Reviewerly/exHarmony huggingface dataset:
Generating Emotional Controllable Response Based on Multi-Task and Dual Attention Framework

Building an empathic conversation agent in open-domain is a key step towards affective computing and intelligent interactions.However, most current methods either focus on the consistency of content or the controllability of emotion and handling both factors are not yet properly solved.In this paper, we propose the multi-task and dual attentions (MTDA) framework for generating an emotional response.The MTDA framework decomposes the input utterance into the content layer and emotional layer, and then encodes and decodes them separately, which makes this end-to-end model more interpretable and controllable.A multi-task learning based encoder is employed in the MTDA framework, which can obtain the representation of the content and the emotion through unsupervised learning and supervised learning.A dual attention mechanism is adopted for decoding, which ensures that specific emotional responses are coherent with the content and the emotion of the input.We also combine the MTDA framework with state of the art generative models to train emotional generation systems.Extensive experiments show that our model can not only adapt to different target emotion goals but also generate coherent and informative responses.
Color Quantization Application Based on K-Means in Remote Sensing Image Processing

Abstract In this paper, four kinds of RGB remote sensing images are processed using color quantization algorithm based on K-Means to reduce the number of colors in the image. The color quantization algorithm is to select the most representative color and reduce the useless color in the image as much as possible. This paper assumes that the RGB remote sensing image is composed of multiple pixels. Using the K-Means algorithm to perform unsupervised clustering on these pixels with specific colors, color quantization can be realized. The use of K-Means for color quantization of remote sensing images can reduce the number of colors in those images, so that remote sensing images can be reproduced well in lower performance computer equipment. At the same time, color quantization reduces the size of remote sensing images and improves the efficiency of remote sensing image processing.
Improving Lossless Image Compression with Contextual Memory

With the increased use of image acquisition devices, including cameras and medical imaging instruments, the amount of information ready for long term storage is also growing. In this paper we give a detailed description of the state-of-the-art lossless compression software PAQ8PX applied to grayscale image compression. We propose a new online learning algorithm for predicting the probability of bits from a stream. We then proceed to integrate the algorithm into PAQ8PX’s image model. To verify the improvements, we test the new software on three public benchmarks. Experimental results show better scores on all of the test sets.
Service Selection With QoS Correlations in Distributed Service-Based Systems

Service selection is an important research problem in distributed service-based systems, which aims to select proper services to meet user requirements. A number of service selection approaches have been proposed in recent years. Most of them, however, overlook quality-of-service (QoS) correlations, which broadly exist in distributed service-based systems. The concept of QoS correlations involves two aspects: 1) QoS correlations among services and 2) QoS correlations of user requirements. The first aspect means that some QoS attributes of service not only depend on the service itself but also have correlations with other services, e.g., buying service 1 and then getting service 2 with half price. The second aspect means the relationships among QoS attributes of user requirements, e.g., a user can accept a service with fast response time and high service cost or the user can also accept a service with slow response time and low service cost ( <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fig. 1</xref> ). These correlations significantly affect user selection of services. Currently, only a few existing approaches have considered QoS correlations among services, i.e., the first aspect, but they still overlook QoS correlations of user requirements, i.e., the second aspect, which are also very important in distributed service-based systems. In this paper, a novel service selection approach is proposed, which not only considers QoS correlations of services but also accounts for QoS correlations of user requirements. This approach, to the best of our knowledge, is the first one which considers QoS correlations of user requirements. Also, this approach is decentralized which can avoid the single point of failure. The experimental results demonstrate the effectiveness of the proposed approach.
Combining Deep Learning Technologies with Multi-Level Gabor Features for Facial Recognition in Biometric Automated Systems

Face recognition is one of the most important abilities that humans use in their daily lives.It represents a natural, robust and non-intrusive manner for identifying individuals.However, it is also a very challenging problem in the field of computer vision and pattern recognition.A good face recognition algorithm should be able to automatically detect and recognize a face in an image, regardless of lightning, expression, illumination and pose.In this paper, we present a novel approach for the face model representation and matching issues in face recognition.Our approach is based on multi-level Gabor features and Deep Learning techniques.In the experiments presented in this paper, ORL, Caltech, Yale and Yale B databases were used in order to obtain the face recognition rate.The results show that the new face recognition algorithm outperforms the conventional methods such as global Gabor face recognition based on PCA in terms of recognition rate.
Automated Catheter Localization in Volumetric Ultrasound Using 3D Patch-Wise U-Net with Focal Loss

