Reviewerly/exHarmony huggingface dataset:
Reconstructing Personal Stories in Virtual Reality as a Mechanism to Recover the Self

Advances in virtual reality present opportunities to relive experiences in an immersive medium that can change the way we perceive our life stories, potentially shaping our realities for the better. This paper studies the role of virtual reality as a tool for the creation of stories with the concept of the self as a narrator and the life of the self as a storyline. The basis of the study is the philosophical notion of the self-narrative as an explanatory story of the events in one’s life that constitutes the notion of one’s self. This application is suitable for cases when individuals need to recreate their self, such as during recovery after traumatic events. The analysis of the effects of virtual reality shows that it enables a person to engage in a process of deeper self-observation to understand and explain adverse events and to give meaning to these events to form a new story, which can complement the therapeutic outcomes of exposure treatments. This study proposes concrete examples of immersive scenarios used to reconstruct personal stories. Several possible levels of experience are proposed to suggest that recovery can be achieved through the gradual retelling of the self-narrative, addressing all of the underlying narratives. Considering the ethical challenges that might arise, this paper explores the ways in which immersion in virtual reality can benefit a person’s view toward life as a story and his or her self as its author, comparing this idea with previous research on the application of virtual reality for trauma treatment. The analysis also emphasizes the perception of narrative authorship in virtual reality as an essential method for recovering the self-narrative and improving a patient’s mental health during self-actualization.
On the incorporation of box-constraints for ensemble Kalman inversion

The Bayesian approach to inverse problems is widely used in practice to infer unknown parameters from noisy observations. In this framework, the ensemble Kalman inversion has been successfully applied for the quantification of uncertainties in various areas of applications. In recent years, a complete analysis of the method has been developed for linear inverse problems adopting an optimization viewpoint. However, many applications require the incorporation of additional constraints on the parameters, e.g. arising due to physical constraints. We propose a new variant of the ensemble Kalman inversion to include box constraints on the unknown parameters motivated by the theory of projected preconditioned gradient flows. Based on the continuous time limit of the constrained ensemble Kalman inversion, we discuss a complete convergence analysis for linear forward problems. We adopt techniques from filtering, such as variance inflation, which are crucial in order to improve the performance and establish a correct descent. These benefits are highlighted through a number of numerical examples on various inverse problems based on partial differential equations.
Calibration method for a line-structured light vision sensor based on a single cylindrical target

This paper presents a new, to the best of our knowledge, calibration method for line-structured light vision sensors. The laser stripe intersecting the cylindrical target and the line-structured light is captured by the camera, and the light plane parameters of the line-structured light are obtained by combining the cross-sectional characteristics of the ellipse. The nonlinear optimization of the light plane parameters use multiple locations to get the optimal solution of the light plane equation. This method requires only a single cylindrical target, which in turn greatly simplifies the calibration process. The results of simulation experiments and physical experiments show that the proposed calibration method can achieve higher calibration accuracy and measurement accuracy. The effectiveness of the new calibration method is verified.
Procedural Generation of Quests for Games Using Genetic Algorithms and Automated Planning

The following topics are dealt with: computer games; virtual reality; genetic algorithms; rendering (computer graphics); learning (artificial intelligence); diseases; medical disorders; medical computing; serious games (computing); evolutionary computation.
Lüders channels and the existence of symmetric-informationally-complete measurements

The L\"uders rule provides a way to define a quantum channel given a quantum measurement. Using this construction, we establish an if-and-only-if condition for the existence of a $d$-dimensional symmetric-informationally-complete quantum measurement (a SIC) in terms of a particular depolarizing channel. Moreover, the channel in question satisfies two entropic optimality criteria.
Online Active Extreme Learning Machine With Discrepancy Sampling for PolSAR Classification

