bluuebunny/crossref_metadata_2025 huggingface dataset:
Tillträdeskrav i allmännyttan 2013–2022 : SBV Working Paper Series 23:3
Authors: Grander Martin , Sjöland Kozlovic Martina
Published: August 2023

Den här rapporten tar upp en central aspekt kring allmännyttans roll på den svenska bostadsmarknaden, nämligen vilka krav och regler som påverkar vilka som får tillträde till dessa bostäder. Allmännyt­tans särställning som en garant för en generell tillgång till bra bostäder för alla avgörs i praktiken av lokala regler hos de kommunala allmännyttiga bostadsbolagen. Rapporten presenterar en systematisk uppföljning år 2022 av en tidigare rikstäckande undersökning från 2013 kring lokala tillträdeskrav och syftar till att följa upp tillträdeskraven i den svenska allmännyttiga bostadssektorn med fokus på förändringar i kraven mellan åren 2013–2022 samt möjliga orsaker till sådana förändringar. Undersökningen har genomförts inom ramen för det Formas-finansierade projektet “Allmännyttan och blandningspolitikens praktik” (2018–2023), som har syftat till att skapa en fördjupad kunskap om allmännyttans roll på svensk bostadsmarknad, särskilt när det gäller dess funktion att medverka till en allsidig sammansättning av hushåll i kommunerna. Flera intres­santa skillnader i kraven framträder i jämförel­sen. En första slutsats är att kravet på inkomst har sänkts och förändrat karaktär i många av de kommunala bostadsföretagen. En andra slutsats är att andra typer av krav, som kan sammanfattas som skötsamhetskrav, har blivit vanligare. Utifrån denna iakttagelse går det att resone­ra om huruvida det är samma krav som ställs men i en annorlunda utformning. Mycket ty­der på att det är samma grupper som stängs ute från de kommunala hyresmarknaden 2022 som 2013. Sammantaget ger denna studie vid handen att trösklarna fortsatt är höga in på den allmännyttiga hyresmarknaden, vilket i sin tur begränsar möjligheterna för vissa grupper av hushåll på den svenska bostadsmarknaden i allmänhet. Det finns fortfarande finns en ”omvänd selektivitet” i svensk allmännytta, det vill säga att allmännyttan i många kommuner vänder sig främst till de hushåll som har goda ekonomiska och sociala resurser, snarare än de som har det sämst ställt.
Desobediência civil: origens, evolução e aplicação na Constituição Federal de 1988
Authors: Ribeiro Bianchini Caroline , Saueressig Joel
Published: August 2023

Trata o presente trabalho de uma leitura do instituto da desobediência civil. Para tanto se faz necessário um apanhado histórico da aplicação do antigo direito à revolução e suas consequências no direito ocidental, em especial da formação do Estado Liberal burguês. Uma vez definidas as bases do liberalismo, o outrora direito à revolução permaneceu em alguns documentos como um direito de resistência à opressão. Para tanto são analisados processos históricos de três países que se mostraram importantes para o Liberalismo como Inglaterra, Estados Unidos e França. Finalmente, se faz uma análise do instituto da desobediência civil como uma ramificação do direito de resistência e sua conceituação, especialmente das origens desse direito até a sua receptividade pela Constituição Federal de 1988 no art. 5º, parágrafo segundo.
Compositional Zero-Shot Artistic Font Synthesis
Authors: Li Xiang , Wu Lei , Wang Changshuo , Meng Lei , Meng Xiangxu
Published: August 2023

Recently, many researchers have made remarkable achievements in the field of artistic font synthesis, with impressive glyph style and effect style in the results. However, due to less exploration in style disentanglement, it is difficult for existing methods to envision a kind of unseen style (glyph-effect) compositions of artistic font, and thus can only learn the seen style compositions. To solve this problem, we propose a novel compositional zero-shot artistic font synthesis gan (CAFS-GAN), which allows the synthesis of unseen style compositions by exploring the visual independence and joint compatibility of encoding semantics between glyph and effect. Specifically, we propose two contrast-based style encoders to achieve style disentanglement due to glyph and effect intertwining in the image. Meanwhile, to preserve more glyph and effect detail, we propose a generator based on hierarchical dual styles AdaIN to reorganize content-styles representations from structure to texture gradually. Extensive experiments demonstrate the superiority of our model in generating high-quality artistic font images with unseen style compositions against other state-of-the-art methods. The source code and data is available at moonlight03.github.io/CAFS-GAN/.
Low-Confidence Samples Mining for Semi-supervised Object Detection
Authors: Liu Guandu , Zhang Fangyuan , Pan Tianxiang , Yong Jun-Hai , Wang Bin
Published: August 2023

