These slides were presented during the WG2 meeting of COST Action IC1203 ENERGIC (http://vgibox.eu) held in Paris on December 3-4, 2015, which was focused on the evaluation of OSM quality through the comparison with official IGN data. The presentation describes an application of an open source GRASS-based procedure - including a Web Processing Service - to compare OSM and authoritative road network datasets (https://github.com/MoniaMolinari/OSM-roads-comparison) in the Paris case study.
A GRASS-based automated procedure to compare OpenStreetMap and authoritative ...Marco Minghini
These slides were presented during the XVII meeting of the Italian users of GRASS and FOSS4G, held in Parma on February 11-12, 2015. The presentation describes an automated GRASS-based procedure to compare OSM and authoritative road network datasets (https://github.com/MoniaMolinari/OSM-roads-comparison). An application is presented focused on Paris city, where the OSM road network is compared with the official road network provided by IGN. A Web Processing Service (WPS) is also under development.
Qualità dei dati OpenStreetMap: sperimentazioni sulla città di Milano e risul...Marco Minghini
These slides were presented during the Italian OpenStreetMap conference - OSMit 2016 (http://conf.openstreetmap.it), held in Milan (Italy) on May 20-21, 2016. They include a description of some ongoing research works on OpenStreetMap which are under development at the GEOlab (http://geolab.como.polimi.it) of Politecnico di Milano.
Maria Antonia Brovelli, Carolina Arias Muñoz, Marco Minghini, Giorgio Zamboni.
https://drive.google.com/file/d/0B3xWOhmJOx-_am5Ld3c4dnFPUUE/view
https://www.youtube.com/watch?v=dQ-EdwoPMVQ&feature=youtu.be
An accurate retrieval through R-MAC+ descriptors for landmark recognitionFederico Magliani
The landmark recognition problem is far from being solved, but with the use of features extracted from intermediate layers of Convolutional Neural Networks (CNNs), excellent results have been obtained. In this work, we propose some improvements on the creation of R-MAC descriptors in order to make the newly-proposed R-MAC+ descriptors more representative than the previous ones. However, the main contribution of this paper is a novel retrieval technique, that exploits the fine representativeness of the MAC descriptors of the database images. Using this descriptors called "db regions" during the retrieval stage, the performance is greatly improved. The proposed method is tested on different public datasets: Oxford5k, Paris6k and Holidays. It outperforms the state-of-the- art results on Holidays and reached excellent results on Oxford5k and Paris6k, overcame only by approaches based on fine-tuning strategies.
GPS Instrumental Biases Estimation Using Continuous Operating Receivers NetworkCSCJournals
Precise Total Electron Content (TEC) are required to produce accurate spatial and temporal resolution of Global Ionosphere Maps (GIMs). Receivers and Satellites Instrumental Biases (IBs) are one of the main error sources in estimating precise TEC from Global Positioning Systems (GPS) data. Recently, researchers are interested in developing models and algorithms to compute IBs of receivers and satellites close to those computed from the Ionosphere Associated Analysis Centers (IAAC). Here we introduce a MATLAB code called Multi Station IBs Estimation (MSIBE) to calculate satellites and codeless tracking receivers IBs from GPS data. MSIBE based on spherical harmonic function and geometry free combination of GPS carrier phase and pseudo-range code observations and weighted least square were applied to solve observation equations, to improve estimation of IBs values. There are many factors affecting estimated value of IBs. The premier factor is the observations weighting function which relying on the satellite elevation angle. The second factor concerned with estimating IBs using single GPS Station Precise Point Positioning (PPP) or using GPS network. The third factor is the number of GPS receivers in the network. Results from MSIBE were evaluated and compared with data from IAAC and other codes like M_DCB and ZDDCBE. The results of weighted (MSIBE) least square shows an improvement for estimated IBs, where mean differences from CODE less than 0.746 ns. IBs estimated from Continuous Operating Receivers (CORs) GPS network shows a good agreement with IAAC than IBs estimated from PPP where the mean differences are less than 0.1477 ns and 1.1866 ns, respectively. The mean differences of computed IBs improved by increasing number of GPS stations in the network.
Monitoring traffic in urban areas is an important task for intelligent transport applications to alleviate the traffic problems like traffic jams and long trip times. The traffic flow in urban areas is more complicated than the traffic flow in highway, due to the slow movement of vehicles and crowded traffic flows in urban areas. In this paper, a vehicle detection and classification system at intersections is proposed. The system consists of three main phases: vehicle detection, vehicle tracking and vehicle classification. In the vehicle detection, the background subtraction is utilized to detect the moving vehicles by employing mixture of Gaussians (MoGs) algorithm, and then the removal shadow algorithm is developed to improve the detection phase and eliminate the undesired detected region (shadows). After the vehicle detection phase, the vehicles are tracked until they reach the classification line. Then the vehicle dimensions are utilized to classify the vehicles into three classes (cars, bikes, and trucks). In this system, there are three counters; one counter for each class. When the vehicle is classified to a specific class, the class counter is incremented by one. The counting results can be used to estimate the traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The system is applied to videos obtained by stationary cameras. The results obtained demonstrate the robustness and accuracy of the proposed system.
