Prototype-driven few-shot learning (FSL) holds promise for cross-domain hyperspectral image (HSI) classification but encounters two key limitations: 1) over-reliance on visual features overlooks deep semantic information, hindering the differentiation of visually similar categories and 2) inade quate semantic connections between local and holistic features restrict the utilization of spatial-spectral characteristics. To tackle these issues, we propose a multimodal prototype correction and multidimensional knowledge distillation framework for cross-domain few-shot hyperspectral image classification MCMD-CFSC. This framework implements a multimodal prototype correction strategy that leverages pre-trained language models to extract spatial and spectral text features from spatial and spectral text descriptions, thereby aligning image spatial prototypes with spatial text features and spectral prototypes with spectral text features to enhance the recognition of diverse ground objects. In addition, a multidimensional knowledge distillation mechanism is developed to ensure semantic consistency between local and holistic features across spatial-spectral dimensions, bolstering the model's discriminative power and generalization ability. Experimental results demonstrate that MCMD-CFSC significantly surpasses existing deep learning and FSL techniques on four hyperspectral datasets. The code is available at https://github.com/Li-ZK/MCMD-CFSC-2025.

There are many interference factors in the real world that are not related to facial expressions, such as background, lighting intensity, and changes in image resolution. In order to address the problem of facial expression recognition, this article proposes a fuzzy neural network enabled deep subspace domain adaptive fusion (FNN-DSDAF) by integrating the fuzzy logic module into the convolutional network structure. The fuzzy logic module integrates the powerful learning ability of deep networks can learn a set of robust Gaussian membership functions through a series of learnable Gaussian membership functions and logical operations. When training the classical normalized exponential loss function to force different categories of samples to maintain a certain distance in the learned feature space, FNN-DSDAF further uses the local hold loss function to make the local clusters within each category of samples more compact. The experimental results demonstrated that the fuzzy neural network had good feature decoupling ability and was suitable for facial expression understanding in real-world scenarios, and improve the accuracy, reliability, and robustness.

课程思政改革教学已成为新型教学改革举措.信息技术高速发展的社会环境下,数据已是第一生产力要素.数据库原理专业课程的教学尤显重要.为了让课程内容更贴合思政教学要求,推进全方位育人.本文从数据库课程思政建设总体思路出发,围绕教学内容、挖掘课程思政元素,采用多路径并举,探究课程思政微课教学方案设计,对数据库思政微课内容设计要点进行了阐述.

In recent years, breakthroughs have been made in the fields of image processing, natural language understanding, speech recognition, and online advertising. Integrate deep learning into the recommendation system, study the construction of self-service learning ability for business English majors based on heterogeneous data, and verify the experimental hypothesis that “reflection can promote the achievement of deep learning goals” through the analysis of the difference between written tests and work evaluations., Through the analysis of the difference between the written test and the work evaluation, verify the experimental hypothesis that “reflection can promote the achievement of deep learning goals.”

In the actual communication environment, the accuracy of spectrum detection is easily affected by the uncertainty factors such as multipath fading, shadow effect, hidden terminal, channel signal-to-noise ratio, and noise variance fluctuation. Cooperative spectrum sensing technology makes full use of the spatial gain generated by sensor nodes in different geographical locations, and obtains more accurate combination decision results than single node decision. In cooperative sensing, fusion center (FC) will analyze and process the collected local detection statistics or sensing results according to a certain data fusion rule, and make the final decision on the current channel state according to the fusion results. Data fusion strategy plays an important role in perception results and system reliability. In this paper, we summarize several conventional data fusion schemes in cooperative spectrum sensing for cognitive radio networks and conduct performance analysis.

Due to the negative impact from spatial correlation, spatially correlated cognitive radio (CR) based devices participating in cooperative spectrum sensing may be harmful to the detection performance. In this paper, we propose an energy-efficient cooperative spectrum sensing scheme based on spatial correlation for cognitive Internet of Things (CIoT). To mitigate the communication overhead and ensure sufficient sensing accuracy, the CR-based devices (CRDs) can be grouped into several clusters. The member nodes undertake cooperative spectrum sensing tasks in turn, and send the local test statistic to their cluster head nearby. Then, by exploiting the spatial correlation of the members, the cluster head combines the sensing results and makes use of likelihood ratio test to obtain the cluster decision. After receiving the decisions from all clusters, the fusion center employs hard fusion scheme to make the final decision about spectrum occupancy. The simulation results show that our scheme not only provides the better sensing performance, but also improve the energy efficiency.

