Reviewerly/exHarmony huggingface dataset:
Apparel Recommendation System using Content-Based Filtering

Nowadays, people are constantly moving towards various fashion products as a result the e-commerce market for garments is growing rapidly. Online stores must update their features according to user requirements and preferences. However, there are too many options for users to select from these online stores which may leave them in a dilemma to identify the correct outfit, save the user time, and increase sales, efficient recommendation systems are becoming a necessity for online retailers. In this paper, we proposed an Apparel Recommendation System that generates recommendations for users based on their input. We used a real-world data set taken from the online market giant Amazon using Amazon’s Product Advertising API. We aim to use keywords like brand, color, size, etc., to recommend. Data exploration to get detailed information about our dataset, Data Cleaning(pre-processing) to remove invalid sections, Model selection (We have compared different feature extraction techniques like bag of words, TF-IDF, and word2vec model) to find out efficient techniques and Deployment of the model that could facilitate recommendation system to simplify the task of apparel recommendation system. The accuracy of the model is identified using the response time and content matching.
Improved Salsa20 Stream Cipher Diffusion Based on Random Chaotic Maps

To enhance stream ciphers, numerous studies have concentrated on the randomness, unpredictable nature, and complexity of keystream. Numerous stream algorithms have been put forth. Most of them require a significant amount of computational power. Salsa20 is a high-performance stream encryption solution that works on computers with fewer resources and uses a secure method that is faster than AES. They suggest Salsa20 for encryption in common cryptographic applications. Users who value speed over certainty should utilize the Salsa20 family of reduced-round ciphers, such as the (8,12) round cipher. It uses a 256-bit key and a hash algorithm. A successful fusion makes use of both the Salsa20 algorithm's and the random maps' advantages to improve the Salsa20 algorithm's shortcomings by increasing its unpredictability. Particularly now that Salsa20/7 has been hacked and Salsa20/12 is no longer as secure as it previously was. As a result, Salsa20 needs to achieve a high level of diffusion to withstand known attacks. Right now, salsa20 and its shortened versions rank among the fastest ciphers. This study presents a novel lightweight approach to construct a strong keystream that is sufficiently random to avoid being predicted by adversaries, achieve good diffusion, and withstand known assaults. A NIST test found that the performance of the (Salsa20-chaotic maps) approach in terms of data integrity and secrecy is nearly 0.3131 higher than that of the Salsa20 .
Fuzzy Based Decision Support Model for Health Insurance Claim

Insurance industry in Indonesia has shown promising result based on premium growth in 2014-2018, as recorded in Indonesia General Insurance Market Update 2019. With the increase of premium, the claim rate also grows. Insurance companies face challenges in processing the claims. Many factors need to be carefully considered before making a claim decision. This paper proposes a decision support model (DSM) to score claim cases and to propose claim risk category (CRC) and claim decision (CD). The model was built with 13 parameters, divided into non-fuzzy group and fuzzy group. The analytic hierarchy process (AHP) method was used to determine the priority weight (PW) among parameters. The Tsukamoto's fuzzy logic (FL) method was applied to process the fuzzy parameters. A simple mathematics method (SMM) was exercised to calculate the non-fuzzy parameters, and to aggregate the result into claim risk score (CRS). Finally, CRC and CD were derived from the CRS using a rule base. The model was tested using 19611 actual claim history records. The result was: 6171 (31.47%) accepted with CRC= low, 3459 (17.64%) pending (CRC medium), and 9981 (50.89%) pending (CRC high). The DSM model was implemented in python with Google COLAB and Datapane to create various graphics.
Automatic Question Generation Monolingual Multilingual pre-trained Models using RNN and Transformer in Low Resource Indonesian Language

Although Indonesian is the fourth most frequently used language on the internet, the development of NLP in Indonesian has not been studied intensively. One form of NLP application classified as an NLG task is the Automatic Question Generation task. Generally, the task has proven well, using rule-based and cloze tests, but these approaches depend heavily on the defined rules. While this approach is suitable for automated question generation systems on a small scale, it can become less efficient as the scale of the system grows. Many NLG model architectures have recently proven to have significantly improved performance compared to previous architectures, such as generative pre-trained transformers, text-to-text transfer transformers, bidirectional autoregressive transformers, and many more. Previous studies on AQG in Indonesian were built on RNN-based architecture such as GRU, LSTM, and Transformer. The performance of models in previous studies is compared with state-of-the-art models, such as multilingual models mBART and mT5, and monolingual models such as IndoBART and IndoGPT. As a result, the fine-tuned IndoBART performed significantly higher than either BiGRU and BiLSTM on the SQuAD dataset. Fine-tuned IndoBART on most of the metrics also performed better on the TyDiQA dataset only, which has fewer population than the SQuAD dataset.
Framework for automating requirement elicitation using a chatbot

