Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Java Knowledge Graph Based on Neo4j

Under the background of the information explosion, there are numerous knowledge stars in all walks of life on the Internet. Taking java learning as the main case, you can easily find a lot of learning knowledge by searching “Java” in station B; On the one hand, while enjoying the dividends brought by the times, learners also feel a lot of inconvenience on the other hand, such as selecting high-quality learning materials, storing the source of learning materials, and building other people's knowledge into their own knowledge system. In view of the above problems, this paper proposes to develop and design a java knowledge map system with the advantages of mind mapping, which can effectively record the source of high-quality learning materials and the idea of high-quality personal system. The system adopts the development mode of front-end and back-end separation. The back-end and front-end adopt Spring Boot and Vue architecture respectively, integrate the ORM framework MyBatis, and the database adopts Neo4j diagram database and MySQL relational database for storage. The system shows the system of knowledge in the form of map, effectively records learning materials and transmits learning ideas.
Parallelization of Local Neighborhood Difference Pattern Feature Extraction using GPU

One of the various techniques employed for image feature extraction is the Local Neighborhood Difference Pattern, also called as LNDP. LNDP considers the relationship between neighbors of a central pixel with its adjacent pixels and transforms this mutual relationship of all the neighboring pixels into a binary pattern. It has proven to be a powerful and effective descriptor for texture analysis. A parallel implementation of LNDP using Compute Unified Device Architecture (CUDA) has been proposed in this paper. A speedup of about 1000 times has been achieved through a shared memory parallel implementation for large images. Thus, an efficacious and efficient implementation has resulted in an increased execution speed and reduced execution time.
Application of VR in Art Exhibition Design

Art exhibition is mainly the exhibition, display, release and other activities of art works, which is the main way of dissemination of art works. Therefore, art display design plays a vital role in the success of art display, which will improve the quality, grade and grade of art display design. With the rapid development of Internet technology, digital technology has been applied to daily life, which can help us experience new things from a new perspective. Therefore, VR has become a common technology in life, which has been applied to military, games, education, exhibition and other fields. By simulating real space with three-dimensional technology, people can break through the limitations of time and space by combining images, sounds and external equipment, which can have an immersive feeling in vision, hearing, smell and touch. Therefore, VR can convey information intuitively and quickly. However, the virtual display design has not formed a certain standard and guidance, which urgently needs to generate a complete theoretical evaluation system. By meeting the needs of users, we can complete the evaluation of virtual display design system. Firstly, this paper analyzes the importance of VR. Then, this paper analyzes the main algorithms of AHP. Finally, this paper puts forward the application of VR in art exhibition design.
Face Detection and Identification based on 2D and 3D Deep Texture Features

The majority of conventional biometric systems have emphasized the use of manually created features for facial identification. Multimodal biometric systems, however, incorporate a number of traits. Given how challenging it is to create dependable features that can adapt to changes in the input images, feature extraction is unquestionably a crucial step for the effectiveness of these approaches. In this study, we introduce novel methods for identifying deep-face textures for the purpose to recognize people. The proposed methods are based on texture analysis, LBP algorithm, skewness coefficients, and morphological transformations using heatmap visualizations of GradCAM, GradCAM++, and ScoreCam models. They allow recognizing people using facial location from 2D or 3D images with texture masking and face bartering.
A Novel Approach of Smart Contract based Distributed Ledger Technology using Deep Learning Techniques to Secure Medical Images

Internet of Medical Things (IoMT) is on-demand research area, generally utilized in most of medical applications. Security is a challenging problem in decentralized platform while handling with medical data or images. An effective deep learning-based blockchain framework with reduced transaction cost is proposed to enhance the security of medical images in IoMT. The proposed study involves four different stages like image acquisition, encryption, optimal key generation, secured storing. The input images initially are collected in the image acquisition stage. Then, the collected medical images are encrypted using coupled map lattice (CML). This encryption process assists to preserve the input medical images from the attackers. In order to provide more confidentiality to the encrypted images, optimal keys are generated using opposition-based sparrow search optimization (O-SSO) algorithm. These encrypted images are stored using distributed ledger technology (DLT) and smart contract based blockchain technology. This blockchain technology enhances the data integrity and authenticity and allows secured transmission of medical images. After decrypting the image, the disease is diagnosed in the classification stage using proposed Recurrent Generative Neural Network (RGNN) model. The proposed study used python tool for simulation analysis and the medical images are gathered from CT images in COVID-19 dataset.
A Simple Approach for Data Cleansing on Hadoop Framework using File Merging Technique

