Reviewerly/exHarmony huggingface dataset:
Coercion by modification — The adaptive capacities of event-sensitive adnominal modifiers

This paper is about coercive meaning adaptions as triggered by modifiers. It pursues two objectives. First, we argue that coercion by modification is rooted in the linguistic system. More specifically, given a pending type-conflict between a modifier and its target, fine-grained lexical typing information is shown to both license and constrain adaptive options. Notably, such a dynamic lexical semantics integrates conceptual knowledge resources into rigid compositional mechanisms without giving up their principled distinction. Second, we reconcile coercion by modification with the standard conception of modification as a type-preserving operation. However, we also argue that the coercion facts cannot be handled within rule-based approaches to modification. The merits of the proposed dynamic approach to lexical semantics are exemplified by a detailed investigation of the subtly differing coercive potentials of event-sensitive adjectives such as schnell ‘quick’, flink ‘nimble’, and rasch ‘rapid’ in German. It is spelled out in terms of Asher’s (2011) Type Composition Logic. EARLY ACCESS
Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders

Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations.We propose an approach to semi-supervised learning of semantic dependency parsers based on the CRF autoencoder framework.Our encoder is a discriminative neural semantic dependency parser that predicts the latent parse graph of the input sentence.Our decoder is a generative neural model that reconstructs the input sentence conditioned on the latent parse graph.Our model is arc-factored and therefore parsing and learning are both tractable.Experiments show our model achieves significant and consistent improvement over the supervised baseline.
A multi-branch hand pose estimation network with joint-wise feature extraction and fusion

The study of 3D hand pose estimation from a single depth image is regarded as a detection-based or regression-based problem among most of the existing deep learning-based methods, and this approach does not fully exploit the geometry of the hand, such as its structural and physical constraints. To overcome these weaknesses, we design a network with three simple parallel branches that correspond to the three functional parts of the hand. This observation is motivated by the biological viewpoint that each finger plays a different role in performing grasping and manipulation. In each branch, we perform a more detailed regression in two stages – top-down joint location regression followed by bottom-up hand pose regression – which fully exploits both the local and global structure of a hand. Finally, we further make use of the hand structure and physical constraints to refine each joint by its auxiliary points. The proposed network is a unified structure and function model that is more appropriate for hand pose estimation. Our system does not require pose pre-processing or feedback since it can directly perform training and predicting from end-to-end. The experimental results on three public datasets demonstrate that the proposed system achieves performance comparable to state-of-the-art methods.
A High Precision Feature Matching Method Based on Geometrical Outlier Removal for Remote Sensing Image Registration

The reliability of feature matching is required for accurate remote sensing image registration.Outliers reduce the accuracy and effectiveness of feature matching.This paper proposes a novel feature matching method that utilizes the global geometric relationship of feature points in two images to eliminate outliers and preserve inliers.A mathematical model is formulated based on the similarity of the geometric relationship of feature points in the reference image and the sensed image.We also find the optimization solution through analysis and simplification of the mathematical model.The corresponding feature matching algorithm based on outlier removal is proposed according to the optimization solution.The experimental results of several remote sensing images demonstrate that our method can preserve more inliers, remove more outliers and obtain a better registration performance with higher accuracy and robustness than the state-of-the-art methods, such as SIFT, SIFT-RANSAC, SIFT-GTM, SIFT-LPM.
PRINCIPLES OF WEBSITE DESIGN FOR PROFESSIONAL PSYCHOLOGICAL DIAGNOSIS IN DIFFERENT SPECIALTIES

The rapid IT development in the 21st century has a significant impact on scientific communications and research in all branches of modern science. Psychological science cannot stay aside this non-stop process. Indeed, the modern IT capabilities provide wide opportunities for psychological research in terms of speed, accuracy, volume and cross-culture perspective. The article describes theoretical, methodological and practical principles of design of websites for professional psychological diagnosis. Adherence to these principles ensures that the psychological diagnostic websites: 1) attract visitors and maintain the necessary level of their confidence and interest; 2) provide anonymity, clarity, convenience and ease of use; 3) have high informational value, content richness and scientific character; 4) differentiate the respondents and help receive feedback from them. The research methods used include the analysis of the national and foreign experience of IT use in psychological research, modelling and online psychological diagnosis organised on two psychological diagnostic websites with 26 psychological diagnostic methods and questionnaires. The detailed structure and developmental rules for diagnostic Internet sites are substantiated. A number of specific aspects concerning diagnostic Internet site application for professional psychological diagnosis are discussed. The scientific foundations developed by the authors for the use of remote professional psychological diagnosis have been successfully tested on two created psychological diagnostic websites http://prof-diagnost.org and http://hr-test.org and confirmed by the research carried out with their help. These sites are aimed at solving conceptually rather different research tasks. The first site has become the basis for fundamental research performed by different scientists, whereas the other has provided an opportunity to adapt 11 modern English-language psychological diagnostic techniques related to the diagnosis of occupational stress for their use in Ukraine.
Energy Modeling and Experimental Validation of Four-Wheel Mecanum Mobile Robots for Energy-Optimal Motion Control

