Reviewerly/exHarmony huggingface dataset:
Uniting Image and Text Deep Networks via Bi-directional Triplet Loss for Retreival

Image and text are heterogeneous data, thus it is difficult to retrieve images with text query or retrieve texts with image query. Thanks to the success of deep learning in recent years, the feature representations of image and text have been made great advances. However, their distances still cannot be compared directly since they are from different modalities. In this paper, we propose a bi-directional triplet constraint for learning image and text deep networks by simultaneously 1) minimizing the distance of relevant image-text pairwise data, 2) pushing the distance of image vs. its irrelevant text and the distance of text vs. its irrelevant image both larger than that of the pairwise data. Our triplet loss could be seen as cross-modal and bi-directional extension of large margin nearest neighbor method, which is for single-modal data classification. For raw image, a fully-connected subnetwork is designed for image representation learning based on ResNet, and the same architecture is designed for text representation learning. The two deep models are jointly learned with the bi-directional triplet loss in an end-to-end manner. Experiments verify the effectiveness of our proposed model on a widely used dataset.
Quarter-Point Codeword Expansion for Product Quantization

Due to its low storage cost and high query accuracy, Product Quantization (PQ) has been widely used for approximate nearest neighbor (ANN) search. However, almost all existing PQ-based methods use the nearest clustering center as the codeword, which might not fully utilize the information of distances from data points to clustering centers. In this paper, we propose a novel codeword expansion method for PQ-based methods, called Quarter-point Codeword Expansion (QCE), by estimating the distances from the query points to the database points using the quarter points instead of the clustering centers. The distances can be computed more precisely and it will result in a lower distortion using QCE, which is also a general method could be used to improve all PQ-based methods. Extensive experiments on approximate nearest neighbor search show that PQ-based methods with QCE can outperform the state-of-the-art.
Item-Based Collaborative Filtering Algorithm Based on Co-occurrence Matrix

The traditional collaborative filtering algorithm in the past is difficult to bear the computational cost when the data volume is large. This paper proposes an item-based collaborative filtering algorithm based on co-occurrence matrix by using the MapReduce programming idea. The movie was taken as the user recommendation object for experiment and compared the performance with the two existing collaborative algorithms. Experimental results show that item-based collaborative filtering algorithm has an obvious advantage in running time, saving 20 times of running time on average.
Parallelization of a Self-adaptive Harmony Search Algorithm on Graphics Processing Units

In recent years, in order to reduce the execution time, some evolutionary algorithms that run on GPUs using Compute Unified Device Architecture (i.e., CUDA) have been proposed. In these evolutionary algorithms, they compared the execution time and precision ofGPU versions with those of CPU versions. In this study, we parallelize aself-adaptive harmony search algorithm and compare with the existing evolutionary algorithms on the same GPU platform. The proposed algorithm is divided into four steps: initialization, improvising, sorting, and updating.In the experiments, we use eight well-known optimization problems to evaluate the proposed algorithm and the other existing algorithms. As a result, our algorithm achieves the best performances among all the algorithms on the single-objective optimization problems with more dimensions or populations.
Philosophical and Anthropological Analysis of the Problem of the Relation Between the Constructive and Destructive Effect of Freedom

The main issue the author‘s attention is concentrated on is the substantiation of optimal sociocultural and personal balance of freedom as a factor of destructive and constructive display minimization of freedom. A wide range of methodological and theoretical approaches of philosophical anthropology, cultural philosophy and social psychology is applied in the research. The author analyses freedom as a factor of: uncertainty and fear intensification; evil manifestation, deviations’ origin and marginalization’s growth; intensification of society’s development anarchy tendency. Basing on this analysis the author substantiates the ways to obtain the freedom of constructive character. It is stated that instilling into personality an ability to use freedom efficiently guarantees that freedom doesn’t become a subjective factor of uncertainty and fear intensification as a personality doesn’t exist out of positive freedom and positive freedom is created by our personality. Philosophical and anthropological analysis of constructive and destructive freedom display in modern sociocultural situation shows that freedom is not an end in itself but performs as the main condition to turn people’s life into constant process of development and realization of their potentiality. The personality that has found itself and its audience that gives positive feedback in sociocultural reality has no need and time to show deliberate distraction. It is concluded that being an owner of autonomous personality energy a human being can freely develop his/her interpersonal potentiality, by which freedom becomes a creative power able to conduct self-transcendence in sociocultural space performing as a factor of human creation. This factor subjects to distraction only the old-fashioned, wrong, obsolete and impeding progressive development of natural and sociocultural space.
Machine Perception MU—Perceptual Transformation

