Reviewerly/exHarmony huggingface dataset:
Image iterative method for handwritten Chinese character recognition

Abstract Although accuracy and calculation time of handwritten Chinese character recognition have been greatly improved, it has not yet reached the practical application standard. Recently literatures, iterative method is used to recognize face images and good results are obtained. Therefore, image recognition is improved based on this idea. In this paper, linear combination of trigonometric functions is used as auxiliary function and image to construct discrete dynamic system. By adding bevel and shift to construct font surface and so on, the system structure is improved to solve problem of system convergence caused by large area flatness of handwritten Chinese character image, and good recognition effect is achieved. Preliminary experiments(for example, extracting 20 Chinese characters written by 9 people in the data set) are carried out, and recognition rate can reach 100% when all of them are trained, and recognition rate can reach 76.7% when each Chinese character is trained to 3 pieces; in addition, experiments were conducted on handwritten Chinese character sets changing from different angles, and the recognition rate reached 80%, which was compared with other algorithms to verify the feasibility of handwritten Chinese character recognition algorithm based on chaotic iterative trajectory characteristics of images.
Edge adaptive hybrid regularization model for image deblurring

Abstract Parameter selection is crucial to regularization-based image restoration methods. Generally speaking, a spatially fixed parameter for the regularization term does not perform well for both edge and smooth areas. A larger parameter for the regularization term reduces noise better in smooth areas but blurs edge regions, while a small parameter sharpens edge but causes residual noise. In this paper, an automated spatially adaptive regularization model, which combines the harmonic and total variation (TV) terms, is proposed for the image reconstruction from noisy and blurred observation. The proposed model detects the edges and then spatially adjusts the parameters of Tikhonov and TV regularization terms for each pixel according to the edge information. Accordingly, the edge information matrix will also be dynamically updated during the iterations. Computationally, the newly-established model is convex, which can be solved by the semi-proximal alternating direction method of multipliers with a linear convergence rate. Numerical simulation results demonstrate that the proposed model effectively preserves the image edges and eliminates the noise and blur at the same time. In comparison to state-of-the-art algorithms, it outperforms other methods in terms of peak signal to noise ratio, structural similarity index and visual quality.
Fuzzy Logic Systems

In this chapter we present some basic concepts about fuzzy systems needed to better understand the ideas and the concepts of this book.
Emerging Technologies at the Events

Events are creating huge demand, have a vital role, and contribute to the tourism industry considerably. Whatever the objective or topic of the event, they are popular attraction to achieve diverse outputs. There are different types of events such as mega-events, special events, festivals, conventions, exhibitions, fairs, concerts, sporting events, and trade shows. They all have different purposes, different target audiences, and different planning processes. But the common thing that they need is to finish an event successfully and beneficially for both organizers and attendees. In this respect, they need to benefit from the advantages of technology. Technology use to plan, perform, and finalize an event is important. In this chapter, both academic studies and industrial articles have been reviews to provide information regarding the emerging technologies at the events with real-world examples. It has been seen that most music and sporting events used new technologies to attract and satisfy the attendees.
Performance of non-CSS LDGM-based quantum codes over the misidentified depolarizing channel

Quantum Low Density Generator Matrix (QLDGM) codes based on Calderbank-Steane-Shor (CSS) constructions have shown unprecedented error correction capabilities in the paradigm of quantum communication. Recently, a strategy based on non-CSS quantum codes derived from QLDGM CSS codes has been shown to surpass other Quantum Low Density Parity Check (QLDPC) schemes proposed in the literature over the depolarizing channel. Given the importance of quantum channel estimation and the impact it has on the performance of QLDPC codes, in this article, we study the behaviour of non-CSS QLDGM codes under the umbrella of channel mismatch. We begin by showing how a relatively accurate estimate of the quantum channel is pivotal for these codes to perform appropriately. We follow this by analyzing an off-line and an on-line quantum channel parameter estimation technique, as well as discussing how these methods affect the Quantum Error Correction (QEC) codes under consideration. Finally, we show how the on-line methodology yields similar performance to the perfect channel knowledge scenario despite its relative simplicity.
Creating Cover Photos (Thumbnail) for Movies and TV Series with Convolutional Neural Network

The latest version and the use of video streaming platforms is increasing day by day, adding new ones. Competition is also increasing in the development of platforms for expanding film and TV series. The purpose of replicating these platforms is to achieve more, better quality on some platforms, and to keep them on one platform even better. Before that, film and TV series platforms use artificial intelligence algorithms. In this study, the aim is to create more attractive cover photos for the user by finding suitable frames from a movie or TV series and converting these frames into cover/thumbnail images on the platform. It is based on eliminating frames that are useless according to closed eyes, blurred frames or non-face images. Also, deep learning used for labeling images with objects and face's emotion and identity.
Is <i>p</i>-value 0.05 enough? <i>A study on the statistical evaluation of classifiers</i>

