Reviewerly/exHarmony huggingface dataset:
Effective preservation of higher-frequency contents in the context of short utterance based children’s speaker verification system

Developing an automatic speaker verification (ASV) system for children is extremely challenging due to the unavailability of children's speech corpora. The challenges are further exacerbated in the case of short utterances. Voice-based biometric systems require adequate amount of speech data for enrollment and verification; otherwise the performance considerably degrades. In this paper, we have focussed on data paucity and preserving the higher-frequency contents in order to enhance the performance of a short-utterance based children's speaker verification system. To deal with data scarcity, several out-of-domain data augmentation techniques have been utilized. Since the out-of-domain data used is from adult speakers which are acoustically very different from children's speech, we have resorted to techniques like prosody modification, formant modification and voice-conversion in order to render it acoustically similar to children's speech prior to augmentation. This helps in not only increasing the amount of training data but also in effectively capturing the missing target attributes. A relative improvement of 33.6% in equal error rate (EER) with respect to the baseline system trained solely on child data-set is achieved when the proposed data augmentation technique is applied. Further to that, for the preservation of the higher-frequency contents, we have resorted to concatenation of the classical Mel-frequency cepstral coefficients (MFCC) features with the linear-frequency cepstral coefficient (LFCC) or with the inverse-Mel-frequency cepstral coefficient (IMFCC) features. The use of Mel-filter-bank leads to poor resolution of higher-frequency components. On the other hand, linear- or inverse-Mel-filter-banks yield better resolution of higher-frequency components. Moreover, MFCC and IMFCC features exhibit low canonical correlation. Consequently, the frame-level concatenation of MFCC and LFCC or IMFCC features leads to better resolution of both lower- as well as higher-frequency components. Therefore, the EER considerably reduces when either LFCC features or IMFCC features are concatenated with MFCC features. The EER for the full test set shows a relative reduction of 10.56% (with respect to the EER for the MFCC features) when IMFCC features are concatenated with the MFCC features. This novel approach of incorporating data augmentation followed by frame-level feature concatenation helps in achieving an overall reduction of 40.6% in EER.
A receipt-free quantum voting protocol based on quantum public key encryption and quantum key agreement

Abstract To better protect the privacy of voters, a receipt-free quantum voting protocol is first proposed, which adopts quantum key agreement and quantum public key encryption with single photons. We employ Lagrange interpolation polynomials and random numbers to blind the voting information on the bulletin board, so that it can minimize the public information. Furthermore, our receipt-free quantum voting protocol is information-theoretically secure, i.e., the eavesdropper cannot get any voting information by collecting both the quantum ciphertext and the public information. In addition, compared with the existing quantum voting protocols, our protocol can meet more security attributes, such as anonymity, unrepeatability, verifiability legitimacy and especially, receipt-freeness.
Sequential fusion estimation for Markov jump systems with heavy-tailed noises

We study a sequential fusion estimation problem for Markov jump multi-sensor systems with heavy-tailed noises. By modelling the noises as Student's t distributions, a sequential fusion estimation algorithm is designed by utilising the interacting multiple model method and Bayes' rule. To improve the robustness against measurement outliers caused by measurement heavy-tailed noise, an F-distribution detection strategy is designed to detect and reject the measurement outliers. Simulation results demonstrate that the designed sequential fusion estimation algorithm can effectively fuse the measurements from multiple sensors, and the accuracy of the designed algorithm is superior to the existing interacting multiple model Student's t batch fusion algorithm and single model adaptive Student's t batch fusion algorithm when there exist model switching and disturbances with heavy-tailed property.
Heuristic task scheduling on heterogeneous UAVs: A combinatorial optimization approach

A fleet of unmanned aerial vehicles (UAVs) provide a new and unique type of distributed computing paradigm and platform. This is a distributed computing environment with mobile servers, where a server (i.e., a UAV) moves around to process tasks. Task scheduling for UAVs has several unique characteristics, such as heterogeneity, mobility, and locality. UAVs are heterogeneous in the sense that they have different initial positions, flight speeds, and execution times. While task assignment and flight planning have been studied extensively, there has been little research of task scheduling on heterogeneous UAVs within the framework of combinatorial optimization, i.e., heuristic algorithms for NP-hard problems and their performance evaluation when compared with optimal solutions. In this paper, we take a combinatorial optimization approach to addressing the issues in task scheduling for heterogeneous UAVs. The main contributions are summarized as follows. We define eight combinatorial optimization problems, i.e., four time-centric problems including the completion time minimization problem, the total time minimization problem, the finished tasks maximization with time constraint problem, the reward maximization with time constraint problem; and four distance-centric problems including the longest distance minimization problem, the total distance minimization problem, the finished tasks maximization with distance constraint problem, the reward maximization with distance constraint problem. We prove that all these problems are NP-hard. We develop two efficient and effective heuristic algorithmic frameworks to solve our problems, one for minimization problems and one for maximization problems. We derive lower/upper bounds for the optimal solutions. We evaluate the performance of our heuristic algorithms by comparing their solutions with optimal solutions and show that they are able to produce near-optimal solutions. To the best of the author's knowledge, this is the first paper which studies task scheduling on heterogeneous UAVs using a combinatorial optimization approach.
A Multigoal Path-Planning Approach for Explosive Ordnance Disposal Robots Based on Bidirectional Dynamic Weighted-A* and Learn Memory-Swap Sequence PSO Algorithm

