Miracle-dz/UniFAR-FTUs huggingface dataset:
Biometric Feature-Type Transformation: Making templates compatible for secret protection

Biometrics refers to physiological (i.e., face, fingerprint, hand geometry, etc.) and behavioral (i.e., speech, signature, keystroke, etc.) traits of a human identity. As these traits are unique to individuals, biometrics can be used to identify users reliably in many authentication applications, such as access control and e-commerce. Most biometric authentication systems offer great convenience without requiring the users to possess or remember any secret credentials. For applications that demand greater security, biometrics can be used in complement with passwords and security tokens to offer a multifactor authentication.
The design and implementation of an OLAP system for sequence data analysis

We design a novel online analytical processing system for sequence data analysis (the S-OLAP system). The biggest distinction of S-OLAP from traditional OLAP is that a sequence can be characterized not only by the attributes' values of its constituting items, but also by the subsequence/substring patterns it possesses. Traditional OLAP systems and techniques were not designed for sequence data and thus they are incapable of supporting sequence data analysis. This paper describes my Ph.D research on the design and implementation of the S-OLAP system. The proposed system is able to support "pattern-based" grouping and aggregation, which is currently not supported by any OLAP system. The preliminary ideas, significant research issues as well as major challenges of this ongoing project are presented.
Towards scalable and reliable group key management

1. INTRODUCTION In secure group communications, members of a group share a symmetric group key. A key server provides access control to the group key as well as reliable rekeying for group members whenever the key changes. Figure 1 shows the functional components of an architecture for group key management. The registration component authenticates users and distributes to each user its individual key, which is shared only between the user and the key server. Authenticated users send their join and leave requests to the rekey encoding component. The encoding component validates the requests by checking whether they are encrypted by individual keys, and generates rekey messages, which are sent to the rekey transport component for delivery. Previous studies, however, have focused mainly on the encoding component, particularly the processing time required by the encoding component in a key server to process each request individually [1, 2]. The performance of batch rekeying and the problem of reliable transport of group rekey messages have not been addressed in the literature. In this paper, we investigate scalability issues of all three components, including a performance analysis of periodic batch rekeying based on the use of key trees [2], the design of a reliable rekey transport protocol, an analysis of server and client bandwidth requirements for the protocol, and an overall performance analysis of our system, called keygem. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. redistribute to lists, requires prior specific permission and/or a fee. While individual rekeying introduces no extra delay to process user requests, it has two issues. First, it is hard for individual rekeying to control the synchronization between keys and data. Furthermore, the delay needed to reliably multicast a rekey message may he too large to implement individual rekeying. Second, individual rekey-ing can be inefficient. For authentication purpose, each rekey message needs to be digitally signed to prove that it originates from the key server. Furthermore, when a key server rekeys upon each request and when forward access control is required, we show that f2(log N) is a lower bound on the amortized number of encrypted keys. In keygem, we alleviate the synchronization problem and improve rekey …
An Agnostic and Practically Useful Estimator of the Stochastic Discount Factor By Kuntara Pukthuanthong and Richard Roll

We propose an estimator for the stochastic discount factor (SDF) that does not require macroeconomic proxies or preference assumptions. It depends only on observed asset returns, yet is immune to the form of the multivariate return distribution, including the distribution’s factor structure. Using US equity data, our estimator satisfies the Hansen/Jagannathan bounds. Authors’ Coordinates Pukthuanthong1 Roll Department of Finance University of Missouri Columbia, MO 65211 Caltech Pasadena, CA 91125 & Emeritus, UCLA 573-884-9785 626-395-3890 pukthuanthongk@missouri.edu rroll@caltech.edu
Foud: Integrating Fog and Cloud for 5G-Enabled V2G Networks

