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Notre Dame University, Lebanon

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7 Papers

Persuasion Meets AI: Ethical Considerations for the Design of Social Engineering Countermeasures

Topics: e-Government; Social Networks and the Psychological Dimension; Ethical and Legal Issues of IT; Information Systems Security and Information Privacy Protection

Authors: Nicolás E. Díaz Ferreyra ¹ , Esma Aïmeur ² , Hicham Hage ³ , Maritta Heisel ¹ and Catherine García van Hoogstraten ⁴

Affiliations: ¹ Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Germany , ² Department of Computer Science and Operations Research (DIRO), University of Montréal, Canada , ³ Computer Science Department, Notre Dame University - Louaize, Lebanon , ⁴ Booking.com, The Netherlands

Digital Object Identifier: 10.5220/0010142402040211

Proceedings of KMIS 2020/IC3K, ISBN: 978-989-758-474-9

Quick Abstract

Privacy in Social Network Sites (SNSs) like Facebook or Instagram is closely related to people’s self-disclosure decisions and their ability to foresee the consequences of sharing personal information with large and diverse audiences. Nonetheless, online privacy decisions are often based on spurious risk judgements that make people liable to reveal sensitive data to untrusted recipients and become victims of social engineering attacks. Artificial Intelligence (AI) in combination with persuasive mechanisms like nudging is a promising approach for promoting preventative privacy behaviour among the users of SNSs. Nevertheless, combining behavioural interventions with high levels of personalization can be a potential threat to people’s agency and autonomy even when applied to the design of social engineering countermeasures. This paper elaborates on the ethical challenges that nudging mechanisms can introduce to the development of AI-based countermeasures, particularly to those addressing unsafe self-disclosure practices in SNSs. Overall, it endorses the elaboration of personalized risk awareness solutions as i) an ethical approach to counteract social engineering, and ii) as an effective means for promoting reflective privacy decisions.

“Majorly Adapted Translator”: Towards Major Adaptation in ITS

Topics: Natural Language Processing; AI and Creativity

Authors: Tina Daaboul and Hicham Hage

Affiliation: Department of Computer Science, Notre Dame University - Louaize, Zouk Mosbeh, Keserwan and Lebanon

Digital Object Identifier: 10.5220/0007296904510457

Proceedings of ICAART 2019, ISBN: 978-989-758-350-6

Quick Abstract

Culturally Aware Learning Systems are intelligent systems that adapt learning materials or techniques to the culture of learners having different “country, hobbies, experiences, etc.”, helping them better understand the topics being taught. In higher education, many learning sessions involve students of different majors. As observed, many instructors tend to manually modify the exercises several times, once for every major to adapt to the culture, which is tedious and impractical. Therefore, in this paper we propose an approach to making learning sessions adaptable to the major of the learner. Specifically, this work introduces an Artificial Intelligent system, “Majorly Adapted Translator (MAT)”, which aims at translating and adapting exercises from one major to another. MAT has two main phases, the first identifies the parts of an exercise that needs changing and creates an exercise template. The second translates and adapts the exercise. This work, highlights the first phase, the Feature Extract phase, which relies on our own relation extraction method to identify variables which extracts relations specific to named entities by using dependency relations and shallow parsing. Moreover, we report the performance of the system that was tested on a number of probability exercises.

Related Ontology Subjects/Areas/Topics (9)

AI and Creativity	Knowledge Engineering and Ontology Development	Pattern Recognition
Applications	Knowledge-Based Systems	Soft Computing
Artificial Intelligence	Natural Language Processing	Symbolic Systems

Modal Specifications for Composition of Agent Behaviors

Topics: Multi-Agent Systems; Planning and Scheduling

Authors: Hikmat Farhat ¹ and Guillaume Feuillade ²

Affiliations: ¹ Notre Dame University-Louaize, Lebanon , ² Universite Paul Sabatier, France

Digital Object Identifier: 10.5220/0004817804370444

Proceedings of ICAART 2014, ISBN: 978-989-758-015-4

Quick Abstract

The goal of the behavior composition problem is to build a complex target behavior using several agent behaviors. We propose two extensions to the framework where agent behaviors are modeled by finite transition system and where the composition is done by coordinating the actions of the agents. The first extension is done by making the composition indirect: instead of choosing the actions of the agent, the composition is done by a controller issuing sets of instructions at each step. This allows to model problems where the agents behaviors are not fully controllable. The second extension is the use of modal specifications as a goal for the composition. These specifications express (infinite) sets of acceptable behaviors. We give an algorithm to solve the extended composition problem and we show that these two extensions retain the important properties of the initial framework and that the synthesis algorithm keep the same complexity.

