Daemo

Gaikwad, S.; Morina, Durim; Nistala, Rohit; Agarwal, Megha; Cossette, Alison; Bhanu, Radhika; Savage, Saiph; Narwal, Vishwajeet; Rajpal, Karan; Regino, Jeff; Mithal, Aditi; Ginzberg, Adam; Nath, Aditi; Ziulkoski, Karolina; Cossette, Trygve; Gamage, Dilrukshi; Richmond-Fuller, Angela; Suzuki, Ryo; Herrejón, Jeerel; Le, Kevin; Flores-Saviaga, Claudia; Thilakarathne, Haritha; Gupta, K. K.; Dai, William; Sastry, A. V. S.; Goyal, Shirish; Rajapakshe, Thejan; Abolhassani, Niki; Xie, Angela; Reyes, Abigail; Ingle, Surabhi; Jaramillo, Verónica Andrade; Godinez, M.; Ángel, Walter; Toxtli, Carlos; Flores, Juan Pablo; Gupta, Asmita; Sethia, Vineet; Padilla, D. G.; Milland, Kristy; Setyadi, Kristiono; Wajirasena, Nuwan; Batagoda, Muthitha; Cruz, R.T.; Damon, James; Nekkanti, Divya; Sarma, Tejas Seshadri; Saleh, Mohamed Nour-El-Din; Gongora-Svartzman, Gabriela; Bateni, Soroosh; Barrera, Gema Toledo; Peña, Alex; Compton, Ryan; Aariff, Deen; Palacios, Luis; Ritter, Manuela Paula; Nisha, K K; Kay, Alan; Uhrmeister, Jana; Nistala, Srivalli; Esfahani, Milad Rabbani; Bakiu, Elsa; Diemert, Christopher; Matsumoto, Luca; Singh, Manik; Patel, Krupa R.; Krishna, Ranjay; Kovacs, Geza; Vaish, Rajan; Bernstein, Michael S.

doi:10.1145/2815585.2815739

Cited by 33 publications

(8 citation statements)

References 3 publications

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“…By reading the extracted paragraphs, crowdworkers are requested to create questions based on the content and to highlight spans in the passage as answers. The creation of question–answer pairs was done through the Daemo platform (Gaikwad et al, 2015). The crowdworkers were encouraged to post questions in their own words and the copy‐paste feature of the paragraph was disabled.…”

Section: Case Study 1: Question Answering Datasetsmentioning

confidence: 99%

Dataset versus reality: Understanding model performance from the perspective of information need

Yu,

Sun

2023

Asso for Info Science & Tech

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Deep learning technologies have brought us many models that outperform human beings on a few benchmarks. An interesting question is: can these models well solve real‐world problems with similar settings (e.g., identical input/output) to the benchmark datasets? We argue that a model is trained to answer the same information need in a similar context (e.g., the information available), for which the training dataset is created. The trained model may be used to solve real‐world problems for a similar information need in a similar context. However, information need is independent of the format of dataset input/output. Although some datasets may share high structural similarities, they may represent different research tasks aiming for answering different information needs. Examples are question–answer pairs for the question answering (QA) task, and image‐caption pairs for the image captioning (IC) task. In this paper, we use the QA task and IC task as two case studies and compare their widely used benchmark datasets. From the perspective of information need in the context of information retrieval, we show the differences in the dataset creation processes and the differences in morphosyntactic properties between datasets. The differences in these datasets can be attributed to the different information needs and contexts of the specific research tasks. We encourage all researchers to consider the information need perspective of a research task when selecting the appropriate datasets to train a model. Likewise, while creating a dataset, researchers may also incorporate the information need perspective as a factor to determine the degree to which the dataset accurately reflects the real‐world problem or the research task they intend to tackle.

