Reviewerly/exHarmony huggingface dataset:
Research Status and Future Development Direction of Chinese Sports and Geographical Environment

Abstract The formation and development of sports events originate from a specific geographical environment. At present, the research on sports and geographical environment in China is relatively weak compared with foreign countries, and the breadth and depth of research are also a certain gap. The related research of the environment is integrated, and the research in this field is mainly concentrated on the following aspects: 1. The impact of the geographical environment on sports, including the relationship between the generation of sports events and the geographical environment, and the geographical environment on the performance of sports competitions Impact, climatic conditions on sports, etc.; 2. The impact of geographic environment on sports participants includes the training methods, methods and physiological functions of athlete groups, and the effects of physical exercisers' physical behavior and physical health; 3. The relationship between sports and environmental protection and other aspects. At the same time, it also made a rough prediction on the future research directions in the field of sports and geographical environment, in order to be able to contribute to the research in this field.
The Planar/Hyper-Planar Rotated Polar Coordinate System and Its Mathematical Solid Vector Addition, Multiplication, Division, Dot Product and Cross Product Operations

In geometry, the curvilinear coordinate systems have curved coordinate lines. They are used to localize physical or virtual quantities in image processing computer science, space science, earth science, cartography, quantum mechanics, relativity, and other engineering sciences. Their Mathematical operations are useful for defining the motion of these quantities and objects under the influence of central forces, and solving equation of curved boundary conditions. The typical ones are the correct 2D Polar mathematical operations; and the wrong 3D Spherical mathematical operations. The problem statement is that there is no available definition of curvilinear one that is 3D and correct, except the Solid Vector Subtraction operation which was recently proposed and used to develop 3D spatial filters of field of vectors for geometrical edge detection in 3D image processing science. In this research, this Solid Vector Subtraction is extended by proposing its 3D and correct definitions of: (1) Rotated Polar coordinate system; (2) 2D, 3D, and hyper dimensional space; and (3) Other whole complementary set of Solid Vector Addition, Multiplication, Division, Dot Product, and Cross Product operations. Because their justification has a long derivation, it is for future work. These Solid Vector operations are 3D and correct.
Controlling entity integrity with key sets

Codd's rule of entity integrity stipulates that every table has a primary key. Key sets can control entity integrity when primary keys do not exist. While key set validation is quadratic, update maintenance for unary key sets is efficient when incomplete values only occur in few key columns. We establish a binary axiomatization for the implication problem, and prove its coNP-completeness. However, the implication of unary by arbitrary key sets has better properties. The fragment enjoys a unary axiomatization and is decidable in quadratic time. Hence, we can minimize overheads before validating key sets. While Armstrong relations do not always exist, we show how to compute them for any instance of our fragment. Similarly, we show how unary keys sets can be mined from relations using hypergraph transversals. Finally, we establish an axiomatization and computational complexity for the implication problem of key sets combined with NOT NULL constraints.
Multi-Classes Emotion Detection for Unbalanced Indonesian Tweets

Research concerning Twitter mining is becoming an exciting research topic in recent years. Emotion detection is one such research area that uses microblogs, such as Twitter, to discover emotions from textual data. There was a modern technique using graph-based was introduced to extract patterns that bear emotion. The system has achieved good performance in different western languages. By adopting the approach, we are motivated to enhance the performance of emotion detection for Indonesian language. We use eight emotions, i.e., joy (senang), sadness (sedih), fear (takut), surprise (terkejut), disgust (jijik), anticipation (antisipasi), trust (percaya), and anger (marah). The data distribution among the emotions is unbalanced, making the Indonesian language system has low precision. This study proposed an adjusting pattern weight to address the unbalanced data problem for the Indonesian language. The experiment results show that the proposed approach can significantly improve the precision of minority classes (i.e., fear, surprise, and disgust).
Region of Interest-Based Encryption of Biomedical Image

Encrypting the particular region of an image is of wiser than encrypting the entire image, which helps in a decrement of the computational time in the encryption process. In this paper, the biomedical images of baby, brain, lungs, and retina are taken, and by utilizing Otsu thresholding and contours, extraction of the region of interest (ROI) is done. After that, generated region of interest is encrypted using RSA, Elgamal, and LEA techniques. The computational times of above mentioned encryption techniques are performed and compared in this manuscript. Python programming is used for compilation and calculating computational times.
A Novel Text Personalized Analysis Model Based on BP Neural Network

