pwc-archive/papers-with-abstracts huggingface dataset:
Stability Based Generalization Bounds for Exponential Family Langevin Dynamics
Authors: Arindam Banerjee, Tiancong Chen, Xinyan Li, Yingxue Zhou
Date: 2022-01-09 00:00:00
Tasks: Generalization Bounds

Recent years have seen advances in generalization bounds for noisy stochastic algorithms, especially stochastic gradient Langevin dynamics (SGLD) based on stability (Mou et al., 2018; Li et al., 2020) and information theoretic approaches (Xu and Raginsky, 2017; Negrea et al., 2019; Steinke and Zakynthinou, 2020). In this paper, we unify and substantially generalize stability based generalization bounds and make three technical contributions. First, we bound the generalization error in terms of expected (not uniform) stability which arguably leads to quantitatively sharper bounds. Second, as our main contribution, we introduce Exponential Family Langevin Dynamics (EFLD), a substantial generalization of SGLD, which includes noisy versions of Sign-SGD and quantized SGD as special cases. We establish data-dependent expected stability based generalization bounds for any EFLD algorithm with a O(1/n) sample dependence and dependence on gradient discrepancy rather than the norm of gradients, yielding significantly sharper bounds. Third, we establish optimization guarantees for special cases of EFLD. Further, empirical results on benchmarks illustrate that our bounds are non-vacuous, quantitatively sharper than existing bounds, and behave correctly under noisy labels.
Medication Error Detection Using Contextual Language Models
Authors: Yu Jiang, Christian Poellabauer
Date: 2022-01-09 00:00:00

Medication errors most commonly occur at the ordering or prescribing stage, potentially leading to medical complications and poor health outcomes. While it is possible to catch these errors using different techniques; the focus of this work is on textual and contextual analysis of prescription information to detect and prevent potential medication errors. In this paper, we demonstrate how to use BERT-based contextual language models to detect anomalies in written or spoken text based on a data set extracted from real-world medical data of thousands of patient records. The proposed models are able to learn patterns of text dependency and predict erroneous output based on contextual information such as patient data. The experimental results yield accuracy up to 96.63% for text input and up to 79.55% for speech input, which is satisfactory for most real-world applications.
Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication
Authors: Abhishek Gupta, Himanshu Soni, Raunak Joshi, Ronald Melwin Laban
Date: 2022-01-09 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, Classification, Dimensionality Reduction, Supervised dimensionality reduction

A lot of prognostication methodologies have been formulated for early detection of Polycystic Ovary Syndrome also known as PCOS using Machine Learning. PCOS is a binary classification problem. Dimensionality Reduction methods impact the performance of Machine Learning to a greater extent and using a Supervised Dimensionality Reduction method can give us a new edge to tackle this problem. In this paper we present Discriminant Analysis in different dimensions with Linear and Quadratic form for binary classification along with metrics. We were able to achieve good accuracy and less variation with Discriminant Analysis as compared to many commonly used classification algorithms with training accuracy reaching 97.37% and testing accuracy of 95.92% using Quadratic Discriminant Analysis. Paper also gives the analysis of data with visualizations for deeper understanding of problem.
Development of a hybrid machine-learning and optimization tool for performance-based solar shading design
Authors: Maryam Daneshi, Reza Taghavi Fard, Zahra Sadat Zomorodian, Mohammad Tahsildoost
Date: 2022-01-09 00:00:00
Tasks: Hybrid Machine Learning

