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Activities which may be used to investigate a variety of woven patterns are presented. Pattern predicting and analysis of different patterns are discussed. A Basic computer program for the weaving code is given. (CW)

The recognition of the creepage discharge development process of oil–paper insulation under AC–DC combined voltage is the basis for fault monitoring and diagnosis of converter transformers; however, only a few related studies are available. In this study, the AC–DC combined voltage with a ratio of 1:1 was used to develop a recognition method for the creepage discharge development process of an oil–paper insulation under a cylinder–plate electrode structure. First, the pulse current method was used to collect the discharge signals in the creepage discharge development process. Then, 24 characteristic parameters were extracted from four types of creepage discharge characteristic spectra after dimensionality reduction. Finally, based on the online sequential extreme learning machine (OS-ELM) algorithm, these characteristic parameters were used to recognize the development stage of the creepage discharge of the oil–paper insulation. The results showed that when the size of the sample training set used in the OS-ELM algorithm is close to the number of hidden layer neurons, a high recognition accuracy can be obtained, and the type of activation function has little influence on the recognition accuracy. Four stages of the creepage discharge development process were recognized using the OS-ELM algorithm; the trend was the same as that of the characteristic parameters of the entire creepage discharge development process. The recognition accuracy was 91.4%. The algorithm has a high computing speed and high accuracy and can train data in batches. Therefore, it can be widely used in the field of online monitoring and evaluation of electrical equipment status.

Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.

The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students' particulars such as their academic background, current study and student activity records, their attended higher education institution's expectations of graduate attributes and self-assessment of their own generic competencies. The gap between the higher education students' generic competence development and their current statuses such as their academic performance and their student activity involvement was incorporated into the framework to come up with a recommendation for the student activities that lead to their generic competence development. For the formulation of the recommendation framework, the data mining tool Orange with some programming in Python and machine learning models was applied on 14,556 students' activity and academic records in the case higher education institution to find out three major types of patterns between the students' participation of the student activities and (1) their academic performance change, (2) their programmes of studies, and (3) their English results in the public examination. These findings are also discussed in this paper.

Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between early childhood music and maths. The emphasis is on children as intuitive pattern makers as they explore, categorise and imagine their worlds. We argue for the careful listening of childhood languages and reason that music and maths are expressive languages that young children use to investigate and experiment. This concept is based on Malaguzzi's idea of the hundred languages of children. Children's explorations of patterns, whether music, maths or other media will follow the principle of symmetry. We observe the intersectionality of language/s. Data are drawn from video and teaching materials prepared for early childhood teachers in-service training in Australia. Framing the discussion with Vygotskian and developmental theories we identify the patterns a child makes in a music discovery video and categorise the child's actions to combine both musical and mathematical concepts within the one action. The aim of the research is to revisit early childhood curriculum and pedagogy, rethinking sources of knowledge and the value of treating arts and sciences as being of equal importance as children discover and define their worlds.

College students exploit information technology to cheat on papers and assignments, but for the most part university faculty employ few technological techniques to detect cheating. This paper reports on a trial of software for the detection of cheating in a large undergraduate survey class. The paper discusses the decision to adopt electronic means for screening student papers, the techniques used, the outcome, strategic concerns regarding deterrence versus detection of cheating, and the results of a survey of student attitudes about the experience. The paper advances the thesis that easily-adopted techniques not only close a sophistication gap associated with computerized cheating, but can place faculty in a stronger position than they have ever enjoyed historically with regard to the deterrence and detection of some classes of plagiarism. (Contains 3 figures and 5 notes.)

There is an increasing interest and growing practice in Citizen Science (CS) that goes along with the usage of websites for communication as well as for capturing and processing data and materials. From an educational perspective, it is expected that by integrating information about CS in a formal educational setting, it will inspire teachers to create learning activities. This is an interesting case for using bots to automate the process of data extraction from online CS platforms to better understand its use in educational contexts. Although this information is publicly available, it has to follow GDPR rules. This paper aims to explain (1) how CS communicates and is promoted on websites, (2) how web scraping methods and anonymization techniques have been designed, developed and applied to collect information from online sources and (3) how these data could be used for educational purposes. After the analysis of 72 websites, some of the results obtained show that only 24.8% includes detailed information about the CS project and 48.61% includes information about educational purposes or materials.

As we move further into the digital age, the acquisition of digital literacy (DL) and computational thinking (CT) skills is emerging internationally as an essential goal for students in contemporary school curricula. As the world becomes more uncertain and volatile due to impacts of artificial intelligence (AI), international unrest, climate change, global economic instability and unforeseen catastrophes such as the Coronavirus (COVID-19) pandemic, it is necessary to review, revise and refine school education curricula and policies. The issue of what is essential for students to learn, and how they learn it, is of growing importance to international organisations such as the Organisation for Economic Co-operation and Development (OECD) and the United Nations Educational, Scientific and Cultural Organisation (UNESCO) and is emerging as a significant driver for educational reform across the globe. The growing prominence of CT and DL skills across many industry sectors has prompted recent changes to international assessment frameworks such as the Programme for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS). This paper will briefly discuss specific examples of alternative approaches to addressing CT in national curricula for the compulsory years of schooling and explain how CT has been adopted as a driver for mathematics curriculum change in Australia.

This study investigated the influence of CThink4CS2 Module on computational thinking (CT) skills of form four chemistry students. The CThink4CS[superscript]2 Module integrated CT with the Engineering Design Process (EDP) in chemistry class. This study utilized quantitative research methods and quasi-experimental design. Quantitative data were collected using the Computational Thinking Skill Test (CTST) which consisted of algorithmic reasoning, abstraction, decomposition, and pattern recognition constructs. A total of 73 students were in the treatment group (n=39) and control group (n=34). Experimental data were described by means of descriptive analysis and inferential analysis employing two-way MANOVA analysis. The results of the analysis indicated significant differences in CT skills between groups; students in the treatment group demonstrated better results compared to those in the control group. The paper provides insight into the integration of CT and EDP as effective pedagogical strategies for inculcating CT skills.

This paper presents a method for the extraction of contour lines and other geographic information from scanned color images of topographical maps. Although topographic maps are available from many suppliers, this work focuses on United States Geological Survey (USGS) maps. The extraction of contour lines, which are shown with brown color on USGS maps, is a difficult process due to aliasing and false colors induced by the scanning process and due to closely spaced and intersecting/overlapping features inherent to the map. These difficulties render simple approaches such as clustering ineffective. The proposed method overcomes these difficulties using a multistep process. First, a color key set, designed to comprehend color aliasing and false colors, is generated using an eigenvector line-fitting technique in RGB space. Next, area features, representing vegetation and bodies of water, are extracted using RGB color histogram analysis in order to simplify the next stage. Then, linear features corresponding to roads and rivers including contours, are extracted using a valley seeking algorithm operating on a transformed version of the original map. Finally, an A* search algorithm is used to link valleys together to form linear features and to close the gaps caused by intersecting features. The performance of the algorithm is tested on a number of USGS topographic map samples. Index Terms--Color map analysis, map segmentation, topographic map contour line extraction, USGS map analysis, aliasing and false colors.

The present paper examines a patterning activity that was organised within a teaching experiment in order to analyse the different uses of variables by secondary school students. The activity presented in the paper can be categorised as a pictorial/geometric linear pattern. We adopted a student-oriented perspective for our analysis, in order to grasp how students perceive their own generalising actions. The analysis of our data led us to two broad categories for variable use, according to whether the variable is viewed as a generalised number or not. Our results also show that students sometimes treat the variable as closely linked to a referred object, as a superfluous entity or as a constant. Finally, the notion of equivalence, which is an important step towards understanding variables, proved difficult for our students to grasp.

This paper studies how four primary-school in-service teachers develop the mathematical practices of conjecturing and proving. From the consideration of professional development as the legitimate peripheral participation in communities of practice, these teachers' mathematical practices have been characterised by using a theoretical framework (consisting of categories of activities) that describes and explains how a research mathematician develops these two mathematical practices. This research has adopted a qualitative methodology and, in particular, a case study methodological approach. Data was collected in a working session on professional development while the four participants discussed two questions that invoked the development of the mathematical practices of conjecturing and proving. The results of this study show the significant presence of informal activities when the four participants conjecture, while few informal activities have been observed when they strive to prove a result. In addition, the use of examples (an informal activity) differs in the two practices, since examples support the conjecturing process but constitute obstacles for the proving process. Finally, the findings are contrasted with other related studies and several suggestions are presented that may be derived from this work to enhance professional development.

We reviewed studies on recognition technologies published in the last ten years. This review study was aimed toward identifying, appraising, selecting, and synthesizing all high quality research evidence published in the literature related to recognition technologies and on determining how they can assist learning and instruction. This study particularly focuses on summarizing the current state of knowledge in the following dimensions: (1) recognition technology and processes, (2) applications, (3) schemes, and (4) advantages and disadvantages. We reviewed seventy-two papers and identified eighteen recognition technologies. Our results showed that all of the recognition technologies under consideration featured different recognition processes and applications. In most studies, the participants were university students. Questionnaires and tests were the most frequently used data collection methods. Most studies used a group comparison as their research design. Finally, several advantages and disadvantages of the recognition technologies were identified and summarized in the papers. The most frequently cited disadvantage was a low recognition accuracy rate. Based on our results, several suggestions and implications are made for the teaching and research community.

Multimedia programs having a number of elements like Texts, spoken words, sound & music, graphics, animations and still pictures provide different stimuli in their presentations. Art is the field of education that provides a platform for rigorous investigation, representation, expression, and reflection of both scholastic content and the art form itself. The integration of art with other subjects of the school curriculum can open new pathways of learning for students. Vedic Mathematics is an approach to resolve the crisis in education especially in the field of mathematics. It is not simply a collection of new computational techniques; rather, it provides an entirely different approach to the mathematical computation based on pattern recognition. The present paper deals with the development of multimedia packages using techniques of art and Vedic aphorisms on some selected common topics of curriculum of UK and Indian elementary mathematics and the effectiveness of multimedia packages for the development of mathematical skills. The study was conducted using quasi experimental design for research in both countries. The quantitative analysis of data revealed that the multimedia packages developed by using techniques of art and Vedic Aphorisms have significantly improved the mathematical skills of UK elementary school students.

Information Visualisation strategies can be applied in a variety of domains. In the context of temporal networks, i.e., networks in which interactions between individuals occur throughout time, efforts have been conducted to develop visual approaches that allow finding interaction patterns, anomalies, and other behaviours not previously perceived in the data. This paper presents two case studies involving real-world education networks from a primary school and a high school. For this purpose, we used the "Massive Sequence View (MSV)" layout with the "Community-based Node Ordering (CNO)" method, two well established approaches for visual analysis of temporal networks. Our results show that the identified patterns involving students/students and students/ teachers represent important information to benefit and support decision making about school management and teaching strategies, especially those related to strategic group formation.

The recognition of the creepage discharge development process of oil–paper insulation under AC–DC combined voltage is the basis for fault monitoring and diagnosis of converter transformers; however, only a few related studies are available. In this study, the AC–DC combined voltage with a ratio of 1:1 was used to develop a recognition method for the creepage discharge development process of an oil–paper insulation under a cylinder–plate electrode structure. First, the pulse current method was used to collect the discharge signals in the creepage discharge development process. Then, 24 characteristic parameters were extracted from four types of creepage discharge characteristic spectra after dimensionality reduction. Finally, based on the online sequential extreme learning machine (OS-ELM) algorithm, these characteristic parameters were used to recognize the development stage of the creepage discharge of the oil–paper insulation. The results showed that when the size of the sample training set used in the OS-ELM algorithm is close to the number of hidden layer neurons, a high recognition accuracy can be obtained, and the type of activation function has little influence on the recognition accuracy. Four stages of the creepage discharge development process were recognized using the OS-ELM algorithm; the trend was the same as that of the characteristic parameters of the entire creepage discharge development process. The recognition accuracy was 91.4%. The algorithm has a high computing speed and high accuracy and can train data in batches. Therefore, it can be widely used in the field of online monitoring and evaluation of electrical equipment status. [ABSTRACT FROM AUTHOR]

Electronic language corpora, and their attendant computer software, are proving increasingly influential in language teaching as sources of language descriptions and pedagogical materials. However, few teachers are clear about their nature or their relevance to language teaching. This paper defines corpora and their types, discusses their contribution to language learning and teaching, and provides examples of their use in class. It also outlines the changes in knowledge, skills and attitudes that are needed for learners and teachers to take advantage of the opportunities offered by the availability of corpus resources. Finally, the paper discusses the limitations of using corpora in language teaching, and the potential pitfalls arising from their uncritical use. Although the paper refers to research and teaching materials and procedures relevant to English language teaching (ELT) it addresses issues related to language teaching in general.

This paper synthesizes research from three separate studies, analysing how different representations of a mathematical concept may affect young children's engagement with mathematical activities. Children between five and seven years old engaged in counting objects, identifying triangles and completing repeating patterns. The implementation of three counting principles were investigated: the one-to-one principle, the stable-order principle and the cardinal principal. Children's reasoning when identifying triangles was analysed in terms of visual, critical and non-critical attribute reasoning. With regard to repeating patterns, we analyse children's references to the minimal unit of repeat of the pattern. Results are discussed in terms of three functions of multiple external representations: to complement, to constrain and to construct.

