Distant Supervised Relation Extraction with Cost-Sensitive Loss

Recently, many researchers have concentrated on distant supervision relation extraction (DSRE). DSRE has solved the problem of the lack of data for supervised learning, however, the data automatically labeled by DSRE has a serious problem, which is class imbalance. The data from the majority class obviously dominates the dataset, in this case, most neural network classifiers will have a strong bias towards the majority class, so they cannot correctly classify the minority class. Studies have shown that the degree of separability between classes greatly determines the performance of imbalanced data. Therefore, in this paper we propose a novel model, which combines class-to-class separability and cost-sensitive learning to adjust the maximum reachable cost of misclassification, thus improving the performance of imbalanced data sets under distant supervision. Experiments have shown that our method is more effective for DSRE than baseline methods.     

MOOC Learner’s Final Grade Prediction Based on an Improved Random Forests Method

Massive Open Online Course (MOOC) has become a popular way of online learning used across the world by millions of people. Meanwhile, a vast amount of information has been collected from the MOOC learners and institutions. Based on the educational data, a lot of researches have been investigated for the prediction of the MOOC learner’s final grade. However, there are still two problems in this research field. The first problem is how to select the most proper features to improve the prediction accuracy, and the second problem is how to use or modify the data mining algorithms for a better analysis of the MOOC data. In order to solve these two problems, an improved random forests method is proposed in this paper. First, a hybrid indicator is defined to measure the importance of the features, and a rule is further established for the feature selection; then, a Clustering-Synthetic Minority Over-sampling Technique (SMOTE) is embedded into the traditional random forests algorithm to solve the class imbalance problem. In experiment part, we verify the performance of the proposed method by using the Canvas Network Person-Course (CNPC) dataset. Furthermore, four well-known prediction methods have been applied for comparison, where the superiority of our method has been proved.     

Social licence: power imbalances and levels of consciousness – two case studies

ABSTRACT

Social licence to operate (SLO) is a term that is finding increasing acceptance in a number of industries. Like all new terms, its precise meaning and implications are still being investigated. Using data from previous studies, this paper offers an analysis of the SLO of two case studies with each study being viewed separately through the grid of a distinct theoretical framework. Case study 1 looks at the development of differential social licence negotiated in the Hamlet of Cambridge Bay, Nunavut, as a result of an impact and benefit agreement negotiated between Tahera Diamond Corp. and the Kitikmeot Inuit Organisation in 2004. The study demonstrates that general acceptance of resource development can be uneven and reflect an unequal distribution of decision-making power. In case study 2, stakeholders of a failed mineral development project were queried across time about the specifics of the proposed mine development and were queried about resource development across different levels of consciousness. Perhaps, SLO is variable across different levels of consciousness. The paper concludes with observations about the variable nature of SLO acceptance across populations and across levels of consciousness within individuals. Perhaps, the concept of SLO is, in fact, complex, difficult to define and measure and, at this point, of limited utility as a measure of resource development acceptance.     

Analysis of robust optimization for decentralized microgrid energy management under uncertainty

The present paper provides an extended analysis of a microgrid energy management framework based on Robust Optimization (RO). Uncertainties in wind power generation and energy consumption are described in the form of Prediction Intervals (PIs), estimated by a Non-dominated Sorting Genetic Algorithm (NSGA-II) – trained Neural Network (NN). The framework is tested and exemplified in a microgrid formed by a middle-size train station (TS) with integrated photovoltaic power production system (PV), an urban wind power plant (WPP) and a surrounding residential district (D). The system is described by Agent-Based Modelling (ABM): each stakeholder is modeled as an individual agent, which aims at a specific goal, either of decreasing its expenses from power purchasing or increasing its revenues from power selling. The aim of this paper is to identify which is the uncertainty level associated to the “extreme” conditions upon which robust management decisions perform better than a microgrid management based on expected values. This work shows how the probability of occurrence of some specific uncertain events, e.g., failures of electrical lines and electricity demand and price peaks, highly conditions the reliability and performance indicators of the microgrid under the two optimization approaches: (i) RO based on the PIs of the uncertain parameters and (ii) optimization based on expected values.     

