Search results for: architectural design learning

Authors: RAM PAL SINGH, VIKASH CHAUDHARY, MONIKA VERMA

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In this paper, we proposed the implementation of optimization based Extreme Learning Machine (ELM) for watermarking of B-channel of color image in discrete wavelet transform (DWT) domain. ELM, a regularization algorithm, works based on generalized single-hidden-layer feed-forward neural networks (SLFNs). However, hidden layer parameters, generally called feature mapping in context of ELM need not to be tuned every time. This paper shows the embedding and extraction processes of watermark with the help of ELM and results are compared with already used machine learning models for watermarking.Here, a cover image is divide into suitable numbers of non-overlapping blocks of required size and DWT is applied to each block to be transformed in low frequency sub-band domain. Basically, ELM gives a unified leaning platform with a feature mapping, that is, mapping between hidden layer and output layer of SLFNs, is tried for watermark embedding and extraction purpose in a cover image. Although ELM has widespread application right from binary classification, multiclass classification to regression and function estimation etc. Unlike SVM based algorithm which achieve suboptimal solution with high computational complexity, ELM can provide better generalization performance results with very small complexity. Efficacy of optimization method based ELM algorithm is measured by using quantitative and qualitative parameters on a watermarked image even though image is subjected to different types of geometrical and conventional attacks.

Keywords: BER, DWT, extreme leaning machine (ELM), PSNR

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Authors: Emad Khorshid, Habib Awada

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Newly developed Ergonomic speed control hump/Dip designs are conducted. The numerical simulation for the driver-vehicle-hump dynamic system will be performed using computer software. The design problem for which the speed hump or dip should provide: (1) discomfort feeling to the driver if speed is over the specified limit, and (2) normal/good comfort level to the driver (and or other passengers) if the speed is within the limit. For comparison reasons, different vehicles suspension systems (active, semi-active and non-active suspension) are used in the simulation. The measuring of the acceptable range of vibration will be referenced to the British standard BS6841, ISO 2631/1 and the new ISO 2631/5. All these standards are related to human health and comfort level in terms of acceptable range of whole body vibration exposure.

Keywords: speed hump, speed dip, ergonomic design, human health, vehicle modeling

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Authors: Nisreen Ardda

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Fenestrations are one of the main building envelope elements that play an important role in home social-ecological l factors. They play a vital role in providing natural lighting and ventilation, visual, thermal, and acoustical comfort, and also provide weather-tightness, privacy, a feeling of openness. In most home buildings, fenestrations are controlled manually by the occupants, which significantly impacts occupants' comfort and energy use. Culture plays a central role in the Palestinians window operation behavior. Improved windows design that provides the desired privacy while maintaining the appropriate function of fenestration (natural lighting, thermal comfort, and visual openness) is becoming a necessity. Therefore, this paper proposes a window typology to achieve the social and environmental factors in residential buildings in the West Bank. The window typology and reference building were designed in Rivet 2021, and natural ventilation was carried out in Design Builder 4.3.0.039. The results showed that the proposed typology provides the desired privacy and the feeling of openness without compromising natural ventilation as the existing window did.

Keywords: window design, passive design, sustainable built environment, building material

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Authors: Balthazar Temu, Zhao Yan, Bogdan-Petrin Ratiu, Sang Soon Oh, Qiang Li

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Nanowire based Photonic Crystal Surface Emitting Lasers (PCSELs) reported in the literature have been designed using a triangular, square or honeycomb patterns. The triangular and square pattern PCSELs have limited degrees of freedom in tuning the design parameters which hinders the ability to design high quality factor (Q-factor) devices. Nanowire based PCSELs designed using triangular and square patterns have been reported with the lasing thresholds of 130 kW/〖cm〗^2 and 7 kW/〖cm〗^2 respectively. On the other hand the honeycomb pattern gives more degrees of freedom in tuning the design parameters, which can allow one to design high Q-factor devices. A deformed honeycomb pattern device was reported with lasing threshold of 6.25 W/〖cm〗^2 corresponding to a simulated Q-factor of 5.84X〖10〗^5.Despite this achievement, the design principles which can lead to realization of even higher Q-factor honeycomb pattern PCSELs have not yet been investigated. In this work we show that through deforming the honeycomb pattern and tuning the heigh and lattice constants of the nanowires, it is possible to achieve even higher Q-factor devices. Considering three different band edge modes, we investigate how the resonance wavelength changes as the device is deformed, which is useful in designing high Q-factor devices in different wavelength bands. We eventually establish the design and simulation of honeycomb PCSELs operating around the wavelength of 960nm , in the O and the C band with Q-factors up to 7X〖10〗^7. We also investigate the Q-factors of undeformed device, and establish that the mode at the band edge close to 960nm can attain highest Q-factor of all the modes when the device is undeformed and the Q-factor degrades as the device is deformed. This work is a stepping stone towards the fabrication of very high Q-factor, nanowire based honey comb PCSELs, which are expected to have very low lasing threshold.

