Reviewerly/exHarmony huggingface dataset:
Prediction of serious outcomes based on continuous vital sign monitoring of high-risk patients

Continuous monitoring of high-risk patients and early prediction of severe outcomes is crucial to prevent avoidable deaths. Current clinical monitoring is primarily based on intermittent observation of vital signs and the early warning scores (EWS). The drawback is lack of time series dynamics and correlations among vital signs. This study presents an approach to real-time outcome prediction based on machine learning from continuous recording of vital signs. Systolic blood pressure, diastolic blood pressure, heart rate, pulse rate, respiration rate and peripheral blood oxygen saturation were continuously acquired by wearable devices from 292 post-operative high-risk patients. The outcomes from serious complications were evaluated based on review of patients’ medical record. The descriptive statistics of vital signs and patient demographic information were used as features. Four machine learning models K-Nearest-Neighbors (KNN), Decision Trees (DT), Random Forest (RF), and Boosted Ensemble (BE) were trained and tested. In static evaluation, all four models had comparable prediction performance to that of the state of the art. In dynamic evaluation, the models trained from the static evaluation were tested with continuous data. RF and BE obtained the lower false positive rate (FPR) of 0.073 and 0.055 on no-outcome patients respectively. The four models KNN, DT, RF and BE had area under receiver operating characteristic curve (AUROC) of 0.62, 0.64, 0.65 and 0.64 respectively on outcome patients. RF was found to be optimal model with lower FPR on no-outcome patients and a higher AUROC on outcome patients. These findings are encouraging and indicate that additional investigations must focus on validating performance in a clinical setting before deployment of the real-time outcome prediction.
Metadata harmonization–Standards are the key for a better usage of omics data for integrative microbiome analysis

Tremendous amounts of data generated from microbiome research studies during the last decades require not only standards for sampling and preparation of omics data but also clear concepts of how the metadata is prepared to ensure re-use for integrative and interdisciplinary microbiome analysis.In this Commentary, we present our views on the key issues related to the current system for metadata submission in omics research, and propose the development of a global metadata system. Such a system should be easy to use, clearly structured in a hierarchical way, and should be compatible with all existing microbiome data repositories, following common standards for minimal required information and common ontology. Although minimum metadata requirements are essential for microbiome datasets, the immense technological progress requires a flexible system, which will have to be constantly improved and re-thought. While FAIR principles (Findable, Accessible, Interoperable, and Reusable) are already considered, international legal issues on genetic resource and sequence sharing provided by the Convention on Biological Diversity need more awareness and engagement of the scientific community.The suggested approach for metadata entries would strongly improve retrieving and re-using data as demonstrated in several representative use cases. These integrative analyses, in turn, would further advance the potential of microbiome research for novel scientific discoveries and the development of microbiome-derived products.
A Watermarking Optimization Method Based on Matrix Decomposition and DWT for Multi-Size Images

Image watermarking is a key technology for copyright protection, and how to better balance the invisibility and robustness of algorithms is a challenge. To tackle this challenge, a watermarking optimization method based on matrix decomposition and discrete wavelet transform (DWT) for multi-size images is proposed. The DWT, Hessenberg matrix decomposition (HMD), singular value decomposition (SVD), particle swarm optimization (PSO), Arnold transform and logistic mapping are combined for the first time to achieve an image watermarking optimization algorithm. The multi-level decomposition of DWT is used to be adapted to multi-size host images, the Arnold transform, logistic mapping, HMD and SVD are used to enhance the security and robustness, and the PSO optimized scaling factor to balance invisibility and robustness. The simulation results of the proposed method show that the PSNRs are higher than 44.9 dB without attacks and the NCs are higher than 0.98 under various attacks. Compared with the existing works, the proposed method shows high robustness against various attacks, such as noise, filtering and JPEG compression and in particular, the NC values are at least 0.44% higher than that in noise attacks.
On-chip bacterial foraging training in silicon photonic circuits for projection-enabled nonlinear classification

Abstract On-chip training remains a challenging issue for photonic devices to implement machine learning algorithms. Most demonstrations only implement inference in photonics for offline-trained neural network models. On the other hand, artificial neural networks are one of the most deployed algorithms, while other machine learning algorithms such as supporting vector machine (SVM) remain unexplored in photonics. Here, inspired by SVM, we propose to implement projection-based classification principle by constructing nonlinear mapping functions in silicon photonic circuits and experimentally demonstrate on-chip bacterial foraging training for this principle to realize single Boolean logics, combinational Boolean logics, and Iris classification with ~96.7 − 98.3 per cent accuracy. This approach can offer comparable performances to artificial neural networks for various benchmarks even with smaller scales and without leveraging traditional activation functions, showing scalability advantage. Natural-intelligence-inspired bacterial foraging offers efficient and robust on-chip training, and this work paves a way for photonic circuits to perform nonlinear classification.
Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction

We present a study on leveraging multilingual pre-trained generative language models for zero-shot cross-lingual event argument extraction (EAE).By formulating EAE as a language generation task, our method effectively encodes event structures and captures the dependencies between arguments.We design language-agnostic templates to represent the event argument structures, which are compatible with any language, hence facilitating the cross-lingual transfer.Our proposed model finetunes multilingual pre-trained generative language models to generate sentences that fill in the language-agnostic template with arguments extracted from the input passage.The model is trained on source languages and is then directly applied to target languages for event argument extraction.Experiments demonstrate that the proposed model outperforms the current state-of-the-art models on zero-shot cross-lingual EAE.Comprehensive studies and error analyses are presented to better understand the advantages and the current limitations of using generative language models for zero-shot cross-lingual transfer EAE.
An Investigation of SMOTE based Methods for Imbalanced Datasets with Data Complexity Analysis

Many binary class datasets in real-life applications are affected by class imbalance problem. Data complexities like noise examples, class overlap and small disjuncts problems are observed to play a key role in producing poor classification performance. These complexities tend to exist in tandem with class imbalance problem. Synthetic Minority Oversampling Technique (SMOTE) is a well-known method to re-balance the number of examples in imbalanced datasets. However, this technique cannot effectively tackle data complexities and it also has the capability of magnifying the degree of complexities. Also, the performance of the SMOTE is still not satisfactory. Therefore, various SMOTE variants have been proposed to overcome the downsides of SMOTE either by combining SMOTE with other algorithms or modifying the existing SMOTE algorithm. This paper aims to comparatively review the algorithms applied in SMOTE variants and investigate which data complexities are being addressed in what variants. Series of experiments are conducted on 24 binary class imbalanced datasets to observe the changes in the data complexity measures after SMOTE variants were applied in these datasets. The evaluation metrics like G-Mean and F1-Score are also analyzed to investigate the difference in classification performance between SMOTE variants.
Seq2Path: Generating Sentiment Tuples as Paths of a Tree

Aspect-based sentiment analysis (ABSA) tasks aim to extract sentiment tuples from a sentence.Recent generative methods such as Seq2Seq models have achieved good performance by formulating the output as a sequence of sentiment tuples.However, the orders between the sentiment tuples do not naturally exist and the generation of the current tuple should not condition on the previous ones.In this paper, we propose Seq2Path to generate sentiment tuples as paths of a tree.A tree can represent "1-to-n" relations (e.g., an aspect term may correspond to multiple opinion terms) and the paths of a tree are independent and do not have orders.For training, we treat each path as an independent target, and we calculate the average loss of the ordinary Seq2Seq model over paths.For inference, we apply beam search with constrained decoding.By introducing an additional discriminative token and applying a data augmentation technique, valid paths can be automatically selected.We conduct experiments on five tasks including AOPE, ASTE, TASD, UABSA, ACOS.We evaluate our method on four common benchmark datasets including Laptop14, Rest14, Rest15, Rest16.Our proposed method achieves state-of-the-art results in almost all cases.
FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis

Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hindered their applications to speech synthesis. This paper proposes FastDiff, a fast conditional diffusion model for high-quality speech synthesis. FastDiff employs a stack of time-aware location-variable convolutions of diverse receptive field patterns to efficiently model long-term time dependencies with adaptive conditions. A noise schedule predictor is also adopted to reduce the sampling steps without sacrificing the generation quality. Based on FastDiff, we design an end-to-end text-to-speech synthesizer, FastDiff-TTS, which generates high-fidelity speech waveforms without any intermediate feature (e.g., Mel-spectrogram). Our evaluation of FastDiff demonstrates the state-of-the-art results with higher-quality (MOS 4.28) speech samples. Also, FastDiff enables a sampling speed of 58x faster than real-time on a V100 GPU, making diffusion models practically applicable to speech synthesis deployment for the first time. We further show that FastDiff generalized well to the mel-spectrogram inversion of unseen speakers, and FastDiff-TTS outperformed other competing methods in end-to-end text-to-speech synthesis. Audio samples are available at https://FastDiff.github.io/.
Variational autoencoders for anomalous jet tagging