In order to protect people’s lives and property, increasing numbers of explosive disposal robots have been developed. It is necessary for an explosive ordinance disposal (EOD) robot to quickly detect all explosives, especially when the location of the explosives is unknown. To achieve this goal, we propose a bidirectional dynamic weighted-A star (BD-A*) algorithm and a learn memory-swap sequence particle swarm optimization (LM-SSPSO) algorithm. Firstly, in the BD-A* algorithm, our aim is to obtain the shortest distance path between any two goal positions, considering computation time optimization. We optimize the computation time by introducing a bidirectional search and a dynamic OpenList cost weight strategy. Secondly, the search-adjacent nodes are extended to obtain a shorter path. Thirdly, by using the LM-SSPSO algorithm, we aim to plan the shortest distance path that traverses all goal positions. The problem is similar to the symmetric traveling salesman problem (TSP). We introduce the swap sequence strategy into the traditional PSO and optimize the whole PSO process by imitating human learning and memory behaviors. Fourthly, to verify the performance of the proposed algorithm, we begin by comparing the improved A* with traditional A* over different resolutions, weight coefficients, and nodes. The hybrid PSO algorithm is also compared with other intelligent algorithms. Finally, different environment maps are also discussed to further verify the performance of the algorithm. The simulation results demonstrate that our improved A* algorithm has superior performance by finding the shortest distance with less computational time. In the simulation results for LM-SSPSO, the convergence rate significantly improves, and the improved algorithm is more likely to obtain the optimal path.
A Double-Stage 3D U-Net for On-Cloud Brain Extraction and Multi-Structure Segmentation from 7T MR Volumes

The brain is the organ most studied using Magnetic Resonance (MR). The emergence of 7T scanners has increased MR imaging resolution to a sub-millimeter level. However, there is a lack of automatic segmentation techniques for 7T MR volumes. This research aims to develop a novel deep learning-based algorithm for on-cloud brain extraction and multi-structure segmentation from unenhanced 7T MR volumes. To this aim, a double-stage 3D U-Net was implemented in a cloud service, directing its first stage to the automatic extraction of the brain and its second stage to the automatic segmentation of the grey matter, basal ganglia, white matter, ventricles, cerebellum, and brain stem. The training was performed on the 90% (the 10% of which served for validation) and the test on the 10% of the Glasgow database. A mean test Dice Similarity Coefficient (DSC) of 96.33% was achieved for the brain class. Mean test DSCs of 90.24%, 87.55%, 93.82%, 85.77%, 91.53%, and 89.95% were achieved for the brain structure classes, respectively. Therefore, the proposed double-stage 3D U-Net is effective in brain extraction and multi-structure segmentation from 7T MR volumes without any preprocessing and training data augmentation strategy while ensuring its machine-independent reproducibility.
A Blockchain-Based Efficient and Verifiable Attribute-Based Proxy Re-Encryption Cloud Sharing Scheme

When choosing a third-party cloud storage platform, the confidentiality of data should be the primary concern. To address the issue of one-to-many access control during data sharing, it is important to encrypt data with an access policy that enables fine-grained access. The attribute-based encryption scheme can be used for this purpose. Additionally, attribute-based proxy re-encryption (ABPRE) can generate a secret key using the delegatee’s secret key and access policy to re-encrypt the ciphertext, allowing for one-to-many data sharing. However, this scheme still has some flaws, such as low efficiency, inability to update access rules, and private data leakage. To address these issues, we proposed a scheme that combines attribute-based encryption (ABE) and identity-based encryption (IBE) to achieve efficient data sharing and data correctness verification. We also integrated this scheme with blockchain technology to ensure tamper-proof and regulated data storage, addressing issues such as data tampering and lack of supervision on third-party servers. Finally, to demonstrate the security of our scheme, we evaluated the communication overhead and computation overhead. Our results showed that our scheme is more efficient than other schemes and is secure against chosen plaintext attacks with verifiable properties.
Dynamics of quantum-memory assisted entropic uncertainty of a two-spin Heisenberg XXX model under the intrinsic decoherence effect

