Reviewerly/exHarmony huggingface dataset:
Face Recognition Systems: Are you sure they only consider your face?

Face recognition has been one of the major biometric authentication procedures in smart devices that allows users to provide an additional layer of security for accessing their device. The accuracy of image similarity should depend on the face and its expression, as could be extracted from the whole image. Importantly, the background may have a substantial amount of additional information that can potentially pose a threat to the privacy of the user. In this paper, we report the impact of background on the recommended measure of similarity, Euclidean-L2, across different pictures that represent distinguishable emotions and image background. Additionally, we report that this impact of the background varies for different ethnic groups. Our findings are despite the fact that background should not matter for Face Recognition. For each facial image, we perform two preprocessings, gray-scaling and background whitening, and compute the similarity between the original image and the preprocessed image by using the DeepFace Face Recognition System. We have considered six data sets, i) containing 100 blurry images of one American man, ii) and iii) contained 100 images each of one American man in normal settings, iv) contained 50 each of East Asian men and women, v) contained 50 each of Indian men and women, and vi) contained 50 each of African or African-American men and women. We observe that gray scaling or background whitening images makes them dissimilar, often to the point of being unrecognisable. Overall, we report that the information contained in the background of a facial image can be significant and it can have different impacts across different skin complexions and facial structure. Importantly, our initial results bring up an important question of how to identify the images having a higher risk of exposing private information via the background of a facial image.
Towards Improving the Deprecation Process of Web Features through Progressive Web Security

To keep up with the continuous modernization of web applications and to facilitate their development, a large number of new features are introduced to the web platform every year. Although new web features typically undergo a security review, issues affecting the privacy and security of users could still surface at a later stage, requiring the deprecation and removal of affected APIs. Furthermore, as the web evolves, so do the expectations in terms of security and privacy, and legacy features might need to be replaced with improved alternatives. Currently, this process of deprecating and removing features is an ad-hoc effort that is largely uncoordinated between the different browser vendors. This causes a discrepancy in terms of compatibility and could eventually lead to the deterrence of the removal of an API, prolonging potential security threats. In this paper we propose a progressive security mechanism that aims to facilitate and standardize the deprecation and removal of features that pose a risk to users’ security, and the introduction of features that aim to provide additional security guarantees.
Analyzing Sentiments on Indonesia's New National Palace using The Combination of Naive Bayes and Sentiment Scoring

Sentiment Analysis is one of the fields of Natural Language Processing (NLP) that builds a system to recognize and extract opinions in text form with the help of sentiment analysis; previously unstructured information can be changed to be more structured. There are many ways to implement sentiment analysis, one of which is to use the Naive Bayes method to categorize a word and sentence from a sentiment. To determine a sentiment, we need a technique that helps to calculate the negative and positive of a sentence. The method used is sentiment scoring. This research begins by mining the required data, namely the pre-design of the state palace, pre-processing of data with four stages of case folding, tokenizing, stopword, and stemming. Sentiment scoring produces positive and negative labels, and then Naive Bayes is used to entering each category. According to the criteria. The next stage is to implement a sentiment analysis system that has been designed previously for the state palace and ends with the testing stage. Although several studies have been conducted related to sentiment analysis, none have used sentiment assessment for labeling. After implementation, we tested the accuracy by dividing the test data and training data differently. The test is carried out using a fast mining tool. The results obtained an accuracy of 70&#x0025;, and the effects of sentiment on this issue are 28.57&#x0025; positive and 71.43&#x0025; negative. This means that public sentiment towards the design of the new capital city is the number of criticisms that come out. Some provide criticism by providing input, do not support it, though, and provide support briefly. Suggestions for further research are to add and use a better sentiment dictionary and expand case studies on the government to get high accuracy results.
Doctors Dynamic Scheduling For Outpatient, Inpatient, And Surgery Using Genetic Algorithm

Doctor scheduling and rescheduling play an essential factor in the day-to-day operation of the hospital. It is essential in scheduling suitable Doctors to provide the best service to patients with optimized resources. Rescheduling a doctor is unrecognized as an everyday time-consuming management task with different imperatives from scheduling. Poor rescheduling decisions can have quality, cost, and moral implications. This study aims to develop a genetic algorithm that can assist in scheduling and rescheduling doctors for medical resources. Our case study is classified as a re-planning problem using the genetic algorithm method in treating physician scheduling and rescheduling for outpatient, inpatient, and surgery. An integer permutation representation with 200 chromosomes is used, where each digit in the gene represents the work program. The crossover method used is Two-point crossover, reciprocal exchange mutation method, and elitism selection. The tests&#x0027; results include a population size of 50 individuals with an average fitness of 0.4766667, 100 generations with an average fitness of 0.5, and the combination of Cr &#x003D; 0.20 and Mr 0.80 with an average fitness of 0.48. the genetic algorithm can combine further research with a Hybrid Tabu Search Algorithm, Bee Colony Optimization Algorithm, and Harmony search algorithm (HSA) to produce an optimal schedule solution.
Combining Classification and Recommendation Techniques on Selecting New Branch Office: A Case of Heavy Equipment Services Company

