Reviewerly/exHarmony huggingface dataset:
Densely pyramidal residual network for UAV-based railway images dehazing

On purpose of aiding detection and recognition for railway infrastructure and dramatic changes in the environment around railways, visual inspection based on unmanned aerial vehicle (UAV) images is a highlight. However, UAV images often suffer from degradation for fog or haze, which limits the inspection efficiency. Most existing methods depend on a suboptimal two-step network with much more redundant procedures where transmission map and atmospheric light are estimated at first, and then haze-free images can be acquired using a dehazing model. This paper presents a novel end-to-end network for UAV-based railway images dehazing, and focuses on two key issues: network architecture and loss function. With regards to the first aspect, based on a pyramidal network structure, densely pyramidal residual network (DPRnet) consists of dense residual block and enhanced residual blocks, which heavily exploits the feature maps of all preceding layers and considerably increased depth at different scale, respectively. With regards to the second, a new loss function introducing structural similarity index is proposed to preserve more structural information, thereby restore the appealing perceptual quality of the hazy images. Finally, quantitative and qualitative evaluations illustrate that the DPRnet achieves better performance over the classic methods, yet remains efficient and convenient.
Adapting End-to-end Neural Speaker Verification to New Languages and Recording Conditions with Adversarial Training

In this article we propose a novel approach for adapting speaker embeddings to new domains based on adversarial training of neural networks. We apply our embeddings to the task of text-independent speaker verification, a challenging, real-world problem in biometric security. We further the development of end-to-end speaker embedding models by combing a novel 1-dimensional, self-attentive residual network, an angular margin loss function and adversarial training strategy. Our model is able to learn extremely compact, 64-dimensional speaker embeddings that deliver competitive performance on a number of popular datasets using simple cosine distance scoring. One the NIST-SRE 2016 task we are able to beat a strong i-vector baseline, while on the Speakers in the Wild task our model was able to outperform both i-vector and x-vector baselines, showing an absolute improvement of 2.19% over the latter. Additionally, we show that the integration of adversarial training consistently leads to a significant improvement over an unadapted model.
Spatially Variant Linear Representation Models for Joint Filtering

Joint filtering mainly uses an additional guidance image as a prior and transfers its structures to the target image in the filtering process. Different from existing algorithms that rely on locally linear models or hand-designed objective functions to extract the structural information from the guidance image, we propose a new joint filter based on a spatially variant linear representation model (SVLRM), where the target image is linearly represented by the guidance image. However, the SVLRM leads to a highly ill-posed problem. To estimate the linear representation coefficients, we develop an effective algorithm based on a deep convolutional neural network (CNN). The proposed deep CNN (constrained by the SVLRM) is able to estimate the spatially variant linear representation coefficients which are able to model the structural information of both the guidance and input images. We show that the proposed algorithm can be effectively applied to a variety of applications, including depth/RGB image upsampling and restoration, flash/no-flash image deblurring, natural image denoising, scale-aware filtering, etc. Extensive experimental results demonstrate that the proposed algorithm performs favorably against state-of-the-art methods that have been specially designed for each task.
Reversible data hiding in encrypted binary images by pixel prediction

In this paper, reversible data hiding in encrypted binary images (RDHEBI) is proposed. Considering that there is not much redundancy in binary images and arbitrarily flipping pixels usually causes noticeable distortions, the original image is divided into non-overlapping blocks, including uniform blocks (pixels are all-white or all-black) and non-uniform blocks (both white pixels and black pixels exist). A type image is generated to identify these two kinds of blocks. After image rearranging and type image self-embedding, the preprocessed image is encrypted via a standard stream cipher. In the data embedding phase, the secret data are embedded into both uniform blocks and non-uniform blocks. Finally, by means of pixel prediction in "T" patterns, the receiver can obtain the secret data with the data embedding key, or the approximate original image with the encryption key, or the secret data and the original image with both keys. Moreover, the secret data can be extracted in both encrypted domain and decrypted domain. Experimental results show that there are no distortions with a low embedding rate or very few distortions with a high embedding rate. In addition, an extended lossless method is given to achieve complete reversibility independent of embedding rate.
A Method for the Detection of Fake Reviews Based on Temporal Features of Reviews and Comments

