Implementing Selective Signature Scanning to Optimize Malware Detection

Signature scanning is one of the oldest types of malware detection, and it remains an essential lightweight detection method for many antivirus programs. However, signature scanning has unavoidable limitations, including an inevitably increasing runtime as malware signature databases continually expand. In this paper, we discuss the current state of signature scanning, including usage of the open-source signature scanning tool YARA. We test Zemlyanaya et al’s assertion that scanning only the beginning and end of files can reduce the runtime cost of signature database expansion — while maintaining a high level of accuracy — and find it inaccurate in the case of general scanning. However, by examining the behavior of specific rules during head-and-foot scanning, we argue that head-and-foot scanning can provide large runtime improvements with minimal accuracy loss, but only for a specific subset of malware signatures. Finally, we argue for further investigation into the prevalence of malware signatures amenable to head-and-foot scanning, as this may enable analysts to improve the runtime of malware detection tools

Senior Recital: Lucas Gray, Baritone

This recital is presented in partial fulfillment of requirements for the degree Bachelor of Music in Music Education. Mr. Gray studies voice with Jana Young.https://digitalcommons.kennesaw.edu/musicprograms/2248/thumbnail.jp

Session A2- Webber Pond Steeppass Fishway

The Webber Pond dam is a concrete, masonry, and earthen structure located on Sevenmile Stream at the outlet of Webber Pond in Vassalboro, Maine. The dam is owned by the Webber Pond Asociation and is adjacent to a boat launch and beach owned by the Town of Vassalboro. Recently an Alaskan Steeppass fish ladder and sorting pool were installed at the dam, along with a new mechanical stoplog system at the pond outlet. Prior to the installation of the fish ladder and sorting pool the dam impeded the passage of upstream migrating fish such as alewives. Before the fishway was installed the Maine Department of Marine, Resources (MDMR) had been netting adult alewives at the base of the dam and lifting them over the dam into the pond for spawning and phosphorous sequestration. The MDMR wanted to improve upstream fish passage into Webber Pond and include a means of checking and sorting fish before they were allowed to pass into the lake. The new fishway provides a means for sorting and harvesting fish, acceptable hydraulics (velocity, angle of approach and turbulence) at the fish ladder entrance, access for cleaning the fish passage of debris, and the protection of the existing bridge foundation located downstream of the dam. The related improvements to the gate structure at the outlet of Webber Pond also provides safe and downstream passage for out migrating alewives and the ability to maintain lake levels while regulating downstream flow

Statistics of opinion domains of the majority-vote model on a square lattice

The existence of juxtaposed regions of distinct cultures in spite of the fact that people's beliefs have a tendency to become more similar to each other's as the individuals interact repeatedly is a puzzling phenomenon in the social sciences. Here we study an extreme version of the frequency-dependent bias model of social influence in which an individual adopts the opinion shared by the majority of the members of its extended neighborhood, which includes the individual itself. This is a variant of the majority-vote model in which the individual retains its opinion in case there is a tie among the neighbors' opinions. We assume that the individuals are fixed in the sites of a square lattice of linear size $L$ and that they interact with their nearest neighbors only. Within a mean-field framework, we derive the equations of motion for the density of individuals adopting a particular opinion in the single-site and pair approximations. Although the single-site approximation predicts a single opinion domain that takes over the entire lattice, the pair approximation yields a qualitatively correct picture with the coexistence of different opinion domains and a strong dependence on the initial conditions. Extensive Monte Carlo simulations indicate the existence of a rich distribution of opinion domains or clusters, the number of which grows with $L^2$ whereas the size of the largest cluster grows with $\ln L^2$. The analysis of the sizes of the opinion domains shows that they obey a power-law distribution for not too large sizes but that they are exponentially distributed in the limit of very large clusters. In addition, similarly to other well-known social influence model -- Axelrod's model -- we found that these opinion domains are unstable to the effect of a thermal-like noise

Quantum memories based on engineered dissipation

Storing quantum information for long times without disruptions is a major requirement for most quantum information technologies. A very appealing approach is to use self-correcting Hamiltonians, i.e. tailoring local interactions among the qubits such that when the system is weakly coupled to a cold bath the thermalization process takes a long time. Here we propose an alternative but more powerful approach in which the coupling to a bath is engineered, so that dissipation protects the encoded qubit against more general kinds of errors. We show that the method can be implemented locally in four dimensional lattice geometries by means of a toric code, and propose a simple 2D set-up for proof of principle experiments.Comment: 6 +8 pages, 4 figures, Includes minor corrections updated references and aknowledgement

KSU Holiday Concert 2017

Kick off your holiday season with the School of Music as we celebrate the season and present holiday favorites performed by the KSU Symphony Orchestra, Wind Ensemble, and choirs. This performance will feature carols sung by KSU choirs, a special Christmas at the Movies medley performed by the KSU Symphony Orchestra including music from Home Alone, How the Grinch Stole Christmas, Polar Express, and more.https://digitalcommons.kennesaw.edu/musicprograms/1992/thumbnail.jp

Triggering redox activity in a thiophene compound: radical stabilization and coordination chemistry

The synthesis, metalation, and redox properties of an acyclic bis(iminothienyl)methene L− are presented. This π-conjugated anion displays pronounced redox activity, undergoing facile one-electron oxidation to the acyclic, metal-free, neutral radical L* on reaction with FeBr2. In contrast, reaction of L− with CuI forms the unique, neutral Cu2I2(L*) complex of a ligand-centered radical, whereas reaction with the stronger oxidant AgBF4 forms the metal-free radical dication L*2+

The Effects of Color on Cognitive Performance

Typically, assessments are administered on white pieces of paper. This research experiment was designed to test the effects of colored paper on an individual’s cognitive performance. Researchers have administered an IQ test to approximately 60 male and female undergraduate participants. Participants were recruited through the NERD System and were given partial credit in their individual classes. The IQ tests were printed on four different colors of paper and given out to participants at random. It was hypothesized that utilizing colored paper on an administered test will increase the individual’s cognitive performance. Results will be discussed in terms of increasing cognitive performance in educational and work environments

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio