Reviewerly/exHarmony huggingface dataset:
Efficient Data Loading for Deep Neural Network Training

Deep neural networks (DNNs) have achieved outstanding results in a wide range of applications. However, A DNN training is a data- and compute-intensive task to obtain high accuracy. Multiple state-of-the-art AI accelerators such as GPUs can be deployed in a single machine for training, data loading dominates a significant amount of time in the whole training process. As a result, data loading becomes a key bottleneck to limit the whole training performance. In this paper, we present efficient data loading for DNN trainings by using two methods: Based on data access pattern and efficient utilization of disk I/O, we use a block as the unit for data reading. As data are repeated used in training an accurate model, we cache partially loaded data for faster access. We use ImageNet as the dataset and present comprehensive evaluations in terms of running time and accuracy for state-of-the-art deep neural networks on a machine with four V100 GPU cards. The evaluation results demonstrated that while the accuracy is maintained, our optimizations can achieve up to 10 fold performance growth in terms of the whole run time for measured DNN trainings.
DIGITAL CONTACT TRACING LANDSCAPE DURING COVID-19 PANDEMIC: ROLE OF MOBILE APPLICATIONS IN CONTACT TRACING

Severe Acute Respiratory Syndrome (SARS) is a distinctive form of pneumonia recognized for its elevated transmission rate. It originated in Guangdong, China, in November 2002. A major SARS outbreak occurred in Singapore in mid-March 2003, with a connection to a traveler who had returned from Hong Kong. However, the subsequent spread through untraceable contacts ultimately led to its global dissemination, shaping the current Covid-19 pandemic [1]. Contact tracing plays a pivotal role in public health efforts to curb the spread and manage infectious diseases. By addressing the limitations of relying solely on symptomatic detection, contact tracing enhances sensitivity and community preparedness in responding to emerging epidemics.
Imperative Sentence in “The Guidance iPhone Support Website”

This study attempts to describe imperative sentence in “The Guidance iPhone Support Website”. The aim of this study to investigate the types of imperative sentences and to determine the constituent structure by using tree diagram. The data were collected through library research by reading the e-guidance, note taking and classifying the data based on imperative sentence. The analysis of the data was conducted qualitative method and presented descriptively based on the theory proposed by Quirk et al. (1985) in classifying the types of imperative sentence and the theory proposed by Brown and Miller (1991) is used in analysing the constituent structure by using tree diagram. The method that applied in presenting the result of the analysis was used formal and informal methods. There are two types of imperative sentences found In “The Guidance of iPhone Support Website”, as follows: imperative without subject with a total of 242 or 98,4% and negative imperative with a total of 4 or 1,6%. The constituent structure of imperative sentence is described by tree diagram. The constituent of imperative sentence found in data consists of an omitted subject and negative form.
Determining Prospective Recipients of The Indonesia Smart Scholarship Program Using The Support Vector Machine Algorithm and Simple Additive Weighting

Between the period from 2018 to the first semester of 2020, a total of 2.4 million students who owned the Kartu Indonesia Pintar (Indonesia Smart Card) were at risk of not being able to access the Program Indonesia Pintar due to delays in entering recipient data, misdirected targeting, and fund disbursement obstacles. During its implementation, there were issues with inaccurate data of Kartu Indonesia Pintar recipients, resulting in the beneficiaries not being entirely targeted correctly. According to Article 12 of Minister of Education and Culture Regulation No. 10 of 2020, schools, as educational units, are assigned the responsibility of managing the Smart Indonesia Program at the school level. This includes tasks such as proposing eligible student candidates for the Program based on criteria, monitoring and assisting the smooth process of receiving the benefits, and accommodating students who hold the Kartu Indonesia Pintar (KIP). However, schools faced difficulties in obtaining the eligibility data of potential Indonesia Pintar Program recipients through the school proposal format. The objective of this research is to determine the eligibility of potential Indonesia Pintar Program recipients using a combination of Support Vector Machine (SVM) and Simple Additive Weighting (SAW) techniques. The SVM model was trained using kernel tricks and parameter selection (C and gamma) based on data sourced from the DAPODIK application of students in SMK Negeri 1 Kota Bekasi for the academic years 2020/2021 to 2022/2023, with a dataset containing 2499 rows . This dataset was used to determine the eligibility of potential Indonesia Pintar Program recipients. The SAW method was then employed to calculate scores based on the weighting of attributes possessed by the students, which were subsequently ranked from the highest score. The Linear Kernel with parameter values C=0.1 and gamma=0.1 achieved an accuracy of 88.8% during the classification of test data, using 10% of the total dataset, which amounted to 250 rows of data. The combination of SVM and SAW methods resulted in the classification of eligibility for potential Indonesia Pintar Program recipients, ranked based on their scores using the weighted assessment with the SAW method.
Using the 3D Protein Structure as Key to Encrypt Images

