OpenRaiser/Intern-Atlas huggingface dataset:
Active Robot-Assisted Feeding with a General-Purpose Mobile Manipulator: Design, Evaluation, and Lessons Learned
Venue: arXiv cs.RO 2019
Year: 2019

Eating is an essential activity of daily living (ADL) for staying healthy and living at home independently. Although numerous assistive devices have been introduced, many people with disabilities are still restricted from independent eating due to the devices' physical or perceptual limitations. In this work, we present a new meal-assistance system and evaluations of this system with people with motor impairments. We also discuss learned lessons and design insights based on the evaluations. The meal-assistance system uses a general-purpose mobile manipulator, a Willow Garage PR2, which has the potential to serve as a versatile form of assistive technology. Our active feeding framework enables the robot to autonomously deliver food to the user's mouth, reducing the need for head movement by the user. The user interface, visually-guided behaviors, and safety tools allow people with severe motor impairments to successfully use the system. We evaluated our system with a total of 10 able-bodied participants and 9 participants with motor impairments. Both groups of participants successfully ate various foods using the system and reported high rates of success for the system's autonomous behaviors. In general, participants who operated the system reported that it was comfortable, safe, and easy-to-use.
Reframing Threat Detection: Inside esINSIDER
Venue: arXiv cs.CR 2019
Year: 2019

We describe the motivation and design for esINSIDER, an automated tool that detects potential persistent and insider threats in a network. esINSIDER aggregates clues from log data, over extended time periods, and proposes a small number of cases for human experts to review. The proposed cases package together related information so the analyst can see a bigger picture of what is happening, and their evidence includes internal network activity resembling reconnaissance and data collection. The core ideas are to 1) detect fundamental campaign behaviors by following data movements over extended time periods, 2) link together behaviors associated with different meta-goals, and 3) use machine learning to understand what activities are expected and consistent for each individual network. We call this approach campaign analytics because it focuses on the threat actor's campaign goals and the intrinsic steps to achieve them. Linking different campaign behaviors (internal reconnaissance, collection, exfiltration) reduces false positives from business-as-usual activities and creates opportunities to detect threats before a large exfiltration occurs. Machine learning makes it practical to deploy this approach by reducing the amount of tuning needed.
Fast Grid Splitting Detection for N-1 Contingency Analysis by Graph Computing
Venue: arXiv cs.DC 2019
Year: 2019

In this study, a graph-computing based grid splitting detection algorithm is proposed for contingency analysis in a graph-based EMS (Energy Management System). The graph model of a power system is established by storing its bus-branch information into the corresponding vertex objects and edge objects of the graph database. Numerical comparison to an up-to-date serial computing algorithm is also investigated. Online tests on a real power system of China State Grid with 2752 buses and 3290 branches show that a 6 times speedup can be achieved, which lays a good foundation for advanced contingency analysis.
BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures
Venue: arXiv cs.DB 2019
Year: 2019

We introduce BriskStream, an in-memory data stream processing system (DSPSs) specifically designed for modern shared-memory multicore architectures. BriskStream's key contribution is an execution plan optimization paradigm, namely RLAS, which takes relative-location (i.e., NUMA distance) of each pair of producer-consumer operators into consideration. We propose a branch and bound based approach with three heuristics to resolve the resulting nontrivial optimization problem. The experimental evaluations demonstrate that BriskStream yields much higher throughput and better scalability than existing DSPSs on multi-core architectures when processing different types of workloads.
Near-linear time approximation schemes for Steiner tree and forest in low-dimensional spaces
Venue: arXiv cs.CG 2019
Year: 2019

We give an algorithm that computes a $(1+ε)$-approximate Steiner forest in near-linear time $n \cdot 2^{(1/ε)^{O(ddim^2)} (\log \log n)^2}$. This is a dramatic improvement upon the best previous result due to Chan et al., who gave a runtime of $n^{2^{O(ddim)}} \cdot 2^{(ddim/ε)^{O(ddim)} \sqrt{\log n}}$. For Steiner tree our methods achieve an even better runtime $n (\log n)^{(1/ε)^{O(ddim^2)}}$ in doubling spaces. For Euclidean space the runtime can be reduced to $2^{(1/ε)^{O(d^2)}} n \log n$, improving upon the result of Arora in fixed dimension $d$.
Ride N' Rhythm, Bike as an Embodied Musical Instrument to Improve Music Perception for Young Children
Venue: arXiv cs.HC 2019
Year: 2019

