Reviewerly/exHarmony huggingface dataset:
Comparison of the Efficiency of Parallel Algorithms KNN and NLM Based on CUDA for Large Image Processing

Digital image processing is widely used in various fields of science, such as medicine -Xray analysis, magnetic resonance imaging, computed tomography, cosmology -collecting information from satellites, their transmission and analysis.Image noise accompanies any of the stages of image processing -from obtaining them to segmentation and object recognition.In order to process large images in real time, the paper proposes to use CUDA technology to parallelize KNN and NLM algorithms.The subject area of the satellite images is selected for noise reduction.A comparative analysis of the effectiveness of the proposed approach.It is investigated how the computation time of successive versions of algorithms changes with increasing the size of the image itself.Based on a series of numerical experiments, it was possible to achieve an acceleration of 40 times using CUDA technology.It is shown that the calculations on the CPU significantly exceed the time spent on execution compared to the GPU.This is due to the fact that in tasks of this type, several threads on the CPU are not able to compete with thousands of threads of the graphics core.We can also observe that as the number of GPU threads increases, the time decreases significantly.This study is especially relevant in current trends in video cards.that in tasks of this type, several threads on the CPU are not able to compete with thousands of threads of the graphics core.We can also observe that as the number of GPU threads increases, the time decreases significantly.This study is especially relevant in current trends in video cards.that in tasks of this type, several threads on the CPU are not able to compete with thousands of threads of the graphics core.We can also observe that as the number of GPU threads increases, the time decreases significantly.This study is especially relevant in current trends in video cards.
Compliance-as-Code for Cybersecurity Automation in Hybrid Cloud

Automation of cybersecurity processes has become crucial with large scale deployment of sensitive workloads in regulated on-prem, private, and public cloud environments. Regulatory and standards bodies such as Payment Card Industry (PCI), Federal Financial Institutions Examination Council (FFIEC), International Organization for Standardization (ISO), and others govern the minimal set of cybersecurity controls that an organization must implement. To meet such requirements while maintaining business agility, organizations need to modernize from manual document based compliance management to automated processes for continuous compliance. This modernized process is called compliance-as-code. In this paper, we present an architecture for compliance-as-code based on a standardized framework. We identify several design choices and our rationale behind those. Specifically, we introduce a system for manipulating compliance information in a standardized manner and a data interchange protocol for inter-operable communication of compliance information. We demonstrate the scalability of our approach and briefly describe deployment and experimental results in real world settings.
Hybrid mapping algorithm based on 1‐DCM and Lorenz

A hybrid chaotic mapping algorithm(HCM) based on 1-DCM (one-dimensional chaotic map) and Lorenz is proposed to address the security issues of low-dimensional chaotic systems and the complexity of high-dimensional chaotic systems. Firstly, the color images are divided into three high 4-bit planes (BP) and low 4-bit planes. By optimizing the parameters and structure of trigonometric function, the new arcsine-sine (AS) function is designedto generate the 1-DCM and permutation matrix, which is constructed to change the plane position and pixel position of the high 12BP to obtain the scrambling plane, which is encrypted by Lorenz chaotic system to produce the selfencryption matrix, with which the low 12BP is encrypted to generate the low bit encryption planes. Then, the ciphertext image is obtained by combining the high bit scrambling plane and low bit encryption plane. The SHA function is also used as the chaotic system's parameter generator, which improves the correlation between plaintext images and the key and resists selective plaintext attacks. The simulation results show that by modularizing the image's high and low bit planes, the encryption process is simplified and improved. The security and reliability of color image transmission are also improved.
Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization’s functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents’ relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users’ queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.
Virtual Reality for Medical Annotation Tasks: A Systematic Review

