OpenRaiser/Intern-Atlas huggingface dataset:
Bionic Sea Urchin Robot with Foldable Telescopic Actuator
Venue: arXiv cs.RO 2020
Year: 2020

This paper presents a couple of interesting ideas: a telescopic actuator design and a bio-inspired sea urchin robot. The "spines" of the sea urchin robot consist of fourteen telescopic actuators equally distributed over it's spherical body. The telescopic actuation system integrates linked 3D printed rack articulations that are locked in all axes if the pinion moves the links in a forward direction, creating a solid and rigid rack. Thus, the robot is able to propel and move by extending its spines. On the other hand, if the pinion moves the rigid articulations backward, these are unlocked and can be folded in a minimal space, enabling the bionic sea urchin robot to hide its spines inside its constrained spherical-body and be able to roll. Simulations and experiments are presented from both, the sea urchin robotic prototype and different scales of the telescopic actuation system.
Quantum Interference for Counting Clusters
Venue: arXiv cs.LG 2020
Year: 2020

Counting the number of clusters, when these clusters overlap significantly is a challenging problem in machine learning. We argue that a purely mathematical quantum theory, formulated using the path integral technique, when applied to non-physics modeling leads to non-physics quantum theories that are statistical in nature. We show that a quantum theory can be a more robust statistical theory to separate data to count overlapping clusters. The theory is also confirmed from data simulations.This works identify how quantum theory can be effective in counting clusters and hope to inspire the field to further apply such techniques.
An Evaluation of Percentile Measures of Citation Impact, and a Proposal for Making Them Better
Venue: arXiv cs.DL 2020
Year: 2020

Percentiles are statistics pointing to the standing of a paper's citation impact relative to other papers in a given citation distribution. Percentile Ranks (PRs) often play an important role in evaluating the impact of scholars, institutions, and lines of study. Because PRs are so important for the assessment of scholarly impact, and because citation practices differ greatly across time and fields, various percentile approaches have been proposed to time- and field-normalize citations. Unfortunately, current popular methods often face significant problems in time- and field-normalization, including when papers are assigned to multiple fields or have been published by more than one unit (e.g., researchers or countries). They also face problems for estimating citation counts (CCs) for pre-defined PRs (e.g., the 90th PR). We offer a series of guidelines and procedures that, we argue, address these problems and others and provide a superior means to make the use of percentile methods more accurate and informative. In particular, we introduce two approaches, CP-IN and CP-EX, that should be preferred in bibliometric studies because they consider the complete citation distribution. Both approaches are based on cumulative frequencies in percentages (CPs). The paper further shows how bar graphs and beamplots can present PRs in a more meaningful and accurate manner.
Bio-inspired Adaptive Latching System for Towing and Guiding Power-less Floating Platforms with Autonomous Robotic Boats
Venue: arXiv cs.RO 2020
Year: 2020

Autonomous robotic boats are expected to perform several tasks: 1) navigate autonomously in water environments, such as the canals of Amsterdam; 2) perform individual task, such as water monitoring, transporting goods and people; 3) latch together to create floating infrastructure, such as bridges and markets. In this paper we present a novel bio-inspired robotic system for latching, towing and guiding a floating passive-power-less platform. The challenge is to design an adaptive latching mechanism, able to create a secure connection between the entities, easy to attach/detach, even if the boats are affected by water disturbances. But most important, the adaptive latching must be able to restricting the DoF (degrees of freedom) of the latched "dummy" platform. Since, the robotic boat may drive it in narrow water canals and must prevent it from drifting and hitting the wall. This novel adaptive latching mechanism is based on the ball and socket joint that allows rotation and free movements in two planes at the same time. It consists of two parts: the male part that includes a bearing stud (ball) integrated on the floating bin "dummy" and the female part located on the autonomous robotic boat. Which integrates an adaptive framed funnel to guide the male ball into an actuated receptor that traps the ball, creating the ball-socket joint between the boats. In this sense, the adaptive latching mechanism mimics squid's tentacles that can adjust the forces applied to a holding object restricting its degrees of freedom. Experimental results are presented from our swarm robotic boats integrating the adaptive latching system and performing the towing and guiding use cases.
Using Deep Learning to Explore Local Physical Similarity for Global-scale Bridging in Thermal-hydraulic Simulation
Venue: arXiv cs.LG 2020
Year: 2020

Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions, especially when the geometry and boundary conditions are extrapolated beyond the range of test facilities. This paper proposes a data-driven approach, Feature Similarity Measurement FFSM), to establish a technical basis to overcome these difficulties by exploring local patterns using machine learning. The underlying local patterns in multiscale data are represented by a set of physical features that embody the information from a physical system of interest, empirical correlations, and the effect of mesh size. After performing a limited number of high-fidelity numerical simulations and a sufficient amount of fast-running coarse-mesh simulations, an error database is built, and deep learning is applied to construct and explore the relationship between the local physical features and simulation errors. Case studies based on mixed convection have been designed for demonstrating the capability of data-driven models in bridging global scale gaps.
Lower Bounds for Shoreline Searching with 2 or More Robots
Venue: arXiv cs.DM 2020
Year: 2020

