Reviewerly/exHarmony huggingface dataset:
Ant Routing scalability for the Lightning Network

The ambition of the Lightning Network is to provide a second layer to the Bitcoin network to enable transactions confirmed instantly, securely and anonymously with a world scale capacity using a decentralized protocol. Some of the current propositions and implementations present some difficulties in anonymity, scaling and decentalization. The Ant Routing algorithm for the Lightning Network was proposed in \cite{GrunspanPerez} for maximal decentralization, anonymity and potential scaling. It solves several problems of current implementation, such as channel information update and centralization by beacon nodes. Ant Routing nodes play all the same role and don't require any extra information on the network topology beside for their immediate neighbors. The goal of LN transactions are completed instantaneously and anonymously. We study the scaling of the Ant Routing protocol. We propose a precise implementation, with efficient memory management using AVL trees. We evaluate the efficiency of the algorithm and we estimate the memory usage of nodes by local node workload simulations. We prove that the number of transactions per second that Ant Routing can sustain is of the order of several thousands which is enough for a global payment network.
Query Execution Optimization in Spark SQL

Spark SQL is a big data processing tool for structured data query and analysis. However, due to the execution of Spark SQL, there are multiple times to write intermediate data to the disk, which reduces the execution efficiency of Spark SQL. Targeting on the existing issues, we design and implement an intermediate data cache layer between the underlying file system and the upper Spark core to reduce the cost of random disk I/O. By using the query pre-analysis module, we can dynamically adjust the capacity of cache layer for different queries. And the allocation module can allocate proper memory for each node in cluster. According to the sharing of the intermediate data in the Spark SQL workflow, this paper proposes a cost-based correlation merging algorithm, which can effectively reduce the cost of reading and writing redundant data. This paper develops the SSO (Spark SQL Optimizer) module and integrates it into the original Spark system to achieve the above functions. This paper compares the query performance with the existing Spark SQL by experiment data generated by TPC-H tool. The experimental results show that the SSO module can effectively improve the query efficiency, reduce the disk I/O cost and make full use of the cluster memory resources.
Norway made a new deal with Elsevier

In 2019 Norway decided not to renew their deal with the Dutch publisher Elsevier. The reasons were clear: there was no real transition towards Open Access. Now, a new deal has been signed with the same publisher, and the deal is worth around 9-10 million euros. But the question is: What kind of a deal has been made this time around?&#x0D; The new deal is said to be a trial deal, and the pilot will run for two years. It will be giving seven universities and 39 research institutions across Norway access to Elsevier’s world-leading platform for scientific knowledge with more than 16 million publications from over 2,500 journals published by Elsevier and its society partners. It also enables Norwegian researchers to publish their research Open Access.&#x0D; Our guest today is Mona Magnussen, the Head of the Department of Collections at the University Library at UiT The Arctic University of Norway. The host of the program is Erik Lieungh.&#x0D; This episode was first published 29 April 2019.
The Correlation between the Use of Social Media and Academic Writing Mastery of the English Education Students

The objectives of this study were to find out: (1) the social media which is the most frequently used by the English Education Study Program students, (2) the correlation between the use of social media and academic writing mastery of the students, and (3) the contribution between the use of social media and academic writing mastery of the students. Thirty five students were chosen as the sample by using purposive sampling method. The data were collected through questionnaire and documentation. Pearson Product Moment Correlation Coefficient was used to analyze the obtained data. The result showed that there was a significant correlation between the use of social media and academic writing mastery of students of English education study program (
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality

Interest in generative models has grown tremendously in the past decade. However, their training performance can be adversely affected by contamination, where outliers are encoded in the representation of the model. This results in the generation of noisy data. In this paper, we introduce weighted conjugate feature duality in the framework of Restricted Kernel Machines (RKMs). The RKM formulation allows for an easy integration of methods from classical robust statistics. This formulation is used to fine-tune the latent space of generative RKMs using a weighting function based on the Minimum Covariance Determinant, which is a highly robust estimator of multivariate location and scatter. Experiments show that the weighted RKM is capable of generating clean images when contamination is present in the training data. We further show that the robust method also preserves uncorrelated feature learning through qualitative and quantitative experiments on standard datasets.
Le varie fasi di revisione del testo applicate alla traduzione della saggistica divulgativa

