Abstract

Data science and artificial intelligence (DSAI) based methods can process massive amounts of data to extract useful knowledge and can be employed to build decision support systems in various domains. Like other domains, the legal systems in several countries are being digitized and there is a scope to build DSAI-based frameworks to improve the performance of legal systems. In the literature, research efforts are being made to investigate DSAI-based methods to improve justice delivery. The Indian legal system is currently experiencing a major problem with a substantial backlog of cases. This paper provides an overview of DSAI-based efforts in the legal domain related to India. We also listed potential research issues to be explored. We hope the issues will encourage further research to improve justice delivery performance in India and other countries.

Abstract

Dysarthric speech poses significant challenges in developing as- sistive technologies, primarily due to the limited availability of data. Recent advances in neural speech synthesis, especially zero-shot voice cloning, facilitate synthetic speech generation for data augmentation; however, they may introduce biases to- wards dysarthric speech. In this paper, we investigate the effec- tiveness of state-of-the-art F5-TTS in cloning dysarthric speech using TORGO dataset, focusing on intelligibility, speaker sim- ilarity, and prosody preservation. We also analyze potential biases using fairness metrics like Disparate Impact and Parity Difference to assess disparities across dysarthric severity lev- els. Results show that F5-TTS exhibits a strong bias toward speech intelligibility over speaker and prosody preservation in dysarthric speech synthesis. Insights from this study can help integrate fairness-aware dysarthric speech synthesis, fostering the advancement of more inclusive speech technologies.

Abstract

This paper presents a novel Transformer (XFMR) design intended for integrated millimeter-wave (mmWave) applications. The XFMR consists of an 8-shaped primary inductor and a dumbbell-shaped secondary inductor. This structure addresses key challenges in on-chip XFMR design by improving electromagnetic interference (EMI) suppression, reducing unwanted signal coupling, and optimizing magnetic field symmetry. Additionally, the geometry enables better layout efficiency and isolation, contributing to improved performance in tightly integrated environments. To demonstrate the practical advantages of the proposed XFMR, it is implemented in a low-voltage, low-phase-noise (PN) LC VCO designed for 23.8-27.2 GHz operation. Simulation results in TSMC 65~nm CMOS technology show a tuning range of 3.4~GHz, a PN lesser than −110 dBc/Hz at 1 MHz offset, and a figure-of-merit (FoM) greater than 186 dBc/Hz, at 0.6 V supply. These results validate the effectiveness of the XFMR in enhancing VCO performance, highlighting its potential for use in high-performance mmWave design.

Abstract

Recent progress and development of artificial intelligence and machine learning (AI/ML) techniques have enabled addressing complex biomolecular problems. AI/ML models learn the underlying distribution of data they are trained on and when exposed to new inputs, they make predictions based on patterns and relationships previously observed in the training set. Further, generative artificial intelligence (GenAI) can be used to accurately generate protein structure or sequence from specific selected properties. This review specifically focuses on the applications of AI/ML in predicting important functional properties of proteins, and the potential prospects of reverse-engineering in depicting the sequence and structure, from available protein-property information.

Abstract

Motorized two-wheelers (MTW) dominate Indian road but remain underrepresented in driver behavior research. This study presents the first large-scale analysis of MTW driver gaze behavior in naturalistic, heterogeneous urban traffic, using the myEye2Wheeler dataset. A semantic segmentation pipeline (YOLOv11 + SAM2) was used to extract object-level gaze metrics under two attention modes: direct gaze (foveal overlap) and central vision (parafoveal monitoring). Results reveal a functional division: central vision supports broad monitoring, while direct gaze enables brief, selective sampling. Novice riders exhibit road-anchored scanning, returning to the road between object fixations, while experienced riders form longer chains of attention across multiple objects. The findings suggest that experience primarily refines temporal rhythm rather than altering allocation strategy and reduces object-class effects in gaze patterns. These findings offer new insight into MTW attention structures and inform future work on behavior modeling and safety systems.

Abstract

This paper presents an Internet of Things (IoT) enabled system that utilizes machine learning (ML) techniques to estimate the water flow in pipelines based on multi-modal sensor data. Traditional methods of water flow monitoring, mainly digital water flow meters, are prohibitively expensive, making largescale deployments challenging. To address this, an alternative has been proposed that leverages the correlation between water flow rate, motor current consumption, and pipeline pressure. Current consumption data of water motors has been integrated with pipeline pressure data to develop ML-based solutions that estimate the value of water flow without directly measuring it. The proposed approach enables digital flow monitoring at a fraction of the cost of conventional digital flow meters while offering high time granularity and accuracy, making it a scalable and practical solution for smart water management in diverse settings. The system’s robustness has been demonstrated by testing the model against erasures in the data.

