Reviewerly/exHarmony huggingface dataset:
Implementation of Autoencoder for Super Resolution of 3D MRI Imaging using Convolution Neural Network

In today&#x2019;s scenario, acquiring high dimensional image modalities is still extensive area of medicinal and clinical research in healthcare field. The need of increasing the quality of medical imaging for the examination by healthcare domain, medicines or clinical analysis. The elucidation can be the provided by super resolution image to demarcate as higher resolution image structure formed from three-dimensional medical image. The recreation of higher dimensional medical image structure to increase resolution can be done through the notion of convolutional neural networks. The proposed methodology will implement the TensorFlow framework for construction of super resolution MRI (Magnetic resonance image) using convolutional neural network (CNN) in order to minimize loss in image. The understanding of deep learning technique using autoencoder approach for higher resolution will provide the wide-ranging anatomical information of threedimensional image for precise clinical analysis for curative handling.
GAB - Gestures for Artworks Browsing

Hands are an important tool for our daily communication with our peers and the world. They allow us to convey information through particular gestures that are either the product of social conventions or personal expressions. Thanks to the sophistication of sensing and computer vision technologies over the past decade, automated hand recognition can now be more easily used and integrated in simple web applications. In a context of digital artworks collections, it means that gestures can now be envisioned as a new browsing tool that goes beyond simple movements to navigate through a 3D digital space. The paper presents Gestures for Artwork Browsing (GAB), a web application which proposes to use hand motions as a way to directly query pictorial hand gestures from the past. Based on materials from a digitized collection of Renaissance paintings, GAB enables users to record a sequence with the hand movement of their choice and outputs an animation reproducing that same sequence with painted hands. Fostering new research possibilities, the project is a novelty in terms of art database browsing and human-computer interaction, as it does not require traditional search tools such as text-based inputs based on metadata, and allows a direct communication with the content of the artworks.
Tightly Secure PKE Combiner in the Quantum Random Oracle Model

The development of increasingly sophisticated quantum computers poses a long-term threat to current cryptographic infrastructure. This has spurred research into both quantum-resistant algorithms and how to safely transition real-world implementations and protocols to quantum-resistant replacements. This transition is likely to be a gradual process due to both the complexity and cost associated with transitioning. One method to ease the transition is the use of classical–quantum hybrid schemes, which provide security against both classical and quantum adversaries. We present a new combiner for creating hybrid encryption schemes directly from traditional encryption schemes. Our construction is the only existing proposal in the literature with IND-CCA-security in the classical and quantum random oracle models, respectively.
A Blockchain Consensus Optimization-Based Algorithm for Food Traceability

At present, blockchain technology is more and more widely used in the field of food traceability, and good results have been achieved. However, many of the current blockchain technologies and algorithms are not developed for the specific situation of food traceability, resulting in resource waste and low computational efficiency. In view of these problems, this paper analyzes and summarizes the classic distributed consensus mechanism in blockchain technology, focusing on the PBFT (practical Byzantine fault tolerance) consensus mechanism and the existing problems related to the improvement scheme. In order to solve the problem of low efficiency of a consensus algorithm in a food traceability scenario, this paper proposes a blockchain consensus algorithm suitable for the food traceability scenario based on clustering and food credit. In addition, the differences between the improved algorithm and the classical Byzantine consensus algorithm in consensus algorithm time and communication times are analyzed through experiments and simulations. The consensus efficiency of the improved algorithm in this paper is significantly improved, which can greatly reduce the application difficulty of blockchain in food traceability.
A new hash function inspired by sponge construction using chaotic maps

This paper presents a new secure hash function structure inspired by the sponge construction for hash functions. It is designed to avoid the length of the extension attack, multi-collisions attack, and other vulnerabilities that have been found for many of the hash functions based on the older Merkle Damgård construction. The proposed hash function generates a variable-size hash code. It uses three nonlinear functions that generate chaotic maps: Zaslavsky 2D maps, logistic maps, and Henon maps. Other operations include XOR, rotation, multiplication, and addition. The proposed scheme has a high sensitivity to the initial conditions and message, strong and stable diffusion and confusion capabilities, as well as flexibility in terms of hash size and number of rounds.
Data science for service design: An introductory overview of methods and opportunities

