Xinyi Ding

Southern Methodist University | SMU · Department of Computer Science

Existing truth inference methods in crowdsourcing aim to map redundant labels and items to the ground truth. They treat the ground truth as hidden variables and use statistical or deep learning-based worker behavior models to infer the ground truth. However, worker behavior models that rely on ground truth hidden variables overlook workers' behavio...

Crowdsourcing as a practical way of ensuring data quality has achieved great success in fields like image annotation, speech recognition, etc. For structured complex tasks like translation which are usually very intensive, one task need to be split first, otherwise workers may feel overwhelmed. However, if we randomly split one task into evenly siz...

In crowdsourcing, existing efforts mainly use real datasets collected from crowdsourcing as test datasets to evaluate the effectiveness of aggregation algorithms. However, these work ignore the fact that the datasets obtained by crowdsourcing are usually sparse and imbalanced due to limited budget. As a result, applying the same aggregation algorit...

Crowdsourcing has achieved great success in fields like data annotation, social survey, objects labeling, etc. However, enticed by potential high rewards, we have seen more and more malicious behavior like plagiarism, random submission, offline collusion, etc. The existence of such malicious behavior not only increases the cost of handling tasks fo...

Crowdsourcing is a popular way of collecting crowd wisdom and has been deployed in various senarios. Effective answer collection and answer aggregation are two important crowdsourcing topics as workers may give incorrect responses. For difficult tasks, workers tend to implicitly use task related information during answer collection, and those infor...

Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO 2 ) readings that allow for diagno...

Knowledge Tracing is the process of tracking mastery level of different skills of students for a given learning domain. It is one of the key components for building adaptive learning systems and has been investigated for decades. The empirical success of deep neural networks in the past few years has encouraged researchers in the learning science c...

Machine learning-based approaches have greatly accelerated the progresses in the field of micro-expression detection and recognition. However, many models, especially those based on deep learning require very large databases of hand labeled data for training. Existing micro-expression data collection processes usually cost a lot and are time-consum...

Knowledge Tracing is the process of tracking mastery level of different skills of students for a given learning domain. It is one of the key components for building adaptive learning systems and has been investigated for decades. In parallel with the success of deep neural networks in other fields, we have seen researchers take similar approaches i...

Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO$_2$) readings that allow for diagn...

Knowledge tracing allows Intelligent Tutoring Systems to infer which topics or skills a student has mastered, thus adjusting curriculum accordingly. Deep Learning based models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have achieved significant improvements compared with models like Bayesian Knowledge Tracing (BK...

Knowledge tracing allows Intelligent Tutoring Systems to infer which topics or skills a student has mastered, thus adjusting curriculum accordingly. Deep Learning based models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have achieved significant improvements compared with models like Bayesian Knowledge Tracing (BK...

Abstract The tremendous success of deep learning for imaging applications has resulted in numerous beneficial advances. Unfortunately, this success has also been a catalyst for malicious uses such as photo-realistic face swapping of parties without consent. In this study, we use deep transfer learning for face swapping detection, showing true posit...

Most existing research works involving deep learning focus on performance improvement by developing new architectures or regularizers. However, in this paper we study the modeling of uncertainty in recurrent networks for the application of student response modeling, more specifically, knowledge tracing. Knowledge tracing is an application of time s...

The tremendous success of deep learning for imaging applications has resulted in numerous beneficial advances. Unfortunately , this success has also been a catalyst for malicious uses such as photo-realistic face swapping of parties without consent. Transferring one person's face from a source image to a target image of another person, while keepin...

The tremendous success of deep learning for imaging applications has resulted in numerous beneficial advances. Unfortunately, this success has also been a catalyst for malicious uses such as photo-realistic face swapping of parties without consent. Transferring one person's face from a source image to a target image of another person, while keeping...

Knowledge tracing allows Intelligent Tutoring Systems to infer which topics or skills a student has mastered, thus adjusting curriculum accordingly. Deep Knowledge Tracing (DKT) uses recurrent neural networks (RNNs) for knowledge tracing and has achieved significant improvements compared with models like Bayesian Knowledge Tracing (BKT) and Perform...

In this paper, we develop a context-aware, tablet-based learning module for adult education. Specifically, we focus on adult education in healthcare-teaching learners to perform a medical screening procedure. Based upon how learners navigate through the learning module (e.g. swipe-speed and click duration, among others), we use machine learning to...

Arterial oxygen saturation (SaO2) is an indicator of how much oxygen is carried by hemoglobin in the blood. Having enough oxygen is vital for the functioning of cells in the human body. Measurement of SaO2 is typically estimated with a pulse oximeter, but recent works have investigated how smartphone cameras can be used to infer SaO2. In this paper...

Pupillary diameter monitoring has been proven successful at objectively measuring cognitive load that might otherwise be unobservable. This paper compares three different algorithms for measuring cognitive load using commodity cameras. We compare the performance of modified starburst algorithm (from previous work) and propose two new algorithms: 2...

With the rapid growth of various data, large-scale data centers are built to store and analyze them. These data centers are consuming enormous energy every year, while most of them are wasted due to the low utilization of servers. In recent years, many efforts are carried out to reduce the energy cost as well as ensure the QoS, while they do not di...