Reviewerly/exHarmony huggingface dataset:
Stacked hourglass deep learning networks based on attention mechanism in multi-person pose estimation

In order to solve the problem of small-scale key point positioning accuracy in multiplayer pose estimation, using top-down method and advanced yolov4-tiny, this paper proposes an improved multi-person pose estimation method based on stacked hourglass deep learning network. The coordinate attention mechanism is introduced in the original hourglass network residual module to perform feature enhancement, which suppresses useless features and improves useful features, thus improving the recognition rate of small-scale joint points. From experiments, the index PCK@0.5 reaches 88.9% on MPII dataset, which verifies the effectiveness of our proposed method.
Logistics data sharing method based on federated learning

In today's era of big data, the logistics supply chain generates massive amounts of data at all stages, and the privacy issues of logistics data are increasingly prominent. In order to efficiently utilize the logistics data of each enterprise to meet the needs of the enterprise and achieve secure data sharing, a federated learning-based logistics data sharing scheme is proposed. Using federated learning to federate multiple sources of data for modelling, the reputation value of each enterprise is stored on the blockchain and the enterprises that provide high quality data sharing are rewarded. Finally, the effectiveness of the scheme and the impact of data quality and algorithm selection on model training are verified through simulation experiments.
A high performance bitcoin trading strategy prediction model

First released as open source in 2009 by the pseudonym Satoshi Nakamoto, Bitcoin is the longest running and best known cryptocurrency. Bitcoin's transaction history is characterised by openness and transparency, and Bitcoin has become an important part of financial transactions. Therefore, it is increasingly important to be able to make accurate predictions about the development of the Bitcoin market. In this study, we construct a prediction model for bitcoin trading strategies based on the LightGBM algorithm, and show that our model has an accuracy of over 95.1%. The results show that our model achieves an accuracy of over 95.1% and has a higher performance compared to popular machine learning models.
Database management and data mining

Today, driven by new technologies such as cloud computing, big data, the Internet of Things and artificial intelligence, the era of artificial intelligence has arrived. "Digital transformation" has become the only way for enterprises to seek breakthroughs and innovations. As the carrier of information, the applications of data are fundamental. This paper discusses the database construction, data mining, analysis methods and the application of optimizing the database. Each of these applications will be discussed in detail.
Sequential recommendations based on time series and dynamic interest for TV program

Within the background of the rapid development of information technology, cable TV has lost plenty of users under the shock of audio-visual media. While the study of cable TV recommendation technology can be of use to provide users with personalized recommendations. For the problem that the sequence information of cable TV programs is made full use of in the current recommendation technology research, a sequential recommendations model based on time series and dynamic interest (SR-TSDI) is proposed in this paper. It alleviates the sparsity of the data and fully considers the user’s dynamic interest change process. Comprehensive experiment results improved by up to 6.09% in the accuracy indicator and 5.50% in the ranking indicator.
Detection of violent crowd behavior based on mean kinetic streak flow

With the frequent occurrence of global security problems, violent crowd behavior endangers public security seriously. Meanwhile, intelligent surveillance video technology can be applied for violent crowd behavior detection as more and more surveillance cameras are installed in public and sensitive areas. In this paper, we propose a novel mean kinetic violent flow (MKViF) algorithm for violent crowd behavior detection by extracting the kinetic energy feature of video flow. Specifically, A is firstly calculating the mean kinetic energy by streak flow of each corner in each frame. Then, we obtain a binary indicator of kinetic energy change by calculating the amplitude change between sequence frames. Finally, the MKViF vector for a sequence of frames is obtained by averaging these binary indicators of each pixel in all frames. Experimental results show that the proposed MKViF algorithm behaves better in classification performance and real-time processing performance (45 frames per second) than the existing algorithms.
Tracking pedestrians from a moving camera based on Kalman filter

The target tracking and object tracking are defined in this paper and the difference between multi-target tracking and multi-object tracking is also be illustrated. The Bayes filter, Kalman filter, EKF, JPDA and Hungarian Algorithm are introduced with formulars and an example of moving camera to track the pedestrians used by Kalman filter are shown. In this example, the method which is based on Kalman filter that track pedestrians from a moving car which is installed with camera in the field of the multi-object tracking is analyzed with steps. The algorithm initializes boundary boxes to track the pedestrians and predict the pedestrians based on the previous position. Then, update the tracks and delete the useless tracks. The final step is creating the tracks. After displaying the result, the algorithm based on Kalman filter can successfully track the pedestrians with boundary boxes. However, when the camera is moving fast, some of the pedestrians cannot be recognized.
Feature Selection and Parameter Optimization of Optimized Extreme Learning Machine for Motor Fault Detection

