Reviewerly/exHarmony huggingface dataset:
The first application of compositional data analysis (CoDA) in a multivariate perspective for detection of pollution source in sea sediments: The Pozzuoli Bay (Italy) case study

In the last decades, investigating geochemistry of sea sediments has been challenging in the eastern sector of Pozzuoli Bay, source of the metal(loid)s has been a matter of debate and the proposed origin of potentially toxic elements (PTEs) has been occasionally inconsistent. In this study, compositional data analysis (CoDA) was used because the results are independent of the measurement unit, the selected subgroup of elements and the order of chemicals in the dataset. The robust variant of principal component analysis (PCA) indicated that Hg, Cd, Cu, Pb and Zn were positively correlated with mud and organic matter in the sediments deposited in front of the former industrial site. Concentrations of these metals decrease along the cores and in the distal zone. Nevertheless, Al, As, V, Fe, Cr, Ni and sand form an association along the coast which strengthens with increasing distance from fumaroles in the proximal zone. It suggests that arsenic was mainly originated from the pyroclastic deposits of Campi Flegrei and some of the seepages with hydrothermal component, supported by low contribution of the variables in robust PCA of the sediments from distal zone. Therefore, this pioneering article suggests CoDA as a powerful tool for answering the long-lasting questions over sediment geochemistry in polluted areas.
LearningToAdapt with word embeddings: Domain adaptation of Named Entity Recognition systems

The task of Named Entity Recognition (NER) is aimed at identifying named entities in a given text and classifying them into pre-defined domain entity types such as persons, organizations, locations. Most of the existing NER systems make use of generic entity type classification schemas, however, the comparison and integration of (more or less) different entity types among different NER systems is a complex problem even for human experts. In this paper, we propose a supervised approach called L2AWE (Learning To Adapt with Word Embeddings) which aims at adapting a NER system trained on a source classification schema to a given target one. In particular, we validate the hypothesis that the embedding representation of named entities can improve the semantic meaning of the feature space used to perform the adaptation from a source to a target domain. The results obtained on benchmark datasets of informal text show that L2AWE not only outperforms several state of the art models, but it is also able to tackle errors and uncertainties given by NER systems.
Wireless Sensor Networks for Detection and Localization of Subsea Oil Leakages

This work studies the impact of Wireless Sensor Networks (WSNs) for oil spill detection and localization in Subsea Production Systems. The case study is the Goliat FPSO, with a realistic assumption about the presence of a WSN built upon the existing passive acoustic sensors installed on each subsea template to monitor the manifold. The sensors take local binary decisions regarding the presence/absence of a spill by performing an energy test. A Fusion Center (FC) collects such local decisions and provides a more reliable global binary decision. The Counting Rule (CR) and a modified Chair-Varshney Rule (MCVR) are compared. An objective function based on the Receiver Operating Characteristic (ROC) is used for threshold design. The FC, in case of a spill detection, provides an estimated position of the leak source. Four localization algorithms are explored: Maximum A-Posteriori (MAP) estimation, Minimum Mean Square Error (MMSE) estimation, and two heuristic centroid-based algorithms. Detection and localization performances are assessed in comparison to the (position) Clairvoyant Chair-Varshney Rule (CVR) and to the Cramér-Rao Lower Bound (CRLB), respectively. The considered framework requires the prior knowledge of the involved subsea production system in terms of components that in case of failure would cause a leakage and their corresponding failure rates.
Position Information in Transformers: An Overview

Abstract Transformers are arguably the main workhorse in recent natural language processing research. By definition, a Transformer is invariant with respect to reordering of the input. However, language is inherently sequential and word order is essential to the semantics and syntax of an utterance. In this article, we provide an overview and theoretical comparison of existing methods to incorporate position information into Transformer models. The objectives of this survey are to (1) showcase that position information in Transformer is a vibrant and extensive research area; (2) enable the reader to compare existing methods by providing a unified notation and systematization of different approaches along important model dimensions; (3) indicate what characteristics of an application should be taken into account when selecting a position encoding; and (4) provide stimuli for future research.
Wash trading at cryptocurrency exchanges

