Reviewerly/exHarmony huggingface dataset:
BioMedBERT: A Pre-trained Biomedical Language Model for QA and IR

The SARS-CoV-2 (COVID-19) pandemic spotlighted the importance of moving quickly with biomedical research.However, as the number of biomedical research papers continue to increase, the task of finding relevant articles to answer pressing questions has become significant.In this work, we propose a textual data mining tool that supports literature search to accelerate the work of researchers in the biomedical domain.We achieve this by building BioMedBERT, a neural-based deep contextual understanding model for Question-Answering (QA) and Information Retrieval tasks.We also leverage the new BREATHE dataset which is one of the largest available datasets of biomedical research literature, containing abstracts and full-text articles from ten different biomedical literature sources on which we pre-train our BioMedBERT model.Our work achieves state-of-the-art results on the QA fine-tuning task on BioASQ 5b, 6b and 7b datasets.In addition, we observe superior relevant results when BioMedBERT embeddings are used with Elasticsearch for the Information Retrieval task on the intelligently formulated BioASQ dataset.We believe our diverse dataset and our unique model architecture are what led us to achieve the state-of-the-art results for QA and IR tasks.
Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification

In a dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers’ intentions, where dialog act and sentiment can indicate the explicit and the implicit intentions separately. The dialog context information (contextual information) and the mutual interaction information are two key factors that contribute to the two related tasks. Unfortunately, none of the existing approaches consider the two important sources of information simultaneously. In this paper, we propose a Co-Interactive Graph Attention Network (Co-GAT) to jointly perform the two tasks. The core module is a proposed co-interactive graph interaction layer where a cross-utterances connection and a cross-tasks connection are constructed and iteratively updated with each other, achieving to consider the two types of information simultaneously. Experimental results on two public datasets show that our model successfully captures the two sources of information and achieve the state-of-the-art performance. In addition, we find that the contributions from the contextual and mutual interaction information do not fully overlap with contextualized word representations (BERT, Roberta, XLNet).
Numerical finite-key analysis of quantum key distribution

Abstract Quantum key distribution (QKD) allows for secure communications safe against attacks by quantum computers. QKD protocols are performed by sending a sizeable, but finite, number of quantum signals between the distant parties involved. Many QKD experiments, however, predict their achievable key rates using asymptotic formulas, which assume the transmission of an infinite number of signals, partly because QKD proofs with finite transmissions (and finite-key lengths) can be difficult. Here we develop a robust numerical approach for calculating the key rates for QKD protocols in the finite-key regime in terms of two semi-definite programs (SDPs). The first uses the relation between conditional smooth min-entropy and quantum relative entropy through the quantum asymptotic equipartition property, and the second uses the relation between the smooth min-entropy and quantum fidelity. The numerical programs are formulated under the assumption of collective attacks from the eavesdropper and can be promoted to withstand coherent attacks using the postselection technique. We then solve these SDPs using convex optimization solvers and obtain numerical calculations of finite-key rates for several protocols difficult to analyze analytically, such as BB84 with unequal detector efficiencies, B92, and twin-field QKD. Our numerical approach democratizes the composable security proofs for QKD protocols where the derived keys can be used as an input to another cryptosystem.
Gendered Images of us and Them in Anti-Islamic Facebook Groups

Since the turn of the century, anti-Islamic sentiments have become increasingly present in public debate. Scholars conducting research on the far right have even warned that the extreme has become mainstream. A key aspect of this situation is the prominence of gender images in the construction of ‘us and them’ distinctions in arguments against Muslims and Islam. This article examines two anti-Islamic Facebook groups. The main arguments of participants in these groups are that Norwegian women are in need of protection from dangerous Muslim men, and that Muslim women are oppressed but there is no need to feel sorry for them as they have chosen this subordinate position themselves. Further, group members not only make use of the well-known stereotypes of dangerous Muslim men and Muslim women as victims, but do so in explicitly sexist language, so that it is difficult to see them as unequivocal proponents of gender equality. Humour, emotions and jargon contribute to building a group atmosphere of xenophobia. The article analyses the group discussions in the light of theories and concepts of ‘femonationalism’ and right-wing populism.
Explaining CNN and RNN Using Selective Layer-Wise Relevance Propagation