In view of the disadvantage of existing motor fault detection algorithms that they cannot adapt to complex and non-linear fault categories and the accuracy is relatively low., we proposed one method for feature selection and parameter optimization based on simulated annealing-based whale optimization algorithm (SAWOA) optimized extreme learning machine (ELM) are proposed. Based on the ELM classification model., SAWOA is used to optimize the selection of network input variables and parameters of hidden layer nodes of ELM. To verify the effectiveness of the proposed method., it is applied to the real motor fault detection example., and compared with the existing methods under the same conditions. The experiment results show that the method achieves good performance in aspect of the classification accuracy and reliability, and show its effectiveness and application potential.
Correlation analysis and prediction between bitcoin price and its influencing factors

As the oldest and most famous cryptocurrency, the price of Bitcoin has increased nearly 2 million times in the last decade. As a result, predicting the price of Bitcoin through machine learning has become a big hit in recent years. This paper analyzes the correlation between the price of Bitcoin and market or social factors that may affect the price of Bitcoin. Then the author uses these factors with higher correlation to predict the price of bitcoin by LSTM. The experiments show that the average absolute percentage error of the LSTM prediction of bitcoin price decreases from 11.52% to 10.16%, 9.79%, 9.73%, 9.59%, 8.82%, and 8.50%, respectively, after the introduction of external correlation factors.
Vehicle Dynamics Trajectory Planning: Minimum Violation Planning Modifications Reducing Computational Time

State trajectory planning is recently one of the main challenges for self-driving/autonomous vehicles technology. The Minimum Violation Planing (MVP) approach provides a reliable and user intuitive framework for states trajectory planning. It provides possibility of various logic constraints, boundary conditions, dynamic constraints, and many others. The MVP is revisited in this paper with a special focus on the time efficiency of the algorithm. Two modifications of the MVP reducing the calculation time while not significantly compromising the trajectory quality are proposed in this paper. The vehicle yaw, yaw rate, north and east position, velocity, and battery state of charge variables planning is selected to compare the proposed planning framework modifications. The presented modifications and the original MVP algorithm are compared in selected test scenario, where significant calculation time reduction is shown while the plan optimality is not affected remarkably.
An empirical study on the prediction of accountability and transparency score of charities: based on binary logistic regression model

In recent years, charities around the world have experienced frequent trust crises. As the country with the most advanced philanthropy, the United States has experienced many crises of confidence in its history. This paper selects the detailed information of 8,408 different American charities as research samples, conducts data exploration through factor analysis and comparative analysis, and uses binary logistic regression analysis to construct a linear model to obtain the correlation between various financial indicators and Accountability & Transparency Score. The relationship and correlation coefficient have good predictability. At the same time, according to the model results, reminding the financial indicators that charities need to focus on to improve accountability and transparency has certain guiding significance for the development of charities.
Research on personal loan default prediction based on ISSA-XGBoost

In recent years, the scale of loans in China has been increasing, and so has the credit risk. Yet the credit risk assessment system is still in the initial stage of development. To improve the accuracy of default loan prediction, this paper proposes ISSA-XGBoost model based on an improved way of passing input parameters. The model uses the Sparrow Search Algorithm (SSA) to optimize the parameters of eXtreme Gradient Boosting (XGBoost). Since the search strategy of SSA includes convergence to the origin direction, this paper changes the form of input parameters. In the form, the closer the parameters in SSA are to the origin, the more XGBoost under these parameters can avoid overfitting. SSA searches the optimal with the changed parameter form and outputs feasible solutions. Then, the model transforms their solutions back to the original form and inputs them into XGBoost. After the above processes, SSA can avoid overfitting while searching for optimal solutions. Using the improved SSA to optimize XGBoost, the ISSA-XGBoost loan default prediction model is established. The empirical result shows that the model outperforms SSA-XGBoost under the four metrics: ACC, AUC, KS, and BS. And it is significantly better than that of XGBoost optimized by Particle Swarm Optimization (PSO-XGBoost). At the same time, compared with SSA-XGBoost, the AUC score difference between the training set and the test set of ISSA-XGBoost is smaller, which indicates that ISSA-XGBoost can better avoid overfitting.
Analysis of the readiness for change in the implementation of a 3d printing initiative in a catalan tertiary hospital using the normalisation process theory (Preprint)

