Journal of Computer and Forensic Sciences
The Journal of Computer and Forensic Sciences is an open access, peer-reviewed scientific journal published by the University of Criminal Investigation and Police Studies in Belgrade, covering advanced and innovative research across the fields of computer and forensic sciences. The aim of the journal is to provide a platform through which authors can communicate their viewpoints on diverse but often related aspects of computer and forensic sciences and a source of information to support advancing research, education, and practice in these fields.
University of Criminal Investigation and Police Studies
I (we), the author(s), hereby declare under full moral, financial and criminal liability that the manuscript submitted for publication to the Journal of Computer and Forensic Sciences a) is the result of my (our) own original research and that I (we) hold the right to publish it; b) does not infringe any copyright or other third-party proprietary rights; c) complies with the Journal’s research and publishing ethics standards; d) has not been published elsewhere, under this or any other title; e) is not under consideration by another publication, under this or any other title. I (we) also declare under full moral, financial and criminal liability: f) that all conflicts of interest that may directly or potentially influence or impart bias on the work have been disclosed in the manuscript; g) that if the article has been accepted for publishing I (we) will transfer all copyright ownership of the manuscript to the University of Criminal Investigation and Police Studies in Belgrade. Signed by the Corresponding Author on behalf of the all other authors.
Semantic Paraphrase Generation Using Transformer Architectures: A Comparative Study of Pre-trained and Fine-Tuned Models
Semantic paraphrase generation plays a crucial role in academic and technical writing by enabling authors to restate content while preserving its original meaning. Traditional paraphrasing approaches, such as rule-based rewriting and statistical methods, often struggle to maintain semantic consistency and linguistic fluency, especially for complex or longer text segments. Recent advances in transformer-based architectures have significantly improved text generation capabilities by leveraging contextual representations and self-attention mechanisms. This paper presents a comparative study of pre-trained and fine-tuned transformer models for semantic paraphrase generation. We evaluate encoder–decoder–based transformer architectures, with a primary focus on the BART model in both pre-trained and fine-tuned settings, alongside a large generative language model used for paraphrase generation. The fine-tuning process adapts pre-trained models to paraphrasing tasks using task-specific data, enabling improved control over semantic preservation and output consistency. The evaluation is conducted using both quantitative and qualitative analysis, including training and validation loss trends and comparative examination of generated paraphrases. Experimental results demonstrate that fine tuned transformer models produce paraphrases with higher semantic fidelity and structural coherence compared to their pre-trained counterparts, while large generative models offer fluent but less deterministic outputs. The findings highlight the importance of task-specific fine-tuning for controlled and semantically accurate paraphrase generation. This study contributes practical insights into the selection and adaptation of transformer architectures for paraphrasing applications, particularly in academic and research-oriented writing contexts.
Copyright (c) 2026 University of Criminal Investigation and Police Studies, Belgrade, Serbia
A Hybrid Plagiarism Detection Framework Using Lexical and Semantic Similarity with Lightweight Sentence Transformers
Plagiarism detection has become increasingly challenging due to the widespread availability of paraphrasing tools and generative artificial intelligence systems. Traditional plagiarism detection techniques based on lexical similarity, such as TF-IDF and n-gram matching, often fail to identify semantically similar but lexically modified text. This paper presents a hybrid plagiarism detection framework that combines lexical similarity measures with semantic similarity derived from sentence transformer models. The proposed approach integrates TF-IDF–based cosine similarity with lightweight sentence embeddings generated using MiniLM and SBERT models. To enhance semantic detection performance, a MiniLM-based sentence transformer is fine-tuned on the PAN 2011 plagiarism detection corpus. Experimental evaluation demonstrates that the hybrid similarity approach significantly improves detection accuracy compared to purely lexical methods, particularly for paraphrased plagiarism cases. The framework is further validated using threshold-based analysis and real-world web content retrieved through automated scraping. The proposed system provides an efficient and scalable solution for plagiarism detection, balancing computational efficiency with semantic understanding, and is suitable for academic and real-world forensic applications.
