共查询到20条相似文献，搜索用时 750 毫秒
1.
5月教育网整体运行平稳,未发生重大安全事件。高考临近,各类高校的网站,特别是学校的招生网站和各院系的二级网站,又将成为黑客们攻击的重点目标。网站的管理员应该加强对相关网站的安全扫描和监测,对那些存在严重安全隐患（如存在SQL注入漏洞或是存在上传权限配置错误漏洞等）的网站应该及时进行修补, 相似文献
2.
近期教育网运行平稳,未发生严重的安全事件。近期多个相关部门对教育网内的网站进行安全扫描时发现,仍然有大量的网站存在严重的安全漏洞。最突出的还是我们之前多次提到的Apache Struts远程命令执行漏洞。存在该漏洞的网站很多都是学校的信息系统,如学校的迎新系统、贫困生资助管理系统等。 相似文献
3.
针对当前Web应用漏洞攻击方面常见的SQL注入与溢出、ASP攻击与破坏,使得Web网站系统时刻都在面临着各种威胁,通过列举Web应用程序中不同的入侵漏洞及造成的危害程度,详细分析应用程序代码存在的不足及防范措施,试图从代码设计上提出解决漏洞修补的安全技术与方法,总结出一套完整的防御Web应用漏洞攻击的解决方案,确保整个网络应用服务系统的安全运行。 相似文献
4.
针对当前Web应用漏洞攻击方面常见的SQL注入与溢出、ASP攻击与破坏,使得Web网站系统时刻都在面临着各种威胁,通过列举Web应用程序中不同的入侵漏洞及造成的危害程度,详细分析应用程序代码存在的不足及防范措施,试图从代码设计上提出解决漏洞修补的安全技术与方法,总结出一套完整的防御Web应用漏洞攻击的解决方案,确保整个网络应用服务系统的安全运行。 相似文献
5.
介绍了视频网站的三层架构模式以及目前开发网站涉及到的主流技术和数据库等。从用户对此类网站的操作使用、网站的业务逻辑和网站程序的运行方式等角度,对在这些开发技术下实现的视频网站系统的程序安全性进行了分析,根据目前已知的技术漏洞指出存在安全隐患的各个环节。随后根据这些环节提出了加强视频网站安全性的相应对策。 相似文献
6.
程必荣 《中学语文(读写新空间)》2014,(12):34-36
【文题设计】
阅读下面的材料,根据要求作文。
漏洞是硬件、软件、协议的具体实现或系统安全策略上存在的缺陷,黑客和病毒制造者利用漏洞让病毒或木马侵入他人电脑,从而在未被授权的情况下访问或破坏系统。 相似文献
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正6月教育网整体运行平稳,未发现严重的安全事件。6月乌云网络报来的各类网站漏洞中有两类需要引起关注。一类是多所高校使用一个叫做随缘学校管理系统搭建的学校教务系统存在权限绕过漏洞,攻击者只需伪造相应的cookie参数就能绕过系统限制,直接获取后台管理权限而无需用户名和密码。随缘学校管理系统官方已经在3年前就停止对这款产品的更新维护,就是说如 相似文献
9.
何传跃 《中学语文(读写新空间)》2011,(9):38-40
【文题设计】阅读下面的文字,根据要求作文。
漏洞是在硬件、软件、协议的具体实现或系统安全策略上存在的缺陷．那些黑客和病毒制造者就是利用漏洞．让病毒或木马侵入你的电脑．从而可以使攻击者能够在未授权的情况下访问或破坏系统。 相似文献
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Catherine O'Brien 《Educational research; a review for teachers and all concerned with progress in education》2013,55(3):257-301
Background: Data-based decision-making in education often focuses on the use of summative assessment data in order to bring about improvements in student achievement. However, many other sources of evidence are available across a wide range of indicators. There is potential for school leaders, teachers and students to use these diverse sources more fully to support their work on a range of school improvement goals.Purpose and sources of evidence: To explore data-based decision-making for school improvement, this theoretical paper discusses recent research and literature from different areas of data use in education. These areas include the use of formative assessment data, educational research study findings and ‘big data’. In particular, the discussion focuses on how school leaders and teachers can use different sources of data to improve the quality of education.Main argument: Based on the literature reviewed, an iterative model of data use for school improvement is described, consisting of defining goals for data use, collecting different types of data or evidence (e.g. formal data, informal data, research evidence and ‘big data’), sense-making, taking improvement actions and evaluation. Drawing on the literature, research insights are discussed for each of these components, as well as identification of the research gaps that still exist. It is noted that the process of data use does not happen in isolation: data use is influenced by system, organisation and team/individual level factors.Conclusions: When it comes to using data to improve the quality of teaching and learning, it is evident that some of the most important enablers and barriers include data literacy and leadership. However, what is less well understood is how we can promote the enablers and remove the barriers to unlock, more fully, the potential of data use. Only then can data use lead to sustainable school improvement. 相似文献
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ABSTRACTData use is becoming more important in higher education. In this case study, a team of teachers from a teacher education college was supported in data-based decision making by means of the data team procedure. This data team studied the reasons why students drop out. A team's success depends in part on whether the team is able to develop and apply new knowledge based on data. To investigate this, we focused on the depth of inquiry within the data team's conversations, because successful teams discuss data in depth. We observed the data team for 2 years, investigating factors that affected the depth of the conversations in the data team. The results show that depth was influenced by factors related to data and data systems (such as access to relevant data), individual factors (such as belief in data use), and organisational factors (guidance from the data coach). 相似文献
13.
