Reviewerly/exHarmony huggingface dataset:
Cost-effective CNNs-based prototypical networks for few-shot relation classification across domains

This paper studies few-shot relation classification under domain shift, which is quite a challenging inductive task in practice. Previous work focusing on few-shot relation classification usually adopted prototypical networks, whose performance dramatically dropped when adapting to diverse domains. Some researches introduced large pretrained language models, which consume massive time and computation resources. To address the above issues, we propose cost-effective CNNs-based prototypical networks in this paper. Specifically, a multichannel encoder (MCE) is adopted to capture general domain invariant features respectively from the entity and the context, then they are aggregated according to relation classes. When encoding the context, we propose an attention mechanism based on the dependency trees of sentences to effectively select helpful grams. To get further improvements, we leverage the unlabeled data from the target domain by pseudo-labeling and introduce a method to select instances with high confidence via information entropy. We conducted experiments on two public datasets: FewRel 2.0 and FewTAC. The results demonstrate that our approaches not only largely enhance the effectiveness of original prototypical networks, but also achieve competitive results with large pretrained models with faster speeds and much fewer computational costs.
Trust management for service migration in Multi-access Edge Computing environments

Multi-access Edge Computing relies on the concept of moving part of the cloud resources closer to the users to address limitations of the traditional cloud in order to reduce communication latency and increase security. This makes Multi-access Edge Computing (MEC) suitable for time-critical applications such as autonomous vehicles where they can support connectivity with a safe and more efficient experience. Nevertheless, MECs are generally deployed on constrained devices with limited resources, which may affect the infrastructure reliability and thus its trustworthiness. In this paper, we focus on a trust mechanism based on the interactions between MECs to increase reliability in the context of a service migration scenario, where MEC nodes can decide based on a trust score to which node to migrate their services and user information. Our proposed algorithm leverages ideas of the EigenTrust and RLIoT reputation system and combined with a novel correlation concept and a dynamic distance algorithm. From our simulations, our model shows better results in different scenarios and communication ranges. This work provides the core component of a complete trust management system for MECs and could be applied in various use cases.
Protecting high-dimensional entanglement from decoherence via quantum weak measurement and reversal

High-dimensional quantum communication plays an important role in long-distance quantum communication. It is shown that two-qubit quantum entanglement can be protected from amplitude damping decoherence. We study the effect of amplitude damping decoherence on two-qubit high-dimensional quantum entanglement. The negativity for high-dimensional quantum entanglement with or without quantum weak measurement and the reversal in amplitude damping decoherence are presented. We found that nonmaximally entangled state may have better performance than maximally entangled state in amplitude damping decoherence. Moreover, we show that arbitrary high-dimensional quantum entanglement can be protected from amplitude damping decoherence by weak measurement and the reversal. The results of our protocol are in agreement with previous analysis for the case of qubits and qutrits.
Concurrence percolation threshold of large-scale quantum networks

Abstract Quantum networks describe communication networks that are based on quantum entanglement. A concurrence percolation theory has been recently developed to determine the required entanglement to enable communication between two distant stations in an arbitrary quantum network. Unfortunately, concurrence percolation has been calculated only for very small networks or large networks without loops. Here, we develop a set of mathematical tools for approximating the concurrence percolation threshold for unprecedented large-scale quantum networks by estimating the path-length distribution, under the assumption that all paths between a given pair of nodes have no overlap. We show that our approximate method agrees closely with analytical results from concurrence percolation theory. The numerical results we present include 2D square lattices of 200 2 nodes and complex networks of up to 10 4 nodes. The entanglement percolation threshold of a quantum network is a crucial parameter for constructing a real-world communication network based on entanglement, and our method offers a significant speed-up for the intensive computations involved.
Plant Tissue Modelling Using Power-Law Filters

Impedance spectroscopy has became an essential non-invasive tool for quality assessment measurements of the biochemical and biophysical changes in plant tissues. The electrical behaviour of biological tissues can be captured by fitting its bio-impedance data to a suitable circuit model. This paper investigates the use of power-law filters in circuit modelling of bio-impedance. The proposed models are fitted to experimental data obtained from eight different fruit types using a meta-heuristic optimization method (the Water Cycle Algorithm (WCA)). Impedance measurements are obtained using a Biologic SP150 electrochemical station, and the percentage error between the actual impedance and the fitted models' impedance are reported. It is found that a circuit model consisting of a combination of two second-order power-law low-pass filters shows the least fitting error.
Utility-driven assessment of anonymized data via clustering

