Reviewerly/exHarmony huggingface dataset:
Hand-3d-Studio: A New Multi-View System for 3d Hand Reconstruction

This paper proposes a new system named as Hand-3D-Studio to capture the 3D hand pose and shape information. Our system includes 15 synchronized DSLR cameras, which can acquire high quality multi-view 4K resolution color images in a circular manner. We then introduce a 2D hand keypoints guided iterative pixel growth matching strategy for 3D reconstruction, where the 2D keypoints are obtained via convolution neural network. We find that the pre-detected 2D hand keypoints can greatly remove the matching noise, and thus improve the performance of reconstruction. After that, a non-rigid iterative closest points algorithm is performed to drive a template hand to fit the point clouds and register all the hand meshes. As a consequence, we captured more than 20K high quality hand color images, annotated 2D hand key-points, 3D point cloud as well as the registered hand meshes (>200). All the data are public on the website http://www.yangangwang.com for future research.
A Hybrid Model of Hesitant Fuzzy Decision-Making Analysis for Estimating Usable-Security of Software

The main idea for practicing research towards security is to maintain usability of the software as well. This can be achieved by making less complex and high secure software. Though considerable efforts have been made in this context by the developers and security experts, the standard of usable-security is not the same as it should be. Therefore, it is important to research more on the most conversant mechanisms for assessing and increasing the usable-security of software. The quantitative evaluation of usability and security is an important part in increasing the level of secure software. This paper, in particular, is intended to estimate the usable-security of software and achieve the objective of developing software with optimum security while retaining its usability. The decision-makers often find it difficult to integrate security and usability. Hence this paper approaches the integration of usability and security with its contributing attributes. Also, in this league, the present research study suggests a novel technique which is the hybrid of Analytic Hierarchy Process (AHP), Hesitant Fuzzy (HF) sets, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to significantly assess the usability along with security. To test the usefulness of the proposed approach hybrid HF-AHP-TOPSIS technique, the researchers applied it onto software developed for Babasaheb Bhimrao Ambedkar University in India. Furthermore, the procedural sensitivity was also achieved by using the various versions of the method. The findings of the usability along with security assessment insist that this inventive hybrid procedure would be the most conversant mechanism for determining the usable-security of software. Further these findings will be helpful in managing security without affecting the usability for end user.
Variances-constrained weighted extreme learning machine for imbalanced classification

Extreme learning machine (ELM) is a widely used algorithm due to its fast speed and good generalization. But ELM performs well under the assumption that the data is balanced. Because in binary classification with high imbalanced ratio data, the accuracy of samples from the minority class tends to be ignored easily due to the disadvantage in quantity. Therefore, weighted ELM (W-ELM) was proposed for imbalance learning. When samples are transformed through the hidden layer of W-ELM, W-ELM needs to calculate suitable weights to classify the samples linearly. But it is difficult for W-ELM to achieve a good learning performance when the dispersion of the transformed samples is high enough. Besides, W-ELM is limited by its two weighting schemes. Many W-ELM-based methods, such as boosting W-ELM, deep W-ELM and ensemble W-ELM, were also proposed with the limitations of W-ELM. Class-specific cost regulation ELM (CCR-ELM) is an alternative approach to learn imbalanced data. However, to a certain degree, CCR-ELM is similar to ELM. Therefore, in this paper, we will propose variances-constrained weighted extreme learning machine (VW-ELM) to replace W-ELM. VW-WLM takes the dispersion of training results into consideration. From theoretical view, VW-ELM’s learning performance is no lower than W-ELM. We use 21 benchmark datasets to test the learning ability of VW-ELM. Experimental results demonstrate that VW-ELM has an obvious advantage to handle classification problem of imbalanced data, comparing with the state-of-the-arts.
Avoid Oversimplifications in Machine Learning: Going beyond the Class-Prediction Accuracy

Class-prediction accuracy provides a quick but superficial way of determining classifier performance. It does not inform on the reproducibility of the findings or whether the selected or constructed features used are meaningful and specific. Furthermore, the class-prediction accuracy oversummarizes and does not inform on how training and learning have been accomplished: two classifiers providing the same performance in one validation can disagree on many future validations. It does not provide explainability in its decision-making process and is not objective, as its value is also affected by class proportions in the validation set. Despite these issues, this does not mean we should omit the class-prediction accuracy. Instead, it needs to be enriched with accompanying evidence and tests that supplement and contextualize the reported accuracy. This additional evidence serves as augmentations and can help us perform machine learning better while avoiding naive reliance on oversimplified metrics.
Improving Key-Recovery in Linear Attacks: Application to 28-Round PRESENT

