Reviewerly/exHarmony huggingface dataset:
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy

Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions' COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.
Study on automatic detection and classification of breast nodule using deep convolutional neural network system

Conventional ultrasound manual scanning and artificial diagnosis approaches in breast are considered to be operator-dependence, slight slow and error-prone. In this study, we used Automated Breast Ultrasound (ABUS) machine for the scanning, and deep convolutional neural network (CNN) technology, a kind of Deep Learning (DL) algorithm, for the detection and classification of breast nodules, aiming to achieve the automatic and accurate diagnosis of breast nodules.Two hundred and ninety-three lesions from 194 patients with definite pathological diagnosis results (117 benign and 176 malignancy) were recruited as case group. Another 70 patients without breast diseases were enrolled as control group. All the breast scans were carried out by an ABUS machine and then randomly divided into training set, verification set and test set, with a proportion of 7:1:2. In the training set, we constructed a detection model by a three-dimensionally U-shaped convolutional neural network (3D U-Net) architecture for the purpose of segment the nodules from background breast images. Processes such as residual block, attention connections, and hard mining were used to optimize the model while strategies of random cropping, flipping and rotation for data augmentation. In the test phase, the current model was compared with those in previously reported studies. In the verification set, the detection effectiveness of detection model was evaluated. In the classification phase, multiple convolutional layers and fully-connected layers were applied to set up a classification model, aiming to identify whether the nodule was malignancy.Our detection model yielded a sensitivity of 91% and 1.92 false positive subjects per automatically scanned imaging. The classification model achieved a sensitivity of 87.0%, a specificity of 88.0% and an accuracy of 87.5%.Deep CNN combined with ABUS maybe a promising tool for easy detection and accurate diagnosis of breast nodule.
LIG-Doctor: Efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks

The interest for patient trajectory prediction, a sort of computer-aided medicine, has steadily increased with the pace of artificial intelligence innovation. Notwithstanding, the design of effective systems able to predict clinical outcomes based on the history of a patient is far from trivial. Works so far are based on neural architectures with low performance, especially when using low-cardinality datasets; alternatively, complex inference approaches are hard to reproduce and/or extrapolate as they are designed for very specific circumstances. We introduce LIG-Doctor, an artificial neural network architecture based on two Minimal Gated Recurrent Unit networks functioning in a bidirectional parallel manner, benefiting from temporal events both forward and backward. In comparison to state-of-the-art works, consistent improvements were achieved in prognosis prediction, as assessed with metrics [email protected], [email protected], F1-score, and AUC-ROC. Besides the detailed delineation of our architecture, a sequence of experiments is reported with insights that progressively guided design decisions to inspire future works on similar problems. Our results shall contribute to the improvement of computer-aided medicine and, more generally, to processes related to the design of neural network architectures.
A survey of domain name system vulnerabilities and attacks

Aim:The Domain Name System (DNS) plays an integral role in the functionality of the Internet.Clients receive Internet service by mapping domain names into internet protocol addresses, which are routable.DNS provides a scalable and flexible name resolution service to clients easily and quickly.However, DNS was initially developed without security, and the information is not secured.Although DNS security extensions was released in 1999 to protect against vulnerabilities, it is not widely deployed, and DNS continues to suffer from a variety of attacks.The purpose of this study is to provide a comprehensive survey of DNS security. Methods:We describe an overview of DNS vulnerabilities, DNS attacks, and even mitigation systems.In detail, attacks are classified by purpose and methods for defending against these attacks are introduced and assessed.Finally, we conclude with a summary of the current state of DNS security. Results:The main findings of this study is to introduce fundamental vulnerabilities of DNS and classify representative DNS attacks into four categories to efficiently analyze them.Moreover, we describe and assess mitigation systems to defense these attacks. Conclusion:We conclude that DNS is an integral part of Internet operations but is still exposed to various attacks due to its vulnerabilities, low deployment of available mitigation techniques, and limitations of such techniques.
Aprendiendo del tiempo de la COVID-19

Different problematic situations that have arisen from the COVID-19 situation are analyzed, which should lead us to rethink future teaching actions.
Practical Multi-Keyword Ranked Search With Access Control Over Encrypted Cloud Data

