The document discusses the development of a Laboratory Assistant Suite (LAS) database application to manage data from a preclinical cancer model experiment involving implanting patient tumor samples in mice. It provides background on using such preclinical models for personalized cancer medicine. It describes the contributions of two research institutions (IRCC and Politecnico di Torino) to the LAS project and outlines the data flow, requirements, and database design for the LAS application.
Software Defect Prediction Using Radial Basis and Probabilistic Neural NetworksEditor IJCATR
Defects in modules of software systems is a major problem in software development. There are a variety of data mining
techniques used to predict software defects such as regression, association rules, clustering, and classification. This paper is concerned
with classification based software defect prediction. This paper investigates the effectiveness of using a radial basis function neural
network and a probabilistic neural network on prediction accuracy and defect prediction. The conclusions to be drawn from this work is
that the neural networks used in here provide an acceptable level of accuracy but a poor defect prediction ability. Probabilistic neural
networks perform consistently better with respect to the two performance measures used across all datasets. It may be advisable to use
a range of software defect prediction models to complement each other rather than relying on a single technique.
Leveraging Machine Learning Techniques Predictive Analytics for Knowledge Dis...Kevin Mader
Review the basic principles of predictive analytics.
Be exposed to some of the existing validation methodologies to test predictive models.
Understand how to incorporate radiology data sources (PACS, RIS, etc) into predictive modeling
Learn how to interpret results and make visualizations.
Introduction to Machine Learning and Texture Analysis for Lesion Characteriza...Kevin Mader
Review the basic principles of machine learning.
Learn what texture analysis is and how to apply it to medical imaging.
Understand how to combine texture analysis and machine learning for lesion classification tasks.
Learn the how to visualize and analyze results.
Understand how to avoid common mistakes like overfitting and incorrect model selection.
Histolab: an Open Source Python Library for Reproducible Digital PathologyAlessia Marcolini
The histo-pathological analysis of tissue sections is the gold standard to assess the presence of many complex diseases, such as tumors and it is expected to be at the center of the AI revolution in medicine, prevision supported by the increasing success of deep learning applications to digital pathology. The aim of histolab is to provide a tool for Whole Slide Images (WSIs) processing in a reproducible environment to support clinical and scientific research. histolab is designed to handle WSIs, automatically detect the tissue, and retrieve informative tiles.
Verification and validation of knowledge bases using test cases generated by ...Waqas Tariq
Knowledge based systems have been developed to solve many problems. Their main characteristic consists on the use of a knowledge representation of a specific domain to solve problems in such a way that it emulates the reasoning of a human specialist. As conventional systems, knowledge based systems are not free of failures. This justifies the need for validation and verification for this class of systems. Due to the lack of techniques which can guarantee their quality and reliability, this paper proposes a process to support validation of specific knowledge bases. In order to validate the knowledge base, restriction rules are used. These rules are elicit and represented as If Then Not rules and executed using a backward chaining reasoning process. As the result of this process test cases are created and submitted to the knowledge base in order to prove whether there are inconsistencies in the domain representation. Two main advantages can be highlighted here: the use of restriction rules which are considered as meta-knowledge (these rules improve the knowledge representation power of the system) and a process that can generate useful test cases (test cases are usually difficult and expensive to be created).
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
Software test-case generation is the process of identifying a set of test cases. It is necessary to generate the test sequence that satisfies the testing criteria. For solving this kind of difficult problem there were a lot of research works, which have been done in the past. The length of the test sequence plays an important role in software testing. The length of test sequence decides whether the sufficient testing is carried or not. Many existing test sequence generation techniques uses genetic algorithm for test-case generation in software testing. The Genetic Algorithm (GA) is an optimization heuristic technique that is implemented through evolution and fitness function. It generates new test cases from the existing test sequence. Further to improve the existing techniques, a new technique is proposed in this paper which combines the tabu search algorithm and the genetic algorithm. The hybrid technique combines the strength of the two meta-heuristic methods and produces efficient test- case sequence.
