异构复合迁移学习的视频内容标注方法

针对传统的机器学习需要大量的人工标注训练模型的弊端,以及目前多数迁移学习方法只适用于同构空间的问题,提出了一种异构复合迁移学习（HCTL）的视频内容标注方法。首先,借助视频与图像的对应关系,利用典型相关性分析（CCA）来实现图像域（源域）和视频域（目标域）特征空间的同构化;然后,基于这两个特征空间向共同空间投影的代价最小化这一思想,找到源域特征空间向目标域特征空间对齐的矩阵;最后,通过对齐矩阵使得源域特征能够翻译到目标域特征空间中去,进而实现知识迁移,完成视频内容标注任务。所提方法在Kodak数据库上的平均标注准确率达到了35.81%,与标准的支持向量机（S-SVM）领域适应支持向量机（DASVM）、异构直推式迁移学习（HTTL）、跨领域的结构化模型（CDSM）、领域选择机（DSM）、异构源域下的多领域适应（MDA-HS）和判别性相关分析（DCA）方法相比分别提高了58.03%、23.06%、45.04%、6.70%、15.52%、13.07%和6.74%;而在哥伦比亚用户视频（CCV）数据库上达到了20.73%,分别相对提高了133.71%、37.28%、14.34%、24.88%、16.40%、20.73%和12.48%。实验结果表明先同构再对齐的复合迁移思想在异构领域适应问题上能够有效地提升识别准确率。     

Examining different types of collaborative learning in a complex computer-based environment: A cognitive load approach

This study compared the effects of two collaborative learning strategies (Open-ended and Task-based) with an individualized learning strategy on individual learning in a computer-based environment. The experiment sought ecological validity by conducting it under real teaching and homework conditions. Ninety-four students from grade 9 participated in a webpage design task. Cognitive load theory was used to predict that the collaborative approaches would outperform the individualized approach due to reduced cognitive load. This hypothesis was confirmed by performance scores and cognitive load only in the case of the Open-ended collaborative learning condition. Evidence was also found that the Open-ended collaborative learning condition outperformed the Task-based collaborative one. It was concluded that in collaborative learning a more Open-ended task design together with moderate independent sub-task requirements leads to more effective learning.     

OCS: An effective caching scheme for video streaming on overlay networks

Video streaming is vital for many important applications such as distance learning, digital video libraries, and movie-on-demand. Since video streaming requires significant server and networking resources, caching has been used to reduce the demand on these resources. In this paper, we propose a novel collaboration scheme for video caching on overlay networks, called Overlay Caching Scheme (OCS), to further minimize service delays and loads placed on an overlay network for video streaming applications. OCS is not a centralized nor a hierarchical collaborative scheme. Despite its design simplicity, OCS effectively uses an aggregate storage space and capability of distributed overlay nodes to cache popular videos and serve nearby clients. Moreover, OCS is light-weight and adaptive to clients’ locations and request patterns. We also investigate other video caching techniques for overlay networks including both collaborative and non-collaborative ones. Compared with these techniques on topologies inspired from actual networks, OCS offers extremely low average service delays and approximately half the server load. OCS also offers smaller network load in most cases in our study.

Superiority of collaborative learning with complex tasks: A research note on an alternative affective explanation

Kirschner, Paas, and Kirschner (2009c) used the theoretical framework of cognitive load to explain why the learning of a group of collaborating individuals was more efficient than that of individuals learning alone with high-complexity tasks but not with low-complexity tasks. The authors argued that collaboration circumvented the limitations of an individual’s working memory by creating an expanded cognitive capacity and by allowing for the distribution of cognitive load among group members. Inspired by research on efficacy, this study explored an alternative affective explanation of the results. By measuring the amount of mental effort learners expected to invest in working on a learning task before actually carrying out the task, this study showed that learners who had to collaboratively solve a high-complexity problem expected to invest less mental effort than learners who had to solve the problem alone. When confronted with low-complexity tasks, the expected amount of mental effort did not differ.     

Transductive Multi-Instance Multi-Label learning algorithm with application to automatic image annotation

Automatic image annotation has emerged as an important research topic due to its potential application on both image understanding and web image search. Due to the inherent ambiguity of image-label mapping and the scarcity of training examples, the annotation task has become a challenge to systematically develop robust annotation models with better performance. From the perspective of machine learning, the annotation task fits both multi-instance and multi-label learning framework due to the fact that an image is usually described by multiple semantic labels (keywords) and these labels are often highly related to respective regions rather than the entire image. In this paper, we propose an improved Transductive Multi-Instance Multi-Label (TMIML) learning framework, which aims at taking full advantage of both labeled and unlabeled data to address the annotation problem. The experiments over the well known Corel 5000 data set demonstrate that the proposed method is beneficial in the image annotation task and outperforms most existing image annotation algorithms.     

