Reviewerly/exHarmony huggingface dataset:
Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM2.5 Concentration Forecasting: A Case Study of Beijing, China

With the acceleration of urbanization, there is an increasing trend of heavy pollution. PM2.5, also known as fine particulate matter, refers to particles in the atmosphere with a diameter of less than or equal to 2.5 microns. PM2.5 has a serious impact on human life, a sustainable city, national economic development, and so on. How to forecast the PM2.5 concentration accurately, and then formulate a scientific air pollution prevention and monitoring program is of great significance. This paper proposes a hybrid model based on echo state network (ESN) and an improved particle swarm optimization (IPSO) algorithm for the Beijing air pollution problem, and provides a method for PM2.5 concentration forecasting. Firstly, the PSO algorithm is improved to speed up the search performance. Secondly, the optimal subset of the original data is selected by the convergence cross-mapping (CCM) method. Thirdly, the phase space reconstruction (PSR) process is combined with the forecasting model, and some parameters are optimized by the IPSO. Finally, the optimal variable subset is used to predict PM2.5 concentration. The 11-dimensional air quality data in Beijing from January 1 to December 31, 2016 are analyzed by the proposed method. The experimental results show that the hybrid method is superior to other comparative models in several evaluation indicators, both in one-step and multi-step forecasting of PM2.5 time series. The hybrid model has good application prospects in air quality forecasting and monitoring.
Robust bilateral Lp-norm two-dimensional linear discriminant analysis

Traditional linear discriminant analysis (LDA) may suffer from a sensitivity to outliers and the small sample size (SSS) problem, while the Lp-norm measure for 0 < p ≤ 1 is robust in a sense. In this paper, based on the criterion of the Bayes optimality, we propose a matrix-based bilateral Lp-norm two-dimensional linear discriminant analysis (BLp2DLDA) with robust performance, where 0 < p ≤ 1. We prove that the BLp2DLDA criterion is equivalent to an upper bound of the theoretical framework of the Bayes optimality. Compared with the L2-norm 2-directional 2-dimensional LDA ((2D)2LDA), our BLp2DLDA is more robust to outliers and noise. Moreover, unilateral Lp2DLDA can also be easily derived from BLp2DLDA. In addition, a simple but effective iterative technique is introduced to solve BLp2DLDA and the unilateral Lp2DLDA. Experimental results on different types of contaminated human face databases show that the proposed BLp2DLDA outperforms (2D)2LDA.
Synergistic Generic Learning for Face Recognition From a Contaminated Single Sample per Person

Single sample per person face recognition (SSPP FR), i.e., identifying a person (i.e., data subject) with a single face image only for training, has several attractive potential applications, but it is still a challenging problem. Existing generic learning methods usually leverage prototype plus variation (P+V) model for SSPP FR provided that face samples in the biometric enrolment database are variation-free and thus can be treated as the prototypes of data subjects. However, this condition is not satisfied when these samples are contaminated by nuisance facial variations in the wild, such as varied expressions, poor lightings, and disguises (e.g., wearing scarf). We call this new and practical problem SSPP FR with a contaminated biometric enrolment database (SSPP-ce FR). Subsequently, a challenging issue will be raised on estimating proper prototypes from the contaminated enrolment samples in SSPP-ce FR. Moreover, the generated variation dictionary also needs to be enhanced because it is simply based on the subtraction of average face from the samples of the same data subject in the generic set, thus containing individual characteristics that can hardly be shared by other data subjects. To address these two issues, we propose a novel synergistic generic learning (SGL) method to study the SSPP-ce FR problem. Compared with the existing generic learning methods, SGL develops a new “learned P + learned V” model to identify new query samples. Specifically, it learns better prototypes for the contaminated samples in the biometric enrolment database by preserving their more discriminative subject-specific portions and learns a representative variation dictionary by extracting the less discriminative intra-subject variants from an auxiliary generic set. The experiments on various benchmark face datasets demonstrate the effectiveness of the proposed SGL method.
On the Robustness of Semantic Segmentation Models to Adversarial Attacks

