Reviewerly/exHarmony huggingface dataset:
One-to-one Mapping for Unpaired Image-to-image Translation

Recently image-to-image translation has attracted significant interests in the literature, starting from the successful use of the generative adversarial network (GAN), to the introduction of cyclic constraint, to extensions to multiple domains. However, in existing approaches, there is no guarantee that the mapping between two image domains is unique or one-to-one. Here we propose a self-inverse network learning approach for unpaired image-to-image translation. Building on top of CycleGAN, we learn a self-inverse function by simply augmenting the training samples by swapping inputs and outputs during training and with separated cycle consistency loss for each mapping direction. The outcome of such learning is a proven one-to-one mapping function. Our extensive experiments on a variety of datasets, including cross-modal medical image synthesis, object transfiguration, and semantic labeling, consistently demonstrate clear improvement over the CycleGAN method both qualitatively and quantitatively. Especially our proposed method reaches the state-of-the-art result on the cityscapes benchmark dataset for the label to photo un-paired directional image translation.
Integrable nonlinear evolution of the qubit

The nonlinear generalization of the von Neumann equation is studied. The global version of this equation preserves convexity of the space of states. The particular case of the evolution of pure states refers to the nonlinear Schrödinger equation discovered by Gisin. The concrete realization of the nonlinear von Neumann equation is investigated in detail for the density matrix representing the qubit states. Such equation reduces to the integrable classical Riccati system of nonlinear ordinary differential equations. An interesting property of the nonlinear dynamics described by this system is the global asymptotical stability of stationary solutions related to evolution from mixed states to pure states.
A Survey of Topic Models in Text Classification

A massive of text that is generated every minute is increasing dramatically. Therefore, it is more and more important to find an effective model to automatic classify the amount of text. Topic models is the most powerful techniques in text classification. There are many research results in the field of topic model have been published in scholarly journals. The Latent Dirichlet Allocation (LDA) is one of the most popular topic models in text classification. Researchers have proposed many topic evolution models based on LDA to solve some specific problems in applications of text classification. And some joint models which based on topic models combined other algorithms have been studied to enhance the performance of text classification. In this paper, we investigated three categories topic models for text classification and briefly introduced their advantages and disadvantages in the applications of text mining. Also, we introduce the generated process of documents and illustrate the graphical model for each topic models.
Translation Quality and Error Recognition in Professional Neural Machine Translation Post-Editing

This study aims to analyse how translation experts from the German department of the European Commission’s Directorate-General for Translation (DGT) identify and correct different error categories in neural machine translated texts (NMT) and their post-edited versions (NMTPE). The term translation expert encompasses translator, post-editor as well as revisor. Even though we focus on neural machine-translated segments, translator and post-editor are used synonymously because of the combined workflow using CAT-Tools as well as machine translation. Only the distinction between post-editor, which refers to a DGT translation expert correcting the neural machine translation output, and revisor, which refers to a DGT translation expert correcting the post-edited version of the neural machine translation output, is important and made clear whenever relevant. Using an automatic error annotation tool and the more fine-grained manual error annotation framework to identify characteristic error categories in the DGT texts, a corpus analysis revealed that quality assurance measures by post-editors and revisors of the DGT are most often necessary for lexical errors. More specifically, the corpus analysis showed that, if post-editors correct mistranslations, terminology or stylistic errors in an NMT sentence, revisors are likely to correct the same error type in the same post-edited sentence, suggesting that the DGT experts were being primed by the NMT output. Subsequently, we designed a controlled eye-tracking and key-logging experiment to compare participants’ eye movements for test sentences containing the three identified error categories (mistranslations, terminology or stylistic errors) and for control sentences without errors. We examined the three error types’ effect on early (first fixation durations, first pass durations) and late eye movement measures (e.g., total reading time and regression path durations). Linear mixed-effects regression models predict what kind of behaviour of the DGT experts is associated with the correction of different error types during the post-editing process.
User-Guided Path Planning for Redundant Manipulators in Highly Constrained Work Environments

We present a bi-directional tree-search framework for point-to-point path planning for manipulators. By design, it integrates human assistance seamlessly. Our framework consists of six modules: tree selection, focus selection, node selection, target selection, extend selection and connection type selection. Each module consists of a set of interchangeable strategies. By exploiting interaction among these strategies and selecting appropriate strategies based on the contextual cues from the search state, our method computes high quality solutions in a variety of complex scenarios with a low failure rate. We compare our approach with popular methods in a set of very hard scenarios. Without human assistance, our approach reduces the failure rate drastically. With human assistance, our approach has a zero failure rate as well as high solution quality.
Memory Augmented Deep Recurrent Neural Network for Video Question Answering

