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This paper proposes an online multi-user real-time co-reading (OMURCOR) system to promote the performance of co-reading with collaborative learning. The OMURCOR system utilizes WebSocket to perform synchronization controls on co-reading to allow teachers and students to watch streaming videos together with less delay. Moreover, teachers utilize the system to synchronize control operations of videos on the student site, and the OMURCOR system can be integrated into a learning management system to strengthen students' interaction through co-reading. Unlike traditional platforms, the system supports grouping mechanisms during instruction. A survey was conducted with 104 participants. The bootstrapping square and partial least square approaches of the structural equation modeling (PLS-SEM) are employed via the SmartPLS tool to explore evidence of reliability and validity of the revised TAM. Experimental results reflected that six of the seven hypotheses were supported, and the proposed system has a significant impact on students' learning effectiveness during co-reading.

The accuracy of histopathology diagnosis largely depends on the pathologist's experience. It usually takes over 10 years to cultivate a senior pathologist, and small numbers of them lead to a high workload for those available. Meanwhile, inconsistent diagnostic results may arise among different pathologists, especially in complex cases, because diagnosis based on morphology is subjective. Computerized analysis based on deep learning has shown potential benefits as a diagnostic strategy.This research aims to automatically determine the location of gastric cancer (GC) in the images of GC slices through artificial intelligence. We use image data from a regional teaching hospital in Taiwan for training. We collect images of patients diagnosed with GC from January 1, 2019 to December 31, 2020. In this study, scanned images are used to dissect 13,600 images from 50 different patients with GC sections whose GC sections are stained with hematoxylin and eosin (HE stained) through a whole slide scanner, the scanned images from 50 different GC slice patients are divided into 80% training combinations, 2200 images of 40 patients are trained. The remaining 20%, totaling 10 people, are validated from a test set of 550 images.The validation results show that 91% of the correct rates are interpreted as GC images through deep learning. The sensitivity, specificity, PPV, and NPV were 84.9%, 94%, 87.7%, and 92.5%, respectively. After creating a 3D model through the grayscale value, the position of the GC is completely marked by the 3D model. The purpose of this research is to use artificial intelligence (AI) to determine the location of the GC in the image of GC slice.In patients undergoing pancreatectomy for pancreatic cancer, intraoperative infusion of lidocaine did not improve overall or disease-free survival. Reduced formation of circulating NETs was absent in pancreatic tumour tissue.For AI to assist pathologists in daily practice, to help a pathologist making a definite diagnosis is not the prime purpose at present time. The benefits could come from cancer screening and double-check quality control in a heavy workload which could distract the attention of pathologist during the time constraint examination process. We propose a two-steps method to identify cancerous areas in endoscopic gastric biopsy slices via deep learning. Then a 3D model is used to further mark all the positions of GC in the picture, and the model overcomes the problem that deep learning can't catch all GC.

Through three elements of thinking style, learning portfolio, and learning outcome, this study analyzes the correlation and explores the learning results of students based on different thinking styles and learning experiences. Thinking styles are based on “Function”, “Form”, “Level”, “Scope”, and “Leaning” five orientations to distinguish between different styles, from which to identify the individual characteristics of students, and through the student’s learning experience on the Learning Management System (LMS), to understand the different personal characteristics of students’ learning styles and learning preferences. Consequently, the teacher can adaptively teach students with different thinking styles according to their characteristics and adjust the teaching strategy interacting with the students in time.

Education helps increase socioeconomic mobility and is an important way of leaving poverty according to the United Nations, especially since COVID-19 hit the world hard in early 2020. A Mobile System of Formative Tests with Handwriting Revision is proposed in the paper, called the MSFT system. The MSFT is developed from the cognitive apprenticeship theory (CAT) in instructional design. The instruction model can be utilized for higher education mathematics teaching/studying for quiz-oriented instruction inside traditional classrooms as well as for distance-learning modes. The MSFT platform provides college undergraduates and graduates an app for a handheld device, which is used to upload their answer sheets with captured photos to the cloud database server. Moreover, instructors can use the platform to revise or assess answer sheets with instructors’ handwriting through web interfaces or apps. Important features of the integrated platform for teachers are (1) grading answer sheets by handwriting, (2) correcting mistakes in the answer sheets by handwriting, (3) writing down instructors’ comments on students’ answer sheets directly, and (4) choosing examples to demonstrate during class presentation, in a single window through web applications. To evaluate MSFT performance and service level for students, a questionnaire survey was conducted for 51 students and separated into an experimental group and a control group. Results from the experiment show that learning attitudes and learning satisfaction were significantly increased with the MSFT system in the experimental group compared to the control group.

The digital learning has been an important subject for improvement and research in education, especially the development of the Internet and the advancement of computer multimedia tools. This study involves the design of teaching materials and how the role of the teacher acts in digital teaching videos. Thus, two teaching methods are concerned to capture the teaching videos, one is only to capture teaching screen (PPT) without teacher's body language, and the other is to capture teacher's presentation in front of an interactive whiteboard such that the teacher can reveal the situation of problem-solving and logical thinking. Contributions of this research are clearly to state "Teaching Material Favorability" and "Learning Motivation" which will really allow students to cause accompanied effect and reflection in their learning interest from situated impacts.

