Reviewerly/exHarmony huggingface dataset:
Pre-P gravity signals from dynamic earthquake rupture: modelling and observations

Dynamic earthquake rupture is one of the most extensive and devastating fracture phenomena on the Earth. It causes a sudden crustal deformation around a fault and generates seismic waves that induce bulk density variations propagating with them. Both processes constitute rock-mass redistribution, which is expected to induce simultaneous transient gravity perturbations at all distances before the arrival of P-waves. Interest in such pre-P gravity signals has increased both in terms of modelling and observations because of their potential for earthquake early warning. A simple forward model has pioneered the search for the so-called prompt elasto-gravity signals, which led to the first report of a signal from the 2011 M w 9.0 Tohoku-Oki earthquake using a single superconducting gravimeter record. The second report followed using hundreds of broadband seismometers with critical modification of the previous model to consider the pre-P ground acceleration in the measurement of gravity. Post-event analyses have identified prompt elasto-gravity signals from several large earthquakes, and state-of-the-art instruments are now being developed for real-time signal detection. This paper reviews recent progress in the cutting-edge subject of prompt elasto-gravity signals owing to large-scale earthquake rupture. This article is part of the theme issue ‘Fracture dynamics of solid materials: from particles to the globe’.
AutomataDAO: A Blockchain-Based Data Marketplace for Interactive Robot and IoT Data Exchanges Using Ethermint and State Channels

We present AutomataDAO, a decentralized data marketplace for robot and IoT cyberphysical systems. Unlike present literature on blockchain-based data marketplaces, AutomataDAO is implemented as a decentralized autonomous organization (DAO) through a set of governance smart contracts that account for both human and synthetic agents that may take part in the DAO. Concerns over transactional costs and throughput are resolved using state channel technology, and a preliminary decentralized ID (DID) solution is leveraged to establish a reputation system that is considered within the cryptoeconomics of the system; the latter is also accounted for in the market's data pricing model.
An Architecture for Extracting Key Elements from Legal Permits

In many countries worldwide, including Austria, the environmental impact of production facilities is strongly regulated leading to authorities issuing a large number of legal permits on this topic. The access of interested parties to these permits is typically supported by search systems that present a structured view of the permits along their key elements, such as issuing authority or their legal basis. In this paper, we present a real-life use case from Austria's Environment Agency, where the extraction of such key elements represents a non-trivial task for laypersons with limited legal knowledge: the heterogeneity of data, complex language, and implicit information hinder the manual data extraction process and can lead to poor quality in data management. Based on an analysis of the use case's main requirements, we propose an architecture for a system to support the extraction of key elements from legal permits by laypersons. The system combines methods and techniques based on Knowledge Graphs / Semantic Web and Machine Learning technologies and aims to be auditable in terms of its operation.
Massively Parallel Graph Drawing and Representation Learning

To fully exploit the performance potential of modern multi-core processors, machine learning and data mining algorithms for big data must be parallelized in multiple ways. Today's CPUs consist of multiple cores, each following an independent thread of control, and each equipped with multiple arithmetic units which can perform the same operation on a vector of multiple data objects. Graph embedding, i.e. converting the vertices of a graph into numerical vectors is a data mining task of high importance and is useful for graph drawing (low-dimensional vectors) and graph representation learning (high-dimensional vectors). In this paper, we propose MulticoreGEMPE (Graph Embedding by Minimizing the Predictive Entropy), an information-theoretic method which can generate low and high-dimensional vectors. MulticoreGEMPE applies MIMD (Multiple Instructions Multiple Data, using OpenMP) and SIMD (Single Instructions Multiple Data, using AVX-512) parallelism. We propose general ideas applicable in other graph-based algorithms like vectorized hashing and vectorized reduction. Our experimental evaluation demonstrates the superiority of our approach.
Generations Y and X Perception Towards Bitcoin in Malaysia

Traditionally, money served as a medium of financial transaction but now the system advances to the introduction of a new electronic payment through Blockchain technology known as cryptocurrency. The phenomenon of this cryptocurrency in Malaysia is very new and it is still in debate among Malaysians especially Bitcoin’s cryptocurrency. This research is to investigate the perception of generation Y and Generation X to Bitcoin. The research is conducted based on both primary data gathered from questionnaire and secondary data collected from valid sources. The results of the research indicate that the value of Bitcoin and the extending network of operations are considered as significant factors towards the perception towards Bitcoin, while security of Bitcoin has an opposite status. In conclusion, Bitcoin does have high potentials in the near future, and it will change the current monetary system, as it is classically known.
PFWNet: Pretraining neural network via feature jigsaw puzzle for weakly-supervised temporal action localization

