Abstract

The concept of raga in Indian classical music serves as a complex, multifaceted melodic entity that can be approached through various perspectives. Compositions within a raga act as foundational structures, serving as the bedrock for improvisations. Analyzing their textual notations is easier and more objective in comparison with analyzing audio samples. A significant amount of musical insights can be derived from the discrete swara sequences alone. This paper aims to construct an intuitive visualization of raga space, using swara sequences from raga compositions. Notations from public sources are normalized, and their TF-IDF features are projected into a low-dimensional space. This approach allows for qualitative analysis of both Carnatic and Hindustani ragas, mapping them to known raga theory.

Abstract

Edge–cloud coordination offers the chance to mitigate the enormous storage and processing load brought on by a massive increase in traffic at the network’s edge. Though this paradigm has benefits on a large scale, outsourcing the sensitive data from the smart devices deployed in an Internet of Things (IoT) application may lead to privacy leakage. With an attribute-based keyword search (ABKS), the search over ciphertext can be achieved; this reduces the risk of sensitive data explosion. However, ABKS has several issues, like huge computational overhead to perform multi-keyword searches and tracing malicious users. To address these issues and enhance the performance of ABKS, we propose a novel traceable and lightweight attribute-based keyword search technique in an Edge–cloud-assisted IoT, named TL-ABKS, using edge–cloud coordination. With TL-ABKS, it is possible to do effective multi-keyword searches and implement fine-grained access control. Further, TL-ABKS outsources the encryption and decryption computation to edge nodes to enable its usage to resource-limited IoT smart devices. In addition, TL-ABKS achieves tracing user identity who misuse their secret keys. TL-ABKS is secure against modified secret keys, chosen plaintext, and chosen keyword attacks. By comparing the proposed TL-ABKS with the current state-of-the-art schemes, and conducting a theoretical and experimental evaluation of its performance and credibility, TL-ABKS is efficient.

Abstract

Voltage-Controlled Oscillator (VCO) is a crucial component of a Phase-Locked Loop (PLL) system. However, the frequency of a typical Ring-Oscillator-based VCO varies by about 2–3 times across its frequency tuning range and process, voltage and temperature (PVT) conditions. The gain of a Ring-VCO is another crucial parameter that is significantly affected by PVT variations, thereby impacting the stability and loop bandwidth of the PLL. This paper describes a compensation technique to minimize variations in Ring-VCO gain across PVT conditions. A Charge-Pump PLL operating at 4 GHz is implemented in a 28nm CMOS process. The gain of the Ring-VCO varies by ±27% across PVT at 4 GHz. This gain variation of Ring-VCO combined with other PVT-dependent blocks of PLL lead to a deviation of ±60% in Loop Bandwidth and ±21 % in Phase-Margin (PM). Process trimming reduces the VCO gain variation to ±25 %. Subsequently, the temperature compensation technique reduces the VCO gain variation by 3 times to ±8 %, over a temperature range −40∘C to 125∘C . The resulting variations in Loop Bandwidth and Phase-Margin are reduced to ±34%” and ±l5%” respectively.

Abstract

In this work, we introduce an innovative approach to estimate the vital signs of multiple human subjects simultaneously in a non-contact way using a Frequency Modulated Continuous Wave (FMCW) radar-based system. Traditional vital sign monitoring methods often face significant limitations, including subject discomfort with wearable devices, challenges in calibration, and the risk of infection transmission through contact measurement devices. To address these issues, this research is motivated by the need for versatile, non-contact vital monitoring solutions applicable in various critical scenarios. This work also explores the challenges of extending this capability to an arbitrary number of subjects, including hardware and theoretical limitations. Supported by rigorous experimental results and discussions, the paper illustrates the system’s potential to redefine vital sign monitoring. An FPGA-based implementation is also presented as proof of concept for a hardware-based and portable solution, improving upon previous works by offering 2.7x faster execution and 18.4% less Look-Up Table (LUT) utilization, as well as providing over 7400x acceleration compared to its software counterpart.

Abstract

Addressing blockchain's insufficient throughput and scalability is imperative for practical viability. Off-chain approaches, such as state channels (including Hash Time Lock Contract (HTLC), virtual channels), demonstrate enhanced throughput by enabling parallel transaction processing. While virtual channels introduce execution complexity, HTLC suffers from high update delays. Moreover, existing methods face network attacks. We present Sharding-based Hybrid State Channel (SharHSC) to address these issues. Firstly, we introduce a novel off-chain sharding architecture, which partitions proxy nodes into multiple shards. Thus, when the off-chain node count increases, adding shards enhances system throughput. Secondly, each shard establishes a supervisory committee to record latest channel statuses to ensure accurate fund distribution upon channel closure. Thirdly, we combine the strengths of HTLC and virtual channels. In particular, SharHSC constructs a single virtual channel across all the nodes involved in the payment by treating the nodes between payer and payee as an intermediate entity, which utilizes HTLC for fund routing. This realizes both low latency and streamlined complexity. Finally, our work is substantiated by security analysis and experiments. As the node number varies, compared with HTLC and virtual channels, the latency is reduced by 49.32% and 31.82%, and the throughput is increased by 8.93 and 1.89 times.

