The rapid evolution of cloud computing, big data, and Artificial Intelligence (AI) technologies has created an urgent demand for ultra-high bandwidth and ultra-low latency in modern data centers. Although Remote Direct Memory Access (RDMA) technology has been widely adopted, traditional congestion control algorithms such as Priority-based Flow Control (PFC) and Data Center Quantized Congestion Notification (DCQCN) face significant challenges in scalability, deployment flexibility, and tail latency management in complex network environments. To address these limitations, we propose SFCC, a sender-driven rate control solution based on Round-Trip Time (RTT) that requires no switch configuration. SFCC incorporates an adaptive Additive Increase Multiplicative Decrease (AIMD) control loop for enhanced scalability and latency reduction, an RTT re-estimation mechanism to correct deviations, dynamic RTT calculation for optimized queue management, and Negative Acknowledgment (NAK) signal utilization for fast convergence during packet loss. Implemented on commercial RDMA Network Interface Cards (NICs) using a Programmable Congestion Control (PCC) platform, SFCC has been evaluated through small-scale testbeds, storage network testbeds, and NS-3 simulations of ultra-large-scale incast scenarios. Experimental results demonstrate that SFCC significantly improves throughput while reducing switch queue lengths, decreases the 99th percentile tail latency by 55.2 %, maintains high reliability under packet loss conditions, and achieves up to$\mathbf{7 5. 4 \%}$reduction in flow completion time for small flows compared to DCQCN, along with faster convergence speed.

Large Language Model (LLM) training increasingly relies on Remote Direct Memory Access (RDMA) to enable ultra-efficient networking. However, the unique traffic characteristics—sparse yet bandwidth-intensive—often lead to severe load imbalance under Equal-Cost Multi-Path (ECMP) routing. Packet Spraying (PS) offers a promising solution by distributing traffic across multiple paths, but its impact on congestion dynamics remains insufficiently studied. This paper presents a comprehensive study of PS in Artificial Intelligence (AI) clusters using NS-3 simulations, analyzing its effects on congestion distribution, packet reordering, and flow completion time. Our findings show that congestion patterns vary significantly with workload intensity and oversubscription ratios, and existing congestion control schemes are inadequate for general PS networks, where both routing paths and congestion hotspots frequently change. To address this gap, we propose APSCC, a congestion control algorithm that infers congestion locations from out-of-order packets and aggregates Explicit Congestion Notification (ECN) signals across paths for precise rate adaptation. Compared to state-of-the-art mechanisms, APSCC reduces Job Completion Time (JCT) by up to 30 %. The implementation is publicly available at https://github.com/tangjianback/APSCC.

Human visual system has a limitation of sensitivity in detecting small distortion in an image/video and the minimum perceptual threshold is so called Just Noticeable Difference (JND). JND modelling is challenging since it highly depends on visual contents and perceptual factors are not fully understood. In this paper, we propose deep learning based JND and perceptual quality prediction models, which are able to predict the Satisfied User Ratio (SUR) and Video Wise JND (VWJND) of compressed videos with different resolutions and coding parameters. Firstly, the SUR prediction is modeled as a regression problem that fits deep learning tools. Then, Video Wise Spatial SUR method (VW-SSUR) is proposed to predict the SUR value for compressed video, which mainly considers the spatial distortion. Thirdly, we further propose Video Wise Spatial-Temporal SUR (VW-STSUR) method to improve the SUR prediction accuracy by considering the spatial and temporal information. Two fusion schemes that fuse the spatial and temporal information in quality score level and in feature level, respectively, are investigated. Finally, key factors including key frame and patch selections, cross resolution prediction and complexity are analyzed. Experimental results demonstrate the proposed VW-SSUR method outperforms in both SUR and VWJND prediction as compared with the state-of-the-art schemes. Moreover, the proposed VW-STSUR further improves the accuracy as compared with the VW-SSUR and the conventional JND models, where the mean SUR prediction error is 0.049, and mean VWJND prediction error is 1.69 in quantization parameter and 0.84 dB in peak signal-to-noise ratio.

