Reviewerly/exHarmony huggingface dataset:
Using context information to enhance simple question answering

With the rapid development of knowledge bases (KBs), question answering (QA) based on KBs has become a hot research issue. The KB-QA technology can be divided into two technical routes: (1) symbol based representations, such as traditional semantic parsing, and (2) distribution based embedding. With the emergence of deep learning, the development of NLP has greatly promoted. The effect of KB-QA can be improved by combining deep learning with the above two technical routes respectively. In this paper, the impact of the second route (i.e., Distribution Embedding) combined with deep learning is mainly discussed. This route can be divided into pipeline frameworks and end-to-end frameworks. For comprehensive analysis, two frameworks (i.e., a pipeline framework, an end-to-end framework) are proposed to focus on answering single-relation factoid questions. In both of two frameworks, the effect of context information on the quality of QA is studied, such as the entity's notable type, out-degree. In the pipeline framework, it includes two cascaded steps: entity detection and relation detection. In this framework, multiple modules need to be built, and corresponding training data sets must be constructed for them respectively. The entire process of the pipleine framework is complex, costly and has the problem of error propagation. In the end-to-end framework, the two subtasks of entity detection and relation detection are merged together, and then combined into one framework. Questions, entities and relations are mapped into the same semantic space through the encoding of the recurrent neural network. Moreover, the question-entity similarity and the question-relation similarity are calculated, so that the candidate answers can be sorted and selected. Moreover, character-level(char-level) encoding and self-attention mechanisms are combined using weight sharing and multi-task strategies to enhance the accuracy of QA. Experimental results show that context information can get better results of simple QA whether it is the pipeline framework or the end-to-end framework. In addition, the end-to-end framework achieves results competitive with state-of-the-art approaches in terms of accuracy.
Graph-based reasoning model for multiple relation extraction

Linguistic knowledge is useful for various NLP tasks, but the difficulty lies in the representation and application. We consider that linguistic knowledge is implied in a large-scale corpus, while classification knowledge, the knowledge related to the definitions of entity and relation types, is implied in the labeled training data. Therefore, a corpus subgraph is proposed to mine more linguistic knowledge from the easily accessible unlabeled data, and sentence subgraphs are used to acquire classification knowledge. They jointly constitute a relation knowledge graph (RKG) to extract relations from sentences in this paper. On RKG, entity recognition can be regarded as a property value filling problem and relation classification can be regarded as a link prediction problem. Thus, the multiple relation extraction can be treated as a reasoning process for knowledge completion. We combine statistical reasoning and neural network reasoning to segment sentences into entity chunks and non-entity chunks, then propose a novel Chunk Graph LSTM network to learn the representations of entity chunks and infer the relations among them. The experiments on two standard datasets demonstrate our model outperforms the previous models for multiple relation extraction.
Linear Time-Varying MPC for Nonprehensile Object Manipulation with a Nonholonomic Mobile Robot

This paper proposes a technique to manipulate an object with a nonholonomic mobile robot by pushing, which is a nonprehensile manipulation motion primitive. Such a primitive involves unilateral constraints associated with the friction between the robot and the manipulated object. Violating this constraint produces the slippage of the object during the manipulation, preventing the correct achievement of the task. A linear time-varying model predictive control is designed to include the unilateral constraint within the control action properly. The approach is verified in a dynamic simulation environment through a Pioneer 3-DX wheeled robot executing the pushing manipulation of a package.
Content-Dependency Reduction With Multi-Task Learning In Blind Stitched Panoramic Image Quality Assessment

In this work, we investigate deep learning based solutions to blind quality assessment of stitched panoramic images (SPI). The main problem to tackle is that the ground truth data is usually insufficient. As a result, the learned model can easily overfit data with specific content. Because most distortions of SPIs lie within local regions, the problem cannot be alleviated by commonly-used patch-wise training, which assumes local quality equals global quality. We propose a multi-task learning strategy which encourages learned representation to be less dependent on image content. A siamese network with two weight-shared CNN branches is trained to simultaneously compare the quality of two images of the same scene and predict the quality score of each image. Since two images of the same scene are processed by the same CNN, the CNN tends to find their quality differences instead of content differences under the constraint of the quality ranking objective. Because two tasks share the same representations learned by the CNN, the regression task can be further benefited from the quality-sensitive representations. Extensive experiments demonstrate the effectiveness of the proposed model and its superiority over existing SPI quality assessment methods.
Att-DARTS: Differentiable Neural Architecture Search for Attention

