bluuebunny/crossref_metadata_2025 huggingface dataset:
People and Nature of the Urals: Variants of Representation in the Wartime Literature
Authors:   Ural Federal University, Snigireva Tatyana A. ,   Institute of History and Archaeology, Ural Branch of RAS
Published: June 2023

The study is focused on identifying and analysis of the two conceptual blocks that literary represent the Urals in wartime: the Ural character and the Ural nature. The study material consists of works of different genres, from documentary essays to verse journalism. It is shown that the idea of the original Ural character is generally perceived by an independent view of journalists sent to the Urals to report labor feats at the home front (it is akin to the military feats), and writers stranded in the Urals as a result of the evacuation. Optical differences allowed the creation of a sophisticated image of the Ural people. On the one hand, these are soldiers of fabulous courage, and masters of high work culture, but on the other hand, these people are reserved, strict, and not always friendly. The representation of the Urals’ nature is just as ambiguous. The article describes two variants offered by the writers of wartime. The first one fully meets the socialist realism canons: the mighty people of the Urals were born of a majestic nature, and this place is cold and barely adjusted to life. It is emphasized that the conviction of the necessity of living and working under the circumstances in which people found themselves during the wartime, was dominant regarding all literature.
Audience Anthropology: New Dimensions
Authors:   Ural Federal University, Lozovsky Boris N.
Published: June 2023

Based on the analysis of numerous opinions of researchers, media experts, media managers and journalists, the article describes and evaluates the modern features of the audience in its communications with the media, which are attributed to the ongoing political, economic, socio­cultural and technological changes.
District Newspapers: The Results of Ten Years of Development in the Conditions of Digitalization (on the Example of Publications of the Novosibirsk Region)
Authors:   Novosibirsk State University, Belenko Viktoria Е.
Published: June 2023

The article examines how the newspapers of the municipal districts of the Novosibirsk region have grown in the last decade, what their websites are have developed. The data sources for the analysis were sociological studies, conducted annually by order of the regional administration. When analyzing the representation of newspapers on the Internet, we took into account the following: the date of creation of the domain, the number of pages on the site, the number of visitors, and the data on the optimization of sites for desktop and mobile devices. It was found out, that in ten years the audience of printed issues has decreased by 1.5–2 times. The newspapers with the largest audience lost it most of all. In parallel, there were processes of mastering a new information space, not only creating, but also developing websites, systematically updating materials on them, striving to make them visually attractive. Now all (with one exception) of the analyzed newspapers have websites, but their attendance is low: several dozen people a day.
The System of Communicative Elements as the Basis of Modern Corporate Media (on the Newspaper «Dialog UEKHK» and «Znamya»)
Authors:   Ural Federal University, Borozdina Ulyana D.
Published: June 2023

The article raises an issue of professional ethics, highlights the communicative elements in the factory media on the example of the newspaper Dialogue UEIP of the joint stock company of the Ural Electrochemical Plant in Novouralsk and Znamya of the Ural Turbine Plant in Yekaterinburg. The purpose of the study was to develop a system of communicative elements aimed at shaping the corporate culture of enterprises. The theoretical basis for the study of the manifestation of labor relations of corporate media, covering the historical periods of the appearance of the corporate press, its types, functions, stages of development. The author of the article organized and presented six group of semantic concepts that support the rallying of the factory media teams aimed at increasing the prestige of working professions and shaping the image of enterprises. For each concept, cases are found in the factory media, illustrating the functions of the concepts in action.
The Orthodox Periodicals of the Russian Emigration in Argentina during the Cold War
Authors:   Ural Federal University, Antoshin Alexey V. , Antoshin Valery A. ,   Ural Institute of Management of the Russian Academy of National Economy and Public Administration  under the President of the Russian Federation
Published: June 2023

The article is dedicated to the phenomenon of the Orthodox press of the Russian abroad of the Cold War. The focus of the authors of the article is the magazines published in the capital of Argentina Buenos Aires, which after the Second World War became one of the most important centers of Russian emigration. It is proven that Orthodox periodicals are a valuable source on the history of the public consciousness of the Russian abroad.
The June Days Uprising in the Epistolary Heritage of A. I. Herzen
Authors:   Moscow State Regional University, Garifullina Elvira I.
Published: June 2023

The article is devoted to the reflection of the events of June 23–26, 1848 in the 10th letter of the cycle “Letters from France and Italy” by A. I. Herzen and two of his private letters. The aim of the study is to establish the key features of the writer’s epistolary style, as well as to observe the transformation of his reasoning during the transition from one verbal form to another. Taking into account the goal, the author considered a comparative method to be the most reasonable in the context of the chronological approach. A comparative analysis of these sources has been carried out, their structural and content similarity has been revealed, stylistic and compositional features has been analyzed. The result of the work done was the explication of the relatively stable epistolary manner of Herzen, the definition of its main characteristics. In addition, a conclusion was made about the auxiliary role of the writer’s private correspondence in the creation of the open cycle “Letters from Italy and France”.
Zero-Shot Linear Combinations of Grounded Social Interactions with Linear Social MDPs
Authors: Tejwani Ravi , Kuo Yen-Ling , Shu Tianmin , Stankovits Bennett , Gutfreund Dan , Tenenbaum Joshua B. , Katz Boris , Barbu Andrei
Published: June 2023

