Reviewerly/exHarmony huggingface dataset:
Decision support analysis for risk identification and control of patients affected by COVID-19 based on Bayesian Networks

In the context of the outbreak of coronavirus disease (COVID-19), this paper proposes an innovative and systematic decision support model based on Bayesian networks (BNs) to identify and control the risk of COVID-19 patients spreading the virus, which requires the following three steps. First, by consulting the related literature and combining this with expert knowledge, we identify and classify the characteristics (risk factors) of COVID-19 and obtain a conceptual framework for COVID-19 Risk Assessment Bayesian Networks (CRABNs). Second, data on COVID-19 patients with expert scoring results on patient risk levels were collected from hospitals in Hubei Province of China and are used as the training set, and the structure and parameters of the CRABNs model are obtained through machine learning. Finally, we propose two indicators, namely, Model Bias and Model Accuracy, and use the remaining data to verify the feasibility and effectiveness of the CRABNs model to ensure that there are no significant differences between the predicted results of the model and the actual results provided by experts who have relevant experience in treating COVID-19. At the same time, we compared the CRABNs model with the support vector machine (SVM), random forest (RF), and k-nearest neighbour (KNN) models through four indicators: accuracy, sensitivity, specificity, and F-score. The results suggest the reliability of the model and show that it has promising application potential. The proposed model can be used globally by doctors in hospitals as a decision support tool to improve the accuracy of assessing the severity of COVID-19 symptoms in patients. Furthermore, with the further improvement of the model in the future, it can be used for risk assessments in the field of epidemics.
Sequential shape similarity for active contour based left ventricle segmentation in cardiac cine MR image

Delineation of the boundaries of the Left Ventricle (LV) in cardiac Magnetic Resonance Images (MRI) is a hot topic due to its important diagnostic power. In this paper, an approach is proposed to extract the LV in a sequence of MR images. In the proposed paper, all images in the sequence are segmented simultaneously and the shape of the LV in each image is supposed to be similar to that of the LV in nearby images in the sequence. We coined the novel shape similarity constraint, and it is called sequential shape similarity (SSS in short). The proposed segmentation method takes the Active Contour Model as the base model and our previously proposed Gradient Vector Convolution (GVC) external force is also adopted. With the SSS constraint, the snake contour can accurately delineate the LV boundaries. We evaluate our method on two cardiac MRI datasets and the Mean Absolute Distance (MAD) metric and the Hausdorff Distance (HD) metric demonstrate that the proposed approach has good performance on segmenting the boundaries of the LV.
Casual Learn: A linked data-based mobile application for learning about local Cultural Heritage

This paper presents Casual Learn, an application that proposes ubiquitous learning tasks about Cultural Heritage. Casual Learn exploits a dataset of 10,000 contextualized learning tasks that were semiautomatically generated out of open data from the Web. Casual Learn offers these tasks to learners according to their physical location. For example, it may suggest describing the characteristics of the Gothic style when passing by a Gothic Cathedral. Additionally, Casual Learn has an interactive mode where learners can geo-search the tasks available. Casual Learn has been successfully used to support three pilot studies in two secondary-school institutions. It has also been awarded by the regional government and an international research conference. This made Casual Learn to appear in several regional newspapers, radios, and TV channels.
Student’s Learning Profile as a Tool of Personal Learning Logistics

Increase of complexity and uncertainty as well as demand for personalization (including in education) urges universities to pay attention to educational subjectivity and its development; to transfer towards individual / collective-individual educational navigation and flexible systems of educational programs management (including formation of temporary learning groups, supply of required educational resources in due time, protocols of appraisal and mutual offset of educational results), what determines the relevance of the research. Usage of logistic approach enables to distinguish the pedagogical and management objectives of educational activity organization as well as to facilitate personalization of education. The article considers an educational profile as an instrument of personal educational logistics in digital educational environment, presents the preliminary terms “digital track”, “portfolio”, “profile”. The authors also dwell on the requirements to educational profiles development and scenarios of handling them in digital educational environment taking into account domestic and global experience of educational profiles’ implementation.
Adversarial Attacks on Face Recognition Systems

