Reviewerly/exHarmony huggingface dataset:
Modal synthesis of precision control systems

The problem of synthesis of precision modal control systems is considered. It is noted that a common approach to solving this problem is to consistently meet the requirements for the nature of the transient process and for the indicators of its accuracy. This approach to synthesis is faced with the need to make design decisions under incomplete conditions. In practice, this circumstance leads to obtaining synthesis results with undesirable deviations from technical requirements. When designing precision control systems, such deviations are unacceptable. To eliminate the difficulties that arise, a transition to interval methods for formulating and solving modal synthesis problems is proposed. The theoretical possibility of the interval approach is based on the excessive variety of possible placement of eigenvalues in the spectrum of the characteristic matrix of the system. An example of an interval synthesis of a system with a modal controller and additional output feedback is considered. The restrictions on the spectrum of the specified matrix are formed, which determine the fulfillment of the requirements for the monotonicity of the transient process, the regulation time and the accuracy of the response to harmonic influences. It is noted that the variety of solutions obtained creates the preconditions for a multi-alternative approach to modal synthesis of systems.
Smart Health Care Chatbot for Prognosis of Treatments and Disease Diagnosis Using Machine Learning

In this present world there are various diseases for which treatments and remedies are available abundantly. It is impossible for human to remember all the precautions and remedies to cure the disease. There is no relevant platform that could exhibit all the diseases and their respective remedies. Health professionals are not always available to users on all the time. Hence, the necessity of health care Chatbot plays a major role in this current world. In the proposed idea, we have created a HealthCare Chatbot with Artificial Intelligence techniques which can process the text input and predict the diseases associated with the symptoms given by the user. The HealthCare Chatbot implemented here is a user friendly platform which predicts the probable diseases and the home remedies, we can imply to cure based on the symptoms observed by the user in their knowledge.
Polarity Identification for Handwritten Text in Multilingual Documents Using Open Source Optical Character Recognition Tools

Sentimental analysis is a prerequisite for many applications. We propose a model which scans handwritten text in English and Kannada languages by a CamScanner and then translated into editable text by using various Open Source Optical Character Recognition tools. The performances of different OCRs are analyzed and tabulated. Sentimental analysis is performed on the statements written in both English and Kannada languages using Wordnet, Algorithmia Rest API and local dictionaries and we have obtained the satisfied results. The same sentimental analysis module is also applied on customer reviews for the mobile product and reviews are taken from Amazon Web Services. The opinion of the customer about the product can be identified correctly.
Sing Network Slicing And NFV Technology

With the growth of the network and the emergence of 5G networks, it is not possible to achieve a reliable service with proper performance in all cases of use with a single design. Recent advances in virtualization and machine learning techniques have ushered in a new era of network management. By separating network functions from traditional hardware, Network Function virtualization (NFV) is expected to provide more flexible management of network functions and efficient sharing of network resources. Network slicing with Software-Defined Networks (SDN) and NFV creates the flexible deployment of network functions belonging to several Service Function Chains (SFC) on a common infrastructure. In this paper, with the help of NFV capabilities as well as existing machine learning methods, a framework for intelligent network slicing is proposed. In this method, Convolutional Neural Networks (CNN) are used to analyze network traffic and classify them. This method can classify traffic without human intervention for feature extraction. By using CNN results, we were able to make the network slice with 97% accuracy.
Perbandingan Prediksi Kualitas Kopi Arabika dengan Menggunakan Algoritma SGD, Random Forest dan Naive Bayes

Classification is one of the techniques that exist in data mining and is useful for grouping a data based on the attachment of the data with the sample data. The dataset that is used in this study is the coffee dataset taken from Dataset Coffee Quality Institute on the GitHub platform. The attributes that contained in the dataset are Aroma, Aftertaste, Flavor, Acidity, Balance, Body, Uniformity, Sweetness, Clean Cup, and Copper points. There are 3 classification methods that are used in this study, Stochastic Gradient Descent, Random Forest and Naive Bayes. The aim of this study is to find out which algorithm is the most effective to predict the coffee quality in the dataset. After that, the prediction results will be tested using K-Fold Cross Validation and Area Under the Curve (AUC) method. The results show that Stochastic Gradient Descent obtained the best accuracy results compared to the other two methods with an accuracy of 98% and increased to 99% after tested using K-fold Cross Validation and AUC method.
Mirroring Theorems in Free Logic

