Reviewerly/exHarmony huggingface dataset:
Deep Kernel Learning Networks with Multiple Learning Paths

This paper proposes deep kernel learning networks with multiple learning paths (DKL-MLP) for nonlinear function approximation. Leveraging the random feature (RF) mapping technique, kernel methods can be implemented as a two-layer neural network, at drastically reduced workload on weight training. Motivated by the representation power of the deep architecture in deep neural networks, we devise a vanilla deep kernel learning network (DKL) by applying RF mapping at each layer and learn the last layer only. To improve the learning performance of DKL, we add multiple trainable paths to DKL and develop the DKL-MLP method so that some implicit information from earlier hidden layers to the output layer can be learned. We prove that both DKL and DKL-MLP permit universal representation of a wide variety of interesting functions with arbitrarily small error and have no bad local minimum. Numerical experiments on both regression and classification tasks are provided to demonstrate the learning performance and computational efficiency of the proposed methods.
PEAR: Photographic Embedding for Aesthetic Rating

Image aesthetic quality assessment has gained the enormous interest in recent years with the advancement of deep learning and the large-scale datasets. Current state-of-the-art methods generally leverage deep features, while the hand-crafted photographic attributes which are also useful but not always available have not drawn the sufficient attention. In this paper, we propose the Photographic Embedding for Aesthetic Rating (PEAR) framework to assimilate their advantages. In PEAR, a prior network is constructed to smoothly inject the photographic attributes to the latent space, and handle the attribute missing case via knowledge transfer. Simultaneously, aesthetic quality assessment is formulated as multi-task learning of aesthetic rating and image reconstruction to regularize the learning of latent codes. The extensive experiments on two challenging datasets demonstrate PEAR outperforms the state-of-the-art methods.
Heuristic Dropout: An Efficient Regularization Method for Medical Image Segmentation Models

For medical image segmentation in a real scenario, the amount of accurate annotation data at the pixel level is typically small, which tends to cause an overfitting problem. This manuscript goes deep into the research of the Dropout algorithm, which is commonly used in neural networks to alleviate the overfitting problem. From the perspective of solving the co-adaptation problem, this manuscript explains the basic principles of the Dropout algorithm and discusses the existing limitations of its derivative methods. Furthermore, we propose a novel Heuristic Dropout algorithm to address these limitations. The proposed algorithm takes information entropy and variance as heuristic rules. It guides our algorithm to drop features suffering from co-adaptation problem more efficiently and thus can better alleviate the overfitting problem of small-scale medical image segmentation datasets. Experiments on medical image segmentation datasets and models show that the proposed algorithm significantly improves the performance of these models.
Local Context Interaction-Aware Glyph-Vectors for Chinese Sequence Tagging

As hieroglyphics, Chinese characters contain rich semantic and glyphs information, which is beneficial to sequence tagging task. However, it’s difficult for shallow CNNs architecture to extract glyphs information from character data and implement the con-textual interaction of different glyphs information effectively. In this paper, we address these issues by presenting LCIN: a Local Context Interaction-aware Network for glyph-vectors extraction. The network utilizes depthwise separable convolution and attention machine to extract glyphs information from images of Chinese characters. Moreover, we interconnect adjacent attention blocks so that glyphs information can flow within the local context. Experiments on three subtasks for sequence tagging show that our method out-performs other glyph-based models and achieves new SOTA results in a wide range of datasets.
Content Preserving Scale Space Network for Fast Image Restoration from Noisy-Blurry Pairs

Hand-held photography in low-light conditions presents a number of challenges to capture high quality images. Capturing using a high ISO results in noisy images, while capturing using longer exposure results in blurry images. This necessitates post-processing techniques to restore the latent image. Most existing methods try to estimate the latent image either by denoising or by deblurring a single image. Both these approaches are ill-posed and often result in unsatisfactory results. A few methods try to alleviate this ill-posedness using a pair of noisy-blurry images as inputs. However, most of the methods using this approach are computationally very expensive. In this paper, we propose a fast method to estimate a latent image given a pair of noisy-blurry images. To accomplish this, we propose a deep-learning based approach that uses scale space representation of the images. To improve computational efficiency, we process higher scale spaces using shallower networks and the lowest scale using a deeper network. Also, unlike existing scale-space methods that use bi-cubic interpolation, we propose a content preserving scale space transformation for decimation and interpolation. The proposed method generates state-of-the-art results at reduced computational complexity compared to state-of-the-art method. Finally, we also show that computational efficiency can be improved by 90% compared to baseline with only a marginal drop in PSNR.
Distributed Graph Learning With Smooth Data Priors

