Reviewerly/exHarmony huggingface dataset:
The Feasibility Analysis of Constructing a Regional University Library Resource Sharing Platform

This article discusses the feasibility of establishing a regional library resource sharing platform by categorizing and investigating the literature resources of various universities in Wuhan. The results indicate that the resources of college libraries at different levels are unevenly distributed, especially the resources of college libraries are obviously insufficient; the resources of the peer college libraries have their own advantages. Constructing a regional university library resource sharing platform can compensate for the shortage of resources in some college libraries, complement each other's library resources, increase the utilization rate of library resources in colleges and universities, and serve the teaching and scientific research.
An optimized Radio Modulation Classifier Using Deep Neural Network

Automatic Modulation Classification (AMC) is vastly used in civilian and military equipment for instance; the signals in satellite, GSM, Wi-Fi…etc. The identification and handling of the features of these signals became more sophisticated due to the predominating progression of modern communication technology mainly in the past decade. In the last few years, researchers have begun to study the use of Deep Neural Network (DNN) to identify different types of modulation, and by using it the task has become easier, and we can get a significant improvement in the classification performance compared to many traditional methods. In this paper an automatic modulation Classification model is proposed where deep learning is used to classify different types of modulation at different signal to noise ratios (SNRs), where we optimize the conventional convolutional neural network (CNN) architecture of O’Shea (2016) [1] by selecting the values of the CNN hyperparameters that result in obtaining the best accuracy for each SNR. The optimized model uses CNN4 that increases recognition accuracy of radio modulation over O’Shea’s mode.
Linear and Partly-Pseudo-Linear Cryptanalysis of Reduced-Round SPARX Cipher

We propose a new cryptanalytic technique and key recovery attack for the Sparx cipher, Partly-Pseudo-Linear Cryptanalysis, a meet-in-the-middle attack combining linear and pseudo-linear approximations. We observe improvements over the linear hull attacks in the literature for Sparx 128/128 and 128/256. Additionally, we generate another attack for comparison purposes, using the Cho-Pieprzyk property for a fully-linear approximation and a corresponding key recovery attack. We observe improvements on the data complexity, bias, and number of recovered key bits, over all variants of Sparx, when compared to the use of only the Cho-Pieprzyk approximation.
On-The-Fly Syntheziser Programming with Fuzzy Rule Learning

This manuscript explores fuzzy rule learning for sound synthesizer programming within the performative practice known as live coding. In this practice, sound synthesis algorithms are programmed in real time by means of source code. To facilitate this, one possibility is to automatically create variations out of a few synthesizer presets. However, the need for real-time feedback makes existent synthesizer programmers unfeasible to use. In addition, sometimes presets are created mid-performance and as such no benchmarks exist. Inductive rule learning has shown to be effective for creating real-time variations in such a scenario. However, logical IF-THEN rules do not cover the whole feature space. Here, we present an algorithm that extends IF-THEN rules to hyperrectangles, which are used as the cores of membership functions to create a map of the input space. To generalize the rules, the contradictions are solved by a maximum volume heuristics. The user controls the novelty-consistency balance with respect to the input data using the algorithm parameters. The algorithm was evaluated in live performances and by cross-validation using extrinsic-benchmarks and a dataset collected during user tests. The model's accuracy achieves state-of-the-art results. This, together with the positive criticism received from live coders that tested our methodology, suggests that this is a promising approach.
An Optimal Faster-RCNN Algorithm for Intelligent Battlefield Target Recognition

With Its Calculation Method Based on the Natural Advantages of the Human Brain's Way of Thinking, Deep Learning Has a Great Advantage Over Traditional Methods for Image Recognition, Voice Recognition, and Text Processing. This Paper Uses Deep Neural Networks To Train Convolutional Neural Networks on Image Data Obtained From Unmanned Aerial Vehicles. in Addressing the Three Major Problems of the Original Model,i.e., Overfitting, Redundancy Recognition, and Low Recognition Accuracy, We Propose a Model Based on Characteristics of Images Acquired From Unmanned Aerial Vehicles, and Intelligently Generate a Set of Neural Networks that Can Quickly Recognize Multiple Types of Targets on the Battlefield. An Experiment Analyzes the Advantages and Disadvantages in Deep Learning Using Two Typical Models: Faster-Rcnn and Yolo v3. We Obtain Scenarios Applicable To Each Model, and Optimize and Improve the Recognition Accuracy of Faster-Rcnn. the Effectiveness of Our Algorithm Is Validated on a Large Experimental Dataset.
Curriculum Learning Strategies for Hindi-English Code-Mixed Sentiment Analysis

