Reviewerly/exHarmony huggingface dataset:
Massively Parallel Asynchronous Fractal Optimization

Fracta1-based decomposition is a flexible framework representing a family of optimization algorithms based on a hierarchical decomposition of the search space. We have built a software called Zellij, in which we were able to instantiate popular decomposition-based algorithms. Our goal is to tackle optimization problems characterized by computationally expensive objective functions and high dimensional search space. In this paper, we propose a generic asynchronous parallel methodology of fractal-based optimization algorithms on multi-nodes and multi-CPUs distributed environments. Experimental results show a significantly reduced computation time between the mono-threaded version and the asynchronous one. The obtained results are also analyzed according to the various search components such as tree search, exploration, and exploitation strategies.
De-raining a Single Picture Using Multi-scale Recurrent Neural Networks

Remove rain streaks from images using a residual attention-based multi-scale network. The pyramid design removes raindrops of various sizes while preserving the background’s coarse structure and minute details. The proposed work demonstrated that the residual between up-sampling dry and wet images can help detect moist regions. Using this residual, the de-raining network gives precedence to rain-affected locations within the pyramid structure. Each stage of the pyramid applies a reoccurring method to create a small, dependable de-raining network. This method is useful for both qualitative and quantitative evaluations. This technology eliminates rain streaks in photos, making it suitable for autonomous vehicles, security cameras, and landscape photography.
Photon-induced dropletlike bound states in a one-dimensional qubit array

We consider an array of ${N}_{e}$ noninteracting qubits or emitters that are coupled to a one-dimensional cavity array with tunneling energy $J$ and nonlinearity of strength $U$. The number of cavities is assumed to be larger than the number of qubits. Working in the two-excitation manifold, we focus on the band-gap regime where the energy of two excited qubits is off-resonant with the two-photon bound state band. A two-step adiabatic elimination of the photonic degrees of freedom gives rise to a one-dimensional spin Hamiltonian with effective interactions; specifically, the Hamiltonian features constrained single-qubit hopping and pair hopping interactions not only between nearest neighbors but also between next-to-nearest and next-to-next-to-nearest spins. For a regularly arranged qubit array, we identify parameter combinations for which the system supports droplet-like bound states whose characteristics depend critically on the pair hopping. The droplet-like states can be probed dynamically. The bound states identified in our work for off-resonance conditions are distinct from localized hybridized states that emerge for on-resonance conditions.
Bilateral tone mapping scheme for color correction and contrast adjustment in nearly invisible medical images

Abstract Medical images are commonly used to diagnose and screen various diseases. However, medical images taken by operators with different skill levels have a considerable variation in visual quality. Poor illumination of images increases uncertainty in clinical treatment and lead to the risk of misdiagnosis. This paper proposes a novel bilateral tone‐mapped gamma correction to enhance the visual quality of medical images without affecting the natural color quality. First, the proposed tone‐mapped gamma correction is employed to enhance the intensity values of poorly illuminated medical images. Then, bilateral enhancement curve adjustment is performed to effectively improve the global luminance content of the input image with features and richer information. The image quality assessment measures like entropy, blind/reference less image spatial quality evaluator, natural image quality evaluator, perception‐based image quality evaluator, contrast enhancement based image quality parameter, and color contrast fog density evaluates the superior performance of the proposed technique. The experimental results significantly enhance the intensity difference between dark and bright areas and can aid clinical experts and intelligent health fields.
How remote work was created for student assistants during the Covid-19 pandemic

AbstractThe Covid-19 pandemic impacted libraries tremendously and changed the way services were offered to patrons. Dahlgren Memorial Library was no exception and the library strived to find ways to keep their student assistants employed. This article will discuss how the library trained its student assistants to cover the chat system and work on projects for the Resources Department.Keywords: Chatcovid-19 pandemicLibraryH3lpresourcesstudent assistantsstudent management AcknowledgmentsThe authors acknowledge and thank Jett McCann, Senior Associate Dean & Director, and Douglas Varner, Assistant Dean for Information Management/Chief Biomedical Informationist who, along with the authors, fought to keep student staff employed and engaged during the pandemic. Also, thanks to Kerri-Ann Gordon, Senior Resources & Acquisitions Specialist, and our wonderful DML student assistants.
Integration of Natural Language Processing and Augmented Reality: ChatGPT Meets Apple Vision Pro

