Reviewerly/exHarmony huggingface dataset:
Analysis of the impact of cyber events for cyber insurance

The mass adoption of cyber insurance will be predicated on the ability to conduct quantitative cyber risk assessment. This capability is crucial for not only providing insight into the cost of targeted threats but also providing incentives for insured enterprises to invest in protection aimed at preventing exploitation of targeted threats. Research indicates that asymmetric information, correlated loss and interdependent security issues make this difficult if insurers cannot monitor the cybersecurity efforts of the insured enterprises. In this paper, we present an analysis of cyber impacts based on cyber incidents reported in the Advisen cyber loss data feed. We show: (i) how exposure to cyber incidents varies between corporate sectors; (ii) how the type of incident relates to the number of entities and individuals affected by it; (iii) how the type of incident relates to the eventual financial cost; (iv) what type of information is most frequently compromised; (v) a breakdown of the main actors behind cyber incidents; and (vi) how tree-based classifiers can be used to gain insight into cyber risk indicators affecting the cost of incidents.
Saliency-based image correction for colorblind patients

Abstract Improper functioning, or lack, of human cone cells leads to vision defects, making it impossible for affected persons to distinguish certain colors. Colorblind persons have color perception, but their ability to capture color information differs from that of normal people: colorblind and normal people perceive the same image differently. It is necessary to devise solutions to help persons with color blindness understand images and distinguish different colors. Most research on this subject is aimed at adjusting insensitive colors, enabling colorblind persons to better capture color information, but ignores the attention paid by colorblind persons to the salient areas of images. The areas of the image seen as salient by normal people generally differ from those seen by the colorblind. To provide the same saliency for colorblind persons and normal people, we propose a saliency-based image correction algorithm for color blindness. Adjusted colors in the adjusted image are harmonious and realistic, and the method is practical. Our experimental results show that this method effectively improves images, enabling the colorblind to see the same salient areas as normal people.
Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset

Machine reading comprehension has made great progress in recent years owing to largescale annotated datasets.In the clinical domain, however, creating such datasets is quite difficult due to the domain expertise required for annotation.Recently, Pampari et al. (2018) tackled this issue by using expert-annotated question templates and existing i2b2 annotations to create emrQA, the first large-scale dataset for question answering (QA) based on clinical notes.In this paper, we provide an indepth analysis of this dataset and the clinical reading comprehension (CliniRC) task.From our qualitative analysis, we find that (i) emrQA answers are often incomplete, and (ii) emrQA questions are often answerable without using domain knowledge.From our quantitative experiments, surprising results include that (iii) using a small sampled subset (5%-20%), we can obtain roughly equal performance compared to the model trained on the entire dataset, (iv) this performance is close to human expert's performance, and (v) BERT models do not beat the best performing base model.Following our analysis of the emrQA, we further explore two desired aspects of CliniRC systems: the ability to utilize clinical domain knowledge and to generalize to unseen questions and contexts.We argue that both should be considered when creating future datasets. 1
Transition-based Semantic Dependency Parsing with Pointer Networks

Transition-based parsers implemented with Pointer Networks have become the new state of the art in dependency parsing, excelling in producing labelled syntactic trees and outperforming graph-based models in this task.In order to further test the capabilities of these powerful neural networks on a harder NLP problem, we propose a transition system that, thanks to Pointer Networks, can straightforwardly produce labelled directed acyclic graphs and perform semantic dependency parsing.In addition, we enhance our approach with deep contextualized word embeddings extracted from BERT.The resulting system not only outperforms all existing transitionbased models, but also matches the best fullysupervised accuracy to date on the SemEval 2015 Task 18 English datasets among previous state-of-the-art graph-based parsers.
Relation-Based Counterfactual Explanations for Bayesian Network Classifiers

We propose a general method for generating counterfactual explanations (CFXs) for a range of Bayesian Network Classifiers (BCs), e.g. single- or multi-label, binary or multidimensional. We focus on explanations built from relations of (critical and potential) influence between variables, indicating the reasons for classifications, rather than any probabilistic information. We show by means of a theoretical analysis of CFXs’ properties that they serve the purpose of indicating (potentially) pivotal factors in the classification process, whose absence would give rise to different classifications. We then prove empirically for various BCs that CFXs provide useful information in real world settings, e.g. when race plays a part in parole violation prediction, and show that they have inherent advantages over existing explanation methods in the literature.
A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical Attention

