Reviewerly/exHarmony huggingface dataset:
Event Detection With Dynamic Word-Trigger-Argument Graph Neural Networks

The task of ACE Event Detection (ED) often encounters ambiguous and unseen trigger words. Most conventional ED systems exclusively consider the semantic or syntactic patterns as the additional evidence to resolve the problem of the ambiguous and unseen triggers, but rarely consider taking advantages of structured knowledge of the event itself. In this study, we propose Dynamic Word-Trigger-Argument Graph Neural Networks (DWTA-GNN), a novel framework that leverages event structure knowledge to facilitate the two issues simultaneously. In our approach, we utilize words, entities, and event annotations from training to construct an event background graph, which can provide sufficient information of event structure to better disambiguate polysemous triggers and identify unseen triggers. To make full use of the constructed background graph, we further design a knowledge matching module to dynamically match appropriate event structure knowledge and construct a subgraph for each incoming sentence. Besides, an event-selective graph convolution is applied to filter out the noise in the matched knowledge so as to enhance event representation. Experiments on the ACE2005 dataset show that our model achieves competitive performance and advances previous approaches on ambiguous and unseen trigger words, verifying the effectiveness of incorporating event structure knowledge for event detection.
Review

The kernel mode decomposition framework has relations to the fields of additive Gaussian process regression, empirical mode decomposition, and synchrosqueezing. Consequently, here we review these subjects giving context to our work. This chapter is not essential to understanding the book and so can be skipped on first reading.
Design and Analysis of a ChatBot with IPL First Inning Score Prediction

A chat bot aims to make conversations with human and machine both. The use of chat bots has emerged rapidly in many fields, including marketing, support systems, education, health care, cultural heritage, and entertainment. The machine is equipped with the information to identify the sentences and make the decision itself as a response to the question. Response policy compares the input sentence from the user. We have integrated our Chat bot with IPL first inning score prediction. In this paper, details of the last decade IPL seasons from 2008-2017 are used for prediction. Six machine learning techniques are used for prediction and their performance is analyzed. The result shows that the prediction accuracy of Random Forest is better than all other models.
Hyperledger blockchain based secure storage of electronic health record system in edge nodes

Abstract An Electronic Health Record (EHR) is a database for storing patients medical information collected from different sources such as smart wearable devices, smart sensors and diagnostic imaging equipment. An EHR contains sensitive private information for the patients and the treatment of their diseases. Furthermore, it’s often shared among different members consists of healthcare providers, insurance companies, medical researchers, and others. The main difficulty for EHR information management is the result of gathering, storing, and sharing patient healthcare without affecting privacy and security. Blockchain has recently proposed an efficient way to manage EHR data. This paper provides hybrid architecture for EHR data management by using both Hyperledger blockchain network in on-chain and edge node in off-chain. In this architecture is used to authenticate user without affecting sensitive patient’s information and also used to authenticate the encrypted EHR information in edge node. In the on-chain approach EHR activities and Patient authentication activities are recorded in the blockchain for the purpose of accountability and traceability. Edge nodes stored the encrypted EHR data in the off-chain method. So the combination of on-chain and off-chain approaches only allows the authorized data user who has to meet the EHR access activities and to decrypt the EHR data. If the EHR information is alter by an unauthorized user, the hash code is newly generated which is different from old hash code stored in the blockchain. As the result the user can easily detect that their EHR information has been hacked.
Is Systematic Data Sharding able to Stabilize Asynchronous Parameter Server Training?

Over the last years, deep learning has gained an increase in popularity in various domains introducing complex models to handle the data explosion. However, while such model architectures can support the enormous amount of data, a single computing node cannot train the model using the whole data set in a timely fashion. Thus, specialized distributed architectures have been proposed, most of which follow data parallelism schemes, as the widely used parameter server approach. In this setup, each worker contributes to the training process in an asynchronous manner. While asynchronous training does not suffer from synchronization overheads, it introduces the problem of stale gradients which might cause the model to diverge during the training process. In this paper, we examine different data assignment schemes to workers, which facilitate the asynchronous learning approach. Specifically, we propose two different algorithms to perform the data sharding. Our experimental evaluation indicated that when stratification is taken into account the validation results present up to 6X less variance compared to standard sharding creation. When further data exploration for hidden stratification is performed, validation metrics can be slightly optimized. This method also achieves to reduce the variance of training and validation metrics by up to 8X and 2X respectively.
Online Updates of Knowledge Graph Embedding