Software Defect Prediction Using Radial Basis and Probabilistic Neural NetworksEditor IJCATR
Defects in modules of software systems is a major problem in software development. There are a variety of data mining
techniques used to predict software defects such as regression, association rules, clustering, and classification. This paper is concerned
with classification based software defect prediction. This paper investigates the effectiveness of using a radial basis function neural
network and a probabilistic neural network on prediction accuracy and defect prediction. The conclusions to be drawn from this work is
that the neural networks used in here provide an acceptable level of accuracy but a poor defect prediction ability. Probabilistic neural
networks perform consistently better with respect to the two performance measures used across all datasets. It may be advisable to use
a range of software defect prediction models to complement each other rather than relying on a single technique.
Leveraging Machine Learning Techniques Predictive Analytics for Knowledge Dis...Kevin Mader
Review the basic principles of predictive analytics.
Be exposed to some of the existing validation methodologies to test predictive models.
Understand how to incorporate radiology data sources (PACS, RIS, etc) into predictive modeling
Learn how to interpret results and make visualizations.
Introduction to Machine Learning and Texture Analysis for Lesion Characteriza...Kevin Mader
Review the basic principles of machine learning.
Learn what texture analysis is and how to apply it to medical imaging.
Understand how to combine texture analysis and machine learning for lesion classification tasks.
Learn the how to visualize and analyze results.
Understand how to avoid common mistakes like overfitting and incorrect model selection.
Histolab: an Open Source Python Library for Reproducible Digital PathologyAlessia Marcolini
The histo-pathological analysis of tissue sections is the gold standard to assess the presence of many complex diseases, such as tumors and it is expected to be at the center of the AI revolution in medicine, prevision supported by the increasing success of deep learning applications to digital pathology. The aim of histolab is to provide a tool for Whole Slide Images (WSIs) processing in a reproducible environment to support clinical and scientific research. histolab is designed to handle WSIs, automatically detect the tissue, and retrieve informative tiles.
Verification and validation of knowledge bases using test cases generated by ...Waqas Tariq
Knowledge based systems have been developed to solve many problems. Their main characteristic consists on the use of a knowledge representation of a specific domain to solve problems in such a way that it emulates the reasoning of a human specialist. As conventional systems, knowledge based systems are not free of failures. This justifies the need for validation and verification for this class of systems. Due to the lack of techniques which can guarantee their quality and reliability, this paper proposes a process to support validation of specific knowledge bases. In order to validate the knowledge base, restriction rules are used. These rules are elicit and represented as If Then Not rules and executed using a backward chaining reasoning process. As the result of this process test cases are created and submitted to the knowledge base in order to prove whether there are inconsistencies in the domain representation. Two main advantages can be highlighted here: the use of restriction rules which are considered as meta-knowledge (these rules improve the knowledge representation power of the system) and a process that can generate useful test cases (test cases are usually difficult and expensive to be created).
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
Software test-case generation is the process of identifying a set of test cases. It is necessary to generate the test sequence that satisfies the testing criteria. For solving this kind of difficult problem there were a lot of research works, which have been done in the past. The length of the test sequence plays an important role in software testing. The length of test sequence decides whether the sufficient testing is carried or not. Many existing test sequence generation techniques uses genetic algorithm for test-case generation in software testing. The Genetic Algorithm (GA) is an optimization heuristic technique that is implemented through evolution and fitness function. It generates new test cases from the existing test sequence. Further to improve the existing techniques, a new technique is proposed in this paper which combines the tabu search algorithm and the genetic algorithm. The hybrid technique combines the strength of the two meta-heuristic methods and produces efficient test- case sequence.
