Reviewerly/exHarmony huggingface dataset:
GATES

The sheer size of modern knowledge graphs has led to increased attention being paid to the entity summarization task. Given a knowledge graph T and an entity e found therein, solutions to entity summarization select a subset of the triples from T which summarize e's concise bound description. Presently, the best performing approaches rely on sequence-to-sequence models to generate entity summaries and use little to none of the structure information of T during the summarization process. We hypothesize that this structure information can be exploited to compute better summaries. To verify our hypothesis, we propose GATES, a new entity summarization approach that combines topological information and knowledge graph embeddings to encode triples. The topological information is encoded by means of a Graph Attention Network. Furthermore, ensemble learning is applied to boost the performance of triple scoring. We evaluate GATES on the DBpedia and LMDB datasets from ESBM (version 1.2), as well as on the FACES datasets. Our results show that GATES outperforms the state-of-the-art approaches on 4 of 6 configuration settings and reaches up to 0.574 F-measure. Pertaining to resulted summaries quality, GATES still underperforms the state of the arts as it obtains the highest score only on 1 of 6 configuration settings at 0.697 NDCG score. An open-source implementation of our approach and of the code necessary to rerun our experiments are available at https://github.com/dice-group/GATES.
Unsupervised Image Translation in the Wild

The image-to-image translation uses a collection of labeled images for learning to map a source image to a target image. We used the unsupervised image-to-image translation models which have two independent domain sets of images instead of the supervised method which is required to pair corresponding images in different domains. Because the collection of paired image data is a compelling problem. We constructed masked people emoji datasets in the wild and used Flickr Faces datasets for our experiments. We examined the translations between masked faces-unmasked faces and human face-emoji by training UGATIT and AttGAN models to measure success in different wild datasets. As a result of our experiments, we concluded that UGATIT is more successful in style transfer, while AttGAN is more effective in changing face attributes. This result is also compatible with the structure of the architecture.
Learning Paradigms for Neural Networks for Automated Medical Diagnosis

As time passes, we find ourselves facing a new era of automated medical diagnosis, a time when not only accuracy in establishing a diagnosis is important, but also the speed of the entire process. Luckily enough, the extensive development of intelligent systems (IS) algorithms, and especially of neural networks (NNs), have led to the discovery of fast and efficient models for automated medical diagnosis. One direction in building new robust and trustworthy NNs is designing new learning paradigms, with focus on the initialization of the network’s synaptic weights. In this study, our aim is to provide an overview of some learning paradigms for NNs that can be applied in automated medical diagnosis, highlighting their benefits in this crucial domain.
Analysis of Stream Cryptographic Transfer Algorithms for Light (Less-Resource) Cryptographies Defined in ISO/IEC 29192

The third part of the international standard ISO/IEC 29192 Information technology—Security techniques—Lightweight cryptography is devoted to the presentation of stream algorithms of lightweight (low-resource) cryptography, namely: this regulation specifies the specification of Enocoro and Trivium stream ciphers. The Trivium stream encryption algorithm was discussed above, as its specification is also based on the results of the international eSTREAM project for hardware-based SSC. Therefore, in this chapter we consider only the Enocoro algorithm, examine its structure and the main transformations applied. You are also exploring the structure and basic properties of the RC4 stream cryptoconversion algorithm RC4, which is used in popular protocols such as Transport Layer Security (TLS) (for Internet traffic protection) and WEP (for wireless network security).
Research on Teaching Design for Undergraduate Chemistry Major from the Perspective of Embodied Learning

With the development of embodied cognition theory,the research on embodied learning theory has gradually emerged. This learning theory considers that learning is the interaction between the learner’s mind,body and environment,and the experiential,participatory and contextual learning methods under the embodied learning theory bring important enlightenment to the design of undergraduate chemistry teaching,namely,we should pay attention to the embodiedness,contextuality and interactivity of chemistry teaching in the teaching design,which can be realized by combining advanced network technology,focusing on embodied teaching and holding practical activities with chemistry characteristics,so as to make the undergraduate chemistry teaching pay more attention to students’ experience and participation,make the chemistry class more open and interesting,and finally improve the teaching quality of the undergraduate chemistry education and the comprehensive quality of the students.
Does Bitcoin Behave Like a Commodity?

