Object counting is a fundamental task in computer vision, with critical applications in areas such as crowd monitoring and ecological conservation. Traditional methods typically rely on large-scale annotated datasets, which are costly and time-consuming to obtain. Few-shot object counting has emerged as a promising solution, enabling accurate counting with minimal annotated samples. However, in real-world scenarios, objects often exhibit significant scale variations due to factors such as view distortion, varying shooting distances, and inherent size differences. Existing few-shot methods usually struggle to address this challenge effectively. To address these issues, we propose a Scale-Aware Vision Transformer (SAViT) framework. Specifically, we design a multi-scale dilated convolution module in SAViT, which can adaptively adjust convolution kernel sampling rates to handle objects of varying sizes. Additionally, we incorporate a global channel attention mechanism to strengthen the model’s ability to capture robust feature representations, thereby improving detection accuracy. For practical usability, we integrate the Segment Anything Model (SAM) to create an exemplar box selection module, simplifying the process by allowing users to generate precise exemplar boxes with a single line drawn on the target object. Extensive experiments on the FSC-147 dataset demonstrate the effectiveness of our approach, achieving a Mean Absolute Error (MAE) of 8.92 and a Root Mean Squared Error (RMSE) of 31.26. Compared to the state-of-the-art method, CACViT, our model reduces MAE by 0.21 (2.30% improvement) and RMSE by 17.7 (36.15% improvement). Our approach not only provides an effective solution for few-shot object counting but also provides a new practical paradigm for extending few-shot learning to complex vision tasks requiring multi-scale reasoning. The code of our paper is available at https://github.com/BlouseDong/SAViT .

This paper studies cost-effective graph cleaning with a single machine. We adopt a rule-based method that may embed machine learning models as predicates in the rules. Graph cleaning with the rules involves rule discovery, error detection and correction. These tasks are both computation-heavy and I/O-intensive as they repeatedly invoke costly graph pattern matching, and produce a large amount of a large volume of intermediate results, among other things. In light of these, no existing single-machine system is able to carry out these tasks even on not-too-large graphs, even using GPUs. Thus we develop MiniClean, a single-machine system for cleaning large graphs. It proposes (1) a workflow that better fits a single machine by pipelining CPU, GPU and I/O operations; (2) memory footprint reduction with bundled processing and data compression; and (3) a multi-mode parallel model for SIMD, pipelined and independent parallelism, and their scheduling to maximize CPU--GPU synergy. Using real-life graphs, we empirically verify that MiniClean outperforms the SOTA single-machine systems by at least 65.34× and multi-machine systems with 32 nodes by at least 8.09×.

In this paper, we present a neural network-enabled data distribution aware sorting method, coined as NN-sort. Our approach explores the potential of developing deep learning techniques to speed up large-scale sort operations, enabling data distribution aware sorting as a deep learning service. Compared to traditional pairwise comparison-based sorting algorithms, which sort data elements by performing pairwise operations, NN-sort leverages the neural network model to learn the data distribution and uses it to map large-scale data elements into ordered ones. Our experiments demonstrate the significant advantage of using NN-sort. Measurements on both synthetic and real-world datasets show that NN-sort yields 2.18× to 10× performance improvement over traditional sorting algorithms.

Facial image based kinship verification aims to decide whether there exists kinship between the given facial images. In practice, the cross-generation differences will cause adverse effects on kinship verification, which limits the performance. Therefore, how to mine the implied similarity from facial images with large cross-generation divergence is an important problem in kinship verification, which has not yet been well studied. In view of this, we propose a Similarity Mining via Implicit matching pattern LEarning (SMILE) approach for kinship verification. Specifically, SMILE mainly consists of two modules, including a Semi-coupled Multi-pattern Similarity Learning (SMSL) module and a Cross-Generation Feature Normalization (CGFN) module. The SMSL module is designed to learn multiple semi-coupled matching patterns for mining the implicit facial similarity information from different perspectives. The CGFN module aims to reduce the divergence between facial images of parent and child. Extensive experiments demonstrate that the proposed approach outperforms the existing state-of-the-art methods.

