OpenRaiser/Intern-Atlas huggingface dataset:
Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability
Venue: arXiv cs.LG 2022
Year: 2022

Although many methods have been proposed to enhance the transferability of adversarial perturbations, these methods are designed in a heuristic manner, and the essential mechanism for improving adversarial transferability is still unclear. This paper summarizes the common mechanism shared by twelve previous transferability-boosting methods in a unified view, i.e., these methods all reduce game-theoretic interactions between regional adversarial perturbations. To this end, we focus on the attacking utility of all interactions between regional adversarial perturbations, and we first discover and prove the negative correlation between the adversarial transferability and the attacking utility of interactions. Based on this discovery, we theoretically prove and empirically verify that twelve previous transferability-boosting methods all reduce interactions between regional adversarial perturbations. More crucially, we consider the reduction of interactions as the essential reason for the enhancement of adversarial transferability. Furthermore, we design the interaction loss to directly penalize interactions between regional adversarial perturbations during attacking. Experimental results show that the interaction loss significantly improves the transferability of adversarial perturbations.
Towards Using Fully Observable Policies for POMDPs
Venue: arXiv cs.LG 2022
Year: 2022

Partially Observable Markov Decision Process (POMDP) is a framework applicable to many real world problems. In this work, we propose an approach to solve POMDPs with multimodal belief by relying on a policy that solves the fully observable version. By defininig a new, mixture value function based on the value function from the fully observable variant, we can use the corresponding greedy policy to solve the POMDP itself. We develop the mathematical framework necessary for discussion, and introduce a benchmark built on the task of Reconnaissance Blind TicTacToe. On this benchmark, we show that our policy outperforms policies ignoring the existence of multiple modes.
From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach
Venue: arXiv cs.LG 2022
Year: 2022

In multi-label learning, a particular case of multi-task learning where a single data point is associated with multiple target labels, it was widely assumed in the literature that, to obtain best accuracy, the dependence among the labels should be explicitly modeled. This premise led to a proliferation of methods offering techniques to learn and predict labels together, for example where the prediction for one label influences predictions for other labels. Even though it is now acknowledged that in many contexts a model of dependence is not required for optimal performance, such models continue to outperform independent models in some of those very contexts, suggesting alternative explanations for their performance beyond label dependence, which the literature is only recently beginning to unravel. Leveraging and extending recent discoveries, we turn the original premise of multi-label learning on its head, and approach the problem of joint-modeling specifically under the absence of any measurable dependence among task labels; for example, when task labels come from separate problem domains. We shift insights from this study towards building an approach for transfer learning that challenges the long-held assumption that transferability of tasks comes from measurements of similarity between the source and target domains or models. This allows us to design and test a method for transfer learning, which is model driven rather than purely data driven, and furthermore it is black box and model-agnostic (any base model class can be considered). We show that essentially we can create task-dependence based on source-model capacity. The results we obtain have important implications and provide clear directions for future work, both in the areas of multi-label and transfer learning.
Image Denoising Using Convolutional Autoencoder
Venue: arXiv cs.CV 2022
Year: 2022

With the inexorable digitalisation of the modern world, every subset in the field of technology goes through major advancements constantly. One such subset is digital images which are ever so popular. Images can not always be as visually pleasing or clear as you would want them to be and are often distorted or obscured with noise. A number of techniques to enhance images have come up as the years passed, all with their own respective pros and cons. In this paper, we look at one such particular technique which accomplishes this task with the help of a neural network model commonly known as an autoencoder. We construct different architectures for the model and compare results in order to decide the one best suited for the task. The characteristics and working of the model are discussed briefly knowing which can help set a path for future research.
$\textit{FastSVD-ML-ROM}$: A Reduced-Order Modeling Framework based on Machine Learning for Real-Time Applications
Venue: arXiv cs.LG 2022
Year: 2022

