Nowadays, mining opinion or sentiment from the online user-generated texts has become a research hot-spot. Although, a large amount of lexicon-based Chinese polarity detection works have been done, yet the existing methods have one common flaw that even the same word can have opposite polarities among different seed-lexicons, or polarity fuzziness. Therefore, in order to further enhance the performance of Chinese sentiment polarity detection, in this paper, we start from a two-aspect lexicon expansion, specifically, detecting sentiment polarity for new words and revising sentiment polarity for words already defined in seed-lexicons, so that the polarity fuzziness can be avoided. Then, we formulate a novel sentiment polarity detection framework for Chinese (SPDFC) with more effort to fine-grained sentiment processing, involved in symmetrical mapping, sentiment feature pruning and text representation. In this way, the word polarity can be directly taken as features, further penetrating into polarity detection phase. According to the experimental results, compared to other classical and state-of-the-art methods, the proposed the framework SPDFC can achieve the best overall performance from the perspective of Chinese polarity detection, sentiment feature pruning as well as text representation.

Edge computing has been widely researched when 5G network and cloud platforms work together for people’s life. The limitation of energy provided by the battery of the edge device hinders its application. This paper focuses on task scheduling in edge computing combined with the Energy harvesting technology (EH) and Energy Internet (EI). The edge node collects green energy by EH. And nodes exchange energy by EI. Energy Internet obtains green energy from edge nodes. Compared to green energy, we call energy from the power grid (not from the energy of edge nodes by energy harvesting technology) brown energy. How to reduce brown energy consumption is one of the most important problem in our paper. Previous works have not examined the energy attenuation between nodes, neither have they studied the immigration routes of virtual machines (VMs). This paper analyzes VM scheduling and models the energy consumption of VM immigrations, offloading tasks, and green energy transfer in edge computing. The paper proposes a heuristic assumption that there is only one VM in the system, and then presents three heuristics for the system with multiple VMs. The simulation results show that the proposed method-METAR is effective in reducing brown energy and total energy consumption, and improving the utilization rate of green energy. Compared to (EE-PRO) and (MTS), METAR average reduces by 28.23% and 49.50% in (brown) energy consumption. At the same time, METAR average decreases by 5.67% and 11.52% in execution time.

Task offloading from edge devices to the cloud is typically modeled using Directed Acyclic Graphs (DAGs), which assume predefined task details and stable network conditions. However, obtaining detailed task information is often impractical, particularly in dynamic network environments. This paper addresses these challenges by utilizing grey number theory to effectively capture the uncertainties inherent in both task characteristics and network conditions. Through a comprehensive comparative analysis of grey numbers, a Deep Q-Network (DQN) offloading method based on grey numbers is proposed, significantly improving the scheduling process. This study is the first to explore task offloading for edge devices while considering task uncertainty and network dynamics using grey number theory. Simulation results confirm the efficiency of the proposed method in selecting solutions that meet predefined criteria, with the DQN approach demonstrating significant improvements in scheduling performance.