Reviewerly/exHarmony huggingface dataset:
Methods for optimizing the assignment of cloud computing resources and the scheduling of related tasks

Authors:
raw_author_name: Asra Sarwath
raw_affiliation_string: Faculty of Engineering and Technology, Khaja Bandanawaz University (KBNU)

Abstract: &lt;div align="center"&gt;&lt;span&gt;Efficient scheduling algorithms are necessary in the cloud paradigm to optimize service provision to clients while minimizing time duration, energy consumption, and violations of service level agreements (SLAs). Disregarding task appropriateness in resource scheduling can have a detrimental effect on the quality of service provided by cloud providers. Moreover, the utilization of resources in an ineffective manner will necessitate a substantial expenditure of energy to execute activities, leading to prolonged processing duration that adversely affect the temporal duration. Many research projects have focused on employment scheduling problems, and the algorithms used in these studies have offered answers that were deemed nearly flawless. This study presents a chaos bird swarm algorithm (Chaos BSA) approach that use machine learning to consider task priority while allocating tasks to the cloud platform. The method calculates the priorities of task virtual machines and incorporates these values into the scheduler. The scheduler will select tasks that align with the specified priorities and are compatible with the virtual machines. The implementation of the system utilized the openstack cloud platform and the cloudsim tool. The results and comparison with the baseline approach genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) clearly demonstrate that our Chaos BSA outperforms them by 18% in terms of efficiency.&lt;/span&gt;&lt;/div&gt;
Convolutional neural network-based techniques and error level analysis for image tamper detection

Authors:
raw_author_name: Vijaya Shetty Sadanand
raw_affiliation_string: Nitte Meenakshi Institute of Technology

Abstract: &lt;span&gt;Photographs are the foremost powerful and trustworthy media of expression. At present, digital pictures not only serve forged information but also disseminate deceptive information. Users and experts with various objectives edit digital photographs. Images are frequently used as proof of reality or fact, therefore fake news or any publication that makes use of photos that have been altered in any way has a larger chance of deceiving readers. There is a need for a high-resolution image analysis model that processes individual pixels in images and a substantial amount of diverse image data, to detect image falsification. Convolutional neural network (CNN) with error level analysis (ELA) adopted in this research is found to be an ideal deep learning concept for detecting image manipulation. The model exhibited a validation accuracy of 99.6%, 99.7%, and 99.4% for CASIA V1.0, CASIA V2.0 and MICC datasets respectively. The accuracy for handmade tampered images was found to be 99.2%.&lt;/span&gt;
Automatic question generation using extended dependency parsing

Authors:
raw_author_name: Walelign Tewabe Sewunetie
raw_affiliation_string: University of Miskolc
Debre Markos University
raw_author_name: Laszlo Kovacs
raw_affiliation_string: University of Miskolc

Abstract: &lt;span&gt;The importance of automatic question generation (AQG) systems in education is recognized for automating tasks and providing adaptive assessments. Recent research focuses on improving quality with advanced neural networks and machine learning techniques. However, selecting the appropriate target sentences and concepts remains challenging in AQG systems. To address this problem, the authors created a novel system that combined sentence structure analysis, dependency parsing approach, and named entity recognition techniques to select the relevant target words from the given sentence. The main goal of this paper is to develop an AQG system using syntactic and semantic sentence structure analysis. Evaluation using manual and automatic metrics shows good performance on simple and short sentences, with an overall score of 3.67 out of 5.0. As the field of AQG continues to evolve rapidly, future research should focus on developing more advanced models that can generate a wider range of questions, especially for complex sentence structures.&lt;/span&gt;
Academic assistance chatbot-a comprehensive NLP and deep learning-based approaches

Authors:
raw_author_name: Nermin K. Negied
raw_affiliation_string: Nile University, School of Engineering and Applied Science

Abstract: &lt;span&gt;The rapid growth of digital technologies and natural language processing (NLP) have revolutionized the field of education, creating new demand for automated academic assistance systems. In this paper, we present an NLP-based academic assistance chatbot designed to provide comprehensive support to students and researchers using deep learning techniques. &lt;br /&gt; The chatbot incorporates a range of intelligent features to assist with university recommendations, article writing, automatic question answering (QA), and job search. By leveraging sentiment analysis and sarcasm detection models. The proposed chatbot could offer accurate and insightful university recommendations. Additionally, the chatbot incorporates spell and grammar checking, summarization, paraphrasing, and topic modeling capabilities to aid users in enhancing their writing skills. The QA module enables users to obtain quick and precise answers to factoid-based questions. Moreover, the chatbot helps with internships and job search. According to literature, this work presents the first assistance chatbot that encapsulates all features that may be needed by a university student to facilitate and improve his/her learning process. The results demonstrated clearly in the body of the paper showed the success achieved by the academic assistant proposed and built in this work in all its features or modules to offer help to university students and graduates.&lt;/span&gt;
The Nalima: A Multistable Membrane Instrument with Integrated Excitation