To support effective and successful projects, Service Design practitioners rely on insights that mainly build on qualitative research methodology. The literature on data science promises to help transform how design research is done, adding sophisticated quantitative analyses, complementing existing methods with the power of machines. Due to this potential, data science receives widespread attention from both design practitioners and academics. However, the literature is fragmented and specialized, making it hard for designers to engage with data science. This paper addresses the opportunities and challenges for data science to support Service Design projects, evaluating existing technologies from designers’ perspective and providing an entry-level guide for service designers. These methods can help increase the quality of design research, making hidden information accessible and assisting creative processes. Together, these results are expected to inspire organizations to advance their data science resources for Service Design projects.
An empirical analysis of code smells using CRITIC-TOPSIS method

Code Smells indicate the defects in the programming which lead to depreciation in the quality of the software. These defects in the programming can be categorized as &#x2013; architectural, design and implementation code smells. We conducted this study to analyze the source code of the sixteen versions of the open-source software: Apache Tomcat. Eleven metrices of the software were used to apply CRITIC-TOPSIS method. In this study metrices of architectural smells were considered to rank several versions of the Open-Source Software.
Intra-category Aware Hierarchical Supervised Document Hashing

Document hashing is a powerful paradigm for document retrieval, which maps high-dimensional documents to compact hashing codes with preserving the similarity of original data. While fairly successful, the existing document hashing methods do not consider the relevance relationship among different documents from a category and the hierarchical relationship among categories. Intuitively, the intra-category relevance connects related concepts among different documents, which can supplement the omitted information for each document; meanwhile the hierarchical categories can help to identify whether mistakes occur in leaf categories or parent categories, which can be used to reduce the mistakes occurring in parent categories that are often more serious. Inspired by above intuitions, we propose a novel \textbf{I}ntra-category aware \textbf{H}ierarchical supervised \textbf{D}ocument \textbf{H}ashing, called IHDH. Specifically, IHDH is a binary autoencoder architecture equipped with two novel components: intra-category component and hierarchy component. The intra-category component exploits the difference among latent semantic representations of different documents from a category to supplement the omitted information for each document. The hierarchy component utilizes the hierarchical structure to transform the probabilities of leaf categories into the probabilities of parent categories by union operation, and then gives a further parent-level penalty to reduce the mistakes occurring in parent categories.
dSyncPS

The increasing demand of applying machine learning technologies in various domains has driven the involvement of complex machine learning models. To fulfill this demand, distributed machine learning has become the de facto standard computing paradigm for model training. Machine-Learning-as-a-Service (MLaaS) has also emerged as a solution provided by cloud service providers to address this need. With MLaaS, customers can submit their models and training datasets to the service providers, and leverage the existing cloud infrastructure for model training and inference. However, we find that, for end users who require complex and accurate machine learning models but only obtains moderate amount of data, existing solutions are insufficient. The main issue is the lack of support for dynamic deployment of distributed machine learning tasks. To address this issue, we propose a parameter server based framework, called dSyncPS, that allows worker nodes to participate in training dynamically. The key idea is that it separates parameter synchronization from aggregation function in the parameter server nodes, thus resulting in a delayed synchrony.
A polynomial reduction of forks into logic programs

In this research note we present additional results for an earlier published paper [1]. There, we studied the problem of projective strong equivalence (PSE) of logic programs, that is, checking whether two logic programs (or propositional formulas) have the same behaviour (under the stable model semantics) regardless of a common context and ignoring the effect of local auxiliary atoms. PSE is related to another problem called strongly persistent forgetting that consists in keeping a program's behaviour after removing its auxiliary atoms, something that is known to be not always possible in Answer Set Programming. In [1], we introduced a new connective ‘|’ called fork and proved that, in this extended language, it is always possible to forget auxiliary atoms, but at the price of obtaining a result containing forks. We also proved that forks can be translated back to logic programs introducing new hidden auxiliary atoms, but this translation was exponential in the worst case. In this note we provide a new polynomial translation of arbitrary forks into regular programs that allows us to prove that brave and cautious reasoning with forks has the same complexity as that of ordinary (disjunctive) logic programs and paves the way for an efficient implementation of forks. To this aim, we rely on a pair of new PSE invariance properties.
Naive Bayesian Prediction of Japanese Annotated Corpus for Textual Semantic Word Formation Classification

