OpenRaiser/Intern-Atlas huggingface dataset:
Adversarial Examples and the Deeper Riddle of Induction: The Need for a Theory of Artifacts in Deep Learning
Venue: arXiv cs.LG 2020
Year: 2020

Deep learning is currently the most widespread and successful technology in artificial intelligence. It promises to push the frontier of scientific discovery beyond current limits. However, skeptics have worried that deep neural networks are black boxes, and have called into question whether these advances can really be deemed scientific progress if humans cannot understand them. Relatedly, these systems also possess bewildering new vulnerabilities: most notably a susceptibility to "adversarial examples". In this paper, I argue that adversarial examples will become a flashpoint of debate in philosophy and diverse sciences. Specifically, new findings concerning adversarial examples have challenged the consensus view that the networks' verdicts on these cases are caused by overfitting idiosyncratic noise in the training set, and may instead be the result of detecting predictively useful "intrinsic features of the data geometry" that humans cannot perceive (Ilyas et al., 2019). These results should cause us to re-examine responses to one of the deepest puzzles at the intersection of philosophy and science: Nelson Goodman's "new riddle" of induction. Specifically, they raise the possibility that progress in a number of sciences will depend upon the detection and manipulation of useful features that humans find inscrutable. Before we can evaluate this possibility, however, we must decide which (if any) of these inscrutable features are real but available only to "alien" perception and cognition, and which are distinctive artifacts of deep learning-for artifacts like lens flares or Gibbs phenomena can be similarly useful for prediction, but are usually seen as obstacles to scientific theorizing. Thus, machine learning researchers urgently need to develop a theory of artifacts for deep neural networks, and I conclude by sketching some initial directions for this area of research.
Accelerated Analog Neuromorphic Computing
Venue: arXiv cs.NE 2020
Year: 2020

This paper presents the concepts behind the BrainScales (BSS) accelerated analog neuromorphic computing architecture. It describes the second-generation BrainScales-2 (BSS-2) version and its most recent in-silico realization, the HICANN-X Application Specific Integrated Circuit (ASIC), as it has been developed as part of the neuromorphic computing activities within the European Human Brain Project (HBP). While the first generation is implemented in an 180nm process, the second generation uses 65nm technology. This allows the integration of a digital plasticity processing unit, a highly-parallel micro processor specially built for the computational needs of learning in an accelerated analog neuromorphic systems. The presented architecture is based upon a continuous-time, analog, physical model implementation of neurons and synapses, resembling an analog neuromorphic accelerator attached to build-in digital compute cores. While the analog part emulates the spike-based dynamics of the neural network in continuous-time, the latter simulates biological processes happening on a slower time-scale, like structural and parameter changes. Compared to biological time-scales, the emulation is highly accelerated, i.e. all time-constants are several orders of magnitude smaller than in biology. Programmable ion channel emulation and inter-compartmental conductances allow the modeling of nonlinear dendrites, back-propagating action-potentials as well as NMDA and Calcium plateau potentials. To extend the usability of the analog accelerator, it also supports vector-matrix multiplication. Thereby, BSS-2 supports inference of deep convolutional networks as well as local-learning with complex ensembles of spiking neurons within the same substrate.
Milking CowMask for Semi-Supervised Image Classification
Venue: arXiv cs.CV 2020
Year: 2020

Consistency regularization is a technique for semi-supervised learning that underlies a number of strong results for classification with few labeled data. It works by encouraging a learned model to be robust to perturbations on unlabeled data. Here, we present a novel mask-based augmentation method called CowMask. Using it to provide perturbations for semi-supervised consistency regularization, we achieve a state-of-the-art result on ImageNet with 10% labeled data, with a top-5 error of 8.76% and top-1 error of 26.06%. Moreover, we do so with a method that is much simpler than many alternatives. We further investigate the behavior of CowMask for semi-supervised learning by running many smaller scale experiments on the SVHN, CIFAR-10 and CIFAR-100 data sets, where we achieve results competitive with the state of the art, indicating that CowMask is widely applicable. We open source our code at https://github.com/google-research/google-research/tree/master/milking_cowmask
Denial of Service Attacks Detection in Software-Defined Wireless Sensor Networks
Venue: arXiv cs.CR 2020
Year: 2020

Software-defined networking (SDN) is a promising technology to overcome many challenges in wireless sensor networks (WSN), particularly with respect to flexibility and reuse. Conversely, the centralization and the planes' separation turn SDNs vulnerable to new security threats in the general context of distributed denial of service (DDoS) attacks. State-of-the-art approaches to identify DDoS do not always take into consideration restrictions in typical WSNs e.g., computational complexity and power constraints, while further performance improvement is always a target. The objective of this work is to propose a lightweight but very efficient DDoS attack detection approach using change point analysis. Our approach has a high detection rate and linear complexity, so that it is suitable for WSNs. We demonstrate the performance of our detector in software-defined WSNs of 36 and 100 nodes with varying attack intensity (the number of attackers ranges from 5% to 20% of nodes). We use change point detectors to monitor anomalies in two metrics: the data packets delivery rate and the control packets overhead. Our results show that with increasing intensity of attack, our approach can achieve a detection rate close to100% and that the type of attack can also be inferred.
Grounded Situation Recognition
Venue: arXiv cs.CV 2020
Year: 2020

