Sign In

Communications of the ACM

1 - 10 of 164 for bentley

Keeping science on keel when software moves

An approach to reproducibility problems related to porting software across machines and compilers.


Enhancing Affect Detection in Game-Based Learning Environments with Multimodal Conditional Generative Modeling

Accurately detecting and responding to student affect is a critical capability for adaptive learning environments. Recent years have seen growing interest in modeling student affect with multimodal sensor data. A key challenge in multimodal affect detection is dealing with data loss due to noisy, missing, or invalid multimodal features. Because multimodal affect detection often requires large quantities of data, data loss can have a strong, adverse impact on affect detector performance. To address this issue, we present a multimodal data imputation framework that utilizes conditional generative models to automatically impute posture and interaction log data from student interactions with a game-based learning environment for emergency medical training. We investigate two generative models, a Conditional Generative Adversarial Network (C-GAN) and a Conditional Variational Autoencoder (C-VAE), that are trained using a modality that has undergone varying levels of artificial data masking. The generative models are conditioned on the corresponding intact modality, enabling the data imputation process to capture the interaction between the concurrent modalities. We examine the effectiveness of the conditional generative models on imputation accuracy and its impact on the performance of affect detection. Each imputation model is evaluated using varying amounts of artificial data masking to determine how the data missingness impacts the performance of each imputation method. Results based on the modalities captured from students? interactions with the game-based learning environment indicate that deep conditional generative models within a multimodal data imputation framework yield significant benefits compared to baseline imputation techniques in terms of both imputation accuracy and affective detector performance.


SurfaceFleet: Exploring Distributed Interactions Unbounded from Device, Application, User, and Time

Knowledge work increasingly spans multiple computing surfaces. Yet in status quo user experiences, content as well as tools, behaviors, and workflows are largely bound to the current device-running the current application, for the current user, and at the current moment in time. SurfaceFleet is a system and toolkit that uses resilient distributed programming techniques to explore cross-device interactions that are unbounded in these four dimensions of device, application, user, and time. As a reference implementation, we describe an interface built using SurfaceFleet that employs lightweight, semi-transparent UI elements known as Applets. Applets appear always-on-top of the operating system, application windows, and (conceptually) above the device itself. But all connections and synchronized data are virtualized and made resilient through the cloud. For example, a sharing Applet known as a Portfolio allows a user to drag and drop unbound Interaction Promises into a document. Such promises can then be fulfilled with content asynchronously, at a later time (or multiple times), from another device, and by the same or a different user.


Validity frame concept as effort-cutting technique within the verification and validation of complex cyber-physical systems

The increasing performance demands and certification needs of complex cyber-physical systems (CPS) raise the complexity of the engineering process, not only within the development phase, but also in the Verification and Validation (V&V) phase. A proven technique to handle the complexity of CPSs is Model-Based Design (MBD). Nevertheless, the verification and validation of complex CPSs is still an exhaustive process and the usability of the models to front-load V&V activities heavily depends on the knowledge of the models and the correctness of the conducted virtual experiments. In this paper, we explore how the effort (and cost) of the V&V phase of the engineering process of complex CPSs can be reduced by enhancing the knowledge about the system components, and explicitly capturing it within their corresponding validity frame. This effort reduction originates from exploiting the captured system knowledge to generate efficient V&V processes and by automating activities at different model life stages, such as the setup and execution of boundary-value or fault-injection tests. This will be discussed in the context of a complex CPS: a safety-critical adaptive cruise control system.


Towards adaptive abstraction for continuous time models with dynamic structure

Humans often switch between multiple levels of abstraction when reasoning about salient properties of complex systems. These changes in perspective may be leveraged at runtime to improve both performance and explainability, while still producing identical answers to questions about the properties of interest. This technique, which switches between multiple abstractions based on changing conditions in the modelled system, is also known as adaptive abstraction.

The Modelica language represents systems as a-causal continuous equations, which makes it appropriate for the modelling of physical systems. However adaptive abstraction requires dynamic structure modelling. This raises many technical challenges in Modelica since it has poor support for modifying connections during simulation. Its equation-based nature means that all equations need to be well-formed at all times, which may not hold when switching between levels of abstraction. The initialization of models upon switching must also be carefully managed, as information will be lost or must be created when switching abstractions [1].

