Reviewerly/exHarmony huggingface dataset:
Comparison of Path Planning between Improved Informed and Uninformed Algorithms for Mobile Robot

This work is concerned with the Path Planning Algorithms (PPA), which hold an important place in Robotics navigation.Navigation has become indispensable to most modern inventions.Mobile robots have to move to a relevant task point in order to achieve the tasks assigned to them.The actions, which are planned in a structure, may restrict the task duration and even in some situations, the mission tends to be accomplished.This paper aims to study and compare six commonly used informed and uninformed algorithms.Three different maps have been created with gradually increasing difficulty levels related to a number of obstacles in the tested maps.The paper provides a detailed comparison between the algorithms under investigation of several parameters such as: Total steps, straight steps, rotation steps, and search time.The promised results were obtained when the proposed algorithms were applied to a case study.
Machine Translation of British and American Literature Based on Parallel Corpus

Machine translation is a practical technology that uses electronic computers to translate between different languages. With the development of artificial intelligence technology and the advancement of brain science research, machine translation will replace human translators for most of the translation work. The corpus not only contains a rich corpus, but also has powerful retrieval and statistical functions of the computer, which can easily and efficiently discover the potential patterns of the content covered by the corpus and improve the quality and efficiency of machine translation. With the rapid development of computer software and hardware technology, as well as the continuous improvement of corpus construction, machine translation of British and American literature based on parallel corpus has become a new research direction, which improves the quality of British and American literature translation and promotes the dissemination of British and American literature through the knowledge mining of bilingual parallel corpus.
An Overview of Methods and Tools for Extraction of Knowledge for COVID-19 from Knowledge Graphs

The sanitary crisis provoked from the virus COVID-19 push researchers and practitioners to explore and find solutions to stamp the pandemic problem. Therefore many productions of various scientific papers and knowledge graphs are publicly accessible in internet. In this article is defined an overall description of the search engines available for COVID-19 information. A brief review of the knowledge graphs available for COVID-19 information is performed. This paper is an overview of the main relevant knowledge graph-based methods contributing in COVID-19 knowledge extraction and understanding. Furthermore, it is proposed a state-of-the-art of knowledge reasoning methods on COVID-19.
Establishing a Fulfillment Costs Model for the Subscription Box

Most of the companies that have decided on a subscription boxSubscription box model are startups that have gone bankrupt when they were still in their early days. One of the main reasons for this has been the high fulfillment costsFulfillment costs they incurred. As subscription models undergo rapid growth, fulfillment costsFulfillment costs per customer stay the same or even increase in the mid-term due to companies continually forced to increase the variety of items included in the subscription boxesSubscription box, increase the types of boxes they offer, or even improve the logistics offer, for example, by offering greater flexibility in the order delivery windows. To respond to this, we developed a cost modelCost model for the fulfillment operations of a subscription boxSubscription box model, based on a real case study. The model analyzes the evolution of fulfillment costsFulfillment costs, as these businesses expand their market to new populations, increase their number of customers, and vary their offer of products with new subscription boxesSubscription box.
An Interpretable Deep Learning Algorithm for Dynamic Early Warning of Posttraumatic Hemorrhagic Shock Based on Noninvasive Parameter

Objectives: Hemorrhage and hemorrhagic shock are common causes of death after an acute trauma. The mortality from hemorrhagic shock can be significantly reduced through the prophylactic administration of red blood cells or the use of 100% mechanical ventilation. In this study, a dynamic early warning system based on non-invasive parameters was developed and evaluated using deep learning algorithm, aiming to predict the incidence of hemorrhagic shock in patients over the next 4-8 h.Design: An observational cohort study. Setting: The data set was collected from three data sets from 210 different hospitals in the United States and the Netherlands. One of them was publicly available for model development and two were used for testing.Patients: 9659 patients from eicu database, 2942 patients from mimiciii database, 1055 patients from AmsterdamUMC database.Interventions: None
Metamorphic Processes Leading to Gold-Only Deposits

Metamorphic processes can explain and predict the five important characteristics of gold-only deposits outlined in Chapters 4 – 8 as well as the diversity and commonality between deposits. The metamorphic devolatilisation model can be summarised in terms of a source of auriferous fluid, the migration of that fluid, and then deposition of gold. Metamorphism of altered basaltic rocks and some clastic metasedimentary rocks at 400 – 500oC and 10 – 15 km depth produces a fluid dominated by H2O-CO2-H2S that carries gold. This auriferous fluid is produced on a grain-by-grain scale before it amalgamates and moves via a fracture network upwards to around 5 km depth and 350oC. Active deformation and high fluid pressure facilitate fluid-rock interaction and quartz vein formation. The deposition of gold is primarily, but not exclusively, by reduction and wallrock sulfidation.
STUDYING THE MOST EFFECTIVE FPGA NEURAL NETWORKS: ECONOMIC AND MODELING ISSUES