Solar shading design should be done for the desired Indoor Environmental Quality (IEQ) in the early design stages. This field can be very challenging and time-consuming also requires experts, sophisticated software, and a large amount of money. The primary purpose of this research is to design a simple tool to study various models of solar shadings and make decisions easier and faster in the early stages. Database generation methods, artificial intelligence, and optimization have been used to achieve this goal. This tool includes two main parts of 1. predicting the performance of the user-selected model along with proposing effective parameters and 2. proposing optimal pre-prepared models to the user. In this regard, initially, a side-lit shoebox model with variable parameters was modeled parametrically, and five common solar shading models with their variables were applied to the space. For each solar shadings and the state without shading, metrics related to daylight and glare, view, and initial costs were simulated. The database generated in this research includes 87912 alternatives and six calculated metrics introduced to optimized machine learning models, including neural network, random Forrest, support vector regression, and k nearest neighbor. According to the results, the most accurate and fastest estimation model was Random Forrest, with an r2_score of 0.967 to 1. Then, sensitivity analysis was performed to identify the most influential parameters for each shading model and the state without it. This analysis distinguished the most effective parameters, including window orientation, WWR, room width, length, and shading depth. Finally, by optimizing the estimation function of machine learning models with the NSGA II algorithm, about 7300 optimal models were identified. The developed tool can evaluate various design alternatives in less than a few seconds for each.
Rethink the Evaluation for Attack Strength of Backdoor Attacks in Natural Language Processing
Authors: Lingfeng Shen, Haiyun Jiang, Lemao Liu, Shuming Shi
Date: 2022-01-09 00:00:00
Tasks: Backdoor Attack, Text Classification

It has been shown that natural language processing (NLP) models are vulnerable to a kind of security threat called the Backdoor Attack, which utilizes a `backdoor trigger' paradigm to mislead the models. The most threatening backdoor attack is the stealthy backdoor, which defines the triggers as text style or syntactic. Although they have achieved an incredible high attack success rate (ASR), we find that the principal factor contributing to their ASR is not the `backdoor trigger' paradigm. Thus the capacity of these stealthy backdoor attacks is overestimated when categorized as backdoor attacks. Therefore, to evaluate the real attack power of backdoor attacks, we propose a new metric called attack successful rate difference (ASRD), which measures the ASR difference between clean state and poison state models. Besides, since the defenses against stealthy backdoor attacks are absent, we propose Trigger Breaker, consisting of two too simple tricks that can defend against stealthy backdoor attacks effectively. Experiments show that our method achieves significantly better performance than state-of-the-art defense methods against stealthy backdoor attacks.
Auto-Encoder based Co-Training Multi-View Representation Learning
Authors: Run-kun Lu, Jian-wei Liu, Yuan-Fang Wang, Hao-jie Xie, Xin Zuo
Date: 2022-01-09 00:00:00
Tasks: MULTI-VIEW LEARNING, Representation Learning

Multi-view learning is a learning problem that utilizes the various representations of an object to mine valuable knowledge and improve the performance of learning algorithm, and one of the significant directions of multi-view learning is sub-space learning. As we known, auto-encoder is a method of deep learning, which can learn the latent feature of raw data by reconstructing the input, and based on this, we propose a novel algorithm called Auto-encoder based Co-training Multi-View Learning (ACMVL), which utilizes both complementarity and consistency and finds a joint latent feature representation of multiple views. The algorithm has two stages, the first is to train auto-encoder of each view, and the second stage is to train a supervised network. Interestingly, the two stages share the weights partly and assist each other by co-training process. According to the experimental result, we can learn a well performed latent feature representation, and auto-encoder of each view has more powerful reconstruction ability than traditional auto-encoder.
Robust classification with flexible discriminant analysis in heterogeneous data
Authors: Pierre Houdouin, Frédéric Pascal, Matthieu Jonckheere, Andrew Wang
Date: 2022-01-09 00:00:00
Tasks: Classification, parameter estimation, Robust classification

Linear and Quadratic Discriminant Analysis are well-known classical methods but can heavily suffer from non-Gaussian distributions and/or contaminated datasets, mainly because of the underlying Gaussian assumption that is not robust. To fill this gap, this paper presents a new robust discriminant analysis where each data point is drawn by its own arbitrary Elliptically Symmetrical (ES) distribution and its own arbitrary scale parameter. Such a model allows for possibly very heterogeneous, independent but non-identically distributed samples. After deriving a new decision rule, it is shown that maximum-likelihood parameter estimation and classification are very simple, fast and robust compared to state-of-the-art methods.
TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model
Authors: Chunnan Wang, Chen Liang, Xiang Chen, Hongzhi Wang
Date: 2022-01-09 00:00:00
Tasks: Anomaly Detection, AutoML, Prediction, Trajectory Prediction

