Search results for: college student learning experience

Authors: Yen Yen Tan, Lynn Lam, Cynthia Lam, Angela Koh, Edwin Seng

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Entrepreneurial education plays a crucial role in nurturing future innovators and change-makers. Over time, significant progress has been made in refining instructional approaches to develop the necessary skills among learners effectively. Two highly valuable frameworks, Kearney's "4 Principles of Entrepreneurial Pedagogy" and Bloom's "Three Domains of Learning," serve as guiding principles in entrepreneurial education. Kearney's principles align with experiential and student-centric learning, which are crucial for cultivating an entrepreneurial mindset. The potential synergies between these frameworks hold great promise for enhancing entrepreneurial acumen among students. However, despite this potential, their integration remains largely unexplored. This study aims to bridge this gap by building upon the Business Essentials through Action (BETA) module and investigating its contributions to nurturing the entrepreneurial mindset. This study employs a quasi-experimental mixed-methods approach, combining quantitative and qualitative elements to ensure comprehensive and insightful data. A cohort of 235 students participated, with 118 enrolled in the BETA module and 117 in a traditional curriculum. Their Personal Entrepreneurial Competencies (PECs) were assessed before admission (pre-Y1) and one year into the course (post-Y1) using a comprehensive 55-item PEC questionnaire, enabling measurement of critical traits such as opportunity-seeking, persistence, and risk-taking. Rigorous computations of individual entrepreneurial competencies and overall PEC scores were performed, including a correction factor to mitigate potential self-assessment bias. The orchestration of Kearney's principles and Bloom's domains within the BETA module necessitates a granular examination. Here, qualitative revelations surface, courtesy of structured interviews aligned with contemporary research methodologies. These interviews act as a portal, ushering us into the transformative journey undertaken by students. Meanwhile, the study pivots to explore the BETA module's influence on students' entrepreneurial competencies from the vantage point of faculty members. A symphony of insights emanates from intimate focus group discussions featuring six dedicated lecturers, who share their perceptions, experiences, and reflective narratives, illuminating the profound impact of pedagogical practices embedded within the BETA module. Preliminary findings from ongoing data analysis indicate promising results, showcasing a substantial improvement in entrepreneurial skills among students participating in the BETA module. This study promises not only to elevate students' entrepreneurial competencies but also to illuminate the broader canvas of applicability for Kearney's principles and Bloom's domains. The dynamic interplay of quantitative analyses, proffering precise competency metrics, and qualitative revelations, delving into the nuanced narratives of transformative journeys, engenders a holistic understanding of this educational endeavour. Through a rigorous quasi-experimental mixed-methods approach, this research aims to establish the BETA module's effectiveness in fostering entrepreneurial acumen among students at Singapore Polytechnic, thereby contributing valuable insights to the broader discourse on educational methodologies.

Keywords: entrepreneurial education, experiential learning, pedagogical frameworks, innovative competencies

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Authors: Yuanyuan Shi

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Being psychologically flexible is important, especially, under a globalized cultural milieu. Treating psychological flexibility as a form of multicultural competence, we conducted five studies to construct the lay concept of psychological flexibility (Study 1 and 2) and test the association between psychological flexibility and multicultural adaptation (Study 3-5). In Study 1, we first identified the components of psychological flexibility by prototype analysis among lay Chinese (N = 165) and American (N = 165). In Study 2, we examined the convergent validity of the lay concept of psychological flexibility consisted with hypothesized structures via survey among Chinese (N = 172) and American participants (N = 165). Then, we examined the relationship between psychological flexibility and multicultural orientation in American and Chinese contexts (Study 3, N = 6245), and tested the influence of experimentally-manipulated psychological flexibility on foreign cultural accommodation (Study 4 N = 409; Study 5, N = 320). The results showed, higher flexibility was accompanied by higher cognitive flexibility, emotion reappraisal, resilience, and openness to experience, and lower need for cognition closure; besides, people with high psychological flexible turned out to have stronger multicultural orientation and better multicultural adaptations. Our research highlights the importance of psychological flexibility in multicultural situations and extends the understanding of the relationship between multicultural experience and well-being.

