OpenRaiser/Intern-Atlas huggingface dataset:
FNHSM_HRS: Hybrid recommender system using fuzzy clustering and heuristic similarity measure
Venue: arXiv cs.IR 2019
Year: 2019

Nowadays, Recommender Systems have become a comprehensive system for helping and guiding users in a huge amount of data on the Internet. Collaborative Filtering offers to active users based on the rating of a set of users. One of the simplest and most comprehensible and successful models is to find users with a taste in recommender systems. In this model, with increasing number of users and items, the system is faced to scalability problem. On the other hand, improving system performance when there is little information available from ratings, that is important. In this paper, a hybrid recommender system called FNHSM_HRS which is based on the new heuristic similarity measure (NHSM) along with a fuzzy clustering is presented. Using the fuzzy clustering method in the proposed system improves the scalability problem and increases the accuracy of system recommendations. The proposed system is based on the collaborative filtering model and is partnered with the heuristic similarity measure to improve the system's performance and accuracy. The evaluation of the proposed system based results on the MovieLens dataset carried out the results using MAE, Recall, Precision and Accuracy measures Indicating improvement in system performance and increasing the accuracy of recommendation to collaborative filtering methods which use other measures to find similarities.
Backpropagation in the Simply Typed Lambda-calculus with Linear Negation
Venue: arXiv cs.LO 2019
Year: 2019

Backpropagation is a classic automatic differentiation algorithm computing the gradient of functions specified by a certain class of simple, first-order programs, called computational graphs. It is a fundamental tool in several fields, most notably machine learning, where it is the key for efficiently training (deep) neural networks. Recent years have witnessed the quick growth of a research field called differentiable programming, the aim of which is to express computational graphs more synthetically and modularly by resorting to actual programming languages endowed with control flow operators and higher-order combinators, such as map and fold. In this paper, we extend the backpropagation algorithm to a paradigmatic example of such a programming language: we define a compositional program transformation from the simply-typed lambda-calculus to itself augmented with a notion of linear negation, and prove that this computes the gradient of the source program with the same efficiency as first-order backpropagation. The transformation is completely effect-free and thus provides a purely logical understanding of the dynamics of backpropagation.
MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent Labeling
Venue: arXiv cs.CV 2019
Year: 2019

Most of the recent Deep Semantic Segmentation algorithms suffer from large generalization errors, even when powerful hierarchical representation models based on convolutional neural networks have been employed. This could be attributed to limited training data and large distribution gap in train and test domain datasets. In this paper, we propose a multi-level self-supervised learning model for domain adaptation of semantic segmentation. Exploiting the idea that an object (and most of the stuff given context) should be labeled consistently regardless of its location, we generate spatially independent and semantically consistent (SISC) pseudo-labels by segmenting multiple sub-images using base model and designing an aggregation strategy. Image level pseudo weak-labels, PWL, are computed to guide domain adaptation by capturing global context similarity in source and domain at latent space level. Thus helping latent space learn the representation even when there are very few pixels belonging to the domain category (small object for example) compared to rest of the image. Our multi-level Self-supervised learning (MLSL) outperforms existing state-of art (self or adversarial learning) algorithms. Specifically, keeping all setting similar and employing MLSL we obtain an mIoU gain of 5:1% on GTA-V to Cityscapes adaptation and 4:3% on SYNTHIA to Cityscapes adaptation compared to existing state-of-art method.
Factored Probabilistic Belief Tracking
Venue: arXiv cs.AI 2019
Year: 2019

The problem of belief tracking in the presence of stochastic actions and observations is pervasive and yet computationally intractable. In this work we show however that probabilistic beliefs can be maintained in factored form exactly and efficiently across a number of causally closed beams, when the state variables that appear in more than one beam obey a form of backward determinism. Since computing marginals from the factors is still computationally intractable in general, and variables appearing in several beams are not always backward-deterministic, the basic formulation is extended with two approximations: forms of belief propagation for computing marginals from factors, and sampling of non-backward-deterministic variables for making such variables backward-deterministic given their sampled history. Unlike, Rao-Blackwellized particle-filtering, the sampling is not used for making inference tractable but for making the factorization sound. The resulting algorithm involves sampling and belief propagation or just one of them as determined by the structure of the model.
System-level optimization of Network-on-Chips for heterogeneous 3D System-on-Chips
Venue: arXiv cs.AR 2019
Year: 2019

