Mountain Scholar :: Browsing by Author "ACM, publisher"

Browsing by Author "ACM, publisher"

Now showing 1 - 20 of 45

Open Access
A framework for profiling spatial variability in the performance of classification models
(Colorado State University. Libraries, 2024-04-03) Warushavithana, Menuka, author; Barram, Kassidy, author; Carlson, Caleb, author; Mitra, Saptashwa, author; Ghosh, Sudipto, author; Breidt, Jay, author; Pallickara, Sangmi Lee, author; Pallickara, Shrideep, author; ACM, publisher
Scientists use models to further their understanding of phenomena and inform decision-making. A confluence of factors has contributed to an exponential increase in spatial data volumes. In this study, we describe our methodology to identify spatial variation in the performance of classification models. Our methodology allows tracking a host of performance measures across different thresholds for the larger, encapsulating spatial area under consideration. Our methodology ensures frugal utilization of resources via a novel validation budgeting scheme that preferentially allocates observations for validations. We complement these efforts with a browser-based, GPU-accelerated visualization scheme that also incorporates support for streaming to assimilate validation results as they become available.
Open Access
A methodology for evaluating multimodal referring expression generation for embodied virtual agents
(Colorado State University. Libraries, 2023-10-09) Alalyani, Nada, author; Krishnaswamy, Nikhil, author; ACM, publisher
Robust use of definite descriptions in a situated space often involves recourse to both verbal and non-verbal modalities. For IVAs, virtual agents designed to interact with humans, the ability to both recognize and generate non-verbal and verbal behavior is a critical capability. To assess how well an IVA is able to deploy multimodal behaviors, including language, gesture, and facial expressions, we propose a methodology to evaluate the agent's capacity to generate object references in a situational context, using the domain of multimodal referring expressions as a use case. Our contributions include: 1) developing an embodied platform to collect human referring expressions while communicating with the IVA. 2) comparing human and machine-generated references in terms of evaluable properties using subjective and objective metrics. 3) reporting preliminary results from trials that aimed to check whether the agent can retrieve and disambiguate the object the human referred to, if the human has the ability to correct misunderstanding using language, deictic gesture, or both; and human ease of use while interacting with the agent.
Open Access
Algorithm parallelism for improved extractive summarization
(Colorado State University. Libraries, 2023-08-22) Villanueva, Arturo N., Jr., author; Simske, Steven J., author; ACM, publisher
While much work on abstractive summarization has been conducted in recent years, including state-of-the-art summarizations from GPT-4, extractive summarization's lossless nature continues to provide advantages, preserving the style and often key phrases of the original text as meant by the author. Libraries for extractive summarization abound, with a wide range of efficacy. Some do not perform much better or perform even worse than random sampling of sentences extracted from the original text. This study breathes new life to using classical algorithms by proposing parallelism through an implementation of a second order meta-algorithm in the form of the Tessellation and Recombination with Expert Decisioner (T&R) pattern, taking advantage of the abundance of already-existing algorithms and dissociating their individual performance from the implementer's biases. Resulting summaries obtained using T&R are better than any of the component algorithms.
Open Access
Algorithms for parallel generic hp-adaptive finite element software
(Colorado State University. Libraries, 2023-09-19) Fehling, Marc, author; Bangerth, Wolfgang, author; ACM, publisher
The hp-adaptive finite element method—where one independently chooses the mesh size (h) and polynomial degree (p) to be used on each cell—has long been known to have better theoretical convergence properties than either h- or p-adaptive methods alone. However, it is not widely used, owing at least in part to the difficulty of the underlying algorithms and the lack of widely usable implementations. This is particularly true when used with continuous finite elements. Herein, we discuss algorithms that are necessary for a comprehensive and generic implementation of hp-adaptive finite element methods on distributed-memory, parallel machines. In particular, we will present a multistage algorithm for the unique enumeration of degrees of freedom suitable for continuous finite element spaces, describe considerations for weighted load balancing, and discuss the transfer of variable size data between processes. We illustrate the performance of our algorithms with numerical examples and demonstrate that they scale reasonably up to at least 16,384 message passage interface processes. We provide a reference implementation of our algorithms as part of the open source library deal.II.