Linnebo and Shapiro have recently given an analysis of potential infinity using modal logic. A key technical component of their account is to show that under a suitable translation ◊ of nonmodal language into modal language, nonmodal sentences ϕ 1 , … , ϕ n entail ψ just in case ϕ 1 ◊ , … , ϕ n ◊ entail ψ ◊ in the modal logic S4.2. Linnebo and Shapiro establish this result in nonfree logic. In this note I argue that their analysis of potential infinity should be carried out in a free logic. I then extend their key theorems to the setting of negative free logic.
Research on a road target detection method based on improved YOLOv3

Aiming at the inaccurate position of BBox (Bounding-Box) in road target detection of YOLOv3 and its low real-time performance, this paper proposes an improved road target detection network YOLO-G (YOLO-with-GIoU) based on YOLOv3. The prediction accuracy of the model is improved by introducing GIoU (Generalized Intersection over Union) as an optimization goal. The Batch Normalization (BN) layer is consolidated into the convolutional layer, which improves the inference speed of the preceding item of the network to speed up the detection speed. Experiments show that in the road target detection scene, The FPS of YOLO-G under GTX1080Ti graphics card is 62.5 frames/second, which is 10.53 frames/second higher than that of YOLOv3. When the IoU threshold value is 0.5 and 0.8, the accuracy is 87.93% and 56.82%, respectively, which are 0.74% and 11.11% higher than that of YOLOv3.
Iterative Phase Correction Method and Its Application

The iterative Gaussian filter method is proposed to eliminate the phase error of the wrapped phase (which is recovered from the low-quality fringe images). The main approach is regenerating the fringe images from the wrapped phase and performed the iterative Gaussian filter. Generally, the proposed iterative Gaussian filter method can filter the noise without interference from reflectivity, improve the measurement accuracy and recover the wrapped phase information from the low-quality fringe images. The proposed method is verified by the experiment results. For the binocular system, the proposed method can improve the measurement accuracy (the root mean square (RMS) deviations of measurement results can reach 0.0094 mm).
Automatic Classification of Tweets Mentioning a Medication Using Pre-trained Sentence Encoders

This paper describes our submission to the 5th edition of the Social Media Mining for Health Applications (SMM4H) shared task 1. Task 1 aims at the automatic classification of tweets that mention a medication or a dietary supplement. This task is specifically challenging due to its highly imbalanced dataset, with only 0.2% of the tweets mentioning a drug. For our submission, we particularly focused on several pretrained encoders for text classification. We achieve an F1 score of 0.75 for the positive class on the test set.
Subject Ontology of the Common Digital Space of Scientific Knowledge as a source of Scientometric Research

The paper proposes a methodology for assessing the intensity of development of a particular topic related to a given scientific direction, based on the analysis of the dynamics of its subject ontology. It is proposed to evaluate the dynamics of the subject ontology development on the basis of a comparative analysis of the frequency of occurrence of ontology terms in the keywords lists of reflected in the citation databases. The proposed methodology is modeled on the example of the scientific direction "Microbiology".
Development of Electronic Resources on the Formation of Personal Qualities of Schoolchildren

The new technology era is shifting the traditional ways of seeking knowledge and plays a new role in disseminating and sharing knowledge. E-learning systems have been introduced with the emergence of and developments in information technology (IT). However, most e-learning systems focus on allowing students to access and use official educational content, with less attention being paid to involve e-resources linked to curricula to support the educational process and the learners. The main objective of this research is to empower users in an e-learning environment with an Information Communication Technology (ICT) web-based system in order to play a bigger role in seeking and sharing e-resources for a better understanding of the curricula. The article focuses on the problem of teaching students in e-learning. The results of a survey helped us identify the main difficulties facing students when they begin their studies. We also describe a methodology of teaching using e-learning as web-based instruction. The use of e-learning in the educational process improves the quality of practical training and provides a better understanding of the course. This study proposes a web-based system that integrates elements of e-resources within an e-learning environment by using e-classes of 7th, 8th, and 9th grades enrolled at Secondary School as a testbed. The system deals with a set of learning cycles that support the aims of e-learning. The first cycle is the student; the second cycle is the teacher. In this study, quantitative and qualitative approaches have been used to evaluate, investigate, and compare the system's impact. The results of this study revealed that there is a significant difference between the students according to grade. Therefore, students at Secondary School would benefit from a system that exposes them to e-resources linked directly to the curriculum to achieve and navigate knowledge understanding.
CogALex-VI Shared Task: Bidirectional Transformer based Identification of Semantic Relations

