Reviewerly/exHarmony huggingface dataset:
Pandemic Medical Early Warning Score Value to Predict Patient Triage and Mortality During The COVID-19 Pandemic Period

Objective: Pandemics and epidemics have significantly affected public health over the years and have shown us the importance of evaluating the prognosis at the early stage.In the coronavirus disease-19 (COVID-19) pandemic, the necessity of clinical scoring in the triage of patients with similar clinical characteristics in the primary care was highlighted.This study aims to investigate the effectiveness of the Pandemic Medical Early Warning Score (PMEWS), which is a clinical score, in the primary care triage of patients and in distinguishing the risk of 30-day mortality in the COVID-19 pandemic.Materials and Methods: Data from confirmed COVID-19 patients in the emergency department (ED) were analyzed retrospectively between March 2020, and July 2020.The area under the curve (AUC) was used to evaluate the discriminatory power of the PMEWS in predicting all-cause 30-day mortality.Results: Four hundred and fifty-eight patients were included in this study.The median age of the patients was 54.5 years (IQR 32.25 years).There was at least one coexisting disease in 227 (49.6%) of the patients, and it was significantly higher in non-survivor patients compared to survivors (p<0.05).ROC analysis of the PMEWS showed the optimal cutoffs for the 30-day mortality to be 4 (sensitivity 93.33, specificity 82.62).The AUC of the PMEWS for predicting all-cause 30-day mortality was 0.931. Conclusion:PMEWS is a non-disease-specific and physiological-social score at times of high ED admissions in a pandemic and can be a potentially useful triage tool for pre-examination patient triage and for estimating mortality during pandemic periods.More detailed studies are needed to determine the effectiveness of scorings in pandemics.
Addressing Cybersecurity Issues

The protection and preservation of data, confidentiality, integrity, and availability of such data is central to the maintenance of a secure cybersecurity system. While cybersecurity breaches continue to occur, governments and other organizations have established and strengthened cybersecurity regulations to reduce them. In that regard, Malaysia has had its fair share of cybersecurity breaches. Consequently, this chapter seeks to evaluate the components of the embedding ecosystem to assess the developments and readiness of the cybersecurity environment in Malaysia. In doing so, the chapter examines the national cybersecurity policies in Malaysia, which is followed by a discussion on cybersecurity issues arising from the main technology domains in the country. The chapter then addresses the cybersecurity concerns associated with the deployment of IR4.0 technologies in Malaysia.
Generalisation Through Negation and Predicate Invention

The ability to generalise from a small number of examples is a fundamental challenge in machine learning. To tackle this challenge, we introduce an inductive logic programming (ILP) approach that combines negation and predicate invention. Combining these two features allows an ILP system to generalise better by learning rules with universally quantified body-only variables. We implement our idea in NOPI, which can learn normal logic programs with predicate invention, including Datalog programs with stratified negation. Our experimental results on multiple domains show that our approach can improve predictive accuracies and learning times.
DDS: Decoupled Dynamic Scene-Graph Generation Network

Scene-graph generation involves creating a structural representation of the relationships between objects in a scene by predicting subject-object-relation triplets from input data. However, existing methods show poor performance in detecting triplets outside of a predefined set, primarily due to their reliance on dependent feature learning. To address this issue we propose DDS -- a decoupled dynamic scene-graph generation network -- that consists of two independent branches that can disentangle extracted features. The key innovation of the current paper is the decoupling of the features representing the relationships from those of the objects, which enables the detection of novel object-relationship combinations. The DDS model is evaluated on three datasets and outperforms previous methods by a significant margin, especially in detecting previously unseen triplets.
Application of Machine Learning in Prediction of Ethereum-Based Transaction

A cryptocurrency payment platform, allows users to transact in cryptocurrencies on a global scale inside a decentralized environment. Ethereum’s native token has been successfully migrated to the main net. The platform’s digital wallet, Ethereum Wallet, allows users to store and manage their digital assets across several platforms, including computers and mobile devices. Blockchain technology has emerged as a game-changer in aftermath of the success of Bitcoin and other cryptocurrencies. Instead of establishing a new blockchain from scratch as the number of current ones rises, decentralized application developers should focus on finding a solution that best suits their needs and the needs of their decentralized apps. The ownership and transferability of digital financial assets known as “cryptocurrencies” are guaranteed by decentralized cryptographic technology. The increasing market value and popularity of cryptocurrencies pose a variety of difficulties and concerns for global business and industrial economics. The planned study’s main purpose is to validate the correctness of Etherium transactions on the blockchain. The accuracy of the suggested work was compared to that of earlier work in this study. The LSTM-based training strategy has been chosen for the planned study. The training and testing of the Etherium transaction were done with and without considerable record filtering. Both models’ accuracy was evaluated to ensure the dependability of the hybrid strategy, which combined an LSTM model with a specific filtering mechanism.
Research Advancements in Privacy Preserving: A Relative Study Using Bibliometric Analysis in Emerging Areas in Computer Science

