HID RFID标签助力澳大利亚海关与边境保护署资产管理

澳大利亚海关与边境保护署(Australian Customs and Border Protection Service)严密保障澳大利亚边境安全,并与政府部门以及各国机构紧密合作,以检查并堵塞非法出入境的货物与人员,防范贩毒、恐怖、走私等日渐猖獗并威胁社会安全的活动.     

基于防火墙的网络安全技术分析

当前,随着计算机技术的发展,网络安全的问题引起了人们的重视.防火墙作为网络安全技术的重要内容,在防止非法入侵,杜绝非法访问等方面发挥着重要的作用.本文主要从网络安全和防火墙的基本内容开始分析,重点探讨了防火墙技术的关键点,同时就防火墙的发展趋势问题提出了个人的见解.     

多要素约束下的非法游行路径动态预测方法

非法游行事件严重危害社会秩序,非法游行群体移动路径的精准预测对区域警力的高效布控具有重要指导作用.现有相关研究无法同时顾及游行事件中吸引要素和排斥要素对游行群体路径选择的综合影响,导致对游行群体未来移动路径的预测精度低.为此,提出了一种多要素约束下的非法游行路径动态预测方法.首先借助距离衰减理论,耦合游行群体实时位置和地理多要素空间分布构建动态加权无向图,在此基础上建立多要素约束的路径优化数学模型,利用Floyd算法求解游行群体当前位置与目的地间的移动路径.通过对3个非法游行历史事件进行实验对比分析发现,所提方法预测结果与真实游行路径在长度、路段和节点3个维度的吻合度均高于80％,比现有方法更精准有效.     

基于文本挖掘的论坛热点问题时变分析

随着互联网的普及,网络论坛因其开放性和自由性成为了舆论的发源地,真实地反映了当前的新闻热点和当前民众最关心的话题。本文基于网易论坛上的网上谈兵栏目,对相关帖子进行文本挖掘,通过改进传统的网络爬虫技术对指定论坛网页进行爬取,设计了关键词权重计算公式提取关键词,依据关键词归纳总结热点话题,提出话题匹配规则对话题帖子进行定位匹配。通过分析热点话题随时间变化的趋势,把握网络舆论的动向,并对话题进一步的发展趋势做出一定预测,为相关部门及时、准确把握网络舆论动态走向提供了可靠依据。     

僵尸网络综述

近年来,从传统蠕虫和木马发展形成的僵尸网络逐渐成为攻击者手中最有效的攻击平台,甚至可以成为网络战的武器,因此,关注僵尸网络已有研究成果与发展趋势都十分必要.将僵尸网络的发展历程概括为5个阶段,分析各阶段特点和代表性僵尸网络.对僵尸网络进行形式化定义并依据命令控制信道拓扑结构将其划分为4类.同时,将当前僵尸网络研究热点归纳为检测、追踪、测量、预测和对抗5个环节,分别介绍各环节的研究状况,并对每个环节的研究进展进行归纳和分析.通过研究僵尸网络在攻防对抗中的演进规律,提取僵尸网络存在的不可绕过的脆弱性.最后,综合分析当前僵尸网络研究现状,并展望僵尸网络发展趋势.     

使用移动平均线预测云平台服务性能趋势

系统性能预测为有预见性的资源调度提供依据,是云平台管理的重要方面。从数据收集、数据处理和预测方法三个方面总结性能预测方法。提出采用移动平均线方法来预测服务性能的长期发展趋势。针对性能小范围波动时会频繁改变预测信号的问题,进一步改进此方法,引入标准差以有效过滤抖动信号。在亚马逊弹性计算云环境下验证了方法的有效性。     

关于MCAI的研究

深入研究了MCAI的理论基础、媒体类型及特性、开发平台特点和课件制作策略 ,预测了其发展趋势 ,并对当前MCAI存在的不足提出了自己的看法     

欧盟呼吁加强打击垃圾邮件力度

尽管欧盟现有的法律认定垃圾邮件是非法的,但欧盟仍然受到了来自内部和外部非法网络活动的骚扰。欧盟委员会呼吁各国执法机构加强打击垃圾邮件等非法在线活动的力度。     

“北斗“出招

在频繁的经贸往来中,世界范围内的人员流动范围更大、更快捷,其中非法出入境和跨国境犯罪活动也层出不穷,这给许多国家的经济和社会安定带来了很多问题.在经济全球化、信息网络化的今天,人们对安全的理解和需求发生了很大变化.但无论何时,保证国家安全,都是稳定发展的基础.     

