Recommender systems have become an important personalization technique
on the web and are widely used especially in e-commerce applications.
However, operators of web shops and other platforms are challenged by
the large variety of available algorithms and the multitude of their
possible parameterizations. Since the quality of the recommendations that are
given can have a significant business impact, the selection
of a recommender system should be made based on well-founded evaluation
data. The literature on recommender system evaluation offers a large
variety of evaluation metrics but provides little guidance on how to choose
among them. The paper which is presented in this presentation focuses on the often neglected aspect of clearly defining the goal of an evaluation and how this goal relates to the
selection of an appropriate metric. We discuss several well-known
accuracy metrics and analyze how these reflect different evaluation goals. Furthermore we present some less well-known metrics as well as a variation of the area under the curve measure that are particularly suitable for the evaluation of
recommender systems in e-commerce applications.
An improvised similarity measure for generalized fuzzy numbersjournalBEEI
Similarity measure between two fuzzy sets is an important tool for comparing various characteristics of the fuzzy sets. It is a preferred approach as compared to distance methods as the defuzzification process in obtaining the distance between fuzzy sets will incur loss of information. Many similarity measures have been introduced but most of them are not capable to discriminate certain type of fuzzy numbers. In this paper, an improvised similarity measure for generalized fuzzy numbers that incorporate several essential features is proposed. The features under consideration are geometric mean averaging, Hausdorff distance, distance between elements, distance between center of gravity and the Jaccard index. The new similarity measure is validated using some benchmark sample sets. The proposed similarity measure is found to be consistent with other existing methods with an advantage of able to solve some discriminant problems that other methods cannot. Analysis of the advantages of the improvised similarity measure is presented and discussed. The proposed similarity measure can be incorporated in decision making procedure with fuzzy environment for ranking purposes.
The purpose research is to develop the decision model of Multi-Criteria Group Decision Making (MCGDM) into Interval Value Fuzzy Multi-Criteria Group Decision Making (IV-FMCGDM), while the specific purpose is to construct decision-making model of Adaptive Interval Value Fuzzy Analytic Hierarchy Process (AIV- FAHP) uses Triangular Fuzzy Number (TFN) and group decision aggregation functions using Interval Value Geometric Means Aggregation (IV-GMA). The novelty research is to study the concept of group decision making by improving the middle point on the Interval Value Triangular Fuzzy Number (IV TFN). It provides more accurate modeling, and better rating performance, and more effective linguistic representation. This research produced a new decision-making model and algorithm based on AIV-FAHP used to measure the quality of e-learning.
An improvised similarity measure for generalized fuzzy numbersjournalBEEI
Similarity measure between two fuzzy sets is an important tool for comparing various characteristics of the fuzzy sets. It is a preferred approach as compared to distance methods as the defuzzification process in obtaining the distance between fuzzy sets will incur loss of information. Many similarity measures have been introduced but most of them are not capable to discriminate certain type of fuzzy numbers. In this paper, an improvised similarity measure for generalized fuzzy numbers that incorporate several essential features is proposed. The features under consideration are geometric mean averaging, Hausdorff distance, distance between elements, distance between center of gravity and the Jaccard index. The new similarity measure is validated using some benchmark sample sets. The proposed similarity measure is found to be consistent with other existing methods with an advantage of able to solve some discriminant problems that other methods cannot. Analysis of the advantages of the improvised similarity measure is presented and discussed. The proposed similarity measure can be incorporated in decision making procedure with fuzzy environment for ranking purposes.
The purpose research is to develop the decision model of Multi-Criteria Group Decision Making (MCGDM) into Interval Value Fuzzy Multi-Criteria Group Decision Making (IV-FMCGDM), while the specific purpose is to construct decision-making model of Adaptive Interval Value Fuzzy Analytic Hierarchy Process (AIV- FAHP) uses Triangular Fuzzy Number (TFN) and group decision aggregation functions using Interval Value Geometric Means Aggregation (IV-GMA). The novelty research is to study the concept of group decision making by improving the middle point on the Interval Value Triangular Fuzzy Number (IV TFN). It provides more accurate modeling, and better rating performance, and more effective linguistic representation. This research produced a new decision-making model and algorithm based on AIV-FAHP used to measure the quality of e-learning.