Deep learning has recently been applied to various artificial intelligence (AI) fields and has demonstrated excellent performance.However, several models based on deep learning encounter black-box problem that complicates the interpretation of the models and understand their predictions.This makes it difficult to apply deep learning to real problems, especially in critical systems such as those in the defense, aerospace, and security domains.To overcome this issue, the concept of explainable AI was introduced.Various approaches have been proposed to visually explain model predictions for image and text classification.A common approach for visual explanation includes layer-wise relevance propagation (LRP), which produces a heatmap where each pixel value represents a contribution to the prediction of the model.Advanced versions of LRP have been proposed, but these methods have some limitations.In this study, we propose selective layer-wise relevance propagation, which produces a clearer heatmap than the existing methods by combining relevance-based methods and gradient-based methods.The experimental results are presented qualitatively and quantitatively to evaluate the proposed method and verify its effectiveness.
An Enhanced Ensemble Diagnosis of Cervical Cancer: A Pursuit of Machine Intelligence Towards Sustainable Health

Cervical cancer is a potentially life-threatening disease marked by health practitioners. The late diagnosis and treatment, being quite challenging, stake the precious lives of patients. In both developed and undeveloped states, the formal screening for disease identification suffers due to its medical cost, unavailable health facilities, society norms, and late appearance of symptoms. Machine intelligence is cost-effective, computationally inexpensive, and early diagnosis of several types of diseases, including cervical cancer. The patients are not required to pass through contemporary and tedious medical procedures, and early diagnosis of cervical cancer is quite handy with machine-intelligent solutions. The problem with the current machine classification methods for disease identification is the reliance on a single classifier’s prediction accuracy. The adoption of single classification methods doesn’t ensure the optimum prediction due to bias, over-fitting, mishandling of noisy data, and outliers. This research study proposes an Ensemble classification method based on majority voting for an accurate diagnosis addressing the patient’s medical conditions or symptoms. The study experiments a wide range of available classifiers, namely Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Naive Bayes (NB), Multiple Perceptron (MP), J48 Trees, and Logistic Regression (LR) classifiers. The study records a significant enhancement in prediction accuracy of 94% that outperforms the prediction accuracies of single classification methods tested on the same benchmarked datasets. Thus, the proposed model bestows a second opinion to health practitioners for disease identification and timely treatment.
The contribution of stimulating multiple body parts simultaneously to the illusion of owning an entire artificial body

The full-body ownership illusion exploits multisensory perception to induce a feeling of ownership of an entire artificial body. Although previous research has shown that synchronous visuotactile stimulation of a single body part is sufficient for illusory ownership of the whole body, the effect of combining multisensory stimulation across multiple body parts remains unknown. Therefore, 48 healthy adults participated in a full-body ownership illusion with conditions involving synchronous (illusion) or asynchronous (control) visuotactile stimulation to one, two, or three body parts simultaneously (2×3 design). We used questionnaires to isolate illusory ownership of five specific body parts (left arm, right arm, trunk, left leg, right leg) from the full-body ownership experience and sought to test not only for increased ownership in synchronous versus asynchronous conditions but also for potentially varying degrees of full-body ownership illusion intensity related to the number of body parts stimulated. Illusory full-body ownership and all five body-part ownership ratings were significantly higher following synchronous stimulation than asynchronous stimulation ( p -values &lt; .01). Since non-stimulated body parts also received significantly increased ownership ratings following synchronous stimulation, the results are consistent with an illusion that engages the entire body. Furthermore, we noted that ownership ratings for right body parts (which were often but not always stimulated in this experiment) were significantly higher than ownership ratings for left body parts (which were never stimulated). Regarding the effect of stimulating multiple body parts simultaneously on explicit full-body ownership ratings, there was no evidence of a significant main effect of the number of stimulations ( p = .850) or any significant interaction with stimulation synchronicity ( p = .160), as assessed by linear mixed modelling. Instead, median ratings indicated a moderate affirmation (+1) of an illusory full-body sensation in all three synchronous conditions, a finding mirrored by comparable full-body illusion onset times. In sum, illusory full-body ownership appears to be an ‘all-or-nothing’ phenomenon and depends upon the synchronicity of visuotactile stimulation, irrespective of the number of stimulated body parts.
Attribution by Indictment

