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Communications of the ACM

121 - 130 of 206 for bentley

How Do You Feel Online: Exploiting Smartphone Sensors to Detect Transitory Emotions during Social Media Use

Emotions are an intrinsic part of the social media user experience that can evoke negative behaviors such as cyberbullying and trolling. Detecting the emotions of social media users may enable responding to and mitigating these problems. Prior work suggests this may be achievable on smartphones: emotions can be detected via built-in sensors during prolonged input tasks. We extend these ideas to a social media context featuring sparse input interleaved with more passive browsing and media consumption activities. To achieve this, we present two studies. In the first, we elicit participant's emotions using images and videos and capture sensor data from a mobile device, including data from a novel passive sensor: its built-in eye-tracker. Using this data, we construct machine learning models that predict self-reported binary affect, achieving 93.20% peak accuracy. A follow-up study extends these results to a more ecologically valid scenario in which participants browse their social media feeds. The study yields high accuracies for both self-reported binary valence (94.16%) and arousal (92.28%). We present a discussion of the sensors, features and study design choices that contribute to this high performance and that future designers and researchers can use to create effective and accurate smartphone-based affect detection systems.

Enter Your Dinner Now!: Uncovering Persuasive Message Attributes in Tracking Reminders that Motivate Logging

Continuous tracking of information is critical for meaningful self-reflection and self-monitoring, but people often forget to log their information in tracking devices. Research indicates that tracking reminders can successfully remind people to log their information, yet, little is known about what make reminders (in)effective. We extend prior work by identifying message attributes in tracking reminders that people find most effective in motivating them to log. To address this overarching research goal, we conducted two online studies where participants evaluated and designed tracking reminders. In Study 1, participants (N = 135) evaluated a set of tracking reminders for different behaviors (e.g., breakfast, weight) from popular fitness tracking apps on several dimensions such as the persuasiveness of each reminder. We found that participants liked reminders that were straightforward, encouraging, goal specific, and positive. In Study 2, participants (N = 100) designed a reminder for different behaviors (i.e., breakfast, lunch, dinner, weight, and exercise) that would successfully motivate them to log. Through thematic analysis of participants' self-created reminders, we again found prominent message attributes that had emerged in Study 1 and also uncovered novel message attributes, including personalization, humor, and friend-like. Design implications are discussed in light of our findings.

Interactive Parallel Coordinates for Parametric Design Space Exploration

We present an interactive visualization based on parallel coordinates that enables comparison, generation, and modification of multiple parametric design alternatives. Such capabilities are lacking in existing tools. Initial evaluation suggests that our proposal improves usability over existing tools, has novel parameter space exploration capabilities, and also reveals a space for designing direct interactions with visualizations to support parametric exploration.

Context-aware Location Search on Maps

Location searching by keywords has immense demands in location-based services (LBSs). In this paper, we study the context-aware location search problem based on maps. Specifically, given a primary keyword and a set of contexts keywords as constraints, the objective is to search for the best-fit location that meets the user's requirements. In order to improve the performance of the search process, we propose an index structure to reduce the workload of querying. In particular, we consider max distance among the locations corresponding to the primary keyword and all surrounding contexts keywords. Extensive experiments are conducted on multiple datasets to validate the effectiveness of our proposed index structure and searching algorithm.

A Preliminary Study of Emotional Contagion in Live Streaming

Live streaming is an increasingly popular communication medium that allows real-time interaction among a broadcaster and an audience of any size. Using archived YouTube live video transcripts and associated live chat messages, we find evidence for emotional contagion in live streams: sentiment in live video oral transcripts and viewers? text chat is associated with the sentiment in subsequent viewers? comments. This relationship is stronger between viewers? chat messages and the subsequent chat than between the oral messages in the video and the subsequent chat. However, in some types of live streams, negative sentiment in the live video is followed by less negative chat. We conclude with a discussion of future research and potential uses of the dataset.

Advancing Computational Reproducibility in the Dataverse Data Repository Platform

Recent reproducibility case studies have raised concerns showing that much of the deposited research has not been reproducible. One of their conclusions was that the way data repositories store research data and code cannot fully facilitate reproducibility due to the absence of a runtime environment needed for the code execution. New specialized reproducibility tools provide cloud-based computational environments for code encapsulation, thus enabling research portability and reproducibility. However, they do not often enable research discoverability, standardized data citation, or long-term archival like data repositories do. This paper addresses the shortcomings of data repositories and reproducibility tools and how they could be overcome to improve the current lack of computational reproducibility in published and archived research outputs.

Urban Night Scenery Reconstruction by Day-night Registration and Synthesis

Although large-scale 3D reconstruction by photogrammetry has been well studied and applied, the reconstruction of night scenery in urban areas has not been thoroughly considered. At night, low-light conditions often cause the images to lack sharpness and high-dynamic range issue leads to saturation. The SFM reconstruction pipeline that works well in daylight is likely to recover only limited dense points of bright fragmented objects near artificial lighting. Here, we propose a novel solution based on registration and synthesis between the night-time reconstruction and that of the same region in daytime. A registration pipeline is developed for conformal matching of the day and night point clouds. For the coarse registration step, we use detected plane features to search and match 4-plane congruent sets. For the fine registration step, we consider the positions of windows, a commonly-occurring object cue in urban building scenes as markers for accurate positioning. This leads to final registration error less than 0.2 degrees in rotation, and 0.2% in scale and translation. Finally, we synthesize the daytime textured model and the night point clouds to produce vivid visual effects of urban night scenery.

Game Atmosphere: Effects of Audiovisual Thematic Cohesion on Player Experience and Psychophysiology

Game atmosphere and game audio are critical factors linked to the commercial success of video games. However, game atmosphere has been neither operationalized nor clearly defined in games user research literature, making it difficult to study. We define game atmosphere as the emerging subjective experience of a player caused by the strong audiovisual thematic cohesion (i.e., the harmonic fit of sounds and graphics to a shared theme) of video game elements. We studied players' experience of thematic cohesion in two between-subjects, independent-measures experiments (N=109) across four conditions differing in their level of audiovisual thematic fit. Participants' experiences were assessed with physiological and psychometric measurements to understand the effect of game atmosphere on player experience. Results indicate that a lack of thematic fit between audio and visuals lowers the degree of perceived atmosphere, but that while audiovisual thematic dissonance may lead to higher-intensity negative-valence facial events, it does not impact self-reported player experience or immersion.

Bug or Feature? Covert Impairments to Human Computer Interaction

Computer users commonly experience interaction anomalies, such as the text cursor jumping to another location in a document, perturbed mouse pointer motion, or a disagreement between tactile input and touch screen location. These anomalies impair interaction and require the user to take corrective measures, such as resetting the text cursor or correcting the trajectory of the pointer to reach a desired target. Impairments can result from software bugs, physical hardware defects, and extraneous input. However, some designs alter the course of interaction through covert impairments, anomalies introduced intentionally and without the user's knowledge. There are various motivations for doing so rooted in disparate fields including biometrics, electronic voting, and entertainment. We examine this kind of deception by systematizing four different ways computer interaction may become impaired and three different goals of the designer, providing insight to the design of systems that implement covert impairments.

Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic

The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. To this aim, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue.