Reviewerly/exHarmony huggingface dataset:
PUTWorkbench: Analysing Privacy in AI-intensive Systems

AI intensive systems that operate upon user data face the challenge of balancing data utility with privacy concerns. We propose the idea and present the prototype of an open-source tool called Privacy Utility Trade-off (PUT) Workbench which seeks to aid software practitioners to take such crucial decisions. We pick a simple privacy model that doesn't require any background knowledge in Data Science and show how even that can achieve significant results over standard and real-life datasets. The tool and the source code is made freely available for extensions and usage.
Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning

We introduce the problem of Dynamic Real-time Multimodal Routing (DREAMR), which requires planning and executing routes under uncertainty for an autonomous agent. The agent has access to a time-varying transit vehicle network in which it can use multiple modes of transportation. For instance, a drone can either fly or ride on terrain vehicles for segments of their routes. DREAMR is a difficult problem of sequential decision making under uncertainty with both discrete and continuous variables. We design a novel hierarchical hybrid planning framework to solve the DREAMR problem that exploits its structural decomposability. Our framework consists of a global open-loop planning layer that invokes and monitors a local closed-loop execution layer. Additional abstractions allow efficient and seamless interleaving of planning and execution. We create a large-scale simulation for DREAMR problems, with each scenario having hundreds of transportation routes and thousands of connection points. Our algorithmic framework significantly outperforms a receding horizon control baseline, in terms of elapsed time to reach the destination and energy expended by the agent.
Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning

The rapid pace of recent research in AI has been driven in part by the presence of fast and challenging simulation environments. These environments often take the form of games; with tasks ranging from simple board games, to competitive video games. We propose a new benchmark - Obstacle Tower: a high fidelity, 3D, 3rd person, procedurally generated environment. An agent playing Obstacle Tower must learn to solve both low-level control and high-level planning problems in tandem while learning from pixels and a sparse reward signal. Unlike other benchmarks such as the Arcade Learning Environment, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment. In this paper we outline the environment and provide a set of baseline results produced by current state-of-the-art Deep RL methods as well as human players. These algorithms fail to produce agents capable of performing near human level.
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach

In many learning settings, it is beneficial to augment the main features with pairwise interactions. Such interaction models can be often enhanced by performing variable selection under the so-called strong hierarchy constraint: an interaction is non-zero only if its associated main features are non-zero. Existing convex optimization based algorithms face difficulties in handling problems where the number of main features $p \sim 10^3$ (with total number of features $\sim p^2$). In this paper, we study a convex relaxation which enforces strong hierarchy and develop a highly scalable algorithm based on proximal gradient descent. We introduce novel screening rules that allow for solving the complicated proximal problem in parallel. In addition, we introduce a specialized active-set strategy with gradient screening for avoiding costly gradient computations. The framework can handle problems having dense design matrices, with $p = 50,000$ ($\sim 10^9$ interactions)---instances that are much larger than current state of the art. Experiments on real and synthetic data suggest that our toolkit hierScale outperforms the state of the art in terms of prediction and variable selection and can achieve over a 4900x speed-up.
Fine-Grained Temporal Relation Extraction

We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to train models for jointly predicting fine-grained temporal relations and event durations. We report strong results on our data and show the efficacy of a transfer-learning approach for predicting categorical relations.
Asymptotic Theory for Clustered Samples

We provide a complete asymptotic distribution theory for clustered data with a large number of independent groups, generalizing the classic laws of large numbers, uniform laws, central limit theory, and clustered covariance matrix estimation. Our theory allows for clustered observations with heterogeneous and unbounded cluster sizes. Our conditions cleanly nest the classical results for i.n.i.d. observations, in the sense that our conditions specialize to the classical conditions under independent sampling. We use this theory to develop a full asymptotic distribution theory for estimation based on linear least-squares, 2SLS, nonlinear MLE, and nonlinear GMM.
Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge

The main essence of this paper is to investigate the performance of RetinaNet based object detectors on pedestrian detection. Pedestrian detection is an important research topic as it provides a baseline for general object detection and has a great number of practical applications like autonomous car, robotics and Security camera. Though extensive research has made huge progress in pedestrian detection, there are still many issues and open for more research and improvement. Recent deep learning based methods have shown state-of-the-art performance in computer vision tasks such as image classification, object detection, and segmentation. Wider pedestrian detection challenge aims at finding improve solutions for pedestrian detection problem. In this paper, We propose a pedestrian detection system based on RetinaNet. Our solution has scored 0.4061 mAP. The code is available at https://github.com/miltonbd/ECCV_2018_pedestrian_detection_challenege.
Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks

In this paper we present a novel lemmatization method based on a sequence-to-sequence neural network architecture and morphosyntactic context representation. In the proposed method, our context-sensitive lemmatizer generates the lemma one character at a time based on the surface form characters and its morphosyntactic features obtained from a morphological tagger. We argue that a sliding window context representation suffers from sparseness, while in majority of cases the morphosyntactic features of a word bring enough information to resolve lemma ambiguities while keeping the context representation dense and more practical for machine learning systems. Additionally, we study two different data augmentation methods utilizing autoencoder training and morphological transducers especially beneficial for low resource languages. We evaluate our lemmatizer on 52 different languages and 76 different treebanks, showing that our system outperforms all latest baseline systems. Compared to the best overall baseline, UDPipe Future, our system outperforms it on 62 out of 76 treebanks reducing errors on average by 19% relative. The lemmatizer together with all trained models is made available as a part of the Turku-neural-parsing-pipeline under the Apache 2.0 license.
Fast Approximation and Estimation Bounds of Kernel Quadrature for Infinitely Wide Models

An infinitely wide model is a weighted integration $\int \varphi(x,v) d \mu(v)$ of feature maps. This model excels at handling an infinite number of features, and thus it has been adopted to the theoretical study of deep learning. Kernel quadrature is a kernel-based numerical integration scheme developed for fast approximation of expectations $\int f(x) d p(x)$. In this study, regarding the weight $\mu$ as a signed (or complex/vector-valued) distribution of parameters, we develop the general kernel quadrature (GKQ) for parameter distributions. The proposed method can achieve a fast approximation rate $O(e^{-p})$ with parameter number $p$, which is faster than the traditional Barron's rate, and a fast estimation rate $\widetilde{O}(1/n)$ with sample size $n$. As a result, we have obtained a new norm-based complexity measure for infinitely wide models. Since the GKQ implicitly conducts the empirical risk minimization, we can understand that the complexity measure also reflects the generalization performance in the gradient learning setup.
RemNet: Remnant Convolutional Neural Network for Camera Model Identification

Camera model identification (CMI) has gained significant importance in image forensics as digitally altered images are becoming increasingly commonplace. In this paper, a novel convolutional neural network (CNN) architecture is proposed for CMI with emphasis given on the preprocessing task considered to be inevitable for removing the scene content that heavily obscures the camera model fingerprints. Unlike the conventional approaches where fixed filters are used for preprocessing, the proposed remnant blocks, when coupled with a classification block and trained end-to-end minimizing the classification loss, learn to suppress the unnecessary image contents dynamically. This helps the classification block extract more robust camera model-specific features for CMI from the remnant of the image. The whole network, called RemNet, consisting of a preprocessing block and a shallow classification block, when trained on 18 models from the Dresden database, shows 100% accuracy for 16 camera models with an overall accuracy of 97.59% on test images from unseen devices, outperforming the state of the art deep CNNs used in CMI. Furthermore, the proposed remnant blocks, when cascaded with the existing deep CNNs, e.g., ResNet, DenseNet, boost their performances by a large margin. The proposed approach proves to be very robust in identifying the source camera models, even if the original images are post-processed. It also achieves an overall accuracy of 95.11% on the IEEE Signal Processing Cup 2018 dataset, which indicates its generalizability.
Enabling Robots to Infer how End-Users Teach and Learn through Human-Robot Interaction

During human-robot interaction (HRI), we want the robot to understand us, and we want to intuitively understand the robot. In order to communicate with and understand the robot, we can leverage interactions, where the human and robot observe each other's behavior. However, it is not always clear how the human and robot should interpret these actions: a given interaction might mean several different things. Within today's state-of-the-art, the robot assigns a single interaction strategy to the human, and learns from or teaches the human according to this fixed strategy. Instead, we here recognize that different users interact in different ways, and so one size does not fit all. Therefore, we argue that the robot should maintain a distribution over the possible human interaction strategies, and then infer how each individual end-user interacts during the task. We formally define learning and teaching when the robot is uncertain about the human's interaction strategy, and derive solutions to both problems using Bayesian inference. In examples and a benchmark simulation, we show that our personalized approach outperforms standard methods that maintain a fixed interaction strategy.
Guided Self Learning

