Reviewerly/exHarmony huggingface dataset:
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum

Graph Contrastive Learning (GCL), learning the node representations by augmenting graphs, has attracted considerable attentions. Despite the proliferation of various graph augmentation strategies, some fundamental questions still remain unclear: what information is essentially encoded into the learned representations by GCL? Are there some general graph augmentation rules behind different augmentations? If so, what are they and what insights can they bring? In this paper, we answer these questions by establishing the connection between GCL and graph spectrum. By an experimental investigation in spectral domain, we firstly find the General grAph augMEntation (GAME) rule for GCL, i.e., the difference of the high-frequency parts between two augmented graphs should be larger than that of low-frequency parts. This rule reveals the fundamental principle to revisit the current graph augmentations and design new effective graph augmentations. Then we theoretically prove that GCL is able to learn the invariance information by contrastive invariance theorem, together with our GAME rule, for the first time, we uncover that the learned representations by GCL essentially encode the low-frequency information, which explains why GCL works. Guided by this rule, we propose a spectral graph contrastive learning module (SpCo), which is a general and GCL-friendly plug-in. We combine it with different existing GCL models, and extensive experiments well demonstrate that it can further improve the performances of a wide variety of different GCL methods.
Schema Encoding for Transferable Dialogue State Tracking

Dialogue state tracking (DST) is an essential sub-task for task-oriented dialogue systems. Recent work has focused on deep neural models for DST. However, the neural models require a large dataset for training. Furthermore, applying them to another domain needs a new dataset because the neural models are generally trained to imitate the given dataset. In this paper, we propose Schema Encoding for Transferable Dialogue State Tracking (SETDST), which is a neural DST method for effective transfer to new domains. Transferable DST could assist developments of dialogue systems even with few dataset on target domains. We use a schema encoder not just to imitate the dataset but to comprehend the schema of the dataset. We aim to transfer the model to new domains by encoding new schemas and using them for DST on multi-domain settings. As a result, SET-DST improved the joint accuracy by 1.46 points on MultiWOZ 2.1.
Axon: A Language for Dynamic Shapes in Deep Learning Graphs

Axon is a language that enables shape and rank inference for tensors in a Deep Learning graphs. It aims to make shapes implicit and inferred, in a similar manner to how types are implicit and inferred in many functional programming languages. Tensor dimensions are represented by expressions consisting of symbolic variables, constants, and arithmetic operators. Tensor shapes can be expressed as either a sequence of these dimension expressions, as a symbolic variable, or as an appending of other shapes. This allows complex constraints on shapes to be expressed. Axon is functional in style, with a type system similar in to Standard ML, extended to include shape information. It provides a suite of built in operators over tensors, including pointwise arithmetic operators, maps, reduction, loops and user defined functions. We describe a shape inference algorithm based on constraint solving which infers information about shapes, from both shape information provided by the programmer and the structure of the program. This allows fully automatic inference of the shapes of tensors for complex Deep Learning graphs. This approach reduces programmer effort when specifying graphs, as tensor shapes are not explicit, allows composition of Deep Learning graphs while maintaining input and output tensor shape compatibility, and aids in automated error detection by identifying shape mismatches at runtime.
Goal Recognition as a Deep Learning Task: the GRNet Approach

In automated planning, recognising the goal of an agent from a trace of observations is an important task with many applications. The state-of-the-art approaches to goal recognition rely on the application of planning techniques, which requires a model of the domain actions and of the initial domain state (written, e.g., in PDDL). We study an alternative approach where goal recognition is formulated as a classification task addressed by machine learning. Our approach, called GRNet, is primarily aimed at making goal recognition more accurate as well as faster by learning how to solve it in a given domain. Given a planning domain specified by a set of propositions and a set of action names, the goal classification instances in the domain are solved by a Recurrent Neural Network (RNN). A run of the RNN processes a trace of observed actions to compute how likely it is that each domain proposition is part of the agent's goal, for the problem instance under considerations. These predictions are then aggregated to choose one of the candidate goals. The only information required as input of the trained RNN is a trace of action labels, each one indicating just the name of an observed action. An experimental analysis confirms that \our achieves good performance in terms of both goal classification accuracy and runtime, obtaining better performance w.r.t. a state-of-the-art goal recognition system over the considered benchmarks.
IRJIT: A simple, online, information retrieval approach for just-in-time software defect prediction

