An innovative web-based collaborative platform for video annotation

Large scale labeled datasets are of key importance for the development of automatic video analysis tools as they, from one hand, allow multi-class classifiers training and, from the other hand, support the algorithms’ evaluation phase. This is widely recognized by the multimedia and computer vision communities, as witnessed by the growing number of available datasets; however, the research still lacks in annotation tools able to meet user needs, since a lot of human concentration is necessary to generate high quality ground truth data. Nevertheless, it is not feasible to collect large video ground truths, covering as much scenarios and object categories as possible, by exploiting only the effort of isolated research groups. In this paper we present a collaborative web-based platform for video ground truth annotation. It features an easy and intuitive user interface that allows plain video annotation and instant sharing/integration of the generated ground truths, in order to not only alleviate a large part of the effort and time needed, but also to increase the quality of the generated annotations. The tool has been on-line in the last four months and, at the current date, we have collected about 70,000 annotations. A comparative performance evaluation has also shown that our system outperforms existing state of the art methods in terms of annotation time, annotation quality and system’s usability.     

Multiple annotation for biodiversity: developing an annotation framework among biology,linguistics and text technology

Language Resources and Evaluation - Biodiversity information is contained in countless digitized and unprocessed scholarly texts. Although automated extraction of these data has been gaining...     

Enhancement of digital reading performance by using a novel web-based collaborative reading annotation system with two quality annotation filtering mechanisms

Collaboratively annotating digital texts allows learners to add valued information, share ideas, and create knowledge. However, excessive annotations and poor-quality annotations in a digital text may cause information overload and divert attention from the main content. The increased cognitive load ultimately reduces the effectiveness of collaborative annotations in promoting reading comprehension. Thus, this work develops a web-based collaborative reading annotation system (WCRAS-TQAFM) with two quality annotation filtering mechanisms—high-grade and master annotation filters—to promote the reading performance of learners. Ninety-seven students from three classes of a senior high school in Taiwan were invited to participate in an 80-min reading activity in which individual readers use WCRAS with or without annotation filters. Analytical results indicate that digital reading performance is significantly better in readers who use the high-grade annotation filter compared to those who read all annotations. Moreover, the high-grade annotation filter can enhance the reading comprehension of learners in all considered question types (i.e., recall, main idea, inference, and application). Also, the Cohen’s kappa statistics was used for assessing whether the annotation selected by the high-grade annotation filter is in agreement with the annotations selected by a domain expert. The statistic results indicate that the proposed high-grade annotation filter is valid to some degree. Finally, neither of the proposed quality annotation filtering approaches significantly reduces cognitive load.     

CASAM: collaborative human-machine annotation of multimedia

The CASAM multimedia annotation system implements a model of cooperative annotation between a human annotator and automated components. The aim is that they work asynchronously but together. The system focuses upon the areas where automated recognition and reasoning are most effective and the user is able to work in the areas where their unique skills are required. The system’s reasoning is influenced by the annotations provided by the user and, similarly, the user can see the system’s work and modify and, implicitly, direct it. The CASAM system interacts with the user by providing a window onto the current state of annotation, and by generating requests for information which are important for the final annotation or to constrain its reasoning. The user can modify the annotation, respond to requests and also add their own annotations. The objective is that the human annotator’s time is used more effectively and that the result is an annotation that is both of higher quality and produced more quickly. This can be especially important in circumstances where the annotator has a very restricted amount of time in which to annotate the document. In this paper we describe our prototype system. We expand upon the techniques used for automatically analysing the multimedia document, for reasoning over the annotations generated and for the generation of an effective interaction with the end-user. We also present the results of evaluations undertaken with media professionals in order to validate the approach and gain feedback to drive further research.     

Conceptual annotation of text patterns

Patterns are used as a fundamental means for analyzing data in many data mining applications. Many efficient techniques have been developed to discover patterns. However, the excessive number of discovered patterns and the lack of semantic information have made it difficult for a user to interpret and explore the patterns. A rough idea of the meanings of patterns can benefit the user in the process of exploring them. To address this issue, this paper presents a model for automatically annotating patterns with concepts. In addition, in a given context, the relative importance of each term that defines a concept is not the same. To define a context, there are a number of related information sources, such as documents, patterns, concepts, and an ontology. The question is which information sources are useful for estimating the relative importance of the terms? Should the most accurate one to be focused on or all of them be used to define the context? This research investigated these questions and defined an effective annotation context to estimate the relative importance of the terms, where the aim is to improve the performance of a machine that relies on the subject matter of a pattern set. The model is evaluated by comparing it with different baseline models on 2 standard datasets. The results show that the performance of the proposed model is significantly better.     