The extreme learning machine (ELM) has drawn increasing attention in the field of machine learning due to its high accuracy and efficient learning. However, classical ELM works in batch and passive learning paradigms, which cannot deal with sequential data effectively. ELM has been extended to online sequential learning form (OS-ELM) and active learning form (AL-ELM), in which the former is used to improve training efficiency and the latter is mainly adopted to improve accuracy. In order to solve the problem of labeling samples difficulty and costly, poor sample validity, and continuous iterative learning in polarimetric synthetic aperture radar (PolSAR) image classification, we propose an online active extreme learning machine (OA-ELM) algorithm to combine the strengths and make up the weaknesses of OS-ELM and AL-ELM, which improves both efficiency and generalization ability. OA-ELM can learn from sequential data dynamically with low computational complexity and good generalization ability. Specifically, OA-ELM reduces time and memory cost for training via extended recursive least squares for optimization. It also improves accuracy using informative training samples selected by proposed discrepancy sampling (DS), which modifies an active query method called margin sampling (MS). Before applying MS to ELM, real-valued outputs of ELM need to be converted into probabilistic outputs first. Instead, the proposed DS can be applied to ELM directly by calculating the difference between the two largest actual nonprobabilistic outputs of ELM. Experimental results of PolSAR classification demonstrate that OA-ELM is effective and efficient compared with other algorithms in terms of accuracy and running time.
Image Based Tumor Cells Identification Using Convolutional Neural Network and Auto Encoders

The convolutional neural network (CNN) and other neural networks (NNs) provide promising tools for robotized characterization of tumor cells.However, the tumor growth areas in ultrasound images are normally obscure, with uncertain edges.It is not acceptable to prepare ultrasound images straightforwardly with the CNN.To solve the problem, this paper puts forward a faster region-convolutional neural network (R-CNN) to identify tumor cells with the aid of auto encoders.Taking two fully-connected layers with dropout and ReLU enactments as the base, the proposed faster R-CNN adopts 3D convolutional and max pooling layers, enabling the user to extract features from potential tumor growth areas.In addition, the thin and deep layers of the network were connected to facilitate the identification of blurry or small tumor growth areas.Experimental results show that the proposed faster R-CNN with auto encoders outperformed traditional data mining and artificial intelligence (AI) methods in prediction accuracy of tumor cells.
Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization

Grey wolf optimization (GWO) algorithm is a relatively recent and novel optimization approach. GWO showed performance improvement over all competing algorithms. However, the relevant literature identified that the primary GWO due to its position update equation shows superiority in exploitation, but is inefficient in exploration. It shows slow convergence and low precision, too. Motivated by the outlined issues in the primary GWO, this work presents two new and improved GWO algorithms. The first proposed variant modifies all the three models, encircling model of prey, position update equation and the hunting equation of canonical GWO. Further, a new parameter is introduced to scale the encircling and position update equations. As a result, the exploration issue of the algorithm is tackled. Unlike the first variant, the second proposed variant does not modify the position update models, but it incorporates Minkowski’s information into GWO. To the best of our knowledge, no such modifications to GWO have been done before. The proposed modified versions of GWO are tested on a well-known test functions suit and then compared with different population-based algorithms, including fast evolutionary programming and particle swarm optimization. It was identified from the simulation results that proposed algorithms outperform different algorithms in comparison on majority of problems. The sensitivity study of the proposed algorithms to their various parameters is also provided.
High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Segmentation

Magnetic resonance imaging (MRI) provides varying tissue contrast images of internal organs based on a strong magnetic field. Despite the non-invasive advantage of MRI in frequent imaging, the low contrast MR images in the target area make tissue segmentation a challenging problem. This paper demonstrates the potential benefits of image-to-image translation techniques to generate synthetic high tissue contrast (HTC) images. Notably, we adopt a new cycle generative adversarial network (CycleGAN) with an attention mechanism to increase the contrast within underlying tissues. The attention block, as well as training on HTC images, guides our model to converge on certain tissues. To increase the resolution of HTC images, we employ multi-stage architecture to focus on one particular tissue as a foreground and filter out the irrelevant background in each stage. This multi-stage structure also alleviates the common artifacts of the synthetic images by decreasing the gap between source and target domains. We show the application of our method for synthesizing HTC images on brain MR scans, including glioma tumor. We also employ HTC MR images in both the end-to-end and two-stage segmentation structure to confirm the effectiveness of these images. The experiments over three competitive segmentation baselines on BraTS 2018 dataset indicate that incorporating the synthetic HTC images in the multi-modal segmentation framework improves the average Dice scores 0.8%, 0.6%, and 0.5% on the whole tumor, tumor core, and enhancing tumor, respectively, while eliminating one real MRI sequence from the segmentation procedure.
A Filter Based Improved Decision Tree Sentiment Classification Model for RealTime Amazon Product Review Data

