Miracle-dz/UniFAR-FTUs huggingface dataset:
Retroactive Detection of Malware with Applications to Mobile Platforms

We introduce a practical software-based attestation approach. Our new method enables detection of any active malware (e.g., malware that executes or is activated by interrupts) - even if the infection occurred before our security measure was loaded. It works independently of computing platform, and is eminently suited to address the threat of mobile malware, for which the current Anti-Virus paradigm is poorly suited. Our approach is based on memory-printing of client devices. Memory-printing is a novel and light-weight cryptographic construction whose core property is that it takes notably longer to compute a function if given less RAM than for which it was configured. This makes it impossible for a malware agent to remain active (e.g., in RAM) without being detected, when the function is configured to use all space that should be free after all active applications are swapped out. Our approach is based on inherent timing differences for random access of RAM, flash, and other storage; and the time to communicate with external devices. We do not rely on heuristics when arguing our scheme's security. Accordingly, our approach represents a step towards greater rigor and security assurance.
Load forecasting dealing with medium voltage network reconfiguration

Planing the operation in modern power systems requires suitable anticipation of load evolution at different levels of distribution network. Under this perspective, load forecasting performs an important task, allowing the optimization of investments and the adequate exploitation of existing distribution networks. This paper describes the models developed for current intensity forecasting at primary substation feeders. The main goal consists on defining a regression process characterized by good quality estimates of those future intensity values, based on historical database. Anticipation interval shall include from the next hour to one week in advance. The forecasting method shall also be adaptable to power network reconfiguration, whenever planned or not. In this work, artificial neural networks (ANN) were used as the basic regression tool. This paper describes used ANN as well as the premises that led to the implementation of selected forecasting models. At last, some illustrative results attained so far are presented, supporting the adequacy of adopted approach.
Pre-authentication and Selection of Suitable Target Base Station during Handover Procedure in Mobile Wimax Network

WiMAX (Worldwide Interoperability for Microwave Access) is an emerging technology that can provide wireless broadband access at the data rates of 70 Mbps over 50 kilometres to both home and business customers. WiMAX is cost effective and offers higher data rates than other wireless networks. It supports both fixed and mobile applications. WiMAX is easier to deploy as compared to other networks and has flexible network architectures. One of the major challenges concerning the performance of Mobile WiMAX is seamless handover. IEEE 802.16e standard for Mobile WiMAX defines two kind of soft handovers (MDHO and FBSS) and hard handover. But only hard handover mandatory in the standard and Mobile Station cannot transmit or receive data packets during this hard handover. Therefore this long interruption due to hard handover is awful for real-time applications. Re-authentication of Mobile Station and selection of suitable target Base Station are two time-consuming processes within handover procedure. We proposed Pre-authentication scheme in which standard EAP authentication is skipped if Mobile Station handovers from one Base Station to another within he same network so handover latency will be reduced significantly. The modified MOB_BR-ADV message contains useful information e.g. list of neighbouring Base Stations and number of real-time connections each of these Base Stations have etc. which can be helpful to calculate suitability index for selection of target Base Station. In the end we evaluate our proposed scheme by simulation using S-2. The results show that our proposed scheme is more efficient than traditional handover scheme.
Neurochemical Analysis of Focal Ischemia in Rats

Background and Purpose Increases in uric acid follow experimental stroke, which may be related to free radical formation by xanthine oxidase. The present study examined the time course of changes in xanthine and uric acid and their relationship to changes in the free radical scavengers glutathione, cysteine, and ascorbic acid. Methods Focal ischemia was induced by occluding the middle cerebral artery, followed by transient occlusion of the common carotid arteries for 60 minutes. At varying time points, animals were sacrificed, and ischemic cortex was dissected. Neurochemical measurements were made by high-performance liquid chromatography with 16-sensor electrochemical detection. Results Marked increases in uric acid were seen at all time points, with a maximal increase at 1 day and a persistent increase lasting up to 21 days. There were smaller reciprocal decreases in xanthine. Glutathione, cysteine, and ascorbic acid showed significant decreases, consistent with the generation of free radicals. Reductions in levels of cysteine and glutathione were significantly correlated with increases in uric acid levels. Conclusions These findings confirm marked alterations in purine metabolism following focal ischemia and suggest that xanthine oxidase contributes to the generation of free radicals.
The Rho-guanine nucleotide exchange factor Trio controls leukocyte transendothelial migration by promoting docking structure formation

Neutrophils induce endothelial docking structures prior to crossing the blood vessel wall. The Rho guanine nucleotide exchange factor Trio regulates the formation of these structures through ICAM-1 clustering in a filamin-dependent fashion. We show that Trio is a crucial mediator of the signaling pathway that controls leukocyte extravasation through docking structure formation.
Hamiltonian simulation with complexity polylogarithmic in the error