Neural architecture search (NAS) is a promising method to automatically identify neural network architectures. Differentiable architecture search (DARTS) is method that significantly reduces search time and finds architectures that can achieve state-of-the-art performance. For computer vision tasks, DARTS searches convolutional neural networks (CNNs) via stacking convolution layers and pooling operations. Recent studies on neural architectures indicate that attention modules can improve the performances of CNNs by discarding information of no interest, while existing NAS methods have put little focus on it. In this study, we propose Att-DARTS, which searches attention modules as well as convolution and pooling operations simultaneously. In our experiments on CIFAR-10 and CIFAR-100 datasets, we demonstrate that Att-DARTS can find architectures that achieve lower classification error rates and require fewer parameters compared to those found by DARTS.
Sibilant consonants classification comparison with multi‐ and single‐class neural networks

Abstract Many children with speech sound disorders cannot pronounce the sibilant consonants correctly. We have developed a serious game, which is controlled by the children's voices in real time, with the purpose of helping children on practicing the production of European Portuguese (EP) sibilant consonants. For this, the game uses a sibilant consonant classifier. Since the game does not require any type of adult supervision, children can practice producing these sounds more often, which may lead to faster improvements of their speech. Recently, the use of deep neural networks has given considerable improvements in the classification of a variety of use cases, from image classification to speech and language processing. Here, we propose to use deep convolutional neural networks to classify sibilant phonemes of EP in our serious game for speech and language therapy. We compared the performance of several different artificial neural networks that used Mel frequency cepstral coefficients or log Mel filterbanks. Our best deep learning model achieves classification scores of 95.48% using a 2D convolutional model with log Mel filterbanks as input features. Such results are then further improved for specific classes with simple binary classifiers.
Deep Reinforcement Active Learning for Medical Image Classification

In this paper, we propose a deep reinforcement learning algorithm for active learning on medical image data. Although deep learning has achieved great success on medical image processing, it relies on a large number of labeled data for training, which is expensive and time-consuming. Active learning, which follows a strategy to select and annotate informative samples, is an effective approach to alleviate this issue. However, most existing methods of active learning adopt a hand-design strategy, which cannot handle the dynamic procedure of classifier training. To address this issue, we model the procedure of active learning as a Markov decision process, and propose a deep reinforcement learning algorithm to learn a dynamic policy for active learning. To achieve this, we employ the actor-critic approach, and apply the deep deterministic policy gradient algorithm to train the model. We conduct experiments on two kinds of medical image data sets, and the results demonstrate that our method is able to learn better strategy compared with the existing hand-design ones.
Search strategies at the European Patent Office

Prior-art search is a critical step towards determining whether a patent can be granted or not. In 2016, an internal project called Search Workflow Modelling (SWM) was launched at the European Patent Office (EPO) for building a search knowledgebase, which contains a set of models that record not only the current situation on how patent examiners deal with prior-art search (i.e. the as-is models), but also their requirements of being able to do a more efficient and effective search (i.e. the to-be models). We use the Fact-based Modelling (FBM) approach for formalizing search ontologies, which cover a common vocabulary, relations between concepts related to search, and constraints applicable to these relations. We use a hybrid modelling approach of Business Process Modelling Notations and Case Management Model and Notations (BPMN/CMMN) to model search work flows. A patent search strategy typically involves at least one FBM model and at least one BPMN/CMMN model. In this paper, we will illustrate 5 types of existing search strategies (including recursive flow patterns and FBM models for future search features), and future search strategies. The SWM empirical studies in this paper are being put into practice in the ongoing projects concerning search tools at the EPO.
Constrained Sampling-based Trajectory Optimization using Stochastic Approximation

We propose a sampling-based trajectory optimization methodology for constrained problems. We extend recent works on stochastic search to deal with box control constraints, as well as nonlinear state constraints for discrete dynamical systems. Regarding the former, our strategy is to optimize over truncated parameterized distributions on control inputs. Furthermore, we show how non-smooth penalty functions can be incorporated into our framework to handle state constraints. Simulations on cartpole and quadcopter show that our approach outperforms previous methods on constrained sampling-based optimization, in terms of quality of solutions and convergence speed.
Full Reference Image Quality Assessment by Considering Intra-Block Structure and Inter-Block Texture

