OpenRaiser/Intern-Atlas huggingface dataset:
Coordinated Passive Beamforming for Distributed Intelligent Reflecting Surfaces Network
Venue: arXiv cs.IT 2020
Year: 2020

Intelligent reflecting surface (IRS) is a proposing technology in 6G to enhance the performance of wireless networks by smartly reconfiguring the propagation environment with a large number of passive reflecting elements. However, current works mainly focus on single IRS-empowered wireless networks, where the channel rank deficiency problem has emerged. In this paper, we propose a distributed IRS-empowered communication network architecture, where multiple source-destination pairs communicate through multiple distributed IRSs. We further contribute to maximize the achievable sum-rates in this network via jointly optimizing the transmit power vector at the sources and the phase shift matrix with passive beamforming at all distributed IRSs. Unfortunately, this problem turns out to be non-convex and highly intractable, for which an alternating approach is developed via solving the resulting fractional programming problems alternatively. In particular, the closed-form expressions are proposed for coordinated passive beamforming at IRSs. The numerical results will demonstrate the algorithmic advantages and desirable performances of the distributed IRS-empowered communication network.
Online Process Monitoring Using Incremental State-Space Expansion: An Exact Algorithm
Venue: arXiv cs.DB 2020
Year: 2020

The execution of (business) processes generates valuable traces of event data in the information systems employed within companies. Recently, approaches for monitoring the correctness of the execution of running processes have been developed in the area of process mining, i.e., online conformance checking. The advantages of monitoring a process' conformity during its execution are clear, i.e., deviations are detected as soon as they occur and countermeasures can immediately be initiated to reduce the possible negative effects caused by process deviations. Existing work in online conformance checking only allows for obtaining approximations of non-conformity, e.g., overestimating the actual severity of the deviation. In this paper, we present an exact, parameter-free, online conformance checking algorithm that computes conformance checking results on the fly. Our algorithm exploits the fact that the conformance checking problem can be reduced to a shortest path problem, by incrementally expanding the search space and reusing previously computed intermediate results. Our experiments show that our algorithm outperforms comparable state-of-the-art approximation algorithms.
Multi-Level Feature Fusion Mechanism for Single Image Super-Resolution
Venue: arXiv cs.CV 2020
Year: 2020

Convolution neural network (CNN) has been widely used in Single Image Super Resolution (SISR) so that SISR has been a great success recently. As the network deepens, the learning ability of network becomes more and more powerful. However, most SISR methods based on CNN do not make full use of hierarchical feature and the learning ability of network. These features cannot be extracted directly by subsequent layers, so the previous layer hierarchical information has little impact on the output and performance of subsequent layers relatively poor. To solve above problem, a novel Multi-Level Feature Fusion network (MLRN) is proposed, which can take full use of global intermediate features. We also introduce Feature Skip Fusion Block (FSFblock) as basic module. Each block can be extracted directly to the raw multiscale feature and fusion multi-level feature, then learn feature spatial correlation. The correlation among the features of the holistic approach leads to a continuous global memory of information mechanism. Extensive experiments on public datasets show that the method proposed by MLRN can be implemented, which is favorable performance for the most advanced methods.
Algebraic Structure of Blockchains: A Group-Theoretical Primer
Venue: arXiv cs.DC 2020
Year: 2020

Although recent advances of blockchain systems, notably in the form of cryptocurrency, have drawn tremendous interests from both researchers and practitioners, limited studies existed toward the theoretical foundation of blockchains. This paper presents the first study on the algebraic structure of blockchains with an emphasis on the internal properties under algebraic groups. We axiomatically construct a blockchain group and derive some interesting properties that can be potentially taken into the design space and parametric analysis of real-world blockchain systems.
High-Performance High-Order Stencil Computation on FPGAs Using OpenCL
Venue: arXiv cs.DC 2020
Year: 2020

In this paper we evaluate the performance of FPGAs for high-order stencil computation using High-Level Synthesis. We show that despite the higher computation intensity and on-chip memory requirement of such stencils compared to first-order ones, our design technique with combined spatial and temporal blocking remains effective. This allows us to reach similar, or even higher, compute performance compared to first-order stencils. We use an OpenCL-based design that, apart from parameterizing performance knobs, also parameterizes the stencil radius. Furthermore, we show that our performance model exhibits the same accuracy as first-order stencils in predicting the performance of high-order ones. On an Intel Arria 10 GX 1150 device, for 2D and 3D star-shaped stencils, we achieve over 700 and 270 GFLOP/s of compute performance, respectively, up to a stencil radius of four. These results outperform the state-of-the-art YASK framework on a modern Xeon for 2D and 3D stencils, and outperform a modern Xeon Phi for 2D stencils, while achieving competitive performance in 3D. Furthermore, our FPGA design achieves better power efficiency in almost all cases.
Hodge and Podge: Hybrid Supervised Sound Event Detection with Multi-Hot MixMatch and Composition Consistence Training
Venue: arXiv cs.SD 2020
Year: 2020

