Reviewerly/exHarmony huggingface dataset:
Real-time video signal processing methods for feature extraction and analyses

In this thesis, two topics of computer vision are presented. The first topic presents a real-time hand segmentation algorithm that is based on background subtraction and color information. Unlike utilizing one method to segment the hand, the strong points of two methods help each other to segment the hand clearly and robustly. A hand is extracted from a video sequence by background subtraction where unit gradient vectors (UGVs) are used instead of image intensities. Since the UGVs are invariant to change in illumination, the UGV-based background subtraction is more stable dynamic lighting conditions. The color information improves the UGV-based background subtraction result. Therefore, the integration of two results can segment a hand under various lighting conditions robustly. Finally, this algorithm is implemented to a low-cost embedded board Raspberry Pi for a real-time application system.The second topic presents a novel technique for classifying several camera operations in videos. The camera operations have important roles in different research areas such as video indexing, video stabilizing, video encoding, video processing, and video content analysis. To perform the classification, 2D motion vector (MV) fields are firstly measured by block-based motion estimation. Then, the 2D MV fields are described as 2D MV histogram in polar coordinates. The histogram shows how many MV magnitudes and MV orientations in each video frame. These two MV features are utilized simultaneously to classify a total of 16 camera operation classes including single camera operations and combinations of two camera operations. The proposed method can achieve a processing time of 5 – 10 millisecond per frame for a low-resolution video, while it takes 40 – 80 millisecond per frame for a high-resolution video.
Practical use of the digital pedagogical activity simulator: efficiency, innovation, and experience

The article presents the experience of developing and using the digital simulators in teaching and learning at a pedagogical university. Testing the innovative technologies in the course of teachers’ training enables to conclude that this tool is effective when acquiring the professional competencies. The simulators, including the virtual ones, ensure modeling the pedagogical activity in terms of the principles of the activity approach. The analytical part of the article includes the content of the assessment criteria when planning and giving the classes on a digital simulator. The criteria for assessing the operations of a university student on the simulator are consistent with the requirements for the learning outcomes of schoolchildren. The results obtained confirm the relation between the labor operations performed by future teachers and the requirements established by the Federal State Educational Standards for universal learning skills (cognitive, communicative, and regulatory). The content of these requirements involves skills at searching for the study material and assuming the position of an active subject. The requirements content is consistent with the criteria for assessing the operations of the future teacher. The results of testing the simulator enable to analyze the advantages and disadvantages of using a digital simulator in teachers’ training. Its use may involve making some changes in Teaching Methodology at a pedagogical university.
Recognition of Math Expressions & Symbols using Machine Learning

The symbols and expressions used in math are very important and used in daily routines. It is necessary to have them in electronic form to access and produce them easily. The goal of this research is to provide a bridge between the knowledge of the organizer and the user inputs. By making math symbols and expressions visible to the non-native speakers, this research could introduce them to math notation. The method is to be recognized mathematical formula as well as symbols (printed or handwritten) based on feed forward back propagation neural network, support vector machine and K- nearest neighbor classifier. The noise free clean & clear input image can be obtained in preprocessing. The elements associated with math equations can be isolated with the utilization of segmentation. Finally feed forward back propagation neural network, support vector machine and K- nearest neighbor classifier with extracting static and complex features is used to recognize the expressions and symbols on the handwritten and printed image too. The receiving operating characteristics (ROC), confusion matrix & overall recognition system determines the accuracy and efficiency of the proposed system. The recognition of handwritten mathematical equations, symbols, digits provides a lot of real time applications as well as non-real time applications and most important application of math equation recognition system is math talk system for visually impaired people.
Twin support vector machine using kernel function for colorectal cancer detection

Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artiﬁcial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.
The Impact of Information Technology on Supply Chain Management in health sector in Jordan

The medical industry is still finding its place as other industries begin to adapt to information ‎technology (IT).Many sectors benefit from the common field of IT, which involve digitized ‎supply chains; virtual medicine communication; data sharing and ‎improved diagnostics technology in multimedia. It is enough to inspire developing countries ‎such as Jordan to embrace IT to deal with different impacts in the hospital supply networks ‎and compete for e-health that most of these technologies cost (Matar & Alnabhan, 2013)..‎Many sectors benefit from common areas of information technology, including virtual ‎pharmaceutical supply chains, communication with electronic medicines, data sharing and ‎improved interactive diagnostics.‎Moreover, hospital services are increasingly planned and integrated health care systems ‎introduced as essential mechanisms for reducing resource consumption and improving the ‎quality of healthcare. Of course the world of health services operations changed significantly ‎not only in practice but also from a theoretical point of view. ‎Nevertheless, our understanding of the health care sector also tends to be rather limited from a ‎supply chain management perspective. While many healthcare organizations recognize the ‎importance of supply chain management practices, it is often very obviously difficult to apply ‎the strategies, processes and best practices that have been initially developed in an industrial ‎environment
An Application of Non-local Means Algorithm for Denoising Low Dose Computed Tomography

