Computer Science
Loading...
ISSN 1508-2806
e-ISSN: 2300-7036
Issue Date
2021
Volume
Vol. 22
Number
No. 2
Description
Reviewed by: Augusto Ciuffoletti, Lukasz Czekierda, Sebastian Ernst, Rafał Wojciech Grzeszczuk, Mateusz Jarosz, Mohamed Tahar Kimour, Radosław Łazarz, Jerzy Pejaś, Kamil Pietak, Sur Ramkumar, Paweł Topa, Slawomir Zielinski
Journal Volume
Computer Science
Vol. 22 (2021)
Projects
Pages
Articles
A density-based method for the identification of disjoint and non-disjoint clusters with arbitrary and non-spherical shapes
(Wydawnictwa AGH, 2021) Ben Ncir, Chiheb-Eddine
The ability of clustering methods to build both disjoint and non-disjoint partitionings of data has become an important issue in unsupervised learning. Although this problem has been studied during the last decades resulting in several proposed overlapping clustering methods in the literature, most of existing methods fail to look for clusters having arbitrary and non-spherical shapes. In addition, most of these existing methods require to pre-configure the number of clusters in prior, which is not a trivial task in real life application of clustering. To solve all these issues, we propose in this work a new density based overlapping clustering method, referred to as OC-DD, which is able to detect both disjoint and non-disjoint partitioning even when boundaries between clusters have complex separations with arbitrary forms and shapes. The proposed method is based on density and distances to detect highly dense regions and connected groups in data without the necessity to pre-configure the number of clusters. Experiments performed on artificial and real multi-labeled datasets have shown the effectiveness of the proposed method compared to the existing ones.
A novel approach to automated behavioral diagram assessment using label similarity and subgraph edit distance
(Wydawnictwa AGH, 2021) Fauzan, Reza; Siahaan, Daniel Oranova; Rochimah, Siti; Triandini, Evi
The Unified Modeling Language (UML) is one of the standard languages that are used in modeling software, therefore, UML is widely taught in many universities. Generally, teachers assign students to build UML diagram designs based on a predetermined project, however, the assessment of such assignments can be challenging, and teachers may be inconsistent in assessing their students’ answers. Thus, automated UML diagram assessment becomes essential to maintaining assessment consistency. This study uses a behavioral diagram as the object of research, since it is a commonly taught UML diagram. The behavioral diagram can show a dynamic view of the software. This study proposes a new approach to automatically assessing the similarity of behavior diagrams as reliably as experts do. We divide the assessment into two portions: semantic assessment, and structural assessment. Label similarity is used to calculate semantic assessment, while subgraph edit distance is used to calculate structural assessment. The results suggest that the proposed approach is as reliable as an expert in assessing the similarity between two behavior diagrams. The observed agreement value suggests a strong agreement between the use of experts and the proposed approach.
A UML 2.0 activity diagrams/csp integrated approach for modeling and verification of software systems
(Wydawnictwa AGH, 2021) Elmansouri, Raida; Meghzili, Said; Chaoui, Allaoua
This paper proposes an approach that integrates UML 2.0 Activity Diagrams (UML2-ADs) and the communicating sequential process (CSP) for modeling and verifying software systems. A UML2-AD is used for modeling a software system, while a CSP is used for verification purposes. The proposed approach consists of another way of transforming UML2-AD models to CSP models. It also focuses on checking the correctness of some properties of the transformation itself. These properties are specified using linear temporal Logic (LTL) and verified using the GROOVE model checker. This approach is based on model -driven engineering (MDE). The meta-modeling is realized using the AToMPM tool, while the model transformation and the correctness of its properties are realized using the GROOVE tool. Finally, we illustrated this approach through a case study.
Character frequency-based approach for searching for substrings of circular patterns and their conjugates in online text
(Wydawnictwa AGH, 2021) Prasad, Vinod
A fundamental problem in computational biology is dealing with circular patterns. The problem consists of finding a pattern and its rotations in a database. In this paper, we present two online algorithms. The first algorithm reports all of the substrings (factors) of a given pattern in an online text. Then, without losing efficiency, we extend the algorithm to process the circular rotations of the pattern. For a given pattern $P$ of size $M$ and a text $T$ of size $N$, the extended algorithm reports all of the locations in the text where a substring of $P_c$ is found where $P_c$ is one of the rotations of $P$. For an alphabet size σ using $O(M)$ space, the desired goals are achieved in an average $O(MN/ \sigma)$ time, which is $O(N)$ for all patterns with length $M \leq \sigma$. Traditional string-processing algorithms make use of advanced data structures such as suffix trees and automaton. The experimental results we have provided show that basic data structures such as arrays can be used in text-processing algorithms without compromising efficiency.
SARED: self-adaptive active queue management scheme for improving quality of service in network systems
(Wydawnictwa AGH, 2021) Adamu, Aminu; Surajo, Yusuf; Jafar, Muhammad T.
Considering the phenomenal growth of network systems, congestion remains a threat to the quality of the service provided in such systems, hence, research on congestion control is still relevant. The Internet research community regards active queue management (AQM) as an effective approach for addressing congestion in network systems. Most of the existing AQM schemes possess static drop patterns and lack a self-adaptation mechanism, as such they do not work well for networks where the traffic load fluctuates. This paper proposes a self-adaptive random early detection (SARED) scheme that smartly adapts its drop pattern based on a current network’s traffic load in order to maintain improved and stable performance. Under light- to moderate-load conditions, SARED operates in nonlinear modes in order to maximize utilization and throughput, while it switches to a linear mode in order to avoid forced drops and congestion under high-load conditions. Our conducted experiments revealed that SARED provides optimal performance regardless of the condition of the traffic load.