Hong Shen
YOU?
Author Swipe
View article: When Your Boss Is an AI Bot: Exploring Opportunities and Risks of Manager Clone Agents in the Future Workplace
When Your Boss Is an AI Bot: Exploring Opportunities and Risks of Manager Clone Agents in the Future Workplace Open
As Generative AI (GenAI) becomes increasingly embedded in the workplace, managers are beginning to create Manager Clone Agents - AI-powered digital surrogates that are trained on their work communications and decision patterns to perform m…
View article: Can GenAI Move from Individual Use to Collaborative Work? Experiences, Challenges, and Opportunities of Integrating GenAI into Collaborative Newsroom Routines
Can GenAI Move from Individual Use to Collaborative Work? Experiences, Challenges, and Opportunities of Integrating GenAI into Collaborative Newsroom Routines Open
Generative AI (GenAI) is reshaping work, but adoption remains largely individual and experimental rather than integrated into collaborative routines. Whether GenAI can move from individual use to collaborative work is a critical question f…
View article: A Hybrid Harmony Search Algorithm for Distributed Permutation Flowshop Scheduling with Multimodal Optimization
A Hybrid Harmony Search Algorithm for Distributed Permutation Flowshop Scheduling with Multimodal Optimization Open
Distributed permutation flowshop scheduling is an NP-hard problem that has become a hot research topic in the fields of optimization and manufacturing in recent years. Multimodal optimization finds multiple global and local optimal solutio…
View article: Joint Optimization of Caching and Recommendation with Performance Guarantee for Effective Content Delivery in IoT
Joint Optimization of Caching and Recommendation with Performance Guarantee for Effective Content Delivery in IoT Open
Content caching and recommendation for content delivery over the Internet are two key techniques for improving the content delivery effectiveness determined by delivery efficiency and user satisfaction, which is increasingly important in t…
View article: A Hybrid Harmony Search for Distributed Permutation Flowshop Scheduling with Multimodal Optimization
A Hybrid Harmony Search for Distributed Permutation Flowshop Scheduling with Multimodal Optimization Open
Multimodal optimization is to find multiple global and local optimal solutions of a function, rather than a single solution. This study proposes a harmony search algorithm with iterative optimizing operators to solve the NP-hard distribute…
View article: Structuralist Approach to AI Literary Criticism: Leveraging Greimas Semiotic Square for Large Language Models
Structuralist Approach to AI Literary Criticism: Leveraging Greimas Semiotic Square for Large Language Models Open
Large Language Models (LLMs) excel in understanding and generating text but struggle with providing professional literary criticism for works with profound thoughts and complex narratives. This paper proposes GLASS (Greimas Literary Analys…
View article: Tracking top-k structural hole spanners in dynamic networks
Tracking top-k structural hole spanners in dynamic networks Open
Structural Hole (SH) theory states that the node which acts as a connecting link among otherwise disconnected communities gets positional advantages in the network. These nodes are called Structural Hole Spanners (SHS). Numerous solutions …
View article: ML-Empowered Microservice Workload Prediction by Dual-Regularized Matrix Factorization
ML-Empowered Microservice Workload Prediction by Dual-Regularized Matrix Factorization Open
A technical challenge for workload prediction in microservice systems is how to capture both the dynamic features of workload and evolving dependencies among microservices. The existing work focused mainly on modeling dynamic features with…
View article: Mutual Knowledge Distillation-Based Communication Optimization Method for Cross-Organizational Federated Learning
Mutual Knowledge Distillation-Based Communication Optimization Method for Cross-Organizational Federated Learning Open
With the increasing severity of data privacy and security issues, cross-organizational federated learning is facing challenges in communication efficiency and cost. Knowledge distillation, as an effective model compression technique, can r…
View article: Automatic Recognition of Dual-Component Radar Signals Based on Deep Learning