Open Access
An artists' perspectives on natural interactions for virtual reality 3D sketching
(Colorado State University. Libraries, 2024-05-11) Rodriguez, Richard, author; Sullivan, Brian T., author; Machuca, Mayra Donaji Barrera, author; Batmaz, Anil Ufuk, author; Tornatzky, Cyane, author; Ortega, Francisco R., author; ACM, publisher
Virtual Reality (VR) applications like OpenBrush offer artists access to 3D sketching tools within the digital 3D virtual space. These 3D sketching tools allow users to "paint" using virtual digital strokes that emulate real-world mark-making. Yet, users paint these strokes through (unimodal) VR controllers. Given that sketching in VR is a relatively nascent field, this paper investigates ways to expand our understanding of sketching in virtual space, taking full advantage of what an immersive digital canvas offers. Through a study conducted with the participation of artists, we identify potential methods for natural multimodal and unimodal interaction techniques in 3D sketching. These methods demonstrate ways to incrementally improve existing interaction techniques and incorporate artistic feedback into the design.
Open Access
Attacks and defenses for large language models on coding tasks
(Colorado State University. Libraries, 2024-10-27) Zhang, Chi, author; Wang, Zifan, author; Zhao, Ruoshi, author; Mangal, Ravi, author; Fredrikson, Matt, author; Jia, Limin, author; Pasareanu, Corina, author; ACM, publisher
Modern large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities for coding tasks, including writing and reasoning about code. They improve upon previous neural network models of code, such as code2seq or seq2seq, that already demonstrated competitive results when performing tasks such as code summarization and identifying code vulnerabilities. However, these previous code models were shown vulnerable to adversarial examples, i.e., small syntactic perturbations designed to "fool" the models. In this paper, we first aim to study the transferability of adversarial examples, generated through white-box attacks on smaller code models, to LLMs. We also propose a new attack using an LLM to generate the perturbations. Further, we propose novel cost-effective techniques to defend LLMs against such adversaries via prompting, without incurring the cost of retraining. These prompt-based defenses involve modifying the prompt to include additional information, such as examples of adversarially perturbed code and explicit instructions for reversing adversarial perturbations. Our preliminary experiments show the effectiveness of the attacks and the proposed defenses on popular LLMs such as GPT-3.5 and GPT-4.
Open Access
CatalogBank: a structured and interoperable catalog dataset with a semi-automatic annotation tool (DocumentLabeler) for engineering system design
(Colorado State University. Libraries, 2024-08-20) Bank, Hasan Sinan, author; Herber, Daniel R., author; ACM, publisher
In the realm of document engineering and Natural Language Processing (NLP), the integration of digitally born catalogs into product design processes presents a novel avenue for enhancing information extraction and interoperability. This paper introduces CatalogBank, a dataset developed to bridge the gap between textual descriptions and other data modalities related to engineering design catalogs. We utilized existing information extraction methodologies to extract product information from PDF-based catalogs to use in downstream tasks to generate a baseline metric. Our approach not only supports the potential automation of design workflows but also overcomes the limitations of manual data entry and non-standard metadata structures that have historically impeded the seamless integration of textual and other data modalities. Through the use of DocumentLabeler, an open-source annotation tool adapted for our dataset, we demonstrated the potential of CatalogBank in supporting diverse document-based tasks such as layout analysis and knowledge extraction. Our findings suggest that CatalogBank can contribute to document engineering and NLP by providing a robust dataset for training models capable of understanding and processing complex document formats with relatively less effort using the semi-automated annotation tool DocumentLabeler.
Open Access
Character relationship mapping in major fictional works using text analysis methods
(Colorado State University. Libraries, 2023-08-22) Wolyn, Sam, author; Simske, Steven, author; ACM, publisher
Determining the relationships between characters is an important step in analyzing fictional works. Knowing character relationships can be useful when summarizing a work and may also help to determine authorship. In this paper, scores are generated for pairs of characters in fictional works, which can be used for classification tasks if characters have a relationship or not. An SVM is used to predict relationships between characters. Characters farther from the decision boundary often had stronger relationships than those closer to the boundary. The relative rank of the relationships may have additional literary and authorship related purposes.
