Reviewerly/exHarmony huggingface dataset:
Artistic Style Analysis of Root Carving Visual Image Based on Texture Synthesis

Texture synthesis is a hotspot of research in the field of realism and nonphotorealistic rendering, with applications in virtual reality, computer vision, and other areas. It has a wide application prospect in image editing, filling of defective images, data compression, rapid transmission of network data, generation of large-scale scenes, and rendering of realistic and nonrealistic images, and it is used to solve the problems of seam, distortion, and parameter adjustment of traditional methods. Root carving is a one-of-a-kind plastic art form that dates back to the Xia and Shang dynasties and peaked during the Ming and Qing dynasties. Texture synthesis uses existing texture data to create a large-area texture that looks similar. This technology detects matching points in the sample texture and copies them to the output image, resulting in a texture image that is similar to and continuous with the sample texture. Realistic and nonrealistic rendering, image restoration, and computer-aided design are just a few of the applications. Texture can describe a variety of natural phenomena with repeating features, as well as expressing the rich details of an object’s surface. Because of its characteristics of both practicality and artistry, as well as the coexistence of popularity and elegance, the art of root carving retains its unique charm despite its ups and downs as a more than 2,000-year-old art form. A genetic evolution search strategy is used to propose an optimization search algorithm in this paper. We further investigate the ecological aesthetic characteristics of root carving art, in accordance with the nature of its aesthetics. By using a genetic search strategy, you will be able to find what you are looking for faster. A better matching block can be quickly found to complete the search for the best matching block, speeding up the algorithm’s search process and ensuring better synthesis results.
A Spatio-Temporal Resource Description Framework Schema Model for Aeronautical Dynamic Information Based on Semantic Analysis

Aeronautical information service (AIS) involves manifold correlations among aeronautical events. The data mining technology has been used to extract the characteristics of aeronautical information. With the aeronautical dynamic information of the notice to airmen (NOTAM) as the study case, this paper carries out semantic analysis on NOTAMs, and establishes a spatio-temporal resource description framework (RDF) schema model by combining a three-tuple RDF model and semantic analysis to extract features of aeronautical information. The new model is constructed by Protégé and NOTAM texts are employed to verify the model. Experiments showed that our proposed model could effectively match the samples of NOTAM information and extract the characteristic data from the NOTAM information. The study is expected to provide a basis for further aeronautical information mining based on knowledge graph.
Benchmarking the Robustness of Object Detection Based on Near-Real Military Scenes

According to the technical requirements of intelligent development of auxiliary combat system, we construct a visual intelligent test platform. A near-real military scene dataset based on physical rendering is built, which contains 11,000 remote sensing images collected by an analog camera taking pictures in different illumination, weather environment, camera shooting angle, and scene scale condition. Besides, we add a natural style transfer module for a single unmodeled military scene image’s multienvironment generation. We conduct experiments to evaluate the stability of several UAV remote sensing image object detection algorithms. Based on the quality and speed value of the tested algorithms, the adaptability scores in different environments are calculated. Furthermore, we propose a comprehensive evaluation index system of military remote object detection based on a hierarchical model. We envision that our comprehensive benchmark will play a role in the evaluation of algorithm capability for military object detection tasks and the improvement of training algorithm capability.
Medical Imaging using Signal Processing: A Comprehensive Review

Medical imaging is the technology used to achieve images of the body parts for clinical purposes to determine or analyze the diseases. There are a huge number of imaging methodologies done each day all around the planet. Clinical imaging is turning out to be rapidly developing due to the types of progress in Image Processing (IP) procedures including picture analysis, enhancement, and recognition. IP improves the rate and extent of detected group of cells. Use of image processing methodologies plays a major character in giving assistance to experts in diagnosing, physicians and surgeons and to carry out the surgical procedures for the patients. This paper confers the use of both clear and sophisticated picture evaluation procedures in the clinical imaging field, it sums up how to exemplify image evaluation difficulties using algorithms used by various image processing techniques like k-means, ROI based segmentation, and water-shed strategies and also presents the optimal image processing methodologies which is used in medicals.
Institutional Governance Influence Mechanism and Model of Regional Green Development in China

Green has become the underpinning of High-quality development in China. Green development is inseparable from the support of institutional governance. This study uses MVC mode to build institutional governance mechanism, and constructs institutional governance system and model of regional green development. The empirical results show the following: institutional governance has great influence on industrial green development. From the perspective of China's provincial industrial green development, Shandong Province has been maintaining the first position of governance investment. Green governance is better in southern China than in the northern provinces. And the institutional governance intensity of each province is not evenly distributed. Therefore, China's industrial green development still needs to further strengthen the system governance of northwest and northeast regions. Finally, this paper aims to improve the green development level of China's regional industrial economy through the construction of institutional governance system. It provides institutional environment for the development of green innovation.
Quantification of Hepatocellular Mitoses in a Toxicological Study in Rats Using a Convolutional Neural Network

