Reviewerly/exHarmony huggingface dataset:
NOTAM Text Analysis and Classification Based on Attention Mechanism

Abstract As one of the important carriers, Notice to Air Men (NOTAM) provides key aeronautical security information during flight operation. However, a large number of NOTAM texts lack systematic utilization and organization. To improve the application of NOTAM texts, the Natural language processing can be used to classify and analyze NOTAM texts, and their effective processing attaches significance to monitor the air security. In this paper, a corpus set of NOTAM by web crawler technology has been built to generate raw data. Based on the aeronautical intelligence knowledge system, the corpus set is systematically pre-processed and analyzed, while the key information in NOTAM and corpus features are also extracted. Considering the high-dimensional and sparse of corpus set, the traditional bi-directional Recurrent Neural Network (Bi-RNN) architecture is improved by adding an attention layer which not only allows the Bi-RNN to capture hidden dependency to improve sparse features, but also allows the attention mechanism to weigh the key information base on their perceived importance to optimize text representation. Experiments show that that the Attention-based Bi-RNN network outperforms the other four models, achieving a precision of 94.17% and an F1-Score of 93.66%. The model rapidly increases the utilization of text features meanwhile reduces the loss, which demonstrates it is a useful tool for the task of NOTAM texts classification and air security surveillance.
Fundamental Limits of Deep Graph Convolutional Networks for Graph Classification

Graph convolutional networks (GCNs) are a widely used method for graph representation learning. To elucidate their capabilities and limitations for graph classification, we investigate their power to generate well-separated embedding vectors for graphs sampled from different random graph models, which correspond to different class-conditional distributions in a classification problem. It has been recognized that metric properties of learned representations are important for reduction of complexity of classifiers trained on them. Additionally, we show that inability to generate well-separated embedding vectors for two different graph models implies information-theoretic indistinguishability of these models based on noise-perturbed embedding vectors of sample graphs. We consider graph models arising from graphons, which parametrize all infinite exchangeable graph models. We precisely characterize, in terms of degree profile closeness, the set of graphon pairs that are indistinguishable (in metric and information-theoretic senses) by a GCN with depth at least logarithmic in sample graph size. Outside this set, a very simple architecture suffices for distinguishability. We then exhibit a concrete, infinite set of graphon pairs that are well-separated in cut distance and are indistinguishable by a GCN. These results theoretically match empirical observations of several prior works. Finally, we give empirical results on synthetic and real graph classification datasets, giving some indication that degree profile closeness gives rise to indistinguishability of graph distributions in real datasets, even beyond our theoretical framework.
Analysis of Spatial Pyramid Pooling Variations in Semantic Segmentation for Satellite Image Applications

Palm oil is an important export commodity to countries that are situated close to the equator line such as Malaysia, Indonesia, and Brazil. Due to its large demands from all over the world, a lot of new oil palm plantations were set up every year. Hence, the governments need to be aware of the exact size of the plantations to ensure the sustainability of the ecosystems. However, manual mapping of the plantation areas is not a feasible option due to the access difficulty, especially in rural areas. To overcome this limitation, satellite imaging has been used to chart the exact areas of the plantations manually. With once a day sampling frequency, the relevant parties need to perform manual segmentation of the areas every day to produce updated segmentation maps. It is a very tedious task that can be automated by using a machine learning approach. Therefore, this paper proposes a deep learning approach to segment the oil palm plantation areas automatically. Multi-scale capability is embedded in the network by introducing optimized spatial pyramid pooling (SPP) unit. This unit is placed at the bottleneck layer, which happens to be the lowest resolution of the feature maps. Several variants of the SPP unit were experimented with by altering the number of parallel convolutional streams. Each stream will consist of 512 convolutional filters, which will then be combined using a concatenation operator. Three parallel streams of SPP produced the best performance accuracy and mean intersection over union of 73.632 % and 0.5821, respectively. For future improvement, the placement of the SPP unit can be further optimized, not just limited to the bottleneck layer.
Research on The New Drones Design to Improve Transportation of Supplies and Medical Care for Remote Mountain Area in Taiwan

