Reviewerly/exHarmony huggingface dataset:
A Novel Neural Network-Based Method for Medical Text Classification

Medical text categorization is a specific area of text categorization. Classification for medical texts is considered a special case of text classification. Medical text includes medical records and medical literature, both of which are important clinical information resources. However, medical text contains complex medical vocabularies, medical measures, which has problems with high-dimensionality and data sparsity, so text classification in the medical domain is more challenging than those in other general domains. In order to solve these problems, this paper proposes a unified neural network method. In the sentence representation, the convolutional layer extracts features from the sentence and a bidirectional gated recurrent unit (BIGRU) is used to access both the preceding and succeeding sentence features. An attention mechanism is employed to obtain the sentence representation with the important word weights. In the document representation, the method uses the BIGRU to encode the sentences, which is obtained in sentence representation and then decode it through the attention mechanism to get the document representation with important sentence weights. Finally, a category of medical text is obtained through a classifier. Experimental verifications are conducted on four medical text datasets, including two medical record datasets and two medical literature datasets. The results clearly show that our method is effective.
DecAuth: Decentralized Authentication Scheme for IoT Device Using Ethereum Blockchain

Internet of Things (IoT) has lots of attention in the last decade. The connected IoT devices are more than the total world population. Due to its low cost, easy to deploy, and simple to implement, application areas are large like smart city, smart home, smart transportation, environment monitoring, agriculture and many more. There exists some security and privacy challenges in IoT system. The device identification is one of the challenges in any IoT application. Authentication is one of the processes to identify the device. Though some work has been done on this problem, most of these are using a centralized system. In this paper, we have proposed a distributed authentication system using the Blockchain technology The implementation of the proposed authentication is done on Ethereum platform for its better results in order to justify it as a superior scheme.
Curriculum Learning Strategies for IR

Neural ranking models are traditionally trained on a series of random batches, sampled uniformly from the entire training set. Curriculum learning has recently been shown to improve neural models' effectiveness by sampling batches non-uniformly, going from easy to difficult instances during training. In the context of neural Information Retrieval (IR) curriculum learning has not been explored yet, and so it remains unclear (1) how to measure the difficulty of training instances and (2) how to transition from easy to difficult instances during training. To address both challenges and determine whether curriculum learning is beneficial for neural ranking models, we need large-scale datasets and a retrieval task that allows us to conduct a wide range of experiments. For this purpose, we resort to the task of conversation response ranking: ranking responses given the conversation history. In order to deal with challenge (1), we explore scoring functions to measure the difficulty of conversations based on different input spaces. To address challenge (2) we evaluate different pacing functions, which determine the velocity in which we go from easy to difficult instances. We find that, overall, by just intelligently sorting the training data (i.e., by performing curriculum learning) we can improve the retrieval effectiveness by up to 2% (The source code is available at https://github.com/Guzpenha/transformers_cl .).
Dualattn-GAN: Text to Image Synthesis With Dual Attentional Generative Adversarial Network

Recent generative adversarial network based methods have shown promising results for the charming but challenging task of synthesizing images from text descriptions.These approaches can generate images with general shape and color but often produce distorted global structures with unnatural local semantic details.It is due to ineffectiveness of convolutional neural networks in capturing the high-level semantic information for pixel-level image synthesis.In this paper, we propose a Dual Attentional Generative Adversarial Network (DualAttn-GAN) in which the dual attention modules are introduced to enhance local details and global structures by attending to related features from relevant words and different visual regions.As one of the dual modules, the textual attention module is designed to explore the fine-grained interaction between vision and language.On the other hand, visual attention module models internal representations of vision from channel and spatial axes, which can better capture the global structures.Meanwhile, we apply an attention embedding module to merge multi-path features.Furthermore, we present an inverted residual structure to boost representation power of CNNs and apply spectral normalization to stabilize GAN training.With extensive experimental validation on two benchmark datasets, our method significantly improves stateof-the-art models over the evaluation metrics of inception score and Fréchet inception distance.
Morphed Face Detection Based on Deep Color Residual Noise

