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PurposeTo provide better services to customers, especially immediate responses and 24/7 availability, businesses are implementing text-based automated conversational agents, i.e. chatbots on their social platforms and websites. Chatbots are required to not only provide customers with necessary consultancy and guidance but also communicate friendly and socially. Based on the cognitive fit theory, this study attempts to examine the role of chatbot as a decision aid and how the match between information presentation in forms of decisional guidance and communication style and the shopping task influences consumers' perceived cognitive fit and decision performance outcomes.Design/methodology/approachA 2 x 2 x 2 between subject online experiment was conducted to identify which kind of decisional guidance (suggestive and informative guidance) and communication style (task-oriented vs social-oriented style) are the most appropriate for each type of shopping task (searching vs browsing task).FindingsThe findings show that when customers interact with chatbots, they will perceive higher cognitive fit if the chatbots provide them with suggestive guidance and communicate in a friendly style especially when they perform a searching task.Originality/valueThis study is the first attempt to understand the role of chatbots as a decision aid to customers using the communicative language. This study also tries to explore the cognitive fit theory in a novel way, and we propose the information presentation in forms of communicative language rather than matrices, tables and graphs.

Understanding team viability -- a team's capacity for sustained and future success -- is essential for building effective teams. In this study, we aggregate features drawn from the organizational behavior literature to train a viability classification model over a dataset of 669 10-minute text conversations of online teams. We train classifiers to identify teams at the top decile (most viable teams), 50th percentile (above a median split), and bottom decile (least viable teams), then characterize the attributes of teams at each of these viability levels. We find that a lasso regression model achieves an accuracy of .74--.92 AUC ROC under different thresholds of classifying viability scores. From these models, we identify the use of exclusive language such as `but' and `except', and the use of second person pronouns, as the most predictive features for detecting the most viable teams, suggesting that active engagement with others' ideas is a crucial signal of a viable team. Only a small fraction of the 10-minute discussion, as little as 70 seconds, is required for predicting the viability of team interaction. This work suggests opportunities for teams to assess, track, and visualize their own viability in real time as they collaborate., Comment: CSCW 2020 Honorable Mention Award

Intelligent Virtual Assistants (IVA) such as Apple Siri, Google Assistant, are increasingly being used to assist users with performing different tasks. However, their characteristics also raise use...

This paper proposes an end-to-end deep learning framework, termed as motion-aid feature calibration network (MFCN), for video object detection. The key idea is to leverage on the temporal coherence of video features while considering their motion patterns as captured by optical flow. To boost detection accuracy, the framework aggregates the calibrated features both at pixel and instance levels across frames to achieve improved robustness despite appearance variations. The aggregation and calibration are efficiently and adaptively conducted based on an integrated optical flow network. Meanwhile, the entire architecture of the proposed method is end-to-end, thus significantly improving its training and inference efficiency when compared to multi-stage methods for video object detection. Evaluations on KITTI and ImageNet VID indicate that MFCN can improve the results of a strong still-image detector by 11.2% and 7.31% respectively. MFCN also outperforms other competitive video object detectors and achieves a better trade-off between accuracy and runtime speed, demonstrating its potential for use in autonomous driving systems.

Given the thriving of information visualization systems, rich interactivity is expected and essential for users to acquire further and insightful information. Mouseover has been widely used to hint interactivity or details about the hovered elements. However, in a mouseover-click visualization system we designed based on a hierarchical Sankey diagram, we have observed that a mouseover effect interfered users from clicking items and hence reduced the efficacy of the interaction and hid the information in the next levels. This paper examined this situation and studied the effectiveness of different mouseover hints on affordance for inviting clicks. We identified four types of perceived affordances and created seven designs, adopting Mechanical Turk to measure the effectiveness. Results showed that five out of the seven designs significantly improved users’ click-through rate, whereas more than 60% of users failed to click the hovered elements for revealing the next level of information without proper affordance hints, even after they brushed over these elements for multiple times. By comparing the performance of different designs, we proposed guidelines that support the reliability and motivate the interactivity of visualization design.

Periodical time series data can reveal temporal patterns as well as anomalies. Although these patterns appear in different ranges with different measurements, the variables in a dataset can be associated with and affect each other. When the size of datasets becomes large, monitoring and analyzing these data become challenging. We propose a visual analytics framework that is capable of transforming a large number of multivariate time serial data into manageable visual representations that enable analysts to identify and monitor anomalies and trends. After sorting the data in the temporal and categorical directions, we extract the temporal possibility range patterns and anomalies from each variable. Associated variables typically include similar anomalies. Therefore, we employ a hierarchical clustering algorithm to group the variables with similar anomaly appearances. Through our interactive visualization toolset TreeRoses, we find that possibility ranges, anomaly, and its summary, and hierarchical association is perceptible to different degrees. We apply our method to a real-world periodical time series dataset to showcase how our framework effectively monitors anomalies in meteorological data.

