OpenRaiser/Intern-Atlas huggingface dataset:
Backdoor Attacks on Vision Transformers
Venue: arXiv cs.CV 2022
Year: 2022

Vision Transformers (ViT) have recently demonstrated exemplary performance on a variety of vision tasks and are being used as an alternative to CNNs. Their design is based on a self-attention mechanism that processes images as a sequence of patches, which is quite different compared to CNNs. Hence it is interesting to study if ViTs are vulnerable to backdoor attacks. Backdoor attacks happen when an attacker poisons a small part of the training data for malicious purposes. The model performance is good on clean test images, but the attacker can manipulate the decision of the model by showing the trigger at test time. To the best of our knowledge, we are the first to show that ViTs are vulnerable to backdoor attacks. We also find an intriguing difference between ViTs and CNNs - interpretation algorithms effectively highlight the trigger on test images for ViTs but not for CNNs. Based on this observation, we propose a test-time image blocking defense for ViTs which reduces the attack success rate by a large margin. Code is available here: https://github.com/UCDvision/backdoor_transformer.git
Modeling Gender Differences in Membership Change in Open Source Software Projects
Venue: arXiv cs.SE 2022
Year: 2022

Gender diversity in open source software development continues to be a topic of growing interest among researchers, practitioners, and organizations. To date, research has revealed disparities in participation between developers on the basis of gender, with women being significantly underrepresented in open source development. Using a large data set curated for studies of diversity in open source projects, we contribute to this body of work by characterizing the relationship between gender-based participation differences and group composition in GitHub. We found that contributors identified as women and contributors of unknown gender have a shorter tenure in open source projects compared to those identified as men. Additionally, at the team level, we found that project teams with mixed-gender composition were associated with lower turnover and teams with greater disparity in the distribution of platform tenure were associated with higher turnover. Finally, our case study reveals that when looking at the entire team, GitHub users were more likely to remain in a project rather than leave after contributing but when looking at women specifically, users were more likely to leave rather than remain after contributing to a project.
Neural Architecture Adaptation for Object Detection by Searching Channel Dimensions and Mapping Pre-trained Parameters
Venue: arXiv cs.CV 2022
Year: 2022

Most object detection frameworks use backbone architectures originally designed for image classification, conventionally with pre-trained parameters on ImageNet. However, image classification and object detection are essentially different tasks and there is no guarantee that the optimal backbone for classification is also optimal for object detection. Recent neural architecture search (NAS) research has demonstrated that automatically designing a backbone specifically for object detection helps improve the overall accuracy. In this paper, we introduce a neural architecture adaptation method that can optimize the given backbone for detection purposes, while still allowing the use of pre-trained parameters. We propose to adapt both the micro- and macro-architecture by searching for specific operations and the number of layers, in addition to the output channel dimensions of each block. It is important to find the optimal channel depth, as it greatly affects the feature representation capability and computation cost. We conduct experiments with our searched backbone for object detection and demonstrate that our backbone outperforms both manually designed and searched state-of-the-art backbones on the COCO dataset.
Thompson Sampling Achieves $\tilde O(\sqrt{T})$ Regret in Linear Quadratic Control
Venue: arXiv cs.LG 2022
Year: 2022

Thompson Sampling (TS) is an efficient method for decision-making under uncertainty, where an action is sampled from a carefully prescribed distribution which is updated based on the observed data. In this work, we study the problem of adaptive control of stabilizable linear-quadratic regulators (LQRs) using TS, where the system dynamics are unknown. Previous works have established that $\tilde O(\sqrt{T})$ frequentist regret is optimal for the adaptive control of LQRs. However, the existing methods either work only in restrictive settings, require a priori known stabilizing controllers, or utilize computationally intractable approaches. We propose an efficient TS algorithm for the adaptive control of LQRs, TS-based Adaptive Control, TSAC, that attains $\tilde O(\sqrt{T})$ regret, even for multidimensional systems, thereby solving the open problem posed in Abeille and Lazaric (2018). TSAC does not require a priori known stabilizing controller and achieves fast stabilization of the underlying system by effectively exploring the environment in the early stages. Our result hinges on developing a novel lower bound on the probability that the TS provides an optimistic sample. By carefully prescribing an early exploration strategy and a policy update rule, we show that TS achieves order-optimal regret in adaptive control of multidimensional stabilizable LQRs. We empirically demonstrate the performance and the efficiency of TSAC in several adaptive control tasks.
Low-latency Mini-batch GNN Inference on CPU-FPGA Heterogeneous Platform
Venue: arXiv cs.DC 2022
Year: 2022

