Reviewerly/exHarmony huggingface dataset:
Kolaborasi Penulis Pada Media Pustakawan Periode 2017-2019

ABSTRACTMedia Pustakawan period 2017 - 2019 were analyzed using bibliometric methods aimed at finding out the number of articles, author  productivity, authorship patterns, degrees of collaboration, institutions where author work, degree of collaboration  , types of maritime, profession and cities where authors work. The data is downloaded from the Media Pustakawan website, https://ejournal.perpusnas.go.id/mp/index, then the data is processed using Microsof Exel and the results are presented in a table. Based on the results and discussion it can be concluded that during the period 2017 - 2019, Media Pustakawan  published 96 articles, with an average productivity of 1.36 authors / articles, a single author dominated with an average degree of collaboration value of 0.271. Universities  is the highest institution in contributing articles, especially from the State Islamic University. Arif Wicaksoo is the most prolific author with 4 articles, female authors  are more productive than male authors. The librarian profession is in the first position as a contributor to writing, and Jakarta is the place where most authors work.
Using Fetal Myocardial Velocity Recordings to Evaluate an AI Platform to Predict High-risk Deliveries

Diagnosing abnormal fetal cardiac function using ultrasound is a complicated procedure which makes it difficult to obtain high quality results from ultrasound examinations that are performed shortl ...
Application of Geophysics for Quick Determination of Deposit in Mineral Exploration - A Case Study

The Balatindi Mineral Prospect, Guinea, is a multi-element mineral prospect that hosts commercial quantities of gold, uranium and copper.A major challenge for exploration activities within the area is the turnaround time for assay results due to the unavailability of a nearby assay laboratory and the high cost of analysis.This therefore delays assay results which are needed for quick decisions during field work.There is therefore the need to find a faster and cheaper way of delineating mineral anomalies.Magnetic and radiometric geophysical techniques were considered because of the rapid and cost-effective ways in which data is acquired and they yield substantial quantity of accurate data enhancing interpretation and deduction.Down hole drill data for 37 HQ size diamond drilled holes of about 9200 m were used for the analysis.Magnetic susceptibility data were secured from 24 drill hole data for the Central Polymetallic Prospect area, whiles radiometric data were taken for the 23 drill holes in the other areas.The data were correlated with the laboratory assay results for gold and uranium respectively.Strip logs and statistical analysis were used to interpret magnetic susceptibility readings against gold assay values, and radiometric readings against uranium assay values.Maximum and minimum values of -2.46 × 10 -3 and 546.5 × 10 - 3 magnetic susceptibility were respectively recorded whiles 45 cps and 7250 cps were the maximum and minimum radiometric readings obtained.Magnetic susceptibility values did not show any preferential correlation with gold mineralisation.Radiometric cps values showed a very strong direct correlation with uranium mineralisation.This is because the radiometric cps is a function of the radiometric mineral present and therefore, radiometric survey can be used as a faster and cost-effective way of delineating uranium anomaly within the Balatindi prospect.
URL’s Folder Name Length as a Phishing Detection Feature

Phishing is a cybercrime in which, the phishers try to control users’ credentials. The phishers usually construct fake URLs that take to phishing websites where the users might disclose, and loss their credentials. This paper introduces a new feature to detect phishing URLs. The length of folder name of URL’s path is utilized in this work as a phishing detection feature, namely FldrNmLnth. Results from analysis of two email datasets show that many of phishing URLs are constructed using upto 230 characters as names of their folders’ names. The length of folders’ names of legitimate URLs, on the other side, has not exceeded 30 characters. This length variance motivates the use of folder name length as a feature to detect phishing URLs.
Collaborative Drug Discovery with Blockchain Technology

The cost of developing and researching novel molecular compounds in the pharmaceutical industry is very high today. If an actor accidentally releases too much information about a novel molecule to the public the molecule becomes prior art, and as an effect, can not be patented. This has resulted in a fragmented space where collaboration across organizations is virtually non-existent. We propose a blockchain-based platform where participants can partially upload information of molecules while maintaining full ownership of the asset. This way novel molecules do not become prior art and gives the developer of the molecule the ability to reveal to the world that they have a molecule with a certain property, without actually revealing the molecule itself. Larger organizations could use this to auction out molecules that they are not interested in bringing to clinical trials or market, smaller organizations could use it to attract funding for a molecule that they have developed, and researchers could use it to timestamp that they knew the properties or a structure of a molecule at a certain date. A prototype is implemented using the blockchain technology Hyperledger Fabric and is analyzed from security and performance perspectives. Given the right circumstances, Fabric provides a set of functionalities that can be used to make sure that ownership is maintained, integrity is protected, and critical information remains confidential. From a performance perspective, Fabric provides a good throughput and latency in the order of milliseconds. However, the number of participants that can maintain the network seems to be limited in the prototype. These results should be seen as preliminary as no fine-grained optimization was conducted. Further work required to make the prototype production-ready and open problems are also discussed. (Less)
Profound technologies: towards exploring the technological disruption and the challenges for a more humanized and inclusive education

