Reviewerly/exHarmony huggingface dataset:
Family History Information Extraction With Neural Attention and an Enhanced Relation-Side Scheme: Algorithm Development and Validation

Background Identifying and extracting family history information (FHI) from clinical reports are significant for recognizing disease susceptibility. However, FHI is usually described in a narrative manner within patients’ electronic health records, which requires the application of natural language processing technologies to automatically extract such information to provide more comprehensive patient-centered information to physicians. Objective This study aimed to overcome the 2 main challenges observed in previous research focusing on FHI extraction. One is the requirement to develop postprocessing rules to infer the member and side information of family mentions. The other is to efficiently utilize intrasentence and intersentence information to assist FHI extraction. Methods We formulated the task as a sequential labeling problem and propose an enhanced relation-side scheme that encodes the required family member properties to not only eliminate the need for postprocessing rules but also relieve the insufficient training instance issues. Moreover, an attention-based neural network structure was proposed to exploit cross-sentence information to identify FHI and its attributes requiring cross-sentence inference. Results The dataset released by the 2019 n2c2/OHNLP family history extraction task was used to evaluate the performance of the proposed methods. We started by comparing the performance of the traditional neural sequence models with the ordinary scheme and enhanced scheme. Next, we studied the effectiveness of the proposed attention-enhanced neural networks by comparing their performance with that of the traditional networks. It was observed that, with the enhanced scheme, the recall of the neural network can be improved, leading to an increase in the F score of 0.024. The proposed neural attention mechanism enhanced both the recall and precision and resulted in an improved F score of 0.807, which was ranked fourth in the shared task. Conclusions We presented an attention-based neural network along with an enhanced tag scheme that enables the neural network model to learn and interpret the implicit relationship and side information of the recognized family members across sentences without relying on heuristic rules.
A multilevel hybrid anomaly detection scheme for industrial wireless sensor networks

Summary Real‐time sensing plays an important role in ensuring the reliability of industrial wireless sensor networks (IWSNs). Sensor nodes in IWSNs have inherent limitations that give rise to different anomalies in the network. These anomalies can lead to disastrous and harmful situations or even serious system failures. This article presents a formulation to the design of an anomaly detection scheme for detecting the anomalous node along with the type of anomaly. The proposed scheme is divided into two major parts. First, spatiotemporal correlation within a cluster is obtained for the normal and anomalous behavior of sensor nodes. Second, the multilevel hybrid classifier is used by combining the sequential minimal optimization support vector machine (SMO‐SVM) as a binary classifier with optimally pruned extreme learning machine (OP‐ELM) as a multiclass classifier for detection of an anomalous node and type of anomalies, respectively. Mahalanobis distance‐based lightweight K‐Medoid clustering is used to build a new set of training datasets that represents the original training dataset, by significantly reducing the training time of a multilevel hybrid classifier. Results are analyzed using standard WSN datasets. The proposed model shows high accuracy, i.e., 94.79 % and detection rate, i.e., 94.6 % with a reduced false positive rate as compared to existing hybrid methods.
Interdisciplinary Research Maps: A new technique for visualizing research topics

This article introduces Interdisciplinary Research Maps as a novel visualization technique to assist with interdisciplinary research analytics and to map common (and distinct) topics across publications from different disciplines. We detail the method for this technique which is based on entity linking and illustrate its application to a sample of articles sourced from the top business/management and environmental sciences journals. Both fields have separately been criticized for a lack of interdisciplinary research to co-create insights for tackling pressing environmental issues such as climate change. Our mapping approach provides a starting point for exploring similarities and differences in research topics across these fields. The mapping technique introduced here has broader applicability to facilitate the creation and exchange of knowledge across fields. We discuss avenues for visualization techniques to bridge the different fields by focusing on identifying common concepts to provide a basis for future analysis.
Fast 3D Image Reconstruction by Separable Moments based on Hahn and Krawtchouk Polynomials

In this paper, we will present a new method of fast and stable computation of 3D image reconstruction using a new collection of 3D separable discrete orthogonal moments called: the 3D Hahn-Hahn-Krawtchouk moments (HHKMs). The latter are generated as the product of Hahn and Krawtchouk discrete orthogonal polynomials. In this method, we will use the 3D image cuboids representation (ICR) to accelerate the computation time of HHKM and improve the quality of 3D images reconstruction for small orders. Simulation results confirm the effectiveness of the proposed method in terms of the calculation time of HHKMs as well as the speed and quality of 3D image reconstruction, with respect to the global method, and the ability of the 3D image reconstruction using the HHKMs is compared by the classical moments of Hahn and Krawtchouk.
Facial image recognition for biometric authentication systems using a combination of geometrical feature points and low-level visual features