In the previous Chapters the shape classes, the 3D object classes and the picture classes were presented.
Generalized Quantum Correlations in Discrete Variable Systems

This chapter deals with the development of our novel notion of generalized quantum correlations within discrete variable systems. In particular, we do four things: first, we discuss and motivate the need for a generalized definition of quantum correlations; second, we discuss the tool that we use to achieve our goal, that is, we review the concepts of local and global quantum coherence; third, we define nonclassicality from the perspective of quantum computation and coherence theories and establish an equivalence between the two. Finally, through a toy model, we define the notion of quantum correlations and provide analysis of its feasibility and operational significance.
Camera pose estimation based on distorted lines

PNP (Perspective-N-Point) and PNL (Perspective-N-Line) are the most commonly used methods for camera pose estimation. However, most methods are based on the hypothesis of the pinhole camera model, which ignore camera distortion's effects. Considering the influences of camera distortion, we proposed to solve the camera pose directly by using distorted lines. In this paper, we first undistort the lines according to distortion model, then use LSD algorithm extract 2D undistorted lines from the image. Having matched 2D/3D lines, the PNL (Perspective-N-Line) was used finally to solve the camera pose. Through experiments on synthetic images and real images, it turns out that our method is effective and feasible for pose estimation.
A behavior tracking algorithm based on gradient vector flow and linear discriminant analysis

Aiming at the problem of scarcity in traditional tracking algorithms in order to track behavior more rapidly and accurately, a behavior tracking algorithm based on linear discriminant analysis combined with the gradient vector flow is presented. First, the gradient vector flow is calculated in the reference coordinate system, the appropriate curve parameters are set, and the position of the behavior is determined. Then, the discreteness is calculated within the range of behavior, the discriminant function is defined, and the maximum value and the best eigenvector are obtained. Last, the behavior is detected when the value of the dispersion degree is less than the set threshold. The new algorithm is not affected by background changes. Compared with existing algorithms, the simulation results show that the method is easy to realize and has a more stable tracking effect.
Cross-modal Retrieval Based on Stacked Bimodal Auto-Encoder

Deep learning (DL) has achieved excellent results in dealing with various types of single-modal problems, and many researchers have applied DL in the field of cross-modal retrieval, in which the popular approaches are based on two-stage learning. The first stage obtains a separate representation of each modality, and the second stage is responsible for learning the inter-modal correlation, which is the key to retrieval. The traditional solutions to the second stage obtain the inter-modal shared representation through a shallow network structure, which cannot effectively learn the inter-modal multi-level correlation. Motivated by this, in this paper, a novel hybrid deep structure combining two stages was presented for cross-modal retrieval task. To learn the inter-modal correlation, we incorporated a Stacked Bimodal Auto-Encoder (Stacked-BAE) to the third layer. On the one hand, our model introduced Stacked-BAE to learn the rich cross-modal correlation further and enhanced learning ability of the model. On the other hand, we utilized the layer-wise learning method to obtain the inter-modal multi-level correlation, which improves the accuracy of cross-modal retrieval. Extensive experiments on several cross-modal datasets show that our model is superior to the baseline correlation analysis and three common multi-modal deep models regarding cross-modal retrieval tasks.
Universitety tret'ego pokoleniia: realizatsiia innovatsionnoi obrazovatel'noi paradigmy

The relevance of the study is detremined be the necessity to form a new structure of educational activities of universities, focused on the preparation of competitive graduates, whose competencies meet the needs of the modern stage of the changes of technological structures in the economy and the formation of a new post-industrial society. The analysis and generalization of theoretical approaches and practice of educational activity organization in contemporary higher educational institutions is carried out and possible ways of educational process modernization with application of competence approach to definition of educational process results are defined. The prerequisites for changing the educational paradigm in the Russian higher school are studied and the levels of competencies formed in the traditional educational process based on the concept of «supporting education», as well as the levels of competencies that can be formed in students while organizing the educational process using the concept of «innovative education» are determined. The interrelation of higher school teachers' information competence with the possibility of increasing the education system innovative ability is revealed. The ways of creating information and methodological system to support the training environment that accompanies the learning process, using the capabilities of the Internet are proposed in the article. The article describes four blocks of the educational process modernization, combining the basic tools for the innovative educational process implementation at the University, and lists the main conditions for the implementation of the proposed innovations in University practice. Innovative type of teaching methods at the University should be based on the fundamental condition of students' innovative methods of thinking and activity mastering – the principle of the Trinity of educational, research and project activities of students, both independent and in a single team with teachers. The main goal of all methodological technologies is to involve the student in the learning process, to make him an active participant in the learning process, to form his independence, interest in the active acquisition of knowledge, skills of self-education and research abilities.
Blind Audio Watermarking

