OpenRaiser/Intern-Atlas huggingface dataset:
Concepts for End-to-end Augmented Reality based Human-Robot Interaction Systems
Venue: arXiv cs.RO 2019
Year: 2019

The field of Augmented Reality (AR) based Human Robot Interaction (HRI) has progressed significantly since its inception more than two decades ago. With more advanced devices, particularly head-mounted displays (HMD), freely available programming environments and better connectivity, the possible application space expanded significantly. Here we present concepts and systems currently being developed at our lab to enable a truly end-to-end application of AR in HRI, from setting up the working environment of the robot, through programming and finally interaction with the programmed robot. Relevant papers by other authors will also be overviewed. We demonstrate the use of such technologies with systems not inherently designed to be collaborative, namely industrial manipulators. By trying to make such industrial systems easily-installable, collaborative and interactive, the vision of universal robot co-workers can be pushed one step closer to reality. The main goal of the paper is to provide a short overview of the capabilities of HMD-based HRI to researchers unfamiliar with the concepts. For researchers already using such techniques, the hope is to perhaps introduce some new ideas and to broaden the field of research.
Orthogonality Constrained Multi-Head Attention For Keyword Spotting
Venue: arXiv cs.LG 2019
Year: 2019

Multi-head attention mechanism is capable of learning various representations from sequential data while paying attention to different subsequences, e.g., word-pieces or syllables in a spoken word. From the subsequences, it retrieves richer information than a single-head attention which only summarizes the whole sequence into one context vector. However, a naive use of the multi-head attention does not guarantee such richness as the attention heads may have positional and representational redundancy. In this paper, we propose a regularization technique for multi-head attention mechanism in an end-to-end neural keyword spotting system. Augmenting regularization terms which penalize positional and contextual non-orthogonality between the attention heads encourages to output different representations from separate subsequences, which in turn enables leveraging structured information without explicit sequence models such as hidden Markov models. In addition, intra-head contextual non-orthogonality regularization encourages each attention head to have similar representations across keyword examples, which helps classification by reducing feature variability. The experimental results demonstrate that the proposed regularization technique significantly improves the keyword spotting performance for the keyword "Hey Snapdragon".
Language Transfer for Early Warning of Epidemics from Social Media
Venue: arXiv cs.CL 2019
Year: 2019

Statements on social media can be analysed to identify individuals who are experiencing red flag medical symptoms, allowing early detection of the spread of disease such as influenza. Since disease does not respect cultural borders and may spread between populations speaking different languages, we would like to build multilingual models. However, the data required to train models for every language may be difficult, expensive and time-consuming to obtain, particularly for low-resource languages. Taking Japanese as our target language, we explore methods by which data in one language might be used to build models for a different language. We evaluate strategies of training on machine translated data and of zero-shot transfer through the use of multilingual models. We find that the choice of source language impacts the performance, with Chinese-Japanese being a better language pair than English-Japanese. Training on machine translated data shows promise, especially when used in conjunction with a small amount of target language data.
E2FM: an encrypted and compressed full-text index for collections of genomic sequences
Venue: arXiv cs.DB 2019
Year: 2019

Next Generation Sequencing (NGS) platforms and, more generally, high-throughput technologies are giving rise to an exponential growth in the size of nucleotide sequence databases. Moreover, many emerging applications of nucleotide datasets -- as those related to personalized medicine -- require the compliance with regulations about the storage and processing of sensitive data. We have designed and carefully engineered E2FM-index, a new full-text index in minute space which was optimized for compressing and encrypting nucleotide sequence collections in FASTA format and for performing fast pattern-search queries. E2FM-index allows to build self-indexes which occupy till to 1/20 of the storage required by the input FASTA file, thus permitting to save about 95% of storage when indexing collections of highly similar sequences; moreover, it can exactly search the built indexes for patterns in times ranging from few milliseconds to a few hundreds milliseconds, depending on pattern length. Supplementary material and supporting datasets are available through Bioinformatics Online and https://figshare.com/s/6246ee9c1bd730a8bf6e.
Assistive Gym: A Physics Simulation Framework for Assistive Robotics
Venue: arXiv cs.RO 2019
Year: 2019

Autonomous robots have the potential to serve as versatile caregivers that improve quality of life for millions of people worldwide. Yet, conducting research in this area presents numerous challenges, including the risks of physical interaction between people and robots. Physics simulations have been used to optimize and train robots for physical assistance, but have typically focused on a single task. In this paper, we present Assistive Gym, an open source physics simulation framework for assistive robots that models multiple tasks. It includes six simulated environments in which a robotic manipulator can attempt to assist a person with activities of daily living (ADLs): itch scratching, drinking, feeding, body manipulation, dressing, and bathing. Assistive Gym models a person's physical capabilities and preferences for assistance, which are used to provide a reward function. We present baseline policies trained using reinforcement learning for four different commercial robots in the six environments. We demonstrate that modeling human motion results in better assistance and we compare the performance of different robots. Overall, we show that Assistive Gym is a promising tool for assistive robotics research.
On the Effects of Pseudo and Quantum Random Number Generators in Soft Computing
Venue: arXiv cs.LG 2019
Year: 2019