Quantum algorithms for the simulation of quantum systems described by a Hamiltonian provide an exponential improvement in speed over classical algorithms when one considers scaling of the complexity in terms of the dimension. However, one key drawback is that the scaling in terms of the allowable error, ε, is relatively poor. Here we provide a new quantum algorithm whose scaling with respect to the allowable error is exponentially smaller than previous algorithms. That is, the complexity is polylogarithmic in 1/ε, rather than polynomial. The algorithm’s scaling with respect to other parameters—such as dimension and evolution time—also compares well with previous algorithms.
Multiple Imputation of Missing Values

Following the seminal publications of Rubin about thirty years ago, statisticians have become increasingly aware of the inadequacy of “complete-case” analysis of datasets with missing observations. In medicine, for example, observations may be missing in a sporadic way for different covariates, and a complete-case analysis may omit as many as half of the available cases. Hotdeck imputation was implemented in Stata in 1999 by Mander and Clayton. However, this technique may perform poorly when many rows of data have at least one missing value. This article describes an implementation for Stata of the MICE method of multiple multivariate imputation described by van Buuren, Boshuizen, and Knook (1999). MICE stands for multivariate imputation by chained equations. The basic idea of data analysis with multiple imputation is to create a small number (e.g., 5–10) of copies of the data, each of which has the missing values suitably imputed, and analyze each complete dataset independently. Estimates of parameters of interest are averaged across the copies to give a single estimate. Standard errors are computed according to the “Rubin rules”, devised to allow for the between- and within-imputation components of variation in the parameter estimates. This article describes five ado-files. mvis creates multiple multivariate imputations. uvis imputes missing values for a single variable as a function of several covariates, each with complete data. micombine fits a wide variety of regression models to a multiply imputed dataset, combining the estimates using Rubin's rules, and supports survival analysis models (stcox and streg), categorical data models, generalized linear models, and more. Finally, misplit and mijoin are utilities to intercon-vert datasets created by mvis and by the miset program from John Carlin and colleagues. The use of the routines is illustrated with an example of prognostic modeling in breast cancer.
ENST-Drums: an extensive audio-visual database for drum signals processing

One of the main bottlenecks in the progress of the Music Information Retrieval (MIR) research field is the limited access to common, large and annotated audio databases that could serve for technology development and/or evaluation. The aim of this paper is to present in detail the ENST-Drums database, emphasizing on both the content and the recording process. This audiovisual database of drum performances by three professional drummers was recorded on 8 audio channels and 2 video channels. The drum sequences are fully annotated and will be, for a large part, freely distributed for research purposes. The large variety in its content should serve research in various domains of audio signal processing involving drums, ranging from single drum event classification to complex multimodal drum track transcription and extraction from polyphonic music.
Embedding Topic Discovery in Conditional Random Fields Model for Segmenting Nuclei Using Multispectral Data

Segmentation of cells/nuclei is a challenging problem in 2-D histological and cytological images. Although a large number of algorithms have been proposed, newer efforts continue to be devoted to investigate robust models that could have high level of adaptability with regard to considerable amount of image variability. In this paper, we propose a multiclassification conditional random fields (CRFs) model using a combination of low-level cues (bottom-up) and high-level contextual information (top-down) for separating nuclei from the background. In our approach, the contextual information is extracted by an unsupervised topic discovery process, which efficiently helps to suppress segmentation errors caused by intensity inhomogeneity and variable chromatin texture. In addition, we propose a multilayer CRF, an extension of the traditional single-layer CRF, to handle high-dimensional dataset obtained through spectral microscopy, which provides combined benefits of spectroscopy and imaging microscopy, resulting in the ability to acquire spectral images of microscopic specimen. The approach is evaluated with color images, as well as spectral images. The overall accuracy of the proposed segmentation algorithm reaches 95% when applying multilayer CRF model to the spectral microscopy dataset. Experiments also show that our method outperforms seeded watershed, a widely used algorithm for cell segmentation.
Opus: University of Bath Online Publication Store Compensating for Distance Compression in Audiovisual Virtual Environments Using Incongruence

