Anna Georgiadou, Spiros Mouzakitis and Dimitrios Askounis, National Technical University of Athens, Decision Support Systems Laboratory, Iroon Poly-techniou 9, 15780 Zografou, Greece
This paper outlines the design and development of a survey targeting the cyber-security culture assessment of critical infrastructures during the COVID-19 crisis, when living routine was seriously disturbed and working real-ity fundamentally affected. Its foundations lie on a security culture framework consisted of 10 different security dimensions analyzed into 52 domains exam-ined under two different pillars: organizational and individual. In this paper, a detailed questionnaire building analysis is being presented while revealing the aims, goals and expected outcomes of each question. It concludes with the survey implementation and delivery plan following a number of pre-survey stages each serving a specific methodological purpose.
cybersecurity culture, assessment survey, COVID-19 pandemic, criti-cal infrastructures.
Hager Ali Yahia1, Mohammed Zakaria Moustafa2, Mohammed Rizk Mohammed3, Hatem Awad Khater4, 1Department of Communication and Electronics Engineering, ALEXANDRIA University, Alexandria, Egypt, 2Department of Electrical Engineering (Power and Machines Section) ALEXANDRIA University, Alexandria, Egypt, 3Department of Communication and Electronics Engineering, ALEXANDRIA University, Alexandria, Egypt, 4Department of Mechatronics, Faculty of Engineering, Horus University, Egypt
A support vector machine (SVM) learns the decision surface from two different classes of the input points. In many applications, there are misclassifications in some of the input points and each is not fully assigned to one of these two classes. In this paper a bi-objective quadratic programming model with fuzzy parameters is utilized and different feature quality measures are optimized simultaneously. An a-cut is defined to transform the fuzzy model to a family of classical bi-objective quadratic programming problems. The weighting method is used to optimize each of these problems. An important contribution will be added for the proposed fuzzy bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The experimental results, show the effectiveness of the a-cut with the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions.
Support vector machine (SVMs), Classification, Multi-objective problems, Weighting method, fuzzy mathematics, Quadratic programming, Interactive approach.
Nel R. Panaligan1 and Patrick Angelo P. Paasa2, 1Information Technology Department, Southern Philippines Agri-Business and Marine and Aquatic School of Technology, Malita, Davao Occidental, Philippines, 2Computer Science Division, Ateneo de Davao University, Davao City, Philippines
The development of automatic fish counters has been driven by the need for accurate, long-term and cost-effective counting and in terms of object recognition in line with advancement of aquaculture in the country. Non-invasive methods of fish counting are ultimately limited by the properties of the immerging technologies like when candidates for counting are transparent and or small (Bangus Fry). Image processing is one of the most modern approach in automating the counting process. The main objective of the study is to evaluate three image segmentation algorithms in an image (2D image of bangus fry) with touching or overlapping fry whether or not they are capable of segmenting tiny 2 weeks old bangus fry’s’ in an image. The study will be evaluating three (3) Image segmentation algorithms with different methods applied in each, (1) Watershed Algorithm, (2) Hough Transform, (3) Concavity Analysis. This study involves 4 basic steps used in image processing; Image acquisition, Image Pre-Processing, Image segmentation, and Object counting. Result shows that the second method of the Watershed Algorithm which identifies the Local Maxima and the Distance transform performs best with the other algorithm with an accuracy rate of 86.47% and 0 false detection in an experimental data of four sets of 2D image ranging from 100, 200, 300, and 400 bangus fry per test image.
Image Segmentation Algorithms, Watershed Algorithm, Hough Transform, Concavity Analysis, Evaluation.
Marie-Anne Xu1 and Rahul Khanna2, 1Crystal Springs Uplands School, CA, USA, 2University of Southern California, CA, USA
Recent progress in machine reading comprehension and question-answering has allowed machines to reach and even surpass human question-answering. However, the majority of these questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run the top BERT-based models pre-trained for question-answering on this dataset to evaluate their reading comprehension abilities. Among the three types of BERT-based models, RoBERTa exhibits the highest consistent performance. We find that the models perform similar on this new, multi-span dataset (21.492% F1) compared to the multi-span subset of the source dataset (25.0% F1). We conclude that our similarly high model evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in question-answering and improve existing question-answering products and methods.
Natural Language Processing, Question Answering, Machine Reading Comprehension.
Tebatso Moapel1, Sunday Ojo2 and Oludayo Olugbara3, 1Department of Computer Science, University of South Africa, Roodepoort, Florida Park, 2Inclusive African Indigenous Language Technology Institute, Pretoria, Gezina, 3Department of Computer Science, Durban University of Technology, Durban, Greyville
Setswana, an African Bantu language in the Sotho group, is one of the eleven official languages of South Africa. As with other natural languages, Setswana is ambiguous, meaning there are lexical units in Setswana that embodies multiple senses or meaning. This poses a challenge when developing computational Natural Language Processing (NLP) tools such as Machine Translation, Information Extraction, Document Analysis, Word Prediction tools, etc. This paper provides a taxonomy of Setswana ambiguities, a step towards developing Word Sense Disambiguator (WSD) for Setswana. The paper further presents ambiguity challenges faced in computational linguistics when developing linguistic analysis tools such as Part of Speech (POS) Taggers and language translation.
Language Ambiguities, Natural Language Processing, Machine Translation.
Shiyuan Zhang1, Evan Gunnell2, Marisabel Chang2, Yu Sun2, 1Irvine, CA 92620, 2Department of Computer Science, California State Polytechnic University, Pomona
As more students are required to have standardized test scores to enter higher education, developing vocabulary becomes essential for achieving ideal scores. Each individual has his or her own study style that maximizes the efficiency, and there are various approaches to memorize. However, it is difficult to find a specific learning method that fits the best to a person. This paper designs a tool to customize personal study plans based on clients’ different habits including difficulty distribution, difficulty order of learning words, and the types of vocabulary. We applied our application to educational software and conducted a quantitative evaluation of the approach via three types of machine learning models. By calculating crossvalidation scores, we evaluated the accuracy of each model and discovered the best model that returns the most accurate predictions. The results reveal that linear regression has the highest cross validation score, and it can provide the most efficient personal study plans.
Machine learning, study plan, vocabulary.
Evan R.M. Debenham and Roberto Solis-Oba, Department of Computer Science, The University of Western Ontario, Canada
In many computer games checking whether one object is visible from another is very important. Field of Vision (FOV) refers to the set of locations that are visible from a specific position in a scene of a computer game. Once computed, an FOV can be used to quickly determine the visibility of multiple objects from a given position. This paper summarizes existing algorithms for FOV computation, describes their limitations, and presents new algorithms which aim to address these limitations. We first present an algorithm which makes use of spatial data structures in a way which is new for FOV calculation. We then present a novel technique which updates a previously calculated FOV, rather than re-calculating an FOV from scratch. We compare our algorithms to existing FOV algorithms and show that they provide substantial improvements to running time.
Field of Vision (FOV), Computer Games, Visibility Determination, Algorithms.
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