Comparative Analysis of Clustering Textual and Numerical Data Using the K-Means Algorithm
This paper presents a comparative analysis of the application of the K-Means clustering algorithm on two different types of data – textual and numerical. The aim of the research was to examine the reliability, stability, and interpretability of the results when the same algorithm is applied to semantically diverse datasets. The textual data were taken from the articles of the Criminal Code of the Republic of Serbia, where clustering was performed after preprocessing and TF-IDF vectorization. The numerical data refer to traffic accident statistics from 2015 to 2021, analyzing parameters such as the number of property-damage-only accidents, the number of injured persons, and the number of fatalities. The results showed that clustering on textual data produced a relatively clear separation of thematic groups of articles, but with a moderate silhouette coefficient value due to a high degree of semantic similarity among documents. On the other hand, clustering on numerical data demonstrated a more stable structure, where the optimal number of clusters was two, indicating the possibility of distinguishing periods with different intensity and severity of traffic accidents. It was concluded that the K-Means algorithm provides more reliable and interpretable results for numerical data, while in the case of textual data, it requires more precise vector space modeling and possibly the application of semantic models such as Word2Vec or BERT. The paper serves as a basis for further research in the field of integrating machine learning techniques for analyzing heterogeneous data sources.
Application of Time Series Algorithms in Cybersecurity
Abstract: This paper explores the application of time series algorithms to enhance anomaly detection in cybersecurity. Windows log files such as PowerShell Operational, Windows Defender, Firewall, System, and others were analyzed, focusing on those with the highest informational potential and data volume. Various models were used: exponential smoothing (Holt-Winters), Prophet, Fourier analysis, and Kalman filter for modeling seasonal, periodic, and linear patterns in system events. Advanced methods include LSTM and GRU neural networks, as well as ensemble algorithms like Random Forest and XGBoost, which demonstrated high accuracy in detecting unusual behavior. Special emphasis was placed on dynamic models such as Bayesian Structural Time Series to understand system states over time. Experiments show that applying multiple models enables a robust and adaptive approach to log analysis, especially for early detection of attacks and deviations from norms. The proposed framework highlights the importance of predictive analytics in preventive cybersecurity and provides a foundation for developing intelligent systems for real-time monitoring and response.
Copyright (c) 2025 University of Criminal Investigation and Police Studies, Belgrade, Serbia
This research explores the development of a heart rate estimation system that integrates wearable sensors with machine learning techniques to achieve accuracy, low cost, and real-time performance. The project aims to build two-stage phases: a software pipeline for model training and a hardware framework for real-world testing. In the first phase, various machine learning algorithms are trained and fine-tuned using the publicly available PPG DaLiA dataset, which contains physiological data collected during everyday activities. The training process focuses on optimizing performance across different model architectures and configurations. The second phase involves implementing the trained model on a real-time embedded system. An ESP32 microcontroller serves as the central unit to collect data from multiple sensors, including electrocardiography (ECG), photoplethysmography (PPG), galvanic skin response (GSR), temperature, and a 3-axis accelerometer. This data is transmitted wirelessly for preprocessing and inference. The user will see their final predicted heart rate on both an OLED display and a user interface (UI) dashboard.
Comparative Analysis of Machine Learning Models for Real-Time Object Detection
: Object detection is a fundamental task in computer vision with applications ranging from autonomous driving, industrial automation and medical imaging. This report presents a comparative analysis of six well-known object detection models, precisely three small models for edge computing and three large models likely more suited for usage on high-performance systems. The models YOLOv10-Nano, MobileNetV3-SSDLite, EfficientDet-D0, Faster R-CNN, YOLOv10-Large and DETR were evaluated and compared based on their performance in terms of inference speed, accuracy and computational efficiency. The evaluation is conducted through both literature-based benchmarks and empirical tests on two different systems: an Apple Silicon M1 Pro-based system and an NVIDIA RTX 3080Ti-powered computer. Results show that YOLOv10 models consistently outperform the other models in real-time object detection as well as achieving superior accuracy in general while maintaining significantly lower inference times. The analysis further highlights compatibility issues with certain hardware, particularly focusing on PyTorch's MPS backend on Apple Silicon, which leads to serious performance drops in some models. The findings highlight the importance of choosing the right model and appropriate hardware for specific application scenarios.
