Reviewerly/exHarmony huggingface dataset:
Machine learning-based prediction models for home discharge in patients with COVID-19: Development and evaluation using electronic health records

This study aimed to develop and validate predictive models using electronic health records (EHR) data to determine whether hospitalized COVID-19-positive patients would be admitted to alternative medical care or discharged home.We conducted a retrospective cohort study using deidentified data from the University of Florida Health Integrated Data Repository. The study included 1,578 adult patients (≥18 years) who tested positive for COVID-19 while hospitalized, comprising 960 (60.8%) female patients with a mean (SD) age of 51.86 (18.49) years and 618 (39.2%) male patients with a mean (SD) age of 54.35 (18.48) years. Machine learning (ML) model training involved cross-validation to assess their performance in predicting patient disposition.We developed and validated six supervised ML-based prediction models (logistic regression, Gaussian Naïve Bayes, k-nearest neighbors, decision trees, random forest, and support vector machine classifier) to predict patient discharge status. The models were evaluated based on the area under the receiver operating characteristic curve (ROC-AUC), precision, accuracy, F1 score, and Brier score. The random forest classifier exhibited the highest performance, achieving an accuracy of 0.84 and an AUC of 0.72. Logistic regression (accuracy: 0.85, AUC: 0.71), k-nearest neighbor (accuracy: 0.84, AUC: 0.63), decision tree (accuracy: 0.84, AUC: 0.61), Gaussian Naïve Bayes (accuracy: 0.84, AUC: 0.66), and support vector machine classifier (accuracy: 0.84, AUC: 0.67) also demonstrated valuable predictive capabilities.This study's findings are crucial for efficiently allocating healthcare resources during pandemics like COVID-19. By harnessing ML techniques and EHR data, we can create predictive tools to identify patients at greater risk of severe symptoms based on their medical histories. The models developed here serve as a foundation for expanding the toolkit available to healthcare professionals and organizations. Additionally, explainable ML methods, such as Shapley Additive Explanations, aid in uncovering underlying data features that inform healthcare decision-making processes.
A fault-tolerant variational quantum algorithm with limited T-depth

Abstract We propose a variational quantum eigensolver (VQE) algorithm that uses a fault-tolerant (FT) gate-set, and is hence suitable for implementation on a future error-corrected quantum computer. VQE quantum circuits are typically designed for near-term, noisy quantum devices and have continuously parameterized rotation gates as the central building block. On the other hand, an FT quantum computer (FTQC) can only implement a discrete set of logical gates, such as the so-called Clifford+ T gates. We show that the energy minimization of VQE can be performed with such an FT discrete gate-set, where we use the Ross–Selinger algorithm to transpile the continuous rotation gates to the error-correctable Clifford+ T gate-set. We find that there is no loss of convergence when compared to the one of parameterized circuits if an adaptive accuracy of the transpilation is used in the VQE optimization. State preparation with VQE requires only a moderate number of T -gates, depending on the system size and transpilation accuracy. We demonstrate these properties on emulators for two prototypical spin models with up to 16 qubits. This is a promising result for the integration of VQE and more generally variational algorithms in the emerging FT setting, where they can form building blocks of the general quantum algorithms that will become accessible in an FTQC.
Policy-Based Remote User Authentication From Multi-Biometrics

Abstract In this paper, we introduce the first generic framework of policy-based remote user authentication from multiple biometrics. The proposed framework allows an authorized user to remotely authenticate herself to an authentication server using her multiple biometrics, which enhances both the security and usability of user authentications. The authentication server approves a user’s authentication request if and only if the user’s multiple biometrics satisfies an authentication policy. In particular, the authentication policy can be dynamically updated to satisfy different security and usability requirements in practice. We implement an instantiation of the proposed framework and report its performance under various authentication policies.
Keyframe-guided Video Swin Transformer with Multi-path Excitation for Violence Detection

Abstract Violence detection is a critical task aimed at identifying violent behavior in video by extracting frames and applying classification models. However, the complexity of video data and the suddenness of violent events present significant hurdles in accurately pinpointing instances of violence, making the extraction of frames that indicate violence a challenging endeavor. Furthermore, designing and applying high-performance models for violence detection remains an open problem. Traditional models embed extracted spatial features from sampled frames directly into a temporal sequence, which ignores the spatio-temporal characteristics of video and limits the ability to express continuous changes between adjacent frames. To address the existing challenges, this paper proposes a novel framework called ACTION-VST. First, a keyframe extraction algorithm is developed to select frames that are most likely to represent violent scenes in videos. To transform visual sequences into spatio-temporal feature maps, a multi-path excitation module is proposed to activate spatio-temporal, channel and motion features. Next, an advanced Video Swin Transformer-based network is employed for both global and local spatio-temporal modeling, which enables comprehensive feature extraction and representation of violence. The proposed method was validated on two large-scale datasets, RLVS and RWF-2000, achieving accuracies of over 98 and 93%, respectively, surpassing the state of the art.
On Root Cause Localization and Anomaly Mitigation through Causal Inference