Mini-batch inference of Graph Neural Networks (GNNs) is a key problem in many real-world applications. Recently, a GNN design principle of model depth-receptive field decoupling has been proposed to address the well-known issue of neighborhood explosion. Decoupled GNN models achieve higher accuracy than original models and demonstrate excellent scalability for mini-batch inference. We map Decoupled GNNs onto CPU-FPGA heterogeneous platforms to achieve low-latency mini-batch inference. On the FPGA platform, we design a novel GNN hardware accelerator with an adaptive datapath denoted Adaptive Computation Kernel (ACK) that can execute various computation kernels of GNNs with low-latency: (1) for dense computation kernels expressed as matrix multiplication, ACK works as a systolic array with fully localized connections, (2) for sparse computation kernels, ACK follows the scatter-gather paradigm and works as multiple parallel pipelines to support the irregular connectivity of graphs. The proposed task scheduling hides the CPU-FPGA data communication overhead to reduce the inference latency. We develop a fast design space exploration algorithm to generate a single accelerator for multiple target GNN models. We implement our accelerator on a state-of-the-art CPU-FPGA platform and evaluate the performance using three representative models (GCN, GraphSAGE, and GAT). Results show that our CPU-FPGA implementation achieves $21.4-50.8\times$, $2.9-21.6\times$, $4.7\times$ latency reduction compared with state-of-the-art implementations on CPU-only, CPU-GPU and CPU-FPGA platforms.
Active Data Discovery: Mining Unknown Data using Submodular Information Measures
Venue: arXiv cs.CV 2022
Year: 2022

Active Learning is a very common yet powerful framework for iteratively and adaptively sampling subsets of the unlabeled sets with a human in the loop with the goal of achieving labeling efficiency. Most real world datasets have imbalance either in classes and slices, and correspondingly, parts of the dataset are rare. As a result, there has been a lot of work in designing active learning approaches for mining these rare data instances. Most approaches assume access to a seed set of instances which contain these rare data instances. However, in the event of more extreme rareness, it is reasonable to assume that these rare data instances (either classes or slices) may not even be present in the seed labeled set, and a critical need for the active learning paradigm is to efficiently discover these rare data instances. In this work, we provide an active data discovery framework which can mine unknown data slices and classes efficiently using the submodular conditional gain and submodular conditional mutual information functions. We provide a general algorithmic framework which works in a number of scenarios including image classification and object detection and works with both rare classes and rare slices present in the unlabeled set. We show significant accuracy and labeling efficiency gains with our approach compared to existing state-of-the-art active learning approaches for actively discovering these rare classes and slices.
Rectify ViT Shortcut Learning by Visual Saliency
Venue: arXiv cs.CV 2022
Year: 2022

Shortcut learning is common but harmful to deep learning models, leading to degenerated feature representations and consequently jeopardizing the model's generalizability and interpretability. However, shortcut learning in the widely used Vision Transformer framework is largely unknown. Meanwhile, introducing domain-specific knowledge is a major approach to rectifying the shortcuts, which are predominated by background related factors. For example, in the medical imaging field, eye-gaze data from radiologists is an effective human visual prior knowledge that has the great potential to guide the deep learning models to focus on meaningful foreground regions of interest. However, obtaining eye-gaze data is time-consuming, labor-intensive and sometimes even not practical. In this work, we propose a novel and effective saliency-guided vision transformer (SGT) model to rectify shortcut learning in ViT with the absence of eye-gaze data. Specifically, a computational visual saliency model is adopted to predict saliency maps for input image samples. Then, the saliency maps are used to distil the most informative image patches. In the proposed SGT, the self-attention among image patches focus only on the distilled informative ones. Considering this distill operation may lead to global information lost, we further introduce, in the last encoder layer, a residual connection that captures the self-attention across all the image patches. The experiment results on four independent public datasets show that our SGT framework can effectively learn and leverage human prior knowledge without eye gaze data and achieves much better performance than baselines. Meanwhile, it successfully rectifies the harmful shortcut learning and significantly improves the interpretability of the ViT model, demonstrating the promise of transferring human prior knowledge derived visual saliency in rectifying shortcut learning
DFG-NAS: Deep and Flexible Graph Neural Architecture Search
Venue: arXiv cs.LG 2022
Year: 2022