Linear cryptanalysis is one of the most important tools in use for the security evaluation of symmetric primitives. Many improvements and refinements have been published since its introduction, and many applications on different ciphers have been found. Among these upgrades, Collard et al. proposed in 2007 an acceleration of the key-recovery part of Algorithm 2 for last-round attacks based on the FFT. In this paper we present a generalized, matrix-based version of the previous algorithm which easily allows us to take into consideration an arbitrary number of key-recovery rounds. We also provide efficient variants that exploit the key-schedule relations and that can be combined with multiple linear attacks. Using our algorithms we provide some new cryptanalysis on PRESENT, including, to the best of our knowledge, the first attack on 28 rounds.
Feature Selection for Phishing Website Classification

Phishing is an attempt to obtain confidential information about a user or an organization.It is an act of impersonating a credible webpage to lure users to expose sensitive data, such as username, password and credit card information.It has cost the online community and various stakeholders hundreds of millions of dollars.There is a need to detect and predict phishing, and the machine learning classification approach is a promising approach to do so.However, it may take several phases to identify and tune the effective features from the dataset before the selected classifier can be trained to identify phishing sites correctly.This paper presents the performance of two feature selection techniques known as the Feature Selection by Omitting Redundant Features (FSOR) and Feature Selection by Filtering Method (FSFM) to the 'Phishing Websites' dataset from the University of California Irvine and evaluates the performance of phishing webpage detection via three different machine learning techniques: Random Forest (RF) tree, Multilayer Perceptron (MLP) and Naive Bayes (NB).The most effective classification performance of these machine learning algorithms is further rectified based on a selected subset of features set by various feature selection methods.The observational results have shown that the optimized Random Forest (RF PT ) classifier with feature selection by the FSFM achieves the highest performance among all the techniques.
Violent Video Detection Based on Semantic Correspondence

Automatic detection of violent videos has broad application prospects in many fields such as video surveillance and movie grading. However, most existing violent video detection models based on multimodal feature fusion ignore the fact that the audio-visual data in the same violent video may not semantically correspond. Blindly fusing non-corresponding features is not beneficial even potentially harmful to models. In this paper, we propose a novel violent video detection model based on semantic correspondence between audio-visual data from the same video. Deep neural networks are used to extract features of three different modalities: appearance, motion, and audio. After that, we choose the feature-level fusion strategy to fuse these multimodal features via shared subspace learning. Semantic correspondence is used to guide this process through multitask learning and semantic embedding learning. To evaluate the effectiveness of our model, we conduct experiments on several public datasets and our self-built dataset: Violence Correspondence Detection. The results show that our model achieves quite competitive results on both.
Attentional coarse-and-fine generative adversarial networks for image inpainting

Deep learning based image inpainting methods have recently made significant improvements, which can now generate visually realistic and semantically plausible pixels for large missing regions. However, blurry textures and distorted edges are the two main problems of these methods. In nature, they are caused by the ineffectiveness of utilizing features. Thus, we propose a coarse-and-fine structure, which can extract coarse features with a larger receptive field to guarantee the accuracy of global semantic information, and can simultaneously extract fine features with a smaller receptive field at multiple levels to serve as a supplement in a parallel manner. To avoid the artifacts caused by the missing part of image, we flexibly use the gating mechanism and propose an interleaved gated residual block (IGRB) to encourage the useful information flow through the neural network. Moreover, we propose a channel and spatial attention block (CSAB) to alleviate the influence of redundant information and better model the long-range dependency between different regions in the image. Extensive experiments on faces, natural objects and scenes demonstrate that our method outperforms the existing state-of-the-art methods.
FGGAN: Feature-Guiding Generative Adversarial Networks for Text Generation

Text generation is a basic work of natural language processing, which plays an important role in dialogue system and intelligent translation. As a kind of deep learning framework, Generative Adversarial Networks (GAN) has been widely used in text generation. In combination with reinforcement learning, GAN uses the output of discriminator as reward signal of reinforcement learning to guide generator training, but the reward signal is a scalar and the guidance is weak. This paper proposes a text generation model named Feature-Guiding Generative Adversarial Networks (FGGAN). To solve the problem of insufficient feedback guidance from the discriminator network, FGGAN uses a feature guidance module to extract text features from the discriminator network, convert them into feature guidance vectors and feed them into the generator network for guidance. In addition, sampling is required to complete the sequence before feeding it into the discriminator to get feedback signal in text generation. However, the randomness and insufficiency of the sampling method lead to poor quality of generated text. This paper formulates text semantic rules to restrict the token of the next time step in the sequence generation process and remove semantically unreasonable tokens to improve the quality of generated text. Finally, text generation experiments are performed on different datasets and the results verify the effectiveness and superiority of FGGAN.
Border Control Morphing Attack Detection with a Convolutional Neural Network De-morphing Approach

