In this paper, a new algorithm for a high resolution
Direction Of Arrival (DOA) estimation method for multiple
wideband signals is proposed. The proposed method proceeds
in two steps. In the first step, the received signals data is
decomposed in a Toeplitz form using the first-order statistics.
In the second step, The QR decomposition is applied on the
constructed Toeplitz matrix. Compared with existing schemes,
the proposed scheme provides several advantages. First, it
requires computing the triangular matrix R or the orthogonal
matrix Q to find the DOA; these matrices can be computed
with O(n2) operation. However, most of the existing schemes
required eignvalue decomposition (EVD) for the covariance
matrix or singular value decomposition (SVD) for the data
matrix; using EVD or SVD requires much more complex
computational O(n3) operation. Second, the proposed scheme
is more suitable for high-speed communication since it
requires first-order statistics and a single snapshot. Third,
the proposed scheme can estimate the correlated wideband
signals without using spatial smoothing techniques; whereas,
already-existing schemes do not. Accuracy of the proposed
wideband DOA estimation method is evaluated through
computer simulation in comparison with a conventional
method.
Robust Low-rank and Sparse Decomposition for Moving Object DetectionActiveEon
Presentation summary:
* Moving object detection by background modeling and subtraction.
* Solved and unsolved challenges.
* Framework for low-rank and sparse decomposition.
* Some applications of RPCA on:
* * Background modeling and foreground separation.
* * Very dynamic background.
* * Multidimensional and streaming data.
* LRSLibrary1 + demo.
In this paper, a new algorithm for a high resolution
Direction Of Arrival (DOA) estimation method for multiple
wideband signals is proposed. The proposed method proceeds
in two steps. In the first step, the received signals data is
decomposed in a Toeplitz form using the first-order statistics.
In the second step, The QR decomposition is applied on the
constructed Toeplitz matrix. Compared with existing schemes,
the proposed scheme provides several advantages. First, it
requires computing the triangular matrix R or the orthogonal
matrix Q to find the DOA; these matrices can be computed
with O(n2) operation. However, most of the existing schemes
required eignvalue decomposition (EVD) for the covariance
matrix or singular value decomposition (SVD) for the data
matrix; using EVD or SVD requires much more complex
computational O(n3) operation. Second, the proposed scheme
is more suitable for high-speed communication since it
requires first-order statistics and a single snapshot. Third,
the proposed scheme can estimate the correlated wideband
signals without using spatial smoothing techniques; whereas,
already-existing schemes do not. Accuracy of the proposed
wideband DOA estimation method is evaluated through
computer simulation in comparison with a conventional
method.
Robust Low-rank and Sparse Decomposition for Moving Object DetectionActiveEon
Presentation summary:
* Moving object detection by background modeling and subtraction.
* Solved and unsolved challenges.
* Framework for low-rank and sparse decomposition.
* Some applications of RPCA on:
* * Background modeling and foreground separation.
* * Very dynamic background.
* * Multidimensional and streaming data.
* LRSLibrary1 + demo.
PhD Thesis Defense Presentation: Robust Low-rank and Sparse Decomposition for...ActiveEon
Thesis submitted by Andrews Cordolino Sobral at Université de La Rochelle to fulfill the degree of Doctor of Philosophy.
Robust Low-rank and Sparse Decomposition for Moving Object Detection - From Matrices to Tensors
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Un tempo considerato un problema troppo complesso e costoso da risolvere, la governance dei dati registra una nuova fase di interesse. I crescenti volumi di informazioni e i requisiti superiori ad esse associati stanno alimentando la domanda, con il conseguente sviluppo di nuove soluzioni che consentono alle aziende di bilanciare le esigenze a breve termine con una strategia di governance rivolta al futuro.
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Una serie di infografiche per orientarsi nel mondo digital e scoprire come diventare un “Digital” Citizen.
Nella quinta infografica della serie, HP delinea la giornata tipo di un Digital Citizen attraverso l'uso di una serie di app che semplificano la vita di tutti i giorni.
PhD Thesis Defense Presentation: Robust Low-rank and Sparse Decomposition for...ActiveEon
Thesis submitted by Andrews Cordolino Sobral at Université de La Rochelle to fulfill the degree of Doctor of Philosophy.
Robust Low-rank and Sparse Decomposition for Moving Object Detection - From Matrices to Tensors
L’edizione 2014 della CIO Survey, realizzata da NetConsulting e sponsorizzata da Accenture, HP e Telecom Italia, fornisce un quadro delle principali priorità e aree di investimento in ambito ICT delle aziende italiane del settore privato.
Un tempo considerato un problema troppo complesso e costoso da risolvere, la governance dei dati registra una nuova fase di interesse. I crescenti volumi di informazioni e i requisiti superiori ad esse associati stanno alimentando la domanda, con il conseguente sviluppo di nuove soluzioni che consentono alle aziende di bilanciare le esigenze a breve termine con una strategia di governance rivolta al futuro.
"Good to know - Essere “Citizen” nell’era del Digitale"
Una serie di infografiche per orientarsi nel mondo digital e scoprire come diventare un “Digital” Citizen.
Nella quinta infografica della serie, HP delinea la giornata tipo di un Digital Citizen attraverso l'uso di una serie di app che semplificano la vita di tutti i giorni.
131014 yann-gael gueheneuc - quality, patterns, and multi-language systemsPtidej Team
Introduction to software quality. Quality models and studies for quality: patterns, social, and developers studies. Usefulness of quality models and challenges of multi-language systems.
In planet-scale deployments, the Operation and Maintenance (O&M) of cloud platforms cannot be done any longer manually or simply with off-the-shelf solutions. It requires self-developed automated systems, ideally exploiting the use of AI to provide tools for autonomous cloud operations. This talk will explain how deep learning, distributed traces, and time-series analysis (sequence analysis) can be used to effectively detect anomalous cloud infrastructure behaviors during operations to reduce the workload of human operators. The iForesight system is being used to evaluate this new O&M approach. iForesight 2.0 is the result of 2 years of research with the goal to provide an intelligent new tool aimed at SRE cloud maintenance teams. It enables them to quickly detect and predict anomalies thanks to the use of artificial intelligence when cloud services are slow or unresponsive.
Using Met-modeling Graph Grammars and R-Maude to Process and Simulate LRN ModelsWaqas Tariq
Nowadays, code mobility technology is one of the most attractive research domains. Numerous domains are concerned, many platforms are developed and interest applications are realized. However, the poorness of modeling languages to deal with code mobility at requirement phase has incited to suggest new formalisms. Among these, we find Labeled Reconfigurable Nets (LRN) [9], This new formalism allows explicit modeling of computational environments and processes mobility between them. it allows, in a simple and an intuitive approach, modeling mobile code paradigms (mobile agent, code on demand, remote evaluation). In this paper, we propose an approach based on the combined use of Meta-modeling and Graph Grammars to automatically generate a visual modeling tool for LRN for analysis and simulation purposes. In our approach, the UML Class diagram formalism is used to define a meta-model of LRN. The meta-modeling tool ATOM3 is used to generate a visual modeling tool according to the proposed LRN meta-model. We have also proposed a graph grammar to generate R-Maude [22] specification of the graphically specified LRN models. Then the reconfigurable rewriting logic language R-Maude is used to perform the simulation of the resulted R-Maude specification. Our approach is illustrated through examples.
Hardware Implementations of RS Decoding Algorithm for Multi-Gb/s Communicatio...RSIS International
In this paper, we have designed the VLSI hardware for a novel RS decoding algorithm suitable for Multi-Gb/s Communication Systems. Through this paper we show that the performance benefit of the algorithm is truly witnessed when implemented in hardware thus avoiding the extra processing time of Fetch-Decode-Execute cycle of traditional microprocessor based computing systems. The new algorithm with less time complexity combined with its application specific hardware implementation makes it suitable for high speed real-time systems with hard timing constraints. The design is implemented as a digital hardware using VHDL
Identifying concepts in execution traces is a
task often necessary to support program comprehension or
maintenance activities. Several approaches—static, dynamic or
hybrid—have been proposed to identify cohesive, meaningful
sequence of methods in execution traces. However, none of the
proposed approaches is able to label such segments and to
identify relations between segments of the same trace.
This paper present SCAN (Segment Concept AssigNer) an
approach to assign labels to sequences of methods in execution
traces, and to identify relations between such segments. SCAN
uses information retrieval methods and formal concept analysis
to produce sets of words helping the developer to understand
the concept implemented by a segment. Specifically, formal
concept analysis allows SCAN to discover commonalities between segments in different trace areas, as well as terms more
specific to a given segment and high level relations between
segments.
The paper describes SCAN along with a preliminary manual validation—upon execution traces collected from usage
scenarios of JHotDraw and ArgoUML—of SCAN accuracy
in assigning labels representative of concepts implemented by
trace segments.
Second part of the Course "Java Open Source GIS Development - From the building blocks to extending an existing GIS application." held at the University of Potsdam in August 2011
Morichetta, A., Casas, P., & Mellia, M. (2019). EXPLAIN-IT: Towards explainable AI for unsupervised network traffic analysis. In Proceedings of the 3rd ACM CoNEXT Workshop on Big DAta, Machine Learning and Artificial Intelligence for Data Communication Networks (pp. 22–28).
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behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScsitconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
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.
