Search results for: computer assisted language learning

Authors: A. M. Abd-Elfattah, M. H. Abu-Moussa

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In this paper, the estimation of stress-strength parameter R = P(Y < X) is considered when X; Y the strength and stress respectively are two independent random variables of Burr Type XII distribution. The samples taken for X and Y are progressively censoring of type II. The maximum likelihood estimator (MLE) of R is obtained when the common parameter is unknown. But when the common parameter is known the MLE, uniformly minimum variance unbiased estimator (UMVUE) and the Bayes estimator of R = P(Y < X) are obtained. The exact condence interval of R based on MLE is obtained. The performance of the proposed estimators is compared using the computer simulation.

Keywords: Burr Type XII distribution, progressive type-II censoring, stress-strength model, unbiased estimator, maximum-likelihood estimator, uniformly minimum variance unbiased estimator, confidence intervals, Bayes estimator

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Authors: Jiangyong Liu, Xiangxiang Xu, Bote Luo, Xiaoxue Luo, Jiang Zhu, Lingzhi Yi

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To address the problems of non-linearity and high randomness of the original power load sequence causing the degradation of power load forecasting accuracy, a short-term load forecasting method is proposed. The method is based on the Least Square Support Vector Machine optimized by an Improved Sparrow Search Algorithm combined with the Variational Mode Decomposition proposed in this paper. The application of the variational mode decomposition technique decomposes the raw power load data into a series of Intrinsic Mode Functions components, which can reduce the complexity and instability of the raw data while overcoming modal confounding; the proposed improved sparrow search algorithm can solve the problem of difficult selection of learning parameters in the least Square Support Vector Machine. Finally, through comparison experiments, the results show that the method can effectively improve prediction accuracy.

Keywords: load forecasting, variational mode decomposition, improved sparrow search algorithm, least square support vector machine

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Authors: Abdulrahman S. Alturki, John S. Loomis

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This paper discusses a corner detection algorithm for camera calibration. Calibration is a necessary step in many computer vision and image processing applications. Robust corner detection for an image of a checkerboard is required to determine intrinsic and extrinsic parameters. In this paper, an algorithm for fully automatic and robust X-corner detection is presented. Checkerboard corner points are automatically found in each image without user interaction or any prior information regarding the number of rows or columns. The approach represents each X-corner with a quadratic fitting function. Using the fact that the X-corners are saddle points, the coefficients in the fitting function are used to identify each corner location. The automation of this process greatly simplifies calibration. Our method is robust against noise and different camera orientations. Experimental analysis shows the accuracy of our method using actual images acquired at different camera locations and orientations.

Keywords: camera calibration, corner detector, edge detector, saddle points

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Authors: Shoujia Fang, Guoqing Ding, Xin Chen

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The quality of press-fit assembly is closely related to reliability and safety of product. The paper proposed a keypoint detection method based on convolutional neural network to improve the accuracy of keypoint detection in press-fit curve. It would provide an auxiliary basis for judging quality of press-fit assembly. The press-fit curve is a curve of press-fit force and displacement. Both force data and distance data are time-series data. Therefore, one-dimensional convolutional neural network is used to process the press-fit curve. After the obtained press-fit data is filtered, the multi-layer one-dimensional convolutional neural network is used to perform the automatic learning of press-fit curve features, and then sent to the multi-layer perceptron to finally output keypoint of the curve. We used the data of press-fit assembly equipment in the actual production process to train CNN model, and we used different data from the same equipment to evaluate the performance of detection. Compared with the existing research result, the performance of detection was significantly improved. This method can provide a reliable basis for the judgment of press-fit quality.

