tocauthor: Wang_H* : Human-Computer Interaction Bibliography Search

KeDiary: Using Mobile Phones to Assist Patients in Recovering from Drug Addiction Medical Device Sensing / You, Chuang-Wen / Lin, Ya-Fang / Li, Cheng-Yuan / Tsai, Yu-Lun / Huang, Ming-Chyi / Lee, Chao-Hui / Wang, Hao-Chuan / Chu, Hao-Hua Proceedings of the ACM CHI'16 Conference on Human Factors in Computing Systems 2016-05-07 v.1 p.5704-5709

ACM Digital Library Link

Summary: Ketamine is an addictive drug that has been shown to inflict considerable physical and mental damage on users. Due in part to its low cost, ketamine has become one of the most popular club drugs among young adults and teenagers in Southeast Asia. This paper proposes a phone-based support system (KeDiary) with Bluetooth-enabled device for the screening of saliva, as a means of assisting ketamine-dependent patients to self-monitor their ketamine use following acute withdrawal treatment. We also conducted a practical experiment to evaluate the feasibility of the proposed system, wherein three ketamine-dependent patients self-administered tests at least once per day over a period of three weeks. Follow-up interviews with the same users helped in the further refinement of the proposed self-monitoring system.

HandVis: Visualized Gesture Support for Remote Cross-Lingual Communication Late-Breaking Works: Collaborative Technologies / Lin, Kuan-Yu / Yong, Seraphina / Wang, Shuo-Ping / Lai, Chien-Tung / Wang, Hao-Chuan Extended Abstracts of the ACM CHI'16 Conference on Human Factors in Computing Systems 2016-05-07 v.2 p.1236-1242

ACM Digital Library Link

Summary: Effective communication between those who are not fluent in a non-native language can potentially be quite difficult. The common language selected to be used throughout an exchange can encumber those who might not speak it as proficiently as others. Remote communication further heightens the difficulty since less channels are available for communication. We introduce HandVis, a video conferencing interface that visualizes elements of hand gesture, such as trajectory and amount. Gesture is intended to be a communicative tool that can compensate for language deficits. The results of a user study indicate how HandVis can be utilized constructively by less-proficient speakers during cross-lingual communication.

Co-Viewing Room: Mobile TV Content Sharing in Social Chat Late-Breaking Works: Engineering of Interactive Systems / Tu, Pei-Yun / Chen, Mei-Ling / Yang, Chi-Lan / Wang, Hao-Chuan Extended Abstracts of the ACM CHI'16 Conference on Human Factors in Computing Systems 2016-05-07 v.2 p.1615-1621

ACM Digital Library Link

Summary: TV watching is a common leisure activity, and people often use the opportunity of TV watching to socialize with other co-watchers. However, when potential TV co-watchers like friends or family members are distributed in different locations, the social function of TV watching is disrupted. In this paper, we present a mobile TV content sharing system called Co-Viewing Room, which enables distributed users to share three types of TV content, including it whole video sharing, video clips sharing and it snapshots sharing during an online chat. We evaluated the system by comparing the influence of the three types of content sharing on users' experience and social interactions. Our results showed that people were satisfied with remote TV sharing support, and tended to be more responsive to lightweight shared content like snapshots and video clips. Also, people regarded snapshots sharing as a useful support for efficient social chat.

Pactolus: A Method for Mid-Air Gesture Segmentation within EMG Late-Breaking Works: Extending User Capabilities / Chen, Yineng / Su, Xiaojun / Tian, Feng / Huang, Jin / Zhang, Xiaolong (Luke) / Dai, Guozhong / Wang, Hongan Extended Abstracts of the ACM CHI'16 Conference on Human Factors in Computing Systems 2016-05-07 v.2 p.1760-1765

ACM Digital Library Link

Summary: Mid-air gestures have become an important interaction technique in natural user interfaces, especially in augmented reality and virtual reality. Supporting a set of continuous gesture-based commands in mid-air gesture interaction systems, such as selecting and moving then placing an object, however, remains to be a challenge. This is largely because these intentional command gestures are connected through transitional, meaningless gestures, which are often misleading for gesture recognition systems. The inability to separate unintentional movements from intentional command gestures, also called the Midas problem, limits the application of mid-air gestures. This paper addresses the Midas problem via a physiological computing approach. With the help of sensors that capture physiological signals, we present a novel method, Pactolus, for segmenting mid-air gestures using arm electromyography. User studies demonstrate the high accuracy of our approach in segmenting mid-air gestures interleaved by transitional hand or finger movements.

