Reviewerly/exHarmony huggingface dataset:
LEARNING MATHEMATICAL MODELLING WITH AUGMENTED REALITY MOBILE MATH TRAILS PROGRAM: HOW CAN IT WORK?

The aim of this study is to investigate how an augmented reality mobile math trails program can provide opportunities for students to engage in meaningful mathematical modelling activities. An explorative research design was conducted involving two mathematics teachers and 30 eight grades in Semarang, Indonesia. An Augmented Reality Mobile Math Trails App was created, and several math trail tasks were designed, then students run the activity. Data were gathered by means of participatory observation, interviews, questionnaires, tests, and worksheets. Data analysis began with the organisation, annotation, description of the data and statistic tests. The findings indicate that an educational program was successfully designed, which offered students a meaningful mathematical experience. A mobile app was also developed to support this program. The mobile app with augmented reality features is helpful for students as a tool that bridges the gap between real-world situations and mathematical concepts in problem-solving following the mathematical modelling cycle. The program thus contributes to a higher ability in mathematical modelling. The study identified a link between instrumented techniques in programs and mathematical modelling, as built during the instrumentation process. Further studies are essential for project development and implementation in other cities with different situations and aspects of study.
Seismology at School in Nepal: A Program for Educational and Citizen Seismology Through a Low-Cost Seismic Network

Nepal, located above the convergent India-Eurasia plate boundary, has repeatedly experienced devastating earthquakes. During the 2015 magnitude 7.8 Gorkha earthquake, an often-reported experience was that people were not aware of the threatening seismic hazard and have insufficient level of preparedness. An important source of the problem is that earthquake-related topics are not part of the school curriculum. Earthquake education reaching a broad group of the population early in their lives is therefore strongly needed. We established an initiative in Nepal to introduce seismology in schools, with focus on education and citizen seismology. We have prepared educational materials adapted to the Nepali school system, which we distributed and also share on our program’s website: http://seismoschoolnp.org. In selected schools, we also installed a low-cost seismometer to record seismicity and to allow learning-by-doing classroom activities. Our approach was very well received and we hope it will help making earthquake-safe communities across Nepal. The seismic sensor installed in schools is a Raspberry Shake 1D (RS1D), selected based on performance in laboratory tests and adequacy to field conditions. At a test site in Switzerland we were able to record magnitude 1.0 events up to 50 km distance with a RS1D. In Nepal, 22 such seismometers installed in schools create the Nepal School Seismology Network providing online data openly. The seismometer in each school allows students to be informed of earthquakes, visualize the respective waveforms, and estimate distance and magnitude of the event. For significant local and regional events, we provide record sections and network instrumental intensity maps on our program’s website. In 6 months of network operation, more than 194 local and teleseismic earthquakes of M≥4 have been recorded. From a local and a global catalogue, complemented with our own visual identifications, we provide an earthquake wave detectability graph in distance—magnitude space. Based on our observations, we calibrate a new magnitude equation for Nepal, related to the epicentral distance D[km] and to the observed peak ground velocity PGVv[µm/s]. The calibration is done to best fit local catalogue magnitudes, and yields the following result: M = 1.05log10(PGVv) + 1.08log10(D) + 0.75
A survey on sentiment analysis in Urdu: A resource-poor language

The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications. We described the advancements made thus far in this area by categorising the studies along three dimensions, namely: text pre-processing lexical resources and sentiment classification. These pre-processing operations include word segmentation, text cleaning, spell checking and part-of-speech tagging. An evaluation of sophisticated lexical resources including corpuses and lexicons was carried out, and investigations were conducted on sentiment analysis constructs such as opinion words, modifiers, negations. Performance is reported for each of the reviewed study. Based on experimental results and proposals forwarded through this paper provides the groundwork for further studies on Urdu sentiment analysis.
Learning local discriminative representations via extreme learning machine for machine fault diagnosis

Recently, learning data representations have been investigated to reduce the dependences of human intervention and improve the performance of machine fault diagnosis. However, most of the representation learning methods are computationally intensive due to complex training procedures. Extreme learning machine is well-known for its fast training speed and strong generalization ability. It also has been applied to learn data representations for clustering and classification tasks. In this paper, a local discriminant preserving extreme learning machine autoencoder (LDELM-AE) is proposed to learn data representations with the local geometry and local discriminant exploited from the input data. Specifically, LDELM-AE utilizes two graphs to enhance the within-class compactness and between-class separability, respectively. Furthermore, the hierarchical representations can be obtained by stacking several LDELM-AEs. On several benchmark datasets, the proposed method demonstrates better classification accuracies than the state-of-the-art methods. Moreover, the proposed method has been used to diagnostic the rotary machine faults and achieves the diagnostic accuracy of 99.96%, which proves the proposed method is an efficient tool to diagnose machine faults.
Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma

Abstract Background The scoring of Ki-67 is highly relevant for the diagnosis, classification, prognosis, and treatment in breast invasive ductal carcinoma (IDC). Traditional scoring method of Ki-67 staining followed by manual counting, is time-consumption and inter−/intra observer variability, which may limit its clinical value. Although more and more algorithms and individual platforms have been developed for the assessment of Ki-67 stained images to improve its accuracy level, most of them lack of accurate registration of immunohistochemical (IHC) images and their matched hematoxylin-eosin (HE) images, or did not accurately labelled each positive and negative cell with Ki-67 staining based on whole tissue sections (WTS). In view of this, we introduce an accurate image registration method and an automatic identification and counting software of Ki-67 based on WTS by deep learning. Methods We marked 1017 breast IDC whole slide imaging (WSI), established a research workflow based on the (i) identification of IDC area, (ii) registration of HE and IHC slides from the same anatomical region, and (iii) counting of positive Ki-67 staining. Results The accuracy, sensitivity, and specificity levels of identifying breast IDC regions were 89.44, 85.05, and 95.23%, respectively, and the contiguous HE and Ki-67 stained slides perfectly registered. We counted and labelled each cell of 10 Ki-67 slides as standard for testing on WTS, the accuracy by automatic calculation of Ki-67 positive rate in attained IDC was 90.2%. In the human-machine competition of Ki-67 scoring, the average time of 1 slide was 2.3 min with 1 GPU by using this software, and the accuracy was 99.4%, which was over 90% of the results provided by participating doctors. Conclusions Our study demonstrates the enormous potential of automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on WTS, and the automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy. We will provide those labelled images as an open-free platform for researchers to assess the performance of computer algorithms for automated Ki-67 scoring on IHC stained slides.
Robust and accurate detection of image copy-move forgery using PCET-SVD and histogram of block similarity measures

Many block-based detection methods for image copy-move forgery have been reported. However, their performance degrades significantly under different geometric attacks such as rotation and scaling. In this paper, we propose a novel robust and accurate detection scheme for image copy-move forgery. It mainly consists of three steps: firstly, a suspicious image is divided into overlapping circular blocks, and polar complex exponential transform (PCET) is employed to extract geometric invariant feature of each block. Next, singular value decomposition (SVD) is applied to the coefficient matrix composed of extracted geometric invariant moments for dimension reduction. Meanwhile, the histogram of block similarity measures is adopted to estimate the optimal similarity threshold. Finally, the calculated similarity threshold is used for block matching process and consequently more accurate tampered areas are obtained. Experimental results on various datasets show that the proposed image copy-move detection approach outperforms other existing methods in the aspect of resisting geometric rotation and scaling attacks, with the adaptability of similarity threshold and low computational complexity.
Shape Synchronization in Driver-Response of 4-D Chaotic System and Its Application in Image Encryption

In this paper, we put forward on the drive-response synchronization in shape for four-dimensional (4-D) continuous chaotic system, using the basic theory of plane curves in classical differential geometry. For 4-D continuous system, shape synchronization means the six-response systems have the same shape of chaotic attractor as the six projective systems of driver system. Numerical simulations are given to verify the theoretical analysis, which clearly shows that the shape controllers can really make two systems achieve shape synchronization in a quite short time. Moreover, a shape synchronization encryption algorithm for color image is proposed. Simulation results reveal the superiority of the proposed approach.
Benchmarking Semi-supervised Federated Learning

Federated learning promises to use the computational power of edge devices while maintaining user data privacy. Current frameworks, however, typically make the unrealistic assumption that the data stored on user devices come with ground truth labels, while the server has no data. In this work, we consider the more realistic scenario where the users have only unlabeled data and the server has a limited amount of labeled data. In this semi-supervised federated learning (ssfl) setting, the data distribution can be non-iid, in the sense of different distributions of classes at different users. We define a metric, $R$, to measure this non-iidness in class distributions. In this setting, we provide a thorough study on different factors that can affect the final test accuracy, including algorithm design (such as training objective), the non-iidness $R$, the communication period $T$, the number of users $K$, the amount of labeled data in the server $N_s$, and the number of users $C_k\leq K$ that communicate with the server in each communication round. We evaluate our ssfl framework on Cifar-10, SVHN, and EMNIST. Overall, we find that a simple consistency loss-based method, along with group normalization, achieves better generalization performance, even compared to previous supervised federated learning settings. Furthermore, we propose a novel grouping-based model average method to improve convergence efficiency, and we show that this can boost performance by up to 10.79% on EMNIST, compared to the non-grouping based method.
Multi-Robot Coordination for Estimation and Coverage of Unknown Spatial Fields

