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Communications of the ACM

61 - 70 of 3,299 for bentley

Medical Selfies: Emotional Impacts and Practical Challenges

Medical images taken with mobile phones by patients, i.e. medical selfies, allow screening, monitoring and diagnosis of skin lesions. While mobile teledermatology can provide good diagnostic accuracy for skin tumours, there is little research about emotional and physical aspects when taking medical selfies of body parts. We conducted a survey with 100 participants and a qualitative study with twelve participants, in which they took images of eight body parts including intimate areas. Participants had difficulties taking medical selfies of their shoulder blades and buttocks. For the genitals, they prefer to visit a doctor rather than sending images. Taking the images triggered privacy concerns, memories of past experiences with body parts and raised awareness of the bodily medical state. We present recommendations for the design of mobile apps to address the usability and emotional impacts of taking medical selfies.


Modeling User-Centered Page Load Time for Smartphones

Page Load Time (PLT) is critical in measuring web page load performance. However, the existing PLT metrics are designed to measure the Web page load performance on desktops/laptops and do not consider user interactions on mobile browsers. As a result, they are ill-suited to measure mobile page load performance from the perspective of the user. In this work, we present the Mobile User-Centered Page Load Time Estimator (muPLTest), a model that estimates the PLT of users on Web pages for mobile browsers. We show that traditional methods to measure user PLT for desktops are unsuited to mobiles because they only consider the initial viewport, which is the part of the screen that is in the user’s view when they first begin to load the page. However, mobile users view multiple viewports during the page load process since they start to scroll even before the page is loaded. We thus construct the muPLTest to account for page load activities across viewports. We train our model with crowdsourced scrolling behavior from live users. We show that muPLTest predicts ground truth user-centered PLT, or the muPLT, obtained from live users with an error of 10-15% across 50 Web pages. Comparatively, traditional PLT metrics perform within 44-90% of the muPLT. Finally, we show how developers can use the muPLTest to scalably estimate changes in user experience when applying different Web optimizations.


Characterizing the Effect of Audio Degradation on Privacy Perception And Inference Performance in Audio-Based Human Activity Recognition

Audio has been increasingly adopted as a sensing modality in a variety of human-centered mobile applications and in smart assistants in the home. Although acoustic features can capture complex semantic information about human activities and context, continuous audio recording often poses significant privacy concerns. An intuitive way to reduce privacy concerns is to degrade audio quality such that speech and other relevant acoustic markers become unintelligible, but this often comes at the cost of activity recognition performance. In this paper, we employ a mixed-methods approach to characterize this balance. We first conduct an online survey with 266 participants to capture their perception of privacy qualitatively and quantitatively with degraded audio. Given our findings that privacy concerns can be significantly reduced at high levels of audio degradation, we then investigate how intentional degradation of audio frames can affect the recognition results of the target classes while maintaining effective privacy mitigation. Our results indicate that degradation of audio frames can leave minimal effects for audio recognition using frame-level features. Furthermore, degradation of audio frames can hurt the performance to some extend for audio recognition using segment-level features, though the usage of such features may still yield superior recognition performance. Given the different requirements on privacy mitigation and recognition performance for different sensing purposes, such trade-offs need to be balanced in actual implementations.


See What I’m Saying? Comparing Intelligent Personal Assistant Use for Native and Non-Native Language Speakers

Limited linguistic coverage for Intelligent Personal Assistants (IPAs) means that many interact in a non-native language. Yet we know little about how IPAs currently support or hinder these users. Through native (L1) and non-native (L2) English speakers interacting with Google Assistant on a smartphone and smart speaker, we aim to understand this more deeply. Interviews revealed that L2 speakers prioritised utterance planning around perceived linguistic limitations, as opposed to L1 speakers prioritising succinctness because of system limitations. L2 speakers see IPAs as insensitive to linguistic needs resulting in failed interaction. L2 speakers clearly preferred using smartphones, as visual feedback supported diagnoses of communication breakdowns whilst allowing time to process query results. Conversely, L1 speakers preferred smart speakers, with audio feedback being seen as sufficient. We discuss the need to tailor the IPA experience for L2 users, emphasising visual feedback whilst reducing the burden of language production.


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.


