Reviewerly/exHarmony huggingface dataset:
Taking Up Thagard’s Challenge: A Formal Model of Conceptual Revision

Abstract Thagard (1992) presented a framework for conceptual change in science based on conceptual systems. Thagard challenged belief revision theorists, claiming that traditional belief-revision systems are able to model only the two most conservative types of changes in his framework, but not the more radical ones. The main aim of this work is to take up Thagard’s challenge, presenting a belief-revision-like system able to mirror radical types of conceptual change. We will do that with a conceptual revision system, i.e. a belief-revision-like system that takes conceptual structures as units of revisions. We will show how our conceptual revision and contraction operations satisfy analogous of the AGM postulates at the conceptual level and are able to mimic Thagard’s radical types of conceptual change.
Optimization of Probability Distributions for Residual Coding of Screen Content

Probability distribution modeling is the basis for most competitive methods for lossless coding of screen content. One such state-of-the-art method is known as soft context formation (SCF). For each pixel to be encoded, a probability distribution is estimated based on the neighboring pattern and the occurrence of that pattern in the already encoded image. Using an arithmetic coder, the pixel color can thus be encoded very efficiently, provided that the current color has been observed before in association with a similar pattern. If this is not the case, the color is instead encoded using a color palette or, if it is still unknown, via residual coding. Both palette-based coding and residual coding have significantly worse compression efficiency than coding based on soft context formation. In this paper, the residual coding stage is improved by adaptively trimming the probability distributions for the residual error. Furthermore, an enhanced probability modeling for indicating a new color depending on the occurrence of new colors in the neighborhood is proposed. These modifications result in a bitrate reduction of up to 2.9% on average. Compared to HEVC (HM-16.21 + SCM-8.8) and FLIF, the improved SCF method saves on average about 11% and 18% rate, respectively.
A Systematic Review of Exploring the Potential of Teachable Agents in English Learning

Language learning has dramatically influenced the extensiveness of web-based learning, AI applications, smart content, personalized tutoring systems, virtual facilitators. This paper aims to review the empirical evidence on the application of intelligent agents in English learning to define the future research perspectives of Teachable Agents-assisted English education. After a systematic online data search, the investigator analyzed nine empirical studies on how Teachable agents have potential English teaching and learning benefits. The results indicate that (1) most studies conducted with large sample size at university level followed by elementary level, (2) instructional design were in dialogue and image form, (3) emerged two categories- Interaction and integration, (4) most of them used experimental research in different settings. This study suggests that TAs substantially play a supportive role, enhance their involvement, scaffold their learning, and become self-regulated learners. Hence, we prospect the need for a future-oriented vision regarding AI and intelligent technologies- assisted language learning for non-native speakers.
Introduction to reversal fuzzy switch graph

Fuzzy Switch Graphs (FSG) generalize the notion of Fuzzy Graphs by adding high-order arrows and aggregation functions which update the fuzzy values of arrows whenever a zero-order arrow is crossed. In this paper, we propose a more general structure called Reversal Fuzzy Switch Graph (RFSG), which promotes other actions in addition to updating the fuzzy values of the arrows, like activation and deactivation of the arrows. RFSGs are able to model dynamical aspects of some systems which generally appear in engineering, computer science and some other fields. The paper also provides the relationship between RFSGs and fuzzy graphs, a logic to verify properties of the modeled system and closes with an application.
Parameter identification for fractional-fractal model of filtration-consolidation dynamics using artificial neural networks

Artificial neural networks are applied to solve parameters identification problem for one-dimensional fractional-fractal model of filtration consolidation processes in geo-porous media in the conditions of salt transfer. Based on the indicators of the state of the process in a fixed number of observation points, the values of the orders of fractional derivatives with respect to time and space variables were restored. Testing results based on data sets obtained from noised solutions of the direct problem show the adequacy of fractional derivatives orders restoration with at least 25 observation points and noise levels less than 10%.
Electronic Banking (e-Banking) Fraud with Phishing Attack Methods