Online reviews and comments after product sales have become very important for making buying and selling decisions. Fake reviews will affect such decisions due to deceptive information, leading to financial losses for the consumers. Identification of fake reviews has thus received a great deal of attention in recent years. However, most websites have only focused on dealing with problematic reviews and comments. Amazon and Yelp would only remove possible fake reviews without questioning the sellers who could continue posting deceptive reviews for business purposes. In this paper, we propose a method for the detection of fake reviews based on review records associated with products. We first analyze the characteristics of review data using a crawled Amazon China dataset, which shows that the patterns of review records for products are similar in normal situations. In the proposed method, we first extract the review records of products to a temporal feature vector and then develop an isolation forest algorithm to detect outlier reviews by focusing on the differences between the patterns of product reviews to identify outlier reviews. We will verify the effectiveness of our method and compare it to some existing temporal outlier detection methods using the crawled Amazon China dataset. We will also study the impact caused by the parameter selection of the review records. Our work provides a new perspective of outlier review detection and our experiment demonstrates the effectiveness of our proposed method.
Passive Capacitive based Approach for Full Body Gym Workout Recognition and Counting

In this work, we present the design and implementation of a micro watt level power consumption, human body capacitance based sensor for recognizing and counting gym workouts. The concept also works when the device is attached to a body part which is not directly involved in the activity's movement. In contrast, most of the widely used motion sensing based approaches require placing the sensor on the moving body part (e.g. for analyzing leg based gym exercises the sensor needs to be placed on the leg). We described the physical principle behind the ubiquitous electric coupling between human body and environment, and explored the capability of this sensing modality in gym workouts. We evaluated our sensor with 11 subjects, performing 7 popular gym workouts each day over 5 days with our sensor being placed at 3 different body positions, including a non-contact position, where the sensor is placed in the subject's pocket. Results showed that our sensing approach achieved an average counting accuracy of 91%, which is highly competitive with commercial devices on the market. The mean leave one user out workout recognition f-scores obtained were of 63%, 56%, 45% for sensors located on wrist, on calf and in pocket, respectively. As every subject performed activities over multiple days changing shoe height, shoe and clothes type, we demonstrate that full body activity counting and to some extent recognition is feasible, regardless of personal habit of movement speed and scale.
The Influence of Feature Representation of Text on the Performance of Document Classification

In this paper we perform a comparative analysis of three models for a feature representation of text documents in the context of document classification. In particular, we consider the most often used family of bag-of-words models, the recently proposed continuous space models word2vec and doc2vec, and the model based on the representation of text documents as language networks. While the bag-of-word models have been extensively used for the document classification task, the performance of the other two models for the same task have not been well understood. This is especially true for the network-based models that have been rarely considered for the representation of text documents for classification. In this study, we measure the performance of the document classifiers trained using the method of random forests for features generated with the three models and their variants. Multi-objective rankings are proposed as the framework for multi-criteria comparative analysis of the results. Finally, the results of the empirical comparison show that the commonly used bag-of-words model has a performance comparable to the one obtained by the emerging continuous-space model of doc2vec. In particular, the low-dimensional variants of doc2vec generating up to 75 features are among the top-performing document representation models. The results finally point out that doc2vec shows a superior performance in the tasks of classifying large documents.
Spam profiles detection on social networks using computational intelligence methods: The effect of the lingual context

In online social networks, spam profiles represent one of the most serious security threats over the Internet; if they do not stop producing bad advertisements, they can be exploited by criminals for various purposes. This article addresses the nature and the characteristics of spam profiles in a social network like Twitter to improve spam detection, based on a number of publicly available language-independent features. In order to investigate the effectiveness of these features in spam detection, four datasets are extracted for four different language contexts (i.e. Arabic, English, Korean and Spanish), and a fifth is formed by combining them all. We conduct our experiments using a set of five well-known classification algorithms in spam detection field, k-Nearest Neighbours ( k-NN), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT) (J48) and Multilayer Perceptron (MLP) classifiers, along with five filter-based feature selection methods, namely, Information Gain, Chi-square, ReliefF, Correlation and Significance. The results show oscillating performance of each classifier across all datasets, but improved classification results with feature selection. In addition, detailed analysis and comparisons are carried out on two different levels: in the first level, we compare the selected features’ importance among the feature selection methods, whereas in the second level, we observe the relations and the importance of the selected features across all datasets. The findings of this article lead to a better understanding of social spam and improving detection methods by considering the various important features resulting from the different lingual contexts.
Real Photographs Denoising With Noise Domain Adaptation and Attentive Generative Adversarial Network

