Miracle-dz/UniFAR-FTUs huggingface dataset:
A Location Privacy Preserving Authentication Scheme in Vehicular Networks

As an emerging application scenario of wireless technologies, vehicular communications have been initiated not only for enhancing the transportation safety and driving experiences, but also for a new commercial market of on-board Internet services. Due to extraordinarily high mobility of vehicles in a vehicular network, frequent handover requests will be a norm, which initiates the demand for an effective and fast authentication scheme that can maintain the service continuity in presence of the frequent handover events. However, previously reported authentication schemes, although with minimized handover latency and packet loss rate, may disclose the location information of the mobile user to the third party, which will seriously violate the location privacy of the user. In this paper, we propose a location privacy preserving authentication scheme based on blind signature in the elliptic curve domain. The scheme cannot only provide fast authentication, but also guarantee the security and location anonymity to the public. To analyze the proposed scheme, a theoretical traceability analysis is conducted, which shows that the probability of tracing an vehicle's route is negligibly small. We will also examine the authentication speed of the scheme, and show that the scheme can satisfy seamless handover for fast moving vehicles.
Dynamically selecting an appropriate context type for personalisation

Narrowing down the context in the ranking phase of information retrieval has been shown to produce results that are more relevant to searcher's need. We have identified two types of contexts that could be used in the process of personalisation. We research these contexts in the domain of personalised search, but show that our approach can be used for any kind of personalisation or recommendation. We focus on two aspects of the context: temporal context and activity-based context and describe a more general personalisation framework based on lightweight semantics, that can leverage any type of context.
Vibration – Based Statistical Damage Detection For Scale Wind Turbine Blades Under Varying Environmental Conditions

Abstract The problem of damage detection for wind turbine blades under varying environmental conditions and different damage scenarios is considered. A lab–scale blade subject to two levels of damage and conditions is considered under conditions including sprayed water (simulating rain) and temperatures in the range [−20,20]◦C. A vibration–based statistical time series type methodology employing Principal Component Analysis (PCA) is postulated. Two different versions are employed: the first is an output–only scheme based on the vibration response Power Spectral Density (PSD), and the second is an input–output scheme based on the input–output Frequency Response Function (FRF). Detection is based on principal components selected to be independent of environmental conditions, but still sensitive to damage. A single vibration acceleration response is used in the study. The damage detection results are slightly better for the FRF based version, compared to the PSD based one, with detection rates over 96.5% and false alarm rates below 1.5%.
Manage Your 'Blind Flight' - The Optimal Timing for IT Project Re-Evaluation

As the value of an IT project can change over time, management is in "blind flight" about the state of the project until the project has been re-evaluated. As each evaluation causes costs, continuous evaluation is economically unrea- sonable. Nevertheless, the "blind flight" should not take too long, because the project value can considerably deviate from its initial estimation and high losses can occur. To trade off costs of re-evaluation and potential loss of project value, this paper will elaborate upon an economic model that is able to determine the optimal time until re-evaluation considering the risky cash flows of a project. Based on a simulation, we find that it makes good economic sense to optimize the interval of re-evaluation. Therefore, companies are able to avoid financial loss caused by evaluating too early as well as hazarding project value caused by evaluating too late.
Platelet-derived 12-hydroxyeicosatetraenoic acid plays an important role in mediating canine coronary thrombosis by regulating platelet glycoprotein IIb/IIIa activation.

BACKGROUND In the thrombotic process of acute coronary syndromes, the pathophysiological role of thromboxane A2 via cyclooxygenase is well established; however, the role of 12-HETE via 12-lipoxygenase is little known. Therefore, we used OPC-29030, a novel specific inhibitor of 12-HETE synthesis, to test whether platelet-derived 12-HETE is involved in mediating cyclic flow variations (CFVs) and platelet aggregation in stenosed and endothelium-injured canine coronary arteries. METHODS AND RESULTS After developing CFVs, dogs received a vehicle or OPC-29030 intravenously. Plasma and intraplatelet 12-HETE levels increased after CFVs. OPC-29030 but not vehicle reduced CFVs, which was associated with decreases in plasma and intraplatelet 12-HETE levels. Cessation of OPC-29030 restored CFVs in association with increases in plasma and intraplatelet 12-HETE levels. ADP and U46619 induced ex vivo platelet 12-HETE production and aggregation. After OPC-29030 administration, the ADP- and U46619-induced increases in ex vivo platelet 12-HETE production and aggregation were inhibited significantly. Platelet aggregation was linearly correlated with platelet 12-HETE production. OPC-29030 suppressed activation of human platelet glycoprotein IIb/IIIa. CONCLUSIONS OPC-29030 reduced intraplatelet 12-HETE levels, resulting in the inhibition of coronary thrombosis in vivo in dogs. OPC-29030 inhibited human platelet glycoprotein IIb/IIIa activation in vitro. Thus, platelet-derived 12-HETE may play an important role in mediating thrombotic process.
Communicating with the severely brain-damaged adult. II: Establishing communication

