Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy Commitment Scheme for Masked Iris Codes

The fuzzy commitment scheme is one of most popular biometric cryptosystems that aim at securing cryptographic keys using biometrics. Because of the high recognition accuracy exhibited by the iris, iris-based fuzzy commitment schemes, among other modalities, provide the most practical performance rates. Unfortunately, existing iris-based fuzzy commitment schemes do not incorporate noise masks, generated along with iris-codes to highlight unwanted regions of the iris, because there is no way to know the mask of the decoding iris sample in advance. Therefore, the decoding accuracy of iris-based fuzzy commitment schemes is much less than the recognition accuracy of the underlying iris recognition system. This paper presents an iris-based fuzzy commitment scheme that uses the noise mask of the encoding iris sample at both encoding and decoding stages. Experimental results show that the proposed scheme provides a remarkable improvement in the decoding accuracy of iris-based fuzzy commitment schemes.
High-protein–low-carbohydrate diet during pregnancy alters maternal plasma amino acid concentration and placental amino acid extraction but not fetal plasma amino acids in pigs

A high protein–low-carbohydrate diet during pregnancy can cause intra-uterine growth restriction. However, its impact during pregnancy on maternal, umbilical and fetal plasma amino acid (AA) profiles is unknown. A maternal high-protein (30 %)–low-carbohydrate (HP-LC) diet was compared with isoenergetic standard (12·1 % crude protein; ST) and low-protein (6·5 %)–high-carbohydrate (LP-HC) diets fed to nulliparous pregnant sows to examine changes in AA concentrations in maternal, venous and arterial umbilical and fetal plasma in mid and late pregnancy. At 64 and 94 days of pregnancy (dp), sows underwent Caesarean section, and maternal, umbilical and fetal plasma samples were collected. The HP-LC diet mainly affected maternal plasma AA concentrations. Plasma concentrations of Ile and Val were increased and those of Ala, Glu and Gly were decreased (P ≤ 0·05) in HP-LC compared with ST sows at 64 and 94 dp. The LP-HC diet decreased fetal plasma Glu concentration compared with the ST diet at 94 dp. Substantial AA catabolism was reflected by increased (P ≤ 0·05) maternal and fetal plasma urea concentrations with the HP-LC compared with the ST and LP-HC diets at 94 dp. Fractional placental extraction of Val was higher whereas those of Ala, Gln and Glu were lower in the HP-LC compared with the ST sows at 64 and 94 dp (P ≤ 0·05). Reduced fetal mass at 94 dp was accompanied by reduced fetal extraction of Lys and Pro in the HP-LC group (P ≤ 0·05). In conclusion, a maternal HP-LC diet during pregnancy altered maternal plasma composition of many AA and modified placental AA extraction to compensate for imbalanced maternal nutrient intake.
Can Human Movements Explain Heterogeneous Propagation of Dengue Fever in Cambodia?

Background Determining the factors underlying the long-range spatial spread of infectious diseases is a key issue regarding their control. Dengue is the most important arboviral disease worldwide and a major public health problem in tropical areas. However the determinants shaping its dynamics at a national scale remain poorly understood. Here we describe the spatial-temporal pattern of propagation of annual epidemics in Cambodia and discuss the role that human movements play in the observed pattern. Methods and Findings We used wavelet phase analysis to analyse time-series data of 105,598 hospitalized cases reported between 2002 and 2008 in the 135 (/180) most populous districts in Cambodia. We reveal spatial heterogeneity in the propagation of the annual epidemic. Each year, epidemics are highly synchronous over a large geographic area along the busiest national road of the country whereas travelling waves emanate from a few rural areas and move slowly along the Mekong River at a speed of ∼11 km per week (95% confidence interval 3–18 km per week) towards the capital, Phnom Penh. Conclusions We suggest human movements – using roads as a surrogate – play a major role in the spread of dengue fever at a national scale. These findings constitute a new starting point in the understanding of the processes driving dengue spread.
Speech Emotion Recognition Using MFCCs Extracted from a Mobile Terminal based on ETSI Front End

The importance of automatically recognizing emotions from human speech has grown with the increasing role of spoken language interface in man-machine applications. The paper presents a system for the recognition of emotional states based on parameters extracted at the front end of a mobile terminal according to the ETSI ES 202 050 standard. Starting from a vector of various features derived from energy and MFCCs, an approach based on genetic algorithms is used to determine a subset of features that will allow robust speech classification of 7 emotional states: anger, joy, sadness, fear, disgust, boredom and neutral
Active ISP Involvement in Content-Centric Future Internet

As content access and delivery becomes the dominant activity in the Internet, it is increasingly critical to support it in an efficient and scalable way. In this paper, we study how content delivery networks can evolve through an increased presence at the network's edge and a blend of functions to give increased value to users as producers and consumers of content, and present the support node of this new architecture. We also discuss the new opportunities created by this architecture for Internet Service Provider (ISP) to become active participants in the evolution of the content-centric Internet.
Flat clathrin lattices: stable features of the plasma membrane