Abstract In this study, the quantum-memory assisted entropic uncertainty (QM-EU) and entanglement dynamics of the two-qubit Heisenberg XXX chain have been explored in the presence of intrinsic decoherence. The effect of the x -component of Dzyaloshinskii-Moriya (DM) and Kaplan-Shekhtman-Entin-Wohlman-Aharony (KSEA) interactions has been considered. The generation and preservation of quantum memory and entanglement have been examined for various values of the DM, KSEA, spin-spin, and spin coupling strengths. The uncertainty negatively affects the entanglement and both have anti-correlation. The absence and presence of intrinsic decoherence prevail in differing impacts on the dynamics of the system. In the first case, prolonged entanglement preservation, uncertainty suppression, and oscillatory dynamics have been observed. Moreover, in order to achieve the best-prolonged entanglement preservation and relative reduction of the entropic uncertainty, we have analyzed several parameter settings. We find that the effects of raising the DM, KSEA, and spin-spin interaction individually and simultaneously are different. The individual and simultaneous increase of the DM, KSEA, and spin-spin interaction parameters control the degree of entanglement, entropic uncertainty, and primarily the dynamics of the system.
Intelligent Identification Method of Hydrophobic Grade of Composite Insulator Based on Efficient <scp>GA</scp>‐<scp>YOLO</scp> Former Network

Abstract The hydrophobia of composite insulators is a parameter closely related to insulation performance. As an essential index to measure the pollution flashover resistance of composite insulators, the hydrophobic grade is of great significance to maintaining transmission line insulators. To judge the hydrophobic grade of composite insulators accurately and efficiently and to solve the problem of difficult extraction of fine‐grained feature information and high similarity between classes in the data sets of anti‐hydrophobic grade recognition, an end‐to‐end intelligent identification method of the hydrophobic grade of composite insulators based on Efficient GA‐YOLO Former network is proposed. This method introduces Swin Transformer—a multi‐head self‐attention mechanism with shifted windows, into CNN, which helps the model better understand the multi‐scale global semantic information of images through cross‐layer interaction. The efficient BiFPN structure is introduced in the neck part to realize the bidirectional cross‐scale connection and weighted feature fusion in the feature extraction process. By integrating the GAM attention mechanism to amplify the significant cross‐dimensional acceptance region, the model can learn more subtle details that are more difficult to distinguish, improving model efficiency and detection accuracy. Combined with the target detection algorithm, a system for evaluating the hydrophobic grade of composite insulators on transmission lines is designed, which can realize the real‐time measurement of the hydrophobic grade of composite insulators and has practical engineering application value. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
Event-triggered fusion estimation under bounded noises

This paper is devoted to solving the event-triggered multi-sensor fusion estimation problem with bounded noises. Under the centralized fusion framework, a compensation strategy is developed to recover the non-triggered data by analyzing the intrinsic properties of Kalman filter. Then, an optimal centralized fusion estimator is derived by minimizing an upper bound of estimation error at each time. Under the distributed fusion framework, the optimal local estimator on each observable subspace is obtained by the ideas similar to those in the centralized fusion framework. Then, two different distributed fusion estimators are respectively designed by means of a spectral radius inequality and a trace inequality, where the former achieves better estimation performance and the latter is more computationally efficient. Particularly, all the proposed centralized and distributed fusion estimators are shown to be stable under moderate conditions. Finally, a classical target tracking system is utilized to verify the effectiveness and advantages of the proposed methods.
TO GET INSIGHT INTO ISSUANCE MOTIVATIONS BEHIND INTRODUCING CBDC IN INDIA

The era of information and communication technologies has produced numerous opportunities in every field. Finance and business sector is most benefitted field from these recent developments. The spotlight quickly shifted from "Fintech," which emerged in the last ten years, to "crypto currency," then to "CBDC," demonstrating that how quickly the world of digital finance is changing. CBDC is similar to blockchain technology but is controlled and regulated by Reserve Bank of India (RBI). The pilot study of retail segment of CBDC has commenced from the beginning of December 01, 2022. This paper made an attempt to delve into the understanding of Central Bank Digital Currency and to probe the motives behind the issuance of CBDC. RBI is currently working toward a phased deployment plan, progressing gradually through several rounds of pilots before the final launch and concurrently looking at use cases that might be adopted with little to no impact.
GAU U-Net for multiple sclerosis segmentation

Multiple sclerosis is an auto immune disease which affects the brain and nervous system. A total of 2.8 million people are estimated to live with Multiple sclerosis worldwide (35.9 per 100,000 population). The pooled incidence rate across 75 reporting countries is 2.1 per 100,000 persons per year, and the mean age of diagnosis is 32 years. Lesions resulting from the disease can be spotted in the patients MRI scans. In this paper a novel Deep learning architecture GAU-U-net is proposed. The model is inspired from the very famous U-Net architecture used for semantic segmentation and widely used in medical image segmentation. The proposed model consists of 3D U-Net after adding a new attention technique inspired by the Global Attention Upsample unit. By using GAU-unet architecture the Dice coefficient increased from 64% to 72% compared to using 3D-Unet.Also, when compared with Unet- attention network the dice coefficient increased from 69% to around 72% with a considerable incline in the number of model parameters in favor of our architecture, which uses 28 M parameters compared to Unet-attention which uses100M parameters.
Efficient consensus algorithm based on improved DPoS in UAV-assisted mobile edge computing