Related Ontology Subjects/Areas/Topics (15)

Agents	Formal Methods	Multi-Agent Systems
Artificial Intelligence	Informatics in Control, Automation and Robotics	Planning and Scheduling
Artificial Intelligence and Decision Support Systems	Intelligent Control Systems and Optimization	Simulation and Modeling
Distributed and Mobile Software Systems	Knowledge Engineering and Ontology Development	Software Engineering
Enterprise Information Systems	Knowledge-Based Systems	Symbolic Systems

Opinion Mining for Predicting Peer Affective Feedback Helpfulness

Topics: e-Learning; Communication, Collaboration and Information Sharing

Authors: Mouna Selmi ¹ , Hicham Hage ² and Esma Aïmeur ¹

Affiliations: ¹ Université de Montréal, Canada , ² Notre Dame University, Lebanon

Digital Object Identifier: 10.5220/0005158704190425

Proceedings of KMIS 2014/IC3K, ISBN: 978-989-758-050-5

Quick Abstract

Peer feedback has become increasingly popular since the advent of social networks, which has significantly changed the process of learning. Some of today’s e-learning systems enable students to communicate with peers (or co-learners) and ask or provide feedback. However, the highly variable nature of peer feedback makes it difficult for a learner who asked for help to notice and benefit from helpful feedback provided by his peers, especially if he is in emotional distress. Helpful feedback in affective context means positive, motivating and encouraging feedback while an unhelpful feedback is negative, bullying and demeaning feedback. In this paper, we propose an approach to predict the helpfulness of a given affective feedback for a learner based on the feedback content and the learner’s affective state. The proposed approach uses natural language processing techniques and machine learning algorithms to classify and predict the helpfulness of peers’ feedback in the context of an English learning forum. In order to seek the best accuracy possible, we have used several machine learning algorithms. Our results show that Naïve-Bayes provides the best performance with a prediction accuracy of 87.19%.

Related Ontology Subjects/Areas/Topics (11)

Applications	Education/Learning	Knowledge Management and Information Sharing
Artificial Intelligence	e-Learning	Knowledge-Based Systems
Communication, Collaboration and Information Sharing	Enterprise Information Systems	Symbolic Systems
e-Business	Human-Computer Interaction

Creating an Educational Roadmap for Engineering Students via an Optimal and Iterative Yearly Regression Tree using Data Mining

Topics: Intelligent Problem Solving; Knowledge Representation; Applications and Case-Studies; Education and Training Issues

Authors: Marie Khair , Chady El Moucary and Walid Zakhem

Affiliation: Notre Dame University – Louaize, Lebanon

Digital Object Identifier: 10.5220/0004130300430052

Proceedings of KEOD 2012/IC3K, ISBN: 978-989-8565-30-3

Quick Abstract

Targeting high academic standards is required in engineering studies. Advisors usually play an important role in helping students keeping good records and transcripts along their educational path by helping them choose their courses and keeping track of their grades. However, performance in some courses in the curriculum embodies determining repercussions and might inadvertently jeopardize the overall students’ Grade Point Average (GPA) in an irreversible manner. The purpose of this paper is to draw an educational roadmap that helps advisors and students being aware of the turning points that decisively affect their overall cumulative GPA and act upon a current outcome. This roadmap is based on Classification and Regression Trees where nodes and branches denote the aforementioned courses and students’ performance, respectively, with the ultimate outcome being the overall student’s GPA upon graduation. The tree is constructed based on a relatively large number of records with 10-fold cross-validation and testing. Moreover, the tree is produced on a yearly basis with a twofold objective. The first is to secure a high level of precision by applying it over a short period of time and the second is to allow for injecting each-year computed GPA with the remaining courses as to reflect the actual situation with maximum vraisemblance. This iterative and recursive tree achieves a very close tracking of students’ performance and provide a powerful tool to rectify courses’ track and grades for each student individually while aiming at a predefined final GPA. Furthermore, the choice of the optimal tree was carefully examined in the light of the relatively elevated number of attributes. In this context, diverse models were created and performance and/or precision were computed in terms of different values of “pruning levels” and “splitting criteria”. The choice of the best tree to be adopted for advising is thoroughly explained. Besides, it is shown, in this context, that the structure of the tree remains highly versatile in the sense that it can be revisited at any point for further assessment, adjustment, and expansion. Finally, yet importantly, simulation results were carried out using Matlab CART and demonstrated high efficiency and reasonably precise results.

