OpenRaiser/Intern-Atlas huggingface dataset:
Putting Together the Pieces: A Concept for Holistic Industrial Intrusion Detection
Venue: arXiv cs.CR 2019
Year: 2019

Besides the advantages derived from the ever present communication properties, it increases the attack surface of a network as well. As industrial protocols and systems were not designed with security in mind, spectacular attacks on industrial systems occurred over the last years. Most industrial communication protocols do not provide means to ensure authentication or encryption. This means attackers with access to a network can read and write information. Originally not meant to be connected to public networks, the use cases of Industry 4.0 require interconnectivity, often through insecure public networks. This lead to an increasing interest in information security products for industrial applications. In this work, the concept for holistic intrusion detection methods in an industrial context is presented. It is based on different works considering several aspects of industrial environments and their capabilities to identify intrusions as an anomaly in network or process data. These capabilities are based on preceding experiments on real and synthetic data. In order to justify the concept, an overview of potential and actual attack vectors and attacks on industrial systems is provided. It is shown that different aspects of industrial facilities, e.g. office IT, shop floor OT, firewalled connections to customers and partners are analysed as well as the different layers of the automation pyramid require different methods to detect attacks. Additionally, the singular steps of an attack on industrial applications are characterised. Finally, a resulting concept for integration of these methods is proposed, providing the means to detect the different stages of an attack by different means.
Integrated Neural Network and Machine Vision Approach For Leather Defect Classification
Venue: arXiv cs.CV 2019
Year: 2019

Leather is a type of natural, durable, flexible, soft, supple and pliable material with smooth texture. It is commonly used as a raw material to manufacture luxury consumer goods for high-end customers. To ensure good quality control on the leather products, one of the critical processes is the visual inspection step to spot the random defects on the leather surfaces and it is usually conducted by experienced experts. This paper presents an automatic mechanism to perform the leather defect classification. In particular, we focus on detecting tick-bite defects on a specific type of calf leather. Both the handcrafted feature extractors (i.e., edge detectors and statistical approach) and data-driven (i.e., artificial neural network) methods are utilized to represent the leather patches. Then, multiple classifiers (i.e., decision trees, Support Vector Machines, nearest neighbour and ensemble classifiers) are exploited to determine whether the test sample patches contain defective segments. Using the proposed method, we managed to get a classification accuracy rate of 84% from a sample of approximately 2500 pieces of 400 * 400 leather patches.
Fast human motion prediction for human-robot collaboration with wearable interfaces
Venue: arXiv cs.RO 2019
Year: 2019

In this paper, we aim at improving human motion prediction during human-robot collaboration in industrial facilities by exploiting contributions from both physical and physiological signals. Improved human-machine collaboration could prove useful in several areas, while it is crucial for interacting robots to understand human movement as soon as possible to avoid accidents and injuries. In this perspective, we propose a novel human-robot interface capable to anticipate the user intention while performing reaching movements on a working bench in order to plan the action of a collaborative robot. The proposed interface can find many applications in the Industry 4.0 framework, where autonomous and collaborative robots will be an essential part of innovative facilities. A motion intention prediction and a motion direction prediction levels have been developed to improve detection speed and accuracy. A Gaussian Mixture Model (GMM) has been trained with IMU and EMG data following an evidence accumulation approach to predict reaching direction. Novel dynamic stopping criteria have been proposed to flexibly adjust the trade-off between early anticipation and accuracy according to the application. The output of the two predictors has been used as external inputs to a Finite State Machine (FSM) to control the behaviour of a physical robot according to user's action or inaction. Results show that our system outperforms previous methods, achieving a real-time classification accuracy of $94.3\pm2.9\%$ after $160.0msec\pm80.0msec$ from movement onset.
Mechanism Singularities Revisited from an Algebraic Viewpoint
Venue: arXiv cs.RO 2019
Year: 2019

It has become obvious that certain singular phenomena cannot be explained by a mere investigation of the configuration space, defined as the solution set of the loop closure equations. For example, it was observed that a particular 6R linkage, constructed by a combination of two Goldberg 5R linkages, exhibits kinematic singularities at a smooth point in its configuration space. Such problems are addressed in this paper. To this end, an algebraic framework is used in which the constraints are formulated as polynomial equations using Study parameters. The algebraic object of study is the ideal generated by the constraint equations (the constraint ideal). Using basic tools from commutative algebra and algebraic geometry (primary decomposition, Hilbert's Nullstellensatz), the special phenomenon is related to the fact that the constraint ideal is not a radical ideal. With a primary decomposition of the constraint ideal, the associated prime ideal of one primary ideal contains strictly into the associated prime ideal of another primary ideal which also gives the smooth configuration curve. This analysis is extended to shaky and kinematotropic linkages, for which examples are presented.
EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction
Venue: arXiv cs.LG 2019
Year: 2019