Humans and animals engage in rich social interactions. It is often theorized that a relatively small number of basic social interactions give rise to the full range of behavior observed. But no computational theory explaining how social interactions combine together has been proposed before. We do so here. We take a model, the Social MDP, which is able to express a range of social interactions, and extend it to represent linear combinations of social interactions. Practically for robotics applications, such models are now able to not just express that an agent should help another agent, but to express goal-centric social interactions. Perhaps an agent is helping someone get dressed, but preventing them from falling, and is happy to exchange stories in the meantime. How an agent responds socially, should depend on what it thinks the other agent is doing at that point in time. To encode this notion, we take linear combinations of social interactions as defined in Social MDPs, and compute the weights on those combinations on the fly depending on the estimated goals of other agents. This new model, the Linear Social MDP, enables zero-shot reasoning about complex social interactions, provides a mathematical basis for the long-standing intuition that social interactions should compose, and leads to interesting new behaviors that we validate using human observers. Complex social interactions are part of the future of intelligent agents, and having principled mathematical models built on a foundation like MDPs will make it possible to bring social interactions to every robotic application.
CMNet: Contrastive Magnification Network for Micro-Expression Recognition
Authors: Wei Mengting , Jiang Xingxun , Zheng Wenming , Zong Yuan , Lu Cheng , Liu Jiateng
Published: June 2023

Micro-Expression Recognition (MER) is challenging because the Micro-Expressions' (ME) motion is too weak to distinguish. This hurdle can be tackled by enhancing intensity for a more accurate acquisition of movements. However, existing magnification strategies tend to use the features of facial images that include not only intensity clues as intensity features, leading to the intensity representation deficient of credibility. In addition, the intensity variation over time, which is crucial for encoding movements, is also neglected. To this end, we provide a reliable scheme to extract intensity clues while considering their variation on the time scale. First, we devise an Intensity Distillation (ID) loss to acquire the intensity clues by contrasting the difference between frames, given that the difference in the same video lies only in the intensity. Then, the intensity clues are calibrated to follow the trend of the original video. Specifically, due to the lack of truth intensity annotation of the original video, we build the intensity tendency by setting each intensity vacancy an uncertain value, which guides the extracted intensity clues to converge towards this trend rather some fixed values. A Wilcoxon rank sum test (Wrst) method is enforced to implement the calibration. Experimental results on three public ME databases i.e. CASME II, SAMM, and SMIC-HS validate the superiority against state-of-the-art methods.
Denoising after Entropy-Based Debiasing a Robust Training Method for Dataset Bias with Noisy Labels
Authors: Ahn Sumyeong , Yun Se-Young
Published: June 2023

Improperly constructed datasets can result in inaccurate inferences. For instance, models trained on biased datasets perform poorly in terms of generalization (i.e., dataset bias). Recent debiasing techniques have successfully achieved generalization performance by underestimating easy-to-learn samples (i.e., bias-aligned samples) and highlighting difficult-to-learn samples (i.e., bias-conflicting samples). However, these techniques may fail owing to noisy labels, because the trained model recognizes noisy labels as difficult-to-learn and thus highlights them. In this study, we find that earlier approaches that used the provided labels to quantify difficulty could be affected by the small proportion of noisy labels. Furthermore, we find that running denoising algorithms before debiasing is ineffective because denoising algorithms reduce the impact of difficult-to-learn samples, including valuable bias-conflicting samples. Therefore, we propose an approach called denoising after entropy-based debiasing, i.e., DENEB, which has three main stages. (1) The prejudice model is trained by emphasizing (bias-aligned, clean) samples, which are selected using a Gaussian Mixture Model. (2) Using the per-sample entropy from the output of the prejudice model, the sampling probability of each sample that is proportional to the entropy is computed. (3) The final model is trained using existing denoising algorithms with the mini-batches constructed by following the computed sampling probability. Compared to existing debiasing and denoising algorithms, our method achieves better debiasing performance on multiple benchmarks.
Deep Digging into the Generalization of Self-Supervised Monocular Depth Estimation
Authors: Bae Jinwoo , Moon Sungho , Im Sunghoon
Published: June 2023

