Technologies, Methods, and Values: Changes in Empirical Research at CSCW 1990 – 2015

© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the ACM on Human-Computer Interaction 1, 2, Article 106 (November 2017), https://doi.org/10.1145/3134741The field of Computer Supported Cooperative Work has constantly evolved to meet the changing needs of individuals at home, at work, and online. To understand how these changes impacted CSCW research, we systematically reviewed 1209 papers and notes published at the ACM Conference on Computer Supported Cooperative Work between 1990 and 2015. When considered with results from two previous literature reviews, covering 1990 - 1998 and 1998 - 2004 respectively, our analysis provides perspective on 25 years of groupware research. We show that the field has responded to, not anticipated, changes in the computing landscape, long-term trends away from `systems' and explanatory research, and a lack of bibliographic research that synthesizes findings. Finally, we discuss implications of these trends for CSCW research: how results are synthesized across the field, what kinds of research we value, and how multi-device ecologies are studied

Proceedings of Cross-Surface 2015: Workshop on Interacting with Multi-Device Ecologies in the Wild

In this workshop, we reviewed and discussed opportunities, technical challenges and problems with cross-device interactions in real world interactive multi-surface and multi-device ecologies. We aim to bring together researchers and practitioners currently working on novel techniques for cross-surface interactions, focusing both on technical as well as interaction challenges for introducing these technologies into the wild, and highlighting opportunities for further research. The workshop will help to facilitate knowledge exchange on the inherent challenges of building robust and intuitive cross-surface interactions, identify application domains and enabling technologies for cross-surface interactions in the wild, and establish a research community to develop effective strategies for successful design of crossdevice interactions. Please find more details about the workshop, in the submitted proposal. The workshop was held in conjunction with the 2015 ACM International Conference on Interactive Tabletops and Surfaces, that took place from November 15 to 18 in Funchal in Madeira, Portugal

Countering Acoustic Adversarial Attacks in Microphone-equipped Smart Home Devices

Deep neural networks (DNNs) continue to demonstrate superior generalization performance in an increasing range of applications, including speech recognition and image understanding. Recent innovations in compression algorithms, design of efficient architectures and hardware accelerators have prompted a rapid growth in deploying DNNs on mobile and IoT devices to redefine user experiences. Relying on the superior inference quality of DNNs, various voice-enabled devices have started to pervade our everyday lives and are increasingly used for, e.g., opening and closing doors, starting or stopping washing machines, ordering products online, and authenticating monetary transactions. As the popularity of these voice-enabled services increases, so does their risk of being attacked. Recently, DNNs have been shown to be extremely brittle under adversarial attacks and people with malicious intentions can potentially exploit this vulnerability to compromise DNN-based voice-enabled systems. Although some existing work already highlights the vulnerability of audio models, very little is known of the behaviour of compressed on-device audio models under adversarial attacks. This paper bridges this gap by investigating thoroughly the vulnerabilities of compressed audio DNNs and makes a stride towards making compressed models robust. In particular, we propose a stochastic compression technique that generates compressed models with greater robustness to adversarial attacks. We present an extensive set of evaluations on adversarial vulnerability and robustness of DNNs in two diverse audio recognition tasks, while considering two popular attack algorithms: FGSM and PGD. We found that error rates of conventionally trained audio DNNs under attack can be as high as 100%. Under both white- and black-box attacks, our proposed approach is found to decrease the error rate of DNNs under attack by a large margin.Noki

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Immersive interconnected virtual and augmented reality : a 5G and IoT perspective

Despite remarkable advances, current augmented and virtual reality (AR/VR) applications are a largely individual and local experience. Interconnected AR/VR, where participants can virtually interact across vast distances, remains a distant dream. The great barrier that stands between current technology and such applications is the stringent end-to-end latency requirement, which should not exceed 20 ms in order to avoid motion sickness and other discomforts. Bringing AR/VR to the next level to enable immersive interconnected AR/VR will require significant advances towards 5G ultra-reliable low-latency communication (URLLC) and a Tactile Internet of Things (IoT). In this article, we articulate the technical challenges to enable a future AR/VR end-to-end architecture, that combines 5G URLLC and Tactile IoT technology to support this next generation of interconnected AR/VR applications. Through the use of IoT sensors and actuators, AR/VR applications will be aware of the environmental and user context, supporting human-centric adaptations of the application logic, and lifelike interactions with the virtual environment. We present potential use cases and the required technological building blocks. For each of them, we delve into the current state of the art and challenges that need to be addressed before the dream of remote AR/VR interaction can become reality

