Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving face gender classification by adding deliberately misaligned faces to the training data

A novel method of face gender classifier construction is proposed and evaluated. Previously, researchers have assumed that a computationally expensive face alignment step (in which the face image is transformed so that facial landmarks such as the eyes, nose, chin, etc, are in uniform locations in the image) is required in order to maximize the accuracy of predictions on new face images. We, however, argue that this step is not necessary, and that machine learning classifiers can be made robust to face misalignments by automatically expanding the training data with examples of faces that have been deliberately misaligned (for example, translated or rotated). To test our hypothesis, we evaluate this automatic training dataset expansion method with two types of image classifier, the first based on weak features such as Local Binary Pattern histograms, and the second based on SIFT keypoints. Using a benchmark face gender classification dataset recently proposed in the literature, we obtain a state-of-the-art accuracy of 92.5%, thus validating our approach.
2 Sparse Representation-based Classification ( SRC ) 2

In this project, we implement a robust face recognition system via sparse representation and convex optimization. We treat each test sample as sparse linear combination of training samples, and get the sparse solution via L1-minimization. We also explore the group sparseness (L2-norm) as well as normal L1-norm regularization.We discuss the role of feature extraction and classification robustness to occlusion or pixel corruption of face recognition system. The experiments demonstrate the choice of features is no longer critical once the sparseness is properly harnessed. We also verify that the proposed algorithm outperforms other methods.
Non-Audit Services and Auditor Independence : Evidence from 1978-1980

Accounting research has extensively debated the impact of non-audit services (NAS) provided by auditors on auditor independence and on clients’ financial reporting quality. Most research on this debate uses data resulting from a SEC disclosure rule in 2000. We provide evidence on this controversy using data from the years 1978-1980 to re-examine the relation between fees for NAS provided by auditors and (i) their clients’ financial reporting outcomes; and (ii) the stock market’s perception thereof. Apart from providing a new setting to examine a long-standing concern, the 1978-80 data are especially relevant to the current debate because (i) information systems related fees (ISFEE) represent the largest component of NAS in our 1978-80 sample and were of particular concern to the SEC when the current ban on NAS was enacted; and (ii) similar to the SEC requirement of 2000, firms had to obtain prior director approval in 1978-80 to engage auditors for NAS. We find no evidence that financial reporting outcomes are compromised by NAS. In fact, our results suggest that both NAS and ISFEE are associated with higher financial reporting quality. In sum, data drawn from an earlier time period than that examined by recent research fail to support the SEC’s concerns that NAS provided by the auditor affects the quality of financial reporting.
Fast Training of Graph-Based Algorithms for Nonlinear Dimensionality Reduction

Introduction Dimensionality reduction algorithms have long been used ei ther for exploratory analysis of a high-dimensional dataset, to reveal structur e s ch as clustering, or as a preprocessing step, by extracting low-dimensional features that are usef ul for classification or other tasks. Here we focus on dimensionality reduction algorithms where a dat set consisting of N objects is represented by a weighted graph, rather than by a high-dimensiona l feature vector for each object. One type of these methods are spectral methods such as Laplacian eige maps [1], Isomap and others, which have become extremely popular in the last 10 years because of their ease of implementation. Their objective function is quadratic (and subject to quadr atic constraints), so that the minimum can be obtained by solving an eigenproblem using numerical l inear algebra routines. Using a cluster of several hundred machines and various approximations , t is possible to deal with millions of points with Laplacian eigenmaps [2]. Spectral methods can s ometimes find a good low-dimensional representation of a nonlinear dataset, but they severely di stort the data in the presence of noise, variations in density, or when it contains multiple manifol ds. Recently, several methods have been developed that define a non-quadratic objective function, a nd which are able to achieve much better low-dimensional representations than linear or spectral m ethods. These methods include Stochastic Neighbor Embedding (SNE; [3]), t-SNE [4] and the Elastic Emb edding (EE; [5]). However, their practical applicability has been limited to small datasets so far because of the slowness of its optimization. Current implementations have training times o f the order of hours even for datasets of a few thousand points. Our goal is to develop fast training al gorithms that can allow interactive use with small or medium datasets (for example, to update inc r mentally an existing embedding as new high-dimensional data is added to it or removed from it), and relatively short times with large datasets (as well as the ability to create a quick “preview” o f a large dataset and then refine it as needed).
Approximation methods for optimal active fault detection