E-Commerce product features and reviews are considered to be the essential factors in real-time e-commerce sites for product recommendation systems.Due to inaccuracy decision patterns, in most cases e-commerce user fails to predict the products based on the user ratings and review comments.Traditional sentiment classification models are independent of data filtering, transformation and sentiment score computing techniques which require high computing memory, time and mostly leading to false-positive rate.To overcome these issues, a novel sentiment score-based product recommendation model is proposed on the real-time product data.In this model, a new product ranking score, filtering, and hybrid decision tree classifiers are implemented.Initially, real-time amazon product review data is captured using Document Object Model (DOM) parser.The features from the review comments are extracted using lexicon Feature Dictionary (FD) and AFINN, Normalized Product Review Score (NPRS) are generated to compute the class label for product review sentiment prediction.Ranked Principal Component Analysis (RPCA) is used as a feature selection measure to overcome the problem of data sparsity.Random Tree, Hoeffding Tree, Adaboost + Random Tree, the three variants of decision tree classifiers are used for product sentiment classification.The proposed filter-based improved decision tree sentiment classification model for real-time amazon product review data recommends the product based on the user query by prediction using a new novel normalized product review sentiment score and ranked feature selection measure.The proposed product recommendation, the decision-making system maximizes sentiment classification accuracy.Experimental results are compared against the traditional decisionmaking classification models in terms of correctly classified instances, error rate, and PRC, F-measure, kappa statistics.The proposed model experimental results show high efficiency.
Relevance-Promoting Language Model for Short-Text Conversation

Despite the effectiveness of sequence-to-sequence framework on the task of Short-Text Conversation (STC), the issue of under-exploitation of training data (i.e., the supervision signals from query text is ignored) still remains unresolved. Also, the adopted maximization-based decoding strategies, inclined to generating the generic responses or responses with repetition, are unsuited to the STC task. In this paper, we propose to formulate the STC task as a language modeling problem and tailor-make a training strategy to adapt a language model for response generation. To enhance generation performance, we design a relevance-promoting transformer language model, which performs additional supervised source attention after the self-attention to increase the importance of informative query tokens in calculating the token-level representation. The model further refines the query representation with relevance clues inferred from its multiple references during training. In testing, we adopt a randomization-over-maximization strategy to reduce the generation of generic responses. Experimental results on a large Chinese STC dataset demonstrate the superiority of the proposed model on relevance metrics and diversity metrics.1
Coupled-View Deep Classifier Learning from Multiple Noisy Annotators

Typically, learning a deep classifier from massive cleanly annotated instances is effective but impractical in many real-world scenarios. An alternative is collecting and aggregating multiple noisy annotations for each instance to train the classifier. Inspired by that, this paper proposes to learn deep classifier from multiple noisy annotators via a coupled-view learning approach, where the learning view from data is represented by deep neural networks for data classification and the learning view from labels is described by a Naive Bayes classifier for label aggregation. Such coupled-view learning is converted to a supervised learning problem under the mutual supervision of the aggregated and predicted labels, and can be solved via alternate optimization to update labels and refine the classifiers. To alleviate the propagation of incorrect labels, small-loss metric is proposed to select reliable instances in both views. A co-teaching strategy with class-weighted loss is further leveraged in the deep classifier learning, which uses two networks with different learning abilities to teach each other, and the diverse errors introduced by noisy labels can be filtered out by peer networks. By these strategies, our approach can finally learn a robust data classifier which less overfits to label noise. Experimental results on synthetic and real data demonstrate the effectiveness and robustness of the proposed approach.
ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs

A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal. However, in many real-world settings, the size of parallel annotated training data is restricted. Additionally, prior cross-lingual mapping research has mainly focused on the word level. This raises the question of whether such techniques can also be applied to effortlessly obtain cross-lingually aligned sentence representations. To this end, we propose an Adversarial Bi-directional Sentence Embedding Mapping (ABSent) framework, which learns mappings of cross-lingual sentence representations from limited quantities of parallel data. The experiments show that our method outperforms several technically more powerful approaches, especially under challenging low-resource circumstances. The source code is available from https://github.com/zuohuif/ABSent along with relevant datasets.
P-SIF: Document Embeddings Using Partition Averaging

Simple weighted averaging of word vectors often yields effective representations for sentences which outperform sophisticated seq2seq neural models in many tasks. While it is desirable to use the same method to represent documents as well, unfortunately, the effectiveness is lost when representing long documents involving multiple sentences. One of the key reasons is that a longer document is likely to contain words from many different topics; hence, creating a single vector while ignoring all the topical structure is unlikely to yield an effective document representation. This problem is less acute in single sentences and other short text fragments where the presence of a single topic is most likely. To alleviate this problem, we present P-SIF, a partitioned word averaging model to represent long documents. P-SIF retains the simplicity of simple weighted word averaging while taking a document's topical structure into account. In particular, P-SIF learns topic-specific vectors from a document and finally concatenates them all to represent the overall document. We provide theoretical justifications on the correctness of P-SIF. Through a comprehensive set of experiments, we demonstrate P-SIF's effectiveness compared to simple weighted averaging and many other baselines.
Mode-independent quantum entanglement for light

