Reviewerly/exHarmony huggingface dataset:
A Short Text Sentiment Analysis Model Combined with Attention Mechanism of Bidirectional Affective Words

Short text sentiment in social media platforms has important research value. Due to the convolution kernel parameter sharing and pooling operation, convolutional neural network training speed is fast, and the effect is also good. However, most work such as max-pooling, average-pooling, more or less discard some of the secondary features, which makes the final expression of emotion possibly biased. So, in this paper, the attention mechanism applies to the pooling layer of CNN, and a Convolutional Neural Network based on double sentiment word attention pooling (DSA-CNN) is proposed. Each feature has its weight to be calculated. We also note that in the attention mechanism of text classification, the attention query vector is usually randomly initialized during network training, rather than using an existing vector--the semantic information of the previous moment, as in machine translation. Therefore, we use the emotion dictionary and emotion corpus to train bidirectional affective word vectors, so that the emotion-related features can interact with them in the attention. The experiment shows that DSA-CNN has achieved better performance than the classical classification model in a dataset of Weibo nCoV Data, NLPCC 2014, and yf_dianping. The accuracy of the model is 2.18% higher than that of the second-best model. Besides, the convergence rate of DSA-CNN is also significantly improved. The highest accuracy was achieved only in the third epochs, while CNN using the max-pooling needed 14 epochs.
Federated transfer learning with consensus knowledge distillation for intelligent fault diagnosis under data privacy preserving

Abstract Fault diagnosis with deep learning has garnered substantial research. However, the establishment of a model is contingent upon a volume of data. Moreover, centralizing the data from each device faces the problem of privacy leakage. Federated learning can cooperate with each device to form a global model without violating data privacy. Due to the data distribution discrepancy for each device, a global model trained only by the source client with labeled data fails to match the target client without labeled data. To overcome this issue, this research suggests a federated transfer learning method. A consensus knowledge distillation is adopted to train the extended target domain model. A mutual information regularization is introduced to further learn the structure information of the target client data. The source client and the extended target models are aggregated to improve model performance. The experimental results demonstrate that our method has broad application prospects.
Deep Attributed Graph Clustering with Graph Attention Network

Deep clustering has been a popular research topic in recent years. Yet previous deep clustering works are mostly designed for vectorized data or image data, which lack the ability to simultaneously exploit the sample (or node) attributes and their topological structure information and thus are not suitable for attributed graph clustering. In view of this, this paper presents a novel deep attributed graph clustering with graph attention network (DAGAT) approach, which jointly leverages the deep neural network (DNN) and the graph attention network (GAT) with two levels of attention mechanisms, i.e., the representation-wise attention and the node-wise attention. In our DAGAT approach, four modules are specifically incorporated, namely, the feature extraction module (FEM) via the autoencoder, the graph attention network module (GATM) to learn the features with topological information, the representation fusion module (RFM) to adaptively fuse the representations learned by the DNN and the GAT, and the self-supervised learning module (SSLM) for learning clustering-friendly representations. Experimental results have shown that our DAGAT approach outperforms the state-of-the-art approaches on multiple real-world datasets. The code is available at https://github.com/zbw0329/DAGAT.
Space based Earth observation approach for exploring hydrocarbon prospects in an intracratonic rift basin – A study in north Cambay basin, Gujarat

In the present study, we carried out a conjugate analysis of Landsat-8 Operational Land Imager (OLI), Sentinel-2B Multi-Spectral Instrument (MSI), Advanced Land Observation Satellite Phased Array L-band SAR (ALOS PALSAR), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over a study area of northern Cambay Basin to delineate prospective areas for hydrocarbon exploration. In this regard, we utilized feature-oriented principal component (PC) images of Landsat OLI data, PC images of Sentinel-2B visible and near-infrared (VNIR)-short-wave infrared (SWIR) bands, thresholded Cartosat-1 Digital Elevation Model (DEM), and polarization image composite of ALOS PALSAR data to delineate geomorphic and lineament features in the study area. Most of the geomorphic features, such as scroll plain, gullies, elevated pediment, circular drainage anomalies, etc., indicate localized structural uplift. Geomorphic anomalies and lineaments, essential for identifying structural controls of near-surface hydrocarbon accumulation, were identified using enhanced satellite data. Geophysical anomalies derived from gravity datasets supplemented the presence and down-depth continuity of geological structures. Spectral profiles of calcrete-bearing soil samples were used to identify calcrete-rich zones in the optical remote sensing image data as a proxy owing to field evidence of close association of calcrete-rich soils with known subsurface hydrocarbon accumulations. Sub-pixel mapping algorithm Constrained Energy Minimization (CEM) applied to SWIR bands of ASTER data was used to delineate surface proxies of near-surface hydrocarbon seepage. Isopach maps of shallowest reservoir formations (Mid-/ Late Eocene) were used as indicators of favorable near-surface hydrocarbon accumulation. Earth observation-based geomorphic, lineament, and spectral anomalies were integrated with supplementary datasets, such as reservoir isopach and absorbed gas anomalies, as controls using the weighted summation method for quantitative deduction of the hydrocarbon potential map of the study area. The weights for input variables or parameters (geomorphic, spectral, geochemical, etc.) were inferred based on spatial associations of these anomalous units/variables with the existing oil & gas well locations. The identified potential map is in broad synergy with the field observations on localized uplift, geo-botanical anomalies, and calcrete-rich zones within the extent of high & moderate hydrocarbon potential areas.
Research on the Application of AI Frame Compensation Technology in Videos

