An essential application of wireless sensor networks is to successfully respond to user queries. Query packet losses occur in the query dissemination due to wireless communication problems such as interference, multipath fading, packet collisions, et...
Tiered Mobile Wireless Sensor Networks (TMWSNs), consisting of mobile sensor nodes and storage nodes, are widely used in various fields due to their scalability, energy efficiency, and flexibility. Most existing secure query algorithms assume that da...
After the failure of the power system, a large amount of alarm information will flood into the dispatching terminal instantly. At the same time, there are inevitable problems, such as the abnormal operation of the protection and the circuit breaker,...
In recent years, large language models (LLMs) have shown an impressive ability in translating text to SQL queries. However, in real-world applications, standard loss functions frequently fail to capture the complexity of queries adequately. Therefore...
The planning of interventions aimed at preserving the built heritage of inner areas is a complex process due to the fragility of these contexts. It should stem from adequate knowledge to support the recognition of qualities, resources, and potentials...
Unmanned aerial vehicle (UAV) imagery is a core data source for remote sensing interpretation, intelligent transportation, urban monitoring, and disaster assessment, yet its large scale variation, dense object distributions, and complex backgrounds c...
Sustainable and green waste management has become increasingly crucial due to the rising volume of waste driven by urbanization and population growth. Deep learning models based on image recognition offer potential for advanced waste classification a...
High-resolution mass spectrometry (HRMS) enables non-targeted detection of drugs and metabolites in complex matrices. Phase II metabolites—especially glucuronides—are often the only detectable biomarkers in late or postmortem samples but...
Mass multimedia data with geographical information (geo-multimedia) are collected and stored on the Internet due to the wide application of location-based services (LBS). How to find the high-level semantic relationship between geo-multimedia data an...
Background/Objectives: Retinal layer segmentation in optical coherence tomography (OCT) is essential for the diagnosis and monitoring of retinal diseases such as age-related macular degeneration (AMD) and diabetic macular edema (DME). Although deep l...
Pre-trained language model-based neural rankers have been widely applied in information retrieval (IR). However, the robustness issue of current IR models has not received sufficient attention, which could significantly impact the user experience in...
This paper proposes a semi-supervised query consistent transformer for optical remote sensing image object detection (SSOD-QCTR). A detection transformer (DETR)-like model is adopted as the basic network, and it follows the teacher–student trai...
Few-shot synthetic aperture radar automatic target recognition (SAR-ATR) aims to recognize the targets of the images (query images) based on a few annotated images (support images). Such a task requires modeling the relationship between the query and...
Recent advancements in image segmentation have been notably driven by Vision Transformers. These transformer-based models offer one versatile network structure capable of handling a variety of segmentation tasks. Despite their effectiveness, the purs...
Panoptic segmentation, as a key task in the field of computer vision, holds significant importance in practical applications such as autonomous driving and robot vision. Currently, among deep-learning-based panoptic segmentation methods, query-based...
Learning to Rank (L2R) methods that utilize machine learning techniques to solve the ranking problems have been widely studied in the field of information retrieval. Existing methods usually concatenate query and document features as training input,...
In recent years, unmanned aerial vehicles (UAVs) vision systems based on deep neural networks (DNNs) have made remarkable advancements, demonstrating impressive performance. However, due to the inherent characteristics of DNNs, these systems have bec...
Code search is a process that takes a given query as input and retrieves relevant code snippets from a code base. The relationship between query and code is commonly built on code annotation, which is extracted from code comments or other documents....
Existing methods for video instance segmentation (VIS) mostly rely on two strategies: (1) building a sophisticated post-processing to associate frame level segmentation results and (2) modeling a video clip as a 3D spatial-temporal volume with a limi...
This paper describes the data gathered for a paper published in Earth-Science Reviews (DOI: 10.1016/j.earscirev.2023.104339) to address the problem of studies using incorrect equations to calculate rainfall erosivity (R factor), which can lead to iss...
Few-shot relation extraction aims to identify and extract semantic relations between entity pairs using only a small number of annotated instances. Many recently proposed prototype-based methods have shown excellent performance. However, existing pro...
While most of the existing topic models perform a full analysis on a set of documents to discover all topics, it is noticed recently that in many situations users are interested in fine-grained topics related to some specific aspects only. As a resul...
Coreset is usually a small weighted subset of an input set of items, that provably approximates their loss function for a given set of queries (models, classifiers, hypothesis). That is, the maximum (worst-case) error over all queries is bounded. To...
Advancements in multimodal large language models have opened up new possibilities for reasoning-based image segmentation by jointly processing visual and linguistic information. However, existing approaches often suffer from a semantic discrepancy be...
Modern complex systems include products and services that comprise many interconnected pieces of integrated hardware and software, which are expected to serve humans interacting with them. As technology advances, expectations of a smooth, flawless sy...
