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Communications of the ACM

1 - 10 of 225 for bentley

Data-driven Distributionally Robust Optimization For Vehicle Balancing of Mobility-on-Demand Systems

With the transformation to smarter cities and the development of technologies, a large amount of data is collected from sensors in real time. Services provided by ride-sharing systems such as taxis, mobility-on-demand autonomous vehicles, and bike sharing systems are popular. This paradigm provides opportunities for improving transportation systems’ performance by allocating ride-sharing vehicles toward predicted demand proactively. However, how to deal with uncertainties in the predicted demand probability distribution for improving the average system performance is still a challenging and unsolved task. Considering this problem, in this work, we develop a data-driven distributionally robust vehicle balancing method to minimize the worst-case expected cost. We design efficient algorithms for constructing uncertainty sets of demand probability distributions for different prediction methods and leverage a quad-tree dynamic region partition method for better capturing the dynamic spatial-temporal properties of the uncertain demand. We then derive an equivalent computationally tractable form for numerically solving the distributionally robust problem. We evaluate the performance of the data-driven vehicle balancing algorithm under different demand prediction and region partition methods based on four years of taxi trip data for New York City (NYC). We show that the average total idle driving distance is reduced by 30% with the distributionally robust vehicle balancing method using quad-tree dynamic region partitions, compared with vehicle balancing methods based on static region partitions without considering demand uncertainties. This is about a 60-million-mile or a 8-million-dollar cost reduction annually in NYC.


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.


A New Approach for Pedestrian Density Estimation Using Moving Sensors and Computer Vision

An understanding of person dynamics is indispensable for numerous urban applications, including the design of transportation networks and planning for business development. Pedestrian counting often requires utilizing manual or technical means to count individuals in each location of interest. However, such methods do not scale to the size of a city and a new approach to fill this gap is here proposed. In this project, we used a large dense dataset of images of New York City along with computer vision techniques to construct a spatio-temporal map of relative person density. Due to the limitations of state-of-the-art computer vision methods, such automatic detection of person is inherently subject to errors. We model these errors as a probabilistic process, for which we provide theoretical analysis and thorough numerical simulations. We demonstrate that, within our assumptions, our methodology can supply a reasonable estimate of person densities and provide theoretical bounds for the resulting error.


SLEMI: equivalence modulo input (EMI) based mutation of CPS models for finding compiler bugs in Simulink

Finding bugs in commercial cyber-physical system development tools (or "model-based design" tools) such as MathWorks's Simulink is important in practice, as these tools are widely used to generate embedded code that gets deployed in safety-critical applications such as cars and planes. Equivalence Modulo Input (EMI) based mutation is a new twist on differential testing that promises lower use of computational resources and has already been successful at finding bugs in compilers for procedural languages. To provide EMI-based mutation for differential testing of cyber-physical system (CPS) development tools, this paper develops several novel mutation techniques. These techniques deal with CPS language features that are not found in procedural languages, such as an explicit notion of execution time and zombie code, which combines properties of live and dead procedural code. In our experiments the most closely related work (SLforge) found two bugs in the Simulink tool. In comparison, SLEMI found a super-set of issues, including 9 confirmed as bugs by MathWorks Support.


Social Sensing: Assessing Social Functioning of Patients Living with Schizophrenia using Mobile Phone Sensing

Impaired social functioning is a symptom of mental illness (e.g., depression, schizophrenia) and a wide range of other conditions (e.g., cognitive decline in the elderly, dementia). Today, assessing social functioning relies on subjective evaluations and self assessments. We propose a different approach and collect detailed social functioning measures and objective mobile sensing data from N=55 outpatients living with schizophrenia to study new methods of passively accessing social functioning. We identify a number of behavioral patterns from sensing data, and discuss important correlations between social function sub-scales and mobile sensing features. We show we can accurately predict the social functioning of outpatients in our study including the following sub-scales: prosocial activities (MAE = 7.79, r = 0.53), which indicates engagement in common social activities; interpersonal behavior (MAE = 3.39, r = 0.57), which represents the number of friends and quality of communications; and employment/occupation (MAE = 2.17, r = 0.62), which relates to engagement in productive employment or a structured program of daily activity. Our work on automatically inferring social functioning opens the way to new forms of assessment and intervention across a number of areas including mental health and aging in place.


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.


