Search results for: online teaching and learning

Authors: Bandar Alahmadi, Lethia Jackson

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Machine Learning model is used today in many real-life applications. The safety and security of such model is important, so the results of the model are as accurate as possible. One challenge of machine learning model security is the adversarial examples attack. Adversarial examples are designed by the attacker to cause the machine learning model to misclassify the input. We propose a method to generate adversarial examples to attack image classifiers. We are modifying the successfully classified images, so a classifier misclassifies them after the modification. In our method, we do not update the whole image, but instead we detect the important region, modify it, place it back to the original image, and then run it through a classifier. The algorithm modifies the detected region using two methods. First, it will add abstract image matrix on back of the detected image matrix. Then, it will perform a rotation attack to rotate the detected region around its axes, and embed the trace of image in image background. Finally, the attacked region is placed in its original position, from where it was removed, and a smoothing filter is applied to smooth the background with foreground. We test our method in cascade classifier, and the algorithm is efficient, the classifier confident has dropped to almost zero. We also try it in CNN (Convolutional neural network) with higher setting and the algorithm was successfully worked.

Keywords: adversarial examples, attack, computer vision, image processing

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Authors: Sajedeh Salehi, Marco Giacalone

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This paper examines the role of existing online dispute resolution (ODR) mechanisms and their effects on ameliorating access to justice – as a protected right by Art. 47 of the EU Charter of Fundamental Rights – for consumers in EU. The major focus of this study will be on evaluating ODR as the means of dispute resolution for Business-to-Consumer (B2C) cross-border small claims raised in e-commerce transactions. The authors will elaborate the consequences of implementing ODR methods in the context of recent developments in EU regulatory safeguards on promoting consumer protection. In this analysis, both non-judiciary and judiciary ODR redress mechanisms are considered, however, the significant consideration is given to – obligatory and non-obligatory – judiciary ODR methods. For that purpose, this paper will particularly investigate the impact of the EU ODR platform as well as the European Small Claims Procedure (ESCP) Regulation 861/2007 and their role on accelerating the access to justice for consumers in B2C e-disputes. Although, considerable volume of research has been carried out on ODR for consumer claims, rather less (or no-) attention has been paid to provide a combined doctrinal and empirical evaluation of ODR’s potential in resolving cross-border small e-disputes, in EU. Hence, the methodological approach taken in this study is a mixed methodology based on qualitative (interviews) and quantitative (surveys) research methods which will be mainly based on the data acquired through the findings of the Small Claims Analysis Net (SCAN) project. This project contributes towards examining the ESCP Regulation implementation and efficiency in providing consumers with a legal watershed through using the ODR for their transnational small claims. The outcomes of this research may benefit both academia and policymakers at national and international level.

Keywords: access to justice, consumers, e-commerce, small e-Disputes

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Authors: Julian Thrash, Douglas Folk, Michael Ciracy, Audrey C. Tseng, Kristen M. Stromsodt, Amber Younggren, Christopher Maciolek

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The cardiopulmonary system is comprised of the heart, lungs, and many dynamic feedback mechanisms that control its function based on a multitude of variables. The next generation of cardiopulmonary medical devices will involve adaptive control and smart pacing techniques. However, testing these smart devices on living systems may be unethical and exceedingly expensive. As a solution, a comprehensive computational model of the cardiopulmonary system was implemented in Simulink. The model contains over 240 state variables and over 100 equations previously described in a series of published articles. Simulink was chosen because of its ease of introducing machine learning elements. Initial results indicate that physiologically correct waveforms of pressures and volumes were obtained in the simulation. With the development of a comprehensive computational model, we hope to pioneer the future of predictive medicine by applying our research towards the initial stages of smart devices. After validation, we will introduce and train reinforcement learning agents using the cardiopulmonary model to assist in adaptive control system design. With our cardiopulmonary model, we will accelerate the design and testing of smart and adaptive medical devices to better serve those with cardiovascular disease.

Keywords: adaptive control, cardiopulmonary, computational model, machine learning, predictive medicine

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Authors: Amin Nezarat, Naeime Seifadini

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Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

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Authors: Mahmoud Almasri, Xavier Marjou, Fanny Parzysz

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The demand for services provided by Unmanned Aerial Vehicles (UAVs) is increasing pervasively across several sectors including potential public safety, economic, and delivery services. As the number of applications using UAVs grows rapidly, more and more powerful, quality of service, and power efficient computing units are necessary. Recently, cellular technology draws more attention to connectivity that can ensure reliable and flexible communications services for UAVs. In cellular technology, flying with a high speed and altitude is subject to several key challenges, such as frequent handovers (HOs), high interference levels, connectivity coverage holes, etc. Additional HOs may lead to “ping-pong” between the UAVs and the serving cells resulting in a decrease of the quality of service and energy consumption. In order to optimize the number of HOs, we develop in this paper a Q-learning-based algorithm. While existing works focus on adjusting the number of HOs in a static network topology, we take into account the impact of cells deployment for three different simulation scenarios (Rural, Semi-rural and Urban areas). We also consider the impact of the decision distance, where the drone has the choice to make a switching decision on the number of HOs. Our results show that a Q-learning-based algorithm allows to significantly reduce the average number of HOs compared to a baseline case where the drone always selects the cell with the highest received signal. Moreover, we also propose which hyper-parameters have the largest impact on the number of HOs in the three tested environments, i.e. Rural, Semi-rural, or Urban.

