OpenRaiser/Intern-Atlas huggingface dataset:
Selfie Periocular Verification using an Efficient Super-Resolution Approach
Venue: arXiv cs.CV 2022
Year: 2022

Selfie-based biometrics has great potential for a wide range of applications since, e.g. periocular verification is contactless and is safe to use in pandemics such as COVID-19, when a major portion of a face is covered by a facial mask. Despite its advantages, selfie-based biometrics presents challenges since there is limited control over data acquisition at different distances. Therefore, Super-Resolution (SR) has to be used to increase the quality of the eye images and to keep or improve the recognition performance. We propose an Efficient Single Image Super-Resolution algorithm, which takes into account a trade-off between the efficiency and the size of its filters. To that end, the method implements a loss function based on the Sharpness metric used to evaluate iris images quality. Our method drastically reduces the number of parameters compared to the state-of-the-art: from 2,170,142 to 28,654. Our best results on remote verification systems with no redimensioning reached an EER of 8.89\% for FaceNet, 12.14% for VGGFace, and 12.81% for ArcFace. Then, embedding vectors were extracted from SR images, the FaceNet-based system yielded an EER of 8.92% for a resizing of x2, 8.85% for x3, and 9.32% for x4.
Revenue Attribution on iOS 14 using Conversion Values in F2P Games
Venue: arXiv cs.CR 2022
Year: 2022

Mobile app developers use paid advertising campaigns to acquire new users. Marketing managers decide where to spend and how much to spend based on the campaigns' performance. Apple's new privacy mechanisms have a profound impact on how performance marketing is measured. Starting iOS 14.5, all apps must get system permission for tracking explicitly via the new App Tracking Transparency Framework, which shows the users a pop-up asking if they give the app permission to track. If a user does not allow tracking, the required identifier to deterministically find the online advertising campaign that brought the user to install the app is not shared. Instead of relying on individual identifiers, Apple proposed a new performance mechanism called conversion value, which is an integer set by the apps for each user, and the developers can get the number of installs per conversion value for each campaign. However, interpreting how conversion values are used to measure the campaigns performance is not obvious because it requires a method to translate the conversion values to revenue. This paper investigates the task of attributing revenue to advertising campaigns using the reported conversion values per campaign. Our contributions are to formalize the problem, find the theoretically optimal revenue attribution function for any conversion value schema, and show empirical results on past data of a free-to-play mobile game using different conversion value schemas.
$ε$-Arithmetics for Real Vectors and Linear Processing of Real Vector-Valued Signals with Real Vector-Valued Coefficients
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we introduce a new concept, namely $ε$-arithmetics, for real vectors of any fixed dimension. The basic idea is to use vectors of rational values (called rational vectors) to approximate vectors of real values of the same dimension within $ε$ range. For rational vectors of a fixed dimension $m$, they can form a field that is an $m$th order extension $\mathbf{Q}(α)$ of the rational field $\mathbf{Q}$ where $α$ has its minimal polynomial of degree $m$ over $\mathbf{Q}$. Then, the arithmetics, such as addition, subtraction, multiplication, and division, of real vectors can be defined by using that of their approximated rational vectors within $ε$ range. We also define complex conjugate of a real vector and then inner product and convolutions of two real vectors and two real vector sequences (signals) of finite length. With these newly defined concepts for real vectors, linear processing, such as filtering, ARMA modeling, and least squares fitting, with real vector-valued coefficients can be implemented to real vector-valued signals, which will broaden the existing linear processing to scalar-valued signals.
Predicting Lexical Complexity in English Texts: The Complex 2.0 Dataset
Venue: arXiv cs.CL 2022
Year: 2022

Identifying words which may cause difficulty for a reader is an essential step in most lexical text simplification systems prior to lexical substitution and can also be used for assessing the readability of a text. This task is commonly referred to as Complex Word Identification (CWI) and is often modelled as a supervised classification problem. For training such systems, annotated datasets in which words and sometimes multi-word expressions are labelled regarding complexity are required. In this paper we analyze previous work carried out in this task and investigate the properties of CWI datasets for English. We develop a protocol for the annotation of lexical complexity and use this to annotate a new dataset, CompLex 2.0. We present experiments using both new and old datasets to investigate the nature of lexical complexity. We found that a Likert-scale annotation protocol provides an objective setting that is superior for identifying the complexity of words compared to a binary annotation protocol. We release a new dataset using our new protocol to promote the task of Lexical Complexity Prediction.
Contrastive Learning Inverts the Data Generating Process
Venue: arXiv cs.LG 2022
Year: 2022