Virtual reality technologies are broadly used in medicine, including medical educational tasks like surgery training. Annotations are an inseparable part of many medical research and educational tasks. In this systematic review, we investigate the potential of VR for medical tasks with focus on annotation. The questions we pursue are as follows. (Q1) For which healthcare-associated tasks do we find VR-associated investigations and which involve a crowd worker-based annotation? (Q2) To what extent are there gender-specific differences in the usage of VR? To address these questions, we formulated a keyword list and inclusion/exclusion criteria for the collection of recent scientific articles according to the PRISMA Statement 2020. We queried the Medline database and included 59 free full articles available in English and published from 2017 upward. We inspected the abstracts of the retained articles and organized them into 6 categories that referred to VR in the medical context. We identified categories of medicine-related tasks, for which VR is used, and one category associated to cybersickness. We traced technologies used with a higher priority for some tasks, and we found that gender-related investigations are more widespread for some categories than for others. The main findings of our investigation on the role of VR for medical annotation tasks are as follows: VR was used widely for tasks associated with medicine, including medical research and healthcare, but the use of VR for annotation purposes in that context was very limited. Many of the relevant studies concerned VR in education, where annotations may refer to labeling or other enhancements of materials or may refer to exercises. The investigation of gender-related aspects was typically found in studies that encompassed the usage of VR on patients and controls, or on healthy participants in order to assess the potential and limitations of VR for specific tasks/medical assessments or treatments. To fully exploit the VR potential for tasks of medical annotation, especially for the creation of ground truth datasets and similar resources, more research is needed, especially on the interplay of annotator demographics and accessibility to VR technologies.
Towards developer support for merging forked test cases

Developers rely on branching and forking mechanisms of modern versioning systems to evolve and maintain their software systems. As a result, systems often exist in the form of various short-living or even long-living (i.e., clone & own development) variants. Such variants may have to be merged with the main system or other variants, for instance, to propagate features or bug fixes. Within such merging processes, test cases are highly interesting, since they allow to improve the test coverage and hopefully the reliability of the system (e.g., by merging missing tests and bug fixes in test code). However, as all source code, test cases may evolve independently between two or more variants, which makes it non-trivial to decide what changes of the test cases are relevant for the merging. For instance, some test cases in one variant may be irrelevant in another variant (e.g., because the feature shall not be propagated) or may subsume existing test cases. In this paper, we propose a technique that allows for a fine-grained comparison of test cases to support developers in deciding whether and how to merge these. Precisely, inspired by code-clone detection, we use abstract syntax trees to decide on the relations between test cases of different variants. We evaluate the applicability of our technique qualitatively on five open-source systems written in Java (e.g., JUnit 5, Guava). Our insights into the merge potential of 50 pull requests with test cases from these systems indicate that our technique can support the comprehension of differences in variants' test cases, and also highlight future research opportunities.
Texture improvement for human shape estimation from a single image

Current human digitization techniques from a single image are showing promising results when it comes to the quality of the estimated geometry, but they often fall short when it comes to the texture of the generated 3D model, especially on the occluded side of the person, while some others do not even output a texture for the model. Our goal in this paper is to improve the predicted texture of these models without requiring any other additional input more than the original image used to generate the 3D model in the first place. For that, we propose a novel way to predict the back view of the person by including semantic and positional information that outperforms the state-of-the-art techniques. Our method is based on a general-purpose image-to-image translation algorithm with conditional adversarial networks adapted to predict the back view of a human. Furthermore, we use the predicted image to improve the texture of the 3D estimated model and we provide a 3D dataset, V-Human, to train our method and also any 3D human shape estimation algorithms which use meshes such as PIFu.
Towards Adversarial Robust Representation Through Adversarial Contrastive Decoupling

Adversarial training can boost the robustness of the model by aligning discriminative features between natural and generated adversarial samples. However, the generated adversarial samples tend to have more features derived from changed patterns in other categories along with the training process, which prevents better feature alignment between natural and adversarial samples. Unfortunately, existing adversarial training methods ignore such dynamicity of generated adversarial samples. In this paper, we propose Adversarial Contrastive Decoupling (ACD) to filter the features derived from changed patterns. Specificity, we decouple the changed patterns from adversarial samples and then extract robust representations from remaining features. First, we introduce a decoupling module with a dynamic labeling strategy to explore the dynamicity of generated adversarial samples. Then, we propose a siamese network with contrastive learning mechanism to align remaining robust representations between adversarial and natural samples. Extensive experimental results demonstrate the superior performance of ACD over baselines.
Multi-Head Attention Fusion Network for Visual Question Answering