Field Programmable Gate Arrays (FPGAs) are semiconductor devices that are based around a matrix of configurable logic blocks (CLBs) connected via programmable interconnects. FPGAs can be reprogrammed to desired application or functionality requirements after manufacturing. This feature distinguishes FPGAs from Application Specific Integrated Circuits (ASIC), which are custom manufactured for specific design tasks. The purpose of this paper is to study and compare various FPGA neural networks and determine the most effective among them in terms of energy efficiency. Even though various research studies worked on different FPGA neural model designs, very few researchers have considered comparing these designs to determine energy efficiency. This paper provides Ad-MobileNet as an efficient solution, a system constructed on the conventional MobileNet topology that utilizes fewer computer complexity while achieving classification techniques superior to some well-known FPGA-based platforms․ Because of this, we've investigate the Ad-MobileNet model with larger datasets and a range of uses as part of continuing research.
Cross Aggregation Network for Semantic Segmentation

To achieve more accurate prediction, advanced semantic segmentation methods are explored in the way of context modeling. Images in real scenes usually contain multi-scale objects and contents. The feature propagation form in convolution networks is very important to capture multi-scale context and obtain accurate segmentation. This paper proposes a novel pattern of information flow aggregation to rich features expression, called Cross Aggregation Module (CRA), In CRA, the flows of different scales are transmitted to the context aggregation module (CAM) through a parallel-cross connection to generate a feature map containing more information to aggregate multi-scale features and capture remote context information. We further add the low-level features in the later stage in the encoder to enhance the features interaction. Based on these developments, we build a Context Cross Aggregation Network (CRANet) which employs an asymmetric decoder to restore the scale of the predicted feature map. The proposed CRANet is evaluated on two challenging datasets, i.e., Cityscapes and Camvid. The experiments show that the proposed network achieves competitive performance.
Innovative Development Based on Educational Technology in the Era of Big Data

Modern educational technology is not only the result of the development of modern education, but also the product of the era of big data. The advent of the era of big data has also played an important role in promoting the development of educational technology, and has also triggered changes in the technology and thinking of the entire society. It is the introduction of big data and its characteristics that explains the impact of big data on traditional education, and explores the way that education technology can promote education development in the context of big data.
Energy-Based Least Squares Projection Twin SVM

In this paper, we propose energy-based least squares projection twin support vector machines (ELSPTSVM) for classification problems. The proposed ELSPTSVM models seek projection directions for each class in such a manner that the data of each class are well separated from the data of other class in its respective subspace. To further enhance the performance, the proposed ELSPTSVM model uses a recursive procedure to generate multiple projection axis for each class. The proposed ELSPTSVM model uses energy parameters and solves a system of linear equations to boost the classification performance. The experimental results show the efficiency of the proposed ELSPTSVM model on benchmark UCI datasets with binary class and some non-UCI datasets of fecundity estimation for fisheries data of binary class.
Rotational Movement Perception of Spiral Patterns on Dynamic Sphere Surfaces Based on Line Angles

The research on motion perception mainly focuses on flat images, and there is lacked research on 3D aspects. This study discussed the rotational dynamic optical illusion movement perception of a sphere through the dimension of the psychology of vision in the domain of basic modeling. The modeling bodies in prior studies mainly focused on cylinders and cones; therefore, the sphere was set as the main research direction of this paper for studying the line angle optimization of spiral patterns on dynamic sphere surfaces. The study provides five different angles of samples, that angles are 5°, 10°, 15°, 20°, and 25°. This study employed the method of adjustment in experimental methods to study the induced movement effect and movement afterimage effect of spheres through dynamic modeling. The findings showed that the sample at the line angle of 5° brought a better dynamic optical illusion movement perception to the subjects when the sphere was in a rotational movement. The results of this study were expected to provide a reference for future researchers and related art design creators.
Risks of Data Breaches and Mitigating Controls in Financial Sector

In today's technological age, where new inventions are happening everywhere, its side effects are also increasing day by day. All over the world, all the work being done in all the fields is based on the processing of data in some form or the other. There are basically three stages of data; the first is the storage of the data, the second is the movement of the data, and the third is the processing of the data through a tool or technology for a particular type of work. Security of data is essential in all the three stages, so that the basic principles of security, i.e., confidence, integrity and availability can be maintained. This paper presents a view on the financial loss due to data breach and preventive safeguards in the financial sector.
Privacy preserving scheme for document similarity detection