For a system-level design of Networks-on-Chip for 3D heterogeneous System-on-Chip (SoC), the locations of components, routers and vertical links are determined from an application model and technology parameters. In conventional methods, the two inputs are accounted for separately; here, we define an integrated problem that considers both application model and technology parameters. We show that this problem does not allow for exact solution in reasonable time, as common for many design problems. Therefore, we contribute a heuristic by proposing design steps, which are based on separation of intralayer and interlayer communication. The advantage is that this new problem can be solved with well-known methods. We use 3D Vision SoC case studies to quantify the advantages and the practical usability of the proposed optimization approach. We achieve up to 18.8% reduced white space and up to 12.4% better network performance in comparison to conventional approaches.
Black-box Adversarial Attacks with Bayesian Optimization
Venue: arXiv cs.LG 2019
Year: 2019

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs. We use Bayesian optimization~(BO) to specifically cater to scenarios involving low query budgets to develop query efficient adversarial attacks. We alleviate the issues surrounding BO in regards to optimizing high dimensional deep learning models by effective dimension upsampling techniques. Our proposed approach achieves performance comparable to the state of the art black-box adversarial attacks albeit with a much lower average query count. In particular, in low query budget regimes, our proposed method reduces the query count up to $80\%$ with respect to the state of the art methods.
Learning Compact Models for Planning with Exogenous Processes
Venue: arXiv cs.LG 2019
Year: 2019

We address the problem of approximate model minimization for MDPs in which the state is partitioned into endogenous and (much larger) exogenous components. An exogenous state variable is one whose dynamics are independent of the agent's actions. We formalize the mask-learning problem, in which the agent must choose a subset of exogenous state variables to reason about when planning; doing planning in such a reduced state space can often be significantly more efficient than planning in the full model. We then explore the various value functions at play within this setting, and describe conditions under which a policy for a reduced model will be optimal for the full MDP. The analysis leads us to a tractable approximate algorithm that draws upon the notion of mutual information among exogenous state variables. We validate our approach in simulated robotic manipulation domains where a robot is placed in a busy environment, in which there are many other agents also interacting with the objects. Visit http://tinyurl.com/chitnis-exogenous for a supplementary video.
A Self-Healing Hardware Architecture for Safety-Critical Digital Embedded Devices
Venue: arXiv cs.OH 2019
Year: 2019

Digital Embedded Devices of next-generation safety-critical industrial automation systems require high levels of survivability and resilience against the hardware and software failure. One of the concepts for achieving this requirement is the design of resilient and survivable digital embedded systems. In the last two decades, development of self-healing digital systems based on molecular and cellular biology have received attention for the design of robust digital systems. However, many of these approaches have not been architected from the outset with safety in mind, nor have they been targeted for the applications of automation community where a significant need exists. This paper presents a new self-healing hardware architecture, inspired from the way nature responds, defends and heals: the stem cells in the immune system of living organisms, the life cycle of the living cell, and the pathway from Deoxyribonucleic acid (DNA) to protein. The proposed architecture is integrating cellular-based biological concepts, traditional fault tolerance techniques, and operational schematics for the international standard IEC 61131-3 to facilitate adoption in the automation industry and safety-critical applications. To date, two industrial applications have been mapped on the proposed architecture, which are capable of tolerating a significant number of faults that can stem from harsh environmental changes and external disturbances and we believe the nexus of its concepts can positively impact the next generation of critical systems in the automation industry
Analysis and Design of a 32nm FinFET Dynamic Latch Comparator
Venue: arXiv cs.AR 2019
Year: 2019

Comparators have multifarious applications in various fields, especially used in analog to digital converters. Over the years, we have seen many different designs of single stage, dynamic latch type and double tail type comparators based on CMOS technology, and all of them had to make the tradeoff between power consumption and delay time. Meanwhile, to mitigate the short channel effects of conventional CMOS based design, FinFET has emerged as the most promising alternative by owning the tremendous gate control feature over the channel region. In this paper, we have analyzed the performance of some recent dynamic latch type comparators and proposed a new structure of dynamic latch comparator; moreover, 32nm FinFET technology has been considered as the common platform for all of the comparators circuit design. The proposed comparator has shown impressive performance in case of power consumption, time delay, power delay product and offset voltage while compared with the other recent comparators through simulations with LTspice.
Defense in Depth: The Basics of Blockade and Delay
Venue: arXiv cs.CR 2019
Year: 2019