Reliable pseudo labels from unlabeled data play a key role in semi-supervised object detection (SSOD). However, the state-of-the-art SSOD methods all rely on pseudo labels with high confidence, which ignore valuable pseudo labels with lower confidence. Additionally, the insufficient excavation for unlabeled data results in an excessively low recall rate thus hurting the network training. In this paper, we propose a novel Low-confidence Samples Mining (LSM) method to utilize low confidence pseudo labels efficiently. Specifically, we develop an additional pseudo information mining (PIM) branch on account of low-resolution feature maps to extract reliable large area instances, the IoUs of which are higher than small area ones. Owing to the complementary predictions between PIM and the main branch, we further design self-distillation (SD) to compensate for both in a mutually learning manner. Meanwhile, the extensibility of the above approaches enables our LSM to apply to Faster-RCNN and Deformable-DETR respectively. On the MS-COCO benchmark, our method achieves 3.54% mAP improvement over state-of-the-art methods under 5% labeling ratios.
Learning Prototype Classifiers for Long-Tailed Recognition
Authors: Sharma Saurabh , Xian Yongqin , Yu Ning , Singh Ambuj
Published: August 2023

The problem of long-tailed recognition (LTR) has received attention in recent years due to the fundamental power-law distribution of objects in the real-world. Most recent works in LTR use softmax classifiers that are biased in that they correlate classifier norm with the amount of training data for a given class. In this work, we show that learning prototype classifiers addresses the biased softmax problem in LTR. Prototype classifiers can deliver promising results simply using Nearest-Class-Mean (NCM), a special case where prototypes are empirical centroids. We go one step further and propose to jointly learn prototypes by using distances to prototypes in representation space as the logit scores for classification. Further, we theoretically analyze the properties of Euclidean distance based prototype classifiers that lead to stable gradient-based optimization which is robust to outliers. To enable independent distance scales along each channel, we enhance Prototype classifiers by learning channel-dependent temperature parameters. Our analysis shows that prototypes learned by Prototype classifiers are better separated than empirical centroids. Results on four LTR benchmarks show that Prototype classifier outperforms or is comparable to state-of-the-art methods. Our code is made available at https://github.com/saurabhsharma1993/prototype-classifier-ltr.
Domain-Adaptive Self-Supervised Face & Body Detection in Drawings
Authors: Topal Barış Batuhan , Yuret Deniz , Sezgin Tevfik Metin
Published: August 2023

Drawings are powerful means of pictorial abstraction and communication. Understanding diverse forms of drawings, including digital arts, cartoons, and comics, has been a major problem of interest for the computer vision and computer graphics communities. Although there are large amounts of digitized drawings from comic books and cartoons, they contain vast stylistic variations, which necessitate expensive manual labeling for training domain-specific recognizers. In this work, we show how self-supervised learning, based on a teacher-student network with a modified student network update design, can be used to build face and body detectors. Our setup allows exploiting large amounts of unlabeled data from the target domain when labels are provided for only a small subset of it. We further demonstrate that style transfer can be incorporated into our learning pipeline to bootstrap detectors using a vast amount of out-of-domain labeled images from natural images (i.e., images from the real world). Our combined architecture yields detectors with state-of-the-art (SOTA) and near-SOTA performance using minimal annotation effort. Our code can be accessed from https://github.com/barisbatuhan/DASS_Detector.
Align, Perturb and Decouple: Toward Better Leverage of Difference Information for RSI Change Detection
Authors: Wang Supeng , Li Yuxi , Xie Ming , Chi Mingmin , Wang Yabiao , Wang Chengjie , Zhu Wenbing
Published: August 2023

Change detection is a widely adopted technique in remote sense imagery (RSI) analysis in the discovery of long-term geomorphic evolution. To highlight the areas of semantic changes, previous effort mostly pays attention to learning representative feature descriptors of a single image, while the difference information is either modeled with simple difference operations or implicitly embedded via feature interactions. Nevertheless, such difference modeling can be noisy since it suffers from non-semantic changes and lacks explicit guidance from image content or context. In this paper, we revisit the importance of feature difference for change detection in RSI, and propose a series of operations to fully exploit the difference information: Alignment, Perturbation and Decoupling (APD). Firstly, alignment leverages contextual similarity to compensate for the non-semantic difference in feature space. Next, a difference module trained with semantic-wise perturbation is adopted to learn more generalized change estimators, which reversely bootstraps feature extraction and prediction. Finally, a decoupled dual-decoder structure is designed to predict semantic changes in both content-aware and content-agnostic manners. Extensive experiments are conducted on benchmarks of LEVIR-CD, WHU-CD and DSIFN-CD, demonstrating our proposed operations bring significant improvement and achieve competitive results under similar comparative conditions. Code is available at https://github.com/wangsp1999/CD-Research/tree/main/openAPD
Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification
Authors: Wang Zhiwei , Xian Junlin , Liu Kangyi , Li Xin , Li Qiang , Yang Xin
Published: August 2023

