Reviewerly/exHarmony huggingface dataset:
A Transfer Learning Based Method for Incipient Fault Detection

The intelligent incipient fault detection of machines has two requirements. 1) Labeled monitoring data with incipient fault clues are obtainable. 2) The training data and test data are samples of variables with independent and identical distribution (i.i.d). However, the collection of sufficient labeled data is difficult since sensors are not easy to install on some machines. Additionally, even if labeled data can be collected under certain working conditions, the incipient fault detection model based on these data may misdiagnose the data collected from other working conditions. These challenges hinder the development of incipient fault detection. As a promising technic, transfer learning allows a detection model trained in a source domain to run better in a target domain. Based on this technic, we propose an incipient fault detection method. This method is composed of three modules: data preprocessing, feature extraction, and fault detection. The data preprocessing module automatically construct three-channel data by reshaping and stacking the collected one-dimensional signals. The feature extraction module is used to extract incipient fault information utilizing a deep transfer network. In the last module, a fault detector based on a logistic regression model is used to obtain detection results. The effectiveness of the proposed method is verified using the XJTU-SY bearing dataset.
Dynamical evaluation of interference fringe parameters by the Wiener adaptive filtering method

Application of the Wiener adaptive filtering method for estimation of interferometric signal parameters is presented and analyzed. It is shown that the fringe phase, as well as the envelope of low-coherence interference fringes, can be evaluated directly from the adaptive filter impulse response obtained under the criterion of the minimum error variance of a processed fringe signal and predefined reference signal. The method does not limit the number and value of the phase shifts and allows determination of the actual phase step. The accuracy of the method is evaluated and experimental results of dynamical fringe processing in low-coherence interferometry are presented.
Performance Analysis of the Fireworks Algorithm Versions

In the last decades, swarm intelligence algorithms have become a powerful tool for solving hard optimization problems. Nowadays numerous algorithms are proved to be good for different problems. With the overwhelming number of algorithms, it became hard for a common user to choose an appropriate method for solving a certain problem. To provide guidelines, it is necessary to classify optimization metaheuristics according to their capabilities. Deep statistical comparison represents a novel method for comparing and analyzing optimization algorithms. In this paper, the deep statistical comparison method was used for comparing different versions of the widely used fireworks algorithm. The fireworks algorithm was developed and improved in the last ten year, and this paper provides a theoretical analysis of five different versions, a cooperative framework for FWA, bare bones FWA, guided FWA, loser-out tournament based FWA, and dynamic search FWA. Based on the obtained results, the loser-out tournament based FWA has the best performance in the term of the solution quality, while the dynamic search FWA is the best in term of the solutions distribution in the search space.
Intrinsic decoherence effect on dynamics of a Λ-type qutrit interacting nonlinearly with a coherent field

In this paper, an intrinsic decoherence model is solved analytically for a Λ-type qutrit interacting with a coherent field through intensity-dependent coupling. The effects of the qutrit-cavity interaction, the intrinsic decoherence and the initial coherent field intensity are examined on some quantum phenomena as: atom population inversion, mixedness, entanglement as well as total correlation. It is found that the increase of the physical parameters, the regular oscillatory behavior of the qutrit-population collapses and revivals are changed remarkably. The interaction between the three-level atom and the coherent cavity field generates a strong mixture, negativity entanglement and total correlation (classical + quantum). The growth, the regularity and the stability of these quantum effects depend on the decoherence and the initial coherent cavity intensity.
A parallel genetic algorithm to speed up the resolution of the algorithm selection problem

Deciding which optimisation technique to use for solving a particular optimisation problem is an arduous task that has been faced in the field of optimisation for decades. The above problem is known as the Algorithm Selection Problem (ASP). The optimisation techniques considered in previous works have been, mainly, approaches that can be executed rapidly. However, considering more sophisticated optimisation approaches for solving the ASP, such as Evolutionary Algorithms, drastically increases the computational cost. We are interested in solving the ASP by considering different configurations of a Genetic Algorithm (GA) applied to the well-known 0/1 Knapsack Problem (KNP). This involves the execution of a significant number of configurations of the GA, in order to evaluate their performance, when applied to a wide range of instances with different features of the KNP, which is a computationally expensive task. Therefore, the main aim of the current work is to provide, as first step for solving the ASP, an efficient parallel GA, which is able to attain competitive results, in terms of the optimal objective value, in a short amount of time. Computational results show that our approach is able to scale efficiently and considerably reduces the average elapsed time for solving KNP instances.
Diversity collaboratively guided random drift particle swarm optimization

