OpenRaiser/Intern-Atlas huggingface dataset:
ROS Based Visual Programming Tool for Mobile Robot Education and Applications
Venue: arXiv cs.RO 2020
Year: 2020

Visual programming languages (VPLs) provide coding without typing texts. VPL makes coding easy to programmers with automatically adding usually used some code structure. Beginners in coding have generally two main challenges; transforming ideas into logical expressions and syntax errors. Syntax errors are impossible with VPLs because of there is no forgotten parentheses and semicolons. VPLs provide to focus on algorithm for programmers. VPL is a new trend for educational robotic environments. In this study, Robot Operating System (ROS) compatible web based visual programming system has been developed for evarobot. ROS provides libraries and tools to help software developers create robot applications. It provides hardware abstraction, device drivers, libraries, visualizers, message-passing, package management, and more. Blockly has been used as VPL for the study and to generate / use blocks (commucation, sensing etc.). Some applications were generated like teleoperation, SLAM and wander etc. In this system, communication between server and client is supported by rosbridge package. Web page connected to ROS which runs on server using roslibjs library. Rosbridge provides a JSON API to ROS functionality for non-ROS programs.
A study of traits that affect learnability in GANs
Venue: arXiv cs.LG 2020
Year: 2020

Generative Adversarial Networks GANs are algorithmic architectures that use two neural networks, pitting one against the opposite so as to come up with new, synthetic instances of data that can pass for real data. Training a GAN is a challenging problem which requires us to apply advanced techniques like hyperparameter tuning, architecture engineering etc. Many different losses, regularization and normalization schemes, network architectures have been proposed to solve this challenging problem for different types of datasets. It becomes necessary to understand the experimental observations and deduce a simple theory for it. In this paper, we perform empirical experiments using parameterized synthetic datasets to probe what traits affect learnability.
Generalized Pose-and-Scale Estimation using 4-Point Congruence Constraints
Venue: arXiv cs.CV 2020
Year: 2020

We present gP4Pc, a new method for computing the absolute pose of a generalized camera with unknown internal scale from four corresponding 3D point-and-ray pairs. Unlike most pose-and-scale methods, gP4Pc is based on constraints arising from the congruence of shapes defined by two sets of four points related by an unknown similarity transformation. By choosing a novel parametrization for the problem, we derive a system of four quadratic equations in four scalar variables. The variables represent the distances of 3D points along the rays from the camera centers. After solving this system via Groebner basis-based automatic polynomial solvers, we compute the similarity transformation using an efficient 3D point-point alignment method. We also propose a specialized variant of our solver for the case of coplanar points, which is computationally very efficient and about 3x faster than the fastest existing solver. Our experiments on real and synthetic datasets, demonstrate that gP4Pc is among the fastest methods in terms of total running time when used within a RANSAC framework, while achieving competitive numerical stability, accuracy, and robustness to noise.
Design Methodologies for Reliable and Energy-efficient PCM Systems
Venue: arXiv cs.AR 2020
Year: 2020

Phase-change memory (PCM) is a scalable and low latency non-volatile memory (NVM) technology that has been proposed to serve as storage class memory (SCM), providing low access latency similar to DRAM and often approaching or exceeding the capacity of SSD. The multilevel property of PCM also enables its adoption in neuromorphic systems to build high-density synaptic storage. We investigate and describe two significant bottlenecks of a PCM system. First, writing to PCM cells incurs significantly higher latency and energy penalties than reading its content. Second, high operating voltages of PCM impacts its reliable operations. In this work, we propose methodologies to tackle the bottlenecks, improving performance, reliability, energy consumption, and sustainability for a PCM system.
Compiling Spiking Neural Networks to Mitigate Neuromorphic Hardware Constraints
Venue: arXiv cs.NE 2020
Year: 2020

Spiking Neural Networks (SNNs) are efficient computation models to perform spatio-temporal pattern recognition on {resource}- and {power}-constrained platforms. SNNs executed on neuromorphic hardware can further reduce energy consumption of these platforms. With increasing model size and complexity, mapping SNN-based applications to tile-based neuromorphic hardware is becoming increasingly challenging. This is attributed to the limitations of neuro-synaptic cores, viz. a crossbar, to accommodate only a fixed number of pre-synaptic connections per post-synaptic neuron. For complex SNN-based models that have many neurons and pre-synaptic connections per neuron, (1) connections may need to be pruned after training to fit onto the crossbar resources, leading to a loss in model quality, e.g., accuracy, and (2) the neurons and synapses need to be partitioned and placed on the neuro-sypatic cores of the hardware, which could lead to increased latency and energy consumption. In this work, we propose (1) a novel unrolling technique that decomposes a neuron function with many pre-synaptic connections into a sequence of homogeneous neural units to significantly improve the crossbar utilization and retain all pre-synaptic connections, and (2) SpiNeMap, a novel methodology to map SNNs on neuromorphic hardware with an aim to minimize energy consumption and spike latency.
Reducing Discrimination in Learning Algorithms for Social Good in Sociotechnical Systems
Venue: arXiv cs.LG 2020
Year: 2020