ACM RecSys 2011 - Rank and Relevance in Novelty and Diversity Metrics for Rec...Pablo Castells
Slides of the paper presentation at RecSys 2011.
Abstract: The Recommender Systems community is paying increasing attention to novelty and diversity as key qualities beyond accuracy in real recommendation scenarios. Despite the raise of interest and work on the topic in recent years, we find that a clear common methodological and conceptual ground for the evaluation of these dimensions is still to be consolidated. Different evaluation metrics have been reported in the literature but the precise relation, distinction or equivalence between them has not been explicitly studied. Furthermore, the metrics reported so far miss important properties such as taking into consideration the ranking of recommended items, or whether items are relevant or not, when assessing the novelty and diversity of recommendations.
We present a formal framework for the definition of novelty and diversity metrics that unifies and generalizes several state of the art metrics. We identify three essential ground concepts at the roots of novelty and diversity: choice, discovery and relevance, upon which the framework is built. Item rank and relevance are introduced through a probabilistic recommendation browsing model, building upon the same three basic concepts. Based on the combination of ground elements, and the assumptions of the browsing model, different metrics and variants unfold. We report experimental observations which validate and illustrate the properties of the proposed metrics.
Algorithm ExampleFor the following taskUse the random module .docxdaniahendric
Algorithm Example
For the following task:
Use the random module to write a number guessing game.
The number the computer chooses should change each time you run the program.
Repeatedly ask the user for a number. If the number is different from the computer's let the user know if they guessed too high or too low. If the number matches the computer's, the user wins.
Keep track of the number of tries it takes the user to guess it.
An appropriate algorithm might be:
Import the random module
Display a welcome message to the user
Choose a random number between 1 and 100
Get a guess from the user
Set a number of tries to 0
As long as their guess isn’t the number
Check if guess is lower than computer
If so, print a lower message.
Otherwise, is it higher?
If so, print a higher message.
Get another guess
Increment the tries
Repeat
When they guess the computer's number, display the number and their tries count
Notice that each line in the algorithm corresponds to roughly a line of code in Python, but there is no coding itself in the algorithm. Rather the algorithm lays out what needs to happen step by step to achieve the program.
Software Quality Metrics for Object-Oriented Environments
AUTHORS:
Dr. Linda H. Rosenberg Lawrence E. Hyatt
Unisys Government Systems Software Assurance Technology Center
Goddard Space Flight Center Goddard Space Flight Center
Bld 6 Code 300.1 Bld 6 Code 302
Greenbelt, MD 20771 USA Greenbelt, MD 20771 USA
I. INTRODUCTION
Object-oriented design and development are popular concepts in today’s software development
environment. They are often heralded as the silver bullet for solving software problems. While
in reality there is no silver bullet, object-oriented development has proved its value for systems
that must be maintained and modified. Object-oriented software development requires a
different approach from more traditional functional decomposition and data flow development
methods. This includes the software metrics used to evaluate object-oriented software.
The concepts of software metrics are well established, and many metrics relating to product
quality have been developed and used. With object-oriented analysis and design methodologies
gaining popularity, it is time to start investigating object-oriented metrics with respect to
software quality. We are interested in the answer to the following questions:
• What concepts and structures in object-oriented design affect the quality of the
software?
• Can traditional metrics measure the critical object-oriented structures?
• If so, are the threshold values for the metrics the same for object-oriented designs as for
functional/data designs?
• Which of the many new metrics found in the literature are useful to measure the critical
concepts of object-oriented structures?
