Reviewerly/exHarmony huggingface dataset:
Keyword Extraction Algorithm Based on Pre-training and Multi-task Training

The generalization ability of the supervised model is relatively weak in keyword extraction technology. For enhancing the robustness of the model, a keyword extraction method is proposed inspired by the pre-training model. After pre-training with plenty of corpus and fine-tuning with specific datasets, the proposed method performs more robust in keyword extraction tasks. In addition, multi-task training is added in the fine-tuning stage to improve the accuracy of the model. Plenty of comparative experiments show that the proposed method is very significant in improving the robustness and accuracy of the model.
Application of Fuzzy PID Control in UAV Control System

This research studies the application of fuzzy PID control in UAV control system. Based on the modular design concept, in the UAV ground station control system designed in this paper, its structure mainly includes human-computer interaction equipment and UAV instruments, navigation display and flight simulation modules. For the efficient analysis, the fuzzy control model in integrated into the mentioned system. Traditional control methods are widely used in industrial environments, but for systems with large delays and complex nonlinearities, hence, the edge computing and fuzzy clustering is combined as the tool to analyze the comprehensive data and provide the reasonable supporting information. The results show that the designed system is robust.
Determination of average vehicle speed utilizing reverse projection

Reverse projection photogrammetry has long been used to estimate the height of an individual in forensic video examinations. A natural extrapolation would be to apply the same technique on a video to estimate the speed of an object by determining the distance traveled between two points over a set amount of time. To test this theory, five digital video recorders (DVRs) were connected to a single fixed camera to record a vehicle traveling down a track. The vehicle's speed was measured through Doppler radar by a trained operator and the speedometer of the vehicle was also recorded with a video camera. The recorded video was examined and the frames that best depict the beginning and end of the vehicles course were selected. Two reverse projection photogrammetric examinations were performed on the selected frames to establish the position of the vehicle. The distance between the two points was measured, and the time elapsed between the two points was examined. The outcome provided an accurate speed result with a standard degree of uncertainty. This study proves the feasibility of using video data and reverse projection photogrammetry to determine the speed of a vehicle with a limited set of variables. Further research is needed to determine how additional variables would impact the standard degree of uncertainty.
Chess as Sequential Data in a Chess Match Outcome Prediction Using Deep Learning with Various Chessboard Representations

Moves made by chess players during the match are certainly some kind of sequence. In this work, we tried to answer the question if such data could be interpreted as sequential. To achieve that, a model build with LSTM layers only was designed. Results performed by the model are justifying that thesis. A novel model architecture was also proposed and trained on multiple data types—chess moves and chess game metadata. Its purpose was to perform as high classification accuracy as possible—we managed to achieve it with a result close to 69%. Moreover, we compared a couple of chessboard representation methods, more precisely a bitmap input and algebraic input, to check which one is more relevant for the neural networks training process. Contrary to what one might suppose, better scores were reached for bitmap input, which from the theoretical point of view carries out less information than algebraic input.
Tropically planar graphs

We study tropically planar graphs, which are the graphs that appear in smooth tropical plane curves. We develop necessary conditions for graphs to be tropically planar, and compute the number of tropically planar graphs up to genus 7. We provide non-trivial upper and lower bounds on the number of tropically planar graphs, and prove that asymptotically $$0\%$$ of connected trivalent planar graphs are tropically planar.
Exploration geochemistry of surficial media over the high-grade McArthur River uranium deposit, Saskatchewan, Canada

