OpenRaiser/Intern-Atlas huggingface dataset:
Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data
Venue: arXiv cs.LG 2023
Year: 2023

Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios. However, existing time-series forecasting methods often overlook two important characteristics of cumulative data, namely monotonicity and irregularity, which limit their practical applicability. To address this limitation, we propose a principled approach called Monotonic neural Ordinary Differential Equation (MODE) within the framework of neural ordinary differential equations. By leveraging MODE, we are able to effectively capture and represent the monotonicity and irregularity in practical cumulative data. Through extensive experiments conducted in a bonus allocation scenario, we demonstrate that MODE outperforms state-of-the-art methods, showcasing its ability to handle both monotonicity and irregularity in cumulative data and delivering superior forecasting performance.
Identifying Systematic Errors in Object Detectors with the SCROD Pipeline
Venue: arXiv cs.CV 2023
Year: 2023

The identification and removal of systematic errors in object detectors can be a prerequisite for their deployment in safety-critical applications like automated driving and robotics. Such systematic errors can for instance occur under very specific object poses (location, scale, orientation), object colors/textures, and backgrounds. Real images alone are unlikely to cover all relevant combinations. We overcome this limitation by generating synthetic images with fine-granular control. While generating synthetic images with physical simulators and hand-designed 3D assets allows fine-grained control over generated images, this approach is resource-intensive and has limited scalability. In contrast, using generative models is more scalable but less reliable in terms of fine-grained control. In this paper, we propose a novel framework that combines the strengths of both approaches. Our meticulously designed pipeline along with custom models enables us to generate street scenes with fine-grained control in a fully automated and scalable manner. Moreover, our framework introduces an evaluation setting that can serve as a benchmark for similar pipelines. This evaluation setting will contribute to advancing the field and promoting standardized testing procedures.
Lifting Theorems Meet Information Complexity: Known and New Lower Bounds of Set-disjointness
Venue: arXiv cs.CC 2023
Year: 2023

Set-disjointness problems are one of the most fundamental problems in communication complexity and have been extensively studied in past decades. Given its importance, many lower bound techniques were introduced to prove communication lower bounds of set-disjointness. Combining ideas from information complexity and query-to-communication lifting theorems, we introduce a density increment argument to prove communication lower bounds for set-disjointness: We give a simple proof showing that a large rectangle cannot be $0$-monochromatic for multi-party unique-disjointness. We interpret the direct-sum argument as a density increment process and give an alternative proof of randomized communication lower bounds for multi-party unique-disjointness. Avoiding full simulations in lifting theorems, we simplify and improve communication lower bounds for sparse unique-disjointness. Potential applications to be unified and improved by our density increment argument are also discussed.
Semi-Supervised Domain Generalization for Object Detection via Language-Guided Feature Alignment
Venue: arXiv cs.CV 2023
Year: 2023

Existing domain adaptation (DA) and generalization (DG) methods in object detection enforce feature alignment in the visual space but face challenges like object appearance variability and scene complexity, which make it difficult to distinguish between objects and achieve accurate detection. In this paper, we are the first to address the problem of semi-supervised domain generalization by exploring vision-language pre-training and enforcing feature alignment through the language space. We employ a novel Cross-Domain Descriptive Multi-Scale Learning (CDDMSL) aiming to maximize the agreement between descriptions of an image presented with different domain-specific characteristics in the embedding space. CDDMSL significantly outperforms existing methods, achieving 11.7% and 7.5% improvement in DG and DA settings, respectively. Comprehensive analysis and ablation studies confirm the effectiveness of our method, positioning CDDMSL as a promising approach for domain generalization in object detection tasks.
I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnoses
Venue: arXiv cs.CV 2023
Year: 2023

In the field of chest X-ray (CXR) diagnosis, existing works often focus solely on determining where a radiologist looks, typically through tasks such as detection, segmentation, or classification. However, these approaches are often designed as black-box models, lacking interpretability. In this paper, we introduce Interpretable Artificial Intelligence (I-AI) a novel and unified controllable interpretable pipeline for decoding the intense focus of radiologists in CXR diagnosis. Our I-AI addresses three key questions: where a radiologist looks, how long they focus on specific areas, and what findings they diagnose. By capturing the intensity of the radiologist's gaze, we provide a unified solution that offers insights into the cognitive process underlying radiological interpretation. Unlike current methods that rely on black-box machine learning models, which can be prone to extracting erroneous information from the entire input image during the diagnosis process, we tackle this issue by effectively masking out irrelevant information. Our proposed I-AI leverages a vision-language model, allowing for precise control over the interpretation process while ensuring the exclusion of irrelevant features. To train our I-AI model, we utilize an eye gaze dataset to extract anatomical gaze information and generate ground truth heatmaps. Through extensive experimentation, we demonstrate the efficacy of our method. We showcase that the attention heatmaps, designed to mimic radiologists' focus, encode sufficient and relevant information, enabling accurate classification tasks using only a portion of CXR. The code, checkpoints, and data are at https://github.com/UARK-AICV/IAI
Digital Twins and the Future of their Use Enabling Shift Left and Shift Right Cybersecurity Operations
Venue: arXiv cs.CR 2023
Year: 2023

Digital Twins (DTs), optimize operations and monitor performance in Smart Critical Systems (SCS) domains like smart grids and manufacturing. DT-based cybersecurity solutions are in their infancy, lacking a unified strategy to overcome challenges spanning next three to five decades. These challenges include reliable data accessibility from Cyber-Physical Systems (CPS), operating in unpredictable environments. Reliable data sources are pivotal for intelligent cybersecurity operations aided with underlying modeling capabilities across the SCS lifecycle, necessitating a DT. To address these challenges, we propose Security Digital Twins (SDTs) collecting realtime data from CPS, requiring the Shift Left and Shift Right (SLSR) design paradigm for SDT to implement both design time and runtime cybersecurity operations. Incorporating virtual CPS components (VC) in Cloud/Edge, data fusion to SDT models is enabled with high reliability, providing threat insights and enhancing cyber resilience. VC-enabled SDT ensures accurate data feeds for security monitoring for both design and runtime. This design paradigm shift propagates innovative SDT modeling and analytics for securing future critical systems. This vision paper outlines intelligent SDT design through innovative techniques, exploring hybrid intelligence with data-driven and rule-based semantic SDT models. Various operational use cases are discussed for securing smart critical systems through underlying modeling and analytics capabilities.
Changes-Aware Transformer: Learning Generalized Changes Representation
Venue: arXiv cs.CV 2023
Year: 2023

Difference features obtained by comparing the images of two periods play an indispensable role in the change detection (CD) task. However, a pair of bi-temporal images can exhibit diverse changes, which may cause various difference features. Identifying changed pixels with differ difference features to be the same category is thus a challenge for CD. Most nowadays' methods acquire distinctive difference features in implicit ways like enhancing image representation or supervision information. Nevertheless, informative image features only guarantee object semantics are modeled and can not guarantee that changed pixels have similar semantics in the difference feature space and are distinct from those unchanged ones. In this work, the generalized representation of various changes is learned straightforwardly in the difference feature space, and a novel Changes-Aware Transformer (CAT) for refining difference features is proposed. This generalized representation can perceive which pixels are changed and which are unchanged and further guide the update of pixels' difference features. CAT effectively accomplishes this refinement process through the stacked cosine cross-attention layer and self-attention layer. After refinement, the changed pixels in the difference feature space are closer to each other, which facilitates change detection. In addition, CAT is compatible with various backbone networks and existing CD methods. Experiments on remote sensing CD data set and street scene CD data set show that our method achieves state-of-the-art performance and has excellent generalization.
Probabilistic Bounds for Data Storage with Feature Selection and Undersampling
Venue: arXiv cs.IT 2023
Year: 2023

In this paper we consider data storage from a probabilistic point of view and obtain bounds for efficient storage in the presence of feature selection and undersampling, both of which are important from the data science perspective. First, we consider encoding of correlated sources for nonstationary data and obtain a Slepian-Wolf type result for the probability of error. We then reinterpret our result by allowing one source to be the set of features to be discarded and other source to be remaining data to be encoded. Next, we consider neighbourhood domination in random graphs where we impose the condition that a fraction of neighbourhood must be present for each vertex and obtain optimal bounds on the minimum size of such a set. We show how such sets are useful for data undersampling in the presence of imbalanced datasets and briefly illustrate our result using~\(k-\)nearest neighbours type classification rules as an example.
A Probabilistic Model for Data Redundancy in the Feature Domain
Venue: arXiv cs.LG 2023
Year: 2023

In this paper, we use a probabilistic model to estimate the number of uncorrelated features in a large dataset. Our model allows for both pairwise feature correlation (collinearity) and interdependency of multiple features (multicollinearity) and we use the probabilistic method to obtain upper and lower bounds of the same order, for the size of a feature set that exhibits low collinearity and low multicollinearity. We also prove an auxiliary result regarding mutually good constrained sets that is of independent interest.
Combining Two Adversarial Attacks Against Person Re-Identification Systems
Venue: arXiv cs.CV 2023
Year: 2023

The field of Person Re-Identification (Re-ID) has received much attention recently, driven by the progress of deep neural networks, especially for image classification. The problem of Re-ID consists in identifying individuals through images captured by surveillance cameras in different scenarios. Governments and companies are investing a lot of time and money in Re-ID systems for use in public safety and identifying missing persons. However, several challenges remain for successfully implementing Re-ID, such as occlusions and light reflections in people's images. In this work, we focus on adversarial attacks on Re-ID systems, which can be a critical threat to the performance of these systems. In particular, we explore the combination of adversarial attacks against Re-ID models, trying to strengthen the decrease in the classification results. We conduct our experiments on three datasets: DukeMTMC-ReID, Market-1501, and CUHK03. We combine the use of two types of adversarial attacks, P-FGSM and Deep Mis-Ranking, applied to two popular Re-ID models: IDE (ResNet-50) and AlignedReID. The best result demonstrates a decrease of 3.36% in the Rank-10 metric for AlignedReID applied to CUHK03. We also try to use Dropout during the inference as a defense method.
Modelling and Search-Based Testing of Robot Controllers Using Enzymatic Numerical P Systems
Venue: arXiv cs.SE 2023
Year: 2023

The safety of the systems controlled by software is a very important area in a digitalized society, as the number of automated processes is increasing. In this paper, we present the results of testing the accuracy of different lane keeping controllers for an educational robot. In our approach, the robot is controlled using numerical P systems and enzymatic numerical P systems. For tests generation, we used an open-source tool implementing a search-based software testing approach.
A Parallel Dynamic Epistemic Perspective over Muddy Children Puzzle
Venue: arXiv cs.LO 2023
Year: 2023

Epistemic protocols represents a current field of interest, with numerous approaches still being studied. In this paper we formalize parallel sessions of the The Muddy Children Puzzle using Public Observation Logic, a system that allows epistemic update. We consider agents with roles in multiple sessions and the information update in all parallel sessions as new information is discovered in any particular session.
While Loops in Coq
Venue: arXiv cs.PL 2023
Year: 2023

While loops are present in virtually all imperative programming languages. They are important both for practical reasons (performing a number of iterations not known in advance) and theoretical reasons (achieving Turing completeness). In this paper we propose an approach for incorporating while loops in an imperative language shallowly embedded in the Coq proof assistant. The main difficulty is that proving the termination of while loops is nontrivial, or impossible in the case of non-termination, whereas Coq only accepts programs endowed with termination proofs. Our solution is based on a new, general method for defining possibly non-terminating recursive functions in Coq. We illustrate the approach by proving termination and partial correctness of a program on linked lists.
Forecasting large collections of time series: feature-based methods
Venue: arXiv cs.LG 2023
Year: 2023

In economics and many other forecasting domains, the real world problems are too complex for a single model that assumes a specific data generation process. The forecasting performance of different methods changes depending on the nature of the time series. When forecasting large collections of time series, two lines of approaches have been developed using time series features, namely feature-based model selection and feature-based model combination. This chapter discusses the state-of-the-art feature-based methods, with reference to open-source software implementations.
Asynchronous Muddy Children Puzzle (work in progress)
Venue: arXiv cs.LO 2023
Year: 2023

In this work-in-progress paper we explore using the recently introduced VLSM formalism to define and reason about the dynamics of agent-based systems. To this aim we use VLSMs to formally present several possible approaches to modeling the interactions in the Muddy Children Puzzle as protocols that reach consensus asynchronously.
Dual Feature Augmentation Network for Generalized Zero-shot Learning
Venue: arXiv cs.CV 2023
Year: 2023

Zero-shot learning (ZSL) aims to infer novel classes without training samples by transferring knowledge from seen classes. Existing embedding-based approaches for ZSL typically employ attention mechanisms to locate attributes on an image. However, these methods often ignore the complex entanglement among different attributes' visual features in the embedding space. Additionally, these methods employ a direct attribute prediction scheme for classification, which does not account for the diversity of attributes in images of the same category. To address these issues, we propose a novel Dual Feature Augmentation Network (DFAN), which comprises two feature augmentation modules, one for visual features and the other for semantic features. The visual feature augmentation module explicitly learns attribute features and employs cosine distance to separate them, thus enhancing attribute representation. In the semantic feature augmentation module, we propose a bias learner to capture the offset that bridges the gap between actual and predicted attribute values from a dataset's perspective. Furthermore, we introduce two predictors to reconcile the conflicts between local and global features. Experimental results on three benchmarks demonstrate the marked advancement of our method compared to state-of-the-art approaches. Our code is available at https://github.com/Sion1/DFAN.
Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory
Venue: arXiv cs.RO 2023
Year: 2023

LiDAR Odometry is an essential component in many robotic applications. Unlike the mainstreamed approaches that focus on improving the accuracy by the additional inertial sensors, this letter explores the capability of LiDAR-only odometry through a continuous-time perspective. Firstly, the measurements of LiDAR are regarded as streaming points continuously captured at high frequency. Secondly, the LiDAR movement is parameterized by a simple yet effective continuous-time trajectory. Therefore, our proposed Traj-LO approach tries to recover the spatial-temporal consistent movement of LiDAR by tightly coupling the geometric information from LiDAR points and kinematic constraints from trajectory smoothness. This framework is generalized for different kinds of LiDAR as well as multi-LiDAR systems. Extensive experiments on the public datasets demonstrate the robustness and effectiveness of our proposed LiDAR-only approach, even in scenarios where the kinematic state exceeds the IMU's measuring range. Our implementation is open-sourced on GitHub.
PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration
Venue: arXiv cs.CL 2023
Year: 2023

Document-level relation extraction (DocRE) aims to extract relations of all entity pairs in a document. A key challenge in DocRE is the cost of annotating such data which requires intensive human effort. Thus, we investigate the case of DocRE in a low-resource setting, and we find that existing models trained on low data overestimate the NA ("no relation") label, causing limited performance. In this work, we approach the problem from a calibration perspective and propose PRiSM, which learns to adapt logits based on relation semantic information. We evaluate our method on three DocRE datasets and demonstrate that integrating existing models with PRiSM improves performance by as much as 26.38 F1 score, while the calibration error drops as much as 36 times when trained with about 3% of data. The code is publicly available at https://github.com/brightjade/PRiSM.
The Time Traveler's Guide to Semantic Web Research: Analyzing Fictitious Research Themes in the ESWC "Next 20 Years" Track
Venue: arXiv cs.AI 2023
Year: 2023

What will Semantic Web research focus on in 20 years from now? We asked this question to the community and collected their visions in the "Next 20 years" track of ESWC 2023. We challenged the participants to submit "future" research papers, as if they were submitting to the 2043 edition of the conference. The submissions - entirely fictitious - were expected to be full scientific papers, with research questions, state of the art references, experimental results and future work, with the goal to get an idea of the research agenda for the late 2040s and early 2050s. We received ten submissions, eight of which were accepted for presentation at the conference, that mixed serious ideas of potential future research themes and discussion topics with some fun and irony. In this paper, we intend to provide a survey of those "science fiction" papers, considering the emerging research themes and topics, analysing the research methods applied by the authors in these very special submissions, and investigating also the most fictitious parts (e.g., neologisms, fabricated references). Our goal is twofold: on the one hand, we investigate what this special track tells us about the Semantic Web community and, on the other hand, we aim at getting some insights on future research practices and directions.
In-Domain GAN Inversion for Faithful Reconstruction and Editability
Venue: arXiv cs.CV 2023
Year: 2023

Generative Adversarial Networks (GANs) have significantly advanced image synthesis through mapping randomly sampled latent codes to high-fidelity synthesized images. However, applying well-trained GANs to real image editing remains challenging. A common solution is to find an approximate latent code that can adequately recover the input image to edit, which is also known as GAN inversion. To invert a GAN model, prior works typically focus on reconstructing the target image at the pixel level, yet few studies are conducted on whether the inverted result can well support manipulation at the semantic level. This work fills in this gap by proposing in-domain GAN inversion, which consists of a domain-guided encoder and a domain-regularized optimizer, to regularize the inverted code in the native latent space of the pre-trained GAN model. In this way, we manage to sufficiently reuse the knowledge learned by GANs for image reconstruction, facilitating a wide range of editing applications without any retraining. We further make comprehensive analyses on the effects of the encoder structure, the starting inversion point, as well as the inversion parameter space, and observe the trade-off between the reconstruction quality and the editing property. Such a trade-off sheds light on how a GAN model represents an image with various semantics encoded in the learned latent distribution. Code, models, and demo are available at the project page: https://genforce.github.io/idinvert/.
A Cyberpunk 2077 perspective on the prediction and understanding of future technology
Venue: arXiv cs.HC 2023
Year: 2023

Science fiction and video games have long served as valuable tools for envisioning and inspiring future technological advancements. This position paper investigates the potential of Cyberpunk 2077, a popular science fiction video game, to shed light on the future of technology, particularly in the areas of artificial intelligence, edge computing, augmented humans, and biotechnology. By analyzing the game's portrayal of these technologies and their implications, we aim to understand the possibilities and challenges that lie ahead. We discuss key themes such as neurolink and brain-computer interfaces, multimodal recording systems, virtual and simulated reality, digital representation of the physical world, augmented and AI-based home appliances, smart clothing, and autonomous vehicles. The paper highlights the importance of designing technologies that can coexist with existing preferences and systems, considering the uneven adoption of new technologies. Through this exploration, we emphasize the potential of science fiction and video games like Cyberpunk 2077 as tools for guiding future technological advancements and shaping public perception of emerging innovations.