OpenRaiser/Intern-Atlas huggingface dataset:
IoT Skullfort: Exploring the Impact of Internet Connected Cosplay
Venue: arXiv cs.HC 2019
Year: 2019

In this paper, we explore the potential impact of Internet of Things (IoT) technology may have on the cosplay community. We developed a costume (an IoT Skullfort) and embedded IoT technology to enhance its capabilities and user interactions. Sensing technologies are widely used in many different wearable domains including cosplay scenarios. However, in most of these scenarios, typical interaction pattern is that the costume responds to its environment or the player's behaviour (e.g., colour of lights may get changed when player moves hands). In contrast, our research focuses on exploring scenarios where the audience (third party) get to manipulate the costume behaviour (e.g., the audience get to change the colour of the Skullfort using a mobile application). We believe such an audience (third party) influenced cosplay brings new opportunities for enhanced entertainment. However, it also creates significant challenges. We report the results gathered through a focus group conducted in collaboration with cosplay community experts.
Spectral Efficiency Analysis in Dense Massive MIMO Networks
Venue: arXiv cs.IT 2019
Year: 2019

This work considers the uplink of a Massive MIMO network wherein the base stations (BSs) are randomly deployed according to a homogenous Poisson point process of intensity $λ$. Each BS is equipped with $M$ antennas and serves $K$ user equipments. A rigorous stochastic geometry framework with a multi-slope path loss model and pilot-based channel estimation is used to analyze the impact of $λ$ on channel estimation accuracy and spectral efficiency. Both maximum ratio and zero-forcing combiners are considered. Interesting analytical insights are provided into the interplay of network parameters such as $λ$, antenna-UE ratio $M/K$, and pilot reuse factor. The relative strength of pilot contamination and (inter- and intra-cell) interference is analytically and numerically evaluated, as a function of $λ$. It turns out that pilot contamination becomes relevant only for impractical values of $M/K\ge 100$.
Temporally Coherent Full 3D Mesh Human Pose Recovery from Monocular Video
Venue: arXiv cs.CV 2019
Year: 2019

Advances in Deep Learning have recently made it possible to recover full 3D meshes of human poses from individual images. However, extension of this notion to videos for recovering temporally coherent poses still remains unexplored. A major challenge in this regard is the lack of appropriately annotated video data for learning the desired deep models. Existing human pose datasets only provide 2D or 3D skeleton joint annotations, whereas the datasets are also recorded in constrained environments. We first contribute a technique to synthesize monocular action videos with rich 3D annotations that are suitable for learning computational models for full mesh 3D human pose recovery. Compared to the existing methods which simply "texture-map" clothes onto the 3D human pose models, our approach incorporates Physics based realistic cloth deformations with the human body movements. The generated videos cover a large variety of human actions, poses, and visual appearances, whereas the annotations record accurate human pose dynamics and human body surface information. Our second major contribution is an end-to-end trainable Recurrent Neural Network for full pose mesh recovery from monocular video. Using the proposed video data and LSTM based recurrent structure, our network explicitly learns to model the temporal coherence in videos and imposes geometric consistency over the recovered meshes. We establish the effectiveness of the proposed model with quantitative and qualitative analysis using the proposed and benchmark datasets.
On the Fairness Performance of NOMA-based Wireless Powered Communication Networks
Venue: arXiv cs.IT 2019
Year: 2019

The near-far problem causes severe throughput unfairness in wireless powered communication networks (WPCN). In this paper, we exploit non-orthogonal multiple access (NOMA) technology and propose a fairness-aware NOMA-based scheduling scheme to mitigate the near-far effect and to enhance the max-min fairness. Specifically, we sort all users according to their channel conditions and divide them into two groups, the interference group with high channel gains and the noninterference group with low channel gains. The power station (PS) concurrently transmits energy signals with the data transmissions of the users in the interference group. Thus, the users in the noninterference group can harvest more energy and achieve a higher throughput, while the users in the interference group degrade their performance due to the interfering signals from the PS. We then apply order statistic theory to analyze the achievable rates of ordered users, based on which all users are appropriately grouped for NOMA transmission to achieve the max-min fairness of the system. Meanwhile, the optimal number of interfered users that determines the set of users in each group, is derived. Our simulation results validate the significant improvement of both network fairness and throughput via the fairness-aware NOMA-based scheduling scheme.
Super-resolution of Time-series Labels for Bootstrapped Event Detection
Venue: arXiv cs.LG 2019
Year: 2019

