Reviewerly/exHarmony huggingface dataset:
Towards Integrating Interface Behavior Models for Components of Distributed Control Software

Modeling distributed control software facilitates reacting to changing hardware configurations due to the higher abstraction level. Well-designed modules of such systems may be arranged flexibly to perform production tasks, which need to be adapted during the life-cycle of the system. Although IEC 61499 supports modeling the relations between inputs and outputs of its software components as service sequence models, these models are not integrated with the rest of the development process. In this paper, we outline our vision of using the captured information on the interface behavior for development tasks of distributed control software modeled according to IEC 61499. The proposed research directions can contribute to the acceptance of modeling methods in the domain, as appropriate tool support can notably leverage the benefit of models.
Towards the Automatic Generation of Models for Prediction, Monitoring, and Testing of Cyber-Physical Systems

Modeling Cyber-Physical Systems (CPS) requires knowledge from various domains, including computer science, electrical and mechanical engineering, and control theory. In addition, a solid understanding of the application domain, e.g., intralogistics, maritime technology, or grid control technology is required to ensure relevant and accurate models. In order to reduce the knowledge required for modeling CPS, we envision a framework for Automatic Generation of models for CPS (AGenC) supporting design and operation. Typical tasks in design and operation are summarized by the terms prediction, monitoring, and testing. Thus, the proposed framework employs learning techniques to generate models of CPS that predict the system’s performance in various scenarios, monitor the system in real-time to detect anomalies or failures, and automatically generate test cases. This research has the potential to significantly reduce the time and effort required for designing, testing, and maintaining CPS, making them more reliable and efficient.
Transforming Personal Data Transactions with Auditable, Privacy-Preserving Data Exchange Agreements : Fostering Transparency and Trust in Digital Wallet Ecosystems

This paper proposes a comprehensive data exchange framework that addresses key digitalisation challenges and integrates regulatory compliance, usability, auditability, and transparency. The human-centric approach enables auditable data sharing, enhances security and privacy, and applies to centralised and decentralised systems, as evaluated in a digital wallet-based dataspace ecosystem.
Using Graph Clustering Algorithms to Modularize IEC 61499 Control Software

Software size and complexity in all kinds of domains increase rapidly in recent times, making software maintenance and reusability increasingly difficult. Analysis of existing software and subsequent re-modularization can help with these challenges. Although software re-modularization can be time- and resource-intensive, it can support engineers in improving the quality of their systems in the future. Code can contain certain patterns, such as code smells, that define some kind of problematic implementation that is usually inefficient and hinders future development. We explore the use of a semantic clustering algorithm, which aims to minimize the code smell "Feature Envy". We apply this algorithm recursively to create a multi-level hierarchy. With this approach, we explore the re-modularization of existing IEC 61499 control systems. First tests we conducted on example implementations of a capping station and a real-life-sized industrial control system show a significant reduction of Feature Envy and thus the coupling between modules using this approach.
Knowledge-Augmented Anomaly Detection in Small Lot Production for Semantic Temporal Process Data

To mitigate unforeseen operational interruptions caused by potential malfunctions in robotic systems employed in industrial automation, we propose an innovative strategy for anomaly detection that incorporates a Transformer-based reconstruction network for identifying irregularities in skill-oriented manufacturing. Leveraging a semantic representation of processes, products, and resources, a semantic manufacturing execution system synthesizes an appropriate robot program and carries out the process. Our technique utilizes these descriptions to partition and automatically assign pertinent process data, facilitating the automated configuration of the anomaly detection pipeline. To overcome limited data availability, we employ a sliding window technique for data augmentation and capitalize on the attention mechanism of the Transformer to effectively extract semantic interdependencies from the time series data. By examining the discrepancies between the reconstructed time series data and the original, we can detect anomalies related to the manufacturing process. Through experiments conducted on an actual robot workcell, we demonstrate that our approach surpasses alternative competitive concepts.
A software architecture for low-resource autonomous mobile manipulation

Mobile manipulators can significantly contribute to enhance the flexibility of several processes, such as automated order-picking systems and various logistic applications, thanks to their capability to manipulate objects and deliver them to different locations. A primary role is envisaged for them in Smart Factories, as workmates of the operators, if they are able to safely navigate in human-shared environments. This paper proposes a lightweight and flexible ROS1-based software architecture, designed for low-resource mobile manipulators, to make them able to autonomously search for the items requested by a human operator, independently from the starting pose, and pick and place them in a predefined depot location. The validity of the proposed architecture, which is potentially applicable to different low-resource mobile manipulators, is proven through its experimental implementation on a Locobot mobile robot.
A Python Framework for Robot Skill Development and Automated Generation of Semantic Descriptions

