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Book: ICIST 2021 Proceedings

About the book

Subtitle	Proceedings of the 11th International Conference on Information Society and Technology
Book edition	ISOS Conference Proceedings Series
ISBN	978-86-85525-24-7
ISSN (Online)	2738-1447
Publisher	Information Society of Serbia - ISOS
Publisher location	Belgrade, Serbia
Copyright holder	Information Society of Serbia - ISOS
Published	2021
Conference	ICIST 2021 - 11th International Conference on Information Society and Technology
Copyright year	2021

Full book not yet accessible

Book editors

Zdravković, Milan

Faculty of Mechanical Engineering, University of Niš

Trajanović, Miroslav

Faculty of Mechanical Engineering, University of Niš

Konjović, Zora

Singidunum University

Faculty of Technical Sciences, University of Novi Sad

Basic Metrics

Number of book views	1674
Number of book downloads	0
Number of papers in the book	43
Number of countries	6
Number of cities	12
Number of institutions	27
Number of book shares	Facebook: 321 Twitter: 317 Google Plus: 323 Linkedin: 339

Countries

Serbia (101), Slovenia (8), Brazil (6), Germany (2), Greece (2), Montenegro (2)

Cities

Belgrade (33), Novi Sad (32), Niš (23), Kragujevac (10), Ljubljana (8), Curitiba (6), Budva (2), Hannover (2), Ioannina (2), Novi Pazar (2), Sremska Kamenica (1), Trstenik (1)

Institutions

Faculty of Technical Sciences, University of Novi Sad (28)
Faculty of Electronic Engineering, University of Niš (11)
Singidunum University (10)
BIOIRC Bioengineering Research and Development Center (10)
Faculty of Mechanical Engineering, University of Niš (9)
Faculty of Electrical Engineering, University of Ljubljana, Slovenia (8)
Program of Electrical Engineering and Industrial Computing Science (CPGEI), Federal University of Technology (UTFPR) (6)
University of Belgrade, Institute Mihailo Pupin (5)
School of Electrical Engineering, University of Belgrade (5)
Faculty of Information Technology, Metropolitan University (5)
Faculty of Technical Sciences, Computing and Control Department, University of Novi Sad (4)
Faculty of Organizational Sciences, University of Belgrade (3)
TIB-Leibniz Information for Centre for Science and Technology (2)
Foundation for Research and Technology Hellas (2)
State University of Novi Pazar / Department of Technical Sciences (2)
Adriatic University Bar, Faculty for traffic, communication and logistics (2)
Academy of professional studies Sumadija, Department of Trstenik (1)
Faculty of Medicine, University of Niš (1)
Clinical Center Niš, Department of Radiology (1)
Pharmacy „Oaza zdravlja“ (1)
Medical Faculty, University of Niš, Department of Maxillofacial Surgery (1)
Mathematical Institute of the Serbian Academy of Sciences and Arts (1)
Institute of Cardiovascular Diseases in Sremska Kamenica (1)
ICT College for Vocational Studies (1)
General Staff of the Serbian Armed Forces, GARDA (1)
Faculty of Medicine, University of Belgrade (1)
Faculty of Sciences, University of Novi Sad (1)

ICIST 2021 Proceedings - Paper index

ICIST 2021 Proceedings Part I

1. Tooth detection with small panoramic radiograph images datasets and Faster RCNN model

Pages 1-4

Zdravković Milan, Pešić Zoran, Pešić Pavle

Abstract: This paper deals with tooth detection in realistic situations, characterized with relatively small number (in this case, 114 panoramic radiographs) of images for training object detection models, with differing qualities (contrasts, lightness) and sizes. The objective of the work is to define the pipeline and framework of decisions in x-ray images object detection problems. Faster RCNN architecture is used for detection; the approach includes transfer learning, augmentation and normalization (Contrast Limited Adaptive Histogram Equalization) of x-ray images. Resulting model performance is comparable with or exceeding the state of the art in the field, with mean Average Precision for the test set of mAP=0.96-0.97.

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2. Image fusion for obstacle detection optimization

Pages 5-10

Ćirić Ivan, Pavlović Milan

Abstract: This paper presents a new approach for optimization of obstacle detection in railway infrastructure. The approach is oriented at the fusion of IR and night vision images, captured by the SMART obstacle detection system in real world scenarios. Those sensors can operate in low-light and at night conditions. However, due to different light and environmental conditions, object that is not visible on IR image may be visible on image from Night vision system. The fusion process includes artificial intelligence powered extraction and fusing data and features from IR image and image from Night vision system, as well as distance estimation between obstacle detection system and detected obstacles on railway infrastructure. Use of this approach can compensate hardware flaws of sensors and increase accuracy in obstacle detection in different conditions, and those increase safety and reliability of obstacle detection system.

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3. Ontology-Driven Approach for Evidence Admissibility in Network Forensics

Pages 11-16

Matijević Milica, Gostojić Stevan

Abstract: Digital forensic investigators are commonly involved in legal proceedings aiming to support decision making. Therefore, the investigator’s testimony must rely on admissible digital evidence without grounds for impugning. This paper presents an ontology-driven application for assisting digital investigators to conduct a network investigation that leads to court admissible results. The application also enables collaboration among digital forensic experts, thus maintaining awareness of the latest technology, techniques, and tools.

