Reviewerly/exHarmony huggingface dataset:
The Case for In-Memory OLAP on "Wimpy" Nodes

Research projects will often use the latest hardware to achieve orders-of-magnitude performance improvements while ignoring the (usually hefty) associated price tag. Real-world deployments typically follow suit, requiring expensive computing infrastructures that cost even more to power and cool.In this paper, we challenge the conventional wisdom that high-end hardware is absolutely necessary for state-of-the-art performance and instead advocate for a radically different approach based on cheap single-board computers (SBCs). While others have previously explored similar ideas for computationally simple and easily partitionable use cases (e.g., key-value stores), so-called "wimpy" nodes have traditionally been rejected as unsuitable for more complex workloads. We believe, however, that recent hardware advancements driven by the mobile computing market call this orthodoxy into question. For example, our microbenchmarks show that one popular SBC, the Raspberry Pi 3B+, offers single-core compute performance that is surprisingly competitive with many server-grade Intel Xeon and ARM-based CPUs at a fraction of the cost and energy consumption.To make our case, we conducted an extensive experimental study, beginning with a series of microbenchmarks to identify the strengths and weaknesses of SBCs relative to server-grade CPUs. Then, to evaluate the ability of SBCs to handle more complex use cases, we analyzed the performance of an in-memory OLAP workload in both single-node and distributed settings. Overall, our results demonstrate up to several orders of magnitude in cost reductions coupled with substantial energy savings when compared to traditional on-premises and cloud deployments, all without a significant increase in absolute runtimes.
Studying the Evolution of Scientific Topics and their Relationships

We propose a study of the development of scientific topics through time, as well as the relations between them within the scientific field of computational linguistics and across subfields.We use topic modeling to analyze scientific texts published in the ACL Anthology, and introduce a categorization of topics in our field into 3 types: tasks, algorithms, and data.In order to understand how topics emerge, evolve, and gradually disappear over time, we analyze the evolution of these topics across time through several case studies.We further include in our analysis papers published in NeurIPS, and try to understand whether there was any influence between topics in this conference focused on neural methods and computational linguistics conferences, as well as measure the divergence over time between conferences in terms of the topics approached.We additionally look at the relationships between topics, categorizing them into types of competing or cooperating topics.
A spark-based parallel distributed posterior decoding algorithm for big data hidden Markov models decoding problem

&lt;span lang="EN-US"&gt;Hidden &lt;/span&gt;&lt;span lang="IN"&gt;M&lt;/span&gt;&lt;span lang="EN-US"&gt;arkov models (HMMs) are one of machine learning algorithms which have been widely used and demonstrated their efficiency in many conventional applications. This paper proposes a modified posterior decoding algorithm to solve hidden Markov models decoding problem based on MapReduce paradigm and spark’s resilient distributed dataset (RDDs) concept, for large-scale data processing. The objective of this work is to improve the performances of HMM to deal with big data challenges. The proposed algorithm shows a great improvement in reducing time complexity and provides good results in terms of running time, speedup, and parallelization efficiency for a large amount of data, i.e., large states number and large sequences number.&lt;/span&gt;
Between the blabbering noise of individuals or the silent dialogue of many

This article is a multi-authored response to an editorial ‵Postdigital Science and Education′ published in 2018 by Petar Jandrić, Jeremy Knox, Tina Besley, Thomas Ryberg, Juha Suoranta and Sarah Hayes in Educational Philosophy and Theory as a mission statement for the journal Postdigital Science and Education. Nineteen authors were invited to produce their sections, followed by two author-reviewers who examined the article as a whole. Authors’ responses signal the sense of urgency for developing the concept of the postdigital and caution about attempts at simplifying complex relationships between human beings and technology. Whilst the digital indeed seems to become invisible, we simultaneously need to beware of its apparent absence and to avoid overemphasizing its effects. In this attempt, authors offer a wide range of signposts for future research such as ‘the critical postdigital’ and ‘postdigital reflexivity’; they also warn about the group’s own shortcomings such as the lack of ‘real’ sense of collectivity. They emphasize that postdigital education must remain a common good, discuss its various negative aspects such as smartphone addiction and nomophobia, and exhibit some positive examples of postdigital educational praxis. They discuss various aspects of postdigital identities and point towards the need for a postdigital identity theory. With these varied and nuanced responses, the article opens a wide spectrum of opportunity for the development of postdigital approaches to science and education for the future.
Identifikasi Level Pengelolaan Tata Kelola SIPERUMKIM Kota Salatiga berdasarkan COBIT 2019

