Reviewerly/exHarmony huggingface dataset:
Fast Generation of Collision-Free Trajectories for Robot Swarms Using GPU Acceleration

As the capabilities of robots and their control systems improve, we see an increasing number of use cases where the simultaneous operation of robots within a space is advantageous. Although trajectories for individual robots can be computed quickly using the existing methods, when robots operate simultaneously and in close proximity, the requirement for collision avoidance introduces a coupling between robot trajectories and makes the trajectory generation problem difficult to solve quickly. In this paper, we propose a parallelizable formulation of such problems and a method for solving them quickly on modern graphics processing units, using momentum-based gradient descent. We demonstrate the proposed framework in simulation using two case studies: a swarm of 200 quadcopters traversing a maze and a fleet of 100 bicycle robots changing their formation. In both the cases, our method requires just seconds to generate feasible, collision-free trajectories for the entire swarm.
Facial Action Units-Based Image Retrieval for Facial Expression Recognition

Facial expression recognition (FER) is a very challenging problem in computer vision.Although extensive research has been conducted to improve FER performance in recent years, there is still room for improvement.A common goal of FER is to classify a given face image into one of seven emotion categories: angry, disgust, fear, happy, neutral, sad, and surprise.In this paper, we propose to use a simple multi-layer perceptron (MLP) classifier that determines whether the current classification result is reliable or not.If the current classification result is determined as unreliable, we use the given face image as a query to search for similar images.In particular, facial action units are used to retrieve the images with a similar facial expression.Then, another MLP is trained to predict final emotion category by aggregating classification output vectors of the query image and its retrieved similar images.Experimental results on FER2013 dataset demonstrate that the performance of the state-of-the-art networks can be further improved by our proposed method.
An effective feature selection method for web spam detection

Web spam is an illegal and immoral way to increase the ranking of web pages by deceiving search engine algorithms. Therefore, different methods have been proposed to detect and improve the quality of results. Since a web page can be viewed from two aspects of the content and the link, the number of extracting features is high. Thus, selection of features with high separating ability can be considered as a preprocessing step in order to decrease computational time and cost. In this study, a new backward elimination approach is proposed for feature selection. The main idea of this method is measuring the impact of eliminating a set of features on the performance of a classifier instead of a single feature which is similar to the sequential backward selection. This method seeks for the largest feature subset that their omission from whole set features not only reduces the efficiency of the classifier but also improves it. Implementations on WEBSPAM-UK2007 dataset with Naïve Bayes classifier show that the proposed method selects fewer features in comparison with other methods and improves the performance of the classifier in the IBA index about 7%.
A new adaptive boosting total generalized variation (TGV) technique for image denoising and inpainting

In this paper we present a new adaptive boosting technique for total generalized variation (TGV) based image denoising and inpainting. Instead of the strengthening and substracting steps in existing boosting techniques, the proposed technique is iteratively operated by two steps: the first step is to take average of restored image with observed image, and updated parameter; the second step is to operate the TGV restoration algorithm with the average and dynamic parameter. For each iteration, as the input contains more correct information, the restoration algorithm can produce signals with more details. We have solved our boosting TGV model by primal-dual method, and applied the boosting TGV technique for gray/color image denoising and inpainting. Our algorithms have been discussed about influence of parameters, computational cost and compared with several typical existing methods. Plenty of experimental results show that our method can produce images with more structures and prevent staircase artifacts effectively.
A Contactless Insulator Contamination Levels Detecting Method Based on Infrared Images Features and RBFNN

A contactless method uses infrared image features and radial basis function neural network (RBFNN) to detect contamination levels for porcelain insulators is proposed in this paper. First, theory evidence for contamination levels detection by infrared images is inferred. Then, the denoising and image segmentation is implemented to suppress the image noise and eliminate the affection of the background. Nine color moment features related to the contamination levels are extracted from the insulator images. Finally, an RBFNN is constructed to identify the contamination levels. The images features and ambient relative humidity are taken as the inputs of the RBFNN. For improve the precision of the detection, a new method based on the statistical probability of the values of each contamination feature component is proposed to select the initial hidden center parameters for RBFNN hidden nodes. An improved learning algorithm combined with the gradient descent algorithm and a random number control factor are proposed to modify the hidden center parameters and the weights vectors. Testing results show that the selected color moment features are effective on the contamination levels representation and the constructed RBFNN performs better than back propagation neural network (BPNN) and generalized regression neural network (GRNN) on contamination levels identification.
SIREN

The growing volume of digital data stimulates the adoption of recommender systems in different socioeconomic domains, including news industries. While news recommenders help consumers deal with information overload and increase their engagement, their use also raises an increasing number of societal concerns, such as "Matthew effects", "filter bubbles", and the overall lack of transparency. We argue that focusing on transparency for content-providers is an under-explored avenue. As such, we designed a simulation framework called SIREN1 (SImulating Recommender Effects in online News environments), that allows content providers to (i) select and parameterize different recommenders and (ii) analyze and visualize their effects with respect to two diversity metrics. Taking the U.S. news media as a case study, we present an analysis on the recommender effects with respect to long-tail novelty and unexpectedness using SIREN. Our analysis offers a number of interesting findings, such as the similar potential of certain algorithmically simple (item-based k-Nearest Neighbour) and sophisticated strategies (based on Bayesian Personalized Ranking) to increase diversity over time. Overall, we argue that simulating the effects of recommender systems can help content providers to make more informed decisions when choosing algorithmic recommenders, and as such can help mitigate the aforementioned societal concerns.
Fast Pedestrian Detection in Surveillance Video Based on Soft Target Training of Shallow Random Forest

In recent years, deep learning algorithms have achieved top performances in object detection tasks.However, in real-time, systems having memory or computing limitations very wide and deep networks with numerous parameters constitute a major obstacle.In this paper, we propose a fast method for detecting pedestrians in surveillance systems having limited memory and processing units.Our proposed method applies a model compression technique based on a teacher-student framework to a random forest (RF) classifier instead of a wide and deep network because a compressed deep network still demands a large memory for a large amount of parameters and processing resources for multiplication.The first objective of the proposed compression method is to train a student shallow RF (S-RF), which can mimic the teacher RF's performance, by using a softened version of the teacher RF's output.Second, a deep network cannot easily detect small and closely located pedestrians in a surveillance video captured from a high perspective because of frequent convolutions and pooling processes.In this paper, adaptive image scaling and region of interest with S-RF were therefore combined to allow fast and accurate pedestrian detection in a low-specification surveillance system.In experiments, our proposed method achieved up to a 2.2 times faster speed and a 2.68 times higher compression rate than teacher RF and a better detection performance than several stateof-the-art methods on the Performance Evaluation of Tracking and Surveillance 2006, Town Centre, and Caltech benchmark datasets.
Unsupervised feature learning for spam email filtering

An excessive number of features may negatively affect the performance of a learning classifier. In addition, the computational time for processing the data during the training process may be prolonged. Therefore, a preprocessing stage that includes feature extraction and feature reduction processes in the field of machine learning is a vital role for speeding up computation and improving classification accuracy. The problem considered in this study is related to data transformation, prior to machine learning classifiers. Feature representation that preserves class separability with lower dimensional space for identifying spam is being proposed. The major advantage regarding the proposed feature representation is its robustness that enables classifiers like Random Forest, Support Vector Machines, and the decision tree C4.5 to identify an incoming email as spam or non-spam where the feature size is very small with a good generalization irrespective of the data source.
Variational Quantum Simulation for Quantum Chemistry

Abstract Variational quantum‐classical hybrid algorithms are emerging as important tools for simulating quantum chemistry with quantum devices. These algorithms can be applied to evaluate various molecular properties, including potential energy surfaces. Here in, recent progresses on the development of the so‐called variational quantum eigensolver (VQE) are surveyed. The eigensolver aims at reducing the consumption of quantum resources as much as possible. The key feature of VQE is that variation quantum states are optimized by a feedback process, where the measurement of the Hamiltonian is implemented term by term. This approach avoids the need of encoding all of the information about the molecular Hamiltonian in a quantum circuit. The VQE method is also compatible with classical methods in quantum chemistry, such as unitary coupled‐cluster ansatz. Furthermore, basic elements of VQE are covered, such as qubit encoding, mapping rules of the fermionic operators, ansatz preparation, together with several techniques for improving the performance, including constraining, and error mitigation.
Learning transform-aware attentive network for object tracking

Existing trackers often decompose the task of visual tracking into multiple independent components, such as target appearance sampling, classifier learning, and target state inferring. In this paper, we present a transform-aware attentive tracking framework, which uses a deep attentive network to directly predict the target states via spatial transform parameters. During off-line training, the proposed network learns generic motion patterns of target objects from auxiliary large-scale videos. These leaned motion patterns are then applied to track target objects on test sequences. Built on the Spatial Transform Network (STN), the proposed attentive network is fully differentiable and can be trained in an end-to-end manner. Notably, we only fine-tune the pre-trained network in the initial frame. The proposed tracker requires neither online model update nor appearance sampling during the tracking process. Extensive experiments on OTB-2013, OTB-2015, VOT-2014 and UAV-123 datasets demonstrate the competitive performance of our method against state-of-the-art attentive tracking methods.
A novel concurrent relational association rule mining approach