ABSTRACT An orientation survey using surficial media was performed over the high-grade McArthur River unconformity-related U deposit (Saskatchewan, Canada) to test whether or not secondary dispersion of elements related to the ore body or alteration zone can be detected at the surface more than 500 m above the deposit. Organic-rich Ah-horizon soils, Fe-rich B-horizon soils, C-horizon soils, tree cores of Jack pine (Pinus banksiana), and glacially dispersed boulders of Manitou Falls Formation sandstone that host the U deposit were collected in four sampling grids near the mine site. Two of the grids overlaid the trace of the P2 fault that hosts the deposit and extends nearly to the surface, one grid overlaid both the P2 fault and one of the high-grade ore bodies (Zone 4), and one grid was located 2.5 km away from the ore body surface trace in the barren hanging wall of the P2 fault. The grid overlying the Zone 4 ore body had the highest proportion of samples with elevated U and low 207Pb/206Pb ratios, the latter indicative of radiogenic Pb from a high-U source, measured in two size fractions of Ah-horizon soils using Na pyrophosphate leach, pine tree cores using total digestion, and sandstone boulders using 2% HNO3 leach. A handful of pathfinder elements, such as As, Co, Ni, and Pb, are variably associated with the U and radiogenic Pb. Sandstone boulders with an assemblage of dravite + kaolinite ± illite, determined using shortwave infrared (SWIR) spectroscopy and matching the alteration mineralogy in the Manitou Falls Formation above the U deposit, were prevalent in the grid above the Zone 4 ore body and in the adjacent grid in the direction of glacial dispersion. A coarse fraction of the B-horizon soils, leached with 5% HNO3, highlighted the grid above the Zone 4 ore body to a lesser extent, whereas HNO3 leaches and aqua regia digests of C-horizon soil separates did not highlight the P2 fault or ore body trace due to influence by parent till mineralogy. Results of environmental monitoring at the mine site, which was active at the time of sampling, suggest that dust containing U, Pb, and radionuclides from waste rock piles and a ventilation shaft could influence A-horizon soil geochemistry near the mine site, and that U and radiogenic Pb anomalies in B- and C-horizon soils near the water table are close to a treated mine effluent discharge point. However, older trees that record elevated U and radiogenic Pb in annual rings that pre-date mining activity, and alteration mineralogy and geochemistry of boulders that are less susceptible to the influences of mining activity, add confidence that the geochemical anomaly in diverse surficial media above the Zone 4 ore body represents secondary dispersion from the underlying U deposit.
Estimation and Applications of Quantiles in Deep Binary Classification

Conditional quantiles obtained via regression are used as a robust alternative to classical conditional means in econometrics and statistics, as they can capture the uncertainty in a prediction, and model tail behaviors, while making very few distributional assumptions. In this work, we extend the notion of conditional quantiles to the binary classification setting—allowing us to quantify the uncertainty in the predictions, increase resilience to label noise, and provide new insights into the functions learnt by the models. We accomplish this by defining a new loss called binary quantile regression loss. We compute the Lipschitz constant of the proposed loss and show that its curvature is bounded under some regularity conditions. These properties are later used to characterize the error rates of the learning algorithms and to accelerate the training regime with using Lipschitz adaptive learning rates. We leverage the estimated quantiles to obtain individualized confidence scores that provide an accurate measure of a prediction being misclassified. We aggregate these scores to provide two additional metrics, namely, confidence score and retention rate, which can be used to withhold decisions and increase model accuracy. We also study the robustness of the proposed nonparametric binary quantile classification framework, and finally, we demonstrate that quantiles aid in explainability as they can be used to obtain several univariate summary statistics that can be directly applied to existing explanation tools.
Data Augmentation with Illumination Correction in Sematic Segmentation

Abstract In the training process of the deep learning network, data augmentation can effectively improve accuracy. Presently, the commonly used data augmentation methods have been mainly divided into space transformation and color transformation. However, these methods do not take into account the effects of the image’s illumination factor. Under natural conditions, the color, angle, and strength of the light will cause the color of the object to change, which is likely to cause segmentation errors. In this article, we start from the angle of illumination and perform data augmentation on the image. By applying the illumination correction algorithm based on Retinex theory, we are able to enhance the data and achieved good results in the experiment. However, there still remains some problems within the current algorithms based on Retinex theory. Especially for images with uniform illumination conditions, which can be overexposed after processed by these algorithms. In response to this problem, we have improved the relevant algorithm. The improved algorithm further improves the accuracy of semantic segmentation on the original basis.
Senti-BSAS: A BERT-based Classification Model with Sentiment Calculating for Happiness Research

Happiness becomes a rising topic that we all care about recently. It can be described in various forms. For the text content, it is an interesting subject that we can do research on happiness by utilizing natural language processing (NLP) methods. As an abstract and complicated emotion, there is no common criterion to measure and describe happiness. Therefore, researchers are creating different models to study and measure happiness. In this paper, we present a deep-learning based model called Senti-BSAS (BERT embedded bi-lstm with Self-Attention mechanism along with the Sentiment computing). Given a sentence that describes how a person felt happiness recently, the model can classify the happiness scenario in the sentence with two topics: was it controlled by the author (label ‘agency’), and was it involving other people (label ‘social’). Besides language models, we employ the label information through sentiment computing based on lexicon. The model performs with a high accuracy on both ‘agency’ and ‘social’ labels, and we also make comparisons with several popular embedding models like Elmo, GPT. Depending on our work, we can study the happiness at a more fine-grained level.
Training Ensembles of Quantum Binary Neural Networks

