CAST: Cluster-Aware Self-Training for Tabular Data

Self-training has gained attraction because of its simplicity and versatility, yet it is vulnerable to noisy pseudo-labels. Several studies have proposed successful approaches to tackle this issue, but they have diminished the advantages of self-training because they require specific modifications in self-training algorithms or model architectures. Furthermore, most of them are incompatible with gradient boosting decision trees, which dominate the tabular domain. To address this, we revisit the cluster assumption, which states that data samples that are close to each other tend to belong to the same class. Inspired by the assumption, we propose Cluster-Aware Self-Training (CAST) for tabular data. CAST is a simple and universally adaptable approach for enhancing existing self-training algorithms without significant modifications. Concretely, our method regularizes the confidence of the classifier, which represents the value of the pseudo-label, forcing the pseudo-labels in low-density regions to have lower confidence by leveraging prior knowledge for each class within the training data. Extensive empirical evaluations on up to 20 real-world datasets confirm not only the superior performance of CAST but also its robustness in various setups in self-training contexts.Comment: 17 pages with appendi

Encrypted Dynamic Control exploiting Limited Number of Multiplications and a Method using Ring-LWE based Cryptosystem

In this paper, we present a method to encrypt dynamic controllers that can be implemented through most homomorphic encryption schemes, including somewhat, leveled fully, and fully homomorphic encryption. To this end, we represent the output of the given controller as a linear combination of a fixed number of previous inputs and outputs. As a result, the encrypted controller involves only a limited number of homomorphic multiplications on every encrypted data, assuming that the output is re-encrypted and transmitted back from the actuator. A guidance for parameter choice is also provided, ensuring that the encrypted controller achieves predefined performance for an infinite time horizon. Furthermore, we propose a customization of the method for Ring-Learning With Errors (Ring-LWE) based cryptosystems, where a vector of messages can be encrypted into a single ciphertext and operated simultaneously, thus reducing computation and communication loads. Unlike previous results, the proposed customization does not require extra algorithms such as rotation, other than basic addition and multiplication. Simulation results demonstrate the effectiveness of the proposed method.Comment: 11 pages, 4 figures, submitted to IEEE Transactions on Systems, Man, and Cybernetics: System

A Design Method of Distributed Algorithms via Discrete-time Blended Dynamics Theorem

We develop a discrete-time version of the blended dynamics theorem for the use of designing distributed computation algorithms. The blended dynamics theorem enables to predict the behavior of heterogeneous multi-agent systems. Therefore, once we get a blended dynamics for a particular computational task, design idea of node dynamics for individual heterogeneous agents can easily occur. In the continuous-time case, prediction by blended dynamics was enabled by high coupling gain among neighboring agents. In the discrete-time case, we propose an equivalent action, which we call multi-step coupling in this paper. Compared to the continuous-time case, the blended dynamics can have more variety depending on the coupling matrix. This benefit is demonstrated with three applications; distributed estimation of network size, distributed computation of the PageRank, and distributed computation of the degree sequence of a graph, which correspond to the coupling by doubly-stochastic, column-stochastic, and row-stochastic matrices, respectively

A Remotely Operated Science Experiment framework for under-resourced schools

Teaching argumentation with appropriate activities and strategies would support a wide range of goals in science education. Though science labs have been suggested and employed for argumentation activities, such educational expenditures are likely to be beyond the means of most schools in under-resourced areas. Due to the lack of appropriate technological infrastructure and financial support, science education in developing countries is limited to the traditional approach. Teachers and students in the developing world, or other rural areas without sufficient lab resources in developed countries, would adopt argumentation in science classroom if they utilize their wireless infrastructure. We suggest a remote science lab for students in under-resourced schools, and suggest a possible way to engage them in argumentation activities. This paper introduces Remotely Operated Science Experiment (ROSE) and its implementation, and draws on the result of the intervention focusing on the impact of ROSE on students’ argumentation and learning

Toward designing mobile games for visually challenged children

This study attempts to design a mobile learning game for visually challenged children to improve their spatial ability and executive function. Two audible mobile games were designed and tested: (1) Cardinal Direction (CD) and (2) modified Tower of London (TOL). Qualitative (i.e. observational notes and interviews) and quantitative data (i.e. game scores, time logs, and survey data) were collected and analyzed. Results yielded a high level of enjoyment among participants. Findings on collaboration, usability, accessibility, audible feedback, and student success in winning points in the games are discussed in order to provide insights into designing a more comprehensible mobile learning game, with higher collaboration features, for visually challenged users in future

Model Predictive Control Method for Autonomous Vehicles Using Time-Varying and Non-Uniformly Spaced Horizon

This paper proposes an algorithm for path-following and collision avoidance of an autonomous vehicle based on model predictive control (MPC) using time-varying and non-uniformly spaced horizon. The MPC based on non-uniformly spaced horizon approach uses the time intervals that are small for the near future, and time intervals that are large for the distant future, to extend the length of the whole prediction horizon with a fixed number of prediction steps. This MPC has the advantage of being able to detect obstacles in advance because it can see the distant future. However, the presence of longer time interval samples may lead to poor path-following performance, especially for paths with high curvature. The proposed algorithm performs proper adjustment of the prediction interval according to a given situation. For sections with large curvature, it uses the short prediction intervals to increase the path-following performance; further, to consider obstacles over a wider range, it uses the long prediction intervals. This technique allows simultaneous improvement of the path-following performance and the range of obstacle avoidance with fixed computational complexity. The effectiveness of the proposed method is verified through an open-source simulator, CARLA and real-time experiments

Distributed Algorithm for the Network Size Estimation: Blended Dynamics Approach

This paper presents a distributed algorithm for the network size estimation problem. The problem is to find the total number of nodes in the network. The proposed algorithm utilizes the continuous-time dynamics for achieving synchronization, which provides a way to assign each element of the equilibrium point of the dynamics close to the network size. The algorithm guarantees that each node directly estimates the total number of nodes in the network. Moreover, the network size can be estimated regardless of initial condition. We also derive the stopping criteria of the algorithm and extend the result to the case where the network varies intermittently with time.N