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The teaching-learning model that still predominates in higher education is the traditional one, based on the master class taught by the teacher. However, it is necessary to resort to other models of teaching and learning that are more flexible and active for students. For this reason, the main objective of this paper is to apply the flipped classroom teaching-learning model in order to test and compare if the academic results of students with the flipped classroom model improve with respect to the traditional model at different educational levels. In addition, the students' assessment of this teaching-learning model and the use of Information and Communication Technologies is analyzed. This study presents the experience of teaching innovation based on the inverted class model, which has been carried out with 155 students belonging to different educational levels and with six subjects related to the Financial Economics discipline. For this, a descriptive statistical analysis is performed, as well as analysis of contingency tables to detect the degree of association between the educational level and the teaching-learning model applied for the evaluation of the students. Evidence is obtained that university students obtain better academic results with the traditional model, while non-university students achieve better grades with the flipped classroom model. However, the students' assessment of the flipped classroom model has been very positive, regardless of educational level. The results suggest that the flipped classroom model improves the academic results of non-university students because they are more accustomed to the application of new technologies than university students. El modelo de enseñanza-aprendizaje que todavía predomina en la educación superior es el modelo tradicional basado en la clase magistral por parte del profesor. Sin embargo, es necesario recurrir a otros modelos de enseñanza y aprendizaje que sean más flexibles y activos para los estudiantes. Por este motivo, el principal objetivo de este artículo es aplicar el modelo de enseñanza-aprendizaje basado en la clase invertida para contrastar y comparar si mejora los resultados académicos de los estudiantes con respecto al modelo tradicional en diferentes niveles educativos. Además, también se analiza la valoración de los estudiantes sobre este modelo activo de enseñanza-aprendizaje y sobre el uso de las Nuevas Tecnologías de la Información y de la Comunicación. Este estudio presenta la experiencia de innovación docente basada en el modelo de clase invertida, que ha sido llevada a cabo con 155 estudiantes pertenecientes a diferentes niveles educativos y con seis asignaturas relacionadas con la disciplina Economía Financiera. Para ello, se realizan análisis estadísticos descriptivos y análisis de tablas de contingencia para detectar el grado de asociación entre el nivel educativo y la metodología de enseñanza-aprendizaje aplicada para la evaluación de los estudiantes, así como la valoración de los estudiantes sobre esta metodología activa de enseñanza-aprendizaje. Se obtiene evidencia de que los estudiantes universitarios consiguen mejores resultados académicos con la metodología tradicional mientras que los no universitarios logran mejores calificaciones con la metodología basada en la clase invertida. Sin embargo, la valoración de los estudiantes sobre la metodología basada en la clase invertida ha sido muy positiva, independientemente del nivel educativo al que pertenecen. Los resultados sugieren que el modelo basado en la clase invertida mejora los resultados académicos de los estudiantes no universitarios porque ellos están más acostumbrados a la aplicación de nuevas tecnologías que los alumnos universitarios.

Error checking is easy and inexpensive in the initial stages as compared to later stages due to when the development cycle precedes the development cost and efforts also increase. UML class model is a key element of modern software methodologies and creates in the initial stage of software development. Therefore, error detection and rectification of the UML class model may save software development costs and time. This paper presents an overview of UML Class model verification approaches and identifies open issues, current research trends, and other improvement areas. This study uses a systematic literature review as an investigation method with six research questions and assesses 65 papers dated January 1997 to December 2020. From 2124 published research papers, 65 papers are selected and distributed into 7 studies. This work provides an analysis of verification approaches and the automation level of proposed approaches. As a result, it is found that the existing UML class model verification methods provide great efforts to check correctness. However, in some situations (when dealing with large and complex models), they consume a significant amount of time and do not support many important features of the UML class model.

