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Communications of the ACM

1 - 10 of 98 for bentley

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.


VizSciFlow: A Visually Guided Scripting Framework for Supporting Complex Scientific Data Analysis

Scientific workflow management systems such as Galaxy, Taverna and Workspace, have been developed to automate scientific workflow management and are increasingly being used to accelerate the specification, execution, visualization, and monitoring of data-intensive tasks. For example, the popular bioinformatics platform Galaxy is installed on over 168 servers around the world and the social networking space myExperiment shares almost 4,000 Galaxy scientific workflows among its 10,665 members. Most of these systems offer graphical interfaces for composing workflows. However, while graphical languages are considered easier to use, graphical workflow models are more difficult to comprehend and maintain as they become larger and more complex. Text-based languages are considered harder to use but have the potential to provide a clean and concise expression of workflow even for large and complex workflows. A recent study showed that some scientists prefer script/text-based environments to perform complex scientific analysis with workflows. Unfortunately, such environments are unable to meet the needs of scientists who prefer graphical workflows. In order to address the needs of both types of scientists and at the same time to have script-based workflow models because of their underlying benefits, we propose a visually guided workflow modeling framework that combines interactive graphical user interface elements in an integrated development environment with the power of a domain-specific language to compose independently developed and loosely coupled services into workflows. Our domain-specific language provides scientists with a clean, concise, and abstract view of workflow to better support workflow modeling. As a proof of concept, we developed VizSciFlow, a generalized scientific workflow management system that can be customized for use in a variety of scientific domains. As a first use case, we configured and customized VizSciFlow for the bioinformatics domain. We conducted three user studies to assess its usability, expressiveness, efficiency, and flexibility. Results are promising, and in particular, our user studies show that VizSciFlow is more desirable for users to use than either Python or Galaxy for solving complex scientific problems.


APL since 1978

The Evolution of APL, the HOPL I paper by Falkoff and Iverson on APL, recounted the fundamental design principles which shaped the implementation of the APL language in 1966, and the early uses and other influences which shaped its first decade of enhancements.

In the 40 years that have elapsed since HOPL I, several dozen APL implementations have come and gone. In the first decade or two, interpreters were typically born and buried along with the hardware or operating system that they were created for. More recently, the use of C as an implementation language provided APL interpreters with greater longevity and portability.

APL started its life on IBM mainframes which were time-shared by multiple users. As the demand for computing resources grew and costs dropped, APL first moved in-house to mainframes, then to mini- and micro-computers. Today, APL runs on PCs and tablets, Apples and Raspberry Pis, smartphones and watches.

The operating systems, and the software application platforms that APL runs on, have evolved beyond recognition. Tools like database systems have taken over many of the tasks that were initially implemented in APL or provided by the APL system, and new capabilities like parallel hardware have also changed the focus of design and implementation efforts through the years.

The first wave of significant language enhancements occurred shortly after HOPL I, resulting in so-called second-generation APL systems. The most important feature of the second generation is the addition of general arrays—in which any item of an array can be another array—and a number of new functions and operators aligned with, if not always motivated by, the new data structures.

The majority of implementations followed IBM’s path with APL2 “floating” arrays; others aligned themselves with SHARP APL and “grounded” arrays. While the APL2 style of APL interpreters came to dominate the mainstream of the APL community, two new cousins of APL descended from the SHARP APL family tree: J (created by Iverson and Hui) and k (created by Arthur Whitney).

We attempt to follow a reasonable number of threads through the last 40 years, to identify the most important factors that have shaped the evolution of APL. We will discuss the details of what we believe are the most significant language features that made it through the occasionally unnatural selection imposed by the loss of habitats that disappeared with hardware, software platforms, and business models.

The history of APL now spans six decades. It is still the case, as Falkoff and Iverson remarked at the end of the HOPL I paper, that:

Although this is not the place to discuss the future, it should be remarked that the evolution of APL is far from finished.


Locality-Sensitive Hashing Scheme based on Longest Circular Co-Substring

Locality-Sensitive Hashing (LSH) is one of the most popular methods for c-Approximate Nearest Neighbor Search (c-ANNS) in high-dimensional spaces. In this paper, we propose a novel LSH scheme based on the Longest Circular Co-Substring (LCCS) search framework (LCCS-LSH) with a theoretical guarantee. We introduce a novel concept of LCCS and a new data structure named Circular Shift Array (CSA) for k-LCCS search. The insight of LCCS search framework is that close data objects will have a longer LCCS than the far-apart ones with high probability. LCCS-LSH is LSH-family-independent, and it supports c-ANNS with different kinds of distance metrics. We also introduce a multi-probe version of LCCS-LSH and conduct extensive experiments over five real-life datasets. The experimental results demonstrate that LCCS-LSH outperforms state-of-the-art LSH schemes.


