Reviewerly/exHarmony huggingface dataset:
ICTs and Smart Agriculture in Bangladesh: Way of Sustainable Development

This paper analyzes how the farmers of Bangladesh use the modern technology of information and communication for gathering different agricultural data. Farmers of Bangladesh are being encouraged to use smart devices of information and technology such as smart phones, android phones, laptops, desktops, electronic television for getting updates on different agricultural products, harvesting, and competitive markets. Information and communication technology includes all the devices with inbuilt human to human and device to human interaction systems. Thus, information and technology have a direct and indirect impact on rising farmers’ productivity and growth for sustainable management. This study sheds light on the understanding of the effect of information and communication technology in agricultural growth of Bangladesh for sustainable development with the help of conclusive research design and secondary method of data collection has adopted in this research for understanding of the importance of using information and technology in smart agriculture.
The Role of Digital Investment in Economic Growth in Iraq

The economic developments on the global economic stage have contributed to a qualitative shift in economic fields. Many advanced economies now rely on modern technology, with digital economy taking the lead in these countries. This necessitates those developing countries in general, including Iraq, follow the lead of advanced economies in developing their capacities in the knowledge economy. This is crucial for sustaining their economic resources as they transition from rentier economies, which depend on a single sector and have revenues linked to the external world, making their economies vulnerable to fluctuations and instability. The research is based on the hypothesis that the emergence of the new digital investment is a result of the evolution of information and communication technology. It affects both those who possess information technology and those who lack it, consequently influencing the Gross Domestic Product (GDP). The aim is to shed light on the characteristics of this new digital investment, and to present and analyze the key outcomes of investing in the digital economy. The focus is on indicators of digital access, as well as measuring the relationship between indicators of investment in the digital economy and economic growth.
Data Integrity Testing of Digital Evidence Data Capture Results on Private Cloud Computing Services

Private Cloud has better advantages than other cloud services because private cloud is managed and run by the company itself so that cloud needs can be tailored to the company's needs, but allows abuse from within the company itself, as in the case study simulation of a Gojek startup company. This case occurred because of a security weakness in the system so that internal people took advantage of these weaknesses for their own benefit by leaking confidential data, acquisitions were carried out to prove and find evidence of crime, acquisitions used live acquisition techniques, namely acquisitions on an ongoing system, namely monitoring network traffic using Wireshark tools , the method in this case uses the Digital Forensics Investigating Framework (DFIF), data integrity must be properly maintained when acquiring digital evidence because to maintain the authenticity of the digital evidence obtained, then data integrity is tested on the digital evidence obtained, testing is carried out on digital evidence before and after the acquisition to see if there is a change in data integrity, the research results show that there is no change in data integrity.
Air Quality Classification Using Extreme Gradient Boosting (XGBOOST) Algorithm

Air pollution is a serious issue caused by vehicle exhaust, industrial factories, and piles of garbage. The impact is detrimental to human health and the environment. To quickly and accurately monitor classification, techniques are used. One efficient and accurate classification algorithm is XGBoost, a development of the Gradient Decision Tree (GDBT) with several advantages, such as high scalability and prevention of overfitting. The parameters used in the classification include (PM10), (PM2,5),(SO2),(CO),(O3) and (NO2). This study aims to classify air quality into three labels or categories: good, moderate, and unhealthy. In the dataset used to experience an imbalance class, to overcome the imbalance class, techniques will be carried out, namely SMOTE, Random UnderSampling, and Random OverSampling, by producing an accuracy of up to 98,61% with the SMOTE technique for class imbalance. Testing the level of accuracy is done by using the Confusion Matrix.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersianÂ // Â TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//
THE EFFECTIVENESS OF PICTURE SERIES IN TEACHING WRITING RECOUNT TEXT

The purpose of this research is to know whether using picture series is effective in teaching writing recount text. The writer conducted the study at the tenth years students of SMA Negeri 1 Remboken at the 2022/2023 year of academic. The research design used was an experimental design with a quantitative approach, Pre-test, treatment, and post-test procedures were used in this pre-experimental study. A test (pre-test and post-test) with a writing exercise connected to retelling about a holiday was the instrument utilized in this study. The participants in this study were the students of SMA Negeri 1 Remboken. 28 students from class X A served as the research samples. The findings results show that The pre-test mean score was 63.21, and the post-test mean score was 92.75. The mean score on the post-test was higher than it was on the pre-test. The results of the investigation showed that the difference between the 14.500 in Tcount and the 2.052 in Ttable was significant at 5%. This outcome served as proof that the null hypothesis (H0) was rejected and the alternative hypothesis (Ha) was accepted. Therefore, using image series significantly improved the tenth-year students' recount narrative writing at SMA Negeri 1 Remboken.
Carbon Footprint Calculator for Food Waste to Support Behaviour Change

