Reviewerly/exHarmony huggingface dataset:
CrimeID: Towards Crime Prevention and Community Safety in Indonesia using Mobile and Web Technology

The rate of crime incidence in Indonesia is quite high. Some efforts to prevent crime and to create community safety have been already conducted. However, the information technology has not been implemented yet. CrimeID could be a technology tool for both individual and collective crime prevention. The citizen could participate individually as a crowd to annotate the crime data from news article through the crowdsourcing platform using gamification concept. The collected data could be used to raise the awareness among community members of the crime that happens surrounding them by using the location-aware system.
Bootstrapping a Neural Morphological Analyzer for St. Lawrence Island Yupik from a Finite-State Transducer

Morphological analysis is a critical enabling technology for polysynthetic languages. We present a neural morphological analyzer for case-inflected nouns in St. Lawrence Island Yupik, an endangered polysythetic language in the Inuit-Yupik language family, treating morphological analysis as a recurrent neural sequence-to-sequence task. By utilizing an existing finite-state morphological analyzer to create training data, we improve analysis coverage on attested Yupik word types from approximately 75% for the existing finite-state analyzer to 100% for the neural analyzer. At the same time, we achieve a substantially higher level of accuracy on a held-out testing set, from 78.9% accuracy for the finite-state analyzer to 92.2% accuracy for our neural analyzer.
Predicting Software Faults Based on K-Nearest Neighbors Classification

Software defect prediction is one of the most important task during the development of software systems in order to save developers' time and effort.Discovering defects in an early stage of software development will allow programmers and developers to take action and resolve these faulty parts in software before its launch.In this paper, the K-Nearest Neighbor (KNN) machine learning algorithm is used to predict faulty software projects.Experimental studies are conducted on five public datasets with different similarly measures.Results showed that KNN can be used to predict software faults with accuracy rate that can achieve up to 87.2%.
Development of Rule-Based Feature Extraction in Multi-label Text Classification

Hadith is the second main guidelines after the Holy Quran in the Islamic religion, which was revealed through the Messenger of Allah. Today, Hadith can classified by more than one class such as advice class, prohibited, and information to facilitate readers of Hadith in filtering the appropriate classes for each Hadith of Rasulullah SAW. In the course of research, there are many kinds of data involved in a text classification study. Therefore, special handling that fit with the characteristics of certain data is required. This study investigates the handling of multi-label data—Hadith Bukhari in Indonesian translation—focusing on feature extraction, feature weighted, and preprocessing methods. This study uses a rule-based feature extraction combined with several types of preprocessing along with three types of feature-weighted methods: TF-IDF, Word2vec, and Word2vec weighted with TF-IDF, the five preprocessing stages in this research: Case Folding, Tokenization, Remove Punctuation, Stopword Removal, and Stemming. From the 13 experiments conducted in this study consist of 2000 hadiths, it was found that the best performance for multi-label classification of Hadith data produced by the combination of the proposed rule-based feature extraction, Word2vec feature weighted method, and without using Stemming and Stopword Removal in the preprocessing phase. The Hamming Loss value obtained from this combination was 0.0623. The results show that our rule-based feature extraction method better than baseline method.
Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting

Searchable public key encryption supporting conjunctive keywords search (SPE-CKS) enables data users to retrieve the encrypted data of interest from an untrusted server. Based on SPE-CKS, one can realize a multi-keywords search over the encrypted e-mails. In this paper, we propose an efficient SPE-CKS scheme by utilizing a keyword conversion method and the bilinear map technique. Our scheme is proven to be secure against chosen keyword attack under a standard security definition and can also withstand the keywords guessing attack. Furthermore, we design an experiment over a real world e-mail dataset to illustrate that our scheme has a better performance on time and space complexities than the previous schemes. A detailed analysis shows that our scheme is very practical for the encrypted e-mail system in the mobile cloud environment.
MobileTouchDB: Mobile Touch Character Database in the Wild and Biometric Benchmark

In this paper, we introduce a new database of mobile touch on-line data named MobileTouchDB. The database contains more than 64K on-line character samples performed by 217 users, using 94 different smartphone models, with an average of 314 samples per user. In each acquisition session, users had to draw all numbers (from 0 to 9), upper-and lower-case letters (54), different symbols (8), and passwords composed of 4 numbers (6). Regarding the acquisition protocol, MobileTouchDB comprises a maximum of 6 captured sessions per subject with a time gap between them of at least 2 days. This database studies an unsupervised mobile scenario with no restrictions in terms of position, posture, and devices. Users downloaded and used the acquisition app on their own devices freely. In addition, we also report a benchmark evaluation of biometric authentication on MobileTouchDB, providing an easily reproducible framework for two different scenarios of biometric user authentication: i) based on one character, and ii) based on character combinations. The database was collected with three main goals in mind: i) analyse the discriminative power of novel human touch interaction dynamics, ii) enhance traditional password authentication systems through the incorporation of touch biometric information as a second level of user authentication, and iii) analyse the way we interact with mobile devices on a daily basis in order to enhance continuous authentication systems. MobileTouchDB is publicly available in GitHub.
Character Eyes: Seeing Language through Character-Level Taggers

