Reviewerly/exHarmony huggingface dataset:
Remote Sensing

Abstract In this chapter we present the concepts of remote sensing and Earth Observation and, explain why several of their characteristics (volume, variety and velocity) make us consider Earth Observation as Big Data. Thereafter, we discuss the most commonly open data formats used to store and share the data. The main sources of Earth Observation data are also described, with particular focus on the constellation of Sentinel satellites, Copernicus Hub and its six thematic services, as well as other private initiatives like the five Copernicus-related Data and Information Access Services and Sentinel Hub. Next, we present an overview of representative software technologies for efficiently describing, storing, querying and accessing Earth Observation datasets. The chapter concludes with a summary of the Earth Observation datasets used in each DataBio pilot.
Logic Programming for Intelligent Systems

This chapter suggests that logic programs employing recent advances in semantics and in knowledge representation formalisms provide a more robust framework in which to develop very intelligent systems in any domain of knowledge or application. The author has performed work applying this paradigm and these reasoning formalisms in the areas of financial applications, security applications, and enterprise information systems.
Optimizing Quantum Circuits for Arbitrary State Synthesis and Initialization

For practical usage of quantum systems in solving real-world problems, large amounts of classical data are required to be transferred/encoded to the quantum domain. Arbitrary classical data is usually encoded onto quantum devices by synthesizing and initializing a corresponding quantum state. Current techniques of arbitrary state synthesis, however, produce deep and complex quantum circuits, leading to low state fidelity and possible violations of decoherence constraints. In this work, we propose an improved methodology and optimized circuits for synthesizing any arbitrary quantum state from given classical data. Compared to existing methods, the proposed methodology results in circuits with lower gate count, lower circuit depth, and high state fidelity. The proposed methods are evaluated by simulation in MATLAB and IBM qasm, and realistic implementation on an IBM quantum device. The experimental results show a reduction in gate count and circuit depth by a factor of two over existing methods.
Chaotic Function Based ECG Encryption System

Remote health-care monitoring systems communicate biomedical information (e.g. Electrocardiogram (ECG)) over insecure networks. Protection of the integrity, authentication and confidentiality of the medical data is a challenging issue. This chapter proposed an encryption process having a 4-round five steps -encryption structure includes: the random pixel insertion, row separation, substitution of each separated row, row combination and rotation. Accuracy and security analysis of proposed method for 2D ECG encryption is evaluated on MIT-BIH arrhythmia database.
Pocket Cartoons

This study examines the impact of using cartoons shared through mobile devices to promote awareness and to aid in the development of financial literacy among Economics students in Malaysia. The study also investigates the use of these ‘mobile cartoons' to develop students' communication skills. The study involved a quasi-experimental methodology investigating the learning outcomes of 91 undergraduate students. Data was acquired through surveys and interviews. The results showed a higher mean for the experimental group (Mobile Learning Collaborative) than the control group (Conventional Collaborative). Future research may focus on the improvement in the design of the intervention with a particular focus on expanding the range of cartoon characters.
Toward the Automated Construction of Probabilistic Knowledge Graphs for the Maritime Domain

International maritime crime is becoming increasingly sophisticated, often associated with wider criminal networks. Detecting maritime threats by means of fusing data purely related to physical movement (i.e., those generated by physical sensors, or hard data) is not sufficient. This has led to research and development efforts aimed at combining hard data with other types of data (especially human-generated or soft data). Existing work often assumes that input soft data is available in a structured format, or is focused on extracting certain relevant entities or concepts to accompany or annotate hard data. Much less attention has been given to extracting the rich knowledge about the situations of interest implicitly embedded in the large amount of soft data existing in unstructured formats (such as intelligence reports and news articles). In order to exploit the potentially useful and rich information from such sources, it is necessary to extract not only the relevant entities and concepts but also their semantic relations, together with the uncertainty associated with the extracted knowledge (i.e., in the form of probabilistic knowledge graphs). This will increase the accuracy of and confidence in, the extracted knowledge and facilitate subsequent reasoning and learning. To this end, we propose Maritime DeepDive, an initial prototype for the automated construction of probabilistic knowledge graphs from natural language data for the maritime domain. In this paper, we report on the current implementation of Maritime DeepDive, together with preliminary results on extracting probabilistic events from maritime piracy incidents. This pipeline was evaluated on a manually crafted gold standard, yielding promising results.
Implementation of Dynamic Web Server Based on Operating System-Level Virtualization using Docker Stack