Searching for a line on the plane with $n$ unit speed robots is a classic online problem that dates back to the 50's, and for which competitive ratio upper bounds are known for every $n\geq 1$. In this work we improve the best lower bound known for $n=2$ robots from 1.5993 to 3. Moreover we prove that the competitive ratio is at least $\sqrt{3}$ for $n=3$ robots, and at least $1/\cos(π/n)$ for $n\geq 4$ robots. Our lower bounds match the best upper bounds known for $n\geq 4$, hence resolving these cases. To the best of our knowledge, these are the first lower bounds proven for the cases $n\geq 3$ of this several decades old problem.
Beyond Near- and Long-Term: Towards a Clearer Account of Research Priorities in AI Ethics and Society
Venue: arXiv cs.CY 2020
Year: 2020

One way of carving up the broad "AI ethics and society" research space that has emerged in recent years is to distinguish between "near-term" and "long-term" research. While such ways of breaking down the research space can be useful, we put forward several concerns about the near/long-term distinction gaining too much prominence in how research questions and priorities are framed. We highlight some ambiguities and inconsistencies in how the distinction is used, and argue that while there are differing priorities within this broad research community, these differences are not well-captured by the near/long-term distinction. We unpack the near/long-term distinction into four different dimensions, and propose some ways that researchers can communicate more clearly about their work and priorities using these dimensions. We suggest that moving towards a more nuanced conversation about research priorities can help establish new opportunities for collaboration, aid the development of more consistent and coherent research agendas, and enable identification of previously neglected research areas.
Decisiveness of Stochastic Systems and its Application to Hybrid Models
Venue: arXiv cs.LO 2020
Year: 2020

In [ABM07], Abdulla et al. introduced the concept of decisiveness, an interesting tool for lifting good properties of finite Markov chains to denumerable ones. Later, this concept was extended to more general stochastic transition systems (STSs), allowing the design of various verification algorithms for large classes of (infinite) STSs. We further improve the understanding and utility of decisiveness in two ways. First, we provide a general criterion for proving decisiveness of general STSs. This criterion, which is very natural but whose proof is rather technical, (strictly) generalizes all known criteria from the literature. Second, we focus on stochastic hybrid systems (SHSs), a stochastic extension of hybrid systems. We establish the decisiveness of a large class of SHSs and, under a few classical hypotheses from mathematical logic, we show how to decide reachability problems in this class, even though they are undecidable for general SHSs. This provides a decidable stochastic extension of o-minimal hybrid systems. [ABM07] Parosh A. Abdulla, Noomene Ben Henda, and Richard Mayr. 2007. Decisive Markov Chains. Log. Methods Comput. Sci. 3, 4 (2007).
Towards Automated Swimming Analytics Using Deep Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Methods for creating a system to automate the collection of swimming analytics on a pool-wide scale are considered in this paper. There has not been much work on swimmer tracking or the creation of a swimmer database for machine learning purposes. Consequently, methods for collecting swimmer data from videos of swim competitions are explored and analyzed. The result is a guide to the creation of a comprehensive collection of swimming data suitable for training swimmer detection and tracking systems. With this database in place, systems can then be created to automate the collection of swimming analytics.
Live Exploration with Mobile Robots in a Dynamic Ring, Revisited
Venue: arXiv cs.DC 2020
Year: 2020

The graph exploration problem requires a group of mobile robots, initially placed arbitrarily on the nodes of a graph, to work collaboratively to explore the graph such that each node is eventually visited by at least one robot. One important requirement of exploration is the {\em termination} condition, i.e., the robots must know that exploration is completed. The problem of live exploration of a dynamic ring using mobile robots was recently introduced in [Di Luna et al., ICDCS 2016]. In it, they proposed multiple algorithms to solve exploration in fully synchronous and semi-synchronous settings with various guarantees when $2$ robots were involved. They also provided guarantees that with certain assumptions, exploration of the ring using two robots was impossible. An important question left open was how the presence of $3$ robots would affect the results. In this paper, we try to settle this question in a fully synchronous setting and also show how to extend our results to a semi-synchronous setting. In particular, we present algorithms for exploration with explicit termination using $3$ robots in conjunction with either (i) unique IDs of the robots and edge crossing detection capability (i.e., two robots moving in opposite directions through an edge in the same round can detect each other), or (ii) access to randomness. The time complexity of our deterministic algorithm is asymptotically optimal. We also provide complementary impossibility results showing that there does not exist any explicit termination algorithm for $2$ robots. The theoretical analysis and comprehensive simulations of our algorithm show the effectiveness and efficiency of the algorithm in dynamic rings. We also present an algorithm to achieve exploration with partial termination using $3$ robots in the semi-synchronous setting.
On Demand Solid Texture Synthesis Using Deep 3D Networks
Venue: arXiv cs.GR 2020
Year: 2020

