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.

Wind farms generate electricity from wind, offering a sustainable and eco-friendly power source.In wind farms, determining the positions of wind turbines is crucial for high energy production due to complex wake interactions.However, optimizing wind farm layouts for given wind conditions remains challenging.While existing methods try to solve the problem by training a surrogate model and employing optimization algorithms based on the model, these methods require a large number of simulations to obtain highly productive layouts, which is time-consuming in large-scale tasks.Furthermore, they mostly yield less diverse layouts, making the deployment of such layouts into real-world scenarios difficult.To address these challenges, we introduce a novel conditional generative modeling approach to find wind farm layouts that maximize energy production.Our method consists of four stages.First, we collect datasets that consist of layouts and their corresponding annual energy production (AEP).Then, we train a diffusion model conditioned on AEP and wind scenarios using the collected dataset.Specifically, we use a Graph Neural Network as the backbone for the denoising network to ensure permutation invariance.Next, we sample promising layouts from the trained diffusion model by conditioning with a high AEP and given wind scenario.We also introduce a local search algorithm, which enables us to adjust layouts that violate design constraints.Finally, we evaluate the generated layouts and augment the dataset.We repeat these processes iteratively to further optimize the layouts.Throughout these processes, we effectively generate highly productive and diverse wind farm layouts in a sample-efficient manner.Experimental results demonstrate that our approach outperforms state-of-the-art methods in maximizing energy production across various scenarios.

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.

ChronoGraph is a novel system enabling temporal graph traversals. Compared to snapshot-oriented systems, this traversal-oriented system is suitable for analyzing information diffusion over time without violating a time constraint on temporal paths. The cornerstone of ChronoGraph aims at bridging the chasm between point-based semantics and period-based semantics and the gap between temporal graph traversals and static graph traversals. Therefore, our graph model and traversal language provide the temporal syntax for both semantics, and we present a method converting point-based semantics to period-based ones. Also, ChronoGraph exploits the temporal support and parallelism to handle the temporal degree, which explosively increases compared to static graphs. We demonstrate how three traversal recipes can be implemented on top of our system: temporal breadth-first search (tBFS), temporal depth-first search (tDFS), and temporal single source shortest path (tSSSP). According to our evaluation, our temporal support and parallelism enhance temporal graph traversals in terms of convenience and efficiency. Also, ChronoGraph outperforms existing property graph databases in terms of temporal graph traversals. We prototype ChronoGraph by extending Tinkerpop, a de facto standard for property graphs. Therefore, we expect that our system would be readily accessible to existing property graph users.

ChronoGraph is a novel system enabling temporal graph traversals. Compared to snapshot-oriented systems, this traversal-oriented system is suitable for analyzing information diffusion over time without violating a time constraint on temporal paths. The cornerstone of ChronoGraph aims at bridging the chasm between point-based semantics and period-based semantics and the gap between temporal graph traversals and static graph traversals. Therefore, our graph model and traversal language provide the temporal syntax for both semantics, and we present a method converting point-based semantics to period-based ones. Also, ChronoGraph exploits the temporal support and parallelism to handle the temporal degree, which explosively increases compared to static graphs. We demonstrate how three traversal recipes can be implemented on top of our system: temporal breadth-first search (tBFS), temporal depth-first search (tDFS), and temporal single source shortest path (tSSSP). According to our evaluation, our temporal support and parallelism enhance temporal graph traversals in terms of convenience and efficiency. Also, ChronoGraph outperforms existing property graph databases in terms of temporal graph traversals. We prototype ChronoGraph by extending Tinkerpop, a de facto standard for property graphs. Therefore, we expect that our system would be readily accessible to existing property graph users.

In the EPCglobal standards for RFID architecture frameworks and interfaces, the Electronic Product Code Information System (EPCIS) acts as a standard repository storing event and master data that are well suited to Supply Chain Management (SCM) applications. Oliot-EPCIS broadens its scope to a wider range of IoT applications in a scalable and flexible way to store a large amount of heterogeneous data from a variety of sources. However, this expansion poses data security challenge for IoT applications including patients' ownership of events generated in mobile healthcare services. Thus, in this paper we propose Secure-EPCIS to deal with security issues of EPCIS for IoT applications. We have analyzed the requirements for Secure-EPCIS based on real-world scenarios and designed access control model accordingly. Moreover, we have conducted extensive performance comparisons between EPCIS and Secure-EPCIS in terms of response time and throughput, and provide the solution for performance degradation problem in Secure-EPCIS.