Given that individual defenses are rarely sufficient, defense-in-depth is nearly universal and options for individual defensive layers abound. We develop a simple mathematical theory that can help in selecting the type and quantity of defenses for two different defense-in-depth strategies: Blockade and Delay. This theoretical approach accounts for budgetary constraints and the number, skill, and diversity of attackers. We find that defenders have several reasons to be optimistic including that the number of required defenses increases more slowly than the number of attackers, that similar attackers are defended more easily than similar defenses are defeated, and that defenders do not necessarily need to act as quickly as attackers.
How noise affects the Hessian spectrum in overparameterized neural networks
Venue: arXiv cs.LG 2019
Year: 2019

Stochastic gradient descent (SGD) forms the core optimization method for deep neural networks. While some theoretical progress has been made, it still remains unclear why SGD leads the learning dynamics in overparameterized networks to solutions that generalize well. Here we show that for overparameterized networks with a degenerate valley in their loss landscape, SGD on average decreases the trace of the Hessian of the loss. We also generalize this result to other noise structures and show that isotropic noise in the non-degenerate subspace of the Hessian decreases its determinant. In addition to explaining SGDs role in sculpting the Hessian spectrum, this opens the door to new optimization approaches that may confer better generalization performance. We test our results with experiments on toy models and deep neural networks.
Multilingual End-to-End Speech Translation
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we propose a simple yet effective framework for multilingual end-to-end speech translation (ST), in which speech utterances in source languages are directly translated to the desired target languages with a universal sequence-to-sequence architecture. While multilingual models have shown to be useful for automatic speech recognition (ASR) and machine translation (MT), this is the first time they are applied to the end-to-end ST problem. We show the effectiveness of multilingual end-to-end ST in two scenarios: one-to-many and many-to-many translations with publicly available data. We experimentally confirm that multilingual end-to-end ST models significantly outperform bilingual ones in both scenarios. The generalization of multilingual training is also evaluated in a transfer learning scenario to a very low-resource language pair. All of our codes and the database are publicly available to encourage further research in this emergent multilingual ST topic.
Attacking CNN-based anti-spoofing face authentication in the physical domain
Venue: arXiv cs.CR 2019
Year: 2019

In this paper, we study the vulnerability of anti-spoofing methods based on deep learning against adversarial perturbations. We first show that attacking a CNN-based anti-spoofing face authentication system turns out to be a difficult task. When a spoofed face image is attacked in the physical world, in fact, the attack has not only to remove the rebroadcast artefacts present in the image, but it has also to take into account that the attacked image will be recaptured again and then compensate for the distortions that will be re-introduced after the attack by the subsequent rebroadcast process. Subsequently, we propose a method to craft robust physical domain adversarial images against anti-spoofing CNN-based face authentication. The attack built in this way can successfully pass all the steps in the authentication chain (that is, face detection, face recognition and spoofing detection), by achieving simultaneously the following goals: i) make the spoofing detection fail; ii) let the facial region be detected as a face and iii) recognized as belonging to the victim of the attack. The effectiveness of the proposed attack is validated experimentally within a realistic setting, by considering the REPLAY-MOBILE database, and by feeding the adversarial images to a real face authentication system capturing the input images through a mobile phone camera.
An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack
Venue: arXiv cs.LG 2019
Year: 2019

There are two major paradigms of white-box adversarial attacks that attempt to impose input perturbations. The first paradigm, called the fix-perturbation attack, crafts adversarial samples within a given perturbation level. The second paradigm, called the zero-confidence attack, finds the smallest perturbation needed to cause mis-classification, also known as the margin of an input feature. While the former paradigm is well-resolved, the latter is not. Existing zero-confidence attacks either introduce significant ap-proximation errors, or are too time-consuming. We therefore propose MARGINATTACK, a zero-confidence attack framework that is able to compute the margin with improved accuracy and efficiency. Our experiments show that MARGINATTACK is able to compute a smaller margin than the state-of-the-art zero-confidence attacks, and matches the state-of-the-art fix-perturbation at-tacks. In addition, it runs significantly faster than the Carlini-Wagner attack, currently the most ac-curate zero-confidence attack algorithm.
Map as The Hidden Sensor: Fast Odometry-Based Global Localization
Venue: arXiv cs.RO 2019
Year: 2019

Accurate and robust global localization is essential to robotics applications. We propose a novel global localization method that employs the map traversability as a hidden observation. The resulting map-corrected odometry localization is able to provide an accurate belief tensor of the robot state. Our method can be used for blind robots in dark or highly reflective areas. In contrast to odometry drift in long-term, our method using only odometry and the map converges in longterm. Our method can also be integrated with other sensors to boost the localization performance. The algorithm does not have any initial state assumption and tracks all possible robot states at all times. Therefore, our method is global and is robust in the event of ambiguous observations. We parallel each step of our algorithm such that it can be performed in real-time (up to ~ 300 Hz) using GPU. We validate our algorithm in different publicly available floor-plans and show that it is able to converge to the ground truth fast while being robust to ambiguities.
CWAE-IRL: Formulating a supervised approach to Inverse Reinforcement Learning problem
Venue: arXiv cs.LG 2019
Year: 2019