Keywords: adaptation, psychological flexibility, multicultural competence, multicultural orientation

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Authors: Mei Lin, Chang Liu

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Argumentative writing is the most prevalent genre in diverse writing tests. How to construct academic arguments is often regarded as a difficult task by most English as a foreign language (EFL) learners. A failure to generate enough ideas and organise them coherently and logically as well as a lack of competence in supporting their arguments with relevant evidence are frequent problems faced by EFL learners when approaching an English argumentative writing task. Overall, these problems are closely related to planning, and planning an argumentative writing at pre-writing stage plays a vital role in a good academic essay. However, how teachers can effectively guide students to generate ideas is rarely discussed in planning English argumentative writing, apart from brainstorming. Brainstorming has been a common practice used by teachers to help students generate ideas. However, some limitations of brainstorming suggest that it can help students generate many ideas, but ideas might not necessarily be coherent and logic, and could sometimes impede production. It calls for a need to explore effective instructional strategies at pre-writing stage of English argumentative writing. This paper will first examine how a Six Thinking Hats approach can be used to provide a dialogic space for EFL learners to experience and collaboratively generate ideas from multiple perspectives at pre-writing stage. Part of the findings of the impact of a twelve-week intervention (from March to July 2021) on students learning to generate ideas through engaging in group discussions of using Six Thinking Hats will then be reported. The research design is based on the sociocultural theory. The findings present evidence from a mixed-methods approach and fifty-nine participants from two first-year undergraduate natural classes in a Chinese university. Analysis of pre- and post- questionnaires suggests that participants had a positive attitude toward the Six Thinking Hats approach. It fosters their understanding of prewriting and argumentative writing, helps them to generate more ideas not only from multiple perspectives but also in a systematic way. A comparison of participants writing plans confirms an improvement in generating counterarguments and rebuttals to support their arguments. Above all, visual and transcripts data of group discussion collected from different weeks throughout the intervention enable teachers and researchers to ‘see’ the hidden process of learning to generate ideas in action.

Keywords: argumentative writing, innovative pedagogy, six thinking hats, dialogic space, prewriting, higher education

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Authors: Tokihiko Akita, Seiichi Mita

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A millimeter-wave radar is the most robust against adverse environments, making it an essential environment recognition sensor for automated driving. However, the reflection signal is sparse and unstable, so it is difficult to obtain the high recognition accuracy. Deep learning provides high accuracy even for them in recognition, but requires large scale datasets with ground truth. Specially, it takes a lot of cost to annotate for a millimeter-wave radar. For the solution, utilizing a simulator that can generate an annotated huge dataset is effective. Simulation of the radar is more difficult to match with real world data than camera image, and recognition by deep learning with higher-order features using the simulator causes further deviation. We have challenged to improve the accuracy of traffic participant classification by fusing simulator and real-world data with domain adaptation technique. Experimental results with the domain adaptation network created by us show that classification accuracy can be improved even with a few real-world data.

Keywords: millimeter-wave radar, object classification, deep learning, simulation, domain adaptation

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Authors: Troy A. Tyre

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Organizational behavior and organizational change are elements of the ever-changing global business environment. Leadership and organizational behavior are 21st century disciplines. Network marketing organizations need to understand the ever-changing nature of global business and be ready and willing to adapt to the environment. Network marketing organizations have a challenge keeping up with a rapid escalation in global growth. Network marketing growth has been steady and global. Network marketing organizations have been slow to develop a 21st century global strategy to manage the rapid escalation of growth degrading organizational behavior, job satisfaction, increasing attrition, and degrading customer service. Development of an organizational behavior and leadership theory for the 21st century to help network marketing develops a global business strategy to manage the rapid escalation in growth that affects organizational behavior. Managing growth means organizational leadership must develop and adapt to the organizational environment. Growth comes with an open mind and one’s departure from the comfort zone. Leadership growth operates in the tacit dimension. Systems thinking and adaptation of mental models can help shift organizational behavior. Shifting the organizational behavior requires organizational learning. Organizational learning occurs through single-loop, double-loop, and triple-loop learning. Triple-loop learning is the most difficult, but the most rewarding. Tools such as theory U can aid in developing a landscape for organizational behavioral development. Additionally, awareness to espoused and portrayed actions is imperatives. Theories of motivation, cross-cultural diversity, and communications are instrumental in founding an organizational behavior suited for the 21st century.