Providing explanations along with predictions is crucial in some text processing tasks. Therefore, we propose a new self-interpretable model that performs output prediction and simultaneously provides an explanation in terms of the presence of particular concepts in the input. To do so, our model's prediction relies solely on a low-dimensional binary representation of the input, where each feature denotes the presence or absence of concepts. The presence of a concept is decided from an excerpt i.e. a small sequence of consecutive words in the text. Relevant concepts for the prediction task at hand are automatically defined by our model, avoiding the need for concept-level annotations. To ease interpretability, we enforce that for each concept, the corresponding excerpts share similar semantics and are differentiable from each others. We experimentally demonstrate the relevance of our approach on text classification and multi-sentiment analysis tasks.
Maximal correlation and the rate of Fisher information convergence in the Central Limit Theorem
Venue: arXiv cs.IT 2019
Year: 2019

We consider the behaviour of the Fisher information of scaled sums of independent and identically distributed random variables in the Central Limit Theorem regime. We show how this behaviour can be related to the second-largest non-trivial eigenvalue associated with the Hirschfeld--Gebelein--Rényi maximal correlation. We prove that assuming this eigenvalue satisfies a strict inequality, an $O(1/n)$ rate of convergence and a strengthened form of monotonicity hold.
FireNet: A Specialized Lightweight Fire & Smoke Detection Model for Real-Time IoT Applications
Venue: arXiv cs.CV 2019
Year: 2019

Fire disasters typically result in lot of loss to life and property. It is therefore imperative that precise, fast, and possibly portable solutions to detect fire be made readily available to the masses at reasonable prices. There have been several research attempts to design effective and appropriately priced fire detection systems with varying degrees of success. However, most of them demonstrate a trade-off between performance and model size (which decides the model's ability to be installed on portable devices). The work presented in this paper is an attempt to deal with both the performance and model size issues in one design. Toward that end, a `designed-from-scratch' neural network, named FireNet, is proposed which is worthy on both the counts: (i) it has better performance than existing counterparts, and (ii) it is lightweight enough to be deploy-able on embedded platforms like Raspberry Pi. Performance evaluations on a standard dataset, as well as our own newly introduced custom-compiled fire dataset, are extremely encouraging.
Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
Venue: arXiv cs.ET 2019
Year: 2019

Spiking neural networks (SNN) are artificial computational models that have been inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventional artificial neural networks, though their full computational capabilities are yet to be explored. Recently, computational memory architectures based on non-volatile memory crossbar arrays have shown great promise to implement parallel computations in artificial and spiking neural networks. In this work, we experimentally demonstrate for the first time, the feasibility to realize high-performance event-driven in-situ supervised learning systems using nanoscale and stochastic phase-change synapses. Our SNN is trained to recognize audio signals of alphabets encoded using spikes in the time domain and to generate spike trains at precise time instances to represent the pixel intensities of their corresponding images. Moreover, with a statistical model capturing the experimental behavior of the devices, we investigate architectural and systems-level solutions for improving the training and inference performance of our computational memory-based system. Combining the computational potential of supervised SNNs with the parallel compute power of computational memory, the work paves the way for next-generation of efficient brain-inspired systems.
Empirical Review of Java Program Repair Tools: A Large-Scale Experiment on 2,141 Bugs and 23,551 Repair Attempts
Venue: arXiv cs.SE 2019
Year: 2019

In the past decade, research on test-suite-based automatic program repair has grown significantly. Each year, new approaches and implementations are featured in major software engineering venues. However, most of those approaches are evaluated on a single benchmark of bugs, which are also rarely reproduced by other researchers. In this paper, we present a large-scale experiment using 11 Java test-suite-based repair tools and 5 benchmarks of bugs. Our goal is to have a better understanding of the current state of automatic program repair tools on a large diversity of benchmarks. Our investigation is guided by the hypothesis that the repairability of repair tools might not be generalized across different benchmarks of bugs. We found that the 11 tools 1) are able to generate patches for 21% of the bugs from the 5 benchmarks, and 2) have better performance on Defects4J compared to other benchmarks, by generating patches for 47% of the bugs from Defects4J compared to 10-30% of bugs from the other benchmarks. Our experiment comprises 23,551 repair attempts in total, which we used to find the causes of non-patch generation. These causes are reported in this paper, which can help repair tool designers to improve their techniques and tools.
Video-to-Video Translation for Visual Speech Synthesis
Venue: arXiv cs.CV 2019
Year: 2019