Music plays a crucial role in young children's development. Current research lacks the design of an interactive system for younger children that could generate dynamic music change in response to the children's body movement. In this paper, we present the design of bike as an embodied musical instrument for young children 2-5 years old to improve their music perception skills. In the Ride N' Rhythm prototype, the rider's body position maps to the music volume; and the speed of the bike maps to the tempo. The design of the prototype incorporates the Embodied Music Cognition theory and Dalcroze Eurhythmics pedagogy, and aims to internalize the 'intuitive' knowing and musical understanding via the combination of music and body movement.
On-line and on-board planning and perception for quadrupedal locomotion
Venue: arXiv cs.RO 2019
Year: 2019

We present a legged motion planning approach for quadrupedal locomotion over challenging terrain. We decompose the problem into body action planning and footstep planning. We use a lattice representation together with a set of defined body movement primitives for computing a body action plan. The lattice representation allows us to plan versatile movements that ensure feasibility for every possible plan. To this end, we propose a set of rules that define the footstep search regions and footstep sequence given a body action. We use Anytime Repairing A* (ARA*) search that guarantees bounded suboptimal plans. Our main contribution is a planning approach that generates on-line versatile movements. Experimental trials demonstrate the performance of our planning approach in a set of challenging terrain conditions. The terrain information and plans are computed on-line and on-board.
Higher-Level Hardware Synthesis of The KASUMI Algorithm
Venue: arXiv cs.AR 2019
Year: 2019

Programmable Logic Devices (PLDs) continue to grow in size and currently contain several millions of gates. At the same time, research effort is going into higher-level hardware synthesis methodologies for reconfigurable computing that can exploit PLD technology. In this paper, we explore the effectiveness and extend one such formal methodology in the design of massively parallel algorithms. We take a step-wise refinement approach to the development of correct reconfigurable hardware circuits from formal specifications. A functional programming notation is used for specifying algorithms and for reasoning about them. The specifications are realised through the use of a combination of function decomposition strategies, data refinement techniques, and off-the-shelf refinements based upon higher-order functions. The off-the-shelf refinements are inspired by the operators of Communicating Sequential Processes (CSP) and map easily to programs in Handel-C (a hardware description language). The Handel-C descriptions are directly compiled into reconfigurable hardware. The practical realisation of this methodology is evidenced by a case studying the third generation mobile communication security algorithms. The investigated algorithm is the KASUM} block cipher. In this paper, we obtain several hardware implementations with different performance characteristics by applying different refinements to the algorithm. The developed designs are compiled and tested under Celoxica's RC-1000 reconfigurable computer with its 2 million gates Virtex-E FPGA. Performance analysis and evaluation of these implementations are included.
Supervised-Learning for Multi-Hop MU-MIMO Communications with One-Bit Transceivers
Venue: arXiv cs.IT 2019
Year: 2019

This paper considers a nonlinear multi-hop multi-user multiple-input multiple-output (MU-MIMO) relay channel, in which multiple users send information symbols to a multi-antenna base station (BS) with one-bit analog-to-digital converters via intermediate relays, each with one-bit transceiver. To understand the fundamental limit of the detection performance, the optimal maximum-likelihood (ML) detector is proposed with the assumption of perfect and global channel state information (CSI) at the BS. This multi-user detector, however, is not practical due to the unrealistic CSI assumption and the overwhelming detection complexity. These limitations are addressed by presenting a novel detection framework inspired by supervised-learning. The key idea is to model the complicated multihop MU-MIMO channel as a simplified channel with much fewer and learnable parameters. One major finding is that, even using the simplified channel model, a near ML detection performance is achievable with a reasonable amount of pilot overheads in a certain condition. In addition, an online supervised-learning detector is proposed, which adaptively tracks channel variations. The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labelled) data. Lastly, a multi-user detector using a deep neural network is proposed. Unlike the model-based approaches, this model-free approach enables to remove the errors in the simplified channel model, while increasing the computational complexity for parameter learning. Via simulations, the detection performances of classical, model-based, and model-free detectors are thoroughly compared to demonstrate the effectiveness of the supervised-learning approaches in this channel.
Real-time Soft Body 3D Proprioception via Deep Vision-based Sensing
Venue: arXiv cs.RO 2019
Year: 2019

Soft bodies made from flexible and deformable materials are popular in many robotics applications, but their proprioceptive sensing has been a long-standing challenge. In other words, there has hardly been a method to measure and model the high-dimensional 3D shapes of soft bodies with internal sensors. We propose a framework to measure the high-resolution 3D shapes of soft bodies in real-time with embedded cameras. The cameras capture visual patterns inside a soft body, and a convolutional neural network (CNN) produces a latent code representing the deformation state, which can then be used to reconstruct the body's 3D shape using another neural network. We test the framework on various soft bodies, such as a Baymax-shaped toy, a latex balloon, and some soft robot fingers, and achieve real-time computation ($\leq$2.5ms/frame) for robust shape estimation with high precision ($\leq$1% relative error) and high resolution. We believe the method could be applied to soft robotics and human-robot interaction for proprioceptive shape sensing. Our code is available at https://ai4ce.github.io/Deep-Soft-Prorioception/
Criteria and Approaches for Virtualization on Modern FPGAs
Venue: arXiv cs.DC 2019
Year: 2019