The goal of the research described in this paper is the design of a system to automatically extract information from paper-based maps and answer queries related to spatial features and structure of geographic data. The foundation to such a system is a set of image analysis algorithms to extract spatial features from images of paper-based maps. Efficient algorithms to detect symbols, identify and track various types of lines, follow closed contours, compute distances, find shortest paths, etc., from simplified map images have been developed. A query processor analyzes the queries presented by the user in a predefined syntax, controls the operation of the image processing algorithms, and interacts with the user. The query processor is written in Lisp and calls image analysis routines written in Fortran. [ABSTRACT FROM AUTHOR]

Organizational patterns can serve as a teaching strategy for instructors and as a learning tool for students to develop their expository writing skills, which are commonly required for assignments (for example, laboratory reports and research papers) in Food Science courses and in their future careers. The article discusses the importance of organizational patterns for teaching expository writing through an interdisciplinary collaboration. The teaching collaboration occurred with professors from Food Science, English, and Anthropology in an introductory Food Science course (FSCI 232) taught at California State University Long Beach (CSULB). In FSCI 232, students learned how to use organizational patterns to interpret and explain the content of an infographic obtained from the Food Technology magazine, published by the Institute of Food Technologists (IFT). The infographic "Global Obesity's Expanding Girth, the World is Getting Fatter" served as a visual stimulus to help students identify these patterns, focusing on inquiry and analysis of scientific data and skills required for technical writing. Furthermore, the article illustrates those other potential applications of organizational patterns using the infographic could extend to interdisciplinary content (that is, Food Anthropology), which facilitates the development of cultural competency and sensitivity in food systems. Additionally, the article provides sample activities for teachers to use in their classrooms. To summarize, organizational patterns can serve as an effective teaching strategy to enhance students' writing skills across Food Science and related disciplines.

This paper explores Computational Thinking (CT) through the experiences and interactions of sixth-grade students as they were engaging in a science lesson utilizing robotics. This robotics unit institutes the shifting from traditional to engaging hands-on activities coupled with CT skills that are exciting, intriguing, and inviting to students. The constructionist philosophy, hands-on application learning, addresses social skills like collaboration, communication, and teamwork to provide an authentic, real-world learning experience. CT brings to the classroom exciting and innovative activities that infuse robotics with hands-on application platforms in the science and mathematics curriculum, but the education system has missed a core set of young open-minded eager students at the intermediate school level. With today's vision in education focusing on the 21st-Century learner, CT is emerging as a key component in the skill set necessary for the new generation of learners. CT poses a strong ideology based on problem-solving equally conveying an essential position in cross-curricular classroom activities. This session exposes CT through a study relating experiences and interactions by students when engaging in a science lesson utilizing robots. Focusing on how students engage the CT key concepts of: (1) abstraction; (2) decomposition; (3) pattern recognition; and (4) algorithm when participating in robotics activity.

The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Ontology Matching Problem (OMP), whose solution is modeled in this paper as a binary pattern classification problem. The latter problem is then solved through the application of our new proposed associative model. The solution proposed here allows the alignment of two different ontologies--both in the Learning Objects Metadata (LOM) format--into a single ontology of LOs for ODL in LOM format, without redundant objects and with all inherent advantages for handling ODL LOs. The proposed model of pattern classification was validated through experiments, which were done on data taken from the Ontology Alignment Evaluation Initiative (OAEI) 2014 campaign, as well as on data taken from two known educative content repositories: ADRIADNE and MERLOT. The obtained results show a high performance when compared against some of the classifier algorithms present in the state of the art.

License plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M. [ABSTRACT FROM AUTHOR]

Drawing on a review of recent work conducted in the area of pattern generalization (PG), this paper makes a case for a distributed view of PG, which basically situates processing ability in terms of convergences among several different factors that influence PG. Consequently, the distributed nature leads to different types of PG that depend on the nature of a given PG task and a host of cognitive, sociocultural, classroom-related, and unexplored factors. Individual learners draw on a complex net of parallel choices, where every choice depends on the strength of ongoing training and connections among factors, with some factors appearing to be more predictable than others.

Featured Application: This paper proposes a method of monitoring the condition of three-phase asynchronous induction motors running with no load based on computer analysis of the instantaneous active electrical power. This paper experimentally reveals some of the resources offered by the instantaneous active electric power in describing the state of three-phase AC induction asynchronous electric motors (with a squirrel-cage rotor) operating under no-load conditions. A mechanical power is required to rotate the rotor with no load, and this mechanical power is satisfactorily reflected in the constant and variable part of instantaneous active electric power. The variable part of this electrical power should necessarily have a periodic component with the same period as the period of rotation of the rotor. This paper proposes a procedure for extracting this periodic component description (as a pattern by means of a selective averaging of instantaneous active electrical power) and analysis. The time origin of this pattern is defined by the time of a selected first passage through the origin of an angular marker placed on the rotor, detectable by a proximity sensor (e.g., a laser sensor). The usefulness of the pattern in describing the state of the motor rotor has been demonstrated by several simple experiments, which show that a slight change in the no-load running conditions of the motor (e.g., by placing a dynamically unbalanced mass on the rotor) has clear effects in changing the shape of the pattern. [ABSTRACT FROM AUTHOR]

