OpenRaiser/Intern-Atlas huggingface dataset:
Single-Server Private Information Retrieval with Side Information Under Arbitrary Popularity Profiles
Venue: arXiv cs.IT 2022
Year: 2022

This paper introduces a generalization of the Private Information Retrieval with Side Information (PIR-SI) problem called Popularity-Aware PIR-SI (PA-PIR-SI). The PA-PIR-SI problem includes one or more remote servers storing copies of a dataset of $K$ messages, and a user who knows $M$ out of $K$ messages -- the identities of which are unknown to the server -- as a prior side information, and wishes to retrieve one of the remaining $K-M$ messages. The goal of the user is to minimize the amount of information they must download from the server while revealing no information about the identity of the desired message. In contrast to PIR-SI, in PA-PIR-SI, the dataset messages are not assumed to be equally popular. That is, given the $M$ side information messages, each of the remaining $K-M$ messages is not necessarily equally likely to be the message desired by the user. In this work, we focus on the single-server setting of PA-PIR-SI, and establish lower and upper bounds on the capacity of this setting -- defined as the maximum possible achievable download rate. Our upper bound holds for any message popularity profile, and is the same as the capacity of single-server PIR-SI. We prove the lower bound by presenting a PA-PIR-SI scheme which takes a novel probabilistic approach -- carefully designed based on the popularity profile -- to integrate two existing PIR-SI schemes. The rate of our scheme is strictly higher than that of the only existing PIR-SI scheme applicable to the PA-PIR-SI setting.
FACTOID: A New Dataset for Identifying Misinformation Spreaders and Political Bias
Venue: arXiv cs.SI 2022
Year: 2022

Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. In this paper, we introduce a new contemporary Reddit dataset for fake news spreader analysis, called FACTOID, monitoring political discussions on Reddit since the beginning of 2020. The dataset contains over 4K users with 3.4M Reddit posts, and includes, beyond the users' binary labels, also their fine-grained credibility level (very low to very high) and their political bias strength (extreme right to extreme left). As far as we are aware, this is the first fake news spreader dataset that simultaneously captures both the long-term context of users' historical posts and the interactions between them. To create the first benchmark on our data, we provide methods for identifying misinformation spreaders by utilizing the social connections between the users along with their psycho-linguistic features. We show that the users' social interactions can, on their own, indicate misinformation spreading, while the psycho-linguistic features are mostly informative in non-neural classification settings. In a qualitative analysis, we observe that detecting affective mental processes correlates negatively with right-biased users, and that the openness to experience factor is lower for those who spread fake news.
Conversational DevBots for Secure Programming: An Empirical Study on SKF Chatbot
Venue: arXiv cs.HC 2022
Year: 2022

Conversational agents or chatbots are widely investigated and used across different fields including healthcare, education, and marketing. Still, the development of chatbots for assisting secure coding practices is in its infancy. In this paper, we present the results of an empirical study on SKF chatbot, a software-development bot (DevBot) designed to answer queries about software security. To the best of our knowledge, SKF chatbot is one of the very few of its kind, thus a representative instance of conversational DevBots aiding secure software development. In this study, we collect and analyse empirical evidence on the effectiveness of SKF chatbot, while assessing the needs and expectations of its users (i.e., software developers). Furthermore, we explore the factors that may hinder the elaboration of more sophisticated conversational security DevBots and identify features for improving the efficiency of state-of-the-art solutions. All in all, our findings provide valuable insights pointing towards the design of more context-aware and personalized conversational DevBots for security engineering.
F3A-GAN: Facial Flow for Face Animation with Generative Adversarial Networks
Venue: arXiv cs.CV 2022
Year: 2022

