Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Car Damage Classification and Detection

In this paper, we worked on the problem of vehicle damage classification/detection which can be used by insurance companies to automate the process of vehicle insurance claims in a quick fashion. The recent advances in computer vision largely due to the adoption of fast, scalable and end-to-end trainable convolutional neural networks make it technically feasible to recognize vehicle damages using deep convolutional networks. We manually collected and annotated images from various online sources containing different types of vehicle damages. Due to the relatively small size of our dataset, we used models pre-trained on a large and diverse dataset to avoid overfitting and learn more general features. Using CNN models pre-trained on ImageNet dataset and using several other techniques to improve the performance of the system, we were able to achieve top accuracy of 96.39%, significantly better than the current results in this work. Furthermore, to detect the region of damage, we used state-of-the-art YOLO object detector and achieving a maximum map score of 77.78% on the held-out test set, demonstrating that the model was able to successfully recognize different vehicle damages. In addition to this, we also propose a pipeline for a more robust identification of the damage in vehicles by combining the tasks of classification and detection. Overall, these results pave the way for further research in this problem domain, and we believe that collection of a more diverse dataset would be sufficient to implement an automated vehicle damage identification system in the near future.
LTRWES: A new framework for security bug report detection

Context: Security bug reports (SBRs) usually contain security-related vulnerabilities in software products, which could be exploited by malicious attackers. Hence, it is important to identify SBRs quickly and accurately among bug reports (BRs) that have been disclosed in bug tracking systems. Although a few methods have been already proposed for the detection of SBRs, challenging issues still remain due to noisy samples, class imbalance and data scarcity. Object: This motivates us to reveal the potential challenges faced by the state-of-the-art SBRs prediction methods from the viewpoint of data filtering and representation. Furthermore, the purpose of this paper is also to provide a general framework and new solutions to solve these problems. Method: In this study, we propose a novel approach LTRWES that incorporates learning to rank and word embedding into the identification of SBRs. Unlike previous keyword-based approaches, LTRWES is a content-based data filtering and representation framework that has several desirable properties not shared in other methods. Firstly, it exploits ranking model to efficiently filter non-security bug reports (NSBRs) that have higher content similarity with respect to SBRs. Secondly, it applies word embedding technology to transform the rest of NSBRs, together with SBRs, into low-dimensional real-value vectors. Result: Experiment results on benchmark and large real-world datasets show that our proposed method outperforms the state-of-the-art method. Conclusion: Overall, the LTRWES is valid with high performance. It will help security engineers to identify SBRs from thousands of NSBRs more accurately than existing algorithms. Therefore, this will positively encourage the research and development of the content-based methods for security bug report detection.
Fast Additive Partially Homomorphic Encryption From the Approximate Common Divisor Problem

This paper presents two efficient partially homomorphic encryption schemes built upon the approximate common divisor problem, believed to be resistant to quantum computer attacks. Both proposals, named FAHE1 and FAHE2, are additively homomorphic and have a symmetric nature, meaning that they are useful in scenarios where encryption and decryption are performed by the same entity. This is the case, for example, of encrypted databases stored in a public cloud. We also evaluate the performance of our proposals in comparison with two alternatives displaying additive homomorphism: the traditional Paillier asymmetric cryptosystem, which is not quantum-resistant; and the XPIR algorithm, which is both quantum-resistant and symmetric. Our experimental results show that both solutions provide considerable speed-ups when compared to Paillier. Namely, encryption and decryption with FAHE1 are, respectively, 120 and 25 times faster than Paillier's, while for FAHE2 both operations run more than 1000 times faster. In addition, when compared with a highly optimized XPIR code, our reference implementation remains quite competitive while producing smaller ciphertexts.
Image Segmentation Based on K-means and Genetic Algorithms

In this paper, we studied image segmentation to which we applied a combination of the genetic algorithm and the cooperation between unsupervised classification by K-means and contour detection by the Sobel filter to improve image segmentation results by the K-means method alone. First, we will apply the segmentation process by combining two methods in the following way: We have hybridized the K-means method which is used to classify pixels into classes (regions), with the Sobel gradient filter which will then detect the edges of these regions, and then we will apply the genetic algorithm and by scanning further in the response space, try to find better quality class centers. This process is withdrawn until they are unable to find two sufficiently similar neighboring regions. The effectiveness of the proposed method was studied on a number of images. It is also compared by the K-means algorithm.
An effective robust and imperceptible blind color image watermarking using WHT