Keywords: keypoint detection, curve feature, convolutional neural network, press-fit assembly

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Authors: Shahnaz Safitri, Rose Mini Agoes Salim, Pratiwi Widyasari

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Intellectual disability is characterized by significant limitations in intellectual functioning and adaptive behavior that appears before the age of 18 years old. The prominent impacts of intellectual disability in adolescents are failure to establish interpersonal relationships as socially expected and lower academic achievement. Meanwhile, it is known that emotion regulation skills have a role in supporting the functioning of individual, either by nourishing the development of social skills as well as by facilitating the process of learning and adaptation in school. This study aims to look for the effectiveness of Dialectical Behavior Therapy (DBT) in developing emotion regulation skills for adolescents with intellectual disability. DBT's special consideration toward clients’ social environment and their biological condition is foreseen to be the key for developing emotion regulation capacity for subjects with intellectual disability. Through observations on client's behavior, conducted before and after the completion of DBT intervention program, it was found that there is an improvement in client's knowledge and attitudes related to the mastery of emotion regulation skills. In addition, client's consistency to actually practice emotion regulation techniques over time is largely influenced by the support received from the client's social circles.

Keywords: adolescent, dialectical behavior therapy, emotion regulation, intellectual disability

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Authors: Mafarja Majdi, Salwani Abdullah, Najmeh S. Jaddi

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One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.

Keywords: rough set theory, attribute reduction, fuzzy logic, memetic algorithms, record to record algorithm, great deluge algorithm

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Authors: Qingqing Gai, Junxing Shen, Yu Luo

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Due to the hardware architecture characteristics, some operators tend to acquire better performance if the input/output tensor dimensions are aligned to a certain minimum granularity, such as convolution and deconvolution commonly used in deep learning. Furthermore, if the requirements are not met, the general strategy is to pad with 0 to satisfy the requirements, potentially leading to the under-utilization of the hardware resources. Therefore, for the convolution and deconvolution whose input and output channels do not meet the minimum granularity alignment, we propose to transfer the W-dimensional data to the C-dimension for computation (W2C) to enable the C-dimension to meet the hardware requirements. This scheme also reduces the number of computations in the W-dimension. Although this scheme substantially increases computation, the operator’s speed can improve significantly. It achieves remarkable speedups on multiple hardware accelerators, including Nvidia Tensor cores, Qualcomm digital signal processors (DSPs), and Huawei neural processing units (NPUs). All you need to do is modify the network structure and rearrange the operator weights offline without retraining. At the same time, for some operators, such as the Reducemax, we observe that transferring the Cdimensional data to the W-dimension(C2W) and replacing the Reducemax with the Maxpool can accomplish acceleration under certain circumstances.

Keywords: convolution, deconvolution, W2C, C2W, alignment, hardware accelerator

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Authors: Cameron Jones

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Emissions, produced by landfill waste from demolished obsolete buildings, have a damaging effect on both the Earth’s climate and human health. The philosophical theory of material vitalism - the potential for materials to react and emit harmful pollutants - therefore defines this construction and demolition waste (CDW) as having vitality. The European Union’s ‘Circular Economic Action Plan’ (CEAP) aims to mitigate the effects of CDW by prioritising the circularity of building materials. This dissertation examines how the philosophical theory of material vitalism can make an environmentally responsible contribution to CDW policy. The CEAP and Silvertown Quays development are used as case studies for the application of vitalism to policy revision. The study concludes that vitalism has a positive role to play in informing CDW policy, although its contribution is stronger in some areas. This is established by first appraising the aspects that relate to the obsolescence of buildings outlined in the EU’s existing CDW policies. Next, these policy directives are compared with the CE principles employed in the Silvertown Quays development. Subsequently, a keyword analysis model is used to categorise the language used in the CEAP, demonstrating how socio-political approaches to the CE and strategies to address resource scarcity could be strengthened to represent the EU’s policy aspirations more effectively. Recommendations are then made on how material vitalism could be utilised to strengthen legislation, arguing that a notable contribution can be made in most policy areas. Finally, theoretical testing of the impact of these revisions to policy on the case study development identified some practicalities for consideration in improving waste management outcomes.

