OpenRaiser/Intern-Atlas huggingface dataset:
Uplink-Downlink Channel Covariance Transformations and Precoding Design for FDD Massive MIMO
Venue: arXiv cs.IT 2019
Year: 2019

A large majority of cellular networks deployed today make use of Frequency Division Duplexing (FDD) where, in contrast with Time Division Duplexing (TDD), the channel reciprocity does not hold and explicit downlink (DL) probing and uplink (UL) feedback are needed in order to achieve spatial multiplexing gain. To support massive MIMO, the overhead incurred by conventional DL probing and UL feedback schemes scales linearly with the number of BS antennas and, therefore, may be very large. In this paper, we present a new approach to achieve a very competitive trade-off between spatial multiplexing gain and probing-feedback overhead in such systems. Our approach is based on two novel methods: (i) an efficient regularization technique based on Deep Neural Networks (DNN) that learns the Angular Spread Function (ASF) of users channels and permits to estimate the DL covariance matrix from the noisy i.i.d. channel observations obtained freely via UL pilots (UL-DL covariance transformation), (ii) a novel "sparsifying precoding" technique that uses the estimated DL covariance matrix from (i) and imposes a controlled sparsity on the DL channel such that given any assigned DL pilot dimension, it is able to find an optimal sparsity level and a corresponding sparsifying precoder for which the "effective" channel vectors after sparsification can be estimated at the BS with a low mean-square error. We compare our proposed DNN-based method in (i) with other methods in the literature via numerical simulations and show that it yields a very competitive performance. We also compare our sparsifying precoder in (ii) with the state-of-the-art statistical beamforming methods under the assumption that those methods also have access to the covariance knowledge in the DL and show that our method yields higher spectral efficiency since it uses in addition the instantaneous channel information after sparsification.
Leveraging Operational Technology and the Internet of Things to Attack Smart Buildings
Venue: arXiv cs.CR 2019
Year: 2019

In recent years, the buildings where we spend most part of our life are rapidly evolving. They are becoming fully automated environments where energy consumption, access control, heating and many other subsystems are all integrated within a single system commonly referred to as smart building (SB). To support the growing complexity of building operations, building automation systems (BAS) powering SBs are integrating consumer range Internet of Things (IoT) devices such as IP cameras alongside with operational technology (OT) controllers and actuators. However, these changes pose important cybersecurity concerns since the attack surface is larger, attack vectors are increasing and attacks can potentially harm building occupants. In this paper, we analyze the threat landscape of BASs by focusing on subsystems which are strongly affected by the advent of IoT devices such as video surveillance systems and smart lightning. We demonstrate how BAS operation can be disrupted by simple attacks to widely used network protocols. Furthermore, using both known and 0-day vulnerabilities reported in the paper and previously disclosed, we present the first (at our knowledge) BAS-specific malware which is able to persist within the BAS network by leveraging both OT and IoT devices connected to the BAS. Our research highlights how BAS networks can be considered as critical as industrial control systems and security concerns in BASs deserve more attention from both industrial and scientific communities. Even within a simulated environment, our proof-of-concept attacks were carried out with relative ease and a limited amount of budget and resources. Therefore, we believe that well-funded attack groups will increasingly shift their focus towards BASs with the potential of impacting the live of thousands of people.
Smartphone Multi-modal Biometric Authentication: Database and Evaluation
Venue: arXiv cs.CV 2019
Year: 2019

Biometric-based verification is widely employed on the smartphones for various applications, including financial transactions. In this work, we present a new multimodal biometric dataset (face, voice, and periocular) acquired using a smartphone. The new dataset is comprised of 150 subjects that are captured in six different sessions reflecting real-life scenarios of smartphone assisted authentication. One of the unique features of this dataset is that it is collected in four different geographic locations representing a diverse population and ethnicity. Additionally, we also present a multimodal Presentation Attack (PA) or spoofing dataset using a low-cost Presentation Attack Instrument (PAI) such as print and electronic display attacks. The novel acquisition protocols and the diversity of the data subjects collected from different geographic locations will allow developing a novel algorithm for either unimodal or multimodal biometrics. Further, we also report the performance evaluation of the baseline biometric verification and Presentation Attack Detection (PAD) on the newly collected dataset.
Hindsight Credit Assignment
Venue: arXiv cs.LG 2019
Year: 2019

We consider the problem of efficient credit assignment in reinforcement learning. In order to efficiently and meaningfully utilize new data, we propose to explicitly assign credit to past decisions based on the likelihood of them having led to the observed outcome. This approach uses new information in hindsight, rather than employing foresight. Somewhat surprisingly, we show that value functions can be rewritten through this lens, yielding a new family of algorithms. We study the properties of these algorithms, and empirically show that they successfully address important credit assignment challenges, through a set of illustrative tasks.
Iterative Policy-Space Expansion in Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

