Reviewerly/exHarmony huggingface dataset:
Maximizing portfolio profitability during a cryptocurrency downtrend: A Bitcoin Blockchain transaction-based approach

The volatile and unpredictable nature of the cryptocurrency market makes it particularly challenging to make profitable investment decisions. different machine learning-based techniques have been employed for forecasting cryptocurrency value. However, although some works have addressed incorporating the Blockchain transactions’ data into the analysis, none of them has provided a hybrid solution, including features obtained through complex network modeling. In this paper, we investigated the use of machine learning and complex network techniques to improve the profitability of a cryptocurrency portfolio during a downtrend period. We extracted features through a complex network-building methodology based on the Bitcoin blockchain transactions, merged them with the historical cryptocurrency values, and generated the predictions using different machine-learning models. The results indicated that incorporating complex network features improved the performance in retaining the initial capital at the end of the experiment, leading to an increment of 7.09% and 4.33% for the CNN and LSTM models, respectively. Our findings suggest that the proposed method enhanced the performance of cryptocurrency investment strategies during downtrend periods.
icsBERTs: Optimizing Pre-trained Language Models in Intelligent Customer Service

Automatic processing of textual information is a growing application area in intelligent customer service platforms due to the large number of customer requests constantly provided in the form of text. Many pre-trained language models have shown their high performance on text processing tasks. However, these pre-training strategies do not leverage domain-specific information. In this paper, we propose icsBERTs optimized for intelligent customer service on both word and sentence levels. Specifically, the automatic business words extraction and multi-task training methods are designed to enhance language representations for our models. Furthermore, we also propose the use of adaptors based on these icsBERTs for more complicated tasks. Our models outperform mainstream pre-trained language models on several tasks in the field of intelligent customer service and these experimental results demonstrate that using targeted strategies can further improve the performance of pre-trained language models in this field.
Asynchronous training of quantum reinforcement learning

The development of quantum machine learning (QML) has received a lot of interest recently thanks to developments in both quantum computing (QC) and machine learning (ML). One of the ML paradigms that can be utilized to address challenging sequential decision-making issues is reinforcement learning (RL). It has been demonstrated that classical RL can successfully complete many difficult tasks. A leading method of building quantum RL agents relies on the variational quantum circuits (VQC). However, training QRL algorithms with VQCs requires significant amount of computational resources. This issue hurdles the exploration of various QRL applications. In this paper, we approach this challenge through asynchronous training of QRL agents. Specifically, we choose the asynchronous training of advantage actor-critic variational quantum policies. We demonstrate the results via numerical simulations that within the tasks considered, the asynchronous training of QRL agents can reach performance comparable to or superior to classical agents with similar model sizes and architectures.
From Natural Towards Representative Decision Making in Sports: A Framework for Decision Making in Virtual and Augmented Environments

Decision making is vital in complex sporting tasks but is difficult to test and train. New technologies such as virtual and augmented reality offer novel opportunities for improving decision making, yet it remains unclear whether training gains using these new approaches will improve decision making on-field. To clarify the potential benefits, a clear conceptualization of decision making is required, particularly for invasive team sports such as football, basketball and field hockey, where decisions are complex with many possible options offered. Therefore, the aim of this position paper is to establish a framework for the design of virtual and augmented environments that help invasive team sport athletes to train their decision-making capacities. To achieve this, we propose a framework for conceptualising 'natural' decision making within the performance environment in invasive team sports that views decision making as a continuous cyclical process where the ball carrier interacts with teammates to create 'windows of opportunity', and where skilled decision makers often delay decisions to create time, and in turn new opportunities, rather than necessarily selecting the first option available to them. Within the framework, we make a distinction between decision making and anticipation, proposing that decision making requires a series of on-going anticipatory judgments. Based on the framework, we subsequently highlight the consequences for testing and training decision making using virtual and augmented reality environments, in particular outlining the technological challenges that need to be overcome for natural decision making to be represented within virtual and augmented environments.
Local Implicit Neural Representations for Multi-Sequence MRI Translation

