scholarly journals Editorial

2021 ◽  
Vol 27 (4) ◽  
pp. 323-323
Author(s):  
Christian Gütl
Keyword(s):  
Financial Support ◽  
Detection System ◽  
Heterogeneous Data ◽  
Spam Detection ◽  
Regular Expressions ◽  
Challenging Problem ◽  
Crucial Issue ◽  
High Quality ◽  
Distributed Information ◽  
Great Support

I am pleased to announce the fourth issue of 2021. As always, I would like to express my sincere appreciation for the great support that makes the continued publication of novel and high quality articles possible. Thus, I would like to thank all authors for their sound research contributions, the reviewers for their very helpful suggestions and the consortium members for their financial support. I would also like to report on further achievements regarding our new platform. We have successfully migrated all the information of the Board of Editors and we have also started to use the new review module. Due to the cooperation with Pensoft Inc., our new platform provider, we will also be able to offer review acknowledgment on the Publons portal in the future. In this regular issue, I am very pleased to introduce four accepted papers from three different countries and 14 involved authors. Martin Berglund, Brink van der Merwe, and Steyn van Litsenborgh from South Africa investigate in their article regular expressions which contain lookaheads in addition to the standard operators of union, concatenation, and Kleene star. Fairouz Fakhfakh, Slim Kallel and Saoussen Cheikhrouhou from Tunisia research and discuss in their work a crucial issue in modern distributed information systems, i.e. how to verify the correctness of Cloud and Fog systems based on formal verification. Marcia Henke, Eulanda Santos, Eduardo Souto, and Altair O. Santin from Brazil introduce their enhanced spam detection system which is based on analyzing the evolution of features. And finally, also from Brazil, Marcelo Aires Vieira, Elivaldo Lozer Fracalossi Ribeiro, Daniela Barreiro Claro, and Babacar Mane investigate the challenging problem of integrating heterogeneous DaaS and DBaaS sources and explore the Data Join (DJ) method for integrating heterogeneous data.

Spam Detection System Using Supervised ML

2021 ◽  
Author(s):  
Abhila B ◽  
Delphin Periyanayagi M ◽  
Koushika M ◽  
Mabel Nirmala Joseph ◽  
Dhanalakshmi R
Keyword(s):  
Detection System ◽  
Spam Detection

scholarly journals Data-driven versus self-similar parameterizations for Stochastic Advection by Lie Transport and Location Uncertainty

2019 ◽  
Author(s):  
Valentin Resseguier ◽  
Wei Pan ◽  
Baylor Fox-Kemper
Keyword(s):  
Fluid Dynamics ◽  
Heterogeneous Data ◽  
Data Driven ◽  
Accurate Data ◽  
High Quality ◽  
Ensemble Forecasts ◽  
Homogeneous Surface ◽  
Location Uncertainty ◽  
Physically Based ◽  
Self Similar

Abstract. Stochastic subgrid parameterizations enable ensemble forecasts of fluid dynamics systems and ultimately accurate data assimilation. Stochastic Advection by Lie Transport (SALT) and models under Location Uncertainty (LU) are recent and similar physically-based stochastic schemes. SALT dynamics conserve helicity whereas LU models conserve kinetic energy. After highlighting general similarities between LU and SALT frameworks, this paper focuses on their common challenge: the parameterization choice. We compare uncertainty quantification skills of a stationary heterogeneous data-driven parameterization and a non-stationary homogeneous self-similar parameterization. For stationary, homogeneous Surface Quasi-Geostrophic (SQG) turbulence, both parameterizations lead to high quality ensemble forecasts. This paper also discusses a heterogeneous adaptation of the homogeneous parameterization targeted at better simulation of strong straight buoyancy fronts.