Video question answering (VideoQA) is a very important but challenging multimedia task, which automatically analyzes questions and videos and generates accurate answers. However, research on VideoQA is still in its infancy. In this article, we propose a novel memory augmented deep recurrent neural network (MA-DRNN) model for VideoQA, which features a new method for encoding videos and questions, and memory augmentation using the emerging differentiable neural computer (DNC). Specifically, we encode textual (questions) information before visual (videos) information, which leads to better visual-textual representations. Moreover, we leverage DNC (with an external memory) for storing and retrieving useful information in questions and videos, and modeling the long-term visual-textual dependence. To evaluate the proposed model, we conducted extensive experiments using the VTW data set and MSVD-QA data set, which are both Widely used large-scale video data sets for language-level understanding. The experimental results have well validated the proposed model and showed that it outperforms the state-of-the-art in terms of various accuracy-related metrics.
An Ensemble Machine Learning Method for Single and Clustered Cervical Cell Classification

Cervical Cancer was in recent history a major cause of death for women of childbearing age. This changed when in the 1950s the Papanicolaou (Pap smear) test was introduced to identify and diagnose cervical cancer in its infancy. The introduction of the Pap smear test dropped cervical cancer related deaths by 60% but still approximately 4,210 women die from cervical cancer in the United State annually. The goal of our research is to aid in the methods of identifying and classifying cervical cancer used in the Pap smear or Liquid-based Cytology (LBC) with cutting edge machine vision, and ensemble learning techniques. The contribution of this research is to develop an automated Pap smear screening system that identifies cells within a cervical cell slide sample and classify cells and clusters of cells as abnormal or normal as defined by the Bethesda System for reporting cervical cytology. Achieving an accuracy of 90.4% when evaluated with a five-fold cross-validation demonstrates promise in the creation of an automated Pap smear screening test.
Online Fast Adaptive Low-Rank Similarity Learning for Cross-Modal Retrieval

The semantic similarity among cross-modal data objects, e.g., similarities between images and texts, are recognized as the bottleneck of cross-modal retrieval. However, existing batch-style correlation learning methods suffer from prohibitive time complexity and extra memory consumption in handling large-scale high dimensional cross-modal data. In this paper, we propose a Cross-Modal Online Low-Rank Similarity function learning (CMOLRS) method, which learns a low-rank bilinear similarity measurement for cross-modal retrieval. We model the cross-modal relations by relative similarities on the training data triplets and formulate the relative relations as convex hinge loss. By adapting the margin in hinge loss with pair-wise distances in feature space and label space, CMOLRS effectively captures the multi-level semantic correlation and adapts to the content divergence among cross-modal data. Imposed with a low-rank constraint, the similarity function is trained by online learning in the manifold of low-rank matrices. The low-rank constraint not only endows the model learning process with faster speed and better scalability, but also improves the model generality. We further propose fast-CMOLRS combining multiple triplets for each query instead of standard process using single triplet at each model update step, which further reduces the times of gradient updates and retractions. Extensive experiments are conducted on four public datasets, and comparisons with state-of-the-art methods show the effectiveness and efficiency of our approach.
LBM: A Security Framework for Peripherals within the Linux Kernel

Modern computer peripherals are diverse in their capabilities and functionality, ranging from keyboards and printers to smartphones and external GPUs. In recent years, peripherals increasingly connect over a small number of standardized communication protocols, including USB, Bluetooth, and NFC. The host operating system is responsible for managing these devices; however, malicious peripherals can request additional functionality from the OS resulting in system compromise, or can craft data packets to exploit vulnerabilities within OS software stacks. Defenses against malicious peripherals to date only partially cover the peripheral attack surface and are limited to specific protocols (e.g., USB). In this paper, we propose Linux (e)BPF Modules (LBM), a general security framework that provides a unified API for enforcing protection against malicious peripherals within the Linux kernel. LBM leverages the eBPF packet filtering mechanism for performance and extensibility and we provide a high-level language to facilitate the development of powerful filtering functionality. We demonstrate how LBM can provide host protection against malicious USB, Bluetooth, and NFC devices; we also instantiate and unify existing defenses under the LBM framework. Our evaluation shows that the overhead introduced by LBM is within 1 μs per packet in most cases, application and system overhead is negligible, and LBM outperforms other state-of-the-art solutions. To our knowledge, LBM is the first security framework designed to provide comprehensive protection against malicious peripherals within the Linux kernel.
Design and Analysis of a Novel Integral Recurrent Neural Network for Solving Time-Varying Sylvester Equation

