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| 1 | Pre-course student performance prediction with multi-instance multi-label learning显示文摘Dear editor,Studying courses is one of the most basic and important tasks for college students.For each new course,the initial period of learning is crucial for students,and seriously influences subsequent learning activities.However,given a large number of classes in universities,it has become impossible for teachers to keep track of the individual performance of each student.In these circumstances,it is desirable to predict each student’s performance on a certain course prior to its commencement. | Yuling MA Chaoran CUI Xiushan NIE Gongping YANG Kashif SHAHEED Yilong YIN | 2019 | Science China(Information Sciences)2019，62，2： | 5 |
| 2 | Anchor-based manifold binary pattern for finger vein recognition显示文摘This paper proposes a novel learning method of binary local features for recognition of the finger vein. The learning methods existing in local features for image recognition intend to maximize the data variance, reduce quantitative errors, exploit the contextual information within each binary code, or utilize the label information, which all ignore the local manifold structure of the original data. The manifold structure actually plays a very important role in binary code learning, but constructing a similarity matrix for large-scale datasets involves a lot of computational and storage cost. The study attempts to learn a map, which can preserve the manifold structure between the original data and the learned binary codes for large-scale situations. To achieve this goal, we present a learning method using an anchor-based manifold binary pattern(AMBP) for finger vein recognition. Specifically, we first extract the pixel difference vectors(PDVs) in the local patches by calculating the differences between each pixel and its neighbors. Second,we construct an asymmetric graph, on which each data point can be a linear combination of its K-nearest neighbor anchors, and the anchors are randomly selected from the training samples. Third, a feature map is learned to project these PDVs into low-dimensional binary codes in an unsupervised manner, where(i) the quantization loss between the original real-valued vectors and learned binary codes is minimized and(ii) the manifold structure of the training data is maintained in the binary space. Additionally, the study fuses the discriminative binary descriptor and AMBP methods at the image representation level to further boost the performance of the recognition system. Finally, experiments using the MLA and PolyU databases show the effectiveness of our proposed methods. | Haiying LIU Gongping YANG Lu YANG Kun SU Yilong YIN | 2019 | Science China(Information Sciences)2019，62，5： | 2 |
| 3 | Multi-task MIML learning for pre-course student performance prediction显示文摘In higher education,the initial studying period of each course plays a crucial role for students,and seriously influences the subsequent learning activities.However,given the large size of a course’s students at universities,it has become impossible for teachers to keep track of the performance of individual students.In this circumstance,an academic early warning system is desirable,which automatically detects students with difficulties in learning(i.e.,at-risk students)prior to a course starting.However,previous studies are not well suited to this purpose for two reasons:1)they have mainly concentrated on e-learning platforms,e.g.,massive open online courses(MOOCs),and relied on the data about students’online activities,which is hardly accessed in traditional teaching scenarios;and 2)they have only made performance prediction when a course is in progress or even close to the end.In this paper,for traditional classroom-teaching scenarios,we investigate the task of pre-course student performance prediction,which refers to detecting at-risk students for each course before its commencement.To better represent a student sample and utilize the correlations among courses,we cast the problem as a multi-instance multi-label(MIML)problem.Besides,given the problem of data scarcity,we propose a novel multi-task learning method,i.e.,MIML-Circle,to predict the performance of students from different specialties in a unified framework.Extensive experiments are conducted on five real-world datasets,and the results demonstrate the superiority of our approach over the state-of-the-art methods. | Yuling Ma Chaoran Cui Jun Yu Jie Guo Gongping Yang Yilong Yin | 2020 | Frontiers of Computer Science2020，14，5： | 1 |
| 4 | A hybrid biometric identification framework for high security applications显示文摘 | Xuzhou LI Yilong YIN Yanbin NING Gongping YANG Lei PAN | 2015 | Frontiers of Computer Science2015，9，3： | 1 |
| 5 | Finger vein recognition based on deformation information显示文摘The measurement of the vessel pattern in fingers is a superior method for identifying individuals owing to its convenience and the security it offers. We introduce in this paper a new perspective to accomplish finger vein recognition. This method, which regards deformations as discriminative information, is distinct from existing methods that attempt to prevent the influence of deformations. The proposed technique is based on the observation that regular deformation, which corresponds to a posture change, can only exist in genuine vein patterns. In terms of methodology, we incorporate optimized matching to generate pixelbased 2 D displacements that correspond to deformations. The texture of uniformity extracted from the displacement fields is taken as the final matching score. Evaluated on two publicly available databases,Poly U and SDU-MLA, extensive experiments demonstrated that the discriminability of the new feature derived from deformations is preferable. The equal error rate(EER) achieved is the lowest compared to that of state-of-the-art techniques. | Xianjing MENG Xiaoming XI Gongping YANG Yilong YIN | 2018 | Science China(Information Sciences)2018，61，5： | 1 |
| 6 | Non-negative locality-constrained vocabulary tree for finger vein image retrieval显示文摘Finger vein image retrieval is a biometric identification technology that has recently attracted a lot of attention. It has the potential to reduce the search space and has attracted a considerable amount of research effort recently. It is a challenging problem owing to the large number of images in biometric databases and the lack of efficient retrieval schemes. We apply a hierarchical vocabulary tree modelbased image retrieval approach because of its good scalability and high efficiency. However, there is a large accumulative quantization error in the vocabulary tree (VT) model that may degrade the retrieval precision. To solve this problem, we improve the vector quantization coding in the VT model by introducing a non-negative locality-constrained constraint: the non-negative locality-constrained vocabulary tree-based image retrieval model. The proposed method can effectively improve coding performanee and the discriminative power of local features. Extensive experiments on a large fused finger vein database demonstrate the superiority of our encoding method. Experimental results also show that our retrieval strategy achieves better performanee than other state-of-theart methods, while maintaining low time complexity. | Kun SU Gongping YANG Lu YANG Peng SU Yilong YIN | 2019 | Frontiers of Computer Science2019，13，2： | 1 |
| 7 | K-Means based fingerprint segmentation with sensor interoperability显示文摘 | Yang Gongping Zhou Guangtong Yin Yilong | 2010 | Eurasip Journal on Advances in Signal Processing2010，，： | 1 |
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