基于大数据的两客一危全过程安全监管模型及平台

随着公路网络的建设完善,区域间运输效率得到极大提升。但是如何提高道路交通运输安全水平,是当前亟需解决的重大课题。在此背景下,针对道路交通运输中"两客一危"营运车辆的交通运行安全行为展开研究,首先分析了"两客一危"营运车辆的运行特征,然后构建了"两客一危"营运车辆非法运营判别算法模型,最后基于大数据技术设计了"两客一危"非法营运动态监管全过程安全监管系统框架。应用表明,基于大数据的"两客一危"非法营运动态监管算法,能够准确识别"两客一危"车辆的疑似非法运营行为,为综合执法提供有效依据。     

Rationale Visualization for Safety and Security

In safety and security domains where objects of interest (OOI), such as people, vessels, or transactions, are continuously monitored, automated reasoning is required due to their sheer number and volume of information. We present a method to visually explain the rationale of a reasoning engine that raises an alarm if a certain situation is reached. Based both on evidence from heterogeneous and possibly unreliable sources, and on a domain specific reasoning structure, this engine concludes with a certain probability that, e.g., the OOI is suspected of smuggling. To support decision making, we visualize the rationale, an abstraction of the complicated reasoning structure. The evidence is displayed in a color‐coded matrix that easily reveals if and where observations contradict. In it, domain and operational experts can quickly understand and find complicated patterns and relate them to real‐world situations. Also, two groups of these experts evaluate our system through maritime use cases based on real data.     

聊天室客户行为距离计算方法

随着互联网的飞速发展网络在人们的生活中扮演着越来越重要的角色,尽管人们使用互联网的目的多种多样,但通过互联网进行人际交往是主要目的之一。互联网的出现极大地改变了人类社会交往的形态,并很快成为人际交往的重要平台。人与人之间心灵上的距离拉近了,也正是由于这种便利,给那些不法分子带来了可乘之机。利用在线聊天进行网络犯罪的案例越来越多,犯罪分子所用的心理上的战术可谓是高招百出,让人防不胜防。因此针对客户的聊天信息进行分析识别的理想方法尤为重要。本文提出了隐语识别、毒语识别、热语识别、恐怖语识别、走私语识别、人贩子语言识别、黄语识别与群类型识别的基本方法,依据基本识别方法建立客户行为定性分析的评价模型,提出了客户行为距离计算方法,为聊天室信息内容识别监控系统及互联网计算机犯罪的预警系统提供了支持基础。     

Using new attribute construction to incorporate the expertise of human experts into a smuggling vessels classification system

Modifying the database originally designed for maritime transportation and harbourside sailing safety management in order to train the data classifier to detect smuggling and illegal immigrant vessels may lead to a lack of class-related attributes, and the accuracy rate of classification may be low. However, some valuable artificial classification rules already exist prior to the building of a data classifier, and it is worth attempting to incorporate them into the database and thus build a more accurate data classifier. With the limiting condition that no extra reference point of the terrain can be added into the system, this research selects the artificial smuggling detection rule “vessels sail fast towards shore” deduced by Coast Guard’s officers, sets the radar station as the reference point of the shore coordinates, and defines a new attribute, the absolute difference between the anti-course and target azimuth, to demonstrate the “vessels sail approximately towards radar station or sea” concept. Matching the original “speed” attribute, the newly-modified database includes the intrinsic meaning of “vessels sail fast towards radar station or sea”. The results show that the accuracy rate of the two data classifiers, the back-propagation neural network (BPN) and support vector machine (SVM), improved by 22% and 14%, respectively. It is significant that incorporation of the expertise of human experts by new attribute construction contributes to the effective learning of the data classifier.     

Inference of multi-variable trends in unmeasured process quantities

In many applications the trends of unmeasured process quantities are of high interest. Often, these trends can be inferred with high confidence from the trends of some measured process quantities. For example, steady-state detection addresses a trend in the unmeasured state variables, but is usually inferred from stationarity of the measurements. This paper provides a method to analyze the confidence with which trends in measurements imply trends in unmeasured quantities. Thus, trends of unmeasured quantities can be inferred from measured trends with known accuracy. The suggested method, for example, facilitates the selection of a trend error in the measurements to bound the loss of profit in real-time optimization due to deviations from steady-state. Furthermore, the method can also be employed to assess candidate measurement configurations in a plant with respect to the achievable accuracy of steady-state or more general trend detection.     

