Search results for: reflective teaching and learning

Authors: Sung Wook Yun, Yun Jong Choi, Kyong-min Lee, Poogyeon Park*

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Repetitive systems stand for a kind of systems that perform a simple task on a fixed pattern repetitively, which are widely spread in industrial fields. Hence, many researchers have been interested in those systems, especially in the field of iterative learning control (ILC). In this paper, we propose a finite-horizon tracking control scheme for linear time-varying repetitive systems with uncertain initial conditions. The scheme is derived both analytically and numerically for state-feedback systems and only numerically for output-feedback systems. Then, it is extended to stable systems with input constraints. All numerical schemes are developed in the forms of linear matrix inequalities (LMIs). A distinguished feature of the proposed scheme from the existing iterative learning control is that the scheme guarantees the tracking performance exactly even under uncertain initial conditions. The simulation results demonstrate the good performance of the proposed scheme.

Keywords: Finite time horizon, linear matrix inequality (LMI), repetitive system, uncertain initial condition.

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Authors: Anneke Harmse, Roelien Goede

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Information and communication technology (ICT) is essential to the operation of business, and create many employment opportunities. High volumes of students graduate in ICT however students struggle to find job placement. A discrepancy exists between graduate skills and industry skill requirements. To address the need for ICT skills required, universities must create programs to meet the demands of a changing ICT industry. This requires a partnership between industry, universities and other stakeholders. This situation may be viewed as a critical systems thinking problem situation as there are various role players each with their own needs and requirements. Jackson states a typical critical systems methods has a pluralistic nature. This paper explores the applicability and suitability of Maslow and Dooyeweerd to guide understanding and make recommendations for change in ICT WIL, to foster an all-inclusive understanding of the situation by stakeholders. The above methods provide tools for understanding softer issues beyond the skills required. The study findings suggest that besides skills requirements, a deeper understanding and empowering students from being a student to a professional need to be understood and addressed.

Keywords: Dooyeweerd, Maslow, Work Integrated Learning.

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Authors: Pantaleon Lutta, Mohamed Sedky, Mohamed Hassan

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The inability of organizations to put in place management control measures for Internet of Things (IoT) complexities persists to be a risk concern. Policy makers have been left to scamper in finding measures to combat these security and privacy concerns. IoT forensics is a cumbersome process as there is no standardization of the IoT products, no or limited historical data are stored on the devices. This paper highlights why IoT forensics is a unique adventure and brought out the legal challenges encountered in the investigation process. A quadrant model is presented to study the conflicting aspects in IoT forensics. The model analyses the effectiveness of forensic investigation process versus the admissibility of the evidence integrity; taking into account the user privacy and the providers’ compliance with the laws and regulations. Our analysis concludes that a semi-automated forensic process using machine learning, could eliminate the human factor from the profiling and surveillance processes, and hence resolves the issues of data protection (privacy and confidentiality).

Keywords: Cloud forensics, data protection laws, GDPR, IoT forensics, machine learning.

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Authors: Elnaz Lashgari, Emel Demircan

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Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: Electrocardiogram, manifold learning, Laplacian Eigenmaps, running pattern.

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Authors: N. I. Bazid, I. N. Umar

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Due to the advancement of Internet technology, online learning is widely used in higher education institutions. Online learning offers several means of communication, including online forum. Through online forum, students and instructors are able to discuss and share their knowledge and expertise without having a need to attend the face-to-face, ordinary classroom session. The purposes of this study are to analyze the students’ levels of participation and critical thinking, types of action and factors influencing their participation in online forum. A total of 41 postgraduate students undertaking a course in educational technology from a public university in Malaysia were involved in this study. In this course, the students participated in a weekly online forum as part of the course requirement. Based on the log data file extracted from the online forum, the students’ type of actions (view, add, update, delete posts) and their levels of participation (passive, moderate or active) were identified. In addition, the messages posted in the forum were analyzed to gauge their level of critical thinking. Meanwhile, the factors that might influence their online forum participation were measured using a 24-items questionnaire. Based on the log data, a total of 105 posts were sent by the participants. In addition, the findings show that (i) majority of the students are moderate participants, with an average of two to three posts per person, (ii) viewing posts are the most frequent type of action (85.1%), and followed by adding post (9.7%). Furthermore, based on the posts they made, the most frequent type of critical thinking observed was justification (50 input or 19.0%), followed by linking ideas and interpretation (47 input or 18%), and novelty (38 input or 14.4%). The findings indicate that online forum allows for social interaction and can be used to measure the students’ critical thinking skills. In order to achieve this, monitoring students’ activities in the online forum is recommended.

