Deep learning demonstrates impressive performance in many medical image analysis tasks. However, its reliability builds on the labeled medical datasets and the assumption of the same distributions between the training data (source domain) and the test data (target domain). Therefore, some unsupervised medical domain adaptation networks transfer knowledge from the source domain with rich labeled data to the target domain with only unlabeled data by learning domain-invariant features. We observe that conventional adversarial-training-based methods focus on the global distributions alignment and may overlook the class-level information, which will lead to negative transfer. In this paper, we attempt to learn the robust features alignment for the cross-domain medical image analysis. Specifically, in addition to a discriminator for alleviating the domain shift, we further introduce an auxiliary classifier to achieve robust features alignment with the class-level information. We first detect the unreliable target samples, which are far from the source distribution via diverse training between two classifiers. Next, a cross-classifier consistency regularization is proposed to align these unreliable samples and the negative transfer can be avoided. In addition, for fully exploiting the knowledge of unlabeled target data, we further propose a within-classifier consistency regularization to improve the robustness of the classifiers in the target domain, which enhances the unreliable target samples detection as well. We demonstrate that our proposed dual-consistency regularizations achieve state-of-the-art performance on multiple medical adaptation tasks in terms of both accuracy and Macro-F1-measure. Extensive ablation studies and visualization results are also presented to verify the effectiveness of each proposed module. For the skin adaptation results, our method outperforms the baseline and the second-best method by around 10 and 4 percentage points. Similarly, for the COVID-19 adaptation task, our model achieves consistently the best performance in terms of both accuracy (96.93%) and Macro-F1 (86.52%).

The security and confidentiality of medical images are of utmost importance due to frequent issues such as leakage, theft, and tampering during transmission and storage, which seriously impact patient privacy. Traditional encryption techniques applied to entire images have proven to be ineffective in guaranteeing timely encryption and preserving the privacy of organ regions separated from the background. In response, this study proposes a specialized and efficient local image encryption algorithm for the medical field. The proposed encryption algorithm focuses on the regions of interest (ROI) within massive medical images. Initially, the Laplacian of Gaussian operator and the outer boundary tracking algorithm are employed to extract the binary image and achieve ROI edge extraction. Subsequently, the image is divided into ROI and ROB (regions outside ROI). The ROI is transformed into a row vector and rearranged using the Lorenz hyperchaotic system. The rearranged sequence is XOR with the random sequence generated by the Henon chaotic map. Next, the encrypted sequence is arranged according to the location of the ROI region and recombined with the unencrypted ROB to obtain the complete encrypted image. Finally, the least significant bit algorithm controlled by the key is used to embed binary image into the encrypted image to ensure lossless decryption of the medical images. Experimental verification demonstrates that the proposed selective encryption algorithm for massive medical images offers relatively ideal security and higher encryption efficiency. This algorithm addresses the privacy concerns and challenges faced in the medical field and contributes to the secure transmission and storage of massive medical images.

Distantly supervised relation extraction is an automatically annotating method for large corpora by classifying a bound of sentences with two same entities and the relation. Recent works exploit sound performance by adopting contrastive learning to efficiently obtain instance representations under the multi-instance learning framework. Though these methods weaken the impact of noisy labels, it ignores the long-tail distribution problem in distantly supervised sets and fails to capture the mutual information of different parts. We are thus motivated to tackle these issues and establishing a long-tail awareness contrastive learning method for efficiently utilizing the long-tail data. Our model treats major and tail parts differently by adopting hyper-augmentation strategies. Moreover, the model provides various views by constructing novel positive and negative pairs in contrastive learning for gaining a better representation between different parts. The experimental results on the NYT10 dataset demonstrate our model surpasses the existing SOTA by more than 2.61% AUC score on relation extraction. In manual evaluation datasets including NYT10m and Wiki20m, our method obtains competitive results by achieving 59.42% and 79.19% AUC scores on relation extraction, respectively. Extensive discussions further confirm the effectiveness of our approach.

Human activity recognition (HAR) aims to collect time series through wearable devices to precisely identify specific actions. However, the traditional HAR method ignores the activity variances among individuals, which will cause low generalization when applied to a new individual and indirectly enhance the difficulties of personalized HAR service. In this paper, we fully consider activity divergence among individuals to develop an end-to-end model, the multi-source unsupervised co-transfer network (MUCT), to provide personalized activity recognition for new individuals. We denote the collected data of different individuals as multiple domains and implement deep domain adaptation to align each pair of source and target domains. In addition, we propose a consistent filter that utilizes two heterogeneous classifiers to automatically select high-confidence instances from the target domain to jointly enhance the performance on the target task. The effectiveness and performance of our model are evaluated through comprehensive experiments on two activity recognition benchmarks and a private activity recognition data set (collected by our signal sensors), where our model outperforms traditional transfer learning methods at HAR.

Body fluid biomarkers are very important, because they can be detected in a non-invasive or minimally invasive way. The discovery of secreted proteins in human body fluids is an essential step toward proteomic biomarker identification for human diseases. Recently, many computational methods have been proposed to predict secreted proteins and achieved some success. However, most of them are based on a manual negative dataset, which is usually biased and therefore limits the prediction performances. In this paper, we first propose a novel positive-unlabeled learning framework to predict secreted proteins in a single body fluid. The secreted protein discovery in a single body fluid is transformed into multiple binary classifications and solved via multi-task learning. Also, an effective convolutional neural network is employed to reduce the overfitting problem. After that, we then improve this framework to predict secreted proteins in multiple body fluids simultaneously. The improved framework adopts a globally shared network to further improve the prediction performances of all body fluids. The improved framework was trained and evaluated on datasets of 17 body fluids, and the average benchmarks of 17 body fluids achieved an accuracy of 89.48%, F1 score of 56.17%, and PRAUC of 58.93%. The comparative results demonstrate that the improved framework performs much better than other state-of-the-art methods in secreted protein discovery.

