A Chinese Text Input Brain–Computer Interface Based on the P300 Speller

In this article the results of a usability study of CONTACT, an application that supports negotiation strategy training via video modeling for enhancing the conflict resolution of adolescents with autism spectrum disorder (ASD), are presented. This application was modified from the NoProblem! prototype based on feedback from two focus groups of typically developing adolescents and therapists and teachers of adolescents with ASD. It has been designed to address social competencies through video modeling and complies with the principles of constructivist learning theory. Twenty-one adolescents with ASD, 12 to 18 years of age, and 27 age- and gender-matched typically developing adolescents participated in this study. The outcome measure used was the Intrinsic Motivation Inventory. Both adolescents with ASD and typically developing adolescents perceived the application to be interesting and enjoyable; they claimed that it did not generate feelings of pressure or tension and that they had independent choices. The results also showed that adolescents with ASD felt significantly more competent than typically developing adolescents when using this application. The findings of this study emphasize the important role that the usability process has when designing and developing new technologies for educational purposes for adolescents with and without ASD.     

Toward Spoken Human–Computer Tutorial Dialogues

Oral discourse is the primary form of human–human communication, hence, computer interfaces that communicate via unstructured spoken dialogues will presumably provide a more efficient, meaningful, and naturalistic interaction experience. Within the context of learning environments, there are theoretical positions supporting a speech facilitation hypothesis that predicts that spoken tutorial dialogues will increase learning more than typed dialogues. We evaluated this hypothesis in an experiment where 24 participants learned computer literacy via a spoken and a typed conversation with AutoTutor, an intelligent tutoring system with conversational dialogues. The results indicated that (a) enhanced content coverage was achieved in the spoken condition; (b) learning gains for both modalities were on par and greater than a no-instruction control; (c) although speech recognition errors were unrelated to learning gains, they were linked to participants' evaluations of the tutor; (d) participants adjusted their conversational styles when speaking compared to typing; (e) semantic and statistical natural language understanding approaches to comprehending learners' responses were more resilient to speech recognition errors than syntactic and symbolic-based approaches; and (f) simulated speech recognition errors had differential impacts on the fidelity of different semantic algorithms. We discuss the impact of our findings on the speech facilitation hypothesis and on human–computer interfaces that support spoken dialogues.     

User Modeling in Human–Computer Interaction

A fundamental objective of human–computer interaction research is to make systems more usable, more useful, and to provide users with experiences fitting their specific background knowledge and objectives. The challenge in an information-rich world is not only to make information available to people at any time, at any place, and in any form, but specifically to say the right thing at the right time in the right way. Designers of collaborative human–computer systems face the formidable task of writing software for millions of users (at design time) while making it work as if it were designed for each individual user (only known at use time). User modeling research has attempted to address these issues. In this article, I will first review the objectives, progress, and unfulfilled hopes that have occurred over the last ten years, and illustrate them with some interesting computational environments and their underlying conceptual frameworks. A special emphasis is given to high-functionality applications and the impact of user modeling to make them more usable, useful, and learnable. Finally, an assessment of the current state of the art followed by some future challenges is given.     

Agency Effects in Human–Computer Interaction

The studies presented in this article explore a human-centered conceptualization of agents and agency based on the observation that people attribute agency to sufficiently complex interactive systems. Although agency attribution appears to be an unconscious human response, findings from social psychology, affective computing, and perceptual-motor studies suggest agency attribution influences human–computer interaction (HCI). Three studies are presented that examine whether recent findings on agency attribution in physical environments also apply in the virtual environments characteristic of HCI. Results of the studies indicate that agency effects operate in desktop computing environments. Agency effects, however, appear to be influenced by learning effects that preserve a previously observed relationship between perception and action but alter how this effect is expressed. Results suggest that there are both bottom-up and top-down contributions to agency effects in HCI.     

Effects of Luminosity Contrast and Stimulus Duration on User Performance and Preference in a P300-Based Brain–Computer Interface

Brain–computer interfaces (BCI) have potential to provide a new channel of communication and control for people with severe motor disabilities. Although many empirical studies exist, few have specifically evaluated the impact of contributing factors on user performance and perception in BCI applications, especially for users with motor disabilities. This article reports the effects of luminosity contrast and stimulus duration on user performance and usage preference in a P300-based BCI application, P300 Speller. Ten participants with neuromuscular disabilities (amyotrophic lateral sclerosis and cerebral palsy) and 10 able-bodied participants were asked to spell six 10-character phrases in the P300 Speller. The overall accuracy was 76.5% for the able-bodied participants and 26.8% for participants with motor disabilities. The results showed that luminosity contrast and stimulus duration have significant effects on user performance. In addition, participants preferred high luminosity contrast with middle or short stimulus duration. However, these effects on user performance and preference varied for participants with and without motor disabilities. The results also indicated that although most participants with motor disabilities can establish BCI control, BCI illiteracy does exist. These results of the study should provide insights into the future research of the BCI systems, especially the real-world applicability of the BCI applications as a nonmuscular communication and control system for people with severe motor disabilities.     