Self-supervised monocular depth estimation has been widely studied recently. Most of the work has focused on improving performance on benchmark datasets, such as KITTI, but has offered a few experiments on generalization performance. In this paper, we investigate the backbone networks (e.g., CNNs, Transformers, and CNN-Transformer hybrid models) toward the generalization of monocular depth estimation. We first evaluate state-of-the-art models on diverse public datasets, which have never been seen during the network training. Next, we investigate the effects of texture-biased and shape-biased representations using the various texture-shifted datasets that we generated. We observe that Transformers exhibit a strong shape bias and CNNs do a strong texture-bias. We also find that shape-biased models show better generalization performance for monocular depth estimation compared to texture-biased models. Based on these observations, we newly design a CNN-Transformer hybrid network with a multi-level adaptive feature fusion module, called MonoFormer. The design intuition behind MonoFormer is to increase shape bias by employing Transformers while compensating for the weak locality bias of Transformers by adaptively fusing multi-level representations. Extensive experiments show that the proposed method achieves state-of-the-art performance with various public datasets. Our method also shows the best generalization ability among the competitive methods.
Hybrid CNN-Transformer Feature Fusion for Single Image Deraining
Authors: Chen Xiang , Pan Jinshan , Lu Jiyang , Fan Zhentao , Li Hao
Published: June 2023

Since rain streaks exhibit diverse geometric appearances and irregular overlapped phenomena, these complex characteristics challenge the design of an effective single image deraining model. To this end, rich local-global information representations are increasingly indispensable for better satisfying rain removal. In this paper, we propose a lightweight Hybrid CNN-Transformer Feature Fusion Network (dubbed as HCT-FFN) in a stage-by-stage progressive manner, which can harmonize these two architectures to help image restoration by leveraging their individual learning strengths. Specifically, we stack a sequence of the degradation-aware mixture of experts (DaMoE) modules in the CNN-based stage, where appropriate local experts adaptively enable the model to emphasize spatially-varying rain distribution features. As for the Transformer-based stage, a background-aware vision Transformer (BaViT) module is employed to complement spatially-long feature dependencies of images, so as to achieve global texture recovery while preserving the required structure. Considering the indeterminate knowledge discrepancy among CNN features and Transformer features, we introduce an interactive fusion branch at adjacent stages to further facilitate the reconstruction of high-quality deraining results. Extensive evaluations show the effectiveness and extensibility of our developed HCT-FFN. The source code is available at https://github.com/cschenxiang/HCT-FFN.
OctFormer: Efficient Octree-Based Transformer for Point Cloud Compression with Local Enhancement
Authors: Cui Mingyue , Long Junhua , Feng Mingjian , Li Boyang , Kai Huang
Published: June 2023

Point cloud compression with a higher compression ratio and tiny loss is essential for efficient data transportation. However, previous methods that depend on 3D convolution or frequent multi-head self-attention operations bring huge computations. To address this problem, we propose an octree-based Transformer compression method called OctFormer, which does not rely on the occupancy information of sibling nodes. Our method uses non-overlapped context windows to construct octree node sequences and share the result of a multi-head self-attention operation among a sequence of nodes. Besides, we introduce a locally-enhance module for exploiting the sibling features and a positional encoding generator for enhancing the translation invariance of the octree node sequence. Compared to the previous state-of-the-art works, our method obtains up to 17% Bpp savings compared to the voxel-context-based baseline and saves an overall 99% coding time compared to the attention-based baseline.
Improving Crowded Object Detection via Copy-Paste
Authors: Deng Jiangfan , Fan Dewen , Qiu Xiaosong , Zhou Feng
Published: June 2023

Crowdedness caused by overlapping among similar objects is a ubiquitous challenge in the field of 2D visual object detection. In this paper, we first underline two main effects of the crowdedness issue: 1) IoU-confidence correlation disturbances (ICD) and 2) confused de-duplication (CDD). Then we explore a pathway of cracking these nuts from the perspective of data augmentation. Primarily, a particular copy- paste scheme is proposed towards making crowded scenes. Based on this operation, we first design a "consensus learning" method to further resist the ICD problem and then find out the pasting process naturally reveals a pseudo "depth" of object in the scene, which can be potentially used for alleviating CDD dilemma. Both methods are derived from magical using of the copy-pasting without extra cost for hand-labeling. Experiments show that our approach can easily improve the state-of-the-art detector in typical crowded detection task by more than 2% without any bells and whistles. Moreover, this work can outperform existing data augmentation strategies in crowded scenario.
Exploit Domain-Robust Optical Flow in Domain Adaptive Video Semantic Segmentation
Authors: Gao Yuan , Wang Zilei , Zhuang Jiafan , Zhang Yixin , Li Junjie
Published: June 2023

