Reviewerly/exHarmony huggingface dataset:
Improvement of Gaussian kernel function for face recognition

Face recognition has been widely used in information security, access control, financial payment, criminal investigation, and other aspects due to its stability, convenience, ease to forge, and other advantages. The Gaussian blur, a fuzzy algorithm, uses the Gaussian distribution to increase the efficiency of face detection. However, the data distribution of the Gaussian blur model is not effective enough for face recognition. In this paper, we improve the one-dimensional Gaussian kernel function to the two-dimensional Gaussian kernel function and use the Gaussian distribution to improve the accuracy of the recognition system. We improve the Model and adopt the Gaussian calculation method. According to the one-dimensional Gaussian function and two-dimensional Gaussian function to carry on the analysis and operation, the conclusion is drawn that the two-dimensional Gaussian function can enhance the efficiency of face recognition when applied to the designed face recognition system. To verify the effectiveness of our method, we compare the proposed method with Gaussian blur on three different datasets. The experimental results show that our method significantly outperforms Gaussian blur. Our analyses illustrate that the recognition degree and range of the improved model are more comprehensive and broader, decreasing the time used for recognition.
Automatic Generation of Debug Headers through BlackBox Equivalence Checking

Modern compiler optimization pipelines are large and complex, and it is rather cumbersome and error-prone for compiler developers to preserve debugging information across optimization passes. An optimization can add, remove, or reorder code and variables, which makes it difficult to associate the generated code statements and values with the source code statements and values. Moreover recent proposals for automatic generation of optimizations (e.g., through search algorithms) have not previously considered the preservation of debugging information.We demonstrate the application of a blackbox equivalence checker to automatically populate the debugging information in the debug headers of the optimized executables compiled from C programs. A blackbox equivalence checker can automatically compute equivalence proofs between the original source code and the optimized executable code without the knowledge of the exact transformations performed by the compiler/optimizer. We present an algorithm that uses these formal equivalence proofs to improve the executable’s debugging headers. We evaluate this approach on benchmarks derived from the Testsuite of Vectorizing Compilers (TSVC) compiled through three different compilers: GCC, ClangLLVM, and ICC. We demonstrate significant improvements in the debuggability of the optimized executable code in these experiments. The benefits of these improvements can be transparently realized through any standard debugger, such as GDB, to debug the updated executable.
Hierarchical lossless coding of light fields with improved random access

The adoption of light field (LF) technology is often hindered by the large amount of data that is necessary to represent such information. To alleviate such burden, specific coding techniques have been under research in recent years. However, most of them require decoding the whole LF data, even in applications where only a region of the LF visual content needs to be accessed. Therefore, encoding schemes that incorporate scalability and random access capabilities would cater to a wider range of LF use cases, particularly those requiring lossless compression. To enable such type of functionalities, an encoder must be designed so that the decoding process can extract only specific views/regions of the complete LF, according to user options or application requirements. This paper proposes hierarchical Minimum Rate Predictors (H-MRP), a lossless light field codec that provides the aforementioned functionalities. The main contributions herein proposed are: (i) the lossless encoding the LF in multiple hierarchical layers; (ii) the flexible selection of reference images for prediction; and (iii) the use disparity compensation between the reference images. The experimental results show that the proposed encoder provides a good compromise in terms of compression efficiency versus random access penalty, surpassing its best competitor by 25% in terms of random access and by 1 bit-per-pixel in terms of compression efficiency. When optimised to provide the best compression efficiency, the method surpasses current state-of-the-art lossless encoding by up to 3 bits-per-pixel. • Light field lossless hierarchical codec with enhanced stream navigation. • Scalability and random access navigation on the decoded sub-aperture images. • Flexible selection of reference images for prediction with disparity compensation. • Better performance than state-of-the-art in terms of bitrate and random access penalty.
Mixed-block neural architecture search for medical image segmentation

