Real-time analytics,incident response process agility and enterprise cybersecurity performance: A contingent resource-based analysis

Emerging paradigms of attack challenge enterprise cybersecurity with sophisticated custom-built tools, unpredictable patterns of exploitation, and an increasing ability to adapt to cyber defenses. As a result, organizations continue to experience incidents and suffer losses. The responsibility to respond to cybersecurity incidents lies with the incident response (IR) function. We argue that (1) organizations must develop ‘agility’ in their IR process to respond swiftly and efficiently to sophisticated and potent cyber threats, and (2) Real-time analytics (RTA) gives organizations a unique opportunity to drive their IR process in an agile manner by detecting cybersecurity incidents quickly and responding to them proactively. To better understand how organizations can use RTA to enable IR agility, we analyzed in-depth data from twenty expert interviews using a contingent resource-based view. The results informed a framework explaining how organizations enable agile characteristics (swiftness, flexibility, and innovation) in the IR process using the key features of the RTA capability (complex event processing, decision automation, and on-demand and continuous data analysis) to detect and respond to cybersecurity incidents as-they-occur which, in turn, improves their overall enterprise cybersecurity performance.     

Analytical attractions and the techno-continuum: Conceptualising data obsessions and consequences in elite sport

The proliferation of sports science and technological innovation within performance settings has precipitated the generation of increasing volumes of data to aid athletes. Copious data production has also perpetuated the privileging of scientific information, and a ‘thirst’ for ‘more data’ as an unproblematic ‘truth’. Of significance is not merely the use of technology for the production of data-for-data's sake, or the utility of data for a greater cause (e.g. the good of the team), but the quest for personalised data for individual athletes to be analysed, and reflected upon ad nauseam. Furthering scholarship on disciplining bodies, we argue that increased technological consumption, and the related excessive quantification of athletes’ bodies via data production, adds further insecurity into performance sports work. Finally, attention is given to the cultural step-change new techno-dispositions may now present.     

The intermediating role of organizational culture and internal analytical knowledge between the capability of big data analytics and a firm’s performance

Firms are increasingly relying on business insights obtained by deploying data analytics. Analytics-driven business decisions have thus taken a strategic imperative role for the competitive advantage of a firm to endure. The extent and effectiveness through which business firms can actually derive benefits by deploying big data-based practices requires deep analysis and calls for extensive research. This study extends the big data analytics capability (BDAC) model by examining the mediatory effects of organizational culture (CL) between internal analytical knowledge (KN) and BDAC, as well as the mediating effects of BDAC between CL and firm performance. The findings bring into focus that CL plays the role of complementary mediation between BDAC and KN to positively impact firm performance (FP); BDAC also plays a similar mediatory role between CL and the performance of a firm.     

Negation recognition in medical narrative reports

Substantial medical data, such as discharge summaries and operative reports are stored in electronic textual form. Databases containing free-text clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are such. When searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the documents retrieved will be irrelevant. Hence, negation is a major source of poor precision in medical information retrieval systems. Previous research has shown that negated findings may be difficult to identify if the words implying negations (negation signals) are more than a few words away from them. We present a new pattern learning method for automatic identification of negative context in clinical narratives reports. We compare the new algorithm to previous methods proposed for the same task, and show its advantages: accuracy improvement compared to other machine learning methods, and much faster than manual knowledge engineering techniques with matching accuracy. The new algorithm can be applied also to further context identification and information extraction tasks.

A compressed self-index using a Ziv–Lempel dictionary

A compressed full-text self-index for a text T, of size u, is a data structure used to search for patterns P, of size m, in T, that requires reduced space, i.e. space that depends on the empirical entropy (H _k or H ₀) of T, and is, furthermore, able to reproduce any substring of T. In this paper we present a new compressed self-index able to locate the occurrences of P in O((m + occ)log u) time, where occ is the number of occurrences. The fundamental improvement over previous LZ78 based indexes is the reduction of the search time dependency on m from O(m ²) to O(m). To achieve this result we point out the main obstacle to linear time algorithms based on LZ78 data compression and expose and explore the nature of a recurrent structure in LZ-indexes, the suffix tree. We show that our method is very competitive in practice by comparing it against other state of the art compressed indexes.

基于主题的SE-TextRank 情感摘要方法

文本情感摘要技术的目的是以简洁的形式准确表达文章的核心情感内容。为解决不同的文档结构及内容特征等问题对摘要结果的影响,提出了一种基于主题的SE-TextRank 情感摘要方法。通过LDA 模型自动获取收敛后的文本主题,利用余弦距离算法进行主题句子分组,使用传统多特征融合以及SE-TextRank 情感摘要算法对组内中心句抽取,最终获取目的摘要。实验表明,采用此方法能够更为高效的获取新闻文本摘要结果。     

Identifying Criminal Justice Faculty Research Interests Using Voyant and NVivo

Abstract

Inspired by Gao’s text analysis of communication studies faculty, this study looks at the trends from 176 faculty publications from the criminal justice department at the University of Nevada, Reno. Criminal justice faculty publications are missing from library science text analysis literature. This study uses Voyant and NVivo 12 to analyze words and phrases from titles and abstracts to identify research interests. Voyant itself was found to be sufficient and identified specific research trends such as organizational commitment, amber alerts, and death qualification. This study can assist liaison librarians in selecting between these two tools based on their needs.     

搜索引擎中分类技术研究

本文提出了一种基于主题采集的Web文档自动分类算法,该算法对朴素贝叶斯分类模型进行了改进。利用该算法,我们实现了一个基于主题信息采集的网页分类系统。文中着重对该系统的页面解析、中文分词和文本分类模块进行了论述,并对改进后的贝叶斯分类方法进行了评估。实验结果表明,该算法对网页分类有较高的准确性。     

高职英语教材主课文易读性对比研究

易读性是教材编写中阅读材料选择的重要标准之一,易读性公式虽有诸多缺陷,仍是目前最客观、最有效的易读性测量方式。为了检验目前高职英语教材的易读性现状,为高职英语教材的编写提供信息和为高职英语教材的使用者提供选择的标准,本文就文本总体统计特征和易读性指数两方面调查了三种有代表性的高职英语教材,发现所调查的高职英语教材在易读性的总体控制方面把握得不错,但在细节方面尚有诸多需要完善的地方,同时发现了几个问题急待进一步的研究。     

论描写翻译学的文本对比分析

面对翻译标准的众说纷纭,描写翻译学从宏观的角度研究翻译,给予各种各样的翻译以正确的定位。在进行文本对比分析时,描写翻译学以开放性视角对待源语和译语之间的信息、功能厦文体等方面的关系,能促进翻译的优化,推动翻译研究的新发展。