Reviewerly/exHarmony huggingface dataset:
Semantic Relation Detection based on Multi-task Learning and Cross-Lingual-View Embedding

Semantic relation extraction automatically is an important task in NLP.Various methods have been developed using either pattern-based approach or distributional approach.However, existing research only focuses on single task modeling without considering the possibility of generalization with other tasks.Besides, the methods that exist only use one view from task language as an input representation that might lack of features.This happens especially in languages that are classified as low resource language.Therefore, in this paper we proposed a framework for semantic relations classification based on multi-task architecture and cross-lingual-view embedding.There are two main stages in this framework, data augmentation based on pseudo parallel corpora and multi-task architecture with cross-lingual-view embedding.Further, extensive experiment of the proposed framework has been conducted.The results show that the use of rich resource language in cross-lingual-view embedding is able to support low-resource languages.This is shown by the results with accuracy and F1-scores of 85.8% and 87.6%, respectively.The comparison result also shows that our proposed model outperforms another state-of-the art.
ELECTRONINC BANKING AND PERFORMANCE OF DEPOSIT MONEY BANKS IN NIGERIA (2011- 2018)

Electronic banking has become a central concept in the banking industry due to its ability to increase operational efficiency and enhance financial performance. Nevertheless, the challenges of huge capital expenditure on development, and the increased cost of operation on the maintenance of e-banking facilities to achieve operational efficiency subsist in Nigeria. This paper examined the effects of electronic banking on the aggregate financial performance of Deposit Money Banks in Nigeria for eight years. The focus was on selected electronic banking products - Mobile payments, Automated teller machine (ATM), Internet banking (WEB) and Point of sale (POS) transactions. Secondary data were collected and analyzed quantitatively. Four hypotheses were formulated and tested at a ninety-five percent confidence interval using the Ordinary Least Square (OLS) inferential statistical tool. The results indicated that e-banking in the aggregate has a positive but insignificant effect on the overall performance of Deposit Money Banks (DMBs) in Nigeria.
Analysis of E-mail Account Probing Attack Based on Graph Mining

Abstract E-mail has become the main carrier of spreading malicious software and been widely used for phishing, even high-level persistent threats. The e-mail accounts with high social reputation are primary targets to be attacked and utilized by attackers, suffering a lot of probing attacks for a long time. In this paper, in order to understand the probing pattern of the e-mail account attacks, we analyse the log of email account probing captured in the campus network based on graph mining. By analysing characteristics of the dataset in different dimensions, we find a kind of e-mail account probing attack and give it a new definition. Based on the analysis results, its probing pattern is figured out. From the point of probing groups and individuals, we find definitely opposite characteristics of the attack. Owing to the probing pattern and its characteristics, attacks can escape from the detection of security devices, which has a harmful effect on e-mail users and administrators. The analysis results of this paper provide support for the detection and defence of such distributed attacks.
Domain-Specific Automatic Scholar ProfilingBased on Wikipedia

Scholar profiling is usually invited to provide junior researchers with domain-specific concepts systematically, but the organization of domain literatures is usually time-consuming. Then, an automatic scholar profiling method is preferred. Firstly, to extract some properties of a given scholar, structured data, like infobox in Wikipedia, are often used as training datasets. But it may lead to serious mis-labeling problems, such as institutions and alma maters, and a Fine-Grained Entity Typing method is expected. Thus, a novel Relation Embedding method based on local context is proposed to enhance the typing performance. Also, to highlight critical concepts in selective bibliographies of scholars, a novel Keyword Extraction method based on Learning to Rank is proposed to bridge the gap that conventional supervised methods fail to provide junior scholars with relative importance of keywords. Categories of experiments were conducted to evaluate the Relation Embedding method and the Keyword Extraction method, which show that the proposed approaches outperform existing baseline methods notably.
Corpus Phonetics for Under-Documented Languages: A Vowel Harmony Example

Corpus phonetics is enabling the comprehensive analysis of large digital speech collections. In this paper, we develop a corpus phonetics workflow that is flexible enough to be easily applied to under-documented languages. To test the capabilities of this workflow we choose a challenging vowel reduction and vowel harmony problem. In Kera (Chadic) it has been shown (Pearce, 2012), that not only is phonetic reduction linked to the phonetic duration of the vowel, but also that reduction is blocked in vowel harmony domains. We are able to replicate previously published experiments by Pearce that were originally completed using manual measurements. We expect that our corpus phonetics workflow will be of value to phonologists working on other languages.
SPECIAL FEATURES IN CREATION AND APPLICATION OF COMPUTER ANIMATED IMAGES IN TEACHING MATHEMATICS

