Reviewerly/exHarmony huggingface dataset:
Blockchain For Facilities Management

Facilities management encompasses assets management, space management, IoT management, regulatory/compliance adherence and purchasing and billing issuance. Assets management requires monitoring, fault detection and the use of IoT for detecting conditions and triggering events. Facilities management involves tracking work orders, schedule maintenance, doing preventive management, and also tracking assets status through equipment warranties, life cycle assessments, contracts and agreements. Data management of facilities can now be done using a blockchain instead of a database. Blockchain is a peer-to-peer distributed digital ledger that records data which is accessible and shared by multiple qualifying parties with security enabled. The beauty of blockchain is it provides transparency in data handling, traceability of all transactions and ease of collaboration between qualifying parties. Since facilities management involves issuance of contracts and agreements, smart contracts can be deployed to provides a transparent and faster way to make transactions since they can be automated in blockchain network.In this paper, we have implemented a simple facilities management model using Hyperledger Composer Playground. To quickly develop and test the blockchain business networks, we have chosen to use Playground. In our model, we have built in employee information, vendor information, equipment tracking, fault report and maintenance report information. A token asset is employed to incentivize work completed to satisfaction. We will discuss how parameters like lifecycle, discount factor and depreciation information of equipment and facilities can be built into the model to present a more holistic view of the overall worth of assets at a snapshot.
What's the Difficulty? Understanding and Incentivizing Bitcoin's Difficulty Adjustment Mechanism

This paper develops a model of the bitcoin market that views the bitcoin as a tradeable commodity whose supply is managed by the Bitcoin protocol. Miners utilize equipment and electricity to solve complex computational problems and the first miner to solve a problem is rewarded with bitcoins. The protocol adjusts the difficulty of the problem to regulate the supply of bitcoins over time. The model demonstrates that an increase (decrease) in the difficulty works in effect like a government's placing an ad valorem tax (subsidy) on the price of a commodity. The rents that would have arisen from limiting supply, however, are wasted as electricity costs that benefit neither a government nor the miners. Ironically, while the bitcoin is esteemed for its nongovernmental design, its system of supply management is far less efficient than if a government were to regulate the quantity of bitcoins by imposing a tax on its price. I show that an actual tax on the price of the bitcoin can incentivize the protocol to minimize the electricity costs it induces and I characterize the cost-minimizing tax. Using data from March 2014 to January 2019, I estimate that the difficulty adjustment mechanism resulted in net social welfare losses of 351.6 million USD and an average initial tax of 119.5% would have minimized the electricity costs during this time period.
Information System for Registering the Result of Scientific Institution Employees’ Intellectual Activity

The article describes a typical object-oriented WEB-system designed for storing and providing various reference and statistical data on the scientific works of employees of an institution (group of institutions), developed by specialists of the JSCC RAS. The system contains information about publications of employees and reports made by them at scientific conferences, symposiums, and seminars. The system is focused on working with objects belonged to classes connected between each other, such as "author", "organization", "publication", "report", "event". The metadata profile of objects of each class includes attributes that are necessary to get detailed information about both an individual object of this class and a group of objects associated with the specified attribute values of objects of other classes. For example, you have to get a list of articles by employees of a given organization published articles in a given journal for a given period of time. A distinctive feature of the system is the introduced concept of "equivalent" objects. Such objects are "persons" corresponding to the same author with different spellings of the last name in the bibliographic descriptions of publications; organizations with different versions of names; articles which are published without changes in different languages. This article describes in detail the features of the system, its user interface, and provides examples of performing specific queries.
Applications of Information Inequalities to Linear Systems : Adaptive Control and Security

This thesis considers the application of information inequalities, Cramer-Rao type bounds, based on Fisher information, to linear systems. These tools are used to study the trade-offs between learn ...
GrabQC: Graph Based Query Contextualization for Automated ICD Coding

Automated medical coding is a process of codifying clinical notes to appropriate diagnosis and procedure codes automatically from the standard taxonomies such as ICD (International Classification of Diseases) and CPT (Current Procedure Terminology). The manual coding process involves the identification of entities from the clinical notes followed by querying a commercial or non-commercial medical codes Information Retrieval (IR) system that follows the Centre for Medicare and Medicaid Services (CMS) guidelines. We propose to automate this manual process by automatically constructing a query for the IR system using the entities auto-extracted from the clinical notes. We propose \textbf{GrabQC}, a \textbf{Gra}ph \textbf{b}ased \textbf{Q}uery \textbf{C}ontextualization method that automatically extracts queries from the clinical text, contextualizes the queries using a Graph Neural Network (GNN) model and obtains the ICD Codes using an external IR system. We also propose a method for labelling the dataset for training the model. We perform experiments on two datasets of clinical text in three different setups to assert the effectiveness of our approach. The experimental results show that our proposed method is better than the compared baselines in all three settings.
Backward Importance Sampling for Online Estimation of State Space Models

