Reviewerly/exHarmony huggingface dataset:
A novel density-based clustering method for effective removal of spurious intersections in bearings-only localization

Abstract In bearings-only localization, clustering-based methods have been widely used to remove spurious intersections by fusing multiple bearing measurements from different observation stations. Existing clustering methods, including fuzzy C-mean (FCM) clustering and density-based spatial clustering of applications with noise (DBSCAN), must specify the number of clusters and the threshold for defining the neighborhood density, respectively, which are always unknown and difficult to estimate. Moreover, in dense radiation source scenes, existing clustering methods for removal of spurious intersections all deteriorate significantly. Therefore, we propose a novel density-based clustering method called K-M-DBSCAN, which combines the minimum K distance algorithm with Mahalanobis distance-based DBSCAN clustering. Firstly, K-M-DBSCAN uses minimum K distance algorithm for preprocessing to remove most of the spurious intersections and reduce the computational complexity of clustering. Mahalanobis distance-based DBSCAN is used for clustering and spurious intersections recognition. In order to adapt the large variations of sample density in clustering, we use Mahalanobis distance to define an explicit neighborhood of DBSCAN instead of traditional Euclidean distance. Simulation results show that the proposed K-M-DBSCAN performs better than FCM and DBSCAN in removing of spurious intersections.
On Monetizing Personal Wearable Devices Data: A Blockchain-based Marketplace for Data Crowdsourcing and Federated Machine Learning in Healthcare

Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights. While wearable device data helps to monitor, detect, and predict diseases and health conditions, some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns. Moreover, wearable devices have been recently available as commercial products; thus large, diverse, and representative datasets are not available to most researchers. In this article, we propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers (e.g., researchers) to make wearable device data more available to healthcare researchers. To secure the data transactions in a privacy-preserving manner, we use a decentralized approach using Blockchain and Non-Fungible Tokens (NFTs). To ensure data originality and integrity with secure validation, our marketplace uses Trusted Execution Environments (TEE) in wearable devices to verify the correctness of health data. The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share. To ensure user participation, we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs. We also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits. If widely adopted, we believe that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives.
<i>C</i>ausality <i>C</i>onstraint on <i>C</i>ircuit <i>C</i>omplexity from <i>COSMOEFT</i>

Abstract In this article, we investigate the physical implications of the causality constraint via the effective speed of sound on Quantum Circuit Complexity (QCC) in the framework of Cosmological Effective Field Theory (COSMOEFT) using the two‐mode squeezed quantum states. This COSMOEFT setup is constructed using the Stckelberg trick with the help of the lowest dimensional operators, which are broken under time diffeomorphism. In this setup, we consider only the contributions from the two derivative terms in the background quasi‐de Sitter metric. Next, we compute the relevant measures of the circuit complexity and their cosmological evolution for different values of by following two different approaches, the Nielsen's approach and the Covariance matrix approach. Using this setup, we also compute the Von‐Neumann and the Rényi entropy, which finally establishes an underlying relationship between the entanglement entropy and the circuit complexity. Considering the scale factor and as parameters, our analysis of the circuit complexity measures and the entanglement entropy suggests several interesting unexplored features within the window, , which is supported by both causality and cosmological observation. Finally, we comment on the connection between the circuit complexity, the entanglement entropy and the equilibrium temperature for different values lying within the mentioned window.
Pixel-Wise Phase Unwrapping With Adaptive Reference Phase Estimation for 3-D Shape Measurement

Phase unwrapping plays an important role in phase-based three-dimensional (3D) shape measurement. This paper proposes an effective pixel-wise phase unwrapping method without requiring additional fringe patterns. Firstly, compared with the geometric constraint-based (GCB) method which needs calibrate the fringe projection system and create a reference phase at the closest plane <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z<sub>min</sub></i> within the measurement volume, the proposed method can adaptively and directly estimate the reference phase from the wrapped phase without system calibration and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z<sub>min</sub></i> estimation. Secondly, based on the finding that the slope angle of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z<sub>min</sub></i> plane ignored by the GCB method has significant impact on the measurement range, thus the phase gradients that highly correlates with the optimal slope angle are involved into reference phase estimation. The wrapped phase can be pixel-by-pixel unwrapped by comparing with the reference phase. Both simulations and experiments have been conducted to demonstrate the efficiency of the proposed method. Compared with traditional two-frequency method and phase-coding method, the proposed method can improve the measuring speed by 2 times. Experiment results show that the proposed method can effectively remove the phase unwrapping errors and achieve the mean absolute phase error of 0.0137 rad, which has obvious advantages relative to traditional methods.
Blockchain Technology: Energy Community and Beyond

