OpenRaiser/Intern-Atlas huggingface dataset:
A Point Cloud-Based Method for Automatic Groove Detection and Trajectory Generation of Robotic Arc Welding Tasks
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, in order to pursue high-efficiency robotic arc welding tasks, we propose a method based on point cloud acquired by an RGB-D sensor. The method consists of two parts: welding groove detection and 3D welding trajectory generation. The actual welding scene could be displayed in 3D point cloud format. Focusing on the geometric feature of the welding groove, the detection algorithm is capable of adapting well to different welding workpieces with a V-type welding groove. Meanwhile, a 3D welding trajectory involving 6-DOF poses of the welding groove for robotic manipulator motion is generated. With an acceptable error in trajectory generation, the robotic manipulator could drive the welding torch to follow the trajectory and execute welding tasks. In this paper, details of the integrated robotic system are also presented. Experimental results prove application value of the presented welding robotic system.
AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching
Venue: arXiv cs.CV 2020
Year: 2020

Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications (e.g., remote healthcare). Existing end-to-end rPPG and heart rate (HR) measurement methods from facial videos are vulnerable to the less-constrained scenarios (e.g., with head movement and bad illumination). In this letter, we explore the reason why existing end-to-end networks perform poorly in challenging conditions and establish a strong end-to-end baseline (AutoHR) for remote HR measurement with neural architecture search (NAS). The proposed method includes three parts: 1) a powerful searched backbone with novel Temporal Difference Convolution (TDC), intending to capture intrinsic rPPG-aware clues between frames; 2) a hybrid loss function considering constraints from both time and frequency domains; and 3) spatio-temporal data augmentation strategies for better representation learning. Comprehensive experiments are performed on three benchmark datasets to show our superior performance on both intra- and cross-dataset testing.
Detecting fake news for the new coronavirus by reasoning on the Covid-19 ontology
Venue: arXiv cs.AI 2020
Year: 2020

In the context of the Covid-19 pandemic, many were quick to spread deceptive information. I investigate here how reasoning in Description Logics (DLs) can detect inconsistencies between trusted medical sources and not trusted ones. The not-trusted information comes in natural language (e.g. "Covid-19 affects only the elderly"). To automatically convert into DLs, I used the FRED converter. Reasoning in Description Logics is then performed with the Racer tool.
Computing server power modeling in a data center: survey,taxonomy and performance evaluation
Venue: arXiv cs.DC 2020
Year: 2020

Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper.
GLUECoS : An Evaluation Benchmark for Code-Switched NLP
Venue: arXiv cs.CL 2020
Year: 2020

Code-switching is the use of more than one language in the same conversation or utterance. Recently, multilingual contextual embedding models, trained on multiple monolingual corpora, have shown promising results on cross-lingual and multilingual tasks. We present an evaluation benchmark, GLUECoS, for code-switched languages, that spans several NLP tasks in English-Hindi and English-Spanish. Specifically, our evaluation benchmark includes Language Identification from text, POS tagging, Named Entity Recognition, Sentiment Analysis, Question Answering and a new task for code-switching, Natural Language Inference. We present results on all these tasks using cross-lingual word embedding models and multilingual models. In addition, we fine-tune multilingual models on artificially generated code-switched data. Although multilingual models perform significantly better than cross-lingual models, our results show that in most tasks, across both language pairs, multilingual models fine-tuned on code-switched data perform best, showing that multilingual models can be further optimized for code-switching tasks.
Towards Feature Space Adversarial Attack
Venue: arXiv cs.LG 2020
Year: 2020

We propose a new adversarial attack to Deep Neural Networks for image classification. Different from most existing attacks that directly perturb input pixels, our attack focuses on perturbing abstract features, more specifically, features that denote styles, including interpretable styles such as vivid colors and sharp outlines, and uninterpretable ones. It induces model misclassfication by injecting imperceptible style changes through an optimization procedure. We show that our attack can generate adversarial samples that are more natural-looking than the state-of-the-art unbounded attacks. The experiment also supports that existing pixel-space adversarial attack detection and defense techniques can hardly ensure robustness in the style related feature space.
The Impact of Presentation Style on Human-In-The-Loop Detection of Algorithmic Bias
Venue: arXiv cs.HC 2020
Year: 2020