This article proposes a new Sequential Monte Carlo algorithm to perform online estimation in the context of state space models when either the transition density of the latent state or the conditional likelihood of an observation given a state is intractable. In this setting, obtaining low variance estimators of expectations under the posterior distributions of the unobserved states given the observations is a challenging task. Following recent theoretical results for pseudo-marginal sequential Monte Carlo smoothers, a pseudo-marginal backward importance sampling step is introduced to estimate such expectations. This new step allows to reduce very significantly the computational time of the existing numerical solutions based on an acceptance–rejection procedure for similar performance, and to broaden the class of eligible models for such methods. For instance, in the context of multivariate stochastic differential equations, the proposed algorithm makes use of unbiased estimates of the unknown transition densities under much weaker assumptions than most standard alternatives. The performance of this estimator is assessed for high-dimensional discrete-time latent data models, for recursive maximum likelihood estimation in the context of Partially Observed Diffusion process (POD), and in the case of a bidimensional partially observed stochastic Lotka-Volterra model. Supplementary materials for this article are available online.
Fast Pattern Spectra Using Tree Representation of the Image for Patch Retrieval

We extend the notion of content based image retrieval to patch retrieval where the goal is to find the similar patches to a query patch in a large image. Naive searching for similar patches by sequentially computing and comparing descriptors of sliding windows takes a lot of time in a large image. We propose a novel method to compute descriptors for all sliding windows independent from number of patches. We rely on tree representation of the image and exploit the histogram nature of pattern spectra to compute all the required descriptors in parallel. Computation time of the proposed method depends only on the number of tree nodes and is free from query selection. Experimental results show the effectiveness of the proposed method to reduce the computation time and its potential for object detection in large images.
Contextual Prior Constrained Deep Networks for Mitosis Detection With Point Annotations

We study the problem of training an accurate deep learning mitosis detection model with only point annotations.To address this challenging label-efficient deep learning problem, we propose a novel contextual prior constraint mechanism and spatial area constrained loss to generate the reference ground truth for segmentation and to restrain incorrectly predicted pixels, respectively.The spatial area constraint mechanism is not strictly cast at the pixel-level and restrains the mitosis and non-mitosis areas as positive/negative bags under the framework of multiple instance learning.The experimental results show that our contextual prior mechanism with PSPNet as the segmentation baseline achieves state-of-the-art performance with an F-score of 69.92%, 56.22%, and 85.29% on the mitosis detection task of AMIDA 2013, ICPR MITOSIS 2014, and point-annotated ICPR MITOSIS 2012, respectively.Especially, using our spatial area constraint mechanism and reference ground truth, the detection result on point-annotated ICPR MITOSIS 2012 even outperforms the result using the same backbone network with pixel-level annotations.The experimental results demonstrate the advancement and effectiveness of our proposed method.In addition, they indicate that our work can definitely improve the performance of mitosis detection on point-annotated datasets and be extended to other medical image analysis tasks with limited annotations.
Verification of Size Invariance in DNN Activations Using Concept Embeddings

The benefits of deep neural networks (DNNs) have become of interest for safety critical applications like medical ones or automated driving. Here, however, quantitative insights into the DNN inner representations are mandatory [10]. One approach to this is concept analysis, which aims to establish a mapping between the internal representation of a DNN and intuitive semantic concepts. Such can be sub-objects like human body parts that are valuable for validation of pedestrian detection. To our knowledge, concept analysis has not yet been applied to large object detectors, specifically not for sub-parts. Therefore, this work first suggests a substantially improved version of the Net2Vec approach [5] for post-hoc segmentation of sub-objects. Its practical applicability is then demonstrated on a new concept dataset by two exemplary assessments of three standard networks, including the larger Mask R-CNN model [9]: (1) the consistency of body part similarity, and (2) the invariance of internal representations of body parts with respect to the size in pixels of the depicted person. The findings show that the representation of body parts is mostly size invariant, which may suggest an early intelligent fusion of information in different size categories.
Comparative Analysis of Texture Patterns on Mammograms for Classification

