This document provides an introduction and overview of the Stat project, which aims to create an open source machine learning framework in Java for text analysis. The Stat framework is designed to be simple, extensible, and performant. It aims to simplify common text analysis tasks for researchers and engineers by providing reusable tools and wrappers for existing NLP and machine learning packages. The document outlines the goals, scope, stakeholders and provides an initial requirements analysis for the Stat framework.
The STAT technical report provides an introduction to the Stat project, which aims to develop an open source machine learning framework in Java called Stat for text analysis. Stat focuses on facilitating common textual data analysis tasks for researchers and engineers. The report outlines the background, motivation, scope, and stakeholders of the project. It also describes an initial survey conducted to understand potential users and their needs in order to prioritize the framework's design and implementation. Finally, the report analyzes two existing toolkits, Weka and MinorThird, and discusses their strengths and limitations for text analysis tasks.
This document provides an overview and requirements for the Stat project, an open source machine learning framework for text analysis. It describes the background, motivation, scope, and stakeholders of the project. Key requirements for the framework include being simplified, reusable, and providing built-in capabilities to naturally support text representation and processing tasks.
This thesis aims to give a theoretical as well as practical overview of an emerging issue in the field of IT security named Format Preserving Encryption (FPE).
Although FPE is not new, it is relatively unknown. It is used in the full-disk encryption and some other areas. Nevertheless, it is to this day even unknown to many cryptographers. Another issue that is on everyone's lips is the Internet of Things (IoT). IoT offers a whole new scope for FPE and could give it possibly a further boost.
Format Preserving Encryption is - as the name says - an encryption in which the format of the encrypted data is maintained. When a plaintext is encrypted with FPE, the ciphertext then has the same format again. As illustrated for example on the cover page: If we encrypt the owner and the number of a credit card with AES we get an unrecognizable string. If we use FPE instead, we might get for example Paul Miller and the number 4000 0838 7507 2846. The advantage is that for man and/or machine nothing changes. The encryption is therefore not noticed without analysis of the data. The advantage can also become a disadvantage. An attacker has with the format of the ciphertext already information about the plaintext.
This thesis starts with an introduction to the Format Preserving Encryption. In doing so, different variants of FPE are shown. In a next step, a Java library is explained and documented, in which we have implemented some of these FPE variants. This library is designed to enable programmers to use FPE without the need for detailed knowledge about the functionality. Then we explain by means of a tutorial and step by step with a concrete and simple example, how a subsequent integration of FPE could look like. In a final part the integration into a more complex and already widely used application is shown, an Android app called OwnTracks.
With this combination of theoretical and practical information a broad basic knowledge should be provided on the topic, which then can serve as a basis on how FPE can be used and whether a use is reasonable.
IRJET - Pseudocode to Python Translation using Machine LearningIRJET Journal
This document describes a system that translates pseudocode written in natural language into executable Python code. It uses recurrent neural networks with sequence-to-sequence translation to first convert the pseudocode into an intermediate XML representation, and then recursively parses that XML to produce the final Python code. The system aims to help students learn programming by allowing them to test algorithms written in pseudocode. It was implemented using Keras and trained on a dataset containing pseudocode statements and their Python translations.
Class Diagram Extraction from Textual Requirements Using NLP Techniquesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The document summarizes a graduate student's project using support vector machines (SVM) for transductive learning to classify RNA-related biological abstracts. The student collected a corpus of 400 abstracts categorized into RNA-related and non-RNA-related groups. Software was developed to preprocess the abstracts, extract features, generate training and test sets for SVM Light, and test its ability to classify abstracts into different RNA categories like mRNA, tRNA, etc. The goal was to improve on keyword searches by using a small number of training examples from a specific dataset to maximize classification precision for that set.
EVALUATION OF SINGLE-SPAN MODELS ON EXTRACTIVE MULTI-SPAN QUESTION-ANSWERINGdannyijwest
Machine Reading Comprehension (MRC), particularly extractive close-domain question-answering, is a prominent field in Natural Language Processing (NLP). Given a question and a passage or set of passages, a machine must be able to extract the appropriate answer from the passage(s). However, the majority of these existing questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Runtime of base models on the entire datasetis approximately one day while the runtime for all models on a third of the dataset is a little over two days. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset compared to the single-span source datasets. While the models tested on the source datasets were slightly fine-tuned in order to return multiple answers, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in question-answering and improve existing question-answering products and methods
The STAT technical report provides an introduction to the Stat project, which aims to develop an open source machine learning framework in Java called Stat for text analysis. Stat focuses on facilitating common textual data analysis tasks for researchers and engineers. The report outlines the background, motivation, scope, and stakeholders of the project. It also describes an initial survey conducted to understand potential users and their needs in order to prioritize the framework's design and implementation. Finally, the report analyzes two existing toolkits, Weka and MinorThird, and discusses their strengths and limitations for text analysis tasks.
This document provides an overview and requirements for the Stat project, an open source machine learning framework for text analysis. It describes the background, motivation, scope, and stakeholders of the project. Key requirements for the framework include being simplified, reusable, and providing built-in capabilities to naturally support text representation and processing tasks.
This thesis aims to give a theoretical as well as practical overview of an emerging issue in the field of IT security named Format Preserving Encryption (FPE).
Although FPE is not new, it is relatively unknown. It is used in the full-disk encryption and some other areas. Nevertheless, it is to this day even unknown to many cryptographers. Another issue that is on everyone's lips is the Internet of Things (IoT). IoT offers a whole new scope for FPE and could give it possibly a further boost.
