Search results for: student-centered teaching and learning

Authors: Ying-Hsiang Ho, Hsiao-Tseng Lin

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Mudan Township is a remote mountainous area in Taiwan. In recent years, due to the migration of the population, inconvenient transportation, digital divide, and low production, agricultural products marketing have become a major issue. This research aims to develop the marketing strategy suitable for the agricultural products of the rural areas. The main objective of this work is to conduct in-depth interviews with scholars and experts in the marketing field, combined with the marketing 4P combination, to analyze and summarize the possible marketing strategies for agricultural products for remote districts. The interviews consist of seven experts from industry who have practical experience in producing, marketing, and selling agricultural products and three professors that have experience in teaching marketing management. The in-depth interviews are conducted for about an hour using a pre-drafted interview outline. The results of the interviews are summarized by semantic analysis and presented in a marketing 4P combination. The results indicate that in terms of products, high-quality products with original characteristics can be added through the implementation of production history, organic certification, and cultural packaging. In the place part, we found that the use of emerging communities, the emphasis on cross-industry alliances, the improvement of information application capabilities of rural households, production and marketing group, and contractual farming system are the development priorities. In terms of promotion, it should be an emphasis on the management of internet social media and word-of-mouth marketing. Mudan Township may consider promoting agricultural products through special festivals such as farmer's market, wild ginger flower season and hot spring season. This research also proposes relevant recommendations for the government's public sector and related industry reference for the promotion of agricultural products for remote area.

Keywords: marketing strategy, remote districts, agricultural products, in-depth interviews

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Authors: Zhao Gao, Eran Edirisinghe

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The production of an accurate sketch of a suspect based on a verbal description obtained from a witness is an essential task for most criminal investigations. The criminal investigation system employs specifically trained professional artists to manually draw a facial image of the suspect according to the descriptions of an eyewitness for subsequent identification. Within the advancement of Deep Learning, Recurrent Neural Networks (RNN) have shown great promise in Natural Language Processing (NLP) tasks. Additionally, Generative Adversarial Networks (GAN) have also proven to be very effective in image generation. In this study, a trained GAN conditioned on textual features such as keywords automatically encoded from a verbal description of a human face using an RNN is used to generate photo-realistic facial images for criminal investigations. The intention of the proposed system is to map corresponding features into text generated from verbal descriptions. With this, it becomes possible to generate many reasonably accurate alternatives to which the witness can use to hopefully identify a suspect from. This reduces subjectivity in decision making both by the eyewitness and the artist while giving an opportunity for the witness to evaluate and reconsider decisions. Furthermore, the proposed approach benefits law enforcement agencies by reducing the time taken to physically draw each potential sketch, thus increasing response times and mitigating potentially malicious human intervention. With publically available 'CelebFaces Attributes Dataset' (CelebA) and additionally providing verbal description as training data, the proposed architecture is able to effectively produce facial structures from given text. Word Embeddings are learnt by applying the RNN architecture in order to perform semantic parsing, the output of which is fed into the GAN for synthesizing photo-realistic images. Rather than the grid search method, a metaheuristic search based on genetic algorithms is applied to evolve the network with the intent of achieving optimal hyperparameters in a fraction the time of a typical brute force approach. With the exception of the ‘CelebA’ training database, further novel test cases are supplied to the network for evaluation. Witness reports detailing criminals from Interpol or other law enforcement agencies are sampled on the network. Using the descriptions provided, samples are generated and compared with the ground truth images of a criminal in order to calculate the similarities. Two factors are used for performance evaluation: The Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). A high percentile output from this performance matrix should attribute to demonstrating the accuracy, in hope of proving that the proposed approach can be an effective tool for law enforcement agencies. The proposed approach to criminal facial image generation has potential to increase the ratio of criminal cases that can be ultimately resolved using eyewitness information gathering.

