Search results for: adopt a culture of continuous learning

Authors: Erva Akin

Abstract:

– The use of copyrighted material for machine learning purposes is a challenging issue in the field of artificial intelligence (AI). While machine learning algorithms require large amounts of data to train and improve their accuracy and creativity, the use of copyrighted material without permission from the authors may infringe on their intellectual property rights. In order to overcome copyright legal hurdle against the data sharing, access and re-use of data, the use of copyrighted material for machine learning purposes may be considered permissible under certain circumstances. For example, if the copyright holder has given permission to use the data through a licensing agreement, then the use for machine learning purposes may be lawful. It is also argued that copying for non-expressive purposes that do not involve conveying expressive elements to the public, such as automated data extraction, should not be seen as infringing. The focus of such ‘copy-reliant technologies’ is on understanding language rules, styles, and syntax and no creative ideas are being used. However, the non-expressive use defense is within the framework of the fair use doctrine, which allows the use of copyrighted material for research or educational purposes. The questions arise because the fair use doctrine is not available in EU law, instead, the InfoSoc Directive provides for a rigid system of exclusive rights with a list of exceptions and limitations. One could only argue that non-expressive uses of copyrighted material for machine learning purposes do not constitute a ‘reproduction’ in the first place. Nevertheless, the use of machine learning with copyrighted material is difficult because EU copyright law applies to the mere use of the works. Two solutions can be proposed to address the problem of copyright clearance for AI training data. The first is to introduce a broad exception for text and data mining, either mandatorily or for commercial and scientific purposes, or to permit the reproduction of works for non-expressive purposes. The second is that copyright laws should permit the reproduction of works for non-expressive purposes, which opens the door to discussions regarding the transposition of the fair use principle from the US into EU law. Both solutions aim to provide more space for AI developers to operate and encourage greater freedom, which could lead to more rapid innovation in the field. The Data Governance Act presents a significant opportunity to advance these debates. Finally, issues concerning the balance of general public interests and legitimate private interests in machine learning training data must be addressed. In my opinion, it is crucial that robot-creation output should fall into the public domain. Machines depend on human creativity, innovation, and expression. To encourage technological advancement and innovation, freedom of expression and business operation must be prioritised.

Keywords: artificial intelligence, copyright, data governance, machine learning

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Authors: C. W. Kan

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Cotton fibre is a commonly-used natural fibre because of its good fibre strength, high moisture absorption behaviour and minimal static problems. However, one of the main drawbacks of cotton fibre is wrinkling after washing, which is recently overcome by wrinkle-resistant treatment. 1,2,3,4-butanetetracarboxylic acid (BTCA) could improve the wrinkle-resistant properties of cotton fibre. Although the BTCA process is an effective method for wrinkle resistant application of cotton fabrics, reduced fabric strength was observed after treatment. Therefore, this paper would explore the use of atmospheric pressure plasma treatment under different discharge powers as a pretreatment process to enhance the application of BTCA process on cotton fabric without generating adverse effect. The aim of this study is to provide learning information to the users to know how the atmospheric pressure plasma treatment can be incorporated in textile finishing process with positive impact.

Keywords: learning materials, atmospheric pressure plasma treatment, cotton, wrinkle-resistant, BTCA

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Motahareh Fadaei

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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: clustering, energy consumption‎, learning automata, underwater sensor networks

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Authors: Amy Gooden

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Various modes of consent have been utilized in health research, but open consent has not been explored in South Africa’s AI research context. Open consent entails the sharing of data without assurances of privacy and may be seen as an attempt to marry open science with informed consent. Because all potential uses of data are unknown, it has been questioned whether consent can be informed. Instead of trying to adapt existing modes of consent, why not adopt a new perspective? This is what open consent proposes and what this research will explore in AI health research in South Africa.

Keywords: artificial intelligence, consent, health, law, research, South Africa

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Authors: Jiahe Ru, Yan Pang, Zhaomiao Liu

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Necking processes of the Janus droplet generation in the cross-junction microchannels are experimentally and theoretically investigated. The two dispersed phases that are simultaneously shear by continuous phases are liquid paraffin wax and 100cs silicone oil, in which 80% glycerin aqueous solution is used as continuous phases. According to the variation of minimum neck width and thinning rate, the necking process is divided into two stages, including the two-dimensional extrusion and the three-dimensional extrusion. In the two-dimensional extrusion stage, the evolutions of the tip extension length for the two discrete phases begin with the same trend, and then the length of liquid paraffin is larger than silicone oil. The upper and lower neck interface profiles in Janus necking process are asymmetrical when the tip extension velocity of paraffin oil is greater than that of silicone oil. In the three-dimensional extrusion stage, the neck of the liquid paraffin lags behind that of the silicone oil because of the higher surface tension, and finally, the necking fracture position gradually synchronizes. When the Janus droplets pinch off, the interfacial tension becomes positive to drive the neck thinning. The interface coupling of the three phases can cause asymmetric necking of the neck interface, which affects the necking time and, ultimately, the droplet volume. This paper mainly investigates the thinning dynamics of the liquid-liquid interface in confined microchannels. The revealed results could help to enhance the physical understanding of the droplet generation phenomenon.

