Reviewerly/exHarmony huggingface dataset:
Zero-Shot Human Activity Recognition Using Non-Visual Sensors

Due to significant advances in sensor technology, studies towards activity recognition have gained interest and maturity in the last few years. Existing machine learning algorithms have demonstrated promising results by classifying activities whose instances have been already seen during training. Activity recognition methods based on real-life settings should cover a growing number of activities in various domains, whereby a significant part of instances will not be present in the training data set. However, to cover all possible activities in advance is a complex and expensive task. Concretely, we need a method that can extend the learning model to detect unseen activities without prior knowledge regarding sensor readings about those previously unseen activities. In this paper, we introduce an approach to leverage sensor data in discovering new unseen activities which were not present in the training set. We show that sensor readings can lead to promising results for zero-shot learning, whereby the necessary knowledge can be transferred from seen to unseen activities by using semantic similarity. The evaluation conducted on two data sets extracted from the well-known CASAS datasets show that the proposed zero-shot learning approach achieves a high performance in recognizing unseen (i.e., not present in the training dataset) new activities.
Random Shapley Forests: Cooperative Game-Based Random Forests With Consistency

The original random forests (RFs) algorithm has been widely used and has achieved excellent performance for the classification and regression tasks. However, the research on the theory of RFs lags far behind its applications. In this article, to narrow the gap between the applications and the theory of RFs, we propose a new RFs algorithm, called random Shapley forests (RSFs), based on the Shapley value. The Shapley value is one of the well-known solutions in the cooperative game, which can fairly assess the power of each player in a game. In the construction of RSFs, RSFs use the Shapley value to evaluate the importance of each feature at each tree node by computing the dependency among the possible feature coalitions. In particular, inspired by the existing consistency theory, we have proved the consistency of the proposed RFs algorithm. Moreover, to verify the effectiveness of the proposed algorithm, experiments on eight UCI benchmark datasets and four real-world datasets have been conducted. The results show that RSFs perform better than or at least comparable with the existing consistent RFs, the original RFs, and a classic classifier, support vector machines.
Accurate detecting concept drift in evolving data streams

Predictive models operating on the evolving data streams are dynamic. The performance of a model will deteriorate eventually when it suffers the effect of concept drift. The learning algorithms require the proper adaptive strategies to cope with the concept drifting data streams. In this paper, we propose a new concept drift detection approach that analyzes the consistency of prequential error rate using Hoeffding’s inequality to detect the concept drifts in data streams. The experimental results show that our proposed method outperforms in comparison with other state-of-the-art detectors in terms of true drift detection, false alarm and delay of detection.
Simple and Effective Retrieve-Edit-Rerank Text Generation

Retrieve-and-edit seq2seq methods typically retrieve an output from the training set and learn a model to edit it to produce the final output.We propose to extend this framework with a simple and effective post-generation ranking approach.Our framework (i) retrieves several potentially relevant outputs for each input, (ii) edits each candidate independently, and (iii) re-ranks the edited candidates to select the final output.We use a standard editing model with simple task-specific reranking approaches, and we show empirically that this approach outperforms existing, significantly more complex methodologies.Experiments on two machine translation (MT) datasets show new state-of-art results.We also achieve near state-of-art performance on the Gigaword summarization dataset, where our analyses show that there is significant room for performance improvement with better candidate output selection in future work.
Partial Multi-Label Learning via Multi-Subspace Representation

Partial Multi-Label Learning (PML) aims to learn from the training data where each instance is associated with a set of candidate labels, among which only a part of them are relevant. Existing PML methods mainly focus on label disambiguation, while they lack the consideration of noise in the feature space. To tackle the problem, we propose a novel framework named partial multi-label learning via MUlti-SubspacE Representation (MUSER), where the redundant labels together with noisy features are jointly taken into consideration during the training process. Specifically, we first decompose the original label space into a latent label subspace and a label correlation matrix to reduce the negative effects of redundant labels, then we utilize the correlations among features to project the original noisy feature space to a feature subspace to resist the noisy feature information. Afterwards, we introduce a graph Laplacian regularization to constrain the label subspace to keep intrinsic structure among features and impose an orthogonality constraint on the correlations among features to guarantee discriminability of the feature subspace. Extensive experiments conducted on various datasets demonstrate the superiority of our proposed method.
Micropillar single-photon source design for simultaneous near-unity efficiency and indistinguishability

