OpenRaiser/Intern-Atlas huggingface dataset:
Leaf-like Origami with Bistability for Self-Adaptive Grasping Motions
Venue: arXiv cs.RO 2020
Year: 2020

The leaf-like origami structure was inspired by geometric patterns found in nature, exhibiting unique transitions between open and closed shapes. With a bistable energy landscape, leaf-like origami is able to replicate the autonomous grasping of objects observed in biological systems like the Venus flytrap. We show uniform grasping motions of the leaf-like origami, as well as various non-uniform grasping motions which arise from its multi-transformable nature. Grasping motions can be triggered with high tunability due to the structure's bistable energy landscape. We demonstrate the self-adaptive grasping motion by dropping a target object onto our paper prototype, which does not require an external power source to retain the capture of the object. We also explore the non-uniform grasping motions of the leaf-like structure by selectively controlling the creases, which reveals various unique grasping configurations that can be exploited for versatile, autonomous, and self-adaptive robotic operations.
Local Relay Selection in Presence of Dynamic Obstacles in Millimeter Wave D2D Communication
Venue: arXiv cs.NI 2020
Year: 2020

Blockage due to obstacles in millimeter wave (mmWave) device to device (D2D) communication is a prominent problem due to their severe penetration losses. Potential user equipments (UEs) in vicinity of the source UE must be explored in order to select a new relay when the current link gets blocked. However, dynamic obstacles are not known in advance and thus may cause unpredictable fluctuations to D2D channel quality causing newly selected relay link also to be susceptible to blockage. This might cause frequent relay switching leading to call drops and high energy consumption. We have proposed the idea of reducing frequency in relay exploration and switching and thus average end-to-end delay (in seconds) at the expense of additional exploration time units (few milliseconds) during beam alignment. We seek to learn the uncertainty in D2D link qualities by modeling the problem as finite horizon partially observable Markov decision process (POMDP) framework locally at each UE. We have derived an optimal threshold policy which maps the state to set of actions. We then give a simplified and easy to implement stationary threshold policy which counts the number of successive acknowledgment successes/failures for making decisions of selecting or not selecting a given relay locally. Through extensive simulation, we validate our theoretical findings and demonstrate that our approach captures the trade-off between average exploration time and average end-to-end (E2E) delay in presence of dynamic obstacles.
Penetrating RF Fingerprinting-based Authentication with a Generative Adversarial Attack
Venue: arXiv cs.CR 2020
Year: 2020

Physical layer authentication relies on detecting unique imperfections in signals transmitted by radio devices to isolate their fingerprint. Recently, deep learning-based authenticators have increasingly been proposed to classify devices using these fingerprints, as they achieve higher accuracies compared to traditional approaches. However, it has been shown in other domains that adding carefully crafted perturbations to legitimate inputs can fool such classifiers. This can undermine the security provided by the authenticator. Unlike adversarial attacks applied in other domains, an adversary has no control over the propagation environment. Therefore, to investigate the severity of this type of attack in wireless communications, we consider an unauthorized transmitter attempting to have its signals classified as authorized by a deep learning-based authenticator. We demonstrate a reinforcement learning-based attack where the impersonator--using only the authenticator's binary authentication decision--distorts its signals in order to penetrate the system. Extensive simulations and experiments on a software-defined radio testbed indicate that at appropriate channel conditions and bounded by a maximum distortion level, it is possible to fool the authenticator reliably at more than 90% success rate.
Two Heads Are Better Than One: A Two-Stage Approach for Monaural Noise Reduction in the Complex Domain
Venue: arXiv cs.SD 2020
Year: 2020

In low signal-to-noise ratio conditions, it is difficult to effectively recover the magnitude and phase information simultaneously. To address this problem, this paper proposes a two-stage algorithm to decouple the joint optimization problem w.r.t. magnitude and phase into two sub-tasks. In the first stage, only magnitude is optimized, which incorporates noisy phase to obtain a coarse complex clean speech spectrum estimation. In the second stage, both the magnitude and phase components are refined. The experiments are conducted on the WSJ0-SI84 corpus, and the results show that the proposed approach significantly outperforms previous baselines in terms of PESQ, ESTOI, and SDR.
Trajectory Generation with Fast Lidar-based 3D Collision Avoidance for Agile MAVs
Venue: arXiv cs.RO 2020
Year: 2020