Keywords: vitalism, construction waste, obsolescence, political ecology, exceptionalism

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Authors: Pataraporn Kheawwan

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Preceptorship represents an important aspect in new nurse orientation. However, there was no formal preceptor training program developed for nurse preceptor in Thailand. The purposes of this study were to develop and evaluate formal preceptor training program for nurse preceptors in King Chulalongkorn Memorial Hospital, Thailand. A research and development study design was utilized in this study. Participants were 37 nurse preceptors. The program contents were delivered by e-learning material, class lecture, group discussion followed by simulation training. Knowledge of the participants was assessed pre and post program. Skill and critical thinking were assessed using Preceptor Skill and Decision Making Evaluation form at the end of program. Statistical significant difference in knowledge regarding preceptor role and coaching strategies between pre and post program were found. All participants had satisfied skill and decision making score after completed the program. Most of participants perceived benefits of preceptor training course. In conclusion, The results of this study reveal that the newly developed preceptorship course is an effective formal training course for nurse preceptors.

Keywords: preceptor, preceptorship, new nurse, clinical education

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Authors: N. Songumpai, W. Sumranwanich, S. Chatmaneerungcharoen

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This research aims to study the understanding of 8 aspects of nature of science (NOS). The research participants were 39 General Science student teachers who were selected by purposive sampling. In 2015 academic year, they enrolled in the course of Science Education Learning Management. Qualitative research was used as research methodology to understand how the student teachers propose on NOS. The research instruments consisted of open-ended questionnaires and semi-structure interviews that were used to assess students’ understanding of NOS. Research data was collected by 8 items- questionnaire and was categorized into students’ understanding of NOS, which consisted of complete understanding (CU), partial understanding (PU), misunderstanding (MU) and no understanding (NU). The findings reveal the majority of students’ misunderstanding of NOS regarding the aspects of theory and law(89.7%), scientific method(61.5%) and empirical evidence(15.4%) respectively. From the interview data, the student teachers present their misconceptions of NOS that indicate about theory and law cannot change; science knowledge is gained through experiment only (step by step); science is the things that are around humans. These results suggest that for effective science teacher education, the composition of design of NOS course needs to be considered. Therefore, teachers’ understanding of NOS is necessary to integrate into professional development program/course for empowering student teachers to begin their careers as strong science teachers in schools.

Keywords: nature of science, student teacher, no understanding, misunderstanding, partial understanding, complete understanding

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Authors: I. Salas-Menotti

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Problem-solving courts were created to assist offenders with specific needs that were not addressed properly in traditional courts. Problem-solving courts' main objective is to pursue solutions that will benefit the offender, the victim, and society as well. These courts were developed as an innovative response to deal with issues such as drug abuse, mental illness, and domestic violence. In Brooklyn, men who are charged with domestic violence related offenses for the first time are offered plea bargains that include the attendance to a domestic abuse intervention program as a condition to dismiss the most serious charges and avoid incarceration. The desired outcome is that the offender will engage in a program that will modify his behavior avoiding new incidents of domestic abuse, it requires accountability towards the victim and finally, it will hopefully bring down statistic related to domestic abuse incidents. This paper will discuss the effectiveness of the Duluth model as applied to Spanish-speaking men mandated to participate in the program by the specialized domestic violence courts in Brooklyn. A longitudinal study was conducted with 243 Spanish- speaking men who were mandated to participated in the men's program offered by EAC in Brooklyn in the years 2016 through 2018 to determine the recidivism rate of domestic violence crimes. Results show that the recidivism rate was less than 5% per year after completing the program which indicates that the intervention is effective in preventing new abuse allegations and subsequent arrests. It's recommended that comparative study with English-speaking participants is conducted to determine cultural and language variables affecting the program's efficacy.

