Coverage of a given area by means of coordinated autonomous robots is a mission
required in several applications such as, for example, patrolling, monitoring or
environmental sampling. From a mathematical perspective, this can often be
modeled as the need to estimate a scalar field, eventually time varying as in
the security applications. In this paper, the problem is addressed for the
challenging underwater scenario, where localization and communication pose
additional constraints. The solution exploits the appealing properties of the
Voronoi partition of a convex set within a probabilistic framework. In addition,
the algorithm is totally distributed and characterized by a strong engineering
perspective allowing the handling of asynchronous communication or possible loss
or adjunct of vehicles. Beyond the test in dozen of numerical case studies, the
algorithm has been validated by a challenging underwater test in 3 dimension
involving two Autonomous Underwater Vehicles (AUVs). The experiments were run in
the La Spezia harbor, in Italy, in February 2012 as demo
of the European project \co3auvs.
Big Data and Small Devices by Katharina MorikBigMine
How can we learn from the data of small ubiquitous systems? Do we need to send the data to a server or cloud and do all learning there? Or can we learn on some small devices directly? Are smartphones small? Are navigation systems small? How complex is learning allowed to be in times of big data? What about graphical models? Can they be applied on small devices or even learned on restricted processors?
Big data are produced by various sources. Most often, they are distributedly stored at computing farms or clouds. Analytics on the Hadoop Distributed File System (HDFS) then follows the MapReduce programming model. According to the Lambda architecture of Nathan Marz and James Warren, this is the batch layer. It is complemented by the speed layer, which aggregates and integrates incoming data streams in real time. When considering big data and small devices, obviously, we imagine the small devices being hosts of the speed layer, only. Analytics on the small devices is restricted by memory and computation resources.
The interplay of streaming and batch analytics offers a multitude of configurations. In this talk, we discuss opportunities for using sophisticated models for learning spatio-temporal models. In particular, we investigate graphical models, which generate the probabilities for connected (sensor) nodes. First, we present spatio-temporal random fields that take as input data from small devices, are computed at a server, and send results to -possibly different — small devices. Second, we go even further: the Integer Markov Random Field approximates the likelihood estimates such that it can be computed on small devices. We illustrate our learning models by applications from traffic management.
G. Antonelli and F. Arrichiello and F. Caccavale and A. Marino, A decentralized controller-observer scheme for weighted centroid tracking, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Franscisco, CA, pp. 2778--2783, 2011.
G. Antonelli and S. Chiaverini and A. Marino, A coordination strategy for multi-robot sampling of dynamic fields, Proceedings 2012 IEEE International Conference on Robotics and Automation, St Paul, MN, pp. 1113--1118, 2012.
Big Data and Small Devices by Katharina MorikBigMine
How can we learn from the data of small ubiquitous systems? Do we need to send the data to a server or cloud and do all learning there? Or can we learn on some small devices directly? Are smartphones small? Are navigation systems small? How complex is learning allowed to be in times of big data? What about graphical models? Can they be applied on small devices or even learned on restricted processors?
Big data are produced by various sources. Most often, they are distributedly stored at computing farms or clouds. Analytics on the Hadoop Distributed File System (HDFS) then follows the MapReduce programming model. According to the Lambda architecture of Nathan Marz and James Warren, this is the batch layer. It is complemented by the speed layer, which aggregates and integrates incoming data streams in real time. When considering big data and small devices, obviously, we imagine the small devices being hosts of the speed layer, only. Analytics on the small devices is restricted by memory and computation resources.
The interplay of streaming and batch analytics offers a multitude of configurations. In this talk, we discuss opportunities for using sophisticated models for learning spatio-temporal models. In particular, we investigate graphical models, which generate the probabilities for connected (sensor) nodes. First, we present spatio-temporal random fields that take as input data from small devices, are computed at a server, and send results to -possibly different — small devices. Second, we go even further: the Integer Markov Random Field approximates the likelihood estimates such that it can be computed on small devices. We illustrate our learning models by applications from traffic management.
