Reviewerly/exHarmony huggingface dataset:
Medical Entity Relation Verification with Large-scale Machine Reading Comprehension

Medical entity relation verification is a crucial step to build a practical and enterprise medical knowledge graph (MKG) because high-precision medical entity relation is a key requirement for many MKG-based applications. Existing relation verification approaches for general knowledge graphs are not designed for considering medical domain knowledge, although it is central to achieve high-quality entity relation verification for MKG. To this end, in this paper, we introduce a system for medical entity relation verification with large-scale machine reading comprehension. The proposed system is tailored to overcome the unique challenges of medical relation verification including high variants of medical terms, the high difficulty of evidence searching in complex medical documents, and the lack of evidence labels for supervision. To deal with the problem of variants of medical terms, we introduce a synonym-aware retrieve model to retrieve the potential evidence implicitly verifying the given claim. To better utilize the medical domain knowledge, a relation-aware evidence detector and a medical ontology-enhanced aggregator are developed to improve the performance of the relation verification module. Moreover, to overcome the challenge of providing high-quality evidence due to the lack of labels, we introduce an interactive collaborative-training method to iteratively improve the evidence accuracy. Finally, we conduct extensive experiments to demonstrate that the performance of our proposed system is superior to all comparable models. We also demonstrate that our system can significantly reduce the annotation time by medical experts in real-world verification tasks. It can help to improve the efficiency by nearly 300%. In particular, our system has been embedded into the Baidu Clinical Decision Support System.
A simple function embedding approach for binary similarity detection

Binary function similarity detection has been an important problem in binary analysis. Recently, several deep learning based techniques show promising results and achieve state of the art performance. In despite of effectiveness, some of them adopt complex network structures which result in slow convergence speed. To reduce the structure complexity, this paper proposes a simple neural network structure based on Bi-RNN(Bidirectional Recurrent Neural Network). To evaluate the effectiveness of proposed method, this paper conducts experiments on both single-architecture and cross-architecture function similarity detection over OpenSSL library. Experimental results show that our approach achieves higher AUC metric and faster convergence speed than GNN(Graph Neural Network)-based techniques. To explore the effectiveness of GNN, another experiment is conducted to explore the impact of the number of GNN layers on performance and the result shows that when combined semantic information, existing GNN-based methods may reduce performance.
Using ParsBert on Augmented Data for Persian News Classification

Text classification is a fundamental task in Natural Language Processing (NLP). Although many works have been done to perform text classification in English, the number of studies on Persian text classification is limited. Previous works on Persian text classification often use classic machine learning methods such as Naive Bayes, Support Vector Machines, Decision Trees, etc. While these methods are fast and straightforward, they need feature engineering, and their performance heavily depends on the selected features. In this paper, we first augment the input words with their stem form and then use a pre-trained language model for the Persian language (ParsBERT) to classify the text. Augmenting the input words with their stem form enables the proposed classifier to generalize well to the new unseen data. We compare the performance of our proposed model with that of traditional machine learning algorithms. The results show that the proposed model achieves a 0.91 accuracy and outperforms the traditional machine learning algorithm by at least +0.4 absolute on both accuracy and F1 score.
The development of andro-webcomic media based on problem-based learning to improve analytical thinking ability and scientific attitude

Abstract The research aimed to describe the validity, practicality, and effectiveness of Andro-Webcomic media based on problem-based learning as a learning media to improve students’ analytical thinking ability and scientific attitudes. The development design used was Research and Development (R&amp;D) and used the ADDIE model, limited to the ADD (Analysis, Design, Development) stage. At the product development stage, small-scale trials are carried out, which this phrase was limited to 9 students of class XI MIPA at one of the high schools in Jepara. The instruments used were validation sheets, test sheets, observation sheets for students’ scientific attitudes, student and teacher questionnaire responses. The results of this study indicate that: (1) the validity of the Andro-Webcomic media is 95.58%; (2) effective in improving analytical thinking of 88.89% with an n-gain score of 67% and a scientific attitude of 87.19%; (3) practicality based on student responses of 98.39% and teacher responses of 97.36%; (4) media component that supports improving competencies is on the webcomic and investigation menu. Based on the research results, it can be concluded that the Andro-Webcomic media is valid, practical, and effective in improving students’ analytical thinking ability and scientific attitudes.
Smart Online Korean Language Guiding Effect Evaluation System Based on Spectrum Visualization