Popularization essays are borderline works which combine scientific accuracy and high literary quality. The first task of an editor is therefore to select a translator who is able to preserve both aspects in the target text. The revision of the translation is then organized in a series of levels or phases: a comparison with the original text, which implies checking the technical terminology and the research actually carried out; a stylistic revision of the target text; a further reading focusing on the clarity and precision of contents; the check of intertextual references (table of contents, headings, footnotes, references within the text, index); a check of the compliance with the editorial guidelines; the correction of typos and the control of the layout, which in these volumes is often elaborate and sometimes includes pictures. These reading levels, although interacting, should be examined individually in a sequence of different phases, a methodology often hampered by the strict schedules required today by the publishing market. This article focuses on the techniques and problems related to each phase.
Forensic evidence – a rape and murder case

Sexual crime is very alarming in most of the countries and India is not exception to it.As per National Crime Records Bureau (NCRB), MHA, Govt. of India data, every day ninety three women are being raped in the country.There is also gradual increase in the number of rapes reported in India.But incident of rape-sexual homicide cases are very less, as such the data on commission of two different unrelated crimes together are not available in literature.In one case, a girl age about 20-year was found lying dead in half naked condition being strangulated with a full pant tied around her neck in an abandoned place.The incident was reported to the police and investigating officer along with forensic team visited the undisturbed violent crime scene to study the crime scene behavior and collect different forensic evidence.Further, autopsy report and analysis of the forensic evidence could help to establish the crime and apprehend the rapist.The details have been discussed in this paper.
Using Major Element Data

Major element data in geochemistry are used in rock classification, in plotting variation diagrams and in plotting diagrams in which the rock chemistry is shown together with the results of experimentally determined data. The main approaches to major element rock classification in igneous and sedimentary rocks include the use of oxide–oxide plots, the calculated normative mineralogy and the rock composition recast as cations. Binary and ternary variation diagrams are widely used to display geochemical data, and a major part of the interpretation of geochemical data is the identification and the replication, through modelling, of trends on these diagrams. The wealth of experimental data on igneous rocks means that natural rock compositions may be compared with experimental data gathered at particular pressures and temperatures to understand melting and fractionation processes in the Earth’s mafic and felsic crusts and mantle.
Order-Dependent Convergent Search for Efficient Search of Feature Patterns

A novel algorithm, order-dependent convergent search, for efficient search of interest patterns is proposed. Order or sequence in searching plural patterns is analyzed for designing an efficient algorithm in computation cost, and then some schema for reducing the searching area are proposed. For the realization of experimental programs, a matching algorithm of robustness against illumination fluctuation is utilized. For testing the performance of the proposed algorithm, the experimental evaluation using both of synthesized data and real images of industrial products recognized the cost reduction in their registration.
Building A Natural Disaster Knowledge Base Expert System based on the Distributed and Incremental Crawling Technology

Abstract China is one of the countries with the most natural disasters in the world. Natural disasters seriously endanger people’s safety and economic situations. Therefore, it is very necessary to strengthen the prediction and research on the characteristics and the causes of various natural disasters. Based on the Internet distributed and incremental crawling technology, this system crawls the knowledge literatures, disaster news and professional disaster data from domestic and overseas on natural disasters, as well as drawing on the advanced knowledge management concept, the captured data will be displayed in the form of knowledge tree. Integrating the authoritative global typhoon live maps and orthophotos which acquired by UAV to better monitor and predict typhoons; This system determined that the SSM framework is used as the system framework, combined MySQL and Redis database to store data stably and efficiently. In addition, the Echarts component can be used to analyze and visualize the crawled data. Big data analysis techniques such as knowledge graphs and related algorithms for literature analysis, data cleaning to better data store and display. In the end, it will enable the researchers to more easily and efficiently carry out the knowledge sharing, collaborative cooperation and interactive communication, which has the certain research significance and use value.
Impact of Climatic Factors on Settlement Formation

The article considers the influence of various natural and climate conditions of the Republic of Tajikistan on the formation of new towns and cities. The climate conditions are closely interconnected with the landscape, namely valleys, foothills, low mountains, and the highland nature of the terrain, and requires various architectural and planning adjustments.
A Face Recognition System Based on Improved Convolutional Neural Network

