The Profound Revelation of Human Social Relations and the Profound Realization of Information Right of Citizens——The social significance of network technique advancement in the perspective of “Entity Cube”

The R＆D group of relationship searching in Microsoft Asia Research Institute explains ＂Entity Cube＂ launched in 2008 as follows：

A Review on the Recent Trends of Image Steganography for VANET Applications

Image steganography is a technique of concealing confidential information within an image without dramatically changing its outside look.Whereas vehicular ad hoc networks(VANETs),which enable vehicles to communicate with one another and with roadside infrastructure to enhance safety and traffic flow provide a range of value-added services,as they are an essential component of modern smart transportation systems.VANETs steganography has been suggested by many authors for secure,reliable message transfer between terminal/hope to terminal/hope and also to secure it from attack for privacy protection.This paper aims to determine whether using steganography is possible to improve data security and secrecy in VANET applications and to analyze effective steganography techniques for incorporating data into images while minimizing visual quality loss.According to simulations in literature and real-world studies,Image steganography proved to be an effectivemethod for secure communication on VANETs,even in difficult network conditions.In this research,we also explore a variety of steganography approaches for vehicular ad-hoc network transportation systems like vector embedding,statistics,spatial domain(SD),transform domain(TD),distortion,masking,and filtering.This study possibly shall help researchers to improve vehicle networks’ability to communicate securely and lay the door for innovative steganography methods.

Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India

Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world.The number of landslides and the level of damage across the globe has been increasing over time.Therefore,landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region.Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study.The prime goal of the study is to prepare landslide susceptibility maps(LSMs)using computer-based advanced machine learning techniques and compare the performance of the models.To properly understand the existing spatial relation with the landslide,twenty factors,including triggering and causative factors,were selected.A deep learning algorithm viz.convolutional neural network model(CNN)and three popular machine learning techniques,i.e.,random forest model(RF),artificial neural network model(ANN),and bagging model,were employed to prepare the LSMs.Two separate datasets including training and validation were designed by randomly taken landslide and nonlandslide points.A ratio of 70:30 was considered for the selection of both training and validation points.Multicollinearity was assessed by tolerance and variance inflation factor,and the role of individual conditioning factors was estimated using information gain ratio.The result reveals that there is no severe multicollinearity among the landslide conditioning factors,and the triggering factor rainfall appeared as the leading cause of the landslide.Based on the final prediction values of each model,LSM was constructed and successfully portioned into five distinct classes,like very low,low,moderate,high,and very high susceptibility.The susceptibility class-wise distribution of landslides shows that more than 90%of the landslide area falls under higher landslide susceptibility grades.The precision of models was examined using the area under the curve(AUC)of the receiver operating characteristics(ROC)curve and statistical methods like root mean square error(RMSE)and mean absolute error(MAE).In both datasets(training and validation),the CNN model achieved the maximum AUC value of 0.903 and 0.939,respectively.The lowest value of RMSE and MAE also reveals the better performance of the CNN model.So,it can be concluded that all the models have performed well,but the CNN model has outperformed the other models in terms of precision.

On the Phenomenon of Information Silo in the E- government Construction in China

In the past decade,the e-government affairs keep developing,which prom otes the reform of governmental affairs and the enhancement of administrative efficiency.However,the phenomenon of inform ation silo has bad influence upon the governm ental information resources sharing and the efficiency of the e-government affairs,hindering inform ation connectivity,so that the government management cannot meet the need of society,nor can the e-governm ent develop in the long run.This paper attempts to analyze the embodiment and causes of the phenomenon of information silo,putting forward measures of solving the problem of inform ation silo.

基于改进两步法采样策略和卷积神经网络的崩塌易发性评价

机器学习在崩塌滑坡泥石流地质灾害易发性分析评价领域已得到广泛的研究性应用,非灾害样本的选取是易发性建模过程中的关键问题,传统随机抽样和手工标注方法可能存在随机性和主观性。将土质崩塌易发性评价视为正例无标记(positive and unlabeled,简称PU)学习,提出了一种结合信息量(information value,简称IV)和间谍技术(Spy)的两步卷积神经网络(convolutional neural networks,简称CNN)框架(ISpy-CNN)。以广州市黄埔区崩塌编录和15类基础环境因子,通过信息量模型筛选出部分低信息量样本;采用间谍技术训练CNN模型,从低信息量样本中识别出具有高置信度的可靠负例划分为非崩塌样本;分别基于该学习框架、传统间谍技术和随机抽样,使用支持向量机(support vector machine,简称SVM)和随机森林(random forest,简称RF)对比验证。结果表明,ISpy-CNN框架在验证集上的准确率、F1值、敏感度和特异度较随机采样分别提升了6.82%,6.82%,6.82%,8.23%,较传统Spy技术分别提升了2.86%,2.89%,2.86%,2.31%;PU学习中第2步采用CNN模型的预测精度高于RF和SVM模型;与传统Spy技术相比,增加相同数量训练样本,ISpy-CNN框架筛选的样本集表现出较高的稳定性、预测精度和增长率。本研究提出的ISpy-CNN框架能更好地辅助选取高质量非灾害样本,且崩塌易发性分区结果更符合实际的崩塌空间分布。

