Reviewerly/exHarmony huggingface dataset:
<i>CAGFuzz:</i> Coverage-Guided Adversarial Generative Fuzzing Testing for Image-Based Deep Learning Systems

Deep Neural Network (DNN) driven technologies have been extensively employed in various aspects of our life. Nevertheless, the applied DNN always fails to detect erroneous behaviors, which may lead to serious problems. Several approaches have been proposed to enhance adversarial examples for automatically testing deep learning (DL) systems, such as image-based DL systems. However, the approaches contain the following two limitations. First, existing approaches only take into account small perturbations on adversarial examples, they design and generate adversarial examples for a certain particular DNN model. This might hamper the transferability of the examples for other DNN models. Second, they only use shallow features (e.g., pixel-level features) to judge the differences between the generated adversarial examples and the original examples. The deep features, which contain high-level semantic information, such as image object categories and scene semantics, are completely neglected. To address these two problems, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAGFuzz</i> , a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> overage-guided <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> dversarial <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> enerative <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fuzz</u> ing testing approach for image-based DL systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAGFuzz</i> is able to generate adversarial examples for mainstream DNN models to discover their potential errors. First, we train an Adversarial Example Generator ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AEG</i> ) based on general datasets. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AEG</i> only considers the data characteristics to alleviate the transferability problem. Second, we extract the deep features of the original and adversarial examples, and constrain the adversarial examples by cosine similarity to ensure that the deep features of the adversarial examples remain unchanged. Finally, we use the adversarial examples to retrain the models. Based on several standard datasets, we design a set of dedicated experiments to evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAGFuzz</i> . The experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAGFuzz</i> can detect more hidden errors, enhance the accuracy of the target DNN models, and generate adversarial examples with higher transferability.
Deep learning-based energy-efficient relay precoder design in MIMO-CRNs

This paper investigates the problem of energy efficient relay precoder design in multiple-input multiple-output cognitive relay networks (MIMO-CRNs). This is a non-convex fractional programming problem, which is traditionally solved using computationally expensive optimization methods. In this paper, we propose a deep learning (DL) based approach to compute an approximate solution. Specifically, a deep neural network (DNN) is employed and trained using offline computed optimal solution. The proposed scheme consists of an offline data generation phase, an offline training phase, and an online deployment phase. The numerical results show that the proposed DNN provides comparable performance at significantly lower computational complexity as compared to the conventional optimization-based algorithm that makes the proposed approach suitable for real-time implementation.
Research on Radar Active Deception Jamming Identification Method Based on RESNET and Bispectrum Features

In the complex electromagnetic environment of modern electronic warfare, active deception jamming poses a serious threat to radar. This paper proposes the identification of active deception jamming based on residual neural network (RESNET), and studies the bispectral characteristics of the signal and the method of identifying the active deception jamming of RESNET. This algorithm firstly extracts the bispectral features of the jamming signal, and uses the bispectral diagonal slice as the input of RSNET to realize the recognition of active deception jamming. Simulation results show that the algorithm has high recognition accuracy, not only has good anti-noise performance, but also has excellent robustness under low JSR.
Advances in flexible precision aspheric form measurement using axially scanned interferometry

A well-established solution for measurement of aspheric surfaces relies on translating the test part along the optical axis of a precision laser Fizeau interferometer while collecting interference fringes from areas of low slope relative to a spherical reference surface. The data are assembled into a final surface profile using the instrument geometry and scan history. This principle has been the foundation of a commercial instrument for nearly two decades. Here we report on several key advances for this instrument: 1. Optical configuration management for improved automated alignment for flats, spheres, aspheres, and torics. 2. Upfront and re-callable distortion and magnification calibrations for each transmission sphere/zoom combination. 3. Job-centric user interface with improved ease-of-use, including importing of asphere definitions directly from standard lens design software files. We give an overview of the new system, show measurement examples, and discuss how the improvements mentioned above affect the metrology and the user experience.
Investigation on deep learning for handwritten English character recognition

