Reviewerly/exHarmony huggingface dataset:
The OpenDeID corpus for patient de-identification

For research purposes, protected health information is often redacted from unstructured electronic health records to preserve patient privacy and confidentiality. The OpenDeID corpus is designed to assist development of automatic methods to redact sensitive information from unstructured electronic health records. We retrieved 4548 unstructured surgical pathology reports from four urban Australian hospitals. The corpus was developed by two annotators under three different experimental settings. The quality of the annotations was evaluated for each setting. Specifically, we employed serial annotations, parallel annotations, and pre-annotations. Our results suggest that the pre-annotations approach is not reliable in terms of quality when compared to the serial annotations but can drastically reduce annotation time. The OpenDeID corpus comprises 2,100 pathology reports from 1,833 cancer patients with an average of 737.49 tokens and 7.35 protected health information entities annotated per report. The overall inter annotator agreement and deviation scores are 0.9464 and 0.9726, respectively. Realistic surrogates are also generated to make the corpus suitable for distribution to other researchers.
Causal Inference Q-Network: Toward Resilient Reinforcement Learning

Deep reinforcement learning (DRL) has demonstrated impressive performance in various gaming simulators and real-world applications. In practice, however, a DRL agent may receive faulty observation by abrupt interferences such as black-out, frozen-screen, and adversarial perturbation. How to design a resilient DRL algorithm against these rare but mission-critical and safety-crucial scenarios is an important yet challenging task. In this paper, we consider a resilient DRL framework with observational interferences. Under this framework, we discuss the importance of the causal relation and propose a causal inference based DRL algorithm called causal inference Q-network (CIQ). We evaluate the performance of CIQ in several benchmark DRL environments with different types of interferences. Our experimental results show that the proposed CIQ method could achieve higher performance and more resilience against observational interferences.
Issues and Models Concerning the Processing of a Finite Number of Inputs 1

The broadest and most critical arena of investigation and contention with regard to these two matters has always been consciousness itself. Broadbent unveiled a theoretical structure that allowed collation of a substantial body of experimental literature and that was seminal in its influence on later developments. Hybrid models have been of limited current theoretical interest, probably due in part to the difficulty in testing them experimentally. As remarked earlier, the quite special case of seriality versus parallelism is difficult enough to discriminate experimentally. Among such hybrid models are those that represent processing as being serial part of the time and partially parallel within trials. There are many occasions in perceptual and memorial experiments where the information sufficient to make a correct response is embedded in only part of the total stimulus pattern presented to the subject. A finding of independence of total completion times, for instance, although perhaps more intuitively associated with parallel models, can be predicted by serial models.
DEL-based Epistemic Planning for Human-Robot Collaboration: Theory and Implementation

Epistemic planning based on Dynamic Epistemic Logic (DEL) allows agents to reason and plan from the perspective of other agents. The framework of DEL-based epistemic planning thereby has the potential to represent significant aspects of Theory of Mind in autonomous robots, and to provide a foundation for human-robot collaboration in which coordination is achieved implicitly through perspective shifts. In this paper, we build on previous work in epistemic planning with implicit coordination. We introduce a new notion of indistinguishability between epistemic states based on bisimulation, and provide a novel partition refinement algorithm for computing unique representatives of sets of indistinguishable states. We provide an algorithm for computing implicitly coordinated plans using these new constructs, embed it in a perceive-plan-act agent loop, and implement it on a robot. The planning algorithm is benchmarked against an existing epistemic planning algorithm, and the robotic implementation is demonstrated on human-robot collaboration scenarios requiring implicit coordination.
Comparative Opinion Summarization via Collaborative Decoding

Opinion summarization focuses on generating summaries that reflect popular subjective information expressed in multiple online reviews.While generated summaries offer general and concise information about a particular hotel or product, the information may be insufficient to help the user compare multiple different choices.Thus, the user may still struggle with the question "Which one should I pick?"In this paper, we propose the comparative opinion summarization task, which aims at generating two contrastive summaries and one common summary from two different candidate sets of reviews.We develop a comparative summarization framework COCOSUM, which consists of two base summarization models that jointly generate contrastive and common summaries.Experimental results on a newly created benchmark COCOTRIP show that COCOSUM can produce higher-quality contrastive and common summaries than stateof-the-art opinion summarization models.The dataset and code are available at https:// github.com/megagonlabs/cocosum.
Taming the Chaos in Neural Network Time Series Predictions