Keywords: Critical thinking, learning management system, level of online participation, online forum.

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Authors: Duangkamol Thitivesa

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This paper focuses on the use of project work as a pretext for applying the conventions of writing, or the correctness of mechanics, usage, and sentence formation, in a content-based class in a Rajabhat University. Its aim was to explore to what extent the student teachers’ academic achievement of the basic writing features against the 70% attainment target after the use of project is. The organization of work around an agreed theme in which the students reproduce language provided by texts and instructors is expected to enhance students’ correct writing conventions. The sample of the study comprised of 38 fourth-year English major students. The data was collected by means of achievement test and student writing works. The scores in the summative achievement test were analyzed by mean score, standard deviation, and percentage. It was found that the student teachers do more achieve of practicing mechanics and usage, and less in sentence formation. The students benefited from the exposure to texts during conducting the project; however, their automaticity of how and when to form phrases and clauses into simple/complex sentences had room for improvement.

Keywords: Project-Based Learning, Project Work, Writing Conventions.

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Authors: Thammasak Thianniwet, Satidchoke Phosaard, Wasan Pattara-Atikom

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We proposed a technique to identify road traffic congestion levels from velocity of mobile sensors with high accuracy and consistent with motorists- judgments. The data collection utilized a GPS device, a webcam, and an opinion survey. Human perceptions were used to rate the traffic congestion levels into three levels: light, heavy, and jam. Then the ratings and velocity were fed into a decision tree learning model (J48). We successfully extracted vehicle movement patterns to feed into the learning model using a sliding windows technique. The parameters capturing the vehicle moving patterns and the windows size were heuristically optimized. The model achieved accuracy as high as 99.68%. By implementing the model on the existing traffic report systems, the reports will cover comprehensive areas. The proposed method can be applied to any parts of the world.

Keywords: intelligent transportation system (ITS), traffic congestion level, human judgment, decision tree (J48), geographic positioning system (GPS).

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Authors: Aldalalah, M. Osamah, Soon Fook Fong

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The purpose of this study was to investigate the effects of computer–based instructional designs, namely modality and redundancy principles on the attitude and learning of music theory among primary pupils of different Music Intelligence levels. The lesson of music theory was developed in three different modes, audio and image (AI), text with image (TI) and audio with image and text (AIT). The independent variables were the three modes of courseware. The moderator variable was music intelligence. The dependent variables were the post test score. ANOVA was used to determine the significant differences of the pretest scores among the three groups. Analyses of covariance (ANCOVA) and Post hoc were carried out to examine the main effects as well as the interaction effects of the independent variables on the dependent variables. High music intelligence pupils performed significantly better than low music intelligence pupils in all the three treatment modes. The AI mode was found to help pupils with low music intelligence significantly more than the TI and AIT modes.

Keywords: Modality, Redundancy, Music theory, Cognitivetheory of multimedia learning, Cognitive load theory, Musicintelligence.

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Authors: Pedro Padilha Gonçalves, Doris C. C. K. Kowaltowski, Benjamin Cleveland

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Trends in education affect schooling, needing incorporation into design concepts to support desired learning processes with appropriate and stimulating environments. A design process for school architecture demands research, debates, reflections, and efficient decision-making methods. This paper presents research on evidence-based design, related to middle schools, based on a systematic literature review and the elaboration of a set of architectural design patterns, through a graphic translation of new concepts for classroom configurations, to support programming debates and the synthesis phase of design. The investigation resulted in nine patterns that configure the concepts of boundaries, flexibility, levels of openness, mindsets, neighborhoods, movement and interaction, territories, opportunities for learning, and sightlines for classrooms. The research is part of a continuous investigation of design methods, on contemporary school architecture to produce an architectural pattern matrix based on scientific information translated into an insightful graphic design language.