Keywords: neck interface, interface coupling, janus droplets, multiphase flow

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Authors: Azyz Sharafy

Abstract:

3D Text Toys is an innovative and creative approach that utilizes 3D text objects to enhance creativity, literacy, and basic learning in an enjoyable and gamified manner. By using 3D Text Toys, children can develop their creativity, visually learn words and texts, and apply their artistic talents within their creative abilities. This process incorporates haptic engagement with 2D and 3D texts, word building, and mechanical construction of everyday objects, thereby facilitating better word and text retention. The concept involves constructing visual objects made entirely out of 3D text/words, where each component of the object represents a word or text element. For instance, a bird can be recreated using words or text shaped like its wings, beak, legs, head, and body, resulting in a 3D representation of the bird purely composed of text. This can serve as an art piece or a learning tool in the form of a 3D text toy. These 3D text objects or toys can be crafted using natural materials such as leaves, twigs, strings, or ropes, or they can be made from various physical materials using traditional crafting tools. Digital versions of these objects can be created using 2D or 3D software on devices like phones, laptops, iPads, or computers. To transform digital designs into physical objects, computerized machines such as CNC routers, laser cutters, and 3D printers can be utilized. Once the parts are printed or cut out, students can assemble the 3D texts by gluing them together, resulting in natural or everyday 3D text objects. These objects can be painted to create artistic pieces or text toys, and the addition of wheels can transform them into moving toys. One of the significant advantages of this visual and creative object-based learning process is that students not only learn words but also derive enjoyment from the process of creating, painting, and playing with these objects. The ownership and creation process further enhances comprehension and word retention. Moreover, for individuals with learning disabilities such as dyslexia, ADD (Attention Deficit Disorder), or other learning difficulties, the visual and haptic approach of 3D Text Toys can serve as an additional creative and personalized learning aid. The application of 3D Text Toys extends to both the English language and any other global written language. The adaptation and creative application may vary depending on the country, space, and native written language. Furthermore, the implementation of this visual and haptic learning tool can be tailored to teach foreign languages based on age level and comprehension requirements. In summary, this creative, haptic, and visual approach has the potential to serve as a global literacy tool.

Keywords: 3D text toys, creative, artistic, visual learning for world literacy

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Authors: Aregamalage Sujeewa Vijayanthi Polgampala, Fang Huang

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This study explores the genesis and development of concept mapping as a useful tool for science education and its effectiveness as technique for teaching and learning and evaluation for secondary science in schools and the role played by National College of Education science teachers. Concept maps, when carefully employed and executed serves as an integral part of teaching method and measure of effectiveness of teaching and tool for evaluation. Research has shown that science concept maps can have positive influence on student learning and motivation. The success of concept maps played in an instruction class depends on the type of theme selected, the development of learning outcomes, and the flexibility of instruction in providing library unit that is equipped with multimedia equipment where learners can interact. The study was restricted to 6 male and 9 female respondents' teachers in third-year internship pre service science teachers in Gampaha district Sri Lanka. Data were collected through 15 item questionnaire provided to learners and in depth interviews and class observations of 18 science classes. The two generated hypotheses for the study were rejected, while the results revealed that significant difference exists between factors influencing teachers' choice of concept maps, its usefulness and problems hindering the effectiveness of concept maps for teaching and learning process of secondary science in schools. It was examined that concept maps can be used as an effective measure to evaluate students understanding of concepts and misconceptions. Even the teacher trainees could not identify, key concept is on top, and subordinate concepts fall below. It is recommended that pre service science teacher trainees should be provided a thorough training using it as an evaluation instrument.

Keywords: concept maps, evaluation, learning science, misconceptions

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Authors: Ben Tappin, Ryan McKay