Transit Oriented Development and Land Value Capture: A Concept NoteHarshada Chavan
An optimal TOD scenario to reduce negative transportation impacts and enhance land value. The value generated will be captured using the Land Value Capture (LVC) mechanism, which could be utilized as a sustainable source of funding.
A GRASS-based automated procedure to compare OpenStreetMap and authoritative ...Marco Minghini
These slides were presented during the XVII meeting of the Italian users of GRASS and FOSS4G, held in Parma on February 11-12, 2015. The presentation describes an automated GRASS-based procedure to compare OSM and authoritative road network datasets (https://github.com/MoniaMolinari/OSM-roads-comparison). An application is presented focused on Paris city, where the OSM road network is compared with the official road network provided by IGN. A Web Processing Service (WPS) is also under development.
Qualità dei dati OpenStreetMap: sperimentazioni sulla città di Milano e risul...Marco Minghini
These slides were presented during the Italian OpenStreetMap conference - OSMit 2016 (http://conf.openstreetmap.it), held in Milan (Italy) on May 20-21, 2016. They include a description of some ongoing research works on OpenStreetMap which are under development at the GEOlab (http://geolab.como.polimi.it) of Politecnico di Milano.
Maria Antonia Brovelli, Carolina Arias Muñoz, Marco Minghini, Giorgio Zamboni.
https://drive.google.com/file/d/0B3xWOhmJOx-_am5Ld3c4dnFPUUE/view
https://www.youtube.com/watch?v=dQ-EdwoPMVQ&feature=youtu.be
An accurate retrieval through R-MAC+ descriptors for landmark recognitionFederico Magliani
The landmark recognition problem is far from being solved, but with the use of features extracted from intermediate layers of Convolutional Neural Networks (CNNs), excellent results have been obtained. In this work, we propose some improvements on the creation of R-MAC descriptors in order to make the newly-proposed R-MAC+ descriptors more representative than the previous ones. However, the main contribution of this paper is a novel retrieval technique, that exploits the fine representativeness of the MAC descriptors of the database images. Using this descriptors called "db regions" during the retrieval stage, the performance is greatly improved. The proposed method is tested on different public datasets: Oxford5k, Paris6k and Holidays. It outperforms the state-of-the- art results on Holidays and reached excellent results on Oxford5k and Paris6k, overcame only by approaches based on fine-tuning strategies.
GPS Instrumental Biases Estimation Using Continuous Operating Receivers NetworkCSCJournals
Precise Total Electron Content (TEC) are required to produce accurate spatial and temporal resolution of Global Ionosphere Maps (GIMs). Receivers and Satellites Instrumental Biases (IBs) are one of the main error sources in estimating precise TEC from Global Positioning Systems (GPS) data. Recently, researchers are interested in developing models and algorithms to compute IBs of receivers and satellites close to those computed from the Ionosphere Associated Analysis Centers (IAAC). Here we introduce a MATLAB code called Multi Station IBs Estimation (MSIBE) to calculate satellites and codeless tracking receivers IBs from GPS data. MSIBE based on spherical harmonic function and geometry free combination of GPS carrier phase and pseudo-range code observations and weighted least square were applied to solve observation equations, to improve estimation of IBs values. There are many factors affecting estimated value of IBs. The premier factor is the observations weighting function which relying on the satellite elevation angle. The second factor concerned with estimating IBs using single GPS Station Precise Point Positioning (PPP) or using GPS network. The third factor is the number of GPS receivers in the network. Results from MSIBE were evaluated and compared with data from IAAC and other codes like M_DCB and ZDDCBE. The results of weighted (MSIBE) least square shows an improvement for estimated IBs, where mean differences from CODE less than 0.746 ns. IBs estimated from Continuous Operating Receivers (CORs) GPS network shows a good agreement with IAAC than IBs estimated from PPP where the mean differences are less than 0.1477 ns and 1.1866 ns, respectively. The mean differences of computed IBs improved by increasing number of GPS stations in the network.