Parent-child relationship quality and emotional adjustment among college students: The role of parental financial support

2020 ◽  
pp. 026540752096487
Author(s):  
Anna K. Lindell ◽  
Sarah E. Killoren ◽  
Nicole Campione-Barr
Keyword(s):  
College Students ◽  
Relationship Quality ◽  
Financial Support ◽  
Emotional Adjustment ◽  
Well Being ◽  
Anxiety Symptoms ◽  
High Quality ◽  
Parent Child ◽  
Quality Relationships

Many emerging adults experience increases in well-being as they exit adolescence, but college students are at particular risk for emotional adjustment problems, including depression and anxiety. Although receiving financial support from parents may reduce stress and aid emotional adjustment, not all parents are able to provide financial support. Maintaining high-quality relationships with parents may be particularly important for emotional adjustment in these instances. The present study examined whether the quality of parent-emerging adult relationships differed depending on level of parental financial support, and whether parental financial support moderated associations between relationship quality and emotional adjustment. Participants were 260 college students who completed questionnaires during their first and fourth year of college about the quality of their relationships with mothers and fathers, depressive and anxiety symptoms, and parental financial support. On average, parent-child relationships were high-quality, especially when parents provided more financial support. Furthermore, high-quality relationships with parents were related to fewer depressive and anxiety symptoms 3 years later for female students, especially when they received less financial support. However, high-quality relationships along with greater financial support was related to increased anxiety among male students. Results may help colleges and universities developing parent programming understand the nuanced implications of parental support for student mental health.

scholarly journals VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Hisham A. Kholidy ◽  
Abdelkarim Erradi
Keyword(s):  
Power Systems ◽  
Large Volume ◽  
High Speed ◽  
Detection System ◽  
Heterogeneous Data ◽  
Area Measurement ◽  
Volume Data ◽  
Detection Systems ◽  
Key Events ◽  
Reduction Approach

The Cypher Physical Power Systems (CPPS) became vital targets for intruders because of the large volume of high speed heterogeneous data provided from the Wide Area Measurement Systems (WAMS). The Nonnested Generalized Exemplars (NNGE) algorithm is one of the most accurate classification techniques that can work with such data of CPPS. However, NNGE algorithm tends to produce rules that test a large number of input features. This poses some problems for the large volume data and hinders the scalability of any detection system. In this paper, we introduce VHDRA, a Vertical and Horizontal Data Reduction Approach, to improve the classification accuracy and speed of the NNGE algorithm and reduce the computational resource consumption. VHDRA provides the following functionalities: (1) it vertically reduces the dataset features by selecting the most significant features and by reducing the NNGE’s hyperrectangles. (2) It horizontally reduces the size of data while preserving original key events and patterns within the datasets using an approach called STEM, State Tracking and Extraction Method. The experiments show that the overall performance of VHDRA using both the vertical and the horizontal reduction reduces the NNGE hyperrectangles by 29.06%, 37.34%, and 26.76% and improves the accuracy of the NNGE by 8.57%, 4.19%, and 3.78% using the Multi-, Binary, and Triple class datasets, respectively.

scholarly journals Table of Contents and Foreword

2013 ◽  
Vol 8 (2) ◽  
pp. 64-70
Author(s):  
Marcelo Antonio Pavanello ◽  
Fernando Gehm Moraes
Keyword(s):  
Financial Support ◽  
Extended Version ◽  
Valuable Contribution ◽  
High Quality ◽  
Scientific Level ◽  
Microelectronics Technology

In this issue of JICS some of the papers have been selected from the presented at SBMicro2012 (27th Symposium on Microelectronics Technology and Devices), which has been held in Brasília, Brazil, in 2012. Among the contributions presented at the s ymposium, only a few best rated were selected by the JICS Editorial Board and have been invited to submit an extended version to the Journal. These extended papers have passed through the usual reviewing process before acceptance. In addition to the best papers presented at the conference, spontaneous submissions passed through the usual reviewing process and have been accepted as regular papers. We would like to thank the authors for their effort in preparing these high quality papers, as well as the reviewers for their valuable contribution on paper evaluation and selection, which guarantees the scientific level of this issue. We sincerely hope that JICS readers will enjoy these contributions. We also would like to thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the financial support for this JICS Issue.Marcelo Antonio Pavanello - JICS Editor-in-chiefFernando Gehm Moraes - JICS Co-Editor

scholarly journals Spam Detection Framework using ML Algorithm

2020 ◽  
Vol 8 (6) ◽  
pp. 5326-5329
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Information Sharing ◽  
Detection System ◽  
Reliable Data ◽  
Machine Learning Algorithms ◽  
Spam Detection ◽  
Function Type ◽  
Use Of Social Media

