OpenRaiser/Intern-Atlas huggingface dataset:
A Novel Fully Annotated Thermal Infrared Face Dataset: Recorded in Various Environment Conditions and Distances From The Camera
Venue: arXiv cs.CV 2022
Year: 2022

Facial thermography is one of the most popular research areas in infrared thermal imaging, with diverse applications in medical, surveillance, and environmental monitoring. However, in contrast to facial imagery in the visual spectrum, the lack of public datasets on facial thermal images is an obstacle to research improvement in this area. Thermal face imagery is still a relatively new research area to be evaluated and studied in different domains.The current thermal face datasets are limited in regards to the subjects' distance from the camera, the ambient temperature variation, and facial landmarks' localization. We address these gaps by presenting a new facial thermography dataset. This article makes two main contributions to the body of knowledge. First, it presents a comprehensive review and comparison of current public datasets in facial thermography. Second, it introduces and studies a novel public dataset on facial thermography, which we call it Charlotte-ThermalFace. Charlotte-ThermalFace contains more than10000 infrared thermal images in varying thermal conditions, several distances from the camera, and different head positions. The data is fully annotated with the facial landmarks, ambient temperature, relative humidity, the air speed of the room, distance to the camera, and subject thermal sensation at the time of capturing each image. Our dataset is the first publicly available thermal dataset annotated with the thermal sensation of each subject in different thermal conditions and one of the few datasets in raw 16-bit format. Finally, we present a preliminary analysis of the dataset to show the applicability and importance of the thermal conditions in facial thermography. The full dataset, including annotations, are freely available for research purpose at https://github.com/TeCSAR-UNCC/UNCC-ThermalFace
MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we introduce Masked Anomaly Detection (MAD), a general self-supervised learning task for multivariate time series anomaly detection. With the increasing availability of sensor data from industrial systems, being able to detecting anomalies from streams of multivariate time series data is of significant importance. Given the scarcity of anomalies in real-world applications, the majority of literature has been focusing on modeling normality. The learned normal representations can empower anomaly detection as the model has learned to capture certain key underlying data regularities. A typical formulation is to learn a predictive model, i.e., use a window of time series data to predict future data values. In this paper, we propose an alternative self-supervised learning task. By randomly masking a portion of the inputs and training a model to estimate them using the remaining ones, MAD is an improvement over the traditional left-to-right next step prediction (NSP) task. Our experimental results demonstrate that MAD can achieve better anomaly detection rates over traditional NSP approaches when using exactly the same neural network (NN) base models, and can be modified to run as fast as NSP models during test time on the same hardware, thus making it an ideal upgrade for many existing NSP-based NN anomaly detection models.
Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar
Venue: arXiv cs.CV 2022
Year: 2022

This paper presents novel hybrid architectures that combine grid- and point-based processing to improve the detection performance and orientation estimation of radar-based object detection networks. Purely grid-based detection models operate on a bird's-eye-view (BEV) projection of the input point cloud. These approaches suffer from a loss of detailed information through the discrete grid resolution. This applies in particular to radar object detection, where relatively coarse grid resolutions are commonly used to account for the sparsity of radar point clouds. In contrast, point-based models are not affected by this problem as they process point clouds without discretization. However, they generally exhibit worse detection performances than grid-based methods. We show that a point-based model can extract neighborhood features, leveraging the exact relative positions of points, before grid rendering. This has significant benefits for a subsequent grid-based convolutional detection backbone. In experiments on the public nuScenes dataset our hybrid architecture achieves improvements in terms of detection performance (19.7% higher mAP for car class than next-best radar-only submission) and orientation estimates (11.5% relative orientation improvement) over networks from previous literature.
ATDD: Fine-Grained Assured Time-Sensitive Data Deletion Scheme in Cloud Storage
Venue: arXiv cs.CR 2022
Year: 2022