Related Ontology Subjects/Areas/Topics (10)

Applications and Case-studies	Intelligent Problem Solving	Knowledge-Based Systems
Artificial Intelligence	Knowledge Engineering and Ontology Development	Software Engineering
Education and Training Issues	Knowledge Representation	Symbolic Systems
Enterprise Software Technologies

MINING THE WEB FOR LEARNING THE ONTOLOGY

Topic: Semantic Web Technologies

Authors: Bassam M. Aoun and Marie Khair

Affiliation: Notre Dame University, Lebanon

Digital Object Identifier: 10.5220/0001348101890192

Proceedings of ICSOFT 2007, ISBN: 978-989-8111-07-4

Quick Abstract

The Semantic Web is a network of information linked up in such a way as to be easily processed by machines, on a global scale. To reach semantic web, current web resources should be automatically translated into semantic web resources. This is usually performed through semantic web mining, which aims at combining the two fast-developing research areas, the Semantic Web and Web Mining. A major step to be performed is the ontology-learning phase, where rules are mined from unstructured text and used later on to fill the ontology. Making sure that all rules are found and no additional and inaccurate rules are inserted, remains a critical issue since it constitutes the basis for building the semantic web. The mostly used algorithm for this task is the Apriori algorithm, which is inherited from classical data mining. However, due to the nature of the semantic web, some important rules can be dropped. This paper presents an enhanced version of the Apriori algorithm, En_Apriori, which uses the Apriori algorithm in combination with the maximal association and the X2 test to generate association rules from web/textual documents. This provides a major refinement to the classical ontology learning approach.

Related Ontology Subjects/Areas/Topics (5)

Cloud Computing		Semantic Web Technologies		Software Agents and Internet Computing
Enterprise Information Systems		Services Science

THE CONCEPT OF ETHICS IN ELECTRONIC QUALITATIVE RESEARCH

Topics: Security, Freedom and Privacy; Human Factors

Authors: Nouhad J. Rizk ¹ and Elias M. Choueiri ²

Affiliations: ¹ Notre Dame University, Lebanon , ² Lebanese University; Lebanese Association for Public Safety; WSO Liaison Officer to the United Nations, United States

Digital Object Identifier: 10.5220/0002448301260134

Proceedings of ICEIS 2006, ISBN: 978-972-8865-43-6

Quick Abstract

As a key form of communications technology, the internet has created new methodological approaches for social science research. This study focuses on moral issues created by information technology for qualitative research environments. The primary concern is with ethical analysis and legal issues and how both are applied to, although not limited to, issues of privacy, intellectual property, information access, interpersonal communication, moral and civil rights, responsibility and liability, and professional codes as well as some social implications of technology. The Internet is now exposed to a growing number and a wider variety of threats and vulnerabilities. Moreover, Internet-based research raises several ethical questions and introduces new ethical challenges, especially pertaining to privacy, informed consent and confidentiality and anonymity. This study aims to highlight the main ethical issues in electronic qualitative research and to provide some guidance for those doing or reviewing such research. While recognizing the reservations held about strict ethical guidelines for electronic qualitative research, this study opens the door for further debate of these issues so that the social science research community can move towards the adoption of agreed standards of good practice. In addition, it suggests that empirical research is desirable in order to quantify the actual risks to participants in electronic qualitative studies.

Related Ontology Subjects/Areas/Topics (10)

Enterprise Information Systems	Information Systems Analysis and Specification	Physiological Computing Systems
Formal Methods	Methodologies and Technologies	Security
Human Factors	Operational Research	Simulation and Modeling
Human-Computer Interaction

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