An examination of two mental workload measurement approaches to understanding multimedia learning

This study reports on an examination of two measures of mental workload: the NASA-TLX and Paas’ Subjective Cognitive Load (SCL) measure. The goal was to assess the relative efficacy of the measures in the design and research of multimedia learning environments. Benchmarks based on the literature as to the goals for mental workload measurement in learning research are established. A multifaceted study was conducted which manipulated various aspects of mental workload in order to study the utility of these two measures in detecting changes in load and their relationship to learning outcomes. The results indicate that the weighted version of the NASA-TLX provided little additional value over the unweighted version of the measure. While both the NASA-TLX and SCL measures were sensitive to changes in both intrinsic and extraneous load, the study revealed differences in the measures based on levels of each of these load factors. The study also concludes that a better understanding of the third factor, germane load, will be needed to both expand the theoretical framework about mental workload in instructional settings and further understand the utility of these two measures.     

An empirical study of Web browsing behaviour: Towards an effective Website design

Prior studies have suggested that a good Website design which facilitates a user’s Web browsing behaviour would generally lead to better user performance. In this research, we examine user Website behaviour as a way to understand Website design using a “think aloud” protocol analysis. Main theoretical contributions of this research are the illustration of the flow of cognitive processes during the Website browsing and the establishment of Website design dimensions – the meaning and content implied by Website content, its outward form, and the structure and navigation – in relation to user performance.     

Individual and group-based learning from complex cognitive tasks: Effects on retention and transfer efficiency

The effects of individual versus group learning (in triads) on efficiency of retention and transfer test performance in the domain of biology (heredity) among 70 high-school students were investigated. Applying cognitive load theory, the limitations of the working memory capacity at the individual level were considered an important reason to assign complex learning tasks to groups rather than to individuals. It was hypothesized that groups will have more processing capacity available for relating the information elements to each other and by doing so for constructing higher quality cognitive schemata than individuals if the high cognitive load imposed by complex learning tasks could be shared among group members. In contrast, it was expected that individuals who learn from carrying out the same complex tasks would need all available processing capacity for remembering the interrelated information elements, and, consequently, would not be able to allocate resources to working with them. This interaction hypothesis was confirmed by the data on efficiency of retention and transfer test performance; there was a favorable relationship between mental effort and retention test performance for the individual learners as opposed to a favorable relationship between transfer test performance and mental effort for the students who learned in groups.     

AVCD-FRA: A novel solution to automatic video cut detection using fuzzy-rule-based approach

Video shot boundary detection (SBD) is a fundamental step in automatic video content analysis toward video indexing, summarization and retrieval. Despite the beneficial previous works in the literature, reliable detection of video shots is still a challenging issue with many unsolved problems. In this paper, we focus on the problem of hard cut detection and propose an automatic algorithm in order to accurately determine abrupt transitions from video. We suggest a fuzzy rule-based scene cut identification approach in which a set of fuzzy rules are evaluated to detect cuts. The main advantage of the proposed method is that, we incorporate spatial and temporal features to describe video frames, and model cut situations according to temporal dependency of video frames as a set of fuzzy rules. Also, while existing cut detection algorithms are mainly threshold dependent; our method identifies cut transitions using a fuzzy logic which is more flexible. The proposed algorithm is evaluated on a variety of video sequences from different genres. Experimental results, in comparison with the most standard cut detection algorithms confirm our method is more robust to object and camera movements as well as illumination changes.     

Automatic annotation of tennis games: An integration of audio,vision, and learning

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level.     

Adaptive leadership in trauma resuscitation teams: a grounded theory approach to video analysis

The detailed analysis of team interactions can be a source of insight into team processes and how teams interact with technology. Video recordings afford an exciting medium for such analysis. We describe a study of team leadership in the highly dynamic, high-stakes environment of trauma resuscitation. The study was conducted through video recording team activities in actual work settings and analysing the video data using a grounded theory approach. The primary research questions were: what are the functions of team leadership and how do they vary according to task situations? A corpus of 152 video segments from 18 trauma patient resuscitation cases was compiled to address these research questions. A catalog of team leadership functions was developed, along with a categorisation of the task situations in which team leadership occurred. The implications of this catalog and the mapping between leadership and task situations are discussed in relation to the findings from an interview study and a survey study on team leadership. The methodological advantages of a grounded theory approach for in-context video analysis for studying work are also discussed.     

