Reviewerly/exHarmony huggingface dataset:
Project Sustainability and Research Platforms: The Archives Unleashed Cloud Project

This work is primarily supported by the Andrew W. Mellon Foundation. Other financial and in-kind support comes from the Social Sciences and Humanities Research Council, Compute Canada, the Ontario Ministry of Research, Innovation, and Science, York University Libraries, Start Smart Labs, and the Faculty of Arts and David R. Cheriton School of Computer Science at the University of Waterloo.
Using synthetic data and dimensionality reduction in high-dimensional classification via logistic regression

Traditional logistic regression  is plugged with degenerates and violent behavior in high-dimensional classification, because of   the problem of non-invertible matrices in estimating model parameters. In this paper, to overcome the high-dimensionality of data,  we introduce  two new algorithms. First, we  improve the efficiency of finite population Bayesian bootstrapping logistic regression classifier by using the rule of  majority vote.  Second, using simple random sampling without replacement to select a smaller number of covariates rather than the sample size and applying traditional logistic regression, we introduce the other new algorithm  for high-dimensional binary classification.   We compare the proposed algorithms with the regularized logistic regression  models and two other  classification algorithms, i.e., naive Bayes and K-nearest neighbors using both simulated and real data.
Learning Correlation Filter With Detection Response For Visual Tracking

Correlation Filter (CF) has been a powerful tool for real-time visual object tracking. Although enormous improvements have been made since the first CF tracker was proposed, most of the existing CF tracking schemes adopt a fixed Gaussian label to train correlation filter. We argue that it is not optimal for challenging scenarios. Additionally, existing training label managing strategies are too complex to maintain the speed advantage of CF algorithms. In this work, we propose a novel label to supervise filter training. Our method is capable of being integrated into most CF trackers conveniently without increasing computational complexity. We employ the proposed scheme for four existing CF trackers and conduct extensive experiments on popular benchmark. The encouraging experimental results validate both the effi-ciency and efficacy of our proposed method. What’s more, we provide a detailed analysis for challenging applications and hyperparameter setting.
On the Alleged Contradiction in Scientific Rationality

The paper discusses value dimension of scientific research. The author claims that the most promising way here is to analyze scientific rationality as a set of interrelated attitudes within the institutional framework of the big science. She shows that there is no contradiction between the search for truth and the technological enhancement in science.
KLASIFIKASI TEKS BERBASIS ONTOLOGI UNTUK DOKUMEN TUGAS AKHIR BERBAHASA INDONESIA

In this research, the classification of the final project document in Informatics Engineering UMM. The problem faced is the difficulty of finding relevant information and the difficulty of categorizing the TA documents according to areas of interest if it should be done manually. The purpose of this research is to get information based on abstract TA according to the category and make it easier to classify the TA document according to the field of interest. The categories used are the areas of interest in the study program: RPL, Computer Networking, Intelligent Game and Data Science. The data used TA documents as many as 500 data. The stage is to build and model ontology rules according to the data obtained with reference data of UMM Informatics Engineering Curriculum 2017 sourced from the Association for Computing Machinery (ACM) of the IEEE Computer Society. Ontology aims to classify the objects that exist in the collection without requiring the data train. To support the classification process used dao method. The dao method is used to calculate the similarity between documents from an existing node on the ontology by looking at the closest distance. Stage testing system using accuracy obtained result of 87%. This shows that ontologists are able to classify documents without using train data.
Strategies for Developing Process Reference Models: An Analysis of Selected Cases from the Product Development and Management Domain

Reference models available for process assessment and improvement in various domains are often criticized to be too generic or unable to capture industry, domain, or company specific requirements. Although typically a plethora of process reference-like models and other best practice resources are available to potentially close the criticized gaps, companies are reluctant to move towards the implied multi-model-based process management approaches or to customize or extend existing models according their proprietary needs. Approaches and methods to develop, enhance or customize available models or to integrate multiple models are often not satisfactorily understood and far away from being industry practice. The goal of the research underlying this paper is thus to support the development and enhancement of process reference models for complex industry contexts. As a sample domain the area of product development and management in the software and systems engineering domain is used. The paper analyses the development of four industry-driven model development cases to identify their model development contexts and goals as well as development strategies, approaches, and related rationales. The aim of the paper is to contribute to the understanding of how process reference models are developed and how they can be enhanced or adapted to broader and more complex contexts in order to deliver value and competitive advantage to the respective businesses.
Learning Visually Aligned Semantic Graph for Cross-Modal Manifold Matching