Trajectory Prediction (TP) is an important research topic in computer vision and robotics fields. Recently, many stochastic TP models have been proposed to deal with this problem and have achieved better performance than the traditional models with deterministic trajectory outputs. However, these stochastic models can generate a number of future trajectories with different qualities. They are lack of self-evaluation ability, that is, to examine the rationality of their prediction results, thus failing to guide users to identify high-quality ones from their candidate results. This hinders them from playing their best in real applications. In this paper, we make up for this defect and propose TPAD, a novel TP evaluation method based on the trajectory Anomaly Detection (AD) technique. In TPAD, we firstly combine the Automated Machine Learning (AutoML) technique and the experience in the AD and TP field to automatically design an effective trajectory AD model. Then, we utilize the learned trajectory AD model to examine the rationality of the predicted trajectories, and screen out good TP results for users. Extensive experimental results demonstrate that TPAD can effectively identify near-optimal prediction results, improving stochastic TP models' practical application effect.
Causal Discovery from Sparse Time-Series Data Using Echo State Network
Authors: Haonan Chen, Bo Yuan Chang, Mohamed A. Naiel, Georges Younes, Steven Wardell, Stan Kleinikkink, John S. Zelek
Date: 2022-01-09 00:00:00
Tasks: Causal Discovery, regression, Time Series, Time Series Analysis

Causal discovery between collections of time-series data can help diagnose causes of symptoms and hopefully prevent faults before they occur. However, reliable causal discovery can be very challenging, especially when the data acquisition rate varies (i.e., non-uniform data sampling), or in the presence of missing data points (e.g., sparse data sampling). To address these issues, we proposed a new system comprised of two parts, the first part fills missing data with a Gaussian Process Regression, and the second part leverages an Echo State Network, which is a type of reservoir computer (i.e., used for chaotic system modelling) for Causal discovery. We evaluate the performance of our proposed system against three other off-the-shelf causal discovery algorithms, namely, structural expectation-maximization, sub-sampled linear auto-regression absolute coefficients, and multivariate Granger Causality with vector auto-regressive using the Tennessee Eastman chemical dataset; we report on their corresponding Matthews Correlation Coefficient(MCC) and Receiver Operating Characteristic curves (ROC) and show that the proposed system outperforms existing algorithms, demonstrating the viability of our approach to discover causal relationships in a complex system with missing entries.
Open-Set Recognition of Breast Cancer Treatments
Authors: Alexander Cao, Diego Klabjan, Yuan Luo
Date: 2022-01-09 00:00:00
Tasks: Open Set Learning, Robust classification

Open-set recognition generalizes a classification task by classifying test samples as one of the known classes from training or "unknown." As novel cancer drug cocktails with improved treatment are continually discovered, predicting cancer treatments can naturally be formulated in terms of an open-set recognition problem. Drawbacks, due to modeling unknown samples during training, arise from straightforward implementations of prior work in healthcare open-set learning. Accordingly, we reframe the problem methodology and apply a recent existing Gaussian mixture variational autoencoder model, which achieves state-of-the-art results for image datasets, to breast cancer patient data. Not only do we obtain more accurate and robust classification results, with a 24.5% average F1 increase compared to a recent method, but we also reexamine open-set recognition in terms of deployability to a clinical setting.
λ-Scaled-Attention: A Novel Fast Attention Mechanism for Efficient Modeling of Protein Sequences
Authors: Ashish Ranjan, Md Shah Fahad, Akshay Deepak
Date: 2022-01-09 00:00:00
Tasks: Protein Function Prediction