Format Preserving Encryption is - as the name says - an encryption in which the format of the encrypted data is maintained. When a plaintext is encrypted with FPE, the ciphertext then has the same format again. As illustrated for example on the cover page: If we encrypt the owner and the number of a credit card with AES we get an unrecognizable string. If we use FPE instead, we might get for example Paul Miller and the number 4000 0838 7507 2846. The advantage is that for man and/or machine nothing changes. The encryption is therefore not noticed without analysis of the data. The advantage can also become a disadvantage. An attacker has with the format of the ciphertext already information about the plaintext.
This thesis starts with an introduction to the Format Preserving Encryption. In doing so, different variants of FPE are shown. In a next step, a Java library is explained and documented, in which we have implemented some of these FPE variants. This library is designed to enable programmers to use FPE without the need for detailed knowledge about the functionality. Then we explain by means of a tutorial and step by step with a concrete and simple example, how a subsequent integration of FPE could look like. In a final part the integration into a more complex and already widely used application is shown, an Android app called OwnTracks.
With this combination of theoretical and practical information a broad basic knowledge should be provided on the topic, which then can serve as a basis on how FPE can be used and whether a use is reasonable.
IRJET - Pseudocode to Python Translation using Machine LearningIRJET Journal
This document describes a system that translates pseudocode written in natural language into executable Python code. It uses recurrent neural networks with sequence-to-sequence translation to first convert the pseudocode into an intermediate XML representation, and then recursively parses that XML to produce the final Python code. The system aims to help students learn programming by allowing them to test algorithms written in pseudocode. It was implemented using Keras and trained on a dataset containing pseudocode statements and their Python translations.
Class Diagram Extraction from Textual Requirements Using NLP Techniquesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The document summarizes a graduate student's project using support vector machines (SVM) for transductive learning to classify RNA-related biological abstracts. The student collected a corpus of 400 abstracts categorized into RNA-related and non-RNA-related groups. Software was developed to preprocess the abstracts, extract features, generate training and test sets for SVM Light, and test its ability to classify abstracts into different RNA categories like mRNA, tRNA, etc. The goal was to improve on keyword searches by using a small number of training examples from a specific dataset to maximize classification precision for that set.
EVALUATION OF SINGLE-SPAN MODELS ON EXTRACTIVE MULTI-SPAN QUESTION-ANSWERINGdannyijwest
Machine Reading Comprehension (MRC), particularly extractive close-domain question-answering, is a prominent field in Natural Language Processing (NLP). Given a question and a passage or set of passages, a machine must be able to extract the appropriate answer from the passage(s). However, the majority of these existing questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Runtime of base models on the entire datasetis approximately one day while the runtime for all models on a third of the dataset is a little over two days. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset compared to the single-span source datasets. While the models tested on the source datasets were slightly fine-tuned in order to return multiple answers, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in question-answering and improve existing question-answering products and methods
IRJET- A Novel Approch Automatically Categorizing Software TechnologiesIRJET Journal
This document proposes an automatic approach called Witt to categorize software technologies based on their descriptions. Witt takes a sentence describing a technology as input and outputs a general category (e.g. integrated development environment) along with qualifying attributes. It applies natural language processing and the Levenshtein distance algorithm to compare string similarities and categorize technologies from large datasets. The system architecture first obtains data on software methodologies and labels. It then applies NLP and Levenshtein distance to find hypernyms and transform them into categories with attributes for classification.
This document summarizes an experiment on using pair programming with students in a Java lab. Pair programming involves two programmers working together at one workstation, with one typing (driver) while the other reviews the work (navigator). The experiment found that students performed better on various metrics like participation, debugging skills, and perseverance when using pair programming compared to solo programming. An algorithm called PPPA (Pair Programming Performance Algorithm) is also presented to assess pair programming efforts based on factors like effort, time, cohesion, coupling, complexity, and bugs. Empirical evidence from questionnaires given to students after the experiment supported the benefits of pair programming identified by the PPPA.
#ATAGTR2019 Presentation "Re-engineering perfmance strategy of deep learning ...Agile Testing Alliance
Pallavi Shetty and Anjali Sharma will present on re-engineering performance strategies for deep learning applications using TensorFlow. They will discuss techniques for capturing performance metrics and optimizing TensorFlow and application code. They will also explore performance measures, optimization techniques, and case studies demonstrating improved training times and CPU usage through TensorFlow tuning. The goal is to provide a complete guide to performance engineering of deep learning applications.
Benchmarking transfer learning approaches for NLPYury Kashnitsky
Call for collaboration in applied transfer learning for text classification tasks https://www.kaggle.com/kashnitsky/exploring-transfer-learning-for-nlp
This document summarizes a PhD thesis titled "Answer Set Programming: Founded Bounds and Model Counting" by Rehan Abdul Aziz from the University of Melbourne. The thesis extends answer set programming (ASP) in two ways: [1] It presents an approach called Bound Founded ASP that generalizes ASP to allow reasoning over numeric bounds and more complex rules, removing grounding bottlenecks. [2] It develops an algorithm for stable model counting and applies it to probabilistic logic programming and projected model counting. The thesis contributes novel theories, implementations, and applications in extending ASP's capabilities.