Due to a wide spectrum of applications in the real world, such as security, financial surveillance, and health risk, various deep anomaly detection models have been proposed and achieved state-of-the-art performance. However, besides being effective, in practice, the practitioners would further like to know what causes the abnormal outcome and how to further fix it. In this work, we propose RootCLAM, which aims to achieve Root Cause Localization and Anomaly Mitigation from a causal perspective. Especially, we formulate anomalies caused by external interventions on the normal causal mechanism and aim to locate the abnormal features with external interventions as root causes. After that, we further propose an anomaly mitigation approach that aims to recommend mitigation actions on abnormal features to revert the abnormal outcomes such that the counterfactuals guided by the causal mechanism are normal. Experiments on three datasets show that our approach can locate the root causes and further flip the abnormal labels.
Attacking Neural Networks with Neural Networks: Towards Deep Synchronization for Backdoor Attacks

Backdoor attacks inject poisoned samples into training data, where backdoor triggers are embedded into the model trained on the mixture of poisoned and clean samples. An interesting phenomenon can be observed in the training process: the loss of poisoned samples tends to drop significantly faster than that of clean samples, which we call the early-fitting phenomenon. Early-fitting provides a simple but effective evidence to defend against backdoor attacks, where the poisoned samples can be detected by selecting the samples with the lowest loss values in the early training epochs. Then, two questions naturally arise: (1) What characteristics of poisoned samples cause early-fitting? (2) Does a stronger attack exist which could circumvent the defense methods? To answer the first question, we find that early-fitting could be attributed to a unique property among poisoned samples called synchronization, which depicts the similarity between two samples at different layers of a model. Meanwhile, the degree of synchronization could be controlled based on whether it is captured by shallow or deep layers of the model. Then, we give an affirmative answer to the second question by proposing a new backdoor attack method, Deep Backdoor Attack (DBA), which utilizes deep synchronization to reverse engineer trigger patterns by activating neurons in the deep layer of a base neural network. Experimental results validate our propositions and the effectiveness of DBA. Our code is available at https://github.com/GuanZihan/Deep-Backdoor-Attack.
Self-supervised Contrastive Enhancement with Symmetric Few-shot Learning Towers for Cold-start News Recommendation

Nowadays, news spreads faster than it is consumed. This, alongside the rapid news cycle and delayed updates, has led to a challenging news cold-start issue. Likewise, the user cold-start problem, due to limited user engagement, has long hindered recommendations. To tackle both of them, we introduce the Symmetric Few-shot Learning framework for Cold-start News Recommendation (SFCNR), built upon self-supervised contrastive enhancement. Our approach employs symmetric few-shot learning towers (SFTs) to transform warm user/news attributes into their behavior/content features during training. We design two innovative feature alignment strategies to enhance towers training. Subsequently, this tower generates virtual features for cold users/news during inference, leveraging tower-stored prior knowledge through a personalized gating network. We assess the SFCNR on four quality news recommendation models, conducting comprehensive experiments on two kinds of News dataset. Results showcase significant performance boosts for both warm and cold-start scenarios compared to baseline models.
Hierarchical cumulative network for unsupervised medical image registration

Unsupervised deep learning techniques have gained increasing popularity in deformable medical image registration However, existing methods usually overlook the optimal similarity position between moving and fixed images To tackle this issue, we propose a novel hierarchical cumulative network (HCN), which explicitly considers the optimal similarity position with an effective Bidirectional Asymmetric Registration Module (BARM). The BARM simultaneously learns two asymmetric displacement vector fields (DVFs) to optimally warp both moving images and fixed images to their optimal similar shape along the geodesic path. Furthermore, we incorporate the BARM into a Laplacian pyramid network with hierarchical recursion, in which the moving image at the lowest level of the pyramid is warped successively for aligning to the fixed image at the lowest level of the pyramid to capture multiple DVFs. We then accumulate these DVFs and up-sample them to warp the moving images at higher levels of the pyramid to align to the fixed image of the top level. The entire system is end-to-end and jointly trained in an unsupervised manner. Extensive experiments were conducted on two public 3D Brain MRI datasets to demonstrate that our HCN outperforms both the traditional and state-of-the-art registration methods. To further evaluate the performance of our HCN, we tested it on the validation set of the MICCAI Learn2Reg 2021 challenge. Additionally, a cross-dataset evaluation was conducted to assess the generalization of our HCN. Experimental results showed that our HCN is an effective deformable registration method and achieves excellent generalization performance.
FrankenMask: Manipulating semantic masks with transformers for face parts editing

