OpenRaiser/Intern-Atlas huggingface dataset:
Unified Framework for the Adaptive Operator Selection of Discrete Parameters
Venue: arXiv cs.NE 2020
Year: 2020

We conduct an exhaustive survey of adaptive selection of operators (AOS) in Evolutionary Algorithms (EAs). We simplified the AOS structure by adding more components to the framework to built upon the existing categorisation of AOS methods. In addition to simplifying, we looked at the commonality among AOS methods from literature to generalise them. Each component is presented with a number of alternative choices, each represented with a formula. We make three sets of comparisons. First, the methods from literature are tested on the BBOB test bed with their default hyper parameters. Second, the hyper parameters of these methods are tuned using an offline configurator known as IRACE. Third, for a given set of problems, we use IRACE to select the best combination of components and tune their hyper parameters.
How Reliable is Smartphone-based Electronic Contact Tracing for COVID-19?
Venue: arXiv cs.NI 2020
Year: 2020

Smartphone-based electronic contact tracing is currently considered an essential tool towards easing lockdowns, curfews, and shelter-in-place orders issued by most governments around the world in response to the 2020 novel coronavirus (SARS-CoV-2) crisis. While the focus on developing smartphone-based contact tracing applications or apps has been on privacy concerns stemming from the use of such apps, an important question that has not received sufficient attention is: How reliable will such smartphone-based electronic contact tracing be? This is a technical question related to how two smartphones reliably register their mutual proximity. Here, we examine in detail the technical prerequisites required for effective smartphone-based contact tracing. The underlying mechanism that any contact tracing app relies on is called Neighbor Discovery (ND), which involves smartphones transmitting and scanning for Bluetooth signals to record their mutual presence whenever they are in close proximity. The hardware support and the software protocols used for ND in smartphones, however, were not designed for reliable contact tracing. In this paper, we quantitatively evaluate how reliably can smartphones do contact tracing. Our results point towards the design of a wearable solution for contact tracing that can overcome the shortcomings of a smartphone-based solution to provide more reliable and accurate contact tracing. To the best of our knowledge, this is the first study that quantifies, both, the suitability and also the drawbacks of smartphone-based contact tracing. Further, our results can be used to parameterize a ND protocol to maximize the reliability of any contact tracing app that uses it.
Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation
Venue: arXiv cs.RO 2020
Year: 2020

Autonomous harvesting and transportation is a long-term goal of the forest industry. One of the main challenges is the accurate localization of both vehicles and trees in a forest. Forests are unstructured environments where it is difficult to find a group of significant landmarks for current fast feature-based place recognition algorithms. This paper proposes a novel approach where local observations are matched to a general tree map using the Delaunay triangularization as the representation format. Instead of point cloud based matching methods, we utilize a topology-based method. First, tree trunk positions are registered at a prior run done by a forest harvester. Second, the resulting map is Delaunay triangularized. Third, a local submap of the autonomous robot is registered, triangularized and matched using triangular similarity maximization to estimate the position of the robot. We test our method on a dataset accumulated from a forestry site at Lieksa, Finland. A total length of 2100\,m of harvester path was recorded by an industrial harvester with a 3D laser scanner and a geolocation unit fixed to the frame. Our experiments show a 12\,cm s.t.d. in the location accuracy and with real-time data processing for speeds not exceeding 0.5\,m/s. The accuracy and speed limit is realistic during forest operations.
Featured Games
Venue: arXiv cs.LO 2020
Year: 2020