Smart online Korean language guiding effect evaluation system based on spectrum visualization is designed and implemented in this paper. The greedy algorithm in sparse representation has been widely used in speech compression and also noise reduction processing. Based on this popular method, this research work has designed the novel spectrum visualization model. The data collection model is designed and the data analytic framework is then optimized. The evaluation system considers the fuzzy and neural computing. The experiment is conducted on the collected data set. Compared with the other approaches, the proposed model has the better evaluation performance.
Automated Cell Nuclei Segmentation on Cervical Smear Images Using Structure Analysis

Cervical cancer is a common cancer that affects women around the world, and it is also the most common cancer in the developing countries. The cancer burden has increased due to several factors, such as population growth and ageing. In the early century, the systematization of cervical cancer cells takes some time to process manually, and the result that comes out is also inaccurate. This article presents a new nucleus segmentation on pap smear cell images based on structured analysis or morphological approach. Morphology is a broad set of image processing operations that process images based on shape, size and structure. This operation applies a structural element of the image to create an output image of the same size. The most basic of these operations are dilation and erosion. The results of the numerical analysis indicate that the proposed method achieved about 94.38% (sensitivity), 82.56% (specificity) and 93% (accuracy). Also, the resulting performance was compared to a few existing techniques such as Bradley Method, Nick Method and Sauvola Method. The results presented here may facilitate improvements in the detection method of the pap smear cell image to resolve the time-consuming issue and support better system performance to prevent low precision result of the Human Papilloma Virus (HPV) stages. The main impact of this paper is will help the doctor to identify the patient disease based on Pap smear analysis such as cervical cancer and increase the percentages of accuracy compared to the conventional method. Successful implementation of the nucleus detection techniques on Pap smear image can become a standard technique for the diagnosis of various microbiological infections such as Malaria and Tuberculosis.
Intra-Class Uncertainty Loss Function For Classification

Most classification models can be considered as the process of matching templates. However, when intra-class uncertainty/variability is not considered, especially for datasets containing unbalanced classes, this may lead to classification errors. To address this issue, we propose a loss function with intra-class uncertainty following Gaussian distribution. Specifically, in our framework, the features extracted by deep networks of each class are characterized by independent Gaussian distribution. The parameters of distribution are learned with a likelihood regularization along with other network parameters. The means of the Gaussian play a similar role as the center anchor in existing methods, and the variance describes the uncertainty of different classes. In addition, similar to the inter-class margin in traditional loss functions, we introduce a margin to intra-class uncertainty to make each cluster more compact and reduce the imbalance of feature distribution from different categories. Based on MNIST, CIFAR, ImageNet, and Long-tailed CIFAR analyses, the proposed approach shows improved classification performance, through learning a better class representation.
Use of Natural Language Inference in Optimizing Reviews and Providing Insights to end Consumers

Natural Language Inference or NLI adds extraordinary advantages to already existing Natural Language Processing use cases available. NLI has the potential to add syllogistic reasoning over the already existing facts and develop a new kind of knowledge dataset in itself. In this paper, we have demonstrated a Recognizing Textual Entailment wherein the task is to recognize whether a given hypothesis is true (Entailment), false (Contradiction) or unrelated(neutral) with respect to the sentence called premise. The task is performed by training MNLI corpus along with the manually collected dataset from Amazon Product Reviews each having hypothesis and premise pairs with corresponding labels. With this use case, we propose to bring sustainable development in the classification methods used by major E-commerce companies.
Gender and Age Detection using Deep Learning

For the past few years, gender and age detection has been an active area of study and researchers have been putting a lot of effort to contribute quality research in this area. Starting from preprocessing of data to building a model which gives high precision results is tedious task for researchers. There is a immense dormant field of study as it can be used in monitoring, surveillance, human-computer interaction and security. However, there is still a lack of the performance of existing methods on real live images. Many difficult tasks such as computer vision, speech recognition, and natural language processing are easily solved with deep learning. Therefore, the approach of deep learning remarkably growing and this also takes place in image classification. Therefore, to analyses and focuses on comparative study of different algorithms for gender and age recognition system to give elevated degree of precision is required.
A Zero Watermarking Scheme for Encrypted Medical Images Based on Tetrolet-DCT

