Discrete hashing with triple supervision learning

In recent years, discrete supervised hashing methods have attracted increasing attention because of their high retrieval efficiency and precision. However, in these methods, some effective semantic information is typically neglected, which means that all the information is not sufficiently utilized. Moreover, these methods often only decompose the first-order features of the original data, ignoring the more fine-grained higher-order features. To address these problems, we propose a supervised hashing learning method called discrete hashing with triple supervision learning (DHTSL). Specifically, we integrate three aspects of semantic information into this method: (1) the bidirectional mapping of semantic labels; (2) pairwise similarity relations; (3) second-order features from the original data. We also design a discrete optimization method to solve the proposed objective function. Moreover, an out-of-sample extension strategy that can better maintain the independence and balance of hash codes is employed to improve retrieval performance. Extensive experiments on three widely used datasets demonstrate its superior performance.     

Multi-factor person entity relation extraction model based on distant supervision

Aiming at the problem that the basic assumption of distant supervision was too strong and easy to produce noise data,a model of the person entity relation extraction which could automatically filter the training data generated by distant supervision was proposed.For training data generation,the data produced by distant supervision would be filtered by multiple instance learning and the method of TF-IDF-based relation keyword detecting,which tried to make the training data has the manual annotation quality.Furthermore,the model combined lexical and syntactic features to extract the effective relation feature vector from two angles of words and semantics for classifier.The experiment results on large scale real-world datasets show that the proposed model outperforms other relation extraction methods which based on distant supervision.     

一种集成式CMC一体机在HFC接入网中的应用

C-DOCSIS相关技术标准的发布极大地促进了其在HFC网络中的部署。但C-DOCSIS系统头端设备覆盖规模和光纤深化的趋势之间存在矛盾。本文从带宽模型、带宽容量、频率资源三个方面进行分析，提出了一种集成式的CMC一体机的设备架构，兼顾了覆盖规模和投资成本，为C-DOCSIS在HFC网络中的大规模部署提供了有效的解决方案。     

RoHS指令在企业中的实施

指导中小型出口企业认识和实施RoHS指令,建立符合RoHS要求的管理体系,实施物质隔离方法,防止符合RoHS的物质受到污染,满足了客户的RoHS工厂审查和第三方RoHS工厂审查,体系有效。     

单压吸收式制冷机中气泡泵稳态工作特性研究

搭建可视化的气泡泵性能实验系统,以常压饱和水为工质,对单压吸收式制冷机中气泡泵的稳态工作过程进行实验研究,就加热功率和沉浸比对气泡泵提升性能的影响进行了分析。实验结果表明,相同沉浸比下,液体的提升量随着加热功率增加而增加,但增加的速率却随加热功率的增加而减缓;较大的沉浸比有利于降低启动加热功率,提高气泡泵的效率;分析结果对气泡泵的优化设计提供了实验依据,对单压吸收式制冷机性能的提高具有重要意义。     

基于元胞自动机的电子商务平台知识产权监管的演化分析

为了系统地探究电子商务平台知识产权监管的复杂动态行为,本文从复杂系统的角度出发,通过元胞自动机构建电子商务平台知识产权监管的模型并进行演化分析。首先给出了元胞的属性和状态,接着在博弈收益矩阵分析的基础上进行电子商务平台知识产权监管的模型构建与仿真,最后通过对仿真结果的分析给出了启示。本文的研究对电子商务平台知识产权监管具有较好的参考意义。     

基于消费者行为的食品溯源信息监管策略研究

溯源信息真实性直接影响食品可溯源体系建设和消费者对食品安全信心。本文基于消费者行为理论,构建溯源信息掺假与不掺假以及掺假比例大小、政府监管力度强弱情形时企业最优定价决策模型。结果表明,无论消费者购买可溯源产品的溯源信息量高低,企业均选择溯源信息掺假,此时政府两种策略:一是加强监管力度和惩罚力度,迫使企业不敢溯源信息掺假;二是提升产品低溯源信息程度,缩小高低溯源信息程度差异,促使企业无可掺假信息空间。通过高低溯源信息程度对企业最优决策影响研究,低监管情形下企业最优决策受到高低溯源信息程度影响较为显著,高监管情形下企业最优决策受其影响较小。     

The advantage of indices of dispersion in queueing approximations

A recent robust queueing approximation for open queueing networks exploits partial characterizations of each arrival process by its rate and index of dispersion for counts (IDC), which is a scaled version of the variance–time curve. Even though only means and variances (as functions of time) are involved, we show that the IDC provides a basis for more accurate approximations than traditional two-moment partial characterizations. For the $GI∕GI∕1$ queue, this approach applied to the arrival and service processes fully characterizes the model.     

谈农网集抄改造后工程状况及安全隐患

本文通过对农网集抄改造后目前工程的具体状况以及存在的安全问题进行分析探讨.