Reviewerly/exHarmony huggingface dataset:
A Recommendation System for Article Submission for Researchers

Finding the best publication for article submission is a challenge because of the need to ensure that the publication is relevant. The relevancy of the publication is important because an article published in a less relevant publication will have less exposure to the intended target audience. Publications can be characterized by a “finger print” or “signature” that can be a composition of the abstracts of articles already accepted by these publications. In turn, these abstracts can be characterized by vector representations obtained through the methods of natural language processing. Using publication abstract and the obtained finger print to search the publication space for the best fit is the aim of this research. There is a lack of substantial research related to discovering an recommending appropriate journals to those seeking to publish research papers. In this research, we extend the current state of the art and propose a robust and generic article matching tool for all publishers, using Web of Science data.
Accessing Media Via an Audio-only Communication Channel: A Log Analysis

Studies of interaction log analysis are a common tool to investigate behavioural data and contribute to insights into users’ interaction patterns with a system [11, 18]. We present a log analysis from a bespoke conversational system, RealSAM1, an audio-only interaction media assistant in which users can navigate and interact with media content through natural language. The novel assistant is designed for people with a vision impairment or other disability that prevents a person from accessing printed material. The exploratory analysis was conducted to provide an initial insight into the communication and interaction behaviours. We focus on understanding how users utilise the application. The results are twofold, we highlight the (i) implications for the design of future voice-enabled systems such as “infinite-reading” mode, enhanced interaction management enabling file navigation or time-compression techniques, and (ii) challenges of analysing conversational logs and suggest guidelines making these logs more accessible for future research.
Current issues of standardization of requirements for the development and issuing of printed teaching aids

Reforming the national education system, implementation of the Concept of State Educational Policy "New Ukrainian School" put forward the task to create a developmental educational space that will suitable for the formation and growth of a personality. The developmental content environment is one of the key components of an innovative educational space. It is created through the development and implementation of state-of-the-art learning tools and equipment, in particular printed auxiliary facilities of studies. Such as students’ albums and drawing-books, sets of paper and cardboard are a material basis for the tasks of the curriculum for drawing, drafting, appliqués, art work, etc. There are no modern standard approaches to their creation. Therefore, there is a need to identify technical and pedagogical requirements for these learning tools and further to consolidate them in the national standard of Ukraine.&#x0D; The study of educational products samples, the directions of scientific search were determined on the basis of the analysis of specialized scientific and methodological literature, normative documents of national standardization and in the field of education. As a result, the types of student albums and drawing-books, paper and cardboard sets that will become the subject of standardization are outlined. They are defined at the level of terms and definitions. Regulatory dimensions and volumes for each of these types of facilities were specified. Characteristics of the first and foremost technical and pedagogical requirements to them were produced, namely: quality of the materials for making albums, drawings-books, sets according to the educational purpose of products, standards of color standardization, design and configuration products, ways to fasten sheets in blocks, requirements for the artistic and textual design of covers and folders. The results, which were obtained, will form the basis for further applied research of the development and approval of the national standard of Ukraine – “Facilities of studies. Student's albums and drawing books. Student's sets of paper and cardboard. General specifications”. The practical significance of this regulatory document will also be a determination of the mandatory requirements for the labeling, packaging, storage, transportation of ready products, as well as the requirements of safety, environmental protection at the enterprises of the printing industry.
The Treatise On a Sphere by Gerbert of Reims

Aither is a double-blind peer review, Open Access online academic journal published by the Philosophical Faculty of the Palacký University in Olomouc in cooperation with the Philosophical Institute of the Academy of Sciences of the Czech Republic.
Few-shot Object Detection with Camouflage Animals

Abstract Object detection is a hot issue in the field of computer vision, which is widely used in intelligent video surveillance, medical image analysis, and practice in the fields of military strategy. Previous object detection is based on a lot of training data, and for some special target, it is difficult to find enough annotation data to training model, so few-shot object detection was born. In this paper, a data set of animals with protective colors is constructed(CAD), which contains ten categories of different objects with high quality annotations. We combine the existing attention RPN and Feature Reweighting module to train our net, the experimental results prove that the Camouflage animals in nature can be well identified, and our results obtain 1.9% AP50, 1.2% AP75 improvement compared with before work.
Aristotelian Dialectic, Argumentation Theory and Artificial Intelligence

