Reviewerly/exHarmony huggingface dataset:
How to determine the optimal balance for geochemical pattern recognition and anomaly mapping based on compositional balance analysis

Balance analysis of two groups of parts within a whole has become an important method for compositional data analysis. A compositional balance is a particular orthonormal coordinate that is depicted by the log-ratio between two groups of components. Two available approaches to compositional balance analysis (CoBA) can be adopted to generate targeted balances for geochemical pattern analysis and anomaly identification, so-called data-driven CoBA and knowledge-driven CoBA. For data-driven CoBA, the balance is produced strictly by the rules of sequential binary partition, while for knowledge-driven CoBA, the first group within a balance is composed of the interesting parts of the whole and the second group is defined by the remaining parts of the whole. Commonly, it is difficult to conceptualize balances, particularly for high-dimensional data, because this will produce a large number of orthonormal bases or balances based on CoBA. For a certain geochemical pattern, it might be represented by multiple compositional balances generated by data-driven and knowledge-driven CoBA. Thus, how to determine an optimal balance for geochemical pattern analysis and anomaly identification needs to be further explored. In the present study, this question was thoroughly investigated based on a case study from the Chinese Western Tianshan region. Fourteen compositional balances and three principal factors associated with different geochemical patterns including gold and copper mineralization, and particular lithological units, were selected for comparative studies to illustrate how to determine the optimal balances from the perspective of CoBA and multivariate statistical analysis. Supplementary material: Appendix 1 shows the sequential binary partition of 38 geochemical variables, while Appendix 2 shows the compositional balances of the 38 geochemical variables; available at https://doi.org/10.6084/m9.figshare.c.6083724 Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis
DeepRS: A Library of Recommendation Algorithms Based on Deep Learning

Abstract In recent years, recommendation systems have become more complex with increasing research on user preferences. Recommendation algorithm based on deep learning has attracted a lot of attention from researchers in academia and industry, and many new algorithm models are proposed every year. Researchers often need to implement the proposed model to compare the results, which is a great challenge. Even if some papers provide source code, there are a variety of programming languages or deep learning frameworks, and it is not easy to compare the results in the different frameworks. In view of the lack of easily extensible deep learning-based recommendation algorithm libraries, based on the common analysis of deep learning algorithms in attention factorization machine (AFM), neural factorization machine (NFM), deep factorization machine (DeepFM) and deep cross-network (DCN), a recommendation algorithm library based on deep learning (DeepRS for short) is designed and implemented. It consists of three levels: framework level, abstract level and algorithm level. The framework level adopts the Tensorflow open source framework, which provides interfaces, such as automatic differentiation, tensor computing, GPU computing, and numerical optimization algorithms. The abstraction level uses the interface of the framework level to realize the embedding layer (EL), the full connection layer (FCL), the multi-layer perceptron layer (MLPL), the prediction layer (PL), the factorization machine layer (FML), the attention network layer (ANL), the cross-layer (CL) and the cross-network layer (CNL). The algorithm level implements the deep learning-based recommendation algorithms, such as AFM, NFM, DeepFM and DCN, on the basis of the abstraction level and the framework level. Experiments show that the proposed algorithm library has good scalability, ease of use and correctness.
A Bayesian Quality-of-Experience Model for Adaptive Streaming Videos

The fundamental conflict between the enormous space of adaptive streaming videos and the limited capacity for subjective experiment casts significant challenges to objective Quality-of-Experience (QoE) prediction. Existing objective QoE models either employ pre-defined parametrization or exhibit complex functional form, achieving limited generalization capability in diverse streaming environments. In this study, we propose an objective QoE model, namely, the Bayesian streaming quality index (BSQI), to integrate prior knowledge on the human visual system and human annotated data in a principled way. By analyzing the subjective characteristics towards streaming videos from a corpus of subjective studies, we show that a family of QoE functions lies in a convex set. Using a variant of projected gradient descent, we optimize the objective QoE model over a database of training videos. The proposed BSQI demonstrates strong prediction accuracy in a broad range of streaming conditions, evident by state-of-the-art performance on four publicly available benchmark datasets and a novel analysis-by-synthesis visual experiment.
Detection of Key Points in Mice at Different Scales via Convolutional Neural Network

