Reviewerly/exHarmony huggingface dataset:
From Feasibility Tests to Path Planners for Multi-Agent Pathfinding

Multi-agent pathfinding is an important challenge that relates to combinatorial search and has many applications, such as warehouse management, robotics and computer games. Finding an optimal solution is NP-hard and raises scalability issues for optimal solvers. Interestingly, however, it takes linear time to check the feasibility of an instance. These linear-time feasibility tests can be extended to provide path planners but to the best of the authors’ knowledge no such solver has been provided for general graphs. This work first describes a path planner that is inspired by a linear-time feasibility test for multi-agent pathfinding on general graphs. Initial experiments indicated reasonable scalability but worse path quality relative to existing suboptimal solutions. This led to the development of an algorithm that achieves both efficient running time and path quality relative to the alternatives and which finds a solution on available benchmarks. The paper outlines the relation of the final method to the feasibility tests and existing suboptimal planners. Experimental results evaluate the different algorithms, including an optimal solver.
SciChallenge: A Social Media Aware Platform for Contest-Based STEM Education and Motivation of Young Students

Scientific and technological innovations have become increasingly important as we face the benefits and challenges of both globalization and a knowledge-based economy. Still, enrolment rates in STEM degrees are low in many European countries and consequently there is a lack of adequately educated workforce in industries. We believe that this can be mainly attributed to pedagogical issues, such as the lack of engaging hands-on activities utilized for science and math education in middle and high schools. In this paper, we report our work in the SciChallenge European project, which aims at increasing the interest of preuniversity students in STEM disciplines, through its distinguishing feature, the systematic use of social media for providing and evaluation of the student-generated content. A social media-aware contest and platform were thus developed and tested in a panEuropean contest that attracted >700 participants. The statistical analysis and results revealed that the platform and contest positively influenced participants STEM learning and motivation, while only the gender factor for the younger study group appeared to affect the outcomes (confidence level - p<;.05).
On the application of Danskin’s theorem to derivative-free minimax problems

Motivated by Danskin’s theorem, gradient-based methods have been applied with empirical success to solve minimax problems that involve non-convex outer minimization and non-concave inner maximization. On the other hand, recent work has demonstrated that Evolution Strategies (ES) algorithms are stochastic gradient approximators that seek robust solutions. In this paper, we address black-box (gradient-free) minimax problems that have long been tackled in a coevolutionary setup. To this end and guaranteed by Danskin’s theorem, we employ ES as a stochastic estimator for descent directions. The proposed approach is validated on a collection of black-box minimax problems. Based on our experiments, our method’s performance is comparable with its coevolutionary counterparts and favorable for high-dimensional problems. Its efficacy is demonstrated on a real-world application.
Classical benchmarking of Gaussian Boson Sampling on the Titan supercomputer

Gaussian Boson Sampling (GBS) is a model of photonic quantum computing where single-mode squeezed states are sent through linear-optical interferometers and measured using single-photon detectors. In this work, we employ a recent exact sampling algorithm for GBS with threshold detectors to perform classical simulations on the Titan supercomputer. We determine the time and memory resources as well as the amount of computational nodes required to produce samples for different numbers of modes and detector clicks. It is possible to simulate a system with 800 optical modes postselected on outputs with 20 detector clicks, producing a single sample in roughly 2 h using 40% of the available nodes of Titan. Additionally, we benchmark the performance of GBS when applied to dense subgraph identification, even in the presence of photon loss. We perform sampling for several graphs containing as many as 200 vertices. Our findings indicate that large losses can be tolerated and that the use of threshold detectors is preferable over using photon-number-resolving detectors postselected on collision-free outputs.
Fast High-Fidelity Readout of a Single Trapped-Ion Qubit via Machine-Learning Methods

