Reviewerly/exHarmony huggingface dataset:
Embracing New Trends in Scholarly Communication: From Competency Requirements in the Workplace to LIS Curriculum Presence

INTRODUCTION Scholarly communication has undergone dramatic change in the digital era as a result of rapidly evolving digital technology. It is within this context of evolving scholarly communication that this paper reports on an inquiry into (1) the extent to which university libraries in South Africa are actively embracing new and emerging trends in scholarly communication; and (2), the extent to which LIS school curricula in South Africa are responding to new and emerging scholarly communication competencies required in university libraries. METHODS This qualitative study, located within an interpretivist epistemological worldview, was informed by the Operational Elements of Scientific Communication aspect of Khosrowjerdi’s (2011) Viable Scientific Communication Model. Data was collected using summative content analysis of university library job advertisements over a four-year period; South African university libraries’ organizational organograms; and course descriptions available on the websites of South Africa’s LIS schools. RESULTS &amp;amp; DISCUSSION A review of job advertisements and organograms shows that on the whole university libraries in South Africa are embracing the new and emerging trends in scholarly communication, but some university libraries are performing better than others in adopting emerging scholarly communication services such as RDM, digital humanities, or research landscape analysis. Course description analysis provides evidence that LIS schools’ curricula, as per global trend reported in the literature, do not seem to be keeping pace with developments in scholarly communication. CONCLUSION The ambivalent nature of an evolving scholarly communications field with unclear definitions and boundaries necessitates professional practitioners who are adaptable and open to change as well as an LIS education curriculum that is in constant review to seamlessly embrace an evolving field propelled by advancing digital technologies.
Learning to Embed Sentences Using Attentive Recursive Trees

Sentence embedding is an effective feature representation for most deep learning-based NLP tasks. One prevailing line of methods is using recursive latent tree-structured networks to embed sentences with task-specific structures. However, existing models have no explicit mechanism to emphasize taskinformative words in the tree structure. To this end, we propose an Attentive Recursive Tree model (AR-Tree), where the words are dynamically located according to their importance in the task. Specifically, we construct the latent tree for a sentence in a proposed important-first strategy, and place more attentive words nearer to the root; thus, AR-Tree can inherently emphasize important words during the bottomup composition of the sentence embedding. We propose an end-to-end reinforced training strategy for AR-Tree, which is demonstrated to consistently outperform, or be at least comparable to, the state-of-the-art sentence embedding methods on three sentence understanding tasks.
E-LEARNING IN THE MIDDLE EAST AND NORTH AFRICA (MENA) REGION: REVIEW OF TRENDS FROM THE FIRST COMPREHENSIVE REGIONAL HANDBOOK

E-LEARNING IN THE MIDDLE EAST AND NORTH AFRICA (MENA) REGION: REVIEW OF TRENDS FROM THE FIRST COMPREHENSIVE REGIONAL HANDBOOK
Maumahara Papahou: A mobile augmented reality memory treasure box based on Māori mnemonic aids

He aha te mea nui o te ao?He tangata, he tangata, he tangata.What is the most important thing in the world?It is people, it is people, it is people.Traumatic brain injury (TBI) is a worldwide "silent epidemic" causing a variety of memory issues for those affected.Although several software approaches aim to aid memory recollection, they are inadequate for many TBI survivors and none address aspects of cultural awareness in New Zealand.We explored design concepts of traditional Mäori cultural artefacts and techniques and how they helped Mäori people recall past information.This paper describes our research and development of Maumahara Papahou, a mobile app that uses augmented reality features to create a digital memory treasure box based on the concepts of Mäori mnemonic aids.
Data Mining di Bidang Pendidikan untuk Analisa Prediksi Kinerja Mahasiswa dengan Komparasi 2 Model Klasifikasi pada STMIK Jabar

Until now many universities that helped the government in accelerating improving the quality of education so the creation of a competitive environment.. The abundance of data contained in the College can be put to good use in accordance with the needs and processed into useful information so as to find out the relationship between the attributes of the data in it can be on the analysis and expected output in the form of student performance has related to the period of study i.e. can be categorized into appropriate or too late in the anticipated period of study. Data mining can be used for educational institutions or institutions and often called as Educational Data Mining (EDM). In the study carried out using two models of Naive Bayes Classifier i.e. algorithms and C 4.5. as for the value of the best accuracy in the Naive Bayes Classifier algorithm model (NBC) was 86.83% with ratio 80% training data, whereas in model algorithm C 4.5 was 88.10% with 90% training data ratio. Application of EDM and expected to be maximized and developed so that it can contribute to and progress in the world of education especially in data mining.
Choosing Between Alternative Models

