Recently, social media platforms like Weibo have introduced mandatory IP address displays to enhance information evaluation, but their effectiveness remains unclear. In this study, we conducted an online experiment with 722 participants to examine the effectiveness of mandatory IP address displays under different conditions (none, matched, unmatched, unknown) on social media. This study investigated how these conditions influence users’ trust in social media information and whether this process is moderated by users’ patterns of social media usage. Results showed that matched IPs increased trust, unmatched IPs reduced it, and unknown IP labels were perceived as neutral. Frequent social media use and active participation correlated with higher trust in the presented information, indicating susceptibility to misinformation. Trust in technology also amplified trust in social media information under unknown or mismatched IPs. These findings highlight risks in over-relying on social media features, emphasizing the need for careful design to counter misinformation.

Falls are significant hazard to the health and safety of elderly individuals. Designing an alarm system capable of detecting fall events is crucial to mitigate these life-threatening risks. Compared to traditional methods, WiFi-based solutions have garnered extensive attention in recent years due to non-contact sensing nature and low-cost deployment advantages. While existing approaches have achieved high fall recognition accuracy, they still encounter inevitable delays in practice. These delays are mainly caused by limitations in signal segmentation, leading to recognition outcomes only after the activity has concluded. Moreover, existing methods overlook the continuity of human behaviors, reducing model robustness and increasing application constraints. To address these issues, this paper proposes PreFall, a WiFi-based fall detection method that achieves ongoing fall recognition before the activity ends. We adopt a deep model with attentional sentence embedding and propose a hybrid loss strategy. They are supposed to minimize the required sample observation for lower latency and guide the model to correctly detect fall events when multiple activities occur consecutively. Experimental results indicate that PreFall advances fall detection time by an average of 1.5 seconds and maintains a recognition accuracy of 88% when user behaves continuously.

Optical coherence tomography angiography (OCTA) is a non-invasive imaging technique that can reveal blood flow and the detailed structure of the retinal and choroidal vasculature. However, uneven image quality and the high complexity of blood vessel distribution bring great challenges to accurate blood vessel segmentation. In this paper, we propose an OCTA blood vessel segmentation network based on the classic encoder-decoder architecture called TTCNet, which integrates a dual-branch encoder to capture global context information effectively. In addition, Snake Detail Attention Block is designed to enhance the perception of the tubular structure of blood vessels and provide richer feature representation in the network. We also design a module named Pyramid Feature Attention Module in each level of skip connection to better extract feature information at different scales. Experiments are conducted on two public datasets, OCTA_6M and OCTA_3M. G-mean Score and Area Under the Curve of our network on these two datasets are 93.05, 93.18, and 95.21, 94.95%, respectively, achieving better results than other state-of-the-art methods.

Medical image segmentation’s accuracy is crucial for clinical analysis and diagnosis. Despite progress with U-Net-inspired models, they often underuse multi-scale encoding layers crucial for enhancing detailing visual features and overlooking the importance of merging multi-scale features within the channel dimension to enhance decoder complexity. To address these limitations, we introduce a dual-scale hybrid encoder network DSENet for medical image segmentation. Our network design is characterized by the strategic employment of dual-scale convolutional kernels at each encoder level, integrating the robust feature extraction capabilities of CNNs with the contextual awareness of Transformer models. This synergy enables the precise capture of both granular and broader context features throughout the encoding phase. We further enhance the model by integrating a channel attention fusion (CAF) mechanism within the skip connections. This innovation effectively integrates dual-scale features, subsequently integrating them with the same-level decoder feature map, thereby reinforcing the feature representation. To refine the predicted segmentation, we employ a novel strategy that merges dual-scale feature maps from the initial encoder stage with the segmentation map through a cascade operation. The output fusion feature map is then processed by a self-attention Transformer structure, ensuring a meticulous refinement of the segmentation output, preserving essential details, and enhancing segmentation accuracy. Our proposed DSENet has been evaluated on three distinct medical image datasets, and the experimental results demonstrate that it achieves more accurate segmentation performance and adaptability to varying target segmentation, making it more competitive compared to existing SOTA methods.

