Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Computer Accounting Applications with Microsoft Excel VBA (Macro) in The Preparation of Financial Statements of Micro, Small and Medium Enterprises (MSMES) Coconut Wood Furniture

Micro, small and medium enterprises (MSMEs) are one of the economic activities carried out by most Indonesians. MSMEs have a very significant contribution in employment, forming Gross Domestic Product, national export value and national investment. Until 2019, the number of MSMEs in Indonesia reached 56.5 million. In this study, it found that the accounting information system was weak and not in accordance with Financial Accounting Standards at the Technical Education Training Center (BLPT) GMIM Kaaten Tomohon. Seeing the importance of accounting information systems today to the progress of business organizations, and the number of orders for goods at BLPT GMIM Kaaten Tomohon is currently increasing, a fast, accurate and accountable recording process is needed in order to make decisions appropriately based on Financial Accounting Standards. The purpose of this study is to help MSMEs develop the application of a Microsoft Excel VBA (Macro) based accounting information system on BLPT GMIM Kaaten Tomohon. The development method in this study uses the Borg and Gall Research and Development (R&D) model, through this model aims to produce a computer application-based Accounting information system. Data is obtained directly from the company through interviews and documentation. Based on the data obtained, it is stated that the BLPT GMIM Kaaten Tomohon accounting information system has several weaknesses, such as the non-implementation of a computer-based accounting information system. From these weaknesses, researchers provide advice to use a Microsoft Excel VBA (Macro) based accounting information system that is easy to operate and in accordance with SAK EMKM.
The cultural formation of S/M: History and analysis

The cultural visibility of sadomasochism (S/M) has grown markedly in the last 30 years. Is this simply the most recent manifestation of an erotic style that predates terminology such as ‘sadomasochism? ’ or is it a unique reflection of contemporary Western culture? This study uses a cultural theory perspective to frame the history of S/M. Synthesising previous research, it presents a new theoretical model of cultural formation and function. The model predicts that sexual cultures develop in five distinct stages, reflect historical and social conditions, and serve specific functions for their members. This study finds that the evolution of S/M conforms to this model and suggests that S/M constitutes both a new sexual culture and a new sexual identity.
Indonesian Soil Data Base and Predicted Stock of Soil Carbon

Indonesian soil data collection has been carried out for more than a hundred years. However, a digital soil database management (SDBM) system of ICALRD was just started around the 80's. Since then, a large number of digital data in spatial, tabular and metadata formats have been collected and generated. There are some application softwares of soil database to manage such a large amount of data, i.e. : Side & Horizon (SHDE4), Soil Sample Analysis (SSA), and Land Unit in dbf file, while Site and Horizon is in DataEase formats. The database contains soil physics and chemical property data of each soil horizon from surface to effective soil depth, climate, land surface conditions, and other parameters required for soil classification. Until now, soil identification and mapping in Indonesia has covered about 72% of the whole land area at the 1:250.000 scale and about 21% at higher scales. But about 10% of the soil inventory and mapping data based on surveys earlier than 1988 have not been digitized yet. Soil organic carbon, C org , and soil bulk density, BD (the two parameters required for calculating soil carbon stock) data are also stored in the database, but only less than 10% of the surveys included bulk density data. Therefore, our approach is to use BD data from various research of the similar soil type. We assess soil organic carbon stock of the 0-30 cm for Indonesia based on site-specific and generalized BD data and site specific data of soil organic carbon. We came up with BD of 0-30 cm soil depth for Oxisols, Ultisos, Andisols, Inceptisols and Spodosols of (Mg m -3 ) 1.04 + 0.18, 1.16 + 0.11, 0.80 + 0.05, 1.16 + 0.16, 0.97 + 0.17, respectively. Unavailable data of BD are for Alfisols, Entisols, Mollisols, and Histosols, and based on various research reports we found the BD of these soils of 1.23 + 0.08, 1.18 + 0.24, 1.34 + 0.10, 0.44 + 0.04 Mg m -3 , respectively The respective C org of these soils are about 2.06 + 1.15, 1.86 + 1.38, 5.24 + 4.17, 2.34 + 1.57, 3.16 + 2.54, 1.43 + 0.73, 3.18 + 2.45, 42.08 + 10.23, and 3.28 + 1.75 % (by weight). Thus, based on the availability of data, estimated national carbon stock of 0-30 cm depth for the 162 million ha mineral soil is around 17.6 Gt for 81.5% of land (soil data of 18.5% area is not available). For peat soil, organic matter is high throughout the profile. With an estimate of 50 kg C m -3 of peat soil, the first 30 cm peat layer of the 21 million ha Indonesian peat land stocks around 3.15 Gt C.
Spatial interpolation for climate data : the use of GIS in climatology and meterology

