Reviewerly/exHarmony huggingface dataset:
Research on the College Students’ Venture Risk Assessment Model Based on the LightGBM Algorithm

There are numerous risk factors in the school students’ entrepreneurial process. In order to advance the accuracy of school students’ entrepreneurial risk assessment within entrepreneurial practice background and reduce risk management factors, a model of school students’ entrepreneurial risk assessment grounded on the LightGBM algorithm is proposed. We construct the statistical probability density distribution and constraint parameter model of college students’ entrepreneurial risk distribution in an entrepreneurial practice environment. Moreover, we adopt the factor hierarchical classification technology of the evaluation index system to realize the framework model construction and big data fusion processing of college students’ entrepreneurial risk indicators in the entrepreneurial practice environment. Then, we extract the joint similarity feature set of college students’ entrepreneurial risk factors in the entrepreneurial practice environment and adopt the LightGBM feature detection and fuzzy matching technology. The self-adaptive optimization and fuzzy clustering analysis are realized in the procedure of school students’ entrepreneurial risk assessment under the entrepreneurial practice environment, and a fuzzy Bayesian network assessment model for school students’ entrepreneurial risk assessment under the entrepreneurial practice environment is constructed. The Delphi method is used to determine the pairwise influence relationship between factors, and the LightGBM algorithm is used to optimize the evaluation of college students’ entrepreneurial risk factors under the entrepreneurial practice environment and match rough set features. According to the comprehensive evaluation of entrepreneurial risk factors, the intelligent entrepreneurial risk assessment under the entrepreneurial practice environment is comprehended. The simulation outcomes confirm that the intelligence of college students’ entrepreneurial risk assessment in the entrepreneurial practice environment is improved, and the precision of risk assessment is greater than other approaches.
Cross-Section Dimension Measurement of Construction Steel Pipe Based on Machine Vision

Currently, the on-site measuring of the size of a steel pipe cross-section for scaffold construction relies on manual measurement tools, which is a time-consuming process with poor accuracy. Therefore, this paper proposes a new method for steel pipe size measurements that is based on edge extraction and image processing. Our primary aim is to solve the problems of poor accuracy and waste of labor in practical applications of construction steel pipe inspection. Therefore, the developed method utilizes a convolutional neural network and image processing technology to find an optimum solution. Our experiment revealed that the edge image that is proposed in the existing convolutional neural network technology is relatively rough and is unable to calculate the steel pipe’s cross-sectional size. Thus, the suggested network model optimizes the current technology and combines it with image processing technology. The results demonstrate that compared with the richer convolutional features (RCF) network, the optimal dataset scale (ODS) is improved by 3%, and the optimal image scale (OIS) is improved by 2.2%. At the same time, the error value of the Hough transform can be effectively reduced after improving the Hough algorithm.
Application of Embedded Intelligent Target Detection System in Basketball Movement Test

With the development of society, basketball has been deeply loved by people all over the world. Meanwhile, basketball has become one of the most watched events in the world. In order to adapt to the development of basketball and make people better understand the laws of basketball, this paper aims to study the application of embedded intelligent object detection system in basketball sports test and use the embedded intelligent object detection system to separate basketball sports. Compared with the general computer processing system, the embedded system has great differences. It cannot realize the large-capacity storage function because there is no matching large-capacity medium. Strengthen the learning of basketball theoretical knowledge through learning rules. Firstly, the research background of basketball and the research significance of moving target detection are introduced, and the embedded Linus operating system is introduced, including hardware design and software design. Then, four moving target detection algorithms are proposed. Object detection, also called object extraction, is an image segmentation based on the geometric and statistical features of objects, and its accuracy and real-time performance are an important capability of the entire system. Experiments show that when using the YCrCb domain detection algorithm, the average time-consuming is 292 ms, and the conversion frame rate is about 3.5fps. The YCrCb domain detection algorithm has good detection effect and high efficiency.
Learning Embedding for Signed Network in Social Media with Hierarchical Graph Pooling