数据结构课程设计评价是课程设计中的一个重要环节.对传统数据结构课程设计评价中遇到的问题进行深入分析,结合工程教育专业认证的大背景,重点研究数据结构课程设计的形成性评价方法改革.分别从课程评价框架、细粒度考核指标设计、及时且具体性的反馈体系等方面进行阐述.

The spectrum sensing performance depends on the accuracy of the detection about whether primary users are busy or idle. Previous studies on cognitive radio spectrum sensing have shown that the cooperation between secondary users can improve their spectrum detection performance in real cognitive networks. Aiming at the problem of threshold mismatch of energy detectors under noise power uncertainty, a cooperative spectrum sensing method with dynamic dual threshold is proposed. Firstly, the utility function is defined with the objective of minimizing the error probability of spectrum sensing, and the optimum threshold of energy detector is derived. Secondly, in order to mitigate the influence derived from noise uncertainty, an effective dynamic dual threshold adjustment mechanism is presented, and the optimizing combinative fusion rule is discussed with the prerequisite of the minimum global error probability. In addition, in view of insufficient number of cognitive users whose sensing results lie in decision zones, the parameter of credibility is defined to choose the secondary users with reliable local detection for final fusion. Simulation results show that our proposed method can mitigate the influence of noise uncertainty and increase the spectrum sensing accuracy compared with other existing methods.

To detect the primary user’s activity accurately in cognitive radio sensor networks, cooperative spectrum sensing is recommended to improve the sensing performance and the reliability of spectrum-sensing process. However, spectrum-sensing data falsification attack being launched by malicious users may lead to fatal mistake of global decision about spectrum availability at the fusion center. It is a tough task to mitigate the negative effect of spectrum-sensing data falsification attack and even eliminate these attackers from the network. In this article, we first discuss the randomly false attack model and analyze the effects of two classes of attacks, individual and collaborative, on the global sensing performance at the fusion center. Afterwards, a linear weighted combination scheme is designed to eliminate the effects of the attacks on the final sensing decision. By evaluating the received sensing result, each user can be assigned a weight related to impact factors, which includes result consistency degree and data deviation degree. Furthermore, an adaptive reputation evaluation mechanism is introduced to discriminate malicious and honest sensor node. The evaluation is conducted through simulations, and the results reveal the benefits of the proposed in aspect of mitigation of spectrum-sensing data falsification attack.

For resource-constrained IoT systems, data collection is one of the fundamental operations to reduce the energy dissipation of sensor nodes and improve the network lifetime. However, an anomaly or deviation will exert a great influence on the quality of data collected, especially for a data aggregation scheme. By taking into account data-aware clustering and detection of anomalous events, a similarity-aware data aggregation using a fuzzy c-means approach for wireless sensor networks is proposed. Firstly, by using a fuzzy c-means approach, the clustering process can be performed to organize sensors into clusters based on data similarity. Next, an effective support degree function is defined for further outlier diagnosis. Afterwards, the appropriate weight of valid data can be obtained by taking advantage of the probability distribution characteristics of normal samples within a certain period. Finally, the aggregation result in the cluster can be estimated. Practical database-based simulations have confirmed that the proposed data aggregation method can achieve better performance than traditional methods in terms of data outlier detection accuracy and relative recovery error.

针对云计算系统中多任务并发模式下引发的资源竞争,本文提出了一种基于改进的粒子群优化的云计算资源调度分配模型,以提高资源利用率.首先,对云计算系统中的资源调度问题进行形式化描述,构建以任务的总完成时间为优化对象的目标函数.其次,求解时采用粒子群优化算法,为保证收敛速度且避免粒子群在搜索过程中陷入局部最优,定义了惯性权重函数.另外,引入一个调整算子以优化位置更新.仿真结果表明,本文提出的资源调度分配模型能够有效提高云计算资源利用率,大幅减少任务的处理时间.

为了评价不同失真类型图像的质量,提出了一种基于兴趣区域和自然图像统计特性的无参考图像质量评价方法。该方法对Itti模型进行改进,并利用改进的Itti模型提取失真图像的感兴趣区域和非感兴趣区域,在非下采样Contourlet域提取图像的统计特性,通过计算失真图像的不同区域与自然图像统计特性的差异来获得图像的质量分数。在LIVE数据集上与已有方法进行对比,实验结果表明,提出的方法和主观感知具有较好的一致性。