Endocytosis via clathrin-coated pits is a well-understood process; however, clathrin also assembles into large, flat clathrin lattices (FCLs), which remain poorly described. Quantitative electron, superresolution, and live-cell microscopy reveal that FCLs provide stable platforms for the recruitment of endocytic cargo.
A co-experience approach to improving international students’ classroom experience: a practice report from within an Australian higher education setting

Based on my experience teaching in a large metropolitan university in Australia, I discuss why we need to stop looking at international students through a deficit model, and look at the rich range of knowledge that our classrooms can gain from international students; many of them have been top performers in their home countries before they come here, but quickly get disheartened upon arrival in Australia when they find themselves stereotyped in a negative way, on top of all the culture shock and other adjustments they go through. I argue that the solutions go beyond just teachers and university support systems; we need to involve domestic students in making international students’ experience more positive, for they play a big part in an international students’ day-to-day experience in the classroom. Based on a participatory action research project within my own teaching practice, I argue that giving room for flexibility in content choice, assessment choice, and choice of modalities within the scope of a subject’s learning outcomes can yield positive results for all students as detailed in this paper.
Visual servo control of cable-driven soft robotic manipulator

Aim at enhancing dexterous and safe operation in unstructured environment, a cable-driven soft robotic manipulator is designed in this paper. Due to soft material it made of and nearly infinite degree of freedom it owns, the soft robotic manipulator has higher security and dexterity than traditional rigid-link manipulator, which make it suitable to perform tasks in complex environments that is narrow, confined and unstructured. Though the soft robotic manipulator possesses advantages above, it is not an easy thing for it to achieve precise position control. In order to solve this problem, a kinematic model based on piecewise constant curvature hypothesis is proposed. Through building up three spaces and two mappings, the relationship between the length variables of 4 cables and the position and orientation of the soft robotic manipulator end-effector is obtained. Afterwards, a depth-independent image Jacobian matrix is introduced and an image-based visual servo controller is presented. Applied by adaptive algorithm, the controller could estimate unknown position of the feature point online, and then Lyapunov theory is used to prove the stability of the proposed controller. At last, experiments are conducted to demonstrate rationality and validity of the kinematic model and adaptive visual servo controller.
Federal Reserve Bank of Minneapolis Research Department Staff Report 351 Latin America in the Rearview Mirror

Latin American countries are the only Western countries that are poor and that aren't gaining ground on the United States. This paper evaluates why Latin America has not replicated Western economic success. We find that this failure is primarily due to TFP differences. Latin America's TFP gap is not plausibly accounted for by human capital differences, but rather reflects inefficient production. We argue that competitive barriers are a promising channel for understanding low Latin TFP. We document that Latin America has many more international and domestic competitive barriers than do Western and successful East Asian countries. We also document a number of microeconomic cases in Latin America in which large reductions in competitive barriers increase productivity to Western levels. for their comments. We thank Sanghoon Lee, Casey Otsu, and Mauro Rodrigues for their able research assistance. Finally, we especially thank Alan Stockman, who has long served as a mentor, guide, and inspiration to many of us. The views expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of Minneapolis or the Federal Reserve System.
The Bright and Dark Sides of Microfinance

This study analyzes the impact of microcredit on the life of women. Most of the literature has described the bright side of microfinance and how it helps women to improve their micro-firms. We argue that there is a dark side of microcredit that needs to be addressed. This dark side includes women being forced to take credit by their family, issues surrounding financial education, complaints about the Grameen model, marital disharmony, problems with children, etc. Additionally, benefits are kept mainly in the microfinance institutions due to the high interest rates they charge. To avoid this, some solutions are to have microfinance institutions involved in the community or having the father or legal guardian of the children acting as bank guarantor, inter alia. We tested these ideas on a sample of women micro-entrepreneurs in Mexico.
The NYU Ultracomputer&#8212;Designing an MIMD Shared Memory Parallel Computer

We present the design for the NYU Ultracomputer, a shared-memory MIMD parallel machine composed of thousands of autonomous processing elements. This machine uses an enhanced message switching network with the geometry of an Omega-network to approximate the ideal behavior of Schwartz's paracomputer model of computation and to implement efficiently the important fetch-and-add synchronization primitive. We outine the hardware that would be required to build a 4096 processor system using 1990's technology. We also discuss system software issues, and present analytic studies of the network performance. Finally, we include a sample of our effort to implement and simulate parallel variants of important scientific p̀rograms.
Dynasa: adapting grid applications to safety using fault-tolerant methods

