Reviewerly/exHarmony huggingface dataset:
Utilizability of Navigation2/ROS2 in Highly Automated and Distributed Multi-Robotic Systems for Industrial Facilities

The field of mobile robotics is a rapidly evolving space with a constant stream of new advancements. As the available technology matures and new features get accessible for lower prices, the scope of applicability for autonomous mobile robots naturally expands. Apart from simple home appliances, a particular area that currently attracts a surge of transformative research is that of industrial facilities and intelligent factories. A higher emphasis is gradually being placed on autonomous operations, where mobile robots start being suitable complements to human workers. Such an application, however, requires complex mechanisms to be able to handle even simple tasks, such as transferring cargo from one place to another while navigating a changing environment and avoiding dynamic obstacles. Robust localization and navigation solutions utilizing the most effective algorithms are necessary for such long and continuous operations that require a high degree of reliability and low error rates. Robot Operating System (ROS) is a highly flexible open-source framework enjoying wide adoption over the years. Its next-generation implementation (ROS2), together with the Navigation2 project (created as a successor to the original ROS Navigation Stack), provide substantial conceptual enhancements with a strong focus on reliability, security, and performance, promising better compatibility with numerous sensor types, higher modularity, and a more controllable deterministic behavior. This paper will guide the reader through necessary steps to deploy the Navigation2 stack onto an existing custom-built ROS2-operating robotic test-bed platform, serving as a concept for future research.
Automatization of Microscopy Malaria Diagnosis Using Computer Vision and Random Forest Method

Malaria is an infectious disease caused by Plasmodium species parasites transferred by infected females of Anopheles mosquitoes. Around half of the world population is exposed to some risk of malaria, with the highest incidence in sub-Saharan Africa. High mortality rates and the spread of this disease require a fast and efficient mode of diagnosis. This paper presents a model for automatization of microscopy analysis of malaria suspect blood smears and diagnosis using Random Forest method. The results are evaluated through accuracy, recall and precision metrics, with achieved performance of 90% for all three metrics. This shows the potential of machine learning methods inclusion in the malaria detection process for faster and less costly diagnosis.
A CNN Model for Early Leukemia Diagnosis

Blood cancer (leukemia) is one of the most serious diseases that affect blood-forming tissues. It usually involves white blood cells. The early detection of this severe disease helps doctors to provide efficient treatment. However, the discovery of this sickness at its first stages is often not easy due to similar morphological characteristics of malignant and healthy blood cells. Flow cytometry was the only used technique for early detection of leukemia, but it is very expensive and usually unavailable in hospitals. Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans learn by examples. In the last few years, deep learning has achieved great successes to solve concrete problems. In particular, it has proven successful in medical imaging classification. In this work, we propose a Convolutional Neural Network (CNN) experiment for the classification of malignant white blood cells from normal ones using a dataset of microscopic images. The proposed approach leads to a balanced model that reaches a high level accuracy.
Extractive-Abstractive: A Two-Stage Model for Long Text Summarization

Currently, the mainstream text summarization techniques are divided into extractive and abstractive methods. Extractive method is suitable for long texts with a clear structure, while abstractive method is suitable for short texts. In this paper, we aim to address the problems of missing key words and incomplete overview that are usually caused by abstractive method in the face of long texts. To solve this problem, we propose a two-stage model that uses both extractive and abstractive methods for generating summaries. Firstly, we use multi-layer BiLSTM for long text summary extraction. Secondly, we use the classical UniLM as the base model while adding a novel copy mechanism to tackle out-of-vocabulary (OOV) problem and using the sparse softmax to avoid overfitting. Extensive experiments demonstrate that our models perform better than other baseline models, and our models can generate higher quality summaries.
A Two-Channel Chinese Enterprise Abbreviation Generation Method Based on an Enterprise Component and Single-Character Strategy