Abstract Face recognition has been widely used for identity verification both in supervised and unsupervised access control applications. The advancement in deep neural networks has opened up the possibility of scaling it to multiple applications. Despite the improvement in performance, deep network-based Face Recognition Systems (FRS) are not well prepared against adversarial attacks at the deployment level. The output performance of such FRS can be drastically impacted simply by changing the trained parameters, for instance, by changing the number of layers, subnetworks, loss and activation functions. This chapter will first demonstrate the impact on biometric performance using a publicly available face dataset. Further to this, this chapter will also present some strategies to defend against such attacks by incorporating defense mechanisms at the training level to mitigate the performance degradation. With the empirical evaluation of the deep FRS with and without a defense mechanism, we demonstrate the impact on biometric performance for the completeness of the chapter.
Sentence Boundary Extraction from Scientific Literature of Electric Double Layer Capacitor Domain: Tools and Techniques

Given the growth of scientific literature on the web, particularly material science, acquiring data precisely from the literature has become more significant. Material information systems, or chemical information systems, play an essential role in discovering data, materials, or synthesis processes using the existing scientific literature. Processing and understanding the natural language of scientific literature is the backbone of these systems, which depend heavily on appropriate textual content. Appropriate textual content means a complete, meaningful sentence from a large chunk of textual content. The process of detecting the beginning and end of a sentence and extracting them as correct sentences is called sentence boundary extraction. The accurate extraction of sentence boundaries from PDF documents is essential for readability and natural language processing. Therefore, this study provides a comparative analysis of different tools for extracting PDF documents into text, which are available as Python libraries or packages and are widely used by the research community. The main objective is to find the most suitable technique among the available techniques that can correctly extract sentences from PDF files as text. The performance of the used techniques Pypdf2, Pdfminer.six, Pymupdf, Pdftotext, Tika, and Grobid is presented in terms of precision, recall, f-1 score, run time, and memory consumption. NLTK, Spacy, and Gensim Natural Language Processing (NLP) tools are used to identify sentence boundaries. Of all the techniques studied, the Grobid PDF extraction package using the NLP tool Spacy achieved the highest f-1 score of 93% and consumed the least amount of memory at 46.13 MegaBytes.
Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition

Over the last few years, several researchers have worked on handwritten character recognition and have proposed various techniques to improve the performance of Indic and non-Indic scripts recognition. Here, a Deep Convolutional Neural Network has been proposed that learns deep features for offline Gurmukhi handwritten character and numeral recognition (HCNR). The proposed network works efficiently for training as well as testing and exhibits a good recognition performance. Two primary datasets comprising of offline handwritten Gurmukhi characters and Gurmukhi numerals have been employed in the present work. The testing accuracies achieved using the proposed network is 98.5% for characters and 98.6% for numerals.
Egocentric Action Recognition by Automatic Relation Modeling

Egocentric videos, which record the daily activities of individuals from a first-person point of view, have attracted increasing attention during recent years because of their growing use in many popular applications, including life logging, health monitoring and virtual reality. As a fundamental problem in egocentric vision, one of the tasks of egocentric action recognition aims to recognize the actions of the camera wearers from egocentric videos. In egocentric action recognition, relation modeling is important, because the interactions between the camera wearer and the recorded persons or objects form complex relations in egocentric videos. However, only a few of existing methods model the relations between the camera wearer and the interacting persons for egocentric action recognition, and moreover they require prior knowledge or auxiliary data to localize the interacting persons. In this work, we consider modeling the relations in a weakly supervised manner, i.e., without using annotations or prior knowledge about the interacting persons or objects, for egocentric action recognition. We form a weakly supervised framework by unifying automatic interactor localization and explicit relation modeling for the purpose of automatic relation modeling. First, we learn to automatically localize the interactors, i.e., the body parts of the camera wearer and the persons or objects that the camera wearer interacts with, by learning a series of keypoints directly from video data to localize the action-relevant regions with only action labels and some constraints on these keypoints. Second, more importantly, to explicitly model the relations between the interactors, we develop an ego-relational LSTM (long short-term memory) network with several candidate connections to model the complex relations in egocentric videos, such as the temporal, interactive, and contextual relations. In particular, to reduce human efforts and manual interventions needed to construct an optimal ego-relational LSTM structure, we search for the optimal connections by employing a differentiable network architecture search mechanism, which automatically constructs the ego-relational LSTM network to explicitly model different relations for egocentric action recognition. We conduct extensive experiments on egocentric video datasets to illustrate the effectiveness of our method.
Bassa-ML — A Blockchain and Model Card Integrated Federated Learning Provenance Platform