Keywords: School architecture, design process, design patterns, evidence-based design.

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Authors: Tatjana Eitrich, Bruno Lang

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This work deals with aspects of support vector machine learning for large-scale data mining tasks. Based on a decomposition algorithm for support vector machine training that can be run in serial as well as shared memory parallel mode we introduce a transformation of the training data that allows for the usage of an expensive generalized kernel without additional costs. We present experiments for the Gaussian kernel, but usage of other kernel functions is possible, too. In order to further speed up the decomposition algorithm we analyze the critical problem of working set selection for large training data sets. In addition, we analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our tests and conclusions led to several modifications of the algorithm and the improvement of overall support vector machine learning performance. Our method allows for using extensive parameter search methods to optimize classification accuracy.

Keywords: Support Vector Machine Training, Multi-ParameterKernels, Shared Memory Parallel Computing, Large Data

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Authors: Thanida Sujarittham, Narumon Emarat, Jintawat Tanamatayarat, Kwan Arayathanitkul, Suchai Nopparatjamjomras

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Electric field is an important fundamental concept in electrostatics. In high-school, generally Thai students have already learned about definition of electric field, electric field due to a point charge, and superposition of electric fields due to multiple-point charges. Those are the prerequisite basic knowledge students holding before entrancing universities. In the first-year university level, students will be quickly revised those basic knowledge and will be then introduced to a more complicated topic—electric field due to continuous charged distributions. We initially found that our freshman students, who were from the Faculty of Science and enrolled in the introductory physic course (SCPY 158), often seriously struggled with the basic physics concepts—superposition of electric fields and inverse square law and mathematics being relevant to this topic. These also then resulted on students’ understanding of advanced topics within the course such as Gauss's law, electric potential difference, and capacitance. Therefore, it is very important to determine students' understanding of electric field due to continuous charged distributions. The open-ended question about sketching net electric field vectors from a uniformly charged insulating rod was administered to 260 freshman science students as pre- and post-tests. All of their responses were analyzed and classified into five levels of understandings. To get deep understanding of each level, 30 students were interviewed toward their individual responses. The pre-test result found was that about 90% of students had incorrect understanding. Even after completing the lectures, there were only 26.5% of them could provide correct responses. Up to 50% had confusions and irrelevant ideas. The result implies that teaching methods in Thai high schools may be problematic. In addition for our benefit, these students’ alternative conceptions identified could be used as a guideline for developing the instructional method currently used in the course especially for teaching electrostatics.

Keywords: Electrostatics Electric field due to continuous charged distributions, inverse square law, superposition principle, levels of student understandings.

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Authors: Fatemeh Amiri, Caro Lucas, Nasser Yazdani

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As the network based technologies become omnipresent, demands to secure networks/systems against threat increase. One of the effective ways to achieve higher security is through the use of intrusion detection systems (IDS), which are a software tool to detect anomalous in the computer or network. In this paper, an IDS has been developed using an improved machine learning based algorithm, Locally Linear Neuro Fuzzy Model (LLNF) for classification whereas this model is originally used for system identification. A key technical challenge in IDS and LLNF learning is the curse of high dimensionality. Therefore a feature selection phase is proposed which is applicable to any IDS. While investigating the use of three feature selection algorithms, in this model, it is shown that adding feature selection phase reduces computational complexity of our model. Feature selection algorithms require the use of a feature goodness measure. The use of both a linear and a non-linear measure - linear correlation coefficient and mutual information- is investigated respectively

Keywords: anomaly Detection, feature selection, Locally Linear Neuro Fuzzy (LLNF), Mutual Information (MI), liner correlation coefficient.

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. Earlier we predicted the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven datasets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: Software Metrics, Fault prediction, Cross project, Within project.

