An Approach to Select Cost-Effective Risk Countermeasures Exemplified in CORAS

Risk is unavoidable in business and risk management is needed amongst others to set up good security policies. Once the risks are evaluated, the next step is to decide how they should be treated. This involves managers making decisions on proper countermeasures to be implemented to mitigate the risks. The countermeasure expenditure, together with its ability to mitigate risks, is factors that affect the selection. While many approaches have been proposed to perform risk analysis, there has been less focus on delivering the prescriptive and specific information that managers require to select cost-effective countermeasures. This paper proposes a generic approach to integrate the cost assessment into risk analysis to aid such decision making. The approach makes use of a risk model which has been annotated with potential countermeasures, estimates for their cost and effect. A calculus is then employed to reason about this model in order to support decision in terms of decision diagrams. We exemplify the instantiation of the generic approach in the CORAS method for security risk analysis.Comment: 33 page

An Approach for Decision Support on the Uncertainty in Feature Model Evolution

Abstract-Software systems could be seen as a hierarchy of features which are evolving due to the dynamic of the working environments. The companies who build software thus need to make an appropriate strategy, which takes into consideration of such dynamic, to select features to be implemented. In this work, we propose an approach to facilitate such selection by providing a means to capture the uncertainty of evolution in feature models. We also provide two analyses to support the decision makers. The approach is exemplified in the Smart Grid scenario

Assessing a requirements evolution approach: Empirical studies in the air traffic management domain

In this paper, we report the results of the empirical evaluation of a novel approach for modeling and reasoning on evolving requirements. We evaluated the effectiveness of the approach in modeling requirements evolution by means of a series of empirical studies in the air traffic management (ATM) domain

Literature Review of Knowledge Sharing and Issues Raised for Vietnamese Universities

This study aims to review previous studies in the field of knowledge sharing. Data being used in this study was collected from researches related to the topic of knowledge sharing. We summarize literature on knowledge sharing in terms of (i) Necessity of knowledge sharing, (ii) Supporting from information and communication technology tools (ICT), (iii) Context of knowledge sharing, (iv) Participants knowledge sharing, (v) Receiver knowledge and (vi) advantages and disadvantages when participating in knowledge sharing. Some implications are recommended for Vietnamese universities to better support knowledge sharing activities in Vietnam and around the world. Keywords: Knowledge management, Knowledge sharing, Information technology, Management information system. DOI: 10.7176/JESD/10-18-17 Publication date:September 30th 201

The Impact of Social Media Marketing on Brand Awareness and Purchase Intention: Case Study of Vietnam's domestic fashion brands

The study aimed to examine the impact of social media marketing on brand awareness and purchase intention for Vietnamese domestic fashion brands. Quantitative research was conducted on 302 Vietnamese people of Generation Z. The questionnaire designed on Google forms was sent to research samples who were willing to participate. Research results determined the role and benefits of social media marketing in 2 aspects: (1) information about the brand of social media marketing on social networks and (2) brand engagement on social networks. Social media marketing has a positive impact on brand awareness and purchase intention of Vietnamese domestic fashion brands. In particular, brand information when communicating on social networks has a direct and positive impact on brand awareness and purchase intention. Brand engagement on social networks has a positive direct impact on brand awareness and a positive indirect impact on purchase intention through brand awareness. The research results show that Vietnamese domestic fashion brands do quite well in social media marketing, and are highly appreciated by the online community of generation Z in Vietnam. In the future, in order to improve brand awareness and purchase intention, Vietnamese domestic fashion brands need to pay attention to the brand information properties of social media marketing programs and need to invest more in brand engagement characteristics of social networks. Keywords: social media marketing, brand awareness, purchase intentio

Improvement in neoantigen prediction via integration of RNA sequencing data for variant calling

IntroductionNeoantigen-based immunotherapy has emerged as a promising strategy for improving the life expectancy of cancer patients. This therapeutic approach heavily relies on accurate identification of cancer mutations using DNA sequencing (DNAseq) data. However, current workflows tend to provide a large number of neoantigen candidates, of which only a limited number elicit efficient and immunogenic T-cell responses suitable for downstream clinical evaluation. To overcome this limitation and increase the number of high-quality immunogenic neoantigens, we propose integrating RNA sequencing (RNAseq) data into the mutation identification step in the neoantigen prediction workflow.MethodsIn this study, we characterize the mutation profiles identified from DNAseq and/or RNAseq data in tumor tissues of 25 patients with colorectal cancer (CRC). Immunogenicity was then validated by ELISpot assay using long synthesis peptides (sLP).ResultsWe detected only 22.4% of variants shared between the two methods. In contrast, RNAseq-derived variants displayed unique features of affinity and immunogenicity. We further established that neoantigen candidates identified by RNAseq data significantly increased the number of highly immunogenic neoantigens (confirmed by ELISpot) that would otherwise be overlooked if relying solely on DNAseq data.DiscussionThis integrative approach holds great potential for improving the selection of neoantigens for personalized cancer immunotherapy, ultimately leading to enhanced treatment outcomes and improved survival rates for cancer patients

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Managing the Uncertainty of the Evolution of Requirements Models

Evolution is an inevitable phenomenon during the life time of a long-lived software systems due to the dynamic of their working environment. Software systems thus need to evolve to meet the changing demands. A key point of evolution is its uncertainty since it refers to potential future changes to software artifacts such as requirements models. Thus, the selection of evolution-resilient design alternatives for the systems is a significant challenge. This dissertation proposes a framework for modeling evolution and reasoning about it and its uncertainty in requirements models to facilitate the decision making process. The framework provides evolution rules as a means to capture requirements evolution, and a set of evolution metrics to quantify design alternatives of the system. This enables more useful information about to what extent design alternatives could resist to evolution. Thus, it helps decision makers to make strategic moves. Both evolution rules and evolution metrics are backed up with a formal model, which is based on a game-theoretic interpretation, so that it allows a formal semantics understanding of the meaning of the metrics in different scenarios. The proposed framework is supported by a series of algorithms, which automates the calculation of metrics, and a proof-of-concept Computer Aided Software Engineering (CASE) tool. The algorithms calculate metric values for each design alternative, and enumerate possible design alternatives with the best metric values, i.e., winner alternatives. The algorithms have been designed to incrementally react to every single change made to requirements models in an efficient way. The proposed framework is evaluated in a series of empirical studies that took place over a year to evaluate the modeling part of the framework. The evaluation studies used scenarios taken from industrial projects in the Air Traffic Management (ATM) domain. The studies involve different types of participants with different expertise in the framework and the domain. The results from the studies show that the modeling approach is effective in capturing the evolution of complex systems. It is reasonably possible for people, if they are supplied with appropriate knowledge (i.e., knowledge of method for domain experts, knowledge of domain for method experts, and knowledge of both domain and method for novices), to build significantly large models, and identify possible ways for these models to evolve. Moreover, the studies show that obviously there is a difference between domain experts, method experts, and students on the “baseline" (initial) model, but when it comes to model the changes with evolution rules, there is no significant difference. The proposed framework is not only applicable to requirements model, but also other system models like risk assessment. The framework has been adapted to deal with evolving risks in long-lived software systems at a high level of abstraction. It thus could work with many existing risk-assessment methods. In summary, the contribution of this dissertation to the early phase of system development should allow system designers to improve the evolution resilience of long-lived systems