Micro aerial vehicles (MAVs), are frequently used for exploration, examination, and surveillance during search and rescue missions. Manually piloting these robots under stressful conditions provokes pilot errors and can result in crashes with disastrous consequences. Also, during fully autonomous flight, planned high-level trajectories can be erroneous and steer the robot into obstacles. In this work, we propose an approach to efficiently compute smooth, time-optimal trajectories MAVs that avoid obstacles. Our method first computes a trajectory from the start to an arbitrary target state, including position, velocity, and acceleration. It respects input- and state-constraints and is thus dynamically feasible. Afterward, we efficiently check the trajectory for collisions in the 3D-point cloud, recorded with the onboard lidar. We exploit the piecewise polynomial formulation of our trajectories to analytically compute axis-aligned bounding boxes (AABB) to speed up the collision checking process. If collisions occur, we generate a set of alternative trajectories in real-time. Alternative trajectories bring the MAV in a safe state, while still pursuing the original goal. Subsequently, we choose and execute the best collision-free alternative trajectory based on a distance metric. The evaluation in simulation and during a real firefighting exercise shows the capability of our method.
Towards Conceptual Modeling Semantics: Eventizing Tarski's Truth Schema
Venue: arXiv cs.SE 2020
Year: 2020

Modeling languages in software engineering (e.g., UML) evolved from software systems modeling where denotational and operational kinds of semantics are the traditional subjects of research and practice. According to some authors, although a large portion of the static semantics (e.g., UML) seems to have reached a consensus, the dynamic semantics of activities, interactions, and state machines poses a major challenge. Central to semantics is the relationship between a sentence and the (actual) world. Carefully examining semantics-related issues in the modeling languages field to avoid problems that may affect practical applicability is important. One effort in this direction is OMG s release of a 2020 draft specification for Foundational UML (fUML), with the base semantics specifying executions that are executable in the same sense as a program in a traditional programming language. Additionally, efforts within academia have sought to develop an alternative approach to modeling languages using formal semantics (e.g., using Russell s theory of types and Tarski s declarative semantics). This paper aims at a similar exploratory venture of developing semantics, only for a much more modest diagrammatic modeling language, called the thinging machine model. The model promotes a deep understanding of the scrutinized modeling language and leads to considerably fruitful questions. Constructing the thinging machine model seems to facilitate progress in this direction, and the initial results in this paper indicate the viability of the approach.
Centrality Measures: A Tool to Identify Key Actors in Social Networks
Venue: arXiv cs.SI 2020
Year: 2020

Experts from several disciplines have been widely using centrality measures for analyzing large as well as complex networks. These measures rank nodes/edges in networks by quantifying a notion of the importance of nodes/edges. Ranking aids in identifying important and crucial actors in networks. In this chapter, we summarize some of the centrality measures that are extensively applied for mining social network data. We also discuss various directions of research related to these measures.
On the primitivity of Lai-Massey schemes
Venue: arXiv cs.CR 2020
Year: 2020

In symmetric cryptography, the round functions used as building blocks for iterated block ciphers are often obtained as the composition of different layers providing confusion and diffusion. The study of the conditions on such layers which make the group generated by the round functions of a block cipher a primitive group has been addressed in the past years, both in the case of Substitution Permutation Networks and Feistel Networks, giving to block cipher designers the receipt to avoid the imprimitivity attack. In this paper a similar study is proposed on the subject of the Lai-Massey scheme, a framework which combines both Substitution Permutation Network and Feistel Network features. Its resistance to the imprimitivity attack is obtained as a consequence of a more general result in which the problem of proving the primitivity of the Lai-Massey scheme is reduced to the simpler one of proving the primitivity of the group generated by the round functions of a strictly related Substitution Permutation Network.
Towards Automated Anamnesis Summarization: BERT-based Models for Symptom Extraction
Venue: arXiv cs.CL 2020
Year: 2020

Professionals in modern healthcare systems are increasingly burdened by documentation workloads. Documentation of the initial patient anamnesis is particularly relevant, forming the basis of successful further diagnostic measures. However, manually prepared notes are inherently unstructured and often incomplete. In this paper, we investigate the potential of modern NLP techniques to support doctors in this matter. We present a dataset of German patient monologues, and formulate a well-defined information extraction task under the constraints of real-world utility and practicality. In addition, we propose BERT-based models in order to solve said task. We can demonstrate promising performance of the models in both symptom identification and symptom attribute extraction, significantly outperforming simpler baselines.
Economic Principles of PoPCoin, a Democratic Time-based Cryptocurrency
Venue: arXiv cs.CY 2020
Year: 2020

