Heuristics-Driven Link-of-Analogy Prompting: Enhancing Large Language Models for Document-Level Event Argument Extraction

In this study, we investigate in-context learning (ICL) in document-level event argument extraction (EAE). The paper identifies key challenges in this problem, including example selection, context length limitation, abundance of event types, and the limitation of Chain-of-Thought (CoT) prompting in non-reasoning tasks. To address these challenges, we introduce the Heuristic-Driven Link-of-Analogy (HD-LoA) prompting method. Specifically, we hypothesize and validate that LLMs learn task-specific heuristics from demonstrations via ICL. Building upon this hypothesis, we introduce an explicit heuristic-driven demonstration construction approach, which transforms the haphazard example selection process into a methodical method that emphasizes task heuristics. Additionally, inspired by the analogical reasoning of human, we propose the link-of-analogy prompting, which enables LLMs to process new situations by drawing analogies to known situations, enhancing their adaptability. Extensive experiments show that our method outperforms the existing prompting methods and few-shot supervised learning methods, exhibiting F1 score improvements of 4.53% and 9.38% on the document-level EAE dataset. Furthermore, when applied to sentiment analysis and natural language inference tasks, the HD-LoA prompting achieves accuracy gains of 2.87% and 2.63%, indicating its effectiveness across different tasks

Research on the Experimental Teaching Method of Vibration Damping Fastener for Undergraduates Majoring in Rail Transit

Experiment is an important teaching link in talent training. Aiming at the current situation and problems of the experimental teaching of rail transit major, taking the experimental teaching of vibration damping fastener drop weight for railway engineering major of Central South University as an example, the specific methods of the new experimental teaching mode for undergraduates majoring in rail transit are expounded: Improve the subject experimental system, build an open experimental platform, and improve the school-enterprise resource sharing system, etc. This model is conducive to the reform and development of the experimental teaching model for rail transit majors and related science and engineering majors

Research on Practical Teaching of Railway Engineering Specialty Based on Temperature Test of Rubber Sleepers

Experimental teaching plays an important role in cultivating college students' innovative ability. This paper takes the practical teaching of the temperature test of the new rubber sleeper as an example to analyze the current situation and problems of the practical teaching of railway engineering. The specific measures of the new system of practical teaching of railway engineering are put forward: Build a practical teaching curriculum system, improve the practical teaching evaluation mechanism, and promote the sharing of school-enterprise resources, so as to cultivate outstanding railway engineering talents with engineering ability and innovative spirit

Optimal tuned mass-damper-inerter (TMDI) design in wind-excited tall buildings for occupants’ comfort serviceability performance and energy harvesting

The tuned mass-damper-inerter (TMDI) couples the classical tuned mass-damper (TMD), with an inerter device developing a resisting force proportional to the relative acceleration of its ends by the “inertance” constant. Previous works demonstrated that the inclusion of the TMDI leads to more efficient broadband vibration control for a range of different structures under different actions. This paper proposes a novel optimal TMDI design formulation to address occupants’ comfort in wind-excited slender tall buildings susceptible to vortex shedding (VS) effects and to explore optimal TMDI’s potential for transforming part of the windinduced kinetic energy to usable electricity in tall buildings. Attention is focused on investigating benefits of TMDIs with different inertial properties (i.e., secondary mass/weight and inertance) configured in different topologies defined by the number of floors spanned by the inerter device to connect the secondary mass to the building structure. Optimally designed TMDIs for a wide range of inertial properties and three different topologies are obtained through numerical solution of the underlying optimization problem for a benchmark 305.9m tall building with more than 6 height-to-width ratio subjected to experimentally calibrated spatiallycorrelated across-wind force field accounting for VS effects. Performance-based design (PBD) graphs on the TMDI inertial (mass-inertance) plane are furnished demonstrating that any fixed structural performance level in terms of occupants’ comfort (i.e., peak top floor acceleration) can be achieved through lightweight TMDIs if compared with classical TMDs as long as sufficient inertance is provided. Further, TMDI robustness to host structure properties and to reference wind velocity is shown to increase by increasing inertance or by spanning more floors in connecting the secondary mass with the host structure by the inerter. Lastly, it is found that increased available energy for harvesting in wind excited tall buildings is achieved by incorporating electromagnetic motors in TMDIs with varying damping property, while concurrent reduced floor acceleration and increased available energy for harvesting is accomplished by TMDI topologies with inerters spanning more floors