Leveraging Resources on Anonymous Mobile Edge Nodes

Smart devices have become an essential component in the life of mankind. The quick rise of smartphones, IoTs, and wearable devices enabled applications that were not possible few years ago, e.g., health monitoring and online banking. Meanwhile, smart sensing laid the infrastructure for smart homes and smart cities. The intrusive nature of smart devices granted access to huge amounts of raw data. Researchers seized the moment with complex algorithms and data models to process the data over the cloud and extract as much information as possible. However, the pace and amount of data generation, in addition to, networking protocols transmitting data to cloud servers failed short in touching more than 20% of what was generated on the edge of the network. On the other hand, smart devices carry a large set of resources, e.g., CPU, memory, and camera, that sit idle most of the time. Studies showed that for plenty of the time resources are either idle, e.g., sleeping and eating, or underutilized, e.g. inertial sensors during phone calls. These findings articulate a problem in processing large data sets, while having idle resources in the close proximity. In this dissertation, we propose harvesting underutilized edge resources then use them in processing the huge data generated, and currently wasted, through applications running at the edge of the network. We propose flipping the concept of cloud computing, instead of sending massive amounts of data for processing over the cloud, we distribute lightweight applications to process data on users\u27 smart devices. We envision this approach to enhance the network\u27s bandwidth, grant access to larger datasets, provide low latency responses, and more importantly involve up-to-date user\u27s contextual information in processing. However, such benefits come with a set of challenges: How to locate suitable resources? How to match resources with data providers? How to inform resources what to do? and When? How to orchestrate applications\u27 execution on multiple devices? and How to communicate between devices on the edge? Communication between devices at the edge has different parameters in terms of device mobility, topology, and data rate. Standard protocols, e.g., Wi-Fi or Bluetooth, were not designed for edge computing, hence, does not offer a perfect match. Edge computing requires a lightweight protocol that provides quick device discovery, decent data rate, and multicasting to devices in the proximity. Bluetooth features wide acceptance within the IoT community, however, the low data rate and unicast communication limits its use on the edge. Despite being the most suitable communication protocol for edge computing and unlike other protocols, Bluetooth has a closed source code that blocks lower layer in front of all forms of research study, enhancement, and customization. Hence, we offer an open source version of Bluetooth and then customize it for edge computing applications. In this dissertation, we propose Leveraging Resources on Anonymous Mobile Edge Nodes (LAMEN), a three-tier framework where edge devices are clustered by proximities. On having an application to execute, LAMEN clusters discover and allocate resources, share application\u27s executable with resources, and estimate incentives for each participating resource. In a cluster, a single head node, i.e., mediator, is responsible for resource discovery and allocation. Mediators orchestrate cluster resources and present them as a virtually large homogeneous resource. For example, two devices each offering either a camera or a speaker are presented outside the cluster as a single device with both camera and speaker, this can be extended to any combination of resources. Then, mediator handles applications\u27 distribution within a cluster as needed. Also, we provide a communication protocol that is customizable to the edge environment and application\u27s need. Pushing lightweight applications that end devices can execute over their locally generated data have the following benefits: First, avoid sharing user data with cloud server, which is a privacy concern for many of them; Second, introduce mediators as a local cloud controller closer to the edge; Third, hide the user\u27s identity behind mediators; and Finally, enhance bandwidth utilization by keeping raw data at the edge and transmitting processed information. Our evaluation shows an optimized resource lookup and application assignment schemes. In addition to, scalability in handling networks with large number of devices. In order to overcome the communication challenges, we provide an open source communication protocol that we customize for edge computing applications, however, it can be used beyond the scope of LAMEN. Finally, we present three applications to show how LAMEN enables various application domains on the edge of the network. In summary, we propose a framework to orchestrate underutilized resources at the edge of the network towards processing data that are generated in their proximity. Using the approaches explained later in the dissertation, we show how LAMEN enhances the performance of applications and enables a new set of applications that were not feasible

Designing for Cross-Device Interactions

Driven by technological advancements, we now own and operate an ever-growing number of digital devices, leading to an increased amount of digital data we produce, use, and maintain. However, while there is a substantial increase in computing power and availability of devices and data, many tasks we conduct with our devices are not well connected across multiple devices. We conduct our tasks sequentially instead of in parallel, while collaborative work across multiple devices is cumbersome to set up or simply not possible. To address these limitations, this thesis is concerned with cross-device computing. In particular it aims to conceptualise, prototype, and study interactions in cross-device computing. This thesis contributes to the field of Human-Computer Interaction (HCI)—and more specifically to the area of cross-device computing—in three ways: first, this work conceptualises previous work through a taxonomy of cross-device computing resulting in an in-depth understanding of the field, that identifies underexplored research areas, enabling the transfer of key insights into the design of interaction techniques. Second, three case studies were conducted that show how cross-device interactions can support curation work as well as augment users’ existing devices for individual and collaborative work. These case studies incorporate novel interaction techniques for supporting cross-device work. Third, through studying cross-device interactions and group collaboration, this thesis provides insights into how researchers can understand and evaluate multi- and cross-device interactions for individual and collaborative work. We provide a visualization and querying tool that facilitates interaction analysis of spatial measures and video recordings to facilitate such evaluations of cross-device work. Overall, the work in this thesis advances the field of cross-device computing with its taxonomy guiding research directions, novel interaction techniques and case studies demonstrating cross-device interactions for curation, and insights into and tools for effective evaluation of cross-device systems

Networking Solutions for Integrated Heterogeneous Wireless Ecosystem

This work targets at applying computer networking techniques to address challenges in modern wireless networks and in various environments built around these networks. The main focus of the work is on designing and implementing prototypes and demonstrators to support research in domains of heterogeneous networks (HetNets). These research domains include centralized radio resource management in emerging cellular network architectures, network assistance role in device-to-device (D2D) communications, and studying prospective services in these networks. Within the research group the author was tasked with designing network architectures and demonstrating certain connectivity and functionality interesting for the research. The author was responsible for modifying commercial off-the-shelf equipment to become suitable for target research scenarios, selecting network technologies to achieve connectivity requirements, deploying network architecture entities within the research group's cloud platform. For HetNet track, the primary goal was to design a platform that would mimic a device connected through a heterogeneous network, allowing researchers to experiment with traffic flow optimization in an environment close to the envisioned next-generation network architecture. Prototype solution and testbed were designed building on software defined network principles of automation, abstraction and software based flow switching, and were implemented using overlay networks and virtual network functions. Within D2D communications research, the task was to design architecture demonstrating feasibility of traffic offloading from infrastructure network to direct links. Prototype was implemented with automated routing control in overlay network. To demonstrate novel services enabled by advanced security frameworks, D2D platform was augmented and a new network application has been implemented, also suitable for wearable electronics