Preface. Part 1. GIS to Manage and Distribute Climate Data. Chapter 1. GIS, climatology and meteorology (Antonio Perdigao). Chapter 2. SIGMA: a Web-based GIS for environmental applications (Carlos Frederico Angelis, Fabiano Morelli, Luiz Augusto Toledo Machado and Cintia Pereira de Freitas). Chapter 3. Web mapping: different solutions using GIS (Pawel Madej, Malgorzata Barszczynska and Danuta Kubacka). Chapter 4. Comparison of geostatistical and meteorological interpolation methods (what is what? ) (Tamas Szentimrey, Zita Bihari and Sandor Szalai). Chapter 5. Uncertainty from spatial sampling: a case study in the French Alps (Pierre Dumolard). Part 2. Spatial Interpolation of Climate Data. Chapter 6. The Developments in spatialization of meteorological and climatological elements (Ole Einar Tveito). Chapter 7. The spatial analysis of the selected meteorological fields in the example of Poland (Izabela Dyras and Zbigniew Ustrnul). Chapter 8. Optimizing the interpolation of temperatures by GIS: a space analysis approach (Jean-Christophe Loubier). Chapter 9. Daily winter air temperature mapping in mountainous areas (Remi Lhotellier). Chapter 10. Aspects concerning the spatialization of radiation balance components (Cristian Valeriu Patriche). Part 3. Demo Projects. Chapter 11. The use of GIS applications in meteorology and climatology: a need for the application of regional ecological modeling approaches (Martin Wegehenkel). Chapter 12. GIS application to daily fire risk mapping (Alvaro Silva). Chapter 13. Application of GIS technology on the comparisons of climatological databases: an overview of winter precipitation over Spain (M.Y. Luna, M.L. Martin, M.G. Sotillo, C. Almarza, F. Valero and J. de la Cruz). Chapter 14. Drought sensitivity research in Hungary and influence of climate change on drought sensitivity (Sandor Szalai, Szabolcs Bella and Akos Nemeth). Chapter 15. First steps towards a new temperature climatology of the Greater Alpine Region (GAR) (Wolfgang Schoner, Ingeborg Auer and Reinhard Bohm). Chapter 16. XRWIS: a new GIS paradigm for winter road maintenance (John E. Thornes, Lee Chapman and Steve White). Part 4. Climate-related Applications. Chapter 17. The use of GIS in climatology: challenges in fine scale applications: examples in agrometeorological and urban climate studies (Claude Kergomard). Chapter 18. Climate impact on the winter land use and land cover management in Brittany (S. Corgne, H. Quenol, O. Planchon and T. Corpetti). Chapter 19. A tool for the integrated use of remote sensing with ground truth data: DEMETER Project (A. Perdigao, A. Jochum, A. Calera, L. Pessanha, A. Chinita and J. Maia) Chapter 20. Assessing population exposure to odorous pollution from a landfill over complex terrain (Caroline Riesenmey, Charles Chemel, Herve Vaillant and Mireille Batton-Hubert). Chapter 21. Disaggregated estimation of N2O fluxes from agricultural soils of the Italian region by modelization in GIS environment (Anna Carfora, Simona Castaldi, Marco Vigliotti and Riccardo Valentini). List of Authors. Index.
Structural, kinematic and metallogenic evolution of selected domains of the Pilbara granitoid-greenstone terrain : implications for mid Archean tectonic regimes

The Archean period (4000-2500 Ma) is one of the more enigmatic periods of the Earth's evolution. The oldest preserved rocks on Earth stem from this period, as does the oldest evidence of life and water on Earth. Much about the Archean is, however, still unknown. One of the main questions that has dominated studies in Archean terrains is when plate tectonic processes, as we know them in the present Earth, started to operate and whether or not they were active in the Archean (Condie, 1981; Kroner, 1981; Windley, [984; Nisbet, [987; Condie, [994). In geological studies of the Phanerozoic, the uniformitarian approach is usually adopted when interpreting geological data. The plate tectonic setting is commonly used as a key to understand geological processes that occurred in the past in a particular terrain. For example, an ophiolite complex is a reliable indication for oceanization, thick turbidite sequences usually form in trenches associated with subduction and at continental margins and calc-alkaline volcanism is a good indication for a volcanicarc setting with subduction of oceanic crust. This uniformitarian approach can not be used as rigorously in Archean terrains because there still is no consensus on whether the current style of plate tectonics was actually active. The reason for this uncertainty is not only based on theoretical considerations such as an expected higher heat production in the Archean, but also on differences observed in the Archean rock record. In many respects, including rock types, geochemistry of rocks, degree of alteration, intensity of deformation and largescale geometry, Archean terrains are different from modern terrains (see Chapter 2). The question of whether or not present day style plate tectonic processes operated in the (mid) Archean is one of the central themes of modem Archean geology. As a consequence it is the topic that underlies much of the research presented in this thesis.
P-T conditions of earliest Late Cretaceous metamorphism in the Western Sonobari Complex, northwestern Mexico: tectonic implications

