Joy Kim

Bio: Joy Kim is an academic researcher from Stanford University. The author has contributed to research in topics: Crowdsourcing & Creative brief. The author has an hindex of 13, co-authored 16 publications receiving 545 citations. Previous affiliations of Joy Kim include University of Washington.

MicroRNA Profiling in Prostate Cancer - The Diagnostic Potential of Urinary miR-205 and miR-214

Abstract: Prostate cancer (PCa) is the most common type of cancer in men in the United States, which disproportionately affects African American descents. While metastasis is the most common cause of death among PCa patients, no specific markers have been assigned to severity and ethnic biasness of the disease. MicroRNAs represent a promising new class of biomarkers owing to their inherent stability and resilience. In the present study, we investigated potential miRNAs that can be used as biomarkers and/or therapeutic targets and can provide insight into the severity and ethnic biasness of PCa. PCR array was performed in FFPE PCa tissues (5 Caucasian American and 5 African American) and selected differentially expressed miRNAs were validated by qRT-PCR, in 40 (15 CA and 25 AA) paired PCa and adjacent normal tissues. Significantly deregulated miRNAs were also analyzed in urine samples to explore their potential as non-invasive biomarker for PCa. Out of 8 miRNAs selected for validation from PCR array data, miR-205 (p<0.0001), mir-214 (p<0.0001), miR-221(p<0.001) and miR-99b (p<0.0001) were significantly downregulated in PCa tissues. ROC curve shows that all four miRNAs successfully discriminated between PCa and adjacent normal tissues. MiR-99b showed significant down regulation (p<0.01) in AA PCa tissues as compared to CA PCa tissues and might be related to the aggressiveness associated with AA population. In urine, miR-205 (p<0.05) and miR-214 (p<0.05) were significantly downregulated in PCa patients and can discriminate PCa patients from healthy individuals with 89% sensitivity and 80% specificity. In conclusion, present study showed that miR-205 and miR-214 are downregulated in PCa and may serve as potential non-invasive molecular biomarker for PCa.

Ensemble: exploring complementary strengths of leaders and crowds in creative collaboration

Abstract: In story writing, the diverse perspectives of the crowd could support an author's search for the perfect character, setting, or plot. However, structuring crowd collaboration is challenging. Too little structure leads to unfocused, sprawling narratives, and too much structure stifles creativity. Motivated by the idea that individual creative leaders and the crowd have complementary creative strengths, we present an approach where a leader directs the high-level vision for a story and articulates creative constraints for the crowd. This approach is embodied in Ensemble, a novel collaborative story-writing platform. In a month-long short story competition, over one hundred volunteer users on the web started over fifty short stories using Ensemble. Leaders used the platform to direct collaborator work by establishing creative goals, and collaborators contributed meaningful, high-level ideas to stories through specific suggestions. This work suggests that asymmetric creative contributions may support a broad new class of creative collaborations.

Mechanical Novel: Crowdsourcing Complex Work through Reflection and Revision

Abstract: Crowdsourcing systems accomplish large tasks with scale and speed by breaking work down into independent parts. However, many types of complex creative work, such as fiction writing, have remained out of reach for crowds because work is tightly interdependent: changing one part of a story may trigger changes to the overall plot and vice versa. Taking inspiration from how expert authors write, we propose a technique for achieving interdependent complex goals with crowds. With this technique, the crowd loops between reflection, to select a high-level goal, and revision, to decompose that goal into low-level, actionable tasks. We embody this approach in Mechanical Novel, a system that crowdsources short fiction stories on Amazon Mechanical Turk. In a field experiment, Mechanical Novel resulted in higher-quality stories than an iterative crowdsourcing workflow. Our findings suggest that orienting crowd work around high-level goals may enable workers to coordinate their effort to accomplish complex work.

Mechanical Novel: Crowdsourcing Complex Work through Reflection and Revision

Abstract: Crowdsourcing systems accomplish large tasks with scale and speed by breaking work down into independent parts. However, many types of complex creative work, such as fiction writing, have remained out of reach for crowds because work is tightly interdependent: changing one part of a story may trigger changes to the overall plot and vice versa. Taking inspiration from how expert authors write, we propose a technique for achieving interdependent complex goals with crowds. With this technique, the crowd loops between reflection, to select a high-level goal, and revision, to decompose that goal into low-level, actionable tasks. We embody this approach in Mechanical Novel, a system that crowdsources short fiction stories on Amazon Mechanical Turk. In a field experiment, Mechanical Novel resulted in higher-quality stories than an iterative crowdsourcing workflow. Our findings suggest that orienting crowd work around high-level goals may enable workers to coordinate their effort to accomplish complex work.

Motif: Supporting Novice Creativity through Expert Patterns

Abstract: Creating personal narratives helps people build meaning around their experiences. However, novices lack the knowledge and experience to create stories with strong narrative structure. Current storytelling tools often structure novice work through templates, enforcing a linear creative process that asks novices for materials they may not have. In this paper, we propose scaffolding creative work using storytelling patterns extracted from stories created by experts. Patterns are modular sets of related camera shots that expert videographers commonly use to achieve a specific narrative function. After identifying a set of patterns from high-quality storytelling videos, we created Motif, a mobile video storytelling application that allows users to construct video stories by combining these patterns. By making existing solutions used by experts available to novices, we encourage capturing shots with story structure and narrative goals in mind. In a controlled study where we asked participants to create travel video stories, videos created with patterns conveyed stronger narrative structure and were considered higher quality by expert evaluators than videos created without patterns.

