Reviewerly/exHarmony huggingface dataset:
TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction

When TextRank algorithm based on graph model constructs graph associative edges, the co-occurrence window rules only consider the relationships between local terms. Using the information in the document itself is limited. In order to solve the above problems, an improved TextRank keyword extraction algorithm based on rough data reasoning combined with word vector clustering, RDD-WRank, was proposed. Firstly, the algorithm uses rough data reasoning to mine the association between candidate keywords, expands the search scope, and makes the results more comprehensive. Then, based on Wikipedia online open knowledge base, word embedding technology is used to integrate Word2Vec into the improved algorithm, and the word vector of TextRank lexical graph nodes is clustered to adjust the voting importance of nodes in the cluster. Compared with the traditional TextRank algorithm and the Word2Vec algorithm combined with TextRank, the experimental results show that the improved algorithm has significantly improved the extraction accuracy, which proves that the idea of using rough data reasoning can effectively improve the performance of the algorithm to extract keywords.
Hardware architecture of a digital piecewise linear chaotic map with perturbation for pseudorandom number generation

Piecewise linear chaotic map (PWLCM) is a simple chaotic system popularly used for generating pseudorandom numbers (PRN). Digital implementation of PWLCM degrades the randomness of the pseudorandom number due to the finite precision effect. The current paper presents a digital 1-dimensional PWLCM that mitigates the degradation of digital chaos by cascading the chaotic map with a three-stage XORed shift register that perturbs the output of the PWLCM. We further perturb the output of the shift register using a one-stage XOR post-processing. We also present the hardware architecture of the proposed system to generate a high throughput PRN sequence. The designed architecture is synthesized on Xilinx Zynq 7000 Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC) using TSMC (Taiwan Semiconductor Manufacturing Company) 90 nm CMOS technology. We experimentally validate the randomness of the output sequence using NIST test suite 800–22. In addition, we also perform security analyses such as phase space, key sensitivity, correlation, and information entropy. The statistical and security analysis confirms the efficiency of the proposed design in terms of randomness. The synthesis result demonstrates the achieved throughput as 1.296 Gbps at 81 MHz operating frequency on Zynq 7000 FPGA, whereas a throughput of 1.696 Gbps at 106 MHz operating frequency is achieved on ASIC.
SiamSMDFFF: Siamese network tracker based on shallow-middle-deep three-level feature fusion and clustering-based adaptive rectangular window filtering

With the development of machine vision, target trackers based on Siamese networks have demonstrated excellent performance in balancing speed and accuracy compared to traditional methods. Traditional trackers usually use only the deep features extracted from the last convolutional neural network (CNN) layer, which contain semantic information, to complete the similarity matching. In addition, a cosine window is usually used to filter the score map, which leads to the limited suppression effect of the tracker on the interference of the objects similar to the target and the poor robustness of the tracker. In this paper, a Siamese network tracker named SiamSMDFFF, which combines shallow-middle-deep feature fusion with a clustering-based adaptive rectangular window filter, is proposed. SiamSMDFFF uses the features to fuse at the feature level to obtain the complementary feature maps and then uses the score maps calculated from the complementary feature maps via correlation to fuse at the score level and obtain the final score maps. Then, the peak points in the score map are used as the initial clustering centers to complete the clustering, and the distance between the clustering center and the farthest clustering point is calculated. Finally, the distance is used to control the change in the size of the rectangular window to filter the score map in order to overcome the negative impact of the similar targets interfering in the tracking process and improve the robustness of the tracker. The experimental results demonstrate that SiamSMDFFF is significantly improved in several aspects compared to the conventional tracker.
Detecting and Augmenting Missing Key Aspects in Vulnerability Descriptions

Security vulnerabilities have been continually disclosed and documented. For the effective understanding, management, and mitigation of the fast-growing number of vulnerabilities, an important practice in documenting vulnerabilities is to describe the key vulnerability aspects, such as vulnerability type, root cause, affected product, impact, attacker type, and attack vector. In this article, we first investigate 133,639 vulnerability reports in the Common Vulnerabilities and Exposures (CVE) database over the past 20 years. We find that 56%, 85%, 38%, and 28% of CVEs miss vulnerability type, root cause, attack vector, and attacker type, respectively. By comparing the differences of the latest updated CVE reports across different databases, we observe that 1,476 missing key aspects in 1,320 CVE descriptions were augmented manually in the National Vulnerability Database (NVD) , which indicates that the vulnerability database maintainers try to complete the vulnerability descriptions in practice to mitigate such a problem. To help complete the missing information of key vulnerability aspects and reduce human efforts, we propose a neural-network-based approach called PMA to predict the missing key aspects of a vulnerability based on its known aspects. We systematically explore the design space of the neural network models and empirically identify the most effective model design in the scenario. Our ablation study reveals the prominent correlations among vulnerability aspects when predicting. Trained with historical CVEs, our model achieves 88%, 71%, 61%, and 81% in F1 for predicting the missing vulnerability type, root cause, attacker type, and attack vector of 8,623 “future” CVEs across 3 years, respectively. Furthermore, we validate the predicting performance of key aspect augmentation of CVEs based on the manually augmented CVE data collected from NVD, which confirms the practicality of our approach. We finally highlight that PMA has the ability to reduce human efforts by recommending and augmenting missing key aspects for vulnerability databases, and to facilitate other research works such as severity level prediction of CVEs based on the vulnerability descriptions.
Human behavior recognition model based on improved EfficientNet

