Search results for: computer- supported collaborative learning

Authors: Talal Alsulaiman, Khaldoun Khashanah

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In this paper, we provided a literature survey on the artificial stock problem (ASM). The paper began by exploring the complexity of the stock market and the needs for ASM. ASM aims to investigate the link between individual behaviors (micro level) and financial market dynamics (macro level). The variety of patterns at the macro level is a function of the AFM complexity. The financial market system is a complex system where the relationship between the micro and macro level cannot be captured analytically. Computational approaches, such as simulation, are expected to comprehend this connection. Agent-based simulation is a simulation technique commonly used to build AFMs. The paper proceeds by discussing the components of the ASM. We consider the roles of behavioral finance (BF) alongside the traditionally risk-averse assumption in the construction of agent's attributes. Also, the influence of social networks in the developing of agents’ interactions is addressed. Network topologies such as a small world, distance-based, and scale-free networks may be utilized to outline economic collaborations. In addition, the primary methods for developing agents learning and adaptive abilities have been summarized. These incorporated approach such as Genetic Algorithm, Genetic Programming, Artificial neural network and Reinforcement Learning. In addition, the most common statistical properties (the stylized facts) of stock that are used for calibration and validation of ASM are discussed. Besides, we have reviewed the major related previous studies and categorize the utilized approaches as a part of these studies. Finally, research directions and potential research questions are argued. The research directions of ASM may focus on the macro level by analyzing the market dynamic or on the micro level by investigating the wealth distributions of the agents.

Keywords: artificial stock markets, market dynamics, bounded rationality, agent based simulation, learning, interaction, social networks

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Authors: Hernán G. Cortés-Mora, José I. Péna-Reyes, Alfonso Herrera-Jiménez

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This article describes the implementation experience of Project-Based Learning (PBL) in an engineering course of the Universidad Nacional de Colombia, with the aim of strengthening student skills necessary for the exercise of their profession under a sustainability framework. Firstly, we present a literature review on the education for sustainability field, emphasizing the skills and knowledge areas required for its development, as well as the commitment of the Faculty of Engineering of the Universidad Nacional de Colombia, and other engineering faculties of the country, regarding education for sustainability. This article covers the general aspects of the course, describes how students team were formed, and how their experience was during the first semester of 2017. During this period two groups of students decided to develop their course project aiming to solve a problem regarding a Non-Governmental Organization (NGO) that works with head-of-household mothers in a low-income neighborhood in Bogota (Colombia). Subsequently, we show how sustainability is involved in the course, how tools are provided to students, and how activities are developed as to strengthen their abilities, which allows them to incorporate sustainability in their projects while also working on the methodology used to develop said projects. Finally, we introduce the results obtained by the students who sent the prototypes of their projects to the community they were working on and the conclusions reached by them regarding the course experience.

Keywords: sustainability, project-based learning, engineering education, higher education for sustainability

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Authors: Daphne Alroy-Thiberge

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Trauma-impacted individuals present unique treatment challenges that include high reactivity, hyper-and hypo-arousal, poor adherence to therapy, as well as powerful transference and counter-transference experiences in therapy. This work provides an overview of the clinical tenets most often encountered in trauma-impacted individuals. Further, it provides readily applicable clinical techniques to optimize therapeutic rapport and facilitate accelerated positive mental health outcomes. Finally, integrated neuroscience and clinical evidence-based data are discussed to shed new light on crisis states in trauma-impacted individuals. This knowledge is utilized to provide effective and concrete interventions towards rapid and successful de-escalation of the impacted individual. A highly interactive, adult-learning-principles-based modality is utilized to provide an organic learning experience for participants. The information and techniques learned aim to increase clinical effectiveness, reduce staff injuries and burnout, and significantly enhance positive mental health outcomes and self-determination for the trauma-impacted individuals treated.

Keywords: clinical competencies, crisis interventions, psychotherapy techniques, trauma informed care

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Authors: Akinfiresoye W. A., Olarewaju O. O., Okunola, Okunola I. O.