In online interactive platform, text analysis has greatly changed people's communication, thinking, and promoted the explosive growth of user-generated information. A large number of texts generated by users have become one of the most representative data sources of big data in recent years. Mining and analyzing user-generated information has become essential part on research of social development. The sentiment analysis on social media text as an information processing technology for analyzing, processing, summarizing and reasoning subjective texts with emotions has received extensive attention in academia and industry in recent years, and has been used among many areas of social media and many applications. The traditional text sentiment analysis research work mainly focuses on analyzing emotions from texts, but ignores the individualized differences of users in emotional expression, thus affecting the quality of analysis results. To solve the problems, this paper is about solving the problem of personalized sentiment analysis of social media texts. Considering the wide application of BP neural network technology in social media was proposed to solve the possible challenges of social media text personalized sentiment analysis.
Theoretical-Pragmatic Overview of Learning Motivation

The article discusses the motivation to learn as a part of the subject-subject pedagogical interaction. It is presented as an active multifaceted pedagogical, psycho-age and social problem. And also as a problem related to the overall development of school education. The problem of motivation in the school environment is considered theoretically. On this basis, references are made to the various practical situational models proposed in the scientific literature. Motivation is shaped as a specific problem area that is directly related to the school environment; the intellectual and cognitive development of the student; methodical and didactic skills of the teacher.
Trade-off Between Efficiency and Effectiveness: A Late Fusion Multi-view Clustering Algorithm

Late fusion multi-view clustering (LFMVC) algorithms aim to integrate the base partition of each single view into a consensus partition.Base partitions can be obtained by performing kernel k-means clustering on all views.This type of method is not only computationally efficient, but also more accurate than multiple kernel k-means, and is thus widely used in the multi-view clustering context.LFMVC improves computational efficiency to the extent that the computational complexity of each iteration is reduced fromwhere n is the number of samples).However, LFMVC also limits the search space of the optimal solution, meaning that the clustering results obtained are not ideal.Accordingly, in order to obtain more information from each base partition and thus improve the clustering performance, we propose a new late fusion multi-view clustering algorithm with a computational complexity of O n 2 ð Þ.Experiments on several commonly used datasets demonstrate that the proposed algorithm can reach quickly convergence.Moreover, compared with other late fusion algorithms with computational complexity of O n ð Þ, the actual time consumption of the proposed algorithm does not significantly increase.At the same time, comparisons with several other state-of-the-art algorithms reveal that the proposed algorithm also obtains the best clustering performance.
Development of a Model for Predicting the Demand for Bilingual Teachers in Elementary Schools to Support Multicultural Families—Based on NEIS Data

Education-related administrative agencies intend to analyze NEIS data that has been in operation for over 15 years by applying big data and artificial intelligence technologies to predict social issues and establish policies. Therefore, this study developed a test model using big data to analyze the data of school sites and data held by administrative agencies and applied the actual data to this model to analyze the policy’s appropriateness and effectiveness. From 2013 to 2018, a learning model was created based on the number of bilingual teachers for elementary school students in multicultural families in Gyeongsangnam-do. As a result of predicting the number of bilingual teachers in 2015, there were 40, but the test model was analyzed to require 42. It was confirmed that the test model’s accuracy could be further improved if more data on the learning model is accumulated. This study’s results can help establish and implement policies in various educational fields through big data analysis.
Multi-Label Crowd Size And Location Recognition From Images

Excessive crowd concentration or movement may have devastating consequences. Technology-assisted crowd size monitoring systems can provide early warnings and apply preemptive safety measures. Existing automatic head-counting techniques are computationally complex, time-consuming, and rely on high image resolution. The study proposes a rapid crowd-size assessment from images that classifies crowd size in terms of three qualitative labels (large, small, and no crowd). In addition, it gives a simultaneous recognition of the crowd location (airport, mall, train station, and beach). At the first stage, the classification process applies convolutional neural networks (CNNs) trained to classify crowd size and location. At the second stage, a shallow neural network (NN) meta classifier is trained on the probability vectors generated by the first-stage CNNs to give further refinement of the classification outcomes. Validation experiments show that the second stage improves upon the first stage classification results. A comparison with baseline classification techniques using five different CNN architectures (Alex Net, ResNet50, ResNet101, InceptionV3, and Inception-ResNet) is included. The effect of the CNN complexity and the application of a single versus multiple first-stage classifiers on the overall classification results are discussed.
A Differentially Private Incentive Design for Traffic Offload to Public Transportation