Speech interfaces have become an increasingly popular input method for smartphone-based virtual assistants, smart speakers, and Internet of Things (IoT) devices. While they facilitate rapid and natural interaction in the form of voice commands, current speech interfaces lack natural methods for command correction. We present ScratchThat, a method for supporting command-agnostic speech repair in voice-driven assistants, suitable for enabling corrective functionality within third-party commands. Unlike existing speech repair methods, ScratchThat is able to automatically infer query parameters and intelligently select entities in a correction clause for editing. We conducted three evaluations to (1) elicit natural forms of speech repair in voice commands, (2) compare the interaction speed and NASA TLX score of the system to existing voice-based correction methods, and (3) assess the accuracy of the ScratchThat algorithm. Our results show that (1) speech repair for voice commands differ from previous models for conversational speech repair, (2) methods for command correction based on speech repair are significantly faster than other voice-based methods, and (3) the ScratchThat algorithm facilitates accurate command repair as rated by humans (77% accuracy) and machines (0.94 BLEU score). Finally, we present several ScratchThat use cases, which collectively demonstrate its utility across many applications.

Despite the increasing popularity of mobile applications in commerce, there exists an emerging trend where users are abandoning these applications due to the abuse of push notification. This study applies the stress-coping theory to investigate this phenomenon. A 2 × 2 × 2 factorial design experiment with manipulations on perceived information overload, perceived intrusiveness, and perceived reward is conducted in this study. The results show that users are affected by perceived information overload and perceived intrusiveness from push notifications in shopping applications and they deal with these stresses by using disturbance handling strategies and self-preservation strategies. The application varies with different reward systems. Specifically, high levels of reward can encourage disturbance handling strategies and decrease self-preservation strategies. In addition, with high perceived information control and self-efficacy in using mobile devices, users prefer to take actions to handle the disturbances. Finally, disturbance handling strategies prohibit discontinuance behavior, whereas self-preservation strategies encourage quitting behavior. The results of this study benefit both mobile shopping apps users who are in danger of stressors to develop proper strategies and actions to protect themselves and retailers who use applications on smartphones to provide products and services to obtain access to users more efficiently.

As wearable devices move toward the face (i.e. smart earbuds, glasses), there is an increasing need to facilitate intuitive interactions with these devices. Current sensing techniques can already detect many mouth-based gestures; however, users' preferences of these gestures are not fully understood. In this paper, we investigate the design space and usability of mouth-based microgestures. We first conducted brainstorming sessions (N=16) and compiled an extensive set of 86 user-defined gestures. Then, with an online survey (N=50), we assessed the physical and mental demand of our gesture set and identified a subset of 14 gestures that can be performed easily and naturally. Finally, we conducted a remote Wizard-of-Oz usability study (N=11) mapping gestures to various daily smartphone operations under a sitting and walking context. From these studies, we develop a taxonomy for mouth gestures, finalize a practical gesture set for common applications, and provide design guidelines for future mouth-based gesture interactions., 14 page, 5 figures; Accepted to DIS 2021

Augmented Reality (AR) technology has been increasingly utilised in mobile shopping apps to enhance consumers’ mobile shopping experiences, thereby affecting consumers’ impulse buying behaviour. Drawing upon the Spatial Presence Theory and the Elaboration Likelihood Model, the current study aims to empirically investigate AR-related factors (vividness, spatial accuracy, local presence) and their impacts on impulse buying behaviour related factors (arousal, perceived diagnosticity, and urge to buy impulsively) in the context of AR mobile shopping apps. In this study, an experiment employing a full 2X2 factorial between-subject experimental design was carried out. There were a total of 137 participants actively involved in this experiment. The data analysis results and findings, along with the implications of this study, are provided in the paper.

In recent years, cruise travel has become an important branch of tourism. However, the existing cruise function system is designed based on the Western lifestyle and is not entirely suitable for Chinese tourists. Therefore, it is necessary to carry out user research on the usage preferences of Chinese tourists. Considering the specicularity and complexity of cruise space, this study adopts a novel user research method: Behavior Mapping (BM) method, which can be used to understand the unique behavior path of Chinese tourists and analyze their demands. In addition, the BM method was also used combined with the observation method and in-depth interview method in this study. The results show that BM is a good supplement to other research methods and can make the research results more comprehensive and accurate.

We present our CareerVis system, an interactive visualization tool to aid career education for high school and freshman college students. In additional to its practical use, we believe our design approach has potential to inspire the design community to develop simple visualizations that convey complex information to novice users.