Aiming at the problem of medical image leakage in the transmission process, in order to enhance the concealment of patient data, a Tetrolet-DCT based encrypted medical image zero watermarking scheme is proposed. Firstly, DFT and Logistic mapping are used to encrypt the original medical image to obtain the encrypted medical image. Then, Tetrolet-DCT is used to extract the features of the encrypted medical image. After that, zero watermark technology is applied to embed and extract the watermark information of patients. Experimental results show that the proposed scheme ensures the concealment of the original medical image, and can effectively extract the watermark information. It has good robustness both against traditional attacks and geometric attacks.
MMCD VAE Model for Deep Facial Scale-Invariant-Feature-Translation

This paper is utilizing the joint mean and covariance discrepancy as a solution for domain adaption by using a deep Variational Auto Encoder (VAE). The proposed model is tested for the Scale Invariant Feature Transform (SIFT) of facial images. SIFT based face recognition relies heavily on the detection of facial features as the descriptor. The existence of sufficient and accurate scale invariant features can lead to better matching between model image and query image and result in robust recognition. Deep variational auto encoders have shown appreciable results in the extraction of hierarchical latent representation based domain adaptation in face recognition frameworks. Conventionally VAEs are trained to learn variance and mean of input distribution as the latent representation. Maximum mean discrepancy (MMD) uses second-order statistics in reproducing kernel Hilbert space as the representation trait. This research work proposes a novel VAE model to consider maximum mean covariance discrepancy (MMCD). While the VAE model looks for a discrepancy in the spread of variance around the mean value, MMCD measures directional discrepancy in the variance. To verify the efficacy of MMCD based VAE over conventional VAE, SIFT for face images are carried out. Quantitative evaluation of domain adaptation and use of covariance discrepancy are the two major contributions of this work. It is observed that MMCD VAE not only provides more number of SIFT feature but also better correspondent matching of feature points. Images from a created Bollywood database and the publicly available LFW database are used for comparative analysis.
Blur Invariant Kernel-Adaptive Network for Single Image Blind Deblurring

We present a novel, blind single image deblurring method that utilizes information regarding blur kernels. Our model solves the deblurring problem by dividing it into two successive tasks: (1) blur kernel estimation and (2) sharp image restoration. We first introduce a kernel estimation network that produces adaptive blur kernels based on the analysis of the blurred image. The network learns the blur pattern of the input image and trains to generate the estimation of image-specific blur kernels. Subsequently, we propose a deblurring network that restores sharp images using the estimated blur kernel. To use the kernel efficiently, we propose a kernel-adaptive AE block to apply the kernel information on the feature. We evaluate our model on REDS, GOPRO and Flickr2K datasets using various Gaussian blur kernels. Experiments show that our model can achieve state-of-the-art results on each dataset.
The program of the advanced training course for teachers of mathematics and informatics "Graphs and graph models: methods of visual processing "

The method of visual presentation of educational information for solving problems of mathematics and informatics is effective for the development of algorithmic, logical and computational thinking of schoolchildren. Technical progress, informatization of education, the emergence of modern software for visualization of information change the activities of teachers who need to master new technologies of information visualization for use in the classroom and in work with gifted children. Visual models for presenting educational information and methods of their processing with the use of computer programs are also relevant in extracurricular activities, allowing to develop the intellectual abilities of schoolchildren. Teachers are required to teach children to create projects in which visibility is a necessary component and must be represented by an electronic product created using modern information visualization tools. The article proposes a variant of the advanced training course for teachers of mathematics and informatics on teaching methods for visualization of solving problems using graphs and the free software “Graphoanalyzator”. The relevance of the course is due to the need to form the competency to carry out targeted work with gifted children in the use of software for creating and processing graphs based on the graph visualization program “Graphoanalyzator”. The authors believe that the training of teachers on this course will contribute to the formation of their skills to solve problems of mathematical modeling in informatics and mathematics, to apply information technologies to solve pedagogical problems in the context of informatization of education.
Text Extraction from Book Cover Using MSER

Detecting text from natural images is an ongoing field of research. In this paper, we propose a text-extraction and detection algorithm pipeline for obtaining information about a particular book by using computer vision. Features of the book such as its reviews, rating and, the price can be displayed to the end user, thus helping people make an informed decision about the book on which they are going to spend time reading. The text detection algorithm uses edge-enhanced Maximally Stable External Region for identifying the text-blob segments accompanied by various non-text area filtering algorithms to find the bounding boxes. These bounding boxes are then chained together and undergo OCR, performed by the Tesseract engine. The results of the extracted text are further improved by performing post-processing NLP techniques such as domain-based OCR and typo correction. The method proposed in this paper has extended use cases in different areas of text detection from natural images.
An intelligent power user data analysis platform based on Spark