Every segment of the society was affected with the developments in technology, though it has empowered financial activities to be carried out quickly in the internet environment.Banking service, is one of the sectors with the most exhaustive use of technology, which have turned their services into online applications in order to respond quickly to the demands of individuals, commercial companies and to get more shares from the market area in an environment of economic competition.Although this innovation offers many advantages but it has also created many damsel for mischievous people.The most popular and well known outbreaks is phishing.Phishing attacks were designed to obtain a target person's key information, such as their password or credit card.For phishing attacks, fake web pages such as e-mail services, official institution web pages, banks, social media sites are organized and victims are expected to fall into this trap.Due to the high success rate of phishing attacks and the simplicity of preparation, attackers often resort to this method to achieve their goals.Even though security measures continue to be taken to battle this crime, the size of the threat continues to increase day by day.In this study, a forensic analysis of a Phishing attack case sample prepared using electronic banking (e-banking) fake website is performed.Detailed information about the fake website prepared from the analysis results was obtained and information about the attacker was obtained.It is evaluated that the approach and results used in the study will contribute to both the fight against this crime and future studies.
Automatic image annotation based on feature fusion and cost-sensitive learning

In order to solve the problem that it is difficult to recognize small-scale objects in the process of image annotation, an automatic image annotation method based on multi-scale features is proposed. This method combines transfer learning and deep learning, adds a feature fusion layer to the convolutional neural network model, establishes a top-down feature fusion mechanism, and builds a convolutional neural network model that integrates multi-layer convolutional features. The experiment chooses the IAPR TC-12 image annotation data set. Compared with the Tag Prop model, the method in this paper improves the average recall rate by 13%, and improves the average correct rate by 10% compared with the convolutional neural network (CNN-MSE) using the mean square error function. Percentage points. The experimental results show that the convolutional neural network model fused with multi-layer convolutional features can enhance the accuracy of labeling smaller objects.
The Effect of Similarity and Dissimilarity on Information Network Formation and Their Implications in Accurate Information Identification

In the context of the Bitcoin market and its virtual investment communities, this paper demonstrates that both similarity and dissimilarity in social media messages' contents can be associated with high quotation probability among them. This finding advocates new understandings beyond the mainstream conclusion that network connections increase with similarity. It is also found that message networks offer significant implications to distinguish between accurate information and noise. Evidence shows that popular messages frequently quoted by others and redundant messages confirmed from different sources contain more accurate information for Bitcoin market predictions. Theoretical contributions and practical implications are discussed.
Recognition and Location Based on Fusion of FAST Algorithm and SIFT Algorithm

SLAM technology is the core technology of robot positioning and navigation, and its application in indoor positioning is a new popular trend. Starting from the application of SLAM in indoor positioning, this paper focuses on the performance improvement of SLAM front-end. This paper mainly analyzes the front-end feature extraction algorithm of vision SLAM technology, and proposes a hybrid algorithm of FAST-SIFT image local feature extraction. In the process of image feature extraction, the fusion of FAST algorithm and SIFT algorithm can eliminate more useless feature points, improve the matching speed, make the target recognition more accurate, and then improve the accuracy of positioning. This algorithm can optimize FAST algorithm without rotation invariance, SIFT algorithm with many useless feature points, slow operation and low matching accuracy, and improve the accuracy and real-time of indoor positioning system. The simulation results show that the hybrid algorithm of FAST and SIFT can effectively improve the accuracy and real-time of image feature extraction, and improve the stability of positioning system.
Autonomous Recovery from Spacecraft Plan Failures by Regulatory Repair While Retaining Operability

Pre-designed spacecraft plans suffer from failure due to the uncertain space environment. In this case, instead of spending a long time waiting for ground control to upload a feasible plan in order to achieve the mission goals, the spacecraft could repair the failed plan while executing another part of the plan. This paper proposes a method called Isolation and Repair Plan Failures (IRPF) for a spaceship with durable, concurrent, and resource-dependent actions. To enable the spacecraft to perform some actions when a plan fails, IRPF separates all defective actions from executable actions in the pre-designed plan according to causal analysis between the failure state and the established plan. Then, to address the competition between operation and repair during the partial execution of the plan, IRPF sets up several regulatory factors associated with the search process for a solution, and then repairs the broken plan within the limits of these factors. Experiments were carried out in simulations of a satellite and a multi-rover system. The results demonstrate that, compared with replanning and other plan-repair methods, IRPF creates an execution plan more quickly and searches for a recovery plan with fewer explored state nodes in a shorter period of time.
RESA: Relation Enhanced Self-Attention for Low-Resource Neural Machine Translation

