Reviewerly/exHarmony huggingface dataset:
Federated Multi-task Hierarchical Attention Model for Sensor Analytics

Sensors are an integral part of modern Internet of Things (IoT) applications. There is a critical need for the analysis of heterogeneous multivariate temporal data obtained from the individual sensors of these systems. In this paper we particularly focus on the problem of the scarce amount of training data available per sensor. We propose a novel federated multi-task hierarchical attention model (FATHOM) that jointly trains classification/regression models from multiple sensors. The attention mechanism of the proposed model seeks to extract feature representations from the input and learn a shared representation focused on time dimensions across multiple sensors. The underlying temporal and non-linear relationships are modeled using a combination of attention mechanism and long-short term memory (LSTM) networks. We find that our proposed method outperforms a wide range of competitive baselines in both classification and regression settings on activity recognition and environment monitoring datasets. We further provide visualization of feature representations learned by our model at the input sensor level and central time level.
IMPLEMENTASI ALGORITMA A-STAR PADA PERMAINAN KOMPUTER ROGUELIKE BERBASIS UNITY

&lt;p&gt;&lt;em&gt;There are many genres found in video games, one of which is role-playing games (RPGs). Roguelike was originally a type of RPG subgenre whose game was based on a risk-aware pattern. The Roguelike game is synonymous with games that require players to play efficiently in order to minimize the risk of losing game progress. This research is in the form of making a Roguelike game by implementing the A-star algorithm to search for the shortest path in the process of chasing enemies against player characters. Overall, this final assignment research is carried out by applying a prototype system development model. The results of this study are able to prove that using the A-star algorithm is one of the right methods for the shortest search in the process of chasing enemies against player characters.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;: &lt;/em&gt;&lt;em&gt;A-star, Roguelike, unity&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
A Novel Hybrid Optical Character Recognition Approach for Digitizing Text in Forms

The huge amount of document-based processes has considerably contributed to the need of automated systems which are able to appropriately digitize text in documents concerning forms. For example, the text in scanned administrative forms is not accessible without an adequate conversion from pixels to editable text. Against this background, many organizations tap the potential of Optical Character Recognition (OCR) as it is capable of supporting the digitization of text in documents. However, there is still a lack of integrated OCR approaches, considering both handwritten and machine printed texts, which are both of major importance in the context of digitizing text in forms. To address this problem, we propose a new hybrid OCR approach recognizing handwritten and machine printed text based on neural networks in an integrated perspective. We demonstrate the practical applicability of our approach using publicly available forms on which the approach could be successfully applied. Finally, we evaluate our novel hybrid approach in comparison to existing state-of-the-art approaches.
TEACHING ENVIRONMENTAL EDUCATION FOR SUSTAINABLE DEVELOPMENT: STRATEGIES AND CHALLENGES

Building on a serious concern over excessive increase ofproduction garbage waste from one year to another, this articleargues that the garbage waste produced by cities around the globehas led to an environment crisis and the planet earth has faceddesertification, drought, and degradation. Within this scenario, itis high time we brought this awareness by teaching environmentliteracy. To this end, the article discusses climate change as aninconvenient scenario, key challenges, tale of two dying rivers,education for sustainable development, and then approaches toenvironmental education.
Weighting Words Using Bi-Normal Separation for Text Classification Tasks with Multiple Classes

An important usage of natural language processing is creating vector representations of documents as features in a classification task. The traditional bag-of-word approach uses one-hot vector representations of words that aggregate into sparse vector document representation. This representation can be enhanced by weighting words that contribute the most to a classification task. In this paper, we propose a generalization of the Bi-Normal Separation metric that enhances vector representations of documents and outperforms TF-IDF scaling algorithms for one-of-m classification tasks.
Diagnosing Data Pipeline Failures Using Action Languages

This paper discusses diagnosis of industrial data processing pipelines using action languages. Solving the problem requires reasoning about actions, effects of the actions and mechanisms for accessing outside data sources. To satisfy these requirements, we introduce an action language, Hybrid $$\mathcal {AL}E$$ that combines elements of the action language Hybrid $$\mathcal {AL}$$ [6] and the action language $$C_{TAID}$$ [8]. We discuss some of the practical aspects of implementing Hybrid $$\mathcal {AL}E$$ and describe an example of its use.
ASH Search: Binary Search Optimization

