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Communications of the ACM

121 - 130 of 1,735 for bentley

Mobile wellness: collecting, visualizing and interacting with personal health data

Mobile devices are now able to connect to a variety of sensors and provide personalized information to help people reflect on and improve their health. For example, pedometers, heart-rate sensors, glucometers, and other sensors can all provide real-time data to a variety of devices. Collecting and interacting with personal health or well-being data is a growing research area. This workshop will focus on the ways in which our mobile devices can aggregate and visualize these types of data and how these data streams can be presented to encourage interaction, increased awareness and positive behavior change.

Moving Beyond a "one-size fits all": Exploring Individual Differences in Privacy

As our lives become increasingly digitized, how people maintain and manage their networked privacy has become a formidable challenge for academics, practitioners, and policy-makers. A shift toward people-centered privacy initiatives has shown promise; yet many applications still adopt a "one-size fits all" approach, which fails to consider how individual differences in concerns, preferences, and behaviors shape how different people interact with and use technology. The main goal of this workshop is to highlight individual differences (e.g., age, culture, personal preference) that influence users' experiences and privacy-related outcomes. We will work towards best practices for research, design, and online privacy regulation policies that consider these differences.

Learning how to flock: deriving individual behaviour from collective behaviour with multi-agent reinforcement learning and natural evolution strategies

This work proposes a method for predicting the internal mechanisms of individual agents using observed collective behaviours by multi-agent reinforcement learning (MARL). Since the emergence of group behaviour among many agents can undergo phase transitions, and the action space will not in general be smooth, natural evolution strategies were adopted for updating a policy function. We tested the approach using a well-known flocking algorithm as a target model for our system to learn. With the data obtained from this rule-based model, the MARL model was trained, and its acquired behaviour was compared to the original. In the process, we discovered that agents trained by MARL can self-organize flow patterns using only local information. The expressed pattern is robust to changes in the initial positions of agents, whilst being sensitive to the training conditions used.

Being Photo-Visual in HCI and Design

This paper does two things. (1) First, it describes the role of the photo-visual in HCI and Design. The paper appeals primarily to the literature within HCI relating to photo-visual contributions. It appeals also to literatures outside of HCI, but it does not do so exhaustively. (2) Second, it illustrates the role of the photo-visual with two photographic essays that are comprised of both original arranged photographs and original accompanying texts. The photographs are intended to engage the reader as much as the text. The paper seeks to add constructively to the growing acceptance of the photo- visual as a methodologically sound manner of creating and recording design knowledge in HCI and Design. The paper leaves to future reporting much that the reviewers have thoughtfully recommended, owing to the lack of any room for additional material. In particular, more guidance about how this work may be applied and how photographic skills may be taught in design-oriented HCI is forthcoming.

Rectangle-efficient aggregation in spatial data streams

We consider the estimation of aggregates over a data stream of multidimensional axis-aligned rectangles. Rectangles are a basic primitive object in spatial databases, and efficient aggregation of rectangles is a fundamental task. The data stream model has emerged as a de facto model for processing massive databases in which the data resides in external memory or the cloud and is streamed through main memory. For a point p, let n(p) denote the sum of the weights of all rectangles in the stream that contain p. We give near-optimal solutions for basic problems, including (1) the k-th frequency moment Fk = ∑ points p|n(p)|k, (2)~the counting version of stabbing queries, which seeks an estimate of n(p) given p, and (3) identification of heavy-hitters, i.e., points p for which n(p) is large. An important special case of Fk is F0, which corresponds to the volume of the union of the rectangles. This is a celebrated problem in computational geometry known as "Klee's measure problem", and our work yields the first solution in the streaming model for dimensions greater than one.

Exact WCRT Analysis for Message-Processing Tasks on Gateway-Integrated In-Vehicle CAN Clusters

A typical automotive integrated architecture is a controller area network (CAN) cluster integrated by a central gateway. This study proposes a novel and exact worst-case response time (WCRT) analysis method for message-processing tasks in the gateway. We first propose a round search method to obtain lower bound on response time (LBRT) and upper bound on response time (UBRT), respectively. We then obtain the exact WCRT belonging to the scope of the LBRT and UBRT with an effective non-exhaustive exploration. Experimental results on a real CAN message set reveal that the proposed exact analysis method can reduce 99.99999% combinations on large-scale CAN clusters.

Maple: scalable multi-dimensional range search over encrypted cloud data with tree-based index

Cloud computing promises users massive scale outsourced data storage services with much lower costs than traditional methods. However, privacy concerns compel sensitive data to be stored on the cloud server in an encrypted form. This posts a great challenge for effectively utilizing cloud data, such as executing common SQL queries. A variety of searchable encryption techniques have been proposed to solve this issue; yet efficiency and scalability are still the two main obstacles for their adoptions in real-world datasets, which are multi-dimensional in general. In this paper, we propose a tree-based public-key Multi-Dimensional Range Searchable Encryption (MDRSE) to overcome the above limitations. Specifically, we first formally define the leakage function and security of a tree-based MDRSE. Then, by leveraging an existing predicate encryption in a novel way, our tree-based MDRSE efficiently indexes and searches over encrypted cloud data with multi-dimensional tree structures (i.e., R-trees). Moreover, our scheme is able to protect single-dimensional privacy while previous efficient solutions fail to achieve. Our scheme is selectively secure, and through extensive experimental evaluation on a large-scale real-world dataset, we show the efficiency and scalability of our scheme.

An internet-scale idea generation system

A method of organizing the crowd to generate ideas is described. It integrates crowds using evolutionary algorithms. The method increases the creativity of ideas across generations, and it works better than greenfield idea generation. Specifically, a design space of internet-scale idea generation systems is defined, and one instance is tested: a crowd idea generation system that uses combination to improve previous designs. The key process of the system is the following: A crowd generates designs, then another crowd combines the designs of the previous crowd. In an experiment with 540 participants, the combined designs were compared to the initial designs and to the designs produced by a greenfield idea generation system. The results show that the sequential combination system produced more creative ideas in the last generation and outperformed the greenfield idea generation system. The design space of crowdsourced idea generation developed here may be used to instantiate systems that can be applied to a wide range of design problems. The work has both pragmatic and theoretical implications: New forms of coordination are now possible, and, using the crowd, it is possible to test existing and emerging theories of coordination and participatory design. Moreover, it may be possible for human designers, organized as a crowd, to codesign with each other and with automated algorithms.

“It’s a girl thing”: Examining Challenges and Opportunities around Menstrual Health Education in India

Cultural taboos and limiting social norms make it challenging to communicate and teach about menstrual health in India. We present findings from an inquiry of current approaches used to educate adolescents about menstruation, examining the perspectives of young adults, parents, teachers, social workers, and health professionals for identifying design opportunities and potential for impact. Our findings from the content analysis of education and training materials in use, an online survey of 391 adults, 52 interviews, and 2 focus groups indicate that although detailed and descriptive information materials are available for use, there exists a disconnect between parents’ and teachers’ expectations regarding who will introduce these topics to adolescents. We also highlight a clear difference in attitudes regarding who must be taught, how, where, and at what stages. Finally, we articulate factors that shape access and receptivity to this knowledge and engage with the lens of feminist HCI to discuss sociotechnical implications for the design of menstrual health education initiatives.