Reviewerly/exHarmony huggingface dataset:
Pelatihan Teknik Sitasi Menggunakan Aplikasi Mendeley sebagai Direktori Daftar Rujukan Karya Ilmiah bagi Mahasiswa PGSD Universitas Mataram

Writing scientific articles is an obligation for several people, one of which is a college student. College students as prospective teachers must be equipped with the ability to formulate problems to implement the scientific method by publishing their research. In writing scientific papers, there are rules that must be obeyed, one of which is citation and compiling a list of references. The problem obtained is that students have difficulty in carrying out these stages. The solution that can be done is to use the Mendeley application. The target of this activity is the 6th semester of PGSD University of Mataram students who have or are currently taking a scientific paper writing course. The results showed that there was a significant increase in the pre-test to post-test average. This shows that the training provided has had a good impact in terms of knowledge and insight regarding citations and preparation of reference lists with the Mendeley application.
A Human Body Part Semantic Segmentation Enabled Parsing for Human Pose Estimation

Human Body Part Semantic Segmentation and Human Pose estimation are considered to be essential for understanding human behaviours. Both of these tasks are correlated with each other. Employing them together in a unified framework to perform two distinct Human Centric Visual Analysis tasks simultaneously allows benefiting from each other. Taking advantage of the correlation between Human Body Part Semantic Segmentation and Human Pose Estimation, this paper proposes a unified framework that explores efficient context modelling. The framework simultaneously predicts the human body part semantic segmentation and pose estimation with high-quality results. The results extracted from the segmentation are used to predict the pose estimation task. An experimental analysis of the proposed framework is done on the benchmark LIP Dataset. The analysis of the results shows that the proposed framework outperforms the state-of-the-art by 7.3% when evaluated on mean IoU. Moreover, Mean Accuracy, Pixel Accuracy and PCKh are the other metrics used for the evaluation of the framework.
Research on key technologies and application value of private cloud security cloud management platform

Nowadays, with the rapid development and popularization of informatization and intelligence, "cloud computing service" is also born. Among them, "private cloud" is an important part of "cloud computing services". This is also known as the "internal cloud" or "corporate cloud" and plays an important role in the realization of information management within the enterprise. By studying the overall architecture of private cloud security management platform, this paper studies the key technologies of cloud security management platform and the application value of cloud security management platform. It can provide more flexible security solutions for governments and enterprises and provide better security for computer networks.
Credit Card Fraud Detection Using Machine Learning &amp; Python

In recent years, credit cards have taken on important roles in people's lives.The danger of fraud has increased as a result of the unexpected boom in e-commerce and the widespread usage of credit cards for online purchases.Instead, than carrying around a lot of cash, it is easier to keep credit cards on hand.But that's also dangerous now.A serious problem that is now on the increase dramatically is credit card fraud .Currently, new researchers are having a lot of trouble detecting credit card fraud.We put in place a clever algorithm that can spot any type of fraud in a credit card transaction.By identifying a trend for each consumer in between fraudulent and legal transactions, we were able to solve the issue.The pattern of each customer's transactions is predicted using the isolation forest algorithm and the local outlier factor, and decisions are then made in light of that information.All attributes are tagged similarly to avoid data mismatches.
GLYCOLYSIS OF WASTE PET BOTTLES USING ETHYLENE GLYCOL AS A SOLVENT AND A CALCINED SNAIL SHELL AS A CATALYST

With the increasingly widespread use of polyethylene terephthalate (PET) bottles due to their cheap and robust nature, there has been an exponential increase in waste from these bottles in landfills, dumpsites, gutters, and roadsides, which has led to a negative effect on the environment, plants, animals, and human population with land and water pollution.Chemical recycling of the waste PET bottles would reduce the menace and recover the starting monomers for PET production.In this research, waste PET bottles were chemically recycled through glycolysis to produce bis-hydroxyethyl terephthalate (BHET) using ethyl glycol (EG) as a solvent and calcined snail shell as a catalyst within the temperature range of 180-200°C at different EG:PET ratios of 5:1, 6:1, and 6.5:1, while a constant catalyst:PET of 1:100 was used.After reaction and crystallization, a yield of the glycolysis product of 39.72% was obtained.This yield recorded is not as good as using oyster shell (68.6%), sodium acetate (72%), and calcium carbonate (69%) as catalysts.TGA and FTIR indicated that the samples were composed mainly of BHET monomers as functional groups.It is recommended that longer reaction time and varying catalyst:PET ratio be used to determine the optimum temperature and reaction.
Overview of the Benefits Deep Learning Can Provide Against Fake News, Cyberbullying and Hate Speech