Domain adaptive semantic segmentation aims to exploit the pixel-level annotated samples on source domain to assist the segmentation of unlabeled samples on target domain. For such a task, the key is to construct reliable supervision signals on target domain. However, existing methods can only provide unreliable supervision signals constructed by segmentation model (SegNet) that are generally domain-sensitive. In this work, we try to find a domain-robust clue to construct more reliable supervision signals. Particularly, we experimentally observe the domain-robustness of optical flow in video tasks as it mainly represents the motion characteristics of scenes. However, optical flow cannot be directly used as supervision signals of semantic segmentation since both of them essentially represent different information. To tackle this issue, we first propose a novel Segmentation-to-Flow Module (SFM) that converts semantic segmentation maps to optical flows, named the segmentation-based flow (SF), and then propose a Segmentation-based Flow Consistency (SFC) method to impose consistency between SF and optical flow, which can implicitly supervise the training of segmentation model. The extensive experiments on two challenging benchmarks demonstrate the effectiveness of our method, and it outperforms previous state-of-the-art methods with considerable performance improvement. Our code is available at https://github.com/EdenHazardan/SFC.
Progressive Multi-View Human Mesh Recovery with Self-Supervision
Authors: Gong Xuan , Song Liangchen , Zheng Meng , Planche Benjamin , Chen Terrence , Yuan Junsong , Doermann David , Wu Ziyan
Published: June 2023

To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e.g., motion capture, sport analysis) and robustness to single-view ambiguities. Existing solutions typically suffer from poor generalization performance to new settings, largely due to the limited diversity of image/3D-mesh pairs in multi-view training data. To address this shortcoming, people have explored the use of synthetic images. But besides the usual impact of visual gap between rendered and target data, synthetic-data-driven multi-view estimators also suffer from overfitting to the camera viewpoint distribution sampled during training which usually differs from real-world distributions. Tackling both challenges, we propose a novel simulation-based training pipeline for multi-view human mesh recovery, which (a) relies on intermediate 2D representations which are more robust to synthetic-to-real domain gap; (b) leverages learnable calibration and triangulation to adapt to more diversified camera setups; and (c) progressively aggregates multi-view information in a canonical 3D space to remove ambiguities in 2D representations. Through extensive benchmarking, we demonstrate the superiority of the proposed solution especially for unseen in-the-wild scenarios.
Self-Supervised Learning for Multilevel Skeleton-Based Forgery Detection via Temporal-Causal Consistency of Actions
Authors: Hu Liang , Liu Dora D. , Zhang Qi , Naseem Usman , Lai Zhong Yuan
Published: June 2023

Skeleton-based human action recognition and analysis have become increasingly attainable in many areas, such as security surveillance and anomaly detection. Given the prevalence of skeleton-based applications, tampering attacks on human skeletal features have emerged very recently. In particular, checking the temporal inconsistency and/or incoherence (TII) in the skeletal sequence of human action is a principle of forgery detection. To this end, we propose an approach to self-supervised learning of the temporal causality behind human action, which can effectively check TII in skeletal sequences. Especially, we design a multilevel skeleton-based forgery detection framework to recognize the forgery on frame level, clip level, and action level in terms of learning the corresponding temporal-causal skeleton representations for each level. Specifically, a hierarchical graph convolution network architecture is designed to learn low-level skeleton representations based on physical skeleton connections and high-level action representations based on temporal-causal dependencies for specific actions. Extensive experiments consistently show state-of-the-art results on multilevel forgery detection tasks and superior performance of our framework compared to current competing methods.
ClassFormer: Exploring Class-Aware Dependency with Transformer for Medical Image Segmentation
Authors: Huang Huimin , Xie Shiao , Lin Lanfen , Tong Ruofeng , Chen Yen-Wei , Wang Hong , Li Yuexiang , Huang Yawen , Zheng Yefeng
Published: June 2023

Vision Transformers have recently shown impressive performances on medical image segmentation. Despite their strong capability of modeling long-range dependencies, the current methods still give rise to two main concerns in a class-level perspective: (1) intra-class problem: the existing methods lacked in extracting class-specific correspondences of different pixels, which may lead to poor object coverage and/or boundary prediction; (2) inter-class problem: the existing methods failed to model explicit category-dependencies among various objects, which may result in inaccurate localization. In light of these two issues, we propose a novel transformer, called ClassFormer, powered by two appealing transformers, i.e., intra-class dynamic transformer and inter-class interactive transformer, to address the challenge of fully exploration on compactness and discrepancy. Technically, the intra-class dynamic transformer is first designed to decouple representations of different categories with an adaptive selection mechanism for compact learning, which optimally highlights the informative features to reflect the salient keys/values from multiple scales. We further introduce the inter-class interactive transformer to capture the category dependency among different objects, and model class tokens as the representative class centers to guide a global semantic reasoning. As a consequence, the feature consistency is ensured with the expense of intra-class penalization, while inter-class constraint strengthens the feature discriminability between different categories. Extensive empirical evidence shows that ClassFormer can be easily plugged into any architecture, and yields improvements over the state-of-the-art methods in three public benchmarks.
Symmetry-Aware Transformer-Based Mirror Detection
Authors: Huang Tianyu , Dong Bowen , Lin Jiaying , Liu Xiaohui , W.H. Lau Rynson , Zuo Wangmeng
Published: June 2023

