Reviewerly/exHarmony huggingface dataset:
An interpretable attraction recommendation method based on knowledge graph

The attraction recommendation systems not only filter out overwhelming irrelevant information for visitors but also identify potential customers for service providers. However, the current attraction recommendation methods such as content-based methods, collaborative filtering, or deep learning-based methods are either inaccurate due to data sparsity, or lack of interpretability, which results in the users suspicion on the recommendation results. To address the limitations of the current methods, we introduce a novel framework for preference propagation on knowledge graphs (KGs), which utilizes lots of parameters to capture the abundant semantics of existing KGs more comprehensively, and meanwhile explains the results through reasoning the link paths from users history to candidates on KGs. With a multi-view spatiotemporal analysis on real-world travel data, we investigate the geographical characteristics of human tour activities and build a tourism-oriented KG based on open web resources. Then, we propose a KG-aware attraction recommendation method named Geo-RippleNet and implement it with extensive experiments on large-scale datasets. It is argued that the framework for preference propagation on KGs not only absorb rich semantic information to achieve substantial performance gains in the attraction recommendation scenario but also enhance the interpretability of recommendation results with the support of abundant relational knowledge. Moreover, incorporating the spatiotemporal characteristics of human tour activities into the framework for preference propagation further makes the recommendation performance more aligned with the potential interests of visitors.
Calculation of $$\pi $$ on the IBM quantum computer and the accuracy of one-qubit operations

A quantum algorithm for the calculation of $\pi$ is proposed and implemented on the five-qubit IBM quantum computer with superconducting qubits. We find $\pi=3.157\pm0.017$. The error is due to the noise of quantum one-qubit operations and measurements. The results can be used for estimating the errors of the quantum computer and suggest that the errors are purely random.
An Effective and Efficient Heuristic Privacy Preservation Algorithm for Decremental Anonymization Datasets

(α, k)-Anonymity is a well-known anonymization model that is extended from k-Anonymity. It is proposed to address privacy violation issues in published datasets from using identity linkage attacks, attribute linkage attacks, and probability inference linkage attacks. Unfortunately, (α, k)-Anonymity is generally sufficient to preserve the privacy data in datasets that are focused on performing one-time data publishing. Thus, if published datasets, e.g., decremental datasets, are dynamic, i.e., the data of them is always changed by using deletion methods and multiple time data publishing, then the privacy data of users is collected in these published datasets could be violated by using such an appropriate comparison data attacking. To rid this vulnerability of (α, k)-Anonymity, an effective decremental privacy preservation algorithm is available in existence. Although this algorithm can address privacy violation issues in published decremental datasets, it has a vital vulnerability that must be improved, i.e., it is highly complex in terms of transforming the data which is available datasets to satisfy the specific privacy preservation constraints. For this reason, a heuristic privacy preservation algorithm for publishing decremental datasets based on clustering techniques to be proposed in this work. With the proposed algorithm, aside from privacy preservation, the data utility and execution time are also maintained as much as possible. Furthermore, we show the experimental results which indicate that the proposed algorithm is highly effective and efficient.
Accuracy of Marker Tracking on an Optical See-Through Head Mounted Display

This work assesses the accuracy of mono and stereo vision-based marker tracking on the Microsoft HoloLens as a representative of current generation AR devices. This is motivated by the need to employ object tracking in industrial AR applications. We integrate mono and stereo vision-based marker tracking with the HoloLens. A calibration procedure is developed that allows users to assess the accuracy of the calibration alignments by walking around the virtual calibration target. This can be generally applied when calibrating additional tracking systems with ready-made AR systems. Finally, the accuracy that can be achieved with the developed system is evaluated (comparing the influence of different parameters).
Re-Os Ages for the Kourki Porphyry Cu-Mo Deposits, North West Niger (West Africa): Geodynamic Implications

The study area is located at the southern end of the Gorouol greenstone belt, northwestern Niger. This region contains significant deposits of Copper and Molybdenum hosted in intrusive rocks metamorphosed in the green shale facies. This deposit was previously considered a porphyry system of Copper (Cu) and Molybdenum (Mo) without having been the subject of advanced research. The objective of this study is to confirm or refute this hypothesis and to date the mineralisation in an absolute manner in order to readjust the mineralising episode in the history of the West African Craton. The methodology used within the framework of this study is the isotopic dating by the Re-Os method carried out on the pyrites of the host rock. The results of this analysis give an age range between 2158 ± 50 Ma and 2110 ± 51 Ma for the Cu-Mo mineralisation. This age range represents the West African Craton scale to an episode of magmatic accretion. During this accretion, the subduction phenomena between the Crusts (Oceanic and Continental) would have been favourable for the formation of the Cu and Mo mineralisation of Kourki.
BorderDet: Border Feature for Dense Object Detection

