Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children

Research in diabetes, especially when it comes to building data-driven models to forecast future glucose values, is hindered by the sensitive nature of the data. Because researchers do not share the same data between studies, progress is hard to assess. This paper aims at comparing the most promising algorithms in the field, namely Feedforward Neural Networks (FFNN), Long Short-Term Memory (LSTM) Recurrent Neural Networks, Extreme Learning Machines (ELM), Support Vector Regression (SVR) and Gaussian Processes (GP). They are personalized and trained on a population of 10 virtual children from the Type 1 Diabetes Metabolic Simulator software to predict future glucose values at a prediction horizon of 30 minutes. The performances of the models are evaluated using the Root Mean Squared Error (RMSE) and the Continuous Glucose-Error Grid Analysis (CG-EGA). While most of the models end up having low RMSE, the GP model with a Dot-Product kernel (GP-DP), a novel usage in the context of glucose prediction, has the lowest. Despite having good RMSE values, we show that the models do not necessarily exhibit a good clinical acceptability, measured by the CG-EGA. Only the LSTM, SVR and GP-DP models have overall acceptable results, each of them performing best in one of the glycemia regions.
Robust Pinning Constrained Control and Adaptive Regulation of Coupled Chua’s Circuit Networks

This paper addresses the problems of pinning synchronization control and regulation and its circuit realization of a class of perturbed coupled Chua’s circuit networks. Robust constrained control strategies for pinned circuits and adaptive regulation schemes for couplings are developed to eliminate the impact of bounded time-varying perturbations and nonlinearities. Bounded synchronization results of the closed-loop Chua’s circuit networks are obtained based on Lyapunov stability theory. Furthermore, the control strategies and adaptive regulation schemes are implemented physically by circuits to construct the closed-loop Chua’s circuit networks reality. Comparison results based on MULTISIM and MATLAB softwares are given to verify the effectiveness of the proposed control and regulation circuits.
Road Detection via Deep Residual Dense U-Net

Road extraction from aerial images is a hot research topic. With the advancement of convolutional neural network (CNN), several CNN-based road detection methods have been developed. However, most of them do not make full use of the hierarchical features from the original aerial images. In this paper, we propose a novel residual dense U-Net (RDUN), a semantic segmentation network which combines the strengths of residual learning, DenseNet, and U-Net, to overcome the drawback. Our proposed RDUN can fully exploit the hierarchical features from all the convolutional layers, which utilizes the residual dense blocks (RDB) to build up a U-Net architecture. The benefits of our model are two-fold. First, by using the RDB abundant local features can be extracted and fused effectively. Second, based the local features, hierarchical features are constructed by shortcut connections between layers in RDB. Extensive experiments are carried out on a real-world road detection dataset and the results demonstrate the proposed RDUN outperforms state-of-the-art competitors.
Attention-Driven Driving Maneuver Detection System

Driving Maneuver early Detection (DMD) is one of the most important tasks in Advanced Driver Assistance Systems (ADAS), it provides the early notification necessary for ADAS to predict dangerous circumstances and take appropriate actions. The end-to-end architectures such as Recurrent Neural Networks (RNNs) take advantage of deep networks to automatically learn non-linear discriminative features, which significantly boost the performances of DMD systems. However, due to the large number of parameters in the deep architectures, learning effective discriminative features requires millions of labeled images. Moreover the discriminative features are generally meaningless to human being, which makes the diagnose of end-to-end architectures extremely difficult. In this paper, we propose a novel DMD system, denoted as Attention-driven Driving Maneuver Detection system (ADMD), which uses drivers’ attention as an intermediate concept (hint) to explore and understand the causal relation between driving surroundings and driving maneuvers. In the training phase, ADMD distills the knowledge from drivers’ attention prediction model to provide initial search areas for learning effective features, which minimizes the labeled data requirements for training deep architectures and results in a fast optimization process. We compared the performances of ADMD with the state-of-the-art methods on 1953 miles (37 hours) of natural driving data collected from 7 drivers. The experimental results shows ADMD is capable of achieving better performances, which are mostly attributed to the proposed novel learning mechanism used to obtain meaningful interpretations of the driving surroundings that are closely related to the driver’s intend maneuvers.
A Deep Neural Network Model for Predicting User Behavior on Facebook

