Reviewerly/exHarmony huggingface dataset:
A Digital Collection Platform Architecture based on NFTs

In this digital age, the number of NFTs (Non-Fungible Tokens) in the market is growing, and NFT trading platforms are also emerging. However, most of the smart contracts used to manage the ownership, transaction and metadata information of NFT in the platform are different, that is, the management specifications for NFT are also different, which will prevent the sharing of NFT information in different platforms and affect the development of NFT. At the same time, as the number of NFT works continues to increase, when users search for NFTs, the platform needs to quickly and accurately locate the NFTs that users are interested in. Therefore, the platform needs a robust and efficient search system. We proposed a solution for the NFT platform, which manages the functions of registering NFT, searching NFT, and trading NFT of the NFT platform in the way of standardization task, search task, and verification task. The standardization task is used to unify the metadata information of NFT, resulting in the unified specification of NFT created by different users and platforms. The search task uses two methods of content addressing and elastic retrieval to realize the retrieval function of the platform and uses the verification task to learn user identity verification. Finally, the implementation algorithms of the different tools will be presented.
Uncompressed 8K-video-transmission System for Remote Production Secured by Post-quantum Cryptography

We present an uncompressed 8K-video-transmission system for remote production secured by post-quantum cryptography (PQC). To deal with the security threat by quantum computers, secure optical transport based on PQC was applied. As a means of implementation, we used whitebox transponders with a disaggregated architecture. The evaluation experiment demonstrated that an almost 100-Gbps bandwidth was achieved and that the encryption latency was less than 1 µs. Additionally, it was capable of uncompressed 8K-video transmission in 1.4 ms over an 80-km optical fiber. Consequently, the excellent potential to support secured remote production was demonstrated.
Prediction of Video Quality Degradation on a Cloud Gaming Platform

Cloud gaming has become a promising way to play high-quality video games on relatively inexpensive devices. Cloud gaming platforms run interactive video games remotely in the cloud and stream multimedia to the gamer’s device via a telecommunications network. These platforms have many benefits, but also pose several problems and challenges, as they are highly dependent on the network from servers to users. This work aims to study the effect of network impairments on video quality, and to predict video quality degradation in real time. In particular, it was determined that latency is a good predictor of packet loss, which was found to result in visual degradation. Based on this, an algorithm was designed to predict real-time visual degradations before they actually occur.
An Adaptive Video Streaming Delivery Structure based on Mobile Edge Intelligent

With the large-scale deployment and promotion of 5G and edge computing, video service storage and processing nodes gradually sink to edge nodes, providing a smooth real-time playback experience. However, the storage resources of the edge nodes are limited, resulting in a reduction of the service hit ratio. At the same time, the complex and changeable link transmission environment at the edge leads to quality damages of constant bitrate video services, which affects the user experience. To solve above problems, this paper proposes an adaptive video steaming delivery structure based on mobile edge intelligent, which uses the network, storage and computing resources of edge nodes to realize the collaborative processing of network information, cache strategy and intelligent transcoding strategy of video services. The experiment performance of this proposed structure shows that it can provide users with more smooth and high-quality business experience while improving the hit ratio of edge cache against the alternative schemes.
MEConvNN-Designing Memory Efficient Convolution Neural Network for Visual Recognition of Aerial Emergency Situations

Unmanned aerial vehicles (UAVs) play a vital role in calamity and natural disaster management due to their remote sensing capabilities. Specifically, UAVs/drones equipped with visual sensors can have remote access to confined areas wherein human access is limited. Growing inventions in deep learning incubate the efficacy of such UAVs/drones in terms of computational capability with limited resources and lead us to effectively utilize this technology in visual recognition of emergency situations, like floods in urban areas, earthquakes or fires in forests, and traffic accidents on busy highways. This can be beneficial in mitigating the consequences of such events on the environment and people more rapidly with minimum men and material loss. However, most deep learning architectures used in this domain with high accuracy are costly regarding memory and computational resources. This motivates us to propose a framework that can be computationally efficient and can be utilized on an embedded system suitable for smaller platforms. In this work, we formalize and investigate that problem and design a memory-efficient neural network for visual recognition of emergency situations named MEConvNN. To this end, we have effectively used dilated convolutions to extract the spatial representation. The proposed method is experimentally evaluated using Aerial Image Database for Emergency Response (AIDER), showing comparative efficacy with the state-of-the-art methods. Specifically, the proposed method achieves accuracy with less than a 2% drop compared to state-of-art methods but is more memory efficient in contrast to state-of-art methods.
Generalized Net Model with Intuitionistic Fuzzy Estimations of the Humanoid Robot Behavior During Navigation Tasks