Keywords: domestic violence, domestic abuse intervention programs, Problem solving courts, Spanish-speaking offenders

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Authors: Kaleem Kashif, Tayyaba Anam, Yizhi Wu

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This paper introduces a methodology for advancing Italian speech vowels landmark detection within the distinctive feature-based speech recognition domain. Leveraging the legacy tool 'xkl' by integrating combined convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the study presents a comprehensive enhancement to the 'xkl' legacy software. This integration incorporates re-assigned spectrogram methodologies, enabling meticulous acoustic analysis. Simultaneously, our proposed model, integrating combined CNNs and RNNs, demonstrates unprecedented precision and robustness in landmark detection. The augmentation of re-assigned spectrogram fusion within the 'xkl' software signifies a meticulous advancement, particularly enhancing precision related to vowel formant estimation. This augmentation catalyzes unparalleled accuracy in landmark detection, resulting in a substantial performance leap compared to conventional methods. The proposed model emerges as a state-of-the-art solution in the distinctive feature-based speech recognition systems domain. In the realm of deep learning, a synergistic integration of combined CNNs and RNNs is introduced, endowed with specialized temporal embeddings, harnessing self-attention mechanisms, and positional embeddings. The proposed model allows it to excel in capturing intricate dependencies within Italian speech vowels, rendering it highly adaptable and sophisticated in the distinctive feature domain. Furthermore, our advanced temporal modeling approach employs Bayesian temporal encoding, refining the measurement of inter-landmark intervals. Comparative analysis against state-of-the-art models reveals a substantial improvement in accuracy, highlighting the robustness and efficacy of the proposed methodology. Upon rigorous testing on a database (LaMIT) speech recorded in a silent room by four Italian native speakers, the landmark detector demonstrates exceptional performance, achieving a 95% true detection rate and a 10% false detection rate. A majority of missed landmarks were observed in proximity to reduced vowels. These promising results underscore the robust identifiability of landmarks within the speech waveform, establishing the feasibility of employing a landmark detector as a front end in a speech recognition system. The synergistic integration of re-assigned spectrogram fusion, CNNs, RNNs, and Bayesian temporal encoding not only signifies a significant advancement in Italian speech vowels landmark detection but also positions the proposed model as a leader in the field. The model offers distinct advantages, including unparalleled accuracy, adaptability, and sophistication, marking a milestone in the intersection of deep learning and distinctive feature-based speech recognition. This work contributes to the broader scientific community by presenting a methodologically rigorous framework for enhancing landmark detection accuracy in Italian speech vowels. The integration of cutting-edge techniques establishes a foundation for future advancements in speech signal processing, emphasizing the potential of the proposed model in practical applications across various domains requiring robust speech recognition systems.

Keywords: landmark detection, acoustic analysis, convolutional neural network, recurrent neural network

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Authors: Peng Li, Luan Wang, Haifeng Fei, Renhong Xie, Yibin Rui, Shanhong Guo

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The applications of satellite-based automatic identification system (S-AIS) pave the road for wide-range maritime traffic monitoring and management. But the coverage of satellite’s view includes multiple AIS self-organizing networks, which leads to the collision of AIS signals from different cells. The contribution of this work is to propose an improved multi-channel blind source separation algorithm based on Artificial Bee Colony (ABC) and advanced stochastic optimization to perform separation of the mixed AIS signals. The proposed approach adopts modified ABC algorithm to get an optimized initial separating matrix, which can expedite the initialization bias correction, and utilizes the Adaptive Moment Estimation (Adam) to update the separating matrix by adjusting the learning rate for each parameter dynamically. Simulation results show that the algorithm can speed up convergence and lead to better performance in separation accuracy.

Keywords: satellite-based automatic identification system, blind source separation, artificial bee colony, adaptive moment estimation

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Authors: Armijos-Orellana Ana, González-Calle María, Maldonado-Matute Juan, Guerrero-Maxi Pedro

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Nowadays, the digital economy represents a fundamental element to guarantee economic and social development, whose importance increased significantly with the arrival of the COVID-19 pandemic. However, despite the benefits it offers, it can also be detrimental to those developing countries characterized by a wide digital divide. It is for this reason that the objective of this research was to identify and describe the main characteristics, benefits, and obstacles of the digital economy for Latin American countries. Through a bibliographic review, using the analytical-synthetic method in the period 1995-2021, it was determined that the digital economy could give way to structural changes, reduce inequality, and promote processes of social inclusion, as well as promote the construction and participatory development of organizational structures and institutional capacities in Latin American countries. However, the results showed that the digital economy is still incipient in the region and at least three factors are needed to establish it: joint work between academia, the business sector and the State, greater emphasis on learning and application of digital transformation and the creation of policies that encourage the creation of digital organizations.