Monitoring traffic in urban areas is an important task for intelligent transport applications to alleviate the traffic problems like traffic jams and long trip times. The traffic flow in urban areas is more complicated than the traffic flow in highway, due to the slow movement of vehicles and crowded traffic flows in urban areas. In this paper, a vehicle detection and classification system at intersections is proposed. The system consists of three main phases: vehicle detection, vehicle tracking and vehicle classification. In the vehicle detection, the background subtraction is utilized to detect the moving vehicles by employing mixture of Gaussians (MoGs) algorithm, and then the removal shadow algorithm is developed to improve the detection phase and eliminate the undesired detected region (shadows). After the vehicle detection phase, the vehicles are tracked until they reach the classification line. Then the vehicle dimensions are utilized to classify the vehicles into three classes (cars, bikes, and trucks). In this system, there are three counters; one counter for each class. When the vehicle is classified to a specific class, the class counter is incremented by one. The counting results can be used to estimate the traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The system is applied to videos obtained by stationary cameras. The results obtained demonstrate the robustness and accuracy of the proposed system.
Transit Oriented Development and Land Value Capture: A Concept NoteHarshada Chavan
An optimal TOD scenario to reduce negative transportation impacts and enhance land value. The value generated will be captured using the Land Value Capture (LVC) mechanism, which could be utilized as a sustainable source of funding.
Social psychologists like many other fields within psychology, often utilize different perspectives when looking at questions about social behavior.Different perspective has been briefly discussed.
Baromètre sur les pratiques de l'A/B testing et de la personnalisation web Converteo
Converteo et Kameleoon ont le plaisir d’annoncer la parution de leur 3ème baromètre sur les pratiques de l'A/B testing et de la personnalisation web en France.
Cette étude menée auprès de 250 acteurs du digital se divise en 3 grands axes d’analyse :
- Pratiques de l’A/B testing et la personnalisation web
- Modalités d’utilisation de l’A/B testing
- Parties prenantes de l’A/B testing et de la personnalisation
During the winter period ice is likely to form on roads, making pavement surfaces slippery and increasing accident risk. The aim of the “CLEAN-ROADS” project was to forecast surface temperature in advance in order to support road maintenance services in the timely and effective preparation of preventive anti-icing measures. This support was provided through a Decision Support System based, among other components, on the numerical model METRo. METRo is an open source system originally developed and applied in Canada. During the CLEAN-ROADS project it was modified and adapted to an Alpine context, applying the forecast algorithm to a test route along the Adige Valley. This forecast is however local and does not take into account typical peculiarities along road network, such as the presence of road sections that are particularly prone to ice formation. Therefore the fusion with other data sources was foreseen, in order to improve results. Thermal mapping measurements through infrared thermometry permitted to map road sections and thus extend the forecast from a single road and meteorological station to adjacent areas. The processing of thermal mapping signals is however challenging because of random variations in the road surface emissivity. To overcome this several thermal mapping traces were analyzed through automatic algorithms in order to define a “characteristic” thermal fingerprint as a function of all its historical thermal mapping signals. The final result has been used to spatialize local METRo forecasts. Preliminary results suggest the high potential of such a technique for winter road applications. The algorithms were written in Python language and the results were presented on a web page.
Social psychologists like many other fields within psychology, often utilize different perspectives when looking at questions about social behavior.Different perspective has been briefly discussed.
Baromètre sur les pratiques de l'A/B testing et de la personnalisation web Converteo
Converteo et Kameleoon ont le plaisir d’annoncer la parution de leur 3ème baromètre sur les pratiques de l'A/B testing et de la personnalisation web en France.
Cette étude menée auprès de 250 acteurs du digital se divise en 3 grands axes d’analyse :
- Pratiques de l’A/B testing et la personnalisation web
- Modalités d’utilisation de l’A/B testing
- Parties prenantes de l’A/B testing et de la personnalisation
During the winter period ice is likely to form on roads, making pavement surfaces slippery and increasing accident risk. The aim of the “CLEAN-ROADS” project was to forecast surface temperature in advance in order to support road maintenance services in the timely and effective preparation of preventive anti-icing measures. This support was provided through a Decision Support System based, among other components, on the numerical model METRo. METRo is an open source system originally developed and applied in Canada. During the CLEAN-ROADS project it was modified and adapted to an Alpine context, applying the forecast algorithm to a test route along the Adige Valley. This forecast is however local and does not take into account typical peculiarities along road network, such as the presence of road sections that are particularly prone to ice formation. Therefore the fusion with other data sources was foreseen, in order to improve results. Thermal mapping measurements through infrared thermometry permitted to map road sections and thus extend the forecast from a single road and meteorological station to adjacent areas. The processing of thermal mapping signals is however challenging because of random variations in the road surface emissivity. To overcome this several thermal mapping traces were analyzed through automatic algorithms in order to define a “characteristic” thermal fingerprint as a function of all its historical thermal mapping signals. The final result has been used to spatialize local METRo forecasts. Preliminary results suggest the high potential of such a technique for winter road applications. The algorithms were written in Python language and the results were presented on a web page.