Sociotechnical systems within cities are now equipped with machine learning algorithms in hopes to increase efficiency and functionality by modeling and predicting trends. Machine learning algorithms have been applied in these domains to address challenges such as balancing the distribution of bikes throughout a city and identifying demand hotspots for ride sharing drivers. However, these algorithms applied to challenges in sociotechnical systems have exacerbated social inequalities due to previous bias in data sets or the lack of data from marginalized communities. In this paper, I will address how smart mobility initiatives in cities use machine learning algorithms to address challenges. I will also address how these algorithms unintentionally discriminate against features such as socioeconomic status to motivate the importance of algorithmic fairness. Using the bike sharing program in Pittsburgh, PA, I will present a position on how discrimination can be eliminated from the pipeline using Bayesian Optimization.
A Survey of Online Card Payment Fraud Detection using Data Mining-based Methods
Venue: arXiv cs.CR 2020
Year: 2020

Card payment fraud is a serious problem, and a roadblock for an optimally functioning digital economy, with cards (Debits and Credit) being the most popular digital payment method across the globe. Despite the occurrence of fraud could be relatively rare, the impact of fraud could be significant, especially on the cardholder. In the research, there have been many attempts to develop methods of detecting potentially fraudulent transactions based on data mining techniques, predominantly exploiting the developments in the space of machine learning over the last decade. This survey proposes a taxonomy based on a review of existing research attempts and experiments, which mainly elaborates the approaches taken by researchers to incorporate the (i) business impact of fraud (and fraud detection) into their work , (ii) the feature engineering techniques that focus on cardholder behavioural profiling to separate fraudulent activities happening with the same card, and (iii) the adaptive efforts taken to address the changing nature of fraud. Further, there will be a comparative performance evaluation of classification algorithms used and efforts of addressing class imbalance problem. Forty-five peer-reviewed papers published in the domain of card fraud detection between 2009 and 2020 were intensively reviewed to develop this paper.
Online Search with Maximum Clearance
Venue: arXiv cs.DS 2020
Year: 2020

We study the setting in which a mobile agent must locate a hidden target in a bounded or unbounded environment, with no information about the hider's position. In particular, we consider online search, in which the performance of the search strategy is evaluated by its worst case competitive ratio. We introduce a multi-criteria search problem in which the searcher has a budget on its allotted search time, and the objective is to design strategies that are competitively efficient, respect the budget, and maximize the total searched ground. We give analytically optimal strategies for the line and the star environments, and efficient heuristics for general networks.
A Closer Look at the Tropical Cryptography
Venue: arXiv cs.CR 2020
Year: 2020

We examine two public key exchange protocols proposed recently by Grigoriev and Shpilrain (arXiv:1811.06386), which use tropical algebra. We introduce a fast attack on the first protocol, and we show that the second protocol cannot be implemented as described.
Profinet vs Profibus
Venue: arXiv cs.NI 2020
Year: 2020

We present a step by step definition of Profinet and Profibus. We introduced different types of each of the two communication protocols. Then, the topology and performance of each one has been described individually. Finally, the properties of them have been compared to show that which one has a better performance in the industry.
HEDRA: A Bio-Inspired Modular Tensegrity Soft Robot With Polyhedral Parallel Modules
Venue: arXiv cs.RO 2020
Year: 2020

There is a surge of research interest in the field of tensegrity robotics. Robots developed under this paradigm provide many advantages and have distinguishing features in terms of structural compliance, dexterity, safety, and weight reduction. This paper proposes a new robotic mechanism based on tensegrity ('tension-integrity') robots and reconfigurable modular robots. The specific actuation schemes for this tensegrity robot with multiple degrees of freedom are presented. This article describes an easy-to-assemble 350 mm tensegrity based robot prototype by stacking a series of rigid struts linked with tensegrity joints that have no direct rigid contact with each other. The functionality of the proposed robot is validated by the experimental results by integrating the polyhedral parallel structure as its skeleton and series of tensegrity joints. The proposed manipulator is capable of reaching bending angles up to 76 degrees. An adaptive cable driven underactuated robotic gripper is designed and attached to the tensegrity manipulator for grasping objects in different shapes, weights, and sizes.
Active Output Selection Strategies for Multiple Learning Regression Models
Venue: arXiv cs.LG 2020
Year: 2020

Active learning shows promise to decrease test bench time for model-based drivability calibration. This paper presents a new strategy for active output selection, which suits the needs of calibration tasks. The strategy is actively learning multiple outputs in the same input space. It chooses the output model with the highest cross-validation error as leading. The presented method is applied to three different toy examples with noise in a real world range and to a benchmark dataset. The results are analyzed and compared to other existing strategies. In a best case scenario, the presented strategy is able to decrease the number of points by up to 30% compared to a sequential space-filling design while outperforming other existing active learning strategies. The results are promising but also show that the algorithm has to be improved to increase robustness for noisy environments. Further research will focus on improving the algorithm and applying it to a real-world example.
Exploring Deep 3D Spatial Encodings for Large-Scale 3D Scene Understanding
Venue: arXiv cs.CV 2020
Year: 2020

Semantic segmentation of raw 3D point clouds is an essential component in 3D scene analysis, but it poses several challenges, primarily due to the non-Euclidean nature of 3D point clouds. Although, several deep learning based approaches have been proposed to address this task, but almost all of them emphasized on using the latent (global) feature representations from traditional convolutional neural networks (CNN), resulting in severe loss of spatial information, thus failing to model the geometry of the underlying 3D objects, that plays an important role in remote sensing 3D scenes. In this letter, we have proposed an alternative approach to overcome the limitations of CNN based approaches by encoding the spatial features of raw 3D point clouds into undirected symmetrical graph models. These encodings are then combined with a high-dimensional feature vector extracted from a traditional CNN into a localized graph convolution operator that outputs the required 3D segmentation map. We have performed experiments on two standard benchmark datasets (including an outdoor aerial remote sensing dataset and an indoor synthetic dataset). The proposed method achieves on par state-of-the-art accuracy with improved training time and model stability thus indicating strong potential for further research towards a generalized state-of-the-art method for 3D scene understanding.
Offline Reinforcement Learning Hands-On
Venue: arXiv cs.LG 2020
Year: 2020

Offline Reinforcement Learning (RL) aims to turn large datasets into powerful decision-making engines without any online interactions with the environment. This great promise has motivated a large amount of research that hopes to replicate the success RL has experienced in simulation settings. This work ambitions to reflect upon these efforts from a practitioner viewpoint. We start by discussing the dataset properties that we hypothesise can characterise the type of offline methods that will be the most successful. We then verify these claims through a set of experiments and designed datasets generated from environments with both discrete and continuous action spaces. We experimentally validate that diversity and high-return examples in the data are crucial to the success of offline RL and show that behavioural cloning remains a strong contender compared to its contemporaries. Overall, this work stands as a tutorial to help people build their intuition on today's offline RL methods and their applicability.
Reinforcement Learning in Linear Quadratic Deep Structured Teams: Global Convergence of Policy Gradient Methods
Venue: arXiv cs.MA 2020
Year: 2020

In this paper, we study the global convergence of model-based and model-free policy gradient descent and natural policy gradient descent algorithms for linear quadratic deep structured teams. In such systems, agents are partitioned into a few sub-populations wherein the agents in each sub-population are coupled in the dynamics and cost function through a set of linear regressions of the states and actions of all agents. Every agent observes its local state and the linear regressions of states, called deep states. For a sufficiently small risk factor and/or sufficiently large population, we prove that model-based policy gradient methods globally converge to the optimal solution. Given an arbitrary number of agents, we develop model-free policy gradient and natural policy gradient algorithms for the special case of risk-neutral cost function. The proposed algorithms are scalable with respect to the number of agents due to the fact that the dimension of their policy space is independent of the number of agents in each sub-population. Simulations are provided to verify the theoretical results.
State Complexity of the Set of Synchronizing Words for Circular Automata and Automata over Binary Alphabets
Venue: arXiv cs.FL 2020
Year: 2020

Most slowly synchronizing automata over binary alphabets are circular, i.e., containing a letter permuting the states in a single cycle, and their set of synchronizing words has maximal state complexity, which also implies complete reachability.Here, we take a closer look at generalized circular and completely reachable automata. We derive that over a binary alphabet every completely reachable automaton must be circular, a consequence of a structural result stating that completely reachable automata over strictly less letters than states always contain permutational letters. We state sufficient conditions for the state complexity of the set of synchronizing words of a generalized circular automaton to be maximal. We apply our main criteria to the family $\mathscr K_n$ of automata that was previously only conjectured to have this property.
Improved Semantic Role Labeling using Parameterized Neighborhood Memory Adaptation
Venue: arXiv cs.CL 2020
Year: 2020

