Reviewerly/exHarmony huggingface dataset:
Joint Generative Image Deblurring Aided by Edge Attention Prior and Dynamic Kernel Selection

Image deblurring is a classic and important problem in industrial fields, such as aviation photo restoration, object recognition in robotics, and autonomous vehicles. Blurry images in real-world scenarios consist of mixed blurring types, such as a natural motion blurring owing to shaking of the camera. Fast deblurring does not deblur the entire image because it is not the best option. Considering the computational costs, it is also better to have an alternative kernel to deblur different objects at a high-semantic level. To achieve better image restoration quality, it is also beneficial to combine the blurring category location and important structural information in terms of specific artifacts and degree of blurring. The goal of blind image deblurring is to restore sharpness from the unknown blurring kernel of an image. Recent deblurring methods tend to reconstruct prior knowledge, neglecting the influence of blur estimation and visual fidelity on image details and structure. Generative adversarial networks(GANs) have recently been attracting considerable attention from both academia and industry because GAN can perfectly generate new data with the same statistics as the training set. Therefore, this study proposes a generative neural architecture and an edge attention algorithm developed to restore vivid multimedia patches. Joint edge generation and image restoration techniques are designed to solve the low-level multimedia retrieval. This multipath refinement fusion network (MRFNet) can not only perform deblurring of images directly but also individual the frames separately from videos. Ablation experiments validate that our generative adversarial network MRFNet performs better in joint training than in multimodel. Compared to other GAN methods, our two-phase method exhibited state-of-the-art performance in terms of speed and accuracy as well as has a significant visual improvement.
A reliable authentication scheme of personal health records in cloud computing

Abstract A patient-centered personal health records system has been actively promoted in recent years. Its purpose is to maintain long-term personal records and health improvement plans. It combines a cloud computing environment to build a personal health records system to quickly collect personal information and transfer it to the back end for storage for future access. However, in a cloud environment, the message transmission process is more open. Therefore, a lack of an authority security mechanism for the users of such an architecture will result in distrust and doubt by the users. This adversely affects the implementation and quality of long-term health plans. To protect the crucial privacy of the users from malicious attacks or theft, it is necessary to ensure that the users have different authority to access their personal health records under the cloud computing environment and manage the openness of their authority to other users. A secured identify authentication mechanism can ensure that only legitimate users can log in to the system and obtain system service resources through verification. For a personal health records system in the cloud computing environment, this study proposes a secure and reliable user authentication mechanism allowing relevant users access to the user’s PHR in the cloud based on their authority. The proposed authentication method uses a password combined with a smart card, allowing the owner and authorized users to log in to the system and access the relevant personal records. In this study, an authentication method based on bilinear pairing was used to verify the identity of users and to effectively prevent malicious intrusion and theft.
An Ensemble based Modulation Recognition using Feature Extraction

Due to the increase in wireless devices, there has been an increasing demand for spectrum utilization and intelligent radios. This is done by sensing the spectrum to collect important information regarding the received signal and is generally used in military applications to identify or decode enemy signals. Software-defined radio (SDR) and Cognitive radio (CR) are important civilian applications for this field. These types of task fall into the broader study of signal recognition, which is quite difficult due to many different parts of the signal to be identified just by using the received signal. In this paper, we explore the viability of different hand crafted features from various domains such as Time, Frequency, and Statistics for the task of Automatic Modulation Classification (AMC). For the classification we are using an ensemble learning approach with Random Forests (RF) model, and comparing it with Gini-based Decision tree (DT) algorithm. We found that the accuracy of RF model is 10% higher than the DT model for medium to high SNR values and also one feature is dominating the prediction in case of DT approach. Using the scikit-learn machine learning library, we are able to get $\approx$75% accuracy without using any complex Deep Learning architectures or expensive hardware.
Towers of Babel: Combining Images, Language, and 3D Geometry for Learning Multimodal Vision

The abundance and richness of Internet photos of landmarks and cities has led to significant progress in 3D vision over the past two decades, including automated 3D reconstructions of the world’s landmarks from tourist photos. However, a major source of information available for these 3D-augmented collections—namely language, e.g., from image captions—has been virtually untapped. In this work, we present WikiScenes, a new, large-scale dataset of landmark photo collections that contains descriptive text in the form of captions and hierarchical category names. WikiScenes forms a new testbed for multimodal reasoning involving images, text, and 3D geometry. We demonstrate the utility of WikiScenes for learning semantic concepts over images and 3D models. Our weakly-supervised framework connects images, 3D structure, and semantics—utilizing the strong constraints provided by 3D geometry—to associate semantic concepts to image pixels and 3D points. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Artificial Neural Network, Convolutional Neural Network Visualization, and Image Security