An Empirical Comparison and Feature Reduction Performance Analysis of Intrusi...ijctcm
This paper reports on the empirical evaluation of five machine learning algorithm such as J48, BayesNet, OneR, NB and ZeroR using ten performance criteria: accuracy, precision, recall, F-Measure, incorrectly classified instances, kappa statistic, mean absolute error, root mean squared error, relative absolute error, root relative squared error. The aim of this paper is to find out which classifier is better in its performance for intrusion detection system. Machine Learning is one of the methods used in the intrusion detection system (IDS).Based on this study, it can be concluded that J48 decision tree is the most suitable associated algorithm than the other four algorithms. In this paper we compared the performance of Intrusion Detection System (IDS) Classifiers using seven feature reduction techniques.
16th slide set of CECS 542
Quality assurance for requirements documentation
Complete course: http://foss2serve.org/index.php/Requirements_Engineering,_CSU_Long_Beach,_Penzenstadler
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), and Naïve Bayes (NB) classifiers are used for training from the taxonomy of classifiers based on lazy and eager learners. In this paper, Chi-Square, a filter-based feature selection technique, is applied to the UNSW-NB15 dataset to reduce the irrelevant and redundant features. The performance of classifiers is measured in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) with or without feature selection technique and comparative analysis of these machine learning classifiers is carried out.
2020.04.07 automated molecular design and the bradshaw platform webinarPistoia Alliance
This presentation described how data-driven chemoinformatics methods may automate much of what has historically been done by a medicinal chemist. It explored what is reasonable to expect “AI” approaches might achieve, and what is best left with a human expert. The implications of automation for the human-machine interface were explored and illustrated with examples from Bradshaw, GSK’s experimental automated design environment.
Comprehensive Testing Tool for Automatic Test Suite Generation, Prioritizatio...CSCJournals
Testing has been an essential part of software development life cycle. Automatic test case and test data generation has attracted many researchers in the recent past. Test suite generation is the concept given importance which considers multiple objectives in mind and ensures core coverage. The test cases thus generated can have dependencies such as open dependencies and closed dependencies. When there are dependencies, it is obvious that the order of execution of test cases can have impact on the percentage of flaws detected in the software under test. Therefore test case prioritization is another important research area that complements automatic test suite generation in objects oriented systems. Prior researches on test case prioritization focused on dependency structures. However, in this paper, we automate the extraction of dependency structures. We proposed a methodology that takes care of automatic test suite generation and test case prioritization for effective testing of object oriented software. We built a tool to demonstrate the proof of concept. The empirical study with 20 case studies revealed that the proposed tool and underlying methods can have significant impact on the software industry and associated clientele.
A Survey of Security of Multimodal Biometric SystemsIJERA Editor
A biometric system is essentially a pattern recognition system being used in adversarial environment. Since,
biometric system like any conventional security system is exposed to malicious adversaries, who can manipulate
data to make the system ineffective by compromising its integrity. Current theory and design methods of
biometric systems do not take into account the vulnerability to such adversary attacks. Therefore, evaluation of
classical design methods is an open problem to investigate whether they lead to design secure systems. In order
to make biometric systems secure it is necessary to understand and evaluate the threats and to thus develop
effective countermeasures and robust system designs, both technical and procedural, if necessary. Accordingly,
the extension of theory and design methods of biometric systems is mandatory to safeguard the security and
reliability of biometric systems in adversarial environments.
Popular Delusions, Crowds, and the Coming Deluge: end of the Oracle?Bob Binder
Invited Talk at the 20th CREST Open Workshop, The Oracle Problem for Automated Software Testing. University College of London. May 21, 2012
Pragmatic Innovations for test oracles, a new Oracle Taxonomy, Characterization of test oracles, Challenges.
A (vintage) presentation about a database system for the study of gene expression data. Including distributed metadata annotation and some interactive analytics. Some ideas are still actual today.
An Empirical Comparison and Feature Reduction Performance Analysis of Intrusi...ijctcm
This paper reports on the empirical evaluation of five machine learning algorithm such as J48, BayesNet, OneR, NB and ZeroR using ten performance criteria: accuracy, precision, recall, F-Measure, incorrectly classified instances, kappa statistic, mean absolute error, root mean squared error, relative absolute error, root relative squared error. The aim of this paper is to find out which classifier is better in its performance for intrusion detection system. Machine Learning is one of the methods used in the intrusion detection system (IDS).Based on this study, it can be concluded that J48 decision tree is the most suitable associated algorithm than the other four algorithms. In this paper we compared the performance of Intrusion Detection System (IDS) Classifiers using seven feature reduction techniques.
16th slide set of CECS 542
Quality assurance for requirements documentation
Complete course: http://foss2serve.org/index.php/Requirements_Engineering,_CSU_Long_Beach,_Penzenstadler
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), and Naïve Bayes (NB) classifiers are used for training from the taxonomy of classifiers based on lazy and eager learners. In this paper, Chi-Square, a filter-based feature selection technique, is applied to the UNSW-NB15 dataset to reduce the irrelevant and redundant features. The performance of classifiers is measured in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) with or without feature selection technique and comparative analysis of these machine learning classifiers is carried out.
2020.04.07 automated molecular design and the bradshaw platform webinarPistoia Alliance
This presentation described how data-driven chemoinformatics methods may automate much of what has historically been done by a medicinal chemist. It explored what is reasonable to expect “AI” approaches might achieve, and what is best left with a human expert. The implications of automation for the human-machine interface were explored and illustrated with examples from Bradshaw, GSK’s experimental automated design environment.
Comprehensive Testing Tool for Automatic Test Suite Generation, Prioritizatio...CSCJournals
Testing has been an essential part of software development life cycle. Automatic test case and test data generation has attracted many researchers in the recent past. Test suite generation is the concept given importance which considers multiple objectives in mind and ensures core coverage. The test cases thus generated can have dependencies such as open dependencies and closed dependencies. When there are dependencies, it is obvious that the order of execution of test cases can have impact on the percentage of flaws detected in the software under test. Therefore test case prioritization is another important research area that complements automatic test suite generation in objects oriented systems. Prior researches on test case prioritization focused on dependency structures. However, in this paper, we automate the extraction of dependency structures. We proposed a methodology that takes care of automatic test suite generation and test case prioritization for effective testing of object oriented software. We built a tool to demonstrate the proof of concept. The empirical study with 20 case studies revealed that the proposed tool and underlying methods can have significant impact on the software industry and associated clientele.
A Survey of Security of Multimodal Biometric SystemsIJERA Editor
A biometric system is essentially a pattern recognition system being used in adversarial environment. Since,
biometric system like any conventional security system is exposed to malicious adversaries, who can manipulate
data to make the system ineffective by compromising its integrity. Current theory and design methods of
biometric systems do not take into account the vulnerability to such adversary attacks. Therefore, evaluation of
classical design methods is an open problem to investigate whether they lead to design secure systems. In order
to make biometric systems secure it is necessary to understand and evaluate the threats and to thus develop
effective countermeasures and robust system designs, both technical and procedural, if necessary. Accordingly,
the extension of theory and design methods of biometric systems is mandatory to safeguard the security and
reliability of biometric systems in adversarial environments.
Popular Delusions, Crowds, and the Coming Deluge: end of the Oracle?Bob Binder
Invited Talk at the 20th CREST Open Workshop, The Oracle Problem for Automated Software Testing. University College of London. May 21, 2012
Pragmatic Innovations for test oracles, a new Oracle Taxonomy, Characterization of test oracles, Challenges.
A (vintage) presentation about a database system for the study of gene expression data. Including distributed metadata annotation and some interactive analytics. Some ideas are still actual today.