Booming AI (Artificial Intelligence) technology, amid advance in computer science, is assuming a vital role permeating all levels of people's lives and jostling for widespread attention. Asking for perspectiveness by the video industry for production technology labels AI technology as a means of great significance in the creation of videos, especially in frame compensation. This paper is to cotton on features and prevailing AI frame compensation, sum up its application in image creation, and concludes deficiencies and prospects therefrom, so as to chart course for the future development and research of imaging workers and scholars in the field.
Supervised Pradhan Mantri Mudra Yojana Tarun Model Using Neuro-Fuzzy Optimization Techniques

The pandemic has a grave effect on the economy of every country. When a country’s economy is affected, that means the banking sector is one of the biggest victims of it. To support the small-scale industries, the Reserve Bank of India has bought many schemes for industry borrowers. This chapter tries to sum up the industry borrowers’ non-performing issues through the data control note of Small Medium Enterprise Credit Center of State Bank of India. It also finds the factors affecting the Pradhan Mantri Mudra Yojana: “Tarun” scheme. A set of variables are designed from the note from the bank and applied to the supervised neuro-fuzzy model. Credit Underwriting Engine and Package for Accurate, Convenient and Efficient Re-casting scores are applied to big loans non-performing issues. Author suggests the bank apply these score variables to small industries to improve the default prediction results of the designed model. This chapter has considered the performances of the performing customers to reach the benchmark of the qualifying criteria of the borrowers. This strategy will resist the entry of poor or non performing borrowers to the bank. If the bank puts a bar on the non-performing entries, then there will be less count of non-performing assets in banking books. The model is served with 1185 dataset on the platform of python and it is responding well and satisfactory.
Optimization of Landing Attitude Stability for Quadrotor Drones

During the regular landing process of drones, they are often susceptible to the impact of ground effect and wind disturbances, which results in an inability to maintain parallelism with the ground. Drones are subjected to various types of wind disturbances during landing. Therefore, this study aims to analyze and model the varying characteristics of wind speed under natural conditions and introduce it as environmental noise into the system to evaluate the performance of the control algorithm. The goal of this research is to mitigate the impact of wind disturbances, such as oscillations during the landing process. By maintaining the pitch angle within 20%, we can ensure the drone lands quickly and accurately. In terms of algorithm, this research improves upon the existing PID (Proportional-Integral-Derivative) algorithm control for landing, and adopts a modified fuzzy PID algorithm, which can effectively enhance the control performance and response time of the drone.
Multimodal Language Data and Platform Construction for LCTLs Teaching

The progress of artificial intelligence technology has brought great opportunities for the development of Less Commonly Taught Languages (LCTLs) teaching. However, the scarcity of LCTLs resources still plagues the current teaching platform. This paper is committed to building multimodal language data including foreign language vocabulary and translation, bilingual example sentences, phonetic symbols and audio, morphological similarity recommendation, semantic similarity recommendation, etc., and using full-text index and Web Service technology to build a new LCTLs teaching platform. The detailed steps are: (1) acquiring and processing multimodal language data through web pages such as TED and Opensubtitles; (2) constructing the inverted indexes used the HashMap and storing the index files in the cache of the platform, which avoids the heavy database intervention; (3) using the classical Levenshtein distance in the morphological similar words and semantic similar words recommendation algorithm. The platform implemented based on the above data and technology finally supports Japanese, Italian, Indonesian, Malaysian, Vietnamese, Filipino, and Myanmar seven languages.
Study on the Teaching Model of Artificial Intelligence Principles Course Based on Discipline Integration and Industry-Education Integration