ToPIN: A Visual Analysis Tool for Time-anchored Comments in Online Educational Videos Late-Breaking Works: Interaction in Specific Domains / Sung, Ching-Ying / Huang, Xun-Yi / Shen, Yicong / Cherng, Fu-Yin / Lin, Wen-Chieh / Wang, Hao-Chuan Extended Abstracts of the ACM CHI'16 Conference on Human Factors in Computing Systems 2016-05-07 v.2 p.2185-2191

ACM Digital Library Link

Summary: Online videos are widely used to share content for a variety of entertainment, educational and other purposes. To support social interaction, several video-sharing websites Including Ustream, niconico, and Twitch -- allow users to post messages while they are watching videos. As users' comments can be sorted according to the timecode of each video, this is known as time-anchored commenting. We propose a novel visualization method, ToPIN, which is able to analyze and categorize the topics and content types of users' time-anchored comments. We have also developed a visualization interface that combines the visualization techniques of ToPIN and ThemeRiver to generate additional valuable insights for analysts seeking to make sense of time-anchored comments. To test the utility of our approach, we visualized time-anchored commenting data from two online course videos and invited the course instructors to evaluate our system.

Assisting Food Journaling with Automatic Eating Detection Late-Breaking Works: Usable, Useful, and Desirable / Ye, Xu / Chen, Guanling / Gao, Yang / Wang, Honghao / Cao, Yu Extended Abstracts of the ACM CHI'16 Conference on Human Factors in Computing Systems 2016-05-07 v.2 p.3255-3262

ACM Digital Library Link

Summary: In this work we study the feasibility and usability of an assistive food journaling system that sends users just-in-time reminders when unique hand gestures during food consumption are detected using a smartwatch. Our study shows that participants were able to sustain food logging throughout a 2-week period with the help of our eating detection system, as the number of reminders correlate well with the number of food logs. Despite the fact that participants were required to wear the watch on their dominant hand, it was still quite usable and did not interfere with their normal activities. Participant feedback provided additional insights to inform future work to increase detection accuracy, reduce detection delay, and allow for more dietary logging features in the app.

How Social Annotation Affects Second Language Reading Posters / Wang, Hao-Chuan / Han, Cheng-Hsien / Pan, Mei-Hua / Yang, Chi-Lan Companion Proceedings of ACM CSCW 2016 Conference on Computer-Supported Cooperative Work and Social Computing 2016-02-27 v.2 p.429-432

ACM Digital Library Link

Summary: Annotation is a common practice to aid reading. Social annotation may benefit collaborative second language learning in the way that second language readers may share their annotations to help each other read articles of a non-native language. In a laboratory study, we simulated the process of social annotation and examined how seeing others' annotations affects readers' understanding of the content as well as their production and editing of annotations. The preliminary results showed that reading other people's annotations can affect article reading and the modification of annotations especially when readers disagreed with the annotations.

EMV-matchmaker: Emotional Temporal Course Modeling and Matching for Automatic Music Video Generation Poster Session 1 / Lin, Jen-Chun / Wei, Wen-Li / Wang, Hsin-Min Proceedings of the 2015 ACM International Conference on Multimedia 2015-10-26 p.899-902

ACM Digital Library Link

Summary: This paper presents a novel content-based emotion-oriented music video (MV) generation system, called EMV-matchmaker, which utilizes the emotional temporal phase sequence of the multimedia content as a bridge to connect music and video. Specifically, we adopt an emotional temporal course model (ETCM) to respectively learn the relationship between music and its emotional temporal phase sequence and the relationship between video and its emotional temporal phase sequence from an emotion-annotated MV corpus. Then, given a video clip (or a music clip), the visual (or acoustic) ETCM is applied to predict its emotional temporal phase sequence in a valence-arousal (VA) emotional space from the corresponding low-level visual (or acoustic) features. For MV generation, string matching is applied to measure the similarity between the emotional temporal phase sequences of video and music. The results of objective and subjective experiments demonstrate that EMV-matchmaker performs well and can generate appealing music videos that can enhance the viewing and listening experience.