Big data analytics 总被引：1，自引：0，他引：1
V. Rajaraman 《Resonance》2016,21(8):695-716
The volume and variety of data being generated using computers is doubling every two years. It is estimated that in 2015, 8 Zettabytes (Zetta=1021) were generated which consisted mostly of unstructured data such as emails, blogs, Twitter, Facebook posts, images, and videos. This is called big data. It is possible to analyse such huge data collections with clusters of thousands of inexpensive computers to discover patterns in the data that have many applications. But analysing massive amounts of data available in the Internet has the potential of impinging on our privacy. Inappropriate analysis of big data can lead to misleading conclusions. In this article, we explain what is big data, how it is analysed, and give some case studies illustrating the potentials and pitfalls of big data analytics. 相似文献
14.
Mariah Hagadone-Bedir Rick Voithofer Jessica T. Kulp 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(6):1619-1635
This conceptual study uses dynamic systems theory (DST) and phenomenology as lenses to examine data privacy implications surrounding wearable devices that incorporate stakeholder, contextual and technical factors. Wearable devices can impact people's behaviour and sense of self, and DST and phenomenology provide complementary approaches for emphasizing the subjective experiences of individuals that occur with the use of wearable data. Privacy is approached through phenomenology as an individual's lived bodily experience and DST emphasizes the self-regulation and feedback loops of individuals and their uses of wearable data. The data collection, analysis and communication of wearable data to support learning systems alongside privacy implications for each are examined. The IoT, cloud computing, metadata and algorithms are discussed as they relate to wearable data, pointing out privacy risks and strategies to minimize harm.
Practitioner notes
What is already known about this topic
- Data privacy is a complex topic and is approached through different perspectives, influencing the degree of an individual's data autonomy.
- Wearable technology is increasing in the consumer market and offers great potential to learning environments.
What this paper adds
- Extends extant literature on dynamic systems theory and phenomenology, contributing these perspectives to educational research in the context of student data privacy and wearable technologies.
- Provides a framework to understand the complex and contingent ways that privacy can be understood in the collection, analysis, and communication of wearable data to support learning.
Implications for practice and/or policy
- Higher education faculty and educational policymakers should consider various interactions in systems and among systems of how wearable data collection may be analysed, communicated and stored, potentially exposing students to privacy harms.
- Multiple actors in learning systems must engage in continuous and evolving feedback loops around data security, consent, ownership and control to determine who has access to student data, how it is used and for what purposes.
- The EU's General Data Protection and Regulation offers one of the most comprehensive frameworks for higher education institutions and faculty around the world to follow for protecting student data privacy.
15.
数字时代,司法程序越来越“智慧化”,大数据证据的运用也越来越广泛,但这种嬗变给审判场域被告人质证权的行使带来了许多障碍。一方面,对于大数据证据所属证据种类尚存争议,加之被追诉方获取数据渠道的闭塞性和分析数据能力的有限性造成了数字时代新的“控辩鸿沟”,甚至出现了“数据倾倒”,无法实现控辩平等武装。另一方面,法官过度信任大数据证据,对该类证据并不进行充分实质的质证,有违“以审判为中心”的要求。这就需要突破法定证据种类的藩篱,明确大数据证据作为独立的新证据种类的地位并适用补强证据规则,赋予被追诉方数据访问权,强制大数据证据收集方出庭接受质证并允许被追诉方聘请专家对大数据证据发表意见,从而实现对大数据证据的质证。 相似文献
16.