On Facebook, the social media site, liking, commenting and sharing the posts or status of other users are usually considered to be the key mechanisms for exchanging opinions about different topics. Due to the non-availability of data and security constraints, only few research studies have analyzed such behavior. In this paper, we introduced a novel deep neural network model for user behavior prediction (like and comment). We presented an embedding representation method for the textual content of comments and posts based on the contextual recursive auto-encoders model. The users were represented using a deep joint auto-encoders model to fuse the users’ like and comment information, and train the users’ combined embedding representation. Then, the user behaviors towards a given post were embedded into the same feature space of users and posts, using the joint auto-encoders model. Thereafter, we used a fully connected layer for behavior prediction. To train and evaluate the effectiveness of the proposed method, we also constructed a large dataset collected from Facebook. The experimental results show that the proposed method could achieve better results than the previous alternative methods.
VT-GAN: View Transformation GAN for Gait Recognition Across Views

Recognizing gaits without human cooperation is of importance in surveillance and forensics because of the benefits that gait is unique and collected remotely. However, change of camera view angle severely degrades the performance of gait recognition. To address the problem, previous methods usually learn mappings for each pair of views which incurs abundant independently built models. In this paper, we proposed a View Transformation Generative Adversarial Networks (VT-GAN) to achieve view transformation of gaits across two arbitrary views using only one uniform model. In specific, we generated gaits in target view conditioned on input images from any views and the corresponding target view indicator. In addition to the classical discriminator in GAN which makes the generated images look realistic, a view classifier is imposed. This controls the consistency of generated images and conditioned target view indicator and ensures to generate gaits in the specified target view. On the other hand, retaining identity information while performing view transformation is another challenge. To solve the issue, an identity distilling module with triplet loss is integrated, which constrains the generated images inheriting identity information from inputs and yields discriminative feature embeddings. The proposed VT-GAN generates visually promising gaits and achieves promising performances for cross-view gait recognition, which exhibits great effectiveness of the proposed VT-GAN.
Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios

This paper presents an actively semi-supervised multi-layer neuro-fuzzy modeling method, ASSDRB, to classify different lighting conditions for driving scenes. ASSDRB is composed of a massively parallel ensemble of AnYa type 0-order fuzzy rules. It uses a recursive learning algorithm to update its structure when new data items are provided and, therefore, is able to cope with nonstationarities. Different lighting conditions for driving situations are considered in the analysis, which is used by self-driving cars as a safety mechanism. Differently from mainstream Deep Neural Networks approaches, the ASSDRB is able to learn from unseen data. Experiments on different lighting conditions for driving scenes, demonstrated that the deep neuro-fuzzy modeling is an efficient framework for these challenging classification tasks. Classification accuracy is higher than those produced by alternative machine learning methods. The number of algebraic calculations for the present method are significantly smaller and, therefore, the method is significantly faster than common Deep Neural Networks approaches. Moreover, DRB produced transparent AnYa fuzzy rules, which are human interpretable.
A novel hardware-efficient CPG model for a hexapod robot based on nonlinear dynamics of coupled asynchronous cellular automaton oscillators

This paper presents a novel central pattern generator (CPG) model based on nonlinear dynamics of asynchronous cellular automata. It is shown that the presented CPG model can exhibit various synchronization phenomena depending on parameter values. In order to evaluate usefulness of the presented CPG model, this paper focuses on controlling a hexapod robot shown in Fig. 1. Based on intensive analyses of the synchronization phenomena, a parameter tuning method to realize a tripod gait of the hexapod robot is derived. Then the CPG model with a tuned parameter value is implemented in a field programmable gate array and it is shown that the CPG model can realize a tripod gait of the hexapod robot. Also, it is shown that the presented CPG model uses much fewer circuit elements and consumes much less power compared to a conventional CPG model.
Emotion Intensity Estimation from Video Frames using Deep Hybrid Convolutional Neural Networks

Detecting emotional states of human from videos is essential in order to automate the process of profiling human behaviour, which has applications in a variety of domains, such as social, medical and behavioural science. Considerable research has been carried out for binary classification of emotions using facial expressions. However, a challenge exists to automate the feature extraction process to recognise the various intensities or levels of emotions. The intensity information of emotions is essential for tasks such as sentiment analysis. In this work, we propose a metric-based intensity estimation mechanism for primary emotions, and a deep hybrid convolutional neural network-based approach to recognise the defined intensities of the primary emotions from spontaneous and posed sequences. Further, we extend the intensity estimation approach to detect the basic emotions. The frame level facial action coding system annotations and the intensities of action units associated with each primary emotion are considered for deriving the various intensity levels of emotions. The evaluation on benchmark datasets demonstrates that our proposed approach is capable of correctly classifying the various intensity levels of emotions as well as detecting them.
Deep Structure Learning for Rumor Detection on Twitter

