建立Cookie Theft测验的中国常模

目的建立Cookie Theft测验的中国常模。方法 29名正常被试,17例脑卒中患者完成Cookie Theft测验,在错误表述(IS)、不流畅表述(DF)、支持结构(PS)、重复内容(RP)、图片有效信息(CU)、解释图片有效信息(EC)和不相关字词(IR)7个维度上对语言样本进行分析。结果 7个维度上的重测信度值分别为r(IS)=0.92;r(DF)=0.89;r(PS)=0.98;r(RP)=0.84;r(CU)=0.96;r(EC)=0.88和r(IR)=0.99。17例脑卒中患者中,12例(70.6%)被诊断出。结论该常模具有良好的信效度,具有临床和科研应用价值。

基于Cookie Theft测验的脑小血管病患者认知沟通能力数字化测评

为了克服传统Cookie Theft测验费时费力、评测结果受主观影响等不足,提出了一种数字化测评方法.首先,将Cookie Theft测验转化成总分为28分的数字化量表;接着,分析对比了脑小血管病组和健康组分别在无提示语和有提示语条件下的表现差异情况;最后,综合语音相关特征及信息量相关特征,利用随机森林方法建立了脑小血管病智能诊断模型.实验结果显示,综合使用测试过程中记录的语音相关特征和信息量相关特征,脑小血管病智能诊断模型的识别准确率可达82.4%.

Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

Electricity theft is one of the major issues in developing countries which is affecting their economy badly.Especially with the introduction of emerging technologies,this issue became more complicated.Though many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)techniques.Here,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations.Three levels of taxonomy are presented to classify state-of-the-art ETD techniques.Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature.The challenges of different ETD techniques and their mitigation are suggested for future work.It is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft tracking.This can help in the prevention of energy theft,in the future,as well as for ETD.

Adaptive electricity theft detection method based on load shape dictionary of customers

With the application of the advanced measurement infrastructure in power grids,data driven electricity theft detection methods become the primary stream for pinpointing electricity thieves.However,owing to anomaly submergence,which shows that the usage patterns of electricity thieves may not always deviate from those of normal users,the performance of the existing usage-pattern-based method could be affected.In addition,the detection results of some unsupervised learning algorithm models are abnormal degrees rather than“0-1”to ascertain whether electricity theft has occurred.The detection with fixed threshold value may lead to deviation and would not be sufficiently flexible to handle the detection for different scenes and users.To address these issues,this study proposes a new electricity theft detection method based on load shape dictionary of users.A corresponding strategy for tunable threshold is proposed to optimize the detection effect of electricity theft,and the efficacy and applicability of the proposed adaptive electricity theft detection method were verified from numerical experiments.

Energy Theft Identification Using Adaboost Ensembler in the Smart Grids

One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.

Spatial Analysis Applied for Gas Theft Modelling in Tabriz City, Iran

Unauthorized use of energy is the major source of the non-technical losses of the energy in developing countries. Gas theft as a kind of energy theft is an increasing issue in a number of countries particularly in developing countries. This study is an attempt to address the issue of gas theft through the deployment of Geographic Information System (GIS) capabilities (Spatial Analysis) to import external factors into the current gas theft detection methods, improve data mining processes, and offer some management solutions. To achieve the intended goals in the study, two types of data sources were collected and analyzed: internal data such as reported instances of gas theft, and some customer properties, and external data such as some demographic data. In order to analyze and modeling the gas theft and the relationships between variables we used Hotspot analysis, Ordinary Least Squares regression (OLS) and Geographically Weighted Regression (GWR) analysis with ArcGIS tools. The results from clustering test indicated that the gas theft is not a random phenomenon in all areas of Tabriz and there are underlying factors. Mapping clusters by the hotspot techniques suggested the locations of clusters and areas at risk. The results of the regression analysis illustrated the importance of external factors clearly. According to the results, we recommend a conceptual GIS framework to select high risk areas as a subset data for a meter data analysis. Results of this research are of great importance for GIS based spatial analysis and can be used as base of future researches.