Analysis of Educational Robotics activities using a machine learning approachLorenzo Cesaretti
These slides present the preliminary results through the utilisation of machine learning techniques for the analysis of Educational Robotics activities. An experimentation with 197 secondary school students from Italy was con-ducted, through updating Lego Mindstorms EV3 programming blocks in order to record log files containing the coding sequences designed by the students (within team work), during the resolution of a preliminary Robotics’ exercise. We utilised four machine learning techniques (logistic regression, support vec-tor machine, K-nearest neighbors and random forests) to predict the students’ performance, comparing a supervised approach (using twelve indicators ex-tracted from the log files as input for the algorithms) and a mixed approach (ap-plying a k-means algorithm to calculate the machine learning features). The re-sults have highlighted that SVM with the mixed approach outperformed the other techniques, and that three learning styles were predominantly emerged from the data mining analysis.
These slides were presented during the humanitarian OpenStreetMap mapathon held at Politecnico di Milano on November 19, 2015 in the frame of the OpenStreetMap Geography Awareness Week (http://osmgeoweek.org). After a brief presentation of OpenStreetMap (OSM), the slides show how to work with the OSM Tasking Manager (http://tasks.hotosm.org) with regard to the specific mapping tasks agreed with HOT (Humanitarian OpenStreetMap Team) and Missing Maps project members. The slides include also a handful of OSM resources for beginners.
Smart Urban Planning Support through Web Data Science on Open and Enterprise ...Gloria Re Calegari
Prediction of expensive datasets starting from a set of cheap heterogeneous information sources in smart city scenarios.
Prediction of the population and land use of Milano starting from data about Points Of Interest and phone activity.
Accès ouvert aux données météorologiques d’Environnement CanadaVisionGEOMATIQUE2014
Deux services de données géospatiales offerts par le Service météorologique Canada (Environnement Canada) seront présentés.
Le Datamart du SMC permet de télécharger des données brutes produites par le Service météorologique du Canada (SMC) et rend possible la cueillette automatisée des produits et données météorologiques du SMC. Une grande diversité de données s’y retrouvent, tels les avertissements météo, les prévisions publiques en XML, les données brutes de prévision, les observations météorologiques et bien davantage. Le Datamart du SMC jouit d’une grande popularité avec plus de 12 millions d’accès quotidiennement.
Les services web géospatiaux GeoMet permettent au public d’intégrer dans leurs outils SIG, cartes interactives ou appareil mobile, les données brutes de modèles de prévision numérique du temps et de la mosaïque nord-américaine des radars météo par le biais de deux standards de l’Open Geospatial Consortium : le WMS et le KML. Qu’il s’agisse par exemple de la pluie observée par les radar météo ou les prévisions de précipitation de neige ou une animation des prévisions de températures , les utilisations de ces données sont nombreuses et d’une grande valeur pour de nombreux décideurs.
Efficient and Effective Influence Maximization in Social Networks: Hybrid App...NAVER Engineering
발표자: 고윤용(한양대 박사과정)
발표일: 2018.2.
Influence maximization (IM) is the problem of finding a seed set composed of k nodes that maximize their influence spread over a social network. Kempe et al. showed the problem to be NP-hard and proposed a greedy algorithm (referred to as SimpleGreedy) that guarantees 63% influence spread of its optimal solution. However, SimpleGreedy has two performance issues: at a micro level, it estimates the influence spread of a single node by running Monte-Carlo (MC) simulations that are fairly expensive; at a macro level, after selecting one seed at each step, it re-evaluates the influence spread of every node in a social network, leading to significant computational overhead. In this paper, we propose Hybrid-IM that addresses the two issues in both micro and macro levels by combining PB-IM (Path Based Influence Maximization) and CB-IM (Community Based Influence Maximization). Furthermore, we identify two technical issues that could improve the performance of Hybrid-IM more and propose two strategies to address those issues. Through extensive experiments with four real-world datasets, we show that Hybrid-IM achieves great improvement (up to 43 times) in performance over state-of-the-art methods and finds the seed set that provides the influence spread very close to that of the state-of-the-art methods.