Grid applications have been prone to encountering problems such as failures or malicious attacks during execution, due to their distributed and large-scale features. The application itself, however, has limited power to address these problems. This paper presents the design, and implementation of an adaptive framework - Dynasa, which strives to handle security problems using adaptive fault-tolerance (i.e., checkpointing and replication) during the execution of applications according to the status of the grid environments.
The regularity and Neumann problem for non-symmetric elliptic operators

(3) λ|ξ| 6 ξ · Aξ for some λ > 0 and all ξ ∈ R, but A is not necessarily symmetric. The domain Ω = {X = (x, t) ∈ R |φ(x) < t} is the domain above the graph of a Lipschitz function φ. In the sequel we will denote by τ the tangent (1, φ)/(1 + (φ)) 1 2 to ∂Ω and ∂τ = τ · ∇ the derivative along the boundary. The conormal derivative will be ν · A∇. Let us begin by fixing some notation. The spaces C and C 0 are the spaces of smooth functions and smooth functions with compact support, respectively. For clarity, we will often indicate the domain of such functions, for example C∞0 (R). For p ∈ [1,∞] and E a subset of either R or R the space L(E) is the set of measurable functions f : E→ R such that ‖ f ‖Lp(E) is finite, where
Robust and secure beamformer design for MIMO relaying with imperfect eavesdropper CSI

This paper presents a computationally efficient beamforming approach to combat wiretapping in a relay-based multiple-input multiple output (MIMO) communication system which is part of a cognitive radio (CR) network. The system operates in two stages, that is, multiple-access (MA) followed by broadcasting (BC) using physical layer network coding (PNC). The beamforming design is based on minimizing the mean square error (MSE) at the receiving node(s) while enforcing signal-to-interference-plus-noise ratio (SINR) constraints at the eavesdroppers. The constraints take into account uncertainty bounds on eavesdropper channel estimation errors. In each stage of communication, an optimization problem is devised and solved using an iterative procedure, considering two different types of eavesdropper functionality, i.e., selection combining and “blind” beamforming. Numerical results show the convergence of the MSE at the nodes and the SINR distributions at the eavesdroppers for both cases. Comparisons to previously suggested solutions for blind beamforming are also included showing improvements in MSE values in the MA stage and computational efficiency in both stages.
A New Relational Networks Sampling Algorithm Using Topologically Divided Stratums

One popular solution to deal with large-scale relational networks is to derive a representative sample from huge relational networks. We expect this sample could represent the origin relational network well so that the sampled network can be used for simulations and further analysis instead of the origin one. In this paper, we propose a network stratified sampling algorithm using topologically divided stratums for large relational networks, which can maintain the topological similarity well between sampled network and original network. In addition, we evaluate our algorithm on several well-known data sets. The experimental results show that our algorithm outperforms the previous methods.
Modeling Risky Economic Decision-Making with Bounded Rationality

Models of economic decisions often assume people maximize a preference or utility function. While this assumption provides insights into a wide range of economic behavior, in some cases its predictions contradict observed behavior. We illustrate this situation with human-subject experiments of decisions involving risk. We show how the addition of bounded rationality to the economic model can explain the behavior, and we estimate the parameters for our experimental subjects showing they divide into distinct groups with different risk preferences. This combined model illustrates how standard economic models can be extended to a wider range of human decision-making behavior.
Who Is the One Being Disrespectful? Understanding and Deconstructing the Criminalization of Elementary School Boys of Color

This research aims to contribute to understanding what criminalization for boys of color looks like in urban elementary school settings and to offering insights into what we must do to disrupt criminalization in urban schools. Using multiple sources of data from four elementary schools across a 2-year period, we found that boys of color in the study were subjected to criminalization as part of their daily educational experiences. Their bodies and behaviors were hyper-policed, disparately punished, and routinely labeled with criminalizing terms. Furthermore, we found masternarratives framing boys of color as disrespectful and habitually truant to be ambiguous and empirically false.
Image segmentation using a hybrid gradient based watershed transform

A watershed transform segmentation method based on hybrid gradient that combines intensity and texture visual cues is proposed. Firstly a bilateral filtering method derived from robust statistics is used to extract the intensity gradient. Secondly a Gabor filter bank is applied to extract texture features. With a smoothing post process, the texture gradient is extracted. Then by morphological dilation and normalization process texture and intensity gradients are fused to form the hybrid gradient. At last the marked watershed transform on the hybrid gradient image is carried out to segment the image. The experiment results show that the proposed method is effective in generating accurate primitive-objects boundaries and meanwhile reducing the over segmentation of image.
Spectrum Sensing Algorithms for Cognitive Radio Applications

1  Abstract— The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. Challenges associated with spectrum sensing are given and enabling spectrum sensing methods are reviewed. The paper explains the cooperative sensing concept and its various forms. External sensing algorithms and other alternative sensing methods are discussed. Furthermore, statistical modeling of network traffic and utilization of these models for prediction of primary user behavior is studied. Finally, sensing features of some current wireless standards are given.
Interactive Feature Tracking using K-D Trees and Dynamic Programming