Heterogeneous teams of autonomous robots offer a number of benefits for a variety of applications. But deploying such robots is a complex task that requires machine-interpretable descriptions in order to be flexible and adaptable. Formal descriptions in the form of ontologies are increasingly used to describe the functions of such autonomous robots in the form of capabilities and skills. However, these ontological descriptions and a corresponding invocation interface for skills need to be created, causing additional efforts for developers which are complex, time-consuming and error-prone. This contribution presents a Python framework that automates all these additional efforts. It supports a developer in implementing functionalities as skills by having them program only the skill behavior. The framework automatically takes care of generating a standardized state machine, an invocation interface and an ontological description. The presented framework can be used to implement arbitrary functionalities as skills using Python. This is demonstrated using two different evaluation case studies: a simplified behavior of a mobile robot as well as a machine learning algorithm used as an analytical skill for quality control. Both are integrated into an existing skill execution system and can interact with other skills based on their ontological description.
TeachAR-Augmented reality-based Education application

Abstract Augmented reality has come a long way from the concept of science fiction to science-based reality. Until recently, the cost of augmented reality was so high that designers only dreamed of working on an AR related design project. Things have changed now, and augmented reality is also available on mobile phones. An e-learning Augmented Reality application for immersive and practical based learning is proposed in this paper where any student can make use of this platform to learn concepts easily in Augmented Reality. This has been implemented by using technologies like Unity, Zappar and deploying the project on web using python and Django. By incorporating AR technology in education, students can effectively learn and understand complex concepts in a more engaging and interactive way.
Augmented Reality Based Training Module for IC Engine Assembly and Disassembly

Abstract Augmented reality (AR) technology has revolutionized various industries, including education and training. This paper proposes an AR-based training module for IC engine assembly and disassembly, which is aimed at providing an interactive and immersive learning experience to trainees. The module was developed using Solidworks for modeling, Blender for animation, and Unity for game development. Its primary objective is to help trainees improve their understanding and retention of complex procedures, thereby enhancing their technical skills, multi-step tasks, and product knowledge. Our approach intends to eliminate traditional paper-based instructions and replace them with AR-based ones, increasing maintenance efficiency and bridging the knowledge gap between OEMs and internal maintenance operators. The training module was designed for technicians and students who are learning how to assemble and disassemble engines. To make it more accessible and versatile, it was integrated with various devices, such as smartphones or tablets. This allows trainees to access the module from anywhere, at any time, and on any device, thus enabling remote learning. The module can be implemented in various settings, such as training centres, manufacturing facilities, or even through online platforms. Overall, this project represents a significant step forward in AR-based training modules for technical education. The module’s accessibility and versatility make it suitable for various settings, and its structured development process ensures that it is effective and reliable. The use of AR technology in education and training is an exciting field with tremendous potential, and this project demonstrates one of the many ways it can be applied to improve learning outcomes.
Hawai’i

Budgeting on a biennium basis, Hawai’i has two education budgets with appropriations of more than $2 billion for 2022–2023. A strong budget flush with COVID-19 relief funds and higher-than-expected tax revenues are funding priorities that include recovery from the COVID-19 pandemic, meeting pre-pandemic shortfalls, and new advancing education initiatives. Hawai’i uses a weighted student formula with adjustments made at the beginning of each biennium. Weight increases for English learners and homeless students are recent changes to the funding formula. Charter schools serve as one alternative to traditional public schools and students in charter schools receive equalized student funding. Hawai’i is unique among the states because it has a single school system for the entire state and a single university system. The University of Hawai’i system budget is about 6% of the state education budget and P-12 schools, including libraries and charter schools, are about 21% of the state’s budget. One of the most challenging school finance issues facing Hawai’i’s leaders is how to use the excess funds in the current budget to have the maximum impact on schools in a manner that is sustainable in the future.
Different Types of Complex Intellectual Property Objects: Court Practice Issues

