国内Content-based Instruction研究述评

自从20世纪90年代Content-based Instruction进入中国,得到各个学科领域的关注,最近几年相关研究更是呈井喷之势,尤其是实证研究。但是,对中国知网检索文献的分析显示,CBI研究设计科学性不够,忽略了研究的伦理问题,部分研究由于自变量描述不够,影响了研究的效度。分析同样发现,实验中的干预与CBI不符,研究者将CBI与其它教学方式混淆不清。

Course Design of College English Listening with Content-based syllabus

本文基于对非英语专业学生进行的问卷调查,根据发现的听力问题进行分析,然后按照基于内容的教学大纲进行了有针对性的听力课程设计。

Contention-Based Nonorthogonal Massive Access with Massive MIMO

The sporadic communication character of massive machine-type communication systems provides natural advantages to utilize the principle of compressive sensing(CS).However,due to the high computational complexity of CS algorithms,CS-based contention-free access schemes have limited scalability and high computational complexity for massive access with user-specific pilots.To address these problems,in this paper,we propose a new contention-based scheme for CSbased massive access,which can support the sporadic access of massive devices(more than one million devices)with limited resources.Furthermore,an advanced receiver algorithm is designed to solve the optimal solutions for the proposed scheme,which utilizes various prior information to enhance the performance.In specific,the joint sparsity between the channel and data is used to improve the accuracy of pilot detection,and the information of modulation and cyclic redundancy check is exploited for channel correction to improve the performance of data recovery.The simulation results show that the proposed scheme can achieve improved active user detection performance and data recovery accuracy than existing methods.

An Empirical Study on Classroom Learning Anxiety of Secondary Vocational English Learners in Content-based Instruction

This research aims to study the relationship between content-based instruction and secondary vocational English learners.Two classes in one secondary vocational school were chosen as participants.The result shows that CBI teaching has a negative correlation with English learning anxiety and has an impact on alleviating students' anxiety.

An Efficient Deep Learning-based Content-based Image Retrieval Framework

The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provides efficient image retrieval for the given query image.

An E-Commerce Recommender System Based on Content-Based Filtering

Content-based filtering E-commerce recommender system was discussed fully in this paper. Users＇ unique features can be explored by means of vector space model firstly. Then based on the qualitative value of products informa tion, the recommender lists were obtained. Since the system can adapt to the users＇ feedback automatically, its performance were enhanced comprehensively. Finally the evaluation of the system and the experimental results were presented.

New Approach on the Techniques of Content-Based Image Retrieval (CBIR) Using Color, Texture and Shape Features

Digital image collection as rapidly increased along with the development of computer network. Image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and color similarity. Retrieving images based on the content which is color, texture, and shape is called content based image retrieval (CBIR). The content is actually the feature of an image and these features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features. There are two kinds of content based image retrieval which are general image retrieval and application specific image retrieval. For the general image retrieval, the goal of the query is to obtain images with the same object as the query. Such CBIR imitates web search engines for images rather than for text. For application specific, the purpose tries to match a query image to a collection of images of a specific type such as fingerprints image and x-ray. In this paper, the general architecture, various functional components, and techniques of CBIR system are discussed. CBIR techniques discussed in this paper are categorized as CBIR using color, CBIR using texture, and CBIR using shape features. This paper also describe about the comparison study about color features, texture features, shape features, and combined features (hybrid techniques) in terms of several parameters. The parameters are precision, recall and response time.

Towards an Efficient Hierarchical Codebooks for Content-Based Images Retrieval

In space feature quantization, the most important problem is designing an efficient and compact codebook. The hierarchical clustering approach successfully solves the problem of quantifying the feature space in a large vocabulary size. In this paper we propose to use a tree structure of hierarchical self-organizing-map （H-SOM） with the depth length equal to two and a high size of branch factors （50, 100, 200, 400, and 500）. Moreover, an incremental learning process of H-SOM is used to overcome the problem of the curse of the dimensionafity of space. The method is evaluated on three public datasets. Results exceed the current state-of-art retrieval performance on Kentucky and Oxford5k dataset. However, it is with less performance on the Holidays dataset. The experiment results indicate that the proposed tree structure shows significant improvement with a large number of branch factors.

Multi-core based parallel computing technique for content-based image retrieval

In this paper, we propose a parallel computing technique for content-based image retrieval （CBIR） system. This technique is mainly used for single node with multi-core processor, which is different from those based on cluster or network computing architecture. Due to its specific applications （such as medical image processing） and the harsh terms of hardware resource requirement, the CBIR system has been prevented from being widely used. With the increasing volume of the image database, the widespread use of multi-core processors, and the requirement of the retrieval accuracy and speed, we need to achieve a retrieval strategy which is based on multi-core processor to make the retrieval faster and more convenient than before. Experimental results demonstrate that this parallel architecture can significantly improve the performance of retrieval system. In addition, we also propose an efficient parallel technique with the combinations of the cluster and the multi-core techniques, which is supposed to gear to the new trend of the cloud computing.