Graph neural networks (GNNs) have been intensively applied to various graph-based applications. Despite their success, manually designing the well-behaved GNNs requires immense human expertise. And thus it is inefficient to discover the potentially optimal data-specific GNN architecture. This paper proposes DFG-NAS, a new neural architecture search (NAS) method that enables the automatic search of very deep and flexible GNN architectures. Unlike most existing methods that focus on micro-architectures, DFG-NAS highlights another level of design: the search for macro-architectures on how atomic propagation (\textbf{\texttt{P}}) and transformation (\textbf{\texttt{T}}) operations are integrated and organized into a GNN. To this end, DFG-NAS proposes a novel search space for \textbf{\texttt{P-T}} permutations and combinations based on message-passing dis-aggregation, defines four custom-designed macro-architecture mutations, and employs the evolutionary algorithm to conduct an efficient and effective search. Empirical studies on four node classification tasks demonstrate that DFG-NAS outperforms state-of-the-art manual designs and NAS methods of GNNs.
Business Process Model for Interoperability Improvement in the Agricultural Domain Using Digital Twins
Venue: arXiv cs.SE 2022
Year: 2022

A farm generates a lot of data from various systems, which is then stored in a distributed manner, usually in non-standardized formats, which bears the risk of data inconsistencies. This work addresses this issue by using business process management (BPM) to demonstrate that the use of digital twins (DTs) can improve interoperability between services in the agriculture domain. Steps from the BPM lifecycle were applied to a farming use case in Germany. First, the as-is business process model was discovered and modeled without DTs, analyzed and then redesigned into the to-be model according to the DT integration. The to-be model showed a reduction in the number of tasks needed to be performed by the farmer as well as an improvement of process data quality, interoperability, and efficiency. Finally, a comparison of the' average processing times of both models with the help of process simulation revealed improvements in the to-be process.
Minimum Noticeable Difference based Adversarial Privacy Preserving Image Generation
Venue: arXiv cs.CV 2022
Year: 2022

Deep learning models are found to be vulnerable to adversarial examples, as wrong predictions can be caused by small perturbation in input for deep learning models. Most of the existing works of adversarial image generation try to achieve attacks for most models, while few of them make efforts on guaranteeing the perceptual quality of the adversarial examples. High quality adversarial examples matter for many applications, especially for the privacy preserving. In this work, we develop a framework based on the Minimum Noticeable Difference (MND) concept to generate adversarial privacy preserving images that have minimum perceptual difference from the clean ones but are able to attack deep learning models. To achieve this, an adversarial loss is firstly proposed to make the deep learning models attacked by the adversarial images successfully. Then, a perceptual quality-preserving loss is developed by taking the magnitude of perturbation and perturbation-caused structural and gradient changes into account, which aims to preserve high perceptual quality for adversarial image generation. To the best of our knowledge, this is the first work on exploring quality-preserving adversarial image generation based on the MND concept for privacy preserving. To evaluate its performance in terms of perceptual quality, the deep models on image classification and face recognition are tested with the proposed method and several anchor methods in this work. Extensive experimental results demonstrate that the proposed MND framework is capable of generating adversarial images with remarkably improved performance metrics (e.g., PSNR, SSIM, and MOS) than that generated with the anchor methods.
Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift
Venue: arXiv cs.CV 2022
Year: 2022

For many applications, analyzing the uncertainty of a machine learning model is indispensable. While research of uncertainty quantification (UQ) techniques is very advanced for computer vision applications, UQ methods for spatio-temporal data are less studied. In this paper, we focus on models for online handwriting recognition, one particular type of spatio-temporal data. The data is observed from a sensor-enhanced pen with the goal to classify written characters. We conduct a broad evaluation of aleatoric (data) and epistemic (model) UQ based on two prominent techniques for Bayesian inference, Stochastic Weight Averaging-Gaussian (SWAG) and Deep Ensembles. Next to a better understanding of the model, UQ techniques can detect out-of-distribution data and domain shifts when combining right-handed and left-handed writers (an underrepresented group).
AggNet: Learning to Aggregate Faces for Group Membership Verification
Venue: arXiv cs.CV 2022
Year: 2022

In some face recognition applications, we are interested to verify whether an individual is a member of a group, without revealing their identity. Some existing methods, propose a mechanism for quantizing precomputed face descriptors into discrete embeddings and aggregating them into one group representation. However, this mechanism is only optimized for a given closed set of individuals and needs to learn the group representations from scratch every time the groups are changed. In this paper, we propose a deep architecture that jointly learns face descriptors and the aggregation mechanism for better end-to-end performances. The system can be applied to new groups with individuals never seen before and the scheme easily manages new memberships or membership endings. We show through experiments on multiple large-scale wild-face datasets, that the proposed method leads to higher verification performance compared to other baselines.
Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection
Venue: arXiv cs.SE 2022
Year: 2022

