Reviewerly/exHarmony huggingface dataset:
The Nexus between Financial Development and Human Development: The Case of Developing Countries

The purpose of this study to examine the relationship between financial development and human development in the health and welfare dimensions of developing countries. This study aims to determine whether the financial developments of the countries have an effect on the basic human development of the individuals and whether human development indicators have an impact on financial development. In this study, the relationship between financial development and human development has been tried to be revealed by using data obtained from developing countries. Financial development levels of the countries were measured with the developed financial development index. The index is calculated by using M3 / GDP, private sector loans / GDP and loans to banks from private sector / GDP ratios. The human development index is calculated by considering various health indicators and GNP per capita. The data includes annual data for the period 1970-2016. Pedroni and Kao cointegration analysis and Dumitrescu &amp; Hurlin panel causality analysis were performed in the study. According to the results of the study, the cointegration relationship was determined between the two variables. There is also a two-way causality between the variables.
Open Affordable Mixed Reality: A Manifesto

This article describes the main traits of a proposed Mixed Reality (MR) platform designed to be used by the general public in order to create and share MR experiences. The purpose of this MR Platform is to provide interaction with the Internet of Everything (IoE). We embrace the Ubiquitous computing paradigm, where services follow the user seamlessly across physical locations. An emphasis is made on context awareness, which is central in designing a MR application, by anchoring the right multimodal content to the right circumstance. We aim at providing an architecture which splits awareness into four levels with respect to knowledge representation, in order to provide a tool for non-programmers to design applications.
The behaviour of ACS-TSP algorithm when adapting both pheromone parameters using fuzzy logic controller

In this paper, an evolved ant colony system (ACS) is proposed by dynamically adapting the responsible parameters for the decay of the pheromone trails 𝜉 and 𝜌 using fuzzy logic controller (FLC) applied in the travelling salesman problems (TSP). The purpose of the proposed method is to understand the effect of both parameters 𝜉 and 𝜌 on the performance of the ACS at the level of solution quality and convergence speed towards the best solutions through studying the behavior of the ACS algorithm during this adaptation. The adaptive ACS is compared with the standard one. Computational results show that the adaptive ACS with dynamic adaptation of local pheromone parameter 𝜉 is more effective compared to the standard ACS.
Image Retrieval using One-Dimensional Color Histogram Created with Entropy

Image histograms are frequently used as a feature vector in content-based image retrieval (CBIR). The related methodology involves processing of a single channel histogram on gray leve ...
Contributive Representation-Based Reconstruction for Online 3D Action Recognition

Recent years have seen an increasing trend in developing 3D action recognition methods. However, despite the advances, existing models still suffer from some major drawbacks including the lack of any provision for recognizing action sequences with some missing frames. This significantly hampers the applicability of these methods for online scenarios, where only an initial part of sequences are already provided. In this paper, we introduce a novel sequence-to-sequence representation-based algorithm in which a query sample is characterized using a collaborative frame representation of all the training sequences. This way, an optimal classifier is tailored for the existing frames of each query sample, making the model robust to the effect of missing frames in sequences (e.g. in online scenarios). Moreover, due to the collaborative nature of the representation, it implicitly handles the problem of varying styles during the course of activities. Experimental results on three publicly available databases, UTKinect, TST fall, and UTD-MHAD, respectively, show 95.48%, 90.91%, and 91.67% accuracy when using the beginning 75% portion of query sequences and 84.42%, 60.98%, and 87.27% accuracy for their initial 50%.
Factors Affecting Rice Farming Practices among Farmers in Ogun and Niger States, Nigeria

The study examined factors affecting rice farming practices of rural farmers in Ogun and Niger States, Nigeria. Three hundred and twenty respondents were selected through multistage sampling procedure. Frequency distribution, percentages and mean, student’s t-test and regression analysis were used for data. All analysis was done at 5% level of significance. There was a significant difference in the farm practices of the rice farmers in Niger and Ogun States (t = 8.39, p ≤0.05). Factor affecting rice farming practices was determinants of rice production output (β = -0.13, p ≤0.05). There was difference in the farming practices of rice farmers in Ogun and Niger States. Agricultural extension services, particularly, in Ogun State should expedite action in organizing training that can facilitate adoption of better farming practices in order to increase rice production and income generation for the rice farmers, while government and input suppliers should make fertilizers available and affordable.&#x0D; Keywords: factors affecting farming practices; rice production; rural rice farmers;
Design of lightweight pedestrian detection network in railway scenes

