Model editing aims at selectively updating a small subset of a neural model's parameters with an interpretable strategy to achieve desired modifications. It can significantly reduce computational costs to adapt to large language models(LLMs). Given its ability to precisely target critical components within LLMs, model editing shows great potential for efficient fine-tuning applications. In this work, we investigate model editing to serve as an efficient method for adapting LLMs to solve aspect-based sentiment classification. Through causal interventions, we trace and determine which neuron hidden states are essential for the model’s predictions. By performing interventions and restorations on each component of an LLM, we identify the importance of these components for aspect-based sentiment classification. Our findings reveal that a distinct set of mid-layer representations is essential for detecting the sentiment polarity of given aspect words. Leveraging these insights, we develop a model editing approach that focuses exclusively on these critical parts of the LLM, leading to a more efficient method for adapting LLMs. Our in and out of domain experiments demonstrate that this approach achieves competitive results compared to the currently strongest methods with significantly fewer trainable parameters, highlighting a more efficient and interpretable fine-tuning strategy.

Multimodal sentiment analysis for fashion-related social media is essential for understanding how consumers appraise fashion products across platforms like Instagram and Twitter, where both textual and visual elements contribute to sentiment expression. However, a notable challenge in this task is the modality gap, where the different information density between text and images hinders effective sentiment analysis. In this paper, we propose a novel multimodal framework that addresses this challenge by introducing pseudo data generated by a two-stage framework. We further utilize a multimodal fusion approach that efficiently integrates the information from various modalities for sentiment classification of fashion posts. Experiments conducted on a comprehensive dataset demonstrate that our framework significantly outperforms existing unimodal and multimodal baselines, highlighting its effectiveness in bridging the modality gap for more accurate sentiment classification in fashion-related social media posts.

The deployment of edge servers make a significant impact on the service quality of a Mobile Edge Computing (MEC) system. This service quality relies on solving two key sub-problems: 1) interference management between servers 2) the placement of MEC servers. To improve the Quality of Service (QoS), we propose a method based on Graph Partition (GP) and Upper Confidence Bound (UCB) for solving these two sub-problems. Regarding interference management, we use an undirected graph to represent the interference between MEC servers so that the overall graph can be divided into multiple subsets of non-interfering MEC servers. Regarding server placement, we propose a Multiple Choice-Upper Confidence Bound (MC-UCB) algorithm that place an collection of interference aware edge servers in each selection. To evaluate the performance, we define a user's QoS function based on transmission delay, throughput, and user density comprehensively and compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) from previous work. The simulation results show that the performance of the proposed algorithms is improved by more than 4% compared with the GA algorithm and 6% compared with the PSO algorithm.

The Internet of Medical Things (IoMT) and Artificial Intelligence (AI) have brought unprecedented opportunities to meet massive behavioral data access and personalization requirements for Internet of Behavior (IoB). They facilitate the communication and computing resource allocation to guarantee low delay and energy consumption demands in healthcare. This article presents an improved offloading algorithm for Mobile Edge Computing (MEC) based on Deep Q Network (DQN) and Simulated Annealing (SA) for IoB. Firstly, we analyze the network model and establish a task cost function based on processing delay and energy consumption. Secondly, we define a Distributed Optimization Problem (DOP) to maximize individual utilities and system utility, which is proved to be a potential countermeasure. Thirdly, we conduct Markov modeling for the current offloading strategy-making scheme and define the objectives and constraints of the optimization function. At the same time, the SA is introduced into the DQN Algorithm, which improves the capacity of the algorithm by focusing on the exploration in the early stage and following the experience value in the later stage. From the simulation results, we can see that compared with the traditional scheme, the proposed strategy can maximize the utilization of the system and reduce processing delay and energy consumption.

As one of the enabling technologies for E-Health, Internet of Medical Things (IoMT) interconnects various medical devices to collect and exchange healthcare information. To enable low-delay healthcare information processing, Mobile edge computing (MEC) can be incorporated in IoMT which can process various data in proximity of the medical devices. In this paper, we propose a Combinatorial Auction and Improved Particle Swarm Optimization based Computation Offloading Approach (CA-PSO) for e-healthcare to meet the Quality of Service (QoS) requirements of low delay and low energy consumption in healthcare monitoring. Firstly, we formulate a joint optimization problem to minimize the system cost consisting of delay and energy consumption, and transform this problem into a potential game. Secondly, we use combinatorial auction algorithm to analyze the offloading situation for different channels and servers, and combine channels and servers to simplify the original problem. Then we combine the offloading combination with improved Particle Swarm Optimization (PSO) to solve the optimal offloading strategy and server resource allocation. The simulation results show that compared with the comparison algorithm, the CA-PSO algorithm has achieved better performance in terms of average processing cost, delay, and energy consumption.

Massive terminal users have brought explosive need of data residing at edge of overall network. Multiple Mobile Edge Computing (MEC) servers are built in/near base station to meet this need. However, optimal distribution of these servers to multiple users in real time is still a problem. Reinforcement Learning (RL) as a framework to solve interaction problem is a promising solution. In order to apply RL based algorithm into a multi-agent environment, we propose an iterative scheme: select individual users with priorities to interact with the environment iteratively one at a time Furthermore, we tried to optimize the overall system performance based on this scheme. Hence, we construct three objective system performance indicators: average processing cost, delay and energy consumption, improve the existing Deep Q-learning Network (DQN) by using the cost as reward function, changing the fixed exploitation rate into dynamic one that associated with reward and episode time. In order to explore the performance potential of the proposed algorithm, we have simulated the proposed algorithm, DQN algorithm and greedy algorithm under different users and data sizes. The results show that the proposed algorithm had reduced at least 12% of system average processing cost comparing to the greedy algorithm. It also outperform the greedy algorithm and DQN algorithm in delay and energy consumption significantly.