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The political left and right appear to disagree not only over questions of value but, also, over questions of fact—over what is true “out there” in society and the world. Alarmingly, a large body of survey data collected during the past decade suggests that this disagreement tends to be greatest among the most educated and most cognitively sophisticated opposing partisans. In other words, the data show that these individuals display the widest political polarization in their reported factual beliefs. Explanations of this polarization pattern draw heavily on cultural and political factors; yet, the large majority of the evidence originates from one cultural and political context—the United States, a country with a rather unique cultural and political history. One consequence is that widening political polarization conditional on education and cognitive sophistication may be due to idiosyncratic cultural, political or historical factors endogenous to US society—rather than a more general, international phenomenon. We examined widening political polarization conditional on education across Europe, over a topic that is culturally and politically contested; immigration. To do so, we analyzed data from the European Social Survey, a premier survey of countries in and around the European area conducted biennially since 2002. Our main results are threefold. First, we see widening political polarization conditional on education over beliefs about the economic impact of immigration. The foremost countries showing this pattern are the most influential in Europe: Germany and France. However, we also see heterogeneity across countries, with some—such as Belgium—showing no evidence of such polarization. Second, we find that widening political polarization conditional on education is a product of sorting. That is, highly educated partisans exhibit stronger within-group consensus in their beliefs about immigration—the data do not support the view that the more educated partisans are more polarized simply because the less educated fail to adopt a position on the question. Third, and finally, we find some evidence that shocks to the political climate of countries in the European area—for example, the “refugee crisis” of summer 2015—were associated with a subsequent increase in political polarization over immigration conditional on education. The largest increase was observed in Germany, which was at the centre of the so-called refugee crisis in 2015. These results reveal numerous insights: they show that widening political polarization conditional on education is not restricted to the US or native English-speaking culture; that such polarization emerges in the domain of immigration; that it is a product of within-group consensus among the more educated; and, finally, that exogenous shocks to the political climate may be associated with subsequent increases in political polarization conditional on education.

Keywords: beliefs, Europe, immigration, political polarization

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Authors: Bo-Bin Xiaoa, Chia-Yi Liua

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In the task of searching for anomalous radiation sources, personnel holding radiation detectors to search for radiation sources may be exposed to unnecessary radiation risk, and automated search using machines becomes a required project. The research uses various sophisticated algorithms, which are double Q learning, dueling network, and NoisyNet, of deep reinforcement learning to search for radiation sources. The simulation environment, which is a 10*10 grid and one shielding wall setting in it, improves the development of the AI model by training 1 million episodes. In each episode of training, the radiation source position, the radiation source intensity, agent position, shielding wall position, and shielding wall length are all set randomly. The three algorithms are applied to run AI model training in four environments where the training shielding wall is a full-shielding wall, a lead wall, a concrete wall, and a lead wall or a concrete wall appearing randomly. The 12 best performance AI models are selected by observing the reward value during the training period and are evaluated by comparing these AI models with the gradient search algorithm. The results show that the performance of the AI model, no matter which one algorithm, is far better than the gradient search algorithm. In addition, the simulation environment becomes more complex, the AI model which applied Double DQN combined Dueling and NosiyNet algorithm performs better.

Keywords: double Q learning, dueling network, NoisyNet, source searching

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Authors: Nitin Singh, Meng Ling, Talha Ahmed, Tianxia Zhao, Reinier van de Pol

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We propose the use of reinforcement learning (RL) as a viable alternative for optimizing supply chain management, particularly in scenarios with stochasticity in product demands. RL’s adaptability to changing conditions and its demonstrated success in diverse fields of sequential decision-making makes it a promising candidate for addressing supply chain problems. We investigate the impact of demand fluctuations in a multi-product supply chain system and develop RL agents with learned generalizable policies. We provide experimentation details for training RL agents and statistical analysis of the results. We study the generalization ability of RL agents for different demand uncertainty scenarios and observe superior performance compared to the agents trained with fixed demand curves. The proposed methodology has the potential to lead to cost reduction and increased profit for companies dealing with frequent inventory movement between supply and demand nodes.

Keywords: inventory management, reinforcement learning, supply chain optimization, uncertainty

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Authors: Fatima Saeed, Abdullah Mudassar

