Reviewerly/exHarmony huggingface dataset:
Video-text cross-modal retrieval algorithm based on multiple coding

Currently, more and more video data and terminal devices accessing video resources are available to users. Video platforms such as Tiktok and Youtube are gradually rising, and the user scale and video resources are increasing day by day, which brings an urgent practical demand for video-text data cross-modal retrieval. This paper proposes a video-text cross-modal retrieval algorithm based on multiple encoding. By encoding the global features, serial features and local features of video and text, the encoded features are mapped to the common embedding space for training, loss function calculation and optimization. Through experimental verification on MASR-VTT data set and comparison with existing methods, the overall performance R<i>@</i>sum increased by 9.22% and 2.86% respectively, which proved the superiority of this method.
Insights from the spatial variability of (multiple) uncertainties: Earth-ice interactions for East Antarctica

Uncertainty, as applied to geophysical and multivariate initiatives to constrain aspects of Earth-ice interactions for East Antarctica, provides a number of approaches to appraise and interrogate research results.&amp;#160; We discuss a number of use cases: 1) making use of multiple uncertainty metrics; 2) making comparisons between spatially variable maps of inferred properties such as geothermal heat flow; 3) extrapolating crustal structure given the likelihood of tectonic boundaries; and 4) providing research results for interdisciplinary studies in forms that facilitate ensemble approaches.&amp;#160;It proves extremely useful to assess a research finding, such as a mapped geophysical property, through multiple uncertainty metrics (e.g., standard deviation, information entropy, data count).&amp;#160; However, a thoughtful appraisal of multiple metrics could be misleading, i.e., potentially not useful in isolation, in a case where there are significant unquantified uncertainties.&amp;#160; Uncertainties supplied with the mapped geophysical properties can potentially be extended to capture this broader range, but that range in turn could become less helpful as the fine detail in the quantified uncertainty will be lost.&amp;#160;In the case of a property such as geothermal heat flow, indirectly determined for East Antarctica, insights can be drawn by subtracting a forward model map from an empirically determined result (e.g. Aq1) to yield the non-steady state components excluded in the forward model.&amp;#160; In such investigations, including the maximum and minimum possible difference between maps is essential to understand which non-steady state anomalies are real, and which could be artifacts attributable to (quantified) uncertainty.&amp;#160;In further use cases, we show how the few available seismic measurements that constrain the crust and upper mantle structure of East Antarctica can be placed in context, given the likelihood of major tectonic boundaries beneath the ice, and link this to published constraints on the seismic structure (and hence, rheology) of the deeper lithosphere.&amp;#160; In terms of how the solid Earth interacts with the ice sheet above, the impact of fine scale-length variations in spatial uncertainty may be investigated in relation to, for example, ice sheet modelling. For a large region and relatively unexplored region such as East Antarctica, uncertainty yields many and varied insights.&amp;#160;
Cyber risk transfer tools proliferate as breaches rise

Significance However, pricing these policies is tough due to incomplete modelling data about the frequency and cost of breaches, and uncertainty about the scale and interconnectedness of cyber risks. These concerns, alongside spikes in threats such as ransomware, have caused insurers to restrict the coverage they are willing to sell, leading firms to seek out means for transferring cyber risk. Impacts Security firms face rising pressure to provide some warranty if their service fails to prevent or detect a breach. Insuring against state-linked cyberattacks will becoming increasingly complicated as attribution problems mount. Reinsurers will shape the size and growth of the cyber risk transfer market, due to their concerns about catastrophic cyber risk.
Data-driven Attributing of Climate Events with Climate Index Collection based on Model Data (CICMoD)

Machine learning (ML) and in particular artificial neural networks (ANNs) push state-of-the-art solutions for many hard problems e.g., image classification, speech recognition or time series forecasting. In the domain of climate science, ANNs have good prospects to identify causally linked modes of climate variability as key to understand the climate system and to improve the predictive skills of forecast systems. To attribute climate events in a data-driven way with ANNs, we need sufficient training data, which is often limited for real world measurements. The data science community provides standard data sets for many applications. As a new data set, we introduce a collection of climate indices typically used to describe Earth System dynamics. This collection is consistent and comprehensive as we use control simulations from Earth System Models (ESMs) over 1,000 years to derive climate indices. The data set is provided as an open-source framework that can be extended and customized to individual needs. It allows to develop new ML methodologies and to compare results to existing methods and models as benchmark. Exemplary, we use the data set to predict rainfall in the African Sahel region and El Nin&amp;#771;o Southern Oscillation with various ML models. We argue that this new data set allows to thoroughly explore techniques from the domain of explainable artificial intelligence to have trustworthy models, that are accepted by domain scientists. Our aim is to build a bridge between the data science community and researchers and practitioners from the domain of climate science to jointly improve our understanding of the climate system.
Combining convolutional and recurrent neural networks for ShakeMap prediction