Abstract

Cloud-based storage service has emerged as a promising alternative to managing local storage, offering users additional features, such as storage, backup, and restoration of various resources, including software, applications, and sensitive private information, within a virtual database, while ensuring data confidentiality and security. However, the widespread use of cloud services by individuals and organisations raises concerns regarding user accessibility and data security due to increasing social threats and adversarial attacks. Searchable encryption (SE) has been introduced to address these issues, providing a secure environment for a convenient and effective way of data searching and sharing. SE models have been advanced by integrating blockchain functionalities to tackle certain existing vulnerabilities and enhance user accessibility, data privacy, and integrity. This survey work explores various research works on SE techniques leveraging cloud and blockchain functionalities, discussing and categorizing the diverse approaches based on different criteria. This study also discusses the different enhancements made to SE techniques over the years, including the underlying requirements that led to the inclusion of blockchain functionalities. Moreover, this study provides a comparative analysis of existing survey works done in this area, which highlights a lack of recent literature surveys that thoroughly explore blockchain-based public key encryption with keyword search (PEKS) schemes. Particularly, we delve into the technical aspects of PEKS by classifying, analyzing and comparing different functionalities based on various aspects. The survey concludes with a comparative analysis of existing PEKS solutions and a discussion on identified research gaps, aiming to improve future research on PEKS approaches in this emerging field.

Abstract

CubeSats have become a popular platform for low-cost space missions, supporting application ranging from Earth observation to space exploration. Despite their growing adoption, CubeSats face significant challenges in supporting in-orbit software updates with strong authentication and integrity guarantees due to severe constraints on computation, power, and communication bandwidth. Existing approaches either impose unsustainable overhead (e.g., public key cryptography) or focus solely on ground station-to-CubeSat delivery. Prior work such as CSUM offers an efficient hash chain based protocol for broadcast authentication from ground stations. However, it does not address the dissemination of update within a CubeSat cluster, which limits scalability and increase transmission redundancy. In this paper, we propose an enhancement to the CSUM protocol by replacing its custom hash concatenation mechanism with a standardized HMAC construction to achieve stronger resistance against cryptographic attacks, with minimal trade-off in efficiency. Building on this enhancement, we introduce CSUM-G, an extension of CSUM that enables authenticated software update propagation across a CubeSat cluster using inter-satellite links and shared secret cluster keys. In our implementation, CSUM-G achieves 100% update success with only 0.13-0.20 average retries per update, and propagates updates in as little as 0.04 seconds for 6 CubeSats and 3.68 seconds for 600 nodes.

Abstract

The problem of recovering from qubit erasures has recently gained attention as erasures occur in many physical systems such as photonic systems, trapped ions, superconducting qubits and circuit quantum electrodynamics. While several linear-time decoders for error correction are known, their errorcorrecting capability is limited to half the minimum distance of the code, whereas erasure correction allows one to go beyond this limit. As in the classical case, stopping sets pose a major challenge in designing efficient erasure decoders for quantum LDPC codes. In this paper, we show through simulation, that an attractive alternative here, is the use of quantum expander codes in conjunction with the peeling decoder that has linear complexity. We also discuss additional techniques including small-set-flip decoding, that can be applied following the peeling operation, to improve decoding performance and their associated complexity.

Abstract

Dual discriminator generative adversarial networks (D2 GANs) were introduced to mitigate the problem of mode collapse in generative adversarial networks. In D2 GANs, two discriminators are employed alongside a generator: one discriminator rewards high scores for samples from the true data distribution, while the other favors samples from the generator. In this work, we first introduce {dual discriminator α-GANs (D2 α-GANs)}, which combines the strengths of dual discriminators with the flexibility of a tunable loss function, α-loss. We further generalize this approach to arbitrary functions defined on positive reals, leading to a broader class of models we refer to as generalized dual discriminator generative adversarial networks. For each of these proposed models, we provide theoretical analysis and show that the associated min-max optimization reduces to the minimization of a linear combination of an f-divergence and a {reverse} f-divergence. This generalizes the known simplification for D2-GANs, where the objective reduces to a linear combination of the KL-divergence and the reverse KL-divergence. Finally, we perform experiments on 2D synthetic data and use multiple performance metrics to capture various advantages of our GANs.