Reviewerly/exHarmony huggingface dataset:
Super Resolution Image Reconstruction Using Iterative Regularization Method and Feed –Forward Neural Networks

Abstract Super resolution is one of the best existing procedures to acquire high resolution image due to its effortlessness and extensive variety of use in numerous fields of science and engineering. There are various methods exist for super resolution but, this work made an effort by combining the iterative regularization method with neural network and obtained a good result comparatively from the previous method because of its high compatible nature with less and user friendly approach. It takes care of the noise in the initial stage and gets a concrete result when neural network is introduced. In addition to the noise it controls the vulnerable parameters and gets a highly super resolution image as compare to the literature.
Ensemble of Attractor Networks for 2D Gesture Retrieval

This work presents an Ensemble of Attractor Neural Networks (EANN) model for gesture retrieval. 2D single-stroke gestures were captured and tested offline by the ensemble. The ensemble was compared to a single attractor with the same complexity, i.e. with equal connectivity. We show that the ensemble of neural networks improves the gesture retrieval in terms of capacity and quality of the gestures retrieval, regarding the single network. The ensemble was able to improve the retrieval of correlated patterns with a random assignment of pattern subsets to the ensemble modules. Thus, optimizing the ensemble input is a possibility for maximizing the patterns retrieval. The proposed EANN proved to be robust for gesture recognition with large initial noise promising to be robust for gesture invariants.
Integration of Digital Watermarking Technique into Medical Imaging Systems

This paper presents the process of integrating digital watermarking technique into medical imaging workflow to evaluate, validate and verify its applicability and appropriateness to medical domains. This is significant to ensure the ability of the proposed approach to tackle security threats that may face medical images during routine medical practices. This work considers two key objectives within the aim of defining a secure and practical digital medical imaging system: current digital medical workflows are deeply analyzed to define security limitations in Picture Archiving and Communication Systems (PACS) of medical imaging; the proposed watermarking approach is then theoretically tested and validated in its ability to operate in a real-world scenario (e.g. PACS). These have been undertaken through identified case studies related to manipulations of medical images within PACS workflow during acquisition, viewing, exchanging and archiving. This work assures the achievement of the identified particular requirements of digital watermarking when applied to digital medical images and also provides robust controls within medical imaging pipelines to detect modifications that may be applied to medical images during viewing, storing and transmitting.
A. Amalgamative sentiment analysis framework on social networking site

Abstract Sentiment analysis is the most widely used in many applications. Knowing the sentiment analysis for any of the dataset becomes most important to know the sentiment for the given inputs. In this paper, the amalgamative sentiment analysis framework developed for dividing the positive and negative emotions taken from the user’s tweets. In this system, an integrated social networking is developed which is integrated with ASAF. Results show the classification of these tweets.
An Online Tool for Unfolding Symbolic Fuzzy Logic Programs

In many declarative frameworks, unfolding is a very well-known semantics-preserving transformation technique based on the application of computational steps on the bodies of program rules for improving efficiency. In this paper we describe an online tool which allows us to unfold a symbolic extension of a modern fuzzy logic language where program rules can embed concrete and/or symbolic fuzzy connectives and truth degrees on their bodies. The system offers a comfortable interaction with users for unfolding symbolic programs and it also provides useful options to navigate along the sequence of unfolded programs. Finally, the symbolic unfolding transformation is connected with some fuzzy tuning techniques that we previously implemented on the same tool.
Optimal Algebraic Breadth-First Search for Sparse Graphs

There has been a rise in the popularity of algebraic methods for graph algorithms given the development of the GraphBLAS library and other sparse matrix methods. An exemplar for these approaches is Breadth-First Search (BFS). The algebraic BFS algorithm is simply a recurrence of matrix-vector multiplications with the $n \times n$ adjacency matrix, but the many redundant operations over nonzeros ultimately lead to suboptimal performance. Therefore an optimal algebraic BFS should be of keen interest especially if it is easily integrated with existing matrix methods. Current methods, notably in the GraphBLAS, use a Sparse Matrix masked-Sparse Vector (SpMmSpV) multiplication in which the input vector is kept in a sparse representation in each step of the BFS, and nonzeros in the vector are masked in subsequent steps. This has been an area of recent research in GraphBLAS and other libraries. While in theory these masking methods are asymptotically optimal on sparse graphs, many add work that leads to suboptimal runtime. We give a new optimal, algebraic BFS for sparse graphs, thus closing a gap in the literature. Our method multiplies progressively smaller submatrices of the adjacency matrix at each step. Let $n$ and $m$ refer to the number of vertices and edges, respectively. On a sparse graph, our method takes $O(n)$ algebraic operations as opposed to $O(m)$ operations needed by theoretically optimal sparse matrix approaches. Thus for sparse graphs it matches the bounds of the best-known sequential algorithm and on a Parallel Random Access Machine (PRAM) it is work-optimal. Our result holds for both directed and undirected graphs. Compared to a leading GraphBLAS library our method achieves up to 24x faster sequential time and for parallel computation it can be 17x faster on large graphs and 12x faster on large-diameter graphs.
Automatic Extraction of Product Regions from Images on C2C Online Market