The Web has become the main platform where people express their opinions about entities of interest and their associated aspects. Aspect-Based Sentiment Analysis (ABSA) aims to automatically compute the sentiment towards these aspects from opinionated text. In this paper we extend the state-of-the-art Hybrid Approach for Aspect-Based Sentiment Analysis (HAABSA) method in two directions. First we replace the non-contextual word embeddings with deep contextual word embeddings in order to better cope with the word semantics in a given text. Second, we use hierarchical attention by adding an extra attention layer to the HAABSA high-level representations in order to increase the method flexibility in modeling the input data. Using two standard datasets (SemEval 2015 and SemEval 2016) we show that the proposed extensions improve the accuracy of the built model for ABSA.
Toward Automated Story Generation with Markov Chain Monte Carlo Methods and Deep Neural Networks

In this paper, we introduce an approach to automated story generation using Markov Chain Monte Carlo (MCMC) sampling. This approach uses a sampling algorithm based on Metropolis-Hastings to generate a probability distribution which can be used to generate stories via random sampling that adhere to criteria learned by recurrent neural networks. We show the applicability of our technique through a case study where we generate novel stories using an acceptance criteria learned from a set of movie plots taken from Wikipedia. This study shows that stories generated using this approach adhere to this criteria 85%-86% of the time.
Reachable Sets for Safe, Real-Time Manipulator Trajectory Design

For robotic arms to operate in arbitrary environments, especially near people, it is critical to certify the safety of their motion planning algorithms.However, there is often a trade-o↵ between safety and real-time performance; one can either carefully design safe plans, or rapidly generate potentiallyunsafe plans.This work presents a receding-horizon, real-time trajectory planner with safety guarantees, called ARMTD (Autonomous Reachability-based Manipulator Trajectory Design).The method first computes (o✏ine) a reachable set of parameterized trajectories for each joint of an arm.Each trajectory includes a fail-safe maneuver (braking to a stop).At runtime, in each receding-horizon planning iteration, ARMTD constructs a parameterized reachable set of the full arm in workspace and intersects it with obstacles to generate sub-di↵erentiable, provablyconservative collision-avoidance constraints on the trajectory parameters.ARMTD then performs trajectory optimization over the parameters, subject to these constraints.On a 6 degree-of-freedom arm, ARMTD outperforms CHOMP in simulation, never crashes, and completes a variety of real-time planning tasks on hardware.
FracBits: Mixed Precision Quantization via Fractional Bit-Widths

Model quantization helps to reduce model size and latency of deep neural networks. Mixed precision quantization is favorable with customized hardwares supporting arithmetic operations at multiple bit-widths to achieve maximum efficiency. We propose a novel learning-based algorithm to derive mixed precision models end-to-end under target computation constraints and model sizes. During the optimization, the bit-width of each layer / kernel in the model is at a fractional status of two consecutive bit-widths which can be adjusted gradually. With a differentiable regularization term, the resource constraints can be met during the quantization-aware training which results in an optimized mixed precision model. Our final models achieve comparable or better performance than previous quantization methods with mixed precision on MobilenetV1/V2, ResNet18 under different resource constraints on ImageNet dataset.
A Design for a Secure Energy Market Trading System in a National Wholesale Electricity Market

Energy market trading systems are undergoing rapid transformation due to an increasing demand for renewable energy sources to be integrated into the power grid, coupled with the dynamic and evolving needs of future energy customers. In the current energy trading system, which is based on mega power generation, energy is traded by insecure means of communication based on mutual trust. In addition, electricity from both renewable and non-renewable sources is mixed in the grid, impeding customers' ability to definitively track the source of energy dispatched to their premises. Although blockchain technology has been studied for energy trading on a peer-to-peer microgrid trading, to our knowledge none of the previous work focused on using blockchain for trading energy in a national wholesale energy market in macrogrid. In this paper, we address security architectures required of the energy market trading system in an Australian context, we propose a cryptocurrency token-based structure and a smart contract that provides data confidentiality that verifies and audits transactional records. The proposed trading system architecture not only enhances overall system security but provides additional capabilities in the operation of the scheme so that sources of energy dispatched to customer premises are known. The energy market trading system we propose also presents higher security compared to existing trading systems.
Potential of Ambient Sensor Systems for Early Detection of Health Problems in Older Adults