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Introduction: Patient safety remains a global priority in the ever-evolving healthcare landscape. At the forefront of this endeavor are the International Patient Safety Goals (IPSGs), a standardized framework designed to mitigate risks and elevate the quality of care. Doctors, positioned as primary caregivers, wield a pivotal role in upholding and adhering to IPSGs, underscoring the critical significance of their knowledge and understanding of these goals. This research embarks on a comprehensive exploration into the depth of Doctors ' comprehension of IPSGs, aiming to unearth potential gaps and provide insights for targeted educational interventions. Established by influential healthcare bodies, including the World Health Organization (WHO), IPSGs represent a universally applicable set of objectives spanning crucial domains such as medication safety, infection control, surgical site safety, and patient identification. Adherence to these goals has exhibited substantial reductions in adverse events, fostering an overall enhancement in the quality of care. This study operates on the fundamental premise that an informed Doctors workforce is indispensable for effectively implementing IPSGs. A nuanced understanding of these goals empowers Doctors to identify potential risks, advocate for necessary changes, and actively contribute to a safety-centric culture within healthcare institutions. Despite the acknowledged importance of IPSGs, there is a growing concern that nurses may need more knowledge to integrate these goals into their practice seamlessly. Methodology: A Comprehensive research methodology covering study design, setting, duration, sample size determination, sampling technique, and data analysis. It introduces the philosophical framework guiding the research and details material, methods, and the analysis framework. The descriptive quantitative cross-sectional study in teaching care hospitals utilized convenient sampling over six months. Data collection involved written informed consent and questionnaires, analyzed with SPSS version 23, presenting results graphically and descriptively. The chapter ensures a clear understanding of the study's design, execution, and analytical processes. Result: The survey results reveal a substantial distribution across hospitals, with 34.52% in MTIKTH and 65.48% in HMC MTI. There is a notable prevalence of patient safety incidents, emphasizing the significance of adherence to IPSGs. Positive trends are observed, including 77.0% affirming the "time-out" procedure, 81.6% acknowledging effective healthcare provider communication, and high recognition (82.7%) of the purpose of IPSGs to improve patient safety. While the survey reflects a good understanding of IPSGs, areas for improvement are identified, suggesting opportunities for targeted interventions. Discussion: The study underscores the need for tailored care approaches and highlights the bio-socio-cultural context of 'contagion,' suggesting areas for further research amid antimicrobial resistance. Shifting the focus to patient safety practices, the survey chapter provides a detailed overview of results, emphasizing workplace distribution, patient safety incidents, and positive reflections on IPSGs. The findings indicate a positive trend in patient safety practices with areas for improvement, emphasizing the ongoing need for reinforcing safety protocols and cultivating a safety-centric culture in healthcare. Conclusion: In summary, the survey indicates a positive trend in patient safety practices with a good understanding of IPSGs among participants. However, identifying areas for potential improvement suggests opportunities for targeted interventions to enhance patient safety further. Ongoing efforts to reinforce adherence to safety protocols, address identified gaps, and foster a safety culture will contribute to continuous improvements in patient care and outcomes.

Keywords: infection control, international patient safety, patient safety practices, proper medication

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Authors: Ci Lin, Tet Yeap, Iluju Kiringa

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This paper introduces the subspace rotation algorithm (SRA) to train the Restricted Hopfield Network (RHN) as an auto-associative memory. Subspace rotation algorithm is a gradient-free subspace tracking approach based on the singular value decomposition (SVD). In comparison with Backpropagation Through Time (BPTT) on training RHN, it is observed that SRA could always converge to the optimal solution and BPTT could not achieve the same performance when the model becomes complex, and the number of patterns is large. The AUTS case study showed that the RHN model trained by SRA could achieve a better structure of attraction basin with larger radius(in general) than the Hopfield Network(HNN) model trained by Hebbian learning rule. Through learning 10000 patterns from MNIST dataset with RHN models with different number of hidden nodes, it is observed that an several components could be adjusted to achieve a balance between recovery accuracy and noise resistance.

Keywords: hopfield neural network, restricted hopfield network, subspace rotation algorithm, hebbian learning rule

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Authors: Chukwuma S. Ezeonu, Ikechukwu N. E. Onwurah, Uchechukwu U. Nwodo, Chibuike S. Ubani, Chigozie M. Ejikeme

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Trichophyton soudanense and Trichophyton mentagrophyte were isolated from the rice mill environment, cultured and used singly and as di-culture in the treatment of measure quantities of preheated rice husk. Optimized conditions studied showed that carboxymethylcellulase (CMCellulase) activity of 57.61 µg/ml/min was optimum for Trichophyton mentagrophyte heat pretreated rice husk crude enzymes at 50oC and 80oC respectively. Duration of 120 hours (5 days) gave the highest CMcellulase activity of 75.84 µg/ml/min for crude enzyme of Trichophyton mentagrophyte heat pretreated rice husk. However, 96 hours (4 days) duration gave maximum activity of 58.21 µg/ml/min for crude enzyme of Trichophyton soudanense heat pretreated rice husk. Highest CMCellulase activities of 67.02 µg/ml/min and 69.02 µg/ml/min at pH of 5 were recorded for crude enzymes of monocultures of Trichophyton soudanense (TS) and Trichophyton mentagrophyte (TM) heat pretreated rice husk respectively. Biomass components showed that rice husk cooled after heating followed by treatment with Trichophyton mentagrophyte gave 44.50 ± 10.90 (% ± Standard Error of Mean) cellulose as the highest yield. Maximum total lignin value of 28.90 ± 1.80 (% ± SEM) was obtained from pre-heated rice husk treated with di-culture of Trichophyton soudanense and Trichophyton mentagrophyte (TS+TM). The hemicellulose content of 30.50 ± 2.12 (% ± SEM) from pre-heated rice husk treated with Trichophyton soudanense (TS); lignin value of 28.90 ± 1.80 from pre-heated rice husk treated with di-culture of Trichophyton soudanense and Trichophyton mentagrophyte (TS+TM); also carbohydrate content of 16.79 ± 9.14 (% ± SEM) , reducing and non-reducing sugar values of 2.66 ± 0.45 and 14.13 ± 8.69 (% ± SEM) were all obtained from for pre- heated rice husk treated with Trichophyton mentagrophyte (TM). All the values listed above were the highest values obtained from each rice husk treatment. The pre-heated rice husk treated with Trichophyton mentagrophyte (TM) fermented with palmwine yeast gave bio-ethanol value of 11.11 ± 0.21 (% ± Standard Deviation) as the highest yield.

