Reviewerly/exHarmony huggingface dataset:
Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns

Previous studies have shown that combing with color properties may be used as part of the display presented to BCI users in order to improve performance. Build on this, we explored the effects of combinations of face stimuli with three primary colors (RGB) on BCI performance which is assessed by classification accuracy and information transfer rate (ITR). Furthermore, we analyzed the waveforms of three patterns.We compared three patterns in which semitransparent face is overlaid three primary colors as stimuli: red semitransparent face (RSF), green semitransparent face (GSF), and blue semitransparent face (BSF). Bayesian linear discriminant analysis (BLDA) was used to construct the individual classifier model. In addition, a Repeated-measures ANOVA (RM-ANOVA) and Bonferroni correction were chosen for statistical analysis.The results indicated that the RSF pattern achieved the highest online averaged accuracy with 93.89%, followed by the GSF pattern with 87.78%, while the lowest performance was caused by the BSF pattern with an accuracy of 81.39%. Furthermore, significant differences in classification accuracy and ITR were found between RSF and GSF (p < 0.05) and between RSF and BSF patterns (p < 0.05).The semitransparent faces colored red (RSF) pattern yielded the best performance of the three patterns. The proposed patterns based on ERP-BCI system have a clinically significant impact by increasing communication speed and accuracy of the P300-speller for patients with severe motor impairment.
Walking Recognition in Mobile Devices

Presently, smartphones are used more and more for purposes that have nothing to do with phone calls or simple data transfers. One example is the recognition of human activity, which is relevant information for many applications in the domains of medical diagnosis, elderly assistance, indoor localization, and navigation. The information captured by the inertial sensors of the phone (accelerometer, gyroscope, and magnetometer) can be analyzed to determine the activity performed by the person who is carrying the device, in particular in the activity of walking. Nevertheless, the development of a standalone application able to detect the walking activity starting only from the data provided by these inertial sensors is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the smartphone can experience and which have nothing to do with the physical displacement of the owner. In this work, we explore and compare several approaches for identifying the walking activity. We categorize them into two main groups: the first one uses features extracted from the inertial data, whereas the second one analyzes the characteristic shape of the time series made up of the sensors readings. Due to the lack of public datasets of inertial data from smartphones for the recognition of human activity under no constraints, we collected data from 77 different people who were not connected to this research. Using this dataset, which we published online, we performed an extensive experimental validation and comparison of our proposals.
Exascale Deep Learning to Accelerate Cancer Research

Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural network architecture (e.g. number of layers, types of layers, connections between layers, etc.) plays a critical role in determining what, if anything, the neural network is able to learn from the training data. The trend for neural network architectures, especially those trained on ImageNet, has been to grow ever deeper and more complex. The result has been ever increasing accuracy on benchmark datasets with the cost of increased computational demands. In this paper we demonstrate that neural network architectures can be automatically generated, tailored for a specific application, with dual objectives: accuracy of prediction and speed of prediction. Using MENNDL- an HPC-enabled software stack for neural architecture search-we generate a neural network with comparable accuracy to state-of-the-art networks on a cancer pathology dataset that is also 16× faster at inference. The speedup in inference is necessary because of the volume and velocity of cancer pathology data; specifically, the previous state-of-the-art networks are too slow for individual researchers without access to HPC systems to keep pace with the rate of data generation. Our new model enables researchers with modest computational resources to analyze newly generated data faster than it is collected.
The technology of retail central bank digital currency

Central bank digital currencies (CBDCs) promise to provide cash-like safety and convenience for peer-to-peer payments. To do so, they must be resilient and accessible. They should also safeguard the user's privacy, while allowing for effective law enforcement. Different technical designs satisfy these attributes to varying degrees, depending on whether they feature intermediaries, a conventional or distributed infrastructure, account- or token-based access, and retail interlinkages across borders. We set out the underlying trade-offs and the related hierarchy of design choices.
A pruned support vector data description-based outlier detection method: Applied to robust process monitoring

Support vector data description (SVDD) is a boundary-based one-class classifier that has been widely used for process monitoring during recent years. However, in some applications where databases are often contaminated by outliers, the performance of SVDD would become deteriorated, leading to low detection rate. To this end, this paper proposes a pruned SVDD model in order to improve its robustness. In contrast to other robust SVDD models that are developed from the algorithmic level, we prune the basic SVDD from a data level. The rationale is to exclude outlier examples from the final training set as many as possible. Specifically, three different SVDD models are constructed successively with different training sets. The first model is used to extract target points by means of rejecting more suspect outlier examples. The second model is constructed using those extracted target points, and is used to recover some false outlier examples labeled by the first model. We build the third (final) model with the final training set consisting of target examples by the first model and false outlier examples by the second model. We validate our proposed method on 20 benchmark data sets and TE data set. Comparative results show that our pruned model could improve the robustness of SVDD more efficiently.
Generative neural network based spectrum sharing using linear sum assignment problems