Purpose Modern Web browsers all provide a history function that allows users to see a list of URLs they have visited in chronological order. The history log contains rich information but is seldom used because of the tedious nature of scrolling through long lists. This paper aims to propose a new way to improve users’ Web browsing experience by analyzing, clustering and visualizing their browsing history. Design/methodology/approach The authors developed a system called Personal Web Library to help users develop awareness of and understand their Web browsing patterns, identify their topics of interest and retrieve previously visited Web pages more easily. Findings User testing showed that this system is usable and attractive. It found that users can easily see patterns and trends at different time granularities, recall pages from the past and understand the local context of a browsing session. Its flexibility provides users with much more information than the traditional history function in modern Web browsers. Participants in the study gained an improved awareness of their Web browsing patterns. Participants mentioned that they were willing to improve their time management after viewing their browsing patterns. Practical implications As more and more daily activities rely on the internet and Web browsers, browsing data captures a large part of users’ lives. Providing users with interactive visualizations of their browsing history can facilitate personal information management, time management and other meta-level activities. Originality/value This paper aims to help users gain insights into and improve their Web browsing experience, the authors hope that the work they conducted can spur more research contributions in this underdeveloped yet important area.

Visual clutter is a common challenge when visualizing large rank time series data. WikiTopReader, a reader of Wikipedia page rank, lets users explore connections among top-viewed pages by connecting page-rank behaviors with page-link relations. Such a combination enhances the unweighted Wikipedia page-link network and focuses attention on the page of interest. A set of user evaluations shows that the system effectively represents evolving ranking patterns and page-wise correlation.

The multidimensional nature of spatial data poses a challenge for visualization. In this paper, we introduce Phoenixmap , a simple abstract visualization method to address the issue of visualizing multiple spatial distributions at once. The Phoenixmap approach starts by identifying the enclosed outline of the point collection, then assigns different widths to outline segments according to the segments’ corresponding inside regions. Thus, one 2D distribution is represented as an outline with varied thicknesses. Phoenixmap is capable of overlaying multiple outlines and comparing them across categories of objects in a 2D space. We chose heatmap as a benchmark spatial visualization method and conducted user studies to compare performances among Phoenixmap , heatmap , and dot distribution map . Based on the analysis and participant feedback, we demonstrate that Phoenixmap 1) allows users to perceive and compare spatial distribution data efficiently; 2) frees up graphics space with a concise form that can provide visualization design possibilities like overlapping; and 3) provides a good quantitative perceptual estimating capability given the proper legends. Finally, we discuss several possible applications of Phoenixmap and present one visualization of multiple species of birds’ active regions in a nature preserve.

The visual analytics system HeatMosaic aims to reveal spatial-temporal patterns and uncertainty from a large dataset of crowd-sourcing disaster damage reports. The system consists of real-time monitoring and aftermath analysis tools does not empower fast discovering adversity and damage patterns and reliable removal of uncertainty, but also provides just-in-time scanning and rewinding on damage reports. This paper summarises the system's design and architecture, followed by the data interpretation and insights gained by adopting the system.

We proposed a visual analytics tool to analyze social media posts through machine-learning techniques. Latent Dirichlet Allocation and Named Entities Recognition were used to extract semantic information. 12 topics were identified to interpret semantic meanings and reveal spatiotemporal patterns in the dataset. Our visualization consists of topic bubbles, frequency bar chart, stream graph, fisheye list, massage view, map view, word cloud, and social network graph. All the views are linked together and enhanced by efficient interactions. This paper describes the methodology and visualization design in detail.

We present an immersive virtual reality training system cryoVR with passive haptic feedback for training biological scientists preparing bio-sample for Cryo-Electron Microscopy (CryoEM). CryoEM requires the user to conduct careful operations on expensive delicate equipment. To minimize the risk and interruption of crucial research work, we tried to mimic the real lab using VR to let trainer practice in a virtual environment. We used 3D printed objects to provide passive haptic feedback in order to achieve a more realistic training experience. Participants are able to interact with the equipment in the virtual environment by moving or touching physical models. We developed all the necessary operations with haptic feedback, including moving, clicking, pouring, rotating, polling and pushing. By following instructions provided by our virtual reality simulator and interacting with physical objects, trainees will learn how to operate CryoEM with low cost and risk. By developing our training system, we explore the benefits, limitations and precautions of embedding haptic feedback to scientific VR training.

We propose a simple method to measure a driver's fatigue state by detecting the driver's grip force on the steering wheel while driving. We tested the grip force of 36 drivers on the steering wheel in conscious states (Alert) and fatigue states under actual road driving conditions. Using the Stanford sleepiness scale (SSS), we divided drivers into Alert Group A, fatigue Group A, and fatigue Group B. During 20-min real-road driving trials, we measured the steering wheel grip force, electroencephalogram index (R = (α + θ)/β), and blink frequency of each driver synchronously. We found that ΔF, the difference between the maximum/minimum grip force and the standard deviation of the grip force, σF, for each driver, strongly correlated with the driver's fatigue state. In the fatigue state, both ΔF and σF increased significantly. We examined these force indices using analysis of variance (ANOVA) and validated them against the R-value, blink frequency, and the driver's self-reported fatigue state. Using the grip force in fatigue detection, our method can achieve an overall recognition rate of 86.6% and an individual recognition rate of 88.3%. These results indicate that this method can effectively detect a driver's fatigue state during actual road driving. This new method has several advantages, such as a high signal-to-noise ratio, simple data collection, and no influence on daily driving. Thus, our proposed method may provide a theoretical foundation for the development of fatigue-detecting steering wheels

Among online learning factors mentioned in the education literature, it is argued that online participation is the strongest factor which contributes to online learning effectiveness. Drawing from the theory of online learning as online participation, an online learning experiment was conducted in a well-known public University in southern Taiwan. There were seventy-five International MBA students participated in the study. The online learning course contents were specifically designed for studying trust in Information Communication Technologies (ICTs). Furthermore, the online learning course was designed by incorporating active case-based learning to increase students' trust in ICTs. The experiment results showed that high online participation treatments could significantly increase online learning effectiveness, measured by grades, perceived skill development, and the level of students' trust in ICTs. Discussion and limitation of this study are discussed in the paper.

The computer-aided design process is in a complicated non-linear structure involving selections from a pool of configurations with optimized parameters. In order to understand and improve this decision-making process, this paper conducted a user study on students and expert professionals with more than three years of computer-aided design experience. The study revealed the common design problems and challenges faced by CAD designers. The findings also showed that the design approaches students and expert professionals used were different. Additionally, we found that computer-aided designers expect the system to be able to understand vast quantities of multivariate data, control high-quality products for low costs, manage the knowledge personalization and codification within the company, as well as prevent the design mistakes at the design stages. Our findings may lead to the future development of new approaches to improve the computer-aided design process and close up the gap between student and expert professionals in computer-aided design. This paper provides initial support for this future approach.

We extend the interaction space of low-cost mobile virtual reality (VR) by introducing bidirectional scrolling and discrete selection using magnetic sensing. Our design uses the original Google Cardboard v1 input components, modifying only the cardboard mounted on the side. Users slide the magnetized washer around a circular track on the outer layer, which drags a magnet on the inner layer across asymmetric patterned ridges. The phone's magnetometer detects the position of the magnet as it moves around the track and slots into each ridge, emulating a click wheel. The phone's accelerometer is used to recognize center button taps. We compare our system against the current best practice (gaze) with 12 participants across four VR navigation and selection tasks. Finally, we demonstrate our system robustly handles continuous input, despite some minor deterioration of the cardboard, using a motorized rig over an 8-hour period.

Figure 1A screenshot of Contourmap. The bar charts show the overall temporal view of chemical values. Each bar represents the maximum value for one chemical per year/month/day. The spatiotemporal patterns of chemicals can be quickly spotted and compared from the contour map view. we used different color encode different years. The contour lines were created based on inverse distance weighting algorithm. All points along the same contour line are at the same value.This paper introduces a novel visual analytics tool to analyze water contamination data. Bar charts and contour maps are used to inspect the data on an aggregate level. In order to identify both spatial and temporal patterns across all areas, we created a series of contour maps and used isolines to aggregate the readings. We also encoded the contours with color such that newer data were on the warmer spectrum (red/orange) and the older data were on the cooler spectrum (blue/green). Through panning, selecting, brushing, and filtering time scale, the user is able to identify patterns of interest in the data sets and highlight problem areas in the Preserve.

Social data from city level provides unprecedented opportunities to identify urban attributes from various angles. Citizens are likely to evaluate the quality of the neighborhood and make important decisions by their perceptions and the feedback from the environment. In this paper, we studied the correlation between the neighborhood's environment with their properties values and land values. It is profitable to train the algorithm by parallel deep convolutional neural networks on the visual urban scenes data associated with quantitative non-visual urban characteristics in the same views. The result of our approach defines a scenarized profile from building-level details to city-level traits. The main contribution of this work is providing a unique and more accurate perspective to urban valuation estimations, by integrating the scattered sources of pieces of information and implanting into the available images. This urban evaluation approach will be more human-centered, which can be extended to help urban planners and citizens to explore the significant impacts on city decision optimization or spatial economics-related issues like decreasing the cost of properties assessment.

In the face of increased cyber risks, we present our iterative design process and the resulting principles of SpringRain, an information visualization (infoVis) display design concept for large screens in network control rooms (NOCs). It aims to raise team situation awareness by visualizing large-scale multidimensional computer network data sets as a live “rainfall.” We used aesthetic principles and theories of perception to prototype this ambient, yet data-dense, visualization. By applying multiple data dimensions to different properties of a line segment, such as length, motion, and color, the two-dimensional visualization offers analytical affordances (i.e., graphic qualities that make it clear how the display should be “read”) that can be processed pre-attentively (i.e., under 200 ms). This grants that time-sensitive anomalies in the network can be noticed, processed, and addressed in a timely manner. Our design approach was driven by theories rather than existing design works in the hope of encouragi...