Keywords: Trichophyton soudanense, Trichophyton mentagrophyte, biomass, bioethanol, rice husk

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Authors: Sidra Majeed, Irum Ali, Aroosa Ishfaq, Munazzah Parveen

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Objectives: The objective is to compare the effects of high-intensity interval training VS moderate moderate-intensity continuous training on cardiovascular endurance in young healthy females. Method: 30 young, healthy females were collected and randomly assigned into two training groups, HIIT and MICT, each group having a sample size of (n=15). There will be three parameters to be tested, including (VO2max, Resting heart rate, and Rate perceived exertion). Each group will be tested at three different times, e.g. (at Baseline measurement, after two weeks and after four weeks). For the first two weeks, the HIIT group has to perform at 70%HRR and for the third and fourth weeks, at 75%HRR for two minutes, followed by an active resting interval at 30%HRR for two minutes (1:1) with warm-up and cool-down period (2 minutes each period ) on the treadmill. For the first two weeks, the MICT group has to perform at 40%HRR and for the third and fourth weeks at 50% HRR for fifteen minutes continuously on the treadmill, including warm up and cool down period (2 minutes each period). Result: The final assessment of HIIT and MICT groups had shown p values for VO2max (p=.000), RHR (p=.323) and for RPE (p=.085). These values indicating significant improvement in these three parameters in both groups. Conclusion: This study showed that there were significant improvements in both groups but there were more improvements in VO2max in HIIT group so, it is proved that HIIT is more beneficial than MICT in improving cardiovascular endurance.

Keywords: HIIT, MICT, RPE, RHR

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Authors: AnjalI A. Prasad

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Nowadays, social media played an important role in spreading misinformation or fake news. This study analyzes the fake news related to the COVID-19 pandemic spread in social media. This paper aims at evaluating and comparing different approaches that are used to mitigate this issue, including popular deep learning approaches, such as CNN, RNN, LSTM, and BERT algorithm for classification. To evaluate models’ performance, we used accuracy, precision, recall, and F1-score as the evaluation metrics. And finally, compare which algorithm shows better result among the four algorithms.

Keywords: BERT, CNN, LSTM, RNN

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Authors: Khedidja Benaicha Mati

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The present research tries to highlight a very crucial issue which exists at the level of the faculty of Economics and Management at Chlef university. This issue is represented by the challenges and difficulties which face the teaching / learning process in the faculty on the part of the language teachers, the learners, and the administration staff, including mainly the absence of an agreed syllabus, lack of teaching materials, teachers’ qualifications and training, timing, coefficient, and lack of motivation and interest amongst students. All these negative factors make teaching and learning EFL rather ambiguous, ineffective and unsatisfactory. The students at the faculty of Economics and Management are looking for acquiring not only GE but also technical English to respond efficiently to the ongoing changes at the various levels most notably economy, business, technology, and sciences. Therefore, there is a need of ESP programmes which would focus on developing the communicative competence of the learners in their specific field of study or work. The aim of the present research is to explore the ways of improving the actual situation of teaching English in the faculty of Economics and to make the English courses more purposive, fulfilling and satisfactory. The sample population focused on second and third-year students of Economics from different specialties mainly commercial sciences, insurance and banking, accountancy, and management. This is done through a questionnaire which inquires students about their learning weaknesses, difficulties and challenges they encounter, and their expectations of the subject matter.

Keywords: faculty of economics and management, challenges, teaching/ learning process, EFL, GE, ESP, English courses, communicative competence

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Authors: Lassaad Smirani

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In this paper we propose a Neurosymbolic Learning System (NLS) as a channel estimator for Long Term Evolution Advanced (LTE-A) uplink. The proposed system main idea based on Neural Network has modules capable of performing bidirectional information transfer between symbolic module and connectionist module. We demonstrate various strengths of the NLS especially the ability to integrate theoretical knowledge (rules) and experiential knowledge (examples), and to make an initial knowledge base (rules) converted into a connectionist network. Also to use empirical knowledge witch by learning will have the ability to revise the theoretical knowledge and acquire new one and explain it, and finally the ability to improve the performance of symbolic or connectionist systems. Compared with conventional SC-FDMA channel estimation systems, The performance of NLS in terms of complexity and quality is confirmed by theoretical analysis and simulation and shows that this system can make the channel estimation accuracy improved and bit error rate decreased.