Images are the most venerable form of data in terms of security. Daily around 1.8 billion images are added to the Internet and most of the data on the Internet is public. So, it is desired to make sure that images are secure before transmitting through an insecure network like the Internet. Various techniques are used for the protection of these digital images like steganography, watermarking, and encryption. These methods are used for achieving security goals like confidentiality, integrity, and availability. Individually none of the methods can achieve all security goals, so, this paper presents you a blended approach of the above-mentioned methods. A modern approach of using artificial neural network is proposed in this paper. In ANN, we will focus particularly on convolutional neural network (CNN). Since CNN works on the pixels of the image and deals with the subject like object detection, face recognition, so, it will enhance the current methods in terms of efficiency, accuracy, and computational power and will improve the security of image. The method presented in this paper is efficient and secured against the attacks and risks related to data.
Improving Algorithm for the Alignment of Consecutive, Whole-Slide, Immunohistochemical Section Images

Accurate and precise alignment of histopathology tissue sections is a key step for the interpretation of the proteome topology and cell level three-dimensional (3D) reconstruction of diseased tissues. However, the realization of an automated and robust method for aligning nonglobally stained immunohistochemical (IHC) sections is still challenging. In this study, we aim to assess the feasibility of multidimensional graph-based image registration on aligning serial-section and whole-slide IHC section images.An automated, patch graph-based registration method was established and applied to align serial, whole-slide IHC sections at ×10 magnification (average 32,947 × 27,054 pixels). The alignment began with the initial alignment of high-resolution reference and translated images (object segmentation and rigid registration) and nonlinear registration of low-resolution reference and translated images, followed by the multidimensional graph-based image registration of the segmented patches, and finally, the fusion of deformed patches for inspection. The performance of the proposed method was formulated and evaluated by the Hausdorff distance between continuous image slices.Sets of average 315 patches from five serial whole slide, IHC section images were tested using 21 different IHC antibodies across five different tissue types (skin, breast, stomach, prostate, and soft tissue). The proposed method was successfully automated to align most of the images. The average Hausdorff distance was 48.93 μm with a standard deviation of 14.94 μm, showing a significant improvement from the previously published patch-based nonlinear image registration method (average Hausdorff distance of 93.89 μm with 50.85 μm standard deviation).Our method was effective in aligning whole-slide tissue sections at the cell-level resolution. Further advancements in the screening of the proteome topology and 3D tissue reconstruction could be expected.
Performance Analysis of Pseudo Random Bit Generator Using Modified Dual-Coupled Linear Congruential Generator

Pseudo Random Bit Generator (PRBG) is a key element to protect the data in various cryptography applications during transmission. To prove more secure among different previous pseudo random bit generator methods like Linear Feedback Shift Register (LFSR), Linear Congruential Generator (LCG), coupled LCG (CLCG), and Dual Coupled LCG (dual-CLCG) the modified Dual coupled LCG (MDCLCG) is implemented. This method used is to generate a pseudo random bit with less area occupation and with single clock delay. In this paper three different ways of adder topologies ripple carry adder (RCA), carry skip adder (CSKA) and carry increment adder (CIA) are implemented in the place of modulo carry save adder to analyze the area, power and speed performance of the modified Dual Coupled LCG design using Verilog-HDL and prototyped on FPGA device Spartan3E XC3S500E.
Non-central Student-t Mixture of Student-t Processes for Robust Regression and Prediction

The Mixture of Gaussian Processes (MGP) is a general regression model for the data from a general stochastic process. However, there are two drawbacks on the parameter learning of the MGP model. First, it is sensitive to outliers. Actually, when the data are disturbed by heavy noise, its regression results are affected greatly by the noise, which makes it difficult to reflect the overall characteristics of the data. Second, the kernels of Gaussian processes in the MGP model do not have a simple parametric form to represent uncertain intuition by nonparametric prior over the covariance matrix. In order to overcome these problems, we propose the non-central student-t Mixture of student-t Processes (tMtP) model for robust regression and prediction. Specifically, the student-t process takes the invers Wishart distribution as its conjugate prior for the covariance matrix. The learning of the mixture parameters in the tMtP model can be implemented under the general framework of the hard-cut EM algorithm while the learning of the hyperparameters in each student-t process is implemented by maximizing the log-likelihood function of the square exponential kernel in output region. It is demonstrated by the experimental results on synthetic data sets that the tMtP model is effective for robust regression. Moreover, the tMtP model also obtains good prediction performance on a coal production data set.
A Fast Multiscale Clustering Approach Based on DBSCAN