A webserver is a provider that receives and provides data-based services that are sent through a web browser, then given a response in the form of web pages and HTML documents. The main problem that often occurs on a web server is the level of availability, demand affects the performance of the webserver which gets a lot of loads so that the webserver experiences overload, it will cause the web to go down. The Docker stack allows multiple services to connect to each other and run on multiple connected machines. The solution to this is by using operating system-level virtualization that utilizes the cluster system provided by the docker swarm, the docker stack is used to run multi-container images and web server services that will be distributed to other virtual servers. The results of this research show that the operating system-level virtualization supports the failover and load balancing mechanisms in the docker swarm area and the docker stack plays a role in distributing web server services from node manager to node worker as a backup webserver service, testing the best average response time parameters on failover obtained on a node with the time obtained 18.90 milliseconds while in availability through the parameter request test, at 200 requests the system successfully received 80% requests and indicated that the system could not meet the nines criteria at levels of availability.
Industrial Anomaly Classification Method Based on EMD Metric Loss

Anomaly Classification is critical for the efficient and secure operation of industrial equipment. However, the accuracy of many present anomaly classification methods tend to decline, particularly when there are multiple anomalies. In this paper, an anomaly classification method on the basic of Earth Mover’s Distance (EMD) metric Loss is presented. Firstly, the anomaly classification is defined as a case of optimal image matching. Then, a novel EMD metric loss based on triplet is designed for extracting discriminative local features. Finally, a simple classifier support vector machines (SVM) can be adopted to realize the multi-anomalies classification. It is proved that the presented anomaly classification method has better performance than the traditional methods.
Information Extraction for Call for Paper

This paper proposes a system called CFP Manager specialized on IT field and designed to ease the process of searching conference suitable to one's need. At present, the handling of CFP faces two problems: for emails, the huge quantity of CFP received can be easily skimmed through. For websites, the reviewing of some of the main CFP aggregators available online points out the lack of usable criteria. This system proposes to answer to these problems via its architecture consisting of three components: firstly an Information Extraction module extracting relevant information (as date, location, etc...) from CFP using rule based text mining algorithm. The second component enriches the now extracted data with external one from ontology models. Finally the last one displays the said data and allows the end user to perform complex queries on the CFP dataset and thus allow him to only access to CFP suitable for him. In order to validate the authors' proposal, they eventually process the well-known precision / recall metric on our information extraction component with an average of 0.95 for precision and 0.91 for recall on three different 100 CFP dataset. This paper finally discusses the validity of our approach by confronting our system for different queries with two systems already available online (WikiCFP and IEEE Conference Search) and basic text searching approach standing for searching in an email box. On a 100 CFP dataset with the wide variety of usable data and the possibility to perform complex queries we surpass basic text searching method and WikiCFP by not returning the false positive usually returned by them and find a result close to the IEEE system.
Shape Representation

This chapter covers a large number of commonly used shape methods which are broadly classified into two major categories: contourContour -based methods and regionRegion -based methods. It begins with introducing some simple and perceptual shape descriptorsPerceptual shape descriptors followed by several shape signaturesShape signatures in contourContour -based methods. More contourContour -based methods are described in the next including moments, stochastic methodStochastic method , scale spaceScale space method, FDFractal Dimension (FD) , and shape contextShape context . Several structural contourContour methods are also covered including syntactic, chain codeChain code , polygonPolygon , and smooth curve decompositionSmooth curve decomposition . The rest of the chapter covers regionRegion -based shape methods including geometric momentsGeometric moments , complex momentsComplex moments , GFD, shape matrixShape matrix , and shape profilesShape profiles . Structural methods such as convex hullConvex hull and media axisMedia axis are also discussed. Two MPEG-7MPEG-7 shape descriptors are also discussed in the chapter, i.e., CSSCurvature Scale Space (CSS) and ART. Most of the techniques discussed in this chapter are demonstrated with illustrations.
Research on the application of computer technology in legal practice

With the development of science and technology, many industries begin to use computer technology to improve work efficiency. From the facts of many practices, we can see that computer technology is a good technology for many industries and can bring many benefits to the industry. Computer technology is also deepening into legal practice. In legal practice, modeling, simulation and other calculation methods can be used to analyze legal relations, so that legal information can be transformed from traditional analysis to an information and intelligent system with higher efficiency and accuracy. The inclusion classification in legal procedures can be simply summarized as a model, and the eigenvalue table structure is built to make the case enter the computer into data that can be calculated and judged. This method simplifies the case trial and makes the case context clearer. The deep integration of Computational Thinking and legal thinking is conducive to enrich and develop the basic theory of law, improve the scientificity and coordination of various departments in the judicial system, and improve the efficiency of the judicial system. Computer technology plays a positive role in improving legislative and judicial efficiency, coping with and avoiding technical risks.
Context Convolution Dehazing Network With Channel Attention