Abstract The deployment of many advanced pedestrian detection applications is largely hindered by the high computational cost of deep convolutional neural networks (CNNs). In this paper, we propose a two-step pruning method to design a lightweight pedestrian detection network in railway scenes. The first step is feature pyramid network (FPN) pruning, which utilizes the characteristic of pedestrian in railway scenes and the FPN structure in YOLOv3. The second step is regular channel pruning, which utilizes network slimming knowledge and is an accelerator-friendly pruning strategy. Our two-step pruning method gives about 88% reduction in parameters and about 74% reduction in computing complexity with comparable detection accuracy.
Blockchain: A detailed survey to explore innovative implementation of disruptive technology

Blockchain technology is a major disruptive technology breakthroughs in the past two decade.This technology can be easily understood as a ledger of records which are irreversible and verifiable.The greatest impact of this application is in the massive generation of crypto-currencies.Over the years, block chain has proved to be more comprehensive and beneficial in more ways than one; not just as an archive of records or virtual currency domain.This paper comes up with a survey done to bring out the key developments of block chain into other domains of practice.Despite the most prevalent adoption of block chain happened to be financial and banking sector but there are researches and trials been done in many other sector by technology game changers.This paper will explore and present diverse uses of block chain in other domains, its impacts and the future course of implementation practices that may be tested.
Use of Information Technology in Crime Investigation

In litigation, transparency is the primary method of information security.Any citizen can count on legal services, the protection of a lawyer in a case of any complexity.And also on the motivated, reasoned conclusion.The Criminal Code allows the closure of proceedings to ensure the protection of the participant, at the risk of disclosure of intimate aspects of life or state secrets, high-tech ideas and achievements.The investigation should be prompt, evidence-based, so it's important to apply IT's advances, such as Big Data and Data Mining, to identify new connections not lying on the "visible surface" of the case.As well as for reduction of complexity, reduction of versions and prevention, etc.With the emergence of new, systemically and structurally complex tasks, the use of digital technologies and approaches in the investigation of crimes becomes vital not only in a portable sense for some suspects.Society often does not demand punishment ("punishment for the sake of punishment"), it demands prevention of it in the future (public order and confidence in tomorrow).Prevention of criminal acts is possible on the basis of IT, information systems of justice, digital transformations in society, law and economy.This article provides a systematic analysis of ways, methods and problems of IT application in crime investigation.A systematic analysis of the possibilities of updating was carried out, which resulted, in particular, in a list of the most relevant in the investigation of IT crimes.In order to assess the system profile of the suspect, a Bayesian approach is proposed, and as a method of implementing the process -a manyagency method, popular in complex, non-linear problems.
Elementary Number Theory Problems. Part I

Summary In this paper we demonstrate the feasibility of formalizing recreational mathematics in Mizar ([1], [2]) drawing examples from W. Sierpinski’s book “250 Problems in Elementary Number Theory” [4]. The current work contains proofs of initial ten problems from the chapter devoted to the divisibility of numbers. Included are problems on several levels of difficulty.
Color Transfer With Salient Features Mapping via Attention Maps Between Images

Color transfer is a useful image editing technology that transfers the color style of a reference image to a source image. In this paper, a novel automatic perception color transfer method based on salient features mapping was proposed. Firstly, the saliency maps of the reference image and source image are built to predict the human visual attention regions. Secondly, the gradient map is calculated by convolving the image pair with Sobel filter. Thirdly, the saliency map and gradient map are combined to produce a weighted attention map. Fourthly, by thresholding the attention map the reference image and the source image are segmented into regions of salient and non-salient pixels, and mean and standard deviation of these two regions are calculated in the three channels of the YUV space, respectively. Finally, on the salient and non-salient pixels an execution similar to Reinhard's is performed, and the two results are combined to generate the final output. The experiments result show the superiority of our method by its pleasant natural appearance with color similarity and image details preservation, high time efficiency and easy implementation.
Secret Sharing with Reduced Share Size and Data Integrity

The proposed work is to provide security for audio data stored in cloud data centers and online song repositories such as hungama.com,iTunes, where huge number of songs is stored for listening and downloading.When polynomial secret sharing method is applied on secret audio, audio shares are generated each with the size of secret audio.Secret is derived only when a valid subset of members in the group are compromised.The purpose of the proposed work is to reduce the dimensions of shares.This can be achieved by having the amplitude values of the secret audio as the coefficients of the polynomial instead of random values in polynomial based secret sharing scheme.But having amplitude values as coefficients does not generate meaningless shares and reveals information about the secret.In this paper, two methods are proposed for generating meaningless audio shares such that the dimensions of each share are lesser than the dimensions of confidential audio.It gives dealer an advantage to securely store and distribute the shares by hiding the shares into other media.In the first method, secret audio is encrypted and the values of these encrypted audio are taken as the coefficients of the polynomial to generate the shares.Second method is implemented to reduce the burden of encryption and decryption.In this approach, for share construction the leading term in the polynomial will have random values as coefficient and for all other terms the coefficient are amplitude values of the secret audio.In both methods, shares generated are of reduced dimensions and are meaningless.Security of shares is enhanced by providing integrity mechanism to shares and steganography effect.A novel block cipher is implemented as compression function in the hash algorithm to generate checksum which is used for verifying integrity of shares.
The use of Semantic Role Labelling with Triangle-Graph Based Text Summarization

Text summarization aims to create a condensed version of a text, thus retaining the original ideas.In particular, the textual content on the web is increasing at an exponential pace.The ability to interpret valuable information from such a vast volume of data is an essential undertaking and requires an automated system to assist with the current information repository.Text summarization systems aim to help reducing content by retaining the relevant material and filtering out non-important sections of the text.There are two basic approaches in the text summarization systems.Single document summarization is outlined in the first method.In other words, the method uses one document as input and generates a summary version as output.An alternative approach is to take many documents as input and generate a single summary document as output.In terms of performance, the summarization systems are often classified into two groups.One method will be to remove exact sentences from the original document in order to create a summary production.An alternative would be a more nuanced approach in which the text rendered is rephrased version of the original document.A formal semantic representation is provided in this paper, which can capture the algorithms and the text meaning to allow mapping the text documents with the meaning representation.This semantic representation, which focuses on establishing relations amongst concepts of text constituents, is considered to enhance the performance of the single-document extractive summarisation processes.To see how text constituents (i.e.words, phrases and morphemes) are related, a graph semantic model (GSM) was used, which is built using a syntactic and semantic analysis.Semantic Role Labelling (SRL) is the fundamental relationship that a contributor has with the main verb in a sentence.
Position-Aware Recalibration Module: Learning From Feature Semantics and Feature Position

We present a new method to improve the representational power of the features in Convolutional Neural Networks (CNNs). By studying traditional image processing methods and recent CNN architectures, we propose to use positional information in CNNs for effective exploration of feature dependencies. Rather than considering feature semantics alone, we incorporate spatial positions as an augmentation for feature semantics in our design. From this vantage, we present a Position-Aware Recalibration Module (PRM in short) which recalibrates features leveraging both feature semantics and position. Furthermore, inspired by multi-head attention, our module is capable of performing multiple recalibrations where results are concatenated as the output. As PRM is efficient and easy to implement, it can be seamlessly integrated into various base networks and applied to many position-aware visual tasks. Compared to original CNNs, our PRM introduces a negligible number of parameters and FLOPs, while yielding better performance. Experimental results on ImageNet and MS COCO benchmarks show that our approach surpasses related methods by a clear margin with less computational overhead. For example, we improve the ResNet50 by absolute 1.75% (77.65% vs. 75.90%) on ImageNet 2012 validation dataset, and 1.5%~1.9% mAP on MS COCO validation dataset with almost no computational overhead. Codes are made publicly available.
EViLBERT: Learning Task-Agnostic Multimodal Sense Embeddings

The problem of grounding language in vision is increasingly attracting scholarly efforts. As of now, however, most of the approaches have been limited to word embeddings, which are not capable of handling polysemous words. This is mainly due to the limited coverage of the available semantically-annotated datasets, hence forcing research to rely on alternative technologies (i.e., image search engines). To address this issue, we introduce EViLBERT, an approach which is able to perform image classification over an open set of concepts, both concrete and non-concrete. Our approach is based on the recently introduced Vision-Language Pretraining (VLP) model, and builds upon a manually-annotated dataset of concept-image pairs. We use our technique to clean up the image-to-concept mapping that is provided within a multilingual knowledge base, resulting in over 258,000 images associated with 42,500 concepts. We show that our VLP-based model can be used to create multimodal sense embeddings starting from our automatically-created dataset. In turn, we also show that these multimodal embeddings improve the performance of a Word Sense Disambiguation architecture over a strong unimodal baseline. We release code, dataset and embeddings at http://babelpic.org.
Dual Kidney-Inspired Algorithm for Water Quality Prediction and Cancer Detection

