Jiayi Han, Liang Du, Hongwei Du, Xiangguo Zhou, Yiwen Wu, Yuanfang Zhang, Weibo Zheng, Donghong Han. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Abstract Accurate detection of underwater objects is a key indicator technology to effectively enhance the field of marine development and application, and is of great importance to various fields including marine military defense and seafood aquaculture. Efficient and rapid detection of underwater targets is a crucial technological challenge in this field. To meet the challenges posed by these issues, this study applies the convolutional omni‐efficient layer aggregation network (CO‐ELAN) module to the detector backbone to improve the ability of the network structure to acquire underwater objects from image information. The module improves the feature representation of gradient branching through a multi‐dimensional dynamic convolution and attention mechanism. In terms of loss calculation, the optimized normalized Wasserstein distance approach is used to predict the box distribution probabilistic modelling method to determine comparable distances to the ground box and obtain better samples of small target labels. Here, an underwater image enhancement algorithm based on white balance and underwater blur fusion is used to obtain clear images that enable improved detector performance. After the verification experiment on the URPC2018 dataset, it is found that the detector has better underwater detection ability compared with other detectors in the complex underwater environment. The proposed method achieves a 2.4% improvement over the YOLOv7 baseline model, while reducing computation costs by 5%.

Searching tables from poorly maintained data lakes has long been recognized as a formidable challenge in the realm of data management. There are three pivotal tasks: keyword-based, joinable and unionable table search, which form the backbone of tasks that aim to make sense of diverse datasets, such as machine learning. In this demo, we propose LakeCompass, an end-to-end prototype system that maintains abundant tabular data, supports all above search tasks with high efficacy, and well serves downstream ML modeling. To be specific, LakeCompass manages numerous real tables over which diverse types of indexes are built to support efficient search based on different user requirements. Particularly, LakeCompass could automatically integrate these discovered tables to improve the downstream model performance in an iterative approach. Finally, we provide both Python APIs and Web interface to facilitate flexible user interaction.

Discovering tables from poorly maintained data lakes is a significant challenge in data management. Two key tasks are identifying joinable and unionable tables, crucial for data integration, analysis, and machine learning. However, there's a lack of a comprehensive benchmark for evaluating existing methods. To address this, we introduce LakeBench, a large-scale table discovery benchmark. It evaluates effectiveness, efficiency, and scalability of table join & union search methods. With over 16 million real tables, LakeBench is 1,600X larger than existing datasets and 100X larger in storage size. It includes synthesized and real queries with ground truth, totaling more than 10 thousand queries - 10X more than used in any existing evaluation. We spent over 7,500 human hours labeling these queries and constructing diverse query categories for thorough evaluation. Our benchmark thoroughly evaluates state-of-the-art table discovery methods, providing insights into their performance and highlighting research opportunities.

As a common and significant problem in the field of industrial information, the time-varying quaternion matrix equation (TV-QME) is considered in this article and addressed by an improved zeroing neural network (ZNN) method based on the real representation of the quaternion. In the light of an improved dynamic parameter (IDP) and an innovative activation function (IAF), a dynamic parameter noise-tolerant ZNN (DPNTZNN) model is put forward for solving the TV-QME. The presented IDP with the character of changing with the residual error and the proposed IAF with the remarkable performance can strongly enhance the convergence and robustness of the DPNTZNN model. Therefore, the DPNTZNN model possesses fast predefined-time convergence and superior robustness under different noise environments, which are theoretically analyzed in detail. Besides, the provided simulative experiments verify the advantages of the DPNTZNN model for solving the TV-QME, especially compared with other ZNN models. Finally, the DPNTZNN model is applied to image restoration, which further illustrates the practicality of the DPNTZNN model.

