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DUO-GAIT
(2023)
In recent years, there has been a growing interest in developing and evaluating gait analysis algorithms based on inertial measurement unit (IMU) data, which has important implications, including sports, assessment of diseases, and rehabilitation. Multi-tasking and physical fatigue are two relevant aspects of daily life gait monitoring, but there is a lack of publicly available datasets to support the development and testing of methods using a mobile IMU setup. We present a dataset consisting of 6-minute walks under single- (only walking) and dual-task (walking while performing a cognitive task) conditions in unfatigued and fatigued states from sixteen healthy adults. Especially, nine IMUs were placed on the head, chest, lower back, wrists, legs, and feet to record under each of the above-mentioned conditions. The dataset also includes a rich set of spatio-temporal gait parameters that capture the aspects of pace, symmetry, and variability, as well as additional study-related information to support further analysis. This dataset can serve as a foundation for future research on gait monitoring in free-living environments.
Ubiquitous computing has proven its relevance and efficiency in improving the user experience across a myriad of situations. It is now the ineluctable solution to keep pace with the ever-changing environments in which current systems operate. Despite the achievements of ubiquitous computing, this discipline is still overlooked in business process management. This is surprising, since many of today’s challenges, in this domain, can be addressed by methods and techniques from ubiquitous computing, for instance user context and dynamic aspects of resource locations. This paper takes a first step to integrate methods and techniques from ubiquitous computing in business process management. To do so, we propose discovering commute patterns via process mining. Through our proposition, we can deduce the users’ significant locations, routes, travel times and travel modes. This information can be a stepping-stone toward helping the business process management community embrace the latest achievements in ubiquitous computing, mainly in location-based service. To corroborate our claims, a user study was conducted. The significant places, routes, travel modes and commuting times of our test subjects were inferred with high accuracies. All in all, ubiquitous computing can enrich the processes with new capabilities that go beyond what has been established in business process management so far.
In this article, we discuss the notions of experts and expertise in resource discovery in the context of collaborative tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high-quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm, SPEAR (spamming-resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext-induced topic search algorithm and simple statistical measures currently used in most collaborative tagging systems.
This article addresses the problem of estimating the Quality of Service (QoS) of a composite service given the QoS of the services participating in the composition. Previous solutions to this problem impose restrictions on the topology of the orchestration models, limiting their applicability to well-structured orchestration models for example. This article lifts these restrictions by proposing a method for aggregate QoS computation that deals with more general types of unstructured orchestration models. The applicability and scalability of the proposed method are validated using a collection of models from industrial practice.
Text displayed in a video is an essential part for the high-level semantic information of the video content. Therefore, video text can be used as a valuable source for automated video indexing in digital video libraries. In this paper, we propose a workflow for video text detection and recognition. In the text detection stage, we have developed a fast localization-verification scheme, in which an edge-based multi-scale text detector first identifies potential text candidates with high recall rate. Then, detected candidate text lines are refined by using an image entropy-based filter. Finally, Stroke Width Transform (SWT)- and Support Vector Machine (SVM)-based verification procedures are applied to eliminate the false alarms. For text recognition, we have developed a novel skeleton-based binarization method in order to separate text from complex backgrounds to make it processible for standard OCR (Optical Character Recognition) software. Operability and accuracy of proposed text detection and binarization methods have been evaluated by using publicly available test data sets.
Resynthesis of handshake specifications obtained e. g. from BALSA or TANGRAM with speed-independent logic synthesis from STGs is a promising approach. To deal with state-space explosion, we suggested STG decomposition; a problem is that decomposition can lead to irreducible CSC conflicts. Here, we present a new approach to solve such conflicts by introducing internal communication between the components. We give some first, very encouraging results for very large STGs concerning synthesis time and circuit area.
Logic synthesis of speed independent circuits based on signal transition graph (STG) decomposition is a promising approach to tackle complexity problems like state-space explosion. Unfortunately, decomposition can result in components that in isolation have irreducible complete state coding conflicts. In earlier work, the authors showed how to resolve such conflicts by introducing internal communication between components, but only for very restricted specification structures. Here, they improve their former work by presenting algorithms for identifying delay transitions and inserting gyroscopes for specifications having a much more general structure. Thus, the authors are now able to synthesise controllers from real-life specifications. For all algorithms, they present correctness proofs and show their successful application to benchmarks, including very complex STGs arising in the context of control resynthesis.
With the growth of virtualization and cloud computing, more and more forensic investigations rely on being able to perform live forensics on a virtual machine using virtual machine introspection (VMI). Inspecting a virtual machine through its hypervisor enables investigation without risking contamination of the evidence, crashing the computer, etc. To further access to these techniques for the investigator/researcher we have developed a new VMI monitoring language. This language is based on a review of the most commonly used VMI-techniques to date, and it enables the user to monitor the virtual machine's memory, events and data streams. A prototype implementation of our monitoring system was implemented in KVM, though implementation on any hypervisor that uses the common x86 virtualization hardware assistance support should be straightforward. Our prototype outperforms the proprietary VMWare VProbes in many cases, with a maximum performance loss of 18% for a realistic test case, which we consider acceptable. Our implementation is freely available under a liberal software distribution license. (C) 2014 Digital Forensics Research Workshop. Published by Elsevier Ltd. All rights reserved.
Detecting anomalous subsequences in time series is an important task in time series analytics because it serves the identification of special events, such as production faults, delivery bottlenecks, system defects, or heart flicker.
Consequently, many algorithms have been developed for the automatic detection of such anomalous patterns. The enormous number of approaches (i.e., more than 158 as of today), the lack of properly labeled test data, and the complexity of time series anomaly benchmarking have, though, led to a situation where choosing the best detection technique for a given anomaly detection task is a difficult challenge.
In this demonstration, we present TIMEEVAL, an extensible, scalable and automatic benchmarking toolkit for time series anomaly detection algorithms. TIMEEVAL includes an extensive data generator and supports both interactive and batch evaluation scenarios. With our novel toolkit, we aim to ease the evaluation effort and help the community to provide more meaningful evaluations.