Reviewerly/exHarmony huggingface dataset:
EVALUASI IMPLEMENTASI MANAJEMEN PENGETAHUAN DI KATOR PELAYAHAN PAJAK PRATAMA JAKARTA

&lt;p&gt;&lt;em&gt;Knowledge management provides an opportunity for changes and information values and libraries to make themselves relevant to the times. The purpose of this study is to determine the extent of the application of knowledge management at the Pratama Tamansari Dua Jakarta Tax Office. The method in this study is simple qualitative through the technique of observing objects in the field and literature. Based on the results of the research and evaluation it turned out that the Tamansari Dua Tax Service Office had implemented Knowledge Management well and consistently so that the process of achieving goals had a basis for reference in work and had shown significant results in trying to achieve individual and organizational performance. Institutional innovation and development is faster because with the knowledge management cycle pattern all knowledge is well archived and can be easily accessed by all members. However, the willingness of each individual in the member's institution to be explicit in all tacit knowledge is needed so that it can be disseminated to other members.&lt;/em&gt;&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;Keywords&lt;em&gt;: Evaluation, Implementation, Knowledge Management&lt;/em&gt;&lt;/p&gt;
WUT at SemEval-2019 Task 9: Domain-Adversarial Neural Networks for Domain Adaptation in Suggestion Mining

We present a system for cross-domain suggestion mining, prepared for the SemEval-2019 Task 9: Suggestion Mining from Online Reviews and Forums (Subtask B). Our submitted solution for this text classification problem explores the idea of treating different suggestions’ sources as one of the settings of Transfer Learning - Domain Adaptation. Our experiments show that without any labeled target domain examples during training time, we are capable of proposing a system, reaching up to 0.778 in terms of F1 score on test dataset, based on Target Preserving Domain Adversarial Neural Networks.
METODE SVM SEBAGAI PENENTUAN KELULUSAN MAHASISWA PADA PEMBELAJARAN ELEKTRONIK

Electrovic learning system is a web -based communication platform that enables learners , without limitation of place and time , to access a variety of learning tools such as discussion forums , assessment , content repositories , and document sharing system. Electrovic Learning can be just as effective as face-to- face in a conventional classroom teaching and learning , if proper teaching techniques and well-organized. Based on the data processing has been done with membandingakan algorithm and Support Vector Machine Support Vector Machine by using the log data of students . Later in the test to get the accuracy and AUC values ​​of each algorithm so that the highest test results obtained by using support vector machine an accuracy of 85.02%, and AUC value of 0.710.&#x0D;
Iterative Tree Search in General Game Playing with Incomplete Information

General Game Playing (GGP) is concerned with the development of programs capable of effectively playing a game by just receiving its rules and without human intervention. The standard game representation language GDL has recently been extended so as to include games with incomplete information. The so-called Lifted HyperPlay technique, which is based on model sampling, provides a state-of-the-art solution to general game playing with incomplete information. However, this method is known not to model opponents properly, with the effect that it generates only pure strategies and is short-sighted when valuing information. In this paper, we overcome these limitations by adapting the classic idea of fictitious play to introduce an Iterative Tree Search algorithm for incomplete-information GGP. We demonstrate both theoretically and experimentally that our algorithm provides an improvement over existing solutions on several classes of games that have been discussed in the literature.
Better Bootstrapping for Approximate Homomorphic Encryption.

After Cheon et al. (Asiacrypt’ 17) proposed an approximate homomorphic encryption scheme, HEAAN, for operations between encrypted real (or complex) numbers, the scheme is widely used in a variety of fields with needs on privacy-preserving in data analysis. After that, a bootstrapping method for HEAAN is proposed by Cheon et al. (Eurocrypt’ 18) with modulus reduction being replaced by a sine function. In this paper, we generalize the Full-RNS variant of HEAAN proposed by Cheon et al. (SAC, 19) to reduce the number of temporary moduli used in key-switching. As a result, our scheme can support more depth computations without bootstrapping while ensuring the same level of security.
JU_ETCE_17_21 at SemEval-2019 Task 6: Efficient Machine Learning and Neural Network Approaches for Identifying and Categorizing Offensive Language in Tweets

This paper describes our system submissions as part of our participation (team name: JU_ETCE_17_21) in the SemEval 2019 shared task 6: “OffensEval: Identifying and Catego- rizing Offensive Language in Social Media”. We participated in all the three sub-tasks: i) Sub-task A: offensive language identification, ii) Sub-task B: automatic categorization of of- fense types, and iii) Sub-task C: offense target identification. We employed machine learn- ing as well as deep learning approaches for the sub-tasks. We employed Convolutional Neural Network (CNN) and Recursive Neu- ral Network (RNN) Long Short-Term Memory (LSTM) with pre-trained word embeddings. We used both word2vec and Glove pre-trained word embeddings. We obtained the best F1- score using CNN based model for sub-task A, LSTM based model for sub-task B and Lo- gistic Regression based model for sub-task C. Our best submissions achieved 0.7844, 0.5459 and 0.48 F1-scores for sub-task A, sub-task B and sub-task C respectively.
Audio Augmented Reality for Interactive Soundwalks, Sound Art and Music Delivery

Locative Media is an established musical/sound art practice with a rich and diverse history encompassing many musical styles and genres. With the advent of the smartphone and sophisticated tracking systems, audio augmented reality (AAR) has emerged as an effective way to produce highly interactive and widely distributable works. Using their own work as a backdrop, the authors discuss approaches and decision points for building an AAR system as well as the considerations that composers must work through to capitalize on the technology and create a truly engaging, interactive experience for the listener.
On quaternion algebras that split over quadratic number fields

Let $d$ and $m$ be two distinct squarefree integers and $\mathcal{O}_K$ the ring of integers of the quadratic field $K=\mathbb{Q}(\sqrt{d})$. Denote by $ H_K(\alpha, m)$ a quaternion algebra over $K$, where $\alpha\in \mathcal{O}_K$. In this paper we give necessary and sufficient conditions for $ H_K(\alpha, m)$ to split over $K$ for some values of $\alpha$, and we obtain a complete characterization of division quaternion algebras $ H_K(\alpha, m)$ over $K$ whenever $\alpha$ and $m$ are two distinct positive prime integers. Examples are given involving prime Fibonacci numbers.
A Data Masking Utility to Secure Sensitive Production Data in Non-Production Environment

The right to privacy is a fundamental right. However, despite increasing recognition and awareness of data protection, there is still a lack of legal and institutional frameworks, processes, and infrastructure support to protect data and privacy rights. The increasing use of personal data, with the emergence of technologies, enabling new ways of processing and using it, means that regulating an effective data protection framework is more important than ever. Researchers developed a non-deterministic masking technique that provides security to sensitive data, without modifying the appearance of original values of confidential variables. In this study, we develop such a new procedure and discuss its underlying theory. Referred to as non-deterministic masking, this procedure will allow organisations to publish and share data for non-production environment, with minimal disclosure risk. The proposed model is evaluated on a real financial dataset and the results showed masked dataset as similar as the original dataset.
Augmented Reality as a Didactic Tool for Development Spatial Intelligence

Augmented Reality (AR)complements perception and interaction with a semi-real world and allows the user to observe a virtual environment enhanced with additional information assisted by a computer. The use of this technology has been introduced in multiple areas especially in education for spatial intelligence development and students' learning improvement. This paper describes the development of a single didactic material representing Ecuador's historical content through an application of AR in order to improve teaching and learning processes. It shows historical facts on virtual models by superimposing 2D and 3D dynamic graphics which were generated in real time; and description of recognition marks design were utilized to create AR. Thus, it also presents level of acceptance and perception on students using this technology. Students' academic performance was analyzed before and after the didactic material was utilized in the classroom and results were recorded.
Analysis on Reflection Characteristics of the Key Competencies Proposed by the OECD Education 2030 in the 2015 Revised Home Economics Curriculum

