Reviewerly/exHarmony huggingface dataset:
CHARACTER RECOGNITION FROM IMAGE USING TENSORFLOW AND CONVOLUTIONAL NEURAL NETWORKS

Machine Learning (ML) can be defined as the pc algorithms through which a particular system can learn on it's own with help of experiences.It can be consider as the subset of Artificial Intelligence (AI).Machines acquiring knowledge of various algorithms to build a mathematical model which is based upon given (sample) data, considered as "education statistics", in order to perform decisions or predictions.Machine learning algorithms are widely utilized in various programs encompass e-mail spam and malware filtering, virtual assistant, fraud detection, online customer support [9].Many practices have been made to classify information from handwritten statistics, however the laptop alone hasn't been capable of classify statistics efficiently.Hence, the requirement of handwriting recognition system has come up [13,14].In this undertaking we purpose at constructing a handwritten character popularity system using convolutional Neural Network.Which will be capable of understand the handwritten characters and provide the output using the training dataset i.e(IAM) dataset.
Exploring the Use of Immersive Technology in Education to Bring Abstract Theoretical Concepts to Life

Over the years educators have adopted a variety of technologies in a bid to improve student engagement, interest and understanding of abstract topics taught in the classroom. There has been an increasing interest in immersive technology such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). The ability of VR to bring ideas to life in three dimensional spaces in a way that is easy for students to understand the subject matter makes it one of the important tools available today for education. A key feature of VR is the ability to provide multi-sensory visuals and virtual interaction to students wearing a Head Mounted Display thus providing students better learning experience and connection to the subject matter. Virtual Reality has been used for training purposes in the health sector, military, workplace training, gamification and exploration of sites and countless others. With the potential benefits of virtual technology in visualizing abstract concepts in a realistic virtual world, this paper presents a plan to study the use of situated cognition theory as a learning framework to develop an immersive VR application that would be used to train and prepare students studying Telecommunications Engineering for the workplace. This paper presents a review of literature in the area of Virtual Reality in education, offers insight into the motivation behind this research and the planned methodology in carrying out the research.
Cultural viz

Cultural Analytics (C.A.) is an approach for analyzing media and digital culture using data methods and visual computing techniques. This article explores the aesthetic value of C.A. by approaching cultural visualizations as digital artworks. The authors present a variety of techniques developed since 2007 by members of the C.A. lab for creating visualizations of media artifacts and collections of images. Through a series of projects conducted by them, the authors discuss the artistic meaning of media visualizations and their experience in art exhibitions, workshops and seminars.
Smart Guide System for Blind People by Means of Stereoscopic Vision

The main goal of this work is to carry out a system that complements the tasks of detecting the white cane, locating objects or obstacles that are hollow, exceeding their detection and aerial range, through a stereoscopy system implemented in an embedded breadboard Raspberry PI. Thus, the present work consists of obtaining three-dimensional information through two webcams, for the representation of this a disparity map filtered by Weighted Least Squares is calculated and an algorithm based on the averages of three different regions is proposed. In this way, the system makes a decision which is transmitted to the System User through connected headphones. The system alerts to avoid collisions, with voice commands indicating to the user the best route to avoid obstacles. This system is implemented in a base-3D printer which has a compartment for a battery, which will feed the RaspBerry PI.
Smart, and also Reliable and Gas-Efficient, Contracts

A smart contract is a software program that runs on top of a blockchain. It contains a collection of public functions that can be invoked within the transactions launched over the contract by parties interacting with it. Being computer programs, well-studied formal verification techniques can be applied to them. Indeed, smart contracts are a very interesting application domain for validation, verification and optimization techniques since (1) they are relatively small in size, hence the application of these techniques scales better than when applied to larger industrial code, (2) they are valuable (in the corresponding blockchain cryptocurrency), hence software bugs or inefficiencies can cause economical losses and there is much interest in formally proving their safety and security, and (3) they require proving new specific properties to ensure their reliability and efficiency.
The Learning-based Automatic Segmentation Algorithm of Brain MR Images Based on 7T

