Learning today is marked by ubiquitous access to technology and easy information retrieval. That begs the question: how does having all of the answers, often literally in the palm of our hands, affect our memory? Prio...
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ISBN:
(纸本)9781450370929
Learning today is marked by ubiquitous access to technology and easy information retrieval. That begs the question: how does having all of the answers, often literally in the palm of our hands, affect our memory? Prior research suggests that the ease of searching Google leads learners to offload thinking to the internet, despite likely knowing the answers, and even when accessing the internet is made inconvenient [1] [2]. Consequently, search engine use during learning may limit mental effort and critical thinking, thus impeding knowledge retention. Prior research also suggests that actively thinking about yet-to-be-learned material (pre-testing) can enhance subsequent retention of that material, even when initially generated answers are incorrect [3]. The present study applied these insights to the programming education domain to investigate whether thinking about a coding problem before consulting the internet can enhance memory of the searched content. The experiment involved an initial learning phase (variables, print (), if statements, for loops), a programming task, a second learning phase (lists, for loops with lists,.add (), indexing), and a final test. To solve the programming task participants (n=212) had to recall information from learning phase I. However, the task also required information from learning phase II that participants hadn't yet learned. Half of the participants attempted to solve the coding task before seeking Google's help (pre-test), while the other half read the task instructions and immediately consulted Google for answers.
information-Centric Network (ICN) is one of the most promising network architecture to handle the problem of rapid increase of data traffic because it allows in-network cache. ICNs with Linear Network coding (LNC) can...
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ISBN:
(纸本)9781538637906
information-Centric Network (ICN) is one of the most promising network architecture to handle the problem of rapid increase of data traffic because it allows in-network cache. ICNs with Linear Network coding (LNC) can greatly improve the performance of content caching and delivery. In this paper, we propose a Secure Content Caching and Routing (SCCR) framework based on Software Defined Network (SDN) to find the optimal cache management and routing for secure content delivery, which aims to firstly minimize the total cost of cache and bandwidth consumption and then minimize the usage of random chunks to guarantee information theoretical security (ITS). Specifically, we firstly propose the SCCR problem and then introduce the main ideas of the SCCR framework. Next, we formulate the SCCR problem to two Linear Programming (LP) formulations and design the SCCR algorithm based on them to optimally solve the SCCR problem. Finally, extensive simulations are conducted to evaluate the proposed SCCR framework and algorithms.
Terahertz has an emphatic effect on increasing data rates, supporting ultra-dense connections, and realizing low-latency transmission. In this paper, we investigate the peculiarity of the terahertz spectrum, study the...
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ISBN:
(纸本)9781728186160
Terahertz has an emphatic effect on increasing data rates, supporting ultra-dense connections, and realizing low-latency transmission. In this paper, we investigate the peculiarity of the terahertz spectrum, study the propagation characteristics of terahertz technology, and discuss its channel modeling feature. Given the unique spectral characteristics of terahertz bands, physical layer waveforms, modulation, coding schemes are designed accordingly to reduce the peak-to-average power ratio (PAPR), increase the spectral flexibility, meet the backward compatibility and improve the system performance. The baseband signal processing of terahertz signals is analyzed and discussed in this paper, inspiring the design of future terahertz communication systems.
With the development of nanotechnology, Wireless Nano Sensor Networks (WNSN) have been envisioned for many unique applications. In order to optimize the data transmission in nanonetworks, the information rate of indiv...
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Network coding (NC) in wireless sensor network (WSN) has been shown to achieve considerable throughput gains relative to traditional routing networks. While the data throughput capacity of WSNs is unknown, the scaling...
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ISBN:
(纸本)9783038351153
Network coding (NC) in wireless sensor network (WSN) has been shown to achieve considerable throughput gains relative to traditional routing networks. While the data throughput capacity of WSNs is unknown, the scaling of capacity with the number of nodes has recently received increasing attention. This paper proposes a Complex Field Network coding-based Multipath Routing (CFNC-MR) in WSN. The performance of this routing method is studied using NS2 and evaluated in terms of the packet overhead, packet delivery ratio, and packet loss ratio when a packet is transmitted. Simulation shows that the approach is efficient, promising and applicable in WSNs.
An optimized LDPC-product network coding scheme is proposed for a multiple-access relay system with two users, one relay station (RS) and one base station (BS). In the first stage, these two users broadcast their LDPC...
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ISBN:
(纸本)9781467300094;9781467352871
An optimized LDPC-product network coding scheme is proposed for a multiple-access relay system with two users, one relay station (RS) and one base station (BS). In the first stage, these two users broadcast their LDPC-coded data packets in turn to the RS and the BS. In the second stage, the RS uses LDPC product code to combine the decoded packets from these two users and generate extra redundancy instead of employing XOR-based network coding, and then the RS forwards the redundancy packet to the BS. In the third stage, the BS decodes the packets from the users and the RS jointly by the iterative decoding (ID) manner. In this scheme, based on the extrinsic information transfer (EXIT) charts, an optimization algorithm of irregular LDPC product code is also put forward in order to further improve the performance of the system. Simulation results confirm that the proposed scheme significantly outperforms the regular LDPC-product networking coding scheme and the conventional XOR-based network coding scheme on both AWGN and fading channels.