Keywords: RNN, GAN, NLP, facial composition, criminal investigation

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Authors: John Odo Ogah

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The study investigated ways of fostering students’ interest in senior secondary two Biology, using prior knowledge of behavioural objectives and assertive questioning strategies in Benue State of Nigeria. A quasi-experimental research design was adopted; the population comprised 8,571 senior Secondary two students. The sample consisted of 265 SSII biology students selected from six government schools in the study area using a multi-staged sampling technique. Data was generated using the Biology Interest Inventory (BII). The instrument was validated and subjected to reliability analysis using Cronbach’s Alpha formula, which yielded a coefficient of 0.73. Three research questions guided the study, while three hypotheses were formulated and tested. Data collected were analyzed using means, bar graphs, and standard deviations to answer the research questions, while analysis of covariance (ANCOVA) was employed in testing the hypotheses at 0.05 level of significance. The finding revealed that there is a significant difference in the mean interest ratings of students taught cellular respiration and excretory system using assertive questioning strategy, prior knowledge of behavioural objectives strategy and lecture method (p=0.000˂0.05). There is no significant difference in the mean interest ratings of male and female students taught cellular respiration and excretory systems using an assertive questioning strategy (p=0.790>0.05). There is significant difference in the mean interest ratings of male and female students taught cellular respiration and execratory system using prior knowledge of behavioural objectives strategy (p=0.028˂0.05). It was recommended, among others, that teachers should endeavor to utilize prior knowledge of behavioral objectives strategy in teaching biology in order to harness its benefits as it enhances students’ interest.

Keywords: interest, assertive, questioning, prior, knowledge

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Authors: Dominika Collett

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AI applications, such as machine learning, require access to a vast amount of data in the training phase, which can often be the subject of copyright protection. During later usage, the various content with which the application works can be recorded or made available on the basis of which it produces the resulting output. The EU has recently adopted new legislation to secure machine access to protected works under the DSM Directive; but, the issue of machine use of copyright works is not clearly addressed. However, such clarity is needed regarding the increasing importance of AI and its development. Therefore, this paper provides a basic background of the technology used in the development of applications in the field of computer creativity. The second part of the paper then will focus on a legal analysis of machine use of the authors' works from the perspective of existing European and Czech legislation. The main results of the paper discuss the potential collision of existing legislation in regards to machine use of works with special focus on exceptions and limitations. The legal regulation of machine use of copyright work will impact the development of AI technology.

Keywords: copyright, artificial intelligence, legal use, infringement, Czech law, EU law, text and data mining

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Authors: B. Chandra

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In Association Rule Mining, much attention has been paid for developing algorithms for large (frequent/closed/maximal) itemsets but very little attention has been paid to improve the performance of rule generation algorithms. Rule generation is an important part of Association Rule Mining. In this paper, a novel approach named NARG (Association Rule using Antecedent Support) has been proposed for rule generation that uses memory resident data structure named FCET (Frequent Closed Enumeration Tree) to find frequent/closed itemsets. In addition, the computational speed of NARG is enhanced by giving importance to the rules that have lower antecedent support. Comparative performance evaluation of NARG with fast association rule mining algorithm for rule generation has been done on synthetic datasets and real life datasets (taken from UCI Machine Learning Repository). Performance analysis shows that NARG is computationally faster in comparison to the existing algorithms for rule generation.

Keywords: knowledge discovery, association rule mining, antecedent support, rule generation

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Authors: Walid Ibrahim, Abdul Kasem, Sudeendra Doddi, Ilaria Giono, Tareq Sabagh, Muhammad Ammar, Nermin Osman

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Background: wire-guided localization (WL) is the most widely used method for the localization of non-palpable breast lesions. SAVI SCOUT occult lesion localization (SSL) is a new technique in breast-conservative surgery. SSL has the potential benefit of improving radiology workflow as well as accurate localization. Purpose: The purpose of this study is to compare the breast tissue specimen volume and weight and margin excision between WL and SSL. Materials and methods: A single institution retrospective analysis of 377 female patients who underwent wide local breast excision with SAVI SCOUT and or wire-guided technique between 2018 and 2021 in a UK University teaching hospital. Breast department. Breast tissue specimen volume and weight, and margin excision have been evaluated in the three groups of different localization. Results: Three hundred and seventy-seven patients were studied. Of these, 261 had wire localization, 88 had SCOUT and 28 had dual localization techniques. Tumor size ranged from 1 to 75mm (Median 20mm). The pathology specimen weight ranged from 1 to 466gm (Median 46.8) and the volume ranged from 1.305 to 1560cm³ (Median 106.32 cm³). SCOUT localization was associated with a significantly low specimen weight than wire or the dual technique localization (Median 41gm vs 47.3gm and 47gm, p = 0.029). SCOUT was not associated with better specimen volume with a borderline significance in comparison to wire and combined techniques (Median 108cm³ vs 105cm³ and 105cm³, p = 0.047). There was a significant correlation between tumor size and pathology specimen weight in the three groups. SCOUT showed a better >2mm safety margin in comparison to the other 2 techniques (p = 0.031). Conclusion: Preoperative SCOUT localization is associated with better specimen weight and better specimen margin. SCOUT did not show any benefits in terms of specimen volume which may be due to difficulty in getting the accurate specimen volume due to the irregularity of the soft tissue specimen.

