Reviewerly/exHarmony huggingface dataset:
Visual video evaluation association modeling based on chaotic pseudo-random multi-layer compressed sensing for visual privacy-protected keyframe extraction

In current society, artificial intelligence processing technology offers convenient video monitoring, but also raises the risk of privacy leakage. Theoretically, the data used in intelligent video processing methods may directly convey visual information containing private content. For the above problem, this paper uses a multi-layer visual privacy-protected (VPP) coding method to blur private content in the video at the visual level, while avoiding the loss of important visual features contained in the video as much as possible. And this provides a guarantee of the quality of the subsequent keyframe extraction step. Then a visual evaluation algorithm is proposed for assessing the quality of VPP-encoded video privacy protection. And the experiment shows that the results are consistent with those of subjective evaluation. In addition, for VPP-encoded video, we propose an unsupervised two-layer clustering keyframe extraction method with corresponding performance evaluation index. Finally, an association model is established to balance the privacy protection quality and the keyframe extraction performance.
Improved sine cosine algorithm for large-scale optimization problems

Aiming at the shortcomings of the sine cosine algorithm (SCA) in solving the large-scale optimization problems, such as low accuracy, slow convergence speed, and being easy to fall into the dimension disaster, we propose an sine cosine algorithm with Lévy flight (SCAL). By using the element-by-element multiplication of the Lévy flight distribution with the individual position vector of sine and cosine population, the characteristics and information of Lévy flight distribution are integrated into the individual information, so that it can possess the characteristic of random walk of Lévy flight and enhances the ability of local exploitation to escape from local extremum. A novel nonlinear parameter adjustment method based on spatial distance is adopted to balance the local exploitation and global exploration, which improves the convergence speed of the algorithm. On 14 classic test functions with dimensions of 100, 1 000 and 5 000 respectively, SCAL is compared with five swarm intelligence algorithms including SCA, flower pollination algorithm (FPA), particle swarm optimization (PSO) algorithm, sparrow search algorithm (SSA) and whale optimization algorithm (WOA). The experimental results indicate that SCAL has a significant advantage over the five swarm intelligence algorithms in terms of convergence accuracy, convergence speed and robustness. Compared with the improved wolf pack algorithm (IWPA), the improved flower pollination algorithm (IFPA), the improved whale optimization algorithm (IWOA), and the modified whale optimization algorithm (MWOA), which are suitable for solving large scale optimization problems, it is found that the overall optimization result of SCAL is better than the comparison algorithms and thus demonstrate that the proposed algorithm has the obvious advantages and competitiveness for solving large-scale optimization problems.
Image Enhancement and Implementation of CLAHE Algorithm and Bilinear Interpolation

AbstractAbstractDigital cameras have made it possible for people to capture many different images in their daily lives. Some pictures turn out well, while others don't. Additionally, noise causes variations in image quality. It is possible that these noises are caused by low light levels or other challenges with intensity. The primary goal of this proposal is to discuss ways to enhance image attributes. Using image enhancement methods, the main goal is to reduce image noise and improve image quality. HE (Histogram Equalization) is used to improve the image quality, but the results are underwhelming. Image properties are enhanced using Contrast-Limited Adaptive Histogram Equalization (CLAHE). Bilinear interpolation and the CLAHE algorithm will be used to enhance the images in the initial stages of the proposal, with the main goal being noise reduction. Second, approaches for expanding the contrast of an image have been suggested as a strategy to enhance image quality. The findings demonstrate that the suggested methodology creates great noise-free images by lowering the amount of noise in the images and boosting visual contrast.Keywords: Bilinear interpolationCLAHE algorithmcontrast stretchingnoiseless imagenoisy image
Convergence Results in Image Interpolation With the Continuous SSIM

Assessing the similarity of two images is a complex task that attracts significant efforts in the image processing community. The widely used structural similarity index measure (SSIM) addresses this problem by quantifying a perceptual structural similarity. In this paper we consider a recently introduced continuous SSIM (cSSIM), which allows one to analyze sequences of images of increasingly fine resolutions, and further extend the definition of the index to encompass the locally weighted version that is used in practice. For both the local and the global versions, we prove that the continuous index includes the classical SSIM as a special case, and we provide a precise connection between image similarity measured by the cSSIM and by the $L_2$ norm. Using this connection, we derive bounds on the cSSIM by means of bounds on the $L_2$ error, and we even prove that the two error measures are equivalent in certain circumstances. We exploit these results to obtain precise rates of convergence with respect to the cSSIM for several concrete image interpolation methods, and we further validate these findings by different numerical experiments. This newly established connection paves the way to obtain novel insights into the features and limitations of the SSIM, including on the effect of the local weighted window on the index performances.
The Community Game Development Toolkit