This paper presents a natural language processing based automated system called DrawPlus for generating UML diagrams, user scenarios and test cases after analyzing the given business requirement specification which is written in natural language. The DrawPlus is presented for analyzing the natural languages and extracting the relative and required information from the given business requirement Specification by the user. Basically user writes the requirements specifications in simple English and the designed system has conspicuous ability to analyze the given requirement specification by using some of the core natural language processing techniques with our own well defined algorithms. After compound analysis and extraction of associated information, the DrawPlus system draws use case diagram, User scenarios and system level high level test case description. The DrawPlus provides the more convenient and reliable way of generating use case, user scenarios and test cases in a way reducing the time and cost of software development process while accelerating the 70 of works in Software design and Testing phase Janani Tharmaseelan ""Cohesive Software Design"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22900.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/22900/cohesive-software-design/janani-tharmaseelan
Deep Learning libraries and first experiments with TheanoVincenzo Lomonaco
In recent years, neural networks and deep learning techniques have shown to perform well on many
problems in image recognition, speech recognition, natural language processing and many other tasks.
As a result, a large number of libraries, toolkits and frameworks came out in different languages and
with different purposes. In this report, firstly we take a look at these projects and secondly we choose the
framework that best suits our needs: Theano. Eventually, we implement a simple convolutional neural net
using this framework to test both its ease-of-use and efficiency.
This document discusses using Weka, a machine learning toolkit, to perform text classification. It describes loading posts from a database, preprocessing the text by transforming it into word vectors, training two classification models (J48 decision tree and Naive Bayes) on the training data, evaluating the models on test data to measure accuracy, and using the best model via PyWeka to predict categories for new posts. The goal is to build a tool to automatically categorize product listings based on their descriptions.
WEKA is a popular open source machine learning toolkit written in Java. It contains algorithms for data pre-processing, classification, regression, clustering, association rules, and visualization. WEKA has graphical user interfaces for exploring data and evaluating models, as well as tools for performing experiments and comparing machine learning algorithms. It supports common data formats and can operate on datasets stored in files or databases. WEKA is widely used for research and applications involving machine learning and data mining.
This artist loves photography because it allows them to capture moments. They see photography as a form of art and hope others will enjoy their creative works. The artist, Monica Fang, intends to continue focusing on photography and digital art going forward.
This document is a template for Microsoft PowerPoint presentations. It contains 10 placeholder slides with dummy text and instructions to replace the dummy text with the user's own text. Each slide also contains footer text with the page number and logo. The final slide provides terms of use for the template, stating it is for non-commercial use only.
The document discusses the proposed package structure for a statistical machine learning framework. It outlines core packages for handling data structures like corpora and datasets, as well as packages for common machine learning tasks like classification, feature extraction, and modeling. It provides examples of how the framework could be used for tasks like naive Bayes classification with cross-validation.
The document contains an email address but no other substantive information. It appears to be an email address written multiple times without any surrounding context or message.
The document outlines the key components in a text analysis pipeline including:
- CorpusReader which reads text from a source into a Corpus without processing.
- FeatureExtractor which converts text to features and Annotator which adds annotations to the Corpus.
- Dataset contains feature representations of Documents from a Corpus.
- Learner uses the Dataset to learn a Model, which is then used by Classifier to produce Classifications, evaluated by ClassificationEvaluator.
The document discusses exploring the concept of systematically differentiating between dark and light leaders by examining whether a criminal personality profile is possible. It summarizes research on criminal profiling and syndromes associated with criminal behavior. The document proposes analyzing case studies of dark and light leaders using assessment tools to determine if there are patterns suggesting systematic personality differences between the two groups. Specifically, it will use adapted versions of Hare's Psychopathy Checklist, DSM-IV criteria for antisocial personality disorder, and an emotional intelligence scale to rate leaders and identify psychopathic traits or a lack of emotional intelligence.
The document contains an email address but no other substantive information. It appears to be an email address written multiple times without any surrounding context or message.
This document summarizes research on organizational culture and dark leadership. It defines organizational culture and explores how levels of control within an organization can influence deviant behavior. Dark leadership is defined using Edwin Sutherland's concept of white-collar crime. The relationship between CEO and board is discussed. Research on dark leadership frameworks and factors that can enable corrupt organizational cultures like groupthink are summarized.
This document summarizes a research study that aimed to differentiate between "dark" and "light" leaders in a corporate setting. The study found key differences between dark and light leaders, such as dark leaders exhibiting more sociopathic and psychopathic tendencies while lacking emotional intelligence. Dark leadership was explained by the "dark leadership framework." Limitations included a lack of data on light leaders and potential biases. Suggestions for future research included further testing the models and instruments used as well as exploring cognitive and environmental factors.
This document summarizes common clichés and poor practices in PowerPoint presentations. It notes overused quotes, lack of planning, prioritizing speed over quality, repetitive information, unnecessary embellishments, and ideas that should not be shared. The summary criticizes relying on tired conventions rather than original content.
IRJET- A Novel Approch Automatically Categorizing Software TechnologiesIRJET Journal
This document proposes an automatic approach called Witt to categorize software technologies based on their descriptions. Witt takes a sentence describing a technology as input and outputs a general category (e.g. integrated development environment) along with qualifying attributes. It applies natural language processing and the Levenshtein distance algorithm to compare string similarities and categorize technologies from large datasets. The system architecture first obtains data on software methodologies and labels. It then applies NLP and Levenshtein distance to find hypernyms and transform them into categories with attributes for classification.
This document summarizes an experiment on using pair programming with students in a Java lab. Pair programming involves two programmers working together at one workstation, with one typing (driver) while the other reviews the work (navigator). The experiment found that students performed better on various metrics like participation, debugging skills, and perseverance when using pair programming compared to solo programming. An algorithm called PPPA (Pair Programming Performance Algorithm) is also presented to assess pair programming efforts based on factors like effort, time, cohesion, coupling, complexity, and bugs. Empirical evidence from questionnaires given to students after the experiment supported the benefits of pair programming identified by the PPPA.
