We review HCI history from both the perspective of its 1980s split with human factors and its nature as a discipline. We then revisit human augmentation as an alternative to user friendliness that seems particularly relevant in the areas of inclusive design and artificial intelligence. Viewing human-AI interaction as a kind of human augmentation raises issues such as how to promote trust and situation awareness. We also pose the question: Can HCI and human factors engineering work together to solve the increasingly urgent challenges of human-AI technology? In an initial look at this question, we contrast the different approaches of HCI and human factors on emerging AI research. This article concludes by considering other potentially promising paths for HCI. We propose more collaboration between HCI and human factors, or related disciplines, in the future to address the massive challenges posed by the rapid growth in data science and artificial intelligence.

Major depressive disorder (MDD) is the most common mental health disorder and is one of the leading preventable causes of death in the United States (U.S.). It is also recognized as a global problem by the World Health organization (WHO). The persistence of MDD leads to many negative consequences including suicide and disability. The Hamilton Rating Scale for Depression (HAM-D) evaluates depression severity based on 17 risk factors (symptoms) of depression. Risk factor analysis is a process to identify and understand the risk factors contributing to a particular disease, and is an essential component in the development of efficient and effective prevention and intervention efforts. Most existing methods use a one-size-fits-all model to identify the risk factors at the population-level. However, this type of method fails to account for data heterogeneity within a population. To overcome this limitation, we formulate a subpopulation specific MDD risk factors (symptoms) ranking problem, under the framework of multi-task learning (MTL), to identify a ranked list of MDD risk factors for each subpopulation (task) simultaneously while utilizing appropriate shared information across tasks. By synchronously learning multiple related tasks, MTL provides a paradigm to rank risk factors both at the subpopulation and population-level. To the best of our knowledge, this is the first study to investigate HAM-D using MTL.

The Hamilton Rating Scale for Depression (HAM-D) evaluates depression severity based on 17 symptoms of major depressive disorder (MDD). Understanding the clustering of symptoms within MDD is helpful for the identification of depression subgroups that might differ in how they respond to various treatment interventions. In the research reported in this paper, we employed hierarchical clustering to generate a symptom clustering hierarchy. We compare our findings with previously identified factor analytic HAM-D symptom groups. Our findings are comparatively assessed with previously identified HAM-D symptom groups using factor analysis.

Being able to accurately predict the time to event of interest, commonly known as survival analysis, is extremely beneficial in many real-world applications. Traditional commonly used statistical survival analysis methods, e.g., Cox proportional hazards model and parametric censored regressions, are based on strong and sometimes impractical assumptions and can only handle linearity relationship between features and target. Recently, deep learning based formulations have been proposed for survival analysis to handle non-linearity. However, these existing deep learning methods either inherit strong assumptions from their corresponding base models or tailor discrete-time survival analysis. To overcome the limitations within these existing models in the literature, we propose an objective function to guide the training of a deep learning model for continuous-time survival analysis. The objective function combines both ranking based and point-wise regression based losses. The ranking based loss measures the goodness of the orders of the predicted survival time for all instances. The point-wise based loss measures the difference between the predicted survival time and the true survival time for the right censored time-to-event data. More specifically, we derive two versions of the ranking based loss from the smoothed concordance index, and two versions of point-wise based loss based on the normalized mean squared error (MSE) and mean absolute error (MAE). Thus, the proposed formulation is capable of dealing with the continuous-time survival analysis from both global and local perspectives. We conduct experimental analysis over several large-scale real-world time-to-event datasets, and the results demonstrate that our model outperforms the state-of-the-art survival analysis methods. The codes and data used in the experiments are available in the link1.1https://github.com/yanlirock/local_global_survival