Background: The learning-based algorithms provide an ability to automatically estimate and refine GM, WM and CSF. The ground truth manually achieved from the 3T MR image may not be accurate and reliable with poor image intensity contrast. It will seriously influence the classification performance because the supervised learning-based algorithms extremely rely on the ground truth. Recently, the 7T MR images brings about the excellent image intensity contrast, while Structured Random Forest (SRF) performs the pixel-level classification and achieves structural and contextual information in images. Materials and Methods: In this paper, a automatic segmentation algorithm is proposed based on ground truth achieved by the corresponding 7T subjects for segmenting the 3T&amp;1.5T brain tissues using SRF classifiers. Through taking advantage of the 7T brain MR images, we can achieve the highly accuracy and reliable ground truth and then implement the training of SRF classifiers. Our proposed algorithm effectively integrates the T1-weighed images along with the probability maps to train the SRF classifiers for brain tissue segmentation. Results: Specifically, for the mean Dice ratio of all 10 subjects, the proposed method achieved 95.14%±0.9%, 90.17%±1.83%, and 81.96%±4.32% for WM, GM, and CSF. With the experiment results, the proposed algorithm can achieve better performances than other automatic segmentation methods. Further experiments are performed on the 200 3T&amp;1.5T brain MR images of ADNI dataset and our proposed method shows promised performances. Conclusions: The authors have developed and validated a novel fully automated method for 3T brain MR image segmentation.
Gender Recognition in the Wild with Small Sample Size - A Dictionary Learning Approach

In this work we address the problem of gender recognition from facial images acquired in the wild. This problem is particularly difficult due to the presence of variations in pose, ethnicity, age and image quality. Moreover, we consider the special case in which only a small sample size is available for the training phase. We rely on a feature representation obtained from the well known VGG-Face Deep Convolutional Neural Network (DCNN) and exploit the effectiveness of a sparse-driven sub-dictionary learning strategy which has proven to be able to represent both local and global characteristics of the train and probe faces. Results on the publicly available LFW dataset are provided in order to demonstrate the effectiveness of the proposed method.
THE EFFECT OF USING SOCIAL MEDIA TOWARDS STUDENTS’ READING COMPREHENSION

This study aims to determine the effect of the activity of using social media on the ability of college students to reading comprehension. This type of research is quantitative with a correlational method. The sample in this study was taken with simple random sampling. PBSI FKIP UHAMKA active students approximately 600 students who if taken a sample of 25% at random, would choose 150 students. Data collection techniques using questionnaires and reading tests, while data analysis using descriptive statistics and simple linear regression equations. The results of this study indicate that the intensity of the use of social media in the percentage of students is 45% moderate, 25% low, 25% high, and 7% very high, obtained the regression equation . These findings indicate that the more activity on social media, the lower the level of reading comprehension although not significantly.
Web-based methodology for extracting technology words in Chinese process patents

Purpose The purpose of constructing the technology/function matrix is to analyze the patents in the target domain. The extraction of technology words is an important part of the construction of technology/function matrix. This algorithm is used to solve the problem of low efficiency of traditional Chinese process patents technology words extraction. Design/methodology/approach The authors propose a Chinese process patents technology words extraction method based on the improved term frequency–inverse document frequency (TF-IDF) algorithm to help technicians obtain the technology words in the target domain. According to the characteristics of Chinese process patents technology words, the TF value of candidate technology words is divided into four parts, and the corpus of IDF value calculation of candidate technology words is selected. Findings Through the test of Chinese process patents in the domain of path planning, this study shows that the method is feasible and practical. It can help users quickly and accurately obtain the technology words of Chinese process patents in the target domain. Practical implications With the increasing number of patents on the network-based patent information platform, patent analysis of massive Chinese process patents has become a research focus. The method proposed in this paper can facilitate users to extract technology words from massive Chinese process patents for patent analysis. Originality/value This paper aims to improve the efficiency of Chinese process patents technology words extraction. The authors hope that the proposed method can reduce the labor and time cost of Chinese process patents technology words extraction.
Local binary patterns based on α-cutting approach

Local binary patterns (LBP) are well documented in the literature as descriptors of local image texture, and their histograms have been shown to be well-performing texture features. A method for texture description that is based on the α-cutting approach is presented. The presented approach combines basic definitions from the fuzzy set theory with the main concept of LBP descriptors, which resulted in powerful texture features. The general method is introduced and defined and its binary, ternary, and quinary versions evaluated in tests produced excellent results in texture classification. The performance of our method is presented by an extensive evaluation on four datasets—KTH-TIPS2b, UIUC, Virus, and Brodatz. The introduced descriptors are compared with some of the classical approaches—LBP, improved LBP, local ternary pattern, including one very promising LBP variant—median robust extended LBP (MRELBP), as well as with three non-LBP methods, based on deep convolutional neural networks approaches—ScatNet, FV-AlexNet, and fisher vector based very deep VGG. Our method effectively deals with many classification challenges and exceeds most of the other approaches. It outperforms the classical approaches on all datasets, even in its simplest binary version. It outperforms the MRELBP descriptor on the UIUC, KTH-TIPS2b, and Brodatz datasets and reaches a better classification performance than two out of the three deep learning approaches on the KTH-TIPS2b dataset.
GasMet: Profiling Gas Leaks in the Deployment of Solidity Smart Contracts.

