基于D-S证据理论的微博客蕴含交通信息提取方法

微博客消息中经常蕴含大量实时交通信息,有望与现有实时交通信息采集方式形成互补。该文针对微博客消息语义模糊性及用户描述差异性问题,提出了一种微博客消息蕴含交通信息的D-S证据理论提取方法。该方法首先构建微博客消息蕴含交通状态信息评价体系,利用百科知识提高评价精度,然后定义微博客消息源的基本概率分配函数,通过证据合成与证据决策,实现微博客消息蕴含实时交通信息的甄别与融合。实验结果表明,该方法能够对微博客消息蕴含实时交通信息的可信度进行有效判断,并能够在最大程度上利用不同微博客用户发布消息的信息内容,且较之传统的文本聚类融合方法具有更高的准确率。     

基于交易网络的公有链用户识别方法

分析与研究公有链交易数据及系统用户行为对于保证公有链应用安全至关重要。比特币作为公有链的代表性应用,是一种基于P2P网络的电子现金系统。比特币交易地址具有匿名特征,无法关联到用户真实信息,这使得比特币溯源非常困难。为识别比特币中交易地址间的关联关系,推断出用户真实信息,提出一种基于交易网络的用户识别方法。对比特币区块数据进行预处理,通过解析比特币区块数据中的脚本信息,将比特币原始交易数据处理为更加直观的数据格式。衡量交易输入与输出地址间的相似程度,根据交易地址关联信息识别出比特币匿名地址对应的所有用户。在实验中应用真实的比特币区块数据,利用可视化方式对用户识别结果进行分析,结果表明该方法不受交易规则的限制,能对比特币匿名地址进行有效识别,且随着比特币区块数量的增加,识别准确率基本稳定于80%。     

基于比特币的私有信息检索支付协议

私有信息检索（PIR）是一种密码学工具,使用户能够从远程数据库服务器中获取信息,而不会让服务器知道用户获取了什么信息。PIR方案基本上由两种类型组成,即信息论私有信息检索（Information Theoretic PIR,IT-PIR）和计算性私有信息检索（Computational PIR,C-PIR）。IT-PIR方案要求服务器之间不共谋。一旦服务器共谋,就无法保证用户的隐私。共谋问题一直以来都没有一个比较好的解决办法。比特币和区块链的出现为解决公平和信任的问题提供了一种新方法。在本文中,我们创新地使用区块链来处理IT-PIR中的共谋问题,提出了一种基于比特币的PIR支付协议。在此支付协议中,客户通过比特币交易支付服务费。我们通过比特币脚本控制交易兑现的条件,使得如果服务方相互串通,则使服务方受到利益损失。通过这种方式,该支付协议可以在一定程度上降低共谋的可能性。     

基于动机分析的区块链数字货币异常交易行为识别方法

当前区块链数字货币被众多恶意交易者利用,导致了"粉尘"注入、"空投"操作、勒索、骗局等一系列异常交易行为.因此,研究区块链数字货币异常交易行为的识别方法对于规范交易行为、保障网络空间安全具有重要意义.在众多区块链数字货币中,比特币市值超过所有区块链数字货币市值和的一半,具有高代表性.比特币系统的用户数量多、交易规模大、...     

Long-term working memory and interrupting messages in human - computer interaction

The extent to which memory for information content is reliable, trustworthy, and accurate is crucial in the information age. Being forced to divert attention to interrupting messages is common, however, and can cause memory loss. The memory effects of interrupting messages were investigated in three experiments. In Experiment 1, attending to an interrupting message decreased memory accuracy. Experiment 2, where four interrupting messages were used, replicated this result. In Experiment 3, an interrupting message was shown to be most disturbing when it was semantically very close to the main message. Drawing from a theory of long-term working memory it is argued that interrupting messages can both disrupt the active semantic elaboration of content during encoding and cause semantic interference upon retrieval. Properties of the interrupting message affect the extent and type of errors in remembering. Design implications are discussed.     