Keywords: scout, wire, localization, breast

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Authors: Amanda Abbott-Jones

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Adult students with dyslexia in higher education can receive support for their cognitive needs but may also experience negative emotion such as anxiety due to their dyslexia in connection with their studies. This paper aims to test the hypothesis that adult dyslexic learners have a higher prevalence of academic and social anxiety than their non-dyslexic peers. A quantitative approach was used to measure differences in academic and social anxiety between 102 students with a formal diagnosis of dyslexia compared to 72 students with no history of learning difficulties. Academic and social anxiety was measured in a questionnaire based on the State-Trait Anxiety Inventory. Findings showed that dyslexic students showed statistically significant higher levels of academic, but not social anxiety in comparison to the non-dyslexic sample. Dyslexic students in higher education show academic anxiety levels that are well above what is shown by students without dyslexia. The implications of this for the dyslexia practitioner is that delivery of strategies to deal with anxiety should be seen equally as important, if not more so, than interventions to deal with cognitive difficulties.

Keywords: Academic, Anxiety, Dyslexia, Quantitative

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Authors: Hao Zhang, Hongyang Yu

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Scale uncertainty is a well-known challenging problem in visual SLAM. Because RGB-D sensor provides depth information, RGB-D SLAM improves this scale uncertainty problem. However, due to the limitation of physical hardware, the depth map output by RGB-D sensor usually contains a large area of missing depth values. These missing depth information affect the accuracy and robustness of RGB-D SLAM. In order to reduce these effects, this paper completes the missing area of the depth map output by RGB-D sensor and then fuses the completed dense depth map into ORB SLAM2. By adding the process of obtaining pixel-level dense depth maps, a better RGB-D visual SLAM algorithm is finally obtained. In the process of obtaining dense depth maps, a deep learning model of indoor scenes is adopted. Experiments are conducted on public datasets and real-world environments of indoor scenes. Experimental results show that the proposed SLAM algorithm has better robustness than ORB SLAM2.

Keywords: RGB-D, SLAM, dense depth, depth map

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Authors: Ali Asheibi

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The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Preparing raw data to be ready for data mining exploration take up most of the effort and time spent in the whole data mining process. Clustering is an important technique in data mining and machine learning in which underlying and meaningful groups of data are discovered. Large amounts of harmonic data have been collected from an actual harmonic monitoring system in a distribution system in Australia for three years. This amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. In this paper, harmonic data preparation processes to better understanding of the data have been presented. Underlying classes in this data has then been identified using clustering technique based on the Minimum Message Length (MML) method. The underlying operational information contained within the clusters can be rapidly visualised by the engineers. The C5.0 algorithm was used for classification and interpretation of the generated clusters.

Keywords: data mining, harmonic data, clustering, classification

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Authors: Mohamed Mokhtar, Mostafa F. Shaaban

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Among the challenges facing solar photovoltaic (PV) systems in the United Arab Emirates (UAE), dust accumulation on solar panels is considered the most severe problem that faces the growth of solar power plants. The accumulation of dust on the solar panels significantly degrades output from these panels. Hence, solar PV panels have to be cleaned manually or using costly automated cleaning methods. This paper focuses on initiating cleaning actions when required to reduce maintenance costs. The cleaning actions are triggered only when the dust level exceeds a threshold value. The amount of dust accumulated on the PV panels is estimated using an artificial neural network (ANN). Experiments are conducted to collect the required data, which are used in the training of the ANN model. Then, this ANN model will be fed by the output power from solar panels, ambient temperature, and solar irradiance, and thus, it will be able to estimate the amount of dust accumulated on solar panels at these conditions. The model was tested on different case studies to confirm the accuracy of the developed model.

