We propose unsupervised multi-scenario (UMS) person re-identification (ReID) as a new task that expands ReID across diverse scenarios (cross-resolution, clothing change, etc.) within a single coherent framework. To tackle UMS-ReID, we introduce image-text knowledge modeling (ITKM) -- a three-stage framework that effectively exploits the representational power of vision-language models. We start with a pre-trained CLIP model with an image encoder and a text encoder. In Stage I, we introduce a scenario embedding in the image encoder and fine-tune the encoder to adaptively leverage knowledge from multiple scenarios. In Stage II, we optimize a set of learned text embeddings to associate with pseudo-labels from Stage I and introduce a multi-scenario separation loss to increase the divergence between inter-scenario text representations. In Stage III, we first introduce cluster-level and instance-level heterogeneous matching modules to obtain reliable heterogeneous positive pairs (e.g., a visible image and an infrared image of the same person) within each scenario. Next, we propose a dynamic text representation update strategy to maintain consistency between text and image supervision signals. Experimental results across multiple scenarios demonstrate the superiority and generalizability of ITKM; it not only outperforms existing scenario-specific methods but also enhances overall performance by integrating knowledge from multiple scenarios.

Clothing change person re-identification (CC-ReID) aims to match different images of the same person, even when the clothing varies across images. To reduce manual labeling costs, existing unsupervised CC-ReID methods employ clustering algorithms to generate pseudo-labels. However, they often fail to assign the same pseudo-label to two images with the same identity but different clothing—referred to as a clothing change positive pair—thus hindering clothing-invariant feature learning. To address this issue, we propose the identity-clothing similarity modeling (ICSM) framework. To effectively connect clothing change positive pairs, ICSM first performs clothing-aware learning to leverage all discriminative information, including clothing, to obtain compact clusters. It then extracts cluster-level identity and clothing features and performs inter-cluster similarity estimation to identify clothing change positive clusters, reliable negative clusters, and hard negative clusters for each compact cluster. During optimization, we design an adaptive version of existing optimization methods to enhance similarities of clothing change positive pairs, while also introducing text semantics as a supervisory signal to further promote clothing invariance. Extensive experimental results across multiple datasets validate the effectiveness of the proposed framework, demonstrating its superiority over existing unsupervised methods and its competitiveness with some supervised approaches.

Visible-infrared person re-identification (VI-ReID) aims to match visible and infrared images of the same individual. Supervised VI-ReID (SVI-ReID) methods have achieved promising performance under the guidance of manually annotated identity labels. However, the substantial annotation cost severely limits their scalability in real-world applications. As a result, unsupervised VI-ReID (UVI-ReID) methods have attracted increasing attention. These methods typically rely on pseudo-labels generated by clustering and matching algorithms to replace manual annotations. Nevertheless, the quality of pseudo-labels is often difficult to guarantee, and low-quality pseudo-labels can significantly hinder model performance improvements. To address these challenges, we explore the use of attribute arrays extracted by a large vision-language model (LVLM) to enhance VI-ReID, and propose a novel LVLM-driven attribute-aware modeling (LVLM-AAM) approach. Specifically, we first design an attribute-aware reliable labeling strategy, which refines intra-modality clustering results based on image-level attributes and improves inter-modality matching by grouping clusters according to cluster-level attributes. Next, we develop an explicit-implicit attribute fusion module, which integrates explicit and implicit attributes to obtain more fine-grained identity-related text features. Finally, we introduce an attribute-aware contrastive learning module, which jointly leverages static and dynamic text features to promote modality-invariant feature learning. Extensive experiments conducted on VI-ReID datasets validate the effectiveness of the proposed LVLM-AAM and its individual components. LVLM-AAM not only significantly outperforms existing unsupervised methods but also surpasses several supervised methods.

