Optimize public services: explore intelligent decision-making and efficiency improvement

The research aims to evaluate the impact of Generative Pre-training Models (GPT) on public service efficiency and quality. The investigation focuses on two main hypotheses. Firstly, using the GPT model frequently and proficiently has a positive correlation with public service effectiveness. Secondly, the application of the GPT model can enhance transparency and fairness in decision-making. This study used multiple linear regression analysis. It found that frequent and skilful use of the GPT model can significantly improve the efficiency and quality of public services. The practical implications of these findings are important for public administration. They indicate that advanced AI technologies can improve public service delivery efficiently. The study's limitations include insufficient scope and depth of data. The ethical and social implications were not adequately explored. Future research should validate findings on a larger dataset. This will improve generalizability and explore the ethical and social dimensions of GPT application. Additionally, it will comprehensively assess the long-term impact on public policy and social structure.A investigação tem por objetivo avaliar o impacto dos modelos generativos de pré-treino (GPT) na eficiência e na qualidade dos serviços públicos. A investigação centra-se em duas hipóteses principais. Em primeiro lugar, a utilização frequente eproficiente do modelo GPT tem uma correlação positiva com a eficácia do serviço público. Em segundo lugar, a aplicação do modelo GPT pode aumentar a transparência e a equidade na tomada de decisões. Este estudo utilizou a análise de regressão linear múltipla. Concluiu-se que a utilização frequente e competente do modelo CPE pode melhorar significativamente a eficiência e a qualidade dos serviços públicos. As implicações práticas destas conclusões são importantes para a administração pública. Indicam que as tecnologias avançadas de IA podem melhorar a prestação de serviços públicos de forma eficiente. As limitações do estudo incluem o âmbito e a profundidade insuficientes dos dados. As implicações éticas e sociais não foram devidamente exploradas. A investigação futura deve validar os resultados num conjunto de dados maior. Isto melhorará a generalização e explorará as dimensões éticas e sociais da aplicação dos GPT. Além disso, avaliará de forma exaustiva o impacto a longo prazo nas políticas públicas e na estrutura social

A Gray-Box Dynamic Modeling Method for Variable Speed Direct Expansion Systems

In this paper, a gray-box dynamic modeling approach for variable-speed direct-expansion systems is presented. The overall approach incorporates a multi-stage training procedure that consists of 1) identification of component sub-models from quasi-steady-state performance data, 2) system model integration with estimation of refrigerant charge and 3) fine tuning of thermal capacitances of the evaporator and condenser to capture the system dynamic responses. Compared to traditional physics-based models, the proposed modeling approach has advantages including reduced engineering efforts in the model development phase, improved computational efficiency and reduced uncertainties. The modeling method was applied to a 3-ton variable-speed heat pump and proved to be capable of accurately capturing the system transient behaviors over a wide range of operating conditions

Modeling and Tackling Timing Bugs in Multi-threaded Systems and Distributed Systems

Multi-threaded software and distributed cloud software are prevalent as a dominant backbone for modern applications. Although it is extremely important, their reliability is severely threatened by software bugs. Among all types of software bugs, timing bugs are among the most troublesome due to their inherent non-deterministic nature and the huge interleaving space. Timing bugs are caused by unexpected timing among local events in multi-threaded systems (local concurrent bugs or LCbugs) or distributed events, such as message or faults, in distributed systems (distributed concurrency bugs or DCbugs). A timing bug model is critical to guide the design of automated tackling tools, which includes three parts: concurrent source, synchronization mechanisms, and sharing resources. Existing timing bug models mainly focus on the thread interleaving concurrent source, the lock-related synchronization mechanism, and shared global memory resources for LCbugs. To fight timing bugs and improve the concurrent software reliability in multi-threaded systems and distributed systems, this dissertation works on these three parts and makes the following contributions: First, this dissertation conducts an empirical study of timing bugs in multi-threaded systems and distributed cloud service systems to understand how common are timing bugs, what's the resource being competed, and how were they resolved or fixed. Our empirical study includes two parts. (1) we conduct a comprehensive characteristic study on real-world incidents in Microsoft Azure production-run cloud services. The study reveals several main findings: (a) about 15% software bug incidents in our study set are caused by timing bugs; (b) 60% timing bugs in our study set are DCbugs (message timing bugs or fault timing bugs); (c) half of the timing bugs in our study set are racing on persistent data instead of shared global memory variables; (d) mitigation strategy, especially running-environment mitigation, is widely used to resolve timing bug incidents in the cloud. (2) we conduct an empirical study of manual patches for real-world LCbugs in multi-threaded systems to understand the gap between automatically generated patches and manually generated patches. The study finds that (a) lock is the dominant synchronization primitive for enforcing atomicity; lock-related signals/waits are not the dominant primitive for enforcing pairwise ordering in patches. (b) leveraging existing synchronization in software is as common as adding extra primitives. These findings provide many motivation and guidelines for the design of timing bug tackling tools in this field. Second, guided by the empirical study, this thesis proposes new models and detection tools for message timing bugs and fault timing bugs in distributed systems. Our new model captures two new concurrent sources (message interleavings and random faults), the new synchronization mechanisms introduced by them, and new sharing resources, persistent data. Guided by the proposed model, detection tools are designed to predict message timing bugs and fault timing bugs from correct runs. Each step of our detection tool is carefully customized to address the unique challenges for DCbugs in distributed systems. The evaluation result shows that our tool can effectively and efficiently detect message timing bugs and fault timing bugs with low false positive rates. Third, motivated by the findings of \lcbug fixing strategies, we design a fixing tool to model and enforce timing relationship by leveraging existing non-lock synchronization primitives. Evaluation using real-world bugs shows that our tool can automatically generate patches that have matching quality with manual patches and are much simpler than those generated by the previous state of the art techniques