Predicting the direction,maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques

Bitcoin is the most accepted cryptocurrency in the world, which makes it attractive for investors and traders. However, the challenge in predicting the Bitcoin exchange rate is its high volatility. Therefore, the prediction of its behavior is of great importance for financial markets. In this way, recent studies have been carried out on what internal and/or external Bitcoin information is relevant to its prediction. The increased use of machine learning techniques to predict time series and the acceptance of cryptocurrencies as financial instruments motivated the present study to seek more accurate predictions for the Bitcoin exchange rate. In this way, in a first stage of the proposed methodology, different feature selection techniques were evaluated in order to obtain the most relevant attributes for the predictions. In the sequence, it was analyzed the behavior of Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Ensemble algorithms (based on Recurrent Neural Networks and the k-Means clustering method) for price direction predictions. Likewise, the ANN and SVM were employed for regression of the maximum, minimum and closing prices of the Bitcoin. Moreover, the regression results were also used as inputs to try to improve the price direction predictions. The results showed that the selected attributes and the best machine learning model achieved an improvement of more than 10%, in accuracy, for the price direction predictions, with respect to the state-of-the-art papers, using the same period of information. In relation to the maximum, minimum and closing Bitcoin prices regressions, it was possible to obtain Mean Absolute Percentage Errors between 1% and 2%. Based on these results, it was possible to demonstrate the efficacy of the proposed methodology when compared to other studies.     

专业市场税收征管信息系统

运用Oracle9i大型数据库、PowerBuilder8．0、VC＋＋6．0、3-DES数据加密算法、线性回归算法和报文交易技术以及语音卡编程技术开发了。套通用的专业市场税收征管信息系统软件，解决了专业市场管理中普遍存在的逃税漏税严重、征税难、交税难和定额评定难等实际问题，实现了银行与税务异构平台之间的数据实时交换与共享。     

基于有效上下文信息的变体词还原方法

在社交网络上，用户常创造一些变体词来替代部分实体名词，将这些变体词还原为原目标词是自然语言处理中的一项重要工作。针对现有变体词还原方法准确率不够高的问题，提出了基于有效上下文信息的变体词还原方法。该方法利用点互信息抽取出变体词和候选目标词的有效上下文信息，并将其融合进自编码器模型中，获得变体词和候选目标词更准确的编码，并依据此计算相似度进行候选目标词排序，更准确的实现了变体词还原任务。实验表明，该方法较当前主流的几种方法相比效果有显著提升，提高了变体词还原的准确率。     

Improving the match between callers and receivers: A study on the effect of contextual information on cell phone interruptions

A problem with the location-free nature of cell phones is that callers have difficulty predicting receivers' states, leading to inappropriate calls. One promising solution involves helping callers decide when to interrupt by providing them contextual information about receivers. We tested the effectiveness of different kinds of contextual information by measuring the degree of agreement between receivers' desires and callers' decisions. In a simulation, five groups of participants played the role of 'Callers', choosing between making calls or leaving messages, and a sixth group played the role of 'Receivers', choosing between receiving calls or receiving messages. Callers were provided different contextual information about Receivers' locations, their cell phones' ringer state, the presence of others, or no information at all. Callers provided with contextual information made significantly more accurate decisions than those without it. Our results suggest that different contextual information generates different kinds of improvements: more appropriate interruptions or better avoidance of inappropriate interruptions. We discuss the results and implications for practice in the light of other important considerations, such as privacy and technological simplicity.     