The Jeopardy match between the two best human players of all time and the IBM Deep Q/A software, “Watson,” captured the spotlight and stimulated the imagination of the entire world. The subsequent announcement of IBM’s involvement in the creation of “Dr. Watson” has created a high level of interest in the healthcare community about the potential of this breakthrough technology as well as the potential pitfalls of the use of “artificial intelligence” in medicine. Dr. Siegel is currently working together with IBM engineers to explore how Dr. Watson can work together with physicians and medical specialists. His presentation, which was delivered on March 28th, provided a high level overview of the uniqueness of Deep Q/A Software and how it differs from other previous artificial intelligence applications.
The aim of the 3DOR Workshop series is to stimulate researchers from different fields to present state-of-the-art work in the field. 3DOR 2013 took place as the 6th workshop in this series on May 11, 2013 in Girona (Spain), in conjunction with Eurographics 2013. Prof. Henning Muller presented the keynote talk about Medical 3D data retrieval.
Umm, how did you get that number? Managing Data Integrity throughout the Data...John Kinmonth
We live at the intersection of data and people. Data integrity is a function of the decisions that people make throughout the data lifecycle.
Dave De Noia, Pointmarc lead solution architect in data management, gives his take on the processes and people that affect data integrity throughout organizations at DRIVE 2014 (Data, Reporting, Intelligence, and Visualization Exchange)
Whether you're a retailer merging web analytics data with offline numbers or a healthcare company adding new data management software, De Noia explains how to avoid logic wobble and establish shared data structures.
About Dave:
Dave De Noia lives in the balance of chaos and order inherent to working with data. Starting his career at Microsoft building analyses in both SQL and big data environments, Dave later moved onto Redfin where he created and managed data infrastructure for analysis and reporting projects. Dave now serves as the senior solution and data architect at Pointmarc, a Bellevue-based digital analytics consultancy, where he helps some of the world’s largest brands get value from their data. Naturally functioning as a bridge between business and technical teams, Dave’s professional passion lies at the intersection of data and people.
About Pointmarc:
Pointmarc is a leading digital analytics agency providing actionable marketing insight and analytics platform instrumentation services for Fortune 500 clients within retail, technology, financial, media and pharmaceutical industries. With offices in Seattle, Boston, San Francisco and Portland, Pointmarc’s immersive approach to analytics empowers businesses to dive deeper into their data.
Email info@pointmarc.com for more information on data management or analytics instrumentation, and follow @pointmarc on Twitter for the latest in analytics.
Importance of data standards and system validation of software for clinical r...Wolfgang Kuchinke
We present our evaluation of existing data standards for clinical trials. For this purpose a survey about the importance of data standards for clinical trials centers and EDC software companies were conducted. Electronic data capture in clinical trials uses a computerized system designed for the collection of clinical data in electronic form in Case Report Forms (CRF). It also covers medical data captured during clinical trials, safety data related to clinical trials, and patient reported outcome. The degree of implementation of standards, like CDISC ODM in available EDC software products was evaluated. Failure to establish data standards will make it difficult or impossible to connect data between different systems for efficient clinical study execution. The next step after purchasing a software solution is the computer system validation. Validation is about bringing computerized systems into regulatory compliance and making them compliant with GCP, GLP and GMP and other regulations (e.g. data protection). The basis standard for validation is provided by the GAMP Good Practice Guide, which provides a framework of best practices to ensure that computer systems are suitable for use and compliant with the legislation. The newest version uses a risk-based approach to computer system validation A system is evaluated and assigned to a predefined category based on its intended use and complexity. For validation one should define how all elements of the computer system are supposed to work (functional requirements), develop corresponding scripts and test routines to validate it is functioning as it should.
Architectural Styles and Case Studies, Software architecture ,unit–2Sudarshan Dhondaley
Architectural styles; Pipes and filters; Data abstraction and object-oriented organization; Event-based, implicit invocation; Layered systems; Repositories; Interpreters; Process control; Other familiar architectures; Heterogeneous architectures. Case Studies: Keyword in Context; Instrumentation software; Mobile robotics; Cruise control; three vignettes in mixed style.