In this study, clustering is conceived as an auxiliary tool to identify groups of special interest. This approach was applied to a real dataset concerning an entire Portuguese cohort of higher education Law students. Several anonymized clustering scenarios were compared against the original cluster solution. The clustering techniques were explored as data utility models in the context of data anonymization, using k-anonymity and (ε, δ﻿)-differential as privacy models. The purpose was to assess anonymized data utility by standard metrics, by the characteristics of the groups obtained, and the relative risk (a relevant metric in social sciences research). For a matter of self-containment, we present an overview of anonymization and clustering methods. We used a partitional clustering algorithm and analyzed several clustering validity indices to understand to what extent the data structure is preserved, or not, after data anonymization. The results suggest that for low dimensionality/cardinality datasets the anonymization procedure easily jeopardizes the clustering endeavor. In addition, there is evidence that relevant field-of-study estimates obtained from anonymized data are biased.
ARTracker: Compute a More Accurate and Robust Correlation Filter for UAV Tracking

Unmanned aerial vehicle (UAV) tracking focus on tracking moving targets from flying platforms, where the target undergoes a lot of aspect ratio changes, <i>i.e</i>., viewpoint change, rotation. Discriminative correlation filter (DCF) based method shows a promising solution to UAV tracking due to its high computational efficiency. DCF based trackers apply a fixed-bandwidth Gaussian function label for model training and incremental update to adapt to the dramatic appearance changes in target during tracking. However, due to the poor regression ability of correlation filter, DCF trackers is unable to describe the target state when the aspect ratio changes, thus hurting the tracking performance. To alleviate this, we propose a novelty correlation filter, which constructs a Gaussian-like function label for correlation filter training. The label fully considers the aspect ratio distribution of the target, which facilitates the training of a more robust tracker. Furthermore, an accurate incremental update is proposed to mitigate model degradation by combining target samples with adaptive aspect ratios. Extensive experiments are conducted on three popular UAV benchmarks, <i>i.e</i>., VisDrone2018-test-dev, UAV20L and DTB70. Results well demonstrate the superiority of the proposed method over both DCF and deep based trackers. Code will be released soon.
Heterogeneous Defect Prediction Based on Federated Reinforcement Learning via Gradient Clustering

Heterogeneous defect prediction (HDP) refers to using heterogeneous data collected by other projects to build a defect prediction model to predict the software modules prone to defects in a project.Traditional methods usually involve the measurement of the source project and the target project.However, due to the limitations of laws and regulations, these original data are generally not easy to obtain, which forms a data island.As a new machine learning paradigm, federated learning (FL) has great advantages in training heterogeneous and data island.In order to solve the data island and data heterogeneity of HDP, we propose a novel Federated Reinforcement Learning via Gradient Clustering (FRLGC) method in this paper.Firstly, the parameters of the global model are transferred to each dueling deep Q network (dueling DQN) model and each client uses private data to train the dueling model which combines experience replay to increase data efficiency in limited datasets.Secondly, gaussian differential privacy is used to encrypt the model parameters to ensure the privacy and security of the model.Finally, we cluster the clients according to their locally encrypted model parameters and use weighted average to aggregate to create a new global model locally and globally.Experiments on nine projects in three public databases (Relink, NASA and AEEEM) show that FRLGC is superior to the relevant HDP solutions.
Adaptive virtual reality horror games based on Machine learning and player modeling

Fear is a basic human emotion that can be triggered by different situations, which vary from person to person. However, game developers usually design horror games based on a general knowledge about what most players fear, which does not guarantee a satisfying horror experience for every-one. When a horror game aims at intensifying the fear evoked in individual players, having useful information about the fears of the current player is vital to promote more frightening experiences. This work presents a new method to create adaptive virtual reality horror games, which combines player modeling techniques and an adaptive agent-based system that can identify what individual players fear and adapt the content of the game to intensify the fear evoked in players. The main contributions of this work are: (1) a new method to identify individual player’s fears using only gameplay data and machine learning techniques; and (2) a new agent-based adaptive game system that can track the horror intensity experienced by players and moderate the use of the horror elements feared by individual players in the game. The results show that the proposed method is capable of correctly identifying players’ fears (average accuracy of 79.4% for new players). In addition, results of a user study and statistical significance tests (ANOVA and post-hoc analyses) suggest that our method can intensify the fear evoked in players and positively improve immersion and flow.
Adaptive multi-level graph convolution with contrastive learning for skeleton-based action recognition

