Reviewerly/exHarmony huggingface dataset:
Understanding the dynamics of Cyberbully content diffusion using Textual Emotion Mining

With the world going digital and people from all walks of life-different race, colors, ethnicity, geography, literacy, and socio-economic status have come closer. The time to respond to a comment or an expression on the internet, the identity masks leveraged by the social media sites, and lack of moderation, have given way to cyberbullying. Cyberbullying figures are alarming and the concern is grave with the world moving forward with an overlap of the digital and real world. Swearing, use of foul language, expressions of strong disagreement, access, and use of inappropriate content has become more widespread in digital spaces and are considered uncivil and inappropriate in different intensities by different people. The present article investigates the diffusion of cyberbully in one of the popular social media sites, Twitter. It explores how the emotions present in such content drive its diffusion. The outcomes reveal that tweets containing anger, disgust, fear, and guilt as emotions tend to achieve faster and broader diffusion. with anger, hope, and bittersweet being less shared. The findings will aid to build approaches that intend to minimize the diffusion of cyberbully information and thereby protect the emotional and mental health of social media users.
Categorization of Product Feedback using a Deep CNN-based LSTM Model and a Comparative Study of Traditional ML Algorithms

In this era, especially in the pandemic, people and businesses have shifted towards digital platforms from analog ones. These changes have resulted in a blast of information in the marketing sector that needs to be categorized and summarized. Feedback has a significant role in drawing the public towards standard and high-quality products. However, due to the abundance of information, most businesses can’t quickly determine the market value of their products. This is the point where sentimental analysis comes in, which helps in classifying positive and negative feedback. In this paper, the deep learning methods based on CNN and LSTM (Long Short Term Memory) are proposed and trained with the data set from IMDb. In addition, the well-known algorithms of machine learning methods are reviewed based on sentiment analysis. The majority of traditional methods are accurate within a margin of error of 75 to 86 percent. In our experiment, LSTM and CNN showed almost the same accuracy of 89% while the proposed one obtained 92% accuracy. It is evident from the result that, the proposed fusion model outperforms the conventional machine learning algorithms. By deploying the methods with better accuracy, the reviews could be categorized easily and the businesses could stay one step ahead of their competitors.
Representation Learning of Knowledge Graph Integrating Entity Description and Language Morphological Structure Information

Knowledge graph embedding, which projects the symbolic relations and entities onto low-dimension continuous spaces, is the key to knowledge graph completion. The representation learning methods based on translation, such as TransE, TransH and TransR, only consider the triple information of knowledge graph, and fail to make effective use of other information of entity. To solve these problems, in this paper, we propose a knowledge graph representation learning method which integrates entity description and language morphological structure information to deal with complex relations (i.e. 1-N, N-1 and N-N relations). Firstly, the fastText model which considers affix of words is used to get the embedding of all entity description information. Then, the triple embedding, entity description embedding are spliced to obtain the representation of the final entity embedding. In addition, we propose a new score function-dist<inf>cos&#x2013;man</inf>, which considers the similarity of entity vector not only from the value of each dimension, but also from the direction of vectors. Experiments show that our method achieves substantial improvements against state-of-the-art baselines, especially the Hit@10s of head entity prediction for N-1 relations and tail entity prediction for 1-N relations improved by about 11.6% and 17.9% on FB15K database respectively.
DFGAN: Image Deblurring through Fusing Light-Weight Attention and Gradient-Based Filters

Recovering a latent sharp image from a spatially variant blurred image is a challenging task in the field of computer vision especially in blind image deblurring, where the source of the blur kernel is unknown and may vary. To remove the intricate motion blur in the images, recently deep learning-based methods perform latent clean image recovery without the need of knowing the blur kernel explicitly. Unlike conventional blind deblurring methods that assume the blur to be spatially invariant across the image. However, simply stacking convolution layers in deep multi-scale networks does not guarantee the complete removal of motion blur in the images and may lead to a poor performance for blind image deblurring task. Thus, we propose a GAN-based approach for single image blind motion deblurring task in an end-to-end manner, for simplicity its called DeblurFusedGAN (DFGAN). The proposed method improves the performance for image deblurring task by fusing the light-weight attention (LSA) mechanism and gradient-based filters in the generator network. Furthermore, we show the sophisticated performance of our proposed approach both qualitatively and quantitatively in comparison with the other state-of-the-art methods.
STDE: A Single-Senior-Teacher Knowledge Distillation Model for High-Dimensional Knowledge Graph Embeddings

