Reviewerly/exHarmony huggingface dataset:
Text Classification in Architecture Field Based on Naive Bayes Algorithm

With the rapid development of the Internet, it has become a problem to quickly and accurately find the information we need in the massive text information. And the method to solve the problem is text classification. This paper proposes a text classifier for the architectural field. The feature selection adopts the TF-IDF text classification method, which effectively reduces the computing time and creates conditions for improving the computing power of the text classifier. In the classification stage, Naive Bayes classifier, K nearest neighbour classifier and decision tree classifier are introduced for comparative analysis. Experiment shows that the prediction results of Naive Bayes are significantly improved compared with KNN and decision tree. Among the three groups of classifiers, the Naive Bayes classifier has the best performance. The classifier can effectively improve the recall rate and accuracy rate of the text classification in architectural field, and has a good use effect.
P3 Process for Object-Relational Data Migration to NoSQL Document-Oriented Datastore

The exponential growth of complex data in object-relational databases (ORDB) raises the need for efficient storage with scalability, consistency, and partition tolerance. The migration towards NoSQL (not only structured query language) datastores is the best fit for distributed complex data. Unfortunately, very few studies provide solutions for ORDB migration to NoSQL. This paper reports on how to achieve the migration of complex data from ORDB to a document-oriented NoSQL database. The proposed approach focused on the P3 process that involves three major stages: (P1) the preprocessing stage to access and extract the database features using SQL queries, (P2) the processing stage to provide the data mapping by using a list of mapping rules between the source and target models, and (P3) the post-processing stage to store and request the migrated data within the NoSQL context. A thorough experiments on two real-life databases veriðes the P3 process improves the performance of data migration with complex schema structures.
Embedding and Integrating Literals to the HypER Model for Link Prediction on Knowledge Graphs

The link prediction on knowledge graphs is now one of the challenges that are gaining a lot of interest from the academic community. The leading solution for this problem is based on graph embedding. Recently, in embedding approaches, convolutional neural networks (CNN) have produced promising results, especially the HypER model. The HypER model outperforms the preceding approaches to maximize the quantity of information from the source entities and relations. However, HypER and other CNN-based methods only focus on retaining information (i.e., structure) of knowledge graphs in low dimension embedding spaces while ignoring literals of the entities. However, the literals can also have a significant impact on relation construction. As a result, this paper proposes an improved model called HypERLit, which is based on the HypER model and incorporates literals. Experiments prove that the role of literals significantly influences the accuracy of the prediction model on the benchmark datasets, including FB15k, FB15k-237, and YAGO3-10. Furthermore, our model outperforms the HypER and other CNN-based models on almost standard metrics.
Multi-Objective Policy Gradients with Topological Constraints

Multi-objective optimization models that encode ordered sequential constraints provide a solution to model various challenging problems including encoding preferences, modeling a curriculum, and enforcing measures of safety. A recently developed theory of topological Markov decision processes (TMDPs) captures this range of problems for the case of discrete states and actions. In this work, we extend TMDPs towards continuous spaces and unknown transition dynamics by formulating, proving, and implementing the policy gradient theorem for TMDPs. This theoretical result enables the creation of TMDP learning algorithms that use function approximators, and can generalize existing deep reinforcement learning (DRL) approaches. Specifically, we present a new algorithm for a policy gradient in TMDPs by a simple extension of the proximal policy optimization (PPO) algorithm. We demonstrate this on a real-world multiple-objective navigation problem with an arbitrary ordering of objectives both in simulation and on a real robot.
Mayfly-Algorithm Selected Features for Classification of Breast Histology Images into Benign/Malignant Class

Incidence rate of Breast Cancer (BC) is rising globally and the early detection is important to cure the disease. The detection of BC consist different phases from verification to clinical level diagnosis. Confirmation of the cancer and its stage is performed normally with breast biopsy. This research aims to develop a framework to identify Benign/Malignant class images from the Breast Histology Slide (BHS). This technique consist the following phases; (i) Cropping and resizing the image slice, (ii) Deep-feature extraction using pre-trained network, (iii) Discrete Wavelet Transform (DWT) feature mining, (iv) Optimal feature selection with Mayfly algorithm, (v) Serial feature concatenation, and (vi) Binary classification and validation. This work considered the test image with dimension $$896 \times 768 \times 3$$ pixels. During the investigation, every picture is cropped into 25 slices and resized to $$224 \times 224 \times 3$$ pixels. This work implements the following stages; (i) BC detection with deep-features and (ii) BC recognition with concatenated features. In both the cases, a 5-fold cross validation is employed and the experimental investigation of this research confirms that the proposed work helped to achieve an accuracy of 91.39% with deep-feature and 95.56% with concatenation features.
Event Detection from Web Data in Chinese Based on Bi-LSTM with Attention

