Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning based optimization (TLBO), which is known as RSTLBO. The framework to develop the proposed RSTLBO includes numerous stages: (1) acquiring the standard datasets (user reviews of six major U.S. airlines) which are used to validate search result feature selection methods, (2) pre-processing of the dataset using text processing methods. This involves applying text processing methods from natural language processing techniques, combined with linguistic processing techniques to produce high classification results, (3) employing the RSTLBO method, and (4) using the selected features from the previous process for sentiment classification using the Support Vector Machine (SVM) technique. Results show an improvement in sentiment analysis when combining natural language processing with linguistic processing for text processing. More importantly, the proposed RSTLBO feature selection algorithm is able to produce an improved sentiment analysis.     

结合预训练模型和语言知识库的文本匹配方法

针对文本匹配任务,该文提出一种大规模预训练模型融合外部语言知识库的方法。该方法在大规模预训练模型的基础上,通过生成基于WordNet的同义—反义词汇知识学习任务和词组—搭配知识学习任务引入外部语言学知识。进而,与MT-DNN多任务学习模型进行联合训练,以进一步提高模型性能。最后利用文本匹配标注数据进行微调。在MRPC和QQP两个公开数据集的实验结果显示,该方法可以在大规模预训练模型和微调的框架基础上,通过引入外部语言知识进行联合训练有效提升文本匹配性能。     

面向智能客服系统的情感分析技术

该文以阿里小蜜为例,对智能客服系统中的情感分析技术进行比较全面的介绍,包括情感分析算法模型的原理及其在智能客服系统的多个应用场景中的实际落地使用方式和效果分析。智能客服在解决客户高频业务问题的同时,也需要给客户提供多维度的、具有类人能力的助理、导购、语聊和娱乐等服务,提高客户对智能客服机器人的整体满意度。在此过程中,情感分析技术在机器人类人能力建设中起到了至关重要的作用。该文围绕智能客服系统中人机结合的服务形式,从六个维度总结和介绍了情感分析技术在智能客服系统中的应用场景,包括用户情感检测、用户情感安抚、情感生成式语聊、客服服务质检、会话满意度预估和智能人工入口。     

基于Nvivo12 Plus的劳动教育实践基地评定标准分析

劳动教育是培养人全面发展的重要组成部分,劳动教育实践基地评定标准对劳动教育基地建设有着重要的引领指导作用。本文采用Nvivo12 Plus对7个省市的劳动教育实践基地评定标准进行编码和归类分析,发现已设立的评定标准体系具有强调安全运行是劳动教育实践基地建设的重心、设施完备是劳动教育实践基地建设的中心、育人功能是劳动教育实践基地建设的核心等一些共同特征,但也存在如忽视对教学反馈的评价、内容过于笼统不够全面、忽视基地师资力量评价、各省指标趋同缺乏指向性等问题。为更好地发挥评定标准体系的引领作用,应将教育基地的教学反馈纳入评定标准体系,明确将师资力量构成列为评估指标,增加体验新兴劳动形态的教育内容,在评定标准中彰显地方特性,使劳动教育实践基地评定标准体系更好服务于劳动教育发展的需要。     

中国煤矿井下地应力数据库及地应力分布规律

收集了采用井下小孔径水压致裂地应力测量装置获得的煤矿地应力数据，以及其他采用应力解除法、水压致裂法等获得的煤矿地应力数据，共计1 357条。在此基础上建立了“中国煤矿井下地应力数据库”，绘制了中国煤矿矿区地应力分布图。分析了我国煤矿井下地应力分布特征和主要影响因素，取得以下研究成果:① 埋深是影响煤矿井下地应力的重要因素。垂直应力总体上随埋深增加不断增大，但数据存在一定的离散性。最大、最小水平主应力总体上也与埋深呈正相关关系，但数据离散性更大。② 在浅部煤矿，地应力类型主要为逆断型应力状态(σH>σh>σV);在千米深井，主要为正断型应力状态(σV>σH>σh);介于两者之间主要为走滑型应力状态(σH>σV>σh)。③ 水平应力与垂直应力的比值(包括最大、最小水平主应力与垂直应力的比值;平均水平主应力与垂直应力的比值;最大水平主应力与最小水平主应力之差与垂直应力的比值)，埋深越小，这些比值离散性越大，分布范围越广。随着埋深增加，比值的离散性和范围越来越小，并逐渐趋于某一定值。④ 粉砂岩、细砂岩、泥质砂岩和泥岩4类岩性地应力数据统计结果表明，总体上，岩石强度越高，承受的水平应力越大。⑤ 弹性模量较大的岩石，水平应力较高;弹性模量较低的松软破碎岩层，水平应力较低。     

