Reviewerly/exHarmony huggingface dataset:
Hierarchical edge-aware network for defocus blur detection

Abstract Defocus blur detection (DBD) aims to separate blurred and unblurred regions for a given image. Due to its potential and practical applications, this task has attracted much attention. Most of the existing DBD models have achieved competitive performance by aggregating multi-level features extracted from fully convolutional networks. However, they also suffer from several challenges, such as coarse object boundaries of the defocus blur regions, background clutter, and the detection of low contrast focal regions. In this paper, we develop a hierarchical edge-aware network to solve the above problems, to the best of our knowledge, it is the first trial to develop an end-to-end network with edge awareness for DBD. We design an edge feature extraction network to capture boundary information, a hierarchical interior perception network is used to generate local and global context information, which is helpful to detect the low contrast focal regions. Moreover, a hierarchical edge-aware fusion network is proposed to hierarchically fuse edge information and semantic features. Benefiting from the rich edge information, the fused features can generate more accurate boundaries. Finally, we propose a progressive feature refinement network to refine the output features. Experimental results on two widely used DBD datasets demonstrate that the proposed model outperforms the state-of-the-art approaches.
A Dedicated Temporal Erasable-Itemset Mining Algorithm

In recent years, data mining has been widely used to extract information from big data. Frequent-itemset mining is one of the most popular techniques in data mining. Erasable-itemset mining is used for production planning and is one of the various itemset-mining problems. Factories could apply erasable-itemset mining to discover suitable material combinations that are erasable but do not greatly influence the profits. However, as time goes, new products may be entered into the factories. We have previously proposed a unified general algorithm for considering different scenarios of itemset lifetimes. Users can decide their desired scenario according to their requirements. The unified algorithm, however, suffers from its execution time because it needs to consider all the different situations. In this work, we propose a dedicated algorithm for handling a special scenario faster than the unified algorithm. We consider the influence on the profits of the itemsets of products first entering and discuss the influence on the lifespans of different itemsets. Experiments were conducted to compare the performance of the dedicated and the unified proposed approaches under different parameter settings.
Robust dual-graph regularized and minimum redundancy based on self-representation for semi-supervised feature selection

Partial labeled data is ubiquitous in the big data era. Selecting informative features, and avoiding redundant and noise features is an important task for constructing robust learning models. The inherent characteristics of samples and features need to be surveyed simultaneously. In this study, a feature selection method for partial labeled data based on dual-graph regularized is proposed. Self-representation can well mine the relation between features. Then, the self-representation is combined with the framework of sparse learning to well embody the character of data and an adaptive redundancy regularization term based on self-representation is designed to minimize the redundancy between features. In the self-representation based regularization term, the relation between features is updated dynamically during the iteration so as to capture the inherent relation between features more accurately. The manifold structure in both feature space and data space are considered jointly. Moreover, the L2,p-norm is imposed on the feature self-representation regularization term, self-representation coefficient matrix, and feature selection matrix, respectively. These constrains aim to enhance its robustness to outliers, and select the representative features with discriminative and low redundancy. The convex (p=1) and non-convex (0<p<1) are involved in the proposed method. Then, a unified solution for the proposed method is investigated and its convergence is proved. Experimental results on public data sets show that the proposed method is effective for classification tasks when compared with some feature selection methods.
Efficacy improvement of aspect-based sentiment analysis using enhanced rule –based approach and domain-specific lexicon (ERBA-DSL)

Since people express their opinions and feelings more openly than ever before, sentiment analysis proves to be a promising research area that effectively analyses the opinion expressed over the entities. In this context, Sentiment analysis is utilized to gather valuable insights from users’ opinions. These insights would benefit a lot for the business concerns and institutions to improve their respective products/services. Aspect-based sentiment analysis (ABSA) is the most robust technique that offers a more fine-grained analysis. The objective of this paper is to improve the efficacy of ABSA by framing a robust and enhanced set of rules. Several experiments were carried out to detect explicit and implicit aspects. The hybrid approach comprising of enhanced rule-based approach (ERBA) and domain-specific lexicon (DSL) is used to improve the solution of the aspect-based sentiment analysis problem. The proposed approach employs a domain-specific adjective-noun collocation list(DSANCL) tailored to the domain for fine-tuning the process of implicit aspect detection(IAD). The proposed model frames a new nine-point scale for measuring the sentiment strength by introducing a ternary classification of intensifiers based on their degree of intensification. The performance of the proposed model is evaluated using the university reviews dataset.
Construction of Boolean Functions from Hermitian Codes

In 2005, Guillot published a method for the construction of Boolean functions using linear codes through the Maiorana–McFarland construction of Boolean functions. In this work, we present a construction using Hermitian codes, starting from the classic Maiorana–McFarland construction. This new construction describes how the set of variables is divided into two complementary subspaces, one of these subspaces being a Hermitian Code. The ideal theoretical parameters of the Hermitian code are proposed to reach desirable values of the cryptographic properties of the constructed Boolean functions such as nonlinearity, resiliency order, and order of propagation. An extension of Guillot’s work is also made regarding parameters selection using algebraic geometric tools, including explicit examples.
Scalable Consensus Over Finite Capacities in Multiagent IoT Ecosystems

In the real world, consensus-achievement is an adaptive and evolutionary process. Tailor-made mechanisms are engaging in the light of disputes, to resolve emerging contradictions between the atoms. Most of the dominant Blockchain architectures tend to oversee this fact. They come integrated with rigid, overkilling consensus-achievement procedures which in many cases are not necessary or significantly beneficial. They often raise overwhelming demands on the nodes and prohibit the deployment of fully distributed and peer blockchain operation in the IoT world. Still, for the IoT ecosystems and the metaverse to become self-sustainable and self-evolving, robust validity mechanisms must be embedded in the atomic scale. This work investigates the process of building and sustaining scalable consensus policies over the trivial atomic capacities of the IoT ecosystems. For this we deploy the IoT micro-Blockchain framework as an atomic-consistency tier and we define a primary set of validity rules that every node can easily carry out. The initial concept relies on Gödel’s Incompleteness theorems and K. Friston’s free energy principle and is investigated under the perspectives of Graph and Game theory. To study the dynamic behavior of the system, a competitive game among the nodes is run. The Proof of Existence is utilized as a universal proofing-case. Its feasibility is demonstrated, and its complexity is analyzed under various system considerations. The findings prove that finite-capacity atoms can collectively and efficiently support verifiable validity and scalable consensus in the evolutionary IoT ecosystems and the metaverse, even under conditions of high diversity, trivial capacity and eventual consistency.
Classroom Teaching Performance Evaluation Model Guided by Big Data and Mobile Computing

Performance management has evolved rapidly in recent years and has become increasingly dominant in enterprise applications, whereas its application in the field of education has progressed slowly. Because performance management focuses on improving business performance and empowering employees, implementing it in schools helps students develop practical skills. This research focuses on evaluating classroom teaching performance using a big data and mobile computing-driven model. In addition, in the era of educational big data, this paper investigates the general process by which teachers acquire, analyze, and use educational data to improve teaching performance. The data mining method and mobile data capture are organically integrated into the benchmarking analysis to evaluate the classroom teaching performance of local universities, enriching the teaching management theories and methods of local universities. The findings show that benchmarking analysis can produce more meaningful results and provide new data for improving teaching management quality.
The Future of Frequency Capping in Privacy-Centric Digital Advertising

Frequency capping is essential for effective digital marketing. As most of current tools allowing for this use case require third-party cookies, their deprecation leads to doubts about the capping future. Discussion about this topic has been focused on emphasizing its importance, while not discussing the potential alternatives. Market-ready solutions not based on third-party cookies only offer partial support for it. Emerging privacy-centric solutions proposals promise full support for this application. In this article, we discuss frequency capping and its role in digital marketing; why frequency capping is endangered; the current available methods, their applications and limitations; what proposals are under consideration with the potential to address frequency capping needs; and the future of frequency capping in light of today’s trends. We propose key questions to help develop privacy-centric advertising future, and its understanding among market participants.
Improving the performance of automotive vision‐based applications under rainy conditions

Input images are the main source of information for vision-based algorithms. The presence of raindrops in input images degrades their quality and, consequently, reduces the quality of the target vision-based algorithm that consumes them. Many image restoration algorithms were proposed in the literature to remove rain presence in images to improve the input image quality. These algorithms, however, cannot remove all the raindrop presence and sometimes introduce undesirable side-effects, such as the blurring rain-occluded sections of the image and incorrectly de-raining areas in the image that are clear. It is hypothesized that a comparable performance improvement can be achieved by decreasing the sensitivity of vision-based algorithms to noisy input images, rather than denoising these images, through the process of de-raining. To test this hypothesis, the performance of state-of-the-art object detection and semantic segmentation models was evaluated, with de-rained image datasets used as input, and compared it to that performance of the same models, retrained with rained image sets. Results showed that the performance of the retrained models was better than that of the baseline detector with de-rained images used as input.
Detection Of Breast Cancer Metastases by analyzing Whole slide images of Lymph Nodes with Deep Learning Algorithms