With the development of science and technology, drone transportation has gradually become an important transportation and has an impact on many places. In the past, there were many rural and mountain tribes in the world or in Taiwan. They were roadless or had to across the mountains to transport materials with couple hours. This is very inconvenience for the residents in the remote villages and mountain tribes, and even important medical supplies such as blood, medicine or other healthcare items cannot be delivered immediately. This paper proposes a new type of UAV model design to solve the above transportation problem in remote mountainous areas in Taiwan, especially in medical supply delivery. The paper discusses the advantages and disadvantages of the proposed new UAV models. In addition, the flight advantages of the new UAV model in Taiwan mountainous terrain also with be discussed in this paper. The purpose of this research is to use new drones to improve the transportation of supplies and medical care in remote mountain tribes in Taiwan, which will promote more timely, efficient, and economical transportation of supplies and medical care by drones, therefore so that people can get help as soon as possible in the emergency.
Building a corpus: what are key considerations?

This chapter addresses the many aspects that must be taken into consideration when building a corpus. It contains detailed information and clear guidance related to various aspects of corpus building, including issues related to the collection of both spoken and written texts, and file naming conventions are provided. Examples of file names and text processing are included. The importance of identifying a clear research goal prior to collecting a corpus is emphasised as one of the hallmarks of successful corpus construction. In all, the chapter provides a researcher with the concepts and tools to be considered for successful corpus construction.
Non-functional Requirements Elicitation in Agile Base Models

The elicitation of non-functional and functional needs is one of the most critical jobs of a requirement engineer. This scenario involves the imposition of limits on non-functional needs, whereas functional requirements call for the operation of a system in order to carry out functionality. Over the last few years, agile software development approaches have gained widespread acceptance in the software industry as a problem-solving paradigm. Non-functional requirements (NFRs) are frequently cited as a point of contention in non-functional requirements (NFR) approaches. As well as functional requirements like speed and efficiency, security is desired, amongst a host of other things. Aspects like usability, security, and privacy must all be taken into account. Functional needs must be treated as though they were first-class under the current industry standard of practice. Functional requirements are distinguished from non-functional requirements by the fact that only implemented requirements can be evaluated. To give an example, this method attracts the attention of the system's end users to a critical defect in its architecture. Projects of this type frequently fail because to dissatisfaction among the target audience. If you'd like a great demonstration, consider the London Ambulance System. When dealing with non-compliance to the necessary degree of detail, it is feasible to raise the likelihood of software success this is the first study of its kind in its sector to bring attention to the most critical NFR issues. The problems that arise during the elicitation stage of requirement engineering in agile base models. It also outlines the techniques and strategies that are being considered. Proposed in the literature as a means of dealing with these problems.
SEGCN-DCR: A syntax-enhanced event detection framework with decoupled classification rebalance

Event Detection (ED) is a pivotal sub-task of Event Extraction(EE). It aims to locate triggers and categorize them into specific event types. Recent researches on ED have shown that graph convolutional neural networks with syntactic information can achieve advanced performance. However, these methods ignore the implicit importance score of tokens. This will weaken their ability of identifying trigger words. In addition, due to the long-tailed distribution in the corpus, previous methods perform poorly on sparsely labeled trigger words and are prone to overfitting on densely labeled ones. In this paper, we propose a Syntax-Enhanced GCN framework with a Decoupled Classification Rebalance mechanism (SEGCN-DCR) to address the above issues. Specifically, we exploit a tree-structured module based on dependency structure to reduce the noise by capturing global hierarchical syntactic information, and DCR mechanism to rescale the classifier weights, which makes classifier decision boundaries more reasonable. Experiments on benchmark ACE2005 show that the proposed method acquires state-of-the-art performance.
Efficient AutoML via Combinational Sampling