资源约束的传感器网络信息覆盖分析方法

针对传感器网络的覆盖问题,提出一种资源约束的传感器网络信息覆盖分析方法。该方法考虑传感器网络能量和带宽有限的特点,采用分布式估计机制对簇头节点收到的量化离散消息进行最终估计。实验结果表明,该模型与理想条件下的信息覆盖模型相比能更真实、准确地反映实际应用中资源约束传感器网络的覆盖质量。     

Improving the match between callers and receivers: A study on the effect of contextual information on cell phone interruptions

A problem with the location-free nature of cell phones is that callers have difficulty predicting receivers' states, leading to inappropriate calls. One promising solution involves helping callers decide when to interrupt by providing them contextual information about receivers. We tested the effectiveness of different kinds of contextual information by measuring the degree of agreement between receivers' desires and callers' decisions. In a simulation, five groups of participants played the role of ‘Callers’, choosing between making calls or leaving messages, and a sixth group played the role of ‘Receivers’, choosing between receiving calls or receiving messages. Callers were provided different contextual information about Receivers' locations, their cell phones' ringer state, the presence of others, or no information at all. Callers provided with contextual information made significantly more accurate decisions than those without it. Our results suggest that different contextual information generates different kinds of improvements: more appropriate interruptions or better avoidance of inappropriate interruptions. We discuss the results and implications for practice in the light of other important considerations, such as privacy and technological simplicity.     

面向中文短信的信息抽取方法

在手机3D动画自动生成系统中,研究面向中文短信的信息抽取方法.设计一种基于上下文无关文法的模板定义方式,以及对应的模板知识库与模板解析器.在模板解析器处理数据的过程中,通过最左规约算法保证中文短信的信息抽取效率.实验结果表明,该方法在扩展抽取内容范围的同时,能提高信息抽取的准确性.     

面向比特币的区块链扩容:关键技术, 制约因素与衍生问题

比特币是一种利用区块链技术的点对点记账系统.随着比特币的发展,现有的比特币系统架构已经不能满足日益增长的交易需求,亟需扩容以寻求长期发展.比特币是以人为核心的复杂社会经济系统,比特币扩容是涉及多方利益的复杂问题,引起了业界与学术界的广泛关注.本文提出了一个比特币系统扩容问题的研究框架,包括关键技术,制约因素与衍生问题三部分,以深入探讨和研究比特币扩容问题.在该研究框架下,首先介绍链上和链下两类扩容关键技术及发展现状;其次从网络负载和节点瓶颈两方面,总结制约比特币扩容方案的宏观与微观因素;最后,探讨两类衍生问题:从系统安全性的角度,探讨比特币扩容可能引发的安全问题及解决策略;从币值、交易费与矿工收益等方面,阐述比特币扩容涉及的经济问题.     

专家信息语义模型异构数据转换技术

针对专家信息异构数据转换过程中数据之间映射关系的建立问题,提出了一种基于专家信息语义模型的方法。详细阐述了专家数据表语义模型的建立方法,语义相似度计算的方法及基本原理,选取了一种适用于专家数据语义的计算模型,在此模型的基础上对各种影响语义之间相似度的因素进行调整,并对实验结果进行对比分析,找到了一种更加有效、准确的语义相似度计算方法。     

基于LDA主题模型的移动应用相似度构建方法

随着移动互联网的快速发展,如何从大量的移动应用中抽取有效的描述信息继而为移动用户提供有效准确的推荐策略变得尤为迫切。目前,移动应用市场对应用的推荐策略相对传统,大多是根据应用的单一属性进行推荐,如下载量、应用名称、应用分类等。针对推荐粒度过粗和推荐不准确的问题,提出了一种基于潜在狄利克雷分布（LDA）主题模型的移动应用相似度构建方法。该方法从应用的标签入手,构造应用的主题模型分布矩阵,利用该主题分布矩阵构建移动应用的相似度矩阵,同时提出了将移动应用相似度矩阵转化为可行的存储结构的方法。实验结果表明该方法是有效的,相比现有的360应用市场推荐的应用其相似度提升130%。该方法解决了移动应用推荐过程中推荐粒度过粗的问题,可使推荐结果更加准确。     

How are social and mass media different in relation to the stock market? A study on topic coverage and predictive value

