Reviewerly/exHarmony huggingface dataset:
Cross-sentence N-ary relation classification using LSTMs on graph and sequence structures

Relation classification is an important semantic processing task in the field of Natural Language Processing (NLP). The past works mainly focused on binary relations in a single sentence. Recently, cross-sentence N-ary relation classification, which detects relations among n entities across multiple sentences, has been arousing people's interests. The dependency tree based methods and some Graph Neural Network (GNN) based methods have been carried out to convey rich structural information. However, it is challenging for researchers to fully use the relevant information while ignore the irrelevant information from the dependency trees. In this paper, we propose a Graph Attention-based LSTM (GA LSTM) network to make full use of the relevant graph structure information. The dependency tree of multiple sentences is divided into many subtrees whose root node is a word in the sentence and the leaf nodes are regarded as the neighborhood. A graph attention mechanism is used to aggregate the local information in the neighborhood. Using this network, we identify the relevant information from the dependency tree. On the other hand, because the GNNs highly depend on the graph structure of the sentence and lack context sequence structural information, their effectiveness to the task is limited. To tackle this problem, we propose an N-gram Graph LSTM (NGG LSTM) network, which updates the hidden states by aggregating graph neighbor node information and the inherent sequence structural information of sentence. The experimental results show that our methods outperform most of the existing methods.
Opportunities for Explainable Artificial Intelligence in Aerospace Predictive Maintenance

This paper aims to look at the value and the necessity of XAI (Explainable Artificial Intelligence) when using DNNs (Deep Neural Networks) in PM (Predictive Maintenance). The context will be the field of Aerospace IVHM (Integrated Vehicle Health Management) when using DNNs. An XAI (Explainable Artificial Intelligence) system is necessary so that the result of an AI (Artificial Intelligence) solution is clearly explained and understood by a human expert. This would allow the IVHM system to use XAI based PM to improve effectiveness of predictive model. An IVHM system would be able to utilize the information to assess the health of the subsystems, and their effect on the aircraft. Even if the underlying mathematical principles are understood, they lack an understandable insight, hence have difficulty in generating the underlying explanatory structures (i.e. black box). This calls for a process, or system, that enables decisions to be explainable, transparent, and understandable. It is argued that research in XAI would generally help to accelerate the implementation of AI/ML (Machine Learning) in the aerospace domain, and specifically help to facilitate compliance, transparency, and trust. This paper explains the following areas:&#x0D; &#x0D; Challenges &amp; benefits of AI based PM in aerospace&#x0D; Why XAI is required for DNNs in aerospace PM?&#x0D; Evolution of XAI models and industry adoption&#x0D; Framework for XAI using XPA (Explainability Parameters)&#x0D; Discussion about future research in adopting XAI &amp; DNNs in improving IVHM.&#x0D;
Penggunaan Media Literasi Kelas Awal di Sekolah Dasar

The purpose of sending it to the community is to train teachers in using early grade literacy media in elementary schools. The method used in the form of training to teachers in using media literacy. Participants in the service are 18 Air Tawar Selatan elementary school teachers and 25 Air Tawar Selatan public elementary schools, 22 Koto Tangah District Padang City. Based on the training results it can be seen that the average score of the pre-test participants was 69.54 and the average value of reading poetry after the training was 78.18. Thus it can be seen that the ability of 18 Air Tawar Selatan public elementary school teachers and 25 Air Tawar Selatan public elementary school Koto Tangah District, Padang City have improved after the training in the use of early grade literacy media.
Constructing and inferring event logic cognitive graph in the field of big data

Along with the increasing impact of big data on the economic operation mechanism, including global production, circulation, distribution and consumption, event logic (EL) is becoming an important concept in mining and releasing the potential value of big data. Besides its critical role in exploring the clues and rules of digital economy, EL will also help people better understand the future development of the ternary human-cyber-physical universe. Considering the dynamic evolution of the EL is the essential difference between industrial big data and other big data. Therefore, we propose a constructing and inferring EL cognitive graph in the field of big data. Firstly, we introduce the great opportunities offered by the knowledge graph in promoting the high-quality development of the manufacturing industry, leading the economic development of the new digital manufacturing industry, and promoting the cross-development of sociology and information science. Secondly, we present the construction technology of EL cognitive graph and expound the progress and critical technical challenges in the research of the EL cognitive graph at home and abroad, in the aspects of multi-granularity acquisition and representation of EL, multimodal association and fusion, evolution analysis and mining of EL behavior. Finally, combined with the research progress of our works in EL cognitive graph, we summarize the effect of EL cognitive graph in the prevention of network fraud and the control of safe production as well as summarize and anticipate the future research direction and development prospect of the industry.
Counterfeit Anomaly Using Generative Adversarial Network for Anomaly Detection

