Incorporating a knowledge perspective into security risk assessments

Purpose – Many methodologies exist to assess the security risks associated with unauthorized leakage, modification and interruption of information used by organisations. This paper argues that these methodologies have a traditional orientation towards the identification and assessment of technical information assets. This obscures key risks associated with the cultivation and deployment of organisational knowledge. The purpose of this paper is to explore how security risk assessment methods can more effectively identify and treat the knowledge associated with business processes.

Design/methodology/approach – The argument was developed through an illustrative case study in which a well-documented traditional methodology is applied to a complex data backup process. Follow-up interviews were conducted with the organisation’s security managers to explore the results of the assessment and the nature of knowledge “assets” within a business process.

Findings – It was discovered that the backup process depended, in subtle and often informal ways, on tacit knowledge to sustain operational complexity, handle exceptions and make frequent interventions. Although typical information security methodologies identify people as critical assets, this study suggests a new approach might draw on more detailed accounts of individual knowledge, collective knowledge and their relationship to organisational processes.

Originality/value – Drawing on the knowledge management literature, the paper suggests mechanisms to incorporate these knowledge-based considerations into the scope of information security risk methodologies. A knowledge protection model is presented as a result of this research. This model outlines ways in which organisations can effectively identify and treat risks around process knowledge critical to the business.

A naïve, salience-based method for speaker identification in fiction books

This paper presents a salience-based technique for the annotation of directly quoted speech from fiction text. In particular, this paper determines to what extent a naïve (without the use of complex machine learning or knowledge-based techniques) scoring technique can be used for the identification of the speaker of speech quotes. The presented technique makes use of a scoring technique, similar to that commonly found in knowledge-poor anaphora resolution research, as well as a set of hand-coded rules for the final identification of the speaker of each quote in the text. Speaker identification is shown to be achieved using three tasks: the identification of a speech-verb associated with a quote with a recall of 94.41%; the identification of the actor associated with a quote with a recall of 88.22%; and the selection of a speaker with an accuracy of 79.40%.

The association between investigative interviewers' knowledge of question type and adherence to best-practice interviewing

It is well established that not all investigative interviewers adhere to ‘best-practice’ interview guidelines (i.e., the use of open-ended questions) when interviewing child witnesses about abuse. However, little research has examined the sub skills associated with open question usage. In this article, we examined the association between investigative interviewers' ability to identify various types of questions and adherence to open-ended questions in a standardized mock interview. Study 1, incorporating 27 trainee police interviewers, revealed positive associations between open-ended question usage and two tasks; a recognition task where trainees used a structured protocol to guide their response and a recall task where they generated examples of open-ended questions from memory. In Study 2, incorporating a more heterogeneous sample of 40 professionals and a different training format and range of tests, positive relationships between interviewers' identification of questions and adherence to best-practice interviewing was consistently revealed. A measure of interviewer knowledge about what constitutes best-practice investigative (as opposed to knowledge of question types) showed no association with interviewer performance. The implications of these findings for interviewer training programs are discussed.

Reliable knowledge discovery with a minimal causal model inducer

This paper presents a Minimal Causal Model Inducer that can be used for the reliable knowledge discovery. The minimal-model semantics of causal discovery is an essential concept for the identification of a best fitting model in the sense of satisfactory consistent with the given data and be the simpler, less expressive model. Consistency is one of major measures of reliability in knowledge discovery. Therefore to develop an algorithm being able to derive a minimal model is an interesting topic in the are of reliable knowledge discovery. various causal induction algorithms and tools developed so far can not guarantee that the derived model is minimal and consistent. It was proved the MML induction approach introduced by Wallace, Keven and Honghua Dai is a minimal causal model learner. In this paper, we further prove that the developed minimal causal model learner is reliable in the sense of satisfactory consistency. The experimental results obtained from the tests on a number of both artificial and real models provided in this paper confirm this theoretical result.