This paper presents a bidirectional transformer based approach for recognising semantic relationships between a pair of words as proposed by CogALex VI shared task in 2020. The system presented here works by employing BERT embeddings of the words and passing the same over tuned neural network to produce a learning model for the pair of words and their relationships. Afterwards the very same model is used for the relationship between unknown words from the test set. CogALex VI provided Subtask 1 as the identification of relationship of three specific categories amongst English pair of words and the presented system opts to work on that. The resulted relationships of the unknown words are analysed here which shows a balanced performance in overall characteristics with some scope for improvement.
Zero-Shot Neural Machine Translation: Russian-Hindi @LoResMT 2020

Neural machine translation (NMT) is a widely accepted approach in the machine translation (MT) community, translating from one natural language to another natural language. Although, NMT shows remarkable performance in both high and low resource languages, it needs sufficient training corpus. The availability of a parallel corpus in low resource language pairs is one of the challenging tasks in MT. To mitigate this issue, NMT attempts to utilize a monolingual corpus to get better at translation for low resource language pairs. Workshop on Technologies for MT of Low Resource Languages (LoResMT 2020) organized shared tasks of low resource language pair translation using zero-shot NMT. Here, the parallel corpus is not used and only monolingual corpora is allowed. We have participated in the same shared task with our team name CNLP-NITS for the Russian-Hindi language pair. We have used masked sequence to sequence pre-training for language generation (MASS) with only monolingual corpus following the unsupervised NMT architecture. The evaluated results are declared at the LoResMT 2020 shared task, which reports that our system achieves the bilingual evaluation understudy (BLEU) score of 0.59, precision score of 3.43, recall score of 5.48, F-measure score of 4.22, and rank-based intuitive bilingual evaluation score (RIBES) of 0.180147 in Russian to Hindi translation. And for Hindi to Russian translation, we have achieved BLEU, precision, recall, F-measure, and RIBES score of 1.11, 4.72, 4.41, 4.56, and 0.026842 respectively.
Multimodal Context Embedding for Scene Graph Generation

This study proposes a novel deep neural network model that can accurately detect objects and their relationships in an image and represent them as a scene graph. The proposed model utilizes several multimodal features, including linguistic features and visual context features, to accurately detect objects and relationships. In addition, in the proposed model, context features are embedded using graph neural networks to depict the dependencies between two related objects in the context feature vector. This study demonstrates the effectiveness of the proposed model through comparative experiments using the Visual Genome benchmark dataset.
Image Classification Using Graph-Based Representations and Graph Neural Networks

Image classification is an important, real-world problem that arises in many contexts. To date, convolutional neural networks (CNNs) are the state-of-the-art deep learning method for image classification since these models are naturally suited to problems where the coordinates of the underlying data representation have a grid structure. On the other hand, in recent years, there is a growing interest in mapping data from different domains to graph structures. Such approaches proved to be quite successful in different domains including physics, chemoinformatics and natural language processing. In this paper, we propose to represent images as graphs and capitalize on well-established neural network architectures developed for graph-structured data to deal with image-related tasks. The proposed models are evaluated experimentally in image classification tasks, and are compared with standard CNN architectures. Results show that the proposed models are very competitive, and yield in most cases accuracies better or comparable to those of the CNNs.
Development of an Expert Information System for Sports Selection and Orientation Using Fuzzy Logic Methods

The article describes the development of an information and expert system of sports selection and orientation of athletes with the author’s methodology for ranking their results. Before describing the methodology, the identified problems in the development of this information and expert system are indicated and possible ways of their solution are given when designing the system architecture and the data analysis unit. The following are the theses of fuzzy logic used in the development of the methodology, a set of indicators of athletes is determined, consisting of six groups: the level of physical development; health status; functionality; mental features; level of development of psychomotor qualities, sports result. For each group of indicators, a quantitative and qualitative characteristic is given. After that, the process of ranking the indicators of athletes on the basis of fuzzy-logical conclusions systems is described in detail. As a result of the application of this technique, it is supposed to obtain a rating table, which contains the unified data for individual indicators. Athletes rating can be obtained both current and forecast. For each athlete, it is also expected to obtain a generalized assessment for all groups of indicators, which can be observed in dynamics. The approach to the development of an information and expert system of sports selection and orientation, outlined in the article, can serve as the basis for additional research of determining the effectiveness of sports training methods and their implementation with modern digital technologies.
Collusion Attack and Counterattack on the Quantum Key Agreement Via Non-maximally Entangled Cluster States

Recently, Li et al. (Int J Theor Phys: DOI: 10.1007/s10773-020-04588-w, 2020) proposed a multiparty quantum key agreement protocol via non-maximally entangled cluster states. They claimed that the proposed protocol can help all the involved participants have equal influence on the final shared key. However, this study points out a loophole that makes Li et al.'s protocol suffer from a collusion attack, i.e. several dishonest participants can conspire to manipulate the final shared key without being detected by others. To avoid this loophole, an improvement is proposed here.
NUIG-Panlingua-KMI Hindi-Marathi MT Systems for Similar Language Translation Task @ WMT 2020

NUIG-Panlingua-KMI submission to WMT 2020 seeks to push the state-of-the-art in Similar Language Translation Task for Hindi↔Marathi language pair. As part of these efforts, we conducteda series of experiments to address the challenges for translation between similar languages. Among the 4 MT systems prepared under this task, 1 PBSMT systems were prepared for Hindi↔Marathi each and 1 NMT systems were developed for Hindi↔Marathi using Byte PairEn-coding (BPE) into subwords. The results show that different architectures NMT could be an effective method for developing MT systems for closely related languages. Our Hindi-Marathi NMT system was ranked 8th among the 14 teams that participated and our Marathi-Hindi NMT system was ranked 8th among the 11 teams participated for the task.
Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies

Over the past decades, researchers have been pushing the limits of deep reinforcement learning (DRL). Although DRL has attracted substantial interest from practitioners, many are blocked by having to search through a plethora of available methodologies that are seemingly alike, whereas others are still building RL agents from scratch based on classical theories. To address the aforementioned gaps in adopting the latest DRL methods, the author is particularly interested in testing out whether any of the recent technology developed by the leads in the field can be readily applied to a class of optimal trading problems. Unsurprisingly, many prominent breakthroughs in DRL are investigated and tested on strategic games—from AlphaGo to AlphaStar and, at about the same time, OpenAI Five. Thus, in this article, the author shows precisely how to use a DRL library that is initially built for games in a commonly used trading strategy—mean reversion. And by introducing a framework that incorporates economically motivated function properties, they also demonstrate, through the library, a highly performant and convergent DRL solution to decision-making financial problems in general.
Brain CT Image with Motion Artifact Augmentation Based on PGGAN and FBP for Artifact Detection

Synthesis of computed tomographic images with motion artifact has many applications in assisted medical diagnosis such as artifact detection and removal. However, one of the challenges is on how to synthesize high-resolution images with motion artifact while artifact features and tissues are naturally presented within image. In this paper, we propose a new method to solve the problem by combing filtered back-projection (FBP) and progressive growing of generative adversarial networks (PGGAN), while FBP is for artifact generation and feature extraction and PGGAN is for feature augmentation. Finally, we superimpose artifact features onto artifact-free data, so to obtain a set of pre-demanded and diversified images with all kinds of motion artifacts. We quantitatively evaluate the synthetic images by training models with synthetic data for artifact detection. Our extensive experiments demonstrated that the performance of our proposed method is superior over the state-of-the-art methods.
The Discovery of Grounded Theory Practices for Software Engineering Research

Software engineering (SE) research addresses not only technical issues but also human behaviour. SE is considered as an immature discipline because many technical and social issues concerning software development and management have yet to be specified. SE in general is inclined towards quantitative approaches. Nevertheless, qualitative methods are still appropriate for SE research as the methods encourage deep understanding of subject matter. Grounded Theory (GT) is regarded as one of the potential qualitative methods that is applicable to SE research. The method is able to transform less and unknown SE phenomena into cohesive theories through systematic discovery of empirical data from the ground. This paper shares some encounters of using GT in SE research based on the reflection made on several SE research projects covering various phases of software development life cycle. The encounters are then transformed into adaptations and classified as GT practices for SE research, as an effort to inspire the spirit of using GT in SE particularly among novices. The practices embrace aspects concerning formulating research questions, handling preconceptions, utilising software tools, getting access to data and presenting theory and its development process. To illustrate on how the practices were derived, a case study is presented. The proposed GT practices could act as the starting point of adopting GT in SE research. They shall be refined and improved in future to possibly become best practices when more and more experience of using GT in SE are obtained.
Deep Semantic Segmentation for Self-driving Cars

A self-driving car comprises three subsystems in the navigation, viz. lane finding, urban scene understanding and geopositioning. This paper introduces the technique of semantic segmentation for urban scene understanding with various implementations in recent years and proposes a novel approach to fast and accurate semantic segmentation. The architecture of the model is designed using VGG16 as encoder, adopting hierarchical feature fusion technique to perform downsampling followed by a lightweight decoder to perform upsampling. Skip connections are introduced between the encoder and the decoder to improve the information flow. This new approach outperforms the previous implementations by giving 94% accuracy and 8.7 frames per second (FPS) simultaneously.