A Novel Metadata Based Multi-Label Document Classification Technique

From the beginning,the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies,its growth rate is overwhelming.On a rough estimate,more than thirty thousand research journals have been issuing around four million papers annually on average.Search engines,indexing services,and digital libraries have been searching for such publications over the web.Nevertheless,getting the most relevant articles against the user requests is yet a fantasy.It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification.To overcome this issue,researchers are striving to investigate new techniques for the classification of the research articles especially,when the complete article text is not available(a case of nonopen access articles).The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess,“to what extent metadata-based features can perform in contrast to content-based approaches.”In this regard,novel techniques for investigating multilabel classification have been proposed,developed,and evaluated on metadata such as the Title and Keywords of the articles.The proposed technique has been assessed for two diverse datasets,namely,from the Journal of universal computer science(J.UCS)and the benchmark dataset comprises of the articles published by the Association for computing machinery(ACM).The proposed technique yields encouraging results in contrast to the state-ofthe-art techniques in the literature.

Study on strength properties and soil behaviour type classification of Huanghe River Delta silts based on variable rate piezocone penetration test

Fine-grained silt is widely distributed in the Huanghe River Delta(HRD)in China,and the sedimentary structure is complex,meaning that the clay content in the silt is variable.The piezocone penetration test(CPTu)is the most widely approved in situ test method.It can be used to invert soil properties and interpret soil behavior.To analyse the strength properties of surface sediments in the HRD,this paper evaluated the friction angle and its inversion formula through the CPTu penetration test and monotonic simple shear test and other soil unit experiments.The evaluation showed that the empirical formula proposed by Kulhawy and Mayne had better prediction and inversion effect.The HRD silts with clay contents of 9.2%,21.4%and 30.3%were selected as samples for the CPTu variable rate penetration test.The results show as follows.(1)The effects of the clay content on the tip resistance and the pore pressure of silt under different penetration rates were summarized.The tip resistance Q_t is strongly dependent on the clay content of the silt,the B_(q)value of the silt tends to 0 and is not significantly affected by the change of the CPTu penetration rate.(2)Five soil behavior type classification charts and three soil behavior type indexes based on CPTu data were evaluated.The results show that the soil behavior type classification chart based on soil behavior type index ISBT,the Robertson 2010 behavior type classification chart are more suitable for the silty soil in the HRD.

Word Net-based lexical semantic classification for text corpus analysis

Many text classifications depend on statistical term measures to implement document representation. Such document representations ignore the lexical semantic contents of terms and the distilled mutual information, leading to text classification errors.This work proposed a document representation method, Word Net-based lexical semantic VSM, to solve the problem. Using Word Net,this method constructed a data structure of semantic-element information to characterize lexical semantic contents, and adjusted EM modeling to disambiguate word stems. Then, in the lexical-semantic space of corpus, lexical-semantic eigenvector of document representation was built by calculating the weight of each synset, and applied to a widely-recognized algorithm NWKNN. On text corpus Reuter-21578 and its adjusted version of lexical replacement, the experimental results show that the lexical-semantic eigenvector performs F1 measure and scales of dimension better than term-statistic eigenvector based on TF-IDF. Formation of document representation eigenvectors ensures the method a wide prospect of classification applications in text corpus analysis.

Software for automated classification of probe-based confocal laser endomicroscopy videos of colorectal polyps

AIM:To support probe-based confocal laser endomi-croscopy (pCLE) diagnosis by designing software for the automated classification of colonic polyps. METHODS:Intravenous fluorescein pCLE imaging of colorectal lesions was performed on patients under-going screening and surveillance colonoscopies, followed by polypectomies. All resected specimens were reviewed by a reference gastrointestinal pathologist blinded to pCLE information. Histopathology was used as the criterion standard for the differentiation between neoplastic and non-neoplastic lesions. The pCLE video sequences, recorded for each polyp, were analyzed off-line by 2 expert endoscopists who were blinded to the endoscopic characteristics and histopathology. These pCLE videos, along with their histopathology diagnosis, were used to train the automated classification software which is a content-based image retrieval technique followed by k-nearest neighbor classification. The performance of the off-line diagnosis of pCLE videos established by the 2 expert endoscopists was compared with that of automated pCLE software classification. All evaluations were performed using leave-one-patient- out cross-validation to avoid bias. RESULTS:Colorectal lesions (135) were imaged in 71 patients. Based on histopathology, 93 of these 135 lesions were neoplastic and 42 were non-neoplastic. The study found no statistical significance for the difference between the performance of automated pCLE software classification (accuracy 89.6%, sensitivity 92.5%, specificity 83.3%, using leave-one-patient-out cross-validation) and the performance of the off-line diagnosis of pCLE videos established by the 2 expert endoscopists (accuracy 89.6%, sensitivity 91.4%, specificity 85.7%). There was very low power (< 6%) to detect the observed differences. The 95% confidence intervals for equivalence testing were:-0.073 to 0.073 for accuracy, -0.068 to 0.089 for sensitivity and -0.18 to 0.13 for specificity. The classification software proposed in this study is not a "black box" but an informative tool based on the query by example model that produces, as intermediate results, visually similar annotated videos that are directly interpretable by the endoscopist. CONCLUSION:The proposed software for automated classification of pCLE videos of colonic polyps achieves high performance, comparable to that of off-line diagnosis of pCLE videos established by expert endoscopists.

