Reviewerly/exHarmony huggingface dataset:
Solar Wind Speed Prediction With Two‐Dimensional Attention Mechanism

Abstract As more and more high‐technical systems are exposed to the space environment, extreme space weather becomes a great threat to human society. In the solar system, space weather is influenced by the solar wind, such that reliable prediction of solar wind conditions in the near‐Earth environment effectively reduces the impact of space weather on human society. Solar wind speed prediction is improved by making full use of OMNI data measured at Lagrangian Point 1 (L1) by the National Aeronautics and Space Administration (NASA) and image data observed by the Solar Dynamics Observatory (SDO) satellite in this work. Specifically, we propose a model based on the “two‐dimensional attention mechanism” (TDAM) to predict solar wind speed. In this study, we first analyze and preprocess data from 2011 to 2017. Second, considering the characteristics of time series data, we adopt the gated recurrent units (GRU) model which can deal with long‐term dependence as the prediction part of our model. Third, we design a TDAM, which enables our prediction network to focus on important parts. Three performance indices are used: root‐mean‐square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC). By comparing TDAM with other models, we find that the TDAM model achieves the best prediction results, with RMSE of 62.8 km/s, MAE of 47.8 km/s, and CC of 0.789 24 h in advance. The experimental results show that the proposed TDAM model can improve the prediction accuracy of solar wind speed.
Classification of fall directions via wearable motion sensors

Effective fall-detection and classification systems are vital in mitigating severe medical and economical consequences of falls to people in the fall risk groups. One class of such systems is based on wearable sensors. While there is a vast amount of academic work on this class of systems, not much effort has been devoted to the investigation of effective and robust algorithms and like-for-like comparison of state-of-the-art algorithms using a sufficiently large dataset. In this article, fall-direction classification algorithms are presented and compared on an extensive dataset, comprising a total of 1600 fall trials. Eight machine learning classifiers are implemented for fall-direction classification into four basic directions (forward, backward, right, and left). These are, namely, Bayesian decision making (BDM), least squares method (LSM), k-nearest neighbor classifier (k-NN), artificial neural networks (ANNs), support vector machines (SVMs), decision-tree classifier (DTC), random forest (RF), and adaptive boosting or AdaBoost (AB). BDM achieves perfect classification, followed by k-NN, SVM, and RF. Data acquired from only a single motion sensor unit, worn at the waist of the subject, are processed for experimental verification. Four of the classifiers (BDM, LSM, k-NN, and ANN) are modified to handle the presence of data from an unknown class and evaluated on the same dataset. In this robustness analysis, ANN and k-NN yield accuracies above 96.2%. The results obtained in this study are promising in developing real-world fall-classification systems as they enable fast and reliable classification of fall directions.
A comprehensive appraisal of perceptual visual complexity analysis methods in GUI design

• Perceptual visual complexity is a measurable concept and it could be imitated by machines, • Visual complexity analysis without human bias can be performed for graphical user interfaces, • In the near future, machines will be able to control the design of user interfaces. Graphical or Visual User Interface (GUI) is recognized as one of the most important application components for safety critical and business oriented software systems. It is highly advantageous for GUI designers and application developers to analyze the visual complexity of a GUI and predict users’ perception and judgment during the design phase. Although in recent years, various methods have been developed for visual complexity analysis, these have not been widely used due to applicability, practicality and validity issues. In this respect, we have conducted a comprehensive review of studies and methods in visual complexity analysis. After identifying and analyzing 85 research studies, we grouped the visual complexity analysis methods and accordingly a taxonomy is presented. Furthermore, conceptual comparison of the methods is given and gap analysis as well as possible future directions are provided. According to the our findings, major gaps for each visual complexity analysis method may be stated as follows: 1) In metric-model based methods, there is a lack of information about the suitability of the metric-model created for analysis, since the extent to which each metric contributes to visual complexity analysis is still not known exactly. 2) In heuristic- based methods, the extracted rule set is not yet extendable enough beyond the use for specific GUIs. 3) While the visual complexity analysis could be considered as a kind of computer vision task, there exist limited studies that does so. Therefore, generalizable solutions based on machine learning techniques seem to be a promising research direction to develop efficient approaches.
(Mis)managed: A Novel TLB-based Covert Channel on GPUs