Signed network embedding concentrates on learning fixed-length representations for nodes in signed networks with positive and negative links, which contributes to many downstream tasks in social media, such as link prediction. However, most signed network embedding approaches neglect hierarchical graph pooling in the networks, limiting the capacity to learn genuine signed graph topology. To overcome this limitation, this paper presents a unique deep learning-based Signed network embedding model with Hierarchical Graph Pooling (SHGP). To be more explicit, a hierarchical pooling mechanism has been developed to encode the high-level features of the networks. Moreover, a graph convolution layer is introduced to aggregate both positive and negative information from neighbor nodes, and the concatenation of two parts generates the final embedding of the nodes. Extensive experiments on three large real-world signed network datasets demonstrate the effectiveness and excellence of the proposed method.
Outbound Data Legality Analysis in CPTPP Countries under the Environment of Cross-Border Data Flow Governance

The governance of cross-border data flows around the digital economy, data security, and data sovereignty has become a crucial global governance issue. This paper evaluates the legitimacy of data exit rules of CPTPP countries based on machine learning algorithm models under the perspective of cross-border data flow governance. In this study, four machine learning algorithms, namely, logistic regression, decision tree, random forest, and GBDT, are used to build an outbound data assessment and evaluation model. The confusion matrix is used to classify the outbound data legitimacy dichotomously. The recall, precision, and F1 scores are evaluated to compare the empirical results of each model. Based on this, a logistic regression-based outbound data risk scoring model is introduced to quantify the outbound data risk at a deeper level and to classify the outbound data risk level for the reference of regulators to make more scientific and reasonable decisions. The experimental results show that the machine learning models can meet the needs and applications of practical work and make accurate predictions of outbound data risks.
Three-Dimensional Reconstruction of Shoe Soles via Binocular Vision Based on Improved Matching Cost

Aiming at the problem that the toe cap and upper part of the sole of a shoe easily appear missing when using binocular vision to reconstruct the shoe sole in the industrial production process, an improved matching cost calculation method is proposed to reconstruct shoe soles in three dimensions. Firstly, a binocular vision platform is built, and Zhang’s calibration method is used to obtain the calibration parameters. Secondly, the method of fusing Census and BT costs is used to calculate the matching cost of the image, so that the matching cost calculation result is more accurate. On this basis, 4-path aggregation is performed on the obtained cost, and the optimal matching cost is selected in combination with the WTA algorithm. Finally, left–right consistency detection and median filtering are used to optimize the disparity map and combine the camera calibration parameters to reconstruct the shoe sole in three dimensions. The experimental results show that the average mismatch rate of the four images on the Middlebury website in this method is about 6.57%, the reconstructed sole point cloud contour information is complete, and there is no material missing at the toe and heel.
CORPURES: Benchmark corpus for urdu extractive summaries and experiments using supervised learning

Text summarization is the process of shortening the text so that it conveys the key points. Several text summarization methods and benchmark corpora are available for languages like English. A significant hurdle in developing and evaluating existing or new text summarization methods is the unavailability of standardized benchmark corpora, especially for South Asian languages. Among other things, a reference corpus enables researchers to compare existing state-of-the-art methods. Our study addresses this gap by developing a benchmark corpus for one of the widely spoken yet under-resourced language Urdu. The reported corpus contains 161 documents with manually written extractive summaries from the newswire domain. We also perform several experiments on the corpus to show how it can be used to develop, evaluate, and compare text summarization systems using a supervised learning approach for the Urdu language. Our results show that the state of the art classifiers are good candidates for Urdu text summarization when supervised learning techniques are employed. Also, a radical word segmentation technique such as fixed-length segmentation outperforms all other settings (Senetnce Match F1=57%, ROUGE-2 F1=64.4%). On the basic preprocessing of Urdu texts, we observe that tokenization of words on space is a reliable approach until the proper word segmentation tools for Urdu are mature enough. On word similarity features needed for supervised learning, it is observed that a radical stemming such as Ultra stemming with length (1 and 2) works better than the existing stemming and lemmatization tools for Urdu. Finally, the artificially generated datasets do not significantly improve results compared to the original data.
Geological control for in-situ and recoverable resources assessment: A case study on Sarcheshmeh porphyry copper deposit, Iran