Keywords: designing nanowire PCSEL, designing PCSEL on silicon substrates, low threshold nanowire laser, simulation of photonic crystal lasers

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Authors: Chhavi Rana, Sanjay K Jain

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All sides of every education debate agree that quality learning happens when knowledgeable, caring teachers use sound pedagogy. Many deliberations of pedagogy make the fault of considering it as principally being about teaching. There has been lot of research about how to build a positive climate for learning, improve student curiosity, and enhance classroom association. However, these things can only be facilitated when teachers are equipped with better teaching techniques that use sound and accurate pedagogy. Pedagogy is the science and art of education. Its aims range from the full development of the human being to skills acquisition. In India, a project named Mission 10 x has been started by an esteemed IT Corporation Wipro as a faculty development programme (FDP) that particularly focus on elements that facilitated teachers in developing curriculum and new pedagogies that can lead to improvement in student engagement. This paper presents a study of these FDPs and examines (1) the parameters that help teachers in building new pedagogies (2) the extent to which appropriate usage of pedagogy is improved after the conduct of Mission 10 x FDPs, and (3) whether institutions differ in terms of their ability to convert usage of improved pedagogy into academic performance via these FDPs. The sample consisted of 2,236 students at 6 four-year engineering colleges and universities that completed several FDPs during 2012-2014. Many measures of usage of better pedagogy were linked positively with such FDPs, although some of the relationships were weak in strength. The results suggest that the usage of pedagogy were more benefited after conducting these FDPs and application of novel approaches in conducting classes.

Keywords: student engagement, critical thinking; achievement, student learning, pedagogy

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Authors: Brandon Foggo, Nanpeng Yu

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Power distribution circuits undergo frequent network topology changes that are often left undocumented. As a result, the documentation of a circuit’s connectivity becomes inaccurate with time. The lack of reliable circuit connectivity information is one of the biggest obstacles to model, monitor, and control modern distribution systems. To enhance the reliability and efficiency of electric power distribution systems, the circuit’s connectivity information must be updated periodically. This paper focuses on one critical component of a distribution circuit’s topology - the secondary transformer to phase association. This topology component describes the set of phase lines that feed power to a given secondary transformer (and therefore a given group of power consumers). Finding the documentation of this component is call Phase Identification, and is typically performed with physical measurements. These measurements can take time lengths on the order of several months, but with supervised learning, the time length can be reduced significantly. This paper compares several such methods applied to Phase Identification for a large range of real distribution circuits, describes a method of training data selection, describes preprocessing steps unique to the Phase Identification problem, and ultimately describes a method which obtains high accuracy (> 96% in most cases, > 92% in the worst case) using only 5% of the measurements typically used for Phase Identification.

Keywords: distribution network, machine learning, network topology, phase identification, smart grid

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Authors: Ali Almadan, Anoop Krishnan, Ajita Rattani

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Facial recognition is a widely adopted technology in surveillance, border control, healthcare, banking services, and lately, in mobile user authentication with Apple introducing “Face ID” moniker with iPhone X. A lot of research has been conducted in the area of face recognition on datasets captured by surveillance cameras, DSLR, and mobile devices. Recently, face recognition technology has also been deployed on body-worn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic so far, without the availability of any publicly available datasets with a sufficient sample size. This paper aims to advance research in the area of face recognition using body-worn cameras. To this aim, the contribution of this work is two-fold: (1) collection of a dataset consisting of a total of 136,939 facial images of 102 subjects captured using body-worn cameras in in-door and daylight conditions and (2) evaluation of various deep-learning architectures for face identification on the collected dataset. Experimental results suggest a maximum True Positive Rate(TPR) of 99.86% at False Positive Rate(FPR) of 0.000 obtained by SphereFace based deep learning architecture in daylight condition. The collected dataset and the baseline algorithms will promote further research and development. A downloadable link of the dataset and the algorithms is available by contacting the authors.

