Reviewerly/exHarmony huggingface dataset:
Object and subject of scientific or applied analytical research

The article examines the notions «object of research» and «subject of research», shows their significance for research. The article provides an overview of the definitions and interpretations of these notions presented in the scientific literature. The author shows the problems of distinguishing between the object and the subject of research and presents his concept of interpretation and explanation of these notions.
SWAG-V: Explanations for Video using Superpixels Weighted by Average Gradients

CNN architectures that take videos as an input are often overlooked when it comes to the development of explanation techniques. This is despite their use in often critical domains such as surveillance and healthcare. Explanation techniques developed for these networks must take into account the additional temporal domain if they are to be successful. In this paper we introduce SWAG-V, an extension of SWAG for use with networks that take video as an input. In addition we show how these explanations can be created in such a way that they are balanced between fine and coarse explanations. By creating superpixels that incorporate the frames of the input video we are able to create explanations that better locate regions of the input that are important to the networks prediction. We compare SWAG-V against a number of similar techniques using metrics such as insertion and deletion, and weak localisation. We compute these using Kinetics-400 with both the C3D and R(2+1)D network architectures and find that SWAG-V is able to outperform multiple techniques.
PyTraceBugs: A Large Python Code Dataset for Supervised Machine Learning in Software Defect Prediction

Contemporary software engineering tools employ deep learning methods to identify bugs and defects in source code. Being data-hungry, supervised deep neural network models require large labeled datasets for their robust and accurate training. In distinction to, say, Java, there is lack of such datasets for Python. Most of the known datasets containing the labeled Python source code are of relatively small size. Those datasets are suitable for testing built deep learning models, but not for their training. Therefore, larger labeled datasets have to be created based on some well-received algorithmic principles to select relevant source code from the available public codebases. In this work, a large dataset of the labeled Python source code is created named PyTraceBugs. It is intended for training, validating, and evaluating large deep learning models to identify a special class of low-level bugs in source code snippets manifested by throwing error exceptions, reported in standard traceback messages. Here, a code snippet is assumed to be either a function or a method implementation. The dataset contains 5.7 million correct source code snippets and 24 thousands buggy snippets from the Github public repositories. Most represented bugs are: absence of attribute, empty object, index out of range, and text encoding/decoding errors. The dataset is split into training, validation and test samples. Confidence in labeling of the snippets into buggy and correct is about 85&#x0025; according to our estimates. Labeling of the snippets in the test sample is additionally manually validated to be almost 100&#x0025; confident. To demonstrate advantages of our dataset, it is used to train a binary classification model for distinguishing the buggy and correct source code. This model employs the pretrained BERT-like contextual embeddings. Its performances are as follows: precision on the test set is 96 &#x0025; for the buggy source code and 61 &#x0025; for the correct source code whereas recall is 34 &#x0025; and 99 &#x0025; respectively. The model performance is also estimated on the known BugsInPy dataset: here, it reports approximately 14&#x0025; of buggy snippets.
Operation Anomaly Monitoring of Customer Service Data Analysis Platform Based on Improved FP-Growth Algorithm

Abstract Aiming at the problems of long time-consuming monitoring and poor monitoring accuracy in traditional customer service data analysis platform operation abnormality monitoring methods, a customer service data analysis platform operation abnormality monitoring method based on the improved FP-Growth algorithm is designed. Obtain customer service data sets, classify data types, filter customer behavior, identify the operating status of the data analysis platform, improve the FP-Growth algorithm to build a rule configuration model, set the platform safety factor threshold, and keep the reconstruction error of customer service data to a minimum Within the scope, optimize the abnormal monitoring mode. The experimental results show that the average recovery time of the proposed customer service data analysis platform operation anomaly monitoring method is 5.239s, and the average platform operation anomaly accuracy rate is 97.3%, indicating that the customer service data analysis platform integrated with the improved FP-Growth algorithm operates abnormally The monitoring method performs better.
Binary Classification-Based Side-Channel Analysis

We propose a new nonprofiled side-channel analysis (SCA) method called binary classification-based SCA (BCSCA), which accomplishes the tasks of the traditional nonprofiled SCA by solving binary classification problems with the help of neural networks. In addition, to improve the analytical efficiency, we design a new type of neural network and propose a fast implementation of BCSCA. We evaluate BCSCA on both simulated and public side-channel traces and compare BCSCA with correlation power analysis (CPA), mutual information analysis (MIA), and the state-of-the-art nonprofiled SCA method based on neural networks (NNSCA). The experimental results show that BCSCA outperforms NNSCA and MIA in all cases, and it is better than CPA on the nonlinear traces and comparable to CPA on the linear traces.
Contrast-Enhanced Color Visual Cryptography for (k, n) Threshold Schemes

