Reviewerly/exHarmony huggingface dataset:
Multi Scale Region based Advance Scheme for Breast Cancer Detection and Classification

Breast cancer diagnosis is usually performed by doctors based on Digital Mammography (DM) or on Medical Images (MI). In order to assist doctors to process big amount of images for different patients, Breast Cancer Computer Aided Diagnosis (BC- CAD) is becoming, nowadays, an appealing area of research Using image processing techniques for computer-aided diagnosis that involves the feature extraction for cancer detection, so as to help doctors towards making optimal decisions quickly and accurately. Features play an important role in detecting the cancer in the digital mammogram and feature extraction stage is the most vital and difficult stage. In this research, an enhanced feature extraction method named Multi-scale Surrounding Region Method (MSRM) is proposed to be effective in classifying the mammogram images into normal or benign or malignant. This proposed system is based on a four-step procedure: Regions of Interest specification, segmentation base on edge and thresholding, and multi-scale surrounding region dependence matrix computation and feature extraction. After that we apply machine learning mechanism for classify breast cancer on early stage as soon as possible and we also use segmentation approach for that. after Implement Proposed Algorithm achieve more than 93% Accuracy for detect and Classify Breast Cancer with Benign and Malignant type tumors.
Mask Region-Based Convolutional Neural Networks (R-CNN) for Sinhala Sign Language to Text Conversion

Sign Language is the use of various gestures and symbols for communication. It is mainly used by disabled people with communication difficulties due to their speech or hearing impediments. Due to the lack of knowledge on sign language, natural language speakers like us, are not able to communicate with such people. As a result, a communication gap is created between sign language users and natural language speakers. It should also be noted that sign language differs from country to country. With American sign language being the most commonly used, in Sri Lanka, we use Sri Lankan/Sinhala sign language. In this research, the authors propose a mobile solution using a Region Based Convolutional Neural Network for object detection to reduce the communication gap between the sign users and language speakers by identifying and interpreting Sinhala sign language to Sinhala text using Natural Language Processing (NLP). The system is able to identify and interpret still gesture signs in real-time using the trained model. The proposed solution uses object detection for the identification of the signs.
Digitalization of Library: An Overview

The present era is the era of digitalization. Unlike others Library is also not untouched by this many library software has been developed using information technology, which has made all the functions of the library such as acquisition, classification, indexing, cataloguing, inter-library exchange etc. very easy. Today, information from one corner of the world to the other can be transported with the help of various information techniques in an instant. This helps in saving the time and finances of the scholars and readers. The use of library software saves the time of library staff and the students & readers coming to the library. Library services can be kept updated with the use of information techniques.
Algorithms & Antitrust: an overview of EU and national case law

Humans have been creating and using algorithms for thousands of years, but never before have the effects of algorithms been so pervasive in our everyday lives and the functioning of the economy…
Use of Augmented Reality in Assembly Process

Intensive research is currently underway into the concept of intelligent assembly, which integrates production processes, people, hardware and information using both real and virtual methods to achieve significant improvements in productivity, delivery time and combined market turnover. This paper describes the use of augmented reality in the assembly process at the workplace, which by integrating hardware and software equipment will enable an innovative assembly workplace for a manufacturing and development company. The assembly workplace will speed up and facilitate assembly and prevent the creation of failures and restrictions during assembly.
Vote Recommendation System using Aspect based Machine Learning Approach

Over time, the information on WWW has escalated exponentially, paramounting to embryonic research in the field of Data Analysis using Natural Language Processing (NLP) and Machine Learning (ML). As data is increasing day by day there is huge demand for data analysis to get subjective information and analyzing government data is very useful and demanding task. So, in this paper, an application is being developed which will recommend the user to which party to vote will be benignant for themselves and for country, depending on the area of interest of different users. The data is collected from various governmental websites of multiple areas like women empowerment, education, employment, child labor etc. which will enhance the authenticity of the output. The main ground of this research is to lubricate common people and politicians as well. For common people; is for deciding their precious vote, to which party to give will be good for themselves and nation too. For politicians; they will have an idea about themselves and other politicians that which party is preferable and which is not preferable in respective areas, so that the politicians can work accordingly.
Different paths to AI authorship