ABSTRACT The Western Sonobari Complex in northwestern Mexico consists of metamorphosed rocks mostly derived from Palaeozoic (? ) sedimentary and Mesozoic igneous protoliths. Rocks of this complex display amphibolite facies orogenic metamorphism, pervasive foliation, migmatization, and four folding phases. These features are ascribed to a contractional tectonic event with NNW–SSE shortening direction, which caused thrusting, thickening of the crust, and sinking of the lithological units. U–Pb geochronology of migmatitic leucosome bands indicates that peak metamorphic conditions were reached between ~93 and 89 Ma. Post-tectonic Late Cretaceous peraluminous aplite-pegmatite dikes transect the metamorphic foliation. Traditional thermobarometry in the metamorphic rocks yields average pressures and temperatures of 9.0–7.1 kbar and 745–663°C, typical of intermediate P/T Barrovian metamorphism. On the basis of its age and contractional character, the thickening event originating the metamorphism may be related to collision of the Alisitos island arc against crustal blocks of Mexico. Thermobarometric data of post-tectonic intrusives including Late Cretaceous granodiorite and Eocene gabbro indicate emplacement within an overthickened crust, while P-T conditions of post-tectonic dikes point towards an almost isothermal decompression path along the amphibolite facies field. Rock units of similar age and metamorphic character are discontinuously exposed from the Islas Marias offshore the Nayarit coast to the Peninsular Ranges batholith of Baja California, and even extend north into the Sierra Nevada batholith and the Sevier hinterland. This extensive belt of Barrovian metamorphic rocks thus provides a record of middle Cretaceous shortening and crustal thickening related to arc-continent collision followed by subduction resuming.
Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows

Extracting information efficiently from quantum systems is a major component of quantum information processing tasks. Randomized measurements, or classical shadows, enable predicting many properties of arbitrary quantum states using few measurements. While random single qubit measurements are experimentally friendly and suitable for learning low-weight Pauli observables, they perform poorly for nonlocal observables. Prepending a shallow random quantum circuit before measurements maintains this experimental friendliness, but also has favorable sample complexities for observables beyond low-weight Paulis, including high-weight Paulis and global low-rank properties such as fidelity. However, in realistic scenarios, quantum noise accumulated with each additional layer of the shallow circuit biases the results. To address these challenges, we propose the robust shallow shadows protocol. Our protocol uses Bayesian inference to learn the experimentally relevant noise model and mitigate it in postprocessing. This mitigation introduces a bias-variance trade-off: correcting for noise-induced bias comes at the cost of a larger estimator variance. Despite this increased variance, as we demonstrate on a superconducting quantum processor, our protocol correctly recovers state properties such as expectation values, fidelity, and entanglement entropy, while maintaining a lower sample complexity compared to the random single qubit measurement scheme. We also theoretically analyze the effects of noise on sample complexity and show how the optimal choice of the shallow shadow depth varies with noise strength. This combined theoretical and experimental analysis positions the robust shallow shadow protocol as a scalable, robust, and sample-efficient protocol for characterizing quantum states on current quantum computing platforms.
Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology

Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named “posterior parameter ensemble”. We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor.
Video Popularity Prediction by Sentiment Propagation via Implicit Network

Video popularity prediction plays a foundational role in many aspects of life, such as recommendation systems and investment consulting. Because of its technological and economic importance, this problem has been extensively studied for years. However, four constraints have limited most related works' usability. First, most feature oriented models are inadequate in the social media environment, because many videos are published with no specific content features, such as a strong cast or a famous script. Second, many studies assume that there is a linear correlation existing between view counts from early and later days, but this is not the case in every scenario. Third, numerous works just take view counts into consideration, but discount associated sentiments. Nevertheless, it is the public opinions that directly drive a video's final success/failure. Also, many related approaches rely on a network topology, but such topologies are unavailable in many situations. Here, we propose a Dual Sentimental Hawkes Process (DSHP) to cope with all the problems above. DSHP's innovations are reflected in three ways: (1) it breaks the "Linear Correlation" assumption, and implements Hawkes Process; (2) it reveals deeper factors that affect a video's popularity; and (3) it is topology free. We evaluate DSHP on four types of videos: Movies, TV Episodes, Music Videos, and Online News, and compare its performance against 6 widely used models, including Translation Model, Multiple Linear Regression, KNN Regression, ARMA, Reinforced Poisson Process, and Univariate Hawkes Process. Our model outperforms all of the others, which indicates a promising application prospect.
A latent space representation of overdispersed relative propensity in \How many X's do you know?" data