A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective

Abstract: Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough labeled data. Second, unlike traditional machine learning, deep learning techniques automatically generate features, which saves feature engineering costs, but in return may require larger amounts of labeled data. Interestingly, recent research in data collection comes not only from the machine learning, natural language, and computer vision communities, but also from the data management community due to the importance of handling large amounts of data. In this survey, we perform a comprehensive study of data collection from a data management point of view. Data collection largely consists of data acquisition, data labeling, and improvement of existing data or models. We provide a research landscape of these operations, provide guidelines on which technique to use when, and identify interesting research challenges. The integration of machine learning and data management for data collection is part of a larger trend of Big data and Artificial Intelligence (AI) integration and opens many opportunities for new research.

The Experience Sampling Method on Mobile Devices

Abstract: The Experience Sampling Method (ESM) is used by scientists from various disciplines to gather insights into the intra-psychic elements of human life. Researchers have used the ESM in a wide variety of studies, with the method seeing increased popularity. Mobile technologies have enabled new possibilities for the use of the ESM, while simultaneously leading to new conceptual, methodological, and technological challenges. In this survey, we provide an overview of the history of the ESM, usage of this methodology in the computer science discipline, as well as its evolution over time. Next, we identify and discuss important considerations for ESM studies on mobile devices, and analyse the particular methodological parameters scientists should consider in their study design. We reflect on the existing tools that support the ESM methodology and discuss the future development of such tools. Finally, we discuss the effect of future technological developments on the use of the ESM and identify areas requiring further investigation.

Stereotactic Body Radiation Therapy (SBRT) for clinically localized prostate cancer: the Georgetown University experience

Abstract: Background: Stereotactic body radiation therapy (SBRT) delivers fewer high-dose fractions of radiation which may be radiobiologically favorable to conventional low-dose fractions commonly used for prostate cancer radiotherapy. We report our early experience using SBRT for localized prostate cancer. Methods: Patients treated with SBRT from June 2008 to May 2010 at Georgetown University Hospital for localized prostate carcinoma, with or without the use of androgen deprivation therapy (ADT), were included in this retrospective review of data that was prospectively collected in an institutional database. Treatment was delivered using the CyberKnife W with doses of 35 Gy or 36.25 Gy in 5 fractions. Biochemical control was assessed using the Phoenix definition. Toxicities were recorded and scored using the CTCAE v.3. Quality of life was assessed before and after treatment using the Short Form-12 Health Survey (SF-12), the American Urological Association Symptom Score (AUA) and Sexual Health Inventory for Men (SHIM) questionnaires. Late urinary symptom flare was defined as an AUA score ≥ 15 with an increase of ≥ 5 points above baseline six months after the completion of SBRT. Results: One hundred patients (37 low-, 55 intermediate- and 8 high-risk according to the D’Amico classification) at a median age of 69 years (range, 48–90 years) received SBRT, with 11 patients receiving ADT. The median pretreatment prostate-specific antigen (PSA) was 6.2 ng/ml (range, 1.9-31.6 ng/ml) and the median follow-up was 2.3 years (range, 1.4-3.5 years). At 2 years, median PSA decreased to 0.49 ng/ml (range, 0.1-1.9 ng/ml). Benign PSA bounce occurred in 31% of patients. There was one biochemical failure in a high-risk patient, yielding a two-year actuarial biochemical relapse free survival of 99%. The 2-year actuarial incidence rates of GI and GU toxicity ≥grade 2 were 1% and 31%, respectively. A median baseline AUA symptom score of 8 significantly increased to 11 at 1 month (p= 0.001), however returned to baseline at 3 months (p= 0.60). Twenty one percent of patients experienced a late transient urinary symptom flare in the first two years following treatment. Of patients who were sexually potent prior to treatment, 79% maintained potency at 2 years post-treatment. (Continued on next page)

Revolt: Collaborative Crowdsourcing for Labeling Machine Learning Datasets

Abstract: Crowdsourcing provides a scalable and efficient way to construct labeled datasets for training machine learning systems. However, creating comprehensive label guidelines for crowdworkers is often prohibitive even for seemingly simple concepts. Incomplete or ambiguous label guidelines can then result in differing interpretations of concepts and inconsistent labels. Existing approaches for improving label quality, such as worker screening or detection of poor work, are ineffective for this problem and can lead to rejection of honest work and a missed opportunity to capture rich interpretations about data. We introduce Revolt, a collaborative approach that brings ideas from expert annotation workflows to crowd-based labeling. Revolt eliminates the burden of creating detailed label guidelines by harnessing crowd disagreements to identify ambiguous concepts and create rich structures (groups of semantically related items) for post-hoc label decisions. Experiments comparing Revolt to traditional crowdsourced labeling show that Revolt produces high quality labels without requiring label guidelines in turn for an increase in monetary cost. This up front cost, however, is mitigated by Revolt's ability to produce reusable structures that can accommodate a variety of label boundaries without requiring new data to be collected. Further comparisons of Revolt's collaborative and non-collaborative variants show that collaboration reaches higher label accuracy with lower monetary cost.