Reviewerly/exHarmony huggingface dataset:
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification

Modern models for event causality identification (ECI) are mainly based on supervised learning, which are prone to the data lacking problem.Unfortunately, the existing NLPrelated augmentation methods cannot directly produce available data required for this task.To solve the data lacking problem, we introduce a new approach to augment training data for event causality identification, by iteratively generating new examples and classifying event causality in a dual learning framework.On the one hand, our approach is knowledge guided, which can leverage existing knowledge bases to generate well-formed new sentences.On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework, and can interactively adjust the generation process to generate task-related sentences.Experimental results on two benchmarks EventStoryLine and Causal-TimeBank show that 1) our method can augment suitable task-related training data for ECI; 2) our method outperforms previous methods on EventStoryLine and Causal-TimeBank (+2.5 and +2.1 points on F1 value respectively).
An automated solution for measuring the progress toward FAIR research data

<h2>Summary</h2> With a rising number of scientific datasets published and the need to test their Findable, Accessible, Interoperable, and Reusable (FAIR) compliance repeatedly, data stakeholders have recognized the importance of an automated FAIR assessment. This paper presents a programmatic solution for assessing the FAIRness of research data. We describe the translation of the FAIR data principles into measurable metrics and the application of the metrics in evaluating FAIR compliance of research data through an open-source tool we developed. For each metric, we conceptualized and implemented practical tests drawn upon prevailing data curation and sharing practices, and the paper discusses their rationales. We demonstrate the work by evaluating multidisciplinary datasets from trustworthy repositories, followed by recommendations and improvements. We believe our experience in developing and applying the metrics in practice and the lessons we learned from it will provide helpful information to others developing similar approaches to assess different types of digital objects and services.
The Digital Gender Gap in Teacher Education: The TPACK Framework for the 21st Century

The main goal of this research is to explore whether there are any differences by gender regarding the Digital Competence of Teachers (DCT), both in-training and in-service. Simultaneously, the specific goals of the research are to analyse which are the methodologies, including technological, that are being implemented in university classrooms and to evaluate possible new interventions to reduce the digital gender gap. This study is exploratory and descriptive. It relies upon three instruments that have been validated by experts: a questionnaire to collect teachers' in-training perception, a second questionnaire to show in-service teachers' perception regarding their knowledge of technologies, and a rubric to analyse in-service teachers' self-perception regarding methodologies that employ technology. Over three academic years, data were collected from a sample of 914 trainee teachers and 194 professors from several Spanish universities. The results show that, concerning the teaching task, compared to men, the female participants have a very poor self-perception in terms of their Digital Teaching Competence, as well as a lower predisposition towards technologies. We conclude by emphasising the need to transform teaching methodologies in initial teacher education by means of the correct inclusion of ICT tools.
Perceptual Quality Assessment of Colored 3D Point Clouds

3D point clouds have found a wide variety of applications in multimedia processing, remote sensing, and scientific computing. Although most point cloud processing systems are developed to improve viewer experiences, little work has been dedicated to perceptual quality assessment of 3D point clouds. In this work, we build a new 3D point cloud database, namely the Waterloo Point Cloud (WPC) database. In contrast to existing datasets consisting of small-scale and low-quality source content of constrained viewing angles, the WPC database contains 20 high quality, realistic, and omni-directional source point clouds and 740 diversely distorted point clouds. We carry out a subjective quality assessment experiment over the database in a controlled lab environment. Our statistical analysis suggests that existing objective point cloud quality assessment (PCQA) models only achieve limited success in predicting subjective quality ratings. We propose a novel objective PCQA model based on an attention mechanism and a variant of information content-weighted structural similarity, which significantly outperforms existing PCQA models. The database has been made publicly available at https://github.com/qdushl/Waterloo-Point-Cloud-Database.
Open science, data sharing and solidarity: who benefits?

Research, innovation, and progress in the life sciences are increasingly contingent on access to large quantities of data. This is one of the key premises behind the "open science" movement and the global calls for fostering the sharing of personal data, datasets, and research results. This paper reports on the outcomes of discussions by the panel "Open science, data sharing and solidarity: who benefits?" held at the 2021 Biennial conference of the International Society for the History, Philosophy, and Social Studies of Biology (ISHPSSB), and hosted by Cold Spring Harbor Laboratory (CSHL).
Systematically Understanding Cybersecurity Economics: A Survey

Insights in the field of cybersecurity economics empower decision makers to make informed decisions that improve their evaluation and management of situations that may lead to catastrophic consequences and threaten the sustainability of digital ecosystems. By drawing on these insights, cybersecurity practitioners have been able to respond to many complex problems that have emerged within the context of cybersecurity over the last two decades. The academic field of cybersecurity economics is highly interdisciplinary since it combines core findings and tools from disciplines such as sociology, psychology, law, political science, and computer science. This study aims to develop an extensive and consistent survey based on a literature review and publicly available reports. This review contributes by aggregating the available knowledge from 28 studies, out of a collection of 628 scholarly articles, to answer five specific research questions. The focus is how identified topics have been conceptualized and studied variously. This review shows that most of the cybersecurity economics models are transitioning from unrealistic, unverifiable, or highly simplified fundamental premises toward dynamic, stochastic, and generalizable models.
A Local and Global Feature Disentangled Network: Toward Classification of Benign-Malignant Thyroid Nodules From Ultrasound Image

Thyroid nodules are one of the most common nodular lesions. The incidence of thyroid cancer has increased rapidly in the past three decades and is one of the cancers with the highest incidence. As a non-invasive imaging modality, ultrasonography can identify benign and malignant thyroid nodules, and it can be used for large-scale screening. In this study, inspired by the domain knowledge of sonographers when diagnosing ultrasound images, a local and global feature disentangled network (LoGo-Net) is proposed to classify benign and malignant thyroid nodules. This model imitates the dual-pathway structure of human vision and establishes a new feature extraction method to improve the recognition performance of nodules. We use the tissue-anatomy disentangled (TAD) block to connect the dual pathways, which decouples the cues of local and global features based on the self-attention mechanism. To verify the effectiveness of the model, we constructed a large-scale dataset and conducted extensive experiments. The results show that our method achieves an accuracy of 89.33%, which has the potential to be used in the clinical practice of doctors, including early cancer screening procedures in remote or resource-poor areas.
A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system’s ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing.
Machine-learning-based anomaly detection in optical fiber monitoring

Secure and reliable data communication in optical networks is critical for high-speed Internet. However, optical fibers, serving as the data transmission medium providing connectivity to billons of users worldwide, are prone to a variety of anomalies resulting from hard failures (e.g., fiber cuts) and malicious physical attacks [e.g., optical eavesdropping (fiber tapping)]. Such anomalies may cause network disruption, thereby inducing huge financial and data losses, compromising the confidentiality of optical networks by gaining unauthorized access to the carried data, or gradually degrading the network operations. Therefore, it is highly required to implement efficient anomaly detection, diagnosis, and localization schemes for enhancing the availability and reliability of optical networks. In this paper, we propose a data-driven approach to accurately and quickly detect, diagnose, and localize fiber fault anomalies, including fiber cuts and optical eavesdropping attacks. The proposed method combines an autoencoder-based anomaly detection and an attention-based bidirectional gated recurrent unit algorithm, whereby the former is used for fault detection and the latter is adopted for fault diagnosis and localization once an anomaly is detected by the autoencoder. We verify the efficiency of our proposed approach by experiments under various attack anomaly scenarios using real operational data. The experimental results demonstrate that (i) the autoencoder detects any fiber fault or anomaly with an F1 score of 96.86%, and (ii) the attention-based bidirectional gated recurrent unit algorithm identifies the detected anomalies with an average accuracy of 98.2% and localizes the faults with an average root mean square error of 0.19 m.
Federated Learning for Privacy Preservation of Healthcare Data From Smartphone-Based Side-Channel Attacks

Federated learning (FL) has recently emerged as a striking framework for allowing machine and deep learning models with thousands of participants to have distributed training to preserve the privacy of users’ data. Federated learning comes with the pros of allowing all participants the possibility of creating robust models even in the absence of sufficient training data. Recently, smartphone usage has increased significantly due to its portability and ability to perform many daily life tasks. Typing on a smartphone’s soft keyboard generates vibrations that could be abused to detect the typed keys, aiding side-channel attacks. Such data can be collected using smartphone hardware sensors during the entry of sensitive information such as clinical notes, personal medical information, username, and passwords. This study proposes a novel framework based on federated learning for side-channel attack detection to secure this information. We collected a dataset from 10 Android smartphone users who were asked to type on the smartphone soft keyboard. We convert this dataset into two windows of five users to make two clients training local models. The federated learning-based framework aggregates model updates contributed by two clients and trained the Deep Neural Network (DNN) model individually on the dataset. To reduce the over-fitting factor, each client examines the findings three times. Experiments reveal that the DNN model achieves an accuracy of 80.09%, showing that the proposed framework has the potential to detect side-channel attacks.
Algebraic compression of quantum circuits for Hamiltonian evolution

Unitary evolution under a time-dependent Hamiltonian is a key component of simulation on quantum hardware. Synthesizing the corresponding quantum circuit is typically done by breaking the evolution into small time steps, also known as Trotterization, which leads to circuits the depth of which scales with the number of steps. When the circuit elements are limited to a subset of $SU(4)$---or equivalently, when the Hamiltonian may be mapped onto free fermionic models---several identities exist that combine and simplify the circuit. Based on this, we present an algorithm that compresses the Trotter steps into a single block of quantum gates using algebraic relations between adjacent circuit elements. This results in a fixed depth time evolution for certain classes of Hamiltonians. We explicitly show how this algorithm works for several spin models, and demonstrate its use for adiabatic state preparation of the transverse field Ising model.
Motion-Driven Visual Tempo Learning for Video-Based Action Recognition