Machine learning methods, such as Long Short-Term Memory (LSTM) neural networks can predict real-life time series data. Here, we present a new approach to predict time series data combining interpolation techniques, randomly parameterized LSTM neural networks and measures of signal complexity, which we will refer to as complexity measures throughout this research. First, we interpolate the time series data under study. Next, we predict the time series data using an ensemble of randomly parameterized LSTM neural networks. Finally, we filter the ensemble prediction based on the original data complexity to improve the predictability, i.e., we keep only predictions with a complexity close to that of the training data. We test the proposed approach on five different univariate time series data. We use linear and fractal interpolation to increase the amount of data. We tested five different complexity measures for the ensemble filters for time series data, i.e., the Hurst exponent, Shannon's entropy, Fisher's information, SVD entropy, and the spectrum of Lyapunov exponents. Our results show that the interpolated predictions consistently outperformed the non-interpolated ones. The best ensemble predictions always beat a baseline prediction based on a neural network with only a single hidden LSTM, gated recurrent unit (GRU) or simple recurrent neural network (RNN) layer. The complexity filters can reduce the error of a random ensemble prediction by a factor of 10. Further, because we use randomly parameterized neural networks, no hyperparameter tuning is required. We prove this method useful for real-time time series prediction because the optimization of hyperparameters, which is usually very costly and time-intensive, can be circumvented with the presented approach.
Participative Renewable Energy Community—How Blockchain-Based Governance Enables a German Interpretation of RED II

Distributed solar photovoltaic generation is less expensive than the retail price of electricity from the grid in most parts of Europe. Growing shares of variable generation place a focus on Renewable Energy Communities (REC) to increase the economic benefits of local energy systems. Civil society could play an influential and necessary role in the development of such communities, the expansion of renewable energy capacity and the provision of flexibility. However, current RECs models still confine tenants to their role as non-participating consumers. This article provides a concept to enable participative RECs within the German regulatory framework through collective self-consumption by including consumers for a fair allocation of renewable electricity using the blockchain technology.
U-Vectors: Generating Clusterable Speaker Embedding from Unlabeled Data

Speaker recognition deals with recognizing speakers by their speech. Most speaker recognition systems are built upon two stages, the first stage extracts low dimensional correlation embeddings from speech, and the second performs the classification task. The robustness of a speaker recognition system mainly depends on the extraction process of speech embeddings, which are primarily pre-trained on a large-scale dataset. As the embedding systems are pre-trained, the performance of speaker recognition models greatly depends on domain adaptation policy, which may reduce if trained using inadequate data. This paper introduces a speaker recognition strategy dealing with unlabeled data, which generates clusterable embedding vectors from small fixed-size speech frames. The unsupervised training strategy involves an assumption that a small speech segment should include a single speaker. Depending on such a belief, a pairwise constraint is constructed with noise augmentation policies, used to train AutoEmbedder architecture that generates speaker embeddings. Without relying on domain adaption policy, the process unsupervisely produces clusterable speaker embeddings, termed unsupervised vectors (u-vectors). The evaluation is concluded in two popular speaker recognition datasets for English language, TIMIT, and LibriSpeech. Also, a Bengali dataset is included to illustrate the diversity of the domain shifts for speaker recognition systems. Finally, we conclude that the proposed approach achieves satisfactory performance using pairwise architectures.
Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design

Cartesian genetic programming (CGP) represents the most efficient method for the evolution of digital circuits. Despite many successful applications, however, CGP suffers from limited scalability, especially when used for evolutionary circuit design, i.e. design of circuits from a randomly initialized population. Considering the multiplier design problem, for example, the 5 $$\times$$ 5-bit multiplier represents the most complex circuit designed by the evolution from scratch. The efficiency of CGP highly depends on the performance of the point mutation operator, however, this operator is purely stochastic. This contrasts with the recent developments in genetic programming (GP), where advanced informed approaches such as semantic-aware operators are incorporated to improve the search space exploration capability of GP. In this paper, we propose a semantically-oriented mutation operator ( $$\mathrm {SOMO}^k$$ ) suitable for the evolutionary design of combinational circuits. In contrast to standard point mutation modifying the values of the mutated genes randomly, the proposed operator uses semantics to determine the best value for each mutated gene. Compared to the common CGP and its variants, the proposed method converges on common Boolean benchmarks substantially faster while keeping the phenotype size relatively small. The successfully evolved instances presented in this paper include 10-bit parity, 10 + 10-bit adder and 5 $$\times$$ 5-bit multiplier. The most complex circuits were evolved in less than one hour with a single-thread implementation running on a common CPU.
Characterizing wavelet coefficients with decomposition for medical images