Attention-based deep networks have been successfully applied on textual data in the field of NLP. However, their application on protein sequences poses additional challenges due to the weak semantics of the protein words, unlike the plain text words. These unexplored challenges faced by the standard attention technique include (i) vanishing attention score problem and (ii) high variations in the attention distribution. In this regard, we introduce a novel {\lambda}-scaled attention technique for fast and efficient modeling of the protein sequences that addresses both the above problems. This is used to develop the {\lambda}-scaled attention network and is evaluated for the task of protein function prediction implemented at the protein sub-sequence level. Experiments on the datasets for biological process (BP) and molecular function (MF) showed significant improvements in the F1 score values for the proposed {\lambda}-scaled attention technique over its counterpart approach based on the standard attention technique (+2.01% for BP and +4.67% for MF) and state-of-the-art ProtVecGen-Plus approach (+2.61% for BP and +4.20% for MF). Further, fast convergence (converging in half the number of epochs) and efficient learning (in terms of very low difference between the training and validation losses) were also observed during the training process.
Extraction of Product Specifications from the Web -- Going Beyond Tables and Lists
Authors: Govind Krishnan Gangadhar, Ashish Kulkarni
Date: 2022-01-08 00:00:00
Tasks: Attribute, Question Answering

E-commerce product pages on the web often present product specification data in structured tabular blocks. Extraction of these product attribute-value specifications has benefited applications like product catalogue curation, search, question answering, and others. However, across different Websites, there is a wide variety of HTML elements (like <table>, <ul>, <div>, <span>, <dl> etc.) typically used to render these blocks that makes their automatic extraction a challenge. Most of the current research has focused on extracting product specifications from tables and lists and, therefore, suffers from recall when applied to a large-scale extraction setting. In this paper, we present a product specification extraction approach that goes beyond tables or lists and generalizes across the diverse HTML elements used for rendering specification blocks. Using a combination of hand-coded features and deep learned spatial and token features, we first identify the specification blocks on a product page. We then extract the product attribute-value pairs from these blocks following an approach inspired by wrapper induction. We created a labeled dataset of product specifications extracted from 14,111 diverse specification blocks taken from a range of different product websites. Our experiments show the efficacy of our approach compared to the current specification extraction models and support our claim about its application to large-scale product specification extraction.
Lazy Lagrangians with Predictions for Online Learning
Authors: Daron Anderson, George Iosifidis, Douglas J. Leith
Date: 2022-01-08 00:00:00

We consider the general problem of online convex optimization with time-varying additive constraints in the presence of predictions for the next cost and constraint functions. A novel primal-dual algorithm is designed by combining a Follow-The-Regularized-Leader iteration with prediction-adaptive dynamic steps. The algorithm achieves $\mathcal O(T^{\frac{3-\beta}{4}})$ regret and $\mathcal O(T^{\frac{1+\beta}{2}})$ constraint violation bounds that are tunable via parameter $\beta\!\in\![1/2,1)$ and have constant factors that shrink with the predictions quality, achieving eventually $\mathcal O(1)$ regret for perfect predictions. Our work extends the FTRL framework for this constrained OCO setting and outperforms the respective state-of-the-art greedy-based solutions, without imposing conditions on the quality of predictions, the cost functions or the geometry of constraints, beyond convexity.
Reconfigurable Intelligent Surface Enabled Spatial Multiplexing with Fully Convolutional Network
Authors: Bile Peng, Jan-Aike Termöhlen, Cong Sun, Danping He, Ke Guan, Tim Fingscheidt, Eduard A. Jorswieck
Date: 2022-01-08 00:00:00
Tasks: Semantic Segmentation

Reconfigurable intelligent surface (RIS) is an emerging technology for future wireless communication systems. In this work, we consider downlink spatial multiplexing enabled by the RIS for weighted sum-rate (WSR) maximization. In the literature, most solutions use alternating gradient-based optimization, which has moderate performance, high complexity, and limited scalability. We propose to apply a fully convolutional network (FCN) to solve this problem, which was originally designed for semantic segmentation of images. The rectangular shape of the RIS and the spatial correlation of channels with adjacent RIS antennas due to the short distance between them encourage us to apply it for the RIS configuration. We design a set of channel features that includes both cascaded channels via the RIS and the direct channel. In the base station (BS), the differentiable minimum mean squared error (MMSE) precoder is used for pretraining and the weighted minimum mean squared error (WMMSE) precoder is then applied for fine-tuning, which is nondifferentiable, more complex, but achieves a better performance. Evaluation results show that the proposed solution has higher performance and allows for a faster evaluation than the baselines. Hence it scales better to a large number of antennas, advancing the RIS one step closer to practical deployment.
Optimal 1-Wasserstein Distance for WGANs
Authors: Arthur Stéphanovitch, Ugo Tanielian, Benoît Cadre, Nicolas Klutchnikoff, Gérard Biau
Date: 2022-01-08 00:00:00
Tasks: valid