II. METRIC EVALUATION CRITERIA
While metrics for the traditional functional decomposition and data analysis design appro ...
Identifying Thresholds for Distance Design-based Direct Class Cohesion (D3C2)...IJECEIAES
In the several phases of activity in developing a software system, there is design phase. This phase has a purpose to determine and ensure that a software requirement can be realized in accordance with customer needs. The quality of design must be a guarantee at this phase. One of an indicator of quality design is cohesion. Cohesion is the level of relatedness between elements in one component. A Higher value of cohesion can indicate that a component are more modular, has own resources, and less dependent on another component. More independent, components are easy to maintenance. There are many metrics to count how many values of cohesion in a component. One of metric is The Distance Design-Based Direct Class Cohesion (D3C2). But, many practitioners are unable to apply them. Because there is no threshold that can categories the value of cohesion. This study aims to determine the threshold of cohesion metric based on the class diagram. The result showed that the threshold of D3C2 metric is 0.41. 0.41 is the value that has the highest level of agreement with the design expert.
A software system continues to grow in size and complexity, it becomes increasing difficult to
understand and manage. Software metrics are units of software measurement. As improvement in coding tools
allow software developer to produce larger amount of software to meet ever expanding requirements. A method
to measure software product, process and project must be used. In this article, we first introduce the software
metrics including the definition of metrics and the history of this field. We aim at a comprehensive survey of the
metrics available for measuring attributes related to software entities. Some classical metrics such as Lines of
codes LOC, Halstead complexity metric (HCM), Function Point analysis and others are discussed and
analyzed. Then we bring up the complexity metrics methods, such as McCabe complexity metrics and object
oriented metrics(C&K method), with real world examples. The comparison and relationship of these metrics are
also presented.
SPEECH CLASSIFICATION USING ZERNIKE MOMENTScscpconf
Speech recognition is very popular field of research and speech classification improves the performance
for speech recognition. Different patterns are identified using various characteristics or features of speech
to do there classification. Typical speech features set consist of many parameters like standard deviation,
magnitude, zero crossing representing speech signal. By considering all these parameters, system
computation load and time will increase a lot, so there is need to minimize these parameters by selecting
important features. Feature selection aims to get an optimal subset of features from given space, leading to
high classification performance. Thus feature selection methods should derive features that should reduce
the amount of data used for classification. High recognition accuracy is in demand for speech recognition
system. In this paper Zernike moments of speech signal are extracted and used as features of speech signal.
Zernike moments are the shape descriptor generally used to describe the shape of region. To extract
Zernike moments, one dimensional audio signal is converted into two dimensional image file. Then various
feature selection and ranking algorithms like t-Test, Chi Square, Fisher Score, ReliefF, Gini Index and
Information Gain are used to select important feature of speech signal. Performances of the algorithms are
evaluated using accuracy of classifier. Support Vector Machine (SVM) is used as the learning algorithm of
classifier and it is observed that accuracy is improved a lot after removing unwanted features.
Credit Default Swap (CDS) Rate Construction by Machine Learning TechniquesZhongmin Luo
1. Financial institutions need to construct proxy CDS rates for counterparties lacking liquid CDS quotes, which are required for CVA pricing, CVA risk charge calculation, etc;
2. Existing CDS Proxy Methods do not meet regulatory requirements and are vulnerable to arbitrage;
3. After investigating 8 most popular Machine Learning algorithms, we show that Machine Learning techniques can be used to construct reliable CDS proxies that meet regulatory regulations while free from the above problem
4. Feature variable selection can be critical for performance of CDS-proxy construction methods
5. Effects of feature variable correlations on classification performances have to be investigated in the case of financial data
PASS grade must be achievedOutcomesLearner has demonstr.docxherbertwilson5999
PASS grade must be achieved:
Outcomes
Learner has demonstrated the ability to:
Source of evidence
Outcome 1
Understand how total quality management (TQM) systems operate
LO1.1: explain the principles of TQM in relation to a specific application
Task 1
LO1.2: evaluate management structures that can lead to an effective quality organisation
Task 2
LO1.3: analyse the application of TQM techniques in an organisation
Task 3
Outcome 2
Know the key factors of quality assurance (QA) techniques
LO2.1: identify the key factors necessary for the implementation of a QA system within a given process
Task 4
LO2.2: interpret a given internal and external quality audit for control purposes
Task 5
LO2.3: describe the factors affecting costing
Task 6
Outcome 3
Be able to apply quality control (QC) techniques
LO3.1: report on the applications of quality control techniques
Task 7
LO3.2: apply quality control techniques to determine process capability
Task 8
LO3.3: use software packages for data collection and analysis.