Deep Neural Networks (DNNs) have demonstrated exceptional performance on most recognition tasks such as image classification and segmentation. However, they have also been shown to be vulnerable to adversarial examples. This phenomenon has recently attracted a lot of attention but it has not been extensively studied on multiple, large-scale datasets and structured prediction tasks such as semantic segmentation which often require more specialised networks with additional components such as CRFs, dilated convolutions, skip-connections and multiscale processing. In this paper, we present what to our knowledge is the first rigorous evaluation of adversarial attacks on modern semantic segmentation models, using two large-scale datasets. We analyse the effect of different network architectures, model capacity and multiscale processing, and show that many observations made on the task of classification do not always transfer to this more complex task. Furthermore, we show how mean-field inference in deep structured models, multiscale processing (and more generally, input transformations) naturally implement recently proposed adversarial defenses. Our observations will aid future efforts in understanding and defending against adversarial examples. Moreover, in the shorter term, we show how to effectively benchmark robustness and show which segmentation models should currently be preferred in safety-critical applications due to their inherent robustness.
iTM-Net: Deep Inverse Tone Mapping Using Novel Loss Function Considering Tone Mapping Operator

In this paper, we propose a novel inverse tone mapping network, called ''iTM-Net.''For training iTM-Net, we also propose a novel loss function that considers the non-linear relation between low dynamic range (LDR) and high dynamic range (HDR) images.For inverse tone mapping with convolutional neural networks (CNNs), we first point out that training CNNs with a standard loss function causes a problem due to the non-linear relation between the LDR and HDR images.To overcome the problem, the novel loss function non-linearly tone-maps target HDR images into LDR ones on the basis of a tone mapping operator, and the distance between the tone-mapped images and predicted ones are then calculated.The proposed loss function enables us not only to normalize the HDR images but also to reduce the non-linear relation between LDR and HDR ones.The experimental results show that the HDR images predicted by the proposed iTM-Net have higher-quality than the HDR ones predicted by conventional inverse tone mapping methods, including the state of the art, in terms of both HDR-VDP-2.2and PU encoding + MS-SSIM.In addition, compared with loss functions that do not consider the non-linear relation, the proposed loss function is shown to improve the performance of CNNs.
Combining Filter-Based Feature Selection Methods and Gaussian Mixture Model for the Classification of Seismic Events From Cotopaxi Volcano

This paper proposes an exhaustive evaluation of five different filter-based feature selection methods in combination with a Gaussian mixture model classifier for the classification of long-period (LP) and volcano-tectonic (VT) seismic events recorded at Cotopaxi volcano in Ecuador. The experimentation included both exploring and ranking search spaces of seismic-signal-based features, and selecting subsets of optimal features for event classification. The evaluation was carried out by using an experimental dataset formed by 587 LP and 81 VT feature vectors, each composed of 84 statistical, temporal, spectral, and scale-domain features extracted from the original seismic signals. The best result in accuracy, precision, recall, and processing time for LP seismic event classification was obtained by using the Chi2 discretization method with five features, achieving 95.62%, 99.08%, 95.94%, and 3.7 ms, respectively, whereas for VT seismic event classification, the uFilter method with five features reached the scores of 96.71%, 85.23%, 96.00%, and 4.1 ms, respectively. For the classification of both seismic events simultaneously, the uFilter method with five features yielded 96.70%, 97.77%, 96.7%, and 4.1 ms, respectively. According to the Wilcoxon statistical test, these classification schemes provide competitive seismic event classification, while reducing the required processing time.
A revisit to a class of permutation quadrinomials

This paper revisits the quadrinomials x3q+a1x2q+1+a2xq+2+a3x3 over Fq2, where q is a power of 2. We propose a more comprehensive characterization of the coefficients that give rise to new permutation quadrinomials. The new characterization not only contains those coefficients given in [20], but also seems to completely cover all the coefficients that yield permutation quadrinomials, which is evidenced by exhaustive searches on small finite fields.
Enhancing TripleGAN for Semi-Supervised Conditional Instance Synthesis and Classification

Learning class-conditional data distributions is crucial for Generative Adversarial Networks (GAN) in semi-supervised learning. To improve both instance synthesis and classification in this setting, we propose an enhanced TripleGAN (EnhancedTGAN) model in this work. We follow the adversarial training scheme of the original TripleGAN, but completely re-design the training targets of the generator and classifier. Specifically, we adopt feature-semantics matching to enhance the generator in learning class-conditional distributions from both the aspects of statistics in the latent space and semantics consistency with respect to the generator and classifier. Since a limited amount of labeled data is not sufficient to determine satisfactory decision boundaries, we include two classifiers, and incorporate collaborative learning into our model to provide better guidance for generator training. The synthesized high-fidelity data can in turn be used for improving classifier training. In the experiments, the superior performance of our approach on multiple benchmark datasets demonstrates the effectiveness of the mutual reinforcement between the generator and classifiers in facilitating semi-supervised instance synthesis and classification.
Research on neural network chaotic encryption algorithm in wireless network security communication

