Reviewerly/exHarmony huggingface dataset:
On the transitivity of Logical Consequence without Assuming Monotonicity

Authors:
raw_author_name: Lin Chen
raw_affiliation_string: Sun Yat-sen University
raw_author_name: Xuefeng Wen
raw_affiliation_string: Sun Yat-sen University

Abstract: We generalize Ripley’s results on the transitivity of consequence relation, without assuming a logic to be monotonic. Following Gabbay, we assume nonmonotonic consequence relation to be inclusive and cautious monotonic, and figure out the implications between different forms of transitivity of logical consequence. Weaker frameworks without inclusiveness or cautious monotonicity are also discussed. The paper may provide basis for the study of both non-transitive logics and nonmonotonic ones.
Thematic Analysis through Artificial Intelligence (AI)

Authors:
raw_author_name: Prokopis Christou

Abstract: Thematic analysis, a well-enforced qualitative analytic method, is likely to continue evolving with the adoption of AI technologies. This how-to report does not delve into the details of thematic analysis itself, as there are ample existing studies on the topic. Instead, it acknowledges the potential impacts, dynamics, and pitfalls of AI in thematic analysis while offering valuable advice, particularly for novice analysts, on how to incorporate and document AI tools in each phase of a thematic analysis. The author underscores the importance of not allowing AI to overshadow the analyst's critical evaluative and interpretive skills but instead supporting the use of AI as an aid in thematic analysis, enhancing the depth and breadth of analysis, provided certain criteria are adhered to. This approach ensures that AI serves as a complementary tool, augmenting rather than replacing human analytical inquiry.
Adiabatic elimination in the presence of multiphoton transitions in atoms inside a cavity

Authors:
raw_author_name: Prosenjit Maity
raw_affiliation_string: Department of Physics, Ramakrishna Mission Residential College, Narendrapur, Kolkata 700103, West Bengal, India

Abstract: Various approaches have been used in the literature for eliminating nonresonant levels in atomic systems and deriving effective Hamiltonians. Important among these are elimination techniques at the level of probability amplitudes, operator techniques to project the dynamics on to the subspace of resonant levels, Green’s function techniques, the James’ effective Hamiltonian approach, etc. None of the previous approaches is suitable for deriving effective Hamiltonians in intracavity situations. However, the James’ approach does work in the case of only two-photon transitions in a cavity. A generalization of the James’ approach works in the case of three-photon transitions in a cavity, but only under Raman-like resonant conditions. Another important approach for adiabatic elimination is based on an adaptation of the Markov approximation well-known in the theory of system–bath interactions. However, this approach has not been shown to work in intracavity situations. In this paper, we present a method of adiabatic elimination for atoms inside cavities in the presence of multiphoton transitions. We work in the Heisenberg picture, and our approach has the advantage that it allows one to derive effective Hamiltonians even when Raman-like resonance conditions do not hold.
The COVID-19 Tweets Classification Based on Recurrent Neural Network

Authors:
raw_author_name: Arif Dwi Laksito
raw_affiliation_string: Faculty of Computer Science, Universitas Amikom Yogyakarta, Yogyakarta, Indonesia
raw_author_name: Nuruddin Wiranda
raw_affiliation_string: Department of Computer Education, Lambung Mangkurat University, Banjarmasin, Indonesia
raw_author_name: Shofiyati Nur Karimah
raw_affiliation_string: Graduate School of Advanced Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Ishikawa, Japan
raw_author_name: Mardhiya Hayaty
raw_affiliation_string: Faculty of Computer Science, Universitas Amikom Yogyakarta, Yogyakarta, Indonesia