Accurately predicting the time to an event of interest is an important problem in a wide range of real-world applications. However, prediction is often difficult because many medical datasets have a large number of unlabeled (“censored”) instances because labeling is costly and time consuming. Survival analysis focuses on labeled data to predict the time to an event of interest, such as time of death, or conversion to a different stage in a progressive disease. Grouping structure, which naturally exists in medical datasets, can be exploited to improve generalization performance by learning multiple related survival prediction tasks for subgroups collaboratively. Thus a multi-task learning framework can connect multiple survival prediction tasks (for different subgroups) and learn them simultaneously. In order to take into account both censored information, as well as discover the grouping structure, we propose a novel cluster-boosted multitask learning framework for survival analysis that boosts survival prediction performance. We develop an efficient algorithm and demonstrate the performance of the proposed cluster-boosted multi-task survival analysis method on The Cancer Genome Atlas (TCGA) dataset. Our results show that the proposed approach can significantly improve prediction performance in survival analysis while also identifying different subgroups of cancer patients.

Data exfiltration is a serious threat to organizations. Such exfiltrations cause breach events that can lead to millions of dollars of loss. Perimeter defense is not enough by itself since successful exploits from insiders can also be very damaging. Internal network user activities need to be monitored to detect malicious actions. Automatic machine learning methods can be applied for network anomaly detection, but they create a lot of false alarms. Domain experts can identify malicious users, but they are unable to process large volumes of data. Interactive machine learning (iML) deals with this tradeoff by creating an efficient collaboration between domain experts and machine learning algorithms. Previous research in iML has focused mainly on collaboration with non-experts. The design and requirements for expertise-driven iML have yet to be delineated for cybersecurity applications. In this research, we proposed an Active Learning (AL) model trained with outputs from a liberal (outputting many false alarms as well as possible hits) anomaly detection (AD) criterion to study expert-iML collaboration in anomaly detection. The results showed that: iML in this context can prune false alarms and minimize misses; the performance/compatibility tradeoff that typically occurs in conventional machine learning updates may be less salient in iML. We suggest that compatibility between experts and algorithms can be improved by presenting information about feature relevance during the training process.

Closed Captioning (CC) is a service primarily designed for deaf and hard of hearing (D/HoH) viewers. The CC translates spoken speech into text for television or film screen display. The quality assessment methods for live captioning are limited to quantitative measures, while the viewers are still dissatisfied with the current quality. One method to improve the current quality assessment procedure is to include D/HoH viewers in the evaluation procedure for their subjective assessment input. However, it could be costly and impractical to perform evaluations for the entire broadcasted shows. Therefore, it would be helpful to model subjective assessments that could replicate and predict human decisions. In this article, we report on a model of probabilities of D/HoH viewer assessment decisions for CC quality factors based on actual user preferences. An online survey was designed and conducted to collect assessment data for 22 error variation samples from four quality factors: delay, speed, missing words, and paraphrasing of captions. The results are analyzed using the signal detection theory framework to create decision probability models for D/HoH viewers.

Purpose. This study evaluates the predictive ability of patient types (clusters of similar patients) in identifying cancer patients at high risk for emergency department (ED) visits within one year (365 days) following their index date. A descriptive and retrospective cohort study of 45,356 unique cancer patients with only one primary cancer type and at least one ED visit was done using linked administrative sources of health care data. Methods. Three outcomes were investigated in this study. First, the time of ED visit following an index date was predicted using multiple linear regression. Second, those patients who visited an ED within seven days of their index date were detected using logistic regression. In addition to predicting an emergency department visit, vital status of patients was also predicted using logistic regression. We implemented the linear/logistic regression first on unclustered raw data and then on clustered data. The results of these two analyses were then compared. Conclusion. Clustering was found to contribute to a modest improvement in prediction accuracy for all three outcome variables. The results are discussed in terms of the predictive ability of patient types in development of clinical support tools with respect to privacy of patients and their implications for better allocation of resources to cancer patients.

