Antimicrobial peptides (AMPs) have been widely recognized as a promising solution to combat antimicrobial resistance of microorganisms due to the increasing abuse of antibiotics in medicine and agriculture around the globe. In this study, we propose UniAMP, a systematic prediction framework for discovering AMPs. We observe that feature vectors used in various existing studies constructed from peptide information, such as sequence, composition, and structure, can be augmented and even replaced by information inferred by deep learning models. Specifically, we use a feature vector with 2924 values inferred by two deep learning models, UniRep and ProtT5, to demonstrate that such inferred information of peptides suffice for the task, with the help of our proposed deep neural network model composed of fully connected layers and transformer encoders for predicting the antibacterial activity of peptides. Evaluation results demonstrate superior performance of our proposed model on both balanced benchmark datasets and imbalanced test datasets compared with existing studies. Subsequently, we analyze the relations among peptide sequences, manually extracted features, and automatically inferred information by deep learning models, leading to observations that the inferred information is more comprehensive and non-redundant for the task of predicting AMPs. Moreover, this approach alleviates the impact of the scarcity of positive data and demonstrates great potential in future research and applications.

Federated learning enables a large number of clients (such as edge computing devices) to learn a model jointly without data sharing. However, the high amount of communication of the federated learning aggregation algorithm hinders the realisation of artificial intelligence in the last mile. Although FederatedAveraging (FedAvg) is the leading algorithm, its communication cost is still high. The method of communication delay and gradient sparse can reduce the communication cost, but there is no previous work to analyse the relationship and common effects of these two dimensions. Aiming at the problems that federated learning communication is expensive and it has become a training bottleneck, we improve the FedAvg algorithm and propose an adaptive communication frequency FederatedAveraging algorithm (AFedAvg). The gradient sparse operation in the algorithm reduces the quantity of parameters for a single communication, while the communication delay operation allows training to converge faster and obtain smaller losses. The number of sparse parameters is used to select the communication frequency of next round dynamically. Experimental results prove that, the AFedAvg algorithm is superior to the FedAvg and its variants in terms of communication cost. It achieves 2.4X–23.1X communication compression in different data distributions with minimal communication rounds required by the algorithm to converge.

This paper presents a novel single-image object counting method based on block co-saliency density map estimation, called free-to-count everything network (F2CENet). Image block co-saliency attention is introduced to promote density estimation adaptation, allowing to input any image with arbitrary size for accurate counting using the learned model without requiring manually labeled few shots. The proposed network also outperforms existing crowd counting methods based on geometry-adaptive kernels in complex scenes. A novel module generates multilevel & scale block correlation maps to guide the co-saliency density map estimation. Co-saliency attention maps are then fused for accurately locating block-wise salient objects under guidance of the initial cues. Hence, accurate density maps are generated via comprehensive learning of internal relations in block co-salient features and progressive optimization of local details with saliency-oriented scene understanding. Results from extensive experiments on existing density map estimation datasets with arbitrary challenges verify the effectiveness of the proposed F2CENet and show that it outperforms various state-of-the-art few-shot and crowd counting methods. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used as evaluation metrics to measure the accuracy which are commonly used metrics for counting task. The average predicted MAE and RMSE are 10.88% and 8.44% less compared with the state-of-the-art evaluated on dataset contains sufficiently large and diverse categories used for few-shot and crowd counting.

Abstract Given the challenge of pear leaf disease recognition caused by uneven illumination, overlapping leaves, and other green plants in the background, a two‐stage strategy‐based framework for pear leaf segmentation and disease classification is proposed. Initially, a double branch polymerization net fusing features of the low‐level feature branch and semantic branch is constructed to extract the target diseased pear leaf and eliminate background interference. Then an improved lightweight neural network (Inverted‐Inception Efficient‐Excitation‐and‐Filtering‐Bottleneck MobileNet‐v2, I 2 EMv2) is used to capture multi‐scale lesion information and ainhibit invalid feature channels and filter redundant features for the final classification. The experimental results show that the proposed framework can accurately extract the pear leaf region with complete boundary from the complex background, maximize the retention of lesion information, and achieve high‐precision pear leaf disease identification. The mean absolute error, F‐measure, and intersection over union of leaf segmentation are 0.027, 0.947, and 0.880, respectively, and the average recognition accuracy of leaf disease is 92.05%. Compared with others, the method proposed in this paper has superior performance on segmentation and classification, which provides a reference for pear leaf disease classification under complex background.

The widely deployed devices in Internet of Things (IoT) have opened up a large amount of IoT data. Recently, federated learning emerges as a promising solution aiming to protect user privacy on IoT devices by training a globally shared model. However, the devices in the complex IoT environments pose great challenge to federate learning, which is vulnerable to gradient-based reconstruction attacks. In this paper, we discuss the relationships between the security of federated learning model and optimization technologies of decreasing communication overhead comprehensively. To promote the efficiency and security, we propose a defence strategy of federated learning which is suitable to resource-constrained IoT devices. The adaptive communication strategy is to adjust the frequency and parameter compression by analysing the training loss to ensure the security of the model. The experiments show the efficiency of our proposed method to decrease communication overhead, while preventing privacy data leakage.