Reviewerly/exHarmony huggingface dataset:
Avoiding Inferiority: Global Engineering Education Across Japan

Abstract Avoiding Inferiority: Engineering Education Serving JapanThroughout the world, educators are working hard to reform engineering education and training.At first glance, the most prominent activities across different countries look remarkably similar:they aim to prepare mobile engineers for a globalized world. Scratching beneath the surface,however, reveals activities in different countries to have significantly different meanings.Making visible important contrasts demonstrates that preparing students for mobility necessarilymeans preparing them to work with both engineers and non-engineers who understand the largersignificance of their work in very different terms. It also shows that the technical contents ofengineering education carry broader value commitments, which they challenge student to adoptand enact.This ethnographic study accounts for contemporary reform activities across Japan as efforts tohelp the Japanese “household” avoid inferior status vis-à-vis other countries. We first outline keyactivities over the past decade undertaken by the new Japanese Accreditation Board forEngineering Education (JABEE) and the re-formed Japanese Society for Engineering Education(JSEE). This outline draws on interviews undertaken in 2004 and 2010 as well as documentaryevidence. It calls attention to the fact that the first learning criterion for engineers in accreditedprograms is not to demonstrate ability in math and science or engineering analysis but,surprisingly from EuroAmerican points of view, “the ability and intellectual foundation forconsidering issues from a global and multilateral viewpoint.”This objective performs in a new way a persistent fear of Japan becoming, or appearing tobecome, inferior to powerful Western countries, especially the United States. Avoidinginferiority rose to become the dominant image of progress across Japan when the MeijiRestoration transformed it into a country among other countries. Calculations of inferiority drawupon the dominant, yet evolving, Japanese image of the family as a “household” (ie) consistingof both people and property.The balance of the paper draws on the work of historians to produce a retrospective ethnographyof the evolution of engineering education as responses to this dominant image of progress and itschanging metrics. It considers four episodes: (1) before progress: Rogaku/Kogaku learningduring Tokugawa; (2) import and replacement at the Imperial College of Engineering; (3)privatization in service of a military metric; and (4) dramatic expansion following the PacificWar and rise of the commercial metric. Every time the country changed direction, educatorswent to work reforming engineering education to help forestall the latest risk of inferiority.Contemporary struggles among Japanese engineering educators are competitions to help avoidinferiority for the country as its corporations increasingly participate in joint ventures.We conclude by briefly assessing the significance and challenges of making more visible inengineering education the value commitments that bond engineers to their countries. 1
Metasearch Technologies In Reference Work, Oai, And Search Navigation Assistance

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session Number 2441 Metasearch Technologies in Reference Work, OAI, and Search Navigation Assistance William H. Mischo, Mary C. Schlembach Grainger Engineering Library Information Center University of Illinois at Urbana-Champaign Introduction Libraries and librarians will continue to develop and collaborate on access services that better address user information needs. It is important for libraries to focus on providing mechanisms to transform the many distributed digital collections into true “digital libraries” with the essential services that are required to make these digital libraries useful to and productive for users.1 To accomplish this, libraries will focus on providing enhanced gateway and navigation services to guide users through the present distributed, heterogeneous information environment. This distributed scholarly information environment is populated by: silos of full-text repositories maintained by commercial publishers, professional societies; open preprint servers; Abstracting and Indexing (A & I) Services and publisher search and discovery sites; local, regional, and national online catalogs; and publisher, vendor, and library vertical portals, institutional repository systems, and learning management systems. Navigation and database selection functions are extremely important in providing users with more effective and efficient retrieval assistance for these multiple, discrete information resources.2 One technique to provide enhanced access to multiple, distributed information resources is metasearching.3 Metasearch technologies provide simultaneous, asynchronous search capabilities over multiple information resources. Metasearch systems typically employ either broadcast or federated search methods. In a broadcast search, the user search arguments are sent, or “broadcast” asynchronously (all at the same time) to remote, distributed systems and the search results are collected and displayed to the user. In a federated search system, the heterogeneous remote information resources are typically imported or “harvested” (sometimes using Open Archives Initiative or OAI protocols) into a local, central site and the normalized results are placed into a homogeneous database system for search and discovery. The broadcast and federated approaches are not mutually exclusive. Some systems perform broadcast searching over a combination of distributed, heterogeneous systems and systems that are already federated. Technologies Various metasearch system solutions have been developed by libraries, vendors, and information providers for simultaneous searching over online databases, A & I Services, Online Catalogs (OPACs), OAI federated systems, and other information resources.4,5,6,7 Libraries have Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition Copyright @2004, American Society for Engineering Education
Teaching Grammar-In-Context and Its Impact In Minimizing Students’ Grammatical Errors