Feature-based SPL analysis and family-based model checking have seen rapid development. Many model checking problems can be reduced to two-player games on finite graphs. A prominent example is mu-calculus model checking, which is generally done by translating to parity games, but also many quantitative model-checking problems can be reduced to (quantitative) games. In their FASE'20 paper, ter Beek et al.\ introduce parity games with variability in order to develop family-based mu-calculus model checking of featured transition systems. We generalize their model to general featured games and show how these may be analysed in a family-based manner. We introduce featured reachability games, featured minimum reachability games, featured discounted games, featured energy games, and featured parity games. We show how to compute winners and values of such games in a family-based manner. We also show that all these featured games admit optimal featured strategies, which project to optimal strategies for any product. Further, we develop family-based algorithms, using late splitting, to compute winners, values, and optimal strategies for all the featured games we have introduced.
Unpaired Motion Style Transfer from Video to Animation
Venue: arXiv cs.GR 2020
Year: 2020

Transferring the motion style from one animation clip to another, while preserving the motion content of the latter, has been a long-standing problem in character animation. Most existing data-driven approaches are supervised and rely on paired data, where motions with the same content are performed in different styles. In addition, these approaches are limited to transfer of styles that were seen during training. In this paper, we present a novel data-driven framework for motion style transfer, which learns from an unpaired collection of motions with style labels, and enables transferring motion styles not observed during training. Furthermore, our framework is able to extract motion styles directly from videos, bypassing 3D reconstruction, and apply them to the 3D input motion. Our style transfer network encodes motions into two latent codes, for content and for style, each of which plays a different role in the decoding (synthesis) process. While the content code is decoded into the output motion by several temporal convolutional layers, the style code modifies deep features via temporally invariant adaptive instance normalization (AdaIN). Moreover, while the content code is encoded from 3D joint rotations, we learn a common embedding for style from either 3D or 2D joint positions, enabling style extraction from videos. Our results are comparable to the state-of-the-art, despite not requiring paired training data, and outperform other methods when transferring previously unseen styles. To our knowledge, we are the first to demonstrate style transfer directly from videos to 3D animations - an ability which enables one to extend the set of style examples far beyond motions captured by MoCap systems.
CSB-RNN: A Faster-than-Realtime RNN Acceleration Framework with Compressed Structured Blocks
Venue: arXiv cs.DC 2020
Year: 2020

Recurrent neural networks (RNNs) have been widely adopted in temporal sequence analysis, where realtime performance is often in demand. However, RNNs suffer from heavy computational workload as the model often comes with large weight matrices. Pruning schemes have been proposed for RNNs to eliminate the redundant (close-to-zero) weight values. On one hand, the non-structured pruning methods achieve a high pruning rate but introducing computation irregularity (random sparsity), which is unfriendly to parallel hardware. On the other hand, hardware-oriented structured pruning suffers from low pruning rate due to restricted constraints on allowable pruning structure. This paper presents CSB-RNN, an optimized full-stack RNN framework with a novel compressed structured block (CSB) pruning technique. The CSB pruned RNN model comes with both fine pruning granularity that facilitates a high pruning rate and regular structure that benefits the hardware parallelism. To address the challenges in parallelizing the CSB pruned model inference with fine-grained structural sparsity, we propose a novel hardware architecture with a dedicated compiler. Gaining from the architecture-compilation co-design, the hardware not only supports various RNN cell types, but is also able to address the challenging workload imbalance issue and therefore significantly improves the hardware efficiency.
WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge
Venue: arXiv cs.AI 2020
Year: 2020

In this paper, we present the first comprehensive categorization of essential commonsense knowledge for answering the Winograd Schema Challenge (WSC). For each of the questions, we invite annotators to first provide reasons for making correct decisions and then categorize them into six major knowledge categories. By doing so, we better understand the limitation of existing methods (i.e., what kind of knowledge cannot be effectively represented or inferred with existing methods) and shed some light on the commonsense knowledge that we need to acquire in the future for better commonsense reasoning. Moreover, to investigate whether current WSC models can understand the commonsense or they simply solve the WSC questions based on the statistical bias of the dataset, we leverage the collected reasons to develop a new task called WinoWhy, which requires models to distinguish plausible reasons from very similar but wrong reasons for all WSC questions. Experimental results prove that even though pre-trained language representation models have achieved promising progress on the original WSC dataset, they are still struggling at WinoWhy. Further experiments show that even though supervised models can achieve better performance, the performance of these models can be sensitive to the dataset distribution. WinoWhy and all codes are available at: https://github.com/HKUST-KnowComp/WinoWhy.
Approximating Boolean Functions with Disjunctive Normal Form
Venue: arXiv cs.CC 2020
Year: 2020

