基于MVC框架的英语在线学习资源管理系统

由于线下资源和教师精力有限,但学生人数较多,导致英语资源的利用率低和管理效果不好,为此提出基于MVC框架的英语在线学习资源管理系统。构建MVC框架处理英语学习资源的逻辑性数据,从用户和资源双角度分析,建立用户特征模型,凭借密度用户项目矩阵及模糊类似度的优先比方法计算用户间类似性,找出用户间最类似项目以及其次项目。最后采用专业库管理功能、基础信息管理功能、资源管理功能、查询统计管理功能以及系统管理功能模块,完成英语在线学习资源管理系统设计。实验证明:所设计系统稳健性良好,且学习资源利用率较高。

基于Spring MVC的化橘红病虫害防治技术数据库系统的设计

文章设计了化橘红病虫害防治技术数据库系统,并详细分析了设计流程。系统界面设计友好,操作便捷,收集了与化橘红相关的病虫害信息及防治方法,能够为果农提供参考,具有一定的应用价值。

Sparse Reconstructive Evidential Clustering for Multi-View Data

Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.

基于改进GRU与MVC设计模式的数据智能分析算法

针对传统财务异常数据检测方法效率较低、准确度差且坏账率高的问题,文中基于改进的人工智能算法提出了一种异常数据检测方法。由于高维异常数据难以分析,先用孤立森林算法将其剔除,再将处理后的数据经过双向GRU算法的训练,挖掘出数据的时序性特征。对于训练后数据分类准确度较低的问题,通过注意力机制对数据特征权重进行排序,从而得到最终的分类结果。基于MVC设计了软件架构进行实验测试,该算法的训练总时长明显低于对比算法,RMSE及MAPE指标相较Bi-LSTM算法低0.2%和0.15%,且准确率、召回率与F1值在对比算法中也为最优。

Improved Data Stream Clustering Method: Incorporating KD-Tree for Typicality and Eccentricity-Based Approach

Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.

Hyperspectral Image Based Interpretable Feature Clustering Algorithm

Hyperspectral imagery encompasses spectral and spatial dimensions,reflecting the material properties of objects.Its application proves crucial in search and rescue,concealed target identification,and crop growth analysis.Clustering is an important method of hyperspectral analysis.The vast data volume of hyperspectral imagery,coupled with redundant information,poses significant challenges in swiftly and accurately extracting features for subsequent analysis.The current hyperspectral feature clustering methods,which are mostly studied from space or spectrum,do not have strong interpretability,resulting in poor comprehensibility of the algorithm.So,this research introduces a feature clustering algorithm for hyperspectral imagery from an interpretability perspective.It commences with a simulated perception process,proposing an interpretable band selection algorithm to reduce data dimensions.Following this,amulti-dimensional clustering algorithm,rooted in fuzzy and kernel clustering,is developed to highlight intra-class similarities and inter-class differences.An optimized P systemis then introduced to enhance computational efficiency.This system coordinates all cells within a mapping space to compute optimal cluster centers,facilitating parallel computation.This approach diminishes sensitivity to initial cluster centers and augments global search capabilities,thus preventing entrapment in local minima and enhancing clustering performance.Experiments conducted on 300 datasets,comprising both real and simulated data.The results show that the average accuracy(ACC)of the proposed algorithm is 0.86 and the combination measure(CM)is 0.81.

Deep Learning and Tensor-Based Multiple Clustering Approaches for Cyber-Physical-Social Applications

The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed at enhancing their positive aspects and mitigating their negative repercussions. To achieve this, the study introduces a weight learning method grounded in multi-linear attribute ranking. This approach serves to evaluate the significance of attribute combinations across all feature spaces. Additionally, a novel clustering method based on tensors is proposed to elevate the quality of clustering while effectively distinguishing selected features. The methodology incorporates a weighted average similarity matrix and optionally integrates weighted Euclidean distance, contributing to a more nuanced understanding of attribute importance. The analysis of the proposed methods yields significant findings. The weight learning method proves instrumental in discerning the importance of attribute combinations, shedding light on key aspects within feature spaces. Simultaneously, the clustering method based on tensors exhibits improved efficacy in enhancing clustering quality and feature distinction. This not only advances our understanding of attribute importance but also paves the way for more nuanced data analysis methodologies. In conclusion, this research underscores the pivotal role of network platforms in contemporary society, emphasizing their potential for both positive contributions and adverse consequences. The proposed methodologies offer novel approaches to address these dualities, providing a foundation for future research and practical applications. Ultimately, this study contributes to the ongoing discourse on optimizing the utility of network platforms while minimizing their negative impacts.