Secure access control applications like border control rely on the face based verification system by considering its reliability, usability and accuracy in-person verification. However, face recognition systems are vulnerable to morphed face attacks, in which, the morphing process combines two different facial images into a single facial image. The features extracted from the morphed face image will match to those extracted from probe images of both faces. Thus, it is essential to reliably detect the morphed face image attacks on the face recognition systems. In this work, we propose a novel approach to detect morphed face images using residual color noise. The proposed method is designed to capture the noise patterns that are a result of the morphing process. Thus, the proposed method performs first denoising using Deep Convolutional Neural Network (CNN) independently on the Hue Saturation Value (HSV) color space, and then computes the residual noise. The extracted residual noise is further processed using Pyramid Local Binary Patterns (P-LBP), which is further classified using the Spectral Regression Kernel Discriminant Analysis (SRKDA). Extensive experiments are carried out on three different morphed face image datasets. The Morphed Attack Detection (MAD) performance of the proposed method is benchmarked with 13 different state-of-the-art techniques using the ISO IEC 30107-3 evaluation metrics. Based on the obtained quantitative results, the proposed method has indicated the best performance.
Self-paced and auto-weighted multi-view clustering

Multi-view clustering (MVC) methods are effective approaches to enhance clustering performance by exploiting complementary information from multiple views. One main disadvantage of most existing MVC methods is that the corresponding optimization problems are non-convex and thus local optimal solutions are usually obtained. To alleviate this issue, we propose a novel multi-view clustering method equipped with self-paced learning, which first learns the MVC model with easy examples and then progressively considers complex ones from each view. In addition, a soft weighting scheme of self-paced learning is designed to further reduce the negative impact from outliers and noises. Furthermore, to consider the importance of different views, we develop an auto-weighted technique to automatically assign weights to views. The proposed model only needs the number of clusters as input and can be easily solved by an alternating optimization paradigm. Experimental results on various benchmark data sets demonstrate the effectiveness of the proposed model.
Characteristics of the cloud enhancement phenomenon and PV power plants

The cloud enhancement (CE) of solar irradiance is a well-known phenomenon, but its effects on PV power plants are not thoroughly understood. Because of scalability, the diameters of PV generators can vary from some meters up to several hundreds of meters. The output power of a PV generator depends mainly on the irradiance to the PV panels. Therefore, if the irradiance is enhanced, the actual output power might exceed the nominal power. We created a method to estimate the average irradiance over typical land areas of PV generators. The average irradiance over the PV generator land area was used to calculate the frequency, duration and average irradiance of the CE events. The analysis was based on actual irradiance measurement data from an array of pyranometers on an area of 1400 m2, which corresponds to the land area of 0.1 MW PV generator. We found out that the focus areas of the CE events have diameters of the order of tens of meters. Therefore, up to 0.1 MW power range, the land area of the PV generator does not greatly affect the maximum average irradiances. In this power range, the average irradiance was measured to be up to 1.5 times the clear-sky irradiance. The CE events affecting utility-scale PV generators were estimated to have maximum average irradiances of 1.4 times the clear-sky irradiance, but the number of these CE events is considerably lower.
Factors influencing the success of knowledge management process in health care organisations: a literature review

Abstract Introduction Knowledge is a strategic resource in health care organisations and knowledge management (KM) is applied as an effective solution to overcome challenges, such as rising health care cost and increasing demands for improving quality of care. The aim of this review was to identify factors influencing the success of KM process in health care organisations of developed and developing countries. Methods This review was conducted in 2018. The sources used for searching relevant papers were PubMed, Scopus, Science Direct, Web of Knowledge, EMBASE and Google Scholar, and the time frame was between 1980 and 2017. The search terms were KM, health care services administration, health care organisation, hospital and clinic which were combined by using AND/OR Boolean operators. Results In this review, 13 papers were included. The main success factors influencing KM implementation in health care organisations were organisational culture, information technology, organisational structure, and performance evaluation and measurement. In developed countries, organisational culture was the most frequent factor, while in developing countries, organisational culture and information technology were the most frequent ones. Conclusion It seems that in developed and developing countries, organisational factors are the most frequent factors influencing successful KM in health care organisations.
Unsupervised Model for Detecting Plagiarism in Internet-based Handwritten Arabic Documents

Due to the rapid increase of internet-based data, there is urgent need for a robust intelligent documents security mechanism. Although there are many attempts to build a plagiarism detection system in natural language documents, the unlimited variation and different writing styles of each character in Arabic documents make building such systems challenging. Based on its position in a word, the same Arabic letter can be written three different ways, which makes the handwritten character recognition a cumbersome process. This article proposes an intelligent unsupervised model to detect plagiarism in these documents called ASTAP. First, a handwritten Arabic character recognition system is proposed using the Grey Wolf Optimization (GWO) algorithm. Then, a modified Abstract Syntax Tree (AST) is used to match the contents of the Arabic documents to detect any similarity. Compared to the state-of-the-art methods, ASTAP improves the effectiveness of the plagiarism detection in terms of the matched similarity ratio, the precision ratio, and the processing time.
Obesity Prediction with EHR Data: A Deep Learning Approach with Interpretable Elements

