Reviewerly/exHarmony huggingface dataset:
Problematika Keterbacaan Kitab Turats Fiqh Fathu al-Qarib Di Kelas Program Kitab Turats Plus (KTP) Pondok Pesantren Salaf Modern Thohir Yasin

Kitab Turats Fiqh Fathu al-Qarib is a book in the Syafi'iyyah school of thought which is always studied and taught in the world of Islamic boarding schools. Along with the development of time, this book has always been a reference standard in measuring the fluency and understanding of students in reading kitab turats. So the level of readability of this book is closely related to the level of difficulty encountered when reading the book. Therefore, this study aims to analyze the readability problems contained in this book, especially among students of the class of the Kitab Turats Plus (KTP) program at Thohir Yasin Salaf Modern Islamic Boarding School. This study uses a qualitative method with a case study research design. The research data collection instruments were interviews and documentation with fifteen informants. Meanwhile, in analyzing the data, this study used the Atlas.ti 9.0 application using the thematic analysis method. The results of this study found that students still experienced difficulties in analyzing and applying nahwu and sharf knowledge in the kitab Fathu al-Qarib. In addition, the cause of student's difficulties in reading and understanding the kitab Fathu al-Qarib is that there is still a lack of mastery and memorization of vocabulary and there are still difficulties with fiqh terms, especially those found in the chapter on zakat and the latter is due to the lack of parental support, the difference in cognitive level between one student and another and due to the activities of the Islamic boarding school too dense. Therefore, recording and evaluating any difficulties faced by students in reading kitab turats is highly recommended for teachers so that the goals to be achieved can be efficient and on target.
Students’ Writing Error in Parts of Speech: A Case Study of EFL Students

This research was intended to see the phenomena of students capability in producing products of their language target which was English. This was purposed to observe and to find their errors in producing writings. The result then could be used as reflection to improve their skill in writing specifically in parts of speech. From the analysis, it was found out that the errors found in the students writing were dominated by noun with the percentage of 22%, followed by pronoun 19%, adjective 17%, verb 14%, conjunction 14%, adverb 8%, preposition 6%, and lastly interjection 0.2 %. The data then analysed by considering the analysis in error analysis and found out that 33% error in misordering, 23% error in omission, 22% error in misformation, and 22% error in addition. This research showed that error which appear the most was dominated by misordering error and noun in the aspect of parts of speech. The possible cause of how the errors could happened was because of the influence of intralingual transfer. Students cannot differentiate between their experience in using grammar of source language and target language. Thus, the product of target language that provided by the students met errors.
Techniques for improving the performance of unsupervised approach to sentiment analysis

In this work, few techniques were proposed to enhance the performance of unsupervised sentiment analysis method to categorize review reports into sentiment orientations (positive and negative). In review reports, generally negations can change the polarity of other terms in a sentence. Therefore, a new technique for handling negations was proposed. As it is seen that, the positions of terms in a report are also important i.e. the same term appearing at different positions in a report may convey different amount of sentiments. Thus, a new technique was proposed to assign weights to the terms depending on their positions of occurrences within a review. Again, another technique was proposed to use the presence of exclamatory marks in the reviews as the effects of exclamatory marks are equally important in categorizing review reports. After incorporating all these concepts in the first phase of the proposed method, in the second phase, analysis of sentiment orientations was done using cluster ensemble method. The proposed method was tested on a state-of-the-art Movie review dataset and 91.75% accuracy was achieved. A significant improvement over some of the unsupervised and supervised methods in terms of accuracy was achieved with incorporation of the new techniques.
Blockchain Security Through SHA-512 Algorithm Implementation Using Python with NB-IoT Deployment in Food Supply Chain

In this paper, we propose the SHA-512 algorithm, a revised version of using SHA-512 further truncating its output to 256 bits. In comparison to the traditional SHA-256 algorithm, this results in a better and more efficient 256-bit hashing programming algorithm for 64-bit architectures. Furthermore, we address the security concerns associated with the Narrowband Internet of Things (NB-IoT) in food supply chain management by juxtaposing blockchain technology with the aid of NB-IoT. To authenticate data and generate the blockchain, our system employs the SHA-512 algorithm and a Python program, which helps to ensure the accuracy and transparency of the data exchange. The system can also detect damaged agricultural products during transportation, which is critical for preserving food quality. In terms of efficiency and security, the proposed method was evaluated and compared to the traditional SHA-256 algorithm. Our method outperforms the traditional algorithm in terms of efficiency while maintaining the same level of security, according to the results. The study’s findings have important implications for developing reliable and efficient data exchange systems in the Narrowband Internet of Things. Our method is thought to have appealing confidentiality, authentication, accountability, and quality characteristics, making it an ideal method for decentralized and distributed secure transportation with traceability.
A Novel Linear Mathematical Model Based Heuristic for a Class of Classification Problem with Non-linearly Separable Data