Energy-Efficient Content-Based Routing in Internet of Things

The?convergence of the Internet, sensor networks, and Radio Frequency Identification (RFID) systems has ushered to the concept of Internet of Things (IoT) which is capable of connecting daily things, making them smart through sensing, reasoning, and cooperating with other things. Further, RFID technology enables tracking of an object and assigning it a unique ID. IoT has the potential for a wide range of applications relating to healthcare, environment, transportation, cities… Moreover, the middleware is a basic component in the IoT architecture. It handles heterogeneity issues among IoT devices and provides a common framework for communication. More recently, the interest has focusing on developing publish/subscribe middleware systems for the IoT to allow asynchronous communication between the IoT devices. The scope of our paper is to study routing protocols for publish/subscribe schemes that include content and context-based routing. We propose an Energy-Efficient Content-Based Routing (EECBR) protocol for the IoT that minimizes the energy consumption. The proposed algorithm makes use of a virtual topology that is constructed in a centralized manner and then routes the events from the publishers to the intended interested subscribers in a distributed manner. EECBR has been simulated using Omnet++. The simulation results show that EECBR has a significant performance in term of the energy variance compared to the other schemes.

Effect of Fe content on the friction and abrasion properties of copper base overlay on steel substrate by TIG welding

Copper base alloy was overlaid onto 35CrMnSiA steel plate by tungsten inert gas （TIG） welding method. The heat transfer process was simulated, the microstructures of the copper base overlay were analyzed by scanning electron microscopy （SEM） and energy dispersive spectrometer （EDS）, and the friction and abrasion properties of the overlay were measured. The results show that the Fe content increases in the overlay with increasing the welding current. And with the increase of Fe content in the overlay, the friction coefficient increases and the wear mechanism changes from oxidation wear to abrasive wear and plough wear, which is related to the size and quantity of Fe grains in the overlay. While with the increase of Fe content in the overlay, the protection of oxidation layer against the oxidation wear on the melted metal decreases.

A Content-Based Parallel Image Retrieval System on Cluster Architectures

We propose a content-based parallel image retrieval system to achieve high responding ability. Our system is developed on cluster architectures. It has several retrieval. servers to supply the service of content-based image retrieval. It adopts the Browser/Server (B/S) mode. The users could visit our system though web pages. It uses the symmetrical color-spatial features (SCSF) to represent the content of an image. The SCSF is effective and efficient for image matching because it is independent of image distortion such as rotation and flip as well as it increases the matching accuracy. The SCSF was organized by M-tree, which could speedup the searching procedure. Our experiments show that the image matching is quickly and efficiently with the use of SCSF. And with the support of several retrieval servers, the system could respond to many users at mean time. Key words content-based image retrieval - cluster architecture - color-spatial feature - B/S mode - task parallel - WWW - Internet CLC number TP391 Foundation item: Supported by the National Natural Science Foundation of China (60173058)Biography: ZHOU Bing (1975-), male, Ph. D candidate, reseach direction: data mining, content-based image retrieval.

Content-based image retrieval applied to BI-RADS tissue classification in screening mammography

AIM:To present a content-based image retrieval(CBIR) system that supports the classification of breast tissue density and can be used in the processing chain to adapt parameters for lesion segmentation and classification.METHODS:Breast density is characterized by image texture using singular value decomposition(SVD) and histograms.Pattern similarity is computed by a support vector machine(SVM) to separate the four BI-RADS tissue categories.The crucial number of remaining singular values is varied(SVD),and linear,radial,and polynomial kernels are investigated(SVM).The system is supported by a large reference database for training and evaluation.Experiments are based on 5-fold cross validation.RESULTS:Adopted from DDSM,MIAS,LLNL,and RWTH datasets,the reference database is composed of over 10000 various mammograms with unified and reliable ground truth.An average precision of 82.14% is obtained using 25 singular values(SVD),polynomial kernel and the one-against-one(SVM).CONCLUSION:Breast density characterization using SVD allied with SVM for image retrieval enable the development of a CBIR system that can effectively aid radiologists in their diagnosis.

Content-Based Hierarchical Analysis of News Video Using Audio and Visual Information

A schema for content-based analysis of broadcast news video is presented. First, we separate commercials from news using audiovisual features. Then, we automatically organize news programs into a content hierarchy at various levels of abstraction via effective integration of video, audio, and text data available from the news programs. Based on these news video structure and content analysis technologies, a TV news video Library is generated, from which users can retrieve definite news story according to their demands.