Convolutional neural networks (CNNs) have been recognized as valuable tools for rapid quantitative analysis of morphological changes in toxicologic histopathology. We have assessed the performance of CNN-based (Halo-AI) mitotic figure detection in hepatocytes in comparison with detection by pathologists. In addition, we compared with Ki-67 and 5-bromodesoxyuridin (BrdU) immunohistochemistry labeling indices (LIs) obtained by image analysis. Tissues were from an exploratory toxicity study with a glycogen synthase kinase-3 (GSK-3) inhibitor. Our investigations revealed that (1) the CNN achieved similarly accurate but faster results than pathologists, (2) results of mitotic figure detection were comparable to Ki-67 and BrdU LIs, and (3) data from different methods were only moderately correlated. The latter is likely related to differences in the cell cycle component captured by each method. This highlights the importance of considering the differences of the available methods upon selection. Also, the pharmacology of our test item acting as a GSK-3 inhibitor potentially reduced the correlation. We conclude that hepatocyte cell proliferation assessment by CNNs can have several advantages when compared with the current gold standard: it relieves the pathologist of tedious routine tasks and contributes to standardization of results; the CNN algorithm can be shared and iteratively improved; it can be performed on routine histological slides; it does not require an additional animal experiment and in this way can contribute to animal welfare according to the 3R principles.
Links between sonification and generative music

The authors investigate works by four composers who employed technology in the creation of music employing audification, sonification and algorithmic composition techniques. These compositions involve interdisciplinary collaborations either with scientific researchers, in the case of Annea Lockwood's Dusk and Carla Scaletti's hàgg, or artificial intelligence, in George Lewis's Voyager, and Bob Sturm's The Waters of Heanny. Each composer's choice of input data, custom-designed tools and personal compositional processes result in unique, expressive works that challenge the listener to expand their view of music and reality.
Physical Movement Acknowledgment Utilizing Mobile Sensor

The point of this paper is to introduce a novel human movement acknowledgment framework assigned for encompassing helped living conditions. To beat the disadvantages of current arrangements as far as gear costs and computational intricacy, we propose a basic yet powerful action acknowledgment framework that joins the yield of an action order calculation utilizing cell phone sensors, for example, accelerometer and gyroscope to identify more intricate exercises dependent on a choice guideline framework. For the multi-class grouping and for the suitable framework, we propose a troupe-based arrangement consolidating multilayer perceptron with gradient boosted regression and k-nearest neighbors. Our answer has been tried in a controlled home climate arrangement accomplishing a normal confinement blunder of 0.4 m, while for the last movement acknowledgment framework, we report a precision of 93.99% reasoning that the proposed arrangement is sufficiently exact to be incorporated as a checking device office inside surrounding helped living frameworks. Finally, the recognized activity is displayed in the smartphone. Our experimental results of KNN, K-star, naïve Bayes show that the reports a precision of 93.99% reasoning that the proposed arrangement is sufficiently exact to be incorporated with a computing time of 0.96 s.
Using Adaptive Logics for Expression of Context and Interoperability in DL Ontologies

Ontologies are logical theories that are used in computer science for describing different items such as web services, agents in multi-agent systems, or domain knowledge. Many ontologies exist, expressing various domains of knowledge with different abstraction levels (domain ontologies, top-level ontologies, and task ontologies are the usual categories). The conceptualization of the knowledge contained in an ontology is subject to change, whether because the context of its use changes, because the domain evolves, or because an ontology needs to interoperate with other elements using other ontologies. Change in logical theories is a form of defeasible reasoning, in which some formulas need to be added or removed from a knowledge base. Adaptive Logics (AL) is a logic managing defeasible reasoning that we investigate in this paper for managing change in ontologies expressed with Description Logics (DL). The adaptation of AL for DL will help express the context in which formulas remain valid or can be added to a DL knowledge base, and ease the interoperability between ontologies.
Ontology Summit 2021 Communiqué: Ontology generation and harmonization

