Reviewerly/exHarmony huggingface dataset:
Anomaly Detection in Cyber-Physical Systems: Reconstruction of a Prediction Error Feature Space

Cyber-physical systems are infrastructures that use digital information such as network communications and sensor readings to control entities in the physical world. Many cyber-physical systems in airports, hospitals and nuclear power plants are regarded as critical infrastructures since a disruption of its normal functionality can result in negative consequences for the society. In the last few years, some security solutions for cyber-physical systems based on artificial intelligence have been proposed. Nevertheless, knowledge domain is required to properly setup and train artificial intelligence algorithms. Our work proposes a novel anomaly detection framework based on error space reconstruction, where genetic algorithms are used to perform hyperparameter optimization of machine learning methods. The proposed method achieved an F1-score of 87.89% in the SWaT dataset.
Vehicle Re-Identification Based on Global Relational Attention and Multi-Granularity Feature Learning

Vehicle Re-identification (Re-ID) refers to finding the same vehicle shot by other cameras from a given vehicle image library, which can also be regarded as a sub-problem of image retrieval.It plays an important role in intelligent transportation and smart cities.The key of vehicle Re-ID is to extract discriminative vehicle features.To better extract such features from the vehicle image to improve the recognition accuracy, we propose a three-branch adaptive attention network-Global Relational Attention and Multi-granularity Feature Learning (GRMF) to improve feature representation and discrimination.First, we divide the network into three branches, extracting different and useful features from three perspectives: spatial location, channel information, and local information.Second, we propose two effective global relational attention modules, which capture the global structural information for better attention learning.Specifically, to determine the importance level of a node, we use the global relationship between the node and all other nodes to infer the attention weight of the node directly.Third, according to the characteristics of the vehicle re-identification task, we introduce a suitable local partition strategy.It not only can simply capture subtle local information but also solve the problem of misalignment and within-part consistency disruption to a great extent.Extensive experiments demonstrate the effectiveness of our approach, and we achieve state-of-the-art results on two challenging datasets, including VeRi776 and VehicleID.
Artificial intellect and the book library industry: Vectors for problem development

The new National Strategy for Artificial Intellect up to the Year 2030 approved by the RF Presidential Decree in 2019, obligates the libraries to define their position. The author insists that their participation in the Strategy implementation would be beneficial in many aspects. Firstly, the libraries would build user awareness of the Strategy to make the users active stakeholders; secondly, the libraries could become educators teaching the population on artificial intellect principles and methods; thirdly, the large libraries are able to join in building Artificial Intellect Open Library. Fourthly, implementation of artificial intellect technology in libraries would enable to transform the quality of professional education of librarians-to-be who are to create smart libraries. The author makes the suggestions on how to implement artificial intellect technology based on Nikolas Rubakin’s bibliopsychological theory.
Robust object tracking via deformation samples generator

In object tracking applications, it is common for trackers to experience drift problems when the object of interest becomes deformed, which compromises the ability of the tracker to track the object. It is therefore desirable to develop a learning tracker classifier that is robust to deformations. The performance of existing trackers that employ deep classification networks degrades when the amount of training data is limited and does not cover all possible scenarios. While these limitations can be mitigated in part by using larger training datasets, these datasets may still not cover all situations and the positive samples are still monotonous. To overcome this problem, we propose a novel deformation samples generator that generates samples that would normally be difficult for the tracker to classify. In the proposed framework, both the classifier and deformation samples generator learn in a joint manner. Our experiments show that the proposed approach outperforms state-of-the-art methods in both quantitative and qualitative evaluations for the visual object tracking task.
Blockchain-based land registry platforms: a survey on their implementation and potential challenges

Abstract In recent years it has been demonstrated that the use of the traditional property registry models involves the risk of corruption along with long waiting times. This paper points out the main problems associated with conventional models and makes a survey of the new ones that are based on blockchain technology. This type of model is already being developed as a proof of concept by different countries. With the use of this technology in land registry systems, it is possible to improve the transparency of the processes as well as optimize costs and execution time. To show the theoretical results of this study, the Spanish land registry has been taken as an example of a use case scenario.
An Interoperable Open Data Portal for Climate Analysis