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4. Mobile Application for Quantitative Climbing Level Assessment

Pages 17-21

Volontar Matija, Kos Anton, Umek Anton

Abstract: Climbing in general, and especially sport climbing, has become an increasingly popular recreational and competitive activity. Consequently, there are many climbers who would benefit from quantitative insight into their climbing level. After reviewing the research area and existing solutions, we found there are many climbing-specific measurement systems that allow acquisition of quantitative data, from which climbing level can be assessed. The vast majority of measurement systems of this type are scientifically oriented and therefore cannot be used by the general public. The motivation for our work was the development of a multi-purpose and portable climbing-specific prototype measuring system for obtaining quantitative force data with the use of upper limbs and a design of a mobile application for managing the measurements and presenting their results. The measurement systems offers force measurements for a variety of climbing-specific grips. Evaluation of the measuring system was performed with twelve subjects of difierent climbing abilities. Within evaluation, we focussed on the user experience with the mobile application and on quantitative data obtained by the measuring system.

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5. Online testing platform with eye tracking

Pages 22-26

Segedinac Milan, Savić Goran, Majić Igor

Abstract: In this paper we present a platform for online testing which tracks students’ gazes. EyeTrackQuiz enables a teacher to develop quizzes where every individual question can be fine-tuned via the editor which supports freely positioning of different elements (titles, introduction paragraphs, images, possible answers and code fragments). Furthermore, the editor supports defining regions of interests (ROIs). These regions represent rectangle regions of the screen, which the teacher considers important. While the student is taking the test, his eye movements is tracked by an eye tracker which is connected to the computer. Only gazes which fall into the regions of interest are included in the final reports. After the test conclusion, it is possible to see the results and eye tracking reports. Such reports give a better insight into student’s cognitive processes. The solution is implemented as an in-browser application, with an integrated software library for communication with the eye tracker.

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6. Usability of Micro-Credentialing Functionalities

Pages 27-32

Dimitrijevic Sonja, Devedžić Vladan

Abstract: In the last few years, micro-credentials have gained in importance and popularity as small, stockable and shareable (on social media) achievement recognitions. The beginnings have not been easy though. Micro-credentialing technologies in their infancy have shown many limitations including those related to usability, which may have even slowed down their adoption. The objective of this paper is to present some unpublished results of a study on the usability of a couple of micro-credentialing platforms from five years ago. On this basis, other studies available in literature have been searched for, analyzed, summarized and critically evaluated in order to get an insight into how usability of the given technologies has been addressed over time. Some common usability pitfalls and potentially promising approaches on how to recognize and avoid such pitfalls have been identified, analyzed and discussed. Accordingly, the results of this paper are beneficial to current and new adopters of micro-credentials, whether they are issuers, learners/candidates or technology developers.

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7. Empirical evaluation of Machine Learning models for Intrusion Detection

Pages 33-37

Takač Tomislav, Sladić Goran, Kovačević Aleksandar, Slivka Jelena

Abstract: Intrusion detection is vital in ensuring network security. Recently, researchers started focusing on developing machine learning (ML) techniques for intrusion detection as they promise to solve the problems of other techniques: high false positive alarm rates and the inability to detect unknown attacks. For the ML-based intrusion detection model to be useful in practice, it needs to be trained on the dataset that reflects a realistic network setting. Furthermore, as attacks evolve rapidly, it is crucial to use recent datasets that reflect current network environments. Recently, a publicly available dataset UNSW-NB15 was published. This dataset may serve as a recent realistic benchmark for training and evaluating ML models for intrusion detection. Most existing ML-based intrusion detection solutions have been evaluated on older benchmarks, and it is essential to evaluate how these approaches perform on UNSW-NB15. Thus, in this paper, we examine the literature to find which ML algorithms are frequently used for intrusion detection and evaluate them on UNSW-NB15. We discuss and compare our results to other papers using the same dataset.

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8. Enabling visual analysis of tags hierarchy and usage within open data portals

Pages 38-43

Frtunić Gligorijević Milena, Bogdanović Miloš, Veljković Nataša, Stoimenov Leonid

Abstract: Data openness and transparency initiatives have led to a large amount of data being published on open data portals. These portals are focused on making the published data both accessible and discoverable. However, it is not a rare situation that metadata is incomplete, making it difficult for the users to obtain the desired information. To improve the discoverability, it is important to categorize datasets with missing category based on the available information. To do so, it is crucial to understand the usage and hierarchy of datasets’ metadata elements, particularly tags. In this paper we want to address this issue by introducing a tool for interactive visual analysis of the tags’ usage as well as the hierarchy of datasets metadata tags that describe different datasets in a single category of datasets. The tool we present relies on Formal Concept Analysis for creating a hierarchy of the usage of tags and combinations of tags used to describe different datasets. Furthermore, within the tool, we provide visualization of concept lattice that enables analysis of the links between the tags and determining the importance of a tag and a combination of tags within a category.

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9. Multi-threading capability evaluation of the Notification Oriented Programming Language for the x86 Architecture

Pages 44-49

Kaehler Martini Guilherme Henrique, Ronszcka Adriano Francisco, Fabro João Alberto, Simão Jean Marcelo

Abstract: This short paper presents an initial evaluation of the Notification Oriented Programming Language (NOPL) to support multi-threading on x86 computers. This is taken as a necessary advancement to make such language of this new paradigm scalable and usable on modern platforms. The results show that it is possible to achieve such capability. Moreover, the initial numbers indicate that the research should continue. Further work is required to achieve commercial and industrial standards. Keywords multi-threading, computing efficiency, notification oriented paradigm, programming language.

