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This document summarizes software transactional memory (STM) as an alternative to lock-based concurrency. STM allows shared memory access through atomic transactions composed of reads and writes. Transactions are verified and committed to memory if read values match, or aborted if not. While STM addresses problems with lock-based approaches like deadlocks, concerns remain regarding performance overhead and ease-of-use for practitioners. The document reviews STM techniques and research on formal correctness, scalability, and semantic difficulties.
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Robust rule-based parsing
Material of the Natural Language Processing (NLP) Workshop with STIC-Asia representatives and the Nepal team.
August 30-31, 2007.
Patan Dhoka, Lalitpur, Nepal.
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The document discusses requirements for modular ontologies, including semantic soundness, expressivity, and localized semantics. It analyzes approaches like DDL, E-Connections and P-DL based on these requirements. DDL allows directional relations but lacks support for roles and transitive reusability. E-Connections ensures reasoning exactness but has limited expressivity. P-DL supports transitive reusability through compositionally consistent relations but directionality does not always hold and decidability relies on the underlying description logic. The conclusions call for an expressive modular ontology language with both concept and role correspondences across modules while relaxing disjointedness assumptions to improve expressivity and reasoning capabilities.
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limit the amount of unstructured text by clustering based on semantics of log file lines. Next, we only take
the most relevant cluster into account and focus only on those frequent patterns which lead to the desired
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training our model on one system type and applying it to a different system with slightly different log file
patterns, we achieve an accuracy over 99.99%.
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This document presents an adaptive log file parser that uses semantics and hidden Markov models. It first clusters log file lines based on semantics to limit unstructured text. It then builds a hidden Markov model to represent parsing patterns, with log entries as states and extracted values as emissions. When applied to a new system, it adapts the model's transition and emission probabilities to fit the new data. The approach achieves over 99.99% accuracy when trained on one system and applied to another with slightly different log patterns.
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·
· Submit your completed assignment by following the directions linked below. Please check the Course Calendar for specific due dates.
·
· Save your assignment as a Microsoft Word document. (Mac users, please remember to append the ".docx" extension to the filename.) The name of the file should be your first initial and last name, followed by an underscore and the name of the assignment, and an underscore and the date. An example is shown below:
· Jstudent_exampleproblem_101504
1
8
Southeast Missouri State University
Department of Computer Science
Name of the Instructor: Dr. Reshmi Mitra
CS – 609 Graduate Project
December 2021
Team Member List
Jing Ma
Manoj Thapa
Nagendra Mokara
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Machine learning on Stock Predition 0
Abstract 4
1 INTRODUCTION 4
1.1 LSTM Prediction 5
2 BACKGROUND AND RELATED WORK 6
2.1 Overview of the design 7
3 RESEARCH DESIGN AND METHODS 8
3.1 Stock Price Prediction Project using LSTM 8
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4 EVALUTION, VALIDATION ANF KEYRESULTS 9
4.1 Screenshot of Result 10
5 CONCLUSIONS 11
References 12
Sound Classification using Deep Learning ……………………………………………...13
Abstract ………………………………………………………………………………….14
6 INTRODUCTION …………………………………………………………………….14
6.1 Keywords ……………………………………………………………………………15
7 COLLECTION OF DATA FROM THE DATASET …………………………………..15
7.1 Dataset ……………………………………………………………………………….15
7.2 Segregation of Data into Various Folders ..………………………………………….15
8 WRITING CODE AND DATA MODELING/TRAINING……………………………16
8.1 Design Diagram of the Project……………………………………………………….16
9 RESULT ……………………………………………………………………………….17
10 CONCLUSION/FUTURE WORK …………………………………………………..18
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established a dynamic statistical model which learns and adapts processing and parsing rules. First, we
limit the amount of unstructured text by clustering based on semantics of log file lines. Next, we only take
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Module 04 Content
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· In a 4-page paper, describe the procedures for each of the two compliance plans. NOTE: procedures are not the same thing as policies. Policies (Module 03) sets the parameters for decision-making, while procedures explains the "how". The procedures should be step-by-step instructions, and may include a checklist or process steps to follow.
