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Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.

The Internet of Things (IoT) is transforming how devices interact and share data, especially in areas like vehicle monitoring. However, transmitting large volumes of real-time data can result in high latency and substantial energy consumption. In this context, Tiny Machine Learning (TinyML) emerges as a promising solution, enabling the execution of machine-learning models on resource-constrained embedded devices. This paper aims to develop two online multivariate compression approaches specifically designed for TinyML, utilizing the Typicality and Eccentricity Data Analytics (TEDA) framework. The proposed approaches are based on data eccentricity and do not require predefined mathematical models or assumptions about data distribution, thereby optimizing compression performance. The methodology involves applying the approaches to a case study using the OBD-II Freematics ONE+ dataset, which is focused on vehicle monitoring. Results indicate that both proposed approaches, whether parallel or sequential compression, show significant improvements in execution time and compression errors. These findings highlight the approach’s potential to enhance the performance of embedded IoT systems, thereby improving the efficiency and sustainability of vehicular applications.

The technological revolution of past decades has led teaching and learning of evolutionary biology to move away from its naturalist origins. As a result, students’ learning experiences and training on the science of natural history—which entails careful observations and meticulous data curation to generate insight—have been compromised compared with the times of the pioneers in the field. But will technology cause the extinction of natural history in its traditional form? In this essay, we provide a visionary—albeit not yet possible—perspective of the future of natural history in the technological era. We review the main concepts and applications of key state-state-of-the-art technologies to the teaching and learning of Biology including Virtual and Mixed Reality (VMR). Next, we review the current knowledge in artificial life, and describe our visionary model for the future of natural history voyages—the BioVR—which is an immersive world where students can experience evolution in action, and also shape how evolution can occur in virtual worlds. We finish the essay with a cautionary tale as to the known negative sides of using VMR technologies, and why future applications should be designed with care to protect the intended learning outcomes and students’ experience. Our aim is to stimulate debates on how new technologies can revolutionise teaching and learning across scenarios, which can be useful for improving learning outcomes of biological concepts in face-to-face, blended, and distance learning programmes.

This paper presents a robust control method for synchronization of the uncertain fractional-order hyperchaotic systems by using a new self-evolving non-singleton type-2 fuzzy neural network (SE-NT2FNN). The proposed SE-NT2FNNs are used for estimating the unknown functions in the dynamic of system. The effects of approximation error and external disturbance are eliminated by linear matrix inequality control scheme. The proposed SE-NT2FNN has one rule initially, the new rules and membership functions (MFs) are added based on the proposed simple algorithm and unnecessary rules and MFs are deleted. The proposed synchronization scheme is applied in a secure communication scheme. To show the effectiveness of the proposed method, three simulation examples are given. The results are compared with other methods, and it showed that the proposed control scheme results in the better performance than other methods.

In this manuscript, the synchronization of four-dimensional (4D) chaotic systems with uncertain parameters using a self-evolving recurrent interval type-2 Petri cerebellar model articulation controller is studied. The design of the synchronization control system is comprised of a recurrent interval type-2 Petri cerebellar model articulation controller and a fuzzy compensation controller. The proposed network structure can automatically generate new rules or delete unnecessary rules based on the self-evolving algorithm. Furthermore, the gradient-descent method is applied to adjust the proposed network parameters. Through Lyapunov stability analysis, bounded system stability is guaranteed. Finally, the effectiveness of the proposed controller is illustrated using numerical simulations of 4D chaotic systems.

The control of micro- and nanoscale systems is a vital yet challenging endeavor because of their small size and high sensitivity, which make them susceptible to environmental factors such as temperature and humidity. Despite promising methods proposed for these systems in literature, the chattering in the controller, convergence time, and robustness against a wide range of disturbances still require further attention. To tackle this issue, we present an intelligent observer, which accounts for uncertainties and disturbances, along with a chatter-free controller. First, the dynamics of a carbon nanotube (CNT) are examined, and its governing equations are outlined. Then, the design of the proposed controller is described. The proposed approach incorporates a self-evolving neural network-based methodology and the super-twisting sliding mode technique to eliminate the uncertainties’ destructive effects. Also, the proposed technique ensures finite-time convergence of the system. The controller is then implemented on the CNT and its effectiveness in different conditions is investigated. The numerical simulations demonstrate the proposed method’s outstanding performance in both stabilization and tracking control, even in the presence of uncertain parameters of the system and complicated disturbances.