Just-in-Time software defect prediction (JIT-SDP) prevents the introduction of defects into the software by identifying them at commit check-in time. Current defect prediction approaches rely on manually crafted features such as change metrics and involve expensive to train machine learning or deep learning models. These models typically involve extensive training processes that may require significant computational resources and time. These characteristics can pose challenges when attempting to update the models in real-time as new examples become available, potentially impacting their suitability for fast online defect prediction. Furthermore, the reliance on a complex underlying model makes these approaches often less explainable, which means the developers cannot understand the reasons behind models' predictions. An approach that is not explainable might not be adopted in real-life development environments because of developers' lack of trust in its results. To address these limitations, we propose an approach called IRJIT that employs information retrieval on source code and labels new commits as buggy or clean based on their similarity to past buggy or clean commits. IRJIT approach is online and explainable as it can learn from new data without expensive retraining, and developers can see the documents that support a prediction, providing additional context. By evaluating 10 open-source datasets in a within project setting, we show that our approach is up to 23 times faster than the state-of-the-art, offers explainability at the commit and line level, and has comparable performance to the state-of-the-art.
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection

While recent camera-only 3D detection methods leverage multiple timesteps, the limited history they use significantly hampers the extent to which temporal fusion can improve object perception. Observing that existing works' fusion of multi-frame images are instances of temporal stereo matching, we find that performance is hindered by the interplay between 1) the low granularity of matching resolution and 2) the sub-optimal multi-view setup produced by limited history usage. Our theoretical and empirical analysis demonstrates that the optimal temporal difference between views varies significantly for different pixels and depths, making it necessary to fuse many timesteps over long-term history. Building on our investigation, we propose to generate a cost volume from a long history of image observations, compensating for the coarse but efficient matching resolution with a more optimal multi-view matching setup. Further, we augment the per-frame monocular depth predictions used for long-term, coarse matching with short-term, fine-grained matching and find that long and short term temporal fusion are highly complementary. While maintaining high efficiency, our framework sets new state-of-the-art on nuScenes, achieving first place on the test set and outperforming previous best art by 5.2% mAP and 3.7% NDS on the validation set. Code will be released $\href{https://github.com/Divadi/SOLOFusion}{here.}$
Immersive Learning Research from SVR Publications: A Re-conduction of the Systematic Mapping Study

Immersive Learning (iL) is known as a recent area of research that uses three-dimensional virtual environments and multi-sensory devices, also known as immersive technologies, to support the improvement of learning outcomes. This work aims to obtain evidence of theoretical and technological aspects of iL from the Symposium on Virtual and Augmented Reality (SVR) publications. A Systematic Literature Mapping protocol was developed and executed in order to select the primary studies to perform the analysis and data extraction. 76 primary studies helped to answer the research questions. A large part of the contributions by the SVR community are virtual environments that support education in the health area. In addition, some gaps and research opportunities were identified: virtual environments that serve audiences with special needs; development frameworks that consider pedagogical aspects; the use of biometric measures to support the validation of improved learning outcomes and more flexible tools that support educators in the development of immersive virtual environments that do not require specific knowledge in coding or 3D modeling.
Real-time deblurring network for face AR applications

Deblurring is a problem that has been studied for a long time. Extant works have primarily focused on deblurring real-world images. However, face images are different from real-world images. Because face images have fewer textures and weaker edges than real-world images, the deblurring of real-world images focuses on restoring the overall texture of the image; however, restoring the particular face structure (e.g., eyes, nose, and ears) is essential for face images. Recently, a convolutional neural network(CNN)-based deblurring network has been proposed. There are various types of CNN-based deblurring networks. Recently, multiscale architecture has been widely used; however, these types of networks need large amounts of resources. Further, because of the multitude of parameters, it requires a significant amount of time for inference. In this study, we developed a end-to-end network for face image deblurring, wherein novel CNN-based feature attention (FA) blocks are adopted, and a low inference time is achieved. Moreover, discrete Fourier transform (DFT) is employed for high-quality deblurring. FA blocks combine channel attention layer and pixel attention layer for feature extraction. The spectrum obtained using DFT is used as a loss function by comparing the ground truth image with the deblurring image. Experimental results show that the ours network is comparable to other deblurring networks in terms of performance as indicated by the PSNR, SSIM. Moreover we also demonstrated performance improvement by measuring the mean Intersection over Union (mIoU) of the deblurred image using a face-segmentation network.
The Potential of Webcam Based Real Time Eye-Tracking to Reduce Rendering Cost