With the development of social media and the popularity of mobile devices, it becomes increasingly easy to post rumors and spread rumors on social media. Widespread rumors may cause public panic and negative impact on individuals, which makes the automatic detection of rumors become necessary. Most existing methods for automatic rumor detection focus on modeling features related to contents, users and propagation patterns based on feature engineering, but few work consider the existence of graph structural information in the user behavior. In this paper, we propose a model that leverages graph convolutional networks to capture user behavior effectively for rumor detection. Our model is composed of three modules: 1) a user encoder that models users attributes and behaviors based on graph convolutional networks to obtain user representation; 2) a propagation tree encoder, which encodes the structure of the rumor propagation tree as a vector with bridging the content semantics and propagation clues; 3) an integrator that integrates the output of the above modules to identify rumors. Experimental results on two public Twitter datasets show that our model achieves much better performance than the state-of-the-art methods.
Fault Level Studies in a Low Voltage Distribution Network in the Presence of Small Scale Distributed Generations– Case Study for Mauritius

The robustness of a power system is determined by its stability during dynamic system behaviour as well as during fault conditions. Protective schemes are devised to ascertain that the power system remains stable during fault conditions by disconnecting the unhealthy part. One main input for the protective schemes is the fault level along the network. Advanced numerical models are used to calculate the fault levels for the traditional power system characterized by unidirectional current flow. With the popularization of Distributed Generations (DG), more of such units are being connected to the network. They disrupt the traditional model of power flow and alter the fault level in the system. Being sources of energy, they inherently contribute to feeding a fault condition resulting in more intense fault conditions which may become hazardous. This study deals with demonstrating how the fault level is affected by the introduction of Small Scale Distributed Generations (SSDG). An actual residential area in Mauritius is modelled in DIgSILENT Power Factory® using actual site parameters. This model is fed with recorded consumption data as well as photovoltaic and wind generation recorded patterns. The results obtained can be directly applied by any utility having similar residential area and increasing presence of SSDG.
Periodic Neural Networks for Multivariate Time Series Analysis and Forecasting

Designing systems that make accurate forecasts based on time dependent data is always a challenging and significant task. In this regard, a number of statistics and neural network-based models have been proposed for analyzing and forecasting time series datasets. In this paper, we propose a novel machine learning model for handling and predicting multivariate time series data. In our proposed model we focus on supervised learning technique in which (1) some features of time series dataset exhibit periodic behaviour and (2) time t is considered as an input feature. Due to periodic nature of multivariate time series datasets, our model is a simple neural network where the inputs to the single output source are assumed to be in the form A sin(Bt + C)x as opposed to the standard form inputs Ax + B. We train our proposed model on various datasets and compare our model’s performance with standard well-known models used in forecasting multivariate time series datasets. Our results show that our proposed model often outperforms other exiting models in terms of prediction accuracy. Moreover, our results show that the proposed model can handle time series data with missing values and also input data-values that are non-equidistant. We hope that the proposed model will be useful in fostering future research on designing accurate forecasting algorithms.
An Effencient Improvement of RFID Authentication Protocol Using Hash Function ZKP

The applications of Radio Frequency Identification (RFID) technology has been rapidly developed to be used in different fields that require automatic identification of objects and managing information. The advantage of employing RFID systems is to facilitate automatic identification of objects from distance without any interaction with tagged objects and without using a line of sight as compared with barcode. However, security and privacy constitute a challenge to RFID system as RFID systems use the wireless communication. Many researchers have introduced elliptical curve cryptographic (ECC) solutions to the security and privacy in RFID system as an ideal cryptosystem to be implemented with RFID technology. However, most of these solutions do not have provide adequate protection. Moreover, in terms of integrity and confidentiality level, most of these authentication protocols still vulnerable to some of security and privacy attacks. Based on these facts, this paper proposes a mutual authentication protocol that aims at enhancing an existing RFID authentication protocol that suffers from tracking attack and man-in-the-middle attack (MITM). The enhancement is accomplished by improving the security and privacy level against MITM, tracking attack and other related attacks. The proposed protocol is dependent on use the elliptical curve version of Schnorr identification protocol in combination with Keccak hash function. This combination leads to enhance the confidentiality and integrity level of the RFID authentication system and increase the privacy protection.
ASIE

