Miracle-dz/UniFAR-FTUs huggingface dataset:
Data mining workflow templates for intelligent discovery assistance in RapidMiner

: Knowledge Discovery in Databases (KDD) has evolved during the last years and reached a mature stage offering plenty of operators to solve complex tasks. User support for building workflows, in contrast, has not increased proportionally. The large number of operators available in current KDD systems make it difficult for users to successfully analyze data. Moreover, workflows easily contain a large number of operators and parts of the workflows are applied several times, thus it is hard for users to build them manually. In addition, workflows are not checked for correctness before execution. Hence, it frequently happens that the execution of the workflow stops with an error after several hours runtime. In this paper we address these issues by introducing a knowledge-based representation of KDD workflows as a basis for cooperative-interactive planning. Moreover, we discuss workflow templates that can mix executable operators and tasks to be refined later into sub-workflows. This new representation helps users to structure and handle workflows, as it constrains the number of operators that need to be considered. We show that workflows can be grouped in templates enabling re-use and simplifying KDD worflow construction in RapidMiner. Abstract. Knowledge Discovery in Databases (KDD) has grown a lot during the last years. But providing user support for constructing workﬂows is still problematic. The large number of operators available in current KDD systems makes it diﬃcult for a user to successfully solve her task. Also, workﬂows can easily reach a huge number of operators (hundreds) and parts of the workﬂows are applied several times. Therefore, it becomes hard for the user to construct them manually. In addi-tion, workﬂows are not checked for correctness before execution. Hence, it frequently happens that the execution of the workﬂow stops with an error after several hours runtime. In this paper 3 we present a solution to these problems. We introduce a knowledge-based representation of Data Mining (DM) workﬂows as a basis for cooperative-interactive planning. Moreover, we discuss workﬂow templates, i.e. abstract workﬂows that can mix executable operators and tasks to be reﬁned later into sub-workﬂows. This new representation helps users to structure and handle workﬂows, as it constrains the number of operators that need to be considered. Finally, workﬂows can be grouped in templates which foster re-use further simplifying DM workﬂow construction.
An efficient threshold signature scheme based on the elliptic curve cryptosystem

The work presents an efficient identification threshold signature scheme based on the elliptic curve cryptosystem, with a view to optimizing these techniques for implementation within a network. (Any t out of n users in a group can represent this group to sign the group signature) as compared with the existing threshold signature scheme based on the public key system of RSA and ELGmal, our scheme need less parameters but can provide more efficient and super security. Moreover it is constructed on the two base points but other threshold signature scheme constructed on the only one point. The distinguishing feature of the proposed scheme is that the threshold value denotes the minimum number of members required to produce a valid group signature. All message recipients then can verify the signature.
Coner : A Collaborative Approach for Long-Tail Named Entity Recognition in Scientific Publications

Named Entity Recognition (NER) for rare long-tail entities as e.g. often found in domainspecific scientific publications is a challenging task, as typically the extensive training data and test data for fine-tuning NER algorithms is lacking. Recent approaches presented promising solutions relying on training NER algorithms in a iterative distantly-supervised fashion, thus limiting human interaction to only providing a small set of seed terms. Such approaches heavily rely on heuristics in order to cope with the limited training data size. As these heuristics are prone to failure, the overall achievable performance is limited. In this thesis we introduce Coner: A collaborative approach to incrementally incorporate human feedback on the relevance of extracted entities into the training cycle of such iterative NER algorithms. Coner allows to still train new domain specific rare long-tail NER extractors with low costs, but with ever increasing performance while the algorithm is actively used. We do so by employing our intelligent entity selection mechanism that solely selects and visualises extracted entities with the highest potential knowledge gain from users interacting with them and providing feedback on facet relevance. Additionally, users can add new typed entities they deem relevant. Our Coner collaborative human feedback pipeline consists of three novel modules; a document analyser that extracts deep metadata from documents and selects a representative set of publications from a corpus to receive human feedback on, an interactive document viewer that allows users to give feedback on and add new typed entities simply by selecting the relevant text with their mouse and an explicit entity feedback analyser that calculates a facet relevance score through users’ majority vote for each recognised entity. The resulting Coner entity facet relevance scores are then incorporated in the TSE-NER training cycle to boost the expansion and filtering heuristic steps. Remarkably, we revealed that even with limited availability of human resources we were able to boost TSE-NER’s performance by up to 23.1% in terms of recall, up to 5.7% in terms of precision and the F-score with 13.1% depending on the setup of our smart entity selection mechanism and instructions given to evaluators.
Downscaling Vertical GPS Observations to Derive Watershed‐Scale Hydrologic Loading in the Northern Rockies