· Break each procedure section into 2 pages each.
· Remember to support your procedures for each of two plans with a total of three research sources (1-2 per procedure), cited at the end in APA format.
· Write your procedures in a way that all employees will understand at a large medical facility where you are the Compliance Officer.
· Remember, you chose two compliance policy plans under the key compliance areas of Compliance Standards, High-Level Responsibility, Education, Communication, Monitoring/Auditing (for Safety), Enforcement/Discipline, and Response/Prevention. (Check them out if you forget! Remember, you may have written about different policies for the two different compliance plans.)
·
· Submit your completed assignment by following the directions linked below. Please check the Course Calendar for specific due dates.
·
· Save your assignment as a Microsoft Word document. (Mac users, please remember to append the ".docx" extension to the filename.) The name of the file should be your first initial and last name, followed by an underscore and the name of the assignment, and an underscore and the date. An example is shown below:
· Jstudent_exampleproblem_101504
1
8
Southeast Missouri State University
Department of Computer Science
Name of the Instructor: Dr. Reshmi Mitra
CS – 609 Graduate Project
December 2021
Team Member List
Jing Ma
Manoj Thapa
Nagendra Mokara
Contents
Machine learning on Stock Predition 0
Abstract 4
1 INTRODUCTION 4
1.1 LSTM Prediction 5
2 BACKGROUND AND RELATED WORK 6
2.1 Overview of the design 7
3 RESEARCH DESIGN AND METHODS 8
3.1 Stock Price Prediction Project using LSTM 8
3.2 Build the dashboard using plotly 9
4 EVALUTION, VALIDATION ANF KEYRESULTS 9
4.1 Screenshot of Result 10
5 CONCLUSIONS 11
References 12
Sound Classification using Deep Learning ……………………………………………...13
Abstract ………………………………………………………………………………….14
6 INTRODUCTION …………………………………………………………………….14
6.1 Keywords ……………………………………………………………………………15
7 COLLECTION OF DATA FROM THE DATASET …………………………………..15
7.1 Dataset ……………………………………………………………………………….15
7.2 Segregation of Data into Various Folders ..………………………………………….15
8 WRITING CODE AND DATA MODELING/TRAINING……………………………16
8.1 Design Diagram of the Project……………………………………………………….16
9 RESULT ……………………………………………………………………………….17
10 CONCLUSION/FUTURE WORK …………………………………………………..18
Machine learning on stock prediction
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December 2021
Southeast Missouri State University
Advisor: Dr Robert Lowe
Course: Researc ...
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arXiv:2303.01101.
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Language models are few-shot learners. arXiv preprint arXiv:2005.14165.
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This study investigates whether transformer models like ChatGPT (GPT4, MAR2023) can generalize
beyond their training data by examining their performance on the novel Cipher Dataset, which scrambles
token order. The dataset consists of 654 test cases, and the analysis focuses on 51 text examples and 13
algorithmic choices. Results show that the models perform well on low-difficulty ciphers like Caesar and
can unscramble tokens in 77% of the cipher examples. Despite their reliance on training data, the model's
ability to generalize outside of token order is surprising, especially when leveraging large-scale models
with hundreds of billions of weights and a comprehensive text corpus with few examples. The original
contributions of the work focus on presenting a cipher challenge dataset and then scoring historically
significant ciphers for large language models to descramble. The real challenge for these generational
models lies in executing the complex algorithmic steps on new cipher inputs, potentially as a novel
reasoning challenge that relies less on knowledge acquisition and more on trial-and-error or out-ofbounds responses.