The Community Game Development Toolkit is a set of tools that provide an accessible, intuitive work-flow within the Unity game engine for students, artists, researchers and community members to create their own visually rich, interactive 3D stories and immersive environments. The toolkit is designed to support diverse communities to represent their own traditions, rituals and heritages through interactive, visual storytelling, drawing on community members’ own visual assets such as photos, sketches and paintings, without requiring the use of coding or other specialized game-design skills. Projects can be built for desktop, mobile and VR applications. This paper describes the background, implementation and planned future developments of the toolkit, as well the contexts in which it has been used.
CourseExpo: An Immersive Collaborative Learning Ecosystem

Inspired by the need for remote learning technologies due to the Covid-19 pandemic and the isolated sense of lonely learners, we reimagined a remote classroom that fosters collaboration, builds community and yet without the constraints of the physical world. This paper presents a collaborative learning ecosystem that resembles a traditional city square where avatars of learners and facilitators wander, commingle, discover, and learn together. Buildings in the city square are learning modules which include typical knowledge units, assessment booths, or custom collaborative sketching studios. Our attempted prototype at realizing this conceptualization demonstrated initial success and we offer recommendations for future work.
Feature Extension for Chinese Short Text Based on Tongyici Cilin

Since the short text has characteristics such as sparse features, calculating its similarity is a considerable challenge. However, there is less research on the method of Chinese short text feature extension in short text similarity calculation. Therefore, to have a deeper understanding of the method on using feature extension in the similarity of Chinese short texts, this paper adopts a feature extension algorithm based on an external thesaurus Tongyici Cilin (extended) for short texts. The purpose is to solve the feature sparseness problem of Chinese short text feature vectors. Firstly, segment words in the short text according to certain rules with high surface similarity and extract the main difference components in the text. Then, calculate the similarity of the major difference components between the two short texts based on Cilin. Finally, perform feature extension according to the similar results in the corresponding short text. In the large-scale Chinese Question Matching Corpus LCQMC, a variety of unsupervised models are used for testing. The experimental results show that the method in this paper has a certain improvement effect on various spatial vector similarity algorithms. It can achieve accuracy rates and F1-score of about 3% improvements.
Mosquito Type Identification using Convolution Neural Network

Mosquitoes are insects which are most commonly found in places with high water content. There are various types of mosquitos and each type has its own destructive characteristics. In order to completely eradicate mosquitoes from a particular place, people must know what type of mosquito it is. But this is not a task that can be done with naked eyes. To help this situation, this study develops a Deep Learning (DL) model which can predict the type of mosquito with great accuracy. For this purpose, a dataset of images of the Aedes and culex species of mosquito is collected from Kaggle. Following that, this dataset is split into three parts: training, validation, and testing. Then, pixel and image scaling is used to preprocess this dataset. A DL model is produced using the Convolutional Neural Network (CNN) technique. The model is then trained using the preprocessed dataset. The results of the training are also calculated and documented. The results are then put into a graph for easier examination. The highest accuracy of the model is produced during training and it is 99.2%. As for the loss values, the lowest loss value is 0.8% which is so small for a classification algorithm. Next, tests are conducted on the trained and validated models. A confusion matrix is then used to examine the model performance. The model is then assessed using the metrics such as accuracy, True Positive Rate (TPR), True Negative Rate (TNR), False Positive Rate (FPR), False Negative Rate (FNR), precision, and F1 score. These parameters’ final values were also determined to be satisfactory.
Semantic analysis of normalisation by evaluation for typed lambda calculus

Abstract This paper studies normalisation by evaluation for typed lambda calculus from a categorical and algebraic viewpoint. The first part of the paper analyses the lambda definability result of Jung and Tiuryn via Kripke logical relations and shows how it can be adapted to unify definability and normalisation, yielding an extensional normalisation result. In the second part of the paper, the analysis is refined further by considering intensional Kripke relations (in the form of Artin–Wraith glueing) and shown to provide a function for normalising terms, casting normalisation by evaluation in the context of categorical glueing. The technical development includes an algebraic treatment of the syntax and semantics of the typed lambda calculus that allows the definition of the normalisation function to be given within a simply typed metatheory. A normalisation-by-evaluation program in a dependently typed functional programming language is synthesised.
CONTROL OF COMPUTATIONAL TASKS OF A HPC CLUSTER WHEN SOLVING PROBLEMS OF MATERIALS SCIENCE

The paper considers approaches to the organization of the computational process in a HPC cluster, taking into account a wide range of computational loads in the problems of computational materials science. For a number of tasks for which the interactive mode of operation in the computing environment of a high-performance cluster is in demand, an approach is proposed to the dynamic distribution of computing resources between job queues serving the flows of interactive and batch computational tasks, which makes it possible to increase the efficiency of loading resources of a HPC cluster with the combined use of interactive and batch approaches to computing..
Breast Cancer Classification Using CNN Extracted Features: A Comprehensive Review

