This event is approved
and organized by
Artificial Intelligence
Research Group, IUB

International Conference on Intelligent Technologies and Applications
INTAP 2018

October 23–25, 2018 | Bahawalpur, Pakistan


Dr. Letizia Jaccheri
Department of Computer Science, Norwegian University of Science & Technology
Trondheim, Norway

Letizia Jaccheri (Ph.D. from Politecnico di Torino, Italy) is Professor at the Department of Computer Science. Jaccheris research is on: software engineering; entertainment computing; computational creativity; ICT-enabled social innovation. Jaccheri is the Norwegian representative and Vice President of IFIP TC14 on Entertainment Computing. She has published more than 100 papers in International conferences and journals. She has been teaching courses in software engineering at various levels since 1994. She has supervised PhD students, Post-doctoral students and acted as opponent for national and international defenses. From 2015 to April 2018 she was independent director of Reply S.p.A, an IT company with 6000 employees world wide. She has been general chair of IFIP ICEC 2015, co-chair of ACM IDC 2018, and Program Chair of the European Computer Science Summit 2018. She participates to several Horizon 2020 projects, among which INITIATE INnovation through bIg daTa and socIal entrepreneurship; UMI-Sci-Ed Exploiting Ubiquitous Computing, Mobile Computing and the Internet of Things to promote STEM Education; SOCRATIC SOcial CReATive IntelligenCe Platform for achieving Global Sustainability Goals.

From Software through Art to Social Entrepreneurship

Abstract: Software engineering has been in contact with new media art for years, although the connections between these fields have rarely been explicit. In our studies we have identified software engineering issues that appear at the intersection between art and software. Artistic software is often built in small multidisciplinary teams, including collaboration between artists and software developers, as the artworks are heavily dependent on software. Artistic software projects have often a social goal and are highly innovative. Our studies in art and software have given the ground for two research directions:
The first is maker movement for education. The maker culture can be described as a philosophy in which individuals or groups of individuals create artifacts that are recreated and assembled using software and/or physical  objects. Typical topics of interest in maker culture include engineering-oriented pursuits such as electronics, robotics, 3D printing, and computer numerical control tools, as well as more traditional activities such as sewing or arts and crafts. Scholars and educators have  reported a variety of outcomes from the Maker Movement as an instructional process. The advent of programming languages for children (ie, Scratch) combined with accessible programmable hardware platforms (ie, Arduino) makes it possible for teenagers to engage in creative development of digital enriched artifacts, like robots and interactive installations. Together with a group of researchers and artists we have designed, implemented, and evaluated workshop programs. In our studies we have identified the important factors that characterize these development activities and more specifically, what motivates children to participate in such software and hardware intensive  activities?
The second is to harness the power of big data, increase collective and individual awareness about societal problems, existing in multiple levels of society, and ultimately create the needed intelligence that will lead to innovative solutions for societal challenges, such as, frameworks for organizing workshops on entrepreneurship and innovation for immigrants. This will empower the stakeholders that try to solve societal problems to follow data driven decision making process towards a sustainable society. Social Innovation is a great way for understanding and producing social good and change. It may be defined as “A novel solution to a social problem that is more effective, efficient, sustainable, or just than existing solutions and for which the value created accrues primarily to society as a whole rather than private individuals”. Social Innovation is a priority in EU’s strategic framework and is promoted through the Innovation Union Flagship Initiative. Structures like the Social Innovation Europe have been created to foster Social Innovation, and various EU funded projects are working on SI.