The binary search is a method of finding the position of an element in an ordered array.It continuously aims the middle element of array and check if it is the target element or not untills it finds its position.The best case complexity of binary search is O(1), whereas average and worst case time complexity is O(log n), where 'n' is the number of elements in the array.In this paper, I have proposed an algorithm which drastically improves the complexity of search algorithm in sorted array domain outperforming binary search, the paper also compares the proposed solution with other well-known search algorithms.The presented approach minimizes the space complexity, eliminates the need to analyze the scenario and look for the algorithm that best fits the given problem.The proposed algorithm has constant space complexity (O(1)) and time complexity of O(1) (constant time) in best case, O(log(log n)) in average case and O(log(n)) in worst case.Thus, most of the time proposed algorithm works very well as compared to other search algorithms in sorted array domain.
DERMATOLOGICAL IMAGE DENOISING USING ADAPTIVE HENLM METHOD

Abstract. In this paper we propose automatic image denoising method based on Hermite functions (HeNLM). It is an extension of non-local means (NLM) algorithm. Differences between small image blocks (patches) are replaced by differences between feature vectors thus reducing computational complexity. The features are calculated in coordinate system connected with image gradient and are invariant to patch rotation. HeNLM method depends on the parameter that controls filtering strength. To chose automatically this parameter we use a no-reference denoising quality assessment method. It is based on Hessian matrix analysis. We compare the proposed method with full-reference methods using PSNR metrics, SSIM metrics, and its modifications MSSIM and CMSC. Image databases TID, DRIVE, BSD, and a set of dermatological immunofluorescence microscopy images were used for the tests. It was found that more perceptual CMSC and MSSIM metrics give worse correspondence than SSIM and PSNR to the results of information preservation by the non-reference image denoising.
Construction of Generalized Quantum Boolean Functions

The existing construction methods of Quantum Boolean functions (QBFs) are extended and simplified. All QBFs with one qubit and all local QBFs with any qubits are constructed. And we propose the concept of Generalized quantum Boolean functions (GQBFs). We find all GQBFs with one qutrit and all kinds of local GQBFs with any qutrits. The number of each of the four kinds of functions above is uncountably infinitely many. By using diagonal matrices, we obtain uncountably infinitely many non-local QBFs with any qubits and GQBFs with any qutrits. Infinitely many families of GQBFs with any qudits are obtained from the properties of projection matrices of known saturated orthogonal arrays.
A new method for resisting collision attack based on parallel random delay S-box

Collision Attack (CA) has posed a huge threat to the security of AES circuit. To protect sensitive information, it’s necessary to do research on defense strategy of CA. This letter proposes a new method to defense CA through the implementation of random delay based parallel S-box. It can destroy the consistency of the power consumption curves, confuse the judgment of the collision and the setting of the collision threshold to achieve the goal of resisting the CA. Compared to the well-known random mask method and other CA countermeasures, our strategy can defense CA without changing the AES round transformation architecture and bring extra resource overhead.
Desfuncionalizar para Provar

This paper explores the idea of using defunctionalization as a proof technique for higher-order programs. Defunctionalization builds on substituting functional values by a first-order representation. Thus, its interest is that one can use an existing program verification tool, without further extensions in order to support higher-order. This papers illustrates and discusses this approach by means of several running examples, built and verified using the Why3 verification framework.
Question answering via web extracted tables

Question answering (QA) provides answers to a wide range of questions but is still limited in the complexity of reasoning and the breadth of accessible data sources. In this paper, we describe a dataset and baseline results for a question answering system that utilizes web tables. The dataset is derived from commonly asked questions on the web, and their corresponding answers found in tables on websites. Our dataset is novel in that every question is paired with a table of a different signature, so learning must automatically generalize across domains. Each QA training instance comprises a table, a natural language question, and a corresponding structured SQL query. We build our model by dividing question answering into a sequence of tasks, including table retrieval and question element classification, and conduct experiments to measure the performance of each task. In a traditional machine learning design manner, we extract various features specific to each task, apply a neural model, and then compose a full pipeline which constructs the SQL query from its parts. Our work provides quantitative results and error analysis for each task, and identifies in detail the reasoning required to generate SQL expressions from natural language questions. This analysis of reasoning informs future models based on neural machine learning.
Data2paper: Giving Researchers Credit for Their Data

Initially funded as part of the Jisc Data Spring Initiative, a team of stakeholders (publishers, data repository managers, coders) has developed a simple workflow to streamline data paper submission. Metadata about a dataset in a data repository is combined with ORCID metadata about the author to automate and thus greatly reduce the friction of the submission process. Funders are becoming more interested in good data management practice, and institutions are developing repositories to hold the data outputs of their researchers, reducing the individual burden of data archiving. However, to date only a subset of the data produced is associated with publications and thus reliably archived, shared and re-used. This represents a loss of knowledge, leading to the repetition of research (especially in the case of negative observations) and wastes resources. It is laborious for time-poor researchers to fully describe their data via an associated article to maximise its utility to others, and there is little incentive for them to do so. Filling out diverse submission forms, for the repository and journal(s), makes things even lengthier. The app makes the process of associating and publishing data with a detailed description easier, with corresponding citation potential and credit benefits.
Challenges in Building Intelligent Open-domain Dialog Systems

