Reviewerly/exHarmony huggingface dataset:
A Hybrid Method Based on Feature Fusion for Breast Cancer Classification using Histopathological Images

Breast cancer is the most common type of cancer in women today, and it ranks second after lung cancer with a very high mortality rate.If it is detected late, the treatment of breast cancer becomes very difficult.Although there are various methods for the detection of breast cancer, there is still a need for auxiliary diagnosis and treatment methods.In this study, a hybrid method is proposed to investigate the development of basal-like breast tumors and classify basal-like breast cancer types using histopathological images.In the study, firstly, appropriate features that support the accurate classification between tumor and non-tumor regions are extracted from histopathological images.Then the dataset is created by combining the obtained features.In the last stage of the study, the classification of images is carried out by using bag of words (BoW) and deep neural networks (DNN) techniques in a hybrid manner.Generally, immunohistochemical markers are used for this classification, but the performance of these markers remains at 60%.The performance of the classification accuracy of the proposed system is increased with the proposed hybrid classifier based on feature fusion.As a result of the study, 94.5% classification accuracy is achieved on the training set, while 80.8% classification accuracy is succeed on the test set.As a result, it is verified that successful results are achieved in the classification of basal-like breast cancer on histopathological images using the proposed hybrid method based on feature fusion.
Learning network embeddings using small graphlets

Abstract Techniques for learning vectorial representations of graphs ( graph embeddings ) have recently emerged as an effective approach to facilitate machine learning on graphs. Some of the most popular methods involve sophisticated features such as graph kernels or convolutional networks. In this work, we introduce two straightforward supervised learning algorithms based on small-size graphlet counts, combined with a dimension reduction step. The first relies on a classic feature extraction method powered by principal component analysis (PCA). The second is a feature selection procedure also based on PCA. Despite their conceptual simplicity, these embeddings are arguably more meaningful than some popular alternatives and at the same time are competitive with state-of-the-art methods. We illustrate this second point on a downstream classification task. We then use our algorithms in a novel setting, namely to conduct an analysis of author relationships in Wikipedia articles, for which we present an original dataset. Finally, we provide empirical evidence suggesting that our methods could also be adapted to unsupervised learning algorithms.
ESTIMATING WITH A GIVEN ACCURACY OF THE COEFFICIENTS AT NONLINEAR TERMS OF UNIVARIATE POLYNOMIAL REGRESSION USING A SMALL NUMBER OF TESTS IN AN ARBITRARY LIMITED ACTIVE EXPERIMENT

We substantiate the structure of the efficient numerical axis segment an active experiment on which allows finding estimates of the coefficients fornonlinear terms of univariate polynomial regression with high accuracy using normalized orthogonal Forsyth polynomials with a sufficiently smallnumber of experiments. For the case when an active experiment can be executed on a numerical axis segment that does not satisfy these conditions, wesubstantiate the possibility of conducting a virtual active experiment on an efficient interval of the numerical axis. According to the results of the experiment, we find estimates for nonlinear terms of the univariate polynomial regression under research as a solution of a linear equalities system withan upper non-degenerate triangular matrix of constraints. Thus, to solve the problem of estimating the coefficients for nonlinear terms of univariatepolynomial regression, it is necessary to choose an efficient interval of the numerical axis, set the minimum required number of values of the scalarvariable which belong to this segment and guarantee a given value of the variance of estimates for nonlinear terms of univariate polynomial regressionusing normalized orthogonal polynomials of Forsythe. Next, it is necessary to find with sufficient accuracy all the coefficients of the normalized orthogonal polynomials of Forsythe for the given values of the scalar variable. The resulting set of normalized orthogonal polynomials of Forsythe allows us to estimate with a given accuracy the coefficients of nonlinear terms of univariate polynomial regression in an arbitrary limited active experiment: the range of the scalar variable values can be an arbitrary segment of the numerical axis. We propose to find an estimate of the constant and ofthe coefficient at the linear term of univariate polynomial regression by solving the linear univariate regression problem using ordinary least squaresmethod in active experiment conditions. Author and his students shown in previous publications that the estimation of the coefficients for nonlinearterms of multivariate polynomial regression is reduced to the sequential construction of univariate regressions and the solution of the correspondingsystems of linear equalities. Thus, the results of the paper qualitatively increase the efficiency of finding estimates of the coefficients for nonlinearterms of multivariate polynomial regression given by a redundant representation.
Can Designers Take the Driver’s Seat? A New User-Centered Process to Design with Data and Machine Learning

