Miracle-dz/UniFAR-FTUs huggingface dataset:
A Framework for Understanding Dynamic Anti-Analysis Defenses

Malicious code often use a variety of anti-analysis and anti-tampering defenses to hinder analysis. Researchers trying to understand the internal logic of the malware have to penetrate these defenses. Existing research on such anti-analysis defenses tend to study them in isolation, thereby failing to see underlying conceptual similarities between different kinds of anti-analysis defenses. This paper proposes an information-flow-based framework that encompasses a wide variety of anti-analysis defenses. We illustrate the utility of our approach using two different instances of this framework: self-checksumming-based anti-tampering defenses and timing-based emulator detection. Our approach can provide insights into the underlying structure of various anti-analysis defenses and thereby help devise techniques for neutralizing them.
Event Alignment for Cross-Media Feature Extraction in the Football Domain

This paper describes an experiment in creating cross-media descriptors from football-related text and videos. We used video analysis results and combined them with several textual resources - both semi- structured (tabular match reports) and unstructured (textual minute-by-minute match reports). Our aim was to discover the relations among six video data detectors and their behavior during a time window that corresponds to an event described in the textual data. The experiment shows how football events extracted from text can be mapped to corresponding scenes in video and how this may help in extracting event-specific video detectors.
A Real-time Personalized Gesture Interaction System Using Wii Remote and Kinect for Tiled-Display Environment

Gesture interaction is more convenient than traditional input methods in tiled-display environments. As the advance of somatosensory technologies, more and more somatosensory devices such as Wii Remote and Kinect that can be used in gesture interaction are commercially available. We present a real-time personalized gesture interaction system targeting for two-handed gestures for tiled-display environment in this paper. By applying the dynamic time warping (DTW) algorithm, the system does not require a special training stage and is easy to use. Experiment shows that the combination of acceleration data from Wii Remote and skeleton point data from Kinect leads to higher recognition accuracy with lower recognition error rate, and the system can also recognize an ongoing gesture before it ends while the recognition accuracy is kept high enough. Our usage of this system in a real tiled-display environment shows that the system is practical.
Achievable secure degrees of freedom of MISO broadcast channel With alternating CSIT

We study the problem of secure transmission over a two-user Gaussian multi-input single-output (MISO) broadcast channel under the assumption that the channel to each receiver is conveyed either perfectly (P) or with delay (D) to the transmitter. Denoting S<sub>1</sub> and S<sub>2</sub> to be the channel state information at the transmitter (CSIT) of user 1 and user 2, respectively; the overall CSIT can then alternate between four states, i.e., (S<sub>1</sub>, S<sub>2</sub>) ∈ {P,D}<sup>2</sup>. We denote λ<sub>S1S2</sub> be the fraction of time the state S<sub>1</sub>S<sub>2</sub> occurs, and focus on the symmetric case such that λ<sub>S1S2</sub> = λ<sub>S2S1</sub>. Under these assumptions, we first consider the Gaussian MISO wiretap channel and characterize the secure degrees of freedom (SDoF). Next, we generalize this model to the two-user Gaussian MISO broadcast channel and establish an inner bound on the SDoF region. This result shows the synergistic SDoF gains of alternating CSIT and illustrates that, as opposed to encoding separately over different states, an improved SDoF region is achievable by joint encoding across these states.
Tsunami propagation modelling – a sensitivity study

Abstract. Indian Ocean (2004) Tsunami and following tragic consequences demonstrated lack of relevant experience and preparedness among involved coastal nations. After the event, scientific and forecasting circles of affected countries have started a capacity building to tackle similar problems in the future. Different approaches have been used for tsunami propagation, such as Boussinesq and Nonlinear Shallow Water Equations (NSWE). These approximations were obtained assuming different relevant importance of nonlinear, dispersion and spatial gradient variation phenomena and terms. The paper describes further development of original TUNAMI-N2 model to take into account additional phenomena: astronomic tide, sea bottom friction, dispersion, Coriolis force, and spherical curvature. The code is modified to be suitable for operational forecasting, and the resulting version (TUNAMI-N2-NUS) is verified using test cases, results of other models, and real case scenarios. Using the 2004 Tsunami event as one of the scenarios, the paper examines sensitivity of numerical solutions to variation of different phenomena and parameters, and the results are analyzed and ranked accordingly.
Collagen Self-Assembly on Orthopedic Magnesium Biomaterials Surface and Subsequent Bone Cell Attachment