Requirement elicitation determines the success of a project given that it describes the needs of a problem and its context at hand. However, a recent Chaos Report revealed that projects are increasingly failing in large numbers due to the poor quality of requirements resulting from an erroneous requirement elicitation phase (REP). This affects the scalability of the REP given the difficult nature of successfully executing it in complex environments. In this paper, we propose a framework to automate the REP, validating it with a system that consumes the IBM Watson Assistant (IWA) chatbot service. The validation is done by comparing traditional requirement elicitation with requirement elicitation using our system with the help of a Peruvian software company. Finally, we demonstrate that requirement elicitation with our implemented tool is more efficient than traditional requirement elicitation and results in higher quality requirements.
Domain over size: Clinical ELECTRA surpasses general BERT for bleeding site classification in the free text of electronic health records

Bleeding can be a life-threatening condition which occurs for 3.2% of medical patients. Information about previous bleeding and bleeding site is used to predict the risk of future bleeding and guide anticoagulant treatment. However, obtaining this information is a time-consuming task as it is contained in the free text of electronic health records. Previous research has mainly been focused on extracting bleeding events but does not classify the bleeding site which is important for assessing the severity of the bleeding. This study creates the first dataset for developing and evaluating machine learning models for classification of bleeding site. The dataset consists of sentences annotated by medical doctors as belonging to one of ten bleeding sites. The sentences were annotated in 149,523 electronic health record notes from 1,533 patients of Odense University Hospital, Denmark, between 2015 and 2020. We compare different deep learning models on classifying bleeding site and find that a ∼13M parameter ELECTRA model pretrained on clinical text achieves higher accuracy ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.905\ \pm 0.002$</tex> ) than a ∼110M parameter general BERT model ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.884 \pm 0.001$</tex> ) on a balanced test set of 1,500 sentences. We furthermore test different methods for dealing with unbalanced data without finding any significant differences between methods.
Fully Homomorphic Encryption for Cloud Based E-Government Data

Cloud computing is a way of providing services, networks, hardware, storage, and interfaces for the construction of E-Government infrastructure that enables the delivery of services efficiently and achieves cost savings.Cloud services enable individuals and organizations to utilize cloud providers' software and hardware resources stored remotely.The span between the client and the actual location of his data provides a barrier because this data can be obtained by a third party, risking the citizen's data privacy.We examine a method based on the Fully Homomorphic Encryption (FHE) scheme in order to facilitate the processing of sensitive information pertaining to the E-Government that does not involve the disclosure of the original data.In this paper, we consider some general data operations to evaluate the feasibility of the FHE method and show that the accuracy are similar when data operations are applied to homomorphically encrypted data.The results of the experiment highlight the potential of the various privacy-preserving data operations that can be performed under FHE approach.These methods provide results that are equivalent to those achieved by unencrypted data and models within a decent amount of time.
Koopman-Based Spectral Clustering of Directed and Time-Evolving Graphs

Abstract While spectral clustering algorithms for undirected graphs are well established and have been successfully applied to unsupervised machine learning problems ranging from image segmentation and genome sequencing to signal processing and social network analysis, clustering directed graphs remains notoriously difficult. Two of the main challenges are that the eigenvalues and eigenvectors of graph Laplacians associated with directed graphs are in general complex-valued and that there is no universally accepted definition of clusters in directed graphs. We first exploit relationships between the graph Laplacian and transfer operators and in particular between clusters in undirected graphs and metastable sets in stochastic dynamical systems and then use a generalization of the notion of metastability to derive clustering algorithms for directed and time-evolving graphs. The resulting clusters can be interpreted as coherent sets , which play an important role in the analysis of transport and mixing processes in fluid flows. Graphic Abstract
Threshold Cryptography as a Service (in the Multiserver and YOSO Models)

We consider large deployments of threshold cryptographic services that can run in traditional multi-server settings and, at a much larger scale, in blockchain environments. We present a set of techniques that improve performance and meet the requirements of settings with large number of servers and high rate of threshold operations. More fundamentally, our techniques enable threshold cryptographic applications to run in more challenging decentralized permissionless systems, such as contemporary blockchains. In particular, we design and implement a novel threshold solution for the recently introduced YOSO (You Only Speak Once) model. The model builds on ever changing, unpredictable committees that perform ephemeral roles in a way that evades targeting by attackers and enables virtually unlimited scalability in very large networks. Our solution allows for the maintenance of system-wide keys that can be generated, used and proactivized as needed. The specific techniques build on optimized protocols for multi-secret multi-dealer verifiable secret sharing and their adaptation to the YOSO model.
K-12BERT: BERT for K-12 education