A new approach to template tracking is presented, incorporating three distinct contributions. Firstly, an explicit definition for a feature track is given. Secondly, the advantages of an image preprocessing stage are demonstrated and, in particular, the effectiveness of highly compressed image patch data stored in k-d trees for fast and discriminatory image patch searches. Thirdly, the k-d trees are used to generate multiple track hypotheses which are efficiently merged to give the optimal solution using dynamic programming. The explicit separation of feature detection and trajectory determination creates the basis for the novel use of k-d trees and dynamic programming. Multiple appearances and occlusion handling are seamlessly integrated into this framework. Appearance variation through the sequence is robustly handled in an iterative process. The work presented is a significant foundation for a powerful off-line feature tracking system, particularly in the context of interactive applications.
Super-Resolution Employing an Efficient Nonlocal Prior

In this paper, we propose a novel approach for multiframe super-resolution reconstruction by incorporating non-local prior in the maximum a posteriori (MAP) formulation. This prior expresses that recovered images tend to exhibit repetitive structures. A great deal of computation is required in the original non-local prior algorithm dealing with the huge amount of weight calculations. Techniques of weight symmetry, moving averaging filter, limited search window are adopted to speed up non-local filter. Meanwhile, Non-Linear Conjugated Gradient (NLCG) method is introduced to solve simultaneously the high-resolution (HR) image of optimization process and non-local prior adapted to the HR image. Experimental results on extensive synthetic and realistic images demonstrate the superiority of the proposed algorithm to representative algorithms both quantitatively and qualitatively.
The effect of training in Mobile Assisted Language Learning on attitude, beliefs and practices of tertiary students in Pakistan.

x Acknowledgements . xii List of abbreviations . xiii Glossary . xiv Publications arising from this thesis to date . xviii Conference presentations during my PhD journey . xix Chapter 1: Introduction . 1 1.
Studying the Effects of Transcranial Direct-Current Stimulation in Stroke Recovery Using Magnetic Resonance Imaging

Transcranial direct-current stimulation (tDCS) is showing increasing promise as an adjunct therapy in stroke rehabilitation. However questions still remain concerning its mechanisms of action, which currently limit its potential. Magnetic resonance (MR) techniques are increasingly being applied to understand the neural effects of tDCS. Here, we review the MR evidence supporting the use of tDCS to aid recovery after stroke and discuss the important open questions that remain.
Exploiting correlated keywords to improve approximate information filtering

Information filtering, also referred to as publish/subscribe, complements one-time searching since users are able to subscribe to information sources and be notified whenever new documents of interest are published. In approximate information filtering only selected information sources, that are likely to publish documents relevant to the user interests in the future, are monitored. To achieve this functionality, a subscriber exploits statistical metadata to identify promising publishers and index its continuous query only in those publishers. The statistics are maintained in a directory, usually on a per-keyword basis, thus disregarding possible correlations among keywords. Using this coarse information, poor publisher selection may lead to poor filtering performance and thus loss of interesting documents.1 Based on the above observation, this work extends query routing techniques from the domain of distributed information retrieval in peer-to-peer (P2P) networks, and provides new algorithms for exploiting the correlation among keywords in a filtering setting. We develop and evaluate two algorithms based on single-key and multi-key statistics and utilize two different synopses (Hash Sketches and KMV synopses) to compactly represent publishers. Our experimental evaluation using two real-life corpora with web and blog data demonstrates the filtering effectiveness of both approaches and highlights the different tradeoffs.
Lack of Significant Association between Plasma/Serum miR-221 Expression and Poor Survival of Carcinoma: A Meta-Analysis

Background. MicroRNAs (miRNAs) exhibit altered expression levels in cancers, and they may play a potential role as diagnostic and prognostic biomarkers of cancers. The aim of this meta-analysis was to summarize recent advances in miR-221 involvement in a variety of carcinomas and derive a more precise estimation of the relationship between circulating miR-221 level and survival of cancer patients. Methods. We searched online PubMed, EMBASE, and Cochrane Library up to August 2013 to identify relevant studies. Data were collected from studies comparing survival in patients with various carcinomas with higher miR-221 expression to those with lower levels. Pooled hazard ratios (HRs) of miR-221 for survival were calculated. Results. There were 4 studies included in the meta-analysis. The results of meta-analysis suggested that no significant difference in poor overall survival between miR-221 high and low groups (OR = 0.94, 95%, CI = 0.47–1.87, Z = 0.17, and P = 0.863). Conclusions. The current meta-analysis showed the equivalence of high and low plasma/serum miR-221 expression for carcinomas in terms of survival.