Nowadays, blockchain technologies are increasingly adopted for different purposes and in different application domains. Accordingly, more and more applications are developed for running on a distributed ledger technology (i.e., \textit{dApps}). The business logic of a dApp (or part of it) is usually implemented within one (or more) smart contract(s) developed through Solidity, an object-oriented programming language for writing smart contracts on different blockchain platforms, including the popular Ethereum. In Ethereum, once compiled, the smart contracts run on the machines of miners who can earn Ethers (a cryptographic currency like Bitcoin) by contributing their computing resources and the \textit{gas} (in Ether) corresponds to the execution fee compensating such computing resources. However, the deployment and execution costs of a smart contract strictly depend on the choices done by developers while implementing it. Unappropriated design choices -- e.g., in the data structures and the specific instructions used -- could lead to higher gas consumption than necessary. In this paper, we systematically identify a set of 20 Solidity code smells that could affect the deployment and transaction costs of a smart contract, i.e., \textit{cost smells}. On top of these smells, we propose GasMet, a suite of metrics for statically evaluating the code quality of a smart contract, from the gas consumption perspective. In an experiment involving 2,186 real-world smart contracts, we demonstrate that the proposed metrics (i) have direct associations with deployment costs, and (ii) they could be used to properly identify the level of gas consumption of a smart contract without the need for deploying it.
Image Registration Algorithm Based on Manifold Regularization with Thin-Plate Spline Model

In this paper, we propose a new method based on manifold regularization technology with a thin-plate spline for image registration, which is used to remove the outlier by approximating the transformation function. Under a Bayesian framework, we use a latent variable to indicate whether a correspondence is an inlier that should satisfy a mapping function; and then, we formulate the problem as optimizing a posterior problem related to the mapping function. The initial correspondences discard some mismatching points because of the similarity constraint, which may contain important information; however, the manifold regularization (MR) term utilizes all of the feature points and preserve this information as a constraint. In addition, we use the thin-plate spline (TPS) model, which is composed of a global affine transformation and local bending function, to construct the transformation function. Finally, we obtain the solution using the expectation-maximization algorithm. Extensive experiments show that our method outperforms with other comparable state-of-art methods.
Context-Driven Discoverability of Research Data

Research data sharing has been proved to be key for accelerating scientific progress and fostering interdisciplinary research; hence, the ability to search, discover and reuse data items is nowadays vital in doing science. However, research data discovery is yet an open challenge. In many cases, descriptive metadata exhibit poor quality, and the ability to automatically enrich metadata with semantic information is limited by the data files format, which is typically not textual and hard to mine. More generally, however, researchers would like to find data used across different research experiments or even disciplines. Such needs are not met by traditional metadata description schemata, which are designed to freeze research data features at deposition time. In this paper, we propose a methodology that enables “context-driven discovery” for research data thanks to their proven usage across research activities that might differ from the original one, potentially across diverse disciplines. The methodology exploits the collection of publication–dataset and dataset–dataset links provided by OpenAIRE Scholexplorer data citation index so to propagate articles metadata into related research datasets by leveraging semantic relatedness. Such “context propagation” process enables the construction of “context-enriched” metadata of datasets, which enables “context-driven” discoverability of research data. To this end, we provide a real-case evaluation of this technique applied to Scholexplorer. Due to the broad coverage of Scholexplorer, the evaluation documents the effectiveness of this technique at improving data discovery on a variety of research data repositories and databases.
Principle of Cooperation in Human Conversation

In communication, each party involved, namely between the speaker and the speech partner, will always try to convey his speech effectively and efficiently. Grice mentioned that a reasonable discourse could occur if the speaker and hearer are compliant with the principle of cooperative communication. This study aims to explain what it is called maxims in human conversation. The data in this qualitative study were taken from 5 prominent articles which explain and review the theory. This research uses a descriptive qualitative form to analyze and describe the words or sentences used in defining the theory proposed by Grice. The results of this study, in brief, mentioned the principle of cooperationnin conversation consists of four conversational maxims, namely: Maxim of quantity, Maxim of quality, Maxim of Relevance and Maxim of implementation.
A New Common Points Detection Method for Classification of 2D and 3D Texts in Video/Scene Images