In this paper, applications Discrete Laguerre Wavelet Transform were used where satisfactory results were obtained, where the efficiency of our proposed theory was proved, and the examples used will prove this. Three physical samples were selected that were compressed using the proposed wavelets, and good results were obtained that prove the efficiency of the method used. Three physical samples were selected that were compressed using the proposed wavelets, and good results were obtained that prove the efficiency of the method used.
Patient Health Monitoring Using Feed Forward Neural Network With Cloud Based Internet of Things

The healthcare sector is under pressure to embrace new technologies that are available on the market in order to enhance the overall quality of their services. Telecommunications systems are combined with computers, interconnection, mobility, data storage, and information analytics. Technology that is centred on the Internet of Things (IoT) is the order of the day. Because of the limited availability of human resources and infrastructure, it is becoming more necessary to monitor chronic patients on a continual basis as their conditions worsen. A cloud-based architecture, which can handle all of the aforementioned concerns, may offer effective solutions to the health-care sector. In order to create software that combines cloud computing and mobile technologies for health care monitoring systems, we have set a goal of developing software. A technique developed by proposed method is used to extract steady fractal values from electrocardiogram (ECG) data, which has never been tried before by any other researcher in the area of creating a computer-aided diagnostic system for arrhythmia. Based on the findings, it can be concluded that the support vector machine has achieved the highest possible classification accuracy for fractal features. While being compared to the other two classifiers, which are the feed forward and feedback neural network models, the support vector machine outperforms them both. In addition, it should be highlighted that the sensitivity of the feed forward neural network and the support vector machine provide results that are comparable (92.08 percent and 90.36 percent, respectively).
Understanding factors that influence unintentional insider threat: a framework to counteract unintentional risks

Abstract The exploitation of so-called insiders is increasingly recognised as a common vector for cyberattacks. Emerging work in this area has considered the phenomenon from various perspectives including the technological, the psychological and the sociotechnical. We extend this work by specifically examining unintentional forms of insider threat and report the outcomes of a series of detailed Critical Decision Method (CDM) led interviews with those who have experienced various forms of unwitting cybersecurity breaches. We also articulate factors likely to contribute firmly in the context of everyday work-as-done. CDM’s probing questions were used to elicit expert knowledge around how decision making occurred prior, during and post an unintentional cyber breach whilst participants were engaged in the delivery of cognitive tasks. Through the application of grounded theory to data, emerging results included themes of decision making, task factors, accidents and organisational factors. These results are utilised to inform an Epidemiological Triangle to represent the dynamic relationship between three vectors of exploit, user and the work environment that can in turn affect the resilience of cyber defences. We conclude by presenting a simple framework, which for the purposes of this work is a set of recommendations applicable in specific scenarios to reduce negative impact for understanding unintentional insider threats. We also suggest practical means to counteract such threats rooted in the lived experience of those who have fallen prey to them.
A Zero-Trust Methodology for Security of Complex Systems With Machine Learning Components

Abstract Fuelled by recent technological advances, Machine Learning (ML) is being introduced to safety and security-critical applications like defence systems, financial systems, and autonomous machines. ML components can be used either for processing input data and/or for decision making. The response time and success rate demands are very high and this means that the deployed training algorithms often produce complex models that are not readable and verifiable by humans (like multi layer neural networks). Due to the complexity of these models, achieving complete testing coverage is in most cases not realistically possible. This raises security threats related to the ML components presenting unpredictable behavior due to malicious manipulation (backdoor attacks). This paper proposes a methodology based on established security principles like Zero-Trust and defence-in-depth to help prevent and mitigate the consequences of security threats including ones emerging from ML-based components. The methodology is demonstrated on a case study of an Unmanned Aerial Vehicle (UAV) with a sophisticated Intelligence, Surveillance, and Reconnaissance (ISR) module.
Teaching Quantum Computing to High-School-Aged Youth: A Hands-On Approach

