Reviewerly/exHarmony huggingface dataset:
Detection of sarcasm in tweets using hybrid machine learning method

Authors:
raw_author_name: Bellamkonda Rajani
raw_author_name: Sameer Saxena
raw_author_name: Billakurthi Suresh Kumar

Abstract: &lt;p&gt;When one wants to express the contrary of what they mean, especially when insulting someone, sarcasm is used. One of the challenges associated with the control and management of content on social networking sites such as Twitter and other social media sites, is the recognition of sarcasm. Because sarcasm is purposefully conveyed through ambiguous word choice, even for humans it can be challenging to identify. Existing methods for automatically detecting sarcasm rely heavily on lexical as well as linguistic clues the majority of the time. On the other hand, these methods have not yielded a large or even a minor enhancement in terms of the correctness of the mood. The purpose of this study is to increase the accuracy of sentiment analysis by proposing a system that is both reliable and effective at identifying sarcasm. In this investigation, three different types of features are focussed: lexical, sarcastic, and context features. These feature sets are utilised in the process of categorising tweets as either sarcastic or not sarcastic. This research presents a sarcastic feature set coupled with an efficient hybrid machine learning approach, which ultimately results in improved accuracy. The results of the experiments reveal that the recommended hybrid machine learning method achieves 97.3% accuracy for sarcastic feature sets which is better when compared to existing machine learning techniques namely k-nearest neighbor, random forest, support vector machine and decision tree for the selection of the appropriate features.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
Detecting Spam E-mails with Content and Weight-based Binomial Logistic Model

Authors:
raw_author_name: Richa Indu
raw_affiliation_string: Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
raw_author_name: Sushil Chandra Dimri
raw_affiliation_string: Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

Abstract: Spam e-mails are continuously increasing and are a serious threats to a network and its users. Several efficient methods are available regarding this context, but still, it is evolving randomly. Considering this, the proposed approach addresses the problem of spam detection by combining traditional content-matching criteria with the modified version of the binomial logistic algorithm. The work generates seven categories for content-matching, which begins from three basic categories, namely: special words, adult content, and specific symbols and digits. The remaining four categories are derived from various possible combinations of these basic categories. The words selected for each category are carefully curated based on the human psychology of action and reaction. Then, a weight is assigned to each of the categories to signify their importance and a threshold criterion is deployed before implementing the binomial logistic algorithm, which not only increases the efficiency of the proposed algorithm but also reduces the rate of misclassification. The proposed model is tested on six separate datasets of Enron Spam Corpus, where 98.31% and 92.575% are the maximum and minimum accuracies achieved, respectively, in spam e-mail classification. The AUC_ROC scores for the entire Spam Corpus range between 0.927 and 0.983. A comparison is also carried out between the proposed algorithm and the other methods of spam detection that have logistic regression. Finally, the suggested method can adequately handle a large sample size without compromising the efficacy, which is measured using accuracy, precision, recall, F-measure, and AUC_ROC score.
Fine-grained Sentiment-enhanced Collaborative Filtering-based Hybrid Recommender System

Authors:
raw_author_name: Rawaa Alatrash
raw_affiliation_string: Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar – 752054, Odisha, India
raw_author_name: Rojalina Priyadarshini
raw_affiliation_string: Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar – 752054, Odisha, India

Abstract: Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning recommender systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis (FSA) can be leveraged to enrich the recommender system. User-posted reviews and rating data are vital in accurately directing the student to the appropriate e-learning resources based on posted comments by comparable learners. In this work, a new e-learning recommendation system is proposed based on individualization and FSA. A hybrid framework is provided by integrating alternating least square (ALS) based collaborative filtering (CF) with FSA to generate an effective e-content recommendation named HCFSAR. ALS attempts to capture the learner’s latent factors based on their selections of interest to build the learner profile. Three FSA models based on attention mechanisms and bidirectional long short-term memory (bi-LSTM) are suggested and used to train twelve models in order to predict new ratings from learner-posted book reviews based on the extracted learner profile. HCFSAR used multiplication word embeddings for stronger corpus representation that were trained on a dataset generated for an educational context and showed a better accuracy of 93.39% for the best model entitled MHAM based ABHR-2 with multiplication (MHAAM), which performed better than other models. A tailored dataset that has been created by scraping reviews of different e-learning resources is leveraged to train different proposed models and validate against public datasets.
Extended Reality Platform for Metaverse Exhibition