Graph learning is often a necessary step in processing or representing structured data, when the underlying graph is not given explicitly. Graph learning is generally performed centrally with a full knowledge of the graph signals, namely the data that lives on the graph nodes. However, there are settings where data cannot be collected easily or only with a non-negligible communication cost. In such cases, distributed processing appears as a natural solution, where the data stays mostly local and all processing is performed among neighbours nodes on the communication graph. We propose here a novel distributed graph learning algorithm, which permits to infer a graph from signal observations on the nodes under the assumption that the data is smooth on the target graph. We solve a distributed optimization problem with local projection constraints to infer a valid graph while limiting the communication costs. Our results show that the distributed approach has a lower communication cost than a centralised algorithm without compromising the accuracy in the inferred graph. It also scales better in communication costs with the increase of the network size, especially for sparse networks.
Privacy-Preserving Federated Multi-Task Linear Regression: A One-Shot Linear Mixing Approach Inspired By Graph Regularization

We investigate multi-task learning (MTL), where multiple learning tasks are performed jointly rather than separately to leverage their similarities and improve performance. We focus on the federated multi-task linear regression setting, where each machine possesses its own data for individual tasks and sharing the full local data between machines is prohibited. Motivated by graph regularization, we propose a novel fusion framework that only requires a one-shot communication of local estimates. Our method linearly combines the local estimates to produce an improved estimate for each task, and we show that the ideal mixing weight for fusion is a function of task similarity and task difficulty. A practical algorithm is developed and shown to significantly reduce mean squared error (MSE) on synthetic data, as well as improve performance on an income prediction task where the real-world data is disaggregated by race.
Determinantal Point Process Likelihoods for Sequential Recommendation

Sequential recommendation is a popular task in academic research and close to real-world application scenarios, where the goal is to predict the next action(s) of the user based on his/her previous sequence of actions. In the training process of recommender systems, the loss function plays an essential role in guiding the optimization of recommendation models to generate accurate suggestions for users. However, most existing sequential recommendation tech- niques focus on designing algorithms or neural network architectures, and few efforts have been made to tailor loss functions that fit naturally into the practical application scenario of sequential recommender systems.
Human Computer Model for E-learning in Pandemic of Covid-19

The concept of e-learning is not new to the education sector. However, in the recent years, with the outbreak of COVID-19, the importance of e-learning has grown globally. It has completely transformed the way in which learning is imparted to students. Unlike traditional chalk and board method of teaching, e-learning makes learning simpler, easier, and more effective. Earlier the access to knowledge was not accessible to all. Students with economic constraints, geographical boundaries or physical disabilities had scarce opportunities in the academic province. The concept of online education has broken all these barriers. Today's learners are engaged in appropriate, mobile, self-paced, and customized content. This need is satisfied by the online style of learning, where students can study at their own convenience and demand. This research work has proposed an E-Learning Website for Beginners especially designed keeping in mind the students of rural areas and backward classes who may not have access to correct and sufficient resources to learn the tools which are required in day-to-day life. The web-application will provide the user a complete basic knowledge on the various Microsoft Office Tools through videos, images and text materials provided on the platform. There is a quiz at the end of every course, so that students can examine themselves. Students can also download the course material from the platform. Being an open-source platform, this e-learning website can be used by any school or college students.
Comparison of Logistic Regression and Artificial Neural Network for Modelling Credit Card Data Set with the Identification of Precise Fraudulent

Using the Logistic Regression technique, estimate the accuracy % of credit card fraudulent transactions. The accuracy percentage of credit card fraudulent transactions was predicted using a novel decision boundary logistic regression with a sample size of 100 and an artificial neural network (ANN) with a sample size of 100, a 95 percent confidence interval, and a pretest power of 80% iterated at various times. The sigmoid function is used in logistic regression to map values between 0 and 1, which aids in improving the accuracy percentage prediction. Logistic regression has a significantly higher accuracy rate (98.2 percent) than ANNs (88.8 percent). With (p=0.001) (p0.005), there was a statistically significant difference between Logistic regression and ANN. The Logistic Regression algorithm demonstrates a higher predictive accuracy percentage for fraudulent credit card transactions.
ORDER TYPES OF MODELS OF FRAGMENTS OF PEANO ARITHMETIC

