Reviewerly/exHarmony huggingface dataset:
LAPUSKA: Fast Image Super-Resolution via LAPlacian UpScale Knowledge Alignment

Single image super-resolution is important part of computer vision open problems. Recently, deep neural networks have demonstrated excellent performance in this problem. In this work, several cutting edge methods for super-resolution problem using deep neural networks will be considered. Comparison of their effectiveness and evaluation of neural networks architectures with respect to different metrics is one of our main goals for this research. Modern deep learning methods often require large computational cost and load a lot of computer memory, which affects the ease of use of neural networks and the time of generation super-resolution results. In addition to the existing models, we propose a new architecture of neural networks based on best properties of considered architectures and designed to eliminate their shortcomings. Furthermore, we compare the quality of all considered deep learning methods with baseline method of bicubic interpolation.
Study on Semantic Inpainting Deep Learning Models for Artefacts with Traditional Motifs

This paper proposes the use of pre-trained semantic inpainting deep learning architectures to reach a high-fidelity, visually plausible filling content suggestion for the restoration of museum textile objects with traditional motifs. Two state-of-the-art models are selected and their reconstructions are additionally given to an autoencoder trained on a specific collection of textiles. The results show some potential of the tandem and the viability of an automatic support for artefact restoration.
Effective Black Box Adversarial Attack with Handcrafted Kernels

We propose a new, simple framework for crafting adversarial examples for black box attacks. The idea is to simulate the substitution model with a non-trainable model compounded of just one layer of handcrafted convolutional kernels and then train the generator neural network to maximize the distance of the outputs for the original and generated adversarial image. We show that fooling the prediction of the first layer causes the whole network to be fooled and decreases its accuracy on adversarial inputs. Moreover, we do not train the neural network to obtain the first convolutional layer kernels, but we create them using the technique of F-transform. Therefore, our method is very time and resource effective.
Replacing Goniophotometer with Camera and U-Net with Hypercolumn Rescale Block

We deal with replacing a costly and slow goniophotometer device with a standard, inexpensive, and fast camera in the task of evaluating an illuminated area by a car headlamp. This solution is novel, has not yet been solved, and has the potential to speed up the process of prototyping headlamps. The difficulties lie in the significantly different resolutions of the two devices and in the disparity between intensities captured by the camera and goniophotometer due to the nonlinear behavior of the light. We propose to capture images by a camera with various exposure times and handle them as a multispectral image. The image is processed by U-Net architecture where we replaced the standard decoder with a Hypercolumn rescale block. The proposed scheme produces a mean absolute percentage difference between the real goniophotometer and our solution of less than 0.5%.
Offline Substitution Machine Learning Model for the Prediction of Fitness of GA-ARM

Association rule mining (ARM) is one of the most popular tasks in the field of data mining, very useful for decision-making. It is an NP-hard problem for which Genetic algorithms have been widely used. This is due to the obtained competitive results. However, their main drawback is the fitness computation which is time-consuming, especially when working with huge data. To overcome this problem, we propose an offline approach in which we substitute the GA’s fitness computation with a Machine Learning model. The latter will predict the quality of the different generated solutions during the search process. The performed tests on several well-known datasets of different sizes show the effectiveness of our approach.
The Advance and Performance Analysis of MapReduce

Cloud computing is highly praised for its high data reliability, lower cost, and nearly unlimited storage. In cloud computing projects, the MapReduce distributed computing model is prevalent. MapReduce distributed computing model is mainly divided into the Map and Reduce functions. As a mapper, the Map function is responsible for dividing tasks (such as uploaded files) into multiple small tasks executed separately; As a reducer, the Reduce function is responsible for summarizing the processing results of multiple tasks after decomposition. It is a scalable and fault-tolerant data processing tool that can process huge voluminous data in parallel with many low-end computing nodes. This paper implements the wordcount program based on the MapReduce framework and uses different dividing methods and data sizes to test the program. The common faults faced by the MapReduce framework also emerged during the experiment. This paper proposes schemes to improve the efficiency of the MapReduce framework. Finally, building an index or using a machine learning model to alleviate data skew is proposed to improve program efficiency. The application system is recommended to be a hybrid system with different modules to process variant tasks.
Selected Algorithmic Developments