Performance optimisation continues to be a relevant topic both in hardware and software development, with video games producing fully rendered images every 16 or 34 ms, depending on the desired framerate. Human observers close their eyes for about 300 ms an average of twelve times per minute, which means many frames will never be observed. This paper aimed to examine if it would be possible to reduce rendering time by detecting and skipping these unobserved frames. Blinks were identified during runtime by detecting the eye aspect ratio of the observer in low-quality web camera footage. A prototype using this method was tested on a small group of subjects to determine if footage watched this way was perceived as distracting or of lesser quality than unaltered images. Results from a questionnaire suggest that the altered footage did not impact the subjects’ opinions, with no participant reporting any visual disturbances. Because this test used video footage, skipping frames was substituted by a lower resolution render. Altered frames were rendered an average of five percent faster than their unaltered counterparts.
ROAD-R: The Autonomous Driving Dataset with Logical Requirements

Neural networks have proven to be very powerful at computer vision tasks. However, they often exhibit unexpected behaviours, violating known requirements expressing background knowledge. This calls for models (i) able to learn from the requirements, and (ii) guaranteed to be compliant with the requirements themselves. Unfortunately, the development of such models is hampered by the lack of datasets equipped with formally specified requirements. In this paper, we introduce the ROad event Awareness Dataset with logical Requirements (ROAD-R), the first publicly available dataset for autonomous driving with requirements expressed as logical constraints. Given ROAD-R, we show that current state-of-the-art models often violate its logical constraints, and that it is possible to exploit them to create models that (i) have a better performance, and (ii) are guaranteed to be compliant with the requirements themselves.
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models

This paper presents MOAT, a family of neural networks that build on top of MObile convolution (i.e., inverted residual blocks) and ATtention. Unlike the current works that stack separate mobile convolution and transformer blocks, we effectively merge them into a MOAT block. Starting with a standard Transformer block, we replace its multi-layer perceptron with a mobile convolution block, and further reorder it before the self-attention operation. The mobile convolution block not only enhances the network representation capacity, but also produces better downsampled features. Our conceptually simple MOAT networks are surprisingly effective, achieving 89.1% / 81.5% top-1 accuracy on ImageNet-1K / ImageNet-1K-V2 with ImageNet22K pretraining. Additionally, MOAT can be seamlessly applied to downstream tasks that require large resolution inputs by simply converting the global attention to window attention. Thanks to the mobile convolution that effectively exchanges local information between pixels (and thus cross-windows), MOAT does not need the extra window-shifting mechanism. As a result, on COCO object detection, MOAT achieves 59.2% box AP with 227M model parameters (single-scale inference, and hard NMS), and on ADE20K semantic segmentation, MOAT attains 57.6% mIoU with 496M model parameters (single-scale inference). Finally, the tiny-MOAT family, obtained by simply reducing the channel sizes, also surprisingly outperforms several mobile-specific transformer-based models on ImageNet. The tiny-MOAT family is also benchmarked on downstream tasks, serving as a baseline for the community. We hope our simple yet effective MOAT will inspire more seamless integration of convolution and self-attention. Code is publicly available.
Federated Graph-based Networks with Shared Embedding

Nowadays, user privacy is becoming an issue that cannot be bypassed for system developers, especially for that of web applications where data can be easily transferred through internet. Thankfully, federated learning proposes an innovative method to train models with distributed devices while data are kept in local storage. However, unlike general neural networks, although graph-based networks have achieved great success in classification tasks and advanced recommendation system, its high performance relies on the rich context provided by a graph structure, which is vulnerable when data attributes are incomplete. Therefore, the latter becomes a realistic problem when implementing federated learning for graph-based networks. Knowing that data embedding is a representation in a different space, we propose our Federated Graph-based Networks with Shared Embedding (Feras), which uses shared embedding data to train the network and avoids the direct sharing of original data. A solid theoretical proof of the convergence of Feras is given in this work. Experiments on different datasets (PPI, Flickr, Reddit) are conducted to show the efficiency of Feras for centralized learning. Finally, Feras enables the training of current graph-based models in the federated learning framework for privacy concern.
Multi-view Human Body Mesh Translator

