Miracle-dz/UniFAR-FTUs huggingface dataset:
An efficient gait recognition based on a selective neural network ensemble

The neural network ensemble is a learning paradigm where a collection of neural networks is trained for the same task. Generally, the ensemble shows better generalization performance than a single neural network. In this article, a selective neural network ensemble is applied to gait recognition. The proposed method selects some neural network based on the minimization of generalization error. Since the selection rule is directly incorporated into the cost function, we can obtain adequate component networks to constitute an ensemble. Experiments are performed with the NLPR database to show the performance of the proposed algorithm. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 237–241, 2008; Published online in Wiley InterScience (www.interscience.wiley.com).
Defensive Approximation: Enhancing CNNs Security through Approximate Computing

In the past few years, an increasing number of machine-learning and deep learning structures, such as Convolutional Neural Networks (CNNs), have been applied to solving a wide range of real-life problems. However, these architectures are vulnerable to adversarial attacks. In this paper, we propose for the first time to use hardware-supported approximate computing to improve the robustness of machine learning classifiers. We show that our approximate computing implementation achieves robustness across a wide range of attack scenarios. Specifically, for black-box and grey-box attack scenarios, we show that successful adversarial attacks against the exact classifier have poor transferability to the approximate implementation. Surprisingly, the robustness advantages also apply to white-box attacks where the attacker has access to the internal implementation of the approximate classifier. We explain some of the possible reasons for this robustness through analysis of the internal operation of the approximate implementation. Furthermore, our approximate computing model maintains the same level in terms of classification accuracy, does not require retraining, and reduces resource utilization and energy consumption of the CNN. We conducted extensive experiments on a set of strong adversarial attacks; We empirically show that the proposed implementation increases the robustness of a LeNet-5 and an Alexnet CNNs by up to 99% and 87%, respectively for strong grey-box adversarial attacks along with up to 67% saving in energy consumption due to the simpler nature of the approximate logic. We also show that a white-box attack requires a remarkably higher noise budget to fool the approximate classifier, causing an average of 4db degradation of the PSNR of the input image relative to the images that succeed in fooling the exact classifier
Foresee: Attentive Future Projections of Chaotic Road Environments

In this paper, we train a recurrent neural network to learn dynamics of a chaotic road environment and to project the future of the environment on an image. Future projection can be used to anticipate an unseen environment for autonomous driving. Road environment is highly dynamic and complex due to the interaction among traffic participants such as vehicles and pedestrians. Even in such a complex environment, a human driver can easily anticipate the environment and is efficacious to drive safely on the chaotic roads. Proliferation in deep learning research has shown the efficacy of neural networks in learning this kind of human behavior. In the same direction, we investigate recurrent neural networks to understand the road environment. We propose Foresee, a unidirectional gated recurrent units (GRUs) network with attention to project future of the environment in the form of images. We have collected several videos on Delhi roads consisting of various traffic participants, background and infrastructure differences (like 3D pedestrian crossing) at various times on various days. We show that our proposed model performs better than state of the art methods (prednet[9], Enc. Dec. LSTM[15]).
Spiking and collapsing in large noise limits of SDE's

We analyze strong noise limit of some stochastic differential equations. We focus on the particular case of Belavkin equations, arising from quantum measurements, where Bauer and Bernard pointed out an intriguing behavior. As the noise grows larger, the solutions exhibits locally a collapsing, that is to say converge to jump processes, very reminiscent of a metastability phenomenon. But surprisingly the limiting jump process is decorated by a spike process. We completely prove these statements for an archetypal one dimensional diffusion. The proof is robust and can easily be adapted to a large class of one dimensional diffusions.
Kent Academic Repository