Cryptocurrency exchanges allegedly use wash trading to falsely signal their liquidity. We monitored twelve exchanges for metrics of web traffic and for their administered user funds. The exchanges were clustered in three distinct groups based on previous findings: (1) accurately-reporting exchanges, (2) exchanges that engaged in wash trading, (3) exchanges with mixed evidence of wash trading. A comparison of the reported to the predicted trading volume, calibrated on the accurately-reporting exchanges, suggests that group 2 exchanges exaggerate their true volume by a factor of 25 to 50, and exchanges of group 3 by a factor of 1.25 to 33.
Relation-Induced Multi-Modal Shared Representation Learning for Alzheimer’s Disease Diagnosis

The fusion of multi-modal data (e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET)) has been prevalent for accurate identification of Alzheimer's disease (AD) by providing complementary structural and functional information. However, most of the existing methods simply concatenate multi-modal features in the original space and ignore their underlying associations which may provide more discriminative characteristics for AD identification. Meanwhile, how to overcome the overfitting issue caused by high-dimensional multi-modal data remains appealing. To this end, we propose a relation-induced multi-modal shared representation learning method for AD diagnosis. The proposed method integrates representation learning, dimension reduction, and classifier modeling into a unified framework. Specifically, the framework first obtains multi-modal shared representations by learning a bi-directional mapping between original space and shared space. Within this shared space, we utilize several relational regularizers (including feature-feature, feature-label, and sample-sample regularizers) and auxiliary regularizers to encourage learning underlying associations inherent in multi-modal data and alleviate overfitting, respectively. Next, we project the shared representations into the target space for AD diagnosis. To validate the effectiveness of our proposed approach, we conduct extensive experiments on two independent datasets (i.e., ADNI-1 and ADNI-2), and the experimental results demonstrate that our proposed method outperforms several state-of-the-art methods.
A Secured Frame Selection Based Video Watermarking Technique to Address Quality Loss of Data: Combining Graph Based Transform, Singular Valued Decomposition, and Hyperchaotic Encryption

The advancement of Internet technologies has led to the availability of audios, images, and videos in different forms. The unauthorized users are exploiting the use of multimedia by transmitting them on various Internet sites to earn money unethically without the intervention of the original copyright holder. Watermarking is a technique used to hide the signal known as watermark inside multimedia data that is not visible to the intruder to manipulate any information. In this paper, a secured watermarking approach is developed to tackle issues related to copyright protection and ownership identification. A Secured Graph Based Transform, Singular Valued Decomposition, and Hyperchaotic Encryption hybrid techniques are proposed. The watermark cannot be embedded in every frame of the video as it adds to the size of the video and watermark can be easily retrieved by an intruder. Therefore, the frame selection algorithm has been proposed in the given work. Adding watermark in the frame adds to the challenge of quality loss. The quality loss is addressed in this work. Various attacks have been applied on the watermarked frames to calculate the performance of the proposed technique using quality metrics: Peak Signal to Noise Ratio, Structural Similarity Index, Normalized Correlation, and Bit Error Rate. The results indicate that the proposed technique is effective against various attack scenarios.
Integrated Neuromorphic Photonics: Synapses, Neurons, and Neural Networks

Ever‐growing demands of bandwidth, computing speed, and power consumption are now accelerating the transformation of computing research, as work‐at‐home becomes a new normal. Brain‐inspired photonic neuromorphic computing for artificial intelligence is raising an urgent need, and it promises orders‐of‐magnitude higher computing speed and energy efficiency compared with digital electronic counterparts. Photonic neuromorphic networks combine the efficiency of neural networks based on a non‐von Neumann architecture and the benefits of photonics to constitute a new computing paradigm. Herein, some recent advances in photonic neural networks are reviewed, including the concept, principle, key photonic components, and architectures that construct the neuromorphic systems, hoping to provide a better understanding of this emerging field.
FedVCP: A Federated-Learning-Based Cooperative Positioning Scheme for Social Internet of Vehicles

Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes network resources. To fill these gaps, this article proposes a vehicle cooperative positioning (CP) system based on federated learning (FedVCP), which makes full use of the potential of social Internet of Things (IoT) and collaborative edge computing (CEC) to provide high-precision positioning correction while ensuring user privacy. To the best of our knowledge, this article is the first attempt to solve the privacy of CP from a perspective of federated learning. In addition, we take the advantages of local cooperation through vehicle-to-vehicle (V2V) communications in data augmentation. For individual differences in vehicle positioning, we utilize transfer learning to eliminate the impact of such differences. Extensive experiments on real data demonstrate that our proposed model is superior to the baseline method in terms of effectiveness and convergence speed.
A blockchain-based trading system for big data