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Section: Case Study 1: Question Answering Datasetsmentioning

confidence: 99%

Dataset versus reality: Understanding model performance from the perspective of information need

Yu,

Sun

2023

Asso for Info Science & Tech

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“…The Pareto analysis help conclude the problem faced by the TopCoder i.e., most of the contributions are made by the small numbers of software developers that may lead to less number of submissions, poor quality of the complex software solutions and dissatisfaction of the customers indicated in different studies [17]- [19], [57]. The Pareto analysis suggest, in TopCoder case out of more than 1.2 million software developers crowd only few take it as serious job.…”

Section: ) 09% Of the Registrants Won 86% Software Challengesmentioning

confidence: 99%

An Empirical Study on Competitive Crowdsource Software Development: Motivating and Inhibiting Factors

et al. 2019

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Crowdsourcing is gaining more and more popularity among the academic and industrial community. Organizations are adopting this technological advent and increasingly crowdsourcing their tasks to the unknown individuals. However, in the context of competitive crowdsourcing software development (CCSD), crowdsourcing is still unexplored. Too little is presently known about what intricate developers to participate in crowdsourcing software development competitions. Most importantly, what kind of developers are more likely to participate? Such open questions remain to be explored. To this end, in this paper, we present the results of an empirical study conducted to investigate what motivates software developers to participate in CCSD and what inhibits software developers to participate in such competitions. An online questionnaire is sent out to more than 300 crowdsource software participants, of which 113 return valid responses. It is also sent to more than 150 industry practitioners, of which 75 return valid responses. The results suggest that the monetary rewards are not significantly important to motivate software developers to participate in CCSD. Instead, learning, social contacts, and peer recognition are more important. Besides the survey, we also analyze the historical data collected from one of the most popular software crowdsourcing platforms. The analysis results reveal that the Pareto principle holds for CCSD as well, and 0.9% of the participants win 86% competitions. The results support the premise that CCSD market is still at an early stage. Most of the professional software engineers do not participate seriously in crowdsourcing software development. Therefore, many crowdsourced tasks, especially complex tasks, may fail to receive any satisfying submission. These findings are worthwhile for the crowdsourcing platforms and companies who want to outsource their software development tasks to the CCSD platforms. INDEX TERMS Crowdsourcing, motivation, inhibiting factors, competitive software development.

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“…In addition, previous studies have noticed unhealthy relationships between requesters and workers. Particularly, workers have to shoulder a lot of invisible labor without being paid [84]- [86]. A major cause is considered to be the lack of a dialogue platform/mechanism where requesters and workers can equally change ideas with each other and discuss affairs regarding either high-level regulations or specific tasks.…”

Section: ) Inter-human Conversationsmentioning

confidence: 99%

Effect of 2024 Al Alloy Insert on the Grain Refinement of a 2024 Al Alloy Prepared via Insert Mold Casting

Zhu

Zhao

Wang

et al. 2019

Metals

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In this study, an insert mold casting was fabricated by inserting 2024 Al extruded rods into a 2024 Al melt. The molds were kept at a 2024 Al melt for different times. The 2024 Al extruded rods were used to refine the 2024 Al alloy grains because the advantage of this method is that it is contamination free compared with other grain refiners. Moreover, we investigated the macro and microstructure of the ingots. Further, we analyzed the refinement mechanism of the 2024 Al rod on the 2024 Al alloy. Our result showed that when the immersion time of the 2024 Al insert was 0 s, a metallurgical bonding was partly formed between the 2024 Al insert and the 2024 Al alloy mold cast. When the immersion time of the 2024 Al insert increased to 5 s, the 2024 solid insert was dissolved in the liquid; the coarse dendritic grains were replaced by fine equiaxed grains. The refinement mechanism for the insertion of a 2024 Al rod on the 2024 Al alloy was to melt the 2024 Al insert and have it decrease the degree of the liquid superheat, which thus increased the cooling rate and provided a large number of small particles that acted as the nucleus of heterogeneous nucleation. However, these particles were melted gradually in the high-temperature liquid after an increase of immersion time. Thus, the refinement effect of 2024 Al insert on the solidified structure was weakened.

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Daemo

Cited by 33 publications

References 3 publications

Dataset versus reality: Understanding model performance from the perspective of information need

Dataset versus reality: Understanding model performance from the perspective of information need

An Empirical Study on Competitive Crowdsource Software Development: Motivating and Inhibiting Factors

Effect of 2024 Al Alloy Insert on the Grain Refinement of a 2024 Al Alloy Prepared via Insert Mold Casting

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