Our daily activities now heavily rely on data, and sometimes are even controlled by them. Integrating InfoVis into people's daily lives can help them to access, explore, understand, and utilize the vast variance of data. This paper aims to explore and discuss the idea exchange between the traditional domain of industrial design and the novel field of InfoVis. There are three potential approaches. Extending InfoVis into a product design can fill up the small screen on the product and make the product more user friendly. Appling the 3D form of industrial design to InfoVis can bring it to the physical world and enhance the information qualify in our lives. We also argue that there could be a harmonious combination of industrial design and InfoVis that integrate the benefits from both. To understand this hybrid domain, we introduce some preliminary research explorations that covers both the industrial design and InfoVis, along with our education practices, including our assessment framework, research outcomes, education approaches, and student design projects.

The visual analytics system ClockPetals aims to reveal the spatiotemporal and sequential patterns of a large traffic record dataset. The system features appealing interactive web graphics that fast illustrate traffic patterns and allow users to locate unusual, anomalous traffic events from multiple demographical and temporal dimensions. ClockPetals also provides the interactive exploration of different vehicle batches via common sequential characteristic clustering. This paper presents the system’s architecture and the benefits of its adoption.

WindNebula joins two datasets, sensor data and meteorological data, to combine wind directions with sensor readings to reveal implicit relations among locations of factories and sensors, pollution intensity, wind direction, and sensor problems. The two major views, vectorial view and temporal view, blend key information of different variables from the IEEE VAST 2017 Mini Challenge 2 dataset. This paper introduces the composition of the WindNebula system, exhibits the components of both views, and discusses the benefits of using this system to gain insights of the dataset.

The data presented in this article are related to the research article entitled: "Information strategies for energy conservation: a field experiment in India" (Chen et al., 2017)[1]. The availability of high-resolution electricity data offers benefits to both utilities and consumers to understand the dynamics of energy consumption for example, between billing periods or times of peak demand. However, few public datasets with high-temporal resolution have been available to researchers on electricity use, especially at the appliance-level. This article describes data collected in a residential field experiment for 19 apartments at an Indian faculty housing complex during the period from August 1, 2013 to May 12, 2014. The dataset includes detailed information about electricity consumption. It also includes information on apartment characteristics and hourly weather variation to enable further studies of energy performance. These data can be used by researchers as training datasets to evaluate electricity usage consumption.

To assist the system architects with fast exploration and performance evaluation of the reconfigurable software/hardware architectures, this paper presents a system-level simulator, named after SimRPU, for the reconfigurable processing unit (RPU), which is the major computing engine in reconfigurable processor. The proposed simulator consists of a simulation kernel, a software compiler, a system profiler providing performance, area and power information for the desired architectures, and a system debugger supporting inspecting and modification of the internal state of the RPU. Object-oriented hierarchical and parameterized architecture modeling techniques are proposed to satisfy the requirements for a fast and comprehensive evaluation. Cycle-accurate simulation mechanisms are developed to improve the accuracy of the profiled performance data. Compared with the traditional register transfer level (RTL) based simulation scheme, the proposed simulator could achieve an average speedup of 18.5× with only 3.5% reduction on performance estimation accuracy. One reconfigurable processor targeted at high-definition multimedia decoding applications (such as H.264, MPEG2, AVS, etc.) is implemented with Taiwan Semiconductor Manufacturing Company 65-nm process using the proposed exploration and design flow. The measured results show that the implemented architecture has obvious advantages in terms of both performance and power consumption than the reference designs in multimedia decoding applications.

In this paper, a TPP (Task-based Parallelization and Pipelining) scheme is proposed to implement AVS (Audio Video coding Standard) video decoding algorithm on REMUS (REconfigurable MUltimedia System), which is a coarse-grained reconfigurable multimedia system. An AVS decoder has been implemented with the consideration of HW/SW optimized partitioning. Several parallel techniques, such as MB (Macro-Block)-based parallel and block-based parallel techniques, and several pipeline techniques, such as MB level pipeline and block level pipeline techniques are adopted by hardware implementation, for performance improvement of the AVS decoder. Also, most computation-intensive tasks in AVS video standards, such as MC (Motion Compensation), IP (Intra Prediction), IDCT (Inverse Discrete Cosine Transform), REC (REConstruct) and DF (Deblocking Filter), are performed in the two RPUs (Reconfigurable Processing Units), which are the major computing engines of REMUS. Owing to the proposed scheme, the decoder introduced here can support AVS JP (Jizhun Profile) 1920×1088@39fps streams when exploiting a 200 MHz working frequency.