The rapid development of wireless network brings a lot of convenience to people's lives, but there are still many problems to be solved in wireless networks. Among them, communication security is the most critical, especially secure transactions on digital currency transactions are even more important. In traditional network communication encryption algorithms such as RSA and ECC, in order to further enhance the reliability of network communication security, the main means is to increase the length of the key, but this brings the complexity and workload of the calculation, and the speed of encryption cannot be realized. Compatible with security, the neural network chaotic encryption algorithm mainly uses the parallelity characteristics of the neural network and the chaotic dynamic characteristics to randomly generate a sequence, which has non-periodic characteristics. Therefore, the wireless network chaotic encryption algorithm is a good wireless communication security encryption algorithm. However, the traditional neural network chaotic encryption algorithm still has some shortcomings in its algorithm's security performance, encryption speed, encryption efficiency, and anti-deciphering performance. At the same time, the research on neural network chaotic encryption algorithm in wireless communication security is relatively less. In this paper, the performance defect of the original neural network chaotic encryption algorithm is optimized. A dynamic key encryption and decryption neural network chaos algorithm for wireless communication security is proposed. The algorithm is mainly based on the Aihara neural network model and introduces chaos, mapping, and hybrid coding. At the end of the paper, the algorithms before and after optimization are compared. The experimental results show that the algorithm proposed in this paper has a significant improvement in the encryption and decryption speed and anti-deciphering ability of the key.
Boosting Targeted Black-Box Attacks via Ensemble Substitute Training and Linear Augmentation

These years, Deep Neural Networks (DNNs) have shown unprecedented performance in many areas. However, some recent studies revealed their vulnerability to small perturbations added on source inputs. Furthermore, we call the ways to generate these perturbations’ adversarial attacks, which contain two types, black-box and white-box attacks, according to the adversaries’ access to target models. In order to overcome the problem of black-box attackers’ unreachabilities to the internals of target DNN, many researchers put forward a series of strategies. Previous works include a method of training a local substitute model for the target black-box model via Jacobian-based augmentation and then use the substitute model to craft adversarial examples using white-box methods. In this work, we improve the dataset augmentation to make the substitute models better fit the decision boundary of the target model. Unlike the previous work that just performed the non-targeted attack, we make it first to generate targeted adversarial examples via training substitute models. Moreover, to boost the targeted attacks, we apply the idea of ensemble attacks to the substitute training. Experiments on MNIST and GTSRB, two common datasets for image classification, demonstrate our effectiveness and efficiency of boosting a targeted black-box attack, and we finally attack the MNIST and GTSRB classifiers with the success rates of 97.7% and 92.8%.
Behavior tree design of intelligent behavior of non-player character (NPC) based on Unity3D

At this stage, the rapid development of computer technology and information technology in China has provided favorable conditions for the development of the game. In order to pursue the game experience, how to use artificial intelligence in the game has become a new research hotspot. Therefore, the current situation of artificial intelligence used in games was investigated, and the principles of Unity3D game engine were studied; then the intelligent behavior model for NPC was established by using the behavior tree as the basic algorithm, and the AI architecture of the agent in the game was designed; moreover, combined with the above analysis, the behavior tree model based on Q learning algorithm was calculated, and the application of Unity3D in the game was completed; finally, a game model was developed in combination with Unity3D game engine. The results show that the behavior tree based on Unity3D game engine can realize NPC’s intelligent behavior simply and efficiently, and the system can run at a good speed, which has theoretical guidance for the follow-up research of game artificial intelligence and simulation training.
An improved fuzzy ARTMAP and Q-learning agent model for pattern classification

The Fuzzy ARTMAP (FAM) network is an online supervised neural network that operates by computing the similarity level between the new sample and those prototype nodes stored in its network against a threshold. In our previous study, we have developed a multi-agent system consisting of an ensemble of FAM networks and Q-learning, known as QMACS, for data classification. In this paper, an Improved QMACS (IQMACS) model with trust measurement using a combination of Q-learning and Bayesian formalism is proposed. A number of benchmark and real-world problems, i.e., motor fault detection and human motion detection, are conducted to evaluate the effectiveness of IQMACS. Statistical features are extracted from real-world case studies and utilized for classification with IQMACS, QMACS, and their constituents. The experimental results indicate that IQMACS produces better classification performance by combining the outcomes of its constituents as compared with those of QMACS and other related methods.
Unsupervised online detection and prediction of outliers in streams of sensor data