Despite the widespread popularity of growth curve analysis, few studies have investigated robust growth curve models. In this article, the t distribution is applied to model heavy-tailed data and contaminated normal data with outliers for growth curve analysis. The derived robust growth curve models are estimated through Bayesian methods utilizing data augmentation and Gibbs sampling algorithms. The analysis of mathematical development data shows that the robust latent basis growth curve model better describes the mathematical growth trajectory than the corresponding normal growth curve model and can reveal the individual differences in mathematical development. Simulation studies further confirm that the robust growth curve models significantly outperform the normal growth curve models for both heavy-tailed t data and normal data with outliers but lose only slight efficiency for normal data. It appears convincing to replace the normal distribution with the t distribution for growth curve analysis. Three information criteria are evaluated for model selection. Online software is also provided for conducting robust analysis discussed in this study. 相似文献
17.
黎永壹 《钦州师范高等专科学校学报》2013,(8):13-16
根据沿岸海洋机动调查测量数据传输的需求,提出以Mono Touch技术实现对海洋机动测量数据的传输,首先在iPhone上使用SQLite嵌入式数据及SOAP、REST方式处理数据;其次使用iPhone SDK定位并显示地图,利用Mono Touch技术显示数据表及其导航;最后由Mono Touch UIDevice类连接传感器,将iPhone终端上的数据表传输到服务器.形成移动终端设备与中央服务器数据的互操作模式,从而实现了海洋数据传输的实时性、可靠性和便捷性. 相似文献
18.
Qinyi Liu Mohammad Khalil 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(6):1715-1747
The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross-regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence-based, thereby increasing the trustworthiness of learning analytics and its usefulness.
Practitioner notes
What is already known about this topic- Research on privacy and data protection in learning analytics has become a recognised challenge that hinders the further expansion of learning analytics.
- Proposals to counter the privacy and data protection issues in learning analytics are blurry; there is a lack of a summary of previously proposed solutions.
- Establishment of what privacy and data protection issues exist at different phases of the learning analytics cycle.
- Identification of how different stakeholders view privacy, similarities and differences, and what factors influence their views.
- Evaluation and comparison of previously proposed solutions that attempt to address privacy and data protection in learning analytics.
- Privacy and data protection issues need to be viewed in the context of the entire cycle of learning analytics.
- Stakeholder views on privacy and data protection in learning analytics have commonalities across contexts and differences that can arise within the same context. Before implementing learning analytics, targeted research should be conducted with stakeholders.
- Solutions that attempt to address privacy and data protection issues in learning analytics should be put into practice as far as possible to better test their usefulness.
19.
对非精确数据包络分析（IDEA）的研究进行了简要的归纳与总结．首先,简要回顾了有关I—DEA的研究进展;其次,给出了非精确数据的定义与分类,将非精确型数据分为：边界数据、弱序数据、强序数据和比值有界数据;同时给出了几个基本假设和相关模型;接着,构建了一个适用于非精确型数据的IDEA模型,并且给出了尺度变换法和变量交替法以实现将非线性规划问题转换成线性规划问题;最后,为了提高求解效率,对Zhu提出的简化变量交替法进行了改进,指出这将减少实际应用中的计算复杂度． 相似文献
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This article is an argument about something that is both important and severely underemphasized in most current science curricula.
The empirical attitude, fundamental to science since Galileo, is a habit of mind that motivates an active search for feedback
on our ideas from the material world. Although more simple views of science manifest the empirical attitude through relation
of theories to data, we describe more recent philosophical scholarship that characterizes the relation of theories to data
through phenomena (regularities in nature’s behavior that can be identified and characterized through data). This view highlights the centrality
of material
practice, in which scientists design data collection events to inform phenomena. Thus manifestation of the empirical attitude in science
is characterized as a design endeavor that involves considerably sophisticated coordination among theories, phenomena, data,
and data collection events. If we want students to learn how to participate in such work, curricula should break down these
complex processes into more basic components at least at the outset. Our recommendation is to begin with design activities
that can focus on the empirical attitude initially without the complex coordination with phenomena and data. We present an
example of such an activity and share results that suggest design activities can target the empirical attitude and be built
upon in curricula to gradually include coordination with phenomena and theories. 相似文献