OpenRaiser/Intern-Atlas huggingface dataset:
Learning Predictive Models for Ergonomic Control of Prosthetic Devices
Venue: arXiv cs.RO 2020
Year: 2020

We present Model-Predictive Interaction Primitives -- a robot learning framework for assistive motion in human-machine collaboration tasks which explicitly accounts for biomechanical impact on the human musculoskeletal system. First, we extend Interaction Primitives to enable predictive biomechanics: the prediction of future biomechanical states of a human partner conditioned on current observations and intended robot control signals. In turn, we leverage this capability within a model-predictive control strategy to identify the future ergonomic and biomechanical ramifications of potential robot actions. Optimal control trajectories are selected so as to minimize future physical impact on the human musculoskeletal system. We empirically demonstrate that our approach minimizes knee or muscle forces via generated control actions selected according to biomechanical cost functions. Experiments are performed in synthetic and real-world experiments involving powered prosthetic devices.
A Study of Domain Generalization on Ultrasound-based Multi-Class Segmentation of Arteries, Veins, Ligaments, and Nerves Using Transfer Learning
Venue: arXiv cs.CV 2020
Year: 2020

Identifying landmarks in the femoral area is crucial for ultrasound (US) -based robot-guided catheter insertion, and their presentation varies when imaged with different scanners. As such, the performance of past deep learning-based approaches is also narrowly limited to the training data distribution; this can be circumvented by fine-tuning all or part of the model, yet the effects of fine-tuning are seldom discussed. In this work, we study the US-based segmentation of multiple classes through transfer learning by fine-tuning different contiguous blocks within the model, and evaluating on a gamut of US data from different scanners and settings. We propose a simple method for predicting generalization on unseen datasets and observe statistically significant differences between the fine-tuning methods while working towards domain generalization.
DeepMind Lab2D
Venue: arXiv cs.AI 2020
Year: 2020

We present DeepMind Lab2D, a scalable environment simulator for artificial intelligence research that facilitates researcher-led experimentation with environment design. DeepMind Lab2D was built with the specific needs of multi-agent deep reinforcement learning researchers in mind, but it may also be useful beyond that particular subfield.
Trajectory Optimization for High-Dimensional Nonlinear Systems under STL Specifications
Venue: arXiv cs.RO 2020
Year: 2020

Signal Temporal Logic (STL) has gained popularity in recent years as a specification language for cyber-physical systems, especially in robotics. Beyond being expressive and easy to understand, STL is appealing because the synthesis problem---generating a trajectory that satisfies a given specification---can be formulated as a trajectory optimization problem. Unfortunately, the associated cost function is nonsmooth and non-convex. As a result, existing synthesis methods scale poorly to high-dimensional nonlinear systems. In this letter, we present a new trajectory optimization approach for STL synthesis based on Differential Dynamic Programming (DDP). It is well known that DDP scales well to extremely high-dimensional nonlinear systems like robotic quadrupeds and humanoids: we show that these advantages can be harnessed for STL synthesis. We prove the soundness of our proposed approach, demonstrate order-of-magnitude speed improvements over the state-of-the-art on several benchmark problems, and demonstrate the scalability of our approach to the full nonlinear dynamics of a 7 degree-of-freedom robot arm.
Bi-Dimensional Feature Alignment for Cross-Domain Object Detection
Venue: arXiv cs.CV 2020
Year: 2020

Recently the problem of cross-domain object detection has started drawing attention in the computer vision community. In this paper, we propose a novel unsupervised cross-domain detection model that exploits the annotated data in a source domain to train an object detector for a different target domain. The proposed model mitigates the cross-domain representation divergence for object detection by performing cross-domain feature alignment in two dimensions, the depth dimension and the spatial dimension. In the depth dimension of channel layers, it uses inter-channel information to bridge the domain divergence with respect to image style alignment. In the dimension of spatial layers, it deploys spatial attention modules to enhance detection relevant regions and suppress irrelevant regions with respect to cross-domain feature alignment. Experiments are conducted on a number of benchmark cross-domain detection datasets. The empirical results show the proposed method outperforms the state-of-the-art comparison methods.
Modelling Attacks in Blockchain Systems using Petri Nets
Venue: arXiv cs.CR 2020
Year: 2020