Solving real-world problems, particularly with deep learning, relies on the availability of abundant, quality data. In this paper we develop a novel framework that maximises the utility of time-series datasets that contain only small quantities of expertly-labelled data, larger quantities of weakly (or coarsely) labelled data and a large volume of unlabelled data. This represents scenarios commonly encountered in the real world, such as in crowd-sourcing applications. In our work, we use a nested loop using a Kernel Density Estimator (KDE) to super-resolve the abundant low-quality data labels, thereby enabling effective training of a Convolutional Neural Network (CNN). We demonstrate two key results: a) The KDE is able to super-resolve labels more accurately, and with better calibrated probabilities, than well-established classifiers acting as baselines; b) Our CNN, trained on super-resolved labels from the KDE, achieves an improvement in F1 score of 22.1% over the next best baseline system in our candidate problem domain.
On the computational complexity of the probabilistic label tree algorithms
Venue: arXiv cs.LG 2019
Year: 2019

Label tree-based algorithms are widely used to tackle multi-class and multi-label problems with a large number of labels. We focus on a particular subclass of these algorithms that use probabilistic classifiers in the tree nodes. Examples of such algorithms are hierarchical softmax (HSM), designed for multi-class classification, and probabilistic label trees (PLTs) that generalize HSM to multi-label problems. If the tree structure is given, learning of PLT can be solved with provable regret guaranties [Wydmuch et.al. 2018]. However, to find a tree structure that results in a PLT with a low training and prediction computational costs as well as low statistical error seems to be a very challenging problem, not well-understood yet. In this paper, we address the problem of finding a tree structure that has low computational cost. First, we show that finding a tree with optimal training cost is NP-complete, nevertheless there are some tractable special cases with either perfect approximation or exact solution that can be obtained in linear time in terms of the number of labels $m$. For the general case, we obtain $O(\log m)$ approximation in linear time too. Moreover, we prove an upper bound on the expected prediction cost expressed in terms of the expected training cost. We also show that under additional assumptions the prediction cost of a PLT is $O(\log m)$.
Neural Bug Finding: A Study of Opportunities and Challenges
Venue: arXiv cs.SE 2019
Year: 2019

Static analysis is one of the most widely adopted techniques to find software bugs before code is put in production. Designing and implementing effective and efficient static analyses is difficult and requires high expertise, which results in only a few experts able to write such analyses. This paper explores the opportunities and challenges of an alternative way of creating static bug detectors: neural bug finding. The basic idea is to formulate bug detection as a classification problem, and to address this problem with neural networks trained on examples of buggy and non-buggy code. We systematically study the effectiveness of this approach based on code examples labeled by a state-of-the-art, static bug detector. Our results show that neural bug finding is surprisingly effective for some bug patterns, sometimes reaching a precision and recall of over 80%, but also that it struggles to understand some program properties obvious to a traditional analysis. A qualitative analysis of the results provides insights into why neural bug finders sometimes work and sometimes do not work. We also identify pitfalls in selecting the code examples used to train and validate neural bug finders, and propose an algorithm for selecting effective training data.
The Principle of Unchanged Optimality in Reinforcement Learning Generalization
Venue: arXiv cs.LG 2019
Year: 2019

Several recent papers have examined generalization in reinforcement learning (RL), by proposing new environments or ways to add noise to existing environments, then benchmarking algorithms and model architectures on those environments. We discuss subtle conceptual properties of RL benchmarks that are not required in supervised learning (SL), and also properties that an RL benchmark should possess. Chief among them is one we call the principle of unchanged optimality: there should exist a single $π$ that is optimal across all train and test tasks. In this work, we argue why this principle is important, and ways it can be broken or satisfied due to subtle choices in state representation or model architecture. We conclude by discussing challenges and future lines of research in theoretically analyzing generalization benchmarks.
A Survey of Asynchronous Programming Using Coroutines in the Internet of Things and Embedded Systems
Venue: arXiv cs.SE 2019
Year: 2019