Complex networks can be modeled as knowledge graphs (KGs) with nodes and edges denoting entities and relations among those entities, respectively. A knowledge graph embedding assigns to each node and edge in a KG a low-dimensional semantic vector such that the original structure and relations in the KG are approximately preserved in these learned semantic vectors. KG embeddings support downstream applications such as KG completion, classification, entity resolution, link prediction, question answering, and recommendation. In the real world, KGs are dynamic and evolve over time. State-of-the-art KG embedding models deal with static KGs. To support dynamic updates (even local), they must be retrained on the whole KG from scratch, which is inefficient. To this end, we propose a new context-aware Online Updates of Knowledge Graph Embedding (OUKE) method, which supports embedding updates in an online manner. OUKE learns two different vectors for each node and edge, i.e., knowledge embedding and context embedding. This strategy effectively limits the impacts of a local update in a smaller region, so that OUKE is able to efficiently update the KG embedding. Experiments on the link prediction in dynamic KGs demonstrate both effectiveness and efficiency of our solution.
Tag-less back-translation

An effective method to generate a large number of parallel sentences for training improved neural machine translation (NMT) systems is the use of the back-translations of the target-side monolingual data. The standard back-translation method has been shown to be unable to efficiently utilize the available huge amount of existing monolingual data because of the inability of translation models to differentiate between the authentic and synthetic parallel data during training. Tagging, or using gates, has been used to enable translation models to distinguish between synthetic and authentic data, improving standard back-translation and also enabling the use of iterative back-translation on language pairs that underperformed using standard back-translation. In this work, we approach back-translation as a domain adaptation problem, eliminating the need for explicit tagging. In the approach -- \emph{tag-less back-translation} -- the synthetic and authentic parallel data are treated as out-of-domain and in-domain data respectively and, through pre-training and fine-tuning, the translation model is shown to be able to learn more efficiently from them during training. Experimental results have shown that the approach outperforms the standard and tagged back-translation approaches on low resource English-Vietnamese and English-German neural machine translation.
Cryptanalysis on ‘An efficient identity-based proxy signcryption using lattice’

In this note, we conduct a cryptanalysis of the paper published by Zhu et al. on Future Generation Computer Systems in 2021. We demonstrate that their quantum-resistant identity-based proxy signcryption scheme cannot satisfy the confidentiality as they claimed.
OCRNet - Light-weighted and Efficient Neural Network for Optical Character Recognition

The developing expertise of neural networks has validated extraordinary outcomes within text detection. The study seeks to enhance the accuracy of textual content identity to improve the existing technology. Two numbers, one additive, text detection, and textual content reputation are used to identify the optical character. This paper provided a way to determine the degree of similarity between every unique character, in order, that each word may also in the end to be diagnosed. Extensive testing of two datasets,TotalText and CTW-1500, indicates that the optical character detection at character level outplays State of the Art. According to findings, this endorsed technique assures that complex textual content pix, which include letters randomly orientated, bent, or distorted, would be recognized as being very adaptable.
RLTree: Website Fingerprinting Through Resource Loading Tree

Website fingerprinting (WF) attack is a type of traffic analysis technique that extracts the unique fingerprint from the traffic of each website demonstrating that the current privacy protection mechanism provided by HTTPS is still fragile. While prior WF attack methods that extract fingerprints only using the web traffic generated by the first TCP session can be easily compromised by the frequent website updates, we observe that it is still possible to identify a website accurately through fingerprinting the resource loading sequence generated by the multiple initial TCP sessions. We record the multiple TCP sessions by visiting a website and analyze its traffic structure. We find that despite the update of the website, the TCP establishment is always kept unchanged, and such TCP sequence can be used to fingerprint a website. Hence, we build a resource loading tree using the multiple TCP sessions and demonstrates its high precision in recognizing a website even under HTTPS protection. We collect data from 20 websites with a total of 7,326 traces, and show that the accuracy can achieve up to 95.9%.
Ordinal Regression Based on the Distributional Distance Between Labels

Ordinal regression is a problem in classifying instances by considering ordinal relations between labels. It is used in various applications. The existing methods tend to decrease accuracy when they adhere to the preservation of the ordinal relation. Therefore, we propose a new deep neural network that can consider ordinal relations while maintaining high accuracy. The proposed method consists of two subnetworks. The first subnetwork classifies the instances by considering the ordering relation. The relation is embedded in the subnetwork using the distributional distance between labels. The second subnetwork modifies the output of the first subnetwork from the viewpoint of cross-entropy error. The accuracy and the relation are adjusted by combining the outputs of the two subnetworks. To evaluate the proposed method, we conducted experiments on two datasets. The results demonstrate that the proposed method is comparable to or superior to the existing methods in terms of accuracy and mean absolute error.
Implementation of a Multitudinous Face Recognition using YOLO.V3

