Extended Equal Service and Differentiated Service Models for Peer-to-Peer File Sharing

Peer-to-Peer (P2P) systems have proved to be the most effective and popular file sharing applications in recent years. Previous studies mainly focus on the equal service and the differentiated service strategies when peers have no initial data before their download. In an upload-constrained P2P file sharing system, we model both the equal service process and the differentiated service process when peers' initial data distribution satisfies some special conditions, and also show how to minimize the time to get the file to any number of peers. The proposed models can reveal the intrinsic relations among the initial data amount, the size of peer set and the minimum last finish time. By using the models, we can also provide arbitrary degree of differentiated service to a certain number of peers. We believe that our analysis process and achieved theoretical results could provide fundamental insights into studies on bandwidth allocation and data scheduling, and can give helpful reference both for improving system performance and building effective incentive mechanism in P2P file sharing systems

Unpacking culture using Delphi

Following a phenomenological Expert Delphi Study of academics and practitioners, findings suggest that: a by-product of post-industrialism, Globalization, and Web2.0 is the value of investigating culture from an associated, rather than a disassociated state; cultural understanding and its application beyond simply defining and classifying has become the rate-determining step; and that national identity, whilst widely used, is not the most insightful unit. Furthermore, culture cannot be judged on a linear scale – it is dynamic, contextual, and perishable. For these reasons it is argued that when culture is measured, it should be viewed as something which is symbiotic and osmotic. The paper reports findings of field work done in decamping culture and branding with establishing their relationship and interdependence

DDD17: End-To-End DAVIS Driving Dataset

Event cameras, such as dynamic vision sensors (DVS), and dynamic and active-pixel vision sensors (DAVIS) can supplement other autonomous driving sensors by providing a concurrent stream of standard active pixel sensor (APS) images and DVS temporal contrast events. The APS stream is a sequence of standard grayscale global-shutter image sensor frames. The DVS events represent brightness changes occurring at a particular moment, with a jitter of about a millisecond under most lighting conditions. They have a dynamic range of >120 dB and effective frame rates >1 kHz at data rates comparable to 30 fps (frames/second) image sensors. To overcome some of the limitations of current image acquisition technology, we investigate in this work the use of the combined DVS and APS streams in end-to-end driving applications. The dataset DDD17 accompanying this paper is the first open dataset of annotated DAVIS driving recordings. DDD17 has over 12 h of a 346x260 pixel DAVIS sensor recording highway and city driving in daytime, evening, night, dry and wet weather conditions, along with vehicle speed, GPS position, driver steering, throttle, and brake captured from the car's on-board diagnostics interface. As an example application, we performed a preliminary end-to-end learning study of using a convolutional neural network that is trained to predict the instantaneous steering angle from DVS and APS visual data.Comment: Presented at the ICML 2017 Workshop on Machine Learning for Autonomous Vehicle

‘The Pinocchio Effect’ – when managing the brand creation process, across cultures

In global marketing and international management, the fields of Branding and Culture are well discussed as separate disciplines; within both academia and industry. However, there appears to be limited supporting literature, examining brands and culture as a collective discipline. In addition, environmental factors such as ethnicity, nationality and religion are also seen to play a significant role. This in itself adds to the challenges encountered, by those looking to critically apply learning and frameworks, to any information gathered. In the first instance, this paper tries to bring aspects together from Branding and Culture and in doing so, aims to find linkages between the two. The main purpose of this paper is to distil current brand thinking and explore what impact cross-cultural, cross-national, and ethnic interactions have on a brand’s creation. The position of the authors is that without further understanding in this field, a brand will experience what has been termed by them as the ‘Pinocchio Effect’. Pinocchio was a puppet who longed to become a real human being; but sadly encountered difficulties. The conclusion presented is that the critical long-term success of a brand lies in three areas: how it is created; the subsequent associated perceptions; and more specifically in the reality of the relationships that it enjoys. Collectively these processes necessitate an appraisal of connecting strategic management procedures and thinking. Finally, this paper looks into proposing future methods for brand evaluation and strategic management. The aim is to stimulate further thinking in a field; which transcends national, ethnic and cultural boundaries - in the interests of developing new insight, and to provide a platform for marketers to develop more effective communications

GODIVA2: interactive visualization of environmental data on the Web

GODIVA2 is a dynamic website that provides visual access to several terabytes of physically distributed, four-dimensional environmental data. It allows users to explore large datasets interactively without the need to install new software or download and understand complex data. Through the use of open international standards, GODIVA2 maintains a high level of interoperability with third-party systems, allowing diverse datasets to be mutually compared. Scientists can use the system to search for features in large datasets and to diagnose the output from numerical simulations and data processing algorithms. Data providers around Europe have adopted GODIVA2 as an INSPIRE-compliant dynamic quick-view system for providing visual access to their data

Socially Aware Motion Planning with Deep Reinforcement Learning

For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.Comment: 8 page

Dynamic Arrival Rate Estimation for Campus Mobility on Demand Network Graphs

Mobility On Demand (MOD) systems are revolutionizing transportation in urban settings by improving vehicle utilization and reducing parking congestion. A key factor in the success of an MOD system is the ability to measure and respond to real-time customer arrival data. Real time traffic arrival rate data is traditionally difficult to obtain due to the need to install fixed sensors throughout the MOD network. This paper presents a framework for measuring pedestrian traffic arrival rates using sensors onboard the vehicles that make up the MOD fleet. A novel distributed fusion algorithm is presented which combines onboard LIDAR and camera sensor measurements to detect trajectories of pedestrians with a 90% detection hit rate with 1.5 false positives per minute. A novel moving observer method is introduced to estimate pedestrian arrival rates from pedestrian trajectories collected from mobile sensors. The moving observer method is evaluated in both simulation and hardware and is shown to achieve arrival rate estimates comparable to those that would be obtained with multiple stationary sensors.Comment: Appears in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). http://ieeexplore.ieee.org/abstract/document/7759357