Mammogram image is important for breast cancer screening, and typically obtained in a dual-view form, i.e., cranio-caudal (CC) and mediolateral oblique (MLO), to provide complementary information for clinical decisions. However, previous methods mostly learn features from the two views independently, which violates the clinical knowledge and ignores the importance of dual-view correlation in the feature learning. In this paper, we propose a dual-view correlation hybrid attention network (DCHA-Net) for robust holistic mammogram classification. Specifically, DCHA-Net is carefully designed to extract and reinvent deep feature maps for the two views, and meanwhile to maximize the underlying correlations between them. A hybrid attention module, consisting of local relation and non-local attention blocks, is proposed to alleviate the spatial misalignment of the paired views in the correlation maximization. A dual-view correlation loss is introduced to maximize the feature similarity between corresponding strip-like regions with equal distance to the chest wall, motivated by the fact that their features represent the same breast tissues, and thus should be highly-correlated with each other. Experimental results on the two public datasets, i.e., INbreast and CBIS-DDSM, demonstrate that the DCHA-Net can well preserve and maximize feature correlations across views, and thus outperforms previous state-of-the-art methods for classifying a whole mammogram as malignant or not.
Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention
Authors: Wu Zizhang , Li Zhuozheng , Fan Zhi-Gang , Wu Yunzhe , Gan Yuanzhu , Pu Jian
Published: August 2023

The monocular depth estimation task has recently revealed encouraging prospects, especially for the autonomous driving task. To tackle the ill-posed problem of 3D geometric reasoning from 2D monocular images, multi-frame monocular methods are developed to leverage the perspective correlation information from sequential temporal frames. However, moving objects such as cars and trains usually violate the static scene assumption, leading to feature inconsistency deviation and misaligned cost values, which would mislead the optimization algorithm. In this work, we present CTA-Depth, a Context-aware Temporal Attention guided network for multi-frame monocular Depth estimation. Specifically, we first apply a multi-level attention enhancement module to integrate multi-level image features to obtain an initial depth and pose estimation. Then the proposed CTA-Refiner is adopted to alternatively optimize the depth and pose. During the CTA-Refiner process, context-aware temporal attention (CTA) is developed to capture the global temporal-context correlations to maintain the feature consistency and estimation integrity of moving objects. In particular, we propose a long-range geometry embedding (LGE) module to produce a long-range temporal geometry prior. Our approach achieves significant improvements (e.g., 13.5% for the Abs Rel metric on the KITTI dataset) over state-of-the-art approaches on three benchmark datasets.
Vision Language Navigation with Knowledge-driven Environmental Dreamer
Authors: Zhu Fengda , Lee Vincent CS , Chang Xiaojun , Liang Xiaodan
Published: August 2023

Vision-language navigation (VLN) requires an agent to perceive visual observation in a house scene and navigate step-by-step following natural language instruction. Due to the high cost of data annotation and data collection, current VLN datasets provide limited instruction-trajectory data samples. Learning vision-language alignment for VLN from limited data is challenging since visual observation and language instruction are both complex and diverse. Previous works only generate augmented data based on original scenes while failing to generate data samples from unseen scenes, which limits the generalization ability of the navigation agent. In this paper, we introduce the Knowledge-driven Environmental Dreamer (KED), a method that leverages the knowledge of the embodied environment and generates unseen scenes for a navigation agent to learn. Generating an unseen environment with texture consistency and structure consistency is challenging. To address this problem, we incorporate three knowledge-driven regularization objectives into the KED and adopt a reweighting mechanism for self-adaptive optimization. Our KED method is able to generate unseen embodied environments without extra annotations. We use KED to successfully generate 270 houses and 500K instruction-trajectory pairs. The navigation agent with the KED method outperforms the state-of-the-art methods on various VLN benchmarks, such as R2R, R4R, and RxR. Both qualitative and quantitative experiments prove that our proposed KED method is able to high-quality augmentation data with texture consistency and structure consistency.
Differentiable Model Selection for Ensemble Learning
Authors: Kotary James , Di Vito Vincenzo , Fioretto Ferdinando
Published: August 2023

Model selection is a strategy aimed at creating accurate and robust models by identifying the optimal model for classifying any particular input sample. This paper proposes a novel framework for differentiable selection of groups of models by integrating machine learning and combinatorial optimization.

The framework is tailored for ensemble learning with a strategy that learns to combine the predictions of appropriately selected pre-trained ensemble models. It does so by modeling the ensemble learning task as a differentiable selection program trained end-to-end over a pretrained ensemble to optimize task performance. The proposed framework demonstrates its versatility and effectiveness, outperforming conventional and advanced consensus rules across a variety of classification tasks.
Solving the Identifying Code Set Problem with Grouped Independent Support
Authors: Latour Anna L.D. , Sen Arunabha , Meel Kuldeep S.
Published: August 2023

An important problem in network science is finding an optimal placement of sensors in nodes in order to uniquely detect failures in the network. This problem can be modelled as an identifying code set (ICS) problem, introduced by Karpovsky et al. in 1998. The ICS problem aims to find a cover of a set S, such that the elements in the cover define a unique signature for each of the elements of S, and to minimise the cover’s cardinality. In this work, we study a generalised identifying code set (GICS) problem, where a unique signature must be found for each subset of S that has a cardinality of at most k (instead of just each element of S). The concept of an independent support of a Boolean formula was introduced by Chakraborty et al. in 2014 to speed up propositional model counting, by identifying a subset of variables whose truth assignments uniquely define those of the other variables.