This paper presents a novel use of a bio-inspired retina model as a scene preprocessing stage for the recognition of letters and numbers on Chilean vehicle license plates. The goal is to improve the effectiveness and ease of pattern recognition. Inspired by the responses of mammalian retinas, this retinal model reproduces both the natural adjustment of contrast and the enhancement of object contours by parvocellular cells. Among other contributions, this paper provides an in-depth exploration of the architecture, advantages, and limitations of the model; investigates the tuning parameters of the model; and evaluates its performance when integrating a convolutional neural network and a spiking neural network into an optical character recognition (OCR) algorithm, using 40 different genuine license plate images as a case study and for testing. The results obtained demonstrate the reduction of error rates in character recognition based on convolutional neural networks (CNNs), spiking neural networks (SNNs), and OCR. It is concluded that this bio-inspired retina model offers a wide spectrum of potential applications to further explore, including motion detection, pattern recognition, and improvement of dynamic range in images, among others. [ABSTRACT FROM AUTHOR]

The vision plays an important role in educational technologies because it can produce and communicate quite important functions in teaching and learning. In this paper, learners' preference for the visual complexity on small screens of mobile computers is studied by neural networks. The visual complexity in this study is divided into five levels, including "very high" complexity, "slightly high" complexity, "medium" complexity, "slightly low" complexity and "very low" complexity. This study focuses on the age effects for vision problems in educational technologies. The age of the tested subjects distributes from 10 to 64, and is uniformly divided into 11 groups with each group composed of 30 tested subjects. For simplicity, the effects of gender, words, colors, and other visual factors are ignored. This study found that only learners of the younger and older age groups have special preference on the picture of very high complexity. Most learners prefer pictures of medium and slightly high complexity. These results are consistent with many existing studies. With the use of neural networks, only about half of the investigation data are required to predict the overall investigation results. Discussions and interpretations on the results are also given in this study. This study will be helpful in vision problems of educational technologies.

Chatbots were an important research subject in the past. A chatbot is a computer program or an artificial intelligence program that participates in a conversation via auditory or textual methods. As the research on chatbots progressed, some important issues regarding them changed over time. Therefore, it is necessary to review the technology with a focus on recent advancements and core research technologies. In this paper, we introduce five different chatbot technologies: natural language processing, pattern matching, semantic web, data mining, and context-aware computer. We also introduce the latest technology for the chatbot researchers to recognize the present situation and channelize it in the right direction. [ABSTRACT FROM AUTHOR]

In this paper, an automatic lesson generation system is presented which is suitable in a learning-by-mimicking scenario where the learning objects can be represented as multiattribute time series data. The dance is used as an example in this paper to illustrate the idea. Given a dance motion sequence as the input, the proposed lesson generation system automatically generates the lesson plan for students. It first extracts patterns from the input dance sequence to form the learning objects. The prerequisite structure is then built by considering the relations between the learning objects. Afterward the knowledge structure is constructed from the prerequisite structure based on the knowledge space theory. Finally, the learning path is derived according to an easy-to-complex manner while respecting the prerequisite relations. A user study that involved 40 students was conducted to evaluate the proposed work. The average learning time required for the treatment group (learning with the proposed system) was found to be lower than that of the control group (learning by free browsing) thus demonstrating the learning efficiency of the proposed system. The feedback from the questionnaires indicated that a majority of the subjects showed positive response toward the usefulness and rationality of our proposed system. (Contains 12 figures and 1 table.)

Building intelligent course designing systems adaptable to the learners' needs is one of the key goals of research in e-learning. This goal is all the more crucial as gaining knowledge in an e-learning environment depends solely on computer mediated interaction within the learner group and among the learners and instructors. The patterns generated out of discussions among e-learners may reveal significant information regarding their learning growth. This paper presents an algorithm to predict the knowledge gain of students by analyzing their online interaction pattern amongst each other. This knowledge gain is used to categorize the students as prospective "gainers" or "non-gainers" through Naive Bayes Classifier. The preferences of non-gainers in terms of instructor-oriented v/s peer-oriented interaction are subsequently obtained. The paper further suggests some of the remedial plans based on proven instructional strategies, to be adopted that may help learners strengthen their weak areas. The actual knowledge gain of learners is evaluated by performing paired t-test on the previous and post-test score pairs. (Contains 3 figures and 6 tables.)

