Reviewerly/exHarmony huggingface dataset:
U-Shaped Densely Connected Convolutions for Left Ventricle Segmentation from CMR Images

Segmentation of cardiac magnetic resonance images (cMRI) remains a challenging task in the field of scientific research due to its significance in the medical assessment of cardiovascular diseases. Ensuring accurate segmentation of the heart structures, mainly the left ventricle cavity, serves to extract important information and has a major impact on the quantitative analysis of the heart function which helps to conduct the proper diagnosis of doctors. The present paper introduces a simple and efficient U-shaped convolutional neural network aiming to accurately segment the LV from cMR images. We applied our architecture for Left Ventricle (LV) segmentation on cardiac MR images (cMRI), from the Automated Cardiac Diagnosis Challenge (ACDC). Obtained results are promising. This simple model based on CNN has significantly fewer parameters rendering it less demanding in terms of computation. Nevertheless, it has provided accurate segmentation. The tested method achieved LV Dice scores of 0.958 at end-systolic time (ES) and 0.979 at end-diastolic time (ED), which yields a mean Dice score of 0.968 on the ACDC dataset.
Automated Classification of Breast Cancer Lesions for Digitised Mammograms via Computer-Aided Diagnosis System

Women with breast cancer have a high risk of death. Digitised mammograms can be used to detect the early stage of breast cancer. However, digitised mammograms suffer low contrast appearances that may lead to misdiagnosis. This paper proposes a Computer-Aided Diagnosis (CAD) system of automated classification of breast cancer lesions using a modified image processing technique of Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) incorporated with Multilevel Otsu Thresholding on digitised mammograms. Four main blocks were used in this CAD system, namely; (i) Pre-processing and Enhancement block; (ii) Segmentation block; (iii) Region of Interests (ROIs) Extraction block; and (iv) Classification block. The CAD system was tested on 30 digitised mammograms retrieved from the Mini-Mammographic Image Analysis Society (MIAS) database with various degrees of severity and background tissues. The proposed CAD system showed a high accuracy of 96.67% for the detection of breast cancer lesions.
Frequency based edge-texture feature using Otsu’s based enhanced local ternary pattern technique for digital image splicing detection

Sharing information through images is a trend nowadays. Advancements in the technology and user-friendly image editing tool make easy to edit the image and spread fake news through different social networking platforms. Forged image has been generated through an advanced image editing tool, so it is very challenging for image forensics to detect the micro discrepancy which distorted the micro pattern. This paper proposes an image forensic detection technique, which implies multi-level discrete wavelet transform to implement digital image filtering. Canny edge detection technique is implemented to detect the edge of the image to implement Otsu’s based enhanced local ternary pattern (OELTP), which can detect forgery-related artifact. DWT is implemented over Cb and Cr components of the image and using edge texture to improve the Otsu global threshold, which is used to extract features using ELTP technique. Support vector machine (SVM) is used for classification to find the image is forged or not. The performance of the work evaluated on three different open available data sets CASIA v1, CASIA v2, and Columbia. Our proposed work gives better results with some of the previous states of the work in terms of detection accuracy.
Generic Plaintext Equality and Inequality Proofs

Given two ciphertexts generated with a public-key encryption scheme, the problem of plaintext equality consists in determining whether the ciphertexts hold the same value. Similarly, the problem of plaintext inequality consists in deciding whether they hold a different value. Previous work has focused on building new schemes or extending existing ones to include support for plaintext equality/inequality. We propose generic and simple zero-knowledge proofs for both problems, which can be instantiated with various schemes. First, we consider the context where a prover with access to the secret key wants to convince a verifier, who has access to the ciphertexts, on the equality/inequality without revealing information about the plaintexts. We also consider the case where the prover knows the encryption’s randomness instead of the secret key. For plaintext equality, we also propose sigma protocols that lead to non-interactive zero-knowledge proofs. To prove our protocols’ security, we formalize notions related to malleability in the context of public-key encryption and provide definitions of their own interest.
What We Value: Trends in Value Appeals of Interstate Oratory Contest Final Round Speeches

Value appeals are crucial to persuasion. However, we wonder if forensic educators prioritize certain values. This content analysis examines the value appeals in the introductions of the three highest-placing persuasive speeches (N = 56) from the 1996-2016 Interstate Oratory Contests. Our two-decade comparison reveals that the diversity of values to which the finalists appealed decreased in the last decade. Drawing upon the Interstate Oratorical Association's historical context, Rokeach's (1973) value appeals, and Social Judgment Theory, we explain this trend and explore practical implications regarding exclusivity and judge bias, as well as methodological implications for future content analyses of value appeals.
Collaborative Tutoring Architecture : A Generic Case Based Reasoning Multi-Agent

This article is a response to the problem of learner desertion encountered by e-learning platforms. We propose to equip the Learning Management System with a generic intelligent tutoring module. This module is based on the combination of Case Based Reasoning (CBR) and Multi-Agent Systems (MAS). The main advantage of this generic module is to offer the learner individualized follow-up and to prevent dropping out of school. Personalized monitoring is carried out by combining machine tutoring and human tutoring. We describe how the combination of CBR and MAS allows the adaptation of the learning process according to the profile of the student.
A NATURAL LANGUAGE PROCESSING APPROACH TO DETERMINE THE POLARITY AND SUBJECTIVITY OF IPHONE 12 TWITTER FEEDS USING TEXTBLOB

Sentiment analysis and opinion mining is a branch of computer science that has gained considerable growth over the last decade. This branch of computer science deals with determining the emotions, opinions, feelings amongst others of a person on a particular topic. Social media has become an outlet for people to voice out their thoughts and opinions publicly about various topics of discussion making it a great domain to apply sentiment analysis and opinion mining. Sentiment analysis and opinion mining employ Natural Language Processing (NLP) in order to fairly obtain the mood of a person’s opinion about any specific topic or product in the case of an ecommerce domain. It is a process involving automatic feature extractions by mode of notions of a person about service and it functions on a series of different expressions for a given topic based on some predefined features stored in a database of facts. In an ecommerce system, the process of analyzing the opinions of customers about products is vital for business growth and customer satisfaction. This proposed research will attempt to implement a model for sentiment analysis and opinion mining on Twitter feeds. In this paper, we address the issues of combining sentiment classification and the domain constraint analysis techniques for extracting opinions of the public from social media. The dataset that was employed in the paper was gotten from Twitter through the tweepy API. The TextBlob library was used for the analysis of the tweets to determine their sentiments. The result shows that more tweets were having a positive subjectivity and polarity on the subject matter.
Adversarial Example Generation Based on Particle Swarm Optimization

As machine learning is widely applied to various domains, its security vulnerability is also highlighted. A PSO (Particle Swarm Optimization) based adversarial example generation algorithm is proposed to reveal the potential security risks of Support Vector Machine (SVM). The adversarial examples, generated by slightly crafting the legitimate samples, can mislead SVM classifier to give wrong classification results. Using the linear separable property of SVM in high-dimensional feature space, PSO is used to find the salient features, and then the average method is used to map back to the original input space to construct the adversarial example. This method makes full use of the easily finding salient features of linear models in the feature space, and the interpretable advantages of the original input space. Experimental results show that the proposed method can fool SVM classifier by using the adversarial example generated by less than 7 % small perturbation, thus proving that SVM has obvious security vulnerability.
Concept Drift and Model Decay Detection using Machine Learning Algorithm

Machine learning mainly focuses on building applications that can automatically learn periodically and improve their working experience without being explicitly programmed again. Data extracted from dynamic distributions for real-world applications lead to a phenomenon called concept drift. This hitch could encounter when the dependency and relationship of the input data features and class variable varies gradually. So, the model must be capable of processing this data and swiftly adapt to these changes. In this context, we opt for an unsupervised approach, which uses a sliding window mechanism to detect drift in the concept by analyzing the variations in the distribution of the new samples. It uses a classifier that does not have any inbuilt drift detection and adoption mechanism. Experimentation is done on various drift detectors using several data sets. The performance of our method comparatively is higher than D3, Adwin, Page-Hinkley, and OCDD methods.
Development of MPPT Algorithm and Wind Rose Model for Wind Farm in Maharashtra

This work focuses on enhancements of the maximum power point tracking for wind energy village. This work also targeted on numerous variables like air density, wind speed and impression of other geographical regions specific ecological elements. The Incremental Conductance along with Fractional open Circuit Voltage algorithms are usually is much less fruitful and still has problem in determining perfect step-size. To eliminate such drawbacks we recommended Hybrid execution of MPPT algorithms for maximum power point tracking to achieve the best possible power operation. The algorithms are presented by using Matlab simulation. This paper also delivers results for wind farm for various climate conditions in Maharashtra region with wind rose analysis. The outcomes attained are ideal for equivalent weather conditions based on air density variations and Hybrid Algorithm execution can be applied to draw out maximum power point. Results shows that recommended method is more efficient maximum power point tracking method with less execution time for any renewable energy resources.
Design and Implementation of Real-Time Flood Early Warning System (FEWS) Based on IoT Blynk Application

Disaster occurrence in Indonesia needs attention and role from all parties including the community to reduce the risks. Disaster mitigation is one of the ways to reduce the disaster risk through awareness, capacity building, and the development of physical facilities, for example by applying disaster mitigation technology (early warning system, EWS). EWS is one of the effective methods to minimize losses due to disasters by providing warning based on certain parameters for disasters which usually occur such as floods. This research promotes a real-time IoT-based EWS flood warning system (Flood Early Warning System, FEWS) using Arduino and Blynk as well as Global System for Mobile Communication network (GSM) as the communication medium. The steps for implementing FEWS system in real locations are also discussed in this paper. Parameters such as water level, temperature, and humidity as well as rain conditions that are read by the EWS sensor can be accessed in real-time by using android based Blynk application that has been created. The result of the measurement of average temperature, humidity, and water level were 28.6 oC, 63.7 %, and 54.5 cm. Based on this analysis, the parameters indicated that the water level is in normal condition and there are no signs indicating that there will be flooding in the 30 days observation. Based on the data collected by the sensor, FEWS can report four conditions, namely Normal, Waspada Banjir (Advisory), Siaga Banjir (Watch), and Awas Banjir (Warning) that will be sent immediately to the Blynk FEWS application user that has been created.
Illegal drug trafficking on the internet

The relevance of the study is due to the change of activity of organized criminal groups involved in the circulation of psychoactive substances towards the organization of the mentioned activity through the Internet. The purpose of this work is to investigate the current problems of combating drug trafficking by using the worldwide web through contactless sale of narcotic drugs and psychotropic substances. It is necessary to strengthen the analytical component of law enforcement activities in the detection and analysis of content on the Internet through the introduction of information technology in law enforcement activities, the dissemination of knowledge about OSINT. The materials of the article are of practical value for the higher education graduates of police specialties, police officers of the criminal block and specialists in the field of operational search activities.
Design and Application of English Grammar Intelligent Question Bank System

The English grammar question bank can reserve a large number of special knowledge points and special categories of questions, which is helpful for English learners. Therefore, we design and verify the application effect of an intelligent question bank system. We construct an English grammar knowledge map, which is used to design the system architecture, including basic layer, data layer, service layer, business layer, and user layer. On the basis of the system structure, the index system of the item bank is established, and the functional modules of the system are designed comprehensively. Finally, we design a system database based on MySQL and give the table of knowledge points of English grammar. In order to verify the performance of the designed English grammar intelligent test bank system, a verification experiment is carried out. The experiment selects the accuracy rate of English grammatical feature extraction, recall rate, convergence, and system configuration accuracy. The experimental results show that the proposed system can extract English grammatical features with higher accuracy, and the recall rate is more satisfactory. Compared with the reference systems, the designed system configuration accuracy is higher.
Target Tracking Algorithm of Basketball Video Based on Improved Grey Neural Network

Considering the limitation of tracking range on sports video target tracking in basketball games, there are some problems, such as poor tracking effect, low accuracy, low anti-interference ability, and being time-consuming. Therefore, this study proposes a target tracking algorithm of basketball video based on improved grey neural network. According to the pixel grey difference of the target image in basketball video, this study applies the adaptive threshold algorithm in order to segment the target image of basketball video and obtain the target area of basketball video. This algorithm can normalize the grey level of the target area, build the generating sequence of the target area, and collect the target data of the basketball video. It obtains the feature output matrix of basketball video target based on the geometric dispersion of the target image and extracts the key feature points of basketball video target by single frame visual difference analysis. In addition, it makes use of the improved grey neural network to track and locate the feature points of basketball video target and reconstruct the basketball video target image with superresolution to realize basketball video target tracking. The experimental results show that the proposed algorithm has good target tracking effect of basketball video, can effectively improve the target tracking accuracy and anti-interference ability, and can shorten the target tracking time.
Expert Review on Mobile Augmented Reality Applications for Language Learning

Many mobile applications that can increase user engagement and promote self-learning have been developed to date.Nevertheless, mobile applications specific to Malay language learning for non-native speakers with relevant materials are still lacking.Moreover, expert reviews are needed to identify usability issues and check whether such applications can meet the learning goal with relevant materials features.This study developed an augmented reality (AR)-based mobile application called RakanBM for learning the Malay language (i.e. the language officially spoken in Malaysia), and then performed an expert review on the application contents, text presentations, learning outcomes, assessments, effectiveness, efficiency, and satisfaction.The expert review was conducted by a panel of six experts from two specific fields, namely the Malay language and Human-Computer Interaction (HCI), using methods such as cognitive walkthrough (CW), semi-structured interviews, thinkaloud protocols, and survey.The results from CW, semistructured interviews, think-aloud protocols shows that enhancement was needed on user interface and user experience in term of aesthetic and interactivity.The survey results were classified into two levels: high (mean > 4.0) and satisfied (mean > 3.5).Application factors that were recorded as satisfied were the application contents, text presentations, and satisfaction, while the factors recorded as high were the learning outcomes, assessments, effectiveness, and efficiency.The comments or suggestions for improvement were mainly around the contents of the application.Nevertheless, the application received good comments on its usefulness and the topics covered, which were suitable and best for non-native speakers.The findings of this study can guide developers and researchers in the development of future applications that can support language learning for non-native speakers in particular.
Automated AUT scoring using a Big Data variant of the Consensual Assessment Technique : Final Technical Report

In this final technical report, we present the results of our Abbas Foundation Test Development Funds project “The development of a valid and usable creativity test requires big-data and psychometrics”. The project consisted of two phases: (1) creating a large database of responses for the Dutch version of the “Alternative Uses Task” (AUT) and (2)developing an automated scoring algorithm based on the Consensual Assessment Technique and testing its reliability and validity. The main aim of the project was to establish a reliable and automated way to score the AUT that will make future data coding faster and more cost-efficient. Meanwhile, the problem of sample-specific scoring would be solved because automated scoring guarantees consistency, i.e. that the same AUT response receives the same creativity score regardless of where the data was collected and scored. We developed an algorithm that essentially scores AUT responses using the Consensual Assessment Technique based on expert ratings of similar responses from our database of over 70,500 AUT responses. Based on two validation studies, the results show that our algorithm was the best ‘rater’ and reliably scores new AUT responses similarly to experts. Furthermore, the test-retest and alternate form reliability as well as convergent, discriminant and predicted validity of automated scoring is on par with that of expert scoring. There is still room for improvement, but the current version of our AUT scoring algorithm is a reliable alternative to the time-intensive and costly expert scoring methods.
Chapter 4. Instagram in the Library

Chapter 4 of Technology Reports (vol. 55, no. 2), in the Library Chapter 4 of Technology Reports (vol. 55, no. 2), “Mastering Mobile through Social Media: Creating Engaging Content on Instagram and Snapchat,” dives into Instagram. As with the discussion of Snapchat in chapter 3, Alfonzo discusses the basic features of the platform, such as types of feed posts and Instagram Stories camera aspects, provides suggestions for optimization, presents tools and ideas to boost Instagram content, and wraps up with an analytics overview. Additionally, while the concepts she presents have been proven to work particularly well on Instagram, they can be tweaked and used on Snapchat, Facebook, Twitter, and beyond.
Assessing the Relationship Between Binarization and OCR in the Context of Deep Learning-Based ID Document Analysis

Text recognition has been one of the areas greatly benefited from deep learning (DL) development, as well as the preprocessing methods contained in its workflow. Within the document analysis field, identity (ID) documents play a crucial role and should be studied in depth regarding this workflow. For this reason, we propose to analyze the relationship between DL-based binarization and recognition methods, specifically for this type of documents. We perform a review of four binarization and seven optical character recognition (OCR) algorithms, and present two sets of experiments assessing the influence of text size for binarization, and the impact of its output in the final text recognition. We show that DL-based binarization solutions are very sensitive to logical text size and they are still not effective in this domain, thus requiring major improvements. Among the evaluated text recognizers, the best performance over the binarization results was shown by the semantic reasoning network (SRN) method.
Improving Pipelining Tools for Pre-processing Data

The last several years have seen the emergence of data mining and its transformation into a powerful tool that adds value to business and research.Data mining makes it possible to explore and find unseen connections between variables and facts observed in different domains, helping us to better understand reality.The programming methods and frameworks used to analyse data have evolved over time.Currently, the use of pipelining schemes is the most reliable way of analysing data and due to this, several important companies are currently offering this kind of services.Moreover, several frameworks compatible with different programming languages are available for the development of computational pipelines and many research studies have addressed the optimization of data processing speed.However, as this study shows, the presence of early error detection techniques and developer support mechanisms is very limited in these frameworks.In this context, this study introduces different improvements, such as the design of different types of constraints for the early detection of errors, the creation of functions to facilitate debugging of concrete tasks included in a pipeline, the invalidation of erroneous instances and/or the introduction of the burst-processing scheme.Adding these functionalities, we developed Big Data Pipelining for Java (BDP4J, https://github.com/sing-group/bdp4j),a fully functional new pipelining framework that shows the potential of these features.