본고는 온라인 및 온오프 혼합 수업에 맞추어 고안된 독서토론 수업 모형을 제안함으로써 대면 수업의 학습 과정을 보완 · 대체할 방안을 마련하고자 하였다. 본 수업 모형은 ‘웹 기반을 활용하기’와 ‘통합적으로 읽고 말하기’의 두 측면을 염두에 두고 설계되었다. 본 수업 모형의 연구 대상은 S 대학교의 기초역량 교과목인〈독서토론세미나〉를 수강하는 학습자들이다. 본 수업은 독서토론의 특성 이해, 선정 도서의 쟁점 도출, 다매체 텍스트 검색 및 분석, 선정 도서와 다매체 텍스트의 결합, 아이디어 생성, 웹 기반의 토론 실시, 평가의 과정으로 진행되었다. 학습자들은 동료들과의 상호 작용을 통해 협업의 중요성을 인식하고, 자신만의 고유한 아이디어를 공유함으로써 개인의 관점을 자유롭게 표현할 수 있었다. 본 수업 모형은 웹의 활용 능력과 다매체를 복합적으로 읽어내는 능력이 중요시되는 오늘날의 교육적 필요성과도 맞물린다고 할 수 있다. 이는 COVID-19 로 온라인 수업을 진행하게 되는 경우 수업 체제의 혼란스러운 상황을 극복하는 동시에 일반적으로 진행되는 대면 수업의 일부 아쉬운 점을 보완할 가능성을 모색하기 위한 시도이다.

The abstract should summarize the contents of the paper in short terms, i.e. 150–250 words. This article proposes a method for assessing the symptoms of tremor in patients at an early stage of Huntington’s disease (Huntington’s syndrome, Huntington’s chorea, HD). This approach includes the development of a data collection methodology using smartphones or tablets, data labelling for Support vector machine (SVM) model, multiple-class classification strategy, training the SVM, automatic selection of model parameters, and selection of training and test data sets. More than 3000 data records were obtained during research from subjects and patients with HD in Lithuania. The proposed SVM model achieved an accuracy of 97.09% in relation to 14 different classes, which were built according to the Shoulson-Fahn Total Functional Capacity (TFC) scale for assessing the patient’s tremor condition.

Background To initiate the development of a machine learning algorithm capable of comparing segments of pre and post pamidronate whole body MRI scans to assess treatment response and to compare the results of this algorithm with the analysis of a panel of paediatric radiologists. Methods Whole body MRI of patients under the age of 16 diagnosed with CNO and treated with pamidronate at a tertiary referral paediatric hospital in United Kingdom between 2005 and 2017 were reviewed. Pre and post pamidronate images of the commonest sites of involvement (distal femur and proximal tibia) were manually selected (n = 45). A machine learning algorithm was developed and tested to assess treatment effectiveness by comparing pre and post pamidronate scans. The results of this algorithm were compared with the results of a panel of radiologists (ground truth). Results When tested initially the machine algorithm predicted 4/7 (57.1%) examples correctly in the multi class model, and 5/7 (71.4%) correctly in the binary group. However when compared to the ground truth, the machine model was able to classify only 33.3% of the samples correctly but had a sensitivity of 100% in detecting improvement or worsening of disease. Conclusion The machine learning could detect new lesions or resolution of a lesion with good sensitivity but failed to classify stable disease accurately. However, further validation on larger datasets are required to improve the specificity and accuracy of the machine model.

Models are central for the development and management of complex systems. In order to be useful along the entire life cycle of software they must provide reliable support and enable automatic management. For that purpose, they must be precise, consistent, correct, and be subject to stringent quality verification and control criteria. Model automation calls for deep formal study of models, so that tools can provide inclusive support to users. This paper deals with optimization of multiplicity constraints in Class Models, i.e., models that provide abstraction on the static structure of software. The paper introduces an in-depth analysis of redundancy of multiplicity constraints, i.e., multiplicities that cannot be realized in any legal instance. The analysis includes: (1) a formal study of gaps of redundancies in multiplicity intervals; (2) algorithmic and rule-based methods for removing redundancies in multiplicity constraints; (3) a formal study of completeness of the algorithmic procedures, with respect to most UML class model constraints. The algorithmic procedures are implemented in our FiniteSatUSE tool. To the best of our knowledge, there is no previous study of properties of multiplicity redundancy, no completeness analysis of tightening methods, and no systematic study of these features with respect to most UML class model constraints.

Challenge: The "flipped classroom" is a pedagogical model in which instructional materials are delivered to learners outside of class, reserving class time for application of new principles with peers and instructors. Active learning has forever been an elusive ideal in medical education, but the flipped class model is relatively new to medical education. What is the evidence for the "flipped classroom," and how can these techniques be applied to the teaching of dermatology to trainees at all stages of their medical careers?

