Reviewerly/exHarmony huggingface dataset:
Research on Joint Extraction Model of Financial Product Opinion and Entities Based on RoBERTa

With the rapid development of the Internet, and its enormous impact on all aspects of life, traditional financial companies increasingly focus on the user’s online reviews, aiming to promote competitiveness and quality of service in the products of this enterprise. Due to the difficulty of extracting comment text compared with structured data itself, coupled with the fact that it is too colloquial, the traditional model insufficiently semantically represents sentences, resulting in unsatisfactory extraction results. Therefore, this paper selects RoBERTa, a pre-trained language model that has exhibited an excellent performance in recent years, and proposes a joint model of financial product opinion and entities extraction based on RoBERTa multi-layer fusion for the two tasks of opinion and entities extraction. The experimental results show that the performance of the proposed joint model on the financial reviews dataset is significantly better than that of the single model.
A New Model of Intelligent Nursing Based on 5G Network Architecture and RFID Technology

RFID technology is an emerging automatic identification technology in recent years. Based on the research on RFID indoor positioning system, this paper proposes a clustering SPA relative positioning algorithm based on nearest neighbours in the 5G network architecture system. After analysing the algorithm process, simulation experiments are carried out. The experimental results show that the algorithm can effectively improve the positioning accuracy of the system. Based on the research on the application of RFID in hospital medical care management, the medical care mobile management system is designed, mainly including the database system of the system, the handheld data acquisition and wireless transmission system based on the 5G network platform, and the management running on the desktop PC. The system has been designed in detail, with a view to RFID technology can be applied to medical care management.
Civilian First Responder mHealth Apps, Interface Rhetoric, and Amplified Precarity

Our article uses case studies of two civilian emergency response mHealth apps—PulsePoint and OD Help—to theorize the ways the mobile mapping functionality embedded in these tools, which is integrated with the Google Maps platform, enables yet also constrains users’ agential practices. Using an interface rhetoric approach, we unpack assumptions related to the embodied contexts of use facilitated by this functionality within the unique scenario of civilian emergency response. We argue that interactions between and among humans and these apps’ mapping interfaces involve complex, negotiated, contextually situated enactments, which align with a posthumanist perspective toward agency. At the same time, these interactions may also inadvertently amplify the precarity of vulnerable groups. Better understanding the ways that mobile mapping technologies shape agential enactments, particularly in ways that affect precarious and dispossessed populations, has important implications for the design of mHealth technologies—and the users who rely on them—moving forward.
Value-Based Practical Reasoning: Modal Logic + Argumentation

Autonomous agents are supposed to be able to finish tasks or achieve goals that are assigned by their users through performing a sequence of actions. Since there might exist multiple plans that an agent can follow and each plan might promote or demote different values along each action, the agent should be able to resolve the conflicts between them and evaluate which plan he should follow. In this paper, we develop a logic-based framework that combines modal logic and argumentation for value-based practical reasoning with plans. Modal logic is used as a technique to represent and verify whether a plan with its local properties of value promotion or demotion can be followed to achieve an agent’s goal. We then propose an argumentation-based approach that allows an agent to reason about his plans in the form of supporting or objecting to a plan using the verification results.
Learning Decoupling Features Through Orthogonality Regularization

Keyword spotting (KWS) and speaker verification (SV) are two important tasks in speech applications. Research shows that the state-of-art KWS and SV models are trained independently using different datasets since they expect to learn distinctive acoustic features. However, humans can distinguish language content and the speaker identity simultaneously. Motivated by this, we believe it is important to explore a method that can effectively extract common features while decoupling task-specific features. Bearing this in mind, a two-branch deep network (KWS branch and SV branch) with the same network structure is developed and a novel decoupling feature learning method is proposed to push up the performance of KWS and SV simultaneously where speaker-invariant keyword representations and keyword-invariant speaker representations are expected respectively. Experiments are conducted on Google Speech Commands Dataset (GSCD). The results demonstrate that the orthogonality regularization helps the network to achieve SOTA EER of 1.31% and 1.87% on KWS and SV, respectively.
Performance of analytical techniques (SWIR imaging, XRD, EPMA) for the identification of minerals frequently formed during natural and technological geothermal processes