Blockchain technology has evolved through many changes and modifications, such as smart-contracts since its inception in 2008. The popularity of a blockchain system is due to the fact that it offers a significant security advantage over other traditional systems. However, there have been many attacks in various blockchain systems, exploiting different vulnerabilities and bugs, which caused a significant financial loss. Therefore, it is essential to understand how these attacks in blockchain occur, which vulnerabilities they exploit, and what threats they expose. Another concerning issue in this domain is the recent advancement in the quantum computing field, which imposes a significant threat to the security aspects of many existing secure systems, including blockchain, as they would invalidate many widely-used cryptographic algorithms. Thus, it is important to examine how quantum computing will affect these or other new attacks in the future. In this paper, we explore different vulnerabilities in current blockchain systems and analyse the threats that various theoretical and practical attacks in the blockchain expose. We then model those attacks using Petri nets concerning current systems and future quantum computers.
A new vertex coloring heuristic and corresponding chromatic number
Venue: arXiv cs.DM 2020
Year: 2020

One method to obtain a proper vertex coloring of graphs using a reasonable number of colors is to start from any arbitrary proper coloring and then repeat some local re-coloring techniques to reduce the number of color classes. The Grundy (First-Fit) coloring and color-dominating colorings of graphs are two well-known such techniques. The color-dominating colorings are also known and commonly referred as {\rm b}-colorings. But these two topics have been studied separately in graph theory. We introduce a new coloring procedure which combines the strategies of these two techniques and satisfies an additional property. We first prove that the vertices of every graph $G$ can be effectively colored using color classes say $C_1, \ldots, C_k$ such that $(i)$ for any two colors $i$ and $j$ with $1\leq i< j \leq k$, any vertex of color $j$ is adjacent to a vertex of color $i$, $(ii)$ there exists a set $\{u_1, \ldots, u_k\}$ of vertices of $G$ such that $u_j\in C_j$ for any $j\in \{1, \ldots, k\}$ and $u_k$ is adjacent to $u_j$ for each $1\leq j \leq k$ with $j\not= k$, and $(iii)$ for each $i$ and $j$ with $i\not= j$, the vertex $u_j$ has a neighbor in $C_i$. This provides a new vertex coloring heuristic which improves both Grundy and color-dominating colorings. Denote by $z(G)$ the maximum number of colors used in any proper vertex coloring satisfying the above properties. The $z(G)$ quantifies the worst-case behavior of the heuristic. We prove the existence of $\{G_n\}_{n\geq 1}$ such that $\min \{Γ(G_n), b(G_n)\} \rightarrow \infty$ but $z(G_n)\leq 3$ for each $n$. For each positive integer $t$ we construct a family of finitely many colored graphs ${\mathcal{D}}_t$ satisfying the property that if $z(G)\geq t$ for a graph $G$ then $G$ contains an element from ${\mathcal{D}}_t$ as a colored subgraph. This provides an algorithmic method for proving numeric upper bounds for $z(G)$.
Classification of Reverse-Engineered Class Diagram and Forward-Engineered Class Diagram using Machine Learning
Venue: arXiv cs.SE 2020
Year: 2020

