Reviewerly/exHarmony huggingface dataset:
Image Texture Analysis and Edge Detection Algorithm Based on Anisotropic Diffusion Equation

This paper uses partial differential equation image processing techniques to establish image texture analysis models based on nonlinear anisotropic diffusion equations for image denoising, image segmentation, and image decomposition. This paper proposes a class of denoising models based on the hybrid anisotropic diffusion equation from the characteristics of different noise types. The model exhibits anisotropic diffusion near the image boundary, which can protect the boundary well, and isotropic diffusion inside the image; so, it can remove the noise effectively. We use the immovable point theory to prove the uniqueness of the model solution and further discuss other properties such as asymptotics of the solution. We propose a class of image texture analysis algorithms based on anisotropic diffusion equations and discrete gray level sets. First, a class of nonconvex generalized functions is proposed to remove the noise from the original image to obtain a smooth image while sharpening the edges. Then, an energy generalization function based on the gray level set is proposed, and the existence of the global minimum of this energy generalization function is discussed. Finally, an equivalent form of this energy generalization is given in the discrete case, and an image texture analysis algorithm is designed based on the equivalent form. The algorithm is improved by initial position optimization, dynamic adjustment of parameters, and adaptive selection of thresholds so that the ants can search along the real edges. Experiments show that the improved algorithm for image edge detection can obtain more complete edges and better detection results. The energy generalization function is calculated directly on the discrete gray level set instead of solving the corresponding partial differential equation, which can avoid the selection of the initial level set and the reinitialization of the level set, thus greatly improving the segmentation efficiency. The new algorithm has a high improvement in segmentation efficiency and can efficiently handle large size complex images.
Method of Improving Semantic Representation of Word Embedding

Word are the smallest unit of language. The word embedding trained by neural network can reflect the semantic relevance between words, better complete a variety of NLP downstream tasks, becoming a new research paradigm and hot research issue. This paper review the methods of incorporating syntactic features, word morphological features, and prior knowledge of knowledge base into neural network models to improve the semantic representation of word embedding in recent years, aims to provide reference for scholars in future research.
Region Quality Based Scale-aware Selection for Hierarchical Image Segmentation

In this paper, we propose a scale selection method for hierarchical image segmentation task, which is based on the quality of regions. Firstly, the tree-like representation of hierarchical segmentation result is used to establish the region level relationship. Then the region quality is calculated based on defined region features and a graphical model is constructed to obtain the optimal scale labels for each of the lowest level regions. Lastly, the final segmentation is achieved by combining the corresponding regions from the optimal hierarchy. The experimental results show that the proposed method outperforms the traditional thresholding method for scale selection, and can improve the local and global segmentation quality. As a post-processing method, it can largely improve the output quality of the hierarchical segmentation in vision tasks.
Analysis of Literary based on Deep Emotional Network

Literary works are mainly reviewed by professional judges, whose evaluation and analysis are limited by their own experiences and preferences, which makes it difficult to find the value of the works. We design a method to analyze the quality of literary works based on deep characters emotional network. Taking the representative works of Nobel Prize in Literature and nominees for literature as the research objects, we extract the entity relationship network. The node is the entity in the works, and the edge is the relationship between entities. With the help of graph embedding algorithm, we analyze the similarities and differences of different works in emotional distribution of characters. The results show that the works of the Nobel Prize in Literature winners in 2012 and 2013 have the characteristics of high concentration of characters and even emotion fluctuation.
Zarządzanie mediami elektronicznymi z wykorzystaniem technologii blockchain w komunikacji naukowej

Electronic Media Management With the Use of Blockchain Technology in Scholarly Communication Blockchain is a revolutionary technology, which has the potential to fundamentally change many areas of activities, including scientific research and scholarly communication, together with the publishing industry. This paper focuses on the blockchain’s potential to transform scholarly communication and research in general. It highlights, how blockchain can touch many critical aspects of scholarly communication, including transparency, trust, reproducibility and credit. Moreover, blockchain could change the future role of every stakeholder, and it could have an important role in all research processes, not only scholarly communication. The paper shows that blockchain technology has the potential to solve some of the most prominent issues the scholarly communication is currently facing, such as issues around costs, trust and credibility, openness, and universal accessibility to scholarly information. The research presents basic information about blockchain technology. Based on the literature, an attempt was made to create a model, how to use this technology as a disruptive innovation in scholarly communication.
An Entity Recognition Approach of GIS Fault Text

