In recent years, the rapid development of artificial intelligence technology has significantly improved the performance of BERT-based deep learning models in text classification tasks . However, these models still have shortcomings in capturing the deep semantics of text, emerging text classification , and handling minority class samples in imbalanced data . To address these challenges, we proposed an innovative BERT-DXLMA hybrid model. This model enhances the semantic feature extraction and representation learning capabilities by integrating xLSTM, and uses semantic fusion technology to reduce the information loss caused by the increase in the number of network layers. In addition, we re-improved the loss function based on focal loss to improve the model’s attention to minority class samples and generalization ability . We conducted extensive experiments and in-depth analysis on six public datasets, and the results showed that BERT-DXLMA outperformed multiple baseline methods in terms of precision and overall accuracy, demonstrating its superiority in text classification tasks .

Measuring the sleep quality is important or even crucial for people who are engaged in dangerous jobs such as the high-speed train drivers. Since the scalp EEG data are generated by the neural activities of the brain cortex, it is collected from subjects with different hours of sleep time (4 hours, 6 hours and 8 hours) to conduct sleep quality evaluation. To suppress the cross-subject variances of EEG data, in this paper, we propose a joint feature auto-weighting and semi-supervised classification model, termed GRLSR, which is formulated by introducing an auto-weighting variable into the least square regression to adaptively and quantitatively measure the importance of each dimension of the feature. Once the model is solved, besides the measurement results, we can use the auto-weighting variable to 1) analyze the importance of each frequency band in sleep quality expression and 2) identify the capacity of different channels connecting to the sleep effect. Therefore, the proposed GRLSR is a pure data-driven computing model for EEG-based cross-subject sleep quality evaluation. Experimental results show its effectiveness.

Interactive applications like web browsing are sensitive to latency. Unfortunately, TCP consumes significant time in its start-up phase and loss recovery. Existing sender-side optimizations use more aggressive start-up strategies to reduce latency, but at the same time they harm safety in the sense that they can damage co-existing flows' performance and potentially the network's overall ability to deliver data. In this paper, we experimentally compare existing solutions' latency performance and more importantly, the trade-off between latency and safety at both the flow level and the application level. We argue that existing solutions are still operating away from the sweet spot on this trade-off plane. Based on the diagnosis of existing solutions, we introduce Halfback, a new short-flow transmission mechanism that operates on a better latency-safety trade-off point: Halfback achieves lower latency than the lowest latency previous solution and at the same time significantly better safety. As Halfback is TCP-friendly and requires only sender-side changes, it is feasible to deploy.

After more than two decades of evolution, TCP and its end host based modifications can still suffer from severely degraded performance under real-world challenging network conditions. The reason, as we observe, is due to TCP family's fundamental architectural deficiency, which hardwires packet-level events to control responses and ignores emprical performance. Jumping out of TCP lineage's architectural deficiency, we propose Performance-oriented Congestion Control (PCC), a new congestion control architecture in which each sender controls its sending strategy based on empirically observed performance metrics. We show through preliminary experimental results that PCC achieves consistently high performance under various challenging network conditions.

This paper presents the design of a novel architecture called CARE (Content-Aware Redundancy Elimination) that enables maximizing the informational value that challenged networks offer their users. We focus on emerging applications for situational awareness in disaster affected regions. Motivated by advances in computer vision algorithms, we propose to incorporate image similarity detection algorithms in the forwarding path of these networks. The purpose is to handle the large generation of redundant content. We outline the many issues involved in such a vision. With a Delay-Tolerant Network (DTN) setup, our simulations demonstrate that CARE can substantially boost the number of unique messages that escape the disaster zone, and it can also deliver them faster. These benefits are achieved despite the energy overhead needed by the similarity detectors.

For interactive networked applications like web browsing, every round-trip time (RTT) matters. We introduce ASAP, a new naming and transport protocol that reduces latency by shortcutting DNS requests and eliminating TCP's three-way handshake, while ensuring the key security property of verifiable provenance of client requests. ASAP eliminates between one and two RTTs, cutting the delay of small requests by up to two-thirds.

For interactive networked applications like web browsing, every round-trip time (RTT) matters. We introduce ASAP, a new naming and transport protocol that reduces latency by shortcutting DNS requests and eliminating TCP's three-way handshake, while ensuring the key security property of verifiable provenance of client requests. ASAP eliminates between one and two RTTs, cutting the delay of small requests by up to two-thirds.

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