Reviewerly/exHarmony huggingface dataset:
Research on Cloud Workflow Engine Supporting Three-Level Isolation and Privacy Protection

With the development of cloud computing, cloud workflow systems are widely accepted by more and more enterprises and individuals (namely tenants). There exists mass tenant workflow instances running in cloud workflow systems. How to implement the three-level (i.e., data, performance, execution ) isolation and privacy protection among these tenant workflow instances is challenging. To address this issue, this paper presents a novel cloud workflow model supporting multi-tenants with privacy protection. With the presented model, a framework of cloud workflow engine based on the extended jBPM4 is proposed by adopting layered management thought, virtualization technology and sandbox mechanism. By extending the jBPM4 (java Business Process Management) engine, the prototype system of the proposed cloud workflow engine is implemented and applied in the ceramic cloud service platform (denoted as CCSP). The application effect demonstrates that our proposal can be used to implement the three-level isolation and privacy protection between mass various tenant workflow instances in cloud workflow systems.
Transfer Learning Between Related Tasks Using Expected Label Proportions

Deep learning systems thrive on abundance of labeled training data but such data is not always available, calling for alternative methods of supervision.One such method is expectation regularization (XR) (Mann and McCallum, 2007), where models are trained based on expected label proportions.We propose a novel application of the XR framework for transfer learning between related tasks, where knowing the labels of task A provides an estimation of the label proportion of task B. We then use a model trained for A to label a large corpus, and use this corpus with an XR loss to train a model for task B. To make the XR framework applicable to large-scale deep-learning setups, we propose a stochastic batched approximation procedure.We demonstrate the approach on the task of Aspectbased Sentiment classification, where we effectively use a sentence-level sentiment predictor to train accurate aspect-based predictor.The method improves upon fully supervised neural system trained on aspect-level data, and is also cumulative with LM-based pretraining, as we demonstrate by improving a BERTbased Aspect-based Sentiment model.
TOWARDS 3D FACIAL RECONSTRUCTION USING DEEP NEURAL NETWORKS

3D facial reconstruction is an emerging and interesting application in the field of computer graphics and computer vision and is been used for 3D printing, creating avatars etc.It is difficult and challenging to reconstruct the 3D model of face from a single photo because of arbitrary poses, non-uniform illumination, expressions and occlusions.None of algorithm provides detailed 3D facial model because every algorithm has some limitations related to profile view, fine detail, accuracy and speed.The major problem is to develop 3D face with texture of large poses, wild faces and occluded faces.Mostly algorithm is used convolution neural network (CNN) and deep learning frameworks to create facial model and 3D dense face alignment (3DDFA) is the first algorithm that constructed the database consisting of 2D images and 3D facial model.In this paper we review 3D face reconstruction algorithm for application such as 3D printing, creating avatars and facial recognition.Different issues, problems and their proposed solutions are discussed in this paper while advantages and disadvantages are highlighted.A comparison of different algorithms is described in the context of texture and poses to find the best solution regarding reconstruction of 3D facial model from single photo.
ANALISIS KECELAKAAN BERLALU LINTAS DI KOTA JAKARTA DENGAN MENGGUNAKAN METODE K-MEANS

In the city area of Jakarta, accidents in traffic enter problems that almost often occur at any time. Not a few victims suffered property damage, but victims also suffered minor injuries, serious injuries and even lost their lives. To analyze the number of accident incidents in traffic around the Jakarta city area which took casualties, researchers used a clustering method with the K-Means algorithm which was processed through rapidminer software. From the clustering of traffic accidents data obtained the level of accuracy in clusters 1 and 2 with the same accuracy that is equal to 20%, while cluster 3 gets an accuracy value of 10%
Analysis of Environmental Factors Affecting Manufacturing Small and Medium-Size Enterprises in Kogi State Nigeria

This study analyzed the environmental factors affecting manufacturing SMEs in Kogi State, Nigeria. The target is to investigate the effects of internal factors as they affect the profitability of manufacturing SMEs in Kogi State. The survey research design was adopted for the study; covered 171 SME owners in Kogi State. The clusters of SMEs were located and utilized for this study. Data were analyzed using descriptive statistics, principal component factor analysis, and binary logit regression. Findings show that business strategy has a significant negative relationship with the profitability of SMEs in Kogi State. Operational strength and debt leveraging significantly and positively relate to the profitability of SMEs in Kogi State. These factors play critical roles in the sustainability of the profitability of manufacturing SMEs in Kogi State. The study recommended that manufacturing SME owners should seek knowledge and skill to crafting and adopting an effective business strategy, boost their operational strength and manage their debt leveraging distinctively to sustain their profitability in the competitive business environment of Kogi State.
Credit Card Fraud Detection using Random Forest Algorithm