It is shown that Aristotelian dialectic can be analyzed as having two parts: a core formal model that has a formal dialogue structure and a set of ten definable supplementary characteristics that lie outside the core structure. Some current argumentation tools used in artificial intelligence and multi-agent systems are applied to the task of extending the core formal model to include the supplementary characteristics. Using these tools it is explained how the structure of a dialogue can be mapped into an argument diagram that can be analyzed and evaluated using standard argumentation techniques such as argumentation schemes, types of dialogue, critical questions and a dialectical concept of burden of proof. Disputed issues on how the elenchus fits the standard dialogue typology are discussed, and it is concluded that it fits best into a type of dialogue called examination dialogue that is closely related to persuasion dialogue.
Research on the emotional polarity of Academic micro-blog knowledge transfer

[Purpose / significance] This paper takes the official tweet of the Chinese Academy of Sciences as an example to explore the emotional polarity tendency of academic tweet in knowledge transfer and the relationship between these different emotional tedecies and its influential data, so as to analyze the current situation of academic public opinion, and through the analysis of the relationship between different indicators, to provide reference and suggestions for better transfer of academic knowledge. [Method / process] Based on the extended sentiment dictionary, the sentiment analysis rules are established, and the program language is used to recognize the sentiment score of comments. After getting the total score of the comments below each tweet, the relationship between the sentiment of the tweet and its corresponding influential data is identified by using the correlation analysis method. [Results / Conclusion] First of all, the comments under the tweet published by the Chinese Academy of sciences are mainly positive emotions, and the overall emotions of the tweet are also positive, but there are also some negative public opinions. Secondly, there is a moderate or above positive correlation between the overall emotion of the tweet and its influential data (number of Forwards, number of Comments, number of Likes), and the greater the number of the influential data is, the higher the emotional positivity of the original text will be. Finally, in the group of indicators of Forward--Comment, they always present a high positive correlation, while in the two groups of indicators of Forward--Like and Comment--Like, the difference in the number of samples will have a great impact on the results, and with the increase of the number of samples, the correlation is decreasing.
Detection of Malaria Using Machine Learning

“Malaria is a blood-borne disease caused by Plasmodium-borne diseases. Common methods of detecting malaria include preparing a blood smear and examining the contaminated blood smear using a microscope to detect Plasmodium virus infection, which relies heavily on the techniques studied. At the bottom of this paper, with the aim of discriminating against malaria parasites, shallow study tools are used against traditional methods, which have other pitfalls related to understanding and disunity. The described method determines the spread of malaria with the help of photographs taken of patients without blood transfusions or the need for specialists.
Result Prediction Using Data Mining

Data mining is being used in various fields to dig out important information; it can be very effective in the field of education as well for gaining important information from a large dataset that can be used to improve the educational environment. This paper is focused on an approach consisting of several well-known and widely used algorithms on training data set to predict students’ grade for a particular course based on his/her previous results. Further analysis has been carried out considering several errors and accuracy factors of the resulted data in comparison with the actual data.
Heterogeneous agent coordination via adaptive quality diversity and specialization

In many real-world multiagent systems, agents must learn diverse tasks and coordinate with other agents. This paper introduces a method to allow heterogeneous agents to specialize and only learn complementary divergent behaviors needed for coordination in a shared environment. We use a hierarchical decomposition of diversity search and fitness optimization to allow agents to speciate and learn diverse temporally extended actions. Within an agent population, diversity in niches is favored. Agents within a niche compete for optimizing the higher level coordination task. Experimental results in a multiagent rover exploration task demonstrate the diversity of acquired agent behavior that promotes coordination.
The use of data in publishing and library acquisition strategies

Publishers of academic books in STEM disciplines endeavour to supply foundational content to support the evolving needs of the research community, by enabling deep learning of scientific concepts. The demand for this content is signalled either through patron requests for specific titles, through usage behaviour, or through analysis of market trends. The uncertainty of what a post-COVID world will look like from a social and economic standpoint makes data all the more useful for both publishers and libraries to make decisions that are in the best interest of their strategy and the needs and desired outcomes of those they serve. This chapter is intended to discuss issues and impacts related to the STEM book publishing and acquisition workflow.
Triply stochastic gradient method for large-scale nonlinear similar unlabeled classification

