pwc-archive/papers-with-abstracts huggingface dataset:
RLCFR: Minimize Counterfactual Regret by Deep Reinforcement Learning
Authors: Huale Li, Xuan Wang, Fengwei Jia, Yi-Fan Li, Yulin Wu, Jiajia Zhang, Shuhan Qi
Date: 2020-09-10 00:00:00
Tasks: counterfactual, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Counterfactual regret minimization (CFR) is a popular method to deal with decision-making problems of two-player zero-sum games with imperfect information. Unlike existing studies that mostly explore for solving larger scale problems or accelerating solution efficiency, we propose a framework, RLCFR, which aims at improving the generalization ability of the CFR method. In the RLCFR, the game strategy is solved by the CFR in a reinforcement learning framework. And the dynamic procedure of iterative interactive strategy updating is modeled as a Markov decision process (MDP). Our method, RLCFR, then learns a policy to select the appropriate way of regret updating in the process of iteration. In addition, a stepwise reward function is formulated to learn the action policy, which is proportional to how well the iteration strategy is at each step. Extensive experimental results on various games have shown that the generalization ability of our method is significantly improved compared with existing state-of-the-art methods.
Aligning Subjective Ratings in Clinical Decision Making
Authors: Annika Pick, Sebastian Ginzel, Stefan Rüping, Jil Sander, Ann Christina Foldenauer, Michaela Köhm
Date: 2020-09-11 00:00:00
Tasks: Decision Making

In addition to objective indicators (e.g. laboratory values), clinical data often contain subjective evaluations by experts (e.g. disease severity assessments). While objective indicators are more transparent and robust, the subjective evaluation contains a wealth of expert knowledge and intuition. In this work, we demonstrate the potential of pairwise ranking methods to align the subjective evaluation with objective indicators, creating a new score that combines their advantages and facilitates diagnosis. In a case study on patients at risk for developing Psoriatic Arthritis, we illustrate that the resulting score (1) increases classification accuracy when detecting disease presence/absence, (2) is sparse and (3) provides a nuanced assessment of severity for subsequent analysis.
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent
Authors: Rahif Kassab, Osvaldo Simeone
Date: 2020-09-11 00:00:00
Tasks: Bayesian Inference, Federated Learning, Scheduling

This paper introduces Distributed Stein Variational Gradient Descent (DSVGD), a non-parametric generalized Bayesian inference framework for federated learning. DSVGD maintains a number of non-random and interacting particles at a central server to represent the current iterate of the model global posterior. The particles are iteratively downloaded and updated by one of the agents with the end goal of minimizing the global free energy. By varying the number of particles, DSVGD enables a flexible trade-off between per-iteration communication load and number of communication rounds. DSVGD is shown to compare favorably to benchmark frequentist and Bayesian federated learning strategies, also scheduling a single device per iteration, in terms of accuracy and scalability with respect to the number of agents, while also providing well-calibrated, and hence trustworthy, predictions.
Multiclass Model for Agriculture development using Multivariate Statistical method
Authors: N Deepa, Mohammad Zubair Khan, Prabadevi B, Durai Raj Vincent P M, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu
Date: 2020-09-12 00:00:00
Tasks: Binary Classification, Classification, feature selection, General Classification

Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signal-to-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twenty-six input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models.
Guided Policy Search Based Control of a High Dimensional Advanced Manufacturing Process
Authors: Amit Surana, Kishore Reddy, Matthew Siopis
Date: 2020-09-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

In this paper we apply guided policy search (GPS) based reinforcement learning framework for a high dimensional optimal control problem arising in an additive manufacturing process. The problem comprises of controlling the process parameters so that layer-wise deposition of material leads to desired geometric characteristics of the resulting part surface while minimizing the material deposited. A realistic simulation model of the deposition process along with carefully selected set of guiding distributions generated based on iterative Linear Quadratic Regulator is used to train a neural network policy using GPS. A closed loop control based on the trained policy and in-situ measurement of the deposition profile is tested experimentally, and shows promising performance.
ReviewViz: Assisting Developers Perform Empirical Study on Energy Consumption Related Reviews for Mobile Applications
Authors: Mohammad Abdul Hadi, Fatemeh H Fard
Date: 2020-09-13 00:00:00
Tasks: BIG-bench Machine Learning, text-classification, Text Classification, Word Embeddings