Any scientific model will have to make simplifying assumptions about reality. Nevertheless, statistical models are important tools to learn from patterns in underlying data. A good model can be used to make accurate predictions for future subjects. We discuss some general issues in choosing a type of model in a prediction context, with illustration in a case study on modeling age–outcome relations in medicine. We also summarize results from some empirical comparisons of alternative models, including classical regression and modern methods related to machine learningMachine learning approaches.
Spec-QP: Speculative Query Planning for Joins over Knowledge Graphs

Organisations store huge amounts of data from multiple heterogeneous sources in the form of Knowledge Graphs (KGs). One of the ways to query these KGs is to use SPARQL queries over a database engine. Since SPARQL follows exact match semantics, the queries may return too few or no results. Recent works have proposed query relaxation where the query engine judiciously replaces a query predicate with similar predicates using weighted relaxation rules mined from the KG. The space of possible relaxations is potentially too large to fully explore and users are typically interested in only top-k results, so such query engines use top-k algorithms for query processing. However, they may still process all the relaxations, many of whose answers do not contribute towards top-k answers. This leads to computation overheads and delayed response times. We propose Spec-QP, a query planning framework that speculatively determines which relaxations will have their results in the top-k answers. Only these relaxations are processed using the top-k operators. We, therefore, reduce the computation overheads and achieve faster response times without adversely affecting the quality of results. We tested Spec-QP over two datasets - XKG and Twitter, to demonstrate the efficiency of our planning framework at reducing runtimes with reasonable accuracy for query engines supporting relaxations.
Pelatihan Akuntansi Pemerintahan Bagi Guru Akuntansi Di Kota Dan Kabupaten Jayapura

State financial reformation, which was followed by the issuance of Accrual-based GovernmentAccounting Standards (SAP), and the Village Law that strongly influenced the teaching skill ofgovernment accounting in Indonesia. This development requires the availability of competent humanresources to participate directly and actively in managing government finances so that goodgovernance in government sector is achieved. Human Resources who are competent in managingstate finances are produced from educational institutions where the teacher is one of the factors thathas the most significant contribution. Therefore, in relation to the development of governmentaccounting today, it is necessary to conduct government accounting training for accounting teachers.The implementation of Community Service activities is carried out by using lecture methods, tutorials,and discussions. Activities have been carried out properly as expected. The training activities werecarried out with the number of 25 (twenty five) participants in accounting teachers in SMA and SMKin Jayapura City, Jayapura Regency, and Keerom Regency. The training was carried out with tutorialand discussion methods as much as 60% of all training and problem training / practice activities asmuch as 40%. Participants feel they have benefited from the activities they have carried out.
Adaptive Exact Learning of Decision Trees from Membership Queries.

In this paper we study the adaptive learnability of decision trees of depth at most $d$ from membership queries. This has many applications in automated scientific discovery such as drugs development and software update problem. Feldman solves the problem in a randomized polynomial time algorithm that asks $\tilde O(2^{2d})\log n$ queries and Kushilevitz-Mansour in a deterministic polynomial time algorithm that asks $ 2^{18d+o(d)}\log n$ queries. We improve the query complexity of both algorithms. We give a randomized polynomial time algorithm that asks $\tilde O(2^{2d}) + 2^{d}\log n$ queries and a deterministic polynomial time algorithm that asks $2^{5.83d}+2^{2d+o(d)}\log n$ queries.
ICT Competencies in Eight Mayan-speaking Communities of Mexico: Preliminary Findings

The skills related to the use of information and communication technologies (ICT) were examined in this study within the context of eight communities with a high degree of vulnerability and diversity.The objectives of the study were: a) to conduct a diagnosis of thirteen competencies related to the use of ICT in eight Mayanspeaking communities in the state of Yucatan, Mexico, and b) to examine such variables as gender, marital status, and the education level of their parents.in relation to the degree of ICT among these youth.
Written evidence submitted by the Science and Justice Research Interest Group, University of Northumbria to the Science and Technology Committee (Commons) inquiry on the work of the Biometrics Commissioner and Forensic Science Regulator (WBC0004)

