Reviewerly/exHarmony huggingface dataset:
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing

It is well-known that classifiers are vulnerable to adversarial perturbations. To defend against adversarial perturbations, various certified robustness results have been derived. However, existing certified robustnesses are limited to top-1 predictions. In many real-world applications, top-$k$ predictions are more relevant. In this work, we aim to derive certified robustness for top-$k$ predictions. In particular, our certified robustness is based on randomized smoothing, which turns any classifier to a new classifier via adding noise to an input example. We adopt randomized smoothing because it is scalable to large-scale neural networks and applicable to any classifier. We derive a tight robustness in $\ell_2$ norm for top-$k$ predictions when using randomized smoothing with Gaussian noise. We find that generalizing the certified robustness from top-1 to top-$k$ predictions faces significant technical challenges. We also empirically evaluate our method on CIFAR10 and ImageNet. For example, our method can obtain an ImageNet classifier with a certified top-5 accuracy of 62.8\% when the $\ell_2$-norms of the adversarial perturbations are less than 0.5 (=127/255). Our code is publicly available at: \url{https://github.com/jjy1994/Certify_Topk}.
Optical image encryption scheme based on apertured fractional Mellin transform

An optical image encryption scheme is proposed by utilizing an apertured nonlinear fractional Mellin transform (FrMT). Due to its nonlinear property, FrMT is utilized for eliminating potential insecurity in an image encryption system caused by known-plaintext and chosen-plaintext attacks. The aperture in the optical system makes it harder for attackers to collect optical signals in the transmission process. The apertured FrMT can be implemented by log-polar transform and Collins diffraction and the key space of the proposed image encryption algorithm is very large. The orders of the FrMT, the radii of the FrMT domains, the order of the FrFT, the phases generated in the further encryption process, wavelength, side-lengths of hard aperture, and the parameters of logistic map are used as cipher keys. Extensive simulation results demonstrate that the proposed algorithm is feasible, sensitive to the keys, and capable of resisting common classical attacks. The encryption effect changes with the size of apertures.
Scalable and Order-robust Continual Learning with Additive Parameter Decomposition

While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, there are issues that remain to be tackled in order to apply them to real-world problem domains. First, a continual learning model should effectively handle catastrophic forgetting and be efficient to train even with a large number of tasks. Secondly, it needs to tackle the problem of order-sensitivity, where the performance of the tasks largely varies based on the order of the task arrival sequence, as it may cause serious problems where fairness plays a critical role (e.g. medical diagnosis). To tackle these practical challenges, we propose a novel continual learning method that is scalable as well as order-robust, which instead of learning a completely shared set of weights, represents the parameters for each task as a sum of task-shared and sparse task-adaptive parameters. With our Additive Parameter Decomposition (APD), the task-adaptive parameters for earlier tasks remain mostly unaffected, where we update them only to reflect the changes made to the task-shared parameters. This decomposition of parameters effectively prevents catastrophic forgetting and order-sensitivity, while being computation- and memory-efficient. Further, we can achieve even better scalability with APD using hierarchical knowledge consolidation, which clusters the task-adaptive parameters to obtain hierarchically shared parameters. We validate our network with APD, APD-Net, on multiple benchmark datasets against state-of-the-art continual learning methods, which it largely outperforms in accuracy, scalability, and order-robustness.
A Little Charity Guarantees Almost Envy-Freeness

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)A Little Charity Guarantees Almost Envy-FreenessBhaskar Ray Chaudhury, Telikepalli Kavitha, Kurt Mehlhorn, and Alkmini SgouritsaBhaskar Ray Chaudhury, Telikepalli Kavitha, Kurt Mehlhorn, and Alkmini Sgouritsapp.2658 - 2672Chapter DOI:https://doi.org/10.1137/1.9781611975994.162PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Fair division of indivisible goods is a very well-studied problem. The goal of this problem is to distribute m goods to n agents in a “fair” manner, where every agent has a valuation for each subset of goods. We assume general valuations. Envy-freeness is the most extensively studied notion of fairness. However, envy-free allocations do not always exist when goods are indivisible. The notion of fairness we consider here is “envy-freeness up to any good” (EFX) where no agent envies another agent after the removal of any single good from the other agent's bundle. It is not known if such an allocation always exists even when n = 3. We show there is always a partition of the set of goods into n + 1 subsets (X1, …, Xn, P) where for i ϵ [n], Xi is the bundle allocated to agent i and the set P is unallocated (or donated to charity) such that we have: (1)envy-freeness up to any good,(2)no agent values P higher than her own bundle, and(3)fewer than n goods go to charity, i.e., |P| < n (typically m ≫ n). Our proof is constructive. When agents have additive valuations and |P| is large (i.e., when |P| is close to n), our allocation also has a good maximin share (MMS) guarantee. Moreover, a minor variant of our algorithm also shows the existence of an allocation which is 4/7 groupwise maximin share (GMMS): this is a notion of fairness stronger than MMS. This improves upon the current best bound of 1/2 known for an approximate GMMS allocation. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Noise can speed backpropagation learning and deep bidirectional pretraining