To minimize the radiation risk in clinical examination using X-ray CT, low dose CT imaging is desirable in clinical practice. Lowering radiation dose degrades the reconstructed image quality and decreases the diagnostic performance of the image. Hence image denoising and signal enhancement in low dose CT imaging is one of the foremost challenging issue. The NLM algorithm has been successfully applied in removing noise and artefacts from corrupted natural images. This paper studies the application of non-local means (NLM) algorithm in removing noise from low dose CT lung images. In this study low dose CT images are denoised by applying NLM filer in two different ways. In the first application NLM filter is used to remove noise from projection data before reconstructing the images. In the second method images are denoised by applying NLM in the image domain after reconstruction. The Performances of both image space and projection space denoising is compared and evaluated quantitatively using PSNR and SSIM. Results shows that NLM filter can successfully applied to denoise low dose CT images and better results can obtained if the filter is applied on reconstructed images.
Reducing Fraudulent News Proliferation using Classification Techniques

The expansion of dishonorable information in normal get entry to social access media retailers like internet based media channels, news web journals, and online papers have made it hard to identify dependable news sources, subsequently growing the need for technique tools able to deliver insights into the reliability of online content substances.. This paper comes up with the applications of Natural language process techniques for detective work the dishonest news, that is, dishonorable news stories that return from the non-reputable sources. Solely by building a model supported mistreatment word tallies or a Term Frequency-Inverse Document Frequency matrix, will solely get you to date. Is it potential for you to make a model which will differentiate between “Real “news and “Fake” news? Thus our planned work is going to be on grouping a knowledge set of each pretend and real news and uses a Naïve mathematician classifier so as to make a model to classify an editorial into pretend or really supported its words and phrases.
Fuzzy Particle Swarm Optimization Feature Selection and Aggrandized Classifier for Uncovering Frauds in Credit Card Deals

In today's economy, credit card plays a very important role. The rise of credit card customers improved, credit card scam cases were also on the rise. Numerous procedures are anticipated to challenge the evolution of the frauds in credit cards. In this research work, proposed an innovative fraud detection method which utilizes the similar cardholder’s behavioral patterns to construct a current cardholder’s interactive profile in order to stay away from the credit card scams. However, the selection of optimal features from the samples and the decision cost for accuracy becomes main important problem. To illuminate these issues this proposed research work presents an innovative fraud detection technique that makes out of four phases: 1. To augment a cardholder’s behavioral styles, first we divide all cardholders into distinctive groups making use of the cardholder’s historical transaction data such that the members of each group have the similar transaction behavior by K-means. 2. Introduces a new Fuzzy Particle Swarm Optimization (FPSO) feature selection for the amplification of fraud detection in credit cards. 3. By means of a prolonged wrapper method, an ensemble classification are performed by Aggrandized Kernel based Support Vector Machine (AKSVM).4.Refreshing the cardholder’s social profile with an input system. This Proposed work adopts the external quality metrics as Accuracy, Recall, Concept drift rate and Fraud feature rate. The UCI dataset is used and is done in MATLAB framework. The analytical measures were used to estimate the routine of the mentioned fraud detection technique. The simulation results show that this proposed innovative fraud detection method provides better accuracy results than other fraud detection techniques. The low concept drift rate results the gain of the innovative method to classify the transactions accurately.
VALIDITAS MACARTHUR-BATES COMMUNICATIVE DEVELOPMENT INVENTORIES (MB-CDI) DALAM MENGUKUR PERKEMBANGAN BAHASA ANAK USIA DINI SISWA TK DI KABUPATEN BONE