Abstract Statistical significance analysis, based on hypothesis tests, is a common approach for comparing classifiers. However, many studies oversimplify this analysis by simply checking the condition p -value &lt; 0.05, ignoring important concepts such as the effect size and the statistical power of the test. This problem is so worrying that the American Statistical Association has taken a strong stand on the subject, noting that although the p -value is a useful statistical measure, it has been abusively used and misinterpreted. This work highlights problems caused by the misuse of hypothesis tests and shows how the effect size and the power of the test can provide important information for better decision-making. To investigate these issues, we perform empirical studies with different classifiers and 50 datasets, using the Student’s t-test and the Wilcoxon test to compare classifiers. The results show that an isolated p -value analysis can lead to wrong conclusions and that the evaluation of the effect size and the power of the test contributes to a more principled decision-making.
Recognition of Russian and Indian sign languages used by the deaf people

In our paper, we consider approaches towards the recognition of sign languages used by the deaf people in Russia and India. The structure of the recognition system for individual gestures is proposed based on the identification of its five components: configuration, orientation, localization, movement and non-manual markers. We overview the methods applied for the recognition of both individual gestures and continuous Indian and Russian sign languages. In particular we consider the problem of building corpuses of sign languages, as well as sets of training data (datasets). We note the similarity of certain individual gestures in Russian and Indian sign languages and specify the structure of the local dataset for static gestures of the Russian sign language. For the dataset, 927 video files with static one-handed gestures were collected and converted to JSON using the OpenPose library. After analyzing 21 points of the skeletal model of the right hand, the obtained reliability for the choice of points equal to 0.61, which was found insufficient. It is noted that the recognition of individual gestures and sign speech in general is complicated by the need for accurate tracking of various components of the gestures, which are performed quite quickly and are complicated by overlapping hands and faces. To solve this problem, we further propose an approach related to the development of a biosimilar neural network, which is to process visual information similarly to the human cerebral cortex: identification of lines, construction of edges, detection of movements, identification of geometric shapes, determination of the direction and speed of the objects movement. We are currently testing a biologically similar neural network proposed by A.V. Kugaevskikh on video files from the Russian sign language dataset.
Automatic Irony Detection for Romanian Online News

In this paper, we propose a new corpus and approach to evaluate and detect the irony in online news published in Romanian. Irony detection is based on subjective perceptions, thus the human experience plays an important role in this field. We propose a supervised machine learning system based on a Romanian ironic and non-ironic news corpus, manually annotated, and a Romanian dictionary of commonly used words. We present a comparison of the best results by applying several classification algorithms, such as Naïve Bayes, Logistic Regression, Linear SVC, Decision Trees, and Random Forest. We present aspects about irony detection and some language resources, considering a Romanian corpus based on 25,841 non-ironic news items and 14,064 ironic news items collected from online sources, which is used by the proposed irony detection system. Furthermore, we present the results of a sentiment analysis system based on a supervised machine learning approach applied to the 8,128 news items representing our evaluation corpus. The scope of the paper is to present the proposed system and a practical solution for automatic irony detection in Romanian real-world applications, which can provide useful information about online media perception. The best result of the compared systems, after having processed the corpus and applied the filtering method is over 91 %, achieved by the Naïve Bayes algorithm.
Ambient Assisted Living Environment based on Blockchain for Elderly Care

In the context of an ageing population and increasing care costs we present a prototype for an ambient assisted living environment targeted towards elderly care. The system focuses on the integration of IoT and Blockchain technologies and how this integration can enable new care scenarios and ensure security. We present the design and implementation of the system and the validation using a casestudy approach, analyzing how the system behaves in relevant scenarios for ambient assisted living.
Cooperative Clustering Missing Data Imputation

Missing data imputation is a critical part of data cleaning tasks and vital for learning from incomplete data. This paper proposes a novel cooperative clustering imputation (CCI) method to estimate missing values. The proposed method aims to find a better clustering model and donor for imputation, comparing with individual clustering algorithms. It makes use of agreements among different clustering algorithms to generate a set of sub-clusters, and, then, merges these sub-clusters based on the matrix of the performance measures of sub-clusters. The proposed method is evaluated using ten public datasets from UCI data repository and V2X communication data with induced missing samples, and compared with three standard clustering based imputation methods, k-means imputation, fuzzy c-means imputation, and partition around medoids imputation. Missing values are induced through each dataset by different missing mechanisms, missing rates, and missing distribution, and, thus, various incomplete datasets are generated. The performance of these methods are checked using normalized root mean square error (NRMSE). The attained experimental results indicate the effectiveness of the proposed missing values imputation method.
Discretisation and Product Distributions in Ring-LWE