Accelerating Large-scale Image Retrieval on Heterogeneous Architectures with Spark Poster Session 1 / Wang, Hanli / Xiao, Bo / Wang, Lei / Wu, Jun Proceedings of the 2015 ACM International Conference on Multimedia 2015-10-26 p.1023-1026

ACM Digital Library Link

Summary: Apache Spark is a general-purpose cluster computing system for big data processing and has drawn much attention recently from several fields, such as pattern recognition, machine learning and so on. Unlike MapReduce, Spark is especially suitable for iterative and interactive computations. With the computing power of Spark, a utility library, referred to as IRlib, is proposed in this work to accelerate large-scale image retrieval applications by jointly harnessing the power of GPU. Similar to the built-in machine learning library of Spark, namely MLlib, IRlib fits into the Spark APIs and benefits from the powerful functionalities of Spark. The main contributions of IRlib lie in two-folds. First, IRlib provides a uniform set of APIs for the programming of image retrieval applications. Second, the computational performance of Spark equipped with multiple GPUs is dramatically boosted by developing high performance modules for common image retrieval related algorithms. Comparative experiments concerning large-scale image retrieval are carried out to demonstrate the significant performance improvement achieved by IRlib as compared with single CPU thread implementation as well as Spark without GPUs employed.

Human Action Recognition With Trajectory Based Covariance Descriptor In Unconstrained Videos Poster Session 2 / Wang, Hanli / Yi, Yun / Wu, Jun Proceedings of the 2015 ACM International Conference on Multimedia 2015-10-26 p.1175-1178

ACM Digital Library Link

Summary: Human action recognition from realistic videos plays a key role in multimedia event detection and understanding. In this paper, a novel Trajectory Based Covariance (TBC) descriptor is proposed, which is formulated along the dense trajectories. To map the descriptor matrix to vector space and trim out the redundancy of data, the TBC descriptor matrix is projected to Euclidean space by the Logarithm Principal Components Analysis (LogPCA). Our method is tested on the challenging Hollywood2 and TV Human Interaction datasets. Experimental results show that the proposed TBC descriptor outperforms three baseline descriptors (i.e., histogram of oriented gradient, histogram of optical flow and motion boundary histogram), and our method achieves better recognition performances than a number of state-of-the-art approaches.

Learning Knowledge Bases for Multimedia in 2015 Tutorials / Xie, Lexing / Wang, Haixun Proceedings of the 2015 ACM International Conference on Multimedia 2015-10-26 p.1323-1324

ACM Digital Library Link

Summary: Knowledge acquisition, representation, and reasoning has been one of the long-standing challenges in artificial intelligence and related application areas. Only in the past few years, massive amounts of structured and semi-structured data that directly or indirectly encode human knowledge became widely available, turning the knowledge representation problems into a computational grand challenge with feasible solutions in sight. The research and development on knowledge bases is becoming a lively fusion area among web information extraction, machine learning, databases and information retrieval, with knowledge over images and multimedia emerging as another new frontier of representation and acquisition. This tutorial aims to present a gentle overview of knowledge bases on text and multimedia, including representation, acquisition, and inference. In particular, the 2015 edition of the tutorial will include recent progress from several active research communities: web, natural language processing, and computer vision and multimedia.