Multiple studies have demonstrated that the information included in the Medical Student Performance Evaluation fails to reliably predict medical students’ future performance. This faulty transfer of information can lead to harm when poorly prepared students fail out of residency or, worse, are shuttled through the medical education system without an honest accounting of their performance. Such poor learner handovers likely arise from two root causes: (1) the absence of agreed-on outcomes of training and/or accepted assessments of those outcomes, and (2) the lack of standardized ways to communicate the results of those assessments. To improve the current learner handover situation, an authentic, shared mental model of competency is needed; high-quality tools to assess that competency must be developed and tested; and transparent, reliable, and safe ways to communicate this information must be created. To achieve these goals, the authors propose using a learner handover process modeled after a patient handover process. The CLASS model includes a description of the learner’s Competency attainment, a summary of the Learner’s performance, an Action list and statement of Situational awareness, and Synthesis by the receiving program. This model also includes coaching oriented towards improvement along the continuum of education and care. Just as studies have evaluated patient handover models using metrics that matter most to patients, studies must evaluate this learner handover model using metrics that matter most to providers, patients, and learners.

This paper presents a flight envelope protection system for NASA’s Transport Class Model. The developed protection scheme is based on a command-limiting architecture that accounts for aircraft adverse aerodynamics, unusual attitude, and structural integrity and is designed to augment a standard gain-scheduled flight control law. The scheme also includes an energy protection system, which relies on an automatic throttle control loop that implements a total energy control law. In the setup adopted, the limits of the protected envelopes are actively adjusted to ensure, on the one hand, that the aircraft always stays in a safe flight condition and, on the other hand, that the pilot has enough control authority to perform aggressive maneuvers, if needed. Batch simulations demonstrate the efficacy of the protection system in maintaining the aircraft within desired safe conditions.

With the advent of big data, the cost of index recommendation (IR) increases exponentially, and the portability of IR model becomes an urgent problem to be solved. In this paper, a fine-grained classification model based on multi-core convolution neural network (CNNIR) is proposed to implement the transferable IR model. Using the strong knowledge representation ability of convolution network, CNNIR achieves the effective knowledge representation from data and workload, which greatly improves the classification accuracy. In the test set, the accuracy of model classification reaches over \(95\%\). CNNIR has good robustness which can perform well under a series of different learning rate settings. Through experiments on MongoDB, the indexes recommended by CNNIR is effective and transferable.

«Resource class», or «Resource zone», is a model of inclusive education for children with autism spectrum disorders that has proved its effectivity in integraing children to comprehensive school system for the past five years. A resource class is a place where the resources for inclusion of a child with autism of other mental disor¬ders into the school community are gathered. Here, the point of ‘resource class’ model is explained, described are the experience of introduction of resource zone technology, risks and problems appearing when creating a resource class in school. Member of the council of the Regional public organization to help children with autism spectrum disorders «Contact», educators, shares her experience in creating a resource class for autistic children with parents’ effort in «School № 2009» in Moscow.

Sanqi is a widely used traditional Chinese medicines (TCM) for its outstanding efficacy. In Chinese market, Sanqi powder is the goal of counterfeiting for a long time. Investigation of Sanqi authenticity is very important in both economic and public health terms. The present work aims at studying the feasibility of combining near-infrared (NIR) spectroscopy with relief-based variable selection and class-modeling for identifying adulterated Sanqi powder. A total of 209 samples including pure and mixed samples, were prepared. Principal component analysis (PCA) was applied for exploratory analysis. The relief algorithm was used to rank all variables, based on which only the first 100 most informative variables were picked out for subsequent class-modeling. By optimizing the parameters such as the number of components, type I and type II errors, the final one-class models were constructed on the training set and evaluated on the test set. Such a procedure is simple and is more in line with actual need. The performance of the models is acceptable. The results indicate that NIR spectroscopy combined with class-modeling and relief-based variable selection is feasible for identifying the adulteration of Sanqi powder.

