Reviewerly/exHarmony huggingface dataset:
DuIE: A Large-Scale Chinese Dataset for Information Extraction

Information extraction is an important foundation for knowledge graph construction, as well as many natural language understanding applications. Similar to many other artificial intelligence tasks, high quality annotated datasets are essential to train a high-performance information extraction system. Existing datasets, however, are mostly built for English. To promote research in Chinese information extraction and evaluate the performance of related systems, we build a large-scale high-quality dataset, named DuIE, and make it publicly available. We design an efficient coarse-to-fine procedure including candidate generation and crowdsourcing annotation, in order to achieve high data quality at a large data scale. DuIE contains 210,000 sentences and 450,000 instances covering 49 types of commonly used relations, reflecting the real-world scenario. We also hosted an open competition based on DuIE, which attracted 1,896 participants. The competition results demonstrated the potential of this dataset in promoting information extraction research.
Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation

Neural Architecture Search (NAS), a framework which automates the task of designing neural networks, has recently been actively studied in the field of deep learning. However, there are only a few NAS methods suitable for 3D medical image segmentation. Medical 3D images are generally very large; thus it is difficult to apply previous NAS methods due to their GPU computational burden and long training time. We propose the resource-optimized neural architecture search method which can be applied to 3D medical segmentation tasks in a short training time (1.39 days for 1 GB dataset) using a small amount of computation power (one RTX 2080Ti, 10.8 GB GPU memory). Excellent performance can also be achieved without retraining (fine-tuning) which is essential in most NAS methods. These advantages can be achieved by using a reinforcement learning-based controller with parameter sharing and focusing on the optimal search space configuration of macro search rather than micro search. Our experiments demonstrate that the proposed NAS method outperforms manually designed networks with state-of-the-art performance in 3D medical image segmentation.
D-PAttNet: Dynamic Patch-Attentive Deep Network for Action Unit Detection

Facial action units (AUs) relate to specific local facial regions. Recent efforts in automated AU detection have focused on learning the facial patch representations to detect specific AUs. These efforts have encountered three hurdles. First, they implicitly assume that facial patches are robust to head rotation; yet non-frontal rotation is common. Second, mappings between AUs and patches are defined a priori, which ignores co-occurrences among AUs. And third, the dynamics of AUs are either ignored or modeled sequentially rather than simultaneously as in human perception. Inspired by recent advances in human perception, we propose a dynamic patch-attentive deep network, called D-PAttNet, for AU detection that i) controls for 3D head and face rotation, ii) learns mappings of patches to AUs, and iii) models spatiotemporal dynamics. D-PAttNet approach significantly improves upon existing state of the art.
Hierarchical Attentive Transaction Embedding With Intra- and Inter-Transaction Dependencies for Next-Item Recommendation

A transaction-based recommender system (TBRS) aims to predict the next item by modeling dependencies in transactional data. Generally, two kinds of dependencies considered are intra-transaction dependency and inter-transaction dependency. Most existing TBRSs recommend next item by only modeling the intra-transaction dependency within the current transaction while ignoring inter-transaction dependency with recent transactions that may also affect the next item. However, as not all recent transactions are relevant to the current and next items, the relevant ones should be identified and prioritized. In this paper, we propose a novel hierarchical attentive transaction embedding (HATE) model to tackle these issues. Specifically, a two-level attention mechanism integrates both item embedding and transaction embedding to build an attentive context representation that incorporates both intraand inter-transaction dependencies. With the learned context representation, HATE then recommends the next item. Experimental evaluations on two real-world transaction datasets show that HATE significantly outperforms the state-ofthe-art methods in terms of recommendation accuracy.
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows

We show that normalising flows become pathological when used to model targets whose supports have complicated topologies. In this scenario, we prove that a flow must become arbitrarily numerically noninvertible in order to approximate the target closely. This result has implications for all flow-based models, and especially Residual Flows (ResFlows), which explicitly control the Lipschitz constant of the bijection used. To address this, we propose Continuously Indexed Flows (CIFs), which replace the single bijection used by normalising flows with a continuously indexed family of bijections, and which can intuitively clean up mass that would otherwise be misplaced by a single bijection. We show theoretically that CIFs are not subject to the same topological limitations as normalising flows, and obtain better empirical performance on a variety of models and benchmarks.
Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions

Abstract Science teams for rover-based planetary exploration missions like the Mars Science Laboratory Curiosity rover have limited time for analyzing new data before making decisions about follow-up observations. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and focus attention on the most promising or novel observations. Several novelty detection methods have been explored in prior work for three-channel color images and non-image datasets, but few have considered multispectral or hyperspectral image datasets for the purpose of scientific discovery. We compared the performance of four novelty detection methods—Reed Xiaoli (RX) detectors, principal component analysis (PCA), autoencoders, and generative adversarial networks (GANs)—and the ability of each method to provide explanatory visualizations to help scientists understand and trust predictions made by the system. We show that pixel-wise RX and autoencoders trained with structural similarity (SSIM) loss can detect morphological novelties that are not detected by PCA, GANs, and mean squared error autoencoders, but that the latter methods are better suited for detecting spectral novelties—i.e., the best method for a given setting depends on the type of novelties that are sought. Additionally, we find that autoencoders provide the most useful explanatory visualizations for enabling users to understand and trust model detections, and that existing GAN approaches to novelty detection may be limited in this respect.
Utilization of Blockchain Technology for Management E-Certificate Open Journal System

The application of blockchain technology in e-certificates in the open journal system is needed to validate and distribute managed e-certificates in each journal issue. With tools in the form of conceptualized and managed blockcerts allows users to use and verify the authenticity of e-certificates that are closely linked to the blockchain. Observation on the open journal system and applied blockchain tools in the form of blockcerts as a place to issue e-certificates with guaranteed security so that e-certificates are verified as well as managed data. Issuance of e-certificates is still considered unsafe because it can be easily forged. Specifically, this study produced two benefits, namely: (1) Issuance of e-certificates in an open journal system is more verified, data is managed and there will not be duplications or even falsification of e-certificates. (2) The reputation of the open journal system is better with a sense of trust. This research uses library study method by implementing e-certificate in an open journal system with blockchain technology tools such as blockcerts. In order that the results of the e-certificate issuance go well and to avoid any crime that threatens the authenticity of the e-certificate, this blockchain is used to guarantee that the e-certificate is safe and not easily falsified, duplicated or even easily replaced by those not granted access rights. By using blockchain technology, the data collected will be well managed and not easily falsified.
Use of the LMS Moodle for an Effective Implementation of an Innovative Policy in Higher Educational Institutions

This article considers the opportunities of using Moodle LMS (learning management system) tools to ensure the effective implementation of an innovative policy of the higher educational institution. In the framework of digital transformation of social space an important and relevant topic for higher educational institutions is the creation of high-quality information and communication environment, which shall correspond to the contemporary state of science and technology development, global educational standards and information society needs. The advantages of the Moodle system are studied. Moodle system provides for a comprehensive educational process through its extensive educational content, a system for control, monitoring and evaluating the knowledge quality. The authors compare the four-phased model of knowledge management with the capacities of LMS Moodle educational environment. The latter is capable of simulating professional actions in a virtual educational space that allows the university to train advanced specialists and ensure a high level of their professional skills based on hard and soft skills. The perceptive and cognitive attitude of university students and teachers towards the implementation of the LMS Moodle educational platform was researched. The research has brought out the subjective assessment model of the innovative process with clear meaningful factors of changes’ perception. Such a model allows understanding the collective attitude to the university’s policy and highlighting mental settings necessary for working with human resources within the social adaptation and diffusion of innovation. The synergy of technological and managerial innovations has been updated to ensure completeness and efficiency in presenting a professional educational product based on the LMS Moodle platform.
PieNet: Personalized Image Enhancement Network

Image enhancement is an inherently subjective process since people have diverse preferences for image aesthetics. However, most enhancement techniques pay less attention to the personalization issue despite its importance. In this paper, we propose the first deep learning approach to personalized image enhancement, which can enhance new images for a new user, by asking him or her to select about 10–20 preferred images from a random set of images. First, we represent various users’ preferences for enhancement as feature vectors in an embedding space, called preference vectors. We construct the embedding space based on metric learning. Then, we develop the personalized image enhancement network (PieNet) to enhance images adaptively using each user’s preference vector. Experimental results demonstrate that the proposed algorithm is capable of achieving personalization successfully, as well as outperforming conventional general image enhancement algorithms significantly. The source codes and trained models are available at https://github.com/hukim1124/PieNet.
CTE 2018 – How cloud technologies continues to transform education