There has been considerable research on developing symptom clusters for cancer patients but finding a consistent and replicable typology of patients in terms of the kinds of symptoms they experience remains elusive (e.g., [1]). One reason for lack of consensus may be that analyses are unfocused, and use different combinations of clustering variables. This paper identifies subgroups of similar cancer patients within a sample of 18,535 patients, based on the Edmonton Symptom Assessment System (ESAS) and also a set of inpatient and outpatient variables using linked administrative sources of Ontario healthcare data. K-means cluster analysis was performed on cancer patients having only one primary cancer type, and who had at least one emergency department (ED) visit. Only variables that had a Pearson correlation of at least 0.1 with the number of days until visiting an ED, after an assessment, were included in the cluster analysis. While information about next emergency department visit was not included as a clustering variable, the number of patient types was chosen so as to minimize the mean absolute error of predictions of next emergency department visits by cancer patients. The next emergency department visit, after the last assessment date of each patient, was predicted for each cluster solution. The cluster solution with maximum accuracy/minimum MAE was used to derive the final set of patient types. With the help of physicians, and guided by the results of these analyses, a description of each patient type was created. Based on our results we grouped cancer patients into four types that differ in terms of type of cancer, stage of cancer, and symptomology. Implications for symptom management and reduction of ED visits are discussed.

New technologies, such as serious games and ambient activities, are being developed to address problems of under-stimulation, anxiety, and agitation in millions of people living with dementia in long term care homes. Frequent interactions with instrumented versions of these technologies may not only be beneficial for long term care residents, but may also provide a valuable new set of multifaceted data related to the health status of residents over time. In this paper, we develop a model for health monitoring in healthcare environments and we report on two studies that show how medically relevant data can be collected from elderly residents and emergency department patients in an unobtrusive way. The first study shows how data related to cognitive abilities can be collected from elderly emergency department patients and the second study shows how detailed data on a range of factors can be collected from ambient activity units designed to provide engaging interactions for long term care residents. In summary, this paper proposes the use of new technologies to transform long term care from a data poor to a data rich environment, where the health status of long term care residents and elderly patients is more closely monitored.

Regular path queries (RPQs) have quickly become a staple to explore graph databases. SPARQL 1.1 includes prop- erty paths, and so now encompasses RPQs as a fragment. Despite the extreme utility of RPQs, it can be exceedingly difficult for even experts to formulate such queries. It is next to impossible for non-experts to formulate such path queries. As such, several visual query systems (VQSs) have been proposed which simplify the task of constructing path queries by directly manipulating visual objects representing the domain elements. The queries generated by VQSs may, however, have many commonalities that can be exploited to optimize globally. We introduce Tasweet, a framework for optimizing “disjunctive” path queries, which detects the commonalities among the queries to find a globally opti- mized execution plan over the plan spaces of the constituent RPQs. Our results show savings in edge-walks / time-to- completion of 59%.

This paper introduces research that addresses the subjective assessment of Quality of Experience (QoE) during the entire life cycle of a video session. We define a video session life cycle as the time from when a user attempts to initiate playback, until such time that the video ends either from normal video conclusion or through a network-induced failure. We provide a detailed description of our assessment methodology designed to discern whether a user's QoE would be impacted by the presence of failures. To accomphsh this, we carefully select various test conditions to take into consideration the rating scale used, the types of impairments and failures seen by the user, and whether impaired videos are seen together with failed videos in multi-video sessions. The selection and creation of source video sequences are also discussed, as well as the use of between-subjects and within-subjects approaches for running our experiments in a controlled laboratory setting. Statistical analysis was carried out to interpret our experimental results. We compared the results of the between-subjects measures and the results of the within-subjects measures, and concluded that the introduction of a scale with an extended lower bound enabled subjects to more clearly express their dissatisfaction of videos with failures when compared to the traditional ITU 5-point rating scale. In addition, we observed that videos that were simply impaired but concluded normally did not have a statistically significant difference when an extended scale was used.