Hadoop framework is known for being top-notch in processing these huge files and providing useful data. Unfortunately, in a scenario with many small files, the framework is inefficient and fails to deliver. These small files cause many issues when the framework's processing criteria and performance levels. Moreover, these small files contain content that is useless or provides no benefit in the key-value decision-making. To overcome this issue of small files and unnecessary content, this paper proposes a simple data cleansing and file merging approach based on specific type and size that will not only be effective but will increase the framework's performance by approx. 68%. This algorithm ensures the output will be a few huge files with essential/important data. The results show that the proposed system not only improves the framework's performance but also reduces deadlocks in the framework processes, which is approximately 68 % improvement over the base Hadoop framework processing.
Offline handwritten Chinese character recognition based on improved capsule network

An improved capsule network model is proposed to solve the problem of difficult feature extraction and low recognition accuracy of handwritten Chinese characters. The advantage of capsule network is to deal with the spatial relationship between feature vectors, while Chinese characters happen to be a spatial structure composed of strokes, radicals and radicals placed in different positions. However, due to the large number of parameters of the capsule network, it is difficult to train large size images and easy to over fit. In view of the shortcomings of capsule network, the following two improvements are proposed: First, the use of PD (Pixel shuffle down sampling) enables the model to train large images without losing pixel information, while reducing image noise. Secondly, the number of primary capsules is reduced by using multi-layer small windows instead of single-layer large windows to extract image features, so as to reduce the number of model parameters. The experimental results show that, with the increase of training picture size and the decrease of capsule network parameters, the recognition accuracy is significantly improved. The accuracy of the improved capsule network for taking 300 Chinese characters from CASIA-HWDB1.1 dataset can reach 95.97%, and the parameter quantity is only 7.95M, which proves the advantages and effectiveness of the improved model.
An Adaptive Approach for defect Prediction using Dynamic Classifiers Selection

Software Defect Prediction (SDP) approaches use learning methods to classify classes/module/files into the defective or non-defective or provide the possibility that a class can show faulty behaviors in the future. Since there are several classifiers that can give optimal results using ensemble learning methods, they are developed to estimate the defect-proneness of a class by combining prediction outcomes obtained from different classifiers. We are employing an ensemble learning technique and building an adaptive approach for performing bug prediction by dynamically selecting one classifier from a set of machine learning classifiers, that predicts if a class is bug prone or not based on characteristics of static software metrics captured for class. We are proposing a new Adaptive Approach for Bug Prediction using Dynamically Classifier Selection (ADCS) which dynamically selects the best base learning that better predicts bug-proneness of a class based on characteristics of the class. We have used datasets obtained from the PROMISE repository (developed by NASA) for 30 Software system. Our results indicates that ADCS perform better compared to 5 different classifiers which are used to predict bug-proneness independently and when Validation and Voting (VV) ensemble technique used to combine classifiers output with majority voting. We found that the ADCS outperforms in 26 projects and avoids class imbalance and overfitting problems.
Development of a day-ahead solar energy forecasting model using seasonal ARIMA for economic load dispatch

Forecasting the short-term solar irradiance is of paramount importance for efficiently planning the electricity distribution in the transmission network to ensure the smooth running of solar power plants. In this paper, a seasonal ARIMA model is implemented for predicting the global solar irradiance in Mauritius for a time horizon of 24 hours. The proposed technique uses three days historical irradiance measurements to predict the next day values in time steps of one hour. The model has been observed to work well in both cloudy and sunny sky regimes, with a higher predictive accuracy noted on average for the sunny sky conditions. A maximum coefficient of determination attaining 97.7% has been observed for one of the sunny day test-case while a maximum value of 98.0% for the coefficient of determination has been noted for one of the cloudy day scenarios. Coupling of the developed model to a solar farm has been discussed with the suggested approach for regularization of electricity distribution in the grid, despite occasional drops in the electricity generation curve arising from the movement of journeying clouds above the facility.
Breast Cancer Classification using Customized ResNet based Convolution Neural Networks