본 연구는 OECD Education 2030 프로젝트에서 제시하고 있는 핵심역량의 반영 특성을 2015 개정 가정과 교육과정에서 분석하고자 하였으며 연구결과는 다음과 같다. 첫째, 핵심역량 범주에서 일반적인 특징은 기능, 태도, 가치 영역은 46.5%로, 학습 개념 프레임워크 영역은 17%, 역량 개발사이클 영역은 24.2%, 복합 역량 영역은 12.5%로 나타났다. 전체적으로 교육과정 항목에서 성취기준(59%), 성격(16.1%), 교수 · 학습 평가 방향(9.4%), 내용체계(8%), 목표(7.6%) 순으로 반영되었으며 학습 개념 프레임워크의 역량은 성취기준에 가장 많이 반영되었다. 둘째, 핵심역량 항목은 중학교 교육과정에서 행동, 문제해결, 의사소통, 존중, 창의적 사고, 갈등해결, 공감, 비판적 사고, 자기관리, 학생 주체성 순으로 나타났다. 고등학교 교육과정에서 행동, 공감, 문제해결, 예측, 글로벌 역량, 자기관리, 학생주체성, 지속가능 발전을 위한 리터러시, 반성, 비판적 사고 순으로 나타났다. 셋째, 열지도를 통해 3, 4단계에 해당되는 역량의 반영 정도가 높게 나타나 핵심역량의 효과적인 실천을 계획하고 지원할 필요가 있었다. 본 연구를 통해 미래를 위한 학습 안내자의 역할로 OECD에서 강조하는 핵심역량과 가정교과 역량 간의 상호관련성을 파악하고 실천 교과로서 개인의 총체적인 역량 함양을 도울 수 있어야 할 것이다.The purpose of this study was to analyze the characteristics reflected in the 2015 revised home economics curriculum for the key competencies presented in the OECD education 2030 project. The results indicate that first, in general, about 46.5% of the competencies could be classified into the skill, attitude and value category; 17% into the learning concept framework category; 24.2% into the competency development cycle category; and 12.5% into the complex competency category. Overall, the competencies of the OECD learning framework are found to be reflected primarily in the achievement standards(59%), followed by characteristics(16.1%), teaching-learning and assessments orientation(9.4%), content system(8%), and goals(7.6%). Second, the key competencies were reflected in the middle school curriculum, more often in the descending order of action, problem-solving, communication, respect, creative thinking, conflict resolution, empathy, critical thinking, self-regulation, and student agency. In the high school curriculum, the competencies were reflected more often in the descending order of action, empathy, problem-solving, anticipation, global competence, self-regulation, student agency, literacy for sustainable development, reflection, and critical thinking. Third, the heat map shows that the competencies corresponding to the third and fourth levels are most frequently reflected in the curriculum. Therefore, it is advisable to develop effective plans to execute and support the reflection of key competencies in the curriculum. Through this study, home economics educators are expected to understand the inter-connectivity between the key competencies emphasized by the OECD learning framework and the competencies of home economics as a practical subject, and to scrutinize how to help individual students develop their overall competencies and be prepared for the future.
Feature selection methods for document clustering: a comparative study and a hybrid solution

The web proliferation makes the exploration and the use of the huge amount of available unstructured text documents challenged, which drives the need of document clustering. Hence, improving the performances of this mechanism by using feature selection seems worth investigation. Therefore, this paper proposes an efficient way to highly benefit from feature selection for document clustering. We first present a review and comparative studies of feature selection methods in order to extract efficient ones. Then we propose a sequential and hybrid combination modes of statistical and semantic techniques in order to benefit from crucial information that each of them provides for document clustering. Extensive experiments prove the benefit of the proposed combination approaches. The performance of document clustering is highest when the measures based on Chi-square statistic and the mutual information are linearly combined. Doing so, it avoids the unwanted correlation that the sequential approach creates between the two treatments.
Identifying error code misuses in complex system

