Reviewerly/exHarmony huggingface dataset:
Asking It All: Generating Contextualized Questions for any Semantic Role

Asking questions about a situation is an inherent step towards understanding it.To this end, we introduce the task of role question generation, which, given a predicate mention and a passage, requires producing a set of questions asking about all possible semantic roles of the predicate.We develop a two-stage model for this task, which first produces a contextindependent question prototype for each role and then revises it to be contextually appropriate for the passage.Unlike most existing approaches to question generation, our approach does not require conditioning on existing answers in the text.Instead, we condition on the type of information to inquire about, regardless of whether the answer appears explicitly in the text, could be inferred from it, or should be sought elsewhere.Our evaluation demonstrates that we generate diverse and well-formed questions for a large, broadcoverage ontology of predicates and roles.
Blockchain-Based Network Concept Model for Reliable and Accessible Fine Dust Management System at Construction Sites

In total, 44.3% of particle matter 10 (PM10) is fugitive dust, and one of the main sources of fugitive dust generation in Korea is construction work (22%). Construction sites account for 84% of the total business places that have reported fugitive dust generation. Currently, the concentration of fine dust at construction sites is being remotely monitored by government inspection agencies through IoT sensors, but it is difficult to trust that appropriate fine dust reduction measures are being taken, because contractors can avoid taking these measures by submitting false reports or photos. In addition, since the fine dust monitoring system under government management is not an open platform and centralized system, residents near construction sites encounter difficulties in accessing information about fine dust. Therefore, in this study, we designed and constructed a blockchain network model to transparently and reliably provide network participants with the information associated with IoT data and fine dust reduction measures. To operate the blockchain network, we designed the chaincode, DApp, and network architecture. In addition, information on fine dust concentration and reduction measure photos were shared with the participants via the blockchain search tool (Hyperledger Explorer). The proposed blockchain network is expected to form a trust protocol among contractors, government inspection agencies, and citizens.
Inductive Link Prediction with Interactive Structure Learning on Attributed Graph

Link prediction is one of the most important tasks in graph machine learning, which aims at predicting whether two nodes in a network have an edge. Real-world graphs typically contain abundant node and edge attributes, thus how to perform link prediction by simultaneously learning structure and attribute information from both interactions/paths between two associated nodes and local neighborhood among node's ego subgraph is intractable.
Pre-train or Annotate? Domain Adaptation with a Constrained Budget

Recent work has demonstrated that pretraining in-domain language models can boost performance when adapting to a new domain.However, the costs associated with pretraining raise an important question: given a fixed budget, what steps should an NLP practitioner take to maximize performance?In this paper, we view domain adaptation with a constrained budget as a consumer choice problem, where the goal is to select an optimal combination of data annotation and pre-training.We measure annotation costs of three procedural text datasets, along with the pre-training costs of several in-domain language models.The utility of different combinations of pretraining and data annotation are evaluated under varying budget constraints to assess which combination strategy works best.We find that for small budgets, spending all funds on annotation leads to the best performance; once the budget becomes large enough, however, a combination of data annotation and in-domain pre-training yields better performance.Our experiments suggest task-specific data annotation should be part of an economical strategy when adapting an NLP model to a new domain. 1
Pseudonymisation of neuroimages and data protection: Increasing access to data while retaining scientific utility

For a number of years, facial features removal techniques such as ‘defacing’, ‘skull stripping’ and ‘face masking/blurring’, were considered adequate privacy preserving tools to openly share brain images. Scientifically, these measures were already a compromise between data protection requirements and research impact of such data. Now, recent advances in machine learning and deep learning that indicate an increased possibility of re-identifiability from defaced neuroimages, have increased the tension between open science and data protection requirements. Researchers are left pondering how best to comply with the different jurisdictional requirements of anonymization, pseudonymisation or de-identification without compromising the scientific utility of neuroimages even further. In this paper, we present perspectives intended to clarify the meaning and scope of these concepts and highlight the privacy limitations of available pseudonymisation and de-identification techniques. We also discuss possible technical and organizational measures and safeguards that can facilitate sharing of pseudonymised neuroimages without causing further reductions to the utility of the data.
A Multistage Blockchain-Based Secure and Trustworthy Smart Healthcare System Using ECG Characteristic