With the rapid development of general cloud services, more and more individuals or collectives use cloud platforms to store data. Assured data deletion deserves investigation in cloud storage. In time-sensitive data storage scenarios, it is necessary for cloud platforms to automatically destroy data after the data owner-specified expiration time. Therefore, assured timesensitive data deletion should be sought. In this paper, a finegrained assured time-sensitive data deletion (ATDD) scheme in cloud storage is proposed by embedding the time trapdoor in Ciphertext-Policy Attribute-Based Encryption (CP-ABE). Timesensitive data is self-destructed after the data owner-specified expiration time so that the authorized users cannot get access to the related data. In addition, a credential is returned to the data owner for data deletion verification. This proposed scheme provides solutions for fine-grained access control and verifiable data self-destruction. Detailed security and performance analysis demonstrate the security and the practicability of the proposed scheme.
Is the Algorithmic Kadison-Singer Problem Hard?
Venue: arXiv cs.CC 2022
Year: 2022

We study the following $\mathsf{KS}_2(c)$ problem: let $c \in\mathbb{R}^+$ be some constant, and $v_1,\ldots, v_m\in\mathbb{R}^d$ be vectors such that $\|v_i\|^2\leq α$ for any $i\in[m]$ and $\sum_{i=1}^m \langle v_i, x\rangle^2 =1$ for any $x\in\mathbb{R}^d$ with $\|x\|=1$. The $\mathsf{KS}_2(c)$ problem asks to find some $S\subset [m]$, such that it holds for all $x \in \mathbb{R}^d$ with $\|x\| = 1$ that \[ \left|\sum_{i \in S} \langle v_i, x\rangle^2 - \frac{1}{2}\right| \leq c\cdot\sqrtα,\] or report no if such $S$ doesn't exist. Based on the work of Marcus et al. and Weaver, the $\mathsf{KS}_2(c)$ problem can be seen as the algorithmic Kadison-Singer problem with parameter $c\in\mathbb{R}^+$. Our first result is a randomised algorithm with one-sided error for the $\mathsf{KS}_2(c)$ problem such that (1) our algorithm finds a valid set $S \subset [m]$ with probability at least $1-2/d$, if such $S$ exists, or (2) reports no with probability $1$, if no valid sets exist. The algorithm has running time \[ O\left(\binom{m}{n}\cdot \mathrm{poly}(m, d)\right)~\mbox{ for }~n = O\left(\frac{d}{ε^2} \log(d) \log\left(\frac{1}{c\sqrtα}\right)\right), \] where $ε$ is a parameter which controls the error of the algorithm. This presents the first algorithm for the Kadison-Singer problem whose running time is quasi-polynomial in $m$, although having exponential dependency on $d$. Moreover, it shows that the algorithmic Kadison-Singer problem is easier to solve in low dimensions. Our second result is on the computational complexity of the $\mathsf{KS}_2(c)$ problem. We show that the $\mathsf{KS}_2(1/(4\sqrt{2}))$ problem is $\mathsf{FNP}$-hard for general values of $d$, and solving the $\mathsf{KS}_2(1/(4\sqrt{2}))$ problem is as hard as solving the $\mathsf{NAE\mbox{-}3SAT}$ problem.
VSCNN: Convolution Neural Network Accelerator With Vector Sparsity
Venue: arXiv cs.AR 2022
Year: 2022

Hardware accelerator for convolution neural network (CNNs) enables real time applications of artificial intelligence technology. However, most of the accelerators only support dense CNN computations or suffers complex control to support fine grained sparse networks. To solve above problem, this paper presents an efficient CNN accelerator with 1-D vector broadcasted input to support both dense network as well as vector sparse network with the same hardware and low overhead. The presented design achieves 1.93X speedup over the dense CNN computations.
Equity and Fairness of Bayesian Knowledge Tracing
Venue: arXiv cs.LG 2022
Year: 2022