#ATAGTR2019 Presentation "Re-engineering perfmance strategy of deep learning ...Agile Testing Alliance
Pallavi Shetty and Anjali Sharma will present on re-engineering performance strategies for deep learning applications using TensorFlow. They will discuss techniques for capturing performance metrics and optimizing TensorFlow and application code. They will also explore performance measures, optimization techniques, and case studies demonstrating improved training times and CPU usage through TensorFlow tuning. The goal is to provide a complete guide to performance engineering of deep learning applications.
Benchmarking transfer learning approaches for NLPYury Kashnitsky
Call for collaboration in applied transfer learning for text classification tasks https://www.kaggle.com/kashnitsky/exploring-transfer-learning-for-nlp
This document summarizes a PhD thesis titled "Answer Set Programming: Founded Bounds and Model Counting" by Rehan Abdul Aziz from the University of Melbourne. The thesis extends answer set programming (ASP) in two ways: [1] It presents an approach called Bound Founded ASP that generalizes ASP to allow reasoning over numeric bounds and more complex rules, removing grounding bottlenecks. [2] It develops an algorithm for stable model counting and applies it to probabilistic logic programming and projected model counting. The thesis contributes novel theories, implementations, and applications in extending ASP's capabilities.
This paper presents a natural language processing based automated system called DrawPlus for generating UML diagrams, user scenarios and test cases after analyzing the given business requirement specification which is written in natural language. The DrawPlus is presented for analyzing the natural languages and extracting the relative and required information from the given business requirement Specification by the user. Basically user writes the requirements specifications in simple English and the designed system has conspicuous ability to analyze the given requirement specification by using some of the core natural language processing techniques with our own well defined algorithms. After compound analysis and extraction of associated information, the DrawPlus system draws use case diagram, User scenarios and system level high level test case description. The DrawPlus provides the more convenient and reliable way of generating use case, user scenarios and test cases in a way reducing the time and cost of software development process while accelerating the 70 of works in Software design and Testing phase Janani Tharmaseelan ""Cohesive Software Design"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22900.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/22900/cohesive-software-design/janani-tharmaseelan
Deep Learning libraries and first experiments with TheanoVincenzo Lomonaco
In recent years, neural networks and deep learning techniques have shown to perform well on many
problems in image recognition, speech recognition, natural language processing and many other tasks.
As a result, a large number of libraries, toolkits and frameworks came out in different languages and
with different purposes. In this report, firstly we take a look at these projects and secondly we choose the
framework that best suits our needs: Theano. Eventually, we implement a simple convolutional neural net
using this framework to test both its ease-of-use and efficiency.
This document discusses using Weka, a machine learning toolkit, to perform text classification. It describes loading posts from a database, preprocessing the text by transforming it into word vectors, training two classification models (J48 decision tree and Naive Bayes) on the training data, evaluating the models on test data to measure accuracy, and using the best model via PyWeka to predict categories for new posts. The goal is to build a tool to automatically categorize product listings based on their descriptions.
WEKA is a popular open source machine learning toolkit written in Java. It contains algorithms for data pre-processing, classification, regression, clustering, association rules, and visualization. WEKA has graphical user interfaces for exploring data and evaluating models, as well as tools for performing experiments and comparing machine learning algorithms. It supports common data formats and can operate on datasets stored in files or databases. WEKA is widely used for research and applications involving machine learning and data mining.
This artist loves photography because it allows them to capture moments. They see photography as a form of art and hope others will enjoy their creative works. The artist, Monica Fang, intends to continue focusing on photography and digital art going forward.
This document is a template for Microsoft PowerPoint presentations. It contains 10 placeholder slides with dummy text and instructions to replace the dummy text with the user's own text. Each slide also contains footer text with the page number and logo. The final slide provides terms of use for the template, stating it is for non-commercial use only.
The document discusses the proposed package structure for a statistical machine learning framework. It outlines core packages for handling data structures like corpora and datasets, as well as packages for common machine learning tasks like classification, feature extraction, and modeling. It provides examples of how the framework could be used for tasks like naive Bayes classification with cross-validation.
The document contains an email address but no other substantive information. It appears to be an email address written multiple times without any surrounding context or message.
The document outlines the key components in a text analysis pipeline including:
- CorpusReader which reads text from a source into a Corpus without processing.
- FeatureExtractor which converts text to features and Annotator which adds annotations to the Corpus.
- Dataset contains feature representations of Documents from a Corpus.
- Learner uses the Dataset to learn a Model, which is then used by Classifier to produce Classifications, evaluated by ClassificationEvaluator.
The document discusses exploring the concept of systematically differentiating between dark and light leaders by examining whether a criminal personality profile is possible. It summarizes research on criminal profiling and syndromes associated with criminal behavior. The document proposes analyzing case studies of dark and light leaders using assessment tools to determine if there are patterns suggesting systematic personality differences between the two groups. Specifically, it will use adapted versions of Hare's Psychopathy Checklist, DSM-IV criteria for antisocial personality disorder, and an emotional intelligence scale to rate leaders and identify psychopathic traits or a lack of emotional intelligence.
The document contains an email address but no other substantive information. It appears to be an email address written multiple times without any surrounding context or message.
This document summarizes research on organizational culture and dark leadership. It defines organizational culture and explores how levels of control within an organization can influence deviant behavior. Dark leadership is defined using Edwin Sutherland's concept of white-collar crime. The relationship between CEO and board is discussed. Research on dark leadership frameworks and factors that can enable corrupt organizational cultures like groupthink are summarized.