With the rapid development of advanced biomedical sensors, the Internet of Things, and modern wireless communication technologies, smart healthcare systems provide feasible solutions to the problems of population aging and telemedicine services. However, physiological information involves personal privacy, and the data security concern during the transmission of information on public channels has become a critical issue that has restricted the wider acceptance of smart healthcare systems. In this article, a secure and trustworthy smart healthcare system interoperating with wireless body area networks based on multistage blockchain is proposed. The security scheme provides a completely secure and trustworthy environment covering the entire data flow from the front-end to the back-end. The evaluation results show that the encryption scheme based on piecewise linear chaotic map can resist common attack methods and reduce encryption time. The system has the advantages of low complexity for encryption scheme, and larger capacity and efficiency for the blockchain-based data transmission and storage system.
CRYPTOCURRENCY AND CLIMATE CHANGE: AN OVERVIEW

Cryptocurrencies in recent years, have emerged as an innovative means of carrying out online financial transactions, but concerns have been raised among experts about their impact on our environmental.Cryptocurrencies place enormous demand on the energy system, and this increases carbon emissions which sequentially adds up to the overall effects of global warming.However, the technology that powers bitcoin (blockchain) should be considered for future adoption.The environmental hazard regarding cryptocurrency usage stems from the large carbon footprint left by such small share of global cashless transactions, and its potential to be broadly integrated into current technologies.This study examines the impact of cryptocurrency on the environment with its focus on climate change (Global warming).Although the fate of bitcoin is currently unpredictable, it can be projected that if its rate of adoption is accelerated, its electricity demand is capable of producing sufficient emissions to surpass 2°C of global warming in a few decades.The study recommends that further development in cryptocurrencies should analytically aim to reduce power demand so as to avoid the potentially demoralizing consequences of 2 O C of global warming.
Privacy-Preserving and Incentivized Contact Tracing for COVID-19 Using Blockchain

Many countries across the world have not been entirely successful in their attempts to contain the spread of the COVID-19 virus, which has caused, quite possibly, one of the greatest disasters of the 21st century. A rich variety of contact tracing applications, each offering its own unique set of features, are currently being used to track the spread of the virus from person to person, and isolate/quarantine suspected individuals. However, existing approaches have not been very successful since they lack incentives to motivate users to use them actively. Although each solution provides some form of privacy protection and data security, loopholes do exist, and there is scope for improvement. In this context, we explore the use of blockchain to help in advancing contact tracing applications. In this article, we describe how blockchain could offer enhanced data security and functionality without compromising users' privacy. The effectiveness of contact tracing applications improves with the user base. In this regard, we explore how blockchain-based smart contracts can be used to offer incentives to users at various stages of the contact tracing process. Next, we lay down a plan for future contact tracing frameworks that provide the advantages of blockchain and user incentives, and all the best features available in current solutions.
Face with Mask Detection in Thermal Images Using Deep Neural Networks

As the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature based on the detected face, which brings new perspectives and opportunities to use such an approach for health control purposes. The goal of this work is to analyze the effectiveness of deep-learning-based face detection algorithms applied to thermal images, especially for faces covered by virus protective face masks. As part of this work, a set of thermal images was prepared containing over 7900 images of faces with and without masks. Selected raw data preprocessing methods were also investigated to analyze their influence on the face detection results. It was shown that the use of transfer learning based on features learned from visible light images results in mAP greater than 82% for half of the investigated models. The best model turned out to be the one based on Yolov3 model (mean average precision—mAP, was at least 99.3%, while the precision was at least 66.1%). Inference time of the models selected for evaluation on a small and cheap platform allows them to be used for many applications, especially in apps that promote public health.
Target-Guided Feature Super-Resolution for Vehicle Detection in Remote Sensing Images