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👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
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In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
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The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
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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.
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CSMR10a.ppt
1. A Heuristic-based Approach
to Identify Concepts
in Execution Traces
Fatemeh Asadi*
Massimiliano Di Penta**
Giuliano Antoniol*
Yann-Gaël Guéhéneuc**
* Ecole Polytechnique de Montréal, Canada
** Dept. Of Engineering -– Univ. of Sannio, Italy
CSMR 2010 Madrid (Spain) 1
2. Motivations
• Software systems lack adequate documentation
• Developers try to understand systems through
– Static analyses, visualizations built upon static data
– Dynamic analyses, requiring the execution of the system
• (Dynamic) concept identification
– Identify sets of method calls in execution traces responsible
for the implementation of domain concepts or user-observable
features
– Existing approaches based on static analysis [Anquetil and
Lethbridge (1998)], dynamic analysis [Wilde and Scully (1995)
Tonella and Ceccato (2004)], IR techniques [Poshyvanyk et
al. (2007)], or hybrid ones [Eaddy et al. (2008)]
CSMR 2010 - Madrid (Spain) 2
3. Proposed approach
A novel approach that analyzes execution traces and
groups together method calls that:
(i) sequentially invoked together/in sequence
(ii) cohesive and decoupled from a conceptual point of view
Assumptions
Let us consider a feature is being executed in a scenario
– e.g., “Open a Web page from a browser”
or “Save an image in a paint application”
The set of methods related to the feature is likely to be:
– (i) conceptually cohesive
– (ii) decoupled from those of other features
– (iii) sequentially invoked
CSMR 2010 - Madrid (Spain) 3
4. Proposed approach
Step I – System instrumentation
Step II – Execution trace collection
Step III – Trace pruning and compression
Step IV – Textual analysis of methods’
source code
Step V – Search-based concept
identification
CSMR 2010 - Madrid (Spain) 4
5. Step I and Step II – Getting Traces
Step I - System instrumentation
System instrumented using the MoDeC instrumentor
– MoDeC tool to extract and model sequence diagrams for
Java systems
Java bytecode instrumentation tool
– Inserts appropriate and dedicated method invocations in the
system to method/constructor entry/exit, points
– Allows for trace tagging
Step II - Execution trace collection
We exercise a system following operation sequences
taken from user manuals or use case descriptions
CSMR 2010 - Madrid (Spain) 5
6. Step III – Trace Pruning and Compression
Removing methods not very useful for feature identification
Methods occurring in many scenarios
– Are often utility methods
– We use the same idea of tf-idf in Information Retrieval
Too frequent methods
– Could be for example related to crosscutting concerns
– We remove methods having a frequency
Q3 + 2 × IQR (75% percentile + 2 × the interquartile range)
Trace compression
Aim: collapse repetitions in execution traces
Purpose: reduce the search space for Step V
Examples:
– m1(); m1(); m1(); m1();
m1; m2();
– m1(); m2(); m1(); m2();
Performed using the Run Length Encoding (RLE)
Applied for sub-sequences having an arbitrary length
CSMR 2010 - Madrid (Spain) 6
7. Step IV
Conceptual cohesion and coupling determined according
to [Marcus et al., 2008] and [Poshyvanyk et al., 2006]
Index identifiers, comments contained in methods
Extraction of identifiers and comment words
Camel-case splitting of composed identifiers
Stop word removal (English + Java keywords)
Stemming using the Porter stemmer
Indexing using tf-idf
Reduce the term-document space into a (smaller) concept-
document space using Latent Semantic Indexing (LSI)
– Helps to cope with synonymy and homonymy
– Concept space=50
CSMR 2010 - Madrid (Spain) 7
8. Step V
We use a search-based optimization technique based on Genetic
Algorithms (GA) to split traces into segments
Representation: a bit-vector where 1 indicates the end of a segment
Trace splitting m1 m2 m1 m3 m4 m1 m4 m6 m1
Representation 0 1 0 0 1 0 0 0 1
Mutation: randomly flips a bit (i.e., splits or merge segments)
0 1 0 0 1 0 0 0 1 0 0
1 0 0 1 0 0 0 1
Crossover: two-points
0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1
0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1
Selection: Roulette Wheel
CSMR 2010 - Madrid (Spain) 8
9. Step V – Quality of the Solution
Fitness Function:
Segment Cohesion is the average (textual) similarity
between any pair of methods in a segment
Segment Coupling is the average (textual) similarity
between a segment and all other segments in the trace
Other GA parameters
200 individuals
2,000 generations for JHotDraw and 3,000 for ArgoUML
5% mutation probability, 70% crossover probability
Distributed GA implementation (across 4 servers)
CSMR 2010 - Madrid (Spain) 9
10. Empirical Study
• Goal: analyze the novel concept location approach based
• Purpose: of evaluating its capability of identifying
meaningful concepts
• Quality focus: accuracy and completeness of the
identified concepts
• Context: an implementation of our approach and
execution traces extracted from two open source
systems, JHotDraw and ArgoUML
CSMR 2010 - Madrid (Spain) 10
11. Research Questions
RQ1: How stable is the GA, through
multiple runs, when identifying concepts
into execution traces?