This work proposes an open interoperable data portal that offers access to a Web-wide climate domain knowledge graph created for Ireland and England&#x2019;s NOAA climate daily data. There are three main components contributing to this data portal: the first is the upper layer schema of the knowledge graph &#x2013;the climate analysis (CA) ontology &#x2013; the second is an ad hoc SPARQL server by which to store the graph data and provide public Web access, the last is a dereferencing engine deployed to resolve URIs for entity information. Our knowledge graph form of NOAA climate data facilitates the supply of semantic climate information to researchers and offers a variety of semantic applications that can be built on top of it.
FPGA Based Novel Architecture for Real-time Video Stitching

This paper presents a novel architecture for stitching video samples in a field programmable gate array (FPGA). Video streams of two distinct camera sensors of the same specifications are taken as input. Feature points in the video frames are uniquely identified based on Scale Invariant Feature Transform (SIFT). With the feature points as a reference, a stitching algorithm is devised and the stitched output is transmitted to a VGA display. An FPGA architecture for video frame capture, SIFT feature detection, video frame stitching, and output display driver is designed in Verilog. The designed architecture is verified by simulation and implemented in FPGA. A comprehensive analysis of the design performance is also done and is proved to be within real-time performance requirements.
Decentralized Attestation and Distribution of Information Using Blockchains and Multi-Protocol Storage

The distribution of information through web protocols is today based on the client-server model. Recently, decentralized protocols with greater availability appear as well as blockchain-based attestation methods, allowing for proving the existence of information. In combination, these methods promise a secure, decentralized and long-term storage. However, there exist two major problems: (1) the scalability of blockchains limits their storage capacity and (2) various (de)centralized web protocols are in use and could alleviate this problem, but they do not support blockchain-based attestations. In this paper, we extend an approach for blockchain-based attestation with compatibility for multi-protocol storage. Instead of specific protocols or blockchains, the extended approach aims to contribute novel concepts to the discussion on blockchain scalability. It augments the capabilities of existing protocols for applications such as certification or timestamping of digital artifacts. With the use of decentralized protocols such as IPFS, further availability and inherent resilience properties are gained, allowing for applications such as open research repositories and digital registries. We discuss the architecture of the extended approach, a possible implementation in a smart contract on the Ethereum blockchain with IPFS and Git, and evaluate the time and cost of attestations.
The puzzle of sharing scientific data

Government funding entities have placed data sharing at the centre of scientific policy. While there is widespread consensus that scientific data sharing benefits scientific progress, there are significant barriers to its wider adoption. We seek a deeper understanding of how researchers from different fields share their data and the barriers and facilitators of such sharing. We draw upon the notions of epistemic cultures and collective action theory to consider the enablers and deterrents that scientists encounter when contributing to the collective good of data sharing. Our study employs a mixed-methods design by combining survey data collected in 2016 and 2018 with qualitative data from two case studies sampled within two scientific communities: high-energy physics and molecular biology. We describe how scientific communities with different epistemic cultures can employ modularity, time delay, and boundary organisations to overcome barriers to data sharing.
Top-Down aircraft detection in large-scale scenes based on multi-source data and FEF-R-CNN

Aircraft is a crucial mode of transport with several strategic objectives. It is important to detect the position and number of aircraft in remote sensing images accurately. Most of the current research on aircraft detection focuses on improving algorithms or networks based on public datasets or a single remote sensing image, with limited application to large-scale remotely sensed scenes. Moreover, the detection of small and weak targets is challenging for current detection algorithms. This paper proposes a top-down method for aircraft detection in large-scale scenes. First, multi-source data and spatial constraint rules are used to search for potential airport areas in a wide range, and U-Net is then used to extract the airport area. Finally, this paper proposes a feature enhancement faster R-CNN (FEF-R-CNN) by adding a feature enhancement module to the original Faster R-CNN to improve target detection. This can enhance the aircraft feature while suppressing the background information to a certain extent, for effectively detecting small and weak aircraft targets in the airport area. Top-down aircraft detection experiments were conducted on Hainan Island and Shanghai, and 10 of the 12 airports were accurately detected. Meanwhile, the average precision (AP) of aircraft detection by FEF-R-CNN in the public dataset and four large airports in the study area reached 97.71% and 95.63%, respectively, which are 2.14% and 11.31% higher than that of the traditional Faster R-CNN, respectively. Experimental results verify the effectiveness of the proposed method for aircraft detection in large-scale remotely sensed scenes.
Hybrid Design Tools for Participatory, Embodied Sensemaking: An Applied Framework