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An indigenous honey-extracting machine was designed, fabricated and evaluated at the workshop of the department of Agricultural Technology, Federal Polytechnic, Ile-Oluji, Nigeria using locally available materials. It has the extraction unit, the presser, the honey collector and the frame. The harvested honeycomb is placed inside the cylindrical extraction unit with perforated holes. The press plate was then placed on the comb while the hydraulic press of 3 tons was placed on it, supported by the frame. The hydraulic press, which is manually operated, forces the oil out of the extraction chamber through the perforated holes into the honey collector positioned at the lowest part of the extraction chamber. The honey-extracting machine has an average throughput of 2.59 kg/min and an efficiency of about 91%. The cost of producing the honey extracting machine is NGN 31, 700: 00, thirty-one thousand and seven hundred nairas only or $70 at NGN 452.8 to a dollar. This cost is affordable to beekeepers and would-be honey entrepreneurs. The honey-extracting machine is easy to operate and maintain without any complex technical know-how.

Keywords: honey, extractor, cost, efficiency

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Authors: Salinthip Thipayang, Achara Chandrachai, Rath Pichyangkura, Sukree Sinthupinyo

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Most of the well-known frameworks and tools to measure and compare organisational innovation of the public or government agencies have been designed and used in the developed economies such as the EU, Nordic Region, Australia, and South Korea. This project is one of the very first attempts to develop a measurement tool to adequately measure the organisational (administrative) innovation of the government agencies in the developing economies in ASEAN. New measurement framework with the components including the intra and international collaborations of these government agencies to other private, public and academic sectors were added to the proposed measurement framework. Questionnaires and in-depth interviews with the experts and the middle to top executives of the participating public agencies in the ASEAN member states were conducted to determine the suitability and develop the indicators that should be included in the measurement model. The results showed that intra and international collaborations of these government organisations to other agencies in the public, private and academic sectors can lead to new changes and greatly impact the ways in which these government agencies in the ASEAN STI ecosystem are operated and administered. Government organisations in less developing countries in ASEAN are ready and willing to learn from their counterparts in other more advanced countries and adjust their internal management to be more innovative and to better handle international collaborative projects and commitments.

Keywords: organisational innovation, administrative innovation, government agencies, public agencies, ASEAN science technology and innovation ecosystem, international collaborations

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Authors: M. Z. Abdul Rofiq Badril Rizal

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This research is aimed to know the improvement of students’ English writing skill by using brainstorming technique. The technique used in writing is able to help the students’ difficulties in generating ideas and to lead the students to arrange the ideas well as well as to focus on the topic developed in writing. The research method used is classroom action research. The data sources of the research are an English teacher who acts as an observer and the students of class X.MIA5 consist of 35 students. The test result and observation are collected as the data in this research. Based on the research result in cycle one, the percentage of students who reach minimum accomplishment criteria (MAC) is 76.31%. It shows that the cycle must be continued to cycle two because the aim of the research has not accomplished, all of the students’ scores have not reached MAC yet. After continuing the research to cycle two and the weaknesses are improved, the process of teaching and learning runs better. At the test which is conducted in the end of learning process in cycle two, all of the students reach the minimum score and above 76 based on the minimum accomplishment criteria. It means the research has been successful and the percentage of students who reach minimum accomplishment criteria is 100%. Therefore, the writer concludes that brainstorming technique is able to improve the students’ English writing skill at the tenth grade of SMAN 2 Jember.

Keywords: brainstorming technique, improving, writing skill, knowledge and innovation engineering

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Authors: Jeremy Schultz

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The purpose of this study was to understand the perceptions of vineyard managers in the Krasnodar Region of Southern Russia located between the city of Kransnodar and the Black Sea. In recent years, wine tourism throughout the region has seen tremendous growth due in part to the concurrent growth in the number of tourists vacationing at the Black Sea. This trend has contributed to the development of large-scale wine operations developing in numerous rural locations along the tourists’ travel path. Niche areas of tourism, such as wine tourism, have proven to provide economic viability for rural communities all around the world. Understanding their shared group characteristics while honoring their unique qualities as individuals aids in responsible wine tourism development that provides a sense of well-being for the communities and stakeholders involved. Semi-structured interviews and lived experience methodologies were used in locations that were associated with wine food tourism operations. By understanding management perspectives, it lends insight into sustainable destination management and wine tourism product development, furthering our progress toward ethical, responsible, and financially feasible operations. This research also represents a collaborative effort between Russia and the United States that supports an agenda of sustainable destination development and management. As a global community, we need to continue to investigate stakeholder perceptions and strategic management techniques that best support the pillars upon which responsible tourism was founded.