Keywords: global, leadership, network marketing, organizational behavior

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Authors: Nidhi Patel, Amol Shinde, Amrin Moger

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Mainstream Hindi cinema also known as Bollywood, is the largest film producing industry in India. The Indian film industry underwent a sea change since last few years. The industry adapted to the latest technologies and creative manpower to improve visual and cinematic effects. The changes helped the industry to improve its creative looks and ease on production budget. The research focuses on this very change, i.e. the use of VFX. There has been growing use of VFX in feature films. The primary focus is on how VFX can make a difference in the experience of watching a movie. The research examines the use of CGI/VFX in the narrative, which delivers a visually fulfilling film. It also focuses on the use of CGI/ VFX as a cost cutting tool. The research was exploratory in nature. It studies the industry’s evolvement, increment in its use by filmmakers and their intention to use it in their films. The researcher used qualitative method for data collection as an in-depth interview of 10 artists from VFX studios in Mumbai was conducted. The finding reveals the way VFX is used in Hindi cinema by the directors. The researcher learnt that VFX is majorly used as a tool to enhance creativity and provide the audience with creative viewing experience.

Keywords: Bollywood, Hindi cinema, VFX, CGI, technology, creativity, cost cutting

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Authors: Vaidehi Iyer, Konstantin Borozdin

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Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.

Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning

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Authors: Nezha Badi

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It has been shown in the literature that Algerian university students suffer from low levels of reading comprehension proficiency, which hinder their overall proficiency in English. This low level is mainly related to the methodology of teaching reading which is employed by the teacher in the classroom (a teacher-centered environment), as well as students’ poor sense of self-efficacy to undertake reading comprehension activities. Arguably, what is needed is an approach necessary for enhancing students’ self-beliefs about their abilities to deal with different reading comprehension activities. This can be done by providing them with opportunities to take responsibility for their own learning (learners’ autonomy). As a result of learning autonomy, learners’ beliefs about their abilities to deal with certain language tasks may increase, and hence, their language learning ability. Therefore, this experimental research study attempts to assess the extent to which an integrated approach combining one particular reading approach known as ‘collaborative strategic reading’ (CSR), and teacher’s attributional feedback (on students’ reading performance and strategy use) can improve the reading comprehension skill and the sense of self-efficacy of EFL Algerian university students. It also seeks to examine students’ main reasons for their successful or unsuccessful achievements in reading comprehension activities, and whether students’ attributions for their reading comprehension outcomes can be modified after exposure to the instruction. To obtain the data, different tools including a reading comprehension test, questionnaires, an observation, an interview, and learning logs were used with 105 second year Algerian EFL university students. The sample of the study was divided into three groups; one control group (with no treatment), one experimental group (CSR group) who received a CSR instruction, and a second intervention group (CSR Plus group) who received teacher’s attribution feedback in addition to the CSR intervention. Students in the CSR Plus group received the same experiment as the CSR group using the same tools, except that they were asked to keep learning logs, for which teacher’s feedback on reading performance and strategy use was provided. The results of this study indicate that the CSR and the attributional feedback intervention was effective in improving students’ reading comprehension proficiency and sense of self-efficacy. However, there was not a significant change in students’ adaptive and maladaptive attributions for their success and failure d from the pre-test to the post-test phase. Analysis of the perception questionnaire, the interview, and the learning logs shows that students have positive perceptions about the CSR and the attributional feedback instruction. Based on the findings, this study, therefore, seeks to provide EFL teachers in general and Algerian EFL university teachers in particular with pedagogical implications on how to teach reading comprehension to their students to help them achieve well and feel more self-efficacious in reading comprehension activities, and in English language learning more generally.