Abstract Low‐light image enhancement is rapidly gaining research attention due to the increasing demands of extreme visual tasks in various applications. Although numerous methods exist to enhance image qualities in low light, it is still undetermined how to trade‐off between the human observation and computer vision processing. In this work, an effective generative adversarial network structure is proposed comprising both the densely residual block (DRB) and the enhancing block (EB) for low‐light image enhancement. Specifically, the proposed end‐to‐end image enhancement method, consisting of a generator and a discriminator, is trained using the hyper loss function. The DRB adopts the residual and dense skip connections to connect and enhance the features extracted from different depths in the network while the EB receives unique multi‐scale features to ensure feature diversity. Additionally, increasing the feature sizes allows the discriminator to further distinguish between fake and real images from the patch levels. The merits of the loss function are also studied to recover both contextual and local details. Extensive experimental results show that our method is capable of dealing with extremely low‐light scenes and the realistic feature generator outperforms several state‐of‐the‐art methods in a number of qualitative and quantitative evaluation tests.

Different distributed real-time systems (DRS) must handle aperiodic and periodic events under diverse sets of requirements. While existing middleware such as Real-Time CORBA has shown promise as a platform for distributed systems with time constraints, it lacks flexible configuration mechanisms needed to manage end-to-end timing easily for a wide range of different DRS with both aperiodic and periodic events. The primary contribution of this work is the design, implementation, and performance evaluation of the first configurable component middleware services for admission control and load balancing of aperiodic and periodic event handling in DRS. Empirical results demonstrate the need for, and the effectiveness of, our configurable component middleware approach in supporting different applications with aperiodic and periodic events, and providing a flexible software platform for DRS with end-to-end timing constraints.

An increasing number of distributed real-time applications are running on multicore platforms. However, existing real-time middleware (e.g., Real-Time CORBA) lacks adequate support for ensuring the timing constraints of soft real-time tasks on multicore platforms, and thus is dependent on (potentially inadequate) support from the underlying operating system. This paper makes three contributions to the state of the art in real-time system software for multicore platforms. First, it offers what is to our knowledge the first experimental analysis of real-time performance of vanilla Linux primitives on multicore platforms. Second, it presents MC-ORB, the first real-time object request broker (ORB) designed to address the nuances of multiprocessor (and especially multicore) platforms with a novel core-aware middleware thread architecture and allocation service for soft real-time tasks. Third, it evaluates MC-ORB's performance on a Linux multicore testbed, the results of which demonstrate its efficiency and effectiveness.

Existing off-line schedulability analysis for real-time systems can only handle periodic or sporadic tasks with known minimum inter-arrival times. Modeling sporadic tasks with fixed minimum inter-arrival times is a poor approximation for systems in which tasks arrive in bursts, but have longer intervals between the bursts. In such cases, schedulability analysis based on the existing sporadic task model is pessimistic and seriously overestimates the task's time demand. In this paper, we propose a generalized sporadic task model that characterizes arrival times more precisely than the traditional sporadic task model, and we develop a corresponding schedulability analysis that computes tighter bounds on worst-case response times. Experimental results show that when arrival time jitter increases, the new analysis more effectively guarantees schedulability of sporadic tasks.

Different distributed cyber-physical systems must handle a periodic and periodic events with diverse requirements. While existing real-time middleware such as Real-Time CORBA has shown promise as a platform for distributed systems with time constraints, it lacks flexible configuration mechanisms needed to manage end-to-end timing easily for a wide range of different cyber-physical systems with both aperiodic and periodic events. The primary contribution of this work is the design, implementation and performance evaluation of the first configurable component middleware services for admission control and load balancing of a periodic and periodic event handling in distributed cyber-physical systems. Empirical results demonstrate the need for, and the effectiveness of, our configurable component middleware approach in supporting different applications with a periodic and periodic events, and providing a flexible software platform for distributed cyber-physical systems with end-to-end timing constraints.

Many mission-critical distributed real-time applications must handle aperiodic tasks with end-to-end deadlines. However, existing middleware (e.g., RT-CORBA) lacks schedulability analysis and run-time enforcement mechanisms needed to give online real-time guarantees for aperiodic tasks. The primary contribution of this work is the design, implementation, and performance evaluation of the first realization of deferrable server and admission control mechanisms for aperiodic tasks in middleware. Empirical results on a KURT-Linux testbed demonstrate the efficiency and effectiveness of our deferrable server and admission control mechanisms in TAO's federated event service.