Considerations and challenges in building an end to-end microbiome workflowEagle Genomics
Many of the data management and analysis challenges in microbiome research are shared with genomics and other life-science big-data disciplines. However there are aspects that are specific: some are intrinsic to microbiome data, some are related to the maturity of the field, with others related to extracting business value from the data.
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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/
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
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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/
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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.
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.
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.
2. Players
Starting May 2011, LAS stems from the joined efforts of IRCC and the Politecnico of Torino
IRCC contribution
• Strategy
• Working- and Data-flow analysis
• User interface definition
• On-site implementation
POLITO contribution
• Database & Data warehouse
• Analytical tools & software features
• IT
4. Context – Personalized Medicine in Oncology
ASSUMPTION II:
If mutations are causative, in general terms their quality is likely to influence the behavior (biology)
of the system, in particular they are predicted to determining responses to perturbations (e.g.
drugs)
5. Context – Personalized Medicine in Oncology
ASSUMPTION III:
Mutations (or their surrogates) can be exploited to stratify patients for therapy
6. Context – Personalized Medicine in Oncology
EVIDENCE I:
Precision cancer medicine works: Selective inhibition of „driver‟ mutations can result in dramatic
clinical benefit
7. Context – Personalized Medicine in Oncology
EVIDENCE II:
a. Precision cancer medicine stands on exceptions
b. „Drivers‟ not always are „targets‟ Exceptions become rules only if confirmed on a population
basis:
• Only 10% of NSCLCs harbour EGFR mutations, and only 40% of EGFR-
mutant tumours respond to EGFR inhibitors:
• overall prevalence of responders: 4%
• Only 4% of NSCLCs harbour ALK translocations, and only 50% of ALK-
translocated tumours respond to ALK inhibitors:
• overall prevalence of responders: 2%
• Response to BRAF or MEK inhibition in BRAF mutant melanoma: 60%
• Response to BRAF or MEK inhibition in BRAF mutant CRC: 2%
8. Context – Personalized Medicine in Oncology
CONSIDERATION I:
Reliable preclinical models are needed to prioritize hypothesis validation in patients (clinical trials)
due to ethical, economical and social constrains.
• Understanding inter-individual tumour heterogeneity needs a reference background:
• Focus on one specific tumour type
• Pinpointing exceptions needs big numbers:
• Collect many cases
• Identifying exceptions (and the contextual mutational milieu) needs integrated
approaches with reliable outcomes:
• Multi-dimensional genomic exploration of high-quality tumour material
9. Context – Personalized Medicine in Oncology
CONSIDERATION II:
Direct transplantation of surgical specimens in immunocompromized mice can generate a high
fidelity preclinical platform for anticipation of clinical results
• Reliable simulation of phase II trials for investigational drugs
• Identification of new predictive biomarkers for approved drugs
• Multi parametric evaluation of genetic determinants for patients
stratification
• Comparative evaluation of alternative treatment protocols
11. Context – Facts & Numbers
N° of
collected
specimens 22 148 235 480 614
Oct 2008 Oct 2010 May 2011 Apr 2012 Jan 2013
CRC Evaluation of LAS LAS
banking commercial LIMS project started
started solution started working
LAS manages (starting April 2012):
• 622 surgical samples collection
• 7158 mice
• 18537 measures of tumour growth
• 1656 mice treated with 44 different protocols&schedules
• 51131 archived aliquots of biological material
15. Waterfall model
• Feasibility study
Requirements • Requirements analysis
• Requirements definition
• Define software system functions
Design • Establish an overall system architecture
• Unified Modeling Language (UML)
• Code generation
Implementation • Definition of logically separable part of the software (units)
• Unit testing done by the developer
• Integration and testing of the complete system
Verification • Testing units against the requirements as specified
• System delivered to the client
• Identification of problems
Maintenance • Errors fixed
• Performance improvements
16. Agile model
• Customer satisfaction by rapid delivery of useful
software
• Welcome changing requirements, even late in
development
• Working software is delivered frequently
• Working software is the principal measure of
progress
• Sustainable development
• Close cooperation
• Face-to-face conversation is the best form of
communication (co-location)
• Continuous attention to technical excellence and
good design
• Simplicity - the art of maximizing the amount of
work not done - is essential
• Self-organizing teams
• Regular adaptation to changing circumstances
17. Database design
• Conceptual design. The purpose is to
represent the informal requirements of an
application in terms of a conceptual schema
that refers to a conceptual data model
• Logical design. Translation of the conceptual
schema, defined in the preceding phase, into
the logical schema of the database that refers
to a logical data model
• Physical design. The logical schema is
completed with the details of the physical
implementation (file organization and indexes)
on a given DBMS. The product is called the
physical schema and refers to a physical data
model
18. The Entity Relationship model
• Conceptual data model
• Provides a series of constructs
capable of describing the data
requirements
• Easy to understand
• Independent of the criteria for the
management and organization of data
on a database system
• For every construct, there is a
corresponding graphical
representation.