Many cross-modal learning tasks, such as reasoning and matching between vision and language, usually need to explicitly learn the mapping function between semantic expressions of different modalities, whose manifold structures are inconsistent. We proposed to solve above cross-modal learning tasks from the perspective of semantic manifold alignment. First, we extract the respective intrinsic manifolds from different modal spaces and express them as semantic graphs. Then, we use a matrix decomposition strategy to learn a visually aligned semantic graph, based on which a nonparametric graph inference method is proposed. It is finally applied to a typical cross-modal learning task, i.e. zero-shot learning (ZSL). Extensive experimental results demonstrate its effectiveness and promising results.
Enhancement of the metrological sensitivity limit through knowledge of the average energy

We consider the problem of quantum phase estimation with access to arbitrary measurements in a single suboptimal basis. The achievable sensitivity limit in this case is determined by the classical Cram\'er-Rao bound with respect to the fixed basis. Here, we show that the sensitivity can be enhanced beyond this limit if knowledge about the energy expectation value is available. The combined information is shown to be equivalent to a direct measurement of an optimal linear combination of the basis projectors and the phase-imprinting Hamiltonian. Application to an atomic clock with oversqueezed spin states yields a sensitivity gain that scales linearly with the number of atoms. Our analysis further reveals that small modifications of the observable can have a strong impact on the sensitivity.
Marine accident learning with fuzzy cognitive maps (MALFCMs) and Bayesian networks: a case study on maritime accidents

Aiming to improve maritime safety, there is a need for a practical method that is capable of identifying the importance weightings for each contributing factor involved in accidents. Hence, Marine Accident Learning with Fuzzy Cognitive Maps (MALFCM) incorporated with Bayesian networks is suggested and applied in this study. MALFCM approach is based on the concept and principles of Fuzzy Cognitive Maps (FCMs) to represent the interrelations amongst accident contributor factors. Hence, in this study, grounding/stranding accidents were investigated with the proposed MALFCM approach. As a result, inadequate leadership and supervision, lack of training and unprofessional behavior were identified as the most probable causes of grounding accident. In addition, in the accident scenario analysis, it was observed that the lack of safety culture contributed most to the system failure based on the posterior to prior failures ratio.
A Refined Margin Distribution Analysis for Forest Representation Learning

In this paper, we formulate the forest representation learning approach called \textsc{CasDF} as an additive model which boosts the augmented feature instead of the prediction. We substantially improve the upper bound of the generalization gap from $\mathcal{O}(\sqrt{\ln m/m})$ to $\mathcal{O}(\ln m/m)$, while the margin ratio of the margin standard deviation to the margin mean is sufficiently small. This tighter upper bound inspires us to optimize the ratio. Therefore, we design a margin distribution reweighting approach for deep forest to achieve a small margin ratio by boosting the augmented feature. Experiments confirm the correlation between the margin distribution and generalization performance. We remark that this study offers a novel understanding of \textsc{CasDF} from the perspective of the margin theory and further guides the layer-by-layer forest representation learning.
Educational-and-cultural processes in Ukraine in XVIIth Century : european tendencies

Tetiana Kochubei Educational-and-cultural processes in Ukraine in XVIIth Century: european tendencies // History of Education & Children’s Literature(HECL)half-yearly journal / rivista semestraleVol. XIV, n. 1, 2019, s 513-534
GHRNet: Guided Hierarchical Refinement Network for Stereo Matching

Recently, deep convolutional neural networks (CNNs) have been well developed in the task of stereo matching. However, most existing CNNs-based methods cannot accurately estimate details especially for some tiny objects and shape boundaries. To solve this problem, in this paper, we propose a guided hierarchical refinement network (GHRNet) with a specially designed guidance block for details refinement. Specifically, an initial low-resolution disparity map is first estimated. It is then fed into a hierarchical architecture to recover disparity details from coarse to fine. The foremost contribution of our proposed method is an embedded guidance block, which fully makes use of clues from input images to refine predicated disparity map. We evaluate our network on several popular datasets. The experimental results demonstrate that the proposed network can significantly improve disparity details, and the overall result also achieves the state-of-the-art performance.
A New Causal Direction Reasoning Method for Decision Making on Noisy Data

A major trend in self-driving technology is to introduce new causal reasoning mechanisms for decision-making. This paper mainly discusses the problem of causal direction reasoning for nonlinear noisy data. The paper presents the errors-in-variables (EIV) system to construct a causality model and the Hilbert-Schmidt independence criterion (HSIC) to compute the dependence between variables. Then, the paper proposes a new method that is based on the EIV model and HSIC. In the proposed method, noise at both the input and output is considered simultaneously. The proposed method has strong robustness and maintains a relatively stable inference accuracy when the observational noise is considerable. Experiments on simulated and real-world data are presented to demonstrate the performance of the proposed method.
Including Fault Seal and Retention in Monte Carlo Models of Traps

