Tapping and Cultivating the Main Force of Rural Ecological Environment Governance

The Third Plenary Session of the Twentieth Central identified the advancement of integrated urban and rural development as a crucial aspect of Chinese-style modernization. The session underscored the importance of refining the institutional framework for integrated urban and rural development as a viable strategy for addressing the challenges faced by agriculture and rural areas, and for fostering the growth of a robust agricultural sector. The revitalization of the countryside is essential for the overall strength and prosperity of the nation. When the countryside flourishes, the nation benefits, and vice versa. To revive the nation, it is imperative to prioritize the revitalization of the countryside. General Secretary underscored the importance of this point, stating: "Agriculture is a fundamental sector of the economy, and the process of modernization in China is inextricably linked to the advancement of the agricultural industry. "To revitalize agriculture, it is essential to integrate scientific and technological advancement." The primary challenges currently facing China's rural ecological environment governance can be broadly classified into three categories: the need to clarify the role of rural ecological environment governance for townspeople, the necessity to cultivate townspeople's enthusiasm for participating in rural affairs, and the importance of identifying a driving force to facilitate this participation. The second issue pertains to the "technology to the countryside" initiative, which has resulted in an increased degree of specialization and a dearth of farmers' own professional knowledge and skills. This contradiction has led to the current state of rural technology construction in the countryside. Secondly, the discrepancy between the heightened specialization of "technology to the countryside" and the dearth of professional expertise among farmers has resulted in a stagnant trajectory in the development and implementation of technology in rural areas. It is imperative to disseminate scientific ecological concepts and accurate technical methods to these grassroots "elites," so that an increasing number of "elites" can master the requisite knowledge and technology. By encouraging the participation of ordinary villagers in neighboring areas, the primary force of rural ecological governance will gradually be released and activated, forming a consistent stream of endogenous power that will significantly advance the realization of rural ecological revitalization

How to Extend 3D GBSM to Integrated Sensing and Communication Channel with Sharing Feature?

Integrated Sensing and Communication (ISAC) is a promising technology in 6G systems. The existing 3D Geometry-Based Stochastic Model (GBSM), as standardized for 5G systems, addresses solely communication channels and lacks consideration of the integration with sensing channel. Therefore, this letter extends 3D GBSM to support ISAC research, with a particular focus on capturing the sharing feature of both channels, including shared scatterers, clusters, paths, and similar propagation param-eters, which have been experimentally verified in the literature. The proposed approach can be summarized as follows: Firstly, an ISAC channel model is proposed, where shared and non-shared components are superimposed for both communication and sensing. Secondly, sensing channel is characterized as a cascade of TX-target, radar cross section, and target-RX, with the introduction of a novel parameter S for shared target extraction. Finally, an ISAC channel implementation framework is proposed, allowing flexible configuration of sharing feature and the joint generation of communication and sensing channels. The proposed ISAC channel model can be compatible with the 3GPP standards and offers promising support for ISAC technology evaluation

Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems

Recommender system (RS) plays an important role in helping users find the information they are interested in and providing accurate personality recommendation. It has been found that among all the users, there are some user groups called “core users” or “information core” whose historical behavior data are more reliable, objective and positive for making recommendations. Finding the information core is of great interests to greatly increase the speed of online recommendation. There is no general method to identify core users in the existing literatures. In this paper, a general method of finding information core is proposed by modelling this problem as a combinatorial optimization problem. A novel Evolutionary Algorithm with Elite Population (EA-EP) is presented to search for the information core, where an elite population with a new crossover mechanism named as ordered crossover is used to accelerate the evolution. Experiments are conducted on Movielens (100k) to validate the effectiveness of our proposed algorithm. Results show that EA-EP is able to effectively identify core users and leads to better recommendation accuracy compared to several existing greedy methods and the conventional collaborative filter (CF). In addition, EA-EP is shown to significantly reduce the time of online recommendation

Evolve Path Tracer: Early Detection of Malicious Addresses in Cryptocurrency

With the ever-increasing boom of Cryptocurrency, detecting fraudulent behaviors and associated malicious addresses draws significant research effort. However, most existing studies still rely on the full history features or full-fledged address transaction networks, thus cannot meet the requirements of early malicious address detection, which is urgent but seldom discussed by existing studies. To detect fraud behaviors of malicious addresses in the early stage, we present Evolve Path Tracer, which consists of Evolve Path Encoder LSTM, Evolve Path Graph GCN, and Hierarchical Survival Predictor. Specifically, in addition to the general address features, we propose asset transfer paths and corresponding path graphs to characterize early transaction patterns. Further, since the transaction patterns are changing rapidly during the early stage, we propose Evolve Path Encoder LSTM and Evolve Path Graph GCN to encode asset transfer path and path graph under an evolving structure setting. Hierarchical Survival Predictor then predicts addresses' labels with nice scalability and faster prediction speed. We investigate the effectiveness and versatility of Evolve Path Tracer on three real-world illicit bitcoin datasets. Our experimental results demonstrate that Evolve Path Tracer outperforms the state-of-the-art methods. Extensive scalability experiments demonstrate the model's adaptivity under a dynamic prediction setting.Comment: In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD23

How to Extend 3D GBSM Model to RIS Cascade Channel with Non-ideal Phase Modulation?

Reconfigurable intelligent surface (RIS) is seen as a promising technology for next-generation wireless communications, and channel modeling is the key to RIS research. However, traditional model frameworks only support Tx-Rx channel modeling. In this letter, a RIS cascade channel modeling method based on a geometry-based stochastic model (GBSM) is proposed, which follows a 3GPP standardized modeling framework. The main improvements come from two aspects. One is to consider the non-ideal phase modulation of the RIS element, so as to accurately include its phase modulation characteristic. The other is the Tx-RIS-Rx cascade channel generation method based on the RIS radiation pattern. Thus, the conventional Tx-Rx channel model is easily expanded to RIS propagation environments. The differences between the proposed cascade channel model and the channel model with ideal phase modulation are investigated. The simulation results show that the proposed model can better reflect the dependence of RIS on angle and polarization.Comment: 5 pages, 5 figure

Similarity Principle and its Acoustical Verification

This study finds a similarity principle the waves emanated from the same source are similar to each other as long as two wave receivers are close enough to each other the closer to each other the wave receivers are the more similar to each other the received waves are We define the similarity mathematically and verify the similarity principle by acoustical experiment

ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced MiniGPT-4

In recent years, large language models (LLMs) have made significant progress in natural language processing (NLP), with models like ChatGPT and GPT-4 achieving impressive capabilities in various linguistic tasks. However, training models on such a large scale is challenging, and finding datasets that match the model's scale is often difficult. Fine-tuning and training models with fewer parameters using novel methods have emerged as promising approaches to overcome these challenges. One such model is MiniGPT-4, which achieves comparable vision-language understanding to GPT-4 by leveraging novel pre-training models and innovative training strategies. However, the model still faces some challenges in image understanding, particularly in artistic pictures. A novel multimodal model called ArtGPT-4 has been proposed to address these limitations. ArtGPT-4 was trained on image-text pairs using a Tesla A100 device in just 2 hours, using only about 200 GB of data. The model can depict images with an artistic flair and generate visual code, including aesthetically pleasing HTML/CSS web pages. Furthermore, the article proposes novel benchmarks for evaluating the performance of vision-language models. In the subsequent evaluation methods, ArtGPT-4 scored more than 1 point higher than the current \textbf{state-of-the-art} model and was only 0.25 points lower than artists on a 6-point scale. Our code and pre-trained model are available at \url{https://huggingface.co/Tyrannosaurus/ArtGPT-4}.Comment: 16 page