RQ2: To what extent the identified
concepts match the ones in the oracle?
RQ3: How accurate is the identification of
concepts in execution traces?
CSMR 2010 - Madrid (Spain) 11
12. RQ1: GA stability
We compute the overlap between segmentations
obtained in multiple runs using the Jaccard overlap
Score
Two segments overlaps when they contain calls in the same position
of the trace
Because a segment of trace T1 overlaps with more segments of T2,
the highest similarity is chosen
Run 1 m1 m2 m1 m3 m4 m1 m4 m6 m1
Run 2 m1 m2 m1 m3 m4 m1 m4 m6 m1
2/3 2/4 3/4
CSMR 2010 - Madrid (Spain) 12
13. RQ1: Results
Average overlap between 72% and 84%
Slightly higher convergence for ArgoUML
Ability of the algorithm to converge, despite the
relatively large search space
CSMR 2010 - Madrid (Spain) 13
14. RQ2: Matching with the Oracle
We manually tag start-end of features while
executing the system
Using the MoDeC instrumentation tool
While executing the instrumented system, the user triggers the
introduction of <Start> and <Stop> tags in the trace
Matching between identified traces and oracle
computed as in RQ1
Run 1 m1 m2 m1 m3 m4 m1 m4 m6 m1
Oracle m1 m2 m1 m3 m4 m1 m4 m6 m1
2/3 2/4 3/4
CSMR 2010 - Madrid (Spain) 14
15. RQ2: Results
High overlap for some features
e.g., Draw rectangle or Draw circle
Lower for features obtained adapting other ones
e.g., Add text obtained adapting Draw rectangle
In other cases, low overlap is due to large segments
split into more smaller and cohesive ones
CSMR 2010 - Madrid (Spain) 15
16. RQ3: Accuracy in trace identification
Computed similarly to RQ2, however we use
Precision instead of Jaccard overlap Score
Run 1 m1 m2 m1 m3 m4 m1 m4 m6 m1
Oracle m1 m2 m1 m3 m4 m1 m4 m6 m1
2/2 2/3 3/4
CSMR 2010 - Madrid (Spain) 16
17. RQ3: Results
Precision often very high
In most cases above 85% and often equal to 100%
Low precision (mean 32%) for Add text
Relatively low (mean 69%) for Draw rectangle
These two features are difficult to be distinguished
CSMR 2010 - Madrid (Spain) 17
18. Inspection of the obtained segments
Add class (ArgoUML)
The approach split this long feature of 199 methods sequence into 5 segments
related to sub-features (creation of objects, adding the project class, handling
namespace, setting object properties, handling persistence of the diagram)
Create note (ArgoUML)
Only the first part (50 methods) of the trace composed of 88 calls was identified
Problems related to multi-threading
Problems related to collapsing (during compression) loops containing variants
Cut rectangle (JHotDraw)
Only the last 39 out of 172 calls were included in the segment
Methods related to adding to the clipboard and showing the rectangle as “cut”
First methods related to GUI events and split in many small segments
Spawn window (JHotDraw)
72 out of 197 methods included
The remaining ones were related to setting up menu command properties
CSMR 2010 - Madrid (Spain) 18
19. Threats to Validity
Construct validity (relation btw. theory and observation)
Multi-threading can change the ordering of calls in multiple
executions of the same scenario
A better assessment of the actual content of the obtained
segments is needed
Internal validity (presence of confounding factors)
Trace tagging may be imprecise, again due to multi-threading
Noise due to utility methods
GA intrinsic randomness
External validity (generalization of findings)
We analyzed two different systems, multiple traces
As usual, further empirical evaluation is needed
CSMR 2010 - Madrid (Spain) 19
20. Conclusions
We proposed a search-based approach to automatically locate
concepts in execution traces
By splitting traces into conceptually cohesive and decoupled segments
Empirical study on traces from JHotDraw and ArgoUML shows that
The approach is stable
Identified segments highly precise
Finer-splitting wrt. high-level features
Limitations due to: multi-threading, GUI events, feature adaptation..
Work-in-progress:
Improve performance
Use enhanced compression techniques
Automatically label identified concepts
Perform an extensive empirical validation
CSMR 2010 - Madrid (Spain) 20
21. Thank You!
Questions?
CSMR 2010 - Madrid (Spain) 21