The automatic generation of Chinese enterprise abbreviations is a task that extracts enterprise abbreviations to represent the enterprise full name. Traditional methods do not divide abbreviations in detail, which leads to a poor generation effect of irregular Chinese enterprise abbreviations generation, and the best selection method of candidate abbreviations among traditional methods is still coarse-grained relationship modeling. To solve the problem of irregular abbreviation generation and abbreviation screening, this paper proposes a two-channel Chinese enterprise automatic abbreviation generation method. First, in the two-channel method, the enterprise component channel outputs regular candidate abbreviations, and the single-character channel outputs irregular candidate abbreviations to improve the processing effect of the method on irregular abbreviations. Then we design a Bayesian filtering model based on the position relationship of abbreviations in enterprise components to improve the final effect of the automatic generation of Chinese enterprise abbreviations. The results show that our effect is the best in the data performance of Chinese enterprises.
Deep Bug Triage Model Based on Multi-head Self-attention Mechanism

In the course of software maintenance and development, bugs are inevitable. At present, Bug tracking system uses bug reports to match bug with fixers. However, the previous bug triage model relies too much about the quality of the text of the bug report, introduces a lot of redundant information in natural language, and ignores the fixer community factor where the meta-field of the bug report, which makes the model performance worse. Aiming at the above problems, we propose a multi-head self-attention deep bug triage (MSDBT), which considers the text content of the bug report and generates a sequence of fixers with the same meta-field. The features of the input text and the fixer sequence are extracted by Bi-directional Long Short-Term Memory network. The multi-head self-attention mechanism is used to perform parallel attention calculation among the internal input elements. The model weakens the redundant information in the bug report, and further quantifies the influence of fixers with similar activities on bug triage through fixer sequence. We conducted texts on four open source software projects. We can get the MSDBT has clear strength over the previous model in recall index.
RCBERT an Approach with Transfer Learning for App Reviews Classification

Nowadays, extracting valuable information from user reviews of mobile applications has been a critical channel to obtaining user requirements in the process of mobile application development. However, it's difficult to extract accurately valuable information from massive reviews that are unstructured and uneven. Existing works classify the user reviews to assist the process of requirement elicitation. However, due to the differences in wording, sentence structure, and requirements of reviews, the generalization of existing methods is poor. Therefore, we propose a Review Classification using BERT model (RCBERT) for user reviews classification. RCBERT contains the great capability of generalization that can complete specific fine-tuning tasks only by providing less training data. We evaluate the performance on an English dataset with 4014 user reviews and a Chinese review dataset that contains 18331 user reviews. The experimental results show that the approach proposed in this paper improves the review classification accuracy effectively (F1-score of up to 88% on the English dataset and 91% on the Chinese dataset).
POLITICAL TURN IN THE PHILOSOPHY OF SCIENCE

The subject of the connection between science and politics is a modern trend in the research of philosophers of science. Focusing on the analysis of the relationship between science and politics from the standpoint of the social philosophy of science, the study aims to represent a political turn in the philosophy of science and epistemology. The methodological basis of the study are the strategies and approaches of the social philosophy of science. This is an interdisciplinary approach that allows to build bridges between the philosophy of science and political philosophy. The methodological strategy of social epistemology is used in understanding knowledge as power game in the post-truth era. The author, based on interdisciplinary links, considers social epistemology and social philosophy of science as modern trends that focus research on understanding the strategy of individual and collective action of scientists. Scientific novelty is to justify a turn to the social and political philosophy of science, which makes it possible to analyze the relationship between science and politics from social approval and justification of science to the problem of trust in science and scientific knowledge, from the politics of science to the politics of knowledge. In the post-truth era, facts play less of a role than subjective judgments, feelings, beliefs, and emotions. Verification of knowledge plays a much smaller role, and the coherence of statements, completeness of information or conscience of its source are of less interest to recipients. Knowledge becomes a game, and truth becomes a reward in this game.
Improved intelligent clonal optimizer based on adaptive parameter strategy

The intelligent clonal optimizer (ICO) is a new evolutionary algorithm, which adopts a new cloning and selection mechanism. In order to improve the performance of the algorithm, quasi-opposition-based and quasi-reflection-based learning strategy is applied according to the transition information from exploration to exploitation of ICO to speed up the convergence speed of ICO and enhance the diversity of the population. Furthermore, to avoid the stagnation of the optimal value update, an adaptive parameter method is designed. When the update of the optimal value falls into stagnation, it can adjust the parameter of controlling the exploration and exploitation in ICO to enhance the convergence rate of ICO and accuracy of the solution. At last, an improved intelligent chaotic clonal optimizer (IICO) based on adaptive parameter strategy is proposed. In this paper, twenty-seven benchmark functions, eight CEC 2104 test functions and three engineering optimization problems are used to verify the numerical optimization ability of IICO. Results of the proposed IICO are compared to ten similar meta-heuristic algorithms. The obtained results confirmed that the IICO exhibits competitive performance in convergence rate and accurate convergence.
Using Value-Based Potentials for Making Approximate Inference on Probabilistic Graphical Models