Keywords: face recognition, body-worn cameras, deep learning, person identification

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Authors: Theresia El Khoury, Georges Badr, Amir Hajjam El Hassani, Stéphane N’Guyen Van Ky

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The widespread adoption of machine learning and artificial intelligence across different domains can be attributed to the digitization of data over several decades, resulting in vast amounts of data, types, and structures. Thus, data processing and preparation turn out to be a crucial stage. However, applying these techniques to S1000D standard-based data poses a challenge due to its complexity and the need to preserve logical information. This paper describes SALERNO, an S1000d AppLicability dEpended pRocessiNg mOdel. This python-based model analyzes and converts the XML S1000D-based files into an easier data format that can be used in machine learning techniques while preserving the different logic and relationships in files. The model parses the files in the given folder, filters them, and extracts the required information to be saved in appropriate data frames and Excel sheets. Its main idea is to group the extracted information by applicability. In addition, it extracts the full text by replacing internal and external references while maintaining the relationships between files, as well as the necessary requirements. The resulting files can then be saved in databases and used in different models. Documents in both English and French languages were tested, and special characters were decoded. Updates on the technical manuals were taken into consideration as well. The model was tested on different versions of the S1000D, and the results demonstrated its ability to effectively handle the applicability, requirements, references, and relationships across all files and on different levels.

Keywords: aeronautics, big data, data processing, machine learning, S1000D

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Authors: M. M. Essa, S. Subash, M. Akbar, S. Al-Adawi, A. Al-Asmi, G. J. Guillemein

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Objective: Transgenic (tg) mice which contain an amyloid precursor protein (APP) gene mutation, develop extracellular amyloid beta (Aβ) deposition in the brain, and severe memory and behavioural deficits with age. These mice serve as an important animal model for testing the efficacy of novel drug candidates for the treatment and management of symptoms of Alzheimer's disease (AD). Several reports have suggested that oxidative stress is the underlying cause of Aβ neurotoxicity in AD. Pomegranates contain very high levels of antioxidants and several medicinal properties that may be useful for improving the quality of life in AD patients. In this study, we investigated the effect of dietary supplementation of Omani pomegranate extract on the memory, anxiety and learning skills along with inflammation in an AD mouse model containing the double Swedish APP mutation (APPsw/Tg2576). Methods: The experimental groups of APP-transgenic mice from the age of 4 months were fed custom-mix diets (pellets) containing 4% pomegranate. We assessed spatial memory and learning ability, psychomotor coordination, and anxiety-related behavior in Tg and wild-type mice at the age of 4-5 months and 18-19 months using the Morris water maze test, rota rod test, elevated plus maze test, and open field test. Further, inflammatory parameters also analysed. Results: APPsw/Tg2576 mice that were fed a standard chow diet without pomegranates showed significant memory deficits, increased anxiety-related behavior, and severe impairment in spatial learning ability, position discrimination learning ability and motor coordination along with increased inflammation compared to the wild type mice on the same diet, at the age of 18-19 months In contrast, APPsw/Tg2576 mice that were fed a diet containing 4% pomegranates showed a significant improvements in memory, learning, locomotor function, and anxiety with reduced inflammatory markers compared to APPsw/Tg2576 mice fed the standard chow diet. Conclusion: Our results suggest that dietary supplementation with pomegranates may slow the progression of cognitive and behavioural impairments in AD. The exact mechanism is still unclear and further extensive research needed.

Keywords: Alzheimer's disease, pomegranates, oman, cognitive decline, memory loss, anxiety, inflammation

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Authors: Jamison Duckworth, Shankarachary Ragi

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Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.

Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands

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Authors: Gabrielle Peck, Ryan Hayes

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The use of a phi meter allows for definition of equivalence ratio during a fire test. Previous phi meter designs have used expensive catalysts and had restricted portability due to the large furnace and requirement for pure oxygen. The new design of the phi meter did not require the use of a catalyst. The furnace design was based on the existing micro-scale combustion calorimetry (MCC) furnace and operating conditions based on the secondary oxidizer furnace used in the steady state tube furnace (SSTF). Preliminary tests were conducted to study the effects of varying furnace temperatures on combustion efficiency. The SSTF was chosen to validate the phi meter measurements as it can both pre-set and independently quantify the equivalence ratio during a test. The data were in agreement with the data obtained on the SSTF. It was also validated by a comparison of CO2 yields obtained from the SSTF oxidizer and those obtained by the phi meter. The phi meter designed and constructed in this work was proven to work effectively on a bench-scale. The phi meter was then used to measure the equivalence ratio on a series of large-scale ISO 9705 tests for numerous fire conditions. The materials used were a range of non-homogenous materials such as polyurethane. The measurements corresponded accurately to the data collected, showing the novel design can be used from bench to large-scale tests to measure equivalence ratio. This cheaper, more portable, safer and easier to use phi meter design will enable more widespread use and the ability to quantify fire conditions of tests, allowing for better understanding of flammability and smoke toxicity.

Keywords: phi meter, smoke toxicity, fire condition, ISO9705, novel equipment

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

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Authors: Hana Maresova, Daniel Ecler

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The paper focuses on the use of 3D multi-user virtual environments (MUVE) in language teaching and presents the results of four years of research at the Faculty of Education, Palacký University in Olomouc (Czech Republic). In the form of an experiment, mother tongue language teaching in the 3D virtual worlds Second Life and Kitely (experimental group) and parallel traditional teaching on identical topics representing teacher's interpretation using a textbook (control group) were implemented. The didactic test, which was presented to the experimental and control groups in an identical form before and after the instruction, verified the effect of the instruction in the experimental group by comparing the results obtained by both groups. Within the three components of mother-tongue teaching (vocabulary, literature, style and communication education), the students in the literature group achieved partially better results (statistically significant in the case of items devoted to the area of visualization of the learning topic), while in the case of grammar and style education the respondents of the control group achieved better results. On the basis of the results obtained, we can conclude that the most appropriate use of MUVE can be seen in the teaching of those topics that provide the possibility of dramatization, experiential learning and group involvement and cooperation, on the contrary, with regard to the need to divide students attention between the topic taught and the control of avatar and movement in virtual reality as less suitable for teaching in the area of memorization of the topic or concepts.

Keywords: distance learning, 3D virtual environments, online teaching, language teaching

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Authors: Kyle De Freitas, Margaret Bernard

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Enabling data mining analysis among a wider audience of educators is an active area of research within the educational data mining (EDM) community. The paper proposes a framework for developing an environment that caters for educators who have little technical data mining skills as well as for more advanced users with some data mining expertise. This framework architecture was developed through the review of the strengths and weaknesses of existing models in the literature. The proposed framework provides a modular architecture for future researchers to focus on the development of specific areas within the EDM process. Finally, the paper also highlights a strategy of enabling analysis through either the use of predefined questions or a guided data mining process and highlights how the developed questions and analysis conducted can be reused and extended over time.

Keywords: educational data mining, learning management system, learning analytics, EDM framework

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Authors: Kuo-Dung Chiou, Yen-Xue Chen, Chia-Ying Chang

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This study uses AI technology to establish an expert system and establish a fruit appearance database for pineapples and custard apples. It collects images based on appearance defects and fruit maturity. It uses deep learning to detect the location of the fruit and can detect the appearance of the fruit in real-time. Flaws and maturity. In addition, a hyperspectral camera was used to scan pineapples and custard apples, and the light reflection at different frequency bands was used to find the key frequency band for pectin softening in post-ripe fruits. Conducted a large number of multispectral image collection and data analysis to establish a database of Pineapple Custard Apple and Big Eyed Custard Apple, which includes a high-definition color image database, a hyperspectral database in the 377~1020 nm frequency band, and five frequency band images (450, 500, 670, 720, 800nm) multispectral database, which collects 4896 images and manually labeled ground truth; 26 hyperspectral pineapple custard apple fruits (520 images each); multispectral custard apple 168 fruits (5 images each). Using the color image database to train deep learning Yolo v4's pre-training network architecture and adding the training weights established by the fruit database, real-time detection performance is achieved, and the recognition rate reaches over 97.96%. We also used multispectral to take a large number of continuous shots and calculated the difference and average ratio of the fruit in the 670 and 720nm frequency bands. They all have the same trend. The value increases until maturity, and the value will decrease after maturity. Subsequently, the sub-bands will be added to analyze further the numerical analysis of sugar content and moisture, and the absolute value of maturity and the data curve of maturity will be found.