Aggregation operators (AOs) are utilized to overcome the effects of attributes under some specific degree of weight in the decision-making (DM) process. The AOs have a large capacity to deal with uncertain and unpredictable information in multi-attribute decision-making (MADM) problems. The Hamy mean (HM) aggregation tools are well-known aggregation models, which are utilized to define correlation among different input arguments adequately. The intuitionistic fuzzy (IF) sets (IFS) can express unpredictable and vague information. The Aczel Alsina aggregation expressions are extensions of triangular norms. Recently, Aczel Alsina aggregation tools attained a lot of attentions from numerous research scholars. By inspiring the robustness and reliability of Aczel Alsina aggregation tools, we expose some appropriate operations of Aczel Alsina expressions under consideration of IF information. In this manuscript, we developed an intuitionistic fuzzy Aczel Alsina HM (IFAAHM) and an intuitionistic fuzzy Aczel Alsina weighted HM (IFAAWHM) operator. We also expressed the theory of Dual HM (DHM) tools and established a series of new approaches including intuitionistic fuzzy Aczel Alsina Dual HM (IFAADHM) and intuitionistic fuzzy Aczel Alsina weighted Dual HM (IFAAWDHM) operators. Some reliable characteristics and special cases of our derived approaches are also presented. The authors solved an application of a MADM technique under consideration of our derived approaches. To check the reliability and dependency of our derived mythologies, we gave an experimental case study to evaluate a desirable construction material based on some specific criteria of different Alternatives. To see the advantages and compatibility of our derived approaches, by comparing the results of existing approaches with the results of currently discussed AOs.

Although the short-text retrieval model by BERT achieves significant performance improvement, research on the efficiency and performance of long-text retrieval still faces challenges. Therefore, this study proposes an efficient long-text retrieval model based on BERT (called LTR-BERT). This model achieves speed improvement while retaining most of the long-text retrieval performance. In particular, The LTR-BERT model is trained by using the relevance between short texts. Then, the long text is segmented and stored off-line. In the retrieval stage, only the coding of the query and the matching scores are calculated, which speeds up the retrieval. Moreover, a query expansion strategy is designed to enhance the representation of the original query and reserve the encoding region for the query. It is beneficial for learning missing information in the representation stage. The interaction mechanism without training parameters takes into account the local semantic details and the whole relevance to ensure the accuracy of retrieval and further shorten the response time. Experiments are carried out on MS MARCO Document Ranking dataset, which is specially designed for long-text retrieval. Compared with the interaction-focused semantic matching method by BERT-CLS, the MRR@10 values of the proposed LTR-BERT method are increased by 2.74%. Moreover, the number of documents processed per millisecond increased by 333 times.

Promoting cooperation in the network has always been a focus of evolutionary game dynamics research. In this paper, a three-strategy local decision model is designed to promote cooperative behavior between agents. The hide right is introduced to accurately describe the agents which tend to choose conservative strategy to achieve stable payoffs. Agents only consider their own and non-defective neighbors’ payoffs information to make their decisions for next move. According to the adaptive dynamics, the equilibrium states under different conditions have been studied. It is found that the introduction of hide strategy helps to reduce the occurrence of defection. The conclusion of this paper will provide a reference for the cooperation promotion mechanism of selfish agents that are closer to the actual situation.

Supply chain digital twin has emerged as a powerful tool in studying the behavior of an actual supply chain. However, most studies in the field of supply chain digital twin have only focused on what-if analysis that compares several different scenarios. This study proposes a data-driven evolutionary algorithm to efficiently solve the service constrained inventory optimization problem using historical data that generated by supply chain digital twins. The objective is to minimize the total costs while satisfying the required service level for a supply chain. The random forest algorithm is used to build surrogate models which can be used to estimate the total costs and service level in a supply chain. The surrogate models are optimized by an ensemble approach-based differential evolution algorithm which can adaptively use different search strategies to improve the performance during the computation process. A three-echelon supply chain digital twin on the geographic information system (GIS) map in real-time is used to examine the efficiency of the proposed method. The experimental results indicate that the data-driven evolutionary algorithm can reduce the total costs and maintain the required service level. The finding suggests that our proposed method can learn from the historical data and generate better inventory policies for a supply chain digital twin.

In this paper, a novel Adaptive Parameter Strategy Differential Evolution (APSDE) algorithm is proposed to overcome the parameters dependence and avoid local optima. The Parameter Update Mechanism (PUM), which has three different strategies, is used to reduce the dependence on parameters of DE. The Adaptive Proportion Adjustment Mechanism (APAM) is used to balance the proportion of PUM strategies in different development terms of exploitation and exploration, and the Random Restart Mechanism (RRM) is used to improve population diversity when exploitation is in stagnation. The proposed algorithm is verified in the CEC2018 test functions and the results show that APSDE has good abilities of exploitation, exploration, convergence, and stability. Secondly, Midcourse Guidance Maneuver Decision-making (MGMD) in Beyond Visual Range (BVR) air combat is studied and transformed into a single objective variational optimization problem, a MGMD system based on APSDE is established. Finally, the simulation of MGMD is carried out. The APSDE ranks first in the typical MGMD scenario experiment. In the adaptive Midcourse guidance confrontation, the winning rate of APSDE is 54%, and the statistical results show that the APSDE has an excellent MGMD ability.