Recent studies have applied different approaches for summarizing software artifacts, and yet very few efforts have been made in summarizing the source code fragments available on web. This paper investigates the feasi...Recent studies have applied different approaches for summarizing software artifacts, and yet very few efforts have been made in summarizing the source code fragments available on web. This paper investigates the feasibility of generating code fragment summaries by using supervised learning algorithms. We hire a crowd of ten individuals from the same work place to extract source code features on a cor- pus of 127 code fragments retrieved from Eclipse and Net- Beans Official frequently asked questions （FAQs）. Human an- notators suggest summary lines. Our machine learning algo- rithms produce better results with the precision of 82% and perform statistically better than existing code fragment classi- fiers. Evaluation of algorithms on several statistical measures endorses our result. This result is promising when employing mechanisms such as data-driven crowd enlistment improve the efficacy of existing code fragment classifiers.展开更多
Accurate plantar pressure mapping systems with low dependence on the external power supply are highly desired for preventative healthcare and medical diagnosis.Herein,we propose a self-powered smart insole system that...Accurate plantar pressure mapping systems with low dependence on the external power supply are highly desired for preventative healthcare and medical diagnosis.Herein,we propose a self-powered smart insole system that can perform both static and dynamic plantar pressure mapping with high accuracy.The smart insole system integrates an insole-shaped sensing unit,a multi-channel data acquisition board,and a data storage module.The smart insole consists of a 44-pixel sensor array based on triboelectric nanogenerators(TENGs)to transduce pressure to the electrical signal.By optimizing the sensor architecture and the system's robustness,the smart insole achieves high sensitivity,good error-tolerance capability,excellent durability,and short response–recovery time.Various gait and mobility patterns,such as standing,introversion/extraversion,throwing,and surpassing obstacles,can be distin-guished by analyzing the acquired electrical signals.This work paves the way for self-powered wearable devices for gait monitoring,which might enable a new modality of medical diagnosis.展开更多
基金We would like to extend our gratitude to the individu- als who dedicated their time and effort to participate in crowdsourcing activ- ity and annotation of our code fragment corpus. This work was supported in part by National Program on Key Basic Research Project （2013CB035906）, in part by the New Century Excellent Talents in University （NCET-13-0073）, and in part by the National Natural Science Foundation of China （Grant Nos. 61175062, 61370144）.
文摘Recent studies have applied different approaches for summarizing software artifacts, and yet very few efforts have been made in summarizing the source code fragments available on web. This paper investigates the feasibility of generating code fragment summaries by using supervised learning algorithms. We hire a crowd of ten individuals from the same work place to extract source code features on a cor- pus of 127 code fragments retrieved from Eclipse and Net- Beans Official frequently asked questions （FAQs）. Human an- notators suggest summary lines. Our machine learning algo- rithms produce better results with the precision of 82% and perform statistically better than existing code fragment classi- fiers. Evaluation of algorithms on several statistical measures endorses our result. This result is promising when employing mechanisms such as data-driven crowd enlistment improve the efficacy of existing code fragment classifiers.
基金Startup Funding from Local Government,Grant/Award Number:827/000544National Natural Science Foundation of China,Grant/Award Number:51973119+4 种基金Program of the China Postdoctoral Science Foundation,Grant/Award Number:2022M712160Program of the National Natural Science Foundation of China,Grant/Award Number:52150009Open Project of Key Lab of Special Functional Materials of Ministry of Education,Henan University,Grant/Award Number:KFKT-2022-10High-Level University Construction Fund,Grant/Award Number:860-000002081205Natural Science Foundation of Guangdong Province,Grant/Award Number:2019A1515011566。
文摘Accurate plantar pressure mapping systems with low dependence on the external power supply are highly desired for preventative healthcare and medical diagnosis.Herein,we propose a self-powered smart insole system that can perform both static and dynamic plantar pressure mapping with high accuracy.The smart insole system integrates an insole-shaped sensing unit,a multi-channel data acquisition board,and a data storage module.The smart insole consists of a 44-pixel sensor array based on triboelectric nanogenerators(TENGs)to transduce pressure to the electrical signal.By optimizing the sensor architecture and the system's robustness,the smart insole achieves high sensitivity,good error-tolerance capability,excellent durability,and short response–recovery time.Various gait and mobility patterns,such as standing,introversion/extraversion,throwing,and surpassing obstacles,can be distin-guished by analyzing the acquired electrical signals.This work paves the way for self-powered wearable devices for gait monitoring,which might enable a new modality of medical diagnosis.