V2G technology is considered a powerful approach enabling renewable energy sources to provide ancillary electrical services, and managing and monitoring power usage in the smart grid. However, owing to the inherent high mobility of EVs, flexible and timely on-demand response services against EV mobility in the V2G system must be provided. Promoted by the advantages of 5G communications, e.g., supporting various connectivity and faster transmission, fogs and clouds are enabled to provide realistic services, and fog computing and cloud computing assisted V2G systems in future 5G mobile networks are expected to be a new paradigm to create a new situation for V2G services. To this end, a fogbased and cloud-based hybrid computing model named "Foud" applied to V2G networks in 5G is proposed. The potential V2G network services provided by Foud are reviewed, and open issues and challenges for future work are also discussed with possible solutions.
Laser-based intelligent surveillance and abnormality detection in extremely crowded scenarios

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in the extremely crowded area has become an urgent need for public security. In this paper, we propose a novel laser-based system which can simultaneously perform the tracking, semantic scene learning and abnormality detection in the large and crowded environment. In our system, a novel abnormality detection model is proposed, and it considers and combines various factors that will influence human activity. Moreover, this model intensively investigate the relationship between pedestrians' social behaviors and their walking scenarios. We successfully applied the proposed system to the JR subway station of Tokyo, which can cover a 60×35m area, robustly track more than 180 targets at the same time and simultaneously perform the online semantic scene learning and abnormality detection with no human intervention.
DeMoSt : a Tool for Exploring the Decomposition and the Modular Structure of OWL Ontologies

Motivation In relevant application fields ontologies are often maintained as huge monolithic collections of axioms in single files, as for some ontologies in the NCBO BioPortal repository4, like the Gene Ontology (∼ 60, 000 axioms). Such representation is not ideal for applications which only require access limited to individual fragments of the ontology. As an example, the reasoning service provided needs just a small part of it to be performed, and loading only the necessary bit to the task can reduce substantially time and memory required, improving the performance of the system. Hence it is important to investigate the possibility of maintaining ontologies in a more flexible form. Recently the notion of module has been developed. Modules are subsets of ontologies that preserve all entailments over a set of terms Σ called seed signature, and they come in many flavours. We focus on modules based on syntactic locality [2] (from now on simply called modules), that are efficiently computable. Modules are useful and used, for example in reuse scenarios, i.e. when a part of a well-established ontology is selected to be reused in a different ontology. However, it is really important to exactly know the seed signature needed, or a single extraction can generate undesired results, as, for example, a module that does not contain all axioms needed, or one that contains too many of them. Hence it is interesting to discover the relations between the extracted module with its superand sub-modules, because a slightly different module could be more interesting than the one extracted. In order to look at these (logic-based) relationships, a succint representation, called Atomic Decomposition, of the modular structure of an ontology has been introduced in [5]. In our paper [3], accepted for the Research Track at ISWC2011, we present the first, to our knowledge, large-scale investigation into decomposability and modular aspects of the NCBO BioPortal ontologies. We consider the Atomic Decompositions of such ontologies and demonstrate that most of them split into small logically coherent parts (atoms), from which modules can be efficiently assembled during reasoning. We discuss such good (on average) decomposability of BioPortal ontologies, its implications for applications, and also comment on
Anxiety and Autism: Towards Personalized Digital Health

For many people living with conditions such as autism, anxiety manifests so powerfully it has a big impact on quality of life. By investigating the suitability of truly customizable wearable health devices we build on prior research that found each experience of anxiety in people with autism is unique, so 'one-suits all' solutions are not suitable. In addition, users desire agency and control in all aspects of the system. The participative approach we take is to iteratively co-develop prototypes with end users. Here we describe a case study of the co-development of one prototype, a digital stretch wristband that records interaction for later reflection called Snap. Snap has been designed to sit within a platform that allows the distributed and sustainable design, manufacture and data analysis of customizable digital health technologies. We contribute to HCI with (1) lessons learned from a DIY co-development process that follows the principles of modularity, participation and iteration and (2) the potential impact of technology in self-management of anxiety and the broader design implications of addressing unique anxiety experiences.
LEFT VENTRICULAR DYSSYNCHRONY IN PATIENTS WITH MODERATE CORONARY STENOSIS AND BORDER LINE FRACTIONAL FLOW RESERVE

