Reviewerly/exHarmony huggingface dataset:
3D Path prediction of moving objects in a video-augmented indoor virtual environment

Augmented virtual environments (AVE) combine real-time videos with 3D scenes in a Digital Earth System or 3D GIS to present dynamic information and a virtual scene simultaneously. AVE can provide solutions for continuous tracking of moving objects, camera scheduling, and path planning in the real world. This paper proposes a novel approach for 3D path prediction of moving objects in a video-augmented indoor virtual environment. The study includes 3D motion analysis of moving objects, multi-path prediction, hierarchical visualization, and path-based multi-camera scheduling. The results show that these methods can give a closed-loop process of 3D path prediction and continuous tracking of moving objects in an AVE. The path analysis algorithms proved accurate and time-efficient, costing less than 1.3 ms to get the optimal path. The experiment ran a 3D scene containing 295,000 triangles at around 35 frames per second on a laptop with 1 GB of graphics card memory, which means the performance of the proposed methods is good enough to maintain high rendering efficiency for a video-augmented indoor virtual scene.
Evaluation of RCNN parameters for Senile dementia diagnosis using ADNI dataset

Many techniques for medical diagnosis have been used in the medical world. For medical image processing of massive datasets, artificial intelligence is the most reliable and efficient process. Alzheimer's detection is also a popular diagnosis that can be simplified with the help of machine learning algorithms. Alzheimer's disease is caused by a malfunction of brain cells, and is also the cause of dementia. This paper investigated various machine learning methods such as RCNN, Quick RCNN, Faster RCNN, and help vector machines in depth (SVM). We used machine learning methods to analyse training and testing times in this article. Deep neural networks are a relatively recent machine learning method that has shown interest in a number of classification tasks. On a number of image recognition activities, the convolutional neural network outperforms the others. Medical image datasets, on the other side, are hard to come by since labelling them necessitates a high level of technological expertise. As a consequence, utilising a convolutional neural network (CNN)-based algorithm on a chest X-ray dataset, this paper explores how to detect pneumonia. Three techniques are tested using experiments.
Exploring the Integration of E2E ASR and Pronunciation Modeling for English Mispronunciation Detection

There has been increasing demand to develop effective computer-assisted language training (CAPT) systems, which can provide feedback on mispronunciations and facilitate second-language (L2) learners to improve their speaking proficiency through repeated practice. Due to the shortage of non-native speech for training the automatic speech recognition (ASR) module of a CAPT system, the corresponding mispronunciation detection performance is often affected by imperfect ASR. Recognizing this importance, we in this paper put forward a two-stage mispronunciation detection method. In the first stage, the speech uttered by an L2 learner is processed by an end-to-end ASR module to produce N-best phone sequence hypotheses. In the second stage, these hypotheses are fed into a pronunciation model which seeks to faithfully predict the phone sequence hypothesis that is most likely pronounced by the learner, so as to improve the performance of mispronunciation detection. Empirical experiments conducted a English benchmark dataset seem to confirm the utility of our method.
Robust and Blind Audio Watermarking Scheme Based on Genetic Algorithm in Dual Transform Domain

In order to protect the copyright of audio media in cyberspace, a robust and blind audio watermarking scheme based on the genetic algorithm (GA) is proposed in a dual transform domain. A formula for calculating the embedding depth is developed, and two embedding depths with different values are used to represent the “1” and “0” states of the binary watermark, respectively. In the extracting process, the embedding depth in each audio fragment will be calculated and compared with the average embedding depth to determine the watermark bit by bit, so this scheme can blindly extract the watermark without the original audio. GA will be applied to optimize the algorithm parameters for meeting the performance requirements in different applications. Besides, the embedding rule is further optimized to enhance the transparency based on the principle of minimal modification to the audio. Experimental results prove that the payload capacity reaches 172.27 bps, the bit error rate (BER) is 0.1% under the premise that its transparency is higher than 25 dB, and its robustness is strong against many attacks. Significantly, this scheme can adaptively select the algorithm parameters to satisfy the specific performance requirements.
Magic Mirror Stealth: Interactive Automatic Picture Editing System

This paper builds an interactive automatic picture editing system based on the most advanced algorithms in the field of multi-modality. It removes a specific area or entity in the image according to the natural language expression, which is like a magic mirror that can make the target invisible. The system is composed of two core processing modules: a referring image segmentation module based on natural language expressions and an automatic image inpainting module. The two modules cooperate to precisely control the editing area and generate the editing results based on the image semantics, which can ensure that the edited images are natural, realistic, and meet the editing needs of users. The two modules of the system are trained on open-source datasets separately. Experimental results demonstrate that the system can help users quickly edit images by a natural language expression.
A New Blind Evaluation Method of Infrared Image Quality