G. Antonelli and F. Arrichiello and F. Caccavale and A. Marino, A decentralized controller-observer scheme for weighted centroid tracking, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Franscisco, CA, pp. 2778--2783, 2011.
G. Antonelli and S. Chiaverini and A. Marino, A coordination strategy for multi-robot sampling of dynamic fields, Proceedings 2012 IEEE International Conference on Robotics and Automation, St Paul, MN, pp. 1113--1118, 2012.
Inverse kinematics is an active research domain in robotics since several years due to its importance in several robotics application. Among the various approaches, differential inverse kinematics is widely used due to the possibility to real-time implementation. Redundant robotic systems exhibit more degrees of freedom than those strictly required to execute a given end-effector task, in such a case, multiple tasks can be handled simultaneously in, e.g., a task-priority architecture. This paper addresses the systematic extension of the multiple tasks singularity robust solution, also known as Null-space Based Behavioral control, to the case of set-based control tasks, i.e., tasks for which a range, rather than a specific value, is assigned. This is the case for several tasks such as, for example, mechanical joint limits of robotic arm as well as obstacle avoidance for any kind of robots. Numerical validation are provided to support the solution proposed.
F. Arrichiello and G. Antonelli and A.P. Aguiar and A. Pascoal, Observability metrics for the relative localization of AUVs based on range and depth measurements: theory and experiments, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Franscisco, CA, pp. 3166--3171, 2011.
A. Marino and G. Antonelli and A.P. Aguiar and A. Pascoal, Multi-robot harbor patrolling: a probabilistic approach, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Algarve, PT, pp. , 2012.
In this paper, a decentralized control strategy for networked multi-robot systems that allows the tracking of the team centroid and the relative formation is presented. The proposed solution consists of a distributed observer-controller scheme where, based only on local information, each robot
estimates the collective state and tracks the two assigned control variables. We provide a formal stability analysis of the observer-controller scheme and we
relate convergence properties to the topology of the connectivity graph. Experiments are presented to validate the approach.
The paper presents an adaptive trajectory tracking control strategy for quadrotor Micro Aerial Vehicles. The proposed approach, while keeping the typical assumption of an orientation dynamics faster than the translational one, removes that of absence of external disturbances and of perfect symmetry of the vehicle. In particular, the trajectory tracking control law is made adaptive with respect to the presence of external forces and moments, and to the uncertainty of dynamic parameters as the position of the center of mass of the vehicle. A stability analysis as well as numerical simulations are provided to support the control design.
Underwater acoustic communication is a technique of sending and receiving message below water.[1] There are several ways of employing such communication but the most common is using hydrophones. Under water communication is difficult due to factors like multi-path propagation, time variations of the channel, small available bandwidth and strong signal attenuation, especially over long ranges. In underwater communication there are low data rates compared to terrestrial communication, since underwater communication uses acoustic waves instead of electromagnetic waves.
An autonomous underwater vehicle (AUV) is a robot which travels underwater without requiring input from an operator. AUVs constitute part of a larger group of undersea systems known as unmanned underwater vehicles, a classification that includes non-autonomous remotely operated underwater vehicles (ROVs) – controlled and powered from the surface by an operator/pilot via an umbilical or using remote control. In military applications AUVs are more often referred to simply as unmanned undersea vehicles (UUVs).
Improving Pheromone Communication for UAV Swarm Mobility ManagementDaniel H. Stolfi
In this article we address the optimisation of pheromone communication used for the mobility management of a swarm of Unmanned Aerial Vehicles (UAVs) for surveillance applications. A genetic algorithm is proposed to optimise the exchange of pheromone maps used in the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model which improves the vehicles' routes in order to achieve unpredictable trajectories as well as maximise area coverage. Experiments are conducted using realistic simulations, which additionally permit to assess the impact of packet loss ratios on the performance of the surveillance system, in terms of reliability and area coverage.
https://doi.org/10.1007/978-3-030-88081-1_17
Assessment of likely consequences of a potential accident is a major concern for loss prevention and
safety promotion in process industry. Loss of confinement on a storage tank, vessel or piping on industrial
sites could imply atmospheric dispersion of toxic or flammable gases. Gas dispersion forecasting is a
difficult task since turbulence modeling at large scale involves expensive calculations. Therefore simpler
models are used but remain inaccurate especially when turbulence is heterogeneous. The present work
aims to study if Artificial Neural Networks coupled with Cellular Automata could be relevant to overcome
these gaps. Two methods are reviewed and compared. An example database was designed from RANS k-
ε CFD model. Both methods were then applied. Their efficiencies are compared and discussed in terms of
quality, real-time applicability and real-life plausibility.