Magnesium (Mg) biomaterials are a new generation of biodegradable materials and have promising potential for orthopedic applications. After implantation in bone tissues, these materials will directly interact with extracellular matrix (ECM) biomolecules and bone cells. Type I collagen, the major component of bone ECM, forms the architecture scaffold that provides physical support for bone cell attachment. However, it is still unknown how Mg substrate affects collagen assembly on top of it as well as subsequent cell attachment and growth. Here, we studied the effects of collagen monomer concentration, pH, assembly time, and surface roughness of two Mg materials (pure Mg and AZ31) on collagen fibril formation. Results showed that formation of fibrils would not initiate until the monomer concentration reached a certain level depending on the type of Mg material. The thickness of collagen fibril increased with the increase of assembly time. The structures of collagen fibrils formed on semi-rough surfaces of Mg materials have a high similarity to that of native bone collagen. Next, cell attachment and growth after collagen assembly were examined. Materials with rough surface showed higher collagen adsorption but compromised bone cell attachment. Interestingly, surface roughness and collagen structure did not affect cell growth on AZ31 for up to a week. Findings from this work provide some insightful information on Mg-tissue interaction at the interface and guidance for future surface modifications of Mg biomaterials.
The constrained-monad problem

In Haskell, there are many data types that would form monads were it not for the presence of type-class constraints on the operations on that data type. This is a frustrating problem in practice, because there is a considerable amount of support and infrastructure for monads that these data types cannot use. Using several examples, we show that a monadic computation can be restructured into a normal form such that the standard monad class can be used. The technique is not specific to monads, and we show how it can also be applied to other structures, such as applicative functors. One significant use case for this technique is domain-specific languages, where it is often desirable to compile a deep embedding of a computation to some other language, which requires restricting the types that can appear in that computation.
Baseline Levels of Influenza-Specific CD4 Memory T-Cells Affect T-Cell Responses to Influenza Vaccines

Background Factors affecting immune responses to influenza vaccines have not been studied systematically. We hypothesized that T-cell and antibody responses to the vaccines are functions of pre-existing host immunity against influenza antigens. Methodology/Principal Findings During the 2004 and 2005 influenza seasons, we have collected data on cellular and humoral immune reactivity to influenza virus in blood samples collected before and after immunization with inactivated or live attenuated influenza vaccines in healthy children and adults. We first used cross-validated lasso regression on the 2004 dataset to identify a group of candidate baseline correlates with T-cell and antibody responses to vaccines, defined as fold-increase in influenza-specific T-cells and serum HAI titer after vaccination. The following baseline parameters were examined: percentages of influenza-reactive IFN-γ+ cells in T and NK cell subsets, percentages of influenza-specific memory B-cells, HAI titer, age, and type of vaccine. The candidate baseline correlates were then tested with the independent 2005 dataset. Baseline percentage of influenza-specific IFN-γ+ CD4 T-cells was identified as a significant correlate of CD4 and CD8 T-cell responses, with lower baseline levels associated with larger T-cell responses. Baseline HAI titer and vaccine type were identified as significant correlates for HAI response, with lower baseline levels and the inactivated vaccine associated with larger HAI responses. Previously we reported that baseline levels of CD56dim NK reactivity against influenza virus inversely correlated with the immediate T-cell response to vaccination, and that NK reactivity induced by influenza virus depended on IL-2 produced by influenza-specific memory T-cells. Taken together these results suggest a novel mechanism for the homeostasis of virus-specific T-cells, which involves interaction between memory helper T-cells, CD56dim NK and DC. Significance These results demonstrate that assessment of baseline biomarkers may predict immunologic outcome of influenza vaccination and may reveal some of the mechanisms responsible for variable immune responses following vaccination and natural infection.
Vortex pair creation on brane-antibrane pair via marginal deformation

