The document discusses detection strategies for software design flaws using metrics-based rules. It proposes applying filters to metrics to detect design fragments conforming to rules. Logical operators can compose filter results. An example detects "God classes" using weighted method count, tight class cohesion, and access to foreign data. The strategies were evaluated on a case study with over 50% automatic classification accuracy and 87% for manual inspection.
A REVIEW ON BLIND STILL IMAGE STEGANALYSIS TECHNIQUES USING FEATURES EXTRACTI...IJCSEIT Journal
Steganography is the technique for hiding secret information in other data such as still, multimedia
images, text, audio. Whereas Steganalysis is the reverse technique in which detection of the secret
information is done in the stego image. Steganalysis can be classified on the basis of the techniques used
classified statistical techniques, pattern classification techniques and visual detection techniques .All the
existing techniques can be broadly classified on the basis of the information required for the designing of
the steganalysis. They are targeted and blind steganalysis In targeted technique, we first look at
steganalysis techniques is designed for a particular steganographic embedding algorithm in mind whereas
in blind steganalysis is general class of steganalysis techniques which can be implemented with any
steganographic embedding algorithm, even an unknown algorithm. In this paper, an extensive review
report is presented chronologically on the Blind Image Steganalysis for the still stego images using the
classification techniques.
Qvarfordt Understanding The Benefits Of Gaze Enhanced Visual SearchKalle
In certain applications such as radiology and imagery analysis, it is important to minimize errors. In this paper we evaluate a structured inspection method that uses eye tracking information as a feedback mechanism to the image inspector. Our two-phase method starts with a free viewing phase during which gaze data is collected. During the next phase, we either segment the image, mask previously seen areas of the image, or combine the two techniques, and repeat the search. We compare the different methods
proposed for the second search phase by evaluating the inspection method using true positive and false negative rates, and subjective workload. Results show that gaze-blocked configurations reduced the subjective workload, and that gaze-blocking without segmentation showed the largest increase in true positive identifications and the largest decrease in false negative identifications of previously unseen objects.
Design and Implementation of Proportional Integral Observer based Linear Mode...IDES Editor
This paper presents an interior-point method (IPM)
based quadratic programming (QP) solver for the solution of
optimal control problem in linear model predictive control
(MPC). LU factorization is used to solve the system of linear
equations efficiently at each iteration of IPM, which renders
faster execution of QP solver. The controller requires internal
states of the system. To address this issue, a Proportional
Integral Observer (PIO) is designed, which estimates the state
vector, as well as the uncertainties in an integrated manner.
MPC uses the states estimated by PIO, and the effect of
uncertainty is compensated by augmenting MPC with PIOestimated
uncertainties and external disturbances. The
approach is demonstrated practically by applying MPC to QET
DC servomotor for position control application. The proposed
method is compared with classical control strategy-PID
control.
A REVIEW ON BLIND STILL IMAGE STEGANALYSIS TECHNIQUES USING FEATURES EXTRACTI...IJCSEIT Journal
Steganography is the technique for hiding secret information in other data such as still, multimedia
images, text, audio. Whereas Steganalysis is the reverse technique in which detection of the secret
information is done in the stego image. Steganalysis can be classified on the basis of the techniques used
classified statistical techniques, pattern classification techniques and visual detection techniques .All the
existing techniques can be broadly classified on the basis of the information required for the designing of
the steganalysis. They are targeted and blind steganalysis In targeted technique, we first look at
steganalysis techniques is designed for a particular steganographic embedding algorithm in mind whereas
in blind steganalysis is general class of steganalysis techniques which can be implemented with any
steganographic embedding algorithm, even an unknown algorithm. In this paper, an extensive review
report is presented chronologically on the Blind Image Steganalysis for the still stego images using the
classification techniques.
Qvarfordt Understanding The Benefits Of Gaze Enhanced Visual SearchKalle
In certain applications such as radiology and imagery analysis, it is important to minimize errors. In this paper we evaluate a structured inspection method that uses eye tracking information as a feedback mechanism to the image inspector. Our two-phase method starts with a free viewing phase during which gaze data is collected. During the next phase, we either segment the image, mask previously seen areas of the image, or combine the two techniques, and repeat the search. We compare the different methods
proposed for the second search phase by evaluating the inspection method using true positive and false negative rates, and subjective workload. Results show that gaze-blocked configurations reduced the subjective workload, and that gaze-blocking without segmentation showed the largest increase in true positive identifications and the largest decrease in false negative identifications of previously unseen objects.
