Reviewerly/exHarmony huggingface dataset:
Framework for Handling Rare Word Problems in Neural Machine Translation System Using Multi-Word Expressions

Machine Translation (MT) systems are now being improved with the use of an ongoing methodology known as Neural Machine Translation (NMT). Natural language processing (NLP) researchers have shown that NMT systems are unable to deal with out-of-vocabulary (OOV) words and multi-word expressions (MWEs) in the text. OOV terms are those that are not currently included in the vocabulary that is used by the NMT system. MWEs are phrases that consist of a minimum of two terms but are treated as a single unit. MWEs have great importance in NLP, linguistic theory, and MT systems. In this article, OOV words and MWEs are handled for the Punjabi to English NMT system. A parallel corpus for Punjabi to English containing MWEs was developed and used to train the different models of NMT. Punjabi is a low-resource language as it lacks the availability of a large parallel corpus for building various NLP tools, and this is an attempt to improve the accuracy of Punjabi in the English NMT system by using named entities and MWEs in the corpus. The developed NMT models were assessed using human evaluation through adequacy, fluency and overall rating as well as automated assessment tools such as the bilingual evaluation study (BLEU) and translation error rate (TER) score. Results show that using word embedding (WE) and MWEs corpus increased the accuracy of translation for the Punjabi to English language pair. The best BLEU score obtained was 15.45 for the small test set, 43.32 for the medium test set, and 34.5 for the large test set, respectively. The best TER rate score obtained was 57.34% for the small test set, 37.29% for the medium test set, and 53.79% for the large test set, repectively.
Adaptive formative assessment system based on computerized adaptive testing and the learning memory cycle for personalized learning

Computerized adaptive testing (CAT) can effectively facilitate student assessment by dynamically selecting questions on the basis of learner knowledge and item difficulty. However, most CAT models are designed for one-time evaluation rather than improving learning through formative assessment. Since students cannot remember everything, encouraging them to repeatedly evaluate their knowledge state and identify their weaknesses is critical when developing an adaptive formative assessment system in real educational contexts. This study aims to achieve this goal by proposing an adaptive formative assessment system based on CAT and the learning memory cycle to enable the repeated evaluation of students' knowledge. The CAT model measures student knowledge and item difficulty, and the learning memory cycle component of the system accounts for students’ retention of information learned from each item. The proposed system was compared with an adaptive assessment system based on CAT only and a traditional nonadaptive assessment system. A 7-week experiment was conducted among students in a university programming course. The experimental results indicated that the students who used the proposed assessment system outperformed the students who used the other two systems in terms of learning performance and engagement in practice tests and reading materials. The present study provides insights for researchers who wish to develop formative assessment systems that can adaptively generate practice tests.
A hybrid-extreme learning machine based ensemble method for online dynamic security assessment of power systems

This manuscript develops a new hybrid-extreme learning machine (ELM) based ensemble model for real-time dynamic security assessment (DSA) of power systems. In order to boost the forecasting accuracy of ELM algorithm, a Levenberg-Marquardt (LM) backpropagation algorithm is used. The Ensemble strategy takes advantage of complementary information to improve the generalization capability of model. To realize an accurate ensemble model, particle swarm optimization (PSO) based weighted averaging is used to combine the individual forecasts. Transient energy function (TEF) terms, generator real and reactive power outputs and fault location are taken as input features for classification as well as prediction of transient energy margin (TEM) and time margin (TM) for a given operating condition and fault location. The potential of the proposed technique is validated on New England 39-bus and 68-bus test power systems. The results demonstrate that the developed model outperforms other state-of-the-art methods.
Multi-task learning with graph attention networks for multi-domain task-oriented dialogue systems

A task-oriented dialogue system (TOD) is an important application of artificial intelligence. In the past few years, works on multi-domain TODs have attracted increased research attention and have seen much progress. A main challenge of such dialogue systems is finding ways to deal with cross-domain slot sharing and dialogue act temporal planning. However, existing studies seldom consider the models’ reasoning ability over the dialogue history; moreover, existing methods overlook the structure information of the ontology schema, which makes them inadequate for handling multi-domain TODs. In this paper, we present a multi-task learning framework equipped with graph attention networks (GATs) to probe the above two challenges. In the method, we explore a dialogue state GAT consisting of a dialogue context subgraph and an ontology schema subgraph to alleviate the cross-domain slot sharing issue. We further construct a GAT-enhanced memory network using the updated nodes in the ontology subgraph to filter out the irrelevant nodes to acquire the needed dialogue states. For dialogue act temporal planning, a similar GAT and corresponding memory network are proposed to obtain fine-grained dialogue act representation. Moreover, we design an entity detection task to improve the capability of soft gate, which determines whether the generated tokens are from the vocabulary or knowledge base. In the training phase, four training tasks are combined and optimized simultaneously to facilitate the response generation process. The experimental results for automatic and human evaluations show that the proposed model achieves superior results compared to the state-of-the-art models on the MultiWOZ 2.0 and MultiWOZ 2.1 datasets.
The manifold regularized SVDD for noisy label detection