Character-level models have been used extensively in recent years in NLP tasks as both supplements and replacements for closedvocabulary token-level word representations.In one popular architecture, character-level LSTMs are used to feed token representations into a sequence tagger predicting token-level annotations such as part-of-speech (POS) tags.In this work, we examine the behavior of POS taggers across languages from the perspective of individual hidden units within the character LSTM.We aggregate the behavior of these units into language-level metrics which quantify the challenges that taggers face on languages with different morphological properties, and identify links between synthesis and affixation preference and emergent behavior of the hidden tagger layer.In a comparative experiment, we show how modifying the balance between forward and backward hidden units affects model arrangement and performance in these types of languages.
Data-Driven Recognition and Extraction of PDF Document Elements

In the age of digitalization, the collection and analysis of large amounts of data is becoming increasingly important for enterprises to improve their businesses and processes, such as the introduction of new services or the realization of resource-efficient production. Enterprises concentrate strongly on the integration, analysis and processing of their data. Unfortunately, the majority of data analysis focuses on structured and semi-structured data, although unstructured data such as text documents or images account for the largest share of all available enterprise data. One reason for this is that most of this data is not machine-readable and requires dedicated analysis methods, such as natural language processing for analyzing textual documents or object recognition for recognizing objects in images. Especially in the latter case, the analysis methods depend strongly on the application. However, there are also data formats, such as PDF documents, which are not machine-readable and consist of many different document elements such as tables, figures or text sections. Although the analysis of PDF documents is a major challenge, they are used in all enterprises and contain various information that may contribute to analysis use cases. In order to enable their efficient retrievability and analysis, it is necessary to identify the different types of document elements so that we are able to process them with tailor-made approaches. In this paper, we propose a system that forms the basis for structuring unstructured PDF documents, so that the identified document elements can subsequently be retrieved and analyzed with tailor-made approaches. Due to the high diversity of possible document elements and analysis methods, this paper focuses on the automatic identification and extraction of data visualizations, algorithms, other diagram-like objects and tables from a mixed document body. For that, we present two different approaches. The first approach uses methods from the area of deep learning and rule-based image processing whereas the second approach is purely based on deep learning. To train our neural networks, we manually annotated a large corpus of PDF documents with our own annotation tool, of which both are being published together with this paper. The results of our extraction pipeline show that we are able to automatically extract graphical items with a precision of 0.73 and a recall of 0.8. For tables, we reach a precision of 0.78 and a recall of 0.94.
Multi-Element Long Distance Dependencies: Using SPk Languages to Explore the Characteristics of Long-Distance Dependencies

In order to successfully model Long Distance Dependencies (LDDs) it is necessary to understand the full-range of the characteristics of the LDDs exhibited in a target dataset.In this paper, we use Strictly k-Piecewise languages to generate datasets with various properties.We then compute the characteristics of the LDDs in these datasets using mutual information and analyze the impact of factors such as (i) k, (ii) length of LDDs, (iii) vocabulary size, (iv) forbidden subsequences, and (v) dataset size.This analysis reveal that the number of interacting elements in a dependency is an important characteristic of LDDs.This leads us to the challenge of modelling multielement long-distance dependencies.Our results suggest that attention mechanisms in neural networks may aide in modeling datasets with multi-element long-distance dependencies.However, we conclude that there is a need to develop more efficient attention mechanisms to address this issue.
Data mining Mandarin tone contour shapes

In spontaneous speech, Mandarin tones that belong to the same tone category may exhibit many different contour shapes.We explore the use of data mining and NLP techniques for understanding the variability of tones in a large corpus of Mandarin newscast speech.First, we adapt a graph-based approach to characterize the clusters (fuzzy types) of tone contour shapes observed in each tone n-gram category.Second, we show correlations between these realized contour shape types and a bag of automatically extracted linguistic features.We discuss the implications of the current study within the context of phonological and information theory.
Gradient-Aware Model-Based Policy Search