Dense object detection is widely used in automatic driving, video surveillance, and other fields. This paper focuses on the challenging task of dense object detection. Currently, detection methods based on greedy algorithms, such as non-maximum suppr...
To address storage, computational, and latency constraints in cotton-field robots and variable-rate spraying, this study proposes CQ-RT-DETR with a two-stage design of structural compression and training-time query calibration. First, the hidden dime...
This paper addresses the challenge of preserving user privacy in location-based services (LBSs) by proposing a novel, complementary approach to existing privacy-preserving techniques such as k-anonymity and l-diversity. Our approach implements collab...
The objects in high-resolution remote sensing images usually exhibit arbitrary orientations, multi-scale variations, dense distributions, and complex background interference, posing significant challenges to oriented object detection. Although existi...
As one of the top ten security threats faced by artificial intelligence, the adversarial attack has caused scholars to think deeply from theory to practice. However, in the black-box attack scenario, how to raise the visual quality of an adversarial...
We developed a novel approximate Bayesian computation (ABC) framework, ABCDP, which produces differentially private (DP) and approximate posterior samples. Our framework takes advantage of the sparse vector technique (SVT), widely studied in the diff...
Adversarial attacks on Graph Neural Networks (GNNs) have emerged as a significant threat to the security of graph learning. Compared with Graph Modification Attacks (GMAs), Graph Injection Attacks (GIAs) are considered more realistic attacks, in whic...
Few-shot object detection (FSOD) is proposed to solve the application problem of traditional detectors in scenarios lacking training samples. The meta-learning methods have attracted the researchers’ attention for their excellent generalization...
Cardinality estimation is critical for database management systems (DBMSs) to execute query optimization tasks, which can guide the query optimizer in choosing the best execution plan. However, traditional cardinality estimation methods cannot provid...
Cross-version music retrieval aims at identifying all versions of a given piece of music using a short query audio fragment. One previous approach, which is particularly suited for Western classical music, is based on a nearest neighbor search using...
Query auto-completion (QAC) serves as a critical functionality in contemporary textual search systems by generating real-time query completion suggestions based on a user’s input prefix. Despite the prevalent use of language models (LMs) in QAC...
Underground mine object detection is challenged by low illumination, blur, dust scattering, and repetitive tunnel clutter, which jointly corrupt backbone features, entangle DETR queries, and weaken localization for small objects. Existing enhancement...
Deduplication in storage systems has gained momentum recently for its capability in reducing data footprint. However, deduplication introduces challenges to storage management as storage objects (e.g., files) are no longer independent from each other...
Instance segmentation (IS) of remote sensing (RS) images can not only determine object location at the box-level but also provide instance masks at the pixel-level. It plays an important role in many fields, such as ocean monitoring, urban management...
Video moment retrieval (VMR) aims to localize the temporal segment in an untrimmed video that semantically matches a natural-language query. Although recent detection transformer (DETR) approaches have improved this task, they primarily emphasize que...
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across vision–language tasks, yet their safety alignment remains vulnerable to adversarial manipulation. Existing jailbreak attacks typically optimize adversaria...
Optical remote sensing images often feature high resolution, dense target distribution, and uneven target sizes, while transformer-based detectors like DETR reduce manually designed components, DETR does not support arbitrary-oriented object detectio...
Existing cross-modal person re-identification datasets still lack full-scale benchmarks for sketch-to-RGB person retrieval. To address this problem, this paper investigates sketch-to-RGB cross-modal person re-identification. First, this paper constru...
Street view image retrieval aims to estimate the image locations by querying the nearest neighbor images with the same scene from a large-scale reference dataset. Query images usually have no location information and are represented by features to se...
Chemotherapy poses both physical and psychological challenges for patients, prompting many to seek answers independently through online resources. This study investigates German Google search behavior regarding chemotherapy-related terms using Google...
Referring video object segmentation (R-VOS) is a fundamental vision-language task which aims to segment the target referred by language expression in all video frames. Existing query-based R-VOS methods have conducted in-depth exploration of the inte...
Fine-grained object detection aims to accurately localize the object bounding box while identifying the specific model of the object, which is more challenging than conventional remote sensing object detection. Transformer-based object detector (DETR...
Background: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) and GLP-1/glucose-dependent insulinotropic polypeptide (GIP) dual agonists have revolutionized the treatment of type 2 diabetes and obesity. The rapid growth in public interest, off-lab...
Generalized category discovery (GCD) aims to classify known categories while discovering novel ones in unlabeled data, yet existing methods lack mechanisms to correct boundary-ambiguous samples that receive noisy pseudo-labels, as they primarily rely...
The safety of batteries has become a major obstacle to the promotion and application of electric vehicles, and the use of cloud-based vehicle practical big data to summarize the fault knowledge of batteries to improve product quality and reduce maint...
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