Reviewerly/exHarmony huggingface dataset:
Implicit Estimation of Paragraph Relevance From Eye Movements

Eye movements were shown to be an effective source of implicit relevance feedback in constrained search and decision-making tasks. Recent research suggests that gaze-based features, extracted from scanpaths over short news articles (g-REL), can reveal the perceived relevance of read text with respect to a previously shown trigger question. In this work, we aim to confirm this finding and we investigate whether it generalizes to multi-paragraph documents from Wikipedia (Google Natural Questions) that require readers to scroll down to read the whole text. We conduct a user study ( n = 24) in which participants read single- and multi-paragraph articles and rate their relevance at the paragraph level with respect to a trigger question. We model the perceived document relevance using machine learning and features from the literature as input. Our results confirm that eye movements can be used to effectively model the relevance of short news articles, in particular if we exclude difficult cases: documents which are on topic of the trigger questions but irrelevant. However, our results do not clearly show that the modeling approach generalizes to multi-paragraph document settings. We publish our dataset and our code for feature extraction under an open source license to enable future research in the field of gaze-based implicit relevance feedback.
Privacy-Preserving Threshold Spatial Keyword Search in Cloud-Assisted IIoT

Cloud-assisted Industrial Internet of Things (IIoT) systems are increasingly deployed in various applications such as location-based services. Outsourcing data to cloud servers can help minimize local data storage and computation overheads, but it may introduce security and privacy concerns. Therefore, privacy-preserving spatial keyword search has been extensively explored in the literature. However, existing solutions still reveal the order of the spatio-textual similarity values between the query point and all data objects, and do not support searching for arbitrary geometric regions. To solve these issues, in this article we propose a privacy-preserving threshold spatial keyword search (TSKS) scheme. Specifically, we use the polynomial fitting technology, vector space model, and randomizable matrix multiplication technology to allow the cloud server to find relevant objects that are within some arbitrary geometric range and contain all query keywords. Finally, formal security analysis proves that our scheme can protect the privacy of data sets and queries, and extensive experiments demonstrate that our scheme is efficient and practical.
The role of ICT development in boosting economic growth in transition economies

During the last decades, the substantial development of information and communication technologies (ICTs) has led to a number of economic and noneconomic changes in all countries worldwide.The ICT progress can significantly influence macroeconomic outcomes and it is widely recognized as an important factor of the transition toward a new economic system.This transition was especially challenging for former command economies.Since these economies are characterized by relatively low standards of living, poor infrastructure and continuous changes in economy structure and regulatory framework, the ICT development can have adverse effect on economic activities.In that sense, this study addresses the role of ICT development in enhancing the economic growth in 11 EU transition economies over the 2000-2019 period, using the linear regression analysis.The obtained results suggest
Magnetic Force Classifier: A Novel Method for Big Data Classification

There are a plethora of invented classifiers in Machine learning literature, however, there is no optimal classifier in terms of accuracy and time taken to build the trained model, especially with the tremendous development and growth of Big data. Hence, there is still room for improvement. In this paper, we propose a new classification method that is based on the well-known magnetic force. Based on the number of points belonging to a specific class/magnet, the proposed magnetic force (MF) classifier calculates the magnetic force at each discrete point in the feature space. Unknown examples are classified using the magnetic forces recorded in the trained model by various magnets/classes. When compared to existing classifiers, the proposed MF classifier achieves comparable classification accuracy, according to the experimental results utilizing 28 different datasets. More importantly, we found that the proposed MF classifier is significantly faster than all other classifiers tested, particularly when applied to Big datasets and hence could be a viable option for structured Big data classification with some optimization.
PEDAGOGICAL ASPECTS OF DIGITALIZATION OF EDUCATION

The article analyses the impact of globalization and technological development of society on research, education, organization and technology of the educational process. Pedagogical priorities of digitalization of education such as combining the efforts of scientists and practical specialists for a comprehensive analysis and interdisciplinary solution of modern problems of digital educational space and pedagogical principles of education digitalization have been identified. The article formulates the conceptual provisions of digital pedagogy and specification of the content of basic pedagogical concepts of digital education, development of digital didactics, revision of forms, methods, means and technologies of teaching, students’ training and development in the digital space. The features and directions of education of children and youth in the digital space have been identified; new forms and methods of organizing the educational impact on children and youth in the digital environment have been revealed; the risks of digitalization of education and ways to overcome them have been outlined. The article shows that the mission of modern education is to provide society and every citizen with a proper transition to the digital age, focusing on other tools and different living environment and to prepare not only young but also middle-aged and older people for new types and kinds of work. An example of solving problems of digitalization of education, which requires joint efforts of scientists and practical specialists, is the creation and work of the Advisory Board on Digital Education and Pedagogy at the Department of General Pedagogy and Philosophy of Education of the NAES of Ukraine. Its functions are to examine current issues of digitalization of education in relation to interdisciplinarity: philosophical, theoretical and methodological, practical aspects of digital education and pedagogy; to сonduct in-depth research of the benefits and threats of digitalization of educational and scientific activities and to make analytical conclusions and recommendations for the management of education.
Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition

The traditional human action recognition (HAR) method is based on RGB video. Recently, with the introduction of Microsoft Kinect and other consumer class depth cameras, HAR based on RGB-D (RGB-Depth) has drawn increasing attention from scholars and industry. Compared with the traditional method, the HAR based on RGB-D has high accuracy and strong robustness. In this paper, using a selective ensemble support vector machine to fuse multimodal features for human action recognition is proposed. The algorithm combines the improved HOG feature-based RGB modal data, the depth motion map-based local binary pattern features (DMM-LBP), and the hybrid joint features (HJF)-based joints modal data. Concomitantly, a frame-based selective ensemble support vector machine classification model (SESVM) is proposed, which effectively integrates the selective ensemble strategy with the selection of SVM base classifiers, thus increasing the differences between the base classifiers. The experimental results have demonstrated that the proposed method is simple, fast, and efficient on public datasets in comparison with other action recognition algorithms.
Numeracy enhances the Literacy of Language Models

Specialized number representations in NLP have shown improvements on numerical reasoning tasks like arithmetic word problems and masked number prediction.But humans also use numeracy to make better sense of world concepts, e.g., you can seat 5 people in your room but not 500.Does a better grasp of numbers improve a model's understanding of other concepts and words?This paper studies the effect of using six different number encoders on the task of masked word prediction (MWP), as a proxy for evaluating literacy.To support this investigation, we develop Wiki-Convert, a 900,000 sentence dataset annotated with numbers and units, to avoid conflating nominal and ordinal number occurrences.We find a significant improvement in MWP for sentences containing numbers, that exponent embeddings are the best number encoders, yielding over 2 points jump in prediction accuracy over a BERT baseline, and that these enhanced literacy skills also generalize to contexts without annotated numbers.We release all code at https://git.io/JuZXn.
Optimum Solar Panel Orientation and Performance: A Climatic Data-Driven Metaheuristic Approach

This study presents an optimization platform based on the climatic data provided by the National Renewable Energy Laboratory (NREL) to determine the optimum solar panel orientation. Our optimization model is simpler to use than the clearness index model since there is no need to calculate the extraterrestrial insolation on a horizontal flat plate and the shape factor. This optimization approach is based on the hourly climatic data. It determines the optimum tilt angle and azimuth angle of a solar panel for the maximum power generation, considering the diurnal variation of climatic conditions. The hourly evaluation of insolation allows setting up a solar panel azimuth angle that responds to the peak power demand. The main data that impacts the solar panel performance consists of the solar direct normal incident (DNI), direct horizontal incident (DHI), global horizontal incident (GHI), ambient temperature, wind speed, and ground albedo, all of which were obtained from the NREL database for over twenty years. The accuracy of the optimization platform introduced in this study is scrutinized by investigating the three locations in the United States with different climatic conditions. The results based on the present optimization model show higher PV power than the general rule of thumb for south-facing panels with title angles the same as the latitude of the location. Moreover, the effect of deviations from optimum panel orientation is discussed to show the versatility of our technique. Our optimization model is easy-to-use, computationally efficient, and capable of being applied to other locations worldwide.
Spectral Reflectance-Based Mangrove Species Mapping from WorldView-2 Imagery of Karimunjawa and Kemujan Island, Central Java Province, Indonesia