This is an introductory text to a collection of papers from the CTE 2018: The 6th Workshop on Cloud Technologies in Education, which was held in Kryvyi Rih, Ukraine, on the December 21, 2018. It consists of short introduction and some observations about the event and its future.
Quantum probing beyond pure dephasing

Quantum probing is the art of exploiting simple quantum systems interacting with a complex environment to extract precise information about some environmental parameters, e.g. the temperature of the environment or its spectral density. Here we analyze the performance of a single-qubit probe in characterizing Ohmic bosonic environments at thermal equilibrium. In particular, we analyze the effects of tuning the interaction Hamiltonian between the probe and the environment, going beyond the traditional paradigm of pure dephasing. In the weak-coupling and short-time regime, we address the dynamics of the probe analytically, whereas numerical simulations are employed in the strong coupling and long-time regime. We then evaluate the quantum Fisher information for the estimation of the cutoff frequency and the temperature of the environment. Our results provide clear evidence that pure dephasing is not optimal, unless we focus attention to short times. In particular, we found several working regimes where the presence of a transverse interaction improves the maximum attainable precision, i.e. it increases the quantum Fisher information. We also explore the role of the initial state of the probe and of the probe characteristic frequency in determining the estimation precision, thus providing quantitative guidelines to design optimized detection to characterize bosonic environments at the quantum level.
Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks

Supertagging is conventionally regarded as an important task for combinatory categorial grammar (CCG) parsing, where effective modeling of contextual information is highly important to this task.However, existing studies have made limited efforts to leverage contextual features except for applying powerful encoders (e.g., bi-LSTM).In this paper, we propose attentive graph convolutional networks to enhance neural CCG supertagging through a novel solution of leveraging contextual information.Specifically, we build the graph from chunks (n-grams) extracted from a lexicon and apply attention over the graph, so that different word pairs from the contexts within and across chunks are weighted in the model and facilitate the supertagging accordingly.The experiments performed on the CCGbank demonstrate that our approach outperforms all previous studies in terms of both supertagging and parsing.Further analyses illustrate the effectiveness of each component in our approach to discriminatively learn from word pairs to enhance CCG supertagging.
Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation

A synthetic image is a critical issue for computer vision. Traffic sign images synthesized from standard models are commonly used to build computer recognition algorithms for acquiring more knowledge on various and low-cost research issues. Convolutional Neural Network (CNN) achieves excellent detection and recognition of traffic signs with sufficient annotated training data. The consistency of the entire vision system is dependent on neural networks. However, locating traffic sign datasets from most countries in the world is complicated. This work uses various generative adversarial networks (GAN) models to construct intricate images, such as Least Squares Generative Adversarial Networks (LSGAN), Deep Convolutional Generative Adversarial Networks (DCGAN), and Wasserstein Generative Adversarial Networks (WGAN). This paper also discusses, in particular, the quality of the images produced by various GANs with different parameters. For processing, we use a picture with a specific number and scale. The Structural Similarity Index (SSIM) and Mean Squared Error (MSE) will be used to measure image consistency. Between the generated image and the corresponding real image, the SSIM values will be compared. As a result, the images display a strong similarity to the real image when using more training images. LSGAN outperformed other GAN models in the experiment with maximum SSIM values achieved using 200 images as inputs, 2000 epochs, and size 32 × 32.
Voice-Based Agents as Personified Things: Assimilation and Accommodation as Equilibration of Doubt

We aim to investigate the nature of doubt regarding voice-based agents by referring to Piaget’s ontological object–subject classification “thing” and “person,” its associated equilibration processes, and influential factors of the situation, the user, and the agent. In two online surveys, we asked 853 and 435 participants, ranging from 17 to 65 years of age, to assess Alexa and the Google Assistant. We discovered that only some people viewed voice-based agents as mere things, whereas the majority classified them into personified things. However, their classification is fragile and depends basically on the imputation of subject-like attributes of agency and mind to the voice-based agents, increased by a dyadic using situation, previous regular interactions, a younger age, and an introverted personality of the user. We discuss these results in a broader context.
Enhancing Aspect-level Sentiment Analysis with Word Dependencies

