Abstract

Cloud Operations (CloudOps) is a rapidly growing field focused on the automated management and optimization of cloud infrastructure which is essential for organizations navigating increasingly complex cloud environments. MontyCloud Inc. is one of the major companies in the CloudOps domain that leverages autonomous bots to manage cloud compliance, security, and continuous operations. To make the platform more accessible and effective to the customers, we leveraged the use of GenAI. Developing a GenAI-based solution for autonomous CloudOps for the existing MontyCloud system presented us with various challenges such as i) diverse data sources; ii) orchestration of multiple processes; and iii) handling complex workflows to automate routine tasks. To this end, we developed MOYA, a multi-agent framework that leverages GenAI and balances autonomy with the necessary human control. This framework integrates various internal and external systems and is optimized for factors like task orchestration, security, and error mitigation while producing accurate, reliable, and relevant insights by utilizing Retrieval Augmented Generation (RAG). Evaluations of our multi-agent system with the help of practitioners as well as using automated checks demonstrate enhanced accuracy, responsiveness, and effectiveness over non-agentic approaches across complex workflows.

Abstract

Location-based services (LBSs) in a mobile network environment that provide personalized and timely information to users entail privacy concerns due to the leakage of user locations to adversaries. In the literature, several cloaking-based privacy preservation approaches have been proposed by considering the P2P environment. However, existing spatial cloaking approaches form a large cluster of mobile users to cloak the user query location. Maintaining such structures in a highly dynamic mobile-P2P network is challenging. In addition to user’s location, the user’s intent is also an important concern and needs to be preserved from an adversary. This paper proposes a location privacy and intent privacy preservation scheme, especially for spatial range queries. The key contributions of our work are three-fold. First, we introduce the concept of ijk-anonymity to achieve improved location privacy. Second, we propose a location-based privacy-preservation approach, which we designate as ijkCloak, for spatial range queries in a mobile network environment. The proposed approach preserves user location and intent information from the LBS provider and nearby peers. Third, we conduct theoretical analysis and experiments on resistance to attacks. We show that ijkCloak effectively facilitates improved user location privacy and intent privacy by employing fewer peers w.r.t. existing approaches.

Abstract

In this work, we introduce and characterize a broad class of quantum operations with a unique fixed point, termed quantum ergodic channels. We derive Lindblad-type master equations for these channels in arbitrary finite dimensions and analyze their non-Markovian dynamics using established measures. When the fixed point is a passive state, the channels exhibit ergotropy dynamics with notable thermodynamic implications. Specifically, under Markovian processes, ergotropy (a measure of the extractable work from a system under unitary evolution) monotonically decreases. However, in non-Markovian dynamics, ergotropy fluctuates, leading to a backflow effect that highlights memory-induced resource recovery. Our findings suggest that this ergotropy backflow could serve as an operationally meaningful indicator of non-Markovianity, offering new perspectives on the interplay between memory effects and thermodynamic behavior in open quantum systems. This study enhances the theoretical framework for understanding energy dynamics under ergodic channels and highlights new avenues for exploring the implications of memory effects in quantum batteries.

Abstract

Over a decade, efforts have been made to extend data science-based approaches to improve the process of drug-target interactions (DTIs) by modeling drugs and targets as graphs. From a drug discovery point of view, a pharmacophore, a subgraph of a drug candidate, is an essential structural and chemical feature necessary for interacting with a specific biological target. Effective identification of potential pharmacophores from a given candidate drug is a research issue. In the literature, efforts are being made to investigate machine learning and Graph Neural Network (GNN) based frameworks to identify the potential drug candidates for a given target. However, the problem of extracting the knowledge of the potential pharmacophore of the given candidate drug has not been explored. There is an opportunity to extract pharmacophores from drug candidates by exploiting the knowledge of potential subgraphs extracted through a trained GNN model. In this paper, we propose a two-phase GNN-based framework for identifying drug candidates and extracting potential pharmacophores from these candidates. The framework consists of two parts. First, we employ a GNN-based model to compute the affinity score for the given drug compound. Second, by employing the Monte Carlo Tree Search (MCTS) algorithm, the trained GNN is leveraged to extract potential subgraphs representing pharmacophores. The experimental results on the Davis, Kiba and Allergy datasets demonstrate the feasibility of the proposed approach to extract pharmacophores of candidate drugs with high performance. The predicted binding affinities of molecular subgraphs extracted from drug candidates are very similar to those of the corresponding drug candidates. The proposed approach helps explore the potential pharmacophore of the given candidate drug, which enhances the explainability of DTI to obtain deeper insights into crucial molecular interactions.

Abstract

Navigation amongst densely packed crowds remains a challenge for mobile robots. The complexity increases further if the environment layout changes, making the prior computed global plan infeasible. In this paper, we show that it is possible to dramatically enhance crowd navigation by just improving the local planner. Our approach combines generative modelling with inference time optimization to generate sophisticated long-horizon local plans at interactive rates. More specifically, we train a Vector Quantized Variational AutoEncoder to learn a prior over the expert trajectory distribution conditioned on the perception input. At run-time, this is used as an initialization for a sampling-based optimizer for further refinement. Our approach does not require any sophisticated prediction of dynamic obstacles and yet provides state-of-the-art performance. In particular, we compare against the recent DRL-VO approach and show a 40% improvement in success rate and a 6% improvement in travel time.