Currently, the use of biometric identification, automated or semiautomated, is a reality. For this reason, the number of attacks has increased in such systems. One of the most common biometric attacks is the presentation attack (PA) because it is relatively easy to perform. Automated border control (ABC) is a clear target for phishers. Concerning biometric attacks, morphing is one of the most threatening attacks because authentication systems are usually unable to correctly detect them. In this attack, a fake face is generated with the morphing and blending of two different subjects (genuine and phisher), and the image result is stored in the passport. These attacks can generate risky situations in cases of border crossings where an ABC system should perform identification tasks. This research work proposes a de-morphing architecture that is founded on a convolutional neural network (CNN) architecture. This technique is based on the use of two images: the potentially morphed image stored in the passport, and the snapshot of the person located in the ABC system. The goal of the de-morphing process is to unravel the chip image. If the chip image is a morphed one, the revealing process between the in vivo image and the morphed chip image will return a different facial identity to the person located in the ABC system, and the impostor will be uncovered in situ. If the chip image is a non-morphing image, the resulting image will be similar to a genuine passenger. Therefore, the information obtained is considered at the border crossing. The equal error rate (EER) achieved is very low compared to the literature values published to date. The accomplished outcomes endorse a robust method that provides high accuracy rates without taking into account the quality of images used. This key point is crucial to plausible deployment plans in areas such as ABC.
Efficient Parallelism of Post-Quantum Signature Scheme SPHINCS

SPHINCS was recently proposed as a stateless, quantum-resilient hash-based signature scheme. However, one possible limitation of SPHINCS is its signing speed, namely, the best known implementation merely produces a few hundred of signatures per second, which is not good enough, e.g., for a social website with a huge amount of users. Aiming at improving the singing throughput, we present highly parallel and optimized implementations of SPHINCS, which can be deployed on various multi-core platforms. As a first step, we give an elementary implementation on ×86/64 processors, which proves the effectiveness and correctness of our implementations. To obtain a significantly higherthroughput, we implement SPHINCS on Graphics Processing Units (GPUs). Furthermore, we develop a few general and hardware-specific techniques to take full advantage of the computing power of targeted platforms. We instantiate the underlying hash functions with three primitives. Our comprehensive benchmark shows that our work outperforms all the state-of-the-art implementations of SPHINCS regarding throughput with reasonable latency, and has scalability on multiple cores and multiple GPU cards. For instance, forthe key generation algorithm instantiated with ChaCha running on a GeForce GTX 1080, we obtain 5152 signatures per second which is 7.88x speedup fasterthan a recent FPGA implementation. When upgrade to TITAN Xp, 6,651 signatures are generated in one second. With four TITAN Xp GPUs, the obtained throughput satisfies vast majority scenarios.
Scale-Aware Hierarchical Detection Network for Pedestrian Detection

Several or even dozens of times spatial scale variation is one of the major bottleneck for pedestrian detection. Although the Region-based Convolutional Neural Network (R-CNN) families have shown promising results for object detection, they are still limited to detect pedestrians with large scale variations due to the fixed receptive field sizes on a single convolutional output layer. In contrast to previous methods that simply combined pedestrian predictions on feature maps with different resolution, we propose a scale-aware hierarchical detection network for pedestrian detection under large scale variations. First, we introduce a cross-scale features aggregation module to accomplish feature augmentation for pedestrian representation through merging the lateral connection, the top-down path and bottom-up path. Specifically, the cross-scale features aggregation module can adaptively fuse hierarchical features to enhance feature pyramid representation for robust semantic and accurate localization. Further, we design a scale-aware hierarchical detection network to effectively integrate multiscale pedestrian detection into a unified framework by adaptively perceiving the augmented feature level for special-scale pedestrian detection. Experimentally, the proposed scale-aware hierarchical detection network forms a more robust and discriminative model for pedestrian instances with different scales on widely-used ETH and Caltech benchmarks. In particular, compared with the state-of-the-art method FasterRCNN+ATT, the log-average miss rate of pedestrian detection is reduced by 11.98% for medium scale pedestrians (between 30-80 pixels in height), and 14.12% for whole scale pedestrians (above 20 pixels in height) on Caltech benchmark.
Automatic Modulation Recognition for Radar Signals via Multi-Branch ACSE Networks