UML Class diagram is very important to visualize the whole software we are working on and helps understand the whole system in the easiest way possible by showing the system classes, its attributes, methods, and relations with other objects. In the real world, there are two types of Class diagram engineers work with namely 1) Forward Engineered Class Diagram (FwCD) which are hand-made as part of the forward-looking development process, and 2). Reverse Engineered Class Diagram (RECD) which are those diagrams that are reverse engineered from the source code. In the software industry while working with new open software projects it is important to know which type of class diagram it is. Which UML diagram was used in a particular project is an important factor to be known? To solve this problem, we propose to build a classifier that can classify a UML diagram into FwCD or RECD. We propose to solve this problem by using a supervised Machine Learning technique. The approach in this involves analyzing the features that are useful in classifying class diagrams. Different Machine Learning models are used in this process and the Random Forest algorithm has proved to be the best out of all. Performance testing was done on 999 Class diagrams.
Memory-Efficient Dataflow Inference for Deep CNNs on FPGA
Venue: arXiv cs.AR 2020
Year: 2020

Custom dataflow Convolutional Neural Network (CNN) inference accelerators on FPGA are tailored to a specific CNN topology and store parameters in On-Chip Memory (OCM), resulting in high energy efficiency and low inference latency. However, in these accelerators the shapes of parameter memories are dictated by throughput constraints and do not map well to the underlying OCM, which becomes an implementation bottleneck. In this work, we propose an accelerator design methodology - Frequency Compensated Memory Packing (FCMP) - which improves the OCM utilization efficiency of dataflow accelerators with minimal reduction in throughput and no modifications to the physical structure of FPGA OCM. To validate our methodology, we apply it to several realizations of medium-sized CIFAR-10 inference accelerators and demonstrate up to 30% reduction in OCM utilization without loss of inference throughput, allowing us to port the accelerators from Xilinx Zynq 7020 to 7012S, reducing application cost. We also implement a custom dataflow FPGA inference accelerator for a quantized ResNet-50 CNN, utilizing on-chip weights, the largest topology ever implemented with this accelerator architecture. We demonstrate that by applying FCMP to the ResNet accelerator, the OCM bottleneck is alleviated which enables the accelerator to be ported from Alveo U250 to the smaller Alveo U280 board with less throughput loss compared to alternative techniques. By providing a finer-grained trade off between throughput and OCM requirements, FCMP increases the flexibility of custom dataflow CNN inference designs on FPGA.
Brief Report on QoSec, Context Aware Security and the Role of Physical Layer Security in 6G Wireless
Venue: arXiv cs.CR 2020
Year: 2020

While the security literature predominantly focuses on the core network, the enhancement of the security of the beyond fifth generation (B5G) access network becomes of critical importance. Despite the strengthening of 5G security protocols with respect to LTE, there are still open issues that have not yet been fully addressed. In parallel as we move gradually away from the standard client-server networking paradigm and enter a new era of truly E2E quality of service (QoS), service level agreements (SLAs) in the near future will be expected to include guarantees about the quality of security (QoSec) as well. Incorporating context awareness in QoSec is projected to allow handle more efficiently aspects related to identifying the risk or threat level and the required security level. Finally, as novel sensing and intelligence capabilities are envisioned in 6G, security solutions from the palette of physical layer security can emerge, particularly for massive machine type communications involving large scale low-end IoT devices.
RecTen: A Recursive Hierarchical Low Rank Tensor Factorization Method to Discover Hierarchical Patterns in Multi-modal Data
Venue: arXiv cs.IR 2020
Year: 2020

How can we expand the tensor decomposition to reveal a hierarchical structure of the multi-modal data in a self-adaptive way? Current tensor decomposition provides only a single layer of clusters. We argue that with the abundance of multimodal data and time-evolving networks nowadays, the ability to identify emerging hierarchies is important. To this effect, we propose RecTen, a recursive hierarchical soft clustering approach based on tensor decomposition. Our approach enables us to: (a) recursively decompose clusters identified in the previous step, and (b) identify the right conditions for terminating this process. In the absence of proper ground truth, we evaluate our approach with synthetic data and test its sensitivity to different parameters. We also apply RecTen on five real datasets which involve the activities of users in online discussion platforms, such as security forums. This analysis helps us reveal clusters of users with interesting behaviors, including but not limited to early detection of some real events like ransomware outbreaks, the emergence of a blackmarket of decryption tools, and romance scamming. To maximize the usefulness of our approach, we develop a tool which can help the data analysts and overall research community by identifying hierarchical structures. RecTen is an unsupervised approach which can be used to take the pulse of the large multi-modal data and let the data discover its own hidden structures by itself.
Tight Distributed Listing of Cliques
Venue: arXiv cs.DC 2020
Year: 2020