Keywords: wine tourism, tourism development, Russia, rural tourism

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Luedech Girdwichai, Suwaree Yordchim, Phusit Phukamchanoad

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This research is aimed to study about motivation that students of Suan Sunandha Rajabhat University follow and happily live according to Sufficiency Economy Philosophy. Having collected 394 questionnaires, the result showed that most students had great motivation to follow this philosophy in high level, especially in terms of righteousness in profession; besides, students’determination and intention to apply this philosophy in everyday lives were so impressive though students’ families were not ready enough. Each of students, in fact, consulted families for plans of any activities without tiredness and discouragement based on the saying” Where there is a will, there is a way.” On the part of universities lives, students interacted with society and created projects that supported income to the community including exercises, sports, recreational activities, and community services.

Keywords: motivation, livelihood, sufficiency economy philosophy, undergraduate students of Suan Sunandha Rajabhat University

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Authors: R. Ramesh, K. K. Shivaraman

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The main aim of this paper is to handle the energy requirement in an efficient manner by merging the advanced digital communication and control technologies for smart grid applications. In order to reduce user home load during peak load hours, utility applies several incentives such as real-time pricing, time of use, demand response for residential customer through smart meter. However, this method provides inconvenience in the sense that user needs to respond manually to prices that vary in real time. To overcome these inconvenience, this paper proposes a convolutional neural network (CNN) with k-means clustering machine learning model which have ability to forecast energy requirement in short term, i.e., hour of the day or day of the week. By integrating our proposed technique with home energy management based on Bluetooth low energy provides predicted value to user for scheduling appliance in advanced. This paper describes detail about CNN configuration and k-means clustering algorithm for short-term energy forecasting.

Keywords: convolutional neural network, fuzzy logic, k-means clustering approach, smart home energy management

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Authors: Prima Fithri, Afri Adnan, Verra Syahmer

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Library as a public service should be able to provide excellent and quality service. The criteria that should be available in the library is having the collection which relevant, actual and reliable, qualified and professional employee, delivery system that prompt and appropriate as well as supported by proper infrastructure. The aim of this study is to show the performance as an effort to provide quality of services that appropriate with the needs and desires of user. Then, in this research has been carried out the calculation of the gap between the perceptions and expectations of user about the services of the library. The Sevqual and QFD methods are used in this study. Servqual method for measuring the value of the gap that occurs in the dimensions of service quality and QFD method for determine priority repairment that need to be done to improve the quality of services that occur in the dimensions of service quality. From 97 questionaires, shows that value of the gap that occurs in the dimensions of service quality using by Servqual is 27.7% dimensions of responsiveness. It show how much user expectations are not met by the quality of existing services. Construction of the library and standard library becomes priority improvements that need to be done to improve the quality of service that occurs in the dimensions of service quality using the QFD.

Keywords: library, service quality, service quality, QFD

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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The interpretation of medical images benefits from anatomical and physiological priors to optimize computer- aided diagnosis applications. Segmentation of liver and liver lesion is regarded as a major primary step in computer aided diagnosis of liver diseases. Precise liver segmentation in abdominal CT images is one of the most important steps for the computer-aided diagnosis of liver pathology. In this papers, a semi- automated method for medical image data is presented for the liver and liver lesion segmentation data using mathematical morphology. Our algorithm is currency in two parts. In the first, we seek to determine the region of interest by applying the morphological filters to extract the liver. The second step consists to detect the liver lesion. In this task; we proposed a new method developed for the semi-automatic segmentation of the liver and hepatic lesions. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to improve the quality of the original image and image gradient by applying the spatial filter followed by the morphological filters. The second step consists to calculate the internal and external markers of the liver and hepatic lesions. Thereafter we proceed to the liver and hepatic lesions segmentation by the watershed transform controlled by markers. The validation of the developed algorithm is done using several images. Obtained results show the good performances of our proposed algorithm

