<abstract><title style='display:none'>Abstract</title>In this paper we introduce a new, high-quality, dataset of images containing fruits. We also present the results of some numerical experiment for training a neural network to detect fruits. We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use such classifier.</abstract>

Fruit recognition from images using deep learning

<abstract>
<title style='display:none'>Abstract</title>
In this article the concept of domination in signed graphs is examined from an alternate perspective and a new definition of the same is introduced. A vertex subset D of a signed graph S is a dominating set, if for each vertex v not in D there exists a vertex u ∈ D such that the sign of the edge uv is positive. The domination number γ (S) of S is the minimum cardinality among all the dominating sets of S. We obtain certain bounds of γ (S) and present a necessary and su cient condition for a dominating set to be a minimal dominating set. Further, we characterise the signed graphs having small and large values for domination number.
</abstract>

On domination in signed graphs

<abstract><title style='display:none'>Abstract</title> The scientific knowledge is disseminated by research papers. Most of the research literature is copyrighted by publishers and avail- able only through paywalls. Recently, some websites offer most of the recent content for free. One of them is the controversial website Sci-Hub that enables access to more than 47 million pirated research papers. In April 2016, Science Magazine published an article on Sci-Hub activity over the period of six months and publicly released the Sci-Hub’s server log data. The mentioned paper aggregates the view that relies on all downloads and for all fields of study, but these findings might be hiding interesting patterns within computer science. The mentioned Sci-Hub log data was used in this paper to analyse downloads of computer science papers based on DBLP’s list of computer science publications. The top downloads of computer science papers were analysed, together with the geographical location of Sci-Hub users, the most downloaded publishers, types of papers downloaded, and downloads of computer science papers per publication year. The results of this research can be used to improve legal access to the most relevant scientific repositories or journals for the computer science field. </abstract>

Analysis of Sci-Hub downloads of computer science papers

<abstract><title style='display:none'>Abstract</title>According to WHO estimates, 400 million people suffer from diabetes, and this number is likely to double by year 2030. Unfortunately, diabetes can have severe complications like glaucoma or retinopathy, which both can cause blindness. The main goal of our research is to provide an automated procedure that can detect retinopathy-related lesions of the retina from fundus images. This paper focuses on the segmentation of so-called white lesions of the retina that include hard and soft exudates. The established procedure consists of three main phases. The preprocessing step compensates the various luminosity patterns found in retinal images, using background and foreground pixel extraction and a data normalization operator similar to Z-transform. This is followed by a modified SLIC algorithm that provides homogeneous superpixels in the image. The final step is an ANN-based classification of pixels using fifteen features extracted from the neighborhood of the pixels taken from the equalized images and from the properties of the superpixel where the pixel belongs. The proposed methodology was tested using high-resolution fundus images originating from the IDRiD database. Pixelwise accuracy is characterized by a 54% Dice score in average, but the presence of exudates is detected with 94% precision.</abstract>

Automatic detection of hard and soft exudates from retinal fundus images

<abstract><title style='display:none'>Abstract</title>Many industries, for example automotive, have well defined product development process definitions and risk evaluation methods. The FMEA (Failure Mode and Effects Analysis) is a first line risk analysis method in design, which has been implemented in development and production since decades. Although the first applications were focusing on mechanical and electrical design and functionalities, today, software components are implemented in many modern vehicle systems. However, standards or industry specific associations do not specify any “best practice” how to design the <italic>interactions</italic> of multiple entities in one model. This case study focuses on modelling interconnections and on the improvement of the FMEA modelling process in the automotive. Selecting and grouping software components for the analysis is discussed, but software architect design patterns are excluded from the study.</abstract>

Improving the effectiveness of FMEA analysis in automotive – a case study

<abstract><title style='display:none'>Abstract</title>In this short survey and case study we want to present our research experience through the project developed by our team, that involves the building of a LEGO MINDSTORMS EV3 robotic arm and tracked robot car which mimics the motion of the human arm and legs. We used 3 interconnected LEGO MINDSTORMS EV3 bricks to reach the desired degrees of freedom. Using a Kinect sensor, the system detects the motion of the human user’s arm and creates the skeletal image of the arm. Coordinate geometry and different approximation methods are used to calculate the rotation angles between the bones connecting the joints. In our project the key is inverse kinematics, whitch makes use of the kinematics equations to determine the joint rotation parameters that provide a desired position for each of the robot’s end-effectors – arms and legs (wheels). The combined motion of the LEGO MINDSTORMS EV3 motors results in a complete robotic forward or backward motion and arm movement which is a perfect mimic of the human arm movement.</abstract>

Gesture-Driven LEGO robots

<abstract>
<title style='display:none'>Abstract</title>
The graph realization problem seeks an answer to how and under what conditions a graph can be constructed if we know the degrees of its vertices. The problem was widely studied by many authors and in many ways, but there are still new ideas and solutions. In this sense, the paper presents the known necessary and su cient conditions for realization with the description in pseudocode of the corresponding algorithms. Two cases to solve the realization problem are treated: finding one solution, and finding all solutions. In this latter case a parallel approach is presented too, and how to exclude isomorphic graphs from solutions. We are also discussing algorithms using binary integer programming and flow networks.
In the case of a bigraphical list with equal out- and in-degree sequences a modified Edmonds–Karp algorithm is presented such that the resulting graph will be always symmetric without containing loops. This algorithm solves the problem of graph realization in the case of undirected graphs using flow networks.
</abstract>

Methods for the graph realization problem

<abstract>
<title style='display:none'>Abstract</title>
We propose a formal approach based on Bigraphical Reactive Systems (BRS) to provide a formal modeling of multi-viewpoint ontology alignment by composition systems’ structure using bigraphs their dynamic behaviors using bigraphical reaction rules. In the first phase of this approach, we address the modeling of the static structure the dynamic behavior of multi-viewpoint ontology alignment systems. We show how bigraphs enable the description of the di erent multi-view point ontology entities. Furthermore, we define a set of bigraphical reaction rules to model the dynamic nature of the alignment. We introduce composition strategies to describe multi-viewpoint ontology alignment systems’ behaviors. Then, we present a case study on which we illustrate the application of our proposed approach. Finally, we combine the logical reflection of Maude language the hierarchical structure of the BRS to provide an executable formal model for multi-viewpoint ontology alignment by composition systems.
</abstract>

Formal modeling of multi-viewpoint ontology alignment by mappings composition

<abstract>
<title style='display:none'>Abstract</title>
Colposcopy imaging is pivotal in cervical cancer diagnosis, a major health concern for women. The computational challenge lies in accurate lesion recognition. A significant hindrance for many existing machine learning solutions is the scarcity of comprehensive training datasets.
To reduce this gap, we present AnnoCerv: a comprehensive dataset tailored for feature-driven and image-based colposcopy analysis. Distinctively, AnnoCerv include detailed segmentations, expert-backed colposcopic annotations and Swede scores, and a wide image variety including acetic acid, iodine, and green-filtered captures. This rich dataset supports the training of models for classifying and segmenting low-grade squamous intraepithelial lesions, detecting high-grade lesions, aiding colposcopy-guided biopsies, and predicting Swede scores – a crucial metric for medical assessments and treatment strategies.
To further assist researchers, our release includes code that demonstrates data handling and processing and exemplifies a simple feature extraction and classification technique.
</abstract>

AnnoCerv: A new dataset for feature-driven and image-based automated colposcopy analysis

<abstract>
<title style='display:none'>Abstract</title>
The analysis of networks involves several crucial parameters. In this paper, we consider the closeness parameter, which is based on the total distance between every pair of vertices. Initially, we delve into a discussion about the applicability of the closeness parameter to Mycielski graphs. Our findings are categorized based on the underlying graph’s diameter. The formula for calculating the closeness of a Mycielski graph is derived for graphs with a diameter of less than or equal to 4. Furthermore, we establish a sharp lower bound for the closeness of a Mycielski graph when the diameter of the underlying graph is greater than 4. To achieve this, the closeness of the Mycielski transformation of a path graph plays an important role. Additionally, leveraging the obtained results, we examine the closeness of a special planar construction composed of path and cycle graphs, as well as its Mycielski transformation.
</abstract>