Contrastive learning has recently seen tremendous success in self-supervised learning. So far, however, it is largely unclear why the learned representations generalize so effectively to a large variety of downstream tasks. We here prove that feedforward models trained with objectives belonging to the commonly used InfoNCE family learn to implicitly invert the underlying generative model of the observed data. While the proofs make certain statistical assumptions about the generative model, we observe empirically that our findings hold even if these assumptions are severely violated. Our theory highlights a fundamental connection between contrastive learning, generative modeling, and nonlinear independent component analysis, thereby furthering our understanding of the learned representations as well as providing a theoretical foundation to derive more effective contrastive losses.
Surveying the Landscape of Ethics-Focused Design Methods
Venue: arXiv cs.HC 2022
Year: 2022

Over the past decade, HCI researchers, design researchers, and practitioners have increasingly addressed ethics-focused issues through a range of theoretical, methodological and pragmatic contributions to the field. While many forms of design knowledge have been proposed and described, we focus explicitly on knowledge that has been codified as "methods," which we define as any supports for everyday work practices of designers. In this paper, we identify, analyze, and map a collection of 63 existing ethics-focused methods intentionally designed for ethical impact. We present a content analysis, providing a descriptive record of how they operationalize ethics, their intended audience or context of use, their "core" or "script," and the means by which these methods are formulated, articulated, and languaged. Building on these results, we provide an initial definition of ethics-focused methods, identifying potential opportunities for the development of future methods to support design practice and research.
Locally Checkable Problems in Rooted Trees
Venue: arXiv cs.DC 2022
Year: 2022

Consider any locally checkable labeling problem $Π$ in rooted regular trees: there is a finite set of labels $Σ$, and for each label $x \in Σ$ we specify what are permitted label combinations of the children for an internal node of label $x$ (the leaf nodes are unconstrained). This formalism is expressive enough to capture many classic problems studied in distributed computing, including vertex coloring, edge coloring, and maximal independent set. We show that the distributed computational complexity of any such problem $Π$ falls in one of the following classes: it is $O(1)$, $Θ(\log^* n)$, $Θ(\log n)$, or $n^{Θ(1)}$ rounds in trees with $n$ nodes (and all of these classes are nonempty). We show that the complexity of any given problem is the same in all four standard models of distributed graph algorithms: deterministic $\mathsf{LOCAL}$, randomized $\mathsf{LOCAL}$, deterministic $\mathsf{CONGEST}$, and randomized $\mathsf{CONGEST}$ model. In particular, we show that randomness does not help in this setting, and the complexity class $Θ(\log \log n)$ does not exist (while it does exist in the broader setting of general trees). We also show how to systematically determine the complexity class of any such problem $Π$, i.e., whether $Π$ takes $O(1)$, $Θ(\log^* n)$, $Θ(\log n)$, or $n^{Θ(1)}$ rounds. While the algorithm may take exponential time in the size of the description of $Π$, it is nevertheless practical: we provide a freely available implementation of the classifier algorithm, and it is fast enough to classify many problems of interest.
Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization
Venue: arXiv cs.LG 2022
Year: 2022

Smooth minimax games often proceed by simultaneous or alternating gradient updates. Although algorithms with alternating updates are commonly used in practice, the majority of existing theoretical analyses focus on simultaneous algorithms for convenience of analysis. In this paper, we study alternating gradient descent-ascent (Alt-GDA) in minimax games and show that Alt-GDA is superior to its simultaneous counterpart~(Sim-GDA) in many settings. We prove that Alt-GDA achieves a near-optimal local convergence rate for strongly convex-strongly concave (SCSC) problems while Sim-GDA converges at a much slower rate. To our knowledge, this is the \emph{first} result of any setting showing that Alt-GDA converges faster than Sim-GDA by more than a constant. We further adapt the theory of integral quadratic constraints (IQC) and show that Alt-GDA attains the same rate \emph{globally} for a subclass of SCSC minimax problems. Empirically, we demonstrate that alternating updates speed up GAN training significantly and the use of optimism only helps for simultaneous algorithms.
Data-Aware Device Scheduling for Federated Edge Learning
Venue: arXiv cs.DC 2022
Year: 2022