To solve a general time-varying Sylvester equation, a novel integral recurrent neural network (IRNN) is designed and analyzed. This kind of recurrent neural networks is based on an error-integral design equation and does not need training in advance. The IRNN can achieve global convergence performance and strong robustness if odd-monotonically increasing activation functions [i.e., the linear, bipolar-sigmoid, power, or sigmoid-power activation functions (SP-AFs)] are applied. Specifically, if linear or bipolar-sigmoid activation functions are applied, the IRNN possess exponential convergence performance. The IRNN has finite-time convergence property by using power activation function. To obtain faster convergence performance and finite-time convergence property, an SP-AF is designed. Furthermore, by using the discretization method, the discrete IRNN model and its convergence analysis are also presented. Practical application to robot manipulator and computer simulation results with using different activation functions and design parameters have verified the effectiveness, stability, and reliability of the proposed IRNN.
Human-Robot Dialogues for Explaining Activities

The paper focuses on dialogue modelling that enables communication between users and social robots, on topics that concern experience and knowledge of people in service industries, especially in elder-care services. We discuss different knowledge types and explication of knowledge using dialogues based on goal-oriented ontologies. We present an architecture and an example of a dialogue system that allows humans to communicate with a robot partner and be engaged in interactions concerning activities related to basic care-giving tasks.
Rogue-Gym: A New Challenge for Generalization in Reinforcement Learning

In this paper, we propose Rogue-Gym, a simple and classic style roguelike game built for evaluating generalization in reinforcement learning (RL). Combined with the recent progress of deep neural networks, RL has successfully trained humanlevel agents without human knowledge in many games such as those for Atari 2600. However, it has been pointed out that agents trained with RL methods often overfit the training environment, and they work poorly in slightly different environments. To investigate this problem, some research environments with procedural content generation have been proposed. Following these studies, we propose the use of roguelikes as a benchmark for evaluating the generalization ability of RL agents. In our Rogue-Gym, agents need to explore dungeons that are structured differently each time they start a new game. Thanks to the very diverse structures of the dungeons, we believe that the generalization benchmark of Rogue-Gym is sufficiently fair. In our experiments, we evaluate a standard reinforcement learning method, PPO, with and without enhancements for generalization. The results show that some enhancements believed to be effective fail to mitigate the overfitting in Rogue-Gym, although others slightly improve the generalization ability.
Enhancing the classification of social media opinions by optimizing the structural information

Sentiment Analysis is an extensively studied task, however an important aspect yet to study is the underlying structural information of opinions. An important aspect to tackle is the analysis underlying structural information of opinions. Social media is a great source of user opinions, which are structured in most of the cases in two sections: the title and the content or body of the opinion. We claim that the structure of social media opinions has useful information for the polarity classification task. We propose a model for optimizing the contribution of that underlying structural information for polarity classification. Our model is built by weighting the contribution of each section, title and body. We develop a modified Support Vector Machine that includes a weight parameter, which is optimized via a line-search strategy. We evaluate our proposal on three datasets of reviews from different domains written in two different versions of the Spanish language. The results show that our model outperforms the classification of the joint or individual classification of each section of the opinion. Therefore, our claim holds.
(k, n) scalable secret image sharing with multiple decoding options

Traditional (k, n) secret image sharing provides all-or-nothing decoding model and (k, n) scalable secret image sharing provides progress decoding model during image reconstruction. Both these two decoding models are significance in various applications. However, the security level for real applica tions would change due to the dynamical environment, only one single decoding model cannot satisfy the changeable security requirement. In this work, we construct scalable secret image sharing schemes that provides both all-or-nothing and progress decoding models to satisfy the dynamical secure requirement. Each participant in our schemes only needs to keep one initial-shadow. During image reconstruction, the dealer selects the decoding model according to current security requirement, if progress model is chosen, initial shadows can achieve image reconstruction in progress model without any modification; else if all-or-nothing model is chosen, the dealer does not need to resent new shadows to participants, the initial-shadows can be updated to satisfy all-or-nothing model efficiently.
An extrinsic calibration method for multiple RGB-D cameras in a limited field of view