Chapter eight presents the directions that AI authorship might take in the future. First, I address the main ‘players’ who have a substantial claim to AI’s authorship: the programmer, the user, and the AI. Second, I discuss the ‘no authorship’ alternatives; these can be framed in different ways, such as public rights or government ownership. Finally, I address two other rights models: author in law, and AI moral rights. My analysis centres around the traditional copyright framework, but I make further policy observations and consider alternative legal frameworks where applicable.
Attack Transferability Characterization for Adversarially Robust Multi-label Classification

Despite of the pervasive existence of multi-label evasion attack, it is an open yet essential problem to characterize the origin of the adversarial vulnerability of a multi-label learning system and assess its attackability. In this study, we focus on non-targeted evasion attack against multi-label classifiers. The goal of the threat is to cause miss-classification with respect to as many labels as possible, with the same input perturbation. Our work gains in-depth understanding about the multi-label adversarial attack by first characterizing the transferability of the attack based on the functional properties of the multi-label classifier. We unveil how the transferability level of the attack determines the attackability of the classifier via establishing an information-theoretic analysis of the adversarial risk. Furthermore, we propose a transferability-centered attackability assessment, named Soft Attackability Estimator (SAE), to evaluate the intrinsic vulnerability level of the targeted multi-label classifier. This estimator is then integrated as a transferability-tuning regularization term into the multi-label learning paradigm to achieve adversarially robust classification. The experimental study on real-world data echos the theoretical analysis and verify the validity of the transferability-regularized multi-label learning method.
TH58. THE ROLE OF TRAUMA IN PSYCHIATRIC DISORDERS USING AN ELECTRONIC HEALTH RECORDS-BASED APPROACH

Robust method of short-term forecast of Covid-19 epidemic in small administrative units (districts) is proposed. By identifying similar sections of epidemic evolutions in the past it is possible to obtain short-term forecast of epidemic in given district. Examples of one and two-weeks forecasts for three cities in Poland during third epidemic wave (March and April 2021) are shown. Difference between epidemic evolutions in third wave and previous waves caused by Covid B.1.1.7 UK variant is observed. Proposed algorithm allows one to manage epidemic locally by entering or releasing anti-Covid restrictions in groups of small administrative units.
Applicability of M-Learning for Basic Arabic Language among Students During Distance Learning

One of the emerging strategies in education is the usage of mobile devices (PdP).Following this line of thought, Arabic courses make a point of emphasising the use of m-learning in PdP at the fundamental level.Due to the Covid-19 epidemic, this study was done to investigateat the most basic level -the applicability of Arabic m-learning among students on distance learning.The research involved 147 individuals who were enrolled in basic Arabic language classes.The students were satisfied with the application of mTasheel in the PdP process, according to the findings.The studies also revealed that Google platforms like Docs, Slides, and Sites were among the best and most dependable applications to utilise during PdP sessions during distance learning especially during the Covid-19 pandemic.
Automated System of Predicting of the Studied Symptoms among Patients in Medicine

A precise prognosis in medicine plays a decisive role in the prevention, treatment, assessment of the effectiveness of treatment methods, and the results of treatment of patients. The most precise predicting method in computational mathematics today is the interpolation method. The computer medical process manager Medical and the software application Medical Commander Prognosis developed earlier by Shoikhedbrod permitted to carry out in real-time automated optimal interpolation prediction of the studied symptoms in medicine (in particular in oncology), based on two principles of interpolation prediction: minimizing the difference (error) between the known and calculated by different methods of polynomial interpolation of the numerical values of the symptom under study; selection as the values of the studied symptom of the maximum number of statistical frequencies of the observed patients in the indicated intervals of the study. The paper presents the developed automated system of predicting the studied symptoms among patients in medicine, permitting to predict in real-time in automatic mode without human intervention any investigated symptom or set of investigated symptoms among patients based on the developed earlier computer medical process manager Medical and computer software application Medical Commander Prognosis. The essence of using the automated system of predicting the studied symptoms among patients in medicine is the prediction in real-time in an automatic mode without human intervention of any investigated symptom or a set of studied symptoms among patients by transmitting digital data from the console (input-output device) through the controller’s input module to the controller for the primary processing of these data using the converter’s block of computer medical process manager Medical Commander, previously recorded in its RAM, for obtaining frequencies of patients, i.e., a percent quantity of patients in the assigned interval of the predictable symptom on relation to all patients on all intervals of the predictable symptom. The obtained data become the nodal points for conducting the optimal interpolating prognostication by computer software application Medical Commander Prognosis, which was also previously recorded in controller’s RAM, due to the call of the computer software application Medical Commander Prognosis at the program level by the converter’s block of computer medical process manager Medical Commander. The prediction results are plotted in the form of a graph on the system’s display screen.
Comparative Review on Efficient Design of Reversible Sequential Circuits based on Optimization Parameters

