Abstract With the increment of the scale of data centers, device failures are inevitable, which requires high reliability of underlying network. Recursively defined structures can construct large-scale and high-performance data center networks (DCNs), where the investigation of reliability in this group of structures is insufficient as in the representative DPCell. In this paper, a comprehensive research on reliability of DPCell structure was carried out, including the diagnosability based on precise and pessimistic diagnostic strategies, $h$-extra connectivity, and $h$-extra conditional diagnosability. Let $r$ denote the number of layers in a DPCell structure. Let $k_{i}$ denote the degree used to connect to a vertex in the $i$th layer with $0 \leq i \leq r$. Let $h$ denote a scalar, where each connected component contains at least $h+1$ vertices in $G-F$. This research proves that (i) under the precise diagnosis strategy, ${DPCell}_{r}$ is $\sum _{i=0}^{r}k_{i}$-diagnosable for $r\geq 2$; (ii) under the pessimistic diagnosis strategy, ${DPCell}_{r}$ is $\left(\frac{k_{0}-1}{2}\right)/\left(\frac{k_{0}-1}{2}\right)$-diagnosable and $(\sum _{i=1}^{r}k_{i}-1)/(\sum _{i=1}^{r}k_{i}-1)$-diagnosable for $r\geq 0$; (iii) the $h$-extra connectivity $\kappa _{h}({DPCell}_{r})=(h+1)(\sum _{i=1}^{r}k_{i}-1)+k_{0}+1$ for $r\geq 1$ and $0\le h\le k_{0}$; and (iv) the $h$-extra conditional diagnosability $\widetilde{t_{h}^{P}}({DPCell}_{r})=(h+1)\sum _{i=1}^{r}k_{i}+k_{0}$ for $r\geq 2$ and $0\le h\le k_{0}$. Based on the research of connectivity and diagnosability, the reliability of DPCell structure can be evaluated quantitatively. This research results in a comprehensive understanding of the reliability of DPCell, which contributes to network performance improvement of recursively defined structures.

Marine mammals are crucial subjects for marine audio studies, particularly in the development of retrieval algorithms for encrypted marine mammal audio.To address the issues of low retrieval performance and high content redundancy, and to ensure security of existing marine mammal audio retrieval algorithms, this paper proposes an encrypted marine mammal audio retrieval algorithm based on biohashing. The server terminal first extracts the sub-band CQT energy-entropy ratio of audio to construct biometric dataset. by querying the key and corresponding classified biometric in the key-address index table constructed based on SVM classification, the key-controlled orthogonal matrix and the corresponding classified biometric generate the biosafety template, and the template is quantified to obtain biohashing. Finally, the dual-threshold audio segmentation and position mapping are adopted to perform hash reconstruction to obtain the hash index. Meanwhile, the Logistic-AES encryption method encrypts audio clips to construct the encrypted audio library. Experimental results demonstrate that sub-band CQT energy-entropy ratio has better robustness and discrimination, which results in the higher precision and recall of the retrieval system. And the proposed audio segmentation algorithm reduces the redundant part of audio, improving the efficiency of retrieval system. Meanwhile, the designed encryption method is sensitive to the key and disrupts audio correlation, which ensures the resistance of encrypted audio to statistical attacks. Furthermore, the biosafety template of biohashing has better recoverability, security and diversity.

Text-to-image (T2I) models enable users worldwide to create high-definition and realistic images through text prompts, where the underrepresentation and potential misinformation of images have raised growing concerns. However, few existing works examine cultural representativeness, especially involving whether the generated content can fairly and accurately reflect global cultures. Combining automated and human methods, we investigate this issue in multiple dimensions quantificationally and conduct a set of evaluations on three prevailing T2I models (DALL-E v2, Stable Diffusion v1.5 and v2.1). Introducing attributes of cultural cluster and subject, we provide a fresh interdisciplinary perspective to bias analysis. The benchmark dataset UCOGC is presented, which encompasses authentic images of unique cultural objects from global clusters. Our results reveal that the culture of a disadvantaged country is prone to be neglected, some specified subjects often present a stereotype or a simple patchwork of elements, and over half of cultural objects are mispresented.

The massive demand for marine exploitation has promoted the thriving Internet of Underwater Things (IoUT). The volume, velocity, and variety (3V) of data produced by sensors, hydrophones, and cameras in IoUT are enormous, which challenges the network in achieving load balancing and Quality-of-Service (QoS) provisioning. This article adopts the “SDN+AI” paradigm to realize a load-balanced and QoS-aware software-defined IoUT from a framework design. We first introduce SDN technology to separate the data plane from the control plane to enhance the network’s scalability and flexibility. Then, a multicontroller load-balancing strategy based on switch migration called CASM is proposed to improve the network’s performance further. With the global view provided by SDN controllers, we proposed a QoS-aware adaptive routing protocol (SQAR) based on reinforcement learning, which can intelligently select route paths to satisfy the QoS requirements of multiple IoUT services. The results show that CASM achieves an efficient load balance while shortening the response time and average control path latency of the switch migration process, which significantly benefits our routing protocol. SQAR outperforms the existing QoS-aware routing protocols regarding QoS satisfaction probability, energy consumption, and convergence rate. Overall, our framework maintains a QoS violation rate below 5% and a load-balancing rate above 90% in a timely manner.

Federated Mobile CrowdSensing is applied to collect massive sensory data and exploits the computing power of mobile devices brought by their embedded specialized computing engines (e.g., Neural Engine in iPhone) to train machine learning (ML) models. However, the heterogeneity of mobile devices includes significant differences in the size and quality of datasets, different computing power, and some unreliable clients using unreliable data for training. The heterogeneity of mobile devices reduces FL’s performance. Therefore, selecting high-quality clients for Federated learning (FL) is vital. This study proposes a client selection scheme based on the reverse auction. First, each client’s training time is predicted, the total FL time threshold is optimized, and the reputation value is calculated based on the historical performance of each client. Then, each client’s current computing power and dataset size are converted into an efficiency value. Finally, the selection value of each client is calculated based on the efficiency value and reputation value. The results of the experiments show that our scheme can select high-quality clients. Compared with FedRep, our scheme can reduce training time by 91.5%. Compared with FedEff, our scheme can reduce communication rounds by 87.5%. In the same communication rounds (5000), our scheme has higher accuracy than RandomFL, and the average accuracy is improved by about 4.4%.

The flexible architectures address the determined server population in the rigid designs of data centers networks. However, the asymmetrical connection of flexible architectures will introduce load imbalance. To address this issue, we take flexibility and symmetry into consideration and propose ComCell. ComCell is recursively constructed on the division of ports in a switch, where all switches play an equal role. By exploring the interconnection properties, we propose divide-and-conquer path and fault-tolerance routing on the proposed architecture. ComCell benefits data canter networks by flexible design space, double-exponential scalability and good fault tolerance, as well as high network performance. Evaluations under various traffic loadings demonstrate the performance of ComCell in routing delay and spreading out network congestion.