OpenRaiser/Intern-Atlas huggingface dataset:
The Future of Work Is Here: Toward a Comprehensive Approach to Artificial Intelligence and Labour
Venue: arXiv cs.CY 2020
Year: 2020

This commentary traces contemporary discourses on the relationship between artificial intelligence and labour and explains why these principles must be comprehensive in their approach to labour and AI. First, the commentary asserts that ethical frameworks in AI alone are not enough to guarantee workers' rights since they lack enforcement mechanisms and the representation of different stakeholders. Secondly, it argues that current discussions on AI and labour focus on the deployment of these technologies in the workplace but ignore the essential role of human labour in their development, particularly in the different cases of outsourced labour around the world. Finally, it recommends using existing human rights frameworks for working conditions to provide more comprehensive ethical principles and regulations. The commentary concludes by arguing that the central question regarding the future of work should not be whether intelligent machines will replace humans, but who will own these systems and have a say in their development and operation.
Complex Wavelet SSIM based Image Data Augmentation
Venue: arXiv cs.CV 2020
Year: 2020

One of the biggest problems in neural learning networks is the lack of training data available to train the network. Data augmentation techniques over the past few years, have therefore been developed, aiming to increase the amount of artificial training data with the limited number of real world samples. In this paper, we look particularly at the MNIST handwritten dataset an image dataset used for digit recognition, and the methods of data augmentation done on this data set. We then take a detailed look into one of the most popular augmentation techniques used for this data set elastic deformation; and highlight its demerit of degradation in the quality of data, which introduces irrelevant data to the training set. To decrease this irrelevancy, we propose to use a similarity measure called Complex Wavelet Structural Similarity Index Measure (CWSSIM) to selectively filter out the irrelevant data before we augment the data set. We compare our observations with the existing augmentation technique and find our proposed method works yields better results than the existing technique.
Applying recent advances in Visual Question Answering to Record Linkage
Venue: arXiv cs.LG 2020
Year: 2020

Multi-modal Record Linkage is the process of matching multi-modal records from multiple sources that represent the same entity. This field has not been explored in research and we propose two solutions based on Deep Learning architectures that are inspired by recent work in Visual Question Answering. The neural networks we propose use two different fusion modules, the Recurrent Neural Network + Convolutional Neural Network fusion module and the Stacked Attention Network fusion module, that jointly combine the visual and the textual data of the records. The output of these fusion models is the input of a Siamese Neural Network that computes the similarity of the records. Using data from the Avito Duplicate Advertisements Detection dataset, we train these solutions and from the experiments, we concluded that the Recurrent Neural Network + Convolutional Neural Network fusion module outperforms a simple model that uses hand-crafted features. We also find that the Recurrent Neural Network + Convolutional Neural Network fusion module classifies dissimilar advertisements as similar more frequently if their average description is bigger than 40 words. We conclude that the reason for this is that the longer advertisements have a different distribution then the shorter advertisements who are more prevalent in the dataset. In the end, we also conclude that further research needs to be done with the Stacked Attention Network, to further explore the effects of the visual data on the performance of the fusion modules.
Physics Successfully Implements Lagrange Multiplier Optimization
Venue: arXiv cs.ET 2020
Year: 2020

Optimization is a major part of human effort. While being mathematical, optimization is also built into physics. For example, physics has the principle of Least Action, the principle of Minimum Entropy Generation, and the Variational Principle. Physics also has physical annealing which, of course, preceded computational Simulated Annealing. Physics has the Adiabatic Principle, which in its quantum form is called Quantum Annealing. Thus, physical machines can solve the mathematical problem of optimization, including constraints. Binary constraints can be built into the physical optimization. In that case the machines are digital in the same sense that a flip-flop is digital. A wide variety of machines have had recent success at optimizing the Ising magnetic energy. We demonstrate in this paper that almost all those machines perform optimization according to the Principle of Minimum Entropy Generation as put forth by Onsager. Further, we show that this optimization is in fact equivalent to Lagrange multiplier optimization for constrained problems. We find that the physical gain coefficients which drive those systems actually play the role of the corresponding Lagrange Multipliers.
Editable AI: Mixed Human-AI Authoring of Code Patterns
Venue: arXiv cs.HC 2020
Year: 2020

Developers authoring HTML documents define elements following patterns which establish and reflect the visual structure of a document, such as making all images in a footer the same height by applying a class to each. To surface these patterns to developers and support developers in authoring consistent with these patterns, we propose a mixed human-AI technique for creating code patterns. Patterns are first learned from individual HTML documents through a decision tree, generating a representation which developers may view and edit. Code patterns are used to offer developers autocomplete suggestions, list examples, and flag violations. To evaluate our technique, we conducted a user study in which 24 participants wrote, edited, and corrected HTML documents. We found that our technique enabled developers to edit and correct documents more quickly and create, edit, and correct documents more successfully.
Two-Stream Deep Feature Modelling for Automated Video Endoscopy Data Analysis
Venue: arXiv cs.CV 2020
Year: 2020

