Reviewerly/exHarmony huggingface dataset:
M<sup>2</sup>R-Net: deep network for arbitrary oriented vehicle detection in MiniSAR images

Purpose The purpose of this paper is to use the most popular deep learning algorithm to complete the vehicle detection in the urban area of MiniSAR image, and provide reliable means for ground monitoring. Design/methodology/approach An accurate detector called the rotation region-based convolution neural networks (CNN) with multilayer fusion and multidimensional attention (M2R-Net) is proposed in this paper. Specifically, M2R-Net adopts the multilayer feature fusion strategy to extract feature maps with more extensive information. Next, the authors implement the multidimensional attention network to highlight target areas. Furthermore, a novel balanced sampling strategy for hard and easy positive-negative samples and a global balanced loss function are applied to deal with spatial imbalance and objective imbalance. Finally, rotation anchors are used to predict and calibrate the minimum circumscribed rectangle of vehicles. Findings By analyzing many groups of experiments, the validity and universality of the proposed model are verified. More importantly, comparisons with SSD, LRTDet, RFCN, DFPN, CMF-RCNN, R3Det, SCRDet demonstrate that M2R-Net has state-of-the-art detection performance. Research limitations/implications The progress in the field of MiniSAR application has been slow due to strong speckle noise, phase error, complex environments and a low signal-to-noise ratio. In addition, four kinds of imbalances, i.e. spatial imbalance, scale imbalance, class imbalance and objective imbalance, in object detection based on the CNN greatly inhibit the optimization of detection performance. Originality/value This research can not only enrich the means of daily traffic monitoring but also be used for enemy intelligence reconnaissance in wartime.
Software Architecture and Software Design

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Simple gravitational particle swarm algorithm for multimodal optimization problems

In real world situations, decision makers prefer to have multiple optimal solutions before making a final decision. Aiming to help the decision makers even if they are non-experts in optimization algorithms, this study proposes a new and simple multimodal optimization (MMO) algorithm called the gravitational particle swarm algorithm (GPSA). Our GPSA is developed based on the concept of "particle clustering in the absence of clustering procedures". Specifically, it simply replaces the global feedback term in classical particle swarm optimization (PSO) with an inverse-square gravitational force term between the particles. The gravitational force mutually attracts and repels the particles, enabling them to autonomously and dynamically generate sub-swarms in the absence of algorithmic clustering procedures. Most of the sub-swarms gather at the nearby global optima, but a small number of particles reach the distant optima. The niching behavior of our GPSA was tested first on simple MMO problems, and then on twenty MMO benchmark functions. The performance indices (peak ratio and success rate) of our GPSA were compared with those of existing niching PSOs (ring-topology PSO and fitness Euclidean-distance ratio PSO). The basic performance of our GPSA was comparable to that of the existing methods. Furthermore, an improved GPSA with a dynamic parameter delivered significantly superior results to the existing methods on at least 60% of the tested benchmark functions.
A VR-System to Assess Stereopsis with Visual Stimulation: A Pilot Study of System Configuration

Stereopsis is the binocular perception of depth. The lack of accurate depth perception can have a high impact on life, from early childhood learning to the jobs and daily tasks we are able to do. One of the human factors that can influence in the aviation safety is the lack of stereoscopic vision. Our main aim was to develop a virtual system that includes interaction and immersion using a VR device of visualization. It included a virtual environment, several 3D objects and a virtual task to assess stereoscopic vision in adults. Two groups of military pilots participated in the experimental study (N = 20). Group 1 were participants without experience (N = 10) and Group 2 were participants with experience (N = 10). The components of our VR-system SAS have allowed to assess the stereopsis through visualization, recognition and number of 3D objects seen. The HMD used, allowed the participants of both groups to feel a more immersive experience, have better depth perception and see 3D objects inside the virtual environment. Regarding to the 3D sensations and satisfaction, the participants found it easier to use the VR-system SAS, and they would also participate again and use this system. Furthermore, with both, the LANG Stereo-test and the VR-system, the participants were able to see the 3D objects shown. The results suggested that our system is valid to assess stereopsis. Moreover, our VR-system could be a very useful technological tool in the field of ophthalmological evaluation and pilot training.
Forecasting Using Back Propagation with 2-Layers Hidden

Abstract A critical indicator of government policy in economics is based on high inflation because it implicates the welfare of the society in a region. Therefore, it is necessary to have a computing system to see the inflation trend or pattern by looking at monthly data each year. This research aims to predict the rate of inflation using the ANN Back Propagation method based on GUI MATLAB with two hidden layers. Input data is the inflation data of NTB province by using monthly data for the last 11 years, the activation function is LOGSIG, and the training function is TRAINRP. The results of training and testing were obtained an average prediction of 0.213 with MSE of 0.0053. These results proved that inflation rate of 0.5% decreased, therefore catagorized on medium inflation, with the highest inflation in April and the lowest one in July. The results showed that the Back Propagation method can be used as an alternative in predicting the rate of inflation in order to support sustainable economic growth so as to improve people’s welfare in the future.
A Machine Learning Based Modeling of the Cytokine Storm as it Relates to COVID-19 Using a Virtual Clinical Semantic Network (vCSN)