• Allows to define an E-R schema
diagrammatically
19. ER constructs
• Entity
• represents classes of objects (facts, things, people, for example) that have properties in
common and an autonomous existence
• Attribute
• describes the elementary properties of entities or relationships
• Relationship
• represents logical links between two or more entities
• Cardinalities
• specified for each entity participating in a relationship
• describe the maximum and minimum number of relationship occurrences in which an
entity occurrence can participate
• for the minimum cardinality, zero or one
• for the maximum cardinality, one or many (N)
20. ER constructs
• Identifiers
• specified for each entity
• describe the concepts (attributes and/or entities) of the schema allowing the
unambiguous identification of the entity occurrences
• internal identifier (key)
• formed by one or more attributes of the entity itself
• external identifier (foreign key)
• when the attributes of an entity are not sufficient to identify its occurrences
unambiguously
• other entities need to be involved in the identification
• the entity to identify participates with cardinality equal to (1,1) into the relationship
• Generalization
• represents logical links between entities (i.e., 1 parent and one or more children)
• the parent entity is more general in the sense that it comprises child entities as a
particular case
21. Logical design
Goals
• Construction of a relational schema
• Representing correctly and efficiently all of the information described by an ER schema
Design steps
• Restructuring of the Entity-Relationship schema
• Optimization of the schema
• Translation into the logical model
Entity1 (ID1, attr_a, attr_b, …)
Entity2 (ID2, attr_x, attr_y, …)
Relationship1 (ID1, ID2, attr_r, …)
28. Querying the database
• SQL (Structured Query Language)
• designed for managing data held in a relational database management systems
• example:
SELECT barcode, mouseStrainName
FROM Mouse M, Explant E
WHERE M.barcode = E.mouseBarcode
AND status = ‘Implanted’;
• ORM (Object-relational mapping)
• programming technique for converting data between incompatible type systems in
object-oriented programming languages
• creates a "virtual object database“ used from within the programming language
• maps database table rows to objects
• allows to establish relations between those objects
30. • High-level Python Web framework that encourages rapid development
and clean, pragmatic design
• Makes it easier to build better Web apps more quickly and with less code
• The Web framework for perfectionists with deadlines
Features
• MVC architecture • Testing framework
• Object- Relation Mapper • Solid security emphasis
• Templating Language • Send emails easily
• Automatic Language • Nice support for forms
• Elegant urls • Great docs
• Unicode support • Friendly community
• Cache framework
31. Build a django project
$ django-admin.py startproject xenopatients
• command-line utility to interact with the Django project
• the actual Python package of the project
• used to import anything inside it
• indicates that this directory is a Python package
• settings/configuration for the project
• URL declarations
• an entry-point for WSGI-compatible webservers to
serve your project
32. Run server
$ ./manage.py runserver
Validating models...