The current era of the fourth industrial revolution places language and technology as a very important skill to be mastered well, this is because all sources of knowledge and information provide language. Therefore, language skills need to be maximized in their early development. In school life, teacher knowledge about children's language is useful for the purposes of planning, implementing, and evaluating learning. Thus the teacher should have extensive knowledge about children's language development and how to develop it, so that one day they have correct and good language skills, both in listening, speaking, reading, and writing.The teacher's demand in knowing and evaluating children's language development from an early age demands the development of a measuring tool that has been spread and used globally by education practitioners in the world, one of which is MacArthur-Bates Communicative Development Inventories (MAB-CDI) Some instruments have long been developed to measure child’s early language development such as Commmunicative Development Inventories (CDI) that developed by MacArthur & Bates . The purpose of this research is to analyze the construct validity of MAB-CDI . Instruments were distributed to 79 students at kindergarden school. Confirmatory Factor Analysis were used to see the good model for dimensions in early communicative development . This validation tried to reveal the suitability of the instruments to measure the kindergarden students' early language development in the Bone context. Keywords: Assessment, Early childhood , Language Developmnet,
Warmer, deeper, wider: new developments at MacCHESS

MacCHESS supports diffraction experiments for structural biology at CHESS (the Cornell High Energy Synchrotron Source).We provide amenities for routine experiments in macromolecular crystallography (MX) and biological small-angle X-ray scattering (BioSAXS), including: High flux X-rays from recently upgraded 3rd generation source.• Fully equipped MX and BioSAXS stations, with EIGER2 detectors, SEC-SAXS capability, data collection and processing software, etc.• Pressure cryocooling capability to improve crystal quality, trap gases.However, our greatest strength is in helping researchers develop and extend new techniques.Current focus areas include:• Performing MX at room temperature, using serial crystallography to obtain complete datasets.• Using high pressure, for both MX and BioSAXS, to answer questions about life in the depths, protein folding, enzyme reaction pathways, food processing... Examining a wide range of samples, including lipids, nanoparticles, metal-oxide-framework (MOF) crystals, embryos...
Blockchain Technology - Based Solutions for IOT Security

Blockchain innovation has picked up expanding consideration from investigating and industry over the later a long time. It permits actualizing in its environment the smart-contracts innovation which is utilized to robotize and execute deals between clients. Blockchain is proposed nowadays as the unused specialized foundation for a few sorts of IT applications. Blockchain would aid avoid the duplication of information because it right now does with Bitcoin and other cryptocurrencies. Since of the numerous hundreds of thousands of servers putting away the Bitcoin record, it’s impossible to assault and alter. An aggressor would need to change the record of 51 percent of all the servers, at the precise same time. The budgetary fetched of such an assault would distantly exceed the potential picks up. The same cannot be said for our private data that lives on single servers possessed by Google and Amazon. In this paper, we outline major Blockchain technology that based as solutions for IOT security. We survey and categorize prevalent security issues with respect to IoT data privacy, in expansion to conventions utilized for organizing, communication, and administration. We diagram security necessities for IoT together with the existing scenarios for using blockchain in IoT applications.
Speech Encryption in Linear Canonical Transform Domain Based on Chaotic Dynamic Modulation

In order to transmit the speech information safely in the channel, a new speech encryption algorithm in linear canonical transform (LCT) domain based on dynamic modulation of chaotic system is proposed. The algorithm first uses a chaotic system to obtain the number of sampling points of the grouped encrypted signal. Then three chaotic systems are used to modulate the corresponding parameters of the LCT, and each group of transform parameters corresponds to a group of encrypted signals. Thus, each group of signals is transformed by LCT with different parameters. Finally, chaotic encryption is performed on the LCT domain spectrum of each group of signals, to realize the overall encryption of the speech signal. The experimental results show that the proposed algorithm is extremely sensitive to the keys and has a larger key space. Compared with the original signal, the waveform and LCT domain spectrum of obtained encrypted signal are distributed more uniformly and have less correlation, which can realize the safe transmission of speech signals.
Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping.