Background: Home monitoring sensor systems are increasingly used to monitor seniors in their apartments for detection of emergency situations. The aim of this study was to deliver a proof-of-concept for the use of multimodal sensor systems with pervasive computing technology for the detection of clinically relevant health problems over longer time periods. Methods: Data was collected with a longitudinal home monitoring study in Switzerland (StrongAge Cohort Study) in a cohort of 24 old and oldest-old, community dwelling adults over a period of one to two years. Physical activity in the apartment, toilet visits, refrigerator use and entrance door openings were quantified using a commercially available passive infrared (PIR) motion sensing system (Domosafety S.A., Switzerland). Heart rate, respiration rate, and sleep quality were recorded with the commercially available EMFIT QS bed sensor device (Emfit Ltd, Finland). Vital signs and contextual data were collected using a wearable sensor on the upper arm (Everion, Biovotion, Switzerland). Sensor data was correlated with health-related data collected from weekly visits of the seniors by health professionals including information about physical, psychological, cognitive and behavior status, health problems, diseases, medication and medical diagnoses. Results: Twenty of the 24 recruited participants (age 88.9 ± 7.5 years, 79% females) completed the study, 2 participants had to stop study participation because of severe health deterioration, whereas 2 participants died during the course of the study. A history of chronic disease was present in 12/24 seniors including heart failure, heart rhythm disturbances, pulmonary embolism, severe insulin-dependent diabetes and Parkinson disease. In total, 242'232 person-hours were recorded. During the monitoring period, 963 health status records were reported and repeated clinical assessments of ageing relevant indicators and outcomes were performed. Several episodes of health deterioration including heart failure worsening and heart rhythm disturbances could be captured by sensor signals from different sources. Conclusions: Our results indicate that monitoring of seniors with a multimodal sensor and pervasive computing system over longer time periods is feasible and well accepted with a great potential for detection of health deterioration. Further studies are necessary to evaluate the full range of the clinical potential of these findings.
A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients

Unlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imaging, true clinical adoption at scale is slower than anticipated. In the U.S., advances in digital pathology are often siloed pursuits by individual stakeholders, and to our knowledge, there has not been a systematic approach to advance the field through a regulatory science initiative. The Alliance for Digital Pathology (the Alliance) is a recently established, volunteer, collaborative, regulatory science initiative to standardize digital pathology processes to speed up innovation to patients. The purpose is: (1) to account for the patient perspective by including patient advocacy; (2) to investigate and develop methods and tools for the evaluation of effectiveness, safety, and quality to specify risks and benefits in the precompetitive phase; (3) to help strategize the sequence of clinically meaningful deliverables; (4) to encourage and streamline the development of ground-truth data sets for machine learning model development and validation; and (5) to clarify regulatory pathways by investigating relevant regulatory science questions. The Alliance accepts participation from all stakeholders, and we solicit clinically relevant proposals that will benefit the field at large. The initiative will dissolve once a clinical, interoperable, modularized, integrated solution (from tissue acquisition to diagnostic algorithm) has been implemented. In times of rapidly evolving discoveries, scientific input from subject-matter experts is one essential element to inform regulatory guidance and decision-making. The Alliance aims to establish and promote synergistic regulatory science efforts that will leverage diverse inputs to move digital pathology forward and ultimately improve patient care.
Recurrent MADDPG for Object Detection and Assignment in Combat Tasks

With the development of artificial intelligence, multiagent algorithms have been applied to many real-time strategy games. Making plans on the human being is gradually passing away, especially in combat scenarios. Cognitive electronic warfare (CEW) is a complex and challenging work due to the sensitivity of the data sources. There are few studies on CEW. In the past, wargame simulations depended on differential equations and war theory, which resulted in high time and human resource costs. In the future, as other artificial intelligence theories are developed, artificial intelligence will play a more critical role in wargames. The capabilities of multiagent modeling to describe complex systems and predict actions in dynamic environments are superior to those of traditional methods. In this paper, we use a 3D wargame engine from China Aerospace System Simulation Technology Co., Ltd. (Beijing) named All Domain Simulation (ACS), which supports land, sea, and air combat scenarios, to simulate combat. In the simulations, there are several unmanned air vehicles (UAVs) as attackers and several radar stations as defenders, and both have the ability to detect the others. In the game, several UAVs need to learn to detect targets and track targets separately, and we train the UAV's behavior by well-designed reward shaping and multiagent reinforcement learning (MARL) with LSTM. We improved the RDPG algorithm and merged the MADDPG and RDPG algorithms. From the experimental results, we can see that the effectiveness and accuracy of the algorithm have been greatly improved.
Evaluating the Factors Affecting QoE of 360-Degree Videos and Cybersickness Levels Predictions in Virtual Reality

