This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs). This task seeks to adjust the models’ responses to opinion-related questions on specified topics since an individual’s personality ...

The goal of KD is to transfer valuable knowledge from a strong teacher model to a weaker student model in order to bridge the performance gap. However, the conventional teacher-student paradigm of KD can not be applied to the teacher model and ...

Industrial multimedia recommendation systems extensively utilize cascade architectures to deliver personalized content for users, generally consisting of multiple stages like retrieval and ranking. However, retrieval models have long suffered from Sample ...

Insulator defect detection is pivotal for the reliable functioning of power transmission and distribution networks. This paper introduces an optimized lightweight model for insulator defect detection, RDB-YOLOv8n, which addresses the limitations ...

Smart homes, powered by the Internet of Things, offer great convenience but also pose security concerns due to abnormal behaviors, such as improper operations of users and potential attacks from malicious attackers. Several behavior modeling methods have ...

Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs), which plays an important role in knowledge engineering. Recently, EA with dangling entities has been proposed as a more realistic setting, which ...

Knowledge tracing (KT) plays a crucial role in predicting students’ future performance by analyzing their historical learning processes. Deep neural networks (DNNs) have shown great potential in solving the KT problem. However, there still exist some ...

As the global network evolves into a large-scale interconnection system with multiple distributed domains, scalability and efficiency have become equally important for Traffic Engineering (TE). Traditional centralized TE suffers from significant ...

Device fingerprinting can be used by Internet Service Providers (ISPs) to identify vulnerable IoT devices for early prevention of threats. However, due to the wide deployment of middleboxes in ISP networks, some important data, e.g., 5-tuples and flow ...

With the rapid popularity of short video applications, a large number of short video transmissions occupy the bandwidth, placing a heavy load on the Internet. Due to the extensive number of short videos and the predominant service for mobile users, ...

Drones connected via the web are increasingly being used for crowd anomaly detection (CAD). Existing solutions, however, face many challenges, such as low accuracy and high latency due to drones' dynamic shooting distances and angles as well as limited ...

With the continuous development of applications, data centers run application traffic from various service providers, and the requirements for data center networks are also increasing. Congestion control has always been a hot topic of discussion. ...

The current medical practice is more responsive rather than proactive, despite the widely recognized value of early disease detection, including improving the quality of care and reducing medical costs. One of the cornerstones of early disease ...

• Explainable real-time risk assessment system for stroke-associated pneumonia (SAP)
• The first Transformer-based model designed for the task of early disease detection and exploiting the fine-grained intra-visit records
• The early ...

Because of many advantages, such as single-sided riveting and no welding, blind rivet nuts are widely used to fasten various metal sheets and pipes. However, fixing the blind rivet nut to the base material is mainly accomplished by manual ...

• We design a morphology-based image binarization method.
• We propose an improved EDCircles algorithm based on the geometric characteristics of circular arcs.
• We design a circle optimization method.
• We perform experiments that ...

Fine-grained visual classification task is challenging due to large variation in the appearance of the same subcategories and similarity in the appearance of different subcategories. To tackle the challenges, locating multiple discriminative ...

Existing IoT Network Anomaly Detection Systems (NADSes) typically treat IoT devices as independent entities and model them by Euclidean space features. These approaches suffer from low accuracies on new attacks (e.g., platform-based attacks and evasion ...

Current one-stage object detection methods use dense prediction to generate classification and regression results at the same point on the feature map. Due to the different task attributes, classification and regression are typically trained using ...$$

Regular expressions (REs) offer one-shot solutions for many networking tasks, e.g., network intrusion detection. However, REs purely rely on expert knowledge and cannot learn from massive ubiquitous network data for automatic management. Today, neural ...

Many security applications require unsupervised anomaly detection, as malicious data are extremely rare and often only unlabeled normal data are available for training (i.e., zero-positive). However, security operators are concerned about the high stakes ...

Recently, the rapid growth of video content and the increasing demand for high Quality of Experience (QoE) have significantly strained the backbone network. Edge caching is a promising approach to alleviate the strain by caching content closer to users. ...