Dynamic Path-decomposed Tries

A keyword dictionary is an associative array whose keys are strings. Recent applications handling massive keyword dictionaries in main memory have a need for a space-efficient implementation. When limited to static applications, there are a number of highly compressed keyword dictionaries based on the advancements of practical succinct data structures. However, as most succinct data structures are only efficient in the static case, it is still difficult to implement a keyword dictionary that is space efficient and dynamic. In this article, we propose such a keyword dictionary. Our main idea is to embrace the path decomposition technique, which was proposed for constructing cache-friendly tries. To store the path-decomposed trie in small memory, we design data structures based on recent compact hash trie representations. Experiments on real-world datasets reveal that our dynamic keyword dictionary needs up to 68% less space than the existing smallest ones, while achieving a relevant space-time tradeoff.


Density-based Algorithms for Big Data Clustering Using MapReduce Framework: A Comprehensive Study

Clustering is used to extract hidden patterns and similar groups from data. Therefore, clustering as a method of unsupervised learning is a crucial technique for big data analysis owing to the massive number of unlabeled objects involved. Density-based algorithms have attracted research interest, because they help to better understand complex patterns in spatial datasets that contain information about data related to co-located objects. Big data clustering is a challenging task, because the volume of data increases exponentially. However, clustering using MapReduce can help answer this challenge. In this context, density-based algorithms in MapReduce have been largely investigated in the past decade to eliminate the problem of big data clustering. Despite the diversity of the algorithms proposed, the field lacks a structured review of the available algorithms and techniques for desirable partitioning, local clustering, and merging. This study formalizes the problem of density-based clustering using MapReduce, proposes a taxonomy to categorize the proposed algorithms, and provides a systematic and comprehensive comparison of these algorithms according to the partitioning technique, type of local clustering, merging technique, and exactness of their implementations. Finally, the study highlights outstanding challenges and opportunities to contribute to the field of density-based clustering using MapReduce.


Preserving Contextual Awareness during Selection of Moving Targets in Animated Stream Visualizations

In many types of dynamic interactive visualizations, it is often desired to interact with moving objects. Stopping moving objects can make selection easier, but pausing animated content can disrupt perception and understanding of the visualization. To address such problems, we explore selection techniques that only pause a subset of all moving targets in the visualization. We present various designs for controlling pause regions based on cursor trajectory or cursor position. We then report a dual-task experiment that evaluates how different techniques affect both target selection performance and contextual awareness of the visualization. Our findings indicate that all pause techniques significantly improved selection performance as compared to the baseline method without pause, but the results also show that pausing the entire visualization can interfere with contextual awareness. However, the problem with reduced contextual awareness was not observed with our new techniques that only pause a limited region of the visualization. Thus, our research provides evidence that region-limited pause techniques can retain the advantages of selection in dynamic visualizations without imposing a negative effect on contextual awareness.


Efficient simulation of macroscopic molecular communication: the pogona simulator

Molecular communication in pipe networks is a novel technique for wireless data exchange. Simulating such networks accurately is difficult because of the complexity of fluid dynamics at centimeter scales, which existing molecular communication simulators do not model. The new simulator we present combines computational fluid dynamics simulation and particle movement predictions. It is optimized to be computationally efficient while offering a high degree of adaptability to complex fluid flows in larger pipe networks. We validate it by comparing the simulation with experimental results obtained in a real-world testbed.


Shining light on molecular communication

Molecules and combinations of molecules are the natural communication currency of microbes; microbes have evolved and been engineered to sense a variety of compounds, often with exquisite sensitivity. The availability of microbial biosensors, combined with the ability to genetically engineer biological circuits to process information, make microbes attractive bionanomachines for propagating information through molecular communication (MC) networks. However, MC networks built entirely of biological components suffer a number of limitations. They are extremely slow due to processing and propagation delays and must employ simple algorithms due to the still limited computational capabilities of biological circuits. In this work, we propose a hybrid bio-electronic framework which utilizes biological components for sensing but offloads processing and computation to traditional electronic systems and communication infrastructure. This is achieved by using tools from the burgeoning field of optogenetics to trigger biosensing through an optoelectronic interface, alleviating the need for computation and communication in the biological domain.