Computer Science
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ISSN 1508-2806
e-ISSN: 2300-7036
Issue Date
2021
Volume
Vol. 22
Number
No. 3
Description
Reviewed by: Aneta Poniszewska-Maranda, Roman Debski, Yossra Ha-Ali, Dariusz Barbucha, Chris Gniady, Konstanty Ryabinin, Ernest Jamro, Miroslaw Jablonski, Pawel Russek, Jose Antonio Garrido Nataren
Journal Volume
Computer Science
Vol. 22 (2021)
Projects
Pages
Articles
Population diversity in ant-inspired optimization algorithms
(Wydawnictwa AGH, 2021) Byrski, Aleksander; Węgrzyński, Krzysztof; Radwański, Wojciech; Starzec, Grażyna; Starzec, Mateusz; Bargiel, Monika; Urbańczyk, Aleksandra; Kisiel-Dorohinicki, Marek
Measuring the diversity in evolutionary algorithms that work in real-value search spaces is often computationally complex, but it is feasible, however, measuring the diversity in combinatorial domains is practically impossible. Nevertheless, in this paper we propose several practical and feasible diversitymeasurement techniques that are dedicated to ant colony optimization algorithms, leveraging the fact that we can focus on a pheromone table even though an analysis of the search space is at least an NP problem where the direct outcomes of the search are expressed and can be analyzed. Besides sketching out the algorithms, we apply them to several benchmark problems and discuss their efficacy.
Formal verification of extension of istar to support big data projects
(Wydawnictwa AGH, 2021) Djeddi, Chabane; Zarour, Nacer Eddine; Charrel, Pierre-Jean
Identifying all of the correct requirements of any system is fundamental for its success. These requirements need to be engineered with precision in the early phases. Principally, late correction costs are estimated to be more than 200 times greater than the cost of corrections during requirements engineering (RE), especially in the big data area due to its importance and characteristics. A deep analysis of the big data literature suggests that current RE methods do not support the elicitation of big data project requirements. In this research, we present BiStar (an extension of iStar) to undertake big data characteris tics such as volume, variety, etc. As a first step, some missing concepts are identified that are not supported by the current methods of RE. Next, BiStar is presented to take big data-specific characteristics into account while dealing with the requirements. To ensure the integrity property of BiStar, formal proofs are made by performing a Bigraph-based description on iStar and BiStar. Fi nally, iStar and BiStar are applied on the same exemplary scenario. BiStar shows promising results, so it is more efficient for eliciting big data project requirements.
Energy redistribution in autonomous hybridization of agent-based computing
(Wydawnictwa AGH, 2021) Godzik, Mateusz
Evolutionary multi-agent systems (EMAS) are very good at dealing with diffi cult, multi-dimensional problems. Research is currently underway to improve this algorithm, giving agents even more freedom not only to solve the problem, but also to make decisions about the behavior of the algorithm. One way is to hybridize this algorithm with other existing algorithms to create the Hybrid Evolutionary Multi Agent-System (HEMAS). Unfortunately, such connections generate problems in the form of unbalanced agent energy levels. One solution is to use an agent energy redistribution operator. The article presents three different proposals for such redistribution operators, compared them with each other and selected the best based on the results of numerous experiments.
Classification of traffic over collaborative IoT/cloud platforms using deep-learning recurrent LSTM
(Wydawnictwa AGH, 2021) Patil, Sonali A.; Raj, Arun L.
The Internet of Things (IoT) and cloud-based collaborative platforms have emerged as new infrastructures over the recent decades. The classification of network traffic in terms of benign and malevolent traffic is indispensable for IoT/cloud-based collaborative platforms for optimally utilizing channel capac ity for transmitting benign traffic and blocking malicious traffic. The traffic classification mechanism should be dynamic and capable enough for classifying network traffic in a quick manner so that malevolent traffic can be identified at earlier stages and benign traffic can be speedily channelized to the destined nodes. In this paper, we present a deep-learning recurrent LSTM RNet-based technique for classifying traffic over IoT/cloud platforms using the Word2Vec approach. Machine-learning techniques (MLTs) have also been employed for comparing the performance of these techniques with the proposed LSTM RNet classification method. In the proposed research work, network traffic is clas sified into three classes: Tor-Normal, NonTor-Normal, and NonTor-Malicious traffic. The research outcome shows that the proposed LSTM RNet accurately classifies such traffic and also helps reduce network latency as well as enhance data transmission rates and network throughput.
SIGN detection and signed integer comparison for three-moduli SET {2n ±1, 2n+k}
(Wydawnictwa AGH, 2021) Torabi, Zeinab; Timarchi, Somayeh
Comparison, division, and sign detection are considered to be complicated op erations in a residue number system (RNS). A straightforward solution is to convert RNS numbers into binary formats and then perform complicated op erations using conventional binary operators. If efficient circuits are provided for comparison, division, and sign detection, the application of RNS can be extended to those cases that include these operations. For RNS comparison in three-moduli set $\tau = \{2^{n}-1, 2^{n+k}, 2^{n}+1\},(0 \leq k \leq n)$, we have found only one hardware realization. In this paper, an efficient RNS comparator is proposed for moduli set τ , which employs a sign-detection method and operates more efficiently than its counterparts. The proposed sign detector and comparator utilize dynamic range partitioning (DRP), which has been recently presented for unsigned RNS comparison. The delay and cost of the proposed comparator are lower than the previous works, which makes it appropriate for RNS applications with limited delay and cost.