In this work, we introduce machine learning methods to implement readout of a single qubit on $^{171}\mathrm{Yb^{+}}$ trapped-ion system. Different machine learning methods including convolutional neural networks and fully-connected neural networks are compared with traditional methods in the tests. The results show that machine learning methods have higher fidelity, more robust readout results in relatively short time. To obtain a 99% readout fidelity, neural networks only take half of the detection time needed by traditional threshold or maximum likelihood methods. Furthermore, we implement the machine learning algorithms on hardware-based field-programmable gate arrays and an ARM processor. An average readout fidelity of 99.5% (with $10^5$ magnitude trials) within 171 $\mu$s is demonstrated on the embedded hardware system for $^{171}\mathrm{Yb^{+}}$ ion trap.
Quantum remote sensing with asymmetric information gain

Typically, the aim of quantum metrology is to sense target fields with high precision utilizing quantum properties. Unlike the typical aim, in this paper, we use quantum properties for adding a new functionality to quantum sensors. More concretely, we propose a delegated quantum sensor (a client-server model) with security inbuilt. Suppose that a client wants to measure some target fields with high precision, but he/she does not have any high-precision sensor. This leads the client to delegate the sensing to a remote server who possesses a high-precision sensor. The client gives the server instructions about how to control the sensor. The server lets the sensor interact with the target fields in accordance with the instructions, and then sends the sensing measurement results to the client. In this case, since the server knows the control process and readout results of the sensor, the information of the target fields is available not only for the client but also for the server. We show that, by using an entanglement between the client and the server, an asymmetric information gain is possible so that only the client can obtain the sufficient information of the target fields. In our scheme, the server generates the entanglement between a solid state system (that can interact with the target fields) and a photon, and sends the photon to the client. On the other hand, the client is required to possess linear optics elements only including wave plates, polarizing beam splitters, and single-photon detectors. Our scheme is feasible with the current technology, and our results pave the way for a novel application of quantum metrology.
ABox Abduction via Forgetting in ALC

Abductive reasoning generates explanatory hypotheses for new observations using prior knowledge. This paper investigates the use of forgetting, also known as uniform interpolation, to perform ABox abduction in description logic (ALC) ontologies. Non-abducibles are specified by a forgetting signature which can contain concept, but not role, symbols. The resulting hypotheses are semantically minimal and consist of a disjunction of ABox axioms. These disjuncts are each independent explanations, and are not redundant with respect to the background ontology or the other disjuncts, representing a form of hypothesis space. The observations and hypotheses handled by the method can contain both atomic or complex ALC concepts, excluding role assertions, and are not restricted to Horn clauses. Two approaches to redundancy elimination are explored in practice: full and approximate. Using a prototype implementation, experiments were performed over a corpus of real world ontologies to investigate the practicality of both approaches across several settings.
Semi-device-independent quantum money with coherent states

The no-cloning property of quantum mechanics allows unforgeability of quantum banknotes and credit cards. Quantum credit card protocols involve a bank, a client, and a payment terminal and their practical implementation typically relies on encoding information on weak coherent states of light. Here, we provide a security proof in this practical setting for semi-device-independent quantum money with classical verification, involving an honest bank, a dishonest client, and a potentially untrusted terminal. Our analysis uses semidefinite programming in the coherent state framework and aims at simultaneously optimizing over the noise and losses introduced by a dishonest party. We discuss secure regimes of operation in both fixed and randomized phase settings, taking into account experimental imperfections. Finally, we study the evolution of protocol security in the presence of a decohering optical quantum memory and identify secure credit card lifetimes for a specific configuration.
Arabic Cursive Text Recognition from Natural Scene Images

This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years’ publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers.
XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera

We present a real-time approach for multi-person 3D motion capture at over 30 fps using a single RGB camera. It operates in generic scenes and is robust to difficult occlusions both by other people and objects. Our method operates in subsequent stages. The first stage is a convolutional neural network (CNN) that estimates 2D and 3D pose features along with identity assignments for all visible joints of all individuals. We contribute a new architecture for this CNN, called SelecSLS Net, that uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy. In the second stage, a fully-connected neural network turns the possibly partial (on account of occlusion) 2D pose and 3D pose features for each subject into a complete 3D pose estimate per individual. The third stage applies space-time skeletal model fitting to the predicted 2D and 3D pose per subject to further reconcile the 2D and 3D pose, and enforce temporal coherence. Our method returns the full skeletal pose in joint angles for each subject. This is a further key distinction from previous work that neither extracted global body positions nor joint angle results of a coherent skeleton in real time for multi-person scenes. The proposed system runs on consumer hardware at a previously unseen speed of more than 30 fps given 512x320 images as input while achieving state-of-the-art accuracy, which we will demonstrate on a range of challenging real-world scenes.
Using a Google Glass-Based Classroom Feedback System to Improve Students to Teacher Communication