In this work, we argue that the implications of Pseudo and Quantum Random Number Generators (PRNG and QRNG) inexplicably affect the performances and behaviours of various machine learning models that require a random input. These implications are yet to be explored in Soft Computing until this work. We use a CPU and a QPU to generate random numbers for multiple Machine Learning techniques. Random numbers are employed in the random initial weight distributions of Dense and Convolutional Neural Networks, in which results show a profound difference in learning patterns for the two. In 50 Dense Neural Networks (25 PRNG/25 QRNG), QRNG increases over PRNG for accent classification at +0.1%, and QRNG exceeded PRNG for mental state EEG classification by +2.82%. In 50 Convolutional Neural Networks (25 PRNG/25 QRNG), the MNIST and CIFAR-10 problems are benchmarked, in MNIST the QRNG experiences a higher starting accuracy than the PRNG but ultimately only exceeds it by 0.02%. In CIFAR-10, the QRNG outperforms PRNG by +0.92%. The n-random split of a Random Tree is enhanced towards and new Quantum Random Tree (QRT) model, which has differing classification abilities to its classical counterpart, 200 trees are trained and compared (100 PRNG/100 QRNG). Using the accent and EEG classification datasets, a QRT seemed inferior to a RT as it performed on average worse by -0.12%. This pattern is also seen in the EEG classification problem, where a QRT performs worse than a RT by -0.28%. Finally, the QRT is ensembled into a Quantum Random Forest (QRF), which also has a noticeable effect when compared to the standard Random Forest (RF)... ABSTRACT SHORTENED DUE TO ARXIV LIMIT
Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)
Venue: arXiv cs.CL 2019
Year: 2019

We present a recurrent neural network based system for automatic quality estimation of natural language generation (NLG) outputs, which jointly learns to assign numerical ratings to individual outputs and to provide pairwise rankings of two different outputs. The latter is trained using pairwise hinge loss over scores from two copies of the rating network. We use learning to rank and synthetic data to improve the quality of ratings assigned by our system: we synthesise training pairs of distorted system outputs and train the system to rank the less distorted one higher. This leads to a 12% increase in correlation with human ratings over the previous benchmark. We also establish the state of the art on the dataset of relative rankings from the E2E NLG Challenge (Dušek et al., 2019), where synthetic data lead to a 4% accuracy increase over the base model.
A Generative Approach Towards Improved Robotic Detection of Marine Litter
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents an approach to address data scarcity problems in underwater image datasets for visual detection of marine debris. The proposed approach relies on a two-stage variational autoencoder (VAE) and a binary classifier to evaluate the generated imagery for quality and realism. From the images generated by the two-stage VAE, the binary classifier selects "good quality" images and augments the given dataset with them. Lastly, a multi-class classifier is used to evaluate the impact of the augmentation process by measuring the accuracy of an object detector trained on combinations of real and generated trash images. Our results show that the classifier trained with the augmented data outperforms the one trained only with the real data. This approach will not only be valid for the underwater trash classification problem presented in this paper, but it will also be useful for any data-dependent task for which collecting more images is challenging or infeasible.
Design and Prototyping of a Bio-inspired Kinematic Sensing Suit for the Shoulder Joint: Precursor to a Multi-DoF Shoulder Exosuit
Venue: arXiv cs.RO 2019
Year: 2019

Soft wearable robots are a promising new design paradigm for rehabilitation and active assistance applications. Their compliant nature makes them ideal for complex joints like the shoulder, but intuitive control of these robots require robust and compliant sensing mechanisms. In this work, we introduce the sensing framework for a multi-DoF shoulder exosuit capable of sensing the kinematics of the shoulder joint. The proposed tendon-based sensing system is inspired by the concept of muscle synergies, the body's sense of proprioception, and finds its basis in the organization of the muscles responsible for shoulder movements. A motion-capture-based evaluation of the developed sensing system showed conformance to the behaviour exhibited by the muscles that inspired its routing and validates the hypothesis of the tendon-routing to be extended to the actuation framework of the exosuit in the future. The mapping from multi-sensor space to joint space is a multivariate multiple regression problem and was derived using an Artificial Neural Network (ANN). The sensing framework was tested with a motion-tracking system and achieved performance with root mean square error (RMSE) of approximately 5.43 degrees and 3.65 degrees for the azimuth and elevation joint angles, respectively, measured over 29000 frames (4+ minutes) of motion-capture data.
Link Prediction via Graph Attention Network
Venue: arXiv cs.SI 2019
Year: 2019

