Reviewerly/exHarmony huggingface dataset:
Analysis Of Right And Wrong Use Of Mask Based On Deep Learning

Pandemic COVID-19 makes it important to apply the proper and correct use of masks. The correct use of a mask where its use can cover the nose mouth and chin. One of the problems in using masks is that there are still many people who have not used masks properly and correctly. The importance of the correct use of masks because the transmission of the Covid-19 itself does not only occur through splashes when sneezing or coughing between humans but can also occur when talking or breathing by spreading through fluid particles less than 0.0002 inches (5 microns) in diameter called aerosols that are emitted when people talk. From these problemsÂ it is necessary to have a computational-based analysis system to be able to identify patterns and make decisions and perform certain tasks automatically so that the results obtained are more efficient and objective. In this study, a deep learning method with a resnetÂ 50 will be used to obtain the correct and incorrect results of using masks. The results of this study indicate that the deep learning method with resnet 50 is able to achieve 98.41% accuracy in classifying the correct and incorrect use of masks.
Numerical Analysis of Variations Distance Formulas on K Nearest Neighbors In Classifying Malaria Parasite Blood Cells

Malaria is one of the numerous acute and chronic diseases. Even malaria can pose a threat to a person's safety. The original cause of malaria was an infection with a protozoan of the genus Plasmodium, which was transmitted by the bite of a mosquito. This Anopheles mosquito parasite infects red blood cells throughout the body, resulting in an enlarged spleen. This research aims to make it easier for physicians to classify blood images as malaria-infected or not. If the input is a blood image, then SURF Feature Extraction will be used to extract the blood image. We therefore obtained weight results based on the extraction results. The weighted results generated by the SURF extraction process will be classified using the KNN Algorithm to determine whether or not an individual is infected with malaria. This study's tests compared various distance formulas utilized by the KNN classification method. Comparing the results of malaria blood image classification using the KNN classification method with variations in the distance formula, it is evident in table 7 that correlation is the optimal distance formula for malaria parasite blood cells recognition, followed by cosine. According to the results of KNN's tests, it is not optimal at classifying blood images containing malaria, but these results are categorized as good
INVESTIGATION OF CLUSTERING AND CLASSIFICATION METHODS FOR INTELLECTUAL ANALYSIS OF LOG FILES

Today, the application of information technology in all areas of our lives has led to wider spread and popularity of cybercrime. In modern industrial control systems and cyber-physical systems, log files are very important in terms of detecting cyber incidents, identifying and preventing threats and anomalies. However, today, a large volume of log files generated in these systems greatly complicates the process of extracting useful information from them. This, in turn, highlights the need for intellectual analysis of log files. To this end, this article explores a number of clustering and classification methods and algorithms for the intellectual analysis of log files. Thus, K-means, CURE, EM, kNN, Naive Bayes and DT algorithms are selected out of these algorithms and their working principle is studied, explained, and the application of each algorithm on KDD CUP 99 data set is studied and compared.
COMPARATIVE ANALYSIS OF CLUSTER VALIDITY INDICES IN TERMS OF CONSISTENCY

Cluster analysis is one of the key issues in Data Mining, the most important stage of the Knowledge Discovery from Data (KDD) process, and is widely used. There are 3 main tasks of cluster analysis: determining the optimal number of clusters, clustering algorithms and evaluating the quality of clustering. One of the most important steps in cluster analysis is to evaluate the quality of clustering. A number of indices have been proposed to assess the outcome of clustering. The analysis shows that these indices, which are used to assess the quality of clustering, often show inconsistent results. Therefore, extensive research has recently been conducted on the study of indices and new indices are proposed. The article examines a number of internal and external evaluation indices. Different size data sets are taken and k-means, k-medoids, agglomerative hierarchical, BIRCH and OPTICS algorithms are applied to them. A number of internal and external evaluation indices are used to assess the results of the experiment, and the results are analyzed comparatively. Experiments show that Ac, Pr, Rc and F-m indices show similar results in in group determining in a given clustering structure.
BITCOIN SEBAGAI INSTRUMEN INVESTASI YANG MENGUNTUNGKAN

This study was conducted to find out: (1) whether bitcoin is a profitable investment instrument; (2) how to make bitcoin a profitable investment instrument. This article was compiled using the “Library Research” approach where the data obtained will be analyzed in depth descriptively. The results of the study show that: (1) Bitcoin is a profitable investment instrument due to the limited supply of Bitcoin, which makes this instrument behave like Gold; (2) In order to become a profitable investment instrument, individuals can carry out mining activities to get Bitcoin prizes from miner applications and individuals can also carry out spot trading activities by taking advantage of the Bitcoin price difference when buying and selling so as to earn a profit.
Factors Declining Interest in English Language Training for Sermo Tourism Village Community, Kulon Progo Yogyakarta

