Reviewerly/exHarmony huggingface dataset:
Structural Information Preserving for Graph-to-Text Generation

The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs.As a crucial defect, the current state-of-the-art models may mess up or even drop the core structural information of input graphs when generating outputs.We propose to tackle this problem by leveraging richer training signals that can guide our model for preserving input information.In particular, we introduce two types of autoencoding losses, each individually focusing on different aspects (a.k.a.views) of input graphs.The losses are then back-propagated to better calibrate our model via multi-task training.Experiments on two benchmarks for graph-to-text generation show the effectiveness of our approach over a state-of-the-art baseline.Our code is available at
Teacher-Student Networks with Multiple Decoders for Solving Math Word Problem

Math word problem (MWP) is challenging due to the limitation in training data where only one “standard” solution is available. MWP models often simply fit this solution rather than truly understand or solve the problem. The generalization of models (to diverse word scenarios) is thus limited. To address this problem, this paper proposes a novel approach, TSN-MD, by leveraging the teacher network to integrate the knowledge of equivalent solution expressions and then to regularize the learning behavior of the student network. In addition, we introduce the multiple-decoder student network to generate multiple candidate solution expressions by which the final answer is voted. In experiments, we conduct extensive comparisons and ablative studies on two large-scale MWP benchmarks, and show that using TSN-MD can surpass the state-of-the-art works by a large margin. More intriguingly, the visualization results demonstrate that TSN-MD not only produces correct final answers but also generates diverse equivalent expressions of the solution.
An Interactive Multi-Task Learning Framework for Next POI Recommendation with Uncertain Check-ins

Studies on next point-of-interest (POI) recommendation mainly seek to learn users' transition patterns with certain historical check-ins. However, in reality, users' movements are typically uncertain (i.e., fuzzy and incomplete) where most existing methods suffer from the transition pattern vanishing issue. To ease this issue, we propose a novel interactive multi-task learning (iMTL) framework to better exploit the interplay between activity and location preference. Specifically, iMTL introduces: (1) temporal-aware activity encoder equipped with fuzzy characterization over uncertain check-ins to unveil the latent activity transition patterns; (2) spatial-aware location preference encoder to capture the latent location transition patterns; and (3) task-specific decoder to make use of the learned latent transition patterns and enhance both activity and location prediction tasks in an interactive manner. Extensive experiments on three real-world datasets show the superiority of iMTL.
A Multi-Perspective Architecture for Semantic Code Search

The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories.In this paper, we propose a novel multiperspective cross-lingual neural framework for code-text matching, inspired in part by a previous model for monolingual text-to-text matching, to capture both global and local similarities.Our experiments on the CoNaLa dataset show that our proposed model yields better performance on this cross-lingual text-to-code matching task than previous approaches that map code and text to a single joint embedding space.
Lexical-Constraint-Aware Neural Machine Translation via Data Augmentation

Leveraging lexical constraint is extremely significant in domain-specific machine translation and interactive machine translation. Previous studies mainly focus on extending beam search algorithm or augmenting the training corpus by replacing source phrases with the corresponding target translation. These methods either suffer from the heavy computation cost during inference or depend on the quality of the bilingual dictionary pre-specified by user or constructed with statistical machine translation. In response to these problems, we present a conceptually simple and empirically effective data augmentation approach in lexical constrained neural machine translation. Specifically, we make constraint-aware training data by first randomly sampling the phrases of the reference as constraints, and then packing them together into the source sentence with a separation symbol. Extensive experiments on several language pairs demonstrate that our approach achieves superior translation results over the existing systems, improving translation of constrained sentences without hurting the unconstrained ones.
Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer

Unsupervised style transfer aims to change the style of an input sentence while preserving its original content without using parallel training data. In current dominant approaches, owing to the lack of fine-grained control on the influence from the target style, they are unable to yield desirable output sentences. In this paper, we propose a novel attentional sequence-to-sequence (Seq2seq) model that dynamically exploits the relevance of each output word to the target style for unsupervised style transfer. Specifically, we first pretrain a style classifier, where the relevance of each input word to the original style can be quantified via layer-wise relevance propagation. In a denoising auto-encoding manner, we train an attentional Seq2seq model to reconstruct input sentences and repredict word-level previously-quantified style relevance simultaneously. In this way, this model is endowed with the ability to automatically predict the style relevance of each output word. Then, we equip the decoder of this model with a neural style component to exploit the predicted wordlevel style relevance for better style transfer. Particularly, we fine-tune this model using a carefully-designed objective function involving style transfer, style relevance consistency, content preservation and fluency modeling loss terms. Experimental results show that our proposed model achieves state-of-the-art performance in terms of both transfer accuracy and content preservation.
Transferable attention networks for adversarial domain adaptation