Digital twins have emerged as a key technology for optimizing the performance of engineering products and systems. High-fidelity numerical simulations constitute the backbone of engineering design, providing an accurate insight into the performance of complex systems. However, large-scale, dynamic, non-linear models require significant computational resources and are prohibitive for real-time digital twin applications. To this end, reduced order models (ROMs) are employed, to approximate the high-fidelity solutions while accurately capturing the dominant aspects of the physical behavior. The present work proposes a new machine learning (ML) platform for the development of ROMs, to handle large-scale numerical problems dealing with transient nonlinear partial differential equations. Our framework, mentioned as $\textit{FastSVD-ML-ROM}$, utilizes $\textit{(i)}$ a singular value decomposition (SVD) update methodology, to compute a linear subspace of the multi-fidelity solutions during the simulation process, $\textit{(ii)}$ convolutional autoencoders for nonlinear dimensionality reduction, $\textit{(iii)}$ feed-forward neural networks to map the input parameters to the latent spaces, and $\textit{(iv)}$ long short-term memory networks to predict and forecast the dynamics of parametric solutions. The efficiency of the $\textit{FastSVD-ML-ROM}$ framework is demonstrated for a 2D linear convection-diffusion equation, the problem of fluid around a cylinder, and the 3D blood flow inside an arterial segment. The accuracy of the reconstructed results demonstrates the robustness and assesses the efficiency of the proposed approach.
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network
Venue: arXiv cs.CV 2022
Year: 2022

Normalizing flow models have been used successfully for generative image super-resolution (SR) by approximating complex distribution of natural images to simple tractable distribution in latent space through Invertible Neural Networks (INN). These models can generate multiple realistic SR images from one low-resolution (LR) input using randomly sampled points in the latent space, simulating the ill-posed nature of image upscaling where multiple high-resolution (HR) images correspond to the same LR. Lately, the invertible process in INN has also been used successfully by bidirectional image rescaling models like IRN and HCFlow for joint optimization of downscaling and inverse upscaling, resulting in significant improvements in upscaled image quality. While they are optimized for image downscaling too, the ill-posed nature of image downscaling, where one HR image could be downsized to multiple LR images depending on different interpolation kernels and resampling methods, is not considered. A new downscaling latent variable, in addition to the original one representing uncertainties in image upscaling, is introduced to model variations in the image downscaling process. This dual latent variable enhancement is applicable to different image rescaling models and it is shown in extensive experiments that it can improve image upscaling accuracy consistently without sacrificing image quality in downscaled LR images. It is also shown to be effective in enhancing other INN-based models for image restoration applications like image hiding.
Improving Bot Response Contradiction Detection via Utterance Rewriting
Venue: arXiv cs.CL 2022
Year: 2022

Though chatbots based on large neural models can often produce fluent responses in open domain conversations, one salient error type is contradiction or inconsistency with the preceding conversation turns. Previous work has treated contradiction detection in bot responses as a task similar to natural language inference, e.g., detect the contradiction between a pair of bot utterances. However, utterances in conversations may contain co-references or ellipsis, and using these utterances as is may not always be sufficient for identifying contradictions. This work aims to improve the contradiction detection via rewriting all bot utterances to restore antecedents and ellipsis. We curated a new dataset for utterance rewriting and built a rewriting model on it. We empirically demonstrate that this model can produce satisfactory rewrites to make bot utterances more complete. Furthermore, using rewritten utterances improves contradiction detection performance significantly, e.g., the AUPR and joint accuracy scores (detecting contradiction along with evidence) increase by 6.5% and 4.5% (absolute increase), respectively.
Machine-checked executable semantics of Stateflow
Venue: arXiv cs.FL 2022
Year: 2022