Much progress has recently been made in understanding the complexity landscape of subgraph finding problems in the CONGEST model of distributed computing. However, so far, very few tight bounds are known in this area. For triangle (i.e., 3-clique) listing, an optimal $\tilde{O}(n^{1/3})$-round distributed algorithm has been constructed by Chang et al.~[SODA 2019, PODC 2019]. Recent works of Eden et al.~[DISC 2019] and of Censor-Hillel et al.~[PODC 2020] have shown sublinear algorithms for $K_p$-listing, for each $p \geq 4$, but still leaving a significant gap between the upper bounds and the known lower bounds of the problem. In this paper, we completely close this gap. We show that for each $p \geq 4$, there is an $\tilde{O}(n^{1 - 2/p})$-round distributed algorithm that lists all $p$-cliques $K_p$ in the communication network. Our algorithm is \emph{optimal} up to a polylogarithmic factor, due to the $\tildeΩ(n^{1 - 2/p})$-round lower bound of Fischer et al.~[SPAA 2018], which holds even in the CONGESTED CLIQUE model. Together with the triangle-listing algorithm by Chang et al.~[SODA 2019, PODC 2019], our result thus shows that the round complexity of $K_p$-listing, for all $p$, is the same in both the CONGEST and CONGESTED CLIQUE models, at $\tildeΘ(n^{1 - 2/p})$ rounds. For $p=4$, our result additionally matches the $\tildeΩ(n^{1/2})$ lower bound for $K_4$-\emph{detection} by Czumaj and Konrad [DISC 2018], implying that the round complexities for detection and listing of $K_4$ are equivalent in the CONGEST model.
Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data
Venue: arXiv cs.LG 2020
Year: 2020

Depression and post-traumatic stress disorder (PTSD) are psychiatric conditions commonly associated with experiencing a traumatic event. Estimating mental health status through non-invasive techniques such as activity-based algorithms can help to identify successful early interventions. In this work, we used locomotor activity captured from 1113 individuals who wore a research grade smartwatch post-trauma. A convolutional variational autoencoder (VAE) architecture was used for unsupervised feature extraction from four weeks of actigraphy data. By using VAE latent variables and the participant's pre-trauma physical health status as features, a logistic regression classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.64 to estimate mental health outcomes. The results indicate that the VAE model is a promising approach for actigraphy data analysis for mental health outcomes in long-term studies.
GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability
Venue: arXiv cs.LG 2020
Year: 2020

We propose an efficient algorithm to visualise symmetries in neural networks. Typically, models are defined with respect to a parameter space, where non-equal parameters can produce the same input-output map. Our proposed method, GENNI, allows us to efficiently identify parameters that are functionally equivalent and then visualise the subspace of the resulting equivalence class. By doing so, we are now able to better explore questions surrounding identifiability, with applications to optimisation and generalizability, for commonly used or newly developed neural network architectures.
Coresets for Robust Training of Neural Networks against Noisy Labels
Venue: arXiv cs.LG 2020
Year: 2020