Open Access
Claim extraction and dynamic stance detection in COVID-19 tweets
(Colorado State University. Libraries, 2023-04-30) Faramarzi, Noushin Salek, author; Chaleshtori, Fateme Hashemi, author; Shirazi, Hossein, author; Ray, Indrakshi, author; Banerjee, Ritwik, author; ACM, publisher
The information ecosystem today is noisy, and rife with messages that contain a mix of objective claims and subjective remarks or reactions. Any automated system that intends to capture the social, cultural, or political zeitgeist, must be able to analyze the claims as well as the remarks. Due to the deluge of such messages on social media, and their tremendous power to shape our perceptions, there has never been a greater need to automate these analyses, which play a pivotal role in fact-checking, opinion mining, understanding opinion trends, and other such downstream tasks of social consequence. In this noisy ecosystem, not all claims are worth checking for veracity. Such a check-worthy claim, moreover, must be accurately distilled from subjective remarks surrounding it. Finally, and especially for understanding opinion trends, it is important to understand the stance of the remarks or reactions towards that specific claim. To this end, we introduce a COVID-19 Twitter dataset, and present a three-stage process to (i) determine whether a given Tweet is indeed check-worthy, and if so, (ii) which portion of the Tweet ought to be checked for veracity, and finally, (iii) determine the author's stance towards the claim in that Tweet, thus introducing the novel task of topic-agnostic stance detection.
Open Access
Combating spatial disorientation in a dynamic self-stabilization task using AI assistants
(Colorado State University. Libraries, 2024-11-24) Mannan, Sheikh Abdul, author; Hansen, Paige, author; Vimal, Vivekanand Pandey, author; Davies, Hannah N., auhtor; DiZio, Paul, author; Krishnaswamy, Nikhil, author; ACM, publisher
Spatial disorientation is a leading cause of fatal aircraft accidents. This paper explores the potential of AI agents to aid pilots in maintaining balance and preventing unrecoverable losses of control by offering cues and corrective measures that ameliorate spatial disorientation. A multi-axis rotation system (MARS) was used to gather data from human subjects self-balancing in a spaceflight analog condition. We trained models over this data to create "digital twins" that exemplified performance characteristics of humans with different proficiency levels. We then trained various reinforcement learning and deep learning models to offer corrective cues if loss of control is predicted. Digital twins and assistant models then co-performed a virtual inverted pendulum (VIP) programmed with identical physics. From these simulations, we picked the 5 best-performing assistants based on task metrics such as crash frequency and mean distance from the direction of balance. These were used in a co-performance study with 20 new human subjects performing a version of the VIP task with degraded spatial information. We show that certain AI assistants were able to improve human performance and that reinforcement-learning based assistants were objectively more effective but rated as less trusted and preferable by humans.
Open Access
Computational modeling for the computer animation of legged figures
(Colorado State University. Libraries, 1985) Girard, Michael, author; Maciejewski, Anthony A., author; ACM, publisher
Modeling techniques for animating legged figures are described which are used in the PODA animation system. PODA utilizes pseudoinverse control in order to solve the problems associated with manipulating kinematically redundant limbs. PODA builds on this capability to synthesize a kinematic model of legged locomotion which allows animators to control the complex relationships between the motion of the body of a figure and the coordination of its legs. Finally, PODA provides for the integration of a simple model of legged locomotion dynamics which insures that the accelerations of a figure's body are synchronized with the timing of the forces applied by its legs.
Open Access
Constant depth circuit complexity for generating quasigroups
(Colorado State University. Libraries, 2024-07-16) Collins, Nathaniel A., author; Grochow, Joshua A., author; Levet, Michael, author; Weiß, Armin, author; ACM, publisher
We investigate the constant-depth circuit complexity of the Isomorphism Problem, Minimum Generating Set Problem (MGS), and Sub(qasi)group Membership Problem (Membership) for groups and quasigroups (=Latin squares), given as input in terms of their multiplication (Cayley) tables. Despite decades of research on these problems, lower bounds for these problems even against depth-2 AC circuits remain unknown. Perhaps surprisingly, Chattopadhyay, Torán, and Wagner (FSTTCS 2010; ACM Trans. Comput. Theory, 2013) showed that Quasigroup Isomorphism could be solved by AC circuits of depth O(log log n) using O(log2 n) nondeterministic bits, a class we denote ∃log2 nFOLL. We narrow this gap by improving the upper bound for these problems to quasiAC0, thus decreasing the depth to constant. In particular, we show that Membership can be solved in NTIME(polylog(n)) … Our results suggest that understanding the constant-depth circuit complexity may be key to resolving the complexity of problems concerning (quasi)groups in the multiplication table model.