Authors:
raw_author_name: Choonsung Shin
raw_affiliation_string: Graduate School of Culture, Chonnam National University, Gwangju, 61186, Republic of Korea
raw_author_name: Seokhee Oh
raw_affiliation_string: Korea Creative Content Agency, Daejeon, 34863, Republic of Korea
raw_author_name: Hieyong Jeong
raw_affiliation_string: Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, Republic of Korea

Abstract: This paper proposes a web-based extended reality (XR) platform integrating exhibitions based on augmented reality (AR) and virtual reality (VR). The proposed platform includes an XR cloud for managing and sharing data in XR spaces, a VR client for users, and an AR client for real-world users. The XR cloud is a web-based VR service responsible for managing objects and spatial information related to real and virtual spaces while simultaneously facilitating real-time information sharing and synchronization between AR and VR users. The VR client offers a virtual exhibition environment based on the virtual space and information for VR users. The AR client overlays virtual information over objects in a real space, allowing for intuitive interaction with them. The proposed XR platform was implemented with Oculus Quest 2 for VR and HoloLens 2 for AR and was evaluated in a small exhibition environment. In this experimental environment, VR and AR users were able to participate in the same exhibition from different spaces and share their experiences in real time.
Extreme Learning Machine Classification Method based on Twin Strategy Salp Swarm Algorithm

Authors:
raw_author_name: Rongguo Qu
raw_author_name: Deqing Ji
raw_author_name: Zhenxing Yu
raw_author_name: Qinwei Fan

Abstract: Extreme Learning Machines (ELM) are a type of Single Hidden Layer Feedforward Neural Network (SLFN). In recent years, they have attracted attention for their powerful approximation ability and fast learning speed. Compared with traditional neural network algorithms, ELM have the advantages of simple structure, fast learning speed, and good generalization performance. However, since the input weights and biases of ELM are randomly generated, there may be some suboptimal or unnecessary input weights and biases. In addition, ELM may require more hidden nodes, which may slow down its response to unknown test data. To address these issues, a twin strategy Salp Swarm Algorithm (TSSA) is proposed to increase the population diversity to improve the convergence speed of the algorithm through chaotic initialization, while shock inertia weights and learning paradigms are introduced into the follower position updating to enhance the stochasticity of the particles. Classification tests are conducted on six datasets using the proposed TSSA, and the experimental results show that the proposed TSSA-ELM has higher accuracy and better generalization performance than some existing ELM variants.
Entropy Error-based Convolutional Neural Network Sparse Optimization and Application

Authors:
raw_author_name: Kaifang Yang
raw_author_name: Dewei Yang
raw_author_name: Fen Wang
raw_author_name: Jing Zhao
raw_author_name: Qinwei Fan

Abstract: Convolutional Neural Networks (CNNs), as quintessential representatives of deep neural networks, have found widespread applications in numerous domains such as image recognition, object detection, and image generation, owing to their robust nonlinear mapping capabilities. However, the practical deployment of CNNs faces challenges such as low learning efficiency and subpar accuracy due to the intricacies in the network structure. This paper introduces the smoothing L1 regularization term atop the conventional entropy loss function, effectively enhancing both the learning efficiency and algorithmic precision of Convolutional Neural Networks. The efficacy of the improved algorithm is substantiated through numerical simulations.
ES Data Aggregation Scheme based on Personalized Local Differential Privacy

Authors:
raw_author_name: Qiong Liu
raw_author_name: Xueyan Liu
raw_author_name: Jia Wang