The theorem states that: Every Boolean function can be $ε-approximated$ by a Disjunctive Normal Form (DNF) of size $O_ε(2^{n}/\log{n})$. This paper will demonstrate this theorem in detail by showing how this theorem is generated and proving its correctness. We will also dive into some specific Boolean functions and explore how these Boolean functions can be approximated by a DNF whose size is within the universal bound $O_ε(2^{n}/\log{n})$. The Boolean functions we interested in are: Parity Function: the parity function can be $ε-approximated$ by a DNF of width $(1 - 2ε)n$ and size $2^{(1 - 2ε)n}$. Furthermore, we will explore the lower bounds on the DNF's size and width. Majority Function: for every constant $1/2 < ε< 1$, there is a DNF of size $2^{O(\sqrt{n})}$ that can $ε-approximated$ the Majority Function on n bits. Monotone Functions: every monotone function f can be $ε-approximated$ by a DNF g of size $2^{n - Ωε(n)}$ satisfying $g(x) \le f(x)$ for all x.
HDD-Net: Hybrid Detector Descriptor with Mutual Interactive Learning
Venue: arXiv cs.CV 2020
Year: 2020

Local feature extraction remains an active research area due to the advances in fields such as SLAM, 3D reconstructions, or AR applications. The success in these applications relies on the performance of the feature detector and descriptor. While the detector-descriptor interaction of most methods is based on unifying in single network detections and descriptors, we propose a method that treats both extractions independently and focuses on their interaction in the learning process rather than by parameter sharing. We formulate the classical hard-mining triplet loss as a new detector optimisation term to refine candidate positions based on the descriptor map. We propose a dense descriptor that uses a multi-scale approach and a hybrid combination of hand-crafted and learned features to obtain rotation and scale robustness by design. We evaluate our method extensively on different benchmarks and show improvements over the state of the art in terms of image matching on HPatches and 3D reconstruction quality while keeping on par on camera localisation tasks.
Noisy Density Evolution With Asymmetric Deviation Models
Venue: arXiv cs.IT 2020
Year: 2020

This paper considers low-density parity-check (LDPC) decoders affected by deviations introduced by the electronic device on which the decoder is implemented. Noisy density evolution (DE) that allows to theoretically study the performance of these LDPC decoders can only consider symmetric deviation models due to the all-zero codeword assumption. A novel DE method is proposed that admits the use of asymmetric deviation models, thus widening the range of faulty implementations that can be analyzed. DE equations are provided for three noisy decoders: belief propagation, Gallager B, and quantized min-sum (MS). Simulation results confirm that the proposed DE accurately predicts the performance of LDPC decoders with asymmetric deviations. Furthermore, asymmetric versions of the Gallager B and MS decoders are proposed to compensate the effect of asymmetric deviations. The parameters of these decoders are then optimized using the proposed DE, leading to better ensemble thresholds and improved finite-length performance in the presence of asymmetric deviations.
A Report on the 2020 Sarcasm Detection Shared Task
Venue: arXiv cs.CL 2020
Year: 2020