Crowdsourcing as an important computing resource for the Crowd-based Cooperative, crowdsourcing workers, due to the complexity of their personnel composition and the ambiguity of group characteristics, make it difficult to accurately recommend suitable worker groups for the task. Recommending crowdsourcing tasks to suitable workers can greatly improve the efficiency and quality of the execution of crowdsourcing tasks. The current crowdsourcing recommendation algorithm is based on the single characteristics of the workers, ignoring the multi-dimensional characteristics of the workers and the community characteristics of the crowd. This paper proposes a worker group recommendation method based on multi-community collaboration (GRMBC) by utilizing the worker characteristics information extracted from crowdsourcing platform. Based on the characteristics of workers'reputation, preference and activity, the method divides the worker group into several characteristics communities with similar behavior to discover the potential multi-community structure in the crowdsourcing worker group, and then selects Top-N worker group for recommendation through the interaction among multi-communities. At the same time, we also proposed two mitigation strategies for the cold start problem of data in crowdsourcing. This paper uses the public data collected by AMT to do the experiments, and compares the aggregation results of the recommendations generated by different algorithms. The results show the recommendations generated by the GRBMC algorithm proposed in this paper performs the best comprehensively. When the GRBMC algorithm that considers both worker attributes and communi-ty characteristics has an accuracy improvement of 0.03 ~ 0.04 compared with not using the recommendation algorithm on each index, the accuracy of 0.01 ~ 0.02 is improved com-pared with the method considering single characteristic. Moreover, compared with the individual recommendation method of the workers, the recommendation method of the workers' group is more in line with the platform requirements, and can well reflect the wisdom of crowd wisdom.

Although the accuracy of load forecasting has been studied by many works, the actual deployability of a model is rarely considered. In this work, we consider the actual deployability of a model from four aspects: 1) the prediction performance of the model; 2) the robustness of the model; 3) the dependence of the model on external data; and 4) the storage size of the model. From these four aspects, we propose a multiple wavelet convolutional neural network (MWCNN) for load forecasting. On two public data sets, we verified the performance and robustness of the MWCNN. The MWCNN only uses load data, and the storage size of the model is only 497 kB, which shows that MWCNN has good deployability. In addition, our MWCNN prediction results are interpretable. The experimental results show that the MWCNN can effectively capture the periodic characteristics of load data.

Pull Request (PR) is a major contributor to external developers of open-source projects in GitHub. PR reviewing is an important part of open-source software developments to ensure the quality of project. Recommending suitable candidates of reviewer to the new PRs will make the PR reviewing more efficient. However, there is not a mechanism of automatic reviewer recommendation for PR in GitHub. In this paper, we propose an automatic core-reviewer recommendation approach, which combines PR topic model with collaborators in the social network. First PR topics will be extracted from PRs by the latent Dirichlet allocation, and then the collaborator–PR network will be constructed with the connection between collaborators and PRs, and the influence of each collaborator will be calculated via the improved PageRank algorithm which combines with HITS. Finally, the relationship between topics and collaborators will also be built by the history of PR reviewing. When a new PR presents, a collaborator will be chosen as a core reviewer according to the influence of collaborators and the relationship between the new PR and collaborators. The experiment results show in the matching score calculation processing, the influence of collaborators shows higher than that with the expert, and the recommendation precision is better than 70%.