Classification is a supervised machine learning technique that is used to predict the class or category of a new observation based on training data. Classification techniques can be broadly categorized into logic-based (decision trees and rule-based classifier, perceptron-based (neural networks), and statistical techniques. In this study, we focus on developing a mathematical model based heuristic for binary classification problems. The application of Operation Research (OR) techniques is quite rare in the literature on classification. We propose a novel Linear Programming (LP) based model for binary classification. A very popular dataset available in the literature, Iris, has been used in our analysis. It is a multi-variate dataset first used by Fisher in [1], which consists of three classes of fifty instances each, where each class refers to a type of Iris plant. The classes are as follows: Iris Setosa, Iris Versicolor, and Iris Virginica. The proposed Linear Programming model consists of additional variables to show the interaction effect between the predictor variables along with the actual predictor variables so that non-linearly separable data points can also be classified by using the proposed LP based binary classifier. The values of the decision variables, obtained from building the classification model using LP on training data set, are used to predict the class of observations on the test data set. The overall accuracy of the proposed 2-stage classifier is found to be comparable with the results reported in the literature.
Normed Spaces

This chapter introduces normed spaces, which are an essential tool in the study of real functions conducted in Chaps. 6 and 7. Normed spaces are a special case of metric spaces, and their study is an excellent exercise in applying a good share of the theory of topological and metric spaces developed in Chaps. 2 and 3. Normed spaces are linear spaces, and an important distinction is whether their dimension is finite or infinite. The case of finite-dimensional normed spaces is the one in which we are mainly interested in these notes. We will see that they share a large amount of properties with the set of real numbers $$\mathbb {R}$$ . In particular, we will extend the Bolzano–Weierstrass and Heine–Borel theorems to finite-dimensional normed spaces. When possible, the results will be derived in total generality, and a few examples of infinite-dimensional spaces will be offered.
Distribution of heavy metals (arsenic, cobalt, copper and zinc) composition and bacterial communities in sediments along tropical river east coast Malaysia

Sediment samples were collected from 19 sampling stations of Pahang River, east coast Malaysia to determine the concentration of arsenic (As), cobalt (Co), copper (Cu), zinc (Zn) and bacterial colony forming unit (CFU) distribution.Sampling was conducted during pre and post of North-East monsoon season for the years 2013 and 2022.Heavy metal concentrations were determined by using inductively coupled plasma mass spectrometry (ICP-MS).The concentration of heavy metals was compared with the world average concentration of shale values.The dry weight concentration of As ranged between 4.387 ± 0.586 to 15.922 ± 8.738 µg•g -1 during pre-monsoon.While it was 3.989 ± 0.758 to 11.336 ± 6.748 µg•g -1 during post-monsoon.The range of dry weight concentration for Co was found between 1.730 ± 0.318 and 4.569 ± 0.586 µg•g -1 during pre-monsoon while 1.476 ± 0.063 and 4.620 ± 0.951 µg•g -1 during post-monsoon.The dry weight concentration of Cu ranged between 1.182 ± 0.510 to 10.722 ± 5.664 µg•g -1 and 0.900 ± 0.222 to 6.514 ± 3.749 µg•g -1 during pre-monsoon and post-monsoon, respectively.The range dry weight concentration for Zn was found between 7.964 ± 4.857 and 26.289 ± 2.636 µg•g -1 during pre-monsoon while 8.187 ± 3.010 and 28.347 ± 15.665 µg•g -1 during post-monsoon.The bacterial community in sediments along Pahang River was determined using culture-based method.The bacterial CFU range was found between 1526.67 ± 64.68 and 16146.67 ± 225.71 CFU/g during pre-monsoon while 1013.33 ± 38.51 and 28826.67 ± 418.47 CFU/g during post-monsoon.This study revealed that the concentration of heavy metals does not influence the distribution of bacteria in the Pahang River sediments.Even though the concentrations of heavy metals studied are typically below reported toxic levels, ongoing monitoring of river ecosystems is an essential to safeguard this ecosystem and ensure that the pollution from heavy metals is under conducive condition throughout these increased industrialization and urbanization.
Decision Support System for Teacher Performance Assessment Using the Weighted Product Method