"All in the Same Boat": Tradeoffs of Voice Assistant Ownership for Mixed-Visual-Ability Families

A growing body of evidence suggests Voice Assistants (VAs) are highly valued by people with vision impairments (PWVI) and much less so by sighted users. Yet, many are deployed in homes where both PWVI and sighted family members reside. Researchers have yet to study whether VA use and perceived benefits are affected in settings where one person has a visual impairment and others do not. We conducted six in-depth interviews with partners to understand patterns of domestic VA use in mixed-visual-ability families. Although PWVI were more motivated to acquire VAs, used them more frequently, and learned more proactively about their features, partners with vision identified similar benefits and disadvantages of having VAs in their home. We found that the universal usability of VAs both equalizes experience across abilities and presents complex tradeoffs for families-regarding interpersonal relationships, domestic labor, and physical safety-which are weighed against accessibility benefits for PWVI and complicate the decision to fully integrate VAs in the home.


Ergonomic Adaptation of Robotic Movements in Human-Robot Collaboration

Musculoskeletal Disorders (MSDs) are common occupational diseases. An interesting research question is whether collaborative robots actively can minimise the risk of MSDs during collaboration. In this work ergonomic adaptation of robotic movements during human-robot collaboration is explored in a first test case, namely, adjustment of work sureface height. Vision based markerless posture estimation is used as input in combination with ergonomic assessment methods to adapt robotic movements in order to facilitate better ergonomic conditions for the human worker.


Assumptions Checked: How Families Learn About and Use the Echo Dot

Users of voice assistants often report that they fall into patterns of using their device for a limited set of interactions, like checking the weather and setting alarms. However, it's not clear if limited use is, in part, due to lack of learning about the device's functionality. We recruited 10 diverse families to participate in a one-month deployment study of the Echo Dot, enabling us to investigate: 1) which features families are aware of and engage with, and 2) how families explore, discover, and learn to use the Echo Dot. Through audio recordings of families' interactions with the device and pre- and post-deployment interviews, we find that families' breadth of use decreases steadily over time and that families learn about functionality through trial and error, asking the Echo Dot about itself, and through outside influencers such as friends and family. Formal outside learning influencers, such as manufacturer emails, are less influential. Drawing from diffusion of innovation theory, we describe how a home-based voice interface might be positioned as a near-peer to the user, and that by describing its own functionality using just-in-time learning, the home-based voice interface becomes a trustworthy learning influencer from which users can discover new functionalities.


Provider Perspectives on Integrating Sensor-Captured Patient-Generated Data in Mental Health Care

The increasing ubiquity of health sensing technology holds promise to enable patients and health care providers to make more informed decisions based on continuously-captured data. The use of sensor-captured patient-generated data (sPGD) has been gaining greater prominence in the assessment of physical health, but we have little understanding of the role that sPGD can play in mental health. To better understand the use of sPGD in mental health, we interviewed care providers in an intensive treatment program (ITP) for veterans with post-traumatic stress disorder. In this program, patients were given Fitbits for their own voluntary use. Providers identified a number of potential benefits from patients' Fitbit use, such as patient empowerment and opportunities to reinforce therapeutic progress through collaborative data review and interpretation. However, despite the promise of sensor data as offering an "objective" view into patients' health behavior and symptoms, the relationships between sPGD and therapeutic progress are often ambiguous. Given substantial subjectivity involved in interpreting data from commercial wearables in the context of mental health treatment, providers emphasized potential risks to their patients and were uncertain how to adjust their practice to effectively guide collaborative use of the FitBit and its sPGD. We discuss the implications of these findings for designing systems to leverage sPGD in mental health care.?


Passively-sensed Behavioral Correlates of Discrimination Events in College Students

A deep understanding of how discrimination impacts psychological health and well-being of students could allow us to better protect individuals at risk and support those who encounter discrimination. While the link between discrimination and diminished psychological and physical well-being is well established, existing research largely focuses on chronic discrimination and long-term outcomes. A better understanding of the short-term behavioral correlates of discrimination events could help us to concretely quantify such experiences, which in turn could support policy and intervention design. In this paper we specifically examine, for the first time, what behaviors change and in what ways in relation to discrimination. We use actively-reported and passively-measured markers of health and well-being in a sample of 209 first-year college students over the course of two academic quarters. We examine changes in indicators of psychological state in relation to reports of unfair treatment in terms of five categories of behaviors: physical activity, phone usage, social interaction, mobility, and sleep. We find that students who encounter unfair treatment become more physically active, interact more with their phone in the morning, make more calls in the evening, and spend more time in bed on the day of the event. Some of these patterns continue the next day. Our results further our understanding of the impact of discrimination and can inform intervention work.


A Reliable and Accurate Multiple Choice Question Answering System for Due Diligence

The problem of answering multiple choice questions, based on the content of documents has been studied extensively in the machine learning literature. We pose the due diligence problem, where lawyers study legal contracts and assess the risk in potential mergers and acquisitions, as a multiple choice question answering problem, based on the text of the contract. Existing frameworks for question answering are not suitable for this task, due to the inherent scarcity and imbalance in the legal contract data available for training. We propose a question answering system which first identifies the excerpt in the contract which potentially contains the answer to a given question, and then builds a multi-class classifier to choose the answer to the question, based on the content of this excerpt. Unlike existing question answering systems, the proposed system explicitly handles the imbalance in the data, by generating synthetic instances of the minority answer categories, using the Synthetic Minority Oversampling Technique. This ensures that the number of instances in all the classes are roughly equal to each other, thus leading to more accurate and reliable classification. We demonstrate that the proposed question answering system outperforms the existing systems with minimal amount of training data.