The clustering approach to malware dataset analysis
The research in the area of malware analysis is very popular with accent on machine learning algorithms that help automate this subject. One of the leader portals that help researchers with dataset problems is Virus Total, providing free academic accounts with hundreds of thousands malware samples with metadata. This work contributes with the analysis of 429058 malware samples from Virus Total in terms of overcoming the problem of inconsistent labeling of the antivirus scan results from different vendors. Two methods were used, LSA and LDA both with automatic calibration of parameters, with purpose of finding the optimal number of clusters - both resulting with 5. The graphical representation of the clusters was done by k-menas clustering in two dimensional space. Additional research on most informative words in each cluster, showed that we can report of 4 similar classes, and one cluster per method (LSA and LDA) that was not related to the cluster in opposite method by wrd meaning in it. The showed results give good approach malware data analysis when dealing with inconsistently labeled dataset.
Copyright (c) 2024 University of Criminal Investigation and Police Studies, Belgrade, Serbia
Comparative Analysis of Certain Clustering Algorithms That Do Not Require Predefined Number of Clusters on the Articles of the Criminal Code of the Republic of Serbia
This paper explores the application of certain clustering algorithms for analyzing textual documents from the Criminal Code (CC) of the Republic of Serbia (RS). Clustering was performed using three popular algorithms: DBSCAN, Mean-Shift, and Hierarchical Clustering (Agglomerative Clustering). The input data consisted of textual documents in .txt format, where each document corresponds to a specific article of the law. The aim of this study is to identify thematic groups within the legislative texts and analyze the advantages and disadvantages of each algorithm in the context of the specific characteristics of legal documents. The clustering results using these three algorithms show that DBSCAN faces challenges with noise, while Mean-Shift effectively detects dense clusters, and Hierarchical Clustering allows for a detailed analysis at various levels of granularity. In conclusion, this paper provides valuable insights into the application of these clustering algorithms to legal texts and offers recommendations for their selection when analyzing similar datasets.
Surface Electromyography-based Gesture Recognition for Robot Hand Control
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QFormat="true" Name="Subtle Emphasis"/> <w:LsdException Locked="false" Priority="21" SemiHidden="false" UnhideWhenUsed="false" QFormat="true" Name="Intense Emphasis"/> <w:LsdException Locked="false" Priority="31" SemiHidden="false" UnhideWhenUsed="false" QFormat="true" Name="Subtle Reference"/> <w:LsdException Locked="false" Priority="32" SemiHidden="false" UnhideWhenUsed="false" QFormat="true" Name="Intense Reference"/> <w:LsdException Locked="false" Priority="33" SemiHidden="false" UnhideWhenUsed="false" QFormat="true" Name="Book Title"/> <w:LsdException Locked="false" Priority="37" Name="Bibliography"/> <w:LsdException Locked="false" Priority="39" QFormat="true" Name="TOC Heading"/> </w:LatentStyles> </xml><![endif]--><!-- [if gte mso 10]> <![endif]-->This paper presents a human-machine interaction interface for a teleoperated control of a three-finger robot hand based on a surface electromyography. Based on the recorded sEMG signals, motion intention of a human operator is recognized and mapped onto corresponding predefined robot hand grasps. First, the experimental setup and underlying methodology of dataset generation are presented. Namely, 4-channel EMG data were collected from the forearm muscles of 4 healthy subjects as they performed a sequence of predefined grasps. Data processing included: data filtering and segmentation, feature extraction both in time and frequency domains, data annotation, and gestures classification. Two types of classifiers (with multiple variations of parameters) were implemented and compared: (i) Support Vector Machines with multiple different kernels and (ii) K-Nearest-Neighbors. The k-cross validation procedure was combined with the holdout method to generate training, validation and test sets. Performance of each classifier is presented in the form of a confusion matrix and a set of statistical measures, including F-measure. Finally, the integration of the proposed sEMG-based HMI interface and the selected classifier with a robot hand motion controller is presented.