The European Commission published in 2015 “Energy Union Strategy” to make energy production safer, more sustainable, competitive, and economical. To allow citizens of a community to cooperate to consume and exchange renewable energy they produce, the use of blockchain technology seems natural. In this note, we will start from a concrete case study to see what the technological challenges (and not only) are to be overcome. Furthermore, in the last sections, it will be shown how the creation of the IT infrastructure necessary for the exchange of energy produced can also be used for other purposes. That is according to the smart city paradigm to create a community able to make the most of the touristic, environmental, and social resources of the territory in which it lives.
Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances

Deep neural networks (DNNs) have rapidly become a de facto choice for medical image understanding tasks. However, DNNs are notoriously fragile to the class imbalance in image classification. We further point out that such imbalance fragility can be amplified when it comes to more sophisticated tasks such as pathology localization, as imbalances in such problems can have highly complex and often implicit forms of presence. For example, different pathology can have different sizes or colors (w.r.t.the background), different underlying demographic distributions, and in general different difficulty levels to recognize, even in a meticulously curated balanced distribution of training data. In this paper, we propose to use pruning to automatically and adaptively identify hard-to-learn (HTL) training samples, and improve pathology localization by attending them explicitly, during training in supervised, semi-supervised, and weakly-supervised settings. Our main inspiration is drawn from the recent finding that deep classification models have difficult-to-memorize samples and those may be effectively exposed through network pruning [15] - and we extend such observation beyond classification for the first time. We also present an interesting demographic analysis which illustrates HTLs ability to capture complex demographic imbalances. Our extensive experiments on the Skin Lesion Localization task in multiple training settings by paying additional attention to HTLs show significant improvement of localization performance by ~2-3%.
Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model

Over recent years, there are an increasing number of incidents in which archival images have been ripped. Leak tracking is one of the key problems for anti-screenshot digital watermarking of archival images. Most of the existing algorithms suffer from low detection rate of watermark, because the archival images have a single texture. In this paper, we propose an anti-screenshot watermarking algorithm for archival images based on Deep Learning Model (DLM). At present, screenshot image watermarking algorithms based on DLM can resist screenshot attacks. However, if these algorithms are applied on archival images, the bit error rate (BER) of the image watermark will increase dramatically. Archival images are ubiquitous, so in order to improve the robustness of archival image anti-screenshot, we propose a screenshot DLM “ScreenNet”. It aims to enhance the background and enrich the texture with style transfer. Firstly, a preprocessing process based on style transfer is added before the insertion of an archival image into the encoder to reduce the influence of the screenshot process of the cover image. Secondly, the ripped images are usually moiréd, so we generate a database of ripped archival images with moiréd by means of moiréd networks. Finally, the watermark information is encoded/decoded through the improved ScreenNet model using the ripped archive database as the noise layer. The experiments prove that the proposed algorithm is able to resist anti-screenshot attacks and achieves the ability to detect watermark information to leak the trace of ripped images.
Objective Hand Complexity Comparison between Two Mandarin Chinese Cued Speech Systems

Recently, a pilot Mandarin Chinese Cued Speech (MCCS) system, called MCCS-1 was proposed with a main characteristic that each vowel is coded by only one specific hand position, without using any hand slides to code diphthongs. Indeed, hand slides are also used in some other languages of CS to code diphthongs. In order to demonstrate that the MCCS-1 system possesses real advantages over systems using hand slides, in this work, we first propose a novel MCCS-2 by introducing hand slides to code diphthongs, and a “push out move” for ending consonants [n] or [ng] of nasalized vowels. Then, we present a multi-parameter hand complexity measure method to compare MCCS-1 and MCCS-2 by measuring three kinematic parameters, which are the time duration of words realization, hand move trajectory length and average speed of hand movements. Moreover, the first MCCS corpus for these two systems is recorded by three speakers specifically for this work. A statistical analysis of this database shows a superior performance of MCCS-1 since it costs less time and takes a shorter hand move trajectory length than MCCS-2. A strong correlation is observed between the complexity of hand movement and the time duration of word realization.
LOCAL MEDIA IN THE STRUCTURE OF MEDIA SYSTEMS. AT THE BORDER BETWEEN INTERPERSONAL AND MASS COMMUNICATION

