Abstract

Despite edge computing reducing communication delays associated with cloud computing, privacy concerns remain a significant challenge when sharing data from edge-based consumer electronics (CE) or Internet-of-Things (IoT) devices. Ciphertext policy attribute-based encryption (CP-ABE) is a cryptographic tool that facilitates intricate and refined access control. It can deliver more flexible, secure, compact, and effective access control policies for the cloud data. In the case of a conventional CP-ABE scheme, the keys can only be issued and disseminated by a trusted central authority (CA). However, if the CA is compromised, the entire system becomes more susceptible to assaults or failures, which leads to a single-point failure. In this article, we propose a Ring-Learning with Errors (Ring-LWE)-based MA-CP-ABE scheme that is effectively resolved by the multi-authority CP-ABE (MA-CP-ABE), and it ensures the security against quantum attacks. The threshold secret sharing by Shamir and the Lagrange interpolation formula are applied in the key-generation and decryption procedures of the proposed scheme, which make it easier to segment and restore the private keys. The proposed scheme is implemented in the CE-enabled IoT-based smart healthcare applications using the blockchain technology as a secure storage. A detailed comparative study, security analysis and experimental results with the existing relevant schemes shows that the proposed scheme exhibits superior security and better efficiency as compared to other schemes, demonstrating its feasibility in practical IoT-based healthcare applications.

Abstract

Multi-signature protocols allow a group of signers to collectively generate a single signature for a shared message. In the context of a decentralized blockchain, multi-signature schemes play a pivotal role in reducing the signature size. Recently, several multi-signature methods have emerged in the literature, some operating in discrete-log settings and others in lattice settings. However, many of the existing lattice-based multi-signature schemes incur high computation costs and online round complexity. Traditional public key-based multi-signature schemes are susceptible to quantum threats, and they are computationally intensive as well. A lattice-based multi-signature can provide robust security, which often falls short in terms of efficiency when it comes to round complexity. In this article, we aim to introduce a single-round lattice-based multi-signature scheme specifically designed for decentralized public blockchains. What sets the proposed scheme apart is its ability to function without the need for trapdoor commitments or sample pre-images, which are common features in existing lattice-based signature methods. Furthermore, we explore some potential applications in a generic Internet of Things (IoT) environment and their integration of the proposed scheme with the blockchain technology. The security of the proposed scheme is based on lattice-hard problems, like Ring-SIS (Shortest Integer Solution) and Ring-LWE (Learning with Errors).

Abstract

The scalability and reliability of any large scale IoT network depends on a multitude of things, the primary one of that, being the communication protocol underlying the system. Wi-Sun is a novel protocol that offers a low power, long range and cost effective solution. It is a mesh protocol such that any node can be configured to be a transceiver. The mesh forms a destination oriented directed acyclic graph (DODAG) with one node configured to be the border router that connects to the internet, enabling cloud access for the entire mesh. However, long connection time and healing time of the DODAG are some of its drawbacks. In this paper, we present experiments on a real-world deployment of a Wi-Sun network where we study patterns in the time required for each additional node to be added in the Wi-Sun network, and investigate the time required to reform a new DODAG when a node with a certain hop count is detached. This deployment can also serve as a testbed for future such evaluations.

Abstract

A system and processor-implemented method for three-dimensional (3D) object retrieval using a self-supervised model is provided. The present system learns an embedding space of the 3D mesh objects in a self-supervised manner without the need for objects annotated with their class or other properties. Effective embeddings of 3D mesh objects are learned using the self-supervised method for ranked retrieval from a large collection of 3D objects. A simple representation of mesh objects and a standard neural network model is used to learn the embedding. The results are retrieved on the basis of the shape of the object which may not belong to the same category but look similar in shape using the embeddings generated by self-supervised model. The system is independent of class labels and uses the entire 3D model for better information extraction.

Abstract

Digital 3D models play a pivotal role in engineering, entertainment, education, and various domains. However, the search and retrieval of these models have not received adequate attention compared to other digital assets like documents and images. Traditional supervised methods face challenges in scalability due to the impracticality of creating large, labeled collections of 3D objects. In response, this paper introduces a self-supervised approach to generate efficient embeddings for 3D mesh objects, facilitating ranked retrieval of similar objects. The proposed method employs a straightforward representation of mesh objects and utilizes an encoder–decoder architecture to learn the embedding. Extensive experiments demonstrate the competitiveness of our approach compared to supervised methods, showcasing its scalability across diverse object collections. Notably, the method exhibits transferability across datasets, implying its potential for broader applicability beyond the training dataset. The robustness and generalization capabilities of the proposed method are substantiated through experiments conducted on varied datasets. These findings underscore the efficacy of the approach in capturing underlying patterns and features, independent of dataset-specific nuances. This self-supervised framework offers a promising solution for enhancing the search and retrieval of 3D models, addressing key challenges in scalability and dataset transferability.

Abstract

Traditionally, dysarthric speech intelligibility assessment systems have focused on speech as the primary input, utilizing methods such as extraction of relevant speech features, classification models, alignment of Automatic Speech Recognition (ASR) outputs, and comparisons between speech representations of dysarthric and healthy speakers. However, to achieve an automated intelligibility assessment that closely mirrors the auditory-perceptual evaluations conducted by clinicians, a model that captures both the acoustic characteristics of dysarthric speech and the linguistic structure related to word pronunciation are needed. Inspired by the practices of clinicians, this study introduces a novel text-guided dysarthric speech intelligibility assessment framework that leverages custom keyword spotting (DySIA-CKWS). The model evaluates intelligibility by detecting specific keywords and is extensively tested using UA-Speech database for speaker-wise analysis and across word groups of varying complexity. To ensure robustness, the system's performance is further validated on TORGO database, demonstrating its adaptability in cross-database settings. Statistical analysis demonstrates strong alignment between predicted and subjective intelligibility scores, with a Pearson Correlation Coefficient (PCC) of 0.9588 and a Spearman's Correlation Coefficient (SCC) of 0.9141, achieved using the proposed system on the UA-Speech database. The findings emphasize the importance of word selection and showcase the model's effectiveness in diagnosing dysarthric speech, offering a significant advancement in intelligibility assessment methodologies.