Computing closeness for some graphs

<abstract>
<title style='display:none'>Abstract</title>
The paper examines the potential of artificial intelligence (AI) in parsing text and conducting sentiment analysis to identify early markers of mental health and neurodegenerative disorders. Through the analysis of textual data, we investigate whether AI can provide a noninvasive, continuous, and objective complement to traditional diagnostic practices.
<italic>Background</italic>: the early detection of mental health (such as depression, anxiety, psychotic disorders, Alzheimer’s disease and dementia) and neurodegenerative disorders (like Parkinson’s disease) remains a critical challenge in clinical practice. Traditional diagnostic methods rely on clinical evaluations that may be subjective and episodic. Recent advancements in AI and natural language processing (NLP) have opened new avenues for precognitive health assessments, suggesting that variations in language and expressed sentiments in written text can serve as potential biomarkers for these conditions.
<italic>Materials and Methods</italic>: the research used a dataset comprising various forms of textual data, including anonymized social media interactions, transcripts from patient interviews, and electronic health records. NLP algorithms were deployed to parse the text, and machine learning models were trained to identify language patterns and sentiment changes. The study also incorporated a sentiment analysis to gauge emotional expression, a key component of mental health diagnostics.
<italic>Results</italic>: the AI models were able to identify language use patterns and sentiment shifts that correlated with clinically validated instances of mental health symptoms and neurodegenerative conditions. Notably, the models detected an increased use of negative a ect words, a higher frequency of first-person singular pronouns, and a decrease in future tense in individuals with depression. For neurode-generative conditions, there was a notable decline in language complexity and semantic coherence over time.
<italic>Conclusions</italic>: the implemented pipeline of AI-parsed text and sentiment analysis appears to be a promising tool for the early detection and ongoing monitoring of mental health and neurodegenerative disorders. However, these methods are supplementary and cannot replace the nuanced clinical evaluation process. Future research must refine the AI algorithms to account for linguistic diversity and context, while also addressing ethical considerations regarding data use and privacy. The integration of AI tools in clinical settings necessitates a multidisciplinary approach, ensuring that technological advancements align with patient-centered care and ethical standards.
</abstract>

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

<abstract>
<title style='display:none'>Abstract</title>
After recent improvements brought the incremental compilation of large industrial test suites down to a few seconds, the first semantic checking of a project became one of the longest-running processes. As multi-core systems are now the standard, we derived a parallelisation using software engineering laws to improve the performance of semantic checking.
Our measurements show that even an outdated laptop is fast enough for daily use. The performance improvements came without performance regressions, and we can’t expect additional massive benefits even from infinitely scaling Cloud resources.
Companies should utilise cheaper machines that still o er enough performance for longer. This approach can help businesses increase profits, reduce electronic waste and promote sustainability while maintaining high-quality software development practices.
</abstract>

Parallelising semantic checking in an IDE: A way toward improving profits and sustainability, while maintaining high-quality software development

<abstract><title style='display:none'>Abstract</title>Several hundreds of thousand humans are diagnosed with brain cancer every year, and the majority dies within the next two years. The chances of survival could be easiest improved by early diagnosis. This is why there is a strong need for reliable algorithms that can detect the presence of gliomas in their early stage. While an automatic tumor detection algorithm can support a mass screening system, the precise segmentation of the tumor can assist medical staff at therapy planning and patient monitoring. This paper presents a random forest based procedure trained to segment gliomas in multispectral volumetric MRI records. Beside the four observed features, the proposed solution uses 100 further features extracted via morphological operations and Gabor wavelet filtering. A neighborhood-based post-processing was designed to regularize and improve the output of the classifier. The proposed algorithm was trained and tested separately with the 54 low-grade and 220 high-grade tumor volumes of the MICCAI BRATS 2016 training database. For both data sets, the achieved accuracy is characterized by an overall mean Dice score &gt; 83%, sensitivity &gt; 85%, and specificity &gt; 98%. The proposed method is likely to detect all gliomas larger than 10 mL.</abstract>

