Reviewerly/exHarmony huggingface dataset:
Analytic Models of a Loosely Coupled GNSS/INS/LiDAR Kalman Filter Considering Update Frequency Under a Spoofing Attack

Hostile spoofing attacks on the global navigation satellite system (GNSS) receiver increase the risk of catastrophic consequences to autonomous driving systems. This article addresses the problem of the vulnerability of the Kalman filter (KF) under spoofing attack. A state-of-the-art spoofing attack method based on maximizing the lateral deviation is utilized for verification and analysis. To analyze the vulnerability in actual road scenarios better, an analytic error model of the mechanism of GNSS spoofing is derived. Except for the uncertainty of the initial MSF state, the uncertainty of light detection and ranging (LiDAR), and the uncertainty of GNSS, a new factor in spoofing attacks, the update frequency of different sensors, is investigated in this article, which, in fact, is a key factor to increase the immunity multisensors’ fusion (MSF) systems. Experiments were performed in a typical urban scenario of the KAIST dataset. When the update frequency ratios between GNSS and LiDAR are 1, 2, 5, and 10, successful spoof attacks can be performed if the standard deviation (STD) of GNSS is smaller than 4, 2.7, 1.1, and 0.7 m, respectively. Therefore, experiments confirm that the uncertainty of GNSS and the update frequency ratio between LiDAR and GNSS are critical for spoofing attacks, which provides an indication for designing a defense strategy in the future.
VoIPChain: A decentralized identity authentication in Voice over IP using Blockchain

Confidentiality, availability, integrity, authentication, and non-repudiation when together became the top priority for secure communication. However, authentication is the first line of defense in all these security parameters. Unfortunately, several authentication mechanisms in the traditional methods cannot provide secure communication due to various vulnerabilities such as single point of failure and privacy issues. Furthermore, key distribution mechanisms such as Certificate Authority (CA) and Trusted Third Party (TTP) distribute keys over the central architecture fail, thus secure communication cannot occur. As a solution to these drawbacks, this paper proposes a new decentralized blockchain-based identity authentication mechanism for VoIP networks, VoIPChain. The proposed scheme utilizes the main features of the blockchain platform, such as immutability, transparency, and fault tolerance, to provide data privacy and secure communication in VoIP applications. The proposed scheme is evaluated as an actual implementation using the virtual Ethereum platform and Python language. The experimental results show that the proposed scheme is an efficient and cost-effective solution for the call process as a decentralized identity authentication system in VoIP. In addition, end-to-end secure call performance outperforms the existing blockchain-based authentication mechanisms by 30%–70% in terms of average time delay. The proposed scheme is almost ten times faster than the TLS process to authenticate between parties. Moreover, compared to fast but less secure basic methods over the SIP authentication the proposed scheme has an acceptable time delay in a call process.
Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution

Convolutional Neural Network (CNN)-based image super-resolution (SR) has exhibited impressive success on known degraded low-resolution (LR) images. However, this type of approach is hard to hold its performance in practical scenarios when the degradation process ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> blur and downsampling) is unknown. Despite existing blind SR methods proposed to solve this problem using blur kernel estimation, the perceptual quality and reconstruction accuracy are still unsatisfactory. In this paper, we analyze the degradation of a high-resolution (HR) image from image intrinsic components according to a degradation-based formulation model. We propose a components decomposition and co-optimization network (CDCN) for blind SR. Firstly, CDCN decomposes the input LR image into structure and detail components in feature space. Then, the mutual collaboration block (MCB) is presented to exploit the relationship between both two components. In this way, the detail component can provide informative features to enrich the structural context and the structure component can carry structural context for better detail revealing via a mutual complementary manner. After that, we present a degradation-driven learning strategy to jointly supervise the HR image detail and structure restoration process. Finally, a multi-scale fusion module followed by an upsampling layer is designed to fuse the structure and detail features and perform SR reconstruction. Empowered by such degradation-based components decomposition, collaboration, and mutual optimization, we can bridge the correlation between component learning and degradation modelling for blind SR, thereby producing SR results with more accurate textures. Extensive experiments on both synthetic SR datasets and real-world images show that the proposed method achieves the state-of-the-art performance compared to existing methods.
Efficient simulation of Gottesman-Kitaev-Preskill states with Gaussian circuits