In the digital world, information is exposed anytime, anywhere, to everybody, hence itsprivacy is a crucial matter. No matter how complicated the encryption algorithm is, itrequires a strong and hard-to-break encryption key. Since the three-dimensional proteinsequence structures are usually highly conserved, even better than DNA sequences, thiswork presents an innovative scheme for implementing the protein sequence andstructural to build protein data tables that are then used to generate extremely strongencryption-keys. An image-encryption scheme designed to implement such encryptionkeys is developed and produced compatible security strength with existing encryptionschemes. Prototype experiments resulted in an average normalized mean absolute errorof 66.84%, an average peak signal to noise ratio of 6.85 dB, and comparable entropywith other cryptosystems. The obtained results make this scheme a promising colorimage protection technique for various applications.
Predictable Local Polarization inside Electrodes By Three-Dimensional Multi-Scale Model in Flow Batteries

Redox flow batteries (RFBs) are regarded as one of the most promising grid-scale energy storage technologies, which can improve the applicability of wind and solar energy by leveling their fluctuating power output. In RFBs, the gas side reactions including hydrogen evolution reaction and oxygen evolution reaction reduce the efficiency and capacity of the batteries and the gas produced may even cause safety problems. Even if the voltage is controlled in practical applications to restrict the gas side reactions, it is still inevitable due to flow dead zones or insufficient reactants locally inside the electrode. Gas side reactions are usually caused by too high local overpotential, i.e., local over polarization. Therefore, in this work, we propose a multi-scale model that allows the rapid prediction of local polarization to limit and manage the gas side reactions. The multi-scale model contains a deep neural network, a pore network model, and a three-dimensional continuum model with the advantages of both accuracy and extensibility. By learning from the sample data provided by the cell-scale model and the pore-scale model, the deep neural network can quickly predict the local polarization under different operating conditions and give the distribution map of the entire electrode. Compared with conventional models, multi-scale models can take into account the geometry of electrode fibers and are suitable for electrode design and modification. Due to the calculation superiority of the pore network model, the multi-scale model can realize three-dimensional local polarization prediction, which enables a more accurate analysis of the internal uniformity of the electrode. Through the developed model, we explore the effects of the interdigitated flow field, flow rate, initial concentration and voltage on local polarization. It is shown that the multi-scale model can accurately predict local polarization. However, the promotion of flow rate cannot completely eliminate the occurrence of local over polarization.
Phishing sites detection using Machine Learning

Phishing links are disseminated by bushwhackers using e-mail, textbook delivery systems, and social media channels. They manipulate drug users into accessing phishing websites and providing crucial information by using social engineering skills. In the end, the stolen personal data is utilized to obtain illicit gains by abusing the confidence of legitimate websites or financial organizations. Some solutions rely on features that rules have forced to be removed, while others need the use of third-party services, which will lead to insecurity and time-consuming problems with the vaccination service.In this study, phishing websites are identified using a machine literacy-grounded frame. The real-time vaticination service combines a number of techniques to improve delicacy, lower the percentage of false alarms, shorten calculation times, and improve machine learning (ML) prediction. Several machine learning models are compared using various datasets in the ML prediction module. The result of the project is that when provided with a link, it can accurately determine whether it is a phishing site or not.Based on the empirical findings, the Logistic Regression model achieved the top accuracy rate of 96.38%, affirming the viability of the proposed solution.
Machine Learning based Extractive Text Summarization Techniques

Automatic Text Summarization (ATS) is a developing area of Natural Language Processing (NLP). It is concerned with simplifying a summary from a given text while retaining key information. Because of the exponential increase of content on the internet, this is a current area that is used in summarizing a text available in books, newsletters, internal document analysis, patent research, e-learning, and so on. Manually summarizing large amounts of text is difficult for humans. As a result, the demand for more complicated and powerful summarizers has skyrocketed. To match the performance of human-generated summaries, various machine learning algorithms are applied. Extractive text summarization finds and connects fundamental segments of a record to make a more limited report that sums up the first archive’s items. A machine learning approach for Extractive text summarization has been explored in this paper.
A Dynamic Model for Blended Learning of University English Based On K-Means Clustering Algorithm

