Reviewerly/exHarmony huggingface dataset:
Explainable Prediction of Medical Codes With Knowledge Graphs

International Classification of Diseases (ICD) is an authoritative health care classification system of different diseases. It is widely used for disease and health records, assisted medical reimbursement decisions, and collecting morbidity and mortality statistics. The most existing ICD coding models only translate the simple diagnosis descriptions into ICD codes. And it obscures the reasons and details behind specific diagnoses. Besides, the label (code) distribution is uneven. And there is a dependency between labels. Based on the above considerations, the knowledge graph and attention mechanism were expanded into medical code prediction to improve interpretability. In this study, a new method called G_Coder was presented, which mainly consists of Multi-CNN, graph presentation, attentional matching, and adversarial learning. The medical knowledge graph was constructed by extracting entities related to ICD-9 from freebase. Ontology contains 5 entity classes, which are disease, symptom, medicine, surgery, and examination. The result of G_Coder on the MIMIC-III dataset showed that the micro-F1 score is 69.2% surpassing the state of art. The following conclusions can be obtained through the experiment: G_Coder integrates information across medical records using Multi-CNN and embeds knowledge into ICD codes. Adversarial learning is used to generate the adversarial samples to reconcile the writing styles of doctor. With the knowledge graph and attention mechanism, most relevant segments of medical codes can be explained. This suggests that the knowledge graph significantly improves the precision of code prediction and reduces the working pressure of the human coders.
Coverless Image Steganography Based on Generative Adversarial Network

Traditional image steganography needs to modify or be embedded into the cover image for transmitting secret messages. However, the distortion of the cover image can be easily detected by steganalysis tools which lead the leakage of the secret message. So coverless steganography has become a topic of research in recent years, which has the advantage of hiding secret messages without modification. But current coverless steganography still has problems such as low capacity and poor quality .To solve these problems, we use a generative adversarial network (GAN), an effective deep learning framework, to encode secret messages into the cover image and optimize the quality of the steganographic image by adversaring. Experiments show that our model not only achieves a payload of 2.36 bits per pixel, but also successfully escapes the detection of steganalysis tools.
Attribute-based Multi-Signature and Encryption for EHR Management: A Blockchain-based Solution

The global Electronic Health Record (EHR) market is growing dramatically and has already hit $31.5 billion in 2018. To safeguard the security of EHR data and privacy of patients, fine-grained information access and sharing mechanisms are essential for EHR management. This paper proposes a hybrid architecture of blockchain and edge nodes to facilitate EHR management. In this architecture, we utilize attribute-based multi-signature (ABMS) scheme to authenticate user's signatures without revealing the sensitive information and multi-authority attribute-based encryption (ABE) scheme to encrypt EHR data which is stored on the edge node. We develop the blockchain module on Hyperledger Fabric platform and the ABMS module on Hyperledger Ursa library. We measure the signing and verifying time of the ABMS scheme under different settings, and experiment with the authentication events and access activities which are logged as transactions in blockchain.
Human hand gesture recognition with convolutional neural networks for K-12 double-teachers instruction mode classroom

Infrared hand gesture images can capture the human accurate gesture information in the weak illumination environment. In this article, we propose a novel hand gesture recognition model with convolutional neural networks for the human behavior analysis in "double teachers" classroom instruction and learning scenario. The recognized instructors' hand gestures can be utilized to analyze teachers' non-verbal behaviors which attract learners' attention as well as enhance their learning outcomes. To extract the features of the infrared hand gesture images, a non-linear neural networks is constructed with four convolution layers. It aims to learn teachers' pointing gestures and provides with the accurate gestures information. To achieve robust hand gesture recognition, the supervised convolutional neural network is constructed with three convolution layers to extract the infrared hand images features. Experiment results demonstrate that the proposed method outperforms the state-of-the-art methods in the term of the recognition accuracy and classification error. From the comparing experiments, the results prove that the developed approach can estimate the hand gesture with more than 92% recognition ratio.
Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data

With climate change driving an increasingly stronger influence over governments and municipalities, sustainable development, and renewable energy are gaining traction across the globe. This is reflected within the EU 2030 agenda, that envisions a future where there is universal access to affordable, reliable and sustainable energy. One of the challenges to achieve this vision lies on the low reliability of certain renewable sources. While both particulars and public entities try to reach self-sufficiency through sustainable energy generation, it is unclear how much investment is needed to mitigate the unreliability introduced by natural factors such as varying wind speed and daylight across the year. In this sense, a tool that aids predicting the energy output of sustainable sources across the year for a particular location can aid greatly in making sustainable energy investments more efficient. In this paper, we make use of Open Data sources, Internet of Things (IoT) sensors and installations distributed across Europe to create such tool through the application of Artificial Neural Networks. We analyze how the different factors affect the prediction of energy production and how Open Data can be used to predict the expected output of sustainable sources. As a result, we facilitate users the necessary information to decide how much they wish to invest according to the desired energy output for their particular location. Compared to state-of-the-art proposals, our solution provides an abstraction layer focused on energy production, rather that radiation data, and can be trained and tailored for different locations using Open Data. Finally, our tests show that our proposal improves the accuracy of the forecasting, obtaining a lower mean squared error (MSE) of 0.040 compared to an MSE 0.055 from other proposals in the literature.
A Unified Framework for Multilingual Speech Recognition in Air Traffic Control Systems

This work focuses on robust speech recognition in air traffic control (ATC) by designing a novel processing paradigm to integrate multilingual speech recognition into a single framework using three cascaded modules: an acoustic model (AM), a pronunciation model (PM), and a language model (LM). The AM converts ATC speech into phoneme-based text sequences that the PM then translates into a word-based sequence, which is the ultimate goal of this research. The LM corrects both phoneme- and word-based errors in the decoding results. The AM, including the convolutional neural network (CNN) and recurrent neural network (RNN), considers the spatial and temporal dependences of the speech features and is trained by the connectionist temporal classification loss. To cope with radio transmission noise and diversity among speakers, a multiscale CNN architecture is proposed to fit the diverse data distributions and improve the performance. Phoneme-to-word translation is addressed via a proposed machine translation PM with an encoder-decoder architecture. RNN-based LMs are trained to consider the code-switching specificity of the ATC speech by building dependences with common words. We validate the proposed approach using large amounts of real Chinese and English ATC recordings and achieve a 3.95% label error rate on Chinese characters and English words, outperforming other popular approaches. The decoding efficiency is also comparable to that of the end-to-end model, and its generalizability is validated on several open corpora, making it suitable for real-time approaches to further support ATC applications, such as ATC prediction and safety checking.
Optical character recognition with neural networks and post-correction with finite state methods

Abstract The optical character recognition (OCR) quality of the historical part of the Finnish newspaper and journal corpus is rather low for reliable search and scientific research on the OCRed data. The estimated character error rate (CER) of the corpus, achieved with commercial software, is between 8 and 13%. There have been earlier attempts to train high-quality OCR models with open-source software, like Ocropy ( https://github.com/tmbdev/ocropy ) and Tesseract ( https://github.com/tesseract-ocr/tesseract ), but so far, none of the methods have managed to successfully train a mixed model that recognizes all of the data in the corpus, which would be essential for an efficient re-OCRing of the corpus. The difficulty lies in the fact that the corpus is printed in the two main languages of Finland (Finnish and Swedish) and in two font families (Blackletter and Antiqua). In this paper, we explore the training of a variety of OCR models with deep neural networks (DNN). First, we find an optimal DNN for our data and, with additional training data, successfully train high-quality mixed-language models. Furthermore, we revisit the effect of confidence voting on the OCR results with different model combinations. Finally, we perform post-correction on the new OCR results and perform error analysis. The results show a significant boost in accuracy, resulting in 1.7% CER on the Finnish and 2.7% CER on the Swedish test set. The greatest accomplishment of the study is the successful training of one mixed language model for the entire corpus and finding a voting setup that further improves the results.
SMKFC-ER: Semi-supervised multiple kernel fuzzy clustering based on entropy and relative entropy

The present study aimed to present a new algorithm called Semi-supervised Multiple Kernel Fuzzy Clustering based on Entropy and Relative entropy (SMKFC-ER) by focusing on external knowledge related to the labeled data. In the proposed method, entropy coefficient and relative entropy divergence measure are applied instead of fuzzifier for unsupervised section and the geometric distance measure for semi-supervised section respectively, by emphasizing on combining unsupervised and semi-supervised sections explicitly. The use of relative entropy and entropy in the objective function results in sharing more consistent concepts for the semi-supervised section, controlling the fuzziness of the extracted clusters, and determining the kernel weights regularly for the unsupervised section. Finally, using relative entropy with entropy simultaneously derives a closed-form solution. The performance and supremacy of the proposed method on non-spherical synthetic and real-world datasets are shown by comparing unsupervised and semi-supervised fuzzy clustering methods.
On the (in)security of ROS