Weakly supervised temporal action localization is a challenging yet interesting task. Existing methods usually apply a few temporal convolutional layers or linear layers to predict classification scores, where the model capacity is limited. Inspired by counterpart researches, increasing model capacity is the potential to improve the localization performance. However, under the weakly supervised paradigm, the video-level classification label is insufficient to learn large-capacity models. The essential reason lies in that most of the inputs to action localization networks are high-level features extracted by video recognition models. In lack of off-the-shelf initialization weights, the action localization networks have to train from scratch and can only explore low-capacity models. In this work, we are inspired by the self-supervised learning paradigm and propose to learn high-quality representative models via solving the feature jigsaw puzzle task. The proposed self-supervised pretraining process can explore networks with large kernel size and deeper layers, which can provide valuable initialization to action localization networks. In the implementation, we first discover potential action scopes via calculating motion intensity. Then, we cut features into snippets and permute them into out-of-order status. We randomly discard frames for boundaries between two snippets to guide the network learning high-level representations and prevent information leakage. Moreover, because the potential permutation number factorially rises with the increase of snippet number, we select a fixed number of permutation operations via the maximum hamming distance criterion, which eases the learning process. Comprehensive experiments on two benchmarks demonstrate the efficiency of pretraining to weakly supervised action localization task, and the proposed method builds new state-of-the-art performance.
Locational privacy-preserving distance computations with intersecting sets of randomly labeled grid points

Abstract Background We introduce and study a recently proposed method for privacy-preserving distance computations which has received little attention in the scientific literature so far. The method, which is based on intersecting sets of randomly labeled grid points, is henceforth denoted as ISGP allows calculating the approximate distances between masked spatial data. Coordinates are replaced by sets of hash values. The method allows the computation of distances between locations L when the locations at different points in time t are not known simultaneously. The distance between $$L_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> and $$L_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> could be computed even when $$L_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> does not exist at $$t_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>t</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> and $$L_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> has been deleted at $$t_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>t</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> . An example would be patients from a medical data set and locations of later hospitalizations. ISGP is a new tool for privacy-preserving data handling of geo-referenced data sets in general. Furthermore, this technique can be used to include geographical identifiers as additional information for privacy-preserving record-linkage. To show that the technique can be implemented in most high-level programming languages with a few lines of code, a complete implementation within the statistical programming language R is given. The properties of the method are explored using simulations based on large-scale real-world data of hospitals ( $$n=850$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>850</mml:mn> </mml:mrow> </mml:math> ) and residential locations ( $$n=13,000$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>13</mml:mn> <mml:mo>,</mml:mo> <mml:mn>000</mml:mn> </mml:mrow> </mml:math> ). The method has already been used in a real-world application. Results ISGP yields very accurate results. Our simulation study showed that—with appropriately chosen parameters – 99 % accuracy in the approximated distances is achieved. Conclusion We discussed a new method for privacy-preserving distance computations in microdata. The method is highly accurate, fast, has low computational burden, and does not require excessive storage.
Evaluation of Hierarchical Visualization for Large and Small Hierarchies

Hierarchical visualization can provide a useful abstraction for exploring and analysing complex datasets. Many different methods for visualization of hierarchical data have been developed and determining which is the most suitable for a given dataset can be a challenge. Previous evaluations of hierarchical visualization methods typically focus on comparing hierarchical structures and less so on how well they represent underlying data. In this paper five different visualization methods for hierarchical data are evaluated: Treemaps, Icicle plots, Sunburst, Circular Treemaps and Bubble Treemaps. Each method was compared at two different sizes of hierarchical structure with questions comparing nodes based on their underlying data features, through size and colour. The initial results from this study indicate that for large hierarchies, Sunburst and Circular Treemaps are the best overall performers, while for small hierarchies, Treemap, Icicle plot and Circular Treemaps performed best.
Development of a Blockchain-Based Energy Trading Scheme for Prosumers