The incorporation of geological controls is essential for an accurate assessment of the in-situ and recoverable resources in an ore deposit, directly impacting the downstream stages of mining projects. Commonly, the mineral resources evaluation is carried out hierarchically, considering the definition of geological domains first and then predicting or simulating the metal grades within each domain. Nevertheless, this approach assumes a weak correlation between the metal grades across the domain boundaries, which could not be suitable when gradual variations of the grades are observed across these boundaries. To account for the latter scenario known as soft boundaries, we compare three approaches: (i) a hierarchical prediction of the geological domain indicators and the grade within each domain, (ii) a direct prediction of partial grades, defined as the product of the grade and a geological domain indicator, and (iii) a joint simulation of the grade and geological domain indicators. A porphyry copper deposit in which the copper grade is controlled by mineralogical and rock type domains is used as a case study. When compared with production data, the proposed approaches generate more precise predictions than the traditional approach consisting in accounting for the hard boundaries between mineralogical domains but ignoring the soft boundaries between rock types. The joint simulation approach provides more realistic grade variations across the rock type boundaries, allows for an unbiased prediction of the recoverable resources, and quantifies the uncertainty on these resources based on multiple grade outcomes. Ultimately, we emphasize that identifying the geological controls and the nature (hard or soft) of the geological boundaries, and then defining the proper approaches to account for them is necessary to accurately assess the in-situ and recoverable resources.
Synthetic Data Generator for Solving Korean Arithmetic Word Problem

A math word problems (MWPs) comprises mathematical logic, numbers, and natural language. To solve these problems, a solver model requires an understanding of language and the ability to reason. Since the 1960s, research on the design of a model that provides automatic solutions for mathematical problems has been continuously conducted, and numerous methods and datasets have been published. However, the published datasets in Korean are insufficient. In this study, we propose a Korean data generator for the first time to address this issue. The proposed data generator comprised problem types and data variations. Moreover, it has 4 problem types and 42 subtypes. The data variation has four categories, which adds robustness to the model. In total, 210,311 pieces of data were used for the experiment, of which 210,000 data points were generated. The training dataset had 150,000 data points. Each validation and test dataset had 30,000 data points. Furthermore, 311 problems were sourced from commercially available books on mathematical problems. We used these problems to evaluate the validity of our data generator on actual math word problems. The experiments confirm that models developed using the proposed data generator can be applied to real data. The proposed generator can be used to solve Korean MWPs in the field of education and the service industry, as well as serve as a basis for future research in this field.
Event-Based Camera Simulation Wrapper for Arcade Learning Environment

Event-based cameras are cameras with high dynamic range that measure changes in light intensity at each pixel instead of capturing frames like traditional cameras. There are several event-based camera simulation software libraries that can convert videos or collections of frames to a stream of simulated camera events. To the authors’ knowledge, with the exception of vehicle control projects, there are no software libraries that simulate event-based camera activity online during an agent’s training phase on other various control applications. This work introduces ALE_EBC, a software wrapper around the Arcade Learning Environment that converts game frames to simulated event-based camera event streams, allowing agents to be trained on a variety of control-based Atari games through the lens of a neuromorphic camera.
Cooperative platforms for curating and managing digitally recorded finds data

Abstract This chapter focuses on archaeological collection management and the curation of digital information through a case study of the practices that lie behind FindSampo, an open-access service under development for archaeological finds made by the public in Finland. Citizen science is at the centre of this study. It reflects the importance of cooperation with metal detectorists and others and the potential that it entails for archaeology and heritage management. FindSampo, developed by the multidisciplinary SuALT project, introduces innovative solutions to enhance the research and management of archaeological find data. The solutions of FindSampo concern semantic computing and Linked Open Data, and, for example, ontology work on archaeological objects and the validation process of reported finds. In conclusion, the chapter emphasizes the importance of the democratization of archaeological information in the management of archaeological collections.
Adversarial robustness in meta-learning and contrastive learning

In this chapter, we study how adversarial robustness can be transferred in two popular machine learning paradigms, meta-learning (metamodel training and few-shot adaptation) and contrastive learning (pretraining and fine-tuning).
Physical adversarial attacks

Physical adversarial attacks refer to the realization of a physical adversarial object that evades the prediction of a machine learning model. This chapter summarizes principles to generate such adversarial objects and introduces several realizations of such physical adversarial examples.
A Critical Review on Hand Gesture Recognition using sEMG: Challenges, Application, Process and Techniques