3D ultrasound (US) imaging has become an attractive option for image-guided interventions. Fast and accurate catheter localization in 3D cardiac US can improve the outcome and efficiency of the cardiac interventions. In this paper, we propose a catheter localization method for 3D cardiac US using the patch-wise semantic segmentation with model fitting. Our 3D U-Net is trained with the focal loss of cross-entropy, which makes the network to focus more on samples that are difficult to classify. Moreover, we adopt a dense sampling strategy to overcome the extremely imbalanced catheter occupation in the 3D US data. Extensive experiments on our challenging ex-vivo dataset show that the proposed method achieves an F-1 score of 65.1% for catheter segmentation, outperforming the state-of-the-art methods. With this, our method can localize RF-ablation catheters with an average error of 1.28 mm.
Image-to-Image Translation Using Identical-Pair Adversarial Networks

We propose Identical-pair Adversarial Networks (iPANs) to solve image-to-image translation problems, such as aerial-to-map, edge-to-photo, de-raining, and night-to-daytime. Our iPANs rely mainly on the effectiveness of adversarial loss function and its network architectures. Our iPANs consist of two main networks, an image transformation network T and a discriminative network D. We use U-NET for the transformation network T and a perceptual similarity network, which has two streams of VGG16 that share the same weights for network D. Our proposed adversarial losses play a minimax game against each other based on a real identical-pair and a fake identical-pair distinguished by the discriminative network D; e.g. a discriminative network D considers two inputs as a real pair only when they are identical, otherwise a fake pair. Meanwhile, the transformation network T tries to persuade the discriminator network D that the fake pair is a real pair. We experimented on several problems of image-to-image translation and achieved results that are comparable to those of some existing approaches, such as pix2pix, and PAN.
FriWalk robotic walker: usability, acceptance and UX evaluation after a pilot study in a real environment

Background: Scientific evidence supports that prevention strategies like multicomponent physical exercise help avoiding functional decline, falls and frailty. The robotic walker FriWalk, developed within the ACANTO project, supports the execution of controlled physical activities during hospital admission to prevent functional deterioration associated to prolonged bedrest. FriWalk shows in a clinical validation study a positive relationship with improvement in physical performance, basic activities of daily living execution and frailty status. Usability, acceptance and user experience (UX) are key aspects to ease the adoption of assistive technologies in the elderly.Objective: This work pursues the evaluation of the usability, acceptance and UX of the FriWalk from the patients and clinical professionals' perspectives.Methods: Data collected during the validation of FriWalk in a real environment have been used. Forty-two patients recruited at Getafe University Hospital (Acute Care and Orthogeriatric Units) and one clinical professional participated. SUS, TAM, UX and ad hoc questionnaires were administered.Results: Patients provided an average SUS of 52.86 and provided valuable information in the qualitative acceptance interviews. The clinical professional provided an averaged SUS and TAM of 67 and 46.6, respectively, and evaluated all UX categories as above average.Conclusions: Usability results do not qualify FriWalk as above average; the reasons explaining this have been identified and point out to the prototypical stage of the hardware. Acceptance and UX were positively evaluated and allowed the research team to propose a new organizational model to deliver the FriWalk-based prevention program. FriWalk will be soon evolved.Implications for rehabilitationFriWalk showed in a randomized clinical trial a positive relationship with improvement in physical performance, basic activities of daily living execution and frailty status.In terms of usability, user experience (UX) and acceptance, participants of the study have valued the FriWalk robotic walker as a promising help, considering that the device that has been under evaluation was still in a prototype stage.Clinical professional reported FriWalk and its corresponding exercise program description software regarding usability, acceptance and UX as satisfactory tool to prescribe and assess a rehabilitation program for hospitalized patients.
Differential evolution for instance based transfer learning in genetic programming for symbolic regression

Transfer learning attracts increasing attention in many fields in recent years. However, studies on transfer learning for symbolic regression are still rare. This work proposes a new instance weighting framework for genetic programming (GP) based symbolic regression for transfer learning. The key idea is to use differential evolution to search for optimal weights during the evolutionary process of GP, which helps GP identify and learn from more useful source domain instances and eliminate the effort of less useful source domain instances. The results show that the proposed method achieves notably better cross-domain generalisation performance in a very stable way than GP without the instance weighting framework and support vector regression.
Feature-Preserving Tensor Voting Model for Mesh Steganalysis