The paper proposes an interactive virtual reality (VR) 360-degree panorama application system which can be applied in education for soil and water conservation. In the development of the system, the interactive VR 360-degree panoramic videos are produced first. The contents of videos come from introductions of facilities built in soil and water conservation outdoor classrooms. The design of video contents and interactions between video contents and users is on the basis of the cognitive theory of multimedia learning. Additionally, the system offers an android app developed using VR technology to offer users with interactions between users and videos. Learners first download the app into a smart phone and then insert the phone into a cheap VR helmet. Subsequently, they play the app with wearing the VR helmet to watch VR 360-degree panoramic videos so as to achieve immersion on watching video in a 3D VR environment. Moreover, learners can move the interaction sign, a single-circle shape, by shaking their VR helmets on the specific marks on screen to trigger corresponding interactive actions such as selecting a video, exiting viewing video, answering questions of true-false items, etc. Experimental results show the way participants received the system has better interaction than that without the system. Furthermore, the interaction portfolios are also collected in the cloud space for future analysis. Finally, the system can be also applied in learning activities of performing flipped classroom strategy when people will visit soil and water conservation outdoor classrooms.

This paper presents a novel facial expression recognition (FER) technique based on support vector machine (SVM) for the FER. Here it is called the FERS technique. First, the FERS technique develops a face detection method that combines the Haar-like features method with the self-quotient image (SQI) filter. As a result, the FERS technique possesses better detection rate because the face detection method gets more accurate in locating face regions of an image. The main reason is that the SQI filter can overcome the insufficient light and shade light. Subsequently, three schemes, the angular radial transform (ART), the discrete cosine transform (DCT) and the Gabor filter (GF), are simultaneously employed in the design of the feature extraction for facial expression in the FERS technique. More specifically, they are employed in constructing a set of training patterns for the training of an SVM. The FERS technique then exploits the trained SVM to recognize the facial expression for a query face image. Finally, experimental results show that the recognition performance of the FERS technique can be better than that of other existing methods under consideration in the paper.

The paper proposes an interactive virtual reality (VR) application in education for soil and water conservation. Here an android app was developed using VR technology, which can be used for reading a picture book for learning concepts in soil and water conservation. A story is presented by the picture book. Learners play the app with VR in a smart phone and then inserting the smart phone into cheap VR helmets. Learners can watch the VR application by wearing the VR helmet so as to achieve immersive reading in a 3D VR environment. Hereafter, watching the VR application is called watching the 3D VR picture book or 3D VR video. Moreover, learners first wear the VR helmet to read the 3D VR video and then they can use the interaction sign, a double-circle shape, to trigger interactive actions such as playing sound for speaking story, going to next page, going to previous page, showing text annotations, displaying questions for getting responses to the questions, etc. Experimental results show the way learners read the 3D VR picture book has better learning effect, interaction. Furthermore, the interactive portfolios are also collected in the cloud space immediately while triggering actions by the app using the interaction sign. Each record is constructed by experience API. Therefore, interactive portfolios can be analyzed in the future for realizing the behaviors while reading 3D VR picture book.

An e-Learning video having more than one screen enables users to compare the various sources with 360-degree video streaming situation learning environment. Moreover, learners can use 360-degree multiple illustrations to develop their interrelated concept and knowledge. This proposed solution could provide learners during self-learning by addressing and enhancing their physical and psychological factors including attention and cognitive load. However, attention is a mental phenomenon that humans cannot always focus on simultaneous presentations of information. Cognitive load may become profoundly heavy while processing rich information from multiple sources simultaneously. Therefore, the aim of this study is to investigate the split-attention effect, worked example effect, and learning achievement of using multiple screens in learning environments for a Discrete Mathematics course. The results of this study showed significant differences of two learning effects and learning achievement of learners between two learning environments. To conclude, this study may provide evidence toward explaining the influences of split attention of learners and their learning with worked examples and the effects of learning in a 360-degree multiple-screen video streaming environment, as well as in providing users with another suggestion for using the multiple - screen environment in e-Learning.

The paper presents a new image classification technique which first extracts rotation-invariant image texture features in singular value decomposition (SVD) and discrete wavelet transform (DWT) domains. Subsequently, it exploits a support vector machine (SVM) to perform image texture classification. For convenience, it is called the SRITCSD method hereafter. First, the method applies the SVD to enhance image textures of an image. Then, it extracts the texture features in the DWT domain of the SVD version of the image. Also, the SRITCSD method employs the SVM to serve as a multiclassifier for image texture features. Meanwhile, the particle swarm optimization (PSO) algorithm is utilized to optimize the SRITCSD method, which is exploited to select a nearly optimal combination of features and a set of parameters utilized in the SVM. The experimental results demonstrate that the SRITCSD method can achieve satisfying results and outperform other existing methods under considerations here. The following structure displays the conceptual design of the SRITCSD method for image texture classification. More specifically, it depicts the structure of the training phase and the testing phase for the SRITCSD method. In the training phase, the DWT-FE component denotes the feature-extraction scheme applied for a DWT version image. The feature set, f D W T j , in Eq. (8) is computed via feeding the DWT-FE component with a DWT version image. Let I S V D j represent that image I j is enhanced via the SVD. Another feature set, f S V D , D W T j , in Eq. (11) is calculated via feeding the DWT-FE component with a DWT version of I S V D j . Also, the SVM performs as a multiclassifier with respect to a set of training patterns which are constructed using image texture features, f D W T j and f S V D , D W T j . Meanwhile, the PSO algorithm is employed to optimize the SRITCSD method, which selects the nearly optimal combination of features and a set of parameters utilized in the SVM. In the testing phase of the SRITCSD method, two feature sets, f D W T q and f S V D , D W T q , are computed for a query image I q . The classification result can be obtained via feeding the trained SVM model with f S V D , D W T q to estimate which category the image I q belongs to.Display Omitted The paper presents an image classification technique which extracts rotation-invariant image texture features in singular value decomposition (SVD) and discrete wavelet transform (DWT) domains.First, the method applies the SVD to enhance image textures of an image. Then, it extracts the texture features in the DWT domain of the SVD version of the image.Also, the SVM serves as a multiclassifier for image texture features.Meanwhile, the particle swarm optimization (PSO) algorithm is exploited to select a nearly optimal combination of features and a set of parameters utilized in the SVM.The experimental results demonstrate that the method can achieve satisfying results and outperform other existing methods.