Artificial intelligence is one of the most promising and influential fields of science and technology in the current era. How to cultivate high-quality talents with artificial intelligence literacy and ability is an important issue faced by the education sector. This paper designs a teaching model of artificial intelligence principles course based on discipline integration and industry-education integration, and introduces the specific content of this teaching model from two aspects: theoretical teaching and practical teaching. The teaching practice effect is evaluated and feedback is given. The results show that this teaching model helps to improve students’ understanding, interest, practice and innovation of artificial intelligence. This paper has certain reference value and enlightenment for promoting the development and reform of artificial intelligence education.
Automatic Pathfinding of the Robotic Arm by A* Algorithm on 2-Dimension in MATLAB Simulink

This project will give people who are new to A* algorithm a general idea about how robotic arms works in Simulink by applying the A*algorithm. Since A*algorithm combined advantage of two effective algorithm, this project decides to choose it as the main algorithm of the robot arms. In this project, the research question is whether a 2-joint robot arm passes through a single obstacle in 2D space to reach its destination and it selects the shortest path. The answer will be found by applying the A* algorithm on the robot arm. By analyzing the GHF values and trajectory graphs generated by MATLAB and Simulink. It has been confirmed that the A* algorithm works on the robot arm. Finally, success rate was calculated to be 81.7%, confirming that the 2-joint robot arm could pass through a single obstacle in 2D space to reach its destination and it selects the shortest path. The result of this project ca be more convincing if it run in 3-dimension.
Image and Signal processing to detect violent content in social media videos

The amount of violent content shared on social networks makes it a very unhealthy space. Hence the birth of a growing research domain that involves filtering social media content using Artificial Intelligence-powered violence detection systems.
A Bucket-aware Asynchronous Single-Source Shortest Path Algorithm on GPU

Single-Source Shortest Path (SSSP) algorithm is a common routine in graph processing and has been extensively studied on Graphics Processing Unit (GPU). Despite the powerful parallelism resources and high memory bandwidth provided by GPU, the performance of the SSSP algorithm is hindered by several bottlenecks, such as irregular memory access, load imbalance, and redundant operations. In this paper, three optimizations are proposed to boost the performance of the SSSP algorithm on GPU, including property-driven reordering, adaptive load balancing, and bucket-aware asynchronous execution. Property-driven reordering is employed to improve the data locality and work efficiency. Adaptive load balancing brings a higher utilization of software and hardware GPU resources. Bucket-aware asynchronous execution presents a bucket-based approach for asynchronous implementation to accelerate the convergence of SSSP search.
Investigation and analysis of Chinese and foreign college students' quality education

Divides my country's university quality education into four stages, Quality education "refers to the comprehensive development of education formed by long-term subjective acceptance or objective influence of the educated under certain environmental conditions. from five aspects of ideology and morality, humanities, psychology, body and practical innovation, respectively. Investigate and analyze the quality education of post-2000 college students, and investigate and analyze the quality education of typical foreign universities such as the United States, Britain, Germany and Japan, so as to form a comparison and learn from it.
Practical Application of Platform Comment Sentiment Binary Classification Based on Deep Learning

The role of product reviews is to display real product information, help users judge whether the real situation of the product is consistent with the merchant's description, and indirectly help users judge the price range of the product. At present, most commodity platforms and accommodation intermediary platforms adopt the method of user star rating, which leads to many invalid positive reviews and malicious negative reviews, thus bringing a lot of disturbing information to consumers. Therefore, this paper introduces a mechanism based on artificial intelligence to judge the emotion expressed by a machine, and further verifies the validity and accuracy of the model on the BERT Hugging face dataset.
Quantum Algorithm for Regularized Spectral Clustering

Spectral clustering is one of the most robust methods of an unsupervised machine learning field. Compared with the well-known k-means, spectral clustering uses the spectral properties of the Laplacian matrix to project data to a low-dimensional clustering with higher efficiency. However, for a sparse network with a substantial degree of heterogeneity, standard spectral clustering often overfits noise in the periphery of a sparse and stochastic graph. In our work, we combined the quantum version of the k-means algorithm with regularized spectral clustering and proposed a new quantum machine learning algorithm. It is more like a combination of classical and quantum algorithms. Compared with the established quantum k-means algorithm, our method has obvious computational advantages in non-convex or nested structures. At this stage, the research of quantum algorithms mainly stays in the simulation of classical computers. Our method does not need to expend a lot of effort to preprocess the data at the initial quantum state stage. Under the assumption of quantum data encoding, we theoretically demonstrate the feasibility of the quantum regularized spectral clustering algorithm. Then combine Q-means algorithm complexity advantages to complete the clustering task. This work aims to pave the path and help realize quantum spectral clustering algorithms on real quantum computers in the future and help create other versions of quantum machine learning algorithms. We provide simulations and data examples to illustrate these results.
College Students&amp;apos; Self-directed English Learning Ability in Non-face-to-face and Face-to-face Classes