We consider the equity and fairness of curricula derived from Knowledge Tracing models. We begin by defining a unifying notion of an equitable tutoring system as a system that achieves maximum possible knowledge in minimal time for each student interacting with it. Realizing perfect equity requires tutoring systems that can provide individualized curricula per student. In particular, we investigate the design of equitable tutoring systems that derive their curricula from Knowledge Tracing models. We first show that many existing models, including classical Bayesian Knowledge Tracing (BKT) and Deep Knowledge Tracing (DKT), and their derived curricula can fall short of achieving equitable tutoring. To overcome this issue, we then propose a novel model, Bayesian-Bayesian Knowledge Tracing (BBKT), that naturally enables online individualization and, thereby, more equitable tutoring. We demonstrate that curricula derived from our model are more effective and equitable than those derived from classical BKT models. Furthermore, we highlight that improving models with a focus on the fairness of next-step predictions might be insufficient to develop equitable tutoring systems.
On Preemptive Scheduling of Unrelated Machines Using Linear Programming
Venue: arXiv cs.DS 2022
Year: 2022

We consider a basic problem of preemptive scheduling of $n$ non-simultaneously released jobs on a group of $m$ unrelated parallel machines so as to minimize maximum job completion time, the makespan. In the scheduling literature, the problem is commonly considered to be solvable in polynomial time by linear programming (LP) techniques proposed in Lawler and Labetoulle \cite{ll78}. The authors in \cite{ll78} give a LP formulation of the version with simultaneously released jobs and show how an optimal solution to this LP can be used to construct an optimal schedule to the latter problem. As the current study shows, for non-simultaneously released jobs, unlikely, there exist a linear program such that a schedule with the minimum makespan can be constructed based on an optimal LP solution. We also prove that, in case no splitting of the same job on a machine is allowed (i.e., job part assigned to a machine is to be processed without an interruption on that machine), the problem is NP-hard. As a side result, we obtain that, whenever job splitting is not allowed, given an optimal LP solution, it is NP-hard to find an optimal schedule with the minimum makespan that agrees with that LP solution. We also extend the schedule construction procedure based on an optimal LP solution from Lawler and Labetoulle \cite{ll78} for non-simultaneously released jobs. The extended procedure, among all feasible schedules that agree with any feasible (not necessarily optimal) LP solution, generates one with the minimum makespan. Such procedure is helpful, in particular, because, as we show, there may exist no optimal schedule that agrees with an optimal LP solution if jobs are non-simultaneously released.
The composition complexity of majority
Venue: arXiv cs.CC 2022
Year: 2022

We study the complexity of computing majority as a composition of local functions: \[ \text{Maj}_n = h(g_1,\ldots,g_m), \] where each $g_j :\{0,1\}^{n} \to \{0,1\}$ is an arbitrary function that queries only $k \ll n$ variables and $h : \{0,1\}^m \to \{0,1\}$ is an arbitrary combining function. We prove an optimal lower bound of \[ m \ge Ω\left( \frac{n}{k} \log k \right) \] on the number of functions needed, which is a factor $Ω(\log k)$ larger than the ideal $m = n/k$. We call this factor the composition overhead; previously, no superconstant lower bounds on it were known for majority. Our lower bound recovers, as a corollary and via an entirely different proof, the best known lower bound for bounded-width branching programs for majority (Alon and Maass '86, Babai et al. '90). It is also the first step in a plan that we propose for breaking a longstanding barrier in lower bounds for small-depth boolean circuits. Novel aspects of our proof include sharp bounds on the information lost as computation flows through the inner functions $g_j$, and the bootstrapping of lower bounds for a multi-output function (Hamming weight) into lower bounds for a single-output one (majority).
Artificial Intelligence and Structural Injustice: Foundations for Equity, Values, and Responsibility
Venue: arXiv cs.CY 2022
Year: 2022

This chapter argues for a structural injustice approach to the governance of AI. Structural injustice has an analytical and an evaluative component. The analytical component consists of structural explanations that are well-known in the social sciences. The evaluative component is a theory of justice. Structural injustice is a powerful conceptual tool that allows researchers and practitioners to identify, articulate, and perhaps even anticipate, AI biases. The chapter begins with an example of racial bias in AI that arises from structural injustice. The chapter then presents the concept of structural injustice as introduced by the philosopher Iris Marion Young. The chapter moreover argues that structural injustice is well suited as an approach to the governance of AI and compares this approach to alternative approaches that start from analyses of harms and benefits or from value statements. The chapter suggests that structural injustice provides methodological and normative foundations for the values and concerns of Diversity, Equity, and Inclusion. The chapter closes with an outlook onto the idea of structure and on responsibility. The idea of a structure is central to justice. An open theoretical research question is to what extent AI is itself part of the structure of society. Finally, the practice of responsibility is central to structural injustice. Even if they cannot be held responsible for the existence of structural injustice, every individual and every organization has some responsibility to address structural injustice going forward.
Dangling-Aware Entity Alignment with Mixed High-Order Proximities
Venue: arXiv cs.CL 2022
Year: 2022

We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of entities, a KG commonly contains some dangling entities that cannot find counterparts in other KGs. Therefore, dangling-aware entity alignment is more realistic than the conventional entity alignment where prior studies simply ignore dangling entities. We propose a framework using mixed high-order proximities on dangling-aware entity alignment. Our framework utilizes both the local high-order proximity in a nearest neighbor subgraph and the global high-order proximity in an embedding space for both dangling detection and entity alignment. Extensive experiments with two evaluation settings shows that our framework more precisely detects dangling entities, and better aligns matchable entities. Further investigations demonstrate that our framework can mitigate the hubness problem on dangling-aware entity alignment.
Surface Reconstruction from Point Clouds: A Survey and a Benchmark
Venue: arXiv cs.CV 2022
Year: 2022

Reconstruction of a continuous surface of two-dimensional manifold from its raw, discrete point cloud observation is a long-standing problem. The problem is technically ill-posed, and becomes more difficult considering that various sensing imperfections would appear in the point clouds obtained by practical depth scanning. In literature, a rich set of methods has been proposed, and reviews of existing methods are also provided. However, existing reviews are short of thorough investigations on a common benchmark. The present paper aims to review and benchmark existing methods in the new era of deep learning surface reconstruction. To this end, we contribute a large-scale benchmarking dataset consisting of both synthetic and real-scanned data; the benchmark includes object- and scene-level surfaces and takes into account various sensing imperfections that are commonly encountered in practical depth scanning. We conduct thorough empirical studies by comparing existing methods on the constructed benchmark, and pay special attention on robustness of existing methods against various scanning imperfections; we also study how different methods generalize in terms of reconstructing complex surface shapes. Our studies help identify the best conditions under which different methods work, and suggest some empirical findings. For example, while deep learning methods are increasingly popular, our systematic studies suggest that, surprisingly, a few classical methods perform even better in terms of both robustness and generalization; our studies also suggest that the practical challenges of misalignment of point sets from multi-view scanning, missing of surface points, and point outliers remain unsolved by all the existing surface reconstruction methods. We expect that the benchmark and our studies would be valuable both for practitioners and as a guidance for new innovations in future research.
Text to artistic image generation
Venue: arXiv cs.CV 2022
Year: 2022

Painting is one of the ways for people to express their ideas, but what if people with disabilities in hands want to paint? To tackle this challenge, we create an end-to-end solution that can generate artistic images from text descriptions. However, due to the lack of datasets with paired text description and artistic images, it is hard to directly train an algorithm which can create art based on text input. To address this issue, we split our task into three steps: (1) Generate a realistic image from a text description by using Dynamic Memory Generative Adversarial Network (arXiv:1904.01310), (2) Classify the image as a genre that exists in the WikiArt dataset using Resnet (arXiv: 1512.03385), (3) Select a style that is compatible with the genre and transfer it to the generated image by using neural artistic stylization network (arXiv:1705.06830).
Optimal Algorithms for Mean Estimation under Local Differential Privacy
Venue: arXiv cs.LG 2022
Year: 2022