To promote the performance of cooperative learning, this study proposes an online multi-user real-time co-reading system. Through this platform, it assists the teacher and students to increase the coordinative degree of learning activities such that the flipped learning can be seamlessly performed inside or outside the classroom. The multi-user real-time co-reading system is based on the skill of WebSocket to let learners simultaneously watch streaming videos on YouTube almost without delay. This can provide the teacher to guide the student watching the learning video and then get back the feedback from whole class or cooperative groups. According to the feedback, the teacher is able to understand immediately the students’ learning condition so that the teacher can adjust the learning material to improve students’ learning effect and interest. When students have a learning problem, they can discuss or share the ideas with other members in the chatroom. In addition, the greatest feature of the system is the grouping mechanism different from Skype and JoinNet. The teacher can rearrange cooperative groups based on students’ learning condition by their learning feedback from system to give an ideal grouping such that most of students can take higher advantage during their flipped learning.

The paper proposes an interactive feedback portfolio (IFEP) system with iBeacon technology which is used in classroom to support the flipped classroom learning activities. The use of the IFEP system aims at promoting high interaction in classroom learning, which consists of APPs students used and web-interface functions teachers utilized. Students enable the APP in their smart phones and then the APP scan iBeacon devices. The detection statuses of iBeacon devices in classrooms can be automatically recorded in the cloud space. As a result, students’ presence in classroom can be obtained automatically. Teachers can use another function the IFEP system offers to promote the interaction in classroom via displaying questions on the front screen in classroom. Meanwhile, it pushes (multicasts) these questions to students’ smart phones via web-socket technology. Subsequently, students employ APP to feedback their responses to these questions. During collecting students’ responses, the system also exhibits the temporary results of students’ responses on screen and displays count-down time signal. This way can inspire students’ interaction and interest to join the quiz-like activities. Teachers can quickly get the results of responses for students’ answers. These results including students’ presence in classroom and correction rates students’ responses can be readily analyzed for their correlations on learning performance in the future. Moreover, teachers can quickly obtain students’ learning achievements in classroom and then teachers may adjust their instructional strategies.

This paper presents a Decision-Based cDNA Microarray Image filter for the removal of impulse noises for complementary deoxyribonucleic acid (cDNA) microarray images. It employs Fuzzy Logic (FL) technique to design a noise detector optimized by Particle Swarm Optimization (PSO), and utilizes center weighted vector median (CWVM) filter for restoring. For convenience, it is called the DBAMI filter hereafter. The proposed filter first employs the FL technique to detect each vector-valued pixel of a cDNA microarray image. If it is considered as noise-corrupted, the DBAMI filter restores it with the CWVM filter. Otherwise, it remains unchanged. Experimental results demonstrate that the DBAMI filter outperforms the existing well-known filters in restoration of cDNA microarray images under considerations here. This paper presents a Decision-Based cDNA Microarray Image filter for the removal of impulse noises for complementary deoxyribonucleic acid (cDNA) microarray images. For convenience, it is called the DBAMI filter hereafter. It employs Fuzzy Logic (FL) technique to design a noise detector optimized by Particle Swarm Optimization (PSO), and utilizes center weighted vector median (CWVM) filter for restoring. The proposed filter first employs the FL technique to detect each vector-valued pixel of a cDNA microarray image. If it is considered as noise-corrupted, the DBAMI filter restores it with the CWVM filter. Otherwise, it remains unchanged.The algorithm of the DBAMI filter is described as follows:Input: a corrupted cDNA microarray image IOutput: a restored cDNA microarray image I ^ Step 1. The SB component performs spot blocking algorithm to obtain spot blocks.Step 2. Each spot block BLmn is further separated into foreground image, Fmn, and background image, Bmn, respectively.Step 3. Each Fmn is detected by the FLND as follows: For each vector-valued pixel x i j m n of w i j m n in FmnDo Steps 4.1 through 4.3LoopGo to Step 5.Step 4.1. The DBAMI filter first respectively employs the VM filter, the ACWVM filter, and the CWVM filter to judge x i j m n whether it is contaminated or not. If yes, x i j m n is restored and x ^ V M , i j m n , x ^ A C W V M , i j m n , and x ^ C W V M , i j m n are obtained, respectively.Step 4.2. Calculate three Euclidean distances ( L 2 norm), d V M , i j , d A C W V M , i j , and d C W V M , i j for x i j m n .Step 4.3. d V M , i j , d A C W V M , i j , and d C W V M , i j are inputted into the FLND which can judge x i j m n . If x i j m n is judged as noise-corrupted, it is restored with x ^ C W V M , i j m n .Step 5. For each Fmn, all vector-valued pixels are restored by sequence with the CWVM filter and the restored image F ? m n can be obtained.Step 6. Input each F ? m n into the CHF to get F ^ m n .Step 7. Each background image Bmn is set to be black and is outputted as B ^ m n .Step 8. Each foreground F ^ m n and each background B ^ m n are merged, and then to obtain the restored image I ^ .Display Omitted This paper presents a DBAMI filter for the removal of impulse noises.It employs FL for a detector optimized by PSO and utilizes CWVM filter for restoring.The method employs the FL to detect each pixel of a cDNA microarray image.If the pixel is noise-corrupted, the DBAMI filter restores it with the CWVM filter.The DBAMI filter outperforms other filters in detection and restoration.

