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Distributed machine learning on edges is widely used in intelligent transportation, smart home, industrial manufacturing, and underground pipe network monitoring to achieve low latency and real time data processing and prediction. However, the presence of ...

Large language models (LLMs) have captured significant interest from both academia and industry due to their impressive performance across various textual tasks. However, the potential of LLMs to analyze physiological time-series data remains an emerging ...

Graph-level representation learning is important in a wide range of applications. Existing graph-level models are generally built on i.i.d. assumption for both training and testing graphs. However, in an open world, models can encounter out-of-...

Cryptocurrencies are rapidly expanding and becoming vital in digital financial markets. However, the rise in cryptocurrency-related illicit activities has led to significant losses for users. To protect the security of these platforms, it is critical to ...

Real-time velocity monitoring is pivotal for fault detection of rotating machinery. However, existing methods rely on either troublesome deployments of optical encoders and IMU sensors or various tachometers delivering coarse-grained velocity measurements ...

Network verification has recently made strides, focusing on the satisfiability of configurations and policies or the performance and versatility of their methods. However, they generally ignore explainability, which is the ability to explain why a network ...

Common law courts need to refer to similar precedents’ judgments to inform their current decisions. Generating high-quality summaries of court judgment documents can facilitate legal practitioners to efficiently review previous cases and assist ...

Recent studies successfully learned static graph embeddings that are structurally fair by preventing the effectiveness disparity of high- and low-degree vertex groups in downstream graph mining tasks. However, achieving structure fairness in dynamic ...

First-principles calculations combined with the Boltzmann transport theory were used to calculate the thermoelectric characteristics of Zr_1−xNiSnTa_x (x = 0, 1/4, 1/8, 1/12, 1/16, 1/24, 1/32, 1/36, 1/48, and 1/64). Ta-doped ZrNiSn can effectively ...

The proliferation of various mobile devices with massive data and improving computing capacity have prompted the rise of edge artificial intelligence (Edge AI). Without revealing the raw data, federated learning (FL) becomes a promising distributed ...

Federated learning has been identified as an efficient decentralized training paradigm for scaling the machine learning model training on a large number of devices while guaranteeing the data privacy of the trainers. FedAvg has become a foundational ...

The use of Artificial Intelligence (AI) has gained momentum in education. However, the use of AI in K-12 education is still in its nascent stages, and further research and development is needed to realize its potential. Moreover, the creation of a ...

Over the past decade, numerous studies and technologies have emerged exploring how to provide the most appropriate interventions to help students with special educational needs (SEN), who need assistance due to cognitive or developmental ...

Due to the contradiction between limited bandwidth and huge transmission parameters, federated Learning (FL) has been an ongoing challenge to reduce the model parameters that need to be transmitted to server in clients for fast transmission. Existing ...

Accurately predicting Origin-Destination(OD) traffic flow is crucial in traffic planning, vehicle dispatching, user travel, etc. However, existing works mainly focus on modeling prolonged spatial-temporal trends of traffic flow, neglecting the ...

Inductive spatial temporal prediction can generalize historical data to predict unseen data, crucial for highly dynamic scenarios (e.g., traffic systems, stock markets). However, external events (e.g., urban structural growth, market crash) and ...

Federated class-incremental learning (FCIL) allows multiple clients in a distributed environment to learn models collaboratively from evolving data streams, where new classes arrive continually at each client. Some existing works in FCIL combine ...

Video analytics at mobile edge servers offers significant benefits like reduced response time and enhanced privacy. However, guaranteeing various quality-of-service (QoS) requirements of dynamic video analysis requests on heterogeneous edge devices ...

Generating appropriate emotions for responses is essential for dialogue systems to provide human-like interaction in various application scenarios. Most previous dialogue systems tried to achieve this goal by learning empathetic manners from anonymous ...