Reviewerly/exHarmony huggingface dataset:
Semi-supervised Text Annotation for Hate Speech Detection using K-Nearest Neighbors and Term Frequency-Inverse Document Frequency

Sentiment analysis can detect hate speech using the Natural Language Processing (NLP) concept.This process requires annotation of the text in the labeling.However, when carried out by people, this process must use experts in the field of hate speech, so there is no subjectivity.In addition, if processed by humans, it will take a long time and allow errors in the annotation process for extensive data.To solve this problem, we propose an automatic annotation process with the concept of semi-supervised learning using the K-Nearest Neighbor algorithm.This process requires feature extraction of term frequency-inverse document frequency (TF-IDF) to obtain optimal results.KNN and TF-IDF were able to annotate and increase the accuracy of < 2% from the initial iteration of 57.25% to 59.68% in detecting hate speech.This process can annotate the initial dataset of 13169 with the distribution of 80:20 of training and testing data.There are 2370 labeled datasets; for testing, there are 1317 unannotated data; after preprocessing, there are 9482.The final results of the KNN and TF-IDF annotation processes have a length of 11235 for annotated data.
Developing A Moodle-Based Learning Management System (LMS) for Slow Learners

This study aimed to develop a Moodle-based Learning Management System (LMS) called eLSIDA (e-Learning STKIP PGRI Sidoarjo Asistif), which is validly used by students slow to study at STKIP PGRI Sidoarjo. This study used 3 out of 5 ADDIE models, namely Analysis, Design, and Development. Expert judgment in this study consists of two experts, material experts, and media experts. The research instrument used to obtain research data is a questionnaire. The data that has been obtained is then analyzed using descriptive analysis. Validation results from material experts obtained a score of 92.5%, while the results of media expert validation obtained a score of 95%. Based on the expert judgment assessment shown that eLSIDA that has been developed in general can be declared valid and worth to be used. The media that has been developed can be accessed through http://lms.elsida.ac.id/Although the development of eLSIDA has been considered valid, the effectiveness of the media on eLSIDA needs to be tested further.
Reusability and discovery models in software systems: a systematic literature review.

Software Engineering principles and practices promote the reuse of existing elements to maximize the performance of development and maintenance processes.The core concepts of reusability are aligned with the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles.From our perspective, FAIR principles are not only related to data in a software system, because there are other resources and parts of a system that are related to data indirectly or require data schemas and models for their description and management.Examples of these resources are services, business logic processes, contracts, security policies, architectures, components, deployment resources, execution rules, or interaction alternatives, among other possible examples.Actually, any resource in a system may need to be discovered either internally by a subprocess, or externally by any client that might be interested in finding, accessing, interoperating, or reusing it.This paper addresses a systematic literature review to analyze the current principles, paradigms, and technologies that can be applied to enable discovering and reusing mechanisms in modern software systems, and how traditional and outdated approaches have been adapted to support present issues.From this analysis, we outlined the challenges and open research lines that can be addressed to achieve a suitable reuse strategy.
Enterprise SaaS Workloads on New-Generation Infrastructure-as-Code (IaC) on Multi-Cloud Platforms

Cloud Computing has become the primary model used by DevOps practitioners and researchers to provision infrastructure in minimal time. But recently, the traditional method of using a single cloud provider has fallen out of favor due to several limitations regarding performance, compliance rules, geographical reach, and vendor lock-in. To address these issues, industry and academia are implementing multiple clouds (i.e., multi-cloud). However, managing the infrastructure provisioning of enterprise SaaS applications faces several challenges, such as configuration drift and the heterogeneity of cloud providers. This has seen Infrastructure-as-Code (IaC) technologies being used to automate the deployment of SaaS applications. IaC facilitates the rapid deployment of new versions of application infrastructures without degrading quality or stability. Therefore, this work presents a vision of uniformly managing the infrastructure provisioning of enterprise SaaS applications that utilize multiple cloud providers. Hence, we introduce an initial design for the IaC-based Multi-Cloud Deployment pattern and discuss how it addresses the relative challenges.
Diverse Image Inpainting with Normalizing Flow