Geotechnical Identification and Classification of Soils as Flexible Pavement Subgrade of the Section Fongo Tongo-Melong

The present study is inscribed within the framework of the geotechnical characterization of the soils of the Santchou plain, their classification for employment as pavement subgrade, various identification tests were carried out on the samples. The results obtained showed that with a wide range of different grain sizes, the studied soils showed low content in clay grains and dominance of either sand grains or silt grains, this can be explaining how most of these soil are poorly graded. According to the USDA textural classification, the grain size distribution of these soils makes them to be classified as Silty Loam types to Sandy Loam types. Despite of their organic matter content which is less than 10%, according to their respective methylene blue values, the soils studied along the section should be mainly loamy soil of medium plasticity to clayed soil, therefore showing a sensibility of its behavior to variation of water content. That last one is confirmed by the consistency parameters of these soils which show intermediate plasticity to highly plastic. Also, the bearing capacity proposed by these soils at their respective optimum dry densities is relatively small, although most of these experimental CBR values of the studied soils are more important than the ones prescribed by the AASHTO Classification system for A5, A6, and A7 types, and the French Highway Earthworks Manual Classifications system (GTR) for the corresponding A2 and A3 types.

An analysis on the formation mechanism of the distributionof high content of chlorophyll-a in the continentalshelf edge waters of East China Sea

On the basis of the data obtained from the comprehensive Kuroshio surveys in 1987-1988,this paper analyses the oceanographic characteristics in the area (125°-130° E,27°-31° N) of the continental shelf edge of the East China Sea (E. C. S. ) and its adjacent waters and discusses the effects of the Kuroshio front,thermocline and upwelling of the Kuroshio subsurface water on the distribution of standing stock of phytoplankton (chlorophyll-a). The distribution of high content of chlorophylly-a has been detected at 20-50 in depth in the water body on the left side of the Kuroshio front in the continental shelf edge waters of the E. C. S. The high content of chlorophyll-a spreads from the shelf area to the Kuroshio area in the form of a tongue and connects with the maximum layer of subsurface chlorophyll-a of the Kuroshio and pelagic sea. The author considers that the formation of the distribution of high content chlorophyll-a in this area results from the bottom topography and oceanic environment and there are close correlations between the high content of chlorophyll-a and the light-nutrient environment.

A TCAM-based Two-dimensional Prefix Packet Classification Algorithm

Packet classification (PC) has become the main method to support the quality of service and security of network application. And two-dimensional prefix packet classification (PPC) is the popular one. This paper analyzes the problem of ruler conflict, and then presents a TCAMbased two-dimensional PPC algorithm. This algorithm makes use of the parallelism of TCAM to lookup the longest prefix in one instruction cycle. Then it uses a memory image and associated data structures to eliminate the conflicts between rulers, and performs a fast two-dimensional PPC.Compared with other algorithms, this algorithm has the least time complexity and less space complexity.

IMPROVEMENT OF TCAM-BASED PACKET CLASSIFICATION ALGORITHM

The feature of Ternary Content Addressable Memories(TCAMs) makes them particularly attractive for IP address lookup and packet classification applications in a router system. However,the limitations of TCAMs impede their utilization. In this paper,the solutions for decreasing the power consumption and avoiding entry expansion in range matching are addressed. Experimental results demonstrate that the proposed techniques can make some big improvements on the performance of TCAMs in IP address lookup and packet classification.