In this paper, we propose a method called Hodge and Podge for sound event detection. We demonstrate Hodge and Podge on the dataset of Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 Challenge Task 4. This task aims to predict the presence or absence and the onset and offset times of sound events in home environments. Sound event detection is challenging due to the lack of large scale real strongly labeled data. Recently deep semi-supervised learning (SSL) has proven to be effective in modeling with weakly labeled and unlabeled data. This work explores how to extend deep SSL to result in a new, state-of-the-art sound event detection method called Hodge and Podge. With convolutional recurrent neural networks (CRNN) as the backbone network, first, a multi-scale squeeze-excitation mechanism is introduced and added to generate a pyramid squeeze-excitation CRNN. The pyramid squeeze-excitation layer can pay attention to the issue that different sound events have different durations, and to adaptively recalibrate channel-wise spectrogram responses. Further, in order to remedy the lack of real strongly labeled data problem, we propose multi-hot MixMatch and composition consistency training with temporal-frequency augmentation. Our experiments with the public DCASE2019 challenge task 4 validation data resulted in an event-based F-score of 43.4\%, and is about absolutely 1.6\% better than state-of-the-art methods in the challenge. While the F-score of the official baseline is 25.8\%.
Mobile Communications, Computing and Caching Resources Optimization for Coded Caching with Device Computing
Venue: arXiv cs.IT 2020
Year: 2020

Edge caching and computing have been regarded as an efficient approach to tackle the wireless spectrum crunch problem. In this paper, we design a general coded caching with device computing strategy for content computation, e.g., virtual reality (VR) rendering, to minimize the average transmission bandwidth with the caching capacity and the energy constraints of each mobile device, and the maximum tolerable delay constraint of each task. The key enabler is that because both coded data and stored data can be the data before or after computing, the proposed scheme has numerous edge computing and caching paths corresponding to different bandwidth requirement. We thus formulate a joint coded caching and computing optimization problem to decide whether the mobile devices cache the input data or the output data, which tasks to be coded cached and which tasks to compute locally. The optimization problem is shown to be 0-1 nonconvex nonsmooth programming and can be decomposed into the computation programming and the coded caching programming. We prove the convergence of the computation programming problem by utilizing the alternating direction method of multipliers (ADMM), and a stationary point can be obtained. For the coded cache programming, we design a low complexity algorithm to obtain an acceptable solution. Numerical results demonstrate that the proposed scheme provides a significant bandwidth saving by taking full advantage of the caching and computing capability of mobile devices.
Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers
Venue: arXiv cs.CV 2020
Year: 2020

The massive amounts of digitized historical documents acquired over the last decades naturally lend themselves to automatic processing and exploration. Research work seeking to automatically process facsimiles and extract information thereby are multiplying with, as a first essential step, document layout analysis. If the identification and categorization of segments of interest in document images have seen significant progress over the last years thanks to deep learning techniques, many challenges remain with, among others, the use of finer-grained segmentation typologies and the consideration of complex, heterogeneous documents such as historical newspapers. Besides, most approaches consider visual features only, ignoring textual signal. In this context, we introduce a multimodal approach for the semantic segmentation of historical newspapers that combines visual and textual features. Based on a series of experiments on diachronic Swiss and Luxembourgish newspapers, we investigate, among others, the predictive power of visual and textual features and their capacity to generalize across time and sources. Results show consistent improvement of multimodal models in comparison to a strong visual baseline, as well as better robustness to high material variance.
The Phantom Steering Effect in Q&A Websites
Venue: arXiv cs.LG 2020
Year: 2020

Badges are commonly used in online platforms as incentives for promoting contributions. It is widely accepted that badges "steer" people's behavior toward increasing their rate of contributions before obtaining the badge. This paper provides a new probabilistic model of user behavior in the presence of badges. By applying the model to data from thousands of users on the Q&A site Stack Overflow, we find that steering is not as widely applicable as was previously understood. Rather, the majority of users remain apathetic toward badges, while still providing a substantial number of contributions to the site. An interesting statistical phenomenon, termed "Phantom Steering," accounts for the interaction data of these users and this may have contributed to some previous conclusions about steering. Our results suggest that a small population, approximately 20%, of users respond to the badge incentives. Moreover, we conduct a qualitative survey of the users on Stack Overflow which provides further evidence that the insights from the model reflect the true behavior of the community. We argue that while badges might contribute toward a suite of effective rewards in an online system, research into other aspects of reward systems such as Stack Overflow reputation points should become a focus of the community.
Programming Paradigms, Turing Completeness and Computational Thinking
Venue: arXiv cs.PL 2020
Year: 2020