Keywords: drones connectivity, reinforcement learning, handovers optimization, decision distance

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Authors: Argyro Maria Skourmalla

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Internationalization and management of linguistic diversity in Higher Education (HE) have gained much attention in research in the last few years. Internationalization policies in HE aims at promoting the dual role of Higher Education Institutions (HEIs), civilization and competitiveness. In the context of the European Union, the European Education Area initiative aims at “inclusive national education and training systems” through networking and exchange between HEIs. However, the use of English as a ‘lingua academica’ in the place of the official, national, and regional/minority languages raises questions regarding linguistic diversity, linguistic rights and concerns that have to do with the scientific weakening of these languages. In fact, the European Civil Society Platform for Multilingualism, in the Declaration for Multilingualism in Higher Education, draws attention to the use of English at the expense of other regional/national languages and the impact of English-only language policy on an epistemological level. The above issues were brought up during semi-structured interviews with lecturing staff coming from three multilingual Universities in Europe. Lecturers shared their experiences and the practices they use to manage linguistic diversity in these three Universities. Findings show that even though different languages are used in teaching across disciplines, English -or ‘Globish’ as mentioned during an interview- is widely used in research. Despite English being accepted as the “lingua academica,” issues regarding loss of identity come up

Keywords: higher education, internationalization, linguistic diversity, teaching, research, English

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Authors: Han Chih Feng, Yi-Cheng Hou, Jing-Huei Wu

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Diabetes is a chronic disease. Nutrition knowledge and skills enable individuals with type 2 diabetes to optimize metabolic self-management and quality of life. This research studies the effect of nutritional education on diabetes inpatients in terms of their cognition, behavior, and attitude. The participants are inpatients diagnosed with diabetes at Taipei Tzu Chi Hospital. A total of 103 participants, 58 male, and 45 females, enrolled in the research between January 2018 and July 2018. The research evaluates cognition, behavior, and attitude level before and after nutritional education conducted by dietitians. The result shows significant improvement in actual consumption (2.5 ± 1.4 vs 3.8 ± 0.7; p<.001), diet control motivation (2.7 ± 0.8 vs 3.4 ± 0.6; p<.001), correct nutrition concept (1.2± 0.4 vs 2.4 ± 0.5; p<.001), learning willingness (2.7± 0.9 vs 3.4 ± 0.6; p<.001), cognitive behaviors (1.4 ± 0.5 vs 2.9 ± 0.7; p<.001). AC sugar (278.5 ± 321.5 vs 152.2 ± 49.1; p<.001) and HbA1C (10.3 ± 2.6 vs 8.6 ± 1.9; p<.001) are significant improvement after nutritional education. After nutritional education, participants oral hypoglycemic agents increased from 16 (9.2%) to 33 (19.0%), insulin decreased from 75 (43.1%) to 68 (39.1%), and hypoglycemic drugs combined with insulin decreased from 83 (47.7%) to 73 (42.0%).Further analysis shows that female inpatients have significant improvement in diet control motivation (3.91 ± 0.85 vs 4.44 ± 0.59; p<0.000), correct nutrition concept (3.24± 0.48 vs 4.47± 0.51; p<0.000), learning willingness (3.89 ± 0.86 vs 4.44 ± 0.59; p<0.000) and cognitive behaviors (2.42 ± 0.58 vs 4.02 ± 0.69; p<0.000); male inpatients have significant improvement in actual food intake (4.41± 0.92 vs 3.97 ± 0.42; p<0.000), diet control motivation (3.62 ± 0.86 vs 4.29 ± 0.62; p<0.000), correct nutrition concept (3.26 ± 0.44 vs 4.36 ± 0.49; p<0.000), learning willingness (3.72± 0.93 vs 4.33± 0.63; p<0.000) and cognitive behaviors (2.45± 0.54 vs 4.03± 0.77; p<0.000). In conclusion, nutritional education proves effective, regardless of gender, in improving an inpatient’s cognition, behavior, and attitude toward diabetes self-management.

Keywords: diabetes, nutrition education, actual consumption, diet control motivation, nutrition concept, learning willingness, cognitive behaviors

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Authors: Parvin Goli, Amirhosein Kefayat, Rezvan Goli

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Background: It is well established that parents can influence their offspring's neurodevelopment. It is shown that paternal environment and lifestyle is beneficial for the progeny's fitness and might affect their metabolic mechanisms; however, the effects of paternal exercise on the brain in the offspring have not been explored in detail. Objective: This study aims to review the impact of paternal physical exercise on memory and learning, neuroplasticity, as well as DNA methylation levels in the off-spring's hippocampus. Study design: In this systematic review and meta-analysis, an electronic literature search was conducted in databases including PubMed, Scopus, and Web of Science. Eligible studies were those with an experimental design, including an exercise intervention arm, with the assessment of any type of memory function, learning ability, or any type of brain plasticity as the outcome measures. Standardized mean difference (SMD) and 95% confidence intervals (CI) were computed as effect size. Results: The systematic review revealed the important role of environmental enrichment in the behavioral development of the next generation. Also, offspring of exercised fathers displayed higher levels of memory ability and lower level of brain-derived neurotrophic factor. A significant effect of paternal exercise on the hippocampal volume was also reported in the few available studies. Conclusion: These results suggest an intergenerational effect of paternal physical activity on cognitive benefit, which may be associated with hippocampal epigenetic programming in offspring. However, the biological mechanisms of this modulation remain to be determined.