The problem of detecting similar documents plays an essential role for many real-world applications, such as copyright protection and plagiarism detection. To protect data privacy, the new version of such a problem becomes more challenging, where the matched documents are distributed among two or more parties and their privacy should be preserved. In this paper, we propose new privacy-preserving document similarity detection schemes by utilizing the locality-sensitive hashing technique, which can handle the misspelled mistakes. Furthermore, the keywords' occurrences of a given document are integrated into its underlying representation to support a better ranking for the returned results. We introduced a new security definition, which hides the exact similarity scores towards the querying party. Extensive experiments on real-world data illustrate that our proposed schemes are efficient and accurate.
Socio-pedagogical support in distance learning: reality and requirements for the organization

Based on the analysis of scientific, regulatory and methodological literature and the definition of the concept of "pedagogical support" with its socio-cultural characteristics, an attempt is made to highlight the specific features of the socio-pedagogical support required by students in the process of mastering distance education programs, as well as its principles and means. Various models of the organization of the educational process and components of technical equipment are considered.
Designing for Interaction: Determining the Most Influential Aesthetic Factors for Effective Visualisation of Uncertainty

Visualisations offer a variety of novel ways to depict data to a wider range of users. Moreover, they provide the ability to transform raw data into intuitive visual mechanisms for communication. Despite the developments in visualisation techniques, an area of depiction that has struggled to advance is uncertainty. The visualisation of uncertainty offers an additional dimension for the data by presenting confidence and error rates. Whilst nearly all predictive data sets visualised contain uncertainties, there is still little impulse to actively represent uncertainty. A growing area of interest in the visualisation world is the field of aesthetics; the authors ask the question 'could aesthetics be applied to address the issues surrounding visualising uncertainty'. This paper reports on the design and delivery of a study to evaluate the effectiveness of aesthetics for the depiction of uncertainty. In particular, the evaluation of how to practically determine how we assess the influence of aesthetic dimensions for the presentation of uncertainty. The paper reports on the strategies employed in this study to assess user's decision around aesthetic designs, whilst determining what aesthetic combinations elicit the most uncertain visual representation. The findings show that certain aesthetic combinations in a line graph visualisation portrayed a higher level of uncertainty than others and that particular combinations triggered affective responses based on how the visualisation influenced/impacted a participant. In detail, how the textured line characteristics can be displayed aesthetically (combined with either emphasis or scale) to encourage optimal user experiences of uncertainty in a diverse participant group. The paper highlights how a user's decision on which texture was most uncertain can be overturned when presented with varying levels of emphasis and scale. In summary, this paper contributes to a more in-depth understanding of how to design for and evaluate aesthetic uncertainty visualisations that encourage interaction with the data.
Maximum Energy Yield of Pv Surfaces in France and Italy from Climate Based Equations for Optimum Tilt at Different Azimuth Angles

In the present paper, the problem of the determination of yearly maximum energy producibility in terms of optimum tilt angle for solar surfaces is addressed with reference to 216 locations in France and Italy representing the entire Countries. Original correlations are proposed to calculate the best surface slope as a correction parameter to be applied to the local latitude angle. The correction factor formulas are based on local climate conditions and have been inferred from local monthly insolation data as the 12-year averages of global and diffuse irradiance data from PV-GIS-SARAH platform. An optimization problem is solved aimed at maximizing the yearly collectable energy by a sloped surface, in a range of azimuth values (from South Facing to East Facing), for all the selected locations. The best tilt has been determined for each case, different equation forms have been investigated and finally compact and accurate formulas have been developed able to provide the best tilt estimation as a function of latitude, surface azimuth and proper clearness angle parameters. The accuracy of the proposed formulas resulted in a correlation coefficient with respect to the “exact” tilt angles based on solar dataset higher than 0.93 for azimuth angles till 60°.
Improved Mask R-CNN with Deformable Convolutions for Accurate Liver Lesion Detection in Multiphase CT Images

Multiphase computed tomography (CT) is widely used for the diagnosis of liver lesions. Accurate detection of lesions in multiphase CT images is important in the diagnosis of liver cancer. However, to detect liver lesion from multiphase CT images, the multiphase images must be registered accurately as a preprocessing step, which is complicated by liver deformation. Therefore, we herein propose an improved mask R-CNN (Region with Convolutional Neural Network), in which deformable convolution modules are used for feature extraction, unlike the use of conventional convolution modules in mask R-CNN. Experimental results show that the proposed method performs better compared to the baseline (i.e., the original Mask R-CNN with ResNet-50). In particular, our method yields an overall AP50 of 4.8. In terms of AP (Average Precision) and AP75, our method yields values that are 1.6 and 2.6 higher than those of the original mask R-CNN, respectively. Then in the mask AP, our method yields values that are 1.8, 3.2, and 1.1 higher than those of the original mask R-CNN, respectively.
Adaptive Robust Kalman Filter Based on MCC and Its Application in Underwater Integrated Navigation