MediaEval 2016 - Placing Images with Refined Language Models and Similarity S...multimediaeval
Presenter: Giorgos Kordopatis-Zilos
Placing Images with Refined Language Models and Similarity Search with PCA-reduced VGG Features In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Giorgos Kordopatis-Zilos, Adrian Popescu, Symeon Papadopoulos, Yiannis Kompatsiaris
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_13.pdf
Video: https://youtu.be/WR4I3CWjcR4
Abstract: We describe the participation of the CERTH/CEA-LIST team in the MediaEval 2016 Placing Task. We submitted five runs to the estimation-based sub-task: one based only on text by employing a Language Model-based approach with several refinements, one based on visual content, using geospatial clustering over the most visually similar images, and three based on a hybrid scheme exploiting both visual and textual cues from the multimedia items, trained on datasets of different size and origin. The best results were obtained by a hybrid approach trained with external training data and using two publicly available gazetteers.
We use the Georeferenced results of the 2010 Census in Mexico to train machine learning algorithms to detect growth in cities and contribute new information to estimate the total population.
El 29 de febrero y el 1 de marzo de 2016, la Fundación Ramón Areces analizó la relación entre 'Big Data y el cambio climático' en unas jornadas. ¿Puede el Big Data ayudar a reducir el cambio climático? ¿Cómo contribuirá ese análisis masivo de datos a prevenir y gestionar catástrofes naturales? Son solo algunas de las preguntas a las que intentarán responder los ponentes. Las ciencias vinculadas al clima tienen en el Big Data una herramienta muy prometedora para afrontar diferentes fenómenos asociados al cambio climático.
Similar to A GRASS-based procedure to compare OSM and IGN Paris road network datasets (20)
Preliminary Assessment of the Global Urban Footprint and the Global Human Set...Marco Minghini
These slides were presented at the AGILE conference held in Wageningen (The Netherlands) on May 9-12, 2017. They show a preliminary assessment for the city of Milan (Italy) of two new global urban products, namely the Global Urban Footprint (GUS) and the Global Human Settlement Layer (GHSL), through an analysis of their intrinsic similarity and their comparison with two EU authoritative datasets: the Global Monitoring for Environment and Security Urban Atlas, and the Land Use Cover Area frame Survey (LUCAS).
These slides (in Italian) were presented during the FOSS4G-IT 2017 conference, held in Genova (Italy) on February 9-10, 2017 (http://www.dicca.unige.it/geomatica/foss4git_2017/index.html). The presentation is about the exploitation of OpenStreetMap (OSM) database in the emergency stage and focuses on the activities of the Italian OSM community after the earthquakes in Central Italy happened in late 2016.
These slides were presented at the MYGEOSS Final Event "Open Data, Open Source, Open Minds" held in Brussels (Belgium) on December 6, 2016, co-organized by DG JRC-DG RTD. MIGRATE – MIGRation pATterns in Europe (http://geomobile.como.polimi.it/migrate) is a Web Mapping application aimed at educating and raising awareness about the phenomenon of migration in Europe through a gamification approach. MIGRATE is fully based on open data and is released under an open source license.
These slides were presented at the GEO-XIII Plenary in St. Petersburg (Russia) on November 8, 2016 (https://www.earthobservations.org). MIGRATE – MIGRation pATterns in Europe (http://geomobile.como.polimi.it/migrate) is an open source Web mapping application aimed at educating and raising awareness about the phenomenon of migration in Europe through a gamification approach. Users interact with the application by playing a map-based game, where questions are asked and answers are provided and explained about a number of topics related to migration. MIGRATE makes full use of open data. The project was funded under the third call of the MyGEOSS Project from the EU Commission (http://digitalearthlab.jrc.ec.europa.eu/mygeoss).
Building Land Use/Land Cover maps from OpenStreetMapMarco Minghini
These slides were presented at the conference "Mapping, Sensing, and Crowdsourcing Geographic Information", organized as the final joint meeting of the two EU COST Actions TD1202 "Mapping and the Citizen Sensors" (http://www.citizensensor-cost.eu) and IC1203 "ENERGIC" (http://vgibox.eu). This work explains the creation of an automated procedure to convert OpenStreetMap (OSM) data into Land Use/Land Cover (LULC) maps, using the nomenclatures of the two most popular European LULC maps, i.e. Urban Atlas and Corine Land Cover. The procedure is implemented using open source software.
How to organize and run your own OSM humanitarian mapathonMarco Minghini
These slides were presented in a webinar held on May 5, 2016 and promoted by the GeoForAll network (http://www.geoforall.org). They include an introduction on OpenStreetMap (OSM) and some practical instructions and suggestions on how you can organize an OSM humanitarian mapathon. The slides provide several links to a number of web resources which are very useful for OSM beginners. The live presentation of these slides is available here: http://tinyurl.com/hp4jyut
The paths of Regina: Web Mapping to promote slow tourismMarco Minghini
This presentation was given at Maptime Milan on February 25, 2016. It presents the geoportal and the Web Mapping applications developed within the Interreg project "I Cammini della Regina" (http://www.viaregina.eu) by the GIS teams of Politecnico di Milano (Italy) and SUPSI (Switzerland). The main purpose of these applications – all developed with open source software – is the promotion of cultural heritage and sustainable tourism in the area of Via Regina (northern Italy).
