Reviewerly/exHarmony huggingface dataset:
Green FinTech: sustainability of Bitcoin

The COVID-19 pandemic has corroborated environmental degradation and climate change as two important problems of the twenty-first century. To turn environmental challenges into opportunities, we need environmentally friendly, green financial technologies. This article aims to shed light on the studies within this field, by building a bridge between financial technology (FinTech) and cryptocurrencies. A cashless society is expected to be fostered as digital currencies become more widespread, which will result in the eventual replacement of notes and coins. When considered from this point of view, cryptocurrencies could be regarded as environmentally friendly. On the other hand, the large amount of energy consumed in the mining process of cryptocurrencies questions their environmental friendliness. Therefore, analyzing whether cryptocurrencies are environmentally friendly or not conducted in a holistic approach.
Closed-loop optimization of fast trapped-ion shuttling with sub-quanta excitation

Abstract Shuttling ions at high speed and with low motional excitation is essential for realizing fast and high-fidelity algorithms in many trapped-ion-based quantum computing architectures. Achieving such performance is challenging due to the sensitivity of an ion to electric fields and the unknown and imperfect environmental and control variables that create them. Here we implement a closed-loop optimization of the voltage waveforms that control the trajectory and axial frequency of an ion during transport in order to minimize the final motional excitation. The resulting waveforms realize fast round-trip transport of a trapped ion across multiple electrodes at speeds of 0.5 electrodes per microsecond (35 m﻿·s −1 for a one-way transport of 210 μm in 6 μs) with a maximum of 0.36 ± 0.08 mean quanta gain. This sub-quanta gain is independent of the phase of the secular motion at the distal location, obviating the need for an electric field impulse or time delay to eliminate the coherent motion.
On the role of deep learning model complexity in adversarial robustness for medical images

Deep learning (DL) models are highly vulnerable to adversarial attacks for medical image classification. An adversary could modify the input data in imperceptible ways such that a model could be tricked to predict, say, an image that actually exhibits malignant tumor to a prediction that it is benign. However, adversarial robustness of DL models for medical images is not adequately studied. DL in medicine is inundated with models of various complexity-particularly, very large models. In this work, we investigate the role of model complexity in adversarial settings.Consider a set of DL models that exhibit similar performances for a given task. These models are trained in the usual manner but are not trained to defend against adversarial attacks. We demonstrate that, among those models, simpler models of reduced complexity show a greater level of robustness against adversarial attacks than larger models that often tend to be used in medical applications. On the other hand, we also show that once those models undergo adversarial training, the adversarial trained medical image DL models exhibit a greater degree of robustness than the standard trained models for all model complexities.The above result has a significant practical relevance. When medical practitioners lack the expertise or resources to defend against adversarial attacks, we recommend that they select the smallest of the models that exhibit adequate performance. Such a model would be naturally more robust to adversarial attacks than the larger models.
Analysis of Blockchain in the Healthcare Sector: Application and Issues

The emergence of blockchain know-how currently presents the opportunity for the health sector to adopt such technologies in electronic health records. Blockchain assists in maintaining and sharing the relevant medical records of the patient with the relevant group of healthcare providers and the hospital. Numerous specific applications include traceability of drug and patient monitoring or Electronic Health Records (EHR). While Blockchain assists in maintaining and sharing the relevant medical records of the patient with the relevant group of healthcare providers and the hospital, it is important to note that the moral consciousness of the healthcare professionals is the main guide of the moral consciousness is ethics. This paper presents an overview of the application of blockchain in the healthcare and medical sector, highlighting the specific challenges and concerns. The study adopted a systematic review of secondary literature in answering the research question.
Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model

With the emergence of social media platforms, most people have changed their way of interacting. Perhaps, sharing day-to-day lifestyle updates is a trend substantially influenced by microblogging sites, specifically Twitter, Facebook, Instagram, and many more. Moreover, text and messages are the most preferred way for such interactions. Twitter is one of the most commonly used microblogging tools that enable people to express their thoughts, opinions, emotions, happiness, sadness, excitement, ideas, mental stress, and so on. Hence, the sentiment prediction furnished by such textual data becomes a complex and challenging task. In this research, the authors proposed a hybridization of the convolutional neural network and bi-directional long short-term memory model (named ConvBidirectional-LSTM), which aims to better the categorization of sentiments of text data. Then, this proposed hybrid ConvBidirectional-LSTM model is compared with the existing state-of-the-art models, GloVe-based CNN-LSTM and Hierarchical Bi-LSTM (HeBiLSTM) models. Furthermore, the performance of the proposed hybrid ConvBidirectional-LSTM model is evaluated on the US airline dataset using various performance parameters like accuracy, precision, recall, and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>f</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> score. The proposed model outperformed the existing state-of-the-art models with an accuracy rate of 93.25% in sentiment prediction.
Cascade Forward Neural Networks-based Adaptive Model for Real-time Adaptive Learning of Stochastic Signal Power Datasets

