Reviewerly/exHarmony huggingface dataset:
DESKRIPSI HEMATOLOGI KELELAWAR DI GUNUNG TANGKOKO BERDASARKAN PEMERIKSAAN DARAH LENGKAP

ABSTRACTThis study aimed to describe the hematological data of several bat species netted on Mount Tangkoko based on a complete blood examination. Purposive Random Sampling method was used for field sampling, Mistnet method was used for bats sampling and Screening Test method was used for hematological examination. Blood was examined using the Abx Micros 60 Hiruba Medical machine. The examined blood components were erythrocytes, leukocytes, haemoglobin, haematocrit, and platelets/thrombocytes. The examination results showed that each blood component had different amount compared to humans, some other mammals with relativity in size and some other birds. The highest leukocyte count was found in T. nigrescens which was 28.00 ± 19.04 × 103/mm3. The highest erythrocyte count was found in T. nigrescens which was 13.45 ± 1.52 × 106/mm3. The highest haemoglobin and haematocrit count were found in R. amplexicaudatus which were 19.70 ± 1.89 g/dl and 65.70 ± 5.65%. The highest platelet/thrombocyte count was found in T. nigrescens which was 975.2 ± 239.03 × 103/mm3.Keywords: Bats, Mount Tangkoko, Erythrocytes, Leukocytes, Haemoglobin, Haematocrit, Platelets ABSTRAKPenelitian ini bertujuan untuk mendeskripsikan data hematologi beberapa spesies kelelawar yang terjaring di Gunung Tangkoko berdasarkan pemeriksaan darah lengkap. Sampling lokasi menggunakan metode Purposive Random Sampling, penangkapan kelelawar menggunakan metode Mistnet dan pemeriksaan hematologi menggunakan metode Screening Test. Darah diperiksa menggunakan mesin Abx Micros 60 Hiruba Medical. Hasil menunjukan lima komponen darah yaitu eritrosit, leukosit, hemoglobin, hematokrit, dan platelet/trombosit masing-masing memiliki perbedaan dengan manusia dan beberapa spesies mamalia lain dengan ukuran yang relatif sama serta beberapa spesies burung sebagai pembanding. Nilai leukosit tertinggi terdapat pada spesies T. nigrescens yaitu 28,00 ± 19,04 × 103/mm3. Nilai eritrosit tertinggi terdapat pada spesies T. nigrescens yaitu 13,45 ± 1,52 × 106/mm3. Nilai hemoglobin dan hematokrit tertinggi terdapat pada spesies R. amplexicaudatus yaitu masing-masing 19,70 ± 1,89 g/dl dan 65,70 ± 5,65%. Nilai platelet/trombosit tertinggi terdapat pada spesies T. nigrescens yaitu 975,2 ± 239,03 × 103/mm3. Kata kunci : Kelelawar, Gunung Tangkoko, Leukosit, Eritrosit, Hemoglobin, Hematokrit, Leukosit
Simulation of Extrasolar Planet Detection with Rotationally Shearing Interferometer at 10 µm

We simulate the interferometric fringe patterns for the case of an extrasolar planet system with two planets. We show that the incidence distributions become more complicated, yet their interpretation is feasible. The generalized fringe distributions rotate when the Dove prism is rotated. Likewise, the number of fringes changes with the angle of rotation of the Dove prism.
Action Type induction from multilingual lexical features

This paper presents a vector representation and a clustering of action concepts based on lexical features extracted from IMAGACT, a multilingual and multimodal ontology of actions in which concepts are represented through video prototypes. We computed vectors for 1,010 action concepts, where the dimensions correspond to verbs in 10 languages. Finally, an unsupervised clustering method has been applied on these data in order to discover action classes based on typological closeness. Those clusters are not language-specific or language-biased, and thus constitute an inter-linguistic classification of action domain.
Consideration about preventing illegal film copy with re-shooting screen

