Reviewerly/exHarmony huggingface dataset:
Scalable Real-Time Analytics for IoT Applications

Large-scale industrial internet of things (IIoT) applications usually access distributed equipment where high-volume sensor streams are processed. The building of scalable analytic queries and models over such streams could potentially enhance various industrial processes management tasks, e.g., distribution, delivery, and predictive online maintenance. To enable real-time and historical analytics over distributed IIoT applications, we have combined an edge data stream management system (EDSMS), sa.engine, with the highly distributed NoSQL database MongoDB. For supporting advanced analytics and high-volume stream injection into MongoDB, we integrated an extended query processing (EQP) system with sa.engine and MongoDB. This work demonstrates how EQP provides a holistic data management solution for IIoT based on a use case for sound anomaly detection of distributed equipment.
Construction of Intelligent Evaluation Model of Power Informatization Monitoring Platform

The traditional intelligent evaluation model of power informatization monitoring platform has the defect of large evaluation error. Therefore, the intelligent evaluation model of electric power information monitoring platform is researched. The neural network is adopted for processing of the electric power information monitoring platform data to obtain the data characteristics. %ased on the characteristics, the intelligent evaluation network of electric power information monitoring platform is constructed to establish the intelligent evaluation model of electric power information monitoring platform. The constructed intelligent evaluation model of electric power information monitoring platform is adopted for the intelligent evaluation of electric power information monitoring platform. The e[periment shows that the evaluation error of the intelligent evaluation model of established electric power information monitoring platform is 2 less than that of the traditional model, which sufficiently describes that the intelligent evaluation model of established electric power information monitoring platform owns better evaluation performance.
What is a Good Visualization for Digital Humanities Researchers? An Exploratory Study

Abstract Visualization in digital humanities (DH) has developed into a charged topic as increasing numbers of humanities researchers begin to work with machine‐readable data. The current research literature on DH visualization has primarily approached the subject from a theoretical perspective, arguing the humanistic visualization should fundamentally differ from scientific visualization to represent the distinct nature of humanities data and inquiries. However, few studies have tried to empirically understand what it means to be a good visualization for humanities researchers and practitioners. This study aims to bridge this research gap by offering an exploratory investigation into researchers' perceptions on visualization, particularly how they evaluate a visualization in humanities research. Through 10 semi‐structured interviews with humanities scholars engaging in digital work, our study demonstrates that perceptions of a quality visualization among the humanities researchers are closely related to researchers' purposes of using visualization and their self‐confidence in visualization knowledge and skills. This study serves as a baseline for future empirical research on DH visualization and potentially informs the best practices for humanistic visualizations.
User Experience Design as a Framework for Environmental and Political Negotiation in Decision-Making about Wind Energy

This case study outlines a user experience design approach, with specific emphasis on persona building, of a web-based mapping and information tool aimed at helping developers site wind energy projects. Relying on philosophies of user experience in the field of technical communication, particularly ones that focus on usefulness, problem solving, and political participation, the authors of this paper present how user experience design was used as a framework for negotiating political differences between stakeholders in wind energy, namely the military and energy developers but also municipal permitting offices and state agencies overseeing energy regulation. In doing so, the authors speculate about the possibility of implementing user experience design as a method for political negotiation, particularly as it pertains to environmental regulation and communication.
Biobanking in the structure of legal regulation of genetic research: experience of the Nordic Countries

The article examines the experience of legal regulation of biobanks in the Nordic countries in the context of the interaction of law and bioethics. The article analyzes the moral possibilities and legal boundaries of access to personal data by the state, society and the research community, provided that the inviolability of private life is strictly observed. On the basis of legislation of individual Nordic countries, as well as the regulatory framework of the European Union, the successes and difficulties in achieving the stated goal are discussed. The issues of opening, functioning and closing of biobanks, as well as the institutional, value and socio-cultural contexts of relevant practices are considered separately.
Employing Sentence Compression to Improve Event Coreference Resolution