GPS time series of vertical displacement include the elastic response of the Earth to a combination of regional and local loading signals arising from hydrologic mass transfer. The regional loading, controlled by seasonal, synoptic precipitation patterns, dominates the displacement of individual stations and is highly correlated among stations with separation distances from 10 to 300 km. The local loading, controlled by small‐scale precipitation and storage variability, has much shorter correlation lengths of <30 km. We develop a new method to separate the regional and local contributions using common mode analysis and show that GPS is capable of measuring the local hydrologic load changes at watershed scales of tens of kilometers. Using this methodology, GPS‐measured displacement provides an integrated measurement of hydrologic load at a spatial scale between the existing long‐wavelength resolution of the Gravity Recovery and Climate Experiment and point measurement resolution of a precipitation station. Thus, GPS time series record critical observations for monitoring integrated hydrologic budgets at scales useful for water management and assessment of the hydro‐ecological response to climate change.
ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning

Robust topological information commonly comes in the form of a set of persistence diagrams, finite measures that are in nature uneasy to affix to generic machine learning frameworks. We introduce a fast, learnt, unsupervised vectorization method for measures in Euclidean spaces and use it for reflecting underlying changes in topological behaviour in machine learning contexts. The algorithm is simple and efficiently discriminates important space regions where meaningful differences to the mean measure arise. It is proven to be able to separate clusters of persistence diagrams. We showcase the strength and robustness of our approach on a number of applications, from emulous and modern graph collections where the method reaches state-of-the-art performance to a geometric synthetic dynamical orbits problem. The proposed methodology comes with a single high level tuning parameter: the total measure encoding budget. We provide a completely open access software.
Journal of Economic Behavior & Organization Information, Irrationality, and the Evolution of Trust

Trust is a central component of social and economic interactions among humans. While rational self-interest dictates that " investors " should not be trusting and " trustees " should not be trustworthy in one-shot anonymous interactions, behavioral experiments with the " trust game " have found that people are both. Here we show how an evolutionary framework can explain this seemingly irrational, altruistic behavior. When individuals' strategies evolve in a context in which (1) investors sometimes have knowledge about trustees before transactions occur and (2) trustees compete with each other for access to investors, natural selection can favor both trust and trustworthiness, even in the subset of interactions in which individuals interact anonymously. We investigate the effects of investors having " fuzzy minds " and making irrationally large demands, finding that both improve outcomes for investors but are not evolutionarily stable. Furthermore, we often find oscillations in trust and trustworthiness instead of convergence to a socially optimal stable equilibrium, with increasing trustworthiness preceeding trust in these cycles. Finally, we show how " partner choice, " or competition among trustees in small group settings , can lead to arbitrarily equitable distributions of the game's proceeds. To complement our theoretical analysis, we performed a novel behavioral experiment with a modified version of the trust game. Our evolutionary framework provides an ultimate mechanism—not just a proximate psychological explanation—for the emergence of trusting behavior and can explain why trust and trustworthiness are sometimes stable and other times unstable.
The Characteristics Associated with Perceived Quality in Schools of Library and Information Science

The purpose of the present study is to determine, by building a model that predicts a judgment of perceived quality, what the profession means by "perceived quality of schools of library and information science. " The study examines quantifiable characteristics of two groups of library schools: those ranked in both of Herbert S. White's perception studies and those not ranked in both. Multiple regression and discriminant analysis were used to build a model that showed clear differences between the two groups of schools. On the basis of several variables that define aspects of a program's size, finances, age, leadership, and rigor, the analysis showed that ranked and unranked schools form two mutually exclusive groups whose membership can be predicted with better than 98 percent accuracy. It also showed the perceived quality of a school's master's degree (M.L.S.) program is associated with the following variables, listed in decreasing order of importance: the half-life of the school's doctoral graduates, its budget and outside income, its age, its faculty's productivity, and the number of its students.
In-depth: Depth Map Estimation From Monocular RGB Image Using Deep Learning

This report addresses the approaches of predicting depth from a single RGB image. Our implementation is based on the approaches presented by Eigen et al. (2) based on a two pass CNN model. This code is made available on Github (3). We evaluate the outcomes of our approaches in defined metrics and loss value. We also present several directions that we experimented in the project, such as data augmentation, batch normalization, dropout, and network architecture changes.
Attentive Dual Embedding for Understanding Medical Concept in Electronic Health Record

—Medical concept embedding is to learn a distributed representation for a medical related entity, e.g. diagnosis, treatment procedure, and medicine, which is a code stored in Electronic Health Record (EHR). The distributed representation is expected to preserve the comprehensive relationships among medical concepts rather than one-hot encoding, and it will be the inputs of machine learning based healthcare analytic tasks. Therefore, the performance of the analytic tasks highly depends on the quality of embedding outputs. To fully utilise the information in EHR, this paper proposes a novel attentive dual embedding method, namely MC2Vec, to intensively capture the proximity relationships among medical concepts. In particular, the proposed MC2Vec method uses a two-step optimisation framework to recursively reﬁne the embedding via two components 1) Skip-gram based method to generate the initial embedding of medical concept, and 2) Attentive CBOW based method to ﬁne-tune the code embedding by adding the temporal information of one patient’s sequential healthcare activities. The experiment studies on two public EHR datasets demonstrate the effectiveness of the proposed MC2Vec method, which performs superior than other ﬁve state-of-the-art embedding methods.
The time-triggered architecture

The Time-Triggered Architecture (TTA) is a computer architecture for distributed real-time systems in safety critical applications, such as computer controlled brakes, or computer assisted steering in an automobile. The TTA is a composable architecture for the design of large real-time systems. Its main characteristics are a common notion of time in all subsystems of the architecture and the provision of fully specified interfaces, called temporal firewalls, between these subsystems. This paper gives an overview of the TTA, discusses the architectural principles, describes the sensor/actuator interfaces in the TTA and informs about the implementation of fault-tolerance in the TTA.
AutoMicroED: A semi-automated MicroED processing pipeline

Despite rapid adaptation of micro-electron diffraction (MicroED) for protein and small molecule structure determination to sub-angstrom resolution, the lack of automation tools for easy MicroED data processing remains a challenge for expanding to the broader scientific community. In particular, automation tools, which are novice user friendly, compatible with heterogenous datasets and can be run in unison with data collection to judge the quality of incoming data (similar to cryosparc LIVE for single particle cryoEM) do not exist. Here, we present AutoMicroED, a cohesive and semi-automatic MicroED data processing pipeline that runs through image conversion, indexing, integration and scaling of data, followed by merging of successful datasets that are pushed through phasing and final structure determination. AutoMicroED is compatible with both small molecule and protein datasets and creates a straightforward and reproducible method to solve single structures from pure samples, or multiple structures from mixed populations. The immediate feedback on data quality, data completeness and more parameters, aids users to identify whether they have collected enough data for their needs. Overall, AutoMicroED permits efficient structure elucidation for both novice and experienced users with comparable results to more laborious manual processing.
the first step toward developing novel formal methods to ensure safety

The prevalence of the Internet of Things and cyberphysical systems (CPSs) has enabled the emergence of smart cities around the world, where a vast amount of sensing data and smart services are utilized to improve citizens’ safety, wellness, and quality of life.1,2 Various smart city operation control centers (for example, Microsoft’s CityNext, IBM’s Rio de Janeiro Operations Center, and Cisco’s Smart+Connected Operations Center) have been developed to support decision making in smart cities based on real-time sensing data about city states (such as traffic and air pollution). While significant research efforts have been spent toward building smarter services, sensors, and infrastructures in cities, the research challenge of how to ensure that a city’s real-time operations satisfy safety and performance requirements has received only scant attention. Failure to check such requirements can lead to conflicts among smart services or even catastrophic consequences.3–5 This article discusses several core challenges in developing novel formal methods for ensuring safety and performance in smart cities. Specifically, we focus on addressing three key research questions. Digital Object Identifier 10.1109/MC.2021. 3082991 Date of current version: 27 August 2021 Toward Formal Methods for Smart Cities
MRI DENOISING USING BM3D EQUIPPED WITH NOISE INVALIDATION DENOISING TECHNIQUE AND VST

Image denoising is very important in medical applications. Magnetic Resonance Imaging (MRI) denoising is the focus of many researches to provide images with both high signal-to-noise ratio and good spatial resolution. In the proposed method, Blockmatching and 3D filtering (BM3D) technique is used for MRI denoising. Main steps used in BM3D are Nonlocal approach, 3D wavelet shrinkage and 3D Wiener filtering. In the proposed method, Noise Invalidation Denoising (NIDe) is used instead of hard thresholding. This new approach provides the optimum wavelet threshold automatically based on the data and noise characteristics and adaptive to the statistical characteristics of data. This is the main advantage of NIDe over the hard thresholding. Variance Stabilization Transform (VST) removes the dependency of the noise variance on the MRI image intensities before denoising. Combining the proposed BM3D technique with Variance Stabilization Transformation (VST) enables the use of the proposed method for Magnetic Resonance Image denoising. Performance metrics such as peak signal to noise ratio (PSNR), mean square error (MSE) and structural similarity index (SSIM) are find out for T1, T2 and PD weighted MR images.
Substantiation into “rock massive – underground gasifier” system adaptability of Solenovskyi site in the Donetsk coal basin

Purpose. Research on stress-deformed state of rock massive around underground gasifier taking into account the change in the continuity of the roof rocks and the thermodynamic stresses in them. methodology. Conducting analytical studies was based on the accepted physical and mathematical models, for the development of which the rock massif movement theory of Professor A. V. Savostianov is adopted, reflecting the features of the rock strata behavior over the gasifier (stratification, the fractures and stratification cavities formation, layers movements relative to each other). findings. Multivariant calculations of the stress-deformed state of the rocks around the underground gasifier are performed with the establishment of the load diagrams parameters for the sub-layers of the massif, depending on geological, technological and temporal factors. Originality. Dependencies of the underground gasifier bearing pressure zones distribution on the adjacent roof level of the coal seam were established, taking into account gasification channel length (30 and 60 m) and the velocity of combustion face advance from 0.5 to 2.0 m/day. For the first time, dependencies representing the rock layers subsidence parameters in the underground gasifier roof from geomechanical factors, as well as thermal stresses along the gasification channel length and the velocity of combustion face advance, were obtained. Practical value. A mathematical mechanism was proposed for the determination of stresses in various rock massif sections in case of borehole underground coal gasification in order to establish the conditions for the “Rock massive – underground gasifier” system adaptability, taking into account geomechanical factors and thermal stresses along the gasification channel length. The obtained dependencies make it possible to predict the necessary velocity of the combustion face advance to ensure technological effectiveness of the gasification process. keywords: underground gasification, combustion face, rock massif, gasification channel, stress-deformed state
Cumulative Growth in User-Generated Content Production: Evidence from Wikipedia

Open content production platforms typically allow users to gradually create content and react to previous contributions. Using detailed edit-level data across a large number of Wikipedia articles, we investigate how past edits shape current editing activity. We find that cumulative past contributions, embodied by the current article length, lead to significantly more editing activity, while controlling for a host of factors such as popularity of the topic and platform-level growth trends. The magnitude of the effect is large; content growth over an eight-year period would have been 45% lower in its absence. Our findings suggest that other open content production environments are likely to also benefit from similar cumulative growth effects. In the presence of such effects, managerial interventions that increase content are amplified because they trigger further contributions. Data, as supplemental material, are available at http://dx.doi. org/10.1287/mnsc.2015. 2253. This paper was accepted by Pradeep Chint.
Statistical inference for partially observed branching processes with application to cell lineage tracking of in vivo hematopoiesis

Single-cell lineage tracking strategies enabled by recent experimental technologies have produced significant insights into cell fate decisions, but lack the quantitative framework necessary for rigorous statistical analysis of mechanistic models describing cell division and differentiation. In this paper, we develop such a framework with corresponding moment-based parameter estimation techniques for continuous-time, multi-type branching processes. Such processes provide a probabilistic model of how cells divide and differentiate, and we apply our method to study hematopoiesis, the mechanism of blood cell production. We derive closed-form expressions for higher moments in a general class of such models. These analytical results allow us to efficiently estimate parameters of much richer statistical models of hematopoiesis than those used in previous statistical studies. After validating the methodology in simulation studies, we apply our estimator to hematopoietic lineage tracking data from rhesus macaques. Our analysis provides a more complete understanding of cell fate decisions during hematopoiesis in non-human primates, which may be more relevant to human biology and clinical strategies than previous findings from murine studies. For example, in addition to previously estimated hematopoietic stem cell self-renewal rate, we are able to estimate fate decision probabilities and to compare structurally distinct models of hematopoiesis using cross validation. These estimates of fate decision probabilities and our model selection results should help biologists compare competing hypotheses about how progenitor cells differentiate. The methodology is transferrable to a large class of stochastic compartmental models and multi-type branching models, commonly used in studies of cancer progression, epidemiology, and many other fields.
PNAS proof Embargoed until 3 PM ET Monday publication weekof Changing climates of conflict : A social network experiment in 56 schools

Theories of human behavior suggest that individuals attend to the behavior of certain people in their community to understand what is socially normative and adjust their own behavior in response. An experiment tested these theories by randomizing an anticonflict intervention across 56 schools with 24,191 students. After comprehensively measuring every school’s social network, randomly selected seed groups of 20–32 students from randomly selected schools were assigned to an intervention that encouraged their public stance against conflict at school. Compared with control schools, disciplinary reports of student conflict at treatment schools were reduced by 30% over 1 year. The effect was stronger when the seed group contained more “social referent” students who, as network measures reveal, attract more student attention. Network analyses of peer-to-peer influence show that social referents spread perceptions of conflict as less socially normative.