Clothing change person re-identification (CC-ReID) is a crucial task in intelligent surveillance, aiming to match images of the same person wearing different clothing. Promising performance in existing CC-ReID methods is achieved at the cost of labor-intensive manual annotation of identity labels. While some researchers have explored unsupervised CC-ReID, these methods still depend on additional deep learning models for preprocessing. To eliminate the need for additional models and improve performance, we propose a joint augmentation and part learning (JAPL) framework that obtains clothing change positive pairs in an unsupervised fashion by synergistically combining augmentation-based invariant learning (AugIL) and part-based invariant learning (ParIL). AugIL first constructs clothing change pseudo-positive pairs and then encourages the model to focus on clothing-invariant information by enhancing feature consistency between the pseudo-positive pairs. ParIL beneficially encourages high similarity between inter-cluster clothing change positive pair using part images and a prediction sharpening loss. PartIL also introduces a soft consistency loss that promotes clothing-invariant feature learning by encouraging consistency of class vectors between the real features actually used for CC-ReID and the part features. Experimental results on multiple ReID datasets demonstrate that the proposed JAPL not only surpasses existing unsupervised methods but also achieves competitive performance compared to some supervised CC-ReID methods.

Cross-resolution person re-identification (CR-ReID) aims to match low-resolution (LR) and high-resolution (HR) images of the same individual. To reduce the cost of manual annotation, existing unsupervised CR-ReID methods typically rely on cross-resolution fusion to obtain pseudo-labels and resolution-invariant features. However, the fusion process requires two encoders and a fusion module, which significantly increases computational complexity and reduces efficiency. To address this issue, we propose a robust labeling and invariance modeling (RLIM) framework, which utilizes a single encoder to tackle the unsupervised CR-ReID problem. To obtain pseudo-labels robust to resolution gaps, we develop cross-resolution robust labeling (CRL), which utilizes two clustering criteria to encourage cross-resolution positive pairs to cluster together and exploit the reliable relationships between images. We also introduce random texture augmentation (TexA) to enhance the model's robustness to noisy textures related to artifacts and backgrounds by randomly adjusting texture strength. During the optimization process, we introduce the resolution-cluster consistency loss, which promotes resolution-invariant feature learning by aligning inter-resolution distances with intra-cluster distances. Experimental results on multiple datasets demonstrate that RLIM not only surpasses existing unsupervised methods, but also achieves performance close to some supervised CR-ReID methods. Code is available at https://github.com/zqpang/RLIM.

Protein-protein interactions (PPIs) are crucial for various cellular activities and disease development, and modulating PPIs using small molecule inhibitors (PPIIs) has gradually become a promising therapeutic strategy. Recently, researchers have proposed several machine learning methods to screen PPIIs, but most of the works focused on unimodal representations of molecules or combining multimodal features in a naive splicing manner. Meanwhile, current research progress is being slowed by the lack of large-scale PPII datasets. To address these issues, we propose MCLPPII, a unified multimodal contrastive learning framework for PPII prediction. MCLPPII extracts comprehensive molecular information from four modalities and effectively combines them through an adaptive feature fusion method. Furthermore, we propose a three-stage training strategy to enhance the PPII prediction capability of MCLPPII by leveraging self-supervised pre-training on a large unlabeled dataset. We evaluate MCLPPII on nine PPI targets and two downstream tasks, including the PPI inhibitor identification task and potency prediction task. Experimental results show that MCLPPII achieves competitive performance. The source code and datasets are freely available at https://github.com/1zzt/MCLPPII.

Cross-resolution person re-identification (CR-ReID) aims to match images of the same person with different resolutions in different scenarios. Existing CR-ReID methods achieve promising performance by relying on large-scale manually annotated identity labels. However, acquiring manual labels requires considerable human effort, greatly limiting the flexibility of existing CR-ReID methods. To address this issue, we propose a dual-resolution fusion modeling (DRFM) framework to tackle the CR-ReID problem in an unsupervised manner. Firstly, we design a cross-resolution pseudo-label generation (CPG) method, which initially clusters high-resolution images and then obtains reliable identity pseudo-labels by fusing class vectors in both resolution spaces. Subsequently, we develop a cross-resolution feature fusion (CRFF) module to fuse features from both high-resolution and low-resolution spaces. The fusion features have the potential to serve as a new form of resolution-invariant features. Finally, we introduce cross-resolution contrastive loss and probability sharpening loss in DRFM to facilitate resolution-invariant learning and effectively utilize ambiguous samples for optimization. Experimental results on multiple CR-ReID datasets demonstrate that the proposed DRFM not only outperforms existing unsupervised methods but also approaches the performance of early supervised methods.