This paper introduces a novel comparative strategy to access, synthesize, and redesign a mobile visual analytics (VA) system. Designing, evaluating, and improving VA tools are challenging because of the exploratory and unpredicted nature of their users' analysis activities in a real context. Often the system development approach is running rounds of iteration based on one or a few design ideas and related references. Inspired by ideation and design selection from design-thinking literature, we start to redesign systems from comparison and filtering based on a broad range of design ideas. This approach focuses on the information interaction design of systems; integrates design principles from information design, sensorial design, and interaction design as guidelines; compares VA systems at the component level; and seeks unique and adaptive design solutions. The Visual Analytics Benchmark Repository provides a rich collection of the Visual Analytics Science and Technology (VAST) challenges submission reports and videos. For each challenge design problem, there are multiple creative and mature design solutions. Based on this resource, we conducted a series of empirical user studies to understand the user experience by comparing different design solutions, enhanced one visual analytics system design MobileAnalymator by synthesizing new features and removing redundant functions, and accessed the redesign outcomes with the same comparative approach.

We introduce a propagation-based parametric symbolic model approach to supporting analytic provenance. This approach combines a script language to capture and encode the analytic process and a parametrically controlled symbolic model to represent and reuse the logic of the analysis process. Our approach first appeared in a visual analytics system called CZSaw. Using a script to capture the analyst’s interactions at a meaningful system action level allows the creation of a parametrically controlled symbolic model in the form of a Directed Acyclic Graph (DAG). Using the DAG allows propagating changes. Graph nodes correspond to variables in CZSaw scripts, which are results (data and data visualizations) generated from user interactions. The user interacts with variables representing entities or relations to create the next step’s results. Graph edges represent dependency relationships among nodes. Any change to a variable triggers the propagation mechanism to update downstream dependent variables and in turn updates data views to reflect the change. The analyst can reuse parts of the analysis process by assigning new values to a node in the graph. We evaluated this symbolic model approach by solving three IEEE VAST Challenge contest problems (from IEEE VAST 2008, 2009, and 2010). In each of these challenges, the analyst first created a symbolic model to explore, understand, analyze, and solve a particular subproblem and then reused the model via its dependency graph propagation mechanism to solve similar subproblems. With the script and model, CZSaw supports the analytic provenance by capturing, encoding, and reusing the analysis process. The analyst can recall the chronological states of the analysis process with the CZSaw script and may interpret the underlying rationale of the analysis with the symbolic model.

Bilingual college students in Ecuador were presented with a Spanish-language website (either a video website or an online newspaper website). Each website contained a banner ad. The ad was for either a high-involvement product (a smartphone) or a low-involvement product (a news magazine). The type of ad (display ad with photograph vs. text-only, Google-style search ad), shape of ad (horizontal vs. vertical ad), and language of the ad (English vs. Spanish) varied for each type of website. The high-involvement product was seen as significantly more appealing than the low-involvement product, regardless of the type of ad. An interaction suggested that the high-involvement product was seen as somewhat more appealing if advertised with display ads rather than text-only ads; the low-involvement product was slightly more appealing if advertised with text-only ads. Both products - but especially the high-involvement product - attracted higher ratings if advertised with Spanish-language ads rather than English ads. Finally, products were somewhat more appealing if advertised on 'highly congruent' websites, where the advertised product was similar to the theme of the website (e.g., a smartphone that plays videos advertised on a video website; a news magazine advertised on a newspaper website).

This paper proposes a task-based hybrid parallel and hybrid pipeline (THPHP) scheme to implement multi-standard video algorithms, including MPEG-2, H.264, and audio video coding standard (AVS), on a heterogeneous coarse-grained reconfigurable processor, called the reconfigurable multimedia system (REMUS). The proposed schemes greatly improve decoding performance and satisfy the real-time requirements of various high-definition (HD) video decoding standards. In THPHP, we propose both a task-based hybrid parallel scheme, in which macro-block (MB)-level, block-level, and sub-block-level decoding tasks are parallelized to improve data processing throughput, and a hybrid pipeline scheme, in which slice-level, MB-level, block-level and sub-block-level computations are pipelined to improve efficiency. Computation-intensive tasks, such as motion compensation, intra prediction, inverse discrete cosine transform, reconstruction, and deblocking filter, are implemented on two reconfigurable processing units, which are the core computing engines of REMUS. Thanks to the proposed schemes, the implementations can achieve H.264 high profile (HP) 1920×1080@30 fps streams, AVS Jizhun profile (JP) 1920×1080@39 fps streams, and MPEG-2 main profile (MP) 1920×1080@41 fps streams when working at 200 MHz frequency. Compared with XPP-III (a commercial reconfigurable processor), when implementing H.264 HD decoding, the performance and energy efficiency on REMUS are improved by 1.81× and 14.3×, respectively.

