Reviewerly/exHarmony huggingface dataset:
Model Transformation of Declarative User Interface from Platform Specific Model to Platform Independent Model using Graphical Notation.

The approach of model-based user interface development (MBUID) paradigm is for building user interfaces using a set of models that take into account the various facets of a user interface at a semantic level. In this work model transformation of user interface has been explored from source model to target model using relational mapping and graphical notation. A framework based on mathematical graphical notation is presented and relational mapping to re-engineer declarative user interface model transformation through a platform independent model (PIM) is presented. For re-engineering user interface three steps; namely source code to platform specific source model (Extraction), platform specific source model to platform specific target model (Transformation) and platform specific target model to platform specific target code (Development) are followed. For achieving independence with respect to platform and language, user interface elements are addressed at the semantic level. A layer of abstraction of PIM has been added for defining few common basic user interface elements along with their attributes in semantic terms using mathematical graphical notation. Transformation has been defined between the source and target model and generic rules are applied for every new element extracted from source model and placed in the target model. By adding a new layer in the re-engineering process efforts required to port the user interface from one platform specific source model to one or more platform specific model are reduced. By defining the user interface elements semantically in graphical notation, diversity in the programming languages has been reduced. Bi-directionality has been achieved between the source model and the target model by defining the relational mapping between the two.
Organization of Student’s Self-Control during Mathematical Classes Delivered Through Information Educational Environment

Nowadays, tapping the potential of digital educational environment makes it possible to solve numerous educational problems efficiently; specifically, problems of organizing diagnostics and control of learning activity’s deliverables. Application of information technologies changes the perception of ways and means of controlling students’ performance. This research explores self-control in students’ learning and educational activity as an integral part of every stage of the process of learning mathematics. Taking into account the capabilities of using Gipermetod remote learning system in the academic process at the Ural Federal University, a method of organizing mathematical training was developed, ensuring students selfassessment of their readiness for material perception, step-by-step self-control and self-diagnostics of the final results of material retention. In Gipermetod remote learning system, a test is considered as the main means of self-control. Doing this test gives students an opportunity to get quick feedback at the stage of self-control of learning results. Additionally, testing automation significantly reduces professors’ workload.
Data Science Mentoring Support using Neural Networks

The increased demand from people to learn Data Science, together with the arrival of the Coronavirus pandemic and the increase in home-office activities, teaching institutions in this area, has to expand online assistance systems to students. Since this assistance is not always restricted to a particular schedule, it produces instability in the daily tasks that teachers have to perform. To avoid this, this study implements a closed-domain Chatbot for Data Science that uses as a Question Answering System and works as an assistant to teachers in specific questions that students may have. In addition, it allows the students to check their knowledge in any related topic by the automatic generation of questions according to the chosen topic. The System also has an automatic correction process that compares the response given by the student with the correct one. Such features were implemented using the latest Deep Learning models oriented to Natural Language Processing (NLP), such as Transformers architectures. Although the response time is relatively long in some cases, it generated good answers as well as questions. In future works, the authors propose to optimize the system in terms of speed and quality of results. To this end, the reduction in the number of steps is required by the system, as well as finding more efficient solutions according to the research progress in NLP.
Adaptive nature-inspired algorithms for the optimal tuning of fuzzy controllers

This chapter describes the adaptation mechanisms in two representative nature-inspired optimization algorithms, namely gravitational search algorithms (GSAs) and charged systems search algorithms. The adaptation in both algorithms is carried out in terms of the 5E learning cycle, and fuzzy logic is inserted in GSAs to adapt two parameters. These algorithms are inserted in the design approach dedicated to the optimal tuning of simple Takagi-Sugeno proportional-integral fuzzy controllers for the position control of servo systems. Four optimization problems are solved and representative results concerning the algorithms’ behavior are outlined.
Bodo to English Machine Translation through Transliteration

Machine Translation (MT) is a technique that automatically translates text from one natural language to another using machine like computer. Machine Transliteration (MTn) is also a technique that converts the script of text from source language to target language without changing the pronunciation of the source text. Both the MT and MTn are the challenging research task in the field of Natural Language Processing (NLP) and Computational Linguistics (CL) globally. English is a high resource natural language, whereas Bodo is a low resource natural language. Though Bodo is a recognized language of India; still not much research work has been done on MT and MTn systems due to the low resources. The primary objective of this paper is to develop Bodo to English Machine Translation system with the help of Bodo to English Machine Transliteration system. The Bodo to English MT system has been developed using the Phrase-based Statistical Machine Translation technique for General and News domains of Bodo-English parallel text corpus. The Bodo to English MTn system has been developed using the Hybrid technique for General and News domains of Bodo-English parallel transliterated words/terms. The translation accuracy of the MT system has been evaluated using BLEU technique.
Sentiment Analysis on User Reaction for Online Food Delivery Services using BERT Model

