Developing an efficient heartbeat monitoring system has become a focal point in numerous healthcare applications. Specifically, in the last few years, heartbeat classification for arrhythmia detection has gained considerable interest from researchers. This paper presents a novel deep representation learning method for the efficient detection of arrhythmic beats. To mitigate the issues associated with the imbalanced data distribution, a novel re-sampling strategy is introduced. Unlike the existing oversampling methods, the proposed technique transforms majority-class samples into minority-class samples with a novel translation loss function. This approach assists the model in learning a more generalized representation of crucially important minority class samples. Moreover, by exploiting an auxiliary feature, an augmented attention module is designed that focuses on the most relevant and target-specific information. We adopted an inter-patient classification paradigm to evaluate the proposed method. The experimental results of this study on the MIT-BIH arrhythmia database clearly indicate that the proposed model with augmented attention mechanism and over-sampling strategy significantly learns a balanced deep representation and improves the classification performance of vital heartbeats.

Computation offloading enables intensive computational tasks in edge computing to be separated into multiple computing resources of the server to overcome hardware limitations. Deep learning derives the inference approach based on the learning approach with a volume of data using a sufficient computing resource. However, deploying the domain-specific inference approaches to edge computing provides intelligent services close to the edge of the networks. In this paper, we propose intelligent edge computing by providing a dynamic inference approach for building environment control. The dynamic inference approach is provided based on the rules engine that is deployed on the edge gateway to select an inference function by the triggered rule. The edge gateway is deployed in the entry of a network edge and provides comprehensive functions, including device management, device proxy, client service, intelligent service and rules engine. The functions are provided by microservices provider modules that enable flexibility, extensibility and light weight for offloading domain-specific solutions to the edge gateway. Additionally, the intelligent services can be updated through offloading the microservices provider module with the inference models. Then, using the rules engine, the edge gateway operates an intelligent scenario based on the deployed rule profile by requesting the inference model of the intelligent service provider. The inference models are derived by training the building user data with the deep learning model using the edge server, which provides a high-performance computing resource. The intelligent service provider includes inference models and provides intelligent functions in the edge gateway using a constrained hardware resource based on microservices. Moreover, for bridging the Internet of Things (IoT) device network to the Internet, the gateway provides device management and proxy to enable device access to web clients.

With the continuous increase in the development and use of artificial intelligence systems and applications, problems due to unexpected operations and errors of artificial intelligence systems have emerged. In particular, the importance of trust analysis and management technology for artificial intelligence systems is continuously growing so that users who desire to apply and use artificial intelligence systems can predict and safely use services. This study proposes trust management requirements for artificial intelligence and a trust management framework based on it. Furthermore, we present challenges for standardization so that trust management technology can be applied and spread to actual artificial intelligence systems. In this paper, we aim to stimulate related standardization activities to develop globally acceptable methodology in order to support trust management for artificial intelligence while emphasizing challenges to be addressed in the future from a standardization perspective.

The Internet of things (IoT) combines different sources of collected data which are processed and analyzed to support smart city applications. Machine learning and deep learning algorithms play a vital role in edge intelligence by minimizing the amount of irrelevant data collected from multiple sources to facilitate these smart city applications. However, the data collected by IoT sensors can often be noisy, redundant, and even empty, which can negatively impact the performance of these algorithms. To address this issue, it is essential to develop effective methods for detecting and eliminating irrelevant data to improve the performance of intelligent IoT applications. One approach to achieving this goal is using data cleaning techniques, which can help identify and remove noisy, redundant, or empty data from the collected sensor data. This paper proposes a deep reinforcement learning (deep RL) framework for IoT sensor data cleaning. The proposed system utilizes a deep Q-network (DQN) agent to classify sensor data into three categories: empty, garbage, and normal. The DQN agent receives input from three received signal strength (RSS) values, indicating the current and two previous sensor data points, and receives reward feedback based on its predicted actions. Our experiments demonstrate that the proposed system outperforms a common time-series-based fully connected neural network (FCDQN) solution, with an accuracy of around 96% after the exploration mode. The use of deep RL for IoT sensor data cleaning is significant because it has the potential to improve the performance of intelligent IoT applications by eliminating irrelevant and harmful data.

Emotion recognition based on electroencephalography (EEG) signals has many potential applications. However, the development of an emotion recognition model for real-world deployment encounters significant setbacks in learning an invariant representation due to data distribution shift. Some prior studies adopted domain adaptation methods to tackle this issue. However, these studies perform marginally to tackle distribution shift in the source data and neglect to focus on the extraction of domain and target-specific features, which are crucial for domain invariant representation learning. In this study, we propose an assisted domain adaptive representation learning (ADARL) method that learns domain invariant and emotion-specific features for recognizing emotions across different subjects. The proposed architecture introduces novel attention modules to segregate domain-specific and emotion-specific feature maps by exploiting the inter-relationship between different channels and bands. The domain-specific residual feature maps (peripheral features) extracted by the proposed attention mechanism are used to assist the domain discriminator that forces the model to learn a generalized representation of EEG signals. To tackle the inter-subject variability in the source data, we introduce a novel class-aware compactness loss function to improve the domain adaptation transfer efficiency. The experiments conducted on three EEG datasets (SEED, SEED-IV, and DEAP) demonstrate that the ADARL method significantly improves emotion recognition performance and efficiently alleviates the domain shift problem by learning a generalized representation of EEG signals.

Computation offloading enables intensive computational tasks to be separated into multiple computing resources for overcoming hardware limitations. Leveraging cloud computing, edge computing can be enabled to apply not only large-scale and personalized data but also intelligent algorithms based on offloading the intelligent models to high-performance servers for working with huge volumes of data in the cloud. In this paper, we propose a getaway-centric Internet of Things (IoT) system to enable the intelligent and autonomous operation of IoT devices in edge computing. In the proposed edge computing, IoT devices are operated by a decision-making model that selects an optimal control factor from multiple intelligent services and applies it to the device. The intelligent services are provided based on offloading multiple intelligent and optimization approaches to the intelligent service engine in the cloud. Therefore, the decision-making model in the gateway is enabled to select the best solution from the candidates. Also, the proposed IoT system provides monitoring and visualization to users through device management based on resource virtualization using the gateway. Furthermore, the gateway interprets scenario profiles to interact with intelligent services dynamically and apply the optimal control factor to the actual device through the virtual resource. For implementing the improved energy optimization using the proposed IoT system, we propose two intelligent models to learn parameters of a user’s residential environment using deep learning and derive the inference models to deploy in the intelligent service engine. The inference models are used for predicting a heater energy consumption that is applied to the heater. The heater updates the environment parameters to reach the user-desired values. Moreover, based on two energy consumption values, the decision-making model brings a smaller value to operate the heater to enable reducing the energy consumption as well as providing a user-desired environment.