Image Inpainting is an ill-posed problem since there are diverse possible counterparts for the missing areas. The challenge of inpainting is to keep the “corrupted region" content consistent with the background and generate a variety of reasonable texture details. However, existing one-stage methods that directly output the inpainting results have to make a trade-off between diversity and consistency. The two-stage methods as the current trend can circumvent such shortcomings. These methods predict diverse structural priors in the first stage and focus on rich texture details generation in the second stage. However, all two-stage methods require autoregressive models to predict the probability distribution of the structural priors, which significantly limits the inference speed. In addition, their discretization assumption of prior distribution reduces the diversity of the inpainting results. We propose Flow-Fill, a novel two-stage image inpainting framework that utilizes a conditional normalizing flow model to generate diverse structural priors in the first stage. Flow-Fill can directly estimate the joint probability density of the missing regions as a flow-based model without reasoning pixel by pixel. Hence it achieves real-time inference speed and eliminates discretization assumptions. In addition, as a reversible model, Flow-Fill can invert the latent variables for a specified region, which allows us to make the inference process as semantic image editing. Experiments on benchmark datasets validate that Flow-Fill achieves superior diversity and fidelity in image inpainting qualitatively and quantitatively.
Software Defect Density Prediction Using Deep Learning

Delivering a reliable and high-quality software system to client is a big challenge in software development and evolution process.One of the software measures that confirm the quality of the system is the defect density.Practitioners usually need this measure during software development process or during a period of operation to indicate the reliability of software system.However, since predicting defect density before testing the modules is time consuming, managers need to build a prediction model that can help in detecting the defective modules.This process can reduce the testing cost and improve testing resources utilizations.The most intrinsic feature of software defect datasets is the data sparsity in the defect density which might bias the final prediction.Therefore, we use deep learning to build defect density prediction models and handle the inherit challenge of data sparsity in defect density.Deep learning has shown to be effective with sparse data.The constructed model has been evaluated against well-known machine learning methods over 28 public datasets.The obtained results confirmed that the deep learning model is generally more adequate than other machine models over the datasets with high and very high sparsity ratios, and competitive choice when the sparsity ratio is either medium or low
A 2D Chaotic Oscillator for Analog IC

In this paper, we have proposed the design of an analog two-dimensional (2D) discretetime chaotic oscillator.2D chaotic systems are studied because of their more complex chaotic behavior compared to one-dimensional (1D) chaotic systems.The already published works on 2D chaotic systems are mainly focused either on the complex analytical combinations of familiar 1D chaotic maps such as Sine map, Logistic map, Tent map, and so on, or off-the-shelf component-based analog circuits.Due to complex hardware requirements, neither of them is feasible for hardware-efficient integrated circuit (IC) implementations.To the best of our knowledge, this proposed work is the first-ever report of an analog 2D discrete-time chaotic oscillator design that is suitable for hardware-constrained IC implementations.The chaotic performance of the proposed design is analyzed with bifurcation plots, the transient response, 2D Lyapunov exponent, and correlation coefficient measurements.It is demonstrated that the proposed design exhibits promising chaotic behavior with low hardware cost.The real-world application of the proposed 2D chaotic oscillator is presented in a random number generator (RNG) design.The applicability of the RNG in cryptography is verified by passing the generated random sequence through four standard statistical tests namely, NIST, FIPS, TestU01, and Diehard.
A Study on Prevention and Automatic Recovery of Blockchain Networks Against Persistent Censorship Attacks

Blockchain techniques have been actively used in a wide range of applications owing to their unforgeable characteristics. Currently, many blockchain platforms are applying a pBFT-based Delegate PoS(DPoS) consensus mechanism in their networks, because it ensures a quick consensus process since only a few validators participate in the consensus process. However, the pBFT-based DPoS consensus mechanism has a limitation that a malicious cartel with more than 1/3 of the total stake can launch persistent censorship attacks which disrupt the consensus process. To address this limitation, we propose a new defense method against persistent censorship attacks. First, we introduce a hierarchical consensus architecture that consists of the main-validators and sub-validators. In our consensus architecture, sub-validators can disagree with the results of the consensus process of main-validators known as existing validators. As the number of disagreeing sub-validators increases, the cost of censorship attacks increases. Second, we propose a behavior-based credit-scoring function that can operate on our consensus architecture. Our proposed function changes the role of the validators based on their behavior, thereby disrupting the formation of malicious cartels and removing the persistence of censorship attacks. We show that our proposed defense method makes a blockchain network more tolerant and resistant to censorship attacks based on experimental results using validator information collected from a real blockchain network. We also demonstrate that our defense method can automatically recover the blockchain network from persistent censorship attacks.
Data breach: analysis, countermeasures and challenges

The increasing use or abuse of online personal data leads to a big data breach challenge for individuals, businesses, and even the government. Due to the scale of online data and the uncertainty of human factors, it is not feasible to build a practical prevention approach for data breach incidents in a real-time manner. In addition, despite the existing research on protecting users' data, a little systematic survey has been published to guide researchers and industrial participants to address the data breach issues. In this paper, we perform a comprehensive review and analysis of typical data breach incidents. We investigate threat actors, security flaws, and vulnerabilities that often lead to data breaches. The paper also includes the consequences of the information disclosures and lessons learned from each incident. Finally, we discuss countermeasures and challenges in preventing potential data breaches.
Characteristics of Understanding URLs and Domain Names Features: The Detection of Phishing Websites With Machine Learning Methods

