OpenRaiser/Intern-Atlas huggingface dataset:
Cutting-Splicing data augmentation: A novel technology for medical image segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Background: Medical images are more difficult to acquire and annotate than natural images, which results in data augmentation technologies often being used in medical image segmentation tasks. Most data augmentation technologies used in medical segmentation were originally developed on natural images and do not take into account the characteristic that the overall layout of medical images is standard and fixed. Methods: Based on the characteristics of medical images, we developed the cutting-splicing data augmentation (CS-DA) method, a novel data augmentation technology for medical image segmentation. CS-DA augments the dataset by splicing different position components cut from different original medical images into a new image. The characteristics of the medical image result in the new image having the same layout as and similar appearance to the original image. Compared with classical data augmentation technologies, CS-DA is simpler and more robust. Moreover, CS-DA does not introduce any noise or fake information into the newly created image. Results: To explore the properties of CS-DA, many experiments are conducted on eight diverse datasets. On the training dataset with the small sample size, CS-DA can effectively increase the performance of the segmentation model. When CS-DA is used together with classical data augmentation technologies, the performance of the segmentation model can be further improved and is much better than that of CS-DA and classical data augmentation separately. We also explored the influence of the number of components, the position of the cutting line, and the splicing method on the CS-DA performance. Conclusions: The excellent performance of CS-DA in the experiment has confirmed the effectiveness of CS-DA, and provides a new data augmentation idea for the small sample segmentation task.
Detect and Classify IoT Camera Traffic
Venue: arXiv cs.CR 2022
Year: 2022

Deployment of IoT cameras in an organization threatens security and privacy policies, and the classification of network traffic without using IP addresses and port numbers has been challenging. In this paper, we have designed, implemented and deployed a system called iCamInspector to classify network traffic arising from IoT camera in a mixed networking environment. We have collected a total of about 36GB of network traffic containing video data from three different types of applications (four online audio/video conferencing applications, two video sharing applications and six IoT camera from different manufacturers) in our IoT laboratory. We show that with the help of a limited number of flow-based features, iCamInspector achieves an average accuracy of more than 98% in a 10-fold cross-validation with a false rate of about 1.5% in testing phase of the system. A real deployment of our system in an unseen environment achieves a commendable performance of detecting IoT camera with an average detection probability higher than 0.9.
SA4U: Practical Static Analysis for Unit Type Error Detection
Venue: arXiv cs.SE 2022
Year: 2022

Unit type errors, where values with physical unit types (e.g., meters, hours) are used incorrectly in a computation, are common in today's unmanned aerial system (UAS) firmware. Recent studies show that unit type errors represent over 10% of bugs in UAS firmware. Moreover, the consequences of unit type errors are severe. Over 30% of unit type errors cause UAS crashes. This paper proposes SA4U: a practical system for detecting unit type errors in real-world UAS firmware. SA4U requires no modifications to firmware or developer annotations. It deduces the unit types of program variables by analyzing simulation traces and protocol definitions. SA4U uses the deduced unit types to identify when unit type errors occur. SA4U is effective: it identified 14 previously undetected bugs in two popular open-source firmware (ArduPilot & PX4.)
Sub-8-bit quantization for on-device speech recognition: a regularization-free approach
Venue: arXiv cs.SD 2022
Year: 2022

For on-device automatic speech recognition (ASR), quantization aware training (QAT) is ubiquitous to achieve the trade-off between model predictive performance and efficiency. Among existing QAT methods, one major drawback is that the quantization centroids have to be predetermined and fixed. To overcome this limitation, we introduce a regularization-free, "soft-to-hard" compression mechanism with self-adjustable centroids in a mu-Law constrained space, resulting in a simpler yet more versatile quantization scheme, called General Quantizer (GQ). We apply GQ to ASR tasks using Recurrent Neural Network Transducer (RNN-T) and Conformer architectures on both LibriSpeech and de-identified far-field datasets. Without accuracy degradation, GQ can compress both RNN-T and Conformer into sub-8-bit, and for some RNN-T layers, to 1-bit for fast and accurate inference. We observe a 30.73% memory footprint saving and 31.75% user-perceived latency reduction compared to 8-bit QAT via physical device benchmarking.
Pixel-Aligned Non-parametric Hand Mesh Reconstruction
Venue: arXiv cs.CV 2022
Year: 2022

Non-parametric mesh reconstruction has recently shown significant progress in 3D hand and body applications. In these methods, mesh vertices and edges are visible to neural networks, enabling the possibility to establish a direct mapping between 2D image pixels and 3D mesh vertices. In this paper, we seek to establish and exploit this mapping with a simple and compact architecture. The network is designed with these considerations: 1) aggregating both local 2D image features from the encoder and 3D geometric features captured in the mesh decoder; 2) decoding coarse-to-fine meshes along the decoding layers to make the best use of the hierarchical multi-scale information. Specifically, we propose an end-to-end pipeline for hand mesh recovery tasks which consists of three phases: a 2D feature extractor constructing multi-scale feature maps, a feature mapping module transforming local 2D image features to 3D vertex features via 3D-to-2D projection, and a mesh decoder combining the graph convolution and self-attention to reconstruct mesh. The decoder aggregate both local image features in pixels and geometric features in vertices. It also regresses the mesh vertices in a coarse-to-fine manner, which can leverage multi-scale information. By exploiting the local connection and designing the mesh decoder, Our approach achieves state-of-the-art for hand mesh reconstruction on the public FreiHAND dataset.
ArtFacePoints: High-resolution Facial Landmark Detection in Paintings and Prints
Venue: arXiv cs.CV 2022
Year: 2022

Facial landmark detection plays an important role for the similarity analysis in artworks to compare portraits of the same or similar artists. With facial landmarks, portraits of different genres, such as paintings and prints, can be automatically aligned using control-point-based image registration. We propose a deep-learning-based method for facial landmark detection in high-resolution images of paintings and prints. It divides the task into a global network for coarse landmark prediction and multiple region networks for precise landmark refinement in regions of the eyes, nose, and mouth that are automatically determined based on the predicted global landmark coordinates. We created a synthetically augmented facial landmark art dataset including artistic style transfer and geometric landmark shifts. Our method demonstrates an accurate detection of the inner facial landmarks for our high-resolution dataset of artworks while being comparable for a public low-resolution artwork dataset in comparison to competing methods.
Improving Contrastive Learning on Visually Homogeneous Mars Rover Images
Venue: arXiv cs.CV 2022
Year: 2022

Contrastive learning has recently demonstrated superior performance to supervised learning, despite requiring no training labels. We explore how contrastive learning can be applied to hundreds of thousands of unlabeled Mars terrain images, collected from the Mars rovers Curiosity and Perseverance, and from the Mars Reconnaissance Orbiter. Such methods are appealing since the vast majority of Mars images are unlabeled as manual annotation is labor intensive and requires extensive domain knowledge. Contrastive learning, however, assumes that any given pair of distinct images contain distinct semantic content. This is an issue for Mars image datasets, as any two pairs of Mars images are far more likely to be semantically similar due to the lack of visual diversity on the planet's surface. Making the assumption that pairs of images will be in visual contrast - when they are in fact not - results in pairs that are falsely considered as negatives, impacting training performance. In this study, we propose two approaches to resolve this: 1) an unsupervised deep clustering step on the Mars datasets, which identifies clusters of images containing similar semantic content and corrects false negative errors during training, and 2) a simple approach which mixes data from different domains to increase visual diversity of the total training dataset. Both cases reduce the rate of false negative pairs, thus minimizing the rate in which the model is incorrectly penalized during contrastive training. These modified approaches remain fully unsupervised end-to-end. To evaluate their performance, we add a single linear layer trained to generate class predictions based on these contrastively-learned features and demonstrate increased performance compared to supervised models; observing an improvement in classification accuracy of 3.06% using only 10% of the labeled data.
A Unified Algorithm for Stochastic Path Problems
Venue: arXiv cs.LG 2022
Year: 2022

We study reinforcement learning in stochastic path (SP) problems. The goal in these problems is to maximize the expected sum of rewards until the agent reaches a terminal state. We provide the first regret guarantees in this general problem by analyzing a simple optimistic algorithm. Our regret bound matches the best known results for the well-studied special case of stochastic shortest path (SSP) with all non-positive rewards. For SSP, we present an adaptation procedure for the case when the scale of rewards $B_\star$ is unknown. We show that there is no price for adaptation, and our regret bound matches that with a known $B_\star$. We also provide a scale adaptation procedure for the special case of stochastic longest paths (SLP) where all rewards are non-negative. However, unlike in SSP, we show through a lower bound that there is an unavoidable price for adaptation.
Vision-Language Pre-training: Basics, Recent Advances, and Future Trends
Venue: arXiv cs.CV 2022
Year: 2022

This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. We group these approaches into three categories: ($i$) VLP for image-text tasks, such as image captioning, image-text retrieval, visual question answering, and visual grounding; ($ii$) VLP for core computer vision tasks, such as (open-set) image classification, object detection, and segmentation; and ($iii$) VLP for video-text tasks, such as video captioning, video-text retrieval, and video question answering. For each category, we present a comprehensive review of state-of-the-art methods, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies. In addition, for each category, we discuss advanced topics being actively explored in the research community, such as big foundation models, unified modeling, in-context few-shot learning, knowledge, robustness, and computer vision in the wild, to name a few.
Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks
Venue: arXiv cs.CV 2022
Year: 2022