While democracy is founded on the principle of equal opportunity to manage our lives and pursue our fortunes, the forms of money we have inherited from millenia of evolution has brought us to an unsustainable dead-end of exploding inequality. PoPCoin proposes to leverage the unique historical opportunities that digital cryptocurrencies present for a "clean-slate" redesign of money, in particular around long-term equitability and sustainability, rather than solely stability, as our primary goals. We develop and analyze a monetary policy for PoPCoin that embodies these equitability goals in two basic rules that maybe summarized as supporting equal opportunity in "space" and "time": the first by regularly distributing new money equally to all participants much like a basic income, the second by holding the aggregate value of these distributions to a constant and non-diminishing portion of total money supply through demurrage. Through preliminary economic analysis, we find that these rules in combination yield a unique form of money with numerous intriguing and promising properties, such as a quantifiable and provable upper bound on monetary inequality, a natural "early adopter's reward" that could incentivize rapid growth while tapering off as participation saturates, resistance to the risk of deflationary spirals, and migration incentives opposite those created by conventional basic incomes.
Comment on `Open is not forever: a study of vanished open access journals'
Venue: arXiv cs.DL 2020
Year: 2020

This is a comment to an article by Laakso, Matthias and Jahn (arXiv:2008.11933).
Problems using deep generative models for probabilistic audio source separation
Venue: arXiv cs.LG 2020
Year: 2020

Recent advancements in deep generative modeling make it possible to learn prior distributions from complex data that subsequently can be used for Bayesian inference. However, we find that distributions learned by deep generative models for audio signals do not exhibit the right properties that are necessary for tasks like audio source separation using a probabilistic approach. We observe that the learned prior distributions are either discriminative and extremely peaked or smooth and non-discriminative. We quantify this behavior for two types of deep generative models on two audio datasets.
Semi-Supervised Cleansing of Web Argument Corpora
Venue: arXiv cs.CL 2020
Year: 2020

Debate portals and similar web platforms constitute one of the main text sources in computational argumentation research and its applications. While the corpora built upon these sources are rich of argumentatively relevant content and structure, they also include text that is irrelevant, or even detrimental, to their purpose. In this paper, we present a precision-oriented approach to detecting such irrelevant text in a semi-supervised way. Given a few seed examples, the approach automatically learns basic lexical patterns of relevance and irrelevance and then incrementally bootstraps new patterns from sentences matching the patterns. In the existing args.me corpus with 400k argumentative texts, our approach detects almost 87k irrelevant sentences, at a precision of 0.97 according to manual evaluation. With low effort, the approach can be adapted to other web argument corpora, providing a generic way to improve corpus quality.
Ensuring Dataset Quality for Machine Learning Certification
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems. We briefly analyse the applicability of some existing standards dealing with data and show that the specificities of the ML context are neither properly captured nor taken into ac-count. As a first answer to this concerning situation, we propose a dataset specification and verification process, and apply it on a signal recognition system from the railway domain. In addi-tion, we also give a list of recommendations for the collection and management of datasets. This work is one step towards the dataset engineering process that will be required for ML to be used on safety critical systems.
Simulating and classifying behavior in adversarial environments based on action-state traces: an application to money laundering
Venue: arXiv cs.AI 2020
Year: 2020

Many business applications involve adversarial relationships in which both sides adapt their strategies to optimize their opposing benefits. One of the key characteristics of these applications is the wide range of strategies that an adversary may choose as they adapt their strategy dynamically to sustain benefits and evade authorities. In this paper, we present a novel way of approaching these types of applications, in particular in the context of Anti-Money Laundering. We provide a mechanism through which diverse, realistic and new unobserved behavior may be generated to discover potential unobserved adversarial actions to enable organizations to preemptively mitigate these risks. In this regard, we make three main contributions. (a) Propose a novel behavior-based model as opposed to individual transactions-based models currently used by financial institutions. We introduce behavior traces as enriched relational representation to represent observed human behavior. (b) A modelling approach that observes these traces and is able to accurately infer the goals of actors by classifying the behavior into money laundering or standard behavior despite significant unobserved activity. And (c) a synthetic behavior simulator that can generate new previously unseen traces. The simulator incorporates a high level of flexibility in the behavioral parameters so that we can challenge the detection algorithm. Finally, we provide experimental results that show that the learning module (automated investigator) that has only partial observability can still successfully infer the type of behavior, and thus the simulated goals, followed by customers based on traces - a key aspiration for many applications today.
Where am I? SLAM for Mobile Machines on A Smart Working Site
Venue: arXiv cs.RO 2020
Year: 2020