Be aimed at the problems of slow training speed and poor precision of traditional face recognition methods, a face recognition method based on a convolutional neural network is researched and implemented based on classical network LeNet-5. The algorithm consists of three layers of a convolutional layer, three layers of the pooling layer, one layer of fully connected layer and output layer, and a new activation function is added to improve the accuracy and convergence speed of the model. The algorithm can automatically perform data dimensionality reduction, feature extraction, and classification results. The experimental results show that the verification and recognition rate of the algorithm is 99.87% in the ORL face database, 98.34% in the LFW face database, and 97.86% in the AR face database. The algorithm has better performance than the commonly used ANNs and CNNS recognition methods. Finally, based on this algorithm, the face recognition system is realized. The system can input facial information and perform face recognition, which can effectively enhance safety management.
A proper generalized decomposition approach for optical flow estimation

This paper introduces the use of the proper generalized decomposition (PGD) method for the optical flow (OF) problem in a classical framework of Sobolev spaces, ie, optical flow methods including a robust energy for the data fidelity term together with a quadratic penalizer for the regularization term. A mathematical study of PGD methods is first presented for general regularization problems in the framework of (Hilbert) Sobolev spaces, and their convergence is then illustrated on OF computation. The convergence study is divided in two parts: (a) the weak convergence based on the Brézis‐Lieb decomposition and (b) the strong convergence based on a growth result on the sequence of descent directions. A practical PGD‐based OF implementation is then proposed and evaluated on freely available OF data sets. The proposed PGD‐based OF approach outperforms the corresponding non‐PGD implementation in terms of both accuracy and computation time for images containing a weak level of information, namely, low image resolution and/or low signal‐to‐noise ratio (SNR).
Enhancing the Authentication Mechanism of Social Media Websites using Face Detection

This paper includes different models used for the authentication of social media account. Importance of security in authentication is described in paper. Different security and privacy models and techniques for authentication are used over the social media. These techniques have limitations. In order to minimize the security risk of authentication and validation, a methodology called face detection and facial recognition is discussed in this paper. It provides the facility to improve security and to avoid unauthorized access of social media accounts.
Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach

Peer-to-peer knowledge transfer in distributed environments has emerged as a promising method since it could accelerate learning and improve team-wide performance without relying on pre-trained teachers in deep reinforcement learning. However, for traditional peer-to-peer methods such as action advising, they have encountered difficulties in how to efficiently expressed knowledge and advice. As a result, we propose a brand new solution to reuse experiences and transfer value functions among multiple students via model distillation. But it is still challenging to transfer Q-function directly since it is unstable and not bounded. To address this issue confronted with existing works, we adopt Categorical Deep Q-Network. We also describe how to design an efficient communication protocol to exploit heterogeneous knowledge among multiple distributed agents. Our proposed framework, namely Learning and Teaching Categorical Reinforcement (LTCR), shows promising performance on stabilizing and accelerating learning progress with improved team-wide reward in four typical experimental environments.
Information-flow Interfaces

Abstract Contract-based design is a promising methodology for taming the complexity of developing sophisticated systems. A formal contract distinguishes between assumptions , which are constraints that the designer of a component puts on the environments in which the component can be used safely, and guarantees , which are promises that the designer asks from the team that implements the component. A theory of formal contracts can be formalized as an interface theory , which supports the composition and refinement of both assumptions and guarantees. Although there is a rich landscape of contract-based design methods that address functional and extra-functional properties, we present the first interface theory that is designed for ensuring system-wide security properties. Our framework provides a refinement relation and a composition operation that support both incremental design and independent implementability. We develop our theory for both stateless and stateful interfaces. We illustrate the applicability of our framework with an example inspired from the automotive domain.
Investigating variations in computer and information literacy and computational thinking

Prior to the International Computer and Information Literacy Study (ICILS), research into learning outcomes related to information and computer technologies (ICT), and factors associated with student achievement in this area had generally been based on a few national studies. ICILS 2018 used multilevel models to assess the extent to which factors at the student and school level were associated with variations in computer and information literacy (CIL) and computational thinking (CT). It investigated access to, use of, and familiarity with ICT, school context factors, as well as variables reflecting the personal and social background of students. There were differences in the variance in both CIL and CT, as well as the proportion of variance between schools, across participating countries. Multilevel models explained much of the variance in CIL and CT at the school level but less variance within schools was explained. Personal and social background factors were shown to be associated with CIL and CT. Female students tended to have higher CIL scores than male students but lower CT scores than male students. The use of the test language at home was a positive predictor of CIL and CT in some countries. Both expected university education and socioeconomic background were consistent positive predictors of both CIL and CT across countries. There were mostly inconsistent effects across countries on CIL and CT by school-level factors related to ICT.
Pedestrian Detection Based on Improved Interface Difference and Double Threshold with Parallel Structure