Clustering-based Active Learning on Sensor Type Classification in Buildings Session 2D: Clustering / Hong, Dezhi / Wang, Hongning / Whitehouse, Kamin Proceedings of the 2015 ACM Conference on Information and Knowledge Management 2015-10-19 p.363-372

ACM Digital Library Link

Summary: Commercial and industrial buildings account for a considerable portion of all energy consumed in the U.S., and thus reducing this energy consumption is a national grand challenge. Based on the large deployment of sensors in modern commercial buildings, many organizations are applying data analytic solutions to the thousands of sensing and control points to detect wasteful and incorrect operations for energy savings. Scaling this approach is challenging, however, because the metadata about these sensing and control points is inconsistent between buildings, or even missing altogether. Moreover, normalizing the metadata requires significant integration effort.
In this work, we demonstrate a first step towards an automatic metadata normalization solution that requires minimal human intervention. We propose a clustering-based active learning algorithm to differentiate sensors in buildings by type, e.g., temperature v.s. humidity. Our algorithm exploits data clustering structure and propagates labels to their nearby unlabeled neighbors to accelerate the learning process. We perform a comprehensive study on metadata collected from over 20 different sensor types and 2,500 sensor streams in three commercial buildings. Our approach is able to achieve more than 92% accuracy for type classification with much less labeled examples than baselines. As a proof-of-concept, we also demonstrate a typical analytic application enabled by the normalized metadata.

An Inference Approach to Basic Level of Categorization Session 3F: Classification 1 / Wang, Zhongyuan / Wang, Haixun / Wen, Ji-Rong / Xiao, Yanghua Proceedings of the 2015 ACM Conference on Information and Knowledge Management 2015-10-19 p.653-662

ACM Digital Library Link

Summary: Humans understand the world by classifying objects into an appropriate level of categories. This process is often automatic and subconscious. Psychologists and linguists call it as Basic-level Categorization (BLC). BLC can benefit lots of applications such as knowledge panel, advertising and recommendation. However, how to quantify basic-level concepts is still an open problem. Recently, much work focuses on constructing knowledge bases or semantic networks from web scale text corpora, which makes it possible for the first time to analyze computational approaches for deriving BLC. In this paper, we introduce a method based on typicality and PMI for BLC. We compare it with a few existing measures such as NPMI and commute time to understand its essence, and conduct extensive experiments to show the effectiveness of our approach. We also give a real application example to show how BLC can help sponsored search.

Central Topic Model for Event-oriented Topics Mining in Microblog Stream Session 8C: Social Media 2 / Peng, Min / Zhu, Jiahui / Li, Xuhui / Huang, Jiajia / Wang, Hua / Zhang, Yanchun Proceedings of the 2015 ACM Conference on Information and Knowledge Management 2015-10-19 p.1611-1620

ACM Digital Library Link

Summary: To date, data generates and arrives in the form of stream to propagate discussions of public events in microblog services. Discovering event-oriented topics from the stream will lead to a better understanding of the change of public concern. However, as the massive scale of the data stream, traditional static topic models, such as LDA, are no longer fit for topic detection and tracking tasks. In this paper, we propose a central topic model (CenTM), where a Multi-view Clustering algorithm with Two-phase Random Walk (MC-TRW) is devised to aggregate the LDA's latent topics into central topics. Furthermore, we leverage the aggregation of central topics alternately with MC-TRW and sequential topic inference to improve the scalability in the stream fashion, so as to derive the dynamic central topic model (DCenTM). Specifically, our model is able to uncover the intrinsic characteristics of the central topics and predict the trend of their intensity along a life cycle. Experimental results demonstrate that the proposed central topic model is event-oriented and of high generalization, it therefore can dispose the topic trend prediction effectively and precisely in massive data stream.

