5,431 research outputs found
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
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