Inverse kinematics is an active research domain in robotics since several years due to its importance in several robotics application. Among the various approaches, differential inverse kinematics is widely used due to the possibility to real-time implementation. Redundant robotic systems exhibit more degrees of freedom than those strictly required to execute a given end-effector task, in such a case, multiple tasks can be handled simultaneously in, e.g., a task-priority architecture. This paper addresses the systematic extension of the multiple tasks singularity robust solution, also known as Null-space Based Behavioral control, to the case of set-based control tasks, i.e., tasks for which a range, rather than a specific value, is assigned. This is the case for several tasks such as, for example, mechanical joint limits of robotic arm as well as obstacle avoidance for any kind of robots. Numerical validation are provided to support the solution proposed.
F. Arrichiello and G. Antonelli and A.P. Aguiar and A. Pascoal, Observability metrics for the relative localization of AUVs based on range and depth measurements: theory and experiments, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Franscisco, CA, pp. 3166--3171, 2011.
A. Marino and G. Antonelli and A.P. Aguiar and A. Pascoal, Multi-robot harbor patrolling: a probabilistic approach, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Algarve, PT, pp. , 2012.
In this paper, a decentralized control strategy for networked multi-robot systems that allows the tracking of the team centroid and the relative formation is presented. The proposed solution consists of a distributed observer-controller scheme where, based only on local information, each robot
estimates the collective state and tracks the two assigned control variables. We provide a formal stability analysis of the observer-controller scheme and we
relate convergence properties to the topology of the connectivity graph. Experiments are presented to validate the approach.
The paper presents an adaptive trajectory tracking control strategy for quadrotor Micro Aerial Vehicles. The proposed approach, while keeping the typical assumption of an orientation dynamics faster than the translational one, removes that of absence of external disturbances and of perfect symmetry of the vehicle. In particular, the trajectory tracking control law is made adaptive with respect to the presence of external forces and moments, and to the uncertainty of dynamic parameters as the position of the center of mass of the vehicle. A stability analysis as well as numerical simulations are provided to support the control design.
Underwater acoustic communication is a technique of sending and receiving message below water.[1] There are several ways of employing such communication but the most common is using hydrophones. Under water communication is difficult due to factors like multi-path propagation, time variations of the channel, small available bandwidth and strong signal attenuation, especially over long ranges. In underwater communication there are low data rates compared to terrestrial communication, since underwater communication uses acoustic waves instead of electromagnetic waves.
An autonomous underwater vehicle (AUV) is a robot which travels underwater without requiring input from an operator. AUVs constitute part of a larger group of undersea systems known as unmanned underwater vehicles, a classification that includes non-autonomous remotely operated underwater vehicles (ROVs) – controlled and powered from the surface by an operator/pilot via an umbilical or using remote control. In military applications AUVs are more often referred to simply as unmanned undersea vehicles (UUVs).
