Reviewerly/exHarmony huggingface dataset:
Gpu-Accelerated Sift-Aided Source Identification of Stabilized Videos

Video stabilization is an in-camera processing commonly applied by modern acquisition devices.While significantly improving the visual quality of the resulting videos, it has been shown that such operation typically hinders the forensic analysis of video signals.In fact, the correct identification of the acquisition source usually based on Photo Response non-Uniformity (PRNU) is subject to the estimation of the transformation applied to each frame in the stabilization phase.A number of techniques have been proposed for dealing with this problem, which however typically suffer from a high computational burden due to the grid search in the space of inversion parameters.Our work attempts to alleviate these shortcomings by exploiting the parallelization capabilities of Graphics Processing Units (GPUs), typically used for deep learning applications, in the framework of stabilised frames inversion.Moreover, we propose to exploit SIFT features to estimate the camera momentum and identify less stabilized temporal segments, thus enabling a more accurate identification analysis, and to efficiently initialize the frame-wise parameter search of consecutive frames.Experiments on a consolidated benchmark dataset confirm the effectiveness of the proposed approach in reducing the required computational time and improving the source identification accuracy.The code is available at https://github.com/AMontiB/GPU-PRNU-SIFT.
Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts

To develop and validate a machine learning (ML) model for the classification of breast lesions on ultrasound images.In the present study, three separate data cohorts containing 1288 breast lesions from three countries (Malaysia, Iran, and Turkey) were utilized for MLmodel development and external validation. The model was trained on ultrasound images of 725 breast lesions, and validation was done separately on the remaining data. An expert radiologist and a radiology resident classified the lesions based on the BI-RADS lexicon. Thirteen morphometric features were selected from a contour of the lesion and underwent a three-step feature selection process. Five features were chosen to be fed into the model separately and combined with the imaging signs mentioned in the BI-RADS reference guide. A support vector classifier was trained and optimized.The diagnostic profile of the model with various input data was compared to the expert radiologist and radiology resident. The agreement of each approach with histopathologic specimens was also determined. Based on BI-RADS and morphometric features, the model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.885, which is higher than the expert radiologist and radiology resident performances with AUC of 0.814 and 0.632, respectively in all cohorts. DeLong's test also showed that the AUC of the ML protocol was significantly different from that of the expert radiologist (ΔAUCs = 0.071, 95%CI: (0.056, 0.086), P = 0.005).These results support the possible role of morphometric features in enhancing the already well-excepted classification schemes.
Feedback‐Assisted Quantum Search by Continuous‐Time Quantum Walks

Abstract The quantum search of a target node on a cycle graph by means of a quantum walk assisted by continuous measurement and feedback are addressed. Unlike previous spatial search approaches, where the oracle is described as a projector on the target state, a dynamical oracle implemented through a feedback Hamiltonian is considered. The idea is based on continuously monitoring the position of the quantum walker on the graph and then applying a unitary feedback operation based on the information obtained from measurement. The feedback changes the couplings between the nodes and it is optimized at each time via a numerical procedure. The stochastic trajectories describing the evolution for graphs of dimensions up to are numerically simulated, and the performance of the protocol is quantified via the average fidelity between the state of the walker and the target node. Different constraints on the control strategy are discussed. For unbounded controls, the protocol is able to quickly localize the walker on the target node. How the performance is lowered by posing an upper bound on the control couplings is then discussed. Finally, it is shown how a digital feedback protocol seems in general as efficient as the continuous bounded one.
Attention-Guided Disentangled Feature Aggregation for Video Object Detection

Object detection is a computer vision task that involves localisation and classification of objects in an image. Video data implicitly introduces several challenges, such as blur, occlusion and defocus, making video object detection more challenging in comparison to still image object detection, which is performed on individual and independent images. This paper tackles these challenges by proposing an attention-heavy framework for video object detection that aggregates the disentangled features extracted from individual frames. The proposed framework is a two-stage object detector based on the Faster R-CNN architecture. The disentanglement head integrates scale, spatial and task-aware attention and applies it to the features extracted by the backbone network across all the frames. Subsequently, the aggregation head incorporates temporal attention and improves detection in the target frame by aggregating the features of the support frames. These include the features extracted from the disentanglement network along with the temporal features. We evaluate the proposed framework using the ImageNet VID dataset and achieve a mean Average Precision (mAP) of 49.8 and 52.5 using the backbones of ResNet-50 and ResNet-101, respectively. The improvement in performance over the individual baseline methods validates the efficacy of the proposed approach.
Path Planning of Multi-Objective Underwater Robot Based on Improved Sparrow Search Algorithm in Complex Marine Environment