Code clones are pairs of code snippets that implement similar functionality. Clone detection is a fundamental branch of automatic source code comprehension, having many applications in refactoring recommendation, plagiarism detection, and code summarization. A particularly interesting case of clone detection is the detection of semantic clones, i.e., code snippets that have the same functionality but significantly differ in implementation. A promising approach to detecting semantic clones is contrastive learning (CL), a machine learning paradigm popular in computer vision but not yet commonly adopted for code processing. Our work aims to evaluate the most popular CL algorithms combined with three source code representations on two tasks. The first task is code clone detection, which we evaluate on the POJ-104 dataset containing implementations of 104 algorithms. The second task is plagiarism detection. To evaluate the models on this task, we introduce CodeTransformator, a tool for transforming source code. We use it to create a dataset that mimics plagiarised code based on competitive programming solutions. We trained nine models for both tasks and compared them with six existing approaches, including traditional tools and modern pre-trained neural models. The results of our evaluation show that proposed models perform diversely in each task, however the performance of the graph-based models is generally above the others. Among CL algorithms, SimCLR and SwAV lead to better results, while Moco is the most robust approach. Our code and trained models are available at https://doi.org/10.5281/zenodo.6360627, https://doi.org/10.5281/zenodo.5596345.
Efficient WiFi LiDAR SLAM for Autonomous Robots in Large Environments
Venue: arXiv cs.RO 2022
Year: 2022

Autonomous robots operating in indoor and GPS denied environments can use LiDAR for SLAM instead. However, LiDARs do not perform well in geometrically-degraded environments, due to the challenge of loop closure detection and computational load to perform scan matching. Existing WiFi infrastructure can be exploited for localization and mapping with low hardware and computational cost. Yet, accurate pose estimation using WiFi is challenging as different signal values can be measured at the same location due to the unpredictability of signal propagation. Therefore, we introduce the use of WiFi fingerprint sequence for pose estimation (i.e. loop closure) in SLAM. This approach exploits the spatial coherence of location fingerprints obtained while a mobile robot is moving. This has better capability of correcting odometry drift. The method also incorporates LiDAR scans and thus, improving computational efficiency for large and geometrically-degraded environments while maintaining the accuracy of LiDAR SLAM. We conducted experiments in an indoor environment to illustrate the effectiveness of the method. The results are evaluated based on Root Mean Square Error (RMSE) and it has achieved an accuracy of 0.88m for the test environment.
A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networks
Venue: arXiv cs.ET 2022
Year: 2022

Deep Learning neural networks are pervasive, but traditional computer architectures are reaching the limits of being able to efficiently execute them for the large workloads of today. They are limited by the von Neumann bottleneck: the high cost in energy and latency incurred in moving data between memory and the compute engine. Today, special CMOS designs address this bottleneck. The next generation of computing hardware will need to eliminate or dramatically mitigate this bottleneck. We discuss how compute-in-memory can play an important part in this development. Here, a non-volatile memory based cross-bar architecture forms the heart of an engine that uses an analog process to parallelize the matrix vector multiplication operation, repeatedly used in all neural network workloads. The cross-bar architecture, at times referred to as a neuromorphic approach, can be a key hardware element in future computing machines. In the first part of this review we take a co-design view of the design constraints and the demands it places on the new materials and memory devices that anchor the cross-bar architecture. In the second part, we review what is knows about the different new non-volatile memory materials and devices suited for compute in-memory, and discuss the outlook and challenges.
Machine Learning-Driven Process of Alumina Ceramics Laser Machining
Venue: arXiv cs.CE 2022
Year: 2022

Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they help enhance the machining quality by offering comprehension of the inter-relationships between the laser processing parameters. On the other hand, experimental processing parameter optimization recommends a systematic, and consequently time-consuming, investigation over the available processing parameter space. An intelligent strategy is to employ machine learning (ML) techniques to capture the relationship between picosecond laser machining parameters for finding proper parameter combinations to create the desired cuts on industrial-grade alumina ceramic with deep, smooth and defect-free patterns. Laser parameters such as beam amplitude and frequency, scanner passing speed and the number of passes over the surface, as well as the vertical distance of the scanner from the sample surface, are used for predicting the depth, top width, and bottom width of the engraved channels using ML models. Owing to the complex correlation between laser parameters, it is shown that Neural Networks (NN) are the most efficient in predicting the outputs. Equipped with an ML model that captures the interconnection between laser parameters and the engraved channel dimensions, one can predict the required input parameters to achieve a target channel geometry. This strategy significantly reduces the cost and effort of experimental laser machining during the development phase, without compromising accuracy or performance. The developed techniques can be applied to a wide range of ceramic laser machining processes.