Mangrove mapping at the species level enables the creation of a detailed inventory of mangrove forest biodiversity and supports coastal ecosystem management. The Karimunjawa National Park in Central Java Province is one of Indonesia’s mangrove habitats with high biodiversity, namely, 44 species representing 25 true mangroves and 19 mangrove associates. This study aims to (1) classify and group mangrove species by their spectral reflectance characteristics, (2) map mangrove species by applying their spectral reflectance to WorldView-2 satellite imagery with the spectral angle mapper (SAM), spectral information divergence (SID), and spectral feature fitting (SFF) algorithms, and (3) assess the accuracy of the produced mangrove species mapping of the Karimunjawa and Kemujan Islands. The collected field data included (1) mangrove species identification, (2) coordinate locations of targeted mangrove species, and (3) the spectral reflectance of mangrove species measured with a field spectrometer. Dendrogram analysis was conducted with the Ward linkage method to classify mangrove species based on the distance between the closest clusters of spectral reflectance patterns. The dendrogram showed that the 24 mangrove species found in the field could be grouped into four levels. They consisted of two, four, and five species groups for Levels 1 to 3, respectively, and individual species for Level 4. The mapping results indicated that the SID algorithm had the highest overall accuracy (OA) at 49.72%, 22.60%, and 15.20% for Levels 1 to 3, respectively, while SFF produced the most accurate results for individual species mapping (Level 4) with an OA of 5.08%. The results suggest that the greater the number of classes to be mapped, the lower the mapping accuracy. The results can be used to model the spatial distribution of mangrove species or the composition of mangrove forests and update databases related to coastal management.
Augmenting Seismic Data Using Generative Adversarial Network for Low-Cost MEMS Sensors

The performance of a deep learning (DL) model depends on sufficient training datasets and is related to the algorithm structure. Seismological research based on DL suffers from the lack of seismic data recorded by low-cost micro-electro-mechanical systems (MEMS) sensors whose data is usually polluted by different types of noise. Therefore, increasing seismic datasets is necessary for intelligently generating seismic data through data-augmentation techniques. However, it is difficult to characterize and measure the evolution process of seismic sequences, making the feature extraction and data generation of seismic sequences still a significant challenge. By combining the framework of Generative Adversarial Network (GAN) with long short-term memory (LSTM), attention mechanism and neural network (NN), a novel deep generation model (DGM) named EQGAN is developed to overcome the challenges, which can automatically capture the different time histories and dimension characteristics of seismic sequences, meanwhile stably generating high-quality seismic data. The reality of generated data is qualitatively clarified through the analysis of frequency domain and data autocorrelation distribution. Based on the High-throughput Screening (HTS) Theory, the quantitative evaluation index of statistical metrics is designed, and the generation performance of different machine learning models (standard GAN, LSTM, NN) is compared to prove the stability and effectiveness of EQGAN. The experimental results denote that the EQGAN has excellent stability and performance (up to 81%, much higher than that of other generation models), which provides a suitable data expansion approach for the field of seismological research.
Data immutability and event management via blockchain in the Internet of things

The Internet of things (IoT) is the key enabler of the smart systems used in many areas, from agriculture to aviation, industrial automation to autonomous vehicles. Most IoT deployments employ cost-efficient lightweight devices with limited resources (e.g., bandwidth, energy, storage). Although an IoT network must be built in its simplest form, engineers include more sophisticated devices like gateways and servers to provide web-based services and benefit from cloud systems. So, although the nodes can be widely distributed geographically or topologically, the system becomes centralized, which causes bottlenecks and single-points-of-failure. Furthermore, providing data integrity, nonrepudiation, and event management becomes tricky. In most IoT scenarios, data usually flow from sensors to storage and processing units, whereas event-driven commands and triggers flow from these units to actuators, if any. Therefore, an attacker who gained access to parts of the centralized systems may leak, alter, or remove critical data and may exploit event handling features. This is where blockchain technology can be extremely useful. Using a decentralized ledger as the data storage unit provides integrity, immutability, and nonrepudiation for any IoT deployment. And a customized smart contract lets the IoT deployment benefit from decentralized and immutable (i.e. nonmanipulatable) event management features, too. Further, decentralization provides resilience against availability attacks to a large extent. With this motivation, we introduced a novel IoT architecture that incorporates an Ethereum-based private (Quorum) blockchain running a unique ad-hoc smart contract and a message queue telemetry transport (MQTT) based communication scheme between sensor and actuator nodes. The scheme, the ledger, and the smart contract have also been implemented with several nodes, a broker, and a server all on a PC using Docker containers, where the server was running a forest fire risk detection algorithm as the use case scenario. The proof-of-concept successfully validates the abovesaid functionality, scalability, and efficiency for the given IoT scenario (and some others). Moreover, performance tests showed that an instance of the system with 1000 nodes could stably process and record incoming (sensor) data up to 12.5 transactions per second (TPS) and distribute commands up to 4 TPS, whereas higher TPS is achievable depending on the network conditions and tolerance to losses. The scheme was shown to have polynomial message and time complexity.
Named Entity Recognition in the Romanian Legal Domain