The damage caused by the pirated films amounts to $1.3 billion a year. The pirated films are mainly created by re-shooting a screen in a theater or duplicating the official DVDs. Films are released in a theater. Before the official DVDs are released, 90% of the films are pirated in a theater and illegally made DVDs are sold. Although counter technologies are required, there is no effective proposals. The important requirement for the technology is that it does not degrade the image quality in a theater. It only degrades the re-shot video. In this paper, we report a novel method to make it possible.
Car FAQ Assistant Based on BILSTM-Siamese Network

With the development of artificial intelligence, the automatic question answering technology has been paid more and more attention.Along with the technology's maturing, the problem is gradually exposed.This technique has several major difficulties.There is semantic recognition.How accurate are the answers to the questions?Because Chinese grammar is relatively complex.There are many different ways of saying the same thing.There is a powerful challenge to semantic recognition.Due to poor semantic recognition, the accuracy of the corresponding questions and answers is low.Aiming at this kind of questions, we build a Q & A library for common use of cars.Firstly, TFIDF method is used to build a basic FAQ retrieval system.Then we use BILSTM-siamese network to construct a semantic similarity model.The accuracy rate on the test set was 99.52%.The final FAQ system: first retrieves 30 most similar questions using the TFIDF model, then uses BILSTM-siamese network matching and returns the answer of the most similar question.The system has a lot to be improved.For example, how to combine this system with speech recognition?How to realize speech recognition will be a great challenge.
Hand and Object Segmentation from Depth Image using Fully Convolutional Network

Semantic segmentation is an important step for hand and object tracking as subsequent tracking algorithms depend heavily on the accuracy of the segmented hand and object. However, current methods for hand and object segmentation are limited in the number of semantic labels, and lack of a large scale annotated dataset to train an end-to-end deep neural network for semantic segmentation. Thus, in this work, we present a framework for generating a publicly available synthetic dataset, that is targeted for upper limb rehabilitation involving hand-object interaction and uses it to train our proposed deep neural network. Experimental results show that even though the network is trained on synthetic depth images, it is able to achieve a mean intersection over union (mIoU) of 70.4% when tested on real depth images. Furthermore, the inference time of the proposed network takes around 6 ms on a GPU, thus making it suitable for real-time applications.
EFL students’ ability in writing reviews for a novel at a university in Medan

This research aimed at investigating the ability of the fourth semester English Education Department students at the University of Potensi Utama, Medan, in writing a review text of a novel entitled ‘Sengsara Membawa Nikmat’ written by Toelis Soetan Sati. This study applied a qualitative approach through writing tests as the data collection instrument. The data were, furthermore, analyzed by identifying and evaluating the students’ writing test. The result reveals that overall, the students’ review texts on the novel were good enough. However, some problems were still noticed in the students’ texts such as the errors in vocabulary, grammar, generic structure, organizing ideas, mechanic (punctuation and spelling) and the summary of the novel. It implies that while the students’ performance was generally satisfying, their ability to write a review text needs to be consistently improved to produce a better piece of a review text on a novel.
Analysis of Appropriate Security Processes to Mitigate Risk in a Popular Election System

The Consejo Nacional Electoral of Ecuador has been seriously questioned with the implementation of the System of Transmission and Publication of Results (STPR). Descriptive tables of threats and controls were analyzed; to determine criteria such as: loss of integrity, loss of availability and loss of confidentiality. The objective was to identify the security protocols, determining the processes that make up the electoral vote. The qualitative documentary analysis and impact of the analyzed threats was used. In this way the results said that the system is highly vulnerable to threats of human origin, the highest risks are the threats of computer criminals or malicious collaborators. It was concluded that the greatest risk lies in the employees, their selection and hiring must be carried out without incidents of the policy, that comply with a professional profile according to the functions or tasks that they will fulfill and that demonstrate values such as honesty and ethics.
Android Based Adventure Games to Enhance Vocational High School Students’ Critical Thinking Skills

