OpenRaiser/Intern-Atlas huggingface dataset:
Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy
Venue: arXiv cs.LG 2020
Year: 2020

Deployment of deep learning in different fields and industries is growing day by day due to its performance, which relies on the availability of data and compute. Data is often crowd-sourced and contains sensitive information about its contributors, which leaks into models that are trained on it. To achieve rigorous privacy guarantees, differentially private training mechanisms are used. However, it has recently been shown that differential privacy can exacerbate existing biases in the data and have disparate impacts on the accuracy of different subgroups of data. In this paper, we aim to study these effects within differentially private deep learning. Specifically, we aim to study how different levels of imbalance in the data affect the accuracy and the fairness of the decisions made by the model, given different levels of privacy. We demonstrate that even small imbalances and loose privacy guarantees can cause disparate impacts.
A block-coupled Finite Volume methodology for problems of large strain and large displacement
Venue: arXiv cs.CE 2020
Year: 2020

A nonlinear block-coupled Finite Volume methodology is developed for large displacement and large strain regime. The new methodology uses the same normal and tangential face derivative discretisations found in the original fully coupled cell-centred Finite Volume solution methodology for linear elasticity, meaning that existing block-coupled implementations may easily be extended to include finite strains. Details are given of the novel approach, including use of the Newton-Raphson procedure on a residual functional defined using the linear momentum equation. A number of 2-D benchmark cases have shown that, compared with a segregated procedure, the new approach exhibits errors with many orders of magnitude smaller and a much higher convergence rate.
Should Decorators Preserve the Component Interface?
Venue: arXiv cs.SE 2020
Year: 2020

Decorator design pattern is a well known pattern that allows dynamical attachment of additional functionality to an object. Decorators have been proposed as flexible alternative to subclassing for extending functionality. Still, the Decorator pattern has certain limitations, especially related to the fact that in its classical form it is constrained to a single interface, which is implicitly defined by the type of the concrete components that we intend to decorate. Another problem associated to the Decorator pattern is related to the linear composition of the decorations, which could lead to problems in accessing the newly added responsibilities. In this context, the paper presents variants of the Decorator pattern: MixDecorator and D2Decorator, and a variant specific only to C++ language based on templates - HybridDecorator. MixDecorator could be considered a new enhanced version of the Decorator pattern that eliminates some constraints of the Decorator pattern, but also it could be used as a base of a general extension mechanism. The main advantage of using MixDecorator is that it allows direct access to all newly added responsibilities, and so, we may combine different interface-responsibilities (newly added public methods) and operate with them directly and in any order, hiding the linear composition of the decorations. D2Decorator is a variant based on a double-dispatch mechanism. The C++ metaprogramming mechanism based on templates allows an interesting hybrid variant of the Decorator - HybridDecorator, which mixes on-demand defined inheritance with composition. Using these variants of the Decorator pattern we are not longer limited to one single interface; the set of the messages that could be sent to an object could be enlarged, and so, we may consider that using them, we can dynamically change the type of objects.
Development of a Dataset and a Deep Learning Baseline Named Entity Recognizer for Three Low Resource Languages: Bhojpuri, Maithili and Magahi
Venue: arXiv cs.CL 2020
Year: 2020

In Natural Language Processing (NLP) pipelines, Named Entity Recognition (NER) is one of the preliminary problems, which marks proper nouns and other named entities such as Location, Person, Organization, Disease etc. Such entities, without a NER module, adversely affect the performance of a machine translation system. NER helps in overcoming this problem by recognising and handling such entities separately, although it can be useful in Information Extraction systems also. Bhojpuri, Maithili and Magahi are low resource languages, usually known as Purvanchal languages. This paper focuses on the development of a NER benchmark dataset for the Machine Translation systems developed to translate from these languages to Hindi by annotating parts of their available corpora. Bhojpuri, Maithili and Magahi corpora of sizes 228373, 157468 and 56190 tokens, respectively, were annotated using 22 entity labels. The annotation considers coarse-grained annotation labels followed by the tagset used in one of the Hindi NER datasets. We also report a Deep Learning based baseline that uses an LSTM-CNNs-CRF model. The lower baseline F1-scores from the NER tool obtained by using Conditional Random Fields models are 96.73 for Bhojpuri, 93.33 for Maithili and 95.04 for Magahi. The Deep Learning-based technique (LSTM-CNNs-CRF) achieved 96.25 for Bhojpuri, 93.33 for Maithili and 95.44 for Magahi.
Bluetooth based Proximity, Multi-hop Analysis and Bi-directional Trust: Epidemics and More
Venue: arXiv cs.CR 2020
Year: 2020