This paper proposes a method for biometric-driven facial image recognition, based on multivariate correlation analysis, which extracts geometrical feature points and low-level visual features. The low-level visual features, such as colour and texture, are extracted at local from the selected prominent regions of the facial image. The geometrical features are captured using the Active Shape Model (ASM). The colour features are extracted from the YCbCr colour model, and the autocorrelation method is deployed to extract the texture features. The extracted features are formed as a feature tensor matrix. The feature matrix of the key face image is compared to the feature matrices of the target face image that are stored in the feature vector database using the Canonical Correlation method. The correlation between the key and target feature matrices is tested, whether it is highly significant or not. If it is significantly correlated, then it is inferred that the key and target face images are the same; otherwise, it is concluded that they are different. The benchmark facial image datasets, GT, LFW, and Pointing '04 datasets, considered for the experiments; in addition to the datasets, a facial image database has been constructed with celebrities on our interest which has also been subjected to experiments. The proposed method resulted in a mean precision score ([email protected]α) of 95.27%, 94.20%, 96.19%, and 96.05 for GT, LFW, Pointing '04, and our datasets, respectively. Also, the F-score was calculated that are, 96.78%, 95.15%, 97.08%, and 96.96% for GT, LFW, Pointing '04, and our datasets, respectively. The results obtained by the proposed method are comparable to the existing methods.
URL-based Web Tracking Detection Using Deep Learning

The pervasiveness of online web tracking poses a constant threat to the privacy of Internet users. Millions of users currently employ content-blockers in their web browsers to block tracking resources in real time. Although content-blockers are based on blacklists, which are known to be difficult to maintain and easy to evade, the research community has not succeeded in replacing them with better alternatives yet. Most of the methods recently proposed in the literature obtain good detection accuracy, but at the expense of increasing their complexity and making them more difficult to maintain and configure by the end user. In this paper, we present a new web tracking detection method, called Deep Tracking Detector (DTD), that analyzes the properties of URL strings to detect tracking resources, without using any other external features. Consequently, DTD can easily be implemented in a browser plugin and operate in real time. Our experimental results, with more than 5M HTTP requests from 100K websites, show that DTD achieves a detection accuracy higher than 97% by looking only at the URL of the resources.
Image stabilization algorithm based on KLT motion tracking

In this paper, the Kanade Lucas Tomasi (KLT) feature tracker, which is used in the moving target tracking algorithm, is used in the motion estimation. This method is more accurate and faster than the general feature-based motion estimation method. The basic idea of electronic image stabilization algorithm based on KLT tracking algorithm is to obtain camera motion parameters through tracking feature points, and then carry out motion compensation, so as to obtain a stable display effect.
Approach to the Use of Language Models BERT and Word2vec in Sentiment Analysis of Social Network Texts

The paper proposes an original algorithm for the training dataset formation for a neural network that provides sentiment analysis of social networks posts. This article also describes the use of a neural network to determine the sentiment values of a social network posts using the word2vec and BERT algorithms. Also conducted experiments confirming the effectiveness of the proposed approaches. Also experiments were carried out that compare the effectiveness of stemming and lemmatization of the text when forming a training set for sentiment analysis of the text.
Online Cooperative Resource Allocation at the Edge: A Privacy-Preserving Approach

Mobile edge computing provides a platform facilitating individual servers to pool their resources locally for cooperative computation. One fundamental problem in this new paradigm is how to effectively allocate crowdsourced edge resources to users competing in a highly unpredicted environment. This, apparently, cannot be realized without a truthful open market. On the other hand, enforcing truthfulness potentially incurs privacy problems. There have been efforts in differentially private auctions, in which exponential mechanism, designed for single-sided single-item auctions, is a common solution. However, such an approach is not applicable in two-sided combinatorial edge markets, further complicated by the extra migration cost on energy-constrained users often imposed by online allocation. In this paper, we propose OPTA, an online privacy-preserving truthful double auction mechanism for dynamic resource cooperation at the edge. Given uncertainties in future market behaviors, we harness competitive analysis by decomposing the online optimization into a series of single-round auctions such that their objectives are iteratively adjusted to capture the temporally-coupled nature of the problem. In each round, by jointly considering the features of exponential mechanism and greedy heuristic, we design a near-optimal allocation policy with efficiency and privacy guarantee. We further implement a critical-value pricing scheme for winners, realizing the truthfulness in expectation. Building upon the single-round results, our overall online algorithm achieves a provable competitive ratio. We validate the desirable properties of OPTA through theoretical analysis and extensive simulations.
Towards Rapid Development of Conversational Virtual Humans using Web3D Technologies