Multiscale brings great benefits for people to observe objects or problems from different perspectives. It has practical significance for clustering on multiscale data. At present, there is a lack of research on the clustering of large-scale data under the premise that clustering results of small-scale datasets have been obtained. If one does cluster on large-scale datasets by using traditional methods, two disadvantages are as follows: (1) Clustering results of small-scale datasets are not utilized. (2) Traditional method will cause more running overhead. Aims at these shortcomings, this paper proposes a multiscale clustering framework based on DBSCAN. This framework uses DBSCAN for clustering small-scale datasets, then introduces algorithm Scaling-Up Cluster Centers (SUCC) generating cluster centers of large-scale datasets by merging clustering results of small-scale datasets, not mining raw large-scale datasets. We show experimentally that, compared to traditional algorithm DBACAN and leading algorithms DBSCAN++ and HDBSCAN, SUCC can provide not only competitive performance but reduce computational cost. In addition, under the guidance of experts, the performance of SUCC is more competitive in accuracy.
Merging Web Tables for Relation Extraction with Knowledge Graphs

We propose methods for extracting triples from Wikipedia’s HTML tables using a reference knowledge graph. Our methods use a distant-supervision approach to find existing triples in the knowledge graph for pairs of entities on the same row of a table, postulating the corresponding relation for pairs of entities from other rows in the corresponding columns, thus extracting novel candidate triples. Binary classifiers are applied on these candidates to detect correct triples and thus increase the precision of the output triples. We extend this approach with a preliminary step where we first group and merge similar tables, thereafter applying extraction on the larger merged tables. More specifically, we propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">observed schema</i> for individual tables, which is used to group and merge tables. We compare the precision and number of triples extracted with and without table merging, where we show that with merging, we can extract a larger number of triples at a similar precision. Ultimately, from the tables of English Wikipedia, we extract 5.9 million novel and unique triples for Wikidata at an estimated precision of 0.718.
Understanding and Simplifying Perceptual Distances

Perceptual metrics based on features of deep Convolutional Neural Networks (CNNs) have shown remarkable success when used as loss functions in a range of computer vision problems and significantly outperform classical losses such as L1 or L2 in pixel space. The source of this success remains somewhat mysterious, especially since a good loss does not require a particular CNN architecture nor a particular training method. In this paper we show that similar success can be achieved even with losses based on features of a deep CNN with random filters. We use the tool of infinite CNNs to derive an analytical form for perceptual similarity in such CNNs, and prove that the perceptual distance between two images is equivalent to the maximum mean discrepancy (MMD) distance between local distributions of small patches in the two images. We use this equivalence to propose a simple metric for comparing two images which directly computes the MMD between local distributions of patches in the two images. Our proposed metric is simple to understand, requires no deep networks, and gives comparable performance to perceptual metrics in a range of computer vision tasks.
A Keyword-Based Multi-label Text Categorization in the Indian Legal Domain Using Bi-LSTM

In this era of information abundance, text segmentation can be used effectively to locate and extract information specific to the user’s needs, within a massive load of documents. Text categorization refers to the process of segregating a document into smaller labeled text chunks based on the semantic commonality of the contents. In general, as legal texts are filled with a lot of semantic information, text segmentation is a very crucial factor in information retrieval from legal documents. Also, such supervised classification demands huge and intense training of data for building an efficient system. Collecting such a high volume of data and manually classifying them is very expensive. Recent advancements in the field of information technology, and in particular, artificial intelligence have opened the gates of automation. This article proposes a model using deep learning techniques to split the judgment text into the issue, facts, arguments, reasoning, decision, majority concurring, and minority dissenting. To evaluate the proposed model, we conducted experiments that revealed that the bidirectional long short-term memory (Bi-LSTM) categorization technique could achieve 97% accuracy and obtained superior categorization performance. The purpose of our automation technique is to provide high-quality legal reference information from past judgment texts and also categorization of the judgment in a cost-effective and time-saving way.
Diversity Evolutionary Policy Deep Reinforcement Learning