A novel method that extracts only regions in products from product images used in consumer-to-consumer (C2C) online marketplaces or auctions is proposed. The proposed method automatically extracts product regions by using GrabCut based on fact that product regions tend to be found in the center part of images. Although GrabCut is known as an interactive segmentation technique, the proposed method automates procedure of extraction by generating an initial seed of GrabCut. To evaluate effectiveness of the proposed method, with 138 product images including 114 images obtained from a C2C online marketplace, the extraction accuracy of the proposed method was compared with that of the interactive GrabCut. The result shows that the extraction accuracy of the proposed method is 2.3 % lower than that of the interactive GrabCut. However, statistically significant difference in the extraction accuracy disappeared. In other words, the proposed method is effective in enabling automation for extraction without significantly degrading the extraction accuracy.
Performance validation of clustering algorithms using selection of attributes and application of filters in terms of data reduction

Summary Clustering in unsupervised learning is a method to find inherent group of set of unlabeled data. Such set of groups are termed as Clusters. Grouping of datasets in to clusters involves minimization of the interclass similarity and maximization of the intraclass similarity. Therefore, clustering the large datasets introduces the concept called Data Reduction. Data reduction is a simple process of identifying a relevant feature subset, which is enough to represent the selected large datasets. Here, the data reduction is done in terms of applying two clustering algorithms like Expectation and Maximization‐EM and K‐Means with filters in unsupervised category (i) Normalize filter in Instance level and (ii) Randomize filter in attribute level and selection of attributes. The results shows that the Livestock dataset applied with selection of attributes and preprocessing filter named Normalize in attribute level and Randomize in instance level on which the EM and K‐Means algorithm are executed, and the results are compared and analyzed in terms of data reduction. Therefore, the K‐Means algorithm applied with Randomize filter at instance level and selection of attributes and finally K‐Means algorithm show good performance in case of large datasets when compared with other clustering algorithms.
Retos jurídicos que plantea la tecnología de la cadena de bloques (Aspectos legales de blockchain)

espanolEntre las herramientas tecnologicas que actualmente suscitan mayor interes se encuentran las cadenas de bloques. Desde que en 2009 se crease la conocida como Blockchain, no son pocas las que han ido apareciendo con posterioridad. Segun se indica, esta novedosa tecnologia permite eliminar intermediarios, pero garantizando –e incluso aumentando– la seguridad que aquellos proporcionaban, a la vez que incrementa la eficiencia y reduce costes.Como toda tecnologia novedosa, tiene que resolver o encauzar los problemas juridicos que suscita su uso. El presente articulo pretende abordar algunos de los retos que plantea la cadena de bloques: la falta de dueno, los limites de la funcion registral que desarrolla, algunos aspectos sobre la identidad de los intervinientes, o la caracterizacion de las criptomonedas y su relacion con los impuestos o el dinero legal. Finalmente, tambien se hace mencion a los smart contractsque se ejecutan utilizando esta tecnologia EnglishAmong those technological tools which currently trigger great interest are so-called «blockchains». Since the launch of Blockchain in 2009, many such systems have been devised. As is apparent, this technology tends to eliminate intermediaries, while guaranteeing and even increase the security they provide. At the same time, it improves efficiency while reducing costs. Like any other new technology, it must address the legal challenges that arise from its use. The goal of this paper is to address some of those referred to public blockchains –specially, Blockchain–, to wit: the lack of ownership, the limits of their registry functions, concerns regarding the identity of the participants, the character of cryptocurrencies and their relationship to tax structure or legal money, and finally, the legal smart contracts that can be executed using this technology.
Hiding Personal Tendency in Set-valued Database Publication

Set-valued database publication has been increasing its importance recently due to its benefit for various applications such as marketing analysis and advertising. However, publishing raw set-valued database may cause individual privacy breach such as the leakage of sensitive information like personal tendencies when data recipients perform data analysis.
Application of Multivariate Statistic of U, Th and Pb Concentrations and Pb Isotopic Signatures in the Assessment of Geogenic and Anthropogenic Sources in a U-Mineralized Area

