Publications

Permanent URI for this collectionhttps://hdl.handle.net/10217/239510

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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
A unified framework for automated code transformation and pragma insertion
(Colorado State University. Libraries, 2025-02-27) Pouget, Stéphane, author; Pouchet, Louis-Noël, author; Cong, Jason, author; ACM, publisher
High-Level Synthesis compilers and Design Space Exploration tools have greatly advanced the automation of hardware design, improving development time and performance. However, achieving a good Quality of Results still requires extensive manual code transformations, pragma insertion, and tile size selection, which are typically handled separately. The design space is too large to be fully explored by this fragmented approach. It is too difficult to navigate this way, limits the exploration of potential optimizations, and complicates the design generation process. To tackle this obstacle, we propose Sisyphus, a unified framework that automates code transformation, pragma insertion, and tile size selection within a common optimization framework. By leveraging Nonlinear Programming, our approach efficiently explores the vast design space of regular loop-based kernels, automatically selecting loop transformations and pragmas that minimize latency. Evaluation against state-of-the-art frameworks, including AutoDSE, NLP-DSE, and ScaleHLS, shows that Sisyphus achieves superior Quality of Results, outperforming alternatives across multiple benchmarks. By integrating code transformation and pragma insertion into a unified model, Sisyphus significantly reduces design generation complexity and improves performance for FPGA-based systems.
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
Automatic hardware pragma insertion in high-level synthesis: a non-linear programming approach
(Colorado State University. Libraries, 2025-02-07) Pouget, Stéphane, author; Pouchet, Louis-Noël, author; Cong, Jason, author; ACM, publisher
High-Level Synthesis enables the rapid prototyping of hardware accelerators, by combining a high-level description of the functional behavior of a kernel with a set of micro-architecture optimizations as inputs. Such optimizations can be described by inserting pragmas e.g., pipelining and replication of units, or even higher level transformations for HLS such as automatic data caching using the AMD/Xilinx Merlin compiler. Selecting the best combination of pragmas, even within a restricted set, remains particularly challenging and the typical state-of-practice uses design-space exploration to navigate this space. But due to the highly irregular performance distribution of pragma configurations, typical DSE approaches are either extremely time consuming, or operating on a severely restricted search space. This work proposes a framework to automatically insert HLS pragmas in regular loop-based programs, supporting pipelining, unit replication, and data caching. We develop an analytical performance and resource model as a function of the input program properties and pragmas inserted, using non-linear constraints and objectives. We prove this model provides a lower bound on the actual performance after HLS. We then encode this model as a Non-Linear Program, by making the pragma configuration unknowns of the system, which is computed optimally by solving this NLP. This approach can also be used during DSE, to quickly prune points with a (possibly partial) pragma configuration, driven by lower bounds on achievable latency. We extensively evaluate our end-to-end, fully implemented system, showing it can effectively manipulate spaces of billions of designs in seconds to minutes for the kernels evaluated.
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
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
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
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.
Open Access
Integrating soft skills training into your course through a collaborative activity
(Colorado State University. Libraries, 2025-02-18) Brieven, Géraldine, author; Moraes, Marcia, author; Pawelczak, Dieter, author; Vasilache, Simona, author; Donnet, Benoit, author; ACM, publisher
Nowadays, employers highly value soft skills, yet many students lack these fundamental abilities. Teaching soft skills involves fostering active student participation and facilitating communication of technical knowledge among peers. This approach presents challenges: (i) creating an engaging learning environment; (ii) ensuring students get timely feedback; (iii) finding an approach that is not too time-consuming for instructors to prepare. The Collaborative Design & Build (CDB) activity, described in this paper, was designed to respond to these challenges. It simulates a real-life scenario, triggering students' interest. The success of this collaborative activity hinges on students working together in a structured chain, where each team builds upon and contributes to the success of the others. This fosters student engagement and accountability as they realize the impact of their actions on the entire chain. This pedagogical approach has already been adopted by four universities abroad. This paper shows how it can be deployed in different courses. Finally, it also discusses how students perceived the activity through four soft skills: collaboration, communication, problem solving and critical thinking. These skills were selected based on their relevance, both in the context of the collaborative activity and in the job market. They are also aligned with the ''4C's of 21st Century skills''. Results show that while students initially struggled with soft skills, consistent practice throughout the semester boosted their confidence, especially in communication. This makes the activity particularly relevant in the classroom, as communication is considered as the most important soft skill for the future.