Along with the means of communication, it has also prompted the birth of more harmful, and challenging websites in the device of information systems, and electronics. According to current estimates, you can deal with a huge budget to arrange detailed information on attackers. Furthermore, only those that are handled similarly to HTML, DOM, and URL based features in the literature are easily manipulated by attackers. To respond to these attacks, we propose a new method that detects phishing websites by categorizing the Internet URL, and domain names of websites with six different classifier algorithms according to eleven predetermined features. For this method, we created a previously unused list. The list was obtained by analyzing an index created with information obtained from internationally reputable intelligence services, and entire organizations. The proposed method simplifies the process of feature extraction, and reduces processing overhead while going beyond analyzing on HTML, DOM, and URL based features by considering URLs, and domain names. To illustrate the highest accuracy rate among six different classification results, we preferred to use the Random Forest algorithm. In this study, we use a dataset with 32,928 data in which 12,134 data without phishing websites, and 20,614 data with phishing websites to be labeled according to eleven predetermined features. Our experimental results show that phishing websites can be detected with as much as 98.90% accuracy with our proposed method. As a result, it has been demonstrated that RF descriptors with SVM representation can be utilized to accurately mark phishing web pages. In addition, characteristic updates can be followed with a continuously updated source.
AMCSFFT-CIE: Alternate Multi-dimensional Chaotic Structure and Fast Fourier Transform based Novel Approach to Color Image Encryption

Low-dimensional chaotic maps are used for encrypting images because of their simplicity and low implementation cost.However, such chaotic systems are less secure and are slow in encryption speed as compared to multi-dimensional chaotic systems.This paper proposes the combination of spatial domain and frequency domain for image encryption.The proposed combination uses Four-dimensional chaotic map Lorenz System (LS) for key generation and alternate logistic structure for permutation-diffusion process.Initially, Lorenz System (LS) generates the security key using matrices color image's R, G, and B components.Secondly, one dimensional and two dimensional chaotic structures are used to generate a chaos matrix i.e. the two logistic maps are iterated alternately for diffusing the matrix.Finally, the diffused plain image matrix is encoded into the phase and amplitude of the composite function which is encrypted with white noise distribution by using the fast Fourier transform.The simulated outcomes describe that the proposed approach increases the key space value, encryption speed and resists brute force attacks.
Enhancement Digital Forensic Approach for Inter-Frame Video Forgery Detection Using a Deep Learning Technique

The digital world has been witnessing a fast progress in technology, which led to an enormous increase in using digital devices, such as cell phones, laptops, and digital cameras. Thus, photographs and videos function as the primary sources of legal proof in courtrooms concerning any incident or crime. It has become important to prove the trustworthiness of digital multimedia. Inter-frame video forgery one of common types of video manipulation performed in temporal domain. It deals with inter-frame video forgery detection that involves frame deletion, insertion, duplication, and shuffling. Deep Learning (DL) techniques have been proven effective in analysis and processing of visual media. Dealing with video data needs to handle the third dimension (the time dimension), which means extracting temporal features as well as spatial features. The proposed model is built based on the Three Dimension Convolution Neural Network (3D-CNN). Through pre-processing operation that introduced difference frames that pick up the difference in successive adjacent frames, which provide a large quantity of temporal information and lead to enhance the effectiveness of the proposed model. The model achieves high accuracy of 99%.
On the Context-Free Ambiguity of Emoji

Due to their pictographic nature, emojis come with baked-in, grounded semantics. Although this makes emojis promising candidates for new forms of more accessible communication, it is still unknown to what degree humans agree on the inherent meaning of emojis when encountering them outside of concrete textual contexts. To bridge this gap, we collected a crowdsourced dataset (made publicly available) of one-word descriptions for 1,289 emojis presented to participants with no surrounding text. The emojis and their interpretations were then examined for ambiguity. We find that, with 30 annotations per emoji, 16 emojis (1.2%) are completely unambiguous, whereas 55 emojis (4.3%) are so ambiguous that the variation in their descriptions is as high as that in randomly chosen descriptions. Most emojis lie between these two extremes. Furthermore, investigating the ambiguity of different types of emojis, we find that emojis representing symbols from established, yet not cross-culturally familiar code books (e.g., zodiac signs, Chinese characters) are most ambiguous. We conclude by discussing design implications.
The application of system usability scale method to measure the usability of electronic learning system (e-learning) of politeknik caltex riau