Examining the Use of Scott’s Formula and Link Expiration Time Metric for Vehicular Clustering

Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms.K-clustering algorithms are simplistic,with fast performance and relative accuracy.However,their implementation depends on the initial selection of clusters number(K),the initial clusters’centers,and the clustering metric.This paper investigated using Scott’s histogram formula to estimate the K number and the Link Expiration Time(LET)as a clustering metric.Realistic traffic flows were considered for three maps,namely Highway,Traffic Light junction,and Roundabout junction,to study the effect of road layout on estimating the K number.A fast version of the PAM algorithm was used for clustering with a modification to reduce time complexity.The Affinity propagation algorithm sets the baseline for the estimated K number,and the Medoid Silhouette method is used to quantify the clustering.OMNET++,Veins,and SUMO were used to simulate the traffic,while the related algorithms were implemented in Python.The Scott’s formula estimation of the K number only matched the baseline when the road layout was simple.Moreover,the clustering algorithm required one iteration on average to converge when used with LET.

Density Clustering Algorithm Based on KD-Tree and Voting Rules

Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.

MVC浓水、软化再生废水及废热的资源化综合利用研究

针对新疆某油田特有的废热、废水条件,通过对蒸发结晶边界条件确定、二价盐除盐率影响因素及含盐水空气蒸发适应性等多个方面的技术研究,制定了“强制循环反应结晶+空气蒸发浓缩”的废水处理工艺技术方案。有效地利用废热对MVC浓水和软化再生废水的混合液进行蒸发,伴随整个蒸发过程对二价盐进行结晶处理,使这两股原本无法深度处理的污水得以再利用,且回收了部分热能。该工艺技术的应用,在完成水质除硬、除硅的同时,既生产出75%的冷凝水回用于锅炉,又产出氯化钠浓度为18%(质量分数)的盐溶液作为再生用盐供给软化器,解决了稠油开采水平衡的困难,实现资源回收利用,降低软化器的运行成本。其中,污水减排达99%,热能利用90%,热能回收10%。以研究单位现有外排水量及各类费用标准计,每年可产生经济效益约2300万元。

Contrastive Consistency and Attentive Complementarity for DeepMulti-View Subspace Clustering

Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.

A Study of Triangle Inequality Violations in Social Network Clustering

Clustering a social network is a process of grouping social actors into clusters where intra-cluster similarities among actors are higher than inter-cluster similarities. Clustering approaches, i.e. , k-medoids or hierarchical, use the distance function to measure the dissimilarities among actors. These distance functions need to fulfill various properties, including the triangle inequality (TI). However, in some cases, the triangle inequality might be violated, impacting the quality of the resulting clusters. With experiments, this paper explains how TI violates while performing traditional clustering techniques: k-medoids, hierarchical, DENGRAPH, and spectral clustering on social networks and how the violation of TI affects the quality of the resulting clusters.

Low-Rank Multi-View Subspace Clustering Based on Sparse Regularization

Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.