Knowledge-based BERT word embedding fine-tuning for emotion recognition

Emotion recognition has received considerable attention in recent years, with the popularity of social media. It is noted, however, that the state-of-the-art language models such as Bidirectional Encoder Representations from Transformers (BERT) may not produce the best performance in emotion recognition. We found the main cause of the problem is that the embedding of emotional words from the pre-trained BERT model may not exhibit high between-class difference and within-class similarity. While BERT model fine-tuning is a common practice when it is applied to specific tasks, this may not be practical in emotion recognition because most datasets are small and some texts are short and noisy, without containing much useful contextual information. In this paper, we propose to use the knowledge of emotion vocabulary to fine-tune embedding of emotional words. As a separate module independent of the embedding learning model, the fine-tuning model aims to produce emotional word embedding with improved within-class similarity and between-class difference. By combining the emotionally discriminative fine-tuned embedding with contextual information-rich embedding from pre-trained BERT model, the emotional features underlying the texts could be more effectively captured in the subsequent feature learning module, which in turn leads to improved emotion recognition performance. The knowledge-based word embedding fine-tuning model is tested on five datasets of emotion recognition, and the results and analysis demonstrate the effectiveness of the proposed method.National Research Foundation (NRF)This research is supported by the National Research Foundation Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme

Research on the Green Effect of Environmental Policies—From the Perspective of Policy Mix

Environmental protection policy serves as an effective means for the government to curb environmental pollution and promote high-quality economic development. The government must weigh the effects of different policy mixes. From the perspective of policy combination, this paper discusses the green effect of environmental protection policy theoretically and empirically. First and foremost, this paper sorts out the reforming time of environmental protection taxes and the situation of the low-carbon pilot city, and puts forward two hypotheses. Furthermore, by referring to the environmental protection tax, the policy for the low-carbon pilot city, and the urban air quality indicator from 2014 to 2020, this paper explores the green effect of the environmental protection policy and further validates the consolidation effect of the policy mix on the green effect. The study reveals a significant decrease in the air pollution level in regions with higher standards for levying an environmental protection tax. The conclusion remains robust via parallel trend testing and substitution of the subject variables. Furthermore, an analysis of the policy mix of an environmental protection tax indicates that the policy mix of an environmental protection tax and low-carbon city produces a significant green effect, which not only curbs air pollution but also reduces greenhouse gas emissions. An in-depth analysis shows that an environmental protection tax has the best green effect in the first and second areas of a low-carbon pilot market. The synergies of low-carbon pilot effects are higher in areas with low and middle tax rates

Tailored text augmentation for sentiment analysis

In synonym replacement-based data augmentation techniques for natural language processing tasks, words in a sentence are often sampled randomly with equal probability. In this paper, we propose a novel data augmentation technique named Tailored Text Argumentation (TTA) for sentiment analysis. It has two main operations. The first operation is the probabilistic word sampling for synonym replacement based on the discriminative power and relevance of the word to sentiment. The second operation is the identification of words irrelevant to sentiment but discriminative for the training data, and application of zero masking or contextual replacement to these words. The first operation expands the coverage of discriminative words, while the second operation alleviates the problem of misfitting. Both operations tend to improve the model's generalization capability. Extensive experiments on simulated low-data regimes demonstrate that TTA yields notable improvements over six strong baselines. Finally, TTA is applied to public sentiment analysis on measures against Covid-19, which again proves the effectiveness of the new data augmentation algorithm.National Research Foundation (NRF)This work is an outcome of the Future Resilient Systems project at Singapore-ETH Centre (SEC) supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme

Recommending relevant projects via user behaviour: an exploratory study on github

Social coding sites (e.g., Github) provide various features like Forking and Sending Pull-requests to support crowd- based software engineering. When using these features, a large amount of user behavior data is recorded. User behavior data can reflect developers preferences and interests in software development activities. Online service providers in many fields have been using user behavior data to discover user preferences and interests to achieve various purposes. In the field of software engineering however, there has been few studies in mining large amount of user behavior data. Our goal is to design an approach based on user behavior data, to recommend relevant open source projects to developers, which can be helpful in activities like searching for the right open source solutions to quickly build prototypes. In this paper, we explore the possibilities of such a method by conducting a set of experiments on selected data sets from Github. We find it a promising direction in mining projects' relevance from user behavior data. Our study also obtain some important issues that is worth considering in this method. Copyright 2014 ACM.EI25-3