Digital image watermarking is the most widely used recognized approach to protect data against piracy. This paper proposes a new blind color image watermarking based on the Walsh Hadamard Transform (WHT). The proposed method subdivides the image into 4 × 4 non-overlapping blocks, which are then transformed using WHT. The color image watermark is embedded in the third and fourth-row WHT coefficients using the proposed technique as a slight change in those rows may not profoundly affect the visual quality of an image. Then the difference of the third and fourth row WHT coefficients of the watermarked image is obtained. The watermark information is extracted using the attained difference value. The performance evaluation of the proposed scheme outperforms in terms of embedding capacity, Peak Signal-to-Noise Ratio (PSNR), Weighted Peak Signal-to-Noise Ratio (WPSNR), Normalized cross-correlation (NC), and Structural Similarity Index (SSIM) than the conventional watermarking methods. This proposed method provides more robustness against several manipulations and lossy attacks.
SEADer++: social engineering attack detection in online environments using machine learning

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OEbBOA: A Novel Improved Binary Butterfly Optimization Approaches With Various Strategies for Feature Selection

Binary butterfly optimization approach (bBOA) is a recent high performing feature selection algorithm presented in 2018 which is based on the food foraging behavior of butterflies.This paper tries to improve the structure of the bBOA to enhance its classification accuracy, dimension reduction and reliability in feature selection task for who are interested in the fields of data mining and pattern recognition.The new initialization strategy and differential evolution strategy are applied to reduce the randomness of bBOA's initialization and local search process.Then, a new parameter is added to make the bBOA's transfer function more adaptive to the change of exploration and exploitation.Besides, evolution population dynamics (EPD) mechanism is employed as an extension of bBOA.The new method called optimization and extension of binary butterfly optimization approaches (OEbBOA) is tested with the K nearest neighbor classier in which twenty UCI datasets and seven recent algorithms are utilized to assess the performance of the OEbBOA algorithm.The experimental results and nonparametric Wilcoxons rank sum test confirm the efficiency of the proposed OEbBOA in maximizing classification accuracy while minimizing the number of features selected.
Attentive Item2vec: Neural Attentive User Representations

Factorization methods for recommender systems tend to represent users as a single latent vector. However, user behavior and interests may change in the context of the recommendations that are presented to the user. For example, in the case of movie recommendations, it is usually true that earlier user data is less informative than more recent data. However, it is possible that a certain early movie may become suddenly more relevant in the presence of a popular sequel movie. This is just a single example of a variety of possible dynamically altering user interests in the presence of a potential new recommendation. In this work, we present Attentive Item2vec (AI2V) - a novel attentive version of Item2vec (I2V). AI2V employs a context-target attention mechanism in order to learn and capture different characteristics of user historical behavior (context) with respect to a potential recommended item (target). The attentive context-target mechanism enables a final neural attentive user representation. We demonstrate the effectiveness of AI2V on several datasets, where it is shown to outperform other baselines.
Neural embedding-based specificity metrics for pre-retrieval query performance prediction

In information retrieval, the task of query performance prediction (QPP) is concerned with determining in advance the performance of a given query within the context of a retrieval model. QPP has an important role in ensuring proper handling of queries with varying levels of difficulty. Based on the extant literature, query specificity is an important indicator of query performance and is typically estimated using corpus-specific frequency-based specificity metrics However, such metrics do not consider term semantics and inter-term associations. Our work presented in this paper distinguishes itself by proposing a host of corpus-independent specificity metrics that are based on pre-trained neural embeddings and leverage geometric relations between terms in the embedding space in order to capture the semantics of terms and their interdependencies. Specifically, we propose three classes of specificity metrics based on pre-trained neural embeddings: neighborhood-based, graph-based, and cluster-based metrics. Through two extensive and complementary sets of experiments, we show that the proposed specificity metrics (1) are suitable specificity indicators, based on the gold standards derived from knowledge hierarchies (Wikipedia category hierarchy and DMOZ taxonomy), and (2) have better or competitive performance compared to the state of the art QPP metrics, based on both TREC ad hoc collections namely Robust'04, Gov2 and ClueWeb'09 and ANTIQUE question answering collection. The proposed graph-based specificity metrics, especially those that capture a larger number of inter-term associations, proved to be the most effective in both query specificity estimation and QPP. We have also publicly released two test collections (i.e. specificity gold standards) that we built from the Wikipedia and DMOZ knowledge hierarchies.
Augmented Adversarial Training for Cross-Modal Retrieval