Detecting sarcasm and verbal irony is critical for understanding people's actual sentiments and beliefs. Thus, the field of sarcasm analysis has become a popular research problem in natural language processing. As the community working on computational approaches for sarcasm detection is growing, it is imperative to conduct benchmarking studies to analyze the current state-of-the-art, facilitating progress in this area. We report on the shared task on sarcasm detection we conducted as a part of the 2nd Workshop on Figurative Language Processing (FigLang 2020) at ACL 2020.
Constructions of complementary sequence sets and complete complementary codes by 2-level autocorrelation sequences and permutation polynomials
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, a recent method to construct complementary sequence sets and complete complementary codes by Hadamard matrices is deeply studied. By taking the algebraic structure of Hadamard matrices into consideration, our main result determine the so-called $δ$-linear terms and $δ$-quadratic terms. As a first consequence, a powerful theory linking Golay complementary sets of $p$-ary ($p$ prime) sequences and the generalized Reed-Muller codes by Kasami et al. is developed. These codes enjoy good error-correcting capability, tightly controlled PMEPR, and significantly extend the range of coding options for applications of OFDM using $p^n$ subcarriers. As another consequence, we make a previously unrecognized connection between the sequences in CSSs and CCCs and the sequence with 2-level autocorrelation, trace function and permutation polynomial (PP) over the finite fields.
The IOA System for Deep Noise Suppression Challenge using a Framework Combining Dynamic Attention and Recursive Learning
Venue: arXiv cs.SD 2020
Year: 2020

This technical report describes our system that is submitted to the Deep Noise Suppression Challenge and presents the results for the non-real-time track. To refine the estimation results stage by stage, we utilize recursive learning, a type of training protocol which aggravates the information through multiple stages with a memory mechanism. The attention generator network is designed to dynamically control the feature distribution of the noise reduction network. To improve the phase recovery accuracy, we take the complex spectral mapping procedure by decoding both real and imaginary spectra. For the final blind test set, the average MOS improvements of the submitted system in noreverb, reverb, and realrec categories are 0.49, 0.24, and 0.36, respectively.
Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts
Venue: arXiv cs.CV 2020
Year: 2020

Digitization has led to smart, connected technologies be an integral part of businesses, governments and communities. For manufacturing digitization, there has been active research and development with a focus on Cloud Manufacturing (CM) and the Industrial Internet of Things (IIoT). This work presents a computer vision toolkit (CV Toolkit) for non-invasive digitization of the factory floor in line with Industry 4.0 requirements for factory data collection. Currently, technical challenges persist towards digitization of legacy systems due to the limitation for changes in their design and sensors. This novel toolkit is developed to facilitate easy integration of legacy production machinery and factory floor artifacts with the digital and smart manufacturing environment with no requirement of any physical changes in the machines. The system developed is modular, and allows real-time monitoring of production machinery. Modularity aspect allows the incorporation of new software applications in the current framework of CV Toolkit. To allow connectivity of this toolkit with manufacturing floors in a simple, deployable and cost-effective manner, the toolkit is integrated with a known manufacturing data standard, MTConnect, to "translate" the digital inputs into data streams that can be read by commercial status tracking and reporting software solutions. The proposed toolkit is demonstrated using a mock-panel environment developed in house at the University of Cincinnati to highlight its usability.
Artificial life properties of directed interaction combinators vs. chemlambda
Venue: arXiv cs.AI 2020
Year: 2020

We provide a framework for experimentation at https://mbuliga.github.io/quinegraphs/ic-vs-chem.html#icvschem with two artificial chemistries: directed interaction combinators (dirIC, defined in section 2) and chemlambda. We are interested if these chemistries allow for artificial life behaviour: replication, metabolism and death. The main conclusion of these experiments is that graph rewrites systems which allow conflicting rewrites are better than those which don't, as concerns their artificial life properties. This is in contradiction with the search for good graph rewrite systems for decentralized computing, where non-conflicting graph rewrite systems are historically preferred. This continues the artificial chemistry experiments with chemlambda, lambda calculus or interaction combinators, available from the entry page at https://chemlambda.github.io/index.html and described in arXiv:2003.14332.
Apple Defect Detection Using Deep Learning Based Object Detection For Better Post Harvest Handling
Venue: arXiv cs.CV 2020
Year: 2020