Many Internet of Things and embedded projects are event-driven, and therefore require asynchronous and concurrent programming. Current proposals for C++20 suggest that coroutines will have native language support. It is timely to survey the current use of coroutines in embedded systems development. This paper investigates existing research which uses or describes coroutines on resource-constrained platforms. The existing research is analysed with regard to: software platform, hardware platform and capacity; use cases and intended benefits; and the application programming interface design used for coroutines. A systematic mapping study was performed, to select studies published between 2007 and 2018 which contained original research into the application of coroutines on resource-constrained platforms. An initial set of 566 candidate papers were reduced to only 35 after filters were applied, revealing the following taxonomy. The C & C++ programming languages were used by 22 studies out of 35. As regards hardware, 16 studies used 8- or 16-bit processors while 13 used 32-bit processors. The four most common use cases were concurrency (17 papers), network communication (15), sensor readings (9) and data flow (7). The leading intended benefits were code style and simplicity (12 papers), scheduling (9) and efficiency (8). A wide variety of techniques have been used to implement coroutines, including native macros, additional tool chain steps, new language features and non-portable assembly language. We conclude that there is widespread demand for coroutines on resource-constrained devices. Our findings suggest that there is significant demand for a formalised, stable, well-supported implementation of coroutines in C++, designed with consideration of the special needs of resource-constrained devices, and further that such an implementation would bring benefits specific to such devices.
Beyond the VCG Mechanism: Truthful Reverse Auctions for Relay Selection with High Data Rates, High Base Station Utility and Low Interference in D2D Networks
Venue: arXiv cs.GT 2019
Year: 2019

Device-to-Device communication allows a cellular user (relay node) to relay data between the base station (BS) and another cellular user (destination node). We address the problem of designing reverse auctions to assign a relay node to each destination node, when there are multiple potential relay nodes and multiple destination nodes, in the scenarios where the transmission powers of the relay nodes are: 1) fixed, 2) selected to achieve the data rates desired by destination nodes, and 3) selected so as to approximately maximize the BS's utility. We show that auctions based on the widely used Vickrey-Clarke-Groves (VCG) mechanism have several limitations in scenarios 1) and 2); also, in scenario 3), the VCG mechanism is not applicable. Hence, we propose novel reverse auctions for relay selection in each of the above three scenarios. We prove that all the proposed reverse auctions can be truthfully implemented as well as satisfy the individual rationality property. Using numerical computations, we show that in scenarios 1) and 2), our proposed auctions significantly outperform the auctions based on the VCG mechanism in terms of the data rates achieved by destination nodes, utility of the BS and/ or the interference cost incurred to the BS.
Multiple Drones driven Hexagonally Partitioned Area Exploration: Simulation and Evaluation
Venue: arXiv cs.MA 2019
Year: 2019

In this paper, we simulated a distributed, cooperative path planning technique for multiple drones (~200) to explore an unknown region (~10,000 connected units) in the presence of obstacles. The map of an unknown region is dynamically created based on the information obtained from sensors and other drones. The unknown area is considered a connected region made up of hexagonal unit cells. These cells are grouped to form larger cells called sub-areas. We use long range and short range communication. The short-range communication within drones in smaller proximity helps avoid re-exploration of cells already explored by companion drones located in the same subarea. The long-range communication helps drones identify next subarea to be targeted based on weighted RNN (Reverse nearest neighbor). Simulation results show that weighted RNN in a hexagonal representation makes exploration more efficient, scalable and resilient to communication failures.
The Number of Minimum $k$-Cuts: Improving the Karger-Stein Bound
Venue: arXiv cs.DS 2019
Year: 2019