Abstract

Reliable assessment of oral reading fluency (ORF) is of great importance in foundational literacy missions globally. For the design of level appropriate testing passages, text difficulty has traditionally been based on coarse-grained measures of readability like the Flesch–Kincaid score. We present a novel study where we deploy psycholinguistic measures of reading difficulty from Natural Language Processing to predict the duration of words in Hindi read-aloud speech. We test the hypotheses that expectation based measures of linguistic complexity are significant predictors of word duration in Hindi read-aloud speech. We validate the stated hypotheses by estimating surprisal measures inspired from Surprisal Theory of sentence comprehension and introduce a novel measure of orthographic complexity to model the intricacies of the Hindi script. Cognitive modelling experiments were conducted on a dataset of six Hindi short stories read aloud by 5 expert readers, containing 2 measures of word duration. Our results show that both surprisal as well as the orthographic complexity measures are significant predictors of word duration. In contrast with long words, we find duration reducing with increased orthographic complexity in the case of short words. The variation between individual speakers in terms of word duration is very low and the variance in the data is caused by the properties of the words used in the text. Finally, we reflect on the implications of our work for cognitive models of language production and for ORF assessment.

Abstract

In decision making settings such as medical diagnosis, investment choices, or sentencing in a court of law, an individual’s background and experience influence their decisions. In the legal domain, multiple factors like personal bias/beliefs, recent events, and the contemporary state of mind could affect decision-making, leading to inconsistencies in judgments between and within jurisdictions. It is widely reported in the literature that anomalies and disparities exist in judicial decisions, such as bail grants and sentence impositions, stemming from implicit bias or other contextual factors. Notably, in domains like sales and marketing, data cube-based systems are being used to extract interesting trends and anomalies in subspaces from large multidimensional databases. In this paper, we extend the data cube framework to explore possible anomalies and disparities in court sentences by conducting experiments on a sample Indian judgment dataset of criminal cases. The results show that the data cube-based framework could identify anomalies in the legal domain. We hope this work will encourage researchers to investigate a compre- hensive data cube-based framework to reduce disparities and improve justice delivery worldwide.

Abstract

The widespread adoption of machine learning (ML) has brought forth diverse models with varying architectures, data requirements, introducing new challenges in integrating these systems into real-world applications. Traditional solutions often struggle to manage the complexities of connecting heterogeneous models, especially when dealing with varied technical specifications. These limitations are amplified in large-scale, collaborative projects where stakeholders contribute models with different technical specifications. To address these challenges, we developed LoCoML, a low-code framework designed to simplify the integration of diverse ML models within the context of the Bhashini Project - a large-scale initiative aimed at integrating AI-driven language technologies such as automatic speech recognition, machine translation, text-to-speech, and optical character recognition to support seamless communication across more than 20 languages. Initial evaluations show that LoCoML adds only a small amount of computational load, making it efficient and effective for large-scale ML integration. Our practical insights show that a low-code approach can be a practical solution for connecting multiple ML models in a collaborative environment.

Abstract

As Machine Learning (ML) makes its way into aviation, ML-enabled systems—including low-criticality systems—require a reliable certification process to ensure safety and performance. Traditional standards, like DO-178C, which are used for critical software in aviation, don’t fully cover the unique aspects of ML. This paper proposes a semi-automated certification approach, specifically for low-criticality ML systems, focusing on data and model validation, resilience assessment, and usability assurance while integrating manual and automated processes. Key aspects include structured classification to guide certification rigor on system attributes, an Assurance Profile that consolidates evaluation outcomes into a confidence measure the ML component, and methodologies for integrating human oversight into certification activities. Through a case study with a YOLOv8-based object detection system designed to classify military and civilian vehicles in real-time for reconnaissance and surveillance aircraft, we show how this approach supports the certification of ML systems in low-criticality airborne applications.

Abstract

Large Language Models (LLMs) often inherit biases from the web data they are trained on, which contains stereotypes and prejudices. Current methods for evaluating and mitigating these biases rely on bias-benchmark datasets. These benchmarks measure bias by observing an LLM’s behavior on biased statements. However, these statements lack contextual considerations of the situations they try to present. To address this, we introduce a contextual reliability framework, which evaluates model robustness to biased statements by considering the various contexts in which they may appear. We develop the Context-Oriented Bias Indicator and Assessment Score (COBIAS) to measure a biased statement’s reliability in detecting bias, based on the variance in model behavior across different contexts. To evaluate the metric, we augmented 2,291 stereotyped statements from two existing benchmark datasets by adding contextual information. We show that COBIAS aligns with human judgment on the contextual reliability of biased statements (Spearman’s 𝜌 = 0.65, 𝑝 = 3.4 ∗ 10−60) and can be used to create reliable benchmarks, which would assist bias mitigation works.