It has been conjectured that the vortex solution on a D-brane anti-D-brane system represents a D-brane of two lower dimension. We establish this result by first identifying a series of marginal deformations which create the vortex-antivortex pair on the brane-antibrane system, and then showing that under this series of marginal deformations the original D-brane anti-D-brane system becomes a D-brane anti-D-brane system with two lower dimensions. Generalization of this construction to the case of solitons of higher codimension is also discussed.
Computing prime implicates by pruning the search space and accelerating subsumption

The prime implicate trie (pi-trie) of a logical formula is a tree whose branches are labeled with the prime implicates of the formula. An algorithm that builds the pi-trie from a logical formula was introduced in [12]; it builds the trie recursively and makes extensive use of subsumption testing. A more efficient version in which the use of subsumption is reduced is presented in this paper. The improved algorithm is illustrated with experiments. The algorithm naturally lends itself to selecting restricted sets — for example, only positive prime implicates or only those of bounded length, both useful properties for some applications. Such restrictions can provide significant advantages in efficiency since the set of all prime implicates of a logical formula is typically exponential in size.
Human aspects, gamification, and social media in collaborative software engineering

Software engineering is inherently a collaborative venture. In open-source software (OSS) development, such collaborations almost always span geographies and cultures. Because of the decentralised and self-directed nature of OSS as well as the social diversity inherent to OSS communities, the success of an OSS project depends to a large extent on the social aspects of distributed collaboration and achieving coordination over distance. The goal of this dissertation research is to raise our understanding of how human aspects (e.g., gender or cultural diversity), gamification and social media (e.g., participation in social environments such as Stack Overflow or GitHub) impact distributed collaboration in OSS.
EEGVIS: A MATLAB Toolbox for Browsing, Exploring, and Viewing Large Datasets

Recent advances in data monitoring and sensor technology have accelerated the acquisition of very large data sets. Streaming data sets from instrumentation such as multi-channel EEG recording usually must undergo substantial pre-processing and artifact removal. Even when using automated procedures, most scientists engage in laborious manual examination and processing to assure high quality data and to indentify interesting or problematic data segments. Researchers also do not have a convenient method of method of visually assessing the effects of applying any stage in a processing pipeline. EEGVIS is a MATLAB toolbox that allows users to quickly explore multi-channel EEG and other large array-based data sets using multi-scale drill-down techniques. Customizable summary views reveal potentially interesting sections of data, which users can explore further by clicking to examine using detailed viewing components. The viewer and a companion browser are built on our MoBBED framework, which has a library of modular viewing components that can be mixed and matched to best reveal structure. Users can easily create new viewers for their specific data without any programming during the exploration process. These viewers automatically support pan, zoom, resizing of individual components, and cursor exploration. The toolbox can be used directly in MATLAB at any stage in a processing pipeline, as a plug-in for EEGLAB, or as a standalone precompiled application without MATLAB running. EEGVIS and its supporting packages are freely available under the GNU general public license at http://visual.cs. utsa.edu/eegvis.
A Non-Parametric Approach to Multi-Product Pricing

Developed by General Motors (GM), the Auto Choice Advisor web site (http://www.auto choiceadvisor.com) recommends vehicles to consumers based on their requirements and budget constraints. Through the web site, GM has access to large quantities of data that reflect consumer preferences. Motivated by the availability of such data, we formulate a non-parametric approach to multi-product pricing. We consider a class of models of consumer purchasing behavior, each of which relates observed data on a consumer’s requirements and budget constraint to subsequent purchasing tendencies. To price products, we aim at optimizing prices with respect to a sample of consumer data. We offer a bound on the sample size required for the resulting prices to be near optimal with respect to the true distribution of consumers. The bound exhibits a dependence of O(n log n) on the number n of products being priced, showing that – in terms of sample complexity – the approach is scalable to large numbers of products. With regards to computational complexity, we establish that computing optimal prices with respect to a sample of consumer data is NP-complete in the strong sense. However, when prices are constrained by a price ladder – an ordering of prices defined prior to price determination – the problem becomes one of maximizing a supermodular function with real-valued variables. It is not yet known whether this problem is NP-hard. We provide a heuristic for our price-ladder constrained problem together with encouraging computational results. Finally, we apply our approach to a dataset from the Auto Choice Advisor web site. Our analysis provides insights into the current pricing policy at GM and suggests enhancements that may lead to a more effective pricing strategy. 1School of Operations Research and Industrial Engineering, Cornell University, Ithaca, NY, 14853. e-mail: paatrus@orie.cornell. edu 2Department of Management Science and Engineering, Stanford University, Stanford, CA, 94305. e-mail: bvr@stanford.edu 3Department of Management Science and Engineering, Stanford University, Stanford, CA, 94305. e-mail: glynn@stanford.edu Budget Recommended Vehicles Rank Honda Accord Value Sedan 1st Saturn L100 (GM) 2nd $18,000 Dodge Stratus SXT Sedan 3rd Chevrolet Malibu Sedan (GM) 4th Mitsubishi Lancer LS 5th Table 1: An example of consumer preference data collected by the ACA web site.
Opposing FGF and retinoid pathways: a signalling switch that controls differentiation and patterning onset in the extending vertebrate body axis

Construction of the trunk/caudal region of the vertebrate embryo involves a set of distinct molecules and processes whose relationships are just coming into focus. In addition to the subdivision of the embryo into head and trunk domains, this “caudalisation” process requires the establishment and maintenance of a stem zone. This sequentially generates caudal tissues over a long period which then undergo differentiation and patterning in the extending body axis. Here we review recent studies that show that changes in the signalling properties of the paraxial mesoderm act as a switch that controls onset of differentiation and pattern in the spinal cord. These findings identify distinct roles for different caudalising factors; in particular, Fibroblast Growth Factor (FGF) inhibits differentiation in the caudal stem zone, while Retinoic acid (RA) provided rostrally by somitic mesoderm is required for neuronal differentiation and establishment of ventral neural pattern. Furthermore, the mutual opposition of FGF and RA pathways controls not only neural differentiation but also mesoderm segmentation and might also underlie the progressive assignment of rostrocaudal identity by regulating Hox gene availability and activation. BioEssays 26:857–869, 2004. © 2004 Wiley Periodicals, Inc.
Visual Servoing Trajectory Tracking of Nonholonomic Mobile Robots Without Direct Position Measurement

Localization is one of the most difficult and costly problems in mobile robotics. To avoid this problem, this paper presents a new controller for the trajectory tracking of nonholonomic mobile robots using visual feedback without direct position measurement. This controller works on the basis of a novel adaptive algorithm for estimating the global position of the mobile robot online using natural visual features measured by a vision system and its orientation and velocity measured by odometry and Attitude and Heading Reference System (IMU&Compass) sensors. The nonholonomic motion constraint of mobile robots is fully taken into account, compared with most of the existing visual servo controllers for mobile robots. The Lyapunov theory is used to prove that the proposed adaptive visual servo controller gives rise to asymptotic tracking of a desired trajectory and convergence of the position estimation to the actual position. A graphical processing unit is adopted to implement the proposed adaptive controller in parallel to achieve real-time detection and tracking of visual features. Experiments on a mobile robot are conducted to validate the effectiveness and robust performance of the proposed controller.
Dendrodendritic Inhibition in the Olfactory Bulb Is Driven by NMDA Receptors

