Reviewerly/exHarmony huggingface dataset:
The Quantization Parameter Adjustment Scheme Based on Prediction Mode of Coded Prediction Block

Rate control is an important part of video coding technology, which is used to control the encoder to output the code rate of a specific target and minimize the distortion of the coded video. Based on the rate control algorithm audio video coding standard 2.0 (AVS2), the fuzzy rate control has its limitations, and does not consider the correlation of video images. In this paper, an adjustment scheme of the quantization parameter (QP) is proposed. The QP of current encoding is adjusted according to the ratio of the inter-frame prediction block to the total coding block. First, when coding block division, to count the number and the pixels of the block using inter-frame prediction. Second, the final QP is adjusted according to the ratio of the statistical result to the total coding pixels. Finally, the newly obtained QP is substituted into the subsequent encoding process. The experimental results show that, under the common test condition, the new scheme can achieve significant Bjøntegaard delta bit rate (BD-Rate) gain and it is not difficult to achieve.
Unidirectional Cyclic Network Architecture for Distributed Evolution

How to distribute individuals in a population spatially is the most important issue in distributed evolutionary computation. In this paper, a novel distributed evolutionary model named UC model is proposed, in which the network architecture is circular, and the information propagation is unidirectional other than bidirectional. The proposed model is essentially a neighborhood model, but the communication mode between individuals is quite different, and the number of neighborhoods available is more free. As a specific algorithm for model implementation, differential evolution (DE) algorithm is incorporated into the UC model, and a distributed evolutionary algorithm called ucDE is formed. In the experiment, the benchmark functions of CEC’05 are used to test the performance. Experimental results show that the proposed model is a competitive and promising one.
Blind Image Quality Assessment Based Automatical Motion Blur Restoration Algorithm

Image quality has a profound influence on human visual information acquisition. For the distorted image with motion blur, how to restore the image accurately is a difficult problem. Firstly, this paper introduces the cause of motion blur, establishes the degradation model, and finally restores the image with wiener filter and blind image quality index. In previous studies, the original image will be used as a reference when the wiener filter is used to remove ambiguity. By finding the minimum mean square error, the image of good quality can be found. In this paper, no-reference image quality index is used to measure the image quality. A series of relations between image blur parameters and image quality index T (L, α) is obtained. These data points are fitted, and then the optimal data are selected to find the extreme point. The parameters corresponding to the extreme point are used to restore the image. By searching for the best quality index value, the automatic optimization of image definition is finally achieved, and the sharp image is obtained. Experiments show that our method improves the restoration performance than some part of the state method.
How to Protect Your Privacy? A Framework for Counter-Adversarial Decision Making

We consider a counter-adversarial sequential decision-making problem where an agent computes its private belief (posterior distribution) of the current state of the world, by filtering private information. According to its private belief, the agent performs an action, which is observed by an adversarial agent. We have recently shown how the adversarial agent can reconstruct the private belief of the decision-making agent via inverse optimization. The main contribution of this paper is a method to obfuscate the private belief of the agent from the adversary, by performing a suboptimal action. The proposed method optimizes the trade-off between obfuscating the private belief and limiting the increase in cost accrued due to taking a suboptimal action. We propose a probabilistic relaxation to obtain a linear optimization problem for solving the trade-off. In numerical examples, we show that the proposed methods enable the agent to obfuscate its private belief without compromising its cost budget.
Unsupervised Visual Learning: From Pixels to Seeing