Keywords: hyperspectral image, fruit firmness, deep learning, automatic detection, automatic measurement, intelligent labor saving

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Authors: Jack R. McKenzie, Peter A. Appleby, Thomas House, Neil Walton

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Cold-start is a notoriously difficult problem which can occur in recommendation systems, and arises when there is insufficient information to draw inferences for users or items. To address this challenge, a contextual bandit algorithm – the Fast Approximate Bayesian Contextual Cold Start Learning algorithm (FAB-COST) – is proposed, which is designed to provide improved accuracy compared to the traditionally used Laplace approximation in the logistic contextual bandit, while controlling both algorithmic complexity and computational cost. To this end, FAB-COST uses a combination of two moment projection variational methods: Expectation Propagation (EP), which performs well at the cold start, but becomes slow as the amount of data increases; and Assumed Density Filtering (ADF), which has slower growth of computational cost with data size but requires more data to obtain an acceptable level of accuracy. By switching from EP to ADF when the dataset becomes large, it is able to exploit their complementary strengths. The empirical justification for FAB-COST is presented, and systematically compared to other approaches on simulated data. In a benchmark against the Laplace approximation on real data consisting of over 670, 000 impressions from autotrader.co.uk, FAB-COST demonstrates at one point increase of over 16% in user clicks. On the basis of these results, it is argued that FAB-COST is likely to be an attractive approach to cold-start recommendation systems in a variety of contexts.

Keywords: cold-start learning, expectation propagation, multi-armed bandits, Thompson Sampling, variational inference

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Authors: Shri Krishna Mishra

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In an educational context, technology can prompt interactive plight only when it is used in conjunction with interactive plight methods. This study, therefore, examines the relationships among components that contribute to higher levels of interactive plight and execution, such as interactive Plight methods, technology, intrinsic motivation and deep learning. 526 students participated in this study. With structural equation modelling, the authors test the conceptual model and identify satisfactory model fit. The results indicate that interactive Plight methods, technology and intrinsic motivation have significant relationship with interactive Plight; deep learning mediates the relationships of the other variables with Execution.

Keywords: searching the relationship among components, contribute to interactive plight, educational execution, intrinsic motivation

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Authors: Shrikrishna Mishra, Badri Yadav

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In this complex society of today where experiences are innumerable and varied, it is not at all possible to present every situation in its original colors hence the opportunities for learning by actual experiences always are not at all possible. It is only through the use of proper audio visual aids that the life situation can be trough in the class room by an enlightened teacher in their simplest form and representing the original to the highest point of similarity which is totally absent in the verbal or lecture method. In the presence of audio aids, the attention is attracted interest roused and suitable atmosphere for proper understanding is automatically created, but in the existing traditional method greater efforts are to be made in order to achieve the aforesaid essential requisite. Inspire of the best and sincere efforts on the side of the teacher the net effect as regards understanding or learning in general is quite negligible.

Keywords: Audio-Visual Aids, the secondary school teaching, complex society, audio

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Authors: P. Abhishesh, B. S. Ryuh, Y. S. Oh, H. J. Moon, R. Akanksha

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This paper discusses about the conceptual design and development of the control system software using Programmable logic controller (PLC) for autonomous driving and agricultural operations of Multipurpose Agricultural Robot Platform (MARP). Based on given initial conditions by field analysis and desired agricultural operations, the structural design development of MARP is done using modelling and analysis tool. PLC, being robust and easy to use, has been used to design the autonomous control system of robot platform for desired parameters. The robot is capable of performing autonomous driving and three automatic agricultural operations, viz. hilling, mulching, and sowing of seeds in the respective order. The input received from various sensors on the field is later transmitted to the controller via ZigBee network to make the changes in the control program to get desired field output. The research is conducted to provide assistance to farmers by reducing labor hours for agricultural activities by implementing automation. This study will provide an alternative to the existing systems with machineries attached behind tractors and rigorous manual operations on agricultural field at effective cost.