Supervised Learning (SL) celebrates a lot of research topics in machine learning (ML) and provides a large number of applications in multimedia. The effectiveness and performance of supervised learning are highly dependent on the quality of labeled samples, and poor or mislabeled labels always damage pre-knowledge and produce disastrous results. One of the notable works is the development of a model for detecting noisy labels in large-scale data sets for supervised classification. This paper introduces a new method for noisy label detection that combines the support vector data description (SVDD) model with manifold regularization. The proposed method is able to detect the noisy label in the testing set. In addition, we adopt K nearest neighbors to obtain the K-nearest neighbor graph, and the k-core algorithm is used to select noise nodes by selecting the influential nodes with larger k values in the K-NN graph. Experimental results over UCI data sets show that the manifold regularized SVDD can effectively detect the noise labels data, and presents better performance over most methods in terms of the outlier detection accuracy, and the average accuracy is mostly above 50%, even 100%. Moreover, manifold regularized SVDD has good performance in detecting the most influential top-k nodes compared with other methods.
Role design considerations of conversational agents to facilitate discussion and systems thinking

Conversational agents can facilitate learning discussions by applying natural language understanding to process students' discourse. Agents can assume the roles of figures such as peers or mentors, to promote actions similar to human interactions. In this study, we explore how and for whom different role designs of a text-based agent (i.e., chatbot) can facilitate discussion patterns and systems thinking in small-group discussions. Participants included 172 students in 9th grade (ages 13–14). Participants were randomly assigned to groups of five students and interacted with no agent, an expert agent, or a less knowledgeable peer agent. Results suggest that both agents facilitated learning of systems mechanisms by enhancing transactive exchange, where students built on prior ideas in their discussion groups. We also found differences in the agents' effects on discussion and learning outcomes based on groups' variation in systems thinking pre-test. Findings highlight the importance of role design considerations of agents in group settings.
An efficient implementation for spatial–temporal Gaussian process regression and its applications

Spatial–temporal Gaussian process regression is a popular method for spatial–temporal data modeling. Its state-of-art implementation is based on the state-space model realization of the spatial–temporal Gaussian process and its corresponding Kalman filter and smoother, and has computational complexity O ( N M 3 ) , where N and M are the number of time instants and spatial input locations, respectively, and thus can only be applied to data with large N but relatively small M . In this paper, our primary goal is to show that by exploring the Kronecker structure of the state-space model realization of the spatial–temporal Gaussian process, it is possible to further reduce the computational complexity to O ( M 3 + N M 2 ) and thus the proposed implementation can be applied to data with large N and moderately large M . The proposed implementation is illustrated over applications in weather data prediction and spatially-distributed system identification. Our secondary goal is to design a kernel for both the Colorado precipitation data and the GHCN temperature data, such that while having more efficient implementation, better prediction performance can also be achieved than the state-of-art result.
ViGAT: Bottom-Up Event Recognition and Explanation in Video Using Factorized Graph Attention Network

In this paper a pure-attention bottom-up approach, called ViGAT, that utilizes an object detector together with a Vision Transformer (ViT) backbone network to derive object and frame features, and a head network to process these features for the task of event recognition and explanation in video, is proposed. The ViGAT head consists of graph attention network (GAT) blocks factorized along the spatial and temporal dimensions in order to capture effectively both local and long-term dependencies between objects or frames. Moreover, using the weighted in-degrees (WiDs) derived from the adjacency matrices at the various GAT blocks, we show that the proposed architecture can identify the most salient objects and frames that explain the decision of the network. A comprehensive evaluation study is performed, demonstrating that the proposed approach provides state-of-the-art results on three large, publicly available video datasets (FCVID, Mini-Kinetics, ActivityNet).
Chinese Named Entity Recognition Based on BERT and Lightweight Feature Extraction Model

