Miracle-dz/UniFAR-FTUs huggingface dataset:
Human-based dynamics of mental workload in complicated systems

As a dynamic system in which different factors affect human performance via dynamic interactions, mental workload needs a dynamic measure to monitor its factors and evidence in a complicated system, an approach that is lacking in the literature. The present study introduces a system dynamics-based model for designing feedback mechanisms related to the mental workload through literature review and content analysis of the previous studies. A human-based archetype of mental workload was detected from the data collection process. The archetype is presented at various stages, including dynamic theory, behavior over time, leverage points and model verification. The real validation of the dynamic model was confirmed in an urban train simulator. The dynamic model can be used to analyze the long-term behavior of the mental workload. Decision-makers can benefit from the developed archetypes in evaluating the dynamic impact of their decisions on accident prevention in the complicated systems.
Fintechs and the New wave of Financial Intermediaries

The financial services industry is undergoing a massive transformation similar to what was observed when other industries underwent digitization. The FinTech revolution has given rise to a vast number of technology-oriented market entrants who challenge many parts of the financial services industry. This research seeks to provide a better understanding of how FinTechs across various business functions fundamentally impact the value chain in this industry. To this end, we built on top of financial intermediation theory, and developed a taxonomy of FinTechs’ intermediating functions. The following hierarchical clustering analysis identified six archetypes of FinTech intermediaries as observed in the real world, i.e. the different ways in which FinTechs across business functions act as financial intermediaries by transforming assets, reducing transaction cost, and alleviating information asymmetries. Finally, we discuss how FinTechs impact financial intermediation in itself, and to what extent the notion of FinTechs disintermediating the financial value chain is accurate.
A New Representation of the Extended Fermi-Dirac and Bose-Einstein Functions Research

In [22] the authors discuss the extended Fermi-Dirac and Bose-Einstein functions. In this paper, we discuss a new representation of these functions as a series of complex delta functions. This leads to a similar representation for the Hurwitz and Riemann zeta functions. MSC: Primary 46FXX, 60B15, 11M06, 11M35; Secondary 33B15, 33CXX.
Study on Algorithms for Local Outlier Detection

Outlier detection has attracted much attention recently. There are two kinds of outliers: global outliers and local outliers. In many scenarios, the detection of local outliers is more valuable than that of global outliers. To mine local outliers, it is more meaningful to assign to each object a degree of being an outlier. Some existing representative algorithms currently used for solving this problem are compared in detail, and their disadvantages are pointed out such as poor efficiency and the detection accuracy depending on the parameters given by the user. In general, the attributes of each data object can be categorized as the inherent attributes and the context attributes, the inherent attributes characterize the data object while the context attributes embody the relationship between this data object and the neighbor data objects. The context attributes is not intrinsic to the data object. In order to overcome those disadvantages mentioned above, this paper proposes to use the context attributes to determine the object neighborhood and use the inherent attributes to compute the outlier score. For spatial data, the attributes comprise the non-spatial dimensions and the spatial dimensions. The spatial attributes provide a location index to the data object. The neighborhood in the Euclidean space plays a very important role in the analysis of spatial data. The spatial attributes are used to determine spatial neighborhood and the non-spatial dimensions are used to compute the spatial outlier score. This paper also proposes a novel measure, spatial local outlier factor (SLOF), which captures the local behavior of datum in its spatial neighborhood. The experimental results show that proposed SLOF algorithm outperforms the other existing algorithms in detection accuracy, user dependency, scalability and efficiency.
Comparison of Berkeley Earth , NASA GISS , and Hadley CRU averaging techniques on ideal synthetic data

This document will examine the accuracy of the land-surface temperature mapping and averaging techniques employed by Berkeley Earth, NASA GISS, and the Hadley Centre / Climatic Research Unit groups. The ability of algorithms to estimate the global properties of weather fields from sparse data is a fundamental limitation on the achievable accuracy of climate reconstructions. Here, these algorithms are tested by creating simulated weather station data from the temperature field of a global climate model (GCM) and then measuring the effectiveness of each method at reproducing the properties of the underlying GCM field. We examine both the ability to estimate the global land average and the typical local error in the mapped field. In nearly all cases, the Berkeley Earth averaging methodology is shown to have greater accuracy at reproducing both the global and local details of the temperature field.
A PETROLOGIC EVALUATION OF THE LAYOU IGNIMBRITE AND MORNE TROIS PITON LAVA DOME: HOW DO CHANGES IN PRE-ERUPTIVE CONDITIONS AFFECT ERUPTIVE BEHAVIOR?

One of the most active volcanic centers in the Central Lesser Antilles is the island of Dominica, which features nine active volcanic centers, periodic earthquake swarms and seismic activity, as well as phreatic eruptions (non-magmatic explosive steam eruption) (Lindsay et al., 2005). The frequency with which Dominica features voluminous eruptions of pyroclastic andesite flows presents an immediate hazard for communities on Dominica and underscores the importance of investigating the pre-eruptive storage conditions experienced by previous eruptions to determine whether or not mechanisms that can induce eruption can be identified. Here, we evaluate the Layou Ignimbrite and the Morne Trois Piton dome to discern any variations in their pre-eruptive intensive variables, which may explain the shift in eruptive behavior.
Generative Adversarial Network-Based Face Recognition Dataset Generation

Facial recognition technique has many advantages than other biometric recognition solutions and recent studies and outcomes of automation process level almost the same as a human does. Applying Deep-Learning solution in this region is very common these days, but there are many obstacles to put in. This paper deals one of them of which the preparation of a certain scale of a dataset by combining existing dataset and another dataset this paper suggests. Celeb A and 2nd version of VGG face dataset are the base dataset that the discriminator agent of Generative Adversarial Network can be trained, and the generator will refer the new dataset with thousands of western portraits we added. This suggested new dataset is tested with Deep-face network as the one of existing facial recognition solutions, and we confirmed that we can use this technique for other similar dataset preprocessing layers. There are some facts to need to consider when it applies to other targets, as analyzed differences between the real facial pictures and the ones was generated.
Geometricmultigridmethods for Darcy – Forchheimer flow in fractured porousmedia

In this paper, we present a monolithic multigrid method for the efficient solution of flow problems in fractured porous media. Specifically, we consider a mixed-dimensional model which couples Darcy flow in the porous matrix with Forchheimer flow within the fractures. A suitable finite volume discretization permits to reduce the coupled problem to a system of nonlinear equations with a saddle point structure. In order to solve this system, we propose a full approximation scheme (FAS) multigrid solver that appropriately deals with the mixed-dimensional nature of the problem by using mixeddimensional smoothing and inter-grid transfer operators. Numerical experiments show that the proposed multigrid method is robust with respect to the fracture permeability, the Forchheimer coefficient and the mesh size. The case of several possibly intersecting fractures in a heterogeneous porous medium is also discussed. © 2019 Elsevier Ltd. All rights reserved.
Pengaruh Feed Rate dan Kecepatan Putar Pin Tool Friction Stir Welding (FSW) terhadap Kekuatan Tarik dan Kekerasan Aluminium 5052

Abstrak Friction Stir Welding (FSW) adalah proses pengelasan yang memanfaatkan alat bundar yang menimpa dua potong pelat yang akan disambung. Hasil pengelasan dipengaruhi oleh input panas yang dihasilkan antara alat pin dan bahan las. Tujuan dari penelitian ini adalah mengetahui pengaruh laju umpan dan rotasi alat pin FSW terhadap sifat mekanik Aluminium 5052. Lembaran logam dihubungkan dengan metode FSW dengan variasi laju umpan 20, 60, 120 , 180 mm/menit dan rotasi pin tool 1500, 2500, 360 rpm menggunakan holder cylinder tool pin dengan panjang 22mm dan 4mm. Pengujian mekanis yang dilakukan adalah uji tarik, uji kekerasan Vickers mikro dan pengamatan struktur mikro menggunakan mikroskop optik dan SEM. Hasil penelitian menunjukkan bahwa semakin cepat laju umpan, kekuatan tarik semakin rendah, tetapi regangan tarik meningkat. Pada laju umpan 600 mm/menit, kekuatan tarik diperoleh 187 MPa dengan regangan 12,4% dan laju umpan 180 mm/menit kuat tarik 103 MPa dengan regangan 3,17%. Rotasi lebih cepat dari alat pin, kekuatan tarik meningkat, koneksi solid dan menyatu, dan pada 1500 rpm kekuatan tarik dari 112 MPa menjadi 207 MPa pada 3600 rpm. Semakin besar laju umpan dan rotasi alat pin, nilai kekerasan meningkat di mana tingkat umpan 20 mm/menit kekerasan 44,8 VHN menjadi 86,4 VHN pada laju umpan 120 mm/menit, sedangkan alat putaran pin 1500 Kekerasan rpm 51,3 VHN menjadi 69,6 VHN pada 3.600 rpm. Abstract Friction Stir Welding (FSW) is a welding process that utilizes a round tool that impinges on the two pieces of pelates that will be spliced. Welding results are influenced by heat input which is generated between the pin tool with the material of the weld. The purpose of the research was to determine the influence of feed rate and rotation of the pin tool of the FSW on mechanical properties of Aluminum 5052. Metal Sheet connected with the method of the FSW with the variation of the feed rate 20, 60, 120, 180 mm/minute and rotation of the pin tool 1500, 2500, 360 rpm using the holder cylinder 22mm long pin tool 4mm. Mechanical testing carried out are tensile test, hardness test micro Vickers and microstructure observation using optical microscope and SEM. The results showed that the faster the feed rate then the tensile strength more low however, the tensile strain increased. At feed rate of 600 mm/minute tensile strength obtained 187 MPa with a strain of 12.4% and a feed rate of 180 mm/minute tensile strength of 103 MPa with a strain of 3.17%. The Faster rotation of the pin tool, the tensile strength increases, the connection is solid and fused, where at 1500 rpm tensile strength of 112 MPa to 207 MPa at 3600 rpm. The greater the feed rate and rotation of the pin tool then the value of hardness is increasing where the feed rate of 20 mm/minute hardness by 44.8 VHN to 86.4 VHN at a feed rate of 120 mm/minute, while the round pin tool 1500 rpm hardness of 51.3 VHN become 69,6 VHN at 3600 rpm
Prediction error identification in physical networks

