OpenRaiser/Intern-Atlas huggingface dataset:
Enabling End-Users to Deploy Flexible Human-Robot Teams to Factories of the Future
Venue: arXiv cs.RO 2020
Year: 2020

Human-Robot Teams offer the flexibility needed for partial automation in small and medium-sized enterprises (SMEs). They will thus be an integral part of Factories of the Future. Our research targets a particularly flexible teaming mode, where agents share tasks dynamically. Such approaches require cognitive robots with reasoning and sensing capabilities. This results in hardware maintenance demands in terms of sensor calibration. In contrast to intuitive end-user programming, system setup and maintenance are rarely addressed in literature on robot application in SMEs. In this paper, we describe a prototype software toolchain that covers the initial setup, task modelling, and online operation of human-robot teams. We further show, that end-users can setup the system quickly and operate the whole toolchain effortlessly. All in all, this work aims to reduce the concern, that deploying human-robot teams comes with high costs for external expertise.
CLASH WRIST -- A hardware to increase the capability of CLASH fruit gripper to use environment constraints exploration
Venue: arXiv cs.RO 2020
Year: 2020

Humans use environmental constraints (EC) in manipulation to compensate for uncertainties in their world model. The same principle was recently applied to robotics, so that soft underactuated hands improve their grasping capability by using environmental constraints exploitation (ECE) [1]. Due to orientation of the robotic hand for example in the EC wall grasp, the length of the robot wrist plus the hand length gets quite important, if objects are grasp out of a box [2] . Most of the modern cobots have quite long wrist, so we have constructed a two degree of freedom wrist for the CLASH [3], to solve this problem (Fig. 1).
Go Fetch: Mobile Manipulation in Unstructured Environments
Venue: arXiv cs.RO 2020
Year: 2020

With humankind facing new and increasingly large-scale challenges in the medical and domestic spheres, automation of the service sector carries a tremendous potential for improved efficiency, quality, and safety of operations. Mobile robotics can offer solutions with a high degree of mobility and dexterity, however these complex systems require a multitude of heterogeneous components to be carefully integrated into one consistent framework. This work presents a mobile manipulation system that combines perception, localization, navigation, motion planning and grasping skills into one common workflow for fetch and carry applications in unstructured indoor environments. The tight integration across the various modules is experimentally demonstrated on the task of finding a commonly available object in an office environment, grasping it, and delivering it to a desired drop-off location. The accompanying video is available at https://youtu.be/e89_Xg1sLnY.
Spotting political social bots in Twitter: A use case of the 2019 Spanish general election
Venue: arXiv cs.SI 2020
Year: 2020

While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election. Throughout our study, we classified involved users as social bots or humans, and examined their interactions from a quantitative (i.e., amount of traffic generated and existing relations) and qualitative (i.e., user's political affinity and sentiment towards the most important parties) perspectives. Results demonstrated that a non-negligible amount of those bots actively participated in the election, supporting each of the five principal political parties.
Benchmarking End-to-End Behavioural Cloning on Video Games
Venue: arXiv cs.AI 2020
Year: 2020

Behavioural cloning, where a computer is taught to perform a task based on demonstrations, has been successfully applied to various video games and robotics tasks, with and without reinforcement learning. This also includes end-to-end approaches, where a computer plays a video game like humans do: by looking at the image displayed on the screen, and sending keystrokes to the game. As a general approach to playing video games, this has many inviting properties: no need for specialized modifications to the game, no lengthy training sessions and the ability to re-use the same tools across different games. However, related work includes game-specific engineering to achieve the results. We take a step towards a general approach and study the general applicability of behavioural cloning on twelve video games, including six modern video games (published after 2010), by using human demonstrations as training data. Our results show that these agents cannot match humans in raw performance but do learn basic dynamics and rules. We also demonstrate how the quality of the data matters, and how recording data from humans is subject to a state-action mismatch, due to human reflexes.
DeepSumm -- Deep Code Summaries using Neural Transformer Architecture
Venue: arXiv cs.LG 2020
Year: 2020