Graph Convolutional Networks (GCNs) have been widely used in skeleton-based action recognition with remarkable achievements. Many recent studies model the human body as a topological graph and extract action features using GCNs, however, the inherent shared and static characteristics of topological graph during training limit the performance of the model. In this work, we propose an adaptive multi-level graph convolution network (AML-GCN), which uses two different levels of spatial convolution to extract spatial features and enhances the feature representation by contrastive learning, then uses multiscale temporal convolution to capture advanced temporal features. We first propose dynamic topological spatial graph convolution (DTSGC) using dynamic graph topology, which can be continuously updated during training and has a strong generalization ability. To further enhance flexibility of the network, we present adaptive spatial graph convolution (ASGC) using non-shared graph topology, which is unique in different layers and can extract more diverse features. Meanwhile, we introduce contrastive learning to maximize mutual information between the two modules, which are trained together to promote each other and enhance feature representation. Extensive experiments on the three large datasets verify that the proposed model achieves excellent recognition accuracy compared to most current models.
Elite-ordinary synergistic particle swarm optimization

In particle swarm optimization (PSO), the canonical learning exemplars and information sharing mechanism are often criticized due to the loss of population diversity. Aiming at preserving population diversity and promoting search ability of PSO, this paper introduces an elite-ordinary synergistic particle swarm optimization (EOPSO). In EOPSO, particles are divided into elite and ordinary members based on their fitness performance. Each elite individual learns from itself to maintain population diversity and achieve high-level global exploration. The ordinary ones fly toward a unified target and carry out some assistant local exploitation. In addition, an information interaction based jump-out strategy is designed to overcome particles stagnation situations. The benchmark functions in CEC2017 are employed to compare the performance between the proposed EOPSO with 18 optimization methods (8 state-of-the-art PSO variants and 10 recently proposed non-PSO methods). Experimental comparisons demonstrate that, in EOPSO, the particles have the abilities to reasonably adjust population diversity, effectively avoid local optima, and accurately converge to the global optimum.
Exploring the information content of cyber breach reports and the relationship to internal controls

A number of institutions make reports available regarding the types, impacts, or origins of cybersecurity breaches. The information content of cyber breach reports is examined in light of Principle 15 of the 2017 Committee on Sponsoring Organizations Enterprise Risk Management (COSO ERM) information security control framework to understand the degree to which cyber breach reports reflect the established COSO internal control framework. This study utilizes the COSO ERM internal control framework to examine whether current cyber breach reports contain information that may influence a firm’s ability to assess substantial change within its industry due to external forces (COSO ERM Principle 15). As such, this study focuses on data breaches, a special type of cyber incident, which may result in the loss of confidential information. Cyber decision makers rely on this type of information to calibrate information security programs to ensure coverage of relevant threats and the efficient use of available funds. These reports may be used for the purposes of cybersecurity risk assessment and strategic planning. We compare, contrast, and analyzie the reports to identify their utility in such contexts. We also provide an overview of the current cybersecurity reporting environment and suggest revisions to US national cyber policy with the intent of increasing the benefit to reporters and consumers of the data. This study is focused on education as to the current structure of breach reporting based upon our review and synthesis of publicly-available breach reports. In this study, we review nine (9) reports that meet four (4) criteria. We relate these criteria to the framework provided by COSO ERM Principle 15 by analyzing and placing the criteria into a taxonomy developed for this purpose. We analyze the degree to which the reports are complementary, reflect potential improvements of internal controls, and provide recommendations for ways in which these types of reports might be used by practitioners, while highlighting potential limitations. Our findings indicate that the sample reports contain little information that may be incorporated to improve the risk profile of an entity. We provide recommendations to improve the information content and timeliness of breach reports.
Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study

Electronic health records (EHRs) with large sample sizes and rich information offer great potential for dementia research, but current methods of phenotyping cognitive status are not scalable.The aim of this study was to evaluate whether natural language processing (NLP)-powered semiautomated annotation can improve the speed and interrater reliability of chart reviews for phenotyping cognitive status.In this diagnostic study, we developed and evaluated a semiautomated NLP-powered annotation tool (NAT) to facilitate phenotyping of cognitive status. Clinical experts adjudicated the cognitive status of 627 patients at Mass General Brigham (MGB) health care, using NAT or traditional chart reviews. Patient charts contained EHR data from two data sets: (1) records from January 1, 2017, to December 31, 2018, for 100 Medicare beneficiaries from the MGB Accountable Care Organization and (2) records from 2 years prior to COVID-19 diagnosis to the date of COVID-19 diagnosis for 527 MGB patients. All EHR data from the relevant period were extracted; diagnosis codes, medications, and laboratory test values were processed and summarized; clinical notes were processed through an NLP pipeline; and a web tool was developed to present an integrated view of all data. Cognitive status was rated as cognitively normal, cognitively impaired, or undetermined. Assessment time and interrater agreement of NAT compared to manual chart reviews for cognitive status phenotyping was evaluated.NAT adjudication provided higher interrater agreement (Cohen κ=0.89 vs κ=0.80) and significant speed up (time difference mean 1.4, SD 1.3 minutes; P<.001; ratio median 2.2, min-max 0.4-20) over manual chart reviews. There was moderate agreement with manual chart reviews (Cohen κ=0.67). In the cases that exhibited disagreement with manual chart reviews, NAT adjudication was able to produce assessments that had broader clinical consensus due to its integrated view of highlighted relevant information and semiautomated NLP features.NAT adjudication improves the speed and interrater reliability for phenotyping cognitive status compared to manual chart reviews. This study underscores the potential of an NLP-based clinically adjudicated method to build large-scale dementia research cohorts from EHRs.
Contrastive Learning based Multi-task Network for Image Manipulation Detection