融合知识图谱的影视视频标签分类算法研究

鉴于视频感知方式的多样性,视频标签层级分类算法均从视觉和文本模态入手,训练联合模型共同推断视频内容。但现有研究通常只适用于粗粒度的分类,针对影视剧名的分类,则需要更加细粒度的识别。提出了一个融合知识图谱的影视视频标签分类算法。首先,使用了基于大规模通用数据训练的多模态预训练模型提取了视觉和文本的特征,训练了一个多任务的视频标签预测模型,得到视频的类型、题材和实体三级标签;通过在多任务学习网络中引入相似性任务提高分类模型训练的难度,使得同类样本特征更加紧密,且更好地表达样本差异。其次,对于最细粒度的实体标签,提出了一个局部注意力头扩展的实体纠错模型,引入外部知识图谱的共现信息对前置模型的预测结果做修正,得到更准确的实体标签预测结果。采集豆瓣的半结构化数据构建了影视知识图谱并对影视视频标签分类模型进行了实证研究。视频标签分类的实验结果表明,首先,基于多任务网络结构,在训练分类任务时加入交叉熵损失函数和相似性损失函数对模型进行共同约束优化了特征表达,在类型、题材、实体标签的Top-1分类准确率上分别提升了3.70%、3.35%和16.57%;其次,针对前置模型的困难样本提出的全局-局部注意力机制模型,在引入了知识图谱信息之后,实体标签的Top-1分类准确率从38.7%提升到45.6%。该研究是使用图片-文本对数据在多模态视频标签分类问题上新的尝试,为少量数据样本情况下的短视频标签分类提供了新的研究思路。

Morphologic characters and element content during development of Pinus tabuliformis seeds

An embryo classification system for Pinus tabuliformis Carr. was established by time-tracing sam- pling and observation of zygotic embryos. The zygotic embryos were divided into nine stages. Key elements of the zygotic embryo and female gametophyte （FG） tissue of P. tabuliformis were analyzed, using inductively coupled plasma-emission spectroscopy. Several elements--includ- ing aluminum, iron, sodium, and copper--are found in both embryo and FG tissue. Boron, phosphorus, magnesium,zinc, and calcium are also required for zygotic embryo development and therefore accumulated. Manganese is selectively excluded from the embryo. The zygotic embryo development needs a low-sodium and high-potassium nutrition proportion. The results of elemental analysis from zygotic embryos and FGs can provide the mineral targets for optimizing the formulation of culture medium for somatic embryogenesis.

EDCMS:A Content Management System for Engineering Documents

Engineers often need to look for the right pieces of information by sifting through long engineering documents, It is a very tiring and time-consuming job. To address this issue, researchers are increasingly devoting their attention to new ways to help information users, including engineers, to access and retrieve document content. The research reported in this paper explores how to use the key technologies of document decomposition （study of document structure）, document mark-up （with EXtensible Mark- up Language （XML）, HyperText Mark-up Language （HTML）, and Scalable Vector Graphics （SVG））, and a facetted classification mechanism. Document content extraction is implemented via computer programming （with Java）. An Engineering Document Content Management System （EDCMS） developed in this research demonstrates that as information providers we can make document content in a more accessible manner for information users including engineers.The main features of the EDCMS system are： 1） EDCMS is a system that enables users, especially engineers, to access and retrieve information at content rather than document level. In other words, it provides the right pieces of information that answer specific questions so that engineers don＇t need to waste time sifting through the whole document to obtain the required piece of information. 2） Users can use the EDCMS via both the data and metadata of a document to access engineering document content. 3） Users can use the EDCMS to access and retrieve content objects, i.e. text, images and graphics （including engineering drawings） via multiple views and at different granularities based on decomposition schemes. Experiments with the EDCMS have been conducted on semi-structured documents, a textbook of CADCAM, and a set of project posters in the Engineering Design domain. Experimental results show that the system provides information users with a powerful solution to access document content.

