Intelligent assistants, such as navigation, recommender, and expert systems, are most helpful in situations where users lack domain knowledge. Despite this, recent research in cognitive psychology has revealed that lower-skilled individuals may maintain a sense of illusory superiority, which might suggest that users with the highest need for advice may be the least likely to defer judgment. Explanation interfaces - a method for persuading users to take a system's advice - are thought by many to be the solution for instilling trust, but do their effects hold for self-assured users? To address this knowledge gap, we conducted a quantitative study (N=529) wherein participants played a binary decision-making game with help from an intelligent assistant. Participants were profiled in terms of both actual (measured) expertise and reported familiarity with the task concept. The presence of explanations, level of automation, and number of errors made by the intelligent assistant were manipulated while observing changes in user acceptance of advice. An analysis of cognitive metrics lead to three findings for research in intelligent assistants: 1) higher reported familiarity with the task simultaneously predicted more reported trust but less adherence, 2) explanations only swayed people who reported very low task familiarity, and 3) showing explanations to people who reported more task familiarity led to automation bias.

As networked devices and applications make their way into our battlefields, their behaviors need to take into account these highly adversarial cyber-physical environments. On the dark side of the spectrum, undesired side-channels put our sensitive data at risk; hence, side-channel-protected devices and implementations should be promoted. On the bright side of the spectrum, side-channel analysis may be correlated with observed and hidden events, and enable causality inference and watermarking. This paper describes some unique risks and benefits that may be obtained from side-channel analyses in battlefields.

Learning an event classifier is challenging when the scenes are semantically different but visually similar. However, as humans, we typically handle such tasks painlessly by adding our background semantic knowledge. Motivated by this observation, we aim to provide an empirical study about how additional information such as semantic keywords can boost up the discrimination of such events. To demonstrate the validity of this study, we first construct a novel Malicious Crowd Dataset containing crowd images with two events, benign and malicious, which look visually similar. Note that the primary focus of this paper is not to provide the state-of-the-art performance on this dataset but to show the beneficial aspects of using semantically-driven keyword information. By leveraging crowd-sourcing platforms, such as Amazon Mechanical Turk, we collect semantic keywords associated with images and then subsequently identify a subset of keywords (e.g. police, fire, etc.) unique to specific events. We first show that by using recently introduced attention models, a naive CNN-based event classifier actually learns to primarily focus on local attributes associated with the discriminant semantic keywords identified by the Turks. We further show that incorporating the keyword-driven information into early-and late-fusion approaches can significantly enhance malicious event classification.

Managing the extremely large volume of information generated by Internet-of-Things (IoT) devices, estimated to be in excess of 400 ZB per year by 2018, is going to be an increasingly relevant issue. Most of the approaches to IoT information management proposed so far, based on the collection of IoT-generated raw data for storage and processing in the Cloud, place a significant burden on both communications and computational resources, and introduce significant latency. IoT applications would instead benefit from new paradigms to enable definition and deployment of dynamic IoT services and facilitate their use of computational resources at the edge of the network for data analysis purposes, and from smart dissemination solutions to deliver the processed information to consumers. This paper presents SPF (as in “Sieve, Process, and Forward”), an SDN solution which extends the reference ONF architecture replacing the Data Plane with an Information Processing and Dissemination Plane. By leveraging programmable information processors deployed at the Internet/IoT edge and disruption tolerant information dissemination solutions, SPF allows to define and manage IoT applications and services and represents a promising architecture for future urban computing applications.

In the near term, a multitude of IoT applications are expected, each taking advantage of heterogeneous device collections ranging from environmental sensors to smartphones. However, approaches taken in many IoT systems - based on the paradigm of Cloud computing - face challenges of both high latency and network utilization. A clear demand now exists for new paradigms to facilitate IoT application usage of computational resources at the edge of the network for data analysis purposes, as well as smart dissemination solutions to deliver information to consumers. This paper presents SPF (Sieve, Process, and Forward), a Software Defined Networking (SDN) solution for creating and managing IoT applications and services. By leveraging programmable information processors deployed at the Internet/IoT edge, the SDN approach introduced by SPF represents a promising architecture for future urban computing applications.

Analysts who are interested in quickly identifying new and emerging scientific advancements have numerous challenges as the breadth, depth, and volume of scientific literature increases. Network analysis and mining is key to the success in this task. The ARBITER system seeks to identify indicators of emergence and provide a system that is capable of analyzing corpora of full text and metadata to identify emerging science topics and explain its reasoning and conclusions. In this paper, we describe a network-modeling framework that is used in the ARBITER system, and describe our novel hybrid approach using probabilistic foundations in combination with semantic technology and introduce our explanation infrastructure. We include a discussion of some challenges and opportunities related to explaining hybrid approaches to indicator-based analysis and emergence detection.

We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our “Generalized Integrated Learning Architecture” (GILA) is a set of heterogeneous independent learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are weakly coupled in the sense that all coordination during learning and performance happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During performance, each ILR proposes partial solutions to subproblems posed by the MRE, which are then selected from and pieced together by the MRE to produce a complete solution. The heterogeneity of the learner-reasoners allows both learning and problem solving to be more effective because their abilities and biases are complementary and synergistic. We describe the application of this novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspaces need to be deconflicted, reconciled, and managed automatically. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.

The Open Government Directive is making US government data available via websites such as Data.gov for public access. In this paper, we present a Semantic Web based approach that incrementally generates Linked Government Data (LGD) for the US government. In focusing on the trade-off between high quality LGD generation (requiring non-trivial human expert input) and massive LGD generation (requiring low human processing cost), our work is highlighted by the following features: (i) supporting low-cost and extensible LGD publishing for massive government data; (ii) using Social Semantic Web (Web3.0) technologies to incrementally enhance published LGD via crowdsourcing, and (iii) facilitating mash-ups by declaratively reusing cross-dataset mappings which usually are hard-coded in applications.