System identification problems utilizing a prediction error approach are typically considered in an input/output setting, where a directional cause-effect relationship is presumed and transfer functions are used to estimate the causal relationships. In more complex interconnection structures, as e.g. appearing in dynamic networks, the cause-effect relationships can be encoded by a directed graph. Physical dynamic networks are most commonly described by diffusive couplings between node signals, implying that cause-effect relationships between node signals are symmetric and therefore can be represented by an undirected graph. This paper shows how prediction error methods developed for linear dynamic networks can be configured to identify components in (undirected) physical networks with known topology.
Demetrios Prize 2019: Machine Learning as an Adjunct to Medical Decision Making

One of the winners, Nicolas Woitzik from Germany, evaluated in his thesis the textual clinical recommender system which represents a computerized clinical decision support system (CDSS). It analyses the patient’s discharge summaries with the help of information retrieval and natural language processing methods. It provides the user with a similar patient case out of a database to include this information into the user's decision-making process. We conducted an experiment to validate the correlation between the computed similarities by the new CDSS and the similarity judgment of medical experts, junior doctors, and medical students. Taken together, the retrieval system still needs improvements, either based on an improved retrieval algorithm or by additional features. However, it is likely that the performance of the system will improve the more discharge summaries a database contains like it was shown in this thesis. Our data suggest that the simrec software might indeed become an important clinical tool to share clinical experience between hematologists and possibly also other medical specialties.
Reproductive senescence in polar bears in a variable environment

Reproductive senescence is ubiquitous in mammals. However, patterns of senescence vary across reproductive traits, even within populations, perhaps because of differences in selection pressures, physiological constraints, and responses to environmental conditions. We investigated reproductive senescence in wild female polar bears (Ursus maritimus), using 31 years of capture-recapture data from the Svalbard area. We studied the influence of environmental conditions on age-specific litter production and litter size using generalized linear mixed models. Further, using a capture-recapture model that handles the dependency between vital rates of individuals belonging to the same family unit, we assessed maternal-age-related changes in first year cub and litter survival. We provide clear evidence for reproductive senescence in female polar bears. Litter production and litter size peaked in middle-aged females and declined sharply afterward. By contrast cub and litter survival did not decline after prime age. We found no evidence of terminal investment. The reproductive output of all females was affected by sea-ice conditions during the previous year and the Arctic Oscillation, with some effects differing greatly between age groups. Old females were affected the most by environmental conditions. Our results suggest that the decline in reproductive output is a combination of fertility and body-condition senescence, with a weak contribution of maternal-effect senescence, possibly due to benefits of experience. Further, as predicted by evolutionary theory, senescence appears to be a consequence of failures in early stages of the reproductive cycle rather than in late stages, and environmental variation affected old females more than prime-aged females. Our study emphasizes the need to study several reproductive traits and account for environmental variation when investigating reproductive senescence. Differences in senescence patterns across reproductive traits should be interpreted in light of evolutionary theory and while considering underlying physiological drivers.
USING MACHINE LEARNING TO EXPLAIN PRICE DISPERSION

Theory suggests two sources of price dispersion: market frictions or perceived product heterogeneity. We collected eBay posted-price listings for homogenous products from several product categories. We can explain 26%-67% of the dispersion within each category by applying machine learning to a richer set of variables extracted from raw HTML pages. The high degree of predictability, which implicitly bounds the role of market frictions in generating dispersion, significantly improves on the bounds provided by the prior literature. We also find that the listings’ style, which is a signal of professionalism, is important for explaining price dispersion.
Constructing Software Knowledge Graph from Software Text

API documentation provides important knowledge about the functionality and usage of APIs. API caveats are natural-language statement in API documentation that develpers should be aware of in order to avoid unintented use of an API. However, our formative study of Stack Overflow questions suggests that API caveats are often scattered in multiple API documents, and are buried in lengthy textual descriptions. These characteristics make the API caveats less discoverable. To address this issue, we propose natural language processing techniques to extract ten subcategories of API caveat sentences from API documentation and link these sentences to API entities in an API caveats knowledge graph. The API caveats knowledge graph can support information retrieval based or entity-centric search of API caveats. As a proof-of-concept, we construct an API caveats knowledge graph for Android APIs from the API documentation on the Android Developers website. We study the abundance of different subcategories of API caveats and use a sampling method to manually evaluate the quality of the API caveats knowledge graph. We also conduct a user study to validate whether and how the API caveats knowledge graph may improve the accessibility of API caveats in API documentation.
Blind source separation of many sounds in the frequency domain 澤田宏,向井良,荒木章子,牧野昭二

This paper describes the frequency-domain approach to the blind source separation (BSS) of convolutively mixed acoustic signals. The advantage of the frequency-domain approach is that convolutive mixtures in the time domain can be approximated as multiple simple mixtures in the frequency domain. However the permutation ambiguity should be solved to group the frequency components of the same source together. This paper presents effective methods to align the permutation ambiguity. Based on the methods, we succeeded in separating many sources in real-world situations.
Microsoft Access 2010 In Depth

Access 2010 In Depth is the beyond-the-basics, beneath-the-surface guide for everyone who wants to streamline their work with Access 2010, and get more done in less time. Legendary Access expert Roger Jennings provides specific, tested, proven solutions to the problems Access database users and developers run into every day: challenges other books ignore or oversimplify. Jennings thoroughly covers all facets of working with Access 2010, and adds new chapters on integration and collaboration with Microsoft SharePoint and emulating Table Triggers with Access Data Macros. New coverage also includes: customizable Ribbon and Themes; the revamped Macro Designer; Quick Start Fields; IntelliSense support in Expression Builder; Application Parts and Navigation Forms; conditional formatting and data bars in reports; and new web database publishing techniques. As with all In Depth books, Access 2010 In Depth presents comprehensive coverage, breakthrough techniques, exclusive shortcuts, quick access to information, troubleshooting help for tough problems, and real-world examples with nothing glossed over or left out. Step-by-step instructions with icons guide readers through essential tasks such as designing tables, entering data, importing external data, designing and executing queries, and designing data entry forms and printed reports. Additional chapters on advanced form and report design emphasize data entry efficiency and presentation clarity. By Roger Jennings, a world-renowned Access expert who has sold more than 1.25 million books! Covers all aspects of working with Access 2010, from the customizable Ribbon and Themes to conditional formatting, advanced web database publishing to SharePoint collaboration For everyone who wants to get the most out of Access 2010, from hobbyists to power users to corporate developers
Evaluation of local and global atrophy measurement techniques with simulated Alzheimer's disease data

The main goal of this work was to evaluate several well-known methods which provide global (BSI and SIENA) or local (Jacobian integration) estimates of atrophy in brain structures using Magnetic Resonance images. For that purpose, we have generated realistic simulated Alzheimer's disease images in which volume changes are modelled with a Finite Element thermoelastic model, which mimic the patterns of change obtained from a cohort of 19 real controls and 27 probable Alzheimer's disease patients. SIENA and BSI results correlate very well with gold standard data (BSI mean absolute error <0.29%; SIENA <0.44%). Jacobian integration was guided by both fluid and FFD-based registration techniques and resulting deformation fields and associated Jacobians were compared, region by region, with gold standard ones. The FFD registration technique provided more satisfactory results than the fluid one. Mean absolute error differences between volume changes given by the FFD-based technique and the gold standard were: sulcal CSF <2.49%; lateral ventricles 2.25%; brain <0.36%; hippocampi <0.42%.
RoboCup Rescue 2019 TDP Virtual Robot Simulation ATR ( US )

ATR Kent is a research team from Kent State University, located in Kent, Ohio. The lab is oriented towards research in telerobotics, with emphasis on immersion and telepresence. The first robotics competition we participated in was the Disaster Response Challenge, which was a part of the World Robot Summit (WRS) 2018 in Tokyo, Japan. The 2019 RoboCup Rescue Simulation League presents us with an opportunity to expand our research in robot simulation and share our knowledge with fellow competitors, with the ultimate goal of innovating the field of robotics.