Reviewerly/exHarmony huggingface dataset:
A Framework for English and Malay Cross-lingual Document Alignment Method

Issues of information divide in multilingual information retrieval are usually being solved by translating users' queries to a language that the users understand.But dictionaries or other translation knowledge in some of the Asian languages are scarce.The objective of this study was to automatically align the English and Malay news documents to become a comparable corpus, which could contribute as a translation resource to improve the query translation in cross-lingual information retrieval.This study proposes a direct alignment framework by utilizing the textual features similarity of each document itself while attempting a novel approach of using the similarity of the documents sentiment in improving the effectiveness of the alignment method.The proposed sentiment-based approach outperformed existing alignment methods and improved the effectiveness in differentiating the related and unrelated documents.These aligned comparable documents can further be utilised in translation research for the English and Malay cross-lingual information retrieval tasks.
Exploring Multi-Objective Optimization for Multi-Label Classifier Ensembles

Multi-label classification deals with the task of predicting multiple class labels for a given sample. Several performance metrics are designed in the literature to measure the quality of any multi-label classification technique. In general existing multi-label classification approaches focus on optimizing only a single performance measure. The current work builds on the hypothesis that a weighted ensemble of multiple multi-label classifiers will lead to obtain improved results. The appropriate weight combinations for combining the outputs of multiple classifiers can be selected after simultaneously optimizing different multi-label classification metrics like micro F1, hamming loss, 0/1 loss, accuracy, etc. The problem is posed as a multi-objective optimization problem where a set of 13 conflicting objective functions are simultaneously optimized using the search capability of a multi-objective genetic algorithm based technique, namely NSGA-II. The weights of votes for a classifier for different class labels vary over a range quantifying the degrees of confidence of a classifier for different class labels. Several base classifiers are utilized for solving the problem of multi-objective multi-label classification. The effectiveness of the proposed multi-objective based multi-label classifier ensemble approach is shown for 10 data sets of varying complexities, and comparisons are reported for several existing techniques. Obtained experimental results clearly illustrate the efficacy of the proposed technique.
Based on Rough Set and RBF Neural Network Power Grid Fault Diagnosis

Abstract In order to improve the accuracy of troubleshooting results and save diagnostic time, rough set and RBF neural network are used to diagnosed fault of power grid.Rough sample reduction program is used to reduce the fault samples, and the reduction decision table is obtained as the input of the RBF neural network, get the training results.The fault samples without rough set reduction are input into RBF for training, and the two training results are compared. It is found that the samples using the rough set reduction have the same diagnostic ability as the initial decision table.Obviously, the rough decision set initial decision table can greatly reduce the size of the training sample and save diagnosis time.
A Method of Stable Extended Kalman filter

Abstract The extend kalman filter provides an stability solution to the approximate nonlinear-Gaussian filter problem. Due to the limitation of the word length of the processor, the rounding errors is easy to occur in the calculation, which causes the covariance matrix to lose its positive definite and convergence. In order to solve this problem, The proposed stable extended kalman filter (SEKF) method is applied to the problem of non-positive and divergence in recursive filter. This method introduces the matrix decomposition and constructs a covariance square root adaptive filter which is used to solve the numerical stability problem of the extend kalman filter. Experimental results show that SEKF can effectively guarantee symmetric positive definite in recursive calculation, and perform remarkably better than EKF algorithm.
Heterogeneous Graph Based Similarity Measure for Categorical Data Unsupervised Learning

Different from numerical attributes, measuring the similarity between categorical attributes is more complex due to their non-inherently ordered characteristic, especially in an unsupervised scheme. This work, therefore, presents a new method, Heterogeneous Graph-based Similarity measure (HGS), to measure the similarity between categorical data for unsupervised learning. In order to capture the possible complex relationships hidden among attributes, a heterogeneous weighted graph is creatively constructed by extracting the information from categorical data. Both objects and attribute values are represented as nodes and their occurrence and co-occurrence relationships are shown as edges. Based on a derived node-pair graph, three rules are used to iteratively update the similarity scores between object pairs and attribute-value pairs until the scores converge. We also analyze its complexities and validate the metric properties and convergence. In experiment validation, five state-of-the-art measures are compared with HGS based on 20 UCI datasets and 6 high-dimensional datasets in the medical domain in both k-modes and spectral clustering and similarity search experiments. The results show although no measure can outperform all other measures on all datasets, HGS can perform better in both clustering and similarity search tasks on the whole. Finally, six studies further discuss the convergence, time cost, and parameter sensitivity of the HGS, explore its application to imbalanced class distribution, and compare it with its variants by different initialization and graph construction.
[DC] Dimensionality of Augmented Reality Spatial Interfaces