Abstract Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for battery management systems. Deep learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series data. However, the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be improved. To address this challenge, this paper proposes a novel deep learning model, the MLP-Mixer and Mixture of Expert (MMMe) model, for RUL prediction. The MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level features. Additionally, we devise an ensemble predictor based on a Mixture-of-Experts (MoE) architecture to generate reliable RUL predictions. The experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods, providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation process. Our code and dataset are available at the website of github.

Accurate prediction of Drug-Target binding Affinity (DTA) is a daunting yet pivotal task in the sphere of drug discovery. Over the years, a plethora of deep learning-based DTA models have emerged, rendering promising results in predicting the binding affinities between drugs and their target proteins. However, in contrast to the conventional approach of modeling binding affinity in vector spaces, we propose a more nuanced modeling process in a continuous space to account for the diversity of input samples. Initially, the drug is encoded using the Simplified Molecular Input Line Entry System (SMILES), while the target sequences are characterized via a pretrained language model. Subsequently, highly correlative information is extracted utilizing residual gated convolutional neural networks. In a departure from existing deep learning-based models, our model learns the hidden representations of the drugs and targets jointly. Instead of employing two vectors, our hidden representations consist of two Gaussian distributions. To validate the effectiveness of our proposal, we conducted evaluations on commonly utilized benchmark datasets. The experimental outcomes corroborated that our method surpasses the state-of-the-art vectorial representation methods in terms of performance. This approach, therefore, offers potential enhancements in the precision of DTA predictions, potentially contributing to more efficient drug discovery processes.

Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality image retrieval task. Compared to visible modality person re-identification that handles only the intra-modality discrepancy, VI-ReID suffers from an additional modality gap. Most existing VI-ReID methods achieve promising accuracy in a supervised setting, but the high annotation cost limits their scalability to real-world scenarios. Although a few unsupervised VI-ReID methods already exist, they typically rely on intra-modality initialization and cross-modality instance selection, despite the additional computational time required for intra-modality initialization. In this paper, we study the fully unsupervised VI-ReID problem and propose a novel cross-modality hierarchical clustering and refinement (CHCR) method by promoting modality-invariant feature learning and improving the reliability of pseudo-labels. Unlike conventional VI-ReID methods, CHCR does not rely on any manual identity annotation and intra-modality initialization. First, we design a simple and effective cross-modality clustering baseline that clusters between modalities. Then, to provide sufficient inter-modality positive sample pairs for modality-invariant feature learning, we propose a cross-modality hierarchical clustering algorithm to promote the clustering of inter-modality positive samples into the same cluster. In addition, we develop an inter-channel pseudo-label refinement algorithm to eliminate unreliable pseudo-labels by checking the clustering results of three channels in the visible modality. Extensive experiments demonstrate that CHCR outperforms state-of-the-art unsupervised methods and achieves performance competitive with many supervised methods.

RGB (visible), near-infrared (NI), and thermal infrared (TI) imaging modalities are commonly combined for round-the-clock surveillance. We introduce a novel unsupervised multi-modality person re-identification (MM-ReID) task, which, based on an individual’s image in any one modality, seeks to identify matches in the other two modalities. Compared to prior MM-ReID problem formulations, unsupervised MM-ReID significantly reduces labeling cost and imaging constraints. To address the unsupervised MM-ReID task, we propose a novel inter-modality similarity learning (IMSL) framework consisting of four synergistic interconnected modules: modality mean clustering (MMC), multi-modality reliability estimation (MMRE), shape-based mutual reinforcement (SMR), and modality-aware invariant learning (MIL). MMC iterates with SMR and MIL in a mutually beneficial manner to provide pseudo-labels that are robust to modality gap. MMRE normalizes sample weights, mitigating the impact of noisy labels in the multi-modality setting. SMR emphasizes shape information to implicitly enhance the model’s robustness to the modality gap and is additionally guided by pseudo-labels provided by MMC to attend to identity-related details. MIL explicitly encourages learning of modality-invariant and identity-related features via contrastive feedback for the MMC module. Extensive experimental results on the multi-modality and cross-modality datasets demonstrate that IMSL provides substantial performance gains over existing methods. Code is made available at https://github.com/zqpang/IMSL.

Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task-specific. Recently, deep learning models based on graph neural networks have made remarkable progress in molecular representation learning. However, many graph-based approaches ignore molecular hierarchical structure modeling guided by domain knowledge. In chemistry, the functional groups of a molecule determine its interaction with specific targets. Therefore, we propose a hierarchical graph neural network framework (called HiGPPIM) for predicting PPIMs by integrating atom-level and functional group-level features of molecules. HiGPPIM constructs atom-level and functional group-level graphs based on chemical knowledge and learns graph representations using graph attention networks. Furthermore, a hypergraph attention network is designed in HiGPPIM to aggregate and transform two-level graph information. We evaluate the performance of HiGPPIM on eight PPI families and two prediction tasks, namely PPIM identification and potency prediction. Experimental results demonstrate that HiGPPIM achieves state-of-the-art performance on both tasks and that using functional group information to guide PPIM prediction is effective.

Identifying synergistic drug combinations is paramount significance in addressing complex diseases while reducing the risks of toxicities and other adverse effects. Although a plethora of computational methods have been proposed in this domain, most of them are underpinned only by physicochemical or biological features. Recently, Chemical Language Models (CLMs) are shown to be capable of learning better representations that hold utility across diverse tasks, from molecular property prediction, de novo drug design, drug-target interaction, and more. In this study, we proposed CLMSyn, a continuous prompt for CLM aided synergistic drug combinations prediction. Unlike existing works employ CLMs for downstream tasks, we adopt the prompt learning to fine-tune CLM, that is, only train small-scale prompt while keeping CLM fixed. Furthermore, we harness the the multi-head attention mechanism to fuse the learned vector from the CLM, chemical descriptors and gene expression of cell line. A comprehensive array of experiments conducted on a benchmark dataset, encompassing four distinct synergy types, substantiates the superior performance of CLMSyn when contrasted against existing state-of-the-art methods. These empirical findings provide compelling evidence attesting to the efficacy of CLMSyn as a potent instrumentality in expediting the identification of pioneering combination therapies.

MOTIVATION: Accurate prediction of drug-target binding affinity (DTA) is crucial for drug discovery. The increase in the publication of large-scale DTA datasets enables the development of various computational methods for DTA prediction. Numerous deep learning-based methods have been proposed to predict affinities, some of which only utilize original sequence information or complex structures, but the effective combination of various information and protein-binding pockets have not been fully mined. Therefore, a new method that integrates available key information is urgently needed to predict DTA and accelerate the drug discovery process. RESULTS: In this study, we propose a novel deep learning-based predictor termed DataDTA to estimate the affinities of drug-target pairs. DataDTA utilizes descriptors of predicted pockets and sequences of proteins, as well as low-dimensional molecular features and SMILES strings of compounds as inputs. Specifically, the pockets were predicted from the three-dimensional structure of proteins and their descriptors were extracted as the partial input features for DTA prediction. The molecular representation of compounds based on algebraic graph features was collected to supplement the input information of targets. Furthermore, to ensure effective learning of multiscale interaction features, a dual-interaction aggregation neural network strategy was developed. DataDTA was compared with state-of-the-art methods on different datasets, and the results showed that DataDTA is a reliable prediction tool for affinities estimation. Specifically, the concordance index (CI) of DataDTA is 0.806 and the Pearson correlation coefficient (R) value is 0.814 on the test dataset, which is higher than other methods. AVAILABILITY AND IMPLEMENTATION: The codes and datasets of DataDTA are available at https://github.com/YanZhu06/DataDTA.