PT. Mustwill is a company engaged in heavy equipment repair services. The number of consumers outside the city or island causes the repair time to take a long time, therefore PT. Mustwill needs a new branch to reduce repair time. In opening a new branch, several things must be considered, such as the number of consumers in each city, income in each city, the area of the shophouse land, the area of the shophouse building, and the price for the shophouse rental. This study uses the Naive Bayes method and the Simple Additive Weighting method. The Naive Bayes method is used to classify feasible or feasible cities in determining the location of new branch openings. In contrast, the simple additive weighting method is used for ranking based on the criteria weighting values of cities that are declared eligible by the Naive Bayes method. Although there have been studies that have used these two methods, no studies have used these two methods to determine recommendations for opening new branches. After implementing, we conducted an accuracy test to test the merging of the two methods for recommendations for opening new branches. Accuracy testing was carried out using rapidminer with an accuracy of 83&#x0025;. Suggestions for further research are to add several criteria such as population, street names, and coordinates so that the results of the recommendations are more specific.
The Use of Electronic Communication Technology by Terrorist Movement in Indonesia

This paper explains the use of electronic communication technology of chat application particularly Telegram for terrorism movement in Indonesia through a terrorist group in Batam called Katibah Gongong Rebus (KGR). Through an interdisciplinary qualitative studies of terrorism studies and communication and technology studies, this article uses communication technology perspective to have better understanding of electronic communication technology of Telegram. From the case of KGR movement through social media of Telegram, it shows the media of communication, the ways of communication, the content of communications, and the relations among the terrorists as well as the links among some terrorist groups in Indonesia. Telegram has been used by them for: 1) recruiting followers, 2) planning actions, 3) communication and coordination among group members, 4) networking and connecting among terrorist groups across countries, and 5) spreading messages. The movement of terrorism becomes more massive through the use of ECT.
Paper Recommendation for Research References in Data Mining using Content-Based Filtering

The recommendation system predicts items that can identify and provide high-potential content selected by the user based on the filtering information. In this study, a reference recommendation system was made to recommend reading papers for students in their final project. This system makes it easier for students to find the correct absolute assignment reading reference according to the topic taken. This study uses the content-based filtering method and the TF -IDF algorithm to find the availability of existing content. Previous studies have researched reading recommendations for students working on their final project using the Collaborative filtering method. The method requires a rating parameter to recommend something so that if there is a new item, the item is not recommended. However, there is a possibility that the thing from each content is similar. Therefore, this research will answer this problem by creating a recommendation system using Content-Based Filtering. After implementation, we tested the accuracy by dividing the test data and training data differently. System testing is done by using a confusion matrix. The results that have been tested get an accuracy of 78&#x0025;. Further research suggests adding paper data and themes in recommending reading references to make the recommendations more optimal.
Utilization of the Gamification Method on the Website Journal System

In the era of the Industrial Revolution 4.0, technological advances are very important in two areas of publishing educational journals. In science, scientific research is the most important symptom of scientific publications. Today, scientific publications undergo very significant changes. Journal publishing platform or commonly called journal publishing is a journal service system container that is used to disseminate information about scientific research data through the Open Journal System (OJS). One application of gamification techniques in journal publications can be applied to the display board. Due to gamification, users or researchers become more motivated because of the points collected. Display becomes more informative and attractive so that research indicators increase. Users or researchers can also find out information or journals that are often read by other users.
Recommendation On Drugs Prescription For Diabetic Patient Using Case based Reasoning

As medical workers, we are often required to be maximal in treating patients. The knowledge possessed by medical personnel, in this case, doctors, must be demanded to the maximum so that there are no errors in providing treatment to patients. Therefore we need a system to minimize the mistakes, namely a recommendation system. So this study builds a drug recommendation system for patients who have diabetes. One method applied in a recommendation is Case Base Reasoning (CBR). The CBR method calculates based on the similarity of the old case to the new issue. In calculating the similarity of the problems using the Nearest Neighbors algorithm. Although there have been many studies on drug recommendations, no studies have added risk factor variables. Risk factors can influence results more accurately. The results of this study are in the form of a prescription drug recommendation system for diabetic patients based on the type of diabetes they suffer from. They are testing the accuracy of this system using the Confusion Matrix with an accuracy of 80.60&#x0025;. Suggestions for further research are to add several types of similarity. It is necessary to research different kinds of diseases. It is expected to produce an integrated system that can provide drug recommendations for various conditions.
The application of unsupervised learning techniques to the clustering method in use of cell phones in Indonesia