We study the problem of mean estimation of $\ell_2$-bounded vectors under the constraint of local differential privacy. While the literature has a variety of algorithms that achieve the asymptotically optimal rates for this problem, the performance of these algorithms in practice can vary significantly due to varying (and often large) hidden constants. In this work, we investigate the question of designing the protocol with the smallest variance. We show that PrivUnit (Bhowmick et al. 2018) with optimized parameters achieves the optimal variance among a large family of locally private randomizers. To prove this result, we establish some properties of local randomizers, and use symmetrization arguments that allow us to write the optimal randomizer as the optimizer of a certain linear program. These structural results, which should extend to other problems, then allow us to show that the optimal randomizer belongs to the PrivUnit family. We also develop a new variant of PrivUnit based on the Gaussian distribution which is more amenable to mathematical analysis and enjoys the same optimality guarantees. This allows us to establish several useful properties on the exact constants of the optimal error as well as to numerically estimate these constants.
Alignahead: Online Cross-Layer Knowledge Extraction on Graph Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

Existing knowledge distillation methods on graph neural networks (GNNs) are almost offline, where the student model extracts knowledge from a powerful teacher model to improve its performance. However, a pre-trained teacher model is not always accessible due to training cost, privacy, etc. In this paper, we propose a novel online knowledge distillation framework to resolve this problem. Specifically, each student GNN model learns the extracted local structure from another simultaneously trained counterpart in an alternating training procedure. We further develop a cross-layer distillation strategy by aligning ahead one student layer with the layer in different depth of another student model, which theoretically makes the structure information spread over all layers. Experimental results on five datasets including PPI, Coauthor-CS/Physics and Amazon-Computer/Photo demonstrate that the student performance is consistently boosted in our collaborative training framework without the supervision of a pre-trained teacher model. In addition, we also find that our alignahead technique can accelerate the model convergence speed and its effectiveness can be generally improved by increasing the student numbers in training. Code is available: https://github.com/GuoJY-eatsTG/Alignahead
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog
Venue: arXiv cs.CL 2022
Year: 2022

A typical end-to-end task-oriented dialog system transfers context into dialog state, and upon which generates a response, which usually faces the problem of error propagation from both previously generated inaccurate dialog states and responses, especially in low-resource scenarios. To alleviate these issues, we propose BORT, a back and denoising reconstruction approach for end-to-end task-oriented dialog system. Squarely, to improve the accuracy of dialog states, back reconstruction is used to reconstruct the original input context from the generated dialog states since inaccurate dialog states cannot recover the corresponding input context. To enhance the denoising capability of the model to reduce the impact of error propagation, denoising reconstruction is used to reconstruct the corrupted dialog state and response. Extensive experiments conducted on MultiWOZ 2.0 and CamRest676 show the effectiveness of BORT. Furthermore, BORT demonstrates its advanced capabilities in the zero-shot domain and low-resource scenarios.
Unmanned Aerial Vehicles Meet Reflective Intelligent Surfaces to Improve Coverage and Secrecy
Venue: arXiv cs.CR 2022
Year: 2022

The high configurability and low cost of Reflective Intelligent Surfaces (RISs) made them a promising solution for enhancing the capabilities of Beyond Fifth-Generation (B5G) networks. Recent works proposed to mount RISs on Unmanned Aerial Vehicles (UAVs), combining the high network configurability provided by RIS with the mobility brought by UAVs. However, the RIS represents an additional weight that impacts the battery lifetime of the UAV. Furthermore, the practicality of the resulting link in terms of communication channel quality and security have not been assessed in detail. In this paper, we highlight all the essential features that need to be considered for the practical deployment of RIS-enabled UAVs. We are the first to show how the RIS size and its power consumption impact the UAV flight time. We then assess how the RIS size, carrier frequency, and UAV flying altitude affects the path loss. Lastly, we propose a novel particle swarm-based approach to maximize coverage and improve the confidentiality of transmissions in a cellular scenario with the support of RISs carried by UAVs.