Medical imaging (MI) techniques have rapidly emerged as an important tool for clinicians. It has all the more as of late been utilized for preventive medication or evaluating for different illnesses like malignancy. It is especially pertinent that ordinary two-dimensional X-beams are not able to catch the imperfections. Thusly, an assortment of methods are utilized, contingent upon the speculated irregularity. Thus, tomography has to be further explored to improve breast cancer detection. The neural networks (NNs) help to predict the defect, which cannot be usually captured from conventional MI tools. Doctor insight of diagnosing and distinguishing bosom disease can be helped by utilizing some modernized highlights extraction and grouping calculations. This chapter presents an overview of different machine learning algorithms and examination between them which one is ideal to detect faster, and which are utilized to improve the exactness of foreseeing and predicting cancer.
Framing the Female Voice in Doom Metal

The introduction of female vocalist Anneke van Giersbergen on The Gathering's third album, Mandylion (1995), is marked as an innovative moment in the history of doom metal. This chapter explores how the opening track—“Strange Machines”—is structured and sonically shaped. I examine how the vocal and instrumental layers are balanced to occupy specific registral positions in the full sonic spectrum and how they are staged within the stereo field. Using sonic visualization tools (spectrograms) from iZotope RX, I illustrate my analyses in a dynamic engagement with both formal and sonic attributes. My aim is to demonstrate how sonic elements—timbre and space—intersect with the formal features of the song—form, temporal elements, and pitch materials. My analysis explores an emergent dialogue between sonic attributes and musical structure, bringing forward moments where sonic space and timbre are paramount to the crystallization of musical structure and expression. I map these sonic attributes to illuminate Van Giersbergen's powerful expressive role in the genre of doom metal.
Artificial Intelligence without Digital Computers: Programming Matter at a Molecular Scale

Artificial Intelligence In article number 2200157, Li Lin and Michael Keidar present a chemical system with enough complexity which can be used as a programmable intelligent material. The chemical reaction pathway network is mathematically similar to an artificial neural network where the species concentrations are neurons, and the parameters of reaction rate coefficients are weights. A low-temperature plasma is trained to play a board game, showing that a material can have its own intelligence.
Soccer captioning: dataset, transformer-based model, and triple-level evaluation

This work aims at generating captions for soccer videos using deep learning. The paper introduces a novel dataset, model, and triple-level evaluation. The dataset consists of 22k caption-clip pairs and three visual features (images, optical flow, inpainting) for 500 hours of SoccerNet videos. The model is divided into three parts: a transformer learns language, ConvNets learn vision, and a fusion of linguistic and visual features generates captions. The suggested evaluation criterion of captioning models covers three levels: syntax (the commonly used evaluation metrics such as BLEU-score and CIDEr), semantics (the quality of descriptions for a domain expert), and corpus (the diversity of generated captions). The paper shows that the diversity of generated captions has improved (from 0.07 reaching 0.18) with semantics-related losses that prioritize selected words. Semantics-related losses and the utilization of more visual features (optical flow, inpainting) improved the normalized captioning score by 27%.
How to Use Social VR in Higher Education: Case Study of the JYUXR Campus in Finland

This chapter focuses on the use of social virtual reality in higher education. The use of virtual reality (VR) environments in, particularly, teacher education is still in its early days. We argue that teacher education plays an essential role in the adoption of immersive technologies in schools and societies in general. Based on the first experiences of the University of Jyväskylä's extended reality campus JYUXR in 2020–2021, we argue that despite the many bottlenecks that still exist, social VR has particularly good potential for use in future teacher education given that it already offers affordable, workable and flexible environments for testing and pilots.
An Empirical View of Genetic Machine Learning based on Evolutionary Learning Computations

The only prerequisite in the past era was human intelligence, but today's world is full of artificial intelligence and its obstacles, which must still be overcome. It could be said that anything from cars to household items must be artificially intelligent. Everyone needs smartphones, vehicles, and machines. Some kind of intelligence is required by all at all times. Since computers have become such an integral part of our lives, it has become essential to develop new methods of human-computer interaction. Finding an intelligent way of machine and user interaction is one of the most crucial steps in meeting the requirement. The motivations for developing artificial intelligence and artificial life can be traced back to the dawn of the computer era. As always, evolution is a case of shifting phenomena. Adaptive computer systems are explicitly designed to search for problem-specific solutions in the face of changing circumstances. It has been said before that evolution is a massively parallel quest method that never works on a single species or a single solution at any given time. Many organisms are subjected to experiments and modifications. As a result, this write-up aims to create Artificial Intelligence, superior to machine learning that can master these problems, ranging from traditional methods of automatic reasoning to interaction strategies with evolutionary algorithms. The result is evaluated with a piece of code for predicting optimal test value after learning.
A kind of detection method of the water content of crude oil in the porous medium