The objective of this chapter is to analyse the profound technologies in our contemporary society, the process of humanization and inclusion as well as life forms based on coexistence, solidarity and the distributed participation of those who teach and learn through technologies.
The El Coronavirus

During the year 2019 and this 2020, humanity has been attacked by a series of natural disasters (wildfires), pandemics (COVID 19) and a noticeable effect on the inhabitants of climate change and global warming.In this article, it is disclosed what the coronavirus is, where it originates from, what it produces in the human body, how the disease is transmitted and how we can prevent it. Everything in a simple and clear language, for the understanding of a large number of people; so that we stop fearing something we don't know, since knowledge gives us power and takes away fear.Because we will all get ahead together.This article is a bibliographic review, since coronaviruses originate from the Latin name “corona”, which in turn is a word taken from the Greek “κορώνη”, which means crown or garland.They are large pleomorphic spherical particles, with bulbous surfaces, the average diameter of the envelope is 80nm (nanometers) and the spicules (or beaks) are 20nm long. The envelope consists of a double layer of lipids (fats), where three types of structural proteins are anchored (the membrane type M, the envelope type E and the spike type S); however, another subset of coronavirus has another surface protein, called HE, of hemagglutinin esterase.In humans, they were discovered in 1960, in patients with the common cold (or so the human doctors of the time thought). They named them human coronavirus 229E and human coronavirus OC43 and the first photo taken of both viruses was taken by Scottish virologist June Almeida (virologist), at Saint Thomas Hospital in London. Since that time, SARS-CoV in 2003, JCoV NL63 in 2004, HKU1 in 2005, MERS CoV in 2012 and SARS-CoV-2 in 2019 (COVD 19) have appeared; most of them having serious respiratory infections.
LibGuides: Communication: Search

This Communications Research Guide provides a list of library resources for research purposes.
Guides: Resources for Home Learning: Science

During this time when many students are learning from home, we've complied a guide for free online resources to help children learn and play at home.
A classification and predictive model of the complex REE mineral system

&amp;lt;p&amp;gt;We model the intermittent, non-linear interactions and feedback loops of the complex rare earth elements (REE) mineral system applying the self-organized criticality concept.&amp;amp;#160; Our semantic knowledge model (REE_MinSys ontology) represents dynamic primary and secondary processes that occur over a wide range of spatial and temporal scales and produce the emergent REE deposits and their geometry, tonnage, and grade. These include the scale-invariant, out-of-equilibrium geodynamic and magmatic processes that lead to the formation of orthomagmatic (carbonatite, alkaline igneous rocks) and syn- and post-magmatic hydrothermal REE deposits. The ontology also represents the redistribution of the REE from these primary ores by metamorphic fluids and/or post-depositional surface and supergene processes in sedimentary basins, fluvial channels, coast areas, and/or regolith around or above them. The ontology applies concepts of the complex systems theory to represent the spatial and spatio-temporal elements of the REE mineral system such as source, driver, threshold barriers, trigger, avalanche, conduit, relaxation, critical point attractor, and self-organization for the alkaline igneous, Iron oxide (subcategory of IOCG), hydrothermal, marine placers, alluvial placers (including paleo-placers), phosphorite, laterite, and ion-adsorption clays REE deposits. The ontology is instantiated with diverse data drawn from globally-distributed types of well-studied small to giant REE deposits to build the REE_MinSys knowledge base. Users can query the data in the knowledge base to extract explicit and inferred facts in each type of REE deposit, for example by asking: &amp;amp;#8220;Which rare earth elements are in REE phosphate deposits?&amp;amp;#8221;; &amp;amp;#8220;Which rare earth elements are largely explored in REE placer deposits?&amp;amp;#8221; &amp;amp;#160;Data from the knowledge base will be divided into training and testing sets after they are preprocessed and trends and data patterns are identified through data analytical procedures. The training and test datasets will be used to build models applying machine learning algorithms to predict potential REE deposits of different kinds in unexposed or covered areas.&amp;lt;/p&amp;gt;
The Role of Critical Thinking in Fostering Professional Capacity for Pedagogical Students in Vietnam Nowadays

