Reviewerly/exHarmony huggingface dataset:
An Intelligent Athlete Signal Processing Methodology for Balance Control Ability Assessment with Multi-Headed Self-Attention Mechanism

In different kinds of sports, the balance control ability plays an important role for every athlete. Therefore, coaches and athletes need accurate and efficient assessments of the balance control ability to improve the athletes’ training performance scientifically. With the fast growth of sport technology and training devices, intelligent and automatic assessment methods have been in high demand in the past years. This paper proposes a deep-learning-based method for a balance control ability assessment involving an analysis of the time-series signals from the athletes. The proposed method directly processes the raw data and provides the assessment results, with an end-to-end structure. This straight-forward structure facilitates its practical application. A deep learning model is employed to explore the target features with a multi-headed self-attention mechanism, which is a new approach to sports assessments. In the experiments, the real athletes’ balance control ability assessment data are utilized for the validation of the proposed method. Through comparisons with different existing methods, the accuracy rate of the proposed method is shown to be more than 95% for all four tasks, which is higher than the other compared methods for tasks containing more than one athlete of each level. The results show that the proposed method works effectively and efficiently in real scenarios for athlete balance control ability evaluations. However, reducing the proposed method’s calculation costs is an important task for future studies.
A Novel Multi-Source Domain Adaptation Method with Dempster–Shafer Evidence Theory for Cross-Domain Classification

In this era of big data, Multi-source Domain Adaptation (MDA) becomes more and more popular and is employed to make full use of available source data collected from several different, but related domains. Although multiple source domains provide much information, the processing of domain shifts becomes more challenging, especially in learning a common domain-invariant representation for all domains. Moreover, it is counter-intuitive to treat multiple source domains equally as most existing MDA algorithms do. Therefore, the domain-specific distribution for each source–target domain pair is aligned, respectively. Nevertheless, it is hard to combine adaptation outputs from different domain-specific classifiers effectively, because of ambiguity on the category boundary. Subjective Logic (SL) is introduced to measure the uncertainty (credibility) of each domain-specific classifier, so that MDA could be bridged with DST. Due to the advantage of information fusion, Dempster–Shafer evidence Theory (DST) is utilized to reduce the category boundary ambiguity and output reasonable decisions by combining adaptation outputs based on uncertainty. Finally, extensive comparative experiments on three popular benchmark datasets for cross-domain image classification are conducted to evaluate the performance of the proposed method via various aspects.
Enhancing adaptation of digital entrepreneurship to consumers’ online purchasing in the COVID-19 pandemic era

This study focused on enhancing the adaptation of digital entrepreneurship to benefit from consumers’ shift to online purchasing in the COVID-19 pandemic era. It aimed to identify the readiness and ability of digital entrepreneurs, especially small ones, to deal with customers’ shift to online purchasing. It also aimed to determine the means and mechanisms that enable digital entrepreneurs to digitally adapt to take advantage of this opportunity. An electronic questionnaire was distributed to the study sample to collect the necessary data to test the study's hypotheses. The focus of the study was on the Kingdom of Saudi Arabia, that reached 384. And the study uses multiple regression analysis to test this hypothesis. The study concluded that digital entrepreneurs could adapt digitally by targeting customers properly and digitally engaging customers to establish an interaction with them, which is one of the most important foundations of digital business activities. They can also adapt digitally through search engine optimisation; adaptation to smartphones, one of the basics of digital adaptation in digital entrepreneurship; and shifting from the traditional supply chain to the digital value chain.
Detecting practical quantum attacks for continuous-variable quantum key distribution using density-based spatial clustering of applications with noise

Continuous-variable quantum key distribution (CVQKD) has been proven to be secure theoretically. However, the practical CVQKD system may still be subject to various quantum attacks due to the imperfections of devices. In this paper, we suggest a general machine learning-based defense strategy against practical quantum attacks by taking advantage of density-based spatial clustering of applications with noise (DBSCAN), which we called DBSCAN-based attack detection scheme (DADS). Specifically, we first construct a set of features that can well reflect the behaviors of different attacks, then DBSCAN is applied to obtain several clusters. This clustering result can explicitly indicate whether the CVQKD system is being eavesdropped or not. Simulation experiments show that the proposed DADS cannot only detect most of known attacks, but also has ability to identify various unknown attacks, thereby improving practical security of the CVQKD system. We also show that the overestimated secret key rate caused by ignoring practical quantum attacks can be amended by DADS so that a reasonable tighter secure bound of the practical CVQKD system can be obtained.
Internet as a Field