Abstract: In modern society, with the rapid development of technology, research on Epidemiological Survey Data (ESD) has become increasingly crucial. As various diseases continue to evolve and spread, there is a growing need for a profound understanding of the patterns and trends of disease transmission. Epidemiological investigation, as a key method, provides fundamental data support for devising effective prevention and control strategies by tracking cases, contacts, and potential infectees. However, accompanying this progress is the issue of dealing with a large amount of privacy-sensitive data. Once these data are compromised, it may pose severe harm to individuals and society. To address this challenge, we propose a uOUE-based epidemiological survey data aggregation scheme for the collection and processing of ESD, aiming to enhance the efficiency and accuracy of data coding. We ensure the secure, efficient, and accurate aggregation processing of ESD. Through rigorous validation and comparative analysis, our algorithm complies with the requirements of local differential privacy and unbiased estimation. It demonstrates good practicality and accuracy in ESD collection, providing users with a reliable privacy protection effect.
Correlation Avoidance in Single-Photon Detecting Quantum Random Number Generators by Dead Time Overestimation

Authors:
raw_author_name: Balázs Solymos
raw_affiliation_string: Budapest University of Technology and Economics
raw_author_name: Ágoston Schranz
raw_affiliation_string: Budapest University of Technology and Economics
raw_author_name: Miklós Telek
raw_affiliation_string: Budapest University of Technology and Economics

Abstract: Abstract In the case of quantum random number generators based on single photon arrivals, the physical properties of single-photon detectors, such as time-tagger clocks and dead time, influence the stochastic properties of the generated random numbers. This can lead to unwanted correlations among consecutive samples. We present a method based on extending the insensitive periods after photon detections. This method eliminates the unwanted stochastic effects at the cost of reduced generation speed. We calculate performance measures for our presented method and verify its correctness with computer simulations and measurements conducted on an experimental setup. Our algorithm has low complexity, making it convenient to implement in QRNG schemes, where the benefits of having uncorrelated output intervals exceed the disadvantages of the decreased rate.
Observation of the non-Hermitian skin effect and Fermi skin on a digital quantum computer

Authors:
raw_author_name: Ching Hua Lee
raw_affiliation_string: National University of Singapore
raw_author_name: Tianqi Chen
raw_affiliation_string: National University of Singapore
raw_author_name: Bo Yang
raw_affiliation_string: Nanyang Technological University

Abstract: Abstract Non-Hermitian physics has attracted considerable attention in the recent years, in particular the non-Hermitian skin effect (NHSE) for its extreme sensitivity and non-locality. While the NHSE has been physically observed in various classical metamaterials and even ultracold atomic arrays, its highly-nontrivial implications in many-body dynamics have never been experimentally investigated. In this work, we report the first observation of the NHSE on a universal quantum processor, as well as its characteristic but elusive Fermi skin from many-fermion statistics. To implement NHSE dynamics on a quantum computer, the effective time-evolution circuit not only needs to be non-reciprocal and non-unitary, but must also be scaled up to a sufficient number of lattice qubits to achieve spatial non-locality. We show how such a non-unitary operation can be systematically realized by post-selecting multiple ancilla qubits, as demonstrated through two paradigmatic non-reciprocal models on a noisy IBM quantum processor, with clear signatures of asymmetric spatial propagation and many-body Fermi skin accumulation. To minimize errors from inevitable device noise, time evolution is performed using a trainable optimized quantum circuit produced with variational quantum algorithms. Our study represents a critical milestone in the quantum simulation of non-Hermitian lattice phenomena on present-day quantum computers, and can be readily generalized to more sophisticated many-body models with the remarkable programmability of quantum computers.
Prediction of turbulent flows behind cylinder arrays via Echo State Networks

Authors:
raw_author_name: Mohammad Sharifi Ghazijahani
raw_affiliation_string: Technische Universität Ilmenau
raw_author_name: Christian Cierpka
raw_affiliation_string: Technische Universität Ilmenau