Abstract Privacy protection in the computer vision field has attracted increasing attention. Generative adversarial network-based methods have been explored for identity anonymization, but they do not take into consideration semantic information of images, which may result in unrealistic or flawed facial results. In this paper, we propose a Semantic-aware De-identification Generative Adversarial Network (SDGAN) model for identity anonymization. To retain the facial expression effectively, we extract the facial semantic image using the edge-aware graph representation network to constraint the position, shape and relationship of generated facial key features. Then the semantic image is injected into the generator together with the randomly selected identity information for de-Identification. To ensure the generation quality and realistic-looking results, we adopt the SPADE architecture to improve the generation ability of conditional GAN. Meanwhile, we design a hybrid identity discriminator composed of an image quality analysis module, a VGG-based perceptual loss function, and a contrastive identity loss to enhance both the generation quality and ID anonymization. A comparison with the state-of-the-art baselines demonstrates that our model achieves significantly improved de-identification (De-ID) performance and provides more reliable and realistic-looking generated faces. Our code and data are available on https://github.com/kimhyeongbok/SDGAN

Fully unsupervised person re-identification (ReID) methods aim to learn discriminative features without using labeled ReID data. Because these methods are easily affected by camera discrepancies, similar studies have typically designed optimization methods to enable the model to learn camera-invariant features. However, they often ignore the impact of camera discrepancies on clustering results. Specifically, camera discrepancies will reduce the intra-class camera diversity and promote the generation of noise labels. To solve the above problems, we propose a unified unsupervised learning framework: camera invariant feature learning (CIFL) framework. First, we designed a novel DBSCAN-NN algorithm in the CIFL framework that improves the intra-class camera diversity by forcibly merging samples from different cameras. Then, we designed feature ensemble clustering that improves the accuracy of the pseudo-labels by clustering feature ensembles. In addition, we designed an optimization method for camera discrepancies: stochastic pulled loss. With the stochastic pulled loss, the ReID model is forced to learn camera-invariant features. We verified the effectiveness and generalization of CIFL on four ReID datasets (Market-1501, DukeMTMC-reID, MSMT17 and CUHK03-NP). The experimental results show that CIFL not only outperforms the existing fully unsupervised methods but also is superior to the unsupervised domain adaptation methods.

Measuring the semantic similarity between Gene Ontology (GO) terms is a fundamental step in numerous functional bioinformatics applications. To fully exploit the metadata of GO terms, word embedding-based methods have been proposed recently to map GO terms to low-dimensional feature vectors. However, these representation methods commonly overlook the key information hidden in the whole GO structure and the relationship between GO terms. In this paper, we propose a novel representation model for GO terms, named GT2Vec, which jointly considers the GO graph structure obtained by graph contrastive learning and the semantic description of GO terms based on BERT encoders. Our method is evaluated on a protein similarity task on a collection of benchmark datasets. The experimental results demonstrate the effectiveness of using a joint encoding graph structure and textual node descriptors to learn vector representations for GO terms.

The compound-protein binding affinity (CPA) pre-diction is vital for drug discovery and drug repurposing. Deep learning methods have been developed to model the complicated relationship between CPA and the sequences or structures of proteins and molecules. This study proposes a novel deep learning method, SeqGO-CPA, integrating protein function knowledge represented by Gene Ontology (GO) annotations in the CPA prediction. To capture the semantic information of GO annotations, a fine-tuned natural language processing model for biomedical domains is utilized to encode the set of GO terms. Meanwhile, based on the observation that CPA often occurs in sub-structures, our method uses the tokenization algorithm to learn sub-structure information of proteins and compounds from a large number of unlabeled sequences. Further, a deep neural network architecture involving the jointly-feature representation and a highway block is developed to enhance the CPA prediction ability. The proposed model was evaluated on two public benchmark datasets in both standard cross-validation and blinding split settings. The experimental results demonstrate our method outperforms the deep learning-based baselines, meanwhile the incorporating of GO information further improves the prediction performance.

