-------- Forwarded Message -------- Subject: IJBDI latest issue published (Vol. 7, No. 3, 2020) and Call-For-Papers of 2021 Thematic Issues Date: Tue, 29 Sep 2020 18:27:22 +0800 From: CSPlus csplus@asia.edu.tw Reply-To: CSPlus csplus@asia.edu.tw To: neumann@wu-wien.ac.at

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Dear Distinguished Colleagues,

The International Journal of Big Data Intelligence (IJBDI) delighted to announce the publication of the latest issue. We hope these papers will be appealing to research, academia, and industry experts and inspire them for further advancements in the several existing and upcoming areas of data science.

=========================== IJBDI Vol. 7 No. 3 (2020) =========================== https://www.inderscience.com/info/inarticletoc.php?jcode=ijbdi&year=2020...

Article #1 Title: Transaction sampling algorithms for real-time crypto block dependability Authors: Abhilash Kancharla; Hyeyoung Kim; Nohpill Park Int. J. of Big Data Intelligence, Vol. 7, No. 3, 2020, pp.127 - 136

Abstract: This paper presents various transaction sampling algorithms for the proposed real-time crypto computing, and analytical model to assure their dependability under stringent real-time requirement. Efficacy of the algorithms is assessed in terms of the block dependability that expresses the probability for the pending transactions to be posted within the current or the target block delay. Algorithms on prioritising and sampling transactions from pool, to facilitate execution of those transactions within their deadline requirements, such as normal, random, sorted, and stratified, are proposed and simulated. Performance variables such as the number of pending transactions, average speed, gas fees, deadlines, number of miners, are identified and taken into the block dependability in order to reveal the influence of those variables. Extensive parametric simulation results are presented and discussed in the cases of the random and sorted transaction sampling algorithms along with a prototype built based on the Ethereum open source.

Keywords: blockchain; Ethereum; real-time; dependability; crypto computing.

Article #2 Title: DeepICU: imbalanced classification by using deep neural networks for network intrusion detection Authors: Allen Yang; Boxiang Dong; Dawei Li; Weifeng Sun; Bharath K. Samanthula Int. J. of Big Data Intelligence, Vol. 7, No. 3, 2020, pp.137 - 147

Abstract: Cyber intrusions are becoming more commonplace, more dangerous, and more sophisticated. Therefore, there is a desperate need for a robust intrusion detection system. In a healthy network environment, a majority of the connections are initiated by benign behaviours. Despite a wide variety of attacks, they only occupy a limited fraction of the observed network traffic. The imbalanced class distribution implicitly forces conventional classifiers to be biased toward the majority/benign class, thus leave many attack incidents undetected. In this paper, we design a new intrusion detection system named DeepICU based on deep neural networks. To address the class imbalance issue, we design two novel loss functions, i.e., attack-sharing loss and attack-discrete loss, that can effectively move the decision boundary towards the attack classes. Extensive experimental results on three benchmark datasets demonstrate the high detection accuracy of DeepICU. In particular, compared with eight state-of-the-art approaches, DeepICU always provides the best class-balanced accuracy.

Keywords: intrusion detection; deep learning; imbalanced classification; hard sample mining.

Article #3 Title: Optimised parallel implementation with dynamic programming technique for the multiple sequence alignment Authors: T. Gururaj; G.M. Siddesh Int. J. of Big Data Intelligence,Vol. 7, No. 3, 2020, pp.148 - 156

Abstract: Gene sequencing techniques are very useful in analysing various diseases, especially cancer. The various techniques have been applied for the gene sequence for the effective analysis. These technique help also in reducing the computation time. Most existing methods are of low efficiency in the gene sequence alignment due to lack of proper technique to reduce the gap penalty. In this research, the optimised Needleman-Wunsch (ONW) algorithm is applied for multiple sequence alignment (MSA). The ONW technique uses Needleman-Wunsch (NW) algorithm in parallel implementation for multiple genes. The dynamic programming technique such as the backtracking algorithm is applied for reducing the gap penalty in the gene alignment. The proposed ONW algorithm is applied in the case study and being analysed for its performance. This proves that the proposed ONW algorithm has higher performance compared to the other existing method in the MSA techniques. The proposed method has an average similarity of 88.85%, while the existing method has a similarity of 60.23%.

Keywords: backtracking algorithm; gap penalty; multiple sequence alignment; MSA; optimised Needleman-Wunsch algorithm; parallel implementation.

Article #4 Title: Distributed log management for dynamically changing computing environments on cloud Authors: Takayuki Kushida Int. J. of Big Data Intelligence, Vol. 7, No. 3, 2020, pp.157 - 168

Abstract: The cloud logging service is a core component for the operation and management of the production system. The service is usually a central server deployment whereby the dedicated central servers accept all log messages from leaf computing nodes. As the number of applications and solutions on the cloud changes dynamically, the amount of log messages that are forwarded to the logging service is also changed. The paper proposes a distributed logging service (DLS) that distributes log messages to multiple leaf computing nodes. No central server is required to manage the logging service. DLS also provides alert notifications, authentication, lifetime management and resilience, which are required for the production system. Results of an evaluation of the emulated environment show that DLS is suitable for use with applications and solutions which are used for production usages.

Keywords: distributed log; logging service; log management; cloud management; distributed hash table; DHT.

Article #5 Title: A survey about legible Arabic fonts for young readers Authors: Anoual El Kah; Abdelhak Lakhouaja Int. J. of Big Data Intelligence, Vol. 7, No. 3, 2020, pp.169 - 175

Abstract: Reading is an interconnected cognitive process including recognition and comprehension. The objective of the reading act could not be achieved unless the text is legible enough to interpret. For that reason, legibility is crucial for the reading mechanism, it will affect reading speed and the recognition of the graphs in the right way. Based on the fact that fonts and the way the text is presented influence children's reading performance and fluency, the current paper investigates different Arabic fonts in order to determine the optimal font for a fluent reading for children with a low rate of errors in both printed and on-screen texts. This study recruits 33 primary Moroccan school students of third grade and investigates the reading fluency and error rates for five Arabic font types. This paper recommends, as a result, the use of Simplified Arabic font for reducing reading errors due to graphs presentation for either printed or on-screen texts.

Keywords: reading; legibility; Arabic language; fonts; primary schools; Simplified Arabic.

=== Call For Papers - 2021 Thematic Issues ===

Big Data Intelligence for Medical AI and Healthcare (Submission deadline Nov. 30, 2020) Big Data Intelligence for 5G Applications and enhanced connectivity (Submission deadline Dec. 31, 2020) Big Data Intelligence for Smart Cities, Transportation and AIoT (Submission deadline Jan. 31, 2021) Big Data Intelligence for Extended Reality, Smart Education and Multimedia (Submission deadline Feb. 28, 2021)

=== About IJBDI ===

International Journal of Big Data Intelligence (IJBDI) is a peer reviewed journal publishing original and high-quality articles covering a wide range of topics in big data intelligence. IJBDI provides a rapid forum for the dissemination of original research as well as review/survey articles. The journal has a distinguished editorial board with extensive academic qualifications, ensuring high scientific standards.

The IJBDI invites renowned researchers from various branches of the fields to submit manuscripts for publication in the journal. Accepted papers of IJBDI will undergo language copyediting, typesetting, and reference validation in order to provide the highest publication quality. The average reviewing process is about 10 weeks.

Note: There is no submission or publication fees for manuscripts submitted to the International Journal of Big Data Intelligence (IJBDI).

Journal homepage: https://www.inderscience.com/jhome.php?jcode=ijbdi

Submission Portal: http://www.indersciencesubmissions.com

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