We present a detailed study on variational autoencoders (VAEs) for anomalous jet tagging at the Large Hadron Collider. By taking in low-level jet constituents' information and training with background QCD jets in an unsupervised manner, the VAE is able to encode important information for reconstructing jets, while learning an expressive posterior distribution in the latent space. When using the VAE as an anomaly detector, we present different approaches to detect anomalies: directly comparing in the input space or, instead, working in the latent space. A comprehensive series of test sets are generated to fully examine the anomalous tagging performance in different jet types. In order to facilitate general search approaches such as bump hunt, mass-decorrelated VAEs based on distance correlation regularization are also studied. We find that the naive mass-decorrelated VAEs fail at maintaining proper detection performance, by assigning higher probability to some anomalous samples. To build a performant mass-decorrelated anomalous jet tagger, we propose the outlier exposed VAE (OE-VAE), for which some outlier samples are introduced in the training process to guide the learned information. OE-VAEs are employed to achieve two goals at the same time: increasing sensitivity of outlier detection and decorrelating jet mass from the anomaly score. We succeed in reaching excellent results from both aspects.
SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic

iSarcasmEval is the first shared task to target intended sarcasm detection: the data for this task was provided and labelled by the authors of the texts themselves.Such an approach minimises the downfalls of other methods to collect sarcasm data, which rely on distant supervision or third-party annotations.The shared task contains two languages, English and Arabic, and three subtasks: sarcasm detection, sarcasm category classification, and pairwise sarcasm identification given a sarcastic sentence and its non-sarcastic rephrase.The task received submissions from 60 different teams, with the sarcasm detection task being the most popular.Most of the participating teams utilised pre-trained language models.In this paper, we provide an overview of the task, data, and participating teams.
Quantum-Inspired Real-Time Optimization for 6G Networks: Opportunities, Challenges, and the Road Ahead

It is envisioned that 6G, unlike its predecessor 5G, will depart from connected machines and connected people to connected intelligence. The main goal of 6G networks is to support massive connectivity for time-sensitive and computation-sensitive services in mission-critical applications. The creation of real-time optimisation (RTO) enabled by the fast growing data analytic and machine learning will seize the opportunities for 6G wireless networks to support such immersive services such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and tactile Internet. Recently, with the rapid development of quantum computers, quantum-inspired optimisation and machine learning algorithms have been exploited as efficient solutions for future wireless networks. In this article, we provide a comprehensive view on the new concept of quantum-inspired RTO and its application to the optimal resource allocation for 6G wireless networks. Our main contributions are to introduce some of the initial research results and introduce the potentiality of quantum-inspired RTO on some 6G emerging technologies. Not only do we review the fundamental principles; we also explore the challenges and opportunities of this exciting research direction.
A new algorithm for support vector regression with automatic selection of hyperparameters

The hyperparameters in support vector regression (SVR) determine the effectiveness of the support vectors with fitting and predictions. However, the choice of these hyperparameters has always been challenging in both theory and practice. The ν-support vector regression eliminates the need to specify an ϵ value elegantly, but at the cost of specifying or postulating a ν value. We propose an extended primal objective function arising from probability regularization leading to an automatic selection of ϵ, and we can express ν as an explicit function of ϵ. The resultant hyperparameter values can be interpreted as ‘working’ values required only in training but not testing or prediction. This regularized algorithm, namely ϵ*-SVR, automatically provides a data-dependent ϵ and is found to have a close connection to the ν-support vector regression in the sense that ν as a fraction is a sensible function of ϵ. The ϵ*-SVR automatically selects both ν and ϵ values. We illustrate these findings with some public benchmark datasets.
AIParsing: Anchor-Free Instance-Level Human Parsing

Most state-of-the-art instance-level human parsing models adopt two-stage anchor-based detectors and, therefore, cannot avoid the heuristic anchor box design and the lack of analysis on a pixel level. To address these two issues, we have designed an instance-level human parsing network which is anchor-free and solvable on a pixel level. It consists of two simple sub-networks: an anchor-free detection head for bounding box predictions and an edge-guided parsing head for human segmentation. The anchor-free detector head inherits the pixel-like merits and effectively avoids the sensitivity of hyper-parameters as proved in object detection applications. By introducing the part-aware boundary clue, the edge-guided parsing head is capable to distinguish adjacent human parts from among each other up to 58 parts in a single human instance, even overlapping instances. Meanwhile, a refinement head integrating box-level score and part-level parsing quality is exploited to improve the quality of the parsing results. Experiments on two multiple human parsing datasets (i.e., CIHP and LV-MHP-v2.0) and one video instance-level human parsing dataset (i.e., VIP) show that our method achieves the best global-level and instance-level performance over state-of-the-art one-stage top-down alternatives.
Standardizing Smart Contracts