Aiming at the problems of low decentralization, low motivation for node voting, and malicious behavior of nodes for the traditional DPoS consensus mechanism in the blockchain-based UAV-assisted mobile edge computing environment, this paper proposes an improved DPoS-based consensus mechanism approach. First, the framework of a blockchain-based UAV-assisted mobile edge computing system is given, and the consensus mechanism design problem in this framework is analyzed. Second, a proxy node selection model is established based on the rights and votes obtained by the blockchain nodes, and the proxy nodes are selected to participate in the consensus process of the current cycle. Then, the voting behavior, block generation behavior, and block verification behavior of nodes are classified into positive and malicious behaviors to reward and punish the reputation value of nodes. Finally, a blockchain-based UAV-assisted mobile edge computing experimental environment is built, and the TDPoS algorithm, ADRP algorithm, and RDPoS algorithm are used as benchmark algorithms to experimentally compare with the proposed improved DPoS consensus-based algorithm. The experimental results show that the algorithms in this paper can improve network throughput, reduce block-out delay, and increase the proportion of secure proxy nodes.
Multi-layer and Multi-scale feature aggregation for DIBR-Synthesized image quality assessment

Depth-Image-Based Rendering (DIBR) is one of the main fundamental techniques for generating new viewpoints in 3D video applications such as multi-viewpoint video (MVV), free viewpoint video (FVV) and virtual reality (VR). Due to the imperfections of color images, depth maps or texture restoration techniques, several types of distortions occur in synthesized views. However, most of related works evaluated the quality of DIBR-synthesized views by only detecting a specific type of distortion, such as stretching, black holes, blurring, etc., which were unable to accurately evaluate the quality of DIBR-synthesized views. In this paper, a new no-reference image quality assessment method is proposed to evaluate the quality of DIBR-synthesized images by combining multi-layer and multi-scale features of images. To be specific, the distortions introduced by different stages of virtual viewpoint synthesis are first analyzed, and then multi-layer and multi-scale features are extracted to estimate the degree of texture and structure distortions. As a result, individual quality scores associated with two types of distortions (e.g., structural distortion and texture distortion) are aggregated to an overall image quality. Experimental results on two publicly available DIBR datasets show that the method has better performance than the state-of-the-art models. Index Terms: image quality assessment, DIBR-synthesized image, distortion correction, BIQA.
Valuing vicinity: Memory attention framework for context-based semantic segmentation in histopathology

The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely. The identification of subtypes of tumour tissue complicates the issue as the sharpness of separation decreases and the pathologist's reasoning is even more guided by spatial context. However, the identification of detailed tissue types is crucial for providing personalized cancer therapies. Due to the high resolution of whole slide images, existing semantic segmentation methods, restricted to isolated image sections, are incapable of processing context information beyond. To take a step towards better context comprehension, we propose a patch neighbour attention mechanism to query the neighbouring tissue context from a patch embedding memory bank and infuse context embeddings into bottleneck hidden feature maps. Our memory attention framework (MAF) mimics a pathologist's annotation procedure - zooming out and considering surrounding tissue context. The framework can be integrated into any encoder-decoder segmentation method. We evaluate the MAF on two public breast cancer and liver cancer data sets and an internal kidney cancer data set using famous segmentation models (U-Net, DeeplabV3) and demonstrate the superiority over other context-integrating algorithms - achieving a substantial improvement of up to 17% on Dice score. The code is publicly available at https://github.com/tio-ikim/valuing-vicinity.
Feature aggregation with transformer for RGB-T salient object detection

The main purpose of RGB-T salient object detection (SOD) is to fully integrate and exploit the information from the complementary fusion of modalities to address the underperformance of RGB SOD in some challenging scenes. In this paper, we propose a novel feature aggregation network that can fully mine multi-scale and multi-modal information for complete and accurate RGB-T SOD. Subsequently, a cross-attention fusion module is proposed to adaptively integrate high-level features by using the attention mechanism in the Transformer. Then we design a simple yet effective fast feature aggregation module to fuse low-level features. Through the combined work of the above modules, our network can perform well in some complex scenes by effectively fusing features from RGB and thermal modalities. Finally, several experiments on publicly available datasets such as VT821, VT1000, and VT5000 demonstrate that our method outperforms state-of-the-art methods. And our code has been released at:https://github.com/ELOESZHANG/FANet.