We describe a flipped class proposal in the Teacher in Physical Education of the Universidad Nacional of Rio Cuarto, Argentina. The experience was designed and implemented using the social network Facebook and the selection of short videos, it was carried out in two groups with different teachers. The perceptions andsuggestions on the flipped class model were recovered with an ad hoc questionnaire of closed and open questions, frequencies of exchanges, publications and comments on Facebook were also analyzed. The results show highly positive aspects such as the autonomy of the students, the guiding role and facilitator of the teacher. It is concluded that the planning of flipped classes and the sustained accompaniment topromote self-regulation in students is important.

One problem of exploiting information captured by an OO system model such as class models is mapping user queries on the model. This is particularly due to the informal character of the user queries. This paper presents an approach based on text mining for identifying and interpreting user queries. This would enable a matching between the interpreted user query and the corresponding entities specified by the system model. Therefore, navigation expressions in OCL can be derived from the system model that have a formal character and are able to answer the user questions against the class model.

Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2- class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.

We propose a method for localization and classification of brand logos in natural images. The system has to overcome multiple challenges such as perspective deformations, warping, variations of the shape and colors, occlusions, background variations. To deal with perspective variation, we rely on homography matching between the SIFT keypoints of logo instances of the same class. To address the changes in color, we construct a weighted graph of logo interconnections that is further analyzed to extract potentially multiple instances of the class. The main instance is built by grouping the keypoints of the graph connected logos onto the central image. The secondary instance is needed for color inverted logos and is obtained by inverting the orientation of the main instance. The constructed logo recognition system is tested on two databases (FlickrLogos-32 and BelgaLogos), outperforming state of the art with more than 10 % accuracy.

The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging (fMRI) network. Finally, the result shows that the best model only is based on anatomical distance.

A method based on one class support vector machine (OCSVM) is proposed for class incremental learning. Several OCSVM models divide the input space into several parts. Then, the 1VS1 classifiers are constructed for the confuse part by using the support vectors. During the class incremental learning process, the OCSVM of the new class is trained at first. Then the support vectors of the old classes and the support vectors of the new class are reused to train 1VS1 classifiers for the confuse part. In order to bring more information to the certain support vectors, the support vectors are at the boundary of the distribution of samples as much as possible when the OCSVM is built. Compared with the traditional methods, the proposed method retains the original model and thus reduces memory consumption and training time cost. Various experiments on different datasets also verify the efficiency of the proposed method., 6 pages, 4 figures, Under review as a conference paper at International Conference on Pattern Recognition 2018

Computational thinking is to use heuristic reasoning to seek the solution of the problem, and has a strong ability to innovate. In the teaching of computer courses, it is one of the important subjects of teaching research to cultivate students’ computational thinking ability. The traditional teaching model ignores the cognitive role of the students, which is not conducive to the cultivation of students’ innovative thinking and creative ability. Blending learning is an inevitable trend of development in the field of higher education, and a hot research topic in the field of education. Inspired by “Database Principles” course model at University of Missouri, Kansas City, a multi-dimensional blending learning flipped class model of computer courses in colleges and universities based on students’ computational thinking ability is proposed to construct teaching model and design theoretical and practical teaching system and diversified teaching process evaluation system. In the course of “Database Principles”, the teaching practice is carried out to verify the teaching effect.

This article presents the authors' experience with flipping a fundamental concepts of nursing course for students in an undergraduate baccalaureate program. Authors describe implementing a flipped class, practical strategies to transform students' learning experience, and lessons learned. This article serves as a guide to faculty and programs seeking to develop and implement the flipped class model in nursing education.

Teacher as a profession, has its own specialty. To make teachers have strong professional, the quality of teacher education is the key. The teaching mode called "flip class" is beneficial to the development of the teachers' theory quality and technological literacy. If we bring it into the teacher education curriculum teaching, it’ll promote teachers' professional specialization development.

The physical class is always out of the door. This class model is different from the traditional class. As this special characteristic, how to ensure the weight of the sports test course achievement evaluation becomes necessary and important. The application of AHP makes it possible to carry out the quantity evaluation in the sports research. The study makes a comprehensive evaluation with AHP and puts forward a method for relative analysis and a thereunder for decision-maker. From the necessary and sufficient conditions of completely consistent matrix, the criterion of consistency has been improved. We create a new improved AHP method, namely MCAHP (matrix correction analytic hierarchy process). And we apply it to examine the physical class in determining the weights of evaluation indicators by students. Examples show that the improved algorithm is better than the original AHP in significantly reduced computation. And this method improves the running speed. The first part is the introduction. The second part is the steps of AHP. The third part is the study on the MCAHP algorithm. The fourth part is the exploration of the MCAHP algorithm. The fifth part is the numerical experiment and the last part is the conclusion.

