The concept of modeling multiple complex adaptive systems (CAS) as if they were voting processes proposes that an Error Resilient Data Fusion (ERDF) method can help to mitigate the effects of emergent properties in CAS system-of-systems (SoS). The property of emergence in a CAS composed of multiple, multi-modal sensors poses specific problems for fusion processes due to the difficulty in predicting and accounting for sensor performance under disparate environmental conditions. This paper compares the voting and Choquet integral fusion methods in the context of a multi-modal sensor ERDF SoS.

If computational social science is to find practical application in informing policy decisions and proportionately analyzing courses of action, then it will have to make progress in the area of composition of social models.  Since a single simulation cannot hold a world of information, policy makers need to switch in and out modules in federations of simulations to test policies against all possible social environments.  Voting processes as they occur in nature, both in the form of cognition in a human mind of disparate world views, and in the form of equilibria seeking coevolution of species, inform how to combine model results externally and deeply, respectively.  These algorithms, which use the same principles of soft computation found in nature, enable any models to mesh together, even if they have different ontologies, or their data conflict, regardless of the degree they overlap.  A whiteboard architecture in which models report in their own ontologies how other models may inform them and what they have to offer other models, is a framework for the arbitrary meshing of social models.

Research in open-domain commonsense reasoning has been hindered by the lack of evaluation metrics for judging progress and comparing alternative approaches. Taking inspiration from large-scale question sets used in natural language processing research, we authored one thousand English-language questions that directly assess commonsense causal reasoning, called the Choice Of Plausible Alternatives (COPA) evaluation. Using a forced-choice format, each question gives a premise and two plausible causes or effects, where the correct choice is the alternative that is more plausible than the other. This paper describes the authoring methodology that we used to develop a validated question set with sufficient breadth to advance open-domain commonsense reasoning research. We discuss the design decisions made during the authoring process, and explain how these decisions will affect the design of high-scoring systems. We also present the performance of multiple baseline approaches that use statistical natural language processing techniques, establishing initial benchmarks for future systems.

We examine the performance of Total Variation (TV) smoothing for processing of noisy Electrocardiogram (ECG) recorded by an ambulatory device. The TV smoothing is compared with traditionally-used band-pass filtering using ECG with artificially added noise, as well as with real-world noise obtained during physiological monitoring. The fundamental difference between TV smoothing and traditional band-pass filtering is that TV smoothing allow preserving sharp slopes in the ECG, while traditional smoothing dampens them. Since the QRS complex represents a feature with steep slopes, the TV smoothing is a better choice ECG filtering. We found that TV smoothing outperforms traditional filtering on ECG signals recorded under different conditions and can be used as effective filtering tool in popular QRS detection algorithms.

Most of the developing countries are re-inventing the wheel in their efforts to launch egovernment initiatives — especially in the areas of healthcare, education, economic development, supply chains for food distribution, and emergency services. A Computer Aided Strategic Planner, part of the UN eNabler Toolset, has been developed to quickly and effectively produce detailed strategic plans for a wide range of egovernment services based on best practices and standards. The generated plan is highly customized for the type of service as well as the country/region by using the latest thinking in AI, ontologies, and patterns. The Planner, available through the UN-GAID initiative, can be and has been used very effectively to educate as well as assist the government officials of developing countries to accelerate progress in crucial areas.

There is an urgent need for interventions to assist teenagers and young adults in appreciating the physical and social risks of binge drinking. While research on the health risks associated with alcohol abuse is well developed, the translation and communication of this knowledge to young people is not. This paper describes a prototype visualisation tool, an Alcohol Risk Calculator, that provides personalised information on risks associated with alcohol consumption based on individual drinking habits. Its design is informed by studies of graphical literacy, evidence on forms of presenting risk that aid understanding, and theory that provides insight into changing health damaging behaviour.

A multi-robot system, like a robot formation, contains information that is distributed throughout the system. As the collective increases in numbers or explores distant or difficult areas, obtaining collective situational awareness becomes critical. We propose a method for extracting system and environmental information distributed over a collective of robots.

Trust is an approach to managing the uncertainty about autonomous entities and the information they store, and so can play an important role in any decentralized system. As a result, trust has been widely studied in multiagent systems and related fields such as the semantic web. Here we introduce a simple approach to reasoning about trust with logi

Many approaches have been introduced for representing and solving multiagent coordination problems. Unfortunately, these methods make assumptions that limit their usefulness when combined with human operators and real-life hardware and software. In this paper, we discuss the problem of using agents in conjunction with human operators to improve coordination as well as possible models that could be used in these problems. Our approach — Space Collaboration via an Agent Network (SCAN) — enables proxy agents to represent each of the stakeholder agencies in a space setting and shows how the SCAN agent network could facilitate collaboration by identifying opportunities and methods of collaboration. We discuss this approach as well as the challenges in extending models to 1) take advantage of human input, 2) deal with the limited and uncertain information that will be present and 3) combat the scalability issues in solution methods for a large number of decentralized agents. As a first step toward providing rich models for these domains, we describe a method to bound the solution quality due to bounded model uncertainty.

Our research is directed towards agile supply chains enabling enterprises to quickly respond to individual customer demand. From this perspective, agility encompasses three dimensions of adaptivity: space, time, and economy. Supply chain agility can be achieved by exploiting the most fundamental resource of any enterprise: knowledge. Studying supply chains, we regard all their tiers, participants, and potential relationships, as the search space for fulfilling individual customer demand. We study supply chains from a knowledge-based coordination perspective and regard logistics as the guiding conceptualization. The contribution of this research is a logistics knowledge infrastructure. We report about applying parts of this infrastructure to coordination problems in three selected case studies.