Reviewerly/exHarmony huggingface dataset:
Terminological Maps of Publications on the Transformation of Students' Professional Orientations in the Context of their Internet Socialization

The problem of identifying the components of the transformation of professional orientations of students at the interdisciplinary level is urgent. It is connected with their Internet socialization. For the first time terminology maps on the problem of research have been considered. Raw data were downloaded from Scopus and Web of Science. The selected tool is the program VOSviewer. New terms, relationships between articles were identified and terminology maps were created. The results are applicable to the researchers of the problem.
Monitoring and control of polymer production line based on machine learning

Abstract The work is devoted to the development of an application for monitoring and controlling the state of equipment (extruder) for the petrochemical industry based on sensor readings using a machine learning model. The statistical relationships of the technological process parameters are analyzed, the most significant parameters influencing the occurrence of failures are determined using SHAP values. The hypotheses regarding the effectiveness of various machine learning algorithms in relation to the real problem of predicting the technical state of the extruder are tested. A gradient boosting model has been developed to predict the probability of extruder shutdown due to the formation of polypropylene agglomerates. The developed application allows interpreting the results of the model, which makes it possible to select the most important process parameters that have the greatest impact on the probability of extruder failure, and also proposing a prototype of an extruder monitoring system based on sensor readings using a machine learning model.
The Role of Marketing Knowledge Management in Enhancing Digital Financial Innovation in Commercial Banks

This study examines the role of marketing knowledge management (assets and capabilities) in enhancing digital financial innovation through the moderating role of employees' demographic characteristics (age, education, experience, and position) in commercial banks operating in Jordan. To accomplish this aim, a conceptual framework based on knowledge-based theory and literature review was developed. A total of 336 responses to a questionnaire survey were collected from the managers and employees working on commercial banks operating in Jordan. The empirical findings revealed that marketing knowledge management capabilities positively and significantly affect digital financial innovation. The findings also support the moderating effect of only two demographic characteristics: of employees: education and position on the relationship between marketing knowledge management and digital financial innovation in commercial banks in Jordan.
EW2BPaaS: A Framework for Effects Web-based Battle Platform as A Service

At present, the implementation of Decision-Centered Warfare faces important challenges, and the Mosaic Warfare provides a solution to its challenges, while the Effects Web construction is a core element of the Mosaic Warfare, which is the key and difficult point of Mosaic Warfare implementation. Current research on the Effects Web construction in Mosaic Warfare is very scarce and mostly stays at the conceptual stage. To make up for the lack of current research on the Effects Web, we proposed EW2BPaaS, a framework for Effects Web-based battle platform as a service, drawing on the theoretical and practical foundations of IoT service architecture, and specify the framework by taking time-sensitive target strikes as an example. Our research provides a reference solution to deepen the understanding of and advance the research of Effects Web.
Ontology Intelligent Construction Technology for NOTAM

With the coming of the new information epoch, the quantity of aeronautical intelligence information increases exponentially. In the effort to improve the efficiency and security of the aeronautical industry, it has become a key factor to determine how to acquire, extract, and represent knowledge from a large set of aeronautical intelligence information and forming a knowledge base to guide the intelligent development of aeronautical intelligence information management work. In order to promote integration and sharing of information regarding aeronautical intelligence information domain and obtain the deeper information and knowledge, the construction and application of NOTAM (Notice to Air Men) ontology are developed based on text mining. The NOTAM text is collected and analyzed by web crawler technology. Combined with the professional term in specific domain, we successfully extract the key concepts of the ontology by TF-IDF (term frequency-inverse document frequency) text features. Furthermore, the hierarchical and non-hierarchical relations are automatically extracted by text cluster methods and specific domain knowledge system. Finally, the ontology editor—protégé helps us to visualize the key concepts and the relations in the ontology. Meanwhile, a NOTAM text is instanced to verify the efficiency and precision of the NOTAM ontology.
An application-level failure detection algorithm based on a robust and efficient torus-tree for HPC

Applications must be fault-tolerant for future expansion to Exascale, and failure detection is an important prerequisite for the implementation of many fault-tolerant technologies. Implementing failure detection on large-scale High Performance Computing(HPC) systems is often difficult and expensive. And there is a high probability that applications running on super-scale systems may fall into long and ineffective operation due to some undetectable faults. Therefore, we designed a failure detection algorithm at the application level that enables the application to detect abnormally slow or crashed processes in time. The processes invoked by an HPC application communicate according to some efficient topological overlays, however, the faulty processes can destroy the original communication structure. In this paper, we designed a torus-tree topology for the failure detection algorithm. If there are processes that fail, the surviving processes are reconnected to maintain the connectivity of the communication graph and execute fault propagation based on the repaired communication structure. We designed the torus-tree with the features of easy repair, robustness, and high communication efficiency, and used the detection idea of combining local timing and global counting to overcome the difficulty for process fault determination on the HPC system model. We experimentally verify the effectiveness of the detection algorithm.
T-PKI for Anonymous Attestation in TPM

