OpenRaiser/Intern-Atlas huggingface dataset:
Energy-Efficient Hardware-Accelerated Synchronization for Shared-L1-Memory Multiprocessor Clusters
Venue: arXiv cs.AR 2020
Year: 2020

The steeply growing performance demands for highly power- and energy-constrained processing systems such as end-nodes of the internet-of-things (IoT) have led to parallel near-threshold computing (NTC), joining the energy-efficiency benefits of low-voltage operation with the performance typical of parallel systems. Shared-L1-memory multiprocessor clusters are a promising architecture, delivering performance in the order of GOPS and over 100 GOPS/W of energy-efficiency. However, this level of computational efficiency can only be reached by maximizing the effective utilization of the processing elements (PEs) available in the clusters. Along with this effort, the optimization of PE-to-PE synchronization and communication is a critical factor for performance. In this work, we describe a light-weight hardware-accelerated synchronization and communication unit (SCU) for tightly-coupled clusters of processors. We detail the architecture, which enables fine-grain per-PE power management, and its integration into an eight-core cluster of RISC-V processors. To validate the effectiveness of the proposed solution, we implemented the eight-core cluster in advanced 22nm FDX technology and evaluated performance and energy-efficiency with tunable microbenchmarks and a set of real-life applications and kernels. The proposed solution allows synchronization-free regions as small as 42 cycles, over 41 times smaller than the baseline implementation based on fast test-and-set access to L1 memory when constraining the microbenchmarks to 10% synchronization overhead. When evaluated on the real-life DSP-applications, the proposed SCU improves performance by up to 92% and 23% on average and energy efficiency by up to 98% and 39% on average.
Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence
Venue: arXiv cs.SI 2020
Year: 2020

Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings. This work analyzes the usefulness of social media imagery content to perform rapid damage assessment during a real-world disaster. An automatic image processing system, which was activated in collaboration with a volunteer response organization, processed ~280K images to understand the extent of damage caused by the disaster. The system achieved an accuracy of 76% computed based on the feedback received from the domain experts who analyzed ~29K system-processed images during the disaster. An extensive error analysis reveals several insights and challenges faced by the system, which are vital for the research community to advance this line of research.
Scalable Autonomous Vehicle Safety Validation through Dynamic Programming and Scene Decomposition
Venue: arXiv cs.RO 2020
Year: 2020

An open question in autonomous driving is how best to use simulation to validate the safety of autonomous vehicles. Existing techniques rely on simulated rollouts, which can be inefficient for finding rare failure events, while other techniques are designed to only discover a single failure. In this work, we present a new safety validation approach that attempts to estimate the distribution over failures of an autonomous policy using approximate dynamic programming. Knowledge of this distribution allows for the efficient discovery of many failure examples. To address the problem of scalability, we decompose complex driving scenarios into subproblems consisting of only the ego vehicle and one other vehicle. These subproblems can be solved with approximate dynamic programming and their solutions are recombined to approximate the solution to the full scenario. We apply our approach to a simple two-vehicle scenario to demonstrate the technique as well as a more complex five-vehicle scenario to demonstrate scalability. In both experiments, we observed an increase in the number of failures discovered compared to baseline approaches.
Quadrupedal Locomotion via Event-Based Predictive Control and QP-Based Virtual Constraints
Venue: arXiv cs.RO 2020
Year: 2020