The COVID-19 disaster, which was felt globally in all countries including Indonesia, has in general changed the series of business process arrangements in almost all fields, including the field of education. One of the affected areas of education is the Caltex Riau Polytechnic (PCR) campus. The impact of this disaster has forced the academic community (lecturers and students) to carry out the online lecture process since the Odd semester of the 2020/2021 academic year. The online lecture process is facilitated by an electronic learning system (e-learning). Changes in the pattern of lectures, which previously were held face-to-face, then had to be conducted online using e-learning, gave rise to several usability problems. The usability measurement of e-learning PCR was carried out using the System Usability Scale (SUS) questionnaire, involving 196 participants. The results of the usability analysis of e-learning PCR obtained a score of 58.75. With the assessment criteria on the adjective, the rating is okay, the grade scale is D value, and the acceptable ranges are at a marginal low. This means that e-learning still needs improvement and evaluation of the utilization of the features/services provided, so that it can be used more efficiently, effectively, and satisfactorily for its users.
A Blockchain-Based Regulatory Framework for mHealth

Mobile health (mHealth) is playing a key role in facilitating health services for patients. Such services may include remote diagnostics and monitoring, chronic conditions management, preventive medicine, and health promotion. While mHealth has gained significant traction during the COVID-19 pandemic, they may pose safety risks to patients. This entails regulations and monitoring of shared data and management of potential safety risks of all mHealth applications continuously and systematically. In this study, we propose a blockchain-based framework for regulating mHealth apps and governing their safe use. We systematically identify the needs, stakeholders, and requirements of the current mHealth practices and regulations that may benefit from blockchain features. Further, we exemplify our framework on a diabetes mHealth app that supports safety risk assessment and incident reporting functions. Blockchain technology can offer a solution to achieve this goal by providing improved security, transparency, accountability, and traceability of data among stakeholders. Blockchain has the potential to alleviate existing mHealth problems related to data centralization, poor data quality, lack of trust, and the absence of robust governance. In the paper, we present a discussion on the security aspects of our proposed blockchain-based framework, including limitations and challenges.
A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation

Breast cancer is one of the leading causes of death and most frequently diagnosed cancer amongst women. Annually, almost half a million women do not survive the disease and die from breast cancer. Machine learning is a subfield of artificial intelligence (AI) and computer science that uses data and algorithms to mimic how humans learn, and gradually improving its accuracy. In this work, simple machine learning methods are used to classify breast cancer microarray data to normal and relapse. The data is from the gene expression omnibus (GEO) website namely GSE45255 and GSE15852. These two datasets are integrated and combined to form a single dataset. The study involved three machine learning algorithms, random forest (RF), extra tree (ET), and support vector machine (SVM). Grid search cross validation (CV) is applied for hyperparameter tuning of the algorithms. The result shows that the tuned SVM is best among the tested algorithms with accuracy of 97.78%. In the future it is recommended to include feature selection method to get the optimal features and better classification accuracies.
Continuous LWE is as Hard as LWE &amp; Applications to Learning Gaussian Mixtures

We show direct and conceptually simple reductions between the classical learning with errors (LWE) problem and its continuous analog, CLWE (Bruna, Regev, Song and Tang, STOC 2021). This allows us to bring to bear the powerful machinery of LWE-based cryptography to the applications of CLWE. For example, we obtain the hardness of CLWE under the classical worst-case hardness of the gap shortest vector problem. Previously, this was known only under quantum worst-case hardness of lattice problems. More broadly, with our reductions between the two problems, any future developments to LWE will also apply to CLWE and its downstream applications. As a concrete application, we show an improved hardness result for density estimation for mixtures of Gaussians. In this computational problem, given sample access to a mixture of Gaussians, the goal is to output a function that estimates the density function of the mixture. Under the (plausible and widely believed) exponential hardness of the classical LWE problem, we show that Gaussian mixture density estimation in $\mathbb{R}^{n}$ with roughly $\log n$ Gaussian components given poly $(n)$ samples requires time quasi-polynomial in n. Under the (conservative) polynomial hardness of LWE, we show hardness of density estimation for $n^{\epsilon}$ Gaussians for any constant $\epsilon>0$, which improves on Bruna, Regev, Song and Tang (STOC 2021), who show hardness for at least $\sqrt{n}$ Gaussians under polynomial (quantum) hardness assumptions. Our key technical tool is a reduction from classical LWE to LWE with k-sparse secrets where the multiplicative increase in the noise is only $O(\sqrt{k})$, independent of the ambient dimension n.