Low and high grade glioma segmentation in multispectral brain MRI data

<abstract><title style='display:none'>Abstract</title> For a graph G with vertex set V(G) = {v1, v2, . . . , vn}, the extended double cover G* is a bipartite graph with bipartition (X, Y), X = {x1, x2, . . . , xn} and Y = {y1, y2, . . . , yn}, where two vertices xi and yj are adjacent if and only if i = j or vi adjacent to vj in G. The double graph D[G] of G is a graph obtained by taking two copies of G and joining each vertex in one copy with the neighbors of corresponding vertex in another copy. In this paper we study energy and Laplacian energy of the graphs G* and D[G], L-spectra of Gk* the k-th iterated extended double cover of G. We obtain a formula for the number of spanning trees of G*. We also obtain some new families of equienergetic and L-equienergetic graphs.</abstract>

Eötvös Loránd University Faculty of Informatics Budapest

Energy, Laplacian energy of double graphs and new families of equienergetic graphs

<abstract><title style='display:none'>Abstract</title>Behavioural biometrics provides an extra layer of security for user authentication mechanisms. Among behavioural biometrics, mouse dynamics provides a non-intrusive layer of security. In this paper we propose a novel convolutional neural network for extracting the features from the time series of users’ mouse movements. The effect of two preprocessing methods on the performance of the proposed architecture were evaluated. Different training types of the model, namely transfer learning and training from scratch, were investigated. Results for both authentication and identification systems are reported. The Balabit public data set was used for performance evaluation, however for transfer learning we used the DFL data set. Comprehensive experimental evaluations suggest that our model performed better than other deep learning models. In addition, transfer learning contributed to the better performance of both identification and authentication systems.</abstract>

Mouse dynamics based user recognition using deep learning

<abstract><title style='display:none'>Abstract</title>Nowadays users leave numerous comments on different social networks, news portals, and forums. Some of the comments are toxic or abusive. Due to numbers of comments, it is unfeasible to manually moderate them, so most of the systems use some kind of automatic discovery of toxicity using machine learning models. In this work, we performed a systematic review of the state-of-the-art in toxic comment classification using machine learning methods. We extracted data from 31 selected primary relevant studies. First, we have investigated when and where the papers were published and their maturity level. In our analysis of every primary study we investigated: data set used, evaluation metric, used machine learning methods, classes of toxicity, and comment language. We finish our work with comprehensive list of gaps in current research and suggestions for future research themes related to online toxic comment classification problem.</abstract>

Machine learning methods for toxic comment classification: a systematic review

<abstract><title style='display:none'>Abstract</title>Time-series are ordered sequences of discrete-time data. Due to their temporal dimension, anomaly detection techniques used in time-series have to take into consideration time correlations and other time-related particularities. Generally, in order to evaluate the quality of an anomaly detection technique, the confusion matrix and its derived metrics such as precision and recall are used. These metrics, however, do not take this temporal dimension into consideration. In this paper, we propose three metrics that can be used to evaluate the quality of a classification, while accounting for the temporal dimension found in time-series data.</abstract>

Evaluation metrics for anomaly detection algorithms in time-series

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Acta Universitatis Sapientiae, Informatica

The Journal of Sapientia Hungarian University of Transylvania

Acta Universitatis Sapientiae Informatica publishes peer-reviewed, high-quality original papers and surveys in English, in all areas of Computer Science and allied fields. The papers must be...

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Acta Universitatis Sapientiae Informatica publishes peer-reviewed, high-quality original papers and surveys in English, in all areas of Computer Science and...