Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have sh...
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Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views(i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named graph pooling contrast(GPS) to address these *** by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts.
Human activity recognition is a crucial domain in computerscience and artificial intelligence that involves the Detection, Classification, and Prediction of human activities using sensor data such as accelerometers, ...
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Human action recognition is applicable in different domains. Previously proposed methods cannot appropriately consider the sequence of sub-actions. Herein, we propose a semantical action model based on the sequence of...
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In an Internet of Things (IoT) assisted Wireless Sensor Network (WSN), the location of the Base Station (BS) remains important. BS serves as the central hub for data collection, aggregation and communication within th...
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Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-...
Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-world problems like the distributed scheduling [4], sensor network management [5], [6], multi-robot coordination [7], and smart grid [8]. However, DCOPs were not well suited to solve the problems with continuous variables and constraint cost in functional form, such as the target tracking sensor orientation [9], the air and ground cooperative surveillance [10], and the sensor network coverage [11].
Background: Human physical activity recognition is challenging in various research eras, such as healthcare, surveillance, senior monitoring, athletics, and rehabilitation. The use of various sensors has attracted out...
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The authors consider the property of detectability of discrete event systems in the presence of sensor attacks in the context of *** authors model the system using an automaton and study the general notion of detectab...
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The authors consider the property of detectability of discrete event systems in the presence of sensor attacks in the context of *** authors model the system using an automaton and study the general notion of detectability where a given set of state pairs needs to be(eventually or periodically)distinguished in any estimate of the state of the *** authors adopt the ALTER sensor attack model from previous work and formulate four notions of CA-detectability in the context of this attack model based on the following attributes:strong or weak;eventual or *** authors present verification methods for strong CA-detectability and weak *** authors present definitions of strong and weak periodic CA-detectability that are based on the construction of a verifier automaton called the augmented *** development also resulted in relaxing assumptions in prior results on D-detectability,which is a special case of CA-detectability.
Disastrous situations pose a formidable challenge, testing our resilience against nature's fury and the race against time to prevent the loss of human life. It is noted that in such situations that Microblogging p...
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Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and t...
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Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.
Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online *** tackle this challenge,our study introduces a new ap...
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Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online *** tackle this challenge,our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers(BERT)base model(cased),originally pretrained in *** model is uniquely adapted to recognize the intricate nuances of Arabic online communication,a key aspect often overlooked in conventional cyberbullying detection *** model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media(SM)tweets showing a notable increase in detection accuracy and sensitivity compared to existing *** results on a diverse Arabic dataset collected from the‘X platform’demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods.E-BERT shows a substantial improvement in performance,evidenced by an accuracy of 98.45%,precision of 99.17%,recall of 99.10%,and an F1 score of 99.14%.The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications,offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.
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