The development of technology spread widely to many other sectors, one of them is education.In this competitive 21st century, education is developed integrated with technology.This article aimed to find whether or not using android based game puzzle adventure learning media can enhance students' critical thinking skill.The method used in this article is previous study through literature study stage and field study.In general, the whole learning process that happened in the class had not been supported by some interesting and innovative medias.The students used to accept the explanation from their teacher, so that they could not enhance their critical thinking skill in optimum way.One of innovations in learning media to enhance students' critical thinking skill is by using android based game puzzle adventure.This educational game can stimulate and train the students to solve their problems and cases through game play which full of adventure and puzzle.If the students are enthusiastic and meticulous in joining the lesson to solve problems in some questions, for sure it would strengthen their critical thinking skill.Therefore, it would help the students in enhancing their critical thinking skill.
Scaling in Concurrent Evolutionary Algorithms

The concept of channel, a computational mechanism used to convey state to different threads of process execution, is at the core of the design of multi-threaded concurrent algorithms. In the case of concurrent evolutionary algorithms, channels can be used to communicate messages between several threads performing different evolution tasks related to genetic operations or mixing of populations. In this paper we study to what extent the design of these messages in a communicating sequential process context may influence scaling and performance of concurrent evolutionary algorithms. For this aim, we designed a channel-based concurrent evolutionary algorithm that is able to effectively solve different benchmark binary problems (e.g. OneMax, LeadingOnes, RoyalRoad), showing that it provides a good basis to leverage the multi-threaded and multi-core capabilities of modern computers. Although our results indicate that concurrency is advantageous to scale-up the performance of evolutionary algorithms, they also highlight how the trade–off between concurrency, communication and evolutionary parameters affect the outcome of the evolved solutions, opening-up new opportunities for algorithm design.
Fast image dehazing based on multi-scale guided filtering

We propose a fast image dehazing algorithm based on multi-scale guided filtering. We first construct an image pyramid from a hazy input image. Then, based on the observation that the most time-consuming procedure is the estimation of the transmission map and atmospheric light, we estimate them only at the coarsest level. Then, we obtain the transmission map at the finest level by upsampling and guided image filtering. Experimental results show that the proposed algorithm is capable of real-time operation, while providing comparable or even better performance than the state-of-the-art algorithms.
Prediction of Patient-specific Acute Hypotensive Episodes in ICU Using Deep Models

Forecasting acute hypotensive episodes (AHE) in intensive care patients has been of recent interest to researchers in the healthcare domain. Advance warning of an impending AHE may give care providers additional information to help mitigate the negative clinical impact of a serious event such as an AHE or prompt a search for an evolving disease process. However, the currently accepted definition of AHE is restrictive does not account for inter-patient variability. In this paper, we propose a novel definition of an AHE based on patient-specific features of blood pressure recordings. Next, we utilize a deep learning-based method to predict the onset of an AHE from multiple physiological readings for different definitions of the prediction task including variable input and gap lengths. Using a cohort of 538 patients, our model was able to successfully predict the onset of an AHE with an accuracy and AUC score of 0.80 and 0.87 respectively. Compared to a baseline logistic regression model, our model outperforms the baseline in most of the definitions of the prediction task.
Testing Adequacy of Convolutional Neural Network Based on Mutation Testing

It is difficult to apply traditional testing adequacy criteria when measuring the adequacy of convolutional neural network applications. However, only a small number of test cases applied to the CNN model can achieve neuron coverage of almost 100%, overturning the effectiveness of the neuronal coverage criteria. In this paper, we propose a model coverage criterion based on mutation testing for CNN, and applying model coverage criterion to a common CNN image classification models (LeNet-5). we focus on the testing accuracy of model. Experiments show that our method can find the local optimal model and play an important role in improving the testing adequacy of the set of models.
Romanian Part of Speech Tagging using LSTM Networks