Improving the energy efficiency of mobile applications is a topic that has gained a lot of attention recently. It has been addressed in a number of ways such as identifying energy bugs and developing a catalog of energy patterns. Previous work shows that users discuss the battery-related issues (energy inefficiency or energy consumption) of the apps in their reviews. However, there is no work that addresses the automatic extraction of battery-related issues from users' feedback. In this paper, we report on a visualization tool that is developed to empirically study machine learning algorithms and text features to automatically identify the energy consumption specific reviews with the highest accuracy. Other than the common machine learning algorithms, we utilize deep learning models with different word embeddings to compare the results. Furthermore, to help the developers extract the main topics that are discussed in the reviews, two states of the art topic modeling algorithms are applied. The visualizations of the topics represent the keywords that are extracted for each topic along with a comparison with the results of string matching. The developed web-browser based interactive visualization tool is a novel framework developed with the intention of giving the app developers insights about running time and accuracy of machine learning and deep learning models as well as extracted topics. The tool makes it easier for the developers to traverse through the extensive result set generated by the text classification and topic modeling algorithms. The dynamic-data structure used for the tool stores the baseline-results of the discussed approaches and is updated when applied on new datasets. The tool is open-sourced to replicate the research results.
Play MNIST For Me! User Studies on the Effects of Post-Hoc, Example-Based Explanations & Error Rates on Debugging a Deep Learning, Black-Box Classifier
Authors: Courtney Ford, Eoin M. Kenny, Mark T. Keane
Date: 2020-09-10 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

This paper reports two experiments (N=349) on the impact of post hoc explanations by example and error rates on peoples perceptions of a black box classifier. Both experiments show that when people are given case based explanations, from an implemented ANN CBR twin system, they perceive miss classifications to be more correct. They also show that as error rates increase above 4%, people trust the classifier less and view it as being less correct, less reasonable and less trustworthy. The implications of these results for XAI are discussed.
Fraud Detection using Data-Driven approach
Authors: Arianit Mehana, Krenare Pireva Nuci
Date: 2020-09-08 00:00:00
Tasks: Fraud Detection

The extensive use of the internet is continuously drifting businesses to incorporate their services in the online environment. One of the first spectrums to embrace this evolution was the banking sector. In fact, the first known online banking service came in 1980. It was deployed from a community bank located in Knoxville, called the United American Bank. Since then, internet banking has been offering ease and efficiency to costumers in completing their daily banking tasks. The ever increasing use of internet banking and a large number of online transactions increased fraudulent behavior also. As if fraud increase was not enough, the massive number of online transactions further increased the data complexity. Modern data sources are not only complex but generated at high speed and in real-time as well. This presents a serious problem and a definite reason why more advanced solutions are desired to protect financial service companies and credit cardholders. Therefore, this research paper aims to construct an efficient fraud detection model which is adaptive to customer behavior changes and tends to decrease fraud manipulation, by detecting and filtering fraud in real-time. In order to achieve this aim, a review of various methods is conducted, adding above a personal experience working in the Banking sector, specifically in the Fraud Detection office. Unlike the majority of reviewed methods, the proposed model in this research paper is able to detect fraud in the moment of occurrence using an incremental classifier. The evaluation of synthetic data, based on fraud scenarios selected in collaboration with domain experts that replicate typical, real-world attacks, shows that this approach correctly ranks complex frauds. In particular, our proposal detects fraudulent behavior and anomalies with up to 97\% detection rate while maintaining a satisfyingly low cost.
High correlated variables creator machine: Prediction of the compressive strength of concrete
Authors: Aydin Shishegaran, Hessam Varaee, Timon Rabczuk, Gholamreza Shishegaran
Date: 2020-09-11 00:00:00

In this paper, we introduce a novel hybrid model for predicting the compressive strength of concrete using ultrasonic pulse velocity (UPV) and rebound number (RN). First, 516 data from 8 studies of UPV and rebound hammer (RH) tests was collected. Then, high correlated variables creator machine (HVCM) is used to create the new variables that have a better correlation with the output and improve the prediction models. Three single models, including a step-by-step regression (SBSR), gene expression programming (GEP) and an adaptive neuro-fuzzy inference system (ANFIS) as well as three hybrid models, i.e. HCVCM-SBSR, HCVCM-GEP and HCVCM-ANFIS, were employed to predict the compressive strength of concrete. The statistical parameters and error terms such as coefficient of determination, root mean square error (RMSE), normalized mean square error (NMSE), fractional bias, the maximum positive and negative errors, and mean absolute percentage error (MAPE), were computed to evaluate and compare the models. The results show that HCVCM-ANFIS can predict the compressive strength of concrete better than all other models. HCVCM improves the accuracy of ANFIS by 5% in the coefficient of determination, 10% in RMSE, 3% in NMSE, 20% in MAPE, and 7% in the maximum negative error.
Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers
Authors: Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Neel Sundaresan
Date: 2020-09-11 00:00:00
Tasks: software testing