The kidney-inspired algorithm (KA) was presented in a recent research paper as a metaheuristic search algorithm.The KA imitates the physiological process of the kidneys in the human body.The second kidney in the human body filters all the solutes if the other kidney fails.If the second kidney also gets damaged, dialysis can be performed as a treatment.The failure of a kidney is proved by the Glomerular Filtration Rate (GFR) calculation.In this paper, a Dual-population Kidney-inspired Algorithm (Dual-KA), which contributes to the enhancement of a diversity of solutions and, subsequently, better exploration of the search space, is proposed, as a research objective, in the form of a novel simulation of cooperation between kidneys in human body.If GFR is greater than 60 in each iteration, the process in Dual-KA is continued as normal.If this number is between 15 and 60, some treatment is performed.Else, if the GFR is less than 15 and the current sub-population is the first sub-population with a GFR of less than 15, all the solutions in the current sub-population are sent to the other sub-population in the current iteration.Otherwise, a dialysis or transplant process is carried out.Dual-KA is tested on 12 known test functions, standard classification and time series datasets.Water quality prediction and cancer detection are two real-world applications of this algorithm.The mentioned algorithm is run on these problems and its high ability is proved.
Design and Application of Civil Engineering Experimental Teaching Management Platform in Mobile Environment

This paper explains the research and design of the experimental teaching system of civil engineering. Teachers, students and administrators can use this APP to access and manage teaching resources, and interact with each other in real time through interactive answering module. The system is mainly developed by Android Studio and it adopts the idea of layered design. It includes two bottom modules: network communication and message push, and five functional modules: experiment arrangement, job management, interactive question answering, user management and setting. After testing, the experimental teaching management system can meet the functional needs of users. The design of Android course teaching system provides a good reference for the development of similar courses, and achieves the expected development purpose.
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion.

Datasets containing sensitive information are often sequentially analyzed by many algorithms. This raises a fundamental question in differential privacy regarding how the overall privacy bound degrades under composition. To address this question, we introduce a family of analytical and sharp privacy bounds under composition using the Edgeworth expansion in the framework of the recently proposed f-differential privacy. In contrast to the existing composition theorems using the central limit theorem, our new privacy bounds under composition gain improved tightness by leveraging the refined approximation accuracy of the Edgeworth expansion. Our approach is easy to implement and computationally efficient for any number of compositions. The superiority of these new bounds is confirmed by an asymptotic error analysis and an application to quantifying the overall privacy guarantees of noisy stochastic gradient descent used in training private deep neural networks.
Combining Contextualized and Non-contextualized Query Translations to Improve CLIR

In cross-language information retrieval using probabilistic structured queries (PSQ), translation probabilities from statistical machine translation act as a bridge between the query and document vocabulary. These translation probabilities are typically estimated from a sentence-aligned corpus on a word to word basis without taking into account the context. Neural methods, by contrast, can learn to translate using the context around the words, and this can be used as a basis for estimating context-dependent translation probabilities. However, sparsity limits the accuracy of context-specific translation probabilities for rare words, which can be important in retrieval applications. This paper presents evidence that combining such context-dependent translation probabilities with context-independent translation probabilities learned from the same parallel corpus can yield improvements in the effectiveness of cross-language ranked retrieval.
Collaborative Generative Adversarial Network with Visual perception and memory reasoning

In order to address such negative issues as GAN’s mediocre image quality, high-demand for training samples and computation resources, this paper proposes the collaborative Generative Adversarial Network with Visual perception and memory reasoning (ESA-CGAN). This not only makes use of the vision self-attention mechanism and objects salience model to analyze the global information and detailed features of objects, but also designs cross-correlation self-attention module so as to make a balance between the computation efficiency and computational on the one hand and the statistical efficiency and the ability to simulate remote dependencies on the other hand. Based on convolutional long-term and short-term memory network, this paper optimizes the attention feature map so as to highlight the features of objects themselves and improve their generative abilities. Meanwhile, a cooperative learning mechanism between generators is designed, which combines self-constructed generation model and pre-training generation model to form a generation model group. It not only improves effectively the generation ability of the model, improve the computing efficiency, but also restrains the collapse of the model from another angle. In fact, the model proposed here has completed numerical experiments on multiple common standard datasets and self-configuring datasets, and has made comparisons with several mainstream generation antagonistic network models in terms of the performance of image data augmentation. The experimental results demonstrate that the model has excellent simulation ability to enable itself to effectively realize the augmentation of data, thus making it highly applicable in the future.