In the early named entity recognition models, most text processing focused only on the representation of individual words and character vectors, and paid little attention to the semantic relationships between the preceding and following text in an utterance, which led to the inability to handle the problem of multiple meanings of a word during recognition. To address this problem, most models introduce the attention mechanism of Transformer model to solve the problem of multiple meanings of a word in text. However, the traditional Transformer model leads to a high computational overhead due to its fully connected structure. Therefore, this paper proposes a new model, the BERT-Star-Transformer-CNN-BiLSTM-CRF model, to solve the problem of the computational efficiency of the traditional Transformer. First, the input text is dynamically generated into a character vector using the BERT model pre-trained in large-scale preconditioning to solve the problem of multiple meanings of words, and then the lightweight Star-Transformer model is used as the feature extraction module to perform local feature extraction on the word vector sequence, while the CNN-BiLSTM joint model is used to perform global feature extraction on the context in the text. The obtained feature sequences are fused. Finally, the fused feature vector sequences are input to CRF for prediction of the final results. After the experiments, it is shown that the model has a significant improvement in precision, recall and F1 value compared with the traditional model, and the computational efficiency is improved by nearly 40%.
Tracking visual attention during learning of complex science concepts with augmented 3D visualizations

The main purpose of the study was to explore how different augmented 3D visualizations may affect the learning of complex science concepts using the eye tracking method. An Augmented Reality (AR) learning system incorporated with different 3D visualizations was developed to assist conceptual learning in the topic of “molecular shapes.” Learners' visual attention during the AR learning was recorded using a mobile eye tracking system. Participants were 32 10th grade students. Pre- and post-tests were used to assess students' learning achievements. Learners were grouped into low and high achievers according to the posttest result. To analyze the associations between learners' visual attentions and learning achievements, correlation, t-test, correlation, and regression analyses were applied. Additionally, we conducted lag sequential analysis (LSA) to compare the visual transitions of different groups of achievers. The results showed that learners’ conceptual understanding was improved after the AR learning activity. Among the different 3D visualizations, learners attended more to the static and dynamic types in the basic learning stage and to the static and interactive types in the advanced learning stage. Attention to the 3D static model was found to associate more to the attention to the interactive type. The correlation and regression analyses found that the cognitive effort to integrate information about the static 3D model was the key predictor for the learning achievement. LSA showed that high achievers displayed a higher chance of looking back to the static 3D model from other AOIs. The visual pattern analysis suggested that a guidance to connect information delivered by the static and the animated visual representations is necessary for complex conceptual learning.
Integrated photoelasticity in a soft material: phase retardation, azimuthal angle, and stress-optic coefficient

Integrated photoelasticity is investigated for a soft material subjected to a three-dimensional stress state. In the experiment, a solid sphere is pressed against a gelatin gel (Young's modulus is about 4.2 kPa) that deforms up to 4.5 mm depending on the loading forces. The resulting photoelastic parameters (phase retardation, azimuthal angle, and stress-optic coefficient) in the gel are measured using a polarization camera. The measured retardation and azimuth are compared with the analytical prediction based on Hertzian contact theory. Remarkably, experimental and analytical results of the photoelastic parameters show a reasonable agreement not only in the retardation but also in the azimuth that is related to the direction of principal stresses and but rarely validated in previous studies, is essential for reconstructing three-dimensional stress fields in soft materials. The stress-optic coefficient of the gelatin gel used is 3.12$\times10^{-8}$ 1/Pa. Such findings proved that integrated photoelasticity is useful for measuring the three-dimensional stress field in soft materials, which is of importance in biomedical engineering and cell printing applications.
TD-Net: Trans-Deformer network for automatic pancreas segmentation

Accurate and efficient pancreas segmentation is the basis for subsequent diagnosis and qualitative treatment of pancreatic cancer. Segmenting the pancreas from abdominal CT images is a challenging task because the morphology of the pancreas varies greatly among different individuals and may be affected by problems such as the unbalanced category and blurred boundaries. This paper proposes a two-stage Trans-Deformer network to solve these problems of pancreas segmentation. To be specific, we first use 2D Unet for coarse segmentation to generate candidate regions of the pancreas. In the fine segmentation stage, we propose to integrate deformable convolution into Vision Transformer (VIT) for solving the deformation problem of the pancreas. For the problem of blurred boundaries caused by low contrast in the pancreas, a multi-input module based on wavelet decomposition is proposed to make our network pay more attention to high-frequency texture information. In addition, we propose using the Scale Inter-active Fusion (SIF) module to merge local features and global features. Our method was evaluated on the public NIH dataset including 82 abdominal contrast-enhanced CT volumes and the public MSD dataset including 281 abdominal contrast-enhanced CT volumes via fourfold cross-validation. We have achieved the average Dice Similarity Coefficient (DSC) values of 89.89 ± 1.82 % on the NIH dataset, and 91.22 ± 1.37 % on the MSD dataset, outperforming other exiting state-of-the-art pancreas segmentation methods.
Data-driven gold potential maps for the Chibougamau area, Abitibi greenstone belt, Canada