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
A GRASS-based procedure to compare OSM and IGN Paris road network datasets
1. GEOlab, Como Campus
A GRASS-based procedure to compare
OpenStreetMap and IGN Paris road
network datasets
1
Politecnico di Milano, Como Campus, DICA, via Valleggio 11, 22100 Como (Italy)
2
Maynooth University, Dept. of Computer Science, Maynooth, Co. Kildare (Ireland)
Maria Antonia Brovelli1
, Marco Minghini1
, Monia Molinari1
,
Peter Mooney2
, Gabriele Prestifilippo1
COST Action IC1203 – Paris, December 3-4 2015
2. 2
✔ Literature provides plenty of works assessing or comparing OSM quality
against that of authoritative datasets:
Motivation of the work – OSM comparisons
➔ strongly focused on road network
➔ mainly investigating OSM positional accuracy
➔ OSM compared to data from NMA (UK Ordnance Survey, French
NMA, USGS TNM/TIGER, etc.) and CSC (Navteq, TeleAtlas, etc.)
➔ semi- or fully-automated
➔ results from poor to very good
✔ Comparison techniques are very strong and fit for purpose, but mostly
application and dataset specific:
➔ hard to replicate
➔ difficult to extend to other dataset comparisons
GEOlab, Politecnico di Milano – Como Campus
3. 3
✔ Novel methodology to compare OSM and authoritative road datasets:
Our methodology
➔ fully automated
➔ focused on spatial accuracy and completeness
➔ flexible, i.e. not developed for a specific dataset
➔ built with FOSS4G (Free and Open Source Software for Geospatial)
✗ made of required and optional operations
✗ users can define the value of the parameters involved to adapt the
procedure to their specific authoritative datasets
✗ users are supposed to be familiar with the authoritative dataset used
as reference
✗ reusable and extensible in case of need
GEOlab, Politecnico di Milano – Como Campus
4. 4
➔ 1. Preliminary comparison of the datasets and computation of
global statistics
➔ 2. Geometric preprocessing of the OSM dataset to extract a subset
which is fully comparable with the IGN dataset
➔ 3. Evaluation of OSM spatial accuracy using a grid-based approach
✔ Currently developed as 3 GRASS GIS modules:
Our methodology – Overview
➔ written in Python
➔ available with a Graphical User Interface (GUI)
✔ Comparison between OSM and reference (IGN) road network datasets
composed of 3 consecutive steps:
GEOlab, Politecnico di Milano – Como Campus
5. 5
✔ Import and select the OSM and IGN datasets [required]
Step 1: Preliminary comparison of the datasets
GEOlab, Politecnico di Milano – Como Campus
7. 7
✔ Import and select the OSM and IGN datasets [required]
✔ If the extent of the OSM and/or IGN datasets is larger than the one of
interest, import a vector layer to be used as clipping mask [optional]
Step 1: Preliminary comparison of the datasets
GEOlab, Politecnico di Milano – Como Campus
8. 8
✔ Apply a set of buffers of user-specified width around both the IGN and
OSM datasets, to compute the length and the length percentage of the
OSM and IGN datasets included in the buffer [required]
Step 1: Preliminary comparison of the datasets
GEOlab, Politecnico di Milano – Como Campus
9. 9
✔ Compute also the total length of OSM and IGN datasets and their length
difference, both in map units and percentage [required]
Step 1: Preliminary comparison of the datasets
➔ output values are returned in a text file
GEOlab, Politecnico di Milano – Como Campus
10. 10
✔ Compute also the total length of OSM and IGN datasets and their length
difference, both in map units and percentage [required]
Step 1: Preliminary comparison of the datasets
➔ output values are returned in a text file
GEOlab, Politecnico di Milano – Como Campus
11. 11
✔ Compute also the total length of OSM and IGN datasets and their length
difference, both in map units and percentage [required]
Step 1: Preliminary comparison of the datasets
➔ output values are returned in a text file
GEOlab, Politecnico di Milano – Como Campus
✗ ≅450 km more in OSM than IGN dataset!