Sharing of personal health information is subject to multiple constraints, which may dissuade some organizations from sharing their data. Summarized deidentified data, such as that derived from k-means cluster analysis, is subject to far fewer privacy-related constraints. In this paper, we examine the extent to which analysis of clustered patient types can match predictions made by analyzing the entire dataset at once. After reviewing relevant literature, and explaining how data are summarized in each cluster of similar patients, we compare the results of predicting death, and length of stay (LOS) in the ICU1 using regression analysis on original and clustered data from the MIMIC II dataset. Clustering improved regression prediction accuracy for both death and LOS. We then show that clustering prior to regression also improved prediction of number of days to next emergency room visit for cancer patients. Thus, in all three prediction tasks that we investigated (involving two very different datasets), we found that clustering prior to regression analysis improved prediction accuracy. We discuss the results in terms of their implications for the future use of health-repository-based data analytics to provide a supplement to existing methods of clinical decision support.

Quality of Experience (QoE) is a crucial guiding factor for network management of an end-to-end service session. The network provider can control the resources allocated to sessions and in doing so, influence the Technical Quality (TQ), which covers the technical aspects of signal quality during the session. On the other hand, the network provider has no control over the Content Quality (CQ), which pertains to the user's level of interest in a particular video. Together TQ and CQ influence the Overall eXperience (OX) in a session. In this paper, we present results from a user subjective study in which the impact of TQ and CQ on OX was investigated for Over-The-Top (OTT) video sessions from the perspective of a network provider. This perspective places a focus on those elements of QoE that can be controlled by the provider. Various studies have shown that very high interest in a content can strongly influence QoE independent of other factors, so our study uses videos that are neutral with respect to content. We assess the TQ, CQ and OX for video sessions that contain Integrity impairments (in the form of image freezing) and failures in terms of session Accessibility and Retainability. Our findings indicate that TQ and CQ have a strong impact on OX in the presence of impairments, but no failures. On the other hand, TQ is the main determinant of OX when failures are present.

Customer satisfaction is an important factor governing adoption and retention of multimedia products and services, such as Over-The-Top(OTT) video transmission. Quality of Experience involves user-centric evaluation of various services. However, users differ in terms of their ratings of service quality. Some rating differences are due to unreliability (outlier users who are not motivated, or are not sensitive to differences in quality), but others are systematic differences in rating that may reflect different perspectives on quality. In this paper, we explore the use of outlier analysis and clustering as tools for interpreting QoE data. We report on experimental results demonstrating the use of outlier analysis and clustering. In interpreting the clusters, we examine users' opinions on different types of video disruption, and their ability to distinguish the different levels of impairments/failures.

In this paper, we study the relationships between Quality of Service (QoS) and Quality of Experience (QoE) in a session-based Over-The-Top (OTT) video service. A number of Performance Metrics (PMs) with and without the existence of failures during a video are examined. As QoE factors, Technical Quality (TQ) and Acceptability are used. We analyze the correlation between QoS performance metrics and QoE factors, and find new PMs should be employed because failures are included in QoE evaluation. We also summarize the relationships between QoS metrics and QoE factors through machine learning approaches. Using decision tree, we have a general idea about the relationships between PMs and QoE factors. We also understand the impact caused by failures and the value of rating scales.

Accurate Quality of Experience measurement for streaming video has become more crucial with the increase in demand for online video viewing. Quantifying video Quality of Experience is a challenging task. Significant efforts to quantify video Quality of Experience have primarily focused on the measurement of Quality of Experience for videos with network and compression related impairments. These impairments, however, may not always be the only main factors affecting Quality of Experience in an entire video viewing session. In this paper, we evaluate Quality of Experience for entire video viewing sessions, from the beginning to the end. In doing so, we evaluate videos with temporary interruptions as well as those with permanent interruptions or failures. We consider two types of failures, namely Accessibility and Retainability failures, and present the results of two subjective studies. These results indicate: (a) Accessibility and Retainability failures are rated lower compared to temporary interruption impairments; (b) Accessibility failures are rated close to the lowest value on the rating scale; and (c) the traditionally used 5-point scale to measure video Quality of Experience is not sufficient in the presence of Accessibility and Retainability failures.