Keywords: machine learning, dust, PV panels, renewable energy

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Authors: Ana Maghfiroh

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Learning a foreign language needs a big effort from the learner itself to make their language ability grows better day by day. Among those, they also need a support from all around them including teacher, friends, as well as activities which support them to speak the language. When those activities developed well as a habit which are done regularly, it will help improving the students’ language competence. It was a qualitative research which aimed to find out and describe some activities implemented in Pesantren Al Mawaddah, Ponorogo, in order to teach the students a foreign language. In collecting the data, the researcher used interview, questionnaire, and documentation. From the study, it was found that Pesantren Al Mawaddah had successfully built the language habit on the students to speak the target language. More than 15 hours a day students were compelled to speak foreign language, Arabic or English, in turn. It aimed to habituate the students to keep in touch with the target language. The habit was developed through daily language activities, such as dawn vocabs giving, dictionary handling, daily language use, speech training and language intensive course, daily language input, and night vocabs memorizing. That habit then developed the students awareness towards the language learned as well as promoted their language mastery.

Keywords: habit, communicative competence, daily language activities, Pesantren

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Authors: Shaden S. Attia

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This study investigated foreign language anxiety (FLA) and teachers’ awareness of its presence in the Egyptian ESL classrooms and how FLA correlates with different variables such as four language skills, students' sex, and activities used in class. A combination of quantitative and qualitative instruments was used in order to investigate the previously mentioned variables, which included five interviews with teachers, six classroom observations, a survey for teachers, and a questionnaire for students. The findings of the study revealed that some teachers were aware of the presence of FLA, with some of them believing that other teachers, however, are not aware of this phenomenon, and even when they notice anxiety, they do not always relate it to learning a foreign language. The results also showed that FLA was affected by students’ sex, different language skills, and affective anxieties; however, teachers were unaware of the effect of these variables. The results demonstrated that both teachers and students preferred group and pair work to individual activities as they were more relaxing and less anxiety-provoking. These findings contribute to raising teachers' awareness of FLA in ESL classrooms and how it is affected by different variables.

Keywords: foreign language anxiety, situation specific anxiety, skill-specific anxiety, teachers’ perceptions

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Authors: Amin Ranjbar, Amir Arsalan Jalili Zolfaghari, Amir Abolfazl Suratgar, Mehrdad Khajavi

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This paper describes two stages of learning-based fault detection procedure in alternators. The procedure consists of three states of machine condition namely shortened brush, high impedance relay and maintaining a healthy condition in the alternator. The fault detection algorithm uses Wigner-Ville distribution as a feature extractor and also appropriate feature classifier. In this work, ANN (Artificial Neural Network) and also SVM (support vector machine) were compared to determine more suitable performance evaluated by the mean squared of errors criteria. Modules work together to detect possible faulty conditions of machines working. To test the method performance, a signal database is prepared by making different conditions on a laboratory setup. Therefore, it seems by implementing this method, satisfactory results are achieved.

Keywords: alternator, artificial neural network, support vector machine, time-frequency analysis, Wigner-Ville distribution

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Authors: Zeynep Turhan

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Digital tools can improve knowledge and awareness of strategies and skills for healthy and respectful intimate relationships. The website of the Healthy and Respectful Relationship Program has been developed and included five key videos about how to build healthy intimate relationships. This study examined the perspectives about informative videos by focusing on how individuals learn new information or challenge their preconceptions or attitudes regarding male privilege and women's oppression. Five individuals who received no-contact orders and attended group intervention were the sample of this study. The observation notes were the major methodology examining how participants responded to video tools. The data analysis method was the interpretative phenomenological analysis. The results showed that many participants found the tools useful in learning the types of violence and communication strategies. Nevertheless, obstacles to implementing some techniques were found in their relationships. These digital tools might enhance healthy and respectful relationships despite some limitations.

Keywords: healthy relationship, digital tools, intimate partner violence, perpetrators, video interventions

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Authors: Gülcan Taşkıran, Ayla Bayık Temel

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Background: Florence Nightingale, the modern founder of the nursing, is most famous for her role as a nurse. But not so much known about her contributions as a mathematician and statistician. Aim: In this conceptual article it is aimed to examine Florence Nightingale's statistics education, how she used her passion for statistics and applied statistical data in nursing care and her scientific contributions to statistical science. Design: Literature review method was used in the study. The databases of Istanbul University Library Search Engine, Turkish Medical Directory, Thesis Scanning Center of Higher Education Council, PubMed, Google Scholar, EBSCO Host, Web of Science were scanned to reach the studies. The keywords 'statistics' and 'Florence Nightingale' have been used in Turkish and English while being screened. As a result of the screening, totally 41 studies were examined from the national and international literature. Results: Florence Nightingale has interested in mathematics and statistics at her early ages and has received various training in these subjects. Lessons learned by Nightingale in a cultured family environment, her talent in mathematics and numbers, and her religious beliefs played a crucial role in the direction of the statistics. She was influenced by Quetelet's ideas in the formation of the statistical philosophy and received support from William Farr in her statistical studies. During the Crimean War, she applied statistical knowledge to nursing care, developed many statistical methods and graphics, so that she made revolutionary reforms in the health field. Conclusions: Nightingale's interest in statistics, her broad vision, the statistical ideas fused with religious beliefs, the innovative graphics she has developed and the extraordinary statistical projects that she carried out has been influential on the basis of her professional achievements. Florence Nightingale has also become a model for women in statistics. Today, using and teaching of statistics and research in nursing care practices and education programs continues with the light she gave.