The paper tests bitcoin returns on predictions made by a commodity theoretical model by Routledge et al (RM). RM predicts the spot price, futures, and implied volatilities behavior to the immediate-use-demand and inventory of a commodity. I find that bitcoins futures and implied volatility behave like any other commodity. However, I do not find predictable behavior in spot prices. Instead, I find a positive relationship between the number of new participants and spot price, which indicates a behavior like an asset driven by network effect versus a commodity.
Spatial scene similarity assessment based on deep learning

Similarity is the assessment of deviation from equivalence. Spatial scene similarity assessment is a hot topic in Geographic Information Science and has wide prospect in spatial analysis, spatial query, and map generalization, etc. Spatial scene similarity is usually employed to measure similarity between query spatial scene and various candidate scenes. The spatial relation between geographic objects are consisted of topological, directional, and distance. As they capture the essence of a scene's structure, conventional spatial scene similarity computation method builds synthetic evaluation models by adjusting coefficient among different parameters. In this paper, deep neural networks were introduced to assess the spatial scene similarity. More than 50 million map search case was used to train the model. The case data was from Tusou system, which was developed to search map via spatial scene by users' sketch. Experimental implement show that the new model understands users' intension better than current model.
SYSTEM IDENTIFICATION USING NEWTON–RAPHSON METHOD BASED ON SYNERGY OF HUBER AND PSEUDO–HUBER FUNCTIONS

In real situations the presence of outliers is unavoidable and that is why the distribution of a disturbance is non-Gaussian. A synthesis of an algorithm of identification based on the Newton-Raphson method is considered for this case. The method requires that the loss function should be twice differentiable. Huber loss function, relevant for the treatment of outliers, has just the first derivative. In order to overcome the problem, the pseudo- Huber loss function is introduced. This function behaves similarly to the Huber loss function and has derivatives of an arbitrary order. In this paper, the pseudo- Huber loss function is used for the second derivative of functional in the Newton-Raphson procedure. The main contributions of the paper are: (i) Design of a new robust recursive algorithm based on the synergy of Huber and pseudo – Huber functions; (ii) The convergence analysis.
Adaptive Density Peak Algorithm Based on K-nearest Neighbors with Pre-screening Strategy

Clustering by fast search and find of density peaks (DPC) is a new kind of clustering algorithm based on density and distance, which can obtain arbitrary shape of the clusters efficiently. It chooses the cluster centers based on the high value of density and distance. However, in the process of calculating density and distance, the wrong selection of cutoff distance will lead to the clustering performance because initial cluster centers are not selected correctly. In addition, the allocation strategy of one-step is not robust and easy to cause subsequent assignment errors. To overcome these defects, the density peak algorithm based on K-nearest neighbors with pre-screening strategy (PADPC) is proposed in this paper which can effectively increase the robustness of the original algorithm and achieve better experimental results. Experiments on classic datasets show the proposed clustering algorithm outperforms other competitive clustering algorithm greatly.
The comparison of handwritten digital image classification based on different neural networks

Based on the wide application of neural network in image recognition and character recognition, this paper introduces three kinds of neural network models, compares and analyzes the performance of different neural network models in practical application. This paper first introduces the development of neural networks, especially the convolutional neural network, and the principles of the three neural network models in detail. Then building and training the three models that are based on the analysis. Finally, through experimental simulation, it shows that convolutional neural network has an outstanding advantage in handwritten digital image recognition.
Detecting Energy Levels of Spin Systems on IBM’s Quantum Computer by Evolution of Mean Value of Physical Quantity

Finding energy levels of a quantum system is an important problem that can be solved using quantum computers. We showed that transition energies of a quantum system are related with the evolution of mean value of a physical quantity. In particular case when the operator of a physical quantity anticommutes with the Hamiltonian of a system we found that studies of the evolution of the mean value of this physical quantity give a possibility to determine the energy levels of the system and their degeneracy. On the basis of the results the energy levels of spin systems were detected on IBMs quantum computers ibmq manila and ibmq melbourne. It is worth noting that spin systems are one of the most suitable systems for modeling them on a quantum computer. We considered a spin in magnetic field, spin chain with Ising interaction, spin square-lattice with isotropic and spatially anisotropic Ising interactions. The results obtained on the quantum devices correspond to the analytical one. It is important to note that finding the maximal or the minimal energy of the Ising model on square lattice with spatially anisotropic interaction is not a trivial problem. Therefore, the proposed algorithm for obtaining the energy levels opens a possibility to achieve the quantum supremacy in solving this problem on many qubit quantum computers.
Traffic Intersection Lane Control Using Radio Frequency Identification and 5G Communication