Two mineral prospectivity maps (MPM) focusing on a general Au model for the Chibougamau region of Quebec, Canada, have been produced in this paper. The random forest algorithm has been used to integrate 35 evidence (predictor) layers which represent vectors to Au mineralization. The MPMs generated are robust as evidenced by efficiency of classification curves. The known zones of Au mineralization are well predicted; furthermore, new prospective areas with no known Au have been identified which are prime for exploration follow-up. The Chibougamau area is somewhat fertile with respect to Au and the strongest predictors are fault density and intersection density, contact density and Au anomalies in rock.
MCLNet: An multidimensional convolutional lightweight network for gastric histopathology image classification

As global cancer, gastric cancer is a severe threat to the health of people all over the world. In China, young people easily misdiagnose gastric cancer, and its misdiagnosis rate can be as high as 27%. To improve the accuracy and efficiency of gastric cancer detection and the fit goodness of the convolutional neural network, we propose a multidimensional convolutional lightweight network, named MCLNet , based on ShuffleNetV2 . ShuffleNetV2 is a model with low computational complexity, memory consumption, and high GPU parallelism. However, ShuffleNetV2 has too few convolutional layers, only two-dimensional convolution, resulting in insufficient extracted features is not sufficient. Therefore, we consider the association between pixels of the same category in an image. To tap this association, we one-dimensionalize the image and introduce one-dimensional convolution. Since one-dimensional convolution can extract the association of words in a sentence, applying it to images can extract the association between image elements. Adding one-dimensional convolution expands the information exchange between channels and enriches the information, which complements the lack of two-dimensional convolution in global feature extraction. In addition, we compare the proposed MCLNet with the state-of-the-art (SOTA) method and illustrate the best results of the proposed MCLNet model through experiments. • Proposes and implements a lightweight network architecture named MCLNet. • Allows the trained model to be deployed on mobile or small devices, more easily assisting medical personnel. • We design a new network structure using one-dimensional convolution to extract the association between images’ pixels. • We propose an integrated method of one-dimensional convolution and two-dimensional convolution. This approach complements the extracted features so that these features can be best applied to subsequent tasks such as classification. • Tests are performed on the patch level dataset GasHisSDB to demonstrate the accuracy and robustness of the model classification.
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS

Deep learning is a key approach for the environment perception function of Cooperative Intelligent Transportation Systems (C-ITS) with autonomous vehicles and smart traffic infrastructure. The performance of the object recognition and detection strongly depends on the data volume in the model training, which is usually not sufficient due to the limited data collected by a typically small fleet of test vehicles. In today's C-ITS, smart traffic participants are capable of timely generating and transmitting a large amount of data. However, these data can not be used for model training directly due to privacy constraints. In this paper, we introduce a federated learning framework coping with Hierarchical Heterogeneity (H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Fed), which can notably enhance the conventional pre-trained deep learning model. The framework exploits data from connected public traffic agents in vehicular networks without affecting user data privacy. By coordinating existing traffic infrastructure, including roadside units and road traffic clouds, the model parameters are efficiently disseminated by vehicular communications and hierarchically aggregated. Considering the individual heterogeneity of data distribution, computational and communication capabilities across traffic agents and roadside units, we employ a novel method that addresses the heterogeneity in different aggregation layers of the framework architecture, i.e., aggregation in layers of roadside units and cloud. The experimental results indicate that our method can well balance the learning accuracy and stability according to the knowledge of heterogeneity in current communication networks. Comparing to other baseline approaches, the evaluation on federated datasets shows that our framework is more general and capable especially in application scenarios with low communication quality. Even when 90% of the agents are timely disconnected, the pre-trained deep learning model can still be forced to converge stably, and its accuracy can be enhanced from 68% to over 90% after convergence.