Spectrum management and resource allocation (RA) problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks. The traditional approaches for solving such problems usually consume time and memory, especially for large-size problems. Recently different machine learning approaches have been considered as potential promising techniques for combinatorial optimization problems, especially the generative model of the deep neural networks. In this work, we propose a resource allocation deep autoencoder network, as one of the promising generative models, for enabling spectrum sharing in underlay device-to-device (D2D) communication by solving linear sum assignment problems (LSAPs). Specifically, we investigate the performance of three different architectures for the conditional variational autoencoders (CVAE). The three proposed architecture are the convolutional neural network (CVAE-CNN) autoencoder, the feed-forward neural network (CVAE-FNN) autoencoder, and the hybrid (H-CVAE) autoencoder. The simulation results show that the proposed approach could be used as a replacement of the conventional RA techniques, such as the Hungarian algorithm, due to its ability to find solutions of LASPs of different sizes with high accuracy and very fast execution time. Moreover, the simulation results reveal that the accuracy of the proposed hybrid autoencoder architecture outperforms the other proposed architectures and the state-of-the-art DNN techniques.
Deciphering the Complex Mineralogy of River Sand Deposits through Clustering and Quantification of Hyperspectral X-Ray Maps

Alluvial mineral sands rank among the most complex subjects for mineral characterization due to the diverse range of minerals present in the sediments, which may collectively contain a daunting number of elements (&gt;20) in major or minor concentrations (&gt;1 wt%). To comprehensively characterize the phase abundance and chemistry of these complex mineral specimens, a method was developed using hyperspectral x-ray and cathodoluminescence mapping in an electron probe microanalyser (EPMA), coupled with automated cluster analysis and quantitative analysis of clustered x-ray spectra. This method proved successful in identifying and quantifying over 40 phases from mineral sand specimens, including unexpected phases with low modal abundance (&lt;0.1%). The standard-based quantification method measured compositions in agreement with expected stoichiometry, with elemental detection limits in the range of &lt;10–1,000 ppm, depending on phase abundance, and proved reliable even for challenging mineral species, such as the multi-rare earth element (REE) bearing mineral xenotime [(Y,REE)PO 4 ] for which 24 elements were analyzed, including 12 overlapped REEs. The mineral identification procedure was also capable of characterizing mineral groups that exhibit significant compositional variability due to the substitution of multiple elements, such as garnets (Mg, Ca, Fe, Mn, Cr), pyroxenes (Mg, Ca, Fe), and amphiboles (Na, Mg, Ca, Fe, Al).
Joint Keyphrase Chunking and Salience Ranking with BERT.

An effective keyphrase extraction system requires to produce self-contained high quality phrases that are also key to the document topic. This paper presents BERT-JointKPE, a multi-task BERT-based model for keyphrase extraction. JointKPE employs a chunking network to identify high-quality phrases and a ranking network to learn their salience in the document. The model is trained jointly on the chunking task and the ranking task, balancing the estimation of keyphrase quality and salience. Experiments on two benchmarks demonstrate JointKPE's robust effectiveness with different BERT variants. Our analyses show that JointKPE has advantages in predicting long keyphrases and extracting phrases that are not entities but also meaningful. The source code of this paper can be obtained from this https URL
MPL-GAN: Toward Realistic Meteorological Predictive Learning Using Conditional GAN

Meteorological imagery prediction is an important and challenging problem for weather forecasting. It can also be seen as a video frame prediction problem that estimates future frames based on observed meteorological imageries. Despite it is a widely-investigated problem, it is still far from being solved. Current state-of-the-art deep learning based approaches mainly optimise the mean square error loss resulting in blurry predictions. We address this problem by introducing a Meteorological Predictive Learning GAN model (in short MPL-GAN) that utilises the conditional GAN along with the predictive learning module in order to handle the uncertainty in future frame prediction. Experiments on a real-world dataset demonstrate the superior performance of our proposed model. Our proposed model is able to map the blurry predictions produced by traditional mean square error loss based predictive learning methods back to their original data distributions, hence it is able to improve and sharpen the prediction. In particular, our MPL-GAN achieves an average sharpness of 52.82, which is 14% better than the baseline method. Furthermore, our model correctly detects the meteorological movement patterns that traditional unconditional GANs fail to do.
When Bert Forgets How To POS: Amnesic Probing of Linguistic Properties and MLM Predictions