Entity recognition of GIS fault text contributes to the manual experience sharing and the execution efficiency of GIS operation and maintenance. However, traditional models have poor performance on such entity recognition tasks. In this paper, a novel entity recognition model is proposed. The model consists of several single-type entity recognizers, which increase the accuracy of the model. Bidirectional Encoder Representation from Transformers (BERT) is also applied to understand the characters in the text. Experiments show that the proposed model can achieve F1-score of 88.0%. Compared with other traditional entity recognition models, the performance of the proposed model is improved by about 20%.
Characterizing GPU Overclocking Faults

Graphics Processing Units (GPUs) are powerful parallel processors that are becoming common on computers. They are used in many high-performance tasks such as crypto-mining and neural-network training. It is common to overclock a GPU to gain performance, however this practice may introduce calculation faults. In our work, we lay the foundations to exploiting these faults, by characterizing their formation and structure. We find that temperature is a contributing factor to the fault rate, but is not the sole cause. We also find that faults are a byte-wide phenomenon: individual bit-flips are rare. Surprisingly, we find that the vast majority of byte faults are in fact byte-flips: all 8 bits are simultaneously negated. Finally, we find strong evidence that faults are triggered by memory-remnant reads at an alignment of a 32 byte memory transaction size.
Sonar Image Segmentation of Seabed Targets Using a Variational Approach

Imaging sonars are widely used in underwater intelligent transportation, underwater intelligent perception, intelligent underwater robot navigation, etc. The image segmentation is often a key step in sonar image processing. There have been many approaches of segmenting sonar images, and each method has its own characteristics. The approaches of segmenting images using the variational methods have attracted widespread attentions from scholars. A segmentation method for sonar images using a variational method is given in this paper, and it is compared with other commonly used methods for the image segmentation. The experimental results indicate that the method given in this paper is able to obtain the target contours and has fewer false contours, and the contours are closed. However, the method requires iterative operations and requires a large amount of calculation and has poor real-time performance.
Onlife Drama: Towards a Reference Framework for Hyper-Connected Activity

An important aspect of ICT, identified 25 years ago within the user interface design community, is dramatic interaction: The deep engagement promoted by digital technologies that can be better explored by adopting a conceptual framework traditionally used to describe and study theater. This framework offers a wider perspective that demonstrates a deep connection between the qualities of our hyper-connected era and drama as an art of representing action. These concepts transcend the prevailing technical mentality when addressing ICT. They imply that we all participate as “interactors” on the “onlife stage” where other agents (either humans or computer-controlled) are also present. By promoting deep experiences, the hyper-connected environment in which we live in, changes our metaphysics and self-conception. A dramatic framework can explain the power of ICT and help us work towards the development of an equilibrium both personally and collectively: When used to enrich our experiences and extend our agencies, ICT can be considered as an enhancement of reality. When, on the other side, they are used to promote a false reality experience, they should be rectified. Important ethical and anthropological concerns are framed on the same philosophical ground as ancient drama. Ancient drama was a major pillar of Ancient Democracy and served the need to educate citizens with empathy in order to participate as responsible actors in decision making processes.
A Systematic Review of Self-leadership Related Research Trends in Korea －Focusing on Education Field

According to the rapidly changing social structure and the characteristics of the times, the paradigm has changed from leadership to ‘self-leadership’. In existing organizations and teams, if leaders exert influence on team members to achieve organizational goals and improve performance, those members now have more opportunities to make decisions and to solve complex problems by themselves. Indeed, in recent years much attention has been paid to self-leadership. Not surprisingly then, there has been a good deal of domestic research on self-leadership, especially in the field of nursing. However, to date there is no research analyzing previous related research into the field of education. This study aims to analyze the research trends of self-leadership with a focus on domestic research in the education field, and to suggest implications for future research. For this purpose, 191 domestic research papers (academic articles published since 2005) were collected using the Academic Research Information Service and the National Assembly Library Search Service. All 191 research papers have been reviewed and classified according to analytical criteria, such as year of publication, research method, research field, research subjects and related variables. Our findings show that firstly, related research on self-leadership has been growing since 2005. Secondly, there was more research conducted using the quantitative method compared to research that made use of other research methods. Thirdly, concerning the subjects of study, university students were studied more than other groups of elementary, middle and high school students. Fourthly, regarding the analysis results on self-leadership related factors, 84.9% of the research involved diverse correlates. Based on these findings, it is necessary to continue studying self-leadership using various research methods, as well as to examine this research from diverse perspectives. Also, suggestions were made regarding self-leadership education designed within career education with a special focus on the various characteristics of the learners, along with lifelong education, in order to improve the self-leadership curriculum.
Comparison of the compilation speed of the SCSS and LESS preprocessors

