Research on the economic effect of employment structure change in heterogeneous regions: evidence from resource-based cities in China

The Report on the Work of the Chinese Government in 2021 emphasised that stable employment is the foundation of national development. Therefore, adjustment of the employment structure is one of the main routes for sustainable development of resource-based cities. However, the impact of employment structure on sustained economic growth, particularly in heterogeneous regions, has not yet been determined. This study analyses China’s employment structure’s spatial evolution, using panel data from 2004 to 2018 of 115 prefecture-level resource-based cities. It explores the driving factors and spatial effects of employment structure changes on economic growth through an extended two-sector economic growth model and spatial econometric model, and proposes solutions for heterogeneous regions. The results show that the labour productivity of the employed population in the secondary industry is the most important factor affecting economic growth, but the spatial effects of employment structure adjustment on economic growth are different in heterogeneous regions. They further reveal that improving the productivity of the employed population in the secondary industry and building an industrial system according to regional advantages are the top priorities for developing the sustainable economy of resource-based cities

Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation

Recommender systems have been gaining increasing research attention over the years. Most existing recommendation methods focus on capturing users' personalized preferences through historical user-item interactions, which may potentially violate user privacy. Additionally, these approaches often overlook the significance of the temporal fluctuation in item popularity that can sway users' decision-making. To bridge this gap, we propose Popularity-Aware Recommender (PARE), which makes non-personalized recommendations by predicting the items that will attain the highest popularity. PARE consists of four modules, each focusing on a different aspect: popularity history, temporal impact, periodic impact, and side information. Finally, an attention layer is leveraged to fuse the outputs of four modules. To our knowledge, this is the first work to explicitly model item popularity in recommendation systems. Extensive experiments show that PARE performs on par or even better than sophisticated state-of-the-art recommendation methods. Since PARE prioritizes item popularity over personalized user preferences, it can enhance existing recommendation methods as a complementary component. Our experiments demonstrate that integrating PARE with existing recommendation methods significantly surpasses the performance of standalone models, highlighting PARE's potential as a complement to existing recommendation methods. Furthermore, the simplicity of PARE makes it immensely practical for industrial applications and a valuable baseline for future research.Comment: 9 pages, 5 figure

Are your comments outdated? Towards automatically detecting code-comment consistency

In software development and maintenance, code comments can help developers understand source code, and improve communication among developers. However, developers sometimes neglect to update the corresponding comment when changing the code, resulting in outdated comments (i.e., inconsistent codes and comments). Outdated comments are dangerous and harmful and may mislead subsequent developers. More seriously, the outdated comments may lead to a fatal flaw sometime in the future. To automatically identify the outdated comments in source code, we proposed a learning-based method, called CoCC, to detect the consistency between code and comment. To efficiently identify outdated comments, we extract multiple features from both codes and comments before and after they change. Besides, we also consider the relation between code and comment in our model. Experiment results show that CoCC can effectively detect outdated comments with precision over 90%. In addition, we have identified the 15 most important factors that cause outdated comments, and verified the applicability of CoCC in different programming languages. We also used CoCC to find outdated comments in the latest commits of open source projects, which further proves the effectiveness of the proposed method

Logging identification of the Longmaxi mud shale reservoir in the Jiaoshiba area, Sichuan Basin

AbstractCompared with conventional gas reservoirs, shale gas reservoirs are not sensitive to petrophysical properties, making it much difficult to identify this kind of reservoirs with well logging technologies. Therefore, through a comparison of the logging curves of the Lower Silurian Longmaxi marine shale in the Jiaoshiba area, Sichuan Basin, it is found that the mud shale on conventional log curves generally features high gamma ray, high uranium, low thorium, low kalium, relative high resistivity, high interval transit time, low neutron, low density and low photoelectric absorption cross section index, while on elements logging curves, it features an increase of silicon content and a decrease of aluminum and iron content. Based on the logging response characteristics of mud shale, the logging curves most sensitive to shale, gamma ray, neutron and density logging were selected and overlaid to identify mud shale effectively. On the basis of qualitative identification, the density logging value can identify the non-organic-rich mud shale from organic-rich mud shale, because the former has a density of 2.61–2.70 g/cm3, while the latter has a density of less than 2.61 g/cm3. The identification results agree well with the results of field gas content test, TOC experiment, and gas logging, so this study can provide reference for the logging interpretation

Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations

Extracting generalized and robust representations is a major challenge in emotion recognition in conversations (ERC). To address this, we propose a supervised adversarial contrastive learning (SACL) framework for learning class-spread structured representations. The framework applies contrast-aware adversarial training to generate worst-case samples and uses a joint class-spread contrastive learning objective on both original and adversarial samples. It can effectively utilize label-level feature consistency and retain fine-grained intra-class features. To avoid the negative impact of adversarial perturbations on context-dependent data, we design a contextual adversarial training strategy to learn more diverse features from context and enhance the model's context robustness. We develop a sequence-based method SACL-LSTM under this framework, to learn label-consistent and context-robust emotional features for ERC. Experiments on three datasets demonstrate that SACL-LSTM achieves state-of-the-art performance on ERC. Extended experiments prove the effectiveness of the SACL framework.Comment: 16 pages, accepted by ACL 202

Collaborative Edge Caching: a Meta Reinforcement Learning Approach with Edge Sampling

Current learning-based edge caching schemes usually suffer from dynamic content popularity, e.g., in the emerging short video platforms, users' request patterns shift significantly over time and across different edges. An intuitive solution for a specific local edge cache is to collect more request histories from other edge caches. However, uniformly merging these request histories may not perform satisfactorily due to heterogeneous content distributions on different edges. To solve this problem, we propose a collaborative edge caching framework. First, we design a meta-learning-based collaborative strategy to guarantee that the local model can timely meet the continually changing content popularity. Then, we design an edge sampling method to select more "valuable" neighbor edges to participate in the local training. To evaluate the proposed framework, we conduct trace-driven experiments to demonstrate the effectiveness of our design: it improves the average cache hit rate by up to $10.12\%$ (normalized) compared with other baselines.Comment: Published on IEEE International Conference on Multimedia and Expo 2023 (ICME2023