Source code summarizing is a task of writing short, natural language descriptions of source code behavior during run time. Such summaries are extremely useful for software development and maintenance but are expensive to manually author,hence it is done for small fraction of the code that is produced and is often ignored. Automatic code documentation can possibly solve this at a low cost. This is thus an emerging research field with further applications to program comprehension, and software maintenance. Traditional methods often relied on cognitive models that were built in the form of templates and by heuristics and had varying degree of adoption by the developer community. But with recent advancements, end to end data-driven approaches based on neural techniques have largely overtaken the traditional techniques. Much of the current landscape employs neural translation based architectures with recurrence and attention which is resource and time intensive training procedure. In this paper, we employ neural techniques to solve the task of source code summarizing and specifically compare NMT based techniques to more simplified and appealing Transformer architecture on a dataset of Java methods and comments. We bring forth an argument to dispense the need of recurrence in the training procedure. To the best of our knowledge, transformer based models have not been used for the task before. With supervised samples of more than 2.1m comments and code, we reduce the training time by more than 50% and achieve the BLEU score of 17.99 for the test set of examples.
Objects of violence: synthetic data for practical ML in human rights investigations
Venue: arXiv cs.CV 2020
Year: 2020

We introduce a machine learning workflow to search for, identify, and meaningfully triage videos and images of munitions, weapons, and military equipment, even when limited training data exists for the object of interest. This workflow is designed to expedite the work of OSINT ("open source intelligence") researchers in human rights investigations. It consists of three components: automatic rendering and annotating of synthetic datasets that make up for a lack of training data; training image classifiers from combined sets of photographic and synthetic data; and mtriage, an open source software that orchestrates these classifiers' deployment to triage public domain media, and visualise predictions in a web interface. We show that synthetic data helps to train classifiers more effectively, and that certain approaches yield better results for different architectures. We then demonstrate our workflow in two real-world human rights investigations: the use of the Triple-Chaser tear gas grenade against civilians, and the verification of allegations of military presence in Ukraine in 2014.
Topological Properties of Multi-Party Blockchain Transactions
Venue: arXiv cs.CR 2020
Year: 2020

The cross-blockchain transaction remains one of the most challenging problems in blockchains. The root cause of the challenge lies in the nondeterministic nature of blockchains: A $n$-party transaction across multiple blockchains might be partially rolled back due to the potential forks in any of the participating blockchains---eventually, only one fork will survive in the competition among miners. While some effort has recently been made to developing hierarchically distributed commit protocols to make multi-party transactions progress, there is no systematic method to reason about the transaction outcome. This paper tackles this problem from a perspective of point-set topology. We construct multiple topological spaces for the transactions and blockchain forks, and show that these spaces are internally related through either homeomorphism or continuous functions. Combined together, these tools allow us to reason about the cross-blockchain transactions through the growing-fork topology, an intuitive representation of blockchains.
Improving Confidence in the Estimation of Values and Norms
Venue: arXiv cs.AI 2020
Year: 2020

Autonomous agents (AA) will increasingly be interacting with us in our daily lives. While we want the benefits attached to AAs, it is essential that their behavior is aligned with our values and norms. Hence, an AA will need to estimate the values and norms of the humans it interacts with, which is not a straightforward task when solely observing an agent's behavior. This paper analyses to what extent an AA is able to estimate the values and norms of a simulated human agent (SHA) based on its actions in the ultimatum game. We present two methods to reduce ambiguity in profiling the SHAs: one based on search space exploration and another based on counterfactual analysis. We found that both methods are able to increase the confidence in estimating human values and norms, but differ in their applicability, the latter being more efficient when the number of interactions with the agent is to be minimized. These insights are useful to improve the alignment of AAs with human values and norms.
Learning to cooperate: Emergent communication in multi-agent navigation
Venue: arXiv cs.LG 2020
Year: 2020

Emergent communication in artificial agents has been studied to understand language evolution, as well as to develop artificial systems that learn to communicate with humans. We show that agents performing a cooperative navigation task in various gridworld environments learn an interpretable communication protocol that enables them to efficiently, and in many cases, optimally, solve the task. An analysis of the agents' policies reveals that emergent signals spatially cluster the state space, with signals referring to specific locations and spatial directions such as "left", "up", or "upper left room". Using populations of agents, we show that the emergent protocol has basic compositional structure, thus exhibiting a core property of natural language.
An Attention-Based Deep Learning Model for Multiple Pedestrian Attributes Recognition
Venue: arXiv cs.CV 2020
Year: 2020

The automatic characterization of pedestrians in surveillance footage is a tough challenge, particularly when the data is extremely diverse with cluttered backgrounds, and subjects are captured from varying distances, under multiple poses, with partial occlusion. Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with two-fold contributions: 1) considering the strong semantic correlation between the different full-body attributes, we propose a multi-task deep model that uses an element-wise multiplication layer to extract more comprehensive feature representations. In practice, this layer serves as a filter to remove irrelevant background features, and is particularly important to handle complex, cluttered data; and 2) we introduce a weighted-sum term to the loss function that not only relativizes the contribution of each task (kind of attributed) but also is crucial for performance improvement in multiple-attribute inference settings. Our experiments were performed on two well-known datasets (RAP and PETA) and point for the superiority of the proposed method with respect to the state-of-the-art. The code is available at https://github.com/Ehsan-Yaghoubi/MAN-PAR-.
On the Principles of Differentiable Quantum Programming Languages
Venue: arXiv cs.PL 2020
Year: 2020