CCF: a framework for collaborative computing

The Collaborative Computing Frameworks toolkit enables cooperative computer-based work among collaborators at different sites. The collaborators are typically engaged in scientific endeavors, but CCF offers integrated support for any collaboration that depends heavily on distributed computational transformations and data management     

Developing a collaborative learning environment using a web-based design

Abstract Today, technology is being used to support teachers in designing interesting and stimulating learning environments for students. In this paper, a web-based design project is used to create a collaborative learning environment with the aim of inculcating collaborative skills into the learners and increasing their problem-solving and critical thinking skills. The design project was created using multimedia tools such as Dreamweaver and Adobe-Photoshop. Students worked in groups and were actively responsible for their own learning processes. Results showed that students engaged in collaborative learning enhanced their problem-solving and critical thinking skills, learned to work in a team and became more autonomous learners.     

The algorithms for preliminary text processing: Decomposition,annotation, morphological analysis

This paper considers the existing algorithms and suggests new algorithms for preliminary text processing that permit its quality to be increased, including: the deduction-inversion architecture of decomposition, modified algorithm of bidirectional interference, and morphological analysis based on preliminary annotation with tags of parts of speech.     

A web-based platform for biosignal visualization and annotation

With the advent of wearable sensing and mobile technologies, biosignals have seen an increasingly growing number of application areas, leading to the collection of large volumes of data. One of the difficulties in dealing with these data sets, and in the development of automated machine learning systems which use them as input, is the lack of reliable ground truth information. In this paper we present a new web-based platform for visualization, retrieval and annotation of biosignals by non-technical users, aimed at improving the process of ground truth collection for biomedical applications. Moreover, a novel extendable and scalable data representation model and persistency framework is presented. The results of the experimental evaluation with possible users has further confirmed the potential of the presented framework.     

Content annotation for the semantic web: an automatic web-based approach

Semantic Annotation is required to add machine-readable content to natural language text. A global initiative such as the Semantic Web directly depends on the annotation of massive amounts of textual Web resources. However, considering the amount of those resources, a manual semantic annotation of their contents is neither feasible nor scalable. In this paper we introduce a methodology to partially annotate textual content of Web resources in an automatic and unsupervised way. It uses several well-established learning techniques and heuristics to discover relevant entities in text and to associate them to classes of an input ontology by means of linguistic patterns. It also relies on the Web information distribution to assess the degree of semantic co-relation between entities and classes of the input domain ontology. Special efforts have been put in minimizing the amount of Web accesses required to evaluate entities in order to ensure the scalability of the approach. A manual evaluation has been carried out to test the methodology for several domains showing promising results.     

Sync: a Java framework for mobile collaborative applications

Introducing the factors of wireless mobile systems into the development of collaborative applications complicates developers' lives significantly. Application frameworks targeted for coordinating wireless mobile applications simplify development. The authors describe Sync, a development framework that provides high-level primitives that enable programmers to create arbitrarily complex, synchronized, replicated data objects. Designed for wireless networks, Sync enables applications to share changes at a granularity as small as updates to basic types and so enables better performance on low-bandwidth connections     

Enhancing digital reading performance with a collaborative reading annotation system

As children now spend considerable time reading electronic media, digital reading skills and good reading comprehension are essential. However, many studies agree that screen-based reading leads to shallow reading, short attention spans, and poor comprehension. Therefore, this work presents a collaborative reading annotation system with a reading annotation and interactive discussion scaffold (CRAS-RAIDS) for improving reading performance in collaborative digital reading environments. This study used a quasi-experimental design. Fifty-three Grade 5 students were recruited from two classes of an elementary school in Taoyuan County, Taiwan. One class was randomly designated the experimental group used the proposed CRAS-RAIDS support for collaborative reading. The other class was designated the control group and used the traditional paper-based reading annotation method and face-to-face discussions. The two groups were then compared in terms of reading attitude, reading comprehension, and use of reading strategy in an active reading context. Analytical results show that the experimental group significantly outperformed the control group in direct and explicit comprehension, inferential comprehension performance, and use of reading strategy. Moreover, the experimental group, but not the control group, had a significantly improved reading attitude in the total dimensions and in the behavioral and affective sub-dimensions. Additionally, the experimental group showed positive interest and high learning satisfaction.     