Cross-modal retrieval has received considerable attention in recent years. The core of cross-modal retrieval is to find a representation space to align data from different modalities according to their semantics. In this paper, we propose a cross-modal retrieval method that aligns data from different modalities by transferring one source modality to another target modality with augmented adversarial training. To preserve the semantic meaning in the modality transfer process, we employ the idea of conditional GANs and augment it. The key idea is to incorporate semantic information from the label space into the adversarial training process by sampling more semantic relevant and irrelevant source-target sample pairs. The augmented sample pairs improve the alignment from two aspects. First, relevant source-target sample pairs provide more training samples, leading to a better guidance of the alignment of fake targets and true paired targets. Second, relevant and irrelevant source-target sample pairs teach the discriminator to better distinguish true relevant pairs from fake relevant pairs, which guides the generator to better transfer from the source modality to the target modality. Extensive experiments compared with state-of-the-art methods show the promising power of our approach.
Pose-guided spatiotemporal alignment for video-based person Re-identification

Spatiotemporal alignment is a critical problem in video-based person re-identification (re-id) tasks. To address this problem, we propose a pose-guided spatiotemporal alignment (PISA) method to align video sequences for video-based person re-id. First, to perform precise temporal alignment, we align a video sequence to a series of image sets, corresponding to a series of reference pedestrian poses. Each image set is obtained by selecting the images with the same pose and high quality. Furthermore, to perform spatial alignment, we decompose a pedestrian image into human body parts, and accordingly compute the representations over the parts. Finally, we evaluate the similarity between two video sequences by aggregating the distances of the pose-corresponded image sets rather than all pairs. The experimental results on two public datasets show that the proposed method performs favorably against state-of-the-art methods, even deep learning-based approaches.
Effectiveness of Video Prompting Delivered via Augmented Reality for Teaching Transition-Related Math Skills to Adults With Intellectual Disabilities

The purpose of this study was to determine the effectiveness of a video-based instruction packet for teaching math-based vocational skills delivered through augmented reality (AR) to three adults with intellectual disabilities. The dependent variable was the percentage of steps performed correctly to solve each selected type of math problem. The independent variable was the video-based math intervention delivered via AR, which modeled the individual steps for solving three different multistep math problems: (1) adjusting a recipe to serve a different number of people, (2) calculating salary, and (3) calculating unit prices. Visual and statistical analyses demonstrated a functional relationship between the video-based math intervention and an increase in the percentage of steps completed correctly for each type of problem. All three participants showed significant gains immediately after receiving the intervention and maintained the learned skills following withdrawal of the intervention. Implications for practitioners and further research are discussed.
Understanding the decisions of CNNs: An in-model approach

With the outstanding predictive performance of Convolutional Neural Networks on different tasks and their widespread use in real-world scenarios, it is essential to understand and trust these black-box models. While most of the literature focuses on post-model methods, we propose a novel in-model joint architecture, composed by an explainer and a classifier. This architecture outputs not only a class label, but also a visual explanation of such decision, without the need for additional labelled data to train the explainer besides the image class. The model is trained end-to-end, with the classifier taking as input an image and the explainer's resulting explanation, thus allowing for the classifier to focus on the relevant areas of such explanation. Moreover, this approach can be employed with any classifier, provided that the necessary connections to the explainer are made. We also propose a three-phase training process and two alternative custom loss functions that regularise the produced explanations and encourage desired properties, such as sparsity and spatial contiguity. The architecture was validated in two datasets (a subset of ImageNet and a cervical cancer dataset) and the obtained results show that it is able to produce meaningful image- and class-dependent visual explanations, without direct supervision, aligned with intuitive visual features associated with the data. Quantitative assessment of explanation quality was conducted through iterative perturbation of the input image according to the explanation heatmaps. The impact on classification performance is studied in terms of average function value and AOPC (Area Over the MoRF (Most Relevant First) Curve). For further evaluation, we propose POMPOM (Percentage of Meaningful Pixels Outside the Mask) as another measurable criteria of explanation goodness. These analyses showed that the proposed method outperformed state-of-the-art post-model methods, such as LRP (Layer-wise Relevance Propagation).
Self-Supervised Deep Learning for Fisheye Image Rectification

To rectify fisheye distortion from a single image, we advance self-supervised learning strategies and propose a unique deep learning model of Fisheye GAN (FE-GAN). Our FE-GAN learns pixel-level distortion flow from sets of fisheye distorted images and distortion-free ones (but not requiring such correspondences), with unique cross-rotation and intra-warping consistency introduced. With such novel self-supervised learning techniques, our FEGAN is able to recover the distortion-free image directly from the single fisheye image input. Our experiments quantitatively and qualitative confirm the effectiveness and robustness of our proposed model, which performs favorably against recent GAN-based image translation models.
An Autuencoder-based Data Augmentation Strategy for Generalization Improvement of DCNNs