Cloth-Changing Person Re-Identification (CC-ReID) refers to the technology that can identify the same individual in surveillance scenarios, even after changing their clothes. The core of CC-ReID is to exact cloth-irrelevant identity features. Existing methods usually extract them from body contours, skeletons, human faces, etc. However, information extracted from a single modality is often insufficient. Therefore, we propose a novel Multimodal Interaction Fusion (MIF) framework that could facilitate the deep integration of multimodal information, thereby enhancing the generalization ability of CC-ReID. Specifically, we design a triple-interaction collaborative module that strategically integrates features captured from body posture, original RGB images, and clothes-erased images, maximizing the advantages of each modality and overcoming their limitations. Additionally, we develop an Adapt-Blend Pool (ABP) module that effectively improves pooling methods to enhance the model’s ability to process images with complex backgrounds and varied features. Following training, our framework effectively harnesses a variety of modal inputs to capture identity representations independent of clothing accurately. Extensive experimental results on PRCC, LTCC, and VC-Clothes datasets demonstrate the effectiveness of the proposed method.

This paper develops Planar (Plug and play PRAM), a single-machine system for graph analytics by reusing existing PRAM algorithms, without the need for designing new parallel algorithms. Planar supports both out-of-core and in-memory analytics. When a graph is too big to fit into the memory of a machine, Planar adapts PRAM to limited resources by extending a fixpoint model with multi-core parallelism, using disk as memory extension. For an in-memory task, it dedicates all available CPU cores to the task, and allows parallelly scalable PRAM algorithms to retain the property, i.e. , the more cores are available, the less runtime is taken. We develop a graph partitioning and work scheduling strategy to accommodate subgraph I/O, balance memory usage and reduce runtime, beyond traditional partitioners for multi-machine systems. Using real-life graphs, we empirically verify that Planar outperforms SOTA in-memory and out-of-core systems in efficiency and scalability.

This paper studies rule-based blocking in Entity Resolution (ER). We propose HyperBlocker, a GPU-accelerated system for blocking in ER. As opposed to previous blocking algorithms and parallel blocking solvers, HyperBlocker employs a pipelined architecture to overlap data transfer and GPU operations. It generates a data-aware and rule-aware execution plan on CPUs, for specifying how rules are evaluated, and develops a number of hardware-aware optimizations to achieve massive parallelism on GPUs. Using real-life datasets, we show that HyperBlocker is at least 6.8× and 9.1× faster than prior CPU-powered distributed systems and GPU-based ER solvers, respectively. Better still, by combining HyperBlocker with the state-of-the-art ER matcher, we can speed up the overall ER process by at least 30% with comparable accuracy.

BACKGROUND: As an irreversible post-translational modification, protein carbonylation is closely related to many diseases and aging. Protein carbonylation prediction for related patients is significant, which can help clinicians make appropriate therapeutic schemes. Because carbonylation sites can be used to indicate change or loss of protein function, integrating these protein carbonylation site data has been a promising method in prediction. Based on these protein carbonylation site data, some protein carbonylation prediction methods have been proposed. However, most data is highly class imbalanced, and the number of un-carbonylation sites greatly exceeds that of carbonylation sites. Unfortunately, existing methods have not addressed this issue adequately. RESULTS: In this work, we propose a novel two-way rebalancing strategy based on the attention technique and generative adversarial network (Carsite_AGan) for identifying protein carbonylation sites. Specifically, Carsite_AGan proposes a novel undersampling method based on attention technology that allows sites with high importance value to be selected from un-carbonylation sites. The attention technique can obtain the value of each sample's importance. In the meanwhile, Carsite_AGan designs a generative adversarial network-based oversampling method to generate high-feasibility carbonylation sites. The generative adversarial network can generate high-feasibility samples through its generator and discriminator. Finally, we use a classifier like a nonlinear support vector machine to identify protein carbonylation sites. CONCLUSIONS: Experimental results demonstrate that our approach significantly outperforms other resampling methods. Using our approach to resampling carbonylation data can significantly improve the effect of identifying protein carbonylation sites.