Mirror detection aims to identify the mirror regions in the given input image. Existing works mainly focus on integrating the semantic features and structural features to mine specific relations between mirror and non-mirror regions, or introducing mirror properties like depth or chirality to help analyze the existence of mirrors. In this work, we observe that a real object typically forms a loose symmetry relationship with its corresponding reflection in the mirror, which is beneficial in distinguishing mirrors from real objects. Based on this observation, we propose a dual-path Symmetry-Aware Transformer-based mirror detection Network (SATNet), which includes two novel modules: Symmetry-Aware Attention Module (SAAM) and Contrast and Fusion Decoder Module (CFDM). Specifically, we first adopt a transformer backbone to model global information aggregation in images, extracting multi-scale features in two paths. We then feed the high-level dual-path features to SAAMs to capture the symmetry relations. Finally, we fuse the dual-path features and refine our prediction maps progressively with CFDMs to obtain the final mirror mask. Experimental results show that SATNet outperforms both RGB and RGB-D mirror detection methods on all available mirror detection datasets.
AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio
Authors: Huang Xiaoyang , Wang Yanjun , Liu Yang , Ni Bingbing , Zhang Wenjun , Liu Jinxian , Li Teng
Published: June 2023

Spatial audio, which focuses on immersive 3D sound rendering, is widely applied in the acoustic industry. One of the key problems of current spatial audio rendering methods is the lack of personalization based on different anatomies of individuals, which is essential to produce accurate sound source positions. In this work, we address this problem from an interdisciplinary perspective. The rendering of spatial audio is strongly correlated with the 3D shape of human bodies, particularly ears. To this end, we propose to achieve personalized spatial audio by reconstructing 3D human ears with single-view images. First, to benchmark the ear reconstruction task, we introduce AudioEar3D, a high-quality 3D ear dataset consisting of 112 point cloud ear scans with RGB images. To self-supervisedly train a reconstruction model, we further collect a 2D ear dataset composed of 2,000 images, each one with manual annotation of occlusion and 55 landmarks, named AudioEar2D. To our knowledge, both datasets have the largest scale and best quality of their kinds for public use. Further, we propose AudioEarM, a reconstruction method guided by a depth estimation network that is trained on synthetic data, with two loss functions tailored for ear data. Lastly, to fill the gap between the vision and acoustics community, we develop a pipeline to integrate the reconstructed ear mesh with an off-the-shelf 3D human body and simulate a personalized Head-Related Transfer Function (HRTF), which is the core of spatial audio rendering. Code and data are publicly available in https://github.com/seanywang0408/AudioEar.
Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation
Authors: Kim Daehan , Seo Minseok , Park Kwanyong , Shin Inkyu , Woo Sanghyun , Kweon In So , Choi Dong-Geol
Published: June 2023

Mixup provides interpolated training samples and allows the model to obtain smoother decision boundaries for better generalization. The idea can be naturally applied to the domain adaptation task, where we can mix the source and target samples to obtain domain-mixed samples for better adaptation. However, the extension of the idea from classification to segmentation (i.e., structured output) is nontrivial. This paper systematically studies the impact of mixup under the domain adaptive semantic segmentation task and presents a simple yet effective mixup strategy called Bidirectional Domain Mixup (BDM). In specific, we achieve domain mixup in two-step: cut and paste. Given the warm-up model trained from any adaptation techniques, we forward the source and target samples and perform a simple threshold-based cut out of the unconfident regions (cut). After then, we fill-in the dropped regions with the other domain region patches (paste). In doing so, we jointly consider class distribution, spatial structure, and pseudo label confidence. Based on our analysis, we found that BDM leaves domain transferable regions by cutting, balances the dataset-level class distribution while preserving natural scene context by pasting. We coupled our proposal with various state-of-the-art adaptation models and observe significant improvement consistently. We also provide extensive ablation experiments to empirically verify our main components of the framework. Visit our project page with the code at https://sites.google.com/view/bidirectional-domain-mixup
Frequency Selective Augmentation for Video Representation Learning
Authors: Kim Jinhyung , Kim Taeoh , Shim Minho , Han Dongyoon , Wee Dongyoon , Kim Junmo
Published: June 2023