Congressional scholars have long analyzed legislative behavior by examining roll call votes. The study of roll call votes has rarely been extended to particular policy areas, with a few exceptions, such as abortion and environmental issues. This study examines roll call voting in the area of higher education policy. In examining voting patterns regarding higher education, this study extends research of legislative voting behavior to a policy area that was once considered to be uncontroversial. This study also sheds light on an important aspect of higher education policymaking. Using a framework described by Clinton et al. (2004), this paper answers several questions regarding senators' voting patterns on higher education policy. These include: (1) What are the characteristics of the voting patterns of individual senators on higher education issues?; (2) To what extent do senators from each party differ on higher education issues?; (3) What issues result in votes along party lines?; and (4) Have divisive votes become more common recent years? The results of this paper suggest that senators fall along a recognizable left-right continuum in their ideal point preferences. Senators who have been described as liberal in other studies have quite different ideal points than those described elsewhere as conservative. The use of Bayesian inference and estimation to analyze roll call data has illuminated many important aspects of Congressional behavior. This study adds to this growing field by demonstrating that this form of analysis also reveals partisan differences in an area which had been long assumed to be non-partisan. (Contains 5 figures and 3 notes.)

Purpose: This paper seeks to examine the spatial patterns of student use of machines in each laboratory to whether there are underlying commonalities. Design/methodology/approach: The research was carried out by assessing the user behaviour in 16 computer laboratories at a regional university in Australia. Findings: The study found that computers within easy access to doors are disproportionately more used than computer that are further away, irrespective of other "incentive" such as windows, wall anchoring or security camera positioning. Practical implications: This paper has implications for any division within a university environment responsible for the spatial positioning of computer in a student laboratory. Originality/value: Previous research of the use of computer laboratories in schools and universities has focussed on educational issues. None of the studies so far have considered matters of situational territoriality and spatial patterning that govern human behaviour. (Contains 5 figures and 4 tables.)

Most colleges and universities have adopted course management systems (e.g., Blackboard, WebCT). Worldwide faculty and students use them for class communications and discussions. The discussion tools provided by course management systems, while powerful, often do not offer adequate capabilities to appraise communication patterns, online behaviors, group processes, or critical thinking. This paper discusses a Web-based program that represents temporal data as maps to illustrate behaviors of online discussants. Depicting discussions in this manner may help instructors and researchers study, moderate, and/or facilitate group discussions. The paper presents the rationale for program's development and provides a review of its technical specifications. (Contains 4 figures.)

A generic approach in automated essay scoring produces scores that have the same meaning across all prompts, existing or new, of a writing assessment. This is accomplished by using a single set of linguistic indicators (or features), a consistent way of combining and weighting these features into essay scores, and a focus on features that are not based on prompt-specific information or vocabulary. This approach has both logistical and validity-related advantages. This paper evaluates the performance of generic scores in the context of the e-rater[R] automated essay scoring system. Generic scores were compared with prompt-specific scores and scores that included prompt-specific vocabulary features. These comparisons were performed with large samples of essays written to three writing assessments: The GRE General Test argument and issue tasks and the TOEFL independent task. Criteria for evaluation included level of agreement with human scores, discrepancy from human scores across prompts, and correlations with other available scores. Results showed small differences between generic and prompt-specific scores and adequate performance of both types of scores compared to human performance. (Contains 3 tables.)

This study examines the historical and contemporary identity development patterns of first, second, and third generation students to determine the attributes students bring with them and how they develop through their experiences in a multicultural counselor training program. The paper examines patterns between groups, followed by a discussion of implications and recommendations for multicultural counseling and education. (Contains 3 tables.)

Recent educational research has turned increasing attention to the structural development of young students' mathematical thinking. Early algebra, multiplicative reasoning, and spatial structuring are three areas central to this research. There is increasing evidence that an awareness of mathematical structure is crucial to mathematical competence among young children. The purpose of this paper is to propose a new construct, Awareness of Mathematical Pattern and Structure (AMPS), which generalises across mathematical concepts, can be reliably measured, and is correlated with general mathematical understanding. We provide supporting evidence drawn from a study of 103 Grade 1 students. (Contains 3 figures and 1 table.)

This paper surveys important aspects of Web Intelligence (WI) in the context of Artificial Intelligence in Education (AIED) research. WI explores the fundamental roles as well as practical impacts of Artificial Intelligence (AI) and advanced Information Technology (IT) on the next generation of Web-related products, systems, services, and activities. As a direction for scientific research and development, WI can be extremely beneficial for the field of AIED. Some of the key components of WI have already attracted AIED researchers for quite some time--ontologies, adaptivity and personalization, and agents. The paper covers these issues only very briefly. It focuses more on other issues in WI, such as intelligent Web services, semantic markup, and Web mining, and proposes how to use them as the basis for tackling new and challenging research problems in AIED. (Contains 1 table and 5 figures.)