Improving Pheromone Communication for UAV Swarm Mobility ManagementDaniel H. Stolfi
In this article we address the optimisation of pheromone communication used for the mobility management of a swarm of Unmanned Aerial Vehicles (UAVs) for surveillance applications. A genetic algorithm is proposed to optimise the exchange of pheromone maps used in the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model which improves the vehicles' routes in order to achieve unpredictable trajectories as well as maximise area coverage. Experiments are conducted using realistic simulations, which additionally permit to assess the impact of packet loss ratios on the performance of the surveillance system, in terms of reliability and area coverage.
https://doi.org/10.1007/978-3-030-88081-1_17
Assessment of likely consequences of a potential accident is a major concern for loss prevention and
safety promotion in process industry. Loss of confinement on a storage tank, vessel or piping on industrial
sites could imply atmospheric dispersion of toxic or flammable gases. Gas dispersion forecasting is a
difficult task since turbulence modeling at large scale involves expensive calculations. Therefore simpler
models are used but remain inaccurate especially when turbulence is heterogeneous. The present work
aims to study if Artificial Neural Networks coupled with Cellular Automata could be relevant to overcome
these gaps. Two methods are reviewed and compared. An example database was designed from RANS k-
ε CFD model. Both methods were then applied. Their efficiencies are compared and discussed in terms of
quality, real-time applicability and real-life plausibility.
Machinery signal separation using non-negative matrix factorization with real...journalBEEI
A big challenge in detecting damage occurs when the sound of a machine mixes with the sound of another machine. This paper proposes the separation of mixed acoustic signals using Non-negative Matrix Factorization (NMF) method for fault diagnosis. The NMF method is an effective solution for finding hidden parameters when the number of observations obtained by the sensor is less than the number of sources. The real mixing process is done by placing two microphones in front of the machine. Two microphones will be used as sensors to capture a mixture of four machinery signals. Performance testing of signal separation is done by comparing baseline signals with estimated signals through the mean log spectral distance (LSD) and the mean square error (MSE). The smallest spectral distance between the estimated signal and the baseline signal is found in Ŝ2 with an average LSD of 1.26. The estimated signal Ŝ2 is the closest to the baseline signal with MSE of 1.15 x 10-2. The pattern of bearing damage in the male screw compressor can be identified from the spectrum of estimated signal through harmonic frequencies as in the estimated signal Ŝ3 which is seen at 11x fundamental frequency, 12x fundamental frequency, 15x fundamental frequency, and 16x fundamental frequency.
SIMULATION & VANET: TOWARDS A NEW RELIABLE AND OPTIMAL DATA DISSEMINATION MODELpijans
Ad hoc networks was developed in the 2000s, they was highly used in dynamic environment, particularly
for inter- vehicular communication (VANETs : Vehicular Ad hoc Networks).
Since that time, many researches and developments process was dedicated to VANET networks. These were
motivated by the current vehicular industry trend that is leading to a new transport system generation
based on the use of new communication technologies in order to provide many services to passengers, the
fact that improves the driving and travel’s experience.
These systems require traffic information sharing and dissemination, such as the alert message emitting,
that be exchanged for drivers protection on the road. Sharing such information between vehicles helps to
anticipate potentially dangerous situations, as well as planning better routes during congestion situations.
The current paper attempts to model and simulate VANET Networks, aiming to analyze and evaluate
security information dissemination approaches and mechanisms used in this type of networks in several
exchanges conditions. The second objective is to identify their limitations and suggest a new improved
approach. This study was conducted as part of our research project entitled “Simulation & VANETs”,
where we justify and validate our approach using modeling and simulation techniques and tools used in
this domain.
In the present scenario, research conducted is mostly based on determining the duration of green light.
Moreover the research papers published on Adaptive Traffic Management did not focus much on the
concept of handling Emergency Vehicles. This major role of this project is as a continuation to the
existing research papers published on this topic. Here we not only handle traffic effectively but also
elaborate on effective management of highly prioritized vehicles through all possible phases. In this
particular research paper, Wireless Sensor Networks (WSN) is assumed to be the source of input.
AnaVANET: an experiment and visualization tool for vehicular networksManabu Tsukada
The experimental evaluation of wireless and mobile networks is a challenge that rarely substitutes simulation in research works. This statement is even more evident in vehicular communications, due to the equipment and effort needed to obtain significant and realistic results. In this paper, key issues in vehicular experimental evaluation are analyzed by an evaluation tool called AnaVANET, especially designed for assessing the performance of vehicular networks. This software processes the output of well-known testing tools such as ping or iperf, together with navigation information, to generate geo-aware performance figures of merit both in numeric and graphical forms. Its main analysis capabilities are used to validate the good performance in terms of delay, packet delivery ratio and throughput of NEMO, when using a road-side segment based on IPv6 GeoNetworking.