The mathematical forces at work behind Generative Adversarial Networks raise challenging theoretical issues. Motivated by the important question of characterizing the geometrical properties of the generated distributions, we provide a thorough analysis of Wasserstein GANs (WGANs) in both the finite sample and asymptotic regimes. We study the specific case where the latent space is univariate and derive results valid regardless of the dimension of the output space. We show in particular that for a fixed sample size, the optimal WGANs are closely linked with connected paths minimizing the sum of the squared Euclidean distances between the sample points. We also highlight the fact that WGANs are able to approach (for the 1-Wasserstein distance) the target distribution as the sample size tends to infinity, at a given convergence rate and provided the family of generative Lipschitz functions grows appropriately. We derive in passing new results on optimal transport theory in the semi-discrete setting.
A fall alert system with prior-fall activity identification
Authors: Pisol Ruenin, Sarayut Techakaew, Patsakorn Towatrakool, Jakarin Chawachat
Date: 2022-01-08 00:00:00

Falling, especially in the elderly, is a critical issue to care for and surveil. There have been many studies focusing on fall detection. However, from our survey, there is still no research indicating the prior-fall activities, which we believe that they have a strong correlation with the intensity of the fall. The purpose of this research is to develop a fall alert system that also identifies prior-fall activities. First, we want to find a suitable location to attach a sensor to the body. We created multiple-spot on-body devices to collect various activity data. We used that dataset to train 5 different classification models. We selected the XGBoost classification model for detecting a prior-fall activity and the chest location for use in fall detection from a comparison of the detection accuracy. We then tested 3 existing fall detection threshold algorithms to detect fall and fall to their knees first, and selected the 3-phase threshold algorithm of Chaitep and Chawachat [3] in our system. From the experiment, we found that the fall detection accuracy is 88.91%, the fall to their knees first detection accuracy is 91.25%, and the average accuracy of detection of prior-fall activities is 86.25%. Although we use an activity dataset of young to middle-aged adults (18-49 years), we are confident that this system can be developed to monitor activities before the fall, especially in the elderly, so that caretakers can better manage the situation.
ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems
Authors: Qi Pang, Yuanyuan Yuan, Shuai Wang, Wenting Zheng
Date: 2022-01-08 00:00:00
Tasks: Federated Learning, Privacy Preserving, Vertical Federated Learning

Vertical federated learning (VFL) system has recently become prominent as a concept to process data distributed across many individual sources without the need to centralize it. Multiple participants collaboratively train models based on their local data in a privacy-aware manner. To date, VFL has become a de facto solution to securely learn a model among organizations, allowing knowledge to be shared without compromising privacy of any individuals. Despite the prosperous development of VFL systems, we find that certain inputs of a participant, named adversarial dominating inputs (ADIs), can dominate the joint inference towards the direction of the adversary's will and force other (victim) participants to make negligible contributions, losing rewards that are usually offered regarding the importance of their contributions in federated learning scenarios. We conduct a systematic study on ADIs by first proving their existence in typical VFL systems. We then propose gradient-based methods to synthesize ADIs of various formats and exploit common VFL systems. We further launch greybox fuzz testing, guided by the saliency score of ``victim'' participants, to perturb adversary-controlled inputs and systematically explore the VFL attack surface in a privacy-preserving manner. We conduct an in-depth study on the influence of critical parameters and settings in synthesizing ADIs. Our study reveals new VFL attack opportunities, promoting the identification of unknown threats before breaches and building more secure VFL systems.