Network coding provides a powerful mechanism for improving performance of wireless networks. In this paper, we present an analytical approach for end-to-end delay analysis in wireless networks that employ inter-sessio...
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ISBN:
(纸本)9780769543642
Network coding provides a powerful mechanism for improving performance of wireless networks. In this paper, we present an analytical approach for end-to-end delay analysis in wireless networks that employ inter-session coding. Prior work on performance analysis in wireless network coding mainly focuses on the throughput of the overall network. Our approach aims to analyze the end-to-end delay performance of each flow in the network. The theoretical basis of our approach is network calculus. In order to apply network calculus to the analysis of wireless network coding, we address three specific problems: identifying traffic flows, characterizing broadcast links, and measuring coding opportunities. We make three main contributions. First, we obtain theoretical formulations for computing the delay bounds of bursty flows in wireless networks employing network coding. Second, based on the formulations, we figure out the factors that affect the end-to-end delays, and find an interesting phenomenon that, as traffic grows, the overall delay can potentially decrease. Third, in order to exploit the benefit of our findings, we introduce a new scheduling scheme that can improve the performance of current practical wireless network coding.
To achieve a p anoptic machine vision, recognition of images from disparate classes like person, car, building, et cetera is of p rimal importance. The Locally-connected Neural Pyramid (LCNP) was proposed earlier to a...
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ISBN:
(纸本)9781479925711
To achieve a p anoptic machine vision, recognition of images from disparate classes like person, car, building, et cetera is of p rimal importance. The Locally-connected Neural Pyramid (LCNP) was proposed earlier to achieve a robust and a time efficient training of large datasets of images from these disparate classes. The objective of this paper is to propose a technique for fast training of the LCNP. Y1Q co ding is used to ex tract the color based information of the images as it separates the color information from the luminance information. As R GB to YIQ conversion is an embarrassingly parallel situation, this recoding can give a tremendous speed-up over the previous approach-where PCA de-correlation of RGB channels was carried out. Also, the use of Y1Q coding has entailed a reduction in the complexity of the LCNP, thu s, reducing the computations considerably. This will further boost the time performance of the training. Despite a considerable reduction in the complexity of the LCNP and the use of YIQ coding, the recognition performance achieved by this approach is similar to the previous approach. A recognition rate of 85.62% is achieved for the testing samples of the LabelMe-12-50K dataset We p ropose that if the previous method of de-correlating RGB ch annels using PCA is rep laced with Y1Q coding, tremendous speed-up will be achieved.
With the rapid development of location-aware mobile devices, location-based services have been widely used. When LBS (Location Based Services) bringing great convenience and profits, it also brings great hidden troubl...
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ISBN:
(纸本)9781728147628
With the rapid development of location-aware mobile devices, location-based services have been widely used. When LBS (Location Based Services) bringing great convenience and profits, it also brings great hidden trouble, among which user privacy security is one of them. The paper builds a LBS privacy protection model and develops algorithm depend on the technology of one dimensional coding of Geohash geographic information. The results of experiments and data measurements show that the model the model has reached k-anonymity effect and has good performance in avoiding attacking from the leaked information in a continuous query with the user's background knowledge. It also has a preferable performance in time cost of system process.
Deep Neural Network (DNN) models are undergoing active development, and the efforts to achieve higher task performance have caused models to become more complex and larger in size. This limitation causes difficulties ...
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ISBN:
(纸本)9781665499248
Deep Neural Network (DNN) models are undergoing active development, and the efforts to achieve higher task performance have caused models to become more complex and larger in size. This limitation causes difficulties of being used in low-power edge devices. To solve this issue, MPEG (Moving Picture Experts Group) of ISO/IEC has developed NNC (Neural Network coding), a standard that can reduce model complexity and size, thus making facilitation of DNN models in low-power edge devices possible. This standard is designed as a toolbox of compression methods, including Stochastic Binary Ternary (SBT) quantization. SBT quantization is an approximation method that achieves extremely high coding gains by approximating all non-zero values within a weight tensor to the tensor's mean. However, this technique can cause significant performance loss due to its inaccurate estimate of values. We therefore propose Variance based Optimal Averaging (VOA) and Standard Normal Distribution based Optimization (SNDO), which are two different skills to enhance the performance of SBT quantization by searching for more accurate estimates of parameters. In this paper, we explain the mechanism of these two methods, and confirm their performance through experimental results.
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