In this work, we propose a symmetry approach and design a convolutional neural network for mouse pose estimation under scale variation. The backbone adopts the UNet structure, uses the residual network to extract features, and adds the ASPP module into the appropriate residual units to expand the perceptual field, and uses the deep and shallow feature fusion to fuse and process the features at multiple scales to capture the various spatial relationships related to body parts to improve the recognition accuracy of the model. Finally, a set of prediction results based on heat map and coordinate offset is generated. We used our own built mouse dataset and obtained state-of-the-art results on the dataset.
Performance and Information Leakage in Splitfed Learning and Multi-Head Split Learning in Healthcare Data and Beyond

Machine learning (ML) in healthcare data analytics is attracting much attention because of the unprecedented power of ML to extract knowledge that improves the decision-making process. At the same time, laws and ethics codes drafted by countries to govern healthcare data are becoming stringent. Although healthcare practitioners are struggling with an enforced governance framework, we see the emergence of distributed learning-based frameworks disrupting traditional-ML-model development. Splitfed learning (SFL) is one of the recent developments in distributed machine learning that empowers healthcare practitioners to preserve the privacy of input data and enables them to train ML models. However, SFL has some extra communication and computation overheads at the client side due to the requirement of client-side model synchronization. For a resource-constrained client side (hospitals with limited computational powers), removing such conditions is required to gain efficiency in the learning. In this regard, this paper studies SFL without client-side model synchronization. The resulting architecture is known as multi-head split learning (MHSL). At the same time, it is important to investigate information leakage, which indicates how much information is gained by the server related to the raw data directly out of the smashed data-the output of the client-side model portion-passed to it by the client. Our empirical studies examine the Resnet-18 and Conv1-D architecture model on the ECG and HAM-10000 datasets under IID data distribution. The results find that SFL provides 1.81% and 2.36% better accuracy than MHSL on the ECG and HAM-10000 datasets, respectively (for cut-layer value set to 1). Analysis of experimentation with various client-side model portions demonstrates that it has an impact on the overall performance. With an increase in layers in the client-side model portion, SFL performance improves while MHSL performance degrades. Experiment results also demonstrate that information leakage provided by mutual information score values in SFL is more than MHSL for ECG and HAM-10000 datasets by 2×10-5 and 4×10-3, respectively.
Evading Big Brother: Using visual methods to understand children’s perception of sensors and interest in subverting digital surveillance

In relation to this Special Issue’s focus on ugly information, this article examines children’s perception of the often invisible interactions they have with sensor-enabled digital devices and, when prompted, their interest in subverting or blocking these sensors to evade surveillance. The authors report on a study of 12 children, aged 8–12 years, that investigated their knowledge of the sensing abilities of commonly used digital devices (smart phones, smart watches, smart speakers and games consoles), and their attitudes towards having active agency over sensors. In line with this journal’s readership, visual methods used for data collection and analysis are described. Specifically, within semi-structured focus groups, drawing was used to understand what children thought was inside digital devices and the extent of their awareness of digital sensors. Child participants were invited to model speculative tools for deceiving digital sensors in order to explore their interest in having agency over digital surveillance. Data in the form of drawings, photographs of models and video recordings were analysed using experimental visual methods that included 3D rendering and comics, as well as visual content and thematic analysis. These drew out four key themes: (1) the role of inference in sensor awareness; (2) misunderstanding of device components and sensing capabilities; (3) attitudes to surveillance; and (4) children’s interest in subverting rather than blocking sensors. We discuss how technology companies’ desire to create ‘magical experiences’ may contribute to incorrect inferences about information gathering systems, how this reduces children’s agency over the information they share and how it puts them at greater risk from digital surveillance. The article makes an original contribution to knowledge in this area by calling for a two-pronged approach from technology companies and educators to address these issues by making sensor presence more visible, educating children about the full extent of sensor capability and bringing critical discussion of them into curricula.
A Systematic Literature Review of Augmented Reality in Engineering Education : Hardware, Software, Student Motivation &amp; Development Recommendations.

Augmented Reality (AR) is a technology that has benefited from the massification of computational devices, putting it in the focus of researchers as a novel teaching aid in engineering. For this very reason, a great amount of information about AR in engineering education is emerging constantly. To synthesize the information, a Systematic Literature Review (SLR) was carried out and 4 research questions were raised. It was found out the researcher´s trend for the development and testing of software that takes advantage of AR for engineering students. It was also found that Germany and India are the leading nations on investigations about the investigated topic. On the software side, Unity is the most used tool for creating applications and that the target hardware is smartphone. Finally, the high interest of researchers to increase motivation in students is evidenced in this SLR and recommendations were made based on the researcher’s findings.
MGM-4-FL: Combining federated learning and model gossiping in WSNs

