CAAI Transactions on Intelligence Technology最新文献

Offline reinforcement learning (RL) aims to learn policies entirely from passively collected datasets, making it a data-driven decision method. One of the main challenges in offline RL is the distribution shift problem, which causes the algorithm to visit out-of-distribution (OOD) samples. The distribution shift can be mitigated by constraining the divergence between the target policy and the behaviour policy. However, this method can overly constrain the target policy and impair the algorithm's performance, as it does not directly distinguish between in-distribution and OOD samples. In addition, it is difficult to learn and represent multi-modal behaviour policy when the datasets are collected by several different behaviour policies. To overcome these drawbacks, the authors address the distribution shift problem by implicit policy constraints with energy-based models (EBMs) rather than explicitly modelling the behaviour policy. The EBM is powerful for representing complex multi-modal distributions as well as the ability to distinguish in-distribution samples and OODs. Experimental results show that their method significantly outperforms the explicit policy constraint method and other baselines. In addition, the learnt energy model can be used to indicate OOD visits and alert the possible failure.

Myocarditis is a significant public health concern because of its potential to cause heart failure and sudden death. The standard invasive diagnostic method, endomyocardial biopsy, is typically reserved for cases with severe complications, limiting its widespread use. Conversely, non-invasive cardiac magnetic resonance (CMR) imaging presents a promising alternative for detecting and monitoring myocarditis, because of its high signal contrast that reveals myocardial involvement. To assist medical professionals via artificial intelligence, the authors introduce generative adversarial networks - multi discriminator (GAN-MD), a deep learning model that uses binary classification to diagnose myocarditis from CMR images. Their approach employs a series of convolutional neural networks (CNNs) that extract and combine feature vectors for accurate diagnosis. The authors suggest a novel technique for improving the classification precision of CNNs. Using generative adversarial networks (GANs) to create synthetic images for data augmentation, the authors address challenges such as mode collapse and unstable training. Incorporating a reconstruction loss into the GAN loss function requires the generator to produce images reflecting the discriminator features, thus enhancing the generated images' quality to more accurately replicate authentic data patterns. Moreover, combining this loss function with other regularisation methods, such as gradient penalty, has proven to further improve the performance of diverse GAN models. A significant challenge in myocarditis diagnosis is the imbalance of classification, where one class dominates over the other. To mitigate this, the authors introduce a focal loss-based training method that effectively trains the model on the minority class samples. The GAN-MD approach, evaluated on the Z-Alizadeh Sani myocarditis dataset, achieves superior results (F-measure 86.2%; geometric mean 91.0%) compared with other deep learning models and traditional machine learning methods.

Cloud computing has drastically changed the delivery and consumption of live streaming content. The designs, challenges, and possible uses of cloud computing for live streaming are studied. A comprehensive overview of the technical and business issues surrounding cloud-based live streaming is provided, including the benefits of cloud computing, the various live streaming architectures, and the challenges that live streaming service providers face in delivering high-quality, real-time services. The different techniques used to improve the performance of video streaming, such as adaptive bit-rate streaming, multicast distribution, and edge computing are discussed and the necessity of low-latency and high-quality video transmission in cloud-based live streaming is underlined. Issues such as improving user experience and live streaming service performance using cutting-edge technology, like artificial intelligence and machine learning are discussed. In addition, the legal and regulatory implications of cloud-based live streaming, including issues with network neutrality, data privacy, and content moderation are addressed. The future of cloud computing for live streaming is examined in the section that follows, and it looks at the most likely new developments in terms of trends and technology. For technology vendors, live streaming service providers, and regulators, the findings have major policy-relevant implications. Suggestions on how stakeholders should address these concerns and take advantage of the potential presented by this rapidly evolving sector, as well as insights into the key challenges and opportunities associated with cloud-based live streaming are provided.

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target-search feature correlation by self and/or cross attention operations, thus the model complexity will grow quadratically with the number of input images. To alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer-based trackers, we propose a dual pooling transformer tracking framework, dubbed as DPT, which consists of three components: a simple yet efficient spatiotemporal attention model (SAM), a mutual correlation pooling Transformer (MCPT) and a multiscale aggregation pooling Transformer (MAPT). SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi-frame templates along space-time dimensions. MCPT aims to capture multi-scale pooled and correlated contextual features, which is followed by MAPT that aggregates multi-scale features into a unified feature representation for tracking prediction. DPT tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on TrackingNet while maintaining a shorter sequence length of attention tokens, fewer parameters and FLOPs compared to existing state-of-the-art (SOTA) Transformer tracking methods. Extensive experiments demonstrate that DPT tracker yields a strong real-time tracking baseline with a good trade-off between tracking performance and inference efficiency.

Due to the characteristics of high resolution and rich texture information, visible light images are widely used for maritime ship detection. However, these images are susceptible to sea fog and ships of different sizes, which can result in missed detections and false alarms, ultimately resulting in lower detection accuracy. To address these issues, a novel multi-granularity feature enhancement network, MFENet, which includes a three-way dehazing module (3WDM) and a multi-granularity feature enhancement module (MFEM) is proposed. The 3WDM eliminates sea fog interference by using an image clarity automatic classification algorithm based on three-way decisions and FFA-Net to obtain clear image samples. Additionally, the MFEM improves the accuracy of detecting ships of different sizes by utilising an improved super-resolution reconstruction convolutional neural network to enhance the resolution and semantic representation capability of the feature maps from YOLOv7. Experimental results demonstrate that MFENet surpasses the other 15 competing models in terms of the mean Average Precision metric on two benchmark datasets, achieving 96.28% on the McShips dataset and 97.71% on the SeaShips dataset.

The epidemic characters of Omicron (e.g. large-scale transmission) are significantly different from the initial variants of COVID-19. The data generated by large-scale transmission is important to predict the trend of epidemic characters. However, the results of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission. In consequence, these inaccurate results have negative impacts on the process of the manufacturing and the service industry, for example, the production of masks and the recovery of the tourism industry. The authors have studied the epidemic characters in two ways, that is, investigation and prediction. First, a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters. Second, the β-SEIDR model is established, where the population is classified as Susceptible, Exposed, Infected, Dead and β-Recovered persons, to intelligently predict the epidemic characters of COVID-19. Note that β-Recovered persons denote that the Recovered persons may become Susceptible persons with probability β. The simulation results show that the model can accurately predict the epidemic characters.

The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge-based learning systems. Smart healthcare systems leverage knowledge-based learning to become more context-aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X-rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge-based learning systems to foster structured decision-making and enhance the learning abilities of AI. Moreover, in knowledge-driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit-plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high-quality encryption, a large key space, key sensitivity, and resistance to statistical attacks.