This work presents a Principal Component Analysis (PCA) study using Pbisotope signatures and U, Th and Pb concentrations from groundwater, sediments and rocks (granites and orthogneisses) of the Complex of Lagoa Real (Bahia, Brazil).This area is naturally enriched in U and Th, with the occurrence of Pb derived from the radioactive decay of the elements ( 238 U, 235 U and 232 Th) in the form of their stable isotopes 206 Pb, 207 Pb and 208 Pb in addition to the natural isotope 204 Pb.Sampling was carried out in the rainy season (December to January) and the points were selected according to regional hydrology and geology.Thirty samples were analyzed: 12 of groundwater (AP) and 18 of sediments (S).The results show that the use of isotopic ratios allows discrimination between geogenic and anthropogenic samples.This information is not obtained using only the analysis of concentration data.Statistically, the isotopic data of Pb stand out as an efficient tool in the characterization of sources in the scenario investigated, allowing an effective environmental monitoring and a better management of the mining activities.
Brain Storm Optimization Algorithms for Solving Equations Systems

This chapter refers to the use of Brain Storm Optimization (BSO) algorithms in solving equations systems (ES). BSO algorithm is a swarm intelligence algorithm, which simulates the human brainstorming process, a form of human collective creativity. Mainly, in this chapter, two algorithms are proposed: the first for ES preconditioning and second for solving ES. First, is proposed a BSO method aiming the bandwidth reduction of sparse matrices, a process that can improve a lot of computing processes, such as solving large systems of linear equations. The other one proposes a method for solving equations systems that uses BSO. For the second problem, a new crossover strategy as well as a hybridization of BSO with graph theory elements are proposed. Serial and parallel variants of both algorithms are presented. Experimental results obtained illustrate the fact that the proposed algorithms lead to good results, with respect to other methods.
Child Development and Interaction Design

This course will cover child development frameworks from the "classics" that have had a significant impact on interaction design, such as Piaget, Vygotsky, and Papert, to more recent ideas, such as dynamic systems approaches. The materials presented will include concepts such as embodiment, emergence, and plasticity. Hourcade will examine how these frameworks can inform the design, implementation, and evaluation of technologies for children with the goal of promoting healthy development.
Real-Time Topic Detection with Dynamic Windows

Abstract Microblog is a popular social network in which hot topics propagate online rapidly. Real-time topic detection can not only understand public opinion well but also bring high commercial value. We design a method for real-time microblog data analysis in order to detect popular long lasting events as well as emerging events. Firstly, a mining frequent items algorithm on microblog data stream is proposed to count approximate word frequency. This mining frequent items algorithm can find the frequent words for some time. Secondly, the windows size of the monitored words is adjusted dynamically according to the duration time and the evolution of events. Lastly, new topics and trends of existing topics can be detected by using dynamic clustering algorithm based on vector space model. Experimental results show that the proposed algorithms can improve performance in terms of running time and accuracy.
Prevention of SQL Injection Attacks to Login Page of a Website Application using Prepared Statement Technique

The rise of digital transaction of different business and internet users for web service, mobile or desktop web application is increasing every day. Backend database is use to retrieved data for all web application and should be readily available for all the users around the world. SQL injection attack is one of the most serious security vulnerabilities in Web application system. Most of these vulnerabilities are caused by lack of input validations and SQL parameters. Furthermore, for each type of this vulnerability, the researchers explained how each type of the said attack works on the input validations and SQL parameters. Likewise, the prepared statement technique was utilized against SQL Injection. On the implementation of the said technique on the login page of a library website, possible scenarios were analysed, simulated, and implemented to prolong confidentiality, integrity, and availability of securing the data. By the use of prepared statement technique, unauthorized user can't perform SQL injection attacks such as tautology and comment-line in login page.
The design of a novel small waterplane area pillar buoy based on rolling analysis

As one of the main ways to obtain marine data, marine buoys have the advantages of low cost and little environmental restrictions. So far, a variety of buoys with different functions have been developed and buoy internal loading, power systems, mooring systems and hydrodynamic performances have become research hotspots, but few scholars pay close attention to buoy design. Therefore, this paper focuses on the design for pillar buoys to improve their stability in rolling. To obtain the geometric parameters, first the mathematical models is established for an ideal floating structure and then the rolling analysis of small waterplane area pillar buoy (SWAP) and novel small waterplane area pillar buoy (NSWAP) are performed. It shows that small waterplane area can help reduce the rolling angle. The prototype of NSWAP is built and its experiments in water flume are carried out at the wave height of 0.1 m, 0.2 m and 0.3 m. Taking the maximum rolling angle as an indicator, the analysis of experiments data proves that the experiment basically meets the design value and NWAP shows good rolling stability. This paper can be a guideline for further research in rolling analysis and floating structure design.
Adopting information system security services in online clothing marketing system using rup methodology under php

