A Cognitive Framework to Secure Smart Cities

The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms

Development of smart governance in Croatian cities - the size of a city as a determinant of smart governance

Purpose: The paper discusses the role and importance of smart governance as a modern form of urban development, identifies the key determinants of smart governance, analyzes models, evaluation and measurement indicators in smart and sustainable cities, and ranks 127 Croatian cities, regardless of city size. Methodology: A comprehensive database was prepared for the preparation of the study, including ten indicators of key smart governance determinants related to political participation of citizens, delivery of quality services to citizens, and sustainable functioning of city administration, in line with a review of models and indicators from previous studies. Results: The main goal of this research is to determine a correlation between the size of the city according to the number of inhabitants and statistically significant indicators of smart governance and, based on the value of the correlation coefficients, to determine the weights for the indicators in the process of city ranking. By aggregating the weighted z-scores, the Smart Governance Index was created for all Croatian cities and that index is not related to the size of a city. Conclusion: Statistically significant indicators for the formation of the Smart Governance Index for 127 cities in Croatia are the indicators of political participation and sustainable functioning of city administration. It is necessary to include as many indicators as possible in the future period so that the ranking results are as relevant as possible

Applying Deep Learning To Airbnb Search

The application to search ranking is one of the biggest machine learning success stories at Airbnb. Much of the initial gains were driven by a gradient boosted decision tree model. The gains, however, plateaued over time. This paper discusses the work done in applying neural networks in an attempt to break out of that plateau. We present our perspective not with the intention of pushing the frontier of new modeling techniques. Instead, ours is a story of the elements we found useful in applying neural networks to a real life product. Deep learning was steep learning for us. To other teams embarking on similar journeys, we hope an account of our struggles and triumphs will provide some useful pointers. Bon voyage!Comment: 8 page

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Joint Geo-Spatial Preference and Pairwise Ranking for Point-of-Interest Recommendation

Recommending users with preferred point-of-interests (POIs) has become an important task for location-based social networks, which facilitates users' urban exploration by helping them filter out unattractive locations. Although the influence of geographical neighborhood has been studied in the rating prediction task (i.e. regression), few work have exploited it to develop a ranking-oriented objective function to improve top-N item recommendations. To solve this task, we conduct a manual inspection on real-world datasets, and find that each individual's traits are likely to cluster around multiple centers. Hence, we propose a co-pairwise ranking model based on the assumption that users prefer to assign higher ranks to the POIs near previously rated ones. The proposed method can learn preference ordering from non-observed rating pairs, and thus can alleviate the sparsity problem of matrix factorization. Evaluation on two publicly available datasets shows that our method performs significantly better than state-of-the-art techniques for the top-N item recommendation task

An exploratory analysis of pedestrian accidents patterns in Lisbon

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceMobility is one of the pillars of Smart Cities, being one of the most important issues for the development and growth of a city. Nowadays, with the massive flow of populations to large urban centers, mobility and its dangers require special attention. The number of pedestrian accidents has been increasing over the past few years, and it is important to understand what causes and factors contribute to them. The main objective of this work is to identify and classify the pedestrian accidents in the city of Lisbon and segment the accident patterns based on the application of clustering methods. The data used in this work was provided by Lisboa Aberta. The work begins with a literature review about the causes of pedestrian accidents previously identified and the reference of clustering methods used in similar studies. Then, a deep dive will be made of the data provided by Lisboa Aberta, selecting the most relevant variables used for the Cluster analysis. The Cluster analysis will be done using the K-Means and KMedoids methods. At the end of the work, the results of both methods will be compared, where the winning method will be chosen and the conclusions of which are the determining patterns in pedestrian accidents in the city of Lisbon will be presented

Data-Based Urban Heritage Policy Assessment: Evaluating Tel Aviv’s Preservation Plan

Urban heritage policies are rarely assessed on a regular or continuous basis. Formal indicator guidelines and scholarly work address some possible evaluation methods for urban heritage policies, but a gap exists between the generalized work and limited on-site implementation. Spatial and non-spatial datasets should contribute to our understanding and refinement of such policies. Yet, in practice, data and the proper assessment mechanisms are often lacking. This research presents Tel Aviv’s 2650b preservation plan as a case study to explore possible assessment methods of policy effectiveness. Tel Aviv is the second-largest city in Israel. In 2003 UNESCO declared the White City of Tel Aviv, the center-city area, a World Heritage Site (WHS). UNESCO based the designation on an outstanding synthesis of the Modern architecture movement and an outstanding example of new town planning of the 20th century. Municipal plan 2650b was enacted in 2008 and is linked to the WHS, protecting modern architecture and mainly focusing on the center-city area. The Plan classifies properties into two preservation levels and three architectural styles. The city’s online building archive facilitates analysis and evaluation of plan 2650b. The Plan has been in place for over a decade, during which no data-driven comprehensive evaluation or monitoring processes occurred. Relying upon Kitchin’s definition of effectiveness, in the context of urban indicators, as “whether goals and objectives are being met – doing the right things,” this research asks: what factors correlate with the effectiveness of Tel Aviv’s preservation plan? Three sub-questions lead the research: Are the Plan’s goals being met? Are they being met in the same way throughout the Plan area? And, how, if at all, are pre-existing characteristics of the properties addressed by the Plan? The study assesses the outlined goals and presents a roadmap for constructing indicators and spatial analysis for a specific policy. The study approach uses an author-created property-level database to assess proposed customized indicators and run spatial analysis. It finds that the prevalence of preservation varies across space, among architectural types, and between the two preservation-restriction levels. In particular, the Plan is relatively less effective at preserving Modernist buildings. These findings reveal the inconsistency of the Plan at protecting the Modernist architecture at the core of the global designation. The results stress the need for data collection, setting numeric objectives, monitoring plan outcomes, and potential future research to realign incentives with preservation goals