Exploiting Structural Similarities to Classify Citations

Citations play an important role in the scientific community by assisting in measuring multifarious policies like the impact of journals,researchers,institutions,and countries.Authors cite papers for different reasons,such as extending previous work,comparing their study with the state-of-the-art,providing background of the field,etc.In recent years,researchers have tried to conceptualize all citations into two broad categories,important and incidental.Such a categorization is very important to enhance scientific output in multiple ways,for instance,(1)Helping a researcher in identifying meaningful citations from a list of 100 to 1000 citations(2)Enhancing the impact factor calculation mechanism by more strongly weighting important citations,and(3)Improving researcher,institutional,and university rankings by only considering important citations.All of these uses depend upon correctly identifying the important citations from the list of all citations in a paper.To date,researchers have utilized many features to classify citations into these broad categories:cue phrases,in-text citation counts,and metadata features,etc.However,contemporary approaches are based on identification of in-text citation counts,mapping sections onto the Introduction,Methods,Results,and Discussion(IMRAD)structure,identifying cue phrases,etc.Identifying such features accurately is a challenging task and is normally conducted manually,with the accuracy of citation classification demonstrated in terms of these manually extracted features.This research proposes to examine the content of the cited and citing pair to identify important citing papers for each cited paper.This content similarity approach was adopted from research paper recommendation approaches.Furthermore,a novel section-based content similarity approach is also proposed.The results show that solely using the abstract of the cited and citing papers can achieve similar accuracy as the stateof-the-art approaches.This makes the proposed approach a viable technique that does not depend on manual identification of complex features.

SwCS: Section-Wise Content Similarity Approach to Exploit Scientific Big Data

The growing collection of scientific data in various web repositories is referred to as Scientific Big Data,as it fulfills the four“V’s”of Big Data—volume,variety,velocity,and veracity.This phenomenon has created new opportunities for startups;for instance,the extraction of pertinent research papers from enormous knowledge repositories using certain innovative methods has become an important task for researchers and entrepreneurs.Traditionally,the content of the papers are compared to list the relevant papers from a repository.The conventional method results in a long list of papers that is often impossible to interpret productively.Therefore,the need for a novel approach that intelligently utilizes the available data is imminent.Moreover,the primary element of the scientific knowledge base is a research article,which consists of various logical sections such as the Abstract,Introduction,Related Work,Methodology,Results,and Conclusion.Thus,this study utilizes these logical sections of research articles,because they hold significant potential in finding relevant papers.In this study,comprehensive experiments were performed to determine the role of the logical sections-based terms indexing method in improving the quality of results(i.e.,retrieving relevant papers).Therefore,we proposed,implemented,and evaluated the logical sections-based content comparisons method to address the research objective with a standard method of indexing terms.The section-based approach outperformed the standard content-based approach in identifying relevant documents from all classified topics of computer science.Overall,the proposed approach extracted 14%more relevant results from the entire dataset.As the experimental results suggested that employing a finer content similarity technique improved the quality of results,the proposed approach has led the foundation of knowledge-based startups.

Evaluation of the Effect of Lime Content on the California Bearing Ratio of Silty Soils: Case of Fombap District

The present study is inscribed within the framework of the amelioration of the soils of the Santchou plain for employment as pavement subgrade. The bearing capacity proposed by these soils at their respective optimum dry densities is relatively small, although most of these experimental California Bearing Ratio (CBR) values of the studied soils are more important than the ones prescribed by the American Association of State Highway and Transportation Officials Classification system (AASHTO) for A5, A6, and A7 types. The stabilization of this soils with lime has been chosen to improve the bearing capacity and by association, their resilient modulus. The results of this study show that the increase of lime content is not proportional with the increase of the expected mechanical performances. In fact, the literature explains that when the lime content arrives at an optimum, the mechanical parameters no longer increase, but decrease significantly. After this optimum, the soil stabilization no longer shows advantages in the increase of geo-mechanical properties of soils.

Indexing of Content-Based Image Retrieval System with Image Understanding Approach

This paper presents a novel efficient semantic image classification algorithm for high-level feature indexing of high-dimension image database. Experiments show that the algorithm performs well. The size of the train set and the test set is 7 537 and 5 000 respectively. Based on this theory, another ground is built with 12,000 images, which are divided into three classes: city, landscape and person, the total result of the classifications is 88.92%, meanwhile, some preliminary results are presented for image understanding based on semantic image classification and low level features. The groundtruth for the experiments is built with the images from Corel database, photos and some famous face databases.