In this paper, based on the classical "two-frame difference method" and "three-frame difference method", an improved pedestrian detection scheme based on "three-frame difference method" is proposed. Two improvements were made based on the conventional three-frame difference method. The first is to improve the "adjacent frame difference method "of the original three-frame difference to "three-frame frame mutual difference", effectively avoiding the influence of pedestrian behavior on the system. The second is to introduce the "double threshold parallel structure" to transform the image, which relatively increases the useful information contained in the image, and increases the accuracy of the system.
Penerapan Data Mining Untuk Memprediksi Pemesanan Bibit Pohon Dengan Regresi Linear Berganda

Data mining, often also called knowledge discovery in database (KDD), is an activity that includes collecting, using historical data to find order, patterns or relationships in large data sets. Outputs from data mining can be used to improve future decision making. Problems that often occur in BPDASHL are estimation problems such as weather, difficulties in planting, lack of labor, lack of experience in tree nurseries, and different soil conditions. Another problem that is found in agencies is that they do not have a system to predict estimated tree seedlings orders every year so that a method is needed, namely the Multiple Linear Regression Algorithm. So with this was made the Application of Data Mining To Predict Ordering Tree Seeds With Multiple Linear Regression. Multiple Linear Regression Algorithms which are methods that support estimating or predicting order targets for the coming period. Algorithm testing is done using SPSS software. From the results of the research that has been done, it can help BPDASHL to make it easier to predict the ordering of seeds using SPSS Software
Pengenalan Alfabet American Sign Language Menggunakan K-Nearest Neighbors Dengan Ekstraksi Fitur Histogram Of Oriented Gradients

Sign Language use to communicate to people with dissabilities. American Sign Language (ASL) one of popular sign language. Histogram of Oriented Gradient (HOG) can be use as feature extraction. Then feature stored in database. K-Nearest Neighbor use to measure distance between feature train and feature test. There are three distance use in this paper consist of Euclidean Distance, Manhattan Distance and Chebychev Distance. The best result are 0,99 when using Euclidean Distance and Manhattan Distance with k=3 dan k=5
Design of Command Effectiveness Evaluation Software for Overseas Anti-terrorism Operation Command System

Due to the complexity of overseas anti-terrorism operations, the effectiveness of its command system is evaluated by the method of constructing evaluation system software. The evaluation system software can be divided into five levels: infrastructure layer, resource layer, support layer, application layer and standard specification layer. The functions consist of five subsystems: data collection and information, static evaluation, dynamic evaluation, expert evaluation and interaction, and comprehensive processing and visualization. The specific evaluation methods include N-ADC static evaluation method and GERT network dynamic simulation method. Finally, the main page composition and evaluation process of the evaluation system software is described.
The Efficiency of Human Cognition Reflects Planned Information Processing

Planning is useful. It lets people take actions that have desirable long-term consequences. But, planning is hard. It requires thinking about consequences, which consumes limited computational and cognitive resources. Thus, people should plan their actions, but they should also be smart about how they deploy resources used for planning their actions. Put another way, people should also "plan their plans". Here, we formulate this aspect of planning as a meta-reasoning problem and formalize it in terms of a recursive Bellman objective that incorporates both task rewards and information-theoretic planning costs. Our account makes quantitative predictions about how people should plan and meta-plan as a function of the overall structure of a task, which we test in two experiments with human participants. We find that people's reaction times reflect a planned use of information processing, consistent with our account. This formulation of planning to plan provides new insight into the function of hierarchical planning, state abstraction, and cognitive control in both humans and machines.
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement Learning

The combination of Formal Methods with Reinforcement Learning (RL) has recently attracted interest as a way for single-agent RL to learn multiple-task specifications. In this paper we extend this convergence to multi-agent settings and formally define Extended Markov Games as a general mathematical model that allows multiple RL agents to concurrently learn various non-Markovian specifications. To introduce this new model we provide formal definitions and proofs as well as empirical tests of RL algorithms running on this framework. Specifically, we use our model to train two different logic-based multi-agent RL algorithms to solve diverse settings of non-Markovian co-safe LTL specifications.