Keywords: hippocampal plasticity, learning ability, memory, parental exercise

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Authors: S. Chami, J. Chauvin, T. Demarest, Stan Ng, M. Straus, W. Jahner

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Biometrics tools such as fingerprint and iris are widely used in industry to protect critical assets. However, their vulnerability and lack of robustness raise several worries about the protection of highly critical assets. Biometrics based on Electrocardiographic (ECG) signals is a robust identification tool. However, most of the state-of-the-art techniques have worked on clinical signals, which are of high quality and less noisy, extracted from wearable devices like a smartwatch. In this paper, we are presenting a complete machine learning pipeline that identifies people using ECG extracted from an off-person device. An off-person device is a wearable device that is not used in a medical context such as a smartwatch. In addition, one of the main challenges of ECG biometrics is the variability of the ECG of different persons and different situations. To solve this issue, we proposed two different approaches: per person classifier, and one-for-all classifier. The first approach suggests making binary classifier to distinguish one person from others. The second approach suggests a multi-classifier that distinguishes the selected set of individuals from non-selected individuals (others). The preliminary results, the binary classifier obtained a performance 90% in terms of accuracy within a balanced data. The second approach has reported a log loss of 0.05 as a multi-class score.

Keywords: biometrics, electrocardiographic, machine learning, signals processing

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Authors: Swapna Bhargavi Gantasala

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If teamwork is the key to organizational learning, productivity, and growth, then, why do some teams succeed in achieving these, while others falter at different stages? Building teams in higher education institutions has been a challenge and an open-ended constructivist approach was considered on an experimental basis for this study to address this challenge. For this research, teams of students from the MBA program were chosen to study the effect of teamwork in learning, the motivation levels among student team members, and the effect of collaboration in achieving team goals. The teams were built on shared vision and goals, cohesion was ensured, positive induction in the form of faculty mentoring was provided for each participating team and the results have been presented with conclusions and suggestions.

Keywords: teamwork, leadership, motivation and reinforcement, collaboration

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Authors: Vidhu Mitha Goutham, Ruma Mukherjee

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The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.

Keywords: attention, encoder-decoder, Fairseq, Seq2Seq, Spanish, translation

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Authors: M. A. Zaki Ewiss, S. Afifi

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Nahda University (NUB) believes that internationalisation of higher educational is able to provide global society with an education that meets current needs and that can respond efficiently to contemporary demands and challenges, which are characterized by globalisation, interdependence, and multiculturalism. In this paper, we will discuss the the challenges of the Egyptian Higher Education system and the future vision to improve this system> In this report, the following issues will be considered: Increasing knowledge on the development of specialized programs of study at the university. Developing international cooperation programs, which focus on the development of the students and staff skills, and providing academic culture and learning opportunities. Increasing the opportunities for student mobility, and research projects for faculty members. Increased opportunities for staff, faculty and students to continue to learn foreign universities, and to benefit from scholarships in various disciplines. Taking the advantage of the educational experience and modern teaching methods; Providing the opportunities to study abroad without increasing the period of time required for graduation, and through greater integration in the curricula and programs; More cultural interaction through student exchanges.Improving and providing job opportunities for graduates through participation in the global labor market. This document sets out NUB strategy to move towards that vision. We are confident that greater explicit differentiation, greater freedom and greater collaboration are the keys to delivering the further improvement in quality we shall need to retain and strengthen our position as one of the world’s leading higher education systems.

Keywords: technology transfer higher education, knowledge transfer, internationalisation, mobility

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Authors: Chasity O'Connell

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The use of social media and networking sites play a significant role in the lives of adolescents and adults. While research supports that social support and connectedness in general is beneficial; the nature of communication and interaction through social media and its subsequent benefits and impacts could be arguably different. As such, this research aims to explore a specific facet of social media interaction called fear of missing out, or 'FOMO' and investigate its relationship within the context of life stressors, social media usage, anxiety and depressive-symptoms, mindfulness, and psychological well-being. FOMO is the 'uneasy and sometimes all-consuming feeling that you’re missing out—that your peers are doing, in the know about, or in possession of more or something better than you'. Research suggests that FOMO can influence an individual’s level of engagement with friends and social media consumption, drive decisions on participating in various online or offline activities, and ultimately impact mental health. This study hopes to explore the potentially mitigating influence of mindfulness and values-based interventions in reducing the discomfort and distress that can accompany FOMO and increase the sense of psychological well-being in allowing for a more thoughtful and deliberate engagement in life. This study will include an intervention component wherein participants (comprised of university students and adults in the community) will partake in a six-week, group-based intervention focusing on learning practical mindfulness skills and values-exploration exercises (along with a waitlist control group). In doing so, researchers hope to understand if interventions centered on increasing one’s awareness of the present moment and one’s internal values impact decision-making and well-being with regard to social interaction and relationships.

