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Communications of the ACM

21 - 30 of 3,299 for bentley

‘It’s not a romantic relationship’: Stories of Adoption and Abandonment of Smart Speakers at Home

Smart speakers become increasingly ubiquitous in our homes. Consequently, we need to study how smart speakers affect the members of a household. Understanding the adoption of a smart speaker can assure it does not negatively influence the social dynamics within a household and create opportunities for further assistance. We deployed an Amazon Echo dot in nine households with 20 inhabitants who were new smart speaker users. We conducted multiple interviews, inquiring how a smart speaker was integrated into a household from day one. We investigated the development of social rules around using the device and how the smart speaker was appropriated. Users developed different strategies of using the device which altered social behaviours in some households. Further, we identified barriers and unmet requirements in introducing smart speakers to home environments. Our work contributes to an understanding of ubiquitous assistance for user groups at home.


PoisonIvy: (In)secure Practices of Enterprise IoT Systems in Smart Buildings

The rise of IoT devices has led to the proliferation of smart buildings, offices, and homes worldwide. Although commodity IoT devices are employed by ordinary end-users, complex environments such as smart buildings, government, or private smart offices, conference rooms, or hospitality require customized and highly reliable solutions. Those systems called Enterprise Internet of Things (EIoT) connect such environments to the Internet and are professionally managed solutions usually offered by dedicated vendors (e.g., Control4, Crestron, Lutron, etc.). As EIoT systems require specialized training, software, and equipment to deploy, many of these systems are closed-source and proprietary in nature. This has led to very little research investigating the security of EIoT systems and their components. In effect, EIoT systems in smart settings such as smart buildings present an unprecedented and unexplored threat vector for an attacker. In this work, we explore EIoT system vulnerabilities and insecure development practices. Specifically, focus on the usage of drivers as an attack mechanism, and introduce PoisonIvy, a number of novel attacks that demonstrate how it is possible for an attacker to easily attack and command EIoT system controllers using malicious drivers. Specifically, we show how drivers used to integrate third-party services and devices to EIoT systems can be trivially misused in a systematic fashion. To demonstrate the capabilities of attackers, we implement and evaluate PoisonIvy using a testbed of real EIoT devices in a smart building setting. We show that an attacker can easily perform DoS attacks, gain remote control, and maliciously abuse system resources (e.g., bitcoin mining) of EIoT systems. Further, we discuss the (in)securities in drivers and possible countermeasures. To the best of our knowledge, this is the first work to analyze the (in)securities of EIoT deployment practices and demonstrate the associated vulnerabilities in this ecosystem. With this work, we raise awareness on the (in)secure development practices used for EIoT systems, the consequences of which can largely impact the security, privacy, safety, reliability, and performance of millions, if not billions, of EIoT systems worldwide


Hydrogel-based Bio-nanomachine Transmitters for Bacterial Molecular Communications

Bacterial quorum sensing can be engineered with a view to the design of biotechnological applications based on their intrinsic role as a means of communication. We propose the creation of a positive feedback loop that will promote the emission of a superfolded green fluorescence protein from a bacterial population that will flow through hydrogel, which is used to encapsulate the cells. These engineered cells are heretofore referred to as bio-nanomachine transmitters and we show that for lower values of diffusion coefficient, a higher molecular output signal power can be produced, which supports the use of engineered bacteria contained within hydrogels for molecular communications systems. In addition, our wet lab results show the propagation of the molecular output signal, proving the feasibility of engineering a positive feedback loop to create a bio-nanomachine transmitter that can be used for biosensing applications.