Task 9
MERIT grade descriptors that may be achieved for this assignment:
Merit Grade Descriptors
Indicative Characteristics
Source of evidence
M1
Identify and apply strategies to find appropriate solutions
Specify and explore the relevant British or International Standards for the TQM principles defined in task 1.
Provide the organisational structure of the engineering business you selected and specify key elements/responsibilities that lead to a quality product and contribute to a total quality management
Task 1,
Task 2
and
references
M2
Select/design and apply appropriate methods/techniques
Provide evidence to support your answers in task 3.
Investigate the use of another quality control technique to the selected engineering application
Task 3,
Task 7,
and
References
M3
Present and communicate appropriate findings
Throughout the report, the solutions are coherently presented using technical language appropriately and in a professional manner
All Tasks
and
References
DISTINCTION grade descriptors that may be achieved for this assignment:
Merit Grade Descriptors
Indicative Characteristics
Source of evidence
D1
Use critical reflection to evaluate own work and justify valid conclusions
Critically evaluate the application of quality control techniques for the selected process capability of task 8
Tasks 8.
D2
Take responsibility for managing and organising activities
Substantial activities and investigations have been managed and organised in answering tasks 5.
Critically evaluate the effects of quality assurance factors on the overall cost of the final product of the selected engineering business.
Tasks 5
Task 6
D3
Demonstrate convergent/lateral/creative thinking
Ideas have been generated and decisions taken
Self-evaluation has taken place.
Tasks 9
General Information
All submissions to be electronic in MS Word format with a minimum of 20 typed words. All answers must be clearly id.
Maintaining the quality of the software is the major challenge in the process of software development.
Software inspections which use the methods like structured walkthroughs and formal code reviews involve
careful examination of each and every aspect/stage of software development. In Agile software
development, refactoring helps to improve software quality. This refactoring is a technique to improve
software internal structure without changing its behaviour. After much study regarding the ways to
improve software quality, our research proposes an object oriented software metric tool called
“MetricAnalyzer”. This tool is tested on different codebases and is proven to be much useful.
ACM RecSys 2011 - Rank and Relevance in Novelty and Diversity Metrics for Rec...Pablo Castells
Slides of the paper presentation at RecSys 2011.
Abstract: The Recommender Systems community is paying increasing attention to novelty and diversity as key qualities beyond accuracy in real recommendation scenarios. Despite the raise of interest and work on the topic in recent years, we find that a clear common methodological and conceptual ground for the evaluation of these dimensions is still to be consolidated. Different evaluation metrics have been reported in the literature but the precise relation, distinction or equivalence between them has not been explicitly studied. Furthermore, the metrics reported so far miss important properties such as taking into consideration the ranking of recommended items, or whether items are relevant or not, when assessing the novelty and diversity of recommendations.
We present a formal framework for the definition of novelty and diversity metrics that unifies and generalizes several state of the art metrics. We identify three essential ground concepts at the roots of novelty and diversity: choice, discovery and relevance, upon which the framework is built. Item rank and relevance are introduced through a probabilistic recommendation browsing model, building upon the same three basic concepts. Based on the combination of ground elements, and the assumptions of the browsing model, different metrics and variants unfold. We report experimental observations which validate and illustrate the properties of the proposed metrics.