Sentiment Analysis and other semantic tasks are commonly used for social media textual analysis to gauge public opinion and make sense from the noise on social media. The language used on social media not only commonly diverges from the formal language, but is compounded by code-mixing between languages, especially in large multilingual societies like India. Traditional methods for learning semantic NLP tasks have long relied on end to end task specific training, requiring expensive data creation process, even more so for deep learning methods. This challenge is even more severe for resource scarce texts like code-mixed language pairs, with lack of well learnt representations as model priors, and task specific datasets can be few and small in quantities to efficiently exploit recent deep learning approaches. To address above challenges, we introduce curriculum learning strategies for semantic tasks in code-mixed Hindi-English (Hi-En) texts, and investigate various training strategies for enhancing model performance. Our method outperforms the state of the art methods for Hi-En code-mixed sentiment analysis by 3.31% accuracy, and also shows better model robustness in terms of convergence, and variance in test performance.
Multidimensional Measurement of Virtual Human Bodies Acquired with Depth Sensors

Obtaining 3D measurements of the human body requires precise scanning of the body, as well as methods for extracting these 1D/2D and 3D measurements from the selected volumes. The analysis of these 3D measurements and their monitoring over time (4D) in patients undergoing dietary treatment is a field that poses multidisciplinary challenges such as obtaining precise body models, automating the measurement process and analysing the data from a medical point of view. In this work, we propose a framework to acquire 3D models of patients and obtain measurements on these models. This framework incorporates computational methods for extracting 3D models that faithfully represent the human body, as well as methods for obtaining accurate measurements from those 3D models. An analysis of the accuracy of the proposed methods for obtaining measurements with both synthetic and real objects has been carried out. The low level of error observed in the experimentation on synthetic objects allows to attribute most of it to the scanning module. Experiments with real objects and body models show an error level comparable to other scanning systems based on RGB-D technologies. The main contribution of the work is to provide a framework to obtain in a selective and automatic way the 3D measurements of the human body, allowing the analysis of its evolution (4D) during the treatment of obesity.
Segmentation and Assessment of Leukocytes Using Entropy-Based Procedure

Image evaluation plays a necessary dependability in medical discipline to support the sickness inspection. In this work, the assessment of White Blood Cell (WBC) is performed by using the Digital-Microscope-Pictures (DMP) of thin blood smears. In this study, a soft-computing procedure; Chaotic Firefly Algorithm and Kapur’s Entropy (CFA + KE) focused bi-level threshold is considered to improve the test picture. Further, Distance Regularized Level Set (DRLS) Segmentation is considered to mine the WBC region. Later, the mined WBC region is then evaluated with the expert’s Ground Truth (GT), and the well-known Picture-Quality-Parameters (PQP) is computed to substantiate the exactness of the proposed process. The experimental examination is implemented using the benchmark LISC images, and the results confirm that the proposed procedure helps to accomplish better values of PQP (average value >88%).
Survivability Analysis and Optimization of Dependent Networks Based on Natural Connectivity

In order to more fully evaluate the anti-destructive performance of the dependent network under different attacks, three one-to-one, one-to-many, and many-to-many edge-connection methods, as well as three types of degree homology, degree heterogeneity and randomness Coupling way to construct different network models. Under four different attack methods, the natural connectivity is used as a measure of the indestructibility of the dependent network, and the dependent network models of different connected edges are compared to each other based on the intentional attack of the degree and the median when they face different attack strategies. The anti-destructive effect is greater, and the dependent network has the best anti-destructive performance when the number of attack nodes is small. In addition, by changing the coupling strength between the sub-networks, it is found that the anti-destructiveness of the dependent network also increases as the coupling strength increases. Based on natural connectivity, the concept of node dispersion was proposed to evaluate the comprehensive survivability of dependent networks, and it was found that the dependent network was the most robust when one-to-many homogeneous coupling was used.
Smart abnormal emotion analysis from English sentences

Smart abnormal emotion analysis refers to identifying abnormal sentiments, opinions, or attitudes from massive patterns automatically. The abnormal emotion may be hidden in a paragraph of text to reflect the sentiment suddenly changes. The suddenly changed emotion should be identified in time to avoid severe consequence. For instance, a customer would not buy a product any more if he or she has a negative emotion to this product. With the popularization of social media, more and more information is available. For example, feedbacks, comments, or opinions widely exist on Twitter. Identifying abnormal emotion by analyzing texts in social media becomes a hot topic, which enables companies or government organizations to take prevention strategies in time if needing. In this paper, we propose a multivariate Gaussian model based abnormal emotion detection method. In multivariate Gaussian model, whether a user has abnormal emotion is determined by the joint probability density. The distribution test shows that the negative, positive, and neutral emotions of a user follow a normal distribution, while the surprise and anger emotions do not follow. The emotions of texts from social media posted by a group follow a “power law distribution”, while the individual users do not. The abnormal emotion can be detected by multivariate Gaussian about 84.60%.
Residual Adaptive Mask Generative Adversarial Network for Image Raindrop Removal