Carbon emission is carbon gas that is released every time food is produced. Food waste is food that completes the food supply chain until the final product and suitable for human consumption but discarded regardless of whether it is rotten or expired. Wasting food means that greenhouse gas emissions that is released during food production are being wasted. Bakery is the biggest contributor in the food industry that contributes to food waste. Therefore, this project developed a carbon footprint calculator for bakery to effectively monitor carbon released from food waste. User interface design for the web application using persuasive techniques which are reduction, suggestion, conditioning, self-monitoring, and surveillance. The effectiveness of the system is decided by evaluation that consists of monitoring their carbon footprint and behaviour change that is conducted to three bakery owners for three weeks. Two phases of testing were conducted that involved pre-evaluation and post-evaluation. As a conclusion, web-based carbon footprint calculators with persuasive technology techniques manage to motivate bakery owners to change their behaviour in reducing food waste.
Separability and classification of multipartite quantum states

Abstract We study separability in arbitrary multipartite quantum systems based on principal base matrices. Necessary conditions are presented for different kinds of separable states. These conditions can give a complete classification of multipartite quantum states. While the usual Bloch representation of a density matrix uses three types of generators, the representation with principal base matrices has one uniform type of generator which simplifies computation. In this paper, we take advantage of this simplicity to derive useful and operational criteria to detect multipartite separability. We first obtain criteria on detecting <?CDATA $1-3$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>1</mml:mn> <mml:mo>−</mml:mo> <mml:mn>3</mml:mn> </mml:math> separable, <?CDATA $2-2$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>2</mml:mn> <mml:mo>−</mml:mo> <mml:mn>2</mml:mn> </mml:math> separable, <?CDATA $1-1-2$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>1</mml:mn> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> <mml:mo>−</mml:mo> <mml:mn>2</mml:mn> </mml:math> separable and fully separable four-partite quantum states. We then study k -separability for multipartite quantum states in arbitrary dimensions. Detailed examples are given to show that our criteria are able to detect more entanglement states than some existing criteria.
From knowledge to reasoning, a cognitive perspective on personal knowledge graphs

Work on personal knowledge graphs would greatly benefit from decades of progress in the cognitive sciences. Knowledge is about understanding information based upon past experience, where understanding enables reasoning and decision-making. Today&apos;s applications, however, embed limited understanding in application code, as deductive logic is not adequate for human-like reasoning. Humans are always learning and never attain perfect knowledge, and our reasoning out of necessity has to deal with uncertain, incomplete, imprecise, and inconsistent knowledge. This chapter will introduce a cognitive approach to personal knowledge graphs, including plausible reasoning, System 1 for intuitive thinking, including effortlessly and rapidly generating coherent explanations, e.g., for natural language understanding, and System 2 for effortful slower deliberative thought, and how this can enable human-like memory, reasoning and decision-making.
Methodology for Reliable Assessment of Water Quality. V. Statistical Methods for Water Quality Research: Parametric Approach

The possibility of using a parametric tolerance interval to improve the accuracy of studying water quality indicators was analyzed. It was demonstrated that the proposed approach provides advantages over non-parametric interval rank, but not in all cases. The advantages of the new method due to the use of more complete experimental information were substantiated, and the reasons for the decrease in the accuracy of water quality assessment at its application were analysed.
Application of Data Mining with K-Nearest Neighbors Algorithm for Shallot Price Prediction

Shallots are an important and widely consumed bulb crop in Indonesia, both for medicinal and culinary purposes. However, shallot yield is substantially affected by its supply, often leading to significant price fluctuations that greatly impact consumers and producers, especially farmers. Farmers who cannot accurately predict shallot prices often incur losses when selling to shallot distributors. If this problem is not resolved, it may discourage farmers from cultivating shallots. Therefore, a prediction system is needed to forecast shallot prices in the future, thus helping farmers make the right decisions. This research uses the K-Nearest Neighbors (KNN) algorithm for shallot price prediction. KNN classifies data into specific categories based on the closest distance to a set of k patterns for each category, using the Euclidean distance formula to calculate the distance. The dataset consists of 303 entries with five features: farmer price, seller price, retail price, seed price, and yield. The test results of the Shallot Price Prediction System in North Sumatra Province, Indonesia, using the K-Nearest Neighbors Algorithm, showed the best performance when using 80% training data and 20% testing data, with a value of k=2, resulting in a Mean Absolute Error (MAE) of 25,786 and a Mean Squared Error (MSE) of 72. This system empowers farmers to predict the future price of shallots before selling their crops to distributors.
Detecting Breast Cancer in X-RAY images using image segmentation algorithm and neural networks

