Reviewerly/exHarmony huggingface dataset:
Tracking Multiple Targets Using Bearing-Only Measurements in Underwater Noisy Environments

This article handles tracking multiple targets using bearing-only measurements in underwater noisy environments. For tracking multiple targets in underwater noisy environments, the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter provides good performance with its low computational load. Bearing-only measurements are passive and do not provide position information of a target. Note that the nonlinearity of the bearing-only measurements can be handled by Extended Kalman Filters (EKF) when applying the GM-PHD filter. However, range uncertainty of the target is large for bearing-only measurements. Thus, a single EKF leads to poor performance when it is applied in the GM-PHD. In this article, every bearing measurement gives birth to multiple target samples, which are distributed considering the feasible range of the passive sensor. Thereafter, every target sample is updated utilizing the measurement update step of the EKF. In this way, we run multiple EKFs associated to multiple target samples, instead of running a single EKF. To the best of our knowledge, our article is novel in tracking multiple targets in noisy environments, using the observer with bearing-only measurements. The effectiveness of the proposed GM-PHD is verified utilizing MATLAB simulations.
Learning Augmented Memory Joint Aberrance Repressed Correlation Filters for Visual Tracking

With its outstanding performance and tracking speed, discriminative correlation filters (DCF) have gained much attention in visual object tracking, where time-consuming correlation operations can be efficiently computed utilizing the discrete Fourier transform (DFT) with symmetric properties. Nevertheless, the inherent issues of boundary effects and filter degradation, as well as occlusion and background clutter, degrade the tracking performance. In this work, we proposed an augmented memory joint aberrance repressed correlation filter (AMRCF) for visual tracking. Based on the background-aware correlation filter (BACF), we introduced adaptive spatial regularity to mitigate the boundary effect. Several historical views and the current view are exploited to train the model together as a way to reinforce the memory. Furthermore, aberrance repression regularization was introduced to suppress response anomalies due to occlusion and deformation, while adopting the dynamic updating strategy to reduce the impact of anomalies on the appearance model. Finally, extensive experimental results over four well-known tracking benchmarks indicate that the proposed AMRCF tracker achieved comparable tracking performance to most state-of-the-art (SOTA) trackers.
A U-Net Based Progressive GAN for Microscopic Image Augmentation

Dealing with limited medical imagery data by deep neural networks is of a great concern. Obtaining large-scale labelled images requires expertise, is laborious and time consuming, and remains a challenge in medical applications. In this paper, we present a data augmentation method to cope with scarcely available medical imagery data. We propose a U-Net based generative adversarial network to synthesise microscopic images. We adopt a progressive training strategy to guide the synthesising process at multiple resolutions. This also stabilises the training process. The proposed model has been tested on three public datasets and quantitatively evaluated in terms of classification, detection and segmentation performances. Results suggest that training with the proposed augmentation method can provide significant improvements on limited and imbalanced datasets.
STAMP: A Self-training Student-Teacher Augmentation-Driven Meta Pseudo-Labeling Framework for 3D Cardiac MRI Image Segmentation

Medical image segmentation has significantly benefitted thanks to deep learning architectures. Furthermore, semi-supervised learning (SSL) has led to a significant improvement in overall model performance by leveraging abundant unlabeled data. Nevertheless, one shortcoming of pseudo-labeled based semi-supervised learning is pseudo-labeling bias, whose mitigation is the focus of this work. Here we propose a simple, yet effective SSL framework for image segmentation-STAMP (Student-Teacher Augmentation-driven consistency regularization via Meta Pseudo-Labeling). The proposed method uses self-training (through meta pseudo-labeling) in concert with a Teacher network that instructs the Student network by generating pseudo-labels given unlabeled input data. Unlike pseudo-labeling methods, for which the Teacher network remains unchanged, meta pseudo-labeling methods allow the Teacher network to constantly adapt in response to the performance of the Student network on the labeled dataset, hence enabling the Teacher to identify more effective pseudo-labels to instruct the Student. Moreover, to improve generalization and reduce error rate, we apply both strong and weak data augmentation policies, to ensure the segmentor outputs a consistent probability distribution regardless of the augmentation level. Our extensive experimentation with varied quantities of labeled data in the training sets demonstrates the effectiveness of our model in segmenting the left atrial cavity from Gadolinium-enhanced magnetic resonance (GE-MR) images. By exploiting unlabeled data with weak and strong augmentation effectively, our proposed model yielded a statistically significant 2.6% improvement (p<0.001) in Dice and a 4.4% improvement (p<0.001) in Jaccard over other state-of-the-art SSL methods using only 10% labeled data for training.
Politeness in Online Educational Discourse: Exploring Relational Work Strategies in Online Teacher-Student Interactions