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10. Automatic Transformation of Plain-text Legislation into Machine-readable Format

Pages 50-55

Cvejić Andrija, Grujić Katarina-Glorija, Cvejić Aleksandar, Marković Marko, Gostojić Stevan

Abstract: Legislative documents are an important class of legal documents that regulate almost every area of people’s lives. The introduction of modern information technologies in the legal domain brings numerous benefits for the legal profession, but the lack of a machine-readable representation of legal documents brings some difficulties. This paper proposes a method for a regulation transformation from the plain text format into the machine-readable Akoma Ntoso format. The XML tagging process is implemented in three layers: metadata layer, structural layer and text layer. To apply structural and semantic markup, we use a combination of rulebased methods and methods based on neural networks. The output of our system is XML documents that comply with the Akoma Ntoso schema. These documents are technologyneutral and machine-readable.

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11. Application of generic programming for the development of a C++ framework for the Notification Oriented Paradigm

Pages 56-61

dos Santos Neves Felipe, Simão Jean Marcelo, Ribeiro Linhares Robson

Abstract: Modern software development mainly relies on the use of the Imperative Paradigm (IP) and Declarative Paradigm (DP). However, despite widespread use of IP/DP, they have their drawbacks, such as the presence of structural and temporal redundancy, as well as cod coupling. As an alternative, the Notification Oriented Paradigm (NOP) introduces a new approach for developing software, based on the use of small reactive notifiable entities. Therefore, the NOP facilitates software development and allows achieving features such as responsiveness by redundancy avoidance and distributiveness by code decoupling. Some frameworks have been implemented to allow the development of NOP software applications in various programming languages, thereby changing the usual modus operandi of these languages. However, those NOP frameworks present some shortcomings, such as type limitation. This paper evaluates such shortcomings and proposes a new framework version based on generic programming, called NOP C++ Framework 4.0, showing how it improves in performance and usability over the most stable existing framework, the NOP C++ Framework 2.0.

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12. An overview of Hyperledger blockchain technologies and their uses

Pages 62-65

Milićević Vladimir, Jović Jovana, Zdravković Nemanja

Abstract: Hyperledger is an open source community focused on developing a suite of stable frameworks, tools and libraries for enterprise-grade blockchain deployments. It presents an umbrella term comprising different blockchain technologies (BCTs), including distributed ledgers, libraries, and tools. Due to the variety of technologies, it is often not so easy to choose the correct BCT depending on the needs of a blockchain-based application. In this paper, we present an overview of the most (and less) popular Hyperledger distributed ledgers, comparing their similarities and differences between themselves and with Bitcoin and Ethereum, with the ultimate goal of simplifying the choice of ledger for a given application.

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13. A generative model for the creation of a synthetic dataset for semantic segmentation

Pages 66-71

Vidović Mladen, Nešić Nebojša, Radosavljević Ivan, Mitrović Aleksandra, Obradović Đorđe

Abstract: The acquisition of large, annotated image datasets, required for the training of semantic segmentation models, is often an arduous task. This is because of the timeconsuming, complicated and error-prone nature of the process of manual image labelling. This process also often requires specialized software and domain knowledge. These problems can be circumvented by utilizing a generative model to create synthetic automatically labelled datasets. In this paper, we propose a generative model in the form of a 3D scene, representing an urban environment. A virtual camera setup is used to acquire labelled images from the virtual urban environment. Each image is stored as a multichannel EXR file, containing RGB data as well as an additional channel for each object class. These channels contain binary values which indicate whether a pixel belongs to the target class. These images are used to form a dataset for the training of semantic segmentation models. The viability of the generated dataset is evaluated by testing the trained semantic segmentation model on real world manually annotated images.

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14. Experiment-driven system for machine learning based research management

Pages 72-76

Radosavljević Ivan, Obradović Đorđe, Konjović Zora, Mitrović Aleksandra, Gavrić Stanko, Vidović Mladen, Nešić Nebojša

Abstract: Due to the recent rapid expansion of machine learning and its more frequent use as a valuable tool in scientific research the lack of tools that incorporate research project management and tools for machine learning is becoming apparent. Although there are many tools that can be used for project management or machine learning experiments, most of them are intended for commercial use and usually do not support features necessary for scientific research such as experiment versioning, reviewing and publishing of the experiment results. In this paper the authors propose a software solution that provides a unified environment for running machine learning experiments and research project management. The solution is a microservice oriented web platform that can be used to manage research projects, catalogue datasets, define, validate, and execute machine learning experiments. Some of the advantages of the presented solution are its ability to integrate experiments directly into the research project management process, and to automatically version and validate those experiments with respect to the constraints imposed by the authors or reviewers of the experiments. An additional advantage of the solution is the use of the graphical DSL to define experiments, thus allowing researchers who are not skilled in programming to use the platform.