This paper aims to develop a hierarchical nonlinear control algorithm, based on model predictive control (MPC), quadratic programming (QP), and virtual constraints, to generate and stabilize locomotion patterns in a real-time manner for dynamical models of quadrupedal robots. The higher level of the proposed control scheme is developed based on an event-based MPC that computes the optimal center of mass (COM) trajectories for a reduced-order linear inverted pendulum (LIP) model subject to the feasibility of the net ground reaction force (GRF). The asymptotic stability of the desired target point for the reduced-order model under the event-based MPC approach is investigated. It is shown that the event-based nature of the proposed MPC approach can significantly reduce the computational burden associated with the real-time implementation of MPC techniques. To bridge the gap between reduced- and full-order models, QP-based virtual constraint controllers are developed at the lower level of the proposed control scheme to impose the full-order dynamics to track the optimal trajectories while having all individual GRFs in the friction cone. The analytical results of the paper are numerically confirmed on full-order simulation models of a 22 degree of freedom quadrupedal robot, Vision 60, that is augmented by a robotic manipulator. The paper numerically investigates the robustness of the proposed control algorithm against different contact models.
Learning sums of powers of low-degree polynomials in the non-degenerate case
Venue: arXiv cs.CC 2020
Year: 2020

We develop algorithms for writing a polynomial as sums of powers of low degree polynomials. Consider an $n$-variate degree-$d$ polynomial $f$ which can be written as $$f = c_1Q_1^{m} + \ldots + c_s Q_s^{m},$$ where each $c_i\in \mathbb{F}^{\times}$, $Q_i$ is a homogeneous polynomial of degree $t$, and $t m = d$. In this paper, we give a $\text{poly}((ns)^t)$-time learning algorithm for finding the $Q_i$'s given (black-box access to) $f$, if the $Q_i's$ satisfy certain non-degeneracy conditions and $n$ is larger than $d^2$. The set of degenerate $Q_i$'s (i.e., inputs for which the algorithm does not work) form a non-trivial variety and hence if the $Q_i$'s are chosen according to any reasonable (full-dimensional) distribution, then they are non-degenerate with high probability (if $s$ is not too large). Our algorithm is based on a scheme for obtaining a learning algorithm for an arithmetic circuit model from a lower bound for the same model, provided certain non-degeneracy conditions hold. The scheme reduces the learning problem to the problem of decomposing two vector spaces under the action of a set of linear operators, where the spaces and the operators are derived from the input circuit and the complexity measure used in a typical lower bound proof. The non-degeneracy conditions are certain restrictions on how the spaces decompose.
An efficient deception architecture for cloud-based virtual networks
Venue: arXiv cs.CR 2020
Year: 2020

Emerging deceptive systems present a new promise for the uprising security problems in cloud-based virtual networks, especially those operated by small and medium enterprises. The main goal of deceptive systems is to form a layer of defensive nodes in an Internet-accessible cloud-based virtual network to distract and deceive malicious clients. While numerous approaches provide distinct models for developing decisive systems, misery digraphs present a promising decisive model for distracting powerful remote intrusions. Misery digraphs can delay access to targets deep in a cloud-based virtual network. A central challenge to the theory of misery digraphs is verifying their applicability in prominent cloud computing platforms as well as measuring the efficiency of networks that adapt them. Thus, architectural support is needed that can be realized with long-term support technologies and can be deployed for large networks. This work presents and analyzes a high-throughput architecture for misery digraphs, embarking on implementation details and performance analysis. Full implementation of the architecture in Amazon Web Services imposes modest performance delays in the request processing, while highly delaying stealth intrusions in the network.
Improving Many-Objective Evolutionary Algorithms by Means of Edge-Rotated Cones
Venue: arXiv cs.NE 2020
Year: 2020

Given a point in $m$-dimensional objective space, any $\varepsilon$-ball of a point can be partitioned into the incomparable, the dominated and dominating region. The ratio between the size of the incomparable region, and the dominated (and dominating) region decreases proportionally to $1/2^{m-1}$, i.e., the volume of the Pareto dominating orthant as compared to all other volumes. Due to this reason, it gets increasingly unlikely that dominating points can be found by random, isotropic mutations. As a remedy to stagnation of search in many objective optimization, in this paper, we suggest to enhance the Pareto dominance order by involving an obtuse convex dominance cone in the convergence phase of an evolutionary optimization algorithm. We propose edge-rotated cones as generalizations of Pareto dominance cones for which the opening angle can be controlled by a single parameter only. The approach is integrated in several state-of-the-art multi-objective evolutionary algorithms (MOEAs) and tested on benchmark problems with four, five, six and eight objectives. Computational experiments demonstrate the ability of these edge-rotated cones to improve the performance of MOEAs on many-objective optimization problems.
The impact of Industry 4.0 technologies on production and supply chains
Venue: arXiv cs.CY 2020
Year: 2020