Journal Article Shared Input Multimodal Mobile Interfaces: Interaction Modality Effects on Menu Selection in Single-Task and Dual-Task Environments Get access Shengdong Zhao, Shengdong Zhao * 1Department of Computer Science, National University of Singapore, 13 Computing Drive, Computing 2, #01-04, Singapore 117417 *Corresponding author: [email protected] Search for other works by this author on: Oxford Academic Google Scholar Duncan P. Brumby, Duncan P. Brumby 2UCL Interaction Centre, University College London, Gower Street, London WC1E 6BT, UK Search for other works by this author on: Oxford Academic Google Scholar Mark Chignell, Mark Chignell 3Knowledge Media Design Institute (KMDI), University of Toronto, 27 King's College Circle, Toronto, Ont., Canada M5S 1A1 Search for other works by this author on: Oxford Academic Google Scholar Dario Salvucci, Dario Salvucci 4Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA Search for other works by this author on: Oxford Academic Google Scholar Sahil Goyal Sahil Goyal 5National University of Singapore, 13 Computing Drive, Computing 2, #01-04, Singapore 117417 Search for other works by this author on: Oxford Academic Google Scholar Interacting with Computers, Volume 25, Issue 5, September 2013, Pages 386–403, https://doi.org/10.1093/iwc/iws021 Published: 06 February 2013 Article history Received: 29 December 2011 Revision received: 11 October 2012 Accepted: 13 November 2012 Published: 06 February 2013

Measures of emotion should accurately characterize the nature of an emotional experience and determine whether that experience is universal or unique to a subgroup or culture. We investigated the value of assessing emotion through skin conductance (an easy-to-interpret physiological measure) and sliders (frequently used and direct measures of perceived emotion). This paper describes findings from two experiments. The first evaluated various slider configurations and found that measured emotions successfully characterized the emotional nature of short videos. The second experiment collected the slider and skin conductance measures of emotion while one sample of Japanese participants and another sample of Canadian participants viewed longer videos. The measures were sensitive enough to identify cultural differences consistent with existing literature and were also able to identify parts of the experience where members from different cultures reacted consistently, pinpointing content that provoked a universal experience. We offer a toolkit of data interpretation techniques to gain more insight into the implicit and explicit emotion data: analyses for expressiveness and agreement that can infer states such as engagement and fatigue. We summarize the aspects of our measurement approach and toolkit in a model: the ability to distinguish the emotional nature of stimuli, individuals, and affective interaction.

We measured multitasking performance across a range of device interfaces and investigated the relationship between task performance and three measures of cognitive capacity (assessing the executive processes of shifting, inhibition, and updating, respectively). In the first experiment, higher levels of ability on the three executive processes of shifting, updating and inhibition were associated with improved multitasking performance. However, the impact of cognitive demands was reduced when touch input and combined visual and audio output was used in the device interaction task. When a simulated driving task was added in the second experiment, the impact of cognitive demands increased, and the use of combined audio and video output no longer reduced the impact of Central Executive (CE) function ability on performance.

We present the design and evaluation of earPod: an eyes-free menu technique using touch input and reactive auditory feedback. Studies comparing earPod with an iPod-like visual menu technique on reasonably-sized static menus indicate that they are comparable in accuracy. In terms of efficiency (speed), earPod is initially slower, but outperforms the visual technique within 30 minutes of practice. Our results indicate that earPod is potentially a reasonable eyes-free menu technique for general use, and is a particularly exciting technique for use in mobile device interfaces.

Searching audio data can potentially be facilitated by the use of automatic speech recognition (ASR) technology to generate text transcripts which can then be easily queried. However, since current ASR technology cannot reliably generate 100% accurate transcripts, additional techniques for fluid browsing and searching of the audio itself are required. We explore the impact of transcripts of various qualities, dichotic presentation, and time-compression on an audio search task. Results show that dichotic presentation and reasonably accurate transcripts can assist in the search process, but suggest that time-compression and low accuracy transcripts should be used carefully.