The purpose of the image quality assessment is to evaluate image quality consistently with human's subjective evaluation. In image quality assessment, many structural features have been used to measure the quality degradation of an image. However, most of the existing image quality assessment methods are block-based, which ignores the features among neighboring blocks. In this paper, we argue that the human visual system perceives distortions not only depends on local structural (intra-block structure) distortions, but also relates to the structural distortions of their neighborhoods (inter-block texture). Based on this insight, we propose a novel image quality assessment method, called the diffusion speed structure similarity (DSSIM), by considering both intra-block structure and inter-block texture. Specifically, to characterize the inter-block texture, we devise a novel visual feature based on the image diffusion speed. To measure the changes of the intra-block structure, we adopt the image gradient magnitude. Furthermore, to differentiate the importance of a local region, we devise a weighting function based on the image diffusion speed. Extensive experimental results on six benchmark databases demonstrate that our proposed method yields a better performance than the state-of-the-art methods.
A Persian Medical Question Answering System

A question answering system is a type of information retrieval that takes a question from a user in natural language as the input and returns the best answer to it as the output. In this paper, a medical question answering system in the Persian language is designed and implemented. During this research, a dataset of diseases and drugs is collected and structured. The proposed system includes three main modules: question processing, document retrieval, and answer extraction. For the question processing module, a sequential architecture is designed which retrieves the main concept of a question by using different components. In these components, rule-based methods, natural language processing, and dictionary-based techniques are used. In the document retrieval module, the documents are indexed and searched using the Lucene library. The retrieved documents are ranked using similarity detection algorithms and the highest-ranked document is selected to be used by the answer extraction module. This module is responsible for extracting the most relevant section of the text in the retrieved document. During this research, different customized language processing tools such as part of speech tagger and lemmatizer are also developed for Persian. Evaluation results show that this system performs well for answering different questions about diseases and drugs. The accuracy of the system for 500 sample questions is 83.6%.
Exploring Fitness Tracker Visualisations to Avoid Rumination

Fitness trackers encourage users to set goals to improve personal wellbeing, but these goals sometimes remain unmet. Understanding how improved ways of communicating failure to meet fitness goals could help prevent negative thought cycles (rumination) and avoid reduced motivation for physical activity. To address this challenge, we studied how unmet goals can be presented in apps. We designed prototypes that showed unmet fitness goals. Radial and regular bar charts, single-coloured and multicoloured were used in the study. In a survey (n = 165), we compared the four versions and a textual description of the unmet goals. Then, we conducted follow-up interviews (n = 20) to gain a detailed understanding of the perceptions and feelings evoked by the prototypes. We found that bar graphs offered a significantly better potential for reflection and multicoloured charts triggered significantly more rumination. We contribute in-depth insights into designing systems that use goals and avoid potential negative effects of personal tracking.
A Fast Image Segmentation Algorithm Based on Saliency Map and Neutrosophic Set Theory

Due to more or less deviations in the imaging system, there will be noise in the image, which makes the image segmentation inaccurate. To divide a natural image into a more accurate binary image, the target and background of the image are effectively separated to achieve a more effective segmentation result. Therefore, this paper proposes an image segmentation algorithm combining a saliency map and neutrosophic set (NS) theory. First, to overcome the problem of weak edges in the image, we highlight the details and use the guided filter to filter the various channels of the natural image. Then, the initial saliency map is generated. After the weighted superposition of the initial saliency map, the local entropy map and the gray scale map, the final saliency map can be generated using the nonlinear function, and it can effectively highlight the foreground information of the image. Second, the saliency map is transformed to the NS domain and interpreted by three subsets: true (T), indeterminate (I), and false (F). According to NS theory, the indeterminacy is reduced, and the segmentation results are finally obtained by using the method of threshold. Various experiments were done and compared with other state-of-the-art approaches. These experiments demonstrate the effect of the proposed work, which is fast and effective for de-noising.
CONSTRUCTIVIST THEORY AS A FOUNDATION FOR THE UTILIZATION OF DIGITAL TECHNOLOGY IN THE LIFELONG LEARNING PROCESS