Artificial Neural Networks (NNs) are applied to solve problems in several fields such as translation, image processing, and diagnosis of diseases. Training NNs and searching weights parameters for them is a costly task that depends on weights initialization. This problem cost increases when it is necessary to train an Ensemble of Neural Networks (ENNs). Some works explore ways to train Binary Neural Networks (BNNs) using quantum techniques with success. This paper shows a hybrid approach to train an Ensemble of Binary Neural Networks with the same computational cost to train one variational quantum circuit.
Blockchain and Decentralized Modeling for Corporate Tax Planning

In recent years, blockchain technology is receiving more and more people's attention. The reason why it has received so many people's attention is that through blockchain technology, the openness and transparency of information can be effectively guaranteed, and because of the immutability of blockchain, so that it has great application value in various fields. In corporate financial management, the application of blockchain technology to corporate tax planning and management will have a profound impact on corporate tax planning. Based on the analysis of the definition and characteristics of the blockchain, this paper can study the impact of the application of the blockchain in corporate tax planning through decentralized modeling for in-depth research. This article first introduces the steps, key points and research status of corporate tax planning, then introduces the application, development and characteristics of blockchain technology, and finally models and simulates corporate tax planning based on blockchain technology, and the results prove the reliability of the model.
Language Modeling and Text Generation Using Hybrid Recurrent Neural Network

The increase in development of machines that have capability to understand the complicated behavior to solve the human brain involvement problems, the auto text generation application also gets the wide attention. The Language modeling or text generation is a task of next character or word prediction in a sequence with analysis of input data. The ATG enable the machines to write and provide the help to reduce the human brain effort. The ATG is also useful for understanding and analysis of languages and provide the techniques that enable the machines to exchange information in natural languages. At the large scale the text data are created everywhere (whatsApp, facebook, and tweets etc.) and freely online available therefore an effective system is needed for automation of text generation process and analysis of the text data for extracting meaningful information from it so in this work, a case study is presented on how develop a text generation model using hybrid recurrent neural network for English language. The explore model find the dependencies between characters and the conditional probabilities of character in sequences from the available input text data and generate the wholly new sequences of characters like human beings writing (correct in meaning, spelling and sentence structure). A comprehensive comparison between these models, namely, LSTM, deep LSTM, GRU and HRNN is also presented. Previously the RNN models are used for text predictions or auto text generation but these models created the problem of vanishing gradient (short memory) when process long text, therefore the GRU and LSTM models were created for solving this problem. The text generated by GRU and LSTM have many spellings error, incorrect sentence structure, therefore, filling this gap the HRNN model is explore. The HRNN model is the combination of LSTM, GRU and a dense layer. The experiments performed on Penn Treebank, Shakespeare, and Nietzsche datasets. The perplexity of HRNN model is 3.27, the bit per character is 1.18 and average word prediction accuracy is 0.63. As compare with baseline work and previous models (LSTM, deep LSTM and GRU), our model (HRNN) perplexity and bit per character are less. The texts generated by HRNN have fewer spelling errors and sentence structure mistakes. A closer analysis of explored models’ performance and efficiency is described with the help of graph plots and generated texts by taking some input strings. These graphs explain the performance for each model.
ANN-Time Varying GARCH Model for Processes with Fixed and Random Periodicity

Financial Time Series Forecasting is an important tool to support both individual and organizational decisions.Periodic phenomena are very popular in econometrics.Many models have been built aiding capture of these periodic trends as a way of enhancing forecasting of future events as well as guiding business and social activities.The nature of real-world systems is characterized by many uncertain fluctuations which makes prediction difficult.In situations when randomness is mixed with periodicity, prediction is even much harder.We therefore constructed an ANN Time Varying Garch model with both linear and non-linear attributes and specific for processes with fixed and random periodicity.To eliminate the need for time series linear component filtering, we incorporated the use of Artificial Neural Networks (ANN) and constructed Time Varying GARCH model on its disturbances.We developed the estimation procedure of the ANN time varying GARCH model parameters using non parametric techniques.
Towards Semantic Interoperability in Historical Research: Documenting Research Data and Knowledge with Synthesis

A vast area of research in historical science concerns the documentation and study of artefacts and related evidence. Current practice mostly uses spreadsheets or simple relational databases to organise the information as rows with multiple columns of related attributes. This form offers itself for data analysis and scholarly interpretation, however it also poses problems including i) the difficulty for collaborative but controlled documentation by a large number of users, ii) the lack of representation of the details from which the documented relations are inferred, iii) the difficulty to extend the underlying data structures as well as to combine and integrate data from multiple and diverse information sources, and iv) the limitation to reuse the data beyond the context of a particular research activity. To support historians to cope with these problems, in this paper we describe the Synthesis documentation system and its use by a large number of historians in the context of an ongoing research project in the field of History of Art. The system is Web-based and collaborative, and makes use of existing standards for information documentation and publication (CIDOC-CRM, RDF), focusing on semantic interoperability and the production of data of high value and long-term validity.
Weakly-Supervised Temporal Action Localization by Background Suppression