In this era of the information age, a major number of people spend their time on social networking sites. Among different social networking sites, Facebook is one of the most popular due to its accessibility and many other reasons. Nowadays, people use Facebook not only for general purposes but also for business purposes as it provides free opportunity to promote products and services. Users of the products or services share their opinion and feedback on the Facebook page. The amount of the users' data regarding the opinion and feedback is huge. This is very essential to analyze this vast amount of data and extract knowledge from it. For business owners, it is important to consider their clients' sentiments as proper analysis of customers' feedback helps them to take better future planning. In this research work, we proposed a noble strategy to predict users' sentiments from their Facebook comments on online food delivery services. To accomplish this research work, we have mainly considered the BERT machine learning technique. To understand the performance of the BERT in this context, we have applied Char-CNN, Graph-CNN, LSTM, and Bi-LSTM machine learning techniques also. Lastly, the obtained result of the BERT is compared with other four applied techniques in terms of performance evaluation techniques. It is found that BERT outperforms the other applied techniques with achieving the accuracy of 92.86%.
Quantum inspired meta‐heuristic approaches for automatic clustering of colour images

In this article, quantum inspired incarnations of two swarm based meta-heuristic algorithms, namely, Crow Search Optimization Algorithm and Intelligent Crow Search Optimization Algorithm have been proposed for automatic clustering of colour images. The performance and effectiveness of the proposed algorithms have been judged by experimenting on 15 Berkeley images and five publicly available real life images of different sizes. The validity of the proposed algorithms has been justified with the help of four different cluster validity indices, namely, Pakhira Bandyopadhyay Maulik, I-index, Silhouette and CS-measure. Moreover, Sobol's sensitivity analysis has been performed to tune the parameters of the proposed algorithms. The experimental results prove the superiority of proposed algorithms with respect to optimal fitness, computational time, convergence rate, accuracy, robustness, t -test and Friedman test. Finally, the efficacy of the proposed algorithms has been proved with the help of quantitative evaluation of segmentation evaluation metrics.
An improved OS-ELM based Real-time prognostic method towards singularity perturbation phenomenon

Reliable and comprehensive historical data are fundamental for data-driven based reliability prognostic. In practice, the degradation process of electronic products is usually accompanied with singularities caused by intermittent or transient interference, in the past, singularities data often need to be detected in advance or even be regarded as noise being eliminated or ignored, before estimation process. Such traditional ways would lead to waste of time and resources, decrease the accuracy of reliability prognostic as well. Real-time models are being introduced to capture complete degradation trend on time, overcome the limitations of early detection and increase forecasting performance, recently. In this study, a novel real-time fusion approach based on spline function and optimized online sequential extreme learning machine (OS-ELM) is developed to address the above singularity perturbation issue. As the approximation model, the cubic non - polynomial spline function could track the singularities’ feature by window-slide dataset and trigonometric polynomial, form the initial prediction cell frames. ELM is introduced as an effective estimation model with simple structure to save computing resources. To address the uncertainty issue caused by its inherent randomness of ELM model. Quantum behaved particle swarm optimization (QPSO) and optimally pruning method were applied to optimize the parameters of ELM hidden network, and redundant network neurons, respectively. In the verification stage, different experimental results show that the proposed fusion model could capture singularities’ characteristics, achieve better prognostic performance in terms of accuracy.
WITHDRAWN: Chaos based image encryption using 3-Dimension logistic map

Abstract The importance of digital media has been increased in this pandemic; the security of the media generated is huge issue. This led to development of cryptographic techniques that are secure and fast. Chaotic image encryption is gaining popularity considering its ergodic properties. In this paper a Chaotic map-based cryptography based on iterations with three parameters is proposed, the generated chaotic sequence is mixed with the key to achieve partial security. Then the new sequence is used for scrambling and diffusion of pixel values of image. The result was evaluated by using many measures like NPCR, UACI, MSE, Key sensitivity analysis. The demonstrated result shows the proposed scheme is reliable and is resistant to differential cryptoanalysis attack.
MULA: A Just-In-Time Multi-labeling System for Issue Reports