This book is about unsupervised learningUnsupervised learning. That is one of the most challenging puzzles that we must solve and put together, piece by piece, in order to decode the secrets of intelligence. Here, we move closer to that goal by connecting classical computational models to newer deep learningDeep learning ones, and then build based on some fundamental and intuitive unsupervised learning principlesPrinciples of unsupervised learning. We want to reduce the unsupervised learningUnsupervised learning problem to a set of essential ideas and then develop the computational tools needed to implement them in the real world. Eventually, we aim to imagine a universal unsupervised learning machineUniversal unsupervised learning machine, the Visual Story NetworkVisual Story Network (VSN). The book is written for young students as well as experienced researchers, engineers, and professors. It presents computational models and optimization algorithms in sufficient technical detail, while also creating and maintaining a big intuitive picture about the main subject. Different tasks, such as graph matchingGraph matching and clustering, feature selection, classifier learning, unsupervised object discoveryUnsupervised object discovery and segmentation in video, teacher-studentTeacher-student unsupervised learning system learning over multiple generations as well as recursive graph neural networks are brought together, chapter by chapter, under the same umbrella of unsupervised learningUnsupervised learning. In the current chapter, we introduce the reader to the overall story of the book, which presents a unified image of the different topics that will be presented in detail in the chapters to follow. Besides sharing that main goal of learning without human supervision, the problems and tasks presented in the book also share common computational graph models and optimization methods, such as spectral graph matchingSpectral graph matching, spectral clustering, and the integer projected fixed point methodInteger Projected Fixed Point (IPFP) method. By bringing together similar mathematical formulations across different tasks, all guided by common intuitive principles towards a universal unsupervised learningUnsupervised learning system, the book invites the reader to absorb and then participate in the creation of the next generation of artificial intelligence.
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Crypto currency overcomes the limitation that the existing virtual currency cannot escape from the exchange method based on the centralized management...
Realization of Re-configurable True Random Number Generator on FPGA

True random number generation (TRNG) is one of the prominent research areas in present scenario of cryptography and security. It has been reported in the recent past that even TRNG encounters security threats. In order to ensure the security of the random numbers, entropy of random numbers being generated should be high. There are different approaches to generate the random numbers from the physical processes, ranging from jitter to chaos. Various schemes employing the jitter as entropy source have been reported. The usage of jitter in ring oscillator aids in obtaining a high speed real-time random number generation (RNG). On the other hand, the asynchronous architecture ensures high security, which has been implemented in the work. Re-configuring these two architectures develops a RNG with high-speed and security. The statistical tests along with internal tests are conducted to ensure security in the architecture. National Institute of Standards and Technology (NIST) tests validated the unpredictability and randomness of the true random number (TRN) generated.
Accessing and evaluating information sources for health and hygiene practices

Information is an element of knowledge that is publicly available, which can be shared and pass through the world. For any meaningful information to be provided, certain relevant information sources must be consulted keeping in mind the problems associated with use of different information sources. Thus the present study was undertaken with the objectives to assess information sources used by women, evaluation of various information sources, and find out the problems associated with use of information sources. The study was conducted in Ludhiana district of Punjab. Two blocks and two zones were selected purposively from Ludhiana district. Further two villages from each block and two localities from each zone were selected randomly. Twenty-five women from each selected village as well as from each locality in the age of 25-50 years were selected on random basis. Thus, total 200 women formed the sample for the study and data was collected with the help of an interview schedule. Health and hygiene practices were studied as personal, household and food related health and hygiene practices. Information sources were taken as informal, formal and mass media (print and electronic) sources. The results of the study showed that the informal sources (i.e. family, friends, neighbour and relatives) were the most preferred sources for seeking information on all health and hygiene practices. Information sources were never evaluated by the majority of the respondents for household and food related health and hygiene practices while these were sometimes evaluated in case of personal and overall health and hygiene practices. Major problems faced by the respondents related to informal, formal, print and electronic sources were unreliability, expensiveness, non-availability and lack of knowledge in using source respectively. There is need to motivate women to carefully select and evaluate information sources before seeking information, so that authenticated and appropriate information could be sought.
Content Analysis of Teachers’ Suggestions about Childcare Practicum

The research was to inquiry in-service teachers’ suggestions about childcare practicum and to provide the implications for it’s improvement. The participants were 222 childcare teachers working in Busan, and asked open questions on the practicum - What do you think about the childcare practicum? What would you like to suggest for the practicum improvement? The collected data were transcribed to the text, and analyzed quantitatively and qualitatively according to the content analysis procedure. As a result, the 481 units were classified into 5 categories and 12 themes. The participants proposed the revision concerning the practicum hours, childcare center, tutor, implementation, and support. On the basis of the results, the improvement of the practicum reflecting the childcare realities was discussed.
THE ECONOMIC BENEFITS OF PUBLICLY FUNDED BASIC RESEARCH: A CRITICAL REVIEW