Breast cancer is a type of cancer that develops from breast tissue. Mortality increases in breast cancer due to Metastases; it is the process of developing cancer cells from primary tumor to secondary tumor that can be formed in the brain, lungs and bones. PET/CT scan is Biomarkers that will detect the tumor after formation. Our proposed work is to detect the metastases in advance so that the person&#x0027;s lifespan can increase by proper medication. We have taken the whole slide images (WSI) of lymph nodes of breast cancer patients. We can detect the metastases in advance by analyzing the lymph node biopsy images using certain machine learning techniques.
Efficient Scale-Recurrent Network Using Generative Adversarial Network for Image Deblurring

Ubiquitous blurry images degrade viewing experiences and performance of video analysis. To be applicable to consumer electronics, the large amount of parameters and high computational load of deblurring network have to be avoided. The existing SRN+ has a small number of parameters (3.9M) and comparable performance. To improve the quality of outputs of a deblurring network (e.g., SRN+) without changing its architecture, this paper proposes the pseudo label based order task for training the discriminator. The proposed funnel soft label further reduces the problem of vanishing gradient during training SRN+ (generator), and the adversarial loss combined with weighted scale-level losses improves quality of deblurring. For GoPro dataset, the proposed scheme outperforms the light version of the state-of-the-art MPRNet in PSNR (+1dB) and number of parameters (70&#x0025;).
Introduction to Artificial Neural Networks

(Artificial) neural networks are information processing systems, whose structure and operation principles are inspired by the nervous system and the brain of animals and humans. They consist of a large number of fairly simple units, the so-called neurons, which are working in parallel. These neurons communicate by sending information in the form of activation signals, along directed connections, to each other.
Multi‐stage attention network for video‐based person re‐identification

Video-based person re-identification (Re-ID) has received increasing attention in video surveillance analysis in recent years. To extract relevant information of the target, many existing methods utilise the attention mechanism in the residual block of the ResNet. However, these methods only focus on the residual block and ignore the output of the shortcut part, which also contains rich information about the person. To solve this problem, a different aspect of network design is investigated: the insert position of the attention module. To simultaneously explore the discriminative information in both the residual block and the shortcut, a novel multi-stage attention method is proposed by inserting the attention mechanism between stages of ResNet. Using this method can effectively extract the rich discriminative features of the target to better distinguish different pedestrians and improve the feature extraction capabilities of the model. Extensive experiments are conducted on four popular video-based person Re-ID datasets to demonstrate the effectiveness of the authors’ proposed method and display its superiority with the existing video-based person Re-ID methods.
Text-based sequential image generation

Despite recent impressive results of generative adversarial networks on text-to-image generation, the generation of complex scenes with multiple objects in the complicated background remains challenging; moreover, end-to-end text-toimage generation still suffers from poor image quality. In this work, we propose a sequential algorithm of text-to-image generation, which allows synthesizing high-quality images (more than 1024x1024 pixels). The proposed approach consists of location inference, key objects extraction, image search, layout generation, and image harmonization stages. We compare the suggested approach with state-of-the-art image generation model DALL-E with text-to-image mapping. Our approach demonstrates the effectiveness and visual plausibility of the generated images based on golden section layouts.
A Cognitive Sample Consensus Method for the Stitching of Drone-Based Aerial Images Supported by a Generative Adversarial Network for False Positive Reduction

When using drone-based aerial images for panoramic image generation, the unstableness of the shooting angle often deteriorates the quality of the resulting image. To prevent these polluting effects from affecting the stitching process, this study proposes deep learning-based outlier rejection schemes that apply the architecture of the generative adversarial network (GAN) to reduce the falsely estimated hypothesis relating to a transform produced by a given baseline method, such as the random sample consensus method (RANSAC). To organize the training dataset, we obtain rigid transforms to resample the images via the operation of RANSAC for the correspondences produced by the scale-invariant feature transform descriptors. In the proposed method, the discriminator of GAN makes a pre-judgment of whether the estimated target hypothesis sample produced by RANSAC is true or false, and it recalls the generator to confirm the authenticity of the discriminator's inference by comparing the differences between the generated samples and the target sample. We have tested the proposed method for drone-based aerial images and some miscellaneous images. The proposed method has been shown to have relatively stable and good performances even in receiver-operated tough conditions.
Proving Reliability of Image Processing Techniques in Digital Forensics Applications

