Reviewerly/exHarmony huggingface dataset:
Role of visual analytics in supporting mental healthcare systems research and policy: A systematic scoping review

The availability of healthcare data has exponentially grown, both in quantity and complexity. The speed of this evolution has generated new challenges for translating complex data into effective evidence-informed policy. Visual analytics offers new capacity to analyze healthcare systems and support better decision-making. We conducted a systematic scoping review to look for evidence of visual analytics approaches being applied to mental healthcare systems and their use in driving policy. We found 79 relevant studies and categorized them in two ways: by study purpose and by type of visualization. The majority (67.1%) of the studies used geographical maps, and 11% conducted highly complex studies requiring novel visualizations. Significantly, only 15% of the studies provided information indicating high levels of usability for policy and planning. Our findings suggest that while visual analytics continues to evolve rapidly, there is a need to ensure this evolution reflects the practical needs of policy makers.
Simulating all quantum measurements using only projective measurements and postselection

We report an alternative scheme for implementing generalized quantum measurements that does not require the usage of an auxiliary system. Our method utilizes solely (a) classical randomness and postprocessing, (b) projective measurements on a relevant quantum system, and (c) postselection on nonobserving certain outcomes. The scheme implements arbitrary quantum measurement in dimension $d$ with the optimal success probability $1/d$. We apply our results to bound the relative power of projective and generalized measurements for unambiguous state discrimination. Finally, we test our scheme experimentally on an IBM quantum processor. Interestingly, due to noise involved in the implementation of entangling gates, the quality with which our scheme implements generalized qubit measurements outperforms the standard construction using an auxiliary system.
Invisible Adversarial Attack against Deep Neural Networks: An Adaptive Penalization Approach

Recent studies demonstrated that deep neural networks (DNNs) are vulnerable to adversarial examples, which would seriously threaten security-sensitive applications. Existing works synthesized the adversarial examples by perturbing the original/benign images by leveraging the <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_p$</tex-math></inline-formula> -norm to penalize the perturbations, which restricts the pixel-wise distance between the adversarial images and correspondingly benign images. However, they added perturbations globally to the benign images without explicitly considering their content/spacial structure, resulting in noticeable artifacts especially in those originally clean regions, e.g., sky and smooth surface. In this paper, we propose an invisible adversarial attack, which synthesizes adversarial examples that are visually indistinguishable from benign ones. We adaptively distribute the perturbation according to human sensitivity to a local stimulus in the benign image, i.e., the higher insensitivity, the more perturbation. Two types of adaptive adversarial attacks are proposed: 1) coarse-grained and 2) fine-grained. The former conducts <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_p$</tex-math></inline-formula> -norm regularized by the novel spatial constraints, which utilizes the rich information of the cluttered regions to mask perturbation. The latter, called Just Noticeable Distortion (JND)-based adversarial attack, utilizes the proposed JND <inline-formula><tex-math notation="LaTeX">$_p$</tex-math></inline-formula> metric for better measuring the perceptual similarity, and adaptively sets penalty by weighting the pixel-wise perceptual redundancy of an image. We conduct extensive experiments on the MNIST, CIFAR-10 and ImageNet datasets and a comprehensive user study with 50 participants. The experimental results demonstrate that JND <inline-formula><tex-math notation="LaTeX">$_p$</tex-math></inline-formula> is a better metric for measuring the perceptual similarity than <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_p$</tex-math></inline-formula> -norm, and the proposed adaptive adversarial attacks can synthesize indistinguishable adversarial examples from benign ones and outperform the state-of-the-art methods.
Learning towards Abstractive Timeline Summarization

Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods.In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events.Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order.To tackle this challenge, we propose a memory-based timeline summarization model (MTS).Concretely, we propose a time-event memory to establish a timeline, and use the time position of events on this timeline to guide generation process.Besides, in each decoding step, we incorporate event-level information into word-level attention to avoid confusion between events.Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations.
Adversarial Removal of Demographic Attributes Revisited

Elazar and Goldberg (2018) showed that protected attributes can be extracted from the representations of a debiased neural network for mention detection at above-chance levels, by evaluating a diagnostic classifier on a heldout subsample of the data it was trained on.We revisit their experiments and conduct a series of follow-up experiments showing that, in fact, the diagnostic classifier generalizes poorly to both new in-domain samples and new domains, indicating that it relies on correlations specific to their particular data sample.We further show that a diagnostic classifier trained on the biased baseline neural network also does not generalize to new samples.In other words, the biases detected in Elazar and Goldberg (2018) seem restricted to their particular data sample, and would therefore not bias the decisions of the model on new samples, whether in-domain or out-of-domain.In light of this, we discuss better methodologies for detecting bias in our models.
Open Event Extraction from Online Text using a Generative Adversarial Network