L'A. examine l'interaction entre un adulte ayant subi une lesion cerebrale importante et deux professionnels du domaine medical. La question etudiee concerne la maniere dont la communication est etablie et maintenue dans une interaction avec un individu dont l'aptitude a participer a l'interaction est tres limitee
The Immune System in Children with Malnutrition—A Systematic Review

Background Malnourished children have increased risk of dying, with most deaths caused by infectious diseases. One mechanism behind this may be impaired immune function. However, this immune deficiency of malnutrition has not previously been systematically reviewed. Objectives To review the scientific literature about immune function in children with malnutrition. Methods A systematic literature search was done in PubMed, and additional articles identified in reference lists and by correspondence with experts in the field. The inclusion criteria were studies investigating immune parameters in children aged 1–60 months, in relation to malnutrition, defined as wasting, underweight, stunting, or oedematous malnutrition. Results The literature search yielded 3402 articles, of which 245 met the inclusion criteria. Most were published between 1970 and 1990, and only 33 after 2003. Malnutrition is associated with impaired gut-barrier function, reduced exocrine secretion of protective substances, and low levels of plasma complement. Lymphatic tissue, particularly the thymus, undergoes atrophy, and delayed-type hypersensitivity responses are reduced. Levels of antibodies produced after vaccination are reduced in severely malnourished children, but intact in moderate malnutrition. Cytokine patterns are skewed towards a Th2-response. Other immune parameters seem intact or elevated: leukocyte and lymphocyte counts are unaffected, and levels of immunoglobulins, particularly immunoglobulin A, are high. The acute phase response appears intact, and sometimes present in the absence of clinical infection. Limitations to the studies include their observational and often cross-sectional design and frequent confounding by infections in the children studied. Conclusion The immunological alterations associated with malnutrition in children may contribute to increased mortality. However, the underlying mechanisms are still inadequately understood, as well as why different types of malnutrition are associated with different immunological alterations. Better designed prospective studies are needed, based on current understanding of immunology and with state-of-the-art methods.
Likelihood-based estimation and model selection for ACT-R cognitive models

ACT-R cognitive models are examples of complex hidden Markov processes with a large state space and a sparse transition matrix with absorbing states. While these models are designed to be theoretically interpretable as the thought processes generating observable data, mathematical model estimation and evaluation methods are based on simulation and prediction. There is little methodology applied to ACTR that addresses questions of model complexity, identifiability and generalizability – all topics that can be addressed through study of the likelihood. In this paper I develop likelihood-based estimation using MCMC methods for a class of ACT-R models. I compare selection of ACT-R models for scatterplot generation using current predictive methods and using likelihood-based estimation and Bayes Factors. I propose extending this methodology to include a larger range of ACT-R models and to incorporate other model selection criteria (BIC, MDL, etc.) and decision theoretic approaches, with applications to ACT-R models including individual differences, and general hidden Markov processes.
Augmenting recommendation systems using a model of semantically-related terms extracted from user behavior

Common diculties like the cold-start problem and a lack of sucient information about users due to their limited interactions have been major challenges for most recommender systems (RS). To overcome these challenges and many similar ones that result in low accuracy (precision and recall) recommendations, we propose a novel system that extracts semantically-related search keywords based on the aggregate behavioral data of many users. These semantically-related search keywords can be used to substantially increase the amount of knowledge about a specic user’s interests based upon even a few searches and thus improve the accuracy of the RS. The proposed system is capable of mining aggregate user search logs to discover semantic relationships between key phrases in a manner that is language agnostic, human understandable, and virtually noise-free. These semantically related keywords are obtained by looking at the links between queries of similar users which, we believe, represent a largely untapped source for discovering latent semantic relationships between search terms.
Distributed leadership of school curriculum change: an integrative approach