The core issue of text emotion classification is how to represent the emotion semantics of the text effectively. However,most current methods only consider the emotion semantics in the text content,ignoring the user and product information related to the text content. Existing methods that incorporate user and product information still exist two problems:(1)The methods can not effectively represent the user and product information.(2)The semantic representation model is too simple and can not effectively represent the context semantic information in the text.To address these issues, this paper puts forward the corresponding solutions:(1)According to the user and the product data, the Singular Value Decomposition(SVD)method is used to obtain the prior information of user and product.This method avoids the training of user and product parameters,which solves the problem that the iterative speed of the model is very slow.(2)Using the bidirectional Gated Recurrent Unit(GRU)model instead of the original simple model,the context semantic information in the text is more effectively combined in the document feature representation. The experimental results show that the proposed method has good classification performances,even achieves the best state-of-the-art result at some experiment datasets.Meanwhile it improves the training speed of the model.

In wireless sensor networks,malicious nodes will tamper or falsify the data to affect the final fusion results.To solve this problem,an effective method for detecting malicious nodes based on secure data aggregation in clustered wireless sensor networks is proposed,which is able to quantify the uncertainty of node's monitoring data and judge whether the node is credible or not.Besides,the specific malicious node's detection and processing method is introduced.Simulation results show that the model is capable of detecting malicious nodes effectively,and achieving high detection rate and low false detection rate.

Aiming at the shortage of traditional software development mode of training talents, the advantages and disadvantages of creative studio learning model in talent training are discussed in this paper, and a sustainable development model of undergraduate Innovation Studio is introduced. Firstly, the theoretical study mode of combining practice and innovation is elaborated in detail. Then, the studio system construction, students' initiative and interactivity are combined to realize the cultivation of team spirit. Finally, the guidance and core role of the enterprise tutors in the students' studio is put forward to achieve a sustainable and virtuous circle for the development of innovative Studios.

分析了当前高校物联网工程专业课程体系建设存在的问题,基于CDIO一体化课程体系的构建思想,针对如何培养真正适用于物联网产业的创新复合型人才提出了面向创新能力培养的课程体系建设思路,讨论了物联网专业课程体系建设和改革的关键内容,并给出了详细的能力培养教学方案,以切实提高学生的综合实力.

In wireless sensor networks, the high density of node’s distribution will result in transmission collision and energy dissipation of redundant data. To resolve the above problems, an energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks (ESSM) is proposed, which will schedule the sensors into the active or sleep mode to reduce energy consumption effectively. Firstly, the optimal competition radius is estimated to organize the all sensor nodes into several clusters to balance energy consumption. Secondly, according to the data collected by member nodes, a fuzzy matrix can be obtained to measure the similarity degree, and the correlation function based on fuzzy theory can be defined to divide the sensor nodes into different categories. Next, the redundant nodes will be selected to put into sleep state in the next round under the premise of ensuring the data integrity of the whole network. Simulations and results show that our method can achieve better performances both in proper distribution of clusters and improving the energy efficiency of the networks with prerequisite of guaranteeing the data accuracy.

The increasingly growing popularity of the collaboration among researchers and the increasing information overload in big scholarly data make it imperative to develop a collaborator recommendation system for researchers to find potential partners. Existing works always study this task as a link prediction problem in a homogeneous network with a single object type (i.e., author) and a single link type (i.e., co-authorship). However, a real-world academic social network often involves several object types, e.g., papers, terms, and venues, as well as multiple relationships among different objects. This paper proposes a RWR-CR (standing for random walk with restart-based collaborator recommendation) algorithm in a heterogeneous bibliographic network towards this problem. First, we construct a heterogeneous network with multiple types of nodes and links with a simplified network structure by removing the citing paper nodes. Then, two importance measures are used to weight edges in the network, which will bias a random walker's behaviors. Finally, we employ a random walk with restart to retrieve relevant authors and output an ordered recommendation list in terms of ranking scores. Experimental results on DBLP and hep-th datasets demonstrate the effectiveness of our methodology and its promising performance in collaborator prediction.

Attribute selection is an effective data preprocessing method.Aiming at removing redundant or noisy attributes from the multivariate time series attribute set and selecting an attribute subset containing enough original information to improve accuracy,an attribute selection algorithm based on correlation density is proposed.The algorithm employed in the correlation matrix to represent the original multivariate time series,the local density of each attribute to show its representative ability,the distance discriminant between attributes as their discriminant degree.Moreover,attributes with larger representativeness and discriminant degree were filtered according to the distribution of the decision graph.Experiments with SVM classifier on four different datasets from the UCI repository were performed.The experimental results demonstrate the great improvement of the proposed algorithm in classification accuracy and time efficiency when compared with the existing algorithms.