Most previous studies on event coreference resolution usually focused on measuring the similarity between two event sentences. However, a sentence may contain more than one event and the redundant event information will interfere with the calculation of event similarity. To address the above issue, this paper proposes an event coreference resolution framework based on event sentence compression mechanism, which used an AutoEncoder-based model to compress the extracted event sentences based on the event triggers. Meanwhile, the information interaction between the compressed sentences and their original event sentences is used to supplement the missing important information in the compressed sentences. Experimental results on both KBP 2016 and KBP 2017 datasets show that our proposed model outperforms several state-of-the-art baselines.
An Optimal Text Watermarking Method for Sensitive Detecting of Illegal Tampering Attacks

Due to the rapid increase in the exchange of text information via internet networks, the security and authenticity of digital content have become a major research issue. The main challenges faced by researchers are how to hide the information within the text to use it later for authentication and attacks tampering detection without effects on the meaning and size of the given digital text. In this paper, an efficient text-based watermarking method has been proposed for detecting the illegal tampering attacks on the Arabic text transmitted online via an Internet network. Towards this purpose, the accuracy of tampering detection and watermark robustness has been improved of the proposed method as compared with the existing approaches. In the proposed method, both embedding and extracting of the watermark are logically implemented, which causes no change in the digital text. This is achieved by using the third level and alphanumeric strategy of the Markov model as a text analysis technique for analyzing the Arabic contents to obtain its features which are considered as the digital watermark. This digital watermark will be used later to detecting any tampering of illegal attack on the received Arabic text. An extensive set of experiments using four data sets of varying lengths proves the effectiveness of our approach in terms of detection accuracy, robustness, and effectiveness under multiple random locations of the common tampering attacks.
Implementation of Visual Sentiment Analysis on Flickr Images

Visual sentiment analysis is the way to automatically recognize positive and negative emotions from images, videos, graphics and stickers. To estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment, most of the state of the art works exploit the text associated with a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user, which usually includes text useful to maximize the diffusion of the social post. This System will extract three views: visual view, subjective text view and objective text view of Flickr images and will give sentiment polarity positive, negative or neutral based on the hypothesis table. Subjective text view gives sentiment polarity using VADER (Valence Aware Dictionary and sEntiment Reasoner) and objective text view gives sentiment polarity with three convolution neural network models. This system implements VGG-16, Inception-V3 and ResNet-50 convolution neural networks with pre pre-trained ImageNet dataset. The text extracted through these three convolution networks is given to VADER as input to find sentiment polarity. This system implements visual view using a bag of visual word model with BRISK (Binary Robust Invariant Scalable Key points) descriptor. System has a training dataset of 30000 positive, negative and neutral images. All the three views’ sentiment polarity is compared. The final sentiment polarity is calculated as positive if two or more views gives positive sentiment polarity, as negative if two or more views gives negative sentiment polarity and as neutral if two or more views gives neutral sentiment polarity. If all three views give unique polarity then the polarity of the objective text view is given as output sentiment polarity.
Towards Language-Guided Visual Recognition via Dynamic Convolutions

In this paper, we are committed to establishing a unified and end-to-end multi-modal network via exploring language-guided visual recognition. To approach this target, we first propose a novel multi-modal convolution module called Language-guided Dynamic Convolution (LaConv). Its convolution kernels are dynamically generated based on natural language information, which can help extract differentiated visual features for different multi-modal examples. Based on the LaConv module, we further build a fully language-driven convolution network, termed as LaConvNet, which can unify the visual recognition and multi-modal reasoning in one forward structure. To validate LaConv and LaConvNet, we conduct extensive experiments on seven benchmark datasets of three vision-and-language tasks, i.e., visual question answering, referring expression comprehension and segmentation. The experimental results not only show the competitive or better performance of LaConvNet against existing multi-modal networks, but also witness the merits of LaConvNet as an unified structure, including compact network, low computational cost and high generalization ability. Our source code is released in SimREC project: https://github.com/luogen1996/LaConvNet .
Spin coherent states, Bell states, spin Hamilton operators, entanglement, Husimi distribution, uncertainty relation and Bell inequality

Abstract We investigate spin Hamilton operators and compare spin coherent states and Bell states concerning entanglement, Husimi distributions, uncertainty relation and Bell inequality. The distances between spin coherent states and Bell states are derived. The Rayleigh quotients of spin Hamilton operators for spin coherent states and Bell states are evaluated and compared.
Text Classification under Attention Mechanism Based on Label Embedding