Convolutional neural networks have become state-of-the-art in a wide range of image recognition tasks. The interpretation of their predictions, however, is an active area of research. Whereas various interpretation methods have been suggested for image classification, the interpretation of image segmentation still remains largely unexplored. To that end, we propose SEG-GRAD-CAM, a gradient-based method for interpreting semantic segmentation. Our method is an extension of the widely-used Grad-CAM method, applied locally to produce heatmaps showing the relevance of individual pixels for semantic segmentation.
The Influence of Reading Interest in Buginese on the Learning Achievement of the Bugis Language of Class Xi Students of SMA Negeri 2 Barru

Abstract: The Influence of Reading Interest in Buginese on the Learning Achievement of the Bugis Language of Class Xi Students of SMA Negeri 2 Barru . This research is a quantitative research that aims to: (1) describe the interest in reading Bugis language of class XI students of SMA Negeri 2 Barru; (2) describe the learning achievement of Bugis language students of class XI SMA Negeri 2 Barru; (3) to prove whether or not there is an influence of interest in reading Bugis language on the achievement of learning Bugis language for class XI students of SMA Negeri 2 Barru. The population in this study were all students of Class XI SMA Negeri 2 Barru, totaling 162 students, while the sample consisted of 61 students as the sample. The results showed that, (1) students' interest in reading Bugis language was in the low category, the average score was 62.89; (2) Bugis language learning achievement of class XI students of SMA Negeri 2 Barru is in the high category, the average value is 83.66; (3) the influence of interest in Bugis reading on Bugis language learning achievement of class XI students of SMA Negeri 2 Barru proves that there is no significant influence between interest in reading Bugis language on students' Bugis language learning achievement, with data analysis showing tcount < ttable (1,229< 1.671) with an error level of 0.05 withthe number of N = 61.
Analisis cluster dengan menggunakan metode hierarki untuk pengelompokan kecamatan di kabupaten Langkat berdasarkan indikator kesehatan

Cluster analysis is a method used to group objects based on similarity of characteristics they have. Cluster analysis using the hierarchy method is a method with a grouping process that is used in stages. Health is a condition where a person is not sick, has no complaints, and can carry out daily activities. To find out information about the level of health in Langkat Regency, it is necessary to use the grouping method. The grouping was carried out in 23 districts in Langkat Regency. The purpose of this study is to classify sub-districts in Langkat Regency which have similar characteristics based on health indicators through the square euclidian distance is used to measure the similarity between object pairs and the ward method. From the results of cluster analysis using ward method, the results are 3 clusters of sub-districts that merge based on factors that affect the health status in Kabuapaten Langkat, including morbidity, birth support and life expectancy, where the 3 clusters consist of cluster 1 consisting of 10 subdistricts with health l
When Life Gives You LMs, Probe Them for Knowledge With Automatically Generated Prompts

Author(s): Shin, Taylor | Advisor(s): Singh, Sameer | Abstract: Determining the knowledge captured by pretrained language models is an important challenge, and is commonly tackled by probing model representations using classifiers. However, it is difficult to design probes for semantic knowledge such as facts. Reformulating these semantic tasks as cloze tests (i.e., fill-in-the-blank problems) is a promising method for probing such knowledge, but, it requires manually crafting textual prompts that elicit the desired knowledge, thus limiting its use. In this paper, we develop an automated, task-agnostic method to create cloze prompts for any classification task based on a gradient-guided search. Applying our technique, we find prompts that demonstrate MLMs have an inherent capability to perform fact retrieval and relation extraction. We show that our prompts elicit more accurate factual knowledge from MLMs compared to manual prompts, and further, MLMs can be used as relation extractors out of the box, when prompted with suitable prompts, more effectively than recent supervised RE models.
ACCESSIBILITY AND UTILIZATION OF ELECTRONIC VERSUS PRINT BOOKS BY UNDERGRADUATE MEDICAL STUDENTS

Objective: The purpose of this study was to find out the differences of accessibility and utilization of electronic (e-books) and print (p-books) books among undergraduate medical (MBBS) students. Study Design: Corss sectional study. Setting: At Shalamar Medical & Dental College (SMDC), Lahore. Period: Sep 2019 to Jan 2020. Material and Method: There were 600 st MBBS students from 1st year to final year and the data was collected from all enrolled MBBS students of SMDC. The data was analyzed using Statistical Package for Social Sciences (SPSS), 20th version. Results: The study found out that the MBBS students used the e-books for study purpose. In the context of Prof. M. Akhtar Khan Library, SMDC, almost all the study books are available in both formats (e-books and p-books). The accessibility of both formats remains round the clock. It has been noted that, although accessibility to both formats is available yet ease of use factor remained tilted towards p-books. There are indications that the use of e-books may increase in the future while p-books may remain in priority as a learning tool and as reading preference. Conclusion: Most of the students were satisfied with usage and accessibility of ebooks but they preferred p-books. It is also found that participants preferred hybrid libraries due to accessibility and usage of both types of books.