Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Investigation of Afghanistan network infrastructure for cyber security

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Anahtar Kelimeler: Siber güvenlik, siber saldırılar, siber savaşlar, güvenlik açığı, gizlilik, bütünlük, ağ altyapısı, iletişim ve bilgi sistemleri. Global endüstriler büyük ölçüde bilgi ve veri güvenliğine yatırım yapıyor. Sanal iletişim zamanında, herhangi bir topolojisinde, öncelikle geçerlik ve güvenliği garanti altına almalı. Aksi takdirde bu tür iletişim karmaşık sorunlara ve kaynakların ağlar üzerinde zarar görmesine neden olur. Halbuki iletişim sistemleri savunmasızdır, Ülkenin bilgi bütünlüğüne, gizliliğine ve kullanılabilirliğine güvenmesi, siber güvenliğinin yetersizliğinden tam tersidir. Aslında, iletişim sistemleri veya internet öncelikle odaklı veya insan zihnindeki güvenlikle tasarlanmamıştır. Diğer bir deyişle, çok sayıda ağ bileşeninin koordinasyonu, öncelikle hava-arayüzü üzerinden kurulan veya ağ üzerinden önceden tanımlanmış protokoller altında fiziksel olarak entegre edilmiş güvenli bir bağlantıya ihtiyaç duyar. Ayrıca, bir hükümetin gerçekleştirme sorumluluğundan biri, siber ortamda ya da gerçekçi saldırı ve tehditlerle mücadele etmek için bir caydırma ekibi ya da teşkilatı oluşturmaktır. Modern iletişim sistemlerinde, siber saldırılar casusluk açısından gittikçe artmaktadır ve bilgi sistemlerine ciddi zarar vermek suretiyle siber alanın geleceğinde büyük bir sorun çıkarmaktadır. Öte yandan, Afganistan hükümeti, herhangi bir dışa bağımlı siber saldırılara karşı iyi tanımlanmış bir stratejiye sahip değilken, casusluktan sorumlu olan ve Afganistan'daki siber alanda katastrofik sorunlar çıkaran ülkelerden aktarılan değiştirilebilir verilerin büyük bir çoğunluğu bulunmaktadır. Bu sorunlar dikkate alındığında, bu çalışma Afganistan'da siber saldırılar ve siber istismar, bilgi güvenliği ile ilgili zorluklar, siber saldırıların mevcut Afganistan ağ altyapıları üzerindeki etkileri ve analizleri de dahil olmak üzere siber tehditlerle ilgilidir. Siberayla ilgili belirgin ve belirgin olmayan siber saldırılar için bir şekilde çözümün yanı sıra, mevcut ve gelecekteki siber krizin, modellerin ve simülasyon özelliklerinin bu raporun kısmen bir bölümünde analizi tanımlanmıştır. Bununla birlikte, güvenlik açısından Afganistan'ın mevcut siber durumuna, yaygın gelecekteki siber güvenlik ve siber güvenlik zorluklarına ilişkin sorunlar da bu raporda gösterilmektedir.Global industries are investing heavily in information and data security. At the time of virtual communication under any types of topologies, firstly, the validity and security must be guaranteed. Otherwise, such communication cause complex problems and resources damage over the networks. However, communication systems are vulnerable, the nation's reliance on the integrities, confidentialities, and availabilities of information stand in stark contrast to the inadequacy of their cybersecurity. In fact, communication systems or internet was not primarily designed with security in oriented or human minds. On the other word, coordinating of huge numbers of network components, first of all, need to a secure connection, either such connection established via air-interface or integrated physically under predefined protocols over the network. Additionally, one of the accomplishment responsibility of a government is creating a deterrence team or military to combat any types of attack and threat either on cyberspace or on realistic. In modern communication systems cyber-attacks becoming increasingly in terms of espionage, and it would make a big challenge in the future of cyberspace by causing serious damage to information systems. From the other hand, the government of Afghanistan does not have a well-defined strategy against any types of outsider cyberattacks while the huge amount of the exchangeable data transferring from the countries who are in charge of espionage and attempt to make catastrophic problems on Afghanistan's cyberspace. In consideration to these issues, this study concerned in Afghanistan's cyber-threats including cyber-attacks and cyber-exploit, information security challenges, analysis and effects of cyber-attacks on current Afghanistan network infrastructures. Definition of somewhat solution for distinctive and non-distinctive cyber-attacks over cyberspace, as well as the analysis of current and future cyberspace crisis, models and simulations aspect in some partial part of this report, has been also covered. However, current cyberspace status of Afghanistan in term of security, challenges of prevalent future cyber security and cyber security difficulties have also illustrated in this report