We present an algorithm for multi-robot coverage of an initially unknown spatial scalar field characterized by a density function, whereby a team of robots simultaneously estimates and optimizes its coverage of the density function over the domain.The proposed algorithm borrows powerful concepts from Bayesian Optimization with Gaussian Processes that, when combined with control laws to achieve centroidal Voronoi tessellation, give rise to an adaptive sequential sampling method to explore and cover the domain.The crux of the approach is to apply a control law using a surrogate function of the true density function, which is then successively refined as robots gather more samples for estimation.The performance of the algorithm is justified theoretically under slightly idealized assumptions, by demonstrating asymptotic no-regret with respect to the coverage obtained with a known density function.The performance is also evaluated in simulation and on the Robotarium with small teams of robots, confirming the good performance suggested by the theoretical analysis.
Decision Support System For Supplementary Food Recipients (PMT) By Using The Simple Additive Weighting (SAW) Method

Additional Food Recipients (PMT) is a program that is held in every village, especially in the Kubah Sentang Village, which is budgeted annually by the Village Officials through the APBN Fund. The importance of PMT for this Kubah Sentang village is because the poverty rate is quite high reaching 65% and the nutritional intake obtained is very limited especially for toddlers aged 6-59 months. This has led to the need to build a system that can facilitate government agencies, especially in the Kubah Sentang Village Office to select additional food recipients using a Decision Support System with the Simple Additive Weighting (SAW) method. Decision support systems are very necessary in government agencies, especially in the Kubah Sentang Village Office to select recipients of supplementary food (PMT) for the community. The determination of the criteria for additional food recipients is determined from the age of the baby, nutritional status, income of parents, and class of electric power. This study also produced a system that was able to display recommendations for prospective supplementary food recipients with the results of criteria and preference weights that were determined by the system's needs.
AutoFeature: Searching for Feature Interactions and Their Architectures for Click-through Rate Prediction

Click-through rate prediction is an important task in commercial recommender systems and it aims to predict the probability of a user clicking on an item. The event of a user clicking on an item is accompanied by several user and item features. As modelling the feature interactions effectively can lead to better predictions, it has been the focus of many recent approaches including deep learning-based models. However, the existing approaches either (i) model all possible feature interactions for a given order, or (ii) manually select which feature interactions to model. Besides, they use the same network structure or function to model all the feature interactions while ignoring the difference of complexity among them. To address these issues, we propose a neural architecture search based approach called AutoFeature that automatically finds essential feature interactions and selects an appropriate structure to model each of these interactions. Specifically, we first define a flexible architecture search space for the CTR prediction task which covers many popular designs such as PIN, PNN and DeepFM, etc., and enables higher-order interactions. Then we propose an efficient neural architecture search algorithm that recursively refines the search space by partitioning it into several subspaces and samples from higher quality ones. Extensive experiments on multiple CTR prediction benchmarks show the superiority of our AutoFeature over the state-of-the-art baselines. In addition, our experiments show that the learned architectures use fewer flops/parameters and hence can efficiently incorporate higher-order feature interactions. This further boosts the performance. Finally, we show that AutoFeature can find meaningful feature interactions.
Incomplete Utterance Rewriting as Semantic Segmentation

Recent years the task of incomplete utterance rewriting has raised a large attention.Previous works usually shape it as a machine translation task and employ sequence to sequence based architecture with copy mechanism.In this paper, we present a novel and extensive approach, which formulates it as a semantic segmentation task.Instead of generating from scratch, such a formulation introduces edit operations and shapes the problem as prediction of a word-level edit matrix.Benefiting from being able to capture both local and global information, our approach achieves state-ofthe-art performance on several public datasets.Furthermore, our approach is four times faster than the standard approach in inference.
Deep Learning-based Mammogram Classification for Breast Cancer

