Cross-modal image-text retrieval remains a fundamental challenge at the intersection of vision and language. While existing methods leveraging large-scale pre-trained models such as CLIP and BERT have achieved notable progress, they often struggle with the inherent modality gap, background clutter in images, and computational inefficiency. In this paper, we propose an end-to-end framework for efficient cross-modal retrieval using Distribution Alignment and Token Reduction (DATR). The model employs a feature extraction network to extract multi-level representations, which are preliminarily aligned via a distribution calibration module. This module maps features into a Gaussian latent space and maximizes inter-modal mutual information through InfoNCE loss, effectively bridging the semantic gap. Furthermore, we incorporate a differentiable token reduction module inspired by GroupViT to dynamically cluster redundant visual tokens into semantic groups, significantly reducing computational overhead. Similarity computation integrates global and local alignments for robust matching. Extensive experiments on Flickr30K and MSCOCO datasets demonstrate state-of-the-art performance, with image-to-text retrieval R@1 reaching 89.6% and text-to-image retrieval R@1 achieving 77.2% on Flickr30K. The source codes are available at https://github.com/Wuziyi123/DATR.

To express complex query requirements and intentions, natural language has become the preferred way due to its intuitiveness. LLM is a powerful semantic parser for natural language queries. To facilitate intelligent and user-friendly trajectory queries, we proposed an LLM-based generative approach to semantic trajectory queries (LLMTrajQuery). This approach includes a new method named trajectory textualization, which converts numerical trajectories into semantic descriptions by segmenting trajectories, extracting semantics, and constructing documents. The subsequent phase, semantic trajectory query, refines query specifications, retrieves and reorders the extracted semantic trajectories, and generates natural language responses. Finally, multiple experiments demonstrated the advantages of LLMTrajQuery in improving precision and relevance and verified its robustness and scalability. Overall, LLMTrajQuery enables users to retrieve trajectories through natural language queries and receive natural language responses corresponding to numerical trajectory data and user queries.

With the proliferation of mobile devices, spatial crowdsourcing has emerged as a promising paradigm for facilitating location-based services, encompassing various applications across academia and industries. Recently, pioneering works have attempted to infer workers' mobility patterns from historical data to improve the quality of task assignment. However, these studies have overlooked or under-examined issues such as the dynamic mobility patterns of crowd workers, especially in the context of newcomers, the misalignment between the objectives of mobility prediction and task assignment, and the effective utilization of predicted mobility patterns. In this paper, we investigate a problem we term Task Assignment in Mobility Prediction-aware Spatial Crowdsourcing (TAMP). To address the TAMP problem, we first propose a task-adaptive meta-learning algorithm, which trains a set of specific meta-knowledge for workers' mobility prediction models through game theory-based learning task clustering and meta-training within each cluster. Then, we design a task assignment-oriented loss function and develop a task assignment algorithm that incorporates prediction performance, prioritizing assignments with higher confidence of completion. Extensive experiments on real-world datasets validate that our proposed methods can effectively improve the quality of task assignment.

In long-range small UAV detection, challenges such as small target size, low contrast, and insufficient information make the detection task highly difficult. Particularly when the algorithm is deployed in edge environments for real-time processing, extensive optimization is required to balance accuracy and speed under limited computational resources. To address these issues, this paper proposes a novel detection method-Coordinated Attention and Multi-Scale Fusion for Small UAV Detection YOLO (CMS-YOLO). CMS-YOLO incorporates two innovative lightweight modules: the Collaborative Attention Module (CAM) and the Multi-Scale Adaptive Context Fusion Module (MACF). CAM significantly enhances the model's ability to perceive key features by integrating channel attention, spatial attention, and pixel attention, while MACF dynamically adjusts the weights of multi-scale features via an adaptive mechanism, effectively improving small target detection accuracy. In addition, we removed the low-resolution detection head and introduced a high-resolution detection head specifically for small target detection, further optimizing the network structure. Experimental validation on the public small target detection dataset AI-TOD and the self-built long-range small UAV dataset S-Drones shows that CMS-YOLO achieves significant performance improvements while reducing the model size by 20.3%. On the AI-TOD dataset, [email protected] increased by 3.1%, and on the S-Drones dataset, [email protected] increased by 7.1%, significantly outperforming multiple benchmark models and state-of-the-art small target detection methods.