The integration of Natural Language Processing (NLP) and Augmented Reality (AR) technologies has the potential to revolutionize the way we interact with digital content and information.This research paper explores the integration of OpenAI ChatGPT, a powerful NLP model, with the Apple Vision Pro, an AR platform, to create immersive and interactive experiences.The paper provides an overview of the current state of NLP and AR technologies, discusses the potential benefits and applications of their integration, and examines the challenges and limitations involved.By leveraging the capabilities of ChatGPT, which utilizes advanced deep learning techniques, and the object recognition and tracking capabilities of Apple Vision Pro, the integration process involves preprocessing the textual data from the AR environment, performing natural language understanding analysis, enabling user interaction through voice commands and natural language queries, dynamically augmenting AR content based on NLP analysis, ensuring real-time updates between ChatGPT and Apple Vision Pro, and addressing ethical considerations such as user control and privacy.Through this integration, seamless interactions between humans and virtual environments are made possible, bridging the gap between the physical and digital worlds.
COURSE RECOMMENDATION SYSTEM USING MACHINE LEARNING

Education system plays a vital role in any one's carrier.Selecting the suitable course for the final year degree students is a challenging task in the current education sector.In some educational institutes carrier counseling is done to know the student's area of interest in higher course selection.Career counseling is more about knowing and understanding about students and their capabilities and abilities.In the final year degree, students think about selecting the suitable course (higher studies) based on their interest.It is this time every student gets a lot of guidance from various circles (parents, teachers, other educational specialists, etc.).Course selection of the students depends on so many education factors like semester results, gender factor, age factor, attendance factor etc. In this proposed system we develop automation for Course recommendation for the final year students.Course recommendation is done using Machine Learning algorithms.Efficient ML algorithm used to process the educational data and Course recommendation is done.
YouTube Comment Scraping and Sentiment Analysis Using Python and Machine Learning

YouTube has become a massive platform for video sharing and discussion, with millions of users expressing their thoughts and opinions through comments. For content producers, marketers, and academics, gleaning significant insights from these comments can yield useful data. This project explores the combination of machine learning and web scraping techniques to scrape YouTube comments and perform sentiment analysis. By leveraging machine learning algorithms, we aim to analyze sentiments, opinions, and trends within the vast amount of textual data generated on the platform. The process involves collecting comments using the YouTube Data API or web scraping libraries, followed by preprocessing steps to clean and transform the data. Next, Machine learning methods like Naive Bayes, SVM, or deep learning models are used to train sentiment analysis models. These approaches include Bag-of-Words, TF-IDF, and word embedding. Our ability to comprehend the overall emotion conveyed by viewers is subsequently made possible by the trained models, which are then utilized to anticipate the sentiment of fresh comments. Additionally, the ability to identify very favorable or negative remarks is made possible by sorting comments based on sentiment scores. It is essential to consider the complexities of language and contextual nuances when conducting sentiment analysis. For many players in the YouTube ecosystem, this technique does provide useful insights into audience feedback, content strategy, and data-driven decision-making.
Development of Web-Based Plant Sensor Tool Data Processing Application

The rapid development of technology in the 4.0 era brings changes in the use of technology. The industrial 4.0 era makes IoT (Internet of Things) media a technology that can support all sectors of activity including the agriculture or farming sector. IoT media takes place in the form of plant sensor device that can be used to measure changes in environmental parameters. These changes include light intensity, temperature, humidity, soil fertility, are the supporting factors for plant growth. This study aims to design a website-based application that can read plant sensor data. Sensor device data is collected and downloaded in CSV (Comma Separated Values) format. Data processing requires the Naïve Bayes algorithm as a method for grouping parameter data. The results of the study are in the form of a website design that can help analyze the ideal environmental parameters for farming activities.&#x0D; &#x0D;
Analysis of User Satisfaction with E-Learning Services During the Covid-19 Pandemic Using the PIECES Framework

During the Covid-19 pandemic, the education sector has been able to provide assistance for the continuation of the learning process through e-Learning services. E-Learning is an online-based learning and teaching process that utilizes information technology services. E-Learning is specially designed by the institution and integrated with academic progress data to offer the best support to students who have become increasingly familiar with information technology during the pandemic, addressing its limitations. The objective of this research is to measure user satisfaction with the e-Learning service using the PIECES framework. The PIECES framework consists of Performance, Information/data, Control/security, Efficiency, and Service categories. The PIECES framework is employed for analyzing the information system and consists of six variables: performance, information, economic, control, efficiency, and services. Data was collected through questionnaires distributed to 368 students from different graduation years, spanning from 2018 to 2021, who are users of the e-Learning service. Based on the gathered data analysis, the average satisfaction levels for each variable are as follows: performance scored 3.75, information scored 3.82, economic scored 3.84, control scored 3.74, efficiency scored 3.7, and services scored 3.84. Combining these values and referencing Kaplan and Norton, it can be concluded that the overall user satisfaction level with e-Learning falls into the satisfied category
Chapter 16. Discovering Visual Concepts and Rules in Convolutional Neural Networks