In this paper, we propose a trust layer on top of Bluetooth and similar wireless communication technologies that can form mesh networks. This layer as a protocol enables computing trust scores based on proximity and bi-directional transfer of messages in multiple hops across a network of mobile devices. We describe factors and an approach for determining these trust scores and highlight its applications during epidemics such as COVID-19 through improved contact-tracing, better privacy and verification for sensitive data sharing in the numerous Bluetooth and GPS based mobile applications that are being developed to track the spread.
Simplex: Repurposing Intel Memory Protection Extensions for Information Hiding
Venue: arXiv cs.CR 2020
Year: 2020

With the rapid increase in software exploits, the last few decades have seen several hardware-level features to enhance security (e.g., Intel MPX, ARM TrustZone, Intel SGX, Intel CET). Due to security, performance and/or usability issues these features have attracted steady criticism. One such feature is the Intel Memory Protection Extensions (MPX), an instruction set architecture extension promising spatial memory safety at a lower performance cost due to hardware-accelerated bounds checking. However, recent investigations into MPX have found that is neither as performant, accurate, nor precise as cutting-edge software-based spatial memory safety. As a direct consequence, compiler and operating system support for MPX is dying, and Intel has begun to manufacture desktop CPUs without MPX. Nonetheless, given how ubiquitous MPX is, it provides an excellent yet under-utilized hardware resource that can be aptly salvaged for security purposes. In this paper, we propose Simplex, a library framework that re-purposes MPX registers as general purpose registers. Using Simplex, we demonstrate how MPX registers can be used to store sensitive information (e.g., encryption keys) directly on the hardware. We evaluate Simplex for performance and find that its overhead is small enough to permit its deployment in all but the most performance-intensive code. We refactored the string.h buffer manipulation functions and found a geometric mean 0.9% performance overhead. We also modified the deepsjeng and lbm SPEC CPU2017 benchmarks to use Simplex and found a 1% and 0.98% performance overhead respectively. Finally, we investigate the behavior of the MPX context with regards to multi-process and multi-thread programs.
Beyond Accuracy: ROI-driven Data Analytics of Empirical Data
Venue: arXiv cs.LG 2020
Year: 2020

This vision paper demonstrates that it is crucial to consider Return-on-Investment (ROI) when performing Data Analytics. Decisions on "How much analytics is needed"? are hard to answer. ROI could guide for decision support on the What?, How?, and How Much? analytics for a given problem. Method: The proposed conceptual framework is validated through two empirical studies that focus on requirements dependencies extraction in the Mozilla Firefox project. The two case studies are (i) Evaluation of fine-tuned BERT against Naive Bayes and Random Forest machine learners for binary dependency classification and (ii) Active Learning against passive Learning (random sampling) for REQUIRES dependency extraction. For both the cases, their analysis investment (cost) is estimated, and the achievable benefit from DA is predicted, to determine a break-even point of the investigation. Results: For the first study, fine-tuned BERT performed superior to the Random Forest, provided that more than 40% of training data is available. For the second, Active Learning achieved higher F1 accuracy within fewer iterations and higher ROI compared to Baseline (Random sampling based RF classifier). In both the studies, estimate on, How much analysis likely would pay off for the invested efforts?, was indicated by the break-even point. Conclusions: Decisions for the depth and breadth of DA of empirical data should not be made solely based on the accuracy measures. Since ROI-driven Data Analytics provides a simple yet effective direction to discover when to stop further investigation while considering the cost and value of the various types of analysis, it helps to avoid over-analyzing empirical data.
Utility-Optimized Synthesis of Differentially Private Location Traces
Venue: arXiv cs.CR 2020
Year: 2020

Differentially private location trace synthesis (DPLTS) has recently emerged as a solution to protect mobile users' privacy while enabling the analysis and sharing of their location traces. A key challenge in DPLTS is to best preserve the utility in location trace datasets, which is non-trivial considering the high dimensionality, complexity and heterogeneity of datasets, as well as the diverse types and notions of utility. In this paper, we present OptaTrace: a utility-optimized and targeted approach to DPLTS. Given a real trace dataset D, the differential privacy parameter epsilon controlling the strength of privacy protection, and the utility/error metric Err of interest; OptaTrace uses Bayesian optimization to optimize DPLTS such that the output error (measured in terms of given metric Err) is minimized while epsilon-differential privacy is satisfied. In addition, OptaTrace introduces a utility module that contains several built-in error metrics for utility benchmarking and for choosing Err, as well as a front-end web interface for accessible and interactive DPLTS service. Experiments show that OptaTrace's optimized output can yield substantial utility improvement and error reduction compared to previous work.
ColVis: Collaborative Visualization Design Workshops for Diverse User Groups
Venue: arXiv cs.CY 2020
Year: 2020