Deep learning is a feasible technology and is the best replacement for traditional means to prevent fake news, cyberbullying and hate speech. Traditional methods to prevent fake news, cyberbullying, and hate speech include using real life personnel to go through messages and remove them. The research analyses other researchers’ discoveries relative to deep learning. It is important to conduct this research so that we are all aware of how close we are to be able to protect our future generations of children and adults from having their mental and physical health affected. This research aims to analyse the current deep learning techniques used to prevent fake news, cyberbullying and hate speech. A comparison is included in this research to identify the state-of-the-art technique.
COMPARISON BETWEEN EGYPT AND THE UAE IN MEASURING THE EFFECTS OF THE MINISTRY OF HEALTH ORGANIZATIONAL LEARNING ON PROACTIVE BEHAVIOR DURING THE COVID-19 PANDEMIC

With the emergence of the Covid-19 crisis, the world mobilized to deal with this crisis, and each of them dealt with this crisis, dealing with it according to his appropriate organizational learning and his ability to anticipate mechanisms in dealing with it, and the researcher presented several previous Arab studies that spoke on this subject in order to be realistic with the environment Arabic with a convergent culture, which helps in understanding the research problem.This research identified the concept of organizational learning and its use in anticipating mechanisms to deal with the crisis.With the crisis, the researcher made a questionnaire for the United Arab Emirates and Egypt and distributed it electronically to reach the largest number of respondents.A questionnaire tool was created that helped compare organizational and proactive learning between Egypt and the UAE through questionnaire questions.High consistency among the questions of the questionnaire, which helped to rely on it, gender, and nationality were taken into account as the basis for comparison, and the result of the research was that the UAE achieved better positions.According to the factors of organizational and proactive learning, the researcher concluded that there are other factors that caused this, and at the end of the research, the researcher presented academic recommendations to analyze the factors affecting gender and nationality in organizational learning, an increase in academic studies on that, and practical recommendations.They must be followed by organizations to inculcate the concepts of organizational and proactive learning.The main objective of the research was to compare the UAE and Egypt with regard to proactivity in dealing with crises, to find out the reasons for the failure to adopt the proactive method in dealing with crises, and this is of great importance due to the scarcity of research that deals with this concept, and the mythology of dealing with this paper was a mixture of qualitative and quantitative approaches, as the literature related to this topic was used with the design of a questionnaire of 567 questionnaires (294 Egyptian employees and 273 UAE employees).The results indicated that organizational learning had a positive impact on proactive behaviour.However, this effect was modified by nationality and gender.Specifically, Egyptian Ministry of Health employees were less likely to engage in proactive behavior than Emirati Awad.Comparison between Egypt and the UAE in Measuring the Effects of the Ministry of Health Organizational Learning on Proactive Behavior during the COVID-19 Pandemic, Vol.58 No. 3 June 2023 12employees.In addition, female MOH employees were more likely to engage in aggressive behavior than male MOH employees.
Blockchain in AI

Blockchain and artificial intelligence are currently two growing technologies. These two technologies are very different development parties and applications, but researchers have experimented with combining them into one. Blockchain is a decentralized distributed ledger used to store encrypted data, and AI is the brain that enables analysis and decision-making from the collected data. Both technologies can affect and manipulate data in very different ways, and when combined, can take data usage to another level. Blockchain makes AI more consistent and easier to understand. You can also understand and identify why machine learning decisions are made. In addition, AI can improve blockchain efficiency much better than humans and is better than standard computers. This report looks at how blockchain runs on a standard computer and demonstrates that it requires a lot of processing power to perform basic tasks. It also gives a detailed overview of blockchain applications for AI. We'll go over the literature, vocabulary, and the creation of AI-specific block-chaining apps, platforms, and protocols.
Integration of lightweight cryptography and watermarking with compression for high speed and reliable communication of digital images in IoT

Abstract The Internet of Things (IoT) has grown rapidly over the past few years. The main challenges for people who design and make these systems are to make them use less energy and use as small hardware as possible. Recording and sending compressed images in IoT systems must be secure. Secure image transmission requires encryption, watermarking, and compression. In choosing these algorithms, one should always consider energy efficiency and low hardware complexity. So, we propose a secure way to send data in IoT systems by combining encryption, watermarking, and compression. Depending on what is needed, we use a lightweight encryption algorithm, discrete wavelet transform (DWT) watermarking, and Discrete cosine transform + Lempel‐Ziv‐Welch (DCT+LZW) compression. In this method, the main contribution is to make a security pack that is small, strong, and easy to implement for secure image transmission on IoT systems. In the experiments, several images are used to judge how well the proposed integrated method works and how well it compares to other methods. When the proposed method was compared to other methods, the peak signal‐to‐noise ratio (PSNR) parameter improved by more than 20% in the Lena image and by more than 11% in the Baboon image.
EFEKTIVITAS PENGGUNAAN SISTEM INFORMASI KEPEGAWAIAN DALAM MANAJEMEN PNS DI KANTOR WILAYAH KEMENTERIAN HUKUM DAN HAK ASASI MANUSIA LAMPUNG