In this paper, we describe a new design process to use data and machine learning (ML) as design materials in generating new, user-centered adaptive systems.Through a case study, we show the possibilities and limits of designing with ML, how UX and ML aspects need to be handled in parallel when envisioning and developing new solutions, and their mutual influence.We argue that designers can autonomously envision and design user-centered, ML-enabled systems if they acquire basic knowledge of ML principles.However, some steps require close collaboration with ML experts.In this new process, designers are involved in both human-and datacentered activities and should use ad-hoc tools to properly operate in this field.The resulting process described in this paper is characterized by uncertainty and risk of failure, which raise concerns about its applicability in any design context.However, it provides a possible path for design-driven innovation through data and ML.
How long do you want to maintain this thing? Understanding the Challenges Faced by WebXR Creators

The WebXR Device API allows the creation of web browser-based eXtended Reality (XR) applications, i.e. Virtual Reality (VR) and Augmented Reality (AR) applications, by providing access to input and output capabilities from AR and VR devices. WebXR applications can be experienced through the WebXR supported browsers of standalone and PC-connected VR headsets, AR headsets, mobile devices with or without headsets, and personal computers or desktops. WebXR has been growing in popularity since its introduction due to the several benefits it promises such as allowing creators to utilise WebGL's rich development ecosystem; create cross-platform and future proof XR applications; and create applications that can be experienced in VR or AR with minimal code changes. However, research has not been conducted to understand the experiences of WebXR creators. To address this gap, we conducted a qualitative study that involved interviews with 11 WebXR creators with diverse backgrounds aimed at understanding their experiences, and in this paper, we present 8 key challenges reported by these creators.
Implementation of Election System Using Blockchain Technology

Online voting system the most suitable replacement for the older generations paper ballot system and the nowadays trending Electronic Voting Machines (EVM). Every year by year the technology tries to reach its peak with immense exploration by revolutionizing the technology which makes the human life with more coloring. Today's Internet gives us access to a wide range of resources, information easily in a second. Among other such experiments is Blockchain. With its special features such as consistency and architecture, many companies, applications are increasingly moving towards them. One major usage of blockchain can be found in electronic-voting system. We can ensure the transparency of the election by placing all messages or details in the Ethereum blockchain, which keeps the privacy of each voter protected with maximum security and provides an effective sign-up process. Blockchain advantages on online-voting systems includes nonchangeable feature called immutable ledger of votes casted using time stamp method, security of voting system, instant validation of votes and counting of votes and updating in public ledger which does not depending upon the number of nodes in the blockchain network.
Critical Raw Materials Deposits Map of Mainland Portugal: New Mineral Intelligence in Cartographic Form

Reliable and unhindered access to raw materials is a growing concern within the EU and across the globe and the demand for Critical Raw Materials (CRM) plays a crucial economic role in most developed countries around the world. These are of extreme importance for supply chains regarding new technologies, sustainability issues and carbon footprint reduction. The definition of a continuously updated list of CRM by the European Commission led to the first CRM Map of Europe in 2016. Following this, several countries have been surveying, preparing, and evaluating their mineral occurrences to create a resources/deposits database and, therefore, to create a CRM map of their own. With this purpose in mind, we present and explain the first Critical Raw Materials Deposits Map of mainland Portugal, at 1:700,000 scale. This paper describes the scientific, technical, and graphical methodologies involved in its design.
ONLINE SCHOOLING IN RURAL AREAS IN ROMANIA DURING THE PANDEMIC: TEACHERS’ PERSPECTIVES

Because of the threat of the Coronavirus pandemic, schooling in Romania moved in distance regime in March 2020. During the next school year, each municipality chose the scenario for each school – face-to-face schooling or online learning – according to the local number of infections. However, online education encountered many difficulties in terms of material and human resources, especially in rural areas. In this study we aim to explore rural teachers’ perspectives about online schooling in Romania, during the Coronavirus pandemic. The data – obtained through a series of interviews and questionnaires – were collected from rural teachers working in various parts of the country. In this study we propose to follow two main directions. First, we explore teachers’ experiences concerning the access to material resources in the school and at home. The absence of proper digital equipment and the quality of the internet connection are two of the main concerns for policy makers. Second, the study focuses on educational practice, discussing teachers’ accounts and experiences during two periods: March-June 2020 and the next school year, until present. We intend to explore rural teachers’ experiences in depth, as rural was always disadvantaged in Romania’s education system. This study is a starting point for further research of rural teachers’ preparedness in Romania and the implications of online learning on rural teachers and students.
Heuristic-based Approach for Dynamic Consolidation of Software Licenses in Cloud Data Centers