Abstract The complete characterisation of order types of non-standard models of Peano arithmetic and its extensions is a famous open problem. In this paper, we consider subtheories of Peano arithmetic (both with and without induction), in particular, theories formulated in proper fragments of the full language of arithmetic. We study the order types of their non-standard models and separate all considered theories via their possible order types. We compare the theories with and without induction and observe that the theories without induction tend to have an algebraic character that allows model constructions by closing a model under the relevant algebraic operations.
Perception of Age and Gender Detection by using Hierarchical Deep Learning Architecture through Vision

When it comes to software systems that analyze face photos, gender detection is a crucial feature. This research provides a quantum machine learning-based approach for automated gender categorization from face photos utilizing a hybrid classical-quantum neural network, which is based on quantum machine learning. Using the knowledge of a pre-trained off-the-shelf Deep Neural Network (DNN)in conjunction with the transfer learning of a quantum variational circuit, an accurate binary classifier has been produced in this paper. An example of a hybrid network is the binary classifier, which is composed of a convolutional base of the Deep Neural Network (DNN) that has been compressed with a dressed quantum circuit and a dressed quantum circuit. In the classification of a publicly accessible collection of face photographs, better results than that have been previously reported have been obtained.
Intelligent Capture and Analysis of Data Stream Images of Rural E-Commerce Live Broadcast Platform based on Deep Stochastic Neural Network

Intelligent capture and analysis of data stream images of rural e-commerce live broadcast platform based on deep stochastic neural network is the focus of this study. In the designed data stream image analytic model, statistical analysis of the encryption algorithm is carried out. By analyzing the histogram of the encrypted image, the correlation of adjacent pixels, and the information entropy, the confusion and diffusion performance of the algorithm and the ability to resist statistical attacks are tested. The key point of FPGA for image processing is that it can perform real-time pipeline operations to achieve the highest real-time performance. Hecne, the hardware optimization combining the deep stochastic neural network is discussed. The application scenario is selected to be rural e-commerce live broadcast platform. Therough the experimental analysis, the performance is validated.
Domain Transferred Image Recognition via Generative Adversarial Network

Recent studies have demonstrated that neural networks exhibit excellent performance in information hiding and image domain transfer. Considering the tremendous progress that deep learning has made in image recognition, we explore whether neural networks can recognize the imperceptible image in the transferred domain. Our target is to transfer natural images into images that belong to a different domain, while at the same time, the attribute of natural images can be recognized on domain transferred images directly. To address this issue, we proposed domain transferred image recognition to achieve image recognition directly on the transferred images without the original images. In our proposed system, a generator is designed for the domain transfer and a recognizer is responsible for image recognition. To be flexible for the natural image restoration in some cases, we also incorporate an additional generator in our method. In addition, a discriminator will play an indispensable role in the image domain transfer. Finally, we demonstrate that our method can successfully identify the natural images on transferred images without access to original images.
Chinese Spelling Text Generation of Mathematical Formulas

Recently, speech assistants have brought convenience to our lives from many aspects. In the education field, speech assistants can also help teachers to reduce their burdens. However, there is no suitable solution to synthesize speeches for mathematical formulas although there have been lots of good techniques for text-to-speech (TTS) in the general domain. One possible solution is that we can convert mathematical formulas expressed in the LaTeX format to spelling texts and synthesize them into speech. In this paper, we investigated text generation methods that translate mathematical formulas in LaTex into Chinese spelling texts. For this purpose, we first constructed a parallel corpus of mathematical formulas and Chinese spelling texts, then compared the existing commonly used text generation methods, such as rule-based, Seq2Seq, Transformer and Graph2Seq, and finally proposed a novel model. As far as we know, this is the first study for Chinese spelling text generation of mathematical formulas. Experiment results on the annotated corpus show that our proposed model significantly outperforms the existing commonly used generation models.
OpenCV Implementation of Image Processing Optimization Architecture of Deep Learning Algorithm based on Big Data Processing Technology

Through the in-depth analysis of artificial neural network algorithm technology, particle swarm algorithm technology, and image matching algorithm, the article briefly analyzes the theoretical principle of the algorithm, analyzes the characteristics of the algorithm, and analyzes the application of the algorithm in image processing optimization technology. Using the iterative process of neurons to process image data, after dimensionality reduction, reduction, and white point removal, the optimization of image processing is increased by 7.5%. Using deep learning algorithms to optimize the processing of the drawbacks and difficulties of big data, build an image optimization architecture framework and OpenCV modeling, the results showthat image noise is reduced by 12%.
Techvar: Classification of Similarity in Software Detection Model using Deep Learning