HEGEL: Hypergraph Transformer for Long Document Summarization

Extractive summarization for long documents is challenging due to the extended structured input context. The long-distance sentence dependency hinders cross-sentence relations modeling, the critical step of extractive summarization. This paper proposes HEGEL, a hypergraph neural network for long document summarization by capturing high-order cross-sentence relations. HEGEL updates and learns effective sentence representations with hypergraph transformer layers and fuses different types of sentence dependencies, including latent topics, keywords coreference, and section structure. We validate HEGEL by conducting extensive experiments on two benchmark datasets, and experimental results demonstrate the effectiveness and efficiency of HEGEL.Comment: EMNLP 202

Extractive Summarization via ChatGPT for Faithful Summary Generation

Extractive summarization is a crucial task in natural language processing that aims to condense long documents into shorter versions by directly extracting sentences. The recent introduction of ChatGPT has attracted significant interest in the NLP community due to its remarkable performance on a wide range of downstream tasks. However, concerns regarding factuality and faithfulness have hindered its practical applications for summarization systems. This paper first presents a thorough evaluation of ChatGPT's performance on extractive summarization and compares it with traditional fine-tuning methods on various benchmark datasets. Our experimental analysis reveals that ChatGPT's extractive summarization performance is still inferior to existing supervised systems in terms of ROUGE scores. In addition, we explore the effectiveness of in-context learning and chain-of-thought reasoning for enhancing its performance. Furthermore, we find that applying an extract-then-generate pipeline with ChatGPT yields significant performance improvements over abstractive baselines in terms of summary faithfulness. These observations highlight potential directions for enhancing ChatGPT's capabilities for faithful text summarization tasks using two-stage approaches.Comment: Work in progres

Contrastive Hierarchical Discourse Graph for Scientific Document Summarization

The extended structural context has made scientific paper summarization a challenging task. This paper proposes CHANGES, a contrastive hierarchical graph neural network for extractive scientific paper summarization. CHANGES represents a scientific paper with a hierarchical discourse graph and learns effective sentence representations with dedicated designed hierarchical graph information aggregation. We also propose a graph contrastive learning module to learn global theme-aware sentence representations. Extensive experiments on the PubMed and arXiv benchmark datasets prove the effectiveness of CHANGES and the importance of capturing hierarchical structure information in modeling scientific papers.Comment: CODI at ACL 202

SummIt: Iterative Text Summarization via ChatGPT

Existing text summarization systems have made significant progress in recent years but typically generates summaries in a single step. The one-shot summarization setting is sometimes inadequate, however, as the generated summary may contain hallucinations or overlook important details related to the reader's interests. In this paper, we address this limitation by proposing SummIt, an iterative text summarization framework based on large language models like ChatGPT. Our framework enables the model to refine the generated summary iteratively through self-evaluation and feedback, closely resembling the iterative process humans undertake when drafting and revising summaries. We also explore using in-context learning to guide the rationale generation and summary refinement. Furthermore, we explore the potential benefits of integrating knowledge and topic extractors into the framework to enhance summary faithfulness and controllability. We evaluate the performance of our framework on three benchmark summarization datasets through empirical and qualitative analyses. We also conduct a human evaluation to validate the effectiveness of the model's refinements and find a potential issue of over-correction. Our code is available at \url{https://github.com/hpzhang94/summ_it}.Comment: work in progres

Gray box dynamic modeling of vapor compression systems for control optimization

Buildings account for 75% of electricity use in the U.S. and more than 24% of building electrical energy is consumed by vapor compression equipment, including air-conditioners, refrigerators/freezers and heat pumps. Dynamic modeling of vapor compression systems (VCS) is particularly important for developing and validating optimal control strategies to maximize the system efficiency and reliability. However, existing modeling techniques are rarely used in control practices because of the significant model development effort and requirement of high computational resources. This dissertation presents an efficient and robust gray-box dynamic modeling approach for VCS to support control optimization. The presented methodology allows automated construction of data-driven VCS models with minimum training data and human inputs. The overall approach incorporates a multi-stage training procedure with separate estimation of the steady-state and dynamic model parameters along with a finite control volume scheme to achieve good model identifiability while ensuring adequate prediction accuracy. To improve model reliability, the modeling approach incorporates sensitivity analysis and de-correlating steps in a pre-conditioning procedure to avoid over-parameterization. The system-level training identifies the refrigerant charge that minimizes the steady-state simulation errors while the dynamic modeling stage transforms the established steady-state system model into a dynamic counterpart, in which the optimal thermal capacitances of the heat exchanger walls are identified to best reproduce system transient responses

Prioritized experience replay-based DDQN for Unmanned Vehicle Path Planning

Path planning module is a key module for autonomous vehicle navigation, which directly affects its operating efficiency and safety. In complex environments with many obstacles, traditional planning algorithms often cannot meet the needs of intelligence, which may lead to problems such as dead zones in unmanned vehicles. This paper proposes a path planning algorithm based on DDQN and combines it with the prioritized experience replay method to solve the problem that traditional path planning algorithms often fall into dead zones. A series of simulation experiment results prove that the path planning algorithm based on DDQN is significantly better than other methods in terms of speed and accuracy, especially the ability to break through dead zones in extreme environments. Research shows that the path planning algorithm based on DDQN performs well in terms of path quality and safety. These research results provide an important reference for the research on automatic navigation of autonomous vehicles.Comment: 4 pages, 6 figures, 2024 5th International Conference on Information Science, Parallel and Distributed System