Abstract In this sophisticated era of globalization, people are more preferring to shop through online easily. Online consumers are constantly presented with situations in various types of web security vulnerabilities. Eventually web applications not able to meet the expected target then business will become negative. However this paper dealt about Online Clothing Marketing System and it will be a standalone web application which allows users to securely purchase clothes online. The primary features of this website allowing users to securely purchase product online by validating the customer input with multiple layers of validation. Also, there will be data transaction between client and server taking place through encryption algorithm, secure socket layer, password hashing and captcha verification. The process followed in captcha will be implemented to encrypt the whole data transaction process to ensure the security level of the system. Hence, through this web application, problems will be reduced with a great quality and can able to reduce the attackers from stealing the information.
DataMinerSchema

DataMinerSchema is a Mobile learning application intended for students who are taking up data mining and warehousing course. The purpose of this study is to inform and give knowledge to the user about data warehousing specifically three types of analytical modeling primer such as star schema, snowflake schema, and fact constellation schema. DataMinerSchema includes discussion, images, graphics, and video presentation. Augmented Reality (AR) technology was implemented in data mining and data warehousing (DMDW) book to display information about the analytical modeling primer. The developers evaluated IT professionals, mobile application developers, and IT students. DataMinerSchema was evaluated using ISO 9126 Standard measuring its quality in terms of functionality, reliability, usability, and portability. Based on Likert's scale, the users' evaluation result was strongly agreed.
Privacy preserving for global data using ensemble approach

Abstract Security becomes the most important aspect in current world and also in many applications. In data mining, privacy preserving is one of the global techniques to secure data from various attacks and to providing the data integrity for the data. It is very important that for every data there is need of data sensitivity is required to secure the information and to avoid the data spillage and also the information duplication. In this paper, the Ensemble approach which is integrated with an ensemble privacy preserving for data, maintaining the data sensitivity, providing the security for the data by using encryption technique and preventing the data de-duplication. Results show the performance of the proposed system.
Echo State Networks with Artificial Astrocytes and Hebbian Connections

For the last few decades, the neuroscientific research has highlighted the importance of astrocytes, a type of glial cells, in the information processing capabilities. By dynamic bidirectional communication with neurons, astrocytes regulate their excitability through a variety of mechanisms. Traditional artificial neural networks (ANNs) are connectionist models that describe how information passes throughout layer of neurons abstracting from low-level mechanisms. However, very little research has addressed artificial astrocytes and their incorporation into ANNs. In this paper, we present an echo state network (ESN) extended with astrocytes which influence the neurons by fixed or Hebbian connections. By systematic analysis we investigate their role on five classification tasks and show that they can outperform the standard ESN without astrocytes. Although the model with fixed astrocytic weights yields from none to little improvement, the model with Hebbian weights from astrocytes to neurons is significantly superior.
EVALUATIONS OF SECURITY MEASURES AND IMPACT OF GLOBALIZATION ON CHARACTERISTICS OF PARTICULAR PROPERTY CRIMES

In the epoch when terrorism and other serious crimes are cross-border problem, both the European Union and its Member States are responsible to their citizens for full ensuring of area of their internal security.Criminal offences against property or property crimes endanger the property of individuals and legal entities, as well as jeopardize their property interests.Consequently, the property rights, which are jeopardized by the crimes, are guaranteed by the State and are ensured by the Law Enforcement Authorities.Quantitatively, the largest group of criminal offences is composed by the crimes that can only be done on purpose of greediness, and most of them express themselves as the unlawful expropriation of the unfamiliar property from the lawful possession, in order to deal with that asset as their own.Such crimes are theft, robbery, extortion, fraud, and they together form more than half of all offences committed.For their research, the authors of the article have chosen the crimes with high public hazard degree from the offences against property.These offences are most of all affected by the globalization processes in the world, and they are: crimes related to a transport vehicle as a threat object; thefts from cultural objects where the objects of danger are churches or religious articles; modern frauds, when during their realization modern technical tools and new technologies are used.During the research, the co-author of the article -Prof.Dr. Jānis Ivančiks, has deceased suddenly.However, the years of joint work, discussions and scientific disputes, that accompanied the writing of this article and other written works allow co-authors to maintain the position and scientific views of this outstanding scientist in the field of forensic investigations and operational activities of special divisions.The authors show in this work the creative activity in the field of legal norms which affect personal property of individuals and their feeling, when the lawmakers eliminate the unnecessary rules, create new or improve existing ones in accordance with the country's political, economic situation and globalization processes.
Sums of Key Functions Generating Cryptosystems

The paper develops an algorithm based on derivative disproportion functions (DDF) for modeling a cryptosystem for transmitting and receiving devices. The transmitted symbols are encoded with the aid of sums of at least two of those functions weighted with random coefficients. Some important properties of the derivative disproportion functions are also discussed. Numerical experiments demonstrate that the algorithm is quite reliable and robust.