Bibliometric Analysis on Privacy Preserving concept during the years 2014 to 2022 is performed on Scopus database. Total documents under study were 22887. The parameters used for the analysis include source, year, area, Country, University, author and funding agency. Other parameters used for Network analysis were Co-authorship, Co-occurrence, Citation, Co-citation and Bibliographic coupling. The tool supported this analysis was VOSviewer 1.6.16. All Tables and figures were taken from www.scopus.com, The analysis was carried on 08th October, 2021.
Taking advantage of noise in quantum reservoir computing

The biggest challenge that quantum computing and quantum machine learning are currently facing is the presence of noise in quantum devices. As a result, big efforts have been put into correcting or mitigating the induced errors. But, can these two fields benefit from noise? Surprisingly, we demonstrate that under some circumstances, quantum noise can be used to improve the performance of quantum reservoir computing, a prominent and recent quantum machine learning algorithm. Our results show that the amplitude damping noise can be beneficial to machine learning, while the depolarizing and phase damping noises should be prioritized for correction. This critical result sheds new light into the physical mechanisms underlying quantum devices, providing solid practical prescriptions for a successful implementation of quantum information processing in nowadays hardware.
Benchmarking multi-qubit gates -- II: Computational aspects

An important step in developing multi-qubit gates is to construct efficient benchmarking protocols for them. In our previous paper (arXiv: 2210.04330), we developed metrological protocols to measure the reduced Choi matrix i.e., the completely positive (CP) maps induced on a subset S of the qubits, by the multi-qubit gate. Here, we show a set of classically verifiable properties that the Choi matrix satisfies if it is a reduction of a multi-qubit unitary and use them to develop benchmarks. We identify three types of errors that affect the implementation of a multi-qubit unitary, based on their mathematical properties and physical origin. Although a target multi-qubit gate is a unitary operator, errors turn it into a general completely positive (CP) map. Errors due to coupling to a thermal bath result in the multi-qubit gate being CP-divisible (Markovian), deviating from a unitary. The reduced Choi matrix of a multi-qubit gate has a property known as double stochasticity, which is violated in the presence of Markovian errors. We construct a benchmark using double-stochasticity violation and show that it is sensitive to coupling to any thermal bath at a finite temperature. Further, errors due to shot-to-shot fluctuations result in a non-markovian, i.e., CP-indivisible quantum process. We prove a new property, which we call the \rank property of the reduced Choi matrix, the violation of which implies a CP-indivisible error. A third category of errors comes from systematics in the implementation of a multi-qubit gate, resulting in no deviation from unitarity. We refer to this as unitary errors. This corresponds to the most challenging type of error to benchmark. We develop a partial-benchmarking protocol for such errors using symmetries of the multi-qubit gate being applied.
On ring-like event systems in quantum logic

A class of ring-like event systems (RLSEs) is studied that generalizes Boolean rings. Quantum logics represented by orthomodular lattices are characterized within this class and the correspondence between Boolean algebras and Boolean rings is enlarged to orthomodular lattices. The structure of RLSEs and various subclasses is analysed and classical logics are especially identified. Moreover, sets of numerical events within different contexts of physical problems are described. A numerical event is defined as a function p from a set S of states of a physical system to [0,1] such that p(s) is the probability of the occurrence of an event when the system is in state s\in S. In particular, the question is answered whether a given (small) set of numerical events will give rise to the assumption that one deals with a classical physical system or a quantum mechanical one.
The Feasibility of Algorithmic Detection and Decentralised Moderation for Protecting Women from Online Abuse

Online abuse is becoming an increasingly prevalent issue in modern-day society, with 41 percent of Americans having experienced online harassment in some capacity in 2021. People who identify as women, in particular, can be subjected to a wide range of abusive behavior online, with gender-specific experiences cited broadly in recent literature across fields such as blogging, politics, and journalism. In response to this rise in abusive content, platforms have been found to largely employ "individualistic moderation" approaches, aiming to protect users from harmful content through the screening and management of singular interactions or accounts. Yet, previous work performed by the author of this paper has shown that in the cases of women in particular, these approaches can often be ineffective; failing to protect users from multi-dimensional abuse spanning prolonged time periods, different platforms, and varying interaction types. In recognition of its increasing complexity, platforms are beginning to outsource content moderation to users in a new and decentralized approach. The goal of this research is to examine the feasibility of using multidimensional abuse indicators in a Twitter-based moderation algorithm aiming to protect women from female-targeted online abuse. This research outlines three indicators of multidimensional abuse, explores how these indicators can be extracted as features from Twitter data, and proposes a technical framework for deploying an end-to-end moderation algorithm using these features.
RSK-Complete Cycle Decompositions

