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检索条件"主题词=artificial intelligence and robotics"

共 112 条记录，以下是51-60 订阅

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Breaking Audio Captcha Using Machine Learning/Deep Learning And Related Defense Mechanism

Breaking Audio Captcha Using Machine Learning/Deep Learning ...

引用

作者： Heemany Shekhar San Jose State University

学位级别：硕士

CAPTCHA is a web-based authentication method used by websites to distinguish between humans (valid users) and bots(attackers). Audio captcha is an accessible captcha meant for the visually disabled section of users such as color-blind, blind, near-sighted users. In this project, I analyzed the security of audio captchas from attacks that employ machine learning and deep learning models. Audio captchas of varying lengths (5, 7 and 10) and varying background noise (no noise, medium noise or high noise) were analyzed. I found that audio captchas with no background noise or medium background noise were easily attacked with 99% - 100% accuracy. Whereas, audio captchas with high noise were relatively more secure with breaking accuracy of 85%. I also propose that adversarial example attacks can be used in favor of audio captcha, that is, adversarial example attacks can be used to defend audio captcha from attackers. I explored two adversarial examples attack algorithms: Basic Iterative Method (BIM) and DeepFool method to create new adversarial audio captcha. Finally, I analyzed the security of these newly created adversarial audio captcha by simulating Level I and Level II defense scenarios. Level I defense is a defense against pre- trained models that have never seen adversarial examples before. Whereas a Level II defense is a defense against models that have been re-trained on adversarial examples. My experiments show that Level I defense can prevent nearly 100% of attacks from pre-trained models. It also proves that Level II defense increases security of audio captcha by 57% to 67%. Real world scenarios such as multi-retries are also studied and related defense mechanism are suggested.

关键词： audio captchas BIM DeepFool artificial intelligence and robotics Information Security

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Tsar: A System For Defending Hate Speech Detection Models Against Adversaries

Tsar: A System For Defending Hate Speech Detection Models Ag...

引用

作者： Brian Tuan Khieu San Jose State University

学位级别：硕士

Although current state-of-the-art hate speech detection models achieve praiseworthy results, these models have shown themselves to be vulnerable to attack. Easy to execute lexical manipulations such as the removal of whitespace from a given text create significant issues for word-based hate speech detection models. In this paper, we reproduce the results of five cutting edge models as well as four significant evasion schemes from prior work. Only a limited amount of evasion schemes that also maintain readability exists, and this works to our advantage in the recreation of the original data. Furthermore, we demonstrate that each lexical attack or evasion scheme can be overcome with our new defense mechanisms with some reducing the effectiveness of the scheme to 1%. We also propose a new evasion scheme that outperforms the those in previous work along with a corresponding defense. Using our results as a foundation, we contend that hate speech detection models can be defended against lexically attacked data without the need for significant retraining.

关键词： hate speech detection social networks artificial intelligence and robotics Other Computer Sciences

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Detecting Crispr Arrays Using Long-Short Term Memory Network

Detecting Crispr Arrays Using Long-Short Term Memory Network

引用

作者： Shantanu Deshmukh San Jose State University

学位级别：硕士

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat) is a se- quence found in the DNA sequence of an organism. It provides provides immunity to the organism. Recently, it was found that the CRISPR-based immunity mechanism can be manipulated to perform genome editing. The problem is, it is hard to know the specificity of this system and in turn, making it highly specific is difficult. More re- search is required to improve this CRISPR-based genome editing. Detecting CRISPR arrays in the DNA sequence is the first step towards this research. In this work, a CRISPR array detection pipeline, CRISPRLstm, is proposed. CRISPRLstm leverages the power of artificial intelligence to improve its performance over existing CRISPR array detection programs. Why and how artificial intelligence, or specifically, Long- Short Term Memory (LSTM) models, can be used to tackle this problem effectively is explained in this report. The CRSIPR arrays detected by CRISPRLstm are in good agreement with other widely used and freely available CRISPR array detection tools. CRISPRLstm is available in form of a web-tool. It visualizes the detected CRISPR arrays in a highly interactive interface with options to view secondary structure of the repeat and spacer sequences, blast them, create sequence logos of repeat sequences, and more.

