Reviewerly/exHarmony huggingface dataset:
Authorship Attribution of Scientific Abstracts

Plagiarism detection, and identifying the author(s) of scholarly articles are important for determining the actual credit of author(s). Authorship attribution of long texts mostly depends on manual feature engineering for the extraction of relevant features. In this paper, we have developed a capsule-based convolutional neural network (CNN) architecture, which takes term frequency-inverse document frequency (TF-IDF) vector as input, for solving the task of authorship attribution. The TF-IDF vectors are used to represent the articles. The generated TF-IDF vector is fed to a CNN framework, which uses a capsule for learning the spatial features. Our proposed approach reduces the extra effort of hand-crafted feature extraction. The developed model is tested on the created scholarly abstract-based dataset. From the obtained results, it can be concluded that TF-IDF based capsule-CNN method outperforms previous approaches which had utilized hand-engineered features with the neural architectures. Different multi-tasking-based models are also developed to analyze the effect of category information in the authorship prediction task. From the results, it is revealed that multi-tasking-based models yield comparative results as compared to the single-tasking-based system. To illustrate the relevance of our developed system, different qualitative analyses have also been carried out, which clearly reveals the effectiveness of utilizing category information, and capsule networks. The source codes for the proposed approach will be shared after the acceptance.
Transformation of the competence model of scientific and pedagogical workers in the context of digitalization

The purpose of the study was to analyze the practice of transforming the competence model of scientific and pedagogical workers in the context of digitalization. The study is based on the application of the method of theoretical generalization. The need to develop practices for applying the competence model for further interuniversity exchange of experience determines the relevance of the study. The competence-based approach in the educational system of the Russian Federation has been actively introduced relatively recently. The classical education system aimed at transferring a certain set of knowledge, formed in accordance with the request of state bodies and built into a strict system, from teacher to student. Additional requirements are imposed on the qualifications of a teacher, implying the need to demonstrate competence not only in their professional field, but also in the field of IT technologies, independently organize their workplace and be responsible for the quality of the educational services provided, regardless of their housing and technical conditions. Application of the competence-based approach has actualized the active introduction of a mixed form of education, which involves the use of distance learning elements in the implementation of educational programs. The period from March 2020, associated with forced self-isolation in order to prevent the spread of the COVID-19 pandemic, has accelerated this process and gave it a new quality. The results of this study indicate that in modern conditions it is necessary to take into account the ability of teachers to carry out high-quality educational activities in a blended learning model when planning the teaching load for the new academic year and admitting teachers to competitive selection. The study allowed us to generalize the specific problems of application of the competence-based approach in the context of digital transformation.
Fisher Information Matrix and its Application of Bipolar Activation Function Based Multilayer Perceptrons With General Gaussian Input

For the widely used multilayer perceptrons (MLPs), there exist singularities in the parameter space where Fisher information matrix (FIM) degenerates on these subspaces. The singularities seriously influence the learning dynamics of MLPs which have attracted many researchers’ attentions. As FIM plays key role in investigating the singular learning dynamics of MLPs, it is very important to obtain the analytical form of FIM. In this paper, for the bipolar activation function based MLPs with general Gaussian input, by choosing bipolar error function as the activation function, the analytical form of FIM are obtained. Then the validity of obtained results are verified by taking two experiments.
Aerial Object Detection and Tracking using YOLOv4 and DeepSORT

Aerial Surveillance can be used to monitor impor-tant governmental offices, restricted zones and border patrolling too. Drones can give exact location of object and hence can be used for surveillance. Earlier traditional computer vision techniques were used to do the feature extraction. Later the features were given to a machine learning classifier for detection and classification. These algorithms used were highly inaccurate and generated false detection and misclassifications. The Deep Learning algorithms are able to extract more information and provide better accuracy as compared to traditional algorithms. For object detection YOLOv4 is used which is one of the state-of-the-art algorithms. It uses Darknet 53 which is a type of CNN as a backbone for feature extraction. DeepSORT is used for real-time tracking of detected objects. Here, the YOLOv4 based proposed system detect and localize vehicles present in the restricted zone and then geotag and later DeepSORT is used to track them.
E-commerce Website User Engagement Analysis: An Eye Tracking Viewpoint