Keywords: FOMO, mindfulness, values, stress, psychological well-being, intervention, distress

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Authors: Vinicius G. Goecks, Gregory M. Gremillion, William D. Nothwang

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Modern approaches to train intelligent agents rely on prolonged training sessions, high amounts of input data, and multiple interactions with the environment. This restricts the application of these learning algorithms in robotics and real-world applications, in which there is low tolerance to inadequate actions, interactions are expensive, and real-time processing and action are required. This paper addresses this issue introducing CyberSteer, a novel approach to efficiently design intrinsic reward functions based on human intention to guide deep reinforcement learning agents with no environment-dependent rewards. CyberSteer uses non-expert human operators for initial demonstration of a given task or desired behavior. The trajectories collected are used to train a behavior cloning deep neural network that asynchronously runs in the background and suggests actions to the deep reinforcement learning module. An intrinsic reward is computed based on the similarity between actions suggested and taken by the deep reinforcement learning algorithm commanding the agent. This intrinsic reward can also be reshaped through additional human demonstration or critique. This approach removes the need for environment-dependent or hand-engineered rewards while still being able to safely shape the behavior of autonomous robotic agents, in this case, based on human intention. CyberSteer is tested in a high-fidelity unmanned aerial vehicle simulation environment, the Microsoft AirSim. The simulated aerial robot performs collision avoidance through a clustered forest environment using forward-looking depth sensing and roll, pitch, and yaw references angle commands to the flight controller. This approach shows that the behavior of robotic systems can be shaped in a reduced amount of time when guided by a non-expert human, who is only aware of the high-level goals of the task. Decreasing the amount of training time required and increasing safety during training maneuvers will allow for faster deployment of intelligent robotic agents in dynamic real-world applications.

Keywords: human-robot interaction, intelligent robots, robot learning, semisupervised learning, unmanned aerial vehicles

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Authors: Abdulsamad Qadir Hussien

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The development of new technology in recent years has offered ordinary people various online digital platform tools and internet access to provide news stories, information, and subjects of public interest in the Kurdistan Region Government-Iraq (KRI). This shifting aspect has offered more chances for ordinary people to engage with other individuals on many issues in order to discuss and argue matters relating to their everyday lives. The key purpose of this research project will examine the role of citizen journalism in the increase of public awareness in the Kurdish community in the KRi; particularly, citizen journalism provides a new opportunity for ordinary people to raise their voices about problems and public matters in the KRI. The sample of this research project encompasses ordinary people who use social media platforms as sources of information and news concerning the KRI government policy. In the research project, the focus is on the ordinary people who are interacting with the blogs, posts, and footage that are produced by citizen journalism. The questionnaire was sent to more than 1,000 participants in the Kurdish community; this aspect produces statistically acceptable numbers to obtain a significant result for this research project. The sampling process is mainly based on the survey method in this study. The online questionnaire form includes many sections, which are divided into four key sections. The first section contains socio-demographic questions, including gender, age, and level of education. The research project applied the survey method in order to gather data and information surrounding the role of citizen journalism in increasing awareness of individuals in the Kurdish community. For this purpose, the researcher designed a questionnaire as the primary tool for the data collection process from ordinary people who use social media as a source of news and information. During the research project, online questionnaires were mailed in two ways – via Facebook and email – to participants in the Kurdish community, and this questionnaire looked for answers to questions from ordinary people, such as to what extent citizen journalism helps users to obtain information and news about public affairs and government policy. The research project found that citizen journalism has an essential role in increasing awareness of the Kurdish community, especially mainstream journalism has helped ordinary people to raise their voices in the KRI. Furthermore, citizen journalism carries more advantages as digital sources of news, footage, and information related to public affairs. This study provides useful tools to fore the news stories that are unreachable to professional journalists in the KRI.

Keywords: citizen journalism, public awareness, demonstration and democracy, social media news

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Authors: Geoffrey A. Wright

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In an attempt to address shortcomings revealed in international assessments and lamented in legislation, many schools are reducing or eliminating elective courses, applying the rationale that replacing "non-essential" subjects with core subjects, such as mathematics and language arts, will better position students in the global market. However, there is evidence that systematically pairing a core subject with another complementary subject may lead to greater overall learning in both subjects. In this paper, we outline the methods and preliminary findings from a study we conducted analyzing the influence learning programming has on student mathematical comprehension and ability. The purpose of this research is to demonstrate in what ways two subjects might complement each other, and to better understand the principles and conditions that encourage what we call lateral transfer, the synergistic effect that occurs when a learner studies two complementary subjects.

Keywords: programming, engineering, technology, complementary subjects

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Authors: Nathan Labiosa, Aryan Kohli

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This study introduces a hybrid machine learning model for classifying proteins, developed to address the complexities of protein sequence and structural analysis. Utilizing an architecture that combines a lightweight transformer with a concurrent neural network, the hybrid model leverages both sequential and intrinsic physical properties of proteins. Trained on a comprehensive dataset from the Research Collaboratory for Structural Bioinformatics Protein Data Bank, the model demonstrates a classification accuracy of 95%, outperforming existing methods by at least 15%. The high accuracy achieved demonstrates the potential of this approach to innovate protein classification, facilitating advancements in drug discovery and the development of personalized medicine. By enabling precise protein function prediction, the hybrid model allows for specialized strategies in therapeutic targeting and the exploration of protein dynamics in biological systems. Future work will focus on enhancing the model’s generalizability across diverse datasets and exploring the integration of more machine learning techniques to refine predictive capabilities further. The implications of this research offer potential breakthroughs in biomedical research and the broader field of protein engineering.