Deep Neural Networks (DNNs) have the potential for making various clinical procedures more time-efficient by automating medical image segmentation. Due to their strong, in some cases human-level, performance, they have become the standard approach in this field. The design of the best possible medical image segmentation DNNs, however, is task-specific. Neural Architecture Search (NAS), i.e., the automation of neural network design, has been shown to have the capability to outperform manually designed networks for various tasks. However, the existing NAS methods for medical image segmentation have explored a quite limited range of types of DNN architectures that can be discovered. In this work, we propose a novel NAS search space for medical image segmentation networks. This search space combines the strength of a generalised encoder-decoder structure, well known from U-Net, with network blocks that have proven to have a strong performance in image classification tasks. The search is performed by looking for the best topology of multiple cells simultaneously with the configuration of each cell within, allowing for interactions between topology and cell-level attributes. From experiments on two publicly available datasets, we find that the networks discovered by our proposed NAS method have better performance than well-known handcrafted segmentation networks, and outperform networks found with other NAS approaches that perform only topology search, and topology-level search followed by cell-level search.
Detection of Multiple Drones in a Time-Varying Scenario Using Acoustic Signals

Detection of unauthorized drones is mandatory for defense organizations and also for human life protection. Currently, detection methods based on thermal, video, radio frequency (RF) and acoustic signals exist. In previous research, we presented an acoustic signals-based multiple drones detection technique utilizing independent component analysis (ICA) in the presence of interfering sources. In this paper, a method is proposed in which the mixed signals are first separated taking the ICA technique into account. After extracting the features, the support vector machines (SVM) and the k-nearest neighbors (KNN) are used to identify multiple drones in the field. This technique can detect multiple drones in static and quasi-static mixing scenarios, while failing in time-varying scenarios. In this paper, a time-varying drone detection technique (TVDDT) is proposed that first stores a data set of the mixed signals in a time-varying scenario, where time variations occur within the processing data blocks. After estimating the mixing matrices, we developed a technique to track variations in the channel. This technique is based on variations in the mixing coefficients. The proposed channel tracking technique performs classification and detection based on minimum variation criteria in the channel. The proposed TVDDT technique is evaluated through simulations and its superior performance is observed.
Mapping Distinct Source and Target Domains on Amazon Product Customer Critiques with Cross Domain Sentiment Analysis

Sentiment Analysis is the computational investigation of individuals’ conduct, inclinations, judgment, and assessments about people, issues, elements, points, occasions, items as well as their characteristics. In addition, the Internet has become the most significant spot for communicating opinions about items and administrations, just as for remarking on social issues and executive blueprint. The analysis of this kind of information is exceptionally valuable for an entire scope of useful applications, yet it is challenging as well. And the indispensable obstacle in sentiment analysis is that it is highly domain-centralized. Hence, a model that performs satisfactorily in one domain might not perform in another. This work aims to use cross-domain sentiment classification using Machine Learning on an Amazon product dataset to try to overcome these challenges and build on and attempt to improve the previous work carried out. The dataset is first preprocessed to clean the noisy data, fill the missing values, etc, and then normalized according to the requirements. It then uses feature extraction to develop the labeled feature vectors from the source domain and train a model which allows it to pick important features in a comprehensive manner. Next, it uses the trained classifier to classify reviews in the target domain. The proposed method is expected to be a significant improvement and aims to generalize the underlying method to solve other types of domain-dependent tasks in the future.
Towards Higher-Level Abstractions for Quantum Computing

Quantum Computing (QC) has emerged as a field of ever-increasing activity as it promises to revolutionize computation and enable the solution of computational problems that we (realistically) cannot solve with Classical Computing to date. However, existing quantum programming environments mostly require an in-depth understanding of the basic QC building blocks, that is, quantum states, superposition, entanglement and measurement as well as the changing of quantum states using basic quantum gates. The present state of quantum programming reminds us of how Classical Computing was about 70 years ago when computing machines such as the ENIAC required significant effort to program solely using very basic digital building blocks. Over the decades, though, increasingly higher-level abstractions have been created on top of the basic building blocks of Classical Computing and made computation much more accessible and wide-spread. In order to make Quantum Computing more accessible, we argue that Software Engineering for QC needs to embark on a similar journey and create abstractions that shield developers from the basic QC building blocks as much as possible so that they can focus their attention on solving problems and less on how to manipulate quantum states using quantum circuits. Based on our experience of developing a scalable quantum n-queens solver, this paper aims to formulate recommendations for raising the level of abstraction in Quantum Software Engineering.
Convolutional neural network-based automatic classification for incomplete antibody reaction intensity in solid phase anti-human globulin test image