Currently, a large number of creative works are produced with the use of computer technologies. These can be complex works consisting of several heterogeneous objects that are combined into one complex object. Since the law does not provide for a concept of a complex intellectual property object but new complex objects appear, the author examines new works created with computer technologies, as well as NFTs, and provides an analysis of Russian judicial practice aiming to possibly classify such results as complex objects. The paper analyzes the types of complex intellectual property objects that are not named in Art. 1240 of the Civil Code of the Russian Federation but refer to multimedia products, audiovisual works. In addition, cases developed in judicial practice regarding the varieties of multimedia products, audiovisual works are considered. Examples of foreign regulation of the issues discussed in the article are also given.
Publishing Brand and Its Communication Potential

In the context of a decline in publishing circulation, there is a need to activate existing methods and ways to correct negative trends. Traditional in overcoming the crisis is the introduction of progressive forms and methods of book trade, but the development of business processes on online platforms leveled the importance of bookselling enterprises and led to a reduction in the bookselling network. However, the online book trade at this stage has not reached the required level of monetization and did not live up to expectations of management. The purpose of this material is to comprehend the publishing brand as a holistic concept, which includes not just a complex of such elements as the author’s name, title of the work, genre, but as a combination of the brand of the author’s name (author’s brand), the brand of the title of the work (brand naming), the brand of the genre of the work. Among the tasks to identify the problem of the impact of a branded publishing product on the consumer of fiction, children’s literature; definition of a publishing brand as a marketing strategy tool for a modern publishing house. The article discusses the role of the publishing brand in the market business model, the main elements of the publishing brand, the degree of influence on consumer activity, as well as the impact of digital technologies on the creation of a cross-media product on Internet platforms.
Geometric Modal Logic

The purpose of this paper is to generalize Kripke semantics for propositional modal logic by geometrizing it, that is, by considering the space underlying the collection of all possible worlds as an important semantic feature in its own right, so as to take the idea of accessibility seriously. The resulting new modal semantics is worked out in a setting coming from Riemannian geometry, where Kripke semantics is shown to correspond to a particular case, namely, the discrete one. Several correspondence results, established between variants of well-known modal systems and corresponding geometric properties, illustrate the import of this new framework.
Study on computerized measurement-control system for determining the condition of electrical network insulation and permitted connections for electrical energy consumption

In this article electrical distribution in order to ensure the reliability and safe operation of networks, information is provided about the computerized measurement control system that allows determining the state of their insulation. In the article, the functional scheme of the computerized measurement and control system, which allows to determine the state of electrical network insulation and unauthorized connections to electricity, the structural diagram of the connection of the computerized measurement and control device to the electrical network, the structural diagram of the program developed for calculating the electrical network sizes, and the development of mathematical models developed.
Creation, Implementation and Facilitation of an Educational Room of Mysteries in the 1st Class

In the following article I present my experience in approbation of a new practice in primary school – an educational escape room. The project aims to provoke learning in students by providing them with the opportunity to leave their comfort zone and use knowledge from our curricular classes that can be transferred into skills. The students need a more flexible curriculum. The educational escape room is a great tool with which the students can work on multiple activities in a set time in order for them to achieve a specific goal. I present a working best practice with my students in 1st 'B' grade at Saint Ivan Rilski Primary school, Haskovo.
Improvement of Counting Sorting Algorithm

By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting sort while maintaining the original stability. Compared with the original counting sort, it has a wider scope of application and better time and space efficiency. In addition, the accuracy of the above conclusions can be proved by a large amount of experimental data.
Hybrid Deep Face Music Recommendation Using Emotions

Facial emotion recognition is a biometric model which uses facial features to find out the emotion and state of a person. Facial expressions are important as emotions are unique forms of psychological processes. Importantly, emotions are expressed through nonverbal behavior in the face, voice, and body, and different emotions are expressed through different, specific, unique facial configurations that are universal to all cultures, regardless of race, nation, ethnicity, religion, gender, or any other characteristic of the population. This paper explains how to use OpenCV in Python to build and create a facial emotion recognition system using the pre-trained model deep face. deep face is a deep learning facial recognition system, and it has an accuracy of 97%. Based on the user's current emotion, a music playlist is generated using K-means clustering. It is often difficult for people to decide which songs to listen to ease this confusion. This model has been developed. Here, the person’s image is captured using a Webcam, the emotion is recognized, and then, a playlist is recommended along with the link of the song to listen to. Most available methods include manually playing music, employing wearable computing devices, or categorizing based on auditory attributes. In this paper, we recommend that the manual sorting and playing be replaced by recommending a song playlist and providing a YouTube link for the songs. This has an accuracy of 99% for both training and testing the dataset of songs.
An Intelligent Human Pose Recommendation System Using Feature Fusion Technique