Abstract: Due to its extensive use in both public and commercial contexts, sentiment analysis on Twitter has recently received much attention, particularly concerning tweets about COVID-19. Information about COVID-19 has been widely spread over social media, resulting in various views, opinions, and emotions about this pandemic, significantly impacting people's health. It is exceedingly challenging for the authorities to find these rumors on these public platforms manually. This paper proposes a framework for text classification using the RNN model and its updates, such as LSTM, BiLSTM, and GRU. This study aims to determine the best recurrent network model for handling cases of Twitter data classification. We utilized Twitter data relevant to COVID-19 and the lockdown with four classification classes (sad, joy, fear, and anger). In addition, this study aims to prove whether GloVe pre-trained word embedding can increase the accuracy of model predictions. The training and testing datasets were split into 80% and 20%, respectively. Therefore, in this experiment an early stopping technique was used with a limit of 15 epochs and a minimum delta of 0.01, meaning that training will be stopped if there is no improvement of 0.1% accuracy after 15 epochs. We used the f1-score average to measure the accuracy of the classification task results. The test results show that the BiLSTM model with GloVe word embedding yields the best f1-score compared to other models. Moreover, in all model testing, the f1-score value of the 'fear' class displays the highest accuracy compared to other classes.
Enhancing the Zebra Optimization Algorithm with Chaotic Sinusoidal Map for Versatile Optimization

Authors:
raw_author_name: None Osamah Ibrahim Khalaf
raw_affiliation_string: Department of Solar ,Al-Nahrain Research Center for Renewable Energy, Al-Nahrain University, Jadriya, Baghdad, Iraq
raw_author_name: None G Rajesh Chandra
raw_affiliation_string: Department of Computer Science and Engineeirng, KKR & KSR Institute of Technology and Sciences, Andhra Pradesh, India.

Abstract: In this study, the Chaotic Sinusoidal Map (CSM)-enhanced Zebra Optimization Algorithm (CZOA)is introduced. CZOA combines CSM's integration strengths with ZOA's optimization skills. ZOA already exhibitsgreat optimization capabilities, but the addition of CSM increases its potential even more. This addition greatlystrengthens ZOA's exploration and exploitation skills and increases its flexibility for various optimization tasks.CZOA outperforms both the original ZOA and contemporary optimisation methods on 23 benchmark functions,including high-dimensional (FD), multimodal (MM), and unimodal (UM) challenges. Using the chaos of CSM toinvestigate regional optimal and determine better convergence and exploration-exploitation equilibrium are shownby CZOA, which also shows more profitable solution locations. CZOA demonstrates its resilience and versatilitythrough multiple benchmark activities, underscoring its potential as an adaptable optimisation tool. CZOAbecomes a potent metaheuristic by combining biological inspiration and chaotic dynamics to solve difficultoptimisation problems. Inspired by the natural behaviour of zebras, the Zebra Optimisation Algorithm (ZOA) is arelatively new optimisation technique. It makes use of a herd behaviour mechanism and the ideas of leadership andfollowing, in which members of the population—zebras in this case—cooperate to solve optimisation issues in thebest possible ways
Shape and orientation classification of objects based on their electromagnetic signatures using convolutional neural networks

Authors:
raw_author_name: Benoit Miramond
raw_affiliation_string: Laboratory of Electronics, Antennas and Telecommunications, Universite Cote d'Azur, Campus SophiaTech, 930 Route des Colles, Sophia Antipolis, Provence Alpes Côte d'Azur, 06903, FRANCE

Abstract: Abstract This study addresses the classification of objects using their electromagnetic signatures with Convolutional Neural Networks (CNNs) trained on noiseless data. The singularity expansion method (SEM) was applied to establish a compact model that accurately represents the ultra-wideband scattered field of an object, independently of its orientation and observation angle. To perform the classification, we used a CNN associated with a noise-robust SEM technique to classify different objects based on their characteristic parameters. To validate this approach, we compared the performance of the classifier with and without SEM pre-processing of the scattered field for different noise levels and for object sizes not present in the training set. Moreover, we propose a procedure that determines the direction of the receiving antenna and orientation of an object based on the residues associated with each complex natural resonance. This classification procedure using pre-processed SEM data is promising, especially when generalizing to object sizes not included in the training set.
Penyuluhan Bahaya Narkoba Di Kalangan Remaja Khususnya Pelajar SMKN 1 Kisaran

Authors:
raw_author_name: None Suriani Suriani
raw_affiliation_string: Universitas Asahan

Abstract: The purpose of this community service activity is to educate students at SMK Negeri 1 Kisaran on how to avoid drug abuse. drug abuse. This community service activity used an counseling strategy; all activities were planned and conducted with 25 student representatives from each grade and level. The comparison results between pre-test and post-test showed that the PKM can improve the knowledge of participants about the dangers of drugs and drug abuse prevention efforts; the number of participants who were able to mention the types of drugs, the dangers of drugs, and efforts to prevent drug drug abuse prevention efforts increased from 30% to 95%, and the average score reached 87 out of the maximum score. reached 87 out of the maximum score.
Enhanced Multi-Class Newsgroup Document Classification Using N-Gram Approach

Authors:
raw_author_name: Ali Abbas
raw_author_name: Shreya Agarwal
raw_author_name: Prajna Jha
raw_author_name: Tanveer J. Siddiqui

Abstract: With the advancement of technology, consumption of data and information over web is increasing day by day. This has led to rising dependence of our lives on internet data at personal and professional fronts. Being a populous nation with high internet penetration, India is a huge market in digital media. The adaptability to online news, has created a large number of text news data which requires classification into categories for further applications. To address the work for efficient news document classification in the field of computational linguistics, we have proposed a novel approach for Newsgroup classification by combining tokens with a context window size of one, two and three known as unigram, bigram and trigram respectively. &#x0D; In our research work, we explored the relevance of trigram by experimenting over different collection of n-grams using three supervised classifiers namely Perceptron, Nearest Centroid, and Random Forest. In the experiment, we found that inclusion of trigram along with unigram and bigram achieves the best result in terms of F1-macro score and accuracy among all three classifiers. Perceptron shows the best results in comparison with Random Forest and Perceptron classifiers, with an accuracy of 0.846945 and an F1-macro score of 0.83055. Significant time was spent on the validation of our efficient approach with the existing literature.
RANCANG BANGUN APLIKASI E-VOTING DI SMK NURUL HUDA PETIR BERBASIS WEB DENGAN SENSOR RADIO FREQUENCY IDENTIFICATION (RFID)

Authors:
raw_author_name: R Kania
raw_author_name: Waliadi Gunawan
raw_affiliation_string: Universitas Banten Jaya
raw_author_name: Tifani Intan Solihati

Abstract: Information technology is developing rapidly in all aspects of life and until now its application has created conditions conducive to community work. One of the activities that can now take advantage of information technology is e-voting in the election of the chairman and deputy chairman of the organization. SMK Nurul Huda Petir has more than 200 students. The democratic process of selecting the head of this school organization is still used manually. There will be many obstacles, namely data security risks, which will reduce learning time, and require large implementation costs. The problems that arose created an information system called e-voting. E-voting is a system for creating ballots, casting, counting, and displaying ballot results, and generating and maintaining audit trails electronically and digitally. This research aims to speed up, improve security, and reduce implementation costs in the process of election activities at Nurul Huda Petir Vocational School. The application development process-voting uses the method Waterfall with Unified Modelling Language (UML) for system design. System e-voting its build-based web with Framework Codeigniter and added an RFID sensor (Radio Frequency Identification). The results of this research found that the application of e-voting can reduce 80% of implementation costs, improve voice data security, and speed up the process of collecting and calculating voice data.
KLASIFIKASI KEMATANGAN BUAH ALPUKAT MENTEGA BERDASARKAN FITUR WARNA MENGGUNAKAN SUPPORT VECTOR MACHINE

Authors:
raw_author_name: Amir Hamzah
raw_affiliation_string: Jurusan Informatika Fakultas Teknologi Informasi dan Bisnis, Institut Sains & Teknologi AKPRIND Yoagyakarta
raw_author_name: Erma Susanti
raw_affiliation_string: Institut Sains & Teknologi AKPRIND Yogyakarta

