Publications

By Chidansh Bhatt (Clear Search)

2019
Publication Details
  • ACM SIGMOD/PODS workshop on Human-In-the-Loop Data Analytics (HILDA)
  • Jun 30, 2019

Abstract

Close
Manufacturing environments require changes in work procedures and settings based on changes in product demand affecting the types of products for production. Resource re-organization and time needed for worker adaptation to such frequent changes can be expensive. For example, for each change, managers in a factory may be required to manually create a list of inventory items to be picked up by workers. Uncertainty in predicting the appropriate pick-up time due to differences in worker-determined routes may make it difficult for managers to generate a fixed schedule for delivery to the assembly line. To address these problems, we propose OPaPi, a human-centric system that improves the efficiency of manufacturing by optimizing parts pick-up routes and schedules. OPaPi leverages frequent pattern mining and the traveling salesman problem solver to suggest rack placement for more efficient routes. The system further employs interactive visualization to incorporate an expert’s domain knowledge and different manufacturing constraints for real-time adaptive decision making.
2018
Publication Details
  • ACM Intl. Conf. on Multimedia Retrieval (ICMR)
  • Jun 11, 2018

Abstract

Close
Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their rigid, predetermined curricula. Increasingly, professionals consume this content to augment or update specific skills rather than complete degree or certification programs. To better address the needs of this emergent user population, we describe a visual recommender system called MOOCex. The system recommends lecture videos {\em across} multiple courses and content platforms to provide a choice of perspectives on topics. The recommendation engine considers both video content and sequential inter-topic relationships mined from course syllabi. Furthermore, it allows for interactive visual exploration of the semantic space of recommendations within a learner's current context.
Publication Details
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Apr 21, 2018

Abstract

Close
Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their rigid, predetermined curricula. This paper presents MOOCex, a technique that can offer a more flexible learning experience for MOOCs. MOOCex can recommend lecture videos across different courses with multiple perspectives, and considers both the video content and also sequential inter-topic relationships mined from course syllabi. MOOCex is also equipped with interactive visualization allowing learners to explore the semantic space of recommendations within their current learning context. The results of comparisons to traditional methods, including content-based recommendation and ranked list representation, indicate the effectiveness of MOOCex. Further, feedback from MOOC learners and instructors suggests that MOOCex enhances both MOOC-based learning and teaching.
Publication Details
  • Multimedia Modeling 2018
  • Feb 5, 2018

Abstract

Close
This paper examines content-based recommendation in domains exhibiting sequential topical structure. An example is educational video, including Massive Open Online Courses (MOOCs) in which knowledge builds within and across courses. Conventional content-based or collaborative filtering recommendation methods do not exploit courses' sequential nature. We describe a system for video recommendation that combines topic-based video representation with sequential pattern mining of inter-topic relationships. Unsupervised topic modeling provides a scalable and domain-independent representation. We mine inter-topic relationships from manually constructed syllabi that instructors provide to guide students through their courses. This approach also allows the inclusion of multi-video sequences among the recommendation results. Integrating the resulting sequential information with content-level similarity provides relevant as well as diversified recommendations. Quantitative evaluation indicates that the proposed system, \textit{SeqSense}, recommends fewer redundant videos than baseline methods, and instead emphasizes results consistent with mined topic transitions.
2017
Publication Details
  • TRECVID Workshop
  • Mar 1, 2017

Abstract

Close
This is a summary of our participation in the TRECVID 2016 video hyperlinking task (LNK). We submitted four runs in total. A baseline system combined on established vectorspace text indexing and cosine similarity. Our other runs explored the use of distributed word representations in combination with fine-grained inter-segment text similarity measures.
2016
Publication Details
  • ACM International Conference on Multimedia Retrieval (ICMR)
  • Jun 6, 2016

Abstract

Close
We propose a method for extractive summarization of audiovisual recordings focusing on topic-level segments. We first build a content similarity graph between all segments of all documents in the collection, using word vectors from the transcripts, and then select the most central segments for the summaries. We evaluate the method quantitatively on the AMI Meeting Corpus using gold standard reference summaries and the Rouge metric, and qualitatively on lecture recordings using a novel two-tiered approach with human judges. The results show that our method compares favorably with others in terms of Rouge, and outperforms the baselines for human scores, thus also validating our evaluation protocol.