This paper describes a novel approach for on demand volumetric texture synthesis based on a deep learning framework that allows for the generation of high quality 3D data at interactive rates. Based on a few example images of textures, a generative network is trained to synthesize coherent portions of solid textures of arbitrary sizes that reproduce the visual characteristics of the examples along some directions. To cope with memory limitations and computation complexity that are inherent to both high resolution and 3D processing on the GPU, only 2D textures referred to as "slices" are generated during the training stage. These synthetic textures are compared to exemplar images via a perceptual loss function based on a pre-trained deep network. The proposed network is very light (less than 100k parameters), therefore it only requires sustainable training (i.e. few hours) and is capable of very fast generation (around a second for $256^3$ voxels) on a single GPU. Integrated with a spatially seeded PRNG the proposed generator network directly returns an RGB value given a set of 3D coordinates. The synthesized volumes have good visual results that are at least equivalent to the state-of-the-art patch based approaches. They are naturally seamlessly tileable and can be fully generated in parallel.
Rethinking Curriculum Learning with Incremental Labels and Adaptive Compensation
Venue: arXiv cs.CV 2020
Year: 2020

Like humans, deep networks have been shown to learn better when samples are organized and introduced in a meaningful order or curriculum. Conventional curriculum learning schemes introduce samples in their order of difficulty. This forces models to begin learning from a subset of the available data while adding the external overhead of evaluating the difficulty of samples. In this work, we propose Learning with Incremental Labels and Adaptive Compensation (LILAC), a two-phase method that incrementally increases the number of unique output labels rather than the difficulty of samples while consistently using the entire dataset throughout training. In the first phase, Incremental Label Introduction, we partition data into mutually exclusive subsets, one that contains a subset of the ground-truth labels and another that contains the remaining data attached to a pseudo-label. Throughout the training process, we recursively reveal unseen ground-truth labels in fixed increments until all the labels are known to the model. In the second phase, Adaptive Compensation, we optimize the loss function using altered target vectors of previously misclassified samples. The target vectors of such samples are modified to a smoother distribution to help models learn better. On evaluating across three standard image benchmarks, CIFAR-10, CIFAR-100, and STL-10, we show that LILAC outperforms all comparable baselines. Further, we detail the importance of pacing the introduction of new labels to a model as well as the impact of using a smooth target vector.
Visual Storytelling via Predicting Anchor Word Embeddings in the Stories
Venue: arXiv cs.CV 2020
Year: 2020

We propose a learning model for the task of visual storytelling. The main idea is to predict anchor word embeddings from the images and use the embeddings and the image features jointly to generate narrative sentences. We use the embeddings of randomly sampled nouns from the groundtruth stories as the target anchor word embeddings to learn the predictor. To narrate a sequence of images, we use the predicted anchor word embeddings and the image features as the joint input to a seq2seq model. As opposed to state-of-the-art methods, the proposed model is simple in design, easy to optimize, and attains the best results in most automatic evaluation metrics. In human evaluation, the method also outperforms competing methods.
Deep Learning Stereo Vision at the edge
Venue: arXiv cs.CV 2020
Year: 2020

We present an overview of the methodology used to build a new stereo vision solution that is suitable for System on Chip. This new solution was developed to bring computer vision capability to embedded devices that live in a power constrained environment. The solution is constructured as a hybrid between classical Stereo Vision techniques and deep learning approaches. The stereoscopic module is composed of two separate modules: one that accelerates the neural network we trained and one that accelerates the front-end part. The system is completely passive and does not require any structured light to obtain very compelling accuracy. With respect to the previous Stereo Vision solutions offered by the industries we offer a major improvement is robustness to noise. This is mainly possible due to the deep learning part of the chosen architecture. We submitted our result to Middlebury dataset challenge. It currently ranks as the best System on Chip solution. The system has been developed for low latency applications which require better than real time performance on high definition videos.
Companion Unmanned Aerial Vehicles: A Survey
Venue: arXiv cs.RO 2020
Year: 2020