Algorithm ExampleFor the following taskUse the random module .docxdaniahendric
Algorithm Example
For the following task:
Use the random module to write a number guessing game.
The number the computer chooses should change each time you run the program.
Repeatedly ask the user for a number. If the number is different from the computer's let the user know if they guessed too high or too low. If the number matches the computer's, the user wins.
Keep track of the number of tries it takes the user to guess it.
An appropriate algorithm might be:
Import the random module
Display a welcome message to the user
Choose a random number between 1 and 100
Get a guess from the user
Set a number of tries to 0
As long as their guess isn’t the number
Check if guess is lower than computer
If so, print a lower message.
Otherwise, is it higher?
If so, print a higher message.
Get another guess
Increment the tries
Repeat
When they guess the computer's number, display the number and their tries count
Notice that each line in the algorithm corresponds to roughly a line of code in Python, but there is no coding itself in the algorithm. Rather the algorithm lays out what needs to happen step by step to achieve the program.
Software Quality Metrics for Object-Oriented Environments
AUTHORS:
Dr. Linda H. Rosenberg Lawrence E. Hyatt
Unisys Government Systems Software Assurance Technology Center
Goddard Space Flight Center Goddard Space Flight Center
Bld 6 Code 300.1 Bld 6 Code 302
Greenbelt, MD 20771 USA Greenbelt, MD 20771 USA
I. INTRODUCTION
Object-oriented design and development are popular concepts in today’s software development
environment. They are often heralded as the silver bullet for solving software problems. While
in reality there is no silver bullet, object-oriented development has proved its value for systems
that must be maintained and modified. Object-oriented software development requires a
different approach from more traditional functional decomposition and data flow development
methods. This includes the software metrics used to evaluate object-oriented software.
The concepts of software metrics are well established, and many metrics relating to product
quality have been developed and used. With object-oriented analysis and design methodologies
gaining popularity, it is time to start investigating object-oriented metrics with respect to
software quality. We are interested in the answer to the following questions:
• What concepts and structures in object-oriented design affect the quality of the
software?
• Can traditional metrics measure the critical object-oriented structures?
• If so, are the threshold values for the metrics the same for object-oriented designs as for
functional/data designs?
• Which of the many new metrics found in the literature are useful to measure the critical
concepts of object-oriented structures?
II. METRIC EVALUATION CRITERIA
While metrics for the traditional functional decomposition and data analysis design appro ...
Identifying Thresholds for Distance Design-based Direct Class Cohesion (D3C2)...IJECEIAES
In the several phases of activity in developing a software system, there is design phase. This phase has a purpose to determine and ensure that a software requirement can be realized in accordance with customer needs. The quality of design must be a guarantee at this phase. One of an indicator of quality design is cohesion. Cohesion is the level of relatedness between elements in one component. A Higher value of cohesion can indicate that a component are more modular, has own resources, and less dependent on another component. More independent, components are easy to maintenance. There are many metrics to count how many values of cohesion in a component. One of metric is The Distance Design-Based Direct Class Cohesion (D3C2). But, many practitioners are unable to apply them. Because there is no threshold that can categories the value of cohesion. This study aims to determine the threshold of cohesion metric based on the class diagram. The result showed that the threshold of D3C2 metric is 0.41. 0.41 is the value that has the highest level of agreement with the design expert.
A software system continues to grow in size and complexity, it becomes increasing difficult to
understand and manage. Software metrics are units of software measurement. As improvement in coding tools
allow software developer to produce larger amount of software to meet ever expanding requirements. A method
to measure software product, process and project must be used. In this article, we first introduce the software
metrics including the definition of metrics and the history of this field. We aim at a comprehensive survey of the
metrics available for measuring attributes related to software entities. Some classical metrics such as Lines of
codes LOC, Halstead complexity metric (HCM), Function Point analysis and others are discussed and
analyzed. Then we bring up the complexity metrics methods, such as McCabe complexity metrics and object
oriented metrics(C&K method), with real world examples. The comparison and relationship of these metrics are
also presented.