In traditional visual cryptography schemes (VCSs), pixel expansion remains to be an unsolved challenge. To alleviate the impact of pixel expansion, several colored-black-and-white VCSs, called CBW-VCSs, were proposed in recent years. Although these methods could ease the effect of pixel expansion, the reconstructed image obtained by these methods may also suffer from low contrasts. To address this issue, we propose a contrast-enhanced (k, n) CBW-VCS based on random grids, named (k,n) RG-CBW-VCS, in this article. By applying color random grids, a binary secret image is encrypted into n color shares that have no pixel expansion. When any k 1 (k 1 &gt; k ) color shares are collected together, the stacked results of them can be identified as the secret image; whereas the superposition of any k 2 ( k 2 &lt; k ) color shares shows nothing. Through theoretical analysis and experimental results, we justify the effectiveness of the proposed (k, n) RG-CBW-VCS. Compared with related methods in feature, contrast, and pixel expansion, the results indicate that the proposed method generally achieves better performance.
Character Recognition Tamil Language in Printed Images using Convolutional Neural Network (CNN) analysis

In this paper, we suggested a system for handwritten character recognition in printed images of the Tamil language. The current work is being implemented using Optical character Recognition (OCR) in step one of the projects. The most recognized issues are poor print and paper quality and unknown font faces. OCR is also not accurate in acknowledging the handwritten text and the fonts. Also, the implementation is carried out using the Convolutional Neural Network (CNN) model with handwritten digit recognition. CNN has the potential to recognize handwritten picture characters clearly and robustly. For Tamil handwritten character classification, we have considered the CNN in this paper without any feature collection. In terms of test accuracy, the proposed approach provides comparable output with the other existing methods. And it was checked on a major data set as well. For Tamil handwritten character recognition, experiments on a large data set showed the robustness of this model. The outcome of the proposed model for handwritten Tamil character recognition using CNN gives an accuracy of 98.00&#x0025
Sentiment Analysis Using Hybrid CNN-LSTM Approach

Sentiment Analysis which is also referred to as opinion mining focuses to use the tools that can automatically detect different types of emotions such as feelings, attitudes, and opinions expressed in a text. Sentiment Analysis is a field of text mining in which research is ongoing. This survey paper includes insights into various proposed Sentiment Analysis algorithms and a brief idea of the last update in this field. The fields related to data science like emotion detection, building resources are also included in this paper. Mainly this paper includes categorization of different articles in a sophisticated manner and a brief introduction to the recent trend of research in this field and its related areas. It also includes an analysis of movie reviews using a hybrid CNN-LSTM approach.
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In this paper, we propose a L0 Structure-Prior Assisted Blur-intensity Aware method for efficient video deblurring. To efficiently generate sharp frames, we utilize separate streams to estimate content and structure information, both two streams effectively extract features under dedicated constraints using light-weight architecture. In content estimation, to better extract features from frames with non-uniform blur distribution, we introduce a blur-intensity detection module to generate a blur-intensity mask. The mask reflects region-wise blur degrees similar to an attention map and provides preliminary information to the content decoder. For structure estimation, to generate frames with less structural distortion, we utilize L0 smoothed frames as supervision to estimate structure information. A pyramid module with a deformable convolution layer in the last scale is designed in this stream. The estimated content and structure information are then merged with the frame synthesis module to generate perceptually favorable frames. Experiments demonstrate that our method could achieve favorable results on both synthetic and real-world datasets in terms of objective metrics and perceptual quality, with fewer input frames and less time-consuming.
Microstrip Bandstop Filter for Preventing Conduction Electromagnetic Information Leakage of High-Power Transmission Line

The research of transient electromagnetic pulse emission monitoring technology (TEMPEST) protection is essential. Based on microstrip bandstop filter (MSBSF), a method to prevent electromagnetic information leakage of high-power transmission lines is proposed in this paper. The MSBSF with a high insertion loss and carrying large current in the frequency of 2.40–2.49 GHz is designed, fabricated, and experimentally measured. The fabricated MSBSF with the insertion loss of 38 dB at 2.41–2.49 GHz can carry a current greater than 10A and withstand a voltage of 1.7 kV. Compared with the traditional electromagnetic interference (EMI) filter, the MSBSF has the advantages of preventing conducted electromagnetic information leakage in the high-frequency bands, carrying greater current subject to higher voltage, and possessing lighter weight. The MSBSF may be used to prevent the leakage of high-frequency electromagnetic information of high-power transmission lines.
Smart Contract-Based Distributed Access Control for Smart Home

