Reviewerly/exHarmony huggingface dataset:
Detecting AutoAttack Perturbations in the Frequency Domain

Recently, adversarial attacks on image classification networks by the AutoAttack (Croce and Hein, 2020b) framework have drawn a lot of attention. While AutoAttack has shown a very high attack success rate, most defense approaches are focusing on network hardening and robustness enhancements, like adversarial training. This way, the currently best-reported method can withstand about 66% of adversarial examples on CIFAR10. In this paper, we investigate the spatial and frequency domain properties of AutoAttack and propose an alternative defense. Instead of hardening a network, we detect adversarial attacks during inference, rejecting manipulated inputs. Based on a rather simple and fast analysis in the frequency domain, we introduce two different detection algorithms. First, a black box detector that only operates on the input images and achieves a detection accuracy of 100% on the AutoAttack CIFAR10 benchmark and 99.3% on ImageNet, for epsilon = 8/255 in both cases. Second, a whitebox detector using an analysis of CNN feature maps, leading to a detection rate of also 100% and 98.7% on the same benchmarks.
Demo: Traffic Splitting for Tor – A Defense against Fingerprinting Attacks

Website fingerprinting (WFP) attacks on the anonymity network Tor have become ever more effective. Furthermore, research discovered that proposed defenses are insufficient or cause high overhead. In previous work, we presented a new WFP defense for Tor that incorporates multipath transmissions to repel malicious Tor nodes from conducting WFP attacks. In this demo, we showcase the operation of our traffic splitting defense by visually illustrating the underlying Tor multipath transmission using LED-equipped Raspberry Pis.
Semantic Text Summarization

Automatic Text Summarization is the process of generating automatic summaries from a document by extracting its principal statements and preserving the overall semantics of the text at the same time. Text Summarization is a potential solution to the problem of information overload the world is facing at the moment. Numerous text summarizers already exist in the literature, but they fail to preserve the semantics of the text. This research uses this semantic feature as a fundamental technique to find high quality summaries of documents. We use the distributional Semantic Model called word2Vec and compare the results with other state-of-the-art summarizers. We evaluated our results using ROUGE on DUC 2007 dataset. This system outperformed all other reference summarizers proving that using semantics as a feature increases the efficiency of the system.
VHS North and South LibGuides: VHS North Media Center: Procedures, Service Learning Information, Ebooks and Card Catalog: Welcome

Here you will find VHS North and South MackinVia and Destiny Catalogs. You will be able to search for books and do research via Webpath Express and Databases.
Application of Mathematics in Research, Education and Medical Science

This research discusses a variety of applications, especially advances in mathematics and different engineering principles. In this work mathematics like 3D tomography, EPRI, TEM and others are also highlighted. Now a day’s mathematics is one of the important parts of the research. Each and every algorithm requires mathematical modelling for the analysis of it. If some research is performed experimentally but if it failed to verify it mathematically then that research can not be considered up to the mark. In this research work, we have focused on a lot applications of mathematics in various fields
Predicting the predicting brain

Comprehenders use contextual information to guidelanguage processing. One influential hypothesis is that thesecontextual effects arise from predictions based on knowledge aboutpreviously experienced language inputs. In this dissertation, weexamine the extent to which language processing involvespredictions about lexico-semantic and word-form information. InChapter 1, we review the theoretical debates over this issue andthe empirical evidence for lexico-semantic and word-formpredictions. We then inspect this question empirically. In Chapter2, we examine pre-nominal effects arising from word-formpredictions. We propose a computational index of anticipatoryprocessing, Bayesian surprise. We show that, for studies of thetype discussed here, Bayesian surprise can be closely approximatedby the surprisal of the article. We find that the surprisal of thearticle does correlate with the amplitudes of N400 on the article,compatible with the hypothesis that word-form information ispre-activated. In Chapter 3 and 4, we examine predictiveprocessing in more natural processing environments where subjectsread sentences from novels (Frank et al., 2015). We develop novelanalytical tools for ERPs that look for the emergence of effects.Using these tools, we again find evidence for lexico-semantic andword-form predictions. Lastly, we discuss in Chapter 5 how futurestudies can benefit from the theoretical and methodological toolsdeveloped in this dissertation.
Toward Reusing the Numerical Association Rule Mining Models