Automatic modulation recognition (AMR) for radar signals plays a significant role in electronic warfare. Conventional recognition methods may suffer from the recognition accuracy and the computation complexity under low signal-to-noise ratio (SNR) conditions. In this paper, a novel multi-branch Asymmetric Convolution Squeeze-and-Excitation (ACSE) networks using multi-domain features and fusion strategy based on a support vector machine is proposed to recognize eight kinds of radar signals. First, features of radar signals in the frequency domain, the autocorrelation domain, and the time-frequency domain are extracted. Then the obtained multi-domain features are converted as the input of the proposed networks which owns the representational power and learning ability. Finally, the outputs of multi-branch ACSE networks are fused via the fusion strategy to obtain the final results. Via simulations, the robustness and effectiveness of the fusion strategy are verified. The results on the simulation dataset prove that the proposed method can achieve more than 93% accuracy at -10dB for all modulations. Compared with four newly proposed networks, the multi-branch ACSE networks achieves better performance under low SNR conditions. And the results on measured signals show that the proposed method outperforms other comparison methods, especially for binary frequency-shift keying (BFSK) signals.
A high-resolution suitability index for solar farm location in complex landscapes

The introduction of renewable energy legislation in the European Union aims to encourage development of utility-scale renewable energy sources. Site location is a key part of strategic planning for renewables such as solar energy and is hampered in complex landscapes due to high spatial heterogeneity. This heterogeneity needs to be accounted for and incorporated into decision-making processes. This study used high-resolution spatial data in a Geographic Information System (GIS) alongside Analytical Hierarchy Process (AHP) and Multi-Criteria Decision Making (MCDM) to determine the potential for large-scale solar developments using Northern Ireland as a case study. Criteria were analysed using a weighted sum overlay, with weights calculated using a pairwise comparison method. The resulting Suitability Index model defined the level of suitability for 10% of the region with 392 km2 identified as very suitable. Two potential site size criteria were then defined, based on manually mapped extents of current solar developments that identified 462 potential sites. A validation exercise identified modelled potential sites had high coincidence with existing and planned solar developments in the study area. The methodology can be applied across regions with complex landscape and settlement patterns with high accuracy and can be utilised by policymakers or renewable energy developers to efficiently target potential utility-scale solar developments.
An improved spotted hyena optimizer for PID parameters in an AVR system

&lt;abstract&gt; &lt;p&gt;In this paper, an improved spotted hyena optimizer (ISHO) with a nonlinear convergence factor is proposed for proportional integral derivative (PID) parameter optimization in an automatic voltage regulator (AVR). In the proposed ISHO, an opposition-based learning strategy is used to initialize the spotted hyena individual's position in the search space, which strengthens the diversity of individuals in the global searching process. A novel nonlinear update equation for the convergence factor is used to enhance the SHO's exploration and exploitation abilities. The experimental results show that the proposed ISHO algorithm performed better than other algorithms in terms of the solution precision and convergence rate.&lt;/p&gt; &lt;/abstract&gt;
Textual-Visual Reference-Aware Attention Network for Visual Dialog

Visual dialog is a challenging task in multimedia understanding, which requires the dialog agent to answer a series of questions that are based on an input image. The critical issue to produce an exact answer is how to model the mutual semantic interaction among feature representations of the image, question-answer history, and current question. In this study, we propose a textual-visual Reference-Aware Attention Network (RAA-Net), which aims to effectively fuse Q (question), H (history), Vl (local vision), and Vg (global vision) to infer the exact answer. In the multimodal feature flows, RAA-Net first learns the textual context through multi-head attention between Q and H and then guides the textual reference semantics to the image to capture visual reference semantics by self-and cross-reference-aware attention in and between Vl and Vg. In the proposed RAA-Net, we exploit the two-stage (intraand inter-) visual reasoning mechanism on Vl and Vg. Extensive experiments on the VisDial v0.9 and v1.0 datasets show that RAA-Net achieves state-of-the-art performance. Visualization results on both visual and textual attention maps further validate the remarkable interpretability achieved by our solution.
Machine Learning Based Sentiment Text Classification for Evaluating Treatment Quality of Discharge Summary

Patients’ discharge summaries (documents) are health sensors that are used for measuring the quality of treatment in medical centers. However, extracting information automatically from discharge summaries with unstructured natural language is considered challenging. These kinds of documents include various aspects of patient information that could be used to test the treatment quality for improving medical-related decisions. One of the significant techniques in literature for discharge summaries classification is feature extraction techniques from the domain of natural language processing on text data. We propose a novel sentiment analysis method for discharge summaries classification that relies on vector space models, statistical methods, association rule, and extreme learning machine autoencoder (ELM-AE). Our novel hybrid model is based on statistical methods that build the lexicon in a domain related to health and medical records. Meanwhile, our method examines treatment quality based on an idea inspired by sentiment analysis. Experiments prove that our proposed method obtains a higher F1 value of 0.89 with good TPR (True Positive Rate) and FPR (False Positive Rate) values compared with various well-known state-of-the-art methods with different size of training and testing datasets. The results also prove that our method provides a flexible and effective technique to examine treatment quality based on positive, negative, and neutral terms for sentence-level in each discharge summary.