Abstract: Abstract This study aims at the prediction of the turbulent flow behind cylinder arrays by the application of Echo State Networks (ESN). Three different arrangements of arrays of seven cylinders are chosen for the current study. Three different wake flow regimes are represented by these arrays: single bluff body flow, transient flow, and co-shedding flow, which allows inherently similar, yet very different, turbulent flows to be studied. The data is reduced by Proper Orthogonal Decomposition (POD), and then the Time Coefficients of the POD Modes (TCPM) are predicted by the ESN. The ESN is optimized with respect to its three main hyperparameters, Input Scaling (INS), Spectral Radius (SR), and Leaking Rate (LR), in order to produce the best predictions in terms of Weighted Prediction Score (WPS). In general, the ESN is capable of imitating the complex dynamics of turbulent flows even for long periods. Furthermore, the mutual interdependencies of the TCPM are well preserved. However, the ESN’s performance in different hyperparameters depends on the flow characteristics. Generally, as flow dynamics become faster and more intermittent, larger LR and INS values make better predictions, whereas less clear trends in SR values tend to emerge.
SMART EDUCATION TECHNOLOGIES IN HIGHER EDUCATION: POSSIBILITIES AND PROSPECTS OF APPLICATION IN UKRAINE

Authors:
raw_author_name: Olexandr Chernenko
raw_affiliation_string: Central Ukrainian State University named V. Vynnychenko, UKRAINE

Abstract: The Ukrainian education system is actively engaged in the processes of Eurointegration, prompting leaders, researchers, and educators from many domestic educational institutions to explore the possibilities and prospects of implementing SMART education technologies in the learning process. This issue is relevant as it aligns with the contemporary trends in the development of higher education in Ukraine and globally. SMART education allows for the high-level resolution of a wide range of issues related to the quality management of educational services.
ENHANCING ICT CURRICULUM FOR A MASTER'S DEGREE PROGRAMME IN PROFESSIONAL EDUCATION AND COMPUTER TECHNOLOGIES IN UKRAINE

Authors:
raw_author_name: Maksym Pavlenko
raw_affiliation_string: Berdyansk State Pedagogical University, UKRAINE
raw_author_name: Liliia Pavlenko
raw_affiliation_string: Berdyansk State Pedagogical University, UKRAINE
raw_author_name: Yevhen Pavlenko
raw_affiliation_string: Berdyansk State Pedagogical University, UKRAINE

Abstract: The Russian war against Ukraine and the restrictions of the COVID-19 pandemic have forced the Ukrainian education system to make an urgent transition to distance learning [1]. This has highlighted the urgent need for teachers and students to use information and communication technologies (ICTs) effectively for teaching, communication, and collaboration. Professional education programmes must equip graduates not only to use ICT, but also to integrate it seamlessly into their work [2]. This study focusses on improving the ICT curriculum for a Master's Degree Programme in Professional Education and Computer Technologies in Ukraine to meet the pressing needs of distance education.
Persepsi Kesiapan, Sikap dan Motivasi Belajar Mandiri terhadap Pembelajaran Matematika secara Kolaboratif Berbasis Online

Authors:
raw_author_name: Muhammad Ilyas
raw_affiliation_string: Universitas Cokroaminoto Palopo

Abstract: The research aims to describe the readiness, attitude and motivation of self-learning towards collaborative online based mathematics learning during the covid-19 pandemic. The researcher applies a quantitative descriptive methods in this study. Data collection has been done by giving three kinds of questionnaires including readiness, attitude and self learning motivation during covid-19 pandemic, and has distributed to 80 students in 3 Senior High School and 1 Vacational High School at Nuha district. The result of this research shows that the students are ready for Mathematics learning collaborative online based during covid-19 pandemic. That is supported by their ability to operate the computer and the usage of internet as well as their online learning experience they have been through for 2 years in online learning situation. Students' motivation towards learning mathematics through the implementation of online based collaborative learning is inconsistent even though it is in moderate level of motivation. Some students prefer the face to face learning process to online learning process and think that the previous learning will provide better learning outcomes. This is a challenge for teachers to prepare for the new normal phase with more interesting teaching material, strategies and learning methods by the development of digitalization of learning
ARVMex: A web application for obtaining acoustic reference values for forensic studies in Spanish