Keywords: amino acids, deep learning, enzymes, neural networks, protein classification, proteins, transformers

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Authors: Xiaohua Zou, Yongxin Su

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The reasonable operation of heating, ventilation, and air conditioning (HVAC) is of great significance in improving the security, stability, and economy of power system operation. However, the uncertainty of the operating environment, thermal comfort varies by users and rapid decision-making pose challenges for HVAC demand response optimization. In this regard, this paper proposes a reinforcement learning-based method for HVAC demand response optimization considering few-shot personalized thermal comfort (PTC). First, an HVAC DR optimization framework based on few-shot PTC model and DRL is designed, in which the output of few-shot PTC model is regarded as the input of DRL. Then, a few-shot PTC model that distinguishes between awake and asleep states is established, which has excellent engineering usability. Next, based on soft actor criticism, an HVAC DR optimization algorithm considering the user’s PTC is designed to deal with uncertainty and make decisions rapidly. Experiment results show that the proposed method can efficiently obtain use’s PTC temperature, reduce energy cost while ensuring user’s PTC, and achieve rapid decision-making under uncertainty.

Keywords: HVAC, few-shot personalized thermal comfort, deep reinforcement learning, demand response

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Authors: Md Rafiqul Islam, Ashir Ahmed, Anwaar Ulhaq, Abu Raihan M. Kamal, Yuan Miao, Hua Wang

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Mental health of people is equally important as of their physical health. Mental health and well-being are influenced not only by individual attributes but also by the social circumstances in which people find themselves and the environment in which they live. Like physical health, there is a number of internal and external factors such as biological, social and occupational factors that could influence the mental health of people. People living in poverty, suffering from chronic health conditions, minority groups, and those who exposed to/or displaced by war or conflict are generally more likely to develop mental health conditions. However, to authors’ best knowledge, there is dearth of knowledge on the impact of workplace (especially the highly stressed IT/Tech workplace) on the mental health of its workers. This study attempts to examine the factors influencing the mental health of tech workers. A publicly available dataset containing more than 65,000 cells and 100 attributes is examined for this purpose. Number of machine learning techniques such as ‘Decision Tree’, ‘K nearest neighbor’ ‘Support Vector Machine’ and ‘Ensemble’, are then applied to the selected dataset to draw the findings. It is anticipated that the analysis reported in this study would contribute in presenting useful insights on the attributes contributing in the mental health of tech workers using relevant machine learning techniques.

Keywords: mental disorder, diagnosis, occupational stress, IT workplace

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Authors: Valerie Porr, Sydney A. DeCaro

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TARA for borderline personality disorder (BPD), an education and advocacy organization, helps families to compassionately and effectively deal with troubling BPD behaviors. Our psychoeducational programs focus on understanding underlying neurobiological features of BPD and evidence-based methodology integrating dialectical behavior therapy (DBT) and mentalization based therapy (MBT,) clarifying the inherent misunderstanding of BPD behaviors and improving family communication. TARA4BPD conducts online surveys, workshops, and topical webinars. For over 25 years, we have collected data from BPD helpline callers. This data drew our attention to particular childhood idiosyncrasies that seem to characterize many of the children who later met the criteria for BPD. The idiosyncrasies we observed, heightened sensory sensitivity and hypervigilance, were included in Adolf Stern’s 1938 definition of “Borderline.” This aspect of BPD has not been prioritized by personality disorder researchers, presently focused on emotion processing and social cognition in BPD. Parents described sleep reversal problems in infants who, early on, seem to exhibit dysregulation in circadian rhythm. Families describe children as supersensitive to sensory sensations, such as specific sounds, heightened sense of smell, taste, textures of foods, and an inability to tolerate various fabrics textures (i.e., seams in socks). They also exhibit high sensitivity to particular words and voice tones. Many have alexithymia and dyslexia. These children are either hypo- or hypersensitive to sensory sensations, including pain. Many suffer from fibromyalgia. BPD reactions to pain have been studied (C. Schmahl) and confirm the existence of hyper and hypo-reactions to pain stimuli in people with BPD. To date, there is little or no data regarding what comprises a normative range of sensitivity in infants and children. Many parents reported that their children were tested or treated for sensory processing disorder (SPD), learning disorders, and ADHD. SPD is not included in the DSM and is treated by occupational therapists. The overwhelming anecdotal data from thousands of parents of children who later met criteria for BPD led TARA4BPD to develop a sensitivity survey to develop evidence of the possible role of early sensory perception problems as a pre-cursor to BPD, hopefully initiating new directions in BPD research. At present, the research community seems unaware of the role supersensory sensitivity might play as an early indicator of BPD. Parents' observations of childhood sensitivity obtained through family interviews and results of an extensive online survey on sensory responses across various ages of development will be presented. People with BPD suffer from a sense of isolation and otherness that often results in later interpersonal difficulties. Early identification of supersensitive children while brain circuits are developing might decrease the development of social interaction deficits such as rejection sensitivity, self-referential processes, and negative bias, hallmarks of BPD, ultimately minimizing the maladaptive methods of coping with distress that characterizes BPD. Family experiences are an untapped resource for BPD research. It is hoped that this data will give family observations the critical credibility to inform future treatment and research directions.