Automating the analysis of imagery of the Gastrointestinal (GI) tract captured during endoscopy procedures has substantial potential benefits for patients, as it can provide diagnostic support to medical practitioners and reduce mistakes via human error. To further the development of such methods, we propose a two-stream model for endoscopic image analysis. Our model fuses two streams of deep feature inputs by mapping their inherent relations through a novel relational network model, to better model symptoms and classify the image. In contrast to handcrafted feature-based models, our proposed network is able to learn features automatically and outperforms existing state-of-the-art methods on two public datasets: KVASIR and Nerthus. Our extensive evaluations illustrate the importance of having two streams of inputs instead of a single stream and also demonstrates the merits of the proposed relational network architecture to combine those streams.
A Three-limb Teleoperated Robotic System with Foot Control for Flexible Endoscopic Surgery
Venue: arXiv cs.RO 2020
Year: 2020

Flexible endoscopy requires high skills to manipulate both the endoscope and associated instruments. In most robotic flexible endoscopic systems, the endoscope and instruments are controlled separately by two operators, which may result in communication errors and inefficient operation. We present a novel teleoperation robotic endoscopic system that can be commanded by a surgeon alone. This 13 degrees-of-freedom (DoF) system integrates a foot-controlled robotic flexible endoscope and two hand-controlled robotic endoscopic instruments (a robotic grasper and a robotic cauterizing hook). A foot-controlled human-machine interface maps the natural foot gestures to the 4-DoF movements of the endoscope, and two hand-controlled interfaces map the movements of the two hands to the two instruments individually. The proposed robotic system was validated in an ex-vivo experiment carried out by six subjects, where foot control was also compared with a sequential clutch-based hand control scheme. The participants could successfully teleoperate the endoscope and the two instruments to cut the tissues at scattered target areas in a porcine stomach. Foot control yielded 43.7% faster task completion and required less mental effort as compared to the clutch-based hand control scheme. The system introduced in this paper is intuitive for three-limb manipulation even for operators without experience of handling the endoscope and robotic instruments. This three-limb teleoperated robotic system enables one surgeon to intuitively control three endoscopic tools which normally require two operators, leading to reduced manpower, less communication errors, and improved efficiency.
Pose-aware Adversarial Domain Adaptation for Personalized Facial Expression Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Current facial expression recognition methods fail to simultaneously cope with pose and subject variations. In this paper, we propose a novel unsupervised adversarial domain adaptation method which can alleviate both variations at the same time. Specially, our method consists of three learning strategies: adversarial domain adaptation learning, cross adversarial feature learning, and reconstruction learning. The first aims to learn pose- and expression-related feature representations in the source domain and adapt both feature distributions to that of the target domain by imposing adversarial learning. By using personalized adversarial domain adaptation, this learning strategy can alleviate subject variations and exploit information from the source domain to help learning in the target domain. The second serves to perform feature disentanglement between pose- and expression-related feature representations by impulsing pose-related feature representations expression-undistinguished and the expression-related feature representations pose-undistinguished. The last can further boost feature learning by applying face image reconstructions so that the learned expression-related feature representations are more pose- and identity-robust. Experimental results on four benchmark datasets demonstrate the effectiveness of the proposed method.
fenicsR13: A Tensorial Mixed Finite Element Solver for the Linear R13 Equations Using the FEniCS Computing Platform
Venue: arXiv cs.CE 2020
Year: 2020

We present a mixed finite element solver for the linearized R13 equations of non-equilibrium gas dynamics. The Python implementation builds upon the software tools provided by the FEniCS computing platform. We describe a new tensorial approach utilizing the extension capabilities of FEniCS's Unified Form Language (UFL) to define required differential operators for tensors above second degree. The presented solver serves as an example for implementing tensorial variational formulations in FEniCS, for which the documentation and literature seem to be very sparse. Using the software abstraction levels provided by the UFL allows an almost one-to-one correspondence between the underlying mathematics and the resulting source code. Test cases support the correctness of the proposed method using validation with exact solutions. To justify the usage of extended gas flow models, we discuss typical application cases involving rarefaction effects. We provide the documented and validated solver publicly.
Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?
Venue: arXiv cs.LG 2020
Year: 2020

