Despite the astounding progress made in semi-supervised learning (SSL) and imbalanced supervised learning (ISL), there has been little attention devoted to the research of imbalanced semi-supervised learning (ISSL). The ‘Matthew effect’, a phenomenon where a disparity in data representation becomes more severe in a class-imbalanced dataset during training, could be amplified in a semi-supervised setting. In this study, we addressed two key challenges in ISSL: maintaining the reliability of pseudo-labels and ensuring a balanced representation of features. Specifically, we propose a class-aware feature-diffusion constraint and reliable pseudo-labeling (DCRP) framework to address these issues. In the DCRP, we counteract the overconfidence problem of softmax by adding an extra class to the typical K class problem without the need for additional parameters. Moreover, we introduced a flexible class-aware feature diffusion constraint in the feature extractor, promoting a more balanced feature diversity. Experimental validations on various datasets, such as CIFAR10-LT, CIFAR100-LT, SVHN-LT, and Small ImageNet-127, demonstrated consistent improvements in accuracy with our DCRP method. In particular, we achieved a steady improvement in accuracy of approximately 1% under the newly published ACR prototype across most settings. The code is available athttps://github.com/guoxiaoyuatbjtu/DCRP.

Background:Multi-source cross-project defect prediction (MSCPDP) attempts to transfer defect knowledge learned from multiple source projects to the target project. MSCPDP has drawn increasing attention from academic and industry communities owing to its advantages compared with single-source cross-project defect prediction (SSCPDP). However, two main problems, which are how to effectively extract the transferable knowledge from each source dataset and how to measure the amount of knowledge transferred from each source dataset to the target dataset, seriously restrict the performance of existing MSCPDP models.Objective:In this paper, we propose a novel multi-source transfer weighted ensemble learning (MASTER) method for MSCPDP.Method:MASTER measures the weight of each source dataset based on feature importance and distribution difference and then extracts the transferable knowledge based on the proposed feature-weighted transfer learning algorithm. Experiments are performed on 30 software projects. We compare MASTER with the latest state-of-the-art MSCPDP methods with statistical test in terms of famous effort-unaware measures (i.e., PD, PF, AUC, and MCC) and two widely used effort-aware measures (Popt20% and IFA).Result:The experiment results show that: 1) MASTER can substantially improve the prediction performance compared with the baselines, e.g., an improvement of at least 49.1% in MCC, 48.1% in IFA; 2) MASTER significantly outperforms each baseline on most datasets in terms of AUC, MCC,Popt20% and IFA; 3) MSCPDP model significantly performs better than the mean case of SSCPDP model on most datasets and even outperforms the best case of SSCPDP on some datasets.Conclusion:It can be concluded that 1) it is very necessary to conduct MSCPDP, and 2) the proposed MASTER is a more promising alternative for MSCPDP.

With the rise of Foundation models, Text-to-Image models, as one of its important branches, have been increasingly applied. While focusing on the impressive generation capabilities of these models, it is also crucial to pay attention to the robustness of the models against attacks. In this paper, we shift our focus towards studying the vulnerability of Text-to-Image (T2I) models. To this end, we propose a black-box attack method and demonstrate that T2I models are susceptible to adversarial text attacks. Specifically, this method can disrupt T2I models by making subtle modifications to the model’s input (i.e., prompt) without accessing the model parameters, resulting in the generation of incorrect images. It is worth mentioning that we discovered the ability to switch different types of tokenizers within this black-box framework to handle text, furthermore, this attack framework can be applied to target different versions of T2I models. The experiments indicate that the images generated through adversarial text exhibit noticeable errors. We also employ CLIP score, a metric used to evaluate the similarity between images and image descriptions, to assess the results. The findings demonstrate a significant decrease in the visual-textual similarity after the model is subjected to attacks. Additionally, we have identified a specific type of error that T2I models tend to make when facing attacks – when confronted with unrecognizable text, the model often interprets it as human-related content. This paper not only highlights the vulnerability of T2I models to adversarial text attacks but also further discusses potential methods that could enhance the robustness of these attack techniques. This provides a valuable reference for future research directions in this field.

Forecasting crowd movement accurately across an urban area is crucial for efficient traffic control and ensuring public security. Current methods involve transforming the city’s roadmap into a grid-based map, enabling Convolutional Neural Networks (CNNs) or Graph Convolutional Networks (GCNs) to capture spatio-temporal relationships efficiently. However, this approach overlooks the connection between irregularly shaped real-world areas, which can be categorized into various functional zones. In this article, we introduce a novel approach for predicting urban crowd flow named STHGN, which utilizes hypergraph convolutional networks. By constructing 3-level hypergraphs from irregular areas and adopting Hyper-GCN, we capture mobility among irregular regions. We construct the hypergraphs based on hour, day, and week, simultaneously using gated-based mechanisms to fuse various embeddings. We evaluate the efficacy of our model by contrasting it with 11 other approaches, including the most sophisticated STGs. After conducting numerous experiments, we find that STHGN outperforms these methods with higher accuracy, resulting in a reduction of approximately 6-9% in mean absolute error (MAE) for crowd flow prediction.