Inverse reinforcement learning (IRL) is used to infer the reward function from the actions of an expert running a Markov Decision Process (MDP). A novel approach using variational inference for learning the reward function is proposed in this research. Using this technique, the intractable posterior distribution of the continuous latent variable (the reward function in this case) is analytically approximated to appear to be as close to the prior belief while trying to reconstruct the future state conditioned on the current state and action. The reward function is derived using a well-known deep generative model known as Conditional Variational Auto-encoder (CVAE) with Wasserstein loss function, thus referred to as Conditional Wasserstein Auto-encoder-IRL (CWAE-IRL), which can be analyzed as a combination of the backward and forward inference. This can then form an efficient alternative to the previous approaches to IRL while having no knowledge of the system dynamics of the agent. Experimental results on standard benchmarks such as objectworld and pendulum show that the proposed algorithm can effectively learn the latent reward function in complex, high-dimensional environments.
Exploring how Component Factors and their Uncertainty Affect Judgements of Risk in Cyber-Security
Venue: arXiv cs.CR 2019
Year: 2019

Subjective judgements from experts provide essential information when assessing and modelling threats in respect to cyber-physical systems. For example, the vulnerability of individual system components can be described using multiple factors, such as complexity, technological maturity, and the availability of tools to aid an attack. Such information is useful for determining attack risk, but much of it is challenging to acquire automatically and instead must be collected through expert assessments. However, most experts inherently carry some degree of uncertainty in their assessments. For example, it is impossible to be certain precisely how many tools are available to aid an attack. Traditional methods of capturing subjective judgements through choices such as \emph{high}, \emph{medium} or \emph{low} do not enable experts to quantify their uncertainty. However, it is important to measure the range of uncertainty surrounding responses in order to appropriately inform system vulnerability analysis. We use a recently introduced interval-valued response-format to capture uncertainty in experts' judgements and employ inferential statistical approaches to analyse the data. We identify key attributes that contribute to hop vulnerability in cyber-systems and demonstrate the value of capturing the uncertainty around these attributes. We find that this uncertainty is not only predictive of uncertainty in the overall vulnerability of a given system component, but also significantly informs ratings of overall component vulnerability itself. We propose that these methods and associated insights can be employed in real world situations, including vulnerability assessments of cyber-physical systems, which are becoming increasingly complex and integrated into society, making them particularly susceptible to uncertainty in assessment.
Boosting Image Recognition with Non-differentiable Constraints
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we study the problem of image recognition with non-differentiable constraints. A lot of real-life recognition applications require a rich output structure with deterministic constraints that are discrete or modeled by a non-differentiable function. A prime example is recognizing digit sequences, which are restricted by such rules (e.g., \textit{container code detection}, \textit{social insurance number recognition}, etc.). We investigate the usefulness of adding non-differentiable constraints in learning for the task of digit sequence recognition. Toward this goal, we synthesize six different datasets from MNIST and Cropped SVHN, with three discrete rules inspired by real-life protocols. To deal with the non-differentiability of these rules, we propose a reinforcement learning approach based on the policy gradient method. We find that incorporating this rule-based reinforcement can effectively increase the accuracy for all datasets and provide a good inductive bias which improves the model even with limited data. On one of the datasets, MNIST\_Rule2, models trained with rule-based reinforcement increase the accuracy by 4.7\% for 2000 samples and 23.6\% for 500 samples. We further test our model against synthesized adversarial examples, e.g., blocking out digits, and observe that adding our rule-based reinforcement increases the model robustness with a relatively smaller performance drop.
Speech-to-speech Translation between Untranscribed Unknown Languages
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we explore a method for training speech-to-speech translation tasks without any transcription or linguistic supervision. Our proposed method consists of two steps: First, we train and generate discrete representation with unsupervised term discovery with a discrete quantized autoencoder. Second, we train a sequence-to-sequence model that directly maps the source language speech to the target language's discrete representation. Our proposed method can directly generate target speech without any auxiliary or pre-training steps with a source or target transcription. To the best of our knowledge, this is the first work that performed pure speech-to-speech translation between untranscribed unknown languages.