Spoken language is an important and natural way for people to communicate with computers. Nonetheless, habitable, reliable, and efficient human-machine dialogue remains difficult to achieve. This paper describes a multi-threaded semi-synchronous architecture for spoken dialogue systems. The focus here is on its utterance interpretation module. Unlike most architectures for spoken dialogue systems, this new one is designed to be robust to noisy speech recognition through earlier reliance on context, a mixture of rationales for interpretation, and fine-grained use of confidence measures. We report here on a pilot study that demonstrates its robust understanding of users’ objectives, and we compare it with our earlier spoken dialogue system implemented in a traditional pipeline architecture. Substantial improvements appear at all tested levels of recognizer performance.

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.

Sustainable design is as an important movement in design. Biologically inspired design is a major paradigm for sustainable design. In this paper, we analyze a corpus of biologically inspired design projects in terms of sustainability. We then describe a case study of analogical design of a fog harvesting net, and abstract from it the patterns of Hydrophobia and Hydrophilia. We indicate how these two function-mechanism design patterns occur in several design projects in our corpus. This analysis indicates how biologically inspired sustainable design can be analyzed in terms of cross-domain analogical transfer of design patterns.

The Jobs Puzzle, introduced in a book about automated reasoning, is a logic puzzle solvable by some "intelligent sixth graders," but the formalization of the puzzle by the authors was, according to them, "sometimes difficult and sometimes tedious." The puzzle thus presents a triple challenge: 1) formalize it in a non-difficult, non-tedious way; 2) formalize it in a way that adheres closely to the English statement of the puzzle; 3) have an automated general-purpose commonsense reasoner that can accept that formalization and solve the puzzle quickly. In this paper, I present and discuss three formalizations that are less difficult and less tedious than the original. However, none satisfy all three requirements as well as might be desired, and there are a significant number of automated reasoners that cannot solve the puzzle using any of the formalizations. So the Jobs Puzzle remains an interesting challenge.

The increasing agility of business requires an accelerated adaptation of organizations to continuously changing conditions. Individual and organizational learning are prominent means to achieve this. Hereby learning is always accompanied by the development of knowledge artifacts. For the entire of learning and artifact development the term knowledge maturing has been introduced recently, which focuses on these three manifestations of knowledge: cognifacts, sociofacts, and artifacts. In this paper we will focus on sociofacts as the subject-bound knowledge manifestation of social actions. Sociofacts are rooted in respective cognifacts play an independent role due to their binding to collective actions and subjects. These are particularly difficult to grasp but play a decisive role for the performance of organizations and the collaboration in there.The presented paper approaches the notion of sociofacts, discusses them on a theoretical level and establishes a first formal notation for sociofacts. We use the case of a merger between two companies to describe the advantages of sociofact analysis for such process. Some sociofact related problems during a merger are described and possible solutions are presented. We identify technical approaches for seizing sociofacts from tool-mediated social interaction and discuss open question for future research.

Formations are often seen in nature, and bring many benefits for the group as a whole. They can allow a group to explore a large area more effectively, can ease movement of the group through the environment, and can increase group perceptual coverage and increase defensive capabilities, for example. The benefits of any particular formation vary and are obtained from the structure the formation provides. Robotic formations can have similar applications. To date, the techniques used and formations employed in robotic applications are significantly simpler than those seen in nature. Current techniques often require some level of global knowledge, central processing or other unrealistic assumptions. We seek to develop a formation control technique that has as few of these limitations as possible. Each agent under our approach has only local knowledge of the environment, uses no broadcast communication, and can communicate only over a limited range. Formations are achieved by organizing agents into a graph structure, where agents occupying the vertices take on the role of maintaining an appropriate number of agents on each edge, thus preserving the formation's shape and scale. We do not assume a known or static population: the evolving formation acts as a physical data structure to assist in placing and rearranging agents as the population changes. This approach does not require a global coordinate system, fixed positions within the formation, or any single lead agent. All agents within our approach are peers, and any can adopt any role within the formation.

Considering few treatments are available to slow or stop neurodegenerative disorders, such as Alzheimer’s disease and related dementias (ADRD), modifying lifestyle factors to prevent disease onset are recommended. The Voice, Activity, and Location Monitoring system for Alzheimer’s disease (VALMA) is a novel ambulatory sensor system designed to capture natural behaviours across multiple domains to profile lifestyle risk factors related to ADRD. Objective measures of physical activity and sleep are provided by lower limb accelerometry. Audio and GPS location records provide verbal and mobility activity, respectively. Based on a familiar smartphone package, data collection with the system has proven to be feasible in community-dwelling older adults. Objective assessments of everyday activity will impact diagnosis of disease and design of exercise, sleep, and social interventions to prevent and/or slow disease progression.

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.

The fundamental assumption being, any hypothesis found to approximate well over a sufficiently large Surface Electromyogram (SEMG) signals are physiological set of training examples will also approximate well over signals processed to assess the intensity of activity and the other unobserved examples (Mitchell 1997), belonging to fatigue state of the muscles, non-invasively (Kumar, Pah, the same distribution as the training data. But if this basic and Bradley 2003; Georgakis, Stergioulas, and Giakas 2003; assumption is violated as in the case of SEMG data over Koumantakis et al. 2001; Gerdle, Larsson, and Karlsson multiple subjects, direct application of traditional data mining 2000). However researches observed significant difference and machine learning methods would not work. Figure 1 between the data collected from different subjects shows a typical distribution of SEMG data for three different though they performed the same activity under similar experimental subjects, collected over a fatiguing exercise at varying speed conditions (Contessa, Adam, and Luca 2009; representing the four physiological phases corresponding to Gerdle, Larsson, and Karlsson 2000). Because of their four classes (l) low intensity of activity and low fatigue, (2) highly subject specific nature the SEMG based fatigue assessment high intensity of activity and moderate fatigue, (3) low intensity requires subject specific calibration and are hence of activity and moderate fatigue and (4) high intensity confined to clinical environments related to training and rehabilitation. of activity and high fatigue.

Puck Clustering, a particularly widely studied problem domain for self-organized multi-robot systems, involves gathering spatially distributed objects, called pucks, into piles within a planar workspace. Structures in the environment (partially formed clusters) encode information about where clusters should be formed, and their positions are involved in the mechanics of subsequent cluster formation. In this paper, we consider questions regarding the spatial distribution of robots and clusters, and their relation to the boundaries of the workspace. Prior theoretical analysis has assumed a uniform distribution of robots for gathering all objects into a single pile. Yet, in some instances, a disproportionate amount of time may be spent by robots on the boundary. Also, others have documented that the boundary can cause cluster growth itself. This paper considers the problem of clustering square boxes in the center of the workspace. The flat edges of these objects appear to exacerbate the affinity for cluster growth near boundaries. However, by exploiting the shape of our objects, we show that novel "Twisting" and "Digging" operations overcome this effect and enhance production of central clusters. We analyze the dynamics of boundary versus central puck clusters, and investigate how the spatial distribution of the robots changes along with the clustering process: showing stark differences between the standard mode of clustering and the mode we introduce here.