Abstract A statistical framework applicable to Ring-LWE was outlined by Murphy and Player (IACR eprint 2019/452). Its applicability was demonstrated with an analysis of the decryption failure probability for degree-1 and degree-2 ciphertexts in the homomorphic encryption scheme of Lyubashevsky, Peikert and Regev (IACR eprint 2013/293). In this paper, we clarify and extend results presented by Murphy and Player. Firstly, we make precise the approximation of the discretisation of a Normal random variable as a Normal random variable, as used in the encryption process of Lyubashevsky, Peikert and Regev. Secondly, we show how to extend the analysis given by Murphy and Player to degree- k ciphertexts, by precisely characterising the distribution of the noise in these ciphertexts.
SLIT: An Energy-Efficient Reconfigurable Hardware Architecture for Deep Convolutional Neural Networks

Convolutional neural networks (CNNs) have dominated a range of applications, from advanced manufacturing to autonomous cars. For energy cost-efficiency, developing low-power hardware for CNNs is a research trend. Due to the large input size, the first few convolutional layers generally consume most latency and hardware resources on hardware design. To address these challenges, this paper proposes an innovative architecture named SLIT to extract feature maps and reconstruct the first few layers on CNNs. In this reconstruction approach, total multiply-accumulate operations are eliminated on the first layers. We evaluate new topology with MNIST, CIFAR, SVHN, and ImageNet datasets on image classification application. Latency and hardware resources of the inference step are evaluated on the chip ZC7Z020-1CLG484C FPGA with Lenet-5 and VGG schemes. On the Lenet-5 scheme, our architecture reduces 39% of latency and 70% of hardware resources with a 0.456 W power consumption compared to previous works. Even though the VGG models perform with a 10% reduction in hardware resources and latency, we hope our overall results will potentially give a new impetus for future studies to reach a higher optimization on hardware design. Notably, the SLIT architecture efficiently merges with most popular CNNs at a slightly sacrificing accuracy of a factor of 0.27% on MNIST, ranging from 0.5% to 1.5% on CIFAR, approximately 2.2% on ImageNet, and remaining the same on SVHN databases.
DIGITAL UNIVERSITY CAMPUS – CHANGE THE EDUCATION SYSTEM APPROACH TO MEET THE 21ST CENTURY NEEDS

Technologies appear because of the quality of the education and research process, and human intelligence completes a successful future. The latest events of 2020 have shown us the benefits of technologies created by scientists, in the innovative approach to university education - the transition from teaching and conventional research to the digital campus, through EdTech. Actual movements in socio-economic life around the world impose e-learning and e-presence. However, moving digital is more than online teaching. Present research analysis was conducted on two different types of institutions: public and private universities from Europe, with different teaching and learning system: American and European. Information was congregated through face-to-face and online interaction with representative people from universities. The present research paper aims to demonstrate how a digital campus, with all its aspects, can perform a crucial role in enriching the university campus’s structure and culture to ensure the quality assurance of teaching, research, and administrative management using actual innovative technologies.&#x0D; &#x0D; &lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0721a.php" alt="Hit counter" /&gt;&lt;/p&gt;
An Android-Based Vocabulary Model For Autism

> The characteristics of autistic children that tend to focus on their world encourage the writer to conduct this research. Their communication weaknesses have become a problem that faced by parents and teachers. This fact is reasonable because autistic children often create their own ways to communicate, such as using a word that has unique meaning for them or by making their own words to convey their needs or convey their intentions. The development of the world of technology and information is considered solve the learning problems for autism community, especially to improve the mastery of a foreign language (English language). An Android-based learning software/ application or learning model create for autistic children is one of examples that the author believe can help autistic students in their communication skills through a special language learning media or method that based on mobile learning / android app. A learning innovation that aims to help communication / improve foreign language mastery in this case vocabulary for autism children is one of the language learning innovations that want to be designed through this research as a guide or learning guide for them because autistic children prefer to do something based on their own way with a media or tool that have responsive value to attracts their attention.
Weighted Slope One with Threshold Filtering