To extract the structured representations of open-domain events, Bayesian graphical models have made some progress.However, these approaches typically assume that all words in a document are generated from a single event.While this may be true for short text such as tweets, such an assumption does not generally hold for long text such as news articles.Moreover, Bayesian graphical models often rely on Gibbs sampling for parameter inference which may take long time to converge.To address these limitations, we propose an event extraction model based on Generative Adversarial Nets, called Adversarial-neural Event Model (AEM).AEM models an event with a Dirichlet prior and uses a generator network to capture the patterns underlying latent events.A discriminator is used to distinguish documents reconstructed from the latent events and the original documents.A byproduct of the discriminator is that the features generated by the learned discriminator network allow the visualization of the extracted events.Our model has been evaluated on two Twitter datasets and a news article dataset.Experimental results show that our model outperforms the baseline approaches on all the datasets, with more significant improvements observed on the news article dataset where an increase of 15% is observed in F-measure.
Cryptanalysis and Improvement in a Plaintext-Related Image Encryption Scheme Based on Hyper Chaos

Recently, a plaintext-related image encryption scheme based on a hyper-chaotic system has been proposed by Li et al.In their encryption scheme, the permutation process is related to the sum of plaintext pixel values, and the diffusion process is related to the values of nine special positions in the permuted image.This can resist the existing chosen plaintext attack due to the ever-changing permutation matrix and diffusion matrix when the plaintext is changed.Here, we show that Li's encryption scheme contains two vulnerabilities.First, the gray values of the nine specific pixels do not change during diffusion.Second, the permutation process is reversible.Therefore, the diffusion process can be attacked and the permuted image can be obtained by constructing a special image, where the nine special position values in the special image's permuted image are the same as those in the cipher image.Then, if the obtained permuted image is chosen as plaintext, we can get its permuted image by attacking the diffusion process.Since the permutation process is reversible, the permuted image is the original plaintext image.In addition, some improvements for Li's encryption scheme are provided to enhance the security.
Restoring ancient text using deep learning: a case study on Greek epigraphy

Ancient History relies on disciplines such as Epigraphy, the study of ancient inscribed texts, for evidence of the recorded past.However, these texts, "inscriptions", are often damaged over the centuries, and illegible parts of the text must be restored by specialists, known as epigraphists.This work presents PYTHIA, the first ancient text restoration model that recovers missing characters from a damaged text input using deep neural networks.Its architecture is carefully designed to handle longterm context information, and deal efficiently with missing or corrupted character and word representations.To train it, we wrote a nontrivial pipeline to convert PHI, the largest digital corpus of ancient Greek inscriptions, to machine actionable text, which we call PHI-ML.On PHI-ML, PYTHIA's predictions achieve a 30.1% character error rate, compared to the 57.3% of human epigraphists.Moreover, in 73.5% of cases the ground-truth sequence was among the Top-20 hypotheses of PYTHIA, which effectively demonstrates the impact of this assistive method on the field of digital epigraphy, and sets the state-of-the-art in ancient text restoration.
A Unified Neural Coherence Model

Recently, neural approaches to coherence modeling have achieved state-of-the-art results in several evaluation tasks.However, we show that most of these models often fail on harder tasks with more realistic application scenarios.In particular, the existing models underperform on tasks that require the model to be sensitive to local contexts such as candidate ranking in conversational dialogue and in machine translation.In this paper, we propose a unified coherence model that incorporates sentence grammar, inter-sentence coherence relations, and global coherence patterns into a common neural framework.With extensive experiments on local and global discrimination tasks, we demonstrate that our proposed model outperforms existing models by a good margin, and establish a new state-of-the-art.
Memory-oriented Decoder for Light Field Salient Object Detection

Light field data have been demonstrated in favor of many tasks in computer vision, but existing works about light field saliency detection still rely on hand-crafted features. In this paper, we present a deep-learning-based method where a novel memory-oriented decoder is tailored for light field saliency detection. Our goal is to deeply explore and comprehensively exploit internal correlation of focal slices for accurate prediction by designing feature fusion and integration mechanisms. The success of our method is demonstrated by achieving the state of the art on three datasets. We present this problem in a way that is accessible to members of the community and provide a large-scale light field dataset that facilitates comparisons across algorithms. The code and dataset will be made publicly available.
Single image dehazing via self-constructing image fusion