Joint Modeling of User Check-in Behaviors for Point-of-Interest Recommendation Session 8D: Recommendation / Yin, Hongzhi / Zhou, Xiaofang / Shao, Yingxia / Wang, Hao / Sadiq, Shazia Proceedings of the 2015 ACM Conference on Information and Knowledge Management 2015-10-19 p.1631-1640

ACM Digital Library Link

Summary: Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting locations, especially when users travel out of town. However, extreme sparsity of user-POI matrix creates a severe challenge. To cope with this challenge, a growing line of research has exploited the temporal effect, geographical-social influence, content effect and word-of-mouth effect. However, current research lacks an integrated analysis of the joint effect of the above factors to deal with the issue of data-sparsity, especially in the out-of-town recommendation scenario which has been ignored by most existing work.
In light of the above, we propose a joint probabilistic generative model to mimic user check-in behaviors in a process of decision making, which strategically integrates the above factors to effectively overcome the data sparsity, especially for out-of-town users. To demonstrate the applicability and flexibility of our model, we investigate how it supports two recommendation scenarios in a unified way, i.e., home-town recommendation and out-of-town recommendation. We conduct extensive experiments to evaluate the performance of our model on two real large-scale datasets in terms of both recommendation effectiveness and efficiency, and the experimental results show its superiority over other competitors.

Defragging Subgraph Features for Graph Classification Short Papers: Databases / Wang, Haishuai / Zhang, Peng / Tsang, Ivor / Chen, Ling / Zhang, Chengqi Proceedings of the 2015 ACM Conference on Information and Knowledge Management 2015-10-19 p.1687-1690

ACM Digital Library Link

Summary: Graph classification is an important tool for analysing structured and semi-structured data, where subgraphs are commonly used as the feature representation. However, the number and size of subgraph features crucially depend on the threshold parameters of frequent subgraph mining algorithms. Any improper setting of the parameters will generate many trivial short-pattern subgraph fragments which dominate the feature space, distort graph classifiers and bury interesting long-pattern subgraphs. In this paper, we propose a new Subgraph Join Feature Selection (SJFS) algorithm. The SJFS algorithm, by forcing graph classifiers to join short-pattern subgraph fragments, can defrag trivial subgraph features and deliver long-pattern interesting subgraphs. Experimental results on both synthetic and real-world social network graph data demonstrate the performance of the proposed method.

Large-Scale Question Answering with Joint Embedding and Proof Tree Decoding Short Papers: Information Retrieval / Wang, Zhenghao / Yan, Shengquan / Wang, Huaming / Huang, Xuedong Proceedings of the 2015 ACM Conference on Information and Knowledge Management 2015-10-19 p.1783-1786

ACM Digital Library Link

Summary: Question answering (QA) over a large-scale knowledge base (KB) such as Freebase is an important natural language processing application. There are linguistically oriented semantic parsing techniques and machine learning motivated statistical methods. Both of these approaches face a key challenge on how to handle diverse ways natural questions can be expressed about predicates and entities in the KB. This paper is to investigate how to combine these two approaches. We frame the problem from a proof-theoretic perspective, and formulate it as a proof tree search problem that seamlessly unifies semantic parsing, logic reasoning, and answer ranking. We combine our word entity joint embedding learned from web-scale data with other surface-form features to further boost accuracy improvements. Our real-time system on the Freebase QA task achieved a very high F1 score (47.2) on the standard Stanford WebQuestions benchmark test data.

Improving Collaborative Filtering via Hidden Structured Constraint Short Papers: Knowledge Management / Zhang, Qing / Wang, Houfeng Proceedings of the 2015 ACM Conference on Information and Knowledge Management 2015-10-19 p.1935-1938

ACM Digital Library Link

Summary: Matrix factorization models, as one of the most powerful Collaborative Filtering approaches, have greatly advanced the recommendation tasks. However, few of them are able to explicitly consider structured constraint for modeling user interests. To solve this problem, we propose a novel matrix factorization model with adaptive graph regularization framework, which can automatically discover latent user communities jointly with learning latent user representations, to enhance the discriminative power for recommendation. Experiments on real-world datasets demonstrate the effectiveness of the proposed method.