Nowadays, it is no secret that modern machine learning methods are amongst the more computationally-intensive learning methods. The rise in the applications of computationally-intensive deep learning, automated machine learning methods, and even metaheuristics for optimization, have increased the consumption of electrical energy dramatically. Consequently, we can predict that the numbers of global carbon footprints, arising as a byproduct of the increased consumption of electrical energy during intensive computation, will be higher and higher in the near future. Fortunately, the research community is aware of these problems, and is, thus, looking for solutions of how to reduce the carbon footprint in the sense of the so-called Green AI. In line with this, the paper introduces a reusable model for Numerical Association Rule Mining which is also one of the hardest computationally-intensive learning methods. In the classical Numeric Association Rule Mining, the model (i.e., the archive of mined association rules) is created anew, when the new incoming association rule are emerging. The proposed reusable model only modifies the existing model accordingly by incoming new rules, while the complexity of computation decreases remarkably.
GENDER DETECTION USING CONVOLUTION NEURAL NETWORK

Gender detection system is used to recognize gender of a human using their facial features. The human face is captured by web camera, CCTV or from image. Gender detection system can improve the performance of many applications like surveillance, security and social media. A good gender detection system can provide valuable information like gender, age and ethnicity. Gender detection system can be implemented with the help of methods like support vector machine, Linear discriminant analysis and Convolutional neural network. This system proposes gender detection based on machine learning approach using convolutional neural network. This system starts with an input image and finally detects the gender with the help of convolution neural network. The proposed system is implemented using OpenCV, matplotlib of Python and provides good accuracy.
Evaluation Of Breast Lesions By Fine Needle Aspiration Cytology And Correlation With Histopathological Examination

INTRODUCTION: Breast cancer is the most common cancer in worldwide between age of  20-59 years females. It is responsible for 15% of total deaths due to cancers. FNAC is one of  the important components of ‘triple approach’, which has been widely accepted for the  preoperative diagnosis of breast lesions.  AIM: To establish utility and effectiveness in diagnosis of breast lesion by FNAC and find the  incidence of breast cancer in palpable lump in different age groups.  MATERIAL AND METHODS: In this study100 cases of breast lump referred by the Surgery  Department of LG Hospital was included in study period fromAugust2016toOctober2018. RESULT: Out of the 100 cases, in 99 cases the aspirates were adequate for interpretation. Out  of 100, 90cases underwent a biopsy and could be correlated histopathologically. According to  NHSBSP, there was 1 case in C1 category,64 cases in C2 category, 3 cases in C3 category, 2  cases in C4 category and 30casesin C5 category.  CONCLUSION: The present study categorized by NHSBSP criteria conclude 100%  confirmation in C4 and C5 category in age group above 40 years showing malignancy by  histopathology. The overall sensitivity in the study was 83.33%, specificity was 100%, positive  predictive value in disease was 100% and the false negative percentage was 16.66%. FNAC is  an accurate and reliable preoperative diagnostic tool for breast lesions.
Article 7—Common Provisions

Abstract This chapter discusses the common provisions in Article 7 of Directive 2019/790, the European directive on copyright. It explains the application of the Directive to special cases that do not conflict with the normal exploitation of works or other subject matter and do not unreasonably prejudice the legitimate interests of the rightholders. It also seeks to achieve a fair balance between the rights and interests of authors, other rightholders, and users. The chapter mentions the protection of technological measures established in Directive 2001/29/EC, which remains essential to ensure the protection and the effective exercise of the rights granted to authors and other rightholders under Union law. It stresses the maintenance of protection while ensuring the use of technological measures that do not prevent the enjoyment of the exceptions and limitations provided in Directive 2019/790.
ILLOCUTIONARY ACTS USED BY ENGLISH TEACHER WHILE TEACHING AND LEARNING PROCESS

The goal is to find out what English teachers do in the teaching and learning process at SMA Negeri 1 Candi Laras Selatan in the academic year 2018/2019, according to Searle's theory of speech law. This is a quality education. The information or goal of this study is words, phrases, and words spoken by English teachers. The information from the recording will be accompanied by the audio recording. From there, they will be translated in writing, chosen according to the study objectives recorded in the data table, interpreted and analyzed using the verbal theory of Searle's theory. Analyze the data using the interactive qualitative methods proposed by Miles and Huberman (1994). The coding system is also used to make researchers more easily identify the data. Researchers use theoretical triangles. Compare the data with Searle's theory. The results show that among the 326 words, the most commonly used behavior mimics are references (64.044%), which occurred 195 times, while the top 100 are the most rare (2.247%) 7 times. The use of advice can be understood because the relationships between teachers and students are not equal. Using questions by teachers helps students become more involved in the lesson. When asking questions, the teacher will know whether students understand the lesson. At the same time, questions are also used to test students' basic knowledge
Linked Data Usages in DataBio