Vehicle detection in remote sensing images remains a challenge because most vehicles are small and cover only a relatively small area due to the low ground sample distance. Although image super-resolution can improve small object detection performance as a preprocessing step, methods for improving the quality of the entire image tend to focus on the majority backgrounds that are not important for detection and involve high computational cost. Inspired by the promising feature-level super-resolution method, in this letter, we propose a novel anchor-free vehicle detection network for small vehicle detection in remote sensing images. Specifically, a target-guided feature super-resolution network is proposed to enhance the features of the potential target. Besides, we propose a novel feature fusion module to improve the feature representation of shallow layers, which accounts for small object detection. Extensive experiments on three public remote sensing detection datasets [cars overhead with context (COWC), Vehicle Detection in Aerial Imagery (VEDAI), and UCAS-AOD] amply demonstrate that our method can achieve significant performance with a mean average precision of 0.933, 0.756, and 0.961, respectively.
Textometr: an online tool for automated complexity level assessment of texts for Russian language learners

Evaluation of text accessibility seems to be an extremely urgent and labor-consuming task in the process of preparing texts for teaching Russian as a foreign language. On the other hand, the procedure of assigning a text to one of the levels on the CEFR scale (from A1 to C2) is well-formalized and described in the professional literature, which opens opportunities for its automation. This paper presents Textometr - a new free web-based tool for estimating CEFR level and other key statistics from any given text in Russian that can be relevant for adapting it for foreign students. The automated assessment of the text level here is based on a regression model, trained on the dataset of more than 800 texts from Russian textbooks for foreigners, applying several machine learning and natural language processing methods. In addition to the CEFR level, the tool provides information relevant for adapting the text to educational tasks: lists of keywords and words for a potential vocabulary list, statistics on the text coverage by frequency lists and CEFR-graded vocabulary lists (lexical minima), a frequency list of the text, a forecast of the time needed for reading. The tool shortages at the current stage of development and suggested ways to solve them are also discussed. Finally, the results of the test on the tool quality and the vectors for its further development are reported. Textometr can provide helpful information not only to teachers and guidance teachers, but to authors of textbooks and publishers to check the compliance of the text content with the declared level and educational goals.
Accentron: Foreign accent conversion to arbitrary non-native speakers using zero-shot learning

Foreign accent conversion (FAC) aims to create a new voice that has the voice identity of a given second-language (L2) speaker but with a native (L1) accent. Previous FAC approaches usually require training a separate model for each L2 speaker and, more importantly, generally require considerable speech data from each L2 speaker for training. To address these limitations, we propose Accentron, an approach that can generate accent-converted speech for arbitrary L2 speakers unseen during training. In the proposed approach, we first train a speaker-independent acoustic model on L1 corpora to extract bottleneck features that represent the linguistic content of utterances. Then, we develop a speaker encoder and an accent encoder to generate embedding vectors for the desired voice identity (L2 speaker’s) and accent (L1 accent), respectively. Lastly, we use a sequence-to-sequence model to transform bottleneck-features to Mel-spectrograms, conditioned on the L2 speaker embedding and the L1 accent embedding. We conducted experiments on the L2-ARCTIC corpus under two testing conditions: the standard FAC setting where test L2 speakers were seen during training, and a zero-shot FAC setting where test L2 speakers were unseen during training. Accentron achieves over 27% relative improvement in accentedness ratings compared to two state-of-the-art FAC systems in the standard FAC setting. More importantly, our results show that Accentron generalizes to the zero-shot FAC setting with no performance loss. Therefore, in practical use scenarios (e.g., computer-assisted pronunciation training software), Accentron can effectively avoid the need to adapt or retrain the model, which significantly reduces computations and the users’ waiting time.
Vehicle Parking Security System with Face Recognition Detection Based on Eigenface Algorithm

RFID (Radio Frequency Identification) card technology is used for intelligent parking systems. Officers no longer need to record and check manually and repeatedly every vehicle that comes in and out with this system. The system has a weakness, namely if the RFId card is dropped or lost, the person who finds it can use the card. For the purpose of increasing security for users, one of the latest technologies is the use of facial recognition methods. The eigenface algorithm system is an algorithm used for face recognition. The algorithm is used as a training process for the previously inputted employee faces. The results of the training data are stored in a database which is then used as a key to recognize the face of the owner of the motorized vehicle who took the motorcycle. In the trial results of the data application, motorized vehicle owners enter when the facial recognition level is above 70%.
Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques

Solar power is generated using photovoltaic (PV) systems all over the world. Because the output power of PV systems is alternating and highly dependent on environmental circumstances, solar power sources are unpredictable in nature. Irradiance, humidity, PV surface temperature, and wind speed are only a few of these variables. Because of the unpredictability in photovoltaic generating, it’s crucial to plan ahead for solar power generation as in solar power forecasting is required for electric grid. Solar power generation is weather-dependent and unpredictable, this forecast is complex and difficult. The impacts of various environmental conditions on the output of a PV system are discussed. Machine Learning (ML) algorithms have shown great results in time series forecasting and so can be used to anticipate power with weather conditions as model inputs. The use of multiple machine learning, Deep learning and artificial neural network techniques to perform solar power forecasting. Here in this regression models from machine learning techniques like support vector machine regressor, random forest regressor and linear regression model from which random forest regressor beaten the other two regression models with vast accuracy.
Unsupervised Speech Segmentation and Variable Rate Representation Learning Using Segmental Contrastive Predictive Coding

Typically, unsupervised segmentation of speech into the phone- and word-like units are treated as separate tasks and are often done via different methods which do not fully leverage the inter-dependence of the two tasks. Here, we unify them and propose a technique that can jointly perform both, showing that these two tasks indeed benefit from each other. Recent attempts employ self-supervised learning, such as contrastive predictive coding (CPC), where the next frame is predicted given past context. However, CPC only looks at the audio signal&#x2019;s frame-level structure. We overcome this limitation with a segmental contrastive predictive coding (SCPC) framework to model the signal structure at a higher level, e.g., phone level. A convolutional neural network learns frame-level representation from the raw waveform via noise-contrastive estimation (NCE). A differentiable boundary detector finds variable-length segments, which are then used to optimize a segment encoder via NCE to learn segment representations. The differentiable boundary detector allows us to train frame-level and segment-level encoders jointly. Experiments show that our single model outperforms existing phone and word segmentation methods on TIMIT and Buckeye datasets. We analyze the impact of the threshold on boundary detector performance, and our results suggest that automatically learning the boundary threshold can be as effective as manually tuning that threshold. We discover that phone class impacts the boundary detection performance, and the boundaries between successive vowels or semivowels are the most difficult. Finally, we use SCPC to extract speech features at the segment level rather than at the uniformly spaced frame level (e.g., 10 ms) and produce variable rate representations that change according to the contents of the utterance. We can lower the feature extraction rate from the typical 100 Hz to as low as 14.5 Hz on average while still outperforming the hand-crafted features such as MFCC on the linear phone classification task.
The Security of Internet of Vehicles Network: Adversarial Examples for Trajectory Mode Detection

In recent years, the number of vehicles in the city is increasing. However, the increase in the number of cars caused a series of traffic problems, such as road congestion, traffic accidents, environmental pollution, and so on. To handle the problems, Internet of Vehicles (IoV) is emerging. As we know, deep learning has acquired significant success in many fields, and it is also applied in IoV. However, some studies show that deep learning is vulnerable to the crafted samples formed by adding small perturbations to original samples. Thus, the vulnerability of deep learning may pose a huge security threat to IoV. To ensure the security of deep learning, we conduct experiments to investigate whether there are adversarial samples in the IoV field. We try to generate adversarial examples by GPS data. The models we attack are trajectory mode detection models. Since both GPS trajectory data and image data are continuous data, we adopt the algorithm of Computer Vision to generate adversarial examples. We utilize white-box attack algorithms (Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) algorithms) and black-box attack algorithm (One Pixel Attack) to generate adversarial examples. We adopt Dynamic Time Warping (DTW) to measure the similarity between adversarial examples and original trajectory data. Experimental results show that a small perturbation can successfully fool deep neural networks with high confidence.
A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text.For sentiment analysis, annotated data is a basic requirement.Generally, this data is manually annotated.Manual annotation is time consuming, costly and laborious process.To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis.Dataset is created from the reviews of ten most popular songs on YouTube.Reviews of five aspects-voice, video, music, lyrics and song, are extracted.An N-Gram based technique is proposed.Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds (575 h) if it was annotated manually.For the validation of the proposed technique, a sub-dataset-Voice, is annotated manually as well as with the proposed technique.Cohen's Kappa statistics is used to evaluate the degree of agreement between the two annotations.The high Kappa value (i.e., 0.9571%) shows the high level of agreement between the two.This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost.This research also contributes in consolidating the guidelines for the manual annotation process.