Data mining techniques are intensively used to uncover relevant patterns in large volumes of complex data which are continuously extended with newly arrived data instances. Relational association rules (RARs), a data analysis and mining concept, have been introduced as an extension of classical association rules (ARs) for capturing various relationships between the attributes characterizing the data. Due to its NP-completeness, the problem of mining all the interesting RARs within a data set is computationally difficult. As the dimensionality of the data set to be mined increases, the classical algorithm Discovery of Relational Association Rules (DRAR) for RARs mining fails in providing the set of rules in reasonable time. This paper introduces a new approach named CRAR (Concurrent Relational Association Rule mining) which uses concurrency for the RARs discovery process and thus significantly reduces the mining time. The effectiveness of CRAR is empirically validated on nine open source data sets. The reduction in mining time when using CRAR against DRAR emphasizes that it can be successfully applied in various practical data mining scenarios.
Adaptive multi-view subspace clustering for high-dimensional data

With the rapid development of multimedia technologies, we frequently confront with high-dimensional data and multi-view data, which usually contain redundant features and distinct types of features. How to efficiently cluster such kinds of data is still a great challenge. Traditional multi-view subspace clustering aims to determine the distribution of views by extra empirical parameters and search the optimal projection matrix by eigenvalue decomposition, which is impractical for real-world applications. In this paper, we propose a new adaptive multi-view subspace clustering method to integrate heterogenous data in the low-dimensional feature space. Concretely, we extend K-means clustering with feature learning to handle high-dimensional data. Besides, for multi-view data, we evaluate the weights of distinct views according to their compactness of the cluster structure in the low-dimensional subspace. We apply the proposed method to four benchmark datasets and compare it with several widely used clustering algorithms. Experimental results demonstrate the effectiveness of the proposed method.
Large-Margin Regularized Softmax Cross-Entropy Loss

Softmax cross-entropy loss with L2 regularization is commonly adopted in the machine learning and neural network community.Considering that the traditional softmax cross-entropy loss simply focuses on fitting or classifying the training data accurately but does not explicitly encourage a large decision margin for classification, some loss functions are proposed to improve the generalization performance by solving the problem.However, these loss functions enhance the difficulty of model optimization.In addition, inspired by regularized logistic regression, where the regularized term is responsible for adjusting the width of decision margin, which can be seen as an approximation of support vector machine, we proposed a large-margin regularization method for softmax cross-entropy loss.The advantages of the proposed loss are twofold as follows: the first is the generalization performance improvement, and the second is easy optimization.The experimental results on three small-sample datasets show that our regularization method achieves good performance and outperforms the existing popular regularization methods of neural networks.
Multi-Domain Gated CNN for Review Helpfulness Prediction

Consumers today face too many reviews to read when shopping online. Presenting the most helpful reviews, instead of all, to them will greatly ease purchase decision making. Most of the existing studies on review helpfulness prediction focused on domains with rich labels, not suitable for domains with insufficient labels. In response, we explore a multi-domain approach that learns domain relationships to help the task by transferring knowledge from data-rich domains to data-deficient domains. To better model domain differences, our approach gates multi-granularity embeddings in a Neural Network (NN) based transfer learning framework to reflect the domain-variant importance of words. Extensive experiments empirically demonstrate that our model outperforms the state-of-the-art baselines and NN-based methods without gating on this task. Our approach facilitates more effective knowledge transfer between domains, especially when the target domain dataset is small. Meanwhile, the domain relationship and domain-specific embedding gating are insightful and interpretable.
Improving visual question answering using dropout and enhanced question encoder

Using dropout in Visual Question Answering (VQA) is a common practice to prevent overfitting. However, the current way to use dropout in multi-path networks may cause two problems: the co-adaptations of neurons and the explosion of output variance. In this paper, we propose coherent dropout and siamese dropout mechanism to solve the two problems, respectively. Specifically, in coherent dropout, the relevant dropout layers in multiple paths are forced to work coherently to maximize the ability of preventing neuron co-adaptations. We show that the coherent dropout is simple in implementation but very effective to overcome overfitting. As for the explosion of output variance, we develop a siamese dropout mechanism to explicitly minimize the difference between the two output vectors produced from the same input data during training phase. Such mechanism can reduce the gap between training and inference phases and make the VQA model more robust. With the help of the two techniques, we further design an enhanced question encoder called Multi-path Stacked Residual RNNs which is deeper and wider and more powerful than current shallow question encoder. Extensive experiments are conducted to verify the effectiveness of coherent dropout, siamese dropout and the enhanced question encoder. And the results show that our methods can bring clear improvements to the state-of-the-art VQA models on VQA-v1 and VQA-v2 datasets.
Depth-based subgraph convolutional auto-encoder for network representation learning