Side-channel information leaks of Z-wave smart home IoT devices: demo abstract

Z-Wave is one of the key access protocols of the Internet of Things (IoT). It is highly popular in home automation and security system applications due to its minimum power consumption, reliability, and cost effectiveness. With an estimate of over 100 million deployed Z-Wave devices around the globe, it is essential to understand their security landscape. For instance, Z-Wave devices can leak personal information about the home dwellers as well as their possessions and buglers can use compromised Z-Wave devices to disable security systems or even to feed incorrect information. In this paper, we present an experiment setup and early results of side-channel information leaks of Z-Wave. We show that Z-Wave traffic despite being encrypted, leaks information through side-channels and an attacker who can passively capture Z-Wave frames by simply being in the vicinity of a house can identify Z-Wave devices inside the house.


Domestic Robots for Individuals Living With Loneliness: A Long-Term In-Home Interaction Study Design

A growing area of human-robot interaction explores how robots, for example as companions, can be used to help people manage loneliness. However, we do not yet have research results indicating if people are ready to accept companion robots in their daily lives, and thus if companion robots can actually be successful broadly in society. We present a novel long-term in-home interaction study design that will explore how people accept these robots in their homes and how the robots impact loneliness.


BodyWire-HCI: Enabling New Interaction Modalities by Communicating Strictly During Touch Using Electro-Quasistatic Human Body Communication

Communication during touch provides a seamless and natural way of interaction between humans and ambient intelligence. Current techniques that couple wireless transmission with touch detection suffer from the problem of selectivity and security, i.e., they cannot ensure communication only through direct touch and not through close proximity. We present BodyWire-HCI, which utilizes the human body as a wire-like communication channel, to enable human–computer interaction, that for the first time, demonstrates selective and physically secure communication strictly during touch. The signal leakage out of the body is minimized by utilizing a novel, low frequency Electro-QuasiStatic Human Body Communication (EQS-HBC) technique that enables interaction strictly when there is a conductive communication path between the transmitter and receiver through the human body. Design techniques such as capacitive termination and voltage mode operation are used to minimize the human body channel loss to operate at low frequencies and enable EQS-HBC. The demonstrations highlight the impact of BodyWire-HCI in enabling new human–machine interaction modalities for variety of application scenarios such as secure authentication (e.g., opening a door and pairing a smart device) and information exchange (e.g., payment, image, medical data, and personal profile transfer) through touch (


Mining assumptions for software components using machine learning

Software verification approaches aim to check a software component under analysis for all possible environments. In reality, however, components are expected to operate within a larger system and are required to satisfy their requirements only when their inputs are constrained by environment assumptions. In this paper, we propose EPIcuRus, an approach to automatically synthesize environment assumptions for a component under analysis (i.e., conditions on the component inputs under which the component is guaranteed to satisfy its requirements). EPIcuRus combines search-based testing, machine learning and model checking. The core of EPIcuRus is a decision tree algorithm that infers environment assumptions from a set of test results including test cases and their verdicts. The test cases are generated using search-based testing, and the assumptions inferred by decision trees are validated through model checking. In order to improve the efficiency and effectiveness of the assumption generation process, we propose a novel test case generation technique, namely Important Features Boundary Test (IFBT), that guides the test generation based on the feedback produced by machine learning. We evaluated EPIcuRus by assessing its effectiveness in computing assumptions on a set of study subjects that include 18 requirements of four industrial models. We show that, for each of the 18 requirements, EPIcuRus was able to compute an assumption to ensure the satisfaction of that requirement, and further, ≈78% of these assumptions were computed in one hour.


A Tutorial on Learned Multi-dimensional Indexes

Recently, Machine Learning (ML, for short) has been successfully applied to database indexing. Initial experimentation on Learned Indexes has demonstrated better search performance and lower space requirements than their traditional database counterparts. Numerous attempts have been explored to extend learned indexes to the multi-dimensional space. This makes learned indexes potentially suitable for spatial databases. The goal of this tutorial is to provide up-to-date coverage of learned indexes both in the single and multi-dimensional spaces. The tutorial covers over 25 learned indexes. The tutorial navigates through the space of learned indexes through a taxonomy that helps classify the covered learned indexes both in the single and multi-dimensional spaces.


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.