In this paper, we propose a framework for the rapid development of conversational virtual humans (VHs) via the web. Web3D formats, such as WebGL, have enabled the use of interactive VHs on the web. Web-based conversational VHs are found to have great potential in improving population health. However, the development of web-based VHs needs significant time and effort which can lead health experts to avoid VH technology. In this paper, we present a case study to highlight the challenges that lead to significant time and effort in the development of web-based conversational VHs. Based on our findings, we propose a framework that uses Web3D formats and has the potential to address identified challenges in the rapid development of web-based conversational VH systems.
DASH QoE Performance Evaluation Framework with 5G Datasets

Fifth Generation (5G) networks provide high throughput and low delay, contributing to enhanced Quality of Experience (QoE) expectations. The exponential growth of multimedia traffic pose dichotomic challenges to simultaneously satisfy network operators, service providers, and end-user expectations. Building QoE-aware networks that provide run-time mechanisms to satisfy end-users' expectations while the end-to-end network Quality of Service (QoS) varies is challenging, and motivates many ongoing research efforts. The contribution of this work is twofold. Firstly, we present a reproducible data-driven framework with a series of pre-installed Dynamic Adaptive Streaming over HTTP (DASH) tools to analyse state-of-art Adaptive Bitrate Streaming (ABS) algorithms by varying key QoS parameters in static and mobility scenarios. Secondly, we introduce an interactive Jupyter notebook and Binder service providing a live analytical environment, which processes the output dataset of the framework and compares the relationship of five QoE models, three QoS parameters (RTT, throughput, packets), and seven different video KPIs.
Micro-cracks Detection of Polycrystalline Solar Cells with Transfer Learning

Abstract As the photovoltaic (PV) systems are universally utilized in power systems, the defect of solar cells, the core components of PV system requires to be detected in a low-cost and high-efficiency method, because of its wide application and direct influence on power generation efficiency and system stability. The principal aim of this study is to apply transfer learning in defect detection of polycrystalline cells with image-level labelled mono images and unlabelled poly images. The experiment was conducted with DAN models based on a MK-MMD to represent the distance between source domain and target domain and Resnet-50 backbone to learn transferable features. Three contrast methods were designed to make comparison with DAN. It has been proved that DAN model based on MK-MMD and Resnet-50 shows a promising performance on transferring from monocrystalline tasks to polycrystalline tasks.
Access Control System Based on Face Recognition

Face recognition is a technology that uses face image of someone to verify his identity by finding this person in a given photos database. It becomes very practical in access control systems as it does not require any physical interaction for gaining access as traditional ways with keys. Moreover, these systems only require a camera for recognition and are easy to install and use. This is why they are already in use by companies as access control to their offices, in home automation systems, etc.In this paper, different approaches to face recognition are studied. The first step of any face recognition system is face detection and cropping so we analyzed classical Viola-Jones face detection and MultiTask Convolutional Neural Networks (MTCNN) in terms of detection quality and processing time.The final classifier obtained is capable of matching face from the online camera to image from a given database. We also considered decreasing the vulnerability of standalone face recognition by adding a spoof detection method so that the system does not react to every approach to bypass the system like showing a photo of an allowed person shown on a phone's screen.
Clustering-Based Deep Autoencoders for Network Anomaly Detection

A novel hybrid approach between clustering methods and autoencoders (AEs) is introduced for detecting network anomalies in a semi-supervised manner. A previous work has developed regularized AEs, namely Shrink AE (SAE) and Dirac Delta Variational AE (DVAE) that learn to represent normal data into a very small region being close to the origin in their middle hidden layers (latent representation). This work based on the assumption that normal data points may share some common characteristics, so they can be forced to distribute in a small single cluster. In some scenarios, however, normal network data may contain data from very different network services, which may result in a number of clusters in the normal data. Our proposed hybrid model attempts to automatically discover these clusters in the normal data in the latent representation of AEs. At each iteration, an AE learns to map normal data into the latent representation while a clustering method tries to discover clusters in the latent normal data and force them being close together. The co-training strategy can help to reveal true clusters in normal data. When a querying data point coming, it is first mapped into the latent representation of the AE, and its distance to the closest cluster center can be used as an anomaly score. The higher anomaly score a data point has, the more likely it is anomaly. The method is evaluated with four scenarios in the CTU13 dataset, and experiments illustrate that the proposed hybrid model often out-performs SAE on three out of four scenarios.
Phishing Web Page Detection Using Optimised Machine Learning