Federated Edge Learning (FEEL) involves the collaborative training of machine learning models among edge devices, with the orchestration of a server in a wireless edge network. Due to frequent model updates, FEEL needs to be adapted to the limited communication bandwidth, scarce energy of edge devices, and the statistical heterogeneity of edge devices' data distributions. Therefore, a careful scheduling of a subset of devices for training and uploading models is necessary. In contrast to previous work in FEEL where the data aspects are under-explored, we consider data properties at the heart of the proposed scheduling algorithm. To this end, we propose a new scheduling scheme for non-independent and-identically-distributed (non-IID) and unbalanced datasets in FEEL. As the data is the key component of the learning, we propose a new set of considerations for data characteristics in wireless scheduling algorithms in FEEL. In fact, the data collected by the devices depends on the local environment and usage pattern. Thus, the datasets vary in size and distributions among the devices. In the proposed algorithm, we consider both data and resource perspectives. In addition to minimizing the completion time of FEEL as well as the transmission energy of the participating devices, the algorithm prioritizes devices with rich and diverse datasets. We first define a general framework for the data-aware scheduling and the main axes and requirements for diversity evaluation. Then, we discuss diversity aspects and some exploitable techniques and metrics. Next, we formulate the problem and present our FEEL scheduling algorithm. Evaluations in different scenarios show that our proposed FEEL scheduling algorithm can help achieve high accuracy in few rounds with a reduced cost.
Deep Gait Recognition: A Survey
Venue: arXiv cs.CV 2022
Year: 2022

Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk. Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations. Gait recognition methods based on deep learning now dominate the state-of-the-art in the field and have fostered real-world applications. In this paper, we present a comprehensive overview of breakthroughs and recent developments in gait recognition with deep learning, and cover broad topics including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. We first review the commonly used gait datasets along with the principles designed for evaluating them. We then propose a novel taxonomy made up of four separate dimensions namely body representation, temporal representation, feature representation, and neural architecture, to help characterize and organize the research landscape and literature in this area. Following our proposed taxonomy, a comprehensive survey of gait recognition methods using deep learning is presented with discussions on their performances, characteristics, advantages, and limitations. We conclude this survey with a discussion on current challenges and mention a number of promising directions for future research in gait recognition.
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods
Venue: arXiv cs.LG 2022
Year: 2022

We present AI-SARAH, a practical variant of SARAH. As a variant of SARAH, this algorithm employs the stochastic recursive gradient yet adjusts step-size based on local geometry. AI-SARAH implicitly computes step-size and efficiently estimates local Lipschitz smoothness of stochastic functions. It is fully adaptive, tune-free, straightforward to implement, and computationally efficient. We provide technical insight and intuitive illustrations on its design and convergence. We conduct extensive empirical analysis and demonstrate its strong performance compared with its classical counterparts and other state-of-the-art first-order methods in solving convex machine learning problems.
Center Smoothing: Certified Robustness for Networks with Structured Outputs
Venue: arXiv cs.LG 2022
Year: 2022

The study of provable adversarial robustness has mostly been limited to classification tasks and models with one-dimensional real-valued outputs. We extend the scope of certifiable robustness to problems with more general and structured outputs like sets, images, language, etc. We model the output space as a metric space under a distance/similarity function, such as intersection-over-union, perceptual similarity, total variation distance, etc. Such models are used in many machine learning problems like image segmentation, object detection, generative models, image/audio-to-text systems, etc. Based on a robustness technique called randomized smoothing, our $\textit{center smoothing}$ procedure can produce models with the guarantee that the change in the output, as measured by the distance metric, remains small for any norm-bounded adversarial perturbation of the input. We apply our method to create certifiably robust models with disparate output spaces - from sets to images - and show that it yields meaningful certificates without significantly degrading the performance of the base model. Code for our experiments is available at: https://github.com/aounon/center-smoothing.
Back Translation Survey for Improving Text Augmentation
Venue: arXiv cs.CL 2022
Year: 2022

Natural Language Processing (NLP) relies heavily on training data. Transformers, as they have gotten bigger, have required massive amounts of training data. To satisfy this requirement, text augmentation should be looked at as a way to expand your current dataset and to generalize your models. One text augmentation we will look at is translation augmentation. We take an English sentence and translate it to another language before translating it back to English. In this paper, we look at the effect of 108 different language back translations on various metrics and text embeddings.
A flow-based IDS using Machine Learning in eBPF
Venue: arXiv cs.CR 2022
Year: 2022

eBPF is a new technology which allows dynamically loading pieces of code into the Linux kernel. It can greatly speed up networking since it enables the kernel to process certain packets without the involvement of a userspace program. So far eBPF has been used for simple packet filtering applications such as firewalls or Denial of Service protection. We show that it is possible to develop a flow based network intrusion detection system based on machine learning entirely in eBPF. Our solution uses a decision tree and decides for each packet whether it is malicious or not, considering the entire previous context of the network flow. We achieve a performance increase of over 20% compared to the same solution implemented as a userspace program.
On Calibration and Out-of-domain Generalization
Venue: arXiv cs.LG 2022
Year: 2022