Given an edge-weighted graph, how many minimum $k$-cuts can it have? This is a fundamental question in the intersection of algorithms, extremal combinatorics, and graph theory. It is particularly interesting in that the best known bounds are algorithmic: they stem from algorithms that compute the minimum $k$-cut. In 1994, Karger and Stein obtained a randomized contraction algorithm that finds a minimum $k$-cut in $O(n^{(2-o(1))k})$ time. It can also enumerate all such $k$-cuts in the same running time, establishing a corresponding extremal bound of $O(n^{(2-o(1))k})$. Since then, the algorithmic side of the minimum $k$-cut problem has seen much progress, leading to a deterministic algorithm based on a tree packing result of Thorup, which enumerates all minimum $k$-cuts in the same asymptotic running time, and gives an alternate proof of the $O(n^{(2-o(1))k})$ bound. However, beating the Karger--Stein bound, even for computing a single minimum $k$-cut, has remained out of reach. In this paper, we give an algorithm to enumerate all minimum $k$-cuts in $O(n^{(1.981+o(1))k})$ time, breaking the algorithmic and extremal barriers for enumerating minimum $k$-cuts. To obtain our result, we combine ideas from both the Karger--Stein and Thorup results, and draw a novel connection between minimum $k$-cut and extremal set theory. In particular, we give and use tighter bounds on the size of set systems with bounded dual VC-dimension, which may be of independent interest.
Feature-Based Q-Learning for Two-Player Stochastic Games
Venue: arXiv cs.LG 2019
Year: 2019

Consider a two-player zero-sum stochastic game where the transition function can be embedded in a given feature space. We propose a two-player Q-learning algorithm for approximating the Nash equilibrium strategy via sampling. The algorithm is shown to find an $ε$-optimal strategy using sample size linear to the number of features. To further improve its sample efficiency, we develop an accelerated algorithm by adopting techniques such as variance reduction, monotonicity preservation and two-sided strategy approximation. We prove that the algorithm is guaranteed to find an $ε$-optimal strategy using no more than $\tilde{\mathcal{O}}(K/(ε^{2}(1-γ)^{4}))$ samples with high probability, where $K$ is the number of features and $γ$ is a discount factor. The sample, time and space complexities of the algorithm are independent of original dimensions of the game.
Effects of Different Hand-Grounding Locations on Haptic Performance With a Wearable Kinesthetic Haptic Device
Venue: arXiv cs.RO 2019
Year: 2019

Grounding of kinesthetic feedback against a user's hand can increase the portability and wearability of a haptic device. However, the effects of different hand-grounding locations on haptic perception of a user are unknown. In this letter, we investigate the effects of three different hand-grounding locations-back of the hand, proximal phalanx of the index finger, and middle phalanx of the index finger-on haptic perception using a newly designed wearable haptic device. The novel device can provide kinesthetic feedback to the user's index finger in two directions: along the finger-axis and in the finger's flexion-extension movement direction. We measure users' haptic perception for each grounding location through a psychophysical experiment for each of the two feedback directions. Results show that among the studied locations, grounding at proximal phalanx has a smaller average just noticeable difference for both feedback directions, indicating a more sensitive haptic perception. The realism of the haptic feedback, based on user ratings, was the highest with grounding at the middle phalanx for feedback along the finger axis, and at the proximal phalanx for feedback in the flexion-extension direction. Users identified the haptic feedback as most comfortable with grounding at the back of the hand for feedback along the finger axis and at the proximal phalanx for feedback in the flexion-extension direction. These findings show that the choice of grounding location has a significant impact on the user's haptic perception and qualitative experience. The results provide insights for designing next-generation wearable hand-grounded kinesthetic devices to achieve better haptic performance and user experience in virtual reality and teleoperated robotic applications.
Ara: A 1 GHz+ Scalable and Energy-Efficient RISC-V Vector Processor with Multi-Precision Floating Point Support in 22 nm FD-SOI
Venue: arXiv cs.AR 2019
Year: 2019