This study compares the performance of three analytical methods, hyperspectral imaging in the short-wave infrared range (SWIR), X-ray powder diffraction (XRD), and electron-microprobe analysis (EMPA) for the identification of minerals frequently formed during natural and technological geothermal processes. The samples from three geothermal sites in Indonesia contain a spectrum of minerals, which are characteristic for geothermal activities (alunite, jarosite, cristobalite, tridymite) and/or are involved in scaling-forming processes in geothermal plants (barite, celestine, common opal, calcite). We show and discuss the strengths and weaknesses of the individual techniques with respect to properly identifying these minerals and to approximate their relative proportions. The SWIR camera permits an unambiguous identification of only a small selection of the geothermally relevant minerals, which restricts its employment as routine tool in the context of geothermics. XRD and EPMA are more powerful techniques and deliver complementary information. In case that SWIR inactive species occur, (b) the entire spectrum of minerals present in a sample has to be unambiguously identified, (c) amorphous substances are present, (d) accurate mineral compositions are required, and (e) detailed information on minerals occurring in low abundance and grain size is warranted, the combined use of XRD and EMPA is mandatory.
The Breznik epithermal Au occurrence, Western Srednogorie - Bulgaria: an “atypical” (?) low-sulphidation hydrothermal system

The Breznik Au occurrence formed in an Upper Cretaceous volcano-intrusive structure with extrusive andesite-basaltic volcanoclastics and a gabbrodiorite body at 300-350m depth. Zonally developed argillic alterations (quartz-kaolinite-alunite, quartz-sericite, propylite) are signs of the first hydrothermal event. Abundant quartz-sericite-carbonate Au-bearing mineralisation of LS type overprinted earlier altered rocks. Sub-economic Au ore occurs in linear zones in the southern part of the structure among propylitic alteration. Scarce HS (enargite-luzonite) mineralisation developed under and into quartz-kaolinite-alunite and quartz-sericite alterations. It is suggested that such a close spatial formation of LS and HS mineralisations caused the influence of interaction of a LS hydrothermal system with different rock-buffers resulting in precipitation of different in sulphidation state ore minerals: acidic – HS; acidic to neutral – IS; alkaline – LS.
Research on Cooperative Search Trajectory Planning for Rotorcraft UAVs Based on Scrolling Planning Algorithm

Abstract The UAV trajectory planning algorithm for scrolling planning is proposed for the objective of multi-UAVs searching for multiple targets in an uncertain environment. Information about the unknown environment is described using infographic, while for the huge computational effort in multi-UAVs scrolling planning, an improved genetic algorithm is used to solve the optimal solution for the UAV population. Simulation results show that the scrolling planning algorithm proposed in this paper can achieve a better search for the target in the uncertain region, and the algorithm framework is reasonable and feasible with high planning efficiency, which can be used for the cooperative search task of multi-UAVs in the uncertain area.
New data on the origin of the Peña Colorada iron deposit (Colima, Mexico): orthomagmatic vs. skarn

In this paper we present previous field description and petrography of the different iron ore bodies that are mined at the Peña Colorada deposit. Our new data suggests that previous interpretations on the origin of the deposit as a skarn is not correct and that a magmatic origin, like in the Cerro de Mercado iron deposit, is more plausible.
Automatic assessment of adverse drug reaction reports with interactive visual exploration

Abstract A large number of adverse drug reaction (ADR) reports are collected yearly through the spontaneous report system (SRS). However, experienced experts from ADR monitoring centers (ADR experts, hereafter) reviewed only a few reports based on current policies. Moreover, the causality assessment of ADR reports was conducted according to the official approach based on the WHO-UMC system, a knowledge- and labor-intensive task that highly relies on an individual’s expertise. Our objective is to devise a method to automatically assess ADR reports and support the efficient exploration of ADRs interactively. Our method could improve the capability to assess and explore a large volume of ADR reports and aid reporters in self-improvement. We proposed a workflow for assisting the assessment of ADR reports by combining an automatic assessment prediction model and a human-centered interactive visualization method. Our automatic causality assessment model (ACA model)—an ordinal logistic regression model—automatically assesses ADR reports under the current causality category. Based on the results of the ACA model, we designed a warning signal to indicate the degree of the anomaly of ADR reports. An interactive visualization technique was used for exploring and examining reports extended by automatic assessment of the ACA model and the warning signal. We applied our method to the SRS report dataset of the year 2019, collected in Guangdong province, China. Our method is evaluated by comparing automatic assessments by the ACA model to ADR reports labeled by ADR experts, i.e., the ground truth results from the multinomial logistic regression and the decision tree. The ACA model achieves an accuracy of 85.99%, a multiclass macro-averaged area under the curve (AUC) of 0.9572, while the multinomial logistics regression and decision tree yield 80.82%, 0.8603, and 85.39%, 0.9440, respectively, on the testing set. The new warning signal is able to assist ADR experts to quickly focus on reports of interest with our interactive visualzation tool. Reports of interest that are selected with high scores of the warning signal are analyzed in details by an ADR expert. The usefulness of the overall method is further evaluated through the interactive analysis of the data by ADR expert. Our ACA model achieves good performance and is superior to the multinomial logistics and the decision tree. The warning signal we designed allows efficient filtering of the full ADR reports down to much fewer reports showing anomalies. The usefulness of our interactive visualization is demonstrated by examples of unusual reports that are quickly identified. Our overall method could potentially improve the capability of analyzing ADR reports and reduce human labor and the chance of missing critical reports.
Visual aggregation of large multivariate networks with attribute-enhanced representation learning