ABSTRACT Since its inception in 1999, the distributed leadership framework of Spillane, Halverson, and Diamond [2004. “Towards a Theory of Leadership Practice: A Distributed Perspective. ” Journal of Curriculum Studies 36 (1): 3–34. doi:10.1080/0022027032000106726] has supported research into leadership and change in schools. Whilst the conceptual integrity of the framework is sound, research based upon its premises has attracted some criticism. The research has had a narrowed focus which has not generally reflected the inseparability of the elements of the framework and the situation within which leadership is enacted. In addition, it often fails to account for the complexity of critical elements of the environment such as micropolitics. This position paper proposes a reconfiguration of the distributed leadership framework as a research framework in response to this critique. The paper asserts that this new perspective that is presented is able to respond to the critique through an integrated approach that encompasses situation, social distribution and task enactment and which supports an integrated approach to research methodology. This adapted framework has the potential to more fully support research design that is actuality holistic and embedded in the context.
Messing with Android's Permission Model

Permission models have become very common on smartphone operating systems to control the rights granted to installed third party applications (apps). Prior to installing an app, the user is typically presented with a dialog box showing the permissions requested by the app. The user has to decide either to accept all of the requested permissions, or choose not to proceed with the installation. Most regular users are not able to fully grasp which set of permissions granted to the application is potentially harmful. In addition to the knowledge gap between user and application programmer, the missing granularity and alterability of most permission model implementations help an attacker to circumvent the permission model. In this paper we focus on the permission model of Google's Android platform. We detail the permission model, and present a selection of attacks that can be composed to fully compromise a user's device using inconspicuously looking applications requesting non-suspicious permissions.
Multi-layered image compression

The main contribution of this work is a new paradigm for image compression. We describe a new multi-layered representation technique for images. An image is encoded as the superposition of one main approximation, and a sequence of residuals. The strength of the multi-layered method comes from the fact that we use different bases to encode the main approximation and the residuals. For instance, we can use: - a wavelet basis to encode a coarse main approximation of the image, - wavelet packet bases to encode textured patterns, - brushlet bases to encode localized oriented textured features, etc.
A discriminated correlation classifier for face recognition

In this paper, a discriminated correlation classifier is proposed to improve the performance of the two-dimensional (2-D) face recognition algorithm. Until now, many methods have been proposed to address the problems encountered by face recognition system, such as small number problem, pose and illumination variation, etc. All these works are aiming at enhancing the performance of the face recognition system. However, as far as we know, few work are concerning about how to improve the classifier, whose performance directly determines the final recognition accuracy. So, in this paper, our motivation is to improve the performance of correlation classifier, which is widely used in face recognition problems, to improve the recognition accuracy. Inspired by a correlation filter design method called Minimal Average Correlation Energy(MACE) filter, we propose a novel classifier called Discriminated Semi-Normalized Correlation (DSNC) classifier using our discriminative learning method. Compared with the classical discriminative learning methods that need many intra-class samples and can only be applied on close set recognition problems, our method needs only one intra-class sample and can be performed on open set face recognition problem. The validity of our method is tested on two benchmark face database(FRGC2.0 and FERET), and a private face database(THFaceID).
PARALLELIZATION OF VIDEO PROCESSING : FROM PROGRAMMING MODELS TO APPLICATIONS

The explosive growth of digital video content from commodity devices and on the Internet has precipitated a renewed interest in video processing technology, which broadly encompasses the compression, enhancement, analysis, and synthesis of digital video. Video processing is computationally intensive and often has accompanying real-time or super-real-time requirements. For example, surveillance and monitoring systems need to robustly analyze video from multiple cameras in real time in order to automatically detect and signal unusual events. Beyond today’s known applications, continued growth of functionality and speed of video processing systems will likely further enable novel applications.
Pulse Bifurcations in Stochastic Neural Fields

We study the effects of additive noise on traveling pulse solutions in spatially extended neural fields with linear adaptation. Neural fields are evolution equations with an integral term characterizing synaptic interactions between neurons at different spatial locations of the network. We introduce an auxiliary variable to model the effects of local negative feedback and consider random fluctuations by modeling the system as a set of spatially extended Langevin equations whose noise term is a $Q$-Wiener process. Due to the translation invariance of the network, neural fields can support a continuum of spatially localized bump solutions that can be destabilized by increasing the strength of the adaptation, giving rise to traveling pulse solutions. Near this criticality, we derive a stochastic amplitude equation describing the dynamics of these bifurcating pulses when the noise and the deterministic instability are of comparable magnitude. Away from this bifurcation, we investigate the effects of additive .
Forbidding Below Cost Pricing for Retailers: a Strategic Inflationary Mechanism