Keywords: attributions, attributional feedback, collaborative strategic reading, self-efficacy

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Authors: Hernández Yasmín, Ochoa Alberto, Hurtado Diego

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The aim of this study is developing an intelligent tutoring system for electrical operators training with virtual reality systems at the laboratory center of partials discharges LAPEM. The electrical domain requires efficient and well trained personnel, due to the danger involved in the partials discharges field, qualified electricians are required. This paper presents an overview of the intelligent tutor adaptive learning design and user interface with VR. We propose the develop of constructing a model domain of a subset of partial discharges enables adaptive training through a trainee model which represents the affective and knowledge states of trainees. According to the success of the intelligent tutor system with VR, it is also hypothesized that the trainees will able to learn the electrical domain installations of partial discharges and gain knowledge more efficient and well trained than trainees using traditional methods of teaching without running any risk of being in danger, traditional methods makes training lengthily, costly and dangerously.

Keywords: intelligent tutoring system, artificial intelligence, virtual reality, partials discharges, adaptive learning

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Authors: Ashi Gautam, Isha Shukla, Akhil Seghal

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Biosensors are analytical devices that use a biological sensing element to detect and measure a specific chemical substance or biomolecule in a sample. These devices are widely used in various fields, including medical diagnostics, environmental monitoring, and food analysis, due to their high specificity, sensitivity, and selectivity. In this research paper, a machine learning model is proposed for predicting the suitability of skin care products based on biosensor readings. The proposed model takes in features extracted from biosensor readings, such as biomarker concentration, skin hydration level, inflammation presence, sensitivity, and free radicals, and outputs the most appropriate skin care product for an individual. This model is trained on a dataset of biosensor readings and corresponding skin care product information. The model's performance is evaluated using several metrics, including accuracy, precision, recall, and F1 score. The aim of this research is to develop a personalised skin care product recommendation system using biosensor data. By leveraging the power of machine learning, the proposed model can accurately predict the most suitable skin care product for an individual based on their biosensor readings. This is particularly useful in the skin care industry, where personalised recommendations can lead to better outcomes for consumers. The developed model is based on supervised learning, which means that it is trained on a labeled dataset of biosensor readings and corresponding skin care product information. The model uses these labeled data to learn patterns and relationships between the biosensor readings and skin care products. Once trained, the model can predict the most suitable skin care product for an individual based on their biosensor readings. The results of this study show that the proposed machine learning model can accurately predict the most appropriate skin care product for an individual based on their biosensor readings. The evaluation metrics used in this study demonstrate the effectiveness of the model in predicting skin care products. This model has significant potential for practical use in the skin care industry for personalised skin care product recommendations. The proposed machine learning model for predicting the suitability of skin care products based on biosensor readings is a promising development in the skin care industry. The model's ability to accurately predict the most appropriate skin care product for an individual based on their biosensor readings can lead to better outcomes for consumers. Further research can be done to improve the model's accuracy and effectiveness.

Keywords: biosensors, data model, machine learning, skin care

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Authors: Liang Wang, Wei Zhang

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Nowadays, sustainable development has increasingly become an important research topic of engineering education. Existing research on Engineering Education for Sustainable Development (EESD) has highlighted the importance of perceptions for ethical responsibility to address sustainable development in practice. However, whether and how the professional engineering experience affects those perceptions has not been proved, especially in a Chinese context. Our study fills this gap by investigating the perceptions bias of EESD between experienced engineers and engineering students. We specifically examined what EESD means for experienced engineers and engineering students using a triple-dimensional model to understand if there are obvious differences between the two groups. Our goal is to make the benefits of these experiences more accessible in school context. The data (n=438) came from a questionnaire created and adapted from previously published studies containing 288 students from mechanical or civil engineering and 150 civil engineers with rich working experience, and the questionnaire was distributed during Fall 2020. T-test was used to find the difference in different dimensions between the two groups. The statistical results show that there is a significant difference in the perceptions of EESD between experienced engineers and inexperienced engineering students in China. Experienced engineers tend to consider sustainable development from ecological, economic, and social perspectives, while engineering students' answers focus more on ecology and ignore economic and social dimensions to some extend. The findings provide empirical evidence that professional experience is helpful to cultivate the cognition and ability of sustainable development in engineering education. The results of this work indicate that more practical content should be added to engineering education to promote sustainable development. In addition, for the design of engineering courses and professional practice systems for sustainable development, we should not only pay attention to the ecological aspects but also emphasize the coordination of ecological, economic, and socially sustainable development (e.g., engineer's ethical responsibility).