Keywords: channel estimation, SC-FDMA, neural network, hybrid system, BER, LTE-A

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Authors: Gi Ryung Song, Kyoung Seok Kim

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This study reviewed the literature that focused on similarity of various characteristics such as values, personality, or demographics between employee and other elements in its organization for example employee with leader, job, and organization. We divided a body of this study into two parts and organized and demonstrated recent studies in first part. Three issues appeared in this part, which are statistical ways of measuring similarity, supervisor-subordinate similarity, and person-organization fit with person-job fit. In the latter part, based on the three issues of recent studies, we suggested three propositions about points that the recent studies missed or the studies did not orient. First proposition argued about the direction of similarity, which could also be interpreted as there is causal relation between employee and its workplace environments. Second, we suggested a consideration of eliminating common variance buried in one’s characteristics or its profiles. Third proposition was about the similarity of extra role behavior between individual and organization, and we treated this organization’s level of extra role behavior as a kind of its culture. In doing so, similarity of individual’s extra role behavior and organization’s has the meaning that individual’s congruence against their organization culture.

Keywords: similarity, person-organization fit, supervisor-subordinate similarity, literature review

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Authors: Hoi Nga Ng, Kam Weng Boey, Chi Wai Kwan

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Supporters of Inclusive education proclaim that all students, regardless of disabilities or special educational needs (SEN), have the right to study in the normal school setting. It is asserted that students with SEN would benefit in academic performance and psychosocial adjustment via participation in common learning activities within the ordinary school system. When more and more students of SEN completed their early schooling, institute of higher education become the setting where students of SEN continue their learning. This study aimed to investigate the school well-being, social relationship, and academic self-concept of students of SEN in higher education. The Perception of Inclusion Questionnaire (PIQ) was used as the measuring instruments. PIQ was validated and incorporated in a questionnaire designed for online survey. Participation was voluntary and anonymous. A total of 90 students with SEN and 457 students without SEN responded to the online survey. Results showed no significant differences in school well-being and social relationship between students with and without SEN, but students with SEN, particularly those with learning and development impairment and those with mental illness and emotional problems, were significantly poorer in academic self-concept. Implications of the findings were discussed.

Keywords: ccademic self-concept, school well-being, social relationship, special educational needs

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Authors: Benjamin Bonsu Bruce, Marian Dorcas Quain David Appiah-Kubi, Gertrude Osei-Diko, Harrison Kwame Dapaah

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Taro [Colocasia esculenta (L.) Schott] is a major staple food and remains a significant crop to many cultural and agricultural customs worldwide. In Ghana, taro is mostly propagated using vegetative material, which is conserved in field collection and recycled from their farms to establish new fields. However, this practice promotes the accumulation of systemic pathogens. Prior exposure to pests and subsequent expression of disease symptoms can also be a huge constraint to sustainable conservation and utilization of taro genetic resources. In vitro, slow growth is one of the most promising techniques to be utilized for conservation. The objective of this study was to find a medium-term in vitro conservation protocol for local and exotic taro genotypes. The medium-term conservation study was conducted using actively growing shoots obtained from in vitro cultures. Explants were cultured to full strength in complete Murashige and Skoog medium supplemented with Mannitol at different concentrations (0g/l, 20g/l, 25g/l, and 30g/l). Another medium that was tested as an additional treatment is the White’s medium. The highest number of shoots (6.33) and leaves (22.67) occurred on medium containing 20 and 25g/l mannitol in genotype SAO 006 as compared to other genotypes, whereas 30g/l mannitol was the best to restrict growth for the entire 6 months period in terms of shoot height (22.50cm). The study reveals that mannitol supplemented culture media could reduce the growth of Colocasia plantlets, especially in stem height. Culture growth following 6 months of conservation, showed that healthy shoot cultures of Taro were obtained after 6 months of storage in a medium containing 20gl⁻¹ and 25gl⁻¹ mannitol.

Keywords: complete murashige, skoog medium, culture conditions, mannitol, slow growth conservation

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Authors: Fayiz Saifurahman, Noori Fida Mohammad

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Afghanistan produces the highest rate of wheat, it is the first source of food, and food security in Afghanistan is dependent on the availability of wheat. Although Afghanistan is the main producer of wheat, on the other hand, Afghanistan is the largest importers of flour. The objective of this study is to assess the structure and dynamics of the wheat market in Afghanistan, can compute with foreign markets, and increase the level of production. To complete this, a broad series of secondary data was complied with, group discussions and interviews with farmers, agricultural and market experts. The research findings propose that; the government should adopt different policies to support the local market. The government should distribute the seed, support financially and technically to increase wheat production.