This paper explores the indirect inflationary mechanism allowed by loss leaders banning laws. In a double duopoly model where producers compete to sell differentiated products through differentiated retailers, we show that the ban of resale at a loss can be pro-collusive : colluding producers can use this rule strategically to set higher wholesale prices, and pay the retailers through negotiated listing fees. The ban turns wholesale prices into floor prices, thus increasing resale price and lessening consumers welfare. However, if producers do not collude, this mechanism does not hold. These results are robust even if the listing fees are two-part tariff.
Kinase Activity and Mitochondrial Death Signaling-terminal 2 UV-Induced Apoptosis by Inhibiting c-Jun NH The Prosurvival Activity of p 53 Protects Cells from Updated

The cytoprotective function of p53 recently has been exploited as a therapeutic advantage for cancer prevention; agents activating the prosurvival activity of p53 are shown to prevent UV-induced damages. To explore the mechanisms of p53-mediated protection from UV-induced apoptosis, we have established stable clones of H1299 lung carcinoma cells expressing a temperature-sensitive p53 mutant, tsp53. At the permissive temperature of 32°C, the tsp53-expressing cells were arrested in G1 phase without the occurrence of apoptosis; consistent with this is the preferential induction of genes related to growth arrest and DNA damage repair. Previous expression of functional tsp53 for >18 hours inhibited the release of proapoptotic molecules from mitochondria and protected the cells from UV-induced apoptosis; moreover, it suppressed the activation of c-Jun NH2-terminal kinase (JNK) signaling and relieved the effect of UV on p53 target gene activation. p53 associated with JNK and inhibited its kinase activity. Using the p53-null H1299 cells, we showed that inhibition of JNK blocked the UV-elicited mitochondrial death signaling and caspase activation. Our results suggest that the ability of p53 to bind and inactivate JNK, together with the activation of the p53 target genes related to cell cycle arrest and DNA damage repair, is responsible for its protection of cells against UV-induced apoptosis.
The influence of cardiac trabeculae on ventricular mechanics

Cardiac trabeculae are cylindrical structures arranged in a complex shape which cover the endocardial surfaces of ventricles. The trabeculae consist of axially arranged cardiac fibers and represent a significant percentage of the ventricular mass (from 12% to 17%). The aim of the project is to study the influence of the trabecular mass on heart performances. A finite-element model of the left ventricle was developed. To compare different models (with or without trabecular structures) the ventricle was simplified as a truncated ellipsoid and the trabeculae, if present, as cylindrical strands oriented along the ventricular axis direction laying onto the endocardium. The total muscular mass and the intraventricular volume were kept constant in all the models. To model the mechanical behavior of the cardiac tissue, the Holzapfel constitutive relation was chosen. Cardiac fibers were oriented helically in the compact ventricular wall and axially in the trabeculae. Physiologic atrial pressure was set during the ventricular filling phase, while an adequate RCR model was connected downstream the ventricle to simulate the systemic circulation. The results show a significant role of trabeculae on ventricular behavior: the presence of trabecular mass contributes to the ventricular filling, allowing a higher ventricular stroke volume at the same atrial pressure.
VINCI -"Volume imaging in Neurological Research, Co-Registration and ROIs included"

VINCI (“Volume Imaging in Neurological Research, Co-Registration and ROIs included”) was designed for the visualization and analysis of volume data generated by medical tomographical systems with special emphasis on the needs for brain imaging with Positron Emission Tomography (PET). VINCI is highly modular, extensible, compact and runs well on current PCs, no installation is required. We achieve this with a plugin architecture; VINCI can be remotely controlled through several high-level language interfaces, at the basis of which is our own XML-based scripting language. VINCI is entirely true color based and allows online fusion and contour rendering of several images, more than 50 studies can be displayed simultaneously in orthogonal views on current machines. We also have a fully automatic registration tool which is suitable for routine usage and gives online feedback of a running registration.
Conceptualizing Perceived Usefulness In E-Learning Context And Investigating Its Role In Improving Students' Academic Performance

Most of the prior research on e-learning systems focused on the adoption of these systems. Two major research gaps in prior literature are weak conceptualization of perceived usefulness and ignorance of possible performance outcomes of using e-learning systems. This paper conceptualizes perceived usefulness as a combination of perceived learning assistance and perceived community building assistance and proposes a research model for assessing the possible performance outcomes of e-learning systems utilization. The model was tested by collecting data from university students (n = 249) participating in hybrid courses using partial least squares (PLS) analysis. The findings suggest that e-learning utilization, perceived learning assistance and perceived community building assistance are predicted by perceived task-technology fit. In turn, e-learning utilization, perceived learning assistance and perceived community building assistance predict students’ perceived academic performance and satisfaction.