Abstract Hand gesture recognition systems are gaining popularity these days due to the ease with which humans and machines can communicate. The goal of hand gesture development is to improve interactions between humans and computers for the purpose of transmitting ideas. In a typical HGR systems, the main steps followed are, data collection, pre-processing, feature extraction and classification. For every stage, a significant number of techniques are available with various other sub steps. This study gives an overview of modern hand gesture recognition techniques, its Physiological and Anatomical Background, working and challenges faced by these systems. Moreover, the role of artificial intelligence in optimizing the performance of HGR systems is also delineated in this paper. Also, the precision and accuracy of the HGR approaches gets affected by the complexity and diversity of various hand movements, therefore, the need for implementing AI based ML and DL methods keeps on rising. Keeping this in mind, the performance of various ML algorithms in recognizing the visual and sensor-based hand gestures is investigated. Moreover, the commonly utilized framework in detecting hand gestures has been explored in numerous standard datasets.
An Approach to Pattern Recognition for Identification of Devnagari Script Based on Fingertips and Palm

Abstract In this paper, we present an finger point based signed language symbol to text identification and classification algorithm that is based upon RGB image datasets. The palm sized images based upon different sizes, backgrounds, orientation are captured to be preprocessed as per the requirements of developing a convolution neural network based algorithm. This algorithm utilizes Alexnet for the preprocessing requisites where in 47 symbols of Devanagari script are augmented based on the reference rulebook created for our requirements as highlighted in the paper. At the primary level this algorithm provides an excellent classification rate which promises upliftment for our research in the upcoming future. We have provided detailed steps and discussion on the classification parameters considered for our algorithm which is implemented on MATLAB platform with the help of machine learning solution libraries.
Improve Content-based Image Retrieval using Deep learning model

Abstract The complexity of multimedia has expanded dramatically as a result of recent technology breakthroughs, and retrieval of similar multimedia material remains an ongoing research topic. Content-based image retrieval (CBIR) systems search huge databases for pictures that are related to the query image (QI). Existing CBIR algorithms extract just a subset of feature sets, limiting retrieval efficacy. The sorting of photos with a high degree of visual similarity is a necessary step in any image retrieval technique. Because a single feature is not resilient to image datasets modifications, feature combining, also known as feature fusion, is employed in CBIR to increase performance. This work describes a CBIR system in which combining DarkNet-19 and DarkNet-53 information to retrieve images. Experiments on the Wang (Corel 1K) database reveal a considerable improvement in precision over state-of-the-art classic techniques as well as Deep Convolutional Neural Network(DCNN).
Implementing the Adaptation Procedure in Misinformation Games

In this paper we consider scenarios where players interact having a possibly incorrect information regarding the interaction they participate. Building upon the recently proposed framework of misinformation games, we develop a time-discrete iterative procedure, where in each step players make their decisions according to the (possibly erroneous) information they have. Then the joint decision is revealed to anyone, alongside the payoffs it has for each player, according to actual specifications of the interaction. With this at hand, players update their information and (possibly) adapt their behaviour in the next step. We call this process the Adaptation Procedure. After the formal establishment of the Adaptation Procedure we implement this methodology using Logic Programming, and more specifically Answer Set Programming. Hence, we provide an integrated pipeline that analyses interactions where participants engulf new information and re-evaluate their behaviour.
Human Activity Recognition using Time Series Feature Extraction and Active Learning

Today, portable devices like smartwatches and smartphones have made a great impact in human's wellbeing. From sleep monitoring to exercise scheduling, Human Activity Recognition had played a major role in the habits of the people. In this work, we exploit a Time Series dataset that describes a Human Activity Recognition signal. In the beginning, we extract the features oriented on Spectral, Statistical and Temporal domains. Then, we construct a dataset for each domain and we calculate the classification results using a number of different classifiers. In the sequel, we apply Active Learning techniques and calculate their classification accuracy performance using a small portion of the original datasets as initial labeled set. Finally, we compare the original results with the ones produced with Active Learning methods.
Early Fusion of Visual Representations of Skeletal Data for Human Activity Recognition

In this work we present an approach for human activity recognition which is based on skeletal motion, i.e., the motion of skeletal joints in the 3D space. More specifically, we propose the use of 4 well-known image transformations (i.e., DFT, FFT, DCT, DST) on images that are created based on the skeletal motion. This way, we create “activity” images which are then used to train four deep convolutional neural networks. These networks are then used for feature extraction. The extracted features are fused, scaled and upon a dimensionality reduction step they are given as input to a support vector machine for classification. We evaluate our approach using two well-known, publicly available, challenging datasets and we demonstrate the superiority of the fusion approach.