This chapter provides an overview of ERIC (Extracting Relations Inferred from Convolutions), a solution to the extraction of explanations and human-comprehensible knowledge from Convolutional Neural Networks (CNNs). ERIC reduces the behaviour of one or more convolutional layers to a discrete logic program over a set of atoms, each corresponding to individual convolutional kernels. Extracted programs yield performances that correlate with those of the original model. When the logic rules are analysed alongside the data as a visual concept learner, ERIC has demonstrated the discovery of relevant concepts when applied to classification tasks, including those in fields with specialised knowledge such as radiology. Concepts with sharper edges seem to have a positive influence on the fidelity of extracted programs to the extent that ERIC was able to yield high fidelity on MNIST and in a traffic sign classification task of up to 43 classes. Also, extracted concepts may be transferred to a different CNN trained on a related but different problem in the same domain. For example, concepts identified for pleural effusion were transferable to a COVID-19 classification task. Also in the medical domain, ERIC has demonstrated the capability of identifying concepts used by CNNs that are not justified anatomically or used by medical doctors in their decision making. This chapter also briefly reviews Elite Backpropagation (EBP), which trains CNNs so that each class is associated with a small set of elite kernels and improves the performance of ERIC by inducing more compact rules while maintaining high fidelity.
Chapter 28. Effective Reasoning over Neural Networks Using Łukasiewicz Logic

In this chapter, we present a research line whose aim is to represent neural networks in Łukasiewicz Infinitely-valued Logic (Ł∞) so that one might reason about such neural networks by means of logical machinery. We focus on a class of neural networks with inputs and outputs in the unit interval [0,1] concentrating the study on the ones that compute piecewise linear functions, which is not a strong constraint since such functions may densely approximate any continuous function. For that, we introduce the concept of representation modulo satisfiability, that enlarges the representational power of Ł∞. We derive an algorithm for building such representations, which terminates in polynomial time as long as the input is given in a suitable format. As applications of representation modulo satisfiability, we proceed by showing how properties of neural networks, such as reachability and robustness, may be encoded in Ł∞, giving rise to formal verification techniques. Finally, we present a case study where some formal verifications are performed in real-world neural network.
Chapter 8. Neuro-Causal Models

This chapter proposes neuro-causal models, a novel neuro-symbolic model architecture that uses a synthesis of deep generative models and causal graphical models to automatically infer higher level symbolic information from lower level “raw features”, while also allowing for rich relationships among the symbolic variables. Neuro-causal models retain the flexibility of modern deep neural network architectures while simultaneously capturing statistical semantics such as identifiability and causality, which are important to discuss ideal, target representations and their tradeoffs. We consider a general setting for this problem: No assumptions are placed on these relationships, and the number of hidden variables, their state spaces, and their relationships are presumed unknown. The primary objective is to provide explicit conditions under which all of this can be recovered uniquely, and to develop practical algorithms for learning these representations from data.
Chapter 25. Learning Logic Explanations by Neural Networks

The opaqueness of deep neural networks hinders their employment in safety-critical applications. This issue is driving the research community to focus on eXplainable Artificial Intelligence (XAI) techniques. XAI algorithms can be categorized into two types: those that explain the predictions of black-box models and those that create interpretable models from the start. While interpretable models foster user trust, their performance is normally inferior to traditional black-box models like neural networks. To fill this gap, this chapter presents an extensive framework introducing special neural networks known as Logic Explained Networks (LENs). The most notable advantage of this approach is that LENs achieve performances that are comparable to the state-of-the-art neural networks while providing concise First-Order Logic explanations. Moreover, LENs are designed to learn various types of explanations, such as local or global, by associating related task functions or directly explaining a single class based on the others and/or the input data. They can offer explanations for their behaviour or that of other black-box models. Finally, LENs can be applied in many learning scenarios: they can be trained using supervised or unsupervised learning and applied across different domains like tabular data, computer vision, natural language processing, and relational tasks.
Chapter 10. Connectionist Neuroarchitectures in Cognition and Consciousness Theory Based on Integrative (Synchronization) Mechanisms