Nowadays, deep convolutional neural networks(CNNs) based methods have achieved favorable performance in synthetic noisy image denoising, but they are very limited in real photographs denoising since it's hard to obtain ground truth clean image to generate paired training data. Besides, the existing training datasets for real photographs denoising are too small. To solve this problem, we construct a new dataset and obtain corresponding ground truth by averaging, and then extend them through noise domain adaptation. Furthermore, we propose a attentive generative network by injecting visual attention into the generative network. During the training, visual attention map learn noise regions. The generative network will pay more attention to noise regions, which contributes to balancing between noise removal and texture preservation. Extensive experiments show that our method outperforms several state-of-the-art methods quantitatively and qualitatively.
Verifying Asynchronous Interactions via Communicating Session Automata

This paper proposes a sound procedure to verify properties of communicating session automata (csa), i.e., communicating automata that include multiparty session types. We introduce a new asynchronous compatibility property for csa, called k-multiparty compatibility (k-mc), which is a strict superset of the synchronous multiparty compatibility used in theories and tools based on session types. It is decomposed into two bounded properties: (i) a condition called k-safety which guarantees that, within the bound, all sent messages can be received and each automaton can make a move; and (ii) a condition called k-exhaustivity which guarantees that all k-reachable send actions can be fired within the bound. We show that k-exhaustivity implies existential boundedness, and soundly and completely characterises systems where each automaton behaves equivalently under bounds greater than or equal to k. We show that checking k-mc is pspace-complete, and demonstrate its scalability empirically over large systems (using partial order reduction).
Face morphing attacks: Investigating detection with humans and computers

In recent years, fraudsters have begun to use readily accessible digital manipulation techniques in order to carry out face morphing attacks. By submitting a morph image (a 50/50 average of two people's faces) for inclusion in an official document such as a passport, it might be possible that both people sufficiently resemble the morph that they are each able to use the resulting genuine ID document. Limited research with low-quality morphs has shown that human detection rates were poor but that training methods can improve performance. Here, we investigate human and computer performance with high-quality morphs, comparable with those expected to be used by criminals.Over four experiments, we found that people were highly error-prone when detecting morphs and that training did not produce improvements. In a live matching task, morphs were accepted at levels suggesting they represent a significant concern for security agencies and detection was again error-prone. Finally, we found that a simple computer model outperformed our human participants.Taken together, these results reinforce the idea that advanced computational techniques could prove more reliable than training people when fighting these types of morphing attacks. Our findings have important implications for security authorities worldwide.
CaptionNet: Automatic End-to-End Siamese Difference Captioning Model With Attention

Several deep learning techniques have been intensively reviewed for captioning tasks, enabling the possibility of textual understanding, and description of both simple and complex images. In advancing this knowledge, this paper proposes a multimodal end-to-end siamese difference captioning model (SDCM) to automatically generate a natural language description of differences in an image pair. The proposed supervised learning model combines several deep learning techniques in exploring the practicability of capturing, aligning, and computing the disparities between two image features, for the purpose of creating corresponding language model probability distribution. First, a deep siamese convolutional neural network is used to extract the feature vector discrepancies of an image pair, and then an attention mechanism enables the detection of salient regions of the feature vector which effectively allows a bidirectional long short-term memory decoder to generate a matching and semantically associated textual sequence. The evaluation of the model is tested on the spot-the-diff baseline dataset which consists of pairs of images and their equivalent captions. The results indicate that our proposed model demonstrates a highly competitive performance in comparison to the state of the art.
Pose-Guided R-CNN for Jersey Number Recognition in Sports

Recognizing player jersey number in sports match video streams is a challenging computer vision task. The human pose and view-point variations displayed in frames lead to many difficulties in recognizing the digits on jerseys. These challenges are addressed here using an approach that exploits human body part cues with a Region-based Convolutional Neural Network (R-CNN) variant for digit level localization and classification. The paper first adopts the Region Proposal Network (RPN) to perform anchor classification and bounding-box regression over three classes: background, person and digit. The person and digit proposals are geometrically related and fed to a network classifier. Subsequently, it introduces a human body key-point prediction branch and a pose-guided regressor to get better bounding-box offsets for generating digit proposals. A novel dataset of soccer-match video frames with corresponding multi-digit class labels, player and jersey number bounding boxes, and single digit segmentation masks is collected. Our framework outperforms all existing models on jersey number recognition task. This work will be essential to the automation of player identification across multiple sports, and releasing the dataset will ease future research on sports video analysis.
How to Invest my Time