Events are important activities people are involved in real life, and the information about events may be fascinating and important for people to understand and keep abreast with the key developments of some important social and individual subjects. In the big data era, event detection methods can help people efficiently and quickly extract specific information from massive Web information. However, the existing methods usually load the entire Web page information as the input into the models, and the rich noise and irrelevant information on Web pages will seriously impact the event detection performance of these methods. Also, the existing methods mostly used static models, which fail to consider the dynamics of information on the Web. To improve the performance of event detection and classification, we propose in this paper a new method that partitions the Web pages into multiple text blocks and utilizes Bi-LSTM with the attention mechanism for fine-grained event detection from Chinese Web pages. We also propose a dynamic method that updates the data as well as the model regularly and incrementally, making our model more adaptive to the ongoing changes of the Webpage data. The experimental results show that our model outperforms existing methods in event detection in terms of detection performance, the associated computational overhead, and the ability to deal with evolving Webpage information.
SIM-P—A Simplified Consensus Protocol Simulator: Applications to Proof of Reputation-X and Proof of Contribution

Blockchain is a distributed ledger in which participating users with varying levels of trust agree on the ledger’s content using a consensus mechanism called consensus protocols. There has been a rising interest in the design of consensus protocols since they play a central role in blockchain architecture. However, many recently proposed consensus protocols lack experimental verification which hampers the possible deployment of these protocols in real-world blockchain networks. In this article, we propose a simple tool called simplified consensus protocol simulator (SIM-P) that can accurately simulate the behavior of these consensus protocols with ease. It is an agent-based stochastic simulator that relies on the sequential Monte Carlo method to model how block publishers are selected. The likelihood of each node (represented as agents) being selected as a block publisher is represented by independent trials in a binomial experiment. We provide a base SIM-P model that simulates Proof of Work (PoW) for benchmarking purposes. The PoW model also serves as the basic structure of the simulator that can be adapted to other protocols. We showcase the flexibility of SIM-P by proposing two additional simulation models for Proof of Reputation-X and Proof of Contribution, both of which lack experimental verification in their original design specifications. We show how the simulator can be used to produce vital metrics, such as throughput, resistance against the 51% attack, and energy consumption. We verify the accuracy of SIM-P by comparing PoW’s simulated results with theoretical estimates and historical Bitcoin data.
Blockchain-Enabled Hierarchical Federated Learning in Resource-Constrained Power Systems

Power system contains a lot of user data. When using distributed machine learning for joint modeling, there is a risk of data privacy leakage. These problems mainly show that data is exposed in the public environment, leading to attackers using big data analysis and other means to mine important information they are interested in. At the same time, complex edge wireless environment, such as unreliable wireless environment and limited network resources, leads to a long process of federated learning and training and low resource utilization. To solve the problems of data privacy, limited power network resources and unclear ownership of power data, we adopt a hierarchical federated learning architecture based on blockchain. In this architecture, we propose a balanced clustering approach that distributes edge nodes into different clusters. Edge nodes in the same cluster send their local update information to the cluster’s leader, which is then aggregated by each cluster’s leader and sent to the cloud server. Then the global aggregation is implemented on the cloud server. We propose an algorithm to determine the optimal tradeoff between local update and global aggregation to minimize functional loss with limited resource budgets. Finally, the corresponding data is verified and tracked on the blockchain. The performance of the algorithm is evaluated by a large number of experiments on real data sets, and studied on different models and data sets, and a large number of simulation results are obtained.
Deep Reinforcement Learning Method of UAV Behavior Planning for Optimizing Geometric Dilution Precision Distribution

In order to improve the positioning accuracy of fixed radar stations, UAV is used to replace fixed stations. This paper uses deep reinforcement learning to model the environment, and expounds the model element configuration, reward mechanism and implementation steps. The training is divided into four periods according to the passage of time steps. Facing the positioning target of the fixed route, the agent adjusts its strategy from changing the shape of the positioning effective area greatly to find the best point to maximize the coverage of the effective area. The training made the positioning system works for up to 30 hours.
Sistem Absensi Karyawan Secara Realtime Berbasis Fingerprint Menggunakan Metode Rapid Application Development