The notion of programming paradigms, with associated programming languages and methodologies, is a well established tenet of Computer Science pedagogy, enshrined in international curricula. However, this notion sits ill with Kuhn's classic conceptualisation of a scientific paradigm as a dominant world view, which supersedes its predecessors through superior explanatory power. Furthermore, it is not at all clear how programming paradigms are to be characterised and differentiated. Indeed, on closer inspection, apparently disparate programming paradigms are very strongly connected. Rather, they should be viewed as different traditions of a unitary Computer Science paradigm of Turing complete computation complemented by Computational Thinking.
Robust Contract Evolution in a TypeSafe MicroServices Architecture
Venue: arXiv cs.PL 2020
Year: 2020

Microservices architectures allow for short deployment cycles and immediate effects but offer no safety mechanisms when service contracts need to be changed. Maintaining the soundness of microservice architectures is an error-prone task that is only accessible to the most disciplined development teams. We present a microservice management system that statically verifies service interfaces and supports the seamless evolution of compatible interfaces. We define a compatibility relation that captures real evolution patterns and embodies known good practices on the evolution of interfaces. Namely, we allow for the addition, removal, and renaming of data fields of a producer module without breaking or needing to upgrade consumer services. The evolution of interfaces is supported by runtime generated proxy components that dynamically adapt data exchanged between services to match with the statically checked service code.The model was instantiated in a core language whose semantics is defined by a labeled transition system and a type system that prevents breaking changes from being deployed. Standard soundness results for the core language entail the existence of adapters, hence the absence of adaptation errors and the correctness of the management model. This adaptive approach allows for gradual deployment of modules, without halting the whole system and avoiding losing or misinterpreting data exchanged between system nodes. Experimental data shows that an average of 69% of deployments that would require adaptation and recompilation are safe under our approach.
Spectrum Translation for Cross-Spectral Ocular Matching
Venue: arXiv cs.CV 2020
Year: 2020

Cross-spectral verification remains a big issue in biometrics, especially for the ocular area due to differences in the reflected features in the images depending on the region and spectrum used. In this paper, we investigate the use of Conditional Adversarial Networks for spectrum translation between near infra-red and visual light images for ocular biometrics. We analyze the transformation based on the overall visual quality of the transformed images and the accuracy drop of the identification system when trained with opposing data. We use the PolyU database and propose two different systems for biometric verification, the first one based on Siamese Networks trained with Softmax and Cross-Entropy loss, and the second one a Triplet Loss network. We achieved an EER of 1\% when using a Triplet Loss network trained for NIR and finding the Euclidean distance between the real NIR images and the fake ones translated from the visible spectrum. We also outperform previous results using baseline algorithms.
Mapping Motor Cortex Stimulation to Muscle Responses: A Deep Neural Network Modeling Approach
Venue: arXiv cs.LG 2020
Year: 2020

A deep neural network (DNN) that can reliably model muscle responses from corresponding brain stimulation has the potential to increase knowledge of coordinated motor control for numerous basic science and applied use cases. Such cases include the understanding of abnormal movement patterns due to neurological injury from stroke, and stimulation based interventions for neurological recovery such as paired associative stimulation. In this work, potential DNN models are explored and the one with the minimum squared errors is recommended for the optimal performance of the M2M-Net, a network that maps transcranial magnetic stimulation of the motor cortex to corresponding muscle responses, using: a finite element simulation, an empirical neural response profile, a convolutional autoencoder, a separate deep network mapper, and recordings of multi-muscle activation. We discuss the rationale behind the different modeling approaches and architectures, and contrast their results. Additionally, to obtain a comparative insight of the trade-off between complexity and performance analysis, we explore different techniques, including the extension of two classical information criteria for M2M-Net. Finally, we find that the model analogous to mapping the motor cortex stimulation to a combination of direct and synergistic connection to the muscles performs the best, when the neural response profile is used at the input.
BLCS: Brain-Like based Distributed Control Security in Cyber Physical Systems
Venue: arXiv cs.NI 2020
Year: 2020

