Abstract

— Multi-domain vehicle to grid (V2G) is a network environment in which numerous service providers offer charging and discharging services to EV users. This can enhance energy management and traffic flow for efficient intelligent transportation systems (ITS). However, the combination of multiple domains can suffer from various security vulnerabilities, highlighting the need for robust countermeasures. Moreover, existing multidomain V2G protocols utilized a central trusted authority (TA) which can create a single point of failure (SPOF), or required high computational resources. In this paper, we propose a multi-domain authentication protocol for secure and efficient V2G services using consortium blockchain. The proposed protocol provides lightweight intra-domain authentication using hash functions and XOR operators. Furthermore, the proposed protocol ensures secure cross-domain authentication by integrating elliptic curve cryptography (ECC) and physical unclonable function (PUF). Therefore, the proposed protocol can establish trust, enable efficient communications, and prevent congestion at charging stations. To validate security robustness, comprehensive evaluations are conducted using “Real-Or-Random (ROR) model”, “Scyther tool”, and informal analyses. Comparative computational overheads of the proposed and related protocols are measured using “Multiprecision Integer and Rational Arithmetic Cryptographic Library (MIRACL)” testbed experiments. Additionally, a simulation of the practical deployment is conducted using “Network Simulator-3 (NS-3)”. Results indicate that the proposed protocol can improve ITS by providing secure and efficient services for multi-domain V2G environments

Abstract

Unmanned aerial vehicles (UAVs) integrated with the internet of things (IoT) guarantee useful advantages such as facilitating ground communications in regions where the availability of connectivity is restricted owing to physical obstacles. However, the data transmitted by sensors and IoT embedded in UAVs are facing new security issues and privacy challenges with the known security attacks over time. To address these security attacks and threats and meet lightweight UAV communication requirements, a secure and lightweight authentication and key agreement (AKA) scheme is essential. Recently, researchers have designed a lightweight blockchain-enabled AKA scheme with privacy-preserving for UAVs to provide useful and reliable services. However, we prove that the existing scheme is fragile to various security attacks and does not ensure mutual authentication. Thus, we propose a robust and lightweight blockchain-based AKA scheme for PUF-enabled UAVs, called RLBA-UAV to enhance the security problems of the existing scheme. We demonstrate the security of RLBA-UAV by using informal/formal security analyses such as the ROR oracle model and AVISPA simulation. Moreover, we demonstrate the performance comparison analysis between RLBA-UAV and related schemes for UAVs. We demonstrate an implementation of a network simulator (NS) 3 compliant with IEEE 802.11p standards to show its validation and feasibility that RLBA-UAV is appropriate for practical UAVs. Thus, RLBA-UAV offers enhanced security and operational efficiency compared to related schemes and can be applied to practical blockchain-based AKA systems for UAVs.

Abstract

Personalised Interactive Music Systems (PIMS) are emerging as promising devices for enhancing physical activity and exercise outcomes. By leveraging real-time data and adaptive technologies, PIMS align musical features, such as tempo and genre with users’ physical activity patterns, including frequency and intensity, enhancing their overall experience. This systematic review and exploratory meta-analysis evaluates the effectiveness of PIMS across physical, psychophysical, and affective domains. Searches across nine databases identified 18 eligible studies, of which six (comprising 17 intervention arms) contained sufficient data for meta-analysis. Random-effects meta-analyses and meta-regression were performed to assess outcomes for physical activity levels, physical exertion, ratings of perceived exertion (RPE), and affective valence. Results showed significant improvements in physical activity levels (g = 0.49, CI [0.07, 0.91], p = .02, k = 4) and affective valence (g = 1.68, CI [0.15, 3.20], p = .03, k = 4), with faster music tempo identified as a significant moderator (p = 0.04). No significant effects were observed for RPE (g = 0.72, CI [-0.14, 1.59], p = .10, k = 3) or physical exertion (g = 0.79, CI [-0.64, 2.10], p = .28, k = 5). Substantial heterogeneity and limited study quality indicate the need for more robust, randomised controlled trials to establish the efficacy of PIMS in diverse populations.