帮助信息网络犯罪活动罪司法扩张的反思与限缩

为了依法打击借助互联网实施的犯罪,《刑法修正案(九)》增设帮助信息网络犯罪活动罪罪名。但在司法适用中,该罪的构成要件存在司法扩张现象。主观要件“明知”在实务中被扩张为“应当知道”甚至“可能知道”。客观要件的“帮助”行为在实务中被扩张至对从犯、胁从犯乃至对帮助者行为的帮助,“帮助”对象被扩张至对一般违法行为的帮助。入罪标准“情节严重”的司法解释与司法实务仅对数额作出规定或判断,而缺乏对“情节严重”进行实质判断。帮助信息网络犯罪活动罪的司法扩张易使帮信罪沦为互联网时代的口袋罪,也有悖于宽严相济的刑事政策。因此,应对帮信罪的构成要件加以限缩。“主观明知”宜理解为“明确知道”,并通过主客观综合认定来确定“明确知道”。“客观帮助”宜限定为对正犯的帮助,且正犯必须利用信息网络实施犯罪或至少实施刑法分则规定的行为。“情节严重”的司法解释宜在客观数额的基础上增添其他考量因素,且实务认定亦需基于综合标准,同时“情节严重”的出罪通道也必须畅通。

帮助信息网络犯罪活动罪司法适用误区及匡正

帮助信息网络犯罪活动罪的设立具有积极作用。然而该罪的司法适用存在对“主观明知”的判定随意,不能正确理解帮信罪中“犯罪”一词的含义,对“情节严重”的说理不充分,与相关罪名特别是掩饰、隐瞒犯罪活动罪的竞合问题突出等困境。为匡正适用误区,应在“包含说”基础上进一步界定“明知”的认定标准,确定被帮助者的“犯罪”行为只需符合客观构成要件标准,基于限缩目的细化司法解释、及时公布指导性案例和通过强制说理明确“情节严重”的标准,坚持帮信罪和掩隐罪应当排他适用并应继续坚持主客观相统一的认定标准等,以促进本罪在实践中的正确适用,推动相关网络犯罪的治理。

帮助信息网络犯罪活动罪中“情节严重”的体系性解释

帮助信息网络犯罪活动罪中“情节严重”的认定标准和具体内涵都较为含糊,为保障司法适用的准确性,须从刑法教义学角度进行深入分析。“情节严重”应当定位于犯罪论体系中实质违法性的积极条件。由于特有的依附属性和独立属性,该罪具有上游信息网络犯罪所侵害的内层法益和帮助行为本身所侵害的外层法益信息网络安全管理秩序。因此,该罪“情节严重”应以内外双层法益为依托进行限缩解释:“情节严重”中的“情节”应仅限于客观方面的表明法益侵害程度的要素;帮助行为应当对其上游网络犯罪行为的实害法益产生危险性影响;对于实质违法性积极条件缺位的帮助行为,应发挥行刑衔接效能,不可动用刑罚处罚。

帮助信息网络犯罪活动罪的入罪限制

近两三年来,帮助信息网络犯罪活动罪的数量出现大幅度上升的趋势,有口袋化之嫌。本罪的规范属性是帮助行为正犯化,该帮助行为确具有独立性,但是只承认独立性容易滥用该罪名,从属性是帮助行为固有的属性,对本罪的研究不可忽视该属性。以帮助行为的从属性为出发点对本罪的构成要件进行合理解释,该帮助行为不包括间接帮助和主观模糊的中立帮助行为。明知的时间节点,在帮助对象着手实施犯罪之后,实行行为结束之前成立,谨慎适用明知的推定,禁止根据推定的事实进行再次推定,允许提出合理怀疑。同时,遵循情节严重双重限制的标准,贯彻情节严重限制入罪的机能。

Analytic design of information granulation-based fuzzy radial basis function neural networks with the aid of multiobjective particle swarm optimization

Purpose–The purpose of this paper is to consider the concept of Fuzzy Radial Basis Function Neural Networks with Information Granulation(IG-FRBFNN)and their optimization realized by means of the Multiobjective Particle Swarm Optimization(MOPSO).Design/methodology/approach–In fuzzy modeling,complexity,interpretability(or simplicity)as well as accuracy of the obtained model are essential design criteria.Since the performance of the IG-RBFNN model is directly affected by some parameters,such as the fuzzification coefficient used in the FCM,the number of rules and the orders of the polynomials in the consequent parts of the rules,the authors carry out both structural as well as parametric optimization of the network.A multi-objective Particle Swarm Optimization using Crowding Distance(MOPSO-CD)as well as O/WLS learning-based optimization are exploited to carry out the structural and parametric optimization of the model,respectively,while the optimization is of multiobjective character as it is aimed at the simultaneous minimization of complexity and maximization of accuracy.Findings–The performance of the proposed model is illustrated with the aid of three examples.The proposed optimization method leads to an accurate and highly interpretable fuzzy model.Originality/value–A MOPSO-CD as well as O/WLS learning-based optimization are exploited,respectively,to carry out the structural and parametric optimization of the model.As a result,the proposed methodology is interesting for designing an accurate and highly interpretable fuzzy model.

