Reviewerly/exHarmony huggingface dataset:
Optimization of Expert System Based on Interpolation, Forward Chaining, and Certainty Factor for Diagnosing Abdominal Colic

Abdominal colic is a common condition that affects infants and it can be difficult to diagnose because it shares many symptoms with other conditions, such as gastric disease and appendicitis. Limitations of existing diagnostic methods include the unreliability of physical examinations and medical histories and the high cost and time-consuming nature of imaging tests. This research proposes an expert system based on interpolation, forward chaining, and certainty factors for diagnosing abdominal colic. This system has the potential to provide a more accurate and efficient way to diagnose abdominal colic, which could lead to better patient outcomes. This research proposes an expert system based on interpolation, forward chaining, and certainty factors for diagnosing abdominal colic. This system is implemented as a web application model. The forward chaining method is used to establish rules for the expert system. The rules are based on the symptoms and diseases that are included in the system&#039;s knowledge base. The interpolation method is used to normalize lab results and the certainty factor method is used to process medical history and physical examinations. This is necessary because medical history and physical examinations can be imprecise. The expert system was tested on a dataset of 100 cases and it was able to accurately diagnose 96 patients, achieving a 96% accuracy rate. This suggests that the expert system has the potential to provide a more accurate and efficient way to diagnose abdominal colic, which could lead to better patient outcomes.
Playing With Monte-Carlo Tree Search [AI-eXplained]

This paper provides an accessible explanation of the working mechanism of Monte-Carlo Tree Search, an influential search algorithm. The paper summarizes the procedure of Monte-Carlo Tree Search, including selection, expansion, simulation, and backpropagation. Additionally, immersive examples based on Tic-Tac-Toe, Go, and Sokoban, two two-player competitive games and a classic single-player puzzle game, are presented to illustrate how Monte-Carlo Tree Search works. The full article with interactive contents is published on IEEE Xplore.
Omniscient Video Super-Resolution with Explicit-Implicit Alignment

When considering the temporal relationships, most previous video super-resolution (VSR) methods follow the iterative or recurrent framework. The iterative framework adopts neighboring low-resolution (LR) frames from a sliding window, while the recurrent framework utilizes the output generated in the previous SR procedure. The hybrid framework combines them but still cannot fully leverage the temporal relationships. Meanwhile, the existing methods are limited in the receptive field of the optical flow or lack semantic constrains on motion information. In this work, we propose an omniscient framework to fully explore the temporal relationships in the video, which encompasses both LR frames and SR outputs from the past, present, and future. The omniscient framework is more generic because the iterative, recurrent, and hybrid frameworks can be regarded as its special cases. Besides, when addressing the motion information, most previous VSR methods adopt the explicit motion estimation and compensation, while many recent methods turn to implicit alignment. In implicit alignment methods, because basic non-local means suffers from heavy computational costs, we improve it by capturing the non-local correlations in a relatively local manner to reduce the complexity. Moreover, we integrate the explicit and implicit methods into an explicit-implicit alignment module to better utilize motion information. We have conducted extensive experiments on public datasets, which show that our method is superior over the state-of-the-art methods in objective metrics, subjective visual quality, and complexity. In particular, on datasets of Vid4 and UDM10, our method improves PSNR by 0.19 dB, 0.49 dB against the most advanced method BasicVSR++, respectively.
Knowledge graph embedding in a uniform space

Knowledge graph embedding (KGE) is typically used for link prediction to automatically predict missing links in knowledge graphs. Current KGE models are mainly based on complicated mathematical associations, which are highly expressive but ignore the uniformity behind the classical bilinear translational model TransE, a model that embeds all entities of knowledge graphs in a uniform space, enabling accurate embeddings. This study analyses the uniformity of TransE and proposes a novel KGE model called ConvUs that follows uniformity with expressiveness. Based on the convolution neural network (CNN), ConvUs proposes constraints on convolution filter values and employs a multi-layer, multi-scale CNN architecture with a non-parametric L2 norm-based scoring function for the calculation of triple scores. This addresses potential uniformity-related issues in existing CNN-based KGE models, allowing ConvUs to maintain a uniform embedding space while benefiting from the powerful expressiveness of CNNs. Furthermore, circular convolution is applied to alleviate the potential orderliness contradictions, making ConvUs more suitable for conducting uniform space KGE. Our model outperformed the base model ConvKB and several baselines on the link prediction benchmark WN18RR and FB15k-237, demonstrating strong applicability and generalization and indicating that the uniformity of embedding space with high expressiveness enables more efficient knowledge graph embeddings.
Corrigendum to “Omnisolver: An extensible interface to Ising spin–glass and QUBO solvers” [SoftwareX 24 (2023) 101559]

The authors regret imprecise statement of acknowledgements. The acknowledgement section should contain the following statement: “This work is supported by the project “Near-term quantum computers Challenges, optimal implementations and applications” under Grant Number POIR.04.04.00–00–17C1/18–00, which is carried out within the Team-Net programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund.” The authors would like to apologise for any inconvenience caused. Omnisolver: An extensible interface to Ising spin–glass and QUBO solversSoftwareXVol. 24PreviewWe introduce a new framework for implementing Binary Quadratic Model (BQM) solvers called Omnisolver. The framework provides an out-of-the-box dynamically built command-line interface as well as an input/output system, thus heavily reducing the effort required for implementing new algorithms for solving BQMs. The proposed software should be of benefit for researchers focusing on quantum annealers or discrete optimization algorithms as well as groups utilizing discrete optimization as a part of their daily work. Full-Text PDF Open Access
Optimized in-loop filtering in versatile video coding using improved fast guided filter