Blind evaluation of digital image quality has always been one of the most challenging problems in artificial intelligence and computer vision. Because of the limitation of imaging mechanism, the overall quality of infrared reconnaissance image is lower than that of visible image. It will seriously restrict the efficiency of subsequent image processing, seriously affect the performance of detection efficiency. The contours, textures and point features that can represent the characteristics of infrared radiation are analyzed and extracted, and a simple infrared image quality evaluation model combining mixed features and target saliency is constructed. Taking real infrared images as samples and subjective evaluation as the benchmark, the relative comparative verification is carried out. The results show that this method can achieve better evaluation performance than state-of-the-art methods.
A Comparison of Machine Learning Models in Photovoltaic Power Generation Forecasting

The amount of new renewable energy generation is increasing worldwide every year. Among many new renewable energy sources, solar energy generation using solar energy accounts for the highest proportion of new renewable energy generation. There is a variation in power production because solar power generation is more affected by climate conditions compared to power generation using crude oil or oil. In order to accurately predict solar energy generation dependent on climate variables, this study compares the performance of machine learning-based solar power generation prediction models using weather forecast data from the current forecast technology, Numeric Weather Prediction (NWP). In this study, we experimented on two NWP types, and 7 machine learning models depending on 21 photovoltaic(pv) power stations. Based on results, we select the model with the lowest statistical indicators nMAE(%) by region as the optimal model for the region. Finally, experimental results show that the 7-Block ANN model devised in this study is better than conventional machine learning models.
A Study on Multiword Expression Features in Emotion Detection of Code-Mixed Twitter Data

The need for automated detection of emotions in microblogs such as Twitter is growing as many organizations realize the need to take the emotions of their customers and the public in general into consideration in their problem solving and decision making. In multilingual countries such as Malaysia, tweets are typically written in code-mixed Malay-English with some informal Romanized dialect texts. These tweets are often embedded with multiword expressions. Although there have been studies on multiword expressions, there has not been substantial work on the use of multiword expressions as features to detect emotion of text. This paper studies the use of multiword expressions extracted from WordNet and WordNet Bahasa as features in the emotion detection of code-mixed Twitter data.
Partial Order Transactions on Permissioned Blockchains for enhanced Scalability

Ordering service in a permissioned blockchain platform like Hyperledger Fabric groups the transactions into various blocks. These blocks are linked together via a hash chain to provide immutability. Transactions with less block height get validated before the transactions with more block height. Within a single block, the transactions get validated in the same order they are arranged inside the block. This structure imposes a total order among transactions. This total ordering ensures that all the peer nodes reach the same final state once all the transactions are validated. This total ordering prevents parallel validation of transactions. A given transaction may not be dependent on all the transactions preceding it. However, such a transaction still cannot be validated before completion of validation for all the preceding transactions. This paper presents a blockchain system to support partial ordering among non-dependent transactions. The proposed blockchain system aims to achieve high transaction throughput by enabling parallel transaction validation and reducing the transaction turnaround time by eliminating the need for the transactions to wait for non-dependent predecessor transactions. The proposed blockchain system ensures that all peer nodes reach the same final state even if the order in which they validate the transactions is different.
Nahuatl Neural Machine Translation Using Attention Based Architectures: A Comparative Analysis for RNNs and Transformers as a Mobile Application Service

Machine Translation is a problem that consists of automating the task of translating a sentence into another target language done by a computer, and is still in research, especially with low-resource languages. The neoteric introduction of attention techniques inside the Natural Language Processing (NLP) field in coalescence with a broader disposal of word-segmentation and Web Scrapping techniques; including the lack of a proper online tool translation for Nahuatl dialect, inspired this work in an effort to produce such a tool. Once availability of suitable corpus via Web Scrapping is searched for with scrutiny, therefore, doubling the state of the art in parallel phrases; several vocabulary files were produced using two sorts of word segmentation tools in order to extract the morphemes and break down the agglutination Nahuatl contains. By performing a comparative analysis between Recurrent Neural Networks (RNNs) and Transformers, incorporating two segmentation techniques and two different corpus, it is possible to improve the state of the art regarding Nahuatl by more than four times the BLEU score (66.45) with second validation by using a Fuzzy similarity library. Such experiments confirmed the hypothesis that by increasing the corpus size by double, using transformers and sub-word segmentation, a translation from Spanish to Nahuatl is the best approach that can be accomplished so far with the current tools; outperforming many times Statistical Machine Translation (SMT) and RNNs which do not contain attention, plus the deployment of an application that serves as a platform for the language.
Hexagonal Image Compressionusing Singular Value Decomposition in Python