The purpose of the paper is to develop a MObile Handwriting TEst Revision (MOHTER) system which can be applied in an instructional design called a cognitive apprenticeship instruction model for mathematics learning in higher education. The MOHTER system offers a mobile-device APP for students who use the APP in uploading their image version of answer sheets to the cloud server of the system. Subsequently, teachers utilize the proposed system to revise answer sheets via teachers’ handwriting through web interface or web APP. Here, a significant feature of the system is to integrate four operations, revising answer sheets with handwriting, assigning scores for answer sheets, writing teachers’ comments on students’ answer sheets, and selecting answer sheets to be modelling on class, in a single window as well as to operate these four actions through web applications. The system can support a cognitive apprenticeship instruction model for mathematics learning.

This paper presents a novel rotation-invariant texture image retrieval using particle swarm optimization (PSO) and support vector regression (SVR), which is called the RTIRPS method. It respectively employs log-polar mapping (LPM) combined with fast Fourier transformation (FFT), Gabor filter, and Zernike moment to extract three kinds of rotation-invariant features from gray-level images. Subsequently, the PSO algorithm is utilized to optimize the RTIRPS method. Experimental results demonstrate that the RTIRPS method can achieve satisfying results and outperform the existing well-known rotation-invariant image retrieval methods under considerations here. Also, in order to reduce calculation complexity for image feature matching, the RTIRPS method employs the SVR to construct an efficient scheme for the image retrieval.

This paper presents an adaptive image-watermarking technique based on just-noticeable distortion (JND) profile and fuzzy inference system (FIS) optimized with genetic algorithm (GA). Here it is referred to as the AIWJFG technique. During watermark embedding, it embeds a water- mark into an image by referring the JND profile of the image so as to make the watermark more imperceptible. It employs image features and local statistics in the construction of an FIS, and then exploits the FIS to extract watermarks without original images. In addition, the FIS can be further optimized by a GA to improve its watermark-extraction performance remarkably. Experimental re- sults demonstrate that the AIWJFG technique not only makes the embedded watermarks further imperceptible but also possesses adaptive and robust capabilities to resist on image-manipulation attacks being considered in the paper.

This paper develops a multi-user streaming feedback system for cooperative learning in classroom learning. The system offers Android Apps to perform mobile interactive learning. Therefore, the system can be applied for interactive teaching. It also provides a game-based approach to improve students' cooperative learning. On the cognitive development of the game-based learning, the students use mobile devices camera and record their screen activity to play from streaming server and broadcast the contest on the streaming video wall such that the whole class can be able to see the contest. Moreover, the teacher can observe students' learning status and problems in real-time. That students have more willing to concentrate on their learning in the lecture. In other words, the streaming feedback system for cooperative learning in game-based theory classroom learning increases interactive between the teacher and students. Therefore, it improves learners' interest and efficiency in learning.

This paper presents a content-based image retrieval method using three kinds of visual features and 12 distance measurements, which is optimized by particle swarm optimization (PSO) algorithm. For convenience, it is called the CBIRVP method hereafter. First, the CBIRVP method extracts three kinds of features: color, texture, and shape features of images. Subsequently, it employs appropriate distance measurements for each kind of features to calculate the similarities between a query image and others in the database D. Also, the PSO algorithm is utilized to optimize the CBIRVP method via searching for nearly optimal combinations between the features and their corresponding similarity measurements, as well as finding out the approximately optimal weights for three similarities with respect to three kinds of features. Finally, experimental results demonstrate that the CBIRVP method outperforms other existing methods under consideration here.