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15. Providing End-user Oriented OBDA over Existing Relational Databases

Pages 77-81

Jeremić Aleksandar, Čeliković Milan, Dimitrieski Vladimir, Kordić Slavica, Luković Ivan

Abstract: Ontology-based data access (OBDA) is one possible approach to data management. In OBDA, the data is stored in external sources, such as the databases or files. The end-user view of the data is realized though ontologies, and queried as such. In the case of relational data sources, ontologies abstract away the technical details relating to the relational data model, and provide end-user with a more intuitive, object-oriented view of the data. The data layer of such systems may use many heterogenous data storage mechanisms. Relational databases are the most common, but other data storage mechanisms such as NoSQL databases, structured and semi-structured files may be used as well. The view layer of OBDA system is usually implemented though OWL ontologies, queried via SPARQL language. OWL ontologies in OBDA systems only serve to provide the conceptual view of the data stored in the database. Therefore the ontology only contains concept definitions, and not the data itself. This separation of the data (in the database) and its descriptions (in the ontology) imposes some challenges in the development of OBDA systems. For example, mappings need to be created that will connect data definitions in the data sources to the elements of the ontology. Also, using these mappings to accurately and efficiently answer end-user queries is an intricate task in itself. In this work, we present an automated system for bootstrapping a generic OBDA architecture, based on the ontop tool. Our system is able to generate the ontology that will serve as a conceptual view of the provided data sources, and map the data in the data sources to the elements of the generated ontology. An open-source tool ontop is used to subsequently provide a SPARQL endpoint for quering the data over the generated ontology. We put emphasis on generating an ontology that will describe the underlying data in a best possible way, by using database constraints to refine concept descriptions. Also, we provide a web-based front-end application, to aid in understanding and verifying the generated ontology by the end-user.

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16. Conditionally Autonomous Drive from a Driver’s Perspective: A Survey

Pages 82-86

Gruden Timotej, Jakus Grega

Abstract: Since one of the key factors for successful introduction of conditionally autonomous vehicles is a properly designed user interface (UI) for take-over requests (TOR), we believe a systematical approach including drivers’ opinion and preferences should be pursued. We have conducted a short online survey with 126 participants asking for drivers’ preferences of UI modality, level of transferred information and vehicle behavior during TOR. The participants’ answers were in favor of auditory UIs (90%) and followed the “simple is the best” principle – preferring only a single alert, i.e., a beep (37%), for TOR.

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17. A literature overview of real-time biofeedback applications in sport and rehabilitation

Pages 87-91

Hribernik Matevž, Umek Anton, Kos Anton

Abstract: This paper presents a concise literature overview in the field of biofeedback applications and systems in sports and rehabilitation. We searched for papers in three research databases: Scopus, Web of Science, and PubMed. The initial search yielded 1728 papers. After a rigorous and exacting elimination process, 112 papers were finally included into this literature overview. We focused on full papers describing applications and systems with a complete real-time feedback loop, which includes the use of sensors (inertial, motion, force), real-time processing, and feedback to the user (visual, auditory, haptic). A number of research questions were raised and papers were studied and accordingly. We present the first results of this literature overview.

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18. Unsupervised anomaly detection using bidirectional neural networks

Pages 92-95

Vještica Jovan, Predić Bratislav

Abstract: Anomaly detection is a very important class of data processing techniques and as such has found wide applications in many different fields. One of the problems often faced when developing systems for anomaly detection is the absence of labeled datasets, which means that unsupervised learning techniques need to be developed. Anomaly detection is most often used on data that exhibits some type of locality. This, in effect, means that anomalies are context-dependent. This paper analyzes existing approaches for extracting context information and defines a novel approach based on a bidirectional recurrent neural network. This paper defines an end-to-end system that is capable of autonomously flagging anomalies with no human interaction needed.

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19. An Automatic Generation of Production Documentation from MultiProLan Models

Pages 96-101

Todorović Milica, Ivanišević Đorđije, Vještica Marko, Dimitrieski Vladimir, Luković Ivan

Abstract: In the traditional manufacturing industry, technical documentation comes in various forms and plays an important role at all stages of a product development and production. In recent years, the idea of Industry 4.0 has been popularized. One of the core elements of Industry 4.0 is a digital twin representing a virtual model of physical objects and processes in a factory. An initial version of the digital twin is based on the documentation, and all changes that later occur in the lifetime of the digital twin must also be reflected in the corresponding documents. Current forms of the documentation are unsuitable for both the efficient creation of the initial digital twin version, and for consequent updates. To bridge this gap, a novel approach is proposed in this paper. The core element of this approach is a domain-specific modeling language – MultiProLan. Its models serve as centralized representation of knowledge about the production processes. Different types of technical documentation can be automatically generated from the models, resulting in increased document consistency, quality and minimized number of errors introduced by humans. We also explore the possibility of automatic generation of different types of technical documentation from such models, as well as different production process modeling aspects that must be covered.

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20. Dance Tempo Estimation Using a 3D MEMS Accelerometer

Pages 102-104

Stančin Sara, Tomažič Sašo

Abstract: Being an engaging physical activity, creative dance requires high levels of particular skills and body motion control. Automatically detecting dancing tempo has the potential of supporting many practical applications, from assessing dance move timing and overall performance to monitoring progress in the learning process. In this article, we present and evaluate a dance tempo estimation methodology that resides on a single, orientation independent, leg-attached 3D MEMS accelerometer. As the solution avoids using video cameras or IR imaging sensors, it is computational efficient, user-friendly and applicable for a variety of dancing situations - when the dancer is dancing alone, in the crowd, or in front of an audience. We focus our attention on Solo jazz dancing. Initial validation performed for a professional dancer dancing at seven different tempos ranging from 120 to 240 bpm with a 20 bpm tempo increment showed that the methodology is accurate up to 0.5 bpm. Further investigation, including different professional and amateur dancers, dancing following actual jazz music and not only metronome tempo is necessary in order to fully evaluate the presented methodology.