Technological developments have changed the way we live. Banks have introduced the concept of credit cards. Due to the advancement in the electronic commerce technology, the use of credit cards has increased and it has become a popular mode of payment for both online and offline purchases. In spite of this enormous popularity the cards are not free of risk.However, the vast majority of learning algorithms that have been proposed for fraud detection rely on assumptions that hardly hold in a real-world fraud-detection system. Our project mainly focussed on credit card fraud detection in real world. Initially we will collect the credit card datasets for trained dataset. Then we will provide the user credit card queries for testing data set. After classification process of dataset random forest algorithm is used for analysing data set and current dataset provided by the user. After final optimization the results indicates about the optimal accuracy for Random Forest Algorithm is 98.6% of the accuracy of the result data.
COMPARATIVE REVIEW OF REGULATORY FRAMEWORK OF VIRTUAL CURRENCY (CRYPTOCURRENCY) IN SELECTED COUNTRIES

In the era of rapid technological progress, the legislators of individual countries face the challenge of submitting new technological phenomena to the legal framework. The business operation in the field of virtual currencies (cryptocurrencies),widely, in the financial technology sector (hereinafter referred to in as “fintech”), create a chance to attract investors and develop the economy. On the other hand, the same might be misused for illegal purposes, such as, in particular,money laundering or financing of terrorism. This study provides for an overview of the legal regulations of selected states in the field of virtual currencies (cryptocurrencies). Although, presented laws and regulations herein are in favour of thecryptocurrencies, the scope of implemented acts varies significantly.
Gravitational Search For Designing A Fuzzy Rule-Based Classifiers For Handwritten Signature Verification

Handwritten signatures are used in authentication systems as a universal biometric identifier. Signature authenticity verification requires building and training a classifier. This paper describes a new approach to the verification of handwritten signatures by dynamic characteristics with a fuzzy rule-based classifier. It is suggested to use the metaheuristic Gravitational Search Algorithm for the selection of the relevant features and tuning fuzzy rule parameters. The efficiency of the approach was tested with an original dataset; the type II errors in finding the signature authenticity did not exceed 0.5% for the worst model and 0.08% for the best model.
System Description: The Submission of FOKUS to the WMT 19 Robustness Task

This paper describes the systems of Fraunhofer FOKUS for the WMT 2019 machine translation robustness task.We have made submissions to the EN-FR, FR-EN, and JA-EN language pairs.The first two were made with a baseline translator, trained on clean data for the WMT 2019 biomedical translation task.These baselines improved over the baselines from the MTNT paper by 2 to 4 BLEU points, but where not trained on the same data.The last one used the same model class and training procedure, with induced typos in the training data to increase the model robustness.
Confirming the Non-compositionality of Idioms for Sentiment Analysis

An idiom is defined as a non-compositional multiword expression, one whose meaning cannot be deduced from the definitions of the component words.This definition does not explicitly define the compositionality of an idiom's sentiment; this paper aims to determine whether the sentiment of the component words of an idiom is related to the sentiment of that idiom.We use the Dictionary of Affect in Language augmented by WordNet to give each idiom in the Sentiment Lexicon of IDiomatic Expressions (SLIDE) a component-wise sentiment score and compare it to the phrase-level sentiment label crowdsourced by the creators of SLIDE.We find that there is no discernible relation between these two measures of idiom sentiment.This supports the hypothesis that idioms are not compositional for sentiment along with semantics and motivates further work in handling idioms for sentiment analysis.
AN ADAPTIVE IMAGE SHARPENING SCHEME

Image enhancement is one important process for digital images.This processing gives a better subjective visual appearance.The contours and texture are key features that contain important information essential to the visual quality aspect.Adaptive image enhancement schemes are proven better than global methods.An adaptive image sharpening method based on spatial statistics is proposed in this report to effectively sharpen image structures.A noisy Lena was pre-filtered using a low-pass filter to produce a blurred Lena.The residual image was obtained using average filtering and then subtracted from the original image.The high-pass residual image should be a combination of structure and noise.The autocorrelation of each pixel is calculated on the residual image and the image then is sharpened using a nimble filter based on the adaptive autocorrelation values.The PFOM results show that the similarity of adaptive sharpening is better than the global scheme.The proposed method will be further developed and applied to improve image acuity, de-noising and image quality improvement.
EFFECT OF THE SERVICE QUALITY ON TUTORING CUSTOMER’S WORD OF MOUTH AND SATISFACTION IN MALANG CITY