This chapter presents details on the data acquisition and information extraction algorithms that are used in the reviewer and expert recommendation system and the innovation promotion system, Inventorum. The chapter’s primary objectives are to show these algorithms’ underlying details and to justify their selection among other possible solutions.
Efficient Blind Image Super-Resolution

A hybrid method to Single Image Super-resolution is proposed. We used zero-shot super-resolution method to reconstruct high-resolution image from low-resolution one based on the degradation trained on unpaired high-resolution and low-resolution samples. This approach gives the benefits of internal networks, such as extracting features from a particular picture, as well as external methods working with high-resolution and low-resolution image distributions. The proposed scheme would be of high-interest for super-resolution of single images from a specific devices with the same degradations.
Recommending Reviewers and Experts

This chapter focuses on the recommendation of reviewers and experts for the evaluation of scholarly articles and research and development projects. Its main objective is to propose a recommendation system of reviewers and experts—specifically, its architecture and recommendation algorithm, by presenting case studies concerning the architecture and functions of each of its modules.
Performance Analysis of Matrix Multiplication Based on Serverless Platform

Over the past few years, serverless computing has become increasingly important as a new field that dramatically reduces costs, reduces latency, improves scalability, and eliminates server-side management. Big data is also increasingly used in various areas, such as finance, transportation, and telecommunications. Large amounts of data information must be stored in high-dimensional matrices for large-scale scientific computations. This paper combined serverless computing with high-dimensional matrices by using the MapReduce model to implement a serverless architecture on Aliyun to multiply high-dimensional matrices. This paper used observation, comparison, and control variable methods to conduct experiments and concluded by analyzing a large amount of experimental data. This paper found that the change in the number of workers will affect the total working time, the ratio among total working time, calculation time and communication time. The matrix size change will affect the computation time ratio to total working time and communication time to total working time. Moreover, this paper infers an optimal number of workers for each size matrix, which minimizes the total execution time.
Literature Review

This chapter is a review of literature on knowledge recommendation. It emphasises reviewer and expert recommendation, innovation support, and selected information extraction algorithms that are used to create individual profiles. The literature review is based on previous works of the author [62–66, 74, 107]. The information included in those works has been reinterpreted and supplemented with data found in the most recent publications.
Underwater Image Clearing Algorithm Based on the Laplacian Edge Detection Operator

When shooting underwater, limited by different camera hardware and lighting, underwater images are generally of low quality and blurred details. Moreover, due to the different absorption rates of the R, G, and B three-color wavelengths, the color of underwater photos is distorted. This affects the feasibility of studying underwater images. The majority of currently available techniques for improving underwater photographs mainly solve the problem of overall denoising and brightness enhancement of underwater images while ignoring the edge details of the image. As a solution to the aforementioned issues, we suggest a method using a double extraction network structure model based on the Laplacian operator, which can dehaze underwater images more quickly. One is to use downsampling and upsampling to reduce the excessive distortion of the image during training, and the other is to use the Laplacian operator to reduce the blurring of the edge to enhance the clarity of the edge and use the blue‒green model for multiscale fusion. We adopted MSE loss and proved its superiority through experiments and index analysis by training images of different underwater scenes.
Flexible Runtime Security Enforcement with Tagged C

We introduce Tagged C, a novel C variant with built-in tag-based reference monitoring that can be enforced by hardware mechanisms such as the PIPE (Processor Interlocks for Policy Enforcement) processor extension. Tagged C expresses security policies at the level of C source code. It is designed to express a variety of dynamic security policies, individually or in combination, and enforce them with compiler and hardware support. Tagged C supports multiple approaches to security and varying levels of strictness. We demonstrate this range by providing examples of memory safety, compartmentalization, and secure information flow policies. We also give a full formalized semantics and a reference interpreter for Tagged C.
Path Planning Based on Multi-parameter Adaptive Improved Elite Ant Colony Algorithm

The traditional elite ant colony algorithm has strong randomness, easy to fall into local optimal solution, slow convergence and other problems in path planning. This paper proposes that the information heuristic factor α expectation heuristic factor β, pheromone volatilization coefficient ρ, and the pheromone increase coefficient e of the optimal path is changed into a dynamic adaptive parameter, which changes with the number of iterations. In this paper, the grid method is used to model the path environment in space, and an adaptive parameter mathematical model is constructed and verified. By comparing the simulation experiments of different scale map environments, it is found that the improved elite ant colony algorithm has significantly improved the path optimization ability, smoothness and robustness.
Introduction