With the rapid development of Japanese information processing technology, problems such as polysemy and ambiguity at the text and dialogue level, as well as unregistered words, have become increasingly prominent because computers cannot fully “understand” the semantics of words. How to make the computer “understand” the semantics of words accurately requires the computer to “understand” the rules of converting and integrating words into words from the perspective of semantics. Traditional Japanese text classification mostly adopts the text representation method of vector space model, which has the problem of confusing classification effect. Therefore, this paper proposes the topic of constructing a semantic word formation pattern prediction model based on a large-scale annotated corpus. This paper proposes a solution that combines Japanese semantic word formation rules with pattern recognition algorithms. Aiming at this scheme, a variety of pattern recognition algorithms were compared and analyzed, and the naive Bayesian model was decided to predict semantic word formation patterns. This paper further improves the accuracy of computer prediction of Japanese semantic word formation patterns by adding part of speech. Before modeling, the parts of speech of words are automatically tagged and manually checked based on the original annotated corpus. In the research on predicting Japanese semantic word formation patterns, this paper builds a semantic word formation pattern prediction model based on Naive Bayes and conducts simulation experiments. We divide the eight types of semantic word formation patterns in the annotated corpus into two groups, and divide the obtained sample sets into training sets and test sets, so that the Naive Bayes model first learns semantic word formation rules based on the training sets of each group. Semantic word formation patterns are predicted on the test set for each group. The simulation results show that the prediction model of semantic word formation mode has a generally high degree of fit and prediction accuracy. The prediction model of semantic word formation pattern based on this theory can ensure that the computer can judge the semantic word formation pattern more accurately.
Approximation by a class of neural network operators on scattered data

Scattered data are a class of common data in the real world. Naturally, how to efficiently process scattered data is important. This paper uses a class of feedforward neural networks with four layers as tool to fit scattered data and establishes the estimates of the approximation error. In particular, an inverse theorem of the approximation is established. Concretely, we first extend an existed result on to the case of arbitrary bounded convex set in . Secondly, we introduce a modified feedforward neural network with four layers, which is a class of quasi‐interpolation operators and can keep the smoothness of the objective function. By establishing two Bernstein‐type inequalities for the operators, we establish both the direct and converse results of the approximation by the operator, which follows the equivalence characterization theorem of the approximation.
Categorization and Visualization of Issue Tickets to Support Understanding of Implemented Features in Software Development Projects

Background: In most software projects, new members must comprehend the features already implemented since they are usually assigned during the project period. They often read software documents (e.g., flowcharts and data models), but such documents tend not to be updated after they are created. Herein we focus on tickets issued because they are created as a project evolves and include the latest information of the implemented features. Aim: The purpose of this paper is to clarify the way of helping new members understand the implemented features of a project by using tickets. Methodology: We propose a novel method to categorize tickets by clustering and visualizing the characteristics of each category via heatmapping and principal component analysis (PCA). Our method estimates the number of categories and categorizes issue tickets (tickets) automatically. Moreover, it has two visualizations. Ticket lifetime visualization shows the time series change to review tickets quickly, while ticket feature visualization shows the relationships among ticket categories and keywords of ticket categories using heatmapping and PCA. Results: To evaluate the effectiveness of our method, we implemented a case study. Specifically, we applied our method to an industrial software development project and interviewed the project members and external experts. Furthermore, we conducted an experiment to clarify the effectiveness of our method compared with a non-tool-assist method by letting subjects comprehend the target project, which is the same as that of the case study. These studies confirm our method supports experts’ and subjects’ comprehension of the project and its features by examining the ticket category lifetimes and keywords. Implication: Newcomers during project onboarding can utilize tickets to comprehend implemented features effectively if the tickets are appropriately structured and visualized. Conclusions: The original contribution of this paper is the proposal of the project feature comprehension method by visualizing the multi-dimensional nature of requirements in an organized and structured way based on available tickets and the result of its application to the industrial project.
Application Research of Time Delay System Control in Mobile Sensor Networks Based on Deep Reinforcement Learning