GPUs are now commonly available in most modern computing platforms. They are increasingly being adopted in cloud platforms and data centers due to their immense computing capability. In response to this growth in usage, manufacturers continuously try to improve GPU hardware by adding new features. However, this increase in usage and the addition of utility-improving features can create new, unexpected attack channels. In this paper, we show that two such features-unified virtual memory (UVM) and multi-process service (MPS)-primarily introduced to improve the programmability and efficiency of GPU kernels have an unexpected consequence-that of creating a novel covert-timing channel via the GPU's translation lookaside buffer (TLB) hierarchy. To enable this covert channel, we first perform experiments to understand the characteristics of TLBs present on a GPU. The use of UVM allows fine-grained management of translations, and helps us discover several idiosyncrasies of the TLB hierarchy, such as three-levels of TLB, coalesced entries. We use this newly-acquired understanding to demonstrate a novel covert channel via the shared TLB. We then leverage MPS to increase the bandwidth of this channel by 40×. Finally, we demonstrate the channel's utility by leaking data from a GPU-accelerated database application.
Unsupervised Adaptive Embedding for Dimensionality Reduction

High-dimensional data are highly correlative and redundant, making it difficult to explore and analyze. Amount of unsupervised dimensionality reduction (DR) methods has been proposed, in which constructing a neighborhood graph is the primary step of DR methods. However, there exist two problems: 1) the construction of graph is usually separate from the selection of projection direction and 2) the original data are inevitably noisy. In this article, we propose an unsupervised adaptive embedding (UAE) method for DR to solve these challenges, which is a linear graph-embedding method. First, an adaptive allocation method of neighbors is proposed to construct the affinity graph. Second, the construction of affinity graph and calculation of projection matrix are integrated together. It considers the local relationship between samples and global characteristic of high-dimensional data, in which the cleaned data matrix is originally proposed to remove noise in subspace. The relationship between our method and local preserving projections (LPPs) is also explored. Finally, an alternative iteration optimization algorithm is derived to solve our model, the convergence and computational complexity of which are also analyzed. Comprehensive experiments on synthetic and benchmark datasets illustrate the superiority of our method.
Framework and performance analysis of college English testing system based on data mining technology

At present, the college English testing system has become an indispensable system in many universities. However, the English test system is not highly humanized due to problems such as unreasonable framework structure. This paper combines data mining technology to build a college English test framework. The college English test system software based on data mining mainly realizes the computer program to automatically generate test papers, set the test time to automatically judge the test takers’ test results, and give out results on the spot. The test takers log in to complete the test through the test system software. The examination system software solves the functions of printing test papers, arranging invigilation classrooms, invigilating teachers, invigilating process, collecting test papers, scoring and analyzing test papers in traditional examinations. Finally, this paper analyzes the performance of this paper through experimental research. The research results show that the system constructed in this paper has certain practical effects.
Two-stage supervised ranking for emotion cause extraction

Textual emotion analysis is a challenging research topic in the field of natural language processing (NLP), which plays an important role in related NLP tasks, such as opinion mining and personalized recommendation. Existing research on emotion analysis has focused mostly on detecting types of emotions, and has solved problems using classification-based methods. Recently, fine-grained emotion analysis has attracted the attention of researchers for probing the essential elements of emotions, such as the causes, experiencers and results of emotion events, which could help further elucidate textual emotions in more depth. In this paper, we focus on the task of emotion cause extraction, aiming to recognize the causes in sentences that provoke certain emotions. We propose a two-stage supervised ranking method for accurately extracting the emotion causes based on information retrieval techniques. In the first stage, we measure the complexity of provoked emotions using query performance predictors to distinguish the number of causes for each emotion in contexts. In the second stage, we incorporate the emotion complexity into learning an autoencoder-enhanced ranking model for accurately extracting the causal clauses. We also extract abundant emotion-level clause features for clause representations as the learning samples. We evaluate the proposed method on an existing dataset for emotion cause extraction and demonstrate that our method significantly outperforms the state-of-the-art baseline methods. The proposed method is effective in extracting textual emotion causes in sentences, which can greatly benefit in-depth emotion analysis for effective cognitive computing.
Poisson-Skellam distribution based regularization conditional random field method for photon-limited Poisson image denoising

Photon-limited Poisson image denoising is urgent demand in many application fields, but is particularly challenging because the image structures are often damaged seriously. The effective using of the image prior is very important for improving the quality of the denoised image. In this paper, we exploit the prior of inner statistical relationships among pixels in local spatial neighborhood of the Poisson noisy image and introduce a new Skellam distribution based inner interaction potential function as a distance measurement between the pixels. In the framework of conditional random field (CRF) modeling, we propose a novel Poisson-Skellam distribution based regularization CRF model for photon-limited Poisson noise removal. Using the alternating direction method of multipliers technique, we extend our method to a flexible plug-and-play scheme in which we can combine powerful Gaussian denoising method to improve the denoising performance better.
Quantum effects induced by two classical fields in a coherent cavity field containing two two-level atoms

