Reviewerly/exHarmony huggingface dataset:
Real-time route planning of unmanned aerial vehicles based on improved soft actor-critic algorithm

With the application and development of UAV technology and navigation and positioning technology, higher requirements are put forward for UAV maneuvering obstacle avoidance ability and real-time route planning. In this paper, for the problem of real-time UAV route planning in the unknown environment, we combine the ideas of artificial potential field method to modify the state observation and reward function, which solves the problem of sparse rewards of reinforcement learning algorithm, improves the convergence speed of the algorithm, and improves the generalization of the algorithm by step-by-step training based on the ideas of curriculum learning and transfer learning according to the difficulty of the task. The simulation results show that the improved SAC algorithm has fast convergence speed, good timeliness and strong generalization, and can better complete the UAV route planning task.
Automatic ROI Setting Method Based on LSC for a Traffic Congestion Area

Congested regions in videos put forward higher requirements for target detection algorithms, and the key detection of congested regions provides optimization directions for improving the accuracy of detection algorithms. In order to make the target detection algorithm pay more attention to the congested area, an automatic selection method of a traffic congestion area based on surveillance videos is proposed. Firstly, the image is segmented with superpixels, and a superpixel boundary map is extracted. Then, the mean filtering method is used to process the superpixel boundary map, and a fixed threshold is used to filter pixels with high texture complexity. Finally, a maximin method is used to extract the traffic congestion area. Monitoring data of night and rainy days were collected to expand the UA-DETRAC data set, and experiments were carried out on the extended data set. The results show that the proposed method can realize automatic setting of the congestion area under various weather conditions, such as full light, night and rainy days.
Human Recognition Model Using CNN

Since the ever increasing demand of social platforms and social media, it is highly recommended to check age and gender of the person using social websites to avoid any age restricted content. Automatic age and gender classification offers variety of applications majorly authentication, visual surveillance, customer care, biometrics. A convolutional neural network (CNN) is emerging tool for image processing and human identification. In this paper, deep- convolutional neural networks (CNN) is proposed to learn structures of human’s face and hence improve performance for recognition of human’s gender and age significantly. Here, we propose a less complex convolutional neural network design which can be effectively used in case of a small amount of learning data too. The attempts include lowering the number of parameters, boosting the network's depth, and adjusting the dropout rate to have better matching with current state-of-the-art methods using the Adience benchmark.
Theoretical and Numerical Estimate of Signal-to-Noise Ratio in the Analysis of Josephson Junctions Lifetime for Photon Detection

The performances of a Josephson junction employed to reveal a train of pulses (a rough model for single photon detection) are analyzed with a theoretical estimate that exploits an index employed in statistical decision theory, the Kumar–Carroll index. The approximate analysis compares the numerically simulated performances of the device through the receiver operating characteristics, that offer an overview of the rate of false detection, as well as the probability to miss a signal (in this case, a pulse train). It is thus demonstrated the usefulness and the limits of the succinct Kumar–Carroll parameter. On the first side, it is proven that an increase of the parameter corresponds to an improvement of the detection. However, on the side of the limitations, the expected performances are not quite accurate, for the actual performances are systematically worse than the theoretical estimates. The results may be relevant to characterize Josephson junctions as detectors of weak signals, as those stemming from axions.
Metaverse or Not Metaverse

This chapter examines how Turkish educational researchers used the Metaverse concept during the Covid-19 pandemic. A thorough examination of the term metaverse is conducted from its emergence, evolution, past and current usage within the academic circles, along with the terms edufication and gamification. Document analysis method is chosen to do a systematic literature review on academic journals. Search was conducted with added keywords to the Metaverse within the literature in Turkish and English. Although the term is a relatively new entrant to the academic research, it is continuously evolving along with the technological advancements its applications to the industry and social life; hence, the concept and its usage will be a continuous subject of further research. The result of the analysis showed that Turkish researchers mainly prefer ‘three-dimensional virtual world' instead of the term Metaverse, and the ones found to use Metaverse mainly misuse the term out of its internationally accepted definition.
Wavelet oriented SAR image despeckling using fractional-order TV and a non-convex sparse prior