基于改进Informer的蒸汽管网流量预测

提出了一种基于改进Informer的蒸汽管网流量预测模型Informer_BEST。该模型通过引入扩展的因果卷积、相对位置和季节特征编码以及梯度中心化技术等3种算法,对Informer模型进行了改进和优化。对5种预测模型进行实验,结果表明,所提出的Informer_BEST预测模型展现了令人满意的测试结果,预测曲线与实际数据的变化趋势相吻合,对供热调度决策具有良好的指导作用。

电子信息工程中计算机网络技术的实践研究

计算机网络技术作为一门关键的学科和研究领域,在实践研究方面扮演着重要角色。文章在分析计算机网络技术的分类后,详细探讨了电子信息工程中计算机网络技术的实践应用,主要包括资源共享、电子信息工程维护、设备开发等方面,以期为相关研究提供借鉴。

网络环境下图书馆用户信息需求调查分析

网络环境下图书馆信息用户需求是国内外图书馆理论界长期研究探讨的重大现实问题,文章根据调研数据,针对用户信息需求调查分析、用户需求的新特点及图书馆的应对方略等方面加以分析研究,为同行们提供一点工作参考。

网络群体行为的演化博弈模型与分析方法

随着社会关系网络和在线社会网络应用的不断发展,如何理解和分析群体合作行为的稳定维持和演化是社会关系网络研究中面临的最大的挑战之一.文中综述了网络群体行为和随机演化博弈模型与分析方法等方面的研究工作,介绍了可用于网络群体行为分析评价的指标和模型,总结了网络群体行为和随机演化博弈模型的若干研究和分析方法,探讨了应用随机演化博弈模型进行网络群体行为研究的可行性.基于这些讨论,展望了社会关系网络中的网络群体行为研究所面临的主要挑战.

基于信息理论与技术的地区电网辅助决策系统设计

设计了较为实用的面向地区电网的辅助决策系统。在设计过程中利用了信息技术和信息论:运用信息技术设计了数据中心、数据仓库、符合CIM标准的集成化方案,以及基于数据挖掘技术的负荷相似日分析功能;运用电力信息论,设计了故障诊断和拓扑检错功能。对于所设计的系统,利用实际电网数据进行了部分功能的测试,验证了设计方案的合理性,并为后续工程建设积累了一定经验。这种辅助决策系统即将在浙江省杭州市地区电网得到应用。

物联网应用于江苏农业的发展分析

物联网被视为战略新兴产业和新的经济增长点,在现代农业发展中具有广阔的应用前景。文章首先介绍了物联网的内涵,分析了物联网在中国农业应用的发展基础和趋势;然后结合江苏省高效农业发展面临的问题,分析了江苏省农业方面应用物联网的需求,并提出了江苏省农业方面应用物联网的重点发展领域。

林业及生态效益遥感信息动态监测系统建设探讨

采用“６Ｓ”（即：空间信息管理系统的卫星通讯、地理信息系统、全球定位系统、遥感、三维分析可视化和专家系统）集成信息技术，充分开发现有资料，多学科有机结合，通过先进的技术手段建立可交互的网络空间数据库管理系统。采用三维可视化技术，实现基于地图方式的林业资源环境网络信息系统。

构建我国农业信息化技术支持体系的探讨

阐述了农业信息化的基本特征,介绍了农业信息化技术支持体系的发展现状,分析了存在的问题,指出现阶段加快农业信息技术体系建设应当重点抓好农业信息贮藏技术的研究、农业信息应用技术的开发、农业信息传播网络的建设。

基于模糊神经网络的航海信息融合技术研究

根据航海信息融合的要求,建立了航海信息融合处理模型,讨论了各种传感器数据的融合处理;采用改进的自适应模糊系统的径向基函数网络的信息融合算法实现目标信息融合,建立数学模型,给出了具体的算法,并通过计算机仿真,验证了该信息融合算法对提高目标数据的精度和可靠性的有效性。

基于Bayes网络的惯导系统多源试验信息融合方法

提出了一种基于Bayes网络的惯导系统多源试验信息融合方法,旨在为惯导系统的精度鉴定工作提供新的研究思路。首先利用环境折合因子建立不同类别试验信息间的相互联系,然后通过Bayes网络构建反映所有变量关系的图形映射模型。根据试验信息,Bayes网络中的MCMC算法可以快速获得环境折合因子等变量的验后统计特性,继而可以实现不同试验条件下惯导系统误差模型的信息折合。针对惯导试验数据的小样本特性,还讨论了两种不同的信息折合方法。地面标定试验和车载试验信息融合的仿真结果表明了上述方法的有效性。