Text classification is one of key tasks for representing the semantic information of documents. There have been a lot of studies that applied Long Short-Term Memory (LSTM) to this task. This paper develops a novel model to improve the traditional LSTM for text classification, which is based on label embedding and attention mechanism. We use Convolutional Neural Network (CNN) to learn the compatibility between words and labels, which is used as attention value to improve the sequence output generated by Bi-LSTM to ensure that the weight of related words is higher than that of unrelated words. Experimental results on five text datasets demonstrate that the proposed architecture is very competitive compared with the state-of-the-art models.
A Satellite-Based Method for Forecasting Solar Radiation Part I: Cloud Motion and Trajectory Modeling

Advanced imagery from instruments onboard geostationary and/or polar orbit satellites provide measurements of electromagnetic radiation emitted from the earth's atmosphere at visible, near-infrared, and infrared channels. In Asia, the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite provides a unique opportunity to exploit its high frequency imagery capabilities (every 10 min) that can be used to detect and characterize cloud physical and optical characteristics as well as to enhance cloud motion/transport algorithms with possible applications to short and medium term solar radiation forecasting.
Target Registration of Electric Power Installation Based on Hybrid Network Model Under the Application of Intelligent Inspection Application

In the on-line monitoring of power equipment, it is necessary to identify the appearance image of the target, and the image fault recognition must first carry out the accurate matching of the image, so there is an increasingly urgent demand for the application technology of the visual positioning of the target detection equipment. In this paper, a hybrid network model based target seeking algorithm for power equipment is proposed, which combines two research routes: template matching of twin network and image registration based on feature point matching. In the aspect of network depth, the algorithm uses less feature size compression to reduce pooling. In order to ensure that the size of the feature map is the same as that of the original image, the detection resolution can be reduced. Methods the sub neighborhood partition of feature descriptor is improved to simplify the algorithm complexity. In order to avoid over fitting of the model, dropout strategy was used to ignore some neurons randomly during training. At the same time, the exponential descent learning rate curve is used to consider the convergence speed of the model in the early stage and the convergence in the later stage. The experimental results show that the algorithm can achieve the goal-seeking task of power equipment reliably and effectively in the complex and changeable substation detection environment.
Tackling the Imbalance for GNNs

Different from deep neural networks for non-graph data classification, graph neural networks (GNNs) leverage the information exchange between nodes (or samples) when representing nodes. The category distribution shows an imbalance or even a highly-skewed trend on nearly all existing benchmark GNN data sets. The imbalanced distribution will cause misclassification of nodes in the minority classes, and even cause the classification performance on the entire data set to decrease. This study explores the effects of the imbalance problem on the performances of GNNs and proposes new methodologies to solve it. First, a node-level index, namely, the label difference index ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$LDI$</tex> ), is defined to quantitatively analyze the relationship between imbalance and misclassification. The less samples in a class, the higher the value of its average <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$LDI$</tex> ; the higher the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$LDI$</tex> of a sample, the more likely the sample will be misclassified. We define a new loss and propose four new methods based on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$LDI$</tex> . Experimental results indicate that the classification accuracies of the three among our proposed four new methods are better in both transductive and inductive settings. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$LDI$</tex> can be applied to other GNNs.
Gland context networks: A novel approach for improving prostate cancer identification

Prostate cancer (PCa) is a pervasive condition that is manifested in a wide range of histologic patterns in biopsy samples. Given the importance of identifying abnormal prostate tissue to improve prognosis, many computerized methodologies aimed at assisting pathologists in diagnosis have been developed. It is often argued that improved diagnosis of a tissue region can be obtained by considering measurements that can take into account several properties of its surroundings, therefore providing a more robust context for the analysis. Here we propose a novel methodology that can be used for systematically defining contextual features regarding prostate glands. This is done by defining a Gland Context Network (GCN), a representation of the prostate sample containing information about the spatial relationship between glands as well as the similarity between their appearance. We show that such a network can be used for establishing contextual features at any spatial scale, therefore providing information that is not easily obtained from traditional shape and textural features. Furthermore, it is shown that even basic features derived from a GCN can lead to state-of-the-art classification performance regarding PCa. All in all, GCNs can assist in defining more effective approaches for PCa grading.
Design of Physical Training Motion Simulation System Based on Virtual Reality Technology