The next wave of computing is visible on the horizon, heralding the widespread adoption of augmented reality spatial information manipulation on wearable and environment integrated form factors such as head-worn displays. This application for the doctoral consortium at IEEE VR 2019 describes the research conducted and planned in the context of a PhD thesis, which aims at determining a multidimensional model of user performance in spatial user interaction in augmented reality for training.
Science and technology for the Alert Group Disaster “Tumik Singgalang” East Malalak Nagari, Malalak District, Agam Regency, West Sumatra

Abstract In the implementation of devotion in Jorong Saskand obtained a commitment on the part of districts, villages and ellipse as a service area. The commitment is obtained based on some identification problems faced by people who are important to be solved collectively. Society needs an explanation in terms of the academic science related to the potential danger to human settlements that are prone to landslides. The survey was conducted using a test to determine the type of layer geoelectric lithology / rock. Based on the results of the interpretation of electrical resistivity tomography. Wenner configuration showed layers of clay and pumice tuff at this track. Scientific analysis is translated in simple language to convey to the public. The solution to the problems facing society is the improvement of information systems at the community level while considering the potential and local wisdom. Public understanding of scientific terms that are directly involved in the technical processes related to geological conditions in disaster areas and this becomes the basis for an early warning system to collect and effective mitigation mechanisms to do. Improvement of early warning information system for landslides that involve elements that exist in society. To strengthen the information system related to early warning, then the required agreement among participants so that generated Mechanism of Landslide Mitigation Participative Approach. This method is outlined in the form of Standard Operating Procedures (SOP) early warning system for landslides Jorong Saskand, East Malalak, Agam Regency.
On the Impact of the Objective Function on Imbalanced Data using Cartesian Genetic Programming Neuroevolutionary Approaches

The training of machine learning models for imbalanced data classification is a challenging task. Several metrics have been used to assess the performance of the classifiers. Each metric is appropriate for a class of problems, and some users often do not have a clear notion of which metric to use. In such cases, it is desirable that the chosen objective function provides good overall performance for most of the existing metrics. Here, three neuroevolutionary approaches based on Cartesian Genetic Programming are used in order to investigate the impact of optimizing accuracy, G-mean, F β -score, and the area under the Receiver Operating Characteristic (ROC) curve when creating classifiers based on Artificial Neural Networks applied to imbalanced data classification problems. The results suggest that the optimization of G-mean and F β -score generate models that present a superior overall performance in all metrics.
CARS: A Wrappings-Based Test Bed for Self* Cyber-Physical Systems and Their Integration

CARS (Computational Architectures for Reflective Systems) is a test bed for integration of heterogeneous autonomous cyber-physical systems (robotic vehicles). The test bed is organized around four "games" which test individual operation, competition, and both time-asynchronous and time-synchronous collaboration. We describe our design philosophy and then briefly discuss the design of the physical agents and the Wrappings software used to control them, as well as the method used to evaluate performance. Finally, we relate some results of our experiments so far and suggest both topics for future work on the CARS test bed and larger challenges for interwoven systems.
Tendon Vibration Increases Vision-induced Kinesthetic IIIusions in a Virtual Environment

In virtual reality (VR) systems, a user avatar is typically manipulated based on actual body motion in order to present natural immersive sensations resulted from proprioceptors. It is useful to be able to feel a large body motion in a virtual environment while it is actually smaller in the real environment. As a method to achieve this, we have been working on kinesthetic illusions induced by tendon vibration. In this article, we report on a user study focusing on the effects of a combination of visual stimulus and tendon vibration. In the user study, we measured subjects' perceived elbow angles while they observed the corresponding virtual arm bending to 90 degrees, with different rotation amplification ratios between 1.0 and 1.5, with and without tendon vibration on the wrist joint. The results show that the perceived angle is increased by up to 8 degrees when tendon vibration is applied, roughly in proportion to the rotation amplification ratio. Our study suggests that the vision-induced kinesthetic illusion is further increased by tendon vibration, and that our technique can be applied to VR applications to make users feel larger body motion than that in the real environment.
State of University━Employer Interaction Models in Russia

The relevance of the research topic is due to the internal reforms of the Russian education system in accordance with the Bologna Declaration and external changes in the situation on the labor market, since the education at the University For the majority of students should lead to employment in the specialty. ‎The aim of this study is to identify the promising development of Russian universities to improve the ‎quality of education and employment of graduates. And for this purpose, traditional and innovative options for cooperation between universities and employers are considered. Based on the analysis of the data set (the legislation of the Russian Federation in the field of education, the experience of Russian and foreign universities, methodological developments, sociological surveys), proposals are made to improve the structure of the educational process in order to ensure the demand for young professionals (graduates) in the labor market in the future. The study is of practical importance for higher education institutions in the development of methodological programs and strategic planning of educational activities. Further research can be continued by focusing on the chosen educational direction, taking into account the specifics of the future profession, and providing detailed content of the educational process for the selected subject block.
Generating High-Resolution Fashion Model Images Wearing Custom Outfits