Predicting phase durations of traffic lights using live open traffic lights dataBrecht Van de Vyvere
Paper: https://brechtvdv.github.io/Article-Predicting-traffic-light-phases/
Dynamic traffic lights change their current phase duration according to the situation on the intersection, such as crowdedness. In Flanders, only the minimum and maximum duration of the current phase is published. When route planners want to reuse this data they have to predict how long the current phase will take in order to route over these traffic lights. We tested for a live Open Traffic Lights dataset of Antwerp how frequency distributions of phase durations (i) can be used to predict the duration of the current phase and (ii) can be generated client-side on-the-fly with a demonstrator. An overall mean average error (MAE) of 5.1 seconds is reached by using the median for predictions. A distribution is created for every day with time slots of 20 minutes. This result is better than expected, because phase durations can range between a few seconds and over two minutes. When taking the remaining time until phase change into account, we see a MAE around 10 seconds when the remaining time is less than a minute which we still deem valuable for route planning. Unfortunately, the MAE grows linear for phases longer than a minute making our prediction method useless when this occurs. Based on these results, we wish to present two discussion points during the workshop.
A Strategy for Reliability Evaluation and Fault Diagnosis of Autonomous Under...Koorosh Aslansefat
Underwater vehicles contribute significantly to exploiting great maritime resources. Autonomous vehicles are one of the various kinds of underwater vehicles which are able to perform operations without operator's interference. Autonomous underwater vehicles can be classified according to their propulsion systems. Autonomous Underwater Gliders (AUG) are among autonomous underwater vehicles which fall under the category of glide type underwater vehicles. They are designed in a way that they benefit low energy consumption and a wide survey range. Their reliable design is one of the challenges facing their manufacturing. Fault tolerance is one of the important attributes in designing reliable systems. Recognizing, evaluating and facing the faults are of great importance in designing fault tolerant systems. This paper studies underwater Glider vehicles' subsystems, considers their faults and causes, and provides a typical fault tree for these vehicles form which glider reliability and the effects of glider subsystems on its failure can be driven.
Data verification for collective adaptive systems: spatial model-checking of...FoCAS Initiative
Vincenzo Ciancia, Stephen Gilmore, Diego Latella, Michele Loreti, Mieke Massink from 2nd FoCAS Workshop on Fundamentals of Collective Adaptive Systems at SASO 2014
The development of wireless technology currently allows extending the notion of mobility for access to
information and communication anywhere and anytime. With the emergence of sensor networks
(Traditional (WSN) and vehicular (VSN)), new themes have been opened and new challenges have emerged
to meet the needs of individuals and the requirements of several application areas. Research today is much
focused on vehicular sensor networks (VSN), considerable efforts have emerged to introduce intelligence
into transport systems whose aim is to improve safety, efficiency and usability in road transport. These
networks will play an important role in building the Future Internet, where they will serve as a support for
various communication applications and integrated into our daily lives. In this paper, we surveyed the main
characteristic and applications of two type of Ad hoc networks WSN and VSN.
Smart and efficient system for the detection of wrong cars parkingjournalBEEI
This paper presents a smart and efficient car-parking detection system. The proposed system is comprised of two cameras connected to a mobile system that is devised with Arduino, four DC motors, and PIR sensor placed strategically to monitor parking space, especially within its painted rectangular lines of each parking lot. The mobile monitoring system is automatically responsive to any move they detected as vehicles within the parking space along the rows of parking lots. Once detected, the captured images are processed using the MATLAB software. Any improperly parked cars detected, the cameras will identify their plate numbers, and snap and record it in a database. The designed prototype of the proposed system was tested in five presumed cases. In each case, ten images were processed, thus 50 images were eventually obtained. Out of the 50 images, 48 images corresponded to correct detection whereas the other two images corresponded to wrong detection. Accordingly, the efficiency rate of the proposed smart car-parking monitoring system is 96%. This system offers suitable solution in assisting drivers to park properly within each parking lot and owners of parking area to keep it organized via remote monitoring system.