This study investigates the elements that contribute to the community's waning interest in English language training conducted by the "Bule Mengajar" Community in Sermo Tourism Village, Hargowilis, Kulon Progo. A qualitative method using a descriptive approach was adopted. Situations, objects, or social phenomena are described when conducting qualitative descriptive research. Apart from observations and other supporting materials, informants become a source for acquiring data through organized interviews with trainers and training participants. When it comes to data veracity, triangulation is applied. Data gathering, data reduction, display, and conclusions were all part of the interactive analysis. According to research, the Sermo Tourism Village community's lack of interest in English training is attributable to a lack of proper training.
IMPLEMENTASI AlGORITMA NAIVE BAYES UNTUK MEMPREDIKSI TINGKAT PENYEBARAN COVID

The development of Information Technology (IT) is now very rapid and has been used in various aspects of life both in the field of government, banking, socio- cultural, industrial, education, and even health. One type of disease that gets attention for the application of IT is corona virus or better known as Covid 19 because the spread is quite widespread throughout the country, especially in the territory of Indonesia. Corona virus disease development in Indonesia is growing, based on WHO data as of today on August 30, 2020 positive cases have reached 172,053 people, cases died 7,343 people and recovered 124,185 people and the number of cases is increasing every day. Based on these conditions, Central Aceh is in a state of alert against the threat of corona virus. then a form of prevention of the widespread spread of the virus can be done by breaking the chain of transmission by doing social distancing. In this study, a system will be designed to anticipate the Covid-19 pandemic by predicting the rate of spread of covid-19, especially in central Aceh districts using the Naive Bayes Classifier method. The accuracy level of this system is a positive case of 60%.
STUDENTS' PERCEPTIONS ON ONLINE LEARNING FOR INTERMEDIATE LISTENING COURSES THROUGH E-LEARNING DURING THE COVID-19 PANDEMIC

This study aims to determine the perceptions of English Education students at Nurul Jadid University, Probolinggo towards online learning for intermediate listening courses via E-learning during the Covid-19 pandemic. After one semester's learning process, the qualitative descriptive method was carried out by distributing questionnaires using Google Form to 48 2nd semester students of English Education at Nurul Jadid University, Probolinggo. The results of this study stated that the performance aspects of using UNUJA's E-learning for Intermediate Listening courses are as expected, the effort aspect of using E-Learning UNUJA for Intermediate Listening courses are as expected, social factors of using E-Learning UNUJA for Intermediate Listening courses are still less than expected, aspects of conditions that facilitate the use of E-Learning UNUJA for Intermediate Listening courses are considered very good, student interest in using E-Learning UNUJA for Intermediate Listening courses is very high, and behavioural aspects of using E-Learning UNUJA for Intermediate Listening is good.
Center Prediction Loss for Re-identification

The training loss function that enforces certain training sample distribution patterns plays a critical role in building a re-identification (ReID) system. Besides the basic requirement of discrimination, i.e., the features corresponding to different identities should not be mixed, additional intra-class distribution constraints, such as features from the same identities should be close to their centers, have been adopted to construct losses. Despite the advances of various new loss functions, it is still challenging to strike the balance between the need of reducing the intra-class variation and allowing certain distribution freedom. In this paper, we propose a new loss based on center predictivity, that is, a sample must be positioned in a location of the feature space such that from it we can roughly predict the location of the center of same-class samples. The prediction error is then regarded as a loss called Center Prediction Loss (CPL). We show that, without introducing additional hyper-parameters, this new loss leads to a more flexible intra-class distribution constraint while ensuring the between-class samples are well-separated. Extensive experiments on various real-world ReID datasets show that the proposed loss can achieve superior performance and can also be complementary to existing losses.
Tracking Counterfeit Cryptocurrency End-to-end

The production of counterfeit money has a long history. It refers to the creation of imitation currency that is produced without the legal sanction of government. With the growth of the cryptocurrency ecosystem, there is expanding evidence that counterfeit cryptocurrency has also appeared. In this paper, we empirically explore the presence of counterfeit cryptocurrencies on Ethereum and measure their impact. By analyzing over 190K ERC-20 tokens (or cryptocurrencies) on Ethereum, we have identified 2, 117 counterfeit tokens that target 94 of the 100 most popular cryptocurrencies. We perform an end-to-end characterization of the counterfeit token ecosystem, including their popularity, creators and holders, fraudulent behaviors and advertising channels. Through this, we have identified two types of scams related to counterfeit tokens and devised techniques to identify such scams. We observe that over 7,104 victims were deceived in these scams, and the overall financial loss sums to a minimum of $ 17 million (74,271.7 ETH). Our findings demonstrate the urgency to identify counterfeit cryptocurrencies and mitigate this threat.
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models