Dense object detectors rely on the sliding-window paradigm that predicts the object over a regular grid of image. Meanwhile, the feature maps on the point of the grid are adopted to generate the bounding box predictions. The point feature is convenient to use but may lack the explicit border information for accurate localization. In this paper, We propose a simple and efficient operator called Border-Align to extract "border features" from the extreme point of the border to enhance the point feature. Based on the BorderAlign, we design a novel detection architecture called BorderDet, which explicitly exploits the border information for stronger classification and more accurate localization. With ResNet-50 backbone, our method improves single-stage detector FCOS by 2.8 AP gains (38.6 v.s. 41.4). With the ResNeXt-101-DCN backbone, our BorderDet obtains 50.3 AP, outperforming the existing state-of-the-art approaches. The code is available at (https://github.com/Megvii-BaseDetection/BorderDet).
Nonlinear generalization of the monotone single index model

Abstract Single index model is a powerful yet simple model, widely used in statistics, machine learning and other scientific fields. It models the regression function as $g(\left &amp;lt;{a},{x}\right&amp;gt;)$, where $a$ is an unknown index vector and $x$ are the features. This paper deals with a nonlinear generalization of this framework to allow for a regressor that uses multiple index vectors, adapting to local changes in the responses. To do so, we exploit the conditional distribution over function-driven partitions and use linear regression to locally estimate index vectors. We then regress by applying a k-nearest neighbor-type estimator that uses a localized proxy of the geodesic metric. We present theoretical guarantees for estimation of local index vectors and out-of-sample prediction and demonstrate the performance of our method with experiments on synthetic and real-world data sets, comparing it with state-of-the-art methods.
Bibliometric Analysis of the Russian Journal of Library Science “Bibliotekovedenie” and Foreign Journals in Library Science: Search for Professional Discourse

The article reflects the main results of comparative bibliometric analysis of the scientific-practical Russian Journal of Library Science “Bibliotekovedenie” and foreign journals in the library sphere. The purpose of the study is to determine the growth points and development directions of the Russian Journal of Library Science for the formation of international scientific communication practices and professional discourse. The object of the research was metadata array of fourteen foreign English-language library journals included in the international scientometric databases Scopus and Web of Science, and the journal “Bibliotekovedenie” for 2015—2019. The authors used content analysis method, the method of comparison and correlation, as well as criterion method based on determination of relevant indicators and development of criteria for evaluating journals to identify parameters of similarity and difference, followed by the use of method of data interpretation based on modern cultural-philosophical and cultural-theoretical principles and concepts. As a result, the study identified the typical topics for the most frequently cited articles in the foreign journals. The authors show that the journal “Bibliotekovedenie” publishes the articles that can be attributed to most of the presented topics. However, most often they are characterized by another emphasis, presentation style and structure of the text, as well as approaches to conducting research. Trends in the citation of articles in Russian publications indicate the difference in a set of priority topics. The authors conclude that these differences depend not only and not so much on the “collision” of traditional and modern vision of the situation from the point of view of scientific research, but rather on deeper reasons based on cultural differences. These reasons are driven by the postmodern increase of the impact of culture on the development of science, reflecting these differences in the languages of cultures and in the translations, as well as by other, sometimes opposite, trends in the development of socio-cultural situation in different countries, related to the understanding of place and role of libraries, books and information in modern society. Based on the obtained results, there was adjusted the development plan of scientific-practical journal “Bibliotekovedenie”, as well as its name in English, starting 2019 (no. 6) — Russian Journal of Library Science.
OntoAndalus: An ontology of Islamic artefacts for terminological purposes

OntoAndalus aims at constituting a shared conceptualisation of the domain within a future multilingual terminological resource targeted at experts and students of Islamic archaeology. The present version of OntoAndalus is aligned with DOLCE+DnS Ultralite (DUL), an established top-level ontology for the Semantic Web. This article describes the modelling assumptions underlying OntoAndalus, as well as the more relevant design patterns (i.e. artefact types, events and individuals). The latter are exemplified through relevant case studies in the domain, namely those of lighting artefacts, the life cycle of pottery and the several descriptions of Vaso de Tavira.
Achievements analysis of mooc English course based on fuzzy statistics and neural network clustering