This document summarizes a research study that aimed to differentiate between "dark" and "light" leaders in a corporate setting. The study found key differences between dark and light leaders, such as dark leaders exhibiting more sociopathic and psychopathic tendencies while lacking emotional intelligence. Dark leadership was explained by the "dark leadership framework." Limitations included a lack of data on light leaders and potential biases. Suggestions for future research included further testing the models and instruments used as well as exploring cognitive and environmental factors.
This document summarizes common clichés and poor practices in PowerPoint presentations. It notes overused quotes, lack of planning, prioritizing speed over quality, repetitive information, unnecessary embellishments, and ideas that should not be shared. The summary criticizes relying on tired conventions rather than original content.
This document discusses requirements for designing a framework to analyze text datasets. It identifies several key variations in importing datasets related to file sources, formats and schemas. It then proposes using high-level reader classes to handle different datasets. The document outlines the STAT domain model which includes concepts like RawCorpus to represent raw document collections, Processor to process data, Corpus to represent data for machine learning, Trainer for algorithms, Model to store learned parameters, Classifier to classify documents, Prediction for output classifications, Evaluator to evaluate predictions and Evaluation for results.
The document outlines the requirements analysis phase of a project, including analyzing related software, collecting stakeholder opinions, and specifying design guidelines for the next phase. It notes that a list of related packages has been created and a preliminary review and survey draft are prepared. Findings so far indicate the related software may be too complex, deep, and coupled with too many interfaces/classes and lack of generic input/output formats. Next steps are to complete documentation, interviews, surveys and outline preliminary design guidelines.
Weka is a collection of machine learning algorithms and data pre-processing tools developed at the University of Waikato. It contains tools for data pre-processing, classification, regression, clustering, association rule mining, and visualization. Weka is open source, free to use, and popular for research and applications. It has a graphical user interface and supports a variety of data formats including ARFF files.
WEKA is machine learning software written in Java that is used for data mining tasks. It contains tools for pre-processing data, building classifiers, clustering data, finding associations, attribute selection, and visualizing data. WEKA also allows users to perform experiments to compare the performance of different learning algorithms on classification and regression problems. It has graphical user interfaces that make it easy to set up and run machine learning experiments by connecting different components in a workflow.
Classification and Clustering Analysis using Weka Ishan Awadhesh
This Term Paper demonstrates the classification and clustering analysis on Bank Data using Weka. Classification Analysis is used to determine whether a particular customer would purchase a Personal Equity PLan or not while Clustering Analysis is used to analyze the behavior of various customer segments.
This document presents a new method for extracting class diagrams from textual requirements using natural language processing (NLP) techniques. It proposes the Requirements Analysis and Class diagram Extraction (RACE) system, which uses tools like the OpenNLP parser, a stemming algorithm, and WordNet to extract concepts and identify classes, attributes and relationships. The RACE system applies heuristic rules and a domain ontology to the output of the NLP tools to refine and finalize the extracted class diagram. The paper concludes that the RACE system demonstrates the effective use of NLP techniques to automate the extraction of class diagrams from informal natural language requirements specifications.
The document summarizes a workshop on Service-Oriented Programming (SOP). SOP is a new programming methodology that allows developing software applications by connecting and composing existing services, facilitating software reuse. The workshop is divided into two parts: the first part describes SOP concepts and motivation, and the second introduces teaching materials through a demonstration of SOP techniques. The qualifications of the three presenters are also provided, including their research interests and experience in computer science education.
IRJET- Factoid Question and Answering SystemIRJET Journal
This document describes a factoid question answering system that uses neural networks and the Tensorflow framework. The system takes in a text document and question as input. It then processes the input using techniques like gated recurrent units and support vector machines to classify the question. The system calculates attention between facts and the question, modifies its memory, and identifies the word closest to the answer to output as the response. Key aspects of the system include training a question answering engine with Tensorflow, storing and retrieving data, and generating the final answer.
The document discusses some of the promises and perils of mining software repositories like Git and GitHub for research purposes. It notes that while these sources contain rich data on software development, there are also challenges to consider. For example, decentralized version control systems like Git allow private collaboration that may be missed. And most GitHub projects are personal and inactive, while it is also used for storage and hosting. The document recommends researchers approach these data sources carefully and provides lessons on how to properly analyze and interpret the data from repositories like Git and GitHub.
This document introduces object-oriented programming (OOP). It discusses the software crisis and need for new approaches like OOP. The key concepts of OOP like objects, classes, encapsulation, inheritance and polymorphism are explained. Benefits of OOP like reusability, extensibility and managing complexity are outlined. Real-time systems, simulation, databases and AI are examples of promising applications of OOP. The document was presented by Prof. Dipak R Raut at International Institute of Information Technology, Pune.
IRJET- Voice to Code Editor using Speech RecognitionIRJET Journal
This document presents a summary of a research paper on developing a voice-controlled code editor using speech recognition. A team of students and a professor from S.B Jain Institute of Technology, Management and Research created a Java program editor that allows users to write code using voice commands. The editor takes advantage of the natural human ability to speak language and allows coding more accurately and intuitively compared to manual typing. It analyzes the user's speech using acoustic and language modeling with Hidden Markov Models to accurately recognize commands. The proposed voice-controlled code editor is designed to reduce typing errors, improve coding speed, and enable people with disabilities to operate a computer. It will support basic editing tasks and allow switching between voice and manual input.