In Industry 4.0, Deep Learning techniques have become an important research tool in many area namely healthcare, automobiles, video analysis, audio analytics, software systems etc. In recent years, many research are performed in software analysis using modern technologies. CrosLSim, SimMax, and atrpos models are used to review the software similarity detection techniques for different software systems. The existing models for similarity detection are not efficient to be used in major software projects. In this paper, the Techvar-DNN system which performs enhanced Probability, maintainability, testability, and reusability has been proposed. When compared with other methods namely Random Forest and Support vector machines, the proposed system provides increased recall, a smaller function size, and more efficient computing. Moreover, the proposed model results show better F-measure, precision and recall to improve the software similarity detection in a more efficient manner.
Bidirectional Encoder Approach for Abstractive Text Summarization of Urdu Language

The fast pace of accumulating textual data in the online sphere has made it laborious to get out handy information from a profuse amount of information. NLP’s area: automate text summarization, yields a great quality and considerable gist; abstracts and summaries of written texts of myriad human languages. Several attempts have been carried out previously in extractive summarization systems; however, research in abstractive summarization in the Urdu language has not been studied well so far. Urdu is a very rich language in terms of literary sources and it requires serious research efforts to generate abstractive summaries. In this research, we employ a composition of abstractive and extractive algorithms in an automated text summarization system for the Urdu language. In extractive summaries, we use word frequency, Sentence weight, and TF-IDF algorithms. Further, a hybrid method is introduced to improve the results of extractive summaries. Bidirectional Encoder Representations from Transformers (BERT) model is used to process the summaries generated by hybrid method for generation of abstractive summary. To evaluate the system-generated summaries, the assistance of the experts of Urdu language is reaped.
A Novel Hyperparameter Tuned Deep Learning Model for Content based Image Retrieval

Content based image retrieval (CBIR) becomes useful in several real time applications and the size of the image databases gets significantly improved. At the same time, it is hard to undergo a comparison of the feature of the query image (QI) with all images from the dataset. Due to the advancements of deep learning (DL) models, the CBIR models can be designed to accomplish maximum retrieval performance. With this motivation, this paper presents a novel hyperparameter tuned DL model for CBIR (HPTDL-CBIR) models to effectively retrieve the images in the image database. The presented HPTDL-CBIR model involves a VGG-16 model as a feature extractor for deriving a meaningful collection of feature vectors for the QI and the images in the database. At the same time, water wave optimization (WWO) techniques are applied to properly determine the hyperparameter of the VGG-16 model in such a way that the retrieval performance gets enhanced. Finally, Manhattan distance metric is applied in the query matching procedure to determine the similarity level among the images. In order to validate the better retrieval outcome of the presented HPTDL-CBIR method, a series of simulation takes place. The experimental results showcased that the HPTDL-CBIR method has gained maximum outcome.
Blockchain Networks for Solar PV Electric Vehicles Charging Station to Support and Foster Clean Energy Transition

Blockchain technology holds many promises for the energy sector due to its features of immutability, consensus, security, decentralization, and automation by leveraging smart contracts. The Internet of Energy (IoE) is an emerging technology that provides the regulation of incoming data from different edge devices such as renewable sources, smart meters, and electric vehicles in a smart grid network and enables connectivity between them using the existing communication protocols. This paper proposes and implements a blockchainbased energy management system for an electric vehicle charging station at the University of Sharjah powered with a solar photovoltaic (PV) system. By leveraging smart contracts, we can manage and store the production data of the solar PV panels, and the power consumption data of the electric vehicles during the charging session. This study essentially focuses on the integration of blockchain in sustainable transportation to accelerate and promote renewable energies.
Multimodal and Multilabel Genre Classification of Movie Trailers

Movies are a diverse form of art and expressions. Unlike pictures and short clips, movies consist of a story-line which is deliberately made quite complex in order to engage the target audience. This paper presents evaluation of the usefulness of visual, textual and metadata-based functions for predicting the genre of a movie using movie trailers and analyzing it's visual features. The trailers were dissected into individual frames and were evaluated for key characteristics in order to divide them into different genres. Because previous articles employ an impractically large number of parameters to analyse the trailer, this approach aims to keep the number of parameters used to a minimum. The Moviescope dataset has been used which consists of about 5,000 movies with relevant information such as movie trailers, posters, plots and metadata. Linear Regression, KNN (K Nearest Neighbours), Decision Tree, Random Forest and Artificial Neural Networks are just a few of the classification algorithms this research study has used and compared.