Both the forensic science and biometrics ‘strategy’ documents lacked detail, underlying principles, and indeed, any discernible ‘strategy’. Adequate governance and oversight of both forensic science and biometrics is urgent. The importance of creating a transparent and accountable regime for the use of biometrics, and forensic science, that is simultaneously legally justifiable, socially acceptable and ethically sound, has been the core message of multiple reports and publications for many years. There remain serious questions over the effectiveness of the England and Wales model of forensic regulation. Although there has been significant progress with initial aims and objectives and broad stakeholder engagement, both the Forensic Science Regulator (FSR) and Biometrics Commissioner lack meaningful powers, and significant gaps in regulation and oversight remain. Accreditation across the forensic science sector is not only inconsistent but is superficial. Both also face serious resource restrictions with debilitating limitations on their capacities, while wider austerity measures throughout the criminal justice system hamper efforts to raise standards in forensic science. Both areas suffer from the lack of responsiveness of the law, with legal governance and guidance lagging behind the development of technology. Legal principles, such as proportionality, need to be properly accounted for in the necessary public and parliamentary debates around the acceptable use of biometrics and forensic technologies.
Comparing Pipelined and Integrated Approaches to Dialectal

When translating diglossic languages such as Arabic, situations may arise where we would like to translate a text but do not know which dialect it is. A traditional approach to this problem is to design dialect identification systems and dialect-specific machine translation systems. However, under the recent paradigm of neural machine translation, shared multi-dialectal systems have become a natural alternative. Here we explore under which conditions it is beneficial to perform dialect identification for Arabic neural machine translation versus using a general system for all dialects.
Quantum Sensing 1/f Noise via Pulsed Control of a Two-Qubit Gate

Dynamical decoupling sequences are a convenient tool to reduce decoherence due to intrinsic fluctuations with 1 / f power spectrum hindering quantum circuits. We study the possibility to achieve an efficient universal two-qubit gate in the presence of 1 / f noise by periodic and Carr-Purcell dynamical decoupling. The high degree of selectivity achieved by these protocols also provides a valuable tool to infer noise characteristics, as the high-frequency cut off and the noise variance. Different scalings of the gate error with noise variance signal the contribution of different noise statistical properties to the gate error.
Blockchain Beyond Crypto

As you are approaching the last pages of this book, I wanted to inspire you and help you see what’s possible in the coming years from the blockchain technology. Crypto paved the way by providing an alternative to fiat currency with the introduction of bitcoin. Although the invention of bitcoin was more than a decade ago (it was introduced in 2008), blockchain is still in its infancy. However, it’s poised to potentially become one the most useful technological innovations of the 21st century.
Innovation in Synthesizing Big Data: The Electronic Registration Information Center (ERIC)

Maintaining accurate, complete voter lists has been a significant and intractable problem. Many eligible voters are not on the lists, which can also be fraught with inaccuracies. These issues lead to major problems downstream in the elections process, and since so much of election reform is viewed through a partisan lens, the inaccuracies can lead to polarizing divisions. Bringing together a group of experts and election officials, a multistate data center was created, called the Electronic Registration Information Center, or “ERIC.” ERIC delivers information on voters who have moved, while also allowing for outreach to those not yet registered. Born in 2012, ERIC has grown from 7 to 26 states and the District of Columbia, including states as red as Alabama, Louisiana, and Utah and states as blue as Connecticut, Illinois, and Oregon. The ERIC states govern ERIC, and pay the full costs of its operations, and security of all data is maintained through rigorous methods. Since its inception, ERIC has enabled states to update nearly 10 million out-of-date voter records, and reach out to over 34 million eligible but unregistered voters, with millions of these new voters registered.
On the relationship between variational inference and adaptive importance sampling.

The importance weighted autoencoder (IWAE) (Burda et al., 2016) andreweighted wake-sleep (RWS) algorithm (Bornschein and Bengio, 2015) are popularapproaches which employ multiple samples to achieve bias reductions compared tostandard variational methods. However, their relationship has hitherto beenunclear. We introduce a simple, unified framework for multi-sample variationalinference termed adaptive importance sampling for learning (AISLE) and showthat it admits IWAE and RWS as special cases. Through a principled applicationof a variance-reduction technique from Tucker et al. (2019), we also show thatthe sticking-the-landing (STL) gradient from Roeder et al. (2017), whichpreviously lacked theoretical justification, can be recovered as a special caseof RWS (and hence of AISLE). In particular, this indicates that the breakdownof RWS -- but not of STL -- observed in Tucker et al. (2019) may not beattributable to the lack of a joint objective for the generative-model andinference-network parameters as previously conjectured. Finally, we argue thatour adaptive-importance-sampling interpretation of variational inference leadsto more natural and principled extensions to sequential Monte Carlo methodsthan the IWAE-type multi-sample objective interpretation.
Controllability and observability of fuzzy matrix discrete dynamical systems