Exploring the Efficiency of the Program using OOAD MetricsIRJET Journal
This document proposes a methodology to analyze the efficiency of object-oriented programs using OOAD (Object Oriented Analysis and Design) metrics. The methodology involves compiling a program successively until it is error-free, recording the error rate at each compilation. These results are then compared to determine how many compilations were needed for the program to be error-free, indicating its efficiency. The methodology is experimentally validated on a sample Java program, with results showing the error rate decreasing with each compilation until the program is error-free after the 8th compilation, demonstrating good efficiency.
This document summarizes OR/MS (operations research and management science) software available for microcomputers. It lists software packages for several common OR/MS techniques like linear programming, forecasting, and project management. It evaluates packages based on their functionality, size limitations, input and output features. The summary focuses on software for IBM PC and compatible machines running MS-DOS, as they have the greatest variety of software available. It aims to help readers select appropriate OR/MS software for their needs and applications.
IRJET - Mobile Chatbot for Information SearchIRJET Journal
This document summarizes a research paper on developing a mobile chatbot using IBM Watson services to allow students to search for their exam scores. The chatbot uses Watson Assistant for natural language processing, a SQL database as a knowledge base to store score information, and text-to-speech and speech-to-text for input and output. It was built with Android Studio and Java to provide an intuitive mobile interface for users to interact with the chatbot.
This document discusses using machine learning algorithms to predict employee attrition and understand factors that influence turnover. It evaluates different machine learning models on an employee turnover dataset to classify employees who are at risk of leaving. Logistic regression and random forest classifiers are applied and achieve accuracy rates of 78% and 98% respectively. The document also discusses preprocessing techniques and visualizing insights from the models to better understand employee turnover.
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Requirment
1. Requirement Analysis Version 0.1
by the Stat Team
Mehrbod Sharifi
Jing Yang
The Stat Project, guided by
Professor Eric Nyberg and Anthony Tomasic
Feb. 25, 2009
2. Chapter 1
Introduction to STAT
In this chapter, we introduce the Stat project, its motivation and scope and also define the target
audience and stakesholders. We will start the discussion of why we believe such a framework will
be useful for the software engineers and computer science researchers but we will provide more
details and evidence in the later chapters.
1.1 Overview
Stat is an open source machine learning framework in Java for text analysis. Original the work
Stat was abbreviating Semi-Supervised Text Analysis Toolkit which refer to the implementation
of some semi-supervised algorithms in this package, however later on we evolved to defining a
framework as opposed to our particular implementation and therefore the first S can now be in-
terpreted as ”Simple” or ”Statistical”.
Applying machine learning approaches to extract information and uncover patterns from tex-
tual data has become extremely popular in recent years. Accordingly, many software have been
developed to enable people to utilize machine learning for text analytics and automate such pro-
cess. Users, however, find many of these existing software difficult to use, even if they just want
to carry out a simple experiment; they have to spend much time learning those software and may
finally find out they still need to write their own programs to preprocess data to get their target
software running.
We notice this situation and observe that many of these can be simplified. A new software
framework should be developed to ease the process of doing text analytics; we believe researchers
or engineering using our framework for textual data analysis would feel the process convenient,
conformable, and probably, enjoyable.
Existing software with regard to using machine learning for linguistic analysis have tremen-
dously helped researchers and engineers make new discoveries based on textual data, which is
unarguably one of the most form of data in the real world.
As a result, many more researchers, engineers, and possibly students are increasingly inter-
ested in using machine learning approaches in their text analytics. Those people, some of which
even being experienced users, find existing software packages are not generally easy to learn and
convenient to use.
In the next section, we will outline our design goal and provide a summary of how this differ-
entiates Stat from the exiting software packages. We will also defined the scope or our work and
our audience in the sections that follows.
1
3. 1.2 Goals
Here is the outline of our design goal for the new framework. These points will be clarified mostly
in the upcoming chapters but we will state them with brief introduction in this section:
• Simplicity: This is the most important consideration. Essentially, we will reduce the com-
plexity of the API by limiting the hierarchy and number of domain objects and their inter-
action. We achieve this by defining a clear distinction of responsibilities and the evaluate
our success by how quickly someone completely unfamiliar with text analysis and machine
learning can understand the toolkit and start using it. This is explained further in the next
sections and chapters.
• Extensibility: We put the focus on how to facilitate the extension of the package or in
other words: implementing within our framework. Combined with the simplicity, we hope
that this will encourage more people to contribute and enable the kinds proven success as
can be seen in Matlab or R for example.
• Performance: As it is widely know, dealing with text is computationally intensive and
we will take this into consideration from ground up (e.g., using Java primitives instead of
objects)
• Features: In the presence of extensibility we will give lowers priority to implementing many
features for this package. Instead, we will demonstrate how the package generalizes the
approaches by many other packages by ”wrapping” those tools so they can be used in the
simplified manner and also implicitly providing some training for them if the users would
rather continue by moving to any of those packages. As stated previously, we will provide
implementation of unsupervised and semi-supervised methods which is what lacks in this
domain.
These objectives shows how Stat will be different than existing software package in this do-
main. For example, although Weka has a comprehensive suite of machine learning algorithms, it
is not designed for text analysis, lacking of naturally supported capabilities for linguistic concepts
representation and processing. MinorThird, on the other hand, though designed specifically as a
package for text analysis, turns out to be rather complicated and difficult to learn. It also does not
support semi-supervised and unsupervised learning, which are becoming increasingly important
machine learning approaches.