The current optimization approaches of construction machinery are mainly based on internal sensors. However, the decision of a reasonable strategy is not only determined by its intrinsic signals, but also very strongly by environmental information, especially the terrain. Due to the dynamically changing of the construction site and the consequent absence of a high definition map, the Simultaneous Localization and Mapping (SLAM) offering the terrain information for construction machines is still challenging. Current SLAM technologies proposed for mobile machines are strongly dependent on costly or computationally expensive sensors, such as RTK GPS and cameras, so that commercial use is rare. In this study, we proposed an affordable SLAM method to create a multi-layer gird map for the construction site so that the machine can have the environmental information and be optimized accordingly. Concretely, after the machine passes by, we can get the local information and record it. Combining with positioning technology, we then create a map of the interesting places of the construction site. As a result of our research gathered from Gazebo, we showed that a suitable layout is the combination of 1 IMU and 2 differential GPS antennas using the unscented Kalman filter, which keeps the average distance error lower than 2m and the mapping error lower than 1.3% in the harsh environment. As an outlook, our SLAM technology provides the cornerstone to activate many efficiency improvement approaches.
Contextualisation of eCommerce Users
Venue: arXiv cs.SI 2020
Year: 2020

A scaleable modelling framework for the consumer intent within the setting of e-Commerce is presented. The methodology applies contextualisation through embeddings borrowed from Natural Language Processing. By considering the user session journeys throughough the pages of a website as documents, we capture contextual relationships between pages, as well as the topics of the of user visits. Finally, we empirically study the consistency and the stability of the presented framework.
A Dynamics Simulator for Soft Growing Robots
Venue: arXiv cs.RO 2020
Year: 2020

Simulating soft robots in cluttered environments remains an open problem due to the challenge of capturing complex dynamics and interactions with the environment. Furthermore, fast simulation is desired for quickly exploring robot behaviors in the context of motion planning. In this paper, we examine a particular class of inflated-beam soft growing robots called "vine robots", and present a dynamics simulator that captures general behaviors, handles robot-object interactions, and runs faster than real time. The simulator framework uses a simplified multi-link, rigid-body model with contact constraints. To narrow the sim-to-real gap, we develop methods for fitting model parameters based on video data of a robot in motion and in contact with an environment. We provide examples of simulations, including several with fit parameters, to show the qualitative and quantitative agreement between simulated and real behaviors. Our work demonstrates the capabilities of this high-speed dynamics simulator and its potential for use in the control of soft robots.
Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning
Venue: arXiv cs.LG 2020
Year: 2020

The success of deep learning in recent years have led to a significant increase in interest and prevalence for its adoption to tackle financial services tasks. One particular question that often arises as a barrier to adopting deep learning for financial services is whether the developed financial deep learning models are fair in their predictions, particularly in light of strong governance and regulatory compliance requirements in the financial services industry. A fundamental aspect of fairness that has not been explored in financial deep learning is the concept of trust, whose variations may point to an egocentric view of fairness and thus provide insights into the fairness of models. In this study we explore the feasibility and utility of a multi-scale trust quantification strategy to gain insights into the fairness of a financial deep learning model, particularly under different scenarios at different scales. More specifically, we conduct multi-scale trust quantification on a deep neural network for the purpose of credit card default prediction to study: 1) the overall trustworthiness of the model 2) the trust level under all possible prediction-truth relationships, 3) the trust level across the spectrum of possible predictions, 4) the trust level across different demographic groups (e.g., age, gender, and education), and 5) distribution of overall trust for an individual prediction scenario. The insights for this proof-of-concept study demonstrate that such a multi-scale trust quantification strategy may be helpful for data scientists and regulators in financial services as part of the verification and certification of financial deep learning solutions to gain insights into fairness and trust of these solutions.
Rearrangement: A Challenge for Embodied AI
Venue: arXiv cs.AI 2020
Year: 2020

We describe a framework for research and evaluation in Embodied AI. Our proposal is based on a canonical task: Rearrangement. A standard task can focus the development of new techniques and serve as a source of trained models that can be transferred to other settings. In the rearrangement task, the goal is to bring a given physical environment into a specified state. The goal state can be specified by object poses, by images, by a description in language, or by letting the agent experience the environment in the goal state. We characterize rearrangement scenarios along different axes and describe metrics for benchmarking rearrangement performance. To facilitate research and exploration, we present experimental testbeds of rearrangement scenarios in four different simulation environments. We anticipate that other datasets will be released and new simulation platforms will be built to support training of rearrangement agents and their deployment on physical systems.