关键词： CRISPR array detection LSTM artificial intelligence and robotics Other Computer Sciences

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An Ensemble Model For Click Through Rate Prediction

An Ensemble Model For Click Through Rate Prediction

引用

作者： Muthaiah Ramanathan San Jose State University

学位级别：硕士

Internet has become the most prominent and accessible way to spread the news about an event or to pitch, advertise and sell a product, globally. The success of any advertisement campaign lies in reaching the right class of target audience and eventually convert them as potential customers in the future. Search engines like the Google, Yahoo, Bing are a few of the most used ones by the businesses to market their product. Apart from this, certain websites like the *** that has more traffic also offer services for B2B customers to set their advertisement campaign. The look of the advertisement, the maximum bill per day, the age and gender of the audience, the bid price for the position and the size of the advertisement are some of the key factors that are available for the businesses to tune. The businesses are predominantly charged based the number of clicks that they received for their advertisement while some websites also bill them with a fixed charge per billing cycle. This creates a necessity for the advertising platforms to analyze and study these influential factors to achieve the maximum possible gain through the advertisements. Additionally, it is equally important for the businesses to customize these factors rightly to achieve the maximum clicks. This research presents a click through rate prediction system that analyzes several of the factors mentioned above to predict if an advertisement will receive a click or not with improvements over the existing systems in terms of the sampling the data, the features used, and the methodologies handled to improve the accuracy. We used the ensemble model with weighted scheme and achieved an accuracy of 0.91 on a unit scale and predicted the probability for an advertisement to receive a click form the user.

关键词： Click-through rrate prediction online advertising artificial intelligence and robotics Databases and Information Systems

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Stock Market Prediction Using Ensemble Of Graph Theory, Machine Learning And Deep Learning Models

Stock Market Prediction Using Ensemble Of Graph Theory, Mach...

引用

作者： Pratik Patil San Jose State University

学位级别：硕士

Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices. Even though some studies claim to get prediction accuracy higher than a random guess, they consider nothing but a proper selection of stocks and time interval in the experiments. In this project, a novel approach is proposed using graph theory. This approach leverages Spatio- temporal relationship information between different stocks by modeling the stock market as a complex network. This graph-based approach is used along with two techniques to create two hybrid models. Two different types of graphs are constructed, one from the correlation of the historical stock prices and the other is a causation-based graph constructed from the financial news mention of that stock over a period. The first hybrid model leverages deep learning convolutional neural networks and the second model leverages a traditional machine learning approach. These models are compared along with other statistical models and the advantages and disadvantages of graph-based models are discussed. Our experiments conclude that both graph-based approaches perform better than the traditional approaches since they leverage structural information while building the prediction model.

关键词： Stock market machine learning deep learning graph theory financial networks time series forecasting spatio-temporal artificial intelligence and robotics Other Computer Sciences

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Learning To Play The Trading Game

Learning To Play The Trading Game

引用

作者： Neeraj Kulkarni San Jose State University

学位级别：硕士

Can we train a stock trading bot that can take decisions in high-entropy envi- ronments like stock markets to generate profits based on some optimal policy? Can we further extend this learning for any general trading problem? Quantitative Al- gorithms are responsible for more than 75% of the stock trading around the world. Creating a stock market prediction model is comparatively easy. But creating a prof- itable prediction model is still considered as a challenging task in the field of machine learning and deep learning due to the unpredictability of the financial markets. Us- ing biologically inspired computing techniques of reinforcement learning (RL) and artificial neural networks(ANN), this project attempts to train an agent who takes decisions based on the optimal decision policies learned. Different existing RL tech- niques and their slightly modified variants will be used to train the agent, and the trained model is then tested against different stock prices and also stock portfolio settings to see if the agent has learned the rules of the game and can it act optimally irrespective of the testing data provided. This work aims to provide general users with simple recommendations about the possible investment decisions of selected stocks in the portfolio. Results of the implemented approaches do seem to work somewhat well on specific periods of stock market time series, but they are observed to be fragile. Selected strategies do not guarantee similar results on all historical time-periods, nor they are guaranteed to provide exceptional performance on unpredictable future stock market time-series data.