With the development of science and technology, the petroleum industry plays an increasingly important role in people's lives. The water content of crude oil can not only predict the oil layer location and water level of oil wells, but also further infer the output and value of the remaining recoverable crude oil in the oil field, and formulate corresponding development plans. To sum up, it is of indispensable significance to monitor the water content of crude oil. This paper mainly introduces a method for calculating the water content of crude oil based on the improved ELM algorithm. Compared with the traditional machine learning algorithm, the algorithm not only optimizes the activation function with the Gaussian kernel function but also adopts the method of segmental modeling to ensure that the oil-water two-phase mixed state can be sufficiently usable. The experimental results show that the method has achieved good results in the application of the water content of crude oil detection devices based on the conductometric method, which is superior to the general BP neural network, the ELM algorithm, and the ELM algorithm that is only piece-wised but not optimized.
HAPTIC TECHNOLOGIES IN VIRTUAL UPSKILLING AND TRAINING SCENARIOS: AN UMBRELLA REVIEW

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 4411-4418ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.1067Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
IMPROVING ONLINE LANGUAGE LEARNING INTERACTIVITY WITH MULTIUSER VIRTUAL REALITY ENVIRONMENTS: PREPARING FOR THE METAVERSE

IMPROVING ONLINE LANGUAGE LEARNING INTERACTIVITY WITH MULTIUSER VIRTUAL REALITY ENVIRONMENTS: PREPARING FOR THE METAVERSE
Application of Block Chain Technology by Aggregators in Complex Supply Chains

Aggregation is the most prevalent emerging business model of E-commerce. Block Chain is an emerging technology leveraging an open, tamper-evident ledger, which can be used in several situations requiring transparent establishment of trust between transacting parties. This offers several benefits like reduced transaction costs, transparency, traceability, and speed of execution. Most existing applications of the aggregation model involve simple supply chains on the supplier side (eg., Uber, OYO, etc.). This article explores how the application of block chain technology in aggregation models involving a complex supply chain scenario to achieve various benefits of merging the two technologies.
SMS Spam Detection Using Deep Learning Approach

The popularity of mobile phones has increased drastically in the recent years which is making users vulnerable to various threats like SMS spam, where the user is deceived into revealing private information that could result in a security breach. The motivation of this research is to curb the attackers, hackers, etc., from using SMS spam to exploit mobile device users. Several researchers proposed various machine learning models to automatically detect spam, but they could not achieve a commendable accuracy rate. In this research, several machine learning and deep learning models are utilized to detect SMS spam. A dataset from UCI is used and deep learning models are developed to detect and classify SMS spam using LSTM and BERT. The results are compared with the previous models in SMS spam detection. The proposed deep learning approach obtained the highest accuracy of 99.28% using BERT and 98.84% using LSTM. We utilized Python for all implementations.
Synset2Node: A new synset embedding based upon graph embeddings

Due to the advances made in recent years, embedding methods caused a significant increase in the accuracy of text or graph processing methods. Embedding methods exhibit a compact vector representation of the basic elements (words, synsets, nodes,..) of the underlying system to encode the semantic information between the elements. Of course, due to the polysemous nature of words, in some NLP tasks, the use of sense/synset embedding is better than word embedding. However, in the literature, the introduction of embedding for synsets has received less attention. Existing synset embedding methods have complex calculations to calculate synset embedding based on word embeddings or base upon a defined pairwise synset similarity. In this paper, considering the graphical structure of the WordNet and the high-level knowledge encoded in it, we will create a synset embedding directly from the WordNet graph and its synset relations. Node2Vec graph embedding is used to map nodes of this graph to a vector space. We evaluate the performance of different graph structures (e.g. weighted/weightless, directed/undirected graphs). Moreover, we propose a weighting strategy to weight different synset relation types in the resulting WordNet graph. Experimental results of evaluation of the proposed synset embedding on the task of measuring lexical semantic similarities shows that mean squared error of similarities for the proposed synset embedding method on MEM and WordSim353 datasets are 0.065 and 0.035, resp., which is better than the mean squared error of Word2Vec on these datasets, (0.073 and 0.045, resp.). Furthermore, we use the Pearson correlation and Spearman correlation to compare the performance of the proposed synset embedding method with the state-of-the-art ones. The obtained results show the efficiency of the proposed method on various datasets. .The spearman correlation of the SimLex999 is improved by 0.02, while it improves WordSim353 Pearson correlation by 0.14.