We give an overview of the scripting languages used in existing cryptocurrencies, and in particular we review in some detail the scripting languages of Bitcoin, Nxt and Ethereum, in the context of a high-level overview of Distributed Ledger Technology and cryptocurrencies. We survey different approaches, and give an overview of critiques of existing languages. We also cover technologies that might be used to underpin extensions and innovations in scripting and contracts, including technologies for verification, such as zero knowledge proofs, proof-carrying code and static analysis, as well as approaches to making systems more efficient, e.g. Merkelized Abstract Syntax Trees.
Sucesos raros en Ingeniería de Tráfico

espanolLa Ingenieria de Trafico, como rama de la ingenieria del transporte y de la ingenieria civil trata la planificacion, diseno y operacion de trafico en las calles, carreteras y autopistas, sus redes, infraestructuras, tierras colindantes y su relacion con los diferentes medios de transporte para conseguir una movilidad segura, eficiente y conveniente tanto de personas como de mercancias. La ingenieria de trafico estudia los accidentes de trafico, estos por ser sucesos de muy baja probabilidad, pueden ser considerados sucesos raros. Asi, este trabajo recoge un resumen de resultados de la aplicacion del modelo Naive-Poisson a la Ingenieria de Trafico enfocado a, mediante la estimacion de accidentes de trafico, propuestas de mejora en el diseno de algunas carreteras espanolas. EnglishTraffic Engineering is a part of transportation engineering and civil engineering and it is the planning, design and operation of traffic on the streets, roads and highways, their networks, infrastructure, adjoining land and its relationship with the different modes of transport to get thi safe, efficient and convenient for people and goods. Traffic engineering studies the traffic accidents, these are very low probability events and can be considered rare events. Thus, this paper presents a summary of results from the application of Naive-Poisson model to the Traffic Engineering focused, by estimating traffic accidents, suggestions for improvements in the design of some Spanish roads.
Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access Track in DSTC9

Most prior work on task-oriented dialogue systems are restricted to a limited coverage of domain APIs, while users oftentimes have domain related requests that are not covered by the APIs. This challenge track aims to expand the coverage of task-oriented dialogue systems by incorporating external unstructured knowledge sources. We define three tasks: knowledge-seeking turn detection, knowledge selection, and knowledge-grounded response generation. We introduce the data sets and the neural baseline models for three tasks. The challenge track received a total of 105 entries from 24 participating teams. In the evaluation results, the ensemble methods with different large-scale pretrained language models achieved high performances with improved knowledge selection capability and better generalization into unseen data.
Study of the aluminium content in AGB winds using ALMA Indications for the presence of gas-phase (Al2O3)n clusters

Context. The condensation of inorganic dust grains in the winds of evolved stars is poorly understood. As of today, it is not yet known which molecular clusters form the first dust grains in oxygen-rich (C/O 34) can be the potential agents of the broad 11 mu m feature in the SED and in the interferometric data and we propose potential formation mechanisms for these large clusters.
Machine Learning for Yield Curve Feature Extraction: Application to Illiquid Corporate Bonds

This paper studies an application of machine learning in extracting features from the historical market implied corporate bond yields. We consider an example of a hypothetical illiquid fixed income market. After choosing a surrogate liquid market, we apply the Denoising Autoencoder (DAE) algorithm to learn the features of the missing yield parameters from the historical data of the instruments traded in the chosen liquid market. The DAE algorithm is then challenged by two "point-in-time" inpainting algorithms taken from the image processing and computer vision domain. It is observed that, when tested on unobserved rate surfaces, the DAE algorithm exhibits superior performance thanks to the features it has learned from the historical shapes of yield curves.
Path scheduling for multiple mobile actors in wireless sensor network