With the advent of multimedia technologies in last two decades, there is a widespread need for efficient storage and transmission of data. Dealing with the vast information interchange in this digital era, image compression for reduction in byte size of graphics image file without loss of image quality to an acceptable level becomes the large interest area. Inspired from the biological models of human fovea, hexagonal image processing has gained a lot of attention in artificial intelligence era that deals with the application of image processing system that combines the benefits of biologically motivated structures. In this paper a singular value decomposition (SVD) over hexagonal image compression which is a missing stone in computer vision which provides higher packing density, higher angular symmetry and uniform connectivity. Due to lack of developments in hexagonal imaging devices, different resampling methods like alternate pixel shift method, half pixel shift method, pseudo hexagonal pixel method for sourcing hexagonal images. SVD is one of the powerful cutting-edge technology for image compression algorithms. SVD based image compression is performed on hexagonal grid and is compared with square grid using different parameters like compression ratio, compression size, PSNR and MSE using PYTHON's SVD function. SVD based hexagonal image achieves the goal of compression by preserving good image quality at higher compression ratios, high computational efficiency, provides low mean square error (MSE), acceptable compression size depending on application and high peak to signal ratio (PSNR).
Digital Text Security with Steganography Least Significant Bit and Audio Feature Extraction

Abstract The study developed a digital text security system by inserting the classified message or text into digital audio (stego audio) with the least significant bit (LSB) steganography and audio feature extraction. The text bits were inserted into each cover audio frame based on the value of feature extraction energy and zero-crossing rate (ZCR). The study has demonstrated that the least significant bit and audio feature extraction can cover the digital text in stego audio and then be extracted back to the original digital text. There is a small difference in feature extraction energy value between stego audio and cover audio. While there is no difference in feature extraction zero-crossing rate between stego audio and cover audio. The study also measured signal-to-noise ratio (SNR); the detected noise in stego audio because of the change in the least significant bit. The SNR is 111.97 – 130.00 dB.
Model-based Method to Utilize a Catalogue of Quality Requirements in Software Development

Complex software-based systems must comply with both functional and non-functional requirements (NFRs) to provide usefulness. This paper presents a structured catalogue of quality requirements and a model-based approach to collect NFRs from the catalogue in a given project context. The NFR catalogue is structured according to the quality criteria from the ISO 25000 series of standards and can be further extended. This catalogue can be applied in specific software development or modernization projects and in the preparation of tenders. This application to a specific project context is achieved by using the BPMN-NFR method presented in this paper. In this method, pattern recognition in system models is used to build a soft-goal model that serves as a filter mechanism for selecting relevant quality requirements from the catalogue. Through enrichment with context information concrete system related non-functional requirements are derived, which can be used for the system development. This model-based method was developed and applied in the context of the modernization of the budgetary procedures of Germany’s federal government.
INTELLIGENT DYSLEXIA PREDICTION EMPOWERED WITH OPTIMIZATION TECHNIQUES

Specific learning disability is a neurological disorder that affects a person's ability to think, speak, read, write or work with calculations. Dyslexia is one of the specific learning disability that manifests in the difficulty of an individual to read, write, spell and perform simple calculations. Almost 10 to 15% of the world's population is affected with Dyslexia and if not detected at an early stage, it hinders the learning capacity of an individual adversely. There are many methods that can be used for predicting the dyslexic condition and one of the methods is the use of eye tracking. Eye tracking captures the gaze point of the eye in motion and the readings captured that way can be used for many different applications. The dataset used in this research paper is the eye tracking dataset collected from Karolinska Institute, Sweden. I-VT algorithm or the velocity threshold identification algorithm was used to extract useful metrics from the raw eye tracking data. Many different machine learning algorithms like LogisticRegression, RandomForest, MLPClassifier and SVC have been used on the dataset to understand the accuracy of the prediction. It was found that feature selection using Particle Swarm Optimization increased the prediction accuracy of these classifiers, especially SVC and MLPClassifier. It is observed that machine learning algorithms can be applied on the recorded eye movement data to develop models with significant predictive accuracy. Such models can be used as a screening tool to identify high risk of dyslexia in individuals.
Method of multi-domain synchronization network planning via quantum annealing

A quantum-inspired method for multi-domain synchronization network planning is explored and proposed, formulated to multi-variable quadratic models via quantum annealing approach proving better experimental results of optimal performance and completing time comparing with simulated annealing.
Hybrid Evolutionary Computing-based Association Rule Mining