Automated machine learning (AutoML) aims to automatically produce the best machine learning pipeline, i.e., a sequence of operators and their optimized hyperparameter settings, to maximize the performance of an arbitrary machine learning problem. Typically, AutoML based Bayesian optimization (BO) approaches convert the AutoML optimization problem into a Hyperparameter Optimization (HPO) problem, where the choice of algorithms is modeled as an additional categorical hyperparameter. In this way, algorithms and their local hyperparameters are referred to as the same level. Consequently, this approach makes the resulting initial sampling less robust. In this study, we describe a first attempt to formulate the AutoML optimization problem as its nature instead of transfer it into a HPO problem. To take advantage of this paradigm, we propose a novel initial sampling approach to maximize the coverage of the AutoML search space to help BO construct a robust surrogate model. We experiment with 2 independent scenarios of AutoML with 2 operators and 6 operators over 117 benchmark datasets. Results of our experiments demonstrate that the performance of BO significantly improved by using our sampling approach.
DART: Towards a role-based trust management system on blockchain

In the past years, trust management systems have been proven suitable for solving the authorization problem in distributed systems, such as peer to peer systems, social networks, cloud, mobile ad-hoc networks, and Internet of things. Trust management systems could be either managed by a central authority or decentralized. In both cases the entity or, respectively, the set of entities managing the system need to be trusted for all users. To overcome this limitation, this paper brings blockchain technology into trust management systems, proposing a novel implementation of the Role-based Trust management framework (RT) on blockchain. The approach relies on smart contracts to represent user trust networks and to infer new trust relations through the chain discovery algorithm. We evaluated a prototype implemented on Ethereum on a representative set of policies related to different scenarios.
An Empirical Analysis Of Using Blockchain Technology In E-Voting Systems

An analysis on the use of blockchain technology in e-voting systems has been done to compare its performance and security to that of a centralized e-voting system. Blockchain technology is one of the emerging technologies of today as a distributed, decentralized database technology, providing much promise to transparency and reducing cybersecurity risks. A Blockchain-based E-Voting System (BEVS) and a Centralized E-Voting System (CEVS) were developed, and compared in terms of performance and security testing. The results show that the BEVS processes requests slightly slower than the CEVS. This is due to the additional processes of block validation and creation in a blockchain network, whereas utilizing a local private blockchain as the database of an e-voting system handles requests faster than utilizing a public blockchain network. However, the BEVS is more reliable when it comes to the efficiency of an e-voting system as it has a complete 0.00% error rate compared to the CEVS with its errors generated by the internal server. The speed at which the BEVS may also be affected by heavy loads, however, all requests were fulfilled, and no system crashes occurred. The BEVS also proves to be a more secure e-voting system with less vulnerabilities detected.
Temporal reasoning and query answering with preferences and probabilities for medical decision support

Knowledge-based decision support systems have a long tradition within the medical area. In particular, in the last decades, many Computer-Interpretable Guidelines (CIG) systems have been built to provide evidence-based and knowledge-based support to physicians. Since CIGs are, by definition, devoted to the management of specific diseases, the treatment of comorbid patients constitutes a challenging task in the area, involving (i) the detection of the possible interactions between (the effects of) the actions recommended by multiple CIGs (one for each disease of the patient), (ii) the management of such interactions and, finally, (iii) the conciliation of (the recommendations of) different CIGs. This paper focuses on issue (i) above, and specifically, on an innovative approach to support interaction detection along the temporal dimension. Practically, interactions can only occur between effects that intersect in time. Therefore, interaction detection involves the representation of temporal information (temporal constraints), and temporal reasoning (to propagate such constraints). Additionally, query answering facilities are important to support physicians in the investigation of the results of temporal reasoning. Current CIG approaches that face such issues take into account only "crisp" temporal constraints, i.e., they consider all temporal constraints as equally probable\preferred. However, preferences about the temporal constraints between CIGs actions may be available, as well as knowledge about the probabilistic distribution of the effects of CIGs actions in time. Considering such additional pieces of information can provide crucial advantages, in term of the flexibility and informativeness of the support provided by the CIG system to physicians. In this paper, we propose the first homogeneous approach to represent and reason with (propagate) temporal constraints with both preferences and probabilities. We ground our approach on the widely-used Simple Temporal Problem (STP) framework, which supports temporal reasoning on temporal constraints about possible distances between events. We extend (i) the representation formalism to associate preferences and\or probabilities to the possible distances, and (ii) the operations to propagate the constraints to combine also preferences and probabilities. We also (iii) provide an experimental evaluation of our approach, and (iv) propose a wide range of query-answering supports, to facilitate physicians in the analysis of the results of temporal reasoning in general, and in the temporal detection of possible interactions in particular. Finally, (v) we also show how such a temporal framework is integrated in GLARE-SSCPM, a CIG system to treat comorbid patients, and show the advantages of our approach considering a running example.
Sign Language Converter Using Feature Extractor and PoseNet