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21. Linked Open Data Approach to Publishing Legal Information

Pages 105-108

Vuković Marko, Jakovljević Filip, Marković Marko, Gostojić Stevan

Abstract: Knowledge of legislation and regulation is of great importance, especially to legal workers. However, finding a document that contains the relevant information is not a trivial task. Existing services that enable storage, retrieval, and browsing of legislation and regulation usually offer simple search features. They do not utilize metadata to the fullest possible extent and do not link metadata with other data published on the (semantic) web. In this paper, we present a software solution based on linked data principles and technologies that offers key features that the existing legislation and regulation retrieval and browsing services have but also introduces advanced features such as advanced search using document metadata and better handling of document references and links. The content is semantically enriched by using data from DBpedia, a project aiming to extract structured knowledge from the content created in various Wikimedia projects, which makes it more usable by other systems. However, the solution has its constraints because it requires documents to follow a specific legal document format.

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22. Landmark-driven Statistical Morphometry of the Human Ilium bone as a Base for Obtaining Subject Specific 3D Model

Pages 109-112

Tufegdžić Milica, Trajanović Miroslav

Abstract: The human ilium bone, as an entity of the hip bone, represents a very complex morphological structure of irregular shape. Building accurate subject specific 3D model requires complete geometric morphometry of given bone. Therefore, it is necessary to define reliable anatomical landmarks as points which have unique and consistent positions at the each bone, defined by coordinate values. Due to the fact that the number of these point depends on the complexity of the shape, in the case of the ilium bone 15 bilateral landmarks are separated. Based on these landmarks 26 parameters are determined as linear distances. Using statistical approach it is possible to predict coordinate values for 11 landmarks, based on the values for 4 points whose positions are easy to determine. Input data in the form of coordinate values are taken from anatomical points, localized at the sample of 32 polygonal models of the ilium bone. The tools of descriptive statistic and regression analysis are used for establishing proper dependencies between coordinate values, which results in 33 mathematical equations (6 linear, 18 squared and 9 logarithmic). These results are statistically significant, due to the the value of variance R2 (up to 0.83511) and the p-value which is less than 0.01 for regression coefficients. Based on measured and predicted coordinate values it is possible to calculate values for all parameters, using an expression for distance between two points in 3D, in proper Graphical User Interface (GUI) developed for the purpose of this study. The results of study, tested on randomly chosen male hip bone, proved proper accuracy. Landmark-driven approach presented here allows simple and fast prediction of the subject specific morphometry as the first step in building 3D bone surface model.

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23. Comparison of entropy-based and machine learning approaches in intrusion detection

Pages 113-118

Gajin Slavko, Timčenko Valentina

Abstract: This paper provides an analysis and comparison of the most important characteristics of entropy-based techniques and different categories of machine learning approaches. The main goal is to better understand the existing techniques and results, which can be found in a wide range of scientific studies. Although both classes of approaches rely on the network traffic structure analysis, the entropy-based techniques are inherently much simpler for the application, and in some cases can easily deal with the detection of the zero-day attacks. On the other side, due to the lack or presence of the labels, the machine learning algorithms can be applied for specific attack cases, and under such conditions providing more accuracy when compared with some entropy-based methods.

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24. Reuse of Semantic Models for Emerging Smart Grids Applications

Pages 119-123

Janev Valentina, Popadić Dušan, Pujić Dea, Vidal Maria Esther, Endris Kemele

Abstract: Data in the energy domain grows at unprecedented rates. Despite the great potential that IoT platforms and other big data-driven technologies have brought in the energy sector, data exchange and data integration are still not wholly achieved. As a result, fragmented applications are developed against energy data silos, and data exchange is limited to few applications. Therefore, this paper identifies semantic models that can be reused for building interoperable energy management services and applications. The ambition is to innovate the Institute Mihajlo Pupin proprietary SCADA system and to enable integration of PUPIN services/applications in the European Union (EU) Energy Data Space. The selection of reusable models has been done based on a set of scenarios related to electricity balancing services, predictive maintenance services, and services for residential, commercial and industrial sector.

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25. Identification of Air Pollution Sources using Predictive Models and Vehicular Sensor Networks

Pages 124-127

Gavrić Aleksandar, Stanimirović Aleksandar, Stoimenov Leonid

Abstract: Observation of air pollution levels at certain points in space and time is done by using mobile and static sensor networks. The values of air pollution levels at points where no measurements were made are mostly assumed by numerous types of interpolation between known values at measured points. The authors of this paper propose techniques for predicting air pollution levels in points in space where there are no measurements. The proposed techniques are based on the analysis of measurements from the sensor network that are affected by the same sources of pollution. Three approaches for identifying unknown air pollution sources by collecting measures from sensors mounted on public service vehicles are defined, implemented, and evaluated. The first approach can be treated as the optimization problem, the second approach is based on clustering in a multidimensional space and the third one is a fast and light method for a specific simplified case of the problem. The system is also implemented for a distributed computer cluster that applies machine learning algorithms over data streams for efficient estimation of dominant pollution sources in real-time.