0 errors found
March 07, 2013 - 15:50:53
Django version 1.5, using settings ‘xenopatients.settings'
Development server is running at http://127.0.0.1:8000/
Quit the server with CONTROL-C.
33. Create application
$ python manage.py startapp xenos
• application belonging to the Django project
• indicates that this directory is a Python package
• defines python classes mapped on database tables
• simple routines to check the operation of the code
• defines a “type” of Web page to serve a specific
function with a specific template
• each view is represented by a simple Python function
34. Define the model
Edit the file /xenos/models.py
class Mice(models.Model):
barcode = models.BigIntegerField(primary_key=True, editable=False)
birth_date = models.DateField(db_column= 'birthdate', blank=True)
death_date = models.DateField(db_column= 'deathdate', blank=True)
gender = models.CharField(max_length=1)
status = models.CharField(max_length=20)
id_mouse_strain = models.ForeignKey(‘Mouse_strain’, blank=True,
db_column='id_mouse_strain')
def __unicode__(self):
return self.barcode
class Mouse_strain(models.Model):
id_strain = models.BigIntegerField(primary_key=True, editable=False)
mouse_strain_name = models.CharField(max_length=45, unique=True)
description = models.TextField()
linkToDoc = models.CharField(max_length=80)
def __unicode__(self):
return self.mouse_strain_name
35. Define the urls and views
Edit the file /xenos/urls.py
urlpatterns = patterns('',
(r'^$', views.index),
(r'^miceloading/$', views.miceLoading),
(r'^miceStatus/$', views.changeStatus),
…
Edit the file /xenos/views.py
@login_required
def index(request):
if request.method == 'GET':
name = request.user.username
return render_to_response('index.html', {'name':name},
RequestContext(request)) …
36. Activate the admin site
Edit the file /xenopatients/urls.py
from django.conf.urls.defaults import *
# Uncomment the next two lines to enable the admin:
from django.contrib import admin
admin.autodiscover()
urlpatterns = patterns('',
# Uncomment the next line to enable the admin:
(r'^admin/', include(admin.site.urls)),
38. References
• Software Engineering
• I. Sommerville (2010) “Software Engineering (9th Edition)”
• I. Sommerville (2007) “Ingegneria del software”
• R. Miles, K. Hamilton (2006) “Learning UML 2.0”
• M. Fowler (2010) “UML distilled. Guida rapida al linguaggio di modellazione standard”
• Database
• C. Coronel, S. Morris, P. Rob (2012) “Database Systems: Design, Implementation, and
Management”
• P. Atzeni, S. Ceri, S. Paraboschi, R. Torlone (2009) “Basi di dati – Modelli e linguaggi di
interrogazione”
• Python & Django
• A. Martelli (2006) “Python in a Nutshell, Second Edition”
• M. Lutz (2009) “Learning Python: Powerful Object-Oriented Programming”
• M. Dawson (2010) “Python Programming for the Absolute Beginner, 3rd Edition”
• Django website https://www.djangoproject.com/
• A. Holovaty, J. Kaplan-Moss (2009) “The Definitive Guide to Django: Web Development
Done Right”
• M. Beri (2009) “Sviluppare applicazioni web con Django”
39. References (context)
• Personalized medicine in oncology
• Hait WN, Cancer Discov 1, 383 (2011).
• MacConaill LE et al., Cancer Discov 1, 297 ( 2011).
• Haber Da, Gray NS, Baselga J, Cell 145, 19 (2011).
• Unmet needs and preclinical models
• de Bono JS, Ashworth A, Nature 467, 543 (2010).
• Tentler JJ et al., Nat Rev Clin Oncol 9, 338 (2012).
• Our work
• Baralis E et al., J Med Systems ( 2012).
• Migliardi G et al., Clin Cancer Res 18, 2515 ( 2012).
• Bertotti A et al., Cancer Discov 1, 508 (2011).
• Galimi F et al., Clin Cancer Res 17, 3146 ( 2011).