Errors are a natural part of predictive algorithms, but may discourage users from relying on algorithms. We conduct two experiments to demonstrate that reliance on a predictive algorithm following a substantial error is affected by (i) when the error ...

This paper explores the prescribed performance tracking control problem of nonlinear systems with triangular structure. To obtain the desired transient performance and precise estimations of uncertain terms, the techniques of neural network control, ...

Most of the research on personal networking and deep learning have been conducted separately. Crossovers between the two fields have just emerged. This article provides a quick introduction to the fundamentals of deep learning, as well as the most ...

On the issue of low utilisation and acceptance of current adaptive cruise control (ACC), a multi‐objective adaptive cruise control (MO‐ACC) algorithm is developed in this study. Based on model predictive control theory, comprehensively considering the ...

The ability of artificial intelligence techniques to build synthesized brand new videos or to alter the facial expression of already existing ones has been efficiently demonstrated in the literature. The identification of such new threat generally known ...

The big data phenomenon has gained much attention in the wireless communications field. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. In ...

Designing immersion is the key challenge in virtual reality; this challenge has driven advancements in displays, rendering and recently, haptics. To increase our sense of physical immersion, for instance, vibrotactile gloves render the sense of touching, ...

It is often useful to predict opponent behavior when playing a general-sum two-player normal form game. However best-responding to an inaccurate prediction can lead to a strategy which is vulnerable to exploitation. This paper proposes a novel method, ...

Reversible watermarking is a method of hiding the watermark in digital media in such a way that visually its effect is almost negligible and after extracting the watermark, digital media can be restored to its original form bit-by-bit. Reversible ...

Intrinsic motivation and novelty search are promising approaches to deal with plateaus, deceptive functions and other exploration problems where using only the main objective function is insufficient. However, it is not clear until now how and if ...

Histogram Shifting (HS) is one of the most popular reversible data hiding techniques that has received tremendous attention from the research community in recent years. While histogram shifting offers many advantages, it suffers from relatively low ...

Based on histogram shifting and histogram modification of difference images, this paper proposed a method of histogram shifting reversible data hiding by pixel pair's average predictions on gray images. Under the premise that the average remains ...

We study the accuracy of evaluation metrics used to estimate the efficacy of predictive models. Offline evaluation metrics are indicators of the expected model performance on real data. However, in practice we often experience substantial discrepancy ...

This proposed scheme reversibly embeds data into image prediction-errors by using histogram-pair method with the following four thresholds for optimal performance: embedding threshold, fluctuation threshold, left- and right-histogram shrinking ...

Specification testing, which involves the direct measurement of the circuit performance parameters is the conventional practice for testing analog/RF devices. While this approach is highly accurate, it often incurs extremely high testing costs. A ...

Soft sensors are widely used to estimate process variables that are difficult to measure online. However, their predictive accuracy gradually decreases with changes in the state of the plants. We have been constructing soft sensor models based on the ...

Current machine learning algorithms perform well on many problem domains, but in risk-sensitive decision making, for example in medicine and finance, common evaluation methods that give overall assessments of models fail to gain trust among experts, as ...

The instrumental variable (IV) method is one of the most renowned methods for parameter estimation. Its bigger advantage is that it is applicable for open-loop as well as for closed-loop systems. The main difficulty in closed-loop identification is due ...

Injecting weight noise during training has been a simple strategy to improve the fault tolerance of multilayer perceptrons (MLPs) for almost two decades, and several online training algorithms have been proposed in this regard. However, there are some ...

This study applies dynamic diffusion theory to forecast Foreign Direct Investments (FDIs) of Taiwan IC industry into China. We compare the FDI external and internal influence factors among IC design, packaging and testing industries. The internal impact ...