Recent self-supervised video representation learning methods focus on maximizing the similarity between multiple augmented views from the same video and largely rely on the quality of generated views. However, most existing methods lack a mechanism to prevent representation learning from bias towards static information in the video. In this paper, we propose frequency augmentation (FreqAug), a spatio-temporal data augmentation method in the frequency domain
for video representation learning. FreqAug stochastically removes specific frequency components from the video so that learned representation captures essential features more from the remaining information for various downstream tasks. Specifically, FreqAug pushes the model to focus more on dynamic features rather than static features in the video via dropping spatial or temporal low-frequency components. To verify the generality of the proposed method, we experiment with FreqAug on multiple self-supervised learning frameworks along with standard augmentations. Transferring the improved representation to five video action recognition and two temporal action localization downstream tasks shows consistent improvements over baselines.
Pose-Guided 3D Human Generation in Indoor Scene
Authors: Kim Minseok , Kang Changwoo , Park Jeongin , Joo Kyungdon
Published: June 2023

In this work, we address the problem of scene-aware 3D human avatar generation based on human-scene interactions. In particular, we pay attention to the fact that physical contact between a 3D human and a scene (i.e., physical human-scene interactions) requires a geometrical alignment to generate natural 3D human avatar. Motivated by this fact, we present a new 3D human generation framework that considers geometric alignment on potential contact areas between 3D human avatars and their surroundings. In addition, we introduce a compact yet effective human pose classifier that classifies the human pose and provides potential contact areas of the 3D human avatar. It allows us to adaptively use geometric alignment loss according to the classified human pose. Compared to state-of-the-art method, our method can generate physically and semantically plausible 3D humans that interact naturally with 3D scenes without additional post-processing. In our evaluations, we achieve the improvements with more plausible interactions and more variety of poses than prior research in qualitative and quantitative analysis. Project page: https://bupyeonghealer.github.io/phin/.
Intersection Coordination with Priority-Based Search for Autonomous Vehicles
Authors: Li Jiaoyang , Hoang The Anh , Lin Eugene , Vu Hai L. , Koenig Sven
Published: June 2023

The development of connected and autonomous vehicles opens an opportunity to manage intersections without signals. One promising approach is to use a central autonomous intersection manager to optimize the movement of the vehicles in the intersection. Existing work uses Mixed Integer Linear Programming (MILP) to find optimal solutions for this problem but is time-consuming and cannot be applied in real-time. On the other hand, the coordination of the vehicles is essentially a Multi-Agent Path Finding (MAPF) problem, for which dozens of efficient algorithms have been proposed in recent years. Inspired by these MAPF algorithms, we propose a three-level algorithm called PSL to solve the intersection coordination problem. Theoretically, PSL is complete and polynomial-time in the number of vehicles. Empirically, PSL runs significantly faster with only a slight compromise in the solution quality than the optimal MILP method. It also generates significantly better solutions with a slightly larger runtime than the traditional First-Come-First-Served strategy.
Resource Sharing through Multi-Round Matchings
Authors: Trabelsi Yohai , Adiga Abhijin , Kraus Sarit , Ravi S. S. , Rosenkrantz Daniel J.
Published: June 2023

Applications such as employees sharing office spaces over a workweek
can be modeled as problems where agents are matched to resources
over multiple rounds. Agents' requirements limit the set of compatible
resources and the rounds in which they want to be matched. Viewing such an
application as a multi-round matching problem on a bipartite compatibility
graph between agents and resources, we show that a solution
(i.e., a set of matchings, with one matching per round) can be found
efficiently if one exists. To cope with situations where a solution does not exist, we consider two extensions. In
the first extension, a benefit function is defined for each agent and the
objective is to find a multi-round matching to maximize the total benefit. For a
general class of benefit functions satisfying certain properties (including
diminishing returns), we show that this multi-round matching problem is
efficiently solvable. This class includes utilitarian and Rawlsian welfare
functions.
For another benefit function, we show that the maximization
problem is NP-hard.
In the second extension, the objective is to generate advice to
each agent (i.e., a subset of requirements to be relaxed) subject to a
budget constraint so that the agent can be matched.
We show that this budget-constrained advice generation problem is NP-hard.
For this problem, we develop an integer linear programming formulation as well
as a heuristic based on local search.
We experimentally evaluate our algorithms on
synthetic networks and apply them to two real-world situations: shared
office spaces and matching courses to classrooms.
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principles
Authors: Zeng Xianghua , Peng Hao , Li Angsheng
Published: June 2023