The role of human resources (employees) in the company is very important because it functions as the main driver of all activities of the company in achieving its goals. One of the things that can affect employee performance is the number of wages given by the company. The number of wages given by the company to employees can be determined, one of which is through the presence of employees in one month. Therefore, employee attendance data is something that is very important for a company. However, some companies still recap attendance, calculate overtime hours, and make employee attendance reports manually. This work is certainly less effective because it requires a lot of time and effort, where the results are not necessarily accurate, so there is often unrest, dissatisfaction and injustice in the provision of employee wages. Therefore, we need a system that can recapitulate employee attendance hours automatically. This study aims to build an attendance system and calculate employee overtime hours in real time based on employee attendance through fingerprints, as well as conduct trials and evaluations of the attendance system created. The process of managing employee attendance data can be done through a website-based information system by implementing the Rapid Application Development (RAD) method. The attendance information system has been developed to be implemented at PT. Sriwijaya Delapan Tujuh Malang, and based on the results of testing through a questionnaire, the system is easy to use and can help the process of providing employee wages through the results of a recap of attendance and overtime every day.
Intelligent Quality Inspection of Secondary Wiring based on Rotating Object Detection and Pairwise Matching Strategy

As a routine work in substations, the quality inspection of secondary wiring is currently done manually by quality inspectors, which is not only time-consuming and laborious, but also boring, making the quality inspectors extremely fatigued and prone to errors. To overcome the above shortcomings of manual quality inspection, we propose an intelligent quality inspection method of substation secondary wiring based on rotating object detection and pairwise matching strategy. Firstly, a data set is constructed, and the image data is collected during on-site inspection of secondary wiring in the substation. The collected images are augmented, and the annotation sample data set is constructed by using annotation tools. Then, the rotating object detection model is trained to locate and recognize the strings on the terminal number plates (TNPs) and wiring caps. However, due to the irregularity of the secondary wiring and inevitable visual distortion in the process of data collection, it is impossible to complete the checking task by using string localization and recognition results directly. Therefore, this paper further designs an effective pairwise matching strategy to judge whether TNPs and wiring caps are correctly paired based on the domain prior knowledge. Finally, the experimental results on the real dataset verify the effectiveness of our method, which can assist manual quality inspection to a certain extent, reduce the work intensity of quality inspectors, and improve work efficiency.
Comparison between Jaro-Winkler Distance Algorithm and Winnowing Algorithm in detecting word similarities in Indonesian documents

Plagiarism is copying one's work and making it seem as if it is his own work, so it can be said to be a crime that is very rampant in the academic world in Indonesia. The scope in plagiarism is very wide, one of which is the similarity of the contents of a document containing words, sentences or paragraphs taken from the document or writings of others. The initial level in minimizing the act of plagiarizing in a document, namely by detecting the similarity of the document by performing calculations using algorithms that can detect the similarity of words in the document. Therefore, to solve the problem, it is necessary to detect the similarity of words in the document. Researchers performed several stages starting from the preprocessing stage, then carried out the process of calculating similarities using Jaro Winkler distance algorithm and winnowing algorithm. The calculation process is done manually and using the simulator. After testing the two algorithms, the Winnowing Algorithm is better than the Jaro Winkler Distance Algorithm seen from the accuracy value obtained by the Winnowing Algorithm of 94.4% and for the Jaro Winkler Distance Algorithm of 88.8%.
Image generation of traditional Chinese window grilles based on generative adversarial networks

Window grille is one of the expressions of traditional Chinese folk arts, which has unique stylistic characteristics and rich symbolic meanings. Studying how to extract the style characteristics of window grilles and generate new window grilles is beneficial to the inheritance and development of traditional Chinese arts. In recent years, the innovative development of generative adversarial networks (GANs) has made it possible to capture the intrinsic distribution of data and generate images that look like real ones. On the basis of researching existing three types of style-based generative adversarial networks (StyleGANs) and adaptive discriminator augmentation (ADA), we use StyleGAN2-ADA to train window grille datasets and generate new window grille images. Finally, multiple image quality evaluation metrics are used to analyze the generated images. The result shows that StyleGAN2-ADA has a good effect on the automatic generation of window grille images. In addition, by comparing the results of different size datasets, we found that the size of dataset has a significant impact on the quality of the generated window grilles.
Foreground Object Detection in Visual Surveillance With Spatio-Temporal Fusion Network

