OpenRaiser/Intern-Atlas huggingface dataset:
Answer Extraction for Why Arabic Questions Answering Systems: EWAQ
Venue: arXiv cs.CL 2019
Year: 2019

With the increasing amount of web information, questions answering systems becomes very important to allow users to access to direct answers for their requests. This paper presents an Arabic Questions Answering Systems based on entailment metrics. The type of questions which this paper focuses on is why questions. There are many reasons lead us to develop this system: generally, the lack of Arabic Questions Answering Systems and scarcity Arabic Questions Answering Systems which focus on why questions. The goal of the proposed system in this research is to extract answers from re-ranked retrieved passages which are retrieved by search engines. This system extracts the answer only to why questions. This system is called by EWAQ: Entailment based Why Arabic Questions Answering. Each answer is scored with entailment metrics and ranked according to their scores in order to determine the most possible correct answer. EWAQ is compared with search engines: yahoo, google and ask.com, the well-established web-based Questions Answering systems, using manual test set. In EWAQ experiments, it is showed that the accuracy is increased by implementing the textual entailment in re-raking the retrieved relevant passages by search engines and deciding the correct answer. The obtained results show that using entailment based similarity can help significantly to tackle the why Answer Extraction module in Arabic language.
Entropic Regularization of Markov Decision Processes
Venue: arXiv cs.LG 2019
Year: 2019

An optimal feedback controller for a given Markov decision process (MDP) can in principle be synthesized by value or policy iteration. However, if the system dynamics and the reward function are unknown, a learning agent must discover an optimal controller via direct interaction with the environment. Such interactive data gathering commonly leads to divergence towards dangerous or uninformative regions of the state space unless additional regularization measures are taken. Prior works proposed bounding the information loss measured by the Kullback-Leibler (KL) divergence at every policy improvement step to eliminate instability in the learning dynamics. In this paper, we consider a broader family of $f$-divergences, and more concretely $α$-divergences, which inherit the beneficial property of providing the policy improvement step in closed form at the same time yielding a corresponding dual objective for policy evaluation. Such entropic proximal policy optimization view gives a unified perspective on compatible actor-critic architectures. In particular, common least-squares value function estimation coupled with advantage-weighted maximum likelihood policy improvement is shown to correspond to the Pearson $χ^2$-divergence penalty. Other actor-critic pairs arise for various choices of the penalty-generating function $f$. On a concrete instantiation of our framework with the $α$-divergence, we carry out asymptotic analysis of the solutions for different values of $α$ and demonstrate the effects of the divergence function choice on common standard reinforcement learning problems.
Streaming 1.9 Billion Hypersparse Network Updates per Second with D4M
Venue: arXiv cs.DC 2019
Year: 2019

The Dynamic Distributed Dimensional Data Model (D4M) library implements associative arrays in a variety of languages (Python, Julia, and Matlab/Octave) and provides a lightweight in-memory database implementation of hypersparse arrays that are ideal for analyzing many types of network data. D4M relies on associative arrays which combine properties of spreadsheets, databases, matrices, graphs, and networks, while providing rigorous mathematical guarantees, such as linearity. Streaming updates of D4M associative arrays put enormous pressure on the memory hierarchy. This work describes the design and performance optimization of an implementation of hierarchical associative arrays that reduces memory pressure and dramatically increases the update rate into an associative array. The parameters of hierarchical associative arrays rely on controlling the number of entries in each level in the hierarchy before an update is cascaded. The parameters are easily tunable to achieve optimal performance for a variety of applications. Hierarchical arrays achieve over 40,000 updates per second in a single instance. Scaling to 34,000 instances of hierarchical D4M associative arrays on 1,100 server nodes on the MIT SuperCloud achieved a sustained update rate of 1,900,000,000 updates per second. This capability allows the MIT SuperCloud to analyze extremely large streaming network data sets.
Melody Generation using an Interactive Evolutionary Algorithm
Venue: arXiv cs.NE 2019
Year: 2019

Music generation with the aid of computers has been recently grabbed the attention of many scientists in the area of artificial intelligence. Deep learning techniques have evolved sequence production methods for this purpose. Yet, a challenging problem is how to evaluate generated music by a machine. In this paper, a methodology has been developed based upon an interactive evolutionary optimization method, with which the scoring of the generated melodies is primarily performed by human expertise, during the training. This music quality scoring is modeled using a Bi-LSTM recurrent neural network. Moreover, the innovative generated melody through a Genetic algorithm will then be evaluated using this Bi-LSTM network. The results of this mechanism clearly show that the proposed method is able to create pleasurable melodies with desired styles and pieces. This method is also quite fast, compared to the state-of-the-art data-oriented evolutionary systems.
Translating neural signals to text using a Brain-Machine Interface
Venue: arXiv cs.HC 2019
Year: 2019

