IDEA: Interactive DoublE Attentions from Label Embedding for Text Classification

Current text classification methods typically encode the text merely into embedding before a naive or complicated classifier, which ignores the suggestive information contained in the label text. As a matter of fact, humans classify documents primarily based on the semantic meaning of the subcategories. We propose a novel model structure via siamese BERT and interactive double attentions named IDEA ( Interactive DoublE Attentions) to capture the information exchange of text and label names. Interactive double attentions enable the model to exploit the inter-class and intra-class information from coarse to fine, which involves distinguishing among all labels and matching the semantical subclasses of ground truth labels. Our proposed method outperforms the state-of-the-art methods using label texts significantly with more stable results.Comment: Accepted by ICTAI202

ADBench: Anomaly Detection Benchmark

Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data? In this work, we answer these key questions by conducting (to our best knowledge) the most comprehensive anomaly detection benchmark with 30 algorithms on 57 benchmark datasets, named ADBench. Our extensive experiments (98,436 in total) identify meaningful insights into the role of supervision and anomaly types, and unlock future directions for researchers in algorithm selection and design. With ADBench, researchers can easily conduct comprehensive and fair evaluations for newly proposed methods on the datasets (including our contributed ones from natural language and computer vision domains) against the existing baselines. To foster accessibility and reproducibility, we fully open-source ADBench and the corresponding results.Comment: NeurIPS 2022. All authors contribute equally and are listed alphabetically. Code available at https://github.com/Minqi824/ADBenc

ADGym: Design Choices for Deep Anomaly Detection

Deep learning (DL) techniques have recently found success in anomaly detection (AD) across various fields such as finance, medical services, and cloud computing. However, most of the current research tends to view deep AD algorithms as a whole, without dissecting the contributions of individual design choices like loss functions and network architectures. This view tends to diminish the value of preliminary steps like data preprocessing, as more attention is given to newly designed loss functions, network architectures, and learning paradigms. In this paper, we aim to bridge this gap by asking two key questions: (i) Which design choices in deep AD methods are crucial for detecting anomalies? (ii) How can we automatically select the optimal design choices for a given AD dataset, instead of relying on generic, pre-existing solutions? To address these questions, we introduce ADGym, a platform specifically crafted for comprehensive evaluation and automatic selection of AD design elements in deep methods. Our extensive experiments reveal that relying solely on existing leading methods is not sufficient. In contrast, models developed using ADGym significantly surpass current state-of-the-art techniques.Comment: NeurIPS 2023. The first three authors contribute equally. Code available at https://github.com/Minqi824/ADGy

Label Confusion Learning to Enhance Text Classification Models

Representing the true label as one-hot vector is the common practice in training text classification models. However, the one-hot representation may not adequately reflect the relation between the instance and labels, as labels are often not completely independent and instances may relate to multiple labels in practice. The inadequate one-hot representations tend to train the model to be over-confident, which may result in arbitrary prediction and model overfitting, especially for confused datasets (datasets with very similar labels) or noisy datasets (datasets with labeling errors). While training models with label smoothing can ease this problem in some degree, it still fails to capture the realistic relation among labels. In this paper, we propose a novel Label Confusion Model (LCM) as an enhancement component to current popular text classification models. LCM can learn label confusion to capture semantic overlap among labels by calculating the similarity between instance and labels during training and generate a better label distribution to replace the original one-hot label vector, thus improving the final classification performance. Extensive experiments on five text classification benchmark datasets reveal the effectiveness of LCM for several widely used deep learning classification models. Further experiments also verify that LCM is especially helpful for confused or noisy datasets and superior to the label smoothing method

Location-based Service Composition Algorithm in a Wireless Ad hoc Network

Location is increasingly becoming a crucial criterion of Quality of Service (QoS) in mobile computing environments due to the widespread adoption of location-sensing devices. Although some previous work has studied the service composition protocols based on QoS in ad hoc networks, they do not explicitly take services’ location into account. In this paper, we present a distributed service composition algorithm suitable for ad hoc networks to find the optimal composite service with the lowest cost while satisfying distance constraint. Based on the idea of a source-initiated on-demand protocol, a transmission approach of service request messages is exploited to dynamically discover and compose the appropriate basic services in a service network. To solve the broadcast storm problem of control messages, a message filtering algorithm is proposed to effectively discard the unqualified control messages. Simulation results demonstrate that the proposed algorithm significantly outperforms the traditional algorithms in terms of success rate, cost and control message overhead

The Vascular Endothelial Growth Factors-Expressing Character of Mesenchymal Stem Cells Plays a Positive Role in Treatment of Acute Lung Injury In Vivo

Recently, mesenchymal stem cells (MSC) have been proved to be beneficial in acute respiratory distress syndrome (ARDS). Vascular endothelial growth factor (VEGF) is an important angiogenesis factor that MSC release. However, the precise role of VEGF-expressing character of MSC in the MSC treatment for ARDS remains obscure. Here, we firstly knocked down the gene VEGF in MSC (MSC-ShVEGF) with lentiviral transduction. Then we injected the MSC-ShVEGF to rats with lipopolysaccharide-induced acute lung injury (ALI) via the tail vein. Data showed that MSC transplantation significantly increased VEGF levels in the lung, reduced lung permeability, protected lung endothelium from apoptosis, facilitated VE-cadherin recovery, controlled inflammation, and attenuated lung injury. However, VEGF gene knockdown in MSC led to relatively insufficient VEGF expression in the injured lung and significantly diminished the therapeutic effects of MSC on ALI, suggesting an important role of VEGF-expressing behavior of MSC in the maintenance of VEGF in the lung and the MSC treatment for ALI. Hence, we conclude that MSC restores the lung permeability and attenuates lung injury in rats with ALI in part by maintaining a “sufficient” VEGF level in the lung and the VEGF-expressing character of MSC plays a positive role in the therapeutic effects of MSC on ARDS