Variational Quantum Circuits (VQCs), or the so-called quantum neural-networks, are predicted to be one of the most important near-term quantum applications, not only because of their similar promises as classical neural-networks, but also because of their feasibility on near-term noisy intermediate-size quantum (NISQ) machines. The need for gradient information in the training procedure of VQC applications has stimulated the development of auto-differentiation techniques for quantum circuits. We propose the first formalization of this technique, not only in the context of quantum circuits but also for imperative quantum programs (e.g., with controls), inspired by the success of differentiable programming languages in classical machine learning. In particular, we overcome a few unique difficulties caused by exotic quantum features (such as quantum no-cloning) and provide a rigorous formulation of differentiation applied to bounded-loop imperative quantum programs, its code-transformation rules, as well as a sound logic to reason about their correctness. Moreover, we have implemented our code transformation in OCaml and demonstrated the resource-efficiency of our scheme both analytically and empirically. We also conduct a case study of training a VQC instance with controls, which shows the advantage of our scheme over existing auto-differentiation for quantum circuits without controls.
Predicting Injectable Medication Adherence via a Smart Sharps Bin and Machine Learning
Venue: arXiv cs.LG 2020
Year: 2020

Medication non-adherence is a widespread problem affecting over 50% of people who have chronic illness and need chronic treatment. Non-adherence exacerbates health risks and drives significant increases in treatment costs. In order to address these challenges, the importance of predicting patients' adherence has been recognised. In other words, it is important to improve the efficiency of interventions of the current healthcare system by prioritizing resources to the patients who are most likely to be non-adherent. Our objective in this work is to make predictions regarding individual patients' behaviour in terms of taking their medication on time during their next scheduled medication opportunity. We do this by leveraging a number of machine learning models. In particular, we demonstrate the use of a connected IoT device; a "Smart Sharps Bin", invented by HealthBeacon Ltd.; to monitor and track injection disposal of patients in their home environment. Using extensive data collected from these devices, five machine learning models, namely Extra Trees Classifier, Random Forest, XGBoost, Gradient Boosting and Multilayer Perception were trained and evaluated on a large dataset comprising 165,223 historic injection disposal records collected from 5,915 HealthBeacon units over the course of 3 years. The testing work was conducted on real-time data generated by the smart device over a time period after the model training was complete, i.e. true future data. The proposed machine learning approach demonstrated very good predictive performance exhibiting an Area Under the Receiver Operating Characteristic Curve (ROC AUC) of 0.86.
Evaluating the Calibration of Knowledge Graph Embeddings for Trustworthy Link Prediction
Venue: arXiv cs.AI 2020
Year: 2020

Little is known about the trustworthiness of predictions made by knowledge graph embedding (KGE) models. In this paper we take initial steps toward this direction by investigating the calibration of KGE models, or the extent to which they output confidence scores that reflect the expected correctness of predicted knowledge graph triples. We first conduct an evaluation under the standard closed-world assumption (CWA), in which predicted triples not already in the knowledge graph are considered false, and show that existing calibration techniques are effective for KGE under this common but narrow assumption. Next, we introduce the more realistic but challenging open-world assumption (OWA), in which unobserved predictions are not considered true or false until ground-truth labels are obtained. Here, we show that existing calibration techniques are much less effective under the OWA than the CWA, and provide explanations for this discrepancy. Finally, to motivate the utility of calibration for KGE from a practitioner's perspective, we conduct a unique case study of human-AI collaboration, showing that calibrated predictions can improve human performance in a knowledge graph completion task.
Towards PTAS for Precedence Constrained Scheduling via Combinatorial Algorithms
Venue: arXiv cs.DS 2020
Year: 2020