In the present study we propose an approach to model the humanoid service robot behavior during a specific navigation task based on two mathematical formalisms, the Intuitionistic Fuzzy Sets (IFSs) and the Generalized Nets (GNs). Moreover, we include some possibilities for the possible ways for evaluating the overall performance of the robot. To do this we can apply estimations of the intuitionistic fuzzy sets. The estimations, which signify the degree of successfulness (µ) and non-successfulness (ν) of the current navigation task, are represented by ordered pairs <µ, ν> of real numbers from the set [0, 1]. In addition, we study the degree of uncertainty π = 1 − µ − ν that appears in those cases when the navigation task is not fully performed by some reason, either due to the environment or the robot itself. The intuitionistic fuzzy estimations in the proposed model give information for the possible reasons of the task failure, the accuracy of the performance of the robot and the efficiency of the robot when performing navigation tasks.
InterCriteria Analysis as an Intelligent Tool for Intuitionistic Fuzzy Decision Making: Case Study of Statistics for Science, Technology and Information Society of Turkish Statistical Institute

In the current paper Science, Technology and Information Society datasets are analyzed. They are downloaded from Turkish Statistical Institute. The aim of the research is to determine unseen relationships between the enterprises, individuals and their usage of ICT facilities. InterCriteria analysis (ICA) is applied over the datasets to determine dependencies and independencies between the data. ICA is performed using ICrAData software. The results of ICA application are visualized into intuitionistic fuzzy triangle and those of the research determine dependencies between enterprises. The relationships between facilities provided via web pages are also analyzed.
Intuitionistic Fuzzy Evaluations of Garbage Sorting Using a Robotic Arm

Garbage sorting is an important part of the recycling process. Recycling is vital for green and circular economics and can be improved drastically by using modern technologies such as robotics and AI. This paper demonstrates the calculation of Intuitionistic Fuzzy Evaluations (IFEs) for a batch of garbage that contains mixed materials, such as paper, plastic, metals, and other types of waste. A robotic arm equipped with a sensor analyzes each item of garbage and determines whether it is recyclable. This process is modeled and illustrated using DOBOT Magician and a conveyor belt. The different types of garbage are represented by cubes of different colors on the conveyor belt. Based on the color sensor data, an IFE is given for the batch of items on their recyclability.
Investigation the Activity of Medical Institutions for hospital care in Bulgaria with the InterCriteria Analysis Method

The activity of the hospitals of the medical establishments is analyzed annually. In the paper an information about the activity of the inpatient facilities of the medical institutions - for hospital care and other medical institutions (mental health centers, centers for skin-venereal diseases and complex oncology centers) was investigated. To analyze the data in the study, an intelligent multi-criteria decision making tool named Intercriteria Analysis (ICA) method was applied. The paper presents three applications of the ICA method: for the analysis of the bed stock in the medical institutions in Bulgaria; for the analysis of the dependencies of the medical institutions according to the bed stock and patient status in the Burgas region; for the analysis of data related to the passed patients in the wards and clinics of the University Hospital for Active Treatment in Burgas - 32 in number, of which 15 therapeutic, 10 surgical and 7 wards with Covid beds.
Mathematics, Scientific Reasoning and Fuzzy Logic

This article tries to show the relationship among general principles of mathematics, human scientific reasoning and Fuzzy Logic (FL). In applied sciences, in engineering and in social sciences Fuzzy Logic can be used in solution of real life problems. Two main types of mathematical reasoning; induction and deduction and also abduction are essential ways of FL. If we take into consideration dedication we can assume it is always a valid process, but it is not an infallible method. Before the concept FL, in Western World, logic was based on the principle of the bivalent Logic. Ideas of multivalued or even infinite valued logic is based on the mathematical theory of Fuzzy Sets (FS). It means one can pass from bivalent logic to infinitely many values lying in the interval [0,1]. Philosophy of Mathematics and Scientific Reasoning gives us the opportunity to discuss the mathematical background of FL. Rather than being strictly on engineering problems, FL provides a number of broader applications; Artificial intelligence, neural networks, genetic algorithms, biological processes are some of these applications. What kind of mathematical tool we use, in application? We have to show that instead of deterministic or probabilistic solution, FL gives better result.
Pathfinding with Dynamic Obstacles

Pathfinding algorithms are often at the center of any AI movement system. It is required to provide agents mobility in developing a realistic artificial intelligence (AI) for real-world scenarios. Its main goal is to find the optimized path from source to destination by avoiding obstacles. Finding a path through a maze is a major issue as it can be complex due to maze size, number of obstacles and have multiple routes that may lead to dead end. This paper proposed a search algorithm that finds the shortest path with dynamically created obstacles between the source and destination. It uses the A star algorithm and Manhattan distance as heuristic measure. The experiment was performed on grids of 10 × 10 to 100 × 100 size with step size of 10 × 10 on different obstacles ranges. The algorithm’s overall performance is respectable, and it can determine the quickest route between any two points. The proposed algorithm will always return a path for less than 30% obstacles and in cases where feasible paths can be generated.
An Automatic Rating System Based on Review Sentiments and Intuitionistic Fuzzy Sets