In this work, we introduce an extended version of independent support, grouped independent support (GIS), and show how to reduce the GICS problem to the GIS problem. We then propose a new solving method for finding a GICS, based on finding a GIS. We show that the prior state-of-the-art approaches yield integer-linear programming (ILP) models whose sizes grow exponentially with the problem size and k, while our GIS encoding only grows polynomially with the problem size and k. While the ILP approach can solve the GICS problem on networks of at most 494 nodes, the GIS-based method can handle networks of up to 21 363 nodes; a ∼40× improvement. The GIS-based method shows up to a 520× improvement on the ILP-based method in terms of median solving time. For the majority of the instances that can be encoded and solved by both methods, the cardinality of the solution returned by the GIS-based method is less than 10% larger than the cardinality of the solution found by the ILP method.
CSGCL: Community-Strength-Enhanced Graph Contrastive Learning
Authors: Chen Han , Zhao Ziwen , Li Yuhua , Zou Yixiong , Li Ruixuan , Zhang Rui
Published: August 2023

Graph Contrastive Learning (GCL) is an effective way to learn generalized graph representations in a self-supervised manner, and has grown rapidly in recent years. However, the underlying community semantics has not been well explored by most previous GCL methods. Research that attempts to leverage communities in GCL regards them as having the same influence on the graph, leading to extra representation errors. To tackle this issue, we define ''community strength'' to measure the difference of influence among communities. Under this premise, we propose a Community-Strength-enhanced Graph Contrastive Learning (CSGCL) framework to preserve community strength throughout the learning process. Firstly, we present two novel graph augmentation methods, Communal Attribute Voting (CAV) and Communal Edge Dropping (CED), where the perturbations of node attributes and edges are guided by community strength. Secondly, we propose a dynamic ''Team-up'' contrastive learning scheme, where community strength is used to progressively fine-tune the contrastive objective. We report extensive experiment results on three downstream tasks: node classification, node clustering, and link prediction. CSGCL achieves state-of-the-art performance compared with other GCL methods, validating that community strength brings effectiveness and generality to graph representations. Our code is available at https://github.com/HanChen-HUST/CSGCL.
GPLight: Grouped Multi-agent Reinforcement Learning for Large-scale Traffic Signal Control
Authors: Liu Yilin , Luo Guiyang , Yuan Quan , Li Jinglin , Jin Lei , Chen Bo , Pan Rui
Published: August 2023

The use of multi-agent reinforcement learning (MARL) methods in coordinating traffic lights (CTL) has become increasingly popular, treating each intersection as an agent. However, existing MARL approaches either treat each agent absolutely homogeneous, i.e., same network and parameter for each agent, or treat each agent completely heterogeneous, i.e., different networks and parameters for each agent. This creates a difficult balance between accuracy and complexity, especially in large-scale CTL. To address this challenge, we propose a grouped MARL method named GPLight. We first mine the similarity between agent environment considering both real-time traffic flow and static fine-grained road topology. Then we propose two loss functions to maintain a learnable and dynamic clustering, one that uses mutual information estimation for better stability, and the other that maximizes separability between groups. Finally, GPLight enforces the agents in a group to share the same network and parameters. This approach reduces complexity by promoting cooperation within the same group of agents while reflecting differences between groups to ensure accuracy. To verify the effectiveness of our method, we conduct experiments on both synthetic and real-world datasets, with up to 1,089 intersections. Compared with state-of-the-art methods, experiment results demonstrate the superiority of our proposed method, especially in large-scale CTL.
Open Anomalous Trajectory Recognition via Probabilistic Metric Learning
Authors: Gao Qiang , Wang Xiaohan , Liu Chaoran , Trajcevski Goce , Huang Li , Zhou Fan
Published: August 2023

Typically, trajectories considered anomalous are the ones deviating from usual (e.g., traffic-dictated) driving patterns. However, this closed-set context fails to recognize the unknown anomalous trajectories, resulting in an insufficient self-motivated learning paradigm. In this study, we investigate the novel Anomalous Trajectory Recognition problem in an Open-world scenario (ATRO) and introduce a novel probabilistic Metric learning model, namely ATROM, to address it. Specifically, ATROM can detect the presence of unknown anomalous behavior in addition to identifying known behavior. It has a Mutual Interaction Distillation that uses contrastive metric learning to explore the interactive semantics regarding the diverse behavioral intents and a Probabilistic Trajectory Embedding that forces the trajectories with distinct behaviors to follow different Gaussian priors. More importantly, ATROM offers a probabilistic metric rule to discriminate between known and unknown behavioral patterns by taking advantage of the approximation of multiple priors. Experimental results on two large-scale trajectory datasets demonstrate the superiority of ATROM in addressing both known and unknown anomalous patterns.
Targeting Minimal Rare Itemsets from Transaction Databases
Authors: Hidouri Amel , Raddaoui Badran , Jabbour Said
Published: August 2023