A very important function of an issue tracking system is to assign labels to issue reports, such as bug, feature, enhancement, etc., in order to categorize issues to facilitate various development activities. In practice, it is very common that an issue has multiple labels. However, current works are mainly based on single-label prediction, which are not suitable for just-in-time multi-labeling services, due to the low efficiency. Therefore, in this paper, we propose MULA, a just-in-time MUlti-LAbeling system, which learns and automatically assigns multiple labels to issue reports. We have built a dataset with 81,601 entries and 11 labels, as the first benchmark for this task, and implemented a GitHub app. To the best of our knowledge, this is the first work and tool for online multi-labeling GitHub issues based on their categories. We conduct a comprehensive empirical study, including comparisons with five commonly adopted labeling models that show the superiority of MULA, as well as an evaluation that shows high consistency between MULA’s suggestions and developers’ opinions.
Application and Strengthening Strategies of Network Resources in the Construction of Teaching Platform

Abstract Colleges and universities are at the forefront of popularizing and applying information technology, so it is necessary and responsible to create abundant teaching and learning resources, so as to provide a platform for teachers to carry out diversified teaching and students to carry out autonomous learning. In accelerating the process of educational informationzation, we should strengthen the development and application of high-quality educational resources, strengthen the construction of network teaching resources system, establish an open and flexible public service platform for educational resources, and promote the popularization and sharing of high-quality educational resources. In teaching, students should establish information concept, enhance information awareness, integrate, explore and apply computer professional knowledge with other courses, and effectively improve their information technology literacy and operational application ability. When using network resources, teachers need to search and download resources repeatedly with the help of search engines. In order to improve the availability of existing resources and integrate and share the scattered teaching resources, this paper proposes the development of the sharing information platform of network teaching resources in Colleges and universities.
Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients

In recent years, machine learning methods have been rapidly implemented in the medical domain. However, current state-of-the-art methods usually produce opaque, black-box models. To address the lack of model transparency, substantial attention has been given to develop interpretable machine learning methods. In the medical domain, counterfactuals can provide example-based explanations for predictions, and show practitioners the modifications required to change a prediction from an undesired to a desired state. In this paper, we propose a counterfactual explanation solution for predicting the survival of cardiovascular ICU patients, by representing their electronic health record as a sequence of medical events, and generating counterfactuals by adopting and employing a text style-transfer technique. Experimental results on the MIMIC-III dataset strongly suggest that text style-transfer methods can be effectively adapted for the problem of counterfactual explanations in healthcare applications and can achieve competitive performance in terms of counterfactual validity, BLEU-4 and local outlier metrics.
Twisted Group Codes

We investigate right ideals as codes in twisted group algebras. Such codes are called twisted group codes. It turns out that many interesting codes belong to this class; for instance, the ternary extended Golay code, Hamming codes and constacyclic codes. In particular we characterize all linear codes which are twisted group codes in terms of their automorphism group.
A Generative Model for Partial Label Learning

Partial label (PL) learning tackles the problem where each training instance is associated with a set of candidate labels, among which only one is the true label. In this paper, we propose a novel generative model PL-CGAN, which tackles the partial label learning problem with Conditional Generative Adversarial Nets (CGAN). Specially, PL-CGAN introduces a teacher model to refine a more reliable soft label vector of each training instance by iteratively ensembling the current learned prediction network with the formal one in an online manner. Besides, it adopts a MixUp data augmentation scheme to prevent the prediction network from overfitting to the noisy labels. In addition, it deploys a CGAN to generate training instances with its corresponding label vectors, which form a feature alignment based on consistency cost to enhance it’s label refinement capacity. Extensive experiments are conducted on synthesized and real-world partial label learning datasets, while the proposed approach demonstrates the state-of-the-art performance for partial label learning.
CyberBERT: A Deep Dynamic-State Session-Based Recommender System for Cyber Threat Recognition