Abstract

No Results Found

Abstract

In this work, we study the natural monotone analogues of various equivalent definitions of : a well studied class (Poizat 2008, Koiran & Perifel 2009, Malod 2011, Mahajan & Rao 2013) that is believed to be larger than . We observe that these monotone analogues are not equivalent unlike their non-monotone counterparts, and propose monotone ( ) to be defined as the monotone analogue of Poizat's definition. With this definition, turns out to be exponentially stronger than and also satisfies several desirable closure properties that the other analogues may not. Our initial goal was to understand the monotone complexity of transparent polynomials, a concept that was recently introduced by Hrubeš & Yehudayoff (2021). In that context, we show that transparent polynomials with large support are hard for the monotone analogues of all the known definitions of , except for the one due to Poizat.

Abstract

n personalizing interactions with smart devices, identifying keywords in an open-vocabulary context is crucial. Previous methods for open-vocabulary keyword spotting relied on a shared embedding space created by audio and text encoders. However, they suffered from heterogeneous modality representations, causing audio-text mismatch. To tackle this issue, our proposed framework utilizes knowledge from a pre-trained text-to-speech (TTS) system. This knowledge transfer incorporates awareness of audio projections into text representations derived from the text encoder. Consequently, this approach aids in preventing false triggers in scenarios such as closely related pronunciations of audio-text pairs. Additionally, our proposed approach benefits from the keyword embedding calculation only during keyword enrollment phase. The proposed system gives consistent performance across all the word lengths.

Abstract

No Results Found

Abstract

Many real-world speech processing applications depend on the accurate estimation of epoch location. In a real-world scenario, speech may contain multiple speakers or multiple emotions in a single utterance. In the literature, the performance of epoch extraction algorithm was evaluated on speech utterance consisting of a single speaker or single emotion alone. In this study, speech utterance from different speakers, and emotions are combined to simulate multi-speaker and multi-emotion scenario, respectively. Five state-of-the-art epoch extraction methods were used to evaluate the performance for multi-speaker, multi-emotion, and voice disorder scenarios. We also analysed the performance of state-of-the-art epoch extraction methods after applying the region-wise approach on existing methods. CMU arctic, and Berlin emotional database is used in this study to simulate the multi-speaker, and multi-emotion scenarios, respectively for analysing the performance of epoch extraction algorithms. Saarbruecken voice disorder database is used to perform the experiments for voice disorder scenario. The result of this study indicates that performance of state-of-the art epoch extraction methods (which depend on pitch period) degrades in terms of false alarm rate (FAR) for these three scenarios. By applying the region-wise approach on these methods for extraction of epoch locations, FAR is reduced by 5% to 8% for the real-world scenario. It was also observed from this study, that the performance of dynamic programming based methods remains unchanged even after applying the region-wise approach on existing methods.

Abstract

The language acquisition skills are predominant during childhood. Due to the lack of resources, a gap often arises in the language communication abilities of students. To bridge this gap, it is necessary to consider automatic methods for improving the children language skills. Our IIITH Ucchar e-Sudharak web based tool, deployed in a real environment, helps schoolgoing children to practice English reading skills with a teacher in the loop. The choice of English as the target language is strategic, given its status as a global lingua franca. Within the tool, children are provided with class and chapter-specific English sentences for practice tailored by their teachers. During practice sessions, the tool provides real-time feedback in the form of sentence and word-level scores, which are computed by comparing against teacher (expert) audio recordings. These scores offer insights into word-level pronunciation correctness and sentence-level quality by measuring fluency. Moreover, the tool provides a key feature where teachers can assign specific sentences to individual students and assess their performance. This functionality empowers teachers to personalize their instruction, catering to the specific needs of each student. As far as we know, this is a one-of-a-kind tool specifically designed to meet the needs of school-going children.

Abstract

The Robotic Mobile Fulfillment System (RMFS) is an automated technology to fulfill various types of orders (e.g., vehicle assembly) in modern warehouses or factories. In particular, battery-equipped mobile robots move racks that contain various components (Stock Keeping Unit) by visiting replenishment or picking stations to complete orders. We formulate the preliminary system model to optimize the order throughput and energy consumption of mobile robots, which consists of (i) rack allocation, (ii) task allocation, and (iii) route routing.