The combination of consumer owned Distributed Energy Resources, new Information and Communication Technologies (ICT), as well as changes to the national electricity regulations have created new opportunities for consumer engagement in the electricity sector. In this paper, this combination of technologies and regulations is examined in the Portuguese context. The new regulations dealing with self-consumption from prosumers are combined with smart contracts and distributed ledger technology to formulate an automated energy trading system for residential end-users in local energy markets. Results show that including prosumers in the local energy market brings significant benefits to all market participants. Additionally, results show that the newly created regulatory role of a Market Facilitator is beneficial to these type of local energy exchanges.
A Unified Deep Learning Approach for Foveated Rendering &amp; Novel View Synthesis from Sparse RGB-D Light Fields

Near-eye light field displays provide a solution to visual discomfort when using head mounted displays by presenting accurate depth and focal cues. However, light field HMDs require rendering the scene from a large number of viewpoints. This computational challenge of rendering sharp imagery of the foveal region and reproduce retinal defocus blur that correctly drives accommodation is tackled in this paper. We designed a novel end-to-end convolutional neural network that leverages human vision to perform both foveated reconstruction and view synthesis using only 1.2% of the total light field data. The proposed architecture comprises of log-polar sampling scheme followed by an interpolation stage and a convolutional neural network. To the best of our knowledge, this is the first attempt that synthesizes the entire light field from sparse RGB-D inputs and simultaneously addresses foveation rendering for computational displays. Our algorithm achieves fidelity in the fovea without any perceptible artifacts in the peripheral regions. The performance in fovea is comparable to the state-of-the-art view synthesis methods, despite using around 10x less light field data.
Fuzzy control based on genetic algorithm in intelligent psychology teaching system

The advancement of science has made computer technology and the education industry more and more closely related, and the development of intelligent teaching systems has also opened a new path for classroom teaching. This paper studies the application of fuzzy control based on genetic algorithms in the intelligent psychology teaching system. Facing the complicated variables in the teaching process, the improved genetic algorithm can better realize dynamic teaching decisions through fuzzy control. This article aims to improve the quality of psychology classroom teaching, and develops an intelligent psychology teaching system based on the fuzzy control theory of genetic algorithm. Combined with the current development of fuzzy control theory, the problems existing in the intelligent teaching system are studied and analyzed, and they have been optimized and improved. This paper proposes a control algorithm based on a teaching management system. The algorithm can implement fuzzy control on student models, knowledge organization structure, intelligent test papers and teaching decision-making. While restoring the real teaching process, it can better realize teaching students in accordance with their aptitude and improve teaching. The intelligence of the system. According to the system test data, the proportions of the difficulty of the system’s automatic test paper are 30.1%, 51.6%, 18.3%, which are in line with the designer’s set expectation of 3 : 5:2, which shows the improved genetic algorithm. It can realize the intelligent volume group function very well.
Research on sentiment classification of micro-blog short text based on topic clustering

Abstract Aiming at the shortage of research on micro-blog short text fine-grained sentiment classification, a fine-grained sentiment classification method about micro-blog short text based on PLSA model and K-means clustering model was proposed. PLSA is used to calculate the probability matrix between documents and topics, words and topics in the corpus. In terms of the probability distribution of words and topics, K-means algorithm is used to cluster the probability distribution of words on topics and merge the similar topics. Based on the sentiment ontology library, emotion recognition is carried out for the merged topics. Then, according to the merged document and topic probability matrix, the document sentiment category is classified. The experimental results show that the sentiment analysis method integrated with PLSA and K-means can obtain higher classification accuracy than the PLSA model method alone.
A method for constructing a global motion path and planning a route for a self-driving vehicle

Abstract The article presents methods for constructing an unmanned vehicle (UV) motion path in an environment with obstacles to solve the problem of finding a path for a non-holonomic vehicle in an environment with obstacles. A combined approach is described and individual components are considered that allow solving the problem of planning a path for a non-holonomic vehicle. The practical use example of the combined approach for solving the problem of planning the UV path is given.
Fundamental Matrix Computing Based on 3D Metrical Distance

To reconstruct point geometry from multiple images, computation of the fundamental matrix is always necessary. With a new optimization criterion, i.e., the re-projective 3D metric geometric distance rather than projective space under RANSAC (Random Sample And Consensus) framework, our method can reveal the quality of the fundamental matrix visually through 3D reconstruction. The geometric distance is the projection error of 3D points to the corresponding image pixel coordinates in metric space. The reasonable visual figures of the reconstructed scenes are shown but only some numerical result were compared, as is standard practice. This criterion can lead to a better 3D reconstruction result especially in 3D metric space. Our experiments validate our new error criterion and the quality of fundamental matrix under the new criterion.
Investigating the Robustness of Reading Difficulty Models for Russian Educational Texts