This paper aims to propose a design method of interval type-2 wavelet cerebellar model articulation controller (IT2WCMAC) and applies it to control uncertain nonlinear systems. The proposed controller incorporates an IT2WCMAC as the main controller to mimic an ideal controller, and a robust compensator is used to eliminate the approximation error between the IT2WCMAC and the ideal controller. The self-evolving algorithm is used to automatically construct the network structure from the blank rule-base. In the proposed control scheme, the steepest descent gradient algorithm is applied to online tune the network parameters, and a Lyapunov stability theorem is applied to guarantee the system’s stability. Moreover, the learning rates of the parameter adaptive laws can be optimized by the particle swarm optimization (PSO) to promote the parameter learning efficiency. Finally, the proposed control system is applied to control nonlinear chaotic systems to verify the control performance and to show the superiority of the proposed algorithm than the other control methods.

Background: Over the past decade, the potential advantages of employing deep learning models and leveraging auxiliary data in data-driven end-to-end (E2E) frameworks to enhance inventory decision-making have gained recognition. However, current approaches predominantly rely on feed-forward networks, which may have difficulty capturing temporal correlations in time series data and identifying relevant features, resulting in less accurate predictions. Methods: Addressing this gap, we introduce novel E2E deep learning frameworks that combine Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for resolving single-period inventory ordering decisions, also termed the Newsvendor Problem (NVP). This study investigates the performance drivers of hybrid CNN-LSTM architectures, coupled with an evolving algorithm for optimizing network configuration. Results: Empirical evaluation of real-world retail data demonstrates that our proposed models proficiently extract pertinent features and interpret sequential data characteristics, leading to more accurate and informed ordering decisions. Notably, results showcase substantial benefits, yielding up to an 85% reduction in costs compared to a univariate benchmark and up to 40% savings compared to a feed-forward E2E deep learning architecture. Conclusions: This confirms that, in practical scenarios, understanding the impact of features on demand empowers decision-makers to derive tailored, cost-effective ordering decisions for each store or product category.

The increasing number of Android devices and users has been attracting the attention of different types of attackers. Malware authors create new versions of malware from previous ones by implementing code obfuscation techniques. Obfuscated malware is potentially contributed to the exponential increase in the number of generated malware variants. Detection of obfuscated malware is a continuous challenge because it can easily evade the signature-based malware detectors, and behaviour-based detectors are not able to detect them accurately. Therefore, an efficient technique for obfuscated malware detection in Android-based smartphones is needed. In the literature on Android malware classification, few malware detection approaches are designed with the capability of detecting obfuscated malware. However, these malware detection approaches were not equipped with the capacity to improve their performance by learning and evolving their malware detection rules. Based on the concept of evolving soft computing systems, this paper proposes an evolving hybrid neuro-fuzzy classifier (EHNFC) for Android malware classification using permission-based features. The proposed EHNFC not only has the capability of detecting obfuscated malware using fuzzy rules, but can also evolve its structure by learning new malware detection fuzzy rules to improve its detection accuracy when used in detection of more malware applications. To this end, an evolving clustering method for adapting and evolving malware detection fuzzy rules was modified to incorporate an adaptive procedure for updating the radii and centres of clustered permission-based features. This modification to the evolving clustering method enhances cluster convergence and generates rules that are better tailored to the input data, hence improving the classification accuracy of the proposed EHNFC. The experimental results for the proposed EHNFC show that the proposal outperforms several state-of-the-art obfuscated malware classification approaches in terms of false negative rate (0.05) and false positive rate (0.05). The results also demonstrate that the proposal detects the Android malware better than other neuro-fuzzy systems (viz., the adaptive neuro-fuzzy inference system and the dynamic evolving neuro-fuzzy system) in terms of accuracy (90%).

Due to the heterogeneity of investor structure between the Chinese mainland stock market (A-share market) and the Hong Kong stock market (H-share market) as well as the limitations on arbitrage activities, most cross-listed stocks in the two markets (AH stocks) have the characteristics of “one asset, two prices”, in which AH stocks with the same vote rights and dividend streams are traded at different prices in different markets. Based on the VAR (LA-VAR as well) model and a four-variable system including AH stock indices (AHXA, AHXH), the China Securities Index 300 (CSI 300), and the Hang Seng Index (HSI), this paper applies a new time-varying causality test to examine the causalities in prices and volatilities for two pairings (AXHA-AHXH pairing and CSI 300-HSI pairing) during the sample period spanning from 4 January 2010 to 21 May 2021. The empirical results exhibit statistically significant time-varying causalities of the two pairings. Specifically, at the price level, AHXH has a significant negative causal effect on AHXA from October 2017 to February 2020 except for several months in 2018, while AHXA merely has a negative impact on AHXA during a short period from March 2017 to May 2017. Of note, the direction of causalities in volatilities between AHXA and AHXH reverses. A positive causality is found from AHXA to AHXH at the 5% significance level during the period of April 2014 through May 2021, while no causality is detected in the opposite direction during the whole sample period. Meanwhile, the volatilities of CSI 300 significantly Granger cause those of HSI over the whole sample period, but not vice versa. Implications of our results are discussed.