Abstract

The widespread adoption of Large Language Models (LLMs) and awareness around multilingual LLMs have raised concerns regarding the potential risks and repercussions linked to the misapplication of AI-generated text, necessitating increased vigilance. While these models are primarily trained for English, their extensive training on vast datasets covering almost the entire web, equips them with capabilities to perform well in numerous other languages. AI-Generated Text Detection (AGTD) has emerged as a topic that has already received immediate attention in research, with some initial methods having been proposed, soon followed by the emergence of techniques to bypass detection. In this paper, we report our investigation on AGTD for an indic language Hindi. Our major contributions are in four folds: i) examined 26 LLMs to evaluate their proficiency in generating Hindi text, ii) introducing the AI-generated news article in Hindi (AG_hi) dataset, iii) evaluated the effectiveness of five recently proposed AGTD techniques: ConDA, J-Guard, RADAR, RAIDAR and Intrinsic Dimension Estimation for detecting AI-generated Hindi text, iv) proposed Hindi AI Detectability Index (ADI_hi) which shows a spectrum to understand the evolving landscape of eloquence of AI-generated text in Hindi. The code and dataset is available at https://github.com/ishank31/Counter_Turing_Test

Abstract

The objective of the MTIL track in FIRE 2023 was to encourage the development of Indian Language to Indian Language (IL-IL) Neural Machine Translation models. The languages covered in the track included Hindi, Gujarati, Kannada, Odia, Punjabi, Urdu, Telugu, Kashmiri, and Sindhi. The track consists of two tasks: (i) a General Translation Task and (ii) a Domain specific Translation Task with Governance and Healthcare being the chosen domains. For the listed languages, we proposed 12 diverse language directions for the general domain translation task and 8 each for healthcare and governance domains. Participants were encouraged to submit models for one or more language pairs. We witnessed the creation of 34 distinct models spanning various language pairs and domains. Model assessments were conducted using five evaluation metrics: BLEU, CHRF, CHRF++, TER, and COMET. The submitted model outputs were ranked based on the CHRF score.

Abstract

The implementation of Internet of Things (IoT) as Cyber-Physical Systems (CPS) is essential for the advancement of smart healthcare within a highly digital environment. However, the development of medical CPS (MCPS) poses various challenges, particularly in terms of dependability and security. The complex nature of medical IoT components encompasses both computational and physical aspects including secure reliability. To mitigate these issues, this article introduces a cyber engineering, an innovative approach, that combines principles from complex system design in order to establish a systematic framework for combining the cyber and physical layers of IoT and MCPS together. This integration aims to provide an unified system designing approach that can produce more secure and robust systems. By embracing the cyber engineering ideology, the healthcare industry can overcome reliability challenges and pave the way for the development of secure and dependable smart healthcare solutions, while ensuring security by design perspective. The approach also provides ideal future possibilities to the upcoming researchers.

Abstract

This paper contributes to the expanding research on toxicity in online games, exploring the relationship between game design and player behaviours in India. Using an ethnographic approach and in-depth interviews with gamers, we capture firsthand accounts of toxic gaming experiences, delving into game design, game mechanics, and player interactions intersecting to shape player behaviours. While previous research on toxicity in gaming environments has largely focused on the role of game design, aspects of gender and, to a lesser extent, race, primarily within the context of the Global North, our study shifts the focus to India and, by extension, the Global South. We seek to uncover broader dimensions of gaming toxicity that go beyond design and gender. Our analysis examines the underlying factors contributing to the prevalence and persistence of toxic behaviours in Indian gaming environments by highlighting the influence of cultural and contextual elements in producing toxic behaviours. Expanding the scope of research to include these overlooked aspects, our study aims to provide a more comprehensive understanding of the complexities of toxicity in online gaming spaces within the Indian context.

Abstract

Bidding in a periodic double auction (PDA) is challenging due to its sequential nature, where one needs to consider current as well as future auctions to decide the bids. Monte-Carlo Tree Search (MCTS), which is a state-of-the-art online planning algorithm for tackling sequential problems, seems a perfect fit for bidding in PDAs. However, the success stories of MCTS are largely limited to discrete action spaces, and its efficacy diminishes when dealing with continuous actions. Conventional methods often resort to overly simplistic discretizations that limit exploration and fail to provide valuable insights into unexplored actions. In this work, we propose a novel bidding strategy for PDAs, Regression-MCTS, that is built upon MCTS for a continuous action space of bid prices. Unlike conventional methods, our novel MCTS method leverages information obtained from explored actions to enhance the understanding of the larger action set within the continuous domain to place bids in the auctions, thus generalizing the information about action quality between a wider action space for faster learning. To test the efficacy of our proposed method, we design an efficient PDA simulator that closely resembles real-world PDAs. Our analysis verifies that the increase in the number of rollouts improves its performance. Furthermore, our experimental results demonstrate that our approach outperforms existing MCTS-based bidding strategies and the majority of state-of-the-art PDA bidding strategies, showcasing its superior performance in PDAs.