Keywords: anisotropic diffusion filter, CT images, hepatic lesion segmentation, Liver segmentation, morphological filter, the watershed algorithm

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Authors: Nioofar Margarite Rouhani

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Using a case study methodology, this study explores the lived experiences of elite women footballers (soccer) team in the United Arab Emirates (UAE) and the challenges and enablers women in this country encountered in their journey to competing at an international level. Through a series of face-to-face interviews with members of the first all-Emirati (people with UAE passport) women’s football team, members of the team’s coaching staff and key policymakers, the study sought to explore the social and cultural conditions that enabled the emergence of this team. A key aim of the study was to analyze the cultural shifts that have seemingly facilitated changes to gender relations in the UAE and to highlight possibilities for future gender equality work. The study explores the significance of sport in UAE society and its role in disrupting traditional gender boundaries. To do this, the study identifies and analyses contemporary social (religion, class, and culture) conditions that facilitate, and/or restrict, women’s sports participation in the public sphere of sport. Drawing on a feminist poststructural framework the study sought to analyse the discursive enactment of (disruptive) gender identity positions, using lenses such as ‘discourse’ and ‘power’. With a particular focus on elite women’s sport, the study sought to build knowledge around the advance of female participation in what has long been considered as a masculine domain. Here, the study sought to explore the lived experience of social change through a series of face-to-face interviews with members of the first all-Emirati- women’s football team and key support personnel. To maintain representational integrity, the principles of narrative methodology were employed for their ability to privilege the voices of participants while integrating contextual forces that comprised the stories they told about their experiences and the key people who participated in them. This approach supported a key aim of the study, being to analyse the cultural shifts that have supported changes in gender performance in the UAE and to highlight possibilities for future gender disruption. While the results of the study convey a growing sense of opportunity for aspiring sportswomen in the UAE, they also reveal that the participant pathways were full of contestation and restriction. What we learn from the stories of the first Emirate women’s football team is that where the will is strong enough, there can be a way. While it is reasonable to assume that such pathways will become easier in the future, as the participation of women in such sporting arenas becomes less exceptional, there are factors that are likely to enable and disable such journeys. Prominent here is the presence of a ‘powerful’ guardian and mentor who can offer sustained support, and influence. In a society where males continue to have disproportionate access to social and domestic power, such support can be extremely influential. Guardians and mentors can play a crucial role in garnering the support of dominant male figures, or helping to find ways to work around it.

Keywords: gender equality, women, sport, Middle East

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Authors: Qi Wang

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Chinese has become a powerful worldwide language and millions of learners are studying it all over the words. Chinese is a tone language with unique meaningful characters, which makes foreign learners master it with more difficulties. On the other hand, as each foreign language, the learners of Chinese first will learn the basic Chinese Sound Structure (the initials and finals, tones, Neutral Tone and Tone Sandhi). It’s quite common that in the following studies, teachers made a lot of efforts on drilling and error correcting, in order to help students to pronounce correctly, but ignored the training of suprasegmental features (e.g. stress, intonation). This paper analysed the oral data based on our graduation students (two-year program) from 2006-2013, presents the intonation pattern of our graduates to speak Chinese as second language -high and plain with heavy accents, without lexical stress, appropriate stop endings and intonation, which led to the misunderstanding in different real contexts of communications and the international official Chinese test, e.g. HSK (Chinese Proficiency Test), HSKK (HSK Speaking Test). This paper also demonstrated how the Chinese to use the suprasegmental features strategically in different functions and moods (declarative, interrogative, imperative, exclamatory and rhetorical intonations) in order to train the learners to achieve better Communicative Performance.