Increasingly large trip demands have strained urban transportation capacity, which consequently leads to traffic congestion and rapid growth of greenhouse gas emissions. In this work, we focus on achieving sustainable transportation by incentivizing passengers to switch from private cars to public transport. We address the following challenges. First, the passengers incur inconvenience costs when changing their transit behaviors due to delay and discomfort, and thus need to be reimbursed. Second, the inconvenience cost, however, is unknown to the government when choosing the incentives. Furthermore, changing transit behaviors raises privacy concerns from passengers. An adversary could infer personal information (e.g., daily routine, region of interest, and wealth) by observing the decisions made by the government, which are known to the public. We adopt the concept of differential privacy and propose privacy-preserving incentive designs under two settings, denoted as two-way communication and one-way communication. Under two-way communication, passengers submit bids and then the government determines the incentives, whereas in one-way communication, the government simply sets a price without acquiring information from the passengers. We formulate the problem under two-way communication as a mixed integer linear program and propose a polynomial-time approximation algorithm. We show the proposed approach achieves truthfulness, individual rationality, social optimality, and differential privacy. Under one-way communication, we focus on how the government should design the incentives without revealing passengers’ inconvenience costs while still preserving differential privacy. We formulate the problem as a convex program and propose a differentially private and near-optimal solution algorithm. A numerical case study using the Caltrans Performance Measurement System (PeMS) data source is presented as evaluation. The results show that the proposed approaches achieve a win-win situation in which both the government and passengers obtain non-negative utilities.
Analysis of Min-max Algorithm and PSO Algorithm for Data Clustering

This paper propounded the approach of PSO (Particle Swarm Optimization) to cluster data. It has helped us to advent the use of PSO in finding the centroids of a cluster specification by the users. It then looked into two sections of PSO where the first part is using K-means clustering for seeding initial swarm and the second chunk uses PSO to sift the cluster formation by K-means. Unlike previous papers, our evaluation has been extended to some new datasets. Also the use and application of PSO is very pertinent in many sectors (Banking, Telecommunication, E-commerce, IT Industry, Hospital) as this algorithm provides comprehensive approach to a particular problem with its properties such as Fast convergence rate, minute amount of parameters and easy implementation without any computational complexities. But it faces certain fallacy and catastrophic results when the algorithm tries to learn on its parameters such as initial weight, acceleration coefficient (r1 and r2) and to avoid that it needs to be incorporated with some other unsupervised and efficient Machine Learning algorithm that is Min-max K-mean. This paper proposed an idea to get the initial cluster inputs from Min-Max K mean algorithm and to use that factor on the improved PSO algorithm where the stress to find the Gbest does not rely only on the initial parameters and the Particle's Best position (XJ)) can iteratively learned and enhanced their performance.
Design of a Linear Quantum Projection Filter

In order to reduce the computational cost for online calculation of the quantum filter, in this paper, a linear quantum projection filtering scheme is developed by projecting the time evolution trajectory of quantum system state onto a lower-dimensional linear submanifold spanned by a set of basis matrices. The lower-dimensional submanifold is obtained through an extended singular value decomposition algorithm. Compared with the existing works on quantum projection filtering, the proposed approach requires no prior knowledge on the structure of the submanifold, and thus can be used to deal with general open quantum systems. Simulation results demonstrate that the proposed linear projection filter is able to approximate the high-dimensional quantum filter within acceptable accuracy.
A Study on Machine-Learning-Based Prediction for Bitcoin’s Price via Using LSTM and SVR

Abstract Bitcoin plays an increasingly crucial role currently, whose of prices are challenged to be predicted when compared to the index of any traditional stock markets. With the accent of machine learning and deep learning, it is urgent to find out whether a machine learning model can or cannot see the way that the changes of Bitcoin’s price or volume interact over some period of time leads to a price increase or decrease within next few days. Testing results indicated that, the proposed two machine learning models built by LSTM and SVR were capable to capture longer-range dependencies and performed accurately by producing state-of-the-art results.
USE OF LINEAR ALGEBRA AND PARTIAL DERIVATIVES IN SUPERVISED LEARNING (ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING)

When we talk about new technologies and the advancement in the field of Computer Science, the first thing that comes to our mind is Artificial Intelligence and Machine Learning. Artificial Intelligence has seen resurgence in the 21 st century because of its ability to mimic functions done by human intelligence like “problem solving” and “learning”. It is slowly becoming the area of interest of the new generation because of its modern capabilities which even human intelligence struggle to perform like competing at highest level in strategic game systems, intelligent routing, operating cars autonomously and simulations. Artificial Intelligence may look easy but there are several tools involved in making it successful. One of the main tool is “Statistical Methods”. Linear algebra and Partial Differential Equations have become the base of this field. The objective of our paper is to throw light on how Statistical Methods and Mathematical optimization provide the base for the working of Supervised Learning. Over years, algorithms inspired by Partial Differential Equations (PDE) and Linear Algebra have had an immense impact on many processing and autonomously performed tasks that involve speech, image and video data. Image processing tasks and intelligent routing done using PDE models has lead to ground-breaking contributions. The reinterpretation of many modern machine capabilities like artificial neural networks through PDE lens has been creating multiple celebrated approaches that benefit a vast area. In this paper, we have established some working of these methods in different subfields of Artificial Intelligence. Guided by well-established theories we demonstrate new insights and algorithms for Supervised Learning and demonstrate the competitiveness of different numerical experiments used in the sub-fields. Not only will we see the wide application of Artificial intelligence but also its ability to slowly replace human works leading to unemployment which are part of its limitation. This research will provide wider insights into the multiple mathematical processes which acts as roots to make the field of Computer Science interesting and successful.
Model Reuse: How to Avoid Hidden Costs and Truly Benefit from Previous Work

In the simulation realm, opportunities for reuse are often plentiful, and it has grown to be an expectation that this is always the best path. Quite often it is, but the answer is not always as straightforward as some assume. There is a lot of allure to leveraging prior successful models as a means of jump starting a new effort. Often the ideas of the credibility or similarity of functionality are especially appealing. This tends to lead to optimistic estimates of implementation and compatibility, but these need to be examined in an honest, reflective way. There are also some key, less considered, facets of simulation models that should be investigated to truly assess a model’s reuse potential and the costs associated with it. The architecture and sustainability of a model relative to the new environment it will be integrated into warrants consideration as these are often significant cost drivers that are overlooked in reuse scenarios. When all of these components are carefully reviewed, it becomes evident that in some cases, it is more beneficial and specifically less expense to begin from a fresh start. On the surface, this seems counter intuitive, because isn’t starting with something always better than starting with nothing? This misconception is subtly propagated by metrics like Technical Readiness Level (TRL) which are monotonically increasing. Often today, engineers feel obliged to reuse without fully considering the topography of the problem, as if they must surely be leaving value on the table or somehow unnecessarily reinventing the wheel. This paper seeks to walk through the definitions, value, and cost considerations of credibility, functionality, architecture, and sustainability to provide a means to generate a situationally relevant assessment of the actual potential value of a reuse opportunity. Reuse is usually the right answer, but sometimes it is not, and that possibility should be fairly left in the decision space. A case study is provided to illustrate the application of intentionality in the choice of whether or not to reuse models.
A third-party replication service for dynamic hidden databases

Abstract Much data on the web is available in hidden databases. Users browse their contents by sending search queries to form-based interfaces or APIs. Yet, hidden databases just return the top-k result entries and limit the number of queries per time interval. Such access restrictions constrict those tasks that require many/specific queries or need to access many/all data entries. For a temporary solution, an unrestricted local snapshot can be created by crawling the hidden database. Yet, keeping the snapshot permanently consistent is challenging due to the access restrictions of its origin. In this paper, we propose a replication approach providing permanent unrestricted access to the local copy of a hidden database with dynamic changes. To this end, we present an algorithm to effectively crawl hidden databases that outperforms the state of the art. Furthermore, we propose a new way to continuously control the consistency of the replicated database in an efficient manner. We also introduce the cloud-based architecture of a replication service for hidden databases. We show the effectiveness of the approach through a variety of reproducible experimental evaluations.
Assessing Image and Text Generation with Topological Analysis and Fuzzy Logic

Objective and interpretable metrics to evaluate current artificial intelligent systems are of great importance, not only to analyze the current state of such systems but also to objectively measure progress in the future. We propose a novel metric, called Fuzzy Topology Impact (FTI), that assesses both the quality and diversity of a generated set using topological representations combined with fuzzy logic. In our synthetic experiments, FTI consistently outperforms current evaluation methods in terms of stability and sensitivity to detect drops in quality and diversity in the generated set, both on image and text generation tasks. Moreover, FTI shows a high degree of correlation to human evaluation on unconditional language generation.
A Comparison of Genetic &amp; Swarm Intelligence-Based Feature Selection Algorithms for Author Identification

Researchers are moving beyond stylometric features to improve author identification systems. They are exploring non-traditional and hybrid feature sets that include areas like sentiment analysis and topic models. This feature set exploration leads to the concern of determining which features are best suited for which systems and datasets. In this paper, we compare Genetic Search and a number of Swarm Intelligence (SI) methods for feature selection. In addition to Genetic Search methods, we compare SI methods including Artificial Bee Colony, Ant System optimization, Glowworm Swarm optimization and Particle Swarm optimization for feature selection.