Modern neural networks have the capacity to overfit noisy labels frequently found in real-world datasets. Although great progress has been made, existing techniques are limited in providing theoretical guarantees for the performance of the neural networks trained with noisy labels. Here we propose a novel approach with strong theoretical guarantees for robust training of deep networks trained with noisy labels. The key idea behind our method is to select weighted subsets (coresets) of clean data points that provide an approximately low-rank Jacobian matrix. We then prove that gradient descent applied to the subsets do not overfit the noisy labels. Our extensive experiments corroborate our theory and demonstrate that deep networks trained on our subsets achieve a significantly superior performance compared to state-of-the art, e.g., 6% increase in accuracy on CIFAR-10 with 80% noisy labels, and 7% increase in accuracy on mini Webvision.
An efficient label-free analyte detection algorithm for time-resolved spectroscopy
Venue: arXiv cs.LG 2020
Year: 2020

Time-resolved spectral techniques play an important analysis tool in many contexts, from physical chemistry to biomedicine. Customarily, the label-free detection of analytes is manually performed by experts through the aid of classic dimensionality-reduction methods, such as Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF). This fundamental reliance on expert analysis for unknown analyte detection severely hinders the applicability and the throughput of these such techniques. For this reason, in this paper, we formulate this detection problem as an unsupervised learning problem and propose a novel machine learning algorithm for label-free analyte detection. To show the effectiveness of the proposed solution, we consider the problem of detecting the amino-acids in Liquid Chromatography coupled with Raman spectroscopy (LC-Raman).
AmphibianDetector: adaptive computation for moving objects detection
Venue: arXiv cs.CV 2020
Year: 2020

Convolutional neural networks (CNN) allow achieving the highest accuracy for the task of object detection in images. Major challenges in further development of object detectors are false-positive detections and high demand of processing power. In this paper, we propose an approach to object detection which makes it possible to reduce the number of false-positive detections by processing only moving objects and reduce the required processing power for algorithm inference. The proposed approach is a modification of CNN already trained for object detection task. This method can be used to improve the accuracy of an existing system by applying minor changes to the algorithm. The efficiency of the proposed approach was demonstrated on the open dataset "CDNet2014 pedestrian". The implementation of the method proposed in the article is available on the GitHub: https://github.com/david-svitov/AmphibianDetector
Aquanims -- Area-Preserving Animated Transitions based on a Hydraulic Metaphor
Venue: arXiv cs.HC 2020
Year: 2020

We propose "Aquanims" as new design metaphors for animated transitions that preserve displayed areas during the transformation. As liquids are incompressible fluids, we use a hydraulic metaphor to convey the sense of area preservation during animated transitions. We study the design space of Aquanims for rectangle-based charts.
Advances in the training, pruning and enforcement of shape constraints of Morphological Neural Networks using Tropical Algebra
Venue: arXiv cs.LG 2020
Year: 2020

In this paper we study an emerging class of neural networks based on the morphological operators of dilation and erosion. We explore these networks mathematically from a tropical geometry perspective as well as mathematical morphology. Our contributions are threefold. First, we examine the training of morphological networks via Difference-of-Convex programming methods and extend a binary morphological classifier to multiclass tasks. Second, we focus on the sparsity of dense morphological networks trained via gradient descent algorithms and compare their performance to their linear counterparts under heavy pruning, showing that the morphological networks cope far better and are characterized with superior compression capabilities. Our approach incorporates the effect of the training optimizer used and offers quantitative and qualitative explanations. Finally, we study how the architectural structure of a morphological network can affect shape constraints, focusing on monotonicity. Via Maslov Dequantization, we obtain a softened version of a known architecture and show how this approach can improve training convergence and performance.
Crowdsharing Wireless Energy Services
Venue: arXiv cs.DC 2020
Year: 2020

We propose a novel self-sustained ecosystem for energy sharing in the IoT environment. We leverage energy harvesting, wireless power transfer, and crowdsourcing that facilitate the development of an energy crowdsharing framework to charge IoT devices. The ubiquity of IoT devices, coupled with the potential ability for sharing energy, provides new and exciting opportunities to crowdsource wireless energy, thus enabling a green alternative for powering IoT devices anytime and anywhere. We discuss the crowdsharing of energy services, open challenges, and proposed solutions.