Authors:
raw_author_name: N. Sofía Huerta-Pacheco
raw_affiliation_string: Consejo Nacional de Ciencia y Tecnología

Abstract: Abstract Forensic speech experts using the auditory-acoustic-phonetic approach face different challenges when dealing with recordings collected under different technical conditions. We developed ARVMex tool to provide acoustic reference values to describe variation due to technical conditions in recordings. The tool employs a corpus of Spanish speakers, generating descriptive statistics based on different attributes, including speaker characteristics, different sounds, and on two technical conditions: face-to-face interviews and telephone conditions. The results indicate that, for the fundamental frequency, the mean values of the interview attribute accurately depict the behavior of each vowel by gender, whereas the mean values of the telephone attribute exhibit deviation from the general average. In relation to vowel formants, reference values per vowel, gender, and technical conditions must be established considering variations among vowels and the overall mean. The tool presented here aims to assist experts by providing insights into the variations in the measurements of acoustic properties due to technical conditions.
Pembuatan Peta Resiko Bencana di Desa Kubang Tangah Kota Sawahlunto dengan Metode Arc Toolbox

Authors:
raw_author_name: Bambang Istijono
raw_affiliation_string: Universitas Andalas
raw_author_name: Taufika Ophiyandri

Abstract: Landslide movement is the activity of a balance disturbance process that causes the movement of masses of soil and/or rock from higher to lower areas. The cause of landslides is that the driving force on the slope is greater than the resisting force. Several factors cause landslides such as rainfall, steep slopes, soil density, rock type, land use and vibration. Sawahlunto City is one of 19 cities/regencies in West Sumatra Province that has a high level of vulnerability to landslides. Areas prone to landslides can be identified by utilizing remote sensing data and geographic information systems. This service is carried out by making maps of areas prone to landslides using parameters that cause landslides, including rainfall, soil type, height, slope and land use. These parameters will be processed and analyzed with the help of the ArcToolbox method, where ArcToolbox can be used to detect landslide areas in Kubang Tangah Village. The analysis process is carried out by using the spatial analysis tools feature found in ArcGIS software. Community service produces a slope map that can illustrate the vulnerability of landslides. Obtain results in the form of a landslide vulnerability level map which has 4 vulnerability classes, including non-prone vulnerability class, low vulnerability class, medium vulnerability class, and high vulnerability class.
ANALYSIS OF STUDENTS’ DIFFICULTIES IN TRANSLATING DESCRIPTIVE TEXT FROM INDONESIAN INTO ENGLISH

Authors:
raw_author_name: Jaya Nur Iman
raw_affiliation_string: English Education Department, Faculty of Teacher Training and Education,  University of Indo Global Mandiri Palembang

Abstract: The aims of this research was to find out the students’ difficulties into English and to know how the students were cope with the difficulties in translating descriptive text from Indonesian into English at the eighth grade students of junior high school Tri Dharma Palembang. The descriptive qualitative research was used in this sample of this research. The sample of this research was the eighth grade students at Junior High School Tri Dharma Palembang. There were fifteen participants of students and there was one participant of their English teacher. The data was taken by using test and interview as the instrument. The research finding showed that the highest percentage of students’ difficulties in translating was grammar (27%). the highest number of the students’ grammar difficulties was phrase (45%). The second difficulty faced by students was clarity and cohesion &amp; naturalness (24%). The third difficulty face by students was spelling &amp; style (14%) and the last difficulty was realizing &amp; understanding (11%). Based on the result of students and their English teacher interview matrix, it showed that students asked their friends and teacher to help solving their problem. It could be concluded that students had difficulties in translating Indonesian descriptive text into English was Grammar and the students asked their friends or teacher to give them a help.