The Transient Public Key Infrastructure or T-PKI is introduced in this paper that allows a transactional approach to attestation, where a Trusted Platform Module (TPM) can stay anonymous to a verifier. In cloud computing and IoT environments, attestation is a critical step in ensuring that the environment is untampered with. With attestation, the verifier would be able to ascertain information about the TPM (such as location, or other system information) that one may not want to disclose. The addition of the Direct Anonymous Attestation added to TPM 2.0 would potentially solve this problem, but it uses the traditional RSA or ECC based methods. In this paper, a Lattice-based approach is used that is both quantum safe, and not dependent on creating a new key pair in order to increase anonymity.
Study Comparison Stemmer to Optimize Text Preprocessing In Sentiment Analysis Indonesian E-Commerce Reviews

Stemming was an essential part of text preprocessing. Stemming serves to map various forms of words into crucial words. Many stemming algorithms have been developed, including Indonesian stemmers. In this study, we will compare the rule-based stemmer algorithm and dictionary on sentiment analysis classification in the Indonesian E-Commerce Review. The purpose of this research is to find out the best preprocessing algorithm for K-Nearest Neighbor (KNN) classification. The results of the two stemmer testing still contain errors. KNN shows that the use of rule-based stemmers has an effect on increasing accuracy than without using a stemmer. In comparison, the dictionary-based method does not have much influence on the performance of the KNN method.
BSEVOTING: A Conceptual Framework to Develop Electronic Voting System using Sidechain

In a democratic society, a citizen's ability to vote is regarded as one of the most significant legal rights he or she may exercise. For e-voting methods, blockchain presents new possibilities to properly meet transparency, integrity, anonymity and many other security properties. As the need for blockchains keeps rising, demand for bigger, more scalable, more adaptable, and more cost-effective multipurpose chain is also high. Conventional blockchains are incapable of meeting all of these demands. To solve the problems (mostly performance) associated with main blockchains, sidechain technology has recently evolved as a separate chain connected to the main chain that runs in parallel with transactions. Our proposed method is designed to operate on a public blockchain, but we separate the storage of voting information of each candidate using a sidechain to offer a cost-effective blockchain-based voting mechanism by ensuring the security properties such as anonymity, integrity, privacy, security, fairness, receipt freeness and so many. In future, we will broadly discuss and implement this voting system.
Hand Gesture Recognition Based Human Computer Interaction to Control Multiple Applications

Human Computer Interaction (HCI) is nothing but a system where humans can interact with the computer more naturally and efficiently. The main aim is to eliminate the generally used controllers such as - mouse, keyboard, pointers, etc. which works as a barrier between humans and computers. This research provides a method for detecting hand gestures using computer vision techniques for controlling various applications in real-time. The proposed method detects all the skin-colored objects from the captured frames and then detects the face by using Haar based classifier. The number of fingers is detected by the convexity defect approach and then the movement of the hand is tracked. These are considered as the features of the hand gesture recognition system. This hand gesture recognition system doesn’t require any dataset, hence this is simpler to develop. The detected face is blocked. After the gesture is recognized, they’re translated into actions. 20 commands are generated from the hand gestures and sent to the computer via the keyboard. Due to this method, multiple applications like-video player, music player, PDF reader, slideshow presentation, etc. whichever application takes input from the keyboard can be controlled with this single system. The system can be used for different purposes like human-robot communication, e-learning, touch-less interaction with the computer, etc.
Comparative Analysis of Machine Learning Techniques in Classification Cervical Cancer Using Isolation Forest with ADASYN