Four-wheel Mecanum mobile robots (FWMRs) are widely used in transportation because of their omnidirectional mobility. However, the FWMR trades off energy efficiency for flexibility. To efficiently predict the energy consumption of the robot movement processes, this paper proposes a power consumption model for the omnidirectional movement of an FWMR. A power consumption model is of great significance for reducing the power consumption, motion control, and path planning of robots. However, FWMRs are highly maneuverable, meaning their control is complicated and their energy modeling is extremely complex. The speed, distance, path, and power consumption of the robot can vary greatly depending on the control method. This energy model was mathematically implemented in MATLAB and validated by our laboratory’s Mecanum wheel robot. The prediction accuracy of the model was over 95% through simulation and experimental verification.
Multi‐view ensemble learning with empirical kernel for heart failure mortality prediction

Abstract Heart failure (HF) refers to the heart's inability to pump sufficient blood to maintain the body's needs, which has a very serious impact on human health. In recent years, the prevalence of HF has remained high. This paper proposes a multi‐view ensemble learning algorithm based on empirical kernel mapping called MVE‐EK, which predicts the mortality of patient through hospital records. Multi‐view ensemble learning can take advantage of the consistency and complementarity of different views. The MVE‐EK first divides the patient's features into multiple views and then divides the samples of each view to multiple subsets through under sampling, which can reduce the imbalance rate of the original dataset and obtain some relatively balanced subsets. Each subset is mapped into kernel space by empirical kernel mapping, which can map samples from linearly inseparable spaces to linearly separable spaces. Finally, the multi‐view ensemble learning is performed by the designed loss of acquaintance between views. The effectiveness of the algorithm is verified on the three datasets of HF patient in the real world. The performance of the algorithm is better than other comparison algorithms. The datasets are collected from Shanghai Shuguang Hospital and involve 10 203 hospitalization records for 4682 HF patients between March 2009 and April 2016. The prediction information provided by the algorithm can assist the clinician in providing a more personalized treatment plan for patients with HF.
Synthetic Propaganda Embeddings To Train A Linear Projection

This paper presents a method of detecting fine-grained categories of propaganda in text.Given a sentence, our method aims to identify a span of words and predict the type of propaganda used.To detect propaganda, we explore a method for extracting features of propaganda from contextualized embeddings without finetuning the large parameters of the base model.We show that by generating synthetic embeddings we can train a linear function with ReLU activation to extract useful labeled embeddings from an embedding space generated by a general-purpose language model.We also introduce an inference technique to detect continuous spans in sequences of propaganda tokens in sentences.A result of the ensemble model is submitted to the first shared task in fine-grained propaganda detection at NLP4IF as Team Stalin.In this paper, we provide additional analysis regarding our method of detecting spans of propaganda with synthetically generated representations.
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior

Despite the remarkable progress on MachineReading Comprehension (MRC) with the help of open-source datasets, recent studies indicate that most of the current MRC systems unfortunately suffer from weak robustness against adversarial samples.To address this issue, we attempt to take sentence syntax as the leverage in the answer predicting process which previously only takes account of phrase-level semantics.Furthermore, to better utilize the sentence syntax and improve the robustness, we propose a Syntactic Leveraging Network, which is designed to deal with adversarial samples by exploiting the syntactic elements of a question.The experiment results indicate that our method is promising for improving the generalization and robustness of MRC models against the influence of adversarial samples, with performance well-maintained.
Revealing and Predicting Online Persuasion Strategy with Elementary Units

In online arguments, identifying how users construct their arguments to persuade others is important in order to understand a persuasive strategy directly.However, existing research lacks empirical investigations on highly semantic aspects of elementary units (EUs), such as propositions for a persuasive online argument.Therefore, this paper focuses on a pilot study, revealing a persuasion strategy using EUs.Our contributions are as follows: (1) annotating five types of EUs in a persuasive forum, the so-called ChangeMyView, (2) revealing both intuitive and non-intuitive strategic insights for the persuasion by analyzing 4612 annotated EUs, and (3) proposing baseline neural models that identify the EU boundary and type.Our observations imply that EUs definitively characterize online persuasion strategies.The annotated dataset, annotation guideline, and implementation of the neural model are available in public. 1
Four Ways Technology Has Negatively Changed Education