The random drift particle swarm optimization (RDPSO) algorithm is an effective random search technique inspired by the trajectory analysis of the canonical PSO and the free electron model in metal conductors placed in an external electric field. However, like other PSO variants, the RDPSO algorithm also inevitably encounters premature convergence when solving multimodal problems. To address this issue, this paper proposes a novel diversity collaboratively guided (DCG) strategy for the RDPSO algorithm that enhances the search ability of the algorithm. In this strategy, two kinds of diversity measures are defined and modified in a collaborative manner. Specifically, the whole search process of the RDPSO is divided into three phases based on the changes in the two diversity measures. In each phase, different values are selected for the key parameters of the update equation in the RDPSO to make the particle swarm perform different search modes. Consequently, the improved RDPSO algorithm with the DCG strategy (DCG-RDPSO) can maintain its diversity dynamically at a certain level, and thus can search constantly without stagnation until the search process terminates. The performance evaluation of the proposed algorithm is done on the CEC-2013 benchmark suite, in comparison with several versions of RDPSO, different variants of PSO and several non-PSO evolutionary algorithms. Experimental results show that the proposed DCG strategy can significantly improve the performance and robustness of the RDPSO algorithm for most of the multimodal problems. Further experiments on economic dispatch problems also verify the effectiveness of the DCG strategy.
Inaccuracy of State-Action Value Function For Non-Optimal Actions in Adversarially Trained Deep Neural Policies

The introduction of deep neural networks as function approximator for the state-action value function has led to the creation of a new research area for self-learning systems that explore policies from high dimensional input. While the success of deep neural policies has resulted in the deployment of these policies in diversified application domains, there are significant concerns regarding their robustness towards specifically crafted malicious perturbations introduced to their inputs. Several studies have focused on making deep neural policies resistant to such perturbations via training with the existence of these perturbations (i.e. adversarial training). In this paper we focus on conducting an investigation on the state-action value function learned by state-of-the-art adversarially trained deep neural policies and vanilla trained deep neural policies. We perform several experiments in the OpenAI Baselines and we show that the state-action value functions learned by vanilla trained deep neural policies have better estimates for the non-optimal actions than the state-of-the-art adversarially trained deep neural policies. We believe our study lays out intriguing properties of adversarial training and could be critical step towards obtaining robust and reliable policies.
Non-Local Representation Based Mutual Affine-Transfer Network for Photorealistic Stylization

Photorealistic stylization aims to transfer the style of a reference photo onto a content photo in a natural fashion, such that the stylized image looks like a real photo taken by a camera. State-of-the-art methods stylize the image locally within each matched semantic region and are prone to global color inconsistency across semantic objects/parts, making the stylized image less photorealistic. To tackle the challenging issues, we propose a non-local representation scheme, constrained with a mutual affine-transfer network (NL-MAT). Through a dictionary-based decomposition, NL-MAT is able to successfully decouple matched non-local representations and color information of the image pair, such that the context correspondence between the image pair is incorporated naturally, which largely facilitates local style transfer in a global-consistent fashion. To the best of our knowledge, this is the first attempt to address the photorealistic stylization problem with a non-local representation scheme, such that no additional models or steps for semantic matching are required during stylization. Experimental results demonstrate that, the proposed method is able to generate photorealistic results with local style transfer while preserving both the spatial structure and global color consistency of the content image.
Multi-criterion decision making-based multi-channel hierarchical fusion of digital breast tomosynthesis and digital mammography for breast mass discrimination

Multifaceted features decoded from mammographic images may describe various perspectives of the breast mass heterogeneity, in this study, we aimed to explore a methodology to effectively integrate multifaceted mass representations extracted from the digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) to enhance breast cancer discrimination. A novel multi-criterion decision making-based multi-channel fusion (MDMF) framework was proposed to fuse different breast mass representations processed in multi-channels built on the deep convolutional neural network (DCNN) and the multilayer perceptron (MLP) at the decision level. A hierarchical framework (HFMM) was also developed for multi-modality images and multi-channel fusion to integrate multimodality information from DBT and FFDM. We retrospectively collected 441 patients with both DBT and FFDM, and the regions of interest (ROIs) covering the malignant, benign, and normal tissues were extracted for validation. The MDMF achieved the area under the receiver operating characteristic curve (AUC) of 93.14%, 91.30%, 97.35% (FFDM) and 93.79%, 95.16%, 99.31% (DBT) respectively for the malignant, benign and normal mass. While the HFMM further boosted the performance to AUC of malignant 94.14%, benign 95.42% and normal mass 99.56% The matthews correlation coefficient (MCC) were 73.15% and 81.02% for FFDM and DBT accomplished by MDMF, and enhanced to 81.72% when integrating the multimodality information from DBT and FFDM via the proposed HFMM. The experimental results suggested that the proposed HFMM achieved superior discriminative performance when compared with the benchmark classification algorithms and fusion architectures, rendering it a practical tool for breast mass discrimination in breast cancer screening.
Comparing lifetime learning methods for morphologically evolving robots