宽带数字接收机IQ不平衡估计与自适应补偿算法

针对宽带调制解调技术中广泛存在的IQ不平衡问题,在IQ不平衡模型及补偿原理的分析基础上提出了一种宽带数字接收机IQ不平衡估计与自适应补偿算法。首先利用解调数据对IQ不平衡参数进行实时估计,然后利用估计参数对接收信号进行自适应补偿。实验结果表明,所提算法可以有效解决宽带调制解调系统中普遍存在的IQ不平衡问题,提升系统误码性能。     

Research on image segmentation of Miao costume patterns based on the RSKP-UNet model北大核心CSCD

The Miao nationality is the sixth largest ethnic group in China which has a history of thousands of years. It has created a unique material culture and spiritual culture in its development process and the Miao costume is a highly condensed collection of the material and spiritual culture of the Miao nationality. As one of the unique symbols of Miao culture the Miao costume has profound cultural heritage and cultural connotations. The patterns of the Miao costume are particularly eye-catching as they not only symbolize the wisdom of the Miao people in thousands of years of production and life but also symbolize the pursuit of the good spirit of the Miao people. However under the impact of modern pop culture and foreign culture these cultural symbols are gradually disappearing. In order to protect and inherit them the Miao costume pattern segmentation has become the most important work. However the Miao costume pattern segmentation is quite difficult. At present there are few studies on the extraction classification identification and preservation of the features of Miao costume pattern segmentation. With the excellent segmentation performance of the U-Net model and the advantages of easy deployment the paper improves the basic structure of the U-Net model and proposes a Miao costume pattern segmentation algorithm based on the RSKP-UNet Residual Selective-Kernel Parallel U-Net model. The algorithm adds Residual modules in the encoder part of the U-Net model to improve the feature extraction capability of the model and embeds the SKNet modules and ParNet modules in the decoder part to enhance the feature expression capability of the model. The paper uses evaluation metrics to measure the segmentation performance of the model and compares it with four segmentation models based on deep learning. The paper not only combines the current research focus-deep learning and attention mechanism but also introduces the Lovász-hinge loss function to effectively solve the problem of class imbalance in the Miao costume patterns. The RSKP-UNet model is better than other models in various segmentation indicators. Compared with the benchmark model U-Net the Dice coefficient IoU precision recall and accuracy are improved by 6. 98% 11. 07% 2. 89% 6. 75% and 3. 92% . The segmentation algorithm proposed in this paper realizes the extraction of the element content of the Miao costume patterns through image segmentation of Miao costume patterns which can be used to build the Miao costume pattern database in this way thus helping designers relevant researchers and organizations to provide research foundation and completing the protection and inheritance of the Miao costume culture. The paper also provides some reference for the segmentation research of other minority costume patterns. © 2022 Authors. All rights reserved.     

工程造价控制过程中的不平衡报价及其对策

就承包商通常采用的不平衡报价法进行投标报价做了分析探讨，并从不平衡报价的形式，操作模式、严重不平衡报价的界定等方面进行了阐述，最后对如何合理限制不平衡报价的制度管理体系提出了相应措施。     

不平衡推力法与Sarma法的讨论

不平衡推力法是我国独创的边坡稳定性分析方法,在滑坡的分析治理中得到了广泛应用,然而对其精度的分析却廖廖无几。目前在与其他方法的对比分析中,发现在某些情况下其误差非常大,如果不加限制地使用该方法,可能会给工程带来巨大的隐患。针对该方法存在的问题,通过理论分析和算例比较,认为折线形滑面的计算精度与滑面控制点处滑面倾角的变化密切相关,通过控制该变化角度可以控制其精度,工程中建议将滑面控制点处的倾角变化小于10°作为该方法的使用条件,超过该限制应对滑面进行处理使它满足使用条件或采用其他的分析方法。针对Sarma法所给条间剪力方程存在的问题,给出了一个新的条间剪力方程。新的条间剪力方程不但符合边坡稳定性问题的合理性要求,且能正确表示任意条块的条间剪力。基于新的条间剪力方程对Sarma法进行了改进。改进后的Sarma法认为条块界面和滑动面上的剪切强度具有不同折减系数。算例分析表明,改进后的Sarma法优于Sarma法,应用它能够得到更合理的安全系数值,即使条块形状是变化的,也能够给出一致的安全系数值,而且能够保证迭代过程稳定收敛。     

工程量清单计价如何控制不平衡报价

文章对在工程清单计价方式下从设计、招投标等阶段如何控制不平衡报价进行了探讨。     

An efficient Western Energy Imbalance Market with conflicting carbon policies

A reform of the Western Energy Imbalance Market should target the right problem. Import leakage is a problem; resource shuffling is a solution. Proposed modifications for the existing EIM design target the wrong problem and would work at cross purposes to the very reasons for the EIM’s existence. There is a better approach that would address the right problem and preserve the critical elements of the existing EIM design.