Mobile social networks (MSNs) enable users to discover and share contents with each other, especially at ephemeral events such as exhibitions and conferences where users could be strangers. Nevertheless, the incentive of users to actively share their contents in MSNs may be lacking if the corresponding cost is high. Besides, as users in MSN share contents in an impromptu way as they move, it makes them vulnerable to malicious users who may want to disseminate false contents. This is because users may not have knowledge about the peers they are socially connecting with in the network. In this paper, we propose MCoST, a mechanism that motivates content sharing in MSN and ensures that only trustworthy contents are shared. The mechanism is built on users' collective bidding, content cost sharing, and trust evaluation while guaranteeing individual rationality. MCoST enables content providers to share contents with multiple users simultaneously by utilizing the broadcast nature of wireless transmission. The cost of the content is collectively compensated by the content receivers through the content bidding mechanism in MCoST. In ensuring that users can establish the trustworthiness of their encounters' contents, MCoST incorporates a robust trust evaluation framework that guarantees that content reviews are immutable and tamper-proof, resistive to sybil, and rejection attacks, and that users cannot have multiple and fake identities in the network or reject negative reviews about their contents. This is achieved by integrating a distributed cryptographic hash-chained content review mechanism in the design of MCoST. Performance evaluation shows that the proposed mechanism efficiently evaluates contents' trustworthiness by detecting and discriminating review-chains under sybil or rejection attacks and reduces the time and cost to collect the desired contents by 86% and 40%, respectively, and improves network utilization by 50%.
Object co-segmentation aims to segment the common objects in images. This paper presents a CNN-based method that is unsupervised and end-to-end trainable to better solve this task. Our method is unsupervised in the sense that it does not require any training data in the form of object masks but merely a set of images jointly covering objects of a specific class. Our method comprises two collaborative CNN modules, a feature extractor, and a co-attention map generator. The former module extracts the features of the estimated objects and backgrounds, and is derived based on the proposed co-attention loss, which minimizes inter-image object discrepancy while maximizing intra-image figure-ground separation. The latter module is learned to generate co-attention maps by which the estimated figure-ground segmentation can better fit the former module. Besides the co-attention loss, the mask loss is developed to retain the whole objects and remove noises. Experiments show that our method achieves superior results, even outperforming the state-of-the-art, supervised methods.
Self-powered intermittent systems featuring nonvolatile processors (NVPs) allow for accumulative execution in unstable power environments. However, frequent power failures may cause incorrect NVP execution results due to invalid data generated intermittently. This paper presents a HW/SW co-design, called HomeRun, to guarantee atomicity by ensuring that an uninterruptible program section can be run through at one execution. We design a HW module to ensure that a power pulse is sufficient for an atomic section, and develop a SW mechanism for programmers to protect atomic sections. The proposed design is validated through the development of a prototype pattern locking system. Experimental results demonstrate that the proposed design can completely guarantee atomicity and significantly improve the energy utilization of self-powered intermittent systems.
The increasingly high display resolution of mobile devices imposes a further burden on energy consumption. Existing schemes manage either OLED or GPU power to save energy. This paper presents the design, algorithm, and implementation of a co-managing scheme called Duet, which automatically trades off perceptual quality for energy efficiency in accordance with static and dynamic visual acuity when users interact with mobile applications. The results of experiments conducted on a commercial smartphone with popular interactive apps show that Duet saves more energy while retaining better visual quality, compared with a joint scheme that simultaneously uses dynamic pixel disabling and dynamic resolution scaling to save OLED and GPU energy in isolation.
In emergency medical services, the lag time between injury and treatment is one of the most critical parameters with respect to patient survivability. Ambulance services aim to maximize the likelihood of prompt medical treatment to prevent death and/or potential non-reversible damages. The emerging Tactile Internet has a vital role to play on that frontier by allowing next generation of ambulances to be equipped with advanced haptic/tactile devices to allow pre-hospital treatment/diagnosis or even remote surgery while en route. In this paper we propose a novel reliable multi-modal e-health high mobility service optimization framework for ambulances utilizing mobile edge clouds to efficiently transport real time patient information to the hospital. The main challenge of the proposed e-health service is to guarantee the heterogeneous QoS requirements of all involved data flows between the ambulance and the medical personnel. To this end, we formulate the service configuration problem as an optimization problem. In addition, a set of low-complexity algorithms are proposed to provide competitive solutions in real-time. A comprehensive set of numerical investigations are presented to characterize the attainable system performance of the proposed schemes.