A 3D scanning system involving multiple RGB-D cameras has the potential to accelerate the reconstruction of an object and improve the measurement accuracy because it can capture an object in a comprehensive way. However, the extrinsic calibration of a multi-RGB-D-camera system is a fundamental and challenging problem, especially in a limited field of view. In this work, a system with four SR300 cameras on a platform with a size of approximately 0.64 m2 and an extrinsic calibration method assisted by a tower calibration pattern with circular markers in a limited field of view are proposed. The Hough transform algorithm is used to identify the centres of the circular markers in a colour image, and then the 3D coordinates are extracted by employing the alignment relationship between the colour image and the depth image. An improved adaptive cuckoo search (IACS) algorithm and unit quaternion are proposed to optimize the extrinsic parameters. The effectiveness of the IACS is verified by a comparison with the singular value decomposition (SVD), standard cuckoo search (CS) and two CS variant algorithms. In addition, the accuracy of the proposed extrinsic calibration method is verified by reconstructing and measuring a human foot and a cube box. The mean errors from the standard values of the length and width of the reconstructed foot mode are 0.18 cm and 0.13 cm, and the diameter of the yellow sphere are 0.17 cm, respectively. All of the experimental results show that the proposed extrinsic calibration method has a high calibration accuracy and good potential application prospects.
Zero-inflated count time series models using Gaussian copula

Count time series data are observed in several applied disciplines such as environmental science, biostatistics, economics, public health, and finance. In some cases, a specific count, usually zero, may occur more often than other counts. However, overlooking the frequent occurrence of zeros could result in misleading inferences. In this article, we develop a copula-based time series regression model for zero-inflated counts. Zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), and zero-inflated Conway-Maxwell-Poisson (ZICMP) distributed marginals will be considered, and the joint distribution is modeled under Gaussian copula with autoregression moving average (ARMA) errors. Sequential sampling likelihood inference is performed. Simulated and real-life data examples are provided and studied to evaluate the proposed method.
Symmetry and motion primitives in model predictive control

Symmetries, e.g. rotational and translational invariances for the class of mechanical systems, allow to characterize solution trajectories of nonlinear dynamical systems. Thus, the restriction to symmetry-induced dynamics, e.g. by using the concept of motion primitives, may be considered as a quantization of the system. Symmetry exploitation is well established in both motion planning and control. However, the linkage between the respective techniques to optimal control is not yet fully explored. In this manuscript, we want to lay the foundation for the usage of symmetries in Model Predictive Control (MPC). To this end, we investigate a mobile robot example in detail where our contribution is twofold: Firstly, we establish asymptotic stability of a desired set point w.r.t. the MPC closed loop, which is also demonstrated numerically by using motion primitives applied to the parallel parking scenario. Secondly, if the optimization criterion is not consistent with the symmetry action, we provide guidelines to rigorously derive stability guarantees based on symmetry exploitation.
Benefits of Blockchain Initiatives for Value-Based Care: Proposed Framework

The potential of blockchain technology to achieve strategic goals, such as value-based care, is increasingly being recognized by both researchers and practitioners. However, current research and practices lack comprehensive approaches for evaluating the benefits of blockchain applications.The goal of this study was to develop a framework for holistically assessing the performance of blockchain initiatives in providing value-based care by extending the existing balanced scorecard (BSC) evaluation framework.Based on a review of the literature on value-based health care, blockchain technology, and methods for evaluating initiatives in disruptive technologies, we propose an extended BSC method for holistically evaluating blockchain applications in the provision of value-based health care. The proposed method extends the BSC framework, which has been extensively used to measure both financial and nonfinancial performance of organizations. The usefulness of our proposed framework is further demonstrated via a case study.We describe the extended BSC framework, which includes five perspectives (both financial and nonfinancial) from which to assess the appropriateness and performance of blockchain initiatives in the health care domain.The proposed framework moves us toward a holistic evaluation of both the financial and nonfinancial benefits of blockchain initiatives in the context of value-based care and its provision.
General Analytical Techniques For Parameter-Based Procedural Content Generators

Most generative systems built in game development are parameter-driven, but the relationship between parameters and the output of the system is often unclear. This makes them frustrating to use for both experts and novices, and as a result generators are often filtered post-hoc, or tweaked through time-consuming trial and error. In this paper we introduce two analytical techniques: smoothness and codependence. We show how these features help analyse the impact of a parameter change on a generative system and suggest ways this could feed back into more intelligent tools that make working with procedural generators more precise and pleasant.
A Blockchain-based System for Online Consumer Reviews

Today's online review systems (used largely by hospitality services, internet retailers, government services, etc.) are highly centralized and subject to tampering and manipulation. Implementing a decentralized, trustworthy, unbiased and transparent review system is a major challenge. In this paper, we present a solution that utilizes Ethereum Blockchain, Smart Contracts, and IPFS to provide a secure, transparent and trusted platform for an online review system with high integrity and resiliency. We discuss key aspects related to architectural design, interactions between system components, algorithms and logic flow. We also show how we implemented and tested the overall system functionalities. Furthermore, we provide vulnerability analysis of the smart contract code. The full smart contract code was made publicly available at Github.