Application of Federated learning (FL) as it is, in Wireless Sensor Networks (WSNs) in impractical. In fact, it requires the transmission of a large number of model parameters. Also, it implies the consumption of a massive amount of energy and communication resources at nodes located closer to the network elements that aggregate the models calculated by the federated learners. This is a well known problem in WSN called funneling effect . Solutions have been recently proposed that decentralize FL operations further by exploiting multihop communications. Nevertheless, the proposed approaches do not focus on the networking operations needed to support such decentralization in an efficient manner. Objective of this paper is to fill this gap by proposing an integrated networking/learning scheme, named Model Gossiping Method for FL , that supports distributed FL efficiently in wireless sensor networks.
DSER: Deep-Sequential Embedding for single domain Recommendation

Recently, Deep Neural Networks (DNNs) have proved their capability to model nonlinear relationships between users and items in recommender systems. Therefore, many researchers have exploited the DNN to model the non-linearity of individual interactions between users and items. However, it has been barely studied to model the non-linearity of a user's sequential item interactions that may be the fundamental clue to represent a user's interest naturally. This paper proposes a novel Deep Sequential Embedding for a single domain Recommendation based on implicit feedback, named DSER. It exploits Doc2vec techniques to convert sequential user–item interactions, rather than textual information, to the feature vectors of users and items. In our framework, a user's interaction is like a document containing items as words in the word embedding techniques. The obtained user and item vectors are then fed to Multi-Layer Perceptron (MLP) to model a high level of non-linearity for user–item interactions. Finally, the non-linearity of MLP is fused with the linear user–item relationships trained by matrix factorization to predict the possibility that a target user likes items. Extensive experiments on four real-world datasets demonstrate significant improvements of our DSER over state-of-the-art approaches. Moreover, empirical evidence shows that using word embedding offers considerably better predictive performance on sparse data of the smart tourism field, whose new user ratio is relatively high.
A Face Recognition Method Using Deep Learning to Identify Mask and Unmask Objects

At the present, the use of face masks is growing day by day and it is mandated in most places across the world. People are encouraged to cover their faces when in public areas to avoid the spread of infection which can minimize the transmission of Covid-19 by 65 percent (according to the public health officials). So, it is important to detect people not wearing face masks. Additionally, face recognition has been applied to a wide area for security verification purposes since its performance, accuracy, and reliability [15] are better than any other traditional techniques like fingerprints, passwords, PINs, and so on. In recent years, facial recognition is becoming a challenging task because of various occlusions or masks like the existence of sunglasses, scarves, hats, and the use of make-up or disguise ingredients. So, the face recognition accuracy rate is affected by these types of masks. Moreover, the use of face masks has made conventional facial recognition technology ineffective in many scenarios, such as face authentication, security check, tracking school, and unlocking phones and laptops. As a result, we proposed a solution, Masked Facial Recognition (MFR) which can identify masked and unmasked people so individuals wearing a face mask do not need to take it out to authenticate themselves. We used the Deep Learning model, Inception ResNet V1 to train our model. The CASIA dataset [17] is applied for training images and the LFW (Labeled Faces in the Wild) dataset [18] is used for model evaluation purposes. The masked datasets are created using a Computer Vision-based approach (Dlib), We received an accuracy of over 96 percent for our three different trained models. As a result, the purposed work could be utilized effortlessly for both masked and unmasked face recognition and detection systems that are designed for safety and security verification purposes without any challenges.
Ship Deck Segmentation In Engineering Document Using Generative Adversarial Networks

Generative adversarial networks (GANs) have become very popular in recent years. GANs have proved to be successful in different computer vision tasks including image-translation, image super-resolution etc. In this paper, we have used GAN models for ship deck segmentation. We have used 2D scanned raster images of ship decks provided by US Navy Military Sealift Command (MSC) to extract necessary information including ship walls, objects etc. Our segmentation results will be helpful to get vector and 3D image of a ship that can be later used for maintenance of the ship. We applied the trained models to engineering documents provided by MSC and obtained very promising results, demonstrating that GANs can be potentially good candidates for this research area.
Multi-Participant Vertical Federated Learning Based Time Series Prediction

