enhance and give coherence to undergraduate education. The entrepreneurship LLC in this study was designed to offer first year college students the opportunity to live and learn with like-minded students from diverse backgrounds. While the university offers other LLC environments, the target residents for this LLC are less easily identified and are less apparently homogenous than residents of other LLCs on campus (e.g., Black Scholars, LatinX, Women in Science and Education), creating unique challenges. The LLC is a collaboration between the university’s College of Business & Public Administration and the Department of Housing and Residential Education (DHRE). Both parties are contributing staff, technology, living and learning spaces, and other student resources. This paper reports the results an ethnographic case study which chronicles the start-up challenges associated with the co-creation of the LLC. SET provides a general framework for examining how resources are valued and exchanged. In this study, SET offers insights into the processes through which parties negotiate tensions resulting from approaching this shared project from very different perspectives and cultures in a novel project environment. These tensions occur due to a variety of factors, including team formation and management, allocation of resources, and differing output expectations. The results are useful to both scholars and practitioners of entrepreneurship education and organizational management. They suggest probably points of conflict and potential paths towards reconciliation.

Keywords: case study, ethnography, interprofessional collaboration, social exchange theory

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Authors: Kenneth D. Aiello, M. Simeone, Manfred Laubichler

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It is increasingly difficult to analyze emerging scientific fields as contemporary scientific fields are more dynamic, their boundaries are more porous, and the relational possibilities have increased due to Big Data and new information sources. In biomedicine, where funding, medical categories, and medical jurisdiction are determined by distinct boundaries on biomedical research fields and definitions of concepts, ambiguity persists between the microbiome, metabolome, and metagenome research fields. This ambiguity continues despite efforts by institutions and organizations to establish parameters on the core concepts and research discourses. Further, the explosive growth of microbiome, metabolome, and metagenomic research has led to unknown variation and covariation making application of findings across subfields or coming to a consensus difficult. This study explores the evolution and variation of knowledge within the microbiome, metabolome, and metagenome research fields related to ambiguous scholarly language and commensurable theoretical frameworks via a semantic analysis of key concepts and narratives. A computational historical framework of cultural evolution and large-scale publication data highlight the boundaries and overlaps between the competing scientific discourses surrounding the three research areas. The results of this study highlight how discourse and language distribute power within scholarly and scientific networks, specifically the power to set and define norms, central questions, methods, and knowledge.

Keywords: biomedicine, conceptual change, history of science, philosophy of science, science of science, sociolinguistics, sociology of knowledge

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Authors: Lida Balilan, Dariush Sattarzadeh, Rana Koorepaz

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In the Ilkhanid era, the mystical school of Tabriz manifested itself as an art school in various aspects, including miniatures, architecture, urban planning and design, simultaneously with the expansion of the many sciences of its time. In this era, mysticism, both in form and in poetry and prose, as well as in works of art reached its peak. Mysticism, as an inner belief and thought, brought the audience to the artistic and aesthetical view using allegorical and symbolic expression of the religion and had a direct impact on the formation of the intellectual and cultural layers of the society. At the same time, Mystic school of Tabriz could create a symbolic and allegorical language to create magnificent works of architecture with the expansion of science in various fields and using various sciences such as mathematics, geometry, science of numbers and by Abjad letters. In this era, geometry is the middle link between mysticism and architecture and it is divided into two categories, including intellectual and sensory geometry and based on its function. Soltaniyeh dome is one of the prominent buildings of the Tabriz school with the shrine land use. In this article, information is collected using a historical-interpretive method and the results are analyzed using an analytical-comparative method. The results of the study suggest that the designers and builders of the Soltaniyeh dome have used shapes, colors, numbers, letters and words in the form of motifs, geometric patterns as well as lines and writings in levels and layers ranging from plans to decorations and arrays for architectural symbolization and encryption to express and transmit mystical ideas.

Keywords: geometry, Tabriz school, mystical thoughts, dome of Soltaniyeh

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Authors: Valmir Baloshi, Fran Gjoka, Nehat Çollaku, Elvin Toromani