Currently, the higher education curriculum has developed two systems, namely the block-based curriculum in the field of medicine and the semester credit-based curriculum. The block-based curriculum is currently applied in medical schools while the semester credit-based curriculum is applied in non-medical faculties. The presence of these two curriculum systems raises problems in the management of academic information systems. One of the problems that occur is that the existing information system has not been able to manage the block curriculum and semester credit simultaneously in one system. So that this causes the academic information system unable to manage the implementation of assessments on the block-based curriculum. This paper proposes a new model in the design of academic information systems that are able to integrate the needs of block-based curriculum and credit. The method used to solve this problem is an object-oriented approach using class diagrams as a model to describe the structure of academic information systems. The results of the development of the new model show that the structure of the block-based curriculum is a general form (generalization) of semester-based credit. Therefore the implementation of the two curriculums can use a similar model class structure to be more efficient and easier to maintain information system software in the future.

This study aims to develop a Modular Object-Oriented Dynamic Learning Environment (Moodle)-based online learning system and derive a class model to enhance learners&rsquo, creative collaboration capabilities. The research method for this is first to analyze the functional elements of the online learning system to enhance creative collaboration among the functions of the Moodle online learning system. Second, a class model is derived to enhance creative collaboration by applying the constructivist teaching&ndash, learning principle. Third, we conduct a verification of the effectiveness of the Moodle-based class model that was developed to enhance creative collaboration. The results of the study show that, among the functions of the Moodle online learning system, forms, real-time conferencing, reciprocal evaluation, Wiki, and blogs have the ability to enhance creative collaboration. Second, the class model for promoting creative collaboration based on a mood-based online learning system consists of self-reflections, learner-driven learning, professional learning, practical task, and the role of the processor. Third, after verifying the application effect of the Moodle-based class model for university students, a significant effect is found in the creative collaboration of university students. The results of this study suggest that: First, the Moodle online learning system should be utilized to provide learners with a variety of online learning opportunities to promote creative collaboration. Second, the teaching&ndash, learning operation manual needs to be developed later in order for the class model to be effectively implemented at the classroom site to promote creative collaboration based on the Moodle online learning system.

For traditional network anomaly detection system, the detection performance is related to the selected features and training dataset. But traditional methods adopt handcraft feature selection, which requires heavy human labour and relies on the experts’ knowledge and experience. Besides, the collected dataset for training is not balanced, which makes the prediction of the trained model tends to be biased to the majority class. In this paper, a one-class network anomaly detection model based on the stacked autoencoders was proposed. We use the stacked autoencoders to select the prominent features from the raw collected data, then apply the one-class classification algorithm support vector data description to train a classifier to identify the network traffic into normal data and anomalous data. The experimental results demonstrate the promising results of our approach for network anomaly detection.

According to the result of PISA and TIMSS, it was reported that interest for Math and Science was far lower compared to high achievement of Them. The purpose of this study is to investigate effects of robot based STEAM education on elementary school students` Math learning behavior and Science motivation. Robot based STEAM education integrated science, mathematics and art with a theme of `Energy` was practiced for test group and For control group, those three subjects were taught separately in order to achieve this purpose. Curriculum of fourth grade second semester`s science, mathematics and art was analysed to teach a robot based STEAM class and STEAM class Model with the theme `Energy was designed and applied to elementary students. In science class, heat transfer experiment was conducted with robots and the result was related to drawing polygonal lines in mathematics. In art class, robot components were used to describe the heat energy in shapes and colors. The research shows that students` Math learning behavior and Science motivation were improved more with robot based STEAM education than with traditional lessons(p

Recently, ultra large scale of ship is being ordered continuously and because of that, the accurate prediction of ship maneuverability in design stage becomes important matter. The model test like PMM test or CFD analysis are representative methods for predicting the maneuverability of ship. In this study, the captive model tests were carried out for predicting maneuverability of MOERI container ship(KCS) which is opened to the public using X-Y Carriage of Ocean Engineering Wide Tank of University of Ulsan. Considering the dimensions of tank, 2m class model ship was used for captive model test. CMT(Circular Motion Test) was performed for obtaining purer hydrodynamic coefficients related to yawing velocity. For getting hydrodynamic coefficients which cannot be obtained using CMT, PMM test(Planar Motion Mechanism test) were also performed. The maneuverability prediction results by simulation are compared with those of other research institutes.