This version is made available in accordance with publisher policies. Please cite only the published version using the reference above. ABSTRACT A key requirement for a sense of presence in Virtual Environments (VEs) is for a user to perceive space as naturally as possible. One critical aspect is distance perception. When judging distances, compression is a phenomenon where humans tend to underestimate the distance between themselves and target objects (termed egocentric or absolute compression), and between other objects (exocentric or relative compression). Results of studies in virtual worlds rendered through head mounted displays are striking, demonstrating significant distance compression error. Distance compression is a mul-tisensory phenomenon, where both audio and visual stimuli are often compressed with respect to their distances from the observer. In this paper, we propose and test a method for reducing crossmodal distance compression in VEs. We report an empirical evaluation of our method via a study of 3D spatial perception within a virtual reality (VR) head mounted display. Applying our method resulted in more accurate distance perception in a VE at longer range, and suggests a modification that could adaptively compensate for distance compression at both shorter and longer ranges. Our results have a significant and intriguing implication for designers of VEs: an incongruent audiovisual display, i.e. where the audio and visual information is intentionally misaligned, may lead to better spatial perception of a virtual scene.
Modeling the Growth of Fingerprints Improves Matching for Adolescents

We study the effect of growth on the fingerprints of adolescents, based on which we suggest a simple method to adjust for growth when trying to recover a juvenile's fingerprint in a database years later. Based on longitudinal data sets in juveniles' criminal records, we show that growth essentially leads to an isotropic rescaling, so that we can use the strong correlation between growth in stature and limbs to model the growth of fingerprints proportional to stature growth as documented in growth charts. The proposed rescaling leads to a 72% reduction of the distances between corresponding minutiae for the data set analyzed. These findings were corroborated by several verification tests. In an identification test on a database containing 3.25 million right index fingers at the Federal Criminal Police Office of Germany, the number of identification errors was reduced from 10 errors in 48 identification attempts to 1 in 48 by rescaling. The presented method is of striking simplicity and can easily be integrated into existing automated fingerprint identification systems.
Ghostbusting Facebook: Detecting and Characterizing Phantom Profiles in Online Social Gaming Applications

A fundamental question when studying underlying friendship and interaction graphs of Online Social Networks (OSNs) is how closely these graphs mirror real-world associations. The presence of phantom or fake profiles dilutes the integrity of this correspondence. This paper looks at the presence of phantom profiles in the context of social gaming, i.e., profiles created with the purpose of gaining a strategic advantage in social games. Through a measurement-based study of a Facebook gaming application that is known to attract phantom profiles, we show statistical differences among a subset of features associated with genuine and phantom profiles. We then use supervised learning to classify phantom profiles. Our work represents a first-step towards solving the more general problem of detecting fake/phantom identities in OSNs.
SlidingWindow based Multi-Join Algorithms over Distributed Data Streams

This paper focuses on multi-way sliding window join (SWJoin) processing over distributed data streams. A novel Join algorithm is proposed based on two distributed data stream transfer models. To reduce the communication cost and lighten the workload on the central processor node, the algorithm filters out tuples that can’t contribute to multiway SWJoin results by transforming the join conditions of SWJoin into filtering conditions during data stream transfer. Furthermore, the algorithm guarantees that all necessary data for generating exact multi-way SWJoin results can be transmitted to the central processor node.
Improvement in Efficiency of Recognition of Handwritten Gurumukhi Script

In this paper we discuss Multi-Layer Perceptron (MLP) networks for recognition of handwritten Gurumukhi Characters (HGCR). For training of MLP networks error back propagation algorithm is used. We present an automatic HGCR system using MLP networks. Many techniques have been used to recognize Gurumukhi characters but accuracy of recognition is not so much efficient as efficiency of recognition of other scripts. Multilayer Perceptron neural network is used for recognition of characters of other scripts. We would like to use MLP for HGCR so that recognition can be done accurately and efficiently.
Incorporating the extended theory of planned behavior in a school travel mode choice model: a case study of Shaoxing, China

ABSTRACT Travel to and from school can have social, economic, and environmental implications for students and their parents. Therefore, understanding school travel mode choice behavior is essential to find policy-oriented approaches to optimizing school travel mode share. Recent research suggests that psychological factors of parents play a significant role in school travel mode choice behavior and the Multiple Indicators and Multiple Causes (MIMIC) model has been used to test the effect of psychological constructs on mode choice behavior. However, little research has used a systematic framework of behavioral theory to organize these psychological factors and investigate their internal relationships. This paper proposes an extended theory of planned behavior (ETPB) to delve into the psychological factors caused by the effects of adults’ cognition and behavioral habits and explores the factors’ relationship paradigm. A theoretical framework of travel mode choice behavior for students in China is constructed. We established the MIMIC model that accommodates latent variables from ETPB. We found that not all the psychological latent variables have significant effects on school travel mode choice behavior, but habit can play an essential role. The results provide theoretical support for demand policies for school travel.
ECG for blind identity verification in distributed systems

