OpenRaiser/Intern-Atlas huggingface dataset:
Coherence Traffic in Manycore Processors with Opaque Distributed Directories
Venue: arXiv cs.DC 2020
Year: 2020

Manycore processors feature a high number of general-purpose cores designed to work in a multithreaded fashion. Recent manycore processors are kept coherent using scalable distributed directories. A paramount example is the Intel Mesh interconnect, which consists of a network-on-chip interconnecting "tiles", each of which contains computation cores, local caches, and coherence masters. The distributed coherence subsystem must be queried for every out-of-tile access, imposing an overhead on memory latency. This paper studies the physical layout of an Intel Knights Landing processor, with a particular focus on the coherence subsystem, and uncovers the pseudo-random mapping function of physical memory blocks across the pieces of the distributed directory. Leveraging this knowledge, candidate optimizations to improve memory latency through the minimization of coherence traffic are studied. Although these optimizations do improve memory throughput, ultimately this does not translate into performance gains due to inherent overheads stemming from the computational complexity of the mapping functions.
Proof of Authenticity of Logistics Information with Passive RFID Tags and Blockchain
Venue: arXiv cs.CR 2020
Year: 2020

In tracing the (robotically automated) logistics of large quantities of goods, inexpensive passive RFID tags are preferred for cost reasons. Accordingly, security between such tags and readers have primarily been studied among many issues of RFID. However, the authenticity of data cannot be guaranteed if logistics services can give false information. Although the use of blockchain is often discussed, it is simply a recording system, so there is a risk that false records may be written to it. As a solution, we propose a design in which a digitally signing, location-constrained and tamper-evident reader atomically writes an evidence to blockchain along with its reading and writing a tag. By semi-formal modeling, we confirmed that the confidentiality and integrity of the information can be maintained throughout the system, and digitally signed data can be verified later despite possible compromise of private keys or signature algorithms, or expiration of public key certificates. We also introduce a prototype design to show that our proposal is viable. This makes it possible to trace authentic logistics information using inexpensive passive RFID tags. Furthermore, by abstracting the reader/writer as a sensor/actuator, this model can be extended to IoT in general.
Perturbation-based exploration methods in deep reinforcement learning
Venue: arXiv cs.LG 2020
Year: 2020

Recent research on structured exploration placed emphasis on identifying novel states in the state space and incentivizing the agent to revisit them through intrinsic reward bonuses. In this study, we question whether the performance boost demonstrated through these methods is indeed due to the discovery of structure in exploratory schedule of the agent or is the benefit largely attributed to the perturbations in the policy and reward space manifested in pursuit of structured exploration. In this study we investigate the effect of perturbations in policy and reward spaces on the exploratory behavior of the agent. We proceed to show that simple acts of perturbing the policy just before the softmax layer and introduction of sporadic reward bonuses into the domain can greatly enhance exploration in several domains of the arcade learning environment. In light of these findings, we recommend benchmarking any enhancements to structured exploration research against the backdrop of noisy exploration.
Automatic Open-World Reliability Assessment
Venue: arXiv cs.CV 2020
Year: 2020

Image classification in the open-world must handle out-of-distribution (OOD) images. Systems should ideally reject OOD images, or they will map atop of known classes and reduce reliability. Using open-set classifiers that can reject OOD inputs can help. However, optimal accuracy of open-set classifiers depend on the frequency of OOD data. Thus, for either standard or open-set classifiers, it is important to be able to determine when the world changes and increasing OOD inputs will result in reduced system reliability. However, during operations, we cannot directly assess accuracy as there are no labels. Thus, the reliability assessment of these classifiers must be done by human operators, made more complex because networks are not 100% accurate, so some failures are to be expected. To automate this process, herein, we formalize the open-world recognition reliability problem and propose multiple automatic reliability assessment policies to address this new problem using only the distribution of reported scores/probability data. The distributional algorithms can be applied to both classic classifiers with SoftMax as well as the open-world Extreme Value Machine (EVM) to provide automated reliability assessment. We show that all of the new algorithms significantly outperform detection using the mean of SoftMax.
Differentially Private Synthetic Data: Applied Evaluations and Enhancements
Venue: arXiv cs.LG 2020
Year: 2020