‘Living’ theory: a pedagogical framework for process support in networked learning

This paper focuses on the broad outcome of an action research project in which practical theory was developed in the field of networked learning through case‐study analysis of learners’ experiences and critical evaluation of educational practice. It begins by briefly discussing the pedagogical approach adopted for the case‐study course and the action research methodology. It then identifies key dimensions of four interconnected developmental processes—orientation, communication, socialisation and organisation—that were associated with ‘learning to learn’ in the course’s networked environment, and offers a flavour of participants’ experiences in relation to these processes. A number of key evaluation issues that arose are highlighted. Finally, the paper presents the broad conceptual framework for the design and facilitation of process support in networked learning that was derived from this research. The framework proposes a strong, explicit focus on support for process as well as domain learning, and progression from tighter to looser design and facilitation structures for process‐focused (as well as domain‐focused) learning tasks

Associative pattern mining for supervised learning

The Internet era has revolutionized computational sciences and automated data collection techniques, made large amounts of previously inaccessible data available and, consequently, broadened the scope of exploratory computing research. As a result, data mining, which is still an emerging field of research, has gained importance because of its ability to analyze and discover previously unknown, hidden, and useful knowledge from these large amounts of data. One aspect of data mining, known as frequent pattern mining, has recently gained importance due to its ability to find associative relationships among the parts of data, thereby aiding a type of supervised learning known as associative learning . The purpose of this dissertation is two-fold: to develop and demonstrate supervised associative learning in non-temporal data for multi-class classification and to develop a new frequent pattern mining algorithm for time varying (temporal) data which alleviates the current issues in analyzing this data for knowledge discovery. In order to use associative relationships for classification, we have to algorithmically learn their discriminatory power. While it is well known that multiple sets of features work better for classification, we claim that the isomorphic relationships among the features work even better and, therefore, can be used as higher order features. To validate this claim, we exploit these relationships as input features for classification instead of using the underlying raw features. The next part of this dissertation focuses on building a new classifier using associative relationships as a basis for the multi-class classification problem. Most of the existing associative classifiers represent the instances from a class in a row-based format wherein one row represents features of one instance and extract association rules from the entire dataset. The rules formed in this way are known as class constrained rules, as they have class labels on the right side of the rules. We argue that this class constrained representation schema lacks important information that is necessary for multi-class classification. Further, most existing works use either the intraclass or inter-class importance of the association rules, both of which sets of techniques offer empirical benefits. We hypothesize that both intra-class and inter-class variations are important for fast and accurate multi-class classification. We also present a novel weighted association rule-based classification mechanism that uses frequent relationships among raw features from an instance as the basis for classifying the instance into one of the many classes. The relationships are weighted according to both their intra-class and inter-class importance. The final part of this dissertation concentrates on mining time varying data. This problem is known as inter-transaction association rule mining in the data-mining field. Most of the existing work transforms the time varying data into a static format and then use multiple scans over the new data to extract patterns. We present a unique index-based algorithmic framework for inter-transaction association rule mining. Our proposed technique requires only one scan of the original database. Further, the proposed technique can also provide the location information of each extracted pattern. We use mathematical induction to prove that the new representation scheme captures all underlying frequent relationships

Mathematical Formula Recognition and Automatic Detection and Translation of Algorithmic Components into Stochastic Petri Nets in Scientific Documents

A great percentage of documents in scientific and engineering disciplines include mathematical formulas and/or algorithms. Exploring the mathematical formulas in the technical documents, we focused on the mathematical operations associations, their syntactical correctness, and the association of these components into attributed graphs and Stochastic Petri Nets (SPN). We also introduce a formal language to generate mathematical formulas and evaluate their syntactical correctness. The main contribution of this work focuses on the automatic segmentation of mathematical documents for the parsing and analysis of detected algorithmic components. To achieve this, we present a synergy of methods, such as string parsing according to mathematical rules, Formal Language Modeling, optical analysis of technical documents in forms of images, structural analysis of text in images, and graph and Stochastic Petri Net mapping. Finally, for the recognition of the algorithms, we enriched our rule based model with machine learning techniques to acquire better results