With the explosive growth of data volume in the cloud computing environment, data owners are increasingly inclined to store their data on the cloud. Although data outsourcing reduces computation and storage costs for them, it inevitably brings new security and privacy concerns, as the data owners lose direct control of sensitive data. Meanwhile, most of the existing ranked keyword search schemes mainly focus on enriching search efficiency or functionality, but lack of providing efficient access control and formal security analysis simultaneously. To address these limitations, in this article we propose an efficient and privacy-preserving <b>M</b> ulti-keyword <b>R</b> anked <b>S</b> earch scheme with <b>F</b> ine-grained access control (MRSF). MRSF can realize highly accurate ciphertext retrieval by combining coordinate matching with Term Frequency-Inverse Document Frequency (TF-IDF) and improving the secure <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN method. Besides, it can effectively refine users’ search privileges by utilizing the polynomial-based access strategy. Formal security analysis shows that MRSF is secure in terms of confidentiality of outsourced data and the privacy of index and tokens. Extensive experiments further show that, compared with existing schemes, MRSF achieves higher search accuracy and more functionalities efficiently.
Understanding Catastrophic Overfitting in Single-step Adversarial Training

Although fast adversarial training has demonstrated both robustness and efficiency, the problem of "catastrophic overfitting" has been observed. This is a phenomenon in which, during single-step adversarial training, the robust accuracy against projected gradient descent (PGD) suddenly decreases to 0% after a few epochs, whereas the robust accuracy against fast gradient sign method (FGSM) increases to 100%. In this paper, we demonstrate that catastrophic overfitting is very closely related to the characteristic of single-step adversarial training which uses only adversarial examples with the maximum perturbation, and not all adversarial examples in the adversarial direction, which leads to decision boundary distortion and a highly curved loss surface. Based on this observation, we propose a simple method that not only prevents catastrophic overfitting, but also overrides the belief that it is difficult to prevent multi-step adversarial attacks with single-step adversarial training.
Automatic Mobile App Identification From Encrypted Traffic With Hybrid Neural Networks

The proliferation of handheld devices has led to an explosive growth of mobile traffic volumes on the Internet. Identifying mobile apps from network traffic has become a crucial task for mobile network management and security. Traditionally, the design of accurate identifiers relies on the deep packet inspection (DPI) techniques. However, such approaches have become less effective with the raising adoption of encrypted protocols in mobile applications (mostly TLS). To address the problem, various machine learning methods have been studied and used. Most of them use linear classifiers on top of hand-engineered features, which are unreliable due to the complexity of mobile traffic. In this article we propose App-Net, an end-to-end hybrid neural network for mobile app identification from encrypted TLS traffic. App-Net is designed by combining RNN and CNN in a parallel way and can automatically learn effective features from raw TLS flows. With coordinated fusion and optimized training, the hybrid and multimodal architecture is able to characterize both flow sequence patterns and app signatures to learn a joint flow-app embedding. We evaluate App-Net on a real-world dataset covering 80 apps. The results show that our method can achieve an excellent performance and outperform the state-of-the-art methods.
Using the full-text content of academic articles to identify and evaluate algorithm entities in the domain of natural language processing

In the era of big data, the advancement, improvement, and application of algorithms in academic research have played an important role in promoting the development of different disciplines. Academic papers in various disciplines, especially computer science, contain a large number of algorithms. Identifying the algorithms from the full-text content of papers can determine popular or classical algorithms in a specific field and help scholars gain a comprehensive understanding of the algorithms and even the field. To this end, this article takes the field of natural language processing (NLP) as an example and identifies algorithms from academic papers in the field. A dictionary of algorithms is constructed by manually annotating the contents of papers, and sentences containing algorithms in the dictionary are extracted through dictionary-based matching. The number of articles mentioning an algorithm is used as an indicator to analyze the influence of that algorithm. Our results reveal the algorithm with the highest influence in NLP papers and show that classification algorithms represent the largest proportion among the high-impact algorithms. In addition, the evolution of the influence of algorithms reflects the changes in research tasks and topics in the field, and the changes in the influence of different algorithms show different trends. As a preliminary exploration, this paper conducts an analysis of the impact of algorithms mentioned in the academic text, and the results can be used as training data for the automatic extraction of large-scale algorithms in the future. The methodology in this paper is domain-independent and can be applied to other domains.
Exploring sensorimotor performance and user experience within a virtual reality golf putting simulator