Since its emergence, cloud computing has continued to evolve thanks to its ability to present computing as consumable services paid by use, and the possibilities of resource scaling that it offers according to client’s needs. Models and appropriate schemes for resource scaling through consolidation service have been considerably investigated, mainly, at the infrastructure level to optimize costs and energy consumption. Consolidation efforts at the SaaS level remain very restrained mostly when proprietary software are in hand. In order to fill this gap and provide software licenses elastically regarding the economic and energy-aware considerations in the context of distributed cloud computing systems, this work deals with dynamic software consolidation in commercial cloud data centers 〖DS〗^3 C. Our solution is based on heuristic algorithms and allows reallocating software licenses at runtime by determining the optimal amount of resources required for their execution and freed unused machines. Simulation results showed the efficiency of our solution in terms of energy by 68.85% savings and costs by 80.01% savings. It allowed to free up to 75% physical machines and 76.5% virtual machines and proved its scalability in terms of average execution time while varying the number of software and the number of licenses alternately.
Enhancing Performance of Lossy Compression on Encrypted Gray Images through Heuristic Optimization of Bitplane Allocation

Nowadays, it remains a major challenge to efficiently compress encrypted images. In this paper, we propose a novel encryption-then-compression (ETC) scheme to enhance the performance of lossy compression on encrypted gray images through heuristic optimization of bitplane allocation. Specifically, in compressing an encrypted image, we take a bitplane as a basic compression unit and formulate the lossy compression task as an optimization problem that maximizes the peak signal-to-noise ratio (PSNR) subject to a given compression ratio. We then develop a heuristic strategy of bitplane allocation to approximately solve this optimization problem, which leverages the asymmetric characteristics of different bitplanes. In particular, an encrypted image is divided into four sub-images. Among them, one sub-image is reserved, while the most significant bitplanes (MSBs) of the other sub-images are selected successively, and so are the second, third, etc., MSBs until a given compression ratio is met. As there exist clear statistical correlations within a bitplane and between adjacent bitplanes, where bitplane denotes those belonging to the first three MSBs, we further use the low-density parity-check (LDPC) code to compress these bitplanes according to the ETC framework. In reconstructing the original image, we first deploy the joint LDPC decoding, decryption, and Markov random field (MRF) exploitation to recover the chosen bitplanes belonging to the first three MSBs in a lossless way, and then apply content-adaptive interpolation to further obtain missing bitplanes and thus discarded pixels, which is symmetric to the encrypted image compression process. Experimental simulation results show that the proposed scheme achieves desirable visual quality of reconstructed images and remarkably outperforms the state-of-the-art ETC methods, which indicates the feasibility and effectiveness of the proposed scheme.
The evaluation of college students’ innovation and entrepreneurship ability based on nonlinear model

Abstract Under the background of ‘mass entrepreneurship and innovation’, Chinese colleges and universities have strengthened students’ entrepreneurial and innovative abilities. The article first analyses the reasons why applied universities should strengthen innovation and entrepreneurship education. On this basis, the evaluation index system of students’ innovation and entrepreneurship ability is constructed. The thesis uses the nonlinear structural model to complete the index weight setting. Finally, the paper verifies the effectiveness of the combined evaluation model through the data on innovation and entrepreneurship of college students in an university where applied. At the same time, the article proposes measures for optimising the ecological environment of innovation and entrepreneurship education for colleges and universities.
Query-Based Extractive Text Summarization Using Sense-Oriented Semantic Relatedness Measure

Abstract This paper presents a query-based extractive text summarization method by using sense-oriented semantic relatedness measure. We have proposed a Word Sense Disambiguation (WSD) technique to find the exact sense of a word present in the sentence. It helps in extracting query relevance sentences while calculating the sense-oriented sentence semantic relatedness score between the query and input text sentence. The proposed method uses five unique features to make clusters of query-relevant sentences. A redundancy removal technique is also put forward to eliminate redundant sentences. We have evaluated our proposed WSD technique with other existing methods by using Senseval and SemEval datasets. Experimental evaluation and discussion signifies the better performance of proposed WSD method over current systems in terms of F-score. We compare our proposed query-based extractive text summarization method with other methods participated in Document Understanding Conference (DUC) and as well as with current methods. Evaluation and comparison state that the proposed query-based extractive text summarization method outperforms many existing methods. As an unsupervised learning algorithm, we obtained highest ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score for all three DUC 2005, 2006 and 2007 datasets. Our proposed method is also quite comparable with other supervised learning based algorithms. We also observe that our query-based extractive text summarization method can recognize query relevance sentences which meet the query need.
Research on Application of Packet Encryption Technology in Information Security Algorithm