The use of augmented reality (AR) to support the learning process has been extensively researched but its use to support the teaching practice has just started to be explored. In this paper, we present a communication system that makes use of a pair of Google Glass to provide the teacher with a constant and private flow of information on the students' current knowledge. The proposed system allows the information sent by the students through their mobiles to overlap with the teachers' live vision of the class. Compared to other feedback systems like clickers or backchannel systems, this AR prototype avoids teachers diverting their gaze and interrupting the class to access the students' feedback. This supports the constant monitoring of potential comprehension problems that might otherwise be overlooked. With the aim of obtaining insights on the teachers' and students' views of the system, we conducted two studies during which the system was used in real classroom settings. The results of both studies suggest that the AR system could report benefits in terms of better communication between students and teachers, and a more adequate rhythm of the class. Also, the use of the AR system in the classroom does not necessarily constitute an element, which will distract and disrupt educational activity.
Relation Extraction using Explicit Context Conditioning

Gaurav Singh, Parminder Bhatia. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Generalized coherent-squeezed-state expansion for the super-radiant phase transition

We develop a coherent-squeezed-state expansion for the ground state of the quantum phase transition in the quantum Rabi model. The wave function undergoes a transition from a localized state in the normal phase to a two-peaks delocalized state in the super-radiant phase. Interestingly, the two displaced wave packets are squeezed in the momentum space, and overlap strongly to form a correlated state. We develop a superposition of coherent-squeezed states to describe the phase transition correctly, which includes the higher-order excitations at the critical value. The corresponding ground-state energy and the average photon number are in agreement with numerical ones, exhibiting an improvement over previous coherent-state expansions. Moreover, our method successfully captures the reduced quantum fluctuations in momentum variable in the phase transition.
Supporting integrated care with a flexible data management framework built upon Linked Data, HL7 FHIR and ontologies

In this paper we present the methodology and decisions behind an implementation of a telehealth data management framework, aiming to support integrated care services for chronic and multimorbid patients. The framework leverages an OWL ontology, built upon HL7 FHIR resources, to provide storage and representation of semantically enriched EHR data following Linked Data principles. This is presented along with the realization of the persistent storage solution and communication web services that allow the management of EHR data, ensuring the validity and integrity of the exchanged patient data as self-describing ontology instances. The framework concentrates on flexibility and reusability, which is addressed by regarding the aforementioned ontology as a single point of change. This solution has been implemented in the scope of the EU project WELCOME for managing data in a telemonitoring system for patients with COPD and co-morbidities and was also successfully deployed for the INLIFE EU project with minimal effort. The results of the two applications suggest it can be adopted and properly adapted in a series of integrated care scenarios with minimal effort.
Makeup Removal via Bidirectional Tunable De-Makeup Network

We present a deep learning-based method for removing makeup effects (de-makeup) in a face image. This problem poses a major challenge due to obscuring of the underlying facial features by cosmetics, which is very important in multimedia applications in the field of security, entertainment, and social networking. To address this task, we propose the bidirectional tunable de-makeup network (BTD-Net), which jointly learns the makeup process to aid in learning the de-makeup process. For tractable learning of the makeup process, which is a one-to-many mapping determined by the cosmetics that are applied, we introduce a latent variable that reflects the makeup style. This latent variable is extracted in the de-makeup process and used as a condition on the makeup process to constrain the one-to-many mapping to a specific solution. Through extensive experiments, our proposed BTD-Net is found to surpass the state-of-art techniques in estimating realistic non-makeup faces that correspond to the input makeup images. We additionally show that applications such as tuning the amount of makeup can be enhanced through the use of this method.
Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations

Latent factor models for recommender systems represent users and items as low dimensional vectors. Privacy risks of such systems have previously been studied mostly in the context of recovery of personal information in the form of usage records from the training data. However, the user representations themselves may be used together with external data to recover private user information such as gender and age. In this paper we show that user vectors calculated by a common recommender system can be exploited in this way. We propose the privacy-adversarial framework to eliminate such leakage of private information, and study the trade-off between recommender performance and leakage both theoretically and empirically using a benchmark dataset. An advantage of the proposed method is that it also helps guarantee fairness of results, since all implicit knowledge of a set of attributes is scrubbed from the representations used by the model, and thus can’t enter into the decision making. We discuss further applications of this method towards the generation of deeper and more insightful recommendations.
Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation

We propose a scalable stochastic variational approach to GP classification building on Pólya-Gamma data augmentation and inducing points. Unlike former approaches, we obtain closed-form updates based on natural gradients that lead to efficient optimization. We evaluate the algorithm on real-world datasets containing up to 11 million data points and demonstrate that it is up to two orders of magnitude faster than the state-of-the-art while being competitive in terms of prediction performance.
A novel transparent and auditable fog-assisted cloud storage with compensation mechanism

This paper introduces a new fog-assisted cloud storage which can achieve much higher throughput compared to the traditional cloud-only storage architecture by reducing the traffics toward the cloud storage. The fog-storage service providers are transparency to end-users and therefore, no modification on the end-user devices is necessary. This new system is featured with (1) a stronger audit scheme which is naturally coupled with the proposed architecture and does not suffer from the replay attack and (2) a transparent and efficient compensation mechanism for the fog-storage service providers. We provide rigorous theoretical analysis on the correctness and soundness of the proposed system. To the best of our knowledge, this is the first paper to discuss about a storage data audit scheme for fog-assisted cloud storage as well as the compensation mechanism for the service providers of the fog-storage service providers.
Neural Generative Rhetorical Structure Parsing

Amandla Mabona, Laura Rimell, Stephen Clark, Andreas Vlachos. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
A Study on various state of the art of the Art Face Recognition System using Deep Learning Techniques

Considering the existence of very large amount of available data repositories and reach to the very advanced system of hardware, systems meant for facial identification ave evolved enormously over the past few decades. Sketch recognition is one of the most important areas that have evolved as an integral component adopted by the agencies of law administration in current trends of forensic science. Matching of derived sketches to photo images of face is also a difficult assignment as the considered sketches are produced upon the verbal explanation depicted by the eye witness of the crime scene and may have scarcity of sensitive elements that exist in the photograph as one can accurately depict due to the natural human error. Substantial amount of the novel research work carried out in this area up late used recognition system through traditional extraction and classification models. But very recently, few researches work focused on using deep learning techniques to take an advantage of learning models for the feature extraction and classification to rule out potential domain challenges. The first part of this review paper basically focuses on deep learning techniques used in face recognition and matching which as improved the accuracy of face recognition technique with training of huge sets of data. This paper also includes a survey on different techniques used to match composite sketches to human images which includes component-based representation approach, automatic composite sketch recognition technique etc.
Optimizing quantum phase estimation for the simulation of Hamiltonian eigenstates

We revisit quantum phase estimation algorithms for the purpose of obtaining the energy levels of many-body Hamiltonians and pay particular attention to the statistical analysis of their outputs. We introduce the mean phase direction of the parent distribution associated with eigenstate inputs as a new post-processing tool. By connecting it with the unknown phase, we find that if used as its direct estimator, it exceeds the accuracy of the standard majority rule using one less bit of resolution, making evident that it can also be inverted to provide unbiased estimation. Moreover, we show how to directly use this quantity to accurately find the energy levels when the initialized state is an eigenstate of the simulated propagator during the whole time evolution, which allows for shallower algorithms. We then use IBM Q hardware to carry out the digital quantum simulation of three toy models: a two-level system, a two-spin Ising model and a two-site Hubbard model at half-filling. Methodologies are provided to implement Trotterization and reduce the variability of results in noisy intermediate scale quantum computers.