Keywords: second language learning, suprasegmental, communication, HSK (Chinese Proficiency Test)

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Authors: Haikal Achmad Thaha, Pujo Laksono, Dhamma Nibbana Putra

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Customer service is one of the core interest in a service sector of a company, whether as the core business or as service part of the operation. Most of the time, the people and the previous research in service industry is focused on finding the best business model solution for the service sector, usually to decide between total in house customer service, outsourcing, or something in between. Conventionally, to take this decision is some important part of the management job, and this is a process that usually takes some time and staff effort, meanwhile market condition and overall company needs may change and cause loss of income and temporary disturbance in the companies operation . However, in this paper we have offer a new concept model to assist decision making process in service industry. This model will featured information platform as central tool to integrate service industry operation. The result is service information model which would ideally increase response time and effectivity of the decision making. it will also help service industry in switching the service solution system quickly through machine learning when the companies growth and the service solution needed are changing.

Keywords: service industry, customer service, machine learning, decision making, information platform

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Authors: Zaoui Ismahene, Bahri Sidi Mohamed, Abbassa Nadira

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Diabetic Retinopathy (DR) is a leading cause of vision impairment and blindness among individuals with diabetes. Early diagnosis is crucial for effective treatment, yet current diagnostic methods rely heavily on manual analysis of retinal images, which can be time-consuming and prone to subjectivity. This research proposes an automated system for the detection of DR using Jacobi wavelet-based feature extraction combined with Support Vector Machines (SVM) for classification. The integration of wavelet analysis with machine learning techniques aims to improve the accuracy, efficiency, and reliability of DR diagnosis. In this study, retinal images are preprocessed through normalization, resizing, and noise reduction to enhance the quality of the images. The Jacobi wavelet transform is then applied to extract both global and local features, effectively capturing subtle variations in retinal images that are indicative of DR. These extracted features are fed into an SVM classifier, known for its robustness in handling high-dimensional data and its ability to achieve high classification accuracy. The SVM classifier is optimized using parameter tuning to improve performance. The proposed methodology is evaluated using a comprehensive dataset of retinal images, encompassing a range of DR severity levels. The results show that the proposed system outperforms traditional wavelet-based methods, demonstrating significantly higher accuracy, sensitivity, and specificity in detecting DR. By leveraging the discriminative power of Jacobi wavelet features and the robustness of SVM, the system provides a promising solution for the automatic detection of DR, which could assist ophthalmologists in early diagnosis and intervention, ultimately improving patient outcomes. This research highlights the potential of combining wavelet-based image processing with machine learning for advancing automated medical diagnostics.

Keywords: iabetic retinopathy (DR), Jacobi wavelets, machine learning, feature extraction, classification

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Authors: Ayu Nabila Kusuma Pradana, Aprinaldi Jasa Mantau, Tomohiko Akahoshi

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The incidence of Disseminated intravascular coagulation (DIC) following gastrointestinal surgery has a poor prognosis. Therefore, it is important to determine the factors that can predict the prognosis of DIC. This study will investigate the factors that may influence the outcome of DIC in patients after gastrointestinal surgery. Eighty-one patients were admitted to the intensive care unit after gastrointestinal surgery in Kyushu University Hospital from 2003 to 2021. Acute DIC scores were estimated using the new Japanese Association for Acute Medicine (JAAM) score from before and after surgery from day 1, day 3, and day 7. Acute DIC scores will be compared with The Sequential Organ Failure Assessment (SOFA) score, platelet count, lactate level, and a variety of biochemical parameters. This study applied machine learning algorithms to predict the prognosis of DIC after gastrointestinal surgery. The results of this study are expected to be used as an indicator for evaluating patient prognosis so that it can increase life expectancy and reduce mortality from cases of DIC patients after gastrointestinal surgery.