Many complex software systems use error codes to differentiate error states. Therefore, it is crucial to ensure those error codes are used correctly. Misuses of error codes can lead to hardly sensible but fatal system failures. These errors are especially difficult to debug, since the failure points are usually far away from the root causes. Existing static analysis approaches to detecting error handling bugs mainly focus on how an error code is propagated or used in a program. However, they do not consider whether an error code is correctly chosen for propagation or usage within different program contexts, and thus miss to detect many error code misuse bugs. In this work, we conduct an empirical study on error code misuses in a mature commercial system. We collect error code issues from the commit history and conclude three main causes of them. To further resolve this problem, we propose a static approach that can automatically detect error code misuses. Our approach takes error code definition and error domain assignment as the input, and uses a novel static analysis method to detect the occurrence of the three categories of error code misuses in the source code.
Central Banks and Regulation of Cryptocurrencies

This paper explores the interface between central banks and cryptocurrencies. Focusing on the European Central Bank (ECB), it identifies the potential threats that the rise of cryptocurrencies would pose to the basic and ancillary tasks of the ECB, in particular, its monetary policy operations and the exercise of its supervisory functions over credit institutions and payment systems. The paper finds that cryptocurrencies can potentially have both direct – through their potential impact on the price stability and monetary policy, and central banks’ monopoly over issuing base money – and indirect effects on central banks, mainly through the institutions and systems that fall under the ECB’s scope of competence.To address the challenges posed by cryptocurrencies, the ECB may take both legal (including supervisory and oversight) measures and non-legal (or technical) measures. With respect to technical measures, the ECB - to the extent falling within the scope of its competence - may focus on improving the efficiency of existing payment systems and addressing the existing frictions in market infrastructures to indirectly affect the cryptocurrency markets. Alternatively, it can venture into issuing Central Bank Digital Currency (CBDC). Regarding legal measures, central banks could envisage regulating cryptocurrencies either directly or indirectly. However, as the most significant potential impact of cryptocurrencies on central banks is likely to be indirect through the impact of cryptocurrencies on the banking and payment systems, and given the limitations on the ECB’s mandate and its regulatory and supervisory tools, it is apposite for the ECB to consider using indirect strategies and tools to influence cryptocurrency markets. This indirect approach can be implemented through the ECB’s existing supervisory and oversight powers over the banking and payment systems. This paper specifies the direct and indirect measures and assesses their merits in addressing the concerns about cryptocurrencies.
Human action recognition based on a single acceleration sensor

Abstract In this paper, based on the acceleration signal acquired by a single triaxial acceleration sensor worn at the bottom of the finger, an algorithm for recognizing the daily movement behavior of the human body is proposed. The algorithm converts the three-axis acceleration into the resultant acceleration, horizontal acceleration and vertical acceleration, and extracts the features from them. The 37 features are sorted by the F-score as a measure of the importance of the feature, and a subset of features that meet the application requirements is found. Model training is performed on the feature subset using multi-class SVM to obtain the final classification model. Experiments show that the model has high recognition accuracy and the accuracy can reach 93.17%.
Perspective-Aware Loss Function for Crowd Density Estimation

Estimation errors caused by perspective distortions are a long-standing problem in the domain of crowd counting. In this paper, we propose a novel loss function to allow filters in convolutional neural networks to learn features that are adaptive to the scale and perspective variation of individuals in crowd images. By exploring the crowd count error from regions close to the vanishing point of a perspective distorted image, we are able to penalize under-estimations. This is useful to train a network that is robust against perspective distortion for accurate density estimation. The proposed method is scene-independent and can be applied effectively to crowd scene with a variety of physical layout. Extensive comparative evaluations demonstrate that our proposed method achieves significant improvement over the state-of-the-art approaches on the challenging ShanghaiTech and UCF-QNRF datasets.
MIPaaL: Mixed Integer Program as a Layer