Quote examiner: verifying quoted images using web-based text similarity

Over the last few years, there has been a rapid growth in digital data. Images with quotes are spreading virally through online social media platforms. Misquotes found online often spread like a forest fire through social media, which highlights the lack of responsibility of the web users when circulating poorly cited quotes. Thus, it is important to authenticate the content contained in the images being circulated online. So, there is a need to retrieve the information within such textual images to verify quotes before its usage in order to differentiate a fake or misquote from an authentic one. Optical Character Recognition (OCR) is used in this paper, for converting textual images into readable text format, but none of the OCR tools are perfect in extracting information from the images accurately. In this paper, a method of post-processing on the retrieved text to improve the accuracy of the detected text from images has been proposed. Google Cloud Vision has been used for recognizing text from images. It has also been observed that using post-processing on the extracted text improved the accuracy of text recognition by 3.5% approximately. A web-based text similarity approach (URLs and domain name) has been used to examine the authenticity of the content of the quoted images. Approximately, 96.26% accuracy has been achieved in classifying quoted images as verified or misquoted. Also, a ground truth dataset of authentic site names has been created. In this research, images with quotes by famous celebrities and global leaders have been used. A comparative analysis has been performed to show the effectiveness of our proposed algorithm.

     

Texterra: A framework for text analysis

A framework for fast text analysis, which is developed as a part of the Texterra project, is described. Texterra provides a scalable solution for the fast text processing on the basis of novel methods that exploit knowledge extracted from the Web and text documents. For the developed tools, details of the project, use cases, and evaluation results are presented.     

A review of web-based collaborative learning: factors beyond technology

Abstract Web-based collaborative learning has a large potential for knowledge acquisition. However, it has different characteristics compared with conventional learning scenarios; especially with respect to the social communication situation, message exchange, cognitive load and participation of the learners. To cope with possible problems resulting from these characteristics suitable instructional means have to be considered, like collaborative learning methods, on-line moderation, appropriate learning tasks or computer-based visualisation tools.     

SignText: a web-based tool for providing accessible text book contents for Deaf learners

Universal Access in the Information Society - Lack of Sign Language-based learning tools is a hindrance in acquiring knowledge for deaf students. Technology-based tools have introduced innovative...     

基于双视角投影空间表示的文本多标签标注方法

在传统的多标签文本标注领域中,面临着维数灾难和双信息源数据的问题。针对这两个问题,提出一种基于双视角投影空间表示的文本多标签标注方法,通过交替方向乘子法和Bregman迭代对目标函数进行优化求解,得到低维的隐语义投影空间。在雅虎和路透社的新闻数据集上的实验结果验证表明,该方法在解决文本多标签标注问题上性能有明显的提高,其在召回率、覆盖误差和排名平均精度方面优于近年来的其他方法,可应用于新闻推荐,标签系统和情感分析等场景中。     

Metamodel-based collaborative optimization framework

This paper focuses on the metamodel-based collaborative optimization (CO). The objective is to improve the computational efficiency of CO in order to handle multidisciplinary design optimization problems utilising high fidelity models. To address these issues, two levels of metamodel building techniques are proposed: metamodels in the disciplinary optimization are based on multi-fidelity modelling (the interaction of low and high fidelity models) and for the system level optimization a combination of a global metamodel based on the moving least squares method and trust region strategy is introduced. The proposed method is demonstrated on a continuous fiber-reinforced composite beam test problem. Results show that methods introduced in this paper provide an effective way of improving computational efficiency of CO based on high fidelity simulation models.     

HAF:a hybrid annotation framework based on expert knowledge and learning technique

Dear editor, The increasing awareness of the potential value hidden in data has resulted in many data mining studies being con-ducted.In the domain of software ...