We introduce Grounded Situation Recognition (GSR), a task that requires producing structured semantic summaries of images describing: the primary activity, entities engaged in the activity with their roles (e.g. agent, tool), and bounding-box groundings of entities. GSR presents important technical challenges: identifying semantic saliency, categorizing and localizing a large and diverse set of entities, overcoming semantic sparsity, and disambiguating roles. Moreover, unlike in captioning, GSR is straightforward to evaluate. To study this new task we create the Situations With Groundings (SWiG) dataset which adds 278,336 bounding-box groundings to the 11,538 entity classes in the imsitu dataset. We propose a Joint Situation Localizer and find that jointly predicting situations and groundings with end-to-end training handily outperforms independent training on the entire grounding metric suite with relative gains between 8% and 32%. Finally, we show initial findings on three exciting future directions enabled by our models: conditional querying, visual chaining, and grounded semantic aware image retrieval. Code and data available at https://prior.allenai.org/projects/gsr.
To Tweet or Not to Tweet: Covertly Manipulating a Twitter Debate on Vaccines Using Malware-Induced Misperceptions
Venue: arXiv cs.CR 2020
Year: 2020

Trolling and social bots have been proven as powerful tactics for manipulating the public opinion and sowing discord among Twitter users. This effort requires substantial content fabrication and account coordination to evade Twitter's detection of nefarious platform use. In this paper we explore an alternative tactic for covert social media interference by inducing misperceptions about genuine, non-trolling content from verified users. This tactic uses a malware that covertly manipulates targeted words, hashtags, and Twitter metrics before the genuine content is presented to a targeted user in a covert man-in-the-middle fashion. Early tests of the malware found that it is capable of achieving a similar goal as trolls and social bots, that is, silencing or provoking social media users to express their opinion in polarized debates on social media. Following this, we conducted experimental tests in controlled settings (N=315) where the malware covertly manipulated the perception in a Twitter debate on the risk of vaccines causing autism. The empirical results demonstrate that inducing misperception is an effective tactic to silence users on Twitter when debating polarizing issues like vaccines. We used the findings to propose a solution for countering the effect of the malware-induced misperception that could also be used against trolls and social bots on Twitter.
Tactile sensing and computing on a random network of conducting fluid channels
Venue: arXiv cs.ET 2020
Year: 2020

Liquid electronic sensors are typically based on regular arrays of channels filled with a conductive liquid. We propose that a random planar network of conducting liquid allows us for a wider spectrum of electrical responses and localisation of tactile stimuli. We also speculate that a computation protocol can be implemented on such a network, featuring mechanical inputs and electrical outputs. Our results pave a way towards future developments on sensing and computing wearables with disordered sensing networks structure.
Data-Driven Inference of Representation Invariants
Venue: arXiv cs.PL 2020
Year: 2020

A representation invariant is a property that holds of all values of abstract type produced by a module. Representation invariants play important roles in software engineering and program verification. In this paper, we develop a counterexample-driven algorithm for inferring a representation invariant that is sufficient to imply a desired specification for a module. The key novelty is a type-directed notion of visible inductiveness, which ensures that the algorithm makes progress toward its goal as it alternates between weakening and strengthening candidate invariants. The algorithm is parameterized by an example-based synthesis engine and a verifier, and we prove that it is sound and complete for first-order modules over finite types, assuming that the synthesizer and verifier are as well. We implement these ideas in a tool called Hanoi, which synthesizes representation invariants for recursive data types. Hanoi not only handles invariants for first-order code, but higher-order code as well. In its back end, Hanoi uses an enumerative synthesizer called Myth and an enumerative testing tool as a verifier. Because Hanoi uses testing for verification, it is not sound, though our empirical evaluation shows that it is successful on the benchmarks we investigated.
Cycle Text-To-Image GAN with BERT
Venue: arXiv cs.CV 2020
Year: 2020

We explore novel approaches to the task of image generation from their respective captions, building on state-of-the-art GAN architectures. Particularly, we baseline our models with the Attention-based GANs that learn attention mappings from words to image features. To better capture the features of the descriptions, we then built a novel cyclic design that learns an inverse function to maps the image back to original caption. Additionally, we incorporated recently developed BERT pretrained word embeddings as our initial text featurizer and observe a noticeable improvement in qualitative and quantitative performance compared to the Attention GAN baseline.
Metrics and Optimization of Internal Poses for Highly Redundant Truss-Like Serialized Parallel Manipulators
Venue: arXiv cs.RO 2020
Year: 2020

