The urban complex material and geometry characteristics result in a 3D thermal heterogeneity and that limits the urban surface temperature (UST) retrieval. In this study, we improved the temperature and emissivity separation (TES) algorithm by incorporating thermal heterogeneity within mixed pixel (MP). The improvement was based on the Discrete Anisotropic Radiative Transfer (DART) model and applied to retrieve land surface temperature (LST) from SDGSAT-1. The TES-MP algorithm was validated with ECOSTRESS and the data simulated by DART model, and the results show that it can reach good accuracy under complex urban conditions. Based on the simulated scenes from the Sheung Wan building in Hong Kong, the RMSE of TES-MP algorithm is 0.85K under thermal homogeneous conditions and 1.13K under thermal heterogeneous conditions. Additionally, new high-reflectivity construction materials are common in urban areas, i.e. metal materials. It shows that the relationship between MMD and minimums emissivity(εmin) is not applicable to these materials. Thus, the impacts of such materials on the UST retrieval were evaluated. The results show that the higher the reflectivity and the fractional abundance of such materials, the larger the LST underestimation. Under nadir observation conditions, the proportion of high-reflectivity walls does not cause significant LST retrieval errors. The geometry and adjacency effects on retrieved LST were evaluated, and results show that the TES-MP algorithm has some resistance to geometry and adjacency effects, thereby reducing errors in LST retrieval. This study provides a new view on retrieving LST of urban MPs, and also suggests that three or more bands should be considered when setting up thermal infrared sensors.

The plethora of online applications and mobile communication systems helps in the increase of the everyday usage of social networks. More people tend to use social media and join social networks. At the same time, several people suffer from mental stress while they either receive or create social media content. In this work, we examine the feasibility of detecting stress related to social media content, with the use of wearable devices. We use Electrodermal Activity (EDA) signals collected from wrist-based devices and we examine any correlation between them and the social media content. We conducted experiments in different environments with self-reported data from the users. According to preliminary results, the relationship between EDA and stress levels related to social media content can be identified.

Several signals can be collected from wearable devices containing important physiological and psychological information. Understanding various physiological signals is significant for computers to recognize human emotional states. Electrodermal Activity (EDA), originating from the spontaneous activation of sweat glands in the skin, is closely related to mood, arousal, and attention and is the most widely used measurement in the physiological response system for emotional state detection. However, extracting valuable features from EDA signals and making accurate emotional classification predictions has always been challenging. With the continuous development of models with representation learning capabilities, the use of deep learning models to automatically learn physiological signal features and perform classification learning is promising. In order to improve the shortcomings of traditional emotion recognition methods, which require a deep understanding of physiological signals and artificial extraction of relevant features, this paper proposed a Recurrent Neural Network (RNN) -based method for automatic feature extraction from EDA's spectrograms. A Convolutional Neural Network (CNN) is used to learn the extracted features further and output the determined emotional state. The results show that the classification accuracy for arousal and valence has reached 83.4% and 81.2%, respectively, which is promising in extracting features automatically and tackling the emotional state classification problem.

Stress is an inevitable part of modern life. While stress can negatively impact a person's life and health, positive and under-controlled stress can also enable people to generate creative solutions to problems encountered in their daily lives. Although it is hard to eliminate stress, we can learn to monitor and control its physical and psychological effects. It is essential to provide feasible and immediate solutions for more mental health counselling and support programs to help people relieve stress and improve their mental health. Popular wearable devices, such as smartwatches with several sensing capabilities, including physiological signal monitoring, can alleviate the problem. This work investigates the feasibility of using wrist-based electrodermal activity (EDA) signals collected from wearable devices to predict people's stress status and identify possible factors impacting stress classification accuracy. We use data collected from wrist-worn devices to examine the binary classification discriminating stress from non-stress. For efficient classification, five machine learning-based classifiers were examined. We explore the classification performance on four available EDA databases under different feature selections. According to the results, Support Vector Machine (SVM) outperforms the other machine learning approaches with an accuracy of 92.9 for stress prediction. Additionally, when the subject classification included gender information, the performance analysis showed significant differences between males and females. We further examine a multimodal approach for stress classifications. The results indicate that wearable devices with EDA sensors have a great potential to provide helpful insight for improved mental health monitoring.

In the fast pace of life, emotion recognition systems are essential to help monitor mental health and well-being. The continuous development of the Internet of Things (IoT) and Human-Computer Interaction (HCI) improve the availability and accessibility to devices that can capture the facial expressions of a user, while wearable devices can also capture physiological signals and use them for emotion recognition. Meanwhile, machine learning and deep learning methods can provide emotion prediction models. However, the training of the models relies heavily on massive amounts of labeled data. The accuracy of data labels affects the success of the overall system. Research targeting emotion recognition uses the participants' self-reports as labels. However, participants often fail to give accurate self-reports, thus affecting the accuracy of the analysis. In this study, we examine the performance of the self-reports and external annotations for emotion recognition based on visual and physiological signals. Specifically, we use video data, as well as the Electrodermal Activity (EDA), Electroencephalogram (EEG), and Electrocardiogram (ECG) signals collected from wearable devices. We use two machine learning and three deep learning methods to process the signals and train the classifiers. The results show that the classifiers trained with external annotations offer better emotion recognition accuracy than self-reports. Also, the classifiers trained on facial expression offer better emotion prediction accuracy than the physiological signals, and the Deep Convolutional Network model shows the best results.