SPEECH CLASSIFICATION USING ZERNIKE MOMENTScscpconf
Speech recognition is very popular field of research and speech classification improves the performance
for speech recognition. Different patterns are identified using various characteristics or features of speech
to do there classification. Typical speech features set consist of many parameters like standard deviation,
magnitude, zero crossing representing speech signal. By considering all these parameters, system
computation load and time will increase a lot, so there is need to minimize these parameters by selecting
important features. Feature selection aims to get an optimal subset of features from given space, leading to
high classification performance. Thus feature selection methods should derive features that should reduce
the amount of data used for classification. High recognition accuracy is in demand for speech recognition
system. In this paper Zernike moments of speech signal are extracted and used as features of speech signal.
Zernike moments are the shape descriptor generally used to describe the shape of region. To extract
Zernike moments, one dimensional audio signal is converted into two dimensional image file. Then various
feature selection and ranking algorithms like t-Test, Chi Square, Fisher Score, ReliefF, Gini Index and
Information Gain are used to select important feature of speech signal. Performances of the algorithms are
evaluated using accuracy of classifier. Support Vector Machine (SVM) is used as the learning algorithm of
classifier and it is observed that accuracy is improved a lot after removing unwanted features.
Credit Default Swap (CDS) Rate Construction by Machine Learning TechniquesZhongmin Luo
1. Financial institutions need to construct proxy CDS rates for counterparties lacking liquid CDS quotes, which are required for CVA pricing, CVA risk charge calculation, etc;
2. Existing CDS Proxy Methods do not meet regulatory requirements and are vulnerable to arbitrage;
3. After investigating 8 most popular Machine Learning algorithms, we show that Machine Learning techniques can be used to construct reliable CDS proxies that meet regulatory regulations while free from the above problem
4. Feature variable selection can be critical for performance of CDS-proxy construction methods
5. Effects of feature variable correlations on classification performances have to be investigated in the case of financial data
PASS grade must be achievedOutcomesLearner has demonstr.docxherbertwilson5999
PASS grade must be achieved:
Outcomes
Learner has demonstrated the ability to:
Source of evidence
Outcome 1
Understand how total quality management (TQM) systems operate
LO1.1: explain the principles of TQM in relation to a specific application
Task 1
LO1.2: evaluate management structures that can lead to an effective quality organisation
Task 2
LO1.3: analyse the application of TQM techniques in an organisation
Task 3
Outcome 2
Know the key factors of quality assurance (QA) techniques
LO2.1: identify the key factors necessary for the implementation of a QA system within a given process
Task 4
LO2.2: interpret a given internal and external quality audit for control purposes
Task 5
LO2.3: describe the factors affecting costing
Task 6
Outcome 3
Be able to apply quality control (QC) techniques
LO3.1: report on the applications of quality control techniques
Task 7
LO3.2: apply quality control techniques to determine process capability
Task 8
LO3.3: use software packages for data collection and analysis.
Task 9
MERIT grade descriptors that may be achieved for this assignment:
Merit Grade Descriptors
Indicative Characteristics
Source of evidence
M1
Identify and apply strategies to find appropriate solutions
Specify and explore the relevant British or International Standards for the TQM principles defined in task 1.
Provide the organisational structure of the engineering business you selected and specify key elements/responsibilities that lead to a quality product and contribute to a total quality management
Task 1,
Task 2
and
references
M2
Select/design and apply appropriate methods/techniques
Provide evidence to support your answers in task 3.