It is a challenging task to explore large-scaled multivariate networks, since nodes of multivariate networks always contain rich information. We propose a novel visual abstraction method to adaptively aggregate nodes and layout multivariate graphs. First, a novel attribute-enhanced graph representation learning model is proposed to transform nodes into an attribute-enhanced vectorized space. Then, we propose a multi-scale aggregation scheme to classify nodes into hierarchical categories with respect to a set of metrics such as structure closeness, attribute homogeneity and cluster account. Further, we design and implement a visualization framework enabling users to conduct attribute-aware visual abstraction, exploration, and clustering of large-scale multivariate graphs. Case studies and quantitative comparisons with three datasets verify the effectiveness of our approach in enhancing the readability of large-scaled multivariate networks.
A Novel Sentiment Analysis Model of Museum User Experience Evaluation Data Based on Unbalanced Data Analysis Technology

With the development of virtual reality and digital reconstruction technology, digital museums have been widely promoted in various cities. Digital museums offer new ways to display and disseminate cultural heritage. It allows remote users to autonomously browse displays in a physical museum environment in a digital space. It is also possible to reproduce the lost heritage through digital reconstruction and restoration, so as to digitally present tangible cultural heritage and intangible cultural heritage to the public. However, the user’s experience of using digital museums has not been fully and deeply studied at present. In this study, the user’s experience evaluation data of digital museum are classified and processed, so as to analyze the user’s emotional trend towards the museum. Considering that the user’s evaluation data are unbalanced data, this study uses an unbalanced support vector machine (USVM) in the classification of user evaluation data. The main idea of this method is that the boundary of the support vector is continuously shifted to the majority class by repeatedly oversampling some support vectors until the real support vector samples are found. The experimental results show that the classification obtained by the used USVM has a good practical reference value. Based on the classification results of the evaluation data, the construction of the digital museum can be further guided and maintained, thereby improving the user experience satisfaction of the museum. This research will make an important contribution to the construction of the museum and the inheritance of culture.
Mixture survival trees for cancer risk classification

In oncology studies, it is important to understand and characterize disease heterogeneity among patients so that patients can be classified into different risk groups and one can identify high-risk patients at the right time. This information can then be used to identify a more homogeneous patient population for developing precision medicine. In this paper, we propose a mixture survival tree approach for direct risk classification. We assume that the patients can be classified into a pre-specified number of risk groups, where each group has distinct survival profile. Our proposed tree-based methods are devised to estimate latent group membership using an EM algorithm. The observed data log-likelihood function is used as the splitting criterion in recursive partitioning. The finite sample performance is evaluated by extensive simulation studies and the proposed method is illustrated by a case study in breast cancer.
Peningkatan Pengetahuan Kebencanaan Masyarakat Pangandaran Dalam Mewujudkan Masyarakat Tahan Bencana

Pangandaran is an environmentally-based tourism destination that is quite popular in Indonesia, but Pangandaran has a high potential for disaster. Pangandaran is ranked 16th among all districts in Indonesia in terms of disaster potential. Therefore, a program is needed to increase community resilience, community adaptability to disasters, so that people are able to act quickly and precisely when disasters occur and communities are resistant to post-disaster effects. The methods used in this service include lectures, discussions and disaster simulations. With this service activity, it is hoped that the community can: 1) understand and know how to behave when a disaster occurs to reduce disaster risk, around their place of residence; 2) the community knows how and where to look for information on disaster which includes disaster techniques and management; 3) improve the physical and mental preparedness of the community when a disaster occurs; 4) provide an understanding to the community of the importance of preserving the environment in order to avoid disasters.
Energy-yield Assessment Based on the Orientations and the Inclinations of the Solar Photovoltaic Rooftop Mounted in Jakarta, Indonesia