Simulink is a widely used model-based development environment for embedded systems. Stateflow is a component of Simulink for modeling event-driven control via hierarchical state machines and flow charts. However, Stateflow lacks an official formal semantics, making it difficult to formally prove properties of its models in safety-critical applications. In this paper, we define a formal semantics for a large subset of Stateflow, covering complex features such as hierarchical states and transitions, event broadcasts, early return, temporal operators, and so on. The semantics is formalized in Isabelle/HOL and proved to be deterministic. We implement a tactic for automatic execution of the semantics in Isabelle, as well as a translator in Python transforming Stateflow models to the syntax in Isabelle. Using these tools, we validate the semantics against a collection of examples illustrating the features we cover.
Analysis of the deletions of DOIs: What factors undermine their persistence and to what extent?
Venue: arXiv cs.DL 2022
Year: 2022

Digital Object Identifiers (DOIs) are regarded as persistent; however, they are sometimes deleted. Deleted DOIs are an important issue not only for persistent access to scholarly content but also for bibliometrics, because they may cause problems in correctly identifying scholarly articles. However, little is known about how much of deleted DOIs and what causes them. We identified deleted DOIs by comparing the datasets of all Crossref DOIs on two different dates, investigated the number of deleted DOIs in the scholarly content along with the corresponding document types, and analyzed the factors that cause deleted DOIs. Using the proposed method, 708,282 deleted DOIs were identified. The majority corresponded to individual scholarly articles such as journal articles, proceedings articles, and book chapters. There were cases of many DOIs assigned to the same content, e.g., retracted journal articles and abstracts of international conferences. We show the publishers and academic societies which are the most common in deleted DOIs. In addition, the top cases of single scholarly content with a large number of deleted DOIs were revealed. The findings of this study are useful for citation analysis and altmetrics, as well as for avoiding deleted DOIs.
Riemannian Geometry Approach for Minimizing Distortion and its Applications
Venue: arXiv cs.CV 2022
Year: 2022

Given an affine transformation $T$, we define its Fisher distortion $Dist_F(T)$. We show that the Fisher distortion has Riemannian metric structure and provide an algorithm for finding mean distorting transformation -- namely -- for a given set $\{T_{i}\}_{i=1}^N$ of affine transformations, find an affine transformation $T$ that minimize the overall distortion $\sum_{i=1}^NDist_F^{2}(T^{-1}T_{i}).$ The mean distorting transformation can be useful in some fields -- in particular, we apply it for rendering affine panoramas.
Optimizing the Achievable Rate in MIMO Systems Assisted by Multiple Reconfigurable Intelligent Surfaces
Venue: arXiv cs.IT 2022
Year: 2022

In recent years there has been a growing interest in reconfigurable intelligent surfaces (RISs) as enablers for the realization of smart radio propagation environments which can provide performance improvements with low energy consumption in future wireless networks. However, to reap the potential gains of RIS it is crucial to jointly design both the transmit precoder and the phases of the RIS elements. Within this context, in this paper we study the use of multiple RIS panels in a parallel or multi-hop configuration with the aim of assisting a multi-stream multiple-input multiple-output (MIMO) communication. To solve the nonconvex joint optimization problem of the precoder and RIS elements targeted at maximizing the achievable rate, we propose an iterative algorithm based on the monotone accelerated proximal gradient (mAPG) method which includes an extrapolation step for improving the convergence speed and monitoring variables for ensuring sufficient descent of the algorithm. Based on the sufficient descent property we then present a detailed convergence analysis of the algorithm which includes expressions for the step size. Simulation results in different scenarios show that, besides being effective, the proposed approach can often achieve higher rates than other benchmarked schemes.
A Reference Data Model for Process-Related User Interaction Logs
Venue: arXiv cs.AI 2022
Year: 2022