Open Access
Cross-layer design for AI acceleration with non-coherent optical computing
(Colorado State University. Libraries, 2023-06-05) Sunny, Febin, author; Nikdast, Mahdi, author; Pasricha, Sudeep, author; ACM, publisher
Emerging AI applications such as ChatGPT, graph convolutional networks, and other deep neural networks require massive computational resources for training and inference. Contemporary computing platforms such as CPUs, GPUs, and TPUs are struggling to keep up with the demands of these AI applications. Non-coherent optical computing represents a promising approach for light-speed acceleration of AI workloads. In this paper, we show how cross-layer design can overcome challenges in non-coherent optical computing platforms. We describe approaches for optical device engineering, tuning circuit enhancements, and architectural innovations to adapt optical computing to a variety of AI workloads. We also discuss techniques for hardware/ software co-design that can intelligently map and adapt AI software to improve performance on non-coherent platforms.
Open Access
DeepSoil: a science-guided framework for generating high precision soil moisture maps by reconciling measurement profiles across in-situ and remote sensing
(Colorado State University. Libraries, 2024-10-29) Khandelwal, Paahuni, author; Pallickara, Sangmi Lee, author; Pallickara, Shrideep, author; ACM, publisher
Soil moisture plays a critical role in several domains and can be used to inform decision-making in agricultural settings, drought forecasting, forest fire predictions, and water conservation. Soil moisture is measured using in-situ and remote-sensing equipment. Depending on the type of equipment that is used, some challenges must be reconciled, including the density of observations, the measurement precision, and the resolutions at which these measurements are available. In particular, in-situ measurements are high-precision but sparse, while remote sensing measurements benefit from spatial coverage, albeit at lower precision and coarser resolutions. The crux of this study is to produce higher-precision soil moisture estimates at high resolutions (30m). Our methodology combines scientific models, deep networks, topographical characteristics, and information about ambient conditions alongside both in-situ and remote sensing data to accomplish this. Domain science infuses several aspects of our methodology. Our empirical benchmarks profile several aspects and demonstrate that our methodology accounts for spatial variability while accounting for both static (soil properties and elevation) and dynamically varying phenomena to generate accurate, high-precision 30m resolution soil moisture content maps.
Open Access
Design space exploration for PCM-based photonic memory
(Colorado State University. Libraries, 2023-06-05) Shafiee, Amin, author; Charbonnier, Benoit, author; Pasricha, Sudeep, author; Nikdast, Mahdi, author; ACM, publisher
The integration of silicon photonics (SiPh) and phase change materials (PCMs) has created a unique opportunity to realize adaptable and reconfigurable photonic systems. In particular, the nonvolatile programmability in PCMs has made them a promising candidate for implementing optical memory systems. In this paper, we describe the design of an optical memory cell based on PCMs while exploring the design space of the cell in terms of PCM material choice (e.g., GST, GSST, Sb2Se3), cell bit capacity, latency, and power consumption. Leveraging this design-space exploration for the design of efficient optical memory cells, we present the design and implementation of an optical memory array and explore its scalability and power consumption when using different optical memory cells. We also identify performance bottlenecks that need to be alleviated to further scale optical memory arrays with competitive latency and energy consumption, compared to their electronic counterparts.
Open Access
Ethics in computing education: challenges and experience with embedded ethics
(Colorado State University. Libraries, 2023-06-05) Pasricha, Sudeep, author; ACM, publisher
The next generation of computer engineers and scientists must be proficient in not just the technical knowledge required to analyze, optimize, and create computing systems, but also with the skills required to make ethical decisions during design. Teaching computer ethics in computing curricula is therefore becoming an important requirement with significant ramifications for our increasingly connected and computing-reliant society. In this paper, we reflect on the many challenges and questions with effectively integrating ethics into modern computing curricula. We describe a case study of integrating ethics modules into the computer engineering curricula at Colorado State University.
Open Access
Exploring unimodal notification interaction and display methods in augmented reality
(Colorado State University. Libraries, 2023-10-09) Plabst, Lucas, author; Raikwar, Aditya, author; Oberdörfer, Sebastian, author; Ortega, Francisco, author; Niebling, Florian, author; ACM, publisher
As we develop computing platforms for augmented reality (AR) head-mounted display (HMDs) technologies for social or workplace environments, understanding how users interact with notifications in immersive environments has become crucial. We researched effectiveness and user preferences of different interaction modalities for notifications, along with two types of notification display methods. In our study, participants were immersed in a simulated cooking environment using an AR-HMD, where they had to fulfill customer orders. During the cooking process, participants received notifications related to customer orders and ingredient updates. They were given three interaction modes for those notifications: voice commands, eye gaze and dwell, and hand gestures. To manage multiple notifications at once, we also researched two different notification list displays, one attached to the user's hand and one in the world. Results indicate that participants preferred using their hands to interact with notifications and having the list of notifications attached to their hands. Voice and gaze interaction was perceived as having lower usability than touch.