Highlights? A zero-watermark scheme is proposed using RST invariant features, the SVM and the PSO algorithm against RST attacks for image authentication. ? The SVM-based zero-watermark scheme makes no changes to original images after embedding the owner signature of images. ? The SVM-based zero-watermark scheme requires no original image while retrieving watermarks. ? The particle swarm optimization algorithm is employed to search for a set of nearly optimal parameters of the SVM. ? In average, the SVM-based zero-watermark scheme outperforms other existing methods against RST attacks. This paper proposes a zero-watermark scheme with geometrical invariants using support vector machine (SVM) classifier against geometrical attacks for image authentication. Here geometrical attacks merely address rotation, scale, and translation (RST) operations on images. The proposed scheme is called the SVM-based zero-watermark (SZW) scheme hereafter. The SZW method makes no changes to original images while embedding the owner signature of images so as to achieve high transparency. Moreover, in order to promote the robustness to RST operations, it integrates the discrete Fourier transform (DFT) with the log-polar mapping (LPM) for finding out RST invariants of images. The SZW method then generates the secret key for a host image via performing a logical operation exclusive disjunction, an exclusive-or (XOR) operation, on the original watermark and a set of the characteristics of the RST invariants of the host image. Subsequently, a trained SVM (TSVM) is regarded as a mapping so that it can memorize the relationships between the set of characteristics of RST invariants and the secret key. During the watermark-extraction process of the SZW method, the TSVM is first fed with the set of characteristics of RST invariants of the watermarked image to get the estimated secret key. The SZW method then extracts the estimated watermark by performing the XOR operation on the set of characteristics of RST invariants and the estimated secret key. Consequently, the SZW method requires no original image while retrieving watermarks. In the paper, the particle swarm optimization (PSO) algorithm is also employed to search for a set of nearly optimal parameters of the SVM. Finally, the experimental results show that, in average, the SZW method outperforms other existing methods against RST attacks under consideration here.

This article presents a Color Image Retrieval method based on Combined Color Descriptions and Features, which is called the CIRCCODF method hereafter. A main contribution of the article is that the method first devises a modified-color-feature-extraction algorithm, called Image Vector (IV). Then, another color features, Color Layout Descriptor (CLD), which is used in the MPEG-7, is selected. Subsequently, CLD and IV are effectively combined to represent each color image. Experimental results shows that the CIRCCODF method possesses better retrieval performance than that of the other existing schemes under considerations here.

The paper presents a teaching model using an ebook tool with lecturing function and a game-based student response system in flipped classroom for a seminar course for graduate students. The ebook tool with lecturing function helps presenters (students) to easily display their multimedia materials by dual-codes mode and to interact their presentation with students (audience) by handwriting, a screen including video and powerpoint slides, two-pages swapping, etc. The Game-based Student Response System is utilized in the teaching model to increase more interactions among presenter and audience by answering questions using mobile devices in a game-like fashion.

The paper presents a novel blind watermarking scheme for image copyright protection, which is developed in the discrete wavelet transform (DWT) and is based on the singular value decomposition (SVD) and the support vector regression (SVR). Its embedding algorithm hides a watermark bit in the low-low (LL) subband of a target non-overlap block of the host image by modifying a coefficient of U component on SVD version of the block. A blind watermark-extraction is designed using a trained SVR to estimate original coefficients. Subsequently, the watermark bit can be computed using the watermarked coefficient and its corresponding estimate coefficient. Additionally, the particle swarm optimization (PSO) is further utilized to optimize the proposed scheme. Experimental results show the proposed scheme possesses significant improvements in both transparency and robustness, and is superior to existing methods under consideration here.

The paper presents a system using Decision tree, Particle swarm optimization, and Support vector regression to design a Median-type filter with a 2-level impulse detector for image enhancement, called DPSM filter. First, it employs a varying 2-level hybrid impulse noise detector (IND) to determine whether a pixel is contaminated by impulse noises or not. The 2-level IND is constructed by a decision tree (DT) which is built via combining 10 impulse noise detectors. Also, the particle swarm optimization (PSO) algorithm is exploited to optimize the DT. Subsequently, the DPSM filter utilizes the median-type filter with the support vector regression (MTSVR) to restore the corrupted pixels. Experimental results demonstrate that the DPSM filter achieves high performance for detecting and restoring impulse noises, and also outperforms the existing well-known methods under consideration in the paper.

This work proposes a wavelet-based image watermarking (WIW) technique, based on the human visible system (HVS) model and neural networks, for image copyright protection. A characteristic of the HVS, which is called the just noticeable difference (JND) profile, is employed in the watermark embedding to enhance the imperceptibility of the technique. First, we derive the allowable visibility ranges of the JND thresholds for all coefficients of a wavelet-transformed image. The WIW technique exploits the ranges to compute the adaptive strengths to be superimposed in the wavelet coefficients while embedding watermarks. An artificial neural network (ANN) is then used to memorize the relationships between the original wavelet coefficients and its watermark version. Consequently, the trained ANN is utilized for estimating the watermark without the original image. Many existing schemes require the original image to be involved in the calculation of the JND profile of the image. Finally, computer simulations demonstrate that both transparency and robustness of the WIW technique are superior to that of other proposed methods.

This paper presents a robust lossless watermarking technique, based on @a-trimmed mean algorithm and support vector machine (SVM), for image authentication. SVM is trained to memorize relationship between the watermark and the image-dependent watermark other than embedding watermark into the host image. While needing to authenticate the ownership of the image, the trained SVM is used to recover the watermark and then the recovered watermark is compared with the original watermark to determine the ownership. Meanwhile, the robustness can be enhanced using @a-trimmed mean operator against attacks. Experimental results demonstrate that the technique not only possesses the robustness to resist on image-manipulation attacks under consideration but also, in average, is superior to other existing methods being considered in the paper.