Recognizing entities that follow or closely resemble a regular expression (regex) pattern is an important task in information extraction. Common approaches for extraction of such entities require humans to either write a regex recognizing an entity or manually label entity mentions in a document corpus. While human effort is critical to build an entity recognition model, surprisingly little is known about how to best invest that effort given a limited time budget. To get an answer, we consider an iterative human-in-the-loop (HIL) framework that allows users to write a regex or manually label entity mentions, followed by training and refining a classifier based on the provided information. We demonstrate on 5 entity recognition tasks that classification accuracy improves over time with either approach. When a user is allowed to choose between regex construction and manual labeling, we discover that (1) if the time budget is low, spending all time for regex construction is often advantageous, (2) if the time budget is high, spending all time for manual labeling seems to be superior, and (3) between those two extremes, writing regexes followed by manual labeling is typically the best approach. Our code and data is available at https://github.com/nymph332088/HILRecognizer.
Assessing Deep-learning Methods for Object Detection at Sea from LWIR Images

This paper assesses the performance of three convolutional neural networks for object detection at sea using Long Wavelength Infrared (LWIR) images in the 8- 14µm range. Capturing images from ferries and annotating 20k images, fine-tuning is done of three state of art deep neural networks: RetinaNet, YOLO and Faster R-CNN. Targeting on vessels and buoys as two main classes of interest for navigation, performance is quantified by the cardinality of true and false positives and negatives in a random validation set. Calculating precision and recall as functions of tuning parameters for the three classifiers, noticeable differences are found between the three networks when used for LWIR image object classification at sea. The results lead to conclusions on imaging requirements when classification is used to support navigation.
A Novel Provable Secured Signcryption Scheme ????: A Hyper-Elliptic Curve-Based Approach

Rivest, Shamir, &amp; Adleman (RSA), bilinear pairing, and elliptic curve are well-known techniques/algorithms for security protocols. These techniques suffer from higher computation and communication costs due to increased sizes of parameters, public keys, and certificates. Hyper-elliptic curve has lower parameter size, public key size, and certificate size. The aim of the proposed work is to reduce the computational cost and communication cost. Furthermore, we validate the security properties of our proposed scheme by using the well-known simulation tool called automated validation of Internet security protocols and applications. Our approach ensures security properties such as resistance against replay attack, confidentiality, authenticity, unforgeability, integrity, non-repudiation, public verifiability, and forward secrecy.
Multi-projection deep learning network for segmentation of 3D medical images

Segmentation of three-dimensional (3D) medical images using deep learning is a challenging task due to the lack of a 3D medical image dataset and their ground truth, resource memory limitations, and imbalanced dataset problem. In this paper, we propose advanced deep learning network for segmentation of 3D medical images. The proposed Multi-projection Network can preserve resource memory by applying two-dimensional (2D) kernels while still obtaining the 3D information from the image by incorporating slices from different planar projections of the 3D image to achieve good segmentation results. The proposed network uses a weighted cost function to address the imbalanced dataset problem and introduces an adaptive weight that considers the probability of each class in the image. The experimental results showed that the proposed Multi-projection Network can produce the highest sensitivity (true positive rate) compared to other architectures despite the high class imbalance in the dataset and small amount of training data.
GLR: A graph-based latent representation model for successive POI recommendation

Point-of-Interest (POI) recommendation is an important service in location-based social networks (LBSNs) since it can help a user to discover new POIs for future visiting. In order to provide better recommendation experience, a novel POI recommendation paradigm, named successive POI recommendation, has been proposed. The difference between traditional POI recommendation and successive POI recommendation is that successive POI recommendation focuses on recommending POIs that the target user may like to visit within a time period (e.g., a few hours). To address this problem, we propose a new graph-based latent representation model called GLR, to obtain the latent vectors of temporal successive transition influence and temporal user preference based on the historical check-in records. We also propose a novel method named GLR_GT to employ these latent vectors and geographical influence of POIs to perform successive POI recommendation. Moreover, we also propose an extended method named GLR_GT_LSTM to employ a long short-term memory (LSTM) neural network to model users' complex transition behavior. Several experiments are conducted on some real-world LBSN datasets. Experimental results show that our proposed method GLR_GT_LSTM outperforms the other prior successive POI recommendation methods in terms of precision and recall.