Keywords: alexithymia, dyslexia, hypersensitivity, sensory processing disorder

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Authors: Richa Raje, Anumol Antony

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Playing is the foremost stepping stone for childhood development. Play is an essential aspect of a child’s development and learning because it creates meaningful enduring environmental connections and increases children’s performance. The children’s proficiencies are ever varying in their course of growth. There is innovation in the activities, as it kindles the senses, surges the love for exploration, overcomes linguistic barriers and physiological development, which in turn allows them to find their own caliber, spontaneity, curiosity, cognitive skills, and creativity while learning during play. This paper aims to comprehend the learning in play which is the most essential underpinning aspect of the outdoor play area. It also assesses the trend of playgrounds design that is merely hammered with equipment's. It attempts to derive a relation between the natural environment and children’s activities and the emotions/senses that can be evoked in the process. One of the major concerns with our outdoor play is that it is limited to an area with a similar kind of equipment, thus making the play highly regimented and monotonous. This problem is often lead by the strict timetables of our education system that hardly accommodates play. Due to these reasons, the play areas remain neglected both in terms of design that allows learning and wellbeing. Poorly designed spaces fail to inspire the physical, emotional, social and psychological development of the young ones. Currently, the play space has been condensed to an enclosed playground, driveway or backyard which confines the children’s capability to leap the boundaries set for him. The paper emphasizes on study related to kids ranging from 5 to 11 years where the behaviors during their interactions in a playground are mapped and analyzed. The theory of affordance is applied to various outdoor play areas, in order to study and understand the children’s environment and how variedly they perceive and use them. A higher degree of affordance shall form the basis for designing the activities suitable in play spaces. It was observed during their play that, they choose certain spaces of interest majority being natural over other artificial equipment. The activities like rolling on the ground, jumping from a height, molding earth, hiding behind tree, etc. suggest that despite equipment they have an affinity towards nature. Therefore, we as designers need to take a cue from their behavior and practices to be able to design meaningful spaces for them, so the child gets the freedom to test their precincts.

Keywords: children, landscape design, learning environment, nature and play, outdoor play

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Authors: Michael A. Ifarajimi

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The transition from being a graduate teacher to a highly accomplished teacher has been widely portrayed in literature as challenging. Pre-service teachers are troubled with complex issues such as implementing, assessment, meeting prescribed learning outcomes, taking risks, supporting eco sustainability, etc. This list is not exhaustive as they are further complicated when the concerns extend beyond the classroom into the broader school setting and community. Meanwhile, the pre-service teacher education programme as is currently run in Nigeria, cannot adequately prepare newly trained teachers for the realities of classroom teaching. And there appears to be no formal structure in place for mentoring such teachers by the more seasoned teachers in schools. The central research question of the study, therefore, is which institutional framework can be distinguished for enactment in mentoring practices in teacher education? The study was conducted in five colleges of education in South-West Nigeria, and a sample of 1000 pre-service teachers on their final year practicum was randomly selected from the colleges of education. A pre-service teacher mentorship programme (PTMP) framework was designed and implemented, with a focus on the impact of transformative inquiry on the pre-service teacher support system. The study discovered a significant impact of mentoring on pre-service teacher’s professional transformation. The study concluded that institutionalizing mentorship through transformative inquiry is a means to sustainable teacher education, professional growth, and effective classroom practice. The study recommended that the government should enact policies that will promote mentoring in teacher education and establish a framework for the implementation of mentoring practices in the colleges of education in Nigeria.

Keywords: institutionalization, mentoring, pre-service teachers teacher education, transformative inquiry

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Authors: Ann P. Daunic, Nancy Corbett