The chapter demonstrates how the Facebook platform has become an important field complement to the classical ethnographic genres in the Santal community of Bangladesh. The authors focus on this social network as the most popular one globally regarding the number of registered users and as the leading platform for Santal groups. As of April 2022, about 15 Santal online groups and pages of both open and closed types and differences in numbers of users and activity degrees are registered on Facebook. Despite the internal peculiarities of these Santal groups, the main goal of the communities under consideration is to consolidate the participants. Such a platform creates practical tools for maintaining social ties and space of constant communication beyond being connected to a physical location.
Dual contrastive universal adaptation network for multi-source visual recognition

This paper explores recently proposed Universal Multi-source Domain Adaptation (UniMDA) task. UniMDA task is different from existing domain adaptation (DA) tasks, e.g. , Multi-source DA, Closed set DA or Universal DA. UniMDA not only handles multi-source issue, but also needs no prior knowledge about the overlap between the target and source label sets. UniMDA task has three challenges: (i) Domain shift issue among the multiple source domains. (ii) Domain shift issue between target and source domains. (iii) Category shift between the target and each source. Towards tackling the challenges, we formulate a universal multi-source adaptation network termed as M ulti- S ource D ual C ontrastive N etwork (MSDCN), including a transferability rule and contrastive module. In addition, to handle multi-source scenario, we propose (1) pairwise similarities maximization over the examples from multiple source domains, and (2) alternative optimization strategy for training the ensemble of multiple source classifiers end-to-end. The proposed method can handle UniMDA scenario generally, where the label set in each source domain may be different from the target domain, while maintaining the complexity of the method in different DA scenarios. Experiments are conducted on several real-world multi-source benchmarks. The results show that MSDCN could work stably, and exceed the state-of-the-art performance against existing domain adaptation algorithms. • This is an early work that explores Universal Multi-Source Domain Adaptation (UniMDA) setting. • The proposed method can tackle domain and category shift issues across domains. • Experiments show that proposed method outperforms state-of-the-art methods about +4.26%.
Reinforcement learning-based calibration method for cameras with large FOV

The accuracy of camera calibration is of great importance to vision measurements. Target-based calibration methods should cover the whole field of view (FOV), which leads to complex operation in large FOVs and rely on personal experiences. To overcome the difficulty in accurate calibration, a calibration method based on reinforcement learning is proposed. First, a Markov decision process (MDP) model of the calibration procedure is established. Then, the reward function is designed, combining the requirement of calibration accuracy and the state-space constraint. Finally, the optimized target locations and poses are obtained by continuous interaction with the calibration environment based on Q-learning, which plays a key guiding role in camera calibration. Simulation experiments and real experiments are performed, which indicate that the proposed method effectively improves the success rate of large FOV camera calibration, and solves the problems of relying on personal experiences, low calibration accuracy, and poor stability caused by target placement in the camera calibration process.
Fine-Grained Drug Interaction Extraction Based on Entity Pair Calibration and Pre-Training Model for Chinese Drug Instructions

Existing pharmaceutical information extraction research often focus on standalone entity or relationship identification tasks over drug instructions. There is a lack of a holistic solution for drug knowledge extraction. Moreover, current methods perform poorly in extracting fine-grained interaction relations from drug instructions. To solve these problems, this paper proposes an information extraction framework for drug instructions. The framework proposes deep learning models with fine-tuned pre-training models for entity recognition and relation extraction. In addition, it incorporates an novel entity pair calibration process to promote the performance for fine-grained relation extraction. The framework experiments on more than 60k Chinese drug description sentences from 4000 drug instructions. Empirical results show that the framework can successfully identify drug related entities (F1 3 0.95) and their relations (F1 3 0.83) from the realistic dataset, and the entity pair calibration plays an important role (~5% F1 score improvement) in extracting fine-grained relations.
An intelligent spatial stream processing framework for digital forensics amid the COVID-19 outbreak

In recent times, several strategies to minimize the spread of 2019 novel coronavirus disease (COVID-19) have been adopted. Some recent technological breakthroughs like the drone-based tracking systems have focused on devising specific dynamical approaches for administering public mobility and providing early detection of symptomatic patients. In this paper, a smart real-time image processing framework converged with a non-contact thermal temperature screening module was implemented. The proposed framework comprised of three modules viz. , smart temperature screening system, tracking infection footprint, and monitoring social distancing policies. This was accomplished by employing Histogram of Oriented Gradients (HOG) transformation to identify infection hotspots. Further, Haar Cascade and local binary pattern histogram (LBPH) algorithms were used for real-time facial recognition and enforcing social distancing policies. In order to manage the redundant video frames generated at the local computing device, two holistic models, namely, event-triggered video framing (ETVF) and real-time video framing (RTVF) have been deduced, and their respective processing costs were studied for different arrival rates of the video frame. It was observed that the proposed ETVF approach outperforms the performance of RTVF by providing optimal processing costs resulting from the elimination of redundant data frames. Results corresponding to autocorrelation analysis have been presented for the case study of India pertaining to the number of confirmed COVID-19 cases, recovered cases, and deaths to obtain an understanding of epidemiological spread of the virus. Further, choropleth analysis was performed for indicating the magnitude of COVID-19 spread corresponding to different regions in India.
Approximating stability for applied argument-based inquiry