One of the main problems of networked control systems is that signal transmission delay is inevitable due to long distance transmission. This will affect the performance of the system, such as stability range, adjustment time, and rise time, and in serious cases, the system cannot maintain a stable state. In this regard, a definite method is adopted to realize the compensation of network control system. To improve the control ability of mobile sensor network time delay system, the control model of mobile sensor network time delay system based on reinforcement learning is proposed, and the control objective function of mobile sensor network time delay system is constructed by using high-order approximate differential equation, combined with maximum likelihood estimation method for parameter estimation of mobile sensor network time delay, the convergence of reinforcement learning methods for mobile sensor network control and adaptive scheduling, and sensor network time delay system control model of multidimensional measure information registration in strengthening tracking learning optimization mode to realize the adaptive control of mobile sensor network time delay system. The simulation results show that the proposed method has good adaptability, high accuracy of estimation of delay parameters, and strong robustness of the control process.
English News Text Recommendation Method Based on Hypergraph Random Walk Label Expansion

With the rapid development of multimedia and Internet technology, English news text summary technology has received widespread attention as a way to quickly obtain English news text content. The existing method of English news text summarization based on graph model usually takes English news text as the vertices of the graph, and the relationship between the two vertices is represented by the edge. Although it has achieved good results, it cannot quickly sort out the English Complex relationships between news texts. In order to solve this problem, this paper uses the hypergraph model to model the relationship between English news text, and conducts in-depth research on the application of the hypergraph model in the field of English news text summarization. The high popularity of the Internet has brought about earth-shaking changes to the news industry, which makes the news on the Internet a great way for netizens to get news. However, the public cannot pick out satisfactory events from a large amount of news. In order to solve this problem, news event discovery technology that can help users quickly discover and understand hot news is produced. In addition, user personalized recommendation technology will rely on customer operation habits to provide customers with hot events of interest. The personalized news recommendation method adopted in this article has the advantages of integrating discovery and personalized news recommendation, and provides users with a better experience. Compared with the traditional hierarchical clustering algorithm, the algorithm proposed in this paper significantly improves the accuracy.
Reversible transformation of tetrahedral mesh models for data protection and information hiding

The rapid development of Internet and social media has driven the great need for data sharing and intelligent property protection. As the tetrahedral meshes are widely used in industry simulations where finite element analysis is used to find the approximate solution, how to protect the well-designed mesh models in the Web environment becomes an important issue. To address the problem, a reversible visual transformation scheme is proposed in this paper for information hiding and protection of tetrahedral mesh models. Firstly, a traversal key is employed to determine the order of vertices traversed in a tetrahedral mesh. Then position of each vertex is predicted from its traversed neighbors. For visual transformation and data embedding, vertex coordinates are modified by applying a prediction-error expansion algorithm. Besides, a precision key is adopted to determine the strength of data embedding. With the traversal key, a receiver can roughly reconstruct the tetrahedral mesh that has been transformed but cannot obtain the original one. With both of the traversal key and precision key, the embedded information can be correctly extracted while the original tetrahedral mesh can be exactly recovered.
An image retrieval approach based on feature extraction and self-supervised learning

Today, due to the development of technology and the advent of web 2.0 applications, different users prefer to do many of their personal tasks over the Internet. Due to the huge amount of information on the web, retrieving the appropriate information for each user has become a challenging task. Content-based image retrieval is one of the most important research fields in digital image processing domain, which searches the similar images to the target image by extracting visual content from the query image. In this regard, many studies have been conducted to increase the accuracy of image retrieval systems. However, due to the explosive growth of storage resources and the lack of a responsible system for image retrieval, it is still considered as one of the most attractive fields of research. In this paper, a method is proposed that extracts the appropriate features using a hybrid method, and then searches the images that are similar to the target image. In this way, self-supervised learning approach is utilized to provide the most similar images. Experimental results based on the Corel dataset show that the accuracy of the proposed method has increased compared to the other methods.
An Approach of Categorization and Summarization of News using Topic Modeling

The increasing demand/availability of online content has triggered intensive research in the automatic text summarization. Text summarization is the process of removing less useful text from the document to find the required news quickly. Likewise, text summarization, news summarization is also the process of picking the news content which is most important in perspective of online readers and gives the clear idea of the proposed news. Various traditional algorithms are available that can be used to summarize the text. In this paper, an automatic text summarizer using extractive summarization approach is proposed and implemented by considering topic modelling for categorization of news content and text rank algorithm for summarization. In order to evaluate accuracy, F-measure and recall of the produced summary, various machine learning algorithms are applied. The result produced 99.8&#x0025; of accuracy using topic modelling over K-means clustering.