At present, the field of natural language will also introduce in-depth learning, using the concept of word vector, so that the neural network can also complete the work in the field of statistics. It can be said that the neural network has begun to show its advantages in the field of natural language processing. In this paper, the author analyzes the multimedia English course based on fuzzy statistics and neural network clustering. Different factors were classified, and scores were classified according to the number of characteristics of different categories. It can be seen that with the popularization of the Internet, MOOC teaching meets the requirements of the current college English curriculum, is a breakthrough in the traditional teaching mode, improves students’ participation, and enables students to learn independently. It not only conforms to the characteristics of College students, but also improves their learning effect. In the automatic scoring stage, the quantitative text features are extracted by the feature extractor in the pre-processing stage, and then the weights of network connections obtained in the training stage are used to score the weights comprehensively. This model can better reflect students’ autonomous learning ability and language application ability.
Pemerolehan bahasa anak berkebutuhan khusus (tunarungu) dalam memahami bahasa

Children with hearing impairment are children with hearing loss who are classified into deaf and hard of hearing. The direct impact of disability is the obstruction of verbal / verbal communication, both speaking (expressive) and understanding the conversations of others (receptive). Obtaining the first language of a deaf child can be done with total communication. Total communication is the most effective communication system because in addition to using a form of communication orally or called oral, the activity of reading, writing, reading utterances, is also equipped with a form of cues. The purpose of this study was to determine the acquisition of language of children with special needs (deaf) in understanding language. Subjects in this study are children with special needs who experience speech impairment (hearing impairment) while the object of this study is focused on only one child, Mila Erdita, a 15-year-old child. This research refers to case studies with descriptive research type. Data collection techniques in this study will be done in three ways, namely; observation techniques, interview techniques, and documentation techniques. In this research, data processing that will be done is to describe the speech data of deaf children to see the acquisition of children's vocabulary. The results of this study indicate that deaf children can obtain a language of total communication using a form of communication orally or called oral, with the activities of reading, writing, reading utterances, also equipped with signs
Biomedical and Clinical English Model Packages in the Stanza Python NLP Library

We introduce biomedical and clinical English model packages for the Stanza Python NLP library. These packages offer accurate syntactic analysis and named entity recognition capabilities for biomedical and clinical text, by combining Stanza's fully neural architecture with a wide variety of open datasets as well as large-scale unsupervised biomedical and clinical text data. We show via extensive experiments that our packages achieve syntactic analysis and named entity recognition performance that is on par with or surpasses state-of-the-art results. We further show that these models do not compromise speed compared to existing toolkits when GPU acceleration is available, and are made easy to download and use with Stanza's Python interface. A demonstration of our packages is available at: http://stanza.run/bio.
Accelerated Learning-Based MIMO Detection through Weighted Neural Network Design

In this paper, we introduce a framework for a systematic acceleration of deep neural network (DNN) design for MIMO detection. A monotonically non-increasing function is used to scale the values of the layer weights such that only a certain fraction of the inputs is used for feedforward computation. This enables a dynamic weight scaling across and within the network layers, and it is termed as weight-scaling neural network-based MIMO detector (WeSNet). To increase the robustness against the changes in the activation patterns and additional enhancement in the detection accuracy for the same inference complexity, we introduce trainable weight-scaling functions. Experimental results show the superiority of our proposed method over the benchmark model (DetNet) and classical approaches based on semi-definite relaxation in terms of detection accuracy and computational efficiency.
Fraud Detection Call Detail Record Using Machine Learning in Telecommunications Company

Fraud calls have a serious impact on telecommunications operator revenues.Fraud detection is very important because service providers can feel a significant loss of income.We conducted a fraud research case study on one of the operators that experienced fraud in 2009 and 2018.Call Detail Record (CDR) containing records of customer conversations such as source and destination number, call start time, duration of calls at the operator can be a source of information to use in fraud detection.The method used in this study uses machine learning with unsupervised learning techniques which are quite popular methods used in fraud detection.The purpose of this study is to propose an effective method that can be applied to detect fraud on the CDR.Variables used include caller number, number dialled, duration, fee and destination city of the dataset totalling 11,418 rows from record periods 01 to 31 May 2018.In analyzing our CDR using the K-Means and DBSCAN algorithms, we then evaluate the results to calculate accuracy by comparing to actual fraud data.Based on evaluations using confusion matrix on actual CDR fraud, we obtained the K-Means algorithm to show a better accuracy value to model fraud on telecommunications CDR compared to DBSCAN.
A 13.3 Gbps 9/7M Discrete Wavelet Transform for CCSDS 122.0-B-1 Image Data Compression on a Space-Grade SRAM FPGA