The research objective was to apply unsupervised learning techniques by comparing several clustering methods in data mining to determine the best method for cell phone use cases in Indonesia. The data source comes from the Central Bureau of Statistics with the website url https://www.bps.go.id/. The data category used is the percentage of the population using cellular phones by province from 2012&#x2013;2018 (34 records). The attributes used are the name of the province, the percentage (&#x0025;) of the population using cellular telephones in urban areas and the percentage (&#x0025;) of the population using cellular phones in rural areas. The unsupervised learning methods compared were k-medoids and k-means using the T - Test validity test based on the Davies Bouldin Index (DBI) to evaluate the cluster model. The analysis process uses Rapid Miner software. The mapping label is in the form of the cluster used, namely the high cluster (K1) for cellular phone use and the low cluster (K2) for cellular phone use. The best mapping result for the use of cell phones in urban areas is the k-medoids method with the T - Test result = 0.526 (DBI with <tex>$\mathbf{k}=2$</tex>) where 26 provinces <tex>$(\mathbf{K}1=\mathbf{cluster}_{-}1)$</tex> and 8 provinces <tex>$(\mathbf{K}2\ =\mathbf{cluster}_{-}0)$</tex> &#x2022; Meanwhile, the best mapping result for the use of cellular phones in rural areas is the k-medoids method with the results of T - Test <tex>$=0.431$</tex> (DBI with k <tex>$=2)$</tex> where 31 provinces <tex>$(\mathbf{K}1=\mathbf{cluster}_{-}0)$</tex> and 2 provinces (K2 <tex>$= \mathbf{cluster}_{-}1)$</tex>. Based on the mapping results, the mastery of cellular telephones in rural areas (2 &#x0025;) and in urban areas (24 &#x0025;) means that the understanding of the use of cellular telephones in Indonesia is very good. Overall, the k-medoid method is better at mapping cases of cell phone use in Indonesia. Based on the results, it is expected to be information for the government considering that Indonesia has the opportunity to grow very fast and large with the support of the government so that Indonesia&#x0027;s digital industry can overcome the lag behind other countries.
Analyzing sentiments on official online lending platform in Indonesia with a Combination of Naive Bayes and Lexicon Based Method

The rapid development of social media makes people write their opinions on something, and make the data source for this research one of them from social media, Twitter, which will be analyzed in the form of sentiment analysis which is a process to understand and process data to get the information contained. In the opinion of the sentence. The trend of legal online loan/credit applications widely used by people in Indonesia is a hot topic discussed by the public on Twitter. If you want to know the tendency of public comments on online loan/credit applications, whether positive or negative, then sentiment analysis is carried out. The stages in conducting sentiment analysis in this study are data preprocessing, data processing, classification, and evaluation. The nave Bayes method is used because it has a high level of accuracy in classifying sentiments. Combining it with the lexicon can add precision to organizing emotions compared to other methods. The data used in this study were 4059 data with the distribution of 70&#x0025; training data and 30&#x0025; test data. Sentiment analysis obtained in this research shows that Twitter users in Indonesia give more negative comments. This study shows that by using both methods and testing using RapidMiner, the level of accuracy for classifying positive and negative sentiments achieves entirely satisfactory results, namely 82.06&#x0025;, where the accuracy is higher than using only the lexicon-based saka method by 80&#x0025;.
Klasifikasi Sinyal EKG menggunakan Ciri Statistik dan Parameter Hjorth dengan SVM dan k-NN

ABSTRAKSinyal elektrokardiogram (EKG) dapat dianalisis dengan memperhatikan bentuk, durasi, dan irama. Pada penelitian ini, dikembangkan sebuah metode ekstraksi ciri sinyal EKG dengan menggunakan parameter Hjorth dan ciri statistik. Kedua parameter tersebut diaplikasikan untuk mengekstrak ciri-ciri dari rekaman suara sinyal EKG. Terdapat tiga kondisi rekaman sinyal EKG yang menjadi masukan dari sistem, kondisi normal, atrial fibrillation (AF), dan congestive heart failure (CHF). Set ciri rekaman EKG yang didapatkan kemudian diklasifikasikan dengan menggunakan metode support vector machine (SVM) dan k-Nearest Neighbor (k-NN) untuk dibandingkan performansinya. Hasil pengujian menggunakan semua ciri sebagai prediktor menunjukkan bahwa usulan sistem mampu memberikan akurasi sebesar 100%. Sementara itu pada skenario reduksi ciri dimana hanya dua ciri yaitu skewness dan complexity, performansi sistem tidak berkurang. Komparasi dengan beberapa studi sebelumnya menunjukkan bahwa usulan metode lebih unggul dalam hal akurasi deteksi dan jumlah ciri yang digunakan.Kata kunci: EKG, atrial fibrillation, congestive heart failure, Hjorth, SVM, k-NN ABSTRACTAn electrocardiogram (ECG) signal can be analyzed by paying attention to its shape, duration, and rhythm. In this study, feature extraction for ECG signals is applied using the Hjorth parameter and statistical characteristics. These two parameters are applied to extract the characteristics of the ECG signal sound recording. There are three conditions of ECG signal recording that are used as input for the system. They are normal conditions, atrial fibrillation (AF), and congestive heart failure (CHF). The set of ECG recording features are classified using the support vector machine (SVM) and k-Nearest Neighbor (k-NN) methods. The test results using all features show that the proposed system can achieve 100% of accuracy. On the other hand, by reducing the feature using only skewness and complexity, the system’s performance is not reduced. Comparative studies with several previous studies show that the proposed method is superior in detection accuracy and the number of features used.Keywords: ECG, atrial fibrillation, congestive heart failure, Hjorth, SVM, k-NN
A Mobile Invented Spelling Tutoring System