The purpose of the study was to test and explain the effect of service quality on customer satisfaction and word of mouth customers, as well as the effect of service quality on customer word of mouth mediated by customer satisfaction tutoring in Malang. The study population was students at the three biggest tutorings in Malang City who would take part in preparation for college admission selection in 2016. The research sample was 69 people who were determined by purposive sampling. Data analysis techniques using Partial Least Square ( PLS). The results showed that service quality had a significant effect on customer satisfaction and influenced the word of mouth of customers. However, customer satisfaction is not proven to mediate the influence of service quality on word of mouth because customer satisfaction does not have a significant effect on word of mouth. The quality of services provided by non-formal education in Malang City must be improved because it will have an impact on the increasing level of customer satisfaction.
What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons

Memory neurons of long short-term memory (LSTM) networks encode and process information in powerful yet mysterious ways.While there has been work to analyze their behavior in carrying low-level information such as linguistic properties, how they directly contribute to label prediction remains unclear.We find inspiration from biologists and study the affinity between individual neurons and labels, propose a novel metric to quantify the sensitivity of neurons to each label, and conduct experiments to show the validity of our proposed metric.We discover that some neurons are trained to specialize on a subset of labels, and while dropping an arbitrary neuron has little effect on the overall accuracy of the model, dropping label-specialized neurons predictably and significantly degrades prediction accuracy on the associated label.We further examine the consistency of neuron-label affinity across different models.These observations provide insight into the inner mechanisms of LSTMs.
Instant Mobile Messaging (WhatsApp) and Teaching Translation: Views Elicited from Saudi Translation Teachers and Students

This study investigated the views of Saudi translation teachers and students toward using instant messaging services (such as WhatsApp) in their translation learning and teaching. Using a qualitative research method (content analysis), three teachers&amp;rsquo; and 15 students&amp;rsquo; utterances were analyzed and the pros and cons of using this type of technology described. While some participants raised technological and instructional pitfalls many also reported educational and social gains. The teachers and the students had different reasons for establishing chat groups and for the possible uses of this technology; however, all agreed it was effective for their teaching and learning. Suggestions for the integration of this technology into Saudi and international translation classes are also given.
PROMT Systems for WMT 2019 Shared Translation Task

This paper describes the PROMT submissions for the WMT 2019 Shared News Translation Task. This year we participated in two language pairs and in three directions: English-Russian, English-German and German-English. All our submissions are Marian-based neural systems. We use significantly more data compared to the last year. We also present our improved data filtering pipeline.
Task Assignment of Heterogeneous UAV for Anti-radar Mission Using CTAP Models

In this paper, task assignment about the antiradar cooperative combat with heterogeneous unmanned aerial vehicle (UAV) formation is analyzed. First, the capacitated transshipment assignment problem (CTAP) model is adapted to represent the task assignment, and the fast and efficient largescale minimum cost algorithm combined with the tree search is proposed to solve the final task plan. Specifically, the variable definition and reasonable coordination constraints between UAVs and the tasks are presented in detail. Moreover, we design and realized a simulation system which is called heterogeneous UAV formation cooperative task assignment simulation platform, which can test and analyze the optimization and feasibility of the proposed algorithm.
PENDAMPINGAN PERSIAPAN UJIAN NASIONAL BERBASIS KOMPUTER PADA SISWA SMA MUHAMMADIYAH 3 YOGYAKARTA

English and Mathematics are two subjects been tested in Computer-Based National Examination (UNBK). Considering the importance of those subjects, we as the society servant team, guide the students in the process of that exam preparation. The purpose of this activity to build and strengthen the students’ level of understanding of the subjects to make them more ready to face the exam. The methods are observation, pretest to measure the early competence, and posttest to measure the increasing level of understanding after joining a series of guidance and training. The result is that there is a significant increase in the level of understanding among students regarding those subjects. The Mathematics from 5,0 becomes6,8 and English subject from 5,2 to 7,0.
Professorial forum "Science. Education. Regions" (Moscow, February 6‐7, 2019)

The article presents the report on the professorial forum 2019 "Science. Education. Regions", held on February 6 and 7, 2019 in Moscow, Russia, on the initiative of the Russian Professorial Assembly and the Civic Chamber of the Russian Federation.
Combining Summative and Formative Evaluation Using Automated Assessment

Aim/Purpose: Providing both formative and summative assessment that allows students to learn from their mistakes is difficult in large classes. This paper describes an automated assessment system suitable for courses with even 100 or more students. Background: Assessment is a vital part of any course of study. Ideally students should be given formative assessment with feedback during the course so students and tutors can identify weaknesses and focus on what needs improvement before summative assessment, which results in a grade. This paper describes and automated assessment system that lessens the burden of providing formative assessment in large classes. Methodology: We used Checkpoint, a web-based automated assessment system, to grade assignments in a number of different computer science courses. Contribution: The students come from diverse backgrounds, with a wide range of ages, previous qualifications and technical skills, and our approach allows the students to work at their own pace according to their individual needs, submitting their solutions as many times as they wish up to a deadline, using feedback provided by the system to help identify and correct their mistakes before trying again. Findings: Use of automated assessment allows us to achieve the goals of both summative and formative assessment: we allow students to learn from their mistakes without incurring a penalty, while at the same time awarding them a grade to validate their efforts. The students have an overwhelmingly positive view about our use of automated assessment, and their comments support our views on the assessment process. Recommendations for Practitioners: Because of the increasing number of students in today’s courses, we recommend using automated assessment wherever possible.
Cloud: privacy For Locations Based-services' through Access Control with dynamic multi-level policy