Automatic Recognition of Dual-Component Radar Signals Based on Deep Learning Open
The increasing density and complexity of electromagnetic signals have brought new challenges to multi-component radar signal recognition. To address the problem of low recognition accuracy under low signal-to-noise ratios (SNR) in adapting…
View article: Improved Distributed Backdoor Attacks in Federated Learning by Density-Adaptive Data Poisoning and Projection-Based Gradient Updating
Improved Distributed Backdoor Attacks in Federated Learning by Density-Adaptive Data Poisoning and Projection-Based Gradient Updating Open
While federated learning enables collaborative model training with preserved data locality, it remains vulnerable to evolving backdoor attacks that exploit its distributed architecture. Compared with centralized backdoor attacks, a distrib…
View article: Effective Detection of Cloud Masks in Remote Sensing Images
Effective Detection of Cloud Masks in Remote Sensing Images Open
Effective detection of the contours of cloud masks and estimation of their distribution can be of practical help in studying weather changes and natural disasters. Existing deep learning methods are unable to extract the edges of clouds an…
View article: Optimizing Transmit Power for User-Cooperative Backscatter-Assisted NOMA-MEC: A Green IoT Perspective
Optimizing Transmit Power for User-Cooperative Backscatter-Assisted NOMA-MEC: A Green IoT Perspective Open
Non-orthogonal multiple access (NOMA) enables the parallel offloading of multiuser tasks, effectively enhancing throughput and reducing latency. Backscatter communication, which passively reflects radio frequency (RF) signals, improves ene…
View article: Minion: A Technology Probe for Resolving Value Conflicts through Expert-Driven and User-Driven Strategies in AI Companion Applications
Minion: A Technology Probe for Resolving Value Conflicts through Expert-Driven and User-Driven Strategies in AI Companion Applications Open
AI companions based on large language models can role-play and converse very naturally. When value conflicts arise between the AI companion and the user, it may offend or upset the user. Yet, little research has examined such conflicts. We…
View article: Optimizing Transmit Power for User-Cooperative Backscatter-assisted NOMA-MEC: A Green IoT Perspective
Optimizing Transmit Power for User-Cooperative Backscatter-assisted NOMA-MEC: A Green IoT Perspective Open
Non-orthogonal multiple access (NOMA) enables the parallel offloading of multiuser tasks, effectively enhancing throughput and reducing latency. Backscatter communication, which passively reflects radio frequency (RF) signals, improves ene…
View article: User-Driven Value Alignment: Understanding Users' Perceptions and Strategies for Addressing Biased and Discriminatory Statements in AI Companions
User-Driven Value Alignment: Understanding Users' Perceptions and Strategies for Addressing Biased and Discriminatory Statements in AI Companions Open
Large language model-based AI companions are increasingly viewed by users as friends or romantic partners, leading to deep emotional bonds. However, they can generate biased, discriminatory, and harmful outputs. Recently, users are taking …
View article: Maximizing Computation Rate for Sustainable Wireless-Powered MEC Network: An Efficient Dynamic Task Offloading Algorithm with User Assistance
Maximizing Computation Rate for Sustainable Wireless-Powered MEC Network: An Efficient Dynamic Task Offloading Algorithm with User Assistance Open
In the Internet of Things (IoT) era, Mobile Edge Computing (MEC) significantly enhances the efficiency of smart devices but is limited by battery life issues. Wireless Power Transfer (WPT) addresses this issue by providing a stable energy …
View article: Multi-Agent Deep Reinforcement Learning Based Dynamic Task Offloading in a Device-to-Device Mobile-Edge Computing Network to Minimize Average Task Delay with Deadline Constraints
Multi-Agent Deep Reinforcement Learning Based Dynamic Task Offloading in a Device-to-Device Mobile-Edge Computing Network to Minimize Average Task Delay with Deadline Constraints Open
Device-to-device (D2D) is a pivotal technology in the next generation of communication, allowing for direct task offloading between mobile devices (MDs) to improve the efficient utilization of idle resources. This paper proposes a novel al…