The computerization of many everyday tasks generates vast amounts of data, and this has lead to the development of machine-learning methods which are capable of extracting useful information from the data so that the data can be used in future decision-making processes. For a long time now, a number of fields, such as medicine (and all healthcare-related areas) and education, have been particularly interested in obtaining relevant information from this stored data. This interest has resulted in the need to deal with increasingly complex problems which involve many different variables with a high degree of interdependency. This produces models (and in our case probabilistic graphical models) that are difficult to handle and that require very efficient techniques to store and use the information that quantifies the relationships between the problem variables. It has therefore been necessary to develop efficient structures, such as probability trees or value-based potentials, to represent the information. Even so, there are problems that must be treated using approximation since this is the only way that results can be obtained, despite the corresponding loss of information. The aim of this article is to show how the approximation can be performed with value-based potentials. Our experimental work is based on checking the behavior of this approximation technique on several Bayesian networks related to medical problems, and our experiments show that in some cases there are notable savings in memory space with limited information loss.
The Use of Social Networks in E-Learning Technologies in the Context of Distance Education

The research purpose is to identify trends, opportunities, and limitations in the use of social media in higher education based on the study of how students and teachers use social networks in distance learning. The study involves 500 students and teachers of Sechenov First Moscow State University and Kuban State Technological University. The research methodology is based on an empirical approach. The study contained the authors’ questionnaire with 25 questions addressed to students and teachers. Respondents answered them using a Likert scale (1 to 5). It has been found that Instagram is the most popular network used by students (90%) which is followed by VKontakte and Facebook used by 81% and 49% of the respondents, respectively. The results show that students are much more active users compared to teachers and are more satisfied with distance learning. The results obtained can be used in international practice to improve the quality of the remote learning process, develop more advanced types of content consumption, and improve the channels of personal communication between the teaching staff and students.
Efficient acoustic feature transformation in mismatched environments using a Guided-GAN

We propose a new framework to improve automatic speech recognition (ASR) systems in resource-scarce environments using a generative adversarial network (GAN) operating on acoustic input features. The GAN is used to enhance the features of mismatched data prior to decoding, or can optionally be used to fine-tune the acoustic model. We achieve improvements that are comparable to multi-style training (MTR), but at a lower computational cost. With less than one hour of data, an ASR system trained on good quality data, and evaluated on mismatched audio is improved by between 11.5% and 19.7% relative word error rate (WER). Experiments demonstrate that the framework can be very useful in under-resourced environments where training data and computational resources are limited. The GAN does not require parallel training data, because it utilises a baseline acoustic model to provide an additional loss term that guides the generator to create acoustic features that are better classified by the baseline.
A Deep Learning-Based Person Search System for Real-World Camera Images

&lt;p&gt;A person search system was developed to identify the query person from images captured by cameras at four scenes in the study. This study analyzed three network architectures called Model Basic, Model One, and Model Two. To verify the validity of the model design, the models in the public data set and in the recorded system data set were compared to determine whether the results of the proposed model exhibited consistent performance between the camera images from the public data set and the recorded, unprocessed system data set. The detected pedestrian images then underwent distance matching relative to query person images by using the online instance matching (OIM) loss function. Based on Model Basic, Model One and Model Two were designed to further improve accuracy by incorporating different convolutional neural networks. In CUHK-SYSU data set, the testing results of Model Basic, Model One and Model Two achieved the accuracies of 72.38%, 75.96% and 75.32%, respectively. The testing results of Model Basic, Model One, and Model Two with the system data set achieved accuracies of 63.745%, 68.80%, and 69.33%, respectively.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
A mixed target estimation fusion algorithm based on Gibbs‐GLMB and federated filter