Network representation learning (NRL) aims to map vertices of a network into a low-dimensional space which preserves the network structure and its inherent properties. Most existing methods for network representation adopt shallow models which have relatively limited capacity to capture highly non-linear network structures, resulting in sub-optimal network representations. Therefore, it is nontrivial to explore how to effectively capture highly non-linear network structure and preserve the global and local structure in NRL. To solve this problem, in this paper we propose a new graph convolutional autoencoder architecture based on a depth-based representation of graph structure, referred to as the depth-based subgraph convolutional autoencoder (DS-CAE), which integrates both the global topological and local connectivity structures within a graph. Our idea is to first decompose a graph into a family of K-layer expansion subgraphs rooted at each vertex aimed at better capturing long-range vertex inter-dependencies. Then a set of convolution filters slide over the entire sets of subgraphs of a vertex to extract the local structural connectivity information. This is analogous to the standard convolution operation on grid data. In contrast to most existing models for unsupervised learning on graph-structured data, our model can capture highly non-linear structure by simultaneously integrating node features and network structure into network representation learning. This significantly improves the predictive performance on a number of benchmark datasets.
Effects of Compression on Remote Sensing Image Classification Based on Fractal Analysis

Image compression is essential for remote sensing due to the large volume of produced remote sensing imagery and system's limited transmission or storage capacity. As one of the most important applications, classification might be affected due to the introduced distortion during compression. Hence, we perform a quantitative study on the effects of compression on remote sensing image classification and propose a method to estimate the remote sensing image classification accuracy based on fractal analysis. Multiscale feature extraction is performed and a multiple kernel learning approach is proposed accordingly. The experimental results on our established database indicate that the classification accuracy predicted by our method exhibits high consistency with the ground truth and our method shows its superiority when compared with other classical reference algorithms.
Analysis for post-impact tensile-tensile fatigue damage of 2024-T3 sheets based on tests, digital image correlation (DIC) technique and finite element simulation

In order to investigate the effect of low velocity impact damage on fatigue performance of aluminum alloy 2024-T3 thin sheets used in the airframe, the digital image correlation (DIC) analysis was conducted to evaluate fatigue failure of specimens containing various impact damages induced by hemispherical and U-shaped inserts. 2D and 3D strain fields calculated from digital speckle images were applied to analyze the fatigue crack nucleation and propagation. The effects of impact energy, dent dimension and insert shape on the fatigue life are discussed according to the experimental data which could be fitted to a 2-order empirical equation. Fatigue crack nucleation is intensively dominated by the dent geometry resulting from the impact process, the crack initiated around the dent propagates by a combined failure mode of I and III under dynamic loading. The dent could redistribute the strain field on the surface of dented specimens through DIC nephogram analysis. In addition, the simulation based on ABAQUS/Explicit could provide a reliable strain field distributed on the concave surface of dented specimens, while the fatigue life prediction acquired from Fe-safe shows close agreement with experimental data, which could be further used in fatigue life evaluation of impacted components.
A hybrid network security algorithm based on Diffie Hellman and Text-to-Image Encryption algorithm

AbstractAbstractNowadays, the rapid growth of Internet applications open the doors for people to communicate and do business around the world and thus saving time, efforts and money. However, the success of these applications is based on protecting the data from hackers. It is well known that preventing sensitive data from hackers while they are sent through the global network is a big challenge. There are many techniques used for securing data. Some of these techniques are based on encrypting a readable text into an unreadable text using mathematical operations, while other techniques are based on encrypting a readable text into an image (e.g., the Text-to-Image Encryption algorithm – TTIE) or into musical notes using an encryption key. The encryption key must be secure and should not be sent through the Internet.This paper proposes adding a new security level to the TTIE algorithm, and demonstrates how the encryption key produced by the TTIE algorithm is exchanged with the other party using the Diffie Hellman technique. Thus, this paper proposes a modified TTIE algorithm called the Diffie Hellman Text-to-Image Encryption algorithm (DHTTIE), and tests and analyses the proposed algorithm.Subject Classification: (2010) 94A60Keywords: EncryptionPrivate KeySecured CommunicationText-to-Image EncryptionDiffie Hellman algorithm