Solar rooftop is a new trend in harnessing renewable energy in Indonesia.In this work, energy yield assessment was simulated from the solar photovoltaic (PV) installed on the rooftop from the urban housing in Jakarta, Indonesia.The selection of the capital city is because of the high energy consumption from the residence.In this simulation, the orientations of solar PV were varied from four main cardinal directions with inclination variation six points (i.e., 0, 10 • , 15 • , 20 • , 30 • , and 45 • ), respectively.For the important parameters, we set the installed capacity, the calculation area, the module type, and the number of modules as 2 kWp, 16.8 m 2 , Polysilicon 200 Wp, and 10, respectively.The maximum energy yield of 2497 kWh/year is obtained for North orientation with 10 • inclination, while the minimum of 1740 kWh/year was simulated at South with 45 • inclination.In general South-facing orientation has a weak energy yield compared to others, and the 0 o inclination has relatively high energy yield, and 45 o inclination is the weakest energy yield annually.With those energy yields, the Peak Sun Hours (PSH) average is more than 6 kWh m -2 d -1 .The Cost of Solar Energy (CSE) has been calculated, and it showed that the reduced CSE to US$ 2000 will have a payback time of 4 years with the respective PSH.
A Deep Neural Network-Based Method for Building a Professional Farmer Training Model

In this paper, we analyzed the deep neural network (DNN) model and proposed a DNN-based method to build the training model for professional farmers. This paper aims to construct the vocational farmer cultivation model and seek a better path for transforming farmers into new experienced farmers by analyzing each training body involved in the cultivation of vocational farmers and studying their respective problems and reasons. The conceptual and logical structures of the system database were designed, and MySQL was selected for database implementation to complete the information of professional farmers. The system network topology and logical architecture are created, and the functions of view, control, business logic and data access layers are divided. This paper combines the DNN with the vocational farmer training enhancement decision tree. The experimental results of this model are most intuitive and accurate. This paper reconstructs the neural network model using the global average pooling layer to better model the vocational farmer training model to replace the fully connected layer in the original convolutional neural network. At the same time, to make the network model produce a lower probability of overfitting, a dropout layer is added to the layer after the fully connected layer to improve the efficiency of the neural network further to enhance vocational farmer training. The experimental content of this paper provides a new research direction for the estimation of vocational farmer cultivation. The above modeling is used to improve the vocational farmer cultivation model and accelerate the process of vocational farmer cultivation.
Research on Video Captioning Based on Multifeature Fusion

Aiming at the problems that the existing video captioning models pay attention to incomplete information and the generation of expression text is not accurate enough, a video captioning model that integrates image, audio, and motion optical flow is proposed. A variety of large-scale dataset pretraining models are used to extract video frame features, motion information, audio features, and video sequence features. An embedded layer structure based on self-attention mechanism is designed to embed single-mode features and learn single-mode feature parameters. Then, two schemes of joint representation and cooperative representation are used to fuse the multimodal features of the feature vectors output by the embedded layer, so that the model can pay attention to different targets in the video and their interactive relationships, which effectively improves the performance of the video captioning model. The experiment is carried out on large datasets MSR-VTT and LSMDC. Under the metrics BLEU4, METEOR, ROUGEL, and CIDEr, the MSR-VTT benchmark dataset obtained scores of 0.443, 0.327, 0.619, and 0.521, respectively. The result shows that the proposed method can effectively improve the performance of the video captioning model, and the evaluation indexes are improved compared with comparison models.
A Comprehensive Survey Using Novel Content-Based Filtering Technique to Improve Effectiveness on Social Media Datasets for Group Recommendation System Based on External Social Trust Networks

Aim:To build a Movie Recommendation system for Netflix to improve the accuracy percentage using Content-Based Filtering technique to analyze the prediction rate of movies. Materials and Methods: Two different algorithms are used Content-Based filtering algorithm and External Social Trust Networks to achieve maximum accuracy with a content based filtering with a sample size = 6 and external social trust network with a sample size= 6 was iterated 10 times for accurate outcome. Pre test is calculated by using G-Power tool value of 80% and confidence interval is 95% mean and standard deviation. Result and Discussions: These results proved that Recommendation System for Netflix using Novel Content-Based filtering algorithm gives better accuracy (0.94) in comparison with External Social Trust Networks(0.76). Conclusion: Content-Based Filtering algorithm helps to build a recommendation system gives effective results with the better accuracy rate of percentage.