Out-of-domain (OOD) generalization is a significant challenge for machine learning models. Many techniques have been proposed to overcome this challenge, often focused on learning models with certain invariance properties. In this work, we draw a link between OOD performance and model calibration, arguing that calibration across multiple domains can be viewed as a special case of an invariant representation leading to better OOD generalization. Specifically, we show that under certain conditions, models which achieve \emph{multi-domain calibration} are provably free of spurious correlations. This leads us to propose multi-domain calibration as a measurable and trainable surrogate for the OOD performance of a classifier. We therefore introduce methods that are easy to apply and allow practitioners to improve multi-domain calibration by training or modifying an existing model, leading to better performance on unseen domains. Using four datasets from the recently proposed WILDS OOD benchmark, as well as the Colored MNIST dataset, we demonstrate that training or tuning models so they are calibrated across multiple domains leads to significantly improved performance on unseen test domains. We believe this intriguing connection between calibration and OOD generalization is promising from both a practical and theoretical point of view.
GIST: Distributed Training for Large-Scale Graph Convolutional Networks
Venue: arXiv cs.LG 2022
Year: 2022

The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale both in terms of graph size and the number of model parameters. Although some work has explored training on large-scale graphs (e.g., GraphSAGE, ClusterGCN, etc.), we pioneer efficient training of large-scale GCN models (i.e., ultra-wide, overparameterized models) with the proposal of a novel, distributed training framework. Our proposed training methodology, called GIST, disjointly partitions the parameters of a GCN model into several, smaller sub-GCNs that are trained independently and in parallel. In addition to being compatible with all GCN architectures and existing sampling techniques for efficient GCN training, GIST i) improves model performance, ii) scales to training on arbitrarily large graphs, iii) decreases wall-clock training time, and iv) enables the training of markedly overparameterized GCN models. Remarkably, with GIST, we train an astonishgly-wide 32,768-dimensional GraphSAGE model, which exceeds the capacity of a single GPU by a factor of 8x, to SOTA performance on the Amazon2M dataset.
Improving Action Quality Assessment using Weighted Aggregation
Venue: arXiv cs.CV 2022
Year: 2022

Action quality assessment (AQA) aims at automatically judging human action based on a video of the said action and assigning a performance score to it. The majority of works in the existing literature on AQA divide RGB videos into short clips, transform these clips to higher-level representations using Convolutional 3D (C3D) networks, and aggregate them through averaging. These higher-level representations are used to perform AQA. We find that the current clip level feature aggregation technique of averaging is insufficient to capture the relative importance of clip level features. In this work, we propose a learning-based weighted-averaging technique. Using this technique, better performance can be obtained without sacrificing too much computational resources. We call this technique Weight-Decider(WD). We also experiment with ResNets for learning better representations for action quality assessment. We assess the effects of the depth and input clip size of the convolutional neural network on the quality of action score predictions. We achieve a new state-of-the-art Spearman's rank correlation of 0.9315 (an increase of 0.45%) on the MTL-AQA dataset using a 34 layer (2+1)D ResNet with the capability of processing 32 frame clips, with WD aggregation.
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition
Venue: arXiv cs.LG 2022
Year: 2022

Non-stationarity is one thorny issue in cooperative multi-agent reinforcement learning (MARL). One of the reasons is the policy changes of agents during the learning process. Some existing works have discussed various consequences caused by non-stationarity with several kinds of measurement indicators. This makes the objectives or goals of existing algorithms are inevitably inconsistent and disparate. In this paper, we introduce a novel notion, the $δ$-measurement, to explicitly measure the non-stationarity of a policy sequence, which can be further proved to be bounded by the KL-divergence of consecutive joint policies. A straightforward but highly non-trivial way is to control the joint policies' divergence, which is difficult to estimate accurately by imposing the trust-region constraint on the joint policy. Although it has lower computational complexity to decompose the joint policy and impose trust-region constraints on the factorized policies, simple policy factorization like mean-field approximation will lead to more considerable policy divergence, which can be considered as the trust-region decomposition dilemma. We model the joint policy as a pairwise Markov random field and propose a trust-region decomposition network (TRD-Net) based on message passing to estimate the joint policy divergence more accurately. The Multi-Agent Mirror descent policy algorithm with Trust region decomposition, called MAMT, is established by adjusting the trust-region of the local policies adaptively in an end-to-end manner. MAMT can approximately constrain the consecutive joint policies' divergence to satisfy $δ$-stationarity and alleviate the non-stationarity problem. Our method can bring noticeable and stable performance improvement compared with baselines in cooperative tasks of different complexity.