الواجججج
This document presents a preliminary ontology to identify factors that influence software maintenance. The purpose of the ontology is to provide context for empirical studies of maintenance and help understand contradictory results. The ontology identifies key factors such as the maintained product, maintenance activities, maintenance organization and process, and maintenance engineers. It presents these factors using a UML model and discusses how variations in the factors could impact empirical studies of maintenance productivity, quality or efficiency. Two common maintenance scenarios are also described to demonstrate how the ontology can be used to characterize differences between scenarios.
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This document provides a preface and table of contents for a book on algorithms and data structures. The preface discusses how programming has advanced from a craft to an academic discipline, with contributions from Dijkstra and Hoare. It notes that data structures and algorithms are intertwined subjects. The book begins with a chapter on fundamental data structures to provide objects to operate on before discussing algorithms. It follows Hoare's theory of distinguishing between fundamental and advanced structures. The table of contents outlines chapters on data structures, sorting algorithms, recursive algorithms, and dynamic data structures like trees and hashing.
Algoithems and data structures
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hints for computer system design by Butler Lampson
This document provides hints for designing successful computer systems based on the author's experience designing and studying various systems. Some key hints discussed are: (1) Keep interfaces and abstractions simple by doing one thing well and avoiding unnecessary generalizations, (2) Separate normal and worst-case functionality to improve performance for common cases, and (3) Make actions atomic and use logging to update state to improve fault tolerance and reliability. The hints are illustrated with examples ranging from hardware to operating systems to applications.
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Our research aims to propose a global approach for specification, design and verification of context awareness Human Computer Interface (HCI). This is a Model Based Design approach (MBD). This methodology describes the ubiquitous environment by ontologies. OWL is the standard used for this purpose. The specification and modeling of Human-Computer Interaction are based on Petri nets (PN). This raises the question of representation of Petri nets with XML. We use for this purpose, the standard of modeling PNML. In this paper, we propose an extension of this standard for specification, generation and verification of HCI. This extension is a methodological approach for the construction of PNML with Petri nets. The design principle uses the concept of composition of elementary structures of Petri nets as PNML Modular. The objective is to obtain a valid interface through verification of properties of elementary Petri nets represented with PNML.
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Link to the paper available in Open Access ACM library https://dl.acm.org/citation.cfm?id=3303776
Abstract:
This paper introduces the Visual Inspection Tool (VIT) which supports researchers in the annotation of multimodal data as well as the processing and exploitation for learning purposes. While most of the existing Multimodal Learning Analytics (MMLA) solutions are tailor-made for specific learning tasks and sensors, the VIT addresses the data annotation for different types of learning tasks that can be captured with a customisable set of sensors in a flexible way. The VIT supports MMLA researchers in 1) triangulating multimodal data with video recordings; 2) segmenting the multimodal data into time-intervals and adding annotations to the time-intervals; 3) downloading the annotated dataset and using it for multimodal data analysis. The VIT is a crucial component that was so far missing in the available tools for MMLA research. By filling this gap we also identified an integrated workflow that characterises current MMLA research. We call this workflow the Multimodal Learning Analytics Pipeline, a toolkit for orchestration, the use and application of various MMLA tools.
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SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
The Revolution Of Cloud Computing
Here are the key points about using content-based filtering techniques:
- Content-based filtering relies on analyzing the content or description of items to recommend items similar to what the user has liked in the past. It looks for patterns and regularities in item attributes/descriptions to distinguish highly rated items.
- The item content/descriptions are analyzed automatically by extracting information from sources like web pages, or entered manually from product databases.
- It focuses on objective attributes about items that can be extracted algorithmically, like text analysis of documents.
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Continuous Learning Algorithms - a Research Proposal Paper
General software intelligences are still held to be outside our current capacity to build. While the definition of intelligence which we apply to machine learning and artificial intelligence generally has expanded over time as our practical computational scales increase, little exploration has been conducted around the other aspect of intelligence, which is the capacity to constantly learn and improve through interaction with the environment. If we are to define a software intelligence as an algorithm that is capable of interacting with its environment and adapting to it over time, then this exploration is critical to the development of such a system.
