Reviewerly/exHarmony huggingface dataset:
A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments

As one of the challenging tasks of multiple autonomous underwater vehicles systems, the realization of target hunting is the great significance. The multiple autonomous underwater vehicle target hunting is studied in this article. In some research, because the hunting members cannot reach the hunting point at the same time, the hunting time is long or the target escapes. To improve the efficiency of the target hunting, the leader–follower formation algorithm is introduced. Firstly, the task is assigned based on the distance between the autonomous underwater vehicle and the target. Then, the autonomous underwater vehicles with the same task are formed based on leader–follower mode, and the formation is kept to track the target. In the final capture phase, multiple autonomous underwater vehicle system use angle matching algorithm to round up target. The simulation results show that the proposed algorithm can effectively accomplish the target hunting task, save the hunting time, and avoid the target escape. Compared with the bioinspired neural network algorithm, the proposed algorithm shows better performance.
A calibration method for binocular stereo vision sensor with short-baseline based on 3D flexible control field

Binocular stereo vision sensor (BSVS) based on disparity is an important technique in 3D shape measurement. In the application of large field-of-view (FOV), a relative short baseline distance of BSVS has heightened the demand for calibration. In this paper, a calibration method based on a virtual 3D flexible control field (FCF) for BSVS with short-baseline is proposed. The FCF is constructed by a laser tracker, a precision two-axis turntable and a camera calibration reference target (CRT) which can be exchanged with a standard laser tracker target precisely. Firstly, the turntable coordinate system is built and the position of the fixed CRT is measured by the laser tracker. Then the turntable with BSVS on it rotates in two dimensions regularly, during which the cameras shoot the fixed CRT synchronously, and the 3D FCF is constructed. Finally, the minimum objective function is established according to the BSVS imaging model to realize the calibration of internal and external parameters integrally. The experimental results show that under the conditions that the baseline is 120mm and the working distance is 3m ∼ 5m, the residual error RMS of spatial points is 0.3654mm and the reconstructed distance RMS error is 1.0728mm.
TalkDown: A Corpus for Condescension Detection in Context

Condescending language use is caustic; it can bring dialogues to an end and bifurcate communities.Thus, systems for condescension detection could have a large positive impact.A challenge here is that condescension is often impossible to detect from isolated utterances, as it depends on the discourse and social context.To address this, we present TALKDOWN, a new labeled dataset of condescending linguistic acts in context.We show that extending a language-only model with representations of the discourse improves performance, and we motivate techniques for dealing with the low rates of condescension overall.We also use our model to estimate condescension rates in various online communities and relate these differences to differing community norms.
GECOR: An End-to-End Generative Ellipsis and Co-reference Resolution Model for Task-Oriented Dialogue

Ellipsis and co-reference are common and ubiquitous especially in multi-turn dialogues.In this paper, we treat the resolution of ellipsis and co-reference in dialogue as a problem of generating omitted or referred expressions from the dialogue context.We therefore propose a unified end-to-end Generative Ellipsis and CO-reference Resolution model (GECOR) in the context of dialogue.The model can generate a new pragmatically complete user utterance by alternating the generation and copy mode for each user utterance.A multi-task learning framework is further proposed to integrate the GECOR into an end-to-end task-oriented dialogue.In order to train both the GECOR and the multitask learning framework, we manually construct a new dataset on the basis of the public dataset CamRest676 with both ellipsis and co-reference annotation.On this dataset, intrinsic evaluations on the resolution of ellipsis and co-reference show that the GECOR model significantly outperforms the sequenceto-sequence (seq2seq) baseline model in terms of EM, BLEU and F 1 while extrinsic evaluations on the downstream dialogue task demonstrate that our multi-task learning framework with GECOR achieves a higher success rate of task completion than TSCP, a state-of-the-art end-to-end task-oriented dialogue model (Lei et al., 2018).
Multi-marginal Wasserstein GAN

Multiple marginal matching problem aims at learning mappings to match a source domain to multiple target domains and it has attracted great attention in many applications, such as multi-domain image translation. However, addressing this problem has two critical challenges: (i) Measuring the multi-marginal distance among different domains is very intractable; (ii) It is very difficult to exploit cross-domain correlations to match the target domain distributions. In this paper, we propose a novel Multi-marginal Wasserstein GAN (MWGAN) to minimize Wasserstein distance among domains. Specifically, with the help of multi-marginal optimal transport theory, we develop a new adversarial objective function with inner- and inter-domain constraints to exploit cross-domain correlations. Moreover, we theoretically analyze the generalization performance of MWGAN, and empirically evaluate it on the balanced and imbalanced translation tasks. Extensive experiments on toy and real-world datasets demonstrate the effectiveness of MWGAN.
QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization

Standard accuracy metrics indicate that modern reading comprehension systems have achieved strong performance in many question answering datasets.However, the extent these systems truly understand language remains unknown, and existing systems are not good at distinguishing distractor sentences, which look related but do not actually answer the question.To address this problem, we propose QAInfomax as a regularizer in reading comprehension systems by maximizing mutual information among passages, a question, and its answer.QAInfomax helps regularize the model to not simply learn the superficial correlation for answering questions.The experiments show that our proposed QAInfomax achieves the state-of-the-art performance on the benchmark Adversarial-SQuAD dataset 1 .
Multi-mapping Image-to-Image Translation via Learning Disentanglement

Recent advances of image-to-image translation focus on learning the one-to-many mapping from two aspects: multi-modal translation and multi-domain translation. However, the existing methods only consider one of the two perspectives, which makes them unable to solve each other's problem. To address this issue, we propose a novel unified model, which bridges these two objectives. First, we disentangle the input images into the latent representations by an encoder-decoder architecture with a conditional adversarial training in the feature space. Then, we encourage the generator to learn multi-mappings by a random cross-domain translation. As a result, we can manipulate different parts of the latent representations to perform multi-modal and multi-domain translations simultaneously. Experiments demonstrate that our method outperforms state-of-the-art methods.
A Pilot Study for Chinese SQL Semantic Parsing

The task of semantic parsing is highly useful for dialogue and question answering systems.Many datasets have been proposed to map natural language text into SQL, among which the recent Spider dataset provides crossdomain samples with multiple tables and complex queries.We build a Spider dataset for Chinese, which is currently a low-resource language in this task area.Interesting research questions arise from the uniqueness of the language, which requires word segmentation, and also from the fact that SQL keywords and columns of DB tables are typically written in English.We compare character-and wordbased encoders for a semantic parser, and different embedding schemes.Results show that word-based semantic parser is subject to segmentation errors and cross-lingual word embeddings are useful for text-to-SQL.
How well do NLI models capture verb veridicality?

In natural language inference (NLI), contexts are considered veridical if they allow us to infer that their underlying propositions make true claims about the real world.We investigate whether a state-of-the-art natural language inference model (BERT) learns to make correct inferences about veridicality in verbcomplement constructions.We introduce an NLI dataset for veridicality evaluation consisting of 1,500 sentence pairs, covering 137 unique verbs.We find that both human and model inferences generally follow theoretical patterns, but exhibit a systematic bias towards assuming that verbs are veridical-a bias which is amplified in BERT.We further show that, encouragingly, BERT's inferences are sensitive not only to the presence of individual verb types, but also to the syntactic role of the verb, the form of the complement clause (to-vs.that-complements), and negation.
Multi-View Frustum Pointnet for Object Detection in Autonomous Driving

LIDAR point cloud and RGB images are often used for object detection in autonomous driving scenarios. This paper develops a multi-view version of Frustum PointNet (F-PointNet), to be called MVFP to reduce the rate of missed detection in F-PointNet by adding auxiliary bird's eye view (BEV) detection part. In processing MVFP, initial object detection results are obtained from F-PointNet by combining the RGB image and raw LIDAR point cloud. Simultaneously, raw LIDAR point cloud is encoded into BEV feature maps, from which 2D bounding boxes are predicted. In missed detection judgement, the intersection over union (IoU) is used as a criteria for the matching of preliminary object detection results from F-PointNet and BEV maps prediction results. 2D boxes belonging to missed-detected objects from BEV maps are projected to the pipeline of F-PointNet until all the objects in BEV maps find a matching detection result in the set of F-PointNet detection results. To evaluate the performance of MVFP, 3D object detection experiments are conducted on KITTI benchmark. The experiment results demonstrate that MVFP outperforms the original F-PointNet by 5% and 4% higher recall on the hard mode of pedestrian and cyclist.
Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator

Pointer Generators have been the de facto standard for modern summarization systems.However, this architecture faces two major drawbacks: Firstly, the pointer is limited to copying the exact words while ignoring possible inflections or abstractions, which restricts its power of capturing richer latent alignment.Secondly, the copy mechanism results in a strong bias towards extractive generations, where most sentences are produced by simply copying from the source text.In this paper, we address these problems by allowing the model to "edit" pointed tokens instead of always hard copying them.The editing is performed by transforming the pointed word vector into a target space with a learned relation embedding.On three large-scale summarization dataset, we show the model is able to (1) capture more latent alignment relations than exact word matches, (2) improve word alignment accuracy, allowing for better model interpretation and controlling, (3) generate higherquality summaries validated by both qualitative and quantitative evaluations and (4) bring more abstraction to the generated summaries.
Generative Reversible Data Hiding by Image-to-Image Translation via GANs