Data are an extremely important asset. Governments around the world encourage big data sharing and trading to promote the big data economy. However, existing data trading platforms are not fully trusted. Such platforms face the problems of a single point of failure (SPOF), opaque transactions, uncontrollability, untraceability, and issues of data privacy. Several blockchain-based big data trading methods have been proposed; however, they do not adequately address the security issues introduced by dishonesty in the data provider and data agent or the fairness of data revenue distribution and price bargaining. In this paper, we propose a blockchain-based decentralized data trading system in which data trading is completed by smart contract-based data matching, price negotiation, and reward assigning. Moreover, the proposed data trading system evaluates the data quality on the basis of three metrics, records the evaluation results in a side-chain, and distributes the data users’ application revenue to the data provider according to the evaluated data quality. We verify the security, usability, and efficiency of the proposed big data trading system.
Employing Bilinear Fusion and Saliency Prior Information for RGB-D Salient Object Detection

Multi-modal feature fusion and saliency reasoning are two core sub-tasks of RGB-D salient object detection. However, most existing models employ linear fusion strategies (e.g., concatenation) for multi-modal feature fusion and use a simple coarse-to-fine structure for saliency reasoning. Despite their simpleness, they can neither fully capture the cross-modal complementary information nor exploit the multi-level complementary information among the cross-modal features at different levels. To address these issues, a novel RGB-D salient object detection model is presented, where we pay special attention to the aforementioned two sub-tasks. Concretely, a multi-modal feature interaction module is first presented to explore more interactions between the unimodal RGB and depth features. It helps to capture their cross-modal complementary information by jointly using some simple linear fusion strategies and bilinear fusion ones. Then, a saliency prior information guided fusion module is presented to exploit the multi-level complementary information among the fused cross-modal features at different levels. Instead of employing a simple convolutional layer for the final saliency prediction, a saliency refinement and prediction module is designed to better exploit those extracted multi-level cross-modal information for RGB-D saliency detection. Experimental results on several benchmark datasets verify the effectiveness and superiority of the proposed framework over some state-of-the-art methods.
UVid-Net: Enhanced Semantic Segmentation of UAV Aerial Videos by Embedding Temporal Information

Semantic segmentation of aerial videos has been extensively used for decision making in monitoring environmental changes, urban planning, and disaster management. The reliability of these decision support systems is dependent on the accuracy of the video semantic segmentation algorithms. The existing CNN-based video semantic segmentation methods have enhanced the image semantic segmentation methods by incorporating an additional module such as LSTM or optical flow for computing temporal dynamics of the video which is a computational overhead. The proposed research work modifies the CNN architecture by incorporating temporal information to improve the efficiency of video semantic segmentation. In this work, an enhanced encoder-decoder based CNN architecture (UVid-Net) is proposed for unmanned aerial vehicle (UAV) video semantic segmentation. The encoder of the proposed architecture embeds temporal information for temporally consistent labeling. The decoder is enhanced by introducing the feature-refiner module, which aids in accurate localization of the class labels. The proposed UVid-Net architecture for UAV video semantic segmentation is quantitatively evaluated on extended ManipalUAVid dataset. The performance metric mean Intersection over Union of 0.79 has been observed which is significantly greater than the other state-of-the-art algorithms. Further, the proposed work produced promising results even for the pretrained model of UVid-Net on urban street scene by fine tuning the final layer on UAV aerial videos.
PyDiNet: Pyramid Dilated Network for medical image segmentation

Medical image segmentation is an important step in many generic applications such as population analysis and, more accessible, can be made into a crucial tool in diagnosis and treatment planning. Previous approaches are based on two main architectures: fully convolutional networks and U-Net-based architecture. These methods rely on multiple pooling and striding layers leading to the loss of important spatial information and fail to capture details in medical images. In this paper, we propose a novel neural network called PyDiNet (Pyramid Dilated Network) to capture small and complex variations in medical images while preserving spatial information. To achieve this goal, PyDiNet uses a newly proposed pyramid dilated module (PDM), which consists of multiple dilated convolutions stacked in parallel. We combine several PDM modules to form the final PyDiNet architecture. We applied the proposed PyDiNet to different medical image segmentation tasks. Experimental results show that the proposed model achieves new state-of-the-art performance on three medical image segmentation benchmarks. Furthermore, PyDiNet was very competitive on the 2020 Endoscopic Artifact Detection challenge.
The Effect of Transformational Leadership on Achieving Effective Decisions in the Presence of Psychological Capital as an Intermediate Variable in Private Jordanian Universities in Light of the Corona Pandemic