Statement of the problem. The analysis of publications, dedicated to the application of information technologies in teaching mathematics, shows a relatively low level of use of animated possibilities in computer media for teaching high school algebra and introduction to mathematical analysis. In connection with this, the article focuses on the problem of compiling an educational content in the form of animated images, capable of ensuring effective computer support in teaching high school mathematics. The purpose of the article is to show the role and value of computer animation in teaching arithmetic, algebra and introduction to mathematical analysis on specific examples, realized in GeoGebra. The research methodology implies the study and analysis of literature related with the use of information technologies for teaching mathematics, the synthesis of authors, work experience on approval of the technique for teaching mathematics using computer animation. Research results. For creating animated images applicable in teaching arithmetic, algebra and introduction to mathematical analysis, the GeoGebra is recommended. The basic forms of animation are enumerated, specific examples demonstrate the use of animated images during training, for experimentation, conducting training studies, for organizing experimental research style teaching instructions. Conclusion. The proposed on GeoGebra basis digital educational content, which consists of a series of animated images, will increase the technological equipment and facilities of contemporary teachers of mathematics, and will make it possible to achieve higher educational results in mathematics, namely in arithmetic, algebra and introduction to mathematical analysis.
SAR Ship Target Detection Based on 2D Singularity Power Spectrum

A novel SAR ship target detection method based on two-dimensional singularity power spectrum (2DSPS) analysis is proposed. The 2D-SPS of SAR images explores a variety of coupling features, including the dispersion of singularity exponent and the power distribution in the singularity domain, and has strong ability in feature extraction for SAR images of pure sea surface, false alarm at sea, small targets and multiple targets under the conditions of complex sea surface. The method of SAR target detection based on 2D-SPS are designed. Using the OpenSAR Dataset, in the background of sea clutter, the simulation results show that the proposed method performs better than the traditional method under the condition of extremely low SNR and strong false alarm, especially for small SAR targets and multiple SAR targets.
Sixty seconds on . . . the contact tracing app

I know it seems like we’re hearing about a new one every week (particularly with such an “appy clappy” health secretary) but this one has a very specific purpose.Got it in one. This week, a new contact tracing app is being piloted on the Isle of Wight, before being rolled out more widely later this month. The free app is a key …
An extensive study of class-level and method-level test case selection for continuous integration

Continuous Integration (CI) is an important practice in agile development. With the growth of integration system, running all tests to verify the quality of submitted code, is clearly uneconomical. This paper aims at selecting a proper test subset for continuous integration so as to reduce test cost as much as possible without sacrificing quality. We first propose a static test case selection framework Sapient which aims at selecting a precise test subset towards fully covering all changed code and affected code. There are four major steps: locate changes, build dependency graphs, identify related tests by searching dependency graphs, and extend tests. Based on Sapient, we then develop a class-level test case selection approach FEST and a method-level approach MEST. FEST captures the class-level dependencies, especially the hidden references, which facilitates the modeling of full dependency relations at the class level. MEST combines two dynamic execution rules (i.e., dynamic invocation in reflection and dynamic binding in inheritance) with static dependencies to support building the precise method-level dependencies. Evaluation is conducted on 18 open source projects with 261 continuous integration versions from Eclipse and Apache communities. Results show that both FEST and MEST can detect almost all faults of two baselines (i.e., actual CI testing and ClassSRTS), and find new faults compared with actual CI testing (in 25% and 26% versions) and ClassSRTS (in 18% and 27% versions) respectively. Furthermore, MEST outperforms FEST in reduced test size, fault detection efficiency and test cost, indicating method-level test case selection can be more effective than class-level selection. This study can further speed up the feedback and improve the fault detection efficiency of CI testing, and has good application prospects for the CI testing in large-scale and complex systems.
An interactive approach for solar energy system: design and manufacturing

The energy production in the word is a very complex problem with decreasing the pollution. Therefore, the aim is to find an optimal solution, this research focuses on the development and the optimization of parabolic concentrator using an interactivity approach and virtual design tools. Recently, several works have been developed in this area. In this study, a new conception, design Optimization approach has been involved in system energy design including new concept. The design strategy has been successfully applied to design problems. The optimizer tool developed for based on Heuristic: Gravitational Search Algorithm. The results of the presented in this paper are significant in the system energy design, which presents an effective approach of development by reducing the cost of installation, the time of analysis by increasing the radiation and solar flux concentrated within the parabola generating an increase in accumulated energy.
A Novel Dynamic Programming Based Method for Path Planning with Navigation Error Correction

