With the rapid growth of cloud computing and distributed machine learning, traditional static networks increasingly struggle to meet dynamic communication demands. Optical Circuit Switching brings high bandwidth, low latency, and reconfigurability to data center networks, but the resulting reconfigurable data center networks (RDCNs) introduce dynamic path changes and heterogeneous paths, challenging conventional congestion control protocols. To address this, we propose DATCP, a dynamic and adaptive congestion control protocol designed for RDCNs. DATCP features three key mechanisms: (1) aTTL-based path classification method for accurate and lightweight identification of network types; (2) a receiver-side flow tracking model that detects incast congestion and applies weighted rate control; and (3) an adaptive congestion window algorithm that enables fast convergence and efficient bandwidth utilization. Experiments under synthetic and real-world workloads show that DATCP outperforms existing TCP variants, achieving higher throughput, reducing flow completion time and 99th percentile tail latency by up to 37.8% and 21.1%, respectively, and improving flow fairness.

Learning image compression has aroused active exploration and extensive research in recent years. However, the existing compression models fail to capture the complex texture representation of images adequately, causing the loss of detailed texture in reconstructed images. In this paper, we propose a feature enhanced learning image compression method. First, we introduce a novel recurrent criss-cross attention (RCCA), which could effectively extract the global features and contextual information of the input image. Next, we combine all the generalized divisive normalization (GDN) of the encoder to construct a pyramid feature fusion (PFF) module. Then, we introduce an enhanced inverted bottleneck residual blocks (Enh RBs) at the decoder to expand the high-dimensional channel space of texture detail representation. Finally, the experimental results show that our method can produce superior results compared to the state-of-the-art methods.

Versatile Video Coding (VVC/H.266) greatly improves the compression performance at the cost of extremely high computational complexity compared with High Efficiency Video Coding (HEVC/H.265). Within the context of mobile devices with limited power and computational capabilities, reductions on encoding complexity are important; particularly to encode new data formats such as screen content sequences. Therefore, in this paper, aimed at two brand-new techniques with high complexity, matrix-weighted intra-prediction (MIP) and intra-sub-partition (ISP), we propose a fast intra-prediction scheme for VVC screen content coding based on ultra-lightweight convolutional neural network (CNN). Firstly, an ultra-lightweight CNN is designed to segment the image into the natural content region (NCR) and the screen content region (SCR). Then, an adaptive intra-coding mode pruning scheme based on the ultra-lightweight CNN is proposed to accelerate the VVC screen content coding process. Finally, our proposed method was implemented into VTM-10.0 and experimental results show that our method can save averagely 7.68% and maximum to 13.62% of the encoding time without encoding performance loss.

In the field of mobile crowd sensing (MCS), worker selection is a key research issue and has progressively gained considerable interests in the academic community in recent years. The goal of worker selection is to choose the superior workers for tasks that require high-performance characteristics. To solve the problems of long delay and poor perceived quality, we present a worker selection architecture for a recommendation system applied to the MCS system. A worker selection algorithm with high quality of service (QoS) is designed within the architecture, which considers the worker’s reputation and willingness attributes to address the challenge of efficiently selecting excellent workers. Based on these two attributes, we then compute worker QoS and develop a three-dimensional tensor to optimize the worker’s service. Finally, we get a continuously updated list of workers. Extensive experiments on real-world datasets show that the proposed algorithm performs better than the benchmarks, including random, greedy, and matrix-based algorithm. The results indicate that the proposed algorithm’s efficiency has risen by 31% compared to the matrix-based algorithm.

Stock market charts guide investors in making financial decisions. Online stock market charts are largely interactive, driven by real-time financial data. However, these are not easily accessible via a screen reader. To enable screen reader users to query and effectively use interactive online stock market charts, we are developing a tool called ChartMaster. In this paper, we describe an early study conducted with sixteen visually impaired persons, most of whom were financial novices, for co-designing the interaction interface for C hartMaster. An inclusive design exercise was undertaken to discover alternative interfaces using non-visual modalities to interact with stock market charts. A user-centered process of co-design using HCI methods was carried out to iteratively evaluate and refine three input solutions: audio input, text input and dropdown menu. While the users ultimately declared the dropdown menu to be the most useful of the three solutions, they wanted all possible options to choose from based on task contexts and personal preferences. User feedback confirmed that a one-size-fits-all design is not ideal for accommodating diverse user needs within the widest possible range of contexts. It was also found that the ChartMaster tool with dropdown menu interface holds potential educational value for financial novices.

The effect of asymmetrical body-mass distribution on the stability and dynamics of two-degree-of-freedom quadruped bounding in place is investigated in this study. An analytical stability criterion for bounding of quadrupeds with asymmetrical mass distribution is developed. Bounding is found to be passively stable in the Hamiltonian sense when the dimensionless pitch moment of inertia of the body is less than 1-/spl beta//sup 2/, where /spl beta/ is a dimensionless measure of the asymmetry. The criterion is derived under the assumptions of infinite leg stiffness and no energy loss. With energy dissipation modeled as linear damping in the legs, simulation results show that the criterion is independent of the value of leg stiffness and a conservative estimate of the critical inertia value. Body symmetry appears to be more favorable to stable bounding than asymmetry, but only slightly so in practicality. The results show that asymmetry offers some advantages when the dynamic characteristics of bounding are compared for symmetrical and asymmetrical models. Lower stride frequency, larger vertical displacement, and smaller duty factor are obtained with this more animal-like model.

An analytical stability criterion for bounding of quadrupeds with asymmetrical mass distribution is developed in this work. Bounding is found to be passively stable when the dimensionless pitch moment of inertia of the body is less than 1 - /spl beta//sup 1/, where /spl beta/ is a dimensionless measure of the asymmetry. The criterion is derived under the assumptions of infinite leg stiffness and no energy loss. Simulation results show that the criterion is independent of the value of leg stiffness and a conservative estimate of the critical inertia value when energy losses are modeled with linear damping. Body symmetry appears to be more favorable to stable bounding than asymmetry, but only slightly so in practicality.