The precise classification of incomplete antibody reaction intensity (IARI) in hydrogel chromatography medium high density medium solid-phase Coombs test is essential for haemolytic disease screening. However, an automatic and contactless method is required for accurate classification of IARI. Here, we present a deep ensemble learning model that integrates five different convolutional neural networks into a single model for IARI classification. A dataset, including 1628 IARI images and corresponding labels of IARI categories ((-), (1 +), (2 +), (3 +), and (4 +)), was used. We trained our model using 1302 IARIs and validated its performance using 326 IARIs. The proposed model achieved 100%, 99.4%, 99.4%, 100%, and 100% accuracies in the ( -), (1 +), (2 +), (3 +), and (4 +) categories, respectively. The results were compared with those of manual classification by immunologists (average accuracy: 99.8% vs. 88.3%, p < 0.01). Following model assistance, all three immunologists achieved increased accuracy (average accuracy: + 6.1%), with the average accuracy of junior immunologists maximum increasing by 11.3%. The time required for model classification was 0.094 s·image-1, whereas that required manually was 5.528 s·image-1. The proposed model can thus substantially improve the accuracy and efficiency of IARI classification and facilitate the automation of haemolytic disease screening equipment.
Auto-clustering pairs generation method for Siamese neural networks training

In this work, we present the auto-clustering method which can be used for pattern recognition tasks and applied to the training of a metric convolutional neural network. The main idea is that the algorithm creates clusters consisting of classes similar from a network's point of view. The usage of clusters allows the network to pay more attention to classes that are hard to differ. This method improves the generation of pairs during the training process, which is a current problem because the optimal generation of data significantly affects the quality of training. The algorithm works in parallel with the training process and is fully automatic. To evaluate this method we chose the Korean alphabet with the corresponding PHD08 dataset and compared our auto-clustering with random-mining, hard-mining, distance-based mining. Opensource framework Tesseract OCR 4.0.0 was also considered to evaluate the baseline.
Scalable distributed algorithms for multi-robot near-optimal motion planning

This paper investigates a class of motion planning problems where multiple unicycle robots desire to safely reach their respective goal regions with minimal traveling times. We present a distributed algorithm which integrates decoupled optimal feedback planning and distributed conflict resolution. Collision avoidance and finite-time arrival at the goal regions are formally guaranteed. Further, the computational complexity of the proposed algorithm is independent of the robot number. A set of simulations are conducted to verify the scalability and near-optimality of the proposed algorithm.
Ontology Based Image Retrieval by Utilizing Model Annotations and Content

Study on image indexing and retrieval concepts have evolved the research further on Content Based Image Retrieval (CBIR). The CBIR system analyses the image content features such as shape, texture, and colour. In recent systems, these low level features are combined with high level semantics in order to achieve high performance. Analyzing the image content will increase the time of feature extraction and complexity of the retrieval process. The proposed system describes the content of an image region using Color and texture as visual features. An ontology model is constructed to analyze the contents. Content Analysis comprises image segmentation and annotation, which scrutinizes images by subdividing them into entities, selecting primary entities, and finally extracting feature signifiers possessed by these entities. To explore the mechanism of content analysis system, a new fused framework using content and model based annotations was proposed. The proposed integrated framework for CBIR system was experimented and the performance was assessed over a large image database with number of features, compared against traditional approaches from the literature. From the experimental results, it is evident that the proposed system performs substantially better and faster than the existing systems.
TEYSuR - Text Extraction with YOLO and Super Resolution

In these times where a myriad of industries are becoming digitized and advanced, text recognition is being incorporated time and again to enhance customer satisfaction, improve accessibility and organize business processes. Conventional Deep Learning based text recognition, also termed as Optical Character Recognition (OCR) is based on learning the shapes of the characters in a language. In Text Extraction with YOLO and Super Resolution (TEYSuR) we propose a novel approach where YOLO, an object detection algorithm is used to localise and classify characters in an input image. The characters in the image are treated as individual objects by the algorithm and text is extracted with high accuracy and speed. In order to cater to input images of various font sizes the system proposes an additional Inference module which resizes images using Super Resolution and yields extremely high accuracy results. The word accuracy attained by the YOLO model was 88&#x0025; and coupled with the Inference module TEYSuR attained a word accuracy of 96&#x0025;. Hence, this system can be successfully used for extracting text from images with high efficiency.
Locality-based security bug report identification via active learning

