Reviewerly/exHarmony huggingface dataset:
Predicting Text Readability from Scrolling Interactions

Judging the readability of text has many important applications, for instance when performing text simplification or when sourcing reading material for language learners. In this paper, we present a 518 participant study which investigates how scrolling behaviour relates to the readability of a text. We make our dataset publicly available and show that (1) there are statistically significant differences in the way readers interact with text depending on the text level, (2) such measures can be used to predict the readability of text, and (3) the background of a reader impacts their reading interactions and the factors contributing to text difficulty.
Pre-Trained Models for Heterogeneous Information Networks

In network representation learning we learn how to represent heterogeneous information networks in a low-dimensional space so as to facilitate effective search, classification, and prediction solutions. Previous network representation learning methods typically require sufficient task-specific labeled data to address domain-specific problems. The trained model usually cannot be transferred to out-of-domain datasets. We propose a self-supervised pre-training and fine-tuning framework, PF-HIN, to capture the features of a heterogeneous information network. Unlike traditional network representation learning models that have to train the entire model all over again for every downstream task and dataset, PF-HIN only needs to fine-tune the model and a small number of extra task-specific parameters, thus improving model efficiency and effectiveness. During pre-training, we first transform the neighborhood of a given node into a sequence. PF-HIN is pre-trained based on two self-supervised tasks, masked node modeling and adjacent node prediction. We adopt deep bi-directional transformer encoders to train the model, and leverage factorized embedding parameterization and cross-layer parameter sharing to reduce the parameters. In the fine-tuning stage, we choose four benchmark downstream tasks, i.e., link prediction, similarity search, node classification, and node clustering. PF-HIN consistently and significantly outperforms state-of-the-art alternatives on each of these tasks, on four datasets.
Segmental Pronunciation Errors Made by the Lamaholot Speakers of Junior High School Students

This study is entitled Segmental Pronunciation Errors made by the Lamaholot Speakers of Junior High School Students.It aims at revealing how the pronunciation errors were made and investigating the factors that caused the errors.The data of this study were obtained from the Simak Libat Cakap by the use of bait questions conducted on 10 second-graders of a Junior High School in East Adonara district, East Nusa Tenggara Province, Indonesia.The Simak Libat Cakap Method with the bait question was used to get how the students pronounced the basic English words.The observation method was used to observed the student's articulators.The documentation was used to gain some previous studies related to the phonological contrastive analysis between the Lamaholot language and English.The data of the study were qualitatively analyzed based on three branches of phonetic theory by Crystal (2008) and the factors causing errors by Brown (2000).The analysis of this study was presented in formal and informal method, each problem in this study was descriptively presented.The finding revealed that the students tended to make errors in pronouncing the consonants rather than any vowels.Those consonants pronunciation errors were consistently made by the Lamaholot speakers, which were [v] [z].These consonants have not existed in the phonetic chart of Lamaholot language.Moreover, the speakers also consistently did not pronounce the [p] and [d] in the case of consonant clusters distributed in word-final distribution, which are not recognized in the Lamaholot syllable structure.
現代日本語と韓国語の動詞条件形の語彙化 － 視覚動詞「みる」「보다」の条件形の場合 －

It is well known that if the conditional form of a verb loses its verb-ness in modern Japanese and Korean, then it is changed into either a form that determines the grammatical relationship between syntactic elements or a lexical expression that represents the speaker’s attitude in a variety of ways.This paper deals with how mireba and pomyen, the conditional forms of the visual verbs miru and poda, respectively, lose their verb-ness and become agglutinated to mostly adverbs to serve as complex sentence adverbs. In this case, the sentence adverb that is formed by agglutinating mireba or pomyen is used to represent the perspective or attitude through which certain things are evaluated or judged.Since such adverbs retain the features of a conditional form, the evaluation and judgement of things is only valid from the perspective or attitude presented, and therefore implicate other perspectives or attitudes that may lead to a different evaluation or judgement.
Focal Gradient Harmonized Loss Function for Object Detection

Abstract Mainstream detectors in the field of object detection usually can be divided into one-stage and two-stage detectors. Compared to the two-stage detectors, one-stage detectors are more efficient and elegant, but are more easily disturbed by imbalanced data during the training. Inspired by the gradient harmonized single-stage detector (GHM detector)[1], we propose a focal gradient harmonizing loss function for classification (FGHM-C). FGHM-C loss function optimizes the method of generating example weights during training, thereby alleviating the impact of long-tailed distribution. Our proposed model achieves 42.0 mAP on COCO test-dev set, which is 0.4 points higher than the original GHM detector.
Catalyzing research, building capacity: Research grants and the academic library

In the days before the pandemic, when I attended and introduced myself as the grants and awards librarian from the University of Victoria (UVic) Libraries at professional events and conferences, my title often evoked quizzical looks. Even now as this unprecedented time continues, as far as I know, I am still the only librarian with this professional designation in Canada, perhaps in all of North America—maybe even the world. Since 2012, I’ve helped UVic Libraries to attract and retain funding, collaborators, allies, and donors, while raising our research profile and demonstrating the value of the 21st-century academic library to university administrators.
GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts

Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specifications. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and nodes are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.
An Implementation of Biometric Fingerprint-based Attendance System for Staff Management(BFASSM)

The workforce is the bedrock of growth, development, and expansion in any establishment. Therefore, there is need to ensure fairness in monitoring staff attendance and creating a workforce that is dedicated and committed in higher Institutions of learning. Although the integration and use of biometric technology are getting simpler, quite a few institutions of learning are taking advantage of this technology in Nigeria. In this study, Biometric Fingerprint-based Attendance System for Staff Management (BFASSM) was developed to monitor the staff of higher institution using the Federal Polytechnic, Ilaro, Nigeria as a case study. The application was developed using C# programming language, Macromedia fireworks and Microsoft Visio. Microsoft SQL Server was used for database management at the back end with Windows 7 as the minimum operating system. The implementation of this system in any other institution of higher learning will eradicate manual attendance taking, curb the problem of infringement and manipulation of staff attendance, create an avenue to make staff resume work punctually and also sign out at the appropriate time. It will also enhance proper documentation of attendance record.
Patent Law: An Open-Source Casebook (Chapter 5)

Less than a handful of casebooks are truly open source, in the sense of being fully modifiable. Patent Law: An Open-Source Casebook is the first patent law casebook that provides adopting professors, students, and others the ability to fully modify its contents. This chapter of the casebook covers anticipation and the application of 35 U.S.C. § 102 both pre- and post-AIA. The chapter introduces the concept of anticipation (including the single-source test, inherency, and the enabling reference standard), the loss-of-right doctrines (including those based on documentary prior art, public use, and the on-sale bar), prior art based on prior-filed patent applications, and other forms of patent-defeating prior art (including the concept of derivation and the pre-AIA novelty provisions).
Correction to: Where do university graduates live? – a computer vision approach using satellite images

A Correction to this paper has been published: https://doi.org/10.1007/s10489-021-02433-z
KDExplainer: A Task-oriented Attention Model for Explaining Knowledge Distillation

Knowledge distillation (KD) has recently emerged as an efficacious scheme for learning compact deep neural networks (DNNs). Despite the promising results achieved, the rationale that interprets the behavior of KD has yet remained largely understudied. In this paper, we introduce a novel task-oriented attention model, termed as KDExplainer, to shed light on the working mechanism underlying the vanilla KD. At the heart of KDExplainer is a Hierarchical Mixture of Experts (HME), in which a multi-class classification is reformulated as a multi-task binary one. Through distilling knowledge from a free-form pre-trained DNN to KDExplainer, we observe that KD implicitly modulates the knowledge conflicts between different subtasks, and in reality has much more to offer than label smoothing. Based on such findings, we further introduce a portable tool, dubbed as virtual attention module (VAM), that can be seamlessly integrated with various DNNs to enhance their performance under KD. Experimental results demonstrate that with a negligible additional cost, student models equipped with VAM consistently outperform their non-VAM counterparts across different benchmarks. Furthermore, when combined with other KD methods, VAM remains competent in promoting results, even though it is only motivated by vanilla KD. The code is available at https://github.com/zju-vipa/KDExplainer.
Blockchain in food supply chain and Agriculture

The center reason for this work is to combine the traditional supply chain withthe blockchain to follow a food bundle from farm to fork with a kind character forevery food bundle while keeping it simple for the specialists. It will keep the deals sealed andavailable to partners as indicated by the business arrangements and concurred agreementsof information dividing among the organizations without including any incorporated expertfor checking. Blockchain can get a progressive change in the Food Supply Chain industry.The current Food Supply Chain needs to guarantee the trust of food cleanliness to thebuyers. By following the food we consume at each stage, blockchain can guarantee sanitationand assemble trust in customers. Consequently, our work centers around utilizingblockchain innovation to accomplish information security, integrity, food traceability, andsmart contracts.
A Graph Neural Network Approach for Product Relationship Prediction