Context: Software defect number prediction (SDNP) helps allocate limited testing resources by ranking software modules according to the predicted defect numbers. However, the highly skewed distribution of defects greatly degrades the performance of SDNP models by preventing SDNP models from ranking software modules accurately. Objective: This paper introduces a novel subspace hybrid sampling ensemble (SHSE) method based on feature subspace construction, hybrid sampling , and ensemble learning for building high-performance SDNP models. Method: Specifically, we first construct a series of feature subspace to ensure the diversity of base learners. In each of feature subspace, we then use the proposed hybrid sampling method to balance the training subset without losing too much information and introducing lots of noisy data caused by only using undersampling or oversampling techniques. Finally, we train each base learner and combine them by using the proposed weighted ensemble strategy. Experiments are performed on 27 public defect datasets. We compare SHSE with five state-of-the-art resampling-based models and four zero-inflated/hurdle models in terms of the ranking performance measure fault-percentile-average (FPA). To demonstrate the effectiveness of SHSE, two statistical testing methods including Wilcoxon Signed-rank test and Scott–Knott Effect Size Difference test are utilized. Cliff’s δ is also computed for quantifying the difference when there is significant difference between SHSE and each baseline. Results: The experimental results show that SHSE significantly outperforms the baselines and improves the performance over each baseline with as least medium effect size on most datasets. On average, SHSE improves the performance over the resampling-based methods by 8.7% ∼ 14.4% and the zero-inflate/hurdle models by 10.3% ∼ 15.2%. Conclusion: It can be concluded that SHSE is a more promising alternative for software defect number prediction.

Semi-supervised learning (SSL) has been studied widely in standard benchmark datasets; however, real-world data often exhibit class-imbalanced distributions, which pose significant challenges for deep semi-supervised models. To address this issue, we design a three-stage learning framework, 3LPR, by combining unsupervised feature extraction, graph-based Label Propagation, and mixed data augmentation (MDA)-based label Reassignment. Specifically, we first explore the performance of supervised and unsupervised learning for feature extraction of class-imbalanced data and then establish our first stage of feature extraction through unsupervised learning. Then, we adopt graph network-based offline label propagation and sieving to effectively expand the labeled set to overcome the excessive label bias in the classifier during the training process. Finally, we propose a label reassignment (LRA) algorithm for class-imbalanced semi-supervised learning (CISSL) to train the expanded dataset, where the MDA strategy is adopted but with the label reassigned. The experimental results demonstrate that the proposed 3LPR framework for CISSL outperforms other state-of-the-art methods on various datasets.

With the development of deep learning and the emergence of massive video data, object tracking has great application prospects in many fields. However, most tracking algorithms can hardly get top performance with real-time speed. In this paper, we improved tracking model based on adversarial learning and to accelerate feature extraction we proposed an efficient and accurate method. We also present a Precise ROI Pooling (PrROIPooling) based algorithm for extracting more accurate representations of targets. Furthermore, a novel regularization term is defined to ensure the similarity between the generated features and the real features. Finally, the improved objective function with modulating factors is designed to handle the problem of imbalance in the number of positive and negative samples. Extensive experiments on three datasets have demonstrated our effectiveness and achieved competitive results compared with state-of-the-art methods.

In this paper, we propose a novel tracking method by formulating tracking as a correlation filtering as well as a ridge regression problem. First, we develop a tight correlation filter-based tracking framework from the signal detection perspective. In this formulation, the correlation filter is set as the same size as the target, which can make full use of the relations of the adjacent image patches and effectively exclude the influence of the background. Specifically, we point out that the novel correlation filter model can be regarded as the ridge regression model which takes into account the different importance of the samples and has the consistent objective with tracking. Second, we focus on the scale variation problem in tracking. By making use of the spatial structure of the correlation filter, the multiscale filter banks can be generated via interpolation to handle the scale estimation problem easily. Third, we present a novel distance importance-based confidence calculation model to determine the final tracking result, which not only makes use of the fine discriminability of the correlation filter but also takes the distance importance of the candidate samples into account to alleviate the impact of similar distractors. Experimental results demonstrate that our method is superior to several state-of-the-art trackers and many other correlation filter-based methods in the benchmark datasets.

In the era of the Internet of Things (IoT), mobile edge computing (MEC) has become an effective solution to meet the energy efficiency and delay requirements of IoT applications. In MEC systems, tasks can be offloaded from lightweight mobile devices to edge nodes that are nearer to the users. To improve user experience, we combine remote loading and redirection to accelerate the service migration. By tracing historic access patterns, the proposed method first generates a loading request list that locates the core codes in the image file of service applications for booting. The core codes are then be prefetched and cached automatically. Furthermore, to avoid the potential UI lagging caused by incomplete service migration, edge nodes can continuously load the remaining codes in the image file. Once the image file is completely migrated, the file will be reconstructed. The running virtual machine (VM) then switches data access to the merged image file. Experiments show that this method can observably reduce the loading time of a VM-based application.