Object detection generally shows promising results only using spatial information, but foreground object detection in visual surveillance requires proper use of temporal information in addition to spatial information. Recently, deep learning-based visual surveillance algorithms have shown improved results, in an environment similar to training one, compared to traditional background subtraction (BGS) algorithms. However, in unseen environments, they show poor performance compared to BGS algorithms. This paper proposes an algorithm that improves performance in unseen environments by integrating spatial and temporal information. We propose a spatio-temporal fusion network (STFN) that extracts temporal and spatial information from 3D and 2D networks. Also, we propose a method for stable training of the proposed STFN using a semi-foreground map. STFN can generate a compliant background model image and operate in real-time on a desktop with GPU. The proposed algorithm performs well in an environment different from training and is demonstrated by experiments using various public datasets.
Full Face Texture Generation of Virtual Human

Face texture completion plays a significant role in virtual human research, and the quality of face texture needs to be improved urgently. One of the major obstacles to single-face texture generation is that the generated textures are always incomplete for self-occlusion of the input face, and the other is that pixel details are limited by the illumination. To address this, we propose a method for complete face texture generation based on generative adversarial networks. The face parameters obtained from 3D Morphable Model are processed as conditional vectors in the encoder, and the multivariate Gaussian distribution of the latent code is used in the networks to learn the complete texture features. We established a face texture dataset CFT for training the network. Meanwhile, we show the effectiveness of the proposed approach in qualitative and quantitative experiments. The visual results under different tasks show superior performances compared with the state-of-the-art approaches.
IReF: Improved Residual Feature For Video Frame Deletion Forensics

Frame deletion forensics has been a major area of video forensics in recent years. The detection effect of current deep neural network-based methods outperforms previous traditional detection methods. Recently, researchers have used residual features as input to the network to detect frame deletion and have achieved promising results. We propose an IReF (Improved Residual Feature) by analyzing the effect of residual features on frame deletion traces. IReF preserves the main motion features and edge information by denoising and enhancing the residual features, making it easier for the network to identify the tampered features. And the sparse noise reduction reduces the storage requirement. Experiments show that under the 2D convolutional neural network, the accuracy of IReF compared with residual features is increased by 3.81 %, and the storage space requirement is reduced by 78%. In the 3D convolutional neural network with video clips as feature input, the accuracy of IReF features is increased by 5.63%, and the inference efficiency is increased by 18%.
An Optimization of Bleichenbacher’s Oracle Padding Attack

In the present paper we propose an improvement of Bleichenbacher’s Oracle Padding Attack that makes breaking more restrictive implementations of the PKCS#1 v1.5 standard feasible both theoretically and in practice. It is proven that the current attack requires at most a quarter of the total number of queries used by the original version. Using the proposed algorithm, we conducted experiments on various restrictive oracles for illustrating the theoretical improvement, and attacked a real device that implements the PKCS #11 standard in a reasonable amount of time. Note that the use of the original algorithm would have led to only a partial decryption of the ciphertext in a larger time interval.
How Can the Marketing Sector Benefit from Virtual Reality? A SWOT Analysis

The adoption of virtual-reality (VR) technology in marketing is expanding, with positive outcomes being offered for systems that concern purchasing behavior and consumer interest. This paper provides Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of VR in marketing sector. The SWOT framework is a widely used method in business for examining the elements that affect the competitive situation of a company in the market, with a view to the future. This structured analysis of the factors affecting the future and current state of VR marketing will produce a good summary of the significant challenges and issues that are important to understand and develop this vital application field. The results of this research found that VR technology would become a successful marketing platform to raise revenue, enhance a brand reputation and develop better strategies.
Digital Geometry, Mathematical Morphology, and Discrete Optimization: A Survey

We study difficulties that appear when well-established definitions and results in Euclidean geometry, especially in the theory of convex sets and functions in vector spaces, are translated into a discrete setting. Solutions to these problems are sketched.
Multi-modal Image Super-resolution with Joint Coupled Deep Transform Learning

In this paper, we address the problem of multi-modal image super-resolution (MISR), which aims at improving the resolution of the target modality with the help of high resolution guidance image of another modality. A novel joint coupled deep transform learning framework (JCDTL) based on deep transform learning is proposed which combines the information from multiple modalities for achieving MISR. The formulation and the requisite solution steps are provided. Two publicly available datasets RGB/NIR and RGB/Multispectral are considered for performance evaluation. The proposed approach shows a considerable improvement in performance compared to the state-of-art techniques. Further, an average PSNR improvement of close to 2dB and 1.5dB on RGB/Multispectral and RGB/NIR datasets respectively is observed by increasing the number of layers from one to three.