We study the classical simulatability of Gottesman-Kitaev-Preskill (GKP) states in combination with arbitrary displacements, a large set of symplectic operations and homodyne measurements. For these types of circuits, neither continuous-variable theorems based on the non-negativity of quasi-probability distributions nor discrete-variable theorems such as the Gottesman-Knill theorem can be employed to assess the simulatability. We first develop a method to evaluate the probability density function corresponding to measuring a single GKP state in the position basis following arbitrary squeezing and a large set of rotations. This method involves evaluating a transformed Jacobi theta function using techniques from analytic number theory. We then use this result to identify two large classes of multimode circuits which are classically efficiently simulatable and are not contained by the GKP encoded Clifford group. Our results extend the set of circuits previously known to be classically efficiently simulatable.
Formally Verified Native Code Generation in an Effectful JIT: Turning the CompCert Backend into a Formally Verified JIT Compiler

Modern Just-in-Time compilers (or JITs) typically interleave several mechanisms to execute a program. For faster startup times and to observe the initial behavior of an execution, interpretation can be initially used. But after a while, JITs dynamically produce native code for parts of the program they execute often. Although some time is spent compiling dynamically, this mechanism makes for much faster times for the remaining of the program execution. Such compilers are complex pieces of software with various components, and greatly rely on a precise interplay between the different languages being executed, including on-stack-replacement. Traditional static compilers like CompCert have been mechanized in proof assistants, but JITs have been scarcely formalized so far, partly due to their impure nature and their numerous components. This work presents a model JIT with dynamic generation of native code, implemented and formally verified in Coq. Although some parts of a JIT cannot be written in Coq, we propose a proof methodology to delimit, specify and reason on the impure effects of a JIT. We argue that the daunting task of formally verifying a complete JIT should draw on existing proofs of native code generation. To this end, our work successfully reuses CompCert and its correctness proofs during dynamic compilation. Finally, our prototype can be extracted and executed.
Cloud data security and various cryptographic algorithms

&lt;p&gt;Cloud computing has spread widely among different organizations due to its advantages, such as cost reduction, resource pooling, broad network access, and ease of administration. It increases the abilities of physical resources by optimizing shared use. Clients’ valuable items (data and applications) are moved outside of regulatory supervision in a shared environment where many clients are grouped together. However, this process poses security concerns, such as sensitive information theft and personally identifiable data leakage. Many researchers have contributed to reducing the problem of data security in cloud computing by developing a variety of technologies to secure cloud data, including encryption. In this study, a set of encryption algorithms (advance encryption standard (AES), data encryption standard (DES), Blowfish, Rivest-Shamir-Adleman (RSA) encryption, and international data encryption algorithm (IDEA) was compared in terms of security, data encipherment capacity, memory usage, and encipherment time to determine the optimal algorithm for securing cloud information from hackers. Results show that RSA and IDEA are less secure than AES, Blowfish, and DES). The AES algorithm encrypts a huge amount of data, takes the least encipherment time, and is faster than other algorithms, and the Blowfish algorithm requires the least amount of memory space.&lt;/p&gt;
Automatic approach for mask detection: effective for COVID-19

The outbreak of coronavirus disease 2019 (COVID-19) occurred at the end of 2019, and it has continued to be a source of misery for millions of people and companies well into 2020. There is a surge of concern among all persons, especially those who wish to resume in-person activities, as the globe recovers from the epidemic and intends to return to a level of normalcy. Wearing a face mask greatly decreases the likelihood of viral transmission and gives a sense of security, according to studies. However, manually tracking the execution of this regulation is not possible. The key to this is technology. We present a deep learning-based system that can detect instances of improper use of face masks. A dual-stage convolutional neural network architecture is used in our system to recognize masked and unmasked faces. This will aid in the tracking of safety breaches, the promotion of face mask use, and the maintenance of a safe working environment. In this paper, we propose a variant of a multi-face detection model which has the potential to target and identify a group of people whether they are wearing masks or not.
DroneAttention: Sparse weighted temporal attention for drone-camera based activity recognition

Human activity recognition (HAR) using drone-mounted cameras has attracted considerable interest from the computer vision research community in recent years. A robust and efficient HAR system has a pivotal role in fields like video surveillance, crowd behavior analysis, sports analysis, and human–computer interaction. What makes it challenging are the complex poses, understanding different viewpoints, and the environmental scenarios where the action is taking place. To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Attention (SWTA) module to utilize sparsely sampled video frames for obtaining global weighted temporal attention. The proposed SWTA is comprised of two parts. First, temporal segment network that sparsely samples a given set of frames. Second, weighted temporal attention, which incorporates a fusion of attention maps derived from optical flow, with raw RGB images. This is followed by a basenet network, which comprises a convolutional neural network (CNN) module along with fully connected layers that provide us with activity recognition. The SWTA network can be used as a plug-in module to the existing deep CNN architectures, for optimizing them to learn temporal information by eliminating the need for a separate temporal stream. It has been evaluated on three publicly available benchmark datasets, namely Okutama, MOD20, and Drone-Action. The proposed model has received an accuracy of 72.76%, 92.56%, and 78.86% on the respective datasets thereby surpassing the previous state-of-the-art performances by a margin of 25.26%, 18.56%, and 2.94%, respectively.
Practical Cube Attack against Nonce-Misused Ascon