Anomaly detection aims to detect anomaly with only normal data available for training.It attracts considerable attentions in the medical domain, as normal data is relatively easy to obtain but it is rather difficult to have abnormal data especially for some rare diseases, making training a standard classifier challenging or even impossible.Recently, generative adversarial networks (GANs) become prevalent for anomaly detection and most existing GAN-based methods detect outliers by the reconstruction error.In this paper, we propose a novel framework called adGAN for anomaly detection using GAN.Unlike existing GAN-based methods, adGAN is a discriminative model, which uses the fake data generated from GAN as an abnormal class, and then learns a boundary between normal data and simulated abnormal data.Thus it is able to output the anomaly scores directly similar as one-class SVM (OCSVM), without any reconstruction process.We explicitly design adGAN with two key elements, i.e., fake pool generation and concentration loss.The fake pool is created by incrementally collecting the fake data produced by intermediate-state GAN, which are likely surrounding the normal data distribution.The concentration loss is innovatively introduced to penalize large standard deviations of discriminator outputs for normal data, aiming to make the distribution of normal data more compact and more likely to be separated from the distribution of the potential abnormal data.The trained discriminator is finally used as an anomaly detector.We evaluated adGAN on three datasets, including ab-MNIST for synthetic anomaly detection, the ISIC'2016 for skin lesion detection, and the BraTS'2017 for brain lesion detection.The extensive experiments demonstrate that adGAN is consistently superior to its competitors on all three datasets.
A smartphone-based survey in mHealth to investigate the introduction of the artificial intelligence into cardiology

There is an increasing discussion concerning the integration of artificial intelligence (AI) into medical decision-making. AI science is a branch of engineering that implements novel concepts to resolve complex challenges and defined as the theory and development of computer systems to perform tasks which would normally require human intelligence. AI could aid cardiologists in improving decision-making, workflow, productivity, cost-effectiveness, and ultimately, patient outcomes. The present study proposes a tool for a positioning exercise in cardiology using mobile technology.This study is based on a dedicated tool with electronic surveys that collect the opinions, requirements, and desires of the interested actors including both laypeople and professionals.The tool was tested on 30 cardiologists and 30 subjects not involved in health care. The data-analysis revealed several clear trends on the cardiologists: (I) a high desire to invest in AI; (II) high confidence in the use of AI in several fields of cardiology from risk prevention to diagnostics in medical imaging; (III) low confidence in the use of AI in quality control procedures; (IV) a strong belief that ethical issues are hampering the diffusion of AI to different fields. The data-analysis on the 30 subjects not involved in health care highlighted that AI is still not well known and therefore looked with suspicious.The integration of AI with telemedicine and e-health is a key issue for the health care. The study highlights how the mobile technology-based positioning exercises in mHealth can be useful for health care decision makers.
Security Challenges over Cloud Environment from Service Provider Prospective

Cloud computing becomes very popular and growing rapidly since the last few years, various information technology giants such as Amazon, Google, Microsoft and others speed up their growth in development of cloud computing systems and enhance services for consumers all over the world. Cloud computing provides virtual storage for the clients to avail storage, application, platform and services from their deployed servers over the internet. Various security issues such as; data insecure, data leakage, data availability, attacks; etc. could arise due to poor security policies. This paper discussed in detail security issues in terms of the CIA (Confidentiality, Integrity, and Availability) triangle from a service provider perspective over the services they provide to the end users. By these measurements high security can be achieve in cloud computing. Additionally, to protect confidential data from users on cloud the privacy actions are not updated accordingly. Severally, data backups on cloud cause high security risks. This survey paper analyzes the main security issues which are currently present in the cloud computing and offers best practices to service providers and enterprises hoping to control cloud service.&#x0D;
Underwater targets detection and classification in complex scenes based on an improved YOLOv3 algorithm

The fast detection and classification of underwater targets is a key issue in the operation of intelligent underwater robots. In order to improve the detection speed of underwater targets and reduce the missed detection rate of small targets, an improved YOLOv3 algorithm named YOLOv3-Marine is proposed. The network parameters were reduced and the detection speed was increased due to improving the YOLOv3 network structure. The residual module was optimized to improve the feature extraction capabilities of the network, which greatly reduced the rate of missed detection in the case of densely distributed targets. Finally, the prediction scale module and the loss function were improved to increase the detection accuracy of small underwater targets. The final experimental results showed that the proposed YOLOv3-Marine algorithm has a higher detection speed and detection accuracy than the YOLOv3 algorithm.
A Hybrid Dynamic Encryption Scheme for Multi-Factor Verification: A Novel Paradigm for Remote Authentication

