Asynchronous Information Research Articles

A systematic approach for English education model based on the neural network algorithm

This paper algorithms based on neural network model designed for English education, to develop a model education system with artificial intelligence, summarized the dimensions were can be used for data analysis related indicators. These indicators include not only the contents of the learning behavior, test behavior, cooperation behavior and resource search behavior and other human-computer interaction behavior data, also includes demographic background information, learning ability, learning attitude, and other characteristic data that affect the learning effect. We tried to collect relevant indicators to the maximum extent. An audiovisual fusion method based on Convolutional Neural Network (CNN) is proposed. The independent CNN structure is used to realize independent modeling of audiovisual perception and asynchronous information transmission and obtain the description of audiovisual parallel data in the high-dimensional feature space. Following the shared fully connected structure, it is possible to model the long-term dependence of audiovisual parallel data in a higher dimension. Experiments show that the AVSR system built using a CNN-based audiovisual fusion method can achieve a significant performance improvement, and its recognition error rate is relatively reduced by about 15%. The speech recognition system trained with the cross-domain adaptive method can obtain a significant performance improvement, and its recognition error rate is more than 10% lower than that of the baseline system..

Mechanism analysis of competitive information asynchronous dissemination on social networks

Information dissemination groups, especially those disseminating the same kind of information such as advertising, product promotion, etc., compete with each other when their information spread on social networks. Most of the existing methods analyze the dissemination mechanism mainly upon the information itself without considering human characteristics, e.g. relation networks, cooperation/defection, etc. In this paper, we introduce a framework of social evolutionary game (SEG) to investigate the influence of human behaviors in competitive information dissemination. Coordination game is applied to represent human behaviors in the competition of asynchronous information diffusion. We perform a series of simulations through a specific game model to analyze the mechanism and factors of information diffusion, and show that when the benefits of competitive information is around 1.2 times of the original one, it can compensate the loss of reputation caused by the change of strategy. Furthermore, through experiments on a dataset of two films on Sina Weibo, we described the mechanism of competition evolution over real data of social network, and validated the effectiveness of our model.

Nurses' information practice in municipal health care—A web‐like landscape

To uncover the characteristics of nurses' information practice in municipal health care and to address how, when and why various pieces of information are produced, shared and managed. Nursing documentation in the electronic patient record has repeatedly been found unsatisfactory. Little is known about how the information practice of nurses in municipal health care actually is borne out. In order to understand why nursing documentation continues to fail at living up to the expected requirements, a better understanding of nurses' information practice is needed. A qualitative observational field study. The study complied with the Consolidated Criteria for Reporting Qualitative Research. Empirical data were collected in three Norwegian municipalities through participant observations and individual interviews with 17 registered nurses on regular day shifts. The data were analysed through thematic content analysis. Nurses' information practice in municipal health care can be described as complex. The complexity is reflected in four themes that emerged from the data: (1) web of information sources, (2) knowing the patient and information redundancy, (3) asynchronous information practice and (4) compensatory workarounds. The complex and asynchronous nature of nurses' information practice affected both how and when information was produced, recorded and shared. When available systems lacked functions the nurses wanted, they created compensatory workarounds. Although electronic patient record was an important part of their information practice, nurses in long-term care often knew their patients well, which meant that a lot of information about the patients was in their heads, and that searching for information in the electronic patient record sometimes seemed redundant. This study provides contextual knowledge that might be valuable (a) in the further development of information systems tailored to meet nurses' information needs and (b) when studying patient safety in relation to nurses' information practice.

Integrating acoustic shooter detection into a hearing protection device

Recent advances in the development of hearing protection devices open new fields of applications on the battlefield. While the TCAPS (Tactical Communication And Protective Systems) protect against acute acoustic traumas, they maintain information relative to the acoustic environment of the soldiers. Today, these systems are efficient for both hearing protection and communication. We propose to use the microphones equipping the TCAPS headsets in order to detect and localize shooters on the battlefield. The microphone underneath the hearing protection is used in order to detect the shock and muzzle waves generated by supersonic shots. A meshed network between the TCAPS deployed on the field allows transmitting asynchronous information relative to the detected waves to data fusion nodes that allow estimating the shooter’s position. Solutions are proposed in order to compensate the effects of the presence of the head between the microphones underneath the hearing protection. Results concerning the estimation of the time difference of arrival of a transient wave in free field and in the presence of an artificial head are presented. The data fusion process is tested thanks to simulations in various deployment configurations.

