Mining changing customer segments in dynamic markets

Identifying customer segments and tracking their change over time is an important application for enterprises who need to understand what their customers expect from them – now and in the future. This in particular is important for businesses which operate in dynamic markets with customers who, driven by new innovations and competing products, have highly changing demands and attitudes. Customer segmentation is typically done by applying some form of cluster analysis to obtain a set of segments to which future customers are assigned to. In this paper, we present a system for customer segmentation which accounts for the dynamics of today’s markets. It employs an approach based on the discovery of frequent itemsets and the analysis of their change over time which, finally, results in a change-based notion of segment interestingness. Our approach allows us to detect arbitrary segments and analyse their temporal development. Thereby, our approach is assumption-free and pro-active and can be run continuously. Newly discovered segments or relevant changes will be reported automatically based on the application of several interestingness measures.     

Loop-free hybrid single-path/flooding routing algorithms withguaranteed delivery for wireless networks

In a localized routing algorithm, each node makes forwarding decisions solely based on the position of itself, its neighbors, and its destination. In distance, progress, and direction-based approaches'(reported in the literature), when node A wants to send or forward message m to destination node D, it forwards m to its neighbor C which is closest to D (has best progress toward D, whose direction is closest to the direction of D, respectively) among all neighbors of A. The same procedure is repeated until D, if possible, is eventually reached. The algorithms are referred to as GEDIR, MFR, and DIR when a common failure criterion is introduced: The algorithm stops if the best choice for the current node is the node from which the message came. We propose 2-hop GEDIR, DIR, and MFR methods in which node A selects the best candidate node C among its 1-hop and 2-hop neighbors according to the corresponding criterion and forwards m to its best 1-hop neighbor among joint neighbors of A and C. We then propose flooding GEDIR and MFR and hybrid single-path/flooding GEDIR and MFR methods which are the first localized algorithms (other than full flooding) to guarantee the message delivery (in a collision-free environment). We show that the directional routing methods are not loop-free, while the GEDIR and MFR-based methods are inherently loop free. The simulation experiments, with static random graphs, show that GEDIR and MFR have similar success rates, which is low for low degree graphs and high for high degree ones. When successful, their hop counts are near the performance of the shortest path algorithm. Hybrid single-path/flooding GEDIR and MFR methods have low communication overheads. The results are also confirmed by experiments with moving nodes and MAC layer     

基于时间距离与拓扑距离的连通性模型

网络连通性是描述网络性能的一项重要指标.机会传感网络中,节点移动导致网络的拓扑呈动态变化,这使得机会传感网络连通性的表征面临挑战.建立机会传感网络的连通性模型,有助于对机会传感网络的优化和维护.针对机会传感网络拓扑频繁变化的特点,基于时空图理论构建其连通性模型,描述拓扑演化规律;根据消息的可达性,考虑消息传输的时间特性和空间特性,定义了时间距离和拓扑距离,采用统计产品与服务解决方案软件（SPSS）分析其相关性,结果表明,时间距离和拓扑距离无明显相关性;采用网络快照间的时间距离与拓扑距离构建整网连通性模型.实验结果表明,所提出的模型能够从整体上刻画出机会传感网络的连通性;与基于Katz中心性的网络连通度模型相比,该模型能够更好地反映整网连通度的变化.     

Detecting Sybil attacks in Wireless Sensor Networks using neighboring information

As the prevalence of Wireless Sensor Networks (WSNs) grows in the military and civil domains, the need for network security has become a critical concern. In a Sybil attack, the WSN is subverted by a malicious node which forges a large number of fake identities in order to disrupt the network’s protocols. In attempting to protect WSNs against such an attack, this paper develops a scheme in which the node identities are verified simply by analyzing the neighboring node information of each node. The analytical results confirm the efficacy of the approach given a sufficient node density within the network. The simulation results demonstrate that for a network in which each node has an average of 9 neighbors, the scheme detects 99% of the Sybil nodes with no more than a 4% false detection rate. The experiment result shows that the Sybil nodes can still be identified when the links are not symmetric.     