In the context of the new economic era, both the development and application of new technologies have put forward higher requirements for the cultivation of talents in colleges and universities. In order to address the shortcomings of the existing research on the dynamic model of blended learning of university English, this paper briefly introduces the learning support design and behavioral science theories as well as the K-means clustering algorithm. After the discussion of the selection of data collection and online learning behavior metrics for the university English blended learning platform, the blended learning model of the university English online platform and the evaluation model of the university English blended learning platform based on the K-means clustering algorithm are designed, and the clustering results of students’ learning behaviors calculated by the K-means clustering algorithm show that 128 learners have a completion degree of less than 0.8 in the five indicators. The K-mean clustering algorithm was verified to be effective in mining and analyzing learner’s behavioral characteristics.
Assessing the Impact of Vulnerabilities on Confidentiality, Integrity, and Availability in Smart Systems

As Internet of Things (IoT) technologies proliferate and smart devices become more and more integrated into our daily lives and workplaces, it is becoming more and more important to address the complex issues of data security and privacy within smart homes and buildings. Even though the Internet of Things (IoT) has many benefits, such as remote control, real-time monitoring, and sophisticated security features, it also raises serious concerns about how sensitive data is protected. By examining the particular problems associated with protecting sensitive data in the IoT era and guaranteeing the continuous provision of dependable services within smart environments, this research paper seeks to close a significant knowledge gap. In order to do this, the research focuses on the complex data-centric features of smart ecosystems, including data processing, generation, and storage. This paper thoroughly examines the range of current IoT technologies, data management practices, and privacy challenges by using a qualitative research approach. It also offers a thorough examination of the privacy protections and security measures that are now in place in smart buildings and homes. This research focuses primarily on the interface between technology, security, and privacy of data. This work effectively fills in the significant research vacuum by clarifying the specific issues it seeks to solve. It provides useful advice and insights that can help individuals and organizations navigate the complex world of smart technologies, securely store their data, and realize the full potential of these game-changing advancements.
A Proposed Framework for Financial Institutions Using Blockchain Tecnology

Blockchain technology plays a pivotal role in the banking industry, being recognized as one of the most crucial sectors. With the financial market expanding and the demand for banking services on the rise, the need for a robust banking system capable of offering top-notch services to clients is paramount. Regrettably, the current banking system in Egypt falls short of meeting these requirements. However, there is hope on the horizon, as blockchain technology has garnered widespread interest in the global banking community for its potential to mitigate fraud and other threats to banking operations. This study offers a concise overview of various blockchain architectures and trading systems, delving into their types and the popular platforms currently being utilized worldwide., such as Automated Clearing House (ACH).
Face Mask Detection System Using Machine Learning Algorithms

The project presented in this report is a real-time face mask detection system using computer vision and deep learning techniques. The primary objective of this project is to develop a system that can detect whether a person is wearing a face mask or not, with a focus on real-time performance.&#x0D; The system utilizes pre-trained deep learning models for face detection and mask classification. It leverages the MobileNetV2 architecture as a feature extractor and deploys the model in real-time video streams. When a face is detected, the system classifies it as "Mask" or "No Mask" with associated confidence scores. The project involves key components, including the use of OpenCV for image processing and real-time video capture, TensorFlow/ Keras for deep learning, and the integration of pre-trained models. The code is well- structured, and it demonstrates proficiency in model loading, image preprocessing, and real-time video processing.&#x0D; The findings of the project showcase a practical application for face mask detection, which has gained significance in the context of public health and safety. The system provides a valuable tool for monitoring mask compliance in public spaces and can contribute to efforts to mitigate the spread of contagious diseases. The project demonstrates the importance of combining computer vision and deep learning in real-world applications, and it serves as a reference for those interested in similar projects or applications in the field of image processing and object detection. In summary, this project illustrates the successful implementation of a real-time face mask detection system and underscores its potential contributions to public health and safety measures.
Information security incident management using iso 27035 standard

The data and information management process at BMKG entails certain risks related to information security incidents. There needs to be more emphasis on addressing these incidents regarding policies, procedures, technology, and human resources. This research project aims to develop an incident management system by ISO 27035 standards. Qualitative research methods have been employed for BMKG Communication Network Center case studies. The approach involves aligning information security incident management with BMKG's business processes. The outcome of this research includes creating policies, documents, and standardized procedures designed to enhance BMKG's capabilities in effectively managing and mitigating incident threats.
Analysis of The Influence of Education, Health, and Internet User on Economic Growth With Labor Productivity as an Intervening Variable in Indonesia