The reinforcement learning algorithms based on policy gradient may fall into local optimal due to gradient disappearance during the update process, which in turn affects the exploration ability of the reinforcement learning agent. In order to solve the above problem, in this paper, the cross-entropy method (CEM) in evolution policy, maximum mean difference (MMD), and twin delayed deep deterministic policy gradient algorithm (TD3) are combined to propose a diversity evolutionary policy deep reinforcement learning (DEPRL) algorithm. By using the maximum mean discrepancy as a measure of the distance between different policies, some of the policies in the population maximize the distance between them and the previous generation of policies while maximizing the cumulative return during the gradient update. Furthermore, combining the cumulative returns and the distance between policies as the fitness of the population encourages more diversity in the offspring policies, which in turn can reduce the risk of falling into local optimal due to the disappearance of the gradient. The results in the MuJoCo test environment show that DEPRL has achieved excellent performance on continuous control tasks; especially in the Ant-v2 environment, the return of DEPRL ultimately achieved a nearly 20% improvement compared to TD3.
An AI-Powered Smart Camera for Object Detection

Artificial Intelligence has become very important in the new era. Due to the importance of artificial intelligence, this research work has developed an AI-powered smart camera, which can perform different Artificial Intelligence operations like object recognition, line recognition, face recognition. Once the face is recognized by using the husky lens, the data is transmitted by using the LORA module. This operation is carried out by interfacing the Huskey lens and the LORA module via an Arduino module and the data will be received on the receiver side by using another LORA module. The LORA module can able to transmit the data to long distances in km range without any source like the internet. Hence this can be used in a highly secured area.
A Formal Study of Shot Boundary Detection Approaches—Comparative Analysis

In the past, video recording and video databases were relatively small. This makes the manual keyword indexing and retrieval easy. However, in recent years, videos are now a major source of learning, and shot boundary detection (SBD) is the very first step toward video annotation. Through this paper, we want to throw a light on different SBD approaches proposed recently and analyze them based on some existing measures. We evaluated various methods showing the diversity of SBD and tried to facilitate the identification of manuscripts related to the reader's interests.
Using Genetic Programming to Find Functional Mappings for UMAP Embeddings

Manifold learning is a widely used technique for reducing the dimensionality of complex data to make it more understandable and more efficient to work with. However, current state-of-the-art manifold learning techniques - such as Uniform Manifold Approximation and Projection (UMAP) - have a critical limitation. They do not provide a functional mapping from the higher dimensional space to the lower-dimensional space, instead, they produce only the lower-dimensional embedding. This means they are "black-boxes" that cannot be used in domains where explainability is paramount. Recently, there has been work on using genetic programming to perform manifold learning with functional mappings (represented by tree/s), however, these methods are limited in their performance compared to UMAP. To address this, in this work we propose utilising UMAP to create functional mappings with genetic programming-based manifold learning. We compare two different approaches: one that uses the embedding produced by UMAP as the target for the functional mapping; and the other which directly optimises the UMAP cost function by using it as the fitness function. Experimental results reinforce the value of producing a functional mapping and show promising performance compared to UMAP. Additionally, we visualise two-dimensional embeddings produced by our technique compared to UMAP to further analyse the behaviour of each of the algorithms.
M-estimation based sparse grid quadrature filter and stochastic stability analysis

In this study, a novel M-estimation based sparse grid quadrature filter (MSGQF) is proposed to improve the robust performance of the nonlinear system. We present a systematic formulation of the sparse grid quadrature filter (SGQF), and extend it to the discrete-time nonlinear system with abnormal measurement values. The M-estimation method is introduced in the SGQF, which uses the Huber’s cost function to update the measurement covariance. Convergence on the modified robust SGQF is established and proved. The sufficient conditions are shown to ensure stochastic stability of the MSGQF. A target tracking problem has been conducted to demonstrate the accuracy of the MSGQF. When measurement abnormal values appear, it outperforms the unscented Kalman filter (UKF), the cubature Kalman filter (CKF) and the SGQF. Theoretical analysis and simulation results prove that the MSGQF provides significant performance improvement in the robustness of the nonlinear system.
Towards an Interpretable Deep Driving Network by Attentional Bottleneck

Deep neural networks are a key component of behavior prediction and motion generation for self-driving cars. One of their main drawbacks is a lack of transparency: they should provide easy to interpret rationales for what triggers certain behaviors. We propose an architecture called Attentional Bottleneck with the goal of improving transparency. Our key idea is to combine visual attention, which identifies what aspects of the input the model is using, with an information bottleneck that enables the model to only use aspects of the input which are important. This not only provides sparse and interpretable attention maps (e.g. focusing only on specific vehicles in the scene), but it adds this transparency at no cost to model accuracy. In fact, we find improvements in accuracy when applying Attentional Bottleneck to the ChauffeurNet model, whereas we find that the accuracy deteriorates with a traditional visual attention model.