The rapid growth of e-commerce has intensified the demand for efficient urban logistics. Electric Vehicles (EVs), with their eco-friendly and high-efficiency features, have emerged as a promising solution for improving urban logistics efficiency. However, due to their limited battery capacity, EVs often require recharging during operations, and improper charging decisions may lead to delivery delays, resulting in a loss of platform revenue. In this paper, we explore a novel EV Charging-Aware Task Assignment (ECTA) problem in urban logistics scenarios, where the objective is to maximize platform revenue by ensuring timely task completion while meeting the charging needs of EVs. To address this challenge, we present e-Charge, an efficient two-stage framework that enables real-time optimization of two continuous processes: task assignment and charging decision. For task assignment, which focuses on matching tasks to suitable EVs, we construct a hybrid weight model that incorporates charging penalties to calculate matching weights for EVs in both active and charging states, thus improving task assignment quality. Additionally, we implement an effective vehicle selection strategy to expedite the matching process, ensuring the efficiency of task assignment. For charging decision, which focuses on determining when and where EVs should be charged, we propose a multi-agent reinforcement learning (MARL) approach to dynamically select the charging timing for EVs. To further enhance decision-making quality, we devise a hierarchical communication graph that enables better collaboration between EVs and facilitates adaptive charging decisions. Finally, extensive experiments demonstrate thate-Chargesignificantly outperforms compared methods, achieving higher revenue and task completion ratio across a wide range of parameter settings.

With the widespread adoption of mobile internet and GPS-enabled smartphones, spatial crowdsourcing has emerged as a prevalent computing paradigm. In this paradigm, the human-machine collaborative task assignment mode, which empowers workers to select tasks based on their preferences, has become a preferred approach for various applications such as ridesharing and takeaways. Generally, the platform continuously presents a set of top-$k$tasks to individual workers by taking into account factors like travel distance, and allows workers to select tasks from this set. This decision approach is beneficial to both platform and workers. However, it still faces significant challenges in large-scale dynamic results maintenance, which incurs considerable computational costs. In this paper, we propose a novel solution framework with an adaptive two-layer cache structure to efficiently address the problem of updating dynamic top-$k$results. Additionally, we propose two effective learning-based methods which greatly improve the efficiency of result maintenance. Furthermore, we present a novel approach to identify and process caches that trigger intensive updates within a tight time limit, greatly reducing the peak demand for updating caches. Finally, extensive experimental results on real datasets demonstrate that our proposed algorithms exhibit strong performance across various parameter configurations.

Video super-resolution (VSR) on mobile devices aims to restore high-resolution frames from their low-resolution counterparts, satisfying the requirements of performance, FLOPs and latency. On one hand, partial feature processing, as a classic and acknowledged strategy, is developed in current studies to reach an appropriate trade-off between FLOPs and accuracy. However, the splitting of partial feature processing strategy are usually performed in a blind manner, thereby reducing the computational efficiency and performance gains. On the other hand, current methods for mobile platforms primarily treat VSR as an extension of single-image super-resolution to reduce model calculation and inference latency. However, lacking inter-frame information interaction in current methods results in a suboptimal latency and accuracy trade-off. To this end, we propose a novel architecture, termed Feature Aggregating Network with Inter-frame Interaction (FANI), a lightweight yet considering frame-wise correlation VSR network, which could achieve real-time inference while maintaining superior performance. Our FANI accepts adjacent multi-frame low-resolution images as input and generally consists of several fully-connection-embedded modules, i.e., Multi-stage Partial Feature Distillation (MPFD) for capturing multi-level feature representations. Moreover, considering the importance of inter-frame alignment, we further employ a tiny Attention-based Frame Alignment (AFA) module to promote inter-frame information flow and aggregation efficiently. Extensive experiments on the well-known dataset and real-world mobile device demonstrate the superiority of our proposed FANI, which means that our FANI could be well adapted to mobile devices and produce visually pleasing results.

Flights require a range of ground support services during airport stopovers, of which baggage transportation is an important component. At present, most airports in China still use the manual dispatching method of single vehicle and single flight, which is costly and inefficient. To address the problem, this paper analyzes the baggage trolley scheduling process, determines the scheduling constraints, and establishes the model of airport baggage trolley scheduling. In this paper, based on NSGAII combined with the contribution hypervolume metric, an interactive multi-objective optimization algorithm is proposed, which allows the decision-maker to adjust the weights of the objective function during the process of the algorithm in order to guide the algorithm to find solutions that fit real-world. Experiments on the airport dataset show that the algorithm proposed in this paper can significantly improve efficiency and solution quality.