Another problem for many existing packages is that they often adopt their own specific input
and output format. Real-world textual data, however, are generally in other formats that are not
readily understood by those packages. Researchers and engineers who want to make use of those
packages often find themselves spending much time seeking or writing ad hoc format conversion
code. These ad hoc code, which could have been reusable, are often written over and over again
by different users.
Researchers and engineers, when presented common text analysis tasks, usually want a text-
specific, lightweight, reusable, understandable, and easy-to-learn package that help them get their
works done efficiently and straightforwardly. Stat is designed to meet their requirements. Moti-
vated by the needs of users who want to simplify their work and experiment related to textual data
learning, we initiate the Stat project, dedicating to provide them suitable toolkits to facilitate
their analytics task on textual data.
2
4. In a nutshell, Stat is an open source framework aimed at providing researchers and engi-
neers with a integrated set of simplified, reusable, and convenient toolkits for textual data
analysis. Based on this framework, researchers can carry out their machine learning exper-
iments on textual data conveniently, and engineers can build their own small applications
for text analytics or use the classes designed by others.
1.3 Scope
The previous section may give an impression for an impossible task. In this section, we clearly
state what is and is not included in this project.
The main deliverable for this project is a set of specifications, which defines a simplified frame-
work for text analysis based on NLP and machine learning. We explain how succinctly the frame-
work should be used and how easily it can be extended.
We also provide introductory implementations of the framework, including tools and packages
serving foundation classes of the framework. They are
• Dataset and framework object adaptors: A set of classes that will allow reading and
writing files in various formats, supporting importing and exporting dataset as well as loading
and saving framework objects.
• Linguistic and machine learning packages wrappers: A set of classes that integrate
existing tools for NLP and Machine Learning and can be used within the framework. These
wrappers hides the implementation and variation details of these packages to provide a set
of simplified and unified interfaces to framework users.
• Semi-Supervised algorithms: Implementation of certain Semi-Supervised learning algo-
rithms that are not available from the existing packages.
The goal is NOT to design the most comprehensive machine learning package or compete or
correct the previous packages. We will to focus on the goals stated above to create our framework
from a different perspective.
3
5. 1.4 Stakeholders
Below is the list of stakeholder and how this project will affect them:
• Researchers, particularly in language technology but also in other fields, would be able
to save time by focusing on their experiments instead of dealing with various input/output
format which is routinely necessary in text processing. They can also easily switch between
various tools available and even contribute to STAT so that others can save time by using
their adaptors and algorithms.
• Software engineers, who are not familiar with the machine learning can start using the
package in their program with a very short learning phase. STAT can help them develop clear
concepts of machine learning quickly. They can build their applications using functionality
provided STAT easily and achieve high level performance.
• Developers of learning package, can provide plug-ins for STAT to allow ease of integration
of their package. They can also delegate some of the interoperability needs through this
program (some of which may be more time consuming to be addressed within their own
package).
• Beginners to text processing and mining, who want fundamental and easy to learn
capabilities involving discovering patterns from text. They will be benefited from this project
by saving their time, facilitating their learning process, and sparking their interests to the
area of language technology.
4
7. Chapter 3
Existing Related Software Package
In this chapter, we analyze a few main competitors of our projects. We focus on two academic
toolkits – Weka and MinorThird. We comment on their strengths and explore their limitations, and
discuss why and how we can do better than these competitors.
3.1 Weka
Weka is a comprehensive collection of machine learning algorithms for solving data mining problems
in Java and open sourced under the GPL.
3.1.1 Strengths of Weka
Weka is a very popular software for machine learning, due to the its main strengths:
• Provide comprehensive machine learning algorithms. Weka supports most current
machine learning approaches for classification, clustering, regression, and association rules.
• Cover most aspects for performing a full data mining process. In addition to learn-
ing, Weka supports common data preprocessing methods, feature selection, and visualization.
• Freely available. Weka is open source released under GNU General Public License.
• Cross-platform. Weka is cross-platform fully implemented in Java.
Because of its supports of comprehensive machine learning algorithm, Weka is often used for
analytics in many form of data, including textual data.
3.1.2 Limitations of using Weka for text analysis
However, Weka is not designed specifically for textual data analysis. The most critical drawback
of using Weka for processing text is that Weka does not provide “built-in” constructs for natural
representation of linguistics concepts1 . Users interested in using Weka for text analysis often find
themselves need to write some ad-hoc programs for text preprocessing and conversion to Weka
representation.
• Not good at understanding various text format. Weka is good at understanding its
standard .arff format, which is however not a convenient way of representation text. Users
have to worry about how can they convert textual data in various original format such as
1
Though there are classes in Weka supporting basic natural language processing, they are viewed as auxiliary
utilities. They make performing basic textual data processing using Weka possible, but not conveniently and straight-
forwardly
6
8. raw plain text, XML, HTML, CSV, Excel, PDF, MS Word, Open Office document, etc. to
be understandable by Weka. As a result, they need to spend time seeking or writing external
tools to complete this task before performing their actual analysis.
• Unnecessary data type conversion. Weka is superior in processing nominal (aka, categor-
ical) and numerical type attributes, but not string type. In Weka, non-numerical attributes
are by default imported as nominal attributes, which usually is not a desirable type for text
(imagine treating different chunks of text as different values of a categorical attribute). One
have to explicitly use filters to do a conversion, which could have been done automatically if
it knows you are importing text.
• Lack of specialized supported for linguistics preprocessing. Linguistics preprocessing
is a very important aspect of textual data analysis but not a concern of Weka. Weka does
not (at least, not dedicated to) take care this issue very seriously for users. Weka has a
StringToWordVector class that performs all-in-one basic linguistics preprocessing, including
tokenization, stemming, stopword removal, tf-idf transformation, etc. However, it is less
flexible and lack of other techniques (such as part-of-speech tagging and n-gram processing)
for users who want fined grain and advanced linguistics controls.