Transformer-based Neural Machine Translation models have achieved impressive results on many translation tasks. In the meanwhile, some studies prove that extending syntax information can be explicitly incorporated to provide further improvements especially for some low-resource languages. In this paper, we propose RESA: the relation enhanced self-attention for Transformer which can integrate source side dependency syntax. More specifically, dependency parsing produces two kinds of information: dependency heads and relation labels, compared to the previous works only pay attention to dependency heads information, RESA use two methods to integrate relation labels as well: 1) Hard-way that uses a hyper parameter to control the information percentage after mapping relation labels sequence to continuous representations; 2) Gate-way that employs a gate mechanism to mix word information and relation labels information. We evaluate our methods on low-resource Chinese-Tibetan and Chinese-Mongol translation tasks, and the preliminary experimental results show that the proposed model achieves 0.93 and 0.68 BLEU scores gain compared to the baseline model.
Virtual Makeover and Makeup Recommendation Based on Personal Trait Analysis

The utilization of facial makeup is an important attribute in modern society as a means of self-expression, and as a method to feel more confident during social interactions. A makeover has become a necessity when attending divergent functions, and makeup used on diverse occasions varies in style. Choosing the perfect makeup that best suits a person is challenging unless they have years of expertise with cosmetics. This paper proposes a “Virtual Makeover and Makeup Recommendation System” to eliminate the need to be concerned about the appearance after applying makeup. The proposed system enables real-time makeup simulation in an Augmented Reality (AR) environment and recommends makeup styles considering skin tone, colour of clothing and hair, type of clothing and occasion to be attended with makeup. The personal traits of a user would be automatically detected and processed to generate recommendations for makeup products, namely lipstick, foundation and eyeshadow. Complications of wasting makeup products and time, and cleaning makeup can be mitigated by using a real-time makeup simulation system. Recommendations generated by the application assist the users to decide on makeup styles and provides a better user experience. The proposed system is developed with the aid of Deep Learning (DL) algorithms.
User Activity Sequence Prediction in Smart Homes using Multi-Layer Long Short-Term Memory Networks

Smart homes provide a unique opportunity for utility companies and end-users to improve grid efficiency, reliability and end-user cost through the deployment of demand response programs. By offering variable electricity prices, utilities can shape the customer’s electricity demand provided that there is a Smart Home Energy Management System in place that can schedule programmable electric loads to profit from lower electricity prices. To improve customer acceptability and close the optimality gap, a prediction of the activity sequence could be integrated in the Smart Home Energy Management algorithm. In this framework, this paper presents a user activity prediction model that uses a multi-layer Long Short-Term Memory network. The accuracy of the prediction method is evaluated using Dynamic Time Warping. In addition to accuracy quantification, the measure on sequence distance provided by the analysis can be used for designing the multi-layer neural network. Results show that a training window of 75 days is sufficient to achieve useful forecasting of activity patterns.
Differences in Online Class Participation and Satisfaction according to Nursing Students General Characteristics and Learning Characteristics

Purpose: To understand the differences in class participation and satisfaction according to the general and learning characteristics of nursing students participating in online classes. Methods: In this cross-sectional survey, Google Forms was used to survey 74 nursing students from August to September 2020. Results: For the majority of the participants (79.7%), usual bedtime was between 1 and 3 AM. Further, 52.7% of the participants woke up between 10 AM and 12 PM. Regarding learning style, more than half of students favored diffusion. Participation in and satisfaction with online classes did not significantly differ by general and learning characteristics. However, students who were able to preview the class content for over 10 minutes displayed significantly higher in class satisfaction than those with a usual preview time of less than 10 minutes. Conclusion: The findings demonstrate the significance of a fixed preview time not only to increase participation in and satisfaction with online classes but also to help nursing students establish regular life and learning habits.
Hate Speech Detection using ML algorithms

Social media is a growing platform where different users share their ideas and sentiments towards different topics because users spend a lot of time expressing their thoughts and views. There are various researches going on in detecting the sentiments of the user’s comments but the main sentiment factor remain undiagnosed. In this paper, the aim is to detect hate speeches. The dataset was preprocessed and cleaned and cleaned text was explored to get a better understanding. Salient features were extracted from the data to train our model and to identify the hate sentiments of tweets. The vector model is created using genism to learn the relationship between words and based on that sentence are labeled. Stop words and port stemmer are used to filter unwanted data to build the vocabulary using CountVectorizer before it is used for model building. Using various machine algorithms, comparative study is done to check the performance of algorithms and promising results are attained.
Sample-Efficient Reinforcement Learning Based on Dynamics Models via Meta-policy Optimization