Abstract One of the main goals of DataBio was the provision of solutions for big data management enabling, among others, the harmonisation and integration of a large variety of data generated and collected through various applications, services and devices. The DataBio approach to deliver such capabilities was based on the use of Linked Data as a federated layer to provide an integrated view over (initially) disconnected and heterogeneous datasets. The large amount of data sources, ranging from mostly static to highly dynamic, led to the design and implementation of Linked Data Pipelines. The goal of these pipelines is to automate as much as possible the process to transform and publish different input datasets as Linked Data. In this chapter, we describe these pipelines and how they were applied to support different uses cases in the project, including the tools and methods used to implement them.
Nonparametric Modeling Using Kernel Method for the Estimation of the Covid-19 Data in Indonesia During 2020

One of the most research discussed topics is the prediction or forecasting of the COVID-19 data using the classical time series (such as exponential smoothing) and machine learning methods. In fact, the classical time series method often produces quite large error rates. In this study, the researchers try to use nonparametric modeling with the kernel method to get better results with the smallest error rate. Furthermore, the results of the kernel method are compared with the results of the classical time series method. As a comparison tool, the researchers use MAPE by paying attention to the smallest MAPE value. The data used in this study are the COVID-19 data in Indonesia in which its variable is the total of deaths per day. After comparing the classical time series method with the kernel method, the obtained better results are the results from the kernel method. In this study, the researchers use five kernel functions, namely the Gaussian, Epanechnikov, Triangular, Biweight, and Triweight. Then, these five kernel functions are compared to find the best function. After the comparison process is done, the triweight kernel function was determined as the best function with the smallest error rate with a MAPE value of 0,9%.SKeywords — Covid-19, Kernel Method, Mean Absolute Percentage Error, Time Series, Triweight Kernel Function.
MULTI-PATIENT HEALTH MONITORING USING ARDUINO MEGA

In many countries, considering majorly in underdeveloped and developing countries proper health care service is a vital concern.When compared to the requirement of the people, the health centers are far and even the medical personnel are deficient.This reason has made health services for the people who are unhealthy and need health monitoring on regular basis likely to impossible way.This has led the health monitoring of healthy people left far more behind.It is always desirable to implement some system to solve this issue, so that the citizens will not be deprived of the primary care.Smart Healthcare will be significant for the people who have to undergo continuous monitoring, which is not possible to provide outside hospitals.It is also important that the nearby clinics at rural areas or villages, can be in touch with city hospitals, where they can get about their patient's health condition.This current work presents the smart health monitoring system that uses biomedical sensors to the check patient's condition, and it uses internet to inform the concerned.The biomedical sensors used here are connected to Arduino UNO controller to read the data which in turn is interfaced to an LCD display/serial monitor to see the output.
Linked Data and Metadata

Abstract Data is the basis for creating information and knowledge. Having data in a structured and machine-readable format facilitates the processing and analysis of the data. Moreover, metadata—data about the data, can help discovering data based on features as, e.g., by whom they were created, when, or for which purpose. These associated features make the data more interpretable and assist in turning it into useful information. This chapter briefly introduces the concepts of metadata and Linked Data—highly structured and interlinked data, their standards and their usages, with some elaboration on the role of Linked Data in bioeconomy.
Optimized Data Hiding Technique Using Egyptian Vulture Optimization Algorithm For Image Steganography

Image steganography algorithms have been used extensively for securing sensitive data by hiding it in the cover image. One of the most preferred algorithms for securing the sensitive information is least significant bit (LSB) where a secret data bit usually replaces the least significant bit in the cover image pixel. However, the data hiding process can degrade the image quality and finds attention of an attacker. For enhancing the quality of a cover image, various optimization algorithms have been utilized but there is a need to explore the optimal starting pixel in the cover image for data hiding. Therefore, in this paper, an optimized data hiding technique has been proposed that can quickly explore the optimal starting pixel in the cover image for data hiding. This can be achieved by using an Egyptian Vulture Optimization (EVO) algorithm where three operations are performed in different iterations for determining an optimal solution based on the fitness function. To validate the performance of the suggested technique, simulation results are performed on standard dataset images. The performance of the proposed technique is evaluated against the popular optimization-based data hiding algorithms. It is established that proposed technique is superior in terms of PSNR. Additionally, the secret data can be hidden in original or transform form. Thus, the recovery of secret is impossible in the receiver side until its data form is not identified. The proposed technique is useful in image steganography as it allows the user to quickly explore the optimal starting pixel in the cover image.
Multi-object Tracking Based on Nearest Optimal Template Library

Noisy detection and similar appearance lead to deteriorated mis-identification and id-switch in Multi-Object Tracking (MOT). To address these problems, we propose a novel Nearest Optimal Template Library (NOTL) associated with two tailor-made methods based on the NOTL. Here, the NOTL is a historical sample set of the tracked objects, and the elements in the NOTL are closest to the complete object at the current instant. It provides reliable appearance information of the object. Then, we use the single object tracker (SOT) for position prediction, and spatio-temporal network for appearance modeling. They can alleviate mis-identification and id-switch problems, respectively. Besides, the triplet loss is used to train our spatio-temporal network further improves the performance. The proposed algorithm achieves 55.3% and 55.1% in MOTA on challenging MOT16 and MOT17 benchmark datasets respectively. These results show our method is competitive with the previous state-of-the-art approaches.
Developing supplementary listening materials for vocational high school students using technology, education, and design (ted) online media