Asynchronous side information attack from the edge: an approach to identify participants from anonymous mobility traces

With the increasing adoption of location-based social network applications, a large number of location traces of human mobility have been collected and published for the purpose of assisting mobile system design and scientific research. Most mobility traces are processed to achieve anonymity before publishing by the way of replacing the true IDs and introducing noise interference. In this paper, we show that such anonymous mobility traces are vulnerable to asynchronous side information attack from the edge: if partial movement information is exposed to some compromised edge nodes even after the data collection period, the adversary is able to identify the participant from the anonymous mobility traces with high probability. Our method to identify participants is based on exploring the accumulative temporal and spatial characteristics of individual movement. We introduce \(\delta\)-partition to divide user locations into sub-areas, and \(\epsilon\)-partition to group user activities into time intervals. We illustrate that a mobility trace can be uniquely represented by a set of frequent locations together with their active time intervals. We further derive a similarity measurement to be used by the adversary for asynchronous side information attack. We develop theoretical analysis to prove that an anonymous participant can be correctly identified with high probability under certain condition. Extensive experiments are conducted on three typical mobility datasets corresponding to the movement of bus, taxi and human, which show that the identification success ratio achieves 99%, 45% and 72%, respectively.

Precoder design in downlink CoMP-JT MIMO network via WMMSE and asynchronous ADMM

Precoder design of coordinated multi-point joint transmission (CoMP-JT) multiple-input and multiple-output (MIMO) network aimied at throughput maximization is a challenging problem. In this paper, we propose an asynchronous distributed iterative method to solve this problem. We transform the original throughput maximizing problem to the weighted minimum mean square error (WMMSE) problem, then decompose the problem into a series of subproblems. Based on alternation direction method of multipliers (ADMM), the proposed algorithm can solve the optimal precoder in a distributed manner. With asynchronous information exchange mechanism considered, the convergence rate of our algorithm can be accelerated further. Numerical results demonstrate the increase of throughput and the optimality of the precoding scheme provided by our algorithm.

Multi source asynchronous information fusion technology based on cross prompts and its application

Several modern local high-tech wars have shown that the development of war is determined by the precise strike weapons at both ends of attack and defense. In this paper, the urgent requirement of defense ballistic missile target is taken as traction, and the heterogeneous multi-sensor cross-prompt technology is used as support to study the heterogeneous multi-sensor cross-prompt and its application in target detection. On the basis of fully studying the basic theory of heterogeneous multi-sensor cross-prompting, a cross-prompting network structure model based on typical anti-missile combat mission driving and a data fusion model based on asynchronous information are established, and a multi-sensor cross-prompting method based on fuzzy decision is designed. The model and method proposed in this paper are applied to target detection and tracking, and the model and method involved in this paper are verified by simulation.

A novel simulation strategy for equation-based models of cyber-physical systems

Cyber-physical systems (CPS) is a complex intelligent system consists of discrete asynchronous information and continuous synchronous physical systems that are different in structure and mathematics basis. In this article, a new simulation strategy for CPS model is explored based on the equation-based modeling language Modelica. Firstly, some new semantics, such as Clock, Sampling and Holding, are proposed to model information systems based on Modelica. Secondly, the serialization method is proposed to separate the information equation systems from CPS model and sequence the information and physical equation systems respectively. Thirdly, the unified simulation method, which consists of information system simulation strategy and unified simulation strategy, is present to solve the CPS model in a unified form. An inverted pendulum system is taken to demonstrate the above methods. Differently from the common modeling methods and simulation strategy, where the information system and physical system are modeled and simulated separately in different tools and then linked with interfaces between these tools. The major novelty of proposed strategy is that the CPS model could be built and simulated in a unified framework and the information system and physical system could be linked automatically in a natural way.

A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors.