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As a sediment production mechanism, soil erosion is the main environmental threat to the Bovilla watershed, including the decline of water quality of the Bovilla reservoir that provides drinking water to Tirana city (the capital of Albania). Therefore, an experiment with 25 erosion plots for soil erosion monitoring has been set up since June 2017. The aim was to determine the soil loss on plot and watershed scale in Bovilla watershed (Tirana region) for implementation of soil and water protection measures or payments for ecosystem services (PES) programs. The results of erosion monitoring for the period June 2017 - May 2018 showed that the highest values of surface runoff were noted in bare land of 38829.91 liters on slope of 74% and the lowest values in forest land of 12840.6 liters on slope of 64% while the highest values of soil loss were found in bare land of 595.15 t/ha on slope of 62% and lowest values in forest land of 18.99 t/ha on slope of 64%. These values are much higher than the average rate of soil loss in the European Union (2.46 ton/ha/year). In the same sloping class, the soil loss was reduced from orchard or bare land to the forest land, and in the same category of land use, the soil loss increased with increasing land slope. It is necessary to conduct chemical analyses of sediments to determine the amount of chemical elements leached out of the soil and end up in the reservoir of Bovilla. It is concluded that PES programs should be implemented for rehabilitation of sub-watersheds Ranxe, Vilez and Zall-Bastar of the Bovilla watershed with valuable conservation practices.

Keywords: ANOVA, Bovilla, land cover, slope, soil loss, watershed management

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Authors: Timothy Mossman

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The number of adolescent children accompanying their immigrant parents to Canada has steadily increased since the 1990s. Much of the applied linguistics literature on these so-called ‘Generation 1.5’ youth has focused on their deficiencies as academic writers in US Rhetoric and Composition and ESL contexts in higher education and the stigma of ESL in US K-12 contexts. However, the literature on Generation 1.5 students and identity in Canadian higher education is limited. This qualitative study investigates the processes of identity construction of three Generation 1.5 students studying at a university in Metro Vancouver to find out what types of identities and representations of self and other they make relevant, the meanings they attribute to their identities, and what motivates them to construct these identities. The study analyzes the accounts and experiences of the participants in interviews, focus groups, and texts and as ‘culture-in-action,’ positing that they constructed identities as social categories associated with the languages and social practices of their countries of birth, in liminal spaces among a continuum between Canada and their countries of birth, and a spectrum of related cultural representations. Ideas and beliefs associated with broader ‘macro’ social structures in Canadian society related to language, culture, legitimacy, immigration, power, distinction, and racism were shown to be transcended in and through their representations of themselves and others. Data suggest that moving to Canada caused participants to experience discontinuities between their cultures, languages, and social practices, and in some cases a conflicting sense of self. The study brings implications for finding ways to understand the complexity of immigrant students, avoid reifying and generalizing about them, and not see them as stuck-in-between or lacking.

Keywords: culture-in-action, generation 1.5, identity, membership categorization analysis

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Authors: Hidehiro Endo, Taizo Shigemichi

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Introduction & Objectives: In 2008, the revised course of study for elementary schools was published by MEXT, and from the beginning of the academic year of 2011-2012, foreign language activities (English lessons) became mandatory for 5th and 6th graders in Japanese elementary schools. Foreign language activities are currently offered once a week for approximately 50 minutes by elementary school teachers, assistant language teachers who are native speakers of English, volunteers, among others, with the purpose of helping children become accustomed to functional English. However, the new policy has disclosed a myriad of issues in conducting foreign language activities since the majority of the current elementary school teachers has neither English teaching experience nor English proficiency. Nevertheless, converting foreign language activities into English, as a subject in Japanese elementary schools (for 5th and 6th graders) from 2020 is what MEXT currently envisages with the purpose of reforming English education in Japan. According to their new proposal, foreign language activities will be mandatory for 3rd and 4th graders from 2020. Consequently, gaining better access to English learning opportunities becomes one of the primary concerns even in early childhood education. Thus, in this project, we aim to explore some nursery schools’ attempts at providing toddlers with online English lessons via Skype. The main purpose of this project is to look deeply into what roles online English lessons in the nursery schools play in guiding nursery school children to enjoy learning the English language as well as to acquire English communication skills. Research Methods: Setting; The main research site is a nursery school located in the northern part of Japan. The nursery school has been offering a 20-minute online English lesson via Skype twice a week to 7 toddlers since September 2015. The teacher of the online English lessons is a male person who lives in the Philippines. Fieldwork & Data; We have just begun collecting data by attending the Skype English lessons. Direct observations are the principal components of the fieldwork. By closely observing how the toddlers respond to what the teacher does via Skype, we examine what components stimulate the toddlers to pay attention to the English lessons. Preliminary Findings & Expected Outcomes: Although both data collection and analysis are ongoing, we found that the online English teacher remembers the first name of each toddler and calls them by their first name via Skype, a technique that is crucial in motivating the toddlers to actively participate in the lessons. In addition, when the teacher asks the toddlers the name of a plastic object such as grapes in English, the toddlers tend to respond to the teacher in Japanese. Accordingly, the effective use of Japanese in teaching English for nursery school children need to be further examined. The anticipated results of this project are an increased recognition of the significance of creating English language learning opportunities for nursery school children and a significant contribution to the field of early childhood education.

