VLDB 2026 Research / reviewers in the wild / expert

Aleksandar Jevremovic

dblp:18/9783 · DBLP profile ↗
← Back
9ranked-venue papers
1as first author
6since 2021 · last 2026
0000-0002-5564-8344ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 4 · 1 first-author · 4 since 2021Artificial intelligence and machine learning · 3 · 1 since 2021Databases, data management, data science and information retrieval · 2Applied, interdisciplinary, general and emerging computing · 2Security and privacy · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1
YearPublicationVenuePosition
2026 The MultiplEYE Text Corpus: Towards a Diverse and Ever-Expanding Multilingual Text Corpus
Ramune Kaspere, Anna Bondar, Sergiu Nisioi, Maja Stegenwallner-Schütz, Hanne B. Søndergaard Knudsen, Ana Matic Skoric, Eva Pavlinusic Vilus, Dorota Klimek-Jankowska, Chiara Tschirner, Not Battesta Soliva, Deborah N. Jakobi, Cui Ding, Dima Abu Romi, Cengiz Acartürk, Matilda Agdler, Anton Marius Alexandru, Mohd Faizan Ansari, Annalisa Arcidiacono, Elizabete Ausma Velta Barisa, Ana Bautista, Lisa Beinborn, Yevgeni Berzak, Nedeljka Bjelanovic, Anna Isabelle Bothmann, Jan Brasser, Caterina Cacioli, Anila Çepani, Ilze Ceple, Adelina Çerpja, Dalí Chirino, Jan Chromý, Alessandro Corona Mendozza, Iria de-Dios-Flores, Nazik Dinçtopal Deniz, Ana Dosen, Kristian Elersic, Inmaculada Fajardo, Zigmunds Freibergs, Angelina Ganebnaya, Jessica Gomes, Annjo Klungervik Greenall, Alba Haveriku, Anamaria Hodivoianu, Yu-Yin Hsu, Amanda Isaksen, Andreia Janeiro, Kristine M. Jensen de López, Aleksandar Jevremovic, Vojislav Jovanovic, Hanna Kedzierska, Nik Kharlamov, Sara Kosutar, Nelda Kote, Vanja Kovic, Izabela Krejtz, Thyra Krosness, Oleksandra Kuvshynova, Eilam Lavy, Ella Lion, Marta Lockiewicz, Kaidi Lõo, Paula Luegi, Mircea Mihai Marin, Clara Martin, Svitlana Matvieieva, Diane C. Mézière, Xavier Mínguez-López, Valeriia Modina, Jurgita Motiejuniene, Marie-Luise Müller, Tolgonai Nasipbek kyzy, Jamal Abdul Nasir, Johanne Sofie Krog Nedergård, Aysegül Özkan, Patrizia Paggio, Marijan Palmovic, Maria Christina Panagiotopoulou, Alberto Parola, Helena Pérez, Klaudia Petersen, Anja Podlesek, Eva Pospísilová, Marta Praulina, Mikulás Preininger, Loredana Punga, Diego Rossini, Spela Rot, Habib Sani Yahaya, Irina A. Sekerina, Anne Gabija Skadina, Jordi Solé i Casals, Lonneke van der Plas, Saara M. Varjopuro, Spyridoula Varlokosta, João Veríssimo, Oskari Juhapekka Virtanen, Nemanja Vracar, Mila Dimitrova-Vulchanova, Ahmad Mustapha Wali, Peizheng Wu, Nilgün Yücel, Stefan Frank, Nora Hollenstein, Lena A. Jäger, Somayeh Bakhtiari
 LREC50
2025 The efficiency of ICT suppliers' product security incident response teams in reducing the risk of exploitation of vulnerabilities in the wild
Vladimir Radunovic, Mladen D. Veinovic, Aleksandar Jevremovic
 Comput. Secur.3
2024 Just-in-time Software Distribution in (A)IoT Environments
Srdjan Atanasijevic, Aleksandar Jevremovic, Dragan Perakovic, Mladen D. Veinovic, Tibor Mijo Kuljanic
 Mob. Networks Appl.2
2024 Approaches and Opportunities of Using Machine Learning Methods in Telecommunications and Industry 4.0
Ivan Cvitic, Aleksandar Jevremovic, Petre Lameski
 Mob. Networks Appl.2
2024 Energy-Efficient Edge Intelligence: A Comparative Analysis of AIoT Technologies
Aleksandar Jevremovic, Zona Kostic, Dragan Perakovic
 Mob. Networks Appl.1
2023 An Overview of Smart Home IoT Trends and related Cybersecurity Challenges
Ivan Cvitic, Dragan Perakovic, Marko Perisa, Aleksandar Jevremovic, Andrii Shalaginov
 Mob. Networks Appl.4
2020 Modern Cybercrime Investigation: Technological Advancement of Smart Devices and Legal Aspects of Corresponding Digital Transformation
abstract