Visualizing an outfit is an essential part of shopping for clothes. On fashion e-commerce platforms, only a limited number of outfits are visually represented, as it is impractical to photograph every possible outfit combination, even with a small assortment of garments. In this paper, we broaden the set of articles that can be combined into visualizations by training two Generative Adversarial Network (GAN) architectures on a dataset of outfits, poses, and fashion model images. Our first approach employs vanilla StyleGAN that is trained only on fashion model images. We show that this method can be used to transfer the style and the pose of one randomly generated outfit to another. In order to control the generated outfit, our second approach modifies StyleGAN by adding outfit/pose embedding networks. This enables us to generate realistic, high-resolution images of fashion models wearing a custom outfit under an input body pose.
Potential of machine translation in headline translation: Focusing on Korean-Japanese translation of headlines

We use Google Translate when reading foreign newspapers. Given that it is MT, we expect some degree of awkwardness. However, it still gives us easy and convenient access to the content and information. Today, many Korean media outlets provide articles in foreign languages, by selecting and instantly translating the many articles that are produced every day. The time pressure is extremely high in this kind of translation, and there can be occasional verbatim, hence unnatural, renderings. Yet, overall, the quality of work done by skilled translators tends to be high. If MT continues to advance, however, it seems that MT may be able to replace human translators in the near future, in terms of speed and accuracy. This paper looks into the outcome of MT on news headlines. In this age of overflowing information, headlines can be important indicators for selecting articles. With fewer words, they convey compact information, and it needs to be tested whether MT can serve the same function. While there is existing literature that covers the features of headlines in Korean and Japanese newspapers, and suggests translation strategies assuming human translation, the present study attempts to identify issues in headline translation done by machines. Further, it aims to discuss the potential of MT and implications for post-editing. Headline translation requires the understanding of the entire content of the article to avoid mistranslation, as well as the knowledge of the characteristics of headlines in both working languages. This paper examines headlines from different newspapers and how they are translated, in comparison to their MT versions.
Static cycling postures classification analysis: A data mining approach

The normal, lean forward and standing are three basic postures in the cycling sports studies. Past studies have successfully demonstrated the effects of cycling postures on cycling performance from the ergonomic, medical and mechanical perspectives. However, no works have attempted to identify the cycling postures based on the cyclist’s body joint orientations. Therefore, the goal of this study was to classify the normal, lean forward and standing cycling postures using a data mining approach. In order to examine whether the differences in body joint orientations distinguishes the cycling postures, the dynamics of stationary cycling by six cyclists on normal, lean forward and standing positions were assessed. The data obtained were the coordinates of the body joints: head, shoulder, elbow, wrist, pelvis, knee and ankle along with five joint angles measured from the origin point (centre of the bike front wheel). A total of 19 body joint attributes with a class attribute of 1080 instances were classified on ZeroR, J48, IBk and Naïve Bayes algorithms of Waikato Environment for Knowledge Analysis (WEKA) tool. Findings show that the IBk classifier gives the highest classification accuracy of 87%. The results suggest that although all cyclists perform similar cycling postures, the distinctive individual posture patterns classified on the three cycling postures differ from 41%-100% accuracy. The lower limb joints, in particular, the knee and ankle coordinates of skilled cyclist are more effective to differentiate the normal, lean forward and standing postures.
Analysis harmonic distortion of side outgoing transformer 20 kv/1.2 kv in traction substation of Tanjung Barat KRL Station

Operational Electrical Railway of PT. KAI Commuter Jabodetabek is supplied by overhead contact system 1500 VDC. Traction substation of Tanjung Barat station uses 6 pulse rectifiers to change alternating current (AC) become direct current (DC) with silicon rectifier type. The effect of the rectification process is harmonic current that causes overheating even fire in transformator. This paper aims to resolve the problem on the rectification process by a simulation using an ETAP 12.6.o application by introducing a single tuned passive filter of the 5th order which is customized to the converter circuit load system. The simulation results showed that filter installation caused reduction of average total harmonic distortion (THD) value where 20% load reduced from 20.19% to 1.02%; 40% load reduced from 20.19% to 3.93%, in 60% load reduced from 20.19% to 1.70% and 80% load reduced from 20.20% to 4.91%. It was highlighted that the obtained total harmonic distortion (THD) reduction below 5 % that meet the standards of IEEE 519-1992.
Rapid and Accurate Registration Method between Intraoperative 2D XA and Preoperative 3D CTA Images for Guidance of Percutaneous Coronary Intervention

In this paper, we propose a rapid rigid registration method for the fusion visualization of intraoperative 2D X-ray angiogram (XA) and preoperative 3D computed tomography angiography (CTA) images. First, we perform the cardiac cycle alignment of a patient’s 2D XA and 3D CTA images obtained from a different apparatus. Subsequently, we perform the initial registration through alignment of the registration space and optimal boundary box. Finally, the two images are registered where the distance between two vascular structures is minimized by using the local distance map, selective distance measure, and optimization of transformation function. To improve the accuracy and robustness of the registration process, the normalized importance value based on the anatomical information of the coronary arteries is utilized. The experimental results showed fast, robust, and accurate registration using 10 cases, each of the left coronary artery (LCA) and right coronary artery (RCA). Our method can be used as a computer-aided technology for percutaneous coronary intervention (PCI). Our method can be applied to the study of other types of vessels.
Fraudulent Loan Prediction using Machine Learning Algorithms

There are many frauds that are being done by the people in loans. So, here concern is about fraud loan risk which is common in banks based on various attributes. This work predominantly focused on prediction of fraud loan risk. Fraud loan risk is predicted using classification techniques in machine learning. By detecting the risk of fraud loan will support the bank for early prediction of this risk will be useful. Various data mining classification approaches and machine learning algorithms are applied for prediction of fraud loan in banking dataset. In this study, the dataset of fraud loan is experimented to explore the data mining algorithm to find outperforming algorithm.
Restoration of Polarization Parameters from Data of Vertical Sounding of the Ionosphere by Chirp Signal

The article presents a technique for polarization parameters recovery (ellipticity coefficient and the polarization ellipse inclination angle) for vertical remote sensing with a continuous chirp signal. The multifunctional chirp “Inosonde-MS” data were used for testing.
Proposal and Evaluation of Textual Description Templates for Bar Charts Vocalization

The textual description of data charts is a complex task. A chart presents different visual characteristics for the information represented, which can be influenced by the technique selected and the combination of visual elements. There are crowdsourcing initiatives to create descriptions for charts available on the Web, but the descriptions can have failures, considering that they arise from the understanding of the person. In this context, methods to automatically extract data from chart images allow producing descriptions for use in these scenarios. However, there is no standard way of vocalizing the chart content. For this, the textual description must be based on a template, so that the chart can be completely understood. Thus, this paper presents templates that allow verbalizing the data extracted from vertical and grouped bar charts in an intelligible way. Evaluations were performed with users to verify the ease of understanding textual descriptions. The results showed that the proposed templates were suitable for vocalization the contents of bar charts.
Best Practices for Learning Domain-Specific Cross-Lingual Embeddings

Cross-lingual embeddings aim to represent words in multiple languages in a shared vector space by capturing semantic similarities across languages.They are a crucial component for scaling tasks to multiple languages by transferring knowledge from languages with rich resources to low-resource languages.A common approach to learning cross-lingual embeddings is to train monolingual embeddings separately for each language and learn a linear projection from the monolingual spaces into a shared space, where the mapping relies on a small seed dictionary.While there are high-quality generic seed dictionaries and pretrained cross-lingual embeddings available for many language pairs, there is little research on how they perform on specialised tasks.In this paper, we investigate the best practices for constructing the seed dictionary for a specific domain.We evaluate the embeddings on the sequence labelling task of Curriculum Vitae parsing and show that the size of a bilingual dictionary, the frequency of the dictionary words in the domain corpora and the source of data (task-specific vs generic) influence the performance.We also show that the less training data is available in the low-resource language, the more the construction of the bilingual dictionary matters, and demonstrate that some of the choices are crucial in the zero-shot transfer learning case.
Predicting fatalities among shark attacks: comparison of classifiers

&lt;span lang="EN-US"&gt;This paper presents the comparisons of different classifiers on predicting Shark attack fatalities. In this study, we are comparing two classifiers which are Support vector machines(SVMs) and Bayes Point Machines(BPMs) on Shark attacks dataset. The comparison of the classifiers were based on the accuracy, recall, precision and F1-score as the performance measurement. The results obtained from this study showed that BPMs predicted the fatality of shack attack victim experiment with higher accuracy and precision than the SVMs because BPMs have “average” identifier which can minimize the probabilistic error measure. From this experiment, it is concluded that BPMs are more suitable in predicting fatality of shark attack victim as BPMs is an improvement of SVMs.&lt;/span&gt;