The blind audio watermarking is one of the important techniques where the original audio signal is not required for extraction of watermark image from the watermarked audio signal. In this chapter, the blind audio watermarking techniques in the spatial domain, transform domain, and hybrid domain are discussed. Also, the audio watermarking techniques using advanced signal transforms such as stationary wavelet transform (SWT) and fast discrete curvelet transform (FDCuT) are discussed with its experimental results.
String pattern searching algorithm based on characters indices

In this article we shortly present new string pattern matching algorithm. The algorithm uses novel technique for skipping unnecessary comparisons in pattern searching phase. The pattern searching is applied in almost all branches of science such as bioinformatics, information security, text mining, etc. In the context of continuous increase of data, efficient algorithms are necessary to ensure that one can find a pattern in a sequence in a fast and accurate manner. Pattern searching solves the problem of finding a pattern exhibiting certain properties within a given sequence of symbols. Concept of the new algorithm presented in this article is based on a character index in a pattern, aiming at, but not limited to patterns in DNA sequences.
Real-time Object Detection Based on RGB-D Image

Object detection for environment perception is one of the most critical steps for autonomous driving and driving assistant systems. Currently, considering the size of detection model, there is still a need for further improvement to satisfy the requirement of real-time, high-accuracy in object detection approaches for embedded on-board devices. In this paper, we proposed a novel method that taking advantage of fully convolutional neural network and RGB-D image to enhance the speed of detection and simplify the model by reducing its size. Specifically, we utilized a simulator to obtain a lot of binocular and supplementary RGB-D images for training. The proposed method was evaluated on the KITTI dataset and can achieve an accuracy of 90% for easy task. Moreover, the simulation results demonstrated the efficiency of detection get a speed up to 180 FPS when running on the GPU.
A Distribution on the Simplex Arising from Inverted Chi-square Random Variables with Odd Degrees of Freedom

We consider the random variables Zi=βi2Yi/∑k=1mβk2Yk where Yi,i=1…,m are independent inverted chi-square r.v. with νi degrees of freedom. The probability density function of Z=(Z1,Z2,…Zm) is obtained. When νi, i=1…,m are odd, it is shown how to obtain in a fairly easy way a closed form expression for the expectation of log (∑k=1mβk2Yk). Differentiating this expression with respect to the βi, one can find the moments of the random variables Zi. For the particular case of odd degrees of freedom, closed form expressions for the pdf of the univariate and multivariate marginal distributions of Z are also derived. The distribution of Z may be an alternative to the Dirichlet distribution for modeling compositional data.
Resource Planning for UAV Swarms Based on NSGA-II

With the development of unmanned systems, the scale and application scenarios of unmanned swarms have greatly expanded. At present, the research on swarms mainly focuses on the swarm intelligent bionic strategy and the implementation of obstacle avoidance algorithms for specific task scenarios. In the process of swarm control, due to the complex natural environment and limited number of base station connections, the size of the swarm for specific task scenarios needs to be explored and studied. Different mission scenarios have different requirements for the capability of the drone. How to organize the unmanned nodes that meet the mission capability requirements, and how large the swarm size is the best for the mission completion revenue. This article mainly addresses the above two issues. Based on the target search task, the task requirements and the capability of the UAV group are formally modeled, the mapping between the task requirements and the UAV capability is matched, and the multiobjective optimization problem is solved by the NSGA-II algorithm. The unmanned nodes that meet the mission requirements are organized to form alliances. This model is also applicable to mission scenarios where area coverage is required.
On some new variational problems for image denoising

This paper is devoted to image denoising problems using multiresolution schemes related to variational problems. We start with the linear approach of Donoho and Johnstone, that is related to a well known diffusion‐type variational problem. In order to improve the behavior of this approach, we propose some new nonlinear variational problems more adapted to the problem of denoising. Moreover, the discretization is performed using nonlinear multiresolution schemes. In particular, we obtain some fast and well adapted schemes for the considered problem of denoising.
Self-Representation Convolutional Neural Networks