Resilience is the property that enables a system to continue operating properly when one or more faults occur. Nowadays, as software systems become more and more complex, their hardware execution platforms also become more heterogenous with larger scale. Software systems may fail due to some faults such as node breakdown, communication failure, or data processing failure. In this paper, we propose a ring-based resilience mechanism, which implements fault detection and recovery. (1) To solve the problem that the central server may have high burden of network traffic, we design a ring-based heartbeat algorithm for crash fault detection. (2) We also design a light-weight recovery mechanism to recover from crash faults as compared with the current system-specific mechanisms. To evaluate our mechanism, we use a 3D-online game system as a case study. By injecting faults, we test the effectiveness and overhead of the proposed mechanism. Compared with other mechanisms, the experimental results show that our mechanism can support resilience very well and is better at dealing with the crash fault caused by high cluster workload with acceptable overhead.

QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful technique in recent years. In this paper, we propose a novel two-phase neural network model to identify the untrustworthy Web services. In the first phase, Web services are collected from the published QoS dataset. Then, we design a feedforward neural network model to build the classifier for Web services with different QoS levels. In the second phase, we employ a probabilistic neural network (PNN) model to identify the untrustworthy Web services from each classification. The experimental results show the proposed approach has 90.5% identification ratio far higher than other competing approaches.

Resilience is an important factor in designing web service oriented systems due to frequent failures arising in runtime. These failures derive from the stochastic and uncertainty nature of a composite web service. Service providers need to rapidly address issue when a fault occurs in system running. But it is not easy to locate and fix the faults only using the log generated by the system. In this paper, we propose a resilient framework to automatically generate a fault handling strategy for each failed service to improve the efficiency of fault handling. In the framework, we design and implement three components including exception analyzer, decision maker, and strategy selector. First, The exception analyzer builds a record, derived from the system log generated by an application, for each failed service. Next, the decision maker adopts a k-means clustering approach to construct a decision including the fault handling to each failed service in a scope. Then, the strategy selector uses an integer program solver to generate the solution to strategy selection problem that is boiled down to the optimization problem. The experiment shows that the framework can improve resilience of Web service-oriented systems under acceptable overheads, and meanwhile the accuracy of fault handling strategy is over 95%.

Cloud computing has been a mainstream solution for the processing and storage of mass data, as well as an exciting area for research. As a novel business model, cloud computing has dramatically changed the provision of services and IT capacity by means of the advanced techniques. In recent years, with the increasing research interests and rapid growth of publications, some review papers provide detailed analysis on cloud computing area. In this paper, a bibliometric-based approach is presented and implemented to quantitatively review the progress in global cloud computing research with the related literature during 2007–2013 from the databases of Science Citation Index Expanded (SCI-E), Conference Proceedings Citation Index–Science (CPCI-S), and IEEEXplore. Our work is motivated by the purpose of tracing global advancement in terms of research content, geographic distribution and issue time of the related publications, rather than a specific technological area in cloud computing research. By investigating the characteristics of publications such as keywords, output, geographic distribution and affiliation, we draw some valuable conclusions to guide the further research. The experimental results show that the top 5 active research points of cloud computing concentrate on virtualization, security, mobile cloud, distributed computing, and scheduling. From the location-time aspect, China, USA, and India have published most of the papers, dominate cloud computing research and keep a high level on the international research cooperation. And there is a great increase in publication outputs especially in China and USA. Meanwhile, the analysis results demonstrate the top 3 high-cited research institutes of the University of Melbourne, University of California. Berkeley and University of Vienna in cloud computing research. The mobile cloud will be a future research hotspot and promising application field.

Crowd simulation explores crowd behavior in virtual environments, which has been extensively studied in many areas, such as safety and civil engineering, transportation, social science, and entertainment industry. In this paper, an improved potential field method is proposed to achieve the real-time crowd simulation, which is composed of the global navigation with Dijkstra's algorithm and the potential field based local navigation. First, a region separation is performed to divide the environment into a set of triangles, and thus a topological graph can be built with the triangles as vertices. Then a velocity-density model is introduced for improving the speed controlling mechanism and solving the "maximum speed dilemma" which means the velocity of an individual derived by potential field will be stuck into the maximum due to the ill speed control. Since the movement of an individual in the crowd is influenced by the socio-psychological forces, the individuals' actions express the group attributes. In order to represent the group attributes in the crowd, the repulsive potential function is improved in this paper. Experiments have been carried out and the results show that the improved potential field based method can simulate the crowd in real time and avoid the "maximum speed dilemma".