The computation of minimal rare itemsets is a well known task in data mining, with numerous applications, e.g., drugs effects analysis and network security, among others. This paper presents a novel approach to the computation of minimal rare itemsets. First, we introduce a generalization of the traditional minimal rare itemset model called k-minimal rare itemset. A k-minimal rare itemset is defined as an itemset that becomes frequent or rare based on the removal of at least k or at most (k − 1) items from it. We claim that our work is the first to propose this generalization in the field of data mining. We then present a SAT-based framework for efficiently discovering k-minimal rare itemsets from large transaction databases. Afterwards, by partitioning the k-minimal rare itemset mining problem into smaller sub-problems, we aim to make it more manageable and easier to solve. Finally, to evaluate the effectiveness and efficiency of our approach, we conduct extensive experimental analysis using various popular datasets. We compare our method with existing specialized algorithms and CP-based algorithms commonly used for this task.
KDLGT: A Linear Graph Transformer Framework via Kernel Decomposition Approach
Authors: Wu Yi , Xu Yanyang , Zhu Wenhao , Song Guojie , Lin Zhouchen , Wang Liang , Liu Shaoguo
Published: August 2023

In recent years, graph Transformers (GTs) have been demonstrated as a robust architecture for a wide range of graph learning tasks. However, the quadratic complexity of GTs limits their scalability on large-scale data, in comparison to Graph Neural Networks (GNNs). In this work, we propose the Kernel Decomposition Linear Graph Transformer (KDLGT), an accelerating framework for building scalable and powerful GTs. KDLGT employs the kernel decomposition approach to rearrange the order of matrix multiplication, thereby reducing complexity to linear. Additionally, it categorizes GTs into three distinct types and provides tailored accelerating methods for each category to encompass all types of GTs. Furthermore, we provide a theoretical analysis of the performance gap between KDLGT and self-attention to ensure its effectiveness. Under this framework, we select two representative GTs to design our models. Experiments on both real-world and synthetic datasets indicate that KDLGT not only achieves state-of-the-art performance on various datasets but also reaches an acceleration ratio of approximately 10 on graphs of certain sizes.
Why Rumors Spread Fast in Social Networks, and How to Stop It
Authors: N. Zehmakan Ahad , Out Charlotte , Hesamipour Khelejan Sajjad
Published: August 2023

We study a rumor spreading model where individuals are connected via a network structure. Initially, only a small subset of the individuals are spreading a rumor. Each individual who is connected to a spreader, starts spreading the rumor with some probability as a function of their trust in the spreader, quantified by the Jaccard similarity index. Furthermore, the probability that a spreader diffuses the rumor decreases over time until they fully lose their interest and stop spreading.

We focus on determining the graph parameters which govern the magnitude and pace that the rumor spreads in this model. We prove that for the rumor to spread to a sizable fraction of the individuals, the network needs to enjoy ``strong'' expansion properties and most nodes should be in ``well-connected'' communities. Both of these characteristics are, arguably, present in real-world social networks up to a certain degree, shedding light on the driving force behind the extremely fast spread of rumors in social networks.

Furthermore, we formulate a large range of countermeasures to cease the spread of a rumor. We introduce four fundamental criteria which a countermeasure ideally should possess. We evaluate all the proposed countermeasures by conducting experiments on real-world social networks such as Facebook and Twitter. We conclude that our novel decentralized countermeasures (which are executed by the individuals) generally outperform the previously studied centralized ones (which need to be imposed by a third entity such as the government).
Non-Obvious Manipulability in Extensive-Form Mechanisms: The Revelation Principle for Single-Parameter Agents
Authors: Archbold Thomas , de Keijzer Bart , Ventre Carmine
Published: August 2023

Recent work in algorithmic mechanism design focuses on designing mechanisms for agents with bounded rationality, modifying the constraints that must be satisfied in order to achieve incentive compatibility. Starting with Li's strengthening of strategyproofness, obvious strategyproofness (OSP) requires truthtelling to be "obvious" over dishonesty, roughly meaning that the worst outcome from truthful actions must be no worse than the best outcome for dishonest ones. A celebrated result for dominant-strategy incentive-compatible mechanisms that allows us to restrict attention to direct mechanisms, known as the revelation principle, does not hold for OSP: the implementation details matter for the obvious incentive properties of the mechanism. Studying agent strategies in real-life mechanisms, Troyan and Morrill introduce a relaxation of strategyproofness known as non-obvious manipulability, which only requires comparing certain extrema of the agents' utility functions in order for a mechanism to be incentive-compatible. Specifically a mechanism is not obviously manipulable (NOM) if the best and worst outcomes when acting truthfully are no worse than the best and worst outcomes when acting dishonestly. In this work we first extend the cycle monotonicity framework for direct-revelation NOM mechanism design to indirect mechanisms. We then apply this to two settings, single-parameter agents and mechanisms for two agents in which one has a two-value domain, and show that under these models the revelation principle holds: direct mechanisms are just as powerful as indirect ones.
Deliberation as Evidence Disclosure: A Tale of Two Protocol Types
Authors: Chingoma Julian , Haret Adrian
Published: August 2023

We study a model inspired by deliberative practice, in which agents selectively disclose evidence about a set of alternatives prior to taking a final decision on them. We are interested in whether such a process, when iterated to termination, results in the objectively best alternatives being selected—thereby lending support to the idea that groups can be wise even when their members communicate with each other. We find that, under certain restrictions on the relative amounts of evidence, together with the actions available to the agents, there exist deliberation protocols in each of the two families we look at (i.e., simultaneous and sequential) that offer desirable guarantees. Simulation results further complement this picture, by showing how the distribution of evidence among the agents influences parameters of interest, such as the outcome of the protocols and the number of rounds until termination.
Inferring Private Valuations from Behavioral Data in Bilateral Sequential Bargaining
Authors: Cui Lvye , Yu Haoran
Published: August 2023

