Abstract: Energy consumption is one of the main limiting factors for the design of ultrascale infrastructures. Multi-level hardware and software optimizations must be designed and explored in order to reduce energy consumption for these largescale equipment. This chapter addresses the issue of energy efficiency of ultrascale systems in front of other quality metrics. The goal of this chapter is to explore the design of metrics, analysis, frameworks and tools for putting energy awareness and energy efficiency at the next stage. Significant emphasis will be placed on the idea of “energy complexity,” reflecting the synergies between energy efficiency and quality of service, resilience and performance, by studying computation power, communication/data sharing power, data access power, algorithm energy consumption, etc.

Abstract: Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. Determining the effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Cloud data centers and depends on how Virtual Machines (VMs) are allocated to physical resources. In this paper, we propose a multi-objective framework for dynamic placement of VMs exploiting live-migration mechanisms which simultaneously optimize the resource wastage, overcommitment ratio and migration cost. The optimization algorithm is based on a novel evolutionary meta-heuristic using an island population model underneath. We implemented and validated our method based on an enhanced version of a well-known simulator. The results demonstrate that our approach outperforms other related approaches by reducing up to 57% migrations energy consumption while achieving different energy and QoS goals.

Abstract: In multimedia research, scientific progress is often slowed downby high demands on hard- and software. However, hardware con-tinuously improves and today’s hardware got powerful enoughto meet the performance demands of complex 3D and deep learn-ing applications. With this demo, we demonstrate that utilizingdeep learning and 3D modeling is not a major barrier anymorewhen building prototypes for showcasing research projects. Ourweb-based game, called “HeadbangZ”, showcases a novel gesture-based input methodology realized through deeply learned poseestimation and user interaction in a 3D environment. Since gesture-based inputs increase the immersion in virtual environments, weassume this input methodology to be especially useful for AR/VRapplications and games. Furthermore, we demonstrate that rapidprototyping of applications using novel technologies, such as deeplearning, is even possible within 48 hours by developing a workingdemo within this time frame. Finally, we provide insights into whatwe learned during the development of HeadbangZ to encourageother researchers to make use of novel technologies. In referenceto Stephen Harper’s quote “Having hit a wall, the next logical stepis not to bang our heads against it.”, we hope that the presentationof HeadbangZ encourages researchers to bang their heads rhythmi-cally to rock music instead of angrily against a virtual wall createdby hard- and software limitations.

Abstract: A state of discomfort is known as a disease, also termed as illness or sickness. When the tiniest living things like virus enters our body, it reacts with the cells of the body and results an illness. The Arthritis is very problematic to early forecast. It nurtures with the age and related to the large and small joint pain. The Rheumatoid Arthritis (RA) is chronic disease, its long-term auto-immune and inflammatory disease which damages many joints tissues. It occurs when immune system can't distinguish the cells and tissues. The ANFIS model is used for the prediction of the RA in human mortals. A complete process is mentioned in this study, which helps to a technique for the diagnosis of the Rheumatoid Arthritis in human beings with accuracy 93.5%. This diagnosis is made on the bases of 12 symptoms of RA in human lives like age, stiffness, joint deformity, ESR, CRP, WBC, Uric Acid etc. This paper also compares the ANFIS with Naive Bayes, Bagging algorithm and KNN classifiers.

Abstract: That video and computer games have reached the masses is a well-known fact. However, game streaming and, therefore, watching other people play videogames has also outgrown its humble beginnings by far. Game streams, be it live or recorded, are viewed by millions. Many of the streams are broadcasting competitive multiplayer games. This is called e-sports and it is very similar to sports broadcasting. E-sports is organized in leagues and tournaments in which players can compete in controlled environments and viewers can experience the matches, discuss and criticize just like in physical sports. In this paper, we look into the challenges for computer science in general and multimedia research in particular. The multimedia research community has done a lot of work on video streaming, broadcasting and analyzing the audience, but has missed the opportunity to investigate e-sports in detail. We focus on one particular game we deem representative for e-sports, Counter-Strike: Global Offensive, and investigate how the audience consumes game streams from competitive tournaments.

Abstract: Das Anthropozän bezeichnet ein neues Erdzeitalter, in dem die Menschheit deutliche Spuren hinterlässt. Diese reichen von Gesteinsschichten mit radioaktiven Ablagerungen aus Atomtests über ausgerottete Tier- und Pflanzenarten bis hin zum allgegenwärtigen Klimawandel. Für manche dieser Spuren ist technologischer Fortschritt ein erheblicher Einflussfaktor. Während Mensch und Technik zusammen Spuren hinterlassen, beeinflusst auch die Technik den Menschen. Insbesondere die Digitalisierung könnte einen besonderen Einfluss auf das neue Erdzeitalter nehmen, in dem digitales Grundverständnis und Computational Thinking notwendige Kompetenzen auf dem Weg in die Zukunft darstellen. Wie diese aussieht, ist aufgrund der hohen Dynamik der gegenwärtigen Systeme ungewiss, insbesondere da durch die digitale Vernetzung eine hohe Produktivität einer großen Volatilität bei der Langzeitarchivierung gegenübersteht. In diesem Buchkapitel spannen wir einen Bogen vom Anthropozän über derzeitige Auswirkungen der menschlichen Intervention hin zur Entwicklung und Wirkung von Kommunikations- und Computertechnik in der heutigen Welt, zusammengefasst als digitale (R)evolution. In einem weiteren Schritt beschäftigen wir uns mit der gegenwärtig vorherrschenden und uns täglich umgebenden “digitalen Welt” und der Notwendigkeit zu digitalem Grundverständnis und Computational Thinking. Den Abschluss des Kapitels bildet ein Ausblick in die Zukunft und erläutert mögliche Zukunftsszenarien im digitalen Bereich.

Abstract: Computers are powerful tools capable of solving a great variety of ever so complex problems, yet training them to interpret even the simplest video scenes can prove more challenging than one might imagine. Still being one of the major problems in computer vision, this issue recently is addressed by utilizing promising deep learning approaches in order to recognize objects and their semantics. For achieving this goal, huge artificial networks are fed with many human-created annotations using more or less sophisticated tools for speeding up the otherwise time-consuming task of manual annotation. Purposefully refraining from designing yet another of these annotation tools, in this work we strive for evaluating what makes existing ones great or not, i.e. we aim at determining effectiveness and efficiency of state-of-the-art object annotation tools when employed for annotating different kinds of video content. Our findings in a user study evaluating three comparable tools on three videos of distinct domains indicate a significant difference in annotation effort from a video perspective, yet no significance regarding utilized tools. Further, we determine a significant correlation between annotation time and accuracy.