This study used an adapted the research and development designed by Hyland (2003) (1), which consisted of eight stages, namely(1) Need analysis, (2) Carrying Out Survey, (3) Developing the Product, (4) Experts Validation, (5) Revision I, (6) Product Try Out, (7) Revision II, and (8) Final Product. The instruments used in the need analysis were a questionnaire, an interview guide, and a classroom observation sheet. The students’ data were calculated in percentages, and those from the teacher interview and classroom observation were described qualitatively. The data from the need analysiswere used as a basic consideration for material development. The story of ”Golden Cucumber” was chosen based on the result of need analysis which most of the students did not know about the ”Golden Cucumber” story because moreover, they like to read foreign folktales. From the”Golden Cucumber” story,the students can learn to keep survive when they meet problems and not give up. Questionnaires were provided for the validation of language and the validation of the media. The result of these questionnaires was used to revise the product before the try-out. Some revisions are based on the expert’s validation stage, such as grammatical errors in the narration of the story, worksheet, and unsuitable between text with some pictures. The expert and teacher data were analysed qualitatively, while data from the try-out were analysed qualitatively and quantitatively. The try-out was conducted once on Thursday, 9 October 2020. It was carried out in Class X Networking 4 that consisted of thirty-nine students. The result from the questionnaires showed that the students liked the video animation and could answer the questions. That been showed with the average score of all the students was 86.6. They were interested in watching the video animation entitled Golden Cucumber. The data obtained from the questionnaires also showed that 55% of students said that the media really helped them, and 45% of students said that the video animation quite helped them to understand the story and answer the questions.
Perbandingan Teknik Aplikatif Cat Eyes Eyeliner dengan Winged Eyeliner pada Tata Rias Pengantin Muslim untuk Bentuk Mata Bulat

Eyeliner is a cosmetic that is commonly used on the eye line to emphasize the eye line. The use of eyeliner can also manipulate the shape of the eyes so that the eyes will look more beautiful and defined. Applying the right eyeliner can also minimize the use of lashes to emphasize the shape of the eye. This study aims to (1) determine the results of the comparison of the applied techniques of cat eye eyeliner with winged eyeliner in Muslim bridal make-up for round eyes (2) to find out which applied eyeliner techniques have the overall results make round eyes look neat, proportional and firm. This type of research is experimental research. The subjects in this study were applied techniques of cat eyes eyeliner with winged eyeliner on Muslim bridal make-up for round eye. The data collection method used was observations made by 34 respondents. The data analysis technique used is the Independent Sample T-Test. The results showed that the application of cat eyes eyeliner and winged eyeliner to Muslim bridal make-up for round eyes did not have a significant difference. The best result in this study is the applied winged eyeliner technique. The average result of the applied winged eyeliner technique has the highest score on three aspects of assessment, namely the proportion of the eye shape of 3.3, the aspect of eye firmness of 3.6, and the overall result of eye makeup of 3.5.Keywords: make-up, cat eyes eyeliner, winged eyeliner, round eyes
UPAYA MENINGKATKAN HASIL BELAJAR AQIDAH AKHLAK DENGAN METODE GALLERY WALK SISWA KELAS VIII MTs PARMIATUWASSA’ADAH SAMBIREJO TIMUR TAHUN AJARAN 2019/2020

This study aims to determine whether the learning process using the Gallery Walk learning method can improve student learning outcomes of Aqidah Akhlak students of class VIII MTs Parmiatu Wassa'adah Sambirejo Timur in the A cademic Y ear2019/2020 . This research method is Classroom Action Research (CAR) which is carried out in 2 cycles, in each cycle consisting of four stages , namely; planning, action, observation, and reflection. Data collection techniques in this study used observation and test techniques.The results of this study indicate that the Gallery Walk method can improve student learning outcomes from the value of completeness in the pre-cycle the percentage shows 34.62%. While in the first cycle student achievement showed 61.54% and followed later in the second cycle as much as 100%. The average value of learning outcomes in the pre-cycle is 64.81, while in the first cycle it is 70.96 and to be 87.1. in the second cycle. Thus the results of the study conclude that the application of the Gallery Walk learning method can improve the learning outcomes of class VIII students in the Aqidah Akhlak subject of MTs Parmiatu Wassa'adah Sambirejo Timur students in the Academic Year  2019/2020.