Breast cancer becomes is a nightmare threating woman all over the world, so, all the studies are trying for early detection of it to increase healing of it, it can save 30 percent from infected women which is a big percentage. Dangerous of breast cancer comes from the fact that all the women do not know about it until they have a mammogram image for the breast. It can be detected personally in late stages. That means it is important to make a medical examination periodically to investigate the presence of any cancerous lumps in breast tissue or underarm which can be an indicator for the existence of the tumour. Mammogram rays are an X-RAY applied on the breast which can used to find any problems in the breast like tumor blocks in breast, pain, secretions from nipples. Mammogram rays can detect breast cancer early and decrease the death cases. mammogram imaging starts in 40 age and must done every 3 years to assure the not infection of it. In cases of Genetic disease history, it is important to take the mammogram imaging before 40 age in the state of early tumor detection so it increases the recovery in early stages. This work is a study to create a method to estimate the breast cancer situation in X-RAY images to select an automatic medical solution which passes in three stages, primary aiding, chemical aiding, and eradication.
Presenting at The Lower Decks. A Symposium on Janeway and Open Access Publishing. September 7–8, 2023 | Birkbeck, University of London

I will be presenting on Friday 8th September, 2023 with the title “Making Gatekeeping Transparent: Insights from a Decade of Trying Open Peer Review”.
The relationship between organizational support and work pressure: A field study on employees in the Saudi Telecom Company (STC)

The study aimed to identify the relationship between organizational support and work pressures, a field study on the employees of the Saudi Telecom Company (STC), the study followed the descriptive analytical approach and reliance on the questionnaire as a tool for the study, and the study sample reached (81) of the employees in the Saudi Telecom Company, the statistical processing program was used to analyze the data to measure the main variables in the study. The most important results of the study were that the general average of the availability of dimensions of regulatory support in the Saudi Telecom Company amounted to (3.45) out of 5, which means the availability of dimensions of regulatory support in the telecommunications company to a high degree and that the general average of the availability of dimensions of work stress in the Saudi Telecom Company amounted to (3.29) out of 5, which means the availability of dimensions of work stress in the telecommunications company to a medium degree. Working for employees of the Saudi Telecom Company STC. The study concluded with a number of recommendations, most notably the need to work on discussing workers in the decisions taken in the company and clarify that these decisions are in the interest of work and workers and the need to find effective systems, and use them to ensure that employees perform the expected job as planned, and address deviations as quickly as possible and as a result of the high level of workload in the company, which works to increase job pressures on workers and therefore recommends this study the need to increase the number of Employees in the company, to fill the shortage in the company’s business, and mitigate the increase in workloads, thus reducing the pressures to which employees are exposed. Keywords: Organizational Support – Work Stress – the Saudi Telecom Company (STC)
Malware Mitigation in Cloud Computing Architecture

Cloud computing is one of the decade’s most trending discussions in information technology. The cloud is not an array of software, hardware or services but a vast shared resource that accommodates a large volume of users and offers dynamic access that is dependent on the demands. Despite all the benefits and potentials, cloud computing infrastructure is highly vulnerable to many security challenges, including malware. Therefore, research towards detecting these malware as well as safeguarding the cloud architecture against malware attacks are increasing. In this research, malware attacks and the techniques for safeguarding against malware challenges in cloud computing architecture were analysed. The work reported that the commonly reported security threats include data loss and breaches, malicious insiders, man-in-the-middle attacks, denial of service (DOS), distributed denial of service (DDOS) attacks, cookie poisoning attacks, wrapping attacks, etc., where several variants of malware are responsible for most of these attacks. The malware identified includes Trojan horses, worms, backdoors, viruses, rootkits, botnets, etc. Identified detection techniques include malware detection and prevention systems (MDPS), antiviruses and virtual machine introspection (VMI). Various methods were also identified for safeguarding the cloud against these attacks, including round trip time, address resolution protocol, intrusion prevention system, regular cookie cleanup, firewall implementation, advanced authentication and isolation methodologies between virtual machines. Nevertheless, these methods do not often perform satisfactorily, perhaps because of the continued surfacing of malware. Among others, this research recommends, since there is yet no omnibus approach to all security challenges, regular auditing of the cloud and the design/consideration of approaches that combine multiple detections and/or mitigation techniques.
Variable Clustered Fuzzy Rules for Self-Tuning Scheme for Respiratory Distresses by Similarity and the Phase Plane Trajectory Concept