The joint evolution of morphologies and controllers of robots leads to a problem: Even if the parents have well-matching bodies and brains, the stochastic recombination can break this match and cause a body-brain mismatch in their offspring. This can be mitigated by having newborn robots perform a learning process that optimizes their inherited brain quickly after birth. An adequate learning method should work on all possible robot morphologies and be efficient. In this paper we apply Bayesian Optimization and Differential Evolution as learning algorithms and compare them on a test suite of different robot bodies.
Analysis of Multiplicative Low Entropy Masking Schemes Against Correlation Power Attack

Low Entropy Masking Schemes (LEMS) had been proposed to mitigate the high-performance overhead results from the Full Entropy Masking Schemes (FEMS) while offering good protection against side-channel attacks. The masking schemes usually rely on Boolean masking, however, splitting sensitive variables in a multiplicative way is more amenable to non-linear functions and it had been applied to both software and hardware with a competitive alternative to state-of-the-art masked design. Compared to the comprehensive analysis done for Boolean LEMS, the specific leakage characteristics of Multiplicative LEMS have not yet been analyzed. In this paper, we introduce security models for LEMS to characterize the balance of the mask set. Based on the security model, we present an inherent weakness of Multiplicative LEMS. We prove that this defect of Multiplicative LEMS cannot be compensated by choosing a proper mask set, and the security of FEMS is guaranteed thanks to the Dirac function which is used to resist zero-value attack. Then, we exhibit the leakages in the implementation of Multiplicative LEMS. In particular, we propose a new attack against Multiplicative LEMS more efficient by utilizing the distribution of masked intermediate values. The feasibility of the attack is verified by both simulation and practical experiments.
Three-dimensional reconstruction using variable exponential function regularization for wide-field polarization modulation imaging of surface texture of particles

Shapes from the diffuse polarization method effectively realize the three-dimensional (3D) reconstruction of the object surface by using the polarization information of the diffuse reflection light. However, due to the nonconvexity of the particle surface, the reconstruction often falls into a local optimal solution. Indeed, the depth image obtained by the scanning electron microscope has serious stripe noise, which distorts the surface texture of the particle. In this Letter, a variable exponential function regularization method is proposed to realize 3D reconstruction for the nonconvexity of the surface and inclination of the particles. We focus on the gradient unintegrability caused by the skew and surface undulation of the specimen. An adaptive 3D reconstruction method is proposed based on variable exponential function regularization to fit the surface function of the particle. Experimental results of finite-difference time-domain simulations and actual imaging demonstrate the effectiveness of the method.
Efficient 3D Pancreas Segmentation Using Two-Stage 3D Convolutional Neural Networks

In recent years, 3D segmentation of the pancreas has received a lot of attention from researchers because of its importance for clinical diagnosis and treatment. However, there are many problems with 3D pancreatic segmentation: 1) because the shape of the pancreas is not regular enough compared to other organs in the abdomen, it has a relatively small shape and there is also an excessive background of interference, resulting in inaccurate segmentation results for the pancreas; 2) one of the main drawbacks of 3D convolutional neural networks for segmentation is the excessive memory occupation, which requires censoring of the network structure to fit a given memory budget. To address the above issues, this paper proposes a new coarse-to-fine method based on convolutional neural networks (CNNs). In the first stage, the segmentation is trained to obtain candidate regions. In the second stage, the approximate location of the pancreas is obtained after the first stage, and then the pancreas is finely segmented in this approximate location. The convolutional neural network used in this paper is a modified 3DUnet network, which is improved to require less memory and higher segmentation accuracy compared to the traditional 3DUnet network. This segmentation method requires a less demanding experimental environment than other algorithms, and can also improve accuracy by eliminating a large amount of irrelevant background interference. The combination of our proposed network structure and the two-stage segmentation method achieves advanced performance.
Weighted Hierarchy Mechanism over BERT for Long Text Classification