Outliers are unexpected observations, which deviate from the majority of observations. Outlier detection and prediction are challenging tasks, because outliers are rare by definition. A stream is an unbounded source of data, which has to be processed promptly. This article proposes novel methods for outlier detection and outlier prediction in streams of sensor data. The outlier detection is an independent, unsupervised process, which is implemented using an autoencoder. The outlier detection continuously evaluates if the latest data point $$\mathbf {x}_i$$ from a stream is an inlier or an outlier. This distinction is based on the reconstruction cost accompanied with Chebyshev's inequality and the EWMA (exponentially weighted moving average) model. The outlier prediction uses the results of the outlier detection to form the required training data. The outlier prediction utilizes LR (logistic regression), SGD (stochastic gradient descent) and the hidden representation provided by the autoencoder to predict outliers in streams. The results of the experiments show that the proposed methods (1) provide accurate results, (2) are calculated in reduced computation time and (3) use a low amount of memory. Our proposed methods are suitable for analyzing streams of sensor data and providing results with low latency. The experiments also indicated that the outlier prediction is able to anticipate the occurrence of outliers in streams of sensor data.
Multiscale Edge Detection Using First-Order Derivative of Anisotropic Gaussian Kernels

Spatially scaled edges are ubiquitous in natural images. To better detect edges with heterogeneous widths, in this paper, we propose a multiscale edge detection method based on first-order derivative of anisotropic Gaussian kernels. These kernels are normalized in scale-space, yielding a maximum response at the scale of the observed edge, and accordingly, the edge scale can be identified. Subsequently, the maximum response and the identified edge scale are used to compute the edge strength. Furthermore, we propose an adaptive anisotropy factor of which the value decreases as the kernel scale increases. This factor improves the noise robustness of small-scale kernels while alleviating the anisotropy stretch effect that occurs in conventional anisotropic methods. Finally, we evaluate our method on widely used datasets. Experimental results validate the benefits of our method over the competing methods.
Participatory Design of Affective Technology: Interfacing Biomusic and Autism

The benefits of user-centered and participatory design have been widely acknowledged for the development of technologies that are likely to be appropriated by the product&#x2019;s stakeholders. While participatory design has been applied to some affective technologies, the technical and algorithmic complexity of those based on semi-intelligent information filters (SIIFs) pose distinct challenges. Coincidentally, these technologies raise important and distinct ethical issues that make stakeholder input critical during product design. We present a framework for fostering genuine engagement from stakeholders through the case example of biomusic - a SIIF-based affective technology that translates emotion-related physiological changes into sound. During a 3-day workshop, ethnographic methods were used to collect data about the interface between biomusic and individuals on the autism spectrum. From these data, emergent themes, such as such as privacy, data security, conceptions of assistive technology and representation of emotions were analyzed using a grounded theory approach. In order to illuminate distinct design decisions implicated by these complex and interwoven ethical issues, we propose a design framework consisting of a technological, a human-centered and an ecological lens. This framework and recommendations provide a concrete praxis for engaging stakeholders in the complex issues associated with the design of SIIF-based emotion-oriented systems.
Reading News with a Purpose

Personalized content provided by recommender systems is an integral part of the current online news reading experience. However, news recommender systems are criticized for their 'black-box' approach to data collection and processing, and for their lack of explainability and transparency. This paper focuses on explaining user profiles constructed from aggregated reading behavior data, used to provide content-based recommendations. The paper makes a first step toward consolidating epistemic values of news providers and news readers. We present an evaluation of an explanation interface reflecting these values, and find that providing users with different goals for self-actualization (i.e., Broaden Horizons vs. Discover the Unexplored) influences their reading intentions for news recommendations.
Securing Blockchain based Electronic Health Record using Multilevel Authentication

Electronic health records possess the patient's medication details and their health history. The health records attract the attention of the attackers' as it possesses invaluable information. Loss of electronic health record leads to a wrong medication or surgery. Healthcare systems provide fewer security measures to secure the health records. Blockchain is a distributed and decentralized ledger that plays a vital role in securing the data and transactions. Introduction of blockchain in the healthcare systems protects the health records from the attackers. However, the blockchain faces phishing, dictionary-based, cold wallets, and hot wallets attacks. This paper proposes a multilevel authentication-based scheme to protect the blockchain from the attacks mentioned above.
Abstractive Multi-Document Summarization Based on Semantic Link Network

The key to realize advanced document summarization is semantic representation of documents. This paper investigates the role of Semantic Link Network in representing and understanding documents for multi-document summarization. It proposes a novel abstractive multi-document summarization framework by first transforming documents into a Semantic Link Network of concepts and events and then transforming the Semantic Link Network into the summary of the documents based on the selection of important concepts and events while keeping semantics coherence. Experiments on benchmark datasets show that the proposed summarization approach significantly outperforms relevant state-of-the-art baselines and the Semantic Link Network plays an important role in representing and understanding documents.