In this work, adaptive learning of a monitored real-time stochastic phenomenon over an operational LTE broadband radio network interface is proposed using cascade forward neural network (CFNN) model. The optimal architecture of the model has been implemented computationally in the input and hidden units by means of incremental search process. Particularly, we have applied the proposed adaptive-based cascaded forward neural network model for realistic learning of practical signal data taken from an operational LTE cellular network. The performance of the adaptive learning model is compared with a benchmark feedforward neural network model (FFNN) using a number of measured stochastic SINR datasets obtained over a period of three months at two indoors and outdoors locations of the LTE network. The results showed that proposed CFNN model provided the best adaptive learning performance (0.9310 RMSE; 0.8669 MSE; 0.5210 MAE; 0.9311 R), compared to the benchmark FFNN model (1.0566 RMSE; 1.1164 MSE; 0.5568 MAE; 0.9131 R) in the first studied outdoor location. Similar robust performances were attained for the proposed CFNN model in other locations, thus indicating that it is superior to FFNN model for adaptive learning of real-time stochastic phenomenon.
Local Evaluation of Time Series Anomaly Detection Algorithms

In recent years, specific evaluation metrics for time series anomaly detection algorithms have been developed to handle the limitations of the classical precision and recall. However, such metrics are heuristically built as an aggregate of multiple desirable aspects, introduce parameters and wipe out the interpretability of the output. In this article, we first highlight the limitations of the classical precision/recall, as well as the main issues of the recent event-based metrics -- for instance, we show that an adversary algorithm can reach high precision and recall on almost any dataset under weak assumption. To cope with the above problems, we propose a theoretically grounded, robust, parameter-free and interpretable extension to precision/recall metrics, based on the concept of ``affiliation'' between the ground truth and the prediction sets. Our metrics leverage measures of duration between ground truth and predictions, and have thus an intuitive interpretation. By further comparison against random sampling, we obtain a normalized precision/recall, quantifying how much a given set of results is better than a random baseline prediction. By construction, our approach keeps the evaluation local regarding ground truth events, enabling fine-grained visualization and interpretation of algorithmic results. We compare our proposal against various public time series anomaly detection datasets, algorithms and metrics. We further derive theoretical properties of the affiliation metrics that give explicit expectations about their behavior and ensure robustness against adversary strategies.
A Novel Framework for Smart Systems Using Blockchain-Enabled Internet of Things

The revolution brought about by intelligent Internet of Things (IoT) systems has impacted every sphere of life, including but not limited to smart healthcare, grid, home, industry, logistics, and environmental monitoring. IoT systems traditionally are plagued with security vulnerabilities and scalability issues. In recent years, blockchain technology has widely emerged as a potential solution for secure, trusted, and efficient storing, processing, and data sharing. This article aims to incorporate IoT in conjunction with blockchain by introducing an intermediary layer in the IoT ecosystem. Thus, an intelligent blockchain-enabled IoT (BIoT) framework is proposed for smart applications to provide data reliability, privacy, and scalability in IoT ecosystems. Moreover, several real-life BIoT case studies are highlighted and comparatively analyzed to discuss the contribution of our proposed layer vis-á-vis conventional blockchain-IoT ecosystems. Finally, based on the analysis, we outline some futuristic directions and open challenges for the BIoT.
EMERGENCY HIGHER EDUCATION DIGITAL TRANSFORMATION: UKRAINE’S RESPONSE TO THE COVID-19 PANDEMIC