Recognition of named entities present in text is an important step towards information extraction and natural language understanding.This work presents a named entity recognition system for the Romanian legal domain.The system makes use of the gold annotated Legal-NERo corpus.Furthermore, the system combines multiple distributional representations of words, including word embeddings trained on a large legal domain corpus.All the resources, including the corpus, model and word embeddings are open sourced.Finally, the best system is available for direct usage in the RE-LATE platform.
Performance Evaluation of INT8 Quantized Inference on Mobile GPUs

During the past several years, the need for on-device deep learning has rapidly increased, and the performance of mobile GPUs has significantly increased.As a viable approach for efficient ondevice deep learning, INT8 quantized inference has been actively studied and proposed but there are currently few frameworks that support INT8 quantization for mobile GPUs.This paper presents a unified framework that integrates various INT8 quantization methods, such as symmetric, asymmetric, per-layer, and per-channel, and discuss their impact on accuracy and efficiency on recent mobile GPUs.Moreover, we discuss the performance and accuracy of INT8 quantized Winograd convolution and propose INT8 Winograd convolution with F(2×2, 3×3), where weight tensors are quantized in INT4 and input tensors are quantized in INT6.We evaluated the performance of INT8 methods, including INT8 Winograd, for ResNet50, MobileNet-v1, and VGG16 on Mali G52, G72, and G76 GPUs on Odroid N2, Galaxy S9, and Galaxy Note 10+, respectively.INT8 quantized inference based on General Matrix Multiplication (GEMM) was 1.67× faster than FP32 GEMM for ResNet50 on Mali G52, and was further accelerated by batch normalization folding and by the proposed INT8 Winograd convolution, achieving 2.45× speedup in total with an accuracy loss of only -0.31%p.INDEX TERMS On-device deep learning, INT8 quantization, INT8 Winograd convolution, mobile GPU I. INTRODUCTION 1 Deep learning has become a prevalent approach and de-2 mand for on-device deep learning in particular has rapidly 3 increased in various domains such as autonomous driving, 4 smart IoT, robots, and so forth.On-device deep learning 5 offers several significant advantages compared with cloud 6 based deep learning where the device connects to the cloud 7 whenever it needs to process a deep learning task; (i) Users 8 do not need to send private data to the cloud, and (ii) net-9 work communication is not required, and hence (iii) energy 10 consumption and latency can be reduced.11 However, mobile devices have significantly limited re-12 sources and power consumption constraints compared with 13 cloud based systems.Recent GPUs in the cloud servers have 14 over a hundred streaming multiprocessors, whereas mobile
Design of the Secure Smart Home System Based on the Blockchain and Cloud Service

In this paper, a variety of cloud service combinations is used to form the control core of a smart home, realize data forwarding, storage, and analysis in the cloud, and complete the remote management of smart home devices. This paper studies the secure access control of smart home data combined with the blockchain technology and password technology to realize the secure and efficient access control of smart home data. The system can achieve the goal of independent research and development. Aiming at the problem that the access control of a smart home is generally managed by third-party authorized institutions and there is an unauthorized access, a blockchain-based smart home (BSH) access control scheme is proposed. The scheme extends the attribute-based access control model (ABAC) and applies the blockchain technology to the smart home ABAC model to realize fine-grained access control. In the scheme, the resource provider first publishes the access control policy of the resource to the blockchain. If the resource visitor wants to access the resource, he needs to submit an access request to the blockchain and use an SM2 threshold signature to process the transaction proposal. The endorsement peer node in the blockchain runs the access control policy smart contract to decide whether to grant access rights. This scheme can ensure that resource providers can participate in all the processes of access control and avoid the risk of unauthorized access caused by the centralized management of the third party. Finally, the simulation experiment is carried out on the Hyperledger Fabric alliance chain development platform. The results show that the BSH access control scheme has good applicability in the smart home scenario.
Topic Extraction and Interactive Knowledge Graphs for Learning Resources

Humanity development through education is an important method of sustainable development. This guarantees community development at present time without any negative effects in the future and also provides prosperity for future generations. E-learning is a natural development of the educational tools in this era and current circumstances. Thanks to the rapid development of computer sciences and telecommunication technologies, this has evolved impressively. In spite of facilitating the educational process, this development has also provided a massive amount of learning resources, which makes the task of searching and extracting useful learning resources difficult. Therefore, new tools need to be advanced to facilitate this development. In this paper we present a new algorithm that has the ability to extract the main topics from textual learning resources, link related resources and generate interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks no matter how big or small the texts are. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm’s accuracy was evaluated against Gensim, largely improving its accuracy. This could be a step towards strengthening self-learning and supporting the sustainable development of communities, and more broadly of humanity, across different generations.