Motion segmentation is a critical pre-processing step for autonomous robotic systems to facilitate tracking of moving objects in cluttered environments. Event based sensors are low power analog devices that represent a scene by means of asynchronous information updates of only the dynamic details at high temporal resolution and, hence, require significantly less calculations. However, motion segmentation using spatiotemporal data is a challenging task due to data asynchrony. Prior approaches for object tracking using neuromorphic sensors perform well while the sensor is static or a known model of the object to be followed is available. To address these limitations, in this paper we develop a technique for generalized motion segmentation based on spatial statistics across time frames. First, we create micromotion on the platform to facilitate the separation of static and dynamic elements of a scene, inspired by human saccadic eye movements. Second, we introduce the concept of spike-groups as a methodology to partition spatio-temporal event groups, which facilitates computation of scene statistics and characterize objects in it. Experimental results show that our algorithm is able to classify dynamic objects with a moving camera with maximum accuracy of 92%.

Real-time nowcasting of nominal GDP with structural breaks

This paper provides early assessments of current U.S. Nominal GDP growth, which has been considered as a potential new monetary policy target. The nowcasts are computed using the exact amount of information that policy makers have available at the time predictions are made. However, real time information arrives at different frequencies and asynchronously, which poses the challenge of mixed frequencies, missing data, and ragged edges. This paper proposes a multivariate state space model that not only takes into account asynchronous information inflow it also allows for potential parameter instability (DYMIBREAK). We use small scale confirmatory factor analysis in which the candidate variables are selected based on their ability to forecast nominal GDP. The model is fully estimated in one step using a nonlinear Kalman filter, which is applied to obtain simultaneously both optimal inferences on the dynamic factor and parameters. Differently from principal component analysis, the proposed factor model captures the comovement rather than the variance underlying the variables. We compare the predictive ability of the model with other univariate and multivariate specifications. The results indicate that the proposed model containing information on real economic activity, inflation, interest rates, and Divisia monetary aggregates produces the most accurate real time nowcasts of nominal GDP growth.

Constrained coordinated distributed control of smart grid with asynchronous information exchange

Smart grid constrained optimal control is a complex issue due to the constant growth of grid complexity and the large volume of data available as input to smart device control. In this context, traditional centralized control paradigms may suffer in terms of the timeliness of optimization results due to the volume of data to be processed and the delayed asynchronous nature of the data transmission. To address these limits of centralized control, this paper presents a coordinated, distributed algorithm based on distributed, local controllers and a central coordinator for exchanging summarized global state information. The proposed model for exchanging global state information is resistant to fluctuations caused by the inherent interdependence between local controllers, and is robust to delays in information exchange. In addition, the algorithm features iterative refinement of local state estimations that is able to improve local controller ability to operate within network constraints. Application of the proposed coordinated, distributed algorithm through simulation shows its effectiveness in optimizing a global goal within a complex distribution system operating under constraints, while ensuring network operation stability under varying levels of information exchange delay, and with a range of network sizes.

Multi-Source-Driven Asynchronous Diffusion Model for Video-Sharing in Online Social Networks

Characterizing the video diffusion in online social networks (OSNs) is not only instructive for network traffic engineering, but also provides insights into the information diffusion process. A number of continuous-time diffusion models have been proposed to describe video diffusion under the assumption that the activation latency along social links follows a single parametric distribution. However, such assumption has not been empirically verified. Moreover, a user usually has multiple activated neighbors with different activation times, and it is hard to distinguish the different contributions of these multiple potential sources. To fill this gap, we study the multiple-source-driven asynchronous information diffusion problem based on substantial video diffusion traces. Specifically, we first investigate the latency of information propagation along social links and define the single-source (SS) activation latency for an OSN user. We find that the SS activation latency follows the exponential mixture model. Then we develop an analytical framework which incorporates the temporal factor and the influence of multiple sources to describe the influence propagation process. We show that one's activation probability decreases exponentially with time. We also show that the time shift of the exponential function is only determined by the most recent source (MRS) active user, but the total activation probability is the combination of influence exerted by all active neighbors. Based on these discoveries, we develop a multi-source-driven asynchronous diffusion model (MADM). Using maximum likelihood techniques, we develop an algorithm based on expectation maximization (EM) to learn model parameters, and validate our proposed model with real data. The experimental results show that the MADM obtains better prediction accuracy under various evaluation metrics.