Last decade can be characterized by the rapid integration of smart applications and digitization of all aspects of our life. Cheap, portable and easy to deploy hardware and software components lead to the integration of tiniest Internet of Things (IoT) components in almost every digital household product that is currently on the market. Bringing data processing to the Edge and moving operations into the Cloud, companies try to operationalize novel utilization of IoT. Given altruistic goals to improve quality of life, reduce the amount of manual labour and provide more sustainable technologies, such smart solutions became an increasingly attractive target for adversarial actors. Attack scenarios that were considered as highly unlikely 10 years ago have been implemented and demonstrated on several occasions, including Mirai botnet. Distributed computations, variety of legal standards, cross-border data sharing brings novel obstacles in cybercrime investigation. However, it does not mean that the data and pieces of digital evidence from the IoT ecosystem cannot improve the pro-active response of law enforcement agencies. This paper addresses issues and discusses opportunities of IoT technology to enhance public safety and security in the long run.
Andrii Shalaginov, Marina Shalaginova, Aleksandar Jevremovic, Marko Krstic
 IEEE BigData3
2020 What Image Features Boost Housing Market Predictions?
abstract
The attractiveness of a property is one of the most interesting, yet challenging, categories to model. Image characteristics are used to describe certain attributes, and to examine the influence of visual factors on the price or timeframe of the listing. In this paper, we propose a set of techniques for the extraction of visual features for efficient numerical inclusion in modern-day predictive algorithms. We discuss techniques such as Shannon's entropy, calculating the center of gravity, employing image segmentation, and using Convolutional Neural Networks. After comparing these techniques as applied to a set of property-related images (indoor, outdoor, and satellite), we conclude the following: (i) the entropy is the most efficient single-digit visual measure for housing price prediction; (ii) image segmentation is the most important visual feature for the prediction of housing lifespan; and (iii) deep image features can be used to quantify interior characteristics and contribute to captivation modeling. The set of 40 image features selected here carries a significant amount of predictive power and outperforms some of the strongest metadata predictors. Without any need to replace a human expert in a real-estate appraisal process, we conclude that the techniques presented in this paper can efficiently describe visible characteristics, thus introducing perceived attractiveness as a quantitative measure into the predictive modeling of housing.
Zona Kostic, Aleksandar Jevremovic
 IEEE Trans. Multim.2
2019 Technique for Finding and Investigating the Strongest Combinations of Cyberattacks on Smart Grid Infrastructure
abstract
Recently, smart grids have become a vector of the energy policy of many countries. Due to structural and operation features, smart grids are a constant target of combined and simultaneous cyberattacks. To maximize security and to optimize existing network schemes to prevent cyber intrusion, in this paper, we propose an approach to decision support in finding and identifying the most potent attack combinations that can set the system to maximum damage. The main purpose is to identify the most severe combinations of attacks on smart grid components that potentially can be implemented from the perspective of the attacker. In this context, the problem of finding weaknesses points in the network configuration of a smart grid and assessing the impact of events on cyberinfrastructure is considered. The technique for detecting and investigating the strongest combinations of cyberattacks on the smart grid network is given with an example of the analysis of the spread of pandemic software in a system with arbitrary structure.
Igor Kotsiuba, Inna Skarga-Bandurova, Alkiviadis Giannakoulias, Mykhailo Chaikin, Aleksandar Jevremovic
 IEEE BigData5