While decision makers have begun to employ machine learning, machine learning models may make predictions that bias against certain demographic groups. Semi-automated bias detection tools often present reports of automatically-detected biases using a recommendation list or visual cues. However, there is a lack of guidance concerning which presentation style to use in what scenarios. We conducted a small lab study with 16 participants to investigate how presentation style might affect user behaviors in reviewing bias reports. Participants used both a prototype with a recommendation list and a prototype with visual cues for bias detection. We found that participants often wanted to investigate the performance measures that were not automatically detected as biases. Yet, when using the prototype with a recommendation list, they tended to give less consideration to such measures. Grounded in the findings, we propose information load and comprehensiveness as two axes for characterizing bias detection tasks and illustrate how the two axes could be adopted to reason about when to use a recommendation list or visual cues.
Error Probability Analysis of Non-Orthogonal Multiple Access with Channel Estimation Errors
Venue: arXiv cs.IT 2020
Year: 2020

Non-orthogonal multiple access (NOMA) is very promising for future wireless systems thanks to its spectral efficiency. In NOMA schemes, the effect of imperfect successive interference canceler (SIC) has dominant effect on the error performances. In addition to this imperfect SIC effect, the error performance will get worse with the channel estimation errors just as in all wireless communications systems. However, all literature has been devoted to analyze error performance of NOMA systems with the perfect channel state information (CSI) at the receivers which is very strict/unreasonable assumption. In this paper, we analyze error performance of NOMA systems with imperfect SIC and channel estimation errors, much more practical scenario. We derive exact bit error probabilities (BEPs) in closed-forms. All theoretical analysis is validated via computer simulations. Then, we discuss optimum power allocation for user fairness in terms of error performance of users and propose a novel power allocation scheme which achieves maximum user fairness.
Error Analysis of Decode-Forward Cooperative Relaying NOMA Schemes over Nakagami-m Fading Channels
Venue: arXiv cs.IT 2020
Year: 2020

Non-orthogonal multiple access (NOMA) has attracted great recent attention due to its high spectral efficiency. Besides, NOMA can be easily implemented in physical layer, thus its interplays with other psychical layer techniques have been analyzed widely by researches. Cooperative NOMA schemes are the most investigated among these interplays. However, these studies mostly analyze cooperative NOMA schemes in terms of achievable rate and outage probability whereas bit error probability (BEP) for those systems has not been studied well although it is one of the most important performance metrics. In this paper, we investigate the error performance of cooperative NOMA schemes where a decode-forward relay helps NOMA users. We derive exact BEP expressions in closed-form over Nakagami-m fading channels. All derived expressions are validated via computer simulations.
Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language
Venue: arXiv cs.CL 2020
Year: 2020

To better tackle the named entity recognition (NER) problem on languages with little/no labeled data, cross-lingual NER must effectively leverage knowledge learned from source languages with rich labeled data. Previous works on cross-lingual NER are mostly based on label projection with pairwise texts or direct model transfer. However, such methods either are not applicable if the labeled data in the source languages is unavailable, or do not leverage information contained in unlabeled data in the target language. In this paper, we propose a teacher-student learning method to address such limitations, where NER models in the source languages are used as teachers to train a student model on unlabeled data in the target language. The proposed method works for both single-source and multi-source cross-lingual NER. For the latter, we further propose a similarity measuring method to better weight the supervision from different teacher models. Extensive experiments for 3 target languages on benchmark datasets well demonstrate that our method outperforms existing state-of-the-art methods for both single-source and multi-source cross-lingual NER.
Designing a physically-feasible colour filter to make a camera more colorimetric
Venue: arXiv cs.CV 2020
Year: 2020

Previously, a method has been developed to find the best colour filter for a given camera which results in the new effective camera sensitivities that best meet the Luther condition. That is, the new sensitivities are approximately linearly related to the XYZ colour matching functions. However, with no constraint, the filter derived from this Luther-condition based optimisation can be rather non-smooth and transmit very little light which are impractical for fabrication. In this paper, we extend the Luther-condition filter optimisation method to allow us to incorporate both the smoothness and transmittance bounds of the recovered filter which are key practical concerns. Experiments demonstrate that we can find physically realisable filters which are smooth and reasonably transmissive with which the effective "camera+filter" becomes significantly more colorimetric.
Secure Steganography Technique Based on Bitplane Indexes
Venue: arXiv cs.MM 2020
Year: 2020

This paper is concerned with secret hiding in multiple image bitplanes for increased security without undermining capacity. A secure steganographic algorithm based on bitplanes index manipulation is proposed. The index manipulation is confined to the first two Least Significant Bits of the cover image. The proposed algorithm has the property of un-detectability with respect to stego quality and payload capacity. Experimental results demonstrate that the proposed technique is secure against statistical attacks such as pair of value (PoV), Weighted Stego steganalyser (WS), and Multi Bitplane Weighted Stego steganalyser (MLSB-WS).
Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes
Venue: arXiv cs.CV 2020
Year: 2020