Digital libraries play a crucial role in distance learning. Nowadays, they are one of the fundamental information sources for the students enrolled in this learning system. These libraries contain huge amount of instructional data (text, audio and video) offered by the distance learning program. Organization of the digital libraries is therefore very important for easy and fast access to the desired information. Improper categorization of data may mislead the students searching the library. Since manual categorization of huge amount of data might be challenging, an automatic and reliable method is needed. In this sense, this paper proposes an automated categorization scheme for digital libraries in distance learning. The categorization scheme is designed and developed by a pattern recognition approach. Effectiveness of the proposed scheme is evaluated on widely used Reuters database. The results of the experimental study verify that the proposed scheme is a good candidate for categorization of digital libraries in distance learning programs. (Contains 6 tables and 2 figures.)

Bird's nest defect is an important cause of transmission line faults. To achieve accurate detection of bird nest defects in complex scenarios, a bird nest defect detection model for transmission lines was proposed that combines domain knowledge and occlusion reasoning networks. On the one hand, the model utilized the domain knowledge of the location of the bird's nest, using edge detection to obtain tower area information to constrain the location of candidate frames. This helps to reduce the false detection caused by complex backgrounds. On the other hand, on the basis of analyzing the occlusion characteristics of bird nests, the model employed occlusion reasoning networks that randomly erase features at the feature level to simulate the occlusion of bird nests in real scenes and improve the model's detection capability for occluded targets. Additionally, a multi‐scale feature fusion algorithm was designed in this paper to adapt the model to the scale variations of bird nests in aerial images. Experimental results demonstrate that the model outperforms advanced target detection models and other bird nest defect detection methods, with an AP50 of 78.8% and an AR10 of 72.4% for defect detection. [ABSTRACT FROM AUTHOR]

Educational design for networked learning is becoming more complex but also more inclusive, with teachers and learners playing more active roles in the design of tasks and of the learning environment. This paper connects emerging research on the use of design patterns and pattern languages with a conception of educational design as a conversation between theory and praxis. We illustrate the argument by drawing on recent empirical research and literature reviews from the field of networked learning. (Contains 1 table and 2 figures.)

High levels of academic achievement in Asian educational systems have generated interest in the study of motivational patterns of students in these contexts. The objectives of this paper are firstly, to provide a review of existing literature on the study of motivational styles amongst students and secondly, to identify the occurrence of different motivational styles amongst students in Singapore,. The method of identifying different motivational styles was adapted from a procedure first developed by Craske (1988). The findings of this study indicate that although the distribution of motivational styles amongst the Singaporean students was consistent with that as obtained by Craske, there was a higher tendency for maladaptive motivation amongst the males than the females. In contrast, Craske found no gender differentiation, though earlier researchers had found that maladaptive motivation was more common among the females. (Contains 3 tables.)

This paper is concerned with the use of spreadsheets within mathematical investigational tasks. Considering the learning of both children and pre-service teaching students, it examines how mathematical phenomena can be seen as a function of the pedagogical media through which they are encountered. In particular, it shows how pedagogical apparatus influence patterns of social interaction, and how this interaction shapes the mathematical ideas that are engaged with. Notions of conjecture, along with the particular faculty of the spreadsheet setting, are considered with regard to the facilitation of mathematical thinking. Employing an interpretive perspective, a key focus is on how alternative pedagogical media and associated discursive networks influence the way that students form and test informal conjectures.

In this paper, the aim is to classify torque signals that are received from a 3-DOF manipulator using a pattern recognition neural network (PR-NN). The output signals of the proposed PR-NN classifier model are classified into four indicators. The first predicts that no collisions occur. The other three indicators predict collisions on the three links of the manipulator. The input data to train the PR-NN model are the values of torque exerted by the joints. The output of the model predicts and identifies the link on which the collision occurs. In our previous work, the position data for a 3-DOF robot were used to estimate the external collision torques exerted by the joints when applying collisions on each link, based on a recurrent neural network (RNN). The estimated external torques were used to design the current PR-NN model. In this work, the PR-NN model, while training, could successfully classify 56,592 samples out of 56,619 samples. Thus, the model achieved overall effectiveness (accuracy) in classifying collisions on the robot of 99.95%, which is almost 100%. The sensitivity of the model in detecting collisions on the links "Link 1, Link 2, and Link 3" was 97.9%, 99.7%, and 99.9%, respectively. The overall effectiveness of the trained model is presented and compared with other previous entries from the literature. [ABSTRACT FROM AUTHOR]