ABSTRACT The cutoff values of fractional flow reserve (FFR) to detect physiological myocardial ischemia are still controversial. Some studies have reported that left ventricular (LV) dyssynchrony occurs in patients with coronary artery disease (CAD). The purpose of this study was to investigate LV dyssynchrony in patients with moderate coronary stenosis and borderline FFR, using stress electrocardiographically-gated myocardial perfusion single-photon emission computed tomography (SPECT). The study population comprised 10 patients with moderate (50–75% diameter) stenosis and an FFR in the range 0.75–0.90, who were compared to 10 control subjects. All underwent stress myocardial 99mTc-sestamibi (MIBI) or tetrofosmin SPECT imaging. The regional time to end systole (TES), time to peak ejection (TPE), and time to peak filling (TPF) were obtained as indexes of perfusion and function, using gated SPECT (pFAST) in combination with Cardio Gated SPECT Regional Assessment for LV Function (cardioGRAF). The dyssynchrony index (DI) was also calculated. The DI of post-stress TES was significantly greater than that of rest in patients with moderate CAD (4.8 ± 2.8 vs. 2.7 ± 1.5, P = 0.01), but there were no significant differences in the control subjects (3.0 ± 1.7 vs. 2.9 ± 1.9, P = 0.99). There were no significant differences in TPE and TPF between the groups. In conclusion, LV dyssynchrony may occur after stress in patients with coronary stenosis and borderline FFR, even without a significant reduction in perfusion.
Imputation of household survey data using mixed models

Household surveys collect information about a household and data items relating to one or more people within the household. Developing an efficient strategy for dealing with missing data is essential in the current climate of falling response rates. People within households are more likely to share characteristics than a random group of people and this homogeneity can be used when forming strategies for dealing with nonresponse. Amongst single value imputation methods, linear models and donor models are commonly used, but generally ignore relationships within households. These strategies make use of auxiliary variables available for nonrespondents to replace the missing value with a single value, for example a mean or donor value. Imputation strategies for missing items at person level will be the focus of this thesis. The goal is to make use of correlation structures within households to form improved imputed values for missing data. Imputation models are developed and assessed using the hierarchical
Yoga for persons with severe visual impairment: a feasibility study

This exploratory study aims to establish the feasibility of an Ashtanga-based Yoga Therapy (AYT) program for improving sleep disturbances, balance, and negative psychosocial states, which are prevalent issues for visually impaired (VI) individuals. Ten legally blind adult participants were randomized to an 8-week AYT program. Four subjects in the 1st cohort and three in the 2nd cohort successfully completed the AYT program. They convened for one session per week with an instructor and performed two home-based sessions per week using an audio CD. The Pittsburgh Sleep Quality Index (PSQI), Perceived Stress Scale (PSS), Beck Anxiety Inventory (BAI), and Beck Depression Inventory (BDI) were administered at baseline and post-intervention. A Timed One-Leg balance measure, respiratory rate (RR), and the Philadelphia Mindfulness Scale (PHLMS) were assessed in the 2nd cohort. Both groups completed a qualitative exit survey. Positive exit survey responses (all subjects were extremely or mostly satisfied, and wanted to continue AYT) and good participation rates (7 subjects attended at least 7 of the 8 weekly sessions) support the feasibility of the AYT. PSQI, PSS, BAI and BDI scores changed in the direction of reduced negative symptoms after AYT for the 1st cohort. Changes in PSQI and PSS for the 2nd cohort were varied. Balance, RR and PHLMS awareness trended toward improvement for each individual. This preliminary study provides proof of concept for potential benefits of AYT that may be observed in VI subjects. Larger studies and an active control group are needed to determine efficacy.
A model-based control method for decentralized calibration of wireless sensor networks