Fog and haze weather conditions lead to deterioration of image visual quality. Under these conditions, advanced image processing tasks such as object detection and image segmentation are difficult to perform. To solve related problems, in this paper we propose an end-to-end context dilated convolution dehazing network with channel attention to return to a clear image from a haze image. This model uses context dilated module extracts the multi-scale scene information in the haze image, which can better maintain the original color of the image while removing the haze. The channel attention enables the model to separate the importance of features and boosts the model’s power to adapt to different input scenarios. In the training phase, the model uses contrastive learning to distinguish the potential difference between the haze picture and the clear picture, helping the model to better renewal the clear image. The results of contrastive tests with existing methods indicate the proposed method has excellent dehazing performance.
Search of Web Service Based on Weighted Association Rule

The rapid growth of web services need efficiently discovering the desired web services for the users. Web service interfaces are defined with WSDL that is described by a bag of terms. Many similarity metrics are proposed to solve this problem, it is hardly to resolve the problem that only few pairs of terms between two services have high semantic distance, the semantic distance of other terms between two services are low. Using traditional keyword search metrics may acquire a wrong result that these two web services are similar, in addition, semantics of the web services is hardly to exploit. In this work the authors firstly help the request service to find the services that belong to the same class, and then they use association rule to find terms that are often appear together and find the most similar terms. The authors weaken the weight of the most similar term contained in an association rule and enhance the other terms' weight contained in an association rule to solve the situation above. The experiments show that our approach outperforms some searching methods.
Polish

Abstract This chapter walks the reader through a range of elliptical constructions available in Polish. The choice of constructions is determined by the degree of attention they have received in the ellipsis literature. I focus on those features of these constructions that warrant an ellipsis-based approach and address, as appropriate, the extent to which they fit in with the existing theoretical approaches to ellipsis. The task of explaining the behavior of elliptical constructions, both intralinguistically and cross-linguistically, has been successfully undertaken by proponents of the PF-deletion approach to ellipsis, but Polish continues to pose a challenge for this approach in some respects.
Ship Detection Based on Aerial Images with Modified YOLOv5

Ship detection is a significant application with aerial image detection, which faces the challenges of data imbalance and rotation of the ship objects while the horizontal annotation boxes have a large amount of redundant information. This paper focuses on the scene of real-time ship inspection and YOLOv5 is selected as the baseline for fast detection and identification. Dynamic data enhancement during the training process is designed via loss feedback, which can alleviate the problem of data imbalance. Considering the rotation characteristics of ships, classification method is used to predict angle and a loss function is proposed based on rotation-aware Pixels-IoU (RA-PIoU). This method can solve the problem of angle periodicity in rotation object detection and improve the accuracy of loss calculation, so as to achieve better detection performance. Further, we construct an aerial image dataset with offshore ships. Experiments on the largescale public dataset HRSC2016 and our own dataset show that the proposed method can significantly improve the real-time ship detection performance.
The Polytomous Rasch Model III

A category coefficientCategory coefficient $$ \kappa_{k} $$ is the sum of the exceeded thresholdsThreshold for response category k. The PRM can be rewritten in terms of principal componentsPrincipal components (Guttman) for the thresholdsThreshold instead of the thresholdsThreshold themselves. The principal component $$ \lambda $$ characterizes the spread of the responses, the principal component $$ \eta $$ characterizes the skewness, another principal component characterizes the kurtosis, etc. ThresholdsThreshold can be recovered from principal componentsPrincipal components through the category coefficientsCategory coefficient. Only if a threshold does not discriminate between two adjacent response categories it is justified to combine adjacent response categories through rescoring the responses.
A Pairing-based Homomorphic Encryption Scheme for Multi-User Settings

A new method is presented to privately outsource computation of different users. As a significant cryptographic primitive in cloud computing, homomorphic encryption (HE) can evaluate on ciphertext directly without decryption, thus avoid information leakage. However, most of the available HE schemes are single-user, which means that they could only evaluate on ciphertexts encrypted by the same public key. Adopting the idea of proxy re-encryption, and focusing on the compatibility of computation, the authors provide a pairing-based multi-user homomorphic encryption scheme. The scheme is a somewhat homomorphic one, which can do infinite additions and one multiplication operation. Security of the scheme is based on subgroup decision problem. The authors give a concrete security model and detailed security analysis.
Denoising Method for LiDAR Intensity Image via Combining Adaptive Non-local Means and Exponential Soft Thresholding