The topic of this chapter are cognitive neuroarchitectures, which have been developed since the mid-eighties in connectionism as theoretical models to explain, based on empirical-experimental data, as neurophysiologically plausible as possible perceptual and linguistic performances of self-organizing neuronal networks in the human brain that are related to the binding problem. Thus, neurocognition can be viewed as organized by integrative (phase) synchronization mechanisms that orchestrate the flow of neurocognitive information in self-organizing networks with positive and/or negative feedback loops in subcortical and cortical areas of the brain. This dynamic perspective on cognition contributes significantly to bridging the gap between the discrete, abstract symbolic description of propositions in the mind and their continuous, numerical, and dynamic modeling in terms of cognitive neuroarchitectures in connectionism. This dynamic binding mechanism in connectionist cognitive neuroarchitectures thus has the advantage of enabling more accurate modeling of cognitive processes by conceptualizing these neuroarchitectures as nonlinear dynamical systems. This means that neurocognition is modeled using a neurodynamics in abstract n-dimensional phase spaces in the form of nonlinear vector fields or vector flows.
Chapter 2. Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges

Ontologies are used in various domains, with RDF and OWL being prominent standards for ontology development. RDF is favored for its simplicity and flexibility, while OWL enables detailed domain knowledge representation. However, as ontologies grow larger and more expressive, reasoning complexity increases, and traditional reasoners struggle to perform efficiently. Despite optimization efforts, scalability remains an issue. Additionally, advancements in automated knowledge base construction have created large and expressive ontologies that are often noisy and inconsistent, posing further challenges for conventional reasoners. To address these challenges, researchers have explored neuro-symbolic approaches that combine neural networks’ learning capabilities with symbolic systems’ reasoning abilities. In this chapter, we provide an overview of the existing literature in the field of neuro-symbolic deductive reasoning supported by RDF(S), the description logics EL and ALC, and OWL 2 RL, discussing the techniques employed, the tasks they address, and other relevant efforts in this area.
Chapter 27. Explaining CNNs Using Knowledge Extraction and Graph Analysis

Explainable Artificial Intelligence (XAI) has recently become an active research field due to the need for transparency and accountability when deploying AI models for high-stake decision making. Despite state-of-the-art Convolutional Neural Networks (CNNs) have achieved great performance in computer vision, understanding their decision processes, especially when a mistake occurs, is still a known challenge. The research direction presented in this chapter stems from the idea that combining knowledge with deep representations can be the key to more transparent decision making. Specifically, we have proposed a graph representation, called co-activation graph, that can be used as an intermediate representation between knowledge encoded within a trained CNNs with the semantics contained in external knowledge bases. Given a trained CNN, in this chapter we first show how a co-activation graph can be created and exploited to generate global insights for the inner-workings of the deep model. Then, we illustrate in detail how background knowledge from external knowledge bases can be connected to the graph in order to generate textual local factual and counterfactual explanations based on semantic attributes. Our results indicate that graph analysis approaches such as community analysis, node centrality and link prediction applied to co-activation graphs can reveal important insights into how CNNs work and enable both global and local semantic explanations for Deep Learning models. At the end of the chapter we will discuss interesting research directions that are being investigated in the area of using knowledge graphs and graph analysis for explainability of deep learning models.
An up-to-date comparative analysis of the KNN classifier distance metrics for text categorization

Text categorization (TC) is one of the widely studied branches of text mining and has many applications in different domains.It tries to automatically assign a text document to one of the predefined categories often by using machine learning (ML) techniques.Choosing the best classifier in this task is the most important step in which k-Nearest Neighbor (KNN) is widely employed as a classifier as well as several other well-known ones such as Support Vector Machine, Multinomial Naive Bayes, Logistic Regression, and so on.The KNN has been extensively used for TC tasks and is one of the oldest and simplest methods for pattern classification.Its performance crucially relies on the distance metric used to identify nearest neighbors such that the most frequently observed label among these neighbors is used to classify an unseen test instance.Hence, in this paper, a comparative analysis of the KNN classifier is performed on a subset (i.e., R8) of the Reuters-21578 benchmark dataset for TC.Experimental results are obtained by using different distance metrics as well as recently proposed distance learning metrics under different cases where the feature model and term weighting scheme are different.Our comparative evaluation of the results shows that Bray-Curtis and Linear Discriminant Analysis (LDA) are often superior to the other metrics and work well with raw term frequency weights.
Application of Information Technology Law in India on IoT/IoE With Image Processing

In order to address the issues posed by emerging technologies like IoT, IP, and AI, the legislation governing information technology in India is undergoing a period of fast development (AI). Since these technologies are able to gather, share, and analyse massive quantities of data, regulations are necessary to safeguard individuals' right to privacy about such data. Companies and organizations need to have a full awareness of the legal framework around IoT/IoE, image processing, and AI in order to guarantee that they are in compliance with the laws that have been enacted. Companies and organisations may assist in guaranteeing that they are employing these technologies in a legally responsible way by using legal knowledge gained through understanding the legal consequences of using these technologies. It has been observed that there are several applications of IT law in India on IoT/ IoE. Present research work is considering the role of image processing in IoT environment.