IMC Information Multimedia Communication (Germany)

This article proposes a novel approach to segment instances of bulk material heaps in aerial data using deep learning-based computer vision and transfer learning to automate material inventory procedures in the construction-, mining-, and material-handling industry. The proposed method uses information about color, texture, and surface topography as input features for a supervised computer vision algorithm. The approach neither relies on hand-crafted assumptions on the general shape of heaps, nor does it solely rely on surface material type recognition. Therefore, the method is able to (1) segment heaps with “atypical” shapes, (2) segment heaps that stand on a surface made of the same material as the heap itself, (3) segment individual heaps of the same material type that border each other, and (4) differentiate between artificial heaps and other objects of similar shape like natural hills. To utilize well-established segmentation algorithms for raster-grid-based data structures, this study proposes a pre-processing step to remove all overhanging occlusions from a 3D surface scan and convert it into a 2.5D raster format. Preliminary results demonstrate the general feasibility of the approach. The average F1 score computed on the test set was 0.70 regarding object detection and 0.90 regarding the pixelwise segmentation.

We report two 6-week studies, each with 10 participants, on improving time management. In each study a different interventions was administered, in parallel to otherwise regular work: In the self-tracking setting, participants used only an activity logging tool to track their time use and a reflective practice, namely daily review of time use, to improve time management. In the coaching setting, participants did the same, but additionally received weekly bilateral coaching. In both settings, participants reported learning about time management. This is encouraging, as such self-directed learning is clearly cheaper than coaching. Only participants in the coaching setting however improved their self-assessment with respect to predefined time management best practices.

More, perhaps, than any other industry, the telecommunications business is reliant upon gathering precise information about its customers. After all, hardly any other industry has proved to be as restless and volatile in the last few years as the business of transmitting speech and data. Any operator that is going to hold a steady course in these stormy times must constantly improve its services to keep itself from being pushed out of the market like many other carriers. In 1994 when ISIS was founded, the situation was very different. During that starting phase when the German telecommunications market was just beginning to be liberalised, small, agile, low-priced businesses like ISIS were busy grabbing their first customers from the old unwieldy state-run Deutsche Telekom. In the second phase of market deregulation in 1998, it became possible to acquire private customers as well and there was a real gold-rush spirit among the new companies. However, the cutthroat competition forced prices below any profitability and soon claimed its first victims so that, over the years ISIS, too, had to retreat to its earliest roots and largely leave the mass market to others. As at the start, ISIS is concentrating its efforts on companies, public institutions and administrative bodies once again. The business and the products have also become more individualised and now demand more concentration on the customer.

It has not yet been possible to agree on a generally accepted, comprehensive, definition of an intelligent agent. This is evidence of the inter-disciplinary character of agents, which on the one hand are subject to the effects of different scientific research directions, and, on the other hand, reflect the requirements demanded in practical applications. If one approaches the area of agents primarily from the direction of the artificial intelligence, there are different criteria than those seen from the point of view of the information and communications systems. Similarly, the demands of a social scientist differ from those of the more technically oriented observations made by a computer scientist.

Die Meta Group stellt in dem Worldwide Benchmark Report 1999 fest, das der Mangel an qualifiziertem IT-Personal und damit das Thema Personalbeschaffung jetzt auch in Deutschland massiv an Wichtigkeit gewinnt [1]. Die Einnahmenverluste, die durch den Engpas an IT-Fachkraften verursacht werden, beziffern sich in den USA auf jahrlich 15 Mrd. Dollar [2]. Aufgrund dieser Ressourcenengpasse werden immer mehr IT-Projekte an spezialisierte Dienstleistungsunternehmen outgesourct, die dem-zufolge zweistellige Wachstumsraten verzeichnen konnen. Knappe IT-Personalressourcen verbunden mit zeit- und unternehmenskritischen IT-Projekten, wie zum Beispiel die Jahr 2000-Umstellung, flihren dazu, das samtliche Nicht-IT-Projektbezogenen Aktivitaten auf ein Minimum reduziert werden. So wird der propa-gierte Aufbruch in die Wissensgesellschaft durch eine alarmierend niedrige Trainings-rate ad absurdum gefuhrt. So betrug 1997 die Zahl der Trainings- und Fortbildungsta-ge pro Mitarbeiter nur noch 8,5 Tage, im Vergleich zu 12 Tagen in 1994 [3]. Das ei-genverantwortliche Lernen -oft wahrend der Freizeit — gewinnt somit immer mehr an Bedeutung, um den Anschlus in einer von kurzen Innovationszyklen gekennzeichne-ten Branche halten zu konnen. Um der Forderung nach lebenslangem Lernen gerecht zu werden, ist es also erforderlich, neue Formen der Wissensvermittlung zu etablieren.

As part of its work on the Heidelberg Transport System (HeiTS) for multimedia communication, the IBM European Networking Center (ENC) has implemented the Experimental Internet Stream Protocol, Version 2 (ST-II). ST-II is a connection-oriented internetworking protocol for multiple-destination communication of real-time data such as digital audio and video. Although ST-II fits well into typical multimedia application scenarios, some functions are still missing No mechanisms are provided for receiver-initiated communication, allowing receivers to join streams, specify their quality of service (QOS) requirements, or initiate stream establishment and resource reservation. In this paper, we present some extensions to tile protocol that make it fulfill the receivers' needs move adequately. Our approach allows intermediate nodes to execute stream management functions on behalf of the sender. This way, the protocol scales better with the number of receivers per stream, as the origin does not need to track every target.