&lt;p&gt;Devices with varying display capabilities from a common source may face degradation in video quality because of the limitation in transmission bandwidth and storage. The solution to overcome this challenge is to enrich the video quality. For the mentioned purpose, this paper introduces an improved fast guided filter (IFGF) for the contemporary video coding standard H.266/VVC (versatile video coding), a continuation of H.265/HEVC (high efficiency video coding). VVC includes several types of coding techniques to enhance video coding efficiency over existing video coding standards. Despite that, blocking artifacts are still present in the images. Hence, the proposed method focuses on denoising the image and the increase of video quality, which is measured in terms of peak signal-to-noise (PSNR). The objective is achieved by using an IFGF for in-loop filtering in VVC to denoise the reconstructed images. VTM (VVC test model)-17.2 is used to simulate the various video sequences with the proposed filter. This method achieves a 0.67% Bjontegaard delta (BD)-rate reduction in low-delay configuration accompanied by an encoder run time increase of 4%.&lt;/p&gt;
Security enhancement of cyber-physical system using modified encryption AESGNRSA technique

&lt;span&gt;A cyber-physical system (CPS) is a combination of physical components with computational elements to interact with the physical world. The integration of these two systems has led to an increase in security concerns. Traditional encryption algorithms designed for general-purpose computing environments may not adequately address the distinct challenges of CPS, such as limited processing power, delay, and resource-constrained hardware. Therefore, there is a pressing need to develop an encryption algorithm that is optimized for CPS security without compromising the critical real-time aspects of these systems. This research has designed a modified encryption technique named the advanced encryption standard in galois counter mode with nonce and rivest-shamir-adleman algorithm (AESGNRSA). A smart medical system is designed to monitor the health of remotely located patients. The AESGNRSA algorithm is applied to the three servers of this system. The data of 1.5 lakh patients is fed to this system to verify the effectiveness of the AESGNRSA algorithm. The performance parameters like encryption and decryption time, encryption and decryption throughput, and encrypted file size are calculated for the AESGNRSA algorithm. The comparative analysis proved that AESGNRSA has the highest performance as compared to other algorithms and it can protect CPS against many cyber-attacks.&lt;/span&gt;
Methods for optimizing the assignment of cloud computing resources and the scheduling of related tasks

&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;
Multi-domain aspect-oriented sentiment analysis for movie recommendations using feature extraction

&lt;span&gt;Sentiment analysis is a well-recognized research field that has acknowledged significant attention in recent years. Researchers have made extensive efforts in employing various methodologies to explore these domains. Sentiment classification plays a fundamental role in natural language processing (NLP). However, studies have shown that sentiment classification models heavily depend on the specific domain. In the context of movie industry, where the demand for reliable movie reviews is high and not all movies are of exceptional quality and worthy of viewers time. Therefore, people depend on movie reviews before watching a movie. This explores the use of data from various domains to improve classification performance within each domain, addressing the difficulty of multi-domain sentiment classification in natural language processing. Therefore, it is crucial to effectively utilize shared sentiment knowledge across different domains for real-world applications. To solve these issues, a multi-domain aspect-oriented sentiment analysis for movie recommendation using feature extraction techniques. The main contribution of this work is to eliminate the time for users to go through a lengthy list of movies to make their decision. The novelty of this work is analysis of different movie genres, TV shows genres with accurate results. The presented approach's performance is validated by evaluating various metrics, including precision, recall, mean square error (MSE) and F1-score.&lt;/span&gt;
Convolutional neural network-based techniques and error level analysis for image tamper detection

&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

&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

&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

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*

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>

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
Coding Hotline

This month’s Coding Hotline addresses questions about coding for social determinants of health, repair of laceration of the lip, and 2 distinct E/M services.
Quantum covariance scalar products and efficient estimation of maximum-entropy projections

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.
Online Fan Practices and Informal Language Learning

Extensive and repeated language exposure is essential for second language learning. While not all learners can get such exposure routinely in face-to-face contexts, English language learners today create pathways to opportunities for learning English through participation in social media communities. The informal exposure to English they obtain can be driven by their passionate interest in popular culture and affiliation with communities of fans. The potential of interaction in online fandoms for language learning, however, depends in part on the language that learners are exposed to. In this study, a lexical bundle analysis was conducted to identify frequently used word combinations in YouTube comments posted under South Korean music band BTS fan-edited videos and performance videos. Lexical bundles retrieved from a corpus of 8,000 comments were investigated structurally and functionally. The bundles were found to display structural similarities with spoken registers, even though comments have unique textual characteristics. Functional analysis results showed that many of the bundles express interpersonal meanings, such as positive appraisal, with stance expressions. Findings from the structural and functional analyses of lexical bundles increase knowledge of the type of language that English learners are exposed to and can potentially benefit from by engaging in online fan communities.
Penggunaan Metode Suku Kata Untuk Meningkatkan Hasil Belajar Membaca Pada Mata Pelajaran Bahasa Indonesia Siswa Kelas 1 SDN Tlogomas 2 Kota Malang

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;