The quality of teachers can be seen from the results of their performance assessment whether they have increased or vice versa. Therefore, an objective performance appraisal system is needed, in addition to reviewing performance results, the assessment system can also be a reference for determining the best teacher, so that teachers are more motivated in teaching. In making decisions on teacher performance evaluation at Pelita Insani Special Schools, they still experience problems when the process is done manually and the assessment system is less objective. So we need a Decision Support System that can produce the best alternative, can be done automatically and objectively. Practitioners This study produced a web-based decision support system that can provide alternative decision-making for school principals based on teacher performance results. By using the Weighted Product method. There are six criteria, namely: learning planning, implementing learning, assessing learning outcomes, training and guiding, additional assignments, and developing professional activities, then processed with data on teacher performance results with this method and produces teacher rankings which can be recommendations for decision making for school principals
Penerapan Algoritma Floyd-Warshall pada Jalur Evakuasi Korban Kecelakaan di Boyolali

The Floyd-Warshall algorithm is one of the algorithms that can be used to solve the shoetest route problem and is the easiests to apply because it can find all the shortest routes between each possible pair of position and is part of a dynamic program that is very efficient in solving optimal route promblems. The Floyd-Warshall algorithm works by comparing each possible path on the graph for each node pair and checking the resulting node combination. The problem of the shortest route in daily life is the evacuation of victims of traffic accidents in Boyolali, Central Java. For evacuation to be more effective, a nearby route to the nearest hospital is needed. Based on the results of the research that has been done, it can be concluded that the study produced reference matrix in the form of the shortest trajectory used to determine the shortest route to nearest hospital in Boyolali, Central Java.
Modifikasi Vigenere Cipher Menggunakan Grup Simetri untuk Mengamankan Pesan Teks

Cryptography is widely used to overcome information security problems that are exchanged on the internet network. One of the algorithms in cryptography is the Vigenere Cipher, the algorithm is known as a robust method and is not easily solved. However, the Vigenere Cipher algorithm has a weakness, namely the key is short and is used repeatedly. Therefore, it is necessary to modify the Vigenere Cipher. The purpose of this study is to determine the modification of the Vigenere Cipher using a symmetric group and also to determine the level of security of the encryption results of Vigenere Cipher modification when compared to the Vigenere Cipher. The results obtained from this research is a new algorithm of the modification process of the Vigenere Cipher using symmetric groups. The modified Vigenere Cipher algorithm is proven to be stronger than the Vigenere Cipher. This is because the plaintext is scrambled first using a symmetric group encoding, so that the actual plaintext is safe against attacks by the Kasiski method and exhaustive key search. In addition, the resulting new algorithm also supports the use of capital letters, lowercase letters, numbers, and symbols.
Membangun Super Enkripsi untuk Mengamankan Pesan

The issue of message security or an information is very important. A science that studies aboutsecuring the confidentiality of messages using passwords is called cryptography. To enhance security, twoalgorithmsarecombinedtosecuremessages.Superencryptionisaconceptthatusesacombinationoftwoormoresubstitutionandpermutation(transposition)cryptographytechniquestoobtainanalgorithmthatis more difficult to crack. The first thing to do is to encrypt the message using a substitution technique(Cipher Substitution), then re-encrypt it using a permutation technique (Cipher Transposition). In thisstudy, two cryptographic algorithms will be combined to build super encryption using the Vigenere Cipherand Bifid Cipher algorithms to secure messages. The message encryption process is using the VigenereCipher algorithm for the first encryption process, then continued using the Bifid Cipher algorithm for thesecondencryptionprocess.Theencryptionprocessisdonetheotherwayaround,startingfromthebackoftheencryptionprocess.Thecombinationofthesetwoalgorithmsresultsinmoresecuremessagesecurity.
Lexical Cohesion in Undergraduate Theses: AQuantitative Study in an Indonesian University

This study undertook a detailed analysis of the representation of lexical cohesion within the research background sections of undergraduate theses at an Indonesian university. Utilizing a quantitative approach, the research aimed to investigate the frequency and percentage distribution of lexical cohesion devices employed by students in their writing. The primary data collection tool was note-taking, with data drawn from a selection of undergraduate theses submitted by students of English Language Education. The study revealed that repetition was the predominant lexical cohesion strategy, accounting for 93% of instances. It was followed by lesser-used strategies, such as synonym use (4%), collocation (2%), and superordinate terms (1%). However, the research has its limitations; the findings were specific to one group of students within a single university context and based solely on theses, thus potentially lacking wider applicability. The implications of the study highlight the necessity to increase students' awareness and application of diverse lexical cohesion devices. While repetition was a primary strategy, the sparse use of synonyms, collocations, and superordinates indicates areas for enhanced teaching and practice in academic writing. By encouraging a more varied use of these strategies, we can promote improved coherence, richness, and sophistication in student academic writing. This research underscores the importance of diversifying cohesive devices to enrich students' academic writing skills.
Proposed Watermarking System Using Genetic Algorithm and Security Measures

