Browsing by Author "Ray, Indrakshi, author"

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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
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
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.
Open Access
Sparse binary transformers for multivariate time series modeling
(Colorado State University. Libraries, 2023-08-04) Gorbett, Matt, author; Shirazi, Hossein, author; Ray, Indrakshi, author; ACM, publisher
Compressed Neural Networks have the potential to enable deep learning across new applications and smaller computational environments. However, understanding the range of learning tasks in which such models can succeed is not well studied. In this work, we apply sparse and binary-weighted Transformers to multivariate time series problems, showing that the lightweight models achieve accuracy comparable to that of dense floating-point Transformers of the same structure. Our model achieves favorable results across three time series learning tasks: classification, anomaly detection, and single-step forecasting. Additionally, to reduce the computational complexity of the attention mechanism, we apply two modifications, which show little to no decline in model performance: 1) in the classification task, we apply a fixed mask to the query, key, and value activations, and 2) for forecasting and anomaly detection, which rely on predicting outputs at a single point in time, we propose an attention mask to allow computation only at the current time step. Together, each compression technique and attention modification substantially reduces the number of non-zero operations necessary in the Transformer. We measure the computational savings of our approach over a range of metrics including parameter count, bit size, and floating point operation (FLOPs) count, showing up to a 53x reduction in storage size and up to 10.5x reduction in FLOPs.
Open Access
Synthesizing and analyzing attribute-based access control model generated from natural language policy statements
(Colorado State University. Libraries, 2023-05-24) Abdelgawad, Mahmoud, author; Ray, Indrakshi, author; Alqurashi, Saja, author; Venkatesha, Videep, author; Shirazi, Hosein, author; ACM, publisher
Access control policies (ACPs) are natural language statements that describe criteria under which users can access resources. We focus on constructing NIST Next Generation Access Control (NGAC) ABAC model from ACP statements. NGAC is more complex than RBAC or XACML ABAC as it supports dynamic, event-based policies, as well as prohibitions. We provide algorithms that use spaCy, a NLP library, to extract entities and relations from ACP sentences and convert them into the NGAC model. We then convert this NGAC model into Neo4j representation for the purpose of analysis. We apply the approach to various real-world ACP datasets to demonstrate the feasibility and assess scalability. We demonstrate that the approach is scalable and effectively extracts the NGAC ABAC model from large ACP datasets. We also show that redundancies and inconsistencies of ACP sentences are often found in unclean datasets.
Open Access
Tiled bit networks: sub-bit neural network compression through reuse of learnable binary vectors
(Colorado State University. Libraries, 2024-10-21) Gorbett, Matt, author; Shirazi, Hossein, author; Ray, Indrakshi, author; ACM, publisher
Binary Neural Networks (BNNs) enable efficient deep learning by saving on storage and computational costs. However, as the size of neural networks continues to grow, meeting computational requirements remains a challenge. In this work, we propose a new form of quantization to tile neural network layers with sequences of bits to achieve sub-bit compression of binary-weighted neural networks. The method learns binary vectors (i.e. tiles) to populate each layer of a model via aggregation and reshaping operations. During inference, the method reuses a single tile per layer to represent the full tensor. We employ the approach to both fully-connected and convolutional layers, which make up the breadth of space in most neural architectures. Empirically, the approach achieves near full-precision performance on a diverse range of architectures (CNNs, Transformers, MLPs) and tasks (classification, segmentation, and time series forecasting) with up to an 8x reduction in size compared to binary-weighted models. We provide two implementations for Tiled Bit Networks: 1) we deploy the model to a microcontroller to assess its feasibility in resource-constrained environments, and 2) a GPU-compatible inference kernel to facilitate the reuse of a single tile per layer in memory.