大数据环境下精准客户定位的社交网络分析

大数据为企业进行精准营销提供了重要支撑,精准营销能提升营销效果,提高客户满意度,精准营销的前提是客户识别与选择。通过分析网络个体与群体特征,社交网络分析能够定位核心价值客户。首先对社交网络的中心性进行分析,探讨社交网络节点地位与营销效果的关系,运用社群识别方法,对社交网络进行分群,提出并用MapReduce实现了针对大规模社交网络的社群划分RMCL方法。在此基础上,构建了客户影响度与客户影响因子等指标,并结合中心度指标,定位社群的核心节点,并采用分类回归树方法,研究了社交网络结构与客户消费响应关系,并确定了变量重要性,为企业采取客户差异化营销组合策略提供指导。     

WEB到WAP的转换过程中页面去噪问题的研究

为了保证WEB到WAP转换结果的完备精练,主要针对转换过程中无用信息去除问题,提出一套页面去噪解决方案.首先根据节点大小位置通过算法判断节点是否为核心内容,在此基础上计算节点链接比,同阈值进行对比,进一步明确节点类型,为了避免误删,对于可能的噪音模块,采用正则表达式检测节点中链接的指向,如绝大多数指向其它网站,则断定该节点为噪音节点.最终通过构建项目实验平台,对该解决方案进行评估,证明该方案的有效性和可靠性.     

Actively open-minded thinking is key to combating fake news: A multimethod study

The fake news phenomenon has exposed the vulnerability of individuals and societies to information manipulation in social media. We conducted two studies to understand why people believe in fake news and propose a simple IT intervention method that can help in detecting disinformation. In Study 1, we designed a laboratory experiment using behavioral and neurophysiological tools to test two competing theories in the disinformation literature. Both behavioral and neurophysiological evidence support the classical reasoning account hypotheses and reject the motivated reasoning predictions, suggesting that the lack of actively open-minded thinking (AOT) is linked to the belief in fake news. An intervention method was designed (i.e., performance feedback) that reduces individuals’ overconfidence in their ability to detect fake news and encourages more analytical thinking. In Study 2, we conducted an online survey presenting participants with their performance feedback halfway through the survey. The results show that the intervention increased participants’ performance by 14%. Our study contributes to the research on fake news by providing behavioral and neurophysiological evidence in support of the classical reasoning account. It also offers a simple and practical method that increases users’ ability to detect fake news.     

Competitive Pricing Strategies for Software and SaaS Products

We study the pricing strategies for a software firm and an entrant software-as-a-service (SaaS) firm in two customer markets: the market composed of the incumbent’s past customers and the market of new customers. We build a game theoretical model to investigate how user costs and the quality differential between products affect firms’ pricing in different customer markets. Our findings show that it is not always optimal for the software firm to price discriminate between its old users and the new customers. The entrant firm would be better off acquiring only the new customers when the SaaS quality is sufficiently low.     

Design and analysis of a social botnet

Online Social Networks (OSNs) have attracted millions of active users and have become an integral part of today’s web ecosystem. Unfortunately, in the wrong hands, OSNs can be used to harvest private user data, distribute malware, control botnets, perform surveillance, spread misinformation, and even influence algorithmic trading. Usually, an adversary starts off by running an infiltration campaign using hijacked or adversary-owned OSN accounts, with an objective to connect with a large number of users in the targeted OSN. In this article, we evaluate how vulnerable OSNs are to a large-scale infiltration campaign run by socialbots: bots that control OSN accounts and mimic the actions of real users. We adopted the design of a traditional web-based botnet and built a prototype of a Socialbot Network (SbN): a group of coordinated programmable socialbots. We operated our prototype on Facebook for 8 weeks, and collected data about user behavior in response to a large-scale infiltration campaign. Our results show that (1) by exploiting known social behaviors of users, OSNs such as Facebook can be infiltrated with a success rate of up to 80%, (2) subject to user profile privacy settings, a successful infiltration can result in privacy breaches where even more private user data are exposed, (3) given the economics of today’s underground markets, running a large-scale infiltration campaign might be profitable but is still not particularly attractive as a sustainable and independent business, (4) the security of socially-aware systems that use or integrate OSN platforms can be at risk, given the infiltration capability of an adversary in OSNs, and (5) defending against malicious socialbots raises a set of challenges that relate to web automation, online-offline identity binding, and usable security.     

