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Splash: Learnable Activation Functions For Improving Accuracy And Adversarial Robustness, Mohammadamin Tavakoli, Forest Agostinelli, Pierre Baldi 2021 University of California, Irvine

Splash: Learnable Activation Functions For Improving Accuracy And Adversarial Robustness, Mohammadamin Tavakoli, Forest Agostinelli, Pierre Baldi

Publications

We introduce SPLASH units, a class of learnable activation functions shown to simultaneously improve the accuracy of deep neural networks while also improving their robustness to adversarial attacks. SPLASH units have both a simple parameterization and maintain the ability to approximate a wide range of non-linear functions. SPLASH units are: (1) continuous; (2) grounded (f(0)=0"); (3) use symmetric hinges; and (4) their hinges are placed at fixed locations which are derived from the data (i.e. no learning required). Compared to nine other learned and fixed activation functions, including ReLU and its variants, SPLASH units show superior performance ...


Learn Biologically Meaningful Representation With Transfer Learning, Di He 2021 City University of New York (CUNY)

Learn Biologically Meaningful Representation With Transfer Learning, Di He

Dissertations, Theses, and Capstone Projects

Machine learning has made significant contributions to bioinformatics and computational biol­ogy. In particular, supervised learning approaches have been widely used in solving problems such as bio­marker identification, drug response prediction, and so on. However, because of the limited availability of comprehensively labeled and clean data, constructing predictive models in super­ vised settings is not always desirable or possible, especially when using data­hunger, red­hot learning paradigms such as deep learning methods. Hence, there are urgent needs to develop new approaches that could leverage more readily available unlabeled data in driving successful machine learning ap­ plications in this ...


Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain, Sterling Beason, William Hinton, Yousri A. Salamah, Jordan Salsman 2021 Southern Methodist University

Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain, Sterling Beason, William Hinton, Yousri A. Salamah, Jordan Salsman

SMU Data Science Review

Much progress has been made in text analysis, specifically within the statistical domain of Term Frequency (TF) and Inverse Document Frequency (IDF). However, there is much room for improvement especially within the area of discovering Emerging Trends. Emerging Trend Detection Systems (ETDS) depend on ingesting a collection of textual data and TF/IDF to identify new or up-trending topics within the Corpus. However, the tremendous rate of change and the amount of digital information presents a challenge that makes it almost impossible for a human expert to spot emerging trends without relying on an automated ETD system. Since the U ...


Airbnb Price Prediction With Sentiment Classification, Peilu Liu 2021 San Jose State University

Airbnb Price Prediction With Sentiment Classification, Peilu Liu

Master's Projects

Airbnb is an online platform that provides arrangements for short-term local home renting services. It is a challenging task for the house owner to price a rental home and attract customers. Customers also need to evaluate the price of the rental property based on the listing details. This paper demonstrates several existing Airbnb price prediction models using machine learning and external data to improve the prediction accuracy. It also discusses machine learning and neural network models that are commonly used for price prediction. The goal of this paper is to build a price prediction model using machine learning and sentiment ...


Benchmarking Clustering And Classification Tasks Using K-Means, Fuzzy C-Means And Feedforward Neural Networks Optimized By Pso, Adam Pickens, Adam Pickens 2021 Murray State University

Benchmarking Clustering And Classification Tasks Using K-Means, Fuzzy C-Means And Feedforward Neural Networks Optimized By Pso, Adam Pickens, Adam Pickens

Honors College Theses

Clustering is a widely used unsupervised learning technique across data mining and machine learning applications and finds frequent use in diverse fields ranging from astronomy, medical imaging, search and optimization, geology, geophysics and sentiment analysis to name a few. It is therefore important to verify the effectiveness of the clustering algorithms in question and to make reasonably strong arguments for the acceptance of the end results generated by the validity indices that measure the compactness and separability of clusters. This work aims to explore the successes and limitations of popular clustering mechanisms such as K-Means and Fuzzy C-Means by comparing ...


Seen And Heard, 2021 DePaul University

Seen And Heard

In The Loop

IRL Programs Debut; Short & Sweet Pandemic Film Fest; New MS in Artificial Intelligence; Virtual Experts Talks; DePaul Trustee Producing Documentary; DemonHacks Hackathon


Alumna Profile: Code Warrior, 2021 DePaul University

Alumna Profile: Code Warrior

In The Loop

Competing in triathlons helped Ovetta Sampson (CDM MS ’16) stride past personal setbacks. The DePaul graduate’s career path evokes that athletic competition as well. She has moved from journalist to principal creative director at Microsoft, where she leads a team she says tackles “big, human-centered problems for big companies” in artificial intelligence, automation, digital transformation and manufacturing.


Data Forgery Detection In Automatic Generation Control: Exploration Of Automated Parameter Generation And Low-Rate Attacks, Yatish R. Dubasi 2021 University of Arkansas, Fayetteville

Data Forgery Detection In Automatic Generation Control: Exploration Of Automated Parameter Generation And Low-Rate Attacks, Yatish R. Dubasi

Computer Science and Computer Engineering Undergraduate Honors Theses

Automatic Generation Control (AGC) is a key control system utilized in electric power systems. AGC uses frequency and tie-line power flow measurements to determine the Area Control Error (ACE). ACE is then used by the AGC to adjust power generation and maintain an acceptable power system frequency. Attackers might inject false frequency and/or tie-line power flow measurements to mislead AGC into falsely adjusting power generation, which can harm power system operations. Various data forgery detection models are studied in this thesis. First, to make the use of predictive detection models easier for users, we propose a method for automated ...


Brave New World Reboot: Technology’S Role In Consumer Manipulation And Implications For Privacy And Transparency, Allie Mertensotto 2021 University of Arkansas, Fayetteville

Brave New World Reboot: Technology’S Role In Consumer Manipulation And Implications For Privacy And Transparency, Allie Mertensotto

Marketing Undergraduate Honors Theses

Most consumers are aware that our data is being obtained and collected through the use of our devices we keep in our homes or even on our person throughout the day. But, it is understated how much data is being collected. Conversations you have with your peers – in a close proximity of a device – are being used to tailor advertising. The advertisements you receive on your devices are uniquely catered to your individual person, due to the fact it consistently uses our data to produce efficient and personal ads. On the flip side, our government is also tapping into our ...


Semi-Supervised Spatial-Temporal Feature Learning On Anomaly-Based Network Intrusion Detection, Huy Mai 2021 University of Arkansas, Fayetteville

Semi-Supervised Spatial-Temporal Feature Learning On Anomaly-Based Network Intrusion Detection, Huy Mai

Computer Science and Computer Engineering Undergraduate Honors Theses

Due to a rapid increase in network traffic, it is growing more imperative to have systems that detect attacks that are both known and unknown to networks. Anomaly-based detection methods utilize deep learning techniques, including semi-supervised learning, in order to effectively detect these attacks. Semi-supervision is advantageous as it doesn't fully depend on the labelling of network traffic data points, which may be a daunting task especially considering the amount of traffic data collected. Even though deep learning models such as the convolutional neural network have been integrated into a number of proposed network intrusion detection systems in recent ...


City Goers: An Exploration Into Creating Seemingly Intelligent A.I. Systems, Matthew Brooke 2021 University of Arkansas, Fayetteville

City Goers: An Exploration Into Creating Seemingly Intelligent A.I. Systems, Matthew Brooke

Computer Science and Computer Engineering Undergraduate Honors Theses

Artificial Intelligence systems have come a long way over the years. One particular application of A.I. is its incorporation in video games. A key goal of creating an A.I. system in a video game is to convey a level of intellect to the player. During playtests for Halo: Combat Evolved, the developers at Bungie noticed that players deemed tougher enemies as more intelligent than weaker ones, despite the fact that there were no differences in behavior in the enemies. The tougher enemies provided a greater illusion of intelligence to the players. Inspired by this, I set out to ...


Dynamic Task Allocation In Partially Defined Environments Using A* With Bounded Costs, James Hendrickson 2021 Embry-Riddle Aeronautical University

Dynamic Task Allocation In Partially Defined Environments Using A* With Bounded Costs, James Hendrickson

PhD Dissertations and Master's Theses

The sector of maritime robotics has seen a boom in operations in areas such as surveying and mapping, clean-up, inspections, search and rescue, law enforcement, and national defense. As this sector has continued to grow, there has been an increased need for single unmanned systems to be able to undertake more complex and greater numbers of tasks. As the maritime domain can be particularly difficult for autonomous vehicles to operate in due to the partially defined nature of the environment, it is crucial that a method exists which is capable of dynamically accomplishing tasks within this operational domain. By considering ...


Working With Smart Machines: Insights On The Future Of Work, Tom DAVENPORT, Steven MILLER 2021 Babson College

Working With Smart Machines: Insights On The Future Of Work, Tom Davenport, Steven Miller

Research Collection School Of Computing and Information Systems

In this article, we share our observations on how and why AI-based systems are being deployed. We look at how these systems have been integrated into existing and new work processes, especially the implications for the changing nature of work and how it will be conducted in future with AI-based smart machines. This will help companies that are in the earlier stages of considering, planning, or deploying these systems to know what to expect from recent developments in practice. We draw our analysis from 24 case studies that we have recently completed on AI system usage in actual operational settings.


Improving Reader Motivation With Machine Learning, Tanner A. Bohn 2021 The University of Western Ontario

Improving Reader Motivation With Machine Learning, Tanner A. Bohn

Electronic Thesis and Dissertation Repository

This thesis focuses on the problem of increasing reading motivation with machine learning (ML). The act of reading is central to modern human life, and there is much to be gained by improving the reading experience. For example, the internal reading motivation of students, especially their interest and enjoyment in reading, are important factors in their academic success.