Keywords: agricultural operations, autonomous driving, MARP, PLC

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Authors: Irfan Ahmed Rind

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Science education has recently become the top priority of government of Pakistan. Number of schemes has been initiated for the improvement of science teaching and learning at primary and secondary levels of education, most importantly training in-service science teachers on inquiry based teaching and learning to empower students and encourage creativity, critical thinking, and innovation among them. Therefore, this approach has been promoted in the recent continuous professional development trainings for the in-service teachers. However, the follow ups on trained science teachers and educators suggest that these teachers fail to implement the inquiry based teaching and learning in their classes. In addition, these trainings also fail to bring any significant change in students’ science content knowledge and understanding as per the annual national level surveys conducted by government and independent agencies. Research suggests that science has been taught using scientific positivism, which supports objectivity based on experiments and mathematics. In contrary, the inquiry based teaching and learning are based on constructivism, which conflicts with the positivist epistemology of science teachers. It was, therefore, assumed that science teachers struggle to implement the inquiry based teaching approach as it conflicts with their basic epistemological beliefs. With this assumption, this research aimed to (i) understand how science teachers conceptualize the nature of science, and how this influence their understanding of learning, learners, their own roles as teachers and their teaching strategies, (ii) identify the conflict of science teachers’ epistemological beliefs with the inquiry based teaching approach, and (iii) find the ways in which science teachers epistemological beliefs may be developed from positivism to constructivism, so that they may effectively use the inquiry based teaching approach in teaching science. Using qualitative case study approach, thirty six secondary and higher secondary science teachers (21 male and 15 female) were selected. Data was collected using interviewed, participatory observations (sixty lessons were observed), and twenty interviews from students for verifications of teachers’ responses. The findings suggest that most of the science teacher were positivist in defining the nature of science. Most of them limit themselves to one fix answer that is provided in the books and that there is only one 'right' way to teach science. There is no room for students’ or teachers’ own opinion or bias when it comes to scientific concepts. Inquiry based teaching seems 'no right' to them. They find it difficult to allow students to think out of the box. However, some interesting exercises were found to be very effective in bringing the change in teachers’ epistemological beliefs. These will be discussed in detail in the paper. The findings have major implications for the teachers, educators, and policymakers.

Keywords: science teachers, epistemology, metacognition, inquiry based teaching

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Authors: Neda Jalaliyoon, Nooh Abu Bakar, Hamed Taherdoost

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The present research recommended balanced scorecard (BSC) framework to appraise the performance of the universities. As the original model of balanced scorecard has four perspectives in order to implement BSC in present research the same model with “financial perspective”, “customer”,” internal process” and “learning and growth” is used as well. With applying fuzzy Delphi method (FDM) and questionnaire sixteen measures of performance were identified. Moreover, with using the analytic network process (ANP) the weights of the selected indicators were determined. Results indicated that the most important BSC’s aspect were Internal Process (0.3149), Customer (0.2769), Learning and Growth (0.2049), and Financial (0.2033) respectively. The proposed BSC framework can help universities to enhance their efficiency in competitive environment.

Keywords: balanced scorecard, higher education, fuzzy delphi method, analytic network process (ANP)

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Authors: Ismail Cengiz, Faruk Elaldi

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Within the scope of the study presented in this paper, load carrying capacity and buckling behavior of a stiffened aluminum panel designed by adopting current ‘buckle-resistant’ design application and ‘Post –Buckling’ design approach were investigated experimentally and numerically. The test specimen that is stabilized by Z-type stiffeners and manufactured from aluminum 2024 T3 Clad material was test under compression load. Buckling behavior was observed by means of 3 – dimensional digital image correlation (DIC) and strain gauge pairs. The experimental study was followed by developing an efficient and reliable finite element model whose ability to predict behavior of the stiffened panel used for compression test is verified by compering experimental and numerical results in terms of load – shortening curve, strain-load curves and buckling mode shapes. While finite element model was being constructed, non-linear behaviors associated with material and geometry was considered. Finally, applicability of aluminum stiffened panel in airframe design against to composite structures was evaluated thorough the concept of ‘Structural Efficiency’. This study reveals that considerable amount of weight saving could be gained if the concept of ‘post-buckling design’ is preferred to the already conventionally used ‘buckle resistant design’ concept in aircraft industry without scarifying any of structural integrity under load spectrum.