Handwritten character recognition mostly depends on human intervention and consumes more time. Optical character recognition is predominantly used in many applications and vast amount of research is decisive in this domain for the past one decade. Deep Convolutional Neural Networks (DCNN) is used to solve optical character recognition problem and have been shown to provide significantly better results the conventional methods. In this proposed work hyper parameters like kernel size, padding, stride size, activation function and number of layers will be tuned to improve the CNN-based handwritten English alphabet recognition. A various combination of learning parameters in LeNet-5 architecture is implemented to achieve a better result in classifying MNIST handwritten English characters. LeNet-5 architecture showed better performance in terms of both training and testing accuracy.
The Security Risk of Lacking Compiler Protection in WebAssembly

WebAssembly is increasingly used as the compilation target for cross-platform applications.In this paper, we investigate whether one can rely on the security measures enforced by existing C compilers when compiling C programs to WebAssembly.We compiled 4,469 C programs with known buffer overflow vulnerabilities to x86 code and to WebAssembly, and observed the outcome of the execution of the generated code to differ for 1,088 programs.Through manual inspection, we identified that the root cause for these is the lack of security measures such as stack canaries in the generated WebAssembly: while x86 code crashes upon a stack-based buffer overflow, the corresponding WebAssembly continues to be executed.We conclude that compiling an existing C program to WebAssembly without additional precautions may hamper its security, and we encourage more research in this direction.
Effects of Surface Visualizations on Depth Perception in Projective Augmented Reality

Depth perception is a common issue in augmented reality (AR). Projective AR, where the spatial relations between the projection surface and displayed virtual contents need to be represented properly, is particularly affected. This is crucial in the medical domain, e.g., for the distances between the patient’s skin and projected inner anatomical structures, but not much research was conducted in this context before. To this end, this work investigates the applicability of surface visualization techniques to support the perception of spatial relations in projective AR. Four methods previously explored in different domains were combined with the projection of inner anatomical structures on a human torso phantom. They were evaluated in a comparative user study (n=21) with respect to a distance estimation and a sorting task. Measures included Task completion time, accuracy, total Head movement and Confidence of the participants. Consistent results across variables show advantages of more occluding surface visualizations for the distance estimation task. Opposite results were obtained for the sorting task. This suggests that the amount of needed surface preservation depends on the use case and individual occlusion compromises need to be explored in future work.
PEWOBS: An efficient Bayesian network learning approach based on permutation and extensible ordering-based search

How to efficiently construct Bayesian network (BN) is a major research hotspot in the field of artificial intelligence. Ordering-based search (OBS) algorithm is a kind of search scoring-based BN structure learning algorithm with good performance. This kind of algorithm constructs the BN structure by optimizing the ordering of variables iteratively. However, it has the problems of poor initial ordering quality, insufficient ordering optimization and low accuracy. In order to solve these problems, this paper proposes a Bayesian network structure learning approach (PEWOBS) based on full permutation and extensible ordering-based search. It firstly uses the local learning and pruning method to process the dataset with small and large number of variables respectively, and obtains the candidate parent sets of each variable efficiently. Then it obtains the high quality of initial ordering in consideration of the global benefits and individual losses. On the basis of the initial optimization of ordering by the extensible window operator, the second optimization of sub-ordering is performed using the full permutation operator. The final BN structure with the largest score will be obtained according to the final ordering. Experimental results show that PEWOBS can get a better network structure in the first iteration. It has better network learning accuracy and efficiency compared with several kinds of existing OBS approaches. Availability and implementation: codes and experiment dataset are available at: http://122.205.95.139/PEWOBS/.
Image Inpainting using Clustered Planar Structure Guidance

This paper presents an effective method using clustered planar structure guidance for image inpainting. Our method concerns restoring the unknown area by clustering structures of related planes. It is employed to obtain precisely similar structures in the surrounding area of missing regions. The approach of our work contains four essential steps: Planar Guidance, Clustering Structures, Feature Localization, and Patch Matching. According to perspective scenes, we first extract vanishing points (vp1, vp2, and vp3) using RAndom SAmple Consensus (RANSAC) algorithm as planar guidance. Then, we cluster the structure lines of each planar into more categories using the integration between K-means++ and Elbow method. Gaussian filter and Hadamard products blend among structure categories in the feature localization. This feature position propagates the surrounding structure information into unknown areas. For completing the unknown area, we employ PatchMatch [2] algorithm to match between the unknown and its surrounding patches. In experiments, our results perform well in various structures and perspective scenes that have foreshortened planes.
Smart Dark Pattern Detection: Making Aware of Misleading Patterns Through the Intended App