The navigation error of aircraft increases in task. Aircraft should correct the navigation error in time during task to avoid path deviation caused by navigation error. Aircraft path planning with navigation error correction is a discrete constrained optimization problem and is a challenge for the traditional path planning methods. In this paper, we propose a novel dynamic programming based method to solve this problem. Our method outputs the optimal path in terms of the number of error correction regions from 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> possible paths and the computational complexity of our method is O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) where n represents the number of error correction regions. We also improve Dijkstra method to compute almost optimal path in term of the length of the path and achieve good performance on simulated data.
The Effect Of Using Jigsaw Technique On Students’ Vocabulary Ability At Smp Swasta Kartika 1-2 Medan

The title of this research is The Effect of Using Jigsaw Technique On Students’ Vocabulary Ability. This research aimed to know how the effect of using jigsaw technique on students’s vocabulary ability at SMP Kartika 1-2 Medan. In this research, the subjects of this research were VII-1 and VII-2’s class with 62 students, VII-1 consist of 31 students called an experimental group and VII-2 consist of 31 students called a control group. This research was a quantitative research. The mean score of the pre test in experimental group was 39,45, the mean score of the post test was 75,90, the mean score of the pre test in control group was 53,41 and the mean score of the post test was 53,93. It concluded that using jigsaw technique is effective and significantly effect to improved students’ vocabulary ability. It proved by the obtained score of T-test, the T-test showed that T-observe was higher than T-table ( 25,53 &gt; 0,05). It meant that Ha was accepted and H0 was rejected.
Enhancing Healthcare Information Sharing with Blockchain Technology

Blockchain, the foundation of many cryptocurrencies, also can be useful for many other industries such as healthcare. Blockchain can enforce immutability and non-repudiation for information stored on it. Although some say the technology is not yet mature enough, others are putting blockchain to the test with programs and pilots. Examining these efforts and dissecting the detail allows us to look at blockchain’s qualities and pitfalls to allow data sharing efforts with medical records.It is widely agreed that sharing medical data outside of the silos where it is captured or created will benefit the individual’s care and outcomes. Regulation and personal humility often stand in the way of this sharing. Blockchain is offering new and novel ways to share data properly and securely with only the providers or researchers who are supposed to receive it. Blockchain is also allowing the patient to take control of their health data and in some cases, even profit from it. This is the concept behind a proposal by the author to allow a patient to share all or part of their data for research and to be justly compensated for that sharing.
SQAL Self-Adaptive System's Quality Assurance Language

Today's software systems tend to be flexible and dynamic by provisioning mechanisms to react quickly to the environment changes and to adapt system configuration accordingly, in order to maintain the required quality of service (QoS). The engineering of system self-adaptation requires new modeling methods and development methodologies that employ the principles of model-driven development in building self-adaptive systems (SASs). To tackle this issue, the present work proposes SQAL (self-adaptive system quality assurance language) a domain specific language for quality-aware SASs. This language allows describing SASs architectural elements and the corresponding interrelations in terms of hierarchical compositions. It also provides concepts for defining SASs behavioral aspects by identifying adaptation actions and mainly weighting them with QoS parameters. SQAL is defined in terms of its abstract and concrete syntaxes. This article associates a PSMaude-based semantics to SQAL in order to quantitatively analyze quality-aware SASs behaviors.
Target Tracking Method Based on Adaptive Structured Sparse Representation With Attention

Considering the problems of motion blur, partial occlusion and fast motion in target tracking, a target tracking method based on adaptive structured sparse representation with attention is proposed. Under the framework of particle filtering, the performance of high-quality templates is enhanced through an attention mechanism. Structure sparseness is used to build candidate target sets and sparse models between candidate samples and local patches of target templates. Combined with the sparse residual method, reconstruction error is reduced. After optimally solving the model, the particle with the highest similarity is selected as the prediction target. The most appropriate scale is selected according to the multiscale factor method. Experiments show that the proposed algorithm has a strong performance when dealing with motion blur, fast motion, partial occlusion.
TOP TEN MOST PROBLEMATIC GRAMMATICAL ITEMS FOR INDONESIAN TERTIARY EFL LEARNER WRITERS