Binary images have found its place in many applications, such as digital forensics involving legal documents, authentication of images, digital books, contracts, and text recognition. Modern digital forensics applications involve binary image processing as part of data hiding techniques for ownership protection, copyright control, and authentication of digital media. Whether in image forensics, health, or other fields, such transformations are often implemented in high-level languages without formal foundations. The lack of formal foundation questions the reliability of the image processing techniques and hence the forensic results loose their legal significance. Furthermore, counter-forensics can impede or mislead the forensic analysis of the digital images. To ensure that any image transformation meet high standards of safety and reliability, more rigorous methods should be applied to image processing applications. To verify the reliability of these transformations, we propose to use formal methods based on theorem proving that can fulfil high standards of safety. To formally investigate binary image processing, in this paper, a reversible formal model of the binary images is defined in the Proof Assistant Coq. Multiple image transformation methods are formalized and their reliability properties are proved. To analyse real-life RGB images, a prototype translator is developed that reads RGB images and translate them to Coq definitions. As the formal definitions and proof scripts can be validated automatically by the computer, this raises the reliability and legal significance of the image forensic applications.
Generation of Black-box Audio Adversarial Examples Based on Gradient Approximation and Autoencoders

Deep Neural Network (DNN) is gaining popularity thanks to its ability to attain high accuracy and performance in various security-crucial scenarios. However, recent research shows that DNN-based Automatic Speech Recognition (ASR) systems are vulnerable to adversarial attacks. Specifically, these attacks mainly focus on formulating a process of adversarial example generation as iterative, optimization-based attacks. Although these attacks make significant progress, they still take large generation time to produce adversarial examples, which makes them difficult to be launched in real-world scenarios. In this article, we propose a real-time attack framework that utilizes the neural network trained by the gradient approximation method to generate adversarial examples on Keyword Spotting (KWS) systems. The experimental results show that these generated adversarial examples can easily fool a black-box KWS system to output incorrect results with only one inference. In comparison to previous works, our attack can achieve a higher success rate with less than 0.004 s. We also extend our work by presenting a novel ensemble audio adversarial attack and testing the attack on KWS systems equipped with existing defense mechanisms. The efficacy of the proposed attack is well supported by promising experimental results.
Recursive-Parallel Algorithm for Solving the Graph-Subgraph Isomorphism Problem

The paper proposes a parallel algorithm for solving the Graph-Subgraph Isomorphism Problem and makes an experimental study of its efficiency. The problem is one of the most famous NP-complete problems. Its solution may be required when solving many practical problems associated with the study of complex structures. We solve the problem in a formulation that requires finding all existing isomorphic substitutions or proving their absence. In view of the high complexity of the problem, it is natural to want to speed up its solution by parallelizing the algorithm.We used the RPM ParLib library, developed by the author, as the main tool to program the algorithm. This library allows us to develop effective applications for parallel computing on a local network under the control of the runtime environment .NET Framework. Thanks to this library, applications have the ability to generate parallel branches of computation directly during program execution and dynamically redistribute work between computing modules. Any language with support of the .NET Framework can be used as a programming language in conjunction with this library. For the numerical experiment, an open database from the Internet was used, which was specially developed to study algorithms for searching for isomorphic substitutions. Also, the author has developed a special application in C# for generating additional sets of initial data with specified characteristics. The aim of the experiment was to study the speedup achieved due to the recursively parallel organization of computations. The paper provides a detailed description of the proposed algorithm, as well as the results obtained during the experiment.
Color Held Illumination Map Estimation using GAN for Low-light Image Enhancement

Recently, museum learning has attracted considerable attention. Guide robots that detect exhibits are being used to enhance learning effects. However, detecting exhibits in the dark areas of the museum is difficult. Therefore, it is necessary to enhance low-light images. In this study, we present an image enhancement method, color held illumination map estimation (CHIME) using generative adversarial networks (GAN) (CHIMEGAN), which can be trained without paired data. Our method estimates a channel-wise illumination map for optimal image enhancement. Furthermore, CHIMEGAN can preserve the optimal color information of images by comparing the reflectance obtained from an elaborate illumination map. Experimental results show that our proposed method outperformed state-of-the-art methods and that it can be used to enhance low-light images of dark areas.