User interaction (UI) logs are high-resolution event logs that record low-level activities performed by a user during the execution of a task in an information system. Each event in a UI log corresponds to a single interaction between the user and the interface, such as clicking a button or entering a string into a text field. UI logs are used for purposes like task mining or robotic process automation (RPA), but each study and tool relies on a different conceptualization and implementation of the elements and attributes that constitute user interactions. This lack of standardization makes it difficult to integrate UI logs from different sources and to combine tools for UI data collection with downstream analytics or automation solutions. To address this, we propose a universally applicable reference data model for process-related UI logs. Based on a review of scientific literature and industry solutions, this model includes the core attributes of UI logs, but remains flexible with regard to the scope, level of abstraction, and case notion. We provide an implementation of the model as an extension to the XES interchange standard for event logs and demonstrate its practical applicability in a real-life RPA scenario.
Dynamic Channel Selection in Self-Supervised Learning
Venue: arXiv cs.CV 2022
Year: 2022

Whilst computer vision models built using self-supervised approaches are now commonplace, some important questions remain. Do self-supervised models learn highly redundant channel features? What if a self-supervised network could dynamically select the important channels and get rid of the unnecessary ones? Currently, convnets pre-trained with self-supervision have obtained comparable performance on downstream tasks in comparison to their supervised counterparts in computer vision. However, there are drawbacks to self-supervised models including their large numbers of parameters, computationally expensive training strategies and a clear need for faster inference on downstream tasks. In this work, our goal is to address the latter by studying how a standard channel selection method developed for supervised learning can be applied to networks trained with self-supervision. We validate our findings on a range of target budgets $t_{d}$ for channel computation on image classification task across different datasets, specifically CIFAR-10, CIFAR-100, and ImageNet-100, obtaining comparable performance to that of the original network when selecting all channels but at a significant reduction in computation reported in terms of FLOPs.
A Piecewise Monotonic Gait Phase Estimation Model for Controlling a Powered Transfemoral Prosthesis in Various Locomotion Modes
Venue: arXiv cs.RO 2022
Year: 2022

Gait phase-based control is a trending research topic for walking-aid robots, especially robotic lower-limb prostheses. Gait phase estimation is a challenge for gait phase-based control. Previous researches used the integration or the differential of the human's thigh angle to estimate the gait phase, but accumulative measurement errors and noises can affect the estimation results. In this paper, a more robust gait phase estimation method is proposed using a unified form of piecewise monotonic gait phase-thigh angle models for various locomotion modes. The gait phase is estimated from only the thigh angle, which is a stable variable and avoids phase drifting. A Kalman filter-based smoother is designed to further suppress the mutations of the estimated gait phase. Based on the proposed gait phase estimation method, a gait phase-based joint angle tracking controller is designed for a transfemoral prosthesis. The proposed gait estimation method, the gait phase smoother, and the controller are evaluated through offline analysis on walking data in various locomotion modes. And the real-time performance of the gait phase-based controller is validated in an experiment on the transfemoral prosthesis.
Quantifying Overheads in Charm++ and HPX using Task Bench
Venue: arXiv cs.DC 2022
Year: 2022

Asynchronous Many-Task (AMT) runtime systems take advantage of multi-core architectures with light-weight threads, asynchronous executions, and smart scheduling. In this paper, we present the comparison of the AMT systems Charm++ and HPX with the main stream MPI, OpenMP, and MPI+OpenMP libraries using the Task Bench benchmarks. Charm++ is a parallel programming language based on C++, supporting stackless tasks as well as light-weight threads asynchronously along with an adaptive runtime system. HPX is a C++ library for concurrency and parallelism, exposing C++ standards conforming API. First, we analyze the commonalities, differences, and advantageous scenarios of Charm++ and HPX in detail. Further, to investigate the potential overheads introduced by the tasking systems of Charm++ and HPX, we utilize an existing parameterized benchmark, Task Bench, wherein 15 different programming systems were implemented, e.g., MPI, OpenMP, MPI + OpenMP, and extend Task Bench by adding HPX implementations. We quantify the overheads of Charm++, HPX, and the main stream libraries in different scenarios where a single task and multi-task are assigned to each core, respectively. We also investigate each system's scalability and the ability to hide the communication latency.
Evolving Recursive Definitions with Applications to Dynamic Programming
Venue: arXiv cs.PL 2022
Year: 2022