Childhood obesity is a major public health challenge. Early prediction and identification of the children at an elevated risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage obesity. Most existing predictive tools for childhood obesity primarily rely on traditional regression-type methods using only a few hand-picked features and without exploiting longitudinal patterns of children's data. Deep learning methods allow the use of high-dimensional longitudinal datasets. In this paper, we present a deep learning model designed for predicting future obesity patterns from generally available items on children's medical history. To do this, we use a large unaugmented electronic health records dataset from a large pediatric health system in the US. We adopt a general LSTM network architecture and train our proposed model using both static and dynamic EHR data. To add interpretability, we have additionally included an attention layer to calculate the attention scores for the timestamps and rank features of each timestamp. Our model is used to predict obesity for ages between 3-20 years using the data from 1-3 years in advance. We compare the performance of our LSTM model with a series of existing studies in the literature and show it outperforms their performance in most age ranges.
A Comparative Analysis of Deep Learning and Machine Learning on Detecting Movement Directions Using PIR Sensors

Machine learning has played a significant role in building intelligent systems in the history of data science. In the recent paradigm where objects in the world will be connected with each other, commonly referred to as the Internet of Things (IoT), people begin to consider the challenges and opportunities to utilize the huge data sets generated, also referred to as Big data. One of the active research topics in dealing with the IoT's big data is the practical feasibility of algorithms used in classical machine learning but also in a newly emerging branch, called deep learning. In this article, we demonstrate a quantitative analysis comparing performance between classical machine learning and deep learning algorithms with a human movement direction detecting application based on analog pyroelectric infrared (PIR) sensor signals. The sensing data acquisition and retrieval system is implemented with the open-source IoT software platforms based on the oneM2M standard. With the analog PIR data sets collected from 30 subjects, we perform experimental studies comparing classical machine learning and deep learning algorithms in terms of economic feasibility, scalability, generality, and real-time detection performance. The results show that classical machine learning shows better performance in real-time detection (i.e., with the sensing values within the first 0.5 s). In contrast, our simple deep learning model achieves about 90% accuracy for detecting moving directions even with the data sets from only three subjects and a single PIR sensor. Moreover, it could be applied to a larger number of subjects without updates.
Towards a new generation of ontology based data access

Ontology Based Data Access (OBDA) refers to a range of techniques, algorithms and systems that can be used to deal with the heterogeneity of data that is common inside many organisations as well as in inter-organisational settings and more openly on the Web. In OBDA, ontologies are used to provide a global view over multiple local datasets; and mappings are commonly used to describe the relationships between such global and local schemas. Since its inception, this area has evolved in several directions. Initially, the focus was on the translation of original sources into a global schema, and its materialisation, including non-OBDA approaches such as the use of Extract Transform Load (ETL) workflows in data warehouses and, more recently, in data lakes. Then OBDA-based query translation techniques, relying on mappings, were proposed, with the aim of removing the need for materialisation, something especially useful for very dynamic data sources. We think that we are now witnessing the emergence of a new generation of OBDA approaches. It is driven by the fact that a new set of declarative mapping languages, most of which stem from the W3C Recommendation R2RML for Relational Databases (RDB), are being created. In this vision paper, we enumerate the reasons why new mapping languages are being introduced. We discuss why it may be relevant to work on translations among them, so as to benefit from the engines associated to each of them whenever one language and/or engine is more suitable than another. We discuss the emerging concept of "mapping translation", the basis for this new generation of OBDA, together with some of its desirable properties: information preservation and query result preservation. We show several scenarios where mapping translation can be or is being already applied, even though this term has not necessarily been used in existing literature.
Blockchain-Based Real Estate Market: One Method for Applying Blockchain Technology in Commercial Real Estate Market

Global real estate (RE) investments account more than twice the size of the stock market. Yet the number of investors in RE are much lower, because of the liquidity and global access. Tenants, owners, and investors are barely satisfied in the current system. In this paper, the goal is to try out the employment of blockchain in RE market and represent the facilities it can give to the RE market. The research to date leads to the following conclusions: Blockchain technology and smart contracts can sort out the classical issues that RE is facing with, and they offer much more meaningful tools for a game theoretic stable-priced market.
Hybrid Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening

Transfer learning using deep pre-trained convolutional neural networks is increasingly used to solve a large number of problems in the medical field. In spite of being trained using images with entirely different domain, these networks are flexible to adapt to solve a problem in a different domain too. Transfer learning involves fine-tuning a pre-trained network with optimal values of hyperparameters such as learning rate, batch size, and number of training epochs. The process of training the network identifies the relevant features for solving a specific problem. Adapting the pre-trained network to solve a different problem requires fine-tuning until relevant features are obtained. This is facilitated through the use of large number of filters present in the convolutional layers of pre-trained network. A very few features out of these features are useful for solving the problem in a different domain, while others are irrelevant, use of which may only reduce the efficacy of the network. However, by minimizing the number of filters required to solve the problem, the efficiency of the training the network can be improved. In this study, we consider identification of relevant filters using the pre-trained networks namely AlexNet and VGG-16 net to detect cervical cancer from cervix images. This paper presents a novel hybrid transfer learning technique, in which a CNN is built and trained from scratch, with initial weights of only those filters which were identified as relevant using AlexNet and VGG-16 net. This study used 2198 cervix images with 1090 belonging to negative class and 1108 to positive class. Our experiment using hybrid transfer learning achieved an accuracy of 91.46%.
IWSCR: An Intelligent Water Surface Cleaner Robot for Collecting Floating Garbage

In this article, a robot system for intelligent water surface cleaner named IWSCR is developed to collect floating plastic garbage. It is able to accomplish three major tasks autonomously, i.e., cruise and detection, tracking and steering, and grasping and collection. The challenges behind these tasks involve how to realize the accurate and real-time garbage detection, how to resist the disturbances while IWSCR conducts vision-based steering, and how to grasp the floating garbage reliably despite the turbulent conditions on the surface of the water. To overcome these difficulties, three key techniques are proposed for IWSCR. First, the YOLOv3 network, which is widely applied in the high speed and accuracy object detection field, is trained on the proposed floating garbage dataset to realize accurate and real-time garbage detection. Next, to improve the ability of resisting disturbances, a control law based on the sliding-mode controller is proposed for vision-based steering. Furthermore, inspired by the stability of floating bottles in fluid, a feasible grasping strategy is utilized for IWSCR. Finally, the experimental results demonstrate that IWSCR is competent to carry out the task of water surface cleaning.
A new method to predict the interference effect in quantum-like Bayesian networks

Recent researches on human decision-making behaviors reveal some interesting phenomenon which are difficult to be explained under the structure of classic probability theory. The quantum-like Bayesian networks, like many other quantum models, have been developed to solve this problem. Powerful as these models are to explain the experimental results, these models usually contain unknown quantum parameters. In quantum-like Bayesian networks, such parameters represent the interference between different decisions. In this paper, a heuristic method is proposed to measure the interference effect. The proposed model also has its connection with the original quantum-like Bayesian networks. The experimental data from several Prisoners dilemma games are used to test the proposed method. The testing results illustrate the efficiency of the proposed method.
An approach for measuring semantic similarity between Wikipedia concepts using multiple inheritances

Wikipedia provides a huge collaboratively made semi-structured taxonomy called Wikipedia category graph (WCG), which can be utilized as a Knowledge Graph (KG) to measure the semantic similarity (SS) between Wikipedia concepts. Previously, several Most Informative Common Ancestor-based (MICA-based) SS methods have been proposed by intrinsically manipulating the taxonomic structure of WCG. However, some basic structural issues in WCG such as huge size, branching factor and multiple inheritance relations hamper the applicability of traditional MICA-based and multiple inheritance-based approaches in it. Therefore, in this paper, we propose a solution to handle these structural issues and present a new multiple inheritance-based SS approach, called Neighborhood Ancestor Semantic Contribution (NASC). In this approach, firstly, we define the neighborhood of a category (a taxonomic concept in WCG) to define its semantic space. Secondly, we describe the semantic value of a category by aggregating the intrinsic IC-based semantic contribution weights of its semantically relevant multiple ancestors. Thirdly, based on our approach, we propose six different methods to compute the SS between Wikipedia concepts. Finally, we evaluate our methods on gold standard word similarity benchmarks for English, German, Spanish and French languages. The experimental evaluation demonstrates that the proposed NASC-based methods remarkably outperform traditional MICA-based and multiple inheritance-based approaches.