Reversible computing, a well known research area in the field of computer science. One of the aims of reversible computing is to design low power digital circuits that dissipates no energy to heat. The main challenge of designing reversible circuits is to optimize the parameters which make the design costly. In this paper, we review different designs of efficient reversible sequential circuits and prepare a comparative statement based on eight optimization parameters such as Quantum Cost (QC), Delay (del), Garbage Output (GO), Constant Input (CI), Gate Level (GL), Number of Gate (NoG), Type of Gate (ToG), Hardware Complexity (HC) of Circuit.
A face recognition system using convolutional feature extraction with linear collaborative discriminant regression classification

Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in CNN and also used to make the model robust than other traditional feature extraction technique called Local Multiple Pattern (LMP). The extracted features are fed into the Linear Collaborative Discriminant Regression Classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e.80% of training and 20% of testing data).
Managing Implementation Risk for Successful Mobile Medicine

This chapter discusses the risks, mitigation strategies, and monitoring and measurement techniques that can serve as signposts on the successful journey. While it might seem counterintuitive to delve into a project to re-evaluate your existing risk management methodology, it is worthwhile to explore the leading frameworks to find nuggets of information that might be particularly helpful in understanding the risks of mobile medicine. When you last updated your risk management methodology, the risks of mobile computing and mobile medicine may not have been a focus; now these risks are on the front burner and it is possible that you might need to improve or modify how the qualitatively or quantitatively assess risk. Robust vulnerability management and remediation eliminates many known vulnerabilities that could potentially be exploited by various threats facing your environment. When evaluating the insider threat management program, it is important to consider these types of Insiders as well.
DETECTING DEPRESSIVE TWEETS USING GATED RECURRENT UNIT NETWORK (GRU)

Twitter adalah layanan microblogging dan jejaring sosial tempat pengguna dapat memposting dan berinteraksi dengan pesan yang dikenal sebagai tweet, memberikan kebebasan kepada pengguna untuk mengekspresikan diri. Banyak kasus penyakit mental tidak terdiagnosis atau tidak dikenali karena stigmatisasi oleh media yang membuat keinginan untuk membantu mereka menjadi terbatas. Oleh karena itu, penelitian ini bertujuan untuk membuat model pendeteksian pada Tweet menggunakan Deep Learning Model RNN dan GRU. Penelitian ini memanfaatkan Word Embeddings yaitu Word2Vec dan GloVe untuk menghasilkan representasi kata yang lebih baik dibandingkan penelitian sebelumnya yang tidak menggunakan Word Embedding. Pengujian dilakukan pada dataset yang berisi total 10.314 tweet dengan label depresif (1) dan positif (0). Penelitian ini menggunakan train & validation split 60/20 dan kurva AUC ROC sebagai variabel evaluasi tambahan. Hasil setelah dilakukan pengujian pada 20% data menggunakan RNN & GRU adalah seri. RNN memperoleh akurasi 99,6% dan GRU pada 99,5% dengan perolehan yang sama, presisi, dan skor f-1 pada 99,5%. Namun, GRU berhasil memperoleh skor kinerja keseluruhan yang lebih besar dalam pelatihan & uji validasi dan skor kurva AUC ROC di mana ia memperoleh hasil yang lebih stabil, lebih tinggi, dan lebih sedikit berfluktuasi. Twitter is a microblogging and social networking service on which users can post and interact with messages known as tweets, giving users freedom to express themselves. Many cases of mental illness go undiagnosed or unrecognized due to stigmatization by the media making the desire to help them limited. Therefore, this research aims to create a detection model on Tweets using Deep Learning Model RNN and GRU. This research makes use of Word Embeddings, namely Word2Vec and GloVe in order to create improved representations of words than previous researches that did not use Word Embedding. The test is done on a dataset containing a total of 10,314 tweets with depressive (1) and positive (0) as the label. This research uses train & validation split of 60/20 and the AUC ROC curve as an additional evaluation variable. The results after testing on 20% data using RNN & GRU was a tie. RNN gained an accuracy of 99.6% and GRU at 99.5% with the same recall, precision and f-1 score at 99.5%. However, GRU managed to obtain a greater overall performance score in the training & validation test and the AUC ROC curve score where it obtained steadier, higher and less fluctuating results.
LibGuides: Databases in the Electronic Text Center: How to Access ETC Titles

A list of all current and former digital titles (on disk or online) housed or once available in the Electronic Text Center in Olin Library. Titles are linked to the online version when available. All titles have records in the library's online catalog.
A Study on the Changes of Vocation Training Education Considering the Key Capabilities Required in 21st Century of Fourth Industrial Revolution - Focusing on Architectural Vista Design -

The era of the Fourth Industrial Revolution will replace millions of existing jobs requiring new skill set among future workers.This era requires ten core competencies including complex problem solving, critical thinking, creativity, human resource management, coordinating with others, emotional intelligence, and logical judgment and decision making.To train today's students with these capabilities, it is necessary to change the teaching-learning process of universities.Professors should be equipped with skills to teach a competency-based education.They should be the partners of students to maximize their individual potential and realize their best value.The development of ICT such as the Internet and computers made access to information and knowledge easier.An education that simply conveys knowledge is meaningless.Academic knowledge are now short-lived as knowledge exists as the half-truth.Students accept and memorize simple academic knowledge delivered by professors and they cannot retain them.Changing the teaching-learning process would guarantee a competency-based education to ensure that students will be equipped with the core competencies of the 4IR.This paper studies the education method of universities based on the 4IR core competencies on what changes should be made.Furthermore, it focuses on the architectural vista design, which is a subject for students majoring in urban planning and architectural planning.
Development of Chemical Learning Media in Reagent Bottle with QR-code

This study aims to develop learning media in the form of a reagent bottle with a label in the form of a QR code.This research is research and development with research stages namely analysis, design, development and evaluation.The data were obtained from the validity test and media readability test.The results of this study are the percentage of media validation tests from material experts, media experts, and student readability are 87.6%,89.5%, and 90.5%, which means that the media is classified as very feasible and very good to use.It can be concluded that the learning media in the form of a reagent bottle with a QR code is declared feasible and very good for use in Basic Chemistry lectures.It is recommended to test the effectiveness and development of media on other materials.
PENGARUH PENDEKATAN HIGH ORDER THINKING SKILL (HOTS) TERHADAP KEMAMPUAN MENULIS TEKS EKSPLANASI

Writing is a language skill in expressing main ideas or ideas in writing. Writing skills are not easy to do in learning, this is related to the level of writing at the highest level position. Learning must be done by choosing an approach that can promote students' critical thinking skills. The explanatory text is a text that can express students' ideas in writing about phenomena. Therefore, this study was conducted on the background of how the learning process of writing explanatory text with an approach that can provide a stimulus for critical thinking. The purpose of this study was to determine the explanatory text writing skills influenced by the HOTS approach. The research method used in this study was descriptive qualitative. The subjects of this study were 26 students of class VIII-B of SMPN 2 Margaasih. The results showed that 16 students got high scores, 7 students got adequate scores and 3 students got low scores. The results of the study concluded that the use of HOTS-based approaches in class VIII-B students could improve their explanatory text writing skills.
Using Chunking Technique To Improve English Reading Comprehension For First-Year English Majors At Thai Nguyen University Of Education

Reading comprehension is incredibly complex to students who use English as a second language because it requires them to interpret not only the main meaning of the text but also the intention of the writer.The English learners always have to struggle to use different reading techniques to grasp the meaning of the passage.One of the most effective techniques which can help students enhance reading comprehension in English is "Chunking technique".This study aims to investigate a) the current situation of the first-year English majors' reading comprehension at Thai Nguyen University of Education; b) the effectiveness of using Chunking technique in order to improve English reading comprehension for first-year English students.The researchers used questionnaires and experiments as two main instruments to collect data, and SPSS 20 (Statistical Package for the Social Sciences 20) was used to analyze the data.In the process of research, 20 students divided into control group and experimental group took part in the experiment within 8 weeks.The findings of this study showed that Chunking technique had a positive impact on students' reading comprehension when the mean score of students' post-tests of experimental group (6.05) was higher than that of the controlled group (5.32).It is also proved that this research was reliable to implement.The researchers clarified the effectiveness of using Chunking technique so that not only teachers but also students can achieve their goals concerning reading comprehension development.