Haze usually degrades the visibility of outdoor images and decreases their visual quality. Previous techniques are not sufficient enough to deal with dehazing problem by using various hand-crafted priors or supervised training on paired manners. In this paper, we propose a self-constructing image fusion method for single image dehazing, which does not rely on the accuracy of global atmospheric light and transmission map. Hence, the proposed method can avoid some visual problems, such as undesirable brightness perception, unsatisfied halo artifacts and edge blur in sky regions or bright objects in dehazed images. To produce several self-constructing images with different exposures, a novel segmentation method is exploited to capture the range of global atmospheric light approximately, and a new adaptive boundary-limited L0 gradient optimization method is employed to optimize the transmission map. An adaptive selective SIFT flow multi-exposure fusion method is constructed by applying the two-layer visual sensory selector. Extensive experimental results on both synthetic and real-world images demonstrate that the proposed algorithm performs favorably against the state-of-the-art algorithms in terms of no-reference and full-reference image quality.
Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices

Radio frequency fingerprinting (RFF) is based on identification of unique features of RF transient signals emitted by radio devices. RF transient signals of radio devices are short in duration, non-stationary and nonlinear time series. This paper evaluates the performance of RF fingerprinting method based on variational mode decomposition (VMD). For this purpose, VMD is used to decompose Bluetooth (BT) transient signals into a series of band-limited modes, and then, the transient signal is reconstructed from the modes. Higher order statistical (HOS) features are extracted from the complex form of reconstructed transients. Then, Linear Support Vector Machine (LVM) classifier is used to identify BT devices. The method has been tested experimentally with BT devices of different brands, models and series. The classification performance shows that VMD based RF fingerprinting method achieves better performance (at least 8% higher) than time-frequency-energy (TFED) distribution based methods such as Hilbert-Huang Transform. This is demonstrated with the same dataset but with smaller number of features (nine features) and slightly lower (2-3 dB) SNR levels.
Thai Agriculture Products Traceability System using Blockchain and Internet of Things

In this paper, we successfully designed and developed Thai agriculture products traceability system using blockchain and Internet of Things.Blockchain, which is the distributed database, is used for our proposed traceability system to enhance the transparency and data integrity.OurSQL is added on another layer to easier query process of blockchain database, therefore the proposed system is a user-friendly system, which cannot be found in ordinary blockchain database.The website and android application have been developed to show the tracking information of the product.The blockchain database coupling with Internet of Things give a number of benefits for our traceability system because all of the collecting information is in real-time and kept in a very secured database.Our system could have a huge impact on food traceability and supply chain management become more reliable as well as rebuild public awareness in Thailand on food safety and quality control.
Segmentation of kidney tumor by multi-resolution VB-nets

Accurate segmentation of kidney tumors can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice.In this work, we propose multi-resolution 3D V-Net networks to automatically segment kidney and renal tumor in computed tomography (CT) images.Specifically, we adopt two resolutions and propose a customized V-Net model called VB-Net for both resolutions.The VB-Net model in the coarse resolution can robustly localize the organs, while the VB-Net model in the fine resolution can accurately refine the boundary of each organ or lesion.We experiment in the KiTS19 challenge, which shows promising performance.
Experimental quantum network coding

Abstract Distributing quantum state and entanglement between distant nodes is a crucial task in distributed quantum information processing on large-scale quantum networks. Quantum network coding provides an alternative solution for quantum-state distribution, especially when the bottleneck problems must be considered and high communication speed is required. Here, we report the first experimental realization of quantum network coding on the butterfly network. With the help of prior entanglements shared between senders, two quantum states can be transmitted perfectly through the butterfly network. We demonstrate this protocol by employing eight photons generated via spontaneous parametric downconversion. We observe cross-transmission of single-photon states with an average fidelity of 0.9685 ± 0.0013, and that of two-photon entanglement with an average fidelity of 0.9611 ± 0.0061, both of which are greater than the theoretical upper bounds without prior entanglement.
AIBench: Towards Scalable and Comprehensive Datacenter AI Benchmarking