This work proposes a fuzzy rule extraction methodology for a self-tuning fuzzy controller based on the fuzzy clustering method (FCM) and the similarity approach technique. The similarity technique and phase plane trajectory method are used to even lower the acquired rules. To show a potential rule extraction scheme, the self-tuning fuzzy-logic-based proportional-derivative (STFLPDC) with 49 expert fuzzy rules and 49 clustered fuzzy gain rules is used. The utility of the approach is validated using common clustering validity indices. With 49 initial clustered fuzzy rules, the suggested method addressed oxygen supply in a human respiratory model. After further reduction using the similarity strategy, 29 and 14 fuzzy rules remained. Parallel to this, a real-time benchmark application using 49, 21, and 16 extracted fuzzy gain rules is used to access the performance of a lab-based overhead crane. Finally, to investigate control performance in both models, the phase plane trajectory concept is used to generate as few as 13 fuzzy gain rules.
THE STRATEGY OF AL UMANAA MODERN ISLAMIC BOARDING SCHOOL IN IMPLEMENTING LITERACY CULTURE

This study aims to obtain facts and evidence against the strategy applied by Al Umanaa Modern Islamic Boarding School Sukabumi for its assignment as a Pioneer for the School Literacy Movement. This study uses a qualitative descriptive approach and uses methods of survey, observation, and interview in collecting data with recording and note-taking techniques. The data were analyzed using agih and direct element division techniques to describe the strategy developed and implemented in Al Umanaa. The result showed that the strategies applied by Al Umanaa were: (1) providing local content literacy subjects; (2) aligning language subjects (Indonesian, English, Arabic) and literacy subjects into speaking and writing practices;(3) continuing the silent and aloud reading program on an ongoing basis under the guidance of the teachers; (4) writing the reading results in to a literacy work or presenting the reading results; (5) allocate budget per semester for book expenditure, receive book grants; and (6) improving the function of the library as a place to read, while those that are not yet available are literacy culture guidebooks and literacy shops in open areas, (7)empowering all potential human resources to realize the vision, mission, and goals to be achieved.
Visual Question Answering (VQA) on Images with Superimposed Text

Superimposed text annotations have been under-investigated, yet are ubiquitous, useful and important, especially in medical images. Medical images also highlight the challenges posed by low resolution, noise and superimposed textual meta-information. Therefore we probed the impact of superimposing text onto medical images on VQA. Our results revealed that this textual meta-information can be added without severely degrading key measures of VQA performance. Our findings are significant because they validate the practice of superimposing text on images, even for medical images subjected to the VQA task using AI techniques. The work helps advance understanding of VQA in general and, in particular, in the domain of healthcare and medicine.
Uncovering Anime Insights: Enhancing Classification Models Through Decision Trees and TF-IDF Embeddings

Having a clear understanding of future trends is essential for producers in the animation industry to navigate effectively, as it can impact various aspects of project deployment, including creativity and employment. In this paper, we first utilize several machine-learning models, including decision tree, K-nearest neighbors (KNN), logistic regression, and MLP classifier, to predict the anime class using web-scraped anime data. Then, we propose a decision tree algorithm using both Gini index and Entropy to explain the clustering and classification models built on tf-idf embeddings on the text information of animes, e.g., name, genre, and type. Specifically, this decision tree algorithm helps us to understand the important features that the clustering and classification models rely on and why certain animes are classified as popular, and vice versa. The overall accuracy of the decision tree with depth 3 is 80.6%. The study reveals that the score of each anime is significantly influenced by its number of episodes and members. Other factors, such as the anime’s name, also contribute to the scoring of anime. We further conducted a word frequency analysis on the names of anime to differentiate between popular and unpopular anime. It is important to note that future research is necessary to investigate other factors that may impact the score of each anime, including malicious comments, untruthful responses, and data collection methods.
Redefining Health Interventions: A User-Centered Value Engineering Approach via Mobile Application

Traditional mobile applications aim to enhance people’s over-all well-being by tracking their behavior and giving feedback to help them change their unhealthy habits. However, these applications often focus solely on assessing users’ physical activities and do not consider their values, which inhibit a full understanding of their lifestyles and health goals. In this paper, we present a design that outlines how to align users’ technological behavior with their values and intended priorities, providing them with interventions to aid them in accomplishing their objectives.