Domain adaptation is one of the fundamental challenges in transfer learning. How to effectively transfer knowledge from labeled source domain to unlabeled target domain is critical for domain adaptation, as it benefits to reduce the considerable performance gap due to domain shift. Existing methods of domain adaptation address this issue via matching the global features across domains. However, not all features are transferable for domain adaptation, while forcefully matching the untransferable features may lead to negative transfer. In this paper, we propose a novel method dubbed transferable attention networks (TAN) to address this issue. The proposed TAN focuses on the feature alignment by utilizing adversarial optimization. Specifically, we utilize the self-attention mechanism to weight extracted features, such that the influence of untransferable features can be effectively eliminated. Meanwhile, to exploit the complex multi-modal structures of domain adaptation, we use learned features and classifier predictions as the condition to train the adversarial networks. Furthermore, we further propose that the accurately transferable features should enable domain discrepancy to minimum. Three loss functions are introduced into the adversarial networks: classification loss, attention transfer loss, and condition transfer loss. Extensive experiments on Office-31, ImageCLEF-DA, Office-Home, and VisDA-2017 datasets testify that the proposed approach yields state-of-the-art results.
When are researchers willing to share their data? – Impacts of values and uncertainty on open data in academia

Background E-science technologies have significantly increased the availability of data. Research grant providers such as the European Union increasingly require open access publishing of research results and data. However, despite its significance to research, the adoption rate of open data technology remains low across all disciplines, especially in Europe where research has primarily focused on technical solutions (such as Zenodo or the Open Science Framework) or considered only parts of the issue. Methods and findings In this study, we emphasized the non-technical factors perceived value and uncertainty factors in the context of academia, which impact researchers’ acceptance of open data–the idea that researchers should not only publish their findings in the form of articles or reports, but also share the corresponding raw data sets. We present the results of a broad quantitative analysis including N = 995 researchers from 13 large to medium-sized universities in Germany. In order to test 11 hypotheses regarding researchers’ intentions to share their data, as well as detect any hierarchical or disciplinary differences, we employed a structured equation model (SEM) following the partial least squares (PLS) modeling approach. Conclusions Grounded in the value-based theory, this article proclaims that most individuals in academia embrace open data when the perceived advantages outweigh the disadvantages. Furthermore, uncertainty factors impact the perceived value (consisting of the perceived advantages and disadvantages) of sharing research data. We found that researchers’ assumptions about effort required during the data preparation process were diminished by awareness of e-science technologies (such as Zenodo or the Open Science Framework), which also increased their tendency to perceive personal benefits via data exchange. Uncertainty factors seem to influence the intention to share data. Effects differ between disciplines and hierarchical levels.
Data and Knowledge Twin Driven Integration for Large-Scale Device-Free Localization

Device-free localization (DFL) is becoming one of the attractive techniques in wireless sensing field, due to its advantage that the target does not need to be attached to any electronic device. However, most of the existed approaches for DFL can only obtain satisfactory localization performance in specific small area, they cannot function well when implemented to complex and large area. In order to tackle this issue, in this article, a hierarchical framework is developed for large-scale DFL based on data and knowledge twin driven integration, which consists of two phases, including the offline training phase and the online localization phase. In the offline training phase, the complex and large monitoring area is first divided into some subdomains by the K-means clustering algorithm, and then training corresponding number of broad learning (BL)-based DFL models for each subdomain using the Fresnel phase difference as the fingerprints. Meanwhile, a class-specific cost regulation extreme learning machine (CCR-ELM) classifier is also trained for determining the attribution of the reference points, which can alleviate the impacts of imbalanced data distribution on classification results. In the online localization phase, the attributions of the testing points are first judged through the trained CCR-ELM classifier, after that, estimating the target's location in the corresponding subdomains using BL-based DFL models. The validity of the proposed hierarchical framework is evaluated both in small and larger areas, respectively.
An entanglement-based quantum network based on symmetric dispersive optics quantum key distribution