In argument-based inquiry, agents jointly construct arguments supporting or attacking a topic claim to find out if the claim can be accepted given the agents’ knowledge bases. While such inquiry systems can be used for various forms of automated information intake, several efficiency issues have so far prevented widespread application. In this paper, we aim to tackle these efficiency issues by exploring the notion of stability: can additional information change the justification status of the claim under discussion? Detecting stability is not tractable for every input, since the problem is CoNP-complete, yet in practical applications it is essential to guarantee efficient computation. This makes approximation a viable alternative. We present a sound approximation algorithm that recognises stability for many inputs in polynomial time and discuss several of its properties. In particular, we show that the algorithm is sound and identify constraints on the input under which it is complete. As a final contribution of this paper, we describe how the proposed algorithm is used in three different case studies at the Netherlands Police.
Improved AdaBoost algorithm using misclassified samples oriented feature selection and weighted non-negative matrix factorization

To improve the classification performance of existing adaptive boosting (AdaBoost) based algorithms effectively, an improved AdaBoost algorithm based on misclassified samples oriented feature selection and weighted non-negative matrix factorization (WNMF) is proposed in this paper. Firstly, in order to consider the effects of sample weights, a misclassified samples oriented feature selection (called MOFS) is proposed to select the most discriminative features which occur in the samples with high weights. Secondly, the explicit features and the part-based features of the training samples are both considered, and the WNMF algorithm is introduced and combined with MOFS to reduce the dimension of the training sample set. Finally, the concept of misclassification degree is introduced and a fine grained sample weight updating method is proposed to distinguish the samples with different misclassification degrees. Numerical experiments show that the proposed MOFS method achieves higher accuracy compared to traditional feature selection methods, and the proposed MOFS and WNMF based AdaBoost method obtains significant improvement on classification accuracy when comparing with typical existing AdaBoost based algorithms using different classifiers.
Advances in Exploratory Data Analysis, Visualisation and Quality for Data Centric AI Systems

It is widely accepted that data preparation is one of the most time-consuming steps of the machine learning (ML) lifecycle. It is also one of the most important steps, as the quality of data directly influences the quality of a model. In this tutorial, we will discuss the importance and the role of exploratory data analysis (EDA) and data visualisation techniques to find data quality issues and for data preparation, relevant to building ML pipelines. We will also discuss the latest advances in these fields and bring out areas that need innovation. To make the tutorial actionable for practitioners, we will also discuss the most popular open-source packages that one can get started with along with their strengths and weaknesses. Finally, we will discuss on the challenges posed by industry workloads and the gaps to be addressed to make data-centric AI real in industry settings.
Few-shot Heterogeneous Graph Learning via Cross-domain Knowledge Transfer

Graph few-shot learning seeks to alleviate the label scarcity problem resulting from the difficulties and high cost of data annotations in graph learning. However, the overwhelming solutions in graph few-shot learning focus on homogeneous graphs, ignoring the ubiquitous heterogeneous graphs (HGs), which represent real-world complex systems and domain knowledge with multi-typed nodes interconnected by multi-typed edges. To this end, we study the cross-domain few-shot learning problem over HGs and develop a novel model for Cross-domain Heterogeneous Graph Meta learning (CrossHG-Meta). The general idea is to promote the HG node classification in the data-scarce target domain by transferring meta-knowledge from a series of HGs in data-rich source domains. The key challenges are to 1) combat the heterogeneity in HGs to acquire the transferable meta-knowledge; 2) handle the domain shifts between the source HG and target HG; and 3) fast adapt to novel target tasks with few-shot annotated examples. Regarding the graph heterogeneity, CrossHG-Meta firstly builds a graph encoder to aggregate heterogeneous neighborhood information from multiple semantic contexts. Secondly, to tackle domain shifts, a cross-domain meta-learning strategy is proposed to include a domain critic, which is designed to explicitly lead cross-domain adaptation for meta-tasks in different domains and improve model generalizability. Last, to further alleviate data scarcity, CrossHG-Meta leverages unlabelled information in source domains with auxiliary self-supervised learning task to provide cross-domain contrastive regularization alongside the meta-optimization process to facilitate node embedding. Extensive experimental results on three multi-domain HG datasets demonstrate that the proposed model outperforms various state-of-the-art baselines for multiple few-shot node classification tasks under the cross-domain setting.
Multimodal AutoML for Image, Text and Tabular Data

Automated machine learning (AutoML) offers the promise of translating raw data into accurate predictions without the need for significant human effort, expertise, and manual experimentation. In this lecture-style tutorial, we demonstrate fundamental techniques that powers up multimodal AutoML. Different from most AutoML systems that focus on solving tabular tasks that contain categorical and numerical features, we consider supervised learning tasks on various types of data including tabular features, text, and image, as well as their combinations. Rather than technical descriptions of how individual ML models work, we emphasize how to best use models within an overall ML pipeline that takes in raw training data and outputs predictions for test data. A major focus of our tutorial is on automatically building and training deep learning models, which are powerful yet cumbersome to manage manually. Hardly any educational material describes their successful automation. Each topic covered in the tutorial is accompanied by a hands-on Jupyter notebook that implements best practices (which will be available on GitHub before and after the tutorial). Most of the code is adopted from AutoGluon (https://auto.gluon.ai/), a recent open-source AutoML toolkit that is both state-of-the-art and easy-to-use.
Robust Tensor Graph Convolutional Networks via T-SVD based Graph Augmentation

Graph Neural Networks (GNNs) have exhibited their powerful ability of tackling nontrivial problems on graphs. However, as an extension of deep learning models to graphs, GNNs are vulnerable to noise or adversarial attacks due to the underlying perturbations propagating in message passing scheme, which can affect the ultimate performances dramatically. Thus, it's vital to study a robust GNN framework to defend against various perturbations. In this paper, we propose a Robust Tensor Graph Convolutional Network (RT-GCN) model to improve the robustness. On the one hand, we utilize multi-view augmentation to reduce the augmentation variance and organize them as a third-order tensor, followed by the truncated T-SVD to capture the low-rankness of the multi-view augmented graph, which improves the robustness from the perspective of graph preprocessing. On the other hand, to effectively capture the inter-view and intra-view information on the multi-view augmented graph, we propose tensor GCN (TGCN) framework and analyze the mathematical relationship between TGCN and vanilla GCN, which improves the robustness from the perspective of model architecture. Extensive experimental results have verified the effectiveness of RT-GCN on various datasets, demonstrating the superiority to the state-of-the-art models on diverse adversarial attacks for graphs.
ML4S

Causal skeleton learning aims to identify the undirected graph of the underlying causal Bayesian network (BN) from observational data. It plays a pivotal role in causal discovery and many other downstream applications. The methods for causal skeleton learning fall into three primary categories: constraint-based, score-based, and gradient-based methods. This paper, for the first time, advocates for learning a causal skeleton in a supervision-based setting, where the algorithm learns from additional datasets associated with the ground-truth BNs (complementary to input observational data). Concretizing a supervision-based method is non-trivial due to the high complexity of the problem itself, and the potential "domain shift" between training data (i.e., additional datasets associated with ground-truth BNs) and test data (i.e., observational data) in the supervision-based setting. First, it is well-known that skeleton learning suffers worst-case exponential complexity. Second, conventional supervised learning assumes an independent and identical distribution (i.i.d.) on test data, which is not easily attainable due to the divergent underlying causal mechanisms between training and test data. Our proposed framework, ML4S, adopts order-based cascade classifiers and pruning strategies that can withstand high computational overhead without sacrificing accuracy. To address the "domain shift" challenge, we generate training data from vicinal graphs w.r.t. the target BN. The associated datasets of vicinal graphs share similar joint distributions with the observational data. We evaluate ML4S on a variety of datasets and observe that it remarkably outperforms the state of the arts, demonstrating the great potential of the supervision-based skeleton learning paradigm.
Toolkit for Time Series Anomaly Detection

Time series anomaly detection is an interesting practical problem that mostly falls into unsupervised learning segment. There has been continuous stream of work being published in top-tier data mining and machine learning conferences. We invented many anomaly algorithms, procedures, and applications while working on real industrial application settings. This tutorial presents a design and implementation of a scikit-compatible system for detecting anomalies from time series data for the purpose of offering a broad range of algorithms to the end user, with special focus on unsupervised/semi-supervised learning.