The traditional convolutional neural networks (CNNs) learn numbers of fixed kernels (filters), which are used to obtain the representations of fixed patterns. Therefore, the knowledge representations of CNNs are limited to the number of kernels. In this paper, we present a Self-Representation Convolutional (SRC) layer to obtain richer knowledge representations of images by fully considering the self-similarity between adjacent pixels. SRC layer comprises a learnable local correlation measurement which measures the importance of adjacent pixels to the current pixel and two learnable linear parameters that perform linear projection on adjacent pixels and the weighted sum vectors, respectively. Compared with regular convolutional layers, the SRC layers can not only obtain comparable knowledge representations, but also reduce by a factor of 3× to 56× in the number of learnable parameters. Empirically, CNNs with SRC layers, called Self-Representation Convolutional Neural Networks (SRCNN), achieve strong performances on a range of visual datasets (SVHN, CIFAR-10 and CIFAR-100) while enjoying significant parameters and FLOPs savings.
Forecasting the Production of Electrical Energy from Photovoltaics

The production of electricity from renewable energy sources (RES) increases every year. For Bulgaria, a serious relative share is that obtained from photovoltaics. Because of its volatile nature, however, it creates a number of problems. One of the most important is related to its forecasting. Although there are different forecasting methods, much is currently being done to increase their reliability and accuracy. This determines the high priority of the present research work.
Autobiographical Approach Study on Experience of Guidance Integrated Instruction in Affiliated School to the College of Education: The Ugly Duckling

This study is autobiographical study on my experience of guidance integrated instruction in Affiliated School to the College of Education. I have given personal guidance to the integrated instruction teacher for 14 years in teaching practice college. This study’s method is Pinar(1975, 1994)’s 4 stages: regressive, progressive, analytical, synthetical. I described my life as guidance professor based on my article, lesson plan by made teacher, my comments, e-mail received by teacher, clips on instruction, proceedings of instruction conference etc.. I recollected my past events through back to the past based on theses materials and I suffered mental anguish on desirable existence of integrated instruction in the future. In these process, I realized I was the ‘ugly duckling’. Upcoming the Ugly Duckling should be live with communication to the teacher and be harmony to the organization school for more development each other.
Hybrid Detection for Vehicle Blind Spot using Fisheye Camera

Many vehicular accidents occur because of a blind spot. Previous studies of blind spot reveal that an algorithm becomes weak if the car is near, car detection is 5 to 10 meters only, and the detection rate is not high. A study on fisheye detection using hybrid algorithms for vehicle blind spots can address the issues about accidents in the national and city roads. The hybrid algorithms involved for vehicle detections are rapid AdaBoost Classifier, Background Subtraction, and Color Edge Detection. This study can be very efficient and can give more accurate vehicle detection. As a result, the study will give the driver's awareness and warning from the incoming threats for any untoward accidents.
Implementation and Demonstration

In this chapter, we implement multimedia QoE evaluation on the big data processing platform and demonstrate evaluation results. Firstly, we give a brief introduction to the framework of big data platform. Then, we describe the realization procedure of multimedia QoE evaluation containing data collection, storage, analysis, and mining. Finally, we introduce the demonstration of multimedia QoE evaluation results.
Towards a 2-Multiple Context-Free Grammar for the 3-Dimensional Dyck Language

We discuss the open problem of parsing the Dyck language of 3 symbols, $$D^3$$ , using a 2-Multiple Context-Free Grammar. We attempt to tackle this problem by implementing a number of novel meta-grammatical techniques and present the associated software packages we developed.
Improving Retrieval of Pathological Liver Images in Multi-phase CT Data Using a Correlation Graph Distance

Accurate retrieval of similar tumor images helps a clinician to suggest a successful treatment planning. In this paper, we employ a correlation graph distance to measure the similarity of the images. After reading a multi-phase input data, we extract the ROI of the corresponding tumors. We represent each phase by individual features of the GLCM texture descriptor and therefore describe the three phases by a 9×1 vector. Instead of a local correlation, we employ a global correlation to initially measure the similarity of two multi-phase data. The correlation graph distance is next constructed using the strongly connected component algorithm through an iterative process. In each iteration, the links between a node and the graph are modified. Then, the connected components are recognized, and unattached nodes are connected to the nearest clusters. Our results revealed that the non-linear approach of the correlation graph improves the Recall index by at least 10% and individual selection of the features enhances the output by at least 4 %.
Improvement and Empirical Study of K-Means Clustering Algorithm Based on Chinese Retrieval

K-means is a classical clustering algorithm, which is a hard clustering algorithm. The algorithm has the advantages of simplicity and speed, especially when dealing with large data sets. It can be more efficient and flexible. However, the K-means algorithm also has some shortcomings and defects, such as relying on the initial clustering center to fall into the local optimal solution, easy to love noise points and isolated point effects. The article will optimize and demonstrate the shortcomings and defects of the K-means algorithm.