Inferring bargainers' private valuations on items from their decisions is crucial for analyzing their strategic behaviors in bilateral sequential bargaining. Most existing approaches that infer agents' private information from observable data either rely on strong equilibrium assumptions or require a careful design of agents' behavior models. To overcome these weaknesses, we propose a Bayesian Learning-based Valuation Inference (BLUE) framework. Our key idea is to derive feasible intervals of bargainers' private valuations from their behavior data, using the fact that most bargainers do not choose strictly dominated strategies. We leverage these feasible intervals to guide our inference. Specifically, we first model each bargainer's behavior function (which maps his valuation and bargaining history to decisions) via a recurrent neural network. Second, we learn these behavior functions by utilizing a novel loss function defined based on feasible intervals. Third, we derive the posterior distributions of bargainers' valuations according to their behavior data and learned behavior functions. Moreover, we account for the heterogeneity of bargainer behaviors, and propose a clustering algorithm (K-Loss) to improve the efficiency of learning these behaviors. Experiments on both synthetic and real bargaining data show that our inference approach outperforms baselines.
Participatory Budgeting: Data, Tools and Analysis
Authors: Faliszewski Piotr , Flis Jarosław , Peters Dominik , Pierczyński Grzegorz , Skowron Piotr , Stolicki Dariusz , Szufa Stanisław , Talmon Nimrod
Published: August 2023

We provide a library of participatory budgeting data (Pabulib) and open source tools (Pabutools and Pabustats) for analysing this data.

We analyse how the results of participatory budgeting elections would change if a different selection rule was applied.

We provide evidence that the outcomes of the Method of Equal Shares would be considerably fairer than those of the Utilitarian Greedy rule that is currently in use. We also show that the division of the projects into districts and/or categories can in many cases be avoided when using proportional rules. We find that this would increase the overall utility of the voters.
The Computational Complexity of Single-Player Imperfect-Recall Games
Authors: Tewolde Emanuel , Oesterheld Caspar , Conitzer Vincent , Goldberg Paul W.
Published: August 2023

We study single-player extensive-form games with imperfect recall, such as the Sleeping Beauty problem or the Absentminded Driver game. For such games, two natural equilibrium concepts have been proposed as alternative solution concepts to ex-ante optimality. One equilibrium concept uses generalized double halving (GDH) as a belief system and evidential decision theory (EDT), and another one uses generalized thirding (GT) as a belief system and causal decision theory (CDT). Our findings relate those three solution concepts of a game to solution concepts of a polynomial maximization problem: global optima, optimal points with respect to subsets of variables and Karush–Kuhn–Tucker (KKT) points. Based on these correspondences, we are able to settle various complexity-theoretic questions on the computation of such strategies. For ex-ante optimality and (EDT,GDH)-equilibria, we obtain NP-hardness and inapproximability, and for (CDT,GT)-equilibria we obtain CLS-completeness results.
Approximate Envy-Freeness in Graphical Cake Cutting
Authors: Yuen Sheung Man , Suksompong Warut
Published: August 2023

We study the problem of fairly allocating a divisible resource in the form of a graph, also known as graphical cake cutting. Unlike for the canonical interval cake, a connected envy-free allocation is not guaranteed to exist for a graphical cake. We focus on the existence and computation of connected allocations with low envy. For general graphs, we show that there is always a 1/2-additive-envy-free allocation and, if the agents' valuations are identical, a (2+\epsilon)-multiplicative-envy-free allocation for any \epsilon > 0. In the case of star graphs, we obtain a multiplicative factor of 3+\epsilon for arbitrary valuations and 2 for identical valuations. We also derive guarantees when each agent can receive more than one connected piece. All of our results come with efficient algorithms for computing the respective allocations.
Can You Improve My Code? Optimizing Programs with Local Search
Authors: Abdollahi Fatemeh , Ameen Saqib , Taylor Matthew E. , Lelis Levi H. S.
Published: August 2023

This paper introduces a local search method for improving an existing program with respect to a measurable objective. Program Optimization with Locally Improving Search (POLIS) exploits the structure of a program, defined by its lines. POLIS improves a single line of the program while keeping the remaining lines fixed, using existing brute-force synthesis algorithms, and continues iterating until it is unable to improve the program's performance. POLIS was evaluated with a 27-person user study, where participants wrote programs attempting to maximize the score of two single-agent games: Lunar Lander and Highway. POLIS was able to substantially improve the participants' programs with respect to the game scores. A proof-of-concept demonstration on existing Stack Overflow code measures applicability in real-world problems. These results suggest that POLIS could be used as a helpful programming assistant for programming problems with measurable objectives.
TDG4Crowd:Test Data Generation for Evaluation of Aggregation Algorithms in Crowdsourcing
Authors: Fang Yili , Shen Chaojie , Gu Huamao , Han Tao , Ding Xinyi
Published: August 2023