Keywords: engineering education, sustainable development, experienced engineers, engineering students

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Authors: Kashika P. H.

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The development of computer vision systems has been greatly aided by the efficient and precise detection of images and videos. Although the ability to recognize and comprehend images is a strength of the human brain, employing technology to tackle this issue is exceedingly challenging. In the past few years, the use of Deep Learning algorithms to treat object detection has dramatically expanded. One of the key issues in the realm of image recognition is the recognition and detection of certain notable people from randomly acquired photographs. Face recognition uses a way to identify, assess, and compare faces for a variety of purposes, including user identification, user counting, and classification. With the aid of an accessible deep learning-based API, this article intends to recognize various faces of people and their facial descriptors more accurately. The purpose of this study is to locate suitable individuals and deliver accurate information about them by using the Amazon Rekognition system to identify a specific human from a vast image dataset. We have chosen the Amazon Rekognition system, which allows for more accurate face analysis, face comparison, and face search, to tackle this difficulty.

Keywords: Amazon rekognition, API, deep learning, computer vision, face detection, text detection

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Authors: Derssie Mebratu

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As a large variety of micro services, web services, social graphic applications, and media applications are continuously developed, it is substantially vital to design and build a reliable, efficient, and faster memory tiering system. Despite limited design, implementation, and deployment in the last few years, several techniques are currently developed to improve a memory tiering system in a cloud. Some of these techniques are to develop an optimal scanning frequency; improve and track pages movement; identify pages that recently accessed; store pages across each tiering, and then identify pages as a hot, warm, and cold so that hot pages can store in the first tiering Dynamic Random Access Memory (DRAM) and warm pages store in the second tiering Compute Express Link(CXL) and cold pages store in the third tiering Non-Volatile Memory (NVM). Apart from the current proposal and implementation, we also develop a new technique based on a machine learning algorithm in that the throughput produced 25% improved performance compared to the performance produced by the baseline as well as the latency produced 95% improved performance compared to the performance produced by the baseline.

Keywords: machine learning, bayesian optimization, memory tiering, CXL, DRAM

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Authors: Adi Gielgun-Katz, Alina S. Rusu

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In the last two decades, education systems worldwide have integrated new pedagogical methods and strategies in lesson plans, such as innovative technologies, social-emotional learning (SEL), gamification, mixed learning, multiple literacies, and many others. Visual language, such as photographs, is known to transcend cultures and languages, and it is commonly used by youth to express positions and affective states in social networks. Therefore, visual language needs more educational attention as a linguistic and communicative component that can create connectedness among the students and their teachers. Nowadays, when SEL is gaining more and more space and meaning in the area of academic improvement in relation to social well-being, and taking and sharing pictures is part of the everyday life of the majority of people, it becomes natural to add the visual language to SEL approach as a reinforcement strategy for connecting education to the contemporary culture and language of the youth. This article presents a program conducted in a high school class in Israel, which combines the five SEL with photography techniques, i.e., Social-Emotional Learning Visual Empowerments (SELVEs) program (experimental group). Another class of students from the same institution represents the control group, which is participating in the SEL program without the photography component. The SEL component of the programs addresses skills such as: troubleshooting, uncertainty, personal strengths and collaboration, accepting others, control of impulses, communication, self-perception, and conflict resolution. The aim of the study is to examine the effects of programs on the level of the five SEL aspects in the two groups of high school students: Self-Awareness, Social Awareness, Self-Management, Responsible Decision Making, and Relationship Skills. The study presents a quantitative assessment of the SEL programs’ impact on the students. The main hypothesis is that the students’ questionnaires' analysis will reveal a better understanding and improvement of the five aspects of the SEL in the group of students involved in the photography-enhanced SEL program.