This paper presents a targeted, machine learning based solution to model the phenomenon known as the 'cytokine storm,' which is suspected to play a major role in explaining the highly variable severity of COVID-19 among patients.It describes how a Natural Language Processing (NLP) approach, augmented by biomedical knowledge databases, can extract pre-existing conditions and relevant clinical markers from Electronic Health Records (EHRs).These extracted variables can be modeled to demonstrate correlation with the severity of infection outcomes, the building blocks of a comprehensive risk assessment and stratification strategy to predict which patients have higher or lower risks in terms of the disease severity and likelihood of hospitalization, exclusively from insights taken from the natural language data.The model has been applied to a cohort of patients from a large database of real, anonymized patients and has displayed demonstrable results.
Polytopic Attack on Round-Reduced Simon32/64 Using Deep Learning

In CRYPTO 2019, Gohr uses the residual network technology of artificial intelligence to build a differential distinguisher, and attacks the reduced-round Speck32/64. We tried this method to recover the keys for ten-round Simon32/64. In this paper, we have three innovations. First, we construct polytope neural network distinguisher. On eight-round Simon32/64, polytope neural network distinguisher could increase the success rate of three neural network distinguishers with 0.76 success rate to 0.92. Second, we propose an attack on Simon32/64 based on the combination of the probability of differential path and polytope neural network distinguisher. This method can only increase the computational complexity of the chosen data as the number of rounds increases. Nine-round polytope neural network distinguisher is used to filter out data, whether it is what we want. Eight-round neural distinguisher is used to recover the final round key. The computational complexity of key recovery on the final key of eleven-round Simon32/64 is $$2^{33.4}$$ . Third, we propose an attack called Bayesian Key Research with Error. With this attack, the computational complexity of key recovery on the final key of eleven-round Simon32/64 is $$2^{30.9}$$ . In our paper, the main idea is combining polytope differences with neural networks. By constructing polytope differential neural network distinguisher, we make a key recovery attack. In order to increase the number of rounds, we first used brute force attack and then proposed Bayesian Key Research with Error. We think this idea can be applied to many cryptographic algorithms.
Semi-Supervised Learning and Feature Fusion for Multi-view Data Clustering

Generative Adversarial Networks GANs have become widely used in Single-view classification tasks. Nowadays, most of the data have multiple views and each view emphasizes a unique feature set of the data. In this paper, we investigate the application of GANs on Multi-view data for the task of clustering and few-shot learning. We propose mvSGAN, a deep learning approach to GAN multi-view clustering, where generator and classifier networks are in a competitive min-max game. A multi-view learning algorithm is implemented with a mini-batch which can handle large data sets. We test the accuracy of our method in clustering real-world data sets. The experimental results show that our method outperforms state-of-the-art research.
Research on Volleyball Image Classification Based on Artificial Intelligence and SIFT Algorithm

Due to the application scenarios of image matching, different scenarios have different requirements for matching performance. Faced with this situation, people cannot accurately and timely find the information they need. Therefore, the research of image classification technology is very important. Image classification technology is one of the important research directions of computer vision and pattern recognition, but there are still few researches on volleyball image classification. The selected databases are the general database ImageNet library and COCO library. First, the color image is converted into a gray image through gray scale transformation, and then the scale space theory is integrated into the image feature point extraction process through the SIFT algorithm. Extract local feature points from the volleyball image, and then combine them with the Random Sample Consensus (RANSAC) algorithm to eliminate the resulting mismatch. Analyze the characteristic data to obtain the data that best reflects the image characteristics, and use the data to classify existing volleyball images. The algorithm can effectively reduce the amount of data and has high classification performance. It aims to improve the accuracy of image matching or reduce the time cost. This research has very important use value in practical applications.
A branch &amp; bound algorithm to determine optimal bivariate splits for oblique decision tree induction