14. 14
✔ Outputs from Step 1 can be used to perform further analysis:
Step 1: Preliminary comparison of the datasets
➔ sensitivity analysis on the buffer width
GEOlab, Politecnico di Milano – Como Campus
15. 15
✔ Outputs from Step 1 can be used to perform further analysis:
Step 1: Preliminary comparison of the datasets
➔ sensitivity analysis on the buffer width
GEOlab, Politecnico di Milano – Como Campus
16. 16
✔ Cleaning of OSM dataset to make it comparable with IGN dataset
Step 2: preprocessing of the OSM dataset
GEOlab, Politecnico di Milano – Como Campus
18. 18
✔ Generalize the IGN dataset with the Douglas-Peucker algorithm [optional]
Step 2: preprocessing of the OSM dataset
➔ users have to enter the threshold for the algorithm
GEOlab, Politecnico di Milano – Como Campus
19. 19
✔ Generalize the IGN dataset with the Douglas-Peucker algorithm [optional]
Step 2: preprocessing of the OSM dataset
✔ Split the line features of the datasets into segments [required]
➔ users have to enter the threshold for the algorithm
GEOlab, Politecnico di Milano – Como Campus
21. 21
✔ Apply a buffer of user-specified width around the IGN dataset [required]
Step 2: preprocessing of the OSM dataset
➔ suitable buffer width derived from Step 1
➔ delete all the OSM roads falling outside the buffer
GEOlab, Politecnico di Milano – Como Campus
22. 22
✔ Apply a buffer of user-specified width around the IGN dataset [required]
Step 2: preprocessing of the OSM dataset
➔ suitable buffer width derived from Step 1
➔ delete all the OSM roads falling outside the buffer
➔ buffer is applied without cap around the terminal nodes
GEOlab, Politecnico di Milano – Como Campus
23. 23
✔ Apply a buffer of user-specified width around the IGN dataset [required]
Step 2: preprocessing of the OSM dataset
➔ suitable buffer width derived from Step 1
➔ delete all the OSM roads falling outside the buffer
➔ buffer is applied without cap around the terminal nodes
GEOlab, Politecnico di Milano – Como Campus
24. 24
✔ Apply a buffer of user-specified width around the IGN dataset [required]
Step 2: preprocessing of the OSM dataset
➔ suitable buffer width derived from Step 1
➔ delete all the OSM roads falling outside the buffer
➔ buffer is applied without cap around the terminal nodes
GEOlab, Politecnico di Milano – Como Campus
25. 25
Step 2: preprocessing of the OSM dataset
✔ Further clean the OSM dataset [required]:
GEOlab, Politecnico di Milano – Como Campus
26. 26
Step 2: preprocessing of the OSM dataset
➔ apply a buffer of user-specified width around each IGN segment
✔ Further clean the OSM dataset [required]:
GEOlab, Politecnico di Milano – Como Campus
27. 27
Step 2: preprocessing of the OSM dataset
➔ compute the angular coefficient of each IGN segment and all the
OSM segments included in the buffer around it
✔ Further clean the OSM dataset [required]:
GEOlab, Politecnico di Milano – Como Campus
28. 28
Step 2: preprocessing of the OSM dataset
➔ compare the difference between IGN and OSM angular coefficients
with a user-specified threshold
✔ Further clean the OSM dataset [required]:
GEOlab, Politecnico di Milano – Como Campus
29. 29
Step 2: preprocessing of the OSM dataset
✔ Further clean the OSM dataset [required]:
GEOlab, Politecnico di Milano – Como Campus
31. 31
✔ Outputs from Step 2 are saved and can be used for further analysis:
Step 2: preprocessing of the OSM dataset
➔ sensitivity analysis on the parameters involved
GEOlab, Politecnico di Milano – Como Campus
32. 32
✔ Outputs from Step 2 are saved and can be used for further analysis:
Step 2: preprocessing of the OSM dataset
➔ sensitivity analysis on the parameters involved
GEOlab, Politecnico di Milano – Como Campus
✗ preprocessed OSM has 50 km less than original OSM≅
✗ preprocessed OSM has still 50 km more than IGN≅
➔ Area 2: generalization threshold = 0.5 m, buffer = 11 m
33. 33
✔ Use a grid to take into account OSM heterogeneous nature [optional]:
Step 3: grid-based evaluation of OSM accuracy
➔ import a vector layer to be used as grid
GEOlab, Politecnico di Milano – Como Campus
34. 34
✔ Use a grid to take into account OSM heterogeneous nature [optional]:
Step 3: grid-based evaluation of OSM accuracy
➔ import a vector layer to be used as grid
➔ manually create a grid
GEOlab, Politecnico di Milano – Como Campus
35. 35
✔ For each grid cell, find the OSM maximum deviation from IGN [optional]:
Step 3: grid-based evaluation of OSM accuracy
➔ enter an upper bound value for the deviation, and the percentage