In this paper, we present Ara, a 64-bit vector processor based on the version 0.5 draft of RISC-V's vector extension, implemented in GlobalFoundries 22FDX FD-SOI technology. Ara's microarchitecture is scalable, as it is composed of a set of identical lanes, each containing part of the processor's vector register file and functional units. It achieves up to 97% FPU utilization when running a 256 x 256 double precision matrix multiplication on sixteen lanes. Ara runs at more than 1 GHz in the typical corner (TT/0.80V/25 oC) achieving a performance up to 33 DP-GFLOPS. In terms of energy efficiency, Ara achieves up to 41 DP-GFLOPS/W under the same conditions, which is slightly superior to similar vector processors found in literature. An analysis on several vectorizable linear algebra computation kernels for a range of different matrix and vector sizes gives insight into performance limitations and bottlenecks for vector processors and outlines directions to maintain high energy efficiency even for small matrix sizes where the vector architecture achieves suboptimal utilization of the available FPUs.
Towards Interactive Training of Non-Player Characters in Video Games
Venue: arXiv cs.LG 2019
Year: 2019

There is a high demand for high-quality Non-Player Characters (NPCs) in video games. Hand-crafting their behavior is a labor intensive and error prone engineering process with limited controls exposed to the game designers. We propose to create such NPC behaviors interactively by training an agent in the target environment using imitation learning with a human in the loop. While traditional behavior cloning may fall short of achieving the desired performance, we show that interactivity can substantially improve it with a modest amount of human efforts. The model we train is a multi-resolution ensemble of Markov models, which can be used as is or can be further "compressed" into a more compact model for inference on consumer devices. We illustrate our approach on an example in OpenAI Gym, where a human can help to quickly train an agent with only a handful of interactive demonstrations. We also outline our experiments with NPC training for a first-person shooter game currently in development.
C2P2: A Collective Cryptocurrency Up/Down Price Prediction Engine
Venue: arXiv cs.LG 2019
Year: 2019

We study the problem of predicting whether the price of the 21 most popular cryptocurrencies (according to coinmarketcap.com) will go up or down on day d, using data up to day d-1. Our C2P2 algorithm is the first algorithm to consider the fact that the price of a cryptocurrency c might depend not only on historical prices, sentiments, global stock indices, but also on the prices and predicted prices of other cryptocurrencies. C2P2 therefore does not predict cryptocurrency prices one coin at a time --- rather it uses similarity metrics in conjunction with collective classification to compare multiple cryptocurrency features to jointly predict the cryptocurrency prices for all 21 coins considered. We show that our C2P2 algorithm beats out a recent competing 2017 paper by margins varying from 5.1-83% and another Bitcoin-specific prediction paper from 2018 by 16%. In both cases, C2P2 is the winner on all cryptocurrencies considered. Moreover, we experimentally show that the use of similarity metrics within our C2P2 algorithm leads to a direct improvement for 20 out of 21 cryptocurrencies ranging from 0.4% to 17.8%. Without the similarity component, C2P2 still beats competitors on 20 out of 21 cryptocurrencies considered. We show that all these results are statistically significant via a Student's t-test with p<1e-5. Check our demo at https://www.cs.dartmouth.edu/dsail/demos/c2p2
Massive Styles Transfer with Limited Labeled Data
Venue: arXiv cs.CL 2019
Year: 2019

Language style transfer has attracted more and more attention in the past few years. Recent researches focus on improving neural models targeting at transferring from one style to the other with labeled data. However, transferring across multiple styles is often very useful in real-life applications. Previous researches of language style transfer have two main deficiencies: dependency on massive labeled data and neglect of mutual influence among different style transfer tasks. In this paper, we propose a multi-agent style transfer system (MAST) for addressing multiple style transfer tasks with limited labeled data, by leveraging abundant unlabeled data and the mutual benefit among the multiple styles. A style transfer agent in our system not only learns from unlabeled data by using techniques like denoising auto-encoder and back-translation, but also learns to cooperate with other style transfer agents in a self-organization manner. We conduct our experiments by simulating a set of real-world style transfer tasks with multiple versions of the Bible. Our model significantly outperforms the other competitive methods. Extensive results and analysis further verify the efficacy of our proposed system.