Federated learning (FL) ensures multi-party can train a model together while avoiding privacy leakage. Our vertical federated learning (VFL) task tackles the following scenarios: i) all parties share the same sample space but differ in feature space, ii) only one party holds the label data. Our contribution is twofold: i) proposing a novel aggregation strategy to show that embedding learning is qualified to handle the challenge of VFL, ii) Incorporating specific strategy of Secure Multi-party Computation (MPC) into the training phase to remain the dataset at each local machine. We focus on time series scenarios and choose Gated Recurrent Unit (GRU) as our basic algorithm. We evaluate our method on both Google stock data for regression prediction and Kyoto University Benchmark Data for classification prediction to illustrate the performance of the results in terms of computational and communication complexities.
Object Detection Based on Deep Learning and B-Spline Level Set in Color Images

Object detection based on deep learning is a popular trend, and it includes object recognition and positioning. This paper proposes a method that can accurately obtain object type and accurate three-dimensional position. The method is divided into three parts: object recognition and coarse positioning based on deep learning, precise positioning based on deep learning combined with B-spline level set in color images, and precise three-dimensional positioning with depth information of RGB-D camera. The precise positioning of the object provides accurate end pose information for the autonomous grasping of the robotic arm, and it has great significance to the gripping of the robotic arm. Performance metrics include mAP (mean average precision) and IOU (intersection of a union). Experimental results show that the mAP value of Yolo-v3 in this paper can reach 87.62%, the average IOU of Yolo-v3 in this paper can reach 66.74%, the average IOU of Yolo-v3 and B-spline level set can reach 100%, and can get accurate 3D location in the real scene. In addition, the experiments comparisons between VOC dataset and our own dataset validate that our dataset can take higher mAP and average IOU values.
BFCRI: A Blockchain-Based Framework for Crowdsourcing With Reputation and Incentive

With the rapid development of cloud computing and the sharing economy, crowdsourcing aroused widespread interest and adoption in providing intelligent and efficient services for humans. The majority of existing works focus on effective crowdsourcing task assignment and privacy protection, mostly relying on central servers and assuming that participants are <inline-formula><tex-math notation="LaTeX">$honest$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$and$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$curious$</tex-math></inline-formula> and proactive. However, in reality, workers may be unwilling to participate, and there may be malicious behavior among participants, thus harming the enthusiasm and interests of other participants. The central server has weaknesses such as single point of failure. To address above problems, we propose a blockchain-based framework for crowdsourcing with reputation and incentive. We first design a worker selection scheme to select credible and capable workers. We leverage reputation as a metric of workers’ credibility, which is calculated through the improved subjective logic model. Then we utilize contract theory to design incentive mechanisms to attract more workers, especially high-quality workers to participate. Experimental results show that our proposed method can detect and prevent malicious participants and resist malicious collusion when the proportion of malicious participants is no more than 1/3. And encourage more workers to actively, honestly and continuously participate in crowdsourcing.
Investigation of Japanese PnG BERT Language Model in Text-to-Speech Synthesis for Pitch Accent Language

End-to-end text-to-speech synthesis (TTS) can generate highly natural synthetic speech from raw text. However, rendering the correct pitch accents is still a challenging problem for end-to-end TTS. To tackle the challenge of rendering correct pitch accent in Japanese end-to-end TTS, we adopt PnG~BERT, a self-supervised pretrained model in the character and phoneme domain for TTS. We investigate the effects of features captured by PnG~BERT on Japanese TTS by modifying the fine-tuning condition to determine the conditions helpful inferring pitch accents. We manipulate content of PnG~BERT features from being text-oriented to speech-oriented by changing the number of fine-tuned layers during TTS. In addition, we teach PnG~BERT pitch accent information by fine-tuning with tone prediction as an additional downstream task. Our experimental results show that the features of PnG~BERT captured by pretraining contain information helpful inferring pitch accent, and PnG~BERT outperforms baseline Tacotron on accent correctness in a listening test.
Student Satisfaction Pilot Experience With Synchronous Classroom Live Streaming Styles During the COVID-19 Pandemic

In this pilot study, was analyzed the perception of synchronous virtual classes by engineering students in Colombia, specifically concerning four live streaming styles used as tools for digital teaching during the COVID-19 pandemic: talking head, speaker next to slide, speaker inside slide, and lightboard. Student opinions were collected using a Likert scale survey and open-response questions, and data were quantified to determine the influence of live streaming styles on teaching processes. Two populations were analyzed to determine whether continuous interaction with live streaming styles influenced student perceptions. Variation among the live streaming styles was observed only for the population with continuous interaction. Results indicate that the experiences between populations using the fourth live streaming style influence student perceptions of live streaming styles. The continuous interaction population preferred lightboard style, while the control population preferred speaker inside slide style. Factors for improvement were identified in all live streaming styles and mainly considered how the quality of audio and interaction impacted perception.