Browsing databases with constraint hierarchies

Electronic markets rely on database technology for efficient search for products, services, customers, and trading partners. Yet, database technology was not designed to build electronic markets, and it often fails to fully support market search activities. Market search often requires browsing and incremental search, attribute and constraint tradeoffs, approximate matches, and incorporation of user and task characteristics into searches. None of these requirements are supported directly by the database technology. Three major extensions to database technology are proposed to effectively support electronic markets. First, incremental search techniques are proposed to allow users to browse databases, and to move from solution to solution by following attribute and constraint tradeoffs. These techniques require systems to have some knowledge of similarity, distance, direction, and tradeoffs. Second, typing and stereotyping techniques are introduced to allow users to relate task and user characteristics to product attributes. These techniques require integration of typing systems with database searches. Third, a comprehensive search strategy is developed that combines incremental search and stereotyping techniques by utilizing product, task, and user ontologies.     

Examining customers’ trust in online vendors and their dropout decisions: An empirical study

Customer dropouts represent a critical challenge to online vendors and have severely constrained the proliferation of electronic retailing, which greatly depends on the customer eventually clicking on the “confirm purchase” button. In this study, we investigate the relationship between customers’ trust in an online vendor and their dropouts in different stages of the purchase process. Using a well-established consumer decision-making process, we analyze salient online trust antecedents for each purchase stage, develop specific hypotheses, and test them empirically. Our results suggest that customers’ trust in an online vendor has significant effects on their decisions to exit from the vendor’s website, and that salient trust antecedents vary in different stages of the consumer decision process. According to our findings, customers by and large depend on general, subjective antecedents pertaining to personal traits or perceptions about the website’s ease of use to assess the trustworthiness of an online vendor in the early stages but focus on specific, objective, transaction-oriented antecedents directly related to their purchase decision making in the later stages. Our results have important implications for research and practice which are also discussed in the paper.     

结合极大似然距离估计的MDS-MAP节点定位算法

基于多维定标的定位算法通常利用节点间的最短路径长度代替欧式距离构建距离矩阵,当网络拓扑结构不规则时,会导致较大的定位误差。针对这一问题,提出了一种结合极大似然距离估计和多维定标的节点定位算法MDS-MAP（MLE）。算法将待测节点的一跳邻居节点信息作为极大似然方法的输入,利用与邻居节点的距离信息计算待测节点的相对坐标,然后根据已知锚节点的坐标,将所有节点的相对坐标映射为绝对坐标。实验结果表明,针对规则网络和不规则网络,MDS-MAP （MLE）算法均可取得较好的定位精度,且当网络连通度在一定范围内变化时,定位误差可保持在较低的稳定区间内。     

UbCadet: detection of compromised accounts in twitter based on user behavioural profiling

Multimedia Tools and Applications - Online Social Networks(OSNs) are generally at the risk of many potential dangers. Malicious attackers use compromised OSN accounts to spread fake news, to send...     

Stationary probability distribution for states of G-networks with constrained sojourn time

We consider an exponential queueing network that differs from a Gelenbe network (with the usual positive and so-called negative customers), first, in that the sojourn time of customers at the network nodes is bounded by a random value whose conditional distribution for a fixed number of customers in a node is exponential. Second, we significantly relax the conditions on possible values of parameters for incoming Poisson flows of positive and negative customers in Gelenbe’s theorem. Claims serviced at the nodes and customers leaving the nodes at the end of their sojourn time can stay positive, become negative, or leave the network according to different routing matrices. We prove a theorem that generalizes Gelenbe’s theorem.     

