A Genetic Algorithmic Approach To Automated Auction Mechanism Design, 2016 CUNY Guttman Community College
A Genetic Algorithmic Approach To Automated Auction Mechanism Design, Jinzhong Niu, Simon Parsons
Publications and Research
In this paper, we present a genetic algorithmic approach to automated auction mechanism design in the context of \cat games. This is a follow-up to one piece of our prior work in the domain, the reinforcement learning-based grey-box approach. Our experiments show that given the same search space the grey-box approach is able to produce better auction mechanisms than the genetic algorithmic approach. The comparison can also shed light on the design and evaluation of similar search solutions to other domain problems.
Formalization Of The Ad Hominem Argumentation Scheme, 2016 University of Windsor
Formalization Of The Ad Hominem Argumentation Scheme, Douglas Walton
In this paper, several examples from the literature, and one central new one, are used as case studies of texts of discourse containing an argumentation scheme that has now been widely investigated in literature on argumentation. Argumentation schemes represent common patterns of reasoning used in everyday conversational discourse. The most typical ones represent defeasible arguments based on nonmonotonic reasoning. Each scheme has a matching set of critical questions used to evaluate a particular argument fitting that scheme. The project is to study how to build a formal computational model of this scheme for the circumstantial ad hominem argument using argumentation ...
Examination Dialogue: An Argumentation Framework For Critically Questioning An Expert Opinion, 2016 University of Windsor
Examination Dialogue: An Argumentation Framework For Critically Questioning An Expert Opinion, Douglas Walton
Recent work in argumentation theory (Walton and Krabbe, 1995; Walton, 2005) and artificial intelligence (Bench-Capon, 1992, 2003; Cawsey, 1992; McBurney and Parsons, 2002; Bench-Capon and Prakken, 2005) uses types of dialogue as contexts of argument use. This paper provides an analysis of a special type called examination dialogue, in which one party questions another party, sometimes critically or even antagonistically, to try to find out what that party knows about something. This type of dialogue is most prominent in law and in both legal and non-legal arguments based on expert opinion. It is also central to dialogue systems for questioning ...
Critical Questions In Computational Models Of Legal Argument, 2016 University of Windsor
Critical Questions In Computational Models Of Legal Argument, Douglas Walton, Thomas F. Gordon
Two recent computational models of legal argumentation, by Verheij and Gordon respectively, have interpreted critical questions as premises of arguments that can be defeated using Pollock’s concepts of undercutters and rebuttals. Using the scheme for arguments from expert opinion as an example, this paper evaluates and compares these two models of critical questions from the perspective of argumentation theory and competing legal theories about proof standardsfor defeating presumptions. The applicable proof standard is found to be a legal issue subject to argument. Verheij’smodel is shown to have problems because the proof stan-dards it applies to different kinds of ...
Energy Consumption Prediction With Big Data: Balancing Prediction Accuracy And Computational Resources, Katarina Grolinger, Miriam Am Capretz, Luke Seewald
Electrical and Computer Engineering Publications
In recent years, advances in sensor technologies and expansion of smart meters have resulted in massive growth of energy data sets. These Big Data have created new opportunities for energy prediction, but at the same time, they impose new challenges for traditional technologies. On the other hand, new approaches for handling and processing these Big Data have emerged, such as MapReduce, Spark, Storm, and Oxdata H2O. This paper explores how findings from machine learning with Big Data can benefit energy consumption prediction. An approach based on local learning with support vector regression (SVR) is presented. Although local learning itself is ...
Dna Analysis Using Grammatical Inference, 2016 San Jose State University
Dna Analysis Using Grammatical Inference, Cory Cook
An accurate language definition capable of distinguishing between coding and non-coding DNA has important applications and analytical significance to the field of computational biology. The method proposed here uses positive sample grammatical inference and statistical information to infer languages for coding DNA.
An algorithm is proposed for the searching of an optimal subset of input sequences for the inference of regular grammars by optimizing a relevant accuracy metric. The algorithm does not guarantee the finding of the optimal subset; however, testing shows improvement in accuracy and performance over the basis algorithm.
Testing shows that the accuracy of inferred languages for ...
Analysis On Alergia Algorithm: Pattern Recognition By Automata Theory, 2016 San Jose State University
Analysis On Alergia Algorithm: Pattern Recognition By Automata Theory, Xuanyi Qi
Based on Kolmogorov Complexity, a finite set x of strings has a pattern if the set x can be output by a Turing machine of length that is less than minimum of all |x|; this Turing machine, that may not be unique, is called a pattern of the finite set of string. In order to find a pattern of a given finite set of strings (assuming such a pattern exists), the ALERGIA algorithm is used to approximate such a pattern (Turing machine) in terms of finite automata. Note that each finite automaton defines a partition on formal language Σ*, ALERGIA ...