The New Language Corpus: Exploring Perception of Graphemes 'P' and 'B' in Serbian Cyrillic and Latin Scripts According to Semantical Context
Language corpus serve as essential instruments in facilitating research endeavors within the domain of natural language processing. As an illustration of such a language corpus, the paper presents part of the conducted research. Notably, the Serbian language demonstrates a deficiency in its linguistic resources, a concern accentuated by its unique status as one of the few languages characterized by a bialphabetical system., i.e. although in official use only the Cyrillic alphabet is used, in the Serbian language the Latin alphabet is also used in parallel. The Serbian Cyrillic alphabet in modern times is threatened by various influences of globalization, i.e. more dominant use of the Latin alphabet in everyday life. The primary goal of the research is to examine the perceptual differences between the Latin and Cyrillic alphabets (for the purposes of the research, a sample of subjects whose native language is not Serbian was used, so that the subjects would not be compelled by previous knowledge of the semantics of the Serbian language - the subjects would have to know at least the basics of the Serbian language and the rules of reading and writing, to be able to understand the questionnaire, simplified with vocabulary for level A2). Within this paper, a segment of this research will be presented, through several compared variables, and predominantly by examining whether the graphemes 'P' and 'B' can cause confusion, because these graphemes in Serbian Cyrillic and Latin have different phonetic pronunciation. The research showed that the answers given by the subjects were in a higher percentage perceived in the reverse script of the questionnaire (if the questionnaire was in Cyrillic, the perception of unclear words by a higher percentage of subjects was in Latin and vice versa).
Merging Control-flow and Dataflow Architectures on a Single Chip
Computing power rises predominantly by increasing the number of cores of modern processors, and the number of cores in cluster and cloud architectures. Along with increasing the processing power, high performance computing requirements also rise. Vast of the computing infrastructure includes control-flow processors that are based on von Neumann paradigm. In contrary, the principle of dataflow architectures is based on the data flowing through the already configured hardware. Dataflow architectures are often implemented using FPGAs. Recent research has proposed hybrid architectures, where both control-flow and dataflow hardware would exist on the same chip die. In this article, a new hybrid control-flow and dataflow architecture is proposed, where control-flow hardware would be similar to modern graphical cards, consisting of thousands of cores, but with a reasonable small amount of dataflow hardware available on each GPU core. The proposed architecture is tested by analyzing the conjugate gradient method executed on both control-flow and dataflow hardware. The execution of a the algorithm is divided onto GPU cores, and the execution of repeated instructions on each GPU core is delegated to the assigned dataflow hardware. Results indicate that it is possible to accelerate the execution of algorithms using the proposed architecture.
An Overview of Image Processing in Biomedicine Using U-Net Convolutional Neural Network Architecture
Image processing in biomedicine is a very broad field, which includes both medical and technical significance. The aim of this work is to investigate the current trends in the domain of application of U-Net architecture in the period from 2018 to 2023. The PRISMA framework was used for the systematic literature review and 4 research questions were asked. For the most part, U-Net architectures are used that can process complex high-resolution images in the fastest way in the context of semantic segmentation. Previous work in image processing has focused on overcoming problems such as the complexity of different architectures, image loss, image resolution, and quality, as well as the size of datasets and noise reduction. The most frequently used groups of datasets are BraTS, Data Science Bowl, and ISIC Challenge. The best general Dice score was obtained for LUNA16, VESSEL12, and The Kaggle Lung datasets with 0.98. It is concluded that the application of the U-net network is growing, with a focus on solving specific challenges in the context of a certain modality and segment of biomedicine.
