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Deep neuro-fuzzy systems (DNFS) have emerged as a hybrid machine learning approach that has found applications in a wide range of fields, including healthcare, transportation, and finance. However, we empirically demonstrate that DNFS is vulnerable to ...

Federated learning enables multi-party model training by utilizing shared models instead of raw data, allowing for effective use of user data while ensuring privacy protection. However, the training process still has potential threats. Guided by ...

In recent years, learning-based color and tone enhancement methods for photos have become increasingly popular. However, most learning-based image enhancement methods just learn a mapping from one distribution to another based on one dataset, lacking the ...

Welcome to the 20th edition of the ACM Conference on Emerging Networking Experiment and Technologies (ACM CoNEXT 2024). CoNEXT is a premier and highly selective venue in computer networking. The first edition of the conference was organized in Toulouse ...

As the primary channel for users to report and resolve service issues, customer care has historically been a critical and resource-intensive aspect of operating cellular networks. However, owing to the inherent complexity in correlating network events ...

Recommending a tempting sequence of items according to a user’s previous history of purchases and clicks, for instance, in the online shopping portals is challenging. And yet it is a crucial task for all service providers. One of the core ...

• Propose a novel sequential model that embeds a Transformer model inside a U-Net, called UET4Rec.
• Introduce a loss function that implements supervised learning, reinforcement learning, and contrastive learning.
• Extensive evaluation ...

SMP Challenge is an annual challenge that seeks top research teams to develop innovative forecasting methods that can enhance social and business applications. We define and introduce the Social Media Popularity Prediction (SMPP) task that predicting the ...

Diffusion models have garnered significant success in generative tasks, emerging as the predominant model in this domain. Despite their success, the substantial computational resources required for training diffusion models restrict their practical ...

Diffusion models have received a lot of attention in the field of generation due to their ability to produce high-quality samples. However, several recent studies indicate that treating all distributions equally in diffusion model training is sub-...

In order to accurately predict user purchasing behavior and help merchants maximize the sales conversion rate and user satisfaction on e-commerce platforms, this paper proposes a user purchasing behavior prediction model based on the combination of the ...

In the realm of fine-grained image classification, discerning subtle distinctions between closely related categories remains a challenge. However, these approaches typically fall short in addressing the deeper causal relationships that underlie ...

To accelerate the convergence of multi-objective optimization algorithm and achieve an optimization solution set with good diversity, this paper proposes the Pacesetter-Lévy Multi-Objective Particle Swarm Optimization using Arnold Chaotic Map ...

Modern social media’s development has dramatically changed how people obtain information. However, the wide dissemination of various false information has severe detrimental effects. Accordingly, many deep learning-based methods have been proposed to ...

Boolean matrix factorization (BMF) has been widely utilized in fields such as recommendation systems, graph learning, text mining, and -omics data analysis. Traditional BMF methods decompose a binary matrix into the Boolean product of two lower-rank ...

Various resources as the essential elements of data centers, and their utilization is vital to resource managers. In terms of the persistence, the periodicity and the spatial-temporal dependence of stream workload, a new Storm scheduler with ...

A wireless federated learning system is investigated by allowing a server and multiple workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via band-limited channels, ...

Imbalanced data classification presents a significant challenge when there is a substantial disparity in sample sizes across different classes. This issue severely affects classifier accuracy in predicting minority classes, hampering numerous ...

Floorplanning is a complex physical design problem that produces initial locations of movable objects, the quality of which has a great impact on downstream tasks such as placement and routing. To improve the efficacy of floorplanning, machine learning ...

• Proposing a powerful adaptive residual extended Kalman filter calibration method for robotic smoothing system that is able to obtain higher calibration accuracy owing to refrain the gradient degradation or gradient vanishing of the EKF and ...

Achieving high absolute positioning accuracy is crucial for obtaining aspheric optical components with remarkable surface quality using a robotic smoothing system. Robot kinematic calibration is an effective means of improving absolute ...

Cross-domain recommendation aims to leverage heterogeneous information to transfers knowledge from a data-sufficient domain (source domain) to a data-scarce domain (target domain). Existing approaches mainly focus on learning single-domain user ...