Despite remarkable success in image-to-image translation that celebrates the advancements of generative adversarial networks (GANs), very limited attempts are known for video domain translation. We study the task of video-to-video translation in the context of visual speech generation, where the goal is to transform an input video of any spoken word to an output video of a different word. This is a multi-domain translation, where each word forms a domain of videos uttering this word. Adaptation of the state-of-the-art image-to-image translation model (StarGAN) to this setting falls short with a large vocabulary size. Instead we propose to use character encodings of the words and design a novel character-based GANs architecture for video-to-video translation called Visual Speech GAN (ViSpGAN). We are the first to demonstrate video-to-video translation with a vocabulary of 500 words.
Certifiably Robust Interpretation in Deep Learning
Venue: arXiv cs.LG 2019
Year: 2019

Deep learning interpretation is essential to explain the reasoning behind model predictions. Understanding the robustness of interpretation methods is important especially in sensitive domains such as medical applications since interpretation results are often used in downstream tasks. Although gradient-based saliency maps are popular methods for deep learning interpretation, recent works show that they can be vulnerable to adversarial attacks. In this paper, we address this problem and provide a certifiable defense method for deep learning interpretation. We show that a sparsified version of the popular SmoothGrad method, which computes the average saliency maps over random perturbations of the input, is certifiably robust against adversarial perturbations. We obtain this result by extending recent bounds for certifiably robust smooth classifiers to the interpretation setting. Experiments on ImageNet samples validate our theory.
Harnessing Slow Dynamics in Neuromorphic Computation
Venue: arXiv cs.LG 2019
Year: 2019

Neuromorphic Computing is a nascent research field in which models and devices are designed to process information by emulating biological neural systems. Thanks to their superior energy efficiency, analog neuromorphic systems are highly promising for embedded, wearable, and implantable systems. However, optimizing neural networks deployed on these systems is challenging. One main challenge is the so-called timescale mismatch: Dynamics of analog circuits tend to be too fast to process real-time sensory inputs. In this thesis, we propose a few working solutions to slow down dynamics of on-chip spiking neural networks. We empirically show that, by harnessing slow dynamics, spiking neural networks on analog neuromorphic systems can gain non-trivial performance boosts on a battery of real-time signal processing tasks.
Strategic Prediction with Latent Aggregative Games
Venue: arXiv cs.LG 2019
Year: 2019

We introduce a new class of context dependent, incomplete information games to serve as structured prediction models for settings with significant strategic interactions. Our games map the input context to outcomes by first condensing the input into private player types that specify the utilities, weighted interactions, as well as the initial strategies for the players. The game is played over multiple rounds where players respond to weighted aggregates of their neighbors' strategies. The predicted output from the model is a mixed strategy profile (a near-Nash equilibrium) and each observation is thought of as a sample from this strategy profile. We introduce two new aggregator paradigms with provably convergent game dynamics, and characterize the conditions under which our games are identifiable from data. Our games can be parameterized in a transferable manner so that the sets of players can change from one game to another. We demonstrate empirically that our games as models can recover meaningful strategic interactions from real voting data.
Leveraging Semantics for Incremental Learning in Multi-Relational Embeddings
Venue: arXiv cs.LG 2019
Year: 2019

Service robots benefit from encoding information in semantically meaningful ways to enable more robust task execution. Prior work has shown multi-relational embeddings can encode semantic knowledge graphs to promote generalizability and scalability, but only within a batched learning paradigm. We present Incremental Semantic Initialization (ISI), an incremental learning approach that enables novel semantic concepts to be initialized in the embedding in relation to previously learned embeddings of semantically similar concepts. We evaluate ISI on mined AI2Thor and MatterPort3D datasets; our experiments show that on average ISI improves immediate query performance by 41.4%. Additionally, ISI methods on average reduced the number of epochs required to approach model convergence by 78.2%.
Robo-Taxi service fleet sizing: assessing the impact of user trust and willingness-to-use
Venue: arXiv cs.MA 2019
Year: 2019