The use of an information system is a must in facing complex organizational challenges.Utilization of the Personnel Management Information System aims to improve work efficiency and decision making related to civil servant management.The purpose of this study was to determine the effectiveness of the use of the Civil Service Administration Information System (SIMPEG) for Civil Servants in the Regional Office of the Ministry of Law and Human Rights in Lampung and the obstacles to its use.The method used in this study is naturalistic qualitative, researchers will collect data in a natural way through interviews with key informants, direct field observations and researching related documents.The results showed that the use of Administrative Information Systems in PNS Management at the Regional Office of the Ministry of Law and Human Rights Lampung is still quite effective because it fulfills the components of measuring the effectiveness of information systems in the form of security (confidentiality, availability, integrity) and output.The obstacles encountered when using the Civil Service Information System (SIMPEG) to meet the needs of civil servant management were that corrupt files were found due to application errors, incomplete data, leadership intervention in making decisions that ruled out the information presented by SIMPEG for consideration, and the internet network is unstable to access SIMPEG so that the staffing service process is obstructed.
Advanced Control Techniques for Unmanned Aerial Vehicle (UAV) Navigation and Flight Control

Unmanned aerial vehicles (UAVs) have drawn a lot of interest from a variety of businesses because of their adaptability and capacity to carry out difficult tasks on their own.Advanced control approaches have been developed to improve the performance, autonomy, and operational capabilities of UAV navigation and flight control.This paper gives a brief review of some of the most popular advanced control techniques used in UAV flight and navigation.This work presents a unique unmanned aerial vehicle (UAV) flight control method that generates graph-based flight pathways based on analysed UAV flight data gathered by a pilot.The goal is to generate paths for UAV flights using hierarchical A* search methods in order to gather images at various surveillance stations.The suggested strategy enables UAVs to autonomously navigate along shorter itineraries compared to those recorded by the pilot by joining partially flown tracks.The concept was put into practise by analysing the UAV flight data that was gathered and creating graph-based pathways with circulation patterns.To plan the UAV flights, hierarchical A* search methods were next used on these paths.In order to enhance UAV navigation and flight control, advanced control approaches such robust control, fuzzy logic control, and neural networkbased control have also been investigated.The UAV can manage disturbances and uncertainty better with robust control approaches.Flexible control in complicated situations is made possible by fuzzy logic control, which allows decision-making based on language norms.Artificial neural networks are used in neural network-based control to learn from historical data and system dynamics and to optimise control actions.
A Holistic Study of the Role of Innovation in Business Growth and Development: An Analytical Perspective

Innovation is pivotal in driving business growth and development in today's dynamic and competitive landscape.It is the practice of introducing new concepts, ways of doing things, goods, or services that generate value and meet the ever-changing requirements of customers.By embracing innovation, businesses can gain a competitive edge, increase efficiency, and open up new opportunities for expansion.Innovative ideas foster creativity, encourage problem-solving, and enable companies to adapt to changing market demands.It enables companies to differentiate themselves from their rivals, which in turn helps them attract new consumers and keep the ones they already have.Moreover, innovation drives productivity improvements, streamlines processes, and enhances operational effectiveness.Through continuous innovation, organisations can stay ahead of the curve, identify emerging trends, and seize market opportunities, fueling sustained growth and development.
Hidden Markov Model

A problem that researchers often face when constructing the models is that the observations obtained are incomplete, either by physical impossibilities or due to the presence of noise in the measurements. A valuable tool to solve this problem is the so-called hidden Markov models because they allow a sequence of observations, determine the real states of the system. This chapter presents the three basic problems that need to be solved to make the model useful in applications. Finally, as an application example, a hidden Markov chain is used to determine the behavior of two sharks from the trajectories traveled by them.
Representation of time-varying and time-invariant EMR data and its application in modeling outcome prediction for heart failure patients

