We explore the feasibility of using crowd workers from Amazon Mechanical Turk to identify and rank sidewalk accessibility issues from a manually curated database of 100 Google Street View images. We examine the effect...
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ISBN:
(纸本)9781450313216
We explore the feasibility of using crowd workers from Amazon Mechanical Turk to identify and rank sidewalk accessibility issues from a manually curated database of 100 Google Street View images. We examine the effect of three different interactive labeling interfaces Point, Rectangle, and Outlineon task accuracy and duration. We close the paper by discussing limitations and opportunities for future work.
iSonic is an interactive sonification tool for vision impaired users to explore geo-referenced statistical data, such as population or crime rates by geographical regions. Users use a keyboard or a smooth surface touc...
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ISBN:
(纸本)1595931597
iSonic is an interactive sonification tool for vision impaired users to explore geo-referenced statistical data, such as population or crime rates by geographical regions. Users use a keyboard or a smooth surface touchpad to interact with coordinated map and table views of the data. The integrated use of musical sounds and speech allows users to grasp the overall data trends and to explore the data to get more details. Scenarios of use are described.
After an historical review of evaluation methods, we describe an emerging research method called Multi-dimensional In-depth Long-term Case studies (MILCs) which seems well adapted to study the creative activities that...
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ISBN:
(纸本)1595935622
After an historical review of evaluation methods, we describe an emerging research method called Multi-dimensional In-depth Long-term Case studies (MILCs) which seems well adapted to study the creative activities that users of information visualization systems engage in. We propose that the efficacy of tools can be assessed by documenting 1) usage (observations, interviews, surveys, logging etc.) and 2) expert users' success in achieving their professional goals. We summarize lessons from related ethnography methods used in HCI and provide guidelines for conducting MILCs for information visualization. We suggest ways to refine the methods for MILCs in modest sized projects and then envision ambitious projects with 3-10 researchers working over 1-3 years to understand individual and organizational use of information visualization by domain experts working at the frontiers of knowledge in their fields. Copyright 2006 ACM.
Widespread interest in discovering features and trends in time- series has generated a need for tools that support interactive *** paper introduces timeboxes: a powerful direct-manipulation metaphor for the specificat...
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Identification of patterns in time series data sets is a task that arises in a wide variety of application domains. This paper presents a user interface for the timebox query model of rectangular regions that specify ...
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ISBN:
(纸本)1581134541
Identification of patterns in time series data sets is a task that arises in a wide variety of application domains. This paper presents a user interface for the timebox query model of rectangular regions that specify constraints over time series data sets. A prototype application based on timeboxes is presented. Collaborations with potential users will guide the design of enhanced functionality. Usability tests and controlled experiments will be conducted to evaluate the timebox query model.
In this paper we describe CTArcade, a web application framework that seeks to engage users through game play resulting in the improvement of computational thinking (CT) skills. Our formative study indicates that CT sk...
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In this paper, we examine hypermedia systems with an emphasis on the types of learning they are appropriate for. We use Bloom's taxonomy of behavioral objectives as the organizing framework for a discussion of edu...
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This paper describes a research effort to support collaborative translation by monolingual speakers, or people that speak only the source or target language. I hypothesize that sharing knowledge across the language ba...
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ISBN:
(纸本)9781605582474
This paper describes a research effort to support collaborative translation by monolingual speakers, or people that speak only the source or target language. I hypothesize that sharing knowledge across the language barrier is possible with a combination of automated (but poor quality) machine translation, language-independent communication, and existing background knowledge. I demonstrate this possibility with proof-of-concept experiments.
In this paper, we present ThumbSpace, a software-based interaction technique that provides general one-handed thumb operation of touchscreenbased mobile devices. Our goal is to provide accurate selection of all interf...
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Tire tracks are crucial evidence for investigating and identifying traffic accident scenes. Intelligent recognition of tire track images enables rapid and precise identification of suspicious vehicles, assisting inves...
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Tire tracks are crucial evidence for investigating and identifying traffic accident scenes. Intelligent recognition of tire track images enables rapid and precise identification of suspicious vehicles, assisting investigators in determining accident causes and liabilities. This enhances both the efficiency and accuracy of case resolution. However, tire track images are complex and diverse, significantly influenced by environmental factors such as lighting and road conditions. Moreover, the limited availability of tire track image samples poses a challenge for training deep learning models in practical applications. To address these challenges, particularly the limited generalization ability of few-shot metric learning models on unseen categories and their poor recognition rates in open-world environments, we propose a novel framework called TireNet for few-shot tire track recognition. First, we adopt a residual network integrated with coordinate attention as the backbone for feature extraction. Secondly, the context-aware features of the support set and the query set are extracted separately through attention-based bi-directional long short-term memory model. Subsequently, the cosine similarity between the features of the support set and the query set is calculated to determine the category of the query image. Finally, to address the class imbalance issue in the tire track image dataset, we utilize an improved Focal Loss for gradient updates. This enables the model to focus more on difficult samples during training, thereby enhancing the training efficiency, particularly in scenarios involving few-shot learning and open-world environments. To validate the proposed method, we constructed a tire track image dataset of 1700 samples, covering various environmental conditions, providing a rich resource for tire track recognition research. Experimental results demonstrate that the proposed method significantly outperforms few-shot image classification and classic machine
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