Face recognition is one of the most functional research in present scenario, with many practical and commercial applications including identification, access control, forensics, medical care, human-computer interactions, security, etc. Face recognition technique is rapidly becoming the mainstay of state of the art technological security solution. One of the crucial applications of face recognition in the current scenario is linked with security. Identifying people from a crowd or a group of people require an exceptional algorithm. One of the most arduous tasks about the existing face recognition system is the processing or prediction time. The current systems focus on accuracy than speed, which leads to an increase in the detection time. There are several techniques in machine learning and deep learning. But deep learning is preferred more than machine learning for detection and recognition applications because of the large availability of data. An algorithm for fast real-time object detecting and recognizing application is required. YOLO (you only look once) is a single shot deep learning object detection algorithm. In this work, the working of the YOLO algorithm and implementing multiple face recognition using YOLO version 3 is explained. A custom dataset is created from taken from Kaggle and google. At the time of testing the model, a processing speed of 30 ms was obtained.
A Demand-Driven SAR Target Sample Generation Method for Imbalanced Data Learning

Since there are differences in the natural frequency of various synthetic aperture radar (SAR) target samples in reality, the problem of imbalanced data on the automatic target recognition (ATR) model has gradually appeared in recent years. The problem makes the classification boundary learned by the ATR model often fuzzy or even wrong. In this article, an SAR target sample generation method was proposed, called demand-driven generative adversarial nets (DDGANs), which provided an effective way to implement imbalanced data learning. When the imbalanced data exacerbated the deterioration of the minority category target samples distribution, the proposed method generated samples to alleviate this negative impact. The proposed method innovatively used two convolutional neural networks to form the discriminator of DDGAN. Among them, a convolutional neural network was used to determine whether the generated sample is real or fake. Moreover, another convolutional neural network can simultaneously dig out the generation demands of different categories of target samples when recognizing the generated samples. The generation demands enabled DDGAN to allocate different generation capabilities to different target samples on demand, thereby alleviating the negative impact of data imbalance. At the same time, DDGAN can autonomously learn the generation demands from imbalanced training sets. Several experimental results based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset showed the advantages of DDGAN. Compared with existing imbalanced learning algorithms, the proposed method had obvious superiority in recognition performance and data generation efficiency.
Problem-oriented CBR: Finding potential problems from lead user communities

• This paper suggests a problem-oriented CBR for defining problems from lead user communities. • This paper improves current textual CBR from input and procedural perspectives. • This paper defined problems using sentiment analysis for improving textual CBR inputs. • This paper integrated SAO analysis and technology tree for improving textual CBR process. Online communities, where lead users openly share their experiences and knowledge on product and technology in the form of a post, have become a fruitful source of innovation. While efforts have been made to provide a way of data-driven case-based reasoning (CBR), existing studies have limitations in reflecting lead users’ characteristics into the CBR process. Current research has emphasized the retrieval and adaptation phase only, which retrieves, reuses, and revises cases. However, what is at the core of lead user characteristics is to find out the problem, and solve the problem by themselves before mass customers. This means CBR needs to focus on uncovering and defining problems and finding relevant solutions for designated problems. In response, this research suggests a novel approach for problem-oriented CBR approach to reflect lead user characteristics. First, this study defines problems, by extracting problem–solution sets related to the specific function using sentiment analysis. Second, this study improves case representation and case retrieval using subject-action-object (SAO) analysis and technology tree respectively. This study demonstrates the approach through a case of drone technology using lead user communities (diydrones.com), and our findings suggest that the approach can help firms broaden the knowledge of existing products to make an improvement.
Ship Detection Method based on Scale Matched R3Det

Given their high detection rates and low false alarm rates, object detection neural networks based on deep learning have been widely used in ship detection. However, the detection in real-world scenario with complex back ground remains a challenge in marine dynamic ship detection, whose performance is limited by the scale of training datasets, where training a model with high-performance usually requires a large number of multi-scale datasets. However, it is difficult to obtain a large-scale dataset in such cases. In addition, R3Det solves the problem that the vertical and horizontal ratio of the object to be detected is large, the objects to be detected are densely arranged, and category asymmetry of objects to be detected have been widely concerned. However, R3Det uses the nearest neighbor interpolation to up-sampling the image, which leads to a blocky effect of the image with a certain probability, which affects the object detection. In order to alleviate these problems, we propose a new model called “Refined Single-Stage Detector with Feature Refinement for Rotating Object based on Scale-match”. The new pre-training strategy of scale match and improved feature pyramid network (IFPN) were introduced. The method not only expands the training data sample set, but also improves the clarity of training pictures, and improves the ship detection rate and reduce the false alarm rate. Experiments with DOTAv1.5 and high-resolution datasets showed that the ship detection rate and false alarm rate are better than baseline methods.
Adaptive Gaussian Distribution Threshold Spatial Frequency Denoising Image