SIPERUMKIM is the digitization process of public service licensing recommendations for housing implementation. The utilization of information technology governance is used to facilitate monitoring and evaluating the performance of SIPERUMKIM information technology which has been implemented in Housing and Settlement Area in Salatiga city. Information technology governance is a process that can manage investment decisions related to information technology within the company to achieve goals and meet company needs. The use of COBIT 2019 analysis in information technology governance aims to help organizations achieve risk optimization, governance, and information technology management. The results of this study are in the design form of corporate information technology governance and knowing the important process recommendations for the Department of Housing and Settlement of the City of Salatiga. These three important process recommendations are APO12, DSS02, and DSS03.&#x0D;
Application of Marketing Techniques in Library Services and Challenges Faced by University Librarians in Sri Lanka

Libraries should explore the marketing strategies to be used to promote their services among users, to entice new users and retain existing users. The objectives of this study were to find out the marketing techniques used in Sri Lankan university libraries and identify the challenges that university librarians face when marketing their services. The study involved 130 academic staff members of the libraries in 15 state universities established under the purview of the University Grants Commission in Sri Lanka. The total population was considered as the sample and the data were collected through self-administered questionnaire. The data were analyzed using the SPSS software package considering the mean and standard deviation. There were 105 valid questionnaires among returns, and the response rate was 80.8%. As per the results, library websites, user orientation seminars, notice boards, and e-mail notifications were most frequently used to promote library services. Mobile applications, blogs and podcasts were rarely used to promote library services in Sri Lankan universities. The mean ratings of perceived challenges in the marketing of library services by university librarians in Sri Lanka were considered. Lack of networking among librarians (3.54) was identified as a significant challenge faced by Sri Lankan university librarians when marketing their services. Other significant challenges were unawareness of library user requirements, insufficient funds for marketing, increased cost of marketing and lack of skills in marketing library services. This concludes that Sri Lankan university librarians market their library services using an array of techniques and come across various challenges while adopting marketing techniques to their services. To overcome these challenges libraries should consider appointing a marketing librarian for each university library and establish a collaboration between them, develop a unique marketing strategy for each library, and allocation of funds for marketing library services.
The Pandemic's Impact On The Learning Process: Case Students Of Mining University

Based on the data of the sociological survey, the consequences of the corona virus epidemic on various aspects of the studying process in the university educational system from the point of view of the student community are identified and analyzed.It is established that not all universities were ready to teach their students in a remote format, but, according to students, quarantine and experience may have triggered the digitalization of the educational process.Therefore, in order to prevent possible negative consequences of the pandemic in the future, it is proposed to consider the availability of a special internal educational platform for remote education based on learning management systems and creation of digital educational resources in all subjects as a mandatory indicator of the university's activity.In addition, universities should make every effort to improve the qualifications of their teaching staff in the application of information and communication technologies in the teaching process and to improve the technical equipment of the students' and professors' working.The positive consequences of the sudden transition to remote teaching include improved skills in working with electronic resources and technical devices, increased self-discipline and self-organization, as well as a reassessment of values related to young people's personal communication and teamwork
Input Representations for Parsing Discourse Representation Structures: Comparing English with Chinese

Neural semantic parsers have obtained acceptable results in the context of parsing DRSs (Discourse Representation Structures).In particular models with character sequences as input showed remarkable performance for English.But how does this approach perform on languages with a different writing system, like Chinese, a language with a large vocabulary of characters?Does rule-based tokenisation of the input help, and which granularity is preferred: characters, or words?The results are promising.Even with DRSs based on English, good results for Chinese are obtained.Tokenisation offers a small advantage for English, but not for Chinese.Overall, characters are preferred as input, both for English and Chinese.
An Inverse-Free and Scalable Sparse Bayesian Extreme Learning Machine for Classification Problems

Sparse Bayesian Extreme Learning Machine (SBELM) constructs an extremely sparse and probabilistic model with low computational cost and high generalization. However, the update rule of hyperparameters (ARD prior) in SBELM involves using the diagonal elements from the inversion of the covariance matrix with the full training dataset, which raises the following two issues. Firstly, inverting the Hessian matrix may suffer ill-conditioning issues in some cases, which hinders SBELM from converging. Secondly, it may result in the memory-overflow issue with computational memory O(L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) ( L: number of hidden nodes) to invert the big covariance matrix for updating the ARD priors. To address these issues, an inverse-free SBELM called QN-SBELM is proposed in this paper, which integrates the gradient-based Quasi-Newton (QN) method into SBELM to approximate the inverse covariance matrix. It takes O(L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) computational complexity and is simultaneously scalable to large problems. QN-SBELM was evaluated on benchmark datasets of different sizes. Experimental results verify that QN-SBELM achieves more accurate results than SBELM with a sparser model, and also provides more stable solutions and a great extension to large-scale problems.
Kids Study Club: Mempelajari Kosa Kata Bahasa Inggris dengan Menggunakan Strategi Fun Learning di Desa Sindang Sari Lk 1 pasa Masa Pandemi Covid-19

The covis-19 virus has spread throughout the world, one of the countries that has been infected is Indonesia. School activities had to be closed and replaced by learning from home. Some children have difficulty learning online, especially in learning English. Therefore, students participating in the Mandiri KKN Muhammadiyah Kotabumi University hold a course program (sw course) that focuses on learning English and seeks to help mastering English vocabulary for elementary school students in Sindang Sari Bawah LK 1 village using the fun strategy. learning or learning fun. This strategy is very effective to use to make it easier for children to learn English vocabulary and to develop children's creativity. This independent KKN activity is expected to help children in learning during the Covid-19 pandemic
Development of the RFID Based Library Management and Anti-Theft System:A Case of East African Community (EAC) Region

Radio Frequency Identification (RFID) Systems are becoming very useful in our daily life due to its advantages such as reduction of human error, theft prevention, time consuming reduction, the auto identification of targeted objects, business processes automation etc. RFID systems has been applied in library to manage items and library operations.Different approaches have been adopted in library management system in the East African region unfortunately some challenges including theft, pages removal, non-customer satisfaction, high cost of used system etc. are still persisting.To address these challenges, an RFID based library management and anti-theft system has been developed to East African Community (EAC) library.It focused on the use of Ultra High Frequency (UHF) band which enable readers and tags to transmit and receive data at long range.The developed system facilitates users to borrow and return library items using RFID modules and enable librarians to monitor, record library activities and prevent no issued item to cross the library entrance or exit.
A New Bounding Scheme for Influence Diagrams

Influence diagrams provide a modeling and inference framework for sequential decision problems, representing the probabilistic knowledge by a Bayesian network and the preferences of an agent by utility functions over the random variables and decision variables. Computing the maximum expected utility (MEU) and the optimizing policy is exponential in the constrained induced width and therefore is notoriously difficult for larger models. In this paper, we develop a new bounding scheme for MEU that applies partitioning based approximations on top of the decomposition scheme called a multi-operator cluster DAG for influence diagrams that is more sensitive to the underlying structure of the model than the classical join-tree decomposition of influence diagrams. Our bounding scheme utilizes a cost-shifting mechanism to tighten the bound further. We demonstrate the effectiveness of the proposed scheme on various hard benchmarks.
User Identity Linkage Method Based on User Online Habit

User Identity Linkage (UIL) across social networks refers to the recognition of the accounts belonging to the same individual among multiple social network platforms.Due to the network user's identities have the characteristics of various sources and real identity cannot be confirmed, it is very easy to become the main means of malicious user to carry out network attacks and spread rumors.User Identity Linkage not only can make the service provider to understand the user and thus to provide better service to the user, but also plays a significant role in improving the ability to find and track malicious users.For the credibility problem on the associated clues of user identification resulted from dynamic IP, shared Internet access and other factors, a user identity linkage method based on user online habit is proposed.This method assumes that the people use multiple network services crosswise when using the internet, converts the association analysis problem of user identification to the frequent pattern mining problem, and performs the optimization from three respective aspects: the online transaction database construction, the fast algorithm for mining frequent patterns and frequent cooccurrence identities consolidation.In order to improve the efficiency of frequent pattern mining, a parallelization of FPGrowth algorithm called MRFP-Growth algorithm is proposed to mine the user identifications of frequent cooccurrence quickly and efficiently.Experiments show that this method can associate multiple accounts of a user in network traffic with more than 85% accuracy in the scenario of dynamic variable IP address with only IP address and online time.
Latent Reasoning for Low-Resource Question Generation

Multi-hop question generation requires complex reasoning and coherent language realization.Learning a generation model for the problem requires extensive multi-hop question answering (QA) data, which are limited due to the manual collection effort.A two-phase strategy addresses the insufficiency of multihop QA data by first generating and then composing single-hop sub-questions.Learning this generating and then composing twophase model, however, requires manually labeled question decomposition data, which is labor intensive.To overcome this limitation, we propose a novel generative approach that optimizes the two-phase model without question decomposition data.We treat the unobserved sub-questions as latent variables and propose an objective that estimates the true sub-questions via variational inference.We further generalize the generative modeling to single-hop QA data.We hypothesize that each single-hop question is a sub-question of an unobserved multi-hop question, and propose an objective that generates single-hop questions by decomposing latent multi-hop questions.We show that the two objectives can be unified and both optimize the two-phase generation model.Experiments show that the proposed approach outperforms competitive baselines on HOTPOTQA, a benchmark multi-hop question answering dataset.
An Approach for Multi-Document Text Summarization Using Extreme Learning Machine and LexRank

Due to the exponential growth of online textual data and the variety of its sources, there is a need to produce an accurate text summary with the least time and effort.Extractive multi-document text summarization methods are intended to automatically generate summaries from a document collection, covering the main content and avoiding redundant information.In this study, a new method for extractive multi-document summarization has been proposed based on the combination of supervised and unsupervised learning.Throughout the supervised learning, a set of seven features was extracted from each sentence in the document collection and introduces to the Extreme Learning Machine (ELM), to distinguish between important and unimportant sentences.A graph of important sentences was generated and assigned scores by the LexRank algorithm during the unsupervised learning.The performance of the proposed method on the DUC-2002 dataset was calculated using ROUGE evaluation metrics.The proposed method achieved a 0.47472 ROUGE for 200-word summaries and 0.54641 ROUGE for 400-word summaries.
Revisiting the Evaluation of End-to-end Event Extraction

Event extraction (EE) aims to harvest event instances from plain text, where each instance is composed of a group of event arguments with specific event roles.Existing end-to-end EE research usually adopts the role-averaged evaluation that produces evaluation measures by averaging evaluation statistics of each event role.However, although this averaged metric can indicate the model performance to some extent, we find that such metric can be pretty misleading to downstream applications that utilize an event instance as a whole, where one wrongly identified event argument can substantially alter the whole meaning of an event instance.To mitigate this gap and provide a more complete understanding of performance, we propose two new evaluation metrics that also consider an event instance as a whole and explicitly penalize wrongly identified event arguments.Moreover, to support diverse preferences of evaluation metrics motivated by different scenarios, we propose a new training paradigm based on reinforcement learning for a typical end-to-end EE model, i.e., Doc2EDAG.Our extensive experiments show that the new training improves the initial one by a large margin (about 10%) under new metrics.Nevertheless, the current performance is still far from satisfactory, and optimizing towards these new metrics calls for more future research.
Research on the Deep Interaction of Online Teaching and Its Value Embodiment

The purpose of this study is to promote deep interaction in online teaching to be paid more attention to, and to provide guidance and suggestions for the implementation of deep interaction in online teaching.Make online teaching interaction break through the limitation of traditional teaching interaction, attach importance to the innovation of the content and form of teaching interaction, and further form the depth of online teaching interaction; In order to adapt to the interactive characteristics of college students in the new era and promote the comprehensive development of students' information quality, a new type of teacher-student relationship can be constructed to trigger students' deep learning.In collection, on the basis of analysis of a large number of research data, analysis of the connotation of the traditional teaching of interaction, think the connotation of the traditional teaching interaction can't cover online interactive teaching content and form, and online interactive teaching is more form diversification, depth of content, the interaction process of digitalization, digital interactive features such as interactive evaluation, Therefore, the connotation of the deep interaction of online teaching is expanded.On the classification of the interactive teaching, previous studies mainly in view of the interaction of traditional classroom teaching, cannot reflect the type of online interactive teaching, this study based on the online interactive teaching depth delay sexual characteristics, the types of online interactive teaching, online interactive teaching depth can be divided into interpersonal interactions, and interpersonal interactive time delay, the known interaction and people skills four types; Finally, this study discusses the value of deep interaction of online teaching in teacher-student relationship, deep learning, adapting to the personality characteristics of modern students, and training students' comprehensive development of information quality.
SciConceptMiner: A system for large-scale scientific concept discovery

Scientific knowledge is evolving at an unprecedented rate of speed, with new concepts constantly being introduced from millions of academic articles published every month.In this paper, we introduce a self-supervised end-toend system, SciConceptMiner, for the automatic capture of emerging scientific concepts from both independent knowledge sources (semi-structured data) and academic publications (unstructured documents).First, we adopt a BERT-based sequence labeling model to predict candidate concept phrases with selfsupervision data.Then, we incorporate rich Web content for synonym detection and concept selection via a web search API.This two-stage approach achieves highly accurate (94.7%) concept identification with more than 740K scientific concepts.These concepts are deployed in the Microsoft Academic 1 production system and are the backbone for its semantic search capability.