Autonomous underwater vehicle (AUV) path planning in complex marine environments meets many chanllenges, such as many influencing factors, complex models and the performance of the optimization algorithm to be improved. To find a path with minimum cost of the actual environmental threats, navigation height changes and the minimum energy consumption of the AUV, an improved sparrow search algorithm is designed under the impact of the time-varying characteristics of the current in the complex marine environment on the AUV and the physical constraints of the AUV movement. In the proposed algorithm, an adaptive weight factor balance strategy is introduced into the position update of the discoverer to improve the convergence speed and search ability. In the follower position, the variable spiral search strategy is improved based on the position update formula to enhance the local jumping ability of the algorithm. After the position update, Levy flying strategy is added, and Cauchy–Gaussian mutation is performed on the optimal individual to increase the population diversity, which improves the anti-stagnation ability of the algorithm. Finally, the AUV global path with minimum cost in the sense of multi-objective weighting is obtained. The simulation results show that the proposed algorithm can plan a path with less cost and quickly converge to an optimized point, and finally it can meet the global path planning requirements of AUV when navigating in complex sea areas.
Deep learning-based framework for slide-based histopathological image analysis

Abstract Digital pathology coupled with advanced machine learning (e.g., deep learning) has been changing the paradigm of whole-slide histopathological images (WSIs) analysis. Major applications in digital pathology using machine learning include automatic cancer classification, survival analysis, and subtyping from pathological images. While most pathological image analyses are based on patch-wise processing due to the extremely large size of histopathology images, there are several applications that predict a single clinical outcome or perform pathological diagnosis per slide (e.g., cancer classification, survival analysis). However, current slide-based analyses are task-dependent, and a general framework of slide-based analysis in WSI has been seldom investigated. We propose a novel slide-based histopathology analysis framework that creates a WSI representation map, called HipoMap, that can be applied to any slide-based problems, coupled with convolutional neural networks. HipoMap converts a WSI of various shapes and sizes to structured image-type representation. Our proposed HipoMap outperformed existing methods in intensive experiments with various settings and datasets. HipoMap showed the Area Under the Curve (AUC) of 0.96±0.026 (5% improved) in the experiments for lung cancer classification, and c-index of 0.787±0.013 (3.5% improved) and coefficient of determination ( $$R^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> ) of 0.978±0.032 (24% improved) in survival analysis and survival prediction with TCGA lung cancer data respectively, as a general framework of slide-based analysis with a flexible capability. The results showed significant improvement comparing to the current state-of-the-art methods on each task. We further discussed experimental results of HipoMap as pathological viewpoints and verified the performance using publicly available TCGA datasets. A Python package is available at https://pypi.org/project/hipomap , and the package can be easily installed using Python PIP. The open-source codes in Python are available at: https://github.com/datax-lab/HipoMap .
Lessons learned to improve the UX practices in agile projects involving data science and process automation

User-Centered Design (UCD) and Agile methodologies focus on human issues. Nevertheless, agile methodologies focus on contact with contracting customers and generating value for them. Usually, the communication between end users (they use the software and have low decision power) and the agile team is mediated by customers (they have high decision power but do not use the software). However, they do not know the actual problems that end users (may) face in their routine, and they may not be directly affected by software shortcomings. In this context, UX issues are typically identified only after the implementation, during user testing and validation. Aiming to improve the understanding and definition of the problem in agile projects, this research investigates the practices and difficulties experienced by agile teams during the development of data science and process automation projects. Also, we analyze the benefits and the teams’ perceptions regarding user participation in these projects. We collected data from four agile teams, in the context of an academia and industry collaboration focusing on delivering data science and process automation solutions. Therefore, we applied a carefully designed questionnaire answered by developers, scrum masters, and UX designers. In total, 18 subjects answered the questionnaire. From the results, we identify practices used by the teams to define and understand the problem and to represent the solution. The practices most often used are prototypes and meetings with stakeholders. Another practice that helped the team to understand the problem was using Lean Inception (LI) ideation workshops. Also, our results present some specific issues regarding data science projects. We observed that end-user participation can be critical to understanding and defining the problem. They help to define elements of the domain and barriers in the implementation. We identified a need for approaches that facilitate user-team communication in data science projects to understand the data and its value to the users’ routine. We also identified insights about the need of more detailed requirements representations to support the development of data science solutions.
Physics-inspired Ising Computing with Ring Oscillator Activated p-bits