This paper proposes the median-type filters with an impulse noise detector using the decision tree and the particle swarm optimization, for the recovery of the corrupted gray-level images by impulse noises. It first utilizes an impulse noise detector to determine whether a pixel is corrupted or not. If yes, the filtering component in this method is triggered to filter it. Otherwise, the pixel is kept unchanged. In this work, the impulse noise detector is an adaptive hybrid detector which is constructed by integrating 10 impulse noise detectors based on the decision tree and the particle swarm optimization. Subsequently, the restoring process in this method respectively utilizes the median filter, the rank ordered mean filter, and the progressive noise-free ordered median filter to restore the corrupted pixel. Experimental results demonstrate that this method achieves high performance for detecting and restoring impulse noises, and outperforms the existing well-known methods.

This paper presents two novel digital watermarking techniques to protect image copyrights. The first technique, called wavelet based image watermarking using rank order and classification (WIWROC), explores the features and distances among rank orders of the wavelet coefficients in the subbands of a wavelet transformation (WT) image. These features are then utilised to develop a classification algorithm which can divide wavelet blocks of the image into three categories. A set of thresholds is exploited to devise three distinct embedding algorithms and their corresponding extraction algorithms for three categories. Each target block is hidden using its corresponding embedding algorithm. The WIWROC technique also uses the set of thresholds to retrieve watermarks without original images. Subsequently, a genetic algorithm (GA) is applied to optimise the WIWROC technique so as to further enhance its performance. The latter is referred to as the GWIWROC technique. Finally, experimental results reveal th...

This work proposes a novel watermarking technique called SVM-based Color Image Watermarking (SCIW), based on support vector machines (SVMs) for the authentication of color images. To protect the copyright of a color image, a signature (a watermark), which is represented by a sequence of binary data, is embedded in the color image. The watermark-extraction issue can be treated as a classification problem involving binary classes. The SCIW method constructs a set of training patterns with the use of binary labels by employing three image features, which are the differences between a local image statistic and the luminance value of the center pixel in a sliding window with three distinct shapes. This set of training patterns is gathered from a pair of images, an original image and its corresponding watermarked image in the spatial domain. A quasi-optimal hyperplane (a binary classifier) can be realized by an SVM. The SCIW method utilizes this set of training patterns to train the SVM and then applies the trained SVM to classify a set of testing patterns. Following the results produced by the classifier (the trained SVM), the SCIW method retrieves the hidden signature without the original image during watermark extraction. Experimental results have demonstrated that the SCIW method is sufficiently robust against several color-image manipulations, and that it outperforms other proposed methods considered in this work.

This study proposes a highly interactive model with the new technology of wireless projector to provide an ideal environment for presenting and discussing by multiple users including the teacher and students during lecture hour of flipped classroom. This model can definitely reduce the transition time and the presentation burden switching among a variety of learning activities to achieve a seamless learning. The TAM statistical analysis method is then exploited in the assessment for ease of use and usefulness for the proposed model. Finally, the experimental results demonstrated that the proposed model could readily support highly interactive learning activities for the flipped learning and have high acceptance of intent of use and usage behavior.

Based the characteristics of the human auditory system (HAS) and the techniques of neural networks, this work proposes an Adaptive Signal-Dependent Audio Watermarking (ASDAW) technique for protecting audio copyrights. First, a signal-dependent watermark for the ASDAW technique is generated by using the characteristics of the HAS (the temporal and frequency maskings). Next, the signal-dependent watermark is hidden in an original audio on the temporal domain. The ASDAW technique can make the signal-dependent audio watermark imperceptive (inaudible) because of the characteristics of the HAS. Moreover, an artificial neural network (ANN) is trained in the ASDAW technique so that the ASDAW technique can memorize the relationships between an original audio and the corresponding watermarked audio. Using the trained ANN (TANN), the ASDAW technique can extract the signal-dependent watermarks without the original audio. The extracted watermarks are then exploited in verifying legal duplications made of an audio during audio authentication. Consequently, the copyright forgery for audio can be suppressed greatly. Furthermore, experimental results illustrate that the ASDAW technique significantly possesses memorized, adaptive, and robust capabilities, making it immune against common audio manipulations and pirate attacks for counterfeiting audio copyrights.

With rapid development of the world globalization, the role of English has become an international language for people conversation. In fact, people know it but they often have no good ways to learn English efficiently. Sometimes, some people think that the procedure of learning English is boring. Therefore, it is an important issue how to engage and encourage people to effectively learn English. Many researches and experiments had indicated that the game-based learning is a joyful approach for learning. Chinese Mahjong is a traditional Chinese game for gambling and entertainment. The game rule of Mahjong is to collect the related cards combining a card sequence or the same cards in a triple pair. In the same, spelling the English vocabulary is also combined the letters to form a word. Thus, this paper tries to design and implement a Mahjong-like multi-party game spelling the English vocabulary for learners in English learning. That is, the learners play the game with other peer learners via the Internet to promote their spelling abilities of English vocabulary in multi-party networked game.