A growing body of work makes use of probing in order to investigate the working of neural models, often considered black boxes. Recently, an ongoing debate emerged surrounding the limitations of the probing paradigm. In this work, we point out the inability to infer behavioral conclusions from probing results, and offer an alternative method which is focused on how the information is being used, rather than on what information is encoded. Our method, Amnesic Probing, follows the intuition that the utility of a property for a given task can be assessed by measuring the influence of a causal intervention which removes it from the representation. Equipped with this new analysis tool, we can now ask questions that were not possible before, e.g. is part-of-speech information important for word prediction? We perform a series of analyses on BERT to answer these types of questions. Our findings demonstrate that conventional probing performance is not correlated to task importance, and we call for increased scrutiny of claims that draw behavioral or causal conclusions from probing results.
SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks

a b s t r a c t SeMi (SEmantic Modeling machIne) is a tool to semi-automatically build large-scale Knowledge Graphs from structured sources such as CSV, JSON, and XML files.To achieve such a goal, SeMi builds the semantic models of the data sources, in terms of concepts and relations within a domain ontology.Most of the research contributions on automatic semantic modeling is focused on the detection of semantic types of source attributes.However, the inference of the correct semantic relations between these attributes is critical to reconstruct the precise meaning of the data.SeMi covers the entire process of semantic modeling: (i) it provides a semi-automatic step to detect semantic types; (ii) it exploits a novel approach to inference semantic relations, based on a graph neural network trained on background linked data.At the best of our knowledge, this is the first technique that exploits a graph neural network to support the semantic modeling process.Furthermore, the pipeline implemented in SeMi is modular and each component can be replaced to tailor the process to very specific domains or requirements.This contribution can be considered as a step ahead towards automatic and scalable approaches for building Knowledge Graphs.
On Learning Sets of Symmetric Elements

Learning from unordered sets is a fundamental learning setup, recently attracting increasing attention. Research in this area has focused on the case where elements of the set are represented by feature vectors, and far less emphasis has been given to the common case where set elements themselves adhere to their own symmetries. That case is relevant to numerous applications, from deblurring image bursts to multi-view 3D shape recognition and reconstruction. In this paper, we present a principled approach to learning sets of general symmetric elements. We first characterize the space of linear layers that are equivariant both to element reordering and to the inherent symmetries of elements, like translation in the case of images. We further show that networks that are composed of these layers, called Deep Sets for Symmetric Elements (DSS) layers, are universal approximators of both invariant and equivariant functions, and that these networks are strictly more expressive than Siamese networks. DSS layers are also straightforward to implement. Finally, we show that they improve over existing set-learning architectures in a series of experiments with images, graphs, and point-clouds.
Human Rights are (Increasingly) Plural: Learning the Changing Taxonomy of Human Rights from Large-scale Text Reveals Information Effects

This manuscript helps to resolve the ongoing debate concerning the effect of information communication technology on human rights monitoring. We reconceptualize human rights as a taxonomy of nested rights that are judged in textual reports and argue that the increasing density of available information should manifest in deeper taxonomies of human rights. With a new automated system, using supervised learning algorithms, we are able to extract the implicit taxonomies of rights that were judged in texts by the US State Department, Amnesty International, and Human Rights Watch over time. Our analysis provides new, clear evidence of change in the structure of these taxonomies as well as in the attention to specific rights and the sharpness of distinctions between rights. Our findings bridge the natural language processing and human rights communities and allow a deeper understanding of how changes in technology have affected the recording of human rights over time.
Blockchain and IoT-based architecture design for intellectual property protection

Purpose The blockchain technology provides a way to record transactions that is designed to be highly secure, transparent, trustable, traceable, auditable and tamper-proof. And, the internet of things (IoT) technology provides the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction, which is able to link computing devices and digitized machines, things, objects, animals and people that are provided with digital unique identifiers (UIDs). This paper aims to explore the combined application of blockchain and IoT-based technologies, especially on the intellectual property protection area. Design/methodology/approach In this paper, the authors propose a high-level architecture design of blockchain and IoT-based intellectual property protection system, which can help to process three types of intellectual property: (1) patents, copyrights, trademarks etc.; (2) industrial design, trade dress, craft works, trade secrets etc.; and (3) plant variety rights, geographical indications, etc. Findings Using blockchain peer-to-peer network and IoT devices, the proposed method can help people to establish a trusted, self-organized, open and ecological intellectual property protection system. Originality/value To the best of the authors’ knowledge, this is the first work that applied blockchain and IoT technologies on traditional intellectual property protection and trade ecosystem.
On Stability of a Class of Filters for Nonlinear Stochastic Systems