Comparing the Similarities and Differences between All Versions of Grand Theft Auto

The article investigates the similarities and differences between all versions of Grand Theft Auto as an adventure game with the widest popularity in the last decade. The game is a story collection, a frame for performance, a virtual museum of vernacular culture and a widely circulated pop culture artifact whose double-voiced aesthetic has given rise to diverse interpretive communities. The aim of comparing the differences and similarities between different versions of the game is to be able to evaluate the game from the user’s point of view. With this aim, whether with the verisimilitude that the different versions offer makes GTA a product of an iterative design process or not will be displayed.

An Analysis of the Female Images in Theft from the Perspective of Feminism

In the field of vision of American Literature in the 20th century,Katherine Anne Porter is highly praised by many readers for her flexible artistic style,accurate and vivid description of characters,profound connotation of her works.As a female writer,Porter is good at creating female characters from a unique female perspective,and reveals the inner activities of female characters in self-development and the female individual consciousness and independent consciousness pursued by women through her works.Although she doesn’t consciously involve feminism,we can see that the heroine’s female consciousness is gradually awakening from her portrayal of female characters and her exploration of female inner world.This paper takes Porter’s short story Theft published in the 1990s as an example to analyze the feminist consciousness in her works from the perspective of feminism.

A novel anti-theft alarm system b ased on detection of magnetic field

This paper proposes a novel bidirect i o nal anti-theft alarm scheme through detecting the magnetic field.The theoretical background and analysis of the approach of anti-theft alarm are pre sented.The circuit of burglar alarm is designed,fabricated and tested,and C language program is implemented and debugged.Feasibility of the de veloped scheme is proved by the experiments.

Electricity Theft Detection Method Based on Ensemble Learning and Prototype Learning

With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is violent,which makes the training of detection model challenging.In this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal level.In this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption data.After calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown samples.In the meanwhile,through training the network by different balanced subsets of training set,the prototype is representative.Compared with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal data.The results show that the proposed method outperforms other state-of-the-art methods.

基于重采样和混合集成学习的不平衡窃电检测

针对电力用户类别不平衡导致窃电检测具有偏向性问题,该文提出一种基于重采样和混合集成学习的不平衡窃电检测模型。首先以Easy-ensemble混合集成学习框架为基础确定最佳采样子集数;然后通过重采样自适应策略,即根据用户用电数据集的不平衡度以及最佳采样子集数确定检测模型的重采样方式,使用电数据达到平衡;最后按照先串行集成减小偏差、后并行集成降低方差的混合集成方式,对重采样后的均衡样本进行窃电检测。算例对比分析表明所提检测模型通过重采样和混合集成有效解决了传统集成算法在不平衡窃电检测中的偏向问题,降低了由于用电数据的不平衡性对集成结果的影响,提高了用户类别不平衡的窃电检测效果,在多种不平衡度下模型的准确率、F1值和G均值均表现优异。

基于STM32的智能家居安防系统设计

物联网应用技术快速发展,智能安防产品受到越来越多人的关注,尤其是智能家居安防产品。为了最大程度确保人身以及财产安全,满足人民群众对自身安全和家庭安防的要求,以STM32为主控芯片,结合各类智能传感器、控制器、报警器及智能手机小程序,设计并实现一套基于STM32的家居安防报警系统,该系统能实时、远程监测家居室内外环境状况,及时、有效防止家居安全事故发生,对保护家庭成员生命、财产安全具有重要的现实意义。

Cyberattack Ramifications, The Hidden Cost of a Security Breach

In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.

Energy-Theft Detection Issues for Advanced Metering Infrastructure in Smart Grid

With the proliferation of smart grid research, the Advanced Metering Infrastructure （AMI） has become the first ubiquitous and fixed computing platform. However, due to the unique characteristics of AMI, such as complex network structure, resource-constrained smart meter, and privacy-sensitive data, it is an especially challenging issue to make AMI secure. Energy theft is one of the most important concerns related to the smart grid implementation. It is estimated that utility companies lose more than S25 billion every year due to energy theft around the world. To address this challenge, in this paper, we discuss the background of AMI and identify major security requirements that AMI should meet. Specifically, an attack tree based threat model is first presented to illustrate the energy-theft behaviors in AMI. Then, we summarize the current AMI energy-theft detection schemes into three categories, i.e., classification-based, state estimation-based, and game theory-based ones, and make extensive comparisons and discussions on them. In order to provide a deep understanding of security vulnerabilities and solutions in AMI and shed light on future research directions, we also explore some open challenges and potential solutions for energy-theft detection.