This article deals with automated urban traffic management, and proposes a new comprehensive infrastructure solution for dynamic traffic direction switching at intersection lines. It was assumed that the currently used solutions based on video monitoring are unreliable. Therefore, the Radio Frequency IDentification (RFID) technique was introduced, in which vehicles are counted and, if necessary, identified in order to estimate the flows on individual lanes. The data is acquired in real time using fifth-generation wireless communications (5G). The Pots and Ising models derived from the theory of statistical physics were used in a novel way to determine the state of direction traffic lights. The models were verified by simulations using data collected from real traffic observations. The results were presented for two exemplary intersections.
Ontology-Based Question Answering System for an Academic Domain

The development of question answering (QA) systems involves methods and techniques from the areas of Information Extraction (EI), Natural Language Processing (NLP), and sometimes speech recognition. A user interface that involves all these tasks requires deep development to improve the interaction between a user and a device. This paper describes a Spanish QA system for an academic domain through a multi-platform user interface. The system uses a voice query to be transformed into text. The semi-structured query is converted into SQWRL language to extract a system of ontologies from an academic domain using patterns. The answer of the ontologies is placed in templates classified according to the type of question. Finally, the answer is transformed into a voice. A method for experimentation is presented focusing on the questions asked in voice and their respective answers by experts from the academic domain in a set of 258 questions, obtaining a 92% accuracy.
German Translation Teaching Reform Based on Computer Aided Translation Software

Abstract With the rapid development of the times, the role of computer in all aspects of society is becoming more and more obvious, and the translation industry has also been greatly affected. Now the traditional translation no longer meets the requirements of the society, the emergence of computer-supported translation software. Relying on the powerful computing and translation storage functions of computers, translation can be managed more effectively. As a new research field, computer-aided foreign language research has been widely concerned by scholars from all walks of life. Among them, due to its application value, computer-aided translation research has been paid more attention by scholars. Computer assisted translation is computer-assisted translation. The basic operation principle is to search the existing translation language reference room, compare the original text with the translation language group, and select the language materials most similar to the original text. In this paper, through the analysis and understanding of computer-aided translation and its software requirements, as well as the investigation of the current application status and future development forecast, and the understanding of the application defects of computer-aided translation, we can know the operability of computer-aided translation software in practical translation work.
Learning Positional Priors for Pretraining 2D Pose Estimators

The target of 2D human pose estimation is to locate the keypoints of body parts from 2D images. State-of-the-art methods for pose estimation usually construct pixel-wise heatmaps from keypoints as labels for learning neural networks, which are usually initialized randomly or using classification models on large dataset, such as ImageNet, for their backbones. According to statistical data, there are strong positional priors for human keypoints, which are highly dependent on their relationship between image patches. To learn positional priors for pretraining pose estimators, we propose Heatmap-Style Jigsaw Puzzles (HSJP) problem as self-supervised pretext task, whose target is to predict the location of each patch from an image composed of shuffled patches. During pretraining, we only use person images in MS-COCO, rather than introducing extra large dataset like ImageNet. A heatmap-style label for patch location is designed and our learning process is in a non-contrastive way. The weights learned by HSJP pretext task are utilised as backbones of 2D human pose estimators, which are then finetuned on MS-COCO human keypoints dataset. With two popular and strong 2D human pose estimators, HRNet and SimpleBaseline, we evaluate mAP score on both MS-COCO validation and test-dev datasets. Our experiments show that downstream pose estimators with our self-supervised pretraining obtain much better performance than those trained from scratch, and are comparable to those using ImageNet classification models as their initial backbones.
Ensemble Approach Based on Bagging and Boosting for Identification the Computer System State

The efficiency of using machine learning technology to detect the state of a computer system has been studied. A set of different classifiers and ensembles of classifiers was developed, their training, cross-checking (testing) on real data were carried out. Based on the research results, two methods for identifying the state of a computer system are proposed using an ensemble of decision trees based on boosting and bagging as a classifier. These classifiers were modified due to a special procedure for selecting the optimal parameters for the functioning of the classifiers as well as through the use of the initial data preprocessing procedure. The developed methods are implemented in the software and are investigated in solving the problem of identifying the state of the computer system. The efficiency of the developed classifiers has been evaluated. Prospects for further research may be the development of an ensemble of fuzzy decision trees based on the proposed methods, optimization of their software implementation.
Integration of Distributed Ledger Technology and Modern Smart Grids: An Outlook