Inspired by computability logic\cite{Jap03}, we refine recursive function definitions into two kinds: blindly-quantified (BQ) ones and parallel universally quantified (PUQ) ones. BQ definitions corresponds to the traditional ones where recursive definitions are $not$ evolving. PUQ definitions are {\it evolving} in the course of computation, leading to automatic memoization. In addition, based on this idea, we propose a new, high-level object-oriented language.
Improving Pseudo Labels With Intra-Class Similarity for Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2022
Year: 2022

Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a different but related fully-unlabeled target domain. To address the problem of domain shift, more and more UDA methods adopt pseudo labels of the target samples to improve the generalization ability on the target domain. However, inaccurate pseudo labels of the target samples may yield suboptimal performance with error accumulation during the optimization process. Moreover, once the pseudo labels are generated, how to remedy the generated pseudo labels is far from explored. In this paper, we propose a novel approach to improve the accuracy of the pseudo labels in the target domain. It first generates coarse pseudo labels by a conventional UDA method. Then, it iteratively exploits the intra-class similarity of the target samples for improving the generated coarse pseudo labels, and aligns the source and target domains with the improved pseudo labels. The accuracy improvement of the pseudo labels is made by first deleting dissimilar samples, and then using spanning trees to eliminate the samples with the wrong pseudo labels in the intra-class samples. We have applied the proposed approach to several conventional UDA methods as an additional term. Experimental results demonstrate that the proposed method can boost the accuracy of the pseudo labels and further lead to more discriminative and domain invariant features than the conventional baselines.
A Multi-Party Dialogue Ressource in French
Venue: arXiv cs.AI 2022
Year: 2022

We present Dialogues in Games (DinG), a corpus of manual transcriptions of real-life, oral, spontaneous multi-party dialogues between French-speaking players of the board game Catan. Our objective is to make available a quality resource for French, composed of long dialogues, to facilitate their study in the style of (Asher et al., 2016). In a general dialogue setting, participants share personal information, which makes it impossible to disseminate the resource freely and openly. In DinG, the attention of the participants is focused on the game, which prevents them from talking about themselves. In addition, we are conducting a study on the nature of the questions in dialogue, through annotation (Cruz Blandon et al., 2019), in order to develop more natural automatic dialogue systems.
COSIME: FeFET based Associative Memory for In-Memory Cosine Similarity Search
Venue: arXiv cs.AR 2022
Year: 2022

In a number of machine learning models, an input query is searched across the trained class vectors to find the closest feature class vector in cosine similarity metric. However, performing the cosine similarities between the vectors in Von-Neumann machines involves a large number of multiplications, Euclidean normalizations and division operations, thus incurring heavy hardware energy and latency overheads. Moreover, due to the memory wall problem that presents in the conventional architecture, frequent cosine similarity-based searches (CSSs) over the class vectors requires a lot of data movements, limiting the throughput and efficiency of the system. To overcome the aforementioned challenges, this paper introduces COSIME, an general in-memory associative memory (AM) engine based on the ferroelectric FET (FeFET) device for efficient CSS. By leveraging the one-transistor AND gate function of FeFET devices, current-based translinear analog circuit and winner-take-all (WTA) circuitry, COSIME can realize parallel in-memory CSS across all the entries in a memory block, and output the closest word to the input query in cosine similarity metric. Evaluation results at the array level suggest that the proposed COSIME design achieves 333X and 90.5X latency and energy improvements, respectively, and realizes better classification accuracy when compared with an AM design implementing approximated CSS. The proposed in-memory computing fabric is evaluated for an HDC problem, showcasing that COSIME can achieve on average 47.1X and 98.5X speedup and energy efficiency improvements compared with an GPU implementation.