The deficiency of 3D segmentation labels is one of the main obstacles to effective point cloud segmentation, especially for scenes in the wild with varieties of different objects. To alleviate this issue, we propose a novel deep graph convolutional network-based framework for large-scale semantic scene segmentation in point clouds with sole 2D supervision. Different with numerous preceding multi-view supervised approaches focusing on single object point clouds, we argue that 2D supervision is capable of providing sufficient guidance information for training 3D semantic segmentation models of natural scene point clouds while not explicitly capturing their inherent structures, even with only single view per training sample. Specifically, a Graph-based Pyramid Feature Network (GPFN) is designed to implicitly infer both global and local features of point sets and an Observability Network (OBSNet) is introduced to further solve object occlusion problem caused by complicated spatial relations of objects in 3D scenes. During the projection process, perspective rendering and semantic fusion modules are proposed to provide refined 2D supervision signals for training along with a 2D-3D joint optimization strategy. Extensive experimental results demonstrate the effectiveness of our 2D supervised framework, which achieves comparable results with the state-of-the-art approaches trained with full 3D labels, for semantic point cloud segmentation on the popular SUNCG synthetic dataset and S3DIS real-world dataset.
Difficulty Translation in Histopathology Images
Venue: arXiv cs.CV 2020
Year: 2020

The unique nature of histopathology images opens the door to domain-specific formulations of image translation models. We propose a difficulty translation model that modifies colorectal histopathology images to be more challenging to classify. Our model comprises a scorer, which provides an output confidence to measure the difficulty of images, and an image translator, which learns to translate images from easy-to-classify to hard-to-classify using a training set defined by the scorer. We present three findings. First, generated images were indeed harder to classify for both human pathologists and machine learning classifiers than their corresponding source images. Second, image classifiers trained with generated images as augmented data performed better on both easy and hard images from an independent test set. Finally, human annotator agreement and our model's measure of difficulty correlated strongly, implying that for future work requiring human annotator agreement, the confidence score of a machine learning classifier could be used as a proxy.
Automatic Textual Evidence Mining in COVID-19 Literature
Venue: arXiv cs.IR 2020
Year: 2020

We created this EVIDENCEMINER system for automatic textual evidence mining in COVID-19 literature. EVIDENCEMINER is a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. It is constructed in a completely automated way without any human effort for training data annotation. EVIDENCEMINER is supported by novel data-driven methods for distantly supervised named entity recognition and open information extraction. The named entities and meta-patterns are pre-computed and indexed offline to support fast online evidence retrieval. The annotation results are also highlighted in the original document for better visualization. EVIDENCEMINER also includes analytic functionalities such as the most frequent entity and relation summarization.
DWT-GBT-SVD-based Robust Speech Steganography
Venue: arXiv cs.MM 2020
Year: 2020

Steganography is a method that can improve network security and make communications safer. In this method, a secret message is hidden in content like audio signals that should not be perceptible by listening to the audio or seeing the signal waves. Also, it should be robust against different common attacks such as noise and compression. In this paper, we propose a new speech steganography method based on a combination of Discrete Wavelet Transform, Graph-based Transform, and Singular Value Decomposition (SVD). In this method, we first find voiced frames based on energy and zero-crossing counts of the frames and then embed a binary message into voiced frames. Experimental results on the NOIZEUS database show that the proposed method is imperceptible and also robust against Gaussian noise, re-sampling, re-quantization, high pass filter, and low pass filter. Also, it is robust against MP3 compression and scaling for watermarking applications.
Demo: MmWave Lens MIMO
Venue: arXiv cs.NI 2020
Year: 2020

In fifth-generation (5G) communication, the RF lens antenna is introduced with advantages of high antenna gain and low hardware cost in analog/digital hybrid beamforming technology. In this paper, we design and manufacture the appropriate RF lens antenna for mmWave communication identified in simulation analysis and present a real-time mmWave lens MIMO testbed. Furthermore, in conventional phase-shift MIMO, beam-squint, a problem caused by the wideband of mmWave communication, is estimated with the fabricated RF lens antenna.
Continuous hand-eye calibration using 3D points
Venue: arXiv cs.RO 2020
Year: 2020