This article compares the compilation speed of the SCSS and LESS preprocessor codes. Each preprocessor has its own syntax, which is transpiled into the CSS stylesheet language in the further development of the web page. These technologies serve the same purpose - to simplify and speed up the writing of page views, but are based on different programming languages - Ruby (SCSS) and JavaScript (LESS). For the purposes of the research, a test application was created, a series of tests was carried out on various sets of codes, the results were collected, which allowed to identify a preprocessor with a faster compilation speed, which turned out to be the LESS preprocessor.
Extremely Tiny Face Detector for Platforms with Limited Resources

Face detection is a fundamental step for face analysis tasks. In recent years, deep learning-based algorithms in face detection have grown rapidly. Most neural networks are computationally expensive and rely on graphics processing units, falling to be applied in practical applications. This paper explores the principles of designing tiny models and proposes an extremely tiny face detector based on the tiny-YOLOv3 framework, introducing new network structures such as Cross-Stage-Partial-connections (CSP), depthwise convolution, and Spatial Pyramid Pooling (SPP). The number of parameters is less than 10k, and the storage is less than 50Kb by using half-precision float point (FP16) for each parameter. Furthermore, each layer’s peak memory usage is under 0.07MB, leading the model to be accessible to various platforms. The experiments on a subset of the WIDER FACE dataset and Open Images Dataset V4 (OID) show that the proposed face detector can achieve comparable performance to the more massive face detectors heavier in model size and floating-point operations.
SMASH: A Semantic-Enabled Multi-agent Approach for Self-adaptation of Human-Centered IoT

As the number of IoT applications increases, IoT devices are becoming more and more ubiquitous. Therefore, they need to adapt their functionality in response to the uncertainties of their environment to achieve their goals. In Human-centered IoT, objects and devices have direct interactions with human beings. Self-adaptation of such applications is a crucial subject that needs to be addressed in a way that respects human goals and human values. This paper presents SMASH: a multi-agent approach for self-adaptation of IoT applications in human-centered environments. SMASH agents are provided with a 4-layer architecture based on the BDI agent model that integrates human values with goal-reasoning, planning, and acting. It also takes advantage of a semantic-enabled platform called Home’In to address interoperability issues among non-identical agents and devices with heterogeneous protocols and data formats. This approach is compared with the literature and is validated by developing a scenario as the proof of concept. The timely responses of SMASH agents show the feasibility of the proposed approach in human-centered environments.
Design and development of student attendance system using QR-Code for UiTM Cawangan Terengganu

Abstract This paper describes the development of student attendance using QR-code forUiTM Kuala Terengganu. The existing or manual attendance in Universiti Teknologi Mara Kuala Terengganu Cawangan Terengganu Kampus Kuala Terengganu (UiTMCTKKT) requires lecturer to records student attendance in attendance sheet, calculate the percentage of student attendance manually and give warning letter to the student by hand. There are several problems may occur if lecturer still using manual method to take student attendance such as lost and damaged the attendance sheet, heavy usage of papers, time limitation, and wrong calculation while calculating the attendance percentage. This project work aims to assist the lecturer in taking student attendance, to automatically calculate attendance percentage, generate warning letters to the student and lastly preventing fraudulent activities by the student. This system is developed by using the System Development Life Cycle (SDLC) waterfall model.
Training deep neural networks for power control in Multiple input multiple output systems

In wireless communication systems reliability can be achieved through spatial diversity and the data rate can be improved with spatial multiplexing. Multiple input multiple output systems otherwise known as MIMO systems achieves both. To improve the efficiency of MIMO systems, the available resources like power and spectrum should be optimally allocated among the users. Water filling algorithm is a classic approach for the same. This paper presents a version of the existing improved water filling technique approximated using deep learning technique in multi user systems inorder to obtain increased capacity and improved accuracy. Comparisons of already existing techniques, approximated with and without deep learning technique with proposed method is performed in single user and multi user based MIMO systems to obtain improved capacity.
Sparse Representation Based Face Recognition Using VGGFace

Face recognition is one of the most important applications of computer vision and it has been used in biometric, surveillance cameras and face tagging. Recently, the convolutional neural network (CNN) model has shown better performance on visual data and it attracted more attention due to automatic feature extraction. Sparse representation-based classification (SRC) has been shown tremendous success in the area of face recognition since the last decade. Convolution neural network is invariant to unconstrained variation whereas SRC is prone. SRC needs discriminative features to represent a test sample. This paper, exploits the significance of both the aforementioned methods and proposed a new CNN-SRC which alleviates the performance of unconstraint variation. The experimental results of the proposed method have shown better performance on different benchmark databases.
Construction of Practical Courses of Financial Sharing Center Under the Background of Cloud Computing