In radiological practice, multi-sequence MRI is routinely acquired to characterize anatomy and tissue. However, due to the heterogeneity of imaging protocols and contraindications to contrast agents, some MRI sequences, e.g. contrast-enhanced T1-weighted image (T1ce), may not be acquired. This creates difficulties for large-scale clinical studies for which heterogeneous datasets are aggregated. Modern deep learning techniques have demonstrated the capability of synthesizing missing sequences from existing sequences, through learning from an extensive multi-sequence MRI dataset. In this paper, we propose a novel MR image translation solution based on local implicit neural representations. We split the available MRI sequences into local patches and assign to each patch a local multi-layer perceptron (MLP) that represents a patch in the T1ce. The parameters of these local MLPs are generated by a hypernetwork based on image features. Experimental results and ablation studies on the BraTS challenge dataset showed that the local MLPs are critical for recovering fine image and tumor details, as they allow for local specialization that is highly important for accurate image translation. Compared to a classical pix2pix model, the proposed method demonstrated visual improvement and significantly improved quantitative scores (MSE 0.86 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> vs. 1.02 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> and SSIM 94.9 vs 94.3).
Evolving Interest with Feature Co-action Network for CTR Prediction

Abstract Recently, many deep learning-based models have been successfully applied to click-through rate prediction. However, most previous models focus only on feature-level interactions between a single user behavior and the target item or only treat the user’s historical behavior as a sequence to uncover the hidden interests behind it when mining user interests. This can lead to user interest that evolves over time dynamically being ignored or the interest shown by a single user’s behavior not being exploited. Based on the above problems, we propose evolving interest with feature co-action network (EIFCN). Specifically, we first design user dynamic interest network to treat the user’s historical behavior as a sequence of information, and tap into the user’s hidden interests over time. In this part, we use a multi-head self-attention mechanism to initially process the data and then pass it into the deep learning network. Then a feature co-action network is designed to mine the user’s single behavior and the displayed feature-level interactions of the target item. Experimental results show that the EIFCN model performs better than other models.
Automated sentiment analysis in social media using Harris Hawks optimisation and deep learning techniques

Sentiment analysis (SA) is a technique used in natural language processing (NLP), which determines the sentiment or emotion expressed in a piece of text. SA technique is frequently implemented on text datasets to assist in understanding customer needs, product sentiment in customer feedback, and businesses monitoring brands. Accordingly, the deep learning (DL) model is evolved as a promising technique that has been widely employed and attained considerable outcomes. Because DL models automatically extract features from the dataset, it can be claimed that intermediate representation extracted from the network is utilized as a relevant feature. Therefore, this article focuses on the design of automated sentiment analysis in social media using Harris Hawks Optimization with deep learning (ASASM-HHODL) model. The presented ASASM-HHODL model primarily processes the raw social media text into a useful format. Besides, the proposed ASASM-HHODL model uses fastText-based word embedding and skip-gram to examine the decrease of language processing dependency on data-pre-processing. Moreover, the attention-based bidirectional long-short-term model (ABiLSTM) is exploited for the classification of sentiments. Furthermore, the primary hyperparameters of the ABiLSTM model are adjusted by the use of the HHO algorithm to improve the classification performance. The performance validation of the proposed ASASM-HHODL model is carried out using a benchmark dataset and the results highlighted the promising performance over recent state of art approaches.
A Survey of Personalized News Recommendation

Abstract Personalized news recommendation is an important technology to help users obtain news information they are interested in and alleviate information overload. In recent years, news recommendation has been increasingly widely studied and has achieved remarkable success in improving the news reading experience of users. In this paper, we provide a comprehensive overview of personalized news recommendation approaches. Firstly, we introduce personalized news recommendation systems according to different needs and analyze the characteristics. And then, a three-part research framework on personalized news recommendation is put forward. Based on the framework, the knowledge and methods involved in each part are analyzed in detail, including news datasets and processing techniques, prediction models, news ranking and display. On this basis, we focus on news recommendation methods based on different types of graph structure learning, including user–news interaction graph, knowledge graph and social relationship graph. Lastly, the challenges of the current news recommendation are analyzed and the prospect of the future research direction is presented.
CD-Net: Histopathology Representation Learning Using Context-Detail Transformer Network