Keywords: social-emotional learning, photography, education program, adolescents

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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With the continuous increment of smart meter installations across the globe, the need for processing of the load data is evident. Clustering-based load profiling is built upon the utilization of unsupervised machine learning tools for the purpose of formulating the typical load curves or load profiles. The most commonly used algorithm in the load profiling literature is the K-means. While the algorithm has been successfully tested in a variety of applications, its drawback is the strong dependence in the initialization phase. This paper proposes a novel modified form of the K-means that addresses the aforementioned problem. Simulation results indicate the superiority of the proposed algorithm compared to the K-means.

Keywords: clustering, load profiling, load modeling, machine learning, energy efficiency and quality

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Authors: Pedro Martinez-Santos, Víctor Gómez-Escalonilla, Silvia Díaz-Alcaide, Esperanza Montero, Miguel Martín-Loeches

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Groundwater monitoring networks are critical in evaluating the vulnerability of groundwater resources to depletion and contamination, both in space and time. Groundwater monitoring networks typically grow over decades, often in organic fashion, with relatively little overall planning. The groundwater monitoring networks in the Madrid area, Spain, were reviewed for the purpose of identifying gaps and opportunities for improvement. Spatial analysis reveals the presence of various monitoring networks belonging to different institutions, with several hundred observation wells in an area of approximately 4000 km2. This represents several thousand individual data entries, some going back to the early 1970s. Major issues included overlap between the networks, unknown screen depth/vertical distribution for many observation boreholes, uneven time series, uneven monitored species, and potentially suboptimal locations. Results also reveal there is sufficient information to carry out a spatial and temporal analysis of groundwater vulnerability based on machine learning applications. These can contribute to improve the overall planning of monitoring networks’ expansion into the future.

Keywords: groundwater monitoring, observation networks, machine learning, madrid

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Yeong-Gyeong Choi, Kyoung-Seok Kim

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This study, literature review research, intends to deal with the problem of conceptual ambiguity among research on emotional labor, and to look into the evolutionary trends and changing aspects of defining the concept of emotional labor. In addition, in existing studies, deep acting and surface acting are highly related to a positive outcome variable and a negative outcome variable, respectively. It was confirmed that for employees performing emotional labor, deep acting and surface acting are highly related to OCB and CWB, respectively. While positive emotion that employees come to experience during job performance process can easily trigger a positive non-task behavior such as OCB, negative emotion that employees experience through excessive workload or unfair treatment can easily induce a negative behavior like CWB. The two management behaviors of emotional labor, surface acting and deep acting, can have either a positive or negative effect on non-task behavior of employees, depending on which one they would choose. Thus, the purpose of this review paper is to clarify the relationship between emotional labor and non-task behavior more specifically.

Keywords: emotion labor, non-task behavior, OCB, CWB

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Authors: Yoke-Mui Lim, Yahaya Ahmad

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Conservation works in Malaysia that is procured by public organisation usually follow the traditional approach where the works are tendered based on Bills of Quantities (BQ). One of the purposes of tendering is to enable the selection of a competent contractor that offers a competitive price. While competency of the contractors are assessed by their technical knowledge, experience and track records, the assessment of pricing will be dependent on the tender amount. However, the issue currently faced by the conservation works sector is the difficulty in assessing the competitiveness and reasonableness of the tender amount due to the high variance between the tenders amount. Thus, this paper discusses the factors that cause difficulty to the tenderers in pricing competitively in a bidding exercise for conservation tenders. Data on tendering is collected from interviews with conservation works contractors to gain in-depth understanding of the barriers faced in pricing tenders of conservation works. Findings from the study lent support to the contention that the variance of tender amount is very high amongst tenderers. The factors identified in the survey are the format of BQ, hidden works, experience and labour and material costs.