The current study explores politeness in computer-mediated communication from the perspective of online educational discourse.Focusing on the use of language in teacher-student interactions, we collect data that consist of 117 naturally-occurring conversations from three online language courses in Dingtalk, one of the most popular online educational platforms in Mainland China.Data are analyzed using an adapted version of politeness theory and the notion of relational work.The results show that the management of relational work and the realization of politeness through certain strategies.The preferred relational work strategies employed by teachers and students are positive relational work strategies, negative relational work strategies, and emoji relational work strategies.The preference for these strategies indicates that teachers and students are concerned about constructing politeness by considering their positive and negative face.In addition to contributing to the existing literature on politeness and relational work in computer-mediated communication, the present study offers practical implications for using relational work strategies to do polite and appropriate relational practices to maintain politeness in the online education community.
Low-Effort Re-identification Techniques Based on Medical Imagery Threaten Patient Privacy

Deriving patients’ identity from medical imagery threatens privacy, as these data are acquired to support diagnosis but not to reveal identity-related features. Still, for many medical imaging modalities, such identity breaches have been reported, however, typically employing a highly specialised image processing and pattern recognition pipeline. In this paper, we demonstrate that surprisingly, a simple and unified deep learning-based technique is able to determine patient identity from two exemplary imaging modalities, i.e., brain MRI and gastrointestinal endoscopic data. This demonstrates that almost anyone with limited resources and knowledge of the field would be able to perform this task, which indicates that according to GDPR, medical image data after pseudonymisation should be considered “information on an identifiable natural person” and thus must not be released to the public without further provisions.
UC2: Universal Cross-lingual Cross-modal Vision-and-Language  Pre-training

Vision-and-language pre-training has achieved impressive success in learning multimodal representations between vision and language. To generalize this success to non-English languages, we introduce UC2, the first machine translation-augmented framework for cross-lingual cross-modal representation learning. To tackle the scarcity problem of multilingual captions for image datasets, we first augment existing English-only datasets with other languages via machine translation (MT). Then we extend the standard Masked Language Modeling and Image-Text Matching training objectives to multilingual setting, where alignment between different languages is captured through shared visual context (i.e, using image as pivot). To facilitate the learning of a joint embedding space of images and all languages of interest, we further propose two novel pre-training tasks, namely Masked Region-to-Token Modeling (MRTM) and Visual Translation Language Modeling (VTLM), leveraging MT-enhanced translated data. Evaluation on multilingual image-text retrieval and multilingual visual question answering benchmarks demonstrates that our proposed framework achieves new state-of-the-art on diverse non-English benchmarks while maintaining comparable performance to monolingual pre-trained models on English tasks.
Anchor-Free Person Search

Person search aims to simultaneously localize and identify a query person from realistic, uncropped images, which can be regarded as the unified task of pedestrian detection and person re-identification (re-id). Most existing works employ two-stage detectors like Faster-RCNN, yielding encouraging accuracy but with high computational overhead. In this work, we present the Feature-Aligned Person Search Network (AlignPS), the first anchor-free framework to efficiently tackle this challenging task. AlignPS explicitly addresses the major challenges, which we summarize as the misalignment issues in different levels (i.e., scale, region, and task), when accommodating an anchor-free detector for this task. More specifically, we propose an aligned feature aggregation module to generate more discriminative and robust feature embeddings by following a "re-id first" principle. Such a simple design directly improves the baseline anchor-free model on CUHK-SYSU by more than 20% in mAP. Moreover, AlignPS outperforms state-of-the-art two-stage methods, with a higher speed. Code is available at https://github.com/daodaofr/AlignPS
UniSpeech: Unified Speech Representation Learning with Labeled and  Unlabeled Data

In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture information more correlated with phonetic structures and improve the generalization across languages and domains. We evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task, i.e., a relative word error rate reduction of 6% against the previous approach.
Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data

Not all real-world data are labeled, and when labels are not available, it is often costly to obtain them. Moreover, as many algorithms suffer from the curse of dimensionality, reducing the features in the data to a smaller set is often of great utility. Unsupervised feature selection aims to reduce the number of features, often using feature importance scores to quantify the relevancy of single features to the task at hand. These scores can be based only on the distribution of variables and the quantification of their interactions. The previous literature, mainly investigating anomaly detection and clusters, fails to address the redundancy-elimination issue. We propose an evaluation of correlations among features to compute feature importance scores representing the contribution of single features in explaining the dataset's structure. Based on Coalitional Game Theory, our feature importance scores include a notion of redundancy awareness making them a tool to achieve redundancy-free feature selection. We show that the deriving features' selection outperforms competing methods in lowering the redundancy rate while maximizing the information contained in the data. We also introduce an approximated version of the algorithm to reduce the complexity of Shapley values' computations.
Fruit Golf