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26. Numerical analysis of hip implant surfaces

Pages 128-130

Vulović Aleksandra, Filipović Nenad

Abstract: Total hip arthroplasty is considered as one of the most successful surgeries. Cementless hip implant surface structure has been identified as a significant factor when choosing the implant that will be inserted in a body. Our aim was to numerically analyze how three different Ti-6Al-4V hip implant surface topographies affect the shear stress distribution under the static load corresponding to single leg stance. Finite Element Analysis was performed for three developed hip implant and bone models, using material properties and boundary conditions adapted from literature. Based on the criteria that shear stress at the implant-bone interface should be minimized to promote bone ingrowth, we were able to conclude which model would be the best choice for the hip implant surface topography as well as to determine if location of surface topography influences the shear stress results.

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27. Automatic Detection of Cardiomyopathy in Cardiac Left Ventricle Ultrasound Images

Pages 131-134

Šušteršič Tijana, Blagojević Anđela, Simović Stefan, Velicki Lazar, Filipović Nenad

Abstract: This paper presents development of an automatic diagnostic tool based on machine learning that analyses cardiac ultrasound images of patients with cardiomyopathy in several views (4 chamber apical, 2 chamber apical and M mode view). The main aim of the developed tool is to perform automatic left ventricle (LV) segmentation and to extract relevant parameters in order to estimate the severeness of cardiomyopathy in patients. Dataset included 1809 images with apical view and 53 images with M view from real patients collected at three Clinical Centers in UK and Serbia. Separate methodologies have been implemented for analyzing apical and M mode view, including U-net for segmentation, after which parameters such as left ventricular length (LVL), internal dimension (LVID), posterior wall thickness (LVPW) and interventricular septum thickness (IVS) are calculated, both in systole and diastole. The tool has also been implemented on the platform with a user-friendly interface, which allows these two modules to be used either separately or combined. In order to validate the model and compare the results between gold standard and developed methodology, two cardiology specialists have independently manually annotated LV and measured relevant parameters. The results show that the model achieves dice coefficient of 92.091% for segmentation and average root mean square error (RMSE) of 0.3052cm for parameter extraction in apical view images and average RMSE of 1.3548cm for parameter extraction in M mode view. Fully automatic detection of cardiomyopathy in cardiac LV ultrasound images can help clinicians in supporting diagnostic decision making and prescribing adequate therapy.

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28. Application of deep learning techniques for segmentation of atherosclerotic carotid arteries by using ultrasound images

Pages 135-138

Arsić Branko, Tomašević Smiljana, Anić Miloš, Saveljić Igor, Končar Igor, Filipović Nenad

Abstract: In the era of personalized medicine and improved prediction power, the cardiovascular diseases such as carotid atherosclerosis have to be analyzed by using advanced machine learning techniques in order to better estimate the patient’s condition and decrease the risk from severe catastrophic events over time, such as stroke and Transitional Ischemic Attack (TIA). Among various diagnostic imaging techniques, the US images were used in this study for the detection of segments of the carotid artery as this technique is low-cost and widespread in examination of carotid atherosclerosis. The clinical US images were analyzed by using deep learning techniques. The automatic segmentation of carotid artery’s lumen has been done by using the U-Net based deep convolutional networks. The US images of carotid arteries underwent the preprocessing, such as resizing and classification of US images, as well as the annotation of lumen area. The whole dataset was randomly divided into, training, validation and testing sub-sets, while the model robustness was tested on unseen images (test dataset). The obtained results show high accuracy in region detection and segmentation (Precision - 0.90, Recall - 0.92, Dice coefficient (F1-score) - 0.91). The automatic extraction of US features such as carotid lumen gives the specific segmentation of individual patient-specific anatomy and can be used for further analysis of the patient, as well as for building up the patient-specific models for computational CFD simulations.

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29. Insilico clinical trials for bioresorbable vascular stents

Pages 139-142

Gacić Marija, Karanasiou Georgia, Fotiadis Dimitris, Filipović Nenad

Abstract: The world stent market has an estimated value of €6.4 billion, of which 37% is generated in the US and 10% in the EU. Coronary stents are now the most commonly implanted medical devices, with more than 1 million implanted annually. Coronary stents are currently the most widely used for treating symptomatic coronary disease. This paper presents the innovative platform and its separate modules that can be used as a standalone tools developed within EU funded project InSilc, for designing, developing and assessing coronary stents. This integrated solution was developed by international consortium in H2020 project www.insilc.eu. After three years of intensive work on different modules, presented in this paper and their integration in the platform which will be offered as a service to interested stakeholders, consortium is planning the commercialization of the solution. In this paper the potential pathways for further exploitation and commercialization are presented together with market analysis, business plan and business model. The technological level of each module is also elaborated, as well as different business models for its market introduction. Future work will include testing and validation on larger databases, and improvement of the tool based on feedback from the users from different categories (stent industry, clinicians, researchers etc.). The main goal is commercialization of the InSilc solution and its fast market uptake.