Keywords: teaching children, English education, early childhood education, nursery school

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Authors: Abdallah Abu Madi, Dongmei Cao, Alexeis Garcia-Perez, Qile He

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The failure of Enterprise Resource Planning (ERP) implementation in higher education institutions (HEIs) worldwide is much higher in comparison to other sectors, such as banking or manufacturing, to our knowledge limited research has been conducted on this issue. To date, prior literature has identified some key success factors (KSFs) mostly either in the domain of information and system (IS) or in the industrial context. However, evidence of ERP implementation in the higher education sector has had little attention in the extant literature. Hence, this paper identifies and categories KSFs of ERP implementation in HEIs. Semi-structured face-to-face interviews were conducted with technicians and managers from three Jordanian HEIs. From these case studies, three new sector- and context-specific KSFs were identified and categorized according to two dimensions: organizational and technical. The first new KSF is the selection of the ERP system, which is an influential factor in the organizational dimension. Results show that an ERP solution that is suitable to one context may be disastrous in another. The second new KSF, which falls under the technical dimension, is the relationship between vendors and HEIs. This must be fair and impartial to enable successful decision-making and thus the achievement of pre-defined goals. Also within the technical dimension is the third factor: in-house maintenance. Once an appropriate system is selected and a strong relationship is established, the ERP system requires continuous maintenance for effective operation. HEIs should ensure that qualified IT support is in place and in-house to avoid excessive running expenses.

Keywords: Enterprise Resource Planning (ERP)implementation, key success factors, higher education institutions, Jordanian higher education

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Authors: Tooraj Karimi, Mohammadreza Sadeghi Moghadam

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Grey set theory has the advantage of using fewer data to analyze many factors, and it is therefore more appropriate for system study rather than traditional statistical regression which require massive data, normal distribution in the data and few variant factors. So, in this paper grey clustering and entropy of coefficient vector of grey evaluations are used to analyze energy consumption in buildings of the Oil Ministry in Tehran. In fact, this article intends to analyze the results of energy audit reports and defines most favorable characteristics of system, which is energy consumption of buildings, and most favorable factors affecting these characteristics in order to modify and improve them. According to the results of the model, ‘the real Building Load Coefficient’ has been selected as the most important system characteristic and ‘uncontrolled area of the building’ has been diagnosed as the most favorable factor which has the greatest effect on energy consumption of building. Grey clustering in this study has been used for two purposes: First, all the variables of building relate to energy audit cluster in two main groups of indicators and the number of variables is reduced. Second, grey clustering with variable weights has been used to classify all buildings in three categories named ‘no standard deviation’, ‘low standard deviation’ and ‘non- standard’. Entropy of coefficient vector of Grey evaluations is calculated to investigate greyness of results. It shows that among the 38 buildings surveyed in terms of energy consumption, 3 cases are in standard group, 24 cases are in ‘low standard deviation’ group and 11 buildings are completely non-standard. In addition, clustering greyness of 13 buildings is less than 0.5 and average uncertainly of clustering results is 66%.

Keywords: energy audit, grey set theory, grey incidence matrixes, grey clustering, Iran oil ministry

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Authors: Zainab Mutlaq Ibrahim, Mohamed Bader-El-Den, Mihaela Cocea

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Students' textual feedback can hold unique patterns and useful information about learning process, it can hold information about advantages and disadvantages of teaching methods, assessment components, facilities, and other aspects of teaching. The results of analysing such a feedback can form a key point for institutions’ decision makers to advance and update their systems accordingly. This paper proposes a data mining framework for analysing end of unit general textual feedback using part of speech feature (PoS) with four machine learning algorithms: support vector machines, decision tree, random forest, and naive bays. The proposed framework has two tasks: first, to use the above algorithms to build an optimal model that automatically classifies the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data. Second task to use the same algorithms to build an optimal model for whole data set, and the new data subsets to automatically detect their sentiment. The significance of this paper is to compare the performance of the above four algorithms using part of speech feature to the performance of the same algorithms using n-grams feature. The paper follows Knowledge Discovery and Data Mining (KDDM) framework to construct the classification and sentiment analysis models, which is understanding the assessment domain, cleaning and pre-processing the data set, selecting and running the data mining algorithm, interpreting mined patterns, and consolidating the discovered knowledge. The results of this paper experiments show that both models which used both features performed very well regarding first task. But regarding the second task, models that used part of speech feature has underperformed in comparison with models that used unigrams and bigrams.

Keywords: assessment, part of speech, sentiment analysis, student feedback

Procedia PDF Downloads 133