Keywords: Crimean war, Florence Nightingale, nursing, statistics

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Authors: Aiswarya Gopal, Kamal Bijlani, Vinoth Rengaraj, R. Jayakrishnan

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In the business world, the term “startup” is frequently ringing the bell with the high frequency of young ventures. The main dilemma of startups is the unsuccessful management of the unique risks that have to be confronted in the present world of competition and technology. This research work tried to bring out a game based methodology to improve enough real-world experience among entrepreneurs as well as management students to handle risks and challenges in the field. The game will provide experience to the player to overcome challenges like market problems, running out of cash, poor management, and product problems which can be resolved by a proper strategic approach in the entrepreneurship world. The proposed serious game works on the life cycle of a new software enterprise where the entrepreneur moves from the planning stage to secured financial stage, laying down the basic business structure, and initiates the operations ensuring the increment in confidence level of the player.

Keywords: business model, game based learning, poor management, start up

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Authors: Michelle Hartanto, Risheka Suthantirakumar

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Introduction Staff redeployment, increased numbers of acutely unwell patients requiring resuscitation decision-making conversations, visiting restrictions, and varying guidance regarding resuscitation for patients with COVID-19 disrupted clinicians’ management of resuscitation and escalation decision-making processes. While it was generally accepted that the COVID-19 pandemic disturbed numerous aspects of the Recommended Summary Plan for Emergency Care and Treatment (ReSPECT) process in the United Kingdom, a process which establishes a patient’s CPR status and treatment escalation plans, the impact of the pandemic on clinicians’ attitudes towards these resuscitation and decision-making conversations was unknown. This was the first study to examine the impact of the COVID-19 pandemic on clinicians’ knowledge, skills, and attitudes towards the ReSPECT process. Methods A cross-sectional survey of clinicians at one acute teaching hospital in the UK was conducted. A questionnaire with a defined five-point Likert scale was distributed and clinicians were asked to recall their pre-pandemic views on ReSPECT and report their current views at the time of survey distribution (May 2020, end of the first COVID-19 wave in the UK). Responses were received from 171 clinicians, and self-reported views before and during the pandemic were compared. Results Clinicians reported they found managing ReSPECT conversations more challenging during the pandemic, especially when conducted over the telephone with relatives, and they experienced an increase in negative emotions before, during, and after conducting ReSPECT conversations. Our findings identified that due to the pandemic there was now a need for clinicians to receive training and support in conducting resuscitation and escalation decision-making conversations over the telephone with relatives and managing these processes.

Keywords: cardiopulmonary resuscitation, COVID-19 pandemic, DNACPR discussion, education, recommended summary plan for emergency care and treatment, resuscitation order

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Authors: Chun-Ming Huang, Yi-Jun Liu, Yi-Jie Hsieh, Jin-Ju Chue, Wei-Lin Lai, Chun-Yu Chen, Chih-Chyau Yang, Chien-Ming Wu

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The market of wearable devices has been growing rapidly in two years. The integration of sensors and wearable devices has become the trend of the next technology products. Thus, the academics and industries are eager to cultivate talented persons in sensing technology. Currently, academic and industries have more and more demands on the integrations of versatile sensors and applications, especially for the teams who focus on the development of sensor circuit architectures. These teams tape-out many MEMs sensors chips through the chip fabrication service from National Chip Implementation Center (CIC). However, most of these teams are only able to focus on the circuit design of MEMs sensors; they lack the key support of further system demonstration. This paper follows the CIC’s main mission of promoting the chip/system advanced design technology and aims to establish the environments of the modularized sensing system platform and the system design flow with the measurement and calibration technology. These developed environments are used to support these research teams and help academically advanced sensor designs to perform the system demonstration. Thus, the research groups can promote and transfer their advanced sensor designs to industrial and further derive the industrial economic values. In this paper, the modularized sensing platform is proposed to enable the system demonstration for advanced sensor chip design. The environment of sensor measurement and calibration is established for academic to achieve an accurate sensor result. Two reference sensor designs cooperated with the modularized sensing platform are given to show the sensing system integration and demonstration. These developed environments and platforms are currently provided to academics in Taiwan, and so that the academics can obtain a better environment to perform the system demonstration and improve the research and teaching quality.