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30. Deep Convolutional Neural Networks for COVID-19 detection from CT scans: A survey

Pages 143-148

Kostić Marija, Petrović Miloš, Drašković Dražen

Abstract: Since the Coronavirus outbreak in December of 2019, people have put a lot of effort into its early diagnosis by assessing signs and symptoms obtained from various studies. To this goal, deep learning models are trained to detect the presence of COVID-19 from three most commonly used lung-imaging modes: X-Ray, Ultrasound, and Computed Tomography (CT) scan. This survey compares current Deep Convolutional Neural Networks approaches for the classification of CT scans considering datasets, neural network architectures, and evaluation metrics. It gives new researchers in this field an excellent starting point providing currently most popular and prosperous CNN architectures and large available diverse datasets.

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ICIST 2021 Proceedings Part II

31. Blockchain implementation for IoT devices, Blockchain of Things

Pages 149-153

Pavlović Nikola, Šarac Marko

Abstract: Internet of Things and Blockchain are considered two major technologies. Internet of Things is facing many challenges such as poor interoperability, security vulnerabilities, privacy, and lack of industry standards. Most Internet of Things Devices needs a constant connection to the internet which brings many challenges in their protection and security the data. This is where Blockchain technology comes into use. The Blockchain provides decentralization and authentication that makes is impossible for third parties to gain access to the network. It provides much needed privacy and flexibility which is currently missing from IoT infrastructure. Our work provides a solution with overview what can be done to implement Blockchain in existing IoT infrastructure and furthermore improve security and privacy of the system.

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32. Design Methodology of a Personalised Wrist Orthosis for Fractures and Rehabilitation

Pages 154-157

Aranđelović Jovan, Korunović Nikola, Stamenković Bojana, Arsić Milica, Trajanović Miroslav

Abstract: The goal of this study is to develop an expeditious methodology for designing an orthosis which can be used both for wrist fracture immobilisation and later rehabilitation. This requires an orthosis that can (depending on the patient’s needs) be flexible or fully stiff. It is possible to achieve this by making the orthosis out of a flexible material and by adding external plates for stiffness (which can be removed when the orthosis is used for rehabilitation purposes). Another reason why the orthosis should be made of a flexible material is to ensure that it can adapt to changes in swelling that may appear during the healing process. The design of the orthosis is expeditious in order to facilitate possible practical application of the methodology in clinics.

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33. Automated process of determining “black points” in traffic using business intelligence systems

Pages 158-162

Atanasijević Jordan, Atanasijević Nevena

Abstract: The basic setting of the paper is related to the improvement of the current way of decision-making by using the business intelligence system, important for decision-making on a specific problem, and in order to achieve benefits in decision-making at the highest level. The improvement must be appropriate to the development of the BI system in conditions when there is already previously accumulated knowledge about the problem. The obtained results should show that the business intelligence system can identify dangerous places on the roads, as the first and very important stage in the process of managing "black points", and is a procedure for detecting specific locations on the road network where appropriate concrete measures need to be taken.

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34. Teaching Approach in Difficult Knowledge Transfer Conditions Due to the Pandemic State of Emergency

Pages 163-167

Mravik Miloš, Šarac Marko

Abstract: This paper represents a teaching approach to the transformation of classical lectures teaching. Paper elaborates use of the online platform that enables students with and without disabilities to follow classes without hindrance during the lecture period. The main advantage of this kind of teaching is the possibility of attending classes from any location and from any device, it is only important to be connected to the Internet connection. After the lecture, students also have the ability to view video and presentation materials. This paper describes a new approach to teaching and illustrates the expected benefits of online teaching. Full integration with the already existing Faculty Information System has been performed. The platforms used in this integration are Microsoft Azure, Microsoft Office 365 Admin, Microsoft Teams, Microsoft Stream and Microsoft SharePoint.

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35. Integration of Mainflux platform into a Multi-Agent based HEMS framework

Pages 168-172

Kaplar Aleksandra, Savić Filip, Kaplar Aleksandar, Vidaković Jovana, Slivka Jelena, Vidaković Milan

Abstract: Smart homes consist of electronic devices that consume electricity from the electricity grid (EG) or renewable energy sources. The system proposed in this paper aims to lower the cost of consumed energy in smart homes. Cost reduction can be achieved by training a smart HEMS (House Energy Management System) to orchestrate the schedule of loads energy consumption according to the time-varying energy price and the residents' preferences. HEMS can be trained by Reinforcement Learning (RL) if provided a realistic environment. In this paper, we propose a simulated household environment with the help of the Typhoon HIL application. To make our simulated environment realistic, we need realistic measurements of external conditions, such as external temperature and solar irradiation. Thus, we use the Mainflux platform that supplies the simulated environment with real-world data. This paper focuses on integrating the Mainflux IoT platform with Typhoon HIL simulation of smart home devices. In this paper, Mainflux provides two real-world parameters: solar irradiation and outdoor temperature, vital inputs for the realistic simulation of smart home devices such as PV panels and Air Conditioners.

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36. An LSTM neural network model for stock market data

Pages 173-177

Radojičić Dragana

Abstract: For each trading day, there is a huge number of trade events registered at the stock market, and thus a large volume of data is recorded. In this research, machine learning approaches are particularly employed in order to learn from stock market data. More precisely, in order to capture the time dependency between rows in the market data, the model based on the Long short-term memory (LSTM) network is employed.