While the global COVID-19 pandemic did not catalyze widespread adoption of virtual reality (VR) technologies across all industries as some had anticipated, studies like Hall et al. from 2022 have demonstrated that public valuation of VR remains strongly in gaming, entertainment, and socializing [Hall et al., 2022].
Study on the effect of extreme learning machine and its variants in differentiating Alzheimer conditions from selective regions of brain MR images

In this study, an attempt has been made to analyze the effect of the Extreme Learning Machine (ELM) classifier and its variants in the differentiation of Healthy Controls (HC) and Alzheimer's Disease (AD) using structural MR images. For this, sub-anatomic brain structures, namely Corpus Callosum (CC) and Lateral Ventricles (LV) are segmented and characterized using morphometric features. Significant features from these regions are subjected to ELM, online sequential ELM, and Self-adaptive Differential Evolution ELM (SaDE-ELM) classifiers for the differentiation of HC and AD. The activation functions and the number of hidden neurons in ELM classifiers are tuned by evaluating the performance using standard metrics. Results indicate that ELM and its variant classifiers are able to identify AD using morphometric features from CC and LV. ELM classifiers achieve an accuracy greater than 90% using the sigmoid activation function for both regions. Among the ELM variants, SaDE-ELM attains a maximum sensitivity of 97% and 94% using LV and CC features respectively with the lowest number of hidden neurons. The extracted features from LV demonstrate higher discriminative power than CC in AD classification. However, a specificity greater than 95% is observed in ELM for CC features. As the proposed approach is able to characterize and differentiate the morphometric alterations in CC and LV due to AD, the study seems to be clinically significant for the differentiation of HC and AD.
Text line segmentation from struck-out handwritten document images

In the case of freestyle everyday handwritten documents, writing, erasing, striking out, and overwriting are common behaviors of the writers. This not cleanly-written text poses significant challenges for text line segmentation. Accurate text line segmentation in handwritten documents is essential to the success of several real-world applications, such as answer script evaluation, fraud document identification, writer identification, document age estimation and writer gender classification, to name a few. This paper proposes the first, to the authors’ best knowledge, text line segmentation approach that is applicable in the presence of both cleanly-written and struck-out text. The approach consists of three steps. In the first step, components - at the word level - are detected in the input handwritten document images (containing both cleanly-written and struck-out text) based on stroke width information estimation, filtering of noise, and morphological operations. In the second step, the struck-out components are identified using the DenseNet deep learning model and treated differently to clean text in further analysis. In the third step, geometrical spatial features, the direction between candidate components and the overall text line, and the common overlapping region between adjacent components are evaluated to progressively form text lines. To evaluate the proposed steps and compare the proposed method to the state-of-the-art, experiments have been conducted on a new problem-focused dataset containing instances of struck-out text in handwritten documents, as well as on two standard datasets (ICDAR2013 text line segmentation contest dataset and ICDAR2019 HDRC dataset) to show the proposed steps are effective and useful, with superior performance compared to existing methods.
Efficient sharing of privacy-preserving sensing data on consortium blockchain via group key agreement

Due to the self-organizing and distributed nature of Wireless Sensor Networks (WSN), valuable data in sensor networks faces security and privacy risks. A blockchain-based approach enables secure and convenient sharing of sensor information among different users. Compared to public and private blockchains, consortium blockchain is widely used across different industries and use cases in WSN due to its auditability and high transaction rate. However, sensing data sharing via consortium blockchain raises the privacy issue. Therefore, the data from the sensor node is encrypted by the key (named sensorkey) shared by the sensor node and the sink node and then sent to the blockchain network to not reveal the privacy of the sensing data. Since the infrastructure in large-scale WSN is usually owned and managed by multiple organizations, encrypted sensing data needs to be authorized by these multiple organizations for computation. Organizations requesting privacy-preserving data are referred to as data sharers. Distributing the sensorkey to each data sharer requires separate encryption of the key using the data sharer’s public key. The sink node needs to be online when each data sharer asks for the sensorkey, and one encryption of the sensorkey for each data sharer consumes precious resources. This work proposes GSChain for efficient privacy-preserving sensing data sharing on consortium blockchain. Multiple data sharers resort to asymmetric group key agreement protocol to maintain a shared group encryption key and their respective group decryption keys, enabling efficient sensorkey retrieval from the consortium blockchain. The sensorkey is encrypted only once by the group encryption key and stored on the consortium blockchain along with the privacy-preserving sensing data. Our scheme improves the efficiency of privacy-preserving data sharing among multiple data sharers while reducing the online demand for sink nodes. Although the data-sharing group should remain stable for a long time, we design the group key update scheme. We also discuss how old and new data sharers access different ranges of privacy-preserving sensing data as the data-sharing group changes. We build a complete implementation of GSChain based on the Hyperledger Fabric framework and conduct a comprehensive set of experimental studies. Our experimental results demonstrate that GSChain improves the privacy-preserving sensing data sharing efficiency with tolerable time and storage overhead. In addition, the time overhead caused by the recovery of the GSChain system is tolerable when the membership of the data-sharing group changes.
Adaptive multinoulli-based Kalman filter with randomly unknown delayed and lost measurements