内乡县李沟矽线石矿床地质特征及矿床成因

李沟矽线石矿床位于“豫西南高铝三石成矿带”中,受区域地层、构造及岩浆活动影响显著,属沉积变质矿床。该矿床赋存于古元古界雁岭沟组片岩内,矿床受地层和褶皱构造控制,矿体呈层状产出,成矿地质条件较好。本文在研究前人地质勘查资料的基础上,总结了该矿床的区域及矿区成矿地质条件,并对该地区矽线石矿的矿床成因、找矿标志进行了探讨。笔者认为李沟矽线石矿床及区域地层、构造、岩浆活动构成了“三位一体”的控矿机制,该矿床的周边地区内矽线石矿床(点)分布较多,李沟矽线石矿床及外围地区具有寻找矽线石矿的较好找矿前景。     

Validity of facial features’ geometric measurements for real-time assessment of mental fatigue in construction equipment operators

Operating construction equipment for extended periods of time may lead to mental fatigue and, as a result, an increased risk of human error-related accidents and jeopardized health problems for the operators. Therefore, to limit the risk of accidents and protect operators' wellbeing, their mental fatigue must be monitored reliably and in real time. Recently, many invasive technologies have been employed to alleviate this problem, but they entail the wearing of physical sensors, which may instigate irritation and discomfort. This study proposes a non-invasive mental fatigue monitoring method using geometric measurements of their facial features that does not require the operators to wear sensors on their body. The study further validates the proposed method by comparing it with wearable electroencephalography (EEG) technology to establish its ecological validity for construction equipment operators. To serve the purpose, a one-hour excavator operation by sixteen construction equipment operators was conducted on a construction site. Ground truth, brain activity using wearable EEG, and geometric measurements of facial features were extracted and analyzed at the baseline and every 20 min for one hour. A considerable temporal variation was found in the reported metrics (eye aspect ratio, eye distance, mouth aspect ratio, face area, and head motion) and were significantly correlated with ground truth and EEG metric. Furthermore, the brain visualization pattern obtained from EEG was also associated with the variations in the facial features. The findings of the study reveal that construction equipment operators’ mental fatigue can be monitored non-invasively using geometrical measurements of facial features.     

Rapidly finding CAD features using database optimization

Automatic feature recognition aids downstream processes such as engineering analysis and manufacturing planning. Not all features can be defined in advance; a declarative approach allows engineers to specify new features without having to design algorithms to find them. Naive translation of declarations leads to executable algorithms with high time complexity. Database queries are also expressed declaratively; there is a large literature on optimizing query plans for efficient execution of database queries. Our earlier work investigated applying such technology to feature recognition, using a testbed interfacing a database system (SQLite) to a CAD modeler (CADfix). Feature declarations were translated into SQL queries which are then executed.The current paper extends this approach, using the PostgreSQL database, and provides several new insights: (i) query optimization works quite differently in these two databases, (ii) with care, an approach to query translation can be devised that works well for both databases, and (iii) when finding various simple common features, linear time performance can be achieved with respect to model size, with acceptable times for real industrial models. Further results also show how (i) lazy evaluation can be used to reduce the work performed by the CAD modeler, and (ii) estimating the time taken to compute various geometric operations can further improve the query plan. Experimental results are presented to validate our main conclusions.     

融合分词隐层特征的汉语基本块识别

该文以字为基本标注单位,构建了一种汉语基本块识别的神经网络学习模型。模型联合分词任务的神经网络学习模型与基本块识别任务模型,将分词任务模型中学习得到的隐层特征融入基本块识别的模型中,两模型相互交替优化学习模型参数,并实现了以整句似然函数(而非单字似然函数)作为优化目标的算法。实验结果表明:1)以整句似然函数为优化目标的基本块识别的F值比单字似然情形要高出1.33%,特别是在多字块识别中,其召回率比单字似然情形要高出4.68%;2)融合分词任务模型中的隐层特征的汉语基本块识别模型的结果比不做融合的模型要高出2.17%,说明融合分词隐层特征的交替联合学习方法是有效的。     

Role of activated carbon on micropollutans degradation by ionizing radiation

The objective of this study was to analyze the influence of the presence of activated carbon on the degradation of the triiodinated contrast medium diatrizoate (DTZ) by the simultaneous use of gamma radiation and activated carbon. Four commercial activated carbons (Ceca, Witco, Sorbo, and Merck) with different textural and chemical characteristics were used for this purpose. The percentage DTZ removal obtained was considerably higher with the gamma radiation/activated carbon (GM/AC) system than with radiolysis in the absence of activated carbon, and it depended on the specific activated carbon employed. First, we optimized the amount of activated carbon required to maximize the amount of DTZ removed by the GM/AC system (0.06 g). The degradation constants were higher with the GM/AC system than with radiolysis alone, evidencing a synergic effect that favors pollutant removal. This synergic effect is independent of the textural but not the chemical characteristics of the activated carbon, observing a higher synergic activity for carbons with a higher surface content of oxygen, specifically quinone groups. We also highlight that the synergic effect of the activated carbon requires adsorbent–adsorbate electrostatic interaction and is absent when this interaction is hindered.