The paper derives an exact solution for a two-qubit system coupling to a quantized cavity field driven by two different types of external fields when the cavity is initially in a coherent field state. The two-qubit quantum coherence via the Jensen–Shannon divergence and the two-qubit entanglement via the negativity are investigated under the effects of the physical parameters. The results are depicted that, the generated two-qubit effects depend on the external-qubit and the external-field detunings as well as the initial cavity coherent intensity. It is found that the increase of the parameters of the initial coherent states, the detuning between the cavity field and the external classical field, and the detuning between the qubit and the external classical fields are played a key role in the dynamics of the population inversion and the generated entropic qubit-cavity entanglement. The oscillation amplitudes of the atomic population can be improved when the classical-field detuning is equal to the qubit detuning, and they can be weakened dramatically when the qubit detuning is smaller than the classical-field detuning. The qubit-cavity entanglement can be improved when the classical-field detuning is larger than the qubit detuning, and vice versa.
<i>TilGAN</i>: GAN for Facilitating Tumor-Infiltrating Lymphocyte Pathology Image Synthesis With Improved Image Classification

Tumor-infiltrating lymphocytes (TILs) act as immune cells against cancer tissues. The manual assessment of TILs is usually erroneous, tedious, costly and subject to inter- and intraobserver variability. Machine learning approaches can solve these issues, but they require a large amount of labeled data for model training, which is expensive and not readily available. In this study, we present an efficient generative adversarial network, TilGAN, to generate high-quality synthetic pathology images followed by classification of TIL and non-TIL regions. Our proposed architecture is constructed with a generator network and a discriminator network. The novelty exists in the TilGAN architecture, loss functions, and evaluation techniques. Our TilGAN-generated images achieved a higher Inception score than the real images (2.90 vs. 2.32, respectively). They also achieved a lower kernel Inception distance (1.44) and a lower Fréchet Inception distance (0.312). It also passed the Turing test performed by experienced pathologists and clinicians. We further extended our evaluation studies and used almost one million synthetic data, generated by TilGAN, to train a classification model. Our proposed classification model achieved a 97.83% accuracy, a 97.37% F1-score, and a 97% area under the curve. Our extensive experiments and superior outcomes show the efficiency and effectiveness of our proposed TilGAN architecture. This architecture can also be used for other types of images for image synthesis.
Nature‐inspired resource management and dynamic rescheduling of microservices in Cloud datacenters

Abstract Distributed Cloud environments are now resorting to Cloud applications composed of heterogeneous microservices. Cloud service providers strive to provide high quality of service (QoS) and response time is one of the key QoS attributes for microservices. The dynamism of microservice ecosystems necessitates runtime adaptations and microservices rescheduling to avoid performance degradation. Existing works target rescheduling in hypervisor‐based systems, while ignoring the influence of configuration parameters of container‐based microservices. In an effort to address these challenges, this article describes a novel microservice rescheduling framework, throttling and interaction‐aware anticorrelated rescheduling for microservices, to proactively perform rescheduling activities whilst ensuring timely service responses. Based on periodic monitoring of the performance attributes, the framework schedules container migrations. Considering the exponentially large solution space, a metaheuristic approach based on multiverse optimization is developed to generate the near‐optimal mapping of microservices to the datacenter resources. Experimental results indicate that our framework provides superior performance with a reduction of up to 13.97% in the average response time, when compared with systems with no support for rescheduling.
The influence of the benchmark corpus on keyword analysis

Abstract The growing popularity of keyword analysis as an applied linguistics methodology has not been matched by an increase in the rigour with which the method is applied. While several studies have investigated the impact of choices made at certain stages of the keyword analysis process, the impact of the choice of benchmark corpus has largely been overlooked. In this paper, we compare a target corpus with several benchmark corpora and show that the keywords generated are different. We also show that certain characteristics of the keyword list and of the keywords themselves vary in relatively predictable ways depending on the benchmark corpus. These variations have implications for the choice of benchmark corpus and how the results of a keyword analysis should be interpreted. Analyzing the keywords from a comparison with a large general corpus or the keyword lists from multiple comparisons may be most appropriate for register studies.
Self-Guided Deep Multi-View Subspace Clustering Network