Cervical cancer is a form of cancer that forms in the cervix area. Majority of this form of cancer are related to human papillomavirus infection. Cervical cancer is linked with a number of risk factors. It is important to consider the importance of cervical cancer test factors when categorizing patients based on the findings. In this paper, we have performed a comprehensive analysis on cervical cancer prediction using various machine learning algorithms. We have applied seven different machine learning algorithms which are artificial neural networks (ANN), logistic regression (LR), decision tree (DT), random forest (RF), gradient boosting classifier (GBC), K-nearest neighbor (KNN), and AdaBoost (ADA) for predicting cervical cancer. Adaptive synthetic sampling method was used in this paper for filling out the missing data. For reducing the dimension of the dataset, linear discriminant analysis was used. In addition, we have used isolation forest outliers detection method for detecting outliers. From the comprehensive analysis, we have concluded that attains the maximum accuracy of 95%. We have performed our experiments on publically available cervical cancer dataset. This dataset is available in UCI machine learning repository. Overall, decision tree algorithm with outlier detection approach has performed comparatively well with comprehensive accuracy for predicting women exhibiting clinical symptoms of cervical cancer.
Graph Modeling for Efficient Retrieval of Power Network Model Change History

Power grids are constantly evolving, and data changes are increasing. Operational technology (OT) is controlled by IT technologies in smart grids, where changes in the physical world impose changes in the software data model, as well as the continuous generation of data points, resulting in time series datasets. The increased need for processing large amounts of data combined with requirements to maintain and increase overall performances has created a significant challenge for traditional database solutions and relational database models. The main idea of this paper was to find and propose a graph model that will allow the retrieval of historical connectivity in a reduced time complexity. Furthermore, the research question was addressed by evaluating three different approaches where the results provide a foundation for the proposed design guidelines related to optimizing graph-based databases for a modern smart grid system. The results of the experiments demonstrated reduced time complexities from 3 to 5 times depending on the typical industry usage patterns and the selected graph model. This suggests that the design decision may severely affect the outcome for given smart grid use cases when using historical features in OT technologies. Therefore, the main contribution of the research is the proposed guidelines on how to design an optimal graph model that satisfies the described smart grid requirements.
BERT-based implicit aspect extraction

The traditional implicit aspect extraction methods require too much manual feature engineering, which are inefficient when processing large-scale data. To address the problem, BERT-based model with different kinds of classifiers is proposed. In this paper, the text are inputted into BERT to get the embeddings. Then the embeddings of the final hidden layer are combined with different classifiers to obtain implicit aspect. Five classific text classifiers are used for comparison. The experimental results show that the method in this paper outperform state-of-the-art works. The accuracy and the macro-F1 score on the SemEval implicit extraction data set can reach 78.11% and 73.19% respectively.
3D Mesh Generation by Introducing Extended Attentive Normalization

In recent years, research on conditional image generation using GANs of the type where conditions are given by class labels or texts has been successful. On the other hand, the generation of conditional 3D models consisting of 3D meshes is still in its infancy. In this research, we add global information based on Attentive Normalization to local information using CNN to improve 3D mesh generation. Specifically, we propose Conditional Attentive Normalization, which is an extension of Attentive Normalization and can add conditional information. Comparative experiments conditioned by class labels and texts have been carried out by using Caltech-UCSD Birds-200-201. It turns out that our proposed method outperforms the conventional methods.
Dynamical Properties of Environmental Prediction Using Human Sensitivity

By making use both of the self-consistent field theory (SCFT) and the Master equation, we investigate dynamic property of a sensor network for environmental prediction via human sensitivity in a small-scale area with an air conditioner. We find that the present method smoothly infers comfortable surroundings accurately around the Bayes-optimal condition that we have perfect knowledges on an assumed true prior and transition probability from each original surrounding to the observed one.
Face Super-Resolution via Meta-Learning Optimization Network

Most current face super-resolution (FSR) methods assume that the degradation from high-resolution (HR) image to low-resolution (LR) image is single and known (e.g., bicubic downsampling), which results in the generalization ability of the model being limited to the ideal conditions for bicubic downsamling. When the actual degradation is different from the current settings, these methods will suffer serious performance degradation. In order to solve this problem, we proposed a face super-resolution via meta-learning optimization network (MLON). Firstly, learning the SR tasks composed of multiple blur kernels to construct an initial SR model with sensitive and transferable parameters; then, the model parameters are optimized with a few gradient steps by learning a new blur kernel; finally, the generalization ability of the current model to the new blur kernel is verified. A large number of experimental results on FEI datasets show that our MLON method is superior to the recent advanced FSR methods.
Comprehensive CNN-Based Approach for Helmet Use Detection of Tracked Motor Cycles