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Authors: A.A Issahanova, S. M. Jakupov, Z. B. Madalieva, A.A. Tolegenova

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In the article the experience of principle new technology development of ethnopsychological experiment on the basis of using other virtual independent experimental variables is presented. It is shown that ethnic prejudices are the result of forming and development of specific semantic barriers, arising up in the conditions of interethnic co-operation and people-s communication. Their overcoming is more successful in the conditions of the special organized process of teaching in a polyethnic environment, characteristic for the modern institute

Keywords: virtual monoethnic group, semantic barrier, general fund of semantic notions, polyethnic environment, ethnic prejudices

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Authors: Alquliyev R.M., Kazimov T.H., Mahmudova Sh.C., Mahmudova R.Sh.

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The given work is devoted to the description of Information Technologies NAS of Azerbaijan created and successfully maintained in Institute. On the basis of the decision of board of the Supreme Certifying commission at the President of the Azerbaijan Republic and Presidium of National Academy of Sciences of the Azerbaijan Republic, the organization of training courses on Computer Sciences for all post-graduate students and dissertators of the republic, taking of examinations of candidate minima, it was on-line entrusted to Institute of Information Technologies of the National Academy of Sciences of Azerbaijan. Therefore, teaching the computer sciences to post-graduate students and dissertators a scientific - methodological manual on effective application of new information technologies for research works by post-graduate students and dissertators and taking of candidate minima is carried out in the Educational Center. Information and communication technologies offer new opportunities and prospects of their application for teaching and training. The new level of literacy demands creation of essentially new technology of obtaining of scientific knowledge. Methods of training and development, social and professional requirements, globalization of the communicative economic and political projects connected with construction of a new society, depends on a level of application of information and communication technologies in the educational process. Computer technologies develop ideas of programmed training, open completely new, not investigated technological ways of training connected to unique opportunities of modern computers and telecommunications. Computer technologies of training are processes of preparation and transfer of the information to the trainee by means of computer. Scientific and technical progress as well as global spread of the technologies created in the most developed countries of the world is the main proof of the leading role of education in XXI century. Information society needs individuals having modern knowledge. In practice, all technologies, using special technical information means (computer, audio, video) are called information technologies of education.

Keywords: Educational Center, post-graduate, database.

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Authors: F. O Anugom

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The general functions of the university amongst other things include teaching, research and community service. Universities are recognized as the apex of learning, accumulating and imparting knowledge and skills of all kinds to students to enable them to be productive, earn their living and to make optimum contributions to national development. This is equivalent to the production of human capital in the form of high level manpower needed to administer the educational society, be useful to the society and manage the economy. Quality has become a matter of major importance for university education in Nigeria. Accreditation is the systematic review of educational programs to ensure that acceptable standards of education, scholarship and infrastructure are being maintained. Accreditation ensures that institution maintain quality. The process is designed to determine whether or not an institution has met or exceeded the published standards for accreditation, and whether it is achieving its mission and stated purposes. Ensuring quality assurance in accreditation process falls in the hands of university management which justified the need for this study. This study examined accreditation and quality assurance: the management imperative. Three research questions and three hypotheses guided the study. The design was a correlation survey with a population of 2,893 university administrators out of which 578 Heads of department and Dean of faculties were sampled. The instrument for data collection was titled Programme Accreditation Exercise scale with high levels of reliability. The research questions were answered with Pearson ‘r’ statistics. T-test statistics was used to test the hypotheses. It was found among others that the quality of accredited programme depends on the level of funding of universities in Nigeria. It was also indicated that quality of programme accreditation and physical facilities of universities in Nigeria have high relationship. But it was also revealed that programme accreditation is positively related to staffing in Nigerian universities. Based on the findings of the study, the researcher recommend that academic administrators should be included in the team of those who ensure quality programs in the universities. Private sector partnership should be encouraged to fund programs to ensure quality of programme in the universities. Independent agencies should be engaged to monitor the activities of accreditation teams to avoid bias.

Keywords: Accreditation, quality assurance, NUC, physical facilities, staffing.