In order to solve the problem of low restoration degree of physical training motion in traditional physical training motion simulation system, a physical training motion simulation system based on virtual reality technology is designed. The main controller in the system hardware mainly outputs and collects the training data in the physical training motion simulation system, and designs a processor. To coordinate the work of each node in the system; Then, USB interface and Ethernet are designed to meet the communication requirements of physical training motion simulation system; Finally, virtual reality technology is used to simulate physical training motions and training scenes, and a database is established. In this way, the design of physical training motion simulation system based on virtual reality technology is completed. The experimental results show that, The physical training motion simulation system designed in this paper has good restoration degree of physical training motion, which has certain practical application significance.
‘Let Us Work Together’– Insights from an Experiment with Conversational Agents on the Relation of Anthropomorphic Design, Dialog Support, and Performance

In the human interaction with CAs, research has shown that elements of persuasive system design, such as praise, are perceived differently when compared to traditional graphical interfaces.In this experimental study, we will extend our knowledge regarding the relation of persuasiveness (namely dialog support), anthropomorphically designed CAs, and task performance. Within a three-conditions-between-subjects design, two instances of the CA are applied within an online experiment with 120 participants. Our results show that anthropomorphically designed CAs increase perceived dialog support and performance but adding persuasive design elements can be counterproductive. Thus, the results are embedded in the discourse of CA design for task support.
GazeMetro: A Gaze-Based Interactive System for Metro Map

In this paper, we propose a gaze-based interactive system for metro map named GazeMetro, which helps explore and interact with the metro map only by eye movements, providing a new experience of interaction. The objective of GazeMetro is to provide metro map viewers with gaze-based interactions to search the metro map without other manual operations. We implement GazeMetro with 4 gaze-based interaction techniques which are Gaze Fisheye, Gaze Scaling and Panning, Gaze Selection and Gaze Hint. We conducted an experiment to evaluate GazeMetro and the results showed a positive evaluation in pragmatic quality and especially in hedonic quality.
Using the Method of Rare Combinations in the Interpretation of Electrical Prospecting Data

The traditional interpretation of field measurements of the natural field and electrical profiling is most often based on qualitative analysis or the simplest methods for assessing the depth and size of anomalous bodies. This leads to ambiguous conclusions about the presence and nature of the phenomena that caused changes in the observed electrical parameters. The rare combination method is proposed to enhance the reliability of data interpretation of the method of the natural field (NF) and the symmetric electroprofiling (SEP). The method is based on the calculation of summary matrix on relative frequencies of occurrence of various parameters. The minimum values of MRC parameter (of sum of relative frequencies) are connected with the most anomalous, in a broad terms, rare combinations of all used features. That is, they are the indicators of the existence of poorly distributed on these area geological formations. The method was tested on the section located within of the mine field of the Verkhnekamsk salt deposit. The mineral deposit is situated in the Perm region on the western mountainside of the Middle Urals in the area of its interface with East European Platform. Statistical characteristics of electrical parameters served as the initial indications for the calculations of the MRC parameters (of values of NF potential and the apparent resistance - AR) calculated in sliding windows. Matrixes of the MRC parameters were counted for various statistic groups, taking into account the spans of the power line AB. The use of MR parameters made it possible to localize (highlight) the areas of expected engineering and geological complications. The contours of these areas have good spatial coincidence with the contours of the classes, which were obtained earlier using non-standard classification procedures. That is, the areas of expected engineering-geological complications are steadily distinguished by two methods with different mathematical apparatus, which increases the reliability of the result.
Distributed Filtering for a Class of Saturated Nonlinear Systems with Measurement Outliers

This paper addresses a distributed filtering problem for saturated systems over sensor network with measurement outliers, where the multiplicative noises are used to characterize the internal behaviors of system model. A χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> detection is first constructed in the distributed filter to detect and remove the outliers existing in the measurement outputs caused by some external environment disturbances or malicious attacks. Moreover, the innovation sequence is applied to design filter as a compensation term. The objective of this paper is to find a locally optimal distributed filter such that, for both the measurement outliers and multiplicative noises, an upper bound of filtering error covariance is given and then a proper filter gain is derived to minimize the obtained upper bound matrix with the help of the matrix analysis theory and the probability knowledge. Finally, a numerical experiment is proposed to verify the validity of the designed distributed filtering algorithm.