Neuromorphic computing based on spiking neural network (SNN) shows good energy-efficiency. However, it is inefficient for SNN to perform the convolution based on frame. It may contain a lot of redundant information in the frame. The output of Dynamic Vision Sensors (DVS) is a stream event based on Address Event Representation (AER). The asynchronous nature of AER events makes the event-based convolution reflect the characteristics of SNN low energy consumption. This article presents an SNN hardware inference engine based on an asynchronous Processing Element (PE) array with AER events as input. The engine uses a convolution algorithm based on AER events. This design also uses distributed storage in the PE array to store the state of neurons to reduce the cost of memory access. The experimental results show that the design can achieve a recognition accuracy of 98.0% for the MNIST AER dataset. The design can perform the reference process more efficiently in the case where the accuracy of the loss is negligible. During the filling and draining processes of the systolic array, the number of active PE units in our PE array is reduced and, thus, the average power consumption per PE unit is drastically decreased.
An Attention-based Hybrid LSTM-CNN Model for Arrhythmias Classification

Electrocardiogram (ECG) signal based arrhythmias classification is an important task in healthcare field. Based on domain knowledge and observation results from large scale data, we find that accurately classifying different types of arrhythmias relies on three key characteristics of ECG: overall variation trends, local variation features and their relative location. However, these key factors are not yet well studied by existing methods. To tackle this problem, we design an attention-based hybrid LSTM-CNN model which is comprised of a stacked bidirectional LSTM (SB-LSTM) and a two-dimensional CNN (TD-CNN). Specifically, SB-LSTM and TD-CNN are utilized to extract the overall variation trends and local features of ECG, respectively. Furthermore, we add a trend attention gate (TAG) to SB-LSTM, meanwhile, add a feature attention mechanism (FAM) and a location attention mechanism (LAM) to TD-CNN. Thus, the effects of important trends and features at key locations in ECG can be enhanced, which is conducive to obtaining a better understanding of the fluctuation pattern of ECG. Experimental results on the MIT-BIH arrhythmias dataset indicate that our model outperforms three state-of-the-art methods, and achieve 99.3% of accuracy, 99.6% of sensitivity and 98.1% of specificity, respectively.
Heating and Freezing Injury to Plant Tissues and Their Effect on Bioimpedance: Experimental Study

Electrochemical Impedance Spectroscopy (EIS) has been used as a technique for the assessment of food attributes. This paper discusses the effect of injuries caused by heating and freezing treatments to plant's bioimpedance. Unlike other studies to these kinds of injuries, experiments are carried out on the whole fruit using non-invasive electrodes keeping the plant tissues unharmed. Moreover, one of the samples under test was chosen to be a ripening fruit to discuss how its bioimpedance behavior differ from non-ripening ones. The experimental results showed how the damage caused by freezing and heating injuries to cell walls can be realized through bioimpedance parameters. The experiment is carried out by BioLogic SP-150 impedance analyzer and the impedance is tested over a frequency range of 10 Hz to 200 kHz. The results showed consistency and matched other studies carried out from the biological point of view.
Quantitative Analysis of Dynamical Complexity in Cultured Neuronal Network Models for Reservoir Computing Applications

Reservoir computing is a machine learning paradigm that was proposed as a model of cortical information processing in the brain. It processes information using the spatiotemporal dynamics of a large-scale recurrent neural network and is expected to improve power efficiency and speed in neuromorphic computing systems. Previous theoretical investigation has shown that brain networks exhibit an intermediate state of full coherence and random firing, which is suitable for reservoir computing. However, how reservoir performance is influenced by connectivity, especially which revealed in recent connectomics analysis of brain networks, remains unclear. Here, we constructed modular networks of integrate-and-fire neurons and investigated the effect of modular structure and excitatory-inhibitory neuron ratio on network dynamics. The dynamics were evaluated based on the following three measures: synchronous bursting frequency, mean correlation, and functional complexity. We found that in a purely excitatory network, the complexity was independent of the modularity of the network. On the other hand, networks with inhibitory neurons exhibited complex network activity when the modularity was high. Our findings reveal a fundamental aspect of reservoir performance in brain networks, contributing to the design of bio-inspired reservoir computing systems.
Agritourism and the adaptive re-use of farm buildings in New Zealand

Abstract This paper reports findings from a study of the adaptive re-purposing of farm buildings for a wide array of agritourism activities. The research is being conducted in New Zealand where the international visitor sector is thriving. In response, an increasing number of farmers are attempting to boost their farm incomes by adding tourism ventures to their business portfolios. In doing so, many of them are using and preserving rural cultural heritage, particularly old agricultural and other rural buildings, while also diversifying farm activity. This element of agritourism therefore has an important role in the protection and adaptive re-use of farm buildings, farm landscape change, and the creation of new value and values in the countryside. In the cases we have studied, this entrepreneurial activity is largely farmer-driven and undertaken with some, but limited, financial support from central and local government. In considering the policy implications of our work, we call for the provision of advisory services to facilitate and enable New Zealand farmers to create profitable and sustainable high-quality tourism services that simultaneously preserve farm buildings.