The first commercial fleets of Robo-Taxis will be on the road soon. Today important efforts are made to anticipate future Robo-Taxi services. Fleet size is one of the key parameters considered in the planning phase of service design and configuration. Based on multi-agent approaches, the fleet size can be explored using dynamic demand response simulations. Time and cost are the most common variables considered in such simulation approaches. However, personal taste variation can affect the demand and consequently the required fleet size. In this paper, we explore the impact of user trust and willingness-to-use on the Robo-Taxi fleet size. This research is based upon simulating the transportation system of the Rouen-Normandie metropolitan area in France using MATSim, a multi-agent activity-based simulator. A local survey is made in order to explore the variation of user trust and their willingness-to-use future Robo-Taxis according to the sociodemographic attributes. Integrating survey data in the model shows the significant importance of traveler trust and willingness-to-use varying the Robo-Taxi use and the required fleet size.
Attention Based Pruning for Shift Networks
Venue: arXiv cs.NE 2019
Year: 2019

In many application domains such as computer vision, Convolutional Layers (CLs) are key to the accuracy of deep learning methods. However, it is often required to assemble a large number of CLs, each containing thousands of parameters, in order to reach state-of-the-art accuracy, thus resulting in complex and demanding systems that are poorly fitted to resource-limited devices. Recently, methods have been proposed to replace the generic convolution operator by the combination of a shift operation and a simpler 1x1 convolution. The resulting block, called Shift Layer (SL), is an efficient alternative to CLs in the sense it allows to reach similar accuracies on various tasks with faster computations and fewer parameters. In this contribution, we introduce Shift Attention Layers (SALs), which extend SLs by using an attention mechanism that learns which shifts are the best at the same time the network function is trained. We demonstrate SALs are able to outperform vanilla SLs (and CLs) on various object recognition benchmarks while significantly reducing the number of float operations and parameters for the inference.
A Method of Expressing the Magnitude of Merit of Being Able to Access a Side Information During Encoding
Venue: arXiv cs.IT 2019
Year: 2019

In general, if there is one device A with the same performance as many devices B, it would be better to replace many devices with one device. In order to determine the number of devices that can be reduced, it is important to determine the number of other devices B having the same performance as one device A. In this paper, based on the concept of "representing the performance of one coding system A_n by the number of other coding systems B_n", we consider the merits of an encoder that can access the side information in the fixed-length source coding in general sources. The purpose of this paper is to characterize the merit of being able to access the side information during encoding with the number k_n of coding systems that can not access the side information during encoding. For this purpose, we derive general formulas for the upper limit of error probability and reliability functions without assuming a specific information theoretical structure. These general formulas are applicable to various fields other than source coding problems, because they have been proved without assuming any particular information theoretical structure. We prove some theorems under the condition that the coding system is subadditive.
Mixed Precision Training With 8-bit Floating Point
Venue: arXiv cs.LG 2019
Year: 2019

Reduced precision computation for deep neural networks is one of the key areas addressing the widening compute gap driven by an exponential growth in model size. In recent years, deep learning training has largely migrated to 16-bit precision, with significant gains in performance and energy efficiency. However, attempts to train DNNs at 8-bit precision have met with significant challenges because of the higher precision and dynamic range requirements of back-propagation. In this paper, we propose a method to train deep neural networks using 8-bit floating point representation for weights, activations, errors, and gradients. In addition to reducing compute precision, we also reduced the precision requirements for the master copy of weights from 32-bit to 16-bit. We demonstrate state-of-the-art accuracy across multiple data sets (imagenet-1K, WMT16) and a broader set of workloads (Resnet-18/34/50, GNMT, Transformer) than previously reported. We propose an enhanced loss scaling method to augment the reduced subnormal range of 8-bit floating point for improved error propagation. We also examine the impact of quantization noise on generalization and propose a stochastic rounding technique to address gradient noise. As a result of applying all these techniques, we report slightly higher validation accuracy compared to full precision baseline.
Rethinking Full Connectivity in Recurrent Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

Recurrent neural networks (RNNs) are omnipresent in sequence modeling tasks. Practical models usually consist of several layers of hundreds or thousands of neurons which are fully connected. This places a heavy computational and memory burden on hardware, restricting adoption in practical low-cost and low-power devices. Compared to fully convolutional models, the costly sequential operation of RNNs severely hinders performance on parallel hardware. This paper challenges the convention of full connectivity in RNNs. We study structurally sparse RNNs, showing that they are well suited for acceleration on parallel hardware, with a greatly reduced cost of the recurrent operations as well as orders of magnitude less recurrent weights. Extensive experiments on challenging tasks ranging from language modeling and speech recognition to video action recognition reveal that structurally sparse RNNs achieve competitive performance as compared to fully-connected networks. This allows for using large sparse RNNs for a wide range of real-world tasks that previously were too costly with fully connected networks.