Our experience with technology is a bitter-sweet one.We relish its presence in our lives, but we dread the effect it may have on our manners, attitudes and social interactions.We open the gates of our schools to all types of technological tools, yet we fear it may badly impact our students' performance.This article investigates the ways through which classroom technology such as iPad, Internet connection, laptops and social media, impacts negatively on education.Relevant research has proven that technology could change education negatively through four paths: deteriorating students' competences of reading and writing, dehumanizing educational environments, distorting social interactions between teachers and students and isolating individuals when using technology.
Image Generation with Gans-based Techniques: A Survey

In recent years, frameworks that employ Generative Adversarial Networks (GANs) have achieved immense results for various applications in many fields especially those related to image generation both due to their ability to create highly realistic and sharp images as well as train on huge data sets.However, successfully training GANs are notoriously difficult task in case ifhigh resolution images are required.In this article, we discuss five applicable and fascinating areas for image synthesis based on the state-of-theart GANs techniques including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Manipulation, 3D Image Synthesis and DeepMasterPrints.We provide a detailed review of current GANs-based image generation models with their advantages and disadvantages.The results of the publications in each section show the GANs based algorithmsAREgrowing fast and their constant improvement, whether in the same field or in others, will solve complicated image generation tasks in the future.
Cross-Layer Autoencoder for Zero-Shot Learning

Zero-shot learning (ZSL) is the task of recognizing samples from their related classes which have never been seen during model training. ZSL is generally realized through learning a common embedding space where both high dimensional visual features and some pre-defined semantics can be mapped. However, this kind of solutions usually suffers from domain shift. In addition, the limitation and subjectivity of manual semantic information can also affect the classification results. To address these challenges, this paper proposes a novel end-to-end deep learning model called Cross-Layer Autoencoder (CLAE), which integrates different ways of semantic mapping and maintains reconstruction information. Besides, a regularized loss function is used to preserve local class manifolds. Extensive experiments for both traditional and generalized ZSL tasks are conducted on several benchmark datasets, and high effectiveness of the proposed method and its superiority over many previous researches are demonstrated.
Thermodynamic analysis of quantum error-correcting engines

Quantum error-correcting codes can be cast in a way which is strikingly similar to a quantum heat engine undergoing an Otto cycle. In this paper, we strengthen this connection further by carrying out a complete assessment of the thermodynamic properties of four-stroke operator-based error-correcting codes. This includes an expression for the entropy production in the cycle which, as we show, contains clear contributions stemming from the different sources of irreversibility. To illustrate our results, we study a classical three-qubit error-correcting code, well suited for incoherent states, and the nine-qubit Shor code capable of handling fully quantum states. We show that the work cost associated with the correction gate is directly associated with the heat introduced by the error. Moreover, the work cost associated with encoding and decoding quantum information is always positive, a fact which is related to the intrinsic irreversibility introduced by the noise. Finally, we find that correcting the coherent (and thus genuinely quantum) part of a quantum state introduces substantial modifications related to the Hadamard gates required to encode and decode coherences.
Pedestrian Attribute Recognition with Graph Convolutional Network in Surveillance Scenarios

Pedestrian attribute recognition is to predict a set of attribute labels of the pedestrian from surveillance scenarios, which is a very challenging task for computer vision due to poor image quality, continual appearance variations, as well as diverse spatial distribution of imbalanced attributes. It is desirable to model the label dependencies between different attributes to improve the recognition performance as each pedestrian normally possesses many attributes. In this paper, we treat pedestrian attribute recognition as multi-label classification and propose a novel model based on the graph convolutional network (GCN). The model is mainly divided into two parts, we first use convolutional neural network (CNN) to extract pedestrian feature, which is a normal operation processing image in deep learning, then we transfer attribute labels to word embedding and construct a correlation matrix between labels to help GCN propagate information between nodes. This paper applies the object classifiers learned by GCN to the image representation extracted by CNN to enable the model to have the ability to be end-to-end trainable. Experiments on pedestrian attribute recognition dataset show that the approach obviously outperforms other existing state-of-the-art methods.
On Application of Case-Based Reasoning to Personalise Learning