Role-based learning is a promising approach to improving the performance of Multi-Agent Reinforcement Learning (MARL). Nevertheless, without manual assistance, current role-based methods cannot guarantee stably discovering a set of roles to effectively decompose a complex task, as they assume either a predefined role structure or practical experience for selecting hyperparameters. In this article, we propose a mathematical Structural Information principles-based Role Discovery method, namely SIRD, and then present a SIRD optimizing MARL framework, namely SR-MARL, for multi-agent collaboration. The SIRD transforms role discovery into a hierarchical action space clustering. Specifically, the SIRD consists of structuralization, sparsification, and optimization modules, where an optimal encoding tree is generated to perform abstracting to discover roles. The SIRD is agnostic to specific MARL algorithms and flexibly integrated with various value function factorization approaches. Empirical evaluations on the StarCraft II micromanagement benchmark demonstrate that, compared with state-of-the-art MARL algorithms, the SR-MARL framework improves the average test win rate by 0.17%, 6.08%, and 3.24%, and reduces the deviation by 16.67%, 30.80%, and 66.30%, under easy, hard, and super hard scenarios.
Privacy Attacks on Schedule-Driven Data
Authors: Fahrenkrog-Petersen Stephan A. , Senderovich Arik , Tichauer Alexandra , Tutak Ali Kaan , Beck J. Christopher , Weidlich Matthias
Published: June 2023

Schedules define how resources process jobs in diverse domains, reaching from healthcare to transportation, and, therefore, denote a valuable starting point for analysis of the underlying system. However, publishing a schedule may disclose private information on the considered jobs. In this paper, we provide a first threat model for published schedules, thereby defining a completely new class of data privacy problems. We then propose distance-based measures to assess the privacy loss incurred by a published schedule, and show their theoretical properties for an uninformed adversary, which can be used as a benchmark for informed attacks. We show how an informed attack on a published schedule can be phrased as an inverse scheduling problem. We instantiate this idea by formulating the inverse of a well-studied single-machine scheduling problem, namely minimizing the total weighted completion times. An empirical evaluation for synthetic scheduling problems shows the effectiveness of informed privacy attacks and compares the results to theoretical bounds on uninformed attacks.
Markov Decision Processes with Time-Varying Geometric Discounting
Authors: Gan Jiarui , Hennes Annika , Majumdar Rupak , Mandal Debmalya , Radanovic Goran
Published: June 2023

Canonical models of Markov decision processes (MDPs) usually consider geometric discounting based on a constant discount factor. While this standard modeling approach has led to many elegant results, some recent studies indicate the necessity of modeling time-varying discounting in certain applications. This paper studies a model of infinite-horizon MDPs with time-varying discount factors. We take a game-theoretic perspective – whereby each time step is treated as an independent decision maker with their own (fixed) discount factor – and we study the subgame perfect equilibrium (SPE) of the resulting game as well as the related algorithmic problems. We present a constructive proof of the existence of an SPE and demonstrate the EXPTIME-hardness of computing an SPE. We also turn to the approximate notion of epsilon-SPE and show that an epsilon-SPE exists under milder assumptions. An algorithm is presented to compute an epsilon-SPE, of which an upper bound of the time complexity, as a function of the convergence property of the time-varying discount factor, is provided.
Safe Interval Path Planning with Kinodynamic Constraints
Authors: Ali Zain Alabedeen , Yakovlev Konstantin
Published: June 2023

Safe Interval Path Planning (SIPP) is a powerful algorithm for solving a single-agent pathfinding problem where the agent is confined to a graph and certain vertices/edges of this graph are blocked at certain time intervals due to dynamic obstacles that populate the environment. The original SIPP algorithm relies on the assumption that the agent is able to stop instantaneously. However, this assumption often does not hold in practice, e.g. a mobile robot moving at a cruising speed cannot stop immediately but rather requires gradual deceleration to a full stop that takes time. In other words, the robot is subject to kinodynamic constraints. Unfortunately, as we show in this work, in such a case, the original SIPP is incomplete. To this end, we introduce a novel variant of SIPP that is provably complete and optimal for planning with acceleration/deceleration. In the experimental evaluation, we show that the key property of the original SIPP still holds for the modified version: it performs much fewer expansions compared to A* and, as a result, is notably faster.
Diversity Maximization in the Presence of Outliers
Authors: Amagata Daichi
Published: June 2023

Given a set X of n points in a metric space, the problem of diversity maximization is to extract a set S of k points from X so that the diversity of S is maximized. This problem is essential in AI-related fields, such as web search, databases, recommender systems, and data mining. Although there have been extensive studies of this problem, these studies assume that X is clean. This usually does not hold, because real-world datasets usually contain outliers. The state-of-the-art algorithm for the diversity maximization problem is based on furthest point retrieval, which is too sensitive to outliers. We therefore address the problem of diversity maximization with outliers and propose two algorithms with performance guarantee. The first algorithm runs in O((k+z)n) time, guarantees 1/2-approximation, and returns no outliers, where z is the number of outliers. The second algorithm runs in O(kz) time (which is independent of n), guarantees 1/6(1+epsilon)-approximation, and returns no outliers with constant probability. We conduct experiments on real datasets to demonstrate the effectiveness and efficiency of our algorithms.
SegFormer: A Topic Segmentation Model with Controllable Range of Attention
Authors: Bai Haitao , Wang Pinghui , Zhang Ruofei , Su Zhou
Published: June 2023