This article critically reviews the literature on the economic benefits of publicly funded basicresearch. In that literature, three main methodological approaches have been adopted —econometric studies, surveys and case studies. Econometric studies are subject to certainmethodological limitations but they suggest that the economic benefits are very substantial.These studies have also highlighted the importance of spill over’s and the existence oflocalisation effects in research. From the literature based on surveys and on case studies, it isclear that the benefits from public investment in basic research can take a variety of forms. Weclassify these into six main categories, reviewing the evidence on the nature and extent of eachtype. The relative importance of these different forms of benefit apparently varies withscientific field, technology and industrial sector. Consequently, no simple model of theeconomic benefits from basic research is possible. We reconsider the rationale for governmentfunding of basic research, arguing that the traditional ‘market failure’ justification needs tobe extended to take account of these different forms of benefit from basic research. The articleconcludes by identifying some of the policy implications that follow from this review.
Mathematical Implementation of Circle Hough Transformation Theorem Model Using C# For Calculation Attribute of Circle

Hough transformation is a method or theorem is used in searching primitive object forms, such as lines and circles.Circular Hough transformation detection has high accuracy rate in the form of the circular object.There are several stages in performing the application of the mathematical model for circular Hough transformation, namely, pre-processing image and the process of applying the theorem.In the process of pre-processing the image, the image will change the format from RGB format to one channel image using edge detection then, the result of the image will be applied to mathematical circular Hough theorem to find the Attribute of circle like center point and the radius of the object
Entity Candidate Network for Whole-Aware Named Entity Recognition

Named Entity Recognition (NER) is a crucial upstream task in Natural Language Processing (NLP). Traditional tag scheme approaches offer a single recognition that does not meet the needs of many downstream tasks such as coreference resolution. Meanwhile, Tag scheme approaches ignore the continuity of entities. Inspired by one-stage object detection models in computer vision (CV), this paper proposes a new no-tag scheme, the Whole-Aware Detection, which makes NER an object detection task. Meanwhile, this paper presents a novel model, Entity Candidate Network (ECNet), and a specific convolution network, Adaptive Context Convolution Network (ACCN), to fuse multi-scale contexts and encode entity information at each position. ECNet identifies the full span of a named entity and its type at each position based on Entity Loss. Furthermore, ECNet is regulable between the highest precision and the highest recall, while the tag scheme approaches are not. Experimental results on the CoNLL 2003 English dataset and the WNUT 2017 dataset show that ECNet outperforms other previous state-of-the-art methods.
Multi-agent Data Mining Using Asymmetric AES Algorithm in Cloud Computing

In the present-day scenario, huge amount of data is available on Web due to which the current-day major challenge in the cloud computing is converting the data to knowledge which can be used in the industry for different reasons and on the other hand to attain the confidence of consumers by imposing privacy and security for generating constructive suggestions by agents in a multi-agent architecture using OLAP for eliminating regulatory constraints over sensitive data. In this paper, we are proposing a novel algorithm that uses basic AES while performing communication between distinct agents in a cloud computing environment.
Combinatorial Network of Dynamic Models: A Method to Improve Bad-quality Models

This work proposes a combinatorial network of dynamic models in order to combine models that doesn't approximate well a system, to obtain an output that have a better performance considering the system behaviour. The network can be useful in situations where a good model cannot be obtained from data, such when there is a bad signal noise ratio in identification data-set or when a specific input cannot be applied to generate identification data. To do this, we present two different approaches: an analytical one and a numerical method, both combining a weighted sum of the bad quality models. The method is tested on models obtained through a multi-objective system identification procedure, and from models obtained through an interval system identification procedure. The combined model has improved the performance in the validation indexes analyzed, reaching a reduction up to 65% in the RMSE index, 95% in the MSE index of the static curve, 87% in the energy of the residues vector and a reduction of 21% in the auto-correlation energy of the residues vector.
Superiority of Bayes estimators over the MLE in high dimensional multinomial models and its implication for nonparametric Bayes theory

The performance of Bayes estimators is examined, in comparison with the MLE, in multinomial models with a relatively large number of cells. The prior for the Bayes estimator is taken to be the conjugate Dirichlet, i.e., the multivariate Beta, with exchangeable distributions over the coordinates, including the non-informative uniform distribution. The choice of the multinomial is motivated by its many applications in business and industry, but also by its use in providing a simple nonparametric estimator of an unknown distribution. It is striking that the Bayes procedure outperforms the asymptotically efficient MLE over most of the parameter spaces for even moderately large dimensional parameter spaces and rather large sample sizes.
Learning Probabilistic Sentence Representations from Paraphrases

Probabilistic word embeddings have shown effectiveness in capturing notions of generality and entailment, but there is very little work on doing the analogous type of investigation for sentences. In this paper we define probabilistic models that produce distributions for sentences. Our best-performing model treats each word as a linear transformation operator applied to a multivariate Gaussian distribution. We train our models on paraphrases and demonstrate that they naturally capture sentence specificity. While our proposed model achieves the best performance overall, we also show that specificity is represented by simpler architectures via the norm of the sentence vectors. Qualitative analysis shows that our probabilistic model captures sentential entailment and provides ways to analyze the specificity and preciseness of individual words.
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The purpose of this study was to analyze the relationship between the performance ability, training values, and career preparation behavior of...
Statistical Analysis of the Most Influential Cryptocurrencies

A comparison of the most 30 influential cryptocurrencies has been made, based on the CoinMarketCap web page. Over the last few years, an increasing number of the world's population is investing in the cryptocurrency market. The emphasis is placed on Bitcoin, which is the absolute leader on this market. The difference between electronic money and virtual currency is explained, followed by the history of crypto values. Finally, the statistical analysis of the most influential cryptocurrencies, during the last year, will be presented.This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO PREDICT AUTHOR’S AGE FROM TEXT

Author's age prediction is the task of determining the author's age by studying the texts written by them. The prediction of author’s age can be enlightening about the different trends, opinions social and political views of an age group. Marketers always use this to encourage a product or a service to an age group following their conveyed interests and opinions. Methodologies in natural language processing have made it possible to predict author’s age from text by examining the variation of linguistic characteristics. Also, many machine learning algorithms have been used in author’s age prediction. However, in social networks, computational linguists are challenged with numerous issues just as machine learning techniques are performance driven with its own challenges in realistic scenarios. This work developed a model that can predict author's age from text with a machine learning algorithm (Naïve Bayes) using three types of features namely, content based, style based and topic based. The trained model gave a prediction accuracy of 80%.
Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation

Unsupervised domain adaptation has received significant attention in recent years. Most of existing works tackle the closed-set scenario, assuming that the source and target domains share the exactly same categories. In practice, nevertheless, a target domain often contains samples of classes unseen in source domain (i.e., unknown class). The extension of domain adaptation from closed-set to such open-set situation is not trivial since the target samples in unknown class are not expected to align with the source. In this paper, we address this problem by augmenting the state-of-the-art domain adaptation technique, Self-Ensembling, with category-agnostic clusters in target domain. Specifically, we present Self-Ensembling with Category-agnostic Clusters (SE-CC) -- a novel architecture that steers domain adaptation with the additional guidance of category-agnostic clusters that are specific to target domain. These clustering information provides domain-specific visual cues, facilitating the generalization of Self-Ensembling for both closed-set and open-set scenarios. Technically, clustering is firstly performed over all the unlabeled target samples to obtain the category-agnostic clusters, which reveal the underlying data space structure peculiar to target domain. A clustering branch is capitalized on to ensure that the learnt representation preserves such underlying structure by matching the estimated assignment distribution over clusters to the inherent cluster distribution for each target sample. Furthermore, SE-CC enhances the learnt representation with mutual information maximization. Extensive experiments are conducted on Office and VisDA datasets for both open-set and closed-set domain adaptation, and superior results are reported when comparing to the state-of-the-art approaches.
Research on Green Energy Management Mechanism from the perspective of social network

Abstract Energy is an important material basis for the survival and development of human society, which determines the quality of national economic development and people’s quality of life.Energy shortage and environmental pollution have always been the two major problems that human beings face for a long time. Green energy management is extremely urgent. It is of great practical significance to develop green energy management from the perspective of social network.This paper finds that through the reform of energy system, strengthening the construction of system innovation, giving full play to the capacity of social collaborative governance, realizing network management through structural positioning and active role, and making full use of energy internet information technology to improve the capacity of technological innovation can significantly improve the capacity of green energy management reform in chian.