Modern field programmable gate arrays (FPGAs) can produce high performance in a wide range of applications, and their computational capacity is becoming abundant in personal computers. Regardless of this fact, FPGA virtualization is an emerging research field. Nowadays, challenges of the research area come from not only technical difficulties but also from the ambiguous standards of virtualization. In this paper, we introduce novel criteria of FPGA virtualization and discuss several approaches to accomplish those criteria. In addition, we present and describe in detail the specific FPGA virtualization architecture that we developed on Intel Arria 10 FPGA. We evaluate our solution with a combination of applications and microbenchmarks. The result shows that our virtualization solution can provide a full abstraction of FPGA device in both user and developer perspective while maintaining a reasonable performance compared to native FPGA.
Fifty years of Hoare's Logic
Venue: arXiv cs.LO 2019
Year: 2019

We present a history of Hoare's logic.
On matrices and $K$-relations
Venue: arXiv cs.DB 2019
Year: 2019

We show that the matrix query language $\mathsf{MATLANG}$ corresponds to a natural fragment of the positive relational algebra on $K$-relations. The fragment is defined by introducing a composition operator and restricting $K$-relation arities to two. We then proceed to show that $\mathsf{MATLANG}$ can express all matrix queries expressible in the positive relational algebra on $K$-relations, when intermediate arities are restricted to three. Thus we offer an analogue, in a model with numerical data, to the situation in classical logic, where the algebra of binary relations is equivalent to first-order logic with three variables.
Jointly Measuring Diversity and Quality in Text Generation Models
Venue: arXiv cs.LG 2019
Year: 2019

Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics such as BLEU only consider the quality of generated sentences and neglect their diversity. For example, repeatedly generation of only one high quality sentence would result in a high BLEU score. On the other hand, the more recent metric introduced to evaluate the diversity of generated texts known as Self-BLEU ignores the quality of generated texts. In this paper, we propose metrics to evaluate both the quality and diversity simultaneously by approximating the distance of the learned generative model and the real data distribution. For this purpose, we first introduce a metric that approximates this distance using n-gram based measures. Then, a feature-based measure which is based on a recent highly deep model trained on a large text corpus called BERT is introduced. Finally, for oracle training mode in which the generator's density can also be calculated, we propose to use the distance measures between the corresponding explicit distributions. Eventually, the most popular and recent text generation models are evaluated using both the existing and the proposed metrics and the preferences of the proposed metrics are determined.
Real or Fake? User Behavior and Attitudes Related to Determining the Veracity of Social Media Posts
Venue: arXiv cs.SI 2019
Year: 2019

Citizens and Emergency Managers need to be able to distinguish ''fake'' (untrue) news posts from real news posts on social media during disasters. This paper is based on an online survey conducted in 2018 that produced 341 responses from invitations distributed via email and through Facebook. It explores to what extent and how citizens generally assess whether postings are ''true'' or ''fake,'' and describes indicators of the trustworthiness of content that users would like. The mean response on a semantic differential scale measuring how frequently users attempt to verify the news trustworthiness (a scale from 1-never to 5-always) was 3.37. The most frequent message characteristics citizens' use are grammar and the trustworthiness of the sender. Most respondents would find an indicator of trustworthiness helpful, with the most popular choice being a colored graphic. Limitations and implications for assessments of trustworthiness during disasters are discussed.
Coupling agent based simulation with dynamic networks analysis to study the emergence of mutual knowledge as a percolation phenomenon
Venue: arXiv cs.MA 2019
Year: 2019

The emergence of mutual knowledge is a major cognitive mechanism for the robustness of complex socio technical systems. It has been extensively studied from an ethnomethodological point of view and empirically reproduced by multi agent simulations. Whilst such simulations have been used to design real work settings the underlying theoretical grounding for the process is vague. The aim of this paper is to investigate whether the emergence of mutual knowledge (MK) in a group of co-located individuals can be explained as a percolation phenomenon. The followed methodology consists in coupling agent-based simulation with dynamic networks analysis to study information propagation phenomena: after using an agent-based simulation we generated and then analysed its traces as networks where agents met and exchanged knowledge. Deep analysis of the resulting networks clearly shows that the emergence of MK is comparable to a percolation process. We specifically focus on how changes at the microscopic level in our agent based simulator affect percolation and robustness. These results therefore provides theoretical basis for the analysis of social organizations.
Understanding communications in medical emergency situations
Venue: arXiv cs.CY 2019
Year: 2019