An Approach to Optimization of Gated Recurrent Unit with Greedy Algorithm
This study focuses on enhancing the performance of Stacked Gated Recurrent Unit (GRU) model in time series data processing, specifically in stock price prediction. The most significant innovation occurs in the integration of a Greedy Algorithm for optimizing hyperparameters such as look-back period, number of epochs, batch size, and units in each GRU layer. Historical stock data from Apple Inc. is utilized for the model's application, emphasizing the model's effectiveness in predicting stock prices. The study methodology involves a sequence of steps, such as data loading, preprocessing, dataset splitting, model construction and evaluation. The role of the Greedy Algorithm's focuses in iteratively adjusting hyperparameters to minimize the Root Mean Squared Error (RMSE) metric, resulting in refining the model's predictive accuracy. The outcomes reveal that the integrated Greedy Algorithm significantly enhances the model's accuracy in predicting stock prices, indicating its potential application in various scenarios requiring precise time series forecasting.
Application of Convolutional Neural Networks to Spoken Words Evaluation Based on Lip Movements without Accompanying Sound Signal
This paper proposes an approach to evaluate spoken words based on lip movements without accompanying sound signals using convolutional neural networks. The main goal of this research is to prove the efficiency of neural networks in the field, where all data is received from an array of images. The modeling and the hypotheses are validated based on the results obtained for a specific case study. Our study reports on speech recognition from only a sequence of images provided, where all crucial data and features are extracted, processed, and used in a model to create artificial consciousness.
Copyright (c) 2022 Journal of Computer and Forensic Sciences
Non-Technical Losses Detection in Power System
The digitization of distribution networks enables the collection of big data from which it is necessary to draw conclusions and detect anomalies among electricity consumers. This paper explains methodologies to detect non-technical losses, commercial losses, and electricity theft. Based on monthly electricity consumption measurements, possible and prevalent cases of anomalies and theft among consumers are identified. Indicators that can detect anomalies have been proposed for such types of load diagrams. The sensitivity of the indicators to different types of consumers was analyzed. The applicability of this methodology was examined for a set of real measurements, and its advantages were pointed out. This concept represents a good recommendation, as it is possible to observe and detect irregularities in electricity consumption.
Performance Modeling of File System Pairs in Hypervisor-Based Virtual Environment Applied on KVM Hypervisor Case Study
This paper proposed an approach to mathematical modeling of the file system performance in a hypervisor-based virtual environment, with special focus on the file system pair interactions. The main goal of this research is to conduct an in-depth analysis of the filesystem pair behavior with respect to the performance costs originating from the employed technologies, such as H-Trees, B-Trees and Copy-on-Write/Overwrite update method, and different application workload types. The modeling provides a collection of hypotheses about the expected behavior. The modeling and the hypotheses are validated based on the results obtained for a specific case study. Our study reports on a file system performance comparison in the context of KVM hypervisor-based full hardware virtualization, application-level benchmarking, and 64-bit Linux filesystems Ext4, XFS, and Btrfs. The Filebench benchmark tool is applied for comprehensive testing of the filesystem performance under fair-play conditions. According to the obtained results, we provide a set of recommendations (i.e., a Knowledge Data Base) for optimal filesystem pair selection for the KVM hypervisor. Finally, it is important to note that the proposed modeling is also applicable to other hypervisor-based virtualizations.
Towards Hybrid Supercomputing Architectures
In light of recent work on combining control-flow and dataflow architectures on the same chip die, a new architecture based on an asymmetric multicore processor is proposed. The control-flow architectures are described as a most commonly used computer architecture today. Both multicore and manycore architectures are explained, as they are based on the same principles. A dataflow computing model assumes that data input flows through hardware as either a software or hardware dataflow implementation. In software dataflow, processors based on the control-flow paradigm process tasks based on their availability from the same queue (if there are any). In hardware dataflow architectures, the hardware is configured for a particular algorithm, and data input is streamed into the hardware, and the output is streamed back to the multicore processor for further processing. Hardware dataflow architectures are usually implemented with FPGAs. Hybrid architectures employ asymmetric multicore and manycore computer architectures that are based on the control-flow and hardware dataflow architecture, all combined on the same chip die. Advantages include faster processing time, lower power consumption (and heating), and less space needed for the hardware.