Similar unlabeled (SU) classification is pervasive in many real-world applications, where only similar data pairs (two data points have the same label) and unlabeled data points are available to train a classifier. Recent work has identified a practical SU formulation and has derived the corresponding estimation error bound. It evaluated SU learning with linear classifiers on medium-sized datasets. However, in practice, we often need to learn nonlinear classifiers on large-scale datasets for superior predictive performance. How this could be done in an efficient manner is still an open problem for SU classification. In this paper, we propose a scalable kernel learning algorithm for SU classification using a triply stochastic optimization framework, called TSGSU. Specifically, in each iteration, our method randomly samples an instance from the similar pairs set, an instance from the unlabeled set, and their random features to calculate the stochastic functional gradient for the model update. Theoretically, we prove that our method can converge to a stationary point at the rate of $$O(1/\sqrt{T})$$ after T iterations. Experiments on various benchmark datasets and high-dimensional datasets not only demonstrate the scalability of TSGSU but also show the efficiency of TSGSU compared with existing SU learning algorithms while retaining similar generalization performance.
HIOA-CPS: Combining Hybrid Input-Output Automaton and Game Theory for Security Modeling of Cyber-Physical Systems

A Cyber-Physical System (CPS) is usually composed of subnetworks where each subnetwork is under ownership of one defender. Security threats on such CPS can be represented by an attack graph where the defenders are required to invest wisely their limited budget in order to protect their critical assets from being compromised. We model such CPS using hybrid input/output automaton (HIOA) where each subnetwork is represented by a HIOA module. We first establish the building blocks needed in our setting. We then present our model that characterizes the continuous time evolution of the investments and discrete transitions between different system’s states (where each state represents a different condition within the system). Finally, we provide a representative real-world CPS to validate our modeling and show its benefit for CPS security.
ASPECTS OF CORPORATE CULTURE FORMATION IN A STUDENT BODY THROUGH SELF-ORGANIZATION OF EXTRA-CURRICULAR ACTIVITIES

The article substantiates the role of corporate culture in the formation of a student body as one of the most important factors in the development of the creative (innovative) activity of a student's personality. The relevance of the topic is due to the influence of corporate culture on the motivation of self-organizing activity of a student association, the possibility of regulating the behavioral attitudes of students on the basis of those values that are acceptable for a university, formation of traditions of a student community. The research problem is the search for new guidelines for the organization of a high-quality educational process in a higher educational institution. The aim of the research is to reveal the aspects that influence the formation of a communicative culture of a student body through self-organization of extracurricular activities. The research is based on the materials of scientific and practical conferences, the results of empirical research, as well as other documents and materials related to the research problem. Questioning and interviewing, study of behavior norms, traditions, and rituals prevailing in student associations have been used as the main research methods. The Results and key findings. Corporate culture of a student body - influences the development of a student's culture, the formation of the values of a student association, its effective adaptation in a student community; - promotes the development of student's social activity through motivation for joint collective activity; - is a stimulating tool that encourages the members of a student body to create projects, to form the traditions of a student community, which contributes to the enhancement of the corporate spirit, the development of the student's personal competencies.
Regulation and Standardization of Blockchain Technology for Improved Benefit Realization

Regulation and standardization are critical success factors for benefit realization and wide adoption of any new technology. Although, Bitcoin, the first Blockchain implementation met with varied resistance, recent non-financial applications of Blockchain technology will play critical roles in the upcoming fourth Industrial Revolution (Industry 4.0) due to its characteristic features of trust, immutability, transparency and decentralization. In this paper, we presented Blockchain technology and highlighted some of its innovative applications in business, government, health, and oil and gas sectors that promise to be disruptive with great benefits. The efforts towards regulation of Blockchain technology by various national governments were examined and it was found that only a handful of countries have adopted and published regulations. Furthermore, the emerging new Blockchain Service Providers offering open source development environments is making it imperative for regulatory policies and standards to be in place for optimum benefit realization. Meanwhile, except for a few published standards on Blockchain technology by the IEEE, the status of standards development by other international organizations; ISO and ITU are still at the preliminary stages. We recommend that for adequate benefit realization of this technology and for the success of Industry 4.0, that the international standardization bodies should accelerate work on standards publication to enable various countries to make regulatory policies for safe and secure use of Blockchain technologies. Additionally, due to the numerous potentials of Blockchain in various areas of endeavor, we recommend an interdisciplinary approach to development of regulatory policies and standards for the technology.
Design and Application of Comprehensive Evaluation System of College Students' Network Literacy