In the evolving context of distributed ledger technologies, the standardization of smart contracts is necessary.Smart contracts are tamper-proof computer programs.Due to their security and flexibility, it is possible to exploit smart contracts in a wide variety of use cases.In particular, it could be possible to automate legally recognized contracts by leveraging smart contracts.To this extent, some standards regarding the proper management of smart contracts are surging.However, there are still many technological misconceptions regarding smart contracts.This study describes smart contracts from multiple perspectives and identifies and clarifies some of the most common misconceptions regarding smart contracts.This study also provides some guidelines and insights on the proper management of smart contracts.This study can be a valuable resource for future standards on smart contracts.
HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection

The High-Resolution Transformer (HRFormer) can maintain high-resolution representation and share global receptive fields. It is friendly towards salient object detection (SOD) in which the input and output have the same resolution. However, two critical problems need to be solved for two-modality SOD. One problem is two-modality fusion. The other problem is the HRFormer output’s fusion. To address the first problem, a supplementary modality is injected into the primary modality by using global optimization and an attention mechanism to select and purify the modality at the input level. To solve the second problem, a dual-direction short connection fusion module is used to optimize the output features of HRFormer, thereby enhancing the detailed representation of objects at the output level. The proposed model, named HRTransNet, first introduces an auxiliary stream for feature extraction of supplementary modality. Then, features are injected into the primary modality at the beginning of each multi-resolution branch. Next, HRFormer is applied to achieve forwarding propagation. Finally, all the output features with different resolutions are aggregated by intra-feature and inter-feature interactive transformers. Application of the proposed model results in impressive improvement for driving two-modality SOD tasks, e.g., RGB-D, RGB-T, and light field SOD. <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/liuzywen/HRTransNet</uri>
Secure Smart Communication Efficiency in Federated Learning: Achievements and Challenges

Federated learning (FL) is known to perform machine learning tasks in a distributed manner. Over the years, this has become an emerging technology, especially with various data protection and privacy policies being imposed. FL allows for performing machine learning tasks while adhering to these challenges. As with the emergence of any new technology, there will be challenges and benefits. A challenge that exists in FL is the communication costs: as FL takes place in a distributed environment where devices connected over the network have to constantly share their updates, this can create a communication bottleneck. This paper presents the state-of-the-art of the conducted works on communication constraints of FL while maintaining the secure and smart properties that federated learning is known for. Overall, current challenges and possible methods for enhancing the FL models’ efficiency with a perspective on communication are discussed. This paper aims to bridge the gap in all conducted review papers by solely focusing on communication aspects in FL environments.
Popularity of the metaverse: Embodied social presence theory perspective

With Facebook’s name changing to Meta, the metaverse concept has become popular again. There are many indications that the current fashionableness of the metaverse is not driven by technical factors, rather related to the public hype. To clarify the reasons for the increasing popularity of the concept, this study develops a model based on embodied social presence theory. We surveyed 292 metaverse users, and analyzed the obtained data using partial least squares structural equation modeling (PLS-SEM). The results show that the main technical factors influencing the metaverse popularity do not significantly predict users’ embodied presence and embodied co-presence, while users’ imagination positively predicts their embodied presence in the metaverse and positively influences users’ willingness to continuously participate through the multiple mediating effects of embodied presence and co-presence. The results of this study confirm, to some extent, that user imagination triggered by public opinion drives the popularity of the metaverse.
Data compression for quantum machine learning

The advent of noisy-intermediate scale quantum computers has introduced the exciting possibility of achieving quantum speedups in machine learning tasks. These devices, however, are composed of a small number of qubits and can faithfully run only short circuits. This puts many proposed approaches for quantum machine learning beyond currently available devices. We address the problem of compressing classical data into efficient representations on quantum devices. Our proposed methods allow both the required number of qubits and depth of the quantum circuit to be tuned. We achieve this by using a correspondence between matrix-product states and quantum circuits and further propose a hardware-efficient quantum circuit approach, which we benchmark on the Fashion-MNIST dataset. Finally, we demonstrate that a quantum circuit-based classifier can achieve competitive accuracy with current tensor learning methods using only 11 qubits.