We study the classic problem of scheduling $n$ precedence constrained unit-size jobs on $m = O(1)$ machines so as to minimize the makespan. In a recent breakthrough, Levey and Rothvoss \cite{LR16} developed a $(1+ε)$-approximation for the problem with running time $\exp\Big(\exp\Big(O\big(\frac{m^2}{ε^2}\log^2\log n\big)\Big)\Big)$, via the Sherali-Adams lift of the basic linear programming relaxation for the problem by $\exp\Big(O\big(\frac{m^2}{ε^2}\log^2\log n\big)\Big)$ levels. Garg \cite{Garg18} recently improved the number of levels to $\log ^{O(m^2/ε^2)}n$, and thus the running time to $\exp\big(\log ^{O(m^2/ε^2)}n\big)$, which is quasi-polynomial for constant $m$ and $ε$. In this paper we present an algorithm that achieves $(1+ε)$-approximation for the problem with running time $n^{O\left(\frac{m^4}{ε^3}\log^3\log n\right)}$, which is very close to a polynomial for constant $m$ and $ε$. Unlike the algorithms of Levey-Rothvoss and Garg, which are based on linear-programming hierarchy, our algorithm is purely combinatorial. For this problem, we show that the conditioning operations on the lifted LP solution can be replaced by making guesses about the optimum schedule.
Series Elastic Force Control for Soft Robotic Fluid Actuators
Venue: arXiv cs.RO 2020
Year: 2020

Fluid-based soft actuators are an attractive option for lightweight and human-safe robots. These actuators, combined with fluid pressure force feedback, are in principle a form of series-elastic actuation (SEA), in which nearly all driving-point (e.g. motor/gearbox) friction can be eliminated. Fiber-elastomer soft actuators offer unique low-friction and low-hysteresis mechanical properties which are particularly suited to force-control based on internal pressure force feedback, rather than traditional external force feedback using force/tactile sensing, since discontinuous (Coulomb) endpoint friction is unobservable to internal fluid pressure. However, compensation of endpoint smooth hysteresis through a model-based feedforward term is possible. We report on internal-pressure force feedback through a disturbance observer (DOB) and model-based feedforward compensation of endpoint friction and nonlinear hysteresis for a 2-DOF lightweight robotic gripper driven by rolling-diaphragm linear actuators coupled to direct-drive brushless motors, achieving an active low-frequency endpoint impedance range ("Z-width") of 50dB.
Knowing What, Where and When to Look: Efficient Video Action Modeling with Attention
Venue: arXiv cs.CV 2020
Year: 2020

Attentive video modeling is essential for action recognition in unconstrained videos due to their rich yet redundant information over space and time. However, introducing attention in a deep neural network for action recognition is challenging for two reasons. First, an effective attention module needs to learn what (objects and their local motion patterns), where (spatially), and when (temporally) to focus on. Second, a video attention module must be efficient because existing action recognition models already suffer from high computational cost. To address both challenges, a novel What-Where-When (W3) video attention module is proposed. Departing from existing alternatives, our W3 module models all three facets of video attention jointly. Crucially, it is extremely efficient by factorizing the high-dimensional video feature data into low-dimensional meaningful spaces (1D channel vector for `what' and 2D spatial tensors for `where'), followed by lightweight temporal attention reasoning. Extensive experiments show that our attention model brings significant improvements to existing action recognition models, achieving new state-of-the-art performance on a number of benchmarks.
RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE
Venue: arXiv cs.NI 2020
Year: 2020

With the rapid development of autonomous driving, collision avoidance has attracted attention from both academia and industry. Many collision avoidance strategies have emerged in recent years, but the dynamic and complex nature of driving environment poses a challenge to develop robust collision avoidance algorithms. Therefore, in this paper, we propose a decentralized framework named RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE. Leveraging a hierarchical architecture we develop an algorithm named Co-DDPG to efficiently train autonomous vehicles. Through a security abiding channel, the autonomous vehicles distribute their driving policies. We use the relative distances obtained by the opponent sensors to build the VANET instead of locations, which ensures the vehicle's location privacy. With a leader-follower architecture and parameter distribution, RACE accelerates the learning of optimal policies and efficiently utilizes the remaining resources. We implement the RACE framework in the widely used TORCS simulator and conduct various experiments to measure the performance of RACE. Evaluations show that RACE quickly learns optimal driving policies and effectively avoids collisions. Moreover, RACE also scales smoothly with varying number of participating vehicles. We further compared RACE with existing autonomous driving systems and show that RACE outperforms them by experiencing 65% less collisions in the training process and exhibits improved performance under varying vehicle density.
Mapping Three Decades of Intellectual Change in Academia
Venue: arXiv cs.DL 2020
Year: 2020

Research on the development of science has focused on the creation of multidisciplinary teams. However, while this coming together of people is symmetrical, the ideas, methods, and vocabulary of science have a directional flow. We present a statistical model of the text of dissertation abstracts from 1980 to 2010, revealing for the first time the large-scale flow of language across fields. Results of the analysis include identifying methodological fields that export broadly, emerging topical fields that borrow heavily and expand, and old topical fields that grow insular and retract. Particular findings show a growing split between molecular and ecological forms of biology and a sea change in the humanities and social sciences driven by the rise of gender and ethnic studies.