Stand der Technik: Anschmierungen in Radial-Zylinderrollenlagern

Forschung im Ingenieurwesen - Anschmierungen innerhalb von Wälzlagern zählen zu den Verschleißarten, die bis heute nicht vollständig verstanden wurden. Bekannt ist, dass...     

Visualizing music similarity: clustering and mapping 500 classical music composers

Scientometrics - This paper applies clustering techniques and multi-dimensional scaling (MDS) analysis to a 500?×?500 composers’ similarity/distance matrix. The objective is...     

Adversarial frontier stitching for remote neural network watermarking

Neural Computing and Applications - The state-of-the-art performance of deep learning models comes at a high cost for companies and institutions, due to the tedious data collection and the heavy...     

Self-reported discomfort when using commercially targeted virtual reality equipment in discomfort distraction

Commercially targeted virtual reality (VR) equipment is gaining popularity and might be a viable tool for pain distraction. This experimental research aimed to discover whether active distraction techniques (such as commercially targeted VR and video games) result in reduced subjective discomfort relative to passive distraction techniques. The study examined a healthy adult population who experienced an experimentally induced discomfort task. Participants were 27 adults, 14 females and 13 males. Participants completed four tasks, a baseline measure of physical discomfort, video clip distraction (passive distraction), video game distraction (active distraction) and exploring a VR world using an Oculus Rift head-mounted display (active distraction). In all four test conditions, participants were asked to sit on a chair holding their non-dominant leg at a height of approximately 30 cm from the floor, up to a maximum of 5 min. Counterbalancing of task order was conducted to reduce effects of participant fatigue. The participants indicated significantly reduced self-reported discomfort in the active distraction tasks when compared to the passive distraction tasks. While the findings demonstrate the effectiveness of a commercially targeted VR technology in increasing pain tolerance, the relative benefits of this technology over non-immersive video games are not apparent.     

Harnessing Artificial Intelligence to Deliver Real-Time Intelligence and Business Process Improvements

As demand for data scientists in audit/Governance, risk management and compliance (GRC), and industry in general, outpaces supply, data science in a box—packaged analytics powered by artificial intelligence (AI) and guided machine learning—can bridge the gap to bring analytics to every major enterprise. Packaged analytics harness the power of AI and machine learning technologies to help operations, finance executives, and GRC professionals do their jobs better; optimize business processes; and deliver actionable insights for better decision making. This article will explore real-world case studies of how companies have used packaged analytics to achieve process improvements, better oversight over financial spend, and significant return on investment. It is a guide to internal auditors and their GRC counterparts on what is available and suggests they can partner or use the products independently and significantly contribute to their companies.     

Applying robotic frameworks in a simulated multi-agent contest

In the Multi-Agent Programming Contest 2017 the TUBDAI team of the Technische Universität Berlin is using the complex multi-agent scenario to evaluate the application of two frameworks from the field (multi-)robot systems. In particular the task-level decision-making and planning framework ROS Hybrid Behaviour Planner (RHBP) is used to implement the execution and decision-making for single agents. The RHBP framework builds on top of the framework Robot Operating System (ROS) that is used to implement the communication and scenario specific coordination strategy of the agents. The united team for the official contest is formed by volunteering students from a project course and their supervisors.     

Information from social networking sites: Context collapse and ambiguity in the hiring process

Forming impressions of job candidates is a challenging process, one characterized by ambiguity brought about by the uncertainty associated with making decisions and judgments. To reduce ambiguity, hiring professionals have established policies and procedures to facilitate the sourcing and use of information about a candidate. However, recently, a public source of information is increasingly being used—information from social networking sites (SNSs). While conventional wisdom says more information is better and can help make decisions less ambiguous, this relationship may not be as straightforward as expected when facing assessments of candidates. This paper examines two such aspects, information‐task quality and context collapse, and their collective impact on ambiguity when making an assessment of a job candidate. Using data from an online survey‐based experiment, the findings suggest information from SNSs can be useful, yet can create ambiguity for decision makers because of context collapse made possible by SNS technologies.     

Geometric transformations parameters estimation from copy-move forgery using image blobs and keypoints

A copy-move forgery is a passive tampering wherein one or more regions have been copied and pasted within the same image. Often, geometric transformations, including scale, rotation, and rotation+scale are applied to the forged areas to conceal the counterfeits to the copy-move forgery detection methods. Recently, copy-move forgery detection using image blobs have been used to tackle the limitation of the existing detection methods. However, the main limitation of blobs-based copy-move forgery detection methods is the inability to perform the geometric transformation estimation. To tackle the above-mentioned limitation, this article presents a technique that detects copy-move forgery and estimates the geometric transformation parameters between the authentic region and its duplicate using image blobs and scale-rotation invariant keypoints. The proposed algorithm involves the following steps: image blobs are found in the image being analyzed; scale-rotation invariant features are extracted; the keypoints that are located within the same blob are identified; feature matching is performed between keypoints that are located within different blobs to find similar features; finally, the blobs with matched keypoints are post-processed and a 2D affine transformations is computed to estimate the geometric transformation parameters. Our technique is flexible and can easily take in various scale-rotation invariant keypoints including AKAZE, ORB, BRISK, SURF, and SIFT to enhance the effectiveness. The proposed algorithm is implemented and evaluated on images forged with copy-move regions combined with geometric transformation from standard datasets. The experimental results indicate that the new algorithm is effective for geometric transformation parameters estimation.

     

An audio enhancement system to improve intelligibility for social-awareness in HRI

Multimedia Tools and Applications - Improving the ability to interact through voice with a robot is still a challenge especially in real environments where multiple speakers coexist. This work has...