Recommender SystemDas, Subrata isPatra, Bidyut Kumar usefulKumar, Jitendra tool for providing personalized suggestion to users. Collaborative filtering is one of the major and popular techniques for recommender system. Slope One algorithms are simple algorithm based on differential popularity also known as deviation. Slope One algorithms can be used for streaming scenario (dynamically updating parameters while new ratings are coming); however it has few drawbacks. By addressing the drawbacks of Slope One algorithms we proposed a variation of Slope One. Proposed approach uses only those ratings which have more weightage while computing new predicted ratings. We tested our proposed approach in two different popular dataset Netflix and MovieLens datasets. Our approach outperforms the existing Slope One algorithms.
An improved mapping method for automated consistency check between software architecture and source code

In daily software development, inconsistencies between architecture and code inevitably occur with the continuous contribution, even under model-driven development which can trace between design and code. Many methods have been proposed for consistency checking, but most require huge human efforts on establishing the mappings between architectural and code elements. Besides, the multi-layered architecture and code increases the difficulties in inconsistency detection, while existing algorithms do not handle this well. Thus, we propose an improved mapping method for automated consistency check between software architecture and Java implementation, with the premises that initial tracing between architecture and code are established. To be specific, during software evolution, our method can automatically re-establish the mappings between architecture and code using initial tracing information. Then, with detailed inconsistency check rules, we detect the inconsistencies heuristically. Experiments with two projects show our method's high effectiveness with more than 98% of recall and 96% of precision.
Multi-Format Document Verification System

The spread of fake documents claiming to be from official sources on social media has led to increasing levels of skepticism and uncertainty in modern society. Currently, there is no easy access method of verification for documents that can be adopted by the public. This paper proposes a method of a multi-format document verification scheme using digital signatures and blockchain. We employ digital signature algorithms to sign document contents extracted using Optical Character Recognition (OCR) methods and attach this signature to the document by converting it into a 2D barcode format. This code can then be used on a shared document to retrieve the document’s digital signature and OCR can be used to verify the signature. In addition to this, we also provide an alternative method of verification in the form of forgery detection techniques. These signed documents are stored in a decentralized storage solution backed by blockchain technology, increasing the solution's overall reliability and security.
Catchword: Blockchains and Enterprise Modeling.

In this catchword article we describe the current technological opportunities that are available through blockchain technologies and outline how the field of enterprise modeling can contribute to these developments as well as benefit itself from them. For this purpose, we discuss the technical foundations of blockchains and derive a framework for relating both sides. Finally, it is reported about recent approaches that already engage in these opportunities.
Towards an Machine Learning-Based Edge Computing Oriented Monitoring System for the Desert Border Surveillance Use Case

The design of border surveillance systems is critical for most countries in the world, having each border specific needs. This paper focuses on an Internet of Things oriented surveillance system to be deployed in the Sahara Desert, which is composed of many unattended fixed platforms, where the nodes in the edge have a Forward Looking InfraRed (FLIR) camera for field monitoring. To reduce communications and decentralise the processing, IR images should be fully computed on the edge by an Automated Target Recognition (ATR) algorithm, tracking and identifying targets of interest. As edge nodes are constrained in energy and computing capacity, this work proposes two ATR systems to be executed in low-power microprocessors. Both proposals are based on using Bag-of-Features for feature extraction and a supervised algorithm for classification, both differing in segmenting the InfraRed image in regions of interest or working directly with the whole image. Both proposals are successfully applied to infer about a dataset generated to this end, getting a trade-off between computing cost and detection capacity. As a result, the authors obtained a detection capacity of up to 97% and a frame rate of up to 5.71 and 59.17, running locally on the edge device and the workstation, respectively.
SISTEM PAKAR DIAGNOSA KERUSAKAN SEPEDA MOTOR YAMAHA MATIC MENGGUNAKAN METODE FORWARD CHAINING

The expert system is a computer-based system that uses knowledge, facts, and reasoning techniques to solve problems that usually only experts in their field can solve. The development of an expert system is aimed to help Yamaha motorbike users to solve damage problems to a Yamaha Automatic motorcycle. Damage to a Yamaha Automatic motorcycle is a problem that requires experts (technicians) to rely on their knowledge to solve the problem. This system is built using the forward chaining method which is used to test the factors that have been entered by using the rules stored in the system until a conclusion is obtained. By using the forward chaining method, it is hoped that it can help the system in diagnosing the damage to the Yamaha Automatic motorcycle engine. Therefore, by applying an expert system for motorcycle engine damage, the problem of Yamaha Automatic motorcycle engine damage can be resolved.