OpenRaiser/Intern-Atlas huggingface dataset:
Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing
Venue: arXiv cs.CY 2020
Year: 2020

Although essential to revealing biased performance, well intentioned attempts at algorithmic auditing can have effects that may harm the very populations these measures are meant to protect. This concern is even more salient while auditing biometric systems such as facial recognition, where the data is sensitive and the technology is often used in ethically questionable manners. We demonstrate a set of five ethical concerns in the particular case of auditing commercial facial processing technology, highlighting additional design considerations and ethical tensions the auditor needs to be aware of so as not exacerbate or complement the harms propagated by the audited system. We go further to provide tangible illustrations of these concerns, and conclude by reflecting on what these concerns mean for the role of the algorithmic audit and the fundamental product limitations they reveal.
The extent and drivers of gender imbalance in neuroscience reference lists
Venue: arXiv cs.SI 2020
Year: 2020

Like many scientific disciplines, neuroscience has increasingly attempted to confront pervasive gender imbalances within the field. While much of the conversation has centered around publishing and conference participation, recent research in other fields has called attention to the prevalence of gender bias in citation practices. Because of the downstream effects that citations can have on visibility and career advancement, understanding and eliminating gender bias in citation practices is vital for addressing inequity in a scientific community. In this study, we sought to determine whether there is evidence of gender bias in the citation practices of neuroscientists. Using data from five top neuroscience journals, we find that reference lists tend to include more papers with men as first and last author than would be expected if gender were not a factor in referencing. Importantly, we show that this overcitation of men and undercitation of women is driven largely by the citation practices of men, and is increasing over time as the field becomes more diverse. We develop a co-authorship network to assess homophily in researchers' social networks, and we find that men tend to overcite men even when their social networks are representative. We discuss possible mechanisms and consider how individual researchers might address these findings in their own practices.
Simulating Ankle Torque during Walking Using a new Bioinspired Muscle Model with Application for Controlling a Powered Exoskeleton
Venue: arXiv cs.RO 2020
Year: 2020

Human-like motion is a primary goal for many robotic assistive devices. Emulating the strategy of the human neuromuscular system may aid the control of such powered devices, yet many challenges remain. In this study, we investigated the potential for using the winding filament model (WFM) of muscle to predict the net muscle moment of the ankle. The long-term goal is to use this model to improve ankle control of a commercial powered exoskeleton. The innovation aspects of this study are: First, there have been no commercialized active ankle exoskeletons available in the market. All the available exoskeletons have passive ankle joints, which cannot mimic human movement, especially in normal and fast walking [1]. Second, the Winding Filament Model Controller (WFMC) is the first control strategy based on a muscle model that does not use an electromyographic (EMG) signal as an input. The activation, which is calculated from EMG, is a crucial input parameter for almost all of the control strategies based on muscle modeling. However, the winding filament muscle model can predict muscle force by using muscle length as the primary input, and the activation input signal could be either a square wave [33] or a simple bell shape function like our study. This is one of the most important benefits of the WFMC strategy, since it makes this bioinspired strategy applicable for all patient populations, even those with impaired muscle activities or without muscle activities (e.g. stroke, spinal cord injury, Parkinson disease, etc.). Third, the WFMC is adaptive to different tasks like walking at different speeds, as well as walking over the incline and over stairs.
Optimal Binary Linear Codes from Maximal Arcs
Venue: arXiv cs.IT 2020
Year: 2020

The binary Hamming codes with parameters $[2^m-1, 2^m-1-m, 3]$ are perfect. Their extended codes have parameters $[2^m, 2^m-1-m, 4]$ and are distance-optimal. The first objective of this paper is to construct a class of binary linear codes with parameters $[2^{m+s}+2^s-2^m,2^{m+s}+2^s-2^m-2m-2,4]$, which have better information rates than the class of extended binary Hamming codes, and are also distance-optimal. The second objective is to construct a class of distance-optimal binary codes with parameters $[2^m+2, 2^m-2m, 6]$. Both classes of binary linear codes have new parameters.
Temporal Tensor Transformation Network for Multivariate Time Series Prediction
Venue: arXiv cs.LG 2020
Year: 2020