Understanding different types of users' needs can even be more critical in today's data visualization field, as exploratory visualizations for novice users are becoming more widespread with an increasing amount of data sources. The complexity of data-driven projects requires input from including interdisciplinary expert and novice users. Our workshop framework helps taking design decisions collaboratively with experts and novice users, on different levels such as outlining users and goals, identifying tasks, structuring data, and creating data visualization ideas. We conducted workshops for two different data visualization projects. For each project, we conducted a workshop with project stakeholders who are domain experts, then a second workshop with novice users. We collected feedback from participants and used critical reflection on the process. Later on, we created recommendations on how this workshop structure can be used by others. Our main contributions are, (1) the workshop framework for designing data visualizations, (2) describing the outcomes and lessons learned from multiple workshops.
Input Hessian Regularization of Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Regularizing the input gradient has shown to be effective in promoting the robustness of neural networks. The regularization of the input's Hessian is therefore a natural next step. A key challenge here is the computational complexity. Computing the Hessian of inputs is computationally infeasible. In this paper we propose an efficient algorithm to train deep neural networks with Hessian operator-norm regularization. We analyze the approach theoretically and prove that the Hessian operator norm relates to the ability of a neural network to withstand an adversarial attack. We give a preliminary experimental evaluation on the MNIST and FMNIST datasets, which demonstrates that the new regularizer can, indeed, be feasible and, furthermore, that it increases the robustness of neural networks over input gradient regularization.
Themes Informed Audio-visual Correspondence Learning
Venue: arXiv cs.AI 2020
Year: 2020

The applications of short-term user-generated video (UGV), such as Snapchat, and Youtube short-term videos, booms recently, raising lots of multimodal machine learning tasks. Among them, learning the correspondence between audio and visual information from videos is a challenging one. Most previous work of the audio-visual correspondence(AVC) learning only investigated constrained videos or simple settings, which may not fit the application of UGV. In this paper, we proposed new principles for AVC and introduced a new framework to set sight of videos' themes to facilitate AVC learning. We also released the KWAI-AD-AudVis corpus which contained 85432 short advertisement videos (around 913 hours) made by users. We evaluated our proposed approach on this corpus, and it was able to outperform the baseline by 23.15% absolute difference.
GIA-Net: Global Information Aware Network for Low-light Imaging
Venue: arXiv cs.CV 2020
Year: 2020

It is extremely challenging to acquire perceptually plausible images under low-light conditions due to low SNR. Most recently, U-Nets have shown promising results for low-light imaging. However, vanilla U-Nets generate images with artifacts such as color inconsistency due to the lack of global color information. In this paper, we propose a global information aware (GIA) module, which is capable of extracting and integrating the global information into the network to improve the performance of low-light imaging. The GIA module can be inserted into a vanilla U-Net with negligible extra learnable parameters or computational cost. Moreover, a GIA-Net is constructed, trained and evaluated on a large scale real-world low-light imaging dataset. Experimental results show that the proposed GIA-Net outperforms the state-of-the-art methods in terms of four metrics, including deep metrics that measure perceptual similarities. Extensive ablation studies have been conducted to verify the effectiveness of the proposed GIA-Net for low-light imaging by utilizing global information.
Variable Binding for Sparse Distributed Representations: Theory and Applications
Venue: arXiv cs.NE 2020
Year: 2020

Symbolic reasoning and neural networks are often considered incompatible approaches. Connectionist models known as Vector Symbolic Architectures (VSAs) can potentially bridge this gap. However, classical VSAs and neural networks are still considered incompatible. VSAs encode symbols by dense pseudo-random vectors, where information is distributed throughout the entire neuron population. Neural networks encode features locally, often forming sparse vectors of neural activation. Following Rachkovskij (2001); Laiho et al. (2015), we explore symbolic reasoning with sparse distributed representations. The core operations in VSAs are dyadic operations between vectors to express variable binding and the representation of sets. Thus, algebraic manipulations enable VSAs to represent and process data structures in a vector space of fixed dimensionality. Using techniques from compressed sensing, we first show that variable binding between dense vectors in VSAs is mathematically equivalent to tensor product binding between sparse vectors, an operation which increases dimensionality. This result implies that dimensionality-preserving binding for general sparse vectors must include a reduction of the tensor matrix into a single sparse vector. Two options for sparsity-preserving variable binding are investigated. One binding method for general sparse vectors extends earlier proposals to reduce the tensor product into a vector, such as circular convolution. The other method is only defined for sparse block-codes, block-wise circular convolution. Our experiments reveal that variable binding for block-codes has ideal properties, whereas binding for general sparse vectors also works, but is lossy, similar to previous proposals. We demonstrate a VSA with sparse block-codes in example applications, cognitive reasoning and classification, and discuss its relevance for neuroscience and neural networks.
Truth or Square: Aspect Ratio Biases Recall of Position Encodings
Venue: arXiv cs.HC 2020
Year: 2020