Quantum key distribution (QKD) is a crucial technology for information security in the future. Developing simple and efficient ways to establish QKD among multiple users is important to extend the applications of QKD in communication networks. Herein, we proposed a scheme of symmetric dispersive optics QKD and demonstrated an entanglement-based quantum network based on it. In the experiment, a broadband entangled photon pair source was shared by end users via wavelength and space division multiplexing. The wide spectrum of generated entangled photon pairs was divided into 16 combinations of frequency-conjugate channels. Photon pairs in each channel combination supported a fully connected subnet with eight users by a passive beam splitter. Eventually, it showed that an entanglement-based QKD network over 100 users could be supported by one entangled photon pair source in this architecture. It has great potential on applications of local quantum networks with large user number.
DISTANCE LEARNING AND OBSTACLES DURING COVID-19 OUTBREAK

This paper aims to know the implementation of distance learning during COVID-19 cases in Pacitan and to find out the obstacles in handling distance learning in Pacitan. This is phenomenological research. The data were collected by using an online interview with parents. There were 48 parents of kindergarten and elementary school students as the sample. The researcher selected the sample by using simple random sampling. The results showed that there were five implementations of distance learning: doing worksheets from school (15 students), joining online class/online sources from the internet (7 students), completing the task from school (18 students), doing free activities without guidance (4 students), doing free activities with guidance (7 students). However, there were eight obstacles faced by parents in implementing distance learning: the parents could not focus to guide children in learning (4 respondents), the children could not focus to their learning (10 respondents), the children got boredom (4 respondents), the children did not want to learn (10 respondents), the children wanted to watch the television or play games (4 respondents), the children asked to go to school (3 respondents), the children could not do online learning (7 respondents), and the children had limited understanding related to the material (3 respondents).
GCSBA-Net: Gabor-Based and Cascade Squeeze Bi-Attention Network for Gland Segmentation

Colorectal cancer is the second and the third most common cancer in women and men, respectively. Pathological diagnosis is the “gold standard” for tumor diagnosis. Accurate segmentation of glands from tissue images is a crucial step in assisting pathologists in their diagnosis. The typical methods for gland segmentation form a dense image representation, ignoring its texture and multi-scale attention information. Therefore, we utilize a Gabor-based module to extract texture information at different scales and directions in histopathology images. This paper also designs a Cascade Squeeze Bi-Attention (CSBA) module. Specifically, we add Atrous Cascade Spatial Pyramid (ACSP), Squeeze Position Attention (SPA) module and Squeeze Channel Attention module (SCA) to model semantic correlation and maintain the multi-level aggregation on the spatial pyramid with different dilations. Besides, to solve the imbalance of data distribution and boundary blur, we propose a hybrid loss function to response the object boudary better. The experimental results show that the proposed method achieves state-of-the-art performance on the GlaS challenge dataset and CRAG colorectal adenocarcinoma dataset, respectively.
A set theory based similarity measure for text clustering and classification

Abstract Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and several other text-processing applications. However, the problem with these measures is that, until recently, there has never been one single measure recorded to be highly effective and efficient at the same time. Thus, the quest for an efficient and effective similarity measure is still an open-ended challenge. This study, in consequence, introduces a new highly-effective and time-efficient similarity measure for text clustering and classification. Furthermore, the study aims to provide a comprehensive scrutinization for seven of the most widely used similarity measures, mainly concerning their effectiveness and efficiency. Using the K-nearest neighbor algorithm (KNN) for classification, the K-means algorithm for clustering, and the bag of word (BoW) model for feature selection, all similarity measures are carefully examined in detail. The experimental evaluation has been made on two of the most popular datasets, namely, Reuters-21 and Web-KB. The obtained results confirm that the proposed set theory-based similarity measure (STB-SM), as a pre-eminent measure, outweighs all state-of-art measures significantly with regards to both effectiveness and efficiency.
Design of a Wearable Wireless Multi-Sensor Monitoring System and Application for Activity Recognition Using Deep Learning

Human activity monitoring and recognition systems assist experts in evaluating various health problems including obesity, cardiac diseases and, sports injury detection.However, these systems have two challenging points; monitoring activities for outdoor applications and extracting relevant features using hand-crafted techniques from multi-dimensional and large datasets.To address these challenges, we have focused on new dataset generation for activity recognition, a novel design of a sensor-based wireless activity monitoring system, and its application to deep learning neural networks.The designed monitoring system consists of one master and four slave devices, and can collect and record acceleration and gyroscope information.The slave devices were attached on arm, chest, thigh, and shank areas of the human body.Activity data were collected and recorded from sixty healthy people for thirteen activity types including drink from cup and cleaning table.These activities were divided into three activity categories as basic, complex, and all, which is the combination of basic and complex activities.Obtained datasets were fed into deep learning neural networks namely convolutional neural network (CNN), long-short term memory (LSTM) neural networks, and convolutional LSTM (ConvLSTM) neural networks.The performance of each neural network for each category type was separately examined.The results show that ConvLSTM outperforms CNN and LSTM as far as activity recognition is concerned.
STUDY AT HOME: ANALISIS KESULITAN BELAJAR MATEMATIKA PADA PROSES PEMBELAJARAN DARING

