Design of College English Halving Classroom Teaching System Based on Big Data

With the development of big data and the continuous advancement of education informatization, the single lecture-based classroom teaching model no longer meets the development requirements of the society for the development of talent education and training. Under the environment of big data technology, how to innovate teaching models and talent training models becomes the focus of the current classroom teaching reform. This article aims to study the design of the college English bisection classroom teaching system based on big data. On the basis of introducing the bisection classroom, the basic theory of teaching system design and the design principles of the bisection classroom teaching system, the theoretical analysis and top-level design of the system login module and the teaching system that adaptively integrates teaching resources according to the status of college students are carried out. Finally, an experimental test of the college English split-class teaching system based on big data has been carried out. The test results show that the system can realize the core of system management, teaching resource release and dynamic configuration.

Dynamic configuration of large-scale cortical networks during an inhibitory task accounts for heterogeneity in attention-deficit/hyperactivity disorder traits

The heterogeneity of attention-deficit/hyperactivity disorder (ADHD) traits (inattention vs. hyperactivity/impulsivity) complicates diagnosis and intervention. Identifying how the configuration of large-scale functional brain networks during cognitive processing correlate with this heterogeneity could help us understand the neural mechanisms altered across ADHD presentations. Here, we recorded high-density EEG while 62 non-clinical participants (ages 18-24; 32 male) underwent an inhibitory control task (Go/No-Go). Functional EEG networks were created using sensors as nodes and across-trial phase-lag index values as edges. Using cross-validated LASSO regression, we examined whether graph-theory metrics applied to both static networks (averaged across time-windows: -500 to 0ms, 0 to500ms) and dynamic networks (temporally layered with 2ms intervals), were associated with hyperactive/impulsive and inattentive traits. Network configuration during response execution/inhibition was associated with hyperactive/impulsive (mean R2 across test sets = .20, SE = .02), but not inattentive traits. Post-stimulus results at higher frequencies (Beta, 14-29Hz; Gamma, 30-90Hz) showed the strongest association with hyperactive/impulsive traits, and predominantly reflected less burst-like integration between modules in oscillatory beta networks during execution, and increased integration/small-worldness in oscillatory gamma networks during inhibition. We interpret the beta network results as reflecting weaker integration between specialized pre-frontal and motor systems during motor response preparation, and the gamma results as reflecting a compensatory mechanism used to integrate processing between less functionally specialized networks. This research demonstrates that the neural network mechanisms underlying response execution/inhibition might be associated with hyperactive/impulsive traits, and that dynamic, task-related changes in EEG functional networks may be useful in disentangling ADHD heterogeneity.

Dynamic Structural Changes in Nickel Single-Atom Catalysts During Electrochemical Reduction of CO2 to CO

Electrochemical CO2 reduction reaction (ECO2RR) is an important route for global carbon abatement. However, a comprehensive picture of the structure evolution of metal active sites is currently lacked in ECO2RR. Here, we present the first full view of Ni single-atom catalyst for ECO2RR over a broad potential range. Comprehensive X-ray absorption spectroscopy (XAS) analyses confirmed the Ni coordinated with pyrrole nitrogen in the form of Ni-N4 attached with an axial O2 ligand. Operando XAS revealed the precise structure of the Ni single-atom catalyst that dynamically changes with the shift of applied potentials. Such changes ultimately contributed to the CO selectivity variation ranging from 20%-99%. Interestingly, the Ni center was found to move toward the N4 plane during the ECO2RR, which played a crucial role of reaching near-unity CO selectivity. Together with theoretical calculations, a clear quantitative correlation between the dynamic configuration and the catalytic properties was established.

Improving Power of DSP and CNN Hardware Accelerators Using Approximate Floating-point Multipliers

Approximate computing has emerged as a promising design alternative for delivering power-efficient systems and circuits by exploiting the inherent error resiliency of numerous applications. The current article aims to tackle the increased hardware cost of floating-point multiplication units, which prohibits their usage in embedded computing. We introduce AFMU (Approximate Floating-point MUltiplier), an area/power-efficient family of multipliers, which apply two approximation techniques in the resource-hungry mantissa multiplication and can be seamlessly extended to support dynamic configuration of the approximation levels via gating signals. AFMU offers large accuracy configuration margins, provides negligible logic overhead for dynamic configuration, and detects unexpected results that may arise due to the approximations. Our evaluation shows that AFMU delivers energy gains in the range 3.6%–53.5% for half-precision and 37.2%–82.4% for single-precision, in exchange for mean relative error around 0.05%–3.33% and 0.01%–2.20%, respectively. In comparison with state-of-the-art multipliers, AFMU exhibits up to 4–6× smaller error on average while delivering more energy-efficient computing. The evaluation in image processing shows that AFMU provides sufficient quality of service, i.e., more than 50 db PSNR and near 1 SSIM values, and up to 57.4% power reduction. When used in floating-point CNNs, the accuracy loss is small (or zero), i.e., up to 5.4% for MNIST and CIFAR-10, in exchange for up to 63.8% power gain.