Natural language processing (NLP) and text mining methodologies are beneficial tools for utilizing from large scale service review datasets. Sentiment analysis (SA) is among them and it is used to label texts according to their polarity degrees. In this paper, a novel review scoring approach is introduced based on sentiment polarity scores and intuitionistic fuzzy sets. The approach is then implemented on a review dataset which is retrieved from several websites for a certain hotel service. The proposed approach presents promising results for more reliable product/service evaluation relatively to crisp rating scores.
Measuring Happiness: Evaluation of Elementary School Students’ Perception of Happiness Assessed by Intuitionistic Fuzzy Logic

The paper explores happiness as an emotional construct, part of people’s emotional intelligence. The motivation for studying the phenomenon is the statement, supported by a number of empirical studies, that the feeling of happiness is a sought-after state to which a person strives hard. Conceptually important is the question of how to define happiness as an emotional state and whether it can be validly measured. An experimental study was conducted with elementary school students regarding the possibility of awareness of this emotion. Various factors that can be predictors of happiness and their relationship in assessing the degree of satisfaction experienced by the surveyed respondents are discussed. The obtained results are processed by making use of intuitionistic fuzzy set (IFS) techniques. The information obtained is a starting point for future studies on the possibilities of achieving a sense of happiness and its objective evaluation.
On an Intelligent Hybrid Intuitionistic Fuzzy Method for Breast Cancer Classification

Breast cancer is the most common type of cancer in women. It is a disease in which abnormal cells begin to grow and multiply uncontrollably. They form a tumor mass that can be either benign (non-cancerous) or malignant. In the present work information for patients with breast cancer was analyzed. The data contains measurements (parameters), calculated from a digitized fine-needle aspirate image of a breast mass: type of formation, radius, perimeter, area, texture, smoothness, symmetry, concavity, etc. For data analysis an InterCriteria Analysis method is used. The method uses indexed matrices and intuitionistic fuzzy estimations. By the method the correlations between each pairs of parameters, explaining the formation in the breast mass were obtained. The obtained correlations are in a form of intuitionistic fuzzy pairs with values in the [0, 1] interval. The aim of the study is to propose a method for reducing the input data about breast cancer at the inputs of a Deep learning neural network. This can be easily done, but the goal here is to achieve a reduction in the number of neural network inputs without affecting the classification accuracy of the data. For this purpose, the obtained intuitionistic fuzzy pairs were used, showing the degree of connection between the measured parameters.
Formation of the Academic Index of a University Teacher Based on Weighted Evaluation Criteria

The assessment of the university teacher largely creates an idea of the quality of the university and its reputation. Nevertheless, in most universities in Azerbaijan, the assessment of the quality of the teachers’ activities is not carried out on a systematic basis. For example, the basis for the ongoing procedure for replacing vacant posts at the department is not a clearly formulated concept on assessing the teacher activities. This complicates practical work and negatively affects the reputation of the university. Currently, various methods for evaluating the teaching staff are being developed and tested, but there is still no generally accepted methodology. Analyzing the reasons for the complexity of developing models and methods for evaluating the teachers’ activities at Azerbaijan universities, the following main factors can be distinguished: 1) the difficulty of assessing the contribution of an individual teacher who performs many functional duties in the training of a specialist, which is inherently collective work; 2) the absence of reliable information about the qualitative results of the university as a whole and teachers, in particular; 3) the absence of some managers and teachers in conducting the objective assessment of the results of scientific and pedagogical activity based on complete verified information. Based on these considerations, the article proposes the method of expert evaluation of the teachers’ activities according to the final result, systematized at the initial stage in the quality committee and university leadership.
Cryptography Based on Fuzzy Graphs

Cryptography is a scientific area, which is in continue progress. It is dictated by the technological development, as well as the complexity of cyber-attacks. For this reason, existing cryptosystems are being improved and new ones are emerging. Recently, the apparatus of fuzzy graphs and its applicability in a wide variety of domains, including cryptography, is explored for achieving a high level of security. The aim of the paper is to present a discussion regarding properties of fuzzy graphs, which are suitable for usage in cryptography and an idea for implementation for a Diffie – Hellman like protocol to be argued. Machine learning models, predicting the man-in-the-middle attack are presented.
First-Order Representations and Calculi of Categorical Propositions