We show that the backpropagation algorithm is a special case of the generalized Expectation–Maximization (EM) algorithm for iterative maximum likelihood estimation. We then apply the recent result that carefully chosen noise can speed the average convergence of the EM algorithm as it climbs a hill of probability or log-likelihood. Then injecting such noise can speed the average convergence of the backpropagation algorithm for both the training and pretraining of multilayer neural networks. The beneficial noise adds to the hidden and visible neurons and related parameters. The noise also applies to regularized regression networks. This beneficial noise is just that noise that makes the current signal more probable. We show that such noise also tends to improve classification accuracy. The geometry of the noise-benefit region depends on the probability structure of the neurons in a given layer. The noise-benefit region in noise space lies above the noisy-EM (NEM) hyperplane for classification and involves a hypersphere for regression. Simulations demonstrate these noise benefits using MNIST digit classification. The NEM noise benefits substantially exceed those of simply adding blind noise to the neural network. We further prove that the noise speed-up applies to the deep bidirectional pretraining of neural-network bidirectional associative memories (BAMs) or their functionally equivalent restricted Boltzmann machines. We then show that learning with basic contrastive divergence also reduces to generalized EM for an energy-based network probability. The optimal noise adds to the input visible neurons of a BAM in stacked layers of trained BAMs. Global stability of generalized BAMs guarantees rapid convergence in pretraining where neural signals feed back between contiguous layers. Bipolar coding of inputs further improves pretraining performance.
Neural memory plasticity for medical anomaly detection

In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, trajectory prediction, object tracking, and language modelling. However, we observe that the attention based knowledge retrieval mechanisms used in current NMNs restrict them from achieving their full potential as the attention process retrieves information based on a set of static connection weights. This is suboptimal in a setting where there are vast differences among samples in the data domain; such as anomaly detection where there is no consistent criteria for what constitutes an anomaly. In this paper, we propose a plastic neural memory access mechanism which exploits both static and dynamic connection weights in the memory read, write and output generation procedures. We demonstrate the effectiveness and flexibility of the proposed memory model in three challenging anomaly detection tasks in the medical domain: abnormal EEG identification, MRI tumour type classification and schizophrenia risk detection in children. In all settings, the proposed approach outperforms the current state-of-the-art. Furthermore, we perform an in-depth analysis demonstrating the utility of neural plasticity for the knowledge retrieval process and provide evidence on how the proposed memory model generates sparse yet informative memory outputs.
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning

Deep Metric Learning (DML) is arguably one of the most influential lines of research for learning visual similarities with many proposed approaches every year. Although the field benefits from the rapid progress, the divergence in training protocols, architectures, and parameter choices make an unbiased comparison difficult. To provide a consistent reference point, we revisit the most widely used DML objective functions and conduct a study of the crucial parameter choices as well as the commonly neglected mini-batch sampling process. Under consistent comparison, DML objectives show much higher saturation than indicated by literature. Further based on our analysis, we uncover a correlation between the embedding space density and compression to the generalization performance of DML models. Exploiting these insights, we propose a simple, yet effective, training regularization to reliably boost the performance of ranking-based DML models on various standard benchmark datasets. Code and a publicly accessible WandB-repo are available at this https URL.
Automated Post-Breach Penetration Testing through Reinforcement Learning

