Acta Universitatis Sapientiae, Informatica Feed

Applications of edge analytics: a systematic review

Tue, 12 Dec 2023 00:00:00 GMT

With the development and expansion of the Internet of Things, computing at the edge is becoming increasingly important, especially for applications where real-time response is important. In this paper, we made a systematic review of the literature on analytics at the edge. We extracted data from 40 selected primary relevant studies from the complete set of 419 papers retrieved from scientific databases. In our analysis of the full text of every primary study we investigated: temporal distribution of primary studies, publication types, domain and application areas of the primary papers, used machine learning and deep learning methods. We also elaborated on the main themes of the primary studies and recommended some possible interesting future research possibilities.

Computing closeness for some graphs

Tue, 12 Dec 2023 00:00:00 GMT

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.

Enhancing healthcare services recommendation through sentiment analysis

Tue, 12 Dec 2023 00:00:00 GMT

As technology advances, most people use social media sites like Twitter, Facebook, and Flickr to share information and communicate with others. The volume of free-text data is growing daily due to the widespread use of these social media platforms. These platforms contain a substantial amount of unstructured information. Patient opinions expressed on social media platforms play a significant role in healthcare improvement and impact health-related policymaking. In this research, we introduce a machine learning approach for the optimal identification of healthcare-related features. This approach is based on a novel synthetic method. Additionally, we employ an entropy-based technique to classify free-text comments from hospital data into positive, negative or neutral. The experimental results and evaluations show 85%, 82.3%, 78.2% and 87% accuracy between ratings of health care. We observed that there is a minor association between our technique, expert opinion and patient interviews. Through the use of machine learning techniques, we achieve an accuracy level that suggests we are capable of providing an accurate and reasonable assessment of the ideal healthcare center for a patient. Our proposed novel framework predicts the healthcare experience at hospitals based on patient reviews posted on social media. This innovative approach outperforms traditional methods, such as surveys and expert opinions.

A generalized fuzzy-possibilistic c-means clustering algorithm

Tue, 12 Dec 2023 00:00:00 GMT

The so-called fuzzy-possibilistic c-means (FPCM) algorithm was introduced as an early mixed-partition method aiming to eliminate some adverse effects present in the behavior of the fuzzy c-means (FCM) and the possibilistic c-means (PCM) algorithms. A great advantage of FPCM was the low number of its parameters, as it eliminated the possibilistic penalty terms used by PCM. Unfortunately, FPCM in its original formulation also has a weak point: the strength of the possibilistic term is in inverse proportion with the number of clustered data items, which makes FPCM act like FCM when clustering large sets of data. This paper proposes a modification of the FPCM algorithm by introducing an extra coefficient into the possibilistic term that allows us to control the strength of the possibilistic effect within the mixture model. The modified clustering model will be referred to as generalized FPCM, since a certain value of the extra parameter reduces it to the original FPCM, or in other words, FPCM is a special case of the proposed algorithm. The proposed method is evaluated using noise-free and noisy data as well.

Formal modeling of multi-viewpoint ontology alignment by mappings composition

Tue, 12 Dec 2023 00:00:00 GMT

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.

Methods for the graph realization problem

Tue, 12 Dec 2023 00:00:00 GMT

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.

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

Tue, 12 Dec 2023 00:00:00 GMT

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.

Average distance colouring of graphs

Tue, 12 Dec 2023 00:00:00 GMT

For a graph G with n vertices, average distance µ(G) is the ratio of sum of the lengths of the shortest paths between all pairs of vertices to the number of edges in a complete graph on n vertices. In this paper, we introduce average distance colouring and find the average distance colouring number of certain classes of graphs.

The eccentricity-based topological indices

Tue, 12 Dec 2023 00:00:00 GMT

The aim of this paper is to obtain some relationships between eccentricity-based topological indices as the eccentric connectivity, connective eccentricity, total eccentricity, second Zagreb eccentricity, first Zagreb eccentricity connectivity, first eccentricity connectivity and first Zagreb eccentricity connectivity of a simple connected graph.

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

Tue, 12 Dec 2023 00:00:00 GMT

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.Background: 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.Materials and Methods: 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.Results: 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.Conclusions: 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.

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

Tue, 12 Dec 2023 00:00:00 GMT

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.

E-super arithmetic graceful labelling of Hi(m, m), Hi(1) (m, m) and chain of even cycles

Tue, 08 Aug 2023 00:00:00 GMT

E-super arithmetic graceful labelling of a graph G is a bijection f from the union of the vertex set and edge set to the set of positive integers (1, 2, 3, … |V(G) ∪ E(G)|) such that the edges have the labels from the set {1, 2, 3, …, |E(G)|} and the induced mapping f* given by f* (uv) = f(u) + f(v) − f(uv) for uv ∈ E(G) has the range {|V(G) ∪ E(G)| + 1, |V(G) ∪ E(G)| + 2, …, |V(G)| + 2|E(G)|}In this paper we prove that Hi(m, m) and Hi(1) (m, m) and chain of even cycles C4,n, C6,n are E-super arithmetic graceful.

Eccentric connectivity index in transformation graph Gxy+

Tue, 08 Aug 2023 00:00:00 GMT

Let G be a connected graph with vertex set V(G)and edge set E(G). The eccentric connectivity index of G is defined as ∑ν∈V(G)ec(ν) deg(ν) \sum\limits_{\nu\in{\rm{V}}\left({\rm{G}}\right)}{{\rm{ec}}\left(\nu\right)\,{\rm{deg}}\left(\nu\right)} where ec(v) the eccentricity of a vertex v and deg(v)is its degree and denoted by ɛc(G). In this paper, we investigate the eccentric connectivity index of the transformation graph Gxy+.