An EEG Brain–Computer Interface Approach for Classifying Vigilance States in Humans: A Gamma Band Focus Supports Low Misclassification Rates

This research investigated a brain–computer interface (BCI) for classifying vigilance states. A BCI approach based on event-related potential (ERP) and spectral features of electroencephalographic (EEG) data derived from an auditory oddball paradigm was employed. A 128-channel EEG system recorded potentials while participants simultaneously completed a prolonged visual match-to-sample task. It was hypothesized that better classification rates would be found for a group of “bored” participants, as compared to the not-bored participants. The best classification rates were found by extracting EEG features from gamma frequencies at middle and late latencies (for the standard tones). Critically, the BCI paradigm was most effective for the bored participants (mean misclassification rate = 13%), as compared to the not-bored participants (mean misclassification rate = 21%). These findings extend our knowledge of reliable latencies and spectral features of ERP data most salient to the classification of vigilance states.     

Computer丛林

DIGITAL 不久前推出的 PC 5510采用了最新技术,将网络集成、错误管理、系统可用性、电源管理和资源管理等功能集于一身,提供安全开机、安全 BIOS 和安全机箱等多种保护措施,并预装 ClientWORKS、Alta、Vista 个人查询及诊断软件,极大地满足了企业计算环境对系统安全性、可靠性和可管理性的要求,同时大大降低了总体成本。DIGITAL PC 5510采用了 Intel 公司最新的 Pentium     

Citizen Science: New Research Challenges for Human–Computer Interaction

Citizen science broadly describes citizen involvement in science. Citizen science has gained significant momentum in recent years, brought about by widespread availability of smartphones and other Internet and communications technologies (ICT) used for collecting and sharing data. Not only are more projects being launched and more members of the public participating, but more human–computer interaction (HCI) researchers are focusing on the design, development, and use of these tools. Together, citizen science and HCI researchers can leverage each other’s skills to speed up science, accelerate learning, and amplify society’s well-being globally as well as locally. The focus of this article is on HCI and biodiversity citizen science as seen primarily through the lens of research in the author’s laboratory. The article is framed around five topics: community, data, technology, design, and a call to save all species, including ourselves. The article ends with a research agenda that focuses on these areas and identifies productive ways for HCI specialists, science researchers, and citizens to collaborate. In a nutshell, while species are disappearing at an alarming rate, citizen scientists who document species’ distributions help to support conservation and educate the public. HCI researchers can empower citizen scientists to dramatically increase what they do and how they do it.     

Evaluation of hydraulic excavator Human–Machine Interface concepts using NASA TLX

This study evaluated newly proposed Human–Machine Interface (HMI) design concepts for improving the ergonomics of hydraulic excavators. The design concepts were based on an augmented interaction technique which involved the use of heads-up display (HUD) and coordinated control as HMI elements. Two alternative HMI designs were elaborated in order to separately evaluate the ergonomic impacts of the head-up display and the coordinated control by comparing them to the standard HMI design. The effectiveness of these three HMI designs in terms of the reduction of the operators' mental and physical workload were assessed by conducting experiments utilizing human subjects, ages 23–35 years. The National Aeronautics and Space Administration's Task Load Index (NASA TLX) method was used for collecting subjective workload scores based on a weighted average of ratings of six factors: Mental Demand, Physical Demand, Temporal Demand, Own Performance, Effort, and Frustration Level. The results showed that the type of HMI design affects different aspects of the operator's workload. Indeed, it showed how the proposed augmented interaction is an effective solution for reducing the ergonomic gaps in terms of mental workload, and to a lesser extent the physical workload, subjected by the standard HMI design.     

Parallel Programming of Multi-processor SoC: A HW–SW Interface Perspective

For the design of classic computers the parallel programming concept is used to abstract HW/SW interfaces during high level specification of application software. The software is then adapted to existing multiprocessor platforms using a low level software layer that implements the programming model. Unlike classic computers, the design of heterogeneous MPSoC includes also building the processors and other kind of hardware components required to execute the software. In this case, the programming model hides both hardware and software refinements. This paper deals with parallel programming models to abstract both hardware and software interfaces in the case of heterogeneous MPSoC design. Different abstraction levels will be needed. For the long term, the use of higher level programming models will open new vistas for optimization and architecture exploration like CPU/RTOS tradeoffs.     

Brain–Computer Interfaces for Multimodal Interaction: A Survey and Principles

For decades, brain–computer interfaces (BCIs) have been used for restoring the communication and mobility of disabled people through applications such as spellers, web browsers, and wheelchair controls. In parallel to advances in computational intelligence and the production of consumer BCI products, BCIs have recently started to be considered as alternative modalities in human–computer interaction (HCI). One of the popular topics in HCI is multimodal interaction (MMI), which deals with combining multiple modalities in order to provide powerful, flexible, adaptable, and natural interfaces. This article discusses the situation of BCI as a modality within MMI research. State-of-the-art, real-time multimodal BCI applications are surveyed in order to demonstrate how BCI can be helpful as a modality in MMI. It is shown that multimodal use of BCIs can improve error handling, task performance, and user experience and that they can broaden the user spectrum. The techniques for employing BCI in MMI are described, and the experimental and technical challenges with some guidelines to overcome these are shown. Issues in input fusion, output fission, integration architectures, and data collection are covered.     

China Computer Federation

ＣｈｉｎａＣｏｍｐｕｔｅｒＦｅｄｅｒａｔｉｏｎ￥／／ＣｈｉｎａＣｏｍｐｕｔｅｒＦｅｄｅｒａｔｉｏｎｗａｓｆｏｕｎｄｅｄｉｎＪＬＩｎｅ，１９６２，ａｎｄａｕｔｈｏｒｉｚｅｄｔｏｂｅｃｏｍｅａｆｉｒｓｔ－－ｃｌａｓｓＦｅｄｅｒａｔｉｏｎｏｎＭａｒｃｈ５，...