Topic segmentation aims to reveal the latent structure of a document and divide it into multiple parts. However, current neural solutions are limited in the context modeling of sentences and feature representation of candidate boundaries. This causes the model to suffer from inefficient sentence context encoding and noise information interference. In this paper, we design a new text segmentation model SegFormer with unidirectional attention blocks to better model sentence representations. To alleviate the problem of noise information interference, SegFormer uses a novel additional context aggregator and a topic classification loss to guide the model to aggregate the information within the appropriate range. In addition, SegFormer applies an iterative prediction algorithm to search for optimal boundaries progressively. We evaluate SegFormer's generalization ability, multilingual ability, and application ability on multiple challenging real-world datasets. Experiments show that our model significantly improves the performance by 7.5% on the benchmark WIKI-SECTION compared to several strong baselines. The application of SegFormer to a real-world dataset to separate normal and advertisement segments in product marketing essays also achieves superior performance in the evaluation with other cutting-edge models.
Zero-Shot Cross-Lingual Event Argument Extraction with Language-Oriented Prefix-Tuning
Authors: Cao Pengfei , Jin Zhuoran , Chen Yubo , Liu Kang , Zhao Jun
Published: June 2023

Event argument extraction (EAE) aims to identify the arguments of a given event, and classify the roles that those arguments play. Due to high data demands of training EAE models, zero-shot cross-lingual EAE has attracted increasing attention, as it greatly reduces human annotation effort. Some prior works indicate that generation-based methods have achieved promising performance for monolingual EAE. However, when applying existing generation-based methods to zero-shot cross-lingual EAE, we find two critical challenges, including Language Discrepancy and Template Construction. In this paper, we propose a novel method termed as Language-oriented Prefix-tuning Network (LAPIN) to address the above challenges. Specifically, we devise a Language-oriented Prefix Generator module to handle the discrepancies between source and target languages. Moreover, we leverage a Language-agnostic Template Constructor module to design templates that can be adapted to any language. Extensive experiments demonstrate that our proposed method achieves the best performance, outperforming the previous state-of-the-art model by 4.8% and 2.3% of the average F1-score on two multilingual EAE datasets.
Preference-Controlled Multi-Objective Reinforcement Learning for Conditional Text Generation
Authors: Chen Wenqing , Tian Jidong , Fan Caoyun , Li Yitian , He Hao , Jin Yaohui
Published: June 2023

Conditional text generation is to generate text sequences conditioning on linguistic or non-linguistic data. The main line of existing work proposed deterministic models to improve the fidelity of the generated text but often ignored the diversity. Another line relied on conditional variational auto-encoders (CVAEs), which increased the diversity over their deterministic backbones. However, CVAEs regard diversity as an implicit objective and may not be optimal. In this paper, we raise two questions: i) Can diversity be further improved with an explicit objective? ii) Since fidelity and diversity are two conflicting objectives, how can we obtain different multi-objective optimal solutions according to user preferences? To answer question i), we propose a multi-objective reinforcement learning (MORL) method which explicitly takes CIDEr and Self-CIDEr scores as the fidelity-oriented and diversity-oriented rewards respectively. To answer question ii), we propose a preference-controlled MORL method, which can obtain infinite multi-objective optimal solutions by tuning the preference variable. We conduct extensive experiments on paraphrasing and image captioning tasks, which show that in the fidelity-diversity trade-off space, our model outperforms both deterministic and CVAE-based baselines.
Learn from Yesterday: A Semi-supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams
Authors: Chen Yongrui , Guo Xinnan , Wu Tongtong , Qi Guilin , Li Yang , Dong Yang
Published: June 2023

Conventional text-to-SQL studies are limited to a single task with a fixed-size training and test set. When confronted with a stream of tasks common in real-world applications, existing methods struggle with the problems of insufficient supervised data and high retraining costs. The former tends to cause overfitting on unseen databases for the new task, while the latter makes a full review of instances from past tasks impractical for the model, resulting in forgetting of learned SQL structures and database schemas. To address the problems, this paper proposes integrating semi-supervised learning (SSL) and continual learning (CL) in a stream of text-to-SQL tasks and offers two promising solutions in turn. The first solution Vanilla is to perform self-training, augmenting the supervised training data with predicted pseudo-labeled instances of the current task, while replacing the full volume retraining with episodic memory replay to balance the training efficiency with the performance of previous tasks. The improved solution SFNet takes advantage of the intrinsic connection between CL and SSL. It uses in-memory past information to help current SSL, while adding high-quality pseudo instances in memory to improve future replay. The experiments on two datasets shows that SFNet outperforms the widely-used SSL-only and CL-only baselines on multiple metrics.