Deep Learning (DL) is a rising field of researches in last decade by exposing a hybrid analysis procedure including advanced level image processing and many efficient supervised classifiers.Robustness of the DL algorithms to the big data enhances the analysis capabilities of machine learning models by feature learning on heterogeneous image database.In this paper, Convolutional Neural Network (CNN) architecture was proposed on simplified feature learning and fine-tuned classifier model to separate cancer-normal cases on mammograms.Breast Cancer is a prevalent and mortal disease appeared resultant mutating of normal tissue into tumor pathology.Mammograms are the common and effective tools for the diagnosis of breast cancer.DL-based computer-assisted systems have capability of detailed analysis for even small pathology that may lead the curing progress for a complete assessment.The proposed DLbased model aimed at assessing the applicability of various feature-learning models and enhancing the learning capacity of the DL models for an operative breast cancer diagnosis using CNN.The mammograms were fed into the DL to evaluate the classification performances in accordance with various CNN architectures.The proposed Deep model achieved high classification performance rates of 92.84%, 95.30%, and 96.72% for accuracy, sensitivity, specificity, and precision, respectively.
RAGAT: Relation Aware Graph Attention Network for Knowledge Graph Completion

Knowledge graph completion (KGC) is the task of predicting missing links based on known triples for knowledge graphs.Several recent works suggest that Graph Neural Networks (GNN) that exploit graph structures achieve promising performance on KGC.These models learn information called messages from neighboring entities and relations and then aggregate messages to update central entity representations.The drawback of existing GNN based models lies in that they tend to treat relations equally and learn fixed network parameters, overlooking the distinction of each relational information.In this work, we propose a Relation Aware Graph ATtention network (RAGAT) that constructs separate message functions for different relations, which aims at exploiting the heterogeneous characteristics of knowledge graphs.Specifically, we introduce relation specific parameters to augment the expressive capability of message functions, which enables the model to extract relational information in parameter space.To validate the effect of relation aware mechanism, RAGAT is implemented with a variety of relation aware message functions.Experiments show RAGAT outperforms state-of-the-art link prediction baselines on standard FB15k-237 and WN18RR datasets.
Unsupervised soft-label feature selection

Unsupervised feature selection is an important task in various research fields. It is difficult to select the discriminative features under unsupervised scenario due to the absence of label guidance. Recent works employ the pseudo labels to guide feature selection. However, they generate pseudo labels from the original feature space, where noises, redundancies and outliers may degrade the quality of pseudo labels. Besides, they ignore data fuzziness and use hard-labels as the semantic supervision of feature selection, thus the selected features suffer from significant information loss and semantic shortage. To tackle these problems, we propose an effective Unsupervised Soft-label Feature Selection (USFS) model, which performs soft-label learning and simultaneously guides the unsupervised feature selection process with the learned soft-labels. Specifically, we transform the data to low-dimensional subspace where the affinity matrix with sparse constraint is learned based on the local distances. The affinity matrix is determined as the soft-label matrix and further employed to guide the ultimate feature selection process. A simple yet efficient optimization method is derived to iteratively solve the formulated problem. Promising experimental results on widely tested benchmarks demonstrate the superiority of the proposed method compared with state-of-the-art approaches. For the purpose of reproducibility, we provide the code and testing datasets at https://github.com/wang-feifei/USFS-code.
Self-organizing deep belief modular echo state network for time series prediction

A deep belief echo state network is an effective deep learning framework for solving time series prediction problems. The suitable structure of the hidden layers determines the prediction performance of the neural network. However, a neural network structure designed using artificial experience has difficulty meeting application requirements. To address this problem, this paper proposes a self-organizing deep belief modular echo state network (SDBMESN) model for time series prediction with high accuracy. The basic framework of this model includes two parts: a deep belief network for deep feature extraction and a modular echo state network with subreservoirs for time series prediction. To find a suitable neural network structure, a neuron significance based on mutual information is designed to measure the degree of information of the neurons, and then a self-organizing mechanism is designed to realize the dynamic adjustment of the hidden layer neurons and subreservoirs. In addition, the robust loss function is used to improve the robustness of the prediction. The simulation results of nonlinear system modeling, sunspot prediction and algal bloom prediction demonstrate that the SDBMESN has good prediction performance and robustness.
GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction

We revisit the classic problem of documentlevel role-filler entity extraction (REE) for template filling.We argue that sentence-level approaches are ill-suited to the task and introduce a generative transformer-based encoderdecoder framework (GRIT) that is designed to model context at the document level: it can make extraction decisions across sentence boundaries; is implicitly aware of noun phrase coreference structure, and has the capacity to respect cross-role dependencies in the template structure.We evaluate our approach on the MUC-4 dataset, and show that our model performs substantially better than prior work.We also show that our modeling choices contribute to model performance, e.g., by implicitly capturing linguistic knowledge such as recognizing coreferent entity mentions.