Recently, access control systems have incorporated blockchain technology to overcome inherent issues in conventional access control schemes in IoT. However, when applied to the smart home system, the existing blockchain-based access control systems generally have limitations, such as limited distribution, lack of security, and privacy. Here, we provide a solution based on the Hyperledger Fabric platform. This solution includes a trust management center and multiple smart contracts. Specifically, an identity contract is used for device registration. The trust contract records the access behavior and evaluation value of the device, from which the trust management center calculates the trust value of the device. And the access control contract makes logical judgments on the access request. Finally, we designed an experiment to verify the algorithm of the trust management center. The function of the smart contract is testified, which ensures that the T-DCAC model can be applied to the smart home environment.
Project Cost Accounting Based on Internet of Things Technology

In order to improve the effect of engineering cost accounting, this paper applies the Internet of Things technology to the engineering cost accounting system, and combines the chaotic data processing method with the Internet of Things technology. Moreover, this paper uses the Internet of Things technology to construct the engineering cost accounting system, uses the Internet of Things technology to collect various data in the real-time process of the project, and builds the engineering cost accounting system based on the actual situation. In addition, this paper combines improved intelligent algorithms to improve system performance to enable the system to collect data, manage data, process data, transmit data, and output data. It can be seen from the research results that the project cost evaluation system constructed in this paper is rated above good, which is higher than the existing project cost evaluation methods. The experimental research shows that the project cost accounting system based on the Internet of Things technology proposed in this paper has a good engineering data processing effect.
Robust Multi-model Personalized Federated Learning via Model Distillation

Federated Learning(FL) is a popular privacy-preserving machine learning paradigm that enables the creation of a robust centralized model without sacrificing the privacy of clients’ data. FL has a wide range of applications, but it does not integrate the idea of independent model design for each client, which is imperative in the areas like healthcare, banking sector, or AI as service (AIaaS), due to the paramount importance of data and heterogeneous nature of tasks. In this work, we propose a Robust Multi-Model FL (RMMFL) framework, an extension to FedMD, which under the same set of assumptions significantly improves the results of each individual model. RMMFL adapted two changes in the FedMD training process: First, a high entropy aggregation method is introduced to soften the output predictions. Second, a weighted ensemble technique is used to weigh the predictions of each client model in line with their performance. Extensive experiments are performed on heterogeneous models using CIFAR/MNIST as benchmark datasets, the simulations results obtained from our study shows that RMMFL exceeds the accuracy by over $$5\%$$ compare to the baseline method.
Sports Video Athlete Detection Based on Associative Memory Neural Network

Aiming at the detection of athletes in sports videos, an automatic detection method based on AMNN is proposed. The background image from the image sequence is obtained, the moving area is extracted, and the color information of pixels to extract the green stadium from the background image is used. In order to improve the accuracy of athletes' detection, the texture similarity measurement method is used to eliminate the shadow in the movement area, the morphological method is used to eliminate the cracks in the area, and the noise outside the stadium is removed according to the stadium information. Combined with the images of nonathletes, a training set is constructed to train the NN classifier. For the input image frames, image pyramids of different scales are constructed by subsampling and the positions of several candidate athletes are detected by NN. The center of gravity of candidate athletes is calculated, a representative candidate athlete is obtained, and then, the final athlete position through a local search process is determined. Experiments show that the system can accurately detect the motion shape of moving targets, can process images in real time, and has good real-time performance.
Density Peaks Clustering Algorithm Based on K Nearest Neighbors

Density peaks clustering algorithms calculate the local density based on the cutoff distance and the global distribution of the sample. They cannot capture the local characteristics of the sample well, and are prone to appear errors in the selection of density peaks; additionally, the allocation strategy has poor fault tolerance. Once a sample is allocated incorrectly, subsequent allocations will magnify the error. Hence, we proposed a density peaks clustering algorithm based on k-nearest neighbors (DPC-KNN). First, the k-nearest neighbors information of the sample is used to define the local density of the sample in order to find the cluster centers accordingly; the sample with the distance between cluster centers and k-nearest neighbors sample less than the set threshold is defined as the core sample, and the core sample is classified into the corresponding cluster to construct the core area of the cluster; after the degree of attribution of the remaining samples and various clusters are calculated, they are allocated to clusters with high degree of attribution. In order to verify the effectiveness of the proposed algorithm, eight synthetic datasets and ten UCI datasets are selected for experiments, and the proposed algorithm is compared with FKNN-DPC, DPCSA, FNDPC, DPC and DBSCAN. The experimental results indicated that the proposed algorithm had better clustering performance.
Optimal Data Allocation for Graph Processing in Processing-in-Memory Systems

