This page has only limited features, please log in for full access.
Depression is a global mental health problem, the worst cases of which can lead to self-injury or suicide. An automatic depression detection system is of great help in facilitating clinical diagnosis and early intervention of depression. In this work, we propose a new automatic depression detection method utilizing speech signals and linguistic content from patient interviews. Specifically, the proposed method consists of three components, which include a Bidirectional Long Short-Term Memory (BiLSTM) network with an attention layer to deal with linguistic content, a One-Dimensional Convolutional Neural Network (1D CNN) to deal with speech signals, and a fully connected network integrating the outputs of the previous two models to assess the depressive state. Evaluated on two publicly available datasets, our method achieves state-of-the-art performance compared with the existing methods. In addition, our method utilizes audio and text features simultaneously. Therefore, it can get rid of the misleading information provided by the patients. As a conclusion, our method can automatically evaluate the depression state and does not require an expert to conduct the psychological evaluation on site. Our method greatly improves the detection accuracy, as well as the efficiency.
Lin Lin; Xuri Chen; Ying Shen; Lin Zhang. Towards Automatic Depression Detection: A BiLSTM/1D CNN-Based Model. Applied Sciences 2020, 10, 8701 .
AMA StyleLin Lin, Xuri Chen, Ying Shen, Lin Zhang. Towards Automatic Depression Detection: A BiLSTM/1D CNN-Based Model. Applied Sciences. 2020; 10 (23):8701.
Chicago/Turabian StyleLin Lin; Xuri Chen; Ying Shen; Lin Zhang. 2020. "Towards Automatic Depression Detection: A BiLSTM/1D CNN-Based Model." Applied Sciences 10, no. 23: 8701.
Among the members of biometric identifiers, the palmprint and the palmvein have received significant attention due to their stability, uniqueness, and non-intrusiveness. In this paper, we investigate the problem of palmprint/palmvein recognition and propose a Deep Convolutional Neural Network (DCNN) based scheme, namely PalmRCNN (short for palmprint/palmvein recognition using CNNs). The effectiveness and efficiency of PalmRCNN have been verified through extensive experiments conducted on benchmark datasets. In addition, though substantial effort has been devoted to palmvein recognition, it is still quite difficult for the researchers to know the potential discriminating capability of the contactless palmvein. One of the root reasons is that a large-scale and publicly available dataset comprising high-quality, contactless palmvein images is still lacking. To this end, a user-friendly acquisition device for collecting high quality contactless palmvein images is at first designed and developed in this work. Then, a large-scale palmvein image dataset is established, comprising 12,000 images acquired from 600 different palms in two separate collection sessions. The collected dataset now is publicly available.
Lin Zhang; Zaixi Cheng; Ying Shen; Dongqing Wang. Palmprint and Palmvein Recognition Based on DCNN and A New Large-Scale Contactless Palmvein Dataset. Symmetry 2018, 10, 78 .
AMA StyleLin Zhang, Zaixi Cheng, Ying Shen, Dongqing Wang. Palmprint and Palmvein Recognition Based on DCNN and A New Large-Scale Contactless Palmvein Dataset. Symmetry. 2018; 10 (4):78.
Chicago/Turabian StyleLin Zhang; Zaixi Cheng; Ying Shen; Dongqing Wang. 2018. "Palmprint and Palmvein Recognition Based on DCNN and A New Large-Scale Contactless Palmvein Dataset." Symmetry 10, no. 4: 78.
Recent years have witnessed a growing interest in developing automatic parking systems in the field of intelligent vehicles. However, how to effectively and efficiently locating parking-slots using a vision-based system is still an unresolved issue. Even more seriously, there is no publicly available labeled benchmark dataset for tuning and testing parking-slot detection algorithms. In this paper, we attempt to fill the above-mentioned research gaps to some extent and our contributions are twofold. Firstly, to facilitate the study of vision-based parking-slot detection, a large-scale parking-slot image database is established. This database comprises 8600 surround-view images collected from typical indoor and outdoor parking sites. For each image in this database, the marking-points and parking-slots are carefully labeled. Such a database can serve as a benchmark to design and validate parking-slot detection algorithms. Secondly, a learning-based parking-slot detection approach, namely PSDL, is proposed. Using PSDL, given a surround-view image, the marking-points will be detected first and then the valid parking-slots can be inferred. The efficacy and efficiency of PSDL have been corroborated on our database. It is expected that PSDL can serve as a baseline when the other researchers develop more sophisticated methods.
Lin Zhang; Xiyuan Li; Junhao Huang; Ying Shen; Dongqing Wang. Vision-Based Parking-Slot Detection: A Benchmark and A Learning-Based Approach. Symmetry 2018, 10, 64 .
AMA StyleLin Zhang, Xiyuan Li, Junhao Huang, Ying Shen, Dongqing Wang. Vision-Based Parking-Slot Detection: A Benchmark and A Learning-Based Approach. Symmetry. 2018; 10 (3):64.
Chicago/Turabian StyleLin Zhang; Xiyuan Li; Junhao Huang; Ying Shen; Dongqing Wang. 2018. "Vision-Based Parking-Slot Detection: A Benchmark and A Learning-Based Approach." Symmetry 10, no. 3: 64.