Keywords: developing countries, digital divide, digital economy, digital literacy, digital transformation

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Authors: Madara Apsalone

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Knowledge management and innovation is key to competitive advantage and sustainable business development in advanced economies. Small and medium-sized enterprises (SMEs) have lower capacity and more constrained resources for long-term and high-uncertainty research and development investments. At the same time, SMEs can implement organizational innovation to improve their performance and further foster other types of innovation. The purpose of this study is to analyze, how socio-cultural factors such as shared values, organizational behaviors, work organization and decision making processes can influence knowledge management and help to develop organizational innovation via an empirical study. Surveying 600 SMEs in Latvia, the author explores the contribution of different socio-cultural factors to organizational innovation and the role of knowledge management and organizational learning in this process. A conceptual model, explaining the impact of organizational team, development, result-orientation and structure is created. The study also proposes insights that contribute to theoretical and practical discussions on fostering innovation of small businesses in small economies.

Keywords: knowledge management, organizational innovation, small and medium-sized enterprises, socio-cultural factors

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Authors: Amir Hossein Allahdadi

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The mystical and intuitive look of Islamic artists inspired by the Koranic and mystical principles and also based on the geometry and mathematics has left unique works whose range extends across the borders of Islam. The relationship between Islamic art and music in the traditional art is of one of the concepts that can be traced back to the other arts by detection of its components. One of the links is the art of painting whose subtleties that can be applicable to both architecture and music. So, architecture and music links can be traced in other arts with a traditional foundation in order to evaluate the equivalents of traditional arts. What is the relationship between physical space of architecture and nonphysical space of music? What is musical architecture? What is the music that tends to architecture? These questions are very small samples of the questions that arise in this category, and these questions and concerns remain as long as the music is played and the architecture is made. Efforts have been made in this area, references compiled and plans drawn. As an example, we can refer to views of ‘Mansour Falamaki’ in the book of architecture and music, as well as the book transition from mud to heart by ‘Hesamodin Seraj’. The method is such that a certain melody is given to an architect and it is tried to design a specified architecture using a certain theme. This study is not to follow the architecture of a particular type of music and the formation of a volume based on a sound. In this opportunity, it is tried to briefly review the relationship between music and architecture in the Iranian original and traditional arts, using the basic definitions of arts. The musician plays, the architect designs, the actor forms his desired space and painter displays his multi-dimensional world in the form of two-dimensions. The expression language is different, but all of them can be gathered in a form, a form which has no clear boundaries. In fact, in any original art, the artist applies his art as a tool to express his insights which are nothing but achieving the world beyond this place and time.

Keywords: architecture, music, geometric proportions, mathematical proportions

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Authors: Anne Eugenia Wright

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This study examines issues in accessibility to maritime cultural heritage. Using the Pillar Dollar Wreck in Biscayne National Park, Florida, this study presents an approach to public outreach based on the concept of Design for All. Design for All advocates creating products that are accessible and functional for all users, including those with visual, hearing, learning, mobility, or economic impairments. As a part of this study, a small exhibit was created that uses 3D products as a way to bring maritime cultural heritage to the public. It was presented to the public at East Carolina University’s Joyner Library. Additionally, this study presents a methodology for 3D printing scaled photogrammetry models of archaeological sites in full color. This methodology can be used to present a realistic depiction of underwater archaeological sites to those who are incapable of accessing them in the water. Additionally, this methodology can be used to present underwater archaeological sites that are inaccessible to the public due to conditions such as visibility, depth, or protected status. This study presents a practical use for 3D photogrammetry models, as well as an accessibility strategy to expand the outreach potential for maritime archaeology.