Abstract

Humans have the ability to utilize visual cues, such as lip movements and visual scenes, to enhance auditory perception, particularly in noisy environments. However, current Automatic Speech Recognition (ASR) or Audio-Visual Speech Recognition (AVSR) models often struggle in noisy scenarios. To solve this task, we propose a model that improves transcription by correlating noise sources to visual cues. Unlike works that rely on lip motion and require the speaker's visibility, we exploit broader visual information from the environment. This allows our model to naturally filter speech from noise and improve transcription, much like humans do in noisy scenarios. Our method re-purposes pretrained speech and visual encoders, linking them with multi-headed attention. This approach enables the transcription of speech and the prediction of noise labels in video inputs. We introduce a scalable pipeline to develop audio-visual datasets, where visual cues correlate to noise in the audio. We show significant improvements over existing audio-only models in noisy scenarios. Results also highlight that visual cues play a vital role in improved transcription accuracy.

Abstract

Automatic syllable stress detection traditionally relies on heuristic features like energy, duration, and pitch. Recent self-supervised models, such as wav2vec 2.0, capture stress cues more effectively, enhancing scalability. These models encode different information across layers, yet existing research focuses mainly on final-layer features, leaving intermediate layers unexplored. Stress detection is particularly challenging for non-native speakers due to deviations from correct stress patterns. This study analyzes all layers of self-supervised models to identify the most effective for stress detection. We evaluate wav2vec 2.0 (base, large, xlsr) on supervised and unsupervised classifiers using the ISLE corpus of non-native German and Italian speakers. Results show that wav2vec 2.0-large achieves the highest accuracy (96.43% for German, 96.68% for Italian) at layers 16 and 13, respectively, improving by 2.9% and 3.3% over the baseline.

Abstract

Brachiation, inspired by ape locomotion, involves swinging from one substrate to another. Existing approaches typically rely on simple grippers and computationally expensive optimal control to compute feasible states and control trajectories. In contrast, learning-based methods often lack physical modeling and require extensive training data. We present a brachiating system using high degree-of-freedom anthropomorphic hands to generate swing trajectories and perform stable grasps in a physics-based simulation environment (MuJoCo). An optimal open-loop trajectory is first generated via trajectory optimization based on a desired grasp location. A tracking controller follows this reference, while a grasping controller activates upon proximity to ensure secure contact. To reduce computational cost, we train a Generative Adversarial Imitation Learning (GAIL) policy using expert trajectories from the optimization framework. The GAIL-based controller generalizes to perturbed conditions and eliminates the need for repeated re-optimization, significantly lowering computation time. It also adapts to varying initial configurations, removing the requirement to rerun optimization for each case. We compare the learned model with a traditional optimal controller and demonstrate marked improvements in both computational efficiency and versatility.

Abstract

Background Heggli et al. (2021) noted that circadian rhythms and individual traits influence diurnal patterns in music preference. Depressive individuals often experience more negative moods in the evening, known as the evening-worsening effect (Rusting & Larsen, 1998). As music is often used for emotion focused-coping during such mood states (Stewart et al., 2019), depressive individuals’ music consumption in the evening are unclear. Aims This study explores the evening-worsening effect in music listening, examining if depressive individuals prefer low-valence, low-arousal music in the evening. Methods A survey of 290 Indians (M=20.35yrs, SD=2.08, 211 males) collected Kessler Psychological Distress scores (K10) and Healthy-Unhealthy music scale (HUMS) scores along with Spotify listening histories over one year. Additional measures like trait empathy and life satisfaction were collected but not analysed. Participants were classified as No-Risk (K10<20, n=114, 82 males) or At-Risk (K10≥29, n=81, 52 males) of depression (Andrews & Slade, 2001). Spotify valence and energy features for top 100-250 songs over the past 4-8 weeks from the time they filled in the survey were analyzed alongside timestamps. K-Means clustering divided the day into early hours, morning, afternoon and evening, and group differences in hourly valence and energy averages were assessed using two-way ANOVA and t-tests. Results At-Risk had significantly higher Unhealthy scores than No-Risk (t=8.15, p<0.001). Significant differences in valence and energy were found between At-Risk and No-Risk only in the afternoon (valence: t=-9.47, energy: t=-6.53) and evening (valence: t=-2.98, energy: t=-11.56)(all p≤ 0.05). Two-way ANOVA showed significant main effects of time, group, and their interaction for valence (time: F(3, 40)=22.86; group: F(1, 40)=12.36; interaction: F(3, 40)=6.65) and energy (time: F(3, 40)=23.22; group: F(1, 40)=98.73; interaction: F(3, 40)=13.57)(all p<0.001), consistent across the top 100-250 songs and the past 4-8 weeks of listening histories. Conclusions This study confirms diurnal patterns in music preferences, with At-Risk individuals favouring low-valence and low-energy music, especially in the afternoon and evening. These results align with Rusting and Larsen’s (1998) evening-worsening effect, linking depressive tendencies to heightened negative moods in the evening. The findings suggest potential maladaptive listening behaviors, needing further research.