Broadening Exposure to Socio-Political Opinions via a Pushy Smart Home Device

Motivated by the effects of the filter bubble and echo chamber phenomena on social media, we developed a smart home device, Spkr, that unpredictably "pushes" socio-political discussion topics into the home. The device utilised trending Twitter discussions, categorised by their socio-political alignment, to present people with a purposefully assorted range of viewpoints. We deployed Spkr in 10 homes for 28 days with a diverse range of participants and interviewed them about their experiences. Our results show that Spkr presents a novel means of combating selective exposure to socio-political issues, providing participants with identifiably diverse viewpoints. Moreover, Spkr acted as a conversational prompt for discussion within the home, initiating collective processes and engaging those who would not often be involved in political discussions. We demonstrate how smart home assistants can be used as a catalyst for provocation by altering and pluralising political discussions within households.


Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design

Artificial Intelligence (AI) plays an increasingly important role in improving HCI and user experience. Yet many challenges persist in designing and innovating valuable human-AI interactions. For example, AI systems can make unpredictable errors, and these errors damage UX and even lead to undesired societal impact. However, HCI routinely grapples with complex technologies and mitigates their unintended consequences. What makes AI different? What makes human-AI interaction appear particularly difficult to design? This paper investigates these questions. We synthesize prior research, our own design and research experience, and our observations when teaching human-AI interaction. We identify two sources of AI's distinctive design challenges: 1) uncertainty surrounding AI's capabilities, 2) AI's output complexity, spanning from simple to adaptive complex. We identify four levels of AI systems. On each level, designers encounter a different subset of the design challenges. We demonstrate how these findings reveal new insights for designers, researchers, and design tool makers in productively addressing the challenges of human-AI interaction going forward.


Cultivating the Cyberinfrastructure Workforce via an Intermediate/Advanced Virtual Residency Workshop

Cyberinfrastructure (CI) Facilitation is the process of helping researchers to use research computing systems and services to advance their computing-intensive/data-intensive research goals. The growing need for CI Facilitation isn't being met by traditional academic degree and certificate programs, so informal education is required. The Virtual Residency (VR) is a program that teaches key CI Facilitation skills to CI Facilitators. Using a combination of (a) workshops, (b) biweekly conference calls, (c) a Grant Proposal Writing Apprenticeship and (d) a new Paper Writing Apprenticeship, the VR has been teaching CI Facilitation since 2015. During the summers of 2015-17, the annual VR workshop was at an introductory level, driving demand for a higher level workshop. In 2018, the VR workshop was presented at a level described as intermediate, but in practice it included a great deal of advanced content, concentrated on institutional CI leadership. The 2018 focus areas were: (1) in-depth CI expertise in areas of rapidly changing technology; (2) CI leadership; (3) funding acquisition skills; (4) outreach strategies, techniques, and skills; (5) communication skills. The 2018 VR workshop served 216 participants from 147 institutions in 42 US states and 2 US territories plus 2 other countries.


Distributed Spatial and Spatio-Temporal Join on Apache Spark

Effective processing of extremely large volumes of spatial data has led to many organizations employing distributed processing frameworks. Apache Spark is one such open source framework that is enjoying widespread adoption. Within this data space, it is important to note that most of the observational data (i.e., data collected by sensors, either moving or stationary) has a temporal component or timestamp. To perform advanced analytics and gain insights, the temporal component becomes equally important as the spatial and attribute components. In this article, we detail several variants of a spatial join operation that addresses both spatial, temporal, and attribute-based joins. Our spatial join technique differs from other approaches in that it combines spatial, temporal, and attribute predicates in the join operator. In addition, our spatio-temporal join algorithm and implementation differs from others in that it runs in commercial off-the-shelf (COTS) application. The users of this functionality are assumed to be GIS analysts with little if any knowledge of the implementation details of spatio-temporal joins or distributed processing. They are comfortable using simple tools that do not provide the ability to tweak the configuration of the algorithm or processing environment. The spatio-temporal join algorithm behind the tool must always succeed, regardless of input data parameters (e.g., it can be highly irregularly distributed, contain large numbers of coincident points, it can be extremely large, etc.). These factors combine to place additional requirements on the algorithm that are uncommonly found in the traditional research environment. Our spatio-temporal join algorithm was shipped as part of the GeoAnalytics Server [12], part of the ArcGIS Enterprise platform from version 10.5 onward.