In this paper, we propose a novel digital watermarking technique based on neural networks for color images. Our technique hides an invisible watermark into a color image, and then effectively cooperates neural networks to learn the characteristics of the embedded watermark related to the watermarked image. Due to neural networks possessing the learning and adaptive capabilities, the trained neural networks almost exactly recover the watermark from the watermarked image against image processing attacks. Extensive experimental results illustrate that our technique significantly possesses robustness to be immune against the attacks.

On the design of multichannel filters, especially in color image restoration, it is not easy to simultaneously achieve three objectives: noise attenuation, chromaticity retention, and edges or details preservation. In this paper we propose a new class of multichannel filters, called genetic-based fuzzy hybrid multichannel (GFHM) filters, to reach these three objectives simultaneously. The design of GFHM filters is mainly based on human concept (heuristic rules) and genetic algorithms. Because the human concept can be readily and efficiently expressed by fuzzy implicative rules, GFHM filters can take the useful characteristics of filtering behavior of three filters: a vector median, a vector directional, and an identity filter. Since genetic algorithms possess the global-searching capability for an optimal solution, they are able to effectively optimize GFHM filters to improve the filtering performance. In color image restoration applications, extensive simulation results illustrate that GFHM filters not only achieve these three objectives but also possess the robust and the adaptive capability; moreover, these simulation results also demonstrate that the performance of GFHM filters outperforms that of other proposed filtering techniques.

On the design of multichannel filters, especially in color image restoration, it is not easy to simultaneously achieve three objectives: noise attenuation, chromaticity retention, and edges or details preservation. In this paper, we propose a new class of multichannel filters called adaptive fuzzy hybrid multichannel (AFHM) filters to achieve these three objectives simultaneously. Our novel approach is mainly based on human concept (heuristic rules) and provides a significant framework to take the merits of filtering behavior of three filters: a vector median (VM) filter, a vector directional (VD) filter, and an identity filter. On the design of an AFHM filter, our approach is a powerful and flexible scheme to achieve these three objectives because human concept can be efficiently expressed by fuzzy implicative rules for improving the filtering performance. The AFHM filters are able to effectively inherit the merits of filtering behaviors of these three filters in color image restoration applications. This is the first paper to include human concept to design multichannel filters. Moreover, a faster convergence property of the learning algorithm is investigated to reduce the time complexity of the AFHM filters. Extensive simulation results illustrate that AFHM filters not only achieve these three objectives but also possess the robust and adaptive capabilities, and demonstrate that the performance of AFHM filters outperforms that of other proposed filtering techniques.

This paper presents an Image Filter with noise detector using Fuzzy Logic and Particle Swarm Optimization (PSO), which is called the IFFLPSO filter, for removal and restoration of impulse noises. In IFFLPSO filter, the fuzzy logic is employed to efficiently design the noise detector. The proposed filter effectively judges the input pixel vector whether it is corrupted or not. Meanwhile, the particle swarm optimization algorithm (PSO) is utilized so as to optimize the noise detectors to enhance the noise detection performance. Subsequently, in order to enhance the restoration performance of proposed filter, the color ratio of spot's region in the restored image is employed to determine the spot's color. Also, the pixel vectors with different color ratios in the spot region are detected. Finally, the vector median filter is utilized to restore the corrupted pixels. Experimental results demonstrate that the proposed image filter outperforms the existing other well-known filters in restoration performance. And the system can be widely applied in microarray image processing.

This paper presents a novel facial emotion recognition (FER) technique, based on support vector machine (SVM), to recognize the facial emotion expression. Here it is called the FERS technique. First, a face detection method, which combines the Haar-like features (HFs) method with the self quotient image (SQI) filter, is used in the FERS technique to accurately locate the face region of an image. It can improve the detection rate due to the use of the SQI filter to overcome the insufficient light and shade light. Subsequently, angular radial transform (ART), discrete cosine transform (DCT) and Gabor filter (GF) are employed in the procedure of facial expression feature extraction. An SVM is trained and then utilized to recognize the facial expression for a queried face image. Finally, experimental results show that the recognition performance of the FERS technique can be better than that of other existing methods.

The paper proposes an Image Filter with a hybrid impulse detector based on Decision Tree (DT) and Particle Swarm Optimization (PSO), which is called the IFDTPSO filter, for the recovery of the corrupted image by impulse noises. The Several impulse noise detectors are combined in the design of the IFDTPSO filter to form an impulse noise detector (IND) which is designed by DT and PSO to effectively detect corrupted pixels of noisy images. The IND can correctly classify pixels as either noise-free or noise-corrupted, and then, restoring process utilizes the median filter to powerfully recover corrupted pixels from noisy images. Experimental results demonstrate that the IFDTPSO filter outperforms the existing well-known methods.

This paper presents a robust lossless watermarking technique using alpha-trimmed mean and support vector machine (SVM), which is called the RLW method hereafter. It does not damage the contents of original images during watermark embedding, because it uses trained SVMs to memorize the watermark or owner signature and then exploits the trained SVMs to estimate the watermark. Meanwhile, its robustness can be enhanced using alpha-trimmed mean operator against attacks. Experimental results demonstrate that the RLW method not only possesses the robust ability to resist on image-manipulation attacks under consideration but also, in average, is superior to other existing methods being considered in the paper.