Formulated as a conditional generation problem, face animation aims at synthesizing continuous face images from a single source image driven by a set of conditional face motion. Previous works mainly model the face motion as conditions with 1D or 2D representation (e.g., action units, emotion codes, landmark), which often leads to low-quality results in some complicated scenarios such as continuous generation and largepose transformation. To tackle this problem, the conditions are supposed to meet two requirements, i.e., motion information preserving and geometric continuity. To this end, we propose a novel representation based on a 3D geometric flow, termed facial flow, to represent the natural motion of the human face at any pose. Compared with other previous conditions, the proposed facial flow well controls the continuous changes to the face. After that, in order to utilize the facial flow for face editing, we build a synthesis framework generating continuous images with conditional facial flows. To fully take advantage of the motion information of facial flows, a hierarchical conditional framework is designed to combine the extracted multi-scale appearance features from images and motion features from flows in a hierarchical manner. The framework then decodes multiple fused features back to images progressively. Experimental results demonstrate the effectiveness of our method compared to other state-of-the-art methods.
Knowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-Identification
Venue: arXiv cs.CV 2022
Year: 2022

Despite the recent success of deep learning architectures, person re-identification (ReID) remains a challenging problem in real-word applications. Several unsupervised single-target domain adaptation (STDA) methods have recently been proposed to limit the decline in ReID accuracy caused by the domain shift that typically occurs between source and target video data. Given the multimodal nature of person ReID data (due to variations across camera viewpoints and capture conditions), training a common CNN backbone to address domain shifts across multiple target domains, can provide an efficient solution for real-time ReID applications. Although multi-target domain adaptation (MTDA) has not been widely addressed in the ReID literature, a straightforward approach consists in blending different target datasets, and performing STDA on the mixture to train a common CNN. However, this approach may lead to poor generalization, especially when blending a growing number of distinct target domains to train a smaller CNN. To alleviate this problem, we introduce a new MTDA method based on knowledge distillation (KD-ReID) that is suitable for real-time person ReID applications. Our method adapts a common lightweight student backbone CNN over the target domains by alternatively distilling from multiple specialized teacher CNNs, each one adapted on data from a specific target domain. Extensive experiments conducted on several challenging person ReID datasets indicate that our approach outperforms state-of-art methods for MTDA, including blending methods, particularly when training a compact CNN backbone like OSNet. Results suggest that our flexible MTDA approach can be employed to design cost-effective ReID systems for real-time video surveillance applications.
Optimal-Degree Polynomial Approximations for Exponentials and Gaussian Kernel Density Estimation
Venue: arXiv cs.CC 2022
Year: 2022

For any real numbers $B \ge 1$ and $δ\in (0, 1)$ and function $f: [0, B] \rightarrow \mathbb{R}$, let $d_{B; δ} (f) \in \mathbb{Z}_{> 0}$ denote the minimum degree of a polynomial $p(x)$ satisfying $\sup_{x \in [0, B]} \big| p(x) - f(x) \big| < δ$. In this paper, we provide precise asymptotics for $d_{B; δ} (e^{-x})$ and $d_{B; δ} (e^{x})$ in terms of both $B$ and $δ$, improving both the previously known upper bounds and lower bounds. In particular, we show $$d_{B; δ} (e^{-x}) = Θ\left( \max \left\{ \sqrt{B \log(δ^{-1})}, \frac{\log(δ^{-1}) }{ \log(B^{-1} \log(δ^{-1}))} \right\}\right), \text{ and}$$ $$d_{B; δ} (e^{x}) = Θ\left( \max \left\{ B, \frac{\log(δ^{-1}) }{ \log(B^{-1} \log(δ^{-1}))} \right\}\right).$$ Polynomial approximations for $e^{-x}$ and $e^x$ have applications to the design of algorithms for many problems, and our degree bounds show both the power and limitations of these algorithms. We focus in particular on the Batch Gaussian Kernel Density Estimation problem for $n$ sample points in $Θ(\log n)$ dimensions with error $δ= n^{-Θ(1)}$. We show that the running time one can achieve depends on the square of the diameter of the point set, $B$, with a transition at $B = Θ(\log n)$ mirroring the corresponding transition in $d_{B; δ} (e^{-x})$: - When $B=o(\log n)$, we give the first algorithm running in time $n^{1 + o(1)}$. - When $B = κ\log n$ for a small constant $κ>0$, we give an algorithm running in time $n^{1 + O(\log \log κ^{-1} /\log κ^{-1})}$. The $\log \log κ^{-1} /\log κ^{-1}$ term in the exponent comes from analyzing the behavior of the leading constant in our computation of $d_{B; δ} (e^{-x})$. - When $B = ω(\log n)$, we show that time $n^{2 - o(1)}$ is necessary assuming SETH.
Computing Programs for Generalized Planning as Heuristic Search
Venue: arXiv cs.AI 2022
Year: 2022