Deep neural models achieve some of the best results for semantic role labeling. Inspired by instance-based learning that utilizes nearest neighbors to handle low-frequency context-specific training samples, we investigate the use of memory adaptation techniques in deep neural models. We propose a parameterized neighborhood memory adaptive (PNMA) method that uses a parameterized representation of the nearest neighbors of tokens in a memory of activations and makes predictions based on the most similar samples in the training data. We empirically show that PNMA consistently improves the SRL performance of the base model irrespective of types of word embeddings. Coupled with contextualized word embeddings derived from BERT, PNMA improves over existing models for both span and dependency semantic parsing datasets, especially on out-of-domain text, reaching F1 scores of 80.2, and 84.97 on CoNLL2005, and CoNLL2009 datasets, respectively.
AWLCO: All-Window Length Co-Occurrence
Venue: arXiv cs.DS 2020
Year: 2020

Analyzing patterns in a sequence of events has applications in text analysis, computer programming, and genomics research. In this paper, we consider the all-window-length analysis model which analyzes a sequence of events with respect to windows of all lengths. We study the exact co-occurrence counting problem for the all-window-length analysis model. Our first algorithm is an offline algorithm that counts all-window-length co-occurrences by performing multiple passes over a sequence and computing single-window-length co-occurrences. This algorithm has the time complexity $O(n)$ for each window length and thus a total complexity of $O(n^2)$ and the space complexity $O(|I|)$ for a sequence of size n and an itemset of size $|I|$. We propose AWLCO, an online algorithm that computes all-window-length co-occurrences in a single pass with the expected time complexity of $O(n)$ and space complexity of $O( \sqrt{ n|I| })$. Following this, we generalize our use case to patterns in which we propose an algorithm that computes all-window-length co-occurrence with expected time complexity $O(n|I|)$ and space complexity $O( \sqrt{n|I|} + e_{max}|I|)$, where $e_{max}$ is the length of the largest pattern.
An Artificial Consciousness Model and its relations with Philosophy of Mind
Venue: arXiv cs.AI 2020
Year: 2020

This work seeks to study the beneficial properties that an autonomous agent can obtain by implementing a cognitive architecture similar to the one of conscious beings. Along this document, a conscious model of autonomous agent based in a global workspace architecture is presented. We describe how this agent is viewed from different perspectives of philosophy of mind, being inspired by their ideas. The goal of this model is to create autonomous agents able to navigate within an environment composed of multiple independent magnitudes, adapting to its surroundings in order to find the best possible position in base of its inner preferences. The purpose of the model is to test the effectiveness of many cognitive mechanisms that are incorporated, such as an attention mechanism for magnitude selection, pos-session of inner feelings and preferences, usage of a memory system to storage beliefs and past experiences, and incorporating a global workspace which controls and integrates information processed by all the subsystem of the model. We show in a large experiment set how an autonomous agent can benefit from having a cognitive architecture such as the one described.
Modelling Verbal Morphology in Nen
Venue: arXiv cs.CL 2020
Year: 2020

Nen verbal morphology is remarkably complex; a transitive verb can take up to 1,740 unique forms. The combined effect of having a large combinatoric space and a low-resource setting amplifies the need for NLP tools. Nen morphology utilises distributed exponence - a non-trivial means of mapping form to meaning. In this paper, we attempt to model Nen verbal morphology using state-of-the-art machine learning models for morphological reinflection. We explore and categorise the types of errors these systems generate. Our results show sensitivity to training data composition; different distributions of verb type yield different accuracies (patterning with E-complexity). We also demonstrate the types of patterns that can be inferred from the training data through the case study of syncretism.
Where Should We Begin? A Low-Level Exploration of Weight Initialization Impact on Quantized Behaviour of Deep Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

With the proliferation of deep convolutional neural network (CNN) algorithms for mobile processing, limited precision quantization has become an essential tool for CNN efficiency. Consequently, various works have sought to design fixed precision quantization algorithms and quantization-focused optimization techniques that minimize quantization induced performance degradation. However, there is little concrete understanding of how various CNN design decisions/best practices affect quantized inference behaviour. Weight initialization strategies are often associated with solving issues such as vanishing/exploding gradients but an often-overlooked aspect is their impact on the final trained distributions of each layer. We present an in-depth, fine-grained ablation study of the effect of different weights initializations on the final distributions of weights and activations of different CNN architectures. The fine-grained, layerwise analysis enables us to gain deep insights on how initial weights distributions will affect final accuracy and quantized behaviour. To our best knowledge, we are the first to perform such a low-level, in-depth quantitative analysis of weights initialization and its effect on quantized behaviour.