A novel robust learning algorithm for optimizing fuzzy neural networks is proposed to address two important issues: how to reduce the outlier effects and how to optimize fuzzy neural networks, in the function approximation. This algorithm is able to reduce the outlier effects by cooperating with a conventional robust approach, and then to optimize fuzzy neural networks by determining the optimal learning rates which can minimize the next-step mean error at each iteration of our algorithm.

A novel character recognition method, called character recognition based on neural network and Dempster-Shafer theory (CRNNDS), is proposed in this paper. The CRNNDS integrates a recurrent neural network (RNN) and Dempster-Shafer (D-S) theory to recognize handwritten characters. It employs an RNN to effectively extract oriented features of a handwritten character and then these features are applied to Dempster-Shafer theory which can powerfully estimate the similarity ratings between a recognized character and sampling characters in the character database. Experimental results demonstrate that the CRNNDS system achieves a satisfying recognition performance.

This paper presents a Decision-based Hybrid Image Watermarking (DHIW) technique, based on the Human Visible System (HVS) and an Artificial Neural Network (ANN), for image copyright protection in wavelet domain. In [1], an image watermarking technique, called the IWNN technique, utilizes an ANN to extract watermarks without original images. However, the IWNN technique performs poorly for highly complicated image textures because the generalization capability of neural networks is powerfully effective in dealing with smooth image textures. Therefore, the PAIW method is proposed to enhance the IWNN technique, which uses the spatial information associated with wavelet-transformed images. The DHIW technique takes advantages of these two techniques by using a decision preprocessor. Experimental results prove that the DHIW technique remarkably outperforms other existing schemes.

Phonetic awareness is a critical and often neglected component in the learning of the English language. It is submitted that good pronunciation can improve upon spelling and reading abilities of children. This paper describes a multimedia (ML) learning system that is directed at children with the aim of enhancing their English pronunciation. The system uses hidden Markov models (HMMs) to analyze phonetic structures, identify and capture pronunciation errors. It provides children with targeted advice in pronunciation, intonation, rhythm and volume that is equivalent to four years of instruction. The system was tested in an informal experiment that involved thirty two elementary students that were divided into two groups: sixteen high and sixteen low achievers. It helped the low achieving group to significantly improve not only their English pronunciation but their spelling and reading abilities.

A robust watermarking (ROW) technique is proposed to protect image authorization. The ROW technique explores features which are the distances among rank orders of the coefficients in subbands of a wavelet-domain image. The features are then utilized to divide wavelet blocks of the image into three categories. A set of thresholds, which corresponds with the three categories, is exploited to develop the embedding and the extraction algorithms. The ROW technique retrieves watermarks without original images. The GROW technique, which employs a genetic algorithm to optimize the ROW technique, to enhance the robustness of the ROW technique. Experimental results illustrate that the performances of the ROW and the GROW techniques are acceptable.

Summary form only given. This paper presents a GA-based adaptive image watermarking technique with just-noticeable distortion (JND) profile and fuzzy inference system (FIS) which is referred to as the GAIWJF technique. During watermark embedding, the GAIWJF technique embeds a watermark into an image by referring to the JND profile of an image so that the technique makes the watermark more imperceptible. The GAIWJF technique employs image features and local statistics to create an FIS containing three fuzzy input variables, eight fuzzy inference rules, and a single fuzzy output variable. During watermark extraction, the GAIWJF technique does not require the information of original images because it employs the FIS to extract watermarks. Also, the FIS is further optimized by a GA so that the performance of watermark extraction can be improved. From experimental results, the GAIWJF technique not only makes the embedded watermark more imperceptible but also possesses adaptive and robust capabilities to resist image manipulations.

Summary form only given, as follows. Image detail preservation and impulse noise attenuation are not easy to simultaneously achieve in image restoration design. This study proposes a novel adaptive type-2 fuzzy median (type-2 FM) filter to accomplish these objects. The novel filter is mainly based on the uncertainty handling ability of the type-2 fuzzy sets to use the filtering behavior merits of two filters: the fuzzy median (FM) filter and the type-1 FM filter. On the design of a type-2 FM filter, the proposed method adopts a powerful scheme to extend the restriction on the derivation of membership functions by the FM filter, and a flexibility inference mechanism to improve the performance in the limited memory usage by the type-1 FM filter. Extensive simulation results illustrate that the type-2 FM filter not only possesses desirable robustness in suppressing noises but also outperforms other proposed filtering approaches.

This paper proposes a novel class of multichannel filters called neuro-fuzzy hybrid multichannel (NFHM) filters to simultaneously achieve three objectives: noise attenuation, chromaticity retention, and edges or details preservation. NFHM filters are characterized by a set of fuzzy rules (structure knowledge) such that they are capable of effectively fusing together the useful filtering merits from vector median, vector directional, and identity filters to further improve the filtering performance of the conventional filters. Moreover, we adequately exploit the functional equivalence between fuzzy inference systems and radial-basis function networks on the optimal design of NFHM filters such that NFHM filters can be optimized by neuro-learning techniques based on the radial-basis function networks to obtain adaptive fuzzy rules for the different window contents. Finally, extensive simulation results demonstrate that the filtering performance of NFHM filters is superior to that of other proposed filters.