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37. Determination of molds isolated from the patient materials, based on their microscopic morphological characteristics

Pages 178-182

Milanović Mina, Milosavljević Aleksandar

Abstract: Based on literature data in recent years the incidence of invasive fungal infection (IFI) caused by molds is on the rise. Diagnostics of those infections can sometimes be inefficient; they require a longer period of time in laboratory procedures and sometimes may lead to late diagnosis or misdiagnosis, which can result in patient’s critical condition or even mortality. Goal of this research is to train a classifier, a neural network model that can be used for classification of molds from patient materials, which can drastically improve the process of diagnosis and prevent fatal cases. Using a ResNet-50 deep convolutional neural network (CNN) and images obtained from Department of Microbiology and Immunology, Medical faculty, University of Niš, Serbia, archives, a classifier has been developed, displaying promising results, which show that with wider dataset it will be possible to train a model that can be used in diagnostics.

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38. Ensuring the Durability and Reliability of Data in Smart Health Services Using Blockchain Technologies

Pages 183-186

Avdić Aldina, Marovac Ulfeta, Janković Dragan

Abstract: The use of modern technologies in all spheres of life offers the possibility of collecting and processing citizens' data in smart cities to improve the quality of their lives through creating smart health services. The collected data should be protected from misuse. This paper describes e-health services in smart cities, as well as the problems of ensuring durability and reliability of patients' data. As a solution to this problem, the use of blockchain technology has been proposed and ways for its application have been given.

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39. An Analysis of the JSON Functionalities Evolution Across Different Versions of Oracle Relational DBMS

Pages 187-190

Bjeladinović Srđa, Asanović Marko, Gospić Nataša

Abstract: Modern web applications, as a necessity, require fast, efficient, and accurate data exchange, which all participants in communication can uniformly interpret. Consistency in interpreting exchanged data can be achieved using text, self-describing and platform-independent file formats such as XML and, recently, the even more popular JSON. JSON represents a common model for NoSQL databases. Also, it has been supported for years in relational databases from leading manufacturers, such as Oracle. This paper systematises, analyses, and compares the functionalities for working with JSON across different versions of Oracle DBMS, starting with version 12c R1, which introduced support for native JSON, concluding with the recently introduced version. For selected use cases, experimental tests were performed, which demonstrated the impact on performance achieved by the supported JSON functionalities from version 12c R1 to 19c of Oracle relational database management systems (DBMSs).

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40. Creation of Exercise Plan Using Rule-based System

Pages 191-196

Travica Milica, Nikolić Siniša, Vejnović Mina, Luburić Nikola

Abstract: Nowadays, the modern world strives to live life healthier. With the influence of the virus COVID-19 on everyday life, it is difficult to maintain motivation and training routine. By using technology, overcoming those difficulties can be easier. This paper presents the implementation of the system for helping users during exercise, filling the role of a digital personal trainer. The system mimics the reasoning of a human expert in proposing a suitable exercise plan based on the current user's condition. Also, the system supports the process of monitoring the current user's status during exercise, acts on changes, and shows certain messages concerning the displayed status. The system uses rules to determine which exercises are most suitable for a particular user, as well as the messages that will be shown during the exercise. The system is implemented using a system based on rules.

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41. Applying Blockchain Technology in eLearning systems: Overview, Analysis and Potential Solutions

Pages 197-201

Dimitrijević Nikola, Bogdanović Milena

Abstract: Technological innovations such as M-Learning, automatic graded systems, virtual and augmented reality, online classes, and the gamification of learning are rapidly being integrated in the field of contemporary education. One of these technologies is Blockchain, which has already proved to be a disruptive in several fields, such as finance, healthcare, human resources and advertising. Multiple organizations from different industries are developing blockchain-based systems, but the application of Blockchain in education is still novel. The goal of this paper is to present an overview of existing literature in this field, and the potential of application of Blockchain technology in education. We present a simple solution of an eLearning system for learning programming on the basis of automatic grading developed in the language Python.

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42. Reinforcement learning usage in the development of recommender systems

Pages 202-205

Cincović Jelica, Drašković Dražen

Abstract: Recommender systems are used for predicting user preferences mostly for commercial purposes. As the need for recommender systems has increased significantly, due to the rapid lifestyle we all lead, machine learning has also found its wide application in the development and improvement of these systems. Reinforcement learning emerged as one of the newer machine learning techniques, which can make recommender systems more flexible using user interactions. This paper focuses on analyzing and comparing existing implementations of recommender systems based on reinforcement learning, and drawing final conclusions when combining these two techniques, as well as defining possible further upgrades.

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43. Comparison of five outlier detection methods in case of OpenClick data set

Pages 206-211

Mitrović Vladimir, Mišić Dragan, Trajanović Miroslav, Vitković Nikola

Abstract: In data analysis, outlier detection is very important for tasks such as filtering data or finding points of special interest. Finding outliers can be quite challenging task, especially when data set represents human behavior or certain action, which can be hard to describe or predict. This is also case in OpenClick project, a platform which provides various tests where human behavior can be analyzed through human-computer interaction. For a single test type on OpenClick project, an individual test can be invalid for various reasons, e.g., a disturbance occurred during a test (such as ringing of a phone) or perhaps a test subject cheated in some way. One way how could we distinguish invalid test from valid is through detection of outliers in data set using outlier detection methods. For this purpose, we chose five outlier detection methods, which we use to detect suspicious data points and to evaluate tests in terms of how much outliers bring disturbance in test results. In addition, we give some observations on the form of the data set of a single test and propose possible way on how to approach a task of labeling tests as valid or not.

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