Cyber-physical system (CPS) has operated, controlled and coordinated the physical systems integrated by a computing and communication core applied in industry 4.0. To accommodate CPS services, fog radio and optical networks (F-RON) has become an important supporting physical cyber infrastructure taking advantage of both the inherent ubiquity of wireless technology and the large capacity of optical networks. However, cyber security is the biggest issue in CPS scenario as there is a tradeoff between security control and privacy exposure in F-RON. To deal with this issue, we propose a brain-like based distributed control security (BLCS) architecture for F-RON in CPS, by introducing a brain-like security (BLS) scheme. BLCS can accomplish the secure cross-domain control among tripartite controllers verification in the scenario of decentralized F-RON for distributed computing and communications, which has no need to disclose the private information of each domain against cyber-attacks. BLS utilizes parts of information to perform control identification through relation network and deep learning of behavior library. The functional modules of BLCS architecture are illustrated including various controllers and brain-like knowledge base. The interworking procedures in distributed control security modes based on BLS are described. The overall feasibility and efficiency of architecture are experimentally verified on the software defined network testbed in terms of average mistrust rate, path provisioning latency, packet loss probability and blocking probability. The emulation results are obtained and dissected based on the testbed.
Layered Embeddings for Amodal Instance Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

The proposed method extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts. A fully convolutional network is trained to produce consistent pixel-level embedding across two layers such that, when clustered, the results convey the full spatial extent and depth ordering of each instance. Results demonstrate that the network can accurately estimate complete masks in the presence of occlusion and outperform leading top-down bounding-box approaches. Source code available at https://github.com/yanfengliu/layered_embeddings
Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling
Venue: arXiv cs.LG 2020
Year: 2020

Despite the wide applications of Adam in reinforcement learning (RL), the theoretical convergence of Adam-type RL algorithms has not been established. This paper provides the first such convergence analysis for two fundamental RL algorithms of policy gradient (PG) and temporal difference (TD) learning that incorporate AMSGrad updates (a standard alternative of Adam in theoretical analysis), referred to as PG-AMSGrad and TD-AMSGrad, respectively. Moreover, our analysis focuses on Markovian sampling for both algorithms. We show that under general nonlinear function approximation, PG-AMSGrad with a constant stepsize converges to a neighborhood of a stationary point at the rate of $\mathcal{O}(1/T)$ (where $T$ denotes the number of iterations), and with a diminishing stepsize converges exactly to a stationary point at the rate of $\mathcal{O}(\log^2 T/\sqrt{T})$. Furthermore, under linear function approximation, TD-AMSGrad with a constant stepsize converges to a neighborhood of the global optimum at the rate of $\mathcal{O}(1/T)$, and with a diminishing stepsize converges exactly to the global optimum at the rate of $\mathcal{O}(\log T/\sqrt{T})$. Our study develops new techniques for analyzing the Adam-type RL algorithms under Markovian sampling.
Historical Document Processing: Historical Document Processing: A Survey of Techniques, Tools, and Trends
Venue: arXiv cs.CV 2020
Year: 2020

Historical Document Processing is the process of digitizing written material from the past for future use by historians and other scholars. It incorporates algorithms and software tools from various subfields of computer science, including computer vision, document analysis and recognition, natural language processing, and machine learning, to convert images of ancient manuscripts, letters, diaries, and early printed texts automatically into a digital format usable in data mining and information retrieval systems. Within the past twenty years, as libraries, museums, and other cultural heritage institutions have scanned an increasing volume of their historical document archives, the need to transcribe the full text from these collections has become acute. Since Historical Document Processing encompasses multiple sub-domains of computer science, knowledge relevant to its purpose is scattered across numerous journals and conference proceedings. This paper surveys the major phases of, standard algorithms, tools, and datasets in the field of Historical Document Processing, discusses the results of a literature review, and finally suggests directions for further research.
Legion: Best-First Concolic Testing
Venue: arXiv cs.SE 2020
Year: 2020

Concolic execution and fuzzing are two complementary coverage-based testing techniques. How to achieve the best of both remains an open challenge. To address this research problem, we propose and evaluate Legion. Legion re-engineers the Monte Carlo tree search (MCTS) framework from the AI literature to treat automated test generation as a problem of sequential decision-making under uncertainty. Its best-first search strategy provides a principled way to learn the most promising program states to investigate at each search iteration, based on observed rewards from previous iterations. Legion incorporates a form of directed fuzzing that we call approximate path-preserving fuzzing (APPFuzzing) to investigate program states selected by MCTS. APPFuzzing serves as the Monte Carlo simulation technique and is implemented by extending prior work on constrained sampling. We evaluate Legion against competitors on 2531 benchmarks from the coverage category of Test-Comp 2020, as well as measuring its sensitivity to hyperparameters, demonstrating its effectiveness on a wide variety of input programs.
Quantum Bandits
Venue: arXiv cs.LG 2020
Year: 2020

We consider the quantum version of the bandit problem known as {\em best arm identification} (BAI). We first propose a quantum modeling of the BAI problem, which assumes that both the learning agent and the environment are quantum; we then propose an algorithm based on quantum amplitude amplification to solve BAI. We formally analyze the behavior of the algorithm on all instances of the problem and we show, in particular, that it is able to get the optimal solution quadratically faster than what is known to hold in the classical case.