An important role of Knowledge Graph Embedding (KGE) is to automatically complete the missing fact in a knowledge base. It is well-known that human society is constantly developing and the knowledge generated by human society will always being increasing. The increasing scale of the knowledge base is a great challenge to the storage and computing resources of downstream applications. At present, the dimensions of most mainstream knowledge graph embedding models are between 200-1000. For a large-scale knowledge base with millions of entities, these embeddings with hundreds of dimensional values are not conducive to rapid and frequent deployment on many kinds of artificial intelligent applications with limited storage and computing resources. To solve this problem, we propose a single-senior-teacher knowledge distillation model for high-dimensional knowledge graph embeddings named STDE, which constructs a low-dimensional student from a trained high-dimensional teacher. In STDE, the senior teacher can help the student learn key knowledge from correct knowledge and indistinguishable wrong knowledge with use of high-quality negative samples of triplets. We apply STDE to four typical KGE models on two famous data sets. Experimental results show that STDE can compress the embedding parameters of high-dimensional KGE models to 1/8 or 1/16 of their original scales. We further verify the effectiveness of "senior teacher" through ablation experiments.
Research on Smart Grid Behaviour Privacy Protection System Based on Spark

With the construction and rapid development of smart grids, short-term load forecasting with high-precision is becoming more and more important. Aiming at the characteristics of massive and heterogeneous grid data, a big data distributed system based on Spark is built to process power grid big data, which realizes the whole process from collection and transmission of grid data, processing, calculation to storage, and the construction of power load forecasting model. However, the ensuing risk of privacy leakage of user behaviour about the electricity usage information has attracted increasing attention. By analysing the electricity consumption information through Non-Intrusive Load Monitoring (NILM) techniques, the attacker can infer the operating status of the user&#x0027;s equipment, and then figure out the user behaviour, which seriously threatens the privacy of individuals and their family. For this reason, we adopted an effective local differential privacy protection method in the power load forecasting process aiming at solving the risk of privacy disclosure. Experiments have shown that this method can guarantee the availability of power data and meanwhile protect the privacy of user behaviour. In the field of smart grid, the system has strong application prospects.
Detection on Safety Helmet Wearing of Distribution Network Construction Based on YOLOv5-Btri Algorithm

In order to monitor whether workers wear helmets correctly on the distribution network construction, and to address the problems of low detection accuracy of existing helmet detection, a new method based on YOLOv5s-Btri model was proposed in this paper. Firstly, the C3 module is improved by replacing the general convolution with improved Ghost convolution, to reduce the parameters. Secondly, the algorithm introduces the Triplet Attention to improve the C3 module, so as to improve the ability of feature extraction. At last, the PANet of the original network is improved by using BiFPN, thus obtaining richer location and semantic information. Fourthly, chooses CIOU as the loss function of the frame regression. The experimental results show that compared with YOLOv5s, the average detection accuracy of YOLOv5s-Btri network reaches 93.9&#x0025;, which is improved by 2.9&#x0025;.
Database Interface Application Based on ODBC Development

In this paper, the structure and application principle of ODBC and the basic method and idea of developing data application program based on ODBC are described in detail. Combined with a concrete example, this paper describes how to develop the simulation software of the dual channel amplitude ratio direction finder, and introduces in detail the link between the application program of the simulation software and the database, the concrete realization of the main button and the realization of key technologies. From the development of the program, the application of ODBC development applications, high development efficiency, strong compatibility, short development cycle, low cost, easy to achieve. It has strong operability and practicability and is widely used in small simulation software.
Research on Course Score Analysis Based on K-Means Clustering Algorithm

Traditional data analysis methods can no longer meet the current demand for course score analysis. Clustering analysis is a traditional and famous data mining tool. With the rapid development of education informatization, it&#x0027;s inevitable that the data mining technology is applied in the education field. The principle and process of the K-means clustering algorithm is described in this paper. And evaluates and analyzes the course scores of a college based on the K-means clustering analysis algorithm, and provides a reference for teachers and students. Based on the results of cluster analysis: For teachers, hierarchical teaching and personalized guidance strategies can be adopted to implement &#x201C;precise&#x201D; teaching of course knowledge points. For students, they can understand their mastery of the various knowledge points of the course, and adjust their time and energy in a targeted manner to improve learning efficiency.
Improvement of Self-supervised Depth and Motion Learning with Vision Transformer