Today, the evolution of electrical grid systems is pacing towards interconnected Smart Grids mainly driven by governmental activities worldwide. This step brings additional challenges from the data storage perspective since conventional centralized systems may not handle the growing data volumes arriving from various sources. Thus, new distributed solutions must be introduced to allow flexible and on-the-fly enablers to such tremendous data flows. One of the potential candidates is integrating Blockchain-powered Distributed Ledger Technology (DLT) to achieve an immutable record of manipulating electricity from production to end-user delivery. In this paper, we study the relation of said diverse technological components inter-operation, related challenges, and solutions proposed by research and federal regulators. Based on the literature review, we draft the recommendation for the wide-scale architecture of the future combined solution.
GANs and VAEs As Methods of Synthetic Data Generation and Augmentation to Enhance Heart Disease Prediction

Heart disease instances are rising at an alarming rate, and it is critical and essential to predict any such ailments in advance. This is a challenging diagnostic that must be done accurately and swiftly. Lack of relevant data is often the impeding factor when it comes to various areas of research. Data augmentation is a strategy for improving the training of discriminative models that may be accomplished in a variety of ways. Deep generative models, which have recently advanced, now provide new approaches to enrich current data sets. Generative Models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are frequently used to generate high quality, realistic, synthetic data essential for machine learning algorithms as they play a critical role in various classification problems. In our case, we were provided with 304 rows of heart disease data to create a robust model for predicting the presence of an ailment in the patient. However, the identification of heart disease would not be efficient given the small amount of available training data. We used GAN, CGAN, and VAE to generate data to tackle this problem, thus augmenting the original data. This additional data will help in increasing the accuracy of the models created using the new dataset. We applied classification-based Machine Learning models such as Logistic Regression, Decision Trees, KNN, and Random Forest. We compared the accuracy of the said models, each of which was supplied with the original dataset and the augmented datasets that used the data generation techniques mentioned above. Our research suggests that using data generation techniques significantly boosts the accuracy of the machine learning techniques applied to them.
Corpus Based Information Extraction Approach for Marine Ontology Development

Extracting information from texts and representing it as a formal knowledge system has become important due to increasing volume of freely available texts, which makes difficult finding relevant information and separating meaningful data from insignificant quickly. These tasks are especially important for industries involving international collaboration such as shipping industry. Shipping industry being a lifeblood of the world economy provides 90% of goods delivery. The industry is regulated by International Maritime organization (IMO) Conventions which are due to regular revising and editing according to changing conditions of trade and multiple parties involved. This pilot research provides a corpus based approach to information extracting and building IMO Conventions Ontology. A Corpus of IMO Convention texts are processed with using semantic approach to extract definitions. Based on core shipping industry definitions extracted from IMO Conventions the Ontology will be build. Developed ontology can be used for intelligent processing of documents and teaching purposes.
Ontology Design for Patient Medication Record

Most of the existing patient medication records used paper card systems to record the medicine prescription in the pharmacy. Different countries use different systems, yet hold the same system workflow. This paper reviews ontology works and their usage in medical applications. As technology evolves, this paper proposes an ontology modeling in developing an electronic patient medication record. This paper uses DOID ontology for its disease and symptom ontology in the proposed methodology. The proposed medication ontology can be embedded in a web application that can be used by patients and doctors. However, the proposed ontology is only specified for the Malaysian environment which covers certain prescribed medicine allowed to be sold at the pharmacy.
Preliminary Design and Realization of the Computer Aided Teaching Software of Volleyball Tactics

At present, volleyball skill training is a hot spot in China. Many volleyball loving athletes pay more attention to the study of volleyball tactics, and colleges and universities also pay more attention to the mastery of volleyball skills. This paper studies the preliminary design and implementation of volleyball tactics computer guidance teaching software. Based on the main points of volleyball tactics, this paper makes computer simulation of students' receiving point and padding posture, and carries out teaching of volleyball tactics from the perspective of artificial intelligence data analysis and prediction, so as to realize the auxiliary teaching and automatic guidance of computer software. In this paper, the students' individual learning situation is recorded, and combined with the skill requirements and key points of international volleyball competition, the students are systematically guided. This software is written by Python language, using the visual library, and supporting high scalability. It can supplement the volleyball teaching points that are not involved at present, and also can arouse students' interest in volleyball teaching. Research shows that in this paper, in the computer-aided teaching software of volleyball tactics, taking a volleyball team in our city as an example, compared with the demonstration teaching, the teaching effect of this method is improved by more than 50%, and the teaching practice can be shortened by 24%, which can be said to be helpful for volleyball auxiliary teaching to a great extent. In teaching, the system can also change the module according to the students' mastery of tactics, which is conducive to improving the skills of students of different levels.