The popularity of image editing techniques and user-friendly editing software have seriously reduced the authenticity of the images. Detection and localization of image manipulations are becoming urgent problems to be solved. Although many existing solutions attempt to address these problems, most works can only solve one specific type of manipulations. Furthermore, some methods need heavy, time-consuming preprocessings and/or postprocessings to localize tampered region, resulting in disconnection and under-optimization of the model. In this paper, a contrastive learning based multi-task network is proposed for the localization of multiple image manipulations. Multi-scale tampered patch classifications and pixel-wise tampered region semantic segmentation are integrated into an end-to-end multi-task network. The consistency of different region statistical properties is measured by contrastive learning to enhance the feature representation ability of the proposed network, improving the performance of tampered patch detection. Various scale tampered patch detections cooperate to localize the tampered region boundaries from coarse to fine. Prediction Pyramid composed of different scale patch detection results provides comprehensive guidance for pixel-wise semantic segmentation of the tampered region. Experimental results on four standard image manipulation datasets demonstrate the better performance of the proposed model.
Multi-Spacecraft Tracking and Data Association Based on Uncertainty Propagation

This paper proposed a novel multi-spacecraft tracking and data association method based on the orbit uncertainty propagation. The proposed method makes full use of the dynamic information and thus the data association performance is enhanced. The proposed method is divided into three portions, i.e., the uncertainty propagation, the data association, and the orbit estimation. The second-order solutions derived for state and measurement prediction, on which to base the optimal association, are set up. The optimal association is solved by the contract network algorithm to reduce the computing cost. Finally, a second-order extended Kalman filter is designed to estimate the orbit of each spacecraft. The proposed method is successfully applied for solving a four-spacecraft tracking problem. Simulations show that all the four targets are well tracked. The method demonstrates close to 100% data association precision. The proposed method is proved to be efficient and effective to solve the multi-spacecraft tracking problem.
Spillovers between Bitcoin and Meme stocks

This paper examines the spillover effect between Bitcoin and Meme stocks, the two types of assets heavily traded by retail investors. A unidirectional wealth transfer phenomenon from Meme stocks to Bitcoin is identified. Moreover, both past own volatility and volatility from Bitcoin contribute to Meme stocks' current volatility. "Bad" news on the two assets affects not only themselves but also spillovers to each other. Our findings demonstrate the great risk of investment in Meme stocks and call for comprehensive protection for retail investors amid their growing active involvement in financial markets. : C22, C5, G11
Making Sense of the Unknown: How Managers Make Cyber Security Decisions

Managers rarely have deep knowledge of cyber security and yet are expected to make decisions with cyber security implications for software-based systems. We investigate the decision-making conversations of seven teams of senior managers from the same organisation as they complete the Decisions &amp; Disruptions cyber security exercise. We use grounded theory to situate our analysis of their decision-making and help us explore how these complex socio-cognitive interactions occur. We have developed a goal-model (using iStar 2.0) of the teams’ dialogue that illustrates what cyber security goals teams identify and how they operationalise their decisions to reach these goals. We complement this with our model of cyber security reasoning that describes how these teams make their decisions, showing how each team members’ experience, intuition, and understanding affects the team’s overall shared reasoning and decision-making. Our findings show how managers with little cyber security expertise are able to use logic and traditional risk management thinking to make cyber security decisions. Despite their lack of cyber security–specific training, they demonstrate reasoning that closely resembles the decision-making approaches espoused in cyber security–specific standards (e.g., NIST/ISO). Our work demonstrates how organisations and practitioners can enrich goal modelling to capture not only what security goals an organisation has (and how they can operationalise them) but also how and why these goals have been identified. Ultimately, non–cyber security experts can develop their cyber security model based on their current context (and update it when new requirements appear or new incidents happen), whilst capturing their reasoning at every stage.