Keywords: post-buckling, stiffened panel, non-linear finite element method, aluminum, structural efficiency

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Authors: Almudena Konrad, Tomás Galguera

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Lack of motivation and interest is a serious obstacle to students’ learning computing skills. A need exists for a knowledge base on effective pedagogy and curricula to teach computer programming. This paper presents results from research evaluating a six-year project designed to teach complex concepts in computer programming collaboratively, while supporting students to continue developing their computer thinking and related coding skills individually. Utilizing a quasi-experimental, mixed methods design, the pedagogical approaches and methods were assessed in two contrasting groups of students with different socioeconomic status, gender, and age composition. Analyses of quantitative data from Likert-scale surveys and an evaluation rubric, combined with qualitative data from reflective writing exercises and semi-structured interviews yielded convincing evidence of the project’s success at both teaching and inspiring students.

Keywords: computational thinking, computing education, computer programming curriculum, logic, teaching methods

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Authors: Geir Sigurðsson

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This paper compares ancient Daoist and Confucian approaches to the human body as a locus for learning, edification or personal cultivation. While pointing out some major differences between ancient Chinese and mainstream Western visions of the body, it seeks at the same time inspiration in some seminal Western phenomenological and post-structuralist writings, in particular from Maurice Merleau-Ponty and Pierre Bourdieu. By clarifying the somewhat dissimilar scopes of foci found in Daoist and Confucian philosophies with regard to the role of and attitude to the body, the conclusion is nevertheless that their approaches are comparable, and that both traditions take the physical body to play a vital role in the cultivation of excellence. Lastly, it will be argued that cosmological underpinnings prevent the Confucian li from being rigid and invariable and that it rather emerges as a flexible learning device to train through active embodiment a refined sensibility for one’s cultural environment.

Keywords: body, Confucianism, Daoism, li (ritual), phenomenology

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Authors: Gaye Senbag

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In English language teaching, feedback is considered as one of the main components of writing instruction. Teachers put a lot of time and effort in order to provide learners with written feedback for effective language learning. At Anadolu University School of Foreign Languages (AUSFL) students are given written feedback for their each piece of writing through online platforms such as Edmodo and Turnitin, and traditional methods. However, little is known regarding how learners value and respond to teacher-provided feedback. As the perceptions of the students remarkably affect their learning, this study examines how they perceive the effectiveness of feedback provided by the teacher. Aiming to analyse it, 30 intermediate level (B1+ CEFR level) students were given a questionnaire, which includes Likert scale questions. The results will be discussed in detail.

Keywords: feedback, perceptions, writing, English Language Teaching (ELT)

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Authors: Annette Cosgrove

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The objectives of this research study is to examine the impact of professional development in Technology Enhanced Learning (TEL) and the digitisation of learning in teaching communities across multiple higher education sites in the ATU (Atlantic Technological University *) ( 2020-2025), including the proposal of an evidence based digital teaching model for use in a future pandemic. The research strategy undertaken for this PhD Study is a multi-site study using mixed methods. Qualitative & quantitative methods are being used in the study to collect data. A pilot study was carried out initially , feedback collected and the research instrument was edited to reflect this feedback, before being administered. The purpose of the staff questionnaire is to evaluate the impact of professional development in the area of TEL, and to capture the practitioners views on the perceived impact on their teaching practice in the higher education sector across ATU (West of Ireland – 5 Higher education locations ). The phenomenon being explored is ‘ the impact of professional development in the area of technology enhanced learning and on teaching practice in a higher education institution.’ The research methodology chosen for this study is an Action based Research Study. The researcher has chosen this approach as it is a prime strategy for developing educational theory and enhancing educational practice . This study includes quantitative and qualitative methods to elicit data which will quantify the impact that continuous professional development in the area of digital teaching practice and technologies has on the practitioner’s teaching practice in higher education. The research instruments / data collection tools for this study include a lecturer survey with a targeted TEL Practice group ( Pre and post covid experience) and semi-structured interviews with lecturers.. This research is currently being conducted across the ATU multisite campus and targeting Higher education lecturers that have completed formal CPD in the area of digital teaching. ATU, a west of Ireland university is the focus of the study , The research questionnaire has been deployed, with 75 respondents to date across the ATU - the primary questionnaire and semi- formal interviews are ongoing currently – the purpose being to evaluate the impact of formal professional development in the area of TEL and its perceived impact on the practitioners teaching practice in the area of digital teaching and learning . This paper will present initial findings, reflections and data from this ongoing research study.