Using text mining to infer the purpose of permission use in mobile apps Understanding and protecting privacy / Wang, Haoyu / Hong, Jason / Guo, Yao Proceedings of the 2015 International Conference on Ubiquitous Computing 2015-09-07 p.1107-1118

ACM Digital Library Link

Summary: Understanding the purpose of why sensitive data is used could help improve privacy as well as enable new kinds of access control. In this paper, we introduce a new technique for inferring the purpose of sensitive data usage in the context of Android smartphone apps. We extract multiple kinds of features from decompiled code, focusing on app-specific features and text-based features. These features are then used to train a machine learning classifier. We have evaluated our approach in the context of two sensitive permissions, namely ACCESS_FINE_LOCATION and READ_CONTACT_LIST, and achieved an accuracy of about 85% and 94% respectively in inferring purposes. We have also found that text-based features alone are highly effective in inferring purposes.

Leveraging User Reviews to Improve Accuracy for Mobile App Retrieval Session 6C: Tasks and Devices / Park, Dae Hoon / Liu, Mengwen / Zhai, ChengXiang / Wang, Haohong Proceedings of the 2015 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2015-08-09 p.533-542

ACM Digital Library Link

Summary: Smartphones and tablets with their apps pervaded our everyday life, leading to a new demand for search tools to help users find the right apps to satisfy their immediate needs. While there are a few commercial mobile app search engines available, the new task of mobile app retrieval has not yet been rigorously studied. Indeed, there does not yet exist a test collection for quantitatively evaluating this new retrieval task. In this paper, we first study the effectiveness of the state-of-the-art retrieval models for the app retrieval task using a new app retrieval test data we created. We then propose and study a novel approach that generates a new representation for each app. Our key idea is to leverage user reviews to find out important features of apps and bridge vocabulary gap between app developers and users. Specifically, we jointly model app descriptions and user reviews using topic model in order to generate app representations while excluding noise in reviews. Experiment results indicate that the proposed approach is effective and outperforms the state-of-the-art retrieval models for app retrieval.

Identifying Appraisal Expressions of Online Reviews in Chinese Social Media for Business / Yin, Pei / Wang, Hongwei / Wang, Wei HCIB 2015: 2nd International Conference on HCI in Business 2015-08-02 p.207-218

Keywords: Online reviews; Appraisal expressions; Product feature; Review feature; Semantic lexicon; Consumers' opinions

Link to Digital Content at Springer

Summary: With the development of Web2.0 technology, an increasing number of consumers are giving comments on products over the Internet, thus opinion mining rises in response to the requirement of retrieving valuable information in speed. After thoroughly analyzing the style of language and the ways of expression in Chinese, this paper proposes a semantic lexicon-based method to identify the appraisal expressions in Chinese online reviews. A comparative experiment based on cellphone online reviews in Chinese is conducted in this research, and the result indicates that the proposed method is quite promising and outperforms the two baselines (a statistic orientation method and a semantic orientation method). Moreover, the method is applied to a comparative evaluation of two popular cellphones, demonstrating the theoretical significance and the practical value of this research.

The Moderating Role of Perceived Effectiveness of Provider Recommendations on Consumers' Satisfaction, Trust, and Online Repurchase Intention Electronic, Mobile and Ubiquitous Commerce / Wang, Hongpeng / Du, Rong / Ai, Shizhong / Chi, Zhe HCIB 2015: 2nd International Conference on HCI in Business 2015-08-02 p.382-391

Keywords: Provider recommendations; Satisfaction; Trust; Online repurchase intention

Link to Digital Content at Springer

Summary: Despite the importance of online provider recommendations in e-commerce transactions, there is still little understanding about how provider recommendations impacts on customer retention. Addressing this gap, this study introduces a key construct, perceived effectiveness of provider recommendations (PEPRs) to investigate the differential moderating effects of PEPRs on the relationships between satisfaction, trust and repeat purchase intention. The research models are designed based on a research model and an online survey is conducted with 130 respondents. We draw conclusions that (1) PEPRs negatively moderate the relationship between satisfaction with vendor and trust in vendor and (2) PEPRs positively moderate the relationship between trust in vendor and repurchase intention. These findings are important theoretical contributions to know that first-hand experience can be to some extent replaced by supplementary information. In addition, we give some managerial countermeasures towards the new situation.