Single image raindrop removal is an extremely challenging task since the raindrop regions of various shapes and sizes are not given and the background information of the occluded regions is completely lost for most part. In this paper, a novel two-stage residual adaptive mask generative adversarial network (RAM-GAN) is developed for single image raindrop removal, in which the raindrop regions can be automatically detected and a restored image without raindrops is generated. Moreover, the residual adaptive mask block (RAMB) structures and residual dense adaptive mask modules (RDAMM) are proposed to be the main components constructing the network. The proposed RAMB structure can serve as a feature selector which adaptively enhances the effective information and suppress the invalid information. Each block is processed into two branches: soft mask branch and trunk branch. A mask is generated by the soft mask branch to softly weigh the features processed by the trunk branch. In addition, RDAMM, the residual densely connected module based on RAMB structure, is proposed to maximize the information flow among different blocks and guarantee better convergence. Our experimental results have demonstrated that our method can effectively remove raindrops while well preserving the image details, which outperforms the state-of-the-art methods quantitatively and qualitatively.
3DMGNet: 3D Model Generation Network Based on Multi-Modal Data Constraints and Multi-Level Feature Fusion

Due to the limitation of less information in a single image, it is very difficult to generate a high-precision 3D model based on the image. There are some problems in the generation of 3D voxel models, e.g., the information loss at the upper level of a network. To solve these problems, we design a 3D model generation network based on multi-modal data constraints and multi-level feature fusion, named as 3DMGNet. Moreover, 3DMGNet is trained by self-supervised method to achieve 3D voxel model generation from an image. The image feature extraction network (2DNet) and 3D feature extraction network (3D auxiliary network) are used to extract the features of the image and 3D voxel model. Then, feature fusion is used to integrate the low-level features into the high-level features in the 3D auxiliary network. To extract more effective features, each layer of the feature map in feature extraction network is processed by an attention network. Finally, the extracted features generate 3D models by a 3D deconvolution network. The feature extraction of 3D model and the generation of voxelization play an auxiliary role in the training of the whole network for the 3D model generation based on an image. Additionally, a multi-view contour constraint method is proposed, to enhance the effect of the 3D model generation. In the experiment, the ShapeNet dataset is adapted to prove the effect of the 3DMGNet, which verifies the robust performance of the proposed method.
Towards Automated Taxonomy Generation for Grouping App Reviews: A Preliminary Empirical Study

App reviews often reflect end-users’ requests, issues or suggestions for supporting app maintenance and evolution. Hence, researchers have evaluated several classification approaches for identifying and classifying such app reviews. However, these classification approaches are driven by manually derived taxonomies. This is a limitation given the burden of human involvement, numerous app reviews and dependency on the availability of domain knowledge to perform classification. In this pilot study, we develop and evaluate a novel approach towards the automatic generation of a dynamic taxonomy that groups related app reviews. Our approach uses natural language processing, feature engineering and word sense disambiguation to automatically generate the taxonomy. We validated the proposed approach with app reviews extracted from the popular My Tracks app, where outcomes revealed a 72% match with a manual taxonomy generated from domain knowledge provided by humans. Our approach shows promise for rapidly supporting software maintenance and evolution.
A-DFPN: Adversarial Learning and Deformation Feature Pyramid Networks for Object Detection

In order to weak the variation of the object instance caused by the scale, we innovatively propose an object detection detector based on adversarial learning and deformation feature pyramid: A-DFPN. Firstly, in the feature extraction stage, the concept of Deformation Feature Pyramid Module is proposed. The outstanding advantage is that it can fully extract object features from different convolution layers and objects of different scales. In addition, Two Stage Module is also proposed, it gradually perfects the adjusted anchors in the previous stage through multi-step regression, and locates the position and shape of the object in each RPN to make the location more accurate. At the same time, Mask Module increases the robustness of the detector by spatially blocking certain feature maps or by manipulating feature responses to generate difficult samples. Finally, the final bounding boxes is filtered by soft-NMS. Under the Resnet-101 network architecture, our algorithm achieves the mean average precision of 81.1034% on the Pascal VOC 2007 dataset and 73.52% on the DETRAC dataset, reaching the state-of-the-art detection level.
Solving Math Problems through the Principles of Scientific Creativity