Predicting cyber attacks to networks is ever present challenges in the security domain. Rapid growth of Artificial Intelligence (AI) has made this even more challenging as machine learning algorithms are now used to attack such systems while defense systems continue to protect them with traditional approaches. Penetration testing (pentest) has long been one way to prevent security breaches by mimicking black hat hackers to expose possible exploits and vulnerabilities. Using trained machine learning agents to automate this process is an important research area that still needs to be explored. The objective of this paper is to apply machine learning in the post-exploitation phase of penetration testing to assess the vulnerability of the system and hence, contribute to the automation process of penetration testing. We train the agent using reinforcement learning by providing an appropriate environment to explore a compromised network and find sensitive files. By utilizing several different network environments during training, we hope to generalize our agent as much as possible, allowing for more widespread application. Extended research may include training our agent for further lateral exploration and exploitation in the system.
Vertex-frequency graph signal processing: A comprehensive review

Graph signal processing deals with signals which are observed on an irregular graph domain. While many approaches have been developed in classical graph theory to cluster vertices and segment large graphs in a signal independent way, signal localization based approaches to the analysis of data on graph represent a new research direction which is also a key to big data analytics on graphs. To this end, after an overview of the basic definitions of graphs and graph signals, we present and discuss a localized form of the graph Fourier transform. To establish analogy with classical signal processing, spectral domain and vertex domain definitions of the localization window are given next. The spectral and vertex localization kernels are then related to the wavelet transform, followed by their polynomial approximations and a study of filtering and inversion operations. For rigor, the analysis of energy representation and frames in the localized graph Fourier transform is extended to the energy forms of vertex-frequency distributions, which operate even without the requirement to apply localization windows. Another link with classical signal processing is established through the concept of local smoothness, which is subsequently related to the paradigm of signal smoothness on graphs, a lynchpin which connects the properties of the signals on graphs and graph topology. This all represents a comprehensive account of the relation of general vertex-frequency analysis with classical time-frequency analysis, an important but missing link for more advanced applications of graph signal processing. The theory is supported by illustrative and practically relevant examples.
Advances in Nowcasting Economic Activity: Secular Trends, Large Shocks and New Data

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
From Speaker Verification to Multispeaker Speech Synthesis, Deep Transfer with Feedback Constraint

High-fidelity speech can be synthesized by end-to-end text-to-speech models in recent years. However, accessing and controlling speech attributes such as speaker identity, prosody, and emotion in a text-to-speech system remains a challenge. This paper presents a system involving feedback constraint for multispeaker speech synthesis. We manage to enhance the knowledge transfer from the speaker verification to the speech synthesis by engaging the speaker verification network. The constraint is taken by an added loss related to the speaker identity, which is centralized to improve the speaker similarity between the synthesized speech and its natural reference audio. The model is trained and evaluated on publicly available datasets. Experimental results, including visualization on speaker embedding space, show significant improvement in terms of speaker identity cloning in the spectrogram level. Synthesized samples are available online for listening. (this https URL)
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning

While neural sequence learning methods have made significant progress in single-document summarization (SDS), they produce unsatisfactory results on multi-document summarization (MDS).We observe two major challenges when adapting SDS advances to MDS: (1) MDS involves larger search space and yet more limited training data, setting obstacles for neural methods to learn adequate representations; (2) MDS needs to resolve higher information redundancy among the source documents, which SDS methods are less effective to handle.To close the gap, we present RL-MMR, Maximal Margin Relevance-guided Reinforcement Learning for MDS, which unifies advanced neural SDS methods and statistical measures used in classical MDS.RL-MMR casts MMR guidance on fewer promising candidates, which restrains the search space and thus leads to better representation learning.Additionally, the explicit redundancy measure in MMR helps the neural representation of the summary to better capture redundancy.Extensive experiments demonstrate that RL-MMR achieves state-of-the-art performance on benchmark MDS datasets.In particular, we show the benefits of incorporating MMR into end-to-end learning when adapting SDS to MDS in terms of both learning effectiveness and efficiency. 1
A User‐based Visual Analytics Workflow for Exploratory Model Analysis

Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an accurate predictive model for future use. In that case, users are more interested in generating of diverse and robust predictive models, verifying their performance on holdout data, and selecting the most suitable model for their usage scenario. In this paper, we consider the concept of Exploratory Model Analysis (EMA), which is defined as the process of discovering and selecting relevant models that can be used to make predictions on a data source. We delineate the differences between EMA and the well-known term exploratory data analysis in terms of the desired outcome of the analytic process: insights into the data or a set of deployable models. The contributions of this work are a visual analytics system workflow for EMA, a user study, and two use cases validating the effectiveness of the workflow. We found that our system workflow enabled users to generate complex models, to assess them for various qualities, and to select the most relevant model for their task.
ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning

Given questions regarding some prototypical situation -such as Name something that people usually do before they leave the house for work?-a human can easily answer them via acquired experiences.There can be multiple right answers for such questions, with some more common for a situation than others.This paper introduces a new question answering dataset for training and evaluating common sense reasoning capabilities of artificial intelligence systems in such prototypical situations.The training set is gathered from an existing set of questions played in a longrunning international game show -FAMILY-FEUD.The hidden evaluation set is created by gathering answers for each question from 100 crowd-workers.We also propose a generative evaluation task where a model has to output a ranked list of answers, ideally covering all prototypical answers for a question.After presenting multiple competitive baseline models, we find that human performance still exceeds model scores on all evaluation metrics with a meaningful gap, supporting the challenging nature of the task.* Equal contribution.
Numerical simulation of artificial spin ice for reservoir computing

Abstract We propose the use of artificial spin ice (ASI) for reservoir computing and evaluate its performance from simulation results. Our ASI reservoir is formed by 72 magnets arranged in a honeycomb lattice and the temperature is 0 K. A pseudo-random binary sequence is sent to the reservoir and its status is updated by external magnetic fields. Short-term memory capacity of 3.5 and nonlinear computational capacity of 2.9 are achieved when the strength of the magnetic fields is near the switching field of the magnets. The performance can be optimized by tuning the aspect ratios of the magnets.
Monocular Quasi-Dense 3D Object Tracking

A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer’s actions in numerous applications such as autonomous driving. We propose a framework that can effectively associate moving objects over time and estimate their full 3D bounding box information from a sequence of 2D images captured on a moving platform. The object association leverages quasi-dense similarity learning to identify objects in various poses and viewpoints with appearance cues only. After initial 2D association, we further utilize 3D bounding boxes depth-ordering heuristics for robust instance association and motion-based 3D trajectory prediction for re-identification of occluded vehicles. In the end, an LSTM-based object velocity learning module aggregates the long-term trajectory information for more accurate motion extrapolation. Experiments on our proposed simulation data and real-world benchmarks, including KITTI, nuScenes, and Waymo datasets, show that our tracking framework offers robust object association and tracking on urban-driving scenarios. On the Waymo Open benchmark, we establish the first camera-only baseline in the 3D tracking and 3D detection challenges. Our quasi-dense 3D tracking pipeline achieves impressive improvements on the nuScenes 3D tracking benchmark with near five times tracking accuracy of the best vision-only submission among all published methods.
Who Are Virtual Reality Headset Owners? A Survey and Comparison of Headset Owners and Non-Owners

The number of people who own a virtual reality (VR) head-mounted display (HMD) has reached a point where researchers can readily recruit HMD owners to participate remotely using their own equipment. However, HMD owners recruited online may differ from the university community members who typically participate in VR research. HMD owners (n=220) and non-owners (n=282) were recruited through two online work sites-Amazon's Mechanical Turk and Prolific-and an undergraduate participant pool. Participants completed a survey in which they provided demographic information and completed measures of HMD use, video game use, spatial ability, and motion sickness susceptibility. In the context of the populations sampled, the results provide 1) a characterization of HMD owners, 2) a snapshot of the most commonly owned HMDs, 3) a comparison between HMD owners and non-owners, and 4) a comparison among online workers and undergraduates. Significant gender differences were found: men reported lower motion sickness susceptibility and more video game hours than women, and men outperformed women on spatial tasks. Men comprised a greater proportion of HMD owners than non-owners, but after accounting for this imbalance, HMD owners did not differ appreciably from non-owners. Comparing across recruitment platform, male undergraduates outperformed male online workers on spatial tests, and female undergraduates played fewer video game hours than female online workers. The data removal rate was higher from Amazon compared to Prolific, possibly reflecting greater dishonesty. These results provide a description of HMD users that can inform researchers recruiting remote participants through online work sites. These results also signal a need for caution when comparing in-person VR research that primarily enrolls undergraduates to online VR research that enrolls online workers.