Multivariate time series prediction has applications in a wide variety of domains and is considered to be a very challenging task, especially when the variables have correlations and exhibit complex temporal patterns, such as seasonality and trend. Many existing methods suffer from strong statistical assumptions, numerical issues with high dimensionality, manual feature engineering efforts, and scalability. In this work, we present a novel deep learning architecture, known as Temporal Tensor Transformation Network, which transforms the original multivariate time series into a higher order of tensor through the proposed Temporal-Slicing Stack Transformation. This yields a new representation of the original multivariate time series, which enables the convolution kernel to extract complex and non-linear features as well as variable interactional signals from a relatively large temporal region. Experimental results show that Temporal Tensor Transformation Network outperforms several state-of-the-art methods on window-based predictions across various tasks. The proposed architecture also demonstrates robust prediction performance through an extensive sensitivity analysis.
DLockout: A Design Lockout Technique for Key Obfuscated RTL IP Designs
Venue: arXiv cs.CR 2020
Year: 2020

Intellectual Property (IP) infringement including piracy and over production have emerged as significant threats in the semiconductor supply chain. Key based obfuscation techniques (i.e., logic locking) are widely applied to secure legacy IP from such attacks. However, the fundamental question remains open whether an attacker is allowed an exponential amount of time to seek correct key or could it be useful to lock out the design in a non-destructive manner after several incorrect attempts. In this paper, we address this question with a robust design lockout technique. Specifically, we perform comparisons on obfuscation logic output that reflects the condition (correct or incorrect) of the applied key without changing the system behaviour. The proposed approach, when combined with key obfuscation (logic locking) technique, increases the difficulty of reverse engineering key obfuscated RTL module. We provide security evaluation of DLockout against three common side channel attacks followed by a quantitative assessment of the resilience. We conducted a set of experiments on four datapath intensive IPs and one crypto core for three different key lengths (32-, 64-, and 128-bit) under typical design corner. On average, DLockout incurs negligible area, power, and delay overheads.
Minimization Problems on Strictly Convex Divergences
Venue: arXiv cs.IT 2020
Year: 2020

The divergence minimization problem plays an important role in various fields. In this note, we focus on differentiable and strictly convex divergences. For some minimization problems, we show the minimizer conditions and the uniqueness of the minimizer without assuming a specific form of divergences. Furthermore, we show geometric properties related to the minimization problems.
Matrix Completion with Prior Subspace Information via Maximizing Correlation
Venue: arXiv cs.IT 2020
Year: 2020

This paper studies the problem of completing a low-rank matrix from a few of its random entries with the aid of prior information. We suggest a strategy to incorporate prior information into the standard matrix completion procedure by maximizing the correlation between the original signal and the prior information. We also establish performance guarantees for the proposed method, which show that with suitable prior information, the proposed procedure can reduce the sample complexity of the standard matrix completion by a logarithmic factor. To illustrate the theory, we further analyze an important practical application where the prior subspace information is available. Both synthetic and real-world experiments are provided to verify the validity of the theory.
The Asymptotic Generalized Poor-Verdu Bound Achieves the BSC Error Exponent at Zero Rate
Venue: arXiv cs.IT 2020
Year: 2020

The generalized Poor-Verdu error lower bound for multihypothesis testing is revisited. Its asymptotic expression is established in closed-form as its tilting parameter grows to infinity. It is also shown that the asymptotic generalized bound achieves the error exponent (or reliability function) of the memoryless binary symmetric channel at zero coding rates.
The Human Visual System and Adversarial AI
Venue: arXiv cs.CV 2020
Year: 2020

This paper applies theories about the Human Visual System to make Adversarial AI more effective. To date, Adversarial AI has modeled perceptual distances between clean and adversarial examples of images using Lp norms. These norms have the benefit of simple mathematical description and reasonable effectiveness in approximating perceptual distance. However, in prior decades, other areas of image processing have moved beyond simpler models like Mean Squared Error (MSE) towards more complex models that better approximate the Human Visual System (HVS). We demonstrate a proof of concept of incorporating HVS models into Adversarial AI.
Enhanced Huffman Coded OFDM with Index Modulation
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we propose an enhanced Huffman coded orthogonal frequency-division multiplexing with index modulation (EHC-OFDM-IM) scheme. The proposed scheme is capable of utilizing all legitimate subcarrier activation patterns (SAPs) and adapting the bijective mapping relation between SAPs and leaves on a given Huffman tree according to channel state information (CSI). As a result, a dynamic codebook update mechanism is obtained, which can provide more reliable transmissions. We take the average block error rate (BLER) as the performance evaluation metric and approximate it in closed form when the transmit power allocated to each subcarrier is independent of channel states. Also, we propose two CSI-based power allocation schemes with different requirements for computational complexity to further improve the error performance. Subsequently, we carry out numerical simulations to corroborate the error performance analysis and the proposed dynamic power allocation schemes. By studying the numerical results, we find that the depth of the Huffman tree has a significant impact on the error performance when the SAP-to-leaf mapping relation is optimized based on CSI. Meanwhile, through numerical results, we also discuss the trade-off between error performance and data transmission rate and investigate the impacts of imperfect CSI on the error performance of EHC-OFDM-IM.
A System for Real-Time Interactive Analysis of Deep Learning Training
Venue: arXiv cs.LG 2020
Year: 2020