Link prediction aims to infer missing links or predicting the future ones based on currently observed partial networks, it is a fundamental problem in network science with tremendous real-world applications. However, conventional link prediction approaches neither have high prediction accuracy nor being capable of revealing the hidden information behind links. To address this problem, we generalize the latest techniques in deep learning on graphs and present a new link prediction model - DeepLinker. Instead of learning node representation with the node label information, DeepLinker uses the links as supervised information. Experiments on five graphs show that DeepLinker can not only achieve the state-of-the-art link prediction accuracy, but also acquire the efficient node representations and node centrality ranking as the byproducts. Although the representations are obtained without any supervised node label information, they still perform well on node ranking and node classification tasks.
Dynamic Spectrum Sharing for Load Balancing in Multi-Cell Mobile Edge Computing
Venue: arXiv cs.IT 2019
Year: 2019

Large-scale mobile edge computing (MEC) systems require scalable solutions to allocate communication and computing resources to the users. In this letter we address this challenge by applying dynamic spectrum sharing among the base stations (BSs), together with local resource allocation in the cells. We show that the network-wide resource allocation can be transformed into a convex optimization problem, and propose a distributed, hierarchical solution with limited information exchange among the BSs. Numerical results demonstrate that the proposed solution is superior to other baseline algorithms, when wireless and computing resource allocation is not jointly optimized, or the wireless resources allocated to the BSs are fixed.
The Detection of Distributional Discrepancy for Text Generation
Venue: arXiv cs.CV 2019
Year: 2019

The text generated by neural language models is not as good as the real text. This means that their distributions are different. Generative Adversarial Nets (GAN) are used to alleviate it. However, some researchers argue that GAN variants do not work at all. When both sample quality (such as Bleu) and sample diversity (such as self-Bleu) are taken into account, the GAN variants even are worse than a well-adjusted language model. But, Bleu and self-Bleu can not precisely measure this distributional discrepancy. In fact, how to measure the distributional discrepancy between real text and generated text is still an open problem. In this paper, we theoretically propose two metric functions to measure the distributional difference between real text and generated text. Besides that, a method is put forward to estimate them. First, we evaluate language model with these two functions and find the difference is huge. Then, we try several methods to use the detected discrepancy signal to improve the generator. However the difference becomes even bigger than before. Experimenting on two existing language GANs, the distributional discrepancy between real text and generated text increases with more adversarial learning rounds. It demonstrates both of these language GANs fail.
Vision-Based Autonomous Vehicle Control using the Two-Point Visual Driver Control Model
Venue: arXiv cs.CV 2019
Year: 2019

This work proposes a new self-driving framework that uses a human driver control model, whose feature-input values are extracted from images using deep convolutional neural networks (CNNs). The development of image processing techniques using CNNs along with accelerated computing hardware has recently enabled real-time detection of these feature-input values. The use of human driver models can lead to more "natural" driving behavior of self-driving vehicles. Specifically, we use the well-known two-point visual driver control model as the controller, and we use a top-down lane cost map CNN and the YOLOv2 CNN to extract feature-input values. This framework relies exclusively on inputs from low-cost sensors like a monocular camera and wheel speed sensors. We experimentally validate the proposed framework on an outdoor track using a 1/5th-scale autonomous vehicle platform.
NASS-AI: Towards Digitization of Parliamentary Bills using Document Level Embedding and Bidirectional Long Short-Term Memory
Venue: arXiv cs.CV 2019
Year: 2019

There has been several reports in the Nigerian and International media about the Senators and House of Representative Members of the Nigerian National Assembly (NASS) being the highest paid in the world. Despite this high-level of parliamentary compensation and a lack of oversight, most of the legislative duties like bills introduced and vote proceedings are shrouded in mystery without an open and annotated corpus. In this paper, we present results from ongoing research on the categorization of bills introduced in the Nigerian parliament since the fourth republic (1999 - 2018). For this task, we employed a multi-step approach which involves extracting text from scanned and embedded pdfs with low to medium quality using Optical Character Recognition (OCR) tools and labeling them into eight categories. We investigate the performance of document level embedding for feature representation of the extracted texts before using a Bidirectional Long Short-Term Memory (Bi-LSTM) for our classifier. The performance was further compared with other feature representation and machine learning techniques. We believe that these results are well-positioned to have a substantial impact on the quest to meet the basic open data charter principles.
Remote Control: A Simple Deadlock Avoidance Scheme for Modular System on Chip
Venue: arXiv cs.AR 2019
Year: 2019