AbstractThe e-learning study at home education system is in accordance with the 2013 curriculum. However, when the e-learning application is too difficult, it is likely that students are not able to learn mathematics optimally, as well as the amount of data that must be delivered but the internet or network access becomes slow, of course it is will disrupt student activities. This study aims to determine the learning difficulties of students towards mathematics e-learning at home. The research method used is descriptive research method. The study involved high school / MA students with data collection techniques and information through a student questionnaire response instrument using Google forms and the research subjects numbered 30 students. The student response contained 31 statement items which were the descriptions of the indicators. Indicator to measure students 'responses regarding students' learning difficulties to mathematics through the e-learning system at home. Based on the average student achievement regarding learning difficulties in mathematics e-learning that is 75% by having the lowest achievement value by students namely the implementation of interactions, assignments and teaching materials in online learning by 73% and the highest indicator achieved by students namely technical signal constraints and the inability to learn online (e-learning) that is equal to 77% and for testing the validity shows that all items are valid statements, this is because 𝑟𝑐𝑜𝑢𝑛𝑡 𝑟𝑡𝑎𝑏𝑒𝑙 and Cronbach Alpha value 0.839 reliability coefficient 0.6. After calculated using SPSS 23.Keywords: Difficulty learning mathematics, E-learning, studying at home
Video stabilization: Overview, challenges and perspectives

Video Stabilization (VS) has been an active area of research in the last two decades. Many approaches have been successfully proposed and it is time to take a step back and offer a glimpse and a critical look to this hot topic. Among the questions that should be answered : ”is VS a solved problem”?. Is there still room for further improvement and at which level and how? The main purpose of this contribution is to answer such questions and to provide a fairly unifying framework to allow a better understanding of the progress of this research subject with appreciable industrial and academic benefits. In this paper we focus on the main challenges, practical aspects and mathematical core concepts of the video stabilization techniques. We also put a special attention to the Video Stabilization Quality Assessment (VSQA) by introducing new methodology inspired by the research results on Image Quality Assessment (IQA) in its broad sense. Finally we also discuss some new research directions to overcome the limitations of the existing methods.
The Effect of COVID-19 in University Tutoring Models

This research is part of the Educational Research Project developed at the University of La Laguna during the academic year 2019–2020, which included the period of confinement by COVID-19. The study was carried out with a sample of 193 student teachers in Early Childhood Education, Primary and four master’s degree programs offered by the Faculty of Education of the University of La Laguna. Four tutoring models were analyzed; in person, by e-mail, using virtual tutoring (Hangout/Google Meet) and WhatsApp. The results confirm the need to enhance synchronous models, as the most effective models for the development of the process of academic orientation and support for students, compared to asynchronous models. These delay the processes of academic decision-making and have a significant effect on them, as well as the pace of study and the motivation of the students, hindering the processes of learning, adaptation and fulfilment.
Coherent Ising machines—Quantum optics and neural network Perspectives

A coherent Ising machine (CIM) is a network of optical parametric oscillators (OPOs), in which the “strongest” collective mode of oscillation at well above threshold corresponds to an optimum solution of a given Ising problem. When a pump rate or network coupling rate is increased from below to above threshold, however, the eigenvectors with the smallest eigenvalue of the Ising coupling matrix [Jij] appear near threshold and impede the machine to relax to true ground states. Two complementary approaches to attack this problem are described here. One approach is to utilize the squeezed/anti-squeezed vacuum noise of OPOs below threshold to produce coherent spreading over numerous local minima via quantum noise correlation, which could enable the machine to access either true ground states or excited states with eigen-energies close enough to that of ground states above threshold. The other approach is to implement a real-time error correction feedback loop so that the machine migrates from one local minimum to another during an explorative search for ground states. Finally, a set of qualitative analogies connecting the CIM and traditional computer science techniques are pointed out. In particular, belief propagation and survey propagation used in combinatorial optimization are touched upon.