A geophysical investigation has been done to identify resistivity variation zone of sulfide mineralization (gold) at Arinem-Papandayan. This investigation used secondary data of Controlled Source Audio Frequency Magnetotelluric (CSAMT) method. Processing data using software CMT-Pro and Surfer-9, resulting 2D modeling resistivity section. From the analysis and interpretation of 2D modeling resistivity section obtain that sulfide mineralization zone was found low resistivity (0??m-100 ??m) and medium resistivity (100 ??m-200 ??m) identify as argilic and silification alteration zones that spread into the NorthEast of investigation area. Electrical respon of rock in CSAMT also show high resistivity more than 1000 ??m in guess as andesite intrusion

We performed a citation analysis on the Web of Science publications consisting of more than 63 million articles and 1.45 billion citations on 254 subjects from 1981 to 2020. We proposed the Article's Scientific Prestige (ASP) metric and compared this metric to number of citations (#Cit) and journal grade in measuring the scientific impact of individual articles in the large-scale hierarchical and multi-disciplined citation network. In contrast to #Cit, ASP, that is computed based on the eigenvector centrality, considers both direct and indirect citations, and provides steady-state evaluation cross different disciplines. We found that ASP and #Cit are not aligned for most articles, with a growing mismatch amongst the less cited articles. While both metrics are reliable for evaluating the prestige of articles such as Nobel Prize winning articles, ASP tends to provide more persuasive rankings than #Cit when the articles are not highly cited. The journal grade, that is eventually determined by a few highly cited articles, is unable to properly reflect the scientific impact of individual articles. The number of references and coauthors are less relevant to scientific impact, but subjects do make a difference.

Nowadays, editors tend to separate different subtopics of a long Wiki-pedia article into multiple sub-articles. This separation seeks to improve human readability. However, it also has a deleterious effect on many Wikipedia-based tasks that rely on the article-as-concept assumption, which requires each entity (or concept) to be described solely by one article. This underlying assumption significantly simplifies knowledge representation and extraction, and it is vital to many existing technologies such as automated knowledge base construction, cross-lingual knowledge alignment, semantic search and data lineage of Wikipedia entities. In this paper we provide an approach to match the scattered sub-articles back to their corresponding main-articles, with the intent of facilitating automated Wikipedia curation and processing. The proposed model adopts a hierarchical learning structure that combines multiple variants of neural document pair encoders with a comprehensive set of explicit features. A large crowdsourced dataset is created to support the evaluation and feature extraction for the task. Based on the large dataset, the proposed model achieves promising results of cross-validation and significantly outperforms previous approaches. Large-scale serving on the entire English Wikipedia also proves the practicability and scalability of the proposed model by effectively extracting a vast collection of newly paired main and sub-articles.

We study the online problem of reading articles that are listed in an aggregated form in a dynamic stream, e.g., in news feeds, as abbreviated social media posts, or in the daily update of new articles on arXiv. In such a context, the brief information on an article in the listing only hints at its content. We consider readers who want to maximize their information gain within a limited time budget, hence either discarding an article right away based on the hint or accessing it for reading. The reader can decide at any point whether to continue with the current article or skip the remaining part irrevocably. In this regard, Reading Articles Online, RAO, does differ substantially from the Online Knapsack Problem, but also has its similarities. Under mild assumptions, we show that any $α$-competitive algorithm for the Online Knapsack Problem in the random order model can be used as a black box to obtain an $(\mathrm{e} + α)C$-competitive algorithm for RAO, where $C$ measures the accuracy of the hints with respect to the information profiles of the articles. Specifically, with the current best algorithm for Online Knapsack, which is $6.65<2.45\mathrm{e}$-competitive, we obtain an upper bound of $3.45\mathrm{e} C$ on the competitive ratio of RAO. Furthermore, we study a natural algorithm that decides whether or not to read an article based on a single threshold value, which can serve as a model of human readers. We show that this algorithmic technique is $O(C)$-competitive. Hence, our algorithms are constant-competitive whenever the accuracy $C$ is a constant.

We propose a summarization approach for scientific articles which takes advantage of citation-context and the document discourse model. While citations have been previously used in generating scientific summaries, they lack the related context from the referenced article and therefore do not accurately reflect the article's content. Our method overcomes the problem of inconsistency between the citation summary and the article's content by providing context for each citation. We also leverage the inherent scientific article's discourse for producing better summaries. We show that our proposed method effectively improves over existing summarization approaches (greater than 30% improvement over the best performing baseline) in terms of \textsc{Rouge} scores on TAC2014 scientific summarization dataset. While the dataset we use for evaluation is in the biomedical domain, most of our approaches are general and therefore adaptable to other domains.

In this paper, we introduce QABISAR, a novel framework for statutory article retrieval, to overcome the semantic mismatch problem when modeling each query-article pair in isolation, making it hard to learn representation that can effectively capture multi-faceted information. QABISAR leverages bipartite interactions between queries and articles to capture diverse aspects inherent in them. Further, we employ knowledge distillation to transfer enriched query representations from the graph network into the query bi-encoder, to capture the rich semantics present in the graph representations, despite absence of graph-based supervision for unseen queries during inference. Our experiments on a real-world expert-annotated dataset demonstrate its effectiveness.