Identification of tree species plays a key role in forestry related tasks like forest conservation, disease diagnosis and plant production. There had been a debate regarding the part of the tree to be used for differentiation, whether it should be leaves, fruits, flowers or bark. Studies have proven that bark is of utmost importance as it will be present despite seasonal variations and provides a characteristic identity to a tree by variations in the structure. In this paper, a deep learning based approach is presented by leveraging the method of computer vision to classify 50 tree species, on the basis of bark texture using the BarkVN-50 dataset. This is the maximum number of trees being considered for bark classification till now. A convolutional neural network(CNN), ResNet101 has been implemented using transfer-learning based technique of fine tuning to maximise the model performance. The model produced an overall accuracy of >94% during the evaluation. The performance validation has been done using K-Fold Cross Validation and by testing on unseen data collected from the Internet, this proved the model's generalization capability for real-world uses.
Learning Texture Transformer Network for Light Field Super-Resolution
Venue: arXiv cs.CV 2022
Year: 2022

Hand-held light field cameras suffer from low spatial resolution due to the inherent spatio-angular tradeoff. In this paper, we propose a method to improve the spatial resolution of light field images with the aid of the Texture Transformer Network (TTSR). The proposed method consists of three modules: the first module produces an all-in focus high-resolution perspective image which serves as a reference image for the second module, i.e. TTSR, which in turn produces a high-resolution light field. The last module refines the spatial resolution by imposing a light field prior. The results demonstrate around 4 dB to 6 dB PSNR gain over a bicubically resized light field image
UMLsec4Edge: Extending UMLsec to model data-protection-compliant edge computing systems
Venue: arXiv cs.SE 2022
Year: 2022

Edge computing enables the processing of data - frequently personal data - at the edge of the network. For personal data, legislation such as the European General Data Protection Regulation requires data protection by design. Hence, data protection has to be accounted for in the design of edge computing systems whenever personal data is involved. This leads to specific requirements for modeling the architecture of edge computing systems, e.g., representation of data and network properties. To the best of our knowledge, no existing modeling language fulfils all these requirements. In our previous work we showed that the commonly used UML profile UMLsec fulfils some of these requirements, and can thus serve as a starting point. The aim of this paper is to create a modeling language which meets all requirements concerning the design of the architecture of edge computing systems accounting for data protection. Thus, we extend UMLsec to satisfy all requirements. We call the resulting UML profile UMLsec4Edge. We follow a systematic approach to develop UMLsec4Edge. We apply UMLsec4Edge to real-world use cases from different domains, and create appropriate deployment diagrams and class diagrams. These diagrams show UMLsec4Edge is capable of meeting the requirements.
A Systematic Study of the Consistency of Two-Factor Authentication User Journeys on Top-Ranked Websites (Extended Version)
Venue: arXiv cs.CR 2022
Year: 2022

Heuristics for user experience state that users will transfer their expectations from one product to another. A lack of consistency between products can increase users' cognitive friction, leading to frustration and rejection. This paper presents the first systematic study of the external, functional consistency of two-factor authentication user journeys on top-ranked websites. We find that these websites implement only a minimal number of design aspects consistently (e.g., naming and location of settings) but exhibit mixed design patterns for setup and usage of a second factor. Moreover, we find that some of the more consistently realized aspects, such as descriptions of two-factor authentication, have been described in the literature as problematic and adverse to user experience. Our results advocate for more general UX guidelines for 2FA implementers and raise new research questions about the 2FA user journeys.
Reconstruction Attack on Differential Private Trajectory Protection Mechanisms
Venue: arXiv cs.CR 2022
Year: 2022

Location trajectories collected by smartphones and other devices represent a valuable data source for applications such as location-based services. Likewise, trajectories have the potential to reveal sensitive information about individuals, e.g., religious beliefs or sexual orientations. Accordingly, trajectory datasets require appropriate sanitization. Due to their strong theoretical privacy guarantees, differential private publication mechanisms receive much attention. However, the large amount of noise required to achieve differential privacy yields structural differences, e.g., ship trajectories passing over land. We propose a deep learning-based Reconstruction Attack on Protected Trajectories (RAoPT), that leverages the mentioned differences to partly reconstruct the original trajectory from a differential private release. The evaluation shows that our RAoPT model can reduce the Euclidean and Hausdorff distances between the released and original trajectories by over 68% on two real-world datasets under protection with $\varepsilon \leq 1$. In this setting, the attack increases the average Jaccard index of the trajectories' convex hulls, representing a user's activity space, by over 180%. Trained on the GeoLife dataset, the model still reduces the Euclidean and Hausdorff distances by over 60% for T-Drive trajectories protected with a state-of-the-art mechanism ($\varepsilon = 0.1$). This work highlights shortcomings of current trajectory publication mechanisms, and thus motivates further research on privacy-preserving publication schemes.
Link and Network-wide Study of Incoherent GN/EGN Models
Venue: arXiv cs.NI 2022
Year: 2022