Nowadays, we are overwhelmed by data. This is caused by the unprecedented influx of news, advertisements, opinions, technical papers, scientific works, and more. This phenomenon results in difficulties finding the right information, expertise, or people when it is necessary. Knowledge recommendation is an urgent and timely subject in research and information services.
The Study for the Influencing Factors of the Box Office and Prediction Based on Machine Learning Models

Without a derivative market, film investments primarily rely on box office receipts as their primary source of revenue. For this reason, early box office forecasting is crucial because it enables investors to anticipate the project's overall success in advance and raise awareness of the movie using the right publicity techniques to boost its overall box office take. In this study, we first extracted a dataset containing 217 missing values that were suitable for analysis with regression and classification models. Then the data are preprocessed, including missing value processing, normalization, weakly correlated data processing and feature scatter analysis. If there is missing data, the data will be deleted or replaced based on whether the missing variables in the observed values are missing. Random forest model, neural network model, decision tree model, gradient lifting tree and extreme random forest are used to express the error measure between observations of the same phenomenon with the average absolute error. In addition, the evaluation index called MAE was used to evaluate the error based on the machine learning models. Finally, through the analysis of the experimental results and the mean absolute error size, it is found that the random forest model has the best performance and the smallest error in predicting the movie box office.
Knowledge Recommendation in Practice

This chapter describes the technical aspects of the reviewer and expert recommendation system and Inventorum, the innovation support system, whose methodological assumptions and algorithms are discussed in detail in Chaps. 3, 4, and 5. The primary objective of this chapter is to explain these systems’ information architectures, technologies, and user interfaces.
Runtime Monitoring of Accidents in Driving Recordings with Multi-type Logic in Empirical Models

Video capturing devices with limited storage capacity have become increasingly common in recent years. As a result, there is a growing demand for techniques that can effectively analyze and understand these videos. While existing approaches based on data-driven methods have shown promise, they are often constrained by the availability of training data. In this paper, we focus on dashboard camera videos and propose a novel technique for recognizing important events, detecting traffic accidents, and trimming accident video evidence based on anomaly detection results. By leveraging meaningful high-level time-series abstraction and logical reasoning methods with state-of-the-art data-driven techniques, we aim to pinpoint critical evidence of traffic accidents in driving videos captured under various traffic conditions with promising accuracy, continuity, and integrity. Our approach highlights the importance of utilizing a formal system of logic specifications to deduce the relational features extracted from a sequence of video frames and meets the practical limitations of real-time deployment.
Supporting Innovativeness and Information Sharing

This chapter focuses on the recommendation and sharing of information on innovations. Its main objective is to propose a recommendation system that supports innovativeness and information sharing. The purpose of such recommendations is to reduce the time necessary for users to find the right innovations, potential business partners, experts, and conferences.
Comparison of Data Processing Performance of Hadoop and Spark Based on Huawei Cloud and Serverless

The optimal choice between serverless computing and traditional server computing, the current frontier in the machine learning and cloud server industries, has always been controversial. In contrast, serverless computing has better resource utilization but is restricted to a certain range of applications. Therefore, the research on serverless computing is not only the future direction of development, but also an essential practice direction. This paper deployed and built an experimental solution based on Huawei Cloud serverless platform, compared and analyzed various data processing algorithms under MapReduce in Huawei Cloud serverless platform, summarized their advantages and disadvantages, and found the areas where each algorithm is promising.
DeDA: Deep Directed Accumulator