Deal selection on Groupon is a typical social learning and decision making process, where the quality of a deal is usually unknown to the customers. The customers must acquire this knowledge through social learning from other social medias such as reviews on Yelp. Additionally, the quality of a deal depends on both the state of the vendor and decisions of other customers on Groupon. How social learning and network externality affect the decisions of customers in deal selection on Groupon is our main interest. We develop a data-driven game-theoretic framework to understand the rational deal selection behaviors cross social medias. The sufficient condition of the Nash equilibrium is identified. A value-iteration algorithm is proposed to find the optimal deal selection strategy. We conduct a year-long experiment to trace the competitions among deals on Groupon and the corresponding Yelp ratings. We utilize the dataset to analyze the deal selection game with realistic settings. Finally, the performance of the proposed social learning framework is evaluated with real data. The results suggest that customers do make decisions in a rational way instead of following naive strategies, and there is still room to improve their decisions with assistance from the proposed framework.
Generating music medleys is about finding an optimal permutation of a given set of music clips. Toward this goal, we propose a self-supervised learning task, called the music puzzle game, to train neural network models to learn the sequential patterns in music. In essence, such a game requires machines to correctly sort a few multisecond music fragments. In the training stage, we learn the model by sampling multiple nonoverlapping fragment pairs from the same songs and seeking to predict whether a given pair is consecutive and is in the correct chronological order. For testing, we design a number of puzzle games with different difficulty levels, the most difficult one being music medley, which requiring sorting fragments from different songs. On the basis of state-of-the-art Siamese convolutional network, we propose an improved architecture that learns to embed frame-level similarity scores computed from the input fragment pairs to a common space, where fragment pairs in the correct order can be more easily identified. Our result shows that the resulting model, dubbed as the similarity embedding network (SEN), performs better than competing models across different games, including music jigsaw puzzle, music sequencing, and music medley. Example results can be found at our project website, https://remyhuang.github.io/DJnet.
Generating music has a few notable differences from generating images and videos. First, music is an art of time, necessitating a temporal model. Second, music is usually composed of multiple instruments/tracks with their own temporal dynamics, but collectively they unfold over time interdependently. Lastly, musical notes are often grouped into chords, arpeggios or melodies in polyphonic music, and thereby introducing a chronological ordering of notes is not naturally suitable. In this paper, we propose three models for symbolic multi-track music generation under the framework of generative adversarial networks (GANs). The three models, which differ in the underlying assumptions and accordingly the network architectures, are referred to as the jamming model, the composer model and the hybrid model. We trained the proposed models on a dataset of over one hundred thousand bars of rock music and applied them to generate piano-rolls of five tracks: bass, drums, guitar, piano and strings. A few intratrack and inter-track objective metrics are also proposed to evaluate the generative results, in addition to a subjective user study. We show that our models can generate coherent music of four bars right from scratch (i.e. without human inputs). We also extend our models to human-AI cooperative music generation: given a specific track composed by human, we can generate four additional tracks to accompany it. All code, the dataset and the rendered audio samples are available at https://salu133445.github.io/musegan/.
Most existing neural network models for music generation use recurrent neural networks. However, the recent WaveNet model proposed by DeepMind shows that convolutional neural networks (CNNs) can also generate realistic musical waveforms in the audio domain. Following this light, we investigate using CNNs for generating melody (a series of MIDI notes) one bar after another in the symbolic domain. In addition to the generator, we use a discriminator to learn the distributions of melodies, making it a generative adversarial network (GAN). Moreover, we propose a novel conditional mechanism to exploit available prior knowledge, so that the model can generate melodies either from scratch, by following a chord sequence, or by conditioning on the melody of previous bars (e.g. a priming melody), among other possibilities. The resulting model, named MidiNet, can be expanded to generate music with multiple MIDI channels (i.e. tracks). We conduct a user study to compare the melody of eight-bar long generated by MidiNet and by Google’s MelodyRNN models, each time using the same priming melody. Result shows that MidiNet performs comparably with MelodyRNN models in being realistic and pleasant to listen to, yet MidiNet’s melodies are reported to be much more interesting.
In this paper, we consider a simultaneous wireless information and power transfer (SWIPT)-enabled cooperative cognitive network that addresses energy scarcity and spectral scarcity, two important issues in 5G wireless communications. In the considered network, the self-sustainable, SWIPT-enabled relay assists primary user's transmission, while the relay itself is also a secondary user with its own information superimposed on the regenerated primary information for transmission. The SWIPT relay employs the proposed energy-assisted decode-and-forward (EDF) protocol, which enhances the conventional decode-and-forward (DF) protocol with energy-dimension-augmented information decoding. We conduct a comparative analysis of the proposed EDF and the conventional DF and amplify-and-forward (AF) protocols in this SWIPT cooperative cognitive framework in terms of capacity, outage probability, and throughput for both primary and secondary networks. Simulation corroborates the analysis and demonstrates performance advantages of EDF over DF/AF from various perspectives.