PurposeThe purpose of this study is to propose two additions to the existing IS success model to influence usefulness and satisfaction, and ultimately customer loyalty. This study posits that process quality and collaboration quality will significantly influence perceptions on satisfaction, since both considerations are expected to improve interactions between the organization and its customers.Design/methodology/approachThe developed research model was tested in the context of B2B e‐commerce users, employing structural equation modelling‐partial least square to analyze and test the research hypotheses.FindingsResults show that adding process and collaboration quality to the model affects the way system, information, and service quality influences usefulness and satisfaction, suggesting a shift in perspectives and priorities when it comes to B2B e‐commerce transactions. Furthermore, results show that process and collaboration quality have significant effects on usefulness and satisfaction, reinforcing the objective of using B2B e‐commerce systems across supply chain members.Practical implicationsThis highlights the importance of improving business processes and enhancing collaboration activities, implying that managers should focus their efforts into enhancing customer service on top of system functionalities and features via B2B to better work with their supply chain partners. However, this study is limited in the context of a Taiwanese supply chain, suggesting that further research consider its applicability in different contexts, culturally and geographically, especially given the fact that B2B e‐commerce enables cross‐border business operations.Originality/valueThis study also extends the IS success model, especially in the context of B2B e‐commerce, by emphasizing behavioural considerations on top of the functional issues present in any information systems.

Faced with the challenge of information explosion, almost everyone have been exposed to some kind of information visualization in varies of forms. Understanding how people read, understand, interpret and distinguish various forms of visualizations helps designers and developers think about how to improve the designs from the perspectives of users. This paper applied a mixed research method of quantitative and qualitative to explore how the designs of visualization forms evolved, and whether those kinds of graphs and charts are easy for users to understand, and how much information the users can get from the visualization. By testing if users are able to easily and accurately reach the information and providing the scales of simple to complex, and easy to hard, we see that basic visualizations like bar, pie, bubble, line, and scatter charts have been distributed in areas which are relatively simple in design and easy to read. Nonetheless, visualizations like the tree, parallel coordinate, sunburst, heat map, box plot and Sankey graphs have been concentrated in the regions of relatively complex in design, and are difficult to understand. In addition, the visualizations, including stacked bar, word cloud, box plot, and theme river that frequently appeared in the middle region of the grid, embodied the transitions of visualization design from simple to complex, and easy to hard.

Visual conventions are perceptually efficient graphic agreements with common-sense like referents and are commonly used in what we interact with in daily life. It becomes a studying-worthy issue on whether such conventions can enhance performances and reduce cognitive load when we perceive and reason about new knowledge. Furthermore, whether the visual conventions can affect experts, who have prior knowledge and design experience about different visual encoding principles the same degree as novice who have no background knowledge in this area, is another research focus in this study. Our research is carried out according to action features when we read visualizations. Four task features are extracted, based on which behavioral and eye-tracking measurement were conducted, that is data localization, simple and complex data comparison, and knowledge inference. Both expert and novice participants were enrolled in our experiment. The result indicates that conventional elements in visualizations can hugely improve performances in more complex tasks involving higher-level cognition, like making comparisons and reasoning about new knowledge. The performance improvement can be seen from shorter response time on achieving conclusions and higher accuracy rates. Meanwhile, cognitive load, which can be measured from shorter total fixation duration and fewer fixation counts in AOIs, is reduced through applying visually conventional features. No statistically significant difference is found in comparing perceptual and inferential outputs of expert and novice group. We draw conclusions that visual conventions in visualizations can better performance in relatively complex activities, and it can be equally perceived and acquired regardless of the user’s knowledge background is. What we conclude in this study can be extended to areas of dynamic data visualization and layout design in the digital interface domain.

Empty nest is a global social phenomenon that is constantly increasing during the recent decades. The loneliness and depression faced by parents and children lead to a decrease in life quality and other health problems especially for the families with international students. Remote family communication is crucial to reduce the symptoms and improve mental and physical health. Traditional verbal communication methods require synchronous interaction with a convenient time and a suitable environment. We aim to work on this issue from a perspective of immersive and transparent interaction through the ambient display. Based on a series of user research and iterations of concept development, our design outcome FAMILINK employs existing technologies and methods to offer a novel non-intrusive information communication experience. It constructs a cohesive system that generalizes collected data into ambient hologram display and projection and informs the user other family members’ real-time statuses in an ambient approach.

Used car online trading is a new trend developed in the vehicle trading market recently. In this paper, we focus on user experience of car searching in a used-car website. The datasets of used vehicles are complicated and multidimensional. To find a satisfying car within the budget, the car buyer needs to compare many cars by exploring and comparing data include but not limit to car maker, type, mileage, price, and key features. Within most current e-commerce systems, to search and evaluate different cars, users often have to jump across detail pages to search pages repeatedly. Due to constraints set in the searching process, it may result in uncontrollable information overload or deficiency. With a user-centered design approach, we first analyzed the car searching behaviors through the user study, then designed a data visualization interface that helps a customer to (1) achieve an overall understanding of the used car market such as relations of car type to price, mileage/model-year to price, and features, and (2) search, filter, and compare cars of interest through simple interactions. The goal of this research is to help users to find out an ideal candidate car through an innovative interface and enhance user experience on used-car trading websites.