Keywords: Afghanistan, wheat, production , import

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Authors: Ibrahim Malik, M. Ikramullah al Kambuna

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Today the crisis of understanding in Islamic values has been extended to the architectural design. The majority of Muslim Architects when they designed Islamic Architectural building always stuck in cultural symbols, forms of facade, carvings calligraphy, and all of things which intimately associated with middle eastern culture. As well as the interpretation of symbols form, by designing a dome in every mosque, calligraphy carving in the interior of the mosque, and the other elements in the building which interpreted to the middle east culture. So here there are problems in understanding the meaning of Islam with Kaffah (overall), which appeared a distortion of understanding to distinguish between cultural and theological values in a design. This paper will try to evaluate the design of a contemporary mosque in Indonesia, with case studies in Jogokariyan Mosque Yogyakarta Indonesia. The building evaluation are focused in building function , aesthetic, comfort, and security. And finally from this research should be found the results of the integrated design evaluation of a contemporary mosque which based on the study of the Quran and Hadith.

Keywords: evaluation, islamic concept, mosque, Hadith, Quran

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Authors: Malik Ghulam Behlol, Alison Fox, Faiza Masood, Sabiha Arshad

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This paper explores the barriers to the application of learning-supportive assessment at teaching practicum while investigating the role of university teachers (UT), cooperative teachers (CT), prospective teachers ( PT) and heads of the practicum schools (HPS) in the selected universities of Pakistan and the UK. It is a qualitative case study and data were collected through the lesson observation of UT in the pre-service teacher education setting and PT in practicum schools. Interviews with UT, HPS, and Focus Group Discussions with PT were conducted too. The study has concluded that as compared to the UK counterpart, PT in Pakistan faces significant barriers in applying learning-supportive assessment in the school practicum settings because of large class sizes, lack of institutionalised collaboration between universities and schools, poor modelling of the lesson, ineffective feedback practices, lower order thinking assignments, and limited opportunities to use technology in school settings.

Keywords: assessment, pre-service teacher education, theory-practice gap, teacher education

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Authors: Tina Stavredes

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Increasingly borderless learning opportunities including online learning are expanding. Singapore University of Social Science (SUSS) conducted research in February of 2017 to determine the level of consumer interest in undertaking a completely online distance learning degree program across three countries in the Asian Pacific region. The target audience was potential bachelor degree and post-degree students from Malaysia, Indonesia, and Vietnam. The results gathered were used to assess the market size and ascertain the business potential of online degree programs in Malaysia, Indonesia and Vietnam. Secondly, the results were used to determine the most receptive markets to prioritise entry and identify the most receptive student segments. In order to achieve the key outcomes, the key points of understanding were as follows: -Motivations for higher education & factors that influence the choice of institution, -Interest in online learning, -Interest in online learning from a Singapore university relative to other foreign institutions, -Key drivers and barriers of interest in online learning. An online survey was conducted from from 7th Feb 2017 to 27th Feb 2017 amongst n=600 respondents aged 21yo-45yo, who have a basic command of English, A-level qualifications and above, and who have an intent to further their education in the next 12 months. Key findings from the study regarding enrolling in an online program include the need for a marriage between intrinsic and extrinsic motivation factors and the flexibility and support offered in an online program. Overall, there was a high interest for online learning. Survey participants stated they are intrinsically motivated to learn because of their interest in the program of study and the need for extrinsic rewards including opportunities for employment or salary increment in their current job. Seven out of ten survey participants reported they are motivated to further their education and expand their knowledge to become more employable. Eight in ten claims that the feasibility of furthering their education depends on cost and maintaining a work-life balance. The top 2 programs of interest are business and information and communication technology. They describe their choice of university as a marriage of both motivational and feasibility factors including cost, choice, quality of support facilities, and the reputation of the institution. Survey participants reported flexibility as important and stated that appropriate support assures and grows their intent to enrol in an online program. Respondents also reported the importance of being able to work while studying as the main perceived advantage of online learning. Factors related to the choice of an online university emphasized the quality of support services. Despite concerns, overall there was a high interest for online learning. One in two expressed strong intent to enrol in an online programme of study. However, unfamiliarity with online learning is a concern including the concern with the lack of face-to-face interactions. Overall, the findings demonstrated an interest in online learning. A main driver was the ability to earn a recognised degree while still being able to be with the family and the ability to achieve a ‘better’ early career growth.