ABSTRACT In wireless sensor network (WSN), energy is the main constraint. In this work we have addressed this issue for single as well as multiple mobile sensor actor network. In this work, we have proposed Rendezvous Point Selection Scheme (RPSS) in which Rendezvous Nodes are selected by set covering problem approach and from that, Rendezvous Points are selected in a way to reduce the tour length. The mobile actors tour is scheduled to pass through those Rendezvous Points as per Travelling Salesman Problem (TSP). We have also proposed novel rendezvous node rotation scheme for fair utilisation of all the nodes. We have compared RPSS with Stationery Actor scheme as well as RD-VT, RD-VT-SMT and WRP-SMT for performance metrics like energy consumption, network lifetime, route length and found the better outcome in all the cases for single actor. We have also applied RPSS for multiple mobile actor case like Multi-Actor Single Depot (MASD) termination and Multi-Actor Multiple Depot (MAMD) termination and observed by extensive simulation that MAMD saves the network energy in optimised way and enhance network lifetime compared to all other schemes.
A First Look at the Year in Computing

In this paper, we discuss students' expectations and experiences in the first term of the Year in Computing, a new programme for non-computing majors at the University of Kent, a public research university in the UK. We focus on the effect of students' home discipline on their experiences in the programme and situate this work within the context of wider efforts to make the study of computing accessible to a broader range of students.
The collective model of household consumption

We provide a nonparametric characterization of a general collective model for household consumption, which includes externalities and public consumption. Next, we establish testable necessary and sufﬁcient conditions for data consistency with collective rationality that only include observed price and quantity information. These conditions have a similar structure as the generalized axiom of revealed preference for the uni- tary model, which is convenient from a testing point of view. In addition, we derive the minimum number of goods and observations that enable the rejection of collectively rational household behavior.
Phytochemical Analysis and Evaluation of Antidiabetic Effects in Alloxan-Induced Diabetic Rats Treated with Aqueous Leaf Extract of Acanthospermum hispidum

The current study is aimed at investigating the antidiabetic activity of the leaves of Acanthospermum hispidum, a medicinal plant traditionally used to treat diabetes mellitus in NorthWestern Nigeria. Material from the plant was extracted using water (guided by the traditional mode of extraction) and the phytochemicals contained in the extract were analyzed qualitatively and quantitatively. Acute toxicity study of the extract was conducted, and diabetic rats induced using alloxan (80 mg/kg administered intraperitoneally once daily for 3 days) were treated with the extract for 28 days at 3 incremental doses (70, 210 and 700 mg/kg). At the end of the experiment, fasting blood glucose and glycogen content of the liver and skeletal muscle were determined. The study findings indicate that the extract was relatively safe with LD 50 above 5000 mg/kg. Treatment with Acanthospermum hispidum extract resulted in a significant (P<0.05) reduction in FBG as well as a significant improvement in oral glucose tolerance at all doses. A significant elevation in the hepatic content of glycogen was also observed at the highest dose (700 mg/kg). Results from this study have demonstrated the potent antidiabetic activity of the aqueous extract of the leaves of Acanthospermum hispidum, thus justifying the traditional claim. Index Excerpta CAB Abstracts, SCOPUS, Global Health Asian Science Index, Veterinarius
-convexity

Given a homeomorphism one can define on the topological space X a set operator through the formula . Such a convexity on X has all the topological, geometric and algebraic properties of the usual convexity on ; up to a change of variable, it is a linear convexity. In the context of convex analysis and optimization theory such operators were considered by Avriel (1972) and Ben Tal (1977). We consider a sequence on homeomorphisms and we study the abstract convexity which is associated to the limit, in the appropriate sense, of the sequence of set operators ; we call the limit-convexity -convexity. On one can loosely say that this -convexity is obtained from the usual linear convexity through the formal substitution . We end this article with some simple applications to duality and “max-programming”.
Chapter 25 Reasoning About Effects : Seeing the Wood Through the Trees ( Extended Version )

Pure functional languages support programming with impure effects by exploiting mathematical notions such as monads, applicative functors, and arrows. However, in contrast to the wealth of research on the use of these notions to write effectful programs, there has been comparatively little progress on reasoning about the resulting programs. In this ar ticle we focus on this problem, using a simple but instructive example concerned with relabelling binary trees.
A Modified Multifractal Formalism for a Class of Self-Similar Measures