Recent papers on Russian readability suggest several formulas aimed at evaluating text reading difficulty for learners of different ages. However, little is known about individual formulas for school subjects and their performance compared to that of existing universal readability formulas. Our goal is to study the impact of the subject both in terms of model quality and on the importance of individual features. We trained 4 linear regression models: an individual formula for each of 3 school subjects (Biology, Literature, and Social Studies) and a universal formula for all the 3 subjects. The dataset was created of schoolbook texts, randomly sampled into pseudo-texts of size 500 sentences. It was split into train and test sets in the ratio of 75 to 25. As for the features, previous papers on Russian readability do not provide proper feature selection. So we suggested a set of 32 features that are possibly relevant to text difficulty in Russian. For every model, features were selected from this set based on their importance. The results obtained show that all the one-subject formulas outperform the universal model and previously developed readability formulas. Experiments with other sample sizes (200 and 900 sentences per sample) prove these results. This is because feature importances vary significantly among the subjects. Suggested readability models might be beneficial for school education for evaluating text relevance for learners and adjusting those texts to target difficulty levels.
Dolby Vision: Advancing the Technology of Cinema and Home Entertainment Transformation of an Industry

Dolby leads the way in how consumers experience cinematic entertainment centered around high dynamic range.
Using Competency Mapping for Skills Assessment in an Introductory Cybersecurity Course

Various courses and trainings aim to teach cybersecurity with only few measures for learning outcomes. Existing metrics such as general grades do not reflect the various competencies and specific outcomes that make up a course. More specific measures targeting competencies are needed: this differentiated approach must reflect course specificities, but also be general enough to be transferable to other courses. This study presents a competency mapping approach based on the NIST NICE framework, demonstrated in a cybersecurity course on bachelor level. The approach enables to evaluate specific competencies and their relevance to various cybersecurity job roles. Additionally, this type of competency mapping can provide useful insights for designing and managing the course content in general.
Image Restoration Method based on Partition and Regularization for Industrial X-ray images

Traditional regularization methods often process the whole region of images, which are suffered from some possible contradictions of detail preservation and denoising. This paper proposed an image restoration method based on partition and regularization for X-ray images of integrated circuits. Firstly, the original X-ray image is divided into detail area and smooth area based on local variance. Secondly, after filtering the smooth area, a new observed image is reconstructed by original detail area and filtered smooth area. Thirdly, Split Bregman method is adopted to solve the TV regularization denoising problem, then the image restoration results come out. Experimental results on X-ray images of LED chips and capacitors show the effectiveness of the proposed method, which is helpful to have a more accurate X-ray detection of integrated circuits.
Long-Term Exploration in Unknown Dynamic Environments

The task of exploration does not end when the robot has covered the entire environment. The world is dynamic and to model this property and to keep the map up to date the robot needs to re-explore. In this work, we present an approach to long-term exploration that builds on prior work on dynamic mapping, volumetric representations of space, and exploration planning. The main contribution of our work is a novel formulation of the information gain function that controls the exploration so that it trades off revisiting highly dynamic areas where changes are very likely with covering the rest of the environment to ensure both coverage and up-to-date estimates of the dynamics. We provide experimental validation of our approach in three different simulated environments.
VM Consolidation based on Overload Detection and VM Selection Policy

Even though cloud computing has been a big boon to the ICT (Information and communication technology) industry, it faces high energy consumption and substantial CO2 emission. Due to the increase in demand for computational resources, it is now necessary and of utmost significance to improve the energy consumption of the cloud system. Virtual Machine (VM) consolidation is one of the powerful tools to improve energy efficiency as it reduces the number of VM migrations by managing VMs from overloaded/underloaded hosts. Implementation of VM consolidation techniques leads to a decrease in the amount of hardware consumption, energy consumption, and data footprints which leads to an increased Quality of Service (QoS). In this paper, an energy aware VM selection algorithm is proposed along with an overload detection algorithm. The proposed algorithm runs on the CloudSim toolkit environment and analyzes it based on different parameters like energy consumption, SLA violation, server shutdown, and the number of VM migrations to analyze energy efficiency improvement. This modified approach exhibited better performance on all the parameters as compared to the existing algorithms.