Achieving high quality recognition result for video and natural scene images that contain both standard 2D text as well as decorative 3D text is challenging. Methods developed for 2D text may fail for 3D text due to the presence of pixels representing shadow and depth in the 3D text. This work aims at classification of 2D and 3D texts in video or scene images such that one can choose an appropriate method in the classified text for achieving better results. The proposed method explores Generalized Gradient Vector Flow (GGVF) for finding dominant points for input 2D and 3D text line images based on opposite direction symmetry. For each dominant point, our approach finds distance between neighbor points and plots a histogram to choose points which contribute to the highest peak as candidates. Distance symmetry between a candidate point and its neighbor points is checked and if a candidate point is visited twice, a common point is created. Statistical features such as the mean and standard deviation of the common points and candidate points are extracted to feed to Neural Network (NN) for classification. Experimental results on dataset of 2D-3D text line images and the dataset collected from standard natural scene images show that the proposed method outperforms exiting methods. Furthermore, recognition experiments before and after classification show recognition performance improves significantly as a result of applying our method.
A Survey on Nonlinear Second-Order Diffusion-Based Techniques for Additive Denoising

An overview of additive noise reduction algorithms using nonlinear second-order partial differential equations (PDEs) is provided here. First, the state-of-the-art anisotropic diffusion models that are used for image restoration are described here. The second-order PDE-based denoising approaches using variational schemes are addressed next. Our most important contributions in these image processing fields are also discussed in this work and method comparison examples are provided, too.
An Improved Convolutional Block Attention Module for Chinese Character Recognition

Recognizing Chinese characters in natural images is a very challenging task, because they usually appear with artistic fonts, different styles, various lighting and occlusion conditions. This paper proposes a novel method named ICBAM (Improved Convolutional Block Attention Module) for Chinese character recognition in the wild. We present the concept of attention disturbance and combine it with CBAM (Convolutional Block Attention Module), which improve the generalization performance of the network and effectively avoid over-fitting. ICBAM is easy to train and deploy due to the ingenious design. Besides, it is worth mentioning that this module does not have any trainable parameters. Experiments conducted on the ICDAR 2019 ReCTS competition dataset demonstrate that our approach significantly outperforms the state-of-the-art techniques. In addition, we also verify the generalization performance of our method on the CTW dataset.
Evaluating the Quality of Recommendation System by Using Serendipity Measure

Recently, several approaches to recommendation systems have been studied. A recommender system aims to provide personalized recommendations to users for specific items. Most of these systems always provide the most relevant items of users or items. Traditionally, the evaluation of recommender system quality has focused on the various predictive accuracy metrics of these. However, recommender system must be not only accurate but also useful to users. User satisfaction with recommender systems as an evaluation criterion of recommender system is related not only to how accurately the system recommends but also to how much it supports the user’s decision making. In particular, highly serendipitous recommendation would help a user to find a surprising and interesting item. Serendipity in this study is defined as a measure of the extent to which the recommended items are both attractive and surprising to the users. Therefore, this paper proposes an application of serendipity measure to recommender systems to improve the performance of recommender systems. In this study we define relevant or attractive unexpectedness as serendipity measure for assessing recommendation systems. That is, serendipity measure is evaluated as the measure indicating how the recommender system can find unexpected and useful items for users. Our experimental results show that highly serendipitous recommendation has better performance than the other recommendations in terms of recommendation accuracy.
Segmentation Based on Particle Swarm Optimization

This paper aims to apply the particle swarm optimization (PSO) in the image segmentation. For this purpose, the popular segmentation methods were reviewed in details, the specific steps of the PSO was introduced, and the PSO-based image segmentation was examined in an experiment. The proposed method was contrasted with Ostu’s algorithm on the standard test image Lena. The results show that PSO-based image segmentation can create segments with great details. The findings of this study sheds new light on the application of the PSO and the image segmentation.
A flexible authentication scheme for smart home networks using app interactions and machine learning

Smartphones have now become ubiquitous for accessing and controlling home appliances in smart homes, a popular application of the Internet of Things. User authentication on smartphones is mostly achieved at initial access. However, without applying a continuous authentication process, the network will be susceptible to unauthorized users. This issue emphasizes the importance of offering a continuous authentication scheme to identify the current user of the device. This can be achieved by extracting information during smartphone usage, including application access patterns. In this paper, we present a flexible machine learning user authentication scheme for smart home networks based on smartphone usage. Considering that users may run their smartphone applications differently during different day time intervals as well as different days of the week, new features are extracted by considering this information. The scheme is evaluated on a real-world dataset for continuous user authentication. The results show that the presented scheme authenticates users with high accuracy.