From objects to subjects: Repositioning teachers as policy actors doing policy work

Within policy discourses, teachers are positioned as objects of reform who enact policies under rational systems of observation and accountability and are, therefore, situated as de-professionalized actors lacking expert knowledge. This research project asks relatively straight-forward questions: If practicing teachers were given a voice in political debates over urban education policy and reform, what would they say? What macro-level policy problems would they identify as being important? Findings indicate the primary policy problems include Systemic Inequity and Bad Policy. The primary policy solutions include, Schools as a Community Resource; Shared Decision-Making; Contextual Goals; and Time, Space and Resources.     

Location extraction from tweets

Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. These events are represented by three main dimensions: time, location and entity-related information. The focus of this paper is location, which is an essential dimension for geo-spatial applications, either when helping rescue operations during a disaster or when used for contextual recommendations. While the first type of application needs high recall, the second is more precision-oriented. This paper studies the recall/precision trade-off, combining different methods to extract locations. In the context of short posts, applying tools that have been developed for natural language is not sufficient given the nature of tweets which are generally too short to be linguistically correct. Also bearing in mind the high number of posts that need to be handled, we hypothesize that predicting whether a post contains a location or not could make the location extractors more focused and thus more effective. We introduce a model to predict whether a tweet contains a location or not and show that location prediction is a useful pre-processing step for location extraction. We define a number of new tweet features and we conduct an intensive evaluation. Our findings are that (1) combining existing location extraction tools is effective for precision-oriented or recall-oriented results, (2) enriching tweet representation is effective for predicting whether a tweet contains a location or not, (3) words appearing in a geography gazetteer and the occurrence of a preposition just before a proper noun are the two most important features for predicting the occurrence of a location in tweets, and (4) the accuracy of location extraction improves when it is possible to predict that there is a location in a tweet.     

Meta-Circuit machine: Inferencing human collaborative relationships in heterogeneous information networks

Human collaborative relationship inference is a meaningful task for online social networks and is called link prediction in network science. Real-world networks contain multiple types of interacting components and can be modeled naturally as heterogeneous information networks (HINs). The current link prediction algorithms in HINs fail to effectively extract training samples from snapshots of HINs; moreover, they underutilise the differences between nodes and between meta-paths. Therefore, we propose a meta-circuit machine (MCM) that can learn and fuse node and meta-path features efficiently, and we use these features to inference the collaborative relationships in question-and-answer and bibliographic networks. We first utilise meta-circuit random walks to obtain training samples in which the basic idea is to perform biased meta-path random walks on the input and target network successively and then connect them. Then, a meta-circuit recurrent neural network (mcRNN) is designed for link prediction, which represents each node and meta-path by a dense vector and leverages an RNN to fuse the features of node sequences. Experiments on two real-world networks demonstrate the effectiveness of our framework. This study promotes the investigation of potential evolutionary mechanisms for collaborative relationships and offers practical guidance for designing more effective recommendation systems for online social networks.     

无线传感网络中低碰撞概率协议研究

传感网络经常采用Flooding协议作为路由管理,但碰撞和重传会导致大量重复的数据包,造成有限资源的浪费.讨论了隐藏节点及邻居节点对碰撞的影响,并进行了仿真.结果表明当发射半径达到一定区域时,隐藏节点引起的碰撞数量达到一个峰值,从而可以通过合适的设计避免高碰撞概率的网络分布区域.另外,还可以通过增加传输延迟来减少节点碰撞.     

聚丙烯酰胺与聚苯乙烯组成的亲水-疏水互穿树脂

用分步法合成了交联聚丙烯酸甲酯(PMA)-交联聚苯乙烯(PS)互穿树脂,然后用二乙烯三胺对其胺解,得到由交联聚丙烯酰胺-交联聚苯乙烯组成的亲水-疏水互穿树脂.利用疏水部分(PS)来调整亲水部分(PAM)的溶胀,来分离具有不同体积的酸.     

配电网理论线损计算算法比较

本文归纳和比较了在配电网中理论线损计算的两类常规方法:基于均方根电流原理的近似计算和潮流计算,概括地阐述了几种常见方法的基本原理和优缺点,并在此基础上提出了一种优化算法的思想.     

A cross-layer design approach on spectrum allocation and resource scheduling in cognitive PMP networks

We propose the spectrum allocation and resource scheduling algorithms in cognitive point to multipoint （PMP） networks with rapid changes of spectrum opportunities and present a media access control （MAC） protocol based on these algorithms. The objective of spectrum allocation is to make efficient use of the spectrum while maintaining the transceiver synchronization on frequency and time in the network. The objective of resource scheduling is to guarantee the quality of service （QoS） requirements of different kinds of connections and to minimize the total energy consumption in the network as well. By sensing only a small set of possible channels in each slot based on the state transition probability of each channel, our spectrum allocation algorithm achieves high spectrum efficiency in the network. The resource scheduling problem is divided into three sub problems and we derive optimal solutions to these problems by greedy algorithm and convex optimization. The simulation results show that our algorithm can make efficient use of the spectrum and the network resources at a cost of low computational complexity.     

一个基于朴素贝叶斯方法的RSS分类器

RSS是一种基于RDF／XML描述的全新的互联网信息获取方式。但目前,RSS文档的分类完全依赖于预先设定,对于适应用户多变的需求存在着明显的不足。朴素贝叶斯分类器是解决自然语言文本文档分类问题的最有效的方法之一。通过构造朴素贝叶斯学习器,用户可以构造出符合自己要求的RSS分类结构。实验结果表明,这种基于朴素贝叶斯方法的分类器。可以很好地实现对RSS文档的自动分类。     

Adaptive ant-based routing in wireless sensor networks using Energy*Delay metrics

To find the optimal routing is always an important topic in wireless sensor networks （WSNs）. Considering a WSN where the nodes have limited energy, we propose a novel Energy^＊Delay model based on ant algorithms （＂E＆D ANTS＂ for short） to minimize the time delay in transferring a fixed number of data packets in an energy-constrained manner in one round. Our goal is not only to maximize the lifetime of the network but also to provide real-time data transmission services. However, because of the tradeoff of energy and delay in wireless network systems, the reinforcement learning （RL） algorithm is introduced to train the model. In this survey, the paradigm of E＆D ANTS is explicated and compared to other ant-based routing algorithms like AntNet and AntChain about the issues of routing information, routing overhead and adaptation. Simulation results show that our method performs about seven times better than AntNet and also outperforms AntChain by more than 150% in terms of energy cost and delay per round.