Although heuristic search is one of the most successful approaches to classical planning, this planning paradigm does not apply straightforwardly to Generalized Planning (GP). This paper adapts the planning as heuristic search paradigm to the particularities of GP, and presents the first native heuristic search approach to GP. First, the paper defines a program-based solution space for GP that is independent of the number of planning instances in a GP problem, and the size of these instances. Second, the paper defines the BFGP algorithm for GP, that implements a best-first search in our program-based solution space, and that is guided by different evaluation and heuristic functions.
Adaptive Block Floating-Point for Analog Deep Learning Hardware
Venue: arXiv cs.LG 2022
Year: 2022

Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep neural network (DNN) inference than their digital counterparts. However, recent studies show that DNNs on AMS devices with fixed-point numbers can incur an accuracy penalty because of precision loss. To mitigate this penalty, we present a novel AMS-compatible adaptive block floating-point (ABFP) number representation. We also introduce amplification (or gain) as a method for increasing the accuracy of the number representation without increasing the bit precision of the output. We evaluate the effectiveness of ABFP on the DNNs in the MLPerf datacenter inference benchmark -- realizing less than $1\%$ loss in accuracy compared to FLOAT32. We also propose a novel method of finetuning for AMS devices, Differential Noise Finetuning (DNF), which samples device noise to speed up finetuning compared to conventional Quantization-Aware Training.
Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers
Venue: arXiv cs.CV 2022
Year: 2022

Augmented reality applications have rapidly spread across online platforms, allowing consumers to virtually try-on a variety of products, such as makeup, hair dying, or shoes. However, parametrizing a renderer to synthesize realistic images of a given product remains a challenging task that requires expert knowledge. While recent work has introduced neural rendering methods for virtual try-on from example images, current approaches are based on large generative models that cannot be used in real-time on mobile devices. This calls for a hybrid method that combines the advantages of computer graphics and neural rendering approaches. In this paper we propose a novel framework based on deep learning to build a real-time inverse graphics encoder that learns to map a single example image into the parameter space of a given augmented reality rendering engine. Our method leverages self-supervised learning and does not require labeled training data which makes it extendable to many virtual try-on applications. Furthermore, most augmented reality renderers are not differentiable in practice due to algorithmic choices or implementation constraints to reach real-time on portable devices. To relax the need for a graphics-based differentiable renderer in inverse graphics problems, we introduce a trainable imitator module. Our imitator is a generative network that learns to accurately reproduce the behavior of a given non-differentiable renderer. We propose a novel rendering sensitivity loss to train the imitator, which ensures that the network learns an accurate and continuous representation for each rendering parameter. Our framework enables novel applications where consumers can virtually try-on a novel unknown product from an inspirational reference image on social media. It can also be used by graphics artists to automatically create realistic rendering from a reference product image.
Modular Baskets Queue
Venue: arXiv cs.DC 2022
Year: 2022

A modular version of the baskets queue of Hoffman, Shalev and Shavit is presented. It manipulates the head and tail using a novel object called load-link/increment-conditional, which can be implemented using only READ/WRITE instructions, and admits implementations that spread contention. This suggests that there might be an alternative to the seemingly inherent bottleneck in previous queue implementations that manipulate the head and the tail using read-modify-write instructions over a single shared register.
Multi-Environment Meta-Learning in Stochastic Linear Bandits
Venue: arXiv cs.LG 2022
Year: 2022