This chapter proposes three novel association rule mining algorithms based on hybrid evolutionary techniques, which obviate the necessity of pre-specifying the minimum support and minimum confidence, unlike Apriori and FP-Growth. Further, these rules are obtained in a single run of these algorithms. These features signal a clear departure from the traditional algorithms without sacrificing the power of the rules. Their effectiveness is tested on a real-life commercial bank dataset from India and five other standard datasets. This chapter opens up a new line of research, wherein hybrid evolutionary techniques can be exploited to their fullest potential in mining association rules from databases.
On Indian English Language Model for Continuous Speech Recognition

Language model is usually used to describe the probability distribution between characters or words. It can effectively recognize continuous speech with pronunciation dictionary and acoustic model. At present, the language model based on n-gram is generally used in the engineering practice of continuous speech recognition (CSR) system, while the state of art is mostly focused on neural networks. Based on the characteristics of Indian English linguistics, this paper constructs a text corpus for language model training and a variety of Indian English language models with n-gram and neural networks, and carries out experimental analysis to explore the suitable language model construction method for Indian English CSR.
Parallel Accelerated Algorithm Optimization for Speckle Matching in Deformation Measurement Based on Stereo Vision

This paper is concerned with the efficiency of speckle match in vision deformation measurement, upon which the CUDA programming architecture, combined with the Visual Studio platform and Mex script files is utilized to implement parallel operations. With the aid of compiling the GPU parallel mode of the CUDA source program through NVCC, the scheme of speckle matching parallel computing are given, which is crucial to improve the real-time performance of vision-based deformation measurement. Consequently, the method in this paper completes the efficient calculation of match of the speckle image sub-regions in the three-dimensional deformation measurement. The proposed strategy solves the obstacle problem when the Mex script and different programming languages interact, and is not restricted by overloaded functions, so that the overall computing performance of the deformation measurement program reaches a better state. Lastly, the experimental results show that the speckle matching has achieved a calculation speedup ratio of 20.39 times.
Real Power Loss Reduction by Melon Fly Optimization and Spontaneous Process Algorithm’s

HIGHLIGHTS Melon Fly algorithm based on the innate events of Melon fly By smell and vision the Melon fly will move to the best location Spontaneous Process Algorithm based onnuclear fission and fusion Nucleus symbolizes the variables and potential solution
APPLICATION OF COMPUTER MODELING POSSIBILITIES IN THE STUDY OF PHYSICAL MODELS

The advantages and disadvantages of using a virtual laboratory and computer models in the educational process in physics are identified. The expediency of using virtual laboratories and computer models in the context of mixed physics education is substantiated. To teach future physics teachers to create and use computer models of physical phenomena and processes is carried out in a professionally oriented author's course "Methods of using computer technology in professional activities." The stages of development of a physical model for the formulation of a mathematical problem are described and the implementation on the example of development and use of the program "Dynamic model of an ideal gas" is shown.
Measurement of transmitted wavefront error and homogeneity with a low-coherence interferometer

We describe high-precision measurements of the transmitted wavefront error and refractive index homogeneity of glass plates using a low-coherence interferometer in a downward-looking configuration. With the low-coherence illumination there are no spurious interference fringes even for a plane-parallel sample. Unbiased results are obtained by using a combination of four different measurement steps. The measurement performance and repeatability are shown for the lowcoherence interferometer OptoFlat with a special fixture incorporating sample holder and return mirror. Its ease of handling and supporting software-based user guidance through the measurement steps greatly facilitate wavefront and homogeneity measurements in the optics shop.
3D-SceneCaptioner: Visual Scene Captioning Network for Three-Dimensional Point Clouds

Currently, image captioning has been widely studied with the development of deep neural networks. However, seldom work has been conducted to develop captioning models for three-dimensional (3D) visual data, for example, point clouds, which are now popularly employed for vision perception. Technically, most of these models first project the 3D shapes into multiple images, and then use the existing or similar framework for image captioning models to fulfill the task. Consequently, within such a technical framework, a large amount of useful information hidden in 3D vision is inevitably lost. In this paper, a captioning model for visual scenes directly based on point clouds is proposed. First, a deep model with densely connected point convolution is developed to extract visual features directly on point clouds, and the multi-task learning method is adopted to improve the visual features. Then, the visual features are converted into sentences through a caption generation module. As a whole, an end-to-end model is constructed for the task of 3D scene captioning. This model makes full use of the rich semantic information in point clouds, and generate more accurate captions. Since there do not exist large-scale datasets for this task, in this paper two new datasets are created on existing point cloud datasets by manually labeling captions. Comprehensive experiments conducted on three datasets (including one public benchmark) indicate the effectiveness of our model.