The multifractal spectrum of a Borel measure $\mu$ in $\mathbb{R}^n$ is defined as \[ f_\mu(\alpha) = \dim_H {x:\lim_{r\to 0} \frac{\log \mu(B(x,r))}{\log r}=\alpha}. \] For self-similar measures under the open set condition the behavior of this and related functions is well-understood; the situation turns out to be very regular and is governed by the so-called ''multifractal formalism''. Recently there has been a lot of interest in understanding how much of the theory carries over to the overlapping case; however, much less is known in this case and what is known makes it clear that more complicated phenomena are possible. Here we carry out a complete study of the multifractal structure for a class of self-similar measures with overlap which includes the 3-fold convolution of the Cantor measure. Among other things, we prove that the multifractal formalism fails for many of these measures, but it holds when taking a suitable restriction.
The Emergence of Shallow Easterly Jets within QBO Westerlies

AbstractA configuration of an idealized general circulation model has been obtained in which a deep, stratospheric, equatorial, westerly jet is established that is spontaneously and quasi-periodically disrupted by shallow easterly jets. Similar to the disruption of the quasi-biennial oscillation (QBO) observed in early 2016, meridional fluxes of wave activity are found to play a central role. The possible relevance of two feedback mechanisms to these disruptions is considered. The first involves the secondary circulation produced in the shear zones on the upper and lower flanks of the easterly jet. This is found to play a role in maintaining the aspect ratio of the emerging easterly jet. The second involves the organization of the eddy fluxes by the mean flow: the presence of a weak easterly anomaly within a tall, tropical, westerly jet is demonstrated to produce enhanced and highly focused wave activity fluxes that reinforce and strengthen the easterly anomalies. The eddies appear to be organized by the .
A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling

Canonical automatic summary evaluation met-001 rics, such as ROUGE, focus on lexical similar-002 ity which cannot well capture semantics nor lin-003 guistic quality and require a reference summary 004 which is costly to obtain. Recently, there have 005 been a growing number of efforts to alleviate ei-006 ther or both of the two drawbacks. In this paper, 007 we present a proof-of-concept study to a weakly 008 supervised summary evaluation approach with-009 out the presence of reference summaries. Mas-010 sive data in existing summarization datasets are 011 transformed for training via simple negative 012 sampling methods. In cross-domain tests, our 013 strategy outperforms baselines with promising 014 improvements, and show a great advantage in 015 gauging linguistic qualities over all metrics. We 016 hope this study can inspire more research using 017 similar strategies. Our code is at https:// 018 anonymous.4open. science/r/37CF . 019
Incoherent Waves in Fluid-Saturated Sintered Granular Systems: Scattering Phenomena

The incoherent transport of ultrasound waves in water-saturated sintered glass bead packings is experimentally investigated. The spectral energy density of scattered high-frequency waves is explained by a diffusion wave equation. Immersion broadband transducers with central frequencies of 1 MHz are positioned at a distance of 73 mm to the porous sample. The diffusion coefficient and quality factor are predicted from a diffusion approximation of the time-dependent intensity curve to the ensemble-averaged measurement data. From the diffusion coefficient, we deduce a mean-free path for scattering events at lz - 0:87 60:03 mm close to the range of particle diameters of the samples (1:0 < dp < 1:2 mm). Results are in good agreement with observations from Jia (2004, "Codalike Multiple Scattering of Elastic Waves in Dense Granular Media," Phys. Rev. Lett., 93(15), p. 154303) observed for nonsintered and consolidated bead packings (0:6 < dp < 0:8 mm). The low-quality factor Q = 190 ± 10 indicates a high amount of intrinsic damping of the scattered waves although water was used as saturating and coupling fluid.
Legitimating Power: The Domestic Politics of U.S. International Hierarchy