A novel adaptive multinoulli-based Kalman filter (AMKF) is proposed to address the filtering problem of a linear system with random one-step delays and unknown measurement loss and delay probabilities. By introducing four discrete random variables with multinoulli distribution, the weighted sum of four Gaussian distributions is converted into exponential multiplicative form. The AMKF exploits a hierarchical Gaussian probability density function, in which the variational Bayesian (VB) method is utilized to deal with the probability density function of the joint distribution. In the simulation, the proposed approach performs better in estimation accuracy in terms of unknown probability of delay and loss compared to the existing approach.
Exploring the Effect of Word Embeddings and Bag-of-Words for Vietnamese Sentiment Analysis

In the field of natural language processing, word embeddings and bag of words are two objects used to represent initial features for many machine learning models. Bag of words is known to be widely used and suitable for traditional models such as SVM, logistic regression, random forest. Meanwhile, word embeddings are used suitable for deep learning models that designed based on Convolutional neural network (CNN) and Long short-term memory (LSTM). Many sentiment analysis studies have effectively used word embeddings. However, they have ignored the role of bag of words in their proposed model, this paper proposes a CNN model that simultaneously exploits the effectiveness of word embeddings and bag of words for vietnamese sentiment analysis. In the experiment, we used 4009 actual textual reviews in the data domain of mobile phone products, the experimental results have demonstrated the ability of the proposed model as well as the role of bag of words.
STEAM project based learning for future designers

The article is devoted to the actual problem of STEAM education in Ukraine in the context of a multidisciplinary approach in modern higher education. The project method is considered as the most promising means of implementing STEAM training during the training of future designers and practical issues of implementing project activities are highlighted. The results of the study of the effectiveness of project implementation in the carrying out of STEAM training in the aspect of developing the abilities and professional competencies of future designers are revealed. In the course of the research, the components of the formation of professional competence (cognitive-intellectual, effective-reflexive, creative and professionally-active) are identified. Quantitative indicators for determining the dynamics of professional competence formation using STEAM project based learning are identified, and the level of development of each of the indicators and the overall level of their formation among future designers are determined. Statistical processing of the results of an empirical study on the use of project activity as a means of forming the professional competence of future designers is carried out. To verify the reliability of the results obtained during the experiment, the Pearson criterion χ2 was used, which allowed us to confirm the statistical significance of changes based on the results of implementing the project method in STEAM training of future designers.
Sentiment analysis of Covid-19 vaccine tweets utilizing Naïve Bayes

COVID-19 is acknowledged as a transmitted from one person to another through contact, coughing, and sneezing. Twitter has served as one of the media outlets to raise awareness regarding COVID-19 problems. One of the government's objectives, based on the rising distribution, is pursued to preserve immunizations in stock. Hence, the vaccine information has become adequately available. However, immunization has sparked a range of reactions, including support and objection for vaccination. Attempts require a mechanism to distinguish tweets addressing immunization-related information. One notable method includes sentiment analysis, expressing a statement's negative, neutral, and positive feelings. A total of 5200 datasets were employed, with 4000 datasets classified as neutral, 300 datasets as negative, and 900 datasets as positive. The Naïve Bayes method and the TF-IDF (Term Frequency – Inverse Document Frequency) word weighting strategy are proposed to model the COVID-19 vaccine dataset, by comparing the three models of: Gaussian, Multinomial, and TF-IDF (Term Frequency – Inverse Document Frequency). According to study employing Naïve Bayes, the best model employing Bernoulli Naive Bayes is 80% with a data splitting of 30%.