Keywords: Underwater Archaeology, 3D Printing, Photogrammetry, Design for All

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Authors: Tawfik Thelaidjia, Salah Chenikher

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Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of the signal’s Kurtosis and features obtained through the preprocessing of the vibration signal samples using Db2 discrete wavelet transform at the fifth level of decomposition. In this way, a 7-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach

Keywords: condition monitoring, discrete wavelet transform, fault diagnosis, kurtosis, machine learning, particle swarm optimization, roller bearing, rotating machines, support vector machine, vibration measurement

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Authors: Adedeji W. Oyediran, Yekini N. Asafe

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Mechatronics is synergistic integration of mechanical engineering, with electronics and intelligent computer control in the design and manufacturing of industrial products and processes. Automobile (auto car, motor car or car is a wheeled motor vehicle used for transporting passengers, which also carries its own engine or motor) is a mechatronic system which served as major means of transportation around the world. Virtually all community has a need for automobile. This makes automobile issues as related to diagnostic and repair interesting to all communities. Consequent to the diversification of skill in diagnosing automobile faults and approaches in solving some problems and innovation in automobile industry. It is appropriate to say that repair and diagnostic of automobile will be better enhanced if community has opportunity of sharing knowledge and idea globally. This paper discussed the desirable elements in automobile as mechatronics system and present conceptual framework of virtual community model for automobile users.

Keywords: automobile, automobile users, knowledge sharing, mechatronics system, virtual community

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Authors: Seif Naoui, Lassaad Latrach, Ali Gharsallah

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This paper is specialized to highlight the method of miniaturization and improvement the patch antenna by using the complementary metamaterial. This method is presented by a simple technique is composed a structure of patch antenna integrated in its surface a cell of complementary split ring resonator. This resonator is placed at the middle of the radiating patch in parallel with the transmission line and with a variable angle of orientation. The objective is to find the ultimate angle where the best results are obtained on improving the characteristics of the considered antenna. This motif widespread at the traceability applications by wireless communication for RFID technology at the operation frequency 2.45 GHz. Our contribution is based on studies empirical often presented in this article. All simulation results were made by the CST Microwave Studio.

Keywords: complimentary split ring resonators, computer simulation technology microwave studio, metamaterials patch antennas, microstrip patch antenna, radio frequency identification

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Authors: Ali Babajanzade, Hossein Bozorgian

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Cognitive load plays an important role in learning in general and L2 listening comprehension in particular. This study is an attempt to investigate the effect of metacognitive strategy intervention through dialogic interaction (MSIDI) on L2 listeners’ cognitive load. A mixed-method design with 50 participants of male and female Iranian lower-intermediate learners between 20 to 25 years of age was used. An experimental group (n=25) received weekly interventions based on metacognitive strategy intervention through dialogic interaction for ten sessions. The second group, which was control (n=25), had the same listening samples with the regular procedure without a metacognitive intervention program in each session. The study used three different instruments: a) a modified version of the cognitive load questionnaire, b) digit span tests, and c) focused group interviews to investigate listeners’ level of cognitive load throughout the process. Results testified not only improvements in listening comprehension in MSIDI but a radical shift of cognitive load rate within this group. In other words, listeners experienced a lower level of cognitive load in MSIDI in comparison with their peers in the control group.

Keywords: cognitive load theory, human mental functioning, metacognitive theory, listening comprehension, sociocultural theory

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Authors: Ezio Bassi, Francesco Vercesi, Francesco Benzi

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The aim of this study is to evaluate the potentiality of synchronous reluctance motors for lift systems by also evaluating the vibroacoustic behaviour of the motor. Two types of synchronous machines are designed, analysed, and compared with an equivalent induction motor, which is the more common solution in such gearbox applications. The machines' performance are further improved with optimization procedures based on multiobjective optimization genetic algorithm (MOGA). The difference between the two synchronous motors consists in the rotor geometry; a symmetric and an asymmetric rotor design were investigated. The evaluation of the vibroacoustic performance has been conducted with a multi-variable model and finite element software taking into account electromagnetic, mechanical, and thermal features of the motor, therefore carrying out a multi-physics analysis of the electrical machine.