<sec> <title>BACKGROUND</title> the high failure rate of innovation projects motivates to understand the perceptions about resistances and barriers of the main stakeholders to improve success rates. </sec> <sec> <title>OBJECTIVE</title> this study aims at analysing the readiness for change in the implementation of a 3D printing project in a Catalan tertiary hospital, prior to its implementation. </sec> <sec> <title>METHODS</title> online, voluntary, and anonymous survey using the Normalisation MeAsure Development questionnaire (NoMAD) to gather views and perceptions among a selected group of healthcare professionals at Germans Trias i Pujol University Hospital. </sec> <sec> <title>RESULTS</title> all groups of professionals involved see the value of the project and are willing to enrol and support it. Respondents show the highest scores in Cognitive Participation (4.45/5) and high scores in Coherence (3.83/5) and Reflective Monitoring (3.78/5). The weakest score is in Collective Action (3,52/5). There is no statistically relevant score differences between roles along the survey. </sec> <sec> <title>CONCLUSIONS</title> the 3D project implementation should pay attention to preparing, defining, sharing, and supporting the operational work involved in its use and implementation as well as to understand, assess and communicate the ways that the new set of practices affect the users and others around them. We suggest that health officers and politicians consider this experience as a solid ground towards development of a more efficient health innovation system and as a catalysator for transformation. </sec>
Spam detection using Catboost integration algorithm

This paper introduces the background and significance of building a CatBoost-based spam detection model and proposes a new research approach on the classical research model. Besides, a large number of network resources are occupied which makes 85% of the system resources of the mail server are used for the identification of spam. It is not only a waste of resources, but may even lead to network congestion and paralysis, affecting the normal business email communication of enterprises. In this study, we use the Enron-Spam dataset, which is currently the most publicly available dataset used in email-related research. First, we use word bagging processing and TF-IDF processing for feature extraction, and then CatBoost integration algorithm is used for training. The final accuracy of the model is more than 98%. Compared with the conventional model, the model has better performance, which can effectively improve the accuracy of spam detection and identification.
Efficient reinforcement learning for reversi AI

Reversi (or Othello) is a simple and popular board game played on an eight-by-eight board. In the field of reinforcement learning, searching of the game tree of Reversi is widely studied as a classic problem, since it has a small board and thus a state space not too complex to analyze. Monte Carlo tree search (MCTS) is a heuristic search algorithm for decision tree search, which is often applied to the AI methods for board games, such as the application of AlphaGo in the field of Go games. We modify and apply the Monte Carlo tree search strategy to Reversi AI. Applying some engineering optimizations (such as multithreading), we achieve significant results with high time efficiency.
Transaction fraud persistence based on graph algorithm

At the time mobile devices and online payment make people’s life more convenient, they caused an increasing number of fraud cases in recent years. In this paper, we represented trading data as graphs by graph machine learning and setting up a high-performance model which could detect fraudulent transactions automatically. The datasets that the paper used were the fraudulent transactions dataset on Kaggle’s credit cards. By random under-sampling, it was processed and shown as bipartite graphs which were substituted into our training models after being processed by graph embedding algorithm. Finally, the optimal model was found by the coming out results. The result reveals that average embedder algorithm could detect fraud more precisely than the other three algorithms.
A novel method of Chinese text content analysis and mining based on statistical models

With the accumulation of various kinds of text data, it is no longer possible to generalize or classify them by manual reading, so how to use statistical models to mine text data reasonably and effectively has become an important issue in academic research and practical work. This paper discusses three problems of Chinese text mining: word separation, keyword extraction and text classification. For the word separation problem, the Cascaded Hidden Markov Model and the WDM that treats the segmentation between words as missing data and solves it with the EM algorithm are introduced. For the keyword extraction problem, this paper proposes a Bayes factor and introduces CCS using sparse regression. For the text classification problem, the method of building a classifier based on the frequency of keywords and the method of building a classifier based on the probability of the topic first are introduced. We give the respective advantages of each method by comparing the above methods with two datasets using SVM and Random Forest and make suggestions of their use.
Harmonic analysis in tide analysis

