Student-centric Model of Learning Management System Activity and Academic Performance: from Correlation to Causation

In recent years, there is a lot of interest in modeling students' digital traces in Learning Management System (LMS) to understand students' learning behavior patterns including aspects of meta-cognition and self-regulation, with the ultimate goal to turn those insights into actionable information to support students to improve their learning outcomes. In achieving this goal, however, there are two main issues that need to be addressed given the existing literature. Firstly, most of the current work is course-centered (i.e. models are built from data for a specific course) rather than student-centered; secondly, a vast majority of the models are correlational rather than causal. Those issues make it challenging to identify the most promising actionable factors for intervention at the student level where most of the campus-wide academic support is designed for. In this paper, we explored a student-centric analytical framework for LMS activity data that can provide not only correlational but causal insights mined from observational data. We demonstrated this approach using a dataset of 1651 computing major students at a public university in the US during one semester in the Fall of 2019. This dataset includes students' fine-grained LMS interaction logs and administrative data, e.g. demographics and academic performance. In addition, we expand the repository of LMS behavior indicators to include those that can characterize the time-of-the-day of login (e.g. chronotype). Our analysis showed that student login volume, compared with other login behavior indicators, is both strongly correlated and causally linked to student academic performance, especially among students with low academic performance. We envision that those insights will provide convincing evidence for college student support groups to launch student-centered and targeted interventions that are effective and scalable.

Planar Manipulation via Learning Regrasping

Regrasping is important for robots to reorient objects in planar manipulation tasks. Different placements of objects can provide robots with alternative grasp configurations, which are used in complex planar manipulation tasks that require multiple pick-rotate-and-place steps due to the constraints of the environment and robot kinematic constraints. In this work, our goal is to generate diverse placements of objects on the plane using deep neural networks. We propose a pipeline with the stages of orientation generation, position refinement, and placement discrimination to obtain accurate and diverse stable placements based on the perception of point clouds. A large-scale dataset is created for training, including simulated placements and contact information between objects and the plane. The simulation results show that our pipeline outperforms the start-of-the-art, achieving an accuracy rate of 90% and a diversity rate of 80% in simulation on generated placements. Our pipeline is also validated in real-robot experiments. With the generated placements, sequential pick-rotate-and-place steps are calculated for the robot to reorient objects to goal poses that are not reachable within one step. Videos and dataset are available at https://sites.google.com/view/pmvlr2022/.

FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset

We present FaceVerse, a fine-grained 3D Neural Face Model, which is built from hybrid East Asian face datasets containing 60K fused RGB-D images and 2K high-fidelity 3D head scan models. A novel coarse-to-fine structure is proposed to take better advantage of our hybrid dataset. In the coarse module, we generate a base parametric model from large-scale RGB-D images, which is able to predict accurate rough 3D face models in different genders, ages, etc. Then in the fine module, a conditional StyleGAN architecture trained with high-fidelity scan models is introduced to enrich elaborate facial geometric and texture details. Note that different from previous methods, our base and detailed modules are both changeable, which enables an innovative application of adjusting both the basic attributes and the facial details of 3D face models. Furthermore, we propose a single-image fitting framework based on differentiable rendering. Rich experiments show that our method outperforms the state-of-the-art methods.

Intent Matching based Customer Services Chatbot with Natural Language Understanding

Customer service is the lifeblood of any business. Excellent customer service not only generates return business but also creates new customers. Looking at the demanding market to provide a 24/7 service to customers, many organisations are increasingly engaged in popular social media and text messaging platforms such as WhatsApp and Facebook Messenger in providing a 24/7 service to customers in the current demanding market. In this paper, we present an intent matching based customer services chatbot (IMCSC), which is capable of replacing the customer service work of sales personnel, whilst interacting in a more natural and human-like manner through the employment of Natural Language Understanding (NLU). The bot is able to answer the most common frequently asked questions and we have also integrated features for the processing and exporting of customer orders to a Google Sheet.

CELLS: Cost-Effective Evolution in Latent Space for Goal-Directed Molecular Generation

Efficiently discovering molecules that meet various property requirements can significantly benefit the drug discovery industry. Since it is infeasible to search over the entire chemical space, recent works adopt generative models for goal-directed molecular generation. They tend to utilize the iterative processes, optimizing the parameters of the molecular generative models at each iteration to produce promising molecules for further validation. Assessments are exploited to evaluate the generated molecules at each iteration, providing direction for model optimization. However, most previous works require a massive number of expensive and time-consuming assessments, e.g., wet experiments and molecular dynamic simulations, leading to the lack of practicability. To reduce the assessments in the iterative process, we propose a cost-effective evolution strategy in latent space, which optimizes the molecular latent representation vectors instead. We adopt a pre-trained molecular generative model to map the latent and observation spaces, taking advantage of the large-scale unlabeled molecules to learn chemical knowledge. To further reduce the number of expensive assessments, we introduce a pre-screener as the proxy to the assessments. We conduct extensive experiments on multiple optimization tasks comparing the proposed framework to several advanced techniques, showing that the proposed framework achieves better performance with fewer assessments.