We present a numerical investigation of the performance of the micropillar cavity single-photon source in terms of collection efficiency and indistinguishability of the emitted photons in the presence of non-Markovian phonon-induced decoherence. We analyze the physics governing the efficiency using a single-mode model, and we optimize efficiency $\ensuremath{\varepsilon}$ and the indistinguishability $\ensuremath{\eta}$ on an equal footing by computing $\ensuremath{\varepsilon}\ensuremath{\eta}$ as function of the micropillar design parameters. We show that $\ensuremath{\varepsilon}\ensuremath{\eta}$ is limited to $\ensuremath{\sim}0.96$ for the ideal geometry due to an inherent tradeoff between efficiency and indistinguishability. Finally, we subsequently consider the influence of realistic fabrication imperfections and Markovian pure dephasing noise on the performance.
Effects of body visualization on performance in head-mounted display virtual reality

Although there are many virtual reality (VR) applications in sports, only a handful of studies visualized the whole body. There is still a lack of understanding, how much of the own body must be visualized in the head-mounted display (HMD) based VR, to ensure fidelity and similar performance outcome as in the real-world. In the current study, 20 young and healthy participants completed three tasks in a real and virtual environment: balance task, grasping task, and throwing task with a ball. The aim was to find out the meaning of the visualization of different body parts for the quality of movement execution and to derive future guidelines for virtual body presentation. In addition, a comparison of human performance between reality and VR, with whole-body visualization was made. Focusing on the main goal of the current study, there were differences within the measured parameters due to the visualization of different body parts. In the balance task, the differences within the VR body visualization consisted mainly through no-body visualization (NB) compared to the other visualization types defined as whole-body (WB), WB except feet (NF), as well as WB except feet and legs (NLF). In the grasping task, the different body visualization seemed to have no impact on the participants' performances. In the throwing task, the whole-body visualization led to higher accuracy compared to the other visualization types. Regarding the comparison between the conditions, we found significant differences between reality and VR, which had a large effect on the parameters time for completion in the balance and grasping task, the number of foot strikes on the beam in the balance task, as well as the subjective estimation of the difficulty for all tasks. However, the number of errors and the quality of the performances did not differ significantly. The current study was the first study comparing sports-related tasks in VR and reality with further manipulations (occlusions of body parts) of the virtual body. For studies analyzing perception and sports performance or for VR sports interventions, we recommend the visualization of the whole body in real-time.
Integrate MSRCR and Mask R-CNN to Recognize Underwater Creatures on Small Sample Datasets

The poor quality of optical imaging caused by the complex and varying underwater environment is a significant challenge to underwater target recognition. Moreover, the insufficiency of relevant datasets may lead to the overfitting problem in target recognition models based on deep learning. Taking the instance segmentation of three underwater creatures (echinus, holothurian, and starfish) as an example, we propose a new method for recognition of underwater creatures. It combines the MSRCR (multi-scale Retinex with color restoration) image enhancement algorithm and the Mask R-CNN (region-based convolutional neural work) framework, and achieves a mAP (mean average accuracy) value higher than 90% on a small sample dataset. This method consists of three major steps. First, the dataset with 84 images is augmented (flip, adding noise, and GAN (generative adversarial networks)) to 430 images, and all images are enhanced with MSRCR to improve their qualities; Second, the model is pre-trained on the COCO (Microsoft common objects in context) dataset to shorten the training time and overcome overfitting; Finally, the pre-trained model is transferred to the underwater dataset, and the whole training process is completed. We achieve 97.46% precision and 94.52% recall, and the mAP (intersection over union (IOU) = 50) is 94.84%. The effectiveness of the proposed method is verified by comparing it with several popular target recognition models, including SSD (Single Shot Detector), YOLOv3 (You only look once), original Mask R-CNN, and a SIFT-based (Scale-invariant feature transform) model.
Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning

Targeted opinion word extraction (TOWE) is a sub-task of aspect based sentiment analysis (ABSA) which aims to find the opinion words for a given aspect-term in a sentence.Despite their success for TOWE, the current deep learning models fail to exploit the syntactic information of the sentences that have been proved to be useful for TOWE in the prior research.In this work, we propose to incorporate the syntactic structures of the sentences into the deep learning models for TOWE, leveraging the syntax-based opinion possibility scores and the syntactic connections between the words.We also introduce a novel regularization technique to improve the performance of the deep learning models based on the representation distinctions between the words in TOWE.The proposed model is extensively analyzed and achieves the state-of-the-art performance on four benchmark datasets.
IoT in Distance Learning during the COVID-19 Pandemic

