Investigative Simulation: Towards Utilizing Graph Pattern Matching for Investigative Search

This paper proposes the use of graph pattern matching for investigative graph search, which is the process of searching for and prioritizing persons of interest who may exhibit part or all of a pattern of suspicious behaviors or connections. While there are a variety of applications, our principal motivation is to aid law enforcement in the detection of homegrown violent extremists. We introduce investigative simulation, which consists of several necessary extensions to the existing dual simulation graph pattern matching scheme in order to make it appropriate for intelligence analysts and law enforcement officials. Specifically, we impose a categorical label structure on nodes consistent with the nature of indicators in investigations, as well as prune or complete search results to ensure sensibility and usefulness of partial matches to analysts. Lastly, we introduce a natural top-k ranking scheme that can help analysts prioritize investigative efforts. We demonstrate performance of investigative simulation on a real-world large dataset.Comment: 8 pages, 6 figures. Paper to appear in the Fosint-SI 2016 conference proceedings in conjunction with the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 201

Impacts of WeChat on Millennials’ Perceptions and Consumption Behaviors in the Hotel Industry

Social media, known as interactive Web 2.0 Internet-based applications, has deeply changed and reformed interpersonal communication and business operation with the wide spread of Internet and the development of technology. In the past few years, since mobile apps are becoming more and more popular, the access of social media is not limited to tablet computers only, but is also available for almost all kinds of smart phone devices, such as iPhone, Android, Symbian and so on. The function of social media is not confined to real- time message transmission or information sharing any more. It has expanded to a widely range of features, such as online purchase and payment, e-commerce business, and service for different types of social events. Social media plays an increasingly important role in daily personal life as well as in business activities. People are not merely considered as social media users, but also the component of social media itself. As a result, it is very crucial for people to realize the importance and impacts of social media, especially for those business operators. WeChat (Weixin in Chinses, literally “micro message”) is a cross-platform instant text and voice messaging communication service for multiple mobile devices, developed by Tecent in China, first released in the January of 2011. It is claimed to provide “the new way to connect” and create “a way of life”. It is free to download, install and register, and support all kinds of smart phone platforms with multiple language versions, such as Chinese, English, Japanese, French, and Spanish. WeChat provides its users different ways to communicate and interact with friends innovatively through instant text messaging, hold-to-talk voice messaging, group messaging, lively video sharing, location sharing, money transferring, and contact information sharing. Among all the WeChat users, Millennials is the majority. With the growing-up of Millennials, they are becoming more and more powerful and important to the society and will be the next target segmentation for most of the industries in the very near future. Especially for the hotel industry, the industry that urges to attract Millennials patrons for further substantial development, how to attract Millennials is becoming a critical issue for those hotel operators

A Simple Iterative Model Accurately Captures Complex Trapline Formation by Bumblebees Across Spatial Scales and Flower Arrangements

PMCID: PMC3591286This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

Co-Clustering Network-Constrained Trajectory Data

Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajectories of vehicles whose movement is restricted by the underlying road network. We model relations between these trajectories and road segments as a bipartite graph and we try to cluster its vertices. We demonstrate our approaches on synthetic data and show how it could be useful in inferring knowledge about the flow dynamics and the behavior of the drivers using the road network

Multicriteria VMAT optimization

Purpose: To make the planning of volumetric modulated arc therapy (VMAT) faster and to explore the tradeoffs between planning objectives and delivery efficiency. Methods: A convex multicriteria dose optimization problem is solved for an angular grid of 180 equi-spaced beams. This allows the planner to navigate the ideal dose distribution Pareto surface and select a plan of desired target coverage versus organ at risk sparing. The selected plan is then made VMAT deliverable by a fluence map merging and sequencing algorithm, which combines neighboring fluence maps based on a similarity score and then delivers the merged maps together, simplifying delivery. Successive merges are made as long as the dose distribution quality is maintained. The complete algorithm is called VMERGE. Results: VMERGE is applied to three cases: a prostate, a pancreas, and a brain. In each case, the selected Pareto-optimal plan is matched almost exactly with the VMAT merging routine, resulting in a high quality plan delivered with a single arc in less than five minutes on average. VMERGE offers significant improvements over existing VMAT algorithms. The first is the multicriteria planning aspect, which greatly speeds up planning time and allows the user to select the plan which represents the most desirable compromise between target coverage and organ at risk sparing. The second is the user-chosen epsilon-optimality guarantee of the final VMAT plan. Finally, the user can explore the tradeoff between delivery time and plan quality, which is a fundamental aspect of VMAT that cannot be easily investigated with current commercial planning systems