Improving the professional capacity of pedagogical students is an urgent issue facing pedagogical universities in Vietnam. Currently, the professional capacity of these students is not only shown in the teaching ability but also in the soft skills fostered and self-trained by students themselves during the university study process. This article clarifies the role and the importance of critical thinking in fostering professional capacity for pedagogical students in Vietnam nowadays. Keywords: critical thinking, professional capacity, pedagogical students, role. DOI: 10.7176/RHSS/10-18-06 Publication date: September 30 th 2020
A Fast Photon-Interface Using a Fiber-Based Atom-Cavity System

Using a cavity assisted two-photon Raman process, we demonstrate the deterministic generation and storage of fast photons (pulse width &lt; atomic excited-state lifetime) in an atomic system coupled to a high-bandwidth cavity (k &gt; 7).
Parameterized Complexity of Manipulating Sequential Allocation

The sequential allocation protocol is a simple and popular mechanism to allocate indivisible goods, in which the agents take turns to pick the items according to a predefined sequence. While this protocol is not strategy-proof, it has been shown recently that finding a successful manipulation for an agent is an NP-hard problem (Aziz et al., 2017). Conversely, it is also known that finding an optimal manipulation can be solved in polynomial time in a few cases: if there are only two agents or if the manipulator has a binary or a lexicographic utility function. In this work, we take a parameterized approach to provide several new complexity results on this manipulation problem. More precisely, we give a complete picture of its parameterized complexity w.r.t. the following three parameters: the number $n$ of agents, the number $\mu(a_1)$ of times the manipulator $a_1$ picks in the picking sequence, and the maximum range $\mathtt{rg}^{\max}$ of an item. This third parameter is a correlation measure on the preference rankings of the agents. In particular, we show that the problem of finding an optimal manipulation can be solved in polynomial time if $n$ or $\mu(a_1)$ is a constant, and that it is fixed-parameter tractable w.r.t. $\mathtt{rg}^{\max}$ and $n+\mu(a_1)$. Interestingly enough, we show that w.r.t. the single parameters $n$ and $\mu(a_1)$ it is W[1]-hard. Moreover, we provide an integer program and a dynamic programming scheme to solve the manipulation problem and we show that a single manipulator can increase the utility of her bundle by a multiplicative factor which is at most 2.
Classifying the Classifier: Dissecting the Weight Space of Neural Networks

This paper presents an empirical study on the weights of neural networks, where we interpret each model as a point in a high-dimensional space -- the neural weight space. To explore the complex structure of this space, we sample from a diverse selection of training variations (dataset, optimization procedure, architecture, etc.) of neural network classifiers, and train a large number of models to represent the weight space. Then, we use a machine learning approach for analyzing and extracting information from this space. Most centrally, we train a number of novel deep meta-classifiers with the objective of classifying different properties of the training setup by identifying their footprints in the weight space. Thus, the meta-classifiers probe for patterns induced by hyper-parameters, so that we can quantify how much, where, and when these are encoded through the optimization process. This provides a novel and complementary view for explainable AI, and we show how meta-classifiers can reveal a great deal of information about the training setup and optimization, by only considering a small subset of randomly selected consecutive weights. To promote further research on the weight space, we release the neural weight space (NWS) dataset -- a collection of 320K weight snapshots from 16K individually trained deep neural networks.
Entanglement Distribution Using a Number State and a Beam Splitter

We show that a photon number state incident on a beam splitter will create a phase- entangled state that violates Bell’s inequality. This approach may have practical applications in quantum communications and quantum key distribution.
A lower bound on determinantal complexity

The determinantal complexity of a polynomial P ∈ F[x1,..., xn] over a field F is the dimension of the smallest matrix M whose entries are affine functions in F[x1,...,xn] such that P = Det(M). We prove that the determinantal complexity of the polynomial Σni=1 xni is at least 1.5n − 3. For every n-variate polynomial of degree d, the determinantal complexity is trivially at least d, and it is a long standing open problem to prove a lower bound which is super linear in max{n, d}. Our result is the first lower bound for any explicit polynomial which is bigger by a constant factor than max{n, d}, and improves upon the prior best bound of n + 1, proved by Alper, Bogart and Velasco [2] for the same polynomial.
Impact of Reading Anxiety on Reading Comprehension Performance in ESL Classroom of Boys’ Degree College Larkana, Sindh, Pakistan