Unit testing represents the foundational basis of the software testing pyramid, beneath integration and end-to-end testing. Automated software testing researchers have proposed a variety of techniques to assist developers in this time-consuming task. In this paper we present an approach to support developers in writing unit test cases by generating accurate and useful assert statements. Our approach is based on a state-of-the-art transformer model initially pretrained on an English textual corpus. This semantically rich model is then trained in a semi-supervised fashion on a large corpus of source code. Finally, we finetune this model on the task of generating assert statements for unit tests. The resulting model is able to generate accurate assert statements for a given method under test. In our empirical evaluation, the model was able to predict the exact assert statements written by developers in 62% of the cases in the first attempt. The results show 80% relative improvement for top-1 accuracy over the previous RNN-based approach in the literature. We also show the substantial impact of the pretraining process on the performances of our model, as well as comparing it with assert auto-completion task. Finally, we demonstrate how our approach can be used to augment EvoSuite test cases, with additional asserts leading to improved test coverage.
Machine Learning for Temporal Data in Finance: Challenges and Opportunities
Authors: Jason Wittenbach, Brian d'Alessandro, C. Bayan Bruss
Date: 2020-09-11 00:00:00
Tasks: BIG-bench Machine Learning, Time Series Analysis

Temporal data are ubiquitous in the financial services (FS) industry -- traditional data like economic indicators, operational data such as bank account transactions, and modern data sources like website clickstreams -- all of these occur as a time-indexed sequence. But machine learning efforts in FS often fail to account for the temporal richness of these data, even in cases where domain knowledge suggests that the precise temporal patterns between events should contain valuable information. At best, such data are often treated as uniform time series, where there is a sequence but no sense of exact timing. At worst, rough aggregate features are computed over a pre-selected window so that static sample-based approaches can be applied (e.g. number of open lines of credit in the previous year or maximum credit utilization over the previous month). Such approaches are at odds with the deep learning paradigm which advocates for building models that act directly on raw or lightly processed data and for leveraging modern optimization techniques to discover optimal feature transformations en route to solving the modeling task at hand. Furthermore, a full picture of the entity being modeled (customer, company, etc.) might only be attainable by examining multiple data streams that unfold across potentially vastly different time scales. In this paper, we examine the different types of temporal data found in common FS use cases, review the current machine learning approaches in this area, and finally assess challenges and opportunities for researchers working at the intersection of machine learning for temporal data and applications in FS.
MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models
Authors: Yixi Xu, Sumit Mukherjee, Xiyang Liu, Shruti Tople, Rahul Dodhia, Juan Lavista Ferres
Date: 2020-09-11 00:00:00

Generative machine learning models are being increasingly viewed as a way to share sensitive data between institutions. While there has been work on developing differentially private generative modeling approaches, these approaches generally lead to sub-par sample quality, limiting their use in real world applications. Another line of work has focused on developing generative models which lead to higher quality samples but currently lack any formal privacy guarantees. In this work, we propose the first formal framework for membership privacy estimation in generative models. We formulate the membership privacy risk as a statistical divergence between training samples and hold-out samples, and propose sample-based methods to estimate this divergence. Compared to previous works, our framework makes more realistic and flexible assumptions. First, we offer a generalizable metric as an alternative to the accuracy metric especially for imbalanced datasets. Second, we loosen the assumption of having full access to the underlying distribution from previous studies , and propose sample-based estimations with theoretical guarantees. Third, along with the population-level membership privacy risk estimation via the optimal membership advantage, we offer the individual-level estimation via the individual privacy risk. Fourth, our framework allows adversaries to access the trained model via a customized query, while prior works require specific attributes.
QRnet: optimal regulator design with LQR-augmented neural networks
Authors: Tenavi Nakamura-Zimmerer, Qi Gong, Wei Kang
Date: 2020-09-11 00:00:00
Tasks: BIG-bench Machine Learning, Physics-informed machine learning

In this paper we propose a new computational method for designing optimal regulators for high-dimensional nonlinear systems. The proposed approach leverages physics-informed machine learning to solve high-dimensional Hamilton-Jacobi-Bellman equations arising in optimal feedback control. Concretely, we augment linear quadratic regulators with neural networks to handle nonlinearities. We train the augmented models on data generated without discretizing the state space, enabling application to high-dimensional problems. We use the proposed method to design a candidate optimal regulator for an unstable Burgers' equation, and through this example, demonstrate improved robustness and accuracy compared to existing neural network formulations.
A Review on Cyber Crimes on the Internet of Things
Authors: Mohan Krishna Kagita, Navod Thilakarathne, Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta, Saurabh Singh
Date: 2020-09-12 00:00:00