Brain-Computer Interfaces (BCI) help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech output by direct neural processing. However, practical implementation of such a system has proven difficult due to limitations in speed, accuracy, and generalizability of the existing interfaces. To this end, we aim to create a BCI system that decodes text directly from neural signals. We implement a framework that initially isolates frequency bands in the input signal encapsulating differential information regarding production of various phonemic classes. These bands then form a feature set that feeds into an LSTM which discerns at each time point probability distributions across all phonemes uttered by a subject. Finally, these probabilities are fed into a particle filtering algorithm which incorporates prior knowledge of the English language to output text corresponding to the decoded word. Performance of this model on data obtained from six patients shows encouragingly high levels of accuracy at speeds and bit rates significantly higher than existing BCI communication systems. Further, in producing an output, our network abstains from constraining the reconstructed word to be from a given bag-of-words, unlike previous studies. The success of our proposed approach, offers promise for the employment of a BCI interface by patients in unfettered, naturalistic environments.
Multiple Knapsack-Constrained Monotone DR-Submodular Maximization on Distributive Lattice --- Continuous Greedy Algorithm on Median Complex ---
Venue: arXiv cs.DS 2019
Year: 2019

We consider a problem of maximizing a monotone DR-submodular function under multiple order-consistent knapsack constraints on a distributive lattice. Since a distributive lattice is used to represent a dependency constraint, the problem can represent a dependency constrained version of a submodular maximization problem on a set. We propose a $1 - 1/e$ approximation algorithm for this problem. To achieve this result, we generalize the continuous greedy algorithm to distributive lattices: We choose a median complex as a continuous relaxation of a distributive lattice and define the multilinear extension on it. We show that the median complex admits special curves, named uniform linear motions, such that the multilinear extension of a DR-submodular function is concave along a positive uniform linear motion, which is a key property of the continuous greedy algorithm.
An Attention Mechanism for Musical Instrument Recognition
Venue: arXiv cs.IR 2019
Year: 2019

While the automatic recognition of musical instruments has seen significant progress, the task is still considered hard for music featuring multiple instruments as opposed to single instrument recordings. Datasets for polyphonic instrument recognition can be categorized into roughly two categories. Some, such as MedleyDB, have strong per-frame instrument activity annotations but are usually small in size. Other, larger datasets such as OpenMIC only have weak labels, i.e., instrument presence or absence is annotated only for long snippets of a song. We explore an attention mechanism for handling weakly labeled data for multi-label instrument recognition. Attention has been found to perform well for other tasks with weakly labeled data. We compare the proposed attention model to multiple models which include a baseline binary relevance random forest, recurrent neural network, and fully connected neural networks. Our results show that incorporating attention leads to an overall improvement in classification accuracy metrics across all 20 instruments in the OpenMIC dataset. We find that attention enables models to focus on (or `attend to') specific time segments in the audio relevant to each instrument label leading to interpretable results.
Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
Venue: arXiv cs.CV 2019
Year: 2019

On-orbit proximity operations in space rendezvous, docking and debris removal require precise and robust 6D pose estimation under a wide range of lighting conditions and against highly textured background, i.e., the Earth. This paper investigates leveraging deep learning and photorealistic rendering for monocular pose estimation of known uncooperative spacecrafts. We first present a simulator built on Unreal Engine 4, named URSO, to generate labeled images of spacecrafts orbiting the Earth, which can be used to train and evaluate neural networks. Secondly, we propose a deep learning framework for pose estimation based on orientation soft classification, which allows modelling orientation ambiguity as a mixture of Gaussians. This framework was evaluated both on URSO datasets and the ESA pose estimation challenge. In this competition, our best model achieved 3rd place on the synthetic test set and 2nd place on the real test set. Moreover, our results show the impact of several architectural and training aspects, and we demonstrate qualitatively how models learned on URSO datasets can perform on real images from space.
Image based Eye Gaze Tracking and its Applications
Venue: arXiv cs.CV 2019
Year: 2019

Eye movements play a vital role in perceiving the world. Eye gaze can give a direct indication of the users point of attention, which can be useful in improving human-computer interaction. Gaze estimation in a non-intrusive manner can make human-computer interaction more natural. Eye tracking can be used for several applications such as fatigue detection, biometric authentication, disease diagnosis, activity recognition, alertness level estimation, gaze-contingent display, human-computer interaction, etc. Even though eye-tracking technology has been around for many decades, it has not found much use in consumer applications. The main reasons are the high cost of eye tracking hardware and lack of consumer level applications. In this work, we attempt to address these two issues. In the first part of this work, image-based algorithms are developed for gaze tracking which includes a new two-stage iris center localization algorithm. We have developed a new algorithm which works in challenging conditions such as motion blur, glint, and varying illumination levels. A person independent gaze direction classification framework using a convolutional neural network is also developed which eliminates the requirement of user-specific calibration. In the second part of this work, we have developed two applications which can benefit from eye tracking data. A new framework for biometric identification based on eye movement parameters is developed. A framework for activity recognition, using gaze data from a head-mounted eye tracker is also developed. The information from gaze data, ego-motion, and visual features are integrated to classify the activities.
A New Stereo Benchmarking Dataset for Satellite Images
Venue: arXiv cs.CV 2019
Year: 2019