Deep learning is the most frequently used tool in the classification of tumors in medical applications. In recent decades, many research works have been done on the Breast Imaging Reporting & Data System (BI-RADS) atlas based classification of Breast cancer. As reported in the existing research works, training the larger datasets is a challenging task. Therefore, a customized ResNet based Convolution Neural Network (cRN-CNN) with batch normalization is proposed in this manuscript for addressing the above mentioned issue. The proposed cRN-CNN method has the advantage of faster training and computationally effective for the classification of BIRADS atlas based MRI breast cancer records, where the proposed model's performance is superior compared to the conventional CNN model. The extensive experiments performed on the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) dataset confirmed that the proposed cRN-CNN method achieved better classification results than the existing methods. In the proposed model, the deformation technique based on elastic deformation is also applied to increase the training size of data that helps to improve the outcomes of prediction up-to 99.80%, because of the efficient strategy of batch normalization as customization and elastic deformation.
Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness

Machine learning models have proven to have the ability to make accurate predictions on complex data tasks such as image and graph data. However, they are vulnerable to various backdoor and data poisoning attacks which adversely affect model behavior. These attacks become more prevalent and complex in federated learning, where multiple local models contribute to a single global model communicating using only local gradients. Additionally, these models tend to make unfair predictions for certain protected features. Previously published works revolve around solving these issues both individually and jointly. However, there has been little study on how the adversary can launch an attack that can control model fairness. Demonstrated in this work, a flexible attack, which we call Un-Fair Trojan, that targets model fairness while remaining stealthy can have devastating effects against machine learning models, increasing their demographic parity by up to 30%, without causing a significant decrease in the model accuracy.
Segmentation-free Offline Handwritten Malayalam Word Recognition using Transfer Learning Based Deep Neural Network

Offline Handwriting Recognition is a challenging open research problem under the domain of document analysis and recognition. In this paper we propose a method for offline handwritten Malayalam word recognition from degraded scanned images. Here the major complexities arise from variations in writing styles, touching of characters and degraded noisy scanned images. To avoid the challenges while decomposing the words into pseudo-characters, we treat the word as an individual entity for the recognition. We created a dataset by considering the repeated Malayalam words from the degraded scanned documents of Police First Information statemenst and increased the volume of samples by collecting handwritten words of the same lexicon. In this lexicon based work, we extract the holistic features of the word image using convolutional neural network based transfer learning method. Six different transfer learning architectures, namely VGG16, ResNet50, Xception, MobileNetV2, InceptionV3 and DenseN et121 are experimented for feature extraction and VGG16 outperforms the others. Then for improved performance, an ensemble learning technique, Extremely Randomized Trees Classifier is used for feature selection from the extracted features. Finally, an 8-layer Deep Neural Network is used for the recognition and achieved 97.98% accuracy.
An assessment of the solar climate of a tropical island having a complex topography

An investigation of five meteorologically-distinct regions based on topography and wind directions is performed, to classify day-to-day variations of solar irradiances into physically-sensible regimes. Descriptive statistical tools which include standard deviation, coefficient of variation, quartiles, minimum, maximum, skewness, and kurtosis are utilized to measure the central tendency and variability of recorded global solar irradiance values for the regions of Barkly, Flacq, Plaine Sophie, Reduit, and Richelieu. Seasonal analysis is performed and reveals that the summer season witnesses on average, higher insolation values at all sites while the winter season reflects a relatively stable solar climate, characteristic of constant solar energy resource for efficient energy conversion. An inter-comparison study on annual scale indicates that the North-West regions of Barkly and Richelieu have inherently higher solar energy potentials with clear to lightly scattered sky cover for about 275 days with global solar irradiance above a threshold value of 300 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> • The annual average daily irradiance at Barkly and Richelieu are 371.8 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 374.2 W/m2, respectively, favoring the development of solar energy projects.
Neural Text Style Transfer with Custom Language Styles for Personalized Communication Systems