In our days of Artificial Intelligence, smartphones and all Internet of Things that provide us with myriads of connected objects at a very early age, education has to be transformed to integrate the searching power these means of communications, in fact first of all means of exploration-incorporation, provide every single individual in our society. They have become the main means of development for masseducation in a knowledge society. I will at first start from the mental architecture of a learner and move to the fundamental transference phenomena that command the motivation of a learner and a selflearner has to live on his own motivation. Then I will move to the role and responsibility of the Conceptor-Designer of a batch of selflearning work based on the choice expressed by the Self-Learner, from the resources provided or suggested by the batch to the tasks to be fulfilled. Everything is hegemonies by the type of needs and activities the learner will have to face and assume in his future professional field. The Self-Learner will have to target what is in his “zone of proximal development” to be most effective. That will lead us to the triadic guided self-learning situation centered on the Self-Learner but with the guidance from the Conceptor-Designer on one side and the real professional world represented by the Coach-Counterpart-Partner- Doppelganger that accompanies the Self-learner in his voyage on the other side. The Internet, in its present development, is the real toolbox the Self-Learner has at the tip of his fingers constantly. That will lead us to the Boolean logic of the Internet that has to be understood and dominated by the Self-Learner, at times against the interests of the search-engine the Self-Learner uses. I will end with one particular batch – called a triad – of self-learning work proposed in Fall 2018 to the CEO of a medical startup, which will bring the type of contract that can be signed by the company and the Conceptor-Designer who only sells Intellectual Property to the company. Keywords: Motivation, autonomy, flexibility, self-learning, professional life, communication, AI, Boolean Logic, Internet, Deep Learning.
Quantum Hoare Logic with Ghost Variables

Quantum Hoare logic allows us to reason about quantum programs. We present an extension of quantum Hoare logic that introduces "ghost variables" to extend the expressive power of pre-/postconditions. Ghost variables are variables that do not actually occur in the program and are allowed to have arbitrary quantum states (in a sense, they are existentially quantified), and be entangled with program variables. Ghost variables allow us to express properties such as the distribution of a program variable or the fact that a variable has classical content. And as a case study, we show how quantum Hoare logic with ghost variables can be used to prove the security of the quantum one-time pad.
Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge

The recent advent of `Internet of Things' (IOT) has increased the demand for enabling AI-based edge computing. This has necessitated the search for efficient implementations of neural networks in terms of both computations and storage. Although extreme quantization has proven to be a powerful tool to achieve significant compression over full-precision networks, it can result in significant degradation in performance. In this work, we propose extremely quantized hybrid network architectures with both binary and full-precision sections to emulate the classification performance of full-precision networks while ensuring significant energy efficiency and memory compression. We explore several hybrid network architectures and analyze the performance of the networks in terms of accuracy, energy efficiency and memory compression. We perform our analysis on ResNet and VGG network architectures. Among the proposed network architectures, we show that the hybrid networks with full-precision residual connections emerge as the optimum by attaining accuracies close to full-precision networks while achieving excellent memory compression, up to 21.8x in case of VGG-19. This work demonstrates an effective way of hybridizing networks which achieve performance close to full-precision networks while attaining significant compression, furthering the feasibility of using such networks for energy-efficient neural computing in IOT-based edge devices.
HashCore: Proof-of-Work Functions for General Purpose Processors

Over the past five years, the rewards associated with mining Proof-of-Work blockchains have increased substantially. As a result, miners are heavily incentivized to design and utilize Application Specific Integrated Circuits (ASICs) that can compute hashes far more efficiently than existing general purpose hardware. Currently, it is difficult for most users to purchase and operate ASICs due to pricing and availability constraints, resulting in a relatively small number of miners with respect to total user base for most popular cryptocurrencies. In this work, we aim to invert the problem of ASIC development by constructing a Proof-of-Work function for which an existing general purpose processor (GPP, such as an x86 IC) is already an optimized ASIC. In doing so, we will ensure that any would-be miner either already owns an ASIC for the Proof-of-Work system they wish to participate in or can attain one at a competitive price with relative ease. In order to achieve this, we present HashCore, a Proof-of-Work function composed of "widgets" generated pseudo-randomly at runtime that each execute a sequence of general purpose processor instructions designed to stress the computational resources of such a GPP. The widgets will be modeled after workloads that GPPs have been optimized for, for example, the SPEC CPU 2017 benchmark suite for x86 ICs, in a technique we refer to as inverted benchmarking. We provide a proof that HashCore is collision-resistant regardless of how the widgets are implemented. We observe that GPP designers/developers essentially create an ASIC for benchmarks such as SPEC CPU 2017. By modeling HashCore after such benchmarks, we create a Proof-of-Work function that can be run most efficiently on a GPP, resulting in a more accessible, competitive, and balanced mining market.
Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements

We present a novel semantic framework for modeling linguistic expressions of generalization---generic, habitual, and episodic statements---as combinations of simple, real-valued referential properties of predicates and their arguments. We use this framework to construct a dataset covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to probe the efficacy of type-level and token-level information---including hand-engineered features and static (GloVe) and contextual (ELMo) word embeddings---for predicting expressions of generalization. Data and code are available at decomp.io.
An Extensive Formal Security Analysis of the OpenID Financial-grade API

Forced by regulations and industry demand, banks worldwide are working to open their customers' online banking accounts to third-party services via web-based APIs. By using these so-called Open Banking APIs, third-party companies, such as FinTechs, are able to read information about and initiate payments from their users' bank accounts. One of the most promising standards in this segment is the OpenID Financial-grade API (FAPI), currently under development in an open process by the OpenID Foundation and backed by large industry partners. The FAPI is a profile of OAuth 2.0 designed for high-risk scenarios and aiming to be secure against very strong attackers. To achieve this level of security, the FAPI employs a range of mechanisms that have been developed to harden OAuth 2.0. In this paper, we perform a rigorous, systematic formal analysis of the security of the FAPI, based on the Web Infrastructure Model (WIM) proposed by Fett, Kuesters, and Schmitz. To this end, we first develop a precise model of the FAPI in the WIM, including different profiles and combinations of security features. We then use our model of the FAPI to precisely define central security properties. In an attempt to prove these properties, we uncover partly severe attacks, breaking authentication, authorization, and session integrity properties. We develop mitigations against these attacks and finally are able to formally prove the security of a fixed version of the FAPI. This analysis is an important contribution to the development of the FAPI since it helps to define exact security properties and attacker models, and to avoid severe security risks. Of independent interest, we also uncover weaknesses in the aforementioned security mechanisms for hardening OAuth 2.0. We illustrate that these mechanisms do not necessarily achieve the security properties they have been designed for.
Decoding surface code with a distributed neural network based decoder

There has been a rise in decoding quantum error correction codes with neural network based decoders, due to the good decoding performance achieved and adaptability to any noise model. However, the main challenge is scalability to larger code distances due to an exponential increase of the error syndrome space. Note that, successfully decoding the surface code under realistic noise assumptions will limit the size of the code to less than 100 qubits with current neural network based decoders. Such a problem can be tackled by a distributed way of decoding, similar to the Renormalization Group (RG) decoders. In this paper, we introduce a decoding algorithm that combines the concept of RG decoding and neural network based decoders. We tested the decoding performance under depolarizing noise with noiseless error syndrome measurements for the rotated surface code and compared against the Blossom algorithm and a neural network based decoder. We show that similar level of decoding performance can be achieved between all tested decoders while providing a solution to the scalability issues of neural network based decoders.
Bayesian nonparametric multiway regression for clustered binomial data

We introduce a Bayesian nonparametric regression model for data with multiway (tensor) structure, motivated by an application to periodontal disease (PD) data. Our outcome is the number of diseased sites measured over four different tooth types for each subject, with subject-specific covariates available as predictors. The outcomes are not well-characterized by simple parametric models, so we use a nonparametric approach with a binomial likelihood wherein the latent probabilities are drawn from a mixture with an arbitrary number of components, analogous to a Dirichlet Process (DP). We use a flexible probit stick-breaking formulation for the component weights that allows for covariate dependence and clustering structure in the outcomes. The parameter space for this model is large and multiway: patients $\times$ tooth types $\times$ covariates $\times$ components. We reduce its effective dimensionality, and account for the multiway structure, via low-rank assumptions. We illustrate how this can improve performance, and simplify interpretation, while still providing sufficient flexibility. We describe a general and efficient Gibbs sampling algorithm for posterior computation. The resulting fit to the PD data outperforms competitors, and is interpretable and well-calibrated. An interactive visual of the predictive model is available at http://ericfrazerlock.com/toothdata/ToothDisplay.html , and the code is available at https://github.com/lockEF/NonparametricMultiway .