The challenges of attributing malicious cyber activity—that is, identifying its authors and provenance with a sufficient degree of certainty—are well documented. This essay focuses on a phenomenon that I call “attribution by indictment.” Since 2014, the United States has issued more than a dozen indictments that implicate four foreign states in malicious cyber activity: China, Iran, Russia, and North Korea. Ten of these indictments were issued in 2018, suggesting that this practice is likely to continue and even intensify in the near term. Attribution by indictment uses domestic criminal law, enforced transnationally, to define and enforce certain norms of state behavior in cyberspace. This essay analyzes the U.S. practice of attribution by indictment as a response to malicious cyber activity.
An Extensive Formal Analysis of Multi-factor Authentication Protocols

Passwords are still the most widespread means for authenticating users, even though they have been shown to create huge security problems. This motivated the use of additional authentication mechanisms in so-called multi-factor authentication protocols. In this article, we define a detailed threat model for this kind of protocol: While in classical protocol analysis attackers control the communication network, we take into account that many communications are performed over TLS channels, that computers may be infected by different kinds of malware, that attackers could perform phishing, and that humans may omit some actions. We formalize this model in the applied pi calculus and perform an extensive analysis and comparison of several widely used protocols—variants of Google 2-step and FIDO’s U2F (Yubico’s Security Key token). The analysis is completely automated, generating systematically all combinations of threat scenarios for each of the protocols and using the P ROVERIF tool for automated protocol analysis. To validate our model and attacks, we demonstrate their feasibility in practice, even though our experiments are run in a laboratory environment. Our analysis highlights weaknesses and strengths of the different protocols. It allows us to suggest several small modifications of the existing protocols that are easy to implement, as well as an extension of Google 2-step that improves security in several threat scenarios.
Transforming and evaluating electronic health record disease phenotyping algorithms using the OMOP common data model: a case study in heart failure

The aim of the study was to transform a resource of linked electronic health records (EHR) to the OMOP common data model (CDM) and evaluate the process in terms of syntactic and semantic consistency and quality when implementing disease and risk factor phenotyping algorithms.Using heart failure (HF) as an exemplar, we represented three national EHR sources (Clinical Practice Research Datalink, Hospital Episode Statistics Admitted Patient Care, Office for National Statistics) into the OMOP CDM 5.2. We compared the original and CDM HF patient population by calculating and presenting descriptive statistics of demographics, related comorbidities, and relevant clinical biomarkers.We identified a cohort of 502 536 patients with the incident and prevalent HF and converted 1 099 195 384 rows of data from 216 581 914 encounters across three EHR sources to the OMOP CDM. The largest percentage (65%) of unmapped events was related to medication prescriptions in primary care. The average coverage of source vocabularies was >98% with the exception of laboratory tests recorded in primary care. The raw and transformed data were similar in terms of demographics and comorbidities with the largest difference observed being 3.78% in the prevalence of chronic obstructive pulmonary disease (COPD).Our study demonstrated that the OMOP CDM can successfully be applied to convert EHR linked across multiple healthcare settings and represent phenotyping algorithms spanning multiple sources. Similar to previous research, challenges mapping primary care prescriptions and laboratory measurements still persist and require further work. The use of OMOP CDM in national UK EHR is a valuable research tool that can enable large-scale reproducible observational research.
A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network

Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset.
A Multi-Perspective Benchmarking Framework for Estimating Usable-Security of Hospital Management System Software Based on Fuzzy Logic, ANP and TOPSIS Methods