The traditional reversible data hiding technique is based on cover image modification which inevitably leaves some traces of rewriting that can be more easily analyzed and attacked by the warder. Inspired by the cover synthesis steganography-based generative adversarial networks, in this paper, a novel generative reversible data hiding (GRDH) scheme by image translation is proposed. First, an image generator is used to obtain a realistic image, which is used as an input to the image-to-image translation model with CycleGAN. After image translation, a stego image with different semantic information will be obtained. The secret message and the original input image can be recovered separately by a well-trained message extractor and the inverse transform of the image translation. The experimental results have verified the effectiveness of the scheme.
Sentiment Analysis Is Not Solved! Assessing and Probing Sentiment Classification

Neural methods for sentiment analysis have led to quantitative improvements over previous approaches, but these advances are not always accompanied with a thorough analysis of the qualitative differences.Therefore, it is not clear what outstanding conceptual challenges for sentiment analysis remain.In this work, we attempt to discover what challenges still prove a problem for sentiment classifiers for English and to provide a challenging dataset.We collect the subset of sentences that an (oracle) ensemble of state-of-the-art sentiment classifiers misclassify and then annotate them for 18 linguistic and paralinguistic phenomena, such as negation, sarcasm, modality, etc. 1 Finally, we provide a case study that demonstrates the usefulness of the dataset to probe the performance of a given sentiment classifier with respect to linguistic phenomena.
Co-saliency detection via integration of multi-layer convolutional features and inter-image propagation

Convolutional neural networks have been successfully applied to detect salient objects in an image. However, how to better use convolutional features for co-saliency detection, which is an emerging branch of saliency detection, is not fully explored. This paper proposes a convolutional neural network based co-saliency detection model, which consists of two key parts including the integration of multi-layer convolutional features extracted from a group of images and the inter-image saliency propagation. Firstly, the input image and its four co-images belonging to the same image category are passed through the VGG16 model, to obtain the multi-layer convolutional features of these images. Secondly, multi-scale synthesized feature maps, which contain both internal features and correlative features, are generated by integrating the multi-layer convolutional features. Thirdly, via the integration of low-level boundary features and high-level semantic features, the multi-scale synthesized feature maps are enhanced and fused together to generate the initial co-saliency map. Finally, an inter-image saliency propagation method is utilized to refine the initial co-saliency map, yielding the final co-saliency map with the improved quality. Experimental results on two public datasets demonstrate that the proposed model achieves the best performance compared to the state-of-the-art co-saliency detection models.
Marginal Replay vs Conditional Replay for Continual Learning

We present a new replay-based method of continual classification learning that we term “conditional replay” which generates samples and labels together by sampling from a distribution conditioned on the class. We compare conditional replay to another replay-based continual learning paradigm (which we term “marginal replay”) that generates samples independently of their class and assigns labels in a separate step. The main improvement in conditional replay is that labels for generated samples need not be inferred, which reduces the margin for error in complex continual classification learning tasks. We demonstrate the effectiveness of this approach using novel and standard benchmarks constructed from MNIST and FashionMNIST data, and compare to the regularization-based elastic weight consolidation (EWC) method [17, 34].
A Large-Scale User Study of an Alexa Prize Chatbot: Effect of TTS Dynamism on Perceived Quality of Social Dialog

This study tests the effect of cognitiveemotional expression in an Alexa text-tospeech (TTS) voice on users' experience with a social dialog system.We systematically introduced emotionally expressive interjections (e.g., "Wow!") and filler words (e.g., "um", "mhmm") in an Amazon Alexa Prize socialbot, Gunrock.We tested whether these TTS manipulations improved users' ratings of their conversation across thousands of real user interactions (n=5,527).Results showed that interjections and fillers each improved users' holistic ratings, an improvement that further increased if the system used both manipulations.A separate perception experiment corroborated the findings from the user study, with improved social ratings for conversations including interjections; however, no positive effect was observed for fillers, suggesting that the role of the rater in the conversation-as active participant or external listener-is an important factor in assessing social dialogs.
PERANAN MODEL PEMBELAJARAN VALUE CLARIFICATION TECHNIQUE BERBANTUAN MEDIA AUDIO VISUAL UNTUK MENINGKATKAN HASIL BELAJAR IPS