Application of piecewise linear regression in the detection of vegetation greenness trends on the Tibetan Plateau

Vegetation plays an important role in regulating the climate system and terrestrial carbon cycle. In recent decades, numerous studies have focused on monitoring vegetation greenness changes at different scales with long-term remote-sensing data. Most previous studies adopted a monotonic linear regression approach (one-piece linear regression) to detect vegetation greenness trends at global and regional scales. However, the monotonic linear regression approach is limited in its ability to detect abrupt changes in vegetation greenness trends because non-monotonic phenomena may exist in these trends. This study applied a piecewise linear regression (PLR) method to detect the non-monotonic trends of vegetation greenness in the Tibetan Plateau from 1982 to 2006. The vegetation greenness was indicated by the normalized difference vegetation index (NDVI) data derived from Global Inventory Modelling and Mapping Studies (GIMMS) Advanced Very High Resolution Radiometer (AVHRR) data. The results implied that the PLR method could detect an abrupt change in the trend with a break point (i.e. the year when the vegetation greenness trend changed abruptly) mainly concentrated in 1989. We chose three typical regions to compare the PLR and monotonic linear regression approach for vegetation trend detection to verify the effectiveness of the PLR method. The PLR method can detect different trends during different periods, but the one-piece linear regression method can only detect monotonic trends. After comparing the results of the two methods for three climatic zones, we found that the greenness trend detected by PLR can be better explained by temperature and precipitation variations. Our results illustrate that the PLR method is superior to the one-piece linear regression method due to its ability to detect non-monotonic trends. It can, therefore, delineate vegetation greenness trends in detail and be applied to other, similar studies.     

基于时间序列的音乐流行趋势预测研究

在大数据环境下,对音乐及听众的历史数据进行分析,可以实现对音乐流行趋势较为准确的预测。在STL、Holt Winters分解模型的基础上,提出TSMP算法。该算法从长期趋势和周期两方面进行分析,对长期趋势编码和分类并基于类别最优值选择法对音乐流行趋势进行预测。基于TSMP算法,进而提出E TSMP算法,该算法基于子序列模式匹配法及对近期发布新专辑的附加处理,实现更精准的预测。在清华大学和阿里云天池大数据竞赛平台承办的“2016中国高校计算机大赛——大数据挑战赛之阿里音乐流行趋势预测”比赛中,参赛团队凭借提出的E TSMP算法对2016年9月～10月艺人的播放量实现了较好的预测,并在此次比赛中夺得亚军。     

An empirical study of programming language trends

Predicting software engineering trends is a strategically important asset for both developers and managers, but it's also difficult, due to the wide range of factors involved and the complexity of their interactions. This paper reveals some interesting trends and a method for studying other important software engineering trends. This article trades breadth for depth by focusing on a small, compact set of trends involving 17 high-level programming languages. We quantified many of their relevant factors, and then collected data on their evolution over 10 years. By applying statistical methods to this data, we aim to gain insight into what does and does not make a language successful.     

嵌入式税控收款机的测试技术

税控收款机是国家实施金税工程以来的又一个新兴产业。税控收款机是指具有特定税控功能的电子收款机。所谓特定税控功能是指能够保证数据的正确性、可靠存储、安全传输,并可实现税务管理和稽查等功能。本文介绍嵌入式税控收款机的特点及其系统测试平台和测试方法;结合相关LED驱动模块为例分析其测试技术。     

The application of polynomial analyses to detect global vegetation dynamics during 1982–2012

Use of the normalized difference vegetation index (NDVI) to build long-term vegetation trends is one of the most effective techniques for identifying global environmental change. Trend identification can be achieved by ordinary least squares (OLS) analysis or the Theil–Sen (TS) procedure with a Mann–Kendall (MK) significance test, and these linear regression approaches have been widely used. However, vegetation changes are not linear, and thus the response of vegetation to global climate change may follow non-linear trends. In this article, a polynomial trend-fitting method, which uses stepwise regression and expands on previous research, is presented. With an improved fitting ability, this procedure may reveal trends that were concealed by linear fitting methods. Globally, the traditional TS-MK method reveals significant greening trends for 37.27% of vegetated land, and significant browning trends for 7.98%. Using the polynomial analysis, 34.62% of pixels were fitted by high-order trends. The significant greening trends covered up to 30% of cultivated land, thus indicating that cultivated vegetation may be increasing faster than natural vegetation. Significant vegetation browning mostly occurred in sparse vegetation areas, which suggests that vegetation growth may be more sensitive to climate change in arid regions. Our results show that use of polynomial analysis can help further elucidate global NDVI trends.     

Identification of trends from patents using self-organizing maps

Patent users such as governments, inventors, and manufacturing organizations strive to identify the directions in which new technology is advancing, and their goal is to outline the boundaries of existing knowledge. The paper analyzes patent knowledge to identify research trends. A model based on knowledge extraction from patents and self-organizing maps for knowledge representation is presented. The model was tested on patents from the United States Patent and Trademark Office. The experiments show that the method provides both an overview of the directions of the trends and a drill-down perspective of current trends.