Keywords: TEL, DTL, digital teaching, digital assessment

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Authors: Vareesha Masood, Ammara Gul, Nabeeha Areej, Muhammad Asif Masood, Hamna Imran

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The universal use of PDF files has prompted hackers to use them for malicious intent by hiding malicious codes in their victim’s PDF machines. Machine learning has proven to be the most efficient in identifying benign files and detecting files with PDF malware. This paper has proposed an approach using a decision tree classifier with parameters. A modern, inclusive dataset CIC-Evasive-PDFMal2022, produced by Lockheed Martin’s Cyber Security wing is used. It is one of the most reliable datasets to use in this field. We designed a PDF malware detection system that achieved 99.2%. Comparing the suggested model to other cutting-edge models in the same study field, it has a great performance in detecting PDF malware. Accordingly, we provide the fastest, most reliable, and most efficient PDF Malware detection approach in this paper.

Keywords: PDF, PDF malware, decision tree classifier, random forest classifier

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Authors: Ozge Yalciner

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Intercultural communication is crucial in the education of multilingual learners in primary grades, significantly influencing their academic and social development. This study explores how intercultural communication intersects with multilingual education, highlighting the importance of culturally responsive teaching practices. It addresses the challenges and opportunities presented by diverse linguistic backgrounds and proposes strategies for creating inclusive and supportive learning environments. The research emphasizes the need for teacher training programs that equip educators with the skills to recognize and address cultural differences, thereby enhancing student engagement and participation. This study was completed in an elementary school in a city in the Midwest, USA. The data was collected through observations and interviews with students and teachers. It discusses the integration of multicultural perspectives in curricula and the promotion of language diversity as an asset. Peer interactions and collaborative learning are highlighted as crucial for developing intercultural competence among young learners. The findings suggest that meaningful intercultural communication fosters a sense of belonging and mutual respect, leading to improved educational outcomes for multilingual students. Prioritizing intercultural communication in primary education is essential for supporting the linguistic and cultural identities of multilingual learners. By adopting inclusive pedagogical approaches and fostering an environment of cultural appreciation, educators can better support their students' academic success and personal growth.

Keywords: diversity, intercultural communication, multilingual learners, primary grades

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Authors: Alexander Guzman Urbina, Atsushi Aoyama

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The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

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Authors: Azizah Che Omar

Abstract:

The study provides a systematic literature review which analyzed the literature for the purpose of looking for concepts, theories, approaches and guidelines in order to propose a conceptual design model of interactive television advertising toward impulse purchase (iTVAdIP). An extensive review of literature was purposely carried out to understand the concepts of interactive television (iTV). Therefore, some elements; iTV guidelines, advertising theories, persuasive approaches, and the impulse purchase elements were analyzed to reach the scope of this work. The extensive review was also a necessity to achieve the objective of this study, which was to determine the concept of iTVAdIP design model. Through systematic review analysis, this study discovered that all the previous models did not emphasize the conceptual design model of interactive television advertising. As a result, the finding showed that the concept of the proposed model should contain the iTV guidelines, advertising theory, persuasive approach and impulse purchase elements. In addition, a summary diagram for the development of the proposed model is depicted to provide clearer understanding towards the concepts of conceptual design model of iTVAdIP.

Keywords: impulse purchase, interactive television advertising, human computer interaction, advertising theories

Procedia PDF Downloads 374