Despite the worldwide physical closing of educational institutions due to the pandemic of COVID-19 in the spring of 2020, the learning process was not interrupted. Learning management systems and digital tools for online collaboration ensured a safe distance and continuity of educational activities. However, the rapid transition to remote learning in electronic environment has created a number of challenges in higher education. In order to derive longterm benefits from the changes in the way of teaching and examining, the paper explores the possibilities of IoT technology for continuous monitoring and flexible management of the learning process. The proposed framework for IoT application in educational activities will facilitate the adaptation of studying process at universities to the new circumstances.
Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge

We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the number of observations, in particular, two versions of best linear unbiased predictor (BLUP), support vector machine (SVM), two shallow convolutional neural networks (CNNs), and the famous ResNet and Inception V1. Ensemble learning was derived from estimating weights via linear regression in a hold-out subset of the training sample. We further evaluated and identified factors that could influence prediction accuracy: choice of algorithm, ensemble learning, and features used as input/MRI image processing. Our prediction error was correlated with age, and absolute error was greater for older participants, suggesting to increase the training sample for this subgroup. Our results may be used to guide researchers to build age predictors on healthy individuals, which can be used in research and in the clinics as non-specific predictors of disease status.
Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation

An effective recruitment evaluation plays an important role in the success of companies, industries and institutions. In order to obtain insight on the relationship between factors contributing to systematic recruitment, the artificial neural network and logic mining approach can be adopted as a data extraction model. In this work, an energy based k satisfiability reverse analysis incorporating a Hopfield neural network is proposed to extract the relationship between the factors in an electronic (E) recruitment data set. The attributes of E recruitment data set are represented in the form of k satisfiability logical representation. We proposed the logical representation to 2-satisfiability and 3-satisfiability representation, which are regarded as a systematic logical representation. The E recruitment data set is obtained from an insurance agency in Malaysia, with the aim of extracting the relationship of dominant attributes that contribute to positive recruitment among the potential candidates. Thus, our approach is evaluated according to correctness, robustness and accuracy of the induced logic obtained, corresponding to the E recruitment data. According to the experimental simulations with different number of neurons, the findings indicated the effectiveness and robustness of energy based k satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward positive recruitment in the insurance agency in Malaysia.
An Analysis of the Emotional Evolution of Large-Scale Internet Public Opinion Events Based on the BERT-LDA Hybrid Model

The purpose of this article is to analyse the emotional evolution of the netizens in reaction to the events of the Anti-ELAB (Anti-Extradition Law Amendment Bill) movement in Hong Kong.We attempt to investigate evolving laws of large-scale Internet public opinion events and provide relevant agencies with a theoretical basis for a public opinion response mechanism.On the basis of improving the Bidirectional Encoder Representations from Transformers (BERT) pre-training task, we add in-depth pre-training tasks, and based on the optimisation results of the LDA topic embedding, we integrate deeply with the LDA model to dynamically present the fine-grained public sentiment of the event.Through the collection of large-scale text data related to the Anti-ELAB Movement from a well-known forum in Hong Kong, a BERT-LDA hybrid model for large-scale network public opinion analysis is constructed in a complex context.Through empirical analysis, we calculate and reveal the emotional change process of netizens and opinion leaders in the three transition stages of the Anti-ELAB Movement with the evolution of the topic word as the core by visualisation.We also analyse the emotional distribution and evolution trend of public opinion under the 'text topic', and deeply analyse the character and role of opinion leaders in Anti-ELAB public opinion events.The improved BERT-LDA model or sentiment classification AUC value exceeds 99.6% in the sentiment classification task for the Anti-ELAB Movement.
Triple Clipped Histogram-Based Medical Image Enhancement Using Spatial Frequency