Graph Identification (GI) has long been researched in graph learning and is essential in certain applications (e.g. social community detection). Specifically, GI requires to predict the label/score of a target graph given its collection of node features and edge connections. While this task is common, more complex cases arise in practice---we are supposed to do the inverse thing by, for example, grouping similar users in a social network given the labels of different communities. This triggers an interesting thought: can we identify nodes given the labels of the graphs they belong to? Therefore, this paper defines a novel problem dubbed Inverse Graph Identification (IGI), as opposed to GI. Upon a formal discussion of the variants of IGI, we choose a particular case study of node clustering by making use of the graph labels and node features, with an assistance of a hierarchical graph that further characterizes the connections between different graphs. To address this task, we propose Gaussian Mixture Graph Convolutional Network (GMGCN), a simple yet effective method that makes the node-level message passing process using Graph Attention Network (GAT) under the protocol of GI and then infers the category of each node via a Gaussian Mixture Layer (GML). The training of GMGCN is further boosted by a proposed consensus loss to take advantage of the structure of the hierarchical graph. Extensive experiments are conducted to test the rationality of the formulation of IGI. We verify the superiority of the proposed method compared to other baselines on several benchmarks we have built up. We will release our codes along with the benchmark data to facilitate more research attention to the IGI problem.
Probabilistic Jacobian-based Saliency Maps Attacks
Venue: arXiv cs.CV 2020
Year: 2020

Neural network classifiers (NNCs) are known to be vulnerable to malicious adversarial perturbations of inputs including those modifying a small fraction of the input features named sparse or $L_0$ attacks. Effective and fast $L_0$ attacks, such as the widely used Jacobian-based Saliency Map Attack (JSMA) are practical to fool NNCs but also to improve their robustness. In this paper, we show that penalising saliency maps of JSMA by the output probabilities and the input features of the NNC allows to obtain more powerful attack algorithms that better take into account each input's characteristics. This leads us to introduce improved versions of JSMA, named Weighted JSMA (WJSMA) and Taylor JSMA (TJSMA), and demonstrate through a variety of white-box and black-box experiments on three different datasets (MNIST, CIFAR-10 and GTSRB), that they are both significantly faster and more efficient than the original targeted and non-targeted versions of JSMA. Experiments also demonstrate, in some cases, very competitive results of our attacks in comparison with the Carlini-Wagner (CW) $L_0$ attack, while remaining, like JSMA, significantly faster (WJSMA and TJSMA are more than 50 times faster than CW $L_0$ on CIFAR-10). Therefore, our new attacks provide good trade-offs between JSMA and CW for $L_0$ real-time adversarial testing on datasets such as the ones previously cited. Codes are publicly available through the link https://github.com/probabilistic-jsmas/probabilistic-jsmas.
A Comparative Study on Polyp Classification using Convolutional Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Colorectal cancer is the third most common cancer diagnosed in both men and women in the United States. Most colorectal cancers start as a growth on the inner lining of the colon or rectum, called 'polyp'. Not all polyps are cancerous, but some can develop into cancer. Early detection and recognition of the type of polyps is critical to prevent cancer and change outcomes. However, visual classification of polyps is challenging due to varying illumination conditions of endoscopy, variant texture, appearance, and overlapping morphology between polyps. More importantly, evaluation of polyp patterns by gastroenterologists is subjective leading to a poor agreement among observers. Deep convolutional neural networks have proven very successful in object classification across various object categories. In this work, we compare the performance of the state-of-the-art general object classification models for polyp classification. We trained a total of six CNN models end-to-end using a dataset of 157 video sequences composed of two types of polyps: hyperplastic and adenomatous. Our results demonstrate that the state-of-the-art CNN models can successfully classify polyps with an accuracy comparable or better than reported among gastroenterologists. The results of this study can guide future research in polyp classification.
Robotic Non-Destructive Testing of Manmade Structures: A Review of the Literature
Venue: arXiv cs.RO 2020
Year: 2020

This literature review investigates how robots can be used for the maintenance of manmade structures, such as pipes, reinforced concrete decks, and space stations as a sampling of the broad spectrum of robotic non-destructive testing (NDT) applications. Robotic NDT can be used to find plaque in pipes, corrosion in steel buildings, and impact damage in space stations, which would normally be invisible to the eye. After inspection, the inspected material is preserved in its original condition. The paper's structure is as follows: first, the definition of NDT is elaborated upon with the discussion of specific methods that will be used in the inspection of the structures mentioned above. Second, an explanation follows on why robots are suited to inspection, specifically focusing on robots' advantages over humans. Third, three real-world examples notify the reader of current progress in robot NDT. Lastly, a summary of robot problems serves as a reminder that testing and development must continue for robot NDT to become mainstream.
OtoWorld: Towards Learning to Separate by Learning to Move
Venue: arXiv cs.SD 2020
Year: 2020