Existing methods for human mesh recovery mainly focus on single-view frameworks, but they often fail to produce accurate results due to the ill-posed setup. Considering the maturity of the multi-view motion capture system, in this paper, we propose to solve the prior ill-posed problem by leveraging multiple images from different views, thus significantly enhancing the quality of recovered meshes. In particular, we present a novel \textbf{M}ulti-view human body \textbf{M}esh \textbf{T}ranslator (MMT) model for estimating human body mesh with the help of vision transformer. Specifically, MMT takes multi-view images as input and translates them to targeted meshes in a single-forward manner. MMT fuses features of different views in both encoding and decoding phases, leading to representations embedded with global information. Additionally, to ensure the tokens are intensively focused on the human pose and shape, MMT conducts cross-view alignment at the feature level by projecting 3D keypoint positions to each view and enforcing their consistency in geometry constraints. Comprehensive experiments demonstrate that MMT outperforms existing single or multi-view models by a large margin for human mesh recovery task, notably, 28.8\% improvement in MPVE over the current state-of-the-art method on the challenging HUMBI dataset. Qualitative evaluation also verifies the effectiveness of MMT in reconstructing high-quality human mesh. Codes will be made available upon acceptance.
Inharmonious Region Localization with Auxiliary Style Feature

With the prevalence of image editing techniques, users can create fantastic synthetic images, but the image quality may be compromised by the color/illumination discrepancy between the manipulated region and background. Inharmonious region localization aims to localize the inharmonious region in a synthetic image. In this work, we attempt to leverage auxiliary style feature to facilitate this task. Specifically, we propose a novel color mapping module and a style feature loss to extract discriminative style features containing task-relevant color/illumination information. Based on the extracted style features, we also propose a novel style voting module to guide the localization of inharmonious region. Moreover, we introduce semantic information into the style voting module to achieve further improvement. Our method surpasses the existing methods by a large margin on the benchmark dataset.
Learning Video-independent Eye Contact Segmentation from In-the-Wild Videos

Human eye contact is a form of non-verbal communication and can have a great influence on social behavior. Since the location and size of the eye contact targets vary across different videos, learning a generic video-independent eye contact detector is still a challenging task. In this work, we address the task of one-way eye contact detection for videos in the wild. Our goal is to build a unified model that can identify when a person is looking at his gaze targets in an arbitrary input video. Considering that this requires time-series relative eye movement information, we propose to formulate the task as a temporal segmentation. Due to the scarcity of labeled training data, we further propose a gaze target discovery method to generate pseudo-labels for unlabeled videos, which allows us to train a generic eye contact segmentation model in an unsupervised way using in-the-wild videos. To evaluate our proposed approach, we manually annotated a test dataset consisting of 52 videos of human conversations. Experimental results show that our eye contact segmentation model outperforms the previous video-dependent eye contact detector and can achieve 71.88% framewise accuracy on our annotated test set. Our code and evaluation dataset are available at https://github.com/ut-vision/Video-Independent-ECS.
Computing maximal generalized palindromes

Palindromes are popular and important objects in textual data processing, bioinformatics, and combinatorics on words. Let $S = XaY$ be a string, where $X$ and $Y$ are of the same length and $a$ is either a single character or the empty string. Then, there exist two alternative definitions for palindromes: $S$ is said to be a palindrome if: Reversal-based definition: $S$ is equal to its reversal $S^R$; Symmetry-based definition: its left-arm $X$ is equal to the reversal of its right-arm $Y^R$. It is clear that if the "equality" ($\approx$) used in both definitions is exact character matching ($=$), then the two definitions are the same. However, if we apply other string-equality criteria $\approx$, including the complementary model for biological sequences, the parameterized model [Baker, JCSS 1996], the order-preserving model [Kim et al., TCS 2014], the Cartesian-tree model [Park et al., TCS 2020], and the palindromic-structure model [I et al., TCS 2013], then are the reversal-based palindromes and the symmetry-based palindromes the same? To the best of our knowledge, no previous work has considered or answered this natural question. In this paper, we first provide answers to this question, and then present efficient algorithms for computing all maximal generalized palindromes that occur in a given string. After confirming that Gusfield's offline suffix-tree based algorithm for computing maximal symmetry-based palindromes can be readily extended to the aforementioned matching models, we show how to extend Manacher's online algorithm for computing maximal reversal-based palindromes in linear time for all the aforementioned matching models.
Stillman's question for twisted commutative algebras

Let $\mathbf{A}_{n, m}$ be the polynomial ring $\text{Sym}(\mathbf{C}^n \otimes \mathbf{C}^m)$ with the natural action of $\mathbf{GL}_m(\mathbf{C})$. We construct a family of $\mathbf{GL}_m(\mathbf{C})$-stable ideals $J_{n, m}$ in $\mathbf{A}_{n, m}$, each equivariantly generated by one homogeneous polynomial of degree $2$. Using the Ananyan-Hochster principle, we show that the regularity of this family is unbounded. This negatively answers a question raised by Erman-Sam-Snowden on a generalization of Stillman's conjecture.