Paradigm of computations on the Petri nets

The paradigm of computations on the Petri nets was shown to appreciably speed up the computations and reduce laboriousness of software development owing to the mass parallelism and asynchronous information processing. A language of the programmed Petri nets was developed, alternative approaches to realizing the paradigm at the micro and macro levels were proposed, and the estimates of complexity of the earlier universal Petri net that are prototypes of the corresponding processor were specified. Indicated were the lines of practical realization of the paradigm such as the development of the technology of programming on the loaded Petri net, technology of translation of the loaded Petri nets to the inhibitory Petri nets, and the efficient hardware processors executing programs in the language of the inhibitory Petri nets. Fast computations are supported by the nets of the Sleptsov class with multiple start of transition at a step.

USING MOBILE DISASTER RESPONSE SYSTEM IN BRIDGE MANAGEMENT

Over the years, various hydro-meteorological disasters have impacted many countries and civilians, thus decreasing the losses caused by these disasters has become important for disaster management. While bridges often are essential to connect the affected disaster areas, this study focuses on bridge management associated with typhoons and rain-storms. Recently, although numerous information technology-based applications are available, three problems (i.e. asynchronous information records, unsteady information transmission, and inaccessible information analysis) still influence responses on disaster sites. Therefore, this study integrated several mobile information techniques to propose a mobile disaster response system. The applied mobile GPS- and GIS-based data reports simplify information generation, AR-based assistance enhances information representation, and mobile 3D graphics accelerates information processes. Based on the tests, this system not only improved the recognized problems but also strengthened the cooperation between construction engineers and workers and disaster managers. Overall, this study is a useful reference for similar applications in bridge management and disaster management.

Rateless Codes and Random Walksfor P2P Resource Discovery in Grids

Peer-to-peer (P2P) resource location techniques in grid systems have been recently investigated to obtain scalability, reliability, efficiency, fault-tolerance, security, and robustness. Query resolution for locating resources and update information on their own resource status in these systems can be abstracted as the problem of allowing one peer to obtain a local view of global information defined on all peers of a P2P unstructured network. In this paper, the system is represented as a set of nodes connected to form a P2P network where each node holds a piece of information that is required to be communicated to all the participants. Moreover, we assume that the information can dynamically change and that each peer periodically requires to access the values of the data of all other peers. A novel approach based on a continuous flow of control packets exchanged among the nodes using the random walk principle and rateless coding is proposed. An innovative rateless decoding mechanism that is able to cope with asynchronous information updates is also proposed. The performance of the proposed system is evaluated both analytically and experimentally by simulation. The analytical results show that the proposed strategy guarantees quick diffusion of the information and scales well to large networks. Simulations show that the technique is effective also in presence of network and information dynamics.

UTAUT Model for Blended Learning: The Role of Gender and Age in the Intention to Use Webinars

Introduction Asynchronous information and communication tools like discussion forums, electronic mails, blogs, and wikis have been used for several decades in many fields. In the education context, asynchronous electronic means were used to reinforce interactions between students and teachers. In 2009, more than 74% of American higher educational institutions agreed that online education is an important component of their long-term strategy (Allen & Seaman, 2010). Even though the results about the acceptance of online instruction by faculties are mixed, Allen and Seaman (2010) have confirmed that the percentage of chief academic officers that think that students' retention is a greater problem for online courses was twice as large as those who disagree. According to some authors (Allen, Bourhis, Burrel, & Mabry, 2002; Muilenburg & Berge, 2001), interaction is known to be a sine qua non condition to students' satisfaction and retention. Synchronous tools were, therefore, seen as an additional component to strengthen live communication between the different stakeholders of the distance-learning environment (Johnson, 2006). The accessibility and ease of use of both asynchronous and synchronous technologies made it easier to respond to various students' needs by providing courses and even programs fully online. In the last decade, there has been a great amount of interest in mixing the advantages of online courses with those of face-to-face courses by offering another type of course delivery mode called blended learning. Blended learning consists of blending face-to-face and online delivery in order to reinforce the interaction and direct contact of students with the other participants in a course (Allen et al., 2002). To do so, technologies like webinars offer support to instructors by ensuring efficient synchronous communications. As technologies represent the spearhead of blended learning, their acceptance by students is essential to the success of their use. However, empirical studies that have tried to evaluate the acceptance of webinars by students are not abundant. We deem it important to explore this avenue because student readiness is one of the success factors of blended learning (Graham, 2006). In this study, we aim to identify factors that motivate students to use webinars in a blended learning course. These factors were further analyzed according to age and sex. Obtained results will not only add scientific evidence to the literature about blended learning, webinars, and technology adoption, but they could also lead to a better practical understanding of the factors that may incite or discourage students to use webinar technologies in blended higher education. The following section includes a background about blended learning, the webinar concept, and the acceptance models used in the information systems field. The research objective and questions are then proposed, followed by the research model, variables, and hypotheses. The description of the sample and the procedure is provided before presenting and discussing the results. Background Blended Learning According to Graham (2006), blended learning is a buzzword that is still ambiguous. Most authors agree on the definition of blended learning systems as a combination of face-to-face instruction with computer-mediated instruction (Graham, 2006; Rooney, 2003; Young, 2002). This definition overrides the broad vision of blended learning as a combination of instructional modalities or methods. As such, blended learning is the result of the convergence of two archetypal learning environments: distributed environments that have been strengthened thanks to the communication and interaction features of new technologies (synchronous and asynchronous) and the traditional face-to-face learning environment (Graham, 2006). These environments were matched in different forms and combinations in order to facilitate teaching and learning (Duhaney, 2004). …

