Intercultural communicative competence in foreign language education: Questions of theory, practice and research

Language teaching and learning has undergone a ‘cultural turn’ since the emergence of ‘the Communicative Approach’ and ‘Communicative Language Teaching’ in the 1970s. The earlier study of language, which involved the study of literary and other texts, had neglected the need for ‘communicative competence’— the ability to use language in socially appropriate ways, often operationalised as ‘politeness’. However, perhaps as a consequence of globalisation, new technologies, and mass economic and refugee migration, it has become clear that communicative language teaching too, with its focus on sociolinguistic appropriateness and politeness, is inadequate to the task of teaching for communication. This new social context requires consideration of the ways in which people of different languages — including language learners themselves — think and act, and how this might impact on successful communication and interaction. The ‘cultural turn’ – the introduction of ‘intercultural competence’ to complement ‘communicative competence’ – has further refined the notion of what it is to be competent for communication with speakers of different languages. Teachers and learners now need to be ‘aware’ of other people’s ‘cultures’ as well as their own, and therefore, the term ‘intercultural (communicative) competence’ has emerged, along with other terms such as ‘cultural awareness’ and ‘transnational competence’

Enhancing Biometric-Capsule-based Authentication and Facial Recognition via Deep Learning

In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based authentication systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based BioCapsule method. The BioCapsule method is provably secure, privacy-preserving, cancellable and flexible in its secure feature fusion design. In this work, we extend BioCapsule to face-based recognition. Moreover, we incorporate state-of-art deep learning techniques into a BioCapsule-based facial authentication system to further enhance secure recognition accuracy. We compare the performance of an underlying recognition system to the performance of the BioCapsule-embedded system in order to demonstrate the minimal effects of the BioCapsule scheme on underlying system performance. We also demonstrate that the BioCapsule scheme outperforms or performs as well as many other proposed secure biometric techniques

An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning

Semi-supervised learning (SSL) has shown great promise in leveragingunlabeled data to improve model performance. While standard SSL assumes uniformdata distribution, we consider a more realistic and challenging setting calledimbalanced SSL, where imbalanced class distributions occur in both labeled andunlabeled data. Although there are existing endeavors to tackle this challenge,their performance degenerates when facing severe imbalance since they can notreduce the class imbalance sufficiently and effectively. In this paper, westudy a simple yet overlooked baseline -- SimiS -- which tackles data imbalanceby simply supplementing labeled data with pseudo-labels, according to thedifference in class distribution from the most frequent class. Such a simplebaseline turns out to be highly effective in reducing class imbalance. Itoutperforms existing methods by a significant margin, e.g., 12.8%, 13.6%, and16.7% over previous SOTA on CIFAR100-LT, FOOD101-LT, and ImageNet127respectively. The reduced imbalance results in faster convergence and betterpseudo-label accuracy of SimiS. The simplicity of our method also makes itpossible to be combined with other re-balancing techniques to improve theperformance further. Moreover, our method shows great robustness to a widerange of data distributions, which holds enormous potential in practice. Codewill be publicly available.<br

Longitudinal and transversal piezoresistive response of granular metals

In this paper, we study the piezoresistive response and its anisotropy for a bond percolation model of granular metals. Both effective medium results and numerical Monte Carlo calculations of finite simple cubic networks show that the piezoresistive anisotropy is a strongly dependent function of bond probability p and of bond conductance distribution width \Delta g. We find that piezoresistive anisotropy is strongly suppressed as p is reduced and/or \Delta g is enhanced and that it vanishes at the percolation thresold p=p_c. We argue that a measurement of the piezoresistive anisotropy could be a sensitive tool to estimate critical metallic concentrations in real granular metals.Comment: 14 pages, 7 eps figure