Lifelong learning, with the help of digital technology, possesses the capacity to offer individual significant advantages to individual learners. This paper examines both the diverse approaches to lifelong learning and the digital tools available to promote these strategies. The paper contains a review of some of the main articles pertaining to the fields of constructivism, digital technology, and the process of lifelong learning. Specifically, the authors intend to critically evaluate the utilization of rapidly developing digital technologies, such as computers, tablets, and mobile devices, within lifelong learning and their role as tools in promoting access to both practical and theoretical knowledge and in facilitating the communication of ideas within a global network, as per the constructivist approach. Hence, this article relies upon a specific definition of lifelong learning and an exploration of the notional foundation of what comprises lifelong learning and the environment in which their knowledge acquisition occurs. Thus, a framework for the present research is established wherein peer-reviewed studies concerning the use of social media by lifelong learners is explored, and extrapolating from the dual tenets of professional development and adult learning theory. Moreover, the authors additionally examine approaches to the notion of PLEs (personal learning environments) and PLNs (personal learning networks) as related to the selection of relevant lifelong learning strategies. The discussion is exemplified by cases ranging from video platforms to blogs and is simultaneously multidisciplinary and spanning diverse fields. Each example has applicability for lifelong learning and represents the characteristics of constructivism and its support within a web-based learning environment. It is thereby suggesting that effective and meaningful strategies supportive of the lifelong learning lifestyle can be achieved via welldesigned PLEs and PLNs.
Approximate dynamics leading to more optimal control: Efficient exact derivatives

Accurate derivatives are important for locally traversing and converging efficiently in quantum optimization landscapes. By deriving analytically exact control derivatives (gradient and Hessian) for unitary control tasks, we show here that the computational feasibility of meeting this accuracy requirement depends on the choice of propagation scheme and problem representation. Even when exact propagation is sufficiently cheap it is, perhaps surprisingly, much more efficient to optimize the (appropriately) approximate propagators: Approximations in the dynamics are traded off for significant complexity reductions in the exact derivative calculations. Importantly, past the initial analytical considerations, only standard numerical techniques are explicitly required with straightforward application to realistic systems. These results are numerically verified for two concrete problems of increasing Hilbert space dimensionality. The best schemes obtain unit fidelity to machine precision, whereas the results for other schemes are separated consistently by orders of magnitude in computation time and in worst-case ten orders of magnitude in achievable fidelity. Since these gaps continually increase with system size and complexity, this methodology allows numerically efficient optimization of very high-dimensional dynamics, e.g., in many-body contexts, operating in the high-fidelity regime.
Practical Random Access to SLP-Compressed Texts

Grammar-based compression is a popular and powerful approach to compressing repetitive texts but until recently its relatively poor time-space trade-offs during real-life construction made it impractical for truly massive datasets such as genomic databases. In a recent paper (SPIRE 2019) we showed how simple pre-processing can dramatically improve those trade-offs, and in this paper we turn our attention to one of the features that make grammar-based compression so attractive: the possibility of supporting fast random access. This is an essential primitive in many algorithms that process grammar-compressed texts without decompressing them and so many theoretical bounds have been published about it, but experimentation has lagged behind. We give a new encoding of grammars that is about as small as the practical state of the art (Maruyama et al., SPIRE 2013) but with significantly faster queries.
Robust Feature Matching with Spatial Smoothness Constraints

Feature matching is to detect and match corresponding feature points in stereo pairs, which is one of the key techniques in accurate camera orientations. However, several factors limit the feature matching accuracy, e.g., image textures, viewing angles of stereo cameras, and resolutions of stereo pairs. To improve the feature matching accuracy against these limiting factors, this paper imposes spatial smoothness constraints over the whole feature point sets with the underlying assumption that feature points should have similar matching results with their surrounding high-confidence points and proposes a robust feature matching method with the spatial smoothness constraints (RMSS). The core algorithm constructs a graph structure from the feature point sets and then formulates the feature matching problem as the optimization of a global energy function with first-order, spatial smoothness constraints based on the graph. For computational purposes, the global optimization of the energy function is then broken into sub-optimizations of each feature point, and an approximate solution of the energy function is iteratively derived as the matching results of the whole feature point sets. Experiments on close-range datasets with some above limiting factors show that the proposed method was capable of greatly improving the matching robustness and matching accuracy of some feature descriptors (e.g., scale-invariant feature transform (SIFT) and Speeded Up Robust Features (SURF)). After the optimization of the proposed method, the inlier number of SIFT and SURF was increased by average 131.9% and 113.5%, the inlier percentages between the inlier number and the total matches number of SIFT and SURF were increased by average 259.0% and 307.2%, and the absolute matching accuracy of SIFT and SURF was improved by average 80.6% and 70.2%.