This paper sheds light on the current development in major industrialized countries (such as Germany, Japan, and Switzerland): the trend towards highly-integrated and autonomous production systems. The question is how such a transition of a production infrastructure can take place most efficiently. This research uses the system dynamics method to address this complex transition process from a legacy production system to a modern and highly integrated production system (an Industry 4.0 system). The findings mainly relate to the identification of system structures that are relevant for an Industry 4.0 perspective. Our research is the first in its kind which presents a causal model that addresses the transition to Industry 4.0.
Visual Descriptor Learning from Monocular Video
Venue: arXiv cs.CV 2020
Year: 2020

Correspondence estimation is one of the most widely researched and yet only partially solved area of computer vision with many applications in tracking, mapping, recognition of objects and environment. In this paper, we propose a novel way to estimate dense correspondence on an RGB image where visual descriptors are learned from video examples by training a fully convolutional network. Most deep learning methods solve this by training the network with a large set of expensive labeled data or perform labeling through strong 3D generative models using RGB-D videos. Our method learns from RGB videos using contrastive loss, where relative labeling is estimated from optical flow. We demonstrate the functionality in a quantitative analysis on rendered videos, where ground truth information is available. Not only does the method perform well on test data with the same background, it also generalizes to situations with a new background. The descriptors learned are unique and the representations determined by the network are global. We further show the applicability of the method to real-world videos.
FOND Planning for LTLf and PLTLf Goals
Venue: arXiv cs.AI 2020
Year: 2020

In this report, we will define a new approach to the problem of non deterministic planning for extended temporal goals. In particular, we will give a solution to this problem reducing it to a fully observable non deterministic (FOND) planning problem and taking advantage of the LTLfToDFA tool. First of all, we will introduce the main idea and motivations supporting our approach. Then, we will give some preliminaries explaining the Planning Domain Definition Language (PDDL) language and the FOND planning problem formally. After that, we will illustrate our FOND4LTLfPLTLf (also available online) approach with the encoding of temporal goals into a PDDL domain and problem. Finally, we will present some of the results obtained through the application of the proposed solution.
Using Experimental Vignettes to Study Early-Stage Automation Adoption
Venue: arXiv cs.HC 2020
Year: 2020

When discussing the future of work and in detail the concerns of workers within and beyond established workplace settings, technology-wise we act on rather new ground. Especially preserving a meaningful work environment gains new importance when introducing disruptive technologies. We sometimes do not even have the technology which effects we are willing to discuss. To measure implications for employees and thus create meaningful design variants we need to test systems and their effects before developing them. Confronted with the same problem we used the experimental vignette method to study the effects of AI use in work contexts. During the workshop, we will report our experiences.
Fair and Efficient Gossip in Hyperledger Fabric
Venue: arXiv cs.DC 2020
Year: 2020

Permissioned blockchains are supported by identified but individually untrustworthy nodes, collectively maintaining a replicated ledger whose content is trusted. The Hyperledger Fabric permissioned blockchain system targets high-throughput transaction processing. Fabric uses a set of nodes tasked with the ordering of transactions using consensus. Additional peers endorse and validate transactions, and maintain a copy of the ledger. The ability to quickly disseminate new transaction blocks from ordering nodes to all peers is critical for both performance and consistency. Broadcast is handled by a gossip protocol, using randomized exchanges of blocks between peers. We show that the current implementation of gossip in Fabric leads to heavy tail distributions of block propagation latencies, impacting performance, consistency, and fairness. We contribute a novel design for gossip in Fabric that simultaneously optimizes propagation time, tail latency and bandwidth consumption. Using a 100-node cluster, we show that our enhanced gossip allows the dissemination of blocks to all peers more than 10 times faster than with the original implementation, while decreasing the overall network bandwidth consumption by more than 40%. With a high throughput and concurrent application, this results in 17% to 36% fewer invalidated transactions for different block sizes.
A Polynomial Interpolation based Quantum Key Reconciliation Protocol: Error Correction without Information Leakage
Venue: arXiv cs.CR 2020
Year: 2020