Analyze Large Multidimensional Datasets Using Algebraic Topology, 2016 San Jose State University
Analyze Large Multidimensional Datasets Using Algebraic Topology, David Le
This paper presents an efficient algorithm to extract knowledge from high-dimensionality, high- complexity datasets using algebraic topology, namely simplicial complexes. Based on concept of isomorphism of relations, our method turn a relational table into a geometric object (a simplicial complex is a polyhedron). So, conceptually association rule searching is turned into a geometric traversal problem. By leveraging on the core concepts behind Simplicial Complex, we use a new technique (in computer science) that improves the performance over existing methods and uses far less memory. It was designed and developed with a strong emphasis on scalability, reliability, and extensibility. This paper ...
Machine Learning On The Cloud For Pattern Recognition, 2016 San Jose State University
Machine Learning On The Cloud For Pattern Recognition, Tien Nguyen
Pattern recognition is a field of machine learning with applications to areas such as text recognition and computer vision. Machine learning algorithms, such as convolutional neural networks, may be trained to classify images. However, such tasks may be computationally intensive for a commercial computer for larger volumes or larger sizes of images. Cloud computing allows one to overcome the processing and memory constraints of average commercial computers, allowing computations on larger amounts of data. In this project, we developed a system for detection and tracking of moving human and vehicle objects in videos in real time or near real time ...
Multi Faceted Text Classification Using Supervised Machine Learning Models, 2016 San Jose State University
Multi Faceted Text Classification Using Supervised Machine Learning Models, Abhiteja Gajjala
In recent year’s document management tasks (known as information retrieval) increased a lot due to availability of digital documents everywhere. The need of automatic methods for extracting document information became a prominent method for organizing information and knowledge discovery. Text Classification is one such solution, where in the natural language text is assigned to one or more predefined categories based on the content. In my research classification of text is mainly focused on sentiment label classification. The idea proposed for sentiment analysis is multi-class classification of online movie reviews. Many research papers discussed the classification of sentiment either positive ...
Supervised Learning For Multi-Domain Text Classification, 2016 San Jose State University
Supervised Learning For Multi-Domain Text Classification, Siva Charan Reddy Gangireddy
Digital information available on the Internet is increasing day by day. As a result of this, the demand for tools that help people in finding and analyzing all these resources are also growing in number. Text Classification, in particular, has been very useful in managing the information. Text Classification is the process of assigning natural language text to one or more categories based on the content. It has many important applications in the real world. For example, finding the sentiment of the reviews, posted by people on restaurants, movies and other such things are all applications of Text classification. In ...
Movie Script Shot Lister, 2016 San Jose State University
Movie Script Shot Lister, David Robert Smith
The making of a motion picture almost always starts with the script, the written version of a story envisioned within the mind of its creator. The script is then broken down into shots. Each individual shot is filmed and then they are edited together to create the motion picture. The goal of the Movie Script Shot Lister thesis project is to be able to read in a script for a movie or television show, and automatically generate a shot list. While a script is text, a shot list is the blue print for how to visualize that script, so the ...
Multiple Sequence Alignment With Pro Le Hidden Markov Models, 2016 San Jose State University
Multiple Sequence Alignment With Pro Le Hidden Markov Models, Shubhangi Rakhonde
The human genome consists of various patterns and sequences that are of biolog- ical signi cance. Capturing these patterns can help us in resolving various mysteries related to the genome, like how genomes evolve, how diseases occur due to genetic mutation, how viruses mutate to cause new disease and what is the cure for these diseases. All these applications are covered in the study of bioinformatics.
One of the very common tasks in bioinformatics involves simultaneous alignment of a number of biological sequences. In bioinformatics, this is widely known as Mul- tiple Sequence Alignment. Multiple sequence alignments help in grouping ...