In order to facilitate further research in stereo reconstruction with multi-date satellite images, the goal of this paper is to provide a set of stereo-rectified images and the associated groundtruthed disparities for 10 AOIs (Area of Interest) drawn from two sources: 8 AOIs from IARPA's MVS Challenge dataset and 2 AOIs from the CORE3D-Public dataset. The disparities were groundtruthed by first constructing a fused DSM from the stereo pairs and by aligning 30 cm LiDAR with the fused DSM. Unlike the existing benckmarking datasets, we have also carried out a quantitative evaluation of our groundtruthed disparities using human annotated points in two of the AOIs. Additionally, the rectification accuracy in our dataset is comparable to the same in the existing state-of-the-art stereo datasets. In general, we have used the WorldView-3 (WV3) images for the dataset, the exception being the UCSD area for which we have used both WV3 and WorldView-2 (WV2) images. All of the dataset images are now in the public domain. Since multi-date satellite images frequently include images acquired in different seasons (which creates challenges in finding corresponding pairs of pixels for stereo), our dataset also includes for each image a building mask over which the disparities estimated by stereo should prove reliable. Additional metadata included in the dataset includes information about each image's acquisition date and time, the azimuth and elevation angles of the camera, and the intersection angles for the two views in a stereo pair. Also included in the dataset are both quantitative and qualitative analyses of the accuracy of the groundtruthed disparity maps. Our dataset is available for download at \url{https://engineering.purdue.edu/RVL/Database/SatStereo/index.html}
On Approximating Partial Set Cover and Generalizations
Venue: arXiv cs.DS 2019
Year: 2019

Partial Set Cover (PSC) is a generalization of the well-studied Set Cover problem (SC). In PSC the input consists of an integer $k$ and a set system $(U,S)$ where $U$ is a finite set, and $S \subseteq 2^U$ is a collection of subsets of $U$. The goal is to find a subcollection $S' \subseteq S$ of smallest cardinality such that sets in $S'$ cover at least $k$ elements of $U$; that is $|\cup_{A \in S'} A| \ge k$. SC is a special case of PSC when $k = |U|$. In the weighted version each set $X \in S$ has a non-negative weight $w(X)$ and the goal is to find a minimum weight subcollection to cover $k$ elements. Approximation algorithms for SC have been adapted to obtain comparable algorithms for PSC in various interesting cases. In recent work Inamdar and Varadarajan, motivated by geometric set systems, obtained a simple and elegant approach to reduce PSC to SC via the natural LP relaxation. They showed that if a deletion-closed family of SC admits a $β$-approximation via the natural LP relaxation, then one can obtain a $2(β+ 1)$-approximation for PSC on the same family. In a subsequent paper, they also considered a generalization of PSC that has multiple partial covering constraints which is partly inspired by and generalizes previous work of Bera et al on the Vertex Cover problem. Our main goal in this paper is to demonstrate some useful connections between the results in previous work and submodularity. This allows us to simplify, and in some cases improve their results. We improve the approximation for PSC to $(1-1/e)(β+ 1)$. We extend the previous work to the sparse setting.
Model Development Process
Venue: arXiv cs.LG 2019
Year: 2019

Predictive modeling has an increasing number of applications in various fields. High demand for predictive models drives creation of tools that automate and support work of data scientist on the model development. To better understand what can be automated we need first a description of the model life-cycle. In this paper we propose a generic Model Development Process (MDP). This process is inspired by Rational Unified Process (RUP) which was designed for software development. There are other approached to process description, like CRISP DM or ASUM DM, in this paper we discuss similarities and differences between these methodologies. We believe that the proposed open standard for model development will facilitate creation of tools for automation of model training, testing and maintaining.
Learning from History: Recreating and Repurposing Sister Harriet Padberg's Computer Composed Canon and Free Fugue
Venue: arXiv cs.MM 2019
Year: 2019