No one middleware communication model completely solves the problem of ensuring schedulability in every DRE system. Furthermore, there have been few studies to date of the trade-offs between alternative middleware communication models under different application scenarios. This paper makes three contributions to the state of the art in middleware for distributed real-time and embedded systems. First, it describes what we believe is the first example of integrating release guards directly with CORBA distributable threads to ensure appropriate release times for sub-tasks along an end-to-end computation. Second, it presents empirical results in which release guards improve schedulability of distributable threads compared to a greedy protocol in which arriving tasks simply begin to run as soon as they can. Third, we offer the first empirical comparisons of the distributable thread and event channel models under three different communication scenarios and then using a randomized workload.

An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes while the remaining nodes stay active to provide continuous service. For the sensor network to operate successfully, the active nodes must maintain both sensing coverage and network connectivity. Furthermore, the network must be able to configure itself to any feasible degree of coverage and connectivity in order to support different applications and environments with diverse requirements. This article presents the design and analysis of novel protocols that can dynamically configure a network to achieve guaranteed degrees of coverage and connectivity. This work differs from existing connectivity or coverage maintenance protocols in several key ways. (1) We present a Coverage Configuration Protocol (CCP) that can provide different degrees of coverage requested by applications. This flexibility allows the network to self-configure for a wide range of applications and (possibly dynamic) environments. (2) We provide a geometric analysis of the relationship between coverage and connectivity. This analysis yields key insights for treating coverage and connectivity within a unified framework; in sharp contrast to several existing approaches that address the two problems in isolation. (3) We integrate CCP with SPAN to provide both coverage and connectivity guarantees. (4) We propose a probabilistic coverage model and extend CCP to provide probabilistic coverage guarantees. We demonstrate the capability of our protocols to provide guaranteed coverage and connectivity configurations through both geometric analysis and extensive simulations.

In an emerging class of open distributed real-time and embedded (DRE) systems with stringent but dynamic QoS requirements, there is a need to propagate QoS parameters and enforce task QoS requirements across multiple endsystems in a way that is simultaneously efficient and adaptable. The object management group's (OMG) real-time CORBA 2.0 specification (RTC2) defines a dynamic scheduling framework for propagating and enforcing QoS parameters dynamically in standard CORBA middleware. We make two contributions to research on middleware for open DRE systems. First, it describes the design and capabilities of the RTC2 dynamic scheduling framework provided by TAO, which is our open-source CORBA standards-based object request broker (ORB). Second, it describes and summarize the results of empirical studies we have conducted to validate our RTC2 framework in the context of open DRE systems. The results of those experiments show that a range of policies for adaptive scheduling and management of distributable threads can be enforced efficiently in standard middleware for open DRE systems.

An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes, while the remaining nodes stay active to provide continuous service. For the sensor network to operate successfully, the active nodes must maintain both sensing coverage and network connectivity. Furthermore, the network must be able to configure itself to any feasible degrees of coverage and connectivity in order to support different applications and environments with diverse requirements. This paper presents the design and analysis of novel protocols that can dynamically configure a network to achieve guaranteed degrees of coverage and connectivity. This work differs from existing connectivity or coverage maintenance protocols in several key ways: 1) We present a Coverage Configuration Protocol (CCP) that can provide different degrees of coverage requested by applications. This flexibility allows the network to self-configure for a wide range of applications and (possibly dynamic) environments. 2) We provide a geometric analysis of the relationship between coverage and connectivity. This analysis yields key insights for treating coverage and connectivity in a unified framework: this is in sharp contrast to several existing approaches that address the two problems in isolation. 3) Finally, we integrate CCP with SPAN to provide both coverage and connectivity guarantees. We demonstrate the capability of our protocols to provide guaranteed coverage and connectivity configurations, through both geometric analysis and extensive simulations.