Agent oriented fault detection, isolation and recovery and aspect-oriented plug-and-play tracking mechanism

Fault detection, isolation, and recovery are some of the most critical activities in which astronauts and flight controllers participate. Recent systems to perform the FDIR activity lack portability and extensibility, and do not provide any explanation of the system's activity. In this research, we explore the use of an agent-oriented paradigm and Java technology for better performance of FDIR activity. Also, we have explored the use of explanation in agent-oriented systems, and designed a system-activity tracking mecha-nism that helps the user to understand the agents' behavior. We have explored different ways to generalize this mechanism for arbitrary agent systems to use. Furthermore, we studied mechanisms to automatically add the tracking mechanism to an existing agent system. By using AspectJ, an aspect-oriented tool, a plug-and-playable tracking system has been built that can add the capability to track the activity of the system to any JACK agent system easily. Our experience can help further research on using aspect-oriented tools with agent-oriented paradigms together to obtain better performance

An Online Sparse Streaming Feature Selection Algorithm

Online streaming feature selection (OSFS), which conducts feature selection in an online manner, plays an important role in dealing with high-dimensional data. In many real applications such as intelligent healthcare platform, streaming feature always has some missing data, which raises a crucial challenge in conducting OSFS, i.e., how to establish the uncertain relationship between sparse streaming features and labels. Unfortunately, existing OSFS algorithms never consider such uncertain relationship. To fill this gap, we in this paper propose an online sparse streaming feature selection with uncertainty (OS2FSU) algorithm. OS2FSU consists of two main parts: 1) latent factor analysis is utilized to pre-estimate the missing data in sparse streaming features before con-ducting feature selection, and 2) fuzzy logic and neighborhood rough set are employed to alleviate the uncertainty between estimated streaming features and labels during conducting feature selection. In the experiments, OS2FSU is compared with five state-of-the-art OSFS algorithms on six real datasets. The results demonstrate that OS2FSU outperforms its competitors when missing data are encountered in OSFS

R.: Active algorithm selection

Abstract Most previous studies on active learning focused on the problem of model selection, i.e., how to identify the optimal classification model from a family of predefined models using a small, carefully selected training set. In this paper, we address the problem of active algorithm selection. The goal of this problem is to efficiently identify the optimal learning algorithm for a given dataset from a set of algorithms using a small training set. In this study, we present a general framework for active algorithm selection by extending the idea of the Hedge algorithm. It employs the worst case analysis to identify the example that can effectively increase the weighted loss function defined in the Hedge algorithm. We further extend the framework by incorporating the correlation information among unlabeled examples to accurately estimate the change in the weighted loss function, and Maximum Entropy Discrimination to automatically determine the combination weights used by the Hedge algorithm. Our empirical study with the datasets of WCCI 2006 performance prediction challenge shows promising performance of the proposed framework for active algorithm selection

Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical Distillation

Large-scale pre-trained language models (PLMs) have shown great potential in natural language processing tasks. Leveraging the capabilities of PLMs to enhance automatic speech recognition (ASR) systems has also emerged as a promising research direction. However, previous works may be limited by the inflexible structures of PLMs and the insufficient utilization of PLMs. To alleviate these problems, we propose the hierarchical knowledge distillation (HKD) on the continuous integrate-and-fire (CIF) based ASR models. To transfer knowledge from PLMs to the ASR models, HKD employs cross-modal knowledge distillation with contrastive loss at the acoustic level and knowledge distillation with regression loss at the linguistic level. Compared with the original CIF-based model, our method achieves 15% and 9% relative error rate reduction on the AISHELL-1 and LibriSpeech datasets, respectively.Comment: Accepted by INTERSPEECH 202

DMRM: A Dual-channel Multi-hop Reasoning Model for Visual Dialog

Visual Dialog is a vision-language task that requires an AI agent to engage in a conversation with humans grounded in an image. It remains a challenging task since it requires the agent to fully understand a given question before making an appropriate response not only from the textual dialog history, but also from the visually-grounded information. While previous models typically leverage single-hop reasoning or single-channel reasoning to deal with this complex multimodal reasoning task, which is intuitively insufficient. In this paper, we thus propose a novel and more powerful Dual-channel Multi-hop Reasoning Model for Visual Dialog, named DMRM. DMRM synchronously captures information from the dialog history and the image to enrich the semantic representation of the question by exploiting dual-channel reasoning. Specifically, DMRM maintains a dual channel to obtain the question- and history-aware image features and the question- and image-aware dialog history features by a mulit-hop reasoning process in each channel. Additionally, we also design an effective multimodal attention to further enhance the decoder to generate more accurate responses. Experimental results on the VisDial v0.9 and v1.0 datasets demonstrate that the proposed model is effective and outperforms compared models by a significant margin.Comment: Accepted at AAAI 202

Inhibitory effects of Jasminum grandiflorum L. essential oil on lipopolysaccharide-induced microglia activation-integrated characteristic analysis of volatile compounds, network pharmacology, and BV-2 cell

Neuroinflammation is considered to have a prominent role in the pathogenesis of Alzheimer’s disease (AD). Microglia are the resident macrophages of the central nervous system, and modulating microglia activation is a promising strategy to prevent AD. Essential oil of Jasminum grandiflorum L. flowers is commonly used in folk medicine for the relief of mental pressure and disorders, and analyzing the volatile compound profiles and evaluating the inhibitory effects of J. grandiflorum L. essential oil (JGEO) on the excessive activation of microglia are valuable for its application. This study aims to explore the potential active compounds in JGEO for treating AD by inhibiting microglia activation-integrated network pharmacology, molecular docking, and the microglia model. A headspace solid-phase microextraction combined with the gas chromatography–mass spectrometry procedure was used to analyze the volatile characteristics of the compounds in J. grandiflorum L. flowers at 50°C, 70°C, 90°C, and 100°C for 50 min, respectively. A network pharmacological analysis and molecular docking were used to predict the key compounds, key targets, and binding energies based on the detected compounds in JGEO. In the lipopolysaccharide (LPS)-induced BV-2 cell model, the cells were treated with 100 ng/mL of LPS and JGEO at 7.5, 15.0, and 30 μg/mL, and then, the morphological changes, the production of nitric oxide (NO) and reactive oxygen species, and the expressions of tumor necrosis factor-α, interleukin-1β, and ionized calcium-binding adapter molecule 1 of BV-2 cells were analyzed. A total of 34 compounds with significantly different volatilities were identified. α-Hexylcinnamaldehyde, nerolidol, hexahydrofarnesyl acetone, dodecanal, and decanal were predicted as the top five key compounds, and SRC, EGFR, VEGFA, HSP90AA1, and ESR1 were the top five key targets. In addition, the binding energies between them were less than −3.9 kcal/mol. BV-2 cells were activated by LPS with morphological changes, and JGEO not only could clearly reverse the changes but also significantly inhibited the production of NO and reactive oxygen species and suppressed the expressions of tumor necrosis factor-α, interleukin-1β, and ionized calcium-binding adapter molecule 1. The findings indicate that JGEO could inhibit the overactivation of microglia characterized by decreasing the neuroinflammatory and oxidative stress responses through the multi-compound and multi-target action modes, which support the traditional use of JGEO in treating neuroinflammation-related disorders