This paper presents a kinematic definition of a serialized Stewart platform designed for autonomous in-space assembly called an Assembler. The Assemblers architecture describes problems inherent to the inverse kinematics of over-actuated mixed kinematic systems. This paper also presents a methodology for optimizing poses. In order to accomplish this with the Assembler system, an algorithm for finding a feasible solution to its inverse kinematics was developed with a wrapper for a nonlinear optimization algorithm designed to minimize the magnitude of forces incurred by each actuator. A simulated version of an Assembler was placed into a number of representative poses, and the positions were optimized. The results of these optimizations are discussed in terms of actuator forces, reachability of the platform, and applicability to high-payload structure assembly capabilities.
A Collective Learning Framework to Boost GNN Expressiveness
Venue: arXiv cs.LG 2020
Year: 2020

Graph Neural Networks (GNNs) have recently been used for node and graph classification tasks with great success, but GNNs model dependencies among the attributes of nearby neighboring nodes rather than dependencies among observed node labels. In this work, we consider the task of inductive node classification using GNNs in supervised and semi-supervised settings, with the goal of incorporating label dependencies. Because current GNNs are not universal (i.e., most-expressive) graph representations, we propose a general collective learning approach to increase the representation power of any existing GNN. Our framework combines ideas from collective classification with self-supervised learning, and uses a Monte Carlo approach to sampling embeddings for inductive learning across graphs. We evaluate performance on five real-world network datasets and demonstrate consistent, significant improvement in node classification accuracy, for a variety of state-of-the-art GNNs.
Incremental Learning Algorithm for Sound Event Detection
Venue: arXiv cs.LG 2020
Year: 2020

This paper presents a new learning strategy for the Sound Event Detection (SED) system to tackle the issues of i) knowledge migration from a pre-trained model to a new target model and ii) learning new sound events without forgetting the previously learned ones without re-training from scratch. In order to migrate the previously learned knowledge from the source model to the target one, a neural adapter is employed on the top of the source model. The source model and the target model are merged via this neural adapter layer. The neural adapter layer facilitates the target model to learn new sound events with minimal training data and maintaining the performance of the previously learned sound events similar to the source model. Our extensive analysis on the DCASE16 and US-SED dataset reveals the effectiveness of the proposed method in transferring knowledge between source and target models without introducing any performance degradation on the previously learned sound events while obtaining a competitive detection performance on the newly learned sound events.
FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks
Venue: arXiv cs.DC 2020
Year: 2020

Convolutional neural networks (CNNs) are becoming more and more important for solving challenging and critical problems in many fields. CNN inference applications have been deployed in safety-critical systems, which may suffer from soft errors caused by high-energy particles, high temperature, or abnormal voltage. Of critical importance is ensuring the stability of the CNN inference process against soft errors. Traditional fault tolerance methods are not suitable for CNN inference because error-correcting code is unable to protect computational components, instruction duplication techniques incur high overhead, and existing algorithm-based fault tolerance (ABFT) techniques cannot protect all convolution implementations. In this paper, we focus on how to protect the CNN inference process against soft errors as efficiently as possible, with the following three contributions. (1) We propose several systematic ABFT schemes based on checksum techniques and analyze their fault protection ability and runtime thoroughly.Unlike traditional ABFT based on matrix-matrix multiplication, our schemes support any convolution implementations. (2) We design a novel workflow integrating all the proposed schemes to obtain a high detection/correction ability with limited total runtime overhead. (3) We perform our evaluation using ImageNet with well-known CNN models including AlexNet, VGG-19, ResNet-18, and YOLOv2. Experimental results demonstrate that our implementation can handle soft errors with very limited runtime overhead (4%~8% in both error-free and error-injected situations).
Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision
Venue: arXiv cs.CL 2020
Year: 2020

We created this CORD-NER dataset with comprehensive named entity recognition (NER) on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus (2020-03-13). This CORD-NER dataset covers 75 fine-grained entity types: In addition to the common biomedical entity types (e.g., genes, chemicals and diseases), it covers many new entity types related explicitly to the COVID-19 studies (e.g., coronaviruses, viral proteins, evolution, materials, substrates and immune responses), which may benefit research on COVID-19 related virus, spreading mechanisms, and potential vaccines. CORD-NER annotation is a combination of four sources with different NER methods. The quality of CORD-NER annotation surpasses SciSpacy (over 10% higher on the F1 score based on a sample set of documents), a fully supervised BioNER tool. Moreover, CORD-NER supports incrementally adding new documents as well as adding new entity types when needed by adding dozens of seeds as the input examples. We will constantly update CORD-NER based on the incremental updates of the CORD-19 corpus and the improvement of our system.
A Hybrid Lagrangian/Eulerian Collocated Advection and Projection Method for Fluid Simulation
Venue: arXiv cs.GR 2020
Year: 2020