&amp;lt;p&amp;gt;&amp;amp;#160; &amp;amp;#160; &amp;amp;#160;Taiwan has a high population density, and is located in the Circum-Pacific Belt, there are countless seismic hazard events.The Earthquake Early Warning (EEW) and Rapid Report Systems have become one of the most important disaster prevention systems to effectively reduce and prevent disasters caused by destructive earthquakes. Currently, according to the Central Weather Bureau&amp;#039;s website, the EEW and repaid reports are produced and distributed 5 to 10 minutes after an earthquake, including the time of the earthquake, the location of the epicenter, the size and depth of the earthquake, and the fast estimated magnitude of the earthquake.&amp;lt;br /&amp;gt;&amp;amp;#160; &amp;amp;#160; &amp;amp;#160;In recent years, deep learning has developed rapidly, especially in the field of image recognition, including image classification, object detection, image generation, etc. This project is expected to use the Taiwan Strong Motion Instrument Program (TSMIP) station data provided by the Central Weather Bureau to generate the ShakeMap. We will use the multi-label classification method to mark the ShakeMap, and use the Recurrent Neural Network (RNN) to embed labels in the image, capture the dependency of labels and combine with a Convolution Neural Network (CNN) to predict the different labels in the shakeMap, which is expected to achieve the purpose of fast prediction of earthquake intensity in different places in the whole Taiwan island.&amp;amp;#160;&amp;lt;/p&amp;gt;
Automatic keyword extraction based on dependency parsing and BERT semantic weighting

It's hard for the classic TextRank algorithm to differentiate the degree of association between candidate keyword nodes. Furthermore, it readily ignores the long-distance syntactic relations and topic semantic information between words while extracting keywords from a document. For the purpose of solving this problem, we propose an improved TextRank algorithm utilizing lexical, grammatical, and semantic features to find objective keywords from Chinese academic text. Firstly, we construct the word graph of candidate keywords after text preprocessing. Secondly, we integrate multidimensional features of candidate words into the primary calculation of the transition probability matrix. In this regard, our approach mines the full text to extract a collection of grammatical and morphological features (such as part-of-speech, word position, long-distance dependencies, and distinguished BERT dynamic semantic information). By introducing the dependency syntax of long sentences, the algorithm's ability to identify low-frequency topic keywords is obviously promotional. In addition, the external semantic information is designed to be imported through the word embedding model. A merged feature-based matrix is then employed to calculate the influence of all candidate keyword nodes with the iterative formula of PageRank. Namely, we attain a set of satisfactory keywords by ranking candidate nodes according to their comprehensive influence scores and selecting the ultimate top N keywords. This paper utilizes public data sets to verify the effectiveness of the proposed algorithm. Our approach achieves comparable f-scores with a 5.5% improvement (4 keywords) over the classic. The experimental results demonstrate that our approach can expand the degree of association differentiation between nodes better by mining synthetic long text features. Besides, the results also show that the proposed algorithm is more promising and its extraction effect is more robust than previously studied ensemble methods.
Click-through rate prediction based on behavioral sequences

As the most important module in recommendation systems, click-through rate prediction has attracted the attention of industry and academia. Due to the powerful learning ability of deep learning, it is widely used in click-through rate prediction. Behavior sequences based on user is an important direction of click-through rate prediction. Although some results have been made in related directions, existing methods still have some problems, such as the inability to learn feature weights better, the presence of noise in user behavior sequences, not fully mining the hidden information in features, etc. In this paper, we propose a method for related problems, named DISFMN, which can dynamically learn the importance of features as well as filter out the noise in user behavior sequences. The method also combines high-order and low-order feature interactions to uncover more valuable information in features. Comparative experiments are conducted on different datasets and the experimental results showed the effectiveness of the proposed method.
Design of gesture recognition system based on machine vision

The project is based on Window10+Python3.6 environment, and uses Python libraries such as OpenCV, Sklearn and PyQt5 to construct a relatively complete gesture recognition and translation system, which can recognize all kinds of static gesture signals in life, and translate them into Chinese or Arabic numerals through image processing.Due to the limitation of the training amount of Support Vector Machines (SVM), this design is only used to recognize gesture actions 1-10, and the interface is designed using PyQt5 for real-time display of the results of gesture recognition translation.The paper focuses on the noise elimination, contour extraction, RGB colorspace and YCrCb feasibility comparison of still images of 1-10 gesture features extracted by computer camera, GUI page design, Fourier operator extraction of gestures, training SVM model, and debugging of the system. The system is also able to be integrated on different development boards and can be embedded in device carriers to meet multi-scene adaptability.
EaDz: a web-based, relational database for detrital zircons from East Asia

