RETRACTED ARTICLE: Tolerance rough set firefly-based quick reduct

In medical information system, there are a lot of features and the relationship among elements is solid. In this way, feature selection of medical datasets gets awesome worry as of late. In this article, tolerance rough set firefly-based quick reduct, is developed and connected to issue of differential finding of diseases. The hybrid intelligent framework intends to exploit the advantages of the fundamental models and, in the meantime, direct their restrictions. Feature selection is procedure for distinguishing ideal feature subset of the original features. A definitive point of feature selection is to build the precision, computational proficiency and adaptability of expectation strategy in machine learning, design acknowledgment and information mining applications. Along these lines, the learning framework gets a brief structure without lessening the prescient precision by utilizing just the chose remarkable features. In this research, a hybridization of two procedures, tolerance rough set and as of late created meta-heuristic enhancement calculation, the firefly algorithm is utilized to choose the conspicuous features of medicinal information to have the capacity to characterize and analyze real sicknesses. The exploratory results exhibited that the proficiency of the proposed system outflanks the current supervised feature selection techniques.

     

Minimizing risk using prediction uncertainty in neural networkestimation fusion and its application to papermaking

The paper presents Bayesian information fusion theory in the context of neural-network model combination. It shows how confidence measures can be combined with individual model estimates to minimize risk through the fusion process. The theory is illustrated through application to the real task of quality prediction in the papermaking industry. Prediction uncertainty estimates are calculated using approximate Bayesian learning. These are incorporated into model combination as confidence measures. Cost functions in the fusion center are used to control the influence of the confidence measures and improve the performance of the resultant committee.     

On detecting co-resident cloud instances using network flow watermarking techniques

Virtualization is the cornerstone of the developing third-party compute industry, allowing cloud providers to instantiate multiple virtual machines (VMs) on a single set of physical resources. Customers utilize cloud resources alongside unknown and untrusted parties, creating the co-resident threat—unless perfect isolation is provided by the virtual hypervisor, there exists the possibility for unauthorized access to sensitive customer information through the exploitation of covert side channels. This paper presents co-resident watermarking, a traffic analysis attack that allows a malicious co-resident VM to inject a watermark signature into the network flow of a target instance. This watermark can be used to exfiltrate and broadcast co-residency data from the physical machine, compromising isolation without reliance on internal side channels. As a result, our approach is difficult to defend against without costly underutilization of the physical machine. We evaluate co-resident watermarkingunder a large variety of conditions, system loads and hardware configurations, from a local laboratory environment to production cloud environments (Futuregrid and the University of Oregon’s ACISS). We demonstrate the ability to initiate a covert channel of 4 bits per second, and we can confirm co-residency with a target VM instance in $<$ 10 s. We also show that passive load measurement of the target and subsequent behavior profiling is possible with this attack. We go on to consider the detectability of co-resident watermarking, extending our scheme to create a subtler watermarking attack by imitating legitimate cloud customer behavior. Our investigation demonstrates the need for the careful design of hardware to be used in the cloud.     

Using Courseware Discussion Boards to Engage Graduate Students in Online Library Workshops

ABSTRACT

Librarians at Oregon State University (OSU) Libraries used the discussion board features of Blackboard courseware to create an interactive experience for graduate students at a distance who could not attend the on-campus “Literature Review Workshops.” These recently developed workshops have been extremely popular with graduate students across the disciplines and have generated a growing demand from distance education graduate students and faculty to offer similar information online. Reluctant to simply deliver content via an online tutorial, librarians sought to duplicate the workshop atmosphere by making the sessions available for a short time-period online, asking participants to respond to discussion questions at specific points in the workshop, and offering audio-mediated online demonstrations of tools and resources. Student feedback and follow-up requests for more workshops support the perception that this approach offered a rewarding learning experience that addressed their specific adult learning needs.     

NATIONAL INSTITUTIONAL FRAMEWORKS AND THE HYBRIDIZATION OF ENTREPRENEURIAL BUSINESS MODELS: THE GERMAN AND UK BIOTECHNOLOGY SECTORS

Given what institutional scholars have described as an inhospitable institutional climate for entrepreneurial business, why has the German biotechnology industry suddenly taken off, while in the UK, where a ''correct'' institutional architecture exists, has the industry shown signs of stagnation? To explain these trends the article develops a firm-centered approach, recognizing that firms work with institutional frameworks - often with help from public policies - to create new business strategies. The argument is developed that such processes are associated with the ''hybridization'' of business strategies at the micro level, combined with the generation of new constellations of particular institutional frameworks within relatively stable national models.     

Quantitative determination of sterols and other alcohols in overland flow from grazing land and possible source materials

Organic marker compounds (biomarkers) can be used to identify the sources of waterborne pollutants. This paper examines sterols and other alcohols in overland flow from pasture-based grazing systems, possible agricultural source materials and water extracts of these source materials as a preliminary step to developing chemical profiles that can be used for tracing pollutants. The biomarkers were quantified using gas chromatography and gas chromatography-mass spectrometry techniques. Analyses of plant material show that some pasture species contain unique compounds, enabling their identification. For example, Arctotheca calendula (capeweed) contains an as yet unidentified compound (Arctotheca m/z 163). Other pasture species that do not contain unique compounds do contain unique ratios of phytol, hexacosanol, octacosanol and 24-ethylcholesterol, enabling their identification. Analyses of faecal samples show that the ratios of sterols to stanols enable faeces to be distinguished from the pasture species, e.g. the ratio of 24-ethylcholesterol to 24-ethylcoprostanol was <1, generally <0.25 for faeces, while for most pasture species this ratio was >4. Using this ratio, qualitative apportioning of the sources of pollutants in overland flow to vegetation or faeces could be performed, but only in extreme cases (i.e. when the ratio <1 or >4). Decaying organic matter and surface soil appear to contain a composite of plant and faecal sterols. Sterols, being sparingly soluble in water and surface active, were not expected to be present in overland flow samples. Surprisingly, cholesterol and 24-ethylcoprostanol were found in both the particulate and filtrate fractions of most overland flow and water extracts of most source materials. Using the ratios of sterols to stanols, particulate organic material in water could be traced back to its broader source, i.e. vegetation or faeces.     

Retraction Note: Tolerance rough set firefly-based quick reduct

Neural Computing and Applications -