130,363 research outputs found
Classification of chestnuts with feature selection by noise resilient classifiers
No full textIn this paper we solve the problem of classifying chestnut plants according to their place of origin. We compare the results obtained by state of the art classifiers, among which, MLP, RBF, SVM, C4.5 decision tree and random forest. We determine which features are meaningful for the classification, the achievable classification accuracy of these classifiers families with the available features and how much the classifiers are robust to noise. Among the obtained classifiers, neural networks show the greatest robustness to noise
Surgically Returning to Randomized lib(c)
No full textTo strengthen systems against code injection attacks, the write or execute only policy (W + X) and address space layout randomization (ASLR) are typically used in combination. The former separates data and code, while the latter randomizes the layout of a process. In this paper we present a new attack to bypass W + X and ASLR. The state-of-the-art attack against this combination of protections is based on brute-force, while ours is based on the leakage of sensitive information about the memory layout of the process. Using our attack an attacker can exploit the majority of programs vulnerable to stack-based buffer overflows surgically, i.e., in a single attempt. We have estimated that our attack is feasible on 95.6% and 61.8% executables (of medium size) for Intel x86 and x86-64 architectures, respectively. We also analyze the effectiveness of other existing protections at preventing our attack. We conclude that position independent executables (PIE) are essential to complement ASLR and to prevent our attack. However, PIE requires recompilation, it is often not adopted even when supported, and it is not available on all ASLR-capable operating systems. To overcome these limitations, we propose a new protection that is as effective as PIE, does not require recompilation, and introduces only a minimal overhead
A methodology for testing CPU emulators
No full textA CPU emulator is a software system that simulates a hardware CPU. Emulators are widely used by computer scientists for various kind of activities (e.g., debugging, profiling, and malware analysis). Although no theoretical limitation prevents developing an emulator that faithfully emulates a physical CPU, writing a fully featured emulator is a very challenging and error prone task. Modern CISC architectures have a very rich instruction set, some instructions lack proper specifications, and others may have undefined effects in corner cases. This article presents a testing methodology specific for CPU emulators, based on fuzzing. The emulator is “stressed” with specially crafted test cases, to verify whether the CPU is properly emulated or not. Improper behaviors of the emulator are detected by running the same test case concurrently on the emulated and on the physical CPUs and by comparing the state of the two after the execution. Differences in the final state testify defects in the code of the emulator. We implemented this methodology in a prototype (named as EmuFuzzer), analyzed five state-of-the-art IA-32 emulators (QEMU, Valgrind, Pin, BOCHS, and JPC), and found several defects in each of them, some of which can prevent proper execution of programs
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Scholarly Communication and Publishing Lunch and Learn Talk #11: The ULS Open Access Author Fee Fund
No full textAt the May 2014 talk, you will learn about the ULS Open Access Author Fee Fund--what it is, why we do it, how it works, and how the program is going so far
The R&D Tax Incentives
No full textThis article sets out some background information and reflections of the author on the R&D tax incentive schemes included in the Common Corporate Tax Base (CCTB) Proposal. In particular the author analyzes the stimulus to private R&D through ad hoc tax incentives included in the CCTB Proposal and dives into the actual provisions included in the Proposal highlighting the most relevant issues connected with their design and interpretation. Moreover, the author explores the interaction between the CCTB Proposal and the granting by Member States of domestic R&D tax incentives
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