The article explores how higher education in Ukraine has responded to the COVID‐19 pandemic, particularly in the case of the Borys Grinchenko Kyiv University’s experience. The research provides a comparative analysis of national transformation trends at various levels – HEIs, lecturers and students during two waves of the COVID-19 pandemic in 2020. It covers two timeline periods: March-May 2020 – the 1-st wave (summer semester); October 2020-January 2021 – the 2-nd wave (winter semester). The research reveals the COVID-19 pandemic impact on Ukrainian higher education in 2020. It presents trends in national HEIs transformation, lecturers and students’ perception of education and exams online. Based on the literature review and conducting surveys (514 students/97 lecturers), the study makes it possible to reveal universal consequences caused by the COVID-19 pandemic in higher education systems worldwide as well as specific trends in national higher education emergency transition to digitally-based teaching-learning. The research shows that global higher education systems faced the COVID-19 pandemic in different conditions due to various technical facilities, governmental and institutional support, financial investment, digitally competent academic staff, students familiar with educational technology. The “better prepared” higher education systems are likely to cope with the Coronavirus impact easier and transit to digitally-based distance teaching-learning smoothly. Ukrainian higher education met the COVID-19 pandemic unready. Though some national HEIs have managed to re-organise the educational process smoothly and effectively due to the academic staff’s enthusiasm and colossal effort. In this regard, positive and negative trends are revealed in the national higher education emergency transition to digitally-based distance learning. The findings contribute to the investigation of higher education transformation in crisis and confirm that the COVID-19 pandemic has accelerated higher education digitalisation worldwide.
Single Image Dehazing Based on Two-Stream Convolutional Neural Network

Objective The haze weather environment leads to the deterioration of the visual effect of the image, and it is difficult to carry out the work of the advanced vision task. Therefore, dehazing the haze image is an important step before the execution of the advanced vision task. Traditional dehazing algorithms achieve image dehazing by improving image brightness and contrast or constructing artificial priors such as color attenuation priors and dark channel priors, but the effect is unstable when dealing with complex scenes. In the method based on convolutional neural network, the image dehazing network of the encoding and decoding structure does not consider the difference before and after the dehazing image, and the image spatial information is lost in the encoding stage. In order to overcome these problems, this paper proposes a novel end-to-end two-stream convolutional neural network for single image dehazing. Method The network model is composed of a spatial information feature stream and a high-level semantic feature stream. The spatial information feature stream retains the detailed information of the dehazing image, and the high-level semantic feature stream extracts the multi-scale structural features of the dehazing image. A spatial information auxiliary module is designed between the feature streams. This module uses the attention mechanism to construct a unified expression of different types of information, and realizes the gradual restoration of the clear image with the semantic information auxiliary spatial information in the dehazing network. A parallel residual twicing module is proposed, which performs dehazing on the difference information of features at different stages to improve the model’s ability to discriminate haze images. Result The peak signal-to-noise ratio and structural similarity are used to quantitatively evaluate the similarity between the dehazing results of each algorithm and the original image. The structure similarity and peak signal-to-noise ratio of the method in this paper reached 0.852 and 17.557dB on the Hazerd dataset, which were higher than all comparison algorithms. On the SOTS dataset, the indicators are 0.955 and 27.348dB, which are sub-optimal results. In experiments with real haze images, this method can also achieve excellent visual restoration effects.
RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking

Single-object tracking (SOT) in satellite videos (SVs) is a promising and challenging task in the remote sensing community. In terms of the object itself and the tracking algorithm, the rotation of small-sized objects and tracking drift are common problems due to the nadir view coupled with a complex background. This article proposes a novel rotation adaptive tracker with motion constraint (RAMC) to explore how the hybridization of angle and motion information can be utilized to boost SV object tracking from two branches: rotation and translation. We decouple the rotation and translation motion patterns. The rotation phenomenon is decomposed into the translation solution to achieve adaptive rotation estimation in the rotation branch. In the translation branch, the appearance and motion information are synergized to enhance the object representations and address the tracking drift issue. Moreover, an internal shrinkage (IS) strategy is proposed to optimize the evaluation process of trackers. Extensive experiments on space-born SV datasets captured from the Jilin-1 satellite constellation and International Space Station (ISS) are conducted. The results demonstrate the superiority of the proposed method over other algorithms. With an area under the curve (AUC) of 0.785 and 0.946 in the success and precision plots, respectively, the proposed RAMC achieves optimal performance while running at real-time speed.
Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System

Despite the acknowledgment that the perception of explanations may vary considerably between end-users, explainable recommender systems (RS) have traditionally followed a one-size-fits-all model, whereby the same explanation level of detail is provided to each user, without taking into consideration individual user's context, i.e., goals and personal characteristics. To fill this research gap, we aim in this paper at a shift from a one-size-fits-all to a personalized approach to explainable recommendation by giving users agency in deciding which explanation they would like to see. We developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations of the recommendations, with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study (N=31) to investigate the relationship between user's personal characteristics and the explanation level of detail, and the effects of these two variables on the perception of the explainable RS with regard to different explanation goals. Our results show that the perception of explainable RS with different levels of detail is affected to different degrees by the explanation goal and user type. Consequently, we suggested some theoretical and design guidelines to support the systematic design of explanatory interfaces in RS tailored to the user's context.
Efficient Model-Driven Network for Shadow Removal