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Background and Significance: Researchers emphasize the role that motivation, self-esteem, and self-regulation play in children’s early adjustment to the school culture, including skills such as identifying their own feelings and understanding the feelings of others. As social-emotional growth, academic learning, and successful integration within culture and society are inextricably connected, the Social-Emotional Learning Foundations (SELF) curriculum was designed to integrate social-emotional learning (SEL) instruction within early literacy instruction (specifically, reading) for Kindergarten and first-grade students at risk for emotional and behavioral difficulties. Storybook reading is a typically occurring activity in the primary grades; thus SELF provides an intervention that is both theoretically and practically sound. Methodology: The researchers will report on findings from the first two years of a three-year study funded by the US Department of Education’s Institute of Education Sciences to evaluate the effects of the SELF curriculum versus “business as usual” (BAU). SELF promotes the development of self-regulation by incorporating instructional strategies that support children’s use of SEL related vocabulary, self-talk, and critical thinking. The curriculum consists of a carefully coordinated set of materials and pedagogy designed specifically for primary grade children at early risk for emotional and behavioral difficulties. SELF lessons (approximately 50 at each grade level) are organized around 17 SEL topics within five critical competencies. SELF combines whole-group (the first in each topic) and small-group lessons (the 2nd and 3rd in each topic) to maximize opportunities for teacher modeling and language interactions. The researchers hypothesize that SELF offers a feasible and substantial opportunity within the classroom setting to provide a small-group social-emotional learning intervention integrated with K-1 literacy-related instruction. Participating target students (N = 876) were identified by their teachers as potentially at risk for emotional or behavioral issues. These students were selected from 122 Kindergarten and 100 first grade classrooms across diverse school districts in a southern state in the US. To measure the effectiveness of the SELF intervention, the researchers asked teachers to complete assessments related to social-emotional learning and adjustment to the school culture. A social-emotional learning related vocabulary assessment was administered directly to target students receiving small-group instruction. Data were analyzed using a 3-level MANOVA model with full information maximum likelihood to estimate coefficients and test hypotheses. Major Findings: SELF had significant positive effects on vocabulary, knowledge, and skills associated with social-emotional competencies, as evidenced by results from the measures administered. Effect sizes ranged from 0.41 for group (SELF vs. BAU) differences in vocabulary development to 0.68 for group differences in SEL related knowledge. Conclusion: Findings from two years of data collection indicate that SELF improved outcomes related to social-emotional learning and adjustment to the school culture. This study thus supports the integration of SEL with literacy instruction as a feasible and effective strategy to improve outcomes for K-1 students at risk for emotional and behavioral difficulties.

Keywords: Socio-cultural context for learning, social-emotional learning, social skills, vocabulary development

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Authors: Luanna B. Prevost, Kelli Carter, Margaurete Romero, Kirsti Martinez

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Writing is an essential scientific practice, yet, in several countries, the increasing university science class-size limits the use of written assessments. Written assessments allow students to demonstrate their learning in their own words and permit the faculty to evaluate students’ understanding. However, the time and resources required to grade written assessments prohibit their use in large-enrollment science courses. This study examined the use of machine learning algorithms to automatically analyze student writing and provide timely feedback to the faculty about students' writing in biology. Written responses to questions about matter and energy transformation were collected from large-enrollment undergraduate introductory biology classrooms. Responses were analyzed using the LightSide text mining and classification software. Cohen’s Kappa was used to measure agreement between the LightSide models and human raters. Predictive models achieved agreement with human coding of 0.7 Cohen’s Kappa or greater. Models captured that when writing about matter-energy transformation at the ecosystem level, students focused on primarily on the concepts of heat loss, recycling of matter, and conservation of matter and energy. Models were also produced to capture writing about processes such as decomposition and biochemical cycling. The models created in this study can be used to provide automatic feedback about students understanding of these concepts to biology faculty who desire to use formative written assessments in larger enrollment biology classes, but do not have the time or personnel for manual grading.

Keywords: machine learning, written assessment, biology education, text mining

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Authors: Ahmed Krobba, Meriem Touzene, Mohamed Debeyche

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The wireless communications system (5G) will provide more diverse applications and higher quality services for users compared to the long-term evolution 4G (LTE). 5G uses a higher carrier frequency, which suffers from information loss in 5G coverage. Most 5G users often cannot obtain high-quality communications due to transmission channel noise and channel complexity. Physical Uplink Control Channel (PUCCH-NR: Physical Uplink Control Channel New Radio) plays a crucial role in 5G NR telecommunication technology, which is mainly used to transmit link control information uplink (UCI: Uplink Control Information. This study based of evaluating the performance of channel physical uplink control PUCCH-NR under low Signal-to-Noise Ratios with various antenna numbers reception. We propose the artificial intelligence approach based on deep neural networks (Deep Learning) to estimate the PUCCH-NR channel in comparison with this approach with different conventional methods such as least-square (LS) and minimum-mean-square-error (MMSE). To evaluate the channel performance we use the block error rate (BLER) as an evaluation criterion of the communication system. The results show that the deep neural networks method gives best performance compared with MMSE and LS

Keywords: 5G network, uplink (Uplink), PUCCH channel, NR-PUCCH channel, deep learning

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Authors: Ida Zubaidah

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With the advancement of information and communication technologies, in many cases, geographical distance is no longer considered as a main barrier in distance education. Geographical distance, even from a continent to another, between students and their instructor or students and their campus can be connected by the Internet, telephone or any other means of communication technology. Managing distance learning in an archipelagic country like Indonesia, however, has some different stories. Comprising more than 17,000 islands and 6.000 of them inhabited, Indonesia is considered as one of the most archipelagic countries in the world. In some areas or islands that have adequate public transportation and communication facilities the courses can be delivered quite well. In other areas that geographically very remote and dispersed islander, Universitas Terbuka, an open university in Indonesia, has to have very different strategies in overcoming the specific and even emergency situations in learning delivery. This ongoing research paper aims to share experiences of how Universitas Terbuka makes serious and unique efforts in overcoming the barriers and obstacles in providing educational service in part of difficult areas, especially in eastern areas of Indonesia. The data collection methods are observation of sample areas and in-depth interview with the head of regional offices of Universitas Terbuka in eastern Indonesia, staff, and tutors. Conducting educational deliveries in in difficult areas with no regular and adequate transportation has made the regional office have specific strategies in making the learning process run as smooth as possible. Sending a tutor to an area to meet some students and conducting a series of tutorial, which are supposed to be weekly, in several days is one of the strategies. Recruiting local people to manage the students in the area is another strategy. The absence of regular transportation from island to island, high tides, hurricanes, are among the obstacles faced by the regional offices in doing their job. Non geographical barriers such as unavailability of qualified tutor, inadequate tutor payment, are problems as well. The learning process, however, has to be done in any way, otherwise the distance education mission to reach unreachable cannot be achieved.