This study investigates the impact of foreign language reading anxiety upon reading comprehension performance, the factors that affect comprehension performance, covering second language (L2).All Participants were Pakistani, studying English as a second language at government boys Degree College, Larkana, Sindh Pakistan.They were asked to fill the foreign language reading anxiety scale (FLRAS; Saito, Horwitz, & Garza, 1999) was developed to measure reading anxiety in L2.Reading comprehension tests were used to investigate students' reading comprehension performance in L2.Correlation analysis indicated that effect of reading anxiety was significant on reading comprehension performance of the students, it can be stated that students of boys Degree College face considerable challenges in comprehending the topics written with English.Students are unable to comprehend topics and concepts as they did not merely have lack of expertise in reading meanwhile, understanding, and pronunciation is also some fundamental challenges that are challenging the reading comprehension of students.In addition, the study found students face considerable challenges in reading comprehension also because they fail to remember letters.It can be stated that challenges for the students in the form of pronunciation, remembering letters, understanding collectively or individually results in the lower self-esteem, confidence and increased reading anxiety.Further, Reading anxiety which is developed due to discussed challenges has a significant impact on foreign reading comprehension performance.The study suggests that because of low self-esteem and confidence, students dislike reading in an ESL classroom.
Towards Multi-Robot Task-Motion Planning for Navigation in Belief Space.

Autonomous robots operating in large knowledgeintensive domains require planning in the discrete (task) space and the continuous (motion) space. In knowledge-intensive domains, on the one hand, robots have to reason at the highestlevel, for example the regions to navigate to or objects to be picked up and their properties; on the other hand, the feasibility of the respective navigation tasks have to be checked at the controller execution level. Moreover, employing multiple robots offer enhanced performance capabilities over a single robot performing the same task. To this end, we present an integrated multi-robot task-motion planning framework for navigation in knowledge-intensive domains. In particular, we consider a distributed multi-robot setting incorporating mutual observations between the robots. The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning. The underlying methodology and its limitations are discussed, providing suggestions for improvements and future work. We validate key aspects of our approach in simulation.
Object Tracking Achieved by Implementing Predictive Methods with Static Object Detectors Trained on the Single Shot Detector Inception V2 Network

In this work, the possibility of realising object tracking by implementing predictive methods with static object detectors is explored. The static object detectors are obtained as models trained on ...
Nielsen’s Unrealised Opera Plans

In addition to the two finished operas of Nielsen, scattered sources show that throughout his life he had at least five other opera projects in mind, which for various reasons were given up at different stages in their progress. The article presents the sources that are available for these projects, comprising the following works: Judith, Psycke, The Silent Woman, Portia, and Fru Marie Grubbe. The focus of the article is on Nielsen’s plan to compose an opera based on Shakespeare’s The Merchant of Venice under the working title Portia. Two important manuscripts in the Royal Library show that Nielsen himself had worked out a complete synopsis for the opera, and that the librettist, Sophus Michaëlis, had made a full text of the first act. A comparison of the two manuscripts and Shakespeare’s play reveals that the opera would have been quite different from the model with its focus on the character of Portia and the leaving out the merchant Antonio. Nielsen apparently gave up the idea, and no music from the work in progress worth mentioning is known.
Study on Schema Theory Applied in College English ESP Curriculum Based on Intelligent Optimization Algorithm

College English ESP course teaching is facing a new round of reforms. Under this situation,scholars can actively explore the new mode of ESP course teaching based on schema theory, and combinethe advanced disciplines to build ESP English course teaching. In this paper, the schema theory based onswarm intelligence method was introduced. Taking the English ESP curriculum teaching as an example, therecommendation algorithm and PSO algorithm were introduced first, and the algorithm was initialized andanalyzed. The operation process was described in detail, and the application process was analyzed, and makeit used in ESP English teaching. Finally, the experiment indicated that the PSO algorithm has a higherdegree of accuracy and can better judge individual behaviors, which is helpful to ebhance the teachingefficiency of college English ESP courses.
Amortized variance reduction for doubly stochastic objectives.

Approximate inference in complex probabilistic models such as deep Gaussian processes requires the optimisation of doubly stochastic objective functions. These objectives incorporate randomness both from mini-batch subsampling of the data and from Monte Carlo estimation of expectations. If the gradient variance is high, the stochastic optimisation problem becomes difficult with a slow rate of convergence. Control variates can be used to reduce the variance, but past approaches do not take into account how mini-batch stochasticity affects sampling stochasticity, resulting in sub-optimal variance reduction. We propose a new approach in which we use a recognition network to cheaply approximate the optimal control variate for each mini-batch, with no additional model gradient computations. We illustrate the properties of this proposal and test its performance on logistic regression and deep Gaussian processes.