of OSM road length to be considered (to take into account outliers)
GEOlab, Politecnico di Milano – Como Campus
36. 36
✔ For each grid cell, find the OSM maximum deviation from IGN [optional]:
Step 3: grid-based evaluation of OSM accuracy
GEOlab, Politecnico di Milano – Como Campus
➔ Area 2: generalization threshold = 0.5 m, buffer = 11 m
5 - 6 m
6 - 7 m
7 - 8 m
8 - 9 m
9 - 10 m
10 - 11 m
38. 38
✔ For each grid cell, evaluate OSM accuracy against one or more threshold
values of OSM deviation from IGN [optional]:
Step 3: grid-based evaluation of OSM accuracy
➔ users have to enter one or more thresholds for deviation
GEOlab, Politecnico di Milano – Como Campus
39. 39
✔ For each grid cell, evaluate OSM accuracy against one or more threshold
values of OSM deviation from IGN [optional]:
Step 3: grid-based evaluation of OSM accuracy
➔ length percentage of OSM roads included in the threshold buffer
➔ Area 2: threshold buffer = 6 m
GEOlab, Politecnico di Milano – Como Campus
85 - 90%
90 - 95%
95 - 100%
40. 40
✔ For each grid cell, evaluate OSM accuracy against one or more threshold
values of OSM deviation from IGN [optional]:
Step 3: grid-based evaluation of OSM accuracy
➔ length percentage of OSM roads included in the threshold buffer
➔ Area 2: threshold buffer = 8 m
GEOlab, Politecnico di Milano – Como Campus
85 - 90%
90 - 95%
95 - 100%
41. 41
✔ For each grid cell, evaluate OSM accuracy against one or more threshold
values of OSM deviation from IGN [optional]:
Step 3: grid-based evaluation of OSM accuracy
➔ length percentage of OSM roads included in the threshold buffer
➔ Area 2: threshold buffer = 10 m
GEOlab, Politecnico di Milano – Como Campus
85 - 90%
90 - 95%
95 - 100%
42. 42
✔ Work in progress, currently available just for Step 1
Transposition of the procedure as a WPS
GEOlab, Politecnico di Milano – Como Campus
➔ available at http://131.175.143.84/WPS
43. 43
✔ User instructions on how to use the tool
Transposition of the procedure as a WPS
GEOlab, Politecnico di Milano – Como Campus
44. 44
✔ Geocoding service to move the map to a specified location
Transposition of the procedure as a WPS
GEOlab, Politecnico di Milano – Como Campus
45. 45
✔ Upload of IGN road network dataset
Transposition of the procedure as a WPS
GEOlab, Politecnico di Milano – Como Campus
50. 50
✔ Retrieval of OSM road network dataset from the current map view
Transposition of the procedure as a WPS
GEOlab, Politecnico di Milano – Como Campus
51. 51
✔ Retrieval of OSM road network dataset from the current map view
Transposition of the procedure as a WPS
GEOlab, Politecnico di Milano – Como Campus
52. 52
Transposition of the procedure as a WPS
GEOlab, Politecnico di Milano – Como Campus
✔ Retrieval of OSM road network dataset from a rectangle drawn on the
map or from the bounding box of an uploaded layer
53. 53
✔ Methodology for comparing OSM and authoritative road datasets:
Conclusions
✔ Future work:
➔ understand the influence of parameters through sensitivity analysis
➔ reduce computational time (especially for Step 2)
➔ extend the procedure to also compare attributes
➔ increase usability through a WPS implementation (also for Steps 2 & 3)
➔ test on different datasets (any test dataset is welcome)!
➔ (hopefully) fills a gap in literature
➔ not tied to a specific reference dataset
➔ generic, flexible and adaptable to any reference dataset
➔ users have a key role in driving the procedure
➔ parameter values should reflect the characteristic of the reference
dataset involved (e.g. nominal scale and accuracy)
GEOlab, Politecnico di Milano – Como Campus
54. 54
Links & publications
GEOlab, Politecnico di Milano – Como Campus
✔ Related publications:
➔ Brovelli M. A., Minghini M., Molinari M. and Mooney P (in press).
Towards an automated comparison of OpenStreetMap with
authoritative road datasets. Transactions in GIS.
➔ Antunes F., Fonte C. C., Brovelli M. A., Minghini M., Molinari M. and
Mooney P. (2015) Assessing OSM Road Positional Quality with
Authoritative Data. Proceedings of the VIII Conferência Nacional de
Cartografia e Geodesia, Lisbon (Portugal), October 29-30, 2015.
➔ Brovelli M. A., Minghini M., Molinari M. and Mooney P. (2015) A
FOSS4G-based procedure to compare OpenStreetMap and
authoritative road network datasets. Geomatics Workbooks 12,
235-238, ISSN 1591-092X.
✔ Links:
➔ source code:https://github.com/MoniaMolinari/OSM-roads-comparison
➔ WPS client: http://131.175.143.84/WPS