Against the three kinds of defaults of categorical propositions in traditional logic: (1) inconsistent interpretations by Euler circles, (2) monadic quantification, and (3) localization of representation and inference, the author advocated “fully expanded categorical propositions (FECPs)” with consistent, dyadic fuzzy quantifiers, and ubiquitous knowledge representations. Here, FECPs and more generally, multi-adic FECPs (FEMCPs) are transformed to be first-order logic (FOL) expressions. Further, the author has proved that FEMCPs are adequate in FOL calculi. The ramification of FEMCPs predicts that this kind of reformed categorical propositions can be written as languages of common mathematical logic and as formal prototype of logic programming languages, making the reformed categorical propositions well integrated in wider knowledge contexts and applications for FOL expressions’ intrinsic features.
Enhancing E-Commerce Query Expansion Using Generative Adversarial Networks (GANs)

In this study, we propose an innovative approach to query expansion (QE) in e-commerce, aiming to enhance the effectiveness of information search. Our method utilizes a generative adversarial network (GAN) called modified QE conditional GAN (mQE-CGAN) to expand queries by generating synthetic queries that incorporate semantic information from textual input. The (mQE-CGAN) framework consists of a generator and a discriminator. The generator is a sequence-to-sequence transformer model trained to produce keywords, while the discriminator is a recurrent neural network model used to classify the generator’s output in an adversarial manner. By incorporating a modified CGAN framework, we introduce various forms of semantic insights from the query-document corpus into the generation process. These insights serve as conditions for the generator model and are instrumental in improving the query expansion task. Through various preliminary experiments, we demonstrate that the utilization of condition structures within the mQE-CGAN framework significantly enhances the semantic similarity between the generated sequences and reference documents. Compared to baseline models, our approach achieves an impressive increase of approximately 5–10% in semantic similarity.
Fuzzy Numbers and Analysis of Radiological Images

Fuzzy sets are a mathematical notion that allows us to represent uncertainty by assigning a degree of membership to a value in a range. This degree of membership represents the degree to which a particular value belongs to the fuzzy set. Fuzzy numbers are fuzzy sets that satisfy specific conditions, and can be applied in the interpretation of the behavior of neural networks. In many real-world problems, the output of a neural network may not be a precise numerical value, but rather a range of values that are subject to uncertainty. In the context of neural networks, fuzzy numbers can be used to represent uncertainty in the output of the network. Fuzzy numbers are used in applications where the representation of ambiguity and uncertainty in numerical data is explicitly desired. This paper will consider discretizations and defuzzifications triangular fuzzy numbers with the use of MATLAB® software. MATLAB® functions were applied to the analysis of kidney images, where the fuzzy numbers represent the tendency value of the degree of redness, which serves as an indicator for classifying the image as normal or abnormal based on different characteristics.
Epic Poetry Summarization and Visualization by Using Sentiment Analysis, Topic Modeling and DALL-E

Epics are the narratives of generally exceptional illustrations of the nation’s cultural heritage and reflect the cultural elements of the era that epic occurred. Epics are also studied by social scientists and linguists and they are about to vanish from the history. Since these are works from centuries ago, there are no images or visual records of the events. However, the detailed descriptions of characters and environments are accessible through the chapters of the epics. This study aims to suggest a framework for epic poetry summary extraction based on text mining and image generation techniques. As a result of combining the results of the used algorithms, a final output was obtained that will reveal the themes of the chapters of the epics. The outputs of the model were given as input to the text-to-image application DALL-E, and the main ideas were visualized. Results show that obtained summaries are in line with the epic literature.
A Co-design Approach for Developing and Implementing Smart Health Technologies and Services

In this chapter we demonstrate how co-design can generate the knowledge needed to develop smart technologies and services in participatory ways. Even though co-design has many benefits and has been successfully used in an array of domains, it is argued that easy to apply methods are needed to guide the co-design process. One useful method for this purpose is Motivational Modelling which importantly, does not limit any aspect of participation and gives all stakeholders a strong voice in the design outcome. We report on two case studies using Motivational Modelling in the co-design process to create solutions for health and wellbeing with a focus on older adults. We argue that through the shared goal definition with attention to users’ emotions, we better understand the demands on smart solutions to create benefits for older adults and other stakeholders in complex and individual everyday contexts and hence increase acceptance and adoption.
Intuitionistic Fuzzy Generalized Net Model of a Human-Robot Interaction

In the present paper we propose an approach to model the humanoid service robot interaction with a human, based on two mathematical formalisms, the Intuitionistic Fuzzy Sets (IFSs) and the Generalized Nets (GNs). Moreover, in the present work we are using one of the extensions of the ordinary GNs, the so called, Intuitionistic Fuzzy GNs of the first type (IFGN1). The input data for the proposed model is namely from the embedded sensors and the peripherals of the robot which give the possibility of multi-modal interaction. The IFGN1-model allows the development of a more detailed and complex model for optimization and improvement of the human-robot interaction in industrial, service, co-manipulation, medical and healthcare applications.