The measurement noise in underwater SINS/USBL integrated navigation is often non-Gaussian due to the influence of the multipath effect. In this case, applying traditional filter algorithms will result in reduced accuracy. To solve this problem, this paper combines the maximum correntropy criterion with Kalman filter, proposes a robust Kalman filter algorithm based on MCC, which can adjust the kernel bandwidth adaptively. Finally, Kalman filter (KF), Huber-KF, MCC-KF, and the proposed algorithm are applied to the SINS/USBL integrated navigation for comparative analysis. The results of the simulated experiment verify that the proposed algorithm can improve navigation accuracy significantly under the environment of non-Gaussian measurement noise.
Data Security Techniques in Cloud Computing

Cloud computing refers to computing services provided on demand to the user. The services can be servers, storage, databases, networking etc. These services are provided by the service providers whose duty is to ensure the availability of service on demand and protect the data the user has stored on the cloud. One of the major issue is data security in cloud computing because the data is present at different locations all over the globe. The techniques used by providers for data security ensures confidentiality, availability and integrity of data. But with an expansion in the cyber world there is an increase in cyber-attacks. Organizations face a lot of data breaches from clouds. The present study discusses the data security concept and techniques used by service providers and why there is a need to introduce new security techniques as the hackers are advancing with new techniques in stealing data from cloud servers.
A Data-Driven Residual Static Correction Method Based on the Early Arrival Waveform in Xyoa Domainb

Summary In the areas of complex near-surface, datum statics and reflection statics cannot solve the static correction problem completely. Especially when the residual statics is large, the first break residual statics methods are used to correct them. The conventional methods rely on the first-break picking time. However, it is difficult to pick up the high-quality first break time in the areas. This paper proposes a data-driven residual static correction method based on the early arrival waveform in XYOA domain. The waveform is chosen according to the low frequency trend of the first-break picking on the seismic data. The method has two main features: First it only fits the trend in the XYOA bin to replace the traditional multi-domain fitting, that can ensure the surface consistency of the seismic data; Second, it applies the data-driven decomposition method, that not only reduces the requirements of quality of the first-break picking, but also enhances the reliability of the early arrival residual statics. The synthetic and field examples prove the effectiveness of the method.
Spatial Data Mining- A tool for Spatial Decision Support System in Agriculture Management

Agriculture and its allied sectors have been generating a huge amount of big data.This data includes the different forms such as structured, semi-structured and unstructured real time data.This has led to impose challenges to mine knowledge.Data Mining has left a vast scope for decision making in government and enterprises.The gap has been bridged by several techniques.Data mining is one of the such technique.The recent advanced information technology techniques such as spatial information technology has the capability of analyzing the wider range of agriculture related resources.The different agriculture related parameters include soil, climatic conditions, irrigation and water availability pattern and various socio-economic variables.The paper aims to systematically review the current researches on geospatial information for making better decisions in agriculture.The study also summarizes the application of geospatial data mining techniques and algorithms in agriculture.The study is an initiative in the current era for building a decision support system in agriculture sector.
Assessing Intelligence Text Classification Techniques

Documents contain a tremendous quantity of important human information. The use of automatic text classification is necessitated by the fast rise in the quantity of machine-readable documents for public or private access. In a standard text categorization system, preprocessing is one of the most important elements. This article will look at the influence of preprocessing on text classification from a variety of perspectives, including classification accuracy, text domain, text language, and dimension reduction. Text categorization is a key difficulty for knowledge-based technologies since it touches on all of artificial intelligence's old demons: the knowledge engineering bottleneck, scalability issues, simple transfer across various applications, and cost-effective system design. By specifying a document, information retrieval (IR) systems have typically avoided all of these difficulties. Message classification is a text categorization problem that has piqued machine learning's interest. The categorization of brief text messages is one element of message classification that poses a special problem. The technique of categorizing documents into specified groups based on their content is known as text classification. It is the process of automatically categorizing natural language documents into preset groups. Text categorization is a prerequisite of both text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which change text in some way, such as creating new documents. Existing supervised learning methods for text classification require a large number of documents to learn correctly.