With the continuous development of AI, human behavior recognition has a wide range of applications in surveillance, smart medical care, smart furniture and so on. In order to solve the disadvantages of traditional human behavior recognition methods such as poor robustness, inconvenience of wearing and privacy violation, this paper proposes a human behavior recognition model based on the improved EfficientNet. The main body of the model is based on EfficientNet, and it is optimized by streamlining the MBConv module, adding Residual structure to the MBConv module and selecting a more suitable activation function. First, the point cloud data of human behavior is collected using 3D LiDAR; to improve the robustness of human behavior recognition, the 3D point cloud is bilaterally filtered and transformed into images to create a LiDAR-based human behavior recognition dataset; the improved EfficientNet model is trained using this dataset, and it is also used with other classification networks such as DenseNet, GoogLeNet, and MobileNetV3. The experimental results show that the improved model is more efficient and finally achieves the optimal recognition accuracy of 99.69%. Compared with other human behavior recognition methods, the method in this paper has stronger environmental robustness and lower human invasiveness.
Strong-lensing source reconstruction with variationally optimized Gaussian processes

ABSTRACT Strong-lensing images provide a wealth of information both about the magnified source and about the dark matter distribution in the lens. Precision analyses of these images can be used to constrain the nature of dark matter. However, this requires high-fidelity image reconstructions and careful treatment of the uncertainties of both lens mass distribution and source light, which are typically difficult to quantify. In anticipation of future high-resolution data sets, in this work we leverage a range of recent developments in machine learning to develop a new Bayesian strong-lensing image analysis pipeline. Its highlights are (a) a fast, GPU-enabled, end-to-end differentiable strong-lensing image simulator; (b) a new, statistically principled source model based on a computationally highly efficient approximation to Gaussian processes that also takes into account pixellation; and (c) a scalable variational inference framework that enables simultaneously deriving posteriors for tens of thousands of lens and source parameters and optimizing hyperparameters via stochastic gradient descent. Besides efficient and accurate parameter estimation and lens model uncertainty quantification, the main aim of the pipeline is the generation of training data for targeted simulation-based inference of dark matter substructure, which we will exploit in a companion paper.
Twitter Sentiment Analysis using Machine Learning and Deep Learning Techniques

Nowadays, Social Media websites have become increasingly popular, hosting millions of users daily and accounting for countless interactions. Among them, Twitter is extraordinarily popular, boasting a huge user base and a preeminent global presence. People from all walks of life use Twitter to express their opinions on a myriad of topics. Thus, by employing Sentiment Analysis techniques on tweets, Organizations can get valuable insights on public opinions, which can allow them to make more effective decisions. In this paper, Machine Learning and Deep Learning models were trained on a dataset containing 1.6 million tweets, to classify them into positive or negative sentiments. The performance of these models was compared with respect to metrics like Accuracy, F1-Score, Recall and Precision.
Improving adversarial robustness of Bayesian neural networks via multi-task adversarial training

Bayesian neural networks (BNNs) are used in many tasks because they provide a probabilistic representation of deep learning models by placing a distribution over the model parameters. Although BNNs are a more robust deep learning paradigm than vanilla deep neural networks, their ability to handle adversarial attacks in practice remains limited. In this study, we propose a novel multi-task adversarial training approach for improving the adversarial robustness of BNNs. Specifically, we first generate diverse and stronger adversarial examples for adversarial training by maximising a multi-task loss. This multi-task loss is a combination of the unsupervised feature scattering loss and supervised margin loss. Then, we find the model parameters by minimising another multi-task loss composed of the feature loss and variational inference loss. The feature loss is defined based on distance ℓp, which measures the difference between the two feature representations extracted from the clean and adversarial examples. Minimising the feature loss improves the feature similarity and helps the model learn more robust features, resulting in enhanced robustness. Extensive experiments are conducted on four benchmark datasets in white-box and black-box attack scenarios. The experimental results demonstrate that the proposed approach significantly improves the adversarial robustness compared with several state-of-the-art defence methods.
Understanding Deep Learning with Statistical Relevance