In this paper, a novel triple clipped histogram model-based fusion approach has been proposed to improve the basics features, brightness preservation and contrast of the medical images. This incorporates the features of the equalized image and input image together. In the initial step, the low-contrast medical image is equalized using the triple clipped dynamic histogram equalization technique for which the histogram of the input medical image is split into three sections on the basis of standard deviation with almost equal number of pixels. The clipping process of the histogram is performed on every histogram section and mapped to a new dynamic range using simple calculations. In the second step, the sub-histogram equalization process is performed separately. Approximation and detail coefficients of equalized and input images are separated using discrete wavelet transform (DWT). Thereafter, the approximation coefficients are modified using some basic calculation-based fusion which involves singular value decomposition (SVD) and its inverse. Detail coefficients are fused using spatial frequency features. This yields modified approximation and detail coefficients for an enhanced image. Finally, inverse discrete wavelet transform (IDWT) has been applied to the modified coefficients which result in an enhanced image with improved visual quality. These improvements are analyzed qualitatively and quantitatively.
Commit2Vec: Learning Distributed Representations of Code Changes

Deep learning methods, which have found successful applications in fields like image classification and natural language processing, have recently been applied to source code analysis too, due to the enormous amount of freely available source code (e.g., from open-source software repositories). In this work, we elaborate upon a state-of-the-art approach to the representation of source code that uses information about its syntactic structure, and we adapt it to represent source changes (i.e., commits). We use this representation to classify security-relevant commits. Because our method uses transfer learning (that is, we train a network on a "pretext task" for which abundant labeled data is available, and then we use such network for the target task of commit classification, for which fewer labeled instances are available), we studied the impact of pre-training the network using two different pretext tasks versus a randomly initialized model. Our results indicate that representations that leverage the structural information obtained through code syntax outperform token-based representations. Furthermore, the performance metrics obtained when pre-training on a loosely related pretext task with a very large dataset ($>10^6$ samples) were surpassed when pretraining on a smaller dataset ($>10^4$ samples) but for a pretext task that is more closely related to the target task.
Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic

This paper provides an overview of the WANLP 2021 shared task on sarcasm and sentiment detection in Arabic. The shared task has two subtasks: sarcasm detection (subtask 1) and sentiment analysis (subtask 2). This shared task aims to promote and bring attention to Arabic sarcasm detection, which is crucial to improve the performance in other tasks such as sentiment analysis. The dataset used in this shared task, namely ArSarcasm-v2, consists of 15,548 tweets labelled for sarcasm, sentiment and dialect. We received 27 and 22 submissions for subtasks 1 and 2 respectively. Most of the approaches relied on using and fine-tuning pre-trained language models such as AraBERT and MARBERT. The top achieved results for the sarcasm detection and sentiment analysis tasks were 0.6225 F1-score and 0.748 F1-PN respectively.
Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate databases. The features selected by each bioinspired algorithm are used to train three back propagation neural networks (BPNN) independently using the conjugate gradient algorithm (CGA). Classifier testing is performed by using the testing set on each trained classifier, and the diagnostic results obtained are used to evaluate the performance of each classifier. The classification results obtained for each instance of the testing set of the three classifiers and the class label associated with each instance of the testing set will be the candidate instances for training and testing the super learner. The training set comprises of 80% of the instances, and the testing set comprises of 20% of the instances. Experimentation has been carried out using seven clinical datasets from the University of California Irvine (UCI) machine learning repository. The super learner has achieved a classification accuracy of 96.83% for Wisconsin diagnostic breast cancer dataset (WDBC), 86.36% for Statlog heart disease dataset (SHD), 94.74% for hepatocellular carcinoma dataset (HCC), 90.48% for hepatitis dataset (HD), 81.82% for vertebral column dataset (VCD), 84% for Cleveland heart disease dataset (CHD), and 70% for Indian liver patient dataset (ILP).
Patch-Wise Attention Network for Monocular Depth Estimation

In computer vision, monocular depth estimation is the problem of obtaining a high-quality depth map from a two-dimensional image. This map provides information on three-dimensional scene geometry, which is necessary for various applications in academia and industry, such as robotics and autonomous driving. Recent studies based on convolutional neural networks achieved impressive results for this task. However, most previous studies did not consider the relationships between the neighboring pixels in a local area of the scene. To overcome the drawbacks of existing methods, we propose a patch-wise attention method for focusing on each local area. After extracting patches from an input feature map, our module generates attention maps for each local patch, using two attention modules for each patch along the channel and spatial dimensions. Subsequently, the attention maps return to their initial positions and merge into one attention feature. Our method is straightforward but effective. The experimental results on two challenging datasets, KITTI and NYU Depth V2, demonstrate that the proposed method achieves significant performance. Furthermore, our method outperforms other state-of-the-art methods on the KITTI depth estimation benchmark.