Self-Attention Networks for Code Search

2021 ◽  
Vol 134 ◽  
pp. 106542
Author(s):  
Sen Fang ◽  
You-Shuai Tan ◽  
Tao Zhang ◽  
Yepang Liu
Keyword(s):  
Attention Networks ◽  
Code Search

Cross-modality co-attention networks for visual question answering

2021 ◽  
Author(s):  
Dezhi Han ◽  
Shuli Zhou ◽  
Kuan Ching Li ◽  
Rodrigo Fernandes de Mello
Keyword(s):  
Question Answering ◽  
Attention Networks ◽  
Visual Question Answering

scholarly journals Superpixel Image Classification with Graph Attention Networks

2020 ◽  
Author(s):  
Pedro H. C. Avelar ◽  
Anderson R. Tavares ◽  
Thiago L. T. da Silveira ◽  
Cliudio R. Jung ◽  
Luis C. Lamb
Keyword(s):  
Image Classification ◽  
Attention Networks

scholarly journals Robust Multimodal Representation Learning with Evolutionary Adversarial Attention Networks

2021 ◽  
pp. 1-1
Author(s):  
Feiran Huang ◽  
Alireza Jolfaei ◽  
Ali Kashif Bashir
Keyword(s):  
Representation Learning ◽  
Attention Networks ◽  
Multimodal Representation

scholarly journals Geometric Scattering Attention Networks

2021 ◽  
Author(s):  
Yimeng Min ◽  
Frederik Wenkel ◽  
Guy Wolf
Keyword(s):  
Attention Networks

scholarly journals Guided pattern mining for API misuse detection by change-based code analysis

2021 ◽  
Vol 28 (2) ◽  
Author(s):  
Sebastian Nielebock ◽  
Robert Heumüller ◽  
Kevin Michael Schott ◽  
Frank Ortmeier
Keyword(s):  
Pattern Mining ◽  
Third Party ◽  
Just In Time ◽  
False Alarms ◽  
Misuse Detection ◽  
Code Search ◽  
Usage Patterns ◽  
Development Processes ◽  
Code Changes ◽  
Api Usage

AbstractLack of experience, inadequate documentation, and sub-optimal API design frequently cause developers to make mistakes when re-using third-party implementations. Such API misuses can result in unintended behavior, performance losses, or software crashes. Therefore, current research aims to automatically detect such misuses by comparing the way a developer used an API to previously inferred patterns of the correct API usage. While research has made significant progress, these techniques have not yet been adopted in practice. In part, this is due to the lack of a process capable of seamlessly integrating with software development processes. Particularly, existing approaches do not consider how to collect relevant source code samples from which to infer patterns. In fact, an inadequate collection can cause API usage pattern miners to infer irrelevant patterns which leads to false alarms instead of finding true API misuses. In this paper, we target this problem (a) by providing a method that increases the likelihood of finding relevant and true-positive patterns concerning a given set of code changes and agnostic to a concrete static, intra-procedural mining technique and (b) by introducing a concept for just-in-time API misuse detection which analyzes changes at the time of commit. Particularly, we introduce different, lightweight code search and filtering strategies and evaluate them on two real-world API misuse datasets to determine their usefulness in finding relevant intra-procedural API usage patterns. Our main results are (1) commit-based search with subsequent filtering effectively decreases the amount of code to be analyzed, (2) in particular method-level filtering is superior to file-level filtering, (3) project-internal and project-external code search find solutions for different types of misuses and thus are complementary, (4) incorporating prior knowledge of the misused API into the search has a negligible effect.

scholarly journals Social exclusion increases the executive function of attention networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Huoyin Zhang ◽  
Shiyunmeng Zhang ◽  
Jiachen Lu ◽  
Yi Lei ◽  
Hong Li
Keyword(s):  
Social Exclusion ◽  
Executive Control ◽  
Brain Regions ◽  
Negative Influence ◽  
Future Research ◽  
Research Attention ◽  
Attention Network ◽  
Attention Networks ◽  
The Social ◽  
And Control

AbstractPrevious studies in humans have shown that brain regions activating social exclusion overlap with those related to attention. However, in the context of social exclusion, how does behavioral monitoring affect individual behavior? In this study, we used the Cyberball game to induce the social exclusion effect in a group of participants. To explore the influence of social exclusion on the attention network, we administered the Attention Network Test (ANT) and compared results for the three subsystems of the attention network (orienting, alerting, and executive control) between exclusion (N = 60) and inclusion (N = 60) groups. Compared with the inclusion group, the exclusion group showed shorter overall response time and better executive control performance, but no significant differences in orienting or alerting. The excluded individuals showed a stronger ability to detect and control conflicts. It appears that social exclusion does not always exert a negative influence on individuals. In future research, attention to network can be used as indicators of social exclusion. This may further reveal how social exclusion affects individuals' psychosomatic mechanisms.

Improve Code Search via Reformulating Queries With Evolving Contexts

2021 ◽  
Author(s):  
P. Niranjan ◽  
Syed Abdul Moeed ◽  
V. Pranitha ◽  
T. Sam Spurgeon ◽  
V. Kavitha ◽  
...  
Keyword(s):  
Code Search
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