Reviewerly/exHarmony huggingface dataset:
PIC 4th Challenge

The task of Dense Video Captioning (DVC) aims to generate captions with timestamps for multiple events in one video. Semantic information plays an important role for both localization and description of DVC. We present a semantic-assisted dense video captioning model based on the encoding-decoding framework. In the encoding stage, we design a concept detector to extract semantic information, which is then fused with multi-modal visual features to sufficiently represent the input video. In the decoding stage, we design a classification head, paralleled with the localization and captioning heads, to provide semantic supervision. Our method achieves significant improvements on the YouMakeup dataset \citewang2019youmakeup under DVC evaluation metrics and achieves high performance in the Makeup Dense Video Captioning (MDVC) task of \hrefhttp://picdataset.com/challenge/task/mdvc/ PIC 4th Challenge.
EPOS Thematic Core Service Anthropogenic Hazards - Open-Access Integrated Infrastructures for Research and Innovation in the Area of Anthropogenic Seismicity Associated with the Exploitation of Georesources

ABSTRACT: EPOS Thematic Core Service ANTHROPOGENIC HAZARDS (TCS AH) integrates, within the framework of the European Plate Observing System (EPOS) (www.epos-eu.org), research infrastructures freely open for scientists, engineers, and all others interested in hazards posed by the seismicity induced by geo-resource exploration and exploitation. The integrated infrastructures consist of three components: •Data gathered in "episodes." An episode is a set of time-correlated geophysical and technological data and other relevant geo-data that comprehensively report anthropogenic seismicity cases and the industrial and geological conditions of these processes. •Applications, i.e., problem-oriented, bespoke, and standard software tools to process and analyze the data. They help relate seismicity and technological factors for hazard assessment and other scientific targets. •The episodes and applications are accessible on the EPISODES e-platform (https://tcs.ah-epos.eu), an IT environment for data modeling and analysis. The platform provides the user’s workspace, adequate hardware resources, including HPC systems and other facilities that grant quick and efficient execution of commissioned tasks. Here we present and describe the structure and details of this open-access facility. On an example of the pilot "Time-Dependent Anthropogenic Seismic Hazard Assessment," we demonstrate the potential of the integrated infrastructures for developing innovative practice solutions, ready-for-implementation in geo-resource exploitation. 1. INTRODUCTION Exploitation of geo-resources alters the stress and strength conditions in the engaged rock mass. In favorable conditions such alterations result in rock fracturing and violent emission of seismic waves. This process known as anthropogenic seismicity is an unwanted response of the rock mass to the technological processes. With rising demands for energy and minerals, anthropogenic seismicity has been appearing in association with diverse technological processes, and often also in previously aseismic regions. The induced or triggered earthquakes can accompany underground and open-pit mining, conventional and unconventional hydrocarbon extraction, geothermal energy production, underground liquid and gas storage including carbon sequestration, impoundment of surface reservoirs of liquids (like artificial lakes of dammed water for hydroelectric plants and ore-floatation waste repositories) and many other technological processes that perturb the boundary conditions in the affected rock (Lasocki and Orlecka-Sikora, 2020).
Different Techniques of Data Fusion in Internet of Things (IoT)

An IoT (Internet of Things) technology is a dynamic area of research, which has been growing at a remarkable rate for the last few years. The IoT is a mammoth network of associated things and people, which accumulates and shares data about how they are used and the environment around them. It is the discernment of connecting any device to the internet and other associated devices. When something is associated with the internet, it can propel or receive information, or both. This ability to propel and/or receive information makes things smart. IoT permits businesses and people to get more insights from the world around them and do more evocative higher-level work. Data fusion techniques are used to extract eloquent information from dissimilar IoT data. It ferocities dissimilar data from sensor sources to mutually find a consequence, which is more dependable, precise, and comprehensive. This chapter briefly designates the IoT by the characteristics of data procurement and data fusion.
Geochemical Occurrence Characteristics of Geogenic Heavy Metals in Korea Evaluated Using Geochemical Map Data