Open Access
Fast and scalable monitoring for value-freeze operator augmented signal temporal logic
(Colorado State University. Libraries, 2024-05-14) Ghorbel, Bassem, author; Prabhu, Vinayak S., author; ACM, publisher
Signal Temporal Logic (STL) is a timed temporal logic formalism that has found widespread adoption for rigorous specification of properties in Cyber-Physical Systems. However, STL is unable to specify oscillatory properties commonly required in engineering design. This limitation can be overcome by the addition of additional operators, for example, signal-value freeze operators, or with first order quantification. Previous work on augmenting STL with such operators has resulted in intractable monitoring algorithms. We present the first efficient and scalable offline monitoring algorithms for STL augmented with independent freeze quantifiers. Our final optimized algorithm has a |ρ|log(|ρ|) dependence on the trace length |ρ| for most traces ρ arising in practice, and a |ρ|2 dependence in the worst case. We also provide experimental validation of our algorithms – we show the algorithms scale to traces having 100k time samples.
Open Access
FedHIL: heterogeneity resilient federated learning for robust indoor localization with mobile devices
(Colorado State University. Libraries, 2023-09-09) Gufran, Danish, author; Pasricha, Sudeep, author; ACM, publisher
Indoor localization plays a vital role in applications such as emergency response, warehouse management, and augmented reality experiences. By deploying machine learning (ML) based indoor localization frameworks on their mobile devices, users can localize themselves in a variety of indoor and subterranean environments. However, achieving accurate indoor localization can be challenging due to heterogeneity in the hardware and software stacks of mobile devices, which can result in inconsistent and inaccurate location estimates. Traditional ML models also heavily rely on initial training data, making them vulnerable to degradation in performance with dynamic changes across indoor environments. To address the challenges due to device heterogeneity and lack of adaptivity, we propose a novel embedded ML framework called FedHIL. Our framework combines indoor localization and federated learning (FL) to improve indoor localization accuracy in device-heterogeneous environments while also preserving user data privacy. FedHIL integrates a domain-specific selective weight adjustment approach to preserve the ML model's performance for indoor localization during FL, even in the presence of extremely noisy data. Experimental evaluations in diverse real-world indoor environments and with heterogeneous mobile devices show that FedHIL outperforms state-of-the-art FL and non-FL indoor localization frameworks. FedHIL is able to achieve 1.62 × better localization accuracy on average than the best performing FL-based indoor localization framework from prior work.
Open Access
Formal verification of source-to-source transformations for HLS
(Colorado State University. Libraries, 2024-04-02) Pouchet, Louis-Noël, author; Tucker, Emily, author; Zhang, Niansong, author; Chen, Hongzheng, author; Pal, Debjit, author; Rodríguez, Gabriel, author; Zhang, Zhiru, author; ACM, publisher
High-level synthesis (HLS) can greatly facilitate the description of complex hardware implementations, by raising the level of abstraction up to a classical imperative language such as C/C++, usually augmented with vendor-specific pragmas and APIs. Despite productivity improvements, attaining high performance for the final design remains a challenge, and higher-level tools like source-to-source compilers have been developed to generate programs targeting HLS toolchains. These tools may generate highly complex HLS-ready C/C++ code, reducing the programming effort and enabling critical optimizations. However, whether these HLS-friendly programs are produced by a human or a tool, validating their correctness or exposing bugs otherwise remains a fundamental challenge. In this work we target the problem of efficiently checking the semantics equivalence between two programs written in C/C++ as a means to ensuring the correctness of the description provided to the HLS toolchain, by proving an optimized code version fully preserves the semantics of the unoptimized one. We introduce a novel formal verification approach that combines concrete and abstract interpretation with a hybrid symbolic analysis. Notably, our approach is mostly agnostic to how control-flow, data storage, and dataflow are implemented in the two programs. It can prove equivalence under complex bufferization and loop/syntax transformations, for a rich class of programs with statically interpretable control-flow. We present our techniques and their complete end-to-end implementation, demonstrating how our system can verify the correctness of highly complex programs generated by source-to-source compilers for HLS, and detect bugs that may elude co-simulation.