&amp;lt;div&amp;gt; &amp;lt;p&amp;gt;Detrital zircon geochronology and geochemistry have become the primary methods for reconstructing the Earth&amp;#039;s tectonic and surficial processes over geologic time. The compilation and dissemination of detrital zircon data are essential to facilitate rapid, innovative, and reliable studies and to drive new research discoveries. To this end, we present EaDz, a new web-based, relational database for detrital zircons from East Asia, which includes geochronological, geochemical, petrological, and stratigraphic datasets. The EaDz currently contains 7083 rock samples representing 570,092 U-Pb ages, 77,394 Lu-Hf isotopes, 35,045 trace elements, and 1773 oxygen isotope data entries. Within the EaDz database, 71% of the samples are siliciclastic rocks, followed by 16% metasedimentary rocks, 6% modern sediments, and 7% other rocks. The database is intended to serve as a repository for new and existing detrital zircon data and a platform for the visualization and analysis of detrital zircon data. Through a publicly accessible interactive website, EaDz allows simultaneous data retrieval, analysis, and visualization in the cloud. Additionally, EaDz provides a means for combining the detrital zircon data with global plate reconstruction models - thus providing deep-time dynamic reconstructions of detrital palaeolocations. As such, the EaDz provides a powerful resource for applications in paleogeography reconstruction, plate tectonic reconstruction, and crustal-mantle evolution. The database will continue to be developed and improved. We encourage users to collaborate on data compilation by submitting data through the website or contacting corresponding authors directly after their data is published.&amp;lt;/p&amp;gt; &amp;lt;/div&amp;gt;
Fraudulent promotion detection on GitHub using heterogeneous neural network

There are fraudulent promotion behaviors in GitHub, which promotes Stars and Forks for specific repositories. It is harmful to the environment of the open source community, while it is not effectively detected by GitHub yet. This paper applies a heterogeneous neural network to detect repositories that are suspected of fraudulent promotion behavior. A heterogenous mini-graph neural network with attention mechanism and hyper-graph generation is proposed to detect repositories with cheating behaviors. Attention mechanism can dynamically balance the weight of semantics in heterogeneous information networks. Hyper-graph generation method can solve the problem of poor connectivity caused by many small graphs in the dataset. The experimental result shows that the model can effectively detect this kind of cheating behavior.
New approach to evaluate the intensity of ancient human activities, based on organic matter characteristics using Rock-Eval® thermal analysis.

Archaeological anthrosols constitute a heritage of long-term carbon storage and soil fertility. Their anthropogenic features are affected by the type and intensity of ancient human activities. Human activities can follow a gradation of disturbance intensity, with lower intensity related to a weak human pollution of natural soil, and stronger intensity related to anthropogenic materials inputs (e.g. refuse pits). Soil properties are indeed deeply modified by the addition of objects (e.g. bones, ceramic) and of organic matter with distinct chemical composition and biological stability (e.g. charcoal). The aim of the study was to establish a new analytical approach to distinguish intensities of human activities, based on organic matter characteristics. To this end, we studied intertropical soil profiles (0-120 cm) from Cameroon, Brazil and Bolivia, with spatial or temporal intensity variations of human activities. We used standard compositional parameters (hydrogen index, HI, and oxygen index, OI) and advanced thermal parameters (I-index and R-index) from Rock-Eval&amp;#174; pyrolysis, as well as magnetic susceptibility, to characterize anthrosols.Results demonstrated the potential of Rock-Eval&amp;#174; pyrolysis parameters to identify human activities changes. First, the deviation of I-index (delta-I) between our samples and a reference value from natural sites informed about the intensity of human impacts, allowing for the distinction between artificial infilling of refuse pits and soil profiles with no or few human impacts. Second, positive HI:OI correlation established the importance of charcoal as main organic C source. The magnetic susceptibility informed about the presence of burnt soils in a Brazilian and one of the Bolivian sites. The combination of all these parameters, when represented with soil depth, allowed for the estimation of temporal changes in Brazilian and Bolivian sites. The topsoils were similar for all sites, relative to a low intensity of human activities or to the resumption of natural pedogenesis, thereby alleviating the effects of ancient human activities on organic matter characteristics. In contrast, the subsoils exhibited higher intensities of ancient human activities, with even higher values of intensities in Bolivian sites, thereby evidencing the long-term conservation of their effects on organic matter characteristics.To conclude, anthropogenic activities may durably affect organic matter characteristics in tropical sites, even after several centuries. Beyond being of interest for archaeological research, this new approach raises questions about the long-term consequences of our current human activities.
Recognition Model for Facial Expression Using Machine Learning