Investigate the use of another quality control technique to the selected engineering application
Task 3,
Task 7,
and
References
M3
Present and communicate appropriate findings
Throughout the report, the solutions are coherently presented using technical language appropriately and in a professional manner
All Tasks
and
References
DISTINCTION grade descriptors that may be achieved for this assignment:
Merit Grade Descriptors
Indicative Characteristics
Source of evidence
D1
Use critical reflection to evaluate own work and justify valid conclusions
Critically evaluate the application of quality control techniques for the selected process capability of task 8
Tasks 8.
D2
Take responsibility for managing and organising activities
Substantial activities and investigations have been managed and organised in answering tasks 5.
Critically evaluate the effects of quality assurance factors on the overall cost of the final product of the selected engineering business.
Tasks 5
Task 6
D3
Demonstrate convergent/lateral/creative thinking
Ideas have been generated and decisions taken
Self-evaluation has taken place.
Tasks 9
General Information
All submissions to be electronic in MS Word format with a minimum of 20 typed words. All answers must be clearly id.
Maintaining the quality of the software is the major challenge in the process of software development.
Software inspections which use the methods like structured walkthroughs and formal code reviews involve
careful examination of each and every aspect/stage of software development. In Agile software
development, refactoring helps to improve software quality. This refactoring is a technique to improve
software internal structure without changing its behaviour. After much study regarding the ways to
improve software quality, our research proposes an object oriented software metric tool called
“MetricAnalyzer”. This tool is tested on different codebases and is proven to be much useful.
GRID COMPUTING: STRATEGIC DECISION MAKING IN RESOURCE SELECTIONIJCSEA Journal
The rapid development of computer networks around the world generated new areas especially in computer instruction processing. In grid computing, instruction processing is performed by external processors available to the system. An important topic in this area is task scheduling to available external resources. However, we do not deal with this topic here. In this paper we intend to work on strategic decision making on selecting the best alternative resources for processing instructions with respect to criteria in special conditions. Where the criteria might be security, political, technical, cost, etc. Grid computing should be determined with respect to the processing objectives of instructions of a program. This paper seeks a way through combining Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to help us in ranking and selecting available resources according to considerable criteria in allocating instructions to resources. Therefore, our findings will help technical managers of organizations in choosing as well as ranking candidate alternatives for processing program instructions.
Similar to Setting Goals and Choosing Metrics for Recommender System Evaluations (20)
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
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.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Home assignment II on Spectroscopy 2024 Answers.pdf
Setting Goals and Choosing Metrics for Recommender System Evaluations
1. Setting Goals and Choosing Metrics for Recommender
System Evaluations
Gunnar Schröder, Maik Thiele, Wolfgang Lehner
Gunnar Schröder UCERSTI 2 Workshop
T-Systems Multimedia Solutions at the 5th ACM Conference on
Dresden University of Technology Recommender Systems
Chicago, October 23th, 2011
2. How Do You Evaluate Recommender Systems?
RMSE
Precision
F1-Measure
Recall MAE
ROC Curves
Qualitative Techniques
Quantitative Techniques
User-Centric Evaluation
Mean Average Precision Area under the Curve
Accuracy Metrics Non-Accuracy Metrics
But why do you do it exactly this way?
Setting Goals and Choosing Metrics for Recommender System Evaluation - Gunnar Schröder
3. Some of the Issues This Paper Tries to Touch
A large variety of metrics have been published
Some metrics are highly correlated [Herlocker 2004]
Little guidance for evaluating recommenders and choosing metrics
Which aspects of the usage scenario and the data influence the choice?
Which metrics are applicable?
What do these metrics express?
What are differences among them?
Which metric represents our use-case best?
How much do the metrics suffer from biases?