Quantum computing is an interdisciplinary field that lies at the intersection of mathematics, quantum physics, and computer science, and finds applications in areas including optimization, machine learning, and simulation of chemical, physical, and biological systems.It has the potential to help solve problems that so far have no satisfying method solving them, and to provide significant speedup to solutions when compared with their best classical approaches.In turn, quantum computing may allow us to solve problems for inputs that so far are deemed practically intractable.With the computational power of quantum computers and the proliferation of quantum development kits, quantum computing is anticipated to become mainstream, and the demand for a skilled workforce in quantum computing is expected to increase significantly.Therefore, quantum computing education is ramping up.This article describes our experiences in designing and delivering quantum computing workshops for youth (Grades 9-12).We introduce students to the world of quantum computing in innovative ways, such as newly designed unplugged activities for teaching basic quantum computing concepts.We also take a programmatic approach and introduce students to the IBM Quantum Experience using Qiskit and Jupyter notebooks.Our contributions are as follows.First, we present creative ways to teach quantum computing to youth with little or no experience in science, technology, engineering, and mathematics areas; second, we discuss diversity and highlight various pathways into quantum computing from quantum software to quantum hardware; and third, we discuss the design and delivery of online and in-person motivational, introductory, and advanced workshops for youth.
How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology?

Data-driven subword segmentation has become the default strategy for open-vocabulary machine translation and other NLP tasks, but may not be sufficiently generic for optimal learning of non-concatenative morphology.We design a test suite to evaluate segmentation strategies on different types of morphological phenomena in a controlled, semisynthetic setting.In our experiments, we compare how well machine translation models trained on subword-and character-level can translate these morphological phenomena.We find that learning to analyse and generate morphologically complex surface representations is still challenging, especially for nonconcatenative morphological phenomena like reduplication or vowel harmony and for rare word stems.Based on our results, we recommend that novel text representation strategies be tested on a range of typologically diverse languages to minimise the risk of adopting a strategy that inadvertently disadvantages certain languages. 1
A Blockchain-Based Audit Trail Mechanism: Design and Implementation

Audit logs are a critical component in today’s enterprise business systems as they provide several benefits such as records transparency and integrity and security of sensitive information by creating a layer of evidential support. However, current implementations are vulnerable to attacks on data integrity or availability. This paper presents a Blockchain-based audit trail mechanism that leverages the security features of Blockchain to enable secure and reliable audit trails and to address the aforementioned vulnerabilities. The architecture design and specific implementation are described in detail, resulting in a real prototype of a reliable, secure, and user-friendly audit trail mechanism.
Federated Learning Model with Adaptive Differential Privacy Protection in Medical IoT

With the proliferation of intelligent services and applications authorized by artificial intelligence, the Internet of Things has penetrated into many aspects of our daily lives, and the medical field is no exception. The medical Internet of Things (MIoT) can be applied to wearable devices, remote diagnosis, mobile medical treatment, and remote monitoring. There is a large amount of medical information in the databases of various medical institutions. Nevertheless, due to the particularity of medical data, it is extremely related to personal privacy, and the data cannot be shared, resulting in data islands. Federated learning (FL), as a distributed collaborative artificial intelligence method, provides a solution. However, FL also involves multiple security and privacy issues. This paper proposes an adaptive Differential Privacy Federated Learning Medical IoT (DPFL-MIoT) model. Specifically, when the user updates the model locally, we propose a differential privacy federated learning deep neural network with adaptive gradient descent (DPFLAGD-DNN) algorithm, which can adaptively add noise to the model parameters according to the characteristics and gradient of the training data. Since privacy leaks often occur in downlink, we present differential privacy federated learning (DP-FL) algorithm where adaptive noise is added to the parameters when the server distributes the parameters. Our method effectively reduces the addition of unnecessary noise, and at the same time, the model has a good effect. Experimental results on real-world data show that our proposed algorithm can effectively protect data privacy.
Changes implemented in the work environment of nurses in the COVID-19 pandemic

ABSTRACT Objective: to describe the changes implemented in the work environment of nurses in university hospitals considering the COVID-19 pandemic. Methods: this qualitative and descriptive research was developed from an online survey with 75 nurses from three Brazilian university hospitals. Data processing occurred through textual analysis with the aid of software IRAMUTEQ. Results: five semantic classes were obtained: Organization of units for exclusive care of patients with COVID-19; Adaptations in the use of personal protective equipment; Physical structure adaptation; Care flow institution; Increased number of beds and training courses. Final considerations: the results show the effort of healthcare and nursing professionals/managers in the development of structural adaptations and reorganizations of care processes, in the hospital context, to respond with quality and efficiency to the demands arising from the COVID-19 pandemic.