Keywords: the survival rate, gastrointestinal surgery, JAAM score, neural network, machine learning, disseminated intravascular coagulation (DIC)

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Authors: W. J. Strydom, K. Puren

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Place-making is viewed here as an empowering process in which people represent, improve and maintain their spatial (natural or built) environment. With the above-mentioned in mind, place-making is multi-dimensional and include a spatial dimension (including visual properties or the end product/plan), a procedural dimension during which (negotiation/discussion of ideas with all relevant stakeholders in terms of end product/plan) and a psychological dimension (inclusion of intrinsic values and meanings related to a place in the end product/plan). These three represent dimensions of place-making. The purpose of this paper is to explore these dimensions of place-making in a case study of a local community in Ikageng, Potchefstroom, North-West Province, South Africa. This case study represents an inclusive process that strives to empower a local community (forcefully relocated due to Apartheid legislation in South Africa). This case study focussed on the inclusion of participants in the decision-making process regarding their daily environment. By means of focus group discussions and a collaborative design workshop, data is generated and ultimately creates a linkage with the theoretical dimensions of place-making. This paper contributes to the field of spatial planning due to the exploration of the dimensions of place-making and the relevancy of this process on spatial planning (especially in a South African setting).

Keywords: community engagement, place-making, planning theory, spatial planning

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Authors: Mehrnoush Tajnia, Simin Sadeghi-Saeb

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Students and teachers have different perceptions of effectiveness of instruction. Comparing students’ and teachers’ beliefs and finding the mismatches between them can increase L2 students’ satisfaction. Few studies have taken into account the beliefs of both students and teachers on different aspects of pedagogy and the effect of learners’ level of education and contexts on effective foreign language teacher practices. Therefore, the present study was conducted to compare students’ and teachers’ perceptions on effective foreign language teaching. A sample of 303 learners and 54 instructors from different private language institutes and universities participated in the study. A questionnaire was developed to elicit participants’ beliefs on effective foreign language teaching and learning. The analysis of the results revealed that: a) there is significant difference between the students’ beliefs about effective teacher practices and teachers’ belief, b) Class level influences students’ perception of effective foreign language teacher, d) There is a significant difference of opinion between those learners who study foreign languages at university and those who study foreign language in private institutes with respect to effective teacher practices. The present paper concludes that finding the gap between students’ and teachers’ beliefs would help both of the groups to enhance their learning and teaching.

Keywords: effective teacher, effective teaching, students’ beliefs, teachers’ beliefs

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Authors: Talita Veldsman, Elzette Fritz

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Many children attending special education present with sensory integration difficulties that hamper their learning and behaviour. These learners can benefit from therapeutic interventions as part of their classroom curriculum that can address sensory development and allow for holistic development to take place. A research study was conducted by utilizing socio-drama as a therapeutic intervention in the classroom in order to develop sensory integration skills. The use of socio-drama as therapeutic intervention proved to be a successful multi-disciplinary approach where education and psychology could build a bridge of growth and integration. The paper describes how socio-drama was used in the classroom and how these sessions were designed. The research followed a qualitative approach and involved six Afrikaans-speaking children attending special secondary school in the age group 12-14 years. Data collection included observations during the session, reflective art journals, semi-structured interviews with the teacher and informal interviews with the adolescents. The analysis found improved self-confidence, better social relationships, sensory awareness and self-regulation in the participants after a period of a year.

Keywords: education, sensory integration, sociodrama, classroom intervention, psychology

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Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

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Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

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Authors: Muhammad Maisam Abbas, Salman Tariq, Usama Riaz, Muhammad Tanveer, Humaira Abdul Ghafoor

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Autism Spectrum Disorder (ASD) refers to a developmental disorder that impairs an individual's communication and interaction ability. Individuals feel difficult to read facial expressions while communicating or interacting. Facial Expression Recognition (FER) is a unique method of classifying basic human expressions, i.e., happiness, fear, surprise, sadness, disgust, neutral, and anger through static and dynamic sources. This paper conducts a comprehensive comparison and proposed optimal method for a continued research project—a system that can assist people who have Autism Spectrum Disorder (ASD) in recognizing facial expressions. Comparison has been conducted on three supervised learning algorithms EigenFace, FisherFace, and LBPH. The JAFFE, CK+, and TFEID (I&II) datasets have been used to train and test the algorithms. The results were then evaluated based on variance, standard deviation, and accuracy. The experiments showed that FisherFace has the highest accuracy for all datasets and is considered the best algorithm to be implemented in our system.

