Activation of the historical memory resource in modern politics

The article contains analysis of the reasons why the resource of the historical memory of civil societies and power elites is beginning to be actively used in the modern foreign and domestic policy of many countries, including Russia. The article describes the positive consequences of such a construction of political motivations, as well as the risks associated with the weakening of the influence of ideologies on political processes. From the author’s point of view, the interest of the subjects of modern politics in using the resource of historical memory is due to the distrust of citizens and elites to the motivational potential of ideologies growing in the modern world and in Russia. It is also caused by the desire to increase the “human capital” of democratic politics and reserve advantages for the national state in controlling the policy of implementing national interests. At the same time, the author argues, the active motivation of politics, both internal and external, by the arguments of historical memory can generate no less risks for its progress today than those that were generated by the dominance of “classical” ideologies in it.

Research on Distance Teaching System of English Course Based on Wireless Network Technology

The development in technology is taking place with an accelerating pace across the globe. The increasing expansion and advancement in the field of information technology and the modern teaching system provide a technical support for the development of a distance teaching system to learn English courses. Multimedia teaching system of English course based on B/S framework (system 1) and English teaching system based on MVC architecture (system 2) were the two most prominent and widely used approaches for the distance teaching system of English learning courses. These systems comprehensively consider the current English teaching needs, develop the existing architectures, discuss the system architecture and functions, and establish the corresponding development environment. However, the mentioned systems have the problem of high proportion of memory resource consumption and high failure rate of the communicating nodes. In order to reduce the proportion of memory resource consumption and node failure rate of distance teaching system and effectively improve the teaching effect, this study designed a distance teaching system of English course based on wireless network technology. In order to analyze the functionality and stability of the wireless network technology in distance teaching of English course, the server-side and client-side modules of the system are designed. The server side is mainly used to maintain and control the overall functions of the system, while the client side is used to access/request the contents from the server. On this basis, the system software module is designed. The memory consumption results are accounted for under 30%, which is significantly lower than the earlier-mentioned systems, and the node failure rate of the system proposed in this paper does not increase significantly and remains below 4% all the time which indeed is a very low amount of failure rate. The experimental results show that the memory resource consumption ratio and node failure rate of the proposed system are very low, and the application feedback effect is significantly better than the other systems.

Multimodal Biometric-Based Authentication with Secured Templates

This paper proposes a multimodal biometric based authentication (verification and identification) with secured templates. Multimodal biometric systems provide improved authentication rate over unimodal systems at the cost of increased concern for memory requirement and template security. The proposed framework performs person authentication using face and fingerprint. Biometric templates are protected by hiding fingerprint into face at secret locations, through blind and key-based watermarking. Face features are extracted from approximation sub-band of Discrete Wavelet Transform, which reduces the overall working plane. The proposed method also shows high robustness of biometric templates against common channel attacks. Verification and identification performances are evaluated using two chimeric and one real multimodal dataset. The same systems, working with compressed templates provides considerable reduction in overall memory requirement with negligible loss of authentication accuracies. Thus, the proposed framework offers positive balance between authentication performance, template robustness and memory resource utilization.

A Probabilistic Prediction Approach for Memory Resource of Complex System Simulation in Cloud Computing Environment

Accurate memory resource prediction can achieve optimal performance for complex system simulation (CSS) using optimistic parallel execution in the cloud computing environment. However, because of the varying memory resource demands of CSS applications caused by the simulation entity scale and frequent optimistic synchronization, the existing approaches are unable to predict the memory resource required by a CSS application accurately, which cannot take full advantage of the elasticity and symmetry of cloud computing. In this paper, a probabilistic prediction approach based on ensemble learning, which regards the entity scale and frequent optimistic synchronization as the important features, is proposed. The approach using stacking strategy consists of a two-layer architecture. The first-layer architecture includes two kinds of base models, namely, back-propagation neural network (BPNN) and random forest (RF). The root mean squared error-based pruning algorithm is designed to choose the optimal subset of the base models. The second-layer is the Gaussian process regression (GPR) model, which is applied to quantify the uncertainty information in the probabilistic prediction for memory resources. A series of experiments are presented to prove that the proposed approach can achieve higher accuracy and performance compared to RF, BPNN, GPR, Bagging ensemble approach, and Regressive Ensemble Approach for Prediction.

Auditory and Visual Durations Load a Unitary Working-Memory Resource

Items in working memory are typically defined by various attributes, such as colour (for visual objects) and pitch (for auditory objects). The attribute of duration can be signalled by multiple modalities, but has received relatively little attention from a working-memory perspective. While the existence of specialist stores (e.g., the phonological loop and visuospatial sketchpad) is often asserted in the wider working-memory literature, the interval-timing literature has more often implied a unitary (amodal) store. Here we combine two modelling frameworks to probe the basis of working memory for duration; a Bayesian-observer framework, previously used to explain behaviour in duration-reproduction tasks, and mixture models, describing distributions of continuous reports about items in working memory. We modelled different storage mechanisms, such as a limited number of fixed-resolution slots or a resource spread between items at a cost to resolution, in order to ask whether items from different sensory modalities are maintained in separate, independent stores. We initially analysed data from 32 participants, who memorised between one and eight items before reproducing the duration of a randomly selected target. In separate blocks, items could be all visual, all auditory, or an alternating mixture of both. A small control experiment included a further condition with precuing of target modality. Certain kinds of slot models, resource models, and combination models incorporating both mechanisms could account for the data. However, looking across all plausible models, the decline in performance with increasing memory load was most consistent with a single store for event durations, regardless of stimulus modality.