The recent pandemic has brought tremendous changes to everyone’s life, causing stress about losing loved ones, losing jobs, and having changes in sleep or eating habits. This study investigates the feasibility of utilizing Electrodermal Activity (EDA) collected from wearable devices to detect people’s stress. EDA can quantify the changes in sympathetic dynamics by measuring sweat produced by our sweat glands. Currently, the adoption of EDA sensors to commercially off-the-shelf smart-watches is still in the infancy stage, and only a few brands have the EDA sensors implemented into their smartwatch. To facilitate our feasibility study, we need the datasets that contain the EDA signals collected from wearable devices. This paper uses two publicly available datasets containing the EDA signals collected from research-grade wearable devices. We cast the stress detection problem as a binary classification problem and trained the classifiers with three popular machine learning methods: K-Nearest Neighbor, Logistic Regression, and Random Forests. According to experimental results, Random Forests achieves an accuracy of 85.7% to classify stress from non-stress status. The results verified that wearable devices with EDA sensors have the potential to predict stress status.

Abstract We investigate the implementation of fault‐tolerant logical Hadamard gates in Reed–Muller quantum codes (RMQCs). During the realization, we consider the influences of random single‐qubit errors and some error‐detecting stabilizers are simplified by the existing syndromes. We first identify the errors and modify the gauge‐fixing syndromes, then refer to the modified syndromes to select the fix operations, and finally perform the error‐correcting and fix operations together. Furthermore, we establish a graph model for the RMQCs and exhibit a progress of finding the fix operations for the unsatisfied stabilizers. For the circuit design, we optimize the choice of gauge operators, the positions of the ancillary qubits and design a parallel circuit for implementing a fault‐tolerant logical Hadamard gate in 15‐qubit RMQC. We simulate the progress of finding corresponding fix operations for 31‐qubit and 63‐qubit RMQCs and the whole process of realizing logical Hadamard gate with random single‐qubit errors for 15‐qubit and 31‐qubit RMQCs. Results show that correct fix operations can be obtained and fault‐tolerant logical Hadamard gates can be realized as expected. The performance comparisons are also given and the results show that our method can achieve a higher success rate with a reasonable higher cost. With the implementation of the logical Hadamard gate, a universal fault‐tolerant gate set is achieved in single RMQC.

The quality and safety of food is a great concern to the whole society because it is the most basic guarantee for human health and social development and stability. Ensuring food quality and safety is a complex process, and all stages of food processing must be considered, from cultivating, harvesting and storage to preparation and consumption. Grading is one of the essential processes to control food quality. This paper proposed a two-layer image processing system based on machine learning for banana grading. Support Vector Machine is the first layer to classify bananas based on an extracted feature vector that is composed of colour and texture features and YOLOv3 follows up for further locating the defected area on the peel and determining if the inputs belong to mid-ripened or well-ripened class. The performance of the first layer achieved an accuracy of 98.5% and the accuracy of the second layer is 85.7%. The overall accuracy is 96.4%.

We investigate how to implement fault-tolerant logical Hadamard gates in Reed-Muller quantum codes(RMQCs) using the gauge-fixing method. During the realization, we consider the influence of random single-qubit errors by performing the error-detecting measurements. Moreover, some error-detecting stabilizers are simplified by the existing syndromes. Then we identify the errors and modify the syndromes, and refer to the modified syndromes to select the fix operations, and finally perform the error-correcting and fix operations together. Further, we establish a graph model for the RMQCs and exhibit a progress of how to find the fix operations for the unsatisfied stabilizers. We simulate the progress of finding corresponding fix operations for 31-quibt and 63-qubit RMQCs and the whole process of realizing logical Hadamard gate with random single-qubit errors for 15-qubit and 31-quibt RMQCs. Results show that correct fix operations can be obtained and fault-tolerant logical Hadamard gates can be realized as expected. With the implementation of the logical Hadamard gate, a universal fault-tolerant gate set is achieved in single RMQC.

The process of component security testing can produce massive amounts of monitor logs. Current approaches to detect implicit security exceptions (those which cannot be identified by visual inspection alone) compare correct execution sequences with fixed patterns mined from the execution of sequential patterns in the monitor logs. However, this is not efficient and is not suitable for mining large monitor logs. To enable effective mining of implicit security exceptions from large monitor logs, this paper proposes a method based on improved variable-length sequential pattern mining. The proposed method first mines the variable-length sequential patterns from correct execution sequences and from actual execution sequences, thus reducing the number of patterns. The sequential patterns are then detected using the Sunday string-searching algorithm. We conducted an experimental study based on this method, the results of which show that the proposed method can efficiently detect the implicit security exceptions of components.