The significance of dark patterns is to deceive consumers when they are exploring the internet. There were social networking sites such as LinkedIn, Facebook, and others, where users discovered dark patterns whose goal was to steal users’ personal information or get them to click on advertisements. Dark patterns are created by using the domains such as UI/UX. Although, there many kinds in which they trap the users’ attention to focus though the advertisements on the websites where users can be trapped and may lose their money. There are a few security concerns that are required to detect the dark patterns through smart dark pattern detection. The intended theme proposed in this case is designing the application through which browsing could be done where any dark pattern advertisement is identified that could be alerted through a dialog box. For detection, a novel dark pattern detection approach is designed and considered as in-built application activity. The performance and accuracy are the main factors that judge the intended theme is designed as per the expectations.
3M-Transformers for Event Coding on Organized Crime Domain

Political scientists and security agencies increasingly rely on computerized event data generation to track conflict processes and violence around the world. However, most of these approaches rely on pattern-matching techniques constrained by large dictionaries that are too costly to develop, update, or expand to emerging domains or additional languages. In this paper, we provide an effective solution to those challenges. Here we develop the 3M-Transformers (Multilingual, Multi-label, Multitask) approach for Event Coding from domain specific multilingual corpora, dispensing external large repositories for such task, and expanding the substantive focus of analysis to organized crime, an emerging concern for security research. Our results indicate that our 3M-Transformers configurations outperform state-of-the-art usual Transformers models (BERT and XLM-RoBERTa) for coding events on actors, actions and locations in English, Spanish, and Portuguese languages.
Retinex based underwater image enhancement using attenuation compensated color balance and gamma correction

Underwater image is an important carrier of marine information. However, the problems of low contrast, color degradation, uneven illumination and detail loss cause the degradation of image quality. In this paper, we design a novel underwater image enhancement pipeline based on retinex. Firstly, we use bilateral filter instead of the traditional gaussian filter to estimate illumination image and obtain the reflectance image. Then, we design an attenuation map guided gray world method to overcome color distortion and use gamma correction to improve the illumination image. Finally, the fusion image is post-processed to further improve the contrast and obtain the final enhanced image. Qualitative and quantitative performance analysis prove that the proposed method has better performance than other underwater image enhancement methods.
Query-Variant Advertisement Text Generation with Association Knowledge

Online advertising is an important revenue source for many IT companies. In the search advertising scenario, advertisement text that meets the need of the search query would be more attractive to the user. However, the manual creation of query-variant advertisement texts for massive items is expensive. Traditional text generation methods tend to focus on the general searching needs with high frequency while ignoring the diverse personalized searching needs with low frequency. In this paper, we propose the query-variant advertisement text generation task that aims to generate candidate advertisement texts for different web search queries with various needs based on queries and item keywords. To solve the problem of ignoring low-frequency needs, we propose a dynamic association mechanism to expand the receptive field based on external knowledge, which can obtain associated words to be added to the input. These associated words can serve as bridges to transfer the ability of the model from the familiar high-frequency words to the unfamiliar low-frequency words. With association, the model can make use of various personalized needs in queries and generate query-variant advertisement texts. Both automatic and human evaluations show that our model can generate more attractive advertisement text than baselines.
Border-SegGCN: Improving Semantic Segmentation by Refining the Border Outline using Graph Convolutional Network

We present Border-SegGCN, a novel architecture to improve semantic segmentation by refining the border outline using graph convolutional networks (GCN).The semantic segmentation network such as Unet or DeepLabV3+ is used as a base network to have pre-segmented output.This output is converted into a graphical structure and fed into the GCN to improve the border pixel prediction of the presegmented output.We explored and studied the factors such as border thickness, number of edges for a node, and the number of features to be fed into the GCN by performing experiments.We demonstrate the effectiveness of the Border-SegGCN on the CamVid and Carla dataset, achieving a test set performance of 81.96% without any post-processing on CamVid dataset.It is higher than the reported state of the art mIoU achieved on CamVid dataset by 0.404%.
Reinforcement Learning: A Friendly Introduction