In view of the problems and difficulties in the information system integration of the small and medium-sized super-hard cutting tools enterprises, the characteristics of the design and manufacture of the CBN turning tool were analyzed. The demand of the enterprises for the 3C integrated system of CBN turning tool was presented. The 3-layer architecture of the 3C integrated system of CBN turning tool was proposed, which included the data layer, the application layer and the user layer. The object-oriented method was adopted to design the system and the class model of the system was constructed. Based on these, the 3C integrated system of CBN turning tool for small and medium-sized enterprises was developed. The key technologies of the system were put forward. The system had been applied to the enterprise and run well.

A key factor motivating the organisation of tutorial classes for adult learners, based on collaboration between the Workers’ Educational Association and universities in England and Wales, was a belief that they offered an educational vehicle for the promotion of liberal concepts of political citizenship. Attempts to disseminate this tutorial class model were not always successful, despite the promotional efforts of Albert Mansbridge and his fellow supporters. This article considers the educational and organizational characteristics of tutorial classes and makes a regional comparison of their meagre development in Scotland with their sustained growth elsewhere between 1907 and 1939. It identifies a complex weave of factors, related to both the nature of student demand and the funding of adult education, that inhibited the growth of tutorial classes in Scotland. These factors also provide insight into general barriers to the development of liberal adult education to promote citizenship in Scotland.

This article summarizes 4 years of data from San Diego Unified School District's Speech Improvement Class model for students with mild articulation needs. The district made a gradual shift from special education to general education. Data demonstrate that most students complete the class in 17-20 treatment hours. Three factors have been fundamental to the success of this program: the Articulation Resource Center, ongoing professional development, and a well-defined set of procedures. Challenges and concerns are also discussed. Overall, speech-language pathologists in San Diego Unified are implementing this class effectively and have branched out into other response to intervention supports.

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised- and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

Current English classes of Polytechnic schools remain little time for the students to study and think by themselves. It is necessary to explore a new teaching model to better improve students’ practical English abilities. Flipped class model and “micro-class” have been widely applied in western countries. The emerging of these new teaching styles transfuses something valuable to English class innovation in China beyond all questions. Aiming at popularizing flipped class model in college English teaching, this thesis falls into five parts: the introduction of current college English teaching conditions, the elaboration of flipped class model, flipped class model applied to college English course, the feasibility of flipped class model applying to college English teaching and a conclusion. Keywords—flipped class model; micro-class; college English teaching; application; feasibility

Given a class model built from a dataset including labeled data, classification assigns a new data object to the appropriate class. In associative classification the class model (i.e., the classifier) is a set of association rules. Associative classification is a promising technique for the generation of highly accurate classifiers. In this article, we present a compact form which encodes without information loss the classification knowledge available in a classification rule set. This form includes the rules that are essential for classification purposes, and thus it can replace the complete rule set. The proposed form is particularly effective in dense datasets, where traditional extraction techniques may generate huge rule sets. The reduction in size of the rule set allows decreasing the complexity of both the rule generation step and the rule pruning step. Hence, classification rule extraction can be performed also with low support, in order to extract more, possibly useful, rules.

Teacher as a profession, has its own specialty. To make teachers have strong professional, the quality of teacher education is the key. The teaching mode called "flip class" is beneficial to the development of the teachers' theory quality and technological literacy. If we bring it into the teacher education curriculum teaching, it’ll promote teachers' professional specialization development.

We propose a method for localizing and recognizing brand logos in natural images. The task is extremely challenging, due to the various changes in the appearance of the logos. We construct class templates by matching features between examples of the same class to build homographies. An interconnections graph is developed for each class and the representative points are added to the class model. Finally, each class is depicted by the reunion of the suitable keypoints and descriptors, thus leading to a high precision of the proposed logo recognition system. Results show that we outperform the state of the art systems on the challenging Flickr-32 database.