Image denoising plays a fundamental role in many computer vision and signal processing tasks. Due to the laser active imaging principle of LiDAR, there is significant speckle noise in LiDAR intensity image. To remove noise and enhance image quality, a new image denosing method based on adaptive non-local means (NLM) and exponential soft thresholding is proposed. First, speckle noise is converted from multiplicative noise to additive noise by homomorphic transform and the tranformed noisy image is decomposed into high-frequency and low-frequency coefficients by discrete wavelet transform (DWT). Second, we present an improved NLM filtering called adaptive NLM to suppress the noise in low-frequency coefficients, and present a new exponential soft thresholding method to remove the noise in high-frequency coefficients. Finally, inverse DWT and homomorphic transform are carried out to reconstruct the denosied intensity image. We take both qualitative and quantitative tests on the proposed method, and make comparisons with three state-of-the-art methods. The experimental results indicate that the proposed method is superior to the other three methods by achieving best trade-off in suppressing noise and keeping details.
Incremental Distributed Learning With JavaScript Agents for Earthquake and Disaster Monitoring

Ubiquitous computing and The Internet-of-Things (IoT) grow rapidly in today's life and evolving to Self-organizing systems (SoS). A unified and scalable information processing and communication methodology is required. In this work, mobile agents are used to merge the IoT with Mobile and Cloud environments seamless. A portable and scalable Agent Processing Platform (APP) provides an enabling technology that is central for the deployment of Multi-Agent Systems (MAS) in strong heterogeneous networks including the Internet. A large-scale use-case deploying Multi-agent systems in a distributed heterogeneous seismic sensor and geodetic network is used to demonstrate the suitability of the MAS and platform approach. The MAS is used for earthquake monitoring based on a new incremental distributed learning algorithm applied to seismic station data, which can be extended by ubiquitous sensing devices like smart phones. Different (mobile) agents perform sensor sensing, aggregation, local learning and prediction, global voting and decision making, and the application.
The E.U.’s Digital Operational Resilience Act: Cloud Services &amp;amp; Financial Companies

Our report begins by providing an overview of the costs and benefits of cloud technology for financial companies; we find that cloud technology can offer significant benefits to financial companies. We then describe the current regulatory frameworks that apply to financial institutions’ use of third-party technology providers, including cloud service providers, in various jurisdictions. Next, we describe key provisions of DORA that apply to cloud and other technology service providers and how such provisions are similar to or diverge from the current frameworks described in the previous section. We conclude by recommending that the EU revise DORA in certain key respects to better align with the approach in other jurisdictions as DORA’s divergences from other jurisdictions’ regulation of cloud and other third-party technology services may unnecessarily discourage the adoption of such services by financial companies.
Brokering Cloud Computing

Cloud computing is a rapidly evolving computational model, which has succeeded in transforming the ICT industry and the economy's production techniques by making corresponding services even more accessible to businesses, offering cost-effective solutions. The cloud broker is a new business model, derived from the necessity of finding the best provider, or the best bundle for the end user. It is a third-party business that assists clients in making the best decision in choosing the most suitable cloud provider and the most effective service bundle for their needs, in terms of performance and price. This chapter analyzes the cloud broker business model and highlights the broker's vital role and the benefits that arise from the use of its services. In that context, it describes cloud brokering and a market analysis, together with the most popular pricing models, together with a comparison among them, concluding with future directions for the expansion of the brokerage model.
Society of Agents

The development of multiple agent systems faces many challenges, including agent coordination and collaboration on tasks. Minsky's The Society of Mind provides a conceptual view for addressing these multi-agent system problems. A new classification ontology is introduced for comparing multi-agent systems. Next, a new framework called the Society of Agents is developed from Minsky's conceptual foundation. A Society of Agents framework-based problem-solving and a Game Society is developed and applied to the domain of single player logic puzzles and two player games. The Game Society solved 100% of presented Sudoku and Kakuro problems and never lost a tic-tac-toe game. The advantage of the Society of Agents approach is the efficient re-utilization of agents across multiple independent game domain problems and a centralized problem-solving architecture with efficient cross-agent information sharing.