In crowdsourcing, existing efforts mainly use real datasets collected from crowdsourcing as test datasets to evaluate the effectiveness of aggregation algorithms. However, these work ignore the fact that the datasets obtained by crowdsourcing are usually sparse and imbalanced due to limited budget. As a result, applying the same aggregation algorithm on different datasets often show contradicting conclusions. For example, on the RTE dataset, Dawid and Skene model performs significantly better than Majority Voting, while on the LableMe dataset, the experiments give the opposite conclusion. It is challenging to obtain comprehensive and balanced datasets at a low cost. To our best knowledge, little effort have been made to the fair evaluation of aggregation algorithms. To fill in this gap, we propose a novel method named TDG4Crowd that can automatically generate comprehensive and balanced datasets. Using Kullback Leibler divergence and Kolmogorov–Smirnov test, the experiment results show the superior of our method compared with others. Aggregation algorithms also perform more consistently on the synthetic datasets generated using our method.
Abstraction of Nondeterministic Situation Calculus Action Theories
Authors: Banihashemi Bita , De Giacomo Giuseppe , Lesperance Yves
Published: August 2023

We develop a general framework for abstracting the behavior of an agent that operates in a nondeterministic domain, i.e., where the agent does not control

the outcome of the nondeterministic actions, based on the nondeterministic situation calculus and the ConGolog programming language. We assume that

we have both an abstract and a concrete nondeterministic basic action theory, and a refinement mapping which specifies how abstract actions, decomposed into agent actions and environment reactions, are implemented by concrete ConGolog programs. This new setting supports strategic reasoning and strategy synthesis, by allowing us to quantify separately on agent actions and environment reactions. We show that if the agent has a (strong FOND) plan/strategy to achieve a goal/complete a task at the abstract level, and it can always execute the nondeterministic abstract actions to completion at the concrete level, then there exist a refinement of it that is a (strong FOND) plan/strategy to achieve the refinement of the goal/task at the concrete level.
Augmenting Automated Spectrum Based Fault Localization for Multiple Faults
Authors: Chatterjee Prantik , Campos Jose , Abreu Rui , Roy Subhajit
Published: August 2023

Spectrum-based Fault Localization (SBFL) uses the coverage of test cases and their outcome (pass/fail) to predict the "suspiciousness'' of program components, e.g., lines of code. SBFL is, perhaps, the most successful fault localization technique due to its simplicity and scalability. However, SBFL heuristics do not perform well in scenarios where a program may have multiple faulty components. In this work, we propose a new algorithm that "augments'' previously proposed SBFL heuristics to produce a ranked list where faulty components ranked low by base SBFL metrics are ranked significantly higher. We implement our ideas in a tool, ARTEMIS, that attempts to "bubble up'' faulty components which are ranked lower by base SBFL metrics. We compare our technique to the most popular SBFL metrics and demonstrate statistically significant improvement in the developer effort for fault localization with respect to the basic strategies.
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning
Authors: Tennant Elizaveta , Hailes Stephen , Musolesi Mirco
Published: August 2023

Practical uses of Artificial Intelligence (AI) in the real world have demonstrated the importance of embedding moral choices into intelligent agents. They have also highlighted that defining top-down ethical constraints on AI according to any one type of morality is extremely challenging and can pose risks. A bottom-up learning approach may be more appropriate for studying and developing ethical behavior in AI agents. In particular, we believe that an interesting and insightful starting point is the analysis of emergent behavior of Reinforcement Learning (RL) agents that act according to a predefined set of moral rewards in social dilemmas.

In this work, we present a systematic analysis of the choices made by intrinsically-motivated RL agents whose rewards are based on moral theories. We aim to design reward structures that are simplified yet representative of a set of key ethical systems. Therefore, we first define moral reward functions that distinguish between consequence- and norm-based agents, between morality based on societal norms or internal virtues, and between single- and mixed-virtue (e.g., multi-objective) methodologies. Then, we evaluate our approach by modeling repeated dyadic interactions between learning moral agents in three iterated social dilemma games (Prisoner's Dilemma, Volunteer's Dilemma and Stag Hunt). We analyze the impact of different types of morality on the emergence of cooperation, defection or exploitation, and the corresponding social outcomes. Finally, we discuss the implications of these findings for the development of moral agents in artificial and mixed human-AI societies.
Tractable Diversity: Scalable Multiperspective Ontology Management via Standpoint EL
Authors: Gómez Álvarez Lucía , Rudolph Sebastian , Strass Hannes
Published: August 2023

The tractability of the lightweight description logic EL has allowed for the construction of large and widely used ontologies that support semantic interoperability. However, comprehensive domains with a broad user base are often at odds with strong axiomatisations otherwise useful for inferencing, since these are usually context dependent and subject to diverging perspectives.

