Personalising Vibrotactile Displays through Perceptual Sensitivity Adjustment

Haptic displays are commonly limited to transmitting a discrete set of tactile motives. In this paper, we explore the transmission of real-valued information through vibrotactile displays. We simulate spatial continuity with three perceptual models commonly used to create phantom sensations: the linear, logarithmic and power model. We show that these generic models lead to limited decoding precision, and propose a method for model personalization adjusting to idiosyncratic and spatial variations in perceptual sensitivity. We evaluate this approach using two haptic display layouts: circular, worn around the wrist and the upper arm, and straight, worn along the forearm. Results of a user study measuring continuous value decoding precision show that users were able to decode continuous values with relatively high accuracy (4.4% mean error), circular layouts performed particularly well, and personalisation through sensitivity adjustment increased decoding precision

Moregrasp: Restoration of Upper Limb Function in Individuals with High Spinal Cord Injury by Multimodal Neuroprostheses for Interaction in Daily Activities

The aim of the MoreGrasp project is to develop a noninvasive, multimodal user interface including a brain-computer interface (BCI) for intuitive control of a grasp neuroprosthesis to support individuals with high spinal cord injury (SCI) in everyday activities. We describe the current state of the project, including the EEG system, preliminary results of natural movements decoding in people with SCI, the new electrode concept for the grasp neuroprosthesis, the shared control architecture behind the system and the implementation of a user-centered design

Goal setting for innovation : Exploring the relation to operational goals

Goal setting is proven to affect the choices and focus of the employees towards certain tasks, therefore it has been suggested for ensuring that sufficient focus and attentions is direct towards explorative activities (Lund & Magnusson, 2015), which are otherwise at risk of being down prioritized (Levinthal & March, 1993). Such effects of goal setting can though not be expected in situations where there is low commitment to the goal program (Latham, 2004). A factor which is important for increasing the goal commitment, is having clear rationales for the goals set. However, because there are few studies on goal setting for innovation, rationales for establishing innovation goals and hinderers affecting goal commitment have still not been researched from an innovation perspective. Moreover, much of the existing goal setting theory has derived from research related to productivity aspects (Latham & Yukl, 1975;Lund & Magnusson, 2015) which have more in common with operational activities, than innovation activities. The purpose of this study is to identify rationales- and hinders to goal commitment when establishing innovation goals, and to examine how goal setting features for exploration (referred to as innovation goals) differs from goal setting features for exploitation (referred to as operational goals). This research has been conducted as case study at Sandvik Coromant, where both qualitative and quantitative data was collected. Interviews and surveys were done with the help of 32 managers from different departments and managerial levels. The findings are limited to R&D manager’s perceptions of goal setting. Findings show that rationales for establishing innovation goals are; to directing attention towards- and inspire exploration, to bring innovation to a concrete level and create knowledge, to satisfy prerequisites of innovation and/or innovation culture, to come up with new ways of working or improving processes, and to generate new ideas or/and present and implementing them. Hinders affecting goal commitment were identified as the following; misconceptions about innovation from defining it as output only, failing to convince individuals that the goal program is important and lack of resources. Further, according to this study goals for innovation differ from goals for operational activities. Goal features preferred for the two were opposites to each other. Goal features for operational activities aim to reduce variance and control the performance outcome, whereas goal features for innovation aim to trigger essential parts of innovation such as creativity, learning and experimentation, and therefore increases variance.

Importing the OEIS library into OMDoc

Abstract. The On-line Encyclopedia of Integer Sequences (OEIS) is the largest database of its kind and an important resource for mathematicians. The database is well-structured and rich in mathematical content but is informal in nature so knowledge management services are not directly applicable. In this paper we provide a partial parser for the OEIS that leverages the fact that, in practice, the syntax used in its formulas is fairly regular. Then, we import the result into OMD to make the OEIS accessible to OMD -based knowledge management applications. We exemplify this with a formula search application based on the M W S system

Comparing driving behavior of humans and autonomous driving in a professional racing simulator.

Motorsports have become an excellent playground for testing the limits of technology, machines, and human drivers. This paper presents a study that used a professional racing simulator to compare the behavior of human and autonomous drivers under an aggressive driving scenario. A professional simulator offers a close-to-real emulation of underlying physics and vehicle dynamics, as well as a wealth of clean telemetry data. In the first study, the participants' task was to achieve the fastest lap while keeping the car on the track. We grouped the resulting laps according to the performance (lap-time), defining driving behaviors at various performance levels. An extensive analysis of vehicle control features obtained from telemetry data was performed with the goal of predicting the driving performance and informing an autonomous system. In the second part of the study, a state-of-the-art reinforcement learning (RL) algorithm was trained to control the brake, throttle and steering of the simulated racing car. We investigated how the features used to predict driving performance in humans can be used in autonomous driving. Our study investigates human driving patterns with the goal of finding traces that could improve the performance of RL approaches. Conversely, they can also be applied to training (professional) drivers to improve their racing line

On Graph Classification Networks, Datasets and Baselines

Graph classification receives a great deal of attention from the non-Euclidean machine learning community. Recent advances in graph coarsening have enabled the training of deeper networks and produced new state-of-the-art results in many benchmark tasks. We examine how these architectures train and find that performance is highly-sensitive to initialisation and depends strongly on jumping-knowledge structures. We then show that, despite the great complexity of these models, competitive performance is achieved by the simplest of models -- structure-blind MLP, single-layer GCN and fixed-weight GCN -- and propose these be included as baselines in future

Passive Haptic Learning for Vibrotactile Skin-Reading: Comparison of Teaching Structures

This paper investigates the effects of using passive haptic learning to train the skill of reading text from vibrotactile patterns. The vibrotactile method of transmitting messages, skin-reading, is effective at conveying rich information but its active training method requires full user attention, is demanding, time-consuming, and tedious. Passive haptic learning offers the possibility to learn in the background while performing another primary task. We present a study investigating the use of passive haptic learning to train for skin-reading. Additionally, a word-based learning structure is typically used for this passive learning method. We expose trends that suggest this word-based incrimental teaching may not be optimal

Implementation Aspects of Anonymous Credential Systems for Mobile Trusted Platforms

Part 1: Research PapersInternational audienceAnonymity and privacy protection are very important issues for Trusted Computing enabled platforms. Protection mechanisms are required in order to hide activities of the trusted platforms when performing cryptography based transactions over the Internet, which would otherwise compromise the platform’s privacy and with it the users’s anonymity. In order to address this problem, the Trusted Computing Group (TCG) has introduced two concepts addressing the question how the anonymity of Trusted Platform Modules (TPMs) and their enclosing platforms can be protected. The most promising of these two concepts is the Direct Anonymous Attestation (DAA) scheme which eliminates the requirement of a remote authority but includes complex mathematical computations. Moreover, DAA requires a comprehensive infrastructure consisting of various components in order to allow anonymous signatures to be used in real-world scenarios. In this paper, we discuss the results of our analysis of an infrastructure for anonymous credential systems which is focused on the Direct Anonymous Attestation (DAA) scheme as specified by the TCG. For the analysis, we especially focus on mobile trusted platforms and their requirements. We discuss our experiences and experimental results when designing and implementing the infrastructure and give suggestions for improvements and propose concepts and models for - from our point of view - missing components