The current use of social media has created incomparable amounts of social data, as it is a cheap and popular information sharing communication platform. Nowadays, a huge percentage of people depend on the accessible material on social networking in their choices (e.g. comments and suggestions about a subject or product). This feature on exchanging knowledge with a wide number of users has quickly prompted social spammers to exploit the network of confidence to distribute spam messages and support personal forums, advertising, phishing, scams and so on. Identifying these spammers and spam material is a hot subject of study, and while large amounts of experiments have recently been conducted to this end, so far the methodologies are only barely able to identify spam feedback, and none of them demonstrates the value of each derived function type. In this study, we have suggested a machine learning-based spam detection system that determines whether or not a specific message in the dataset is spam using a set of machine learning algorithms. Four main features have been used; including user-behavioral, user-linguistic, reviewbehavioral and review-linguistic, to improve the spam detection process and to gather reliable data

scholarly journals CONSTRUCTION OF SYSTEMS OF DETECTION OF INVASIONS INTO THE INFORMATI TON AND TELECOMMUNICATIONS NETWORK ON THE BASIS OF METHODS OF INTELLECTUAL DISTRIBUTION OF DATA

2020 ◽  
pp. 80-89
Author(s):  
S. Toliupa ◽  
O. Pliushch ◽  
I. Parhomenko
Keyword(s):  
Data Mining ◽  
Experimental Research ◽  
Intelligent Systems ◽  
Detection System ◽  
Attack Detection ◽  
Information Network ◽  
Distributed Information ◽  
Network Attack ◽  
Detection Model ◽  
Detection Systems

The article proposes a combinatorial construction of a network attack detection system based on selected methods of data mining and conducts experimental research that confirms the effectiveness of the created detection model to protect the distributed information network. Experiments with a software prototype showed the high quality of detection of network attacks and proved the correctness of the choice of methods of data mining and the applicability of the developed techniques. The state of security of information and telecommunication systems against cyberattacks is analyzed, which allowed to draw conclusions that to ensure the security of cyberspace it is necessary to implement a set of systems and protection mechanisms, namely systems: delimitation of user access; firewall; cryptographic protection of information; virtual private networks; anti-virus protection of ITS elements; detection and prevention of intrusions; authentication, authorization and audit; data loss prevention; security and event management; security management. An analysis of publications of domestic and foreign experts, which summarizes: experience in building attack detection systems, their disadvantages and advantages; of attack and intrusion detection systems based on the use of intelligent systems. Based on the results of the review, proposals were formed on: construction of network attack detection systems on the basis of selected methods of data mining and experimental research, which confirms the effectiveness of the created detection model for the protection of the distributed information network.

scholarly journals Characteristics of grasslands in the Polish Sudetes in view of fodder production potential and grassland protection

2011 ◽  
Vol 15 (1) ◽  
pp. 29-40 ◽  
Author(s):  
Longina Nadolna ◽  
Marta Żyszkowska
Keyword(s):  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Financial Support ◽  
Economic Importance ◽  
Production Potential ◽  
High Quality ◽  
Fodder Production ◽  
Productive Potential ◽  
The Common ◽  
The Eu

Characteristics of grasslands in the Polish Sudetes in view of fodder production potential and grassland protection Mountain region of the Sudetes has a productive potential that allows for obtaining high quality fodder from grasslands. The potential is facilitated by the fact that 94% of grassland area is situated below the elevation of 700 m a.s.l. and 75% of them on slopes inclined less 9°. Meadows and pastures of the highest economic importance cover an area larger than 50 000 ha, most of them situated in the Kłodzko district. The analysis of ruminant and horse stock in 2008 revealed that the possibilities of fodder production largely exceeded the demands. Productive use of meadows and pastures loses importance, particularly in the Jelenia Góra district, despite financial support within the Common Agricultural Policy of the EU.

Sign in / Sign up

Export Citation Format

Share Document