This body of research will attempt to make the first step into the area of continual feedback for a machine learning algorithm, evaluating it against an area which has traditionally been difficult for computers to emulate – Name Matching Analysis. If a machine learning algorithm can be used to ‘tune’ a soft-search name matching algorithm based on continual feedback generated from the results of that engine and the feedback provided by human experts, then this technique of constant feedback not only has immediate practical value but could be explored further in more ambitious research projects.
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Research Review Slides
- 1. Software Transactional Memory as an alternative to lock-based concurrency Hazel McKendrick 2010
- 2. Overview Concurrency method analogous with database transactions Alternative to coarse lock-based methods Shared memory access through atomic transaction composed of reads and writes Transaction process 1. Transaction begins 2. Reads and writes are logged separately 3. Read log is verified against memory contents 4. If they don’t match, transaction is aborted and repeated 5. Otherwise writes are permanently committed
- 3. Problems Problems with lock-based concurrency Composition and scalability Conceptual difficulties Deadlock, livelock and race conditions Does STM adequately resolve these issues? Application Issues 1. Performance overheads 2. Semantics and ease of use for practitioners 3. Non-transactional code access and migration Implementation Issues 1. Locking and safety 2. Failure, side-effects and retrying aborted transactions
- 4. References I C. Cascaval, C. Blundell, M. Michael, H. W Cain, P. Wu, S. Chiras, and S. Chatterjee. Software transactional memory: why is it only a research toy? Communications of the ACM, 51(11), 2008. S. Chandrasekaran, O. Cooper, A. Deshpande, M. J Franklin, J. M Hellerstein, W. Hong, S. Krishnamurthy, S. R Madden, F. Reiss, and M. A Shah. TelegraphCQ: continuous dataflow processing. In Proceedings of the 2003 ACM SIGMOD international conference on Management of data, 2003. D. Dice, O. Shalev, and N. Shavit. Transactional locking II. Distributed Computing, 2006. D. Grossman. The transactional memory/garbage collection analogy. In Proceedings of the 22nd annual ACM SIGPLAN conference on Object-oriented programming systems and applications, 2007. T. Harris and K. Fraser. Language support for lightweight transactions. ACM SIGPLAN Notices, 38(11), 2003.
- 5. References II Tim Harris, Simon Marlow, Simon Peyton-Jones, and Maurice Herlihy. Composable memory transactions. In Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming, Chicago, IL, USA, 2005. ACM. H. T. Kung and J. T Robinson. On optimistic methods for concurrency control. ACM Transactions on Database Systems (TODS), 6(2), 1981. C. J Rossbach, O. S Hofmann, and E. Witchel. Is transactional programming actually easier? In Proceedings of the 15th ACM SIGPLAN symposium on Principles and practice of parallel computing, 2010. T. Shpeisman, V. Menon, A. R Adl-Tabatabai, S. Balensiefer, D. Grossman, R. L Hudson, K. F Moore, and B. Saha. Enforcing isolation and ordering in STM. ACM SIGPLAN Notices, 42(6), 2007. A. Welc, B. Saha, and A. R Adl-Tabatabai. Irrevocable transactions and their applications. In Proceedings of the twentieth annual symposium on Parallelism in algorithms and architectures, 2008.
- 6. Review Structure 1. Introduction 1.1 Brief overview of lock-based concurrency as motivation 1.2 Overview of STM 1.3 Summary of trends in research and implementations 1.4 Summary of concerns in practice 2. Body 2.1 Brief history and development 2.2 Deadlock, livelock and formal correctness 2.3 Scalability, composition, safety and locking 2.4 Conceptual and semantic difficulties - convincing studies? 2.5 Performance overheads and implementations available 3. Conclusion 3.1 Evaluate state of the art for practical implementations 3.2 Future avenues for research