Online education platforms are powered by various NLP pipelines, which utilize models like BERT to aid in content curation. Since the inception of the pre-trained language models like BERT, there have also been many efforts toward adapting these pre-trained models to specific domains. However, there has not been a model specifically adapted for the education domain (particularly K-12) across subjects to the best of our knowledge. In this work, we propose to train a language model on a corpus of data curated by us across multiple subjects from various sources for K-12 education. We also evaluate our model, K12-BERT, on downstream tasks like hierarchical taxonomy tagging.
Adaptive Deep Learning Model for Software Bug Detection and Classification

Software is unavoidable in software development and maintenance.In literature, many methods are discussed which fails to achieve efficient software bug detection and classification.In this paper, efficient Adaptive Deep Learning Model (ADLM) is developed for automatic duplicate bug report detection and classification process.The proposed ADLM is a combination of Conditional Random Fields decoding with Long Short-Term Memory (CRF-LSTM) and Dingo Optimizer (DO).In the CRF, the DO can be consumed to choose the efficient weight value in network.The proposed automatic bug report detection is proceeding with three stages like pre-processing, feature extraction in addition bug detection with classification.Initially, the bug report input dataset is gathered from the online source system.In the pre-processing phase, the unwanted information from the input data are removed by using cleaning text, convert data types and null value replacement.The pre-processed data is sent into the feature extraction phase.In the feature extraction phase, the four types of feature extraction method are utilized such as contextual, categorical, temporal and textual.Finally, the features are sent to the proposed ADLM for automatic duplication bug report detection and classification.The proposed methodology is proceeding with two phases such as training and testing phases.Based on the working process, the bugs are detected and classified from the input data.The projected technique is assessed by analyzing performance metrics such as accuracy, precision, Recall, F_Measure and kappa.
Implementasi Metode Waterfall pada Rancang Bangun Sistem Pengarsipan Surat Berbasis Website

The use of information technology and computers has become a necessity in daily work, both in private and public agencies, especially in terms of service to the community, the use of information technology is certainly very supportive of existing activities. One of the activities at the Department of Population and Civil Registration is the large number of incoming and outgoing letters, this causes problems in archiving, both incoming and outgoing letters, so to overcome this a website-based archive system for incoming and outgoing mail is built. This system was built using the waterfall method. The test results on the archive of incoming and outgoing letters based on this website indicate that this application has been running well, this can be seen in the results of the black box testing which shows that the application is running as expected, and makes it easier for employees to carry out their duties of archiving incoming and outgoing mail.
Student performance in online learning higher education: A preliminary research

The impact of student performance is the focus of online learning because it can determine the success of students and higher education institutions to get good ratings and public trust. This study explores comprehensively the factors that can affect the impact of student performance in online learning. An empirical model of the impact of student performance has been developed from the literature review and previous research. The test of reliability and validity of the empirical model was evaluated through linguist reviews and statistically tested with construct reliability coefficients and confirmatory factor analysis (CFA). Overall, the results of this study prove that the structural model with second-order measurements produces a good fit, while the structural model with first-order measurements shows a poor fit.
A Theory of Composing Protocols

In programming, protocols are everywhere. Protocols describe the pattern of interaction (or communication) between software systems, for example, between a user-space program and the kernel or between a local application and an online service. Ensuring conformance to protocols avoids a significant class of software errors. Subsequently, there has been a lot of work on verifying code against formal protocol specifications. The pervading approaches focus on distributed settings involving parallel composition of processes within a single monolithic protocol description. However we observe that, at the level of a single thread/process, modern software must often implement a number of clearly delineated protocols at the same time which become dependent on each other, e.g., a banking API and one or more authentication protocols. Rather than plugging together modular protocol-following components, the code must re-integrate multiple protocols into a single component. We address this concern of combining protocols via a novel notion of 'interleaving' composition for protocols described via a process algebra. User-specified, domain-specific constraints can be inserted into the individual protocols to serve as 'contact points' to guide this composition procedure, which outputs a single combined protocol that can be programmed against. Our approach allows an engineer to then program against a number of protocols that have been composed (re-integrated), reflecting the true nature of applications that must handle multiple protocols at once. We prove various desirable properties of the composition, including behaviour preservation: that the composed protocol implements the behaviour of both component protocols. We demonstrate our approach in the practical setting of Erlang, with a tool implementing protocol composition that both generates Erlang code from a protocol and generates a protocol from Erlang code. This tool shows that, for a range of sample protocols (including real-world examples), a modest set of constraints can be inserted to produce a small number of candidate compositions to choose from. As we increasingly build software interacting with many programs and subsystems, this new perspective gives a foundation for improving software quality via protocol conformance in a multi-protocol setting.
Higher Classification of Fake Political News Using Decision Tree Algorithm over Random Forest Algorithm