Keywords: distance education, student motivations, online learning, online student needs

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Authors: Leigh Ann Wilson, Melanie Borrego

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The purpose of institutional learning outcomes (ILOs) is to assess what students across the university know and what they do not. The issue is gathering this information in a systematic and usable way. This presentation will explain how one institution has engineered this process for both student success and maximum faculty curriculum and course design input. At Brandman University, there are three levels of learning outcomes: course, program, and institutional. Institutional Learning Outcomes (ILOs) are mapped to specific courses. Faculty course developers write the signature assignments (SAs) in alignment with the Institutional Learning Outcomes for each course. These SAs use a specific rubric that is applied consistently by every section and every instructor. Each year, the 12-member General Education Team (GET), as a part of their work, conducts the calibration and assessment of the university-wide SAs and the related rubrics for one or two of the five ILOs. GET members, who are senior faculty and administrators who represent each of the university's schools, lead the calibration meetings. Specifically, calibration is a process designed to ensure the accuracy and reliability of evaluating signature assignments by working with peer faculty to interpret rubrics and compare scoring. These calibration meetings include the full time and adjunct faculty members who teach the course to ensure consensus on the application of the rubric. Each calibration session is chaired by a GET representative as well as the course custodian/contact where the ILO signature assignment resides. The overall calibration process GET follows includes multiple steps, such as: contacting and inviting relevant faculty members to participate; organizing and hosting calibration sessions; and reviewing and discussing at least 10 samples of student work from class sections during the previous academic year, for each applicable signature assignment. Conversely, the commitment for calibration teams consist of attending two virtual meetings lasting up to three hours in duration. The first meeting focuses on interpreting the rubric, and the second meeting involves comparing scores for sample work and sharing feedback about the rubric and assignment. Next, participants are expected to follow all directions provided and participate actively, and respond to scheduling requests and other emails within 72 hours. The virtual meetings are recorded for future institutional use. Adjunct faculty are paid a small stipend after participating in both calibration meetings. Full time faculty can use this work on their annual faculty report for "internal service" credit.

Keywords: assessment, assurance of learning, course design, institutional learning outcomes, rubrics, signature assignments

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: Pan Long, Bi Dongjie, Li Xifeng, Xie Yongle

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The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals.

Keywords: complex-valued signal processing, synthetic aperture radar, 2-D radar imaging, compressive sensing, sparse Bayesian learning

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Authors: M. Roberts Masillamani

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There is a phenomenal difference, as regard to the teaching methodology adopted at the urban and the rural area colleges. However, bright and talented student may be from rural back ground even. But there is huge dearth of the digitization in the rural areas and lesser developed countries. Today’s students need new skills to compete and successful in the future. Education should be combination of practical, intellectual, and social skills. What does this mean for rural classrooms and how can it be achieved. Rural colleges are not able to hire the best resources, since the best teacher’s aim is to move towards the city. If city is provided everywhere, then there will be no rural area. This is possible by forming university clusters (UC). The University cluster is a group of renowned and accredited universities coming together to bridge this dearth. The UC will deliver the live lectures and allow the students’ from remote areas to actively participate in the classroom. This paper tries to present a plan of action of providing a better live classroom teaching and learning system from the city to the rural and the lesser developed countries. This paper titled “University Clusters using ICT for teaching and learning” provides a true concept of opening live digital classroom windows for rural colleges, where resources are not available, thus reducing the digital divide. This is different from pod casting a lecture or distance learning and eLearning. The live lecture can be streamed through digital equipment to another classroom. The rural students can collaborate with their peers and critiques, be assessed, collect information, acquire different techniques in assessment and learning process. This system will benefit rural students and teachers and develop socio economic status. This will also will increase the degree of confidence of the Rural students and teachers. Thus bringing about the concept of ‘Train the Trainee’ in reality. An educational university cloud for each cluster will be built remote infrastructure facilities (RIF) for the above program. The users may be informed, about the available lecture schedules, through the RIF service. RIF with an educational cloud can be set by the universities under one cluster. This paper talks a little more about University clusters and the methodology to be adopted as well as some extended features like, tutorial classes, library grids, remote laboratory login, research and development.

Keywords: lesser developed countries, digital divide, digital learning, education, e-learning, ICT, library grids, live classroom windows, RIF, rural, university clusters and urban

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Authors: H. J. Joshi, Satyajeet Patil, Parth Dandavate, Mihir Kulkarni, Harshita Agrawal

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As the world looks towards a sustainable future, electric vehicles have become increasingly popular. Millions worldwide are looking to switch to Electric cars over the previously favored combustion engine-powered cars. This demand has seen an increase in Electric Vehicle Charging Stations. The big challenge is that the randomness of electrical energy makes it tough for these charging stations to provide an adequate amount of energy over a specific amount of time. Thus, it has become increasingly crucial to model these patterns and forecast the energy needs of power stations. This paper aims to analyze how different machine learning models perform on Electric Vehicle charging time-series data. The data set consists of authentic Electric Vehicle Data from the Netherlands. It has an overview of ten thousand transactions from public stations operated by EVnetNL.

Keywords: forecasting, smart grid, electric vehicle load forecasting, machine learning, time series forecasting

Procedia PDF Downloads 95