This paper presents MiniGraph, an out-of-core system for querying big graphs with a single machine. As opposed to previous single-machine graph systems, MiniGraph proposes a pipelined architecture to overlap I/O and CPU operations, and improves multi-core parallelism. It also introduces a hybrid model to support both vertex-centric and graph-centric parallel computations, to simplify parallel graph programming, speed up beyond-neighborhood computations, and parallelize computations within each subgraph. The model induces a two-level parallel execution model to explore both inter-subgraph and intra-subgraph parallelism. Moreover, MiniGraph develops new optimization techniques under its architecture. Using real-life graphs of different types, we show that MiniGraph is up to 76.1x faster than prior out-of-core systems, and performs better than some multi-machine systems that use up to 12 machines.

Protein-protein interaction (PPI) plays an important role in almost all life activities. Many protein interaction sites have been confirmed by biological experiments, but these PPI site identification methods are time-consuming and expensive. In this study, a deep learning-based PPI prediction method, named DeepSG2PPI, is developed. First, the protein sequence information is retrieved and the local context information of each amino acid residue is calculated. A two-dimensional convolutional neural network (2D-CNN) model is employed to extract features from a two-channel coding structure, in which an attention mechanism is embedded to assign higher weights to key features. Second, the global statistical information of each amino acid residue and the relationship graph between the protein and GO (Gene Ontology) function annotation are built, and the graph embedding vector is constructed to represent the biological features of the protein. Finally, a 2D-CNN model and two 1D-CNN models are combined for PPI prediction. The comparison analysis with existing algorithms shows that the DeepSG2PPI method has better performance. It provides more accurate and effective PPI site prediction, which will be helpful in reducing the cost and failure rate of biological experiments.

Image-based kinship verification is an important task in computer vision and has many applications in practice, such as missing children search and family album construction, among others. Due to the differences in age, gender, expression and appearance, there usually exists a large discrepancy between the facial images of parent and child. This makes kinship verification a challenging task. In this article, we propose a Distance and Direction Based Deep Discriminant Metric Learning (D 4 ML) approach for kinship verification. The basic idea of D 4 ML is to make full use of the discriminant information contained in the facial images of parent and child such that the network can learn more a discriminating distance metric. Specifically, D 4 ML learns the metric by utilizing the discriminant information from two perspectives: distance-based perspective and direction-based perspective. From the distance-based perspective, the designed loss function is used to minimize the distance between images having kinship and maximize the distance between images without kinship. In practice, the gender difference and large age gap may significantly increase the distance between facial images of parent and child. Therefore, learning the metric only from a distance-based perspective is insufficient. Considering that two vectors with a large distance may appear with high similarity in direction, D 4 ML also employs the direction-based loss function in the training process. Both kinds of loss function work together to improve the discriminability of the learned metric. Experimental results on four small size publicly available datasets demonstrate the effectiveness of our approach. Source code of our approach can be found at https://github.com/lclhenu/D4ML .

This paper studies deep and collective entity resolution (ER). As opposed to a single pass of pairwise comparison of tuples in a single table, deep ER recursively identifies tuples that refer to the same entity by making use of matches in the previous rounds, and collective ER determines matches by correlating information across multiple tables. We propose a fixpoint model for deep and collective ER, by chasing with logic rules that are collectively defined across multiple relations and may embed machine learning classifiers for ER as predicates. While powerful, we show that deep and collective ER is intractable. To scale with large datasets, we develop a data partitioning strategy and a parallel algorithm underlying the fixpoint model, which guarantee to reduce runtime when more processors are used. Using real-life data, we experimentally verify that the approach improves the ER accuracy and is parallelly scalable.

In this paper, we introduce DLB, a Deep Learning based load Balancing mechanism, to effectively address the data skew problem. The key idea of DLB is to replace hash functions in the load balancing mechanisms with deep learning models, which are trained to be able to map different distributions of workloads and data to the servers in a uniformed manner. We implemented DLB and deployed it on a practical Cloud environment using CloudSim. Experimental results using both synthetic and real-world data sets show that compared with traditional hash function based load balancing methods, DLB is able to achieve more balanced mappings, especially when the workload is highly skewed.