Breast cancer is a cancerous tumor that arrives within the tissues of the breast. Women are mostly attacked than men. To detect early cancer medical specialists, suggest mammography for screening. Algorithms in Machine learning were executed on mammogram images to classify whether the tissues are deleterious or not. An analysis is done based on the texture feature extraction using different techniques like Frequency decoded local binary pattern (FDLBP), Local Bit-plane Decoded Pattern (LBDP), Local Diagonal Extrema Pattern (LDEP), Local Directional Order Pattern (LDOP), Local Wavelet Pattern (LWP). The features extracted are tested on 322 images from MIA’s database of three different classes. The algorithms in Machine learning like K-Nearest Neighbor classifier (KNN), Support vector classifier (SVC), Decision Tree classifier (DTC), Random Forest classifier (RFC), AdaBoost classifier (AC), Gradient Boosting classifier (GBC), Gaussian Naive Bayes classifier (GNB), Linear Discriminant Analysis classifier (LDA), Quadratic Discriminant Analysis classifier (QDA) were used to evaluate the accuracy of classification. This paper examines the comparison of accuracy using different texture features. KNN algorithm with LDEP for texture feature extraction gives classification accuracy of 64.61%, SVC with all the texture patterns mentioned gives classification accuracy of 63.07%, DTC with FDLBP, LBDP gives classification accuracy of 47.69, RFC with LBDP and AC with LDOP and GBC with FDLBP gives 61.53% classification accuracy, GNB and LDA with FDLBP gives 60% and 63.07% classification accuracy respectively, QDA with LBDP gives 64.61 classification accuracy. Of all the Algorithms support vector classifier gives good accuracy results with all the texture patterns mentioned.
Tarski’s Undefinability Theorem and the Diagonal Lemma

Abstract We prove the equivalence of the semantic version of Tarski’s theorem on the undefinability of truth with the semantic version of the diagonal lemma and also show the equivalence of a syntactic version of Tarski’s undefinability theorem with a weak syntactic diagonal lemma. We outline two seemingly diagonal-free proofs for these theorems from the literature and show that the syntactic version of Tarski’s theorem can deliver Gödel–Rosser’s incompleteness theorem.
MIXED REALITY APPLICATION FOR SURGICAL PLANNING IN VIDEO-ASSISTED PULMONARY LOBECTOMY

Abstract INTRODUCTION The adoption of surgical planning systems could greatly simplify the performance of video-assisted thoracoscopic surgery and improve its safety. MATERIAL AND METHODS A new application for surgical planning in video-assisted thoracoscopic surgery was developed, making use of the HoloLens (v1) mixed reality device. The information was displayed in the form of interactive holograms, by means of gestural, visual or voice control. The application was validated during a video-assisted right upper lobectomy surgery, including systematic lymphadenectomy for squamous cell carcinoma in the right upper lobe. RESULTS No complications were shown during surgery. Prior to surgery, the system allowed the surgeon to access in real time the patient's medical history, review the computed tomography study, and visualize and manipulate a 3D model of the lung with its respective vascular and bronchial elements, as well as the tumor to be removed. The surgeon’s vision using the device was easily shared with the rest of the surgical team. The surgeon placed the holographic models with the surgical planning information behind the field of view of the operating table for possible reference during the procedure. The weight and heat generated by the device were considered ergonomic aspects to be improved. CONCLUSIONS The system provides real-time access to important patient information for surgical planning during video-assisted lobectomy surgery, without losing the sterility of the surgical act. The surgeon's view can be shared for communication and learning purposes, as well as recorded for later review of surgical complications.
“May we not see God?”: Henry David Thoreau’s Doctrine of Spiritual Senses

Abstract This article argues that Henry David Thoreau believed in the essential unity of the five senses and privileged each as a source of wild and divine knowledge, which, when combined, created a full picture that might result in a true approximation of God in and beyond nature—the hallmark of Thoreau’s fundamentally incarnational theology. Thoreau treated each sense not only as a source of divine knowledge but as a site of theological discourse: for touch, the relationship between sin and grace; for smell, the conundrum of an eternal divinity acting in historical time; for taste, the efficacy of sacraments; for hearing, the possibility of continuing revelation; and for sight, the ability for human beings to actually see God. The senses were the practical entry point to Thoreau’s theological system, which was concerned with the discovery and redemption of internal “wildness” and reconnection to the mysterious, divine source of that wildness, to the unaccountable in nature.
Does Human Capital Matter for Growth in North African Countries? Panel Threshold Regression Approach