Abstract: The ripeness level of the avocado greatly affects the shelf life of the fruit and also determines the taste of the fruit. Determining the proper ripeness of a fruit will play an important role in increasing the nutritional value and ripeness of avocado affects the quality of avocado oil. Classification of fruit maturity manually has many limitations because it is influenced by human subjectivity so that the application of digital image processing needs to be used.This study aims to classify avocado fruit ripeness which is divided into three categories, namely Unripe, Ripe, and Rotten. This research is intended to facilitate the classification of ripeness in fruit through its color. This study used avocado fruit as an experimental material which was carried out using the SVM (Support Vector Machine) method. The colors of the various avocados and in different positions and conditions of light contrast are used as data to classify the types of avocados. Data collection was taken from the selected Keagle Dataset of 150 data. The data is grouped into two data sets, namely dataset-1 (120 training data and 30 test data) and dataset-2 (90 training data and 60 test data). The image will be converted to grayscale to facilitate the classification process and look for HSV (Hue, Saturation, Value) and RGB (Red, Green, Blue) values to classify the fruit. The results from dataset-1 has an accuracy rate of 86%, 90% precision, and 86% Recall, while dataset-2 has an accuracy rate of 85%, 84% precision, and 85% Recall.
SISTEM INFORMASI PENGELOLAAN TOUR DAN TRAVEL BERBASIS WEB APP PADA CV. KENKEN AGENCY

Authors:
raw_author_name: Khasan Asrori
raw_affiliation_string: Universitas Banten Jaya
raw_author_name: Solihin Solihin
raw_affiliation_string: Politeknik PGRI Banten

Abstract: CV. Kenken is a service company in the city of Serang in managing tour and travel reservations still using the manual system. There are many problems including management of tour booking data and WEB-Based Travel On CV. Kenken Agency, still using a ledger, boking data collection and departure schedule is not well recorded, difficulties in tracking documents sent. The method used is the waterfall method, the design tool uses the UML flow map. While the implementation uses the PHP programming language and MySQL data base. With the web-based tour and travel information system on CV. Kenken Agency, is expected to overcome the problems that exist in the CV. Kenken Agency. Therefore, the recording of documents is neat, the departure schedule is well organized, the latest prices, boking and schedules are well recorded.
APPLICATION OF AUGMENTED REALITY IN MOBILE BASED HIJAIYAH LETTER RECOGNITION APPLICATIONS USING MULTIMEDIA DEVELOPMENT METHODS LIFE CYCLE

Authors:
raw_author_name: Sondy C. Kumajas
raw_affiliation_string: Manado State University
raw_author_name: Ferdinan I. Sangkop
raw_affiliation_string: Manado State University

Abstract: Islamic religious education is an important aspect of Muslim life, and learning Arabic and Hijaiyah letters is an integral part of Islamic education. The use of augmented reality (AR) technology in educational setting is rapidly increasing. This study aims to apply the AR concepts to the creation of mobile Hijaiyah letters using Multimedia Development Life Cycle (MDLC) methodology. The application of AR in this application allows users to interactively learn each hijaiyah letter. Through a mobile device camera, users can see hijaiyah letters that appear virtually on the device screen. The development of this application is based on the needs of users in understanding hijaiyah letters in a more attractive and interactive way. Thus, this application is expected to facilitate users, especially children, to learn hijaiyah letters more effectively.
A Novel Method for Hill Cipher Encryption and Decryption Using Gaussian Integers Implemented in Banking Systems

Authors:
raw_author_name: Saba Salman
raw_affiliation_string: Aliraqia university
raw_author_name: None Yasmin Makki Mohialden
raw_affiliation_string: Mustansiriyah University, Baghdad, Iraq
raw_author_name: None Abbas Abdulhameed
raw_affiliation_string: Mustansiriyah University, Baghdad, Iraq
raw_author_name: None Nadia Mahmood Hussien
raw_affiliation_string: Mustansiriyah University, Baghdad, Iraq

Abstract: Cryptographic tools like Hill's encryption algorithm protect digital data. In this work, we present a novel Hill cipher security method that utilizes Gaussian integers from number theory. Using these intriguing mathematical entities to disguise plaintext values dramatically boosts assault resistance and duration. This research includes a three-pass protocol for encryption and decryption without key exchange, ensuring a safe, efficient, and dependable solution. Pandas is used for efficient data processing, and Numpy for computational tasks, notably matrices. Hill cipher-based encryption and decryption can be utilized in real life. It also demonstrates how to save Pandas DataFrame data to Excel. This strategy assures progress in cryptography. Uniquely designed for banking, it emphasizes its applicability and possible influence on present financial systems.
Does digital inclusive finance promote regional green inclusive growth?