Aspect-level sentiment analysis (ASA) has received much attention in recent years.Most existing approaches tried to leverage syntactic information, such as the dependency parsing results of the input text, to improve sentiment analysis on different aspects.Although these approaches achieved satisfying results, their main focus is to leverage the dependency arcs among words where the dependency type information is omitted; and they model different dependencies equally where the noisy dependency results may hurt model performance.In this paper, we propose an approach to enhance aspect-level sentiment analysis with word dependencies, where the type information is modeled by key-value memory networks and different dependency results are selectively leveraged.Experimental results on five benchmark datasets demonstrate the effectiveness of our approach, where it outperforms baseline models on all datasets and achieves state-of-the-art performance on three of them.
Qualifying quantum approaches for hard industrial optimization problems. A case study in the field of smart-charging of electric vehicles

In order to qualify quantum algorithms for industrial NP-Hard problems, comparing them to available polynomial approximate classical algorithms and not only to exact exponential ones is necessary. This is a great challenge as, in many cases, bounds on the reachable approximation ratios exist according to some highly-trusted conjectures of Complexity Theory. An interesting setup for such qualification is thus to focus on particular instances of these problems known to be "less difficult" than the worst-case ones and for which the above bounds can be outperformed: quantum algorithms should perform at least as well as the conventional approximate ones on these instances, up to very large sizes. We present a case study of such a protocol for two industrial problems drawn from the strongly developing field of smart-charging of electric vehicles. Tailored implementations of the Quantum Approximate Optimization Algorithm (QAOA) have been developed for both problems, and tested numerically with classical resources either by emulation of Pasqal's Rydberg atom based quantum device or using Atos Quantum Learning Machine. In both cases, quantum algorithms exhibit the same approximation ratios as conventional approximation algorithms or improve them. These are very encouraging results, although still for instances of limited size as allowed by studies on classical computing resources. The next step will be to confirm them on larger instances, on actual devices, and for more complex versions of the problems addressed.
Real-time multi-task diffractive deep neural networks via hardware-software co-design

Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing efforts on optical neural networks and optical computing based DNNs hardware, which bring significant advantages for deep learning systems in terms of their power efficiency, parallelism and computational speed. Among them, free-space diffractive deep neural networks (D2NNs) based on the light diffraction, feature millions of neurons in each layer interconnected with neurons in neighboring layers. However, due to the challenge of implementing reconfigurability, deploying different DNNs algorithms requires re-building and duplicating the physical diffractive systems, which significantly degrades the hardware efficiency in practical application scenarios. Thus, this work proposes a novel hardware-software co-design method that enables first-of-its-like real-time multi-task learning in D22NNs that automatically recognizes which task is being deployed in real-time. Our experimental results demonstrate significant improvements in versatility, hardware efficiency, and also demonstrate and quantify the robustness of proposed multi-task D2NN architecture under wide noise ranges of all system components. In addition, we propose a domain-specific regularization algorithm for training the proposed multi-task architecture, which can be used to flexibly adjust the desired performance for each task.
Enhancing Zero-shot and Few-shot Stance Detection with Commonsense Knowledge Graph

In this paper, we consider a realistic scenario on stance detection with more application potential, i.e., zero-shot and few-shot stance detection, which identifies stances for a wide range of topics with no or very few training examples.Conventional data-driven approaches are not applicable to the above zero-shot and few-shot scenarios.For human beings, commonsense knowledge is a crucial element of understanding and reasoning.In the absence of annotated data and cryptic expression of users' stance, we believe that introducing commonsense relational knowledge as support for reasoning can further improve the generalization and reasoning ability of the model in the zero-shot and few-shot scenarios.Specifically, we introduce a commonsense knowledge enhanced model to exploit both the structurallevel and semantic-level information of the relational knowledge.Extensive experiments demonstrate that our model outperforms the state-of-the-art methods on zero-shot and fewshot stance detection task.
Image and video processing on mobile devices: a survey

Image processing and computer vision on mobile devices have a wide range of applications such as digital image enhancement and augmented reality. While images acquired by cameras on mobile devices can be processed with generic image processing algorithms, there are numerous constraints and external issues that call for customized algorithms for such devices. In this paper, we survey mobile image processing and computer vision applications while highlighting these constraints and explaining how the algorithms have been modified/adapted to meet accuracy and performance demands. We hope that this paper will be a useful resource for researchers who intend to apply image processing and computer vision algorithms to real-world scenarios and applications that involve mobile devices.