The standard tensor voting technique shows its versatility in tasks such as object recognition and semantic segmentation by recognizing feature points and sharp edges that can segment a model into several patches. We propose a neighborhood-level representation-guided tensor voting model for 3D mesh steganalysis. Because existing steganalytic methods do not analyze correlations among neighborhood faces, they are not very effective at discriminating stego meshes from cover meshes. In this paper, we propose to utilize a tensor voting model to reveal the artifacts caused by embedding data. In the proposed steganalytic scheme, the normal voting tensor (NVT) operation is performed on original mesh faces and smoothed mesh faces separately. Then, the absolute values of the differences between the eigenvalues of the two tensors (from the original face and the smoothed face) are regarded as features that capture intricate relationships among the vertices. Subsequently, the extracted features are processed with a nonlinear mapping to boost the feature effectiveness. The experimental results show that the proposed feature sets prevail over state-of-the-art feature sets including LFS64 and ELFS124 under various steganographic schemes.
Efficient Exploration with Self-Imitation Learning via Trajectory-Conditioned Policy.

Reinforcement learning with sparse rewards is challenging because an agent can rarely obtain non-zero rewards and hence, gradient-based optimization of parameterized policies can be incremental and slow. Recent work demonstrated that using a memory buffer of previous successful trajectories can result in more effective policies. However, existing methods may overly exploit past successful experiences, which can encourage the agent to adopt sub-optimal and myopic behaviors. In this work, instead of focusing on good experiences with limited diversity, we propose to learn a trajectory-conditioned policy to follow and expand diverse past trajectories from a memory buffer. Our method allows the agent to reach diverse regions in the state space and improve upon the past trajectories to reach new states. We empirically show that our approach significantly outperforms count-based exploration methods (parametric approach) and self-imitation learning (parametric approach with non-parametric memory) on various complex tasks with local optima. In particular, without using expert demonstrations or resetting to arbitrary states, we achieve the state-of-the-art scores under five billion number of frames, on challenging Atari games such as Montezuma's Revenge and Pitfall.
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees

Gaussian processes (GPs) offer a flexible class of priors for nonparametric Bayesian regression, but popular GP posterior inference methods are typically prohibitively slow or lack desirable finite-data guarantees on quality. We develop a scalable approach to approximate GP regression, with finite-data guarantees on the accuracy of our pointwise posterior mean and variance estimates. Our main contribution is a novel objective for approximate inference in the nonparametric setting: the preconditioned Fisher (pF) divergence. We show that unlike the Kullback--Leibler divergence (used in variational inference), the pF divergence bounds bounds the 2-Wasserstein distance, which in turn provides tight bounds on the pointwise error of mean and variance estimates. We demonstrate that, for sparse GP likelihood approximations, we can minimize the pF divergence bounds efficiently. Our experiments show that optimizing the pF divergence bounds has the same computational requirements as variational sparse GPs while providing comparable empirical performance---in addition to our novel finite-data quality guarantees.
A Framework for Description, Analysis and Comparison of Creative Systems

Abstract I summarise and attempt to clarify some concepts presented in and arising from Margaret Boden’s descriptive hierarchy of creativity (Boden, M. A., The Creative Mind: Myths and Mechanisms, 2nd Ed., Routledge, London, 2004), by formalising the ideas she proposes. The aim is to move towards a model which allows detailed comparison, and hence better understanding, of systems, whether artificial, natural or hybrid, which exhibit behaviour which would be called ‘creative’ in humans. The framework paves the way for the description of naturalistic, multi-agent creative artificial intelligence systems, which create in a societal context. I demonstrate some simple reasoning about creative behaviour based on the new framework, to show how it might be useful for the analysis and study of creative systems. In particular, I identify some crucial properties of creative systems, in terms of the framework components, some of which may usefully be proven a priori of a given system. Finally, I exemplify the use of the framework by broadly describing the development of art music from the 10th to the 20th century in these more formal terms.
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters

In this paper we propose a boosting based multiview learning algorithm, referred as PB−MVBoost, which iteratively learns (i) weights over view-specific voters capturing view-specific information, and (ii) weights over views by optimizing a PAC-Bayes multiview C-Bound that takes into account the accuracy of view-specific classifiers and the diversity between the views. We derive a generalization bound for this strategy following the PAC-Bayes theory which is a suitable tool to deal with models expressed as weighted combination over a set of voters. Different experiments on three publicly available datasets show the efficiency of the proposed approach with respect to state-of-art models.
Neural Coreference Resolution with Limited Lexical Context and Explicit Mention Detection for Oral