Comparative Performance Evaluation of Suboptimal Binary Search Trees
Three relevant types of suboptimal binary search trees are comparatively evaluated in this paper: two well-known representatives of height-balanced approaches (the AVL and red-black trees) and a popular self-adjusting splay tree. After a brief theoretical background, an evaluation method was described that employs a suitable synthetic workload method capable of producing diverse desired workload characteristics (different distributions and ranges of key values, varying input sequence lengths, etc.). Evaluation analysis was conducted for search, insert, and delete operations separately for each particular type and in appropriate combinations. Experimental results for an average operation cost as well as for tree maintenance cost are comparatively presented and carefully discussed. Finally, the suggested favorable conditions for application of each tree type are summarized.
An Application of Machine Learning Methods for Anomaly Detection in Internet Advertising
This research deals certain issues regarding downloading data from the Internet, i.e. Internet page advertising, and certain mechanisms to take care of the integrity of the data that is put into the dedicated processing context afterwards. The work also relates to e-commerce as well as to the economy in general, as some advertising scenarios provide high error-rates with pricing, which may be unacceptable in various scenarios, such as renting or selling a home. This paper presents a brief overview of the outlier detection methods and machine learning-based classifiers which are used to determine the number of anomalies in the analyzed dataset. This work contributes to the operation of the organizations which are dealing with data accuracy and integrity, such as home renting or selling agencies.
Copyright (c) 2023 University of Criminal Investigation and Police Studies, Belgrade, Serbia
Speech Enhancement by CycleGAN Using Feature Map Regularization
Highly promising speech enhancement results are recently obtained using an unsupervised CycleGAN approach, comparable to paired dataset neural network approach. However, very often, only a limited amount of noisy speech data is available. Therefore, a semi-supervised CycleGAN approach has been proposed, relying on augmented data samples. Another feature map regularized CycleGAN approach has also been proposed and applied in an image-style translation task, obtaining significant improvements on several standard databases. The feature map regularized CycleGAN approach is combined with the aforementioned semi-supervised learning approach and applied within a speech enhancement task. Significant improvements are obtained in terms of several standard measures using the proposed algorithm in comparison with the baseline algorithm as well as the augmented CycleGAN approach.
A system architecture for preventing social engineering attacks via e-mail
Modern business and the expansion of Internet technology have caused a great growth in communication via electronic mail. Bearing in mind that the weakest link of any system is the person himself, it is precisely in this part of the system that the greatest danger of unauthorized access to ICT resources is recognized[6]. For this reason, the greatest attention regarding the protection of ICT systems should be focused on the users themselves and preventive response to phishing campaigns as the most common form of cyber attack. This paper will present the system architecture for preventive response to phishing campaigns. The architecture itself, which will be explained in more detail later in the text, consists of several different modules integrated into a whole. First, a sender analysis module, which would be based on the blacklist principle. Next, an email attachment analysis module, which would perform the functions of static and dynamic analysis of potentially malicious attachments. A link analysis module, which would include the application of CORTEX, an open source intelligence service, and finally, a text analysis module, based on statistical models. Keywords: phishing, Cortex; analysis, cyber attack
iThrust News Certificate: A Blockchain-Based Solution for News Verification and Reputation Management
The proliferation of fake news and misinformation in the digital era poses a significant challenge to news organizations and content creators. In this paper, we introduce iThrust News Certificate, the architecture of an online blockchain-based solution designed to combat fake news, enhance news verification, and maintain reputation within the media ecosystem. Unlike previous attempts, iThrust News Certificate focuses on user-friendly features while ensuring transparency and reliability.
A verifiable model of a minimal market operating sequentially, with price and time discrete
This research presents a minimal computational market model, i.e.,a model of a trading venue, with sequential order matching, in a declarative style, and proceeds to demonstrate how some fundamental properties can be formally proved. It is a challenging task to formally certify properties for a fundamental system in any realm of human endeavor, especially for systems with infinite state space. With the recent development of theoretical frameworks based on formal logic, it is now possible (albeit very difficult) to both formalize and reason about an object system in the same language. This research derives from the previous research presented in [1], and represents a simplification to obtain a minimal model. The computational model of a minimal market, presented here in a declarative style, is of importance from the perspective of both market design and verification.
Copyright (c) 2023 Journal of Computer and Forensic Sciences