Carrier Aircraft support operation is an important component of the carrier aviation support system, and its efficiency is closely related to the aircraft sortie rate. However, the carrier deck faces significant challenges such as limited space, scarce resources and constantly changing environmental conditions. These factors have a profound impact on the aircraft support operations, particularly in complex scenarios, requiring careful and rational scheduling. To solve above problems, we propose a collaborative fast scheduling framework for Multi-Agent support operations, which is based on a distributed partially observable Markov decision process (Dec-POMDP) model. Within this framework, we develop a dynamic scheduling algorithm based on centralized training with decentralized execution framework (CTDE). To evaluate the performance of our approach, we establish a simulation environment for aircraft scheduling and compare our algorithm’s performance with VDN and QMIX in various carrier aircraft scenarios. Experimental results demonstrate that our algorithm outperforms others in terms of both training efficiency and scheduling quality.

In many safety-critical applications, the response to emergencies is one of the most important components in maintaining the stability of workshop production and transportation. However, a robust emergency response relies on a high degree of collaboration between human and machine intelligence. In this paper, we propose a visual analysis system that can improve the collaboration of failure mitigation during the emergency response period. Our system implements 10 different scenarios with distinct types of maintenance agents, failures, and possible hazards. We illustrate our design methodology and the framework of the system in the paper. The proposed system is able to be utilized on conducting detailed failure or hazard analysis for the current scenario and intervene in the dispatch of maintenance agents in real time.

With the continued proliferation of mobile Internet and geo-locating technologies, carpooling as a green transport mode is widely accepted and becoming tremendously popular worldwide. In this paper, we focus on a popular carpooling service calledride hitching, which is typically implemented using a publish/subscribe approach. In a ride hitching service, drivers subscribe ride orders published by riders and continuously receive matching ride orders until one is picked. The current systems (e.g., Didi Hitch) adopt a threshold-based approach to filter ride orders. That is, a new ride order will be sent to all subscribing drivers whose planned trips can match the ride order within a pre-defined detour threshold. A limitation of this approach is that it is difficult for drivers to specify a reasonable detour threshold in practice. In addressing this problem, we propose a novel type of top-$k$subscription queries calledTop-$k$kRideSubscription (TkRS)query, which continuously returns the best$k$ride orders that match drivers’ trip plans to them. We propose two efficient algorithms to enable the top-$k$result maintenance. We also design a novel hybrid grid index and a two-level buffer structure to efficiently track the top-$k$results for allTkRSqueries. Finally, extensive experiments on real-life datasets suggest that our proposed algorithms are capable of achieving desirable performance in practical settings.

City express delivery services (a.k.a.~last-mile delivery) have become more prominent in recent years. Many logistics giants, such as Amazon, JD, and Cainiao, have deployed intelligent express delivery systems to deal with the growing demand for parcel delivery. Existing works adopt queuing or batch processing approaches to assign parcels to couriers. However, these approaches do not fully consider the distribution of parcels and couriers, leading to poor quality of task assignment. In this paper, we investigate a problem of delivery matching based on revenue maximization in real-time city express delivery services. Given a set of couriers and a stream of parcel collection tasks, our problem aims to assign each collection task to a suitable courier to maximize the overall revenue of the platform. The problem is shown to be NP-hard. To tackle the problem efficiently, we present a time-aware batch matching algorithm to offer high-quality courier-task matching in each sliding window. We further theoretically analyze the matching approximation bound. In addition, we propose an efficient deep reinforcement learning-based approach to adaptively determine the sliding window size for better matching results. Finally, extensive experiments demonstrate that our proposed algorithms can achieve desirable effectiveness and efficiency under a wide range of parameter settings.

Image captioning is still a challenging task aiming at describing the contents of image by words. Current image caption methods usually assume the object relation to be important if the semantic and spatial geometric relationships between objects are close and large, but the relations meeting this assumption are not necessarily important to describe the contents of image in a fine-grained way. That is, the importance of fine-grained object relations is not properly taken into account. Besides, current Transformer based image caption models also fail to consider the importance of fine-grained objects, since they generate all the words of a sentence at one time, which cannot Figure out which objects are more important and vice versa. In this paper, we propose a novel Fine-grained Adaptive Object Transformer (FineFormer) network, which can jointly discover the importance of fine-grained objects and object relations for image captioning. Specifically, a new concept of adaptive soft-foreground attention is proposed to highlight the fine-grained objects dominating the descriptive contents. To characterize and calculate the important relations between fine-grained objects, we also propose an adaptive object relation attention to refine the object relation from the generation process of relation. As such, FineFormer can describe the contents of image more accurately, by reducing the interference of unimportant objects in the background. Extensive experiments on the highly-competitive MS-COCO dataset demonstrated the superiority of our FineFormer.