A significant percentage of security research that is conducted suffers from common issues that prevent wide-scale adoption. Common snags of such proposed methods tend to include (i) introduction of additional nodes within the communication architecture, breaking the simplicity of the typical client-server model, or fundamental restructuring of the Internet ecosystem; (ii) significant inflation of responsibilities or duties for the user and/or server operator; and (iii) adding increased risks surrounding sensitive data during the authentication process. Many schemes seek to prevent brute-forcing attacks; they often ignore either partially or holistically the dangers of other cyber-attacks such as MiTM or replay attacks. Therefore, there is no incentive to implement such proposals, and it has become the norm instead to inflate current username/password authentication systems. These have remained standard within client-server authentication paradigms, despite insecurities stemming from poor user and server operator practices, and vulnerabilities to interception and masquerades. Besides these vulnerabilities, systems which revolve around secure authentication typically present exploits of two categories; either pitfalls which allow MiTM or replay attacks due to transmitting data for authentication constantly, or the storage of sensitive information leading to highly specific methods of data storage or facilitation, increasing chances of human error. This paper proposes a more secure method of authentication that retains the current structure of accepted paradigms, but minimizes vulnerabilities which result from the process, and does not inflate responsibilities for users or server operators. The proposed scheme uses a hybrid, layered encryption technique alongside a two-part verification process, and provides dynamic protection against interception-based cyber-attacks such as replay or MiTM attacks, without creating additional vulnerabilities for other attacks such as bruteforcing. Results show the proposed mechanism outperforms not only standardized methods, but also other schemes in terms of deployability, exploit resilience, and speed.
New Insights into Cross-Document Event Coreference: Systematic Comparison and a Simplified Approach

Cross-Document Event Coreference (CDEC) is the task of finding coreference relationships between events in separate documents, most commonly assessed using the Event Coreference Bank+ corpus (ECB+).At least two different approaches have been proposed for CDEC on ECB+ that use only event triggers, and at least four have been proposed that use both triggers and entities.Comparing these approaches is complicated by variation in the systems' use of gold vs. computed labels, as well as variation in the document clustering pre-processing step.We present an approach that matches or slightly beats state-of-the-art performance on CDEC over ECB+ with only event trigger annotations, but with a significantly simpler framework and much smaller feature set relative to prior work.This study allows us to directly compare with prior systems and draw conclusions about the effectiveness of various strategies.Additionally, we provide the first cross-validated evaluation on the ECB+ dataset; the first explicit evaluation of the pairwise event coreference classification step; and the first quantification of the effect of document clustering on system performance.The last in particular reveals that while document clustering is a crucial pre-processing step, improvements can at most provide for a 3 point improvement in CDEC performance, though this might be attributable to ease of document clustering on ECB+.
Multimode entanglement generation with dual-pumped four-wave-mixing of Rubidium Atoms

Multimode entanglement is essential for the generation of quantum networks, which plays a central role in quantum information processing and quantum metrology. Here, we study the spatial multimode entanglement characteristics of the large scale quantum states via a dual-pumped four-wave-mixing (FWM) process of Rubidium atomics vapors. A linear mode transform approach is applied to solve the four- and six-mode Gaussian states and the analytical input-output relations are presented. Moreover, via reconstructing the full covariance matrix of the produced states, versatile entanglement with from two up to six modes is analyzed. The results show that most of the 1 versus n-mode and m versus n-mode states are entangled, and the amount of entanglement can be regulated due to the competitions of mode components caused by different interaction strengths of co-existing FWMs. Our study could be applied for any multimode Gaussian states with a quadratic Hamiltonian.
A Fully Autonomous Search and Rescue System Using Quadrotor UAV

In order to deal with critical missions a growing interest has been shown to the UAVs design.Flying robots are now used fire protection, surveillance and search & rescue (SAR) operations.In this paper, a fully autonomous system for SAR operations using quadrotor UAV is designed.In order to scan the damaged area, speeds up the searching process and detect any possible survivals a new search strategy that combines the standard search strategies with the probability of detection is developed.Furthermore the autopilot is designed using an optimal backstepping controller and this enables the tracking of the reference path with high accuracy and maximizes the flying time.Finally a comparison between the applied strategies is made using a study case of survivals search operation.The obtained results confirmed the efficiency of the designed system.
What’s in an agent?