Keywords: modularized sensing platform, sensor design and calibration, sensor system, sensor system design flow

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Authors: Georges Bou Ghantous

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This research delves into the transformative impact of Artificial Intelligence (AI) on Infrastructure as Code (IaaC) practices, specifically focusing on the redefinition of infrastructure orchestration. By harnessing AI technologies such as machine learning algorithms and predictive analytics, organizations can achieve unprecedented levels of efficiency and optimization in managing their infrastructure resources. AI-driven IaaC introduces proactive decision-making through predictive insights, enabling organizations to anticipate and address potential issues before they arise. Dynamic resource scaling, facilitated by AI, ensures that infrastructure resources can seamlessly adapt to fluctuating workloads and changing business requirements. Through case studies and best practices, this paper sheds light on the tangible benefits and challenges associated with AI-driven IaaC transformation, providing valuable insights for organizations navigating the evolving landscape of digital infrastructure management.

Keywords: artificial intelligence, infrastructure as code, efficiency optimization, predictive insights, dynamic resource scaling, proactive decision-making

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Authors: Thomas L. McCullough, John D. Hague, Marylesa M. Howard, Matthew K. Kiser, Michael A. Mazur, Lance K. McLean, Johanna L. Turk

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Radiological search and mapping depends on the successful recognition of anomalies in large data sets which contain varied and dynamic backgrounds. We present a new algorithmic approach for real-time anomaly detection which is resistant to common detector imperfections, avoids the limitations of a source template library and provides immediate, and easily interpretable, user feedback. This algorithm is based on a continuous wavelet transform for variance reduction and evaluates the deviation between a foreground measurement and a local background expectation using methods from linear algebra. We also present a technique for recognizing and visualizing spectrally similar clusters of data. This technique uses Laplacian Eigenmap Manifold Learning to perform dimensional reduction which preserves the geometric "closeness" of the data while maintaining sensitivity to outlying data. We illustrate the utility of both techniques on real-world data sets.

Keywords: radiological search, radiological mapping, radioactivity, radiation protection

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Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

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For security purposes, it is important to detect passwords entered by unauthorized users. With traditional alphanumeric passwords, if the content of a password is acquired and correctly entered by an intruder, it is impossible to differentiate the password entered by the intruder from those entered by the authorized user because the password entries contain precisely the same character set. However, no two entries for the gesture-based passwords, even those entered by the person who created the password, will be identical. There are always variations between entries, such as the shape and length of each stroke, the location of each stroke, and the speed of drawing. It is possible that passwords entered by the unauthorized user contain higher levels of variations when compared with those entered by the authorized user (the creator). The difference in the levels of variations may provide cues to detect unauthorized entries. To test this hypothesis, we designed an empirical study, collected and analyzed the data with the help of machine-learning algorithms. The results of the study are significant.

Keywords: authentication, gesture-based passwords, shoulder-surfing attacks, usability

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Authors: Farrukh Shahzad

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The rich Islamic resources related to religious text, Islamic sciences, and history are widely available in print and in electronic format online. However, most of these works are only available in Arabic language. In this research, an attempt is made to utilize these resources to create interactive web applications in Arabic, English and other languages. The system utilizes the Pattern Recognition, Knowledge Management, Data Mining, Information Retrieval and Management, Indexing, storage and data-analysis techniques to parse, store, convert and manage the information from authentic Arabic resources. These interactive web Apps provide smart multi-lingual search, tree based search, on-demand information matching and linking. In this paper, we provide details of application architecture, design, implementation and technologies employed. We also presented the summary of web applications already developed. We have also included some screen shots from the corresponding web sites. These web applications provide an Innovative On-line Learning Systems (eLearning and computer based education).