With the rapid development of intelligent devices and real-time positioning systems, spatial crowdsourcing (SC) platforms, one key component of which is assigning tasks to the appropriate workers in real-time, are thriving. In this paper, we propose the Uuman-in-the-loop Real-time Task Allocation (HRTA) problem, where the SC platform dynamically pushes the top-k task lists to workers, and workers can confirm tasks in real-time. To address this problem, we propose a top-k initialization algorithm and two index structures based on edge and grid, respectively. Sufficient experiments on two real-world data sets suggest that our proposed algorithms are efficient and effective.

Huge warship material transfer support operation is one of the critical factors affecting the comprehensive combat effectiveness of warships, which need to deliver essential combat materials to the target position quickly and safely. In this paper, considering the multi-stage and high dynamic characteristics of huge warship material transfer support, we model the operation scheduling optimization problem as a special flexible flow shop scheduling problem with some constraints. A greedy genetic algorithm based on dual-layer integer-coded is proposed to solve the transfer support model with the objective function of minimizing the maximum transfer completion time. Benchmark computation examples and a simulation environment are used in experiments, which verify the effectiveness of the algorithm in the actual transfer support operation.

Distributed processing and analysis of large-scale graph data remain challenging because of the high-level discrepancy among graphs. This study investigates a novel subproblem: the distributed multi-task learning on the graph, which jointly learns multiple analysis tasks from decentralized graphs. We propose a federated multi-task graph learning (FMTGL) framework to solve the problem within a privacy-preserving and scalable scheme. Its core is an innovative data-fusion mechanism and a low-latency distributed optimization method. The former captures multi-source data relatedness and generates universal task representation for local task analysis. The latter enables the quick update of our framework with gradients sparsification and tree-based aggregation. As a theoretical result, the proposed optimization method has a convergence rate interpolates between \( \mathcal {O}(1/T) \) and \( \mathcal {O}(1/\sqrt {T}) \) , up to logarithmic terms. Unlike previous studies, our work analyzes the convergence behavior with adaptive stepsize selection and non-convex assumption. Experimental results on three graph datasets verify the effectiveness and scalability of FMTGL.

Network embedding has been widely used to solve the network analytics problem. Existing methods mainly focus on networks with single-layered homogeneous or heterogeneous networks. However, many real-world complex systems can be naturally represented by multilayer networks, which is another term of heterogeneous networks with multiple edge/relation types. The problem of how to capture and utilize rich interaction information of multi-type relations causes a major challenge of multilayer network embedding. To address this problem, we propose a fast and scalable multilayer network embedding model, called HMNE, to efficiently preserve and learn information of multi-type relations into a unified embedding space. We develop a heuristic 3D interactive walk technique dedicated for multilayer networks, which can leverage rich interactions among distinct layers and effectively capture important information contained in the layered structure. We evaluate our proposed model HMNE on two downstream analytic applications: node classification and link prediction. Experimental results on seven social and biological multilayer network datasets demonstrate that the proposed model outperforms existing competitive baselines with reduced time and memory occupations.

With the continued proliferation of mobile Internet and geo-locating technologies, carpooling as a green transport mode is widely accepted and becoming tremendously popular worldwide. In this paper, we focus on a popular carpooling service called ride hitching, which is typically implemented using a publish/subscribe approach. In a ride hitching service, drivers subscribe the ride orders published by riders and continuously receive the matching ride orders until one is picked. The current systems (e.g., Didi Hitch) adopt a threshold-based approach to filter ride orders. That is, a new ride order will be sent to all subscribing drivers whose planned trips can match the ride order within a pre-defined detour threshold. A limitation of this approach is that it is difficult for drivers to specify a reasonable detour threshold in practice. In addressing this problem, we propose a novel type of top-k subscription queries called Top-k Ride Subscription (TkRS) query, which continuously returns to drivers the best k ride orders that match their trip plans. We propose two efficient algorithms to enable the top-k result maintenance. Finally, extensive experiments on real-life datasets suggest that our proposed algorithms are capable of achieving desirable performance in practical settings.

Convolutional Neural Networks (CNNs)-guided deep models have obtained impressive performance for image representation, however the representation ability may still be restricted and usually needs more epochs to make the model converge in training, due to the useful information loss during the convolution and pooling operations. We therefore propose a general feature recovery layer, termed Low-rank Deep Feature Recovery (LDFR), to enhance the representation ability of the convolutional features by seamlessly integrating low-rank recovery into CNNs, which can be easily extended to all existing CNNs-based models. To be specific, to recover the lost information during the convolution operation, LDFR aims at learning the low-rank projections to embed the feature maps onto a low-rank subspace based on some selected informative convolutional feature maps. Such low-rank recovery operation can ensure all convolutional feature maps to be reconstructed easily to recover the underlying subspace with more useful and detailed information discovered, e.g., the strokes of characters or the texture information of clothes can be enhanced after LDFR. In addition, to make the learnt low-rank subspaces more powerful for feature recovery, we design a fusion strategy to obtain a generalized subspace, which averages over all learnt sub-spaces in each LDFR layer, so that the convolutional feature maps in test phase can be recovered effectively via low-rank embedding. Extensive results on several image datasets show that existing CNNs-based models equipped with our LDFR layer can obtain better performance.