In human-to-human communication, we can use verbal, vocal, and non-verbal signals. Facial expressions are a form of non-verbal communication. Recognizing them helps to understand the expressions of human beings. This chapter proposes a system for pose and illumination-invariant recognition of facial expressions using images. Constrained local models (CLM), which fit a dense model to an unseen picture in an iterative manner, can be used to calculate information about the human face. The system uses a support vector machine (SVM) classifier for the classification of the training set model. The results outperformed human performance and demonstrated motion invariance. The matching process can be affected by changing lighting conditions, which can impair recognition precision. The findings indicate that accurate face emotion identification may be achieved under a variety of illumination situations. This task may be accomplished using a variety of machine learning methods. In order for machine learning algorithms to identify or recognize anything, they must first be trained and then tested on an appropriate data set. Machine learning techniques as well as extraction of feature techniques are examined in this study to see whether they may assist us in recognizing human emotions with greater accuracy. To implement this task, we used to acquire libraries like MATPLOTLIB and NumPY by utilizing OpenCV Python. SVM and RF (random forest) methods are used as machine learning methodologies. SVMs may efficiently conduct nonlinear classification by implicitly translating their inputs into high-dimensional feature spaces, which is known as the kernel technique.
Hand Sign Recognition Using LeNet-like Architecture

The whole concept of technology and research is based on making the existing technologies more efficient and accurate by overcoming the challenges that we are facing in the respective field. It's a never-ending process. One such challenge is to make communication easier for deaf and dumb people. Luckily, sign languages have come to the rescue. Sign language is the communicating mode for deaf-mute. Normal people may not be able to understand sign language to communicate with deaf-mute people and this problem needs to get overcome. There have been some advancements in this field in recent times as well as some applications have been developed for the detection of sign language and many research papers have also been published in the same field. However, one challenge is followed by other challenges and now the race is after coming up with methods more accurate than the already existing ones to communicate with the deaf and dumb people. This chapter proposes a LeNet-like architecture for classification to get high accuracy. We choose the Modified National Institute of Standards and Technology (MNIST) database sign language data set to train and test our model. Our proposed model has given an accuracy of 99.60% that is the admissible accuracy. And this chapter will also deal with the over fitting issues with the regularization method.
Machine learning for physical layer security

Due to the broadcast nature of the wireless medium, an illegitimate node can harm legitimate users with eavesdropping, jamming, and spoofing threats, as indicated in the previous chapters. These threats can be eliminated, detected, and prevented using physical layer security (PLS) approaches. However, providing PLS of the fifth generation and beyond (5GB) communication and sensing systems is challenging due to the requirements of these systems, such as heterogeneity, ultra density, and high mobility. This is especially the case when a large number of parameters are used simultaneously, and the problem or model itself is highly dimensional and complex. Machine learning (ML) and deep learning (DL) have recently emerged as potential tools for reducing the increasing complexity of wireless networks while simultaneously employing multiple parameters in multiple dimensions (improving the performance of wireless security systems). In this chapter, we first review the classic and recent ML and DL algorithms for PLS, the ongoing and anticipated issues, and the primary efforts made in this sector by leading industries and research entities. Then, we summarize the possible research direction for ML-based PLS.
Physical layer security definition and domains

Today&apos;s fast-paced world is becoming increasingly dependent on wireless communications. Whether it is for businesses or individuals, mobility, portability, and instant access (via the internet) to unlimited information are the mantras of our day. In the past few decades, wireless communication has grown exponentially, with a bright future ahead of it. It is anticipated that this technology will have a profound impact on our lives and allow us to accomplish things we had never imagined. A key element of the success of these technologies will be the ability to communicate securely and reliably. Although cryptographic security mechanisms are widely adopted, they cannot cope with increasingly decentralized and heterogeneous networks. As a complementary solution, physical layer security (PLS) exploits the dynamic features of wireless environments to ensure that legitimate transmissions are subject to CIAA requirements. A generalized PLS framework is provided in this chapter, which not only incorporates the existing work but also allows for the development of next generation PLS methods. To reflect this, PLS fabric is divided into observation and modification planes. Furthermore, different domains of PLS are discussed under these plane concepts, allowing a vision of future PLS mechanisms.