Leveraging Unstructured Image Data for Product Quality Improvement

Recently, traditional quality assurance methods, which often require human expertise, have been accompanied by more automated methods that use machine learning technology. These methods offer manufacturers to reduce error rates and, consequently, to increase margins as well. In particular, predictive quality assurance (Pre QA) allows to minimize expenses by feeding back information from product returns and quality checks into the early product development. However, Pre QA requires detailed information about previous quality problems which is not always readily available in a structured form. In this paper, we therefore discuss the potential of leveraging initially unstructured information in the form of images, taken either during quality checks or by customers when returning a product, to the end of product quality improvement. We furthermore show how this might be realized in practice using the case of fashion manufacturing as an example

Learning over Knowledge-Base Embeddings for Recommendation

State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely neglected recently due to the availability of vast amount of data, and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors. A great challenge for using knowledge bases for recommendation is how to integrated large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements on knowledge base embedding sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge. In this work, we propose to reason over knowledge base embeddings for personalized recommendation. Specifically, we propose a knowledge base representation learning approach to embed heterogeneous entities for recommendation. Experimental results on real-world dataset verified the superior performance of our approach compared with state-of-the-art baselines

Intelligent Word Embeddings of Free-Text Radiology Reports

Radiology reports are a rich resource for advancing deep learning applications in medicine by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the ambiguity and subtlety of natural language. We propose a hybrid strategy that combines semantic-dictionary mapping and word2vec modeling for creating dense vector embeddings of free-text radiology reports. Our method leverages the benefits of both semantic-dictionary mapping as well as unsupervised learning. Using the vector representation, we automatically classify the radiology reports into three classes denoting confidence in the diagnosis of intracranial hemorrhage by the interpreting radiologist. We performed experiments with varying hyperparameter settings of the word embeddings and a range of different classifiers. Best performance achieved was a weighted precision of 88% and weighted recall of 90%. Our work offers the potential to leverage unstructured electronic health record data by allowing direct analysis of narrative clinical notes.Comment: AMIA Annual Symposium 201

Strategies for Searching Video Content with Text Queries or Video Examples

The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search. However, metadata is often lacking for user-generated videos, thus these videos are unsearchable by current search engines. Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity problem by directly analyzing the visual and audio streams of each video. CBVR encompasses multiple research topics, including low-level feature design, feature fusion, semantic detector training and video search/reranking. We present novel strategies in these topics to enhance CBVR in both accuracy and speed under different query inputs, including pure textual queries and query by video examples. Our proposed strategies have been incorporated into our submission for the TRECVID 2014 Multimedia Event Detection evaluation, where our system outperformed other submissions in both text queries and video example queries, thus demonstrating the effectiveness of our proposed approaches

Recent Developments in Quality Management in the Era of Digital Transformation – A Review

The purpose of the current exploratory research is to trace the growth and evolution of the Quality Management as a critical function in organizations and as a discipline of study in academia and research. The methodology adapted is to review some of the classical works and research in the area of Quality Management, which indicates direction of growth and evolution. There are several pioneers who have contributed richly for building and shaping the Quality Management principles, practices and methodologies over several decades. The current study involved the task of summarizing significant trends of Quality Management starting from the crafts man era and going up to the current trend of managing Quality as part of digital transformation. In the digital era there is an increased emphasis on automation of all the activities related to product and process quality management. The use of IoT based automation starting from data capturing, archiving and the point of self-diagnostic and autonomous way of managing quality issues is common place in today’s industries Quality 4.0 era. There are several challenges along the way for which quality professionals must be equipped in terms of knowledge, skills and attitude necessary for quality problem solving using modern techniques. This aspect is also researched in this study. Familiarity with technology platforms such as artificial intelligence, machine learning, image processing, sensors and actuators and such other emerging technologies must form the arsenal for analyzing data and data patterns in the face of data deluge. This requires several inter and multi-disciplinary knowledge exchange forums for grooming future quality professional. This article aims at tracing the metamorphosis of quality management with focus on people development and continuous process improvements in the manufacturing and allied sectors