The current project aims to model and compare the performance of fake news detectors using machine learning algorithms to recognize fake news connected to political topics with high accuracy. The Decision Tree algorithm and the Random Forest algorithm are two algorithms. The methods were developed and evaluated on a dataset including 44,000 samples. Implemented each algorithm through programs and performed ten iterations with different scales of false feeds and factual feeds classification were identified. The G-power test is around 80% accurate. For detecting false political news, the Decision Tree algorithm had a mean accuracy of 99.6990, and the Random Forest approach had a mean accuracy of 98.6380, according to the trial results. The significance of accuracy is p=0.001, indicating the efficacy of the classifier. This research aims to use a novel strategy for contemporary Machine Learning Classifiers to predict fake political news. The comparison results reveal that the Decision Tree method outperforms the Random Forest technique.
Malaria Blood Cell Image Classification using Transfer Learning with Fine-Tune ResNet50 and Data Augmentation

Based on the WHO Report related to malaria, it is estimated that there will be 241 million malaria cases and 627,000 deaths from this disease globally in 2020 with the number of deaths increasing yearly. Preventing malaria disease conditions is through early detection. A more quick and precise malaria diagnosis method was required to simplify and reduce the detection process. Medical image classification could be carried out rapidly and precisely using machine learning or deep learning techniques. This research aims to diagnose malaria by classifying images of malaria blood cells using Deep Learning with a Transfer Learning approach. By utilizing various fine-tuning procedures and implementing data augmentation proposed method develops the method from previous studies. Two types of models Frozen ResNet50 and Fine-Tune ResNet50 are being tested. The dataset utilized will be augmented to improve model performance. This study makes use of the "NIH Malaria Cell Images Dataset" a dataset that contains a total of 27,660 image data. It is divided into two classes: parasitized and uninfected. The results are improved from previous research using the fine-tuned VGG16 model with an accuracy of 96% compared to this study using the fine-tuned ResNet50 model which achieved an accuracy score of 98%.&#x0D;
Web Analyzer Software Tool in Order to Identify an Know the Behavior of Users That Visit Website

The goal of our paper is to design and implement a software tool in order to analyze Apache log file of a website. In this project, a software tool is created that is called web analyzer software tool in order to identify and know the behavior of users that visit a website. This information is used to improve the structure of a website.&#x0D; Some facilities of our software tool are that it lets a website owner to know the number of requests per a specific day, number of requests per week, and most visited pages, number of visitors per day, most used operating systems during a log file. Moreover, the website owner can investigate an IP to know if it is blacklisted or not and what an IP had done during a specific day and how much an IP was online and what he/she wants. Based on this valuable info a website can response to the requests appropriately. The software tool can find location of any IP address, which browsers, and Operating systems used by the user and the software tool can determine the new visitors to the website daily. Moreover, we can detect the countries of IP address and we show them graphically on a chart. By using this software, we can measure and analyze the website log files easily. The C# language is used for coding; Java script is used to find the location of the IPs.
GHive

As a popular distributed data warehouse system, Apache Hive has been widely used for big data analytics in many organizations. Meanwhile, exploiting the massive parallelism of GPU to accelerate online analytical processing (OLAP) has been extensively explored in the database community. In this paper, we present GHive, which enhances CPU-based Hive via CPU-GPU heterogeneous computing. GHive is designed for the business intelligence applications and provides the same API as Hive for compatibility. To run SQL queries jointly on both CPU and GPU, GHive comes with three key techniques: (i) a novel data model gTable, which is column-based and enables efficient data movement between CPU memory and GPU memory; (ii) a GPU-based operator library Panda, which provides a complete set of SQL operators with extensively optimized GPU implementations; (iii) a hardware-aware MapReduce job placement scheme, which puts jobs judiciously on either GPU or CPU via a cost-based approach. In the experiments, we observe that GHive outperforms Hive in both query processing speed and operating expense on the Star Schema Benchmark (SSB).