Performing diagnosis or exploratory analysis during the training of deep learning models is challenging but often necessary for making a sequence of decisions guided by the incremental observations. Currently available systems for this purpose are limited to monitoring only the logged data that must be specified before the training process starts. Each time a new information is desired, a cycle of stop-change-restart is required in the training process. These limitations make interactive exploration and diagnosis tasks difficult, imposing long tedious iterations during the model development. We present a new system that enables users to perform interactive queries on live processes generating real-time information that can be rendered in multiple formats on multiple surfaces in the form of several desired visualizations simultaneously. To achieve this, we model various exploratory inspection and diagnostic tasks for deep learning training processes as specifications for streams using a map-reduce paradigm with which many data scientists are already familiar. Our design achieves generality and extensibility by defining composable primitives which is a fundamentally different approach than is used by currently available systems. The open source implementation of our system is available as TensorWatch project at https://github.com/microsoft/tensorwatch.
From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems
Venue: arXiv cs.LG 2020
Year: 2020

Machine learning methods adapt the parameters of a model, constrained to lie in a given model class, by using a fixed learning procedure based on data or active observations. Adaptation is done on a per-task basis, and retraining is needed when the system configuration changes. The resulting inefficiency in terms of data and training time requirements can be mitigated, if domain knowledge is available, by selecting a suitable model class and learning procedure, collectively known as inductive bias. However, it is generally difficult to encode prior knowledge into an inductive bias, particularly with black-box model classes such as neural networks. Meta-learning provides a way to automatize the selection of an inductive bias. Meta-learning leverages data or active observations from tasks that are expected to be related to future, and a priori unknown, tasks of interest. With a meta-trained inductive bias, training of a machine learning model can be potentially carried out with reduced training data and/or time complexity. This paper provides a high-level introduction to meta-learning with applications to communication systems.
Cooperative Initialization based Deep Neural Network Training
Venue: arXiv cs.CV 2020
Year: 2020

Researchers have proposed various activation functions. These activation functions help the deep network to learn non-linear behavior with a significant effect on training dynamics and task performance. The performance of these activations also depends on the initial state of the weight parameters, i.e., different initial state leads to a difference in the performance of a network. In this paper, we have proposed a cooperative initialization for training the deep network using ReLU activation function to improve the network performance. Our approach uses multiple activation functions in the initial few epochs for the update of all sets of weight parameters while training the network. These activation functions cooperate to overcome their drawbacks in the update of weight parameters, which in effect learn better "feature representation" and boost the network performance later. Cooperative initialization based training also helps in reducing the overfitting problem and does not increase the number of parameters, inference (test) time in the final model while improving the performance. Experiments show that our approach outperforms various baselines and, at the same time, performs well over various tasks such as classification and detection. The Top-1 classification accuracy of the model trained using our approach improves by 2.8% for VGG-16 and 2.1% for ResNet-56 on CIFAR-100 dataset.
Multi-Antenna Coded Content Delivery with Caching: A Low-Complexity Solution
Venue: arXiv cs.IT 2020
Year: 2020

We study downlink beamforming in a single-cell network with a multi-antenna base station serving cache-enabled users. Assuming a library of files with a common rate, we formulate the minimum transmit power with proactive caching and coded delivery as a non-convex optimization problem. While this multiple multicast problem can be efficiently solved by successive convex approximation (SCA), the complexity of the problem grows exponentially with the number of subfiles delivered to each user in each time slot, which itself grows exponentially with the number of users. We introduce a low-complexity alternative through time-sharing that limits the number of subfiles received by a user in each time slot. We then consider the joint design of beamforming and content delivery with sparsity constraints to limit the number of subfiles received by a user in each time slot. Numerical simulations show that the low-complexity scheme has only a small performance gap to that obtained by solving the joint problem with sparsity constraints, and outperforms state-of-the-art results at all signal-to-noise ratio (SNR) and rate values with a sufficient number of transmit antennas. A lower bound on the achievable degrees-of-freedom (DoF) of the low-complexity scheme is derived to characterize its performance in the high SNR regime.
A Robust Pose Transformational GAN for Pose Guided Person Image Synthesis
Venue: arXiv cs.CV 2020
Year: 2020