Network intrusion detection identifies malicious activity in the network by analyzing the behavior of network traffic. As an important part of network intrusion detection, feature extraction plays a crucial role in improving the performance of intrusion detection. This research proposes a novel secondary feature extraction method called L-KPCA based on the Liner Discriminant Analysis (LDA) and Kernel Principal Component Analysis (KPCA), to provide efficient features for network intrusion detection. While maintaining the effectiveness of processing nonlinear data in network traffic, the use of LDA effectively compensates for the problem that KPCA only focuses on the analysis of features in terms of variance and ignores the performance of features in terms of mean. Extensive experimental results verify that the use of the proposed, L-KPCA can make the intrusion detection classification model perform better in terms of recognition accuracy and recall.

Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.

Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at the cellular level. However, due to the extremely low levels of transcripts in a single cell and technical losses during reverse transcription, gene expression at a single-cell resolution is usually noisy and highly dimensional; thus, statistical analyses of single-cell data are a challenge. Although many scRNA-seq data analysis tools are currently available, a gold standard pipeline is not available for all datasets. Therefore, a general understanding of bioinformatics and associated computational issues would facilitate the selection of appropriate tools for a given set of data. In this review, we provide an overview of the goals and most popular computational analysis tools for the quality control, normalization, imputation, feature selection and dimension reduction of scRNA-seq data.

Due to the growth and popularity of the internet, cyber security remains, and will continue, to be an important issue. There are many network traffic classification methods or malware identification approaches that have been proposed to solve this problem. However, the existing methods are not well suited to help security experts effectively solve this challenge due to their low accuracy and high false positive rate. To this end, we employ a machine learning-based classification approach to identify malware. The approach extracts features from network traffic and reduces the dimensionality of the features, which can effectively improve the accuracy of identification. Furthermore, we propose an improved SVM algorithm for classifying the network traffic dubbed Optimized Facile Support Vector Machine (OFSVM). The OFSVM algorithm solves the problem that the original SVM algorithm is not satisfactory for classification from two aspects, i.e., parameter optimization and kernel function selection. Therefore, in this paper, we present an approach for identifying malware in network traffic, called Network Traffic Malware Identification (NTMI). To evaluate the effectiveness of the NTMI approach proposed in this paper, we collect four real network traffic datasets and use a publicly available dataset CAIDA for our experiments. Evaluation results suggest that the NTMI approach can lead to higher accuracy while achieving a lower false positive rate compared with other identification methods. On average, the NTMI approach achieves an accuracy of 92.5% and a false positive rate of 5.527%.

Ontologies are essential description tools for biomedical concepts and entities, supporting biomedical fundamental research such as semantic similarity analysis, protein-protein interaction prediction and so on. An increasing amount of ontology-like domain knowledge is published in scientific publications, meanwhile, advanced natural language processing (NLP) techniques have been widespread to extract information from text resources automatically, both of which facilitate the exploration of the semantic representation of biomedical ontologies. We propose a novel distributional semantic representation methodology based on the combination of two pre-trained and domain-specific word embedding tools, the non-contextualized Word2Vec and the context-dependent NCBI-blueBERT, to enhance the encoding ability for biomedical ontologies. Furthermore, we utilize a randomly initialized bidirectional LSTM to project the obtained word vector sequence to a fixed-length sentence vector, facilitating a flexible and uniform way for the computation of downstream tasks. We evaluate our method in two categories of tasks: the similarity access of ontology terms, and the ontology annotation-based protein-protein interaction classification. Experimental results demonstrate that our method provides encouraging results compared to the baselines in all tests. Our approach offers promising opportunities for representing ontologies semantics and in turn characterizing entities including proteins in biomedical research.