Keywords: synchronous reluctance motor, vibro-acoustic, lift systems, genetic algorithm

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Authors: Zheng Zhihao

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Recently, Knowledge Graph Framework Network (KGCN) has developed powerful capabilities in knowledge representation and reasoning tasks. However, traditional KGCN often uses a fixed weight mechanism when aggregating information, failing to make full use of rich structural information, resulting in a certain expression ability of node representation, and easily causing over-smoothing problems. In order to solve these challenges, the paper proposes an new graph neural network model called KGCN-STAR (Knowledge Graph Convolutional Network with Structural Adaptive Receptive Fields). This model dynamically adjusts the perception of each node by introducing a structural adaptive receptive field. wild range, and a subgraph aggregator is designed to capture local structural information more effectively. Experimental results show that KGCN-STAR shows significant performance improvement on multiple knowledge graph data sets, especially showing considerable capabilities in the task of representation learning of complex structures.

Keywords: knowledge graph, graph neural networks, structural adaptive receptive fields, information aggregation

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Authors: Yosra Mefteh Rekik, Younes Boujelbene

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We introduce in this paper a new conceptual model representing the stock market dynamics. This model is essentially based on cognitive behavior of the intelligence investors. In order to validate our model, we build an artificial stock market simulation based on agent-oriented methodologies. The proposed simulator is composed of market supervisor agent essentially responsible for executing transactions via an order book and various kinds of investor agents depending to their profile. The purpose of this simulation is to understand the influence of psychological character of an investor and its neighborhood on its decision-making and their impact on the market in terms of price fluctuations. Therefore, the difficulty of the prediction is due to several features: the complexity, the non-linearity and the dynamism of the financial market system, as well as the investor psychology. The Artificial Neural Networks learning mechanism take on the role of traders, who from their futures return expectations and place orders based on their expectations. The results of intensive analysis indicate that the existence of agents having heterogeneous beliefs and preferences has provided a better understanding of price dynamics in the financial market.

Keywords: artificial intelligence methods, artificial stock market, behavioral modeling, multi-agent based simulation

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Authors: Sobhy Fathy A. Hashesh

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The study was carried out in the High School Classes of Andalus Private Schools, boys section, using control and experimental groups that were randomly assigned. The study investigated the effect of Al-Andalus Improvement Model (AIM) on the development of students’ skills acquiring. The society of the study composed of Al-Andalus Private Schools, high school students, boys Section (N=700), while the sample of the study composed of four randomly assigned groups two groups of teachers (N=16) and two groups of students (N=42) with one experimental group and one control group for teachers and their students respectively. The study followed the quantitative and qualitative approaches in collecting and analyzing data to investigate the study hypotheses. Results of the study revealed that there were significant statistical differences in teachers’ performances and students' skills acquiring for the favor of the experimental groups and there was a strong correlation between the teachers performances and the students skills acquiring. The study recommended the implementation of the AIM model for the sake of teachers performances and students’ learning outcomes.

Keywords: AIM, improvement model, Classera, Al-Andalus Improvement Model.

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Authors: Farshad Zeinali, Sajedeh Norouzi, Nader Mokari, Eduard Jorswieck

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The capacity of fifth-generation (5G) vehicle-to-everything (V2X) networks poses significant challenges. To ad- dress this challenge, this paper utilizes New Radio (NR) and New Radio Unlicensed (NR-U) networks to develop a heterogeneous vehicular network (HetNet). We propose a new framework, named joint BS assignment and resource allocation (JBSRA) for mobile V2X users and also consider coexistence schemes based on flexible duty cycle (DC) mechanism for unlicensed bands. Our objective is to maximize the average throughput of vehicles while guaranteeing the WiFi users' throughput. In simulations based on deep reinforcement learning (DRL) algorithms such as deep deterministic policy gradient (DDPG) and deep Q network (DQN), our proposed framework outperforms existing solutions that rely on fixed DC or schemes without consideration of unlicensed bands.