We propose an end-to-end coreference resolution system obtained by adapting neural models that have recently improved the state-of-the-art on the OntoNotes benchmark to make them applicable to other paradigms for this task. We report the performances of our system on ANCOR, a corpus of transcribed oral French, for which it constitutes a new baseline with proper evaluation.
That’s Mine! Learning Ownership Relations and Norms for Robots

The ability for autonomous agents to learn and conform to human norms is crucial for their safety and effectiveness in social environments. While recent work has led to frameworks for the representation and inference of simple social rules, research into norm learning remains at an exploratory stage. Here, we present a robotic system capable of representing, learning, and inferring ownership relations and norms. Ownership is represented as a graph of probabilistic relations between objects and their owners, along with a database of predicate-based norms that constrain the actions permissible on owned objects. To learn these norms and relations, our system integrates (i) a novel incremental norm learning algorithm capable of both one-shot learning and induction from specific examples, (ii) Bayesian inference of ownership relations in response to apparent rule violations, and (iii) perceptbased prediction of an object’s likely owners. Through a series of simulated and real-world experiments, we demonstrate the competence and flexibility of the system in performing object manipulation tasks that require a variety of norms to be followed, laying the groundwork for future research into the acquisition and application of social norms.
Neural Collaborative Embedding From Reviews for Recommendation

This paper mainly studies the personalized rating prediction task based on review texts for the recommendation. Recently, most of the related researches use convolutional neural networks to capture local context information, but it loses word frequency and global context information. In addition, they simply equate the user (item) embedding to review embedding, which brings some irrelevant information of the review text into user preference or item property. Moreover, they only consider the low-order interactions, which remain the fine-grained user-item interactions to be explored. To solve these problems, we propose a novel method neural collaborative embedding model (NCEM). We first adopt pre-trained BERT model, which has been proven to improve most of the downstream NLP tasks, to simultaneously capture the global context and word frequency information. In addition, a self-attention mechanism is introduced to learn the contribution of each review. Next, we develop a neural form of standard factorization machine, which can model first-order and second-order user-item interactions by stacking multiple layers. The extensive experiments on four public datasets showed that NCEM consistently outperforms the state-of-the-art recommendation approaches. Furthermore, the recommendation interpretability can be improved by showing users the high score reviews accompanied recommended item.
Privileged Authenticity in Reconstruction of Digital Encrypted Shares

Efficient message reconstruction mechanism depends on the entire partial shares received in random manner. This paper proposed a technique to ensure the authenticated accumulation of shares based on the privileged share.Threshold number of received shares inclusive of the privileged share, were being accumulated together to validate the original message. Although attaining threshold number of shares or more excluding the privileged share, it would not be possible to reconstruct the original message. Encryptional procedure has been put into the desired partial shares to confuse the evaesdroppers. Decisive parameter termed as hash tag has been extracted from the cumulative shares and bitwise checking procedure has been carried out. In first appearance of first mismatch, rests of the checking bits were ignored, as test case put under failure transaction. Different statistical tests namely floating frequency, entropy value have proved the robustness of the proposed technique. Thus, extensive experiments were conducted to evaluate the security and efficiency with better productivity.&lt;strong&gt; &lt;/strong&gt;
A Hybrid Rules and Statistical Method for Arabic to English Machine Translation

Arabic is one of the six major world languages. It originated in the area currently known as the Arabian Peninsula. Arabic is the joint official language in Middle Eastern and African states. Large communities of Arabic speakers have existed outside of the Middle East since the end of the last century, particularly in the United States and Europe. So finding a quick and efficient Arabic machine translator has become an urgent necessity, due to the differences between the languages spoken in the world's communities and the vast development that has occurred worldwide. Arabic combines many of the significant challenges of other languages like word order and ambiguity. The word ordering problem because of Arabic has four sentence structures which allow different word orders. Ambiguity in the Arabic language is a notorious problem because of the richness and complexity of Arabic morphology. The core problems in machine translation are reordering the words and estimating the right word translation among many options in the lexicon. The Rule-Based Machine translation (RBMT) approach is the way to reorder words, and the statistical approach, such as Expectation Maximisation (EM), is the way to select right word translations and count word frequencies. Combining RBMT with EM plays an impotent role in generating a good-quality MT. This paper presents a combination of the rule-based machine translation (RBMT) approach with the Expectation Maximisation (EM) algorithm. These two techniques have been applied successfully to word ordering and ambiguity problems in Arabic-to-English machine translation.