Humans and animals solve a difficult problem much more easily when they are presented with a sequence of problems that starts simple and slowly increases in difficulty. We explore this idea in the context of reinforcement learning. Rather than providing the agent with an externally provided curriculum of progressively more difficult tasks, the agent solves a single task utilizing a decreasingly constrained policy space. The algorithm we propose first learns to categorize features into positive and negative before gradually learning a more refined policy. Experimental results in Tetris demonstrate superior learning rate of our approach when compared to existing algorithms.
Polarized Low-Density Parity-Check Codes on the BSC
Venue: arXiv cs.IT 2019
Year: 2019

The connections between variable nodes and check nodes have a great influence on the performance of low-density parity-check (LDPC) codes. Inspired by the unique structure of polar code's generator matrix, we proposed a new method of constructing LDPC codes that achieves a polarization effect. The new code, named as polarized LDPC codes, is shown to achieve lower or no error floor in the binary symmetric channel (BSC)
GPU Computing with Python: Performance, Energy Efficiency and Usability
Venue: arXiv cs.DC 2019
Year: 2019

In this work, we examine the performance, energy efficiency and usability when using Python for developing HPC codes running on the GPU. We investigate the portability of performance and energy efficiency between CUDA and OpenCL; between GPU generations; and between low-end, mid-range and high-end GPUs. Our findings show that the impact of using Python is negligible for our applications, and furthermore, CUDA and OpenCL applications tuned to an equivalent level can in many cases obtain the same computational performance. Our experiments show that performance in general varies more between different GPUs than between using CUDA and OpenCL. We also show that tuning for performance is a good way of tuning for energy efficiency, but that specific tuning is needed to obtain optimal energy efficiency.
Cryptographically Strong Permutations from the Butterfly Structure
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we present infinite families of permutations of $\mathbb{F}_{2^{2n}}$ with high nonlinearity and boomerang uniformity $4$ from generalized butterfly structures. Both open and closed butterfly structures are considered. It appears, according to experiment results, that open butterflies do not produce permutation with boomerang uniformity $4$. For the closed butterflies, we propose the condition on coefficients $α, β\in \mathbb{F}_{2^n}$ such that the functions $$V_i := (R_i(x,y), R_i(y,x))$$ with $R_i(x,y)=(x+αy)^{2^i+1}+βy^{2^i+1}$ are permutations of $\mathbb{F}_{2^n}^2$ with boomerang uniformity $4$, where $n\geq 1$ is an odd integer and $\gcd(i, n)=1$. The main result in this paper consists of two major parts: the permutation property of $V_i$ is investigated in terms of the univariate form, and the boomerang uniformity is examined in terms of the original bivariate form. In addition, experiment results for $n=3, 5$ indicates that the proposed condition seems to cover all coefficients $α, β\in \mathbb{F}_{2^n}$ that produce permutations $V_i$ with boomerang uniformity $4$. However, the experiment result shows that the quadratic permutation $V_i$ seems to be affine equivalent to the Gold function. Therefore, unluckily, we may not to obtain new permutations with boomerang uniformity $4$ from the butterfly structure.
Autonomous Robot Swarms for Off-World Construction and Resource Mining
Venue: arXiv cs.RO 2019
Year: 2019

Kick-starting the space economy requires identification of critical resources that can lower the cost of space transport, sustain logistic bases and communication relay networks between major nodes in the network. One important challenge with this space-economy is ensuring the low-cost transport of raw materials from one gravity-well to another. The escape delta-v of 11.2 km/s from Earth makes this proposition very expensive. Transporting materials from the Moon takes 2.4 km/s and from Mars 5.0 km/s. Based on these factors, the Moon and Mars have the potential to export material into this space economy. Water has been identified as a critical resource both to sustain human-life but also for use in propulsion, attitude-control, power, thermal storage and radiation protection systems.There is also important need for construction materials such as aluminum, iron/steel, and titanium. Based upon these important findings, we have developed an energy model to determine the feasibility of developing a mining base on the Moon and Mars. These mining base mine and principally exports water, aluminum, titanium and steel. Our designs for a mining base utilize renewable energy sources namely photovoltaics and solar-thermal concentrators to provide power to construct the base, keep it operational and export water and other resources using a Mass Driver. Using the energy model developed, we will determine the energy per Earth-day to export 100 tons each of water, titanium, aluminum and low-grade steel into escape velocity of the Moon and Mars. We perform a detailed comparison of the energy required for construction of similar bases on the Moon and Mars, in addition to the operating energy required for regolith excavation, processing, refining and finally transport off-the-body.
In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processes
Venue: arXiv cs.CY 2019
Year: 2019