In this work, we propose a novel key reconciliation protocol for the quantum key distribution (QKD). Based on Newton's polynomial interpolation, the proposed protocol aims to correct all erroneous bits at the receiver without revealing information to the eavesdropper. We provide the exact frame error rate (FER) expression of the proposed protocol. The inherent nature of the proposed algorithm ensures correcting all erroneous bits if the algorithm succeeds. We present an information-theoretical proof that the revealed information during the key reconciliation process is equal to zero. We also provide a numerical comparison of our algorithm with the asymptotic performance of the error-correcting codes and two exemplary low-density-parity-check (LDPC) codes. The results highlight that our algorithm provides superior performance when compared to the LDPC codes, regardless of the distance between Alice and Bob. Furthermore, the proposed key reconciliation protocol is usable for the longer quantum link distances than the state-of-the-art protocols.
DeeSCo: Deep heterogeneous ensemble with Stochastic Combinatory loss for gaze estimation
Venue: arXiv cs.CV 2020
Year: 2020

From medical research to gaming applications, gaze estimation is becoming a valuable tool. While there exists a number of hardware-based solutions, recent deep learning-based approaches, coupled with the availability of large-scale databases, have allowed to provide a precise gaze estimate using only consumer sensors. However, there remains a number of questions, regarding the problem formulation, architectural choices and learning paradigms for designing gaze estimation systems in order to bridge the gap between geometry-based systems involving specific hardware and approaches using consumer sensors only. In this paper, we introduce a deep, end-to-end trainable ensemble of heatmap-based weak predictors for 2D/3D gaze estimation. We show that, through heterogeneous architectural design of these weak predictors, we can improve the decorrelation between the latter predictors to design more robust deep ensemble models. Furthermore, we propose a stochastic combinatory loss that consists in randomly sampling combinations of weak predictors at train time. This allows to train better individual weak predictors, with lower correlation between them. This, in turns, allows to significantly enhance the performance of the deep ensemble. We show that our Deep heterogeneous ensemble with Stochastic Combinatory loss (DeeSCo) outperforms state-of-the-art approaches for 2D/3D gaze estimation on multiple datasets.
Characterizing Research Leadership on Geographical Weighted Collaboration Network
Venue: arXiv cs.DL 2020
Year: 2020

Research collaborations, especially long-distance and cross-border collaborations, have become increasingly prevalent worldwide. Recent studies highlighted the significant role of research leadership in collaborations. However, existing measures of the research leadership do not take into account the intensity of leadership in the co-authorship network. More importantly, the spatial features, which influence the collaboration patterns and research outcomes, have not been incorporated in measuring the research leadership. To fill the gap, we construct an institution-level weighted co-authorship network that has two types of weight on the edges: the intensity of collaborations and the spatial score (the geographic distance adjusted by the cross-border nature). Based on this network, we propose a novel metric, namely the spatial research leadership rank (SpatialLeaderRank), to identify the leading institutions while considering both the collaboration intensity and the spatial features. Harnessing a dataset of 323,146 journal publications in pharmaceutical sciences during 2010-2018, we perform a comprehensive analysis of the geographical distribution and dynamic patterns of research leadership flows at the institution level. The results demonstrate that the SpatialLeaderRank outperforms baseline metrics in predicting the scholarly impact of institutions. And the result remains robust in the field of Information Science & Library Science.
Latin Hypercubes and Cellular Automata
Venue: arXiv cs.DM 2020
Year: 2020