Computer-Supported Collaborative Learning Requiring Immersive Presence (CSCLIP): An Introduction

The dramatic increase in distance learning (DL) enrollments in higher education is likely to continue. However, research on DL that includes psychomotor, cognitive, and affective skills is virtually non-existent. Indeed, DL for psychomotor skills has been viewed as impossible. Laboratory coursework, which we define as including the acquisition of psychomotor, cognitive, and affective skills, has become a limiting factor in the growth of DL. What is needed is a synergistic integration of technologies and Human-Computer Interface (HCI) principles from Computer Supported Collaborative Learning (CSCL), collaborative learning systems, and immersive presence technologies to enable achievement of psychomotor learning objectives. This paper defines the Computer Supported Collaborative Learning requiring Immersive Presence (CSCLIP) research area, and introduces this special issue of Information Systems Frontiers on CSCLIP and the six papers contained herein.This revised version was published online in March 2005 with corrections to the cover date     

VisionGo: Towards video retrieval with joint exploration of human and computer

This paper introduces an effective interactive video retrieval system named VisionGo. It jointly explores human and computer to accomplish video retrieval with high effectiveness and efficiency. It assists the interactive video retrieval process in different aspects: (1) it maximizes the interaction efficiency between human and computer by providing a user interface that supports highly effective user annotation and an intuitive visualization of retrieval results; (2) it employs a multiple feedback technique that assists users in choosing proper method to enhance relevance feedback performance; and (3) it facilitates users to assess the retrieval results of motion-related queries by using motion-icons instead of static keyframes. Experimental results based on over 160 h of news video shows demonstrate the effectiveness of the VisionGo system.     

Computer-supported collaborative learning with digital video cases in teacher education: The impact of teaching experience on knowledge convergence

Case-based learning is ascribed high potential with respect to the education of pre-service teachers as well as the further education of experienced in-service teachers, as it can provide opportunities for the application of professional knowledge to authentic classroom situations. In addition to the application and acquisition of knowledge, the aspect of knowledge convergence (i.e., increasing similarity of collaborative learners with respect to their knowledge) should be taken into account when homogeneous benefits are desired for all participants of a case-based training. A field study with 29 pre-service teachers and 24 in-service teachers from adult education was conducted to examine effects of different levels of teaching experience on knowledge application, knowledge acquisition, and knowledge convergence in a computer-supported collaborative case-based learning environment. An innovative approach to measuring knowledge convergence was introduced, using the positions of annotations (i.e., flags placed on the timeline of the case video) made by learners during their case analyses as an indicator of their shared focus of attention. This measure was found to positively correlate with the application and acquisition of professional knowledge. Teaching experience affected both processes and outcomes of case-based learning, suggesting different means of instructional support for pre-service and in-service teachers.     

Cognitive load and science text comprehension: Effects of drawing and mentally imagining text content

One hundred and eleven 10th graders read an expository science text on the dipole character of water molecules (ca. 1600 words). Reading instruction was varied according to a 2 × 2 experimental design with factors ‘drawing pictures of text content on paper’ (yes, no) and ‘mentally imagining text content while reading’ (yes, no). The results indicate that drawing pictures, mediated through increased cognitive load, decreased text comprehension and, thus, learning (d = −0.37), whereas mental imagery, although decreasing cognitive load, increased comprehension only when students did not have to draw pictures simultaneously (d = 0.72). No evidence was found that the effects were moderated by domain-specific prior knowledge, verbal ability, or spatial ability. The results are in line with cognitive theories of multimedia learning, self-regulated learning, and mental imagery as well as conceptions of science learning that focus on promoting mental model construction by actively visualizing the content to be learned. Constructing mental images seems to reduce cognitive load and to increase comprehension and learning outcome when the mental visualization processes are not disturbed by externally drawing pictures on paper, whereas drawing pictures seems to increase cognitive load resulting in reduced comprehension and learning outcome.     