In design, we often deal with complex problems that require participatory, embodied sensemaking to solve. There are abundant design tools available – both physical and digital – to support these activities. While digital tools have continued to gain presence in the design processes over the last decades, due to, e.g., widened availability, improved flexibility, and the potential to increase productivity, physical tools and analogue practices still hold a solid place in the design process for many designers. To bridge the gap between the physical and digital, and to benefit from the best of both worlds in tools for design, hybrid tools are being developed. This paper aims to identify design opportunities for future hybrid design tools, by exploring the characteristics of designing for participatory, embodied sensemaking, in the context of physical, digital, and hybrid tools.
Third Space, Information Sharing, and Participatory Design

Society faces many challenges in workplaces, everyday life situations, and education contexts. Within information behavior research, there are often calls to bridge inclusiveness and for greater colla
Research on the Application of Computer Image Processing Technology in Painting Creation

The professional painting industry has experienced a dramatic breakthrough with the rapid expansion of computer science and technology. In the current digital era, digital painting art is extending the more significant creative space to add new content. Digital painting is the modern trend of mainstream painting presented to the public as a new generation of visual art. Creativity may show up, and new techniques of creating art can arise infinitely with the assistance of computer intelligence technology. This article explains how computer image processing is used in the production of art. The report offers a painting technique based on Image Rendering (IR), which does not rely on human expertise in the past, and a color image is turned into a photo with a painting effect automated. Image-based rendering is a novel way in which computer graphics and picture processing are drawn and combined with the requirement to build geometric models, get information from the input image simply by interpolating views, deforming images, and reconstructing the desired action. This article proposes the indirect use of picture processing technology and computer technology to produce oil painting. It will investigate the application of contemporary digital picture technology in order not only to maintain traditional tastes, and to keep pace with the pace of the times, to create traditional optimization.
Anomaly-Aware Adaptive Sampling for Electrical Signal Compression

Intelligent electronic devices for power systems often entail high frequency sampling of electric signals, enabled to capture anomalous signal behavior. However, in normal operation this oversampling is redundant and leads to excessive data being stored or transmitted. This gives rise to a new compression problem where the collected samples should be further subsampled and quantized based on the presence of an anomaly in the underlying signal. We propose an Anomaly-aware Compressive Sampler (ACS) which tests the signal for the presence of an anomaly in a block of samples, and subsamples in a hierarchical manner to retain the desired sampling rate. ACS has been designed keeping hardware constraints in mind, using integer operations, an appropriate bit-packing, a simple iterated delta filter, and a streaming data pipeline. We present a mathematical formulation of the problem and analyze the performance of ACS, establishing theoretically its ability to identify anomalies in the signal and adapt the sampling rate. ACS competes with the state-of-the-art algorithm for the better-behaved transmission system data from DOE/EPRI, and outperforms it significantly on real-time distribution system data recorded in our laboratory. Finally, ACS is lightweight and was implemented on an ARM processor.
MATLAB Algorithms for Diameter Measurements of Textile Yarns and Fibers through Image Processing Techniques

Textile yarns are the fundamental building blocks in the fabric industry. The measurement of the diameter of the yarn textile and fibers is crucial in textile engineering as the diameter size and distribution can affect the yarn's properties, and image processing can provide automatic techniques for faster and more accurate determination of the diameters. In this paper, facile and new methods to measure the yarn's diameter and its individual fibers diameter based on image processing algorithms that can be applied to microscopic digital images. Image preprocessing such as binarization and morphological operations on the yarn image were used to measure the diameter automatically and accurately compared to the manual measuring using ImageJ software. In addition to the image preprocessing, the circular Hough transform was used to measure the diameter of the individual fibers in a yarn's cross-section and count the number of fibers. The algorithms were built and deployed in a MATLAB (R2020b, The MathWorks, Inc., Natick, Massachusetts, United States) environment. The proposed methods showed a reliable, fast, and accurate measurement compared to other different image measuring softwares, such as ImageJ.
Dispersive readout with non-Markovian environments

