Association for the Advancement of Artificial Intelligence: AAAI Publications
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Privacy-and-Utility-Aware Publishing of Schedules
No full textScheduling is adopted in various domains to assign jobs to resources, such that an objective is optimized. While schedules enable the analysis of the underlying system, publishing them also incurs a privacy risk. Recently, privacy attacks on schedules have been proposed, which may reveal sensitive information on the jobs by solving an inverse scheduling problem. In this work, we study the protection against such attacks. We formulate the problem of privacy-and-utility preservation of schedules, which bounds both, the privacy leakage and the loss in the utility of the schedule due to obfuscation. We address the problem based on a set of perturbation functions for schedules, study their instantiations for standard scheduling problems, and implement privacy-and-utility-aware publishing of a schedule using constraint programming. Experiments with synthetic and real-world schedules demonstrate the feasibility, robustness, and effectiveness of our mechanism
PDBs Go Numeric: Pattern-Database Heuristics for Simple Numeric Planning
No full textDespite the widespread success of pattern database (PDB) heuristics in classical planning, to date there has been no application of PDBs to planning with numeric variables. In this paper we attempt to close this gap. We address optimal numeric planning involving conditions characterized by linear expressions and actions that modify numeric variables by constant quantities. Building upon prior research, we present an adaptation of PDB heuristics to numeric planning, introducing several approaches to deal with the unbounded nature of numeric variable projections. These approaches aim to restrict the initially infinite projections, thereby bounding the number of states and ultimately constraining the resulting PDBs. We show that the PDB heuristics obtained with our approach can provide strong guidance for the search
Improved Approximation Algorithms for Clustered TSP and Subgroup Planning
No full textIn the Clustered TSP (CTSP), we are given an edge-weighted graph satisfying the triangle inequality property, and a family of pairwise disjoint vertex groups. The goal is to find a minimum weight tour that includes all vertices, ensuring that the vertices within each group appear consecutively on the tour. The subgroup planning problem (SGPP) is an extension of CTSP by relaxing some triangle inequality requirements on edge weights. CTSP and SGPP have plentiful applications in AI and robotics. In this paper, we design three improved approximation algorithms for SGPP and CTSP. First, we propose a polynomial-time 2.167-approximation algorithm for SGPP, improving the previous ratio of 3 (IJCAI 2017). Second, we give an FPT 2.072-approximation algorithm for SGPP parameterized by the maximum group size, improving the previous ratio of 2.5 (IJCAI 2017). Third, we prove an FPT (
Runtime Analysis for Multi-Objective Evolutionary Algorithms in Unbounded Integer Spaces
No full textRandomized search heuristics have been applied successfully to a plethora of problems. This success is complemented by a large body of theoretical results. Unfortunately, the vast majority of these results regard problems with binary or continuous decision variables -- the theoretical analysis of randomized search heuristics for unbounded integer domains is almost nonexistent. To resolve this shortcoming, we start the runtime analysis of multi-objective evolutionary algorithms, which are among the most successful randomized search heuristics, for unbounded integer search spaces. We analyze single- and full-dimensional mutation operators with three different mutation strengths, namely changes by plus/minus one (unit strength), random changes following a law with exponential tails, and random changes following a power-law. The performance guarantees we prove on a recently proposed natural benchmark problem suggest that unit mutation strengths can be slow when the initial solutions are far from the Pareto front. When setting the expected change right (depending on the benchmark parameter and the distance of the initial solutions), the mutation strength with exponential tails yields the best runtime guarantees in our results -- however, with a wrong choice of this expectation, the performance guarantees quickly become highly uninteresting. With power-law mutation, which is an essentially parameter-less mutation operator, we obtain good results uniformly over all problem parameters and starting points. We complement our mathematical findings with experimental results that suggest that our bounds are not always tight. Most prominently, our experiments indicate that power-law mutation outperforms the one with exponential tails even when the latter uses a near-optimal parametrization. Hence, we suggest to favor power-law mutation for unknown problems in integer spaces
Speeding Up the NSGA-II with a Simple Tie-Breaking Rule
No full textThe non-dominated sorting genetic algorithm II (NSGA-II) is the most popular multi-objective optimization heuristic. Recent mathematical runtime analyses have detected two shortcomings in discrete search spaces, namely, that the NSGA-II has difficulties with more than two objectives and that it is very sensitive to the choice of the population size.