RNA structural motifs are recurrent structural elements occurring in RNA molecules. They play essential roles in consolidating RNA tertiary structures and in binding proteins. Recently, we identified a new type of RNA structural motif, namely λ-turn, from ribosomal RNAs. This motif has a helix-internal loop-helix structure. The directions of its two helices are changed ~90° due to the existence of the internal loop. A guanine from the 3′-end of the internal loop extrudes out and forms a base triple with a G-C WC base pair from one helix of the motif. From the global perspective, the λ-turn is often capped by a helix and a tetraloop. A nucleotide between the capped helix and the tetraloop forms a consecutive base triple next to the first one with a G-C pair from the same helix that the first G-C pair resides. The λ-turn motif has a consensus sequence pattern and its 3D structure is conserved across different species. All the identified λ-turns are located on surfaces of ribosomal RNAs. Structures of ribosomes reveal direct interactions between λ-turns and ribosomal proteins. All these observations indicate that λ-turns have an important role in binding with ribosomal proteins.
Huizhu Ren; Ying Shen; Lin Zhang. The λ-Turn: A New Structural Motif in Ribosomal RNA. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 456 -466.
AMA StyleHuizhu Ren, Ying Shen, Lin Zhang. The λ-Turn: A New Structural Motif in Ribosomal RNA. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; ():456-466.
Chicago/Turabian StyleHuizhu Ren; Ying Shen; Lin Zhang. 2015. "The λ-Turn: A New Structural Motif in Ribosomal RNA." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 456-466.
RNA-protein complexes play an essential role in many biological processes. To explore potential functions of RNA-protein complexes, it’s important to identify RNA-binding residues in proteins. In this work, we propose a set of new structural features for RNA-binding residue prediction. A set of template patches are first extracted from RNA-binding interfaces. To construct structural features for a residue, we compare its surrounding patches with each template patch and use the accumulated distances as its structural features. These new features provide sufficient structural information of surrounding surface of a residue and they can be used to measure the structural similarity between the surface surrounding two residues. The new structural features, together with other sequence features, are used to predict RNA-binding residues using ensemble learning technique. The experimental results reveal the effectiveness of the proposed structural features. In addition, the clustering results on template patches exhibit distinct structural patterns of RNA-binding sites, although the sequences of template patches in the same cluster are not conserved. We speculate that RNAs may have structure preferences when binding with proteins.
Huizhu Ren; Ying Shen. RNA-binding residues prediction using structural features. BMC Bioinformatics 2015, 16, 5 .
AMA StyleHuizhu Ren, Ying Shen. RNA-binding residues prediction using structural features. BMC Bioinformatics. 2015; 16 (1):5.
Chicago/Turabian StyleHuizhu Ren; Ying Shen. 2015. "RNA-binding residues prediction using structural features." BMC Bioinformatics 16, no. 1: 5.
Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm.
Lin Zhang; Zhixuan Ding; Hongyu Li; Ying Shen; Jianwei Lu. 3D Face Recognition Based on Multiple Keypoint Descriptors and Sparse Representation. PLOS ONE 2014, 9, e100120 .
AMA StyleLin Zhang, Zhixuan Ding, Hongyu Li, Ying Shen, Jianwei Lu. 3D Face Recognition Based on Multiple Keypoint Descriptors and Sparse Representation. PLOS ONE. 2014; 9 (6):e100120.
Chicago/Turabian StyleLin Zhang; Zhixuan Ding; Hongyu Li; Ying Shen; Jianwei Lu. 2014. "3D Face Recognition Based on Multiple Keypoint Descriptors and Sparse Representation." PLOS ONE 9, no. 6: e100120.
Biometrics based personal authentication is an effective way for automatically recognizing, with a high confidence, a person’s identity. Recently, 3D ear shape has attracted tremendous interests in research field due to its richness of feature and ease of acquisition. However, the existing ICP (Iterative Closet Point)-based 3D ear matching methods prevalent in the literature are not quite efficient to cope with the one-to-many identification case. In this paper, we aim to fill this gap by proposing a novel effective fully automatic 3D ear identification system. We at first propose an accurate and efficient template-based ear detection method. By utilizing such a method, the extracted ear regions are represented in a common canonical coordinate system determined by the ear contour template, which facilitates much the following stages of feature extraction and classification. For each extracted 3D ear, a feature vector is generated as its representation by making use of a PCA-based local feature descriptor. At the stage of classification, we resort to the sparse representation based classification approach, which actually solves an l1-minimization problem. To the best of our knowledge, this is the first work introducing the sparse representation framework into the field of 3D ear identification. Extensive experiments conducted on a benchmark dataset corroborate the effectiveness and efficiency of the proposed approach. The associated Matlab source code and the evaluation results have been made publicly online available at http://sse.tongji.edu.cn/linzhang/ear/srcear/srcear.htm.
Lin Zhang; Zhixuan Ding; Hongyu Li; Ying Shen. 3D Ear Identification Based on Sparse Representation. PLOS ONE 2014, 9, e95506 .
AMA StyleLin Zhang, Zhixuan Ding, Hongyu Li, Ying Shen. 3D Ear Identification Based on Sparse Representation. PLOS ONE. 2014; 9 (4):e95506.
Chicago/Turabian StyleLin Zhang; Zhixuan Ding; Hongyu Li; Ying Shen. 2014. "3D Ear Identification Based on Sparse Representation." PLOS ONE 9, no. 4: e95506.