In this paper we present LSTM based neural network architectures for determining the part of speech (POS) tags for Romanian words. LSTM networks combined with fully-connected output layers are used for predicting the root POS, and sequence-to-sequence models composed of LSTM encoders and decoders are evaluated for predicting the extended MSD and CTAG tags. The highest accuracy achieved for the root POS is 99.18% and for the extended tags is 98.25%. This method proves to be efficient for the proposed task and has the advantage of being language independent, as no expert linguistic knowledge is used in the input features.
Super-Resolution OCT Using Sparse Representations and Heavy-Tailed Models

This paper introduces a new approach to single-image super-resolution in Optical Coherence Tomography (OCT) images. Retinal OCT images can be used to diagnose various diseases, not only peculiar to the eye, but also some systemic diseases. Nevertheless, as with any imaging modality, the acquired images suffer from degradation due to various causes. To overcome this and enhance image quality, Super-Resolution (SR) techniques are widely used. This work explores a convex regularization approach based on a multivariate generalization of the minimax-concave (GMC) scheme in a forward-backward splitting (FBS) scheme. Based on the assumption that sparse representations of OCT images are heavy-tailed, an α-stable dictionary is employed. This approach is implemented with overlapping and non-overlapping patches. Since the Point Spread Function (PSF) of the images used is generally unknown, it is estimated using a method originally proposed for ultrasound images. The algorithm is tested on OCT images of murine eyes. The results show that the proposed convex regularization method provides results that are competitive with the state-of-the-art. Indeed, significant deblurring and quality enhancement are achieved using the proposed algorithm and in most cases it provides the best results, both objectively and subjectively.
Demo

This paper presents a robot for people identification and tracking developed on robot operating system (ROS). It achieves modulized, light-weight, low-cost, and high-performance design goals even with the existence of distributed sensors. The key idea is to utilize wearable devices to enhance the people tracking capability of a robot through instant wireless communications and multi-sensory data fusion. Experimental results in a realistic environment demonstrate that our robot can keep tracking a specific person at a safe distance even without seeing the biological features of the person, who walks in a crowd with complex trajectories.
Design and Implementation of Intelligent Physique Health Monitoring System Under Android Platform

By monitoring the health and physical quality of college students in real time, we can find the problems and adjust the training plan and training methods. This article designs and implements a student physical health monitoring and management system under Android platform. The requirements analysis, architecture design, outline design, detailed design and implementation of the system are described in detail, and the database design of the system is completed. The details of the interface of the module are determined, the implementation process of key technologies are realized, and the implementation of the extensible framework for the application of the system server and the application of the intelligent terminal are also performed. The tests and analysis results show that the whole system meets the requirements of function and performance, which achieves expected effects.
Efficient Searching for Essential API Member Sets based on Inclusion Relation Extraction

API member set frequent pattern mining application development open source repositories Android
Adaptive manoeuvring target tracking algorithm based on acceleration pre-estimation model

For the defect of requiring artificially set manoeuvring parameters in traditional target models, an adaptive manoeuvring frequency tracking algorithm based on acceleration pre-estimation model (APM) was proposed. On the basis of APM, which has a better target tracking performance compared with current statistic model, this study used residual vector to determine variance of target's manoeuvring, and realised an adaptive adjustment for manoeuvring frequency by using a non-linear function, solved the problem of requiring artificially set manoeuvring frequency in APM. The simulation results showed that the improved algorithm effectively ameliorated the target tracking performance.
A Survey on Knowledge of Registered Drugs amongst Patients from the Specialist Clinic, Malaysia