Internet of Things (IoT) devices are rapidly becoming universal. The success of IoT cannot be ignored in the scenario today, along with its attacks and threats on IoT devices and facilities are also increasing day by day. Cyber attacks become a part of IoT and affecting the life and society of users, so steps must be taken to defend cyber seriously. Cybercrimes threaten the infrastructure of governments and businesses globally and can damage the users in innumerable ways. With the global cybercrime damages predicted to cost up to 6 trillion dollars annually on the global economy by cyber crime. Estimated of 328 Million Dollar annual losses with the cyber attacks in Australia itself. Various steps are taken to slow down these attacks but unfortunately not able to achieve success properly. Therefor secure IoT is the need of this time and understanding of attacks and threats in IoT structure should be studied. The reasons for cyber-attacks can be Countries having week cyber securities, Cybercriminals use new technologies to attack, Cybercrime is possible with services and other business schemes. MSP (Managed Service Providers) face different difficulties in fighting with Cyber-crime. They have to ensure that security of the customer as well as their security in terms of their servers, devices, and systems. Hence, they must use effective, fast, and easily usable antivirus and antimalware tools.
Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network
Authors: Nuobei Xie, Kuang Gong, Ning Guo, Zhixing Qin, Jianan Cui, Zhifang Wu, Huafeng Liu, Quanzheng Li
Date: 2020-09-13 00:00:00
Tasks: Denoising

Patlak model is widely used in 18F-FDG dynamic positron emission tomography (PET) imaging, where the estimated parametric images reveal important biochemical and physiology information. Because of better noise modeling and more information extracted from raw sinogram, direct Patlak reconstruction gains its popularity over the indirect approach which utilizes reconstructed dynamic PET images alone. As the prerequisite of direct Patlak methods, raw data from dynamic PET are rarely stored in clinics and difficult to obtain. In addition, the direct reconstruction is time-consuming due to the bottleneck of multiple-frame reconstruction. All of these impede the clinical adoption of direct Patlak reconstruction.In this work, we proposed a data-driven framework which maps the dynamic PET images to the high-quality motion-corrected direct Patlak images through a convolutional neural network. For the patient motion during the long period of dynamic PET scan, we combined the correction with the backward/forward projection in direct reconstruction to better fit the statistical model. Results based on fifteen clinical 18F-FDG dynamic brain PET datasets demonstrates the superiority of the proposed framework over Gaussian, nonlocal mean and BM4D denoising, regarding the image bias and contrast-to-noise ratio.
Advanced Graph-Based Deep Learning for Probabilistic Type Inference
Authors: Fangke Ye, Jisheng Zhao, Vivek Sarkar
Date: 2020-09-13 00:00:00
Tasks: Deep Learning, Graph Neural Network, Vocal Bursts Type Prediction

Dynamically typed languages such as JavaScript and Python have emerged as the most popular programming languages in use. Important benefits can accrue from including type annotations in dynamically typed programs. This approach to gradual typing is exemplified by the TypeScript programming system which allows programmers to specify partially typed programs, and then uses static analysis to infer the remaining types. However, in general, the effectiveness of static type inference is limited and depends on the complexity of the program's structure and the initial type annotations. As a result, there is a strong motivation for new approaches that can advance the state of the art in statically predicting types in dynamically typed programs, and that do so with acceptable performance for use in interactive programming environments. Previous work has demonstrated the promise of probabilistic type inference using deep learning. In this paper, we advance past work by introducing a range of graph neural network (GNN) models that operate on a novel type flow graph (TFG) representation. The TFG represents an input program's elements as graph nodes connected with syntax edges and data flow edges, and our GNN models are trained to predict the type labels in the TFG for a given input program. We study different design choices for our GNN models for the 100 most common types in our evaluation dataset, and show that our best two GNN configurations for accuracy achieve a top-1 accuracy of 87.76% and 86.89% respectively, outperforming the two most closely related deep learning type inference approaches from past work -- DeepTyper with a top-1 accuracy of 84.62% and LambdaNet with a top-1 accuracy of 79.45%. Further, the average inference throughputs of those two configurations are 353.8 and 1,303.9 files/second, compared to 186.7 files/second for DeepTyper and 1,050.3 files/second for LambdaNet.