Bar charts are among the most frequently used visualizations, in part because their position encoding leads them to convey data values precisely. Yet reproductions of single bars or groups of bars within a graph can be biased. Curiously, some previous work found that this bias resulted in an overestimation of reproduced data values, while other work found an underestimation. Across three empirical studies, we offer an explanation for these conflicting findings: this discrepancy is a consequence of the differing aspect ratios of the tested bar marks. Viewers are biased to remember a bar mark as being more similar to a prototypical square, leading to an overestimation of bars with a wide aspect ratio, and an underestimation of bars with a tall aspect ratio. Experiments 1 and 2 showed that the aspect ratio of the bar marks indeed influenced the direction of this bias. Experiment 3 confirmed that this pattern of misestimation bias was present for reproductions from memory, suggesting that this bias may arise when comparing values across sequential displays or views. We describe additional visualization designs that might be prone to this bias beyond bar charts (e.g., Mekko charts and treemaps), and speculate that other visual channels might hold similar biases toward prototypical values.
Learning Hidden Patterns from Patient Multivariate Time Series Data Using Convolutional Neural Networks: A Case Study of Healthcare Cost Prediction
Venue: arXiv cs.LG 2020
Year: 2020

Objective: To develop an effective and scalable individual-level patient cost prediction method by automatically learning hidden temporal patterns from multivariate time series data in patient insurance claims using a convolutional neural network (CNN) architecture. Methods: We used three years of medical and pharmacy claims data from 2013 to 2016 from a healthcare insurer, where data from the first two years were used to build the model to predict costs in the third year. The data consisted of the multivariate time series of cost, visit and medical features that were shaped as images of patients' health status (i.e., matrices with time windows on one dimension and the medical, visit and cost features on the other dimension). Patients' multivariate time series images were given to a CNN method with a proposed architecture. After hyper-parameter tuning, the proposed architecture consisted of three building blocks of convolution and pooling layers with an LReLU activation function and a customized kernel size at each layer for healthcare data. The proposed CNN learned temporal patterns became inputs to a fully connected layer. Conclusions: Feature learning through the proposed CNN configuration significantly improved individual-level healthcare cost prediction. The proposed CNN was able to outperform temporal pattern detection methods that look for a pre-defined set of pattern shapes, since it is capable of extracting a variable number of patterns with various shapes. Temporal patterns learned from medical, visit and cost data made significant contributions to the prediction performance. Hyper-parameter tuning showed that considering three-month data patterns has the highest prediction accuracy. Our results showed that patients' images extracted from multivariate time series data are different from regular images, and hence require unique designs of CNN architectures.
The Cost of Software-Based Memory Management Without Virtual Memory
Venue: arXiv cs.AR 2020
Year: 2020

Virtual memory has been a standard hardware feature for more than three decades. At the price of increased hardware complexity, it has simplified software and promised strong isolation among colocated processes. In modern computing systems, however, the costs of virtual memory have increased significantly. With large memory workloads, virtualized environments, data center computing, and chips with multiple DMA devices, virtual memory can degrade performance and increase power usage. We therefore explore the implications of building applications and operating systems without relying on hardware support for address translation. Primarily, we investigate the implications of removing the abstraction of large contiguous memory segments. Our experiments show that the overhead to remove this reliance is surprisingly small for real programs. We expect this small overhead to be worth the benefit of reducing the complexity and energy usage of address translation. In fact, in some cases, performance can even improve when address translation is avoided.
Bilinear Generalized Vector Approximate Message Passing
Venue: arXiv cs.IT 2020
Year: 2020

We introduce the bilinear generalized vector approximate message passing (BiG-VAMP) algorithm which jointly recovers two matrices U and V from their noisy product through a probabilistic observation model. BiG-VAMP provides computationally efficient approximate implementations of both max-sum and sumproduct loopy belief propagation (BP). We show how the proposed BiG-VAMP algorithm recovers different types of structured matrices and overcomes the fundamental limitations of other state-of-the-art approaches to the bilinear recovery problem, such as BiG-AMP, BAd-VAMP and LowRAMP. In essence, BiG-VAMP applies to a broader class of practical applications which involve a general form of structured matrices. For the sake of theoretical performance prediction, we also conduct a state evolution (SE) analysis of the proposed algorithm and show its consistency with the asymptotic empirical mean-squared error (MSE). Numerical results on various applications such as matrix factorization, dictionary learning, and matrix completion demonstrate unambiguously the effectiveness of the proposed BiG-VAMP algorithm and its superiority over stateof-the-art algorithms. Using the developed SE framework, we also examine (as one example) the phase transition diagrams of the matrix completion problem, thereby unveiling a low detectability region corresponding to the low signal-to-noise ratio (SNR) regime.