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Authors: Sheila R. Bonito

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This study describes the relationship between motivation factors and academic performance among distance education students enrolled in a postgraduate nursing course. Students (n=96) participated in a survey that assesses student's motivational orientations from a cognitive perspective using a selfadministered questionnaire based on Pintrich-s Motivation Strategies for Learning Questionnaire (MLSQ). Results showed students- motivational factors are highest on task value (6.44, 0.71); followed by intrinsic goal orientation (6.20, 0.76), control beliefs (6.02, 0.89); extrinsic goal orientation (5.85, 1.13); self-efficacy for learning and performance (5.62, 0.84), and finally, test anxiety (4.21, 1.37). Weak positive correlations were found between academic performance and intrinsic goal orientation (r=0.13), extrinsic goal orientation (r=0.04), task value (r=0.09), control beliefs (r=0.02), and self-efficacy (r=0.05), while there was weak negative correlation with test anxiety (r=-0.04). Conclusions from the study indicate the need to focus on improving tasks and targeting intrinsic goal orientations of students to courses since these were positively correlated with academic performance and downplay the use of tests since these were negatively correlated with academic performance.

Keywords: Motivation factors, academic performance, distance education

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Authors: Joan Antony

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Outcome in any learning process should target three goals – propelling the underachiever’s engagement in the learning process, enhancing the drive to achieve, and modifying attitudes and beliefs in his/her capabilities. An intervention study with a three-pronged approach incorporating self-regulatory training targeting three categories of strategies – cognitive, metacognitive and motivational – was designed adopting the before and after control-experimental group design. The evaluation of the training process was based on pre- and post-intervention measures obtained through three indices of measurement – academic scores based on grades on school examinations and comprehension tests, affective variables scores and level of strategy use obtained through responses on scales and questionnaires, and content analysis of subjective responses to open-ended probes. The evaluation relied on three sources – student, teacher and parent. The t-test results for the experimental and control groups on the pre- and post-intervention measurements indicate a significant increase on comprehension tasks for the experimental group. Though statistically significant difference was not found on the school examination scores for the experimental group, there was considerable decline in performance for the control group. Analysis of covariance (ANCOVA) was applied on the scores obtained on affective variables, namely, self-esteem, personal achievement goals, personal ego goals, personal task goals, and locus of control. The experimental group showed increase in personal achievement goals and personal ego goals as compared to the control group. Responses given by the experimental group to the open-ended probes on causal attributions indicated a considerable shift from external to internal causes when moving from the pre- to post-intervention stage. ANCOVA results revealed significantly higher use of learning strategies inclusive of mental learning strategies, behavioral learning strategies, self-regulatory strategies, and an improvement in study orientation encompassing study habits and study attitudes among the experimental group students. Parents and teachers reported significant progressive transformation towards constructive engagement with study material and self-imposed regulation. The implications of this study are three-fold: firstly, strategies training (cognitive, metacognitive and motivational) should be embedded into daily classroom routine; secondly, scaffolding by teachers through activities based on curriculum will eventually enable students to rely more on their own judgements of effective strategy use; thirdly, enhanced confidence will radiate to the affective aspects with enduring effects on other domains of life as well. The cyclic nature of the interaction between utilizing one’s resources, managing effort and regulating emotions forms the foundation for academic achievement.

Keywords: Academic achievement, cognitive strategies, metacognitive strategies, motivational strategies.

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Authors: J. K. Adedeji, M. O. Oyekanmi

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This paper has critically examined the use of Machine Learning procedures in curbing unauthorized access into valuable areas of an organization. The use of passwords, pin codes, user’s identification in recent times has been partially successful in curbing crimes involving identities, hence the need for the design of a system which incorporates biometric characteristics such as DNA and pattern recognition of variations in facial expressions. The facial model used is the OpenCV library which is based on the use of certain physiological features, the Raspberry Pi 3 module is used to compile the OpenCV library, which extracts and stores the detected faces into the datasets directory through the use of camera. The model is trained with 50 epoch run in the database and recognized by the Local Binary Pattern Histogram (LBPH) recognizer contained in the OpenCV. The training algorithm used by the neural network is back propagation coded using python algorithmic language with 200 epoch runs to identify specific resemblance in the exclusive OR (XOR) output neurons. The research however confirmed that physiological parameters are better effective measures to curb crimes relating to identities.