Good communication is essential within teams dealing with emergency situations. In this paper we look at communications within a resuscitation team performing cardio-pulmonary resuscitation. Communication underpins efficient collaboration, joint coordination of work, and helps to construct a mutual awareness of the situation. Poor communication wastes valuable time and can ultimately lead to life-threatening mistakes. Although training sessions frequently focus on medical knowledge and procedures, soft skills, such as communication receive less attention. This paper analyses communication problems in the case of CPR and proposes an architecture that merges a situation awareness model and the belief-desire-intention (BDI) approach in multi-agent systems. The architecture forms the basis of an agent-based simulator used to assess communication protocols in CPR teams.
Implementation of a Daemon for OpenBCI
Venue: arXiv cs.HC 2019
Year: 2019

This document describes a technical study of the electroencephalographic (EEG) headset OpenBCI (New York, US). In comparison to research grade EEG, the OpenBCI headset is affordable thus suitable for the general public use. In this study we designed a daemon, that is, a background and continuous task communicating with the headset, acquiring, filtering and analyzing the EEG data. This study was promoted by the IHMTEK Company (Vienne, France) in 2016 within a thesis on the integration of EEG-based brain-computer interfaces in virtual reality for the general public.
Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop
Venue: arXiv cs.CL 2019
Year: 2019

The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category.
Completely Unsupervised Speech Recognition By A Generative Adversarial Network Harmonized With Iteratively Refined Hidden Markov Models
Venue: arXiv cs.CL 2019
Year: 2019

Producing a large annotated speech corpus for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced, but collecting a relatively big unlabeled data set for such languages is more achievable. This is why some initial effort have been reported on completely unsupervised speech recognition learned from unlabeled data only, although with relatively high error rates. In this paper, we develop a Generative Adversarial Network (GAN) to achieve this purpose, in which a Generator and a Discriminator learn from each other iteratively to improve the performance. We further use a set of Hidden Markov Models (HMMs) iteratively refined from the machine generated labels to work in harmony with the GAN. The initial experiments on TIMIT data set achieve an phone error rate of 33.1%, which is 8.5% lower than the previous state-of-the-art.
Identity-preserving Face Recovery from Stylized Portraits
Venue: arXiv cs.CV 2019
Year: 2019

Given an artistic portrait, recovering the latent photorealistic face that preserves the subject's identity is challenging because the facial details are often distorted or fully lost in artistic portraits. We develop an Identity-preserving Face Recovery from Portraits (IFRP) method that utilizes a Style Removal network (SRN) and a Discriminative Network (DN). Our SRN, composed of an autoencoder with residual block-embedded skip connections, is designed to transfer feature maps of stylized images to the feature maps of the corresponding photorealistic faces. Owing to the Spatial Transformer Network (STN), SRN automatically compensates for misalignments of stylized portraits to output aligned realistic face images. To ensure the identity preservation, we promote the recovered and ground truth faces to share similar visual features via a distance measure which compares features of recovered and ground truth faces extracted from a pre-trained FaceNet network. DN has multiple convolutional and fully-connected layers, and its role is to enforce recovered faces to be similar to authentic faces. Thus, we can recover high-quality photorealistic faces from unaligned portraits while preserving the identity of the face in an image. By conducting extensive evaluations on a large-scale synthesized dataset and a hand-drawn sketch dataset, we demonstrate that our method achieves superior face recovery and attains state-of-the-art results. In addition, our method can recover photorealistic faces from unseen stylized portraits, artistic paintings, and hand-drawn sketches.
Evaluating the Arm Ecosystem for High Performance Computing
Venue: arXiv cs.DC 2019
Year: 2019

In recent years, Arm-based processors have arrived on the HPC scene, offering an alternative the existing status quo, which was largely dominated by x86 processors. In this paper, we evaluate the Arm ecosystem, both the hardware offering and the software stack that is available to users, by benchmarking a production HPC platform that uses Marvell's ThunderX2 processors. We investigate the performance of complex scientific applications across multiple nodes, and we also assess the maturity of the software stack and the ease of use from a users' perspective. This papers finds that the performance across our benchmarking applications is generally as good as, or better, than that of well-established platforms, and we can conclude from our experience that there are no major hurdles that might hinder wider adoption of this ecosystem within the HPC community.
A Manifold of Polynomial Time Solvable Bimatrix Games
Venue: arXiv cs.GT 2019
Year: 2019

This paper identifies a manifold in the space of bimatrix games which contains games that are strategically equivalent to rank-1 games through a positive affine transformation. It also presents an algorithm that can compute, in polynomial time, one such rank-1 game which is strategically equivalent to the original game. Through this approach, we substantially expand the class of games that are solvable in polynomial time. It is hoped that this approach can be further developed in conjunction with other notions of strategic equivalence to compute exact or approximate Nash equilibria in a wide variety of bimatrix games.