Keywords: autism spectrum disorder, ASD, EigenFace, facial expression recognition, FisherFace, local binary pattern histogram, LBPH

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Authors: Teja Koduru

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Untreated Lyme disease can lead to neurological, cardiac, and dermatological complications. Rapid diagnosis of the erythema migrans (EM) rash, a characteristic symptom of Lyme disease is therefore crucial to early diagnosis and treatment. In this study, we aim to utilize deep learning frameworks including Tensorflow and Keras to create deep convolutional neural networks (DCNN) to detect images of acute Lyme Disease from images of erythema migrans. This study uses a custom database of erythema migrans images of varying quality to train a DCNN capable of classifying images of EM rashes vs. non-EM rashes. Images from publicly available sources were mined to create an initial database. Machine-based removal of duplicate images was then performed, followed by a thorough examination of all images by a clinician. The resulting database was combined with images of confounding rashes and regular skin, resulting in a total of 683 images. This database was then used to create a DCNN with an accuracy of 93% when classifying images of rashes as EM vs. non EM. Finally, this model was converted into a web and mobile application to allow for rapid diagnosis of EM rashes by both patients and clinicians. This tool could be used for patient prescreening prior to treatment and lead to a lower mortality rate from Lyme disease.

Keywords: Lyme, untreated Lyme, erythema migrans rash, EM rash

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Authors: Yohanes Atan

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This article reinterprets global history by examining the cultural and historical connections between the Southeast Asian legend of Puteri Gunung Ledang and the British monarchy. It hypothesizes that world civilization originated in Southeast Asia following the dispersion of Adam and Eve, emphasizing the Sunda Shelf as a cradle of human development. Through a document analysis of historical records, this study explores the legend of Puteri Gunung Ledang and her speculative westward journey, proposing a genealogical link to the British royal lineage. Additionally, it draws a parallel between the Malay song Asmaradana and the British royal anthem God Save the Queen/King. The findings situate Southeast Asia as a central player in global civilizational narratives, supported by scholarly evidence from Southeast Asian and European sources.

Keywords: Puteri Gunung Ledang, Asian history, British monarch, maritime connectivity

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Authors: Kati Skarp, Keijo Varis, Juha Kettunen

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The purpose of this paper is to examine and evaluate the top-down and bottom-up leadership development programs focused on human capital that improve the performance of a company. This study reports on the external top-down leadership development program supported by a consulting company and the internal participatory action research of the bottom-up program. The sickness rate and the lost time incident failure rate decreased and the ideas produced for cost savings improved, leading to increased earnings during the top-down program. The estimated cost savings potential of the bottom-up program was 3.8 million euro based on the cost savings of meeting habits, maintenance practices and the way of working in production. The results of this study are useful for those who plan and evaluate leadership development and human capital productivity consultation programs to improve the performance of a company.

Keywords: leadership, development, human resources, company, indicators, evaluation

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Authors: Shanshan Zhou, Massimo Battaglia

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Identity plays a critical role when an organization faces disasters. Individuals reflect on their working identities and identify themselves with the group and the organization, which facilitate collective sensemaking under crisis situations and enable coordinated actions to respond to and recover from disasters. In addition, an organization’s identity links it to its regional community, which fosters the mobilization of resources and contributes to rapid recovery. However, identity is also problematic for disaster prevention because of its persistence. An organization’s ego-defenses system prohibits the rethink of its identity and a rigid identity obstructs disaster prevention. This research aims to tackle the ‘problem’ of identity by study in-depth a case of an Italian multi–utility which experienced the 2012 Northern Italy earthquakes. Collecting data from 11 interviews with top managers and key players in the local community and archived materials, we find that the earthquakes triggered the rethink of the organization’s identity, which got reinforced afterward. This research highlighted the importance of identity in disaster response and recovery. More importantly, it explored the solution of overcoming the barrier of ego-defense that is to transform the organization into a learning organization which constantly rethinks its identity.

Keywords: community identity, disaster, identity, organizational learning

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Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.