Keywords: Islamic resources, Muslim scholars, hadith, narrators, history, fiqh

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Authors: Joo Min Kim, Dongyoun Shin

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The project Future Station (FS) seek for a deeper understanding of metro station. The main idea of the project is enhancing the underground environment by combining new architectural design with IoT technology. This research shows the understanding of the metro environment giving references regarding traditional design approaches and IoT combined space design. Based on the analysis, this research presents design alternatives in two metro stations those are chosen for a testbed. It also presents how the FS platform giving a response to travelers and deliver the benefit to metro operators. In conclusion, the project describes methods to build future metro service and platform that understand traveler’s intentions and giving appropriate services back for enhancing travel experience. It basically used contemporary technology such as smart sensing grid, big data analysis, smart building, and machine learning technology.

Keywords: future station, digital lifestyle experience, sustainable metro, smart metro, smart city

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Authors: Avasha Rambiritch, Adelia Carstens

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In especially the context of a writing center, learning takes place during, and as part of, the conversations between the writing center tutor and the student. This interaction or dialogue is an integral part of writing center research and is the focus of this largely qualitative study, employing a politeness lens. While there is some research on positive politeness strategies employed by writing center tutors, there is very little research on specifically praising as a positive politeness strategy. This study attempts to fill this gap by analyzing a corpus of 10 video-recorded consultations to determine how tutors in a writing center utilize the positive politeness strategy of praise. Findings indicate that while tutors exploit a range of politeness strategies, praise is used more often than any other strategy. The research indicates that praise as a politeness strategy is utilized significantly more when commenting on higher-order concerns, as in line with the writing center literature. The benefits of this study include insights into how such analyses can be used to better prepare and equip the tutors (usually postgraduate students appointed as part-time tutors in the writing center) for the work they do on a daily basis.

Keywords: writing center, academic writing, positive politeness, tutor

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Authors: Aynur Charkasova

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The scientific and economic contributions of international students cannot be underestimated. International education continues to be a competitive global industry, and many countries are seeking to recruit the best and the brightest to reinforce scientific innovations, boost intercultural learning, and bring more funding to universities and colleges. Substantial changes in international educational policies and migration regulations have been made in the hopes of recruiting global talent. This paper explores and compares recruitment strategies, employment opportunities, and a legal path to permanent residency policies related to international students in the United States of America and Canada. This study will utilize the legal information available from the government websites of both countries and peer-reviewed scholarly articles and will highlight which approach promises a better path in recruiting and retention of international students. The findings from the study will be discussed and recommendations will be provided.

Keywords: International students, current immigration policies, STEM, employability, visa reforms for international students, Canadian recruitment policy

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Authors: Ambissah Asah Brigitte

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This research is guided by observations in the types of education offered at the secondary level in Cameroon. The secondary education system in Cameroon comprises two types of education, including General Education and Technical and Vocational Education. Although General Education and, Technical and Vocational Education are given equal importance by public authorities, General Education remains on the thriving trend, enjoying the greatest enrolment. In the meantime, Technical and Vocational Education is still to reach the adequate momentum expected to fostering the country’s full-fledged development, as specified in the National Development Strategy, which is the blue print of State policies in Cameroon for the 2020-2030 decade. Vocational Education is credited for its ability to foster a country’s development, since it teaches students the precise skills and knowledge needed to carry out a specific craft, technical skill or trade. Yet, formal training on Vocational Education for teachers offers a pale face in secondary education. This limits the ability of the educational system to nurture vocations and provide the country’s economy with the manpower necessary to achieving development goals. This article seeks to analyse how concretely does the institutional framework spur vocational skills in secondary school teachers. It overviews the instruments instituting Vocational Education at the secondary level in Cameroon, then assesses their effective implementation on the ground. Questionnaires addressed to both active teachers and vocational education policy-makers serve to collect data which are analysed using descriptive statistics. The final objective is to contribute in the debate urging to rethink the role of teachers in bolstering Vocational Education, which is the cornerstone of industrial development. This is true everywhere in the world. In Cameroon and in Africa in general, teachers must be empowered in this field with specific sets of competencies they will need to pass on to learners. They equally need to be given opportunities to acquire and adapt their knowledge and teaching skills accordingly.