We present an algorithm solving the ROS (Random inhomogeneities in a Overdetermined Solvable system of linear equations) problem mod p in polynomial time for $$\ell > \log p$$ dimensions. Our algorithm can be combined with Wagner’s attack, and leads to a sub-exponential solution for any dimension $$\ell $$ with best complexity known so far. When concurrent executions are allowed, our algorithm leads to practical attacks against unforgeability of blind signature schemes such as Schnorr and Okamoto–Schnorr blind signatures, threshold signatures such as GJKR and the original version of FROST, multisignatures such as CoSI and the two-round version of MuSig, partially blind signatures such as Abe–Okamoto, and conditional blind signatures such as ZGP17. Schemes for e-cash and anonymous credentials (such as Anonymous Credentials Light) inspired from the above are also affected.
The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software Refactoring

Refactoring is the process of changing the internal structure of software to improve its quality without modifying its external behavior. Empirical studies have repeatedly shown that refactoring has a positive impact on the understandability and maintainability of software systems. However, before carrying out refactoring activities, developers need to identify refactoring opportunities. Currently, refactoring opportunity identification heavily relies on developers&#x2019; expertise and intuition. In this paper, we investigate the effectiveness of machine learning algorithms in predicting software refactorings. More specifically, we train six different machine learning algorithms (i.e., Logistic Regression, Naive Bayes, Support Vector Machine, Decision Trees, Random Forest, and Neural Network) with a dataset comprising over two million refactorings from 11,149 real-world projects from the Apache, F-Droid, and GitHub ecosystems. The resulting models predict 20 different refactorings at class, method, and variable-levels with an accuracy often higher than 90 percent. Our results show that (i) Random Forests are the best models for predicting software refactoring, (ii) process and ownership metrics seem to play a crucial role in the creation of better models, and (iii) models generalize well in different contexts.
Identification and Quantification of Hazardous Scenarios for Automated Driving

We present an integrated method for safety assessment of automated driving systems which covers the aspects of functional safety and safety of the intended functionality (SOTIF), including identification and quantification of hazardous scenarios. The proposed method uses and combines established exploration and analytical tools for hazard analysis and risk assessment in the automotive domain, while adding important enhancements to enable their applicability to the uncharted territory of safety analyses for automated driving. The method is tailored to support existing safety processes mandated by the standards ISO 26262 and ISO/PAS 21448 and complements them where necessary. It has been developed in close cooperation with major German automotive manufacturers and suppliers within the PEGASUS project ( https://www.pegasusprojekt.de/en ). Practical evaluation has been carried out by applying the method to the PEGASUS Highway-Chauffeur, a conceptual automated driving function considered as a common reference system within the project.
Interactive intelligent teaching and automatic composition scoring system based on linear regression machine learning algorithm

Intelligent education is an intelligent education platform that integrates correct education concept and Internet of things, big data, cloud computing and other technologies. This paper hopes to use the construction of the composition scoring model to further construct a computer scoring system for college English translation, which can give students a translation score and give feedback evaluation based on the quality of the translation. In this paper, according to the knowledge of the existing automatic scoring system at home and abroad, the feature selection method (TF-IDF, IG, CHI) is discussed and analyzed. Moreover, this paper studies the impact of our composition automatic scoring from the perspective of linguistics. In addition, this paper uses the multiple regression method to evaluate the final score. The features considered in this paper mainly include simple linguistic features and complex linguistic features. Finally, performance analysis of the algorithm model is performed by setting up a control experiment. The research results show that the proposed algorithm model has certain effects. The future trend is to form adult auxiliary machines through various human-computer interaction technologies, which will reshape future learning and education and form a new teaching form.
Joint representation learning for multi-view subspace clustering

Multi-view subspace clustering has made remarkable achievements in the field of multi-view learning for high-dimensional data. However, many existing multi-view subspace clustering methods still have two disadvantages. First, most of them only recover the subspace structure from either consistent or specific perspective. Second, they often fail to take advantage of the high-order information among different views. To alleviate these two issues, this paper proposes a novel multi-view subspace clustering method, which aims to learn the view-specific representation as well as the low-rank tensor representation in a unified framework. Particularly, our method learns the view-specific representation from data samples by exploiting the local structure within each view. In the meantime, we generate the low-rank tensor representation from the view-specific representation to capture the high-order correlation across multiple views. Based on the joint representation learning framework, the proposed method is able to explore the intra-view pairwise information and the inter-view complementary information, so that the underlying data structure can be revealed and then the final clustering result can be obtained through the subsequent spectral clustering. Furthermore, in the proposed Joint Representation Learning for Multi-view Subspace Clustering (JRL-MSC) method, a unified objective function is formulated, which can be efficiently optimized by the alternating direction method of multipliers. Experimental results on multiple real-world data sets have demonstrated that our method outperforms the state-of-the-art counterparts.
Discretely modulated continuous-variable quantum key distribution with an untrusted entanglement source