THE DIAGNOSTIC TOOLS OF PERSONALITY NARRATIVE COMPETENCE DETERMINATION

The article presents and analyzes results of the development of diagnostic tools of narrative competence of personality. Empirical study of formation of the narrative competence is carried out on methodological basis of post-non-classical psychology, according to which a personality is considered as an author of oneself, who has a potential for self-regulation and self-development. Within the psychological hermeneutics, narrative competence has been articulated as an ability of personality to identify and interpret narrative statements of the Other, that is to discover narratives in sociocultural and personal discourses and based on them to generate their own narrative constructs. The structural model of narrative competence as a dynamic configuration of pre-semantic, semantic and meta-semantic levels of understanding and generation of narrative expressions by personality is characterized; this model in turn became a basis for developing psycholinguistic diagnostic tools. The author proposed a method for determining formation levels of narrative competence of personality, which involves selection of texts meeting certain criteria for processing; development of diagnostic tasks for these texts; algorithm for determining a certain formation level of narrative competence of respondents depending on their results of proposed tasks. According to the results of approbation of the diagnostic technique, three main and two transitional levels of narrative competence of respondents have been identified. The algorithm for determining of these levels is presented in the form of a structural mathematical model, the validity of which is confirmed by correlations with expert evaluation. The main advantage of the proposed psycholinguistic tools for determining the formation levels of narrative competence of personality is the possibility of its application for diagnostics of respondents based on their processing of fiction texts without restrictions as for the language they speak.

Dynamic Optimization Method for Broadband ADCP Waveform with Environment Constraints

Broadband acoustic Doppler current profiler (ADCP) is widely used in agricultural water resource explorations, such as river discharge monitoring and flood warning. Improving the velocity estimation accuracy of broadband ADCP by adjusting the waveform parameters of a phase-encoded signal will reduce the velocity measurement range and water stratification accuracy, while the promotion of stratification accuracy will degrade the velocity estimation accuracy. In order to minimize the impact of these two problems on the measurement results, the ADCP waveform optimization problem that satisfies the environment constraints while keeping high velocity estimation accuracy or stratification accuracy is studied. Firstly, the relationship between velocity or distance estimation accuracy and signal waveform parameters is studied by using an ambiguity function. Secondly, the constraints of current velocity range, velocity distribution and other environmental characteristics on the waveform parameters are studied. For two common measurement applications, two dynamic configuration methods of waveform parameters with environmental adaptability and optimal velocity estimation accuracy or stratification accuracy are proposed based on the nonlinear programming principle. Experimental results show that compared with the existing methods, the velocity estimation accuracy of the proposed method is improved by more than 50%, and the stratification accuracy is improved by more than 22%.

Cyber-Physical System for Environmental Monitoring Based on Deep Learning

Cyber-physical systems (CPS) constitute a promising paradigm that could fit various applications. Monitoring based on the Internet of Things (IoT) has become a research area with new challenges in which to extract valuable information. This paper proposes a deep learning classification sound system for execution over CPS. This system is based on convolutional neural networks (CNNs) and is focused on the different types of vocalization of two species of anurans. CNNs, in conjunction with the use of mel-spectrograms for sounds, are shown to be an adequate tool for the classification of environmental sounds. The classification results obtained are excellent (97.53% overall accuracy) and can be considered a very promising use of the system for classifying other biological acoustic targets as well as analyzing biodiversity indices in the natural environment. The paper concludes by observing that the execution of this type of CNN, involving low-cost and reduced computing resources, are feasible for monitoring extensive natural areas. The use of CPS enables flexible and dynamic configuration and deployment of new CNN updates over remote IoT nodes.

A Secure and Scalable Smart Home Gateway to Bridge Technology Fragmentation

Internet of Things (IoT) technologies are already playing an important role in our daily activities as we use them and rely on them to increase our abilities, connectivity, productivity and quality of life. However, there are still obstacles to achieving a unique interface able to transfer full control to users given the diversity of protocols, properties and specifications in the varied IoT ecosystem. Particularly for the case of home automation systems, there is a high degree of fragmentation that limits interoperability, increasing the complexity and costs of developments and holding back their real potential of positively impacting users. In this article, we propose implementing W3C’s Web of Things Standard supported by home automation ontologies, such as SAREF and UniversAAL, to deploy the Living Lab Gateway that allows users to consume all IoT devices from a smart home, including those physically wired and using KNX® technology. This work, developed under the framework of the EC funded Plan4Act project, includes relevant features such as security, authentication and authorization provision, dynamic configuration and injection of devices, and devices abstraction and mapping into ontologies. Its deployment is explained in two scenarios to show the achieved technology’s degree of integration, the code simplicity for developers and the system’s scalability: one consisted of external hardware interfacing with the smart home, and the other of the injection of a new sensing device. A test was executed providing metrics that indicate that the Living Lab Gateway is competitive in terms of response performance.