Abstract This article investigates the morphological diversity of agent nouns (ANs) in French. It addresses the questions of which nouns form a semantically coherent class of ANs, what their morphological properties are, and whether these properties correlate with agentive subtypes. To deal with these issues, a distributional semantics approach is adopted. The investigation is based on the distributional study of monosemous deverbal ANs ending in - eur , and on the examination of word similarities in the French Wikipedia corpus. It is shown that French ANs as a homogeneous distributional class display a large variety of morphological profiles. ANs can be affixed, converted, compound nouns, as well as opaque and morphologically simple nouns. Agentive affixes are diverse and correlated to the selection of bases from different lexical categories and semantic types. It appears that agent meaning in the nominal domain is not necessarily imported from the verbal domain, but can develop directly in the semantic structure of nouns. In addition, a distinction between functional, occasional and behavioral ANs, depending on whether they denote agents with an occupational status, agents in a particular event, or agents with a tendency to act in a certain way, is proven to be distributionally relevant. This distinction applies to all ANs, possibly in correlation with specificities as regards morphological type, base and affix selection. The study illustrates that with a careful processing of linguistic data, distributional semantics can help answering basic research questions and support fine-grained theoretical distinctions.
Fast Self-Adaptive Digital Camouflage Design Method Based on Deep Learning

Traditional digital camouflage is mainly designed for a single background and state. Its camouflage performance is appealing in the specified time and place, but with the change of place, season, and time, its camouflage performance is greatly weakened. Therefore, camouflage technology, which can change with the environment in real-time, is the inevitable development direction of the military camouflage field in the future. In this paper, a fast-self-adaptive digital camouflage design method based on deep learning is proposed for the new generation of adaptive optical camouflage. Firstly, we trained a YOLOv3 model that could identify four typical military targets with mean average precision (mAP) of 91.55%. Secondly, a pre-trained deepfillv1 model was used to design the preliminary camouflage texture. Finally, the preliminary camouflage texture was standardized by the k-means algorithm. The experimental results show that the camouflage pattern designed by our proposed method is consistent with the background in texture and semantics, and has excellent camouflage performance in optical camouflage. Meanwhile, the whole pattern generation process takes a short time, less than 0.4 s, which meets the camouflage design requirements of the near-real-time camouflage in the future.
Empowering mobile crowdsourcing apps with user privacy control

Mobile crowdsourcing is being increasingly used by industrial and research communities to build realistic datasets. By leveraging the capabilities of mobile devices, mobile crowdsourcing apps can be used to track participants’ activity and to collect insightful reports from the environment (e.g., air quality, network quality). However, most of existing crowdsourced datasets systematically tag data samples with metadata (e.g., time and location stamps), which may inevitably lead to user privacy leaks by discarding sensitive information in the wild. This article addresses this critical limitation of the state of the art by proposing a software library that empowers legacy mobile crowdsourcing apps to increase user privacy without compromising the overall quality of the crowdsourced datasets. We propose a decentralized approach, named Fougere, to convey data samples from user devices to third-party servers. By introducing an a priori data anonymization process, we show that Fougere defeats state-of-the-art location-based privacy attacks with little impact on the quality of crowdsourced datasets.
Medical Image Denoising using Fast Discrete Curvelet Transform

Now-a-days, medical field plays a very crucial role in our daily life, as a part of it MRI (Magnetic resonance imaging) scans, CT (computed tomography) images, Ultrasound images etc. of the victim which are one of the main things that are to be determined correctly based on which the patient's condition is concluded and treated.The main problem here occurs is for the original image where the image gets noisy and the features of the original image are lost due to many factors.So, here in our paper, we instigate the method of image denoising technique which helps to eliminate the noisy observations and other disturbances and reconstructs the original image very accurately.The image denoising is one of the important preprocessing steps in medical field image processing analysis.For this denoising method, we are going to use the Fast Discrete Curvelet Transform which is a multi-scale geometric transform and is designed to signify the image or video sequences at different scales and angles.also the performances of it by using fast Fourier discrete curve-let transform which is based on ridge-let analysis theory for denoising procedures and makes recommendations with the help of adaptive threshold algorithm which is applied on the image and gets the original image with effectiveness also retrieves the important detail features in the image and also the quality of the image to be recovered by using the parameters like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE).