• Learning from Code and Non-Code Artifacts 

  Henkel, Jordan (2022-08-17)

  Three things are fundamentally true about software: (i) every day that passes we, as a society, generate more software (more code, more documentation, and more software-related artifacts of all kinds), (ii) it is easier ...

• Feature Significance Analysis of the US Adult Income Dataset 

  Chen, Junda (2021-09-01)

  In this paper, we analyze the classic US Adult Income Dataset using logistics regression and random forest to analyze potential factors that contribute to income bias for the 50Kincome bracket(income ≥ 50K per year). Using ...

• CS532 Course Project Activity - Climate Data Fitting and Local Warming Justification 

  Chen, Junda; Zhao, Haoruo; Duan, Doris (2021-09-01)

  The project introduce a simplified model to justify whether global warming is truly an issuein the current society. Student will first intensify their knowledge aboutBasis Matrix– itsconstruction and its application to the ...

• Explore Optimal Degree of Parallelism for Distributed XGBoost Training 

  Chen, Junda; Akash, Aditya Kumar; Suzuki, Yukiko (2021-09-01)

  The XGBoost has been an extremely popular and effective machine learning method which gained its fame throughwinning multiple Kaggle competitions. One of its strengths lies in parallel processing which makes the ...

• Research review on AI and Machine learning related works 

  Zhao, Wei (2021-03)

  In this review, several innovative methods and the associated system were created for agriculture navigation, mapping, object detection, and related vehicles technologies. Based on the experiences with these applications, ...

• Project Report for Image Quilting Reproduction 

  Qi, Bozhao (2021-01)

  The demand for high-quality images has been greater in computer graphics and computer vision. Image-based rendering is playing bigger roles in video games, for instance. Rather than creating the whole physical world from ...

• Mutation Testing: Algorithms and Applications 

  Brown, David (2020)

  Software continues to be vital to the modern world, and as its ubiquity increases, its correctness becomes ever more valuable. Unfortunately, fundamental mathematical constraints on static analysis preclude the possibility ...

• Enhancing Algebraic Program Analysis 

  Breck, Jason (2020-08-20)

  Many programs have important functional-correctness properties that involve sophisticated mathematical relationships between numerical variables. Additionally, many programs have important numerical properties that ...

• Understanding Representation Learning Paradigms with Applications to Low Resource Text Classification 

  Garg, Siddhant (2020-05-21)

  A crucial component of modern machine learning systems is learning input representations which can be used for prediction tasks. The expensive cost of labelling and easy availability of unlabelled data has led to the ...

• Prioritize Winter Crash Severity Influencing Factors in US Midwestern for Autonomous Vehicle 

  Dai, Shenghong (2020-02-28)

  Adverse weather conditions in winter have significant impacts on crash occurrences and risks. Human drivers can adjust driving styles based on the context information of the surrounding traffic and environments. Similar ...

• A New Semantic Approach on Yelp Review-star Rating Classification 

  Wu, Shuang; Wang, Xiaodong; Qi, Bozhao (2020-02-26)

  This paper introduces a new semantic approach for yelp review star rating prediction. Our approach extracts feature vectors from user reviews to develop star prediction models. User review text contains detailed information ...

• On the Geometric and Statistical Interpretation of Data Augmentation 

  Feng, Zhili (2019-05-10)

  Data augmentation (DA) is a common technique in training machine learning models. For example in image classifications, people augment image datasets by random cropping, rotating, and adding random noises. Another trending ...

• Improving Regulatory Network Reconstruction Through Topological Priors, Robust Hyperparameter Exploration, and Multi-Task Learning 

  Periyasamy, Viswesh (2019-05-10)

  Regulatory network reconstruction is an ongoing field of research that biologists have been pressing with considerable effort. Although several computational methods have been investigated, inferred networks still severely ...

• Sensor-Based Risk Perception for Drivers Under Adverse Environment 

  Zhao, Wei (2018-08-18)

  Due to factors such as snow and ice impeding drivers’ vision, automobile accidents significantly rise during winter months. This study sets forth an automated evaluation network of the Risk Perceived Ability (RPA) for ...

• Dynamic Query Re-Planning Using QOOP 

  Mahajan, Kshiteej; Chowdhury, Mosharaf; Akella, Aditya; Chawla, Shuchi (2018-09-27)

  Modern data processing clusters are highly dynamic – both in terms of the number of concurrently running jobs and their resource usage. To improve job performance, recent works have focused on optimizing the cluster scheduler ...

• Exploration on Deep Drug Discovery: Representation and Learning 

  Liu, Shengchao (2018-09-20)

  Virtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the dataset and evaluation strategy. We ...

• A Formula That Generates Hash Collisions 

  Brockmann, Andrew (2018-08-08)

  We present an explicit formula that produces hash collisions for the Merkle-Damgard construction. The formula works for arbitrary choice of message block and irrespective of the standardized constants used in hash functions, ...

• Overreliance on Classical Computing in Quantum Factorization 

  Brockmann, Andrew (2018-08-08)

  A 2012 quantum experiment factored 143 after performing some simplifications classically. Further research demonstrated that that experiment arguably performed the quantum factorizations of other numbers too, such as 56153. ...

• Deep Learning for Entity Matching: A Design Space Exploration 

  Mudgal Sunil Kumar, Sidharth (2018-05-15)

  Entity matching (EM) finds data instances that refer to the same real-world entity. In this thesis we examine applying deep learning (DL) to EM, to understand DL's benefits and limitations. We review many DL solutions that ...

• Error Backprojection Algorithms for Non-Line-of-Sight Imaging 

  La Manna, Marco; Kine, Fiona; Breitbach, Eric; Jackson, Jonathan; Velten, Andreas (2017-10-18)

  Recent advances in computer vision and inverse light transport theory have resulted in several non-line-of-sight imaging techniques. These techniques use photon time-of-flight information encoded in light after multiple, ...

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