To overcome these difficulties, we analyze a simple tie-breaking rule in the selection of the next population. Similar rules have been proposed before, but have found only little acceptance. We prove the effectiveness of our tie-breaking rule via mathematical runtime analyses on the classic OneMinMax, LeadingOnesTrailingZeros, and OneJumpZeroJump benchmarks. We prove that this modified NSGA-II can optimize the three benchmarks efficiently also for many objectives, in contrast to the exponential lower runtime bound previously shown for OneMinMax with three or more objectives. For the bi-objective problems, we show runtime guarantees that do not increase when moderately increasing the population size over the minimum admissible size. For example, for the OneJumpZeroJump problem with representation length n and gap parameter k, we show a runtime guarantee of O(max {n^(k + 1), N n}) function evaluations when the population size is at least four times the size of the Pareto front. For population sizes larger than the minimal choice N = Θ(n), this result improves considerably over the Θ(N n^k) runtime of the classic NSGA-II
DCA: Dividing and Conquering Amnesia in Incremental Object Detection
No full textIncremental object detection (IOD) aims to cultivate an object detector that can continuously localize and recognize novel classes while preserving its performance on previous classes. Existing methods achieve certain success by improving knowledge distillation and exemplar replay for transformer-based detection frameworks, but the intrinsic forgetting mechanisms remain underexplored. In this paper, we dive into the cause of forgetting and discover forgetting imbalance between localization and recognition in transformer-based IOD, which means that localization is less-forgetting and can generalize to future classes, whereas catastrophic forgetting occurs primarily on recognition. Based on these insights, we propose a Divide-and-Conquer Amnesia (DCA) strategy, which redesigns the transformer-based IOD into a localization-then-recognition process. DCA can well maintain and transfer the localization ability, leaving decoupled fragile recognition to be specially conquered. To reduce feature drift in recognition, we leverage semantic knowledge encoded in pre-trained language models to anchor class representations within a unified feature space across incremental tasks. This involves designing a duplex classifier fusion and embedding class semantic features into the recognition decoding process in the form of queries. Extensive experiments validate that our approach achieves state-of-the-art performance, especially for long-term incremental scenarios. For example, under the four-step setting on MS-COCO, our DCA strategy significantly improves the final AP by 6.9%
DiffGrasp: Whole-Body Grasping Synthesis Guided by Object Motion Using a Diffusion Model
No full textGenerating high-quality whole-body human object interaction motion sequences is becoming increasingly important in various fields such as animation, VR/AR, and robotics. The main challenge of this task lies in determining the level of involvement of each hand given the complex shapes of objects in different sizes and their different motion trajectories, while ensuring strong grasping realism and guaranteeing the coordination of movement in all body parts. Contrasting with existing work, which either generates human interaction motion sequences without detailed hand grasping poses or only models a static grasping pose, we propose a simple yet effective framework that jointly models the relationship between the body, hands, and the given object motion sequences within a single diffusion model. To guide our network in perceiving the object's spatial position and learning more natural grasping poses, we introduce novel contact-aware losses and incorporate a data-driven, carefully designed guidance. Experimental results demonstrate that our approach outperforms the state-of-the-art method and generates plausible results
HFF-Tracker: A Hierarchical Fine-grained Fusion Tracker for Referring Multi-Object Tracking
No full textReferring Multi-Object Tracking (RMOT) aims to track multiple objects based on a provided language expression. Although prior studies have sought to accomplish this by integrating an textual module into the multi-object tracker, these methods combine text and image features in a basic way, neglecting the importance of text features. In this study, we propose a Hierarchical Fine-grained text-image Fusion tracker, named HFF-Tracker, which can perform fine-grained fusion of pixel-level visual features and text features across various semantic levels. Specifically, we have devised a Hierarchical Multi-Modal Fusion (HMMF) module to merge text and image features at an early stage in a hierarchical and detailed manner. The Text-Guided Decoder (TGD) is designed to provide the query with prior semantic information during the decoding process. Additionally, we have crafted a Text-Guided Prediction Head (TGPH) that utilizes text information to enhance the performance of the prediction head. Furthermore, we have implemented an adaptive Look-Back training strategy to maximize the utilization of valuable labeled data. Extensive experiments on the Refer-KITTI dataset and the Refer-KITTI-V2 dataset demonstrate that our proposed HFF-Tracker outperforms other state-of-the-art methods with remarkable margins
MMPF: Multi-Modal Perception Framework for Abnormal Medical Condition Detection
No full textAs the global population ages and the incidence of chronic diseases increases, the demand for early detection of abnormal medical conditions is increasing. Traditional health monitoring methods often require significant resources and specialized personnel, limiting their widespread use. Leveraging advancements in AI technologies, this study proposes a non-invasive method for detecting abnormal medical conditions from image data. A multimodal perception framework is introduced, integrating features from various modalities, including facial expressions and body postures, to enhance detection accuracy. The framework employs a Cascaded Squeeze-Excitation (CSE) module, consisting of Adaptive and Multi-modal Squeeze-Excitation components, to capture complex feature dependencies and improve cross-modal performance. Extensive experiments demonstrate the effectiveness of this approach, showing improved performance over existing methods. In addition, a new dataset that encompasses a wide range of medical conditions has been released, providing a valuable resource for future research in this domain
GLIC: General Format Learned Image Compression
No full textLearned image lossy compression techniques have surpassed traditional methods in both subjective vision and quantitative evaluation. However, current models are only applicable to three-channel image formats, limiting their practical application due to the diversity and complexity of image formats. We propose a high-performance learned image compression model for general image formats. We first introduce a transfer method to unify any-channel image formats, enhancing the applicability of neural networks. This method's effectiveness is demonstrated through image information entropy and image homomorphism theory. Then, we introduce an adaptive attention residual block into the entropy model to give it better generalization ability. Meanwhile, we propose an evenly grouped cross-channel context module for progressive preview image decoding. Experimental results demonstrate that our method achieves state-of-the-art (SOTA) in the field of learned image compression in terms of PSNR and MS-SSIM. This work extends the applicability of learned image compression techniques to more practical production environments