Machine learning practitioners frequently seek to leverage the most informative available data, without violating the data owner's privacy, when building predictive models. Differentially private data synthesis protects personal details from exposure, and allows for the training of differentially private machine learning models on privately generated datasets. But how can we effectively assess the efficacy of differentially private synthetic data? In this paper, we survey four differentially private generative adversarial networks for data synthesis. We evaluate each of them at scale on five standard tabular datasets, and in two applied industry scenarios. We benchmark with novel metrics from recent literature and other standard machine learning tools. Our results suggest some synthesizers are more applicable for different privacy budgets, and we further demonstrate complicating domain-based tradeoffs in selecting an approach. We offer experimental learning on applied machine learning scenarios with private internal data to researchers and practioners alike. In addition, we propose QUAIL, an ensemble-based modeling approach to generating synthetic data. We examine QUAIL's tradeoffs, and note circumstances in which it outperforms baseline differentially private supervised learning models under the same budget constraint.
Learning Agile Locomotion Skills with a Mentor
Venue: arXiv cs.RO 2020
Year: 2020

Developing agile behaviors for legged robots remains a challenging problem. While deep reinforcement learning is a promising approach, learning truly agile behaviors typically requires tedious reward shaping and careful curriculum design. We formulate agile locomotion as a multi-stage learning problem in which a mentor guides the agent throughout the training. The mentor is optimized to place a checkpoint to guide the movement of the robot's center of mass while the student (i.e. the robot) learns to reach these checkpoints. Once the student can solve the task, we teach the student to perform the task without the mentor. We evaluate our proposed learning system with a simulated quadruped robot on a course consisting of randomly generated gaps and hurdles. Our method significantly outperforms a single-stage RL baseline without a mentor, and the quadruped robot can agilely run and jump across gaps and obstacles. Finally, we present a detailed analysis of the learned behaviors' feasibility and efficiency.
E-commerce Query-based Generation based on User Review
Venue: arXiv cs.CL 2020
Year: 2020

With the increasing number of merchandise on e-commerce platforms, users tend to refer to reviews of other shoppers to decide which product they should buy. However, with so many reviews of a product, users often have to spend lots of time browsing through reviews talking about product attributes they do not care about. We want to establish a system that can automatically summarize and answer user's product specific questions. In this study, we propose a novel seq2seq based text generation model to generate answers to user's question based on reviews posted by previous users. Given a user question and/or target sentiment polarity, we extract aspects of interest and generate an answer that summarizes previous relevant user reviews. Specifically, our model performs attention between input reviews and target aspects during encoding and is conditioned on both review rating and input context during decoding. We also incorporate a pre-trained auxiliary rating classifier to improve model performance and accelerate convergence during training. Experiments using real-world e-commerce dataset show that our model achieves improvement in performance compared to previously introduced models.
An application of an Embedded Model Estimator to a synthetic non-stationary reservoir model with multiple secondary variables
Venue: arXiv cs.CE 2020
Year: 2020

A method (Ember) for non-stationary spatial modelling with multiple secondary variables by combining Geostatistics with Random Forests is applied to a three-dimensional Reservoir Model. It extends the Random Forest method to an interpolation algorithm retaining similar consistency properties to both Geostatistical algorithms and Random Forests. It allows embedding of simpler interpolation algorithms into the process, combining them through the Random Forest training process. The algorithm estimates a conditional distribution at each target location. The family of such distributions is called the model envelope. An algorithm to produce stochastic simulations from the envelope is demonstrated. This algorithm allows the influence of the secondary variables as well as the variability of the result to vary by location in the simulation.
Docking two multirotors in midair using relative vision measurements
Venue: arXiv cs.RO 2020
Year: 2020

Modular robots have been rising in popularity for a variety of applications, and autonomous midair docking is a necessary task for real world deployment of these robots. We present a state estimator based on the extended Kalman filter for relative localization of one multirotor with respect to another using only onboard sensors, specifically an inertial measurement unit and a camera-marker pair. Acceleration and angular velocity measurements along with relative pose measurements from a camera on the first multirotor looking at a marker on the second multirotor are used to estimate the relative position and velocity of the first multirotor with respect to the second, and the absolute attitude of the first multirotor. We also present a control architecture to use these onboard state estimates to control the first multirotor at a desired setpoint with respect to the second. The performance of the estimator and control architecture are experimentally validated by successfully and repeatably performing midair docking -- a task that requires relative position precision on the order of a centimeter.
Compression Boosts Differentially Private Federated Learning
Venue: arXiv cs.LG 2020
Year: 2020

Federated Learning allows distributed entities to train a common model collaboratively without sharing their own data. Although it prevents data collection and aggregation by exchanging only parameter updates, it remains vulnerable to various inference and reconstruction attacks where a malicious entity can learn private information about the participants' training data from the captured gradients. Differential Privacy is used to obtain theoretically sound privacy guarantees against such inference attacks by noising the exchanged update vectors. However, the added noise is proportional to the model size which can be very large with modern neural networks. This can result in poor model quality. In this paper, compressive sensing is used to reduce the model size and hence increase model quality without sacrificing privacy. We show experimentally, using 2 datasets, that our privacy-preserving proposal can reduce the communication costs by up to 95% with only a negligible performance penalty compared to traditional non-private federated learning schemes.
Optimized Power Control for Over-the-Air Federated Edge Learning
Venue: arXiv cs.IT 2020
Year: 2020