Human beings commune with each other to convey thoughts, expressions, experiences and so on to the people around them. But this case is not the same when it comes to the deaf and mute people. Sign language paves the way for people with such difficulties to communicate with one another. The aim of this paper is to facilitate easy and accurate communication between people who have hearing and speaking disabilities, and those who do not. This paper shows how the communication gap can be bridged using sign language to text and audio converter with the help of Feature Extractor and Posenet with an accuracy of 92%. A webcam is used to capture the sign language shown by a person. Posenet with Artificial Neural Network is used to classify essential words used in day to day life. Various parts of the body are tracked by the webcam and then converted to text and audio to convey what the person is trying to express in real time.
Image Stripe Noise Removal Based on Compressed Sensing

The sensors or electronic components are vulnerable to interference in the camera’s imaging process, usually leading to random directional stripes. Therefore, a method of stripe noise removal based on compressed sensing is proposed. First, the measurement matrix of the image with stripe noise is established, which makes the stripe images equivalent to the observation of the original image. Second, the relationships between the corresponding coefficients of adjacent scales are defined. On this basis, the bivariate threshold function is set in the curvelet sparse domain to represent the features of images. Finally, the Landweber iteration algorithm of alternating convex projection and filtering operation is achieved. Furthermore, to accelerate the noise removal at the initial stage of iteration and preserve the image details later, the exponential threshold function is utilized. This method does not need many samples, which is different from the current deep learning method. The experimental results show that the proposed algorithm represents excellent performance in removing the stripes and preserving the texture details. In addition, the PSNR of the denoised image has been dramatically improved compared with similar algorithms.
Neural Models for Measuring Confidence on Interactive Machine Translation Systems

Reducing the human effort performed with the use of interactive-predictive neural machine translation (IPNMT) systems is one of the main goals in this sub-field of machine translation (MT). Prior works have focused on changing the human–machine interaction method and simplifying the feedback performed. Applying confidence measures (CM) to an IPNMT system helps decrease the number of words that the user has to check through the translation session, reducing the human effort needed, although this supposes losing a few points in the quality of the translations. The effort reduction comes from decreasing the number of words that the translator has to review—it only has to check the ones with a score lower than the threshold set. In this paper, we studied the performance of four confidence measures based on the most used metrics on MT. We trained four recurrent neural network (RNN) models to approximate the scores from the metrics: Bleu, Meteor, Chr-f, and TER. In the experiments, we simulated the user interaction with the system to obtain and compare the quality of the translations generated with the effort reduction. We also compare the performance of the four models between them to see which of them obtains the best results. The results achieved showed a reduction of 48% with a Bleu score of 70 points—a significant effort reduction to translations almost perfect.
A Blockchain-based Contactless Delivery System for Addressing COVID-19 and Other Pandemics

The coronavirus (COVID-19) pandemic has significantly impacted and changed our daily routines. Worldwide, people have had to adapt by undergoing remote work and self-quarantine. This situation has required transforming strategies for various logistics services for a variety of service providers, such as retail stores and restaurants. The concept of contactless delivery has emerged to help prevent the spread of the coronavirus. However, contactless delivery only reduces the direct interaction between the delivery personnel and the customer. In addition to peer-to-peer contact, items still go through insecure interactions between and among the delivery personnel and other unknown third parties. Even if the items are delivered without physical contact, concerns remain about their routes in the supply chain. In this paper, we present a novel blockchain-based framework to enable the traceability of products in the supply chain. This framework records and tracks delivery traces and the medical status of delivery personnel in a privacy-preserved manner, ultimately contributing to COVID-19 prevention and control. We build a Hyperledger Fabric-based blockchain prototype system as our testbed. Several smart contract functions are implemented and evaluated to show the efficiency of the proposed framework. In conjunction with the implementation and evaluation, we also perform comprehensive security and privacy analyses of this framework.