LBSs are Location-Based Services that provide certain service based on the current or past user's location. During the past decade, LBSs have become more popular as a result of the widespread use of mobile devices with position functions. Location information is a secondary information that can provide personal insight about one's life. This issue associated with sharing of data in cloud-based locations. For example, a hospital is a public space and the actual location of the hospital does not carry any sensitive information. However, it may become sensitive if the specialty of the hospital is analyzed. In this paper we proposed design presents a combination of methods for providing data privacy protection for location-based services (LBSs) with the use of cloud service. The work built in zero trust and we start to manage the access to the system through different levels. The proposal is based on a model that stores user location data in supplementary servers and not in non-trustable third-party applications. The approach of the present research is to analyze the privacy protection possibilities through data partitioning. The data collected from the different recourses are distributed into different servers according to the partitioning model based on multi-level policy. Access is granted to third party applications only to designated servers and the privacy of the user profile is also ensured in each server, as they are not trustable.
The Effect of Tarsia Puzzle on Vocabulary Learning in a Primary School Setting

Vocabulary learning is essential in learning a particular language as its mastery will greatly affect how learners comprehend the language.Young English as Second Language (ESL) learners with limited vocabulary competence are at risk of being left behind in developing the four language skills.Thus, educators across the world play a key role to ensure pedagogical improvement in teaching vocabulary moves at a rapid pace in academia.As an alternative to a conventional vocabulary lesson, this study applied the Puzzle-Based Learning (PBL) to identify its effect on young learners' vocabulary acquisition and their perception of the learning tool in an English as a second language (ESL) context.Tarsia Puzzle incorporates the most basic reasoning to accommodate the young learners' English proficiency.Using a mixed methods research design, the study involved 29 Primary 5 pupils (age 10) from a Malaysian rural school in Lahad Datu, Sabah.Data collection was completed through a set of pre-and post-test as well as a semi-structured interview.Scores from the tests were analysed and compared quantitatively and emerging patterns from the participants' interview were explored.The study suggests that Tarsia puzzle has helped to improve the learners' vocabulary acquisition in addition to collecting positive feedback from them regarding the learning tool.
Study of fault tree analysis and expert system principle

The basic concept of Fault Tree Analysis (FTA) is to select the most serious fault point or fault response in the system as the first problem to deal with. Secondly, the other secondary causes in the system fault are decomposed into intermediate events step by step until the failure causes are decomposed into basic events which can not be decomposed again. That is to say, the FTA adopts the method of decomposition and refinement step by step. The cause of the failure is stated.
Robust Visual Tracking via Collaborative and Reinforced Convolutional Feature Learning

Convolutional neural networks are potent models that yield hierarchies of features and have drawn increasing interest in the visual tracking field. In the paper, we design an end-to-end trainable tracking framework based on Siamese network, which proposes to learn the low-level fine-grained and high-level semantic representations simultaneously with the aim of mutual benefit. Due to the distinct and complementary characteristics of the feature hierarchies, different tracking mechanisms are adopted for different feature layers. The low-level features are exploited and updated with a correlation filter layer for adaptive tracking and the high-level features are compared through cross-correlation directly for robust tracking. The two-level features are jointly trained with a multi-task loss function end-to-end. The proposed tracker takes full advantage of the adaptability of the low-level features and the generalization ability of the high-level features. Extensive experimental tracking results on the widely used OTB and TC128 benchmarks demonstrate the superiority of our tracker. Meanwhile, our proposed tracker can achieve a real-time tracking speed.
Ontology Alignment Repair Through 0-1 Programming

In order to solve the problem of lack of effective methods for ontology inconsistency, a user preferences-oriented ontology alignment repair model is proposed. This model uses 0-1 linear programming method to minimize the remove cost; the structure and source of ontology are used to measure the axiom of importance; Finally, by choosing the minimal conflict sets and the user preferences limit repair strategy, the purpose of eliminating inconsistency, reducing semantic loss and guaranteeing credibility is achieved. The experimental results show that this method can effectively solve the problem of ontology inconsistency, and the restored ontology is more suitable for user preferences than traditional methods.