While convolutional neural network can extract well local features of images, it can only process images’ global features at low-resolution feature maps which were down-sampled by pooling layers or convolutional layers. Vision transformer, in another hand, can process global features at high resolution features maps by tokenizing high resolution features maps or images and feeding them into the network. Thus, the vision transformer included backbone can process representations at high resolutions and has a global receptive field. The properties allow this kind of backbone to provide finer-grained and more globally coherent predictions compared to fully-convolutional networks. In this work, we propose to use a convolutional network concatenate with a vision transformer module as the backbone for a self-supervised monocular depth estimation pipeline to improve the quality of predicted depth map. In addition, the weights in multi-component loss function in this pipeline were chosen heuristically in previous works, which were not optimal for the problem of estimating dense depth maps. Thus, we propose a weights analyzing-and-choosing method for multi-component loss function to improve quality of depth map prediction. Our work achieves a competitive result among other self-supervised depth estimation methods.
Labeled Knowledge-Based Decision Making with Assumption-Based Argumentation

Assumption-based Argumentation (ABA) has received a lot of attention and research attribute to its computability and interpretability. However, it is difficult for users to use ABA to represent the practical problems, especially for the data of problems involving natural language text/processing. Furthermore, since most arguments are derived by rules and some commonsense arguments are implied, massive rules need to be generated during representing practical problems by ABA, which reduces execution efficiency. Since the technologies of knowledge extraction from text and knowledge reasoning of knowledge graph are getting more and more efficient, it is possible to solve the above problems by using knowledge graph with ABA. In this paper, we propose a labeled RDF triple representation to enhance the semantics of triple and concise the process of triples extraction, and an abstract labeled knowledge-based decision making framework based on labeled triples for decision making. In order to apply the framework to the specific scene, we first give a detailed description of the definition and usage of labels in medical argumentation. Then we define an active decision framework for detecting the available decisions, and an optimal decision framework for computing the “best” decisions. Both of them build on argumentation-based computational mechanisms. Finally, we compute and explain the selected decisions by experimenting with the real argumentation record and demonstrate the applicability and rationality of our approach.
Natural Image Matting with Low-Level Feature Attention Guidance

Previous natural image matting algorithms have difficulties with transition regions in the foreground and background, such as tiny and detailed structures like hair. This paper argues that more efficient low-level features can help the network recover details with minor increases in network capacity and computational complexity. The proposed method, termed low-level feature channel guidance net LFCGN, has two advantages: 1) it introduces a low-level feature channel attention module designed to make the model parameters more efficient and can even lead to high-level feature map generation. 2) a dynamic upsampling is used in the decoder stage, making the detail part recover more efficiently. Experiments are evaluated on the Composition-1k dataset, and the experimental results show that our method obtained competitive performance compared to the state-of-the-art on task of image matting.
An Evaluation of the Multi-probe Locality Sensitive Hashing for Large-Scale Face Feature Matching

Large-scale face feature matching is a critical issue for many practical applications where the computing process requires a burden of hardware resources and computational time. In this work, the so-called multi-probe locality sensitive hashing (LSH) is exploited to solve the large-scale face feature matching problem. The standard procedure for hashing the dataset into smaller buckets and a matching algorithm with some improvements were implemented. Experimental implementations on a dataset of 12,720,006 images, corresponding to 617,970 person identifications, were carried out. The results show that extended LSH outperforms excellently in terms of accuracy and computational time. In comparison to linear matching, the computing time is reduced from 12446 ms to 267 ms for data type FP32 bits, and from 5,794 ms to 192 ms for data type INT8, while the accuracy is reduced very slightly. The results show the high potential use of the algorithm for practical applications.
Cross Transformer Network for Scale-Arbitrary Image Super-Resolution

Since implicit neural representation methods can be utilized for continuous image representation learning, pixel values can be successfully inferred from a neural network model over a continuous spatial domain. The recent approaches focus on performing super-resolution tasks at arbitrary scales. However, their magnified images are often distorted and their results are inferior compared to single-scale super-resolution methods. This work proposes a novel CrossSR consisting of a base Cross Transformer structure. Benefiting from the global interactions between contexts through a self-attention mechanism of the Cross Transformer, the CrossSR could efficiently exploit cross-scale features. A dynamic position-coding module and a dense MLP operation are employed for continuous image representation to further improve the results. Extensive experimental and ablation studies show that our CrossSR obtained competitive performance compared to state-of-the-art methods, both for lightweight and classical image super-resolution.
Takagi-Sugeno Fuzzy Control for Stabilizing Nonlinear Inverted Pendulum

Fuzzy is an intelligent control technique that is suitable for uncertainty and nonlinear systems. In this work, an advanced fuzzy logic control system is designed for an inverted pendulum, an unstable nonlinear system. First, the dynamic characteristics of the system are expressed through Takagi-Sugeno fuzzy model. Then, a parallel distributed compensation (PDC) controller is developed based on the definition of fuzzy sets. The purpose of this paper is to keep the stability of the pendulum angle. Besides, the linear matrix inequalities (LMI) is used for solving stability problem. Lastly, the efficiency and advantages of the proposed fuzzy controller are verified by simulation results.
Knowledge Concept Recommender Based on Structure Enhanced Interaction Graph Neural Network