户内盗窃的既未遂认定

司法实践中,户内盗窃包括入户盗窃和在户盗窃两种情形,对相关犯罪行为既未遂的判定通常以所窃物品体积大小、行为人是否将物品搬离为标准。这是一种形式化的判别标准,其中的法理依据缺乏合理性证成。通过对个人专属领域概念的辨析与构建,可以为户内盗窃的既未遂判定确立实质性标准。若所窃物品涉及个人专属领域,则即使在户内,亦可认定行为打破了物主占有,建立了行为人自身占有,构成盗窃既遂;反之,则是盗窃未遂。在个人专属领域存在交叉重合时,应坚持“一物一占有”原则,通过引入以人格权为基础的“贴身禁忌”理论来区分物品的占有者,从而认定盗窃行为的既未遂。

动机视角下员工时间偷窃的产生机制及影响

员工时间偷窃往往被认为是负面的职场偏差行为,会给企业带来不容忽视的损失。以往研究尚未系统探讨其产生机制,对其影响的研究更为有限。因此,如何科学地管理时间偷窃尚无定论。本研究从员工视角出发,构建时间偷窃动机的理论模型,并通过质性研究整合时间偷窃的具体动机并编制相应量表。在此基础上,从行为者和观察者视角探讨不同动机驱动的时间偷窃对职场行为的影响及机制,进而探究如何有效干预消极动机驱动的时间偷窃及其影响。本研究旨在全面理解时间偷窃动机,阐明不同动机的时间偷窃带来的差异化影响,为有效预防和干预时间偷窃问题提供理论和实践指导。

基于深度学习的智能电网窃电检测混合模型研究

针对传统窃电检测模型受维度诅咒、类不平衡等问题,提出一种能有效检测智能电网窃电行为的混合深度学习模型,利用深度学习卷积神经网络(AlexNet)处理维度诅咒问题,显著提升数据处理的准确性;通过自适应增强(Ada Boost)对正常和异常用电行为分类,进一步提高分类精度;使用欠采样技术解决类不平衡问题,确保模型在各类数据的均衡性能;利用人工蜂群算法对AdaBoost和AlexNet的超参数进行优化,有效提高整体模型性能。使用真实智能电表数据集评估混合模型的有效性,与同类模型相比,提出的混合深度学习模型在准确率、精确度、召回率、F1分数、马修斯相关系数(MCC)和曲线下面积-接收者操作特征曲线(AUC-ROC)分数上分别达到了88%、86%、84%、85%、78%和91%,不仅提高了用电行为监测的准确性,也为电力系统的智能分析提供了新视角。

基于改进旋转森林算法的窃电检测研究

如何准确检测出用户侧窃电行为是长期存在于各供电公司一个难点,传统的窃电检测方案均存在一定的局限性。针对窃电检测领域正负类样本高度不平衡,且单一分类模型表现不佳的问题,提出一种基于改进旋转森林算法的窃电检测方法。旋转森林算法采用主成分分析(principal component analysis,PCA)进行特征提取,利用原始训练集的所有主成分训练每个基分类器。在经典的旋转森林算法基础上,使用改进合成少数类过采样(synthetic minority oversampling technique,SMOTE)算法平衡样本子集中的正负类样本;使用Bagging算法中的Bootstrap抽样对训练子集进一步抽样;按准确率对基分类器进行选择性集成等3个方面的改进。算例使用华东某地区实际用户数据,结果表明所提窃电检测方法对比单一分类模型和现有集成学习策略,在多项评价指标下均取得更好的效果。