Session-based recommendation is the task of predicting user actions during short online sessions. The user is considered to be anonymous in this setting, with no past behavior history available. Predicting anonymous users' next actions and their preferences in the absence of historical user behavior information is valuable from a cybersecurity and aerospace perspective, as cybersecurity measures rely on the prompt classification of novel threats. Our offered solution builds upon the previous representation learning work originating from natural language processing, namely BERT, which stands for Bidirectional Encoder Representations from Transformers (Devlin et al., 2018). In this paper we propose CyberBERT, the first deep session-based recommender system to employ bidirectional transformers to model the intent of anonymous users within a session. The session-based setting lends itself to applications in threat recognition, through monitoring of real-time user behavior using the CyberBERT architecture. We evaluate the efficiency of this dynamic state method using the Windows PE Malware API sequence dataset (Catak and Yazi, 2019), which contains behavior for 7107 API call sequences executed by 8 classes of malware. We compare the proposed CyberBERT solution to two high-performing benchmark algorithms on the malware dataset: LSTM (Long Short-term Memory) and transformer encoder (Vaswani et al., 2017). We also evaluate the method using the YOOCHOOSE 1/64 dataset, which is a session-based recommendation dataset that contains 37,483 items, 719,470 sessions, and 31,637,239 clicks. Our experiments demonstrate the advantage of a bidirectional architecture over the unidirectional approach, as well as the flexibility of the CyberBERT solution in modelling the intent of anonymous users in a session. Our system achieves state-of-the-art measured by F1 score on the Windows PE Malware API sequence dataset, and state-of-the-art for P@20 and MRR@20 on YOOCHOOSE 1/64. As CyberBERT allows for user behavior monitoring in the absence of behavior history, it acts as a robust malware classification system that can recognize threats in aerospace systems, where malicious actors may be interacting with a system for the first time. This work provides the backbone for systems that aim to protect aviation and aerospace applications from prospective third-party applications and malware.
RegulaTor: A Straightforward Website Fingerprinting Defense

Abstract Website Fingerprinting (WF) attacks are used by local passive attackers to determine the destination of encrypted internet traffic by comparing the sequences of packets sent to and received by the user to a previously recorded data set. As a result, WF attacks are of particular concern to privacy-enhancing technologies such as Tor. In response, a variety of WF defenses have been developed, though they tend to incur high bandwidth and latency overhead or require additional infrastructure, thus making them difficult to implement in practice. Some lighter-weight defenses have been presented as well; still, they attain only moderate effectiveness against recently published WF attacks. In this paper, we aim to present a realistic and novel defense, RegulaTor, which takes advantage of common patterns in web browsing traffic to reduce both defense overhead and the accuracy of current WF attacks. In the closed-world setting, RegulaTor reduces the accuracy of the state-of-the-art attack, Tik-Tok, against comparable defenses from 66% to 25.4%. To achieve this performance, it requires 6.6% latency overhead and a bandwidth overhead 39.3% less than the leading moderate-overhead defense. In the open-world setting, RegulaTor limits a precision-tuned Tik-Tok attack to an F 1 -score of. 135, compared to .625 for the best comparable defense.
Modeling and Analysis of Mamdani Two-Term Controllers Using Non-Uniformly Distributed Multiple Fuzzy Sets and CoA/CoG Defuzzification

While fuzzy controllers with multiple fuzzy sets and non-uniformly distributed membership functions have the potential to provide better system performance, from the literature, it appears to the authors that till now, no one has found exact mathematical models of fuzzy PI/PD controllers with non-uniformly distributed multiple fuzzy sets and Center of Area (CoA) defuzzification. To bridge this gap, in this paper, analytical structures of the general Mamdani type fuzzy PI/PD controllers are presented using unequally distributed multiple fuzzy sets, Minimum (Min) t-norm, Maximum (Max) s-norm, Larsen Product (LP) inference, and CoA defuzzification. For the purpose of comparison, analytical structures of the controllers are also obtained using Center of Sums (CoS) defuzzification. Properties of the newly obtained controllers are analyzed and compared. Since digital computers are often used for simulation, a rough estimate of the computational complexities and memory requirements are provided. To show the applicability of the newly developed controllers, a nonlinear DC series motor and a nonlinear plant with dead-time are considered. The effectiveness of the proposed controllers is finally shown through a detailed system performance comparison.
Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment

An Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in the agricultural plant protection such as watering, sowing, and pesticide spraying. It is essential to develop a Decision-making Support System (DSS) for UAVs to help them choose the correct action in states according to the policy. In an unknown environment, the method of formulating rules for UAVs to help them choose actions is not applicable, and it is a feasible solution to obtain the optimal policy through reinforcement learning. However, experiments show that the existing reinforcement learning algorithms cannot get the optimal policy for a UAV in the agricultural plant protection environment. In this work we propose an improved Q-learning algorithm based on similar state matching, and we prove theoretically that there has a greater probability for UAV choosing the optimal action according to the policy learned by the algorithm we proposed than the classic Q-learning algorithm in the agricultural plant protection environment. This proposed algorithm is implemented and tested on datasets that are evenly distributed based on real UAV parameters and real farm information. The performance evaluation of the algorithm is discussed in detail. Experimental results show that the algorithm we proposed can efficiently learn the optimal policy for UAVs in the agricultural plant protection environment.