Tide is a natural phenomenon which has important impact on port engineering, earthquake forecasting, shipping and many other fields. But in some complex terrains, tide and tide strain are hard to analyze and anticipate. Harmonic analysis plays an important role when researching tide. This passage uses derivation of math and physics to elaborate the essence of tidal strain observation combination and uses it to analyze tidal strain observation combination from some main gauging station in China. Besides, this passage uses t-tide tide analysis tool to analyze and anticipate the tide along the sea area of the Guangdong Province by harmonic analysis and compares it with the observation. The conclusion shows that our analysis on tidal strain has a high accuracy, and it is precise enough for earthquake scholars to research. In addition, using harmonic analysis to forecast tide is precise and stable. The residue error follows the normal distribution, and its average is less than 0.01. The maximum of variance is 0.2294, the minimum is 0.1732. The length of confidence interval is less than 0.01. Using the information of three months and one year is almost unanimous and shows that different seasons will not influence the conclusion.
A Literature Review on Mathematical Morphology Based Image Segmentation Techniques

Digital image processing is an emerging technology and has found vast applications in biomedical imaging, stellar imaging, remote sensing and radar imaging, the study of climatology, seismology, seabed studies and exploration of earth resources. Image segmentation process of any digital image has been found important in the second level of processing and the main aim of this level of processing is to generate and feed the information to the computer machine for subsequent higher level image processing. Morphology based image segmentation has occupied a significant position in state of art image processing and it needs a thorough investigation for the betterment of the subject concerned and for the ease of handling of the massive data by the machines. The present chapter has aimed at to present a thorough and exhaustive literature study on various aspects of segmentation of digital images using morphological approach.
Cultural metacognition: a large, well-stocked, organized and illuminated toolshed in the mind

Since the introduction of cultural intelligence (CQ), extraordinary numbers of articles have been published associated with CQ. To date, there is no clear work to explicate cultural-metacognition (CM) - one of the central elements of CQ. The chapter unpacks CM, identifying the elements and mechanics of CM, and how CM functions using knowledge and skills to adapt and generate new appropriate behaviors within and in different cultures. Cultures are presented as hybrid-systems that socially construct solutions for existing basic problems of survival in societies that are organized in schematic frameworks of interdependent-action-strategies. Cognition and metacognition are discussed in terms of automatic- and deliberative-processes which are involved in activating one’s interdependent network of schemas in cultural metacognition. The metaphor of a large, well-stocked, organised, illuminated toolshed in the mind is used to discuss CM processes, where functionally-equivalent toolkits are mixed-and-matched to create new solutions for problems within cultures and cross-culturally.
Global-to-Local Modeling for Video-based 3D Human Pose and Shape Estimation

Video-based 3D human pose and shape estimations are evaluated by intra-frame accuracy and inter-frame smoothness. Although these two metrics are responsible for different ranges of temporal consistency, existing state-of-the-art methods treat them as a unified problem and use monotonous modeling structures (e.g., RNN or attention-based block) to design their networks. However, using a single kind of modeling structure is difficult to balance the learning of short-term and long-term temporal correlations, and may bias the network to one of them, leading to undesirable predictions like global location shift, temporal inconsistency, and insufficient local details. To solve these problems, we propose to structurally decouple the modeling of long-term and short-term correlations in an end-to-end framework, Global-to-Local Transformer (GLoT). First, a global transformer is introduced with a Masked Pose and Shape Estimation strategy for long-term modeling. The strategy stimulates the global transformer to learn more inter-frame correlations by randomly masking the features of several frames. Second, a local transformer is responsible for exploiting local details on the human mesh and interacting with the global transformer by leveraging cross-attention. Moreover, a Hierarchical Spatial Correlation Regressor is further introduced to refine intra-frame estimations by decoupled global-local representation and implicit kinematic constraints. Our GLoT surpasses previous state-of-the-art methods with the lowest model parameters on popular benchmarks, i.e., 3DPW, MPI-INF-3DHP, and Human3.6M. Codes are available at https://github.com/sxl142/GLoT.
A Note on the Welfare Gap in Fair Ordering

Public blockchains group submitted transactions into batches, called blocks. A natural question is how to determine which transactions are included in these batches. In this note, we show a gap between the welfare of so-called `fair' ordering, namely first-in-first-out (an ideal that a number of blockchain protocols strive to achieve), where the first transactions to arrive are the ones put into the block, and the welfare of `optimal' inclusion that is, at least approximately, welfare-maximizing, such as choosing which transactions are included in a block via an auction. We show this gap is positive under a simple model with mild assumptions where we assume transactions are, roughly speaking, uniformly drawn from a reasonable distribution. Our results formalize a performance metric for blockchain inclusion rules and consequently provide a framework to help design and compare these rules. The results can be directly extended to ordering mechanisms as well.