Precautionary measures for safety on road and traffic have become key roles played by the new Traffic Regulations and Management, especially for two-wheeler riders. It has been made mandatory for the two-wheeler vehicle riders to wear helmets, which is, unfortunately not being followed by a few riders. In such a scenario, several aspects have been taken into account, resulting in the current project, that consists of the technique where, any two-wheeler rider without wearing helmet, can be detected in places it must be worn compulsorily. In this project, helmet detection and recognition have been deployed mainly using algorithms such as YOLOv2 (You Only Look Once version 2) and LeNet. Any kind of helmet can be detected and recognised under several conditions and cases. Whenever a rider is detected without a helmet from the source file in the format of either image or video, the instance of such malpractice is considered and the output is built by analysing the data.. The trained model gives an accurate result at the rate of95% under multiple cases and marks to 1 FPS using CPU.
Identifying players in broadcast videos using graph convolutional network

The person representation problem is a critical bottleneck in the player identification task. However, the current approaches for player identification utilizing the entire image features only are not sufficient to preserve identities due to the reliance on visible visual representations. In this paper, we propose a novel player representation method using a graph-powered pose representation to resolve this bottleneck problem. Our framework consists of three modules: (i.) a novel pose-guided representation module that is able to capture the pose changes dynamically and their associated effects; (ii.) a pose-guided graph embedding module using both the image deep features and the pose structure information for a better player representation inference; (iii.) an identification module as a player classifier. Experiment results on the real-world sport game scenarios demonstrate that our method achieves state-of-the-art identification performance, together with a better player representation.
The Effect of Filtering on Sparse Representation-Based Image Reconstruction

Imaging using sensors is a promising technique in many cases and usually uses a limited number of sensors. The limited number of sensors results in incomplete visibility measurement. So, the incomplete visibility measurement causes image quality to degrade. The existing method to overcome this problem is the sparse representation-based reconstruction by applying <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell_{1}$</tex> -norm. In this paper, we deliver the use of filtering on sparse representation-based image reconstruction, especially in the case of an image that experiences sparse sampling in frequency. This filtering has the advantage of improving the low-resolution (LR) image due to the sparse sampling. In the training process, we use 80 high-resolution (HR) images to build an overcomplete dictionary. While in the reconstruction, we verify eight low-resolution images. Compared to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell_{1}$</tex> -norm without filtering, the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell_{1}$</tex> -norm with filtering is capable of producing RMSE and PSNR improvement. This improvement also appears in the reconstructed image that is better visually when using the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell_{1}$</tex> -norm with filtering.
ScaleDNN: Data Movement Aware DNN Training on Multi-GPU

Training Deep Neural Networks (DNNs) models is a time-consuming process that requires immense amount of data and computation. To this end, GPUs are widely adopted to accelerate the training process. However, the delivered training performance rarely scales with the increase in the number of GPUs. The major reason behind this is the large amount of data movement that prevents the system from providing the GPUs with the required data in a timely fashion. In this paper, we propose ScaleDNN, a framework that systematically and comprehensively investigates and optimizes data-parallel training on two types of multi-GPU systems (PCIe-based and NVLink-based). Specifically, ScaleDNN performs: i) CPU-centric input batch splitting, ii) mini-batch data pre-loading, and iii) model parameter compression to effectively a) reduce the data movement between the CPU and multiple GPUs, and b) hide the data movement overheads by overlapping the data transfer with the GPU computation. Our experimental results show that ScaleDNN achieves up to 39.38%, with an average of 17.96% execution time saving over modern data parallelism on PCIe-based multi-GPU system. The corresponding execution time reduction on NVLink-based multi-GPU system is up to 19.20% with an average of 10.26%.
A New Knowledge Distillation Method for Object Detection Based on EMD

Abstract As a common method of model compression, the knowledge distillation method can distill the knowledge from the complex large model with strong learning ability to student small model with weak learning ability in the training process, to improve the accuracy and performance of the small model. At present, there has been much knowledge distillation methods specially designed for object detection and achieved good results. However, almost all methods failed to solve the problem of performance degradation caused by the high noise in the current detection framework. In this study, we proposed a feature automatic weight learning method based on EMD to solve these two problems. That is, the EMD method is used to process the space vector to reduce the impact of negative transfer and noise as much as possible, and at the same time, the weights are allocated adaptive to reduce student model’s learning from the teacher model with poor performance and make students more inclined to learn from good teachers. The loss (EMD Loss) was redesigned, and the HEAD was improved to fit our approach. We have carried out different comprehensive performance tests on multiple datasets, including PASCAL, KITTI, ILSVRC, and MS-COCO, and obtained encouraging results, which can not only be applied to the one-stage and two-stage detectors but also can be used radiatively with some other methods.