Learning an expressive representation from multi-view data is a key step in various real-world applications. In this paper, we propose a semi-supervised multi-view deep discriminant representation learning (SMDDRL) approach. Unlike existing joint or alignment multi-view representation learning methods that cannot simultaneously utilize the consensus and complementary properties of multi-view data to learn inter-view shared and intra-view specific representations, SMDDRL comprehensively exploits the consensus and complementary properties as well as learns both shared and specific representations by employing the shared and specific representation learning network. Unlike existing shared and specific multi-view representation learning methods that ignore the redundancy problem in representation learning, SMDDRL incorporates the orthogonality and adversarial similarity constraints to reduce the redundancy of learned representations. Moreover, to exploit the information contained in unlabeled data, we design a semi-supervised learning framework by combining deep metric learning and density clustering. Experimental results on three typical multi-view learning tasks, i.e., webpage classification, image classification, and document classification demonstrate the effectiveness of the proposed approach.

With the expansion of data, increasing imbalanced data has emerged. When the imbalance ratio (IR) of data is high, most existing imbalanced learning methods decline seriously in classification performance. In this paper, we systematically investigate the highly imbalanced data classification problem, and propose an uncorrelated cost-sensitive multiset learning (UCML) approach for it. Specifically, UCML first constructs multiple balanced subsets through random partition, and then employs the multiset feature learning (MFL) to learn discriminant features from the constructed multiset. To enhance the usability of each subset and deal with the non-linearity issue existed in each subset, we further propose a deep metric based UCML (DM-UCML) approach. DM-UCML introduces the generative adversarial network technique into the multiset constructing process, such that each subset can own similar distribution with the original dataset. To cope with the non-linearity issue, DM-UCML integrates deep metric learning with MFL, such that more favorable performance can be achieved. In addition, DM-UCML designs a new discriminant term to enhance the discriminability of learned metrics. Experiments on eight traditional highly class-imbalanced datasets and two large-scale datasets indicate that: the proposed approaches outperform state-of-the-art highly imbalanced learning methods and are more robust to high IR.

Kinship verification is an interesting and important problem in the fields of computer vision. In practice, the biggest obstacle in kinship verification is that the representation capability of extracted features may not be powerful due to the significant differences between facial images of family members. To effectively address this problem, we propose a semi-coupled synthesis and analysis dictionary pair learning (SSADL) approach, which can reduce the differences between facial images. Specifically, SSADL jointly learns two view-specific synthesis-analysis dictionary pairs as well as a mapping matrix from the training data of parent and child, with which, the heterogeneous facial images of parent and child can be transformed into coding coefficients of the same subspace, such that the kinship verification task can be conducted using the coding coefficients. Besides, we also design a hard sample based coefficient discriminant term to ensure that the obtained coefficients own favorable discriminability. Experimental results on several publicly used benchmarks show the effectiveness of our proposed approach.

Web service recommendation plays an important role in building service-oriented systems. QoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To accurately predict the QoS values of candidate Web services, Web service recommendation systems usually need to collect historical QoS data from users, which will potentially pose a threat to the user's privacy. However, how to simultaneously protect user's privacy and make an accurate prediction has not been well studied. By taking these two aspects into consideration, we propose a novel QoS prediction approach for Web service recommendation in this paper. Specifically, we first design a similarity-maintaining privacy preservation (SPP) strategy, which aims to protect the user's privacy and maintain the utility of user data in the meanwhile. Then, we propose a location-aware low-rank matrix factorization (LLMF) algorithm, which employs the L1L1-norm low-rank matrix factorization to improve the model's robustness, and combines the matrix factorization model with two kinds of location information (continent, longitude and latitude) in the prediction process. Experimental results on two publicly available real-world Web service QoS datasets demonstrate the effectiveness of our privacy-preserving QoS prediction approach.

Heterogeneous defect prediction (HDP) refers to predicting defect-proneness of software modules in a target project using heterogeneous metric data from other projects. Existing HDP methods mainly focus on predicting target instances with single source. In practice, there exist plenty of external projects. Multiple sources can generally provide more information than a single project. Therefore, it is meaningful to investigate whether the HDP performance can be improved by employing multiple sources. However, a precondition of conducting HDP is that the external sources are available. Due to privacy concerns, most companies are not willing to share their data. To facilitate data sharing, it is essential to study how to protect the privacy of data owners before they release their data. In this paper, we study the above two issues in HDP. Specifically, to utilize multiple sources effectively, we propose a multi-source selection based manifold discriminant alignment (MSMDA) approach. To protect the privacy of data owners, a sparse representation based double obfuscation algorithm is designed and applied to HDP. Through a case study of 28 projects, our results show that MSMDA can achieve better performance than a range of baseline methods. The improvement is 3.4-15.3 percent in g-measure and 3.0-19.1 percent in AUG.