Setting Goals and Choosing Metrics for Recommender System Evaluation - Gunnar Schröder
4. Factors That Influence the Choice of Evaluation Metrics
Objectives for recommender usage
Business goals User interests
Recommender task and interaction
Prediction Classification Ranking Similarity Presentation
Preference data
Explicit Implicit Unary Binary Numerical
Choice of metrics
Setting Goals and Choosing Metrics for Recommender System Evaluation - Gunnar Schröder
5. Major Classes of Evaluation Metrics
Prediction Accuracy Metrics
Ranking Accuracy Metrics
Classification Accuracy Metrics
Non-Accuracy Metrics
5.0 4.8 4.7 4.3 3.8 3.2 2.4 2.1 1.6 1.2
Setting Goals and Choosing Metrics for Recommender System Evaluation - Gunnar Schröder
6. Why Precision, Recall and F1-Measure May Fool You
Ideal recommender (example a – f) vs. Worst-case recommender (ex. g – l )
Four recommendations (R1 – R4) e.g. Precision@4
Ten items with a varying ratio of relevant items (1 – 9 relevant items)
Precision, recall and F1-measure are very sensitive to the ratio of relevant items Figure 3
They fail to distinguish between an ideal recommender and a worst-case recommender if
the ratio of relevant items is varied
Setting Goals and Choosing Metrics for Recommender System Evaluation - Gunnar Schröder
7. What is the Ideal Length for a Top-k Recommendation List?
A typical ranking produced by a recommender on a set of ten item with four items being
relevant
The length of the top-k recommendation list is varied in examples a (k=1) to j (k=10)
Figure 1
Setting Goals and Choosing Metrics for Recommender System Evaluation - Gunnar Schröder
8. What is the Ideal Length for a Top-k Recommendation List?
A typical ranking produced by a recommender on a set of ten item with four items being
relevant
The length of the top-k recommendation list is varied in examples a (k=1) to j (k=10)
2.
1.
2.
2.
3.
part of
Figure 1
Setting Goals and Choosing Metrics for Recommender System Evaluation - Gunnar Schröder
9. What is the Ideal Length for a Top-k Recommendation List?
A typical ranking produced by a recommender on a set of ten item with four items being
relevant
The length of the top-k recommendation list is varied in examples a (k=1) to j (k=10)
2. 3.
1. 2. 1.
3. 1. 2.
3. 3.
3.
part of
Markedness = Precision + InvPrecision – 1 Figure 1
Informedness = Recall + InvRecall – 1
Matthew’s Correlation =
[Powers 2007]
Setting Goals and Choosing Metrics for Recommender System Evaluation - Gunnar Schröder
10. From Simple Classification Measures to Partial Ranking Measures
Moving a single relevant item among the recommenders ranking (examples a - j)
Idea: Consider both classification and ranking for the top-k recommendations Figure 2
Area under the Curve => Limited Area under the Curve
Boolean Kendall’s Tau => Limited Boolean Kendall’s Tau
Setting Goals and Choosing Metrics for Recommender System Evaluation - Gunnar Schröder
11. A Further More Complex Example to Study at Home
Figure 4
Conclusions:
For classification use markedness, informedness and Matthew’s correlation instead
of precision, recall and F1 measure
Limited area under the curve and limited boolean Kendall’s tau are useful metrics for
top-k recommender evaluations
Setting Goals and Choosing Metrics for Recommender System Evaluation - Gunnar Schröder
12. Conclusion and Contributions
Important aspects that influence the metric choice
Objectives for recommender usage
Recommender task and interaction
Aspects of preference data
Some problems of Precision, Recall and F1-Measure
The advantages of markedness, informedness and Matthew’s correlation
Two new metrics that measure the ranking of a limited top-k list
Limited area under the curve, limited boolean Kendall’s tau
Guidelines for choosing a metric (See paper)
Setting Goals and Choosing Metrics for Recommender System Evaluation - Gunnar Schröder
13. Thank You Very Much!
Do not hesitate to contact me, if you have any
questions, comments or answers!
Slides are available via e-mail or slideshare
Setting Goals and Choosing Metrics for Recommender System Evaluation - Gunnar Schröder