We present OtoWorld, an interactive environment in which agents must learn to listen in order to solve navigational tasks. The purpose of OtoWorld is to facilitate reinforcement learning research in computer audition, where agents must learn to listen to the world around them to navigate. OtoWorld is built on three open source libraries: OpenAI Gym for environment and agent interaction, PyRoomAcoustics for ray-tracing and acoustics simulation, and nussl for training deep computer audition models. OtoWorld is the audio analogue of GridWorld, a simple navigation game. OtoWorld can be easily extended to more complex environments and games. To solve one episode of OtoWorld, an agent must move towards each sounding source in the auditory scene and "turn it off". The agent receives no other input than the current sound of the room. The sources are placed randomly within the room and can vary in number. The agent receives a reward for turning off a source. We present preliminary results on the ability of agents to win at OtoWorld. OtoWorld is open-source and available.
Locality Guided Neural Networks for Explainable Artificial Intelligence
Venue: arXiv cs.CV 2020
Year: 2020

In current deep network architectures, deeper layers in networks tend to contain hundreds of independent neurons which makes it hard for humans to understand how they interact with each other. By organizing the neurons by correlation, humans can observe how clusters of neighbouring neurons interact with each other. In this paper, we propose a novel algorithm for back propagation, called Locality Guided Neural Network(LGNN) for training networks that preserves locality between neighbouring neurons within each layer of a deep network. Heavily motivated by Self-Organizing Map (SOM), the goal is to enforce a local topology on each layer of a deep network such that neighbouring neurons are highly correlated with each other. This method contributes to the domain of Explainable Artificial Intelligence (XAI), which aims to alleviate the black-box nature of current AI methods and make them understandable by humans. Our method aims to achieve XAI in deep learning without changing the structure of current models nor requiring any post processing. This paper focuses on Convolutional Neural Networks (CNNs), but can theoretically be applied to any type of deep learning architecture. In our experiments, we train various VGG and Wide ResNet (WRN) networks for image classification on CIFAR100. In depth analyses presenting both qualitative and quantitative results demonstrate that our method is capable of enforcing a topology on each layer while achieving a small increase in classification accuracy
Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection
Venue: arXiv cs.CV 2020
Year: 2020

This paper focuses on Semi-Supervised Object Detection (SSOD). Knowledge Distillation (KD) has been widely used for semi-supervised image classification. However, adapting these methods for SSOD has the following obstacles. (1) The teacher model serves a dual role as a teacher and a student, such that the teacher predictions on unlabeled images may be very close to those of student, which limits the upper-bound of the student. (2) The class imbalance issue in SSOD hinders an efficient knowledge transfer from teacher to student. To address these problems, we propose a novel method Temporal Self-Ensembling Teacher (TSE-T) for SSOD. Differently from previous KD based methods, we devise a temporally evolved teacher model. First, our teacher model ensembles its temporal predictions for unlabeled images under stochastic perturbations. Second, our teacher model ensembles its temporal model weights with the student model weights by an exponential moving average (EMA) which allows the teacher gradually learn from the student. These self-ensembling strategies increase data and model diversity, thus improving teacher predictions on unlabeled images. Finally, we use focal loss to formulate consistency regularization term to handle the data imbalance problem, which is a more efficient manner to utilize the useful information from unlabeled images than a simple hard-thresholding method which solely preserves confident predictions. Evaluated on the widely used VOC and COCO benchmarks, the mAP of our method has achieved 80.73% and 40.52% on the VOC2007 test set and the COCO2014 minval5k set respectively, which outperforms a strong fully-supervised detector by 2.37% and 1.49%. Furthermore, our method sets the new state-of-the-art in SSOD on VOC2007 test set which outperforms the baseline SSOD method by 1.44%. The source code of this work is publicly available at http://github.com/syangdong/tse-t.
Local Editing in LZ-End Compressed Data
Venue: arXiv cs.DS 2020
Year: 2020

This paper presents an algorithm for the modification of data compressed using LZ-End, a derivate of LZ77, without prior decompression. The performance of the algorithm and the impact of the modifications on the compression ratio is evaluated. Finally, we discuss the importance of this work as a first step towards local editing in Lempel-Ziv compressed data.
Steps Towards Best Practices For Robot Videos
Venue: arXiv cs.RO 2020
Year: 2020

There are unwritten guidelines for how to make robot videos that researchers learn from their advisors and pass onto their students. We believe that it is important for the community to collaboratively discuss and develop a standard set of best practices when making robot. We suggest a starting set of maxims for best robot video practices, and highlight positive examples from the community and negative examples only from videos made by the authors of this article. In addition, we offer a checklist that we hope can act as an document that can be given to robotic researchers to inform them of how to make robot videos that truthfully characterize what a robot can and can not do. We consider this a first draft, and are looking for feedback from the community as we refine and grow our maxims and checklist.