Federated learning is a collaborative/distributed machine learning system which is designed to address the privacy issues in centralized machine learning systems. The transparency and provenance of a machine learning model are important aspects of federated learning systems since they impact peoples&#x2019; lives in various domains (e.g., from healthcare to personal finance to employment). However, most of the existing federated learning systems deal with centralized coordinators which are vulnerable to attacks and privacy breaches. Also, they do not provide any standard transparency and provenance mechanisms for the resulting models. In this paper, we propose a blockchain and Model Card-based integrated federated learning system "Bassa-ML" providing enhanced transparency and trust for the models. Model parameter sharing, local model generation, model averaging, and model sharing functions are implemented using smart contracts. The generated models, model training information, and model reports are stored in the blockchain ledger as Model Card Objects. This results in enhanced transparency and auditability to the federated learning process.
Open-Set Learning-Based Hologram Verification System Using Generative Adversarial Networks

In this study, we address the hologram authenticity challenge by introducing a novel deep-learning based end-to-end hologram verification system. The system ultimately makes the decision whether the hologram image captured from a mobile application is fake or not by employing a robust classifier. We built the system by training three major deep networks; generative networks, convolutional networks and region-based convolutional networks. One major challenge in this study was the lack of negative class samples or so-called fake holograms. To the best of our knowledge there are no publicly available fake hologram datasets and it is not clear how the attackers imitate the real holograms. Therefore, the negative class in the practical hologram classification task is actually “unknown” class, as it is unknown how to imitate holograms by attackers. We hereby consider the problem of hologram classification as in a similar logic to open-set recognition. To make hologram classifier more sensitive to forgery, we generate synthetic images using generative adversarial networks (GANs) to represent negative class. We conduct extensive and comparative experiments on the closed-set and open-set using the-state-of-the-art backbone convolutional neural networks (CNNs). The proposed system gives an impressive accuracy 97.5% and 79% for closed-set and open-set samples, respectively. The reported results show the strong generalization performance of the system for unknown samples.
Predictive structured–unstructured interactions in EHR models: A case study of suicide prediction

Abstract Clinical risk prediction models powered by electronic health records (EHRs) are becoming increasingly widespread in clinical practice. With suicide-related mortality rates rising in recent years, it is becoming increasingly urgent to understand, predict, and prevent suicidal behavior. Here, we compare the predictive value of structured and unstructured EHR data for predicting suicide risk. We find that Naive Bayes Classifier (NBC) and Random Forest (RF) models trained on structured EHR data perform better than those based on unstructured EHR data. An NBC model trained on both structured and unstructured data yields similar performance (AUC = 0.743) to an NBC model trained on structured data alone (0.742, p = 0.668), while an RF model trained on both data types yields significantly better results (AUC = 0.903) than an RF model trained on structured data alone (0.887, p &lt; 0.001), likely due to the RF model’s ability to capture interactions between the two data types. To investigate these interactions, we propose and implement a general framework for identifying specific structured-unstructured feature pairs whose interactions differ between case and non-case cohorts, and thus have the potential to improve predictive performance and increase understanding of clinical risk. We find that such feature pairs tend to capture heterogeneous pairs of general concepts, rather than homogeneous pairs of specific concepts. These findings and this framework can be used to improve current and future EHR-based clinical modeling efforts.
Research on Game-Playing Agents Based on Deep Reinforcement Learning

Path planning is a key technology for the autonomous mobility of intelligent robots. However, there are few studies on how to carry out path planning in real time under the confrontation environment. Therefore, based on the deep deterministic policy gradient (DDPG) algorithm, this paper designs the reward function and adopts the incremental training and reward compensation method to improve the training efficiency and obtain the penetration strategy. The Monte Carlo experiment results show that the algorithm can effectively avoid static obstacles, break through the interception, and finally reach the target area. Moreover, the algorithm is also validated in the Webots simulator.
Integrated IoT-Based Secure and Efficient Key Management Framework Using Hashgraphs for Autonomous Vehicles to Ensure Road Safety

Autonomous vehicles offer various advantages to both vehicle owners and automobile companies. However, despite the advantages, there are various risks associated with these vehicles. These vehicles interact with each other by forming a vehicular network, also known as VANET, in a centralized manner. This centralized network is vulnerable to cyber-attacks which can cause data loss, resulting in road accidents. Thus, to prevent the vehicular network from being attacked and to prevent the privacy of the data, key management is used. However, key management alone over a centralized network is not effective in ensuring data integrity in a vehicular network. To resolve this issue, various studies have introduced a blockchain-based approach and enabled key management over a decentralized network. This technique is also found effective in ensuring the privacy of all the stakeholders involved in a vehicular network. Furthermore, a blockchain-based key management system can also help in storing a large amount of data over a distributed network, which can encourage a faster exchange of information between vehicles in a network. However, there are certain limitations of blockchain technology that may affect the efficient working of autonomous vehicles. Most of the existing blockchain-based systems are implemented over Ethereum or Bitcoin. The transaction-processing capability of these blockchains is in the range of 5 to 20 transactions per second, whereas hashgraphs are capable of processing thousands of transactions per second as the data are processed exponentially. Furthermore, a hashgraph prevents the user from altering the order of the transactions being processed, and they do not need high computational powers to operate, which may help in reducing the overall cost of the system. Due to the advantages offered by a hashgraph, an advanced key management framework based on a hashgraph for secure communication between the vehicles is suggested in this paper. The framework is developed using the concept of Leaving of Vehicles based on a Logical Key Hierarchy (LKH) and Batch Rekeying. The system is tested and compared with other closely related systems on the basis of the transaction compilation time and change in traffic rates.
AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection

This paper proposed a novel anomaly detection (AD) approach of high-speed train images based on convolutional neural networks and the Vision Transformer. Different from previous AD works, in which anomalies are identified with a single image using classification, segmentation, or object detection methods, the proposed method detects abnormal difference between two images taken at different times of the same region. In other words, we cast anomaly detection problem with a single image into a difference detection problem with two images. The core idea of the proposed method is that the “anomaly” commonly represents an abnormal state instead of a specific object, and this state should be identified by a pair of images. In addition, we introduced a deep feature difference AD network (AnoDFDNet) which sufficiently explored the potential of the Vision Transformer and convolutional neural networks. To verify the effectiveness of the proposed AnoDFDNet, we gathered three datasets, a difference dataset (Diff dataset), a foreign body dataset (FB dataset), and an oil leakage dataset (OL dataset). Experimental results on the above datasets demonstrate the superiority of the proposed method. In terms of the F1-score, the AnoDFDNet obtained 76.24%, 81.04%, and 83.92% on Diff dataset, FB dataset, and OL dataset, respectively.
Can Prompt Probe Pretrained Language Models? Understanding the Invisible Risks from a Causal View

Prompt-based probing has been widely used in evaluating the abilities of pretrained language models (PLMs).Unfortunately, recent studies have discovered such an evaluation may be inaccurate, inconsistent and unreliable.Furthermore, the lack of understanding its inner workings, combined with its wide applicability, has the potential to lead to unforeseen risks for evaluating and applying PLMs in real-world applications.To discover, understand and quantify the risks, this paper investigates the promptbased probing from a causal view, highlights three critical biases which could induce biased results and conclusions, and proposes to conduct debiasing via causal intervention.This paper provides valuable insights for the design of unbiased datasets, better probing frameworks and more reliable evaluations of pretrained language models.Furthermore, our conclusions also echo that we need to rethink the criteria for identifying better pretrained language models 1 .
Face Recognition and Gender Detection Using SIFT Feature Extraction, LBPH, and SVM

Face recognition and name and gender identification are challenging processes, especially when identifying perpetrators and suspects or when used in authentication systems. Machine learning and computer vision technologies are used in many fields, including security, and play an important role in face recognition and gender detection, offering valuable information to officials to rectify a situation in less time. This study used a few machine learning methods in the Labelled Faces in the Wild (LFW) database to examine their facial recognition and gender detection capacities. The LFW dataset was used to train and evaluate the Scale Invariant Feature Transform (SIFT) feature extraction method along with the Support Vector Machine (SVM) classifier and the Local Binary Pattern Histogram (LBPH) method. The result comparison from the current and other studies showed that the proposed LBPH method had higher accuracy in face recognition, while its accuracy in gender detection was very close to the ones of other, relevant studies.
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering

To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR).However, there still remains a large discrepancy between the provided upstream signals and the downstream question-passage relevance, which leads to less improvement.To bridge this gap, we propose the HyperLink-induced Pre-training (HLP), a method to pre-train the dense retriever with the text relevance induced by hyperlink-based topology within Web documents.We demonstrate that the hyperlink-based structures of dual-link and co-mention can provide effective relevance signals for large-scale pre-training that better facilitate downstream passage retrieval.We investigate the effectiveness of our approach across a wide range of open-domain QA datasets under zero-shot, few-shot, multihop, and out-of-domain scenarios.The experiments show our HLP outperforms the BM25 by up to 7 points as well as other pre-training methods by more than 10 points in terms of top-20 retrieval accuracy under the zero-shot scenario.Furthermore, HLP significantly outperforms other pre-training methods under the other scenarios.