E-Commerce is the current day lifeline for many. After the COVID-19 pandemic the popularity of the E-Commerce increased rapidly people are now more accustomed to it. It is very important to analyze the impact of a E-Commerce web page on the customer in order to understand the customer behaviour. Where the person looks on a E commerce web page means a lot. It is challenging to analyze visual events while tracking them. Additionally, interaction patterns in significant regions of an interactive platform can be found using eye tracking data. Attention modelling for applications has become a new area of study in computer vision. Without temporal information, existing models are unable to capture the dynamic aspects of the actual attention process in free-viewing applications.To solve this problem, a solution based on an application-based saccadic model to simulate human visual dynamics while viewing applications is proposed. Eye fixes and saccades can be used to interpret a person’s intention or goal in a certain circumstance. To track the user’s vision, we have used pupil saccade fixation mechanism in the proposed methodology. We collect the pupil imprints based on the view, analyze the data from view samples, and recommend the optimum location to display an advertisement in any e-commerce websites. The system proves to be a better option to traditional methods as it gives 94% accuracy.
Are High-quality Photos More Popular Than Low-quality Ones? A Quantitative Study

Social media popularity has increased over the years. Millions of images are uploaded and shared on social media daily. Image popularity prediction is extensively studied over past years. Several research works have shown that image content and social context play an important role in predicting image popularity. However, the impact of image aesthetics and quality on popularity has not been studied in detail until now. In this paper, we report a detailed study and analysis for finding the correlation between image quality/aesthetics and image popularity. To solidify the understanding of the impact, we present experimental results that are conducted on SMP 2020 and ICIP image popularity 2020 datasets to investigate if a high quality image or an aesthetically pleasing image is likely to be popular. We found that image aesthetics is moderately correlated with image popularity while quality is less so.
Rotating Target Detection Based on Lightweight Network

Current rotating object detection task achieves good results based on large models. In order to reduce the size of model, we propose a lightweight network SFC (ShuffleNet combines FPN with CSL) for rotating target detection. SFC first introduces circular smooth label (CSL) to detect target rotations, which transforms the traditional angle regression problem into classification problem. Then, the lightweight ShuffleNetV2 is utilized as the backbone to reduce the number of parameters. ShuffleNetV2 is used for feature extraction, and CSL is introduced to address the periodic problem of angles. Comparative experiments were carried out on DOTA 1.5 dataset. The experimental results show that the proposed method reduces the parameter by nearly 90% with a slight loss of accuracy, and increases the inferencing speed by 40% at the same time.
Explaining Deep and ResNet Architecture Choices with Information Flow

Recently, Information Theoretic Learning (ITL) has helped explain the learning dynamics for deep learning models such as multilayer perceptrons (MLP), convolutional neural networks (CNN), and stacked autoencoders (SAE). It is understood that for MLPs and CNNs, where the desired signal and input are independent of each other, the set of consecutive layers in the primary and adjoint networks represent individual Markov Chains (MC) that effect two data processing inequalities (DPI) in their respective directions. For the SAE, the desired signal is the input, so the DPI is only confirmed until the bottleneck layer. In this paper, we propose using the adjoint network to compute conditional mutual information with the backpropagated errors to demonstrate the DPI until the SAE's last layer. Also, we present an ITL-based analysis of the residual network (ResNet) architecture and propose an explanation for why the identity mapping is the optimal shortcut connection.
Developing a Generic Predictive Computational Model using Semantic data Pre-Processing with Machine Learning Techniques and its application for Stock Market Prediction Purposes

In this paper, we present a Generic Predictive Computational Model (GPCM) and apply it by building a Use Case for the FTSE 100 index forecasting. This involves the mining of heterogeneous data based on semantic methods (ontology), graph-based methods (knowledge graphs, graph databases) and advanced Machine Learning methods. The main focus of our research is data pre-processing aimed at a more efficient selection of input features. The GPCM model pipeline's cycles involve the propagation of the (initially raw) data to the Graph Database structured by an ontology and regular updates of the features' weights in the Graph Database by the feedback loop from the Machine Learning Engine. The Graph Database queries output the most valuable features that, in turn, serve as the input for the Machine Learning-based prediction. The end-product of this process is fed back to the Graph Database to update the weights. We report on practical experiments evaluating the effectiveness of the GPCM application in forecasting the FTSE 100 index. The underlying dataset contains multiple parameters related to predicting time-series data, where Long Short-Term Memory (LSTM) is known to be one of the most efficient machine learning methods. The most challenging task here has been to overcome the known restrictions of LSTM, which is capable of analysing one input parameter only. We solved this problem by combining several parallel LSTMs, a Concatenation unit, which merges the LSTMs' outputs (into a time-series matrix), and a Linear Regression Unit. which produces the final result.
Active Learning for Node Classification using a Convex Optimization approach

The recent advancements related to big data analytics in the era of Industry 4.0 are fueled by development and deployment of decision models based on neural network (NN) architectures. In addition to the data represented in Euclidean space, like, images, text, or videos, there are increasing applications which demand data representation in non-Euclidean domains. Such data are typically represented as graphs with complex interactions and interdependencies between various entities. The complexity of graph data imposes substantial challenge on the traditional Deep NN models, and Graph Neural Networks (GNN) are a powerful extension for modeling and analysis of networked data. The training of these computational models requires large amounts of labeled data. Active Learning (AL) helps to overcome this issue by selecting the most informative instances for labeling during the training process. This paper combines AL with GNN for semi-supervised classification of nodes in attributed graphs. The AL framework is portrayed as a convex optimization problem by employing Dissimilarity-based Sparse Modeling Representative Selection (DSMRS). The experimental evaluation demonstrates that using the selected graph-specific metrics (centrality and robustness measures) as AL heuristics leads to an improvement in classification performance by upto 10%.
Temporal Logic Path Planning under Localization Uncertainty

We present a method to find the optimal control strategy for a robot using prior information of localization that maximizes the probability of satisfaction of a temporal logic specification while considering the uncertainty in both motion and sensing, two major causes for localization uncertainty. The specifications are given in the probabilistic computation tree logic (PCTL) formulae over a set of propositions, which capture the presence of the robot in some key locations in the environment. A computation model that can deal with the uncertainty in both motion and sensing is the Partially Observable Markov Decision Process (POMDP), which is computationally expensive. We approximate the underlying POMDP using Augmented Markov Decision Process (AMDP) and present a control synthesis algorithm for AMDP. We carry out numerous experiments on workspaces with sizes up to 100 × 100 and three different PCTL specifications to evaluate the efficacy of our technique. Experimental results show that our technique for computing robot control policy using localization prior can deal with localization uncertainty effectively and scale to large environments.
Comprehensive Intelligent Training Simulation System for Power Communication Transmission Network

The simulation effect of AE system in power communication network data transmission is poor, and the effect of intelligent training decreases accordingly. For this purpose, a comprehensive intelligent training simulation system for power communication transmission network is designed. The POWER chip designed on the hardware has 30 channels, and the AD sampled values are forwarded to the opposite side CPU board to realize the AD re-sampling of the dual CPU boards; 10 independent Ethernet control effects can be realized by using the GOOSE processor; the training simulation of the power grid is analyzed on the software According to the requirements, the functional modules of the system are established to realize the intelligent simulation of power network data. The system test method is used to verify that the simulation effect of the designed comprehensive intelligent training simulation system for power communication transmission network is better, which is of great promotion value.
A SAR Image De-Noising Method Based on Stationary Wavelet Transform Directional Energy Threshold Filtering

Because the speckle noise of SAR image is non-Gaussian distribution, it cannot be directly processed by the denoising technology of the optical imaging system. At present, there is no real ideal algorithm widely used in SAR image denoising. Based on the analysis of the advantages and disadvantages of the current popular denoising methods, this paper proposed a wavelet domain SAR image denoising method. In order to overcome the shortcomings of the discrete wavelet transform, such as lack of translation invariance and limited directional selectivity, this method uses a stationary wavelet transform to decompose the image into a low-frequency approximation signal and high-frequency detail signal; for the approximation signal with less noise information, the enhanced e-lee filter is used to denoise; for the detail signal, adaptive threshold filtering is carried out according to the directional energy characteristics of speckle noise in wavelet domain Finally, the denoised SAR image is reconstructed. Experimental results showed that the method had a strong de-noising ability and good edge preservation.
Diverse Learner: Exploring Diverse Supervision for Semi-supervised Object Detection

