Weakly-Supervised Neural Text Classification

Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this paper, we propose a weakly-supervised method that addresses the lack of training data in neural text classification. Our method consists of two modules: (1) a pseudo-document generator that leverages seed information to generate pseudo-labeled documents for model pre-training, and (2) a self-training module that bootstraps on real unlabeled data for model refinement. Our method has the flexibility to handle different types of weak supervision and can be easily integrated into existing deep neural models for text classification. We have performed extensive experiments on three real-world datasets from different domains. The results demonstrate that our proposed method achieves inspiring performance without requiring excessive training data and outperforms baseline methods significantly.Comment: CIKM 2018 Full Pape

Perceptions of Information Systems Security Compliance: An Empirical Study in Higher Education Setting

Ensuring information systems security policy compliance is an integral part of the security program of any organization. This paper investigated the perceptions of different stakeholder groups towards information security policy compliance constructs of Unified Model of Information Security Compliance (UMISPC) [1] in a higher education environment. The research findings showed that faculty/staff generally has higher tendency towards security policy compliance comparing to students in a higher education institution. In addition, students with security knowledge are more incline to have security policy compliance activities. Our finding not only added to the knowledge base of information systems security compliance research, but also offers practical implications

Performance Limits and Geometric Properties of Array Localization

Location-aware networks are of great importance and interest in both civil and military applications. This paper determines the localization accuracy of an agent, which is equipped with an antenna array and localizes itself using wireless measurements with anchor nodes, in a far-field environment. In view of the Cram\'er-Rao bound, we first derive the localization information for static scenarios and demonstrate that such information is a weighed sum of Fisher information matrices from each anchor-antenna measurement pair. Each matrix can be further decomposed into two parts: a distance part with intensity proportional to the squared baseband effective bandwidth of the transmitted signal and a direction part with intensity associated with the normalized anchor-antenna visual angle. Moreover, in dynamic scenarios, we show that the Doppler shift contributes additional direction information, with intensity determined by the agent velocity and the root mean squared time duration of the transmitted signal. In addition, two measures are proposed to evaluate the localization performance of wireless networks with different anchor-agent and array-antenna geometries, and both formulae and simulations are provided for typical anchor deployments and antenna arrays.Comment: to appear in IEEE Transactions on Information Theor

Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion

Given a small set of seed entities (e.g., ``USA'', ``Russia''), corpus-based set expansion is to induce an extensive set of entities which share the same semantic class (Country in this example) from a given corpus. Set expansion benefits a wide range of downstream applications in knowledge discovery, such as web search, taxonomy construction, and query suggestion. Existing corpus-based set expansion algorithms typically bootstrap the given seeds by incorporating lexical patterns and distributional similarity. However, due to no negative sets provided explicitly, these methods suffer from semantic drift caused by expanding the seed set freely without guidance. We propose a new framework, Set-CoExpan, that automatically generates auxiliary sets as negative sets that are closely related to the target set of user's interest, and then performs multiple sets co-expansion that extracts discriminative features by comparing target set with auxiliary sets, to form multiple cohesive sets that are distinctive from one another, thus resolving the semantic drift issue. In this paper we demonstrate that by generating auxiliary sets, we can guide the expansion process of target set to avoid touching those ambiguous areas around the border with auxiliary sets, and we show that Set-CoExpan outperforms strong baseline methods significantly.Comment: WWW 202

Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers

Instead of relying on human-annotated training samples to build a classifier, weakly supervised scientific paper classification aims to classify papers only using category descriptions (e.g., category names, category-indicative keywords). Existing studies on weakly supervised paper classification are less concerned with two challenges: (1) Papers should be classified into not only coarse-grained research topics but also fine-grained themes, and potentially into multiple themes, given a large and fine-grained label space; and (2) full text should be utilized to complement the paper title and abstract for classification. Moreover, instead of viewing the entire paper as a long linear sequence, one should exploit the structural information such as citation links across papers and the hierarchy of sections and paragraphs in each paper. To tackle these challenges, in this study, we propose FUTEX, a framework that uses the cross-paper network structure and the in-paper hierarchy structure to classify full-text scientific papers under weak supervision. A network-aware contrastive fine-tuning module and a hierarchy-aware aggregation module are designed to leverage the two types of structural signals, respectively. Experiments on two benchmark datasets demonstrate that FUTEX significantly outperforms competitive baselines and is on par with fully supervised classifiers that use 1,000 to 60,000 ground-truth training samples.Comment: 12 pages; Accepted to KDD 2023 (Code: https://github.com/yuzhimanhua/FUTEX

Modified Technique of Pancreaticogastrostomy for Soft Pancreas with Two Continuous Hemstitch Sutures: A Single-Center Prospective Study