Keywords: distance education, Terbuka University, difficult area, geographical barrier, learning services

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Authors: Xiaoyan Dai, Hsieh Yisan

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Barcode processing of captured images is a huge challenge, as different shooting conditions can result in different barcode appearances. This paper proposes a deep learning-based barcode detection using synthetic-to-real data augmentation. We first augment barcodes themselves; we then augment images containing the barcodes to generate a large variety of data that is close to the actual shooting environments. Comparisons with previous works and evaluations with our original data show that this approach achieves state-of-the-art performance in various real images. In addition, the system uses hybrid resolution for barcode “scan” and is applicable to real-time applications.

Keywords: barcode detection, data augmentation, deep learning, image-based processing

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Authors: Esra Ece Var

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Beekeeping plays essential role both in terms of agricultural yields and agricultural economy; they produce honey, wax, royal jelly, apitoxin, pollen, and propolis. Nowadays, these natural products become more importantly suitable and preferable for nutrition, food supplement, medicine, and industry. However, to produce organic honey, majority of the apiaries are located in remote or distant rural areas where utilities such as electricity and Internet network are not available. Additionally, due to colony failures, world honey production decreases year by year despite the increase in the number of beehives. The objective of this paper is to develop a smart beehive monitoring system for apiaries including those that do not have access to Internet network. In this context, temperature and humidity inside the beehive, and ambient temperature were measured with RFID sensors. Control center, where all sensor data was sent and stored at, has a GSM module used to warn the beekeeper via SMS when an anomaly is detected. Simultaneously, using the collected data, an unsupervised machine learning algorithm is used for detecting anomalies and calibrating the warning system. The results show that the smart beehive monitoring system can detect fatal anomalies up to 4 weeks prior to colony loss.

Keywords: beekeeping, smart systems, machine learning, anomaly detection, apiculture

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Authors: Aisha F. Bushager

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Today we are more exposed than ever to cyber threats and attacks at personal, community, organizational, national, and international levels. More aspects of our lives are operating on computer networks simply because we are living in the fifth domain, which is called the Cyberspace. One of the most sensitive areas that are vulnerable to cyber threats and attacks is the Electronic Banking (e-Banking) area, where the banking sector is providing online banking services to its clients. To be able to obtain the clients trust and encourage them to practice e-Banking, also, to maintain the services provided by the banks and ensure safety, cyber security and risks control should be given a high priority in the e-banking area. The aim of the study is to carry out risk assessment on the e-banking services and determine the cyber threats, cyber attacks, and vulnerabilities that are facing the e-banking area specifically in the Kingdom of Bahrain. To collect relevant data, structured interviews were taken place with e-banking experts in different banks. Then, collected data where used as in input to the risk management framework provided by the National Institute of Standards and Technology (NIST), which was the model used in the study to assess the risks associated with e-banking services. The findings of the study showed that the cyber threats are commonly human errors, technical software or hardware failure, and hackers, on the other hand, the most common attacks facing the e-banking sector were phishing, malware attacks, and denial-of-service. The risks associated with the e-banking services were around the moderate level, however, more controls and countermeasures must be applied to maintain the moderate level of risks. The results of the study will help banks discover their vulnerabilities and maintain their online services, in addition, it will enhance the cyber security and contribute to the management and control of risks that are facing the e-banking sector.

Keywords: cyber security, e-banking, risk assessment, threats identification

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Authors: Christos Chaschatzis, Chrysoula Karaiskou, Pantelis Angelidis, Sotirios K. Goudos, Igor Kotsiuba, Panagiotis Sarigiannidis

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Over the past few decades, the rapid growth of the global population has led to the need to increase agricultural production and improve the quality of agricultural goods. There is a growing focus on environmentally eco-friendly solutions, sustainable production, and biologically minimally fertilized products in contemporary society. Precision agriculture has the potential to incorporate a wide range of innovative solutions with the development of machine learning algorithms. YOLOv5 and YOLOv8 are two of the most advanced object detection algorithms capable of accurately recognizing objects in real time. Detecting tree diseases is crucial for improving the food production rate and ensuring sustainability. This research aims to evaluate the efficacy of YOLOv5 and YOLOv8 in detecting the symptoms of Armillaria spp. in sweet cherry trees and determining their health status, with the goal of enhancing the robustness of precision agriculture. Additionally, this study will explore Computer Vision (CV) techniques with machine learning algorithms to improve the detection process’s efficiency.

Keywords: Armillaria spp., machine learning, precision agriculture, smart farming, sweet cherries trees, YOLOv5, YOLOv8

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