Classroom Action Research was carried out to explain the impact of value clarification technique to improve elementary student learning motivation, interest and achievement in social studies. This technique was in two cycles, involving29students, 10 male and and 19 female. The theme discusses was the important family events. The results of pre-cycle learning activities were obtained with a mean grade of 56,38, there are 10 students (34,48%) who achieved the minimum score of mastery. The observationreveals that students who are courageo us and are able to answer the teacher's questions correctly are 9 people or 31,03%. The data from Cycle 1 indicates a better result, with the class average of 68,28, and 17students or 58,62% achieved the minimum score of mastery. The observation also shows an increase of students whowere able to answer the teacher's questions correctly, which is 16 students or 55,17%. The Cycle 2 activity which used Value Clarification Technique (VCT), suplemented audio visual media obtains a better result with a mean of 86,72, and 27 students (93,10 %) who achieved the minimum score of mastery. The observation also shows a better percentage, 26 people or 89.66% who are capable of answering teacher questions correctly. It can be concluded thatValue Clarification Technique (VCT) using audio visual media can improve student motivation and learning achievement.&#x0D; Penelitian Tindakan Kelas (PTK) ini mempunyai tujuan menganalisis penggunaan model value clarification technique untuk meningkatkan motivasi, minat, dan hasil Belajar IPS siswa Sekolah Dasar. Kegiatan pembelajaran Prasiklus menghasilkan rerata kelas 56,38, dengan peserta didik yang tuntas sebayak 10 atau 34,48%. Hasil pengamatan menunjukkan siswa yang memiliki keberanian dan mampu jawab pertanyaan guru dengan benar baru 9 orang atau 31,03%. Dari Kegiatan Siklus 1 diperolehrerata kelas 68,28, dengan peserta didik yang tuntas sebayak 17 orang atau 58,62%. Hasil pengamatan menunjukkan siswa yang memiliki keberanian dan mampu menjawab pertanyaan guru dengan benar meningkat, mencapai 16 orang atau 55,17%. Kegiatan Siklus 2 yang menggunakan Value Clarification Technique (VCT) dengan berbantuan media audio visual diperoleh hasil rerata kelas 86,72 dengan peserta didik yang tuntas sebayak 27 orang peserta didik atau 93,10 Hasil pengamatan menunjukkan siswa yang memiliki keberanian dan mampu jawab pertanyaan guru dengan benar mencapai 26 orang atau 89,66%. Dapat disimpulkan bahwa penggunaan model pembelajaran Menggunakan Value Clarification Technique (VCT) berbantuan audio visual meningkatkan motivasi peserta didik dalam proses belajar dan hasil belajarnya.
Eigenstate extraction with neural-network tomography

A scheme for quantum state tomography based on an efficient neural network representation is demonstrated. The method, tailored to a relevant class of nearly pure states, or simple mixed states, was tested using experimental data from trapped-ion experiments with four to eight qubits.
Identity Authentication Security Management in Mobile Payment Systems

Mobile payment is a new payment method offering users mobility, reachability, compatibility, and convenience. But mobile payment involves great uncertainty and risk given its electronic and wireless nature. Therefore, biometric authentication has been adopted widely in mobile payment in recent years. However, although technology requirements for secure mobile payment have been met, standards and consistent requirements of user authentication in mobile payment are not available. The flow management of user authentication in mobile payment is still at its early stage. Accordingly, this paper proposes an anonymous authentication and management flow for mobile payment to support secure transaction to prevent the disclosure of users' information and to reduce identity theft. The proposed management flow integrates transaction key generation, encryption and decryption, and matching to process users' personal information and biometric characteristics based on mobile equipment authentication carrier.
Non-Local Intrinsic Decomposition With Near-Infrared Priors

Intrinsic image decomposition is a highly under-constrained problem that has been extensively studied by computer vision researchers. Previous methods impose additional constraints by exploiting either empirical or data-driven priors. In this paper, we revisit intrinsic image decomposition with the aid of near-infrared (NIR) imagery. We show that NIR band is considerably less sensitive to textures and can be exploited to reduce ambiguity caused by reflectance variation, promoting a simple yet powerful prior for shading smoothness. With this observation, we formulate intrinsic decomposition as an energy minimisation problem. Unlike existing methods, our energy formulation decouples reflectance and shading estimation, into a convex local shading component based on NIR-RGB image pair, and a reflectance component that encourages reflectance homogeneity both locally and globally. We further show the minimisation process can be approached by a series of multi-dimensional kernel convolutions, each within linear time complexity. To validate the proposed algorithm, a NIR-RGB dataset is captured over real-world objects, where our NIR-assisted approach demonstrates clear superiority over RGB methods.
Natural Language Generation for Effective Knowledge Distillation

Knowledge distillation can effectively transfer knowledge from BERT, a deep language representation model, to traditional, shallow word embedding-based neural networks, helping them approach or exceed the quality of other heavyweight language representation models.As shown in previous work, critical to this distillation procedure is the construction of an unlabeled transfer dataset, which enables effective knowledge transfer.To create transfer set examples, we propose to sample from pretrained language models fine-tuned on taskspecific text.Unlike previous techniques, this directly captures the purpose of the transfer set.We hypothesize that this principled, general approach outperforms rule-based techniques.On four datasets in sentiment classification, sentence similarity, and linguistic acceptability, we show that our approach improves upon previous methods.We outperform OpenAI GPT, a deep pretrained transformer, on three of the datasets, while using a single-layer bidirectional LSTM that runs at least ten times faster.