Ascon is a sponge-based Authenticated Encryption with Associated Data that was selected as both one of the winners of the CAESAR competition and one of the finalists of the NIST lightweight cryptography standardization effort. As this competition comes to an end, we analyse the security of this algorithm against cube attacks. We present a practical cube attack against the full 6-round encryption in Ascon in the nonce-misuse setting. We note right away that this attack does not violate the security claims made by the designers of Ascon, due to this setting.Our cryptanalysis is a conditional cube attack that is capable of recovering the full capacity in practical time; but for Ascon-128, its extension to a key recovery or a forgery is still an open question. First, a careful analysis of the maximum-degree terms in the algebraic normal form of the Ascon permutation allows us to derive linear equations in half of the capacity bits given enough cube sums of dimension 32. Then, depending on the results of this first phase, we identify smaller-degree cubes that allow us to recover the remaining half of the capacity. Overall, our cryptanalysis has a complexity of about 240 adaptatively chosen plaintexts, and about 240 calls to the permutation. We have implemented the full attack and our experiments confirm our claims.Our results are built on a theoretical framework which allows us to easily identify monomials whose cube-sums provide linear equations in the capacity bits. The coefficients of these monomials have a more general form than those used in the previous attacks against Ascon, and our method enables us to re-frame previous results in a simpler form. Overall, it enables to gain a deeper understanding of the properties of the permutation, and in particular of its S-box, that make such state-recoveries possible.
Estimation of Accuracy in Human Gender Identification and Recall Values Based on Voice Signals Using Different Classifiers

This paper presents the estimation of accuracy in male, female, and transgender identification using different classifiers with the help of voice signals. The recall value of each gender is also calculated. This paper reports the third gender (transgender) identification for the first time. Voice signals are the most appropriate and convenient way to transfer information between the subjects. Voice signal analysis is vital for accurate and fast identification of gender. The Mel Frequency Cepstral Coefficients (MFCCs) are used here as an extracted feature of the voice signals of the speakers. MFCCs are the most convenient and reliable feature that configures the gender identification system. Recurrent Neural Network–Bidirectional Long Short-Term Memory (RNN-BiLSTM), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) are utilized as classifiers in this work. In the proposed models, the experimental result does not depend on the text of the speech, the language of the speakers, and the time duration of the voice samples. The experimental results are obtained by analyzing the common voice samples. In this article, the RNN-BiLSTM classifier has single-layer architecture, while SVM and LDA have a k-fold value of 5. The recall value of genders and accuracy of the proposed models also varied according to the number of voice samples in training and testing datasets. The highest accuracy for gender identification is found as 94.44%. The simulation results show that the accuracy of the RNN is always found at a higher value than SVM and LDA. The gender-wise highest recall value of the proposed model is 95.63%, 96.71%, and 97.22% for males, females, and transgender, respectively, using voice signals. The recall value of the transgender is high in comparison to other genders.
Mask-guided modality difference reduction network for RGB-T semantic segmentation

By exploiting the complementary information of RGB modality and thermal modality, RGB-thermal (RGB-T) semantic segmentation is robust to adverse lighting conditions. When fusing features from RGB images and thermal images, the existing methods design different feature fusion strategies, but most of these methods overlook the modality differences caused by different imaging mechanisms. This may result in insufficient usage of complementary information. To address this issue, we propose a novel Mask-guided Modality Difference Reduction Network (MMDRNet), where the mask is utilized in the image reconstruction to ensure that the modality discrepancy within foreground regions is minimized. Doing so enables the generation of more discriminative representations for foreground pixels, thus facilitating the segmentation task. On top of this, we present a Dynamic Task Balance (DTB) method to balance the modality difference reduction task and semantic segmentation task dynamically. The experimental results on the MFNet dataset and the PST900 dataset demonstrate the superiority of the proposed mask-guided modality difference reduction strategy and the effectiveness of the DTB method.
EffResUNet: Encoder Decoder Architecture for Cloud-Type Segmentation