Open Access
Lights, headset, tablet, action: exploring the use of hybrid user interfaces for immersive situated analytics
(Colorado State University. Libraries, 2024-10-24) Zhou, Xiaoyan, author; Lee, Benjamin, author; Ortega, Francisco R., author; Batmaz, Anil Ufuk, author; Yang, Yalong, author; ACM, publisher
While augmented reality (AR) headsets provide entirely new ways of seeing and interacting with data, traditional computing devices can play a symbiotic role when used in conjunction with AR as a hybrid user interface. A promising use case for this setup is situated analytics. AR can provide embedded views that are integrated with their physical referents, and a separate device such as a tablet can provide a familiar situated overview of the entire dataset being examined. While prior work has explored similar setups, we sought to understand how people perceive and make use of visualizations presented on both embedded visualizations (in AR) and situated visualizations (on a tablet) to achieve their own goals. To this end, we conducted an exploratory study using a scenario and task familiar to most: adjusting light levels in a smart home based on personal preference and energy usage. In a prototype that simulates AR in virtual reality, embedded visualizations are positioned next to lights distributed across an apartment, and situated visualizations are provided on a handheld tablet. We observed and interviewed 19 participants using the prototype. Participants were easily able to perform the task, though the extent the visualizations were used during the task varied, with some making decisions based on the data and others only on their own preferences. Our findings also suggest the two distinct roles that situated and embedded visualizations can have, and how this clear separation might improve user satisfaction and minimize attention-switching overheads in this hybrid user interface setup. We conclude by discussing the importance of considering the user's needs, goals, and the physical environment for designing and evaluating effective situated analytics applications.
Open Access
Maximal simplification of polyhedral reductions
(Colorado State University. Libraries, 2025-01-09) Narmour, Louis, author; Yuki, Tomofumi, author; Rajopadhye, Sanjay, author; ACM, publisher
Reductions combine collections of input values with an associative and often commutative operator to produce collections of results. When the same input value contributes to multiple outputs, there is an opportunity to reuse partial results, enabling reduction simplification. Simplification often produces a program with lower asymptotic complexity. Typical compiler optimizations yield, at best, a constant fold speedup, but a complexity improvement from, say, cubic to quadratic complexity yields unbounded speedup for sufficiently large problems. It is well known that reductions in polyhedral programs may be simplified automatically, but previous methods cannot exploit all available reuse. This paper resolves this long-standing open problem, thereby attaining minimal asymptotic complexity in the simplified program. We propose extensions to prior work on simplification to support any independent commutative reduction. At the heart of our approach is piece-wise simplification, the notion that we can split an arbitrary reduction into pieces and then independently simplify each piece. However, the difficulty of using such piece-wise transformations is that they typically involve an infinite number of choices. We give constructive proofs to deal with this and select a finite number of pieces for simplification.
Open Access
Methodology for resiliency analysis of mission-critical systems
(Colorado State University. Libraries, 2024-05-21) Abdelgawad, Mahmoud, author; Ray, Indrakshi, author; ACM, publisher
Mission-critical systems ensure the safety and security of any nation. Attacks on mission-critical systems can have devastating consequences. We need to design missions that can prevent, detect, survive, recover, and respond to faults and cyber attacks. In other words, we must design missions that are cyber-resilient. System engineering techniques must be used to specify, analyze, and understand where adverse events are possible and how to mitigate them while a mission-critical system is deployed. This work introduces an end-to-end methodology for designing cyber-resilient mission-critical systems. The methodology first specifies a mission in the form of a workflow. It then converts the mission workflow into formal representation using Coloured Petri Nets (CPN). The methodology also derives threat models from the mission specification. The threat models are used to form a formal specification of attacks that can be represented in CPN. These CPN attacks are plugged into potential places in the CPN mission to design various attack scenarios. The methodology finally verifies the state transitions of the CPN mission attached to attacks to analyze the resiliency of the mission. It identifies in which state transition the mission succeeds, fails, and is incomplete. The methodology is applied to a drone surveillance system as a motivating example. The result shows that the methodology is practical for resiliency analysis of mission-critical systems. The methodology demonstrates how to restrict a mission to improve the resiliency of mission-critical systems. The methodology provides crucial insights in the early stages of mission specification to achieve cyber resiliency.
Open Access
Paying attention to wildfire: using U-Net with attention blocks on multimodal data for next day prediction
(Colorado State University. Libraries, 2023-10-09) Fitzgerald, Jack, author; Seefried, Ethan, author; Yost, James, author; Pallickara, Sangmi, author; Blanchard, Nathaniel, author; ACM, publisher
Predicting where wildfires will spread provides invaluable information to firefighters and scientists, which can save lives and homes. However, doing so requires a large amount of multimodal data e.g., accurate weather predictions, real-time satellite data, and environmental descriptors. In this work, we utilize 12 distinct features from multiple modalities in order to predict where wildfires will spread over the next 24 hours. We created a custom U-Net architecture designed to train as efficiently as possible, while still maximizing accuracy, to facilitate quickly deploying the model when a wildfire is detected. Our custom architecture demonstrates state-of-the-art performance and trains an order of magnitude more quickly than prior work, while using fewer computational resources. We further evaluated our architecture with an ablation study to identify which features were key for prediction and which provided negligible impact on performance.