We present a hybrid particle/grid approach for simulating incompressible fluids on collocated velocity grids. We interchangeably use particle and grid representations of transported quantities to balance efficiency and accuracy. A novel Backward Semi-Lagrangian method is derived to improve accuracy of grid based advection. Our approach utilizes the implicit formula associated with solutions of Burgers' equation. We solve this equation using Newton's method enabled by $C^1$ continuous grid interpolation. We enforce incompressibility over collocated, rather than staggered grids. Our projection technique is variational and designed for B-spline interpolation over regular grids where multiquadratic interpolation is used for velocity and multilinear interpolation for pressure. Despite our use of regular grids, we extend the variational technique to allow for cut-cell definition of irregular flow domains for both Dirichlet and free surface boundary conditions.
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms
Venue: arXiv cs.LG 2020
Year: 2020

We present a distributional approach to theoretical analyses of reinforcement learning algorithms for constant step-sizes. We demonstrate its effectiveness by presenting simple and unified proofs of convergence for a variety of commonly-used methods. We show that value-based methods such as TD($λ$) and $Q$-Learning have update rules which are contractive in the space of distributions of functions, thus establishing their exponentially fast convergence to a stationary distribution. We demonstrate that the stationary distribution obtained by any algorithm whose target is an expected Bellman update has a mean which is equal to the true value function. Furthermore, we establish that the distributions concentrate around their mean as the step-size shrinks. We further analyse the optimistic policy iteration algorithm, for which the contraction property does not hold, and formulate a probabilistic policy improvement property which entails the convergence of the algorithm.
Weakly Supervised Dataset Collection for Robust Person Detection
Venue: arXiv cs.CV 2020
Year: 2020

To construct an algorithm that can provide robust person detection, we present a dataset with over 8 million images that was produced in a weakly supervised manner. Through labor-intensive human annotation, the person detection research community has produced relatively small datasets containing on the order of 100,000 images, such as the EuroCity Persons dataset, which includes 240,000 bounding boxes. Therefore, we have collected 8.7 million images of persons based on a two-step collection process, namely person detection with an existing detector and data refinement for false positive suppression. According to the experimental results, the Weakly Supervised Person Dataset (WSPD) is simple yet effective for person detection pre-training. In the context of pre-trained person detection algorithms, our WSPD pre-trained model has 13.38 and 6.38% better accuracy than the same model trained on the fully supervised ImageNet and EuroCity Persons datasets, respectively, when verified with the Caltech Pedestrian.
Rolling Horizon Evolutionary Algorithms for General Video Game Playing
Venue: arXiv cs.AI 2020
Year: 2020

Game-playing Evolutionary Algorithms, specifically Rolling Horizon Evolutionary Algorithms, have recently managed to beat the state of the art in win rate across many video games. However, the best results in a game are highly dependent on the specific configuration of modifications and hybrids introduced over several papers, each adding additional parameters to the core algorithm. Further, the best previously published parameters have been found from only a few human-picked combinations, as the possibility space has grown beyond exhaustive search. This paper presents the state of the art in Rolling Horizon Evolutionary Algorithms, combining all modifications described in literature, as well as new ones, for a large resultant hybrid. We then use a parameter optimiser, the N-Tuple Bandit Evolutionary Algorithm, to find the best combination of parameters in 20 games from the General Video Game AI Framework. Further, we analyse the algorithm's parameters and some interesting combinations revealed through the optimisation process. Lastly, we find new state of the art solutions on several games by automatically exploring the large parameter space of RHEA.
Assessing the Security of OPC UA Deployments
Venue: arXiv cs.CR 2020
Year: 2020

To address the increasing security demands of industrial deployments, OPC UA is one of the first industrial protocols explicitly designed with security in mind. However, deploying it securely requires a thorough configuration of a wide range of options. Thus, assessing the security of OPC UA deployments and their configuration is necessary to ensure secure operation, most importantly confidentiality and integrity of industrial processes. In this work, we present extensions to the popular Metasploit Framework to ease network-based security assessments of OPC UA deployments. To this end, we discuss methods to discover OPC UA servers, test their authentication, obtain their configuration, and check for vulnerabilities. Ultimately, our work enables operators to verify the (security) configuration of their systems and identify potential attack vectors.
Planning with Brain-inspired AI
Venue: arXiv cs.AI 2020
Year: 2020

This article surveys engineering and neuroscientific models of planning as a cognitive function, which is regarded as a typical function of fluid intelligence in the discussion of general intelligence. It aims to present existing planning models as references for realizing the planning function in brain-inspired AI or artificial general intelligence (AGI). It also proposes themes for the research and development of brain-inspired AI from the viewpoint of tasks and architecture.