Over-the-air federated edge learning (Air-FEEL) is a communication-efficient solution for privacy-preserving distributed learning over wireless networks. Air-FEEL allows "one-shot" over-the-air aggregation of gradient/model-updates by exploiting the waveform superposition property of wireless channels, and thus promises an extremely low aggregation latency that is independent of the network size. However, such communication efficiency may come at a cost of learning performance degradation due to the aggregation error caused by the non-uniform channel fading over devices and noise perturbation. Prior work adopted channel inversion power control (or its variants) to reduce the aggregation error by aligning the channel gains, which, however, could be highly suboptimal in deep fading scenarios due to the noise amplification. To overcome this issue, we investigate the power control optimization for enhancing the learning performance of Air-FEEL. Towards this end, we first analyze the convergence behavior of the Air-FEEL by deriving the optimality gap of the loss-function under any given power control policy. Then we optimize the power control to minimize the optimality gap for accelerating convergence, subject to a set of average and maximum power constraints at edge devices. The problem is generally non-convex and challenging to solve due to the coupling of power control variables over different devices and iterations. To tackle this challenge, we develop an efficient algorithm by jointly exploiting the successive convex approximation (SCA) and trust region methods. Numerical results show that the optimized power control policy achieves significantly faster convergence than the benchmark policies such as channel inversion and uniform power transmission.
Deep Time Delay Neural Network for Speech Enhancement with Full Data Learning
Venue: arXiv cs.SD 2020
Year: 2020

Recurrent neural networks (RNNs) have shown significant improvements in recent years for speech enhancement. However, the model complexity and inference time cost of RNNs are much higher than deep feed-forward neural networks (DNNs). Therefore, these limit the applications of speech enhancement. This paper proposes a deep time delay neural network (TDNN) for speech enhancement with full data learning. The TDNN has excellent potential for capturing long range temporal contexts, which utilizes a modular and incremental design. Besides, the TDNN preserves the feed-forward structure so that its inference cost is comparable to standard DNN. To make full use of the training data, we propose a full data learning method for speech enhancement. More specifically, we not only use the noisy-to-clean (input-to-target) to train the enhanced model, but also the clean-to-clean and noise-to-silence data. Therefore, all of the training data can be used to train the enhanced model. Our experiments are conducted on TIMIT dataset. Experimental results show that our proposed method could achieve a better performance than DNN and comparable even better performance than BLSTM. Meanwhile, compared with the BLSTM, the proposed method drastically reduce the inference time.
Decentralized Motion Planning for Multi-Robot Navigation using Deep Reinforcement Learning
Venue: arXiv cs.RO 2020
Year: 2020

This work presents a decentralized motion planning framework for addressing the task of multi-robot navigation using deep reinforcement learning. A custom simulator was developed in order to experimentally investigate the navigation problem of 4 cooperative non-holonomic robots sharing limited state information with each other in 3 different settings. The notion of decentralized motion planning with common and shared policy learning was adopted, which allowed robust training and testing of this approach in a stochastic environment since the agents were mutually independent and exhibited asynchronous motion behavior. The task was further aggravated by providing the agents with a sparse observation space and requiring them to generate continuous action commands so as to efficiently, yet safely navigate to their respective goal locations, while avoiding collisions with other dynamic peers and static obstacles at all times. The experimental results are reported in terms of quantitative measures and qualitative remarks for both training and deployment phases.
Sim-To-Real Transfer for Miniature Autonomous Car Racing
Venue: arXiv cs.AI 2020
Year: 2020

Sim-to-real, a term that describes where a model is trained in a simulator then transferred to the real world, is a technique that enables faster deep reinforcement learning (DRL) training. However, differences between the simulator and the real world often cause the model to perform poorly in the real world. Domain randomization is a way to bridge the sim-to-real gap by exposing the model to a wide range of scenarios so that it can generalize to real-world situations. However, following domain randomization to train an autonomous car racing model with DRL can lead to undesirable outcomes. Namely, a model trained with randomization tends to run slower; a higher completion rate on the testing track comes at the expense of longer lap times. This paper aims to boost the robustness of a trained race car model without compromising racing lap times. For a training track and a testing track having the same shape (and same optimal paths), but with different lighting, background, etc., we first train a model (teacher model) that overfits the training track, moving along a near optimal path. We then use this model to teach a student model the correct actions along with randomization. With our method, a model with 18.4\% completion rate on the testing track is able to help teach a student model with 52\% completion. Moreover, over an average of 50 trials, the student is able to finish a lap 0.23 seconds faster than the teacher. This 0.23 second gap is significant in tight races, with lap times of about 10 to 12 seconds.
Security in biometric systems
Venue: arXiv cs.CR 2020
Year: 2020