This study aims to see whether the Labor Productivity variable is ableto become an intervening variable between the Education, Health andInternet Users variable on Indonesia's Economic Growth in the 2015-2019 period. The method used is Path Analysis using the Eviewsprogram. The data used is in the form of panels consisting of 34provinces in Indonesia over a period of 5 (five) years. For modelselection using the Chow Test and Hausman Test and from the two teststhe Fixed Effect Model was selected. For the value of the EducationVariable, the average length of school data is used; the health variableuses life expectancy data and vaternet uses internet user data. Theresults of this study indicate that the Labor Productivity variable is notan intervening variable between the Education, Health, Internet variableand the Economic Growth variable as seen from the Sobel Test resultswhich have a value of less than 1.96 at 𝛼=5%. The direct effect of theEducation variable on Labor Productivity is negative and notsignificant. The direct effect of the Health variable on LaborProductivity is negative and significant. The direct effect of the Internetvariable on Labor Productivity is positive and not significant. Thedirect effect of the Education variable on Economic Growth is negativeand significant. The direct effect of the Health variable on EconomicGrowth is positive and not significant. The direct effect of Internetvariable on economic growth is positive and significant. The directeffect of the Labor Productivity variable on Economic Growth isnegative and not significant.
Legal Study of the Existence of Electronic Evidence in Corruption Crimes

The use of electronic information or documents as evidence in examinations of corruption cases is becoming increasingly important. Law Number 19 of 2016, concerning Amendments to Law Number 11 of 2008 concerning Information and Electronic Transactions, regulates the use of information or electronic documents as valid evidence in the examination process in criminal trials in general. However, there is a lack of regulation regarding documents and electronic transactions as evidence in resolving criminal corruption cases in the Criminal Procedure Code (KUHAP). Therefore, efforts are needed to harmonize the ITE Law and the Criminal Procedure Code regulations regarding the use of information or electronic documents as evidence in corruption cases. The data used in this research is secondary data obtained from various literature sources such as books, laws, literature, and journals. Although the Criminal Procedure Code does not regulate the position of electronic evidence and the definition of evidence itself, Article 39 paragraph (1) of the Criminal Procedure Code states that objects that can be confiscated can be considered as evidence. However, Law Number 11 of 2008 concerning Information and Electronic Transactions and Law Number 20 of 2001 concerning Amendments to Law Number 31 of 1999 concerning the Eradication of Corruption Crimes recognize that electronic information or documents are valid legal evidence in the judge. Although the Criminal Procedure Code does not regulate the position of electronic evidence and the definition of evidence itself, Article 39 paragraph (1) of the Criminal Procedure Code states that objects that can be confiscated can be considered as evidence. However, Law Number 11 of 2008 concerning Information and Electronic Transactions and Law Number 20 of 2001 concerning Amendments to Law Number 31 of 1999 concerning the Eradication of Corruption Crimes recognize that electronic information or documents are valid legal evidence in the judge. Although the Criminal Procedure Code does not regulate the position of electronic evidence and the definition of evidence itself, Article 39 paragraph (1) of the Criminal Procedure Code states that objects that can be confiscated can be considered as evidence. However, Law Number 11 of 2008 concerning Information and Electronic Transactions and Law Number 20 of 2001 concerning Amendments to Law Number 31 of 1999 concerning the Eradication of Corruption Crimes recognize that electronic information or documents are valid legal evidence in the judge.
Trust Model for Cloud Data Service Providers

This research paper provides the researcher with the use of a multivariate logistic regression model in the field of the cloud computing world based on the trust of the data service provider. For this model, different factors are considered to measure the trust of the data service provider. This model gives the relationship between the trust of the service provider and different factors which are considered while uploading the data to the cloud. The value of the parameters of the model gives the share of that particular parameter in the model. According to the fitted model Security, Authentication of the service provider, service providersâ information, and Cost of storage are the key factors to build consumer trust.
Catalogue Generator Using Distributed Crawler Working Using Python and Redis

A price comparison website acts as a platform or medium between the consumers and the sellers. It allows consumers to see different lists of prices for the product chosen by user and it helps consumers to make an informed decision about which to choose in order to save money. It also act as a tool to help consumers increase their price consciousness so that they will not feel cheated by the advertisement from the retailers that claimed they are offering the cheapest price but the reality happened to be otherwise. On the other words, the price comparison website also has the role to promote the retailer/shop/hypermarket/supermarket to the customers. The pressures on time and money especially in the current economic situation where the living cost increases and thereâs only little time to do some shopping for household, a price comparison website will absolutely become a great help towards consumers.