Reinforcement Learning (RL) is a branch of machine learning (ML) that is used to train artificial intelligence (AI) systems and find the optimal solution for problems. This tutorial paper aims to present an introductory overview of the RL. Furthermore, we discuss the most popular algorithms used in RL and the Markov decision process (MDP) usage in the RL environment. Moreover, RL applications and achievements that shine in the world of AI are covered.
Multi-perspective APT Attack Risk Assessment Framework using Risk-Aware Problem Domain Ontology

Cyber-attacks, which affect a wide range of areas such as political, economic, and social organizations, continue to evolve along with rapid changes in the IT environment, becoming more intelligent and complex. Especially, APT attacks have specific targets with obvious goals and use complex strategies from multiple perspectives. To proactively defend against these potential attacks, it is important to assess security risks based on a comprehensive and systematic understanding of APT attacks. In this paper, we propose the APT Attack Risk Assessment Framework to evaluate the security risks for specific APT attacks, using Risk-Aware Problem Domain Ontology. The proposed framework can assess the risks in which a specific APT attack affects an organization within a domain.
Webpage retrieval based on query by example for think tank construction

Think tanks have been proved helpful for decision-making in various communities. However, collecting information manually for think tank construction implies too much time and labor cost as well as inevitable subjectivity. A probable solution is to retrieve webpages of renowned experts and institutes similar to a given example, denoted as query by webpage (QBW). Considering users' searching behaviors, a novel QBW model based on webpages' visual and textual features is proposed. Specifically, a visual feature extraction module based on pre-trained neural networks and a heuristic pooling scheme is proposed, which bridges the gap that existing extractors fail to extract snapshots' high-level features and are sensitive to the noise effect brought by images. Moreover, a textual feature extraction module is proposed to represent textual content in both term and topic grains, while most existing extractors merely focus on the term grain. In addition, a series of similarity metrics are proposed, including a textual similarity metric based on feature bootstrapping to improve model's robustness and an adaptive weighting scheme to balance the effect of different types of features. The proposed QBW model is evaluated on expert and institute introduction retrieval tasks in academic and medical scenarios, in which the average value of MAP has been improved by 10% compared to existing baselines. Practically, useful insights can be derived from this study for various applications involved with webpage retrieval besides think tank construction.
ERNIE-BiLSTM Based Chinese Text Sentiment Classification Method

For the Chinese text sentiment classification task, the preprocessing based on deep learning models cannot retain the information and polysemy of the word in the sentence well. So this paper adopts the newly developed ERNIE [1–2] (Knowledge Enhanced Semantic Representation) pre-training model from Baidu, which is based on word feature input modeling, not only enhances the semantic representation of the word, but also preserves the contextual information of the word and the polysemy of the word. After pre-training by ERNIE model, the output word vector is used as the input of BiLSTM (bidirectional long and short-term memory network) model for training and obtaining sentiment classification results. The accuracy rate of Ernie bilstm model is 92.35% after verification on nlpcc2014 microblog sentiment analysis sample data set, which proves that the model has good performance in Chinese text sentiment classification task.
An Ensemble Learning Approach for Software Defect Prediction in Developing Quality Software Product

Software Defect Prediction (SDP) is a major research field in the software development life cycle. The accurate SDP would assist software developers and engineers in developing a reliable software product. Several machine learning techniques for SDP have been reported in the literature. Most of these studies suffered in terms of prediction accuracy and other performance metrics. Many of these studies focus only on accuracy and this is not enough in measuring the performance of SDP. In this research, we propose a seven-ensemble machine learning model for SDP. The Cat boost, Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XgBoost), boosted cat boost, bagged logistic regression, boosted LGBM, and boosted XgBoost were used for the experimental analysis. We also used the separate individual base model of logistic regression for the analysis on six datasets. This paper extends the performance metrics from only the accuracy, the Area Under Curve (AUC), precision, recall, F-measure, and Matthew Correlation Coefficient (MCC) were used as performance metrics. The results obtained showed that the proposed ensemble Cat boost model gave an outstanding performance for all the three defects datasets as a result of being able to decrease overfitting and reduce the training time.