The inclusion of Computer Vision and Deep Learning technologies in Agriculture aims to increase the harvest quality, and productivity of farmers. During postharvest, the export market and quality evaluation are affected by assorting of fruits and vegetables. In particular, apples are susceptible to a wide range of defects that can occur during harvesting or/and during the post-harvesting period. This paper aims to help farmers with post-harvest handling by exploring if recent computer vision and deep learning methods such as the YOLOv3 (Redmon & Farhadi (2018)) can help in detecting healthy apples from apples with defects.
A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros
Venue: arXiv cs.AI 2020
Year: 2020

Applying latent variable evolution to game level design has become more and more popular as little human expert knowledge is required. However, defective levels with illegal patterns may be generated due to the violation of constraints for level design. A traditional way of repairing the defective levels is programming specific rule-based repairers to patch the flaw. However, programming these constraints is sometimes complex and not straightforward. An autonomous level repairer which is capable of learning the constraints is needed. In this paper, we propose a novel approach, CNet, to learn the probability distribution of tiles giving its surrounding tiles on a set of real levels, and then detect the illegal tiles in generated new levels. Then, an evolutionary repairer is designed to search for optimal replacement schemes equipped with a novel search space being constructed with the help of CNet and a novel heuristic function. The proposed approaches are proved to be effective in our case study of repairing GAN-generated and artificially destroyed levels of Super Mario Bros. game. Our CNet-assisted evolutionary repairer can also be easily applied to other games of which the levels can be represented by a matrix of objects or tiles.
The Scalable Systems Laboratory: a Platform for Software Innovation for HEP
Venue: arXiv cs.DC 2020
Year: 2020

The Scalable Systems Laboratory (SSL), part of the IRIS-HEP Software Institute, provides Institute participants and HEP software developers generally with a means to transition their R&D from conceptual toys to testbeds to production-scale prototypes. The SSL enables tooling, infrastructure, and services supporting the innovation of novel analysis and data architectures, development of software elements and tool-chains, reproducible functional and scalability testing of service components, and foundational systems R&D for accelerated services developed by the Institute. The SSL is constructed with a core team having expertise in scale testing and deployment of services across a wide range of cyberinfrastructure. The core team embeds and partners with other areas in the Institute, and with LHC and other HEP development and operations teams as appropriate, to define investigations and required service deployment patterns. We describe the approach and experiences with early application deployments, including analysis platforms and intelligent data delivery systems.
Attribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification
Venue: arXiv cs.CV 2020
Year: 2020

Vehicle re-identification is one of the core technologies of intelligent transportation systems and smart cities, but large intra-class diversity and inter-class similarity poses great challenges for existing method. In this paper, we propose a multi-guided learning approach which utilizing the information of attributes and meanwhile introducing two novel random augments to improve the robustness during training. What's more, we propose an attribute constraint method and group re-ranking strategy to refine matching results. Our method achieves mAP of 66.83% and rank-1 accuracy 76.05% in the CVPR 2020 AI City Challenge.
LIoTS: League of IoT Sovereignties. A Scalable approach for a Transparent Privacy-safe Federation of Secured IoT Platforms
Venue: arXiv cs.CR 2020
Year: 2020

Internet-of-Things has entered all the fields where data are produced and processed, resulting in a plethora of IoT platforms, typically cloud-based, centralizing data and services management. This has brought to many disjoint IoT silos. Significant efforts have been devoted to integration, recurrently resulting into bigger centralized infrastructures. Such an approach often stumbles upon the reluctance of IoT system owners to loose the dominion over data. We introduce a secured and privacy-safe infrastructure where a federation overlay is distributed among parties and the data control is kept locally. This establishes a league of peers each sovereign of their IoT system and data: League of IoT Sovereignties (LIoTS). LIoTS is scalable by design, allowing iterative formation of domains levels due to the transparency of its federation. Tests show that the overhead is minimal when exchanged data is hefty, and that LIoTS performs better in large IoT deployments than centralized approaches.