A Location Aware Service to Minimize Travel Costs Using Dynamic Information

Automotive navigation systems have become common accessories in vehicles manufactured today. However, the information provided by these systems is quite limited in that many systems only provide static information. As a result, manufacturers of such systems have not been able to fully capitalize from the potential applications for mobile commerce (mcommerce) which is critically dependent on providing consumers with dynamic information. The objective of this paper is to discuss a novel method, known as Dynamic Location Cost Minimization (DLCM), which can be used with a vehicle’s navigation system to determine the optimum location to purchase gas. With the increasing cost of gas and the possibility of higher prices due to proposed gas price taxes, providing a means for consumers to minimize their costs to travel could prove to be very beneficial, and potentially help drive down prices due to increased competition. In addition, the proposed method could also be used in conjunction with mobile phones to facilitate real-time decisions for other services or purchases. Anecdotal evidence presented in this paper merits further investigation into the usability and acceptance of this technology

Leveraging NLP and Social Network Analytic Techniques to Detect Censored Keywords: System Design and Experiments

Internet regulation in the form of online censorship and Internet shutdowns have been increasing over recent years. This paper presents a natural language processing (NLP) application for performing cross country probing that conceals the exact location of the originating request. A detailed discussion of the application aims to stimulate further investigation into new methods for measuring and quantifying Internet censorship practices around the world. In addition, results from two experiments involving search engine queries of banned keywords demonstrates censorship practices vary across different search engines. These results suggest opportunities for developing circumvention technologies that enable open and free access to information

cOOKie, a Tool for Developing RF Communication Systems for the Internet of Things

There is a need for high-efficiency short-range wireless communications to connect IoT devices that have low to medium security requirements. A hardware/software tool was developed to help IoT product developers quickly and easily develop radio frequency (RF) communication systems for IoT devices where previously this was a manual, one-off process. The tool uses Software Defined Radio (SDR) and focuses on On-Off-Keying (OOK) modulation. It can be used by persons with limited knowledge of RF to analyze existing devices and capture its characteristics, which can be used to create and transmit new messages, in effect spoofing it. New device definitions can be implemented in low-cost off-the-shelf hardware for production. OOK has been found to be very efficient at binary RF communications because the transmitter is only powered when a “1” is being transmitted. This efficiency translates into a battery life of up to one year. Implementations of this system could include arrays of sensors that periodically transmit data to a traditionally-powered Internet-connected receiver. Another possible use of this system could be low-cost small transmitters to track animal movements in a defined area. Receivers placed around the area could record the time and signal strength of the transmissions. Software would be used to analyze the data and plot the animal’s movements. Because the RF transmissions have a specific range, the opportunity to intercept, modify or spoof communications is highly variable. For sensitive data, rolling codes and/or public/private key encryption could be used for encoding before modulating with OOK

Linguistic Characteristics of Censorable Language on SinaWeibo

This paper investigates censorship from a linguistic perspective. We collect a corpus of censored and uncensored posts on a number of topics, build a classifier that predicts censorship decisions independent of discussion topics. Our investigation reveals that the strongest linguistic indicator of censored content of our corpus is its readability

Apollo: A System for Tracking Internet Censorship

If it remains debatable whether the Internet has surpassed print media in making information accessible to the public, then it must nevertheless be conceded that the Internet makes the manipulation and censorship of information easier than had been on the printed page. In coming years and in an increasing number of countries, everyday producers and consumers of online information will likely have to cultivate a sense of censorship. It behooves the online community to learn how to detect and evade interference by governments, regimes, corporations, con-artists, and vandals. The contribution of this research is to describe a method and platform to study Internet censorship detection and evasion. This paper presents the concepts, initial theories, and future work

Decision Support for Perceived Threat in the Context of Intrustion Detection Systems

The objective of this research is to propose a novel approach for using a behavioral biometric known as keystroke analysis, to facilitate decision making in the context of an intrusion detection system (IDS). Regardless of the situation, individuals have a specific baseline or disposition to decision making based on two psychological factors: (1) indecisiveness, and (2) intolerance of uncertainty. The IDS provides a probability of intrusion and a set of objective situational characteristics. We propose a decision support system that allows the decision maker to state a level of perceived threat and to vary the security thresholds that determines the false acceptance rates of the IDS. Our hypothesis is that perceived threat depends not only on the keystroke technology but also on the social context and disposition toward decision making of the user. This research tests this hypothesis and provides guidance in the design of better security systems

Linguistic Characteristics of Censorable Language on SinaWeibo

This paper investigates censorship from a linguistic perspective. We collect a corpus of censored and uncensored posts on a number of topics, build a classifier that predicts censorship decisions independent of discussion topics. Our investigation reveals that the strongest linguistic indicator of censored content of our corpus is its readability

Detecting Censorable Content on Sina Weibo: A Pilot Study

This study provides preliminary insights into the linguistic features that contribute to Internet censorship in mainland China. We collected a corpus of 344 censored and uncensored microblog posts that were published on Sina Weibo and built a Naive Bayes classifier based on the linguistic, topic-independent, features. The classifier achieves a 79.34% accuracy in predicting whether a blog post would be censored on Sina Weibo

Dynamics of Information Diffusion and Social Sensing

Statistical inference using social sensors is an area that has witnessed remarkable progress and is relevant in applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This chapter presents a tutorial description of four important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models and risk averse social learning is considered with applications in finance and online reputation systems. Third, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. Finally, the interaction of social sensors with YouTube channel owners is studied using time series analysis methods. All four topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this chapter stem from recent developments in network science, economics and signal processing. At a deeper level, this chapter considers mean field dynamics of networks, risk averse Bayesian social learning filtering and quickest change detection, data incest in decision making over a directed acyclic graph of social sensors, inverse optimization problems for utility function estimation (revealed preferences) and statistical modeling of interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112

SoK: Making Sense of Censorship Resistance Systems

An increasing number of countries implement Internet censorship at different scales and for a variety of reasons. Several censorship resistance systems (CRSs) have emerged to help bypass such blocks. The diversity of the censor’s attack landscape has led to an arms race, leading to a dramatic speed of evolution of CRSs. The inherent complexity of CRSs and the breadth of work in this area makes it hard to contextualize the censor’s capabilities and censorship resistance strategies. To address these challenges, we conducted a comprehensive survey of CRSs-deployed tools as well as those discussed in academic literature-to systematize censorship resistance systems by their threat model and corresponding defenses. To this end, we first sketch a comprehensive attack model to set out the censor’s capabilities, coupled with discussion on the scope of censorship, and the dynamics that influence the censor’s decision. Next, we present an evaluation framework to systematize censorship resistance systems by their security, privacy, performance and deployability properties, and show how these systems map to the attack model. We do this for each of the functional phases that we identify for censorship resistance systems: communication establishment, which involves distribution and retrieval of information necessary for a client to join the censorship resistance system; and conversation, where actual exchange of information takes place. Our evaluation leads us to identify gaps in the literature, question the assumptions at play, and explore possible mitigations