In this paper we introduce Standpoint EL, a multi-modal extension of EL that allows for the integrated representation of domain knowledge relative to diverse, possibly conflicting standpoints (or contexts), which can be hierarchically organised and put in relation to each other. We establish that Standpoint EL still exhibits EL's favourable PTime standard reasoning, whereas introducing additional features like empty standpoints, rigid roles, and nominals makes standard reasoning tasks intractable.
Exploration via Joint Policy Diversity for Sparse-Reward Multi-Agent Tasks
Authors: Xu Pei , Zhang Junge , Huang Kaiqi
Published: August 2023

Exploration under sparse rewards is a key challenge for multi-agent reinforcement learning problems. Previous works argue that complex dynamics between agents and the huge exploration space in MARL scenarios amplify the vulnerability of classical count-based exploration methods when combined with agents parameterized by neural networks, resulting in inefficient exploration. In this paper, we show that introducing constrained joint policy diversity into a classical count-based method can significantly improve exploration when agents are parameterized by neural networks. Specifically, we propose a joint policy diversity to measure the difference between current joint policy and previous joint policies, and then use a filtering-based exploration constraint to further refine the joint policy diversity. Under the sparse-reward setting, we show that the proposed method significantly outperforms the state-of-the-art methods in the multiple-particle environment, the Google Research Football, and StarCraft II micromanagement tasks. To the best of our knowledge, on the hard 3s_vs_5z task which needs non-trivial strategies to defeat enemies, our method is the first to learn winning strategies without domain knowledge under the sparse-reward setting.
On Discovering Interesting Combinatorial Integer Sequences
Authors: Svatoš Martin , Jung Peter , Tóth Jan , Wang Yuyi , Kuželka Ondřej
Published: August 2023

We study the problem of generating interesting integer sequences with a combinatorial interpretation. For this we introduce a two-step approach. In the first step, we generate first-order logic sentences which define some combinatorial objects, e.g., undirected graphs, permutations, matchings etc. In the second step, we use algorithms for lifted first-order model counting to generate integer sequences that count the objects encoded by the first-order logic formulas generated in the first step. For instance, if the first-order sentence defines permutations then the generated integer sequence is the sequence of factorial numbers n!. We demonstrate that our approach is able to generate interesting new sequences by showing that a non-negligible fraction of the automatically generated sequences can actually be found in the Online Encyclopaedia of Integer Sequences (OEIS) while generating many other similar sequences which are not present in OEIS and which are potentially interesting. A key technical contribution of our work is the method for generation of first-order logic sentences which is able to drastically prune the space of sentences by discarding large fraction of sentences which would lead to redundant integer sequences.
GIDnets: Generative Neural Networks for Solving Inverse Design Problems via Latent Space Exploration
Authors: Adornetto Carlo , Greco Gianluigi
Published: August 2023

In a number of different fields, including Engeneering, Chemistry and Physics, the design of technological tools and device structures is increasingly supported by deep-learning based methods, which provide suggestions on crucial architectural choices based on the properties that these tools and structures should exhibit. The paper proposes a novel architecture, named GIDnet, to address this inverse design problem, which is based on exploring a suitably defined latent space associated with the possible designs. Among its distinguishing features, GIDnet is capable of identifying the most appropriate starting point for the exploration and of likely converging into a point corresponding to a design that is a feasible one. Results of a thorough experimental activity evidence that GIDnet outperforms earlier approaches in the literature.
Computing Abductive Explanations for Boosted Regression Trees
Authors: Audemard Gilles , Bellart Steve , Lagniez Jean-Marie , Marquis Pierre
Published: August 2023

We present two algorithms for generating (resp. evaluating) abductive explanations for boosted regression trees. Given an instance x and an interval I containing its value F (x) for the boosted regression tree F at hand, the generation algorithm returns a (most general) term t over the Boolean conditions in F such that every instance x′ satisfying t is such that F (x′ ) ∈ I. The evaluation algorithm tackles the corresponding inverse problem: given F , x and a term t over the Boolean conditions in F such that t covers x, find the least interval I_t such that for every instance x′ covered by t we have F (x′ ) ∈ I_t . Experiments on various datasets show that the two algorithms are practical enough to be used for generating (resp. evaluating) abductive explanations for boosted regression trees based on a large number of Boolean conditions.
Incremental and Decremental Optimal Margin Distribution Learning
Authors: Chen Li-Jun , Zhang Teng , Shi Xuanhua , Jin Hai
Published: August 2023

Incremental and decremental learning (IDL) deals with the tasks where new data arrives sequentially as a stream or old data turns unavailable continually due to the privacy protection. Existing IDL methods mainly focus on support vector machine and its variants with linear-type loss. There are few studies about the quadratic-type loss, whose Lagrange multipliers are unbounded and much more difficult to track. In this paper, we take the latest statistical learning framework optimal margin distribution machine (ODM) which involves a quadratic-type loss due to the optimization of margin variance, for example, and equip it with the ability to handle IDL tasks. Our proposed ID-ODM can avoid updating the Lagrange multipliers in an infinite range by determining their optimal values beforehand so as to enjoy much more efficiency. Moreover, ID-ODM is also applicable when multiple instances come and leave simultaneously. Extensive empirical studies show that ID-ODM can achieve 9.1x speedup on average with almost no generalization lost compared to retraining ODM on new data set from scratch.