This paper produces a proposed watermarking system to be used for protecting images from tampering by attackers and other third parties. This proposed system consists of three stages: pattern generation, watermark embedding and watermarking extracting. In the first stage, two patterns (primary and secondary) are generated from some features of the image that used as a watermark, then applying a genetic algorithm to get random, unique and robust primary pattern, This unique pattern that is generated from image used as a watermark and embeds this pattern in the original cover image instead of embedding the whole watermarking image inside the cover image. The primary pattern will be embedded in different three locations mainly diagonal, vertical and horizontal lines in the middle of the cover image. The receiver can retrieve this pattern to ensure that it is the same pattern that is generated from the watermark image. In addition to generate and embed secondary pattern that used to confirm between the sender and the recipient, if the secondary pattern doesn’t match, the recipient tells the sender to resend the image again. Different experiments are applied to test the system and the results show the watermark is random, imperceptible, and robust enough against image cropping attacks, fully secure, and Peak Signal to Noise Ratio (PSNR) value of the watermarked image is better than the watermarked image of many previous watermarking system when compared with them.
The concentration of heavy metals and pollution indices in a selected abandoned mine in the State of Pahang, Malaysia

Abandoned iron ore mining soil was analyzed for its metal contamination. Pollution indices were calculated and used to examine the potential of the area to be polluted by the metals. The samples were collected in triplicate from ten sampling points spread throughout the mining area. The energy dispersive X-ray fluorescence (EDXRF) analytical method was used to determine the concentrations of Cd, Cu, Co, Pb, Zn, Fe, Cr, Ni, and Mn. The order of the mean metal concentrations (mg/kg) in soil samples taken at random from ten locations near the chosen iron ore mines is Fe, Mn, Cr, Pb, Cu, Zn, Co, Ni, and Cd. The Geoaccumulation Index (Igeo) results showed that the area was moderately contaminated, especially with Fe, whereas the results of the Enrichment Factor (EF) and the Quantification of Contamination (QoC) showed that metal enrichment was strongly related to anthropogenic origins. EF and Quantification of Contamination (QoC) results revealed that metal enrichment was strongly related to anthropogenic origins. The ecological risk based on individual metals followed the following order: Cd &amp;gt; Pb &amp;gt; Co &amp;gt; Cu &amp;gt; Cr &amp;gt; Fe &amp;gt; Ni &amp;gt; Mn &amp;gt; Zn, and the average Potential Ecological Risk Index (PERI) was recorded at the severely high-risk level. The findings clearly showed that the study area might pose a serious threat to environmental health.
Development of IoT-Based Automated Dynamic Emergency Response System Against Fire Incidents in Academic Building

The ongoing advancement of architectural and structural designs, high-ceiling spaces, special spaces have made fire disasters increasingly diverse and difficult to predict. It is demanding the need for improved firefighting systems. This study aims to address the need for improved firefighting systems in academic building by proposing the development of an IoT-based automated emergency response website. The proposed system leverages IoT technology, wireless and bluetooth sensor networks to gather real-time data from various sensors and devices installed in the site and uses machine learning algorithms to predict and prevent potential fire incidents. The system also includes an emergency response website that allows users to access real-time information about the fire incident, location, severity, and evacuation instructions. Additionally, the proposed system incorporates Building Information Modelling (BIM) to optimize evacuation and rescue routes, providing early detection and accurate alarm capabilities, evacuation guidance for endangered individuals, and guidance for firefighters. The integration of BIM allows the system to provide a three-dimensional visualization of the site, enabling a more efficient and effective response to fire incidents. Overall, the proposed system aims to improve the safety and security through real-time monitoring and response capabilities. By leveraging the power of IoT technology, machine learning algorithms and BIM, the proposed system aims to reduce the impact of fire disasters by providing accurate and timely information, route optimization and facilitating effective evacuation and rescue efforts.Keywords: Building Information Modeling, IoT, Sensor, Dijkstra Algorithm, Simulation
Phishing Website Detection Using Machine Learning: A Review

Phishing, a form of cyber attack in which perpetrators employ fraudulent websites or emails to Deceive individuals into divulging sensitive information such as passwords or financial data, can be mitigated through various machine-learning algorithms for website detection.&#x0D; These algorithms, including decision trees, support vector machines, and Random Forest, analyze multiple website features, such as URL structure, website content, and the presence of specific keywords or patterns, to ascertain the likelihood of a website being a phishing site.&#x0D; This comprehensive review elucidates the concept of phishing website detection and the diverse techniques employed while summarizing previous studies, their outcomes, and their contributions. Overall, machine learning algorithms serve as a potent tool in the identification of phishing websites, thereby safeguarding users against falling prey to such malicious attacks.