Keywords: vocational education, cameroon, institutional framework, national development, competencies and skills

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Authors: Olasunkanmi Jame Ayodeji, Adebayo Adeyinka Victor

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In a hyperconnected world, cybersecurity faces a continuous evolution of threats that challenge traditional defence mechanisms. This paper explores emerging cybersecurity threats like malware, ransomware, phishing, social engineering, and the Internet of Things (IoT) vulnerabilities. It delves into the inadequacies of existing cybersecurity defences in addressing these evolving risks and advocates for adaptive defence mechanisms that leverage AI, machine learning, and zero-trust architectures. The paper proposes collaborative approaches, including public-private partnerships and information sharing, as essential to building a robust defence strategy to address future cyber threats. The need for continuous monitoring, real-time incident response, and adaptive resilience strategies is highlighted to fortify digital infrastructures in the face of escalating global cyber risks.

Keywords: cybersecurity, hyperconnectivity, malware, adaptive defences, zero-trust architecture, internet of things vulnerabilities

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Authors: Philip Symonds, Jon Taylor, Zaid Chalabi, Michael Davies

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With the world climate projected to warm and major cities in developing countries becoming increasingly populated and polluted, governments are tasked with the problem of overheating and air quality in residential buildings. This paper presents the development of an adaptable model of these risks. Simulations are performed using the EnergyPlus building physics software. An accurate metamodel is formed by randomly sampling building input parameters and training on the outputs of EnergyPlus simulations. Metamodels are used to vastly reduce the amount of computation time required when performing optimisation and sensitivity analyses. Neural Networks (NNs) are compared to a Radial Basis Function (RBF) algorithm when forming a metamodel. These techniques were implemented using the PyBrain and scikit-learn python libraries, respectively. NNs are shown to perform around 15% better than RBFs when estimating overheating and air pollution metrics modelled by EnergyPlus.

Keywords: neural networks, radial basis functions, metamodelling, python machine learning libraries

Procedia PDF Downloads 445

Authors: Ram Sharan Mehta, Gayanandra Malla, Anita Gurung, Anu Aryal, Divya Labh, Hricha Neupane

Abstract:

Objectives: Lack of resuscitation skills of nurses and doctors in basic life support (BLS) and advanced life support (ALS) has been identified as a contributing factor to poor outcomes of cardiac arrest victims. The objective of this study was to examine retention of life support measures (BLS/ALS) knowledge and skills of nurses following education intervention programme. Materials and Methods: Pre-experimental research design was used to conduct the study among the nurses working in medical units of B.P Koirala Institute of Health Sciences, where CPR is very commonly performed. Using convenient sampling technique total of 20 nurses agreed to participate and give consent were included in the study. The theoretical, demonstration and re-demonstration were arranged involving the trained doctors and nurses during the three hours educational session. Post-test was carried out after two week of education intervention programme. The 2010 BLS & ALS guidelines were used as guide for the study contents. The collected data were analyzed using SPSS-15 software. Results: It was found that there is significant increase in knowledge after education intervention in the components of life support measures (BLS/ALS) i.e. ratio of chest compression to ventilation in BLS (P=0.001), correct sequence of CPR (p <0.001), rate of chest compression in ALS (P=0.001), the depth of chest compression in adult CPR (p<0.001), and position of chest compression in CPR (P=0.016). Nurses were well appreciated the programme and request to continue in future for all the nurses. Conclusions: At recent BLS/ALS courses (2010), a significant number of nurses remain without any such training. Action is needed to ensure all nurses receive BLS training and practice this skill regularly in order to retain their knowledge.

Keywords: pre-experimental, basic and advance life support, nurses, sampling technique

Procedia PDF Downloads 253

Authors: Ardalan Tofighi Soleimandarabi

Abstract:

Artificial intelligence has revolutionized the concrete construction industry and improved processes by increasing efficiency, accuracy, and sustainability. This article examines the applications of artificial intelligence in predicting the compressive strength of concrete, optimizing mixing plans, and improving structural health monitoring systems. Artificial intelligence-based models, such as artificial neural networks (ANN) and combined machine learning techniques, have shown better performance than traditional methods in predicting concrete properties. In addition, artificial intelligence systems have made it possible to improve quality control and real-time monitoring of structures, which helps in preventive maintenance and increases the life of infrastructure. Also, the use of artificial intelligence plays an effective role in sustainable construction by optimizing material consumption and reducing waste. Although the implementation of artificial intelligence is associated with challenges such as high initial costs and the need for specialized training, it will create a smarter, more sustainable, and more affordable future for concrete structures.

Keywords: artificial intelligence, concrete construction, compressive strength prediction, structural health monitoring, stability

Procedia PDF Downloads 14