In this paper the problem of active fault detection for a non-linear discrete-time stochastic system over an infinite time horizon is considered. The faults in the system are modeled by switching between fault-free and finitely many faulty models. The optimal active fault detector is derived by reformulating the original problem and solving corresponding Bellman's functional equation. A suboptimal solution is based on application of the value iteration or the policy iteration algorithms and a representation of the continuous hyper-state space by a uniform grid. A comparison of both algorithms for solving Bellman's functional equation is illustrated in a numerical example.
Contextual Ontology Based Opinion Mining

The Web contains a wealth of product reviews, but sifting through them is daunting task. Context-aware systems are designed to react to the constant changes in the environment. Many users may have different needs in different situations, every individual’s views and suggestions are valuable information in opinion summarization process. Though lots of views and suggestions are available for different products in websites yet we lack in most appropriate opinion for any selective product at a particular situation. In this paper Customer reviews are collected as first phase, and features of products and contextual information in the customer reviews are represented in ontology. Now opinion mining is done to determine the polarity of the product based on the context and features. So contextual ontology based opinion mining system is developed and opinion summarization is done.
The Role of Interleukin-1 Genotype in the Association between Coronary Heart Disease and Periodontitis in a Syrian Population

Objective. To determine whether differences exist between periodontitis subjects with and without Coronary Heart Disease (CHD) in a Syrian population in the distribution of IL-1 alleles at positions IL-1α+4845, IL-1β+3954, IL-1β−511, and IL-1RN VNTR. Background. The role of Interleukin-1 genes in the association between periodontitis and CHD has been demonstrated in previous studies. No study has been carried out on the Syrian population to asses for such a role. Methods. 200 Syrian Arab periodontitis patients (184 males, 16 females; mean age 52.61) were divided into two groups: cases group 100 subjects with CHD (92 males, 8 females; mean age 52.06); controls group 100 subjects without CHD (92 males, 8 females; mean age 53.16). Probing depth (PD), clinical attachment loss (CAL), and alveolar bone loss (ABL) were performed for patients. Blood samples were collected for genotyping analysis of IL-1α+4845, IL-1β+3954, and IL-1β−511 using PCR-RFLP technique and IL-1RN VNTR using normal PCR. Results. An association between both (CAL and ABL) and CHD was shown after adjustment for other confounders (OR: 7.659, P = 0.001; OR: 3.645, P = 0.006, resp.). Also, an association between allele 2 of IL-1α+4845, IL-1β+3954, and IL-1β−511 and ABL was shown. Allele 2 of IL-1α+4845 and IL-1β−511 was associated with ABL among individuals with and without CHD. But after adjustment for other confounders, the association remained only between allele 2 of IL-11α+4845 and both CHD and severe ABL (OR: 0.189, P < 0.001). Conclusion. Allele 2 of IL-11α+4845 may be considered a risk indicator for having both CHD and severe ABL in the investigated Syrian population.
A role for lactate dehydrogenases in the survival of Neisseria gonorrhoeae in human polymorphonuclear leukocytes and cervical epithelial cells.

Lactate is an abundant metabolite, produced by host tissues and commensal organisms, and it represents an important potential carbon source for bacterial pathogens. In the case of Neisseria spp. , the importance of the lactate permease in colonization of the host has been demonstrated, but there have been few studies of lactate metabolism in pathogenic Neisseria in the postgenomic era. We describe herein the characterization of genome-annotated, respiratory, and substrate-level lactate dehydrogenases (LDHs) from the obligate human pathogen Neisseria gonorrhoeae. Biochemical assays using N. gonorrhoeae 1291 wild type and isogenic mutant strains showed that cytoplasmic LdhA (NAD(+)-dependent D-lactate dehydrogenase) and the membrane-bound respiratory enzymes, LdhD (D-lactate dehydrogenase) and LldD (L-lactate dehydrogenase) are correctly annotated. Mutants lacking LdhA and LdhD showed greatly reduced survival in neutrophils compared with wild type cells, highlighting the importance of D-lactate metabolism in gonococcal survival. Furthermore, an assay of host colonization using the well-established human primary cervical epithelial cell model revealed that the two respiratory enzymes make a significant contribution to colonization of and survival within the microaerobic environment of the host. Taken together, these data suggest that host-derived lactate is critical for the growth and survival of N. gonorrhoeae in human cells.
Extending Options by Changing Their Underlying Assets: An Anti-crisis Solution

This article aims to examine a new type of exotic option, namely the external extendible option. Such an option has two characteristics: first, its maturity has the possibility to be extended for a given duration; second, once the option is extended, it will be written on a new underlying asset. For such European-style options, closed-form pricing formulas are derived, and analytical properties are examined. External extendible options have numerous applications in different fields. In particular, they can be used as anti-crisis arrangements within the framework of montage of capital-guaranteed funds.
Optic nerve degeneration in a murine model of juvenile ceroid lipofuscinosis.

PURPOSE To investigate optic nerve degeneration associated with CLN3 deficiency in a murine model of juvenile neuronal ceroid lipofuscinosis (Batten disease). METHODS Using light and electron microscopy, the density and diameter of axons and the thickness of myelin in optic nerve were compared between age-matched cln3 knock-out (cln3-/-) and wild-type (129ev/TAC) mice. Western blot analysis was used to assay expression of Cln3 in mouse and primate retina and optic nerve. RESULTS Morphologically identified mast cells were present in the meningeal sheaths surrounding the cln3-/- nerve and in the nerve itself. The cln3-/- optic nerve exhibited an overall loss of uniformity and integrity. Axon density in cln3-/- optic nerve was only 64% of that in wild-type optic nerve (P < 0.01). Accounting for differences in axon density, the diameter of axons in cln3-/- optic nerve was 1.2 times greater than in wild-type optic nerve (P < 0.01). Electron micrographs revealed large spaces between axons and 32% thinner myelin surrounding axons in cln3-/- mice than in wild type (P < 0.01). Western blot analysis demonstrated that Cln3 was expressed in retinas and optic nerves of mouse and primate. CONCLUSIONS The presence of apparent mast cells in cln3-/- optic nerve suggests compromise of the blood-brain barrier. The absence of Cln3 causes loss of axons, axonal hypertrophy, and a reduction in myelination of retinal ganglion cells. Furthermore, expression of CLN3 in mouse and primate optic nerve links degeneration to loss of Cln3.
Building SWPBIS Capacity in Rural Schools through Building-Based Coaching: Early Findings from a District-Based Model

School-wide Positive Behavioral Interventions and Supports (SWPBIS) is a widely used framework for supporting student social and academic behavior. Implementation science indicates that one effective way to implement and scale-up practices, such as SWPBIS, is through coaching; thus, there is a need for efficient, cost-effective methods to develop and support SWPBIS coaches. Using the literature of implementation science and SWPBIS, we developed and implemented a coaching curriculum to increase and support building-based capacity with SWPBIS. In this article, we present a district-based model for training school personnel to become SWPBIS coaches. Data on initial training outcomes and school outcomes are presented. Findings suggest that the training of school-based coaches in rural schools resulted in improved knowledge of SWPBIS and supported fidelity of SWPBIS implementation in local schools. Lessons learned, as well as implications for practice and research, are discussed.
Clustered folate receptors deliver 5-methyltetrahydrofolate to cytoplasm of MA104 cells