Abstract In light of recent advances in technology, there has been growing interest in virtual reality (VR) simulations for training purposes in a range of high-performance environments, from sport to nuclear decommissioning. For a VR simulation to elicit effective transfer of training to the real-world, it must provide a sufficient level of validity, that is, it must be representative of the real-world skill. In order to develop the most effective simulations, assessments of validity should be carried out prior to implementing simulations in training. The aim of this work was to test elements of the physical fidelity, psychological fidelity and construct validity of a VR golf putting simulation. Self-report measures of task load and presence in the simulation were taken following real and simulated golf putting to assess psychological and physical fidelity. The performance of novice and expert golfers in the simulation was also compared as an initial test of construct validity. Participants reported a high degree of presence in the simulation, and there was little difference between real and virtual putting in terms of task demands. Experts performed significantly better in the simulation than novices ( p = .001, d = 1.23), and there was a significant relationship between performance on the real and virtual tasks ( r = .46, p = .004). The results indicated that the simulation exhibited an acceptable degree of construct validity and psychological fidelity. However, some differences between the real and virtual tasks emerged, suggesting further validation work is required.
Towards the Natural Language Processing as Spelling Correction for Offline Handwritten Text Recognition Systems

The increasing portability of physical manuscripts to the digital environment makes it common for systems to offer automatic mechanisms for offline Handwritten Text Recognition (HTR). However, several scenarios and writing variations bring challenges in recognition accuracy, and, to minimize this problem, optical models can be used with language models to assist in decoding text. Thus, with the aim of improving results, dictionaries of characters and words are generated from the dataset and linguistic restrictions are created in the recognition process. In this way, this work proposes the use of spelling correction techniques for text post-processing to achieve better results and eliminate the linguistic dependence between the optical model and the decoding stage. In addition, an encoder–decoder neural network architecture in conjunction with a training methodology are developed and presented to achieve the goal of spelling correction. To demonstrate the effectiveness of this new approach, we conducted an experiment on five datasets of text lines, widely known in the field of HTR, three state-of-the-art Optical Models for text recognition and eight spelling correction techniques, among traditional statistics and current approaches of neural networks in the field of Natural Language Processing (NLP). Finally, our proposed spelling correction model is analyzed statistically through HTR system metrics, reaching an average sentence correction of 54% higher than the state-of-the-art method of decoding in the tested datasets.
Presence Promotes Performance on a Virtual Spatial Cognition Task: Impact of Human Factors on Virtual Reality Assessment

The use of virtual reality in spatial cognition evaluation has been growing rapidly, mainly because of its potential applications in the training and diagnosis of cognitive impairment and its ability to blend experimental control and ecological validity. However, there are still many grey areas on virtual reality, notably on the sense of presence and its complex relationship to task performance. Performance in VR is often suggested to be influenced by other human factors amongst which cybersickness, gender, video game experience and field dependence. Would an individual experiencing more presence systematically show better performance? This study aimed to be part of a framework of virtual reality as this question is fundamental for rigorous assessment and diagnostic, and particularly in the spatial cognition field. 48 healthy young subjects were recruited to take part in a virtual spatial cognition evaluation. Spatial cognition performance, along with their level of presence, cybersickness, video game experience, gender and field dependence, were measured. Matrix correlations were used, along with linear regressions and mediation analysis. Results show that presence promoted performance on the spatial cognition evaluation, while cybersickness symptoms hindered it, notably among women. The presence – performance relationship was not mediated by other human factors. Video game experience significantly predicted both sense of presence and cybersickness, the latter two being negatively correlated. Even if women experienced more negative symptoms than men, gender appears less informative than cybersickness and video game experience. Field dependence was not associated with any other variable. Results are discussed by confronting two theories of cognition (representational versus ecological), highlighting that virtual reality is not a simple transposition of reality but truly a new paradigm with its own biases favoring some individual more than others, and that some human factors have to be controlled for rigorous uses of virtual environments, particularly for spatial cognition evaluation.
Artificial Intelligence in the Intensive Care Unit