WiFi-based wireless sensing has gained popularity for enabling smart indoor services, one of which is fall detection which plays a vital role in mitigating health risks for elders. Previous approaches have treated daily activities as independent events and built models to distinguish falls from others. However, human activities are usually adjoint in practice, e.g., the elder may suddenly fall when she walks. This adjoining introduces shared features between different activities, thereby affecting the classification performance. To address this problem, we propose Fall-attention, an attention-based fall detection method that can focus on the features related to fall events and suppress interference of irrelevant activities to improve performance. Its basic idea is to produce a task-oriented feature representation of fall events inside the signal using attention-based sentence embedding techniques and Recurrent Neural Network (RNN). We incorporate multi-task learning into Fall-attention by adopting multiple independent classification modules. This enables the model to explore different regions of the signal, capturing the composition of adjoint activities. A series of signal preprocessing and data enhancement techniques are also adopted to promote model training. Experimental results of the dataset containing adjoint activities demonstrate the superiority of Fall-attention over previous methods, which achieves an average accuracy of 95%.

Abstract Optical coherence tomography angiography (OCTA), as a noninvasive imaging modality, has been widely used in clinical ophthalmology. However, the segmentation of retinal vessels in OCTA is under‐studied due to OCTA is a relatively new technology. In this article, an effective feature extraction network, OVS‐Net, is proposed for OCTA vessel segmentation. The OVS‐Net is divided into coarse stage and refine stage which structures are basically the same. In each stage, we utilize OctaveResBlock as the basic block to better extract the hierarchical multifrequency features of OCTA and capture the multiscale semantic features of the vessels. In order to improve the feature characterization, feature enhanced attention block is introduced into the network, which is proved to be more conducive for microvessel segmentation in our experiments. Multiscale feature blocks are introduced into the network to promote the deep integration of semantic features at different scales. Experiments on OCTA‐SS and OCTA‐500 datasets show that our proposed OVS‐Net achieves more competitive segmentation results than the existing methods, especially for microvessel segmentation.

Along with the penetration of smart devices and mobile applications in our daily life, how to effectively manage the mobility issues in wireless networks becomes a challenging task. The ability to continuously and accurately track the target object’s position plays a vital role in mobility management. In this paper, we propose a novel indoor localization algorithm that fuses multiple signal features as the location fingerprints. The rationale that motivates our algorithm design stems from the following observation: although using one special signal feature (e.g., channel state information (CSI)) might achieve statistically higher accuracy than using another signal feature (e.g., received signal strength (RSS)), the accuracy for individual position estimations is usually diversified when only one signal feature is used in localization. For example, using RSS can obtain more accurate location estimation than using CSI for some individual positions. Thus, we propose a novel indoor localization algorithm that fuses multiple types of signal features as fingerprint of positions, which can effectively improve localization accuracy. We designed several fusion schemes and evaluated their performance. Experiments show that our algorithm achieves localization error below 0.5m and 1.1m in two typical indoor environments, about 30% lower than the accuracy of algorithms by fusing multiple signal features.

Cognitive radio technique improves spectrum utilization by allowing secondary users (SUs) to opportunistically exploit the authorized spectrum of primary users (PUs) to transmit their data. In order to avoid colliding with PU, SUs often sense the channel before transmission. As many secondary users are mobile devices, how to preserve energy consumption of the secondary users is considered in this paper. According to a defined utility function, SU dynamically schedules its actions, i.e., sensing, transmission or sleeping. the utility function is optimized based on the reward and penalty of sleeping actions. Those sleeping actions obeying the rule will be rewarded and those opposite actions will be penalized. SU schedules its action of sensing and sleeping so as to reach a tradeoff between the minimum energy consumption and PU's protection. By maximizing the mean benefit, the proposed strategy exhibits a simple threshold-based structure. Simulation results show that the proposed scheme can greatly reduce energy cost and achieve similar PU collision rate as previous works, with only negligible throughput reduction.