The recent development of calibration algorithms has been driven into two major directions: (1) an increasing accuracy of mathematical approaches and (2) an increasing flexibility in usage by reducing the dependency on calibration objects. These two trends, however, seem to be contradictory since the overall accuracy is directly related to the accuracy of the pose estimation of the calibration object and therefore demanding large objects, while an increased flexibility leads to smaller objects or noisier estimation methods. The method presented in this paper aims to resolves this problem in two steps: First, we derive a simple closed-form solution with a shifted focus towards the equation of translation that only solves for the necessary hand-eye transformation. We show that it is superior in accuracy and robustness compared to traditional approaches. Second, we decrease the dependency on the calibration object to a single 3D-point by using a similar formulation based on the equation of translation which is much less affected by the estimation error of the calibration object's orientation. Moreover, it makes the estimation of the orientation obsolete while taking advantage of the higher accuracy and robustness from the first solution, resulting in a versatile method for continuous hand-eye calibration.
On Perturbation Resilience of Non-Uniform $k$-Center
Venue: arXiv cs.DS 2020
Year: 2020

The Non-Uniform $k$-center (NUkC) problem has recently been formulated by Chakrabarty, Goyal and Krishnaswamy [ICALP, 2016] as a generalization of the classical $k$-center clustering problem. In NUkC, given a set of $n$ points $P$ in a metric space and non-negative numbers $r_1, r_2, \ldots , r_k$, the goal is to find the minimum dilation $α$ and to choose $k$ balls centered at the points of $P$ with radius $α\cdot r_i$ for $1\le i\le k$, such that all points of $P$ are contained in the union of the chosen balls. They showed that the problem is NP-hard to approximate within any factor even in tree metrics. On the other hand, they designed a "bi-criteria" constant approximation algorithm that uses a constant times $k$ balls. Surprisingly, no true approximation is known even in the special case when the $r_i$'s belong to a fixed set of size 3. In this paper, we study the NUkC problem under perturbation resilience, which was introduced by Bilu and Linial [Combinatorics, Probability and Computing, 2012]. We show that the problem under 2-perturbation resilience is polynomial time solvable when the $r_i$'s belong to a constant sized set. However, we show that perturbation resilience does not help in the general case. In particular, our findings imply that even with perturbation resilience one cannot hope to find any "good" approximation for the problem.
Information and Causality in Promise Theory
Venue: arXiv cs.MA 2020
Year: 2020

The explicit link between Promise Theory and Information Theory, while perhaps obvious, is laid out explicitly here. It's shown how causally related observations of promised behaviours relate to the probabilistic formulation of causal information in Shannon's theory, and thus clarify the meaning of autonomy or causal independence, and further the connection between information and causal sets. Promise Theory helps to make clear a number of assumptions which are commonly taken for granted in causal descriptions. The concept of a promise is hard to escape. It serves as proxy for intent, whether a priori or by inference, and it is intrinsic to the interpretations of observations in the latter.
Lexically Constrained Neural Machine Translation with Levenshtein Transformer
Venue: arXiv cs.CL 2020
Year: 2020

This paper proposes a simple and effective algorithm for incorporating lexical constraints in neural machine translation. Previous work either required re-training existing models with the lexical constraints or incorporating them during beam search decoding with significantly higher computational overheads. Leveraging the flexibility and speed of a recently proposed Levenshtein Transformer model (Gu et al., 2019), our method injects terminology constraints at inference time without any impact on decoding speed. Our method does not require any modification to the training procedure and can be easily applied at runtime with custom dictionaries. Experiments on English-German WMT datasets show that our approach improves an unconstrained baseline and previous approaches.
Approximate Turing Kernelization for Problems Parameterized by Treewidth
Venue: arXiv cs.DS 2020
Year: 2020

We extend the notion of lossy kernelization, introduced by Lokshtanov et al. [STOC 2017], to approximate Turing kernelization. An $α$-approximate Turing kernel for a parameterized optimization problem is a polynomial-time algorithm that, when given access to an oracle that outputs $c$-approximate solutions in $O(1)$ time, obtains an $(α\cdot c)$-approximate solution to the considered problem, using calls to the oracle of size at most $f(k)$ for some function $f$ that only depends on the parameter. Using this definition, we show that Independent Set parameterized by treewidth $\ell$ has a $(1+\varepsilon)$-approximate Turing kernel with $O(\frac{\ell^2}{\varepsilon})$ vertices, answering an open question posed by Lokshtanov et al. [STOC 2017]. Furthermore, we give $(1+\varepsilon)$-approximate Turing kernels for the following graph problems parameterized by treewidth: Vertex Cover, Edge Clique Cover, Edge-Disjoint Triangle Packing and Connected Vertex Cover. We generalize the result for Independent Set and Vertex Cover, by showing that all graph problems that we will call "friendly" admit $(1+\varepsilon)$-approximate Turing kernels of polynomial size when parameterized by treewidth. We use this to obtain approximate Turing kernels for Vertex-Disjoint $H$-packing for connected graphs $H$, Clique Cover, Feedback Vertex Set and Edge Dominating Set.