We present an efficient method of utilizing pretrained language models, where we learn selective binary masks for pretrained weights in lieu of modifying them through finetuning. Extensive evaluations of masking BERT and RoBERTa on a series of NLP tasks show that our masking scheme yields performance comparable to finetuning, yet has a much smaller memory footprint when several tasks need to be inferred simultaneously. Through intrinsic evaluations, we show that representations computed by masked language models encode information necessary for solving downstream tasks. Analyzing the loss landscape, we show that masking and finetuning produce models that reside in minima that can be connected by a line segment with nearly constant test accuracy. This confirms that masking can be utilized as an efficient alternative to finetuning.
What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features

While discriminative classifiers often yield strong predictive performance, missing feature values at prediction time can still be a challenge. Classifiers may not behave as expected under certain ways of substituting the missing values, since they inherently make assumptions about the data distribution they were trained on. In this paper, we propose a novel framework that classifies examples with missing features by computing the expected prediction with respect to a feature distribution. Moreover, we use geometric programming to learn a naive Bayes distribution that embeds a given logistic regression classifier and can efficiently take its expected predictions. Empirical evaluations show that our model achieves the same performance as the logistic regression with all features observed, and outperforms standard imputation techniques when features go missing during prediction time. Furthermore, we demonstrate that our method can be used to generate "sufficient explanations" of logistic regression classifications, by removing features that do not affect the classification.
On measuring the iconicity of a face

For a given identity in a face dataset, there are certain iconic images which are more representative of the subject than others. In this paper, we explore the problem of computing the iconicity of a face. The premise of the proposed approach is as follows: For an identity containing a mixture of iconic and non iconic images, if a given face cannot be successfully matched with any other face of the same identity, then the iconicity of the face image is low. Using this information, we train a Siamese Multi-Layer Perceptron network, such that each of its twins predict iconicity scores of the image feature pair, fed in as input. We observe the variation of the obtained scores with respect to covariates such as blur, yaw, pitch, roll and occlusion to demonstrate that they effectively predict the quality of the image and compare it with other existing metrics. Furthermore, we use these scores to weight features for template-based face verification and compare it with media averaging of features.
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at: github.com/otkupjnoz/oc-acnn.
Anytime Online-to-Batch Conversions, Optimism, and Acceleration

A standard way to obtain convergence guarantees in stochastic convex optimization is to run an online learning algorithm and then output the average of its iterates: the actual iterates of the online learning algorithm do not come with individual guarantees. We close this gap by introducing a black-box modification to any online learning algorithm whose iterates converge to the optimum in stochastic scenarios. We then consider the case of smooth losses, and show that combining our approach with optimistic online learning algorithms immediately yields a fast convergence rate of $O(L/T^{3/2}+\sigma/\sqrt{T})$ on $L$-smooth problems with $\sigma^2$ variance in the gradients. Finally, we provide a reduction that converts any adaptive online algorithm into one that obtains the optimal accelerated rate of $\tilde O(L/T^2 + \sigma/\sqrt{T})$, while still maintaining $\tilde O(1/\sqrt{T})$ convergence in the non-smooth setting. Importantly, our algorithms adapt to $L$ and $\sigma$ automatically: they do not need to know either to obtain these rates.
Heavy Tailed Horseshoe Priors

Locally adaptive shrinkage in the Bayesian framework is achieved through the use of local-global prior distributions that model both the global level of sparsity as well as individual shrinkage parameters for mean structure parameters. The most popular of these models is the Horseshoe prior and its variants due to their spike and slab behavior involving an asymptote at the origin and heavy tails. In this article, we present an alternative Horseshoe prior that exhibits both a sharper asymptote at the origin as well as heavier tails, which we call the Heavy-tailed Horseshoe prior. We prove that mixing on the shape parameters provides improved spike and slab behavior as well as better reconstruction properties than other Horseshoe variants. A simulation study is provided to show the advantage of the heavy-tailed Horseshoe in terms of absolute error to both the truth mean structure as well as the oracle.
Strong homotopy of digitally continuous functions