Keywords: Biometric characters, facial recognition, neural network, OpenCV.

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Authors: Yenni Anggrayni

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The lack of awareness of the significant prevalence of school absenteeism in Indonesia, which ultimately results in high rates of school dropouts, is an unresolved issue. Therefore, this study aims to investigate the root causes of chronic absenteeism qualitatively and quantitatively using the bioecological systems paradigm in secondary schools for any reason. This study used an open-ended questionnaire to collect data from 1,148 students in six West Java Province districts/cities. Univariate and stepwise multiple logistic regression analyses produced a prediction model for the components. Analysis results show that poor teacher-student relationships, bullying by peers or teachers, negative perception of education, and lack of parental involvement in learning activities are the leading causes of chronic absenteeism. Another finding is to promote home-school partnerships to improve school climate and parental involvement in learning to address chronic absenteeism.

Keywords: Bullying, chronic absenteeism, dropout of school, home-school partnerships, parental involvement.

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Authors: Hyun-Woo Cho

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Real-time or in-line process monitoring frameworks are designed to give early warnings for a fault along with meaningful identification of its assignable causes. In artificial intelligence and machine learning fields of pattern recognition various promising approaches have been proposed such as kernel-based nonlinear machine learning techniques. This work presents a kernel-based empirical monitoring scheme for batch type production processes with small sample size problem of partially unbalanced data. Measurement data of normal operations are easy to collect whilst special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing process monitoring performance. Furthermore, preprocessing of raw process data is used to get rid of unwanted variation of data. The performance of the monitoring scheme was demonstrated using three-dimensional batch data. The results showed that the monitoring performance was improved significantly in terms of detection success rate of process fault.

Keywords: Process Monitoring, kernel methods, multivariate filtering, data-driven techniques, quality improvement.

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Authors: Shanxiong Chen, Xu Han, Xiaolong Wang, Hui Ma

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Yi is an ethnic group mainly living in mainland China, with its own spoken and written language systems, after development of thousands of years. Ancient Yi is one of the six ancient languages in the world, which keeps a record of the history of the Yi people and offers documents valuable for research into human civilization. Recognition of the characters in ancient Yi helps to transform the documents into an electronic form, making their storage and spreading convenient. Due to historical and regional limitations, research on recognition of ancient characters is still inadequate. Thus, deep learning technology was applied to the recognition of such characters. Five models were developed on the basis of the four-layer convolutional neural network (CNN). Alpha-Beta divergence was taken as a penalty term to re-encode output neurons of the five models. Two fully connected layers fulfilled the compression of the features. Finally, at the softmax layer, the orthographic features of ancient Yi characters were re-evaluated, their probability distributions were obtained, and characters with features of the highest probability were recognized. Tests conducted show that the method has achieved higher precision compared with the traditional CNN model for handwriting recognition of the ancient Yi.

Keywords: Recognition, CNN, convolutional neural network, Yi character, divergence.

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Authors: Omer Cahana, Maya Herman, Ofer Levi

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Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.

Keywords: Accelerate MRI scans, image reconstruction, pyramid network, deep learning.

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Authors: Abdenour Bounsiar, Edith Grall, Pierre Beauseroy

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In this paper we are interested in classification problems with a performance constraint on error probability. In such problems if the constraint cannot be satisfied, then a rejection option is introduced. For binary labelled classification, a number of SVM based methods with rejection option have been proposed over the past few years. All of these methods use two thresholds on the SVM output. However, in previous works, we have shown on synthetic data that using thresholds on the output of the optimal SVM may lead to poor results for classification tasks with performance constraint. In this paper a new method for supervised classification with rejection option is proposed. It consists in two different classifiers jointly optimized to minimize the rejection probability subject to a given constraint on error rate. This method uses a new kernel based linear learning machine that we have recently presented. This learning machine is characterized by its simplicity and high training speed which makes the simultaneous optimization of the two classifiers computationally reasonable. The proposed classification method with rejection option is compared to a SVM based rejection method proposed in recent literature. Experiments show the superiority of the proposed method.