We present a novel latent space representation of the relative propensity for a respondent to form ties with members of a particular social group, a quantity related to overdispersion. In many applications collecting complete network data is financially or practically infeasible. Instead, we use data where respondents are asked for the number of ties they have with members of various subpopulations or “How many X’s do you know? ” data. We connect this data with recent work using models which represent dependence in a fully observed network through distance in an unobservable “social space,” known as latent space models (Hoff et al., 2002). This yields a latent space representation of overdispersion, further elucidates how these data measure social structure indirectly, and suggests a latent space model for such data.
Mining tags using social endorsement networks

Entities on social systems, such as users on Twitter, and images on Flickr, are at the core of many interesting applications: they can be ranked in search results, recommended to users, or used in contextual advertising. Such applications assume knowledge of an entity's nature and characteristic attributes. An effective way to encode such knowledge is in the form of tags. An untagged entity is practically inaccessible, since it is hard to retrieve or interact with. To address this, some platforms allow users to manually tag entities. However,while such tags can be informative, they can oftentimes be inadequate, trivial, ambiguous, or even plain false. Numerous automated tagging methods have been proposed to address these issues. However,most of them require pre-existing high-quality tags or descriptive texts for every entity that needs to be tagged. In our work, we propose a method based on social endorsements that is free from such constraints. Virtually every major social networking platform allows users to endorse entities that they find appealing. Examples include "following" Twitter users or "favoriting" Flickr photos. These endorsements are abundant and directly capture the preferences of users. In this paper, we pose and solve the problem of using the underlying social endorsement network to extract useful tags for entities in a social system. Our work leverages techniques from topic modeling to capture the interests of users and then uses them to extract relevant and descriptive tags for the entities they endorse. We perform an extensive evaluation of our proposed approach on real large-scale datasets from both Twitter and Flickr, and show that it significantly outperforms meaningful and competitive baselines.
Indoor coverage estimation from unreliable measurements using spatial statistics

Estimating the coverage of a wireless network is one of the key problems in network planning and management. In outdoor environments this is usually done using modern network planning tools combined with intensive drive tests. However, in indoor environments the problem is much more difficult. Solutions based on propagation modeling require precise building information for accuracy, and even then their performance is highly varying. Refining such predictions using measurements from mobile terminals is a promising possibility, but is not straightforward due to the noisy and unreliable measurement quality. In this paper we study the performance of spatial statistics techniques for coverage prediction in indoor environments. Using data collected in an indoor testbed with 60 low cost radio receivers, we show that such techniques can yield accurate coverage predictions provided suitable preprocessing and filtering of the data is performed. Further, a simple optimization approach enables high prediction accuracy to be achieved using only a small subset of the available measurement devices. These results are also highly relevant to the minimization of drive tests (MDT) approach currently being developed in 3GPP to enable mobile terminals carry out coverage measurements for wireless networks.
Truncated minimum energy path method for finding first order saddle points

I. S. Lobanov, M. N. Potkina, H. Jónsson, V. M. Uzdin ITMO University, Kronverkskiy, 49, St. Petersburg, 197101, Russia Kronverkskiy, 49, St. Petersburg, 197101, Russia St. Petersburg State University, St. Petersburg, 198504, Russia Science Institute and Faculty of Physical Sciences, University of Iceland 107 Reykjavı́k, Iceland Center for Nonlinear Studies, Los Alamos, NM 87545, USA lobanov.igor@gmail.com, hj@hi.is, v uzdin@mail.ru
A Secure and Efficient End-to-End Provenance System ( EEPS )

Work on the End-to-End Provenance System (EEPS) was begun in the later summer of 2009. The EEPS effort seeks to explore the three central questions in provenance systems; a) ”where and how do I design secure hostlevel provenance collecting instruments (called provenance monitors)? ”, b) ”how do I extend completeness and accuracy guarantees to distributed systems and computations? ”, and c) ”what are the costs associated with provenance collection”. This paper discusses our initial exploration into these issues and posits several challenges to the realization of the EEPS vision.