On the asynchronous information embedding for event-driven systems in molecular communications

Molecular communications is emerging as a promising paradigm for nanoscale communications in nanotechnology. Though still at an early stage, research efforts have been devoted and various molecular communication systems have been proposed. However, each proposed system possesses a specific structure to achieve communications in its own way. To our best knowledge, no unified system description exists so far. In this paper, we propose an abstract system structure called an event-driven system; a significant group of molecular communication systems can be classified into such an abstract form. We define event-driven systems and further show that, for these systems, there is a signaling scheme called asynchronous information embedding which carries additional information while at the same time keeping the original communication mechanisms uninfluenced. Instead of investigating asynchronous information embedding in full generality, we consider synchronous type-based systems as a demonstrative example which still captures most of the features. For such type-based systems, we develop an approximation method for obtaining the channel capacity as a general performance measure. Numerical results are provided to show the capacity gain of asynchronous information embedding.

Genotype Division for Shared Memory Parallel Genetic Algorithm Across Platforms and Systems

In this paper we present a concurrent implementation of coevolutionary genetic algorith m (GA ) designed for shared memo ry architectures such as multi-core processor platforms. Our algorith m div ides the chromosome among the processes, and not the population as it is the case for most parallel imp lementations of the GA. This approach results in a division of the problem to be solved by the GA into sub-problems. We analyze the influence on performance and speedup of several parameters defining the algorith m, such as: a synchronous or asynchronous informat ion exchange between processes and the frequency of communication between processes. We also examine how the problem separability influ- ences the general algorith m performance. Finally, we co mpare different operating systems and platforms in the evaluation process. Our paper shows that this approach is a good way to take advantage of mu lti-core processers and improve not only the execution time, but also the fitness in many cases.

IS THERE TWO-WAY ASYNCHRONOUS INFORMATION TRANSMISSION BETWEEN STOCK MARKETS AND STOCK MESSAGE BOARDS?

This study investigates asynchronous information transmission between stock returns and abnormal posting volume on the online stock message boards in China. Based on a robust GARCH model, the study finds that there are significant two-way volatility spillover effects: a positive volatility spillover effect from stock returns to abnormal message posting volume, and a negative volatility spillover effect from abnormal message posting volume to stock returns. The information exchange and communication on stock message boards have a certain role in stabilizing financial markets and improving investor's decision making on financial markets.

A Self-Organizing Strategy for Power Flow Control of Photovoltaic Generators in a Distribution Network

The focus of this paper is to develop a distributed control algorithm that will regulate the power output of multiple photovoltaic generators (PVs) in a distribution network. To this end, the cooperative control methodology from network control theory is used to make a group of PV generators converge and operate at certain (or the same) ratio of available power, which is determined by the status of the distribution network and the PV generators. The proposed control only requires asynchronous information intermittently from neighboring PV generators, making a communication network among the PV units both simple and necessary. The minimum requirement on communication topologies is also prescribed for the proposed control. It is shown that the proposed analysis and design methodology has the advantages that the corresponding communication networks are local, their topology can be time varying, and their bandwidth may be limited. These features enable PV generators to have both self-organizing and adaptive coordination properties even under adverse conditions. The proposed method is simulated using the IEEE standard 34-bus distribution network.