At many central excitatory synapses, AMPA receptors relay the electrical signal, whereas activation of NMDA receptors is conditional and serves a modulatory function. We show here quite a different role for NMDA receptors at dendrodendritic synapses between mitral and granule cells in the rat olfactory bulb. In whole-cell patch-clamp recordings in bulb slices, stimulation of mitral cells elicited slowly decaying, GABAA receptor-mediated reciprocal IPSCs that reflected prolonged GABA release from granule cells. Although granule cells had a normal complement of AMPA and NMDA receptors, the IPSC was completely blocked by the NMDA receptor antagonistd,l-AP-5, suggesting that NMDA receptor activation is an absolute requirement for dendrodendritic inhibition. The AMPA receptor antagonist 1,2,3,4-tetrahydro-6-nitro-2,3-dioxobenzo[f]quinoxaline-7-sulfonamide (NBQX) had no effect on IPSCs in the absence of extracellular magnesium but modestly reduced IPSCs in 1 mm magnesium, indicating that the primary effect of the AMPA receptor-mediated depolarization was to facilitate the unblocking of NMDA receptors. Granule cell voltage recordings indicated that effective spike stimulation in granule cells depended on the slow NMDA receptor kinetics. Granule cells also showed a pronounced delay between synaptic stimulation and action potential generation, suggesting that their intrinsic membrane properties underlie the ineffectiveness of brief AMPA receptor-mediated EPSPs. NMDA receptors also seem to have a central role in dendrodendritic inhibition in vivo, because intraperitoneal dizocilpine maleate (MK-801) injection in young adult rats resulted in disinhibition of mitral cells as measured by the generation of c-fos mRNA. The unique dependence of dendrodendritic inhibition on slow EPSPs generated by NMDA receptors suggests that olfactory information processing depends on long-lasting reciprocal and lateral inhibition.
XWeb : An Architecture for Cross-modal Collaboration

We show how the XWeb architecture can support collaboration across disparate platforms using different interactive modalities. Our collaborative solution imposes limited compatibility requirements on clients and thus enhances the architecture’s scalability to the size of the Internet. The independence of the implementations of interactive clients and servers is essential for pervasive and cross-modal collaboration. Communication about data changes and reasoning about change conflicts can provide such a collaborative substrate. An algorithm for conflict resolution is given. It is shown how the XWeb architecture naturally supports synchronous session management across interactive modalities.
IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events IEEE AASP SCENE CLASSIFICATION CHALLENGE USING HIDDEN MARKOV MODELS AND FRAME BASED CLASSIFICATION

The IEEE AASP Challenge involves the detection and classification of acoustic scenes and events. The scene classification (SC) challenge consists of 10 different scenes of 10 audio files or length 30 seconds each, totaling a number of 100 audio clips. The list of scenes is: busy street, quiet street, park, open-air market, bus, subway-train, restaurant, shop/supermarket, office, and subway station. The goal is to test on a development set that is composed of audio clips of the same scenes as the training set and determine what scene the audio clips originated from. One of the algorithms presented in this paper to discriminate between these different scenes include the use of hidden Markov models (HMMs) and Gaussian mixture models (GMMs). The features that were used include the following: short time Fourier transform, loudness, and spectral sparsity. Using these features yielded 72% correct classification with 10 fold crossvalidation. The other algorithm implemented uses the same features as before plus temporal sparsity to classify individual frames of an audio clip, then vote on the class. This algorithm achieved 62% accuracy.
Learning from Unseen Data

The learner of Lignos et al. (2009) attained excellent performance in English in Morpho Challenge 2009, but its reliance on minimal word pairs in the input to learn which words a rule applies to led to poor performance in other languages. We demonstrate that this learner can perform well across a broader set of languages if it works to infer word forms un-seen in the data. We evaluate approaches to compounding and base word inference to accomplish this goal, improving the learner’s performance greatly in Turkish and Finnish.