Abstract This paper argues that a notion of statistical explanation, based on Salmon’s statistical relevance model, can help us better understand deep neural networks. It is proved that homogeneous partitions, the core notion of Salmon’s model, are equivalent to minimal sufficient statistics, an important notion from statistical inference. This establishes a link to deep neural networks via the so-called Information Bottleneck method, an information-theoretic framework, according to which deep neural networks implicitly solve an optimization problem that generalizes minimal sufficient statistics. The resulting notion of statistical explanation is general, mathematical, and subcausal.
Un-supervised learning for blind image deconvolution via Monte-Carlo sampling

Abstract Deep learning has been a powerful tool for solving many inverse imaging problems. The majority of existing deep-learning-based solutions are supervised on an external dataset with many blurred/latent image pairs. Recently, there has been an increasing interest on developing dataset-free deep learning methods for image recovery without any prerequisite on external training dataset, including blind deconvolution. This paper aims at developing an un-supervised learning method for blind image deconvolution, which does not call any training sample yet provides very competitive performance. Based on the re-parametrization of latent image using a deep network with random weights, this paper proposed to approximate the maximum-a posteriori estimator of the blur kernel using the Monte-Carlo (MC) sampling method. The MC sampling is efficiently implemented by using dropout and random noise layer, which does not require conjugate model as traditional variational inference does. Extensive experiments on popular benchmark datasets for blind image deconvolution showed that the proposed method not only outperformed existing non-learning methods, but also noticeably outperformed existing deep learning methods, including both supervised and un-supervised ones.
Entity sentiment analysis in the news: A case study based on Negative Sentiment Smoothing Model (NSSM)

This paper studied the sentiment on the 45th President of the United States in the news using a new entity sentiment analysis model called Negative Sentiment Smoothing Model (NSSM). Entity refers to a specific object such as a person, a party, or an institution, etc. Entity sentiment analysis aims to calculate sentiments towards an individual entity in a document rather than obtaining a single sentiment score for the whole document. The NSSM model adjusts sentiment score when there are Negative Associated Entities (NAEs) in the document. NAEs were referred to as the entities that are associated with negative sentiments in the data collection. Specifically, three NSSM models were developed based on smoothing algorithm. Choosing “Trump”-the 45th president of US as the target entity, this study evaluated the effectiveness of NSSM models (NSSM-A, NSSM-B, and NSSM-C) in the news context. The data includes a total of 10,993 individual paragraphs of the target entity-related news collected from CNN, FOX, and NPR for three months period from July 1, 2019, to September 30, 2019. Compared to the method without smoothing, the results indicated that NSSM models improved performance based on three evaluation metrics: Euclidean distance, accuracy, and precision. The NSSM models provide a new way to accurately calculate the entity sentiment by reducing the errors caused by other entities and can be further applied to information systems in other application contexts.
Visualizing simulation ensembles of extreme weather events

In the last 20 years, extreme weather-related events like floods, landslides, droughts, and wildfires have caused the death of 1.23 million people and a loss of 2.97 trillion dollars. Studies show that low and lower-middle income countries are the most impacted ones given the lack of investment in disaster risk management. To reduce the impact of these events, weather researchers have been developing numerical weather models that inform public agencies about the impending extreme events in advance. Despite being powerful tools, these models can suffer from several sources of uncertainty, ranging from the approximation of micro-scale physical processes to the location-dependent calibration of parameters, which is especially critical in developing countries. To minimize uncertainty effects, researchers generate several different weather scenarios to compose an ensemble of simulations that typically are inspected using manual, laborious, and error-prone approaches. In this paper, we propose an interactive visual analytics system, called X-Weather, developed in close collaboration with weather researchers from Brazil. Our system contributes a set of statistics and probability-based visualizations that allows the assessment of extreme weather events by effortlessly navigating through and comparing ensemble members. We demonstrate the effectiveness of the system through two case studies analyzing tragic events that happened in the mountain region of Rio de Janeiro in Brazil.
Electric‐Circuit Simulation of Quantum Fast Hitting with Exponential Speedup

Abstract The ultimate goal of developing quantum algorithms and constructing quantum computers is to achieve faster information processing than using current classical computers. Quantum walks are powerful kernels in quantum computing protocols and possess strong capabilities in speeding up various simulation and optimization tasks. One striking example is provided by quantum walkers evolving on unbalanced trees, which demonstrate faster hitting performances than classical random walk. However, direct experimental construction of unbalanced trees to prove quantum advantage with exponential speedup remains a great challenge due to the highly complex arrangements of the structure. This study attempts to simulate quantum algorithm by classical circuit. Inspired by the quantum algorithm, the classical circuit networks are designed and fabricated with unbalanced tree structures. It is then demonstrated, both theoretically and experimentally, that the quantum algorithm for the fast hitting problem can be simulated in the structure. It is shown that the hitting efficiency of electric signals in the circuit networks with unbalanced tree structures is exponentially faster than the corresponding cases of classical random walks. Because classical circuit networks possess good scalability and stability, the results open up a scalable new path toward quantum speedup in complex problems.
Leveraging AI in Photonics and Beyond

Artificial intelligence (AI) techniques have been spreading in most scientific areas and have become a heated focus in photonics research in recent years. Forward modeling and inverse design using AI can achieve high efficiency and accuracy for photonics components. With AI-assisted electronic circuit design for photonics components, more advanced photonics applications have emerged. Photonics benefit a great deal from AI, and AI, in turn, benefits from photonics by carrying out AI algorithms, such as complicated deep neural networks using photonics components that use photons rather than electrons. Beyond the photonics domain, other related research areas or topics governed by Maxwell’s equations share remarkable similarities in using the help of AI. The studies in computational electromagnetics, the design of microwave devices, as well as their various applications greatly benefit from AI. This article reviews leveraging AI in photonics modeling, simulation, and inverse design; leveraging photonics computing for implementing AI algorithms; and leveraging AI beyond photonics topics, such as microwaves and quantum-related topics.
Skeleton-Based Explainable Bodily Expressed Emotion Recognition Through Graph Convolutional Networks

Much of the focus on emotion recognition has gone into the face and voice as expressive channels, whereas bodily expressions of emotions are understudied.Moreover, current studies lack the explainability of computational features of body movements related to emotional expressions.Perceptual research on body parts' movements shows that features related to the arms' movements are correlated the most with human perception of emotions.In this paper, our research aims at presenting an explainable approach for bodily expressed emotion recognition.It utilizes the body joints of the human skeleton, representing them as a graph, which is used in Graph Convolutional Networks (GCNs).We improve the modelling of the GCNs by using spatial attention mechanisms based on body parts, i.e. arms, legs and torso.Our study presents a stateof-the-art explainable approach supported by experimental results on two challenging datasets.Evaluations show that the proposed methodology offers accurate performance and explainable decisions.The methodology demonstrates which body part contributes the most in its inference, showing the significance of arm movements in emotion recognition.
Robotic manipulation based on 3-D visual servoing and deep neural networks

The critical challenge, for robot–object-interaction, is to estimate visually the pose of the target object in a 3D space and combine it into a vision-based control scheme in manipulation applications. This paper proposes a novel reliable framework for deep ConvNet combined with visual servoing using a single RGB camera. We introduce an extensive system called Deep-Visual-Servoing (DVS) that addresses an integration of: (I) training of deep-CNNs using synthetic dataset only and operates successfully in real-world scenario, (II) continuous 3 D object pose estimation as the sensing feedback in a 3D visual servoing control scheme, and (III) design, integration and experimentation of visual servoing approach based on Lyapunov’s theory. The proposed deep based learning approach, the kinematic modeling and controller design are experimentally verified and discussed using the 6 DOF UR5 manipulator.
Crime activities prediction system in video surveillance by an optimized deep learning framework

Abstract The video surveillance technology is used to find crime events in public places and capture live public events. Hence, detecting the criminalist before the crime actions is the most needed event to catch the criminalist. However, the presence of noise content in the trained video has raised the difficulties in crime specification by maximizing the complexity range of the data. To overcome this issue, this research has designed a novel lion‐based deep belief neural paradigm (LbDBNP) to identify criminals by their activities and handling tools. Initially, three types of datasets were trained to the system then the training flaws were eliminated in the preprocessing layer. Hereafter, the cleaned data is imported to the classification module to detect the crime events present in the video. Subsequently, the designed model is implemented using the Python framework in Windows 10 platform. To evaluate the efficiency of the designed model, the attack is launched in the proposed model after those metrics are calculated. In addition, the robustness of the designed system is verified by three datasets, such as UCSDped1, UCSDped2, and avenue crime. Also, the key parameters of the designed model have been evaluated and compared with other existing schemes to verify the proposed model's robustness by achieving the finest outcomes.