Speckle is an inborn deformity caused by the interaction of backscattered waves from the target in active coherent imaging sensors such as synthetic aperture radar (SAR). Its presence severely limits the utility of SAR imagery in terms of detecting ground targets, retrieving information and analyzing scenes. The wavelet-based methods employ a threshold value on the noisy image for sparse wavelet representation. However, the noisy image coefficients exceeding the threshold are the cause of spurious noise spikes around the discontinuities. The edge preserving total variation (TV) regularizer is effective against speckle. Nevertheless, it is associated with staircase artifact. In this work, the wavelet-based forward model is integrated with a fractional order TV (FrTV) regularizer in the presence of a non-convex sparse prior to exploit the benefits of both methods. This combination aids in avoiding staircasing artifacts and preserving discontinuities while eliminating noise. Under some constraints, the optimization problem is convex, and it is solved using the alternating direction method of multipliers (ADMM). The alternating framework is iteratively solved by employing a thresholding operation, a shrinkage function, and quadratic minimization. The proposed method’s convergence is investigated both mathematically and empirically. The experimental results on both simulated and practical SAR data show that the proposed technique outperforms the previous algorithms in terms of speckle removal.
Statistics of the number of defects after quantum annealing in a thermal environment

We study the statistics of the kink number generated by quantum annealing in a one-dimensional transverse Ising model coupled to a bosonic thermal bath. Using the freezing ansatz for quantum annealing in the thermal environment, we show the relation between the ratio of the second to the first cumulant of the kink number distribution and the average kink density. The theoretical result is confirmed thoroughly by numerical simulation using the non-Markovian infinite time-evolving block decimation which we proposed recently. The simulation using D-Wave's quantum annealer is also discussed. This article is part of the theme issue 'Quantum annealing and computation: challenges and perspectives'.
Tunneling through DNS over TLS providers

DNS over TLS (DoT) is one of the approaches for private DNS resolution, which has already gained support by open resolvers. Moreover, DoT is used by default in Android operating systems. This study investigates the possibility of creating DNS covert channels using DoT, which is a security threat that benefits from the increased privacy of encrypted communication. We evaluated the performance and usability of DoT tunnels created via commonly used resolvers. Our results show that the performance characteristics of DoT tunnels differ vastly depending on the used DoT resolver; however, the creation of a DoT tunnel is possible, reaching speeds up to 232 Kbps. Moreover, we successfully transferred data via DoT servers claiming Anti-Virus protection and family-friendly content.
Knowledge diffusion trajectories of agile software development research: A main path analysis

The dramatic growth of agile software development (ASD) research has resulted in a large number of diverse theoretical and empirical publications. The citation relationships among these publications indicate knowledge dissemination across and within academia or scientists. This study offers a comprehensive understanding of the ASD literature by exploring the knowledge diffusion path through the citation network of publications that have made significant contributions to its research development. We employ a quantitative citation-based methodology, main path analysis (MPA), to examine the citation relationship of 1431 scientific articles published in the Web of Science (WoS) between 2001 and 2021 and visualize the MPA results using Pajek software. Through citation analysis this study discovers knowledge diffusion trajectories of publications concerning ASD method. Our key results present 32 publications identified along the key-route main path as the most influential ones in the trajectories of ASD. There are three phases of ASD research development: introduction, evaluation, and deployment and expansion. Using the multiple-global main path, we further uncover the publication trends from a set of recent papers and reveal four sub-themes: tailoring of agile practices, large-scale agile context, challenges and success factors of large-scale organizations, and agile global software development. Although there was little academic interest in the initial phase, ASD-related publication and citation trends have consistently increased over time. The historical development of ASD methods was established in three distinct phases of publications in the domain. Each phase presents a narrative of agile methods’ development with different focuses. The most recent trends of ASD publications tend to focus on the agile tailoring and scaling process in the global and distributed environment.
Image Denoising Using Multi Scaling Aided Double Decker Convolutional Neural Network

The digital image suffers from noise during transmission and reception. By preserving the qualities of the image, image denoising assists in the recovery of high-quality photos without noise (edge, corners, and sharp structures). Deep convolutional neural networks (CNNs) have been successfully employed for picture denoising due to their shifting topologies. Traditional approaches suffer from a number of drawbacks, such as a lack of adaptability when handling spatially invariant noise and the requirement for numerous models to denoise images. In this study, a novel approach that combines the advantages of various algorithms to achieve the objectives listed below. (1) Better performance (2) Faster processing (3) Less complexity in the denoiser (4) Extracting complementary characteristics to increase the denoiser's overall capacity. In terms of qualitative and quantitative analysis it has been evaluated and the simulation findings show that the Multi Scaling Aided Double Decker (MUS-ADD) convolutional neural network strategy is superior to other approaches.
Unbalanced data processing for software defect prediction

How to solve the imbalance of defect classification in software defect prediction and improve the accuracy of prediction is an important problem in software testing. Thus many machining learning based model, such as self-adaptive Robust Synthetic Minority Over-Sampling Technique (RSMOTE), ware presented for software defect prediction. However, the imbalanced data distribution limited the prediction performance. Addressing to this issue, a RSMOTE-based Data Imbalance Processing (RDIP) model is presented in this paper. Specifically, the normalized outlier data is removed according to the European distance between points in data denoising, and then the fuzzy membership and fuzzy labels of each point are calculated using the Computational Class Fuzzy Algorithm (FCMD), which removes the hazard points and noise points according to the selection boundary point algorithm (BRS). Experimental results the date sets of NASA, Promise show that the average F1-measure of software defect prediction method for data imbalance is 6.98% higher than other comparison algorithms, which can effectively solve the problem of defect classification imbalance in software defect prediction.
Analyzing the impact of police mugshots in face verification for crime investigations

During photo-signaling procedures, national police forces routinely collect two pictures (commonly known as mugshots), i.e., the frontal picture and the right profile picture, together with fingerprints and personal information of a subject, for various purposes, ranging from releasing documents to registering criminals. Thus, there is a clear connection between face recognition and this data collection procedure. Despite Automated Fingerprint Identification Systems (AFISs) are being extended by integrating face recognition techniques to provide image analysis capabilities, there is a lack of research in understanding to which extent such techniques are effective in identifying a known subject when only the two standard images of police databases are available as reference samples. This paper makes a step in such direction by comparing the performance of two different Convolutional Neural Networks (CNNs), pretrained on the VGGFace2 dataset, in a face verification task using the Surveillance Cameras Face (SCFace) database. Specifically, we organized subsets of mugshots taken from different angles in the range [-90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> , 90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> ], where -90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> is an image showing the left profile of a subject, 0° is the frontal picture, and 90° is the right profile. We used each subset in a face verification task with images taken from security cameras, trying to understand if the use of more than two mugshot pictures, taken from different points of view, has a positive impact in the identification of suspected subjects during crime investigations. This study might be a first step to assess if a revision of the mugshot collection procedure during photo-signaling is worth the necessary investments, in terms of picture acquisition and storage.
A rational-expansion-based method to compute Gabor coefficients of 2D indicator functions supported on polygonal domain

We propose a method to compute Gabor coefficients of a two-dimensional (2D) indicator function supported on a polygonal domain by means of rational expansion of the Faddeeva function and by solving second-order linear difference equations. This method has the following three attractive features: (1) the problem of computing Gabor coefficients is formulated as the calculation of a sequence of integrals with a uniform structure, (2) a rational expansion based on fast Fourier transform (FFT) is used to approximate the Faddeeva function on the entire complex plane, (3) second-order inhomogeneous linear difference equations are derived for previous integrals and they are solved stably with Olver's algorithm. Numerical quadrature to compute Gabor coefficients is avoided. Numerical examples show this rational-expansion-based method significantly outperforms numerical quadrature in terms of computation time while maintaining accuracy.
Self-paced multi-label co-training

Multi-label learning aims to solve classification problems where instances are associated with a set of labels. In reality, it is generally easy to acquire unlabeled data but expensive or time-consuming to label them, and this situation becomes more serious in multi-label learning as an instance needs to be annotated with several labels. Hence, semi-supervised multi-label learning approaches emerge as they are able to exploit unlabeled data to help train predictive models. This work proposes a novel approach called Self-paced Multi-label Co-Training (SMCT). It leverages the well-known co-training paradigm to iteratively train two classifiers on two views of a dataset and communicate one classifier's predictions on unlabeled data to augment the other's training set. As pseudo labels may be false in iterative training, self-paced learning is integrated into SMCT to rectify false pseudo labels and avoid error accumulation. Concretely, the multi-label co-training model in SMCT is formulated as an optimization problem by introducing latent weight variables of unlabeled instances. It is then solved via an alternative convex optimization algorithm. Experimental evaluations are carried out based on six benchmark multi-label datasets and three metrics. The results demonstrate that SMCT is very competitive in each setting when compared with five state-of-the-art methods.
Multi-Kernel Excitation Network for Video Action Recognition