Dr. Andrzej Najgebauer
Faculty of Cybernetics,  Military University of Technology,
Warsaw, Poland

He was the Vice-President of Military University of Technology for scientific affairs(2008-2012), Warsaw, Poland, Dean of Cybernetics Faculty (2005-2008). He was formerly Chief of Decision Support Systems Department and Professor in the Institute of Computer and Information Systems. He has Master's degree in Computer Science (Military University of Technology, 1981), Ph.D. in Computer Sciences, System Optimization (Military University of Technology,1988), Certificate, Doctor of Science in Computer Science, Decision Support Systems, (Polish habilitation in Warsaw University of Technology, Faculty of Electronics and Information Technology, 1999). His scientific and professional also educational work is connected with theory of systems, artificial intelligence, modeling and simulation, modeling and designing of military decision support systems, conflict analysis, threat prediction, war games designing, exercise and training systems (CAX) – designing and development, cybersecurity and cyberwar. He was project leader of Polish Army Simulation System for CAXes. He is the member of IFORS and member of Polish Society of Operations Research and Systems Analysis, vice-president of Polish Society of Computer Simulation. He is Polish representative of STO/NATO MSG and also leader of NMSG 026 activity in subject of Early Warning Systems for terrorist crisis. He is the project leader of many Polish or international projects on Decision Support Systems in the area of Security and Defense. He is the specialist in Capability Based Planning for Polish Armed Forces in the area of Computer Based Decision Support. A member of Advisory Board of Polish Armed Forces in the area of Information Technology and Computer Simulation. He is an advisor of Polish Defense Holding. He was the supervisor of 9 doctorates, and also an organizer and president of many international scientific conferences in the area of Military Communication and Information Systems and Computational Intelligence. Author of 5 books and over than 100 publications. He is a member of special group of analysts, who participated in the evaluation of possible results of international war game for eastern Europe (horizon: 10 years). He was an expert in the Strategic Defense Review for simulation and optimization analyses of Capability Based Planning and Budgeting of Polish Armed Forces.

Computational Intelligence in Conflicts Analyses: Defense European Frontline

Abstract: The presentation is devoted to modeling and simulation techniques as well as AI techniques to support strategic planning to control international conflicts. My team so called MS4DS Team at Faculty of Cybernetics of MUT, taking part in Polish-American "tabletop" games, has proposed a set of methods and tools to analyze possible moves to resolve a hypothetical conflict along a front line in northeastern Europe. Both the model and some experimental results are presented in the keynote speech. Decision support was proposed as a set of statistical, optimization, simulation and artificial intelligence tools for conflict scenario preparation and analysis. The range of quantitative methods and the choice of appropriate research tools in the experiments will be presented. My short lecture is divided into following points:
1. The strategic game for defense of European Frontline, which was divided into two phases:
    a. D1 – escalation of the conflict in the B states;
    b. D2 – conventional conflict
2. Network model of conflict – strategic level
3. M&S techniques and Cast Logic for the Game Moves evaluation – determination of effectiveness measures, such as the opponent's estimation of the probability that the conflict will escalate (Bayesian Network Model));
4. Simulation of selected incidents: evaluation of effectiveness measures on our side (evaluation of force potential and structure required, simulation analysis of combat clashes)
Coming from the different conflict analyses, considerations in subject of security of NATO countries in Northern-Eastern Flank we can formulate some important purposes of the strategic game for defense of the frontline. As the main there is reinforcement of deterrence on NATO's Frontline however perceived by opponent propaganda as aggressive posture of NATO. Some first-order insights and desired outputs will be presented. The effectiveness of the computational intelligence methods will be discussed in the speech.



Dr. Parisa Kordjamshidi
Assistant Professor 
Department of Computer Science, Tulane University
New Orleans, USA

Research Scientist at IHMC 

Parisa Kordjamshidi is an assistant professor of Computer Science at Tulane University with a joint appointment at Florida Institute for Human Machine Cognition. Her research includes machine learning, natural language processing, and declarative learning-based programming (DeLBP). She has worked on the extraction of formal semantics and structured representations from natural language, especially spatial semantics, and structured output learning models. Her DeLBP research focuses on developing programming paradigms that support modeling complex intelligent systems and facilitate interacting learning and reasoning components with data from heterogeneous resources. She received her Ph.D. from KU Leuven University in 2013 and was a post-doc at University of Illinois at Urbana-Champaign until 2016. She is a member of editorial board of Journal of Artificial Intelligence Research (JAIR) and has organized several international workshops and served as program committee of major conferences in her field.  The results of her research have been published in international peer-reviewed conferences and journals including ACM-TSLP, Journal of Web Semantics, BMC-Bioinformatics, COLING, NAACL and IJCAI.