Authors:
raw_author_name: Yuchen Li
raw_affiliation_string: College of Integration Science, Yanbian University, Yanji 133000, China

Abstract: Based on the construction of theoretical foundations, this study uses panel data from 30 provinces in China from 2011 to 2021, and employs regression analysis and mediation mechanism tests to explore the relationship between digital inclusive finance and regional green inclusive growth. The findings indicate that digital inclusive finance significantly enhances the level of regional green inclusive growth, and the results remain significant after addressing endogeneity issues and conducting robustness tests. The mechanism test reveals that digital inclusive finance effectively promotes regional green inclusive growth by improving the efficiency of financial services in supporting the real economy.
Monocular depth estimation for 2D mapping of simulated environments

Authors:
raw_author_name: A.A. Pyrkin

Abstract: This article addresses the problem of constructing maps for 2D simulated environments. An algorithm based on monocular depth estimation is proposed achieving comparable accuracy to methods utilizing expensive sensors such as RGBD cameras and LIDARs. To solve the problem, we employ a multi-stage approach. First, a neural network predicts a relative disparity map from an RGB flow provided by RGBD camera. Using depth measurements from the same camera, two parameters are estimated that connect the relative and absolute displacement maps in the form of a linear regression relation. Based on a simpler RGB camera, by applying a neural network and estimates of scaling parameters, an estimate of the absolute displacement map is formed, which allows to obtain an estimate of the depth map. Thus, a virtual scanner has been designed providing Cartographer SLAM with depth information for environment mapping. The proposed algorithm was evaluated on a ROS 2.0 simulation of a simple mobile robot. It achieves faster depth prediction compared to other depth estimation algorithms. Furthermore, maps generated by our approach demonstrated a high overlap ratio with those obtained using an ideal RGBD camera. The proposed algorithm can find applicability in crucial tasks for mobile robots, like obstacle avoidance, and path planning. Moreover, it can be used to generate accurate cost maps, enhancing safety and adaptability in mobile robot navigation.
A method of storing vector data in compressed form using clustering

Authors:
raw_author_name: A.V. Platonov

Abstract: The development of the machine learning algorithms for information search in recent years made it possible to represent text and multimodal documents in the form of vectors. These vector representations (embeddings) preserve the semantic content of documents and allow the search to be performed as the calculation of distance between vectors. Compressing embeddings can reduce the amount of memory they occupy and improve computational efficiency. The article discusses existing methods for compressing vector representations without loss of accuracy and with loss of accuracy. A method is proposed to reduce error by clustering vector representations using lossy compression. The essence of the method is in performing the preliminary clustering of vector representations, saving the centers of each cluster, and saving the coordinate value of each vector representation relative to the center of its cluster. Then, the centers of each cluster are compressed without loss of accuracy, and the resulting shifted vector representations are compressed with loss of accuracy. To restore the original vector representations, the coordinates of the center of the corresponding cluster are added to the coordinates of the displaced representation. The proposed method was tested on the fashion-mnist-784-euclidean and NYT-256-angular datasets. A comparison has been made of compressed vector representations with loss of accuracy by reducing the bit depth with vector representations compressed using the proposed method. With a slight (around 10 %) increase in the size of the compressed data, the absolute value of the error from loss of accuracy decreased by four and two times, respectively, for the tested sets. The developed method can be applied in tasks where it is necessary to store and process vector representations of multimodal documents, for example, in the development of search engines.