Open Access
Predicting attrition among software professionals: antecedents and consequences of burnout and engagement
(Colorado State University. Libraries, 2024-12) Trinkenreich, Bianca, author; Santos, Fabio, author; Stol, Klaas-Jan, author; ACM, publisher
In this study of burnout and engagement, we address three major themes. First, we offer a review of prior studies of burnout among IT professionals and link these studies to the Job Demands-Resources (JD-R) model. Informed by the JD-R model, we identify three factors that are organizational job resources and posit that these (a) increase engagement and (b) decrease burnout. Second, we extend the JD-R by considering software professionals' intention to stay as a consequence of these two affective states, burnout and engagement. Third, we focus on the importance of factors for intention to stay, and actual retention behavior. We use a unique dataset of over 13,000 respondents at one global IT organization, enriched with employment status 90 days after the initial survey. Leveraging partial-least squares structural quation modeling and machine learning, we find that the data mostly support our theoretical model, with some variation across different subgroups of respondents. An importance-performance map analysis suggests that managers may wish to focus on interventions regarding burnout as a predictor of intention to leave. The Machine Learning model suggests that engagement and opportunities to learn are the top two most important factors that explain whether software professionals leave an organization.
Open Access
RUBIKS: rapid explorations and summarization over high dimensional spatiotemporal datasets
(Colorado State University. Libraries, 2024-04-03) Mitra, Saptashwa, author; Young, Matt, author; Breidt, Jay, author; Pallickara, Sangmi, author; Pallickara, Shrideep, author; ACM, publisher
Exponential growth in spatial data volumes have occurred alongside increases in the dimensionality of datasets and the rates at which observations are generated. Rapid summarization and explorations of such datasets are a precursor to several downstream operations including data wrangling, preprocessing, hypothesis formulation, and model construction among others. However, researchers are stymied both by the dimensionality and data volumes that often entail extensive data movements, computation overheads, and I/O. Here, we describe our methodology to support effective summarizations and explorations at scale over arbitrary spatiotemporal scopes, which encapsulate the spatial extents, temporal bounds, or combinations thereof over the data space of interest. Summarizations can be performed over all variables representing the dataspace or subsets specified by the user. We extend the concept of data cubes to encompass spatiotemporal datasets with high-dimensionality and where there might be significant gaps in the data because measurements (or observations) of diverse variables are not synchronized and may occur at diverse rates. We couple our data summarization features with a rapid Choropleth visualizer that allows users to explore spatial variations of diverse measures of interest. We validate these concepts in the context of an Environmental Protection Agency dataset which tracks over 4000 chemical pollutants, presenting in natural water sources across the United States from 1970 onwards.
Open Access
SAFE-PASS: stewardship, advocacy, fairness and empowerment in privacy, accountability, security, and safety for vulnerable groups
(Colorado State University. Libraries, 2023-05-24) Ray, Indrajit, author; Thuraisingham, Bhavani, author; Vaidya, Jaideep, author; Mehrotra, Sharad, author; Atluri, Vijayalakshmi, author; Ray, Indrakshi, author; Kantarcioglu, Murat, author; Raskar, Ramesh, author; Salimi, Babak, author; Simske, Steve, author; Venkatasubramanian, Nalini, author; Singh, Vivek, author; ACM, publisher
Our vision is to achieve societally responsible secure and trustworthy cyberspace that puts algorithmic and technological checks and balances on the indiscriminate sharing and analysis of data. We achieve this vision in a holistic manner by framing research directions with four major considerations: (i) Expanding knowledge and understanding of security and privacy perceptions and expectations in vulnerable groups, which significantly contribute to their unwillingness to share data, and use that knowledge to drive research in (a) mitigating missing/imbalanced data problems, (b) understanding and modeling security and privacy risks of data sharing, and (c) modeling utility of data sharing. (ii) Developing a risk-adaptive, policy model capable of capturing and articulating security and privacy expectations of users that are relevant in a particular context and develops associated technology to ensure provenance and accountability. (iii) Developing robust AI/ML algorithms that are transparent and explainable with respect to fairness and bias to reduce/eliminate discrimination, misuse, privacy violations, or other cyber-crimes. (iv) Developing models and techniques for a nuanced, contextually adaptive, and graded privacy paradigm that allows trade-offs between privacy and utility. Towards this, in this paper we present the SAFE-PASS framework to provide Stewardship, Advocacy, Fairness and Empowerment in Privacy, Accountability, Security, and Safety for Vulnerable Groups.

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