Generating photorealistic images of human subjects in any unseen pose have crucial applications in generating a complete appearance model of the subject. However, from a computer vision perspective, this task becomes significantly challenging due to the inability of modelling the data distribution conditioned on pose. Existing works use a complicated pose transformation model with various additional features such as foreground segmentation, human body parsing etc. to achieve robustness that leads to computational overhead. In this work, we propose a simple yet effective pose transformation GAN by utilizing the Residual Learning method without any additional feature learning to generate a given human image in any arbitrary pose. Using effective data augmentation techniques and cleverly tuning the model, we achieve robustness in terms of illumination, occlusion, distortion and scale. We present a detailed study, both qualitative and quantitative, to demonstrate the superiority of our model over the existing methods on two large datasets.
Snappy: Fast On-chain Payments with Practical Collaterals
Venue: arXiv cs.CR 2020
Year: 2020

Permissionless blockchains offer many advantages but also have significant limitations including high latency. This prevents their use in important scenarios such as retail payments, where merchants should approve payments fast. Prior works have attempted to mitigate this problem by moving transactions off the chain. However, such Layer-2 solutions have their own problems: payment channels require a separate deposit towards each merchant and thus significant locked-in funds from customers; payment hubs require very large operator deposits that depend on the number of customers; and side-chains require trusted validators. In this paper, we propose Snappy, a novel solution that enables recipients, like merchants, to safely accept fast payments. In Snappy, all payments are on the chain, while small customer collaterals and moderate merchant collaterals act as payment guarantees. Besides receiving payments, merchants also act as statekeepers who collectively track and approve incoming payments using majority voting. In case of a double-spending attack, the victim merchant can recover lost funds either from the collateral of the malicious customer or a colluding statekeeper (merchant). Snappy overcomes the main problems of previous solutions: a single customer collateral can be used to shop with many merchants; merchant collaterals are independent of the number of customers; and validators do not have to be trusted. Our Ethereum prototype shows that safe, fast (<2 seconds) and cheap payments are possible on existing blockchains.
Hardware/Software Co-verification Using Path-based Symbolic Execution
Venue: arXiv cs.FL 2020
Year: 2020

Conventional tools for formal hardware/software co-verification use bounded model checking techniques to construct a single monolithic propositional formula. Formulas generated in this way are extremely complex and contain a great deal of irrelevant logic, hence are difficult to solve even by the state-of-the-art Satis ability (SAT) solvers. In a typical hardware/software co-design the firmware only exercises a fraction of the hardware state-space, and we can use this observation to generate simpler and more concise formulas. In this paper, we present a novel verification algorithm for hardware/software co-designs that identify partitions of the firmware and the hardware logic pertaining to the feasible execution paths by means of path-based symbolic simulation with custom path-pruning, property-guided slicing and incremental SAT solving. We have implemented this approach in our tool COVERIF. We have experimentally compared COVERIF with HW-CBMC, a monolithic BMC based co-verification tool, and observed an average speed-up of 5X over HW-CBMC for proving safety properties as well as detecting critical co-design bugs in an open-source Universal Asynchronous Receiver Transmitter design and a large SoC design.
Creating Efficient Blockchains for the Internet of Things by Coordinated Satellite-Terrestrial Networks
Venue: arXiv cs.NI 2020
Year: 2020

Blockchain has emerged as a promising technology that can guarantee data consistency and integrity among distributed participants. It has been used in many applications of the Internet of Things (IoT). However, since IoT applications often introduce a massive number of devices into blockchain systems, the efficiency of the blockchain becomes a serious problem. In this article, we analyze the key factors affecting the efficiency of blockchain. Unlike most existing solutions that handle this from the computing perspective, we consider the problem from the communication perspective. Particularly, we propose a coordinated satellite-terrestrial network to create efficient blockchains. We also derive a network scheduling strategy for the proposed architecture. Simulation results demonstrate that the proposed system can support blockchains for higher efficiency. Moreover, several open research issues and design challenges will be discussed.