Radial visualization is an important technique to depict serial periodic data. Circle clock design is intuitive to encode 24-hour cyclical data. However, the biggest limitation of the design is the accuracy of reading time points on circle. Dodecagon is another way to represent time series data. We empirically evaluated the effectiveness of circle and dodecagon clock design in perceiving specific points in time. A post-testing interview was also conducted to understand participants’ strategies to read the times. Results show that dodecagon is more accurate than circle in terms of reading time points. Dodecagon was voted as a powerful approach to read the time points and circle was regarded as a better beautiful visualization method.

In order to explore the icon controlled digital interface for brain-computer interaction, we use Event-Related Potential technology, first of all, collect the icons from various digital interfaces, use the image processing software to process the icon images, the method comprises the following steps: communication between EEG and test computer, EEG experiment, off-line analysis of the original signal, time domain and frequency domain feature extraction of the group average EEG signal, microprocessor processing, and obtain the user computer function control command of the icon picture, then extract time domain and frequency domain characteristics of target icon picture EEG signal, and calculate the similarity between them, finally realize the triggering of control instruction of the target icon picture. This method can effectively realize icon brain-computer control of the digital interface, and provide the important reference for other elements to interact with the digital interface brain-computer interaction.

User experience remains a crucial consideration when assessing the successfulness of information visualization systems. The theory of affordances provides a robust framework for user experience design. In this article, we demonstrate a design case that employs an affordance-based framework and evaluate the information visualization display design. SolarWheels is an interactive information visualization designed for large display walls in computer network control rooms to help cybersecurity analysts become aware of network status and emerging issues. Given the critical nature of this context, the status and performance of a computer network must be precisely monitored and remedied in real time. In this study, we consider various aspects of affordances in order to amplify the user experience via visualization and interaction design. SolarWheels visualizes the multilayer multidimensional computer network issues with a series of integrated circular visualizations inspired by the metaphor of the solar system. To amplify user interaction and experience, the system provides a three-zone physical interaction that allows multiple users to interact with the system. Users can read details at different levels depending on their distance from the display. An expert evaluation study, based on a four-layer affordance framework, was conducted to assess and improve the interactive visualization design.

3D modeling is used in Computer Graphics in various fields. Since the growth of gestures, virtual reality and embodied cognition, there have been various new technologies developed to either improve the modeling efficiency, or to provide more nature intuitive experience to the users. In this paper, from the user experience perspective, we try to compare these methods for navigation of 3D objects in the virtual modeling environment including: simple bare hand gestures, tangible user interfaces (TUI) with object in hand, as well as mouse/keyboard as the primary input. Based on embodied cognition theory, we hypothesis that the object-in-hand method might bring better user experience since the interaction between the object and hand can enhance the user's cognition while navigating a model. We present a conceptual design, with two approaches and three design models which demonstrate differences in user interaction with 3D modeling software.

This research proposes a new active queue management method (or FPID) to improve network congestion, which integrates the random early detection (RED) method and fuzzy proportional integral derivative (FuzzyPID) approach. Fuzzy theory applied together with PID helps control the buffer queue within the target. This article provides the literature and theoretical background of network congestion problems, active queue management methods, automatic control systems, and fuzzy controllers. Following the discussion of the proposed FPID method and the experimental design, a simulation test is conducted and analyzed in comparison to the existing popular active queue management methods. The results prove the superiority of the proposed method.

This study collected empirical data from over 200 Web users and investigated the relationships between three constructs related to privacy of Web users' data: user privacy concern about personal data collection, user expectation of control over collected personal data, and user self-regulation. A structural model showed that significant relationships existed among the three constructs. The results suggest that businesses would be wise to address the need that users feel for increased privacy, and that personal characteristics such as self-regulation may be used to tailor the level of user control over personal data.

For a business school, the selection of its peer schools is an important component of its International Association for Management Education (AACSB) (re)accreditation process. A school typically compares itself with other institutions having similar structural and identity-based attributes. The identification of peer schools is critical and can have a significant impact on a business school's accreditation efforts. For many schools the selection of comparable peer schools is a judgmental process. This study offers an alternative means for selection; a quantitative technique called Kohonen's Self-Organizing Map (SOM) network for clustering. In this research, we first demonstrate the capability of SOM as a clustering tool to visually uncover the relationships among AACSB-accredited schools. The results suggest that SOM is an effective and robust clustering method. Then, we compare the results of SOM with that of other clustering methods, such as K-means, Factor/K-means analysis, and kth nearest neighbor procedure. The objective of this study is to demonstrate that a two-dimensional SOM map can be used to integrate the results of various clustering methods and, thus, act as a visual decision support tool.