Keywords: rejection, Chow's rule, error-reject tradeoff, SupportVector Machine.

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Authors: Aikaterini Fountoulaki, Nikos Karacapilidis, Manolis Manatakis

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This paper proposes an innovative methodology for Acceptance Sampling by Variables, which is a particular category of Statistical Quality Control dealing with the assurance of products quality. Our contribution lies in the exploitation of machine learning techniques to address the complexity and remedy the drawbacks of existing approaches. More specifically, the proposed methodology exploits Artificial Neural Networks (ANNs) to aid decision making about the acceptance or rejection of an inspected sample. For any type of inspection, ANNs are trained by data from corresponding tables of a standard-s sampling plan schemes. Once trained, ANNs can give closed-form solutions for any acceptance quality level and sample size, thus leading to an automation of the reading of the sampling plan tables, without any need of compromise with the values of the specific standard chosen each time. The proposed methodology provides enough flexibility to quality control engineers during the inspection of their samples, allowing the consideration of specific needs, while it also reduces the time and the cost required for these inspections. Its applicability and advantages are demonstrated through two numerical examples.

Keywords: Acceptance Sampling, Neural Networks, Statistical Quality Control.

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Authors: Morteza Pourazar, Fatemeh Mirakhori, Fazlolah Bagherzadeh, Rasool Hemayattalab

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The purpose of study was to examine the effects of external and internal focus of attention in the motor learning of children with cerebral palsy. The study involved 30 boys (7 to 12 years old) with CP type 1 who practiced throwing beanbags. The participants were randomly assigned to the internal focus, external focus, and control groups, and performed six blocks of 10-trial with attentional focus reminders during a practice phase and no reminders during retention and transfer tests. Analysis of variance (ANOVA) with repeated measures on the last factor was used. The results show that significant main effects were found for time and group. However, the interaction of time and group was not significant. Retention scores were significantly higher for the external focus group. The external focus group performed better than other groups; however, the internal focus and control groups’ performance did not differ. The study concluded that motor skills in Spastic Hemiparetic Cerebral Palsy (SHCP) children could be enhanced by external attention.

Keywords: Cerebral Palsy, external attention, internal attention, throwing task.

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Authors: A. Borji, M. Hamidi

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A new method for color image segmentation using fuzzy logic is proposed in this paper. Our aim here is to automatically produce a fuzzy system for color classification and image segmentation with least number of rules and minimum error rate. Particle swarm optimization is a sub class of evolutionary algorithms that has been inspired from social behavior of fishes, bees, birds, etc, that live together in colonies. We use comprehensive learning particle swarm optimization (CLPSO) technique to find optimal fuzzy rules and membership functions because it discourages premature convergence. Here each particle of the swarm codes a set of fuzzy rules. During evolution, a population member tries to maximize a fitness criterion which is here high classification rate and small number of rules. Finally, particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. Our results, using this method for soccer field image segmentation in Robocop contests shows 89% performance. Less computational load is needed when using this method compared with other methods like ANFIS, because it generates a smaller number of fuzzy rules. Large train dataset and its variety, makes the proposed method invariant to illumination noise

Keywords: Comprehensive learning Particle Swarmoptimization, fuzzy classification.

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Authors: Christina Van Wingerden

Abstract:

This paper focuses on a sense of belonging, isolation, and the use of a learning management system as a “third space” for connection and community. Given student use of learning management systems (LMS) for courses on campuses, moderate to high use of social media and hand-held devices, the author explores the possibilities of LMS as a third space. The COVID-19 pandemic has exacerbated student experiences of isolation, and research indicates that students who experience a sense of belonging have a greater likelihood for academic retention and success. The impacts on students of an LMS designed for student employee orientation and training were examined through a mixed methods approach, including a survey, individual interviews, and focus groups. The sample involved 250-450 undergraduate student employees at a US northwestern university. The goal of the study was to find out the efficiency and effectiveness of the orientation information for a wide range of student employees from multiple student affairs departments. And unexpected finding emerged within the study in 2015 and was noted again as a finding in the 2017 study. Students reported feeling like they individually connected to the department, and further to the university because of the LMS orientation. They stated they could see themselves as part of the university community and like they belonged. The orientation, through the LMS, was designed for and occurred online (asynchronous), prior to students traveling and beginning university life for the academic year. The students indicated connection and belonging resulting from some of the design features. With the onset of COVID-19 and prolonged sheltering in place in North America, as well as other parts of the world, students have been precluded from physically gathering to educate and learn. COVID-19 essentially paused face-to-face education in 2020. Media, governments, and higher education outlets have been reporting on widespread college student stress, isolation, loneliness, and sadness. In this context, the author conducted a current mixed methods study (online survey, online interviews) of students in advanced degree programs, like Ph.D. and Ed.D. specifically investigating isolation and sense of belonging. As a part of the study a prototype of a Canvas site was experienced by student interviewees for their reaction of this Canvas site prototype as a “third” space. Some preliminary findings of this study are presented. Doctoral students in the study affirmed the potential of LMS as a third space for community and social academic connection.

Keywords: COVID-19, learning management systems, sense of belonging, third space.

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Authors: Kongrit Jittangthammagul, Sakesun Yampinij, Thapanee Endoo, Nattapong Kramwong

Abstract:

The study entitled “The Construction of Interactive Computer Multimedia Instruction on Basic Japanese Vocabulary" was aimed: 1) To construct the interactive computer multimedia instruction on Basic Japanese Vocabulary, 2) To find out multimedia-s quality, 3) To examine the student-s satisfaction and 4) To study the learning achievement in Basic Japanese vocabulary. The sampling group used in this study was composed of 40 1st year student in Educational Communications and Technology Department, Faculty of Industrial Education and Technology, King Mongkut-s University of Technology Thonburi, in the academic year 2553 B.E. (2010). According to research results, we found that 1). The quality assessment by 3 mass media experts was at 4.72 on average or at high level. 2) In terms of contents, the evaluation by 3 experts was at 4.81 on average or at high level. 3) In terms of achievement, there was a statistical significance between before and after the treatment at the .05 level. 4) The satisfaction of students towards the interactive computer multimedia Instruction on “Basic Japanese Vocabulary" was 4.35 on average, or at high level.

Keywords: Interactive Computer Multimedia on Basic Japanese Vocabulary, Learning Achievement, Quality

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Authors: Qiuyu Dai, Haochong Zhang, Xiangrong Liu

Abstract:

Efficient matrix-vector multiplication with diagonal sparse matrices is pivotal in a multitude of computational domains, ranging from scientific simulations to machine learning workloads. When encoded in the conventional Diagonal (DIA) format, these matrices often induce computational overheads due to extensive zero-padding and non-linear memory accesses, which can hamper the computational throughput, and elevate the usage of precious compute and memory resources beyond necessity. The ’DIA-Adaptive’ approach, a methodological enhancement introduced in this paper, confronts these challenges head-on by leveraging the advanced parallel instruction sets embedded within Machine Learning Units (MLUs). This research presents a thorough analysis of the DIA-Adaptive scheme’s efficacy in optimizing Sparse Matrix-Vector Multiplication (SpMV) operations. The scope of the evaluation extends to a variety of hardware architectures, examining the repercussions of distinct thread allocation strategies and cluster configurations across multiple storage formats. A dedicated computational kernel, intrinsic to the DIA-Adaptive approach, has been meticulously developed to synchronize with the nuanced performance characteristics of MLUs. Empirical results, derived from rigorous experimentation, reveal that the DIA-Adaptive methodology not only diminishes the performance bottlenecks associated with the DIA format but also exhibits pronounced enhancements in execution speed and resource utilization. The analysis delineates a marked improvement in parallelism, showcasing the DIA-Adaptive scheme’s ability to adeptly manage the interplay between storage formats, hardware capabilities, and algorithmic design. The findings suggest that this approach could set a precedent for accelerating SpMV tasks, thereby contributing significantly to the broader domain of high-performance computing and data-intensive applications.

Keywords: Adaptive method, DIA, diagonal sparse matrices, MLU, sparse matrix-vector multiplication.

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