Identification of 100 fundamental ecological questions

Fundamental ecological research is both intrinsically interesting and provides the basic knowledge required to answer applied questions of importance to the management of the natural world. The 100th anniversary of the British Ecological Society in 2013 is an opportune moment to reflect on the current status of ecology as a science and look forward to high-light priorities for future work. To do this, we identified 100 important questions of fundamental importance in pure ecology. We elicited questions from ecologists working across a wide range of systems and disciplines. The 754 questions submitted (listed in the online appendix) from 388 participants were narrowed down to the final 100 through a process of discussion, rewording and repeated rounds of voting. This was done during a two-day workshop and thereafter. The questions reflect many of the important current conceptual and technical pre-occupations of ecology. For example, many questions concerned the dynamics of environmental change and complex ecosystem interactions, as well as the interaction between ecology and evolution. The questions reveal a dynamic science with novel subfields emerging. For example, a group of questions was dedicated to disease and micro-organisms and another on human impacts and global change reflecting the emergence of new subdisciplines that would not have been foreseen a few decades ago. The list also contained a number of questions that have perplexed ecologists for decades and are still seen as crucial to answer, such as the link between population dynamics and life-history evolution. Synthesis. These 100 questions identified reflect the state of ecology today. Using them as an agenda for further research would lead to a substantial enhancement in understanding of the discipline, with practical relevance for the conservation of biodiversity and ecosystem function.

Knowledge and perceptions of newly graduated medical practitioners in Malaysia of their role in medical care of people with developmental disabilities

Improving content and consistency on developmental disabilities in undergraduate medical curricula has been recommended as a means of improving health outcomes for people with developmental disabilities. Although often the subject of studies in Western countries, little is known about content on developmental disabilities in undergraduate curricula in developing countries. A study was undertaken to: (1) explore content and experience with developmental disabilities received in undergraduate training by medical practitioners in Malaysia; and (2) explore perceptions of their role in the identification and management of developmental disabilities in practice. Comparisons were made according to location of training. Data were collected using a 107-item questionnaire that was administered to 230 newly graduated house officers on their first rotations in seven public hospitals in Peninsula Malaysia. Deficits and inconsistencies were indicated in both content and experience of developmental disabilities during training. Uncertainty about their role in the identification and management of developmental disabilities was evident. Greater inconsistencies and deficits were evident for respondents trained in Eastern and Middle Eastern countries compared with those trained in Malaysia and Western countries. Results suggest a need for increased content, consistency, and intentional exposure in relation to developmental disabilities during undergraduate training across all training settings.

Cluster identification and separation in the growing self-organizing map: application in protein sequence classification

Growing self-organizing map (GSOM) has been introduced as an improvement to the self-organizing map (SOM) algorithm in clustering and knowledge discovery. Unlike the traditional SOM, GSOM has a dynamic structure which allows nodes to grow reflecting the knowledge discovered from the input data as learning progresses. The spread factor parameter (SF) in GSOM can be utilized to control the spread of the map, thus giving an analyst a flexibility to examine the clusters at different granularities. Although GSOM has been applied in various areas and has been proven effective in knowledge discovery tasks, no comprehensive study has been done on the effect of the spread factor parameter value to the cluster formation and separation. Therefore, the aim of this paper is to investigate the effect of the spread factor value towards cluster separation in the GSOM. We used simple k-means algorithm as a method to identify clusters in the GSOM. By using Davies–Bouldin index, clusters formed by different values of spread factor are obtained and the resulting clusters are analyzed. In this work, we show that clusters can be more separated when the spread factor value is increased. Hierarchical clusters can then be constructed by mapping the GSOM clusters at different spread factor values.

Iridoviruses of copepods: their identification, estuarine ecology and host histopathology

Zooplankton Invertebrate Iridescent viruses (ZoopIIVs: Iridoviridae) were discovered infecting copepods. Assays were developed for monitoring an infectious ZoopIIV; and the virus ultrastructure and host infection sites were described. The pioneering work has advanced research methods and our knowledge of infectious viruses in the zooplankton that provide critical ecosystem services globally

Identification of novel therapeutics for complex diseases from genome-wide association data

Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials.

Understanding context in knowledge translation: a concept analysis study protocol.

AIM: To conduct a concept analysis of clinical practice contexts (work environments) that facilitate or militate against the uptake of research evidence by healthcare professionals in clinical practice. This will involve developing a clear definition of context by describing its features, domains and defining characteristics. BACKGROUND: The context where clinical care is delivered influences that care. While research shows that context is important to knowledge translation (implementation), we lack conceptual clarity on what is context, which contextual factors probably modify the effect of knowledge translation interventions (and hence should be considered when designing interventions) and which contextual factors themselves could be targeted as part of a knowledge translation intervention (context modification). DESIGN: Concept analysis. METHODS: The Walker and Avant concept analysis method, comprised of eight systematic steps, will be used: (1) concept selection; (2) determination of aims; (3) identification of uses of context; (4) determination of defining attributes of context; (5) identification/construction of a model case of context; (6) identification/construction of additional cases of context; (7) identification/construction of antecedents and consequences of context; and (8) definition of empirical referents of context. This study is funded by the Canadian Institutes of Health Research (January 2014). DISCUSSION: This study will result in a much needed framework of context for knowledge translation, which identifies specific elements that, if assessed and used to tailor knowledge translation activities, will result in increased research use by nurses and other healthcare professionals in clinical practice, ultimately leading to better patient care.