Shearlet Based Video Fingerprint for Content-Based Copy Detection

Content-based copy detection (CBCD) is widely used in copyright control for protecting unauthorized use of digital video and its key issue is to extract robust fingerprint against different attacked versions of the same video. In this paper, the “natural parts” (coarse scales) of the Shearlet coefficients are used to generate robust video fingerprints for content-based video copy detection applications. The proposed Shearlet-based video fingerprint (SBVF) is constructed by the Shearlet coefficients in Scale 1 (lowest coarse scale) for revealing the spatial features and Scale 2 (second lowest coarse scale) for revealing the directional features. To achieve spatiotemporal natural, the proposed SBVF is applied to Temporal Informative Representative Image (TIRI) of the video sequences for final fingerprints generation. A TIRI-SBVF based CBCD system is constructed with use of Invert Index File (IIF) hash searching approach for performance evaluation and comparison using TRECVID 2010 dataset. Common attacks are imposed in the queries such as luminance attacks (luminance change, salt and pepper noise, Gaussian noise, text insertion);geometry attacks (letter box and rotation);and temporal attacks (dropping frame, time shifting). The experimental results demonstrate that the proposed TIRI-SBVF fingerprinting algorithm is robust on CBCD applications on most of the attacks. It can achieve an average F1 score of about 0.99, less than 0.01% of false positive rate (FPR) and 97% accuracy of localization.

Indexing of Content-Based Image Retrieval System with Image Understanding Approach

This paper presents a novel efficient semantic image classification algorithm for high-level feature indexing of high-dimension image database. Experiments show that the algorithm performs well. The size of the train set and the test set is 7 537 and 5 000 respectively. Based on this theory, another ground is built with 12,000 images, which are divided into three classes: city, landscape and person, the total result of the classifications is 88.92%, meanwhile, some preliminary results are presented for image understanding based on semantic image classification and low level features. The groundtruth for the experiments is built with the images from Corel database, photos and some famous face databases.

Combining Block and Corner Features for Content-Based Trademark Retrieval

In order to retrieve a similarly look trademark from a large trademark database, an automatic content based trademark retrieval method using block hit statistic and comer Delaunay Triangulation features was proposed. The block features are derived from the hit statistic on a series of concentric ellipse. The comers are detected based on an enhanced SUSAN （Smallest Univalue Segment Assimilating Nucleus） algorithm and the Delaunay Triangulation of comer points are used as the comer features. Experiments have been conducted on the MPEG-7 Core Experiment CE-Shape-1 database of 1 400 images and a trademark database of 2 000 images. The retrieval results are very encouraging.

Influence of Water Content on Conductivity and Piezoresistivity of Cement-based Material with both Carbon Fiber and Carbon Black

The influence of water content on the conductivity and piezoresistivity of cement-based material with carbon fiber （CF） and carbon black （CB） was investigated. The piezoresistivity of cement-based material with both CF and CB was compared with that of cement-based material with CF only, and the changes in electrical resistivity of cement-based material with both CF and CB under static and loading conditions in different drying and soaking time were studied. It is found that the piezoresistivity of cement-based material with both CF and CB has better repeatability and linearity than that of cement-based material with CF only. The conductivity and the sensitivity of piezoresistive cement-based material with both CF and CB are enhanced as the water content in piezoresistive cement-based material increases.

Microstructure and mechanical properties of Ti(C, N)-based cermets with different molybdenum contents

The microstructure and mechanical properties of Ti(C, N)-based cermets with different content Mo were studied. Different Mo contents were added into Ti(C, N)-based cermets. Effect of sintering temperature on mechanical properties of the cermets was also investigated. Specimens were fabricated by conventional powder metallurgy techniques and vacuum sintered at different temperatures. The microstructure and the fracture morphology were investigated using scanning electron microscope. Transverse strength and hardness were measured. The results show that the microstructure is uniform and the thickness of rim phase is moderate when the content of Mo is 8%. The mechanical properties are the best when the content of Mo is 8% and the sintering temperature is 1450℃.

HIERARCHICAL CONTENT-BASED IMAGE RETRIEVAL

A hierarchical structure method of content based image retrieval was proposed. During image preprocessing stage three semi automatic algorithms were used to extract image regions. String matching can be used to reduce image searching range. Smallest enclose rectangle(SER) and Hausdorff distance under region normalization were used to measure the similarity between trademark images while keeping invariant under transform(translation, rotation and scale) and noise tolerant. The experiment results show its efficiency.