Machine learning for decentralized and flying radio devicesITU
This presentation discusses matters of machine learning for decentralized and flying radio devices. This presentation was given during the ITU-T workshop on Machine Learning for 5G and beyond, held at ITU HQ in Geneva, Switzerland on 29 Jan 18. More information on the workshop can be found here: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/20180129/Pages/default.aspx
Join our upcoming forums and workshops here: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/Pages/default.aspx
https://www.slideshare.net/ITU/ai-and-machine-learning
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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/
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
1. Experimental Results of Coordinated Coverage by
Autonomous Underwater Vehicles
Alessandro Marino, Gianluca Antonelli
Universit`a di Salerno, Italy
Universit`a di Cassino & ISME (Integrated Systems for Marine Environment), Italy
antonelli@unicas.it
http://webuser.unicas.it/lai/robotica
http://www.isme.unige.it/
Marino, Antonelli Karlsruhe, 9 May 2013
2. CO3
AUVs
Cooperative Cognitive Control of Autonomous Underwater Vehicles
fundings : European FP7, Cognitive Systems, Interaction, Robotics
kind : Collaborative Project (STREP)
duration : 3 years, 2009-2012
partners : Jacobs University, DE;
ISME, I;
Instituto Superior T´ecnico, P;
GraalTech, I
http://www.Co3-AUVs.eu
Marino, Antonelli Karlsruhe, 9 May 2013
3. Problem formulation
Multi-robot harbor patrolling
Totally decentralized
Robust to a wide range of failures
communications
vehicle loss
vehicle still
Flexible/scalable to the number of vehicles add vehicles anytime
Possibility to tailor wrt communication capabilities
Not optimal but benchmarking required
Anonymity
To be implemented on a real set-up obstacles. . .
Marino, Antonelli Karlsruhe, 9 May 2013
4. Proposed solution
Proper merge of the Voronoi and Gaussian processes concepts
Motion computed to increase information
Framework to handle
Spatial variability regions with different interest
Time-dependency forgetting factor
Asynchronous spot visiting demand
Mathematically strong overlap with (time varying) coverage,
deployment, resource allocation, sampling, exploration, monitoring, etc.
slight differences depending on assumptions and objective functions
Marino, Antonelli Karlsruhe, 9 May 2013
5. Proposed solution
Proper merge of the Voronoi and Gaussian processes concepts
Motion computed to increase information
Framework to handle
Spatial variability regions with different interest
Time-dependency forgetting factor
Asynchronous spot visiting demand
Mathematically strong overlap with (time varying) coverage,
deployment, resource allocation, sampling, exploration, monitoring, etc.
slight differences depending on assumptions and objective functions
Marino, Antonelli Karlsruhe, 9 May 2013
6. Background
theoretical details
Antonelli, Chiaverini, Marino, A coordination strategy for multi-robot
sampling of dynamic fields, ICRA 2012
experimental validation with surface vehicles
Marino, Antonelli, Aguiar, Pascoal, Multi-robot harbor patrolling: a
probabilistic approach, IROS 2012
Marino, Antonelli Karlsruhe, 9 May 2013
7. Voronoi partitions I
Voronoi partitions (tessellations/diagrams)
Subdivisions of a set S characterized by a metric with respect to a
finite number of points belonging to the set
union of the cells gives back the set
the intersection of the cells is null
computation of the cells is a
decentralized algorithm without
communication needed
Marino, Antonelli Karlsruhe, 9 May 2013
9. Background I
Variable of interest is a Gaussian process
how much do I trust that
a given point is safe?
Given the points of measurements done. . .
Sa = (xa
1 , ta
1 ), (xa
2 , ta
2 ), . . . , (xa
na
, ta
na
)
and one to do. . .