Extracting rich phenotype information, such as cell density and arrangement, from whole slide histology images (WSIs), requires analysis of large fields of view, i.e views providing more contextual information. This can be achieved through analyzing the digital slides at lower resolution. A potential drawback is missing out on details present at a higher resolution. To jointly leverage complementary information from multiple resolutions, we present a novel transformer based Context-Detail Network (CD-Net). CD-Net exploits the WSI pyramidal structure through co-training of proposed Context and Detail Modules, which operate on inputs from multiple resolutions. The residual connections between the modules enable the joint training paradigm while learning self-supervised representation for WSIs. The efficacy of CD-Net is demonstrated in classifying Lung Adenocarcinoma from Squamous cell carcinoma (N=1042 WSIs).
Are We All in a Truman Show? Spotting Instagram Crowdturfing through Self-Training

Influencer Marketing generated $16 billion in 2022. Usually, the more popular influencers are paid more for their collaborations. Thus, many services were created to boost profiles' popularity metrics through bots or fake accounts. However, real people recently started participating in such boosting activities using their real accounts for monetary rewards, generating ungenuine content that is extremely difficult to detect. To date, no works have attempted to detect this new phenomenon, known as crowdturfing (CT), on Instagram. In this work, we propose the first Instagram CT engagement detector. Our algorithm leverages profiles' characteristics through semi-supervised learning to spot accounts involved in CT activities. Compared to the supervised approaches used so far to identify fake accounts, semi-supervised models can exploit huge quantities of unlabeled data to increase performance. We purchased and studied 1293 CT profiles from 11 providers to build our self-training classifier, which reached 95% F1-score. We tested our model in the wild by detecting and analyzing CT engagement from 20 mega-influencers (i.e., with more than one million followers), and discovered that more than 20 % was artificial. We analyzed the CT profiles and comments, showing that it is difficult to detect these activities based solely on their generated content.
Self-Supervised Vision-Language Pretraining for Medial Visual Question Answering

Medical image visual question answering (VQA) is a task to answer clinical questions, given a radiographic image, which is a challenging problem that requires a model to integrate both vision and language information. Due to the limited size of the training data in medical VQA tasks, pretrain-finetune paradigm is a commonly used solution to improve the model generalization. In this paper, we propose a self-supervised method that applies Masked image modeling, Masked language modeling, Image text matching and Image text alignment via contrastive learning (M2I2) for pretraining on medical image caption dataset, and finetunes to downstream medical VQA tasks. The proposed method achieves state-of-the-art performance on all the three public medical VQA datasets. Our codes and models are available at https://github.com/pengfeiliHEU/M2I2.
Double-sided messages improve the acceptance of chatbots

Customers may not wish to use an artificial intelligence (AI) chatbot after knowing its nonhuman identity. Based on the inoculation theory, this study explores how to increase customers' willingness to communicate with AI chatbots by adopting a double-sided message strategy after AI's nonhuman identity is disclosed through one field experiment in a hotel and three online vignette experiments. Results indicate that a double-sided message strategy enhances customers' willingness to interact with AI chatbots via the mediating role of perceived authenticity. Furthermore, the study highlights that customers' attitudes toward interacting with AI chatbots are moderated by customer demand types—complaints and inquiries. This study proposes an effective solution to customers' rejection of AI chatbots.
Cybersecurity Resilience in SMEs. A Machine Learning Approach

This study investigates cybersecurity resilience in small and medium-sized enterprises (SMEs), focusing on three key aspects: the capacity to handle potential cyber incidents, the ability to recover from such incidents, and the capability to adapt in the face of possible cyber threats.Grounded in the Resource-Based View (RBV) framework, we conduct an empirical investigation utilizing a survey of 239 UK SMEs.The study makes a theoretical and methodological contribution, with significant implications for managers.First, the study highlights the lack of SMEs' engagement with the management of cybersecurity and finds cybersecurity incidents to be the most important factor in driving resilience, as compared to cybersecurity capabilities.Moreover, the study also extends the RBV theory, emphasizing the importance of the interaction between cybersecurity capabilities affecting SMEs' cybersecurity resilience.Second, the study showcases the potential of statistical methods, particularly machine learning techniques to identify the relationships between the factors affecting SMEs' cybersecurity.
A Single-layer Microwave Logic Operator Based on Diffraction Neural Network

In this paper, a single-layer logic operator based on diffraction neural network is proposed. By drawing on the architecture of neural networks, we designed a diffraction neural network with a hidden layer and implemented it at the physical level through a single-layer microwave dielectric metasurface. Due to the modulation of the metasurface, the input logic operation modes characterized by different illumination regions are mapped to two different energy focusing areas on the output plane representing "0" and "1," respectively. As a proof of concept, a prototype performing three types of logic operations ("AND," "OR," and "NOT") is configured and numerically demonstrated. The proposed single-layer design further enhances integration while ensuring the functionality of logic operator, and possesses promising prospects in optical computing and wireless communication systems.
Agile Practices for Quantum Software Development: Practitioners’ Perspectives

Quantum software engineering is an emerging genre of software engineering that exploit principles of quantum bits (Qubit) and quantum gates (Qgates) to solve complex computing problems effeciently than their classical counterparts. According to its proponents, agile software development practices have the potential to address many of the problems endemic to the development of quantum software. However, there is a dearth of evidence investigating whether agile practices are suitable for, and can be adopted by, software teams in the context of quantum software development. To address this lack, we conducted an empirical study to investigate the needs and challenges of using agile practices to develop quantum software. While our semi-structured interviews with 26 practitioners across 10 countries highlighted the applicability of agile practices in this domain, the interview findings also revealed new challenges impeding the effective incorporation of these practices. Our research findings provide a springboard for further contextualization and seamless integration of agile practices in quantum software engineering (QSE) to develop emerging and next generation of quantum software systems and application.
Approaching Peak Ground Truth

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the biomedical domain, annotations are subjective and suffer from low inter-and intra-rater reliability. Since annotations only reflect one interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, four categories of PGT-aware strategies to evaluate and improve model performance are reviewed.
Normative Modeling Via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer’s Disease

Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE) to identify brain dysfunction in Alzheimer’s Disease (AD). Specifically, we first train a conditional VAE on the healthy control (HC) group to create a normative model conditioned on covariates like age, gender and intracranial volume. Then we incorporate an adversarial training process to construct a discriminative feature space that can better generalize to unseen data. Finally, we compute deviations from the normal criterion at the patient level to determine which brain regions were associated with AD. Our experiments on OASIS-3 database show that the deviation maps generated by our model exhibit higher sensitivity to AD compared to other deep normative models, and are able to better identify differences between the AD and HC groups.
Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation

Volumetric magnetic resonance (MR) image segmentation plays an important role in many clinical applications. Deep learning (DL) has recently achieved state-of-the-art or even human-level performance on various image segmentation tasks. Nevertheless, manually annotating volumetric MR images for DL model training is labor-exhaustive and time-consuming. In this work, we aim to train a semi-supervised and self-supervised collaborative learning framework for prostate 3D MR image segmentation while using extremely sparse annotations, for which the ground truth annotations are provided for just the central slice of each volumetric MR image. Specifically, semi-supervised learning and self-supervised learning methods are used to generate two independent sets of pseudo labels. These pseudo labels are then fused by the Boolean operation to extract a more confident pseudo label set. The images with either manual or network self-generated labels are then employed to train a segmentation model for target volume extraction. Experimental results on a publicly available prostate MR image dataset demonstrate that, while requiring significantly less annotation effort, our framework generates very encouraging segmentation results. The proposed framework is very useful in clinical applications when training data with dense annotations are difficult to obtain.
Identification, Explanation and Clinical Evaluation of Hospital Patient Subtypes

We present a pipeline in which unsupervised machine learning techniques are used to automatically identify subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning. In parallel, clinicians assessed intra-cluster similarities and inter-cluster differences of the identified patient subtypes within the context of their clinical knowledge. By confronting the outputs of both automatic and clinician-based explanations, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.