Action visual tempo characterizes the dynamics and the temporal scale of an action, which is helpful to distinguish human actions that share high similarities in visual dynamics and appearance. Previous methods capture the visual tempo either by sampling raw videos with multiple rates, which require a costly multi-layer network to handle each rate, or by hierarchically sampling backbone features, which rely heavily on high-level features that miss fine-grained temporal dynamics. In this work, we propose a Temporal Correlation Module (TCM), which can be easily embedded into the current action recognition backbones in a plug-in-and-play manner, to extract action visual tempo from low-level backbone features at single-layer remarkably. Specifically, our TCM contains two main components: a Multi-scale Temporal Dynamics Module (MTDM) and a Temporal Attention Module (TAM). MTDM applies a correlation operation to learn pixel-wise fine-grained temporal dynamics for both fast-tempo and slow-tempo. TAM adaptively emphasizes expressive features and suppresses inessential ones via analyzing the global information across various tempos. Extensive experiments conducted on several action recognition benchmarks, e.g. Something-Something V1 & V2, Kinetics-400, UCF-101, and HMDB-51, have demonstrated that the proposed TCM is effective to promote the performance of the existing video-based action recognition models for a large margin. The source code is publicly released at https://github.com/zphyix/TCM.
Multiomics, artificial intelligence, and precision medicine in perinatology

Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality.
Towards Personalised Gaming via Facial Expression Recognition

In this paper we propose an approach for personalising the space in which a game is played (i.e., levels) dependent on classifications of the user's facial expression — to the end of tailoring the affective game experience to the individual user. Our approach is aimed at online game personalisation, i.e., the game experience is personalised during actual play of the game. A key insight of this paper is that game personalisation techniques can leverage novel computer vision-based techniques to unobtrusively infer player experiences automatically based on facial expression analysis. Specifically, to the end of tailoring the affective game experience to the individual user, in this paper we (1) leverage the proven InSight facial expression recognition SDK as a model of the user's affective state InSight, and (2) employ this model for guiding the online game personalisation process. User studies that validate the game personalisation approach in the actual video game Infinite Mario Bros. reveal that it provides an effective basis for converging to an appropriate affective state for the individual human player.
A Taxonomy for Neural Memory Networks

An increasing number of neural memory networks have been developed, leading to the need for a systematic approach to analyze and compare their underlying memory structures. Thus, in this paper, we first create a framework for memory organization and then compare four popular dynamic models: vanilla recurrent neural network, long short-term memory, neural stack, and neural RAM. This analysis helps to open the dynamic neural networks' black box from the memory usage prospective. Accordingly, a taxonomy for these networks and their variants is proposed and proved using a unifying architecture. With the taxonomy, both network architectures and learning tasks are classified into four classes, and a one-to-one mapping is built between them to help practitioners select the appropriate architecture. To exemplify each task type, four synthetic tasks with different memory requirements are selected. Moreover, we use some signal processing applications and two natural language processing applications to evaluate the methodology in a realistic setting.
Text normalization using memory augmented neural networks

We perform text normalization, i.e. the transformation of words from the written to the spoken form, using a memory augmented neural network. With the addition of dynamic memory access and storage mechanism, we present a neural architecture that will serve as a language-agnostic text normalization system while avoiding the kind of unacceptable errors made by the LSTM-based recurrent neural networks. By successfully reducing the frequency of such mistakes, we show that this novel architecture is indeed a better alternative. Our proposed system requires significantly lesser amounts of data, training time and compute resources. Additionally, we perform data up-sampling, circumventing the data sparsity problem in some semiotic classes, to show that sufficient examples in any particular class can improve the performance of our text normalization system. Although a few occurrences of these errors still remain in certain semiotic classes, we demonstrate that memory augmented networks with meta-learning capabilities can open many doors to a superior text normalization system.
When Do Composed Maps Become Entanglement Breaking?

For many completely positive maps repeated compositions will eventually become entanglement breaking. To quantify this behaviour we develop a technique based on the Schmidt number: If a completely positive map breaks the entanglement with respect to any qubit ancilla, then applying it to part of a bipartite quantum state will result in a Schmidt number bounded away from the maximum possible value. Iterating this result puts a successively decreasing upper bound on the Schmidt number arising in this way from compositions of such a map. By applying this technique to completely positive maps in dimension three that are also completely copositive we prove the so-called PPT squared conjecture in this dimension. We then give more examples of completely positive maps where our technique can be applied, e.g. maps close to the completely depolarizing map, and maps of low rank. Finally, we study the PPT squared conjecture in more detail, establishing equivalent conjectures related to other parts of quantum information theory, and we prove the conjecture for Gaussian quantum channels.
Nonlinear phase estimation: Parity measurement approaches the quantum Cramér-Rao bound for coherent states

Quantum-enhanced phase estimation paves the way for precision measurements and is of great realistic significance. In this paper we theoretically investigate the second-order nonlinear phase estimation using a coherent state and parity measurement. A numerical expression of the parity signal is derived, and its visibility is analyzed. The resolution and sensitivity of the signal are compared to a linear phase estimation protocol with the same input and measurement. Additionally, by using the phase-averaging approach, we make an attempt at unveiling the lowdown on a conclusive sensitivity limit without external phase reference. Finally, the effects of several realistic scenarios on the resolution and sensitivity are studied, including photon loss, imperfect detector, those which are a combination thereof, and unbalanced beam splitter.