Text classification is a very significant task in natural language processing, especially for Chinese language processing task. Recently, huge progress has been obtained in text classification field except long text classification. Although BERT has outperformance in most NLP tasks, it still has obvious defect as it is very resource consuming and has limitation in dealing with long text. This paper focuses on long Chinese text classification. Based on BERT model, we adopt an innovative way to chunk long text into several segments and provide a weighted hierarchy mechanism for weighting these segments. We conduct several contrast experiments over BERT model including BERT+LSTM, BERT+CNN, and BERT+LSTM+CNN. Through optimizing models, the results show that the weighted hierarchy method can improve the accuracy of long Chinese text classification and different parameters of layers of neural networks added can also make some differences, especially the structure of LSTM or CNN over BERT, can improve the final effect based on weighted hierarchy mechanism. This paper provides a useful way for long Chinese text classification and comes up with a novel method on how to deal with long text to overcome the limitation of BERT in training long texts.
Survey on Image Object Detection Algorithms Based on Deep Learning

With the development of image processing technology, computer vision is becoming more and more popular. In recent years, deep learning has flourished, significant progress has been made in object detection. Especially after the R-CNN framework was proposed, the object detection framework based on deep learning has gradually become the mainstream, which can be divided into two categories: region-based and regression-based. Taking these two types of frameworks as the main body, this paper summarizes the research background and then discusses the object detection algorithms based on candidate regions represented by Faster R-CNN and the algorithms based on regression represented by the YOLO series. According to the development history, this paper summarizes the framework proposed in recent years, compares and analyzes the performance of object detection algorithms on public datasets, and introduces the application scenarios of those algorithms. Finally, this paper discusses the current difficulties and challenges in object detection and looks forward to the future development direction.
The power of agents in a dispersed system – The Shapley-Shubik power index

In this paper, dispersed knowledge – accumulated in several decision tables is considered. Dispersion of knowledge is not a part of the work of the system. We assume that the knowledge is already in the dispersed form when it provides to the system. An advanced process of detecting the relations between the decision tables and constructing coalitions is used. The purpose of this paper is to use the measure to determine the strength of the coalition. With this method, a simple method of combining vectors of decisions generated based on local decision tables was applied. The purpose of using the Shapley-Shubik index was to reduce the computational complexity compared to the approach proposed in the earlier papers. In this paper, the results of experiments are presented, and the two approaches are compared. Based on these results, some conclusions have been drawn.
Design and Research of Interactive Animation of Immersive Space Scene Based on Computer Vision Technology

With the development of computational simulation technology, the need for stable and immersive display effects of space scene animation in the field of life experience and visual art has gradually increased. In this case, the requirements for immersive characteristics of space scene animation have also been strengthened. The existing 3D space scene animation has a limited degree of stereoscopic display model and data visualization. However, the current space scene interactive animation adopts a plane layout as a whole, and the size of the view interface is generally fixed, including the size of virtual elements. However, the immersion in this paper interactive animation of spatial scene can effectively solve the problems of incomplete display of 3D effects and unstable view simulation in 3D effects. This paper takes immersive space scene animation as the research object and studies the 3D and characteristics of space scene animation based on computer parallel computing in different immersive space scenes and different virtual space technologies, as well as the animation effects of different scene transformations and art forms. The result of research shows that, with the continuous increase of the degree of virtuality within a certain range, the immersive effect of spatial scene interactive animation gradually becomes better. When the color of the space scene animation is below 15, the virtual immersive effect changes less. When the space scene is in the range of 15–20, the space scene will make people feel the atmosphere of a beautiful and mysterious illusion.
All-in-One Hate Speech Detectors May not be what You Want

The detection of Hate speech has been an increasingly active research topic. The results reported by the state-of-the-art systems to automatically detect hateful contents achieved almost perfect performance on common data sets. However, “hate speech” is a very subjective term, and people with different backgrounds have different levels of tolerance to what constitutes hate. In this paper, we show the limitations of having a single classifier handling the problem of hate speech detection. We then propose to build classifiers customized for different people, instead of a single classifier. The main obstacle towards achieving such a goal is the scarcity of data. Therefore, we use transfer learning to overcome this issue and use very limited amount of annotated data to build these customized classifiers. In a first stage, we build a classifier on a large data set which classifies tweets into 3 classes: hate, offensive, clean, and which we refer to as the general classifier. In the second stage, we asked 3 annotators with different backgrounds to re-annotate a small sub-set of tweets (600 tweets) from the original one. We refer to this newly created data set as “the customized data set.” We then fine-tune the general classifier on the customized data set and build the customized classifier for each annotator. The accuracy of classification of corresponding customized data set got 0.08, 0.06 and 0.11 higher than the general classifier. The result shows that it is possible to start with a general classifier, and adjusted it to each individual despite the very limited amount of the training data for him/her.