This paper discusses ECG biometric recognition in a distributed system, such as smart cards. In a setting where every card is equipped with an ECG sensor to record heart beats from the fingers, and to subsequently perform identity verification, the interest is in protecting the card holder from a set of unknown/unseen biometric traits. Prior works have examined ECG biometrics in settings where a particular subject was to be identified among a set of enrollees. However, this treatment limits the applicability of this biometric. The Autocorrelation - Linear Discriminant Analysis (AC/LDA) is revisited, to propose a strategic extension of the methodology, in order to account for recognition among unknown individuals (blind verification). The discriminant is trained individually for every smart card, on the samples of the subject to be enrolled, as well as a generic dataset of ECG recordings. This enables the recognizer to protect the template against attacks by biometric samples that have not been used to train the discriminant. In addition, we present a methodology for the selection of the matching threshold, which targets to control false acceptance while being experimentally optimized for a particular smart card.
The Neuroanatomy of Herophilus

Herophilus (ca. 330 to ca. 260 BC) was one of Hellenistic -Alexandria's renowned scholars, a leading physician, often named the ‘Father of Anatomy'. From cadaveric dissections and possibly vivisection Herophilus considered the ventricles to be the seat of the soul, intelligence and mental functions. Herophilus introduced the term rete mirabile found in ungulates but not in man, as opposed to Galen, who erroneously believed it a vital human network. A founder of the principles of observations in science, and an exponent of measurements in medicine, his accurate dissections resulted in original anatomical discoveries. He distinguished nerves that produce voluntary motion from blood vessels, and motor from sensory nerves; the nerves of the spinal cord were directly linked to the brain. He identified at least seven pairs of cranial nerves. Herophilus demonstrated the meninges, and ventricles, regarding the fourth as most important. His name is perpetuated by his accounts of the calamus scriptorius and the confluence of venous sinuses the torcular Herophili.
The UMD machine translation systems at IWSLT 2015

We describe the University of Maryland machine translation systems submitted to the IWSLT 2015 French-English and Vietnamese-English tasks. We built standard hierarchical phrase-based models, extended in two ways: (1) we applied novel data selection techniques to select relevant information from the large French-English training corpora, and (2) we experimented with neural language models. Our FrenchEnglish system compares favorably against the organizers’ baseline, while the Vietnamese-English one does not, indicating the difficulty of the translation scenario.
The Neurosciences and Music III: Disorders and Plasticity

Emerging research over the last decade has shown that long-term music training and skill learning can be a strong stimulator for neuroplastic changes in the developing as well as adult brain. Making music places unique demands on the nervous system, leading to strong coupling of perception and action mediated by sensory, motor, and multimodal integrative regions distributed throughout the brain. Furthermore, listening to music and making music (“musicking”) provokes motion, improves and increases between-subject communication and interaction, and is considered to be and experienced as a joyous and rewarding activity. Several reports in this volume have now shown that listening to music and musicking may have the power to make rehabilitation processes not only more enjoyable, but also possibly more effective. Why is music so special and how does listening to and making music achieve its rehabilitative effects? Music is a strong multimodal stimulus that simultaneously transmits visual, auditory, and motoric information to a specialized brain network consisting of frontotemporo-parietal regions whose components are also part of the putative human mirrorneuron system. Among other functions, this system might support the coupling between perceptual events (visual or auditory) and motor
Model Integration For Assessing Future Hydroclimate Impacts On Water Resources, Agricultural Economic Sustainability And Environmental Quality

The US National Assessment of the Potential Consequences of Climate Variability and Change provides compelling arguments for serious consideration of actions and adaptive measures by countries around the world to better manage impacts on water resources, agricultural economic sustainability and environmental quality. National resource planning at this scale requires integrated impact analysis toolboxes to allow linkage and integration of hydroclimate models, linked surface and groundwater hydrologic models, economic and environmental impact models as well as techniques for social impact assessment. The rationale for selection of the simulation models is described as well as the challenges faced in linking the component models of the impacts assessment toolbox. Results from simulations performed with the impacts assessment toolbox are presented and discussed. After initially attempting model integration with a public domain, GIS-based modeling framework MMS/OUI an interactive, map-based web approach was developed instead owing to the nonconformity of database and model temporal scales.
A delay distribution squeezing scheme with speed-adaptive threshold-voltage CMOS (SA-Vt CMOS) for low voltage LSIs

In a speed-adaptive threshold-voltage CMOS (SA-Vt CMOS) circuit, the substrate bias is controlled so that delay in the circuit stays constant. Distributions of device speeds are squeezed under fast-operation conditions. With a ring oscillator using 0.25-µm CMOS devices as a test circuit, we found that the worst-case operating frequency was improved from 20 MHz to 55 MHz, and the fluctuation of the operating frequency was suppressed from 44 % to 15 % while the supply-voltage variation was under 0.1 V with a 1.8 V supply voltage.