This study is conducted to determine the effectiveness of teaching grammar-in-context to minimize students‘ grammatical errors in writing. The design of the study was a quasi-experimental with a non-randomized pretest-posttest control group. The samples of the study were taken from the population of the tenth-grade students. The control group was taught by conventional grammar which was separately given with writing skills. Likewise, the experimental one was treated by teaching grammar-in-context. The results of the study showed that the mean score in the post-test was higher than that in the pretest; and the mean score of experimental group increased 16.20 point after the treatment. This result indicated that teaching grammarin-context is considered to be effective in minimizing students‘ grammatical errors in writing.
Machine Vision And Robotics Laboratory

The modern mechanical engineering technician/technologist must function in an increasingly automated world of design and manufacturing of today's products.The focus of this paper is on how to apply, not how to design, effective automated systems using some of the emerging building blocks of automated manufacturing systems.It will concentrate on the applied emphases of introductory and advanced courses in automated manufacturing systems that can be taken by associate and bachelor degree mechanical engineering technology students.These courses cover both theory and (hands-on) laboratory experiences in basic robots, switches, sensors, vision systems, digital logic, and PLCs used in today's industry.Many of these topics will not be covered here in order to address specifically the integration of machine vision with robotics applications.A short video showing the current automated manufacturing laboratory will be presented in conjunction with this paper.
Perceptions of engineering professors and students regarding the acceptance and use of Moodle

Abstract Perceptions of engineering professors and students regarding the acceptance and use of MoodleWith the current digital age, academic institutions are responsible for providing the latesttechnology for professors and students. Adopted by several organizations around the world, theopen source course management system, Moodle, is designed to assist educators in deliveringtheir courses to their students from a social constructivist perspective. The theoretical frameworkfor this study is drawn from the Unified Theory of Acceptance and Use of Technology (UTAUT)that explains user intentions to use an information system and subsequent usage behavior. Thetheory holds that the following four keys are direct determinants of usage intention: performanceexpectancy, effort expectancy, social influence, and facilitating conditions. Twenty sevenengineering professors and 601 engineering students at the American University of Beirut (AUB)completed an online survey evaluating a set of 30 items on a scale of 5 that reflect the four keysof the UTAUT applied on Moodle. By examining the frequencies, a consistency in theevaluation was observed where both professors and students almost agree on all the items. AnIndependent-Sample t-Test was applied to compare the samples and showed no significancebetween the two groups for all items. Given the non-significance findings between both samples(27 professors and 601 students), all the available 628 observations were combined in one set ofdata in order to investigate users’ perceptions regarding the acceptance of Moodle. Anexploratory Factor Analysis (FA) was employed and generated four factors that were named:Learnability, Satisfaction, system quality, and Community influence. Four new variables werecomputed based on the mean of the items falling under each factor. In order to compare thefactors as rated by users, one-way repeated measures ANOVA was applied on the four variables.Repeated measures ANOVA showed Satisfaction as the highest rated by participants, followedby Community influence, Technical Quality, and Learnability. Although the Post Hoc tests usingBonferroni technique indicated significance between the four factors, this significance cannot beseen as meaningful since the difference is not important on a scale of “5”. Such result suggeststhat the four factors contribute almost equally to the acceptance and use of Moodle. By assessingthe results, users seem to agree that Moodle possesses an adequate acceptance level where all ofthe four attributes were rated above 3.5 on a scale of 5. The current research contributes toknowledge in the field of technology acceptance and demonstrated that CMS quality perceptionsplay an important role in the adoption of technology.
Formas de tratamento e preservação da face em interações verbais online

The forms of address constitute a complex system that regulates the interpersonal relations created in the situation of communication. It is a pragmatic category with direct impact on the relations established by each social and linguistic community. As we have already mentioned in a previous unpublished communication, some forms of pronominal address, the forms Tu, Vós, Você and Vocês are a current social concern, which speakers refer to on social networks but also in more traditional public discourses, such as political discourse and media discourse or academic discourse. We aim to analyse and systematize the way in which speakers represent, in explicit comments, but also implicitly, the functions and values of these forms of address in the construction of a (im)polite speech. Within a discursive-pragmatic approach, the present analysis combines interpersonal relations and the politeness theory, in particular the pragmatic concept of face. Data for analysis were collected from web sites, namely blogs and Facebook.
A general proof certification framework for modal logic