Recent technological advancements in small-scale unmanned aerial vehicles (UAVs) have led to the development of companion UAVs. Similar to conventional companion robots, companion UAVs have the potential to assist us in our daily lives and to help alleviating social loneliness issue. In contrast to ground companion robots, companion UAVs have the capability to fly and possess unique interaction characteristics. Our goals in this work are to have a bird's-eye view of the companion UAV works and to identify lessons learned and guidelines for the design of companion UAVs. We tackle two major challenges towards these goals, where we first use a coordinated way to gather top-quality human-drone interaction (HDI) papers from three sources, and then propose to use a perceptual map of UAVs to summarize current research efforts in HDI. While being simple, the proposed perceptual map can cover the efforts have been made to realize companion UAVs in a comprehensive manner and lead our discussion coherently. We also discuss patterns we noticed in the literature and some lessons learned throughout the review. In addition, we recommend several areas that are worth exploring and suggest a few guidelines to enhance HDI researches with companion UAVs.
Multi-Kernel Polar Codes: Concept and Design Principles
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we propose a new polar code construction by employing kernels of different sizes in the Kronecker product of the transformation matrix, thus generalizing the original construction by Arikan. The proposed multi-kernel polar code allows for more flexibility in terms of the code length, moreover allowing for various new design principles. We describe in detail encoding as well as successive cancellation (SC) decoding and SC list (SCL) decoding, and we provide a novel design method for the frozen set that allows to optimise the performance under list decoding, as opposed to original relability-based code design. Finally, we numerically demonstrate the advantage of multi-kernel polar codes under the new design principles compared to punctured and shortened polar codes.
IoT Network Behavioral Fingerprint Inference with Limited Network Trace for Cyber Investigation: A Meta Learning Approach
Venue: arXiv cs.CR 2020
Year: 2020

The development and adoption of Internet of Things (IoT) devices will grow significantly in the coming years to enable Industry 4.0. Many forms of IoT devices will be developed and used across industry verticals. However, the euphoria of this technology adoption is shadowed by the solemn presence of cyber threats that will follow its growth trajectory. Cyber threats would either embed their malicious code or attack vulnerabilities in IoT that could induce significant consequences in cyber and physical realms. In order to manage such destructive effects, incident responders and cyber investigators require the capabilities to find these rogue IoT and contain them quickly. Such online devices may only leave network activity traces. A collection of relevant traces could be used to infer the IoT's network behaviorial fingerprints and in turn could facilitate investigative find of these IoT. However, the challenge is how to infer these fingerprints when there is limited network activity traces. This research proposes the novel model construct that learns to infer the network behaviorial fingerprint of specific IoT based on limited network activity traces using a One-Card Time Series Meta-Learner called DeepNetPrint. Our research also demonstrates the application of DeepNetPrint to identify IoT devices that performs comparatively well against leading supervised learning models. Our solution would enable cyber investigator to identify specific IoT of interest while overcoming the constraints of having only limited network traces of the IoT.
Partial MDS Codes with Local Regeneration
Venue: arXiv cs.IT 2020
Year: 2020

Partial MDS (PMDS) and sector-disk (SD) codes are classes of erasure codes that combine locality with strong erasure correction capabilities. We construct PMDS and SD codes where each local code is a bandwidth-optimal regenerating MDS code. The constructions require significantly smaller field size than the only other construction known in literature.
On Hard-Decision Decoding of Product Codes
Venue: arXiv cs.IT 2020
Year: 2020

In this paper we review existing hard-decision decoding algorithms for product codes along with different post-processing techniques used in conjunction with the iterative decoder for product codes. We improve the decoder by Reddy and Robinson and use it to create a new post-processing technique. The performance of this new post-processing technique is evaluated through simulations, and these suggest that our new post-processing technique outperforms previously known post-processing techniques which are not tailored for specific codes. The cost of using the new post-processing technique is that the algorithm becomes more complex. However, the post-processing is applied very rarely unless the channel is very noisy, and hence the increase in computational complexity is negligible for most choices of parameters. Finally, we propose a new algorithm that combines existing techniques in a way that avoids the error floor with short relatively high rate codes. The algorithm should also avoid the error floor with long high rate codes, but further work is needed to confirm this.
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach
Venue: arXiv cs.LG 2020
Year: 2020

Federated learning (FL) is an emerging technique for training machine learning models using geographically dispersed data collected by local entities. It includes local computation and synchronization steps. To reduce the communication overhead and improve the overall efficiency of FL, gradient sparsification (GS) can be applied, where instead of the full gradient, only a small subset of important elements of the gradient is communicated. Existing work on GS uses a fixed degree of gradient sparsity for i.i.d.-distributed data within a datacenter. In this paper, we consider adaptive degree of sparsity and non-i.i.d. local datasets. We first present a fairness-aware GS method which ensures that different clients provide a similar amount of updates. Then, with the goal of minimizing the overall training time, we propose a novel online learning formulation and algorithm for automatically determining the near-optimal communication and computation trade-off that is controlled by the degree of gradient sparsity. The online learning algorithm uses an estimated sign of the derivative of the objective function, which gives a regret bound that is asymptotically equal to the case where exact derivative is available. Experiments with real datasets confirm the benefits of our proposed approaches, showing up to $40\%$ improvement in model accuracy for a finite training time.