Many decision-making processes have begun to incorporate an AI element, including prison sentence recommendations, college admissions, hiring, and mortgage approval. In all of these cases, AI models are being trained to help human decision makers reach accurate and fair judgments, but little is known about what factors influence the extent to which people consider an AI-infused decision-making process to be trustworthy. We aim to understand how different factors about a decision-making process, and an AI model that supports that process, influences peoples' perceptions of the trustworthiness of that process. We report on our evaluation of how seven different factors -- decision stakes, decision authority, model trainer, model interpretability, social transparency, and model confidence -- influence ratings of trust in a scenario-based study.
Measurement and analysis of visitors' trajectories in crowded museums
Venue: arXiv cs.HC 2019
Year: 2019

We tackle the issue of measuring and analyzing the visitors' dynamics in crowded museums. We propose an IoT-based system -- supported by artificial intelligence models -- to reconstruct the visitors' trajectories throughout the museum spaces. Thanks to this tool, we are able to gather wide ensembles of visitors' trajectories, allowing useful insights for the facility management and the preservation of the art pieces. Our contribution comes with one successful use case: the Galleria Borghese in Rome, Italy.
Revisiting Few-Shot Learning for Facial Expression Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Most of the existing deep neural nets on automatic facial expression recognition focus on a set of predefined emotion classes, where the amount of training data has the biggest impact on performance. However, in the standard setting over-parameterised neural networks are not amenable for learning from few samples as they can quickly over-fit. In addition, these approaches do not have such a strong generalisation ability to identify a new category, where the data of each category is too limited and significant variations exist in the expression within the same semantic category. We embrace these challenges and formulate the problem as a low-shot learning, where once the base classifier is deployed, it must rapidly adapt to recognise novel classes using a few samples. In this paper, we revisit and compare existing few-shot learning methods for the low-shot facial expression recognition in terms of their generalisation ability via episode-training. In particular, we extend our analysis on the cross-domain generalisation, where training and test tasks are not drawn from the same distribution. We demonstrate the efficacy of low-shot learning methods through extensive experiments.
An Accelerated Correlation Filter Tracker
Venue: arXiv cs.CV 2019
Year: 2019

Recent visual object tracking methods have witnessed a continuous improvement in the state-of-the-art with the development of efficient discriminative correlation filters (DCF) and robust deep neural network features. Despite the outstanding performance achieved by the above combination, existing advanced trackers suffer from the burden of high computational complexity of the deep feature extraction and online model learning. We propose an accelerated ADMM optimisation method obtained by adding a momentum to the optimisation sequence iterates, and by relaxing the impact of the error between DCF parameters and their norm. The proposed optimisation method is applied to an innovative formulation of the DCF design, which seeks the most discriminative spatially regularised feature channels. A further speed up is achieved by an adaptive initialisation of the filter optimisation process. The significantly increased convergence of the DCF filter is demonstrated by establishing the optimisation process equivalence with a continuous dynamical system for which the convergence properties can readily be derived. The experimental results obtained on several well-known benchmarking datasets demonstrate the efficiency and robustness of the proposed ACFT method, with a tracking accuracy comparable to the start-of-the-art trackers.
Designing for Privacy and Confidentiality on Distributed Ledgers for Enterprise (Industry Track)
Venue: arXiv cs.CR 2019
Year: 2019

Distributed ledger technology offers numerous desirable attributes to applications in the enterprise context. However, with distributed data and decentralized computation on a shared platform, privacy and confidentiality challenges arise. Any design for an enterprise system needs to carefully cater for use case specific privacy and confidentiality needs. With the goal to facilitate the design of enterprise solutions, this paper aims to provide a guide to navigate and aid in decisions around common requirements and mechanisms that prevent the leakage of private and confidential information. To further contextualize key concepts, the design guide is then applied to three enterprise DLT protocols: Hyperledger Fabric, Corda, and Quorum.
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport
Venue: arXiv cs.LG 2019
Year: 2019