Picture Wise Just Noticeable Difference (PW-JND), which accounts for the minimum difference of a picture that human visual system can perceive, can be widely used in perception-oriented image and video processing. However, the conventional Just Noticeable Difference (JND) models calculate the JND threshold for each pixel or sub-band separately, which may not reflect the total masking effect of a picture accurately. In this paper, we propose a deep learning based PW-JND prediction model for image compression. Firstly, we formulate the task of predicting PW-JND as a multi-class classification problem, and propose a framework to transform the multi-class classification problem to a binary classification problem solved by just one binary classifier. Secondly, we construct a deep learning based binary classifier named perceptually lossy/lossless predictor which can predict whether an image is perceptually lossy to another or not. Finally, we propose a sliding window based search strategy to predict PW-JND based on the prediction results of the perceptually lossy/lossless predictor. Experimental results show that the mean accuracy of the perceptually lossy/lossless predictor reaches 92%, and the absolute prediction error of the proposed PW-JND model is 0.79 dB on average, which shows the superiority of the proposed PW-JND model to the conventional JND models.

Massive Sequence View(MSV) is an important timeline-based technique for dynamic network visualization. However, it often suffers from severe visual clutter when limited screen space holds excessive network edges. Inspired by the use of graph sampling in static graph analysis, we propose to utilize graph sampling to reduce visual clutter in MSV. An edge sampling method based on accept-reject random sampling is designed for visualizing dynamic network via MSV. The method is able to improve the overall readability of MSV while preserving time varying network behaviors. It is also a preliminary attempt to apply graph-sampling technique into dynamic network analysis.

Pull Request (PR) is the main method for code contributions from the external contributors in GitHub. PR review is an essential part of open source software developments to maintain the quality of software. Matching a new PR for an appropriate integrator will make the PR reviewing more effective. However, PR and integrator matching are now organized manually in GitHub. To make this process more efficient, we propose a Topic-based Integrator Matching Algorithm (TIMA) to predict highly relevant collaborators(the core developers) as the integrator to incoming PRs . TIMA takes full advantage of the textual semantics of PRs. To define the relationships between topics and collaborators, TIMA builds a relation matrix about topic and collaborators. According to the relevance between topics and collaborators, TIMA matches the suitable collaborators as the PR integrator.

The percentage of individuals frequently using their smartphones in work and life is increasing steadily. The interactions between individuals and their smartphones can produce large amounts of usage data, which contain rich information about smartphone owners usage habits and their daily life. In this paper, a visual analytic tool is proposed to discover and understand individual behavior patterns in smartphone usage data. Four cooperated visualization views and many interactions are provided in this tool to visually explore the temporal features of various interactive events between smartphones and their users, the hierarchical associations among event types, and the detailed distributions of massive event sequences. In the case studies, plenty of interesting patterns are discovered by analyzing the data of two smartphone users with different usage styles.

Network security depends heavily on automated Intrusion Detection Systems (IDS) to sense malicious activities. Unfortunately, IDS often generates both too much raw information and a large number of false positive alerts. Information visualization research has been performed to help users discover and analyze information through visual exploration efficiently. Even with the aid of visualization, identifying the attack patterns and recognizing the false positives from a great number of alerts are still challenges. In this paper, we present a novel visualization framework for IDS alerts that can monitor the network and perceive the overall view of the security situation using radial graph in real-time. The framework utilizes five categories of entropy functions to quantitatively analyze the irregular behavioral patterns, and synthesizes interactions, filtering and drill-down to detect the potential intrusions. In conclusion, we describe how this framework was used to analyze the mini-challenges of the 2011 and 2012 VAST challenge.

Cluster ensemble is an effective method to improve the effect in data clustering, but the results of the existing cluster ensemble algorithms are usually not so good when they process the mixed attributes datas, the main reason is that the results of the algorithms are still dispersed. To solve this problem, this paper presents a new weighted cluster ensemble algorithm based on graph theory. It first clusters the datasets and gets cluster members, and then sets weights to each data object with a proposed ensemble function, and determines the relationship between the data-pair by setting weights to the edges between them, so it can get a weighted nearest neighbor graph. At last it does a last-clustering based on graph theory. Experiments show that the accuracy and stability of this cluster ensemble algorithm is better than other clustering ensemble algorithms.