Ke Deng

Zongrui Ji, Zhiming Cui, Na Li et al.

Background:Accurate tooth segmentation from cone beam computed tomography (CBCT) images is crucial for digital dentistry but remains challenging in cases of interdental adhesions, which cause severe anatomical shape distortion. Methods: To address this, we propose a deep learning framework that integrates semantic and shape awareness for shape-preserving segmentation. Our method introduces a target-tooth-centroid prompted multi-label learning strategy to model semantic relationships between teeth, reducing shape ambiguity. Additionally, a tooth-shape-aware learning mechanism explicitly enforces morphological constraints to preserve boundary integrity. These components are unified via multi-task learning, jointly optimizing segmentation and shape preservation. Results: Extensive evaluations on internal and external datasets demonstrate that our approach significantly outperforms existing methods. Conclusions: Our approach effectively mitigates shape distortions and providing anatomically faithful tooth boundaries.

Mingwei Zhang, Jiayuan Liu, Weipu Zhang et al.

We propose a novel software service recommendation model to help users find their suitable repositories in GitHub. Our model first designs a novel context-induced repository graph embedding method to leverage rich contextual information of repositories to alleviate the difficulties caused by the data sparsity issue. It then leverages sequence information of user-repository interactions for the first time in the software service recommendation field. Specifically, a deep-learning based sequential recommendation technique is adopted to capture the dynamics of user preferences. Comprehensive experiments have been conducted on a large dataset collected from GitHub against a list of existing methods. The results illustrate the superiority of our method in various aspects.

Zhengyang Wu, Ke Deng, Judy Qiu et al.

Nowadays, the learning management system (LMS) has been widely used in different educational stages from primary to tertiary education for student administration, documentation, tracking, reporting, and delivery of educational courses, training programs, or learning and development programs. Towards effective learning outcome assessment, the exam script generation problem has attracted many attentions and been investigated recently. But the research in this field is still in its early stage. There are opportunities to further improve the quality of generated exam scripts in various aspects. In particular, two essential issues have been ignored largely by existing solutions. First, given a course, it is unknown yet how to generate an exam script which can result in a desirable distribution of student scores in a class (or across different classes). Second, while it is frequently encountered in practice, it is unknown so far how to generate a pair of high quality exam scripts which are equivalent in assessment (i.e., the student scores are comparable by taking either of them) but have significantly different sets of questions. To fill the gap, this paper proposes ExamGAN (Exam Script Generative Adversarial Network) to generate high quality exam scripts, and then extends ExamGAN to T-ExamGAN (Twin-ExamGAN) to generate a pair of high quality exam scripts. Based on extensive experiments on three benchmark datasets, it has verified the superiority of proposed solutions in various aspects against the state-of-the-art. Moreover, we have conducted a case study which demonstrated the effectiveness of proposed solution in a real teaching scenario.

Mingwei Zhang, Jiawei Zhao, Hai Dong et al.

With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications (apps), app recommendation becomes an emergent task that would benefit both app users and stockholders. How to effectively organize and make full use of rich side information of users and apps is a key challenge to address the sparsity issue for traditional approaches. To meet this challenge, we proposed a novel end-to-end Knowledge Graph Convolutional Embedding Propagation Model (KGEP) for app recommendation. Specifically, we first designed a knowledge graph construction method to model the user and app side information, then adopted KG embedding techniques to capture the factual triplet-focused semantics of the side information related to the first-order structure of the KG, and finally proposed a relation-weighted convolutional embedding propagation model to capture the recommendation-focused semantics related to high-order structure of the KG. Extensive experiments conducted on a real-world dataset validate the effectiveness of the proposed approach compared to the state-of-the-art recommendation approaches.

Yang Yang, Ke Deng, Michael Zhu

Hyperparameters play a critical role in the performances of many machine learning methods. Determining their best settings or Hyperparameter Optimization (HPO) faces difficulties presented by the large number of hyperparameters as well as the excessive training time. In this paper, we develop an effective approach to reducing the total amount of required training time for HPO. In the initialization, the nested Latin hypercube design is used to select hyperparameter configurations for two types of training, which are, respectively, heavy training and light training. We propose a truncated additive Gaussian process model to calibrate approximate performance measurements generated by light training, using accurate performance measurements generated by heavy training. Based on the model, a sequential model-based algorithm is developed to generate the performance profile of the configuration space as well as find optimal ones. Our proposed approach demonstrates competitive performance when applied to optimize synthetic examples, support vector machines, fully connected networks and convolutional neural networks.

Wenlei Bao, Li-Wen Chang, Yang Chen et al.

Quantization has emerged to be an effective way to significantly boost the performance of deep neural networks (DNNs) by utilizing low-bit computations. Despite having lower numerical precision, quantized DNNs are able to reduce both memory bandwidth and computation cycles with little losses of accuracy. Integer GEMM (General Matrix Multiplication) is critical to running quantized DNN models efficiently, as GEMM operations often dominate the computations in these models. Various approaches have been developed by leveraging techniques such as vectorization and memory layout to improve the performance of integer GEMM. However, these existing approaches are not fast enough in certain scenarios. We developed NGEMM, a compiler-based GEMM implementation for accelerating lower-precision training and inference. NGEMM has better use of the vector units by avoiding unnecessary vector computation that is introduced during tree reduction. We compared NGEMM's performance with the state-of-art BLAS libraries such as MKL. Our experimental results showed that NGEMM outperformed MKL non-pack and pack version by an average of 1.86x and 1.16x, respectively. We have applied NGEMM to a number of production services in Microsoft.