The United States has maintained international hierarchies over the Western Hemisphere for more than a century and over Western Europe for nearly seven decades. More recently, it has extended similar hierarchies over states in the Middle East. How does the United States exercise authority over other countries? In a world of juridically sovereign states, how is U.S. rule rendered legitimate? Hierarchy has interstate and intrastate distributional consequences for domestic ruling coalitions and regime types. When the gains from hierarchy are large or when subordinate societies share policy preferences similar to those of the United States, as in Europe, international hierarchy is possible and compatible with democracy. When the gains from hierarchy are small and the median citizen has policy preferences distant from those of the United States, as in Central America, international hierarchy requires autocracy, and the benefits of foreign rule will be concentrated within the governing elite. In the Middle East, the gains from hierarchy also appear small, and policy preferences are distant from those of the United States. As a result, the United States has backed sympathetic authoritarian rulers. Although a global counterinsurgency strategy might be viable over the long term, the costs of establishing effective hierarchies in the region imply that the United States is better off retrenching “East of Suez. ”
Interactions between Superconductivity and Quantum Criticality in CeCoIn5, URhGe and UCoGe

The subject of this thesis is the analyze of the superconducting upper critical field (Hc2) and the interaction between superconductivity and quantum critical points (QCP), for the compounds CeCoIn5, URhGe and UCoGe. In CeCoIn5, study by mean of resistivity of the Fermi liquid domain allows us to localize precisely the QCP at ambient pressure. This analyze rule out the previously suggested pinning of Hc2(0) at the QCP. In a second part, the evolution of Hc2 under pressure is analyzed. The superconducting dome is unconventional in this compound with two characteristic pressures: at 1.6GPa, the superconducting transition temperature is maximum but it is at 0.4GPa that physical properties (maximum of Hc2(0), maximum of the initial slope dHc2/dT, maximum of the specific heat jump DC/C,. ) suggest a QCP. We explain this antagonism with pair-breaking effects in the proximity of the QCP.With these two experiments, we suggest a new phase diagram for CeCoIn5. In a third part, measurements of thermal conductivity on URhGe and UCoGe are presented. We obtained the bulk superconducting phase transition and confirmed the unusual curvature of the slope dHc2/dT observed by resistivity. The temperatures and fields dependence of thermal conductivity allow us to identify a non-electronic contribution for heat transport down to the lowest temperature (50mK) and probably associated with magnon or longitudinal fluctuations. We also identified two different domains in the superconducting region, These domains are compatible with a two bands model for superconductivity. Thermopower measurements on UCoGe reveal a strong anisotropy to current direction and several anomaly under field applied in the b direction. We suggest a Lifshitz transition to explain our observations in these two compounds.
International Journal of Innovation in Teaching and Learning (IJITL) Volume I- Issue I (June 2015)

To meet with the new challenges related to the teachers and teaching system, we need to develop an effective training system for the teachers. So keeping the situation in focus the research was designed to achieve the objective to compare the perception of male and female faculty members towards the teacher training opportunities and the employee’s proactive behavior. Further to assess the effect of teacher training on proactive behavior of the teachers. The design of the study was descriptive. 11,092 teachers serving as the faculty members at higher level educational institutions were considered as the population of the study. Disproportionate stratified sampling technique was used to select the respondents for the purpose of data collection. 154 employees were selected as the sample of the study. The data was collected with the help of two sets of questionnaires designed to assess the in-service teacher training and proactive behavior (DV) of the respondents. The collected data was analyzed with the help of Statistical Package of Social Sciences 21 Edition. The t test and regression analysis were used in the process of analysis. It was found that the there was no statistical difference between the male and the female employees regarding in-service training and proactive behavior. However in-service training had the significant effect on the development of proactive behavior of the employees as well. Further it was found out that the professional competence had the strongest effect on the development of proactive behavior. On the basis of findings it is recommended that training has to be linked with the daily routine problems and issues so that the employees may find it useful in their own situations.