The paper aims to present application of Educational Data Mining and particularly Case-Based Reasoning (CBR) for students profiling and further to design a personalised intelligent learning system.The main aim here is to develop a recommender system which should help the learners to create learning units (scenarios) that are the most suitable for them.First of all, systematic literature review on application of CBR and its possible implementation to personalise learning was performed in the paper.After that, methodology on CBR application to personalise learning is presented where learning styles play a dominate role as key factor in proposed personalised intelligent learning system model based on students profiling and personalised learning process model.The algorithm (the sequence of steps) to implement this model is also presented in the paper.
Large-Scale Hierarchical Alignment for Data-driven Text Rewriting

We propose a simple unsupervised method for extracting pseudo-parallel monolingual sentence pairs from comparable corpora representative of two different text styles, such as news articles and scientific papers.Our approach does not require a seed parallel corpus, but instead relies solely on hierarchical search over pre-trained embeddings of documents and sentences.We demonstrate the effectiveness of our method through automatic and extrinsic evaluation on text simplification from the normal to the Simple Wikipedia.We show that pseudo-parallel sentences extracted with our method not only supplement existing parallel data, but can even lead to competitive performance on their own. 1
User Behavior Clustering Scheme With Automatic Tagging Over Encrypted Data

User behavior clustering analysis has a wide range of applications in business intelligence, information retrieval, and image pattern recognition and fault diagnosis. Most of existing methods of user behavior have some problems such as weak generality and the lack of tags of clustering. With the increasing awareness of privacy protection, user behavior analysis also needs to support for ciphertext to protect user data. Based on clustering algorithm, homomorphic encryption technology and information security, in this paper, we propose a user behavior clustering scheme that supports automatic tags on ciphertext. Firstly, design a security protocol corresponding to the basic operations such as addition, multiplication and comparison and apply to the scheme. Then, the relevant features of the user behavior are merged with the clustering process, the latent factor model, and matrix decomposition. We have implemented our method and evaluated its performance using K-means and K-means++ clustering. The results show that the scheme can auto tags over encrypted data, and the tag also meets the actual situation, which proves the validity and generality of the scheme.
Ontology-based Reasoning Approach for Long-term Behavior Prediction of Road Users

A human driver determines his/her driving action by predicting future behavior of other road users based on the consideration of relative relationships and reactions between all players. To get as close as possible to human driving behavior, it is important to predict the early and long-term behavior of other road users. For this sake, many motion-based methods has been proposed, although only for short-term prediction. On the other hand, reasoning-based methods for long-term prediction have been proposed recently. However, they can only predict the latest behavior to the observation and do not consider uncertainty, which is necessary to realize smooth autonomous driving behavior. Therefore, we propose a method that predicts long-term behavior of other road users with estimated probabilities based on semantic reasoning that considers interactions among various players. For this purpose, we define an ontology that provides a conceptual description of all road users with their interactions, and infer the likely behaviors with their associated probabilities using Markov Logic Network (MLN). This framework is used with a actual vehicle as it approaches intersections, to demonstrate its applicability. Results from the real-time implementation on the vehicle, operating under controlled conditions, indicate that the proposed approach predicts a sequence of behavioral possibilities with their associated probabilities as expected.
DevDocOps: Enabling continuous documentation in alignment with DevOps

Summary The proliferation of DevOps enables significant acceleration and automation of the delivery and deployment of massive software products. Unfortunately, the development of supporting documents that is vital for large‐scale software systems in many cases does not keep pace with the rhythm of feature delivery using DevOps in practice, which becomes the bottleneck for many software organizations to deliver full value to the customers as claimed by the DevOps. This paper proposes, implements, and evaluates an integrated approach, DevDocOps , for continuous automated documentation, in particular for DevOps. With DevDocOps , supporting documents are created along with the development process simultaneously by various roles within a DevOps project, which largely guarantees the accuracy and integrity of documents as well as significantly increases their delivery speed. Within an established delivery chain, a set of templates are created to collect and transform the required information from its origin to the target documents for delivery. A real system, iDoc , is implemented to map, collect, and synthesize the information from document templates and automate the documentation process. DevDocOps has been successfully adopted in a top‐tier global telecommunication enterprise to support more than 5000 users with different roles related to documentation. The lag time between the releases of the product version and its supporting document has been shortened from 1 to 2 months on average to less than 2 days. DevDocOps extends the scope of DevOps and enhances the value delivery by supporting continuous documentation and bridges the gap between feature delivery and document delivery with automation.