There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI. Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connections with users by satisfying the human need for communication, affection, and social belonging. This paper reviews the recent works on neural approaches that are devoted to addressing three challenges in developing such systems: semantics, consistency, and interactiveness. Semantics requires a dialog system to not only understand the content of the dialog but also identify user's social needs during the conversation. Consistency requires the system to demonstrate a consistent personality to win users trust and gain their long-term confidence. Interactiveness refers to the system's ability to generate interpersonal responses to achieve particular social goals such as entertainment, conforming, and task completion. The works we select to present here is based on our unique views and are by no means complete. Nevertheless, we hope that the discussion will inspire new research in developing more intelligent dialog systems.
Measurement-device-independent verification of channel steering

Extending the concept of steerability for quantum states, channel steerability is an ability to remotely control the given channel from a coherently extended party. Verification of channel steering can be understood as certifying coherence of the channel in a one-sided device-independent manner with respect to a bystander. Here we propose a method to verify channel steering in a measurement-device-independent way. To do this, we first obtain Choi matrices from given channels and use the canonical method of measurement-device-independent verification of quantum steering. As a consequence, exploiting channel-state duality which interconverts steerability of channels and that of states, channel steering is verified. We further analyze the effect of imperfect preparation of entangled states used in the verification protocol, and find that the threshold of the undesired noise that we can tolerate is bounded from below by steering robustness.
Feasibility of Audiovisual Media for Literacy Activities of Students of Grade V in Public Elementary School (SDN) 067241 Medan Denai

This study discusses about feasibility of audiovisual media for literacy activities of students of grade V in public elementary school (SDN) 067241 in Medan. This study is conducted in the even semester of Academic Year 2018/2019. The timing of this research is carried out from March to April 2019. The subjects of this study are: (1) students of SDN 067241 Medan Denai class Va as the subject of field trials; and (2) two experts who each have criteria: material experts and media experts. The results shows that a media product of the Tema 4 learning video in the fifth class of Denai Medan. This learning video media product is feasible and valid to be used by Grade V students of SDN 067241 through assessment, revision, and validation from several experts.
Computationally Efficient Feature Significance and Importance for Machine Learning Models

We develop a simple and computationally efficient significance test for the features of a machine learning model. Our forward-selection approach applies to any model specification, learning task and variable type. The test is non-asymptotic, straightforward to implement, and does not require model refitting. It identifies the statistically significant features as well as feature interactions of any order in a hierarchical manner, and generates a model-free notion of feature importance. Experimental and empirical results illustrate its performance.
Identifikasi Faktor-Faktor Yang Mempengaruhi Motivasi Belajar Mahasiswa Pada Program Studi S1 Pendidikan Ekonomi Angkatan 2015 Fakultas Ekonomi Universitas Negeri Gorontalo

This research aims to identification of factors that influencethe motivation of student learning on the study program S1Economic Education, batch of 2015,Faculty of Economics,State University of Gorontalo. By Knowing the factors thatinfluence the motivation to learn can provide useful informationin teaching and learning process. The date in thisstudy was obtained by distributing questionnaires to studentsof Economics Education , batch of 2015 Faculty of Economics,State University of Gorontalo, therefore , the data of thisresearch are primary data.The analytical teachnique used is Factor Analysis. The resultof factor analysis shows that the most influential aspect towardstudents learning motivation is physiology of 93,4%.The next factor that influences is the desire to gain recognition/recognition 87,2%, and social relationships with friends80,6%. While the lowest aspect that affesct the studentslearning motivation sense of security in learning 75,8% andthe desire to self-actualization by 75%.
BeSEPPI: Semantic-Based Benchmarking of Property Path Implementations

In 2013 property paths were introduced with the release of SPARQL 1.1. These property paths allow for describing complex queries in a more concise and comprehensive way. The W3C introduced a formal specification of the semantics of property paths, to which implementations should adhere. Most commonly used RDF stores claim to support property paths. In order to give insight into how well current implementations of property paths work we have developed BeSEPPI, a benchmark for the semantic-based evaluation of property path implementations. BeSEPPI checks whether RDF stores follow the W3Cs semantics by testing the correctness and completeness of query result sets. The results of our benchmark show that only one out of 5 benchmarked RDF stores returns complete and correct result sets for all benchmark queries.
Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification

Extreme multi-label text classification (XMTC) aims at tagging a document with most relevant labels from an extremely large-scale label set. It is a challenging problem especially for the tail labels because there are only few training documents to build classifier. This paper is motivated to better explore the semantic relationship between each document and extreme labels by taking advantage of both document content and label correlation. Our objective is to establish an explicit label-aware representation for each document with a hybrid attention deep neural network model(LAHA). LAHA consists of three parts. The first part adopts a multi-label self-attention mechanism to detect the contribution of each word to labels. The second part exploits the label structure and document content to determine the semantic connection between words and labels in a same latent space. An adaptive fusion strategy is designed in the third part to obtain the final label-aware document representation so that the essence of previous two parts can be sufficiently integrated. Extensive experiments have been conducted on six benchmark datasets by comparing with the state-of-the-art methods. The results show the superiority of our proposed LAHA method, especially on the tail labels.
A Study of Types of Sensors Used in Remote Sensing

Of late, the science of Remote Sensing has been gaining a lot of interest and attention due to its wide variety of applications. Remotely sensed data can be used in various fields such as medicine, agriculture, engineering, weather forecasting, military tactics, disaster management etc. only to name a few. This article presents a study of the two categories of sensors namely optical and microwave which are used for remotely sensing the occurrence of disasters such as earthquakes, floods, landslides, avalanches, tropical cyclones and suspicious movements. The remotely sensed data acquired either through satellites or through ground based- synthetic aperture radar systems could be used to avert or mitigate a disaster or to perform a post-disaster analysis.
American Sign Language Alphabet Recognition using Deep Learning

Tremendous headway has been made in the field of 3D hand pose estimation but the 3D depth cameras are usually inaccessible. We propose a model to recognize American Sign Language alphabet from RGB images. Images for the training were resized and pre-processed before training the Deep Neural Network. The model was trained on a squeezenet architecture to make it capable of running on mobile devices with an accuracy of 83.29%.
Technology Foresight Research of Industrial Robot Based on Patent Analysis

In view of the lack of patent big data in research on technology foresight in the industrial robot field, this paper introduces an improved method based on patent mining and knowledge map.Firstly, SAO structure is extracted from selected patents, secondly, the similarity between patents is calculated based on extracted SAO structure, thirdly, patent network and patent map are drawn based on calculated patent similarity matrix, technology evolution process and future trends of industrial robot are summarized from patent network, and future potential technology opportunities are predicted based on technological vacancies identified from patent map.Finally, this paper identifies six key technical areas and four potential technical opportunities in the field of the industrial robot.
IMPROVING ENGLISH VOCABULARY MASTERY BY USING CROSSWORD PUZZLE

Vocabulary is important in English language. Because, to make a sentence an adequate vocabulary is needed. So, the researcher apply a method in the classroom which can make students motivated to learn vocabulary. The aim of the research is to know that the crossword puzzle is effective to improve students’ vocabulary or not through the use of crossword puzzles among seventh-grade students of MTs At-Taqwa Cihampelas in the academic year of 2018/2019. The method used in this research is quantitative technique and the pre-experimental research design conducted in one class. The population in this research is seventh-grade students of MTs At-Taqwa Cihampelas with the total number of the population are 104 students consisting of 3 classes, and the sample is class VII.A that consist of 30 students. The data is collected with the pretest and posttest as an instrument. The researcher conducted the tests before (pre-test) and after (post-test). After that the researcher give students a treatment consists of five meetings. The result show that the mean score of pre-test was 41.83, and the post-test was 75.67 after gives treatment uses crossword puzzle. It means that teaching English vocabulary using a crossword puzzle was effective media to improve students’ vocabulary at seventh-grade. Keywords: crossword puzzle, students’ vocabulary mastery.
Power-optimal, stabilized entangling gate between trapped-ion qubits

To achieve scalable quantum computing, improving entangling-gate fidelity and its implementation-efficiency are of utmost importance. We present here a linear method to construct provably power-optimal entangling gates on an arbitrary pair of qubits on a trapped-ion quantum computer. This method leverages simultaneous modulation of amplitude, frequency, and phase of the beams that illuminate the ions and, unlike the state of the art, does not require any search in the parameter space. The linear method is extensible, enabling stabilization against external parameter fluctuations to an arbitrary order at a cost linear in the order. We implement and demonstrate the power-optimal, stabilized gate on a trapped-ion quantum computer.