Authors:
raw_author_name: Chris Kiefer
raw_affiliation_string: Experimental Music Technologies Lab, University of Sussex, United Kingdom

Abstract: The Nalima is a self-resonating vibrotactile instrument, a hybrid digital-electro-acoustic system based around interaction with a membrane. It uses a novel method of Integrated Excitation to offer new musical affordances, and makes use of complexity analysis to shape the sonic and interactive behaviour of the instrument, pushing it into musical zones on the edge of chaos. The design and construction of the Nalima is described, along with the theoretical background in cybernetics, complexity theory and multistability.
Color image encryption algorithm based on quantum random walk and multiple reset scrambling*

Authors:
raw_author_name: Dingkang Mou
raw_affiliation_string: Chongqing Normal University, College of Computer and Information Science, Chongqing Normal University, Chongqing, 401331., Chongqing, Chongqing, 401331, CHINA
raw_author_name: Yumin Dong
raw_affiliation_string: Chongqing Normal University, College of Computer and Information Science, Chongqing Normal University, Chongqing, 401331., Chongqing, 401331, CHINA

Abstract: Abstract In order to address the issues of privacy breaches, data tampering, and security threats in the realm of image encryption and information security, it is essential to ensure the confidentiality, integrity, and reliability of image data. In this paper, we propose a novel hybrid color image encryption scheme that increases the complexity of the key space and provides better data integrity protection by combining the quantum random walk and SHA-256 algorithm. At the same time, three chaotic systems were combined, and multiple XOR operations were introduced to effectively scramble the spatial and pixel levels of the color image. Through numerous simulation experiments and security analyses, the results demonstrate that the solution exhibits efficient encryption performance, excellent resistance to attacks, and outstanding privacy protection capabilities.
<i>Demos and Apparatus from the Aichi Physics Circle (Stray Cats) Japanese Physics Teacher’s Website</i>

Authors:
raw_author_name: Dan MacIsaac
raw_affiliation_string: Physics Department, SUNY-Buffalo State University , Buffalo, NY 14222; macisadl@buffalostate.edu

Abstract: Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Dan MacIsaac; Demos and Apparatus from the Aichi Physics Circle (Stray Cats) Japanese Physics Teacher’s Website. Phys. Teach. 1 February 2024; 62 (2): 158. https://doi.org/10.1119/10.0024602 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
Quantum covariance scalar products and efficient estimation of maximum-entropy projections

Authors:
raw_author_name: J. M. Matera
raw_affiliation_string: IFLP - CONICET, Departamento de Física, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, C.C. 67, 1900 La Plata, Argentina

Abstract: The maximum-entropy principle (max-ent) is a valuable and extensively used tool in statistical mechanics and quantum information theory. It provides a method for inferring the state of a system by utilizing a reduced set of parameters associated with measurable quantities. However, the computational cost of employing max-ent projections in simulations of quantum many-body systems is a significant drawback, primarily due to the computational cost of evaluating these projections. In this work, a different approach for estimating max-ent projections is proposed. The approach involves replacing the expensive max-ent induced local geometry, represented by the Kubo-Mori-Bogoliubov scalar product, with a less computationally demanding geometry. Specifically, a new local geometry is defined in terms of the quantum analog of the covariance scalar product for classical random variables. Relations between induced distances and projections for both products are explored. Connections with standard variational and dynamical mean-field approaches are discussed. The effectiveness of the approach is calibrated and illustrated by its application to the dynamic of excitations in a $XX$ Heisenberg spin-$\frac{1}{2}$ chain model.
Penggunaan Metode Suku Kata Untuk Meningkatkan Hasil Belajar Membaca Pada Mata Pelajaran Bahasa Indonesia Siswa Kelas 1 SDN Tlogomas 2 Kota Malang

Authors:
raw_author_name: None Firsta Bagus Sugiharto
raw_affiliation_string: Universitas Tribhuwana Tunggadewi Malang
raw_author_name: None Chusnul Chotimah
raw_affiliation_string: Universitas Tribhuwana Tunggadewi Malang