Keywords: machine learning, audiograms, data imputations, single imputations

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Authors: Mark Williams

Abstract:

The digital revolution has radically transformed our social environment creating vast opportunities for interconnectivity and social interaction. This revolution, however, has also changed the reach and impact of hate crime, with social media providing a new platform to victimize and harass users in their homes. In this way, developments in the information and communication technologies have exacerbated and facilitated the commission of hate crime, increasing its prevalence and impact. Unfortunately, legislators, policymakers and criminal justice professionals have struggled to keep pace with these technological developments, reducing their ability to intervene in, regulate and govern the commission of hate crimes online. This work is further complicated by the global nature of this crime due to the tendency for offenders and victims to reside in multiple different jurisdictions, as well as the need for criminal justice professionals to obtain the cooperation of private companies to access information required for prosecution. Drawing on in-depth interviews with key criminal justice professionals and policymakers with detailed knowledge in this area, this paper examines the specific challenges the police and prosecution services face as they attempt to intervene in and prosecute the commission of hate crimes online. It is argued that any attempt to reduce online othering, such as the commission of hate crimes online, must be multifaceted, collaborative and involve both innovative technological solutions as well as internationally agreed ethical and legal frameworks.

Keywords: cybercrime, digital policing, hate crime, social media

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Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

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Authors: Elena Filova, Ondrej Kaplan, Marie Markova, Helena Dragounova, Roman Matejka, Eduard Brynda, Lucie Bacakova

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Decellularized vessels have been evaluated as small-diameter vascular prostheses. Reseeding autologous cells onto decellularized tissue prior implantation should prolong prostheses function and make them living tissues. Suitable cell types for reseeding are both endothelial cells and bone marrow-derived stem cells, with a capacity for differentiation into smooth muscle cells upon mechanical loading. Endothelial cells assure antithrombogenicity of the vessels and MSCs produce growth factors and, after their differentiation into smooth muscle cells, they are contractile and produce extracellular matrix proteins as well. Fibrin is a natural scaffold, which allows direct cell adhesion based on integrin receptors. It can be prepared autologous. Fibrin can be modified with bound growth factors, such as basic fibroblast growth factor (FGF-2) and vascular endothelial growth factor (VEGF). These modifications in turn make the scaffold more attractive for cells ingrowth into the biological scaffold. The aim of the study was to prepare thin surface-attached fibrin assemblies with bound FGF-2 and VEGF, and to evaluate growth and differentiation of bone marrow-derived mesenchymal stem cells on the fibrin (Fb) assemblies. Following thin surface-attached fibrin assemblies were prepared: Fb, Fb+VEGF, Fb+FGF2, Fb+heparin, Fb+heparin+VEGF, Fb+heparin+FGF2, Fb+heparin+FGF2+VEGF. Cell culture poly-styrene and glass coverslips were used as controls. Human MSCs (passage 3) were seeded at the density of 8800 cells/1.5 mL alpha-MEM medium with 2.5% FS and 200 U/mL aprotinin per well of a 24-well cell culture. The cells have been cultured on the samples for 6 days. Cell densities on day 1, 3, and 6 were analyzed after staining with LIVE/DEAD cytotoxicity/viability assay kit. The differentiation of MSCs is being analyzed using qPCR. On day 1, the highest density of MSCs was observed on Fb+VEGF and Fb+FGF2. On days 3 and 6, there were similar densities on all samples. On day 1, cell morphology was polygonal and spread on all sample. On day 3 and 6, MSCs growing on Fb assemblies with FGF2 became apparently elongated. The evaluation of expression of genes for von Willebrand factor and CD31 (endothelial cells), for alpha-actin (smooth muscle cells), and for alkaline phosphatase (osteoblasts) is in progress. We prepared fibrin assemblies with bound VEGF and FGF-2 that supported attachment and growth of mesenchymal stem cells. The layers are promising for improving the ingrowth of MSCs into the biological scaffold. Supported by the Technology Agency of the Czech Republic TA04011345, and Ministry of Health NT11270-4/2010, and BIOCEV – Biotechnology and Biomedicine Centre of the Academy of Sciences and Charles University” project (CZ.1.05/1.1.00/02.0109), funded by the European Regional Development Fund for their financial supports.

Keywords: fibrin assemblies, FGF-2, mesenchymal stem cells, VEGF

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