Optical Fibre Communication Feature Analysis and Small Sample Fault Diagnosis Based on VMD-FE and Fuzzy Clustering

To solve the problems of a few optical fibre line fault samples and the inefficiency of manual communication optical fibre fault diagnosis,this paper proposes a communication optical fibre fault diagnosis model based on variational modal decomposition(VMD),fuzzy entropy(FE)and fuzzy clustering(FC).Firstly,based on the OTDR curve data collected in the field,VMD is used to extract the different modal components(IMF)of the original signal and calculate the fuzzy entropy(FE)values of different components to characterize the subtle differences between them.The fuzzy entropy of each curve is used as the feature vector,which in turn constructs the communication optical fibre feature vector matrix,and the fuzzy clustering algorithm is used to achieve fault diagnosis of faulty optical fibre.The VMD-FE combination can extract subtle differences in features,and the fuzzy clustering algorithm does not require sample training.The experimental results show that the model in this paper has high accuracy and is relevant to the maintenance of communication optical fibre when compared with existing feature extraction models and traditional machine learning models.

Comprehensive K-Means Clustering

The k-means algorithm is a popular data clustering technique due to its speed and simplicity. However, it is susceptible to issues such as sensitivity to the chosen seeds, and inaccurate clusters due to poor initial seeds, particularly in complex datasets or datasets with non-spherical clusters. In this paper, a Comprehensive K-Means Clustering algorithm is presented, in which multiple trials of k-means are performed on a given dataset. The clustering results from each trial are transformed into a five-dimensional data point, containing the scope values of the x and y coordinates of the clusters along with the number of points within that cluster. A graph is then generated displaying the configuration of these points using Principal Component Analysis (PCA), from which we can observe and determine the common clustering patterns in the dataset. The robustness and strength of these patterns are then examined by observing the variance of the results of each trial, wherein a different subset of the data keeping a certain percentage of original data points is clustered. By aggregating information from multiple trials, we can distinguish clusters that consistently emerge across different runs from those that are more sensitive or unlikely, hence deriving more reliable conclusions about the underlying structure of complex datasets. Our experiments show that our algorithm is able to find the most common associations between different dimensions of data over multiple trials, often more accurately than other algorithms, as well as measure stability of these clusters, an ability that other k-means algorithms lack.

Effective data transmission through energy-efficient clustering and Fuzzy-Based IDS routing approach in WSNs

Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.

基于MVC架构的高校间学分认定与转换系统

针对线下开展学分认定与转换工作存在的问题,结合高校间办理相关业务的需求,介绍了一套应用系统的实现过程。首先对工作流程和系统功能进行分析并画出了对应的流程图和结构图;然后讲解了实现核心功能所需的表的设计要点;最后对系统实现的过程进行介绍,包括MVC架构的控制器、数据模型和视图这三个核心组件的设计与实现以及基于该MVC架构实现应用系统功能的方法。从测试和使用反馈的情况来看,系统能满足在多个学校之间开展学分认定与转换工作的需求,具有界面简洁、使用方便等特点,提高了工作效率,具有较强的实用性。

基于MVC框架的教学管理信息智能调度平台

由于现有教学管理信息调度平台未考虑人机交互目标,导致动态负载均衡效率较低。因此,设计基于MVC框架的教学管理信息智能调度平台。创建一种符合分布式环境的信息访问请求并行调度算法,利用二次调度分配手段实现信息智能调度;运用MVC框架交互性特征,构建平台软件结构,采用不同模块建立教学管理信息智能调度平台。实验结果表明,所建平台具有极强的“健壮性”特征,其动态负载均衡效率、并行效率和加速比都较高。由此可见所建平台可保持优秀的调度性能,能够妥善解决课程调课与教室分配等问题。

基于MVC架构的网络授课系统设计

研究基于MVC架构建立了一套用于高等院校教学工作的网络授课系统,详细介绍系统的整体结构设计方案与系统功能逻辑结构,阐述系统数据库各实体之间的逻辑关系与数据库表设计方案,提出系统各功能模块的设计思路,最终以英语在线考试功能为例展示该模块功能的实现策略与界面实现结果。

复杂商品管理的MVC实现

为了解决电子商务网站中商品的复杂多样,将单一的商品管理换成复杂商品管理是十分必要的。本文重点分析了复杂商品属性,使用SQL Server2008作为数据库,使用ASP.NET+MVC的开发模式,分析了复杂商品管理模块的实现。实践结果表明,该模块能够有效地实现商品多样性的管理,可以为类似模块的开发提供参考。