Internet of Things (IoT) has become prevalent in various fields, especially in the context of home automation (HA). To better control HA-IoT devices, especially to integrate several devices for rich smart functionalit...
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ISBN:
(纸本)9798400702211
Internet of Things (IoT) has become prevalent in various fields, especially in the context of home automation (HA). To better control HA-IoT devices, especially to integrate several devices for rich smart functionalities, trigger-action programming, such as the If This Then That (IFTTT), has become a popular paradigm. Leveraging it, novice users can easily specify their intent in applets regarding how to control a device/service through another once a specific condition is met. Nevertheless, the users may design IFTTT-style integrations inappropriately, due to lack of security experience or unawareness of the security impact of cyber-attacks against individual devices. This has caused financial loss, privacy leakage, unauthorized access and other security issues. To address these problems, this work proposes a systematic framework named MEDIC to model smart home integrations and check their security. It automatically generates models incorporating the service/device behaviors and action rules of the applets, while taking into consideration the external attacks and in-device vulnerabilities. Our approach takes around one second to complete the modeling and checking of one integration. We carried out experiments based on 200 integrations created from a user study and a dataset crawled from ***. To our great surprise, nearly 83% of these integrations have security issues.
NAND Flash Storage Controllers are a crucial component of Solid State Drives (SSDs). They provide an abstraction of Flash packages to the SSD firmware by translating high-level operations, such as a Page Program or a ...
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Reducing the amount of sugar consumed from daily beverages is essential for well-being. However, since a sudden reduction in intake is mentally painful for those who prefer sweetened beverages, solutions are needed to...
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Children with autism spectrum disorder (ASD) experience dysfunctional emotional development leading to negative effects on their social communication. Although interventions are effective in helping children with ASD ...
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ISBN:
(纸本)9781450386357
Children with autism spectrum disorder (ASD) experience dysfunctional emotional development leading to negative effects on their social communication. Although interventions are effective in helping children with ASD improve their social skills over time, they have been found to lack the essential ability to engage children in a real social environment. In this paper, we present "FaceMe," which is an augmented reality (AR) system that uses a virtual agent and a set of tangible toolkits to teach children with ASD about six basic emotions and improve their emotional and communication skills. On the basis of the pilot data, the results suggest that children, especially those with ASD, were willing to socialize with the virtual agent and understand more emotional states. It is hoped that FaceMe can be used as a tool to provide assistance to children with ASD, as well as a way for future interface system design to support emotional development in children.
Cross reality has redefined how individuals interact in the virtual and physical realms, offering a comprehensive paradigm for communication and collaboration. A rigorous examination of the literature from acm, IEEE, ...
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We introduce FRONTMATTER: the largest open-access dataset containing userinterface models of about 160,000 Android apps. FRONTMATTER opens the door for comprehensive mining of mobile userinterfaces, jumpstarting emp...
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ISBN:
(纸本)9781450385626
We introduce FRONTMATTER: the largest open-access dataset containing userinterface models of about 160,000 Android apps. FRONTMATTER opens the door for comprehensive mining of mobile userinterfaces, jumpstarting empirical research at a large scale, addressing questions such as "How many travel apps require registration?", "Which apps do not follow accessibility guidelines?", "Does the userinterface correspond to the description?", and many more. The FRONTMATTER UI analysis tool and the FRONTMATTER dataset are available under an open-source license.
The quality of software products is not only measured by their set of functionality and features, the user Experience (UX) and the quality of the applications' userinterface (UI) gets more and more important. In ...
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Current workflow on co-editing and simultaneous presentation of 3-D shapes is confined to on-screen manipulation, which causes loss of perceived information when presenting perceptual concepts or complex shapes betwee...
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Virtual personal assistant (VPA) services, e.g. Amazon Alexa and Google Assistant, are becoming increasingly popular recently. users interact with them through voice-based apps, e.g. Amazon Alexa skills and Google Ass...
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ISBN:
(纸本)9781450394758
Virtual personal assistant (VPA) services, e.g. Amazon Alexa and Google Assistant, are becoming increasingly popular recently. users interact with them through voice-based apps, e.g. Amazon Alexa skills and Google Assistant actions. Unlike the desktop and mobile apps which have visible and intuitive graphical userinterface (GUI) to facilitate interaction, VPA apps convey information purely verbally through the voice userinterface (VUI), which is known to be limited in its invisibility, single mode and high demand of user attention. This may lead to various problems on the usability and correctness of VPA apps. In this work, we propose a model-based framework named Vitas to handle VUI testing of VPA apps. Vitas interacts with the app VUI, and during the testing process, it retrieves semantic information from voice feedbacks by natural language processing. It incrementally constructs the finite state machine (FSM) model of the app with a weighted exploration strategy guided by key factors such as the coverage of app functionality. We conduct a large-scale testing on 41,581 VPA apps (i.e., skills) of Amazon Alexa, the most popular VPA service, and find that 51.29% of them have weaknesses. They largely suffer from problems such as unexpected exit/start, privacy violation and so on. Our work reveals the immaturity of the VUI designs and implementations in VPA apps, and sheds light on the improvement of several crucial aspects of VPA apps.
Graphical userinterface (GUI) is not merely a collection of individual and unrelated widgets, but rather partitions discrete widgets into groups by various visual cues, thus forming higher-order perceptual units such...
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ISBN:
(纸本)9781450394130
Graphical userinterface (GUI) is not merely a collection of individual and unrelated widgets, but rather partitions discrete widgets into groups by various visual cues, thus forming higher-order perceptual units such as tab, menu, card or list. The ability to automatically segment a GUI into perceptual groups of widgets constitutes a fundamental component of visual intelligence to automate GUI design, implementation and automation tasks. Although humans can partition a GUI into meaningful perceptual groups of widgets in a highly reliable way, perceptual grouping is still an open challenge for computational approaches. Existing methods rely on ad-hoc heuristics or supervised machine learning that is dependent on specific GUI implementations and runtime information. Research in psychology and biological vision has formulated a set of principles (i.e., Gestalt theory of perception) that describe how humans group elements in visual scenes based on visual cues like connectivity, similarity, proximity and continuity. These principles are domain-independent and have been widely adopted by practitioners to structure content on GUIs to improve aesthetic pleasantness and usability. Inspired by these principles, we present a novel unsupervised image-based method for inferring perceptual groups of GUI widgets. Our method requires only GUI pixel images, is independent of GUI implementation, and does not require any training data. The evaluation on a dataset of 1,091 GUIs collected from 772 mobile apps and 20 UI design mockups shows that our method significantly outperforms the state-of-the-art ad-hoc heuristics-based baseline. Our perceptual grouping method creates opportunities for improving UI-related software engineering tasks.
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