The impact of socially responsible human resource management on employees' organizational citizenship behaviour: the mediating role of organizational identification

Based on insights from social exchange and social identity theories, this paper examines the influence of three dimensions of socially responsible human resource management (SR-HRM), namely legal compliance HRM, employee-oriented HRM and general CSR facilitation HRM, on employees' organizational citizenship behaviour (OCB). Structural equation modelling of dyadic data collected from Chinese employees and their direct supervisors in three phases revealed that whilst organizational identification fully mediated the relationship between employee-oriented HRM and employee OCB, general CSR facilitation HRM had a direct effect on employee OCB. In contrast, legal compliance HRM neither influenced employee OCB directly, nor indirectly through organizational identification. The findings highlight the important but complex role played by SR-HRM in eliciting positive employee work outcomes, and contribute to our knowledge of the mechanisms underlying this relationship.

KBRE: a framework for knowledge-based requirements engineering

Detecting inconsistencies is a critical part of requirements engineering (RE) and has been a topic of interest for several decades. Domain knowledge and semantics of requirements not only play important roles in elaborating requirements but are also a crucial way to detect conflicts among them. In this paper, we present a novel knowledge-based RE framework (KBRE) in which domain knowledge and semantics of requirements are central to elaboration, structuring, and management of captured requirements. Moreover, we also show how they facilitate the identification of requirements inconsistencies and other-related problems. In our KBRE model, description logic (DL) is used as the fundamental logical system for requirements analysis and reasoning. In addition, the application of DL in the form of Manchester OWL Syntax brings simplicity to the formalization of requirements while preserving sufficient expressive power. A tool has been developed and applied to an industrial use case to validate our approach.

K-Center: an approach on the multi-source identification of information diffusion

The global diffusion of epidemics, computer viruses, and rumors causes great damage to our society. It is critical to identify the diffusion sources and timely quarantine them. However, most methods proposed so far are unsuitable for diffusion with multiple sources because of the high computational cost and the complex spatiotemporal diffusion processes. In this paper, based on the knowledge of infected nodes and their connections, we propose a novel method to identify multiple diffusion sources, which can address three main issues in this area: 1) how many sources are there? 2) where did the diffusion emerge? and 3) when did the diffusion break out? We first derive an optimization formulation for multi-source identification problem. This is based on altering the original network into a new network concerning two key elements: 1) propagation probability and 2) the number of hops between nodes. Experiments demonstrate that the altered network can accurately reflect the complex diffusion processes with multiple sources. Second, we derive a fast method to optimize the formulation. It has been proved that the proposed method is convergent and the computational complexity is O(mn log α) , where α = α (m,n) is the slowly growing inverse-Ackermann function, n is the number of infected nodes, and m is the number of edges connecting them. Finally, we introduce an efficient algorithm to estimate the spreading time and the number of diffusion sources. To evaluate the proposed method, we compare the proposed method with many competing methods in various real-world network topologies. Our method shows significant advantages in the estimation of multiple sources and the prediction of spreading time.

Asset identification in information security risk assessment: A business practice approach

Organizations apply information security risk assessment (ISRA) methodologies to systematically and comprehensively identify information assets and related security risks. We review the ISRA literature and identify three key deficiencies in current methodologies that stem from their traditional accountancy-based perspective and a limited view of organizational "assets". In response, we propose a novel rich description method (RDM) that adopts a less formal and more holistic view of information and knowledge assets that exist in modern work environments. We report on an in-depth case study to explore the potential for improved asset identification enabled by the RDM compared to traditional ISRAs. The comparison shows how the RDM addresses the three key deficiencies of current ISRAs by providing: 1) a finer level of granularity for identifying assets, 2) a broader coverage of assets that reflects the informal aspects of business practices, and 3) the identification of critical knowledge assets.