Graph processing involves lots of irregular memory accesses and increases demands on high memory bandwidth, making it difficult to execute efficiently on compute-centric architectures. Dedicated graph processing accelerators based on the processing-in-memory (PIM) technique have recently been proposed. Despite they achieved higher performance and energy efficiency than conventional architectures, the data allocation problem for communication minimization in PIM systems (e.g., hybrid memory cubes (HMCs)) has still not been well solved. In this paper, we demonstrate that the conventional “graph data allocation = graph partitioning” assumption is not true, and the memory access patterns of graph algorithms should also be taken into account when partitioning graph data for communication minimization. For this purpose, we classify graph algorithms into two representative classes from a memory access pattern point of view and propose different graph data partitioning strategies for them. We then propose two algorithms to optimize the partition-to-HMC mapping to minimize the inter-HMC communication. Evaluations have proved the superiority of our data allocation framework and the data movement energy efficiency is improved by 4.2-5 × on average than the state-of-the-art GraphP approach.
Generating Anime Characters and Experimental Analysis Based on DCGAN Model

Animation culture in the 20th century has long been free from paper cartoons. From the beginning, when painters drew comics characters with a pen, people can now use a computer to generate anime characters. As with most forms of art, anime characters are designed and created by the artist himself, but if the production of anime characters is automated using the Generative Adversarial Network (GAN), will different forms of anime characters be ended up? Generative Adversarial Networks (GAN) is a deep learning model and one of the most promising methods for unsupervised learning on complex distributions in recent years. The model produces quite good output through mutual game learning of (at least) two modules in the framework: Generative (G) Model and Discriminative (D) Model. The generator is a network that generates images and the Discriminator is a network that identifies as evaluating whether an image is “real” or not. For instance, the generative model gives a series of pictures of dogs and then generates a new picture of a dog that is not in the data set. The discriminant model gives a picture and then judges whether the animal in this picture is a cat or a dog. This paper will also use DCGAN. DCGAN is not only related to GAN but also related to CNN. Both the discriminator and generator of Deep Convolutional Generative Adversarial Networks (DCGAN) use Convolutional Neural Network (CNN) to replace the multilayer perceptron in GAN. DCGAN, as the name implies, a GAN is added to the deep convolution. It mainly constitutes a convolution layer with no maximum pooling or full connection layer. It uses the convergence of Convolutional stride and transpose for downsampling and upsampling.
Ada-GNN

Graph Neural Networks (GNNs) have demonstrated strong power in mining various graph-structure data. Since real-world graphs are usually on a large scale, training scalable GNNs has become one of the research trends in recent years. Existing methods only produce one single model to serve all nodes. However, different nodes may exhibit various properties thus require diverse models, especially when the graph is large. Forcing all nodes to share a unified model will decrease the model's expressiveness. What is worse, some small groups' patterns are prone to be ignored by the model due to their minority, making these nodes unpredictable and even some raising potential unfairness problems. In this paper, we propose a model-agnostic framework Ada-GNN that provides personalized GNN models for specific sets of nodes. Intuitively, it is desirable that every node has its own model. But considering the efficiency and scalability of the framework, we generate specific GNN models at the subgroup-level rather than individual node-level. To be specific, Ada-GNN first splits the original graph into several non-overlapped subgroups and tags each node with its subgroup label. After that, a meta adapter is proposed to adapt a base GNN model to each subgroup rapidly. To better facilitate the global-to-local knowledge adaption, we design a feature enhancement module that captures the distinctions among different subgroups to improve the Ada-GNN's performance. Ada-GNN is model-agnostic and can be equipped to almost all existing scalable GNN based methods such as GraphSAGE, ClusterGCN, SIGN, and SAGN. We conduct extensive experiments with six popular scalable GNN as base methods on two large-scale datasets, and the results consistently demonstrate the generality and superiority of Ada-GNN.
Students' Satisfaction in Online Distance Learning Using Fuzzy Logic and Inference System

Education system can measure the performance of students by their academic assessment which regards them as passed or failed. However, their satisfaction level during the learning process is also one of the important element to measure the students' psychology and their struggle in learning outcomes. Furthermore, the pandemic outbreak has changed the conventional face-to-face learning environment to online distance learning as whole. In this study, fuzzy logic and inference system is used to present an evaluation for students' satisfaction in online distance learning. The main advantage of this method is its capability to express the students throughout based on simple linguistic statements collected from themselves. Six indicators towards satisfaction are determined and it is suggested that 90.27% of students were satisfied and very satisfied with online distance learning.