We study the non-Markovian dispersive readout of a single-mode bosonic (SMB) quantum system coupled to the open cavity with two non-Markovian environments. Assuming that the SMB quantum system is initially prepared in a thermal equilibrium state, the susceptibility of the measured SMB quantum system to the cavity can be obtained by the nonequilibrium linear response theory, which provides the dispersive frequency shift of the cavity. We analytically derive the transmission and reflection of the cavity in the non-Markovian regime and discuss the non-Markovian dispersive readout of the SMB quantum system, which is in good agreement with that obtained by the Markovian approximation. We show that the effect of non-Markovian dynamics on the system's behavior leads to the decrease of the transmission and shifts of the frequency corresponding to the peak of the transmission in the dispersive regime compared with the Markovian case and the increase of the sum of the transmission and reflection due to the excitation backflowing induced by the couplings of the cavity and two non-Markovian environments, which could be applied to quantum secure direct communication with quantum memory. Finally, we generalize the readout theory to the periodically driven SMB quantum system based on the Floquet theory and quantum network, which contain all influences from the non-Markovian environments. The formalism presented in this paper opens an alternative field of possible applications in quantum information and quantum communication with non-Markovianity by manipulating the spectral densities of the environments.
G-DaM: A Blockchain based Distributed Robust Framework for Ground Water Data Management

In this paper, we examine the challenges of groundwater data flow management due to institutional barriers and constraints through technology. We load the groundwater statistics from end-systems towards Distributed Data Storage (DDS) and the blockchain (BC), implementing a dual hashing procedure through Infura gateways and smart contracts. The smart contracts, logical code, and functions give control over access and file sharing. With data secured through crypto puzzles, the quality and integrity are increased while simultaneously preventing higher fee charges for storage on BC with reduced cost and time.
Translational-Randomwalk Embeddings-Based Recommender Systems: A Pragmatic Survey

A recommender system can provide users with relevant items according to their preferences, it produce knowledge from exponential information growth, hence the importance of knowledge graphs which are widely used in recommendation field to address paramount drawbacks such as data sparsity, cold start problem as they offer rich connections between entities, which can be used to strengthen complementarity task and generate pertinent recommendations, many recommendation works are based knowledge graph embeddings such as Translational models and randomwalk techniques wich have shown extremely competitive and promising results in recommendation task. In this survey we provide a background of basics concept, then an analysis of existing literature in this field by comparing and discussing several models.
Analysis of Deep Learning Based Pose Estimation Techniques for Locating Landmarks on Human Body Parts

In the area of AI, the pose estimation become very popular. It can easily find landmarks on human body parts in small amount of time with the use of 2D image. There are various deep learning based pose estimation models available. For particular applications, each model have their own pros and cons. Here, we have discussed OpenPose, BlazePose, DeepPose and Hourglass architectures. Also, we have implemented OpenPose and BlazePose models to locate landmarks on push-up posture of human body. Detailed analysis and comparison between above-mentioned architectures have been presented for better understanding.
Multi‐scale network for remote sensing segmentation

The semantic segmentation of remote sensing images is a critical and challenging task. How to easily and reliably segment useful information from vast remote sensing images is a significant issue. Many methods based on convolutional neural networks have been widely explored to obtain more accurate segmentation from remote sensing images. However, due to the uniqueness of remote sensing images, such as the dramatic changes in the scale of the target object, the results are not satisfactory. To solve the problem, a special network is designed: (1) Create a new backbone network. Compared with ResNet50, the proposed method extracts features of varying sizes more effectively. (2) Reduce spatial information loss. Building a hybrid location module to compensate for the position loss caused by the down-sampling operation. (3) Models with high discriminant ability. In order to improve the discrimination ability of the model, a novel auxiliary loss function is designed to constrain the distance between inter-class and intra-class. The proposed algorithm is tested on remote sensing datasets (e.g., NWPU-45, DLRSD, and WHDLD). The experimental results show that this method obtains the best results and achieves state-of-the-art performance.
Research on YOLOv4 Traffic Sign Detection Algorithm Based on Deep Separable Convolution

In the field of autonomous vehicles and intelligent transportation, the environment is perceived by the cameras around the driving vehicle, and the traffic signs are recognized. The vehicle responds to the recognition results in a timely manner. In order to meet the real-time requirements in autonomous vehicles, this paper proposed an improved YOLOv4 lightweight target detection network. The original YOLOv4 backbone extraction network is improved through depth separable convolution, and a new backbone extraction network is obtained. The experimental results show that the mAP difference between the improved YOLOv4 model and the original YOLOv4 model based on the CSTD traffic sign dataset is only 0.88&#x0025;, but the detection speed is increased by nearly three times, and the number of model parameters is reduced to a certain extent.