One way to allow adaptive abstraction is to represent the system as a multi-mode hybrid Modelica model, a mode being an abstraction that can be switched to based on relevant criteria. Another way is to employ a co-simulation [2] approach, where modes are exported as "black boxes" and orchestrated by a central algorithm that implements adaptivity techniques to dynamically replace components when a switching condition occurs.

This talk will discuss the benefits of adaptive abstraction using Modelica, and the conceptual and technical challenges towards its implementation. As a stand-in for a complex cyber-physical system, an electrical transmission line case study is proposed where attenuation is studied across two abstractions having varying fidelity depending on the signal. Our initial results, as well as our explorations towards employing Modelica models in a co-simulation context using the DEVS formalism [4] are discussed. A Modelica only solution allows to tackle complexity via decomposition, but does not improve performances as all modes are represented as a single set of equations. The co-simulation approach might offer better performances [3], but complicates the workflow.


Constraint handling in genotype to phenotype mapping and genetic operators for project staffing

Project staffing in many organisations involves the assignment of people to multiple projects while satisfying multiple constraints. The use of a genetic algorithm with constraint handling performed during a genotype to phenotype mapping process provides a new approach. Experiments show promise for this technique.


Rethinking Consumer Email: The Research Process for Yahoo Mail 6

This case study follows the research process of rethinking the design and functionality of a personal email client, Yahoo Mail. Over three years, we changed the focus of the product from composing emails towards automatically organizing specific categories of business to consumer email (such as deals, receipts, and travel) and creating experiences unique to each category. To achieve this, we employed iterative user research with over 1,500 in-person interviews in six countries and surveys to many thousands of people around the world. This research process culminated in the launch of Yahoo Mail 6.0 for iOS and Android devices in the fall of 2019.


Exploring the Quality, Efficiency, and Representative Nature of Responses Across Multiple Survey Panels

A common practice in HCI research is to conduct a survey to understand the generalizability of findings from smaller-scale qualitative research. These surveys are typically deployed to convenience samples, on low-cost platforms such as Amazon's Mechanical Turk or Survey Monkey, or to more expensive market research panels offered by a variety of premium firms. Costs can vary widely, from hundreds of dollars to tens of thousands of dollars depending on the platform used. We set out to understand the accuracy of ten different survey platforms/panels compared to ground truth data for a total of 6,007 respondents on 80 different aspects of demographic and behavioral questions. We found several panels that performed significantly better than others on certain topics, while different panels provided longer and more relevant open-ended responses. Based on this data, we highlight the benefits and pitfalls of using a variety of survey distribution options in terms of the quality, efficiency, and representative nature of the respondents and the types of responses that can be obtained.


Evaluating Smartwatch-based Sound Feedback for Deaf and Hard-of-hearing Users Across Contexts

We present a qualitative study with 16 deaf and hard of hearing (DHH) participants examining reactions to smartwatch-based visual + haptic sound feedback designs. In Part 1, we conducted a Wizard-of-Oz (WoZ) evaluation of three smartwatch feedback techniques (visual alone, visual + simple vibration, and visual + tacton) and investigated vibrational patterns (tactons) to portray sound loudness, direction, and identity. In Part 2, we visited three public or semi-public locations where we demonstrated sound feedback on the smartwatch in situ to examine contextual influences and explore sound filtering options. Our findings characterize uses for vibration in multimodal sound awareness, both for push notification and for immediately actionable sound information displayed through vibrational patterns (tactons). In situ experiences caused participants to request sound filtering - particularly to limit haptic feedback - as a method for managing soundscape complexity. Additional concerns arose related to learnability, possibility of distraction, and system trust. Our findings have implications for future portable sound awareness systems.


Promoting Collaborative Skills with Github Project Boards

Teamwork skills are much in demand in the workplace, even more so with the growth of Agile methods. This calls for giving Computer Science students more practice in the kinds of team scenarios they will encounter on the job. Key for success are hands-on experience with planning methods, prioritization techniques, time management and organization. This poster shows how the cooperative tracking tool Github Project Boards helps teams strategize development, track progress, distribute work evenly, and facilitate collaboration. It also shows how instructors can use Github Project Boards to visualize and evaluate a team's development process.