Latin squares and hypercubes are combinatorial designs with several applications in statistics, cryptography and coding theory. In this paper, we generalize a construction of Latin squares based on bipermutive cellular automata (CA) to the case of Latin hypercubes of dimension $k>2$. In particular, we prove that linear bipermutive CA (LBCA) yielding Latin hypercubes of dimension $k>2$ are defined by sequences of invertible Toeplitz matrices with partially overlapping coefficients, which can be described by a specific kind of regular de Bruijn graph induced by the support of the determinant function. Further, we derive the number of $k$-dimensional Latin hypercubes generated by LBCA by counting the number of paths of length $k-3$ on this de Bruijn graph.
Transfer-Learning-Aware Neuro-Evolution for Diseases Detection in Chest X-Ray Images
Venue: arXiv cs.NE 2020
Year: 2020

The neural network needs excessive costs of time because of the complexity of architecture when trained on images. Transfer learning and fine-tuning can help improve time and cost efficiency when training a neural network. Yet, Transfer learning and fine-tuning needs a lot of experiment to try with. Therefore, a method to find the best architecture for transfer learning and fine-tuning is needed. To overcome this problem, neuro-evolution using a genetic algorithm can be used to find the best architecture for transfer learning. To check the performance of this study, dataset ChestX-Ray 14 and DenseNet-121 as a base neural network model are used. This study used the AUC score, differences in execution time for training, and McNemar's test to the significance test. In terms of result, this study got a 5% difference in the AUC score, 3 % faster in terms of execution time, and significance in most of the disease detection. Finally, this study gives a concrete summary of how neuro-evolution transfer learning can help in terms of transfer learning and fine-tuning.
A Study of Blockchain Oracles
Venue: arXiv cs.CR 2020
Year: 2020

The limitation with smart contracts is that they cannot access external data which might be required to control the execution of business logic. Oracles can be used to provide external data to smart contracts. An oracle is an interface that delivers data from external data outside the blockchain to a smart contract to consume. Oracle can deliver different types of data depending on the industry and requirements. In this paper, we study and describe the widely used blockchain oracles. Then, we elaborate on his potential role, technical architecture, and design patterns. Finally, we discuss the human oracle and his key role in solving the truth problem by reaching a consensus about a certain inquiry and tasks.
Musical Features for Automatic Music Transcription Evaluation
Venue: arXiv cs.SD 2020
Year: 2020

This technical report gives a detailed, formal description of the features introduced in the paper: Adrien Ycart, Lele Liu, Emmanouil Benetos and Marcus T. Pearce. "Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription", Transactions of the International Society for Music Information Retrieval (TISMIR), Accepted, 2020.
Gumbel-softmax-based Optimization: A Simple General Framework for Optimization Problems on Graphs
Venue: arXiv cs.LG 2020
Year: 2020

In computer science, there exist a large number of optimization problems defined on graphs, that is to find a best node state configuration or a network structure such that the designed objective function is optimized under some constraints. However, these problems are notorious for their hardness to solve because most of them are NP-hard or NP-complete. Although traditional general methods such as simulated annealing (SA), genetic algorithms (GA) and so forth have been devised to these hard problems, their accuracy and time consumption are not satisfying in practice. In this work, we proposed a simple, fast, and general algorithm framework based on advanced automatic differentiation technique empowered by deep learning frameworks. By introducing Gumbel-softmax technique, we can optimize the objective function directly by gradient descent algorithm regardless of the discrete nature of variables. We also introduce evolution strategy to parallel version of our algorithm. We test our algorithm on three representative optimization problems on graph including modularity optimization from network science, Sherrington-Kirkpatrick (SK) model from statistical physics, maximum independent set (MIS) and minimum vertex cover (MVC) problem from combinatorial optimization on graph. High-quality solutions can be obtained with much less time consuming compared to traditional approaches.