An unprecedented comparison of closed-form incoherent GN (InGN) models is presented with heterogeneous spans and partially loaded links in elastic optical networks. Results reveal that with accumulated dispersion correction and modulation format terms, the InGN shows higher accuracy.
Towards Generating Adversarial Examples on Mixed-type Data
Venue: arXiv cs.LG 2022
Year: 2022

The existence of adversarial attacks (or adversarial examples) brings huge concern about the machine learning (ML) model's safety issues. For many safety-critical ML tasks, such as financial forecasting, fraudulent detection, and anomaly detection, the data samples are usually mixed-type, which contain plenty of numerical and categorical features at the same time. However, how to generate adversarial examples with mixed-type data is still seldom studied. In this paper, we propose a novel attack algorithm M-Attack, which can effectively generate adversarial examples in mixed-type data. Based on M-Attack, attackers can attempt to mislead the targeted classification model's prediction, by only slightly perturbing both the numerical and categorical features in the given data samples. More importantly, by adding designed regularizations, our generated adversarial examples can evade potential detection models, which makes the attack indeed insidious. Through extensive empirical studies, we validate the effectiveness and efficiency of our attack method and evaluate the robustness of existing classification models against our proposed attack. The experimental results highlight the feasibility of generating adversarial examples toward machine learning models in real-world applications.
Improving Low-Resource Cross-lingual Parsing with Expected Statistic Regularization
Venue: arXiv cs.CL 2022
Year: 2022

We present Expected Statistic Regularization (ESR), a novel regularization technique that utilizes low-order multi-task structural statistics to shape model distributions for semi-supervised learning on low-resource datasets. We study ESR in the context of cross-lingual transfer for syntactic analysis (POS tagging and labeled dependency parsing) and present several classes of low-order statistic functions that bear on model behavior. Experimentally, we evaluate the proposed statistics with ESR for unsupervised transfer on 5 diverse target languages and show that all statistics, when estimated accurately, yield improvements to both POS and LAS, with the best statistic improving POS by +7.0 and LAS by +8.5 on average. We also present semi-supervised transfer and learning curve experiments that show ESR provides significant gains over strong cross-lingual-transfer-plus-fine-tuning baselines for modest amounts of label data. These results indicate that ESR is a promising and complementary approach to model-transfer approaches for cross-lingual parsing.
Jo Wilder and the Capitol Case: A taxonomy of uses for a historical inquiry game in 4th grade Classrooms in Wisconsin
Venue: arXiv cs.HC 2022
Year: 2022

In this paper, we study the various ways 3rd-5th grade educators in Wisconsin utilized Jo Wilder and the Capitol Case, a historical inquiry game, as part of their classroom instruction. The 15 educators involved in the study were all grade school teachers in Wisconsin who took part in the "Doing History Fellowship" program, a professional development opportunity offered by the authors, designed to increase their understanding of historical inquiry instruction and game-based learning. As part of the program, the educators planned and implemented the game within their own classroom context and reported their results back to the authors and other educators. Through their reports, surveys and semi-structured interviews we discovered the educators were motivated by five distinct instructional purposes, which influenced how the game was integrated into their curriculum. In this paper, we name and describe these five purposes. We see these findings as useful insights into how educators think about games and how educational video games and corresponding professional development activities may be designed in the future.
Adversarial and Safely Scaled Question Generation
Venue: arXiv cs.IR 2022
Year: 2022

Question generation has recently gained a lot of research interest, especially with the advent of large language models. In and of itself, question generation can be considered 'AI-hard', as there is a lack of unanimously agreed sense of what makes a question 'good' or 'bad'. In this paper, we tackle two fundamental problems in parallel: on one hand, we try to solve the scaling problem, where question-generation and answering applications have to be applied to a massive amount of text without ground truth labeling. The usual approach to solve this problem is to either downsample or summarize. However, there are critical risks of misinformation with these approaches. On the other hand, and related to the misinformation problem, we try to solve the 'safety' problem, as many public institutions rely on a much higher level of accuracy for the content they provide. We introduce an adversarial approach to tackle the question generation safety problem with scale. Specifically, we designed a question-answering system that specifically prunes out unanswerable questions that may be generated, and further increases the quality of the answers that are generated. We build a production-ready, easily-plugged pipeline that can be used on any given body of text, that is scalable and immune from generating any hate speech, profanity, or misinformation. Based on the results, we are able to generate more than six times the number of quality questions generated by the abstractive approach, with a perceived quality being 44% higher, according to a survey of 168 participants.