We characterize the class of cycle decompositions that can achieve all Young tableau shapes (except the trivial ones with a single row or a single column) under the Robinson--Schensted--Knuth (RSK) correspondence, a property that we call RSK-completeness. We prove that for even $n$, cyclic permutations comprise the only fixed cycle decomposition that is RSK-complete. For odd $n$, cyclic permutations and almost cyclic permutations which have a cycle of length $n-1$ are the only RSK-complete cycle decompositions.
Data thinning for convolution-closed distributions

We propose data thinning, an approach for splitting an observation into two or more independent parts that sum to the original observation, and that follow the same distribution as the original observation, up to a (known) scaling of a parameter. This very general proposal is applicable to any convolution-closed distribution, a class that includes the Gaussian, Poisson, negative binomial, gamma, and binomial distributions, among others. Data thinning has a number of applications to model selection, evaluation, and inference. For instance, cross-validation via data thinning provides an attractive alternative to the usual approach of cross-validation via sample splitting, especially in settings in which the latter is not applicable. In simulations and in an application to single-cell RNA-sequencing data, we show that data thinning can be used to validate the results of unsupervised learning approaches, such as k-means clustering and principal components analysis, for which traditional sample splitting is unattractive or unavailable.
Label Inference Attack against Split Learning under Regression Setting

As a crucial building block in vertical Federated Learning (vFL), Split Learning (SL) has demonstrated its practice in the two-party model training collaboration, where one party holds the features of data samples and another party holds the corresponding labels. Such method is claimed to be private considering the shared information is only the embedding vectors and gradients instead of private raw data and labels. However, some recent works have shown that the private labels could be leaked by the gradients. These existing attack only works under the classification setting where the private labels are discrete. In this work, we step further to study the leakage in the scenario of the regression model, where the private labels are continuous numbers (instead of discrete labels in classification). This makes previous attacks harder to infer the continuous labels due to the unbounded output range. To address the limitation, we propose a novel learning-based attack that integrates gradient information and extra learning regularization objectives in aspects of model training properties, which can infer the labels under regression settings effectively. The comprehensive experiments on various datasets and models have demonstrated the effectiveness of our proposed attack. We hope our work can pave the way for future analyses that make the vFL framework more secure.
Class Enhancement Losses with Pseudo Labels for Zero-shot Semantic Segmentation

Recent mask proposal models have significantly improved the performance of zero-shot semantic segmentation. However, the use of a `background' embedding during training in these methods is problematic as the resulting model tends to over-learn and assign all unseen classes as the background class instead of their correct labels. Furthermore, they ignore the semantic relationship of text embeddings, which arguably can be highly informative for zero-shot prediction as seen classes may have close relationship with unseen classes. To this end, this paper proposes novel class enhancement losses to bypass the use of the background embbedding during training, and simultaneously exploit the semantic relationship between text embeddings and mask proposals by ranking the similarity scores. To further capture the relationship between seen and unseen classes, we propose an effective pseudo label generation pipeline using pretrained vision-language model. Extensive experiments on several benchmark datasets show that our method achieves overall the best performance for zero-shot semantic segmentation. Our method is flexible, and can also be applied to the challenging open-vocabulary semantic segmentation problem.
KILDST: Effective Knowledge-Integrated Learning for Dialogue State Tracking using Gazetteer and Speaker Information

Dialogue State Tracking (DST) is core research in dialogue systems and has received much attention. In addition, it is necessary to define a new problem that can deal with dialogue between users as a step toward the conversational AI that extracts and recommends information from the dialogue between users. So, we introduce a new task - DST from dialogue between users about scheduling an event (DST-USERS). The DST-USERS task is much more challenging since it requires the model to understand and track dialogue states in the dialogue between users and to understand who suggested the schedule and who agreed to the proposed schedule. To facilitate DST-USERS research, we develop dialogue datasets between users that plan a schedule. The annotated slot values which need to be extracted in the dialogue are date, time, and location. Previous approaches, such as Machine Reading Comprehension (MRC) and traditional DST techniques, have not achieved good results in our extensive evaluations. By adopting the knowledge-integrated learning method, we achieve exceptional results. The proposed model architecture combines gazetteer features and speaker information efficiently. Our evaluations of the dialogue datasets between users that plan a schedule show that our model outperforms the baseline model.
ViT-AE++: Improving Vision Transformer Autoencoder for Self-supervised Medical Image Representations