The development of smart meters and smart terminals has brought new opportunities to the power industry, providing power enterprises with user data hundreds of times higher than before. There is a lot of value in this data, but power companies currently lack efficient power. The big data analysis platform cannot make effective use of these data. Due to its own particularity and strategy, the power system data cannot use the big data analysis platform provided by other companies. Therefore, it is particularly important to design and build an efficient and usable power system big data analysis platform. Based on the actual scenario, this paper constructs a highly customized power big data analysis platform and integrates user value model analysis services to solve the problems in the actual work of power companies.
A Graph-Based Semi-Supervised Fraud Detection Framework

Credit card payment has become one of the most commonly used consumption methods in modern society, yet risks of fraud transactions using credit cards also increased. Numerous methods have been proposed for credit card fraud detection during past decades. However, most of the existing frameworks mainly focus on directly processing structured data. While they lack inner relations between features of raw descriptions for credit owners, this could lead to information deficiency. Therefore, we proposed a graph-based semi-supervised fraud detection framework. In this work, the structured dataset is translated to graph format through the sample similarity in order to improve the effect of label propagation on the graph. We further adopt the GraphSAGE algorithm which has been demonstrated to show excellent performance on node classification tasks. Experimental results on the real-world dataset show that our graph-based model can outperform state-of-the-art baselines. We argue that our model could be extended to other classification tasks using structured data.
Why do we need to weigh burned daub? Destruction of wattle-and-daub houses and the internal chronology of Neolithic sites

This paper presents a newly developed model that links the destruction processes of wattle-and-daub houses and the internal chronology of sites. By examining the weight and spatial distribution of burned daub, our model explicates the relative chronological sequence of the structures and nearby associated features. A three-step analytical procedure allows for the specification and self-checking of model outcomes. We verify the model by its application to data from Mrowino site 3 (western Poland) and confirm its utility for re-assessing the sequence of Funnel Beaker culture settlement structures. The obtained results are confirmed by previous work detailing the stratigraphy of pits, the distribution and seriation of pottery and radiocarbon dating.
A Cost Matrix Optimization Method Based on Spatial Constraints under Hungarian Algorithm

At present, with the continuous development of single object tracking(SOT) tracker, more and more SOT tracker are applied to multi-object tracking(MOT) tasks. However, in the traditional method of affinity computation, affinity model is used as the metric of Hungarian algorithm, which has the low discrimination rate of similar objects and leads to ID switch easily. In order to solve this problem, we propose a cost matrix optimization method based on spatial constraints under Hungarian algorithm. In the data association stage, Kalman filter is used to estimate the motion vector of the object, so that the current position of the object can be linearly predicted. The weight of the cost matrix is modified according to the spatial relationship between the estimated position and the detection results, which is used for the subsequent re-identification task. It is worth noting that the above methods do not need extra training and can be directly used in other multi-object tracking models. Our method has been evaluated on MOT16(46.7%), MOT17(49.7%), and achieved the effect of SOTA. The entire results can be found on MOTChallenge website1 .
Assessing an Organization Security Culture Based on ENISA Approach

Recently, one of the most promising things for any successful organization has been in establishing a security strategy. It is a distinguished document, which details out a set of steps important for any organization for determining and handling dangers. Developing a security strategy is an exhaustive process that involves an initial assessment, planning, operation, and permanent observation.Assessing the readiness of institutions, in terms of the security culture, is emphasized to correspond with the efforts of governments, organizations, the private sector towards the business administration, and the electronic transformation into a digital society and e-government.This paper proposes an ‘‘Application form’’ derived from the European Network and Information Security Agency (ENISA) safety criteria. The form involves twenty-five high-level security objectives, which are collected in seven domains for reviewing, assessing the readiness of any organization, and recognizing the deficit found in the requirements of information security in the organization.The suggested “Application form” has been tested over various objectives, the acquired findings demonstrate that the suggested system has achieved a great performance and significant competence in terms of determining the weaknesses of the network and the organizational structure of the institution.