In this work we investigate meta-learning (or learning-to-learn) approaches in multi-task linear stochastic bandit problems that can originate from multiple environments. Inspired by the work of [1] on meta-learning in a sequence of linear bandit problems whose parameters are sampled from a single distribution (i.e., a single environment), here we consider the feasibility of meta-learning when task parameters are drawn from a mixture distribution instead. For this problem, we propose a regularized version of the OFUL algorithm that, when trained on tasks with labeled environments, achieves low regret on a new task without requiring knowledge of the environment from which the new task originates. Specifically, our regret bound for the new algorithm captures the effect of environment misclassification and highlights the benefits over learning each task separately or meta-learning without recognition of the distinct mixture components.
Interpretable Climate Change Modeling With Progressive Cascade Networks
Venue: arXiv cs.LG 2022
Year: 2022

Typical deep learning approaches to modeling high-dimensional data often result in complex models that do not easily reveal a new understanding of the data. Research in the deep learning field is very actively pursuing new methods to interpret deep neural networks and to reduce their complexity. An approach is described here that starts with linear models and incrementally adds complexity only as supported by the data. An application is shown in which models that map global temperature and precipitation to years are trained to investigate patterns associated with changes in climate.
An Actionable Framework for Understanding and Improving Developer Experience
Venue: arXiv cs.SE 2022
Year: 2022

Developer experience is an important concern for software organizations as enhancing developer experience improves productivity, satisfaction, engagement and retention. We set out to understand what affects developer experience through semi-structured interviews with 21 developers from industry, which we transcribed and iteratively coded. Our findings elucidate factors that affect developer experience and characteristics that influence their respective importance to individual developers. We also identify strategies employed by individuals and teams to improve developer experience and the barriers that stand in their way. Lastly, we describe the coping mechanisms of developers when developer experience cannot be sufficiently improved. Our findings result in the DX Framework, an actionable conceptual framework for understanding and improving developer experience. The DX Framework provides a go-to reference for organizations that want to enable more productive and effective work environments for their developers.
Deep Learning for Prawn Farming: Forecasting and Anomaly Detection
Venue: arXiv cs.LG 2022
Year: 2022

We present a decision support system for managing water quality in prawn ponds. The system uses various sources of data and deep learning models in a novel way to provide 24-hour forecasting and anomaly detection of water quality parameters. It provides prawn farmers with tools to proactively avoid a poor growing environment, thereby optimising growth and reducing the risk of losing stock. This is a major shift for farmers who are forced to manage ponds by reactively correcting poor water quality conditions. To our knowledge, we are the first to apply Transformer as an anomaly detection model, and the first to apply anomaly detection in general to this aquaculture problem. Our technical contributions include adapting ForecastNet for multivariate data and adapting Transformer and the Attention model to incorporate weather forecast data into their decoders. We attain an average mean absolute percentage error of 12% for dissolved oxygen forecasts and we demonstrate two anomaly detection case studies. The system is successfully running in its second year of deployment on a commercial prawn farm.
Analyzing Impact of Dependency Injection on Software Maintainability
Venue: arXiv cs.SE 2022
Year: 2022

Dependency injection (DI) is generally known to improve maintainability by keeping application classes separate from the library. Particularly within the Java environment, there are many applications using the principles of DI with the aim to improve maintainability. There exists some work that provides an inference on the impact of DI on maintainability, but no conclusive evidence is provided. The fact that there are no publicly available tools for quantifying DI makes such an evidence more difficult to be produced. In this paper, we propose a novel metric, DCBO, to measure the proportion of DI in a project based on weighted couplings. We describe how DCBO can serve as a more meaningful metric in computing maintainability when DI is also considered. The metric is implemented in the CKJM-Analyzer, an extension of the CKJM tool that utilizes static code analysis to detect DI. We discuss the algorithmic approach behind the static analysis and prove the soundness of the tool using a set of open-source Java projects.
$α$-GAN: Convergence and Estimation Guarantees
Venue: arXiv cs.LG 2022
Year: 2022