We propose a novel method of background suppression to solve the issue that background regions are recognized as actions in weakly-supervised temporal action localization. The general attention-based action localization methods tend to use the attention module to generate segment-level attention weights. But there is little difference between the attentions from the background segments similar to the target actions and the attentions of the action segments, which causes the result that many background segments related to the target actions are still recognized as actions. To address this issue, a weakly-supervised temporal action localization network by background suppression (BS-WTAL) is designed. It introduces a filtering module for suppressing the background features and encouraging the action features, a classification module for identifying action categories and a generative attention module for segment-wise representation modeling. This enables BS-WTAL to suppress background to improve localization performance. Furthermore, we conduct ablation studies from different perspectives. Extensive experiments were performed on two datasets – THUMOS14 and ActivityNet1.2. Our approach shows better performance on these two datasets, even comparable with state-of-the-art fully-supervised methods.
Clinical risk prediction models: the canary in the coalmine for artificial intelligence in healthcare?

Clinical risk prediction models (CRPMs) are statistical models that aim to improve medical decision making by providing an objective measure of potential health outcomes based on data.[1][1] In recent years, there has been an explosion in the development of such models across all areas of medical
Automated mineralogical anomaly detection using a categorization of optical maturity trend at lunar surface

The mineralogical anomaly is the mineralogically diagnostic character that differs from its surrounding spectra in terms of absorption features of a spectrum. On the airless planetary body like our Moon, the characteristics of mineral exposures are mostly affected by the physical process over time because of space weathering. This inevitable fact produces mature exposures that act as a background for mineralogical anomaly detection. In this regard, the present study categorizes the optical maturation trend of a site, and the background characterization is integrated with anomaly detection, which makes it more practical, robust, and efficient. These fundamental ideas have been improvised into our automated mineralogical anomaly detection method that uses a modified density-based spatial clustering of applications with noise (Modified DBSCAN) algorithm to categorize the maturity trend from various scatter plots of spectral property. Subsequently, the Reed-Xiaoli detector analyzes the data points belonging to below 33% of maximum point density on the corresponding scatter plot. Experimentally, the proposed method has been validated with previously reported results of literatures over the varietal lunar sites (crater Theophilus, crater Humboldt, Hansteen Alpha silicic dome, western inner ring of Mare Moscoviense, and crater Von Kármán), which have diverse mineralogy in the region. The reflectance spectra of anomalous points explore the mineralogical diversity of the studied region using Chandrayaaan-1 Moon Mineralogy Mapper (M3) data. However, it can also operate with any hyperspectral datasets within the spectral range of 400–3000 nm. This flexibility makes our proposition more attractive for an end-user in the field of planetary science.
Adaptive Confidence Verification for Analysis of BPSK Signals

The existing extreme statistic-based confidence test approaches for the analysis results of binary phase-shift keying (BPSK) signals, i.e., modulation recognition and parameter estimation, do not perform well under transmission impairments, partly due to their insufficient use of extreme samples. This article proposes a copula-based algorithm for the confidence test of BPSK signal analysis by combining the block maxima-based and peak-over-the-threshold-based extreme statistics. The test statistic is developed relying on the one-class detector using the copula probability density function under the null hypothesis, and the threshold is obtained adaptively through the bootstrap algorithm. The proposed statistic is proved to be asymptotically Gaussian distributed. By comparing the statistic with such a threshold, the confidence test is performed. Extensive simulations validate the effectiveness of the proposed algorithm.
Trends of Blockchain Research in Management and Business

Nowadays, more and more people believe blockchains enable transform our economies and societies fundamentally. Scientists discuss blockchain and its application from a technical perspective. This study explores the trends of blockchain research in the management and business domain by use of bibliometric and content analysis. Two hundred eighty-two articles retrieved from SSCI show the study in the area started in 2017 but grew exponentially. The most productive country is the US, China, and the UK. Four main research themes are identified, i.e., impact on business and innovation, impact on the financial industry, impact on operations and supply chains, and technological management. We found researcher's concern has shifted from conceptual explanations and descriptions at a macro-level to highlight specific implementation. Also, we found that the largest body of blockchain research is about the supply chains instead of the financial industry in the management and business domain.