Comprehensive evaluation of college students' network literacy is one of the important contents of students' assessment. Because the existing methods simply superposition the evaluation index results, the evaluation results of different students are too different and not scientific. The comprehensive evaluation system of college students' network literacy is put forward and designed. By using Web technology, the system framework is designed with college students' network literacy module as the core, and the comprehensive evaluation subsystem of college students' network literacy is analyzed in detail. Firstly, the evaluation index system is established, which is divided into four primary indexes: network cognitive literacy, network knowledge literacy, network security literacy and network civilization literacy. Based on the evaluation index, the weight of different evaluation indexes is calculated by analytic hierarchy process, the comprehensive evaluation of network literacy is carried out based on fuzzy evaluation method, and the correlation coefficient is introduced in the calculation process. Avoid the students too different evaluation unscientific problems, achieve system design. The experimental results show that the system has better accuracy and is more scientific than the traditional literature for the comprehensive evaluation of college students' network literacy.
Technical Potential of the Built-Up Area of the Slovak Republic to Produce Electricity from Solar Radiation

The article describes a methodology on the proper evaluation of roof tops' technical potential in the Slovak republic's built-up area to generate electricity from photovoltaic systems. The article looks at statistical data and the possibility of evaluating maps data on available PV evaluations servers. Several conclusions are made in describing potential of covering part (or whole) electricity consumption.
Physiological Biometric an Adequate Security Measure for IoT Applications

Unforeseen upsurge in demand of smart electronic equipment and IoT has bestowed upon the IT fraternity a responsibility to develop a framework which can have control over access and transmission of client's private information. Though the IoT devices use a various types of protocols and standards for authentication but the scenario still demands for a better solution. In present study author has presented a detailed study of available biometric authentications which can be applied on IoT devices to provide a better secure system. Human biometric qualities, for example, confront, finger, iris examining, voice, signature have been discussed and a novel algorithm has been proposed which generate a unique encryption key to be used for authentication of IoT devices
An Effective Method for Recognition of Facial Expressions from Occluded Images

The occluded facial expression recognition (FER) technology is challenge in PC vision space. Numerous strategies have been applied to acquire exact and proficient outcomes in facial expressions. In this paper we proposed Local Binary pattern (LBP) method to recognize the occluded facial expressions from the images or faces. And also doing accuracy based comparison with the Wasserstein Generative Adversarial Network (WGAN) method and LBP method. The LBP which is a surface depiction strategy, that portrays the surface component of an image. The WGAN network which is used to measure the real/fake of the generated images. Finally, after the comparison of LBP and WGAN find which one is better method based on the accuracy performance. That is, the proposed method or already existing method. The exploratory outcome shows that the proposed method is superior to the existing method. Therefore, this method proposed in the paper realizes face recognition with occlusion in complex environment and meets the needs of practical applications. The proposed methods are tested using FER dataset.
Evaluation of Noise Signals in Wavelet Domain

The existing types of noise, which are based on different physical sources, are analyzed. The assessment of the influence of thermal noise on the quality of signal transmission is substantiated. The main statistical characteristics of noise signals are considered. An estimate of the instantaneous values of noise signals in the wavelet region is proposed. A wavelet representation of the energy characteristics of noise signals is recorded. The simulation of the process of estimating the power of noise signals using their wavelet transforms is performed.
Faster URLLC: Deep Learning Waveform Fingerprinting

Ultra-reliable low-latency communication (URLLC) is becoming an important research topic since it can boost mission-critical applications to serve 5G and its beyond. Traditionally, communication latency is determined by multiple factors such as transmission distance, signal duration and signal processing complexity. Among them, receiver side signal processing is the most critical factor. Firstly, a traditional 5G receiver will rely on independent signal processing blocks to decode a message, which is not optimal and would introduce unnecessary processing delay. Secondly, unsuccessful signal decoding will trigger retransmission and extra latency will be caused. This work proposes a waveform fingerprinting (WF) framework, which can easily identify users and resource assignment information based on signal patterns. More importantly, the WF scheme removes the block based 5G signal processing mechanism leading to a faster signal identification. A deep learning based intelligent identifier is integrated to assist the faster signal identification. Simulation results reveal that the proposed WF scheme enables higher reliability than the traditional 5G block based processing especially when noise power is higher than signal power. In addition, the processing latency of the WF scheme is two orders of magnitude shorter than the traditional 5G block based scheme.