The article focuses on local media, which turned out to be closely linked to a number of forms of participatory journalism. The question of the specific status of such means of communication in media systems is raised. Analysis of the nature of mass communication suggests that local media operate on the boundary of two types of communication – the interpersonal and mass, that determines their integration into the life of social groups and the ease with which laymen can enter the process of mass information exchange. It is suggested that local media have the properties of fractals, which allows them to adapt to changes in the social environment.
Banking in Metaverse Opportunities and Challenges

Digital revolution over the last two decades transformed the banking industry on many fronts. While it provided banks with a better system to offer its services over the internet in a seamless cost-effective mode, it also resulted in an emotional disconnect between banks and the customers. This, at times, can be quite costly for banks attempting to retain their customers. Banking in metaverse can provide an effective solution to this problem. In addition, it may further bring limitless opportunities that can improve banks bottom-line significantly. The present article attempts to explore those opportunities of banking in metaverse. It also prescribes strategies for banks to succeed in metaverse along with highlighting the challenges they need to overcome.
Using Technology to Enhance Special Education: An Introduction

Technology has become a very important aspect of our sacred existence as humans. It has penetrated all sections of our society – it is now an enrichment tool for our economy, politics, education, and society. While these enrichments are naturally inclusive, this chapter focuses on the use of technology in enhancing the education of students, especially those with disabilities. Things that were once inaccessible are now accessible to students with disabilities through the use of technology. These students might have some atypical traits; however, they are humans who can learn and function in our society when provided with appropriate learning tools such as technology. With technology, these students' learning and social activities can be enhanced, modified, adapted, and adjusted so that they can maximize their fullest potential. This is the premise of this chapter; and it sets the stage for the other chapters.
Multi-relational graph convolutional networks: Generalization guarantees and experiments

The class of multi-relational graph convolutional networks (MRGCNs) is a recent extension of standard graph convolutional networks (GCNs) to handle heterogenous graphs with multiple types of relationships. MRGCNs have been shown to yield results superior than traditional GCNs in various machine learning tasks. The key idea is to introduce a new kind of convolution operated on tensors that can effectively exploit correlations exhibited in multiple relationships. The main objective of this paper is to analyze the algorithmic stability and generalization guarantees of MRGCNs to confirm the usefulness of MRGCNs. Our contributions are of three folds. First, we develop a matrix representation of various tensor operations underneath MRGCNs to simplify the analysis significantly. Next, we prove the uniform stability of MRGCNs and deduce the convergence of the generalization gap to support the usefulness of MRGCNs. The analysis sheds lights on the design of MRGCNs, for instance, how the data should be scaled to achieve the uniform stability of the learning process. Finally, we provide experimental results to demonstrate the stability results.
Human Activity Recognition With Low-Resolution Infrared Array Sensor Using Semi-Supervised Cross-Domain Neural Networks for Indoor Environment

Low-resolution infrared-based human activity recognition (HAR) attracted enormous interests due to its low-cost and private. In this paper, a novel semi-supervised crossdomain neural network (SCDNN) based on 8 $\times$ 8 low-resolution infrared sensor is proposed for accurately identifying human activity despite changes in the environment at a low-cost. The SCDNN consists of feature extractor, domain discriminator and label classifier. In the feature extractor, the unlabeled and minimal labeled target domain data are trained for domain adaptation to achieve a mapping of the source domain and target domain data. The domain discriminator employs the unsupervised learning to migrate data from the source domain to the target domain. The label classifier obtained from training the source domain data improves the recognition of target domain activities due to the semi-supervised learning utilized in training the target domain data. Experimental results show that the proposed method achieves 92.12\% accuracy for recognition of activities in the target domain by migrating the source and target domains. The proposed approach adapts superior to cross-domain scenarios compared to the existing deep learning methods, and it provides a low-cost yet highly adaptable solution for cross-domain scenarios.
Conservative-Progressive Collaborative Learning for Semi-Supervised Semantic Segmentation

Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels. Addressing that, we propose a novel learning approach, called Conservative-Progressive Collaborative Learning (CPCL), among which two predictive networks are trained in parallel, and the pseudo supervision is implemented based on both the agreement and disagreement of the two predictions. One network seeks common ground via intersection supervision and is supervised by the high-quality labels to ensure a more reliable supervision, while the other network reserves differences via union supervision and is supervised by all the pseudo labels to keep exploring with curiosity. Thus, the collaboration of conservative evolution and progressive exploration can be achieved. To reduce the influences of the suspicious pseudo labels, the loss is dynamic re-weighted according to the prediction confidence. Extensive experiments demonstrate that CPCL achieves state-of-the-art performance for semi-supervised semantic segmentation.
Research on intelligent financial information framework and smarter university campus

China has entered the big data cloud era, and college financial information construction is key. Good financial information in schools and universities promotes an 'Intelligent campus' and improves financial management. This paper reviews current smart city and smart campus successes. The report defines smart campus principles and a tactical approach for its parts. IoT and cloud technology were the app's key extra infrastructure. This paper analyses colleges' financial information concerns from an 'Intelligent campus' perspective. It evaluates elements affecting the smart-university campus adoption of an intelligent financial information system. China's universities were surveyed using a cross-sectional questionnaire. The framework was based on IoT and cloud computing as the main supporting infrastructure, with eight main criteria and 25 sub-applications such as Smart Card or E-Card, Smart Classrooms, Energy Management, Adaptive Learning, Smart Transportation, Security and Safety, Optimisation and Analytics Data Center and Smart Facilities Services and the optimisation strategy to promote the construction of an 'Intelligent campus' in our university.
Online knowledge sharing in blockchains: towards increasing participation

Purpose This paper aims to propose a system dynamics model of blockchain online community knowledge sharing, with the following goals: to reveal the internal mechanism of blockchain technology on community knowledge sharing; to show the impact of blockchain technology on knowledge sharing; and to promote knowledge sharing and the self-development of blockchain online communities. Design/methodology/approach Based on the core characteristic factors of blockchain technology, including incentive mechanism, trust mechanism, information protection mechanism, etc., a knowledge sharing analysis framework is established. Through the use of the Vensim PLE (Personal Learning Version) software, according to the steps of “putting forward a dynamic hypothesis”, “establishing a system dynamic equations”, and then “model testing” and “simulation”, the article analyzes in depth the process and extent of the impact of the above features on online community knowledge sharing. Findings The results show that the blockchain incentive mechanism, trust mechanism and information protection mechanism all contribute to promoting an increase in the number of community knowledge sharing users, as well as in the total amount of knowledge shared. The results also show that the token reward in the incentive mechanism has in fact a higher degree of influence than the trust and information protection mechanisms. Originality/value At present, no research on the internal mechanism of knowledge sharing in blockchain online communities has been carried out. This article plays a complementary role in research in this field, and offers significant guidance for promoting online community knowledge management and online community development.
Clause Fusion-Based Emotion Embedding Model for Emotion-Cause Pair Extraction

The goal of emotion-cause pair extraction (ECPE) is to simultaneously extract all emotion clauses and their corresponding cause clauses in a document. In most existing methods, emotion clause representations and cause clause representations are usually obtained separately and are then fed into neural networks. However, the close relationship between emotion and cause is ignored, resulting in an insufficient representation of the emotion clause and the cause clause. To address this problem, we propose a new model, called the clause fusion-based emotion embedding model, to make full use of emotion-related knowledge by utilizing an emotion embedding method when obtaining the representation of the cause clause. First, the emotion word embedding is processed by the emotion clause encoder to get the emotion feature. Second, in clause fusion based emotion embedding network, the emotion clause-level feature in the sliding-window is fused to fused emotion features. The fused emotion features, cause word-level, and emotion word-level feature representation are embedded to get emotion embedding. Third, the emotion embedding is processed to the cause clause feature representation by a bidirectional long short-term memory. Forth, each emotion clause-level feature representation was paired with each cause clause-level feature representation to produce candidate pairs representation. Finally, in the clause pair encoder, a graph convolutional network is applied to model the pair-level context, and then contextual features are extracted for the candidate pairs. Experimental results show that our model achieves state-of-the-art performance on the Chinese benchmark ECPE corpus.