The drug-target binding affinity(DTA) indicates the strength of the drug-target interaction; therefore, predicting DTA by computational approaches can considerably benefit drug discovery by narrowing down the searching space and pruning those drug-target pairs with low binding affinity scores. In the computational methods, feature representation of proteins is one of the most important parts due to its strong influence on the following regression task. This paper introduces the BERT-based language representation to embed the gene ontology annotations, combined with the raw sequence to characterize a protein by fusing its physical structure and human knowledge. We exploit CNN network stacked over full connected layers to learn the prediction of DTA scores in a supervised manner. This framework enhances the feature representation ability, leading to the improvement of the DTA prediction precision. The evaluation on the Davis and KIBA datasets compared to the state-of-the-art baselines demonstrates our feature representation's superiority.

Adaptive Random Testing (ART) aims at improving detection effectiveness by evenly distributing test cases over the whole input domain. Many ART algorithms introducing clustering techniques (such as k-means Clustering) have been proposed to achieve an even spread of test cases. Though previous studies have demonstrated that ART with k-means clustering could achieve a good enhancement in testing effectiveness, k-means clustering is limited by the value of k, which will have a great impact on the test effectiveness. To improve the testing effectiveness of these techniques for object-oriented software, in this paper, we propose an approach named Determination Method of Optimal k-value based on the Experimental Process (DMOVk-EP) to determine the optimal k-value of k-means clustering and make the ART algorithms using k-means clustering technique achieve the best fault detection capability. The proposed method consists of two parts, one is a solution model for k based on the experimental process, and the other is an optimal k-value algorithm based on the presented model. We integrate this method with k-means clustering in ART and apply it to a set of open-source programs, with the experimental results showing that our approach obtains much more appropriate k, and also achieves much better testing effectiveness than other related methods.

Cell tracking automatically in time-lapse image sequences is important for understanding the dynamic pattern of micro-cell. In this paper, we present a novel method for tracking cell with shape feature based on the delta generalized labeled multi-Bernoulli (delta-GLMB) filter which is of great research significance. The delta-GLMB filter with cell shape parameters can improve the tracking accuracy. This approach is evaluated and compared with raw detection using the generalized optimal sub-pattern assignment (GOSPA) metric on real N2DH-SIM cell sequences. Experiment results show that the delta-GLMB filter can provide the shape information as well as the better estimation than raw detection and KTH method.

The collaborations of the diseases might be the key to understand the mechanism of the diseases since it is difficult to detect the role of complex genes and micro RNA in diseases. With the rapid development of technology, several metabolites of many kinds of diseases could be obtained by the advanced machines. Some diseases are related to several metabolites, and some metabolites have strong relationship with several diseases. Since there is certain relationship between different diseases, firstly we should find the similarity of different diseases. Then the similarity of different metabolites could be calculated by the relationship of diseases. Then a network of metabolites' similarity could be built. After building up the network, the diseases might not only relate to originally several metabolites, but more related metabolites to the disease could be found by the lines of the network. It can be used to explain the mechanism of the diseases more precisely. These metabolites could also be candidates to map the diseases in terms of the similarities. We can also sort these potentially relevant metabolites by similarity. It offers researchers a novel way to find out metabolites which is related to diseases.

Functional similarity between molecules results in similar phenotypes, such as diseases. Therefore, it is an effective way to reveal the function of molecules based on their induced diseases. However, the lack of a tool for obtaining the similarity score of pair-wise disease sets (SSDS) limits this type of application. Here, we introduce DisSetSim, an online system to solve this problem in this article. Five state-of-the-art methods involving Resnik's, Lin's, Wang's, PSB, and SemFunSim methods were implemented to measure the similarity score of pair-wise diseases (SSD) first. And then “pair-wise-best pairs-average” (PWBPA) method was implemented to calculated the SSDS by the SSD. The system was applied for calculating the functional similarity of miRNAs based on their induced disease sets. The results were further used to predict potential disease-miRNA relationships. The high area under the receiver operating characteristic curve AUC (0.9296) based on leave-one-out cross validation shows that the PWBPA method achieves a high true positive rate and a low false positive rate. The system can be accessed from http://bio-annotation.cn/DisSetSim.