AI benchmarking provides yardsticks for benchmarking, measuring and evaluating innovative AI algorithms, architecture, and systems. Coordinated by BenchCouncil, this paper presents our joint research and engineering efforts with several academic and industrial partners on the datacenter AI benchmarks—AIBench. The benchmarks are publicly available from http://www.benchcouncil.org/AIBench/index.html . Presently, AIBench covers 16 problem domains, including image classification, image generation, text-to-text translation, image-to-text, image-to-image, speech-to-text, face embedding, 3D face recognition, object detection, video prediction, image compression, recommendation, 3D object reconstruction, text summarization, spatial transformer, and learning to rank, and two end-to-end application AI benchmarks. Meanwhile, the AI benchmark suites for high performance computing (HPC), IoT, Edge are also released on the BenchCouncil web site. This is by far the most comprehensive AI benchmarking research and engineering effort.
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation

Visual paragraph generation aims to automatically describe a given image from different perspectives and organize sentences in a coherent way. In this paper, we address three critical challenges for this task in a reinforcement learning setting: the mode collapse, the delayed feedback, and the time-consuming warm-up for policy networks. Generally, we propose a novel Curiosity-driven Reinforcement Learning (CRL) framework to jointly enhance the diversity and accuracy of the generated paragraphs. First, by modeling the paragraph captioning as a long-term decision-making process and measuring the prediction uncertainty of state transitions as intrinsic rewards, the model is incentivized to memorize precise but rarely spotted descriptions to context, rather than being biased towards frequent fragments and generic patterns. Second, since the extrinsic reward from evaluation is only available until the complete paragraph is generated, we estimate its expected value at each time step with temporal-difference learning, by considering the correlations between successive actions. Then the estimated extrinsic rewards are complemented by dense intrinsic rewards produced from the derived curiosity module, in order to encourage the policy to fully explore action space and find a global optimum. Third, discounted imitation learning is integrated for learning from human demonstrations, without separately performing the time-consuming warm-up in advance. Extensive experiments conducted on the Standford image-paragraph dataset demonstrate the effectiveness and efficiency of the proposed method, improving the performance by 38.4% compared with state-of-the-art.
Deep Transfer Reinforcement Learning for Text Summarization

Deep neural networks are data hungry models and thus face difficulties when attempting to train on small text datasets. Transfer learning is a potential solution but their effectiveness in the text domain is not as explored as in areas such as image analysis. In this paper, we study the problem of transfer learning for text summarization and discuss why existing state-of-the-art models fail to generalize well on other (unseen) datasets. We propose a reinforcement learning framework based on a self-critic policy gradient approach which achieves good generalization and state-of-the-art results on a variety of datasets. Through an extensive set of experiments, we also show the ability of our proposed framework to fine-tune the text summarization model using only a few training samples. To the best of our knowledge, this is the first work that studies transfer learning in text summarization and provides a generic solution that works well on unseen data.MSC codesTransfer learningtext summarizationself-critic reinforcement learning.
Low-resolution facial expression recognition: A filter learning perspective

Automatic facial expression recognition has attracted increasing attention for a variety of applications. However, the problem of low-resolution generally causes the performance degradation of facial expression recognition methods under real-life environments. In this paper, we propose to perform low-resolution facial expression recognition from the filter learning perspective. More specifically, a novel image filter based subspace learning (IFSL) method is developed to derive an effective facial image representation. The proposed IFSL method mainly includes three steps: Firstly, we embed the image filter learning into the optimization process of linear discriminant analysis (LDA). By optimizing the cost function of LDA, a set of discriminative image filters (DIFs) corresponding to different facial expressions is learned. Secondly, the images filtered by the learned DIFs are added together to generate the combined images. Finally, a regression learning technique is leveraged for subspace learning, where an expression-aware transformation matrix is obtained using the combined images. Based on the transformation matrix, IFSL effectively removes irrelevant information while preserving useful information in the facial images. Experimental results on several facial expression datasets, including CK+, MMI, JAFFE, SFEW and RAF-DB, show the superior performance of the proposed IFSL method for low-resolution facial expression recognition, compared with several state-of-the-art methods.
Multilingual Whispers: Generating Paraphrases with Translation

Naturally occurring paraphrase data, such as multiple news stories about the same event, is a useful but rare resource.This paper compares translation-based paraphrase gathering using human, automatic, or hybrid techniques to monolingual paraphrasing by experts and nonexperts.We gather translations, paraphrases, and empirical human quality assessments of these approaches.Neural machine translation techniques, especially when pivoting through related languages, provide a relatively robust source of paraphrases with diversity comparable to expert human paraphrases.Surprisingly, human translators do not reliably outperform neural systems.The resulting data release will not only be a useful test set, but will also allow additional explorations in translation and paraphrase quality assessments and relationships.