To represent a patient record with both time-invariant and time-varying features as a single vector using an end-to-end deep learning model, and further to predict the kidney failure (KF) status and mortality of heart failure (HF) patients.The time-invariant EMR data included demographic information and comorbidities, and the time-varying EMR data were lab tests. We used a Transformer encoder module to represent the time-invariant data, and refined a long short-term memory (LSTM) with a Transformer encoder attached to the top to represent the time-varying data, taking the original measured values and their corresponding embedding vectors, masking vectors, and two types of time intervals as inputs. The proposed representations of patients with time-invariant and time-varying data were used to predict KF status (949 out of 5268 HF patients diagnosed with KF) and mortality (463 in-hospital deaths) for HF patients. Comparative experiments were conducted between the proposed model and some representative machine learning models. Ablation experiments were also performed around the time-varying data representation, including replacing the refined LSTM with the standard LSTM, GRU-D and T-LSTM, respectively, and removing the Transformer encoder and the time-varying data representation module, respectively. The visualization of the attention weights of the time-invariant and time-varying features was used to clinically interpret the predictive performance. We used the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), and the F1-score to evaluate the predictive performance of the models.The proposed model achieved superior performance, with average AUROCs, AUPRCs and F1-scores of 0.960, 0.610 and 0.759 for KF prediction and 0.937, 0.353 and 0.537 for mortality prediction, respectively. Predictive performance improved with the addition of time-varying data from longer time periods. The proposed model outperformed the comparison and ablation references in both prediction tasks.Both time-invariant and time-varying EMR data of patients could be efficiently represented by the proposed unified deep learning model, which shows higher performance in clinical prediction tasks. The way to use time-varying data in the current study is hopeful to be used in other kinds of time-varying data and other clinical tasks.
Over and Over: quick ways to reuse handy shell one-liners

There are a few one-liners that I use in the shell to do some really nifty stuff. I struggle to quickly find and reuse these and asked for a solution on Mastodon. https://fosstodon.org/@quantixed/109518367449656573 A handy one-liner might be something like (taken from here): find . -iname '*.flac' -exec bash -c 'D=$(dirname "{}"); B=$(basename "{}"); mkdir "$D/mp3/"; ffmpeg -i "{}" -ab 320k -map_metadata 0 -id3v2_version 3 "$D/mp3/${B%.*}.mp3"' \;\n This converts all the flac files...
Motivation for learning English of Thu Dau Mot University’ students

This paper aims to identify the English-learning motivation of Thu Dau Mot University’s students. Based on the questionnaire, the data for analysis were collected from more than 100 students of different majors studying non-majored English at Thu Dau Mot University’s Foreign Language Center. The findings show that students have relatively positive learning motivation. In the relationship between learning achievement and motivation, the motive that comes from the students’ desire to get a better job occupies 58.4% of all the students surveyed because they believe it is essential and decisive to ensure their future income (applying for a job to earn money), and motivation to integrate into modern world life (because English is a global language: 40.4%) are the factors affecting students’ achievement.
A Blockchain-Based Decentralized Document Authentication System for Multiple Organizations

Due to the ease with which false documents can be produced, strict verification and authentication procedures for some original documents may be required. A well-crafted fake document is never easily identified. As a result, a secure method of document verification is crucial. It is possible to cut costs and reduce document verification time from days to a few seconds by utilizing blockchain technology. Blockchain is a decentralized peer-to-peer network that provides security, dependability, authenticity, immutability, and transparency. It is resistant to modifications by design. This work involves developing a complete decentralized blockchain-based document verification system for multiple organizations. The project is built on the Ethereum public blockchain, which makes it flexible and suited for cooperation between various parties. We have used the InterPlanetary File System (IPFS) to store crucial documents. IPFS uses a decentralized protocol, which helps us achieve our goal of making a completely decentralized system. The backend of the system is a website developed on the React framework. The developed system is for multiple organizations. It has an admin verification system, which includes a payment system. Multiple organizations can control their separate platforms with different verifier panels from their own organizations. Only the admin can add verifiers to the system. He/she can choose suitable candidates from all the profiles created by that organization and give the person the authority to check the authenticity of the uploaded documents. The Admin adds a verifier with the person’s wallet address. The uploaded documents get checked by authorized verifiers. The status of the document, whether it is an authentic document or not, is determined by the votes of 50% of the verifiers. This system can verify the authenticity of documents of all kinds more securely than any other conventional system.
Twitcher II: tweet frequency and top tweets

Another post looking at Twitter data in R. It follows this one and this one. I wanted to look again at my tweeting frequency over the 12 years on Twitter, but this time do it in a calendar view. Something like a GitHub commit calendar would be perfect. I have used a library for this in the past. Here, for no particular reason I used a function that I found in this post (code at the end). To generate these plots, it was a case of loading in the data (as described...