In an era where almost everyone owns a mobile phone and most of the pictures taken end up in social media, posing your subject allows you to tell a different story with every frame. At the same time, it helps improve the appearance of the subject in the photo. The difference between professional and unprofessional photos can be easily spotted. Since the pandemic, globally a hike in the number of social media users is observed. It has become a virtual platform for people to express themselves. People want to have the best photos and project themselves in a better way. However, it is hard to guarantee the quality of such uploaded images because of poor image acquisition devices and lack of professionalism in the pictures as not everyone is a professional photographer. In order to tackle these problems, this research work is novel in nature as such this paper proposes an intelligent photo pose recommendation method to recommend a professional photo pose according to the background of the input image. Given a query image with only background, the global features are extracted from it using VGG 16 and ResNet101. Secondly, the local features are extracted using color histograms. These features are concatenated to give a final feature vector. The feature vector then is used to get the most similar image. Open pose is used to extract pose from the most similar image and is superimposed on the query image to give pose recommendation.
Integrating ResNet18 and YOLOv4 for Pedestrian Detection

Ever-since the advent of computer vision-related research field, object detection has been in the forefront of this field. Among all the object detection tasks, pedestrian detection has been the most challenging. The scope and use of pedestrian detection task in surveillance, traffic monitoring, and autonomous cars like The Tesla’s of the world makes it an essential field of research. To this cause, the work done by the YOLO detection algorithms is significant as it detects objects in a single stage, whereas traditional detectors did the same in multiple stages. But the training and computational cost for YOLO are very high. So in our paper, we propose a combination of a simpler YOLOv4 version where the training is done faster, and also the inference times are greatly reduced because of the integration of ResNet18. ResNet18 is used for feature extraction, and YOLOv4 is used for classification.
A Comparison of Machine Translation Methods for Natural Language Processing and Their Challenges

Machine translation (MT) plays an important role in natural language processing (NLP) to automate the translation of different natural languages. Machine translation (MT) comes into existence as computational linguistic subfield of NLP and AI to automate various tasks and generate intelligence in machine like humans. The demand of MT systems growing day by day to make the task easier. By using various approaches of MT one can implement natural language processing concept in various fields like spam filtering, robotics, sentiment analysis automated learning, real-time translation applications like text-to-text or text-to-speech and vice versa, etc. There are various approaches like rule based MT, statistical MT, hybrid or neural MT approaches that are used to achieve MT process like Google Translator currently uses NMT approach to translate many language pairs. But still machine translation is facing numerous challenges to achieve high accuracy states. In this paper, we have discussed some approaches and the current challenges in MT to achieve natural language processing. This paper discusses the existing MT approaches for NLP and their comparative analysis.
A Method for Designing the Requirements of an Information System Using Patterns Under Fuzzy Environment

Software architecture is a decision-making process in which system design problems are solved by software architect by considering the set of alternatives solutions. In the beginning of the software development, the patterns are collected, organized, and selected by the software architect according to the need of the requirements of an information system. Several types of the patterns are available in the literature like interpreter, rule-based system, and virtual machine. The aim of this article is to design the requirements of an information system using patterns under fuzzy environment. The applicability of the proposed method is expressed by using the requirements of an institute examination system. The implementation of one of the requirements of an examination system is also discussed as a part of case study.
Conversion of Satellite Images to Google Maps Using GAN

Automatically generating maps from satellite images is an important task. As a lot of applications like Navigation services significantly rely on up-to-date and accurate maps. Conventional map generation involves labor-intensive methods as well as time-consuming manual efforts, which can restrict the updating frequency of maps to a few years or even longer. In recent years, satellite images have become more ubiquitous, and converting them to map-style images has attracted attention because it updates frequently and its cost-effective in nature this can be done by image-to-image translation is a general name for a task where an image from one domain is converted to a corresponding image in another domain, given sufficient training data. A generative adversarial network that is GAN is a good approach for generating maps as it is automatic satellite-to-map image conversion. We have used two types of GANs for this process of conversion of satellite images to human-readable maps and compared the results by using various similarity metrics.