Animal behavior analysis has, in the past, taken a very low tech approach, with direct observer surveillance and automated video surveillance as the norm. These methods are insufficient when one wants to study interactions between large numbers of animals in their housing environment. In this paper we use a housing environment that has been equipped with a system of RFID sensors. RFID transponders were implanted into the study animal, the naked mole rat. The resulting data was analyzed using principal component analysis and frequent pattern mining. Results showed that these methods can identify time periods of high behavioral activity from that of low activity, along with which groups of animals interacted with one another

In this paper, we investigate the application of data mining to existing techniques for quality control/anomaly detection on weather sensor observations. Specifically we adapt the popular Barnes Spatial interpolation method to use time-series distance rather than spatial distance to develop an online algorithm that uses readings from similar stations based on current and historical observations for interpolation and we demonstrate that this new algorithm exhibits less model error than the Barnes Spatial interpolation-based method. We focus on interpolation, which is a basis for this popular quality control method and other related methods, and examine a dataset of over 233 million temperature observations from California and surrounding areas. Our approach shows improved performance as indicated by mean squared error reduced by approximately one half for predicted values versus reported values.

Protecting our nation’s ports is a critical challenge for homeland security and requires the research, development and deployment of new technologies that will allow for the efficient securing of shipments entering this country. Most approaches look only at statistical irregularities in the attributes of the cargo, and not at the relationships of this cargo to others. However, anomalies detected in these relationships could add to the suspicion of the cargo, and therefore improve the accuracy with which we detect suspicious cargo. This paper proposes an improvement in our ability to detect suspicious cargo bound for the U.S. through a graph-based anomaly detection approach. Using anonymized data received from the Department of Homeland Security, we demonstrate the effectiveness of our approach and its usefulness to a homeland security analyst who is tasked with uncovering illegal and potentially dangerous cargo shipments.

This work investigates the applicability of several dimensionality reduction techniques for large scale solar data analysis. Using the first solar domain-specific benchmark dataset that contains images of multiple types of phenomena, we investigate linear and non-linear dimensionality reduction methods in order to reduce our storage costs and maintain an accurate representation of our data in a new vector space. We present a comparative analysis between several dimensionality reduction methods and different numbers of target dimensions by utilizing different classifiers in order to determine the percentage of dimensionality reduction that can be achieved on solar data with said methods, and to discover the method that is the most effective for solar images.

We propose and give a preliminary test of a new metric for the quality of the commonsense knowledge and reasoning of large AI databases: Using the same measurement as is used for a four-year-old, namely, an IQ test for young children. We report on results obtained us- ing test questions we wrote in the spirit of the questions of the Wechsler Preschool and Primary Scale of Intelligence, Third Edition (WPPSI-III) on the ConceptNet system, which were, on the whole, quite strong.

Artifact capabilities can play an important role in understanding human cognition. Over time humans learn to use artifacts, evolve the knowledge and combine acquired capabilities with others to form complex capabilities. In this study we present a hybrid ontology of artifacts to facilitate learning artifact capabilities. We develop a framework where agents simultaneously exploit a centralized artifact ontology in the environment and a distributed artifact ontology local to each agent. We demonstrate how both ontologies can be used by agents both in the artifact selection process and in learning artifact use. The local ontology serves as domain knowledge gained by the agent as it learns. We illustrate an example to show how an acquired artifact capability can be stored in an agent's local ontology for future use.

Event Management is one of the most lucrative and growing professions today. At present event management is done by humans. With the growing demand for managing large events, there is a rising demand for building intelligent systems to manage events. The so called event management systems today are only data processing systems that are unable to carry out decision making task on their own. Event management systems today do not consider emergencies and risk assessment as part of their execution. In this paper, we present an approach for representing events and monitor their execution. In particular, discuss the exceptions that can occur during an event execution and how they can be managed using event management rules. We present strategies for writing management rules that are used to handle problematic events and to build a DAG based programming system for event management. Our simulation results show how the performance of our event management system performs with the exception management rules.

This paper addresses questions of the transition related to conscious processes and unconscious processes, namely aims to substantiating a primary framework to the following open question: The vast majority of brain activity is non-conscious. What is the criterion to distinguish the non-conscious activities from conscious ones? To support our answers in a principled way, we present a general framework for the study of mental processes resting on two main principles: firstly, we endorse Matte Blanco’s principle of symmetry by giving central stage to the concept of unconscious processes. Secondly, to structure and combine the notions of infinity and part-whole equivalence in a mathematical logic method, moreover we base our work on modern non-classical logics in the disposition of context-dependency, as forcefully put forward by CJS Clarke. In particular, we employ the paraconsistent logic as the underlying logical system for defining the general framework for mental processes, highly structural and formal representation, called bi-logic framework.

Household appliances such as dishwashers, televisions and radios are an indispensable part of the modern household. Yet, people who have some form of physical impairment often find that they are unable to make use of these commonly available appliances, to the detriment of their lifestyle. This paper proposes a gesture interface for home appliances that can be used by people with physical impairments. Two simulated gesture controlled appliances are developed and evaluated by physically impaired people. The results show that this interface is able to allow physically impaired people to make use of modern appliances by gesture.

The increasingly prevalent view that recommendation is a conversation between user and system is driving a renewed interest in approaches to system design that involve the user in meaningful ways. In addition to this the proliferation of mobile devices and the near-ubiquity of sensing technologies means that there are now many opportunities to capture real-life experiences, in real-time, providing a new source of raw material for case-based reasoning. In this paper we consider the availability of real-world exercise information, in this cases corresponding to jogging routes, and meth- ods by which we can involve a user in recommending such routes. We describe the Exercise Builder, a proof-of-concept application that attempts to help visitors to a new city to plan their jogging routes by combining case retrieval, interactive adaptation, and multimedia explanation in a single online service.