Postoperative pancreatic fistula (POPF) remains a persistent problem after pancreaticoduodenectomy (PD), especially in the presence of a soft, nonfibrotic pancreas. To reduce the risk of POPF, pancreaticogastrostomy (PG) is an optional reconstruction technique for surgeons after PD. This study presents a new technique of PG for a soft, nonfibrotic pancreas with double-binding continuous hemstitch sutures and evaluates its safety and reliability. From January 2011 to June 2012, 92 cases of patients with periampullary malignancy with a soft pancreas underwent this technique. A modified technique of PG was performed with two continuous hemstitch sutures placed in the mucosal and seromuscular layers of the posterior gastric wall, respectively. Then the morbidity and mortality was calculated. This technique was applied in 92 patients after PD all with soft pancreas. The median time for the anastomosis was 12 min (range, 8–24). Operative mortality was zero, and morbidity was 16.3 % (n = 15), including hemorrhage (n = 2), biliary fistula (n = 2), pulmonary infection (n = 1), delayed gastric emptying (DGE; n = 5, 5.4 %), abdominal abscess (n = 3, one caused by PF), and POPF (n = 2, 2.2 %). Two patients developed a pancreatic fistula (one type A and one type B) classified according to the International Study Group on Pancreatic Fistula. The described technique is a simple and safe reconstruction procedure after PD, especially for patients with a soft and fragile pancreas. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11605-013-2183-8) contains supplementary material, which is available to authorized users

Therapeutic potential of Erxian decoction and its special chemical markers in depression: a review of clinical and preclinical studies

The increasing prevalence of depression is a major societal burden. The etiology of depression involves multiple mechanisms. Thus, the outcomes of the currently used treatment for depression are suboptimal. The anti-depression effects of traditional Chinese medicine (TCM) formulations have piqued the interest of the scientific community owing to their multi-ingredient, multi-target, and multi-link characteristics. According to the TCM theory, the functioning of the kidney is intricately linked to that of the brain. Clinical observations have indicated the therapeutic potential of the kidney-tonifying formula Erxian Decoction (EXD) in depression. This review aimed to comprehensively search various databases to summarize the anti-depression effects of EXD, explore the underlying material basis and mechanisms, and offer new suggestions and methods for the clinical treatment of depression. The clinical and preclinical studies published before 31 August 2023, were searched in PubMed, Google Scholar, China National Knowledge Infrastructure, and Wanfang Database. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Clinical studies have demonstrated that EXD exhibits therapeutic properties in patients with menopausal depression, postpartum depression, and maintenance hemodialysis-associated depression. Meanwhile, preclinical studies have reported that EXD and its special chemical markers exert anti-depression effects by modulating monoamine neurotransmitter levels, inhibiting neuroinflammation, augmenting synaptic plasticity, exerting neuroprotective effects, regulating the hypothalamic-pituitary-adrenal axis, promoting neurogenesis, and altering cerebrospinal fluid composition. Thus, the anti-depression effects of EXD are mediated through multiple ingredients, targets, and links. However, further clinical and animal studies are needed to investigate the anti-depression effects of EXD and the underlying mechanisms and offer additional evidence and recommendations for its clinical application. Moreover, strategies must be developed to improve the quality control of EXD. This review provides an overview of EXD and guidance for future research direction

Fine-grained Audible Video Description

We explore a new task for audio-visual-language modeling called fine-grained audible video description (FAVD). It aims to provide detailed textual descriptions for the given audible videos, including the appearance and spatial locations of each object, the actions of moving objects, and the sounds in videos. Existing visual-language modeling tasks often concentrate on visual cues in videos while undervaluing the language and audio modalities. On the other hand, FAVD requires not only audio-visual-language modeling skills but also paragraph-level language generation abilities. We construct the first fine-grained audible video description benchmark (FAVDBench) to facilitate this research. For each video clip, we first provide a one-sentence summary of the video, ie, the caption, followed by 4-6 sentences describing the visual details and 1-2 audio-related descriptions at the end. The descriptions are provided in both English and Chinese. We create two new metrics for this task: an EntityScore to gauge the completeness of entities in the visual descriptions, and an AudioScore to assess the audio descriptions. As a preliminary approach to this task, we propose an audio-visual-language transformer that extends existing video captioning model with an additional audio branch. We combine the masked language modeling and auto-regressive language modeling losses to optimize our model so that it can produce paragraph-level descriptions. We illustrate the efficiency of our model in audio-visual-language modeling by evaluating it against the proposed benchmark using both conventional captioning metrics and our proposed metrics. We further put our benchmark to the test in video generation models, demonstrating that employing fine-grained video descriptions can create more intricate videos than using captions.Comment: accpeted to CVPR 2023, Xuyang Shen, Dong Li and Jinxing Zhou contribute equally, code link: github.com/OpenNLPLab/FAVDBench, dataset link: www.avlbench.opennlplab.c