Visual Question Answering (VQA) is a challenging task to answer questions with respect to the image. Most approaches concentrate on utilizing attention networks to focus on crucial objects of the image and key words of the question. However, the attention distribution of these prior attempts tends to lo-cate similar regions, which leads to lack of the ability to derive important entities. To address the issue, we propose a multi-head attention fusion network (MHAFN), which can achieve hierarchical multimodal fusion with various branches to capture the fine-grained and intricate relationship in the perspective of multiple levels: word, region and the interaction of them. Furthermore, it can also capture distinct attention distribution for attending to multiple different visual and textual components that are vital to infer the answer. Extensive ex-periments on the benchmark of VQA-v2 dataset demonstrate that MHAFN significantly outperforms previous methods.
Occluded Vehicle Detection via Multi-Scale Hybrid Attention Mechanism in the Road Scene

The obstruction of vehicles by surrounding vehicles, obstacles, etc. is a common phenomenon in the practical application of automatic driving. In view of the problem that the vehicle’s vision is affected by the occlusion, the vehicle feature information is incomplete, resulting in the low detection accuracy of the occlusion vehicle, and the occlusion vehicle detection method based on the multi-scale hybrid attention mechanism is proposed. The paper aims to fully excavate the advantages of multi-scale feature extraction, channel/space attention and other modules, and to design a multi-scale hybrid attention module suitable for occlusion vehicle detection to improve the detection accuracy of occlusion vehicles. Multi-scale features are enriched by the grouping convolution of different sizes of multi-scale feature extraction networks, and the parallel connection channels and spatial attention modules form different scale hybrid domain attention modules, which enhance the local feature information of the occluded vehicles and realize the reinforcement learning of multi-scale features and the suppression of occlusion interference information. Experimental results show that in the self-made occlusion vehicle dataset and the BDD100K occlusion vehicle dataset, the average mean accuracy of this method is 95.2% and 59.3%, respectively, which is 1.5% and 2.9% higher than that of the baseline network YOLOv5, respectively.
Feature Selection Methods for Extreme Learning Machines

Extreme learning machines (ELMs) have gained acceptance owing to their high efficiency and outstanding generalization ability. As a key component of data preprocessing, feature selection methods can decrease the noise or irrelevant data for ELMs. However, ELMs still do not have their own practical feature selection method for their special mechanism. In this study, we proposed a feature selection method for the ELM, named FELM. The proposed algorithm achieves highly efficient dimensionality reduction due to the feature ranking strategy. The FELM can simultaneously complete the feature selection and classification processes. In addition, by incorporating a memorization–generalization kernel into the FELM, the nonlinear case of it is issued (called FKELM). The FKELM can achieve high classification accuracy and extensive generalization by applying the property of memorization of training data. According to the experimental results on different artificial and benchmark datasets, the proposed algorithms achieve significantly better classification accuracy and faster training than the other methods.
The Siren Call of Content Moderation Formalism

Abstract Systems of online content moderation governance are becoming some of the most elaborate and extensive bureaucracies in history, and they are deeply imperfect and need reform. Would-be reformers of content moderation systems are drawn to a highly rule-bound and formalistic vision of how these bureaucracies should operate, but the sprawling chaos of online speech is too vast, ever-changing, and varied to be brought into consistent compliance with rigid rules. This essay argues that the quest to make content moderation systems ever more formalistic will not fix public and regulatory concerns about the legitimacy and accountability of how platforms moderate content on their services. The largest social media platforms operate massive unelected, unaccountable, and increasingly complex bureaucracies that decide to act or not act on millions of pieces of content uploaded to their platforms every day. A formalistic model, invoking judicial-style norms of reasoning and precedent, is doomed to fail at this scale and level of complexity. As these governance systems mature, it is time to be content moderation realists about the task ahead.
Data Security Challenges with its Defence Strategies of Internet of Things: Critical Review Study

Due to the sensitivity of the data security issue and the lack of studies focused on data security in the Internet of Things (IoT) environment; this paper-through the methodology of critical review of relevant previous and recent studies-aimed to extract the most important challenges or problems and technical and defence strategies and solutions related to securing data in IoT. That is all In order to serve as a practical guide to benefit the concerned ones. As for the results of the studies, there were several things, including: that the most common security issues and challenges within the previous studies were: confidentiality, privacy and encryption effectiveness, and authentication methods are the best methods for securing data by nearly 60%, and the most important of these methods is lightweight authentication, multi-factor authentication, then mutual authentication. In addition, it is necessary to pay attention to securing all layers of the IoT. Especially the perception layer; as it is the main interface for the attack for easy physical access to the end nodes and web interfaces. Moreover, there are several modern technologies and methods that contribute to securing data such as: lightweight systems, digital twins, Software-Defined Network (SDN), edge computing and engineering, fog computing, trust computing, Context aware of accessibility. As for the recommendations, the study recommended all the following. IoT service providers should adopt several security methods, techniques and protocols in order to raise the level of security during data transfer and storage. The concerned organizations and bodies should issue unified laws and frameworks in order to standardize the work in IoT environment and In order to protect the confidentiality and privacy of data.
Computer-Aided Close Reading