Mixed targets are composed of point targets, extended targets, and group targets. The point target can produce one measurement at most, the extended target and the group target can produce multiple measurements, but the sub-goals of the group target have a certain dependency relationship. At this time, the estimated fusion of the group target is converted to the estimated fusion of sub-targets with formation motion structure, and the distance among the sub-targets is very close, which brings difficulties to the estimated fusion of mixed targets. This paper combines the adjacency matrix in graph theory to dynamically model the discernible group target and introduces the concept of deformation. Also, it uses the finite mixture model method to dynamically model the extended target. Then the Gibbs-GLMB algorithm is used to estimate the state and number of the mixed targets. A dynamic detection federated filter fusion algorithm is proposed to fuse the mixed targets state estimates. The effectiveness of the algorithm is verified in the final simulation.
An Experimental Study on Tracking Identity of Moving Shapes: Do Stimulus Complexity and Familiarity Affect Tracking Accuracy of Male and Female Students?

The current study explored the effects of stimulus complexity and familiarity on tracking accuracy between male and female postgraduate students in Malaysia. Since online courses are not restricted by location, the convenience of recording classes has become a steady trend, especially during the COVID-19 period. However, some are also skeptical about the efficacy of online teaching and learning through the digital environment and believe that the stimulus generated on these foundations will result in undesirable consequences (Dhawan, 2020). As student attention determines their tracking accuracy, external factors like stimulus complexity and familiarity tend to be an obstacle for students to be attentive. In this quantitative experimental study named "Simon Effect", 24 postgraduate students (12 males and 12 females) in a Malaysian public research university were used as samples to test whether or not gender plays a role in tracking accuracy. Results show that gender does not influence the tracking accuracy of postgraduate students. At the same time, the stimulus complexity and familiarity do influence the tracking accuracy of the postgraduate students. The implication of the study lies in promoting the teaching and learning sessions not only for a postgraduate student but for all the students in different educational levels during the COVID-19 period and maximizing the educational outcomes.
Implementation of an Educational Chatbot using Rasa Framework

The growth in Artificial Intelligence (AI), Big-data,and Internet-of-Things (IOT) technologies has increased chabot’s application in many areas. Some of the applications of chatbot can be seen in areas such as social media, e-commerce, healthcare, stock market, education, banking sector etc. Most of the high-end chatbots are deployed inside e-commerce, banking and health websites. There is a need to deploy the chatbots in educational website to improve interactivity of the educational platforms. The main target users of this website is rural students. In rural areas, probability of students dropping school after some age is common because, there won’t be proper monitoring of students and also sometimes facilities will be less. With e- learning, anyone can learn everything with limited cost. The key insight of developing this e-learning website is to provide a chatbot which can motivate rural students towards education. Thus a single platform where users can learn different courses, take quizzes, and chat with the bot is developed. It also provides an additional facility of tracking the scores of the quizzes and giving personalized recommendation systems to improve the scores. The chatbot will also help users to find details aboutfaculties and help users to set an appointment with distant faculties in online mode for doubts clarification. Flask micro- framework is used for developing the website. Firebase is used to store the data. RASA framework is used in developing the chatbot. Finally a content based filtering is used to givepersonalized recommendation systems.
Sensitive Information Detection Adopting Named Entity Recognition: A Proposed Methodology

Protecting and safeguarding privacy has become increasingly important, especially in recent years. The increasing possibilities of acquiring and sharing personal information and data through digital devices and platforms, such as apps or social networks, have increased the risks of privacy breaches. In order to effectively respect and guarantee the privacy and protection of sensitive information, it is necessary to develop mechanisms capable of providing such guarantees automatically and reliably. In this paper we propose a methodology able to automatically recognize sensitive data. A Named Entity Recognition was used to identify appropriate entities. An improvement in the recognition of these entities is achieved by evaluating the words contained in an appropriate context window by assessing their similarity to words in a domain taxonomy. This, in fact, makes it possible to refine the labels of the recognized categories using a generic Named Entity Recognition. A preliminary evaluation of the reliability of the proposed approach was performed. In detail, texts of juridical documents written in Italian were analyzed.
Linguistic Foundations of Low-Resource Languages for Speech Synthesis on the Example of the Kazakh Language

The article considers the linguistic foundations of the Kazakh language for the Kazakh speech synthesis system. A formalization of phonological rules is presented for the implementation of an automatic transcription of words into transcriptional notation, which can be applied to speech synthesis and recognition systems.