Spatial and semantic interference on an auditory Stroop test: comparison of young and older adults

Background and Aim: Age related changes in cognitive functioning have been shown to vary depending on the task used. Thus, the study aimed to compare the responses of young and older adults to an auditory Stroop test that asse­ssed spatial (responses to location of the stimuli) and semantic (responses to meaning of the sti­muli) localization. Methods: The “Auditory spatial and semantic localization Stroop test”, developed as a part of the study was administered on 30 young adults aged 18 to 30 years and 30 older adults aged 58 to 70 years having normal hearing. The res­ponse accuracy and reaction time of the parti­cipants were determined for the words “right”, “left”, “front”, and “back.” Results: The older adults had significantly poo­rer response accuracy and reaction time than the young adults for both spatial and semantic loca­lization tasks. Within each participant group, semantic localization had better response accu­racy than spatial localization, while such diffe­rences in reaction time were found only in the older adults. In both groups, a congruency effect was seen for spatial but not for semantic loca­lization when response accuracy was calculated, whereas it was observed only for semantic and not for spatial localization when reaction time was measured. Conclusion: The auditory Stroop test, which measures stimulus interference and cognitive skills, could be used as a simple tool to assess the same for stimuli presented through the audi­tory modality. This would be especially helpful in older adults who may demonstrate cognitive decline with ageing to auditory stimuli. Keywords: Spatial localization; semantic localization; auditory Stroop test; age related changes

Locater

This paper explores the data cleaning challenges that arise in using WiFi connectivity data to locate users to semantic indoor locations such as buildings, regions, rooms. WiFi connectivity data consists of sporadic connections between devices and nearby WiFi access points (APs), each of which may cover a relatively large area within a building. Our system, entitled semantic LOCATion cleanER (LOCATER), postulates semantic localization as a series of data cleaning tasks - first, it treats the problem of determining the AP to which a device is connected between any two of its connection events as a missing value detection and repair problem. It then associates the device with the semantic subregion (e.g., a conference room in the region) by postulating it as a location disambiguation problem. LOCATER uses a bootstrapping semi-supervised learning method for coarse localization and a probabilistic method to achieve finer localization. The paper shows that LOCATER can achieve significantly high accuracy at both the coarse and fine levels.

Adaptive Context-Aware Design Using Context State Information for the Internet of Things Paradigm

In the Internet of Things (IoT) paradigm, context state information plays a critical role in advancing the development of adaptive pervasive applications. Pervasive services and context-aware computing are emerging as the next computing paradigms in which infrastructure and services are seamlessly available anywhere, anytime, and in any format. The IoT paradigm raises new opportunities and demands on the underlying systems, in particular, the need to have systems that are adaptive and context-aware using context state information. In this paper, we introduce a new adaptive context state design technique to model context-aware applications that are sensitive to context state information changes. Each context change event is captured, interpreted and reacted to so that applications and users use only the functionality and adaptability needs that are solutions to their needs. The solution is modeled using Finite State Machine (FSM) and semantic localization so that context state information within the IoT paradigm is aligned to events. The semantic localization process precisely estimates the proximity location of the user along with the quality of context (QoC) attributes using the Bluetooth cell-based approach. This semantic information is useful in determining and inferring the user activities in a location. The QoC attributes are used to determine the confidence of the user location and range of the Bluetooth beacons within the IoT domain. This will, in turn, be used to determine whether the user is in the location or not. The alignment technique in our model represents the proper and new solution concerning functionality and adaptability needs expressed by other user applications in the IoT environment. The experimental scenario results indicate that a user can continue to enjoy their daily activities while the IoT application adapts continuously to their changing needs and notifying service providers of the changes according to the events of the user.