关键词： Reinforcement Learning artificial Neural Networks Deep Learning Recurrent Neural Networks Long Short-Term Memory Time Series Analysis Deep Q-learning Direct Reinforcement Learning artificial intelligence and robotics

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Classification Of Malware Models

Classification Of Malware Models

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作者： Akriti Sethi San Jose State University

学位级别：硕士

Automatically classifying similar malware families is a challenging problem. In this research, we attempt to classify malware families by applying machine learning to machine learning models. Specifically, we train hidden Markov models (HMM) for each malware family in our dataset. The resulting models are then compared in two ways. First, we treat the HMM matrices as images and experiment with convolutional neural networks (CNN) for image classification. Second, we apply support vector machines (SVM) to classify the HMMs. We analyze the results and discuss the relative advantages and disadvantages of each approach.

关键词： Malware model CNN image HMM SVM artificial intelligence and robotics Information Security

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Neural Generative Models and Representation Learning for Information Retrieval

Neural Generative Models and Representation Learning for Inf...

引用

作者： Qingyao Ai University of Massachusetts Amherst

学位级别：博士

Information Retrieval (IR) concerns about the structure, analysis, organization, storage, and retrieval of information. Among different retrieval models proposed in the past decades, generative retrieval models, especially those under the statistical probabilistic framework, are one of the most popular techniques that have been widely applied to Information Retrieval problems. While they are famous for their well-grounded theory and good empirical performance in text retrieval, their applications in IR are often limited by their complexity and low extendability in the modeling of high-dimensional information. Recently, advances in deep learning techniques provide new opportunities for representation learning and generative models for information retrieval. In contrast to statistical models, neural models have much more flexibility because they model information and data correlation in latent spaces without explicitly relying on any prior knowledge. Previous studies on pattern recognition and natural language processing have shown that semantically meaningful representations of text, images, and many types of information can be acquired with neural models through supervised or unsupervised training. Nonetheless, the effectiveness of neural models for information retrieval is mostly unexplored. In this thesis, we study how to develop new generative models and representation learning frameworks with neural models for information retrieval. Specifically, our contributions include three main components: (1) Theoretical Analysis: We present the first theoretical analysis and adaptation of existing neural embedding models for ad-hoc retrieval tasks; (2) Design Practice: Based on our experience and knowledge, we show how to design an embedding-based neural generative model for practical information retrieval tasks such as personalized product search; And (3) Generic Framework: We further generalize our proposed neural generative framework for complicated heterogeneous infor

关键词： artificial intelligence and robotics Databases and Information Systems

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Sensor - Based Human Activity Recognition Using Smartphones

Sensor - Based Human Activity Recognition Using Smartphones

引用

作者： Mustafa Badshah San Jose State University

学位级别：硕士

It is a significant technical and computational task to provide precise information regarding the activity performed by a human and find patterns of their behavior. Countless applications can be molded and various problems in domains of virtual reality, health and medical, entertainment and security can be solved with advancements in human activity recognition (HAR) systems. HAR is an active field for research for more than a decade, but certain aspects need to be addressed to improve the system and revolutionize the way humans interact with smartphones. This research provides a holistic view of human activity recognition system architecture and discusses various problems associated with the design aspects. It further attempts to showcase the reduction in computational cost and significant achievement in accuracy by methods of feature selection. It also attempts to introduce the use of recurrent neural networks to learn features from the long sequences of time series data, which can contribute towards improving accuracy and reducing dependency on domain knowledge for feature extraction and engineering.

关键词： Human activity recognition machine learning mobile sensors accelerometer gyroscope feature selection RNN artificial intelligence and robotics

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Detecting Cars In A Parking Lot Using Deep Learning

Detecting Cars In A Parking Lot Using Deep Learning

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作者： Samuel Ordonia San Jose State University

学位级别：硕士

Detection of cars in a parking lot with deep learning involves locating all objects of interest in a parking lot image and classifying the contents of all bounding boxes as cars. Because of the variety of shape, color, contrast, pose, and occlusion, a deep neural net was chosen to encompass all the significant features required by the detector to differentiate cars from not cars. In this project, car detection was accomplished with a convolutional neural net (CNN) based on the You Only Look Once (YOLO) model architectures. An application was built to train and validate a car detection CNN as well as track the quantity of cars in a parking lot across a period of time. A separate service called Vision maps and standardizes the input data formats for the CNN as well as sanity checks the bounding box labeling with ground truth image annotations. Finally, another serviced called Skynet analyzes and summarizes the car count statistics over time on a series of parking lot images.

关键词： parking lot images Yolo CNN Deep Learning artificial intelligence and robotics

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