Design and Implementation of Proportional Integral Observer based Linear Mode...IDES Editor
This paper presents an interior-point method (IPM)
based quadratic programming (QP) solver for the solution of
optimal control problem in linear model predictive control
(MPC). LU factorization is used to solve the system of linear
equations efficiently at each iteration of IPM, which renders
faster execution of QP solver. The controller requires internal
states of the system. To address this issue, a Proportional
Integral Observer (PIO) is designed, which estimates the state
vector, as well as the uncertainties in an integrated manner.
MPC uses the states estimated by PIO, and the effect of
uncertainty is compensated by augmenting MPC with PIOestimated
uncertainties and external disturbances. The
approach is demonstrated practically by applying MPC to QET
DC servomotor for position control application. The proposed
method is compared with classical control strategy-PID
control.
Study of solution development methodology for small size projects.Joon ho Park
Medium-size system integration or IT Solution Company’s solution development project has limitation as like human resource limitation, budget limitation and expert limitation. Especially it is hard to maintain many IT experts for medium-size and small-size system integration or IT Solution Company. Thus in order to efficiently and beneficially complete projects, medium-size and small-size system integration or IT Solution Company should have appropriate solution development methodology.Solution development projects for medium-size and small-size system integration or IT Solution Company are usually shot-term and small budget so that they need slim and light-weight solution development methodology. But usual medium-size and small-size system integration or IT Solution Company do not have their own appropriate solution development methodology. Thus, if those kinds of solution development methodologies are applied to solution development projects for medium-size and small-size system integration or IT solution company without some modifications, shortage of human resources, incompleteness of solution and deliverables could arouse.Especially unnecessary paper works (deliverables and documentations) to both of projects teams and client’s wastes project resources and time. We analyze previous solution development methodologies and derive mandatory deliverables and optional deliverables. Before deriving them, we newly define procedures and tasks for each project stages which are necessary to projects team and clients, from client and expert of interviews. Our proposed solution development methodology can easily leverage the development overhead of short-term projects. Optional deliverables can be omitted by the contraction between project team and client.
In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in eCommerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play.
Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above. It exploits and orchestrates techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, learning, stochastics, statistics, decision making under uncertainty, as well as combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems.
In this talk, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these challenges. For instance, we will present methods for the optimization of the time and memory performance of diagnosis systems, show efficient techniques for a semi-automatic debugging by interacting with a user or expert, and demonstrate how our algorithms can be effectively leveraged in important application domains such as scheduling or the Semantic Web.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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Study of solution development methodology for small size projects.Joon ho Park
Medium-size system integration or IT Solution Company’s solution development project has limitation as like human resource limitation, budget limitation and expert limitation. Especially it is hard to maintain many IT experts for medium-size and small-size system integration or IT Solution Company. Thus in order to efficiently and beneficially complete projects, medium-size and small-size system integration or IT Solution Company should have appropriate solution development methodology.Solution development projects for medium-size and small-size system integration or IT Solution Company are usually shot-term and small budget so that they need slim and light-weight solution development methodology. But usual medium-size and small-size system integration or IT Solution Company do not have their own appropriate solution development methodology. Thus, if those kinds of solution development methodologies are applied to solution development projects for medium-size and small-size system integration or IT solution company without some modifications, shortage of human resources, incompleteness of solution and deliverables could arouse.Especially unnecessary paper works (deliverables and documentations) to both of projects teams and client’s wastes project resources and time. We analyze previous solution development methodologies and derive mandatory deliverables and optional deliverables. Before deriving them, we newly define procedures and tasks for each project stages which are necessary to projects team and clients, from client and expert of interviews. Our proposed solution development methodology can easily leverage the development overhead of short-term projects. Optional deliverables can be omitted by the contraction between project team and client.
In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in eCommerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play.
Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above. It exploits and orchestrates techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, learning, stochastics, statistics, decision making under uncertainty, as well as combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems.
In this talk, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these challenges. For instance, we will present methods for the optimization of the time and memory performance of diagnosis systems, show efficient techniques for a semi-automatic debugging by interacting with a user or expert, and demonstrate how our algorithms can be effectively leveraged in important application domains such as scheduling or the Semantic Web.
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This will be used as part of your Personal Professional Portfolio once graded.
