An Experience-Connected e-Learning System with a Personalization Mechanism for Learners’ Situations and Preferences

This paper presents an “experience-connected” e- Learning system that facilitates users to learn practical skills of foreign language by associating knowledge and daily-life experiences. “Experience-Connected” means that the users of this system receive personalized and situation-dependent learning materials automatically. Knowledge associated to users’ daily-life has the following advantages: 1) provides opportunities to learn frequently, and 2) provides clear and practical context information about foreign language usage. The unique feature of this system is a dynamic relevance computation mechanism that retrieves learning materials according to both preference relevance and spatiotemporal relevance. Users of this system obtain appropriate learning materials, without manual and time-consuming search processes. This paper proves the feasibility of the system by showing the actual system implementation that automatically broadcasts the media-data of foreign language learning materials to smart-phones

A Geo-Location Context-Aware Mobile Learning System with Adaptive Correlation Computing Methods

AbstractThis paper proposes a context-aware mobile learning system with adaptive correlation computing methods. This system enables users to enhance their knowledge by correlating it with daily experiences. The proposed system contains a hybrid metric vector space to define the correlation between heterogeneous metadata vectors of the user context and learning material. The system integrates heterogeneous metric vector spaces with definitions of the semantic relations between the vector spaces. The significant feature of this system is a hybrid adaptation mechanism for the calculation of correlation. The adaptation mechanism has multidirectional adaptation functions for various learning materials, situations, and learners. We propose a revise-localize-personalize (RLP) adaptation model. In the adaptation mechanism, users only have to improve the metadata or the relations just in their relevant field. The advantage of the system is that the system reduces the time-intensive efforts required for describing direct relations between user contexts and learning materials. This paper presents the feasibility of the context-aware heterogeneous information provision with the hybrid metric vector space, by implementing an actual mobile application system and examining real-world experiments on data provision

Preprogrammed capillarity to passively control system-level sequential and parallel microfluidic flows

In microfluidics, capillarity-driven solution flow is often beneficial, owing to its inherently spontaneous motion. However, it is commonly perceived that, in an integrated microfluidic system, the passive capillarity control alone can hardly achieve well-controlled sequential and parallel flow of multiple solutions. Despite this common notion, we hereby demonstrate system-level sequential and parallel microfluidic flow processing by fully passive capillarity-driven control. After manual loading of solutions with a pipette, a network of microfluidic channels passively regulates the flow timing of the multiple solution menisci in a sequential and synchronous manner. Also, use of auxiliary channels and preprogramming of inlet-well meniscus pressure and channel fluidic conductance allow for controlling the flow direction of multiple solutions in our microfluidic system. With those components orchestrated in a single device chip, we show preprogrammed flow control of 10 solutions. The demonstrated system-level flow control proves capillarity as a useful means even for sophisticated microfluidic processing without any actively controlled valves and pumps.close5

Learned pseudo-random number generator: WGAN-GP for generating statistically robust random numbers.

Pseudo-random number generators (PRNGs) are software algorithms generating a sequence of numbers approximating the properties of random numbers. They are critical components in many information systems that require unpredictable and nonarbitrary behaviors, such as parameter configuration in machine learning, gaming, cryptography, and simulation. A PRNG is commonly validated through a statistical test suite, such as NIST SP 800-22rev1a (NIST test suite), to evaluate its robustness and the randomness of the numbers. In this paper, we propose a Wasserstein distance-based generative adversarial network (WGAN) approach to generating PRNGs that fully satisfy the NIST test suite. In this approach, the existing Mersenne Twister (MT) PRNG is learned without implementing any mathematical programming code. We remove the dropout layers from the conventional WGAN network to learn random numbers distributed in the entire feature space because the nearly infinite amount of data can suppress the overfitting problems that occur without dropout layers. We conduct experimental studies to evaluate our learned pseudo-random number generator (LPRNG) by adopting cosine-function-based numbers with poor random number properties according to the NIST test suite as seed numbers. The experimental results show that our LPRNG successfully converted the sequence of seed numbers to random numbers that fully satisfy the NIST test suite. This study opens the way for the "democratization" of PRNGs through the end-to-end learning of conventional PRNGs, which means that PRNGs can be generated without deep mathematical know-how. Such tailor-made PRNGs will effectively enhance the unpredictability and nonarbitrariness of a wide range of information systems, even if the seed numbers can be revealed by reverse engineering. The experimental results also show that overfitting was observed after about 450,000 trials of learning, suggesting that there is an upper limit to the number of learning counts for a fixed-size neural network, even when learning with unlimited data

Electrically Programmable Surfaces for Configurable Patterning of Cells The authors thank Y.-W. Lin for assistance with electrode lithography. This work was supported by NSF, Yamatake Co., Japan, NIH, and an REA Fellowship from the University of Michigan (C.Y.F.).

No Abstract.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58574/1/1418_ftp.pd