Deep Convolutional Neural Networks (CNNs) based methods have achieved significant breakthroughs in the task of single image shadow removal. However, the performance of these methods remains limited for several reasons. First, the existing shadow illumination model ignores the spatially variant property of the shadow images, hindering their further performance. Second, most deep CNNs based methods directly estimate the shadow free results from the input shadow images like a black box, thus losing the desired interpretability. To address these issues, we first propose a new shadow illumination model for the shadow removal task. This new shadow illumination model ensures the identity mapping among unshaded regions, and adaptively performs fine grained spatial mapping between shadow regions and their references. Then, based on the shadow illumination model, we reformulate the shadow removal task as a variational optimization problem. To effectively solve the variational problem, we design an iterative algorithm and unfold it into a deep network, naturally increasing the interpretability of the deep model. Experiments show that our method could achieve SOTA performance with less than half parameters, one-fifth of floating-point of operations (FLOPs), and over seventeen times faster than SOTA method (DHAN).
Using MaxSAT for Efficient Explanations of Tree Ensembles

Tree ensembles (TEs) denote a prevalent machine learning model that do not offer guarantees of interpretability, that represent a challenge from the perspective of explainable artificial intelligence. Besides model agnostic approaches, recent work proposed to explain TEs with formally-defined explanations, which are computed with oracles for propositional satisfiability (SAT) and satisfiability modulo theories. The computation of explanations for TEs involves linear constraints to express the prediction. In practice, this deteriorates scalability of the underlying reasoners. Motivated by the inherent propositional nature of TEs, this paper proposes to circumvent the need for linear constraints and instead employ an optimization engine for pure propositional logic to efficiently handle the prediction. Concretely, the paper proposes to use a MaxSAT solver and exploit the objective function to determine a winning class. This is achieved by devising a propositional encoding for computing explanations of TEs. Furthermore, the paper proposes additional heuristics to improve the underlying MaxSAT solving procedure. Experimental results obtained on a wide range of publicly available datasets demonstrate that the proposed MaxSAT-based approach is either on par or outperforms the existing reasoning-based explainers, thus representing a robust and efficient alternative for computing formal explanations for TEs.
Clinical-BERT: Vision-Language Pre-training for Radiograph Diagnosis and Reports Generation

In this paper, we propose a vision-language pre-training model, Clinical-BERT, for the medical domain, and devise three domain-specific tasks: Clinical Diagnosis (CD), Masked MeSH Modeling (MMM), Image-MeSH Matching (IMM), together with one general pre-training task: Masked Language Modeling (MLM), to pre-train the model. The CD task helps the model to learn medical domain knowledge by predicting disease from radiographs. Medical Subject Headings (MeSH) words are important semantic components in radiograph reports, and the MMM task helps the model focus on the prediction of MeSH words. The IMM task helps the model learn the alignment of MeSH words with radiographs by matching scores obtained by a two-level sparse attention: region sparse attention and word sparse attention. Region sparse attention generates corresponding visual features for each word, and word sparse attention enhances the contribution of images-MeSH matching to the matching scores. To the best of our knowledge, this is the first attempt to learn domain knowledge during pre-training for the medical domain. We evaluate the pre-training model on Radiograph Diagnosis and Reports Generation tasks across four challenging datasets: MIMIC-CXR, IU X-Ray, COV-CTR, and NIH, and achieve state-of-the-art results for all the tasks, which demonstrates the effectiveness of our pre-training model.
Dual Contrastive Network for Sequential Recommendation

Widely applied in today's recommender systems, sequential recommendation predicts the next interacted item for a given user via his/her historical item sequence. However, sequential recommendation suffers data sparsity issue like most recommenders. To extract auxiliary signals from the data, some recent works exploit self-supervised learning to generate augmented data via dropout strategy, which, however, leads to sparser sequential data and obscure signals. In this paper, we propose D ual C ontrastive N etwork (DCN) to boost sequential recommendation, from a new perspective of integrating auxiliary user-sequence for items. Specifically, we propose two kinds of contrastive learning. The first one is the dual representation contrastive learning that minimizes the distances between embeddings and sequence-representations of users/items. The second one is the dual interest contrastive learning which aims to self-supervise the static interest with the dynamic interest of next item prediction via auxiliary training. We also incorporate the auxiliary task of predicting next user for a given item's historical user sequence, which can capture the trends of items preferred by certain types of users. Experiments on benchmark datasets verify the effectiveness of our proposed method. Further ablation study also illustrates the boosting effect of the proposed components upon different sequential models.