Inspired by the phenomenon that the decoding weights of a well-trained autoencoder contain the information of the training samples, we proposed a data augmentation method by utilizing the decoding weights. Given a batch of training data, the autoencoder is trained and the decoding weights are activated; the decoding weights are then combined with the raw samples to generate augmented samples. Furthermore, we probe its working mechanism in three ways: (i) we prove that the decoding weights and the raw samples are of linear relationship under the transformation of a certain invertible function; (ii) the proposed method can sample from a larger range in both feature dimensions and label dimension, which can be interpreted as a broader distribution vicinity compared with those by other approaches; (iii) the model trained with our data augmentation approach has better representation capability, which is reflected by the higher Fisher’s criteria value in deep feature space. We conduct extensive experiments on image and tabular dataset with multiple network architectures. The proposed method provides significant generalization performance improvement compared with the baseline and better or comparable performance compared with the other state-of-the-art data augmentation approaches.
Analysis of Recurrent Neural Network and Predictions

This paper analyzes the operation principle and predicted value of the recurrent-neural-network (RNN) structure, which is the most basic and suitable for the change of time in the structure of a neural network for various types of artificial intelligence (AI). In particular, an RNN in which all connections are symmetric guarantees that it will converge. The operating principle of a RNN is based on linear data combinations and is composed through the synthesis of nonlinear activation functions. Linear combined data are similar to the autoregressive-moving average (ARMA) method of statistical processing. However, distortion due to the nonlinear activation function in RNNs causes the predicted value to be different from the predicted ARMA value. Through this, we know the limit of the predicted value of an RNN and the range of prediction that changes according to the learning data. In addition to mathematical proofs, numerical experiments confirmed our claims.
Blue Channel and Fusion for Sandstorm Image Enhancement

Due to the scattering and absorption of light by sandstorm particles, images taken in sandstorm environments have low contrast, color distortion and other degradation problems, which seriously affect outdoor computer vision systems. In this paper, we propose a novel method to enhance images captured in sandstorm weather conditions. First, the method builds on the blending of two images that are directly derived from the original degraded image. Second, we use multilayer decomposition technology to enhance image details and use a blue channel and white balancing technology to restore image contrast and chromaticity. Third, we associate weight maps to improve image edge contrast. Finally, the Laplacian pyramid fusion method is used to obtain the fusion results of the sandstorm-free color correction image. The experimental results demonstrate that the proposed method can effectively restore the fade characteristics of sandstorm-degraded images and improve the clarity of the images. Experimental results via subjective and objective evaluations demonstrate that the proposed method can significantly improve images captured during sandstorm weather conditions, and the results are better than other methods.
Multi-modal generative adversarial network for zero-shot learning

In this paper, we propose a novel approach for Zero-Shot Learning (ZSL), where the test instances are from the novel categories that no visual data are available during training. The existing approaches typically address ZSL by embedding the visual features into a category-shared semantic space. However, these embedding-based approaches easily suffer from the “heterogeneity gap” issue since a single type of class semantic prototype cannot characterize the categories well. To alleviate this issue, we assume that different class semantics reflect different views of the corresponding class, and thus fuse various types of class semantic prototypes resided in different semantic spaces with a feature fusion network to generate pseudo visual features. Through the adversarial mechanism of the real visual features and the fused pseudo visual features, the complementary semantics in various spaces are effectively captured. Experimental results on three benchmark datasets demonstrate that the proposed approach achieves impressive performances on both traditional ZSL and generalized ZSL tasks.
SA-NLI: A Supervised Attention based framework for Natural Language Inference

Natural Language Inference (NLI) aims to determine the relationship of a pair of sentences. As a critical component of NLI models, attention mechanism has been proven to be effective in the representation and interaction of sentences. However, the attention methods adopted in the existing NLI models are non-parametric or trained inside the model without explicit supervision, and the attention results are poorly explained. In this paper, we propose a Supervised Attention based Natural Language Inference (SA-NLI) framework to solve this problem. In our framework, the intra attention module is trained to focus on syntactically related tokens, while the inter attention module is constrained to capture alignment between sentences. Moreover, the supervised training of intra attention module and inter attention module are unified with the training of the NLI model by multi-task learning and transfer learning, respectively. We conduct extensive experiments on multiple NLI datasets, and the results demonstrate the effectiveness of our supervised attention based method. Further visual analysis validates the interpretability of attention results, and the extended experimental results indicate the generalization of our SA-NLI framework.