Abstract Univariate decision tree induction methods for multiclass classification problems such as CART, C4.5 and ID3 continue to be very popular in the context of machine learning due to their major benefit of being easy to interpret. However, as these trees only consider a single attribute per node, they often get quite large which lowers their explanatory value. Oblique decision tree building algorithms, which divide the feature space by multidimensional hyperplanes, often produce much smaller trees but the individual splits are hard to interpret. Moreover, the effort of finding optimal oblique splits is very high such that heuristics have to be applied to determine local optimal solutions. In this work, we introduce an effective branch and bound procedure to determine global optimal bivariate oblique splits for concave impurity measures. Decision trees based on these bivariate oblique splits remain fairly interpretable due to the restriction to two attributes per split. The resulting trees are significantly smaller and more accurate than their univariate counterparts due to their ability of adapting better to the underlying data and capturing interactions of attribute pairs. Moreover, our evaluation shows that our algorithm even outperforms algorithms based on heuristically obtained multivariate oblique splits despite the fact that we are focusing on two attributes only.
A novel robust fixed-time convergent zeroing neural network for solving time-varying noise-polluted nonlinear equations

Solving nonlinear equations is a crucial step in the territories of science and engineering, as many practical problems could be mathematically described by nonlinear equations. In this paper, a novel robust fast convergence zeroing neural network (RFCZNN) by utilizing a reconstructed activation function (AF) is presented and investigated for the dynamic nonlinear equations (DNE) solving problems in predictable period. Comparing with recently reported finite-time nonlinear recurrent neural network, the presented RFCZNN solves the DNE in settled theoretical time and possesses better robustness in noise-polluted environments. Unlike the finite-time convergent neural network models, the time consumption of the presented RFCZNN in convergence process can be calculated directly by mathematics without considering modelling initial states. The comparative experimental results for solving high-order (second and third order) DNE and tracking robotic motional trail are presented separately to further represent that the proposed fixed-time convergent RFCZNN model is more robust and efficient.
Third Zadeh’s Intuitionistic Fuzzy Implication

George Klir and Bo Yuan named after Lotfi Zadeh the implication p→q=max(1−p,min(p,q)) (also Early Zadeh implication). In a series of papers, the author introduced two intuitionistic fuzzy forms of Zadeh’s implication and studied their basic properties. In the present paper, a new (third) intuitionistic fuzzy form of Zadeh’s implication is proposed and some of its properties are studied.
Effect of the Chaotic Crossover Operator on Breeding Swarms Algorithm

In this paper we present effect of the chaotic crossover operator with different chaotic maps on the metaheuristic search algorithm Breeding Swarms algorithm which is the Particle Swarm Optimization’s one of the genetic algorithm hybrid form. Some of the many optimization problems could have too many local extrema. Most of the time optimization algorithms could stuck on these extrema therefore these algorithms could have trouble with finding global extremum. To avoiding local extrema and conduct better search on search space, a chaotic number generator is used on Breeding Swarms algorithm’s most of the random procedures. To test efficiency and randomness of the chaotic crossover operator, different chaotic maps are used on the Breeding Swarm algorithm. Test and performance evaluations are conducted on Multimodal and unimodal benchmark functions. This new approach showed us that modified Breeding Swarms algorithm yielded slightly better results than Particle Swarm Optimization and original Breeding Swarms algorithms on tested benchmark functions.
A Weighted Tile-based Approach for Viewport Adaptive 360° Video Streaming

Recently, affordable virtual reality (VR) technology has experienced increased attention as it enables innovative and immersive 360° media applications. However, the expected immersive rich media streaming experience is hindered by excessive bandwidth requirements of high-resolution 360° content. It is essential to devise technologies that enable persistent delivery of such bandwidth-hungry applications over the best-effort packet networks. Tile-based adaptive streaming is one of such techniques that support differentiated streaming of quality-variable tiles depending on their position within the viewport. This paper proposes a weighted tile-based approach for 360° video streaming. First, a content-agnostic viewport prediction approach is proposed. Second, a novel viewport and bitrate adaptation heuristic solution which dynamically selects the coverage of the viewport and its marginal regions and allocates weights to the tiles in these regions is introduced. Finally, suitable video bitrates for each tile based on its weight and available network throughput is allocated. The solution is tested and assessed in comparison with alternative approaches resulting in improved viewport quality and lower bandwidth utilization.
Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique

Upon the working principles of the human neocortex, the Hierarchical Temporal Memory model has been developed which is a proposed theoretical framework for sequence learning. Both categorical and numerical types of data are handled by HTM. Semantic Folding Theory (SFT) is based on HTM to represent a data stream for processing in the form of sparse distributed representation (SDR). For natural language perception and production, SFT delivers a solid structural background for semantic evidence description to the fundamentals of the semantic foundation during the phase of language learning. Anomalies are the patterns from data streams that do not follow the expected behavior. Any stream of data patterns could have a number of anomaly types. In a data stream, a single pattern or combination of closely related patterns that diverges and deviates from standard, normal, or expected is called a static (spatial) anomaly. A temporal anomaly is a set of unexpected changes between patterns. When a change first appears, this is recorded as an anomaly. If this change looks a number of times, then it is set to a “new normal” and terminated as an anomaly. An HTM system detects the anomaly, and due to continuous learning nature, it quickly learns when they become the new normal. A robust anomalous behavior detection framework using HTM-based SFT for improving decision-making (SDR-ABDF/P2) is a proposed framework or model in this research. The researcher claims that the proposed model would be able to learn the order of several variables continuously in temporal sequences by using an unsupervised learning rule.
Maximum Likelihood recursive state estimation using the Expectation Maximization algorithm