Sp = (xp, t)
Synthetic Gaussian representation of the condition distribution
ˆµ = µ(xp, t) + c(xp, t)TΣ−1
Sa(ya − µa)
ˆσ = K(f(xp, t), f(xp, t)) − c(xp, t)TΣ−1
Sac(xp, t)
c represents the covariances of the acquired points vis new one
Marino, Antonelli Karlsruhe, 9 May 2013
10. Description I
The variable to be sampled is a confidence map
Reducing the uncertainty means increasing the highlighted term
ˆµ = µ(xp, t) + c(xp, t)TΣ−1
Sa(ya − µa)
ˆσ = K(f(xp, t), f(xp, t)) − c(xp, t)T
Σ−1
Sac(xp, t)
ξ
− > ξ example
Marino, Antonelli Karlsruhe, 9 May 2013
11. Description II
Distribute the computation among the vehicles
each vehicle in its own Voronoi cell
Compute the optimal motion to reduce uncertainty
Several choices possible:
minimum, minimum over an
integrated path, etc.
Marino, Antonelli Karlsruhe, 9 May 2013
14. Accuracy: example
Only the restriction to V or2 is needed for its movement computation
τs
x1 x2 x3 x4
x
ξ(x)
V or2
Marino, Antonelli Karlsruhe, 9 May 2013
15. Accuracy: example
Merging of all the local restrictions leads to a reasonable approximation
τs
x1 x2 x3 x4
x
ξ(x)
V or2
Marino, Antonelli Karlsruhe, 9 May 2013
17. Numerical validation
Dozens of numerical simulations by changing the key parameters:
vehicles number
faults
obstacles
sensor noise
area shape/dimension
comm. bit-rate
space scale
time scale
2
3 4
Marino, Antonelli Karlsruhe, 9 May 2013
18. Some benchmarking
With a static field the coverage index always tends to one
0 200 400 600 800 1000
0.2
0.4
0.6
0.8
1
step
[]
Coverage Index
Marino, Antonelli Karlsruhe, 9 May 2013
19. Some benchmarking
Comparison between different approaches
00
Lawnmower
Proposed
Random
Deployment0.5
1.5
2
200 400 600 800 1000 1200
1
[]
step
same parameters
lawnmower rigid wrt
vehicle loss
deployment suffers
from theoretical
flaws
Marino, Antonelli Karlsruhe, 9 May 2013
20. Vehicle characteristics
internal diameter .125 m
external diameter .14 m
length 2 m
mass 30 kg
mass variation range .5 kg
(at water density 1.031 kg/m3
)
moving mass max displacement 0.050 m
Lead acid batteries 12 V 72 Ah
autonomy at full propulsion 8 h
diving scope 0–50 m
break point in depth 100 m
speed with jet pump propeller 1.01 m/s 2 knots
speed with blade propeller 2.02 m/s 4 knots
cpu 1GHz, VIA EDEN
dram 1GB, DDR2
Marino, Antonelli Karlsruhe, 9 May 2013
21. Experimental validation
joint experiment with Graaltech NURC (NATO Undersea Research
Center) facilities, La Spezia, Italy
Marino, Antonelli Karlsruhe, 9 May 2013
22. Experimental validation
2 F`olaga, 4 acoustic transponders, 1 gateway buoy
110 × 80 × 4 m
1.5 m/s
33 minutes
WHOI micromodem 80 bps
Time Division Multiple Access
localization: every 8 s
user comm: 31 byte/min with 14 s delay
Marino, Antonelli Karlsruhe, 9 May 2013
23. Experimental validation
Due to poor communication, the algorithm runs by predicting the
movement of the other
# fields size (bytes)
1) vehicle ID 2
2) localization time 4
3) vehicle latitude 4
4) vehicle longitude 4
5) vehicle depth 4
6) target latitude 4
7) target longitude 4
8) target depth 4
Marino, Antonelli Karlsruhe, 9 May 2013
24. Experimental validation - video
Coverage index
200 400 600 800 1000 1200 1400 1600
0.1
0.2
0.3
0.4
[]
0.5
00
time [s] 1800
Marino, Antonelli Karlsruhe, 9 May 2013