The increase in design cost and complexity have motivated designers to adopt modular design of System on Chip (SoC) by integrating independently designed small chiplets. However, it introduces new challenges for correctness validation, increasing chances of forming deadlock in the system involving multiple chiplets. Although there have been many solutions available for deadlock freedom in flat networks, the study on deadlock issue in chiplet-based systems is still in its infancy. A recent study suggests adding extra turn restrictions as a viable solution for this problem. However, imposing extra turn restrictions reduces chiplet design flexibility and interposer design complexity. In addition, it may lead to non-minimal route and traffic imbalance forming hotspots, resulting in high latency and low throughput. We propose Remote Control (RC), a simple routing oblivious deadlock avoidance scheme. Our proposal is based on two key observations. First, packets with destinations in the current chiplet are blocked by packets with destinations outside the chiplet. Second, deadlock always involves multiple boundary routers. Hence, we segregate different traffics to alleviate mutual blocking at the chiplet's boundary routers. Along with guarantee of deadlock freedom and performance enhancements, our simple RC scheme also provides more routing flexibility to both chiplet and SoC designers, as compared to the state-of-the-art.
Threshold Logic in a Flash
Venue: arXiv cs.ET 2019
Year: 2019

This paper describes a novel design of a threshold logic gate (a binary perceptron) and its implementation as a standard cell. This new cell structure, referred to as flash threshold logic (FTL), uses floating gate (flash) transistors to realize the weights associated with a threshold function. The threshold voltages of the flash transistors serve as a proxy for the weights. An FTL cell can be equivalently viewed as a multi-input, edge-triggered flipflop which computes a threshold function on a clock edge. Consequently, it can be used in the automatic synthesis of ASICs. The use of flash transistors in the FTL cell allows programming of the weights after fabrication, thereby preventing discovery of its function by a foundry or by reverse engineering. This paper focuses on the design and characteristics of the FTL cell. We present a novel method for programming the weights of an FTL cell for a specified threshold function using a modified perceptron learning algorithm. The algorithm is further extended to select weights to maximize the robustness of the design in the presence of process variations. The FTL circuit was designed in 40nm technology and simulations with layout-extracted parasitics included, demonstrate significant improvements in the area (79.7%), power (61.1%), and performance (42.5%) when compared to the equivalent implementations of the same function in conventional static CMOS design. Weight selection targeting robustness is demonstrated using Monte Carlo simulations. The paper also shows how FTL cells can be used for fixing timing errors after fabrication.
From Species to Cultivar: Soybean Cultivar Recognition using Multiscale Sliding Chord Matching of Leaf Images
Venue: arXiv cs.CV 2019
Year: 2019

Leaf image recognition techniques have been actively researched for plant species identification. However it remains unclear whether leaf patterns can provide sufficient information for cultivar recognition. This paper reports the first attempt on soybean cultivar recognition from plant leaves which is not only a challenging research problem but also important for soybean cultivar evaluation, selection and production in agriculture. In this paper, we propose a novel multiscale sliding chord matching (MSCM) approach to extract leaf patterns that are distinctive for soybean cultivar identification. A chord is defined to slide along the contour for measuring the synchronised patterns of exterior shape and interior appearance of soybean leaf images. A multiscale sliding chord strategy is developed to extract features in a coarse-to-fine hierarchical order. A joint description that integrates the leaf descriptors from different parts of a soybean plant is proposed for further enhancing the discriminative power of cultivar description. We built a cultivar leaf image database, SoyCultivar, consisting of 1200 sample leaf images from 200 soybean cultivars for performance evaluation. Encouraging experimental results of the proposed method in comparison to the state-of-the-art leaf species recognition methods demonstrate the availability of cultivar information in soybean leaves and effectiveness of the proposed MSCM for soybean cultivar identification, which may advance the research in leaf recognition from species to cultivar.
Rk-means: Fast Clustering for Relational Data
Venue: arXiv cs.LG 2019
Year: 2019

Conventional machine learning algorithms cannot be applied until a data matrix is available to process. When the data matrix needs to be obtained from a relational database via a feature extraction query, the computation cost can be prohibitive, as the data matrix may be (much) larger than the total input relation size. This paper introduces Rk-means, or relational k -means algorithm, for clustering relational data tuples without having to access the full data matrix. As such, we avoid having to run the expensive feature extraction query and storing its output. Our algorithm leverages the underlying structures in relational data. It involves construction of a small {\it grid coreset} of the data matrix for subsequent cluster construction. This gives a constant approximation for the k -means objective, while having asymptotic runtime improvements over standard approaches of first running the database query and then clustering. Empirical results show orders-of-magnitude speedup, and Rk-means can run faster on the database than even just computing the data matrix.