Summary A Monte Carlo modelling approach for including fault seal and fault reactivation risk in trap evaluation is described. The model reduces the problem to a 1D analysis using a structural description depth-referenced to the crest of the prospect. Both juxtaposition and membrane fault seal are calculated, and any potential hydrodynamic effects on fault seal also included. A complementary geomechanical model is used to evaluate fault reactivation and mechanical top seal failure risk. The impact of both fault seal and fault reactivation on trapped column height is calculated and compared in a roll-up of results that also includes the potential columns constrained by capillary and mechanical top seals. The output is a predicted hydrocarbon column height distribution for a trap. The technique additionally offers insight into the potential fluid phase fractionation that may occur due to the active leakage mechanisms, thereby enabling gas vs oil column heights and their respective controls to be included in the prospect evaluation. This latter feature offers an update of the Seal Strength-Trap Closure approach championed by John Sales, by demonstrating how faults impact both the trapped hydrocarbon column and fluid phase partitioning.
PENERAPAN MANAJEMENLAYANAN TEKNOLOGI INFORMASI MENGGUNAKAN FRAMEWORK INFORMATION TECHNOLOGY INFRASTRUCTURE LIBRARY V.3 (studi kasus STMIK Mardira Indonesia)

In the implementation of governance, information security factor is a very important aspect to be noticed, considering the performance of governance will be disrupted if the information as one of the main objects of governance experience information security issues concerning confidentiality, integrity, and availability. Because of this, it is necessary to have operational standard procedures to minimize the level of vulnerability. So we need an adequate information security management for system users to become safer and more comfortable.The operational standards are based on the information technology infrastructure library version 3 sub-service domain design service service catalog management and information security management to support adequate IT management and management.The result of this design is Standard Operating Procedure (SOP) for IT services. We expect this research can help STMIK Mardira Indonesia to improve its service capability and improve its security and role. Keywords : IT Services, ITIL V3, Service Design, Standard Operating Procedure DOI : http://doi.org/10.5281/zenodo.3625315
Morphology Filter for Enhancing the Signal-to-Noise Ratio of the Nuclear Magnetic Resonance Echo Data

Summary During the process of the nuclear magnetic resonance (NMR) echo data measurement, the echo data is often accompanied by lots kinds and amounts of noise. In order to get the inverse result and the petrophysical parameter like porosity and permeability more accurately, it is important to enhance the signal to-noise ratio of the echo data. In this paper, the mathematical morphology method is applied to build the morphology filter for the NMR echo data filtering. And numerical simulation is used to compare the inverse result of echo data before and after the filtering. The root mean square error of the T2 distribution and the relative error of the porosity ξ are the two parameters used for result evaluation. Eventually, the method is proved to be effective and stable based on the numerical simulation.
AI neural networks gets a new memory chip

Scientists work to speed up artificial intelligence neural networks with the material development of resistive switching memory synapses.
The Role of Granularity in Causal Learning

Prior experiments on causal learning have typically investigated how people learn about the relationships between binary variables (e.g., patients either take or do not take a drug, and either exhibit or do not exhibit a particular symptom). Such experiments are often oversimplifications of real-world learning contexts, in which people have to learn about relationships between causes and effects of varying granularities (i.e. how many levels a variable has). In this dissertation, I explored how the granularities of a cause and effect influenced peoples’ estimates of the strength of causal relationships. Four experiments were conducted in which participants learned about a cause-effect relationship by observing a cause and effect over multiple trials and making a judgment about the causal strength. On each trial, participants first viewed the state of the cause and predicted the state of the effect. Participants made stronger causal strength judgments when the effect was more coarse-grained, despite the objective causal strength being fixed (Experiment 1). The influence of the effect’s granularity was due to participants perceiving the prediction task as subjectively easier when it involved a coarse-grained effect, and not due to feedback they received for their predictions (Experiment 2). These findings supported the newly proposed feelings-of-success heuristic; I proposed that participants made judgments of objective causal strength by substituting their subjective feelings of how successfully they made predictions of the effect. In support of this hypothesis, participants’ judgments of how successful they were in the prediction task mediated the relationship between the granularity of the effect and their judgments of objective causal strength (Experiment 3). Finally, the influence of the effect’s granularity was attenuated when participants did not make explicit predictions, suggesting that the effect’s granularity influenced causal strength judgments via the subjective feelings associated with the act of prediction (Experiment 4). Collectively, these studies show that while people are generally accurate when estimating causal strength, real-world factors like the granularity of variables can lead to biases in judgments.
Socially-Attentive Representation Learning for Cold-Start Fraud Review Detection

Fraud reviews consist one of the most serious issues in cyberspace, which dramatically damage users’ decisions yet have great challenges to be detected. Accordingly, effectively detecting fraud reviews is becoming a critical task for cybersecurity. Although various efforts have been put on fraud review detection, they may fail in the case of cold-start where a review is posted by a new user who just pops up on social media. The main reason lies in lacking sufficient historical information of the new user. Recently, limited research has been conducted on fraud review detection with the cold-start problem, in which, however, advanced methods either ignore complex collaborative review manipulations or overlook fraud-related characteristics. As a result, they may easily be deceived by camouflage fraudsters and have low detection precision. This paper presents a novel socially-attentive user representation learning method for fraud review detection with the cold-start problem, namely SATURN, which leverages the fraud-related user reviewing behavior with comprehensive user social couplings for cold-start fraud review detection. SATURN jointly embeds user-item-attitude-review entities relations, explicit and implicit hierarchical social couplings, and fraud-related information into a user vector representation space, in which the fraud-related representation of a new user can be effectively inferred according to the learned socially-attentive entities relation. Subsequently, SATURN effectively detects fraud reviews with the cold-start problem in its learned representation space. Extensive experiments on four large real-world data sets demonstrate SATURN significantly outperforms three state-of-the-art and two baseline competitors in terms of both general and cold-start fraud review detection tasks.
Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes

This paper studies statistical characteristics of multivariate observations with irregular changes in their covariance structures across input space. We propose a unified nonstationary modeling framework to jointly encode the observation correlations to generate a piece-wise representation with a hyper-level Gaussian process (GP) governing the overall contour of the pieces. In particular, we couple the encoding process with automatic relevance determination (ARD) to promote sparsity to account for the inherent redundancy. The hyper GP enables us to share statistical strength among the observation variables over a collection of GPs defined within the observation pieces to characterize the variables' respective local smoothness. Experiments conducted across domains show superior performances over the state-of-the-art methods.
High-Dimensional Embedding Denoising Autoencoding Prior for Color Image Restoration

This work exploits the basic denoising autoencoding (DAE) as enhanced priori for color image restoration (IR). The proposed method consists of two steps: enhanced DAE network learning and iterative restoration. To be special, at the training phase, a denoising network taking 6-dimensional variable as input is trained. Then, the network-driven high-dimensional prior information embedded DAE priori is utilized in the iterative restoration procedure. We first map the intermediate color image to be 6-dimensional and employ the higher-dimensional network to handle its corrupted version. The average operator is used to turn it back to the 3-channel image. The higher-dimensional prior alleviates the issue of the basic DAE that getting trapped in local optimal solution and effectively overcomes the instability. Experimental results on single image super-restoration (SISR) and deblurring demonstrate that the proposed algorithm can achieve good performance and prime visual inspection.
Sampling Networks and Aggregate Simulation for Online POMDP Planning

The paper introduces a new algorithm for planning in partially observable Markov decision processes (POMDP) based on the idea of aggregate simulation. The algorithm uses product distributions to approximate the belief state and shows how to build a representation graph of an approximate action-value function over belief space. The graph captures the result of simulating the model in aggregate under independence assumptions, giving a symbolic representation of the value function. The algorithm supports large observation spaces using sampling networks, a representation of the process of sampling values of observations, which is integrated into the graph representation. Following previous work in MDPs this approach enables action selection in POMDPs through gradient optimization over the graph representation. This approach complements recent algorithms for POMDPs which are based on particle representations of belief states and an explicit search for action selection. Our approach enables scaling to large factored action spaces in addition to large state spaces and observation spaces. An experimental evaluation demonstrates that the algorithm provides excellent performance relative to state of the art in large POMDP problems.
MIXED REALITY AS ONE OF MAIN COMPONENTS OF THE SCIENTIFIC GATEWAY

Data visualization and mixed reality is incredibly important as a tool of communication -whether for hard or softer data.A Science Gateway is a connection between scientists and their computational tools in the form of web portal.It creates a space for communities, collaboration and data sharing and visualization in a comprehensive and e-client manner.The main purpose of such a solution is to allow users to access the computational resources, process and analyze their data and get the results in a uniform and user friendly way.It is very important for us, researcher to communicate our science to non-scientists.Our paper deal with position the mixed reality as a one of main component the scientific gateway and portals.The scientific portals and gateways are cumulative all classes of visualization and Mixed reality.