Model-based reinforcement learning (RL) can acquire remarkable sample efficiency, which makes it a suitable choice for applications where experiment data is hard to collect. However, it is difficult to learn an accurate dynamics model fully matched with the real-world, and the accuracy of the model usually affects the agent’s final performance. In this paper, we propose a novel model-based RL approach called Meta-policy Optimization method with branched rollouts (MPOBR), which gets rid of strong dependency on an accurate model. In MPOBR, meta-learning is used to train a policy prior on an ensemble of learned dynamics models, so that this prior can be rapidly adapted to the environment when combined with environment rollouts. To reduce the affect of model compounding bias, short model-generated rollouts branched from real data are used to update the meta-policy. The experiments on simulated robotic tasks are designed to verify the effectiveness of our method. Results show that our approach can achieve the same asymptotic performance of state-of-the-art model-free algorithms while significantly reducing sample complexity.
IEBench: Benchmarking Streaming Learners on Imbalanced Evolving Data Streams

Nowadays, data coming from electronic devices surround us. Analyzing them all in real-time, also considering the class imbalance and concept drift challenges is a big challenge and the starting point to understand where to innovate. This paper proposes IEBench, an easy-to-use benchmarking environment for comparing streaming learners. We implemented various state-of-art algorithms, and we conducted a comprehensive experimental campaign with IEBench. We evaluated the algorithms on artificial and real data streams with different imbalance levels and concept drift types. We collected empirical evidence of the role and impact of existing methods for rebalancing data streams in improving performance during different types of concept drift. IEBench eases the practitioners’ task of testing existing algorithms on a new data stream and the scientists’ one of developing new algorithms and systematically comparing them with the state-of-the-art.
Development of Autonomous Operation Agent for Normal and Emergency Situations in Nuclear Power Plants

Nuclear power plants (NPPs) are highly automated systems. Nevertheless, the operator’s manual actions are still required when normal (start-up/shutdown) operation and emergency operation. During these operations, operators execute necessary actions (e.g., situation awareness, confirmation of automatic actuation, and manipulation) following the operating procedures. This study suggests a Deep Reinforcement Learning (DRL)-based autonomous agent. The agent can manage the power increase operation from 2% to 100% and reduce the pressure and temperature until the shutdown cooling entry condition after reactor trip caused by loss of coolant accident in NPPs. The DRL-based controller suggested in this study combines a rule-based system and DRL algorithm that involves a Soft Actor-Critic algorithm and deep neural network. The test results using a compact nuclear simulator indicates that the agent can manipulate components to comply with identified constraints for start-up operation and emergency operation.
Mechanisms and Techniques to Enhance the Security of Big Data Analytic Framework with Mongodb and Linux Containers

This paper proposes and implements some security mechanisms and techniques, such as secure authentication, secure authorization, and encryption, to assure the overall security of a big data analytic framework with MongoDB free community edition. This paper presents the fourth phase of our continuous research where in the first phase we proposed a data analytic framework with MongoDB and Linux Containers (LXCs) with basic security requirements. Next, in the second phase we proposed a vulnerability analysis testbed to find vulnerabilities associated with the system. Finally, in the third phase we discussed in detail root causes and some prevention techniques of vulnerabilities found in the system. In addition, this paper introduces a new security mechanism for privacy preserving data handling with MongoDB to ensure the privacy of the data before being processed. Our results show, with our initial model of the analytic framework, how well our newly introduced security mechanisms work and how these security mechanisms and techniques can be used to assure the CIA of any data science project conducted on our proposed analytic framework. In addition, these security mechanisms and techniques help us to strengthen the current system against zero-day attacks where attacks on vulnerabilities that have not been patched or made public yet. Therefore, our vulnerability analysis testbed which is proposed in the second phase of this research will not be able to finds vulnerabilities related to zero-day attacks.
Certifiable AI

Implicit stochastic models, including both ‘deep neural networks’ (dNNs) and the more recent unsupervised foundational models, cannot be explained. That is, it cannot be determined how they work, because the interactions of the millions or billions of terms that are contained in their equations cannot be captured in the form of a causal model. Because users of stochastic AI systems would like to understand how they operate in order to be able to use them safely and reliably, there has emerged a new field called ‘explainable AI’ (XAI). When we examine the XAI literature, however, it becomes apparent that its protagonists have redefined the term ‘explanation’ to mean something else, namely: ‘interpretation’. Interpretations are indeed sometimes possible, but we show that they give at best only a subjective understanding of how a model works. We propose an alternative to XAI, namely certified AI (CAI), and describe how an AI can be specified, realized, and tested in order to become certified. The resulting approach combines ontologies and formal logic with statistical learning to obtain reliable AI systems which can be safely used in technical applications.