We introduce a control algorithm for automated calibration of sensor networks and fault compensation that reduces overall system error. Our approach uses a novel combination of models (for the spatial phenomena and sensor faults) and data-driven methods, and it works under novel conditions of dynamic sensor noise and bias in which the phenomenon of interest is both time- and space-variant. Our algorithm combines noise compensation using the EM (expectation-maximization) algorithm and a gossip-based protocol for sharing calibration information. We demonstrate its effectiveness in simulations that model real-world sensor fault scenarios.
Podocalyxin regulates pronephric glomerular development in zebrafish

Vertebrate glomerular podocytes possess a highly sialylated transmembrane glycoprotein, Podocalyxin. In mammals, the sialic acid of Podocalyxin plays a crucial role in the formation of the characteristic podocyte architecture required for glomerular filtration. We examined the function of Podocalyxin in the developing zebrafish pronephros by disrupting the expression of podocalyxin through the use of morpholino antisense oligonucleotides. Podocalyxin was localized at the apical membrane of podocytes throughout pronephric glomerular development in zebrafish. Translational blocking of podocalyxin expression resulted in pericardial edema and a hypoplastic glomerulus. Whereas regular foot processes with a slit diaphragm covered 66.7 ± 7.8% of the urinary surface of glomerular basement membrane in control fish, only 14.4 ± 7.5% of this area was covered with regular foot processes in the translationally blocked morphants. Splice blocking of podocalyxin exon 2, which partially encodes the bulky mucin domain with extensive sialic acid‐containing sugar chains, resulted in the deletion of 53% of mucin domain‐coding sequence from podocalyxin mRNA. Approximately 40% of these splice‐blocked morphants had mild pericardial edema. Although the pronephric glomerulus in the splice‐blocked morphants exhibited almost normal appearance with developed glomerular capillaries and mesangium, they had only 36.3 ± 6.9% of the area covered with regular foot processes. In conclusion, Podocalyxin is predominantly expressed in the podocytes and plays a distinct role in the formation of the podocyte foot processes with a slit diaphragm during zebrafish pronephric development.
Determining Influential Users with Supervised Random Walks

The emergence of social media and the enormous growth of social networks have initiated a great amount of research in social influence analysis. In this regard, many approaches take into account only structural information while a few have also incorporated content. In this study we propose a new method to rank users according to their topic-sensitive influence which utilizes a priori information by employing supervised random walks. We explore the use of supervision in a PageRank-like random walk while also exploiting textual information from the available content. We perform a set of experiments on Twitter datasets and evaluate our findings.
Pledgability and Liquidity

This paper models the role of assets in facilitating intertemporal exchange: because limited commitment precludes unsecured credit, buyers need to pledge assets as collateral. We develop a general equilibrium model where assets differ in terms of pledgability, and put it to work in applications to finance and macroeconomics. The framework nests standard growth and asset pricing models as special cases. We can price at currency as well as real assets, and analyze how monetary policy affects interest rates, generalizing Fishers approach. We also deliver a Tobin effect of inflation on capital accumulation. We study liquidity differentials along both extensive and intensive margins, making pledgability endogenous, while determining the terms of trade in a general way that captures standard pricing mechanisms as special cases.
Developing FPGA-based Embedded Controllers Using Matlab/Simulink

Field Programmable Gate Arrays (FPGAs) are emerging as suitable platforms for implementing embedded control systems. FPGAs offer advantages such as high performance and concurrent computing which makes them attractive in many embedded applications. As reconfigurable devices, they can be used to build the hardware and software components of an embedded system on a single chip. Traditional FPGA design flows and tools, requiring the use of Hardware Description Languages (HDLs), are in a different domain than standard control system design tools such as MATLAB/Simulink. This paper illustrates development of FPGA-based controllers by utilizing popular tools such as MATLAB/Simulink available for the design and development of control systems. The capability of DSP Builder is extended by developing a custom library of control system building blocks that facilitates rapid development of FPGA-based controllers in the familiar Matlab/Simulink environment. As a case study, this paper presents how the tools can be utilized to develop a FPGA-based controller for a laboratory scale air levitation system.
A Novel Memristor-Based Hardware Security Primitive