Previously, a high affinity, glycosylphosphatidylinositol-anchored receptor for folate and a caveolae internalization cycle have been found necessary for potocytosis of 5-methyltetrahydrofolate in MA104. We now show by cell fractionation that folate receptors also must be clustered in caveolae for potocytosis. An enriched fraction of caveolae from control cells retained 65-70% of the [3H]folic acid bound to cells in culture. Exposure of cells to the cholesterol-binding drug, filipin, which is known to uncluster receptors, shifted approximately 50% of the bound [3H]folic acid from the caveolae fraction to the noncaveolae membrane fraction and markedly inhibited internalization of [3H]folic acid. An mAb directed against the folate receptor also shifted approximately 50% of the caveolae-associated [3H]folic acid to noncaveolae membrane, indicating the antibody perturbs the normal receptor distribution. Concordantly, the mAb inhibited the delivery of 5-methyl[3H]tetrahydrofolate to the cytoplasm. Receptor bound 5- methyl[3H]tetrahydrofolate moved directly from caveolae to the cytoplasm and was not blocked by phenylarsine oxide, an inhibitor of receptor-mediated endocytosis. These results suggest cell fractionation can be used to study the uptake of molecules by caveolae.
Using infrastructure-provided context filters for efficient fine-grained activity sensing

While mobile and wearable sensing can capture unique insights into fine-grained activities (such as gestures and limb-based actions) at an individual level, their energy overheads are still prohibitive enough to prevent them from being executed continuously. In this paper, we explore practical alternatives to addressing this challenge-by exploring how cheap infrastructure sensors or information sources (e.g., BLE beacons) can be harnessed with such mobile/wearable sensors to provide an effective solution that reduces energy consumption without sacrificing accuracy. The key idea is that many fine-grained activities that we desire to capture are specific to certain location, movement or background context: infrastructure sensors and information sources (e.g., BLE beacons) offer practical and cheap ways to identify such context. In this paper, we first explore how various infrastructure, mobile & wearable sensors can be used to identify fine-grained location/movement context (e.g., transiting through a door). We then show, using a couple of illustrative examples (specifically, the detection of `switch pressing' before exiting a room and the identification of `water drinking' after approaching a water cooler) to show that such background context can be predicted, with sufficient accuracy, with sufficient lead time to enable a `triggered' model for mobile/wearable sensing of such microscopic, transient gestures and activities. Moreover, such `triggered' sensing also helps to improve the accuracy of such microscopic gesture recognition, by reducing the set of candidate activity labels. Empirical experiments show that we are able to identify 82.2% of switch-pressing and 91.73% of water-drinking activities in a campus lab setting, with a significant reduction in active sensing time (up to 92.9% compared to continuous sensing).
Creating personalised quizzes both to the learner and to the access device characteristics : the Case of CosyQTI

In this paper, we present CosyQTI, a tool for authoring adaptive assessments, which gives the educator/ author significant flexibility in terms of the adaptation that s/he can incorporate in the assessments s/he builds. The assessments can be accessed via a variety of access devices including desktop and handheld ones. We illustrate the architecture of the CosyQTI tool and advocating that the tool fully conforms to the IMS_QTI and web standard, serving the goal of interoperability.
A Novel Interest Model with Contextual Information on Mobile Platform

Considering the insufficient information used in interest model on mobile platform, we proposed a novel Interest Model (IM) with contextual information. Composite analysis is giving to the contextual information on mobile platform. Continually, an algorithm for contextual information matching based on Case-Based Reasoning (CBR) was raised. Presented hierarchical interest ontology with semantic relationship based on Open Dictionary Project (ODP). Meanwhile, proposed a method to model updating based on Spreading Activation Theory. The experiment showed that the IM could significantly improve the user experience.
Nire eskerrik beroena eman nahi diet master tesi hau burutzen lagundu didaten pertsona guztiei , era berezian honako hauei :