Advances in machine learning and the development of very large knowledge graphs have accompanied a proliferation of ontologies of many types and for many purposes. These ontologies are commonly developed independently, and as a result, it can be difficult to communicate about and between them. To address this difficulty of communication, ontologies and the communities they serve must agree on how their respective terminologies and formalizations relate to each other. The process of coming into accord and agreement is called “harmonization.” The Ontology Summit 2021 examined the overall landscape of ontologies, the many kinds of ontology generation and harmonization, as well as the sustainability of ontologies. The Communiqué synthesizes and summarizes the findings of the summit as well as earlier summits on related issues. One of the major impediments to harmonization is the relatively poor quality of natural language definitions in many ontologies. The summit surveyed the state of the art in natural language definition development, based on lexicographic principles, as well as examples of ongoing projects that are explicitly dealing with harmonization and sustainability.
An end‐to‐end perceptual enhancement method for UHD portrait images

Advances in equipment enable photographers to take ultra-high-definition (UHD) photos, and photo retouching enables them to create an impressive image by artistically adjusting and enhancing the brightness, color etc. However, such image enhancement is an artistic and challenging task, which requires a lot of relevant experience and technology. Thus, using an automated algorithm is an attractive option. In recent years, deep learning has made great progress in image processing, which motivated the authors to explore this application in UHD image enhancement task. In this paper, an end-to-end perceptual enhancement method for UHD portrait images is proposed. Since the artistry of images comes from human perception, including the perception of exposure, structure, color etc., an image preprocessing method is proposed which can help the network learn the brightness change from the original image to the target image more accurately. Then, a composite perceptual loss function is proposed that combines global, structural, and color losses. This loss allows the network to simultaneously optimize the distance and perceptual similarity to a given target image during back-propagation. To preserve the details of the UHD image and reduce the number of network calculations and parameters, a Perceptual Enhancement Network (called PEN) based on dilated convolution is proposed. Extensive experiments on the datasets show that the method excels in baselines in both subjective and objective evaluations.
MPC for $$\mathcal {Q}_2$$ Access Structures over Rings and Fields

We examine Multi-Party Computation protocols in the active-security-with-abort setting for $$\mathcal {Q}_2$$ access structures over small and large finite fields $$\mathbb {F}_p$$ and over rings $$\mathbb {Z}_{p^k}$$ . We give general protocols which work for any $$\mathcal {Q}_2$$ access structure which is realised by a multiplicative Extended Span Program. We generalize a number of techniques and protocols from various papers and compare the different methodologies. In particular we examine the expected communication cost per multiplication gate when the protocols are instantiated with different access structures.
A self-learning cognitive architecture exploiting causality from rewards

Inspired by the human vision system and learning, we propose a novel cognitive architecture that understands the content of raw videos in terms of objects without using labels. The architecture achieves four objectives: (1) Decomposing raw frames in objects by exploiting foveal vision and memory. (2) Describing the world by projecting objects on an internal canvas. (3) Extracting relevant objects from the canvas by analyzing the causal relation between objects and rewards. (4) Exploiting the information of relevant objects to facilitate the reinforcement learning (RL) process. In order to speed up learning, and better identify objects that produce rewards, the architecture implements learning by causality from the perspective of Wiener and Granger using object trajectories stored in working memory and the time series of external rewards. A novel non-parametric estimator of directed information using Renyi's entropy is designed and tested. Experiments on three environments show that our architecture extracts most of relevant objects. It can be thought of as 'understanding' the world in an object-oriented way. As a consequence, our architecture outperforms state-of-the-art deep reinforcement learning in terms of training speed and transfer learning.
Motion Action Analysis at Basketball Sports Scene Based on Image Processing

To solve the problems of low accuracy and high time cost in manual recording and statistics of basketball data, an automatic analysis method of motion action under the basketball sports scene based on the spatial temporal graph convolutional neural network is proposed. By using the graph structure in the data structure to model the joints and limbs of the human body, and using the spatial temporal graph structure to model the posture action, the extraction and estimation of human body posture in basketball sports scenes are realized. Then, training combined with transfer learning, the recognition of motion fuzzy posture is realized through the classification and application of a label subset. Finally, using the self-made OpenCV to collect and calibrate NBA basketball videos, the effectiveness of the proposed method is verified by analyzing the motion action. The results show that the proposed method based on the spatial temporal graph convolutional neural network can recognize all kinds of movements in different basketball scenes. The average recognition accuracy is more than 75%. It can be seen that the method has certain practical application value. Compared with the common motion analysis method feature descriptors, the motion action analysis method based on the spatial temporal graph convolution neural network has higher identification accuracy and can be used for motion action analysis in the actual basketball sports scenes.
Mapping pedagogies: applying cartographic practice to the public sphere