Memristor is an exciting new addition to the repertoire of fundamental circuit elements. Alternatives to many security protocols originally employing traditional mathematical cryptography involve novel hardware security primitives, such as Physically Unclonable Functions (PUFs). In this article, we propose a novel hybrid memristor-CMOS PUF circuit and demonstrate its suitability through extensive simulations of environmental and process variation effects. The proposed PUF circuit has substantially less hardware overhead than previously proposed memristor-based PUF circuits while being inherently resistant to machine learning-based modeling attacks because of challenge-dependent delays of the memristor stages. The proposed PUF can be conveniently used in many security applications and protocols based on hardware-intrinsic security.
Noise reduction on CT set of projections by wiener filtering and wavelet thresholding.

In this paper, we present a comparison of two techniques for noise reduction on CT set of projections. We use for filtering the Pointwise Wiener filter and thresholding of the Wavelet coefficients. We use the Anscombe transformation for noise variance stabilization. The Pointwise Wiener filter was computed with an adaptive windowing scheme for the calculation of local estimates. For the thresholding in Wavelet domain, we compare three families of wavelet bases: Daubechies, Symlets and Coiflets. We also compared four techniques for obtaining thresholds: Universal threshold, Oracle shrink, Minimax threshold and SURE threshold. For the image reconstruction stage we applied the parallel POCS algorithm. The experiments were done with one simulated phantom (Shepp-Logan) and real projections captured by a CT scanner developed by CNPDIA/EMBRAPA. The results were measured with the ISNR and SSIM criteria. In most cases, the best results were obtained with the Pointwise Wiener filter with adaptive windowing.
Thrombin Has Biphasic Effects on the Nitric Oxide-cGMP Pathway in Endothelial Cells and Contributes to Experimental Pulmonary Hypertension

Background A potential role for coagulation factors in pulmonary arterial hypertension has been recently described, but the mechanism of action is currently not known. Here, we investigated the interactions between thrombin and the nitric oxide-cGMP pathway in pulmonary endothelial cells and experimental pulmonary hypertension. Principal Findings Chronic treatment with the selective thrombin inhibitor melagatran (0.9 mg/kg daily via implanted minipumps) reduced right ventricular hypertrophy in the rat monocrotaline model of experimental pulmonary hypertension. In vitro, thrombin was found to have biphasic effects on key regulators of the nitric oxide-cGMP pathway in endothelial cells (HUVECs). Acute thrombin stimulation led to increased expression of the cGMP-elevating factors endothelial nitric oxide synthase (eNOS) and soluble guanylate cyclase (sGC) subunits, leading to increased cGMP levels. By contrast, prolonged exposition of pulmonary endothelial cells to thrombin revealed a characteristic pattern of differential expression of the key regulators of the nitric oxide-cGMP pathway, in which specifically the factors contributing to cGMP elevation (eNOS and sGC) were reduced and the cGMP-hydrolyzing PDE5 was elevated (qPCR and Western blot). In line with the differential expression of key regulators of the nitric oxide-cGMP pathway, a reduction of cGMP by prolonged thrombin stimulation was found. The effects of prolonged thrombin exposure were confirmed in endothelial cells of pulmonary origin (HPAECs and HPMECs). Similar effects could be induced by activation of protease-activated receptor-1 (PAR-1). Conclusion These findings suggest a link between thrombin generation and cGMP depletion in lung endothelial cells through negative regulation of the nitric oxide-cGMP pathway, possibly mediated via PAR-1, which could be of relevance in pulmonary arterial hypertension.