Self-supervised learning has attracted increasing attention as it learns data-driven representation from data without annotations. Vision transformer-based autoencoder (ViT-AE) by He et al. (2021) is a recent self-supervised learning technique that employs a patch-masking strategy to learn a meaningful latent space. In this paper, we focus on improving ViT-AE (nicknamed ViT-AE++) for a more effective representation of 2D and 3D medical images. We propose two new loss functions to enhance the representation during training. The first loss term aims to improve self-reconstruction by considering the structured dependencies and indirectly improving the representation. The second loss term leverages contrastive loss to optimize the representation from two randomly masked views directly. We extended ViT-AE++ to a 3D fashion for volumetric medical images as an independent contribution. We extensively evaluate ViT-AE++ on both natural images and medical images, demonstrating consistent improvement over vanilla ViT-AE and its superiority over other contrastive learning approaches. Codes are here: https://github.com/chinmay5/vit_ae_plus_plus.git.
Learning Interpolations between Boltzmann Densities

We introduce a training objective for continuous normalizing flows that can be used in the absence of samples but in the presence of an energy function. Our method relies on either a prescribed or a learnt interpolation $f_t$ of energy functions between the target energy $f_1$ and the energy function of a generalized Gaussian $f_0(x) = ||x/\sigma||_p^p$. The interpolation of energy functions induces an interpolation of Boltzmann densities $p_t \propto e^{-f_t}$ and we aim to find a time-dependent vector field $V_t$ that transports samples along the family $p_t$ of densities. The condition of transporting samples along the family $p_t$ is equivalent to satisfying the continuity equation with $V_t$ and $p_t = Z_t^{-1}e^{-f_t}$. Consequently, we optimize $V_t$ and $f_t$ to satisfy this partial differential equation. We experimentally compare the proposed training objective to the reverse KL-divergence on Gaussian mixtures and on the Boltzmann density of a quantum mechanical particle in a double-well potential.
DDPEN: Trajectory Optimisation With Sub Goal Generation Model

Differential dynamic programming (DDP) is a widely used and powerful trajectory optimization technique, however, due to its internal structure, it is not exempt from local minima. In this paper, we present Differential Dynamic Programming with Escape Network (DDPEN) - a novel approach to avoid DDP local minima by utilising an additional term used in the optimization criteria pointing towards the direction where robot should move in order to escape local minima. In order to produce the aforementioned directions, we propose to utilize a deep model that takes as an input the map of the environment in the form of a costmap together with the desired goal position. The Model produces possible future directions that will lead to the goal, avoiding local minima which is possible to run in real time conditions. The model is trained on a synthetic dataset and overall the system is evaluated at the Gazebo simulator. In this work we show that our proposed method allows avoiding local minima of trajectory optimization algorithm and successfully execute a trajectory 278 m long with various convex and nonconvex obstacles.
AvoidBench: A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors

Obstacle avoidance is an essential topic in the field of autonomous drone research. When choosing an avoidance algorithm, many different options are available, each with their advantages and disadvantages. As there is currently no consensus on testing methods, it is quite challenging to compare the performance between algorithms. In this paper, we propose AvoidBench, a benchmarking suite which can evaluate the performance of vision-based obstacle avoidance algorithms by subjecting them to a series of tasks. Thanks to the high fidelity of multi-rotors dynamics from RotorS and virtual scenes of Unity3D, AvoidBench can realize realistic simulated flight experiments. Compared to current drone simulators, we propose and implement both performance and environment metrics to reveal the suitability of obstacle avoidance algorithms for environments of different complexity. To illustrate AvoidBench's usage, we compare three algorithms: Ego-planner, MBPlanner, and Agile-autonomy. The trends observed are validated with real-world obstacle avoidance experiments.
AutoFraudNet: A Multimodal Network to Detect Fraud in the Auto Insurance Industry

In the insurance industry detecting fraudulent claims is a critical task with a significant financial impact. A common strategy to identify fraudulent claims is looking for inconsistencies in the supporting evidence. However, this is a laborious and cognitively heavy task for human experts as insurance claims typically come with a plethora of data from different modalities (e.g. images, text and metadata). To overcome this challenge, the research community has focused on multimodal machine learning frameworks that can efficiently reason through multiple data sources. Despite recent advances in multimodal learning, these frameworks still suffer from (i) challenges of joint-training caused by the different characteristics of different modalities and (ii) overfitting tendencies due to high model complexity. In this work, we address these challenges by introducing a multimodal reasoning framework, AutoFraudNet (Automobile Insurance Fraud Detection Network), for detecting fraudulent auto-insurance claims. AutoFraudNet utilizes a cascaded slow fusion framework and state-of-the-art fusion block, BLOCK Tucker, to alleviate the challenges of joint-training. Furthermore, it incorporates a light-weight architectural design along with additional losses to prevent overfitting. Through extensive experiments conducted on a real-world dataset, we demonstrate: (i) the merits of multimodal approaches, when compared to unimodal and bimodal methods, and (ii) the effectiveness of AutoFraudNet in fusing various modalities to boost performance (over 3\% in PR AUC).