Summary In the early phases of software testing, projects may have only limited historical defect data. Learning prediction model with such insufficient training data will limit the efficacy of learned predictor. In practice, there are usually many publicly available fault prediction datasets. Recently, heterogeneous fault prediction (HFP) has been proposed. However, existing HFP models do not investigate how to use mixed project data to predict target. Furthermore, defect data are often imbalanced. The imbalanced data distribution of source usually leads to serious misclassification of fault‐prone instances, which will degrade the predictor's performance. Existing HFP methods do not consider the class imbalance problem in the training stages. In this paper, we propose a novel Cost‐sensitive Label and Structure‐consistent Unilateral Projection (CLSUP) approach for HFP. CLSUP can not only make better use of the within‐project and cross‐project data but also alleviate the class imbalance problem by setting different misclassification costs for fault‐prone and non–fault‐prone instances. Extensive experiments on 30 projects demonstrate the effectiveness of CLSUP.

Video-based person re-identification (re-id) has attracted a lot of research interest. When facing dramatic growth in new pedestrian videos, existing video-based person re-id methods usually need large quantities of labeled pedestrian videos to train a discriminative model. In practice, labeling large quantities of pedestrian videos is a costly and time-consuming task, which will limit the application of these methods in the real environment. Therefore, it is valuable and necessary to investigate how to learn a discriminative re-id model by using limited labeled training pedestrian videos. In this paper, we propose a semi-supervised cross-view projection-based dictionary learning (SCPDL) approach for video-based person re-id. Specifically, SCPDL jointly learns a pair of feature projection matrices and a pair of dictionaries by integrating the information contained in labeled and unlabeled pedestrian videos. With the learned feature projection matrices, the influence of variations within each video to the re-id can be reduced. With the learned dictionary pair, pedestrian videos from two different cameras can be converted into coding coefficients in a common representation space, such that the differences between different cameras can be bridged. In the learning process, the labeled pedestrian videos are used to ensure that the learned dictionaries have favorable discriminability; the large quantities of unlabeled pedestrian videos are used to ensure that SCPDL can better capture the variations between pedestrian videos, such that the learned dictionaries can own stronger representative capability. Experiments on two public pedestrian sequence data sets (iLIDS-VID and PRID 2011) demonstrate the effectiveness of the proposed approach.

Person re-identification plays an important role in video surveillance and forensics applications. In many cases, person re-identification needs to be conducted between image and video clip, e.g., re-identifying a suspect from large quantities of pedestrian videos given a single image of the suspect. We call re-identification in this scenario as image to video person reidentification (IVPR). In practice, image and video are usually represented with different features, and there usually exist large variations between frames within each video. These factors make matching between image and video become a very challenging task. In this paper, we propose a joint feature projection matrix and heterogeneous dictionary pair learning (PHDL) approach for IVPR. Specifically, the PHDL jointly learns an intra-video projection matrix and a pair of heterogeneous image and video dictionaries. With the learned projection matrix, the influence caused by the variations within each video on the matching can be reduced. With the learned dictionary pair, the heterogeneous image and video features can be transformed into coding coefficients with the same dimension, such that the matching can be conducted by using the coding coefficients. Furthermore, to ensure that the obtained coding coefficients own favorable discriminability, the PHDL designs a point-to-set coefficient discriminant term. To make better use of the complementary spatial-temporal and visual appearance information contained in pedestrian video data, we further propose a multi-view PHDL approach, which can fuse different video information effectively in the dictionary learning process. Experiments on four publicly available person sequence data sets demonstrate the effectiveness of the proposed approaches.

Video-based person re-identification (re-id) is an important application in practice. Since large variations exist between different pedestrian videos, as well as within each video, it's challenging to conduct re-identification between pedestrian videos. In this paper, we propose a simultaneous intra-video and inter-video distance learning (SI2DL) approach for video-based person re-id. Specifically, SI2DL simultaneously learns an intravideo distance metric and an inter-video distance metric from the training videos. The intra-video distance metric is used to make each video more compact, and the inter-video one is used to ensure that the distance between truly matching videos is smaller than that between wrong matching videos. Considering that the goal of distance learning is to make truly matching video pairs from different persons be well separated with each other, we also propose a pair separation based SI2DL (P-SI2DL). P-SI2DL aims to learn a pair of distance metrics, under which any two truly matching video pairs can be well separated. Experiments on four public pedestrian image sequence datasets show that our approaches achieve the state-of-the-art performance.

Dictionary learning (DL) is an effective feature learning technique, and has led to interesting results in many classification tasks. Recently, by combining DL with multiple kernel learning (which is a crucial and effective technique for combining different feature representation information), a few multi-kernel DL methods have been presented to solve the multiple feature representations based classification problem. However, how to improve the representation capability and discriminability of multi-kernel dictionary has not been well studied. In this paper, we propose a novel multi-kernel DL approach, named multi-kernel low-rank dictionary pair learning (MKLDPL). Specifically, MKLDPL jointly learns a kernel synthesis dictionary and a kernel analysis dictionary by exploiting the class label information. The learned synthesis and analysis dictionaries work together to implement the coding and reconstruction of samples in the kernel space. To enhance the discriminability of the learned multi-kernel dictionaries, MKLDPL imposes the low-rank regularization on the analysis dictionary, which can make samples from the same class have similar representations. We apply MKLDPL for multiple features based image classification task. Experimental results demonstrate the effectiveness of the proposed approach.

Person re-identification (re-id) plays an important role in video surveillance and forensics applications. In many cases, person re-id needs to be conducted between image and video clip, e.g., re-identifying a suspect from large quantities of pedestrian videos given a single image of him. We call re-id in this scenario as image to video person re-id (IVPR). In practice, image and video are usually represented with different features, and there usually exist large variations between frames within each video. These factors make matching between image and video become a very challenging task. In this paper, we propose a joint feature projection matrix and heterogeneous dictionary pair learning (PHDL) approach for IVPR. Specifically, PHDL jointly learns an intra-video projection matrix and a pair of heterogeneous image and video dictionaries. With the learned projection matrix, the influence of variations within each video to the matching can be reduced. With the learned dictionary pair, the heterogeneous image and video features can be transformed into coding coefficients with the same dimension, such that the matching can be conducted using coding coefficients. Furthermore, to ensure that the obtained coding coefficients have favorable discriminability, PHDL designs a point-to-set coefficient discriminant term. Experiments on the public iLIDS-VID and PRID 2011 datasets demonstrate the effectiveness of the proposed approach.

Heterogeneous defect prediction (HDP) aims to predict defect-prone software modules in one project using heterogeneous data collected from other projects. Recently, several HDP methods have been proposed. However, these methods do not sufficiently incorporate the two characteristics of the defect prediction data: (1) data could be linearly inseparable, and (2) data could be highly imbalanced. These two data characteristics make it challenging to build an effective HDP model. In this paper, we propose a novel Ensemble Multiple Kernel Correlation Alignment (EMKCA) based approach to HDP, which takes into consideration the two characteristics of the defect prediction data. Specifically, we first map the source and target project data into high dimensional kernel space through multiple kernel leaning, where the defective and non-defective modules can be better separated. Then, we design a kernel correlation alignment method to make the data distribution of the source and target projects similar in the kernel space. Finally, we integrate multiple kernel classifiers with ensemble learning to relieve the influence caused by class imbalance problem, which can improve the accuracy of the defect prediction model. Consequently, EMKCA owns the advantages of both multiple kernel learning and ensemble learning. Extensive experiments on 30 public projects show that EMKCA outperforms the related competing methods.

Image set based classification (ISC) has attracted lots of research interest in recent years. Several ISC methods have been developed, and dictionary learning technique based methods obtain state-of-the-art performance. However, existing ISC methods usually transform the image sample of a set into a vector for subsequent processing, which breaks the inherent spatial structure of image sample and the set. In this paper, we utilize tensor to model an image set with two spatial modes and one set mode, which can fully explore the intrinsic structure of image set. We propose a novel ISC approach, named discriminant tensor dictionary learning with neighbor uncorrelation (DTDLNU), which jointly learns two spatial dictionaries and one set dictionary. The spatial and set dictionaries are composed by set-specific sub-dictionaries corresponding to the class labels, such that the reconstruction error is discriminative. To obtain dictionaries with favorable discriminative power, DTDLNU designs a neighbor-uncorrelated discriminant tensor dictionary term, which minimizes the within-class scatter of the training sets in the projected tensor space and reduces tensor dictionary correlation among set-specific sub-dictionaries corresponding to neighbor sets from different classes. Experiments on three challenging datasets demonstrate the effectiveness of DTDLNU.

Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high resolution (HR), while probe images are usually low resolution (LR) in the identification scenarios with large variation of illumination, weather, or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD2L) approach for SR person re-identification task. With the HR and LR dictionary pair and mapping matrices learned from the features of HR and LR training images, SLD2L can convert the features of the LR probe images into HR features. To ensure that the converted features have favorable discriminative capability and the learned dictionaries can well characterize intrinsic feature spaces of the HR and LR images, we design a discriminant term and a low-rank regularization term for SLD2L. Moreover, considering that low resolution results in different degrees of loss for different types of visual appearance features, we propose a multi-view SLD2L (MVSLD2L) approach, which can learn the type-specific dictionary pair and mappings for each type of feature. Experimental results on multiple publicly available data sets demonstrate the effectiveness of our proposed approaches for the SR person re-identification task.

Distance learning (DL) is an effective technique for person reidentification (PR-ID). DL based methods learn the distance metric by exploiting the discriminative information contained in samples. In PR-ID, different types of negative samples own different amounts of discriminative information, and impostor samples usually own more than other well separable negative samples (WSN-samples). Therefore, how to make full use of the different discriminative information conveyed by all negative samples in the DL process is a critical issue to be investigated. In this paper, we propose a novel DL approach for PR-ID. Specifically, for each target sample, we divide its negative samples into impostors and WSN-samples. Then we learn the distance metric by utilizing impostors and WSN-samples differently. For impostors, we design a symmetric triplet constraint, which requires the impostor to be far away from both samples of its corresponding positive sample pair simultaneously; for WSN-samples, we require them to keep their favorable separability. Experimental results on three benchmark datasets demonstrate the effectiveness and efficiency of our approach.

When a company lacks local data in hand, engineers can build an effort model for the effort estimation of a new project by utilizing the training data shared by other companies. However, one of the most important obstacles for data sharing is the privacy concerns of software development organizations. In software engineering, most of existing privacy-preserving works mainly focus on the defect prediction, or debugging and testing, yet the privacy-preserving data sharing problem has not been well studied in effort estimation. In this paper, we aim to provide data owners with an effective approach of privatizing their data before release. We firstly design an Interval Covering based Subclass Division (ICSD) strategy. ICSD can divide the target data into several subclasses by digging a new attribute (i.e., class label) from the effort data. And the obtained class label is beneficial to maintaining the distribution of the target data after obfuscation. Then, we propose a manifold learning based bi-directional data obfuscation (MLBDO) algorithm, which uses two nearest neighbors, which are selected respectively from the previous and next subclasses by utilizing the manifold learning based nearest neighbor selector, as the disturbances to obfuscate the target sample. We call the entire approach as ICSD&MLBDO. Experimental results on seven public effort datasets show that: 1) ICSD&MLBDO can guarantee the privacy and maintain the utility of obfuscated data. 2) ICSD&MLBDO can achieve better privacy and utility than the compared privacy-preserving methods.

Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high-resolution (HR) while probe images are usually low-resolution (LR) in the identification scenarios with large variation of illumination, weather or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD2L) approach for SR person re-identification. For the given training image set which consists of HR gallery and LR probe images, we aim to convert the features of LR images into discriminating HR features. Specifically, our approach learns a pair of HR and LR dictionaries and a mapping from the features of HR gallery images and LR probe images. To ensure that the converted features using the learned dictionaries and mapping have favorable discriminative capability, we design a discriminant term which requires the converted HR features of LR probe images should be close to the features of HR gallery images from the same person, but far away from the features of HR gallery images from different persons. In addition, we apply low-rank regularization in dictionary learning procedure such that the learned dictionaries can well characterize intrinsic feature space of HR and LR images. Experimental results on public datasets demonstrate the effectiveness of SLD2L.