Online education is becoming more and more popular. Although there are many online courses, this brings users trouble: (1) courses with the same name have different emphases; (2) there is dependence between courses in learning order. These phenomena make learners spend a certain amount of energy to find suitable courses, reducing their interest in online education. To help learners better conduct online learning, we study the problem of knowledge concept recommendation. This paper proposes a knowledge concept recommendation model based on a structure-enhanced interactive graph neural network (KCRec-SEIGNN). As for user representation, multiple entities in the knowledge concept recommendation scenario are organized into a heterogeneous graph and then graph convolution based on meta-path guidance to learn user entity representation on the heterogeneous graph. As for knowledge concept representation learning, we capture the co-occurrence information of knowledge points in the interaction sequence by using the knowledge concept interaction sub-sequence as a wizard and then build a knowledge concept interaction graph using the co-occurrence information. When aggregating the neighbor node information, we retain the structure information between the neighbor node and the target node and use an attention mechanism to distinguish the contributions of different neighbor nodes. Lastly, input the representation of user entities and knowledge concepts into a rating layer based on extended matrix decomposition to recommend knowledge concepts by rating. We conduct a series of Experiments on public real-world datasets, XuetangX, showing that the model is more effective for knowledge concept recommendation than some of the latest methods.
BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning

In federated learning (FL), due to the non-i.i.d. nature of distributedly owned local datasets, personalization is an important design goal. In this paper, we investigate FL scenarios in which data owners are related by a network topology (e.g., traffic prediction based on sensor networks). Existing personalized FL approaches cannot take this information into account. To address this limitation, we propose the Bilevel Optimization enhanced Graph-aided Federated Learning (BiG-Fed) approach. The inner weights enable local tasks to evolve towards personalization, and the outer shared weights on the server side target the non-i.i.d problem enabling individual tasks to evolve towards a global constraint space. To the best of our knowledge, BiG-Fed is the first bilevel optimization technique to enable FL approaches to cope with two nested optimization tasks at the FL server and FL clients simultaneously. Theoretical analysis shows that BiG-Fed is guaranteed to converge in an efficient manner. Extensive experiments on both synthetic and real-world data demonstrate significant superior performance of BiG-Fed over seven state-of-the-art methods.
Utilizing Out-Domain Datasets to Enhance Multi-task Citation Analysis

Citations are generally analyzed using only quantitative measures while excluding qualitative aspects such as sentiment and intent. However, qualitative aspects provide deeper insights into the impact of a scientific research artifact and make it possible to focus on relevant literature free from bias associated with quantitative aspects. Therefore, it is possible to rank and categorize papers based on their sentiment and intent. For this purpose, larger citation sentiment datasets are required. However, from a time and cost perspective, curating a large citation sentiment dataset is a challenging task. Particularly, citation sentiment analysis suffers from both data scarcity and tremendous costs for dataset annotation. To overcome the bottleneck of data scarcity in the citation analysis domain we explore the impact of out-domain data during training to enhance the model performance. Our results emphasize the use of different scheduling methods based on the use case. We empirically found that a model trained using sequential data scheduling is more suitable for domain-specific usecases. Conversely, shuffled data feeding achieves better performance on a cross-domain task. Based on our findings, we propose an end-to-end trainable multi-task model that covers the sentiment and intent analysis that utilizes out-domain datasets to overcome the data scarcity.
Logic Constraints to Feature Importance

In recent years, Artificial Intelligence (AI) algorithms have been proven to outperform traditional statistical methods in terms of predictivity, especially when a large amount of data was available. Nevertheless, the “black box” nature of AI models is often a limit for a reliable application in high-stakes fields like diagnostic techniques, autonomous guide, etc. Recent works have shown that an adequate level of interpretability could enforce the more general concept of model trustworthiness [7]. The basic idea of this paper is to exploit the human prior knowledge of the features’ importance for a specific task, in order to coherently aid the phase of the model’s fitting. This sort of “weighted” AI is obtained by extending the empirical loss with a regularization term encouraging the importance of the features to follow predetermined constraints. This procedure relies on local methods for the feature importance computation, e.g. LRP, LIME, etc. that are the link between the model weights to be optimized and the user-defined constraints on feature importance. In the fairness area, promising experimental results have been obtained for the Adult dataset. Many other possible applications of this model agnostic theoretical framework are described.