Ongoing changes in mental and personal characteristics of the modern child initiate the updating of means and forms of education. And they, in their turn, update methodological techniques and approaches to the use of methods during the math class. The source of updating methodological techniques may be the principles used when solving scientific problems. Scientific creativity traditionally systematizes knowledge and skills for their use to a wide range of sciences. Therefore, the principles of scientific creativity have a lot in common with different subjects, showing the student the unity of approaches in working with knowledge. In this regard, the article is aimed at substantiating the principles of scientific creativity as effective methodological techniques for finding solutions to math problems and, on their basis, developing recommendations for conducting classes that prepare for final certification at school in mathematics. The leading research methods in this case are: observation of the methodological work of teachers of mathematics, conversations with teachers, analysis of guidance papers and questionnaires of teachers, statistical processing of research results. In 2018-2019 the experiment in which 19 mathematics teachers took part was conducted. Based on its results the authors of the article succeeded in: highlighting the principles of finding solutions to math problems based on the approaches used in scientific creativity; developing and implementing on the basis of these principles recommendations for conducting classes that prepare for final certification at school in mathematics. The effectiveness of using the principles of finding solutions to math problems was assessed. It allowed the authors to conclude that students have an increase in the speed of finding solutions to math problems by an average of 11%. Practical use of the proposed principles makes it possible to organize training for schoolchildren in solving math problems in traditional forms of teaching, but taking into account the particularities of the development of the modern schoolchild. Methodological recommendations developed by the authors can be used to teach students how to find solutions to math problems during classes that prepare for final certification at school in mathematics.
A Genetic Algorithm with Restart Strategy for Solving Approximate Shortest Vector Problem

The shortest vector problem(SVP) is a famous hard problem in lattice-based cryptography. In this paper, we propose a genetic algorithm for solving approximate shortest vector problem with restart strategy. The genetic algorithm is based on natural number representation proposed by Fukase [1]. We introduce the concept and properties of natural number representation, and encode lattice vector into natural number vector as chromosome. To avoid local stagnation, we propose a restart strategy utilizing good individuals to reduce lattice basis and improve the property of lattice basis, which is enlightened by random sampling reduction algorithms. The genetic algorithm with restart outperforms some SVP challenge records and classic enumeration algorithms on lattices under 100 dimension in practice, and its implementation efficiency is close to the state-of-art enumeration technique of extreme pruning.
A Text Recognition Method for Infrared Images of Electrical Equipment in Substations

As for the operation and maintenance of substations, infrared detection is an effective way of monitoring states of electrical equipment. However, the number of images taking by infrared thermometer increases with more and more equipment putting into operation, which aggravates the burden of recording temperature from infrared images of equipment. To decrease workload and improve work efficiency of operation and maintenance staff, an image recognition method for infrared images based on HSV (Hue, Saturation, Value) color model, CTPN (Connectionist Text Proposal Network) and STRF (Scene Text Recognition Framework) is proposed. Images are processed by HSV to extract the color of text. CTPN is used to transform multi-line text images into single-line text images. STRF is trained to recognize text on the images. Simulations are carried on to demonstrate effectiveness of the proposed method.
The Convergence of a Cooperation Markov Decision Process System

In a general Markov decision progress system, only one agent's learning evolution is considered. However, considering the learning evolution of a single agent in many problems has some limitations, more and more applications involve multi-agent. There are two types of cooperation, game environment among multi-agent. Therefore, this paper introduces a Cooperation Markov Decision Process (CMDP) system with two agents, which is suitable for the learning evolution of cooperative decision between two agents. It is further found that the value function in the CMDP system also converges in the end, and the convergence value is independent of the choice of the value of the initial value function. This paper presents an algorithm for finding the optimal strategy pair (πk0,πk1) in the CMDP system, whose fundamental task is to find an optimal strategy pair and form an evolutionary system CMDP(πk0,πk1). Finally, an example is given to support the theoretical results.
Variational Auto-Encoders Without Graph Coarsening For Fine Mesh Learning

In this paper, we propose a Variational Auto-Encoder able to correctly reconstruct a fine mesh from a very low-dimensional latent space. The architecture avoids the usual coarsening of the graph and relies on pooling layers for the decoding phase and on the mean values of the training set for the up-sampling phase. We select new operators compared to previous work, and in particular, we define a new Dirac operator which can be extended to different types of graph structured data. We show the improvements over the previous operators and compare the results with the current benchmark on the Coma Dataset.