iii Heavy-traffic analysis of a multi-class queue with relative priorities Abstract: In this master thesis we analyze the steady-state queue-length vector in a multi-class single-server queue with relative priorities. Upon service completion, the probability that the next customer to be served is from class k is controlled by class-dependent weights. Once a customer has started service, it is served without interruption until completion. We investigate the system in a heavy-traffic regime i.e. when the system is close to saturation. We first establish a state-space collapse for the scaled queue length vector, that is, in the limit the scaled queue length vector is distributed as the product of an exponentially distributed random variable and a deterministic vector. As a direct consequence, we obtain that the scaled number of customers in the system reduces as classes with smaller mean service requirement obtain relatively larger weights. We then show that the scaled waiting time of a class-k customer is distributed as the product of two exponentially distributed random variables. This allows us to determine the value of the weights that minimize the m-th moment of the scaled waiting time for a customer of arbitrary class. Besides, we simulate this model for two classes in the system and observe that the heavy-traffic results can be a good approximation for the system's performance in a high congested situation.
Energy adjustment of nutrient intakes is preferable to adjustment using body weight and physical activity in epidemiological analyses

Abstract Objective Adjustment for body weight and physical activity has been suggested as an alternative to adjusting for reported energy intake in nutritional epidemiology. We examined which of these approaches would yield stronger correlations between nutrients and their biomarkers. Design A cross-sectional study in which dietary fatty acids, carotenoids and retinol were adjusted for reported energy intake and, separately, for weight and physical activity using the residual method. Correlations between adjusted nutrients and their biomarkers were examined. Setting USA. Subjects Cases and controls from a nested case–control study of erythrocyte fatty acids and CHD (n 442) and of plasma carotenoids and retinol and breast cancer (n 1254). Results Correlations between intakes and plasma levels of trans-fatty acids were 0·30 (energy-adjusted) and 0·16 (weight- and activity-adjusted); for erythrocyte levels, the corresponding correlations were 0·37 and 0·25. Energy-adjusted intakes of linoleic acid and α-linolenic acid were more strongly correlated with their respective biomarkers than weight- and activity-adjusted intakes, but the differences were not significant except for linoleic acid (erythrocyte). Weight- and activity-adjusted DHA intake was slightly more strongly correlated with its plasma biomarker than energy-adjusted intake (0·37 v. 0·34). Neither method made a difference for DHA (erythrocyte), carotenoids and retinol. Conclusions The effect of energy adjustment depends on the nutrient under investigation, and adjustment for energy calculated from the same questionnaire used to estimate nutrient intakes improves the correlation of some nutrients with their biomarkers appreciably. For the nutrients examined, adjustment using weight and physical activity had at most a small effect on these correlations.
A Security evaluation of certain stream ciphers which involve randomness and coding

Following some of the recently reported results, an approach for design of stream ciphers has been considered which is based on joint employment of pseudorandomness, randomness and dedicated wire-tap channel and error correction coding. The wire-tap channel coding controlled by the randomness, provides that an attacker faces not only the traditional problems of cryptanalysis but also the problem of decoding without the secret key which appears as complex as the exhaustive search over the possible secret keys. Security evaluation has been performed based on the algebraic representation of the proposed stream cipher. The given security evaluation has addressed hardness of the algebraic recovering of the secret key in the CPA scenario. It is shown that the secret key recovery is as hard as the LPN∊ problem where ∊ = 1−(1−2)(m−ℓ)/2/2 and m, ℓ and p are the stream cipher parameters.
AN "ALGEBRAIC" RECONSTRUCTION OF PIECEWISE-SMOOTH FUNCTIONS FROM INTEGRAL MEASUREMENTS

This paper presents some results on a well-known problem in Algebraic Sig- nal Sampling and in other areas of applied mathematics: reconstruction of piecewise-smooth functions from their integral measurements (like moments, Fourier coefficients, Radon trans- form, etc.). Our results concern reconstruction (from the moments or Fourier coefficients) of signals in two specific classes: linear combinations of shifts of a given function, and "piecewise D-finite functions" which satisfy on each continuity interval a linear differential equation with polynomial coefficients. In each case the problem is reduced to a solution of a certain type of non-linear algebraic system of equations ("Prony-type system"). We recall some known methods for explicitly solving such systems in one variable, and provide extensions to some multi-dimensional cases. Finally, we investigate the local stability of solving the Prony-type systems.