Clouds play a vital role in Earth’s water cycle and the energy balance of the climate system; understanding them and their composition is crucial in comprehending the Earth–atmosphere system. The dataset “Understanding Clouds from Satellite Images” contains cloud pattern images downloaded from NASA Worldview, captured by the satellites divided into four classes, labeled Fish, Flower, Gravel, and Sugar. Semantic segmentation, also known as semantic labeling, is a fundamental yet complex problem in remote sensing image interpretation of assigning pixel-by-pixel semantic class labels to a given picture. In this study, we propose a novel approach for the semantic segmentation of cloud patterns. We began our study with a simple convolutional neural network-based model. We worked our way up to a complex model consisting of a U-shaped encoder-decoder network, residual blocks, and an attention mechanism for efficient and accurate semantic segmentation. Being an architecture of the first of its kind, the model achieved an IoU score of 0.4239 and a Dice coefficient of 0.5557, both of which are improvements over the previous research conducted in this field.
Complete outlier detection and classification framework for WSNs based on OPTICS

Wireless Sensor Networks (WSNs) consist of multi-functional sensors with limited resources that collect information in various applications (medical, manufacturing, militarily, etc.). However, data gathered by sensors are susceptible to outliers, which need to be detected and classified into errors and events using outlier detection and classification methods. This paper proposes a centralised outlier detection and classification approach for WSNs, which can distinguish between errors due to a faulty sensor and those due to an event. Also, it considers the spatial–temporal correlation between neighbouring sensor nodes and sensors' data values. Our approach, titled OPTICS-K, combines the benefits of the Ordering Points To Identify the Clustering Structure (OPTICS) algorithm for clustering, the Inter-Cluster Distance (ICD) method and the K-Nearest Neighbours (KNN) algorithm for outlier detection. Furthermore, OPTICS-K uses a new method based on Kriging interpolation to classify the outliers. For evaluation, we conduct a comparison study between OPTICS-K and two works from the literature and thus for the multivariate data case. Simulation results with both synthetic and real-life datasets show that the OPTICS-K outperforms the studied techniques in terms of several metrics like Detection Rate (DR), False Alarm Rate (FAR), Accuracy rate (ACC), F1_score and Area Under the Curve (AUC).
Underwater Image Enhancement Based on Color Balance and Multi-Scale Fusion

Underwater light absorption and scattering lead to color deviation, low brightness, fuzzy details and low contrast of underwater images. In this contribution, an underwater image enhancement algorithm based on color balance and multi-scale fusion is proposed. Firstly, a color balance method is used to correct the image color. Then, based on an improved dark channel prior algorithm, the local contrast information of the color balanced image is used to derive two atmospheric lights and transmission maps, which are transformed into the images with enhanced contrast and brightness, respectively. Finally, the multi-scale fusion method is adopted to fuse the contrast enhanced image and the brightness improved image according to the weights. The proposed algorithm is compared with other underwater image enhancement algorithms in qualitative and quantitative evaluation. Experimental results show that the proposed algorithm can effectively eliminate color deviation, remove dark areas, and improve brightness and contrast of underwater images. The enhanced images generated by the proposed algorithm are superior to those generated by other algorithms in indicators of PSNR, SSIM, UIQM and UCIQE. Thus, the proposed algorithm is suitable for underwater image enhancement.
Research on Multi-Objective Multi-Robot Task Allocation by Lin–Kernighan–Helsgaun Guided Evolutionary Algorithms

Multi-robot task allocation (MRTA) and route planning are crucial for a large-scale multi-robot system. In this paper, the problem is formulated to minimize the total energy consumption and overall task completion time simultaneously, with some constraints taken into consideration. To represent a solution, a novel one-chromosome representation technique is proposed, which eases the consequent genetic operations and the construction of the cost matrix. Lin–Kernighan–Helsgaun (LKH), a highly efficient sub-tour planner, is employed to generate prophet generation beforehand as well as guide the evolutionary direction during the proceeding of multi-objective evolutionary algorithms, aiming to promote convergence of the Pareto front. Numerical experiments on the benchmark show the LKH guidance mechanism is effective for two famous multi-objective evolutionary algorithms, namely multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm (NSGA), of which LKH-guided NSGA exhibits the best performance on three predefined indicators, namely C-metric, HV, and Spacing, respectively. The generalization experiment on a multiple depots MRTA problem with constraints further demonstrates the effectiveness of the proposed approach for practical decision making.