Data assimilation for parameter and state estimation in subsurface transport problems remains a significant challenge due to the sparsity of measurements, the heterogeneity of porous media, and the high computational cost of forward numerical models. We present a physics-informed deep neural networks (DNNs) machine learning method for estimating space-dependent hydraulic conductivity, hydraulic head, and concentration fields from sparse measurements. In this approach, we employ individual DNNs to approximate the unknown parameters (e.g., hydraulic conductivity) and states (e.g., hydraulic head and concentration) of a physical system, and jointly train these DNNs by minimizing the loss function that consists of the governing equations residuals in addition to the error with respect to measurement data. We apply this approach to assimilate conductivity, hydraulic head, and concentration measurements for joint inversion of the conductivity, hydraulic head, and concentration fields in a steady-state advection--dispersion problem. We study the accuracy of the physics-informed DNN approach with respect to data size, number of variables (conductivity and head versus conductivity, head, and concentration), DNNs size, and DNN initialization during training. We demonstrate that the physics-informed DNNs are significantly more accurate than standard data-driven DNNs when the training set consists of sparse data. We also show that the accuracy of parameter estimation increases as additional variables are inverted jointly.
Observational Overfitting in Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the observations generated by the Markov Decision Process (MDP). We provide a general framework for analyzing this scenario, which we use to design multiple synthetic benchmarks from only modifying the observation space of an MDP. When an agent overfits to different observation spaces even if the underlying MDP dynamics is fixed, we term this observational overfitting. Our experiments expose intriguing properties especially with regards to implicit regularization, and also corroborate results from previous works in RL generalization and supervised learning (SL).
A Survey on Theorem Provers in Formal Methods
Venue: arXiv cs.SE 2019
Year: 2019

Mechanical reasoning is a key area of research that lies at the crossroads of mathematical logic and artificial intelligence. The main aim to develop mechanical reasoning systems (also known as theorem provers) was to enable mathematicians to prove theorems by computer programs. However, these tools evolved with time and now play vital role in the modeling and reasoning about complex and large-scale systems, especially safety-critical systems. Technically, mathematical formalisms and automated reasoning based-approaches are employed to perform inferences and to generate proofs in theorem provers. In literature, there is a shortage of comprehensive documents that can provide proper guidance about the preferences of theorem provers with respect to their designs, performances, logical frameworks, strengths, differences and their application areas. In this work, more than 40 theorem provers are studied in detail and compared to present a comprehensive analysis and evaluation of these tools. Theorem provers are investigated based on various parameters, which includes: implementation architecture, logic and calculus used, library support, level of automation, programming paradigm, programming language, differences and application areas.
Document Network Embedding: Coping for Missing Content and Missing Links
Venue: arXiv cs.IR 2019
Year: 2019

Searching through networks of documents is an important task. A promising path to improve the performance of information retrieval systems in this context is to leverage dense node and content representations learned with embedding techniques. However, these techniques cannot learn representations for documents that are either isolated or whose content is missing. To tackle this issue, assuming that the topology of the network and the content of the documents correlate, we propose to estimate the missing node representations from the available content representations, and conversely. Inspired by recent advances in machine translation, we detail in this paper how to learn a linear transformation from a set of aligned content and node representations. The projection matrix is efficiently calculated in terms of the singular value decomposition. The usefulness of the proposed method is highlighted by the improved ability to predict the neighborhood of nodes whose links are unobserved based on the projected content representations, and to retrieve similar documents when content is missing, based on the projected node representations.
FlakiMe: Laboratory-Controlled Test Flakiness Impact Assessment. A Case Study on Mutation Testing and Program Repair
Venue: arXiv cs.SE 2019
Year: 2019

Much research on software testing makes an implicit assumption that test failures are deterministic such that they always witness the presence of the same defects. However, this assumption is not always true because some test failures are due to so-called flaky tests, i.e., tests with non-deterministic outcomes. Unfortunately, flaky tests have major implications for testing and test-dependent activities such as mutation testing and automated program repair. To deal with this issue, we introduce a test flakiness assessment and experimentation platform, called FlakiMe, that supports the seeding of a (controllable) degree of flakiness into the behaviour of a given test suite. Thereby, FlakiMe equips researchers with ways to investigate the impact of test flakiness on their techniques under laboratory-controlled conditions. We use FlakiME to report results and insights from case studies that assesses the impact of flakiness on mutation testing and program repair. These results indicate that a 5% of flakiness failures is enough to affect the mutation score, but the effect size is modest (2% - 4% ), while it completely annihilates the ability of program repair to patch 50% of the subject programs. We also observe that flakiness has case-specific effects, which mainly disrupts the repair of bugs that are covered by many tests. Moreover, we find that a minimal amount of user feedback is sufficient for alleviating the effects of flakiness.
A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement
Venue: arXiv cs.LG 2019
Year: 2019

In real world scenarios, objects are often partially occluded. This requires a robustness for object recognition against these perturbations. Convolutional networks have shown good performances in classification tasks. The learned convolutional filters seem similar to receptive fields of simple cells found in the primary visual cortex. Alternatively, spiking neural networks are more biological plausible. We developed a two layer spiking network, trained on natural scenes with a biologically plausible learning rule. It is compared to two deep convolutional neural networks using a classification task of stepwise pixel erasement on MNIST. In comparison to these networks the spiking approach achieves good accuracy and robustness.