Remote sensing is recognized as a cornerstone monitoring technology. The latest high-resolution and high-speed spaceborne imagers provide an explosive growth in data volume and instrument data rates in the range of several Gbps. This competes with the limited on-board storage resources and downlink bandwidth, making image data compression a mission-critical on-board processing task. The Consultative Committee for Space Data Systems (CCSDS) Image Data Compression (IDC) standard CCSDS-122.0-B-1 is a transform-based 2D image compression algorithm designed specifically for use on-board a space platform. In this paper, we introduce a high-performance architecture for a key-part of the CCSDS-IDC algorithm, the 9/7M Integer Discrete Wavelet Transform (DWT). The proposed parallel architecture achieves 2 samples/cycle while the very deep pipeline enables very high clock frequencies. Moreover, it exploits elastic pipeline principles to provide modularity, latency insensitivity and distributed control. The implementation of the proposed architecture on a Xilinx Kintex Ultrascale XQRKU060 space-grade SRAM FPGA achieves state-of-the-art throughput performance of 831 MSamples/s (13.3 Gbps @ 16bpp) allowing seamless integration with next-generation high-speed imagers and on-board data handling networking technology. To the best of our knowledge, this is the fastest implementation of the 9/7M Integer DWT on a space-grade FPGA, outperforming previous implementations.
Deep Generation of Coq Lemma Names Using Elaborated Terms

Coding conventions for naming, spacing, and other essentially stylistic properties are necessary for developers to effectively understand, review, and modify source code in large software projects. Consistent conventions in verification projects based on proof assistants, such as Coq, increase in importance as projects grow in size and scope. While conventions can be documented and enforced manually at high cost, emerging approaches automatically learn and suggest idiomatic names in Java-like languages by applying statistical language models on large code corpora. However, due to its powerful language extension facilities and fusion of type checking and computation, Coq is a challenging target for automated learning techniques. We present novel generation models for learning and suggesting lemma names for Coq projects. Our models, based on multi-input neural networks, are the first to leverage syntactic and semantic information from Coq ’s lexer (tokens in lemma statements), parser (syntax tree s), and kernel (elaborated terms) for naming; the key insight is that learning from elaborated terms can substantially boost model performance. We implemented our models in a toolchain, dubbed Roosterize, and applied it on a large corpus of code derived from the Mathematical Components family of projects, known for its stringent coding conventions. Our results show that Roosterize substantially outperforms baselines for suggesting lemma names, highlighting the importance of using multi-input models and elaborated terms.
Affective engagement in academic reading: What EFL student teachers reveal

Academic reading is an inescapable task in higher education. Due to its importance for study success, students are required to maintain their academic reading engagement. With engagement, they would be enabled to persevere and be more spirited in their reading efforts. However, not all students perceive academic reading positively, particularly in EFL learning environment where English reading is seen as something daunting. Academic reading engagement is essentially determined by a number of dimensions, one of which is affective dimension. This research aims to portray the affective dimension of the academic reading engagement among student teachers of English as a Foreign Language (EFL) in a teacher-training university in Indonesia. Eight EFL student teachers were selected from three classes of an English content course which required a lot of academic reading. These student teachers were interviewed using a semi-structured guide. The results show that many EFL student teachers’ efforts in coping with course-related reading materials were externally driven. They showed low liking for learning, demonstrated little enthusiasm, interest, enjoyment, and confidence in relation to academic reading. Also, they did not always understand the values of academic reading and text relevance for their development as teachers.
Mapping Ensembles of Trees to Sparse, Interpretable Multilayer Perceptron Networks

Abstract Tree-based classifiers provide easy-to-understand outputs. Artificial neural networks (ANN) commonly outperform tree-based classifiers; nevertheless, understanding their outputs requires specialized knowledge in most cases. The highly redundant architecture of ANN is typically designed through an expensive trial-and-error scheme. We aim at (1) investigating whether using ensembles of decision trees to design the architecture of low-redundant, sparse ANN provides better-performing networks, and (2) evaluating whether such trees can be used to provide human-understandable explanations for their outputs. Information about the hierarchy of the features, and how good they are at separating subsets of samples among the classes, is gathered from each branch in an ensemble of trees. This information is used to design the architecture of a sparse multilayer perceptron network. Networks built using our method are called ForestNet . Tree branches corresponding to highly activated neurons are used to provide explanations of the networks’ outputs. ForestNets are able to handle low- and high-dimensional data, as we show on an evaluation using four datasets. Our networks consistently outperformed their respective ensemble of trees and had similar performance to their fully connected counterparts with a significant reduction of connections. Furthermore, our interpretation method seems to provide support for the ForestNet outputs. While ForestNet’s architectures do not allow them yet to capture well the intrinsic variability of visual data, they exhibit very promising results by reducing more than 98% of connections for such visual tasks. Structure similarities between ForestNets and their respective tree ensemble provide means to interpret their outputs.