Abstract As the focus of the development of the whole society in the era of big data, information security is also the main direction of the practice and development of technological innovation. On the basis of understanding the current information transmission security and its algorithm research, this paper analyzes how to use this technology to guarantee information security according to the block encryption principle.
Learning From Mouse CT-Scan Brain Images To Detect MRA-TOF Human Vasculatures

The earlier studies on brain vasculature semantic segmentation used classical image analysis methods to extract the vascular tree from images. Nowadays, deep learning methods are widely exploited for various image analysis tasks. One of the strong restrictions when dealing with neural networks in the framework of semantic segmentation is the need to dispose of a ground truth segmentation dataset, on which the task will be learned. It may be cumbersome to manually segment the arteries in a 3D volumes (MRA-TOF typically). In this work, we aim to tackle the vascular tree segmentation from a new perspective. Our objective is to build an image dataset from mouse vasculatures acquired using CT-Scans, and enhance these vasculatures in such a way to precisely mimic the statistical properties of the human brain. The segmentation of mouse images is easily automatized thanks to their specific acquisition modality. Thus, such a framework allows to generate the data necessary for the training of a Convolutional Neural Network - i.e. the enhanced mouse images and there corresponding ground truth segmentation - without requiring any manual segmentation procedure. However, in order to generate an image dataset having consistent properties (strong resemblance with MRA images), we have to ensure that the statistical properties of the enhanced mouse images do match correctly the human MRA acquisitions. In this work, we evaluate at length the similarities between the human arteries as acquired on MRA-TOF and the "humanized" mouse arteries produced by our model. Finally, once the model duly validated, we experiment its applicability with a Convolutional Neural Network.
Application of dynamic programming approach to computation of atomic functions

The special class of atomic functions is considered. The atomic function is a solution with compact support of linear differential functional equation with constant coefficients and linear transformations of the argument. The functions considered are used in discrete atomic compression (DAC) of digital images. The algorithm DAC is lossy and provides better compression than JPEG, which is de facto a standard for compression of digital photos, with the same quality of the result. Application of high precision values of atomic functions can improve the efficiency of DAC, as well as provide the development of new technologies for data processing and analysis. This paper aims to develop a low complexity algorithm for computing precise values of the atomic functions considered. Precise values of atomic functions at the point of dense grids are the subject matter of this paper. Formulas of V. O. Rvachev and their generalizations are used. Direct application of them to the computation of atomic functions on dense grids leads to multiple calculations of a great number of similar expressions that should be reduced. In this research, the reduction required is provided. The goal is to develop an algorithm based on V. O. Rvachev’s formulas and their generalizations. The following tasks are solved: to convert these formulas to reduce the number of arithmetic operations and to develop a verification procedure that can be used to check results. In the current research, methods of atomic function theory and dynamic programming algorithms development principles are applied. A numerical scheme for computation of atomic functions at the points of the grid with the step, which is less than each predetermined positive real number, is obtained and a dynamic algorithm based on it is developed. Also, a verification procedure, which is based on the properties of atomic functions, is introduced. The following results are obtained: 1) the algorithm developed provides faster computation than direct application of the corresponding formulas; 2) the algorithm proposed provides precise computation of atomic functions values; 3) procedure of verification has linear complexity in the number of values to be checked. Moreover, the algorithms proposed are implemented using Python programming language and a set of tables of atomic functions values are obtained. Conclusions: results of this research are expected to improve existing data processing technologies based on atomic functions, especially the algorithm DAC, and accelerate the development of new ones.
Overview of image mosaic technology by computer vision and digital image processing

Image mosaic technology has become an important research object in the fields of computer vision, computer graphics and digital image processing technology, and has achieved some results. This paper introduces the image mosaic technology and its application fields; Image mosaic technology includes three basic parts: image preprocessing technology, image registration technology and image fusion technology. This paper mainly introduces the basic algorithm of image preprocessing technology, image registration technology and image fusion technology commonly used algorithm, and it is analyzed and summarized; At the end of the paper, the existing problems of image Mosaic technology are put forward, and the development trend of image Mosaic technology is prospected.