Video action recognition based on Convolutional Neural Network (CNN) relies on enough spatial and temporal information captured from the videos by CNN. Conventional 2D CNN-based methods are usually of low computational cost but cannot reason the temporal relationships; 3D CNNs achieve high accuracy accompanied by huge parameters and computational consumption. This paper proposes a piece of spatial-temporal information capturing module, namely Multi-Kernels Excitation (MKE), which can be embedded into 2D CNNs to improve their temporal modeling capability dramatically. The critical part of MKE is Multi-Kernel Attention (MKA), which utilizes attention mechanisms to capture spatial-temporal features. We equip 2D CNNs with the proposed MKE module to construct a simple yet effective MKE-Net. We demonstrate our MKE-Net on ResNet50 and verify its performance on complicated temporal-related datasets (i.e., Something-Something V1 and Something-Something V2).
A dual-flow neural network for medical image registration

Deformable image registration is essential for subsequential medical image analysis. Although the existing convolutional neural network- (CNN) and transformer-based registration methods have achieved promising performance along with fast inference time, the limitations of CNNs and transformers on their own prevent us to promote further registration accuracy. To address this issue, we propose a dual-flow neural network for medical image registration, which consists of two encoders for extracting both local and global features, one is the CNN encoder responsible for mining local differences between the fixed and moving images, and the other is transformer encoder estimating the long-range spatial relationships between distant voxels in two images. Both global and local features are fed into a common decoder to estimate the displacement vector field. To validate the superiority of the dual-flow CNN and transformer network, we compare the proposed method with several state-of-the-art (SOTA) models on Tl-weighted image registration, the results demonstrate that the dual-flow CNN and transformer mixed model outperforms the SOTA methods, it can increase the similarities in surface and boundaries between the fixed and warped images.
An Application of a Fuzzy TOPSIS Multi-Criteria Decision Analysis Algorithm for Augmented Reality Maintenance Aid Systems Selection

In recent years, augmented reality (AR) devices have shown great potential in the field of maintenance. It’s still a work in progress to assess their influence on the maintenance procedure. A few recent studies have looked at this problem utilizing qualitative methodologies based on subjective analysis, as well as after the AR system’s design and/or deployment. This paper presents the first results of an evaluation study that shows the advantages of adopting augmented-reality systems for maintenance activities. In order to provide decision makers with quantitative knowledge to build, maintenance AR applications more efficiently, a multicriteria model based on the Fuzzy TOPSIS technique was suggested. The model combines technical knowledge about AR devices with several criteria to estimate the AR device’s contribution. In order to evaluate the model and describe its worldwide applicability and potentialities, a test, case study regarding the assessment of an AR system on-site repair service has been also explored.
Ship Deck Segmentation Using an Attention Based Generative Adversarial Network

In this paper, we proposed an attention-based generative adversarial network (GAN) model to clean low quality, noisy engineering documents of ship decks provided by the Military Sealift Command (MSC). We proposed four loss functions and optimized the hyperparameters associated with the model and found that each of the parameters had significant influence on the model performance. We trained the proposed model with 154 MSC noisy/clean image pairs with different parameter combinations and applied the trained model to 9 MSC images to identify the best parameter combination. Experiment results show that the proposed model could achieve good results with the limited dataset and the cleaned documents can be used for 3D modeling of ships that will revolutionize ship maintenance.
Prediction of software quality with Machine Learning-Based ensemble methods

Software quality prediction is used at various stages of projects. There are several metrics that provide the quality measure with respect to different types of software. In this study, defect density is used as the feature that represents the quality. The aim of this study is to predict software quality with higher accuracy than previous studies. The study shows that data pre-processing, feature extraction and machine learning algorithms provide more accurate results in predicting the quality of the software. Algorithms were applied on three datasets that include software metrics. The obtained accuracy values were compared with the existing ones in the literature. Ensemble methods, which enable the use of different machine learning algorithms together, contributed to obtaining results with higher accuracy rates in software quality prediction. In EBSPM dataset, logistic regression produced the best accuracy value as 96.67%. On the other hand, in ISBSG dataset and in PROMISE dataset, the ensemble methods Soft Voting & Stacking and Gradient Boosting & Bagging generated the best results respectively with the values 96.31% and 94.59%.