Abstract: The purpose of study was to describe the level of student learning in reading learning outcomes in Indonesian subjects using the syllable method for first-grade students at SDN Tlogomas 2 Malang City. This study used the Classroom Action Research (PTK) method, which consisted of two cycles, each cycle held 2 class meetings. The approach in this research is descriptive qualitative. The subjects of this study were all first-grade students at SDN Tlogomas 2 Malang City totaly of 20 of them 11 male students and 9 female students. Data collection techniques in this study used observation, interviews, and documentation. The results showed that: (1) the application of the Syllable learning method in cycle 1 meeting 1 presentation 65.00% with sufficient category, the second meeting 75.00% with good category, and in the implementation of cycle II meeting one presentation 85.00% with good category, second meeting 92.05% with very good category. (2) the improvement of Indonesian learning outcomes by using the syllable learning method in Indonesian subjects, cycle 1 meeting one presentation was 50% completeness, with an average score of 67.5%, while in the second meeting, the presentation was 60% completeness, with an average score 69% than in the implementation of cycle II experienced an increase at the first meeting the percentage of completeness was 70% with an average value of 79.5%, and at the second meeting the percentage of completeness was 80% with an average value of 85%. Therefore it can be concluded that using the syllable method in the learning process it can increase the activity and learning outcomes of Indonesian students in class I SDN Tlogomas 2 Malang.&#x0D;
Using Appsheet and Apps Script to Develop Management and Implementation Applications for University Education Programs

Authors:
raw_author_name: Pham Ngoc Son
raw_affiliation_string: Hà Nội

Abstract: A smart university management system is developed in a synchronized manner based on various interconnected modules. Managing and implementing the training program is a rigorous and highly synchronized process of school management. In this article, we introduce the use of Appsheet and Apps Script to build an application for managing the implementation of a university's training program. The result of this research, which effectively supports the university's operations and is expandable, integrates with other applications to form a smart educational ecosystem.
Non-Invasive Real-Time Multimodal Deception Detection Using Machine Learning and Parallel Computing Techniques

Authors:
raw_author_name: Baraa M. Albaker
raw_affiliation_string: Al-Iraqia University

Abstract: Abstract Detecting deception reliably has been a sought-after goal for researchers since the early 20th century. This is due to its high stakes nature, especially when considering the setting under which a deception detection system would be utilized, such as law enforcement sectors, judicial bodies, and criminal investigation bodies. Therefore, recent literature has greatly focused on developing systems with ever increasing accuracies to minimize false positives. Attempts to diversify the sources of data being analyzed and classified as well as the reliance on artificial intelligence technologies have shown great success. But little attention was paid to the applicability of these systems in real-life scenarios. an idle deception detection system needs to exhibit accuracy but also perform in real-time, a feature that is lacking in the current state-of-the-art. In this work, a non-invasive real-time multimodal deception detection system is developed using advanced machine learning techniques. It combines data sources from video and audio streams to extract visual, acoustic, and linguistic features. It also utilizes parallel computing techniques to ensure high performance adequate for real-time usage. Furthermore, a user-friendly graphical user interface was built to facilitate the use of the system. Multiple experiments were conducted to determine its accuracy under various circumstances and combinations of features, the system had a detection accuracy of 99.83% under real-life, high-stakes scenarios using visual and acoustic features, and 91.03% accuracy under controlled environments, while an 89.54% accuracy was achieved using mixed environments and using visual, acoustic, and linguistic features.
PENERAPAN PSO UNTUK SENTIMEN ANALISIS PADA REVIEW MATA UANG KRIPTO MENGGUNAKAN METODE NAÏVE BAYES

Authors:
raw_author_name: Nita Merlina
raw_affiliation_string: Universitas Nusa Mandiri
raw_author_name: Nissa Almira Mayangky
raw_affiliation_string: Universitas Nusa Mandiri

Abstract: In the digital age emerging currencies using digital technology called currency crypto money. Many people use cryptocurrencies to invest. This triggered the sentiment in society on social media twitter, there are positive opinions and there are negative opinions. The purpose of this study is to determine the public sentiment regarding the review of crypto currency and then classify it into two sentiments, namely positive and negative sentiments. The classifier method used is Naïve Bayes, Naïve Bayes is a good classifier method but has shortcomings in the selection of features therefore Particle Swarm Optimization (PSO) is applied as a feature selection in order to improve the accuracy value. After conducted experiments using Naïve Bayes method, obtain accuracy value of 66% with AUC 0.482 and after Applied Particle Swarm Optimization (PSO) as feature selection in Naïve Bayes obtain accuracy value of 85% with AUC 0.716 has increased accuracy .
PENERAPAN ALGORITMA CNN MENGGUNAKAN FRAMEWORK YOLO UNTUK DETEKSI OBJEK PRODUK DI PERUSAHAAN MANUFAKTUR

Authors:
raw_author_name: Anis Fitri Nur Masruriyah
raw_affiliation_string: Universitas Buana Perjuangan