GenURL: A General Framework for Unsupervised Representation Learning

Recently unsupervised representation learning (URL) has achieved remarkable progress in various scenarios. However, most methods are specifically designed based on specific data characters or task assumptions. Based on the manifold assumption, we regard most URL problems as an embedding problem that seeks an optimal low-dimensional representation of the given high-dimensional data. We split the embedding process into two steps, data structural modeling and low-dimensional embedding, and propose a general similarity-based framework called GenURL. Specifically, we provide a general method to model data structures by adaptively combining graph distances on the feature space and predefined graphs, then propose robust loss functions to learn the low-dimensional embedding. Combining with a specific pretext task, we can adapt GenURL to various URL tasks in a unified manner and achieve state-of-the-art performance, including self-supervised visual representation learning, unsupervised knowledge distillation, graph embeddings, and dimension reduction. Moreover, ablation studies of loss functions and basic hyper-parameter settings in GenURL illustrate the data characters of various tasks.

Video Person Re-identification using Attribute-enhanced Features

Video-based person re-identification (Re-ID) which aims to associate people across non-overlapping cameras using surveillance video is a challenging task. Pedestrian attribute, such as gender, age and clothing characteristics contains rich and supplementary information but is less explored in video person Re-ID. In this work, we propose a novel network architecture named Attribute Salience Assisted Network (ASA-Net) for attribute-assisted video person Re-ID, which achieved considerable improvement to existing works by two methods.First, to learn a better separation of the target from background, we propose to learn the visual attention from middle-level attribute instead of high-level identities. The proposed Attribute Salient Region Enhance (ASRE) module can attend more accurately on the body of pedestrian. Second, we found that many identity-irrelevant but object or subject-relevant factors like the view angle and movement of the target pedestrian can greatly influence the two dimensional appearance of a pedestrian. This problem can be mitigated by investigating both identity-relevant and identity-irrelevant attributes via a novel triplet loss which is referred as the Pose~\&~Motion-Invariant (PMI) triplet loss.

Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques

The intention of this research is to study and design an automated agriculture commodity price prediction system with novel machine learning techniques. Due to the increasing large amounts historical data of agricultural commodity prices and the need of performing accurate prediction of price fluctuations, the solution has largely shifted from statistical methods to machine learning area. However, the selection of proper set from historical data for forecasting still has limited consideration. On the other hand, when implementing machine learning techniques, finding a suitable model with optimal parameters for global solution, nonlinearity and avoiding curse of dimensionality are still biggest challenges, therefore machine learning strategies study are needed. In this research, we propose a web-based automated system to predict agriculture commodity price. In the two series experiments, five popular machine learning algorithms, ARIMA, SVR, Prophet, XGBoost and LSTM have been compared with large historical datasets in Malaysia and the most optimal algorithm, LSTM model with an average of 0.304 mean-square error has been selected as the prediction engine of the proposed system.

An Automated Knowledge Mining and Document Classification System with Multi-model Transfer Learning

Service manual documents are crucial to the engineering company as they provide guidelines and knowledge to service engineers. However, it has become inconvenient and inefficient for service engineers to retrieve specific knowledge from documents due to the complexity of resources. In this research, we propose an automated knowledge mining and document classification system with novel multi-model transfer learning approaches. Particularly, the classification performance of the system has been improved with three effective techniques: fine-tuning, pruning, and multi-model method. The fine-tuning technique optimizes a pre-trained BERT model by adding a feed-forward neural network layer and the pruning technique is used to retrain the BERT model with new data. The multi-model method initializes and trains multiple BERT models to overcome the randomness of data ordering during the fine-tuning process. In the first iteration of the training process, multiple BERT models are being trained simultaneously. The best model is then selected for the next phase of the training process with another two iterations and the training processes for other BERT models will be terminated. The performance of the proposed system has been evaluated by comparing with two robust baseline methods, BERT and BERT-CNN. Experimental results on a widely used Corpus of Linguistic Acceptability (CoLA) dataset have shown that the proposed techniques perform better than these baseline methods in terms of accuracy and MCC score.

IDEAL: Independent Domain Embedding Augmentation Learning

Many efforts have been devoted to designing sampling, mining, and weighting strategies in high-level deep metric learning (DML) loss objectives. However, little attention has been paid to low-level but essential data transformation. In this paper, we develop a novel mechanism, the independent domain embedding augmentation learning ({IDEAL}) method. It can simultaneously learn multiple independent embedding spaces for multiple domains generated by predefined data transformations. Our IDEAL is orthogonal to existing DML techniques and can be seamlessly combined with prior DML approaches for enhanced performance. Empirical results on visual retrieval tasks demonstrate the superiority of the proposed method. For example, the IDEAL improves the performance of MS loss by a large margin, 84.5\% $\rightarrow$ 87.1\% on Cars-196, and 65.8\% $\rightarrow$ 69.5\% on CUB-200 at Recall$@1$. Our IDEAL with MS loss also achieves the new state-of-the-art performance on three image retrieval benchmarks, \ie, \emph{Cars-196}, \emph{CUB-200}, and \emph{SOP}. It outperforms the most recent DML approaches, such as Circle loss and XBM, significantly. The source code and pre-trained models of our method will be available at\emph{\url{https://github.com/emdata-ailab/IDEAL}}.