Automated Crowdturfing Attacks and Defenses in Online Review Systems

Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers. In this paper, we identify a new class of attacks that leverage deep learning language models (Recurrent Neural Networks or RNNs) to automate the generation of fake online reviews for products and services. Not only are these attacks cheap and therefore more scalable, but they can control rate of content output to eliminate the signature burstiness that makes crowdsourced campaigns easy to detect. Using Yelp reviews as an example platform, we show how a two phased review generation and customization attack can produce reviews that are indistinguishable by state-of-the-art statistical detectors. We conduct a survey-based user study to show these reviews not only evade human detection, but also score high on "usefulness" metrics by users. Finally, we develop novel automated defenses against these attacks, by leveraging the lossy transformation introduced by the RNN training and generation cycle. We consider countermeasures against our mechanisms, show that they produce unattractive cost-benefit tradeoffs for attackers, and that they can be further curtailed by simple constraints imposed by online service providers

A front-end system to support cloud-based manufacturing of customised products

In today’s global market, customized products are amongst an important means to address diverse customer demand and in achieving a unique competitive advantage. Key enablers of this approach are existing product configuration and supporting IT-based manufacturing systems. As a proposed advancement, it considered that the development of a front-end system with a next level of integration to a cloud-based manufacturing infrastructure is able to better support the specification and on-demand manufacture of customized products. In this paper, a new paradigm of Manufacturing-as-a-Service (MaaS) environment is introduced and highlights the current research challenges in the configuration of customizable products. Furthermore, the latest development of the front-end system is reported with a view towards further work in the research

Opportunity Recognition in High Tech and Regulatory Environment: A study of product based Indian Telecom start-ups

Opportunity recognition forms the first step of entrepreneurship. Off late entrepreneurship research has looked at opportunity recognition from varied lenses with entrepreneurial learning forming the core of most scholarly work. However opportunity recognition in high tech sectors is slightly different due to a high component of knowledge intensiveness inherent in such sectors and has been largely ignored in most work. So, we explore a specific high tech sector in the paper to understand and further the existing concepts within opportunity recognition process. We choose the Indian telecom sector as the context of the study and using an inductive case based approach arrive at conceptual combination as the dominant form of idea generation. The regulatory environment was found to acts as an enabler for the new ideas to flourish. We also bring in the idea of dynamic customization as the driving force behind the venture akin to symbiotic relationship present between organisms in the nature.

Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0

The Ubiquitous Interactor - Device Independent Access to Mobile Services

The Ubiquitous Interactor (UBI) addresses the problems of design and development that arise around services that need to be accessed from many different devices. In UBI, the same service can present itself with different user interfaces on different devices. This is done by separating interaction between users and services from presentation. The interaction is kept the same for all devices, and different presentation information is provided for different devices. This way, tailored user interfaces for many different devices can be created without multiplying development and maintenance work. In this paper we describe the system design of UBI, the system implementation, and two services implemented for the system: a calendar service and a stockbroker service