It is important for students to solve problems with specific requirements in the programming teaching. Our teaching system is a Moodle-based interactive teaching platform for C programming. Its online judging system can grade students code automatically. It plays an extremely important role in programming language teaching. This paper is devoted to optimizing and improving the system. We firstly analyze the five problems in the system according to the feedback from the teachers and students: 1) logical errors in students programs cannot be located; 2) a cheating that directly outputs answers cannot be detected; 3) evaluation results lack statistics and visualization; 4) the code submitting procedure is complicated; 5) the feedback of incorrect answers is not detailed. In order to solve these problems, we employ a fault localization algorithm, revise the evaluation logic, introduce third-party visualization plug-ins and refactor the system, respectively. Detailed and exact solutions are given also. After optimizing and improving the system, the user experience is significantly improved. It is convenient for the student to find and correct errors in their programs. Also, it is easier for teachers to acquire valuable feedback and master students' learning situation.

Particle flow algorithms have been developed as an alternative to particle filtering. In these algorithms, there is no importance sampling, and particles are migrated from the prior to the posterior via a flow, described by differential equations. Aside from a few special cases, implementations involve multiple approximations, and their impact on the accuracy of the estimates is not clearly understood. In this paper, we propose algorithms that use particle flow procedures to construct an importance sampling distribution within a standard particle filter. The resultant algorithms retain the statistical consistency of sequential Monte Carlo methods, but acquire the desirable properties of particle flow techniques. We report the results of a multiple target tracking simulation study that combines highly informative measurements with a reasonably high-dimensional state space, leading to a challenging scenario for particle filters. Of the filters we test, the particle flow particle filter provides the smallest tracking error and achieves the largest average effective sample size.

The advent of massive open online courses (MOOC) poses challenges for teaching and learning programming. This paper has analyzed these challenges and thereby proposed a self-motivating learning platform for students in the introductory programming course. Novel mechanisms of online assignments and examination have been introduced. Our platform provides functions for self-motivating learning and practicing in MOOC, which makes it distinguish from the others. For example, self-paced timetable with supervision, self-motivated exercise contents, exercise market, and relative ranking. The automatic grading approach is also a highlight. Programs even with syntactic or semantic errors can be automatically graded. Our platform gains popularity among both students and teachers. The platform has been used together with a programming MOOC. This course is ranked as the third most popular courses among over 500 courses. The platform has also been used by more than 100 other universities. The application of the platform in both MOOC and the traditional classroom courses has shown that students' self-motivation in learning programming has been greatly promoted, and their practical skills have also been significantly improved.

In order to maximize the motivation of students in the programming practice, this paper offers an analysis on the core factors of practice case motivating students put in effort in programming practice, namely, "interest", "usability", and "hierarchy". Furthermore, we present typical practice cases which are carefully designed according to the motivating factors and give a description on the implementation and experience of our programming practice course at Harbin Institute of Technology. The designed programming practice can not only train the students' practical programming skills but also enhance their self-regulated learning skills, creativity and self-efficacy.

Research on biological objects requires tracking hundreds of micro-objects from the microscopy video. We propose an automated tracking framework to extract trajectories of micro-objects. This framework uses a particle probability hypothesis density (PF-PHD) tracker to implement a recursive Bayesian state estimation and trajectories association. In the framework, an ellipse target model is presented to describe the micro-objects with shape parameters instead of point-like targets. Furthermore, an orientation and positional constraint model is developed to deal with the data association of crossing trajectories in multitarget tracking. Using this framework, a significantly larger number of tracks are obtained than manual tracking. The experiments on simulated image sequences of microtubule movement are performed in order to evaluate the proposed PF-PHD tracking method.