Graph Neural Networks have revolutionized many machine learning tasks in recent years, ranging from drug discovery, recommendation systems, image classification, social network analysis to natural language understanding. This paper shows their efficacy in modeling relationships between products and making predictions for unseen product networks. By representing products as nodes and their relationships as edges of a graph, we show how an inductive graph neural network approach, named GraphSAGE, can efficiently learn continuous representations for nodes and edges. These representations also capture product feature information such as price, brand, or engineering attributes. They are combined with a classification model for predicting the existence of the relationship between products. Using a case study of the Chinese car market, we find that our method yields double the prediction performance compared to an Exponential Random Graph Model-based method for predicting the co-consideration relationship between cars. While a vanilla GraphSAGE requires a partial network to make predictions, we introduce an `adjacency prediction model' to circumvent this limitation. This enables us to predict product relationships when no neighborhood information is known. Finally, we demonstrate how a permutation-based interpretability analysis can provide insights on how design attributes impact the predictions of relationships between products. This work provides a systematic method to predict the relationships between products in many different markets.
Diseño de un modelo web intuitivo para una escuela de educación superior

Currently we are in the Information and Communication Society and the web pages in Higher Education institutions represent an impressive showcase to the world, since we cannot say that it is only in their environment, through these web platforms people are informed of the services that are made in each of these schools. This work presents the application of web development tools that facilitate its realization and implementation, giving the expert the opportunity to have enough alternatives to take them ad hoc to school. Providing the scope of projection to be at the fore front and competitiveness. New technologies currently open up the possibility of improving the control of tasks that are required by the user. In this case, the aim is to design an intuitive and comprehensive web model to promote the projection of the institution of higher education, in the East of Michoacán
Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data

This paper investigates the theoretical foundations of the t-distributed stochastic neighbor embedding (t-SNE) algorithm, a popular nonlinear dimension reduction and data visualization method. A novel theoretical framework for the analysis of t-SNE based on the gradient descent approach is presented. For the early exaggeration stage of t-SNE, we show its asymptotic equivalence to power iterations based on the underlying graph Laplacian, characterize its limiting behavior, and uncover its deep connection to Laplacian spectral clustering, and fundamental principles including early stopping as implicit regularization. The results explain the intrinsic mechanism and the empirical benefits of such a computational strategy. For the embedding stage of t-SNE, we characterize the kinematics of the low-dimensional map throughout the iterations, and identify an amplification phase, featuring the intercluster repulsion and the expansive behavior of the low-dimensional map, and a stabilization phase. The general theory explains the fast convergence rate and the exceptional empirical performance of t-SNE for visualizing clustered data, brings forth interpretations of the t-SNE visualizations, and provides theoretical guidance for applying t-SNE and selecting its tuning parameters in various applications.
Image Classification Using Network Inception-Architecture Appications

The organization architecture assumes a significant job in a profound organization's execution and speed to group images. This paper studies the various architecture plans and the variations proposed in Google Net and inception networks. These variations are examined as far as their calculation proficiency, the organization highlights and exhibitions are compared on ImageNet dataset, and necessary audit on inception networks is given.
Job Scheduling in Big Data – A Survey

To quantify the large amount of data stored electronically is not easy. The large amount of data (Data is in the unit of zettabytes or exabytes) is called Big Data. To process these large datasets Hadoop system is used. To collect these big data according to the request Map Reduce program is used. To achieve greater performance in process big data proper scheduling is required. To minimize starvation and maximize resource utilization, Scheduling is a technique of assigning jobs to available resources in a manner. Scheduling technique Performance can be improved by applying deadline constraints on jobs. The objective of the research is to study and analyze various scheduling algorithms for better performance.
Designing a Web Application for Simple and Collaborative Video Annotation That Meets Teaching Routines and Educational Requirements