Phishing is a type of social engineering attack that can affect any company or anyone. This paper explores the effect that different features and optimisation techniques have on the accuracy of intelligent phishing detection using machine learning algorithms. This work looks at both hyperparameter optimisation as well as feature selection optimisation. For hyperparameter tuning, both TPE (Tree-structured Parzen Estimator) and GA (Genetic Algorithm) were tested, with the best option being model dependent. For feature selection, GA, MFO (Moth Flame Optimisation) and PSO (Particle Swarm Optimisation) were used with PSO working best with a Random Forest model. This work used URL (Uniform Resource Locator), DOM (Document Object Model) structure, page rank and page information related features. This research found that the best combination was Random Forest using PSO for feature selection and TPE for hyperparameter optimisation, giving an accuracy of 99.33%.
Gaussian Vector: An Efficient Solution for Facial Landmark Detection

Significant progress has been made in facial landmark detection with the development of Convolutional Neural Networks. The widely-used algorithms can be classified into coordinate regression methods and heatmap based methods. However, the former loses spatial information, resulting in poor performance while the latter suffers from large output size or high post-processing complexity. This paper proposes a new solution, Gaussian Vector, to preserve the spatial information as well as reduce the output size and simplify the post-processing. Our method provides novel vector supervision and introduces Band Pooling Module to convert heatmap into a pair of vectors for each landmark. This is a plug-and-play component which is simple and effective. Moreover, Beyond Box Strategy is proposed to handle the landmarks out of the face bounding box. We evaluate our method on 300W, COFW, WFLW and JD-landmark. That the results significantly surpass previous works demonstrates the effectiveness of our approach.
ICFHR 2020 Competition on Short answer ASsessment and Thai student SIGnature and Name COMponents Recognition and Verification (SASIGCOM 2020)

This paper describes the results of the competition on Short answer ASsessment and Thai student SIGnature and Name COMponents Recognition and Verification (SASIGCOM 2020) in conjunction with the 17th International Conference on Frontiers in Handwriting Recognition (ICFHR 2020). The competition was aimed to automate the evaluation process short answer-based examination and record the development and gain attention to such system. The proposed competition contains three elements which are short answer assessment (recognition and marking the answers to short-answer questions derived from examination papers), student name components (first and last names) and signature verification and recognition. Signatures and name components data were collected from 100 volunteers. For the Thai signature dataset, there are 30 genuine signatures, 12 skilled and 12 simple forgeries for each writer. With Thai name components dataset, there are 30 genuine and 12 skilfully forged name components for each writer. There are 104 exam papers in the short answer assessment dataset, 52 of which were written with cursive handwriting; the rest of 52 papers were written with printed handwriting. The exam papers contain ten questions, and the answers to the questions were designed to be a few words per question. Three teams from distinguished labs submitted their systems. For short answer assessment, word spotting task was also performed. This paper analysed the results produced by their algorithms using a performance measure and defines a way forward for this subject of research. Both the datasets, along with some of the accompanying ground truth/baseline mask will be made freely available for research purposes via the TC10/TC11.
RETRACTED: Research on automatic evaluation method of Mandarin Chinese pronunciation based on 5G network and FPGA

In the automatic evaluation system, need to learn the standard mandarin of scoring method for teaching in native Chinese pronunciation. The most pronounced goal protocols focus on the context in which native speakers are unnatural. The new Hidden Markov Model (HMM) algorithm based on the traditional algorithm likely algorithm for Chinese syllables, whose final initial period is found in the area where evidence for the measurement of weight control has been found. Experiments have also shown that this algorithm is more effective than the traditional posterior recording algorithm of the Mandarin learning method. Force Hidden Markov Model- HMM Align alignment identification for each syllable and associated recording probability for speech evaluation via race-based reliability system applications. These processes could then be formalized as a linear combination after the overall assessment functions: phonics, tone, intensity, and rhythm. Because both linear and non-linear parameters are involved in the overall evaluation functions. Incorporates variation in pronunciation to generate structure through a novel approach that incorporates tons of sub-tones that represent the missing automatic sound models. The word level assessment achieved through the pronunciation is similar to that which in the future showed the singing ability being realized by the evaluation system in full-length pronunciation as a method.
Data Augmentation for Improving SSD Performance in Rainy Weather Conditions

This paper focuses on using data augmentation to improve the performance of Single Shot Multibox Detector (SSD) in rainy weather conditions. Data augmentation is applied by generating synthetic rain on a set of clear images to expand the dataset for training. The experimental results show that the performance of SSD model improves by up to 16.82% when being trained on the proposed augmented synthetic dataset.