Semantic Research of Military Icons Based on Behavioral Experiments and Eye-Tracking Experiments Design Thinking / Chen, Xiao Jiao / Xue, Chengqi / Niu, Yafeng / Wang, Haiyan / Zhang, Jing / Shao, Jiang DUXU 2015: Fourth International Conference on Design, User Experience, and Usability, Part I: Design Discourse 2015-08-02 v.1 p.24-31

Keywords: Aeronautical system; Icons; Semantics; Behavioral experiment; Eye-tracking experiment

Link to Digital Content at Springer

Summary: As a type of symbol, there are four dimensions in icons' symbolic interpretation, namely semantic, syntactic, contextual and pragmatic dimension. Among those dimensions, semantic dimension is the most important one in user's cognitive analysis. Based on the representation of semantics, icons can be classified into four types, namely function-metaphor, operation-metaphor, object-metaphor and meaning-metaphor icon. Here we conducted behavioral experiment and eye-tracking experiment to evaluate those four types of icons selected from military aeronautical system. The behavioral experiment showed that subjects have lowest reaction time to function-metaphor icons and highest accuracy to identify object-metaphor icons. The eye-tracking system showed that subjects have the most fixations when searching for object-metaphor icons and the least fixations when searching for function-metaphor icons. Our research is the first endeavor into the investigation of human's response to different types of icons in the military systems and thus provided novel and valuable guidance to the design of icons in those systems.

Older Adults' Usage of Web Pages: Investigating Effects of Information Structure on Performance Aging, the Web and Social Media / Huang, Jincheng / Zhou, Jia / Wang, Huilin ITAP 2015: First International Conference on Human Aspects of IT for the Aged Population, Part I: Design for Aging 2015-08-02 v.1 p.337-346

Keywords: Information structure; Older adults; Web pages; Navigation

Link to Digital Content at Springer

Summary: This study focuses on older adults' usage of web pages. An experiment consisted of three information structures (the net structure, the tree structure, and the linear structure) was conducted to investigate effects of information structure (IS) on older adult's performance. Three findings were found. First, the number of clicks was the fewest in the net-structure web page among three web pages. Older participants spent less time to complete the tasks in the linear-structure web page than the other two web pages. The number of clicks and the accuracy of participants answered the questions in the tree-structure web page were the highest among three web pages. Second, older participants' performance of card sorting was positively correlated with the task completion time. And there was a positive correlation between spatial ability and the performance of older participants. Third, older participants showed the highest preference of the linear structure among three information structures. They always lost task targets in the tree-structure web page, especially when they needed to transfer from one branch of the tree structure to another branch. This indicated that a simple IS was better used and understood by older participants than a complicated one.

Study on Event-Related Potential of Information Alarm in Monitoring Interface Cognitive Aspects of Display and Information Design / Shao, Jiang / Xue, Chengqi / Wang, Haiyan / Tang, Wencheng / Niu, Yafeng EPCE 2015: 12th International Conference on Engineering Psychology and Cognitive Ergonomics 2015-08-02 p.54-65

Keywords: Information identification; ERP; Human computer interaction

Link to Digital Content at Springer

Summary: Conduct research on the problems caused by the improper design of alarm modes in the digital interface of monitoring system. Based on the behavior data and physiological data obtained by the event-related potential brain electrical experiment, compare the influences of the two alarm modes of interface elements size change and color change on the visual cognition of users, analyze the key elements that cause these reasons and lay the foundation for the improvement of alarm modes of monitoring interface. In the brain electrical components of color change and size change, N100, P200 and P300 are more obvious, and they are focused on the top region, the central left top region and the central right top region. As for the present of digital interface alarm information, in the present method with the same channel, participants is more sensitive to the color code change, although the activation degree of size change on human brain is higher. The data analysis and conclusion of this thesis can provide reference for the design of the digital interface alarm mode in the future, so as to effectively avoid the users' misjudgment and omission on the interface information and improve the use efficiency of system in reality.