Explainable patch-level histopathology tissue type detection with bag-of-local-features models and data augmentation

Tue, 08 Aug 2023 00:00:00 GMT

Automatic detection of tissue types on whole-slide images (WSI) is an important task in computational histopathology that can be solved with convolutional neural networks (CNN) with high accuracy. However, the black-box nature of CNNs rightfully raises concerns about using them for this task. In this paper, we reformulate the task of tissue type detection to multiple binary classification problems to simplify the justification of model decisions. We propose an adapted Bag-of-local-Features interpretable CNN for solving this problem, which we train on eight newly introduced binary tissue classification datasets. The performance of the model is evaluated simultaneously with its decision-making process using logit heatmaps. Our model achieves better performance than its non-interpretable counterparts, while also being able to provide human-readable justification for decisions. Furthermore, the problem of data scarcity in computational histopathology is accounted for by using data augmentation techniques to improve both the performance and even the validity of model decisions. The source code and binary datasets can be accessed at: https://github.com/galigergergo/BolFTissueDetect.

On connectivity of the semi-splitting block graph of a graph

Tue, 08 Aug 2023 00:00:00 GMT

A graph G is said to be a semi-splitting block graph if there exists a graph H such that SB(H) ≌ G. This paper establishes a characterisation of semi-splitting block graphs based on the partition of the vertex set of G. The vertex (edge) connectivity and p-connectedness (p-edge connectedness) of SB(G) are examined. For all integers a, b with 1 < a < b, the existence of the graph G for which κ (G) = a, κ (SB(G)) = b and λ (G) = a, λ (SB(G)) = b are proved independently. The characterization of graphs with κ(SB(G)) = κ (G) and a necessary condition for graphs with κ (SB(G)) = λ (SB(G)) are achieved.

Textual outlier detection with an unsupervised method using text similarity and density peak

Tue, 08 Aug 2023 00:00:00 GMT

Text mining is an intriguing area of research, considering there is an abundance of text across the Internet and in social medias. Nevertheless outliers pose a challenge for textual data processing. The ability to identify this sort of irrelevant input is consequently crucial in developing high-performance models. In this paper, a novel unsupervised method for identifying outliers in text data is proposed. In order to spot outliers, we concentrate on the degree of similarity between any two documents and the density of related documents that might support integrated clustering throughout processing. To compare the e ectiveness of our proposed approach with alternative classification techniques, we performed a number of experiments on a real dataset. Experimental findings demonstrate that the suggested model can obtain accuracy greater than 98% and performs better than the other existing algorithms.

On the spread of the distance signless Laplacian matrix of a graph

Tue, 08 Aug 2023 00:00:00 GMT

Let G be a connected graph with n vertices, m edges. The distance signless Laplacian matrix DQ(G) is defined as DQ(G) = Diag(Tr(G)) + D(G), where Diag(Tr(G)) is the diagonal matrix of vertex transmissions and D(G) is the distance matrix of G. The distance signless Laplacian eigenvalues of G are the eigenvalues of DQ(G) and are denoted by δ1Q(G), δ2Q(G), ..., δnQ(G). δ1Q is called the distance signless Laplacian spectral radius of DQ(G). In this paper, we obtain upper and lower bounds for SDQ (G) in terms of the Wiener index, the transmission degree and the order of the graph.

Some relations between energy and Seidel energy of a graph

Tue, 08 Aug 2023 00:00:00 GMT

The energy E(G) of a graph G is the sum of the absolute values of eigenvalues of G and the Seidel energy ES(G) is the sum of the absolute values of eigenvalues of the Seidel matrix S of G. In this paper, some relations between the energy and Seidel energy of a graph in terms of different graph parameters are presented. Also, the inertia relations between the graph eigenvalue and Seidel eigenvalue of a graph are given. The results in this paper generalize some of the existing results.

SapiPin: Observations on PIN-code typing dynamics

Tue, 08 Aug 2023 00:00:00 GMT

In this paper, we report on PIN-code typing behaviour on touchscreen devices of 112 subjects. Detailed statistical analysis revealed that the major di erence between subjects is in inter-key latency. Key-press duration variations are insignificant compared to inter-key latency variations. Subjects were grouped into meaningful clusters using clustering. The resulting clusters were of slow, medium, and fast typists. The dataset was split randomly into two equal size subsets. The first subset was used to train different synthetic data generators, while the second subset was used to evaluate an authentication attack using the generated synthetic data. The evaluation showed that slow typists were the hardest to attack. Both the dataset and the software are publicly available at https://github.com/margitantal68/sapipin_paper.

On agglomeration-based rupture degree in networks and a heuristic algorithm

Tue, 08 Aug 2023 00:00:00 GMT

The rupture degree is one the most important vulnerability parameter in networks which are modelled by graphs. Let G(V (G),E (G)) be a simple undirected graph. The rupture degree is defined by r(G) = max{w(G–S )–|S |–m(G–S ):S ⊂ V (G) and w(G–S )>1} where m(G–S ) is the order of a largest connected component in G–S and w(G–S ) is the number of components of G–S, respectively. In this paper, we consider the vertex contraction method based on the network agglomeration operation for each vertex of G. Then, we have presented two graph vulnerability parameters called by agglomeration rupture degree and average lower agglomeration rupture degree. Furthermore, the exact values of them for some graph families are given. Finally, we proposed a polynomial time heuristic algorithm to obtain the values of agglomeration rupture degree and average