The exact distance to destination in undirected world

Shortest distance queries are essential not only in graph analysis and graph mining tasks but also in database applications, when a large graph needs to be dealt with. Such shortest distance queries are frequently issued by end-users or requested as a subroutine in real applications. For intensive queries on large graphs, it is impractical to compute shortest distances on-line from scratch, and impractical to materialize all-pairs shortest distances. In the literature, 2-hop distance labeling is proposed to index the all-pairs shortest distances. It assigns distance labels to vertices in a large graph in a pre-computing step off-line and then answers shortest distance queries on-line by making use of such distance labels, which avoids exhaustively traversing the large graph when answering queries. However, the existing algorithms to generate 2-hop distance labels are not scalable to large graphs. Finding an optimal 2-hop distance labeling is NP-hard, and heuristic algorithms may generate large size distance labels while still needing to pre-compute all-pairs shortest paths. In this paper, we propose a multi-hop distance labeling approach, which generates a subset of the 2-hop distance labels as index off-line. We can compute the multi-hop distance labels efficiently by avoiding pre-computing all-pairs shortest paths. In addition, our multi-hop distance labeling is small in size to be stored. To answer a shortest distance query between two vertices, we first generate the query-specific small set of 2-hop distance labels for the two vertices based on our multi-hop distance labels stored and compute the shortest distance between the two vertices based on the 2-hop distance labels generated on-line. We conducted extensive performance studies on large real graphs and confirmed the efficiency of our multi-hop distance labeling scheme.     

A trustworthy online recommendation system based on social connections in a privacy-preserving manner

Certain consumer websites provide reviews from previous buyers to help new customers make purchasing decisions. However, fake reviews can have an adverse impact on user trust. Most previous suggestions for addressing this problem are still subject to various security concerns in terms of privacy, reliability, and authenticity. To ensure the security of online review systems, this paper proposes the development of a secure online-evaluation method based on social connections to establish evaluation authenticity and provide protection against evaluation forgery while preserving the reviewer’s identity. The proposed method enables users to recognize evaluations from their friends to identify reviews from more trustworthy sources, and authenticates online reviews to prevent possible forgery. In addition, it preserves the privacy of friendship relationships from application server and other users and identifier relations between the personal identifier and online identifier. The proposed approach can be applied to Internet auctions and online games, and is shown to be secure and efficient, with sufficient matching probability to be practical.     

CP2: Cryptographic privacy protection framework for online social networks

In Online Social Networks (OSNs), users interact with each other by sharing their personal information. One of the concerns in OSNs is how user privacy is protected since the OSN providers have full control over users’ data. The OSN providers typically store users’ information permanently; the privacy controls embedded in OSNs offer few options to users for customizing and managing the dissipation of their data over the network. In this paper, we propose an efficient privacy protection framework for OSNs that can be used to protect the privacy of users’ data and their online social relationships from third parties. The recommended framework shifts the control over data sharing back to the users by providing them with flexible and dynamic access policies. We employ a public-key broadcast encryption scheme as the cryptographic tool for managing information sharing with a subset of a user’s friends. The privacy and complexity evaluations show the superiority of our approach over previous.     

无线传感器网络中的改进数据聚集调度算法

针对现有聚集数据调度近似算法具有较高延时上界的问题,提出一种改进的聚集数据调度近似算法。建立一棵根在中心结点的广度优先搜索树,分层构造一个最大独立集(MIS),使MIS中相邻的2个结点相距两跳。将MIS中的结点连接起来,形成一棵根在中心结点的数据聚集调度树,使结点按数据聚集调度树进行分层数据调度。在数据聚集调度树的构造过程中,对于任意支配点,以最小的结点连接其相距两跳的支配点。对于2个相邻支配点的公共邻居支配点,通过在距中心点最近的支配点加入数据聚集树,使其在数据调度过程中将数据发送给距中心点最近的支配点,从而降低数据的聚集延时。实验结果表明,与SAS算法、Guo’s算法和IAS算法相比,该算法的数据聚集延时更低,其延时上界为14R+△?10。     

基于OLSR路由协议的HIDA算法

针对Ad Hoc网络中的虫洞攻击,根据最优链路状态路由(OLSR)协议的运行特点,提出检测伪邻居的HELLO间隔分布式算法(HIDA)。仿真结果表明,在网络平均节点数大于4、节点随机最大移动速率大于2 m/s时,HIDA算法能达到80%以上的虫洞攻击检测率。     

Visa moves towards COPAC Mark II

Banks want to build their customer base and they see the ‘unbanked’ population as a large, unexploited market for loans and services. But banks want customers who will make their loan interest payments on time. Many of the unbanked are unbanked because they cannot show a record of servicing loans. Is there a way out of this dilemma?