Traditional model-based reinforcement learning approaches learn a model of the environment dynamics without explicitly considering how it will be used by the agent. In the presence of misspecified model classes, this can lead to poor estimates, as some relevant available information is ignored. In this paper, we introduce a novel model-based policy search approach that exploits the knowledge of the current agent policy to learn an approximate transition model, focusing on the portions of the environment that are most relevant for policy improvement. We leverage a weighting scheme, derived from the minimization of the error on the model-based policy gradient estimator, in order to define a suitable objective function that is optimized for learning the approximate transition model. Then, we integrate this procedure into a batch policy improvement algorithm, named Gradient-Aware Model-based Policy Search (GAMPS), which iteratively learns a transition model and uses it, together with the collected trajectories, to compute the new policy parameters. Finally, we empirically validate GAMPS on benchmark domains analyzing and discussing its properties.
Scientific and Pedagogical Bases of Civic and Patriotic Upbringing of Young Students

Abstract The experience accumulated by pedagogical theory and practice of patriotic and civic upbringing of children and youth is currently undergoing a profound positive conceptualization and evaluation. The relevance of the problem under study is associated with the circumstance that under certain historical conditions there takes place purification, updating and manifestation of Russian values in the interests of the entire Homeland, both “smaller” and “greater” Motherland. The objective of this research is to find out scientific and pedagogical bases for the essence and content of civic and patriotic upbringing of young students at the contemporary stage. The leading principle of studying this problem is the principle of particular historical approach which implies matching the content, forms and methods of patriotic and civic upbringing in a certain historical situation. In this paper, the authors discuss the problems of patriotism and civic spirit in various historical, social and political, and economic conditions depending on their views influenced by the knowledge acquired and the personal civic stance developed under the effect of various factors, as well as attitude to their Homeland.
Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo

This paper describes the models used by our team in SMM4H 2019 shared task (Weissenbacher et al., 2019).We submitted results for subtasks 1 and 2. For task 1 which aims to detect tweets with Adverse Drug Reaction (ADR) mentions we used ELMo embeddings which is a deep contextualized word representation able to capture both syntactic and semantic characteristics.For task 2, which focuses on extraction of ADR mentions, first the same architecture as task 1 was used to identify whether or not a tweet contains ADR.Then, for tweets positively classified as mentioning ADR, the relevant text span was identified by similarity matching with 3 different lexicon sets.
Neural Network to Identify Personal Health Experience Mention in Tweets Using BioBERT Embeddings

This paper describes the system developed by team ASU-NLP for the Social Media Mining for Health Applications(SMM4H) shared task 4. We extract feature embeddings from the BioBERT (Lee et al., 2019) model which has been fine-tuned on the training dataset and use that as inputs to a dense fully connected neural network.We achieve above average scores among the participant systems with the overall F1-score, accuracy, precision, recall as 0.8036, 0.
DeepAttr: Inferring Demographic Attributes via Social Network Embedding

Online Social Networks (OSNs) (e.g., Facebook, Twitter, and Tecent QQ) are popular platforms for people to share information online. The providers of OSNs typically leverage demographic attributes of users in OSNs to perform business behaviors like personalized commodity recommendation, advertisement delivery, etc. Thus, understanding demographic attributes of users in OSNs is of great importance. However, some users refuse to reveal their demographic information due to privacy concerns. Therefore, inferring demographic attributes of users in OSNs with public information is an attractive topic for researchers. Most existing methods mainly focus on individual attribute inference without taking the relationship among attributes into account, which results in low precisions. In this paper, we propose a novel approach, called DeepAttr, to infer users' multiple attributes simultaneously. DeepAttr leverages existing network embedding algorithm to learn the social embedding for each user. Meanwhile, it encodes multiple attributes into structured attribute vectors which are treated as class labels. The core component of DeepAttr is a multi-layer fully connected deep neural network which captures complex nonlinear mapping between the users' social embeddings and the structured attribute vectors. Extensive experiments on a real-world dataset demonstrate that DeepAttr outperforms existing models in both single-attribute inference and multiple-attribute inference as to macro-Precision, macro-Recall, and macro-F1.
Grouping of COST 2100 Indoor Multipaths Using Simultaneous Clustering and Model Selection

Simultaneous Clustering and Model Selection (SCAMS) is introduced to cluster multipaths from COST 2100 channel model (C2CM).SCAMS determines not only the number of clusters but also the membership of the clusters.The study is the first to report clustering of multipaths that consider simultaneously the number of clusters and the membership of the clusters.Cluster identification and cardinality classification are dependent on the values of and , the parameters that weigh the penalty terms to avoid the trivial solution (all 1 matrix) of the affinity matrix.The clustered multipaths are compared with the reference multipaths that can be found in IEEE DataPort.The accuracy of the clustering approach is examined using the Jaccard index ( ).The proposed clustering approach can achieve higher accuracy compared to popular multipath clustering approaches.
Key Schedule Algorithm using 3-Dimensional Hybrid Cubes for Block Cipher