Machine learning components commonly appear in larger decision-making pipelines; however, the model training process typically focuses only on a loss that measures accuracy between predicted values and ground truth values. Decision-focused learning explicitly integrates the downstream decision problem when training the predictive model, in order to optimize the quality of decisions induced by the predictions. It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization. However, these previous applications have uniformly focused on problems from specific classes with simple constraints. Here, we enable decision-focused learning for the broad class of problems that can be encoded as a Mixed Integer Linear Program (MIP), hence supporting arbitrary linear constraints over discrete and continuous variables. We show how to differentiate through a MIP by employing a cutting planes solution approach, which is an exact algorithm that iteratively adds constraints to a continuous relaxation of the problem until an integral solution is found. We evaluate our new end-to-end approach on several real world domains and show that it outperforms the standard two phase approaches that treat prediction and prescription separately, as well as a baseline approach of simply applying decision-focused learning to the LP relaxation of the MIP.
Human-Food Interaction Framework: A New Design Tool Used to Understand Amateur Home Cooks’ Needs

Our present research focuses on gaining a better understanding of the relationships between food and different human stakeholders in order to develop our Human-Food Interaction framework. This paper focuses on the key stakeholders of people new to the kitchen called “amateur cooks.” We noticed there is a gap in research on this group regarding how they transition between taking stored food and choosing how and what to cook. We discovered from initial interviews that more amateur home cooks initially strictly follow recipes and therefore create food waste on the niche ingredients they buy for each recipe. More advanced cooks see recipes as composed of an essential base (like pasta or salad) and extras which are added on to that base (like nuts, sauces or cheese). As a result, we sought to develop a tool to help amateur cooks unleash their creativity and gain confidence while cooking. Our research led us to develop a prototype called Flavor Explorer, a new algorithm and corresponding interface that allows amateur cooks to find the best ingredient pairings depending on what they have stored in their kitchens. With this tool and our research, we thus hope to highlight the importance of the needs of this user group while proposing a novel tool that could actually help amateur cooks around the world.
Measure and Improvement Path of TPACK Context of Professional Teachers of Civil Engineering in Higher Education

In the information age, with the rapid emergence of multimedia computers, smart phones and various Web-based RIA applications and AR and VR technologies, the information and intelligence of the education and teaching environment has continuously enhanced. The innovation of teacher training mode and the development of teacher professionalization should be achieved by continuously improving the teacher's knowledge structure and promoting the integration of technology and teaching. To study how to construct and continuously improve the new teacher knowledge structure system integrating information technology, it will become the basic part of the innovative teacher-training model and the professional development of teachers in the new era. The college civil engineering professional education has been developing with the development of the national social economy; the demand for talents has continuously increased. In addition, the innovation of civil engineering professional teaching is in urgent need of development. It is necessary to improve the quality of the teaching team. It is imperative to study the TPACK level of civil engineering teachers. Based on this, this paper builds a dedicated test scale and retains 34 items after screening. 180 data results were obtained, through the collection of questionnaires. Then we use SPSS statistical analysis software to conduct project analysis on the test results to verify the relevance and reliability of the test items in the test scale. Then, we determine the structural validity of the test scale by factor analysis of the test results. Finally, we analyzed the factors affecting the TPACK context based on the resulting data. The results show that the national policy school environment is a macro factor that affects TPACK, and the teacher's self-efficacy is a micro factor. Based on this, we have proposed different development plans from different angles.
Introduction to this special section: Machine learning applications

Since the last time The Leading Edge devoted a special section to “Data analytics and machine learning” in March 2017, the topic has continued to generate excitement in the field of exploration geophysics and beyond. Today, machine learning is finding new applications within a multitude of industries on an almost daily basis as companies invest heavily in this technology.