Towards Pharmacovigilance Using Machine Learning To Identify Unknown Adverse Reactions Triggered By Drug-Drug Interaction, 2016 Worcester Polytechnic Institute
Towards Pharmacovigilance Using Machine Learning To Identify Unknown Adverse Reactions Triggered By Drug-Drug Interaction, Tabassum Kakar, Xiao Qin, Susmitha Wunnava, Elke A. Rundensteiner
UMass Center for Clinical and Translational Science Research Retreat
Adverse Drug Reactions (ADRs) are a major cause of morbidity and mortality in world. There is thus a growing need of methods facilitating the automated detection of drugs-related ADR; especially ADRs that were not known from clinical trials but later arise due to drug-drug interactions. In this research our goal is to discover the severe unknown Adverse Drug Reactions caused by a combination of drugs, also known as Drug-Drug-Interaction. We propose to use Association Rule Mining to find the ADRs caused by using a combination of drugs yet not known to be caused if these drugs were taken individually. For ...
Hive - An Agent Based Modeling Framework, 2016 San Jose State Universi
Hive - An Agent Based Modeling Framework, Roohi Bharti
This thesis begins by defining agent based modeling. Agent based models are used to model the emergent behavior of complex systems with many interacting components, known as agents. Several model examples are given using NetLogo, which is a popular agent-based modeling platform. A model of concurrent computation is described that uses message passing as the only form of communication between the model’s components, which are called actors. The model is called an actor model. Actors are primitive objects of concurrency in an actor model. In particular, we describe the actor model implemented by Akka, which is Scala’s new ...
An Exercise And Sports Equipment Recognition System, 2016 The University of Western Ontario
An Exercise And Sports Equipment Recognition System, Siddarth Kalra
Electronic Thesis and Dissertation Repository
Most mobile health management applications today require manual input or use sensors like the accelerometer or GPS to record user data. The onboard camera remains underused. We propose an Exercise and Sports Equipment Recognition System (ESRS) that can recognize physical activity equipment from raw image data. This system can be integrated with mobile phones to allow the camera to become a primary input device for recording physical activity. We employ a deep convolutional neural network to train models capable of recognizing 14 different equipment categories. Furthermore, we propose a preprocessing scheme that uses color normalization and denoising techniques to improve ...
Applying Machine Learning To Predict Stock Value, 2016 Central Washington University
Applying Machine Learning To Predict Stock Value, Joseph Lemley, Yishui Liu, Dipayan Banik, Sadia Afroze
Symposium Of University Research and Creative Expression (SOURCE)
The purpose of this study was to compare machine learning techniques for short term stock prediction and evaluate their effectiveness. Stock value analysis is an important element of modern economies. The ability to predict future stock prices from historical price values is of tremendous interest to investors. The prediction of stock performance is still an unsolved problem with a variety of techniques being proposed. Real stock values are affected by many elements, some of which cannot be measured. In this study, we limit our analysis to stock closing prices. We use these prices to predict the future stock value using ...
Automatic Classification Of Perceived Gender From Face Images, 2016 Central Washington University
Automatic Classification Of Perceived Gender From Face Images, Joseph Lemley, Sami Abdul-Wahid, Dipayan Banik
Symposium Of University Research and Creative Expression (SOURCE)
Building software that can visually and accurately perceive gender from face images is an important step in making more intelligent machines. Several approaches to this problem have been suggested in the literature. We evaluate Histogram of Oriented Gradients, Dual Tree Complex Wavelet Transform (DTCWT) Principal Component Analysis (PCA) with Support Vector Machines (SVM) and compare them to Convolutional Neural Networks for this task. We train and test our classifiers with two benchmarks containing thousands of facial images. As expected, convolutional neural networks had the best performance while the performance of DTCWT varied most depending on the dataset used
Detection Of Locations Of Key Points On Facial Images, 2016 San Jose State University
Detection Of Locations Of Key Points On Facial Images, Manoj Gyanani
In field of computer vision research, One of the most important branch is Face recognition. It targets at finding size and location of human face on digital image, by identifying and separating faces from the surrounding objects like building, plants etc. For the purpose of developing an advanced face recognition algorithm, Detection of facial key points is the basic and very important task, basically it is about finding out the location of specific key points on facial images. This key points can be mouths, noses, left eyes, right eyes and so on.
For implementation of solution, I have used amazon ...
Texture Modelling Using Convolutional Neural Networks, 2016 University of Tuebingen
Texture Modelling Using Convolutional Neural Networks, Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
We introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition. Samples from the model are of high perceptual quality demonstrating the generative power of neural networks trained in a purely discriminative fashion. Within the model, textures are represented by the correlations between feature maps in several layers of the network. We show that across layers the texture representations increasingly capture the statistical properties of natural images while making object information more and more explicit. Extending this framework to texture transfer, we introduce A Neural Algorithm of Artistic Style that ...