The use of computational approaches for text analysis in humanities research was primarily driven by the opportunity to analyze large volumes of texts exceeding the analyst’s capacity to read. However, computational techniques have seldom been applied to supplement the close reading of single texts, as such reading has rarely been questioned as a process in need of supplementation. The authors challenge this premise by presenting an interactive visualization environment, DocuScope, designed to present textual information that both contributes to the interpretative efforts of the close reader and yet is not systematically accumulated from close reading. They first provide general background about DocuScope, discuss its lineage from earlier projects, and connect it to other chapters in this volume. They also present the theoretical framework that guided the design of the technology as well as their thinking. In the second half of the chapter, they demonstrate how DocuScope’s visualizations can be used to enhance the serial reading of persuasive texts using the analyses of Lincoln’s Gettysburg Address and two New York Times’ contrasting op-eds on the first impeachment of Donald Trump in September 2019.
Autonomous UAV Navigation via Deep Reinforcement Learning Using PPO

In this paper, a computer vision-based navigation system is proposed for autonomous unmanned aerial vehicles (UAV). The proposed navigation system is based on a deep reinforcement learning-based high-level controller. In this paper, proximal policy optimization (PPO), which is a deep reinforcement learning method, is used to train the artificial neural net-work in an end-to-end way using a continuous reward function. The proposed method has been tested on images obtained from different modalities (RGB and depth) in simulation environments that are created using Unreal Engine and Microsoft AirSim. For the navigation problem that this work is concerned with, a success rate of 96% has been obtained by using RGB cameras. Since RGB cameras are lighter than depth cameras and the trained artificial neural network has a parameter number less than 170.000, the proposed method is suitable to be deployed in micro aerial vehicles. Code is publicly available*.
YOLODrone+: Improved YOLO Architecture for Object Detection in UAV Images

The performance of object detection algorithms running on images taken from Unmanned Aerial Vehicles (UAVs) remains limited when compared to the object detection algorithms running on ground taken images. Due to its various features, YOLO based models, as a part of one-stage object detectors, are preferred in many UAV based applications. In this paper, we are proposing novel architectural improvements to the YO-LOv5 architecture. Our improvements include: (i) increasing the number of detection layers and (ii) use of transformers in the model. In order to train and test the performance of our proposed model, we used VisDrone and SkyData datasets in our paper. Our test results suggest that our proposed solutions can improve the detection accuracy.
Comparison of Transformer-Based Models Trained in Turkish and Different Languages on Turkish Natural Language Processing Problems

Transformer-based pre-trained language models have yielded successful results in natural language processing (NLP) problems in recent years. In these approaches, the models are trained in an unsupervised manner using a large corpus, with the use of mechanisms such as masking and next sentence prediction. In sub-NLP problems, these models are fully or partially updated with a fine-tuning approach, or the vectors obtained from these models are directly mapped to the output with neural network layers added. In this study, transformer-based BERT, RoBERTa, ConvBERT and Electra models, which have given successful results for different languages and problems in the literature, have been applied to Turkish sentiment analysis, text classification and named entity recognition problems, and the results are presented comparatively. One of the contributions of the study is to train the RoBERTa model on a total of 38 GB Turkish corpus to be shared as open source for use in Turkish problems in the field of NLP. Experimental results have shown that the RoBERTaTurk model gives comparable results to the other transformer-based models in Turkish NLP problems. In addition, language models trained in different languages were also tested on Turkish classification problems. According to the obtained results, as the size of the labeled training set increases, the success of the language models trained in different languages becomes close to the success of the language models trained for the Turkish language. It has also been shown that Turkish language models give better results when the number of labeled data is low.