This article conceptualizes how democratized modes of participation in spatial knowledge production via open source mapping platforms translate into educational praxis, detailing educational projects in the Global South using them. To do so, the article gathers findings from a critical discourse analysis of forum articles which discuss grassroots mapping and from digital participant observation work within Humanitarian OpenStreetMap (HOT). The former show how feminist and community-oriented approaches to mapping, which endorse an ethics of craft, care and cultivation over the corporate or outsider research paradigms, are at the forefront of the tactics and practices such articles describe. The latter show how projects such as Crowd2Map Tanzania, The GAL (Global Active Learning) School of Cusco, Peru, Map Lesotho, OpenStreetMap (OSM) Liberia, OSM Nigeria, Public Lab and its Grassroots Mapping Curriculum, and YouthMappers. It details such initiatives to document the mentorship of female students in digital mapping within educational contexts.
Image Sentiment Analysis via Active Sample Refinement and Cluster Correlation Mining

Training an effective image sentiment analysis model using high-quality samples and the implicit cross-modal semantics among heterogeneous features is still challenging. To address this problem, we propose an active sample refinement (ASR) strategy to obtain sufficient high-quality images with definite sentiment semantics. We mine the cluster correlation among the heterogeneous SENet features. Discriminative cross-modal semantics is generated to train an effective but robust image classifier. Ensemble learning is employed to further boost performance. Our method outperforms other competitive baselines, demonstrating its effectiveness and robustness. Meanwhile, the ASR strategy is a useful supplement to the current data augmentation method.
Home IoT device management blockchain platform using smart contracts and a countermeasure against 51% attacks

One of the important problems in Home IoT is security. Attempts to solve the security problem of Home IoT using blockchain have continued. Blockchain is evaluated as a good system for verifying the integrity of data in Home IoT. However, when using a blockchain in Home IoT, it is vulnerable to 51% attacks due to the small number of nodes in one Home IoT system. In this paper, we present a way to ensure Home IoT security and data integrity using smart contracts in blockchain-based Home IoT. It also suggests how users can defend against 51% attacks by setting their own consensus rate. It also investigates the relationship between the consensus rate set by the user and the hacker's attack success rate through simulation. Our suggestions and simulation results suggest how to control nodes when using blockchain for Home IoT and show how to be safe from 51% attack. Therefore, through this paper, a smart contract-based Home IoT blockchain can be configured, and only when the consensus rate is set and the agreement of each node exceeds the consensus rate can a safer network be configured through the preprocessing process.
An Algorithm Substitution Attack on Fiat-Shamir Signatures Based on Lattice

Many evidences have showed that some intelligence agencies (often called big brother) attempt to monitor citizens’ communication by providing coerced citizens a lot of subverted cryptographic algorithms and coercing them to adopt these algorithms. Since legalized services on large number of various applications and system architectures depend on digital signature techniques, in the context some coerced users who use double authentication preventing signatures to design some novel digital signature techniques, have some convincing dissertations to defuse requests from authorities and big brothers creating some corresponding subverted signatures. As rapid progress in quantum computers, National Security Agency advisory memorandum and announcement of National Institute of Standards and Technology procedures from standardization focus on some cryptographic algorithms which are post quantum secure. Motivated by these issues, we design an algorithm substitution attack against Fiat-Shamir family based on lattices (e.g., BLISS, BG, Ring-TESLA, PASSSign and GLP) that are proven post-quantum computational secure. We also show an efficient deterable way to eliminate big brother’s threat by leaking signing keys from signatures on two messages to be public. Security proof shows that our schemes satisfy key extraction, undetectability and deterability. Through parameters analysis and performance evaluation, we demonstrate that our deterring subverted Fiat-Shamir signature is practical, which means that it can be applied to privacy and protection in some system architectures.
Armada: Automated Verification of Concurrent Code with Sound Semantic Extensibility

Safely writing high-performance concurrent programs is notoriously difficult. To aid developers, we introduce Armada, a language and tool designed to formally verify such programs with relatively little effort. Via a C-like language and a small-step, state-machine-based semantics, Armadagives developers the flexibility to choose arbitrary memory layout and synchronization primitives so that they are never constrained in their pursuit of performance. To reduce developer effort, Armadaleverages SMT-powered automation and a library of powerful reasoning techniques, including rely-guarantee, TSO elimination, reduction, and pointer analysis. All of these techniques are proven sound, and Armadacan be soundly extended with additional strategies over time. Using Armada, we verify five concurrent case studies and show that we can achieve performance equivalent to that of unverified code.