We address the problem of the persistence of entanglement of quantum light under mode transformations, where orthogonal modes define the parties between which quantum correlations can occur. Since the representation of a fixed photonic quantum state in different optical mode bases can substantially influence the entanglement properties of the said state, we devise a constructive method to obtain families of states with the genuine feature of remaining entangled for any choice of mode decomposition. In the first step, we focus on two-photon states in a bipartite system and optimize their entanglement properties with respect to unitary mode transformations. Applying a necessary and sufficient entanglement witness criteria, we are then able to prove that the class of constructed states is entangled for arbitrary mode decompositions. Furthermore, we provide optimal bounds to the robustness of the mode-independent entanglement under general imperfections. In the second step, we demonstrate the power of our technique by showing how it can be straightforwardly extended to higher-order photon numbers in multipartite systems, together with providing a generally applicable and rigorous definition of mode-independent separability and inseparability for mixed states.
RobuTrans: A Robust Transformer-Based Text-to-Speech Model

Recently, neural network based speech synthesis has achieved outstanding results, by which the synthesized audios are of excellent quality and naturalness. However, current neural TTS models suffer from the robustness issue, which results in abnormal audios (bad cases) especially for unusual text (unseen context). To build a neural model which can synthesize both natural and stable audios, in this paper, we make a deep analysis of why the previous neural TTS models are not robust, based on which we propose RobuTrans (Robust Transformer), a robust neural TTS model based on Transformer. Comparing to TransformerTTS, our model first converts input texts to linguistic features, including phonemic features and prosodic features, then feed them to the encoder. In the decoder, the encoder-decoder attention is replaced with a duration-based hard attention mechanism, and the causal self-attention is replaced with a "pseudo non-causal attention" mechanism to model the holistic information of the input. Besides, the position embedding is replaced with a 1-D CNN, since it constrains the maximum length of synthesized audio. With these modifications, our model not only fix the robustness problem, but also achieves on parity MOS (4.36) with TransformerTTS (4.37) and Tacotron2 (4.37) on our general set.
Improving Context-Aware Neural Machine Translation Using Self-Attentive Sentence Embedding

Fully Attentional Networks (FAN) like Transformer (Vaswani et al. 2017) has shown superior results in Neural Machine Translation (NMT) tasks and has become a solid baseline for translation tasks. More recent studies also have reported experimental results that additional contextual sentences improve translation qualities of NMT models (Voita et al. 2018; Müller et al. 2018; Zhang et al. 2018). However, those studies have exploited multiple context sentences as a single long concatenated sentence, that may cause the models to suffer from inefficient computational complexities and long-range dependencies. In this paper, we propose Hierarchical Context Encoder (HCE) that is able to exploit multiple context sentences separately using the hierarchical FAN structure. Our proposed encoder first abstracts sentence-level information from preceding sentences in a self-attentive way, and then hierarchically encodes context-level information. Through extensive experiments, we observe that our HCE records the best performance measured in BLEU score on English-German, English-Turkish, and English-Korean corpus. In addition, we observe that our HCE records the best performance in a crowd-sourced test set which is designed to evaluate how well an encoder can exploit contextual information. Finally, evaluation on English-Korean pronoun resolution test suite also shows that our HCE can properly exploit contextual information.
The Power of Computer-Mediated Communication Theories in Explaining the Effect of Chatbot Introduction on User Experience

Chatbots have increasingly penetrated our lives as their behavior growingly imitates a human interlocutor.This paper examines the effect of different methods of selfpresentation of a chatbot on the end-user experience.An interlocutor in a computer-mediated communication (CMC) environment can either introduce itself as a chatbot, a human being, or choose not to identify itself.We conducted an experiment to compare these three methods in terms of end-user experience that comprises of social presence, perceived humanness, and service encounter satisfaction.Our data demonstrate that a chatbot that discloses its virtual identity is scored significantly lower for social presence and perceived humanness than other two choices of self-presentation.Key findings and the associated implications are discussed.