As environmental criteria items are increased or strengthened, cases of heavy metal contamination by geogenic origin are increasing, and the need to distinguish between natural and anthropogenic origins in soil or groundwater exceeding the standard is increasing.In this study, geochemical occurrences of geogenic heavy metals were identified through statistical processing of the national geochemical map data and evaluation of geochemical characteristics of regions with high geoaccumulation indices.Cobalt, Cr, Cu, Ni, Pb, V, and Zn were targeted for which the national geochemical maps were prepared, and Co, Cr, Ni, and V derived from ultrabasic or ultramafic rocks were classified as factor 1. Copper, Pb and Zn of non-ferrous sulfide origin were classified as factor 2. In particular, enrichment of heavy metals by factor 1 occurs mainly in the serpentine distribution areas of the Chungcheong region, and there is a risk of contamination in neighboring areas.In the case of factor 2, geogenic occurrence is concerned not only in non-ferrous metal mineralization areas such as Taebacksan and Gyeongnam mineralization zones, but also in Au-Ag mineralization areas distributed nationwide.
VDL-Surrogate: A View-Dependent Latent-based Model for Parameter Space Exploration of Ensemble Simulations

We propose VDL-Surrogate, a view-dependent neural-network-latent-based surrogate model for parameter space ex- ploration of ensemble simulations that allows high-resolution visualizations and user-specified visual mappings. Surrogate-enabled parameter space exploration allows domain scientists to preview simulation results without having to run a large number of computa- tionally costly simulations. Limited by computational resources, however, existing surrogate models may not produce previews with sufficient resolution for visualization and analysis. To improve the efficient use of computational resources and support high-resolution exploration, we perform ray casting from different viewpoints to collect samples and produce compact latent representations. This latent encoding process reduces the cost of surrogate model training while maintaining the output quality. In the model training stage, we select viewpoints to cover the whole viewing sphere and train corresponding VDL-Surrogate models for the selected viewpoints. In the model inference stage, we predict the latent representations at previously selected viewpoints and decode the latent representations to data space. For any given viewpoint, we make interpolations over decoded data at selected viewpoints and generate visualizations with user-specified visual mappings. We show the effectiveness and efficiency of VDL-Surrogate in cosmological and ocean simulations with quantitative and qualitative evaluations. Source code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/trainsn/VDL-Surrogate</uri> .
Development of a new spraying robot for building waterproof materials

For roof and indoor and outdoor waterproof material coating operations, most companies still rely mainly on manual operations, including painting, rolling, manual spraying, and coil covering. There are phenomena such as low construction efficiency and uneven thickness control. In order to improve the construction process and better promote the application of spraying quick-setting products, this design has developed a robot spraying equipment with high accuracy, fast construction speed and convenient construction. Firstly, according to the area covered and the size of the obstacle, the robot's program will partition the whole area and determine the reciprocating working path. Secondly, design the mechanical structure scheme and clarify the technical route. A rotatable four-wheel mobile chassis is used to build workbenches and spray systems. Finally, this paper proposes a method of combining differential GPS and unit decomposition, combining the small area decomposed with high-precision differential GPS navigation and positioning, and designing a path planning algorithm.
Analysis of Random Cable Bundle Crosstalk Coupling Model Based on Hermite Interpolation Method

In this article, a fast prediction method for statistical characteristics of cable bundle crosstalk, allowing accurate description of cable bundle crosstalk under nondeterministic geometric arrangement, is introduced. Different from the traditional flexible cable model, this method uses the Hermite interpolation method to simulate the spatial pose of the cable bundle according to the structural characteristics of the cable bundle in the actual situation. The condition of the Hermite interpolation method is the known node function value and the first derivative value, using random variables instead of known nodes, can be used to simulate the binding points in the cable bundle and the geometric uncertainty of the bundle. The Hermite interpolation formula under random node $\xi_{i}$ is as follows.
Role of Blockchain in IoT Security

This chapter covers the working of Blockchain, along with its advantages and disadvantages. It discusses examples of Blockchain in Internet of Things (IoT) applications, security aspects of Blockchain, and Blockchain in business. In an IoT network, the Blockchain can keep an immutable record of the history of smart devices. This feature enables the autonomous functioning of smart devices without the need for centralized authority. The concept of BitCoin is mostly derived from the most promising feature of Blockchain, which is decentralization of verification of transactions along with suitable cryptographic procedures. Payment of transaction fees is optional to transactions. The present-day IoT solutions are expensive because of the high infrastructure and maintenance cost associated with centralized clouds, large server farms, and networking equipment. Establishing a peer-to-peer communication model presents a few challenges, but it has emerged out to be a suitable solution to maintain secured data transactions in huge IoT networks.
Surfing the Modeling of pos Taggers in Low-Resource Scenarios