Errors are almost inseparable from learning process, including learning new languages. Though, it is not always considered as negative, instead, it is a part of learning process. This study was conducted to assess studentsâ€™ mastery of grammar in writing, to find what grammatical errors they make, to be an evaluation to overcome the problem in the future. In this study, samples were taken from EFL students of English Education Department of Universitas Muhammadiyah Purwokerto. The grammatical errors were identified under the theory of Politzer-Ramirez which classify errors into two types: morphological and syntactic error. In the end of the study, ten types of grammatical errors were identified as the most common grammatical errors made by the students. Three of them belong to morphological error, while the remaining fall into syntactic error. Those ten errors are: The use of prepositions (75 %), redundancy (75 %), simple past tense incorrect (62.5 %), determiners (58.3 %), number confusion (45.8 %), omission of verb (45.8 %), verb construction (41.67 %), derivatives/wrong part of speech (41.67 %), nominalization (33.3 %), and errors in the use of pronoun (29.2 %). Keywords: Problematic, Grammatical Items, Tertiary, EFL, Learner Writers
Modeling of Quantile Regression to Know the Factors Affecting the High Spread Api Malaria in Indonesia

The OLS method estimation is based on a normal distribution, so it is not appropriate to analyze a number of data that are not symmetrical or contain outliers. Therefore, quantile regression was developed which was not affected by outliers. This study compares quantile regression with OLS in the case of factors affecting malaria in Indonesia. The results show that the value of the Quantil Regression model is 0,832 and the MSE value is 0,182. In addition, the OLS model obtained a value of 0,681 and an MSE value of 0,231. So we get the conclusion that the best model is a quantile regression model. Further results were obtained that the main factors causing the spread of malaria in Indonesia were the factor of livable houses, poor population factors and physician factors.
Perancangan Sistem Monitoring Detak Jantung Bagi Penderita Kardiovaskular Berbasis Internet of Things

The form of an archipelago and densely populated country of Indonesia has its own constraints in efforts to deal with health, especially health for patients with cardiovascular disease. This disease is one of the highest causes of death in Indonesia. Based on data from the Ministry of Health, cardiovascular disease since 2007 is the highest cause of death in Indonesia with more than 220,000 deaths each year. While the number of cases exceeds tuberculosis, which has a death toll of 127,000. The number is increasing every year due to changes in lifestyle of Indonesian people who like to eat high-fat foods and lifestyle factors that affect the risk of cardiovascular disease, including lack of physical activity, smoking, high-fat diets, and alcohol consumption habits. This study aims to monitor the heart rate of cardiovascular sufferers with the internet of things (IoT) approach. Every patient with cardiovascular disease can be monitored using a sensor connected to a PC to record any changes that occur. The research method consists of several stages, namely: Prepare, Plan, Design, Implement, Operate and Optimize (PPDIOO). The results obtained in the form of a prototype heart rate monitoring system for patients with cardiovascular. The patient's health can be monitored at any time by the hospital doctor without the patient having to come regularly to the hospital.
EPNAS: Efficient Progressive Neural Architecture Search

In this paper, we propose Efficient Progressive Neural Architecture Search (EPNAS), a neural architecture search (NAS) that efficiently handles large search space through a novel progressive search policy with performance prediction based on REINFORCE~\cite{this http URL}. EPNAS is designed to search target networks in parallel, which is more scalable on parallel systems such as GPU/TPU clusters. More importantly, EPNAS can be generalized to architecture search with multiple resource constraints, \eg, model size, compute complexity or intensity, which is crucial for deployment in widespread platforms such as mobile and cloud. We compare EPNAS against other state-of-the-art (SoTA) network architectures (\eg, MobileNetV2~\cite{mobilenetv2}) and efficient NAS algorithms (\eg, ENAS~\cite{pham2018efficient}, and PNAS~\cite{Liu2017b}) on image recognition tasks using CIFAR10 and ImageNet. On both datasets, EPNAS is superior \wrt architecture searching speed and recognition accuracy.
A Fuzzy Decision Support Model for Cropland Recommendation of Food Cropping in Indonesia

Indonesia has an incredibly wide area for agriculture.The agriculture area in Indonesia has specific characteristic for each area (e.g., water capacity, land porosity, land height, etc.,).Furthermore, food crop is the most plant planted in Indonesia; rice, corn, red bean, green bean are some important food crops.The study aims to create a Decision Support Model based on fuzzy logic (FDSM).The model is able to recommend the most suitable food crop to be rationally planted in a specified area.The recommendation is based on distance value between plant and area characteristic value.Two main methods are operated in this study, they are fuzzy logic and Euclidean distance measurement.The method fuzzy logic purposely avoids the ambiguity of parameter value and considers the human linguistic based parameter value.Afterward, the Euclidean distance adopted to calculate the fitness value as a fundamental value for decision recommendation.Here, seven geographic parameters (i.e., water availability, temperature, humidity, land height, land slope, rainfall and land porosity) matched to food crop's biotic parameters in finding the fittest value.Finally, the model shows a simulation of recommendation for 514 administrative areas of 34 provinces' district/municipality (in Indonesia) based on five types of food crops (i.e., rice, maize, soybeans, green beans and peanuts).