The Metaverse and its promises are fast becoming reality as maturing technologies are empowering the different facets. One of the highlights of the Metaverse is that it offers the possibility for highly immersive and ...
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People's willingness to share where they are, what they are doing and with whom using location-based technology has led to the emergence of applications which are being used to create new ways to represent and nav...
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People's willingness to share where they are, what they are doing and with whom using location-based technology has led to the emergence of applications which are being used to create new ways to represent and navigate space. One of the most popular applications in this area is Foursquare. In this paper we report the results of a short ethnographic study of the use of Foursquare in East London, combined with further analysis using the developer site to acquire data in order to investigate findings further. We describe how the Foursquare community appropriates the relatively simple communication capabilities of the application to develop their own informal rules of interaction. This behaviour is important to both application designers and businesses using social networking sites to reach and engage with consumers. Specifically we argue that tips are used for more than the intended purpose of commenting on a venue and that mayorships are a vehicle for cheating the leaderboard. We conclude by noting that designing functions to fulfill a specific purpose in media space offers no guarantee that the community will use them solely for the intended purpose.
In an edge-cloud system, mobile devices can offload their computation intensive tasks to an edge or cloud server to guarantee the quality of service or satisfy task deadline requirements. However, it is challenging to...
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ISBN:
(纸本)9781665435413
In an edge-cloud system, mobile devices can offload their computation intensive tasks to an edge or cloud server to guarantee the quality of service or satisfy task deadline requirements. However, it is challenging to determine where tasks should be offloaded and processed, and how much network and computation resources should be allocated to them, such that a system with limited resources can obtain a maximum profit while meeting the deadlines. A key challenge in this problem is that the network and computation resources could be allocated on different servers, since the server to which a task is offloaded (e.g., a server with an access point) may be different from the server on which the task is eventually processed. To address this challenge, we first formulate the task mapping and resource allocation problem as a non-convex Mixed-Integer Nonlinear Programming (MINLP) problem, known as NP-hard. We then propose a zero-slack based greedy algorithm (ZSG) and a linear discretization method (LDM) to solve this MINLP problem. Experiment results with various synthetic tasksets show that ZSG has an average of 2.98% worse performance than LDM with a minimum unit of 5 but has an average of 6.88% better performance than LDM with a minimum unit of 15.
Music Artist Classification is a challenging task in Music Information Retrieval. There exist methods that are based on either signal processing features or deep learning algorithms. While signal processing based appr...
ISBN:
(数字)9781728156880
ISBN:
(纸本)9781728156897
Music Artist Classification is a challenging task in Music Information Retrieval. There exist methods that are based on either signal processing features or deep learning algorithms. While signal processing based approaches do perform well, the challenging task implies that signal processing alone does not suffice to provide good features for the task. Other approaches that rely on deep learning based techniques learn representations from large amount of labelled data. A limitation to this approach is the requirement for large amount of annotated corpus for obtaining a good set of parameters. In this work, we pose auxiliary signal processing based tasks for a deep learning network that includes predicting the harmonic-percussive and vocal-non vocal components of audio files. We show that training on a combination of these tasks provides us with a well trained CNN with a good set of parameters; which can then further be used for the artist classification task. We use a Multi-Task framework and a popular deep learning architecture to train the model jointly on the artists classification and the auxiliary tasks. We observe that vocal-non vocal separation along with alignment prediction proves to be a good auxiliary task for artist classification, improving the baseline by 6%.
A flexible transparent modify dipole antenna printed on PET film is presented in this paper. The proposed antenna was designed to operate at 2.4GHz for ISM applications. The impedance characteristic and the radiation ...
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This work portrays the development of wireless smart shoe for gait analysis. Force sensitive resistors (FSRs) and resistive bend sensor were employed by mounting onto a shoe for detection of pressure distribution bene...
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ISBN:
(纸本)9781467348904
This work portrays the development of wireless smart shoe for gait analysis. Force sensitive resistors (FSRs) and resistive bend sensor were employed by mounting onto a shoe for detection of pressure distribution beneath the foot during normal and abnormal walking. In the present study, we have been interested in 3 walking postures including normal walking, tiptoe walking and dragging foot walking. Wireless sensor network (WSN) based on ZigBee technology was employed in this work for data communication. The Principal component analysis (PCA) was used for pattern recognition in the analysis part. It was shown that the smart shoe was successful to classify between normal gait pattern and some abnormal gait patterns.
Gesture recognition and 3D hand pose estimation are two highly correlated tasks, yet they are often handled separately. In this paper, we present a novel collaborative learning network for joint gesture recognition an...
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Due to the increasing competitiveness in telecom’s market, it has now become more necessary for operators to start building personal relationship with customers for targeted retention strategies. Achieving this goal ...
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Various anatomical objects are tubular in shape. These structures can be modeled by describing their curvilinear path and the cross-sectional shape along the path. However, most research on tubular object segmentation...
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Various anatomical objects are tubular in shape. These structures can be modeled by describing their curvilinear path and the cross-sectional shape along the path. However, most research on tubular object segmentation has focused on vascular systems, and often assumes a circular cross-section. These techniques are not readily applicable to anatomy such as the cochlea, which has a non-circular cross-sectional shape. We present the principal flow filter, which calculates the flow vector (tangential to the path) in a local region of a tubular object with a non-circular cross-section. It can be used to extract the centerline orientation and thus incrementally track along the tube. We present results from generated data with a variety of cross-sectional shapes. The filter is shown to rapidly and robustly converge to the true orientation. We also analyse a CT scan of a human cochlea, with promising results.
Fog devices are beginning to play a key role in relaying data and services within the Internet-of-Things (IoT) ecosystem. These relays may be static or mobile, with the latter offering a new degree of freedom for perf...
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