Abstract: Component products used for manufacturing a machine in manufacturing companies have two types of products, type A and B. The problem that often occurs in the industry is product sorting errors due to the traditional sorting process, using human labor. The disadvantages are limited human labor so that fatigue can occur, causing errors in sorting products and losses for the company. Many studies discuss object detection, Industrial problems in the checking process can be approached with the help of this technology. Object detection works to analyze frames with the method of finding objects. There are methods in digital image processing, CNN algorithms which include methods in computer vision. The growing framework makes the CNN algorithm more powerful. YOLO includes a framework based on the CNN algorithm. YOLOv5 detects objects by taking into account the object's confidence value, the output of the detected object is a bounding box on the object. The problem in the industry in the checking process can be approached with the help of this technology. For this reason, this research aims to create a model for product object detection in manufacturing companies. The process carried out is data collection, image annotation, training, testing, evaluation. The images collected were 137 for training data and 34 for validation data totaling 171 image data. The results of the model using YOLOv5 with epoch 1000 get a precision value of 100%, recall 100% and mAP 99%, the product detection results get an average value of 100%.
CLUSTERING DATA METEOROLOGI WILAYAH INDONESIA TIMUR DENGAN METODE K-MEANS DAN FUZZY C-MEANS

Authors:
raw_author_name: Desi Arisandi
raw_affiliation_string: Universitas Tarumanagara
raw_author_name: Teny Handhayani
raw_affiliation_string: Universitas Tarumanagara

Abstract: Climate change is a global issue that affect human life and the environment. Signs of climate change can be observed from long-term meteorological data. This research uses clustering techniques with the K-Means and Fuzzy C-Means methods to group cities in the Eastern Indonesia region based on numerical daily time series meteorological data from 1 January 2010 to 31 August 2023. The variables are minimum temperature, maximum temperature, temperature average, humidity, rainfall, duration of sunlight, maximum wind speed, and average wind speed. The dataset was collected from 28 meteorological stations. The K-Means and Fuzzy C-Means methods obtained the same results, namely the highest silhouette value of 0.218 with the number of clusters k = 2. In general, the annual trend shows an increase in temperature and a decrease in wind speed which are signs of climate change. This research is an early study of climate change in East Indonesia. The results of this research are expected to contribute to the study of climate change in Indonesia.
Increasing Vocabulary of Grade VII Students at SMPS Miftaahul Khairaat Palu through Crossword Puzzle Game

Authors:
raw_author_name: Arika Nurul Iftitah
raw_affiliation_string: Universitas Tadulako
raw_author_name: Mawardin M. Said
raw_affiliation_string: Universitas Tadulako
raw_author_name: Aminah Suriaman
raw_affiliation_string: Universitas Tadulako

Abstract: The main obstacles encountered by students to communicate in English is the lack of their vocabulary, Therefore, there should be strategic efforts made by teachers to increase students’ vocabulary mastery, like using Crossword Puzzle Game. The objective of this research was to examine whether the application of crossword puzzle game can increase vocabulary of grade VII students at SMPS Miftaahul Khairaat Palu or not. This study utilized the Class Action Research design, implemented in a cyclical process encompassing planning, action, observation, and reflection. The research involved 17 seventh-grade students during the academic year 2023-2024.This research was conducted in two cycles; the first cycle consisted of four meetings and the second one consisted of two meetings. Thus, there were totally six meetings in two cycles (cycle 1 and cycle 2). In collecting the data, there were three types of instruments used in this research: tests, observation sheets, and field notes. The results of quantities data analysis indicate that the mean score of posttests in cycle 2, 81.05 is higher than that of posttest in cycle 1, 69.05, and that of pretest in cycle 1, 60.58, Thus, based on the comparison of the students’ vocabulary achievement in pretest and in posttest in cycle 1 and in cycle 2, it can be concluded that the use crossword puzzle game can increase vocabulary of the grade VII students. While the results of qualitative data indicate that the students’ vocabulary increases from cycle 1 to cycle 2.
Precision camera calibration using known target motions along three perpendicular axes

Authors:
raw_author_name: Thomas E. Shkurti
raw_affiliation_string: Case Western Reserve University, Department of Computer and Data Sciences, Cleveland, Ohio, United States (United States)
raw_author_name: Murat Cenk Çavuşoğlu
raw_affiliation_string: Case Western Reserve University, Department of Electrical and Computer Engineering, Cleveland, Ohio, United States (United States)

Abstract: We present a method for precision intrinsic calibration of pinhole-model cameras. We refer to this method as “3-Axis.” The algorithm employs a target moving with known displacements along three perpendicular axes to decrease the number of unknown terms that must be determined. This method along with new evaluation strategies for comparing the accuracy of pinhole camera calibration algorithms is described in detail. The 3-Axis approach is then compared with the current state-of-the art algorithms in simulated and physical settings and is shown to exceed them in accuracy and computational performance assuming an equal number of samples acquired.