Keywords: vehicle-to-everything (V2X), resource allocation, BS assignment, new radio (NR), new radio unlicensed (NR-U), coexistence NR-U and WiFi, deep deterministic policy gradient (DDPG), deep Q-network (DQN), joint BS assignment and resource allocation (JBSRA), duty cycle mechanism

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Authors: Alexander Goncharenko, Andrey Denisov, Sergey Alyamkin, Evgeny Terentev

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The neural network quantization is highly desired procedure to perform before running neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real life applications require simplification and acceleration of quantization procedure that will maintain accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2 and MNAS. Here we present a method to significantly optimize training with quantization procedure by introducing the trained scale factors for discretization thresholds that are separate for each filter. Using the proposed technique, we quantize the modern mobile architectures of neural networks with the set of train data of only ∼ 10% of the total ImageNet 2012 sample. Such reduction of train dataset size and small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of quantized model (accuracy drop was less than 0.5%). Ready-for-use models and code are available in the GitHub repository.

Keywords: distillation, machine learning, neural networks, quantization

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Authors: Leonardo Herrera, Shield B. Lin, Stephen J. Montgomery-Smith, Ziraguen O. Williams

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A one-directional dynamic model of a Stewart Platform was developed to assist NASA in analyzing the dynamic response in spacecraft docking operations. A simplified mechanical drawing was created, capturing the physical structure's main features. A simplified schematic diagram was developed into a lumped mass model from the mechanical drawing. Three differential equations were derived according to the schematic diagram. A Simulink diagram was created using MATLAB to represent the three equations. System parameters, including spring constants and masses, are derived in detail from the physical system. The model can be used for further analysis via computer simulation in predicting dynamic response in its main docking direction, i.e., up-and-down motion.

Keywords: stewart platform, docking operation, spacecraft, spring constant

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Authors: Syed Misbahuddin, Sagufta Kapadia

Abstract:

Autonomous indoor drone navigation has been posed with various challenges, including the inability to use a Global Positioning System (GPS). As of now, Unmanned Aerial Vehicles (UAVs) either rely on 3D mapping systems or utilize external camera arrays to track the UAV in an enclosed environment. The objective of this paper is to develop an algorithm that utilizes Kalman Filtering to reduce noise, allowing the UAV to be navigated indoors using only the flight controller and an onboard companion computer. In this paper, open-source libraries are used to control the UAV, which will only use the onboard accelerometer on the flight controller to estimate the position through double integration. One of the advantages of such a system is that it allows for low-cost and lightweight UAVs to autonomously navigate indoors without advanced mapping of the environment or the use of expensive high-precision-localization sensors.

Keywords: accelerometer, indoor-navigation, Kalman-filtering, position-control

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Authors: Maaly Jarrah

Abstract:

There is no doubt that reflective practice has become a stable component in continuous professional development (CPD) courses around the world for the purpose of promoting teacher development, meaningful learning, and deliberate teacher personal and professional growth. However, while there is much research on the benefits of integrating reflective practice in teacher CPD courses, not enough research explores EFL teachers’ experiences with engagement in reflective practice in the CPD from their own perspectives. This research employed a case study approach to explore the experience of 7 EFL teachers with engaging in reflective practice in a CPD course that took place in Kuwait.The participating EFL teachers engaged in collaborative dialogue reflections and completed reflection journal entries as part of the course. Data was collected through semi-structured interviews and analyzed thematically. Findings indicate that the participating teachers’ positive experience with reflective practice is associated with their engagement in collaborative dialogue reflections, while challenges and negative feelings are associated with writing their reflection journal entries. Accordingly, the study offers recommendations for CPD courses to help improve EFL teachers’ experiences with engagement in reflective practice.

Keywords: Collaborative dialogue reflections, continuous professional development, EFL teachers, reflection journals, teacher reflective practice

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