Invented spelling is a common exercise administered in kindergarten classrooms where students are asked to produce phonetically correct, but not necessarily absolutely correct spellings. For instance, "jyraf" is a phonetically correct spelling for "giraffe". We present a mobile intelligent tutoring system capable of robustly providing adaptive feedback for invented spelling practice on nearly any target word, and report some initial user testing results.
Multimodal Modeling of Task-Mediated Confusion

In order to build more human-like cognitive agents, systems capable of detecting various human emotions must be designed to respond appropriately.Confusion, the combination of an emotional and cognitive state, is underexplored.In this paper, we build upon prior work to develop models that detect confusion from three modalities: video (facial features), audio (prosodic features), and text (transcribed speech features).Our research improves the data collection process by allowing for continuous (as opposed to discrete) annotation of confusion levels.We also craft models based on recurrent neural networks (RNNs) given their ability to predict sequential data.In our experiments, we find that text and video modalities are the most important in predicting confusion while the explored audio features are relatively unimportant predictors of confusion in our data.
A Database for Modal Semantic Typology

This paper introduces a database for crosslinguistic modal semantics.The purpose of this database is to (1) enable ongoing consolidation of modal semantic typological knowledge into a repository according to uniform data standards and to (2) provide data for investigations in crosslinguistic modal semantic theory and experiments explaining such theories.We describe the kind of semantic variation that the database aims to record, the format of the data, and a current snapshot of the database, emphasizing access and contribution to the database in light of the goals above.We release the database at https://clmbr.shane.st/modal-typology.
Monocular Depth Estimation of Indoor Environments Using Enhanced Rgb-D Dataset

Depth estimation from indoor datasets is more challenging than that from outdoor datasets, both for supervised and self-supervised approaches. Supervised monocular depth estimation methods achieve efficient depth training and exhibit superior performance when compared with self-supervised methods. However, the heavy noise from the indoor ground-truth (GT) depth dataset fundamentally restricts performance improvements in supervised depth estimation for the indoor dataset. In this study, we focused on improving the quality of an indoor GT depth dataset to exploit the advantages of supervised approaches. The inaccurate depth dataset was enhanced based on our previous work, requiring sequentially scanned RGB-depth images. The enhanced GT depth image was generated by aligning a set of consecutive depth frames in a local spatial region; then, the aligned depth frames were averaged at pixel level to reduce the noise and recover the missing values. To verify the contribution of the data quality to performance, we trained the dataset on a baseline-level network and obtained results comparable to those of state-of-the-art supervised methods. Moreover, the comparative supervised methods were retrained separately using the original and enhanced GT datasets, and the results revealed a significant improvement in performance when the method was trained by using the enhanced GT dataset. The experimental results showed that the quality of the GT dataset plays a primary role in performance.
Outlier/Anomaly Detection of Univariate Time Series: A Dataset Collection and Benchmark

In this paper, we present an extensive collection of outlier/anomaly detection tasks to identify unusual series from a given time series dataset. The presented work is based on the popular UCR time series classification archive. In addition to the detection tasks, we provide curated benchmarks, an evaluation scheme and baseline results. The resulting unusual time series detection collection is openly available at: https://outlier-detection.github.io/utsd/ .
Measuring the Impact of Digital Exchange Cyberattacks on Bitcoin Returns

We investigate the impact on Bitcoin returns arising from cyberattacks on digital exchanges. Using several alternative specifications we test the hypothesis that Bitcoin experiences lower returns on the dates associated with cybersecurity breaches of cryptocurrency markets. We find a negative effect where Bitcoin price declines between 1.288% and 1.470% on cyberattack days, depending on model specification. Our results provide evidence of cybersecurity related price risk that may not be widely recognised by cryptocurrency market participants.