• Unnatural representation of textual data learning concepts. Weka is designed for
general purpose machine learning tasks so have to protect too many variations. As a results,
domain concepts in Weka are abstract and high-level, package hierarchy is deep, and the
number of classes explodes. For example, we have to use Instance rather than Document and
Instances rather than Corpus. Concepts in Weka such as Attribute is obscure in meaning
for text processing. First adding many Attribute to a cryptic FastVector which then passed
to a Instances in order to construct a dataset appears very awkward to users processing
text. Categorize filters first according to attribute/instance then supervised /unsupervised
make non-expert users feel confusing and hard to find their right filters. Many users may feel
unconformable programmatically using Weka to carry out their experiments related to text.
In summary, for users who want enjoyable experience at performing text analysis, they need
built-in capabilities to naturally support representing and processing text. They need specialized
and convenient tools that can help them finish most common text analysis tasks straightforwardly
and efficiently. This cannot be done by Weka due to its general-purpose nature, despite its com-
prehensive tools.
7
9. Partial UML Domain Model of Weka (Preliminary)
evaluate
1
1
Evaluation Classifier
1 1
1
built-from classify
evaluate-on
1
1 1
1 tranform-attribute contain
StringToWordVector Instances Instance
1
1 attributeValues
*
1
contain
*
Attribute
NominalToString transform-type 1
1
possibleValues
Note: when you see ClassA quot;containsquot; a number of ClassB,
it is probably that Weka implements it as ClassA maintains a
quot;FastVectorquot; whose elements are instances of ClassB.
Figure 3.1: Partial domain model for Weka for basic text analysis
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10. Chapter 4
Requirements specifications
Here we first explain in detail the major features of our framework.
• Simplified. APIs are clear, consistent, and straightforward. Users with reasonable Java
programming knowledge and basic machine learning concepts can learn our package without
much efforts, understand its logical flow quickly, be able to get started within a small amount
of time, and finish the most common tasks with a few lines of code. Since our framework is
not designed for general purposes and for including comprehensive features, there are space
for us to simplify the APIs to optimize for those most typical and frequent operations.
• Reusable. Built-in modular supports are provided for the core routines across various
phases in text analysis, including text format transformation, linguistic processing, machine
learning, and experimental evaluation. Additional functionalities can be extended on top of
the core framework easily and user-defined specifications are pluggable. Existing code can
be used cross environment and interoperate with external related packages, such as Weka,
MinorThird, and OpenNLP. (I use reusable instead of extendable because it cover a higher
level of concept we might also need and able to follow, what’s your idea? )
• To be added
4.1 Functional Requirements
In this section, we define most common use cases of our framework and address them in the degree
of detail of casual use case. The “functional requirements” of this project are that the users can
use libraries provided by our framework to complete these use cases more easily and comfortably
than not use.
Actors
Since our framework assumes that all users of interests are programming using our APIs, there is
only one role of human actor, namely the programmer. This human actor is always the primary
actor. There are some possible secondary and system actors, namely the external packages our
framework integrates, depending on what specific use cases the primary actor is performing.
Casual Use Cases
Here we present some typical use cases of our framework in a casual format. For better under-
standing and separation of responsibilities, use cases are divided to many categories, where each
category defines a typical step of doing text analysis.
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11. • Dataset importing and exporting. In this category of use cases, a user want to read
file(s) from different kinds of sources in different kinds of formats, to some specific data
structures representing dataset in memory for further processing, or write dataset to files in
other format. Here list sample important use cases:
1. Use case 1. Read a list of raw text files that placed in a specified directory of the local
file system, to a RawCorpus in which a RawDocument represents a text file.
2. Use case 2. Read a list of HTML files that placed in a specified directory of the local file
system, strip the tags, and store to a RawCorpus in which a RawDocument represents
a HTML file.
3. Use case 3. Read a XML file with non-unicode encoding from the Web specified by a
URL to a RawDocument, with fields appropriately populated.
• Object persistence. In this category of use cases, a user want to persist objects in our
framework to disk in our internal format, which can be loaded lately.
1. Use case 1.
2. Use case 2.
3. Use case 3.
• Structured information extraction.
1. Use case 1.
2. Use case 2.
3. Use case 3.
• Linguistic preprocessing.
1. Use case 1.
2. Use case 2.
3. Use case 3.
• Machine learning.
1. Use case 1.
2. Use case 2.
3. Use case 3.
• Experiment and evaluation.
1. Use case 1.
2. Use case 2.
3. Use case 3.
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12. 4.2 Non-functional Requirements
• Open source. It should be made available for public collaboration, allowing users to use,
change, improve, and redistribute the software.
• Portability. It should be consistently installed, configured, and run independent to different
platforms, given its design and implementation on Java runtime environment.
• Documentation. Its code should be readable, self-explained, and documented clearly and
unambiguously for critical or tricky part. It should include an introduction guide for users
to get started, and preferably, provides sample dataset, tutorial, and demos for user to run
examples out of the box.
• Performance. It should be able to response to user within reasonable amount of time given
a limited amount of data (unclear, need specify). Preferably, it can estimate the running
time needed to perform a task and notify user before user actually execute the task (is this
the responsibility for framework designers? )
• Dependency. It is actually a issue. The package integrates other external packages and has
many dependency. How to resolve this issue? How do we distribute our package?
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