Chronic active multiple sclerosis lesions, also referred to as rim+ lesions, are characterized by a hyperintense rim observed at the lesion's edge on quantitative susceptibility maps. Despite their geometrically simple structure, characterized by radially oriented gradients at the lesion edge with a greater gradient magnitude compared to non-rim+ (rim-) lesions, recent studies indicate that the identification performance for these lesions is subpar due to limited data and significant class imbalance. In this paper, we propose a simple yet effective image processing operation, deep directed accumulator (DeDA), which provides a new perspective for injecting domain-specific inductive biases (priors) into neural networks for rim+ lesion identification. Given a feature map and a set of sampling grids, DeDA creates and quantizes an accumulator space into finite intervals and accumulates corresponding feature values. This DeDA operation can be regarded as a symmetric operation to the grid sampling within the forward-backward neural network framework, the process of which is order-agnostic, and can be efficiently implemented with the native CUDA programming. Experimental results on a dataset with 177 rim+ and 3986 rim- lesions show that $$10.1\%$$ of improvement in a partial (false positive rate $$<0.1$$ ) area under the receiver operating characteristic curve (pROC AUC) and $$10.2\%$$ of improvement in an area under the precision recall curve (PR AUC) can be achieved respectively comparing to other state-of-the-art methods. The source code is available online at https://github.com/tinymilky/DeDA .
FedContrast-GPA: Heterogeneous Federated Optimization via Local Contrastive Learning and Global Process-Aware Aggregation

Federated learning is a promising strategy for performing privacy-preserving, distributed learning for medical image segmentation. However, the data-level heterogeneity as well as system-level heterogeneity makes it challenging to optimize. In this paper, we propose to improve Federated optimization via local Contrastive learning and Global Process-aware Aggregation (referred as FedContrast-GPA), aiming to jointly address both data-level and system-level heterogeneity issues. In specific, To address data-level heterogeneity, we propose to learn a unified latent feature space via an intra-client and inter-client local prototype based contrastive learning scheme. Among which, intra-client contrastive learning is adopted to improve the discriminative ability of learned feature embedding at each client, while inter-client contrastive learning is introduced to achieve cross-client distribution perception and alignment in a privacy preserving manner. To address system-level heterogeneity, we further propose a simple yet effective process-aware aggregation scheme to achieve effective straggler mitigation. Experimental results on six prostate segmentation datasets demonstrate large performance boost over existing state-of-the-art methods.
Accountability in Disaster Governance

Government play a very crucial part in responding to disasters. A very pressing concern during the pandemic has been on accountability of governments. Many of the common law countries suffer from the colonial trapping of sovereign immunity, providing legal protection to government actors while working on disasters. In India, we can see that most of the national legislation protects a government actor from judicial scrutiny and failed process rarely sees any repercussion. The European Court of Human Rights, in very few cases, has been able to break away from this limitation under exceptional situations. During the pandemic, some of the legal protection provided was diluted, with courts, civil society, and media demanding accountability when interventions went against the scientific evidence available. People in authority worked on creating engagement with communities and civil society, but the power remained with the government. The chapter considers some of these examples to identify key actors and creating space for accountability of government within the ambit of disaster risk reduction.
FedGrav: An Adaptive Federated Aggregation Algorithm for Multi-institutional Medical Image Segmentation

With the increasingly strengthened data privacy acts and the difficult data centralization, Federated Learning (FL) has become an effective solution to collaboratively train the model while preserving each client's privacy. FedAvg is a standard aggregation algorithm that makes the proportion of the dataset size of each client an aggregation weight. However, it can't deal with non-independent and identically distributed (non-IID) data well because of its fixed aggregation weights and the neglect of data distribution. The paper presents a new aggregation strategy called FedGrav, which is designed to handle non-IID datasets and is inspired by the law of universal gravitation in physics. FedGrav can dynamically adjust the aggregation weights based on the training condition of local models throughout the entire training process, making it an effective solution for non-IID data. The model affinity is creatively proposed by considering both the differences of sample size on the client and the discrepancies among local models. It considers the client sample size as the mass of the local model and defines the model graph distance based on neural network topology. By calculating the affinity among local models, FedGrav can explore internal correlations of them and improve the aggregation weights. The proposed FedGrav has been applied to the CIFAR-10 and the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2021 datasets, and the validation results show that our method outperforms the previous state-of-the-art by 1.54 mean DSC and 2.89 mean HD95. The source code will be available on Github.
Inverse Consistency by Construction for Multistep Deep Registration

Inverse consistency is a desirable property for image registration. We propose a simple technique to make a neural registration network inverse consistent by construction, as a consequence of its structure, as long as it parameterizes its output transform by a Lie group. We extend this technique to multi-step neural registration by composing many such networks in a way that preserves inverse consistency. This multi-step approach also allows for inverse-consistent coarse to fine registration. We evaluate our technique on synthetic 2-D data and four 3-D medical image registration tasks and obtain excellent registration accuracy while assuring inverse consistency .