A Maximum Likelihood recursive state estimator is derived for non-linear state–space models. The estimator iteratively combines a particle filter to generate the predicted/filtered state densities and the Expectation Maximization algorithm to compute the maximum likelihood filtered state estimate. Algorithms for maximum likelihood state filtering, prediction and smoothing are derived. The convergence properties of these algorithms, which are inherited from the Expectation Maximization algorithm and the particle filter, are examined in two examples. For nonlinear state–space systems with linear measurements and additive Gaussian noises, it is shown that the filtering and prediction algorithms reduce to gradient-free optimization in a form of a fixed-point iteration. It is also shown that, with randomized reinitialization, which is feasible because of the simplicity of the algorithm, these methods are able to converge to the Maximum Likelihood Estimate (MLE) of multimodal, truncated and skewed densities, as well as those of disjoint support.
Offline Writer Identification Based on CLBP and VLBP

Writer identification from handwriting is still considered to be challenging task due to homogeneous vision comparing writer of handwritten documents. This paper presents a new method based on two LBPs kinds: Complete Local Binary Patterns (CLBP) and Local Binary Pattern Variance (LBPV) for extracting the features from handwriting documents. The feature vector is then normalized using Probability Density Function (PDF). Classifications are based on the minimization of a similarity criteria based on a distance between two features vectors. A series of evaluations using different combinations of distances metrics are realized high identification rates which are compared with the methods that are participated in the ICDAR 2013 competition.
Object detection through region proposal based techniques

Object Detection is the most significant operation to be performed for applications based on Video surveillance, Medical diagnosis, Image processing, Robotics and many more. Recently many researchers are using an Object detection approach which leverages multiple layers of different CNN architectures to detect significant features. The Generic object detection is performed either through region based proposal technique or regression technique. This paper gives the overview of R-CNN, Fast R-CNN and Faster R-CNN which are region proposal-based object detection techniques. In this paper comparison is done on the basis of Mean Average Precision (MAP) score obtained for various region proposal based object detection methods in IMAGENET Large Scale Visual Recognition Challenge (ILSVRC) and PASCAL VOC Challenges. Also comparison is done among ILSVRC and PASCAL VOC challenges with respect to different parameters and significant differences are reported along with critical review.
Knowledge Graph Anonymization using Semantic Anatomization

As the usage of RDF-based Knowledge Graphs is going mainstream, it becomes necessary for organizations and companies to consider the privacy preservation of the data they are managing and possibly sharing. This is generally performed by anonymization techniques such as triple suppression and generalization. Nevertheless, these techniques have the drawback of reducing the utility of the released datasets. This paper presents semantic anatomization, a novel anonymization technique, that retains all quasi-identifier and sensitive values in the RDF graph. Due to an aggregating mechanism and the exploitation of the semantics contained in ontologies, this technique preserves data correlation and supports high quality analysis from anonymized graphs. We demonstrate the potential of semantic anatomization on large graphs generated from our own extension of the well-established Lehigh university benchmark.
Comparison of Face Classification with Single and Multi-model base on CNN

Since the coronavirus disease 2019 (COVID-19) outbreak has spread across the country, our research applies to remind the people to wear a face mask when we go outside because a facial image detection and classification method will be used to authentication and authorization. This paper has shown that our created models based on CNN can detect the face mask-wearing, glasses-wearing, and gender with comparison two models. We training model with mix public datasets such as WIDER FACE, AFW, and MAFA. Moreover, we use VGG-Face to pre-train the model for the advance detection rate.
Research on the Application of Mind Map in Multimedia Network Teaching Environment

Abstract With the rapid development of Internet technology, multimedia network teaching has become a new teaching mode in the teaching work of major universities. In the context of multimedia technology, the use of mind maps to carry out teaching activities in various disciplines can give full play to the advantages of both, which can use mind maps to present the knowledge learned in an intuitive and vivid way. This not only helps students sort out their knowledge, but also enables students to master important and difficult knowledge faster and more efficiently, and enhance their memory and thinking skills. This is also conducive to the cultivation of students’ divergent systems, systematic thinking and creative logical thinking. Based on this, this article will discuss and study the application of mind mapping in college multimedia network teaching environment. The author hopes that the combination of multimedia technology and mind mapping can maximize the effectiveness of teaching.