Density of States and Conductivity of Granular Metal or Array of Quantum Dots

The conductivity of a granular metal or an array of quantum dots usually has the temperature dependence associated with variable range hopping within the soft Coulomb gap of density of states. This is difficult to explain because neutral dots have a hard charging gap at the Fermi level. We show that uncontrolled or intentional doping of the insulator around dots by donors leads to random charging of dots and finite bare density of states at the Fermi level. Then Coulomb interactions between electrons of distant dots results in the a soft Coulomb gap. We show that in a sparse array of dots the bare density of states oscillates as a function of concentration of donors and causes periodic changes in the temperature dependence of conductivity. In a dense array of dots the bare density of states is totally smeared if there are several donors per dot in the insulator.Comment: 13 pages, 15 figures. Some misprints are fixed. Some figures are dropped. Some small changes are given to improve the organizatio

FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

Pseudo labeling and consistency regularization approaches based on confidencethresholding have made great progress in semi-supervised learning (SSL).However, we argue that existing methods might fail to adopt suitable thresholdssince they either use a pre-defined / fixed threshold or an ad-hoc thresholdadjusting scheme, resulting in inferior performance and slow convergence. Wefirst analyze a motivating example to achieve some intuitions on therelationship between the desirable threshold and model's learning status. Basedon the analysis, we hence propose FreeMatch to define and adjust the confidencethreshold in a self-adaptive manner according to the model's learning status.We further introduce a self-adaptive class fairness regularization penalty thatencourages the model to produce diverse predictions during the early stages oftraining. Extensive experimental results indicate the superiority of FreeMatchespecially when the labeled data are extremely rare. FreeMatch achieves 5.78%,13.59%, and 1.28% error rate reduction over the latest state-of-the-art methodFlexMatch on CIFAR-10 with 1 label per class, STL-10 with 4 labels per class,and ImageNet with 100 labels per class, respectively.<br

A Survey on Biometrics and Cancelable Biometrics Systems

Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results

Phonon and Elastic Instabilities in MoC and MoN

We present several results related to the instability of MoC and MoN in the B1 (sodium chloride) structure. These compounds were proposed as potential superconductors with moderately high transition temperatures. We show that the elastic instability in B1-structure MoN, demonstrated several years ago, persists at elevated pressures, thus offering little hope of stabilizing this material without chemical doping. For MoC, another material for which stoichiometric fabrication in the B1-structure has not proven possible, we find that all of the cubic elastic constants are positive, indicating elastic stability. Instead, we find X-point phonon instabilities in MoC (and in MoN as well), further illustrating the rich behavior of carbo-nitride materials. We also present additional electronic structure results for several transition metal (Zr, Nb and Mo) carbo-nitride systems and discuss systematic trends in the properties of these materials. Deviations from strict electron counting dependencies are apparent.Comment: 5 pages and 4 trailing figures. Submitted to PR

Location Estimation in Wireless Sensor Networks Using Spring-Relaxation Technique

Accurate and low-cost autonomous self-localization is a critical requirement of various applications of a large-scale distributed wireless sensor network (WSN). Due to its massive deployment of sensors, explicit measurements based on specialized localization hardware such as the Global Positioning System (GPS) is not practical. In this paper, we propose a low-cost WSN localization solution. Our design uses received signal strength indicators for ranging, light weight distributed algorithms based on the spring-relaxation technique for location computation, and the cooperative approach to achieve certain location estimation accuracy with a low number of nodes with known locations. We provide analysis to show the suitability of the spring-relaxation technique for WSN localization with cooperative approach, and perform simulation experiments to illustrate its accuracy in localization

Quantum Size Effect transition in percolating nanocomposite films

We report on unique electronic properties in Fe-SiO2 nanocomposite thin films in the vicinity of the percolation threshold. The electronic transport is dominated by quantum corrections to the metallic conduction of the Infinite Cluster (IC). At low temperature, mesoscopic effects revealed on the conductivity, Hall effect experiments and low frequency electrical noise (random telegraph noise and 1/f noise) strongly support the existence of a temperature-induced Quantum Size Effect (QSE) transition in the metallic conduction path. Below a critical temperature related to the geometrical constriction sizes of the IC, the electronic conductivity is mainly governed by active tunnel conductance across barriers in the metallic network. The high 1/f noise level and the random telegraph noise are consistently explained by random potential modulation of the barriers transmittance due to local Coulomb charges. Our results provide evidence that a lowering of the temperature is somehow equivalent to a decrease of the metal fraction in the vicinity of the percolation limit.Comment: 21 pages, 8 figure