In the information age, cloud computing and big data technology have been widely used in all walks of life. Data information is the core content, and cloud computing is a special tool for computing. The combination of cloud computing and financial accounting produces the financial sharing center model, which is a new product of the combination and application of the two in the whole financial accounting field. Since 2016, universities have cooperated with many accounting software companies to launch financial sharing service courses, and the training mode of financial talents is in urgent need of reform and transformation. Based on the background of cloud computing, this paper designs the framework of practice course of Financial Sharing Center, proposes measures to improve the construction of practical courses of financial sharing center, and provides some references and suggestions for promoting the construction of practical courses of financial sharing center in colleges and universities.
Unicode at Gigabytes per Second

We often represent text using Unicode formats (UTF-8 and UTF-16). The UTF-8 format is increasingly popular, especially on the web (XML, HTML, JSON, Rust, Go, Swift, Ruby). The UTF-16 format is most common in Java, .NET, and inside operating systems such as Windows. Software systems frequently have to convert text from one Unicode format to the other. While recent disks have bandwidths of 5 GiB/s or more, conventional approaches transcode non-ASCII text at a fraction of a gigabyte per second. We show that we can validate and transcode Unicode text at gigabytes per second on current systems (x64 and ARM) without sacrificing safety. Our open-source library can be ten times faster than the popular ICU library on non-ASCII strings and even faster on ASCII strings.
Testing using Privileged Information by Adapting Features with Statistical Dependence

Given an imperfect predictor, we exploit additional features at test time to improve the predictions made, without retraining and without knowledge of the prediction function. This scenario arises if training labels or data are proprietary, restricted, or no longer available, or if training itself is prohibitively expensive. We assume that the additional features are useful if they exhibit strong statistical dependence to the underlying perfect predictor. Then, we empirically estimate and strengthen the statistical dependence between the initial noisy predictor and the additional features via manifold de-noising. As an example, we show that this approach leads to improvement in real-world visual attribute ranking.
Language communications skills and academics achievements of the girls in government schools in Rajasthan

Language is the human capacity for acquiring and using complex systems of communication, or to describe the set of rules that makes up these systems, or the set of utterances that can be produced from those rules. The man finds solutions to his problems, gets entertainment, acquires knowledge and gets success in his life through the language. He expresses human feelings of happiness and sorrows with his friends and family through the language. He gets education through the language. The language is the only means through which he studies his syllabus and gets success in his educational life. The man in the society needs a language through which he exchanges his views to others. The man in the society needs a language through which he exchanges his vies to other. The society can neither exist no can it develop in absence of language. The language is important for social, political, economic and moral development of a society.
Data Privacy Protection in Data Fusion

Today, with the rapid development of big data technology, multi data fusion is facing the problem of privacy security, and people pay more and more attention to its data security. Firstly, this paper describes the three most common problems of multi data fusion, and proposes an integrated federated learning for its security, which combines the trusted execution environment (TEE), asymmetric encryption (RSA) and blockchain technology. Trusted execution environment solves the privacy security problem of local client data processing, asymmetric encryption solves the privacy security problem of data sharing and transmission, and blockchain technology solves the privacy security problem of data tampering in centralized server. Finally, the simulation results show that compared with the traditional federated learning, the fusion federated learning has the same test accuracy, smaller training loss and better training accuracy.
Edge-Based Blur Kernel Estimation Using Sparse Representation and Self-similarity

Blind image deconvolution is the problem of recovering the latent image from the only observed blurry image when the blur kernel is unknown. In this paper, we propose an edge-based blur kernel estimation method for blind motion deconvolution. In our previous work, we incorporate both sparse representation and self-similarity of image patches as priors into our blind deconvolution model to regularize the recovery of the latent image. Since almost any natural image has properties of sparsity and multi-scale self-similarity, we construct a sparsity regularizer and a cross-scale non-local regularizer based on our patch priors. It has been observed that our regularizers often favor sharp images over blurry ones only for image patches of the salient edges and thus we define an edge mask to locate salient edges that we want to apply our regularizers. Experimental results on both simulated and real blurry images demonstrate that our method outperforms existing state-of-the-art blind deblurring methods even for handling of very large blurs, thanks to the use of the edge mask.