Style can be thought of as a way where semantics are represented intuitively. Every person has a unique writing style, which can be expressed through certain stylistic features present in a sentence. These features can include the usage of certain common words, contractions, metaphors, slang, and some syntactic structures that when combined determine the language style of a person. Currently, there is no way of applying stylistic variations on text in a dialogue system to adapt to a user or an audience. In this paper, we propose a network architecture to transform machine-like robotic conversations into human-like personalized ones by applying custom text styles to the responses thereby giving them a user-specific tonality, leveraging Language Modeling, Generation, and Style Transfer techniques in a Representation Learning fashion. When executing the process of transferring the text style, our method keeps the original sentence's content intact in the transferred sentence dissociated from its style. This system implementation can be transformed into a software application that can help people use the language styles of their acquaintances and make the AI talk like them.
A Methodology to Locate Image Falsification Using Adaptive Segmentation and Feature Extraction

The requirement for confirming and unwavering quality of computerized images, approving their substance and identifying fabrications is vital. Consequently, noteworthy space in advanced picture verification is Copy-move fraud recognition (CMFD). The Adaptive division technique is one to decide the duplicate move imitation location. The image is adaptively divided into non-covering and uneven fragments using the proposed versatile division computation technique. The component attentions are then extracted as piece determinations from apiece square, and the square choices are then harmonized with one another to identify the labelled component focuses, indicating the alleged fraud or falsification locations. To locate the falsification areas all the more accurately, the imitation district extraction algorithmic is utilized that substitutes the element focuses with little super pixels as highlight pieces so consolidates the neighboring blocks that have proportional shading alternatives into the component squares to get the joined locales. Finally, merge the localities and apply the morphological process to construct the classic faked areas.
Convolutional Neural Network Based Power Information Network Security Situational Awareness Model

At present, most of the external attacks against the power network are through the intrusion of the network layer to tamper with control instructions and sensor data, resulting in abnormal operation of the power system, thus causing serious damage to the power grid process. A variety of defensive measures for security have solved the security problems in the power grid in a variety of ways, but they still can not improve the comprehensive analysis of data in situational awareness. This paper focuses on the collected error or abnormal data, which will lead to abnormal large or small range, and can not normally extract the characteristics of the data. A power load forecasting algorithm based on attention mechanism is proposed by combining the convolutional neural network and GRU. Embedding and convolutional neural networks are applied to prioritize the description of vulnerabilities, which effectively improves forecasting accuracy. The experimental results verify that the model in this paper has a good practical application effect in the power network situation prediction. It can predict the possible attack range and the attack purpose, and establish an effective early warning means to protect the network from attack.
Joint Entity And Relation Extraction Method Fused With Multiple Information

The development of Internet technology has brought massive amounts of unstructured data, extracting entity relation triples from unstructured text is crucial for the construction of large-scale knowledge graph. Most of the existing extraction methods are based on pipeline extraction. But it has the problem of error propagation and the performance of extracting overlapping triples is low. The joint extraction method can solve the above problems, while the existing joint extraction methods ignore the interaction of two subtasks, the enhancement effect of entity type information and the contribution of words under different relations and topics to sentence semantic embedding. In order to solve the above problems, a multi-information fusion entity relation joint extraction model was proposed. The model integrated the predefined entity type description information for head entity recognition, modeled the relationship as a mapping from the beginning entity to the end entity, and used conditional regularization to enhance the sentence representation, and integrated the relationship perception attention mechanism for relationship extraction. Compared with the current SOTA method, the experimental results show that the F1 value is improved by 1%.
Document information similarity calculation method based on multi-feature fusion

In the era of Big Data, the Internet constantly penetrates people's lives, work and research. People are generating new data all the time. In order to separate new data from a large amount of data and get satisfactory results, search engines came into being. One of the essential branches of research in natural language is text similarity computing. This paper proposes a weight-based optimisation algorithm based on a multi-feature fusion algorithm. Using a non-uniform partitioning method, the granularity of the feature fingerprints is varied, resulting in a wide variation of accuracy. The most important feature is the flexible and fast resizing of the partition number to change the vector value. It has been demonstrated experimentally that the method in this paper can save partitioning time. The method in this paper can solve the problem of many vector dimensions in word frequency statistics methods and can better measure the similarity between texts. The method can be widely used in various query scenarios, such as searching taxi maps, orders and routes.