China is a large consumer of meat and meat products. People's daily diets include a variety of meat, but meat food adulteration problems are common. This paper discusses the research progress of the electronic nose and near-infrared spectroscopy in the field of meat adulteration detection. Through the study of dozens of related papers in recent years, it has been found that the use of the electronic nose and near-infrared spectroscopy for meat detection has the advantages of speed, a nondestructive nature, high sensitivity, strong quantitative analysis, high automation, a wide applicability, an improved product quality, and cost reduction over the traditional detection, but it may be limited in detecting the adulteration of a specific meat, and there are issues with the life and stability of the sensors of the electronic nose in the process of detection, along with the problems of the high requirements for the modeling of the data of near-infrared spectroscopy. This paper takes adulterated meat as the research object and briefly summarizes the detection principles of the electronic nose and near-infrared spectroscopy, as well as the types of sensors applied in the electronic nose. The research progress of the electronic nose and near-infrared detection technology in meat adulteration assessment is reviewed, the advantages and disadvantages of the two in practical application are analyzed, the classification of pattern recognition methods and their applications in meat identification are described, and the feasibility and practical significance of the joint application of the two in meat adulteration detection are envisioned. Meanwhile, the challenges faced by the two in meat detection are pointed out. [ABSTRACT FROM AUTHOR]

The papers in this special section were presented at the Computer Vision and Pattern Recognition conference. [ABSTRACT FROM AUTHOR]

Continuous fire models are not suitable for the implementation of hardware units for applications and hence, suitable discrete versions need to be selected. Moreover, the nonlinear components in the neuronal equations reduce system performance (in the case of frequency and number of resources). This research paper focuses on implementing efficient Spiking Neural Networks (SNNs) using Field-Programmable Gate Array (FPGA), with a specific emphasis on the Leaky Integrate and Fire (LIF) neuron model. Its objective is to optimize the mathematical equations of the LIF model by approximating nonlinear functions. This approach enables the development of a simple, cost-effective and high-speed design. Existing LIF Neuron Hardware Blocks (NHBs) are based on the approximation of continuous models by standard difference schemes such as the Euler method or R–K method etc. Mathematically, such approximations do not exactly represent all dynamics of continuous systems. There are good approximations for small step sizes but they behave oddly when the approximation step size increases. Hence, the corresponding discrete, digital versions are not suitable for applications in all cases. This paper utilizes a Nonstandard Finite Difference (NSFD) scheme for the hardware (FPGA) implementation of the exact model of LIF-based NHB that works for all step sizes. The model presented here has a speed of 438.686MHz which is more than other existing models presented in this paper. It is multiplier-less, unlike earlier models. Further, it is implemented for SNN for basic pattern recognition and established that the proposed model works properly for given patterns. The system was evaluated using large datasets such as MNIST handwritten digit recognition, achieving a classification accuracy of 97.8%. Additionally, it underwent testing for COVID-19 chest CT scan image classification, demonstrating an 84% accuracy rate which is 6% more compared to existing Spiking Neural Networks (SNNs). [ABSTRACT FROM AUTHOR]

Head pose estimation is an especially challenging task due to the complexity nonlinear mapping from 2D feature space to 3D pose space. To address the above issue, this paper presents a novel and efficient head pose estimation framework based on particle swarm optimized contrastive learning and multimodal entangled graph convolution network. Firstly, a new network, the region and difference‐aware feature pyramid network (RD‐FPN), is proposed for 2D keypoints detection to alleviate the background interference and enhance the feature expressiveness. Then, particle swarm optimized contrastive learning is constructed to alternatively match 2D and 3D keypoints, which takes the multimodal keypoints matching accuracy as the optimization objective, while considering the similarity of cross‐modal positive and negative sample pairs from contrastive learning as a local contrastive constraint. Finally, multimodal entangled graph convolution network is designed to enhance the ability of establishing geometric relationships between keypoints and head pose angles based on second‐order bilinear attention, in which point‐edge attention is introduced to improve the representation of geometric features between multimodal keypoints. Compared with other methods, the average error of our method is reduced by 8.23%, indicating the accuracy, generalization, and efficiency of our method on the 300W‐LP, AFLW2000, BIWI datasets. [ABSTRACT FROM AUTHOR]

Board games like chess serve as an excellent testbed for human–robot interactions, where advancements can lead to broader human–robot cooperation systems. This paper presents a chess-playing robotic system to demonstrate controlled pick and place operations using a 3-DoF manipulator with image and speech recognition. The system identifies chessboard square coordinates through image processing and centroid detection before mapping them onto the physical board. User voice input is processed and transcribed into a string from which the system extracts the current and destination locations of a chess piece with a word error rate of 8.64%. Using an inverse-kinematics algorithm, the system calculates the joint angles needed to position the end effector at the desired coordinates actuating the robot. The developed system was evaluated experimentally on the 3-DoF manipulator with a voice command used to direct the robot movement in grasping a chess piece. Consideration was made involving both the own pieces as well as capturing the opponent's pieces and moving the captured piece outside the board workspace. [ABSTRACT FROM AUTHOR]