Harriet Padberg wrote Computer-Composed Canon and Free Fugue as part of her 1964 dissertation in Mathematics and Music at Saint Louis University. This program is one of the earliest examples of text-to-music software and algorithmic composition, which are areas of great interest in the present-day field of music technology. This paper aims to analyze the technological innovation, aesthetic design process, and impact of Harriet Padberg's original 1964 thesis as well as the design of a modern recreation and utilization, in order to gain insight to the nature of revisiting older works. Here, we present our open source recreation of Padberg's program with a modern interface and, through its use as an artistic tool by three composers, show how historical works can be effectively used for new creative purposes in contemporary contexts. Not Even One by Molly Jones draws on the historical and social significance of Harriet Padberg through using her program in a piece about the lack of representation of women judges in composition competitions. Brevity by Anna Savery utilizes the original software design as a composition tool, and The Padberg Piano by Anthony Caulkins uses the melodic generation of the original to create a software instrument.
A Short Virtual Reality Mindfulness Meditation Training For Regaining Sustained Attention
Venue: arXiv cs.HC 2019
Year: 2019

The ability to focus one's attention underlies success in many everyday tasks, but voluntary attention cannot be sustained for a long period of time. Several studies indicate that attention training using computer-based exercises can lead to improved attention in children and adults. a major goal of recent research is to create a short (10 minutes) and effective VR Mindfulness meditation particularly designed for regaining or improving sustained attention. In this study, we have created a custom virtually relaxing environment including an archery game with multiple targets. In the experiment, the attention span of 12 adults are tested before and after the virtual reality session by a non-action video game ([19]) score and Muse headband EEG-signals. After the 10-minute virtual reality session participants' game scores increased (according to game experience): for the beginner by 275%, for intermediate by 107%, and for an expert by 17%. For Muse headband data, calm points increased by 250% irrespective of the participants gaming experiences. After the experiment, all participants reported feeling recharged to continue their daily activities.
A Range Matching CAM for Hierarchical Defect Tolerance Technique in NRAM Structures
Venue: arXiv cs.AR 2019
Year: 2019

Due to the small size of nanoscale devices, they are highly prone to process disturbances which results in manufacturing defects. Some of the defects are randomly distributed throughout the nanodevice layer. Other disturbances tend to be local and lead to cluster defects caused by factors such as layer misintegration and line width variations. In this paper, we propose a method for identifying cluster defects from random ones. The motivation is to repair the cluster defects using rectangular ranges in a range matching content-addressable memory (RM-CAM) and random defects using triple-modular redundancy (TMR). It is believed a combination of these two approaches is more effective for repairing defects at high error rate with less resource. With the proposed fault repairing technique, defect recovery results are examined for different fault distribution scenarios. Also the mapping circuit structure required for two conceptual 32*32 and 64*64 bit RAMs are presented and their speed, power and transistor count are reported.
Multi-layer Attention Mechanism for Speech Keyword Recognition
Venue: arXiv cs.LG 2019
Year: 2019

As an important part of speech recognition technology, automatic speech keyword recognition has been intensively studied in recent years. Such technology becomes especially pivotal under situations with limited infrastructures and computational resources, such as voice command recognition in vehicles and robot interaction. At present, the mainstream methods in automatic speech keyword recognition are based on long short-term memory (LSTM) networks with attention mechanism. However, due to inevitable information losses for the LSTM layer caused during feature extraction, the calculated attention weights are biased. In this paper, a novel approach, namely Multi-layer Attention Mechanism, is proposed to handle the inaccurate attention weights problem. The key idea is that, in addition to the conventional attention mechanism, information of layers prior to feature extraction and LSTM are introduced into attention weights calculations. Therefore, the attention weights are more accurate because the overall model can have more precise and focused areas. We conduct a comprehensive comparison and analysis on the keyword spotting performances on convolution neural network, bi-directional LSTM cyclic neural network, and cyclic neural network with the proposed attention mechanism on Google Speech Command datasets V2 datasets. Experimental results indicate favorable results for the proposed method and demonstrate the validity of the proposed method. The proposed multi-layer attention methods can be useful for other researches related to object spotting.
Non-Smooth Newton Methods for Deformable Multi-Body Dynamics
Venue: arXiv cs.RO 2019
Year: 2019

We present a framework for the simulation of rigid and deformable bodies in the presence of contact and friction. Our method is based on a non-smooth Newton iteration that solves the underlying nonlinear complementarity problems (NCPs) directly. This approach allows us to support nonlinear dynamics models, including hyperelastic deformable bodies and articulated rigid mechanisms, coupled through a smooth isotropic friction model. The fixed-point nature of our method means it requires only the solution of a symmetric linear system as a building block. We propose a new complementarity preconditioner for NCP functions that improves convergence, and we develop an efficient GPU-based solver based on the conjugate residual (CR) method that is suitable for interactive simulations. We show how to improve robustness using a new geometric stiffness approximation and evaluate our method's performance on a number of robotics simulation scenarios, including dexterous manipulation and training using reinforcement learning.