We introduce a new type of homotopy relation for digitally continuous functions which we call ``strong homotopy.'' Both digital homotopy and strong homotopy are natural digitizations of classical topological homotopy: the difference between them is analogous to the difference between digital 4-adjacency and 8-adjacency in the plane. We explore basic properties of strong homotopy, and give some equivalent characterizations. In particular we show that strong homotopy is related to ``punctuated homotopy,'' in which the function changes by only one point in each homotopy time step. We also show that strongly homotopic maps always have the same induced homomorphisms in the digital homology theory. This is not generally true for digitally homotopic maps, though we do show that it is true for any homotopic selfmaps on the digital cycle $C_n$ with $n\ge 4$. We also define and consider strong homotopy equivalence of digital images. Using some computer assistance, we produce a catalog of all small digital images up to strong homotopy equivalence. We also briefly consider pointed strong homotopy equivalence, and give an example of a pointed contractible image which is not pointed strongly contractible.
Clicktok: Click Fraud Detection using Traffic Analysis

Advertising is a primary means for revenue generation for millions of websites and smartphone apps (publishers). Naturally, a fraction of publishers abuse the ad-network to systematically defraud advertisers of their money. Defenses have matured to overcome some forms of click fraud but are inadequate against the threat of organic click fraud attacks. Malware detection systems including honeypots fail to stop click fraud apps; ad-network filters are better but measurement studies have reported that a third of the clicks supplied by ad-networks are fake; collaborations between ad-networks and app stores that bad-lists malicious apps works better still, but fails to prevent criminals from writing fraudulent apps which they monetise until they get banned and start over again. This work develops novel inference techniques that can isolate click fraud attacks using their fundamental properties. In the {\em mimicry defence}, we leverage the observation that organic click fraud involves the re-use of legitimate clicks. Thus we can isolate fake-clicks by detecting patterns of click-reuse within ad-network clickstreams with historical behaviour serving as a baseline. Second, in {\em bait-click defence}. we leverage the vantage point of an ad-network to inject a pattern of bait clicks into the user's device, to trigger click fraud-apps that are gated on user-behaviour. Our experiments show that the mimicry defence detects around 81\% of fake-clicks in stealthy (low rate) attacks with a false-positive rate of 110110 per hundred thousand clicks. Bait-click defence enables further improvements in detection rates of 95\% and reduction in false-positive rates of between 0 and 30 clicks per million, a substantial improvement over current approaches.
Self-Dual Skew Cyclic Codes over $\mathbb{F}_{q}+u\mathbb{F}_{q}$

In this paper, we give conditions for the existence of Hermitian self-dual $\Theta-$cyclic and $\Theta-$negacyclic codes over the finite chain ring $\mathbb{F}_q+u\mathbb{F}_q$. By defining a Gray map from $R=\mathbb{F}_q+u\mathbb{F}_q$ to $\mathbb{F}_{q}^{2}$, we prove that the Gray images of skew cyclic codes of odd length $n$ over $R$ with even characteristic are equivalent to skew quasi-twisted codes of length $2n$ over $\mathbb{F}_q$ of index $2$. We also extend an algorithm of Boucher and Ulmer \cite{BF3} to construct self-dual skew cyclic codes based on the least common left multiples of non-commutative polynomials over $\mathbb{F}_q+u\mathbb{F}_q$.
Towards NLP with Deep Learning: Convolutional Neural Networks and Recurrent Neural Networks for Offensive Language Identification in Social Media

This short paper presents the design decisions taken and challenges encountered in completing SemEval Task 6, which poses the problem of identifying and categorizing offensive language in tweets. Our proposed solutions explore Deep Learning techniques, Linear Support Vector classification and Random Forests to identify offensive tweets, to classify offenses as targeted or untargeted and eventually to identify the target subject type.
Predicting Algorithm Classes for Programming Word Problems

We introduce the task of algorithm class prediction for programming word problems. A programming word problem is a problem written in natural language, which can be solved using an algorithm or a program. We define classes of various programming word problems which correspond to the class of algorithms required to solve the problem. We present four new datasets for this task, two multiclass datasets with 550 and 1159 problems each and two multilabel datasets having 3737 and 3960 problems each. We pose the problem as a text classification problem and train neural network and non-neural network-based models on this task. Our best performing classifier gets an accuracy of 62.7 percent for the multiclass case on the five class classification dataset, Codeforces Multiclass-5 (CFMC5). We also do some human-level analysis and compare human performance with that of our text classification models. Our best classifier has an accuracy only 9 percent lower than that of a human on this task. To the best of our knowledge, these are the first reported results on such a task. We make our code and datasets publicly available.