View article: Energy-Efficient Task Offloading in Wireless-Powered MEC: A Dynamic and Cooperative Approach
Energy-Efficient Task Offloading in Wireless-Powered MEC: A Dynamic and Cooperative Approach Open
Mobile Edge Computing (MEC) integrated with Wireless Power Transfer (WPT) is emerging as a promising solution to reduce task delays and extend the battery life of Mobile Devices (MDs). However, maximizing the long-term energy efficiency (E…
View article: Maximizing Computation Rate for Sustainable Wireless Powered MEC network: An Efficient Dynamic Task Offloading Algorithm with User Assistance
Maximizing Computation Rate for Sustainable Wireless Powered MEC network: An Efficient Dynamic Task Offloading Algorithm with User Assistance Open
In the Internet of Things (IoT) era, Mobile Edge Computing (MEC) significantly enhances the efficiency of smart devices but is limited by battery life issues. Wireless Power Transfer (WPT) addresses this issue by providing a stable energy …
View article: Two-Tier Efficient QoE Optimization for Partitioning and Resource Allocation in UAV-Assisted MEC
Two-Tier Efficient QoE Optimization for Partitioning and Resource Allocation in UAV-Assisted MEC Open
Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the quality of experience…
View article: Energy-Efficient Task Offloading in Wireless-Powered MEC: A Dynamic and Cooperative Approach
Energy-Efficient Task Offloading in Wireless-Powered MEC: A Dynamic and Cooperative Approach Open
Mobile Edge Computing (MEC) integrated with Wireless Power Transfer (WPT) is emerging as a promising solution to reduce task delays and extend the battery life of Mobile Devices (MDs). Cooperative user communication, or relay technology, e…
View article: Probabilistic scheduling of dynamic I/O requests via application clustering for burst‐buffers equipped high‐performance computing
Probabilistic scheduling of dynamic I/O requests via application clustering for burst‐buffers equipped high‐performance computing Open
Summary Burst‐buffering is a promising storage solution that introduces an intermediate high‐throughput storage buffer layer to mitigate the I/O bottleneck problem that the current high‐performance computing (HPC) platforms suffer. The exi…
View article: Two-Tier Efficient QoE Optimization for Partitioning and Resource Allocation in UAV-Assisted MEC
Two-Tier Efficient QoE Optimization for Partitioning and Resource Allocation in UAV-Assisted MEC Open
Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the Quality of Experience…
View article: Multi-Agent DRL-Based Dynamic Task Offloading in D2D-MEC Network to Minimize Average Task Delay with Deadline Constraints
Multi-Agent DRL-Based Dynamic Task Offloading in D2D-MEC Network to Minimize Average Task Delay with Deadline Constraints Open
Device to Device (D2D) is a pivotal technology in the next generation of communication, allowing for direct task offloading between mobile devices (MDs) to improve the efficient utilization of idle resources. This paper proposes a novel al…
View article: MSE-Based Training and Transmission Optimization for MIMO ISAC Systems
MSE-Based Training and Transmission Optimization for MIMO ISAC Systems Open
In this paper, we investigate a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system under typical block-fading channels. As a non-trivial extension to most existing works on ISAC, both the training and …
View article: Efficient Binary Task Offloading Optimization in Large-Scale IoT Networks via UAV-Enhanced Mobile Edge Computing
Efficient Binary Task Offloading Optimization in Large-Scale IoT Networks via UAV-Enhanced Mobile Edge Computing Open
Unmanned aerial vehicle (UAV)-enhanced mobile edge computing (U-MEC) integrates flexible deployment and wide coverage, effectively reducing computation latency for edge network devices. Whereas, optimization models that only consider a few…
View article: Enhancing QoE in Large-Scale U-MEC Networks via Joint Optimization of Task Offloading and UAV Trajectories
Enhancing QoE in Large-Scale U-MEC Networks via Joint Optimization of Task Offloading and UAV Trajectories Open
Unmanned Aerial Vehicles (UAVs) have emerged as crucial components in advancing Mobile Edge Computing (MEC), leveraging their proximity to edge nodes and scalable nature. This synergy holds significant promise within the Internet of Things…