The diffusion of electronic health records collecting large amount of clinical, monitoring, and laboratory data produced by intensive care units (ICUs) is the natural terrain for the application of artificial intelligence (AI). AI has a broad definition, encompassing computer vision, natural language processing, and machine learning, with the latter being more commonly employed in the ICUs. Machine learning may be divided in supervised learning models (i.e., support vector machine [SVM] and random forest), unsupervised models (i.e., neural networks [NN]), and reinforcement learning. Supervised models require labeled data that is data mapped by human judgment against predefined categories. Unsupervised models, on the contrary, can be used to obtain reliable predictions even without labeled data. Machine learning models have been used in ICU to predict pathologies such as acute kidney injury, detect symptoms, including delirium, and propose therapeutic actions (vasopressors and fluids in sepsis). In the future, AI will be increasingly used in ICU, due to the increasing quality and quantity of available data. Accordingly, the ICU team will benefit from models with high accuracy that will be used for both research purposes and clinical practice. These models will be also the foundation of future decision support system (DSS), which will help the ICU team to visualize and analyze huge amounts of information. We plea for the creation of a standardization of a core group of data between different electronic health record systems, using a common dictionary for data labeling, which could greatly simplify sharing and merging of data from different centers.
A quantum dot as a source of time-bin entangled multi-photon states

We propose a scheme to make use of recent advances in cavity QED-enhanced resonance fluorescence from quantum dots to generate a stream of entangled and indistinguishable photons. We then demonstrate that we can optically manipulate the state of a trapped hole spin to achieve complete coherent control of a qubit. In combination with the selective cavity enhancement of the resonantly excited transition, we use this capability to perform a proof-of-principle demonstration of our proposal by showing that the time bin of a single photon is dependent on the measured state of the trapped spin.
Deceiving Image-to-Image Translation Networks for Autonomous Driving With Adversarial Perturbations

Deep neural networks (DNNs) have achieved impressive performance on handling computer vision problems, however, it has been found that DNNs are vulnerable to adversarial examples.For such reason, adversarial perturbations have been recently studied in several respects.However, most previous works have focused on image classification tasks, and it has never been studied regarding adversarial perturbations on Image-to-image (Im2Im) translation tasks, showing great success in handling paired and/or unpaired mapping problems in the field of autonomous driving and robotics.This paper examines different types of adversarial perturbations that can fool Im2Im frameworks for autonomous driving purpose.We propose both quasi-physical and digital adversarial perturbations that can make Im2Im models yield unexpected results.We then empirically analyze these perturbations and show that they generalize well under both paired for image synthesis and unpaired settings for style transfer.We also validate that there exist some perturbation thresholds over which the Im2Im mapping is disrupted or impossible.The existence of these perturbations reveals that there exist crucial weaknesses in Im2Im models.Lastly, we show that our methods illustrate how perturbations affect the quality of outputs, pioneering the improvement of the robustness of current SOTA networks for autonomous driving.
HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction

Named Entity Recognition (NER) is a fundamental task in natural language processing.In order to identify entities with nested structure, many sophisticated methods have been recently developed based on either the traditional sequence labeling approaches or directed hypergraph structures.Despite being successful, these methods often fall short in striking a good balance between the expression power for nested structure and the model complexity.To address this issue, we present a novel nested NER model named HIT.Our proposed HIT model leverages two key properties pertaining to the (nested) named entity, including (1) explicit boundary tokens and (2) tight internal connection between tokens within the boundary.Specifically, we design (1) Head-Tail Detector based on the multi-head selfattention mechanism and bi-affine classifier to detect boundary tokens, and (2) Token Interaction Tagger based on traditional sequence labeling approaches to characterize the internal token connection within the boundary.Experiments on three public NER datasets demonstrate that the proposed HIT achieves state-ofthe-art performance.