The nearing end of Moore's Law has been driving the development of domain-specific hardware tailored to solve a special set of problems. Along these lines, probabilistic computing with inherently stochastic building blocks (p-bits) have shown significant promise, particularly in the context of hard optimization and statistical sampling problems. p-bits have been proposed and demonstrated in different hardware substrates ranging from small-scale stochastic magnetic tunnel junctions (sMTJs) in asynchronous architectures to large-scale CMOS in synchronous architectures. Here, we design and implement a truly asynchronous and medium-scale p-computer (with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\approx \mathbf{800}\ \mathbf{p}-\mathbf{bits}$</tex> ) that closely emulates the asynchronous dynamics of sMTJs in Field Programmable Gate Arrays (FP-GAs). Using hard instances of the planted Ising glass problem on the Chimera lattice, we evaluate the performance of the asynchronous architecture against an ideal, synchronous design that performs parallelized (chromatic) exact Gibbs sampling. We find that despite the lack of any careful synchronization, the asynchronous design achieves parallelism with comparable algorithmic scaling in the ideal, carefully tuned and parallelized synchronous design. Our results highlight the promise of massively scaled p-computers with millions of free-running p-bits made out of nanoscale building blocks such as stochastic magnetic tunnel junctions.
ReNAP: Relation network with adaptiveprototypical learning for few-shot classification

Traditional deep learning-based image classification methods often fail to recognize a new class that does not exist in the training dataset, particularly when the new class only has a small number of samples. Such a challenging and new learning problem is referred to as few-shot learning. In few-shot learning, the relation network (RelationNet) is a powerful method. However, in RelationNet and its state-of-the-art variants, the prototype of each class is obtained by a simple summation or average over the labeled samples. These simple sample statistics cannot accurately capture the distinct characteristics of the diverse classes of real-world images. To address this problem, in this paper, we propose the Relation Network with Adaptive Prototypical Learning method (ReNAP), which can learn the class prototypes adaptively and provide more accurate representations of the classes. More specifically, ReNAP embeds an adaptive prototypical learning module constructed by a convolutional network into RelationNet. Our ReNAP achieves superior classification performances to RelationNet and other state-of-the-art methods on four widely used benchmark datasets, FC100, CUB-200-2011, Stanford-Cars, and Stanford-Dogs.
Low light combining multiscale deep learning networks and image enhancement algorithm

Aiming at the lack of reference images for low-light enhancement tasks and the problems of color distortion, texture loss, blurred details, and difficulty in obtaining ground-truth images in existing algorithms, this paper proposes a multi-scale weighted feature low-light based on Retinex theory and attention mechanism. An image enhancement algorithm is proposed. The algorithm performs multi-scale feature extraction on low-light images through the feature extraction module based on the Unet architecture, generates a high-dimensional multi-scale feature map, and establishes an attention mechanism module to highlight the feature information of different scales that are beneficial to the enhanced image, and obtain a weighted image. High-dimensional feature map, the final reflection estimation module uses Retinex theory to build a network model, and generates the final enhanced image through the high-dimensional feature map. An end-to-end network architecture is designed and a set of self-regular loss functions are used to constrain the network model, which gets rid of the constraints of reference images and realizes unsupervised learning. The final experimental results show that the algorithm in this paper maintains high image details and textures while enhancing the contrast and clarity of the image, has good visual effects, can effectively enhance low-light images, and greatly improves the visual quality. Compared with other enhanced algorithms, the objective indicators PSNR and SSIM have been improved.
RISC-V Instruction Set Extensions for Lightweight Symmetric Cryptography

The NIST LightWeight Cryptography (LWC) selection process aims to standardise cryptographic functionality which is suitable for resource-constrained devices. Since the outcome is likely to have significant, long-lived impact, careful evaluation of each submission with respect to metrics explicitly outlined in the call is imperative. Beyond the robustness of submissions against cryptanalytic attack, metrics related to their implementation (e.g., execution latency and memory footprint) form an important example. Aiming to provide evidence allowing richer evaluation with respect to such metrics, this paper presents the design, implementation, and evaluation of one separate Instruction Set Extension (ISE) for each of the 10 LWC final round submissions, namely Ascon, Elephant, GIFT-COFB, Grain-128AEADv2, ISAP, PHOTON-Beetle, Romulus, Sparkle, TinyJAMBU, and Xoodyak; although we base the work on use of RISC-V, we argue that it provides more general insight.
Microseismic Dynamic Response and Multi-Source Warning during Rockburst Monitoring Based on Weight Decision Analysis