One of the biggest challenges that the software industry is facing today is to create highly efficient applications without affecting the quality of healthcare system software.The demand for the provision of software with high quality protection has seen a rapid increase in the software business market.Moreover, it is worthless to offer extremely user-friendly software applications with no ideal security.Therefore a need to find optimal solutions and bridge the difference between accessibility and protection by offering accessible software services for defense has become an imminent prerequisite.Several research endeavours on usable security assessments have been performed to fill the gap between functionality and security.In this context, several Multi-Criteria Decision Making (MCDM) approaches have been implemented on different usability and security attributes so as to assess the usable-security of software systems.However, only a few specific studies are based on using the integrated approach of fuzzy Analytic Network Process (FANP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique for assessing the significant usable-security of hospital management software.Therefore, in this research study, the authors have employed an integrated methodology of fuzzy logic, ANP and TOPSIS to estimate the usablesecurity of Hospital Management System Software.For the intended objective, the study has taken into account 5 usable-security factors at first tier and 16 sub-factors at second tier with 6 hospital management system softwares as alternative solutions.To measure the weights of parameters and their relation with each other, Fuzzy ANP is implemented.Thereafter, Fuzzy TOPSIS methodology was employed and the rating of alternatives was calculated on the foundation of the proximity to the positive ideal solution.
Stabilizing Invertible Neural Networks Using Mixture Models

In this paper, we analyze the properties of invertible neural networks, which provide a way of solving inverse problems. Our main focus lies on investigating and controlling the Lipschitz constants of the corresponding inverse networks. Without such a control, numerical simulations are prone to errors and not much is gained against traditional approaches. Fortunately, our analysis indicates that changing the latent distribution from a standard normal one to a Gaussian mixture model resolves the issue of exploding Lipschitz constants. Indeed, numerical simulations confirm that this modification leads to significantly improved sampling quality in multimodal applications.
Semi-supervised regression using diffusion on graphs

In real-world machine learning applications, unlabeled training data are readily available, but labeled data are expensive and hard to obtain. Therefore, semi-supervised learning algorithms have gathered much attention. Previous studies in this area mainly focused on a semi-supervised classification problem, whereas semi-supervised regression has received less attention. In this paper, we proposed a novel semi-supervised regression algorithm using heat diffusion with a boundary-condition that guarantees a closed-form solution. Experiments from artificial and real datasets from business, biomedical, physical, and social domain show that the boundary-based heat diffusion method can effectively outperform the top state of the art methods.
Dynamic Curve Fitting and BP Neural Network With Feature Extraction for Mobile Specific Emitter Identification

As a critical technology in both civil and military fields, specific emitter identification (SEI) can identify signal sources according to their various features. Existing methods on SEI are mostly based on the prior knowledge of emitters, which are powerless in the non-cooperative scenario. In order to realize the unsupervised identification, the mobile SEI method based on fingerprint set construction and feedback classification algorithm is proposed in this paper. The proposed method first divides signal fingerprints into static features and dynamic features, where the former describe the inherent features of emitters, and the latter represent the moving state features of emitters. Then, the feedback classification algorithm composed of dynamic curve fitting and back propagation (BP) neural network is applied in the classification of signals. The dynamic curve accomplishes the first classification and the results with high credibility are used to train the BP neural network which accomplishes the final classification. Simulation results demonstrate that the proposed method can complete the identification of mobile specific emitter sources in the unsupervised state with more than 95% identification rate.
Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment

This article aims to improve the problem of slow convergence speed, poor global search ability, and unknown time-varying dynamic obstacles in the path planning of ant colony optimization in dynamic environment. An improved ant colony optimization algorithm using time taboo strategy is proposed, namely, time taboo ant colony optimization (TTACO), which uses adaptive initial pheromone distribution, rollback strategy, and pheromone preferential limited update to improve the algorithm's convergence speed and global search ability. For the poor global search ability of the algorithm and the unknown time-varying problem of dynamic obstacles in a dynamic environment, a time taboo strategy is first proposed, based on which a three-step arbitration method is put forward to improve its weakness in global search. For the unknown time-varying dynamic obstacles, an occupancy grid prediction model is proposed based on the time taboo strategy to solve the problem of dynamic obstacle avoidance. In order to improve the algorithm's calculation speed when avoiding obstacles, an ant colony information inheritance mechanism is established. Finally, the algorithm is used to conduct dynamic simulation experiments in a simulated factory environment and is compared with other similar algorithms. The experimental results show that the TTACO can obtain a better path and accelerate the convergence speed of the algorithm in a static environment and can successfully avoid dynamic obstacles in a dynamic environment.