Due to the difficulty of repairing defect, many research efforts have been devoted into automatic defect repair. Given a buggy program that fails some test cases, typical repair technique tries modify make all tests pass. However, since suites in real world projects are usually insufficient, aiming at passing often leads incorrect patches. This problem is known as weak or overfitting. In this paper we aim produce precise patches, is, any patch has relatively high probability be correct. More...

Abstract Studies of EFL teachers’ professional identity have experienced a sociological turn. As result, much research attention has been showered upon exploring construction under the multiple interactions individual and social factors. However, there are few documented studies concerning teachers at tutoring institutions, as marginalized community teachers. To this end, present study drew on narrative inquiry case to indicate dynamic trajectories among institutions “Double Reduction”...

Advances and standards in Internet of Things (IoT) have simplified the realization building automation. However, non-expert IoT users still lack tools that can help them to ensure underlying control system correctness: user-programmable logics match user intention. In fact, necessary know-how domain experts. This paper presents our experience running a automation service based on Salus framework. Complementing efforts simply verify correctness, takes novel steps tackle practical challenges...

Recent advances and industry standards in Internet of Things (IoT) have accelerated the real-world adoption connected devices. To manage this hybrid system digital real-time devices analog environments, has pushed several popular home automation IoT (HA-IoT) frameworks, such as If-This-Then-That (IFTTT), Apple HomeKit, Google Brillo. Typically, users author device interactions by specifying triggering sensor event triggered command. In seemingly simple software system, two dominant factors...

Recent years have witnessed the great potential of attention mechanism in graph representation learning. However, while variants attention-based GNNs are setting new benchmarks for numerous real-world datasets, recent works pointed out that their induced attentions less robust and generalizable against noisy graphs due to lack direct supervision. In this paper, we present a framework which utilizes tool causality provide powerful supervision signal learning process functions. Specifically,...

Skeleton Learning (SL) is the task for learning an undirected graph from input data that captures their dependency relations. SL plays a pivotal role in causal and has attracted growing attention research community lately. Due to high time complexity, anytime emerged which learns skeleton incrementally improves it overtime. In this paper, we first propose advocate reliability requirement be practically useful. Reliability requires intermediately learned have precision persistency. We also...

Due to the difficulty of repairing defect, many research efforts have been devoted into automatic defect repair. Given a buggy program that fails some test cases, typical repair technique tries modify make all tests pass. However, since suites in real world projects are usually insufficient, aiming at passing often leads incorrect patches. In this paper we aim produce precise patches, is, any patch has relatively high probability be correct. More concretely, focus on condition synthesis,...

Person-job fit is an essential part of online recruitment platforms in serving various downstream applications like Job Search and Candidate Recommendation. Recently, pretrained large language models have further enhanced the effectiveness by leveraging richer textual information user profiles job descriptions apart from behavior features metadata. However, general domain-oriented design struggles to capture unique structural within descriptions, leading a loss latent semantic correlations....

Drought-associated tree mortality and forest decline have been increasingly observed across the globe due to warmer drier climates, particularly in drought-prone environments. Understanding how forests respond increasing inter-annual climate variability frequent severe drought events context of global change is great significance for predicting dynamics ecosystems under future warming-drying scenarios. This study combined tree-ring series, xylem anatomy, wood isotope signatures, remote...

Besides natural language processing, transformers exhibit extraordinary performance in solving broader applications, including scientific computing and computer vision. Previous works try to explain this from the expressive power capability perspectives that standard are capable of performing some algorithms. To empower with algorithmic capabilities motivated by recently proposed looped transformer (Yang et al., 2024; Giannou 2023), we design a novel block, dubbed Algorithm Transformer...

Person-job fit is an essential part of online recruitment platforms in serving various downstream applications like Job Search and Candidate Recommendation. Recently, pretrained large language models have further enhanced the effectiveness by leveraging richer textual information user profiles job descriptions apart from behavior features metadata. However, general domain-oriented design struggles to capture unique structural within descriptions, leading a loss latent semantic correlations....

ABSTRACT The role of students' well‐being in their academic development and growth has been reported the literature. Nonetheless, most previous studies have revolved around predictive interpersonal factors well‐being, influence personal is overlooked. To bridge this gap, study probed two attributes, namely mindfulness grit, English well‐being. accomplish this, three valid questionnaires were sent to 623 Chinese as a foreign language (EFL) students chosen from different institutes China....

A lightweight convolutional neural network based psychomotor ability assessment method was proposed in the study, which utilizes EEG signals to analyze participants' abilities. The experimental results indicate that accuracy of evaluation on networks can reach up 94.1%, thus it be effectively used evaluation. In addition, also has advantages simplicity, ease operation, and speed. Experimental research is great significance for deepening understanding characteristics human improving training ability.

Supervised Causal Learning (SCL) aims to learn causal relations from observational data by accessing previously seen datasets associated with ground truth relations. This paper presents a first attempt at addressing fundamental question: What are the benefits supervision and how does it benefit? Starting seeing that SCL is not better than random guessing if learning target non-identifiable priori, we propose two-phase paradigm for explicitly considering structure identifiability. Following...

Both design education and STEAM pay attention to the cultivation of students' innovative consciousness practical ability, they are highly consistent in teaching objectives educational ideas. Based on analysis relevant practice, current research situation basic concepts education, this paper puts forward curriculum principles content evaluation for integrated with STEAM. This constructs link under concept from three aspects: teachers' activities, links activities. finally, it discusses new...