The recent trend toward the application of deep structured techniques has revealed the limits of huge models in natural language processing. This has reawakened the interest in traditional machine learning algorithms, which have proved still to be competitive in certain contexts, particularly in low-resource settings. In parallel, model selection has become an essential task to boost performance at reasonable cost, even more so when we talk about processes involving domains where the training and/or computational resources are scarce. Against this backdrop, we evaluate the early estimation of learning curves as a practical mechanism for selecting the most appropriate model in scenarios characterized by the use of non-deep learners in resource-lean settings. On the basis of a formal approximation model previously evaluated under conditions of wide availability of training and validation resources, we study the reliability of such an approach in a different and much more demanding operational environment. Using as a case study the generation of pos taggers for Galician, a language belonging to the Western Ibero-Romance group, the experimental results are consistent with our expectations.
Apreciación artística para la formación integral de estudiantes universitarios

The intention of this work is to highlight the benefits of using artistic appreciation as a didactic strategy for the strengthening and development of the personality of university students. Recognize the importance of the integral formation of university students, as authentic and individual beings, so that they can decide what they are and what they want to become. From a humanistic education approach, students are expected to confront the diverse artistic manifestations with a critical and reflective attitude that leads them to create their own referential frame of identity, strengthening the development and solidification of their personality.
Prediction of Favorable Areas for Algal Dolomite Development Based on Multiple Attribute Pattern Recognition: A Case Study in Southwest Area of Sichuan Basin

The Leikoupo Formation in the Baimamiao area in southwestern Sichuan is in the early stage of exploration, with few wells drilled in the area and great natural gas exploration potential. Affected by the early Indosinian movement, Leikoupo 43 Sub-member was eroded to varying degrees, and the algal dolomite gradually thinned from the Longmen Piedmont to the basin. Aiming at the problems of few drillings, strong reservoir heterogeneity, and difficulty of thin algal dolomite prediction, Firstly, the stratigraphic division scheme is clarified and the research units are defined. Then, the vertical resolution of seismic data is improved by using spectral inversion technique. On this basis, the seismic waveform characteristics corresponding to different algal-dolomite thickness are analyzed, principal component analysis (PCA) was used to optimize the extracted seismic attributes, and finally, the qualitative prediction of algal dolomite was carried out by adaptive enhanced cluster analysis. The combination of principal component analysis and supervised pattern recognition can eliminate the correlation of multiple attributes and improve the convergence rate and prediction accuracy. Algal dolomite prediction results match well with geological laws and actual drilling results, which confirms the effectiveness of this set of research methods in predicting thin algal dolomite. At the same time, the prediction results obtained by different methods confirm each other, which provides guidance for further oil and gas exploration of weathered crust algal dolomite reservoir at the top of Leikoupo Formation in Southwest Sichuan Basin.
Self-Train: Self-Supervised On-Device Training for Post-Deployment Adaptation

Recent years have witnessed a significant increase in deploying lightweight machine learning (ML) on embedded systems. The list of applications range from self-driving vehicles to smart environmental monitoring. However, the performance of ML models after the deployment degrades because of potential drifting of the device or the environment. In this paper, we propose Self-Train, a self-supervised on-device training method for ML models to adapt to post-deployment drifting without labels. Self-Train employs offline contrastive feature learning and online drift detection with self-supervised adaptation. Experiments on images and real-world sensor datasets demonstrate consistent accuracy improvements over state-of-the-art online unsupervised methods with 2.45× at maximum, while maintaining lower execution time with a maximum of 32.7× speedup.
Smoke Automation and Regression Testing on a peer-to-peer lending Website with the Data-DrivenTesting Method

Software testing is considered as one of the most important processes in software development as it checks whether the system meets the requirements and specifications of the users. The testing process manually or automatically aims to ensure that the main features are not errors and function during development. The manual process frequently causes the publication time of a feature could not to be punctual. This study aims to create an automation script with the data-driven testing method during smoke and regression testing to ensure the quality, especially the main functions or features, can run normally and not be disturbed by the development of new features that are easily understood. One of Various decent automation tools for testing web applications is Katalon Studio which is based on the Selenium tool. The results of the research that have been carried out show that the process of automation of software testing by applying a script automation tool made with Katalon Studio which applies the data-driven testing method is very good with an achievement rate of 80,21%. The automation tools that are built are easy to use, can be learned quickly, are not too complicated and make users want to use it again. &#x0D;
Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio

Abstract Despite their great success in recent years, neural networks have been found to be vulnerable to adversarial attacks. These attacks are often based on slight perturbations of given inputs that cause them to be misclassified. Several methods have been proposed to formally prove robustness of a given network against such attacks. However, these methods typically give rise to high computational demands, which severely limit their scalability. Recent state-of-the-art approaches state the verification task as a minimisation problem, which is formulated and solved as a mixed-integer linear programming (MIP) problem. We extend this approach by leveraging automated algorithm configuration techniques and, more specifically, construct a portfolio of MIP solver configurations optimised for the neural network verification task. We test this approach on two recent, state-of-the-art MIP-based verification engines, $$\mathrm {MIPVerify}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>MIPVerify</mml:mi> </mml:math> and $$\mathrm {Venus}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Venus</mml:mi> </mml:math> , and achieve substantial improvements in CPU time by average factors of up to 4.7 and 10.3, respectively.
DIGITALIZATION AND ITS METHODS FOR PRESERVING AND ENSURING THE CONTINUITY OF SCIENTIFIC TRADITIONS

The article describes digital methods that humanitarians seem to be able to use in their scholarly endeavours. It highlights a new interdisciplinary area of Digital Humanities, which is actively used in the organization of scientific communication and intercultural dialogue in order to preserve scientific, historical and cultural heritage. The usage of digital methods is described on the examples of digitalization projects of the Miklouho-Maclay Foundation for the Preservation of Ethnocultural Heritage during 2017-2022, whose mission is to preserve and ensure the continuity of scientific traditions for their subsequent transfer to new generations of scientists. It is emphasized that modernizing the scientific infrastructure through digitalization significantly facilitates scientific research and contributes to the development of national intellectual assets.
There Is Any Effect Of Board Games For Teaching Reading Second Grade At Junior High School

Game is one of the supporting media in classroom learning. In general, learning media refers to everything that is used to convey messages from the sender of the message to the recipient of the message that can increase or develop the horizons of thinking, feeling, and learning interest of students or students. The purpose of this study was to determine the effect of board game learning media in the classroom when applied as a support for continuous learning. This research was conducted in class VIII SMP PGRI 8 Sidoarjo. The findings of this study use quantitative methods, there are two instruments used by researchers, namely the results of the LKS taken twice. For this reason, the use of board games learning media can help teachers and students to facilitate access to learning media assistance in the form of board game media.
The Effect of Smart Card Media Utilization on Learning Outcomes of Mathematics Subjects for Grade 1 Elementary School

Media helps the learning process to meet the Minimum Completeness Criteria. This research is quantitative with the reliability test results of the smart card capability test of 10 items. Stated reliable r Alpha = 7.57. The results of the data obtained, it is known from the significance value of the calculation of the normality test is 0.01 from the results of the pretest data on 25 students and 0.71 from the results of the post-test data on 25 students. From these results, when viewed from the basis of decision making that the normality value&gt; 0.05, it is concluded that the data is normally distributed. From the results of t arithmetic, it can be seen that H0 there is no difference in the experimental class and the control class on the use of smart cards in mathematics class 1. There is a difference in the experimental class in the experimental class and the first class on the use of smart card media. Based on the t test with a significant level of 0.01, the result is that if the following test criteria are determined, then Ha is accepted
Deep learning-based transformation of H&amp;E stained tissue into special stains

We present a deep learning-based technique to computationally transform H&amp;E-stained tissue sections into different special stains. We also demonstrate that this stain-to-stain transformation framework improves diagnostic accuracy over the use of H&amp;E only.
Producing multiple qubits via spontaneous parametric down-conversion

We have designed Gaussian masks to subdivide the pump beam before passing it through a BBO. In this way, present an approach to possibly produce multiple entangled photon pairs through spontaneous parametric down-conversion (SPDC).
Sentiment analysis on streaming data using parallel computing

Today as everything has become online and everyone has the chance to voice their opinion. Every opinion is important for the success of the company. Therefore, every company has started giving more and more important to this sentiment analysis, thereby making sentiment analysis a huge field in itself and a hot research topic in the field of natural language processing and linguistic communication. As more and more people are having access to the Internet, the Internet is filled with opinions about products, which has led to the data explosion and big data. And as we have to now do the analysis of this large amount of data, we have to find out some mechanism to make this process of analysis faster and more efficient. Parallel computing comes to the rescue in this case. Parallel computing has been a topic of research for a lot of years now. We can use this in the field of sentiment analysis as well. This is what is done in this work. The various ways in which sentiment analysis can be done using parallel computing are compared in terms of efficiency and time taken to compute. Streaming data has become the trend today as data is continuously being added to the Internet. So performing sentiment analysis on streaming data is much more helpful using parallel computing.
Streaming data processing - an introduction

This chapter presents an overview about the basics of streaming data, core components of streaming data processing architecture, challenges associated with stream processing and recent tools for stream processing.