Active class discovery and learning for networked data

With the recent explosion of social network applications, active learning has increasingly become an important paradigm for classifying networked data. While existing research has shown promising results by exploiting network properties to improve the active learning performance, they are all based on a static setting where the number and the type of classes underlying the networked data remain stable and unchanged. For most social network applications, the dynamic change of users and their evolving relationships, along with the emergence of new social events, often result in new classes that need to be immediately discovered and labeled for classification. This paper proposes a novel approach called ADLNET for active class discovery and learning with networked data. Our proposed method uses the Dirichlet process defined over class distributions to enable active discovery of new classes, and explicitly models label correlations in the utility function of active learning. Experimental results on two real-world networked data sets demonstrate that our proposed approach outperforms other state-of-the-art methods

OpenML: networked science in machine learning

Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals. In this paper, we introduce OpenML, a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and collaborate with others to tackle harder problems. We discuss how OpenML relates to other examples of networked science and what benefits it brings for machine learning research, individual scientists, as well as students and practitioners.Comment: 12 pages, 10 figure

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Node discovery in a networked organization

In this paper, I present a method to solve a node discovery problem in a networked organization. Covert nodes refer to the nodes which are not observable directly. They affect social interactions, but do not appear in the surveillance logs which record the participants of the social interactions. Discovering the covert nodes is defined as identifying the suspicious logs where the covert nodes would appear if the covert nodes became overt. A mathematical model is developed for the maximal likelihood estimation of the network behind the social interactions and for the identification of the suspicious logs. Precision, recall, and F measure characteristics are demonstrated with the dataset generated from a real organization and the computationally synthesized datasets. The performance is close to the theoretical limit for any covert nodes in the networks of any topologies and sizes if the ratio of the number of observation to the number of possible communication patterns is large

POISED: Spotting Twitter Spam Off the Beaten Paths

Cybercriminals have found in online social networks a propitious medium to spread spam and malicious content. Existing techniques for detecting spam include predicting the trustworthiness of accounts and analyzing the content of these messages. However, advanced attackers can still successfully evade these defenses. Online social networks bring people who have personal connections or share common interests to form communities. In this paper, we first show that users within a networked community share some topics of interest. Moreover, content shared on these social network tend to propagate according to the interests of people. Dissemination paths may emerge where some communities post similar messages, based on the interests of those communities. Spam and other malicious content, on the other hand, follow different spreading patterns. In this paper, we follow this insight and present POISED, a system that leverages the differences in propagation between benign and malicious messages on social networks to identify spam and other unwanted content. We test our system on a dataset of 1.3M tweets collected from 64K users, and we show that our approach is effective in detecting malicious messages, reaching 91% precision and 93% recall. We also show that POISED's detection is more comprehensive than previous systems, by comparing it to three state-of-the-art spam detection systems that have been proposed by the research community in the past. POISED significantly outperforms each of these systems. Moreover, through simulations, we show how POISED is effective in the early detection of spam messages and how it is resilient against two well-known adversarial machine learning attacks

What is the problem to which interactive multimedia is the solution?

This is something of an unusual paper. It serves as both the reason for and the result of a small number of leading academics in the field, coming together to focus on the question that serves as the title to this paper: What is the problem to which interactive multimedia is the solution? Each of the authors addresses this question from their own viewpoint, offering informed insights into the development, implementation and evaluation of multimedia. The result of their collective work was also the focus of a Western Australian Institute of Educational Research seminar, convened at Edith Cowan University on 18 October, 1994. The question posed is deliberately rhetorical - it is asked to allow those represented here to consider what they think are the significant issues in the fast-growing field of multimedia. More directly, the question is also asked here because nobody else has considered it worth asking: for many multimedia is done because it is technically possible, not because it offers anything that is of value or provides the solution to a particular problem. The question, then, is answered in various ways by each of the authors involved and each, in their own way, consider a range of fundamental issues concerning the nature, place and use of multimedia - both in education and in society generally. By way of an introduction, the following provides a unifying context for the various contributions made here

The challenges of participatory research with 'tech-savvy' youth

This paper focuses on participatory research and how it can be understood and employed when researching children and youth. The aim of this paper is to provide a theoretically and empirically grounded discussion of participatory research methodologies with respect to investigating the dynamic and evolving phenomenon of young people growing up in networked societies. Initially, we review the nature of participatory research and how other researchers have endeavoured to involve young people (children and youth) in their research projects. Our review of these approaches aims to elucidate what we see as recurring and emerging issues with respect to the methodological design of involving young people as co-researchers. In the light of these issues and in keeping with our aim, we offer a case study of our own research project that seeks to understand the ways in which high school students use new media and network ICT systems (Internet, mobile phone applications, social networking sites) to construct identities, form social relations, and engage in creative practices as part of their everyday lives. The article concludes by offering an assessment of our tripartite model of participatory research that may benefit other researchers who share a similar interest in youth and new media