There are many topics in natural language processing (NLP) which can be applied to improving the reading experience in terms of readability, comprehension, reading speed, motivation, etc. Such topics include personalized recommendation, headline optimization, text simplification, and many others. However, to the ...


Generating Effective Sentence Representations: Deep Learning And Reinforcement Learning Approaches, Mahtab Ahmed 2021 The University of Western Ontario

Generating Effective Sentence Representations: Deep Learning And Reinforcement Learning Approaches, Mahtab Ahmed

Electronic Thesis and Dissertation Repository

Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Many Natural Language applications are powered by machine learning models performing a large variety of underlying tasks. Recently, deep learning approaches have obtained very high performance across many NLP tasks. In order to achieve this high level of performance, it is crucial for computers to have an appropriate representation of sentences. The tasks addressed in the thesis are best approached having shallow semantic representations. These representations are vectors that are then embedded in ...


A Lightweight And Explainable Citation Recommendation System, Juncheng Yin 2021 The University of Western Ontario

A Lightweight And Explainable Citation Recommendation System, Juncheng Yin

Electronic Thesis and Dissertation Repository

The increased pressure of publications makes it more and more difficult for researchers to find appropriate papers to cite quickly and accurately. Context-aware citation recommendation, which can provide users suggestions mainly based on local citation contexts, has been shown to be helpful to alleviate this problem. However, previous works mainly use RNN models and their variance, which tend to be highly complicated with heavy-weight computation. In this paper, we propose a lightweight and explainable model that is quick to train and obtains high performance. Our model is based on a pre-trained sentence embedding model and trained with triplet loss. Quantitative ...


Exploring Ai And Multiplayer In Java, Ronni Kurtzhals 2021 Minnesota State University Moorhead

Exploring Ai And Multiplayer In Java, Ronni Kurtzhals

Student Academic Conference

I conducted research into three topics: artificial intelligence, package deployment, and multiplayer servers in Java. This research came together to form my project presentation on the implementation of these topics, which I felt accurately demonstrated the various things I have learned from my courses at Moorhead State University. Several resources were consulted throughout the project, including the work of W3Schools and StackOverflow as well as relevant assignments and textbooks from previous classes. I found this project relevant to computer science and information systems for several reasons, such as the AI component and use of SQL data tables; but it was ...


New Topology Control Base On Ant Colony Algorithm In Optimization Of Wireless Sensor Network, Zana Azeez Kakarash, Sarkhel H.Taher Karim, Nawroz Fadhil Ahmed, Govar Abubakr Omar 2021 Department of Engineering, Faculty of Engineering and Computer Science, Qaiwan International University, Sulaymaniyah, Iraq, Department of Information Technology, Kurdistan Technical Institute, Sulaymaniyah, Kurdistan Region, Iraq

New Topology Control Base On Ant Colony Algorithm In Optimization Of Wireless Sensor Network, Zana Azeez Kakarash, Sarkhel H.Taher Karim, Nawroz Fadhil Ahmed, Govar Abubakr Omar

Passer Journal

Wireless sensor networks (WSNs) have found great appeal and popularity among researchers, especially in the field of monitoring and surveillance tasks. However, it has become a challenging issue due to the need to balance different optimization criteria such as power consumption, packet loss rate, and network lifetime, and coverage. The novelty of this research discusses the applications, structures, challenges, and issues we face in designing WSNs. And proposed new Topology control mechanisms it will focus more on building a reliable and energy efficient network topology step by step through defining available amount of energy for each node within its cluster ...


Administrative Law In The Automated State, Cary Coglianese 2021 University of Pennsylvania Law School

Administrative Law In The Automated State, Cary Coglianese

Faculty Scholarship at Penn Law

In the future, administrative agencies will rely increasingly on digital automation powered by machine learning algorithms. Can U.S. administrative law accommodate such a future? Not only might a highly automated state readily meet longstanding administrative law principles, but the responsible use of machine learning algorithms might perform even better than the status quo in terms of fulfilling administrative law’s core values of expert decision-making and democratic accountability. Algorithmic governance clearly promises more accurate, data-driven decisions. Moreover, due to their mathematical properties, algorithms might well prove to be more faithful agents of democratic institutions. Yet even if an automated ...


Geometric Representation Learning, Luke Vilnis 2021 University of Massachusetts Amherst

Geometric Representation Learning, Luke Vilnis

Doctoral Dissertations

Vector embedding models are a cornerstone of modern machine learning methods for knowledge representation and reasoning. These methods aim to turn semantic questions into geometric questions by learning representations of concepts and other domain objects in a lower-dimensional vector space. In that spirit, this work advocates for density- and region-based representation learning. Embedding domain elements as geometric objects beyond a single point enables us to naturally represent breadth and polysemy, make asymmetric comparisons, answer complex queries, and provides a strong inductive bias when labeled data is scarce. We present a model for word representation using Gaussian densities, enabling asymmetric entailment ...


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