Introduction: One of the solutions to reduce unregistered drugs usage is via awareness and educational programmes.The aim of this study is to determine the significance difference of patients' level of knowledge between control and intervention groups towards registered drugs. Method:The study used a pre-and post-test by answering the validated survey during October-November 2018 at a tertiary hospital in Malaysia.It has two groups as an intervention group (IG) receiving counseling and control group (CG) which did not receive any counseling.This study used systematic random sampling.The data analysis was performed using SPSS version 23.Descriptive data was reported, including the answers each question, score, level of knowledge based on the percentages and the socio-demographic data.The pre and post differences of the score between control and intervention groups were assessed using ANCOVA.Result: A total of 657 respondents were obtained which overall response rate of 93%).Majority of the participants are aged between 18-28 years old (47%), female (58.3),Malay (47.4%),Degree holder (69.1%) and in the government sector (38.3%).However, the majority of participants are in the category of the acceptable level of knowledge towards registered drugs (40.8%) and only 3.5% of the participants are in the category of excellent.However, about 66.1% of the participants have scored above 50%.It can be considered that the majority of the participants have knowledge about registered drugs.IG group displayed significantly higher post-test results compared with CG group (p<0.001). Conclusion:The used of the validated survey during the awareness programmes might give benefit to the public who attends the awareness and educational programmes and finally can improve the effectiveness of the programmes in the future.In the future, the improvement of the design of the campaign might include the use of a survey in order to give more understanding to the public towards registered drugs.
PENERAPAN PERANGKAT PEMBELAJARAN BERBASIS PROYEK MATA DIKLAT SISTEM OPERASI JURUSAN TKJ

This development research aims to development of Project Based Learning on System Operation at computer and networking technic this research methods IDI development research, such as the prosedure of IDI development is: define, develop and evaluate. Types of data is primary data where the data provided by the experts, teachers and students, An analysis of the data used are technical data descriptive analysis was to describe validity, practicality, effectiveness and creative of products using Learning. Based on the data analysis that was did to research methods IDI development research. The results obtained from this research and development in the book teacher and book students after validity as follows: (1) The validity of the Project Based Learning declared valid, (2) The practicalities of Project Based Learning based on teachers after validity revealed practically, (3) Effectiveness of Project Based Learning effective in improving students learning outcomes before with after using Project Based Learning, (4) The creative of Project Based Learning, Based on the findings of this study was concluded that this Project Based Learning was valid, practical effective and creative to be used asa medium of learning was in the system operation.
Gaze-based visual feature extraction via DLPCCA for visual sentiment estimation

This paper presents gaze-based visual feature extraction via Discriminative Locality Preserving Canonical Correlation Analysis (DLPCCA) for visual sentiment estimation. The proposed method calculates novel visual features reflecting users’ visual sentiment by applying DLPCCA to gaze and original visual features. Consequently, accurate visual sentiment estimation becomes feasible by utilizing the novel visual features derived by the proposed method.
Towards CNN-Based Registration of Craniocaudal and Mediolateral Oblique 2-D X-ray Mammographic Images

We investigate methodologies for the automated registration of pairs of 2-D X-ray mammographic images, taken from the two standard mammographic angles. We present two exploratory techniques, based on Convolutional Neural Networks, to examine their potential for co-registration of findings on the two standard mammographic views. To test algorithm performance, our analysis uses a synthetic, surrogate data set for performing controlled experiments, as well as real 2-D X-ray mammogram imagery. The preliminary results are promising, and provide insights into how the proposed techniques may support multi-view X-ray mammography image registration currently and as technology evolves in the future.
Using Graph Pattern Association Rules on Yago Knowledge Base

The use of graph pattern association rules (GPARs) on the Yago knowledge base is proposed. Extending association rules for itemsets, GPARS can help to discover regularities between entities in a knowledge base. A rule-generated graph pattern (RGGP) algorithm was used for extracting rules from the Yago knowledge base and a GPAR algorithm for creating the association rules. Our research resulted in 1114 association rules, with the value of standard confidence at 50.18% better than partial completeness assumption (PCA) confidence at 49.82%. Besides that the computation time for standard confidence was also better than for PCA confidence.