Video annotation and analysis is an important activity for teaching with and about audiovisual media artifacts because it helps students to learn how to identify textual and formal connections in media products. But school teachers lack adequate tools for video annotation and analysis in media education that are easy-to-use, integrate into established teaching organization, and support quick collaborative work. To address these challenges, we followed a design-based research approach and conducted qualitative interviews with teachers to develop TRAVIS GO, a web application for simple and collaborative video annotation. TRAVIS GO allows for quick and easy use within established teaching settings. The web application provides basic analytical features in an adaptable work space. Key didactic features include tagging and commenting on posts, sharing and exporting projects, and working in live collaboration. Teachers can create assignments according to grade level, learning subject, and class size. Our work contributes further insights for the CSCW community about how to implement user demands into developing educational tools.
Correction to: Linking place records using multi-view encoders

A correction to this paper has been published: https://doi.org/10.1007/s00521-021-06076-6
Pedagogía intercultural Inglés-Kichwa “an intercultural world sinchiyachishun ñukanchik kichwa shimikunata”

espanolEl presente trabajo de investigacion analiza la necesidad de una “Pedagogia Intercultural Ingles-Kichwa” dentro del curriculo academico local, porque su aplicacion posibilitara el ejercicio de practicas didacticas desde un eje intercultural inclusivo, para lograr la formacion de docentes que impulsen la recuperacion de la lengua kichwa y asi se incorporen al desarrollo cientifico global de la comunicacion con el aprendizaje del idioma Ingles, profundizando y disenando nuevas estrategias de ensenanza aprendizaje para la transmision y uso de las mismas horizontalmente. La metodologia aplicada tiene un caracter holistico, se elaboro una guia para el efecto; asi tambien la revision y el analisis bibliografico de diferentes autores e investigaciones precedentes. Como producto del estudio se determino que la innovacion pedagogica en sociedades con una fuerte pluralidad cultural, significa construir colectivamente una plataforma basica para intervenir y empujar el desarrollo de habilidades didacticas en docencia y optimizar las condiciones para un sumak kawsay. EnglishThe present research work analyzes the need for an English-Kichwa Intercultural Pedagogy within the local academic curriculum, because its application will allow the practice of didactic practices from an inclusive intercultural axis, to achieve the training of teachers that promote the recovery of the Kichwa language and thus they are incorporated into the global scientific development of communication with the learning of the English language, deepening and designing new teachinglearning strategies for the transmission and use of them horizontally. The applied methodology has a holistic character, a guide for the effect was elaborated; thus also the revision and bibliographic analysis of different authors and previous investigations. As a result of the study, it was determined that pedagogical innovation in societies with a strong cultural plurality means to collectively build a basic platform to intervene and push the development of didactic skills in teaching and optimize the conditions for a sumak kawsay.
Analisis Nilai Lahan Di Kecamatan Jepara Kabupaten Jepara Menggunakan Aplikasi Sistem Informasi Geografis Dan Pengindraan Jauh

The need for land that is always increasing every time can affect the increase in land value and land prices, meanwhile, the demand for land will continue to increase and if accompanied by regional factors that have accessibility can increase attractiveness in an area and qualified facilities can cause a spike in value and land prices are significant in the area while the area of land is unchanged. This study aims to determine the distribution of land values and the number of land prices in each class of land values in the Jepara District, and determine the suitability of land values with land prices and analyze what factors are dominant to land values and land prices in Jepara District. The method used in the study uses survey methods with stratified sampling and purposive side techniques, stratified sampling to get the price of land in each class of land values and purposive side is used for validation of image digitization. The method of analyzing data with quantitative overlay methods through four parameters ranging from land use, positive accessibility, negative accessibility, and public utilities. The results of this study map the distribution of land values with three classes, the distribution of land prices, and the suitability of land values with land prices. High land value in Jepara Subdistrict is 1.95 Km2 or 7.42% of the area of Jepara Subdistrict, medium land value is 16.32 Km2 or 62.07%, and low land value class is 8.02 Km2 or 30.52% of area of Jepara District. The price of high-class land is around Rp.3,357,000.00 - Rp.5,000,000.00, the price of medium-class land is Rp.1,711,000.00 - Rp.3,356,000.00, and the price of low-class land is Rp.65,000, 00 - Rp.1,710,000.00. The value of land by the price of the land area of 64.35 Km2 or 64.35% and that is not appropriate 8.79 Km2 or 8.79%. Dominant factors of high land value classes are land use, public facilities, and positive accessibility; class of land values being positive accessibility and negative accessibility; low land value class negative accessibility.Keywords: land value, land prices, geographic information systems, remote sensing, dominant factors