This study aimed to discover the role of psychological capital in the impact of transformational leadership on taking effective decisions in Jordanian private universities in light of the Corona Pandemic. The sample included (108) respondents while the study population consisted of the lecturers of the (6) private Jordanian universities in the capital Amman. The researchers resorted to multiple and Stepwise linear regression in order to test the effect of transformational leadership on the effectiveness of management decisions. The researchers also used Hierarchical Regression to test the role of psychological capital in this effect as an intermediate variable. After analyzing the study data, it was revealed a high level of awareness of the importance of the leadership style. The results also showed interest in Jordanian universities in taking efficient decisions that contribute to addressing various types of crises, including the current crisis linked to the Corona pandemic. The results of the study also indicated that there is an effect of transformational leadership in terms of its all dimensions (ideal effect, inspirational motivation, intellectual excitement, empowerment) on achieving the effectiveness of decisions in terms of its dimensions (quality of the decision, the timing of decision) in private Jordanian universities in light of the Corona pandemic. Given the direct and indirect impact of each dimension of transformational leadership, psychological capital has an intermediate role in the influence of transformational leadership style on the effectiveness of decisions in private Jordanian universities when studying the dimensions of the use of transformational leadership combined.
CEFEs: A CNN Explainable Framework for ECG Signals

In the healthcare domain, trust, confidence, and functional understanding are critical for decision support systems, therefore, presenting challenges in the prevalent use of black-box deep learning (DL) models. With recent advances in deep learning methods for classification tasks, there is an increased use of deep learning in healthcare decision support systems, such as detection and classification of abnormal Electrocardiogram (ECG) signals. Domain experts seek to understand the functional mechanism of black-box models with an emphasis on understanding how these models arrive at specific classification of patient medical data. In this paper, we focus on ECG data as the healthcare data signal to be analyzed. Since ECG is a one-dimensional time-series data, we target 1D-CNN (Convolutional Neural Networks) as the candidate DL model. Majority of existing interpretation and explanations research has been on 2D-CNN models in non-medical domain leaving a gap in terms of explanation of CNN models used on medical time-series data. Hence, we propose a modular framework, CNN Explanations Framework for ECG Signals (CEFEs), for interpretable explanations. Each module of CEFEs provides users with the functional understanding of the underlying CNN models in terms of data descriptive statistics, feature visualization, feature detection, and feature mapping. The modules evaluate a model's capacity while inherently accounting for correlation between learned features and raw signals which translates to correlation between model's capacity to classify and it's learned features. Explainable models such as CEFEs could be evaluated in different ways: training one deep learning architecture on different volumes/amounts of the same dataset, training different architectures on the same data set or a combination of different CNN architectures and datasets. In this paper, we choose to evaluate CEFEs extensively by training on different volumes of datasets with the same CNN architecture. The CEFEs' interpretations, in terms of quantifiable metrics, feature visualization, provide explanation as to the quality of the deep learning model where traditional performance metrics (such as precision, recall, accuracy, etc.) do not suffice.
Extracting COVID-19 diagnoses and symptoms from clinical text: A new annotated corpus and neural event extraction framework

Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to tracking its spread, describing symptomology, predicting the severity of infection, and forecasting healthcare utilization. Free-text clinical notes contain critical information for resolving these questions. Data-driven, automatic information extraction models are needed to use this text-encoded information in large-scale studies. This work presents a new clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT) Corpus, which comprises 1,472 notes with detailed annotations characterizing COVID-19 diagnoses, testing, and clinical presentation. We introduce a span-based event extraction model that jointly extracts all annotated phenomena, achieving high performance in identifying COVID-19 and symptom events with associated assertion values (0.83-0.97 F1 for events and 0.73-0.79 F1 for assertions). Our span-based event extraction model outperforms an extractor built on MetaMapLite for the identification of symptoms with assertion values. In a secondary use application, we predicted COVID-19 test results using structured patient data (e.g. vital signs and laboratory results) and automatically extracted symptom information, to explore the clinical presentation of COVID-19. Automatically extracted symptoms improve COVID-19 prediction performance, beyond structured data alone.