The discretely modulated continuous-variable quantum key distribution (DM CVQKD) has been proven to be secure, and a basic assumption for the current theoretical security proof of the DM CVQKD is that the signal source cannot be compromised. However, this assumption is quite unpractical in real quantum communication systems. In this paper, we investigate the DM CVQKD with a special configuration in which the entanglement source is placed at the untrusted channel. With this configuration, the source is no longer protected by the sender but is exposed to the vulnerable environment. In particular, we consider the configuration for two typical DM CVQKD protocols, which are the four-state protocol and the eight-state protocol. Security analysis based on linear bosonic channel shows that the DM CVQKD with an untrusted entanglement source is able to defend itself against the most powerful quantum collective attack in a certain distance range and it can still generate positive secret key rate when considering finite-size effect and composable security, thereby providing a theoretical proof for applying the DM CVQKD system to a realistic environment.
Stage-wise Salient Object Detection in 360° Omnidirectional Image via Object-level Semantical Saliency Ranking

The 2D image based salient object detection (SOD) has been extensively explored, while the 360° omnidirectional image based SOD has received less research attention and there exist three major bottlenecks that are limiting its performance. Firstly, the currently available training data is insufficient for the training of 360° SOD deep model. Secondly, the visual distortions in 360° omnidirectional images usually result in large feature gap between 360° images and 2D images; consequently, the widely used stage-wise training-a widely-used solution to alleviate the training data shortage problem, becomes infeasible when conducing SOD in 360° omnidirectional images. Thirdly, the existing 360° SOD approach has followed a multi-task methodology that performs salient object localization and segmentation-like saliency refinement at the same time, being faced with extremely large problem domain, making the training data shortage dilemma even worse. To tackle all these issues, this paper divides the 360° SOD into a multi-staqe task, the key rationale of which is to decompose the original complex problem domain into sequential easy sub problems that only demand for small-scale training data. Meanwhile, we learn how to rank the "object-level semantical saliency", aiming to locate salient viewpoints and objects accurately. Specifically, to alleviate the training data shortage problem, we have released a novel dataset named 360-SSOD, containing 1,105 360° omnidirectional images with manually annotated object-level saliency ground truth, whose semantical distribution is more balanced than that of the existing dataset. Also, we have compared the proposed method with 13 SOTA methods, and all quantitative results have demonstrated the performance superiority.
Blockchain: Is it the future for the tourism and hospitality industry?

Recently, blockchain and cryptocurrencies have become topics of discussion in both research and industry. Iansiti and Lakhani perceive blockchain as a foundational technology rather than a disruptive one, since potentially new economic and social systems can be based on blockchain. Therefore, understanding blockchain and contemplating its impact on the tourism and hospitality industry is essential. The tourism and hospitality industry has to focus not on the technology itself but on how it can be used for the benefit of consumers and suppliers, while at the same time creating new tourism products or systems. The purpose of this study is to explore and identify use cases for blockchain for the tourism and hospitality industry. In addition, an outlook on potential future blockchain applications given the current COVID-19 pandemic is provided.
Evaluation model of classroom teaching quality based on improved RVM algorithm and knowledge recommendation

The intelligent evaluation of classroom teaching quality is one of the development directions of modern education. At present, some teaching quality evaluation models have accuracy problems, and the evaluation process is affected by a variety of interference factors, which leads to inaccurate model results, and it is impossible to find out the specific factors that affect teaching. In order to improve the accuracy of classroom teaching quality evaluation, this study improves RVM based on the method of feature extraction and empirical modal decomposition of ACLLMD method, and establishes classroom theoretical teaching quality evaluation model and experimental teaching quality evaluation model based on RVM algorithm. Moreover, this study uses test data to analyze the accuracy and reliability of the evaluation results to verify the feasibility and reliability of the new method. In addition, this study verifies the reliability of this algorithm by comparing with the manual scoring results. The research results show that RVM can be used to construct classroom theory teaching quality evaluation models and experimental teaching quality evaluation models with high accuracy and good reliability.