Keywords: building conservation, Malaysia, bill of quantities, tender

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Authors: Zhe Li, Aihua Cai

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Aiming at the problem of high-density clutter and interference affecting radar detection target track initiation in ECM and complex radar mission, the traditional radar target track initiation method has been difficult to adapt. To this end, we propose a multi-algorithm fusion learning track initiation algorithm, which transforms the track initiation problem into a true-false track discrimination problem, and designs an algorithm based on 1DCNN(One-Dimensional CNN)combined with Bi-LSTM (Bi-Directional Long Short-Term Memory )for fusion classification. The experimental dataset consists of real trajectories obtained from a certain type of three-coordinate radar measurements, and the experiments are compared with traditional trajectory initiation methods such as rule-based method, logical-based method and Hough-transform-based method. The simulation results show that the overall performance of the multi-algorithm fusion learning track initiation algorithm is significantly better than that of the traditional method, and the real track initiation rate can be effectively improved under high clutter density with the average initiation time similar to the logical method.

Keywords: track initiation, multi-algorithm fusion, 1DCNN, Bi-LSTM

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Shariful Islam, Sharun Akter Khushbu, S. M. Shaqib, Shahriar Sultan Ramit

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In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwear. The suggested method precisely locates these safety items by using the YOLO v7 (You Only Look Once) object detection algorithm. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a [email protected] score of 87.7%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research contributes to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry.

Keywords: deep learning, safety equipment detection, YOLOv7, computer vision, workplace safety

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Authors: Bhawat Chiamjinnawat

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The agri-food market transformation has implied market growth for the fruit industry in Thailand. This article focuses on analysis of farmer competence and farm resources which affect market strategies used by fruit farmers in Chanthaburi province of Thailand. The survey data were collected through the use of face-to-face interviews with structured questionnaires. This study identified 14 drivers related to farmer competence and farm resources of which some had significant effect on the decision to use either high-value markets or traditional markets. The results suggest that farmers who used high-value markets were better educated and they had longer experience and larger sized business. Identifying the important factors that match with the market transformation provides policy with opportunities to support the fruit farmers to increase their market power. Policies that promote business expansion of agricultural cooperatives and knowledge sharing among farmers are recommended to reduce limitations due to limited knowledge, low experience, and small business sizes.

Keywords: farmer competence, farm resources, fruit industry, high-value markets, Thailand

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Authors: Suleyman Karacor

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The main purpose of this study is to investigate tourism marketing in the information age because of the importance and sensitivity. In the twenty-first century as a result of today's the increasing competition and product diversification in the tourism sector, tourism businesses must take into account exogenous variables such as new technological developments, commercial experience and consumer demand. In the information age, tourist product consumers tend to reserve their leisure time and expenditure on more active opportunities for different experiences instead of living the same experience again. Increasing the number of agents in the tourism sector, travel opportunities offering different experiences and more intensive use of modern technology helps to present diversification of leisure activities for tourists. From the perspective of tourists, travel costs are still important for buying the touristic products but maintaining a high level of tourist satisfaction is also of increasing importance. Tourists tend to prefer activities that add value. A real tourist product must be able to create value and new priorities for tourists. Therefore this study aims to review recent significant developments in international tourism marketing research and practices. To this end, this study reviews tourism marketing-focused articles.

Keywords: information age, tourism marketing, tourism marketing mix, management

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Authors: Sajee A. Gamage, Champa J. Wijesinghe, Patricia Burtner, Ananda R. Wickremasinghe

Abstract:

Background: Teachers working in the context of special education have an enormous responsibility of enhancing performance skills of children in their classroom settings. Fine Motor Skills (FMS) are essential functional skills for children to gain independence in Activities of Daily Living. Children with Down Syndrome (DS) are predisposed to specific challenges due to deficits in FMS. This study is aimed to determine the teachers’ experience on improving FMS of children with DS in the context of special education of Southern Province, Sri Lanka. Methodology: A cross-sectional study was conducted among all consenting eligible teachers (n=147) working in the context of special education in government schools of Southern Province of Sri Lanka. A self-administered questionnaire was developed based on literature and expert opinion to assess teachers’ experience regarding deficits of FMS, limitations of classroom activity performance and barriers to improve FMS of children with DS. Results: Approximately 93% of the teachers were females with a mean age ( ± SD) of 43.1 ( ± 10.1) years. Thirty percent of the teachers had training in special educationand 83% had children with DS in their classrooms. Major deficits of FMS reported were deficits in grasping (n=116; 79%), in-hand manipulation (n=103; 70%) and bilateral hand use (n=99; 67.3%). Paperwork (n=70; 47.6%), painting (n=58; 39.5%), scissor work (n=50; 34.0%), pencil use for writing (n=45; 30.6%) and use of tools in the classroom (n=41; 27.9%) were identified as major classroom performance limitations of children with DS. Parental factors (n=67; 45.6%), disease specific characteristics (n=58; 39.5%) and classroom factors (n=36; 24.5%), were identified as major barriers to improve FMS in the classroom setting. Lack of resources and standard tools, social stigma and late school admission were also identified as barriers to FMS training. Eighty nine percent of the teachers informed that training fine motor activities in a special education classroom was more successful than work with normal classroom setting. Conclusion: Major areas of FMS deficits were grasping, in-hand manipulation and bilateral hand use; classroom performance limitations included paperwork, painting and scissor work of children with DS. Teachers recommended regular practice of fine motor activities according to individual need. Further research is required to design a culturally specific FMS assessment tool and intervention methods to improve FMS of children with DS in Sri Lanka.

Keywords: classroom activities, Down syndrome, experience, fine motor skills, special education, teachers

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Authors: Zhepeng Rui, Zhenyu Gu, Caitilin de Bérigny

Abstract:

In the current study, we designed an interactive meditation and brain painting application to cultivate users’ creativity, promote meditation, reduce stress, and improve cognition while attempting to learn a foreign language. User tests and data analyses were conducted on 42 male and 42 female participants to better understand sex-associated psychological and aesthetic differences. Our method utilized brain-computer interfaces to import meditation and attention data to create artwork in meditation-based applications. Female participants showed statistically significantly different language learning outcomes following three meditation paradigms. The art style of brain painting helped females with language memory. Our results suggest that the most ideal methods for promoting memory attention were meditation methods and brain painting exercises contributing to language learning, memory concentration promotion, and foreign word memorization. We conclude that a short period of meditation practice can help in learning a foreign language. These findings provide new insights into meditation, creative language education, brain-computer interface, and human-computer interactions.

Keywords: brain-computer interface, creative thinking, meditation, mental health

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Authors: Joseph Uzuazomaro Oju

Abstract:

The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

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Authors: Bliss Singhal

Abstract:

Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

Abstract:

This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: E. Er, Y. S. Cheng

Abstract:

The role of parents and caregivers in their children’s education is pivotal. Parental involvement (PI) is often associated with a range of student outcomes. This includes academic achievements, socioemotional development, adaptive skills, physical fitness and school attendance. This study is the first in Singapore to (1) explore the relationship between parental involvement and student outcomes; (2) determine the effects of family structure and socioeconomic status (SES) on parental involvement and (3) investigate factors that inform involvement in parents of children with specific developmental disabilities. Approval for the study was obtained from Nanyang Technological University’s Institutional Review Board in Singapore. The revised version of a comprehensive theoretical model on parental involvement was used as the theoretical framework in this study. Parents were recruited from a SPED school in Singapore which caters to school-aged children (7 to 21 years old). Pearson’s product moment correlation, analysis of variance and multiple regression analyses were used as statistical techniques in this study. Results indicate that there are significant associations between parental involvement and educational outcomes in students with developmental disabilities. Next, SES has a significant impact on levels of parental involvement. In addition, parents in the current study reported being more involved at home, in school activities and the community, when teachers specifically requested their involvement. Home-based involvement was also predicted by parents’ perceptions of their time and energy, efficacy and beliefs in supporting their child’s education, as well as their children’s invitations to be more involved. An interesting and counterintuitive inverse relationship was found between general school invitations and parental involvement at home. Research findings are further discussed, and suggestions are put forth to increase involvement for this specific group of parents.

Keywords: autism, developmental disabilities, intellectual disabilities, parental involvement, Singapore

Procedia PDF Downloads 176