Security bug report (SBR) identification is a crucial way to eliminate security-critical vulnerabilities during software development. In recent years, many approaches have utilized supervised machine learning (SML) techniques in the SBR identification. However, such approaches often require a large number of labelled bug reports, which are often hard to obtain in practice. Active learning is a potential approach to reducing the manual labelling cost while maintaining good performance. Nevertheless, the existing active learning-based SBR identification approach still yields poor performance due to ignoring the locality in bug reports. To address the above problems, we propose locality-based SBR identification via active learning. Our approach recommends a small part of instances based on locality in bug reports, asks for their labels, and learns the SBR classifier . Specifically, our approach relies on the locality to construct the initial training set, which is designed to address how to start during active learning. Moreover, our approach applies the locality into the query process, which is designed to improve which instance should be queried next during active learning. We conduct experiments on large-scale bug reports (nearly 125K) from six real-world projects. In comparison with three state-of-the-art SML-based and active learning-based SBR identification approaches, our approach can obtain the maximum values of F-Measure (0.8176) and AUC (0.8631). Moreover, our approach requires 16.60% to 71.40% of all bug reports when achieving the optimal performance in these six projects, which improves three approaches from 9.82% to 64.19% on average. As shown from the experimental results, our approach can be more effective and efficient to identify SBRs than the existing approaches.
A Smart and Robust Automatic Inspection of Printed Labels Using an Image Hashing Technique

This work is focused on the development of a smart and automatic inspection system for printed labels. This is a challenging problem to solve since the collected labels are typically subjected to a variety of geometric and non-geometric distortions. Even though these distortions do not affect the content of a label, they have a substantial impact on the pixel value of the label image. Second, the faulty area may be extremely small as compared to the overall size of the labelling system. A further necessity is the ability to locate and isolate faults. To overcome this issue, a robust image hashing approach for the detection of erroneous labels has been developed. Image hashing techniques are generally used in image authentication, social event detection and image copy detection. Most of the image hashing methods are computationally extensive and also misjudge the images processed through the geometric transformation. In this paper, we present a novel idea to detect the faults in labels by incorporating image hashing along with the traditional computer vision algorithms to reduce the processing time. It is possible to apply Speeded Up Robust Features (SURF) to acquire alignment parameters so that the scheme is resistant to geometric and other distortions. The statistical mean is employed to generate the hash value. Even though this feature is quite simple, it has been found to be extremely effective in terms of computing complexity and the precision with which faults are detected, as proven by the experimental findings. Experimental results show that the proposed technique achieved an accuracy of 90.12%.
Development of Short Term Solar Radiation Forecasting Using AI Techniques

Solar energy (SE) is one of the more popular renewable energy sources, but its integration into the grid is complicated due to its intermittent nature and challenges associated with its generation. South Africa, with an annual global horizontal irradiance of 200W/m<sup>2</sup>, has a great deal of potential for utilizing SE as a source of energy. Accurate SE forecasting is critical for large-scale SE production to grow. Artificial intelligence techniques may prove to be a feasible solution for better forecasting of SE given its intermittent nature and other challenges including weather, temperature, etc. This article presents the view of real-time data collection of various stations located in South Africa and to design the SE forecasting using an an adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) and for forecasting global horizontal irradiance in the short term (GHI). The forecasting models are evaluated via mean absolute square error (MAPE), and root mean square error (RMSE). From the results, it is seen that the ANFIS model outperformed the ANN model for SE accurate forecasting with a lesser value of error. The outcomes of both models for SE are shown via graphical view and also through numerical data.
Developing a framework of beta cryptosystem based on Santilli's isofields second-kind

Society initiated use of several gadgets for various purposes with the expansion in high-tech mechanisms, smart devices etc. So, there is enormous interest of data safety in cyberspace. The present endeavor takes the unique framework to create beta cryptosystem (<tex>$bc$</tex>) built on santilli&#x0027;s isofields second-kind (<tex>$sisk$</tex>), its safety is located in generalized isodiscrete-logarithm-problem (<tex>$gidlp$</tex>), isointeger-factorization-problem (<tex>$iifp$</tex>) and <tex>$sisk$</tex> in the isoproduct isogroup of isofinite <tex>$sisk$</tex>. To break the proposed framework of <tex>$bc$</tex> is to search isonumber from <tex>$sisk$</tex> and numerate both discrete <tex>$gidlp$</tex> and <tex>$iifp$</tex> side by side in the isoproduct isogroup of isofinite <tex>$sisk$</tex> in order to fetch genuine plaintext against the available cipertext and therefore present framework is feasible to succeed a greater level of safety.