Although investors in financial markets have access to information from both mass media and social media, trading platforms that curate and provide this information have little to go by in terms of understanding the difference between these two types of media. This paper compares social media with mass media in the stock market, focusing on information coverage diversity and predictive value with respect to future stock absolute returns. Based on a study of nearly a million stock-related news articles from the Sina Finance news platform and 12.7 million stock-related social media messages from the popular Weibo platform in China, we find that social media covers less stocks than mass media, and this effect is amplified as the volume of media information increases. We find that there is some short-term predictive value from these sources, but they are different. Although mass media information coverage is more predictive than social media information coverage in a one-day horizon, it is the other way around in a two-to five-day horizon. These empirical results suggest that social media and mass media serve stock market investors differently. We draw connections to theories related to how crowds and experts differ and offer practical implications for the design of media-related IS systems.     

基于人工免疫的比特币快捷交易异常检测模型

电子货币交易最重要的问题是双重花费（双花攻击）,比特币预防双花攻击的策略是等待六个确认块（约1 h）,难以适用于快捷支付领域,默认替代策略是等待交易信息传播到卖主的钱包,这无法有效地预防双花攻击。针对比特币快捷交易中双花攻击的检测问题,提出了一种基于人工免疫的比特币快捷交易异常检测模型。在每个传统比特币节点中加入免疫检测模块进行抗原提取,并利用检测器进行异常检测,在威胁控制中心动态演化检测器并分发免疫疫苗以便有效地进行防御。实验结果证明,此检测模型能够有效地检测并预防比特币快捷支付中的双花攻击。     

Information Sharing During Crisis Management in Hierarchical vs. Network Teams

This study examines the differences between hierarchical and network teams in emergency management. A controlled experimental environment was created in which we could study teams that differed in decision rights, availability of information, information sharing, and task division. Thirty‐two teams of either two (network) or three (hierarchy) participants (N=80 in total) received messages about an incident in a tunnel with high‐ranking politicians possibly being present. Based on experimentally induced knowledge, teams had to decide as quickly and as accurately as possible what the likely cause of the incident was: an attack by Al Qaeda, by anti‐globalists, or an accident. The results showed that network teams were overall faster and more accurate in difficult scenarios than hierarchical teams. Network teams also shared more knowledge in the difficult scenarios, compared with the easier scenarios. The advantage of being able to share information that is inherent in network teams is thus contingent upon the type of situation encountered.     

面向PaaS云的信息流控制框架设计与实现

分布式信息流控制(DIFC)是实施端到端数据保护的一种有效方法。现有DIFC方法存在信息流控制粒度单一、需要修改语言运行时环境等问题,不能很好地满足PaaS平台的数据安全需求。基于最典型的PaaS云平台GAE,提出了一个信息流控制框架GIFC,其结合了对象级、消息级和SQL级3种控制粒度。组件内基于Python库来控制调用 对象的方法中所涉及的实体间的信息交互;组件间消息代理根据消息安全标记来过滤消息,以此限制组件可以接收的消息集;组件与数据库之间扩展GAE中的数据模式支持标记信息在datastore中的持久化存储。实验表明,多种IFC粒度相结合有效平衡了信息流控制精度和运行性能。     

Information Overload,Similarity, and Redundancy: Unsubscribing Information Sources on Twitter

The emergence of social media has changed individuals' information consumption patterns. The purpose of this study is to explore the role of information overload, similarity, and redundancy in unsubscribing information sources from users' information repertoires. In doing so, we randomly selected nearly 7,500 ego networks on Twitter and tracked their activities in 2 waves. A multilevel logistic regression model was deployed to test our hypotheses. Results revealed that individuals (egos) obtain information by following a group of stable users (alters). An ego's likelihood of unfollowing alters is negatively associated with their information similarity, but is positively associated with both information overload and redundancy. Furthermore, relational factors can modify the impact of information redundancy on unfollowing.