A geophysical investigation has been done using Induceed Polarization (IP) Frequency Domain and Controlled Source Audio-Frequency Magnetotelluric (CSAMT) method at Papandayan, Garut, West Java. The purpose of this investigation is to identify the sub-surface geology structure potentially have sulfide mineralization contains gold based on 2D modelling resistivity, percent frequency effect (PFE) and geological information. This investigation used secondary data of Induceed Polarization Frequency Domain and CSAMT measured with length 2 ??? 3 km. Processing data using software Surfer 9.0 and CMT-Pro, resulting 2D modelling resistivity and percent frequency effect (PFE) section for IP data processing and 2D resistivity section for CSAMT data processing. Result of interpret 2D modelling resistivity and PFE section suggest that sub-surface of Papandayan was domination in tuff rock, sands and breccia tuff with resistivity less than 300 ??m. This zone was estimate as argilic and silisification alteration zones supposedly as sulfide mineralization that contain gold. At CSAMT section visible andesite intrusion with high resistivity more than 500 ??m

Collaborative content creation inevitably reaches situations where different points of view lead to conflict. We focus on Wikipedia, the free encyclopedia anyone may edit, where disputes about content in controversial articles often reflect larger societal debates. While Wikipedia has a public edit history and discussion section for every article, the substance of these sections is difficult to phantom for Wikipedia users interested in the development of an article and in locating which topics were most controversial. In this paper we present Contropedia, a tool that augments Wikipedia articles and gives insight into the development of controversial topics. Contropedia uses an efficient language agnostic measure based on the edit history that focuses on wiki links to easily identify which topics within a Wikipedia article have been most controversial and when.

In order to better understand the effect of social media in the dissemination of scholarly articles, employing the daily updated referral data of 110 PeerJ articles collected over a period of 345 days, we analyze the relationship between social media attention and article visitors directed by social media. Our results show that social media presence of PeerJ articles is high. About 68.18% of the papers receive at least one tweet from Twitter accounts other than @PeerJ, the official account of the journal. Social media attention increases the dissemination of scholarly articles. Altmetrics could not only act as the complement of traditional citation measures but also play an important role in increasing the article downloads and promoting the impacts of scholarly articles. There also exists a significant correlation among the online attention from different social media platforms. Articles with more Facebook shares tend to get more tweets. The temporal trends show that social attention comes immediately following publication but does not last long, so do the social media directed article views.

-The relationship between budget participation and budget goal clarity with performance still provide uniformity conclusion. This study aims to examine the effect of budget participation and budget goal clarity on the performance of the apparatus and test the effect of commitment and organizational culture in the interaction between participation and budget goal clarity on the performance of the apparatus. The research was conducted on the scope of Makassar City Government. Data collection methods used were questionnaires. Samples were selected by purposive sampling counted 84 respondents spread across 13 agencies and 18 government agencies the city of Makassar. Data were analyzed with regression analysis moderation. The results showed that budget participation significant effect on the performance of personnel, budget goal clarity significantly influence the performance of personnel, organizational commitment moderates the effect of budget participation on the performance of personnel, organizational commitment moderates the effect of budget goal clarity on the performance of personnel, organizational culture moderates the effect of budget participation on the performance apparatus, organizational culture moderates the effect of budget goal clarity on the performance of the apparatus

The generation and spread of fake news within new and online media sources is emerging as a phenomenon of high societal significance. Combating them using data-driven analytics has been attracting much recent scholarly interest. In this study, we analyze the textual coherence of fake news articles vis-a-vis legitimate ones. We develop three computational formulations of textual coherence drawing upon the state-of-the-art methods in natural language processing and data science. Two real-world datasets from widely different domains which have fake/legitimate article labellings are then analyzed with respect to textual coherence. We observe apparent differences in textual coherence across fake and legitimate news articles, with fake news articles consistently scoring lower on coherence as compared to legitimate news ones. While the relative coherence shortfall of fake news articles as compared to legitimate ones form the main observation from our study, we analyze several aspects of the differences and outline potential avenues of further inquiry.

This work was supported by the Solid State Lighting and Energy Electronics Center (SSLEEC) at UCSB and the KACST-KAUST-UCSB Solid State Lighting Program (SSLP). A portion of this work was completed in the UCSB nanofabrication facility, part of the NSF National Nanotechnology Infrastructure Network (NNIN) (ECS-0335765), as well as the UCSB Materials Research Laboratory (MRL) facilities, which are supported under the NSF MRSEC program (DMR-1121053). The authors thank B Lingg for his assistance with the CAIBE tool maintenance, L Megalini and C Forman for informative experiments, and R Farrell, T Margalith, J Speck and C Weisbuch for their useful comments

In recent years, the field of machine learning has seen rapid growth, with applications in a variety of domains, including image recognition, natural language processing, and predictive modeling. In this paper, we explore the application of machine learning to the generation of scientific articles. We present a method for using machine learning to generate scientific articles based on a data set of scientific papers. The method uses a machine-learning algorithm to learn the structure of a scientific article and a set of training data consisting of scientific papers. The machine-learning algorithm is used to generate a scientific article based on the data set of scientific papers. We evaluate the performance of the method by comparing the generated article to a set of manually written articles. The results show that the machine-generated article is of similar quality to the manually written articles.

Statistics of article page views is useful for measuring the impact of individual articles. Analyzing the temporal evolution of article page views, we find that article page views usually decay over time after reaching a peak, especially exhibiting relaxation with nonexponentiality. This finding suggests that relaxation in article page views resembles physical aging as frequently found in complex systems.