Real-world data in emerging applications may suffer from highly-skewed class imbalanced distribution, however how to deal with this kind of problem appropriately through deep learning needs further investigation. In this paper, we mainly propose a novel cross-entropy based loss function, referred to as Additive Scale Loss (ASL), for deep representation learning and imbalanced classification. To deal with the class imbalanced problem, ASL aims at increasing the loss in case of misclassification, which can avoid the superimposed loss values caused by the large amount of easily classified data in the unbalanced database to dominate the loss value of misclassified data. Moreover, in real-world applications, one data source may be used for multiple scenarios, such as classification and embedding learning, however training two separable models to handle these problems is costly, especially in deep learning area. To tackle this issue, we present and integrate a discriminative inter-class separation term into ASL, and propose a discriminative ASL (D-ASL), which can not only improve the classification performance, but also obtain discriminative representations simultaneously. The discriminative inter-class separation term is general, and can be easily integrated to other loss functions, such as CE and FL, as the byproducts. Finally, a new deep convolutional neural network equipped with D-ASL and a fully-connected (FC) layer is proposed, which can classify the imbalanced image data and obtain the discriminative representations at the same time. Extensive experimental results verified the superior performance of our method.

In the past few years ridesharing has largely reshaped the transportation marketplace. It is envisioned as a promising solution to transportation-related problems in metropolitan cities, such as traffic congestion and air pollution. In the current ridesharing research, social ridesharing, which makes use of social relations among drivers and riders to address safety issues, and dynamic pricing are two active directions with important business implications. Simultaneously optimizing social cohesion and revenue is vital to a commercial ridesharing platform's sustainable development, which, however, has not been previously studied. In this paper, we first present a new pricing scheme that better incentivizes drivers and riders to participate in ridesharing, and then propose a novel type of Price-aware Top-$k$Matching (PTkM) queries which retrieve the top-$k$vehicles for a rider's request by taking into account both social relations and revenue. We design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. Moreover, we propose a novel index tailored to our problem to further speed up query processing. Extensive experimental results on real datasets show that our proposed algorithms achieve desirable performance for real-world deployment.

Smog causes low visibility on the road and it can impact the safety of traffic. Modeling traffic in smog will have a significant impact on realistic traffic simulations. Most existing traffic models assume that drivers have optimal vision in the simulations, making these simulations are not suitable for modeling smog weather conditions. In this article, we introduce the Smog Full Velocity Difference Model (SMOG-FVDM) for a realistic simulation of traffic in smog weather conditions. In this model, we present a stadia model for drivers in smog conditions. We introduce it into a car-following traffic model using both psychological force and body force concepts, and then we introduce the SMOG-FVDM. Considering that there are lots of parameters in the SMOG-FVDM, we design a visual verification system based on SMOG-FVDM to arrive at an adequate solution which can show visual simulation results under different road scenarios and different degrees of smog by reconciling the parameters. Experimental results show that our model can give a realistic and efficient traffic simulation of smog weather conditions.

With the increasing demand in using 3D mesh data over networks, supporting effective compression and efficient transmission of meshes has caught lots of attention in recent years. This article introduces a novel compression method for 3D mesh animation sequences, supporting user-defined and progressive transmissions over networks. Our motion-aware approach starts with clustering animation frames based on their motion similarities, dividing a mesh animation sequence into fragments of varying lengths. This is done by a novel temporal clustering algorithm, which measures motion similarity based on the curvature and torsion of a space curve formed by corresponding vertices along a series of animation frames. We further segment each cluster based on mesh vertex coherence, representing topological proximity within an object under certain motion. To produce a compact representation, we perform intra-cluster compression based on Graph Fourier Transform (GFT) and Set Partitioning In Hierarchical Trees (SPIHT) coding. Optimized compression results can be achieved by applying GFT due to the proximity in vertex position and motion. We adapt SPIHT to support progressive transmission and design a mechanism to transmit mesh animation sequences with user-defined quality. Experimental results show that our method can obtain a high compression ratio while maintaining a low reconstruction error.