This objective of this paper is to analyze the impact of human capital on economic growth in North African countries usinga threshold model applied to panel data over the 1985/2017 period. The results revealed the significant impact of the human capital threshold on economic growth, which reflects higher levels of financial development and high trade openness. For a long time, the economic growth in the North African countries has been based mainly based on traditional economic growth factors. On the other hand, the impact of school enrollment is also significant, which implies the existence of a nonlinear relationship. In fact, school enrollment was shown to play an indirect role by increasing the impact of financial development, foreign direct investment and trade openness on economic growth, which showed that higher levels of financial development creates new business opportunities and increases the performance and efficiency of their financial sectors by ameliorating their overall economic performance. To fill this research gap, the present paper showed that the trade openness is positively correlated with economic growth, which implies the importance of globalization and trade liberalization in the valuation of economic growth. The paper finds that the high level of human capital is considered as incentive for domestic companies to easily understand the technical configurations of the technologies adopted by foreign companies. These findings are expected to help policymakers to increase the literacy rate in these countries through the improvement of the educational system to ensure economic growth, it is necessary to orient the economic policies to encourage the accumulation of human capital and improve financial intermediation. Furthermore, our results indicated that the role of education is very important for the economic activity, innovation, social and even political changes and democratic transitions.
Geological and geophysical data compilation for the western Wabigoon and southern Abitibi subprovinces of the Superior Province, Ontario, Canada

The geoscientific data presented in this paper are a foundation for experimental and exploration geological research in the western Wabigoon and southern Abitibi subprovinces of the Superior Province in Ontario, Canada. New geological interpretations, in map and GIS formats, along with compiled mineral deposit information, structural databases, magnetic susceptibility measurements, and reprocessed aeromagnetic grids have been integrated to provide a basis for comparative studies between the two geologically similar yet economically disparate greenstone belts near Dryden and Timmins, Ontario, Canada. Data were acquired from a wide range of publicly sourced data releases and enhanced through the addition of new observations. New geological maps presented for both regions represent the culmination of integrating the multi-disciplinary geoscientific database and recent geological interpretation. Data contained within this publication are co-submitted with Montsion et al. [1].
Research on Discrete Semantics in Continuous Hand Joint Movement Based on Perception and Expression

Continuous movements of the hand contain discrete expressions of meaning, forming a variety of semantic gestures. For example, it is generally considered that the bending of the finger includes three semantic states of bending, half bending, and straightening. However, there is still no research on the number of semantic states that can be conveyed by each movement primitive of the hand, especially the interval of each semantic state and the representative movement angle. To clarify these issues, we conducted experiments of perception and expression. Experiments 1 and 2 focused on perceivable semantic levels and boundaries of different motion primitive units from the perspective of visual semantic perception. Experiment 3 verified and optimized the segmentation results obtained above and further determined the typical motion values of each semantic state. Furthermore, in Experiment 4, the empirical application of the above semantic state segmentation was illustrated by using Leap Motion as an example. We ended up with the discrete gesture semantic expression space both in the real world and Leap Motion Digital World, containing the clearly defined number of semantic states of each hand motion primitive unit and boundaries and typical motion angle values of each state. Construction of this quantitative semantic expression will play a role in guiding and advancing research in the fields of gesture coding, gesture recognition, and gesture design.
Professional self-development of future specialists in the field of ecological psychology in the context of distance learning

Professional self-development as is one of the key targets of the higher education system. The digital transformation of the professional training sphere analyses the features of future specialists’ self-assessment of individual ability to transform themselves, their living conditions and professional activities. The information about the influence of the distance-learning format on professional self-esteem and self-development in future specialists are poorly systematized in the modern scientific literature. The aim of the article is the study of future educational psychologists and social educators’ attitude to the distance learning as a resource for self-development, the analysis of the correlation between future professionals’ quality evaluation of the organization and content of distance learning process in the period of the pandemic and self-assessment of their skills. We used the empirical methods: survey methods to collect data from respondents. The subject is the opportunities for self-development of the future specialist of the psycho-pedagogical fields in the conditions of distance format of obtaining a profession. The mathematical and mathematical statistics methods were used. The sample set of respondents consisted of 57 2-4 years students of full-time and part-time education of the Faculty of Psychology and Social Pedagogy of the State University of Humanities and Technology, Orekhovo-Zuyevo.
Successive Convex Approximation Based Off-Policy Optimization for Constrained Reinforcement Learning

Constrained reinforcement learning (CRL), also termed as safe reinforcement learning, is a promising technique enabling the deployment of RL agent in real-world systems. In this paper, we propose a successive convex approximation based off-policy optimization (SCAOPO) algorithm to solve the general CRL problem, which is formulated as a constrained Markov decision process (CMDP) in context of the average cost. The SCAOPO is based on solving a sequence of convex objective/feasibility optimization problems obtained by replacing the objective and constraint functions in the original problem with convex surrogate functions. The proposed SCAOPO enables reuse of experiences from previous updates, thereby significantly reducing implementation cost when deployed in real-world engineering systems that need to online learn the environment. In spite of the time-varying state distribution and the stochastic bias incurred by off-policy learning, the SCAOPO with a feasible initial point can still provably converge to a Karush-Kuhn-Tucker (KKT) point of the original problem almost surely.