This article develops a comprehensive framework for stability analysis of a broad class of commonly used continuous-and discrete-time filters for stochastic dynamic systems with nonlinear state dynamics and linear measurements under certain strong assumptions.The class of filters encompasses the extended and unscented Kalman filters and most other Gaussian assumed density filters and their numerical integration approximations.The stability results are in the form of time-uniform mean square bounds and exponential concentration inequalities for the filtering error.In contrast to existing results, it is not always necessary for the model to be exponentially stable or fully observed.We review three classes of models that can be rigorously shown to satisfy the stringent assumptions of the stability theorems.Numerical experiments using synthetic data validate the derived error bounds.
Error-mitigated data-driven circuit learning on noisy quantum hardware

Application-level benchmarks measure how well a quantum device performs meaningful calculations. In the case of parameterized circuit training, the computational task is the preparation of a target quantum state via optimization over a loss landscape. This is complicated by various sources of noise, fixed hardware connectivity, and generative modeling, the choice of target distribution. Gradient-based training has become a useful benchmarking task for noisy intermediate-scale quantum computers because of the additional requirement that the optimization step uses the quantum device to estimate the loss function gradient. In this work, we use gradient-based data-driven circuit learning to qualitatively evaluate the performance of several superconducting platform devices and present results that show how error mitigation can improve the training of quantum circuit Born machines with 28 tunable parameters.
Corpus Driven Analysis of News Reports about Covid-19 in a Malaysian Online Newspaper

Covid-19 was declared a global pandemic by the World Health Organization on 11 th March, 2020. Scientists and researchers immediately took interests to find a vaccine. While researchers in various discipline, particularly in medicine, began to study about the virus to help us understand the situation from various perspectives, there is limited research on linguistics analysis to date about Covid-19. Therefore, the present study aims to contribute to research in linguistics about Covid-19, by employing corpus-driven approach for data analysis. Online newspaper reports about Covid-19 were downloaded from the Star online from March 1 st to March 31 st , 2020. A total of 1018 news reports were processed and analysed, with 140 themes being identified.100 top collocates with the MI score set at a minimum of 5, using AntConc software revealed that verb and noun collocates co-occurred frequently with the node word Covid-19. Generally, thecollocates reflect fear, anxiety and uncertainty that majority of Malaysians feel. have. It also shows how the virus is taking a toll on Malaysia and Malaysians with regards to economy and social life. On the other hand, some of the collocates portrayed the government to be in total control of the situation, despite the threat to health and economic situations in the country. Suggestions for future research about Covid-19 include comparison of several newspaper reports from English newspapers in Malaysia, analysis of news report during extended movement control order (ECMO) in Malaysia or discourse analysis of crisis communication by political leaders in ASEAN.
Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality

In this study, we prove that an intrinsic low dimensionality of covariates is the main factor that determines the performance of deep neural networks (DNNs). DNNs generally provide outstanding empirical performance. Hence, numerous studies have actively investigated the theoretical properties of DNNs to understand their underlying mechanisms. In particular, the behavior of DNNs in terms of high-dimensional data is one of the most critical questions. However, this issue has not been sufficiently investigated from the aspect of covariates, although high-dimensional data have practically low intrinsic dimensionality. In this study, we derive bounds for an approximation error and a generalization error regarding DNNs with intrinsically low dimensional covariates. We apply the notion of the Minkowski dimension and develop a novel proof technique. Consequently, we show that convergence rates of the errors by DNNs do not depend on the nominal high dimensionality of data, but on its lower intrinsic dimension. We further prove that the rate is optimal in the minimax sense. We identify an advantage of DNNs by showing that DNNs can handle a broader class of intrinsic low dimensional data than other adaptive estimators. Finally, we conduct a numerical simulation to validate the theoretical results.
Scalable, resilient, and configurable permissioned blockchain fabric

With the advent of Bitcoin, the interest of the database community in blockchain systems has steadily grown. Many existing blockchain applications use blockchains as a platform for monetary transactions, however. We deviate from this philosophy and present ResilientDB, which can serve in a suite of non-monetary data-processing blockchain applications. Our ResilientDB uses state-of-the-art technologies and includes a novel visualization that helps in monitoring the state of the blockchain application.