Reviewerly/exHarmony huggingface dataset:
Converting an Indonesian Constituency Treebank to the Penn Treebank Format

A constituency treebank is a key component for deep syntactic parsing of natural language sentences. For Indonesian, this task is unfortunately hindered by the fact that the only one constituency treebank publicly available is rather small with just over 1000 sentences, and not only that, it employs a format incompatible with readily available constituency treebank processing tools. In this work, we present a conversion of the existing Indonesian constituency treebank to the widely accepted Penn Treebank format. Specifically, the conversion adjusts the bracketing format for compound words as well as the POS tagset according to the Penn Treebank format. In addition, we revised the word segmentation and POS tagging of a number of tokens. Finally, we performed an evaluation on the treebank quality by employing the Shift-Reduce parser from Stanford CoreNLP to create a parser model. A 10-fold cross-validated experiment on the parser model yields an F1-score of 70.90%.
Detection of Arabic Non-Referential Pronouns using Self-Training Method and Similarity Measures

The classification of pronouns as referential or non-referential is necessary for many NLP tasks. However, there are few works interested in this problem in the Arabic language. In this paper, we present a semi-supervised machine learning approach based on a Self-training SVM method for the identification of non-referential pronouns in the Arabic texts. A set of patterns-based and linguistic-based information is used as classification features in our machine learning system. The proposed Self-Training SVM algorithm includes three steps: training, prediction and selection step. It trains SVM classifier on a small set of labeled data, predicts labels of unlabeled data, selects the most accurate and the most informative newly labeled data and adds them to the training dataset. The selection step uses some geometric measures and analyses their relevance on the classification performance. The evaluation of our approach on the training and test data presents good results that can reach up to 96.85% of precision.
Workshop on Multimedia Based Learning for Low Level-Education Teachers

Abstract Education is one aspect that cannot be separated from human life. Even today education can be said to be the primary human need. Formal education in Indonesia starts from the childhood level or is often called kindergarten. To educate children, more interesting methods and media are needed to foster early talent and interest in students. In this case, kindergarten teachers are expected to maximize their communication skills and creativity in preparing to teach. Technology in our life today, many aspects is expected to be able to keep up with technological advances that have increased rapidly from year to year, including kindergarten teachers. Of the many kindergarten teachers present, there are still those who have not mastered the computer skill. However, with the holding of this training, kindergarten teachers slowly became masters of computers skill so that they were more creative and innovative in utilizing ICTs to create learning media especially those on multimedia based.
Visual Feature Space Analyses of Face Morphing Detectors

Face morphing attacks are a serious threat to biometric face recognition systems. If a morphed face image containing biometric characteristics of at least two different subjects is embedded into a personal document, this document can be used for verification by different subjects. Due to the fast dissemination of automated face recognition systems, especially for border control at airports, detection of morphed face images in documents is gaining relevance. Recently proposed morphing detection approaches demonstrate low error rates with datasets used for development and evaluation, but often fail for new datasets. The detectors are often trained and evaluated on images in better quality than images in electronic Machine-Readable Travel Documents (eMRTD). Since these morphing detectors are clearly suitable for fraud detection before the image is compressed to fit into a eMRTD, we study their performance and potential on images compliant with eMRTDs. A visual analysis of feature spaces provide us with a better understanding of a presumable generalization ability of morphing detectors. The questions we endeavour to answer are how the distributions of high resolution face images and face images in eMRTDs differ in a detector's feature space and whether detecting morphed face images in eMRTDs should be considered as a different task than detecting them before they are compressed to fit into an eMRTD.
A Novel Approach Using MPI to Reduce Training Time for Capsule Networks

The deep learning environment is currently developing at an exciting pace as new techniques and new results are consistently being released through new research. One such new development is the capsule network which is a novel new approach to deep learning wherein the standard neurons of a neural network are replaced with capsules. Capsules differ from neurons in that they receive vectors as input and create vectors at their output. Capsule networks additionally are good at classifying objects irrelative of the orientation of the object in a given image. Capsule networks can also train on less data than traditional convolutional neural networks (CNNs). In this paper we present an experiment wherein we aim to reduce the training time of capsule networks, while still retaining acceptable accuracy. We accomplish this by dividing the training set among multiple nodes in a cluster environment and using MPI to allow nodes to communicate their classification results to vote on the correct classification for each image.
Deployment of a cloud pipeline for real‐time visual inspection using fast streaming high‐definition images

Summary We investigate the challenges of building an end‐to‐end cloud pipeline for real‐time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real‐time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high‐definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end‐to‐end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.
A Study on Different Multi-Document Summarization Techniques

In the present situation, the rate of increment of information is growing exponentially in the World Wide Web. As such, bifurcating genuine and noteworthy data from such a colossal size of information has turned into a dull issue. As of late, text summarization is viewed as one of the answers for ousting material data from multiple documents. In recent time, the method which proved to be most accurate for text summarization is Natural Language Processing. Content summary is a method of making an outline by diminishing the range of interesting report and relating basic information of extraordinary record. There is a huge rise in data augmentation on Internet. It increases the need of a highly optimise summary in less time. The system of efficient multi document summary generation can be a best achieving this goal. In this paper a survey on various approaches of summarization presented by the researcher is been discussed and studied.
Research on Fault Line Selection Based on Information Fusion For Small Current Grounding System

Aiming at the problem of low accuracy of the single line selection method for small-current ground faults, this paper proposes a line selection method based on transient and steady-state information fusion. First, the zero-sequence signal is processed by empirical mode decomposition and Fast Fourier Transform to extract the three fault feature quantities of the IMF energy, the fifth harmonic component, and the active power component; then the concept of the fault measure is introduced, and the fault measure function is used to calculate Three fault measures of feature quantities are used, and fault measures are used as feature input vectors for information fusion; finally, least squares support vector machine (LSSVM) classifiers are used as information fusion line selection algorithms to classify the model accuracy and generalization. On the problem of the parameter selection that has a greater impact on the capability, the particle swarm optimization algorithm (PSO) is used to optimize the parameters, and the PSO-LSSVM fault line selection model is established using the optimized parameters. By modeling the small current grounding system in PSCAD and using MATLAB to process data, the results show that the model improves the accuracy of fault line selection.
Social Recommendation Combining Trust Relationship and Distance Metric Factorization

The recommender system predicts user preferences by mining user historical behavior data. This paper proposes a social recommendation combining trust relationship and distance metric factorization. On the one hand, the recommender system has a cold start problem, which can be effectively alleviated by adding social relations. Simultaneously, to improve the problem of sparse trust matrix, we use the Jaccard similarity coefficient and the Dijkstra algorithm to reconstruct the trust matrix and explore the potential user trust relationship. On the other hand, the traditional matrix factorization algorithm is modeled by the user item potential factor dot product, however, it does not satisfy the triangle inequality property and affects the final recommender effect. The primary motivator behind our approach is to combine the best of both worlds, mitigate the inherent weaknesses of each paradigm. Combining the advantages of the two ideas, it has been demonstrated that our algorithm can enhance recommender performance and improve cold start in recommender systems.
Global Optimization for Resource Allocation in a Networked-Surveillance Radar

In this paper, Bayesian optimization is compared to a genetic algorithm as applied to a resource allocation problem in a networked surveillance radar setting. In the chosen setting, the requirement is to optimize the conflicting objectives of both improved detection and improved tracking. As such, the objective function is a weighted sum of two parts. The first part represents the number of undetected targets and the second part represents the sum of tracked target covariance matrix determinants. The Bayesian optimization algorithm outperforms the genetic algorithm, by producing better solutions with the same number of objective function evaluations. Additionally, for a specific choice of objective function weights, better tracking performance and similar detection performance is achieved compared to the case with a uniform scan time over all radar sectors.
Construction of a polynomial invariant annihilation attack of degree 7 for T-310

Cryptographic attacks are typically constructed by black-box methods and combinations of simpler properties, for example in [Generalised] Linear Cryptanalysis. In this article, we work with a more recent white-box algebraic-constructive methodology. Polynomial invariant attacks on a block cipher are constructed explicitly through the study of the space of Boolean polynomials which does not have a unique factorisation and solving the so-called Fundamental Equation (FE). Some recent invariant attacks are quite symmetric and exhibit some sort of clear structure, or work only when the Boolean function is degenerate. As a proof of concept, we construct an attack where a highly irregular product of seven polynomials is an invariant for any number of rounds for T-310 under certain conditions on the long term key and for any key and any IV. A key feature of our attack is that it works for any Boolean function which satisfies a specific annihilation property. We evaluate very precisely the probability that our attack works when the Boolean function is chosen uniformly at random.
Embedding with Pixel Repetition for Reversible Data Hiding

This paper presents a new Pixel-Repetition (PR) based embedding scheme to better utilize the redundancy of image data for a high-capacity Reversible Data Hiding (RDH) scheme. Each pixel of an input image is repeated to create an image-block of size 2×2 in the up-sampled image. The proposed embedding scheme maps the up-sampled pixels using a set of unique conditions to embed 4-bit of data in each 2×2 block. The mapping conditions also ensure the regeneration of the first-pixel of a block from the other pixels in the block, which allows embedding of additional data-bits in every first-pixels of a block. Unlike the existing schemes, the proposed scheme with the modified mapping conditions and embedding in the first pixel in each block ensures better utilization of the redundancy. Thus, the proposed scheme demonstrates a significant improvement in the embedding rate-distortion performance over the relevant RDH schemes.
Performance Enhancement For Gender Recognition Using Trainable Bank of Gabor Filters and NCA

Classifying male and female images based on facial information is a significant application for security issues destined for some innovative evolving fields, such as retail advertising and marketing. In this research work, an image processing technique is used to segment facial images of GENDER-FERET dataset, and choose multi interest prototypes automatically using trainable bank of Gabor filters. Spatial pyramid algorithm with three levels is used to extract the features for each prototype. Enhancement is accomplished in two main steps. First, the descriptors are ranked and most significant ones are identified using Neighborhood Component Analysis feature selection algorithm. Second, Cubic Support Vector Machine (Cubic SVM) classifier is applied. Several classification performance metrics are measured such as accuracy, testing and training time that are recorded better results. A classification rate of around 95.8% was achieved by using the proposed model compared to 93.7% for the related works.
A Method for Analyzing the Activity of Cold Wallets and Identifying Abandoned Cryptocurrency Wallets

The article presents a method for analyzing the activity of cold and abandoned wallets on the example of blockchain Litecoin. The implementation of the software for searching correlations in the behavior of cold wallets using the API resources for interaction with blockchain Litecoin in Python 3.7 is described. Also, the statistics of the approximate ratio of conditionally thrown wallets of cryptocurrency in blockchain at the moment is presented and the analysis of the received data is given.
A Mixed Recommended Algorithm Combining User Properties and Item Features

Abstract In response to the problems of traditional recommended algorithm, such as data sparsity, changing of users’ interest, etc., this thesis proposes a mixed recommended algorithm blending User Properties and item features. First, propose a recommended algorithm based on User Properties after leading in users’ interaction factor and property weight in traditional similarity computation. Second, decompose the user-item matrix, and build the preference matrix and rating frequency matrix of user-item features. And then it proposes a recommended algorithm based on item features through leading in interest’s stable phase and improve the time attenuation function. Lastly, it generates a new mixed recommended algorithm after linearly combining these two algorithms. The Movielens dataset is used in this experiment, which turns out that the mean absolute error of the recommended algorithm proposed in this thesis significantly decreases comparing to traditional recommended algorithm.
Sequence recommendation based on deep learning

Abstract In order to solve the cold start problem of traditional recommendation algorithm, the sequence change of user interaction information and deep learning are gradually considered as a key feature of commodity recommendation system. However, most of the existing recommendation methods based on the sequence changes assume that all the interaction information of users is equally important for recommendation, which is not always applicable in real scenarios, because the interaction process of user items is full of randomness and contingency. In this article, we study how to reduce the randomness and contingency between session sequences, make full use of the association between session sequences in the interaction process of users by Deep Learning. In order to better simulate the change of session sequence in the real scene, we adopt sequence sampling methods to transform the single classification problem into sequence modeling problem. And attention mechanism is added to reduce the interference of the recommendation model in the sequence due to the contingency and randomness of the user in the shopping. Finally, through the verification of real data, the MRR@20 index of the improved model is 20% higher than the benchmark level.
Hand gesture recognition by histogram based kernel using density measure

This paper presents the recognition of hand gesture in the research field of machine vision. Vision based hand gesture recognition has the capacity to develop a tool for Human Machine Interaction (HCI). The automated threshold methods were used as pre-processing steps for extraction of feature vector using chain code histogram (CCH). Then, construct the kernel based on histogram of chain code using density measure to obtain discriminative feature descriptor for efficient recognition of hand gesture using Support Vector Machine (SVM). Cluster based threshold techniques involves Otsu thtresholding (OT), Ridler and Calvard thresholding (RCT), and Kittler and Illingworth thresholding (KIT) are used to segment the region of interest for feature extraction. In this paper, CCH based on various segmentation methods were compared to measure the recognition rate by SVM classifier. The proposed RCT-CCH based kernel method increase the recognition rate of hand posture by 90%, compared with cluster based thresholds.
Contextual Information in Social How-To Questions That Initiate Documentation

This study introduces social question-and-answer (SQA) documentation to technical and professional communication scholarship. It conceptualizes SQA as interactive, user-generated documentation and describes contextual information types within social how-to questions that initiate documentation. It also explores whether contextual information associates with answers that complete the interactive documentation. Results reliably describe 15 information types based on content analysis of 3,529 contextual information types from 500 questions. Exploratory statistical analysis suggests that askers may increase answerability by including less speculative thought, more error messages, and less general situation information. To facilitate complete SQA documentation, the study calls for additional research into question content and answerability.
On Visual-Textual-Knowledge Entity Linking

A picture is worth a thousand words, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed, reveal different and complementary information that, if combined, result in more information than the sum of that contained in the single media. The combination of visual and textual information can be obtained by linking the entities mentioned in the text with those shown in the pictures. To further integrate this with agent background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. This complex task is called Visual-Textual-Knowledge Entity Linking (VTKEL). In this paper, we present a purely unsupervised algorithm for the solution of the VTKEL tasks. The evaluation on the VTKEL dataset -a dataset composed of about 30K pictures, annotated with visual and textual entities, and linked to the YAGO ontology- shows promising results.
Reversible Data Hiding in PDF Document Exploiting Prefix Zeros in Glyph Coordinates

In the contemporary world of information technology, PDF (Portable Document Format) has become the de facto document standard which allows users to exchange and view electronic documents across various platforms. PDF is the most widely exchanged document format since Internet gained popularity. Although PDF has a good authenticity system by making use of digital signatures, the file format is still susceptible to copyright infringement as there are many libraries available on the Internet to bypass the digital signature of a PDF. Therefore, claiming ownership for PDF has become a paramount issue that needs to be addressed. This paper proposes the idea of hiding data in the glyph positioning coordinate value. To suppress bit stream size increment, the reverse zero-run length coding technique is adopted. Experiments are conducted to verify the basic performance of the proposed data hiding method. In the best case scenario, 0.62 bits of data can be embedded into each Byte of the PDF file. The injected leading zeros can be removed to restore the original PDF file.
Relationship between rock physical properties and spectral mineralogy applied to exploration for an unconformity-related uranium deposit (Saskatchewan, Canada)

Infrared (IR) spectroscopy has been used to characterize clay and clay-sized minerals present in drill cores that are associated with unconformity-related uranium deposits. Physical properties have been measured on samples to gain empirical data about the rock types and associated relationships with geophysical survey data. These data can be used to build three-dimensional geological models and constrain geophysical inversions. The objective of this study is to verify whether a relationship exists between rock physical properties and IR spectral mineralogy. Physical properties were measured on 427 core samples collected from the Martin Lake project, which is located in the southeastern Athabasca Basin (Saskatchewan, Canada). Results indicate that resistivity, density, and porosity are correlated to each other, especially within basement units. A comparison of their distribution with the IR spectral mineralogy demonstrates a relationship for each altered and unaltered samples. The samples with low resistivity and density, and high porosity are characterized by the presence of a di-trioctahedral (Al–Mg) chlorite (sudoite) due to the hydrothermal alteration processes. The unaltered samples with higher resistivity and density, and low porosity contain a tri-octahedral (Fe–Mg) chlorite as a result of metamorphic processes. Eleven mineralogical classes can be established based on IR spectroscopy. A percentile-based approach has been proposed and tested to define physical property ranges for each of the classes to predict resistivity and density values downhole.