We prove a two-way correspondence between the min-max optimization of general CPE loss function GANs and the minimization of associated $f$-divergences. We then focus on $α$-GAN, defined via the $α$-loss, which interpolates several GANs (Hellinger, vanilla, Total Variation) and corresponds to the minimization of the Arimoto divergence. We show that the Arimoto divergences induced by $α$-GAN equivalently converge, for all $α\in \mathbb{R}_{>0}\cup\{\infty\}$. However, under restricted learning models and finite samples, we provide estimation bounds which indicate diverse GAN behavior as a function of $α$. Finally, we present empirical results on a toy dataset that highlight the practical utility of tuning the $α$ hyperparameter.
LANTERN-RD: Enabling Deep Learning for Mitigation of the Invasive Spotted Lanternfly
Venue: arXiv cs.CV 2022
Year: 2022

The Spotted Lanternfly (SLF) is an invasive planthopper that threatens the local biodiversity and agricultural economy of regions such as the Northeastern United States and Japan. As researchers scramble to study the insect, there is a great potential for computer vision tasks such as detection, pose estimation, and accurate identification to have important downstream implications in containing the SLF. However, there is currently no publicly available dataset for training such AI models. To enable computer vision applications and motivate advancements to challenge the invasive SLF problem, we propose LANTERN-RD, the first curated image dataset of the spotted lanternfly and its look-alikes, featuring images with varied lighting conditions, diverse backgrounds, and subjects in assorted poses. A VGG16-based baseline CNN validates the potential of this dataset for stimulating fresh computer vision applications to accelerate invasive SLF research. Additionally, we implement the trained model in a simple mobile classification application in order to directly empower responsible public mitigation efforts. The overarching mission of this work is to introduce a novel SLF image dataset and release a classification framework that enables computer vision applications, boosting studies surrounding the invasive SLF and assisting in minimizing its agricultural and economic damage.
Virtually Full-duplex Cell-Free Massive MIMO with Access Point Mode Assignment
Venue: arXiv cs.IT 2022
Year: 2022

We consider a cell-free massive multiple-input multiple-output (MIMO) network utilizing a virtually full-duplex (vFD) mode, where access points (APs) with a downlink (DL) mode and those with an uplink (UL) mode simultaneously serve DL and UL users (UEs). In order to maximize the sum spectral efficiency (SE) of the DL and UL transmissions, we formulate a mixed-integer optimization problem to jointly design the AP mode assignment and power control. This problem is subject to minimum per-UE SE requirements, per-AP power control, and per-UL UE power constraints. By employing the successive convex approximation technique, we propose an algorithm to obtain a stationary solution of the formulated problem. Numerical results show that the proposed vFD approach can provide a sum SE that is $2.5$ and $1.5$ times larger than the traditional half-duplex and heuristic baseline schemes, respectively, in terms of $95\%$-likely sum SE.
Fast Conditional Network Compression Using Bayesian HyperNetworks
Venue: arXiv cs.LG 2022
Year: 2022

We introduce a conditional compression problem and propose a fast framework for tackling it. The problem is how to quickly compress a pretrained large neural network into optimal smaller networks given target contexts, e.g. a context involving only a subset of classes or a context where only limited compute resource is available. To solve this, we propose an efficient Bayesian framework to compress a given large network into much smaller size tailored to meet each contextual requirement. We employ a hypernetwork to parameterize the posterior distribution of weights given conditional inputs and minimize a variational objective of this Bayesian neural network. To further reduce the network sizes, we propose a new input-output group sparsity factorization of weights to encourage more sparseness in the generated weights. Our methods can quickly generate compressed networks with significantly smaller sizes than baseline methods.