In this paper, sufficient conditions for the controllability of the fuzzy dynamical discrete system with the use of fuzzy rule base are established.Further, a sufficient condition for the fuzzy dynamical discrete system to be observable is constructed.The main advantage of this approach is that the rule base for the initial value can be determined without actually solving the system.Difference inclusions approach is adopted in the construction of these conditions.All the established theories are consolidated and explained with the help of examples.
The Association between Digital Literacy and Social Intelligence

This present study sought to identify the association between digital literacy and social intelligence.Samples were gathered from 60 undergraduate students studying in management program at a selected public university in Bangkok.A 14-item of Digital Literacy Awareness and a 21-item of Tromsø Social Intelligence Scale (TSIS) were utilized to evaluate level of digital literacy and social intelligence, respectively.Analysis of Pearson correlation analysis indicated that the overall digital litera cy had no significant relation to social intelligence.Yet, the overall digital literacy was found to have a positive association to social information process (r = .439,p < 0.01), and a negative correlation to social awareness at a low level (r = -.259,p < 0.05).For the limitations, results of this study showed only relationship between digital literacy and some dimensions of social intelligence, but not causation.This study recommended the further study placing more emphasis on studying cause and effects of these variables.For the originality of research, this study was noteworthy to be the first study in this area that attempted to examine the relationship between digital literacy and social intelligence.
Game Learning to Optimize Learning and Trauma Healing in Children After Disaster in Banyumas

The purpose of this study was to determine the effectiveness of learning games to improve learning and reduce trauma to children affected by postdisaster disasters.The research method used was Rn D. The results of the study showed that the utilization of learning games to optimize post-disaster learning in disaster areas.Where from 17 children who were victims of the disaster, they were very fond of game-based learning.On the other hand, the use of learning games can also reduce trauma to children affected by disasters after the disaster.
On the Existence of Kernel Function for Kernel-Trick of k-Means in the Light of Gower Theorem

This paper, constituting an extension to the conference paper [1], corrects the proof of the Theorem 2 from the Gower’s paper [2, page 5]. The correction is needed in order to establish the existence of the kernel function used commonly in the kernel trick e.g. for k-means clustering algorithm, on the grounds of distance matrix. The correction encompasses the missing if-part proof and dropping unnecessary conditions.
Pilot Study to Estimate "Difficult" Area in e-Learning Material by Physiological Measurements

To improve designs of e-learning materials, it is necessary to know which word or figure a learner felt "difficult" in the materials. In this pilot study, we measured electroencephalography (EEG) and eye gaze data of learners and analyzed to estimate which area they had difficulty to learn. The developed system realized simultaneous measurements of physiological data and subjective evaluations during learning. Using this system, we observed specific EEG activity in difficult pages. Integrating of eye gaze and EEG measurements raised a possibility to determine where a learner felt "difficult" in a page of learning materials. From these results, we could suggest that the multimodal measurements of EEG and eye gaze would lead to effective improvement of learning materials. For future study, more data collection using various materials and learners with different backgrounds is necessary. This study could lead to establishing a method to improve e-learning materials based on learners' mental states.
Representing Tables: tibble

The data that the readr package returns are represented as tibble objects. These are tabular data representations similar to the base R data frames but are a more modern version.
Exploiting Vendor-Defined Messages in the USB Power Delivery Protocol

The USB Power Delivery protocol enables USB-connected devices to negotiate power delivery and exchange data over a single connection such as a USB Type-C cable. The protocol incorporates standard commands; however, it also enables vendors to add non-standard commands called vendor-defined messages. These messages are similar to the vendor-specific commands in the SCSI protocol, which enable vendors to specify undocumented commands to implement functionality that meets their needs. Such commands can be employed to enable firmware updates, memory dumps and even backdoors. This chapter analyzes vendor-defined message support in devices that employ the USB Power Delivery protocol, the ultimate goal being to identify messages that could be leveraged in digital forensic investigations to acquire data stored in the devices.
Alexa, How Can I Reason with Prolog?

As with Amazon's Echo and its conversational agent Alexa, smart voice-controlled devices become ever more present in daily life, and many different applications can be integrated into this platform. In this paper, we present a framework that eases the development of skills in Prolog. As Prolog has a long history in natural language processing, we may integrate well-established techniques, such as reasoning about knowledge with Attempto Controlled English, instead of depending on example phrases and pre-defined slots.
The Development and creation of a software system for the monitoring system MAC1

A special system of acoustic detectors was created to search for possible correlations between wide air showers and the signal of elastic oscillations from the depths of the earth's crust. They are intended for joint synchronous work with storm installation.