Designing for operations: Towards a sociotechnical systems and cognitive engineering approach to concurrent engineering

In the practice of concurrent engineering, the factors that are considered early in the product design process include manufacturability, assembly, and cost. A set of issues that are not typically considered revolve around the operational requirements for human workers in the manufacturing system. What tasks will human workers accomplish? How will these tasks be organized and coordinated? What information and resources need to be shared? Will the workers have a coherent set of job responsibilities? How should the manufacturing environment be designed to support effective work practices? How can a manufacturing process be designed that also informs organizational structure and takes into account the quality of working life?

The field of sociotechnical systems theory (STS) focuses on exactly these kinds of issues. Rather than subscribing to the usual view of technological determinism — that a complex human-machine system is designed solely with respect to optimization of technical criteria — the goal of STS is to jointly optimize both human and technological considerations in system design and operation. The spirit of STS has much in common with recent work in cognitive systems engineering that advocates the design of joint cognitive systems in which machines serve as flexible, context-sensitive resources for human problem solving. Furthermore, a focus on design teams necessitates the study of the relationship between group work and technology as studied in the field of computer-supported cooperative work (CSCW). This paper briefly reviews current research in sociotechnical systems theory, computer-supported cooperative work, and cognitive systems engineering and proposes a framework for integrating operational concerns into the concurrent engineering process. Relevance to industry

To be competitive, organizations need to effectively manage human and technological resources. A key issue is the nature of the information and technological infrastructure that both enables and supports ‘best practice’ across the enterprise. This paper describes such an approach in the context of the ‘operational enterprise’ and provides both a philosophical stance as well as specific examples of software support.     

Knowledge-based public service transactions: An intelligent model-driven approach in co-learning contexts

Public administrations pursue the efficiency and quality of administrative services they offer as well as the reduction of time and operational costs in executing service transactions. However, some issues arise when trying to achieve these goals: (a) the lack of procedure formalization to describe public services, (b) a mechanism to guarantee services’ obligatory compliance with frequently changing legal regulations, and (c) the efficient deployment of service transactions in e-government platforms. The aim of this work is to identify the phases of the development cycle of eGovernment transactions and provide the support to automatize them efficiently by using a model driven engineering (MDE) and knowledge-based approach. The main source of the knowledge extraction process comes from a collaborative learning environment where public servants share acquired domain knowledge. A web survey has been conducted to evaluate the approach acceptance degree by software developers and domain experts. The main conclusion is that 61% of the surveyed experts strongly agree that our approach improves actual eGovernment transactions practices and the phases needed to develop them.     

Training adult novices to use computers: Effects of different types of illustrations

Two experiments examined the acquisition of word-processing skills (Experiment 1) and internet usage skills (Experiment 2) by novice adults using three types of illustration, specifically, full-screen illustrations with the text superimposed, icons embedded in the text, and a control, text-only condition. Training with the full-screen or embedded icons yielded significant improvements in performance relative to the text-only condition only in tests that simply required the participants to repeat the step-by-step directions. When tests required that the participants themselves retrieve and reproduce the sequence of steps, the training with illustrations – especially the full-screen illustrations – repeatedly had a negative effect on performance. The results suggest that the illustrations, by making it easy to execute each step of a procedure during training, encouraged superficial processing, which in turn yielded a set of stimulus-response associations rather than a well-linked sequence of actions in long-term memory.     

A cognitive approach to user perception of multimedia quality: An empirical investigation

Whilst multimedia technology has been one of the main contributing factors behind the Web's success, delivery of personalized multimedia content has been a desire seldom achieved in practice. Moreover, the perspective adopted is rarely viewed from a cognitive styles standpoint, notwithstanding the fact that they have significant effects on users’ preferences with respect to the presentation of multimedia content. Indeed, research has thus far neglected to examine the effect of cognitive styles on users’ subjective perceptions of multimedia quality. This paper aims to examine the relationships between users’ cognitive styles, the multimedia quality of service delivered by the underlying network, and users’ quality of perception (understood as both enjoyment and informational assimilation) associated with the viewed multimedia content. Results from the empirical study reported here show that all users, regardless of cognitive style, have higher levels of understanding of informational content in multimedia video clips (represented in our study by excerpts from television programmes) with weak dynamism, but that they enjoy moderately dynamic clips most. Additionally, multimedia content was found to significantly influence users’ levels of understanding and enjoyment. Surprisingly, our study highlighted the fact that Bimodal users prefer to draw on visual sources for informational purposes, and that the presence of text in multimedia clips has a detrimental effect on the knowledge acquisition of all three cognitive style groups.