Current state-of-the-art semi-supervised object detection methods (SSOD) typically adopt the teacher-student framework featured with pseudo labeling and Exponential Moving Average (EMA). Although the performance is desirable, many remaining issues still need to be resolved, for example: (1) the teacher updated by the student using EMA tends to lose its distinctiveness and hence generates similar predictions comparing with student and causes potential noise accumulation as the training proceeds; (2) the exploitation of pseudo labels still has much room for improvement. We present a diverse learner semi-supervised object detection framework to tackle these issues. Concretely, to maintain distinctiveness between teachers and students, our framework consists of two paired teacher-student models with diverse supervision strategy. In addition, we argue that the pseudo labels which are typically regarded as unreliable and obsoleted by many existing methods are of great value. A particular training strategy consisting of Multi-threshold Classification Loss (MTC) and Pseudo Label-Aware Erasing (PLAE) is hence designed to well explore the full set of all pseudo labels. Extensive experimental results show that our diverse learner framework outperforms the previous state-of-the-art method on the MS-COCO dataset by 2.10%, 1.50% and 0.83% when training with only 1%, 5% and 10% labeled data, demonstrating the effectiveness of our proposed framework. Moreover, our approach also performs well with larger amount of data, e.g. using full COCO training set and 123K unlabeled images from COCO, reaching a new state-of-the-art performance of 44.86% mAP.
Fall Detection Method Based on Improved YOLOX Network

The global aging problem is deepening, and the safety care of the elderly will receive wide attention from all walks of life, among which falls are the primary factor leading to disability and death of the elderly. To address the shortcomings of traditional video-based detection methods, which cannot balance detection speed and accuracy, this paper proposes a fall detection method based on YOLOX network, which conducts training tests on three common human actions: standing, falling and sitting, and improves the accuracy of target detection by improving the structure of YOLOX network and adding two attention models to compare experiments. The effect of the improved model is compared with the original network to demonstrate that the proposed detection algorithm has higher accuracy.
Patch Features Reconstruction Transformer for Occluded Person Re-Identification

Occluded person re-identification is one of the challenging areas of computer vision, which faces problems such as inefficient feature representation and low recognition accuracy. Convolutional neural network pays more attention to the extraction of local features, therefore it is difficult to extract features of occluded pedestrians and the effect is not so satisfied. Recently, vision transformer is introduced into the field of re-identification and achieves the most advanced results by constructing the relationship of global features between patch sequences. However, the performance of vision transformer in extracting local features is inferior to that of convolutional neural network. Therefore, we design a partial feature transformer-based person re-identification framework named PFT. The proposed PFT utilizes three modules to enhance the efficiency of vision transformer. (1) Patch full dimension enhancement module. We design a learnable tensor with the same size as patch sequences, which is full-dimensional and deeply embedded in patch sequences to enrich the diversity of training samples. (2) Fusion and reconstruction module. We extract the less important part of obtained patch sequences, and fuse them with original patch sequence to reconstruct the original patch sequences. (3) Spatial Slicing Module. We slice and group patch sequences from spatial direction, which can effectively improve the short-range correlation of patch sequences. Experimental results over occluded and holistic re-identification datasets demonstrate that the proposed PFT network achieves superior performance consistently and outperforms the state-of-the-art methods.
Course-oriented Knowledge State Model

Knowledge state indicates the level of mastery of a learner for each knowledge point in a domain, and Knowledge state model uses learners' performance in the filed of education to predict their future knowledge levels, is a well established problem in computer supported education. In this paper, we use course performance data to track the knowledge state of college students' academic process from the perspective of professional competency requirements. Course information reflects a more comprehensive professional competence of learners, and the use of course performance data with a value range of 0 to 100 makes the model more accurate compared to the binary variables of traditional answer sequences. Firstly classifying courses using hierarchical clustering combined with box-line plots, courses are divided into five categories, and explain the category results with practical implications; Secondly combining learners' future performance on courses with their past responses using an attention mechanism, and visualize the attention weights of knowledge sequences; Finally inputting neural networks to predict students' performance in each category of courses and outputting students' personalized knowledge state visualization chart. The experiments show that the proposed model is able to predict the learner's level of performance more accurately, which is potential for personalization in real-world educational settings.