The objective of biometric systems is to provide an identification mechanism. This identification mechanism can be used to fulfil several objectives. The most common, related to providing security to a resource, is usually authentication or detection of authorized personnel and detection of unauthorized personnel. From the technical point of view, these two objectives can be included in a single point since most functionalities are achieved by making searches of people previously identified in the database of the system in question. In the first case access is given to people entered in the database and in the second case access is given to people who are not entered in the database. Although these are the two most common attacks there are also others that we will discuss in this chapter. The structure of the chapter is as follows. The first part of the chapter gives an overview of the basic types of attacks and describes the usual protection measures (Sections 1, 2 and 3). The second part of the chapter describes several attacks that can be made on systems based on fingerprinting, face recognition, and iris recognition (Sections 4 and 5). Once the attack methodologies have been described, some specific protection measures are also discussed (Sections 4 and 5). Finally, side channel attacks and their usefulness in combination with other possible attacks are described (Section 6).
EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming Events
Venue: arXiv cs.LG 2020
Year: 2020

In this study, we present a dynamic graph representation learning model on weighted graphs to accurately predict the network capacity of connections between viewers in a live video streaming event. We propose EGAD, a neural network architecture to capture the graph evolution by introducing a self-attention mechanism on the weights between consecutive graph convolutional networks. In addition, we account for the fact that neural architectures require a huge amount of parameters to train, thus increasing the online inference latency and negatively influencing the user experience in a live video streaming event. To address the problem of the high online inference of a vast number of parameters, we propose a knowledge distillation strategy. In particular, we design a distillation loss function, aiming to first pretrain a teacher model on offline data, and then transfer the knowledge from the teacher to a smaller student model with less parameters. We evaluate our proposed model on the link prediction task on three real-world datasets, generated by live video streaming events. The events lasted 80 minutes and each viewer exploited the distribution solution provided by the company Hive Streaming AB. The experiments demonstrate the effectiveness of the proposed model in terms of link prediction accuracy and number of required parameters, when evaluated against state-of-the-art approaches. In addition, we study the distillation performance of the proposed model in terms of compression ratio for different distillation strategies, where we show that the proposed model can achieve a compression ratio up to 15:100, preserving high link prediction accuracy. For reproduction purposes, our evaluation datasets and implementation are publicly available at https://stefanosantaris.github.io/EGAD.
Nondeterministic functional transducer inference algorithm
Venue: arXiv cs.FL 2020
Year: 2020

The purpose of this paper is to present an algorithm for inferring nondeterministic functional transducers. It has a lot in common with other well known algorithms such has RPNI and OSTIA. Indeed we will argue that this algorithm is a generalisation of both of them. Functional transducers are all those nondeterministic transducers whose regular relation is a function. Epsilon transitions as well as subsequential output can be erased for such machines, with the exception of output for empty string being lost. Learning partial functional transducers from negative examples is equivalent to learning total from positive-only data.
Filtered Manifold Alignment
Venue: arXiv cs.LG 2020
Year: 2020

Domain adaptation is an essential task in transfer learning to leverage data in one domain to bolster learning in another domain. In this paper, we present a new semi-supervised manifold alignment technique based on a two-step approach of projecting and filtering the source and target domains to low dimensional spaces followed by joining the two spaces. Our proposed approach, filtered manifold alignment (FMA), reduces the computational complexity of previous manifold alignment techniques, is flexible enough to align domains with completely disparate sets of feature and demonstrates state-of-the-art classification accuracy on multiple benchmark domain adaptation tasks composed of classifying real world image datasets.
CalibreNet: Calibration Networks for Multilingual Sequence Labeling
Venue: arXiv cs.CL 2020
Year: 2020

Lack of training data in low-resource languages presents huge challenges to sequence labeling tasks such as named entity recognition (NER) and machine reading comprehension (MRC). One major obstacle is the errors on the boundary of predicted answers. To tackle this problem, we propose CalibreNet, which predicts answers in two steps. In the first step, any existing sequence labeling method can be adopted as a base model to generate an initial answer. In the second step, CalibreNet refines the boundary of the initial answer. To tackle the challenge of lack of training data in low-resource languages, we dedicatedly develop a novel unsupervised phrase boundary recovery pre-training task to enhance the multilingual boundary detection capability of CalibreNet. Experiments on two cross-lingual benchmark datasets show that the proposed approach achieves SOTA results on zero-shot cross-lingual NER and MRC tasks.