The purpose of this study is to explore the difference in self-directed English learning ability between college students taking General English courses in non-face-to-face and face-to-face formats. Additionally, the study aims to examine whether there are variations in self-directed English learning ability based on individual variables among students in both types of classes. A survey was conducted to measure self-directed English learning ability, covering cognitive, affective, and behavioral aspects. Data from 284 freshmen were analyzed using IBM SPSS 27.0. The findings are as follows. First, a difference in self-directed English learning ability was observed between college students taking non-face-to-face and face-to-face General English courses. Notably, the difference in learning when it comes to motivation, continuity of learning activities, and learning about the factors involved in resource interest management was statistically significant. In all seven sub-factors, the average score of college students taking non-face-to-face classes was higher than that of their face-to-face counterparts. Second, the self-directed English learning ability of college students who took non-face-to-face classes differed based on individual variables such as gender, English vocabulary level, interest in English, and average learning time per day. In particular, the difference in average scores by gender showed a certain pattern. Third, the self-directed English learning ability of college students who took face-to-face classes varied depending on individual variables such as gender, English vocabulary level, interest in English, and average learning time per day. Depending on some individual variables, certain patterns were also shown. Based on these findings, similarities and differences in college students' self-directed English learning ability between non-face-to-face and face-to-face classes were identified. Also, educational suggestions to enhance learners' self-directed English learning ability in both settings were provided.
Loopless algorithms to generate maximum length gray cycles wrt. k-character substitutions

Given a binary word relation τ onto A⁎ and a finite language X⊆A⁎, a τ-Gray cycle over X consists in a permutation (w[i])0≤i≤|X|−1 of X such that each word w[i] is an image under τ of the previous word w[i−1]. We define the complexity measure λA,τ(n), equal to the largest cardinality of a language X having words of length at most n, and st. some τ-Gray cycle over X exists. The present paper is concerned with τ=σk, the so-called k-character substitution, st. (u,v)∈σk holds if, and only if, the Hamming distance of u and v is k. We present loopless (resp., constant amortized time) algorithms for computing specific maximum length σk-Gray cycles.
Regional Talent Ecosystem under the Mechanism of "dual-core + dual-sub-centers" in Guangdong Province

Under the application of the theory of dissipation structure to analyze the regional development strategy of "dual-core + dual sub-center" in Guangdong Province, it studies the formation mechanism of the talent ecosystem in the process of Guangzhou-Zhanjiang-Shenzhen-Shantou linkage, and puts forward the basis for determining the dissipation structure. Disgupation structure is the premise of the self-organization of urban talent ecosystem. First, the theoretical analysis of the rationality and characteristics of the dissipation structure of talent ecosystem in the linkage process of Guangzhou-Zhanjiang-Shenzhen-Shantou. Second, the Brusselator model management translation, and establish the mechanism of "dual core + double center" under the regional talent ecosystem evaluation index system, selection of 2016-2020 statistics, in Guangdong province "double core + double center" city talent ecological system dissipation structure formation of empirical judgment, determine the results show that in nearly five years of Guangzhou and Shenzhen talent ecosystem dissipative structure has been formed, but Shantou and Zhanjiang still does not exist the dissipation structure of ecosystem. Finally, through the dissipative analysis, the important factors hindering the orderly development of the talent ecosystem are studied, and the corresponding countermeasures are put forward.
Hyperbolic distance guided multi-positive graph contrastive learning

Graph contrastive learning (GCL) aims to obtain embeddings for graph data in an unsupervised manner. Most existing GCL models are designed for homogeneous graphs, whereas the two critical aspects of heterogeneous GCL models are data augmentation and contrastive sample selection. In this paper, we propose a contrastive learning model for heterogeneous graphs. We take advantage of the property that hyperbolic space is suitable for representing highly hierarchical graph data. We use the normalized hyperbolic distance as an indicator to adaptively select edges to remove in data augmentation to obtain different graph views. In addition, we propose a positive example expansion strategy that integrates topological information and attributes information to obtain more highly relevant positive samples and provide more contrasting information. We conduct node classification experiments on several public heterogeneous graph datasets to evaluate the performance of our model. The results show that our model outperforms all unsupervised comparison methods and even outperforms the best-supervised model on several datasets to reach state-of-the-art.