In this paper, we propose FrankenMask, a novel framework that allows swapping and rearranging face parts in semantic masks for automatic editing of shape-related facial attributes. This is a novel yet challenging task as substituting face parts in a semantic mask requires to account for possible spatial misalignment and the adaptation of surrounding regions. We obtain such a feature by combining a Transformer encoder to learn the spatial relationships of facial parts, with an encoder–decoder architecture, which reconstructs a complete mask from the composition of local parts. Reconstruction and attribute classification results demonstrate the effective synthesis of facial images, while showing the generation of accurate and plausible facial attributes. Code is available at https://github.com/TFonta/FrankenMask_semantic.
Visual Brick model authoring tool for building metadata standardization

The Brick ontology is a unified semantic metadata standard for building assets and their relationships, serving as a key enabler for effective interoperability and automation of building systems and analytics. However, creating a Brick model, in other words, standard semantic metadata based on the Brick ontology for a building dataset, can be a complex task. This paper presents two case studies of the creation of Brick models for real-world residential and commercial building datasets, highlighting the challenges during the Brick model creation process. Additionally, the paper introduces VizBrick, an interactive authoring tool for creating semantic building metadata. VizBrick facilitates the creation of Brick models by providing an intuitive visual interface and interactive capabilities, such as keyword search, automatic mapping suggestions, and recommendations. The use of VizBrick is shown to significantly reduce the time and effort required during the Brick model creation process.
Automated QoS-Aware Service Selection Based on Soft Constraints

QoS attributes are one of the key factors taken into account when selecting services for a composite application. While there are systems for automated service selection based on QoS constraints, most of them are very limited in the preferences the user can state. In this paper we present: a) a simple, yet versatile, language for describing composite applications, b) a rich set of notations for stating complex preferences over the QoS attributes, including checkpoints and invariants, and c) an automatic tool for optimal global QoS-aware service selection based on MiniBrass, a state-of-the-art soft-constraint solver. We provide a running example accompanying the definitions and a preliminary performance analysis showing the practical usefulness of the tools.
Shades of Iteration: From Elgot to Kleene

Notions of iteration range from the arguably most general Elgot iteration to a very specific Kleene iteration. The fundamental nature of Elgot iteration has been extensively explored by Bloom and Esik in the form of iteration theories, while Kleene iteration became extremely popular as an integral part of (untyped) formalisms, such as automata theory, regular expressions and Kleene algebra. Here, we establish a formal connection between Elgot iteration and Kleene iteration in the form of Elgot monads and Kleene monads, respectively. We also introduce a novel class of while-monads, which like Kleene monads admit a relatively simple description in algebraic terms. Like Elgot monads, while-monads cover a large variety of models that meaningfully support while-loops, but may fail the Kleene algebra laws, or even fail to support a Kleen iteration operator altogether.
Why Adjunctions Matter—A Functional Programmer Perspective

For the average programmer, adjunctions are (if at all known) more respected than loved. At best, they are regarded as an algebraic device of theoretical interest only, not useful in common practice. This paper is aimed at showing the opposite: that adjunctions underlie most of the work we do as programmers, in particular those using the functional paradigm. However, functions alone are not sufficient to express the whole spectrum of programming, with its dichotomy between specifications—what is (often vaguely) required—and implementations—how what is required is (hopefully well) implemented. For this, one needs to extend functions to relations. Inspired by the pioneering work of Ralf Hinze on “adjoint (un)folds”, the core of the so-called (relational) Algebra of Programming is shown in this paper to arise from adjunctions. Moreover, the paper also shows how to calculate recursive programs from specifications expressed by Galois connections—a special kind of adjunction. Because Galois connections are easier to understand than adjunctions in general, the paper adopts a tutorial style, starting from the former and leading to the latter (a path usually not followed in the literature). The main aim is to reconcile the functional programming community with a concept that is central to software design as a whole, but rarely accepted as such.
Fischer machine learning for mobile cloud computing in eHealth systems using blockchain mechanism

The Electronic Healthcare (eHealth) systems are competent to ensure effective care engineering and intensified healthcare quality which are user-friendly cache and administration, in Electronic Health Records (EHRs). For secure EHRs of Mobile Cloud-based eHealth systems, ensuring high security and data privacy, Interplanetary File System in healthcare has traditionally been concentrated. However, there has been a recent push towards achieving high quality of e-health services because blockchain-based health care applications require QoS guarantees in terms of requirements such as network latency and end-to-end delay. In this work, an Extended Validation Certification-based Fischer Neural Network Optimization (EVC-FNNO) method for secured Mobile Cloud-based E-Health Systems is proposed. With the identity being the digital certificate, the EVC is provided with the identity to the mobile cloud user who will transact in the network. In this way, the mobile cloud user is being ensured to access the ledger for the transaction. Therefore, both data privacy and security is said to be provided. Next, with Fischer Neural Network Optimization (FNNO), every authenticated mobile cloud user via EVC then possess a copy of shared ledger, therefore resolving data acquisition in cloud server and hence solving network latency. The proposed method is verified by some demonstrative examples in addressing QoS. The empirical results show that the EVC-FNNO method provides an efficient solution by validating the mobile cloud user sensitive health information with digital certificate. Security analysis proves that the EVC-FNNO method is secure. We also conduct comprehensive performance evaluations that demonstrate the high efficiency of the EVC-FNNO method in terms of end-to-end delay, network latency and data privacy, compared to the existing data sharing methods.
Call for papers, JOVACET 7(1), 2024

You are invited to submit an article for the seventh volume of JOVACET to be published in October/November 2024. JOVACET is an accredited publication with the Department of Higher Education and Training.&#x0D; Articles should be topical with regard to current debates/discourses and recent research in the TVET, Adult, and Continuing Education and Training domains. Submissions should be of high quality and follow academic research/writing conventions of journal articles in the social sciences. Specifications can be found on the JOVACET website (www.jovacet.ac.za) or obtained from Dr Catherine Robertson at cathy@tcrobertson.co.za.&#x0D; Articles should comprise a maximum of 8 000 words, including the abstract of approximately 200 words and a list of references, and be submitted in MS Word format via the journal website at www.jovacet.ac.za or emailed to Dr Catherine Robertson at cathy@tcrobertson. co.za. Kindly follow the style guide which is provided on the website.&#x0D; We look forward to receiving your submissions!&#x0D; Due date for full paper submissions: 31 March 2024.
On the Use of Accelerometric Data to Monitor the Seismic Performance of Non-Structural Elements in Existing Buildings: A Case Study

Monitoring of non-structural elements is not usually implemented, despite the seismic vulnerability of these components and the significant cost associated with their replacement in case of damage. By exploiting the limited cost of commercial sensors, accelerometers were installed in an existing building to compare accelerations applied to non-structural elements in case of an earthquake with critical acceleration thresholds. The exceedance of these thresholds would indicate a possible danger for the occupants and the need for a more detailed inspection of the element, guiding prioritisation strategies in the aftermath of the earthquake. Furthermore, the real-time probabilistic assessment of potential damage to non-structural elements can serve to identify escape routes and facilitate rescue operations. Critical acceleration thresholds were defined from probabilistic considerations on the expected seismic performance of each typology of non-structural element, described by appropriately selected fragility curves. The feasibility of the proposed procedure was tested by comparing the identified acceleration thresholds with the design values of floor acceleration provided by the Italian Building Code. As a further application, critical acceleration values of the different non-structural elements were compared with a set of real floor acceleration values recorded at the top level of reinforced concrete buildings, highlighting critical non-structural element typologies.
Using Counterexamples to Improve Robustness Verification in Neural Networks

Given the pervasive use of neural networks in safety-critical systems it is important to ensure that they are robust. Recent research has focused on the question of verifying whether networks do not alter their behavior under small perturbations in inputs. Most successful methods are based on the paradigm of branch-and-bound, an abstraction-refinement technique. However, despite tremendous improvements in the last five years, there are still several benchmarks where these methods fail. One reason for this is that many methods use off-the-shelf methods to find the cause of imprecisions. In this paper, our goal is to develop an approach to identify the precise source of imprecision during abstraction. We present a novel counterexample guided approach that can be applied alongside many abstraction techniques. As a specific case, we implement our technique on top of a basic abstraction framework provided by the tool DeepPoly and demonstrate how we can remove imprecisions in a targetted manner. This allows us to go past DeepPoly’s performance as well as outperform other refinement approaches in literature. Surprisingly, we are also able to verify several benchmark instances on which all leading tools fail.
MoMo: Mouse-Based Motion Planning for Optimized Grasping to Declutter Objects Using a Mobile Robotic Manipulator

The aim of this study is to develop a cost-effective and efficient mobile robotic manipulator designed for decluttering objects in both domestic and industrial settings. To accomplish this objective, we implemented a deep learning approach utilizing YOLO for accurate object detection. In addition, we incorporated inverse kinematics to facilitate the precise positioning, placing, and movement of the robotic arms toward the desired object location. To enhance the robot’s navigational capabilities within the environment, we devised an innovative algorithm named “MoMo”, which effectively utilizes odometry data. Through careful integration of these algorithms, our goal is to optimize grasp planning for object decluttering while simultaneously reducing the computational burden and associated costs of such systems. During the experimentation phase, the developed mobile robotic manipulator, following the MoMo path planning strategy, exhibited an impressive average path length coverage of 421.04 cm after completing 10 navigation trials. This performance surpassed that of other state-of-the-art path planning algorithms in reaching the target. Additionally, the MoMo strategy demonstrated superior efficiency, achieving an average coverage time of just 16.84 s, outperforming alternative methods.