360-degree Virtual Reality (VR) videos have already taken up viewers’ attention by storm. Despite the immense attractiveness and hype, VR conveys a loathsome side effect called “cybersickness” that often creates significant discomfort to the viewers. It is of great importance to evaluate the factors that induce cybersickness symptoms and its deterioration on the end user’s Quality-of-Experience (QoE) when visualizing 360-degree videos in VR. This manuscript’s intent is to subjectively investigate factors of high priority that affect a user’s QoE in terms of perceptual quality, presence, and cybersickness. The content type (fast, medium, and slow), the effect of camera motion (fixed, horizontal, and vertical), and the number of moving targets (none, single, and multiple) in a video can be the factors that may affect the QoE. The significant effect of such factors on end-user QoE under various stalling events (none, single, and multiple) is evaluated in a subjective experiment. The results from subjective experiments show a notable impact of these factors on end-user QoE. Finally, to label the viewing safety concern in VR, we propose a neural network-based QoE prediction method that can predict the degree of cybersickness influenced by 360-degree videos under various stalling events in VR. The performance accuracy of the proposed method is then compared against well-known Machine Learning (ML) algorithms and existing QoE prediction models. The proposed method achieved a 90% prediction accuracy rate and performed well against existing models and other ML methods.
Epidemic mitigation by statistical inference from contact tracing data

Contact-tracing is an essential tool in order to mitigate the impact of pandemic such as the COVID-19. In order to achieve efficient and scalable contact-tracing in real time, digital devices can play an important role. While a lot of attention has been paid to analyzing the privacy and ethical risks of the associated mobile applications, so far much less research has been devoted to optimizing their performance and assessing their impact on the mitigation of the epidemic. We develop Bayesian inference methods to estimate the risk that an individual is infected. This inference is based on the list of his recent contacts and their own risk levels, as well as personal information such as results of tests or presence of syndromes. We propose to use probabilistic risk estimation in order to optimize testing and quarantining strategies for the control of an epidemic. Our results show that in some range of epidemic spreading (typically when the manual tracing of all contacts of infected people becomes practically impossible, but before the fraction of infected people reaches the scale where a lock-down becomes unavoidable), this inference of individuals at risk could be an efficient way to mitigate the epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact. Such communication may be encrypted and anonymized and thus compatible with privacy preserving standards. We conclude that probabilistic risk estimation is capable to enhance performance of digital contact tracing and should be considered in the currently developed mobile applications.
Requirements-Driven Method to Determine Quality Characteristics and Measurements for Machine Learning Software and Its Evaluation

As the applications of machine learning algorithms in various fields are widely demanded, the development of machine learning software systems (MLS) is rapidly increasing. The quality of MLS is different from that of conventional software systems, in the sense that it depends on the amount and distribution of training data in a model learning and input data during operation. This is a major challenge in quality assurance of MLS development for the enterprise. In this paper, we propose a requirements-driven method to determine the quality characteristics of the MLS. Major contributions of this paper include: (1) Extending the quality characteristics of ISO 25010, which defines the conventional software quality, to those unique to MLS; this paper also defines its measuring method. (2) A method to identify requirements, i.e., issues to be determined in the requirements definition, in order to derive the quality characteristics and measurement methods for MLS, since the quality characteristics and the measurement method depend on the goals of the system under development. In order to evaluate the proposed method, we carried out an empirical study of the quality characteristics and measurement methods related to functional correctness and the maturity of the MLS for the enterprise. Based on the study, we compare the quality characteristics and measurement methods derived by the proposed method with those suggested by developers, and demonstrate the effectiveness of the proposed method.
Dynamic Adaptive Cross-Chain Trading Mode for Multi-Microgrid Joint Operation

The emerging blockchain technology has injected new vitality into the energy market, especially the peer-to-peer power trading of microgrid systems. However, with the increase of energy blockchain projects, the difficulty of data communication and value islands between blockchain networks have become open issues. Thus, in this paper, we propose a dynamic adaptive cross-chain trading mode for multi-microgrid joint operation. The novelty is to design a proof of credit threshold consensus mechanism to achieve effective information verification. This consensus mechanism can ensure the adaptive consistency of cross-chain information without changing the existing blockchain architecture of each system. At the same time, we design a corresponding key management interoperability protocol based on RSA algorithm and Chinese remainder theorem, which can realize data transfer and information consensus for cross-chain transactions. The theoretical analysis verifies that the cross-chain communication information is effective and the system is able to protect against the attack of malicious nodes. Finally, a cross-chain simulation experiment is established to analyze the operation efficiency. The result shows that this cross-chain trading takes place within seconds, which basically meets the response requirements for multi-microgrid joint operation.