非学术部门就业对博士生的质量需求——胜任力分类理论视角下基于招聘信息内容的分析

博士生培养规模的扩大以及学术劳动力市场的萎缩使博士生在非学术部门就业渐成主流。本研究通过搜集1317条非学术部门博士生招聘信息并进行内容分析发现,相较于学术职业,非学术部门就业在知识维度上偏重博士生所掌握知识的情境应用与转化,在能力维度上要求博士生具备专业实践能力、可迁移能力、科研创新能力,在潜力维度上重视博士生“为人”“为事”“为学”的综合品质,期待博士生成为面向应用的高端创新人才与领导型人才。基于胜任力分类理论,本研究建构了领域具体性与任务具体性分类框架。面对非学术就业市场对博士生质量提出的新需求,专业学位博士生教育应当扩大规模,建立需求导向的培养反馈机制,优化面向实践情境的博士生培养模式及评价标准;学术学位博士生教育要强化博士生可迁移能力的“融入式”培养,为其多元化发展创设路径,并通过导师指导提升其非认知能力。

效用视角下核心引文识别方法初探

基于引用频次开展的引文分析忽略了引文对施引文献的效用差异,依此展开资源评估、学术影响力判断难免受到“无效”引用干扰。为排除“无效”引文,提升测度及评价数据质量,文章从引文效用的角度,选择引文属性、功能、对象、情感等典型特征,建立引文标注框架。测试逻辑回归与支持向量机等方式对引文自动分类的效果,择优构建“效用视角下核心引文识别模型”。其中,为探索自动化分类方式,在数据标注阶段,除人工标引,还使用大规模预训练语言模型的对话系统ChatGPT进行自动分类标注,并进行建模效果测试,以期为引文自动分类方法及应用实践提供新思路。

基于UAV-RGB的矿区植物分类及其叶绿素含量时空变化分析

相比于高光谱遥感和多光谱遥感,在无人机上搭载RGB相机,不仅操作简单,而且成本低廉。但是,目前基于无人机RGB相机进行矿区典型植物分类的研究较少。另外,开采沉陷对地表植物叶绿素含量的时空扰动规律尚不清楚。为解决上述问题,本研究融合RGB影像的光谱信息、纹理信息和点云的3D特征,使用神经网络、支持向量机、随机森林3种机器学习分类算法,实现了对采煤沉陷区典型植被的分类。基于多期影像的分类结果和植被指数,分析开采沉陷对典型植物叶绿素含量的时空扰动规律。研究表明,最佳的分类算法为支持向量机。多特征融合可以显著提高分类精度,相比于只用光谱特征,多特征融合后的总体分类精度提高了9.45%。总体分类精度可达90%,Kappa系数为0.906,可满足矿区植被调查的需要。通过分析针茅和柠条叶绿素含量的时空变化,发现采煤对拉伸区植被的影响最大,其次是压缩区和中性区。拉伸区应作为生态修复的重点区域。地裂缝是生态修复的重点对象。与针茅相比,柠条能更好地适应采煤引起的干扰,可作为生态恢复的先锋物种。

植保无人驾驶航空器购置补贴分类分档与补贴额研究

植保无人驾驶航空器作为一种先进、高效率的现代农业装备近几年在我国得到快速发展,2017年首次被试点省市纳入农业机械购置补贴范围。由于补贴政策实施时间较短,存在着许多不合理的因素。整理总结2017年以来各试点省市及农业农村部实施的植保无人驾驶航空器发展、补贴现状,综合相关分类分档政策。在此基础上指出基本配置参数、补贴额设定等方面的问题,分析存在质量与补贴额不对等、基本参数限制过少、分档未突出高端产品、补贴未考虑到偏远地区等现象。最后结合实际情况,指出植保无人驾驶航空器的补贴政策将朝着统一质量鉴定、细化分档参数、突出智能化技术、精准补贴标准及根据区域设置补贴等方面发展。

面向融合出版前沿主题发现的学术论文未来工作句挖掘研究

[目的/意义]近年来,随着传统出版与数字出版的不断融合,形成了融合出版的新兴范式。如何科学准确地把握融合出版领域未来研究趋势具有重要研究意义。学术论文中描述未来研究工作的句子(简称“未来工作句”),不但可以辅助预测未来可能出现的前沿主题,还可为科研工作者、特别是初学者选题提供参考。[方法/过程]对融合出版领域论文中的未来工作句进行人工标注和类别划分,构建未来工作句识别与分类语料库。在此基础上,使用支持向量机、朴素贝叶斯和随机森林三种模型结合SelectKBest特征选择方法,来训练未来工作句自动识别模型。[结果/结论]LinearSVC在未来工作句自动识别任务中表现最为出色,其加权F1值达到92.08%。另外,本文对分类语料库中的未来工作句内容及其类别进行分析,得到融合出版领域未来工作句的类别分布及其变化规律。