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Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
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Detection Strategies Metrics-Based Rules for Detecting Design Flaws
1. Introduction Problem Demand Solution Implementation Evaluation Summary
Detection Strategies
Metrics-Based Rules for
Detecting Design Flaws
M.... N.....1
1 Universita della Svizzera Italiana, Switzerland
Software Design and Evolution, WS 2009
Nowak Faculty of Informatics
Detection Strategies
2. Introduction Problem Demand Solution Implementation Evaluation Summary
Author
Dr. Radu Marinescu
Associate Professor -
Department of Computer Science and Engineering
"Politechnica" University at Timisoara
Author of "Object-Oriented Metrics in Practice"
Nowak Faculty of Informatics
Detection Strategies
3. Introduction Problem Demand Solution Implementation Evaluation Summary
Author
Dr. Radu Marinescu
Associate Professor -
Department of Computer Science and Engineering
"Politechnica" University at Timisoara
Author of "Object-Oriented Metrics in Practice"
Ph.D defense
Mircea Lungu, Today, 17.30, A21, Red Building
Nowak Faculty of Informatics
Detection Strategies
4. Introduction Problem Demand Solution Implementation Evaluation Summary
Outline
1 Introduction
2 Problem
3 Demand
4 Solution
5 Implementation
6 Evaluation
7 Summary
Nowak Faculty of Informatics
Detection Strategies
5. Introduction Problem Demand Solution Implementation Evaluation Summary
Metrics
Nowak Faculty of Informatics
Detection Strategies
6. Introduction Problem Demand Solution Implementation Evaluation Summary
Metrics
Metrics
ambiguous definitions
noise
relevance
Nowak Faculty of Informatics
Detection Strategies
7. Introduction Problem Demand Solution Implementation Evaluation Summary
Metrics
Metrics
ambiguous definitions
noise
relevance
Interpretation
experience based
no model
showing symptoms not a disease
Nowak Faculty of Informatics
Detection Strategies
8. Introduction Problem Demand Solution Implementation Evaluation Summary
Strategy
Strategy
"A detection strategy is the quantifiable expression of a rule by
which design fragments that are conforming to that rule can be
detected in the source code"
Nowak Faculty of Informatics
Detection Strategies
9. Introduction Problem Demand Solution Implementation Evaluation Summary
Filters
Semantical
threshold value
direction
For example:
Absolute: HigherThan, LowerThan
Relative: TopValues, BottomValues
Statistical
direction
For example: UpperQuantile, BelowMedian
Nowak Faculty of Informatics
Detection Strategies
10. Introduction Problem Demand Solution Implementation Evaluation Summary
Strategy
Choosing an appropriate filter
1 Absolute semantical filter
2 Relative semantical filter
3 Semantical filter with percentile values
4 Statistical filter
Nowak Faculty of Informatics
Detection Strategies
12. Introduction Problem Demand Solution Implementation Evaluation Summary
Metrics
"God Class" syndrome.
Weighted Method Count (WMC)
Tight Class Cohesion (TCC)
Access to Foreign Data (ATFD)
(WMC(C), TopValues(25%))∧ (1)
(ATFD(C), HigherThan(1))∧ (2)
(TCC(C), BottomValues(25%)) (3)
Nowak Faculty of Informatics
Detection Strategies
13. Introduction Problem Demand Solution Implementation Evaluation Summary
Process
parsing Meta-Model
Sources
(Java, C++)
Metrics using
Detection
Strategy Flaw List
Detection
Manual inspection
Nowak Faculty of Informatics
Detection Strategies
14. Introduction Problem Demand Solution Implementation Evaluation Summary
Filter tuning
1 Experience and Literature
2 Reference Samples (Tuning Machine)
3 Evolution Analysis
Nowak Faculty of Informatics
Detection Strategies
15. Introduction Problem Demand Solution Implementation Evaluation Summary
Case-study
Version 1
93 KLOC, 18 Packages, 152 Classes, 1284 Methods
Version 2
116 KLOC, 29 Packages, 387 Classes, 3446 Methods
Evaluation methods
Automatic Classification (differential between the versions)
Manual Investigation (of the Version 1)
Nowak Faculty of Informatics
Detection Strategies
16. Introduction Problem Demand Solution Implementation Evaluation Summary
Summary
Results
Automatic Classification accuracy over 50% with average
over 67%.
Manual Inspection method resulted in Accuracy of 87%.
Nowak Faculty of Informatics
Detection Strategies
17. Introduction Problem Demand Solution Implementation Evaluation Summary
Summary
Results
Automatic Classification accuracy over 50% with average
over 67%.
Manual Inspection method resulted in Accuracy of 87%.
Conclusion
Method is very promising !
Nowak Faculty of Informatics
Detection Strategies
18. Introduction Problem Demand Solution Implementation Evaluation Summary
Related Work
Quantification of Design Principles and Rules
Using Correlations of Metrics for Design Inspections
Nowak Faculty of Informatics
Detection Strategies
19. Introduction Problem Demand Solution Implementation Evaluation Summary
Discussion
Questions and Discussion.
Nowak Faculty of Informatics
Detection Strategies