To prevent rockburst disasters and improve the accuracy of warnings for rockburst, based on the microseismic data of the 1366 working face of Hengda Coal Mine collected by the microseismic monitoring system, Fourier transform, wavelet packet transform, and Hilbert–Huang transform analysis methods are used for time-frequency domain joint analysis. The time-frequency differences of the main frequency, amplitude, frequency band percentage, and instantaneous energy of the high-energy microseismic event and the events before high-energy microseismic event are obtained. The analysis shows that the high-energy event has obvious low frequency characteristics, and when the high-energy event occurs, the instantaneous energy shows an obvious “inverted V” trend. At the same time, it is found that the acoustoelectric indexes show a trend of “rising” or “inverted V” when the high-energy event occurs. On this basis, the unascertained measure comprehensive evaluation model of rock burst hazard is established by analytic hierarchy process (AHP). Based on the analysis of microseismic data and the acoustoelectric index of the 1366 working face in Hengda coal mine, it is of great significance to determine the warning indicators for rockburst, improve the accuracy of uncertainty quantitative analysis for rockburst, and improve the discrimination accuracy of rockburst risk.
Digital Identity Verification and Management System of Blockchain-Based Verifiable Certificate with the Privacy Protection of Identity and Behavior

Due to the advantages in self-sovereignty identity management and scalability of blockchain, digital identity verification and management systems (DIVMS) of blockchain-based verifiable certificates (VC) are getting more and more attention. However, user privacy in the systems’ traditional architectures cannot be guaranteed. In this paper, the zero-knowledge succinct noninteractive arguments of knowledge (zkSNARKs) referred to as Groth16 are introduced in order to implement privacy protection of the user’s identity and behavior of DIVMS of blockchain-based VC. In the proposed architecture, the malleability attack of Groth16 is considered, and verifications of zero-knowledge proof (ZKP) and the digital signature of an identity provider (IDP) attached to VC and the status management of VC are implemented on the smart contracts of the blockchain to overcome single point failure. Furthermore, a prototype system is designed to verify the proposed architecture’s capability in privacy protection and to evaluate its performances in cost and throughput. Finally, the security of the proposed architecture is discussed, and its comparisons are conducted with those existing blockchain-based DIVMSs, especially those systems using Groth16 of zkSNARKs to improve the privacy of user. All results mentioned above have shown that the proposed system is efficient and safe, and it can improve the privacy of DIVMS of the blockchain based VC while avoiding single point failure.
Research on path planning of mobile robot based on improved genetic algorithm

The basic genetic algorithm usually has the following problems when solving robot path planning problems. First, the chromosome (path) of the first generation is randomly generated, so its fitness is often too high, resulting in too slow convergence speed and even unable to obtain the global optimal solution. Second, in the crossover operator, the selection of crossover points is often random, which cannot ensure that the fitness of cross generated offspring is better than that of their parents. Finally, in the mutation stage, because the traditional mutation strategy is to replace the selected mutation node in the individual chromosome with a random node, the offspring path generated by the mutation operator is likely to be discontinuous, so it cannot reflect the effectiveness of the mutation operator. Based on the mentioned contents, some improved strategies are proposed for the discussed three shortcomings of the basic genetic algorithm. First, a new generation mechanism of the first generation is constructed by simulating the path finding rules of ants in ant colony optimization, which greatly improves the quality of the first generation. Second, during crossover operator, the chromosomes of the two parents are disconnected at the same node, and the optimal chromosome fragments in each segment are combined based on greed, so as to gather all the excellent gene fragments of the parents in an individual as much as possible, improve the quality of the offspring after crossover operator, and speed up the convergence speed. Finally, a new mutation strategy is proposed to eliminate redundant sections and the number of corners, so that the individuals always can be mutated in a good direction. A large number of simulation results show that the improved genetic algorithm is effective in solving the robot path planning problem, and the overall performance is better than the basic genetic algorithm and some other improved genetic algorithms. Finally, by modeling and simulating the real enterprise factory environment based on the ROS platform, the experimental results also verify the effectiveness and feasibility of the improved genetic algorithm.