Declarative Learning based Programming for Spatial Language Understanding

Abstract: Developing intelligent systems that deal with real-world problems requires addressing a range of scientific and engineering challenges. Conventional programming languages offer no help to application programmers that attempt to make use of real-world data, and reason about it in a way that involves learning interdependent concepts, incorporating existing models, and reasoning about them. Natural language understanding is one of such complex problems. In this talk, I discuss two themes of my research. One is about the declarative learning-based programming (DeLBP) paradigm  that aims at facilitating the design and development of intelligent real-world applications that use machine learning and reasoning. The other theme is about using our current DeLBP framework towards spatial language understanding.  Extraction of spatial semantics is a very challenging problem as other semantic natural language tasks due to the polysemy and ambiguity inherent in the language. There is a need to exploit syntactic, semantics and even pragmatics to disambiguate spatial meaning. Moreover, spatial semantics is the most relevant information useful for visualization of the language and, consequently, accompanying visual information could help disambiguation and extraction of the spatial meaning from text. I demonstrate how our DeLBP framework facilitates working with structured data from heterogeneous resources (Vision and language) and helps to put them in a unified graph structure. Designing structured output prediction models over this multimodal graph, considering domain knowledge and spatial ontologies in learning, and experimenting with various learning and inference configurations become seamless in this framework. This paradigm helps to move towards integrating learning and reasoning for solving such AI-complete tasks. 



Dr. Julia Sidorova
Assistant Professor 
Department of Computer Science, Blekinge Institute of Technology
Karlskrona, Sweden

Dr. J. Sidorova was born in Moscow, USSR in 1980. She received the M.S. degrees in linguistics with emphasis on maths and computational aspects of NLP from Moscow State University, in 2002 and the Ph.D. degree in cognitive sciences from Universitat Pompeu Fabra, Barcelona, in 2010. From 2010 to 2014, she was a postdoctoral fellow at Universitat Autonoma de Barcelona and Universidad Carlos III de Madrid, Spain. Since 2015, she has been with Blekinge Institute of Technology, where she is an Assistant Professor with the Department of Computer Science and Engineering, Karlskrona, Sweden. She has been a recipient of several grants from Generalitat de Catalonia, from the Alliance of Spanish universities and Swiss Science Foundation. She is the author of 27 scientific articles with the research interests spanning pattern recognition, artificial intelligence and their industrial applications applications currently in collaboration with Sony and Telenor. Best wishes from Crimea!

Fuzzy Logic to Create a Visual Data Summary of Telecom Operator's Customer Base: Where to Go and Which is the Current Success Rate?

Abstract:As was pointed out by L. Zadeh, the mission of fuzzy logic in the era of big data is to create a relevant summary of huge amounts of data and facilitate decision-making. In this study, fuzzy logic modelling is used to create a visual summary of telecom data: which gives a comprehensive idea concerning the desirability of boosting operator's presence in distinct neighbourhoods and operator's success in different regions. The data, used for framework validation, cover historical mobility in one region of Sweden during a week. Fuzzy logic allows us to model inherently relative characteristics, such as "a tall man" or "a beautiful woman", and, importantly, it also defines "anchors", the situations (characterised with the value of the membership function for the characteristic) under which the relative notion receives a unique crisp interpretation. We propose color coding as a function of the membership value for the relative notions such as "the desirability of boosting operator's presence in the neighbourhood" and "how well the operator is doing in the region". Once the level of granularity is decided by the user, the corresponding regions on the map (as they are defined by the zip code zone) are coloured in different shades passing from green (1) to red (0).