This paper describes an empathic software agent (ESA) interface using eye movement information to facilitate empathy-relevant reasoning and behavior. Eye movement tracking is used to monitor user's attention and interests, and to personalize the agent behaviors. The system reacts to user eye information in real-time, recording eye gaze and pupil dilation data during the learning process. Based on these measures, the ESA infers the focus of attention and motivational status of the learner and responds accordingly with affective (display of emotion) and instructional behaviors. In addition to describing the design and implementation of empathic software agents, this paper will report on some preliminary usability test results concerning how users respond to the empathic functions that are provided.

Recent interest in the use of software character agents raises the issue of how many agents should be used in online learning. In this paper we review evidence concerning the relative effectiveness of multi-agent systems and introduce a multiple agent system that we have developed for online instruction. A user test is carried out that compares one and two agent versions of the learning system. The results are interpreted in terms of their implications for selecting when and how more than one agent should be used in online learning. We conclude with some recommendations on when multiple agents may help online learners to interact with the learning environment more easily and efficiently.

The effect of key size on text entry on a handheld device while walking and standing was examined in order to answer the following questions: 1) Will the additional workload of walking amplify the effect of input difficulty? and 2) Can walking speed be used as a secondary task measure of mental workload during mobile text entry? 13 participants (7 males and 6 females) input well known sayings (sentences) in English into a handheld device in each of four size conditions, with the text input box ranging in width between 2 and 5 millimetres (mm). Text input speed increased with larger size of text box up to a size of 3mm, and text input speed was faster when standing (vs. walking). The effect of size did not depend on whether participants were walking or standing. Errors were significantly higher for the 2mm size condition but did not vary for the wider sizes, while subjective ease of input increased with increasing input box width, only crossing the midpoint of the rating scale (i.e., more easy than difficult) at an input box width of 3mm. Based on these results it is recommended that a minimum text input box width of 3mm be used for handheld text input. Walking speed during text entry in this study was relatively low (with a mean of 1.77 km/h) but width of text input box had no additional effect on walking speed over and above the general slowing caused by text entry. Thus the answers to both of the main questions posed in this study were in the negative, although the fact that people had to enter text slowed walking speed by a fixed amount (independent of level of input difficulty) that varied between individuals. Implications for measuring workload in mobile text entry tasks are discussed.

This paper addresses issues raised by the ever-expanding role of email as a multi-faceted application that combines communication, collaboration, and task management. Individual differences analysis was used to contrast two email user interfaces in terms of their demands on users. The results of this analysis were then interpreted in terms of their implications for designing more inclusive interfaces that meet the needs of users with widely ranging abilities.The specific target of this research is the development of a new type of email message representation that makes pending tasks more visible. We describe a study that compared a new way of representing tasks in an email inbox, with a more standard representation (the Microsoft Outlook inbox). The study consisted of an experiment that examined how people with different levels of three specific cognitive capabilities (flexibility of closure, visual memory, and working memory) perform when using these representations. We then identified combinations of representation and task that are disadvantageous for people with low levels of the measured capabilities.

Abstract With advances in computer graphics, a number of innovative approaches to information visualization have been developed (e.g., Card et al, 1991 ). Some of these approaches create a mapping between information and corresponding structure in a virtual world. The resulting virtual worlds can be fully three dimensional (3D) or they can be implemented as a series of 2D birds‐eye “snapshots” that are traversed as if they were in 3D, using operations such as panning and zooming interactively (2.5D). This paper reports a study that contrasted 3D and 2.5D performance for people with differing levels of spatial and structure learning ability. Four data collection methods were employed: search task scoring; subjective questionnaires; navigational activity logging and analysis; and administration of tests for spatial and structure‐learning abilities. Analysis of the results revealed statistically significant effects of user abilities, and information environment designs. Overall, this research did not find a performance advantage for using a 3D rather than a 2.5D virtual world. In addition, users in the lowest quartile of spatial ability had significantly lower search performance in the 3D environment. The findings suggest that individual differences in traits such as spatial ability may be important in determining the usability and acceptability of 3D environments.

This article reports the results of a study that investigated effects of four user characteristics on users' mental models of information retrieval systems: educational and professional status, first language, academic background, and computer experience. The repertory grid technique was used in the study. Using this method, important components of information retrieval systems were represented by nine concepts, based on four IR experts' judgments. Users' mental models were represented by factor scores that were derived from users' matrices of concept ratings on different attributes of the concepts. The study found that educational and professional status, academic background, and computer experience had significant effects in differentiating users on their factor scores. First language had a borderline effect, but the effect was not significant enough at α = 0.05 level. Specific different views regarding IR systems among different groups of users are described and discussed. Implications of the study for information science and IR system designs are suggested.

Article Information archiving with bookmarks: personal Web space construction and organization Share on Authors: David Abrams Perceptual Robotics, Inc., 1840 Oak Ave., Evanston, IL Perceptual Robotics, Inc., 1840 Oak Ave., Evanston, ILView Profile , Ron Baecker Knowledge Media Design Institute, University of Toronto, Toronto, ON M5S 1A4 Knowledge Media Design Institute, University of Toronto, Toronto, ON M5S 1A4View Profile , Mark Chignell Interactive Media Laboratory, University of Toronto, Toronto, ON M5S 1A4 Interactive Media Laboratory, University of Toronto, Toronto, ON M5S 1A4View Profile Authors Info & Claims CHI '98: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsJanuary 1998 Pages 41–48https://doi.org/10.1145/274644.274651Published:01 January 1998 163citation2,116DownloadsMetricsTotal Citations163Total Downloads2,116Last 12 Months63Last 6 weeks14 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

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We are exploring how virtual reahty theories can be applied toward palmtop computers. In our prototype, called the Chameleon, a small 4-inch hand-held monitor acts as a palmtop computer with the capabihties of a Silicon graphics workstation. A 6D input device and a response button are attached to tbe small monitor to detect user gestures and input selections for issuing commands. An experiment was conducted to evaluate our design and to see how well depth could be perceived in the small screen compared to a large 21-inch screen, and the extent to which movement of the small display (in a palmtop virtual reality condition) could improve depth perception, Results show that with very little training, perception of depth in the palmtop virtual reality condition is about as good as corresponding depth perception in a large (but static) display. Variations to the initial design are also discussed, along with issues to be explored in future research, Our research suggests that palmtop virtual reality may support effective navigation and search and retrieval, in rich and portable information spaces.

Knowledge-based search tactics are discussed in terms of their role in the functioning of a semantically-based search system for bibliographic information retrieval. This prototype system, EP-X, actively assists users in defining or refining their topics of interest. It does so by applying search tactics to a knowledge-base describing topics in a particular domain and a database describing the contents of individual documents.

It is argued that the information processing model represents an incomplete foundation for a human-factors account of cognitive tasks. The authors propose the inclusion of purposive and proactive accounts that are more consistent with the requirements for explaining goal-oriented reasoning and behavior. They review a number of related approaches and sketch out the beginnings of a theoretical foundation for human factors in the form of assumptions that bound human behavior. The initial focus is on basic assumptions governing the availability and use of cognitive resources, but the assumptions suggested elsewhere (e.g. the model human processor approach) are also utilized.>

Research on the nature of knowledge-based systems for bibliographic information retrieval is summarized. Knowledge-based search tactics are then considered in terms of their role in the functioning of a semantically based search system for bibliographic information retrieval, EP-X. This system uses such tactics to actively assist users in defining or refining their topics of interest. It does so by applying these tactics to a knowledge base describing topics in a particular domain and to a database describing the contents of individual documents in terms of these topics. This paper, then, focuses on the two central concepts behind EP-X: semantically based search and knowledge-based search tactics.

In examining the role of time in mental workload, the authors present a different perspective from which to view the problem of assessment. Mental workload is plotted in three dimensions, whose axes represent effective time for action, perceived distance from desired goal state, level of effort required to achieve the time-constrained goal. This representation allows the generation of isodynamic workload contours that incorporate the factors of operator skill and equifinality of effort. An adaptive interface for dynamic task reallocation is described that uses this form of assessment to reconcile the joint aims of stable operator loading and acceptable primary task performance by the total system.>