Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification

Aspect-based sentiment classification is a crucial problem in fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspect according to its context. Previous works have made remarkable progress in leveraging attention mechanism to extract opinion words for different aspects. However, a persistent challenge is the effective management of semantic mismatches, which stem from attention mechanisms that fall short in adequately aligning opinions words with their corresponding aspect in multi-aspect sentences. To address this issue, we propose a novel Aspect-oriented Opinion Alignment Network (AOAN) to capture the contextual association between opinion words and the corresponding aspect. Specifically, we first introduce a neighboring span enhanced module which highlights various compositions of neighboring words and given aspects. In addition, we design a multi-perspective attention mechanism that align relevant opinion information with respect to the given aspect. Extensive experiments on three benchmark datasets demonstrate that our model achieves state-of-the-art results. The source code is available at https://github.com/AONE-NLP/ABSA-AOAN.Comment: 8 pages, 5 figure, ECAI 202

A Novel Two-Layer DAG-based Reactive Protocol for IoT Data Reliability in Metaverse

Many applications, e.g., digital twins, rely on sensing data from Internet of Things (IoT) networks, which is used to infer event(s) and initiate actions to affect an environment. This gives rise to concerns relating to data integrity and provenance. One possible solution to address these concerns is to employ blockchain. However, blockchain has high resource requirements, thereby making it unsuitable for use on resource-constrained IoT devices. To this end, this paper proposes a novel approach, called two-layer directed acyclic graph (2LDAG), whereby IoT devices only store a digital fingerprint of data generated by their neighbors. Further, it proposes a novel proof-of-path (PoP) protocol that allows an operator or digital twin to verify data in an on-demand manner. The simulation results show 2LDAG has storage and communication cost that is respectively two and three orders of magnitude lower than traditional blockchain and also blockchains that use a DAG structure. Moreover, 2LDAG achieves consensus even when 49\% of nodes are malicious

Glucocorticosteroid-sensitive inflammatory eosinophilic pseudotumor of the bladder in an adolescent: a case report

<p>Abstract</p> <p>Introduction</p> <p>Inflammatory eosinophilic pseudotumor of the bladder is a rare inflammatory bladder disease. The etiology and pathophysiology of this condition are still unclear. Few case reports have described inflammatory eosinophilic pseudotumor of the bladder in adults or children. Although benign, this disease is occasionally clinically aggressive and locally invasive, thus open surgical removal or complete transurethral resection is recommended.</p> <p>Case presentation</p> <p>We present the case of a biopsy-proven inflammatory eosinophilic pseudotumor of the bladder in a previously healthy 16-year-old male adolescent with 2-month history of frequent micturition and dysuria with no significant apparent causative factors. The tumor regressed after a 6-week course of glucocorticosteroids.</p> <p>Conclusion</p> <p>To the best of our knowledge, our case is a rare case of inflammatory eosinophilic pseudotumor of the bladder treated with complete conservative management. Due to its glucocorticosteroid-sensitive nature, we postulate that this disease belongs to a subgroup of eosinophilic disorders.</p

A Novel Distance between Vague Sets and Its Applications in Decision Making

A novel distance between vague sets (VSs) is presented after the inadequacies of existing distance measures between vague sets are analyzed by artificial vague sets. The proposed method investigates the assignment of degree of hesitation to the membership and nonmembership degree, and the properties are also discussed. The performances of the new method are illustrated by pattern classification problem. Finally, the proposed method is applied into multicriteria fuzzy decision making, where the linear programming method is taken to generate optimal weights for every criterion and the best alternative is obtained by the weighted sum of distance measures between each alternative and the idea alternative with respect to a set of criteria. The experimental results show the effectiveness of the proposed method

KMT2A promotes melanoma cell growth by targeting hTERT signaling pathway.

Melanoma is an aggressive cutaneous malignancy, illuminating the exact mechanisms and finding novel therapeutic targets are urgently needed. In this study, we identified KMT2A as a potential target, which promoted the growth of human melanoma cells. KMT2A knockdown significantly inhibited cell viability and cell migration and induced apoptosis, whereas KMT2A overexpression effectively promoted cell proliferation in various melanoma cell lines. Further study showed that KMT2A regulated melanoma cell growth by targeting the hTERT-dependent signal pathway. Knockdown of KMT2A markedly inhibited the promoter activity and expression of hTERT, and hTERT overexpression rescued the viability inhibition caused by KMT2A knockdown. Moreover, KMT2A knockdown suppressed tumorsphere formation and the expression of cancer stem cell markers, which was also reversed by hTERT overexpression. In addition, the results from a xenograft mouse model confirmed that KMT2A promoted melanoma growth via hTERT signaling. Finally, analyses of clinical samples demonstrated that the expression of KMT2A and hTERT were positively correlated in melanoma tumor tissues, and KMT2A high expression predicted poor prognosis in melanoma patients. Collectively, our results indicate that KMT2A promotes melanoma growth by activating the hTERT signaling, suggesting that the KMT2A/hTERT signaling pathway may be a potential therapeutic target for melanoma