To cluster the data with complex structures, Deep Subspace Clustering Network (DSCN) extracts the subspace relations among non-linear latent features. However, the performance improvement has encountered bottlenecks due to the lack of supervision. Meantime, in multi-view settings, most of DSCN-based methods underestimate the significance of view-fusion, which always adopt simple tactics. To address these issues, we propose a self-supervised model for simultaneous subspace clustering, consensus construction and self-guided learning, named as Self-Guided Deep Multi-view Subspace Clustering Network (SG-DMSC). We utilize DSCN to learn a complex subspace representation for each single-view. Considering their different importance, we design the view-fusion layer to establish the agreement. We construct a novel loss term, the spectral supervisor, so that the consensus can be more clustering-friendly by the self-guidance of pseudo labels. Theoretical support is provided to reflect the validity of this self-guided strategy. An alternate iterative optimization algorithm is presented to handle SG-DMSC. Experiments on real-world datasets confirm its efficacy compared with others.
A contemplative perspective on federated machine learning: Taxonomy, threats &amp; vulnerability assessment and challenges

Today, the rapid growth of the internet and advancements in mobile technology and increased internet connectivity have brought us to a data-driven economy where an enormous amount of data is being used to train machine learning models to make strategic decisions. However, in the aftermath of a data breach by Facebook in 2018, there are some serious concerns over user data privacy and security being used to train the Machine Learning models. In this context, a new approach, Federated Machine Learning is now one of the most talked-about and recent approaches. Current research primarily focuses on Federated Learning's advantages over the traditional methods and/or its classification. However, being in a nascent stage of development as a method, certain challenges need to be addressed. This paper intends to address the totality of federated learning with a complete vulnerability assessment. During the study of the literature, it is found that security being promised as one of the key advantages of federated learning can still not be guaranteed because of some issues inherently present, and this can lead to poisoning, inference attacks and insertion of backdoors, etc. This paper intends to provide a complete picture by giving an in-depth and comprehensive analysis of Federated Learning and its taxonomy. It also provides a detailed vulnerability assessment and highlights the challenges faced in the current setting and future research directions to make federated learning a more functional, robust and secure method to train machine learning models.
Information Extraction From Free-Form CV Documents in Multiple Languages

This paper proposes two natural language processing models for extracting useful information from multilingual, unstructured (free form) CV documents. The model identifies the relevant document sections (personal information, education, employment, etc.) and the corresponding specific information at the lower hierarchy level (names, addresses, roles, skill competences, etc.). Our approach employs the transformer architecture and its multilingual implementation of the encoder part in the form of the BERT language model. The models are trained and tested on a large, manually annotated CV dataset, achieving high scores on standard accuracy measures. The proposed models exhibit important properties of end-to-end training and interpretability, which was investigated by visualizing the model attention and its vector representations.
Blockchain-Based Cyber Security and Advanced Distribution in Smart Grid

Blockchain commoditizes confidence in a step to grid flexibility and allows decentralized smart contracts to enforce auditable multiparty payments based on established standards among distributed power suppliers and customers. Smart contracts reduce the need to communicate with foreign entities, making it feasible for distributed energy trades and exchanges, including energy flows and financial activity, to be implemented and monetized. It might help decrease the cost of transactive energy and increase protection. This work provides how a renewable energy business is secure and how privacy and anonymity are given for such a system through blockchain implementation.
Blockchain-based Access Control Mechanism of Federated Data Sharing System

Sharing data assets has emerged among organizations with variable interest, especially in the manner of federation. Blockchain has been used in data sharing systems to provide the audit and access control on data because of its tamper-proof characteristics. However, the user's authority is difficult to accurately describe because of the complexity of users' multidimensional roles in the federation. And when developing the decentralized access control mechanism with the support of blockchain techniques, how to improve the efficiency of the decision for access control is the problem that is necessary to be solved. In this paper, we propose a blockchain-based multidimensional user authorization and role-based access control mechanism in the federated data sharing system. The colored coin is defined to identify the user's authority in multidimension, and decentralized access control decision is achieved through the automatic execution of smart contracts, thereby effectively enhancing the mechanism robustness and improving the security decision performance. The system is implemented on the basis of HyperLedger Fabric. Experimental results show that the proposed scheme is feasible and efficient. The decision efficiency is remained stable with the increasing of policy rules.