One of the main issues in proof certification is that different theorem provers, even when designed for the same logic, tend to use different proof formalisms and produce outputs in different formats. The project ProofCert promotes the usage of a common specification language and of a small and trusted kernel in order to check proofs coming from different sources and for different logics. By relying on that idea and by using a classical focused sequent calculus as a kernel, we propose here a general framework for checking modal proofs. We present the implementation of the framework in a Prolog-like language and show how it is possible to specialize it in a simple and modular way in order to cover different proof formalisms, such as labeled systems, tableaux, sequent calculi and nested sequent calculi. We illustrate the method for the logic K by providing several examples and discuss how to further extend the approach.
Implementation of the bin hierarchy method for restoring a smooth function from a sampled histogram

We present $\texttt{BHM}$, a tool for restoring a smooth function from a sampled histogram using the bin hierarchy method. The theoretical background of the method is presented in [arXiv:1707.07625]. The code automatically generates a smooth polynomial spline with the minimal acceptable number of knots from the input data. It works universally for any sufficiently regular shaped distribution and any level of data quality, requiring almost no external parameter specification. It is particularly useful for large-scale numerical data analysis. This paper explains the details of the implementation and the use of the program.
Upper and Lower Tight Error Bounds for Feature Omission with an Extension to Context Reduction

In this work, fundamental analytic results in the form of error bounds are presented that quantify the effect of feature omission and selection for pattern classification in general, as well as the effect of context reduction in string classification, like automatic speech recognition, printed/handwritten character recognition, or statistical machine translation. A general simulation framework is introduced that supports discovery and proof of error bounds, which lead to the error bounds presented here. Initially derived tight lower and upper bounds for feature omission are generalized to feature selection, followed by another extension to context reduction of string class priors (aka language models) in string classification. For string classification, the quantitative effect of string class prior context reduction on symbol-level Bayes error is presented. The tightness of the original feature omission bounds seems lost in this case, as further simulations indicate. However, combining both feature omission andcontext reduction, the tightness of the bounds is retained. A central result of this work is the proof of the existence, and the amount of a statistical threshold w.r.t. the introduction of additional features in general pattern classification, or the increase of context in string classification beyond which a decrease in Bayes error is guaranteed.
Income inequality among entrepreneurs in Ondo State, Nigeria

The study examined the income inequality among entrepreneurs in Ondo State, Nigeria using a questionnaire based survey. Multistage sampling technique was used to select the 200 respondents and the data was subjected to descriptive statistics, Lorenz curve, Gini coefficient and double-log regression model. Findings showed that 40% of the respondents spent at most 12 years in school, while hair dressing (43.5%) and fashion designing (31.5%) were the most common enterprises among the respondents. The findings also revealed that majority of the entrepreneurs earned below ₦200,000 per annum. The result of the Gini coefficient (0.58) showed that there was income inequality as shown by the Lorenz curve while the regression indicated that gender, family size and number of workers significantly affected the income of the entrepreneurs. This study is important as it recommends policies targeted at improving income as well as reducing the income inequality of the entrepreneurs.
Large-Scale Local Online Similarity/Distance Learning Framework Based on Passive/Aggressive

Similarity/distance measures play a key role in many machine learning, pattern recognition, and data mining algorithms, which leads to the emergence of the metric learning field. Many metric learning algorithms learn a global distance function from data that satisfies the constraints of the problem. However, in many real-world datasets, where the discrimination power of features varies in the different regions of input space, a global metric is often unable to capture the complexity of the task. To address this challenge, local metric learning methods are proposed which learn multiple metrics across the different regions of the input space. Some advantages of these methods include high flexibility and learning a nonlinear mapping, but they typically achieve at the expense of higher time requirements and overfitting problems. To overcome these challenges, this research presents an online multiple metric learning framework. Each metric in the proposed framework is composed of a global and a local component learned simultaneously. Adding a global component to a local metric efficiently reduces the problem of overfitting. The proposed framework is also scalable with both sample size and the dimension of input data. To the best of our knowledge, this is the first local online similarity/distance learning framework based on Passive/Aggressive (PA). In addition, for scalability with the dimension of input data, Dual Random Projection (DRP) is extended for local online learning in the present work. It enables our methods to run efficiently on high-dimensional datasets while maintaining their predictive performance. The proposed framework provides a straightforward local extension to any global online similarity/distance learning algorithm based on PA. Experimental results on some challenging datasets from machine vision community confirm that the extended methods considerably enhance the performance of the related global ones without increasing the time complexity.
RANCANG BANGUN ALAT FILTRASI PENYEDIA AIR SIAP MINUM DENGAN SISTEM REVERSE OSMOSIS

Drinking water is a very basic basic need, and cannot be replaced by other elements. The needs of living things for drinking water are very many, not only needed by humans but also needed by animals and plants. To produce good and quality drinking water in accordance with health standards that have been set by the Regulation of the Minister of Health of the Republic of Indonesia No. 492 / MENKES / PER / IV2010, a proper water treatment process is needed. This research activity is aimed at designing a filtration device that is able to process dirty water into clean ready-to-drink water using reverse osmosis (RO) technology. The parameters tested are biological, physical, and chemical parameters in accordance with the requirements for good drinking water set by Permenkes No. 492 of 2010. This study used 3 water source samples to be tested, namely: the first sample was a relatively clear and odorless water sample but the bacteria content was above the standard, the second sample was turbid but odorless water, while the third sample was odorous water. After going through the filtration process with the tool made, ready-to-drink water is able to be produced with the condition that the bacterial level (total coliform) drops from 200/100ml to 0/100ml. In addition, the turbidity level also dropped from 8 NTU to 0 NTU, and the water condition also changed from smelly to odorless. So it can be concluded that the filtration device made and tested is able to produce readyto-drink water according to the standards of Permenkes No. 492 of 2010.
Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling

Recent models have shown that incorporating syntactic knowledge into the semantic role labelling (SRL) task leads to a significant improvement. In this paper, we propose Syntax-aware Graph-to-Graph Transformer (SynG2G-Tr) model, which encodes the syntactic structure using a novel way to input graph relations as embeddings, directly into the self-attention mechanism of Transformer. This approach adds a soft bias towards attention patterns that follow the syntactic structure but also allows the model to use this information to learn alternative patterns. We evaluate our model on both span-based and dependency-based SRL datasets, and outperform previous alternative methods in both in-domain and out-of-domain settings, on CoNLL 2005 and CoNLL 2009 datasets.
SPINEX: Similarity-based Predictions and Explainable Neighbors Exploration for Regression and Classification Tasks in Machine Learning

The field of machine learning (ML) has witnessed significant advancements in recent years. However, many existing algorithms lack interpretability and struggle with high-dimensional and imbalanced data. This paper proposes SPINEX, a novel similarity-based interpretable neighbor exploration algorithm designed to address these limitations. This algorithm combines ensemble learning and feature interaction analysis to achieve accurate predictions and meaningful insights by quantifying each feature's contribution to predictions and identifying interactions between features, thereby enhancing the interpretability of the algorithm. To evaluate the performance of SPINEX, extensive experiments on 59 synthetic and real datasets were conducted for both regression and classification tasks. The results demonstrate that SPINEX achieves comparative performance and, in some scenarios, may outperform commonly adopted ML algorithms. The same findings demonstrate the effectiveness and competitiveness of SPINEX, making it a promising approach for various real-world applications.
ITR: A grammar-based graph compressor supporting fast neighborhood queries

Neighborhood queries are the most common queries on graphs; thus, it is desirable to answer them efficiently on compressed data structures. We present a compression scheme called Incidence-Type-RePair (ITR) for graphs with labeled nodes and labeled edges based on RePair and apply the scheme to network, version, and RDF graphs. We show that ITR performs neighborhood queries and triple queries in only a few milliseconds and thereby outperforms existing solutions while providing a compression size comparable to existing graph compressors.
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction

Information extraction, e.g., attribute value extraction, has been extensively studied and formulated based only on text. However, many attributes can benefit from image-based extraction, like color, shape, pattern, among others. The visual modality has long been underutilized, mainly due to multimodal annotation difficulty. In this paper, we aim to patch the visual modality to the textual-established attribute information extractor. The cross-modality integration faces several unique challenges: (C1) images and textual descriptions are loosely paired intra-sample and inter-samples; (C2) images usually contain rich backgrounds that can mislead the prediction; (C3) weakly supervised labels from textual-established extractors are biased for multimodal training. We present PV2TEA, an encoder-decoder architecture equipped with three bias reduction schemes: (S1) Augmented label-smoothed contrast to improve the cross-modality alignment for loosely-paired image and text; (S2) Attention-pruning that adaptively distinguishes the visual foreground; (S3) Two-level neighborhood regularization that mitigates the label textual bias via reliability estimation. Empirical results on real-world e-Commerce datasets demonstrate up to 11.74% absolute (20.97% relatively) F1 increase over unimodal baselines.