The details of an image with noise can be restored by removing the noise with an appropriate image denoising method. In this work is proposed and tested, an image denoising methods based on the use of an improved generalized adaptive Gaussian distribution threshold in the wavelet domain. Different wavelet transform methods are used in conjunction with an AGGD threshold to experiment with the proposed approach in order to obtain better results for the image denoising process and, consequently, to select the most suitable filter. The wavelet transform working on the frequencies of separate subbands of an image is a powerful method for image analysis. According to this experimental work, the proposed method show better results. The MSE and PSNR values get are used to measure the enhancement of denoised images.
KnowText: Auto-generated Knowledge Graphs for custom domain applications

While industrial Knowledge Graphs enable information extraction from massive data volumes creating the backbone of the Semantic Web, the specialised, custom designed knowledge graphs focused on enterprise specific information are an emerging trend. We present “KnowText”, an application that performs automatic generation of custom Knowledge Graphs from unstructured text and enables fast information extraction based on graph visualisation and free text query methods designed for non-specialist users. An OWL ontology automatically extracted from text is linked to the knowledge graph and used as a knowledge base. A basic ontological schema is provided including 16 Classes and Data type Properties. The extracted facts and the OWL ontology can be downloaded and further refined. KnowText is designed for applications in business (CRM, HR, banking). Custom KG can serve for locally managing existing data, often stored as “sensitive” information or proprietary accounts, which are not on open web access. KnowText deploys a custom KG from a collection of text documents and enable fast information extraction based on its graph based visualisation and text based query methods.
GoogLeNet-like Model for Pedestrian Attribute Detection in Surveillance Environment

Social public security has attracted worldwide attention in these years. Automatic pedestrian attribute detection is of great significance to improve monitoring efficiency. Low resolution of surveillance images and multi-angle problem of image acquisition are the main challenges of current pattern recognition researches. In order to improve the pedestrian attribute detection accuracy, we proposed a GoogLeNet-like model with inception structure which replaced the optimal local construction by a series of dense substructures. The experimental results show that the proposed method outperforms the previous works.
MEDIA COMPETENCE AND MEDIA LITERACY: THE ESSENCE OF CONCEPTS

The analysis of the scientific and pedagogical literature, during which the essence of the concepts of «media literacy» and «media competence» is revealed through different views of scientists is performed in the article. We understand media competence as a set of knowledge, skills, abilities that contribute to the search, selection, interpretation, use, evaluation, critical analysis, creation and transmission of media information. The media competence of future doctors we consider as their competence in the application of media technologies in their professional activities. Media competence includes competence of information, competence of communication, informational and communicative competences. Media competence is formed through media literacy, which, in turn, is achieved in the process of media education. It helps individuals to think critically, which helps to form a certain understanding and make competent decisions in response to media information. We define media literacy as a process of personal development through the media and in order to form media culture, develop communicative and creative abilities, critical thinking, skills of finding, interpreting, evaluating and analyzing media information with the help of media technologies. That is, a media literate person is able to control the media and navigate in the media environment, to find the necessary information, can control erroneous messages.
Ensemble Learning-Based Differential Distinguishers for Lightweight Cipher

Combination of machine learning and cryptoanalysis is a novel filed. The application of machine learning decreases the manual work, time cost and storage space. On the basis of CRYPTO 2019 Gohr's work, we proposed an EL (ensemble learning) based differential distinguishers of AdaBoost, Random Forest, Extremely Randomized Trees and Gradient Boosting Decision Tree which prove stable differential distinguishing result. The result shows the Random Forest based differential distinguisher can distinguish the 6-round Speck with cipher 106 pairs in 89.01 seconds. The Gradient Boosting Decision Tree distinguisher can distinguish 6-round Speck with 106 cipher pairs in 175.60 seconds and proves accuracy up to 0.5840. After our analysis, compared with the analysis method based on machine learning, the amount of data needed by the traditional difference analysis method is about 67 times as much as that of the new method. Our finding shows the ensemble learning method performs well in the differential distinguishing task for lightweight Markov cipher.