A key scheduling algorithm is the mechanism that generates and schedules all session-keys for the encryption and decryption process.The key space of conventional key schedule algorithm using the 2D hybrid cubes is not enough to resist attacks and could easily be exploited.In this regard, this research proposed a new Key Schedule Algorithm based on coordinate geometry of a Hybrid Cube (KSAHC) using Triangular Coordinate Extraction (TCE) technique and 3-Dimensional (3D) rotation of Hybrid Cube surface (HCs) for the block cipher to achieve large key space that are more applicable to resist any attack on the secret key.The strength of the keys and ciphertext are tested using the Hybrid Cube Encryption Algorithm (HiSea) based on Brute Force, entropy, correlation assessment, avalanche effect and NIST randomness test suit which proves the proposed algorithm is suitable for the block cipher.The results show that the proposed KSAHC algorithm has performed better than existing algorithms and we remark that our proposed model may find potential applications in information security systems.
The Research of Random Sample Consensus Matching Algorithm in PCA-SIFT Stereo Matching Method

The stereo matching is the most important part of stereo vision. SIFT algorithm is obviously superior to other character descriptions in the feature point scale, rotation and invariant illumination, so it is extended in this paper. Because of traditional Scale-invariant feature transform (SIFT) method's high dimensions, it is very complex and need much time to calculate. In order to optimize the algorithm, Principal Component Analysis of Traditional Scale-invariant feature transform (PCA-SIFT)+Random Sample Consensus (RANSAC) matching algorithm is proposed. PCA-SIFT uses PCA which can reduce dimension to SIFT in image matching. Although PCA-SIFT can effectively solve the problem of time-consuming, the accuracy of matching is not high. Using PCA-SIFT extract feature points in image and matching them. Firstly, we build Gaussian pyramid, detect extreme value points, and select key points with strong robustness. Using gradient direction distribution features of neighborhood pixels of the key point calculate the direction of key point can be obtained by calculating gradient direction distribution features of neighborhood pixels of the key point. Then using PCA algorithm generates 32d feature descriptors. According to the ratio of nearest neighbor algorithm for image matching, it gets matching points and sort in the size of the ratio of nearest neighbor. RANSAC algorithm is used to carry on the transformation matrix parameters fitting, and the resulting matrix model is used to remove the error matching points. To verify the effectiveness of algorithm, SIFT, PCA-SIFT, PCA-SIFT + RANSAC are compared by using many images and data. The experimental results show that the PCA-SIFT + RANSAC algorithm is more stable, more accurate and more rapid.
Model Prediksi Kebutuhan Air Bersih Berdasarkan Jumlah Penduduk di Kawasan Perkotaan Sentul City

This research aims to create a model for predicting clean water need in the urban area Sentul City currently and next 30 (thirty years). To create the model, it is required data which are useful for forecasting the inhabitant number year to year. There are some general-used models for forecasting the inhabitant number, including to arithmatic, exponential, and least square method. The inhabitant number forecast in this research used an exponential method, as recommended in the Guidance of River Area Water Resource by Directorate General of Water Resource in 2001. The researcher used the software Stella 9.0.1 for simplifying the modeling process, projecting the inhabitant number, predicting the clean water need, and figuring the graphics. Besides this software, Excel programme was also used. According to data analysis result, the equation models to predict water need (m3/year) in Sentul City are stated as follows; for rural area inhabitants: YDn = 1 927 309 (1 + 0.0303)n; for urban area inhabitants: YKn = 1 808 940 + 217 248*n; and for total inhabitants: YSCn = YDn + YKn.Keywords: inhabitant number, geomeric method, clean water need prediction
Pathways to European Policy and Regulation in the Crypto-economy

The EU has yet to develop definitive policies for the crypto-economy, and this article argues that policy development should follow a “systemic” and not “sectoral” approach. This is because the crypto-economy is not merely a financialised space and new productive activity is occurring that would benefit from more holistic policy development than regulation focused on securities and investments. This article proposes that the EU should develop policy for the crypto-economy based more broadly on innovation policy and perhaps feed into the Single Market project.
Finding Hierarchical Structure in Neural Stacks Using Unsupervised Parsing

Neural network architectures have been augmented with differentiable stacks in order to introduce a bias toward learning hierarchysensitive regularities.It has, however, proven difficult to assess the degree to which such a bias is effective, as the operation of the differentiable stack is not always interpretable.In this paper, we attempt to detect the presence of latent representations of hierarchical structure through an exploration of the unsupervised learning of constituency structure.Using a technique due to Shen et al. (2018a,b), we extract syntactic trees from the pushing behavior of stack RNNs trained on language modeling and classification objectives.We find that our models produce parses that reflect natural language syntactic constituencies, demonstrating that stack RNNs do indeed infer linguistically relevant hierarchical structure.