Reviewerly/exHarmony huggingface dataset:
Deep Learning based Enhanced Triplet Nework Model for Landmark Classification in Image Retrieval

Deep learning exist as a successful models for learning, and representing data through semantic method. These are learned as a Section of a classification task. This research work proposes the model of the triplet network, focusing on learning helpful depictions through distance comparisons. Landmark retrieval is a process of restoring a collection of images along its landmarks which is parallel to required images. In the case office reality, studies regarding landmark retrieval concentrates on utilizing the landmarks geometries regards its similarities especially the visual matches. The visual content of social images is of huge diversity in various landmarks, and a few images have similar patterns among various landmarks. At the same time, we noticed that multimodal contents will be there in social images, i.e., visual content and text tags, and landmark will be with its individual characteristic of both of visual as well as the text content. So that the approach on the basis of matching the similar images, may not be highly appealing in this environment. This research work focuses on, whether the visual and text content could be utilized over geographical correlation for landmark retrieval. Particularly, The work for landmark retrieval form an enhanced Triplet Network modal for landmark classification in order to project social image through multimodal content , by a joint model it integrates both landmark classifier and retirement through multimodal contents. The images with geo-tag will normally noted for classifier learning. On the basis of low rank matrix recovery Visual features get refined, and through automatically labeled images it can learn multimodal classification and group sparse. Finally, candidate images are ranked with the consequence of classification as well as semantic consistency between the visual and text content of the combination office.Research on real data visualize the superiority of this approach like comparing with the existing methods.
Crawling Chinese-Myanmar Parallel Corpus: Automatic Collection, Screening and Cleaning Corpus

Abstract The collection of Chinese-Myanmar Parallel Corpus (CMPC) is the key step in the natural language processing (NLP) and training Machine Translation Engine (MTE) of Southeast Asia minority languages. As the scarcity of CMPC resources that efficient corpus collection methods are worth studying extremely. Traditional corpus collection methods include manual collection, text recognition of books and Internet crawlers, etc. Among them, the most efficient method to collect corpus is internet crawler preached by many. Traditional Internet crawler algorithm is interfere easily by a lot of spamming and advertising that lead to the time-consuming and low-precision. We propose a web crawler mechanism combines acquisition automatically technology bilingual website list, crawling corpus and cleaning corpus to obtain high quality parallel corpus. Firstly, using the hyperlinks to recursively access related corpus websites through building the website graph. Furthermore, the breadth-first, Backline and PageRank crawler framework used to build a corpus selection model based on crawling with threshold, matching link, ranking the heat of page, through this, the CMPC can be found accurately. Finally, the corpus cleaning model based on the HTML parsing to determine a set of standardized token sequences. By testing the Chinese-Myanmar reptile algorithm established in this paper, the experimental results show that our benchmarks this model exceeds previous published benchmarks. Up to now, we have obtained 1.1 million parallel corpus pairs of Chinese-Myanmar.
An Application of Abel's Method to the Inverse Radon Transform

A method of approximating the inverse Radon transform on the plane by integrating against a smooth kernel is investigated. For piecewise smooth integrable functions, convergence theorems are proven and Gibbs phenomena are ruled out. Geometric properties of the kernel and their implications for computer implementation are discussed. Suggestions are made for applications and an example is presented.
Far eastern private libraries: preservation and investigation

Personal libraries are an important element of national and regional book culture. Their study helps to reveal similarities and peculiarities in spiritual, cultural and socioeconomic development of different regions of a country. The purpose of the article: to analyze the document flow of the Far Eastern authors, to determine the degree of study of pre-revolutionary personal libraries of the Far Eastern regions in Russia and the problems arising in their study and preservation. As a result, the content analysis revealed 33 publications on the subject for the period 1978-2017, 24 of them were published from 2001 to 2017. Conclusions: up to now there is no a complete list of owners of private libraries, their descriptions, views on the traditions of book-gathering in the region during the period men. This situation is fraught with the loss of a significant part of this segment of cultural heritage. This process can be stopped by the intensification of the identification, allocation and study of pre-revolutionary personal libraries of the far East in the book collections of institutions in the region, as well as the creation of an electronic resource that would accumulate data on the phenomenon under consideration.
Requirement Engineering Challenges in Distributed Software Development

In process of software development, requirement engineering is one of the main pillars and important elements because of the role it plays. Requirements acts as baseline upon which the pacts between the team members, people using it and those purchasing it are affirmed. For an increasing number of globalization and technological reasons, it is imperative to note that software development has grown in recent years and has resulted in several changes that are crucial in the establishment of development projects. The change is supported and geared by the desire to work round the clock and capitalize on the extensive number of available resources, reduce on expenses and the urge to be near or closer to the users but the negative effect of such applications is the risks associated with gaps in communications. The distributed development of software is a complex phenomenon, facing the challenges like: geographical separation of project members, different time zones, different languages, different backgrounds, team structure, organizational structure, communication and technology to be used for communication and coordination.
ipA-MedGAN: Inpainting of Arbitrarily Regions in Medical Modalities.

Local deformations in medical modalities are common phenomena due to a multitude of factors such as metallic implants or limited field of views in magnetic resonance imaging (MRI). Completion of the missing or distorted regions is of special interest for automatic image analysis frameworks to enhance post-processing tasks such as segmentation or classification. In this work, we propose a new generative framework for medical image inpainting, titled ipA-MedGAN. It bypasses the limitations of previous frameworks by enabling inpainting of arbitrarily shaped regions without a prior localization of the regions of interest. Thorough qualitative and quantitative comparisons with other inpainting and translational approaches have illustrated the superior performance of the proposed framework for the task of brain MR inpainting.
Exploring How Novice Programmers Pick Debugging Tactics When Debugging: A Student’s Perspective

Author(s): Lee, Felix | Advisor(s): Jones, James A | Abstract: Novice developers use a variety of debugging tactics to debug. However, how they select a tactic still remains unclear. Many studies in Software Engineering describe programmers using tactics like adding print statements, but only a few studies hint at factors such as knowledge and habits, social environment, and experience that may influence these decisions. To help us understand how novice programmers select debugging tactics, we turned to Information Foraging Theory (IFT) to analyze this decision-making process through the lens of a cost-benefit analysis. We conducted a qualitative study that explored how novice programmers describe their decision-making process when deciding which debugging tactics to use when debugging. We found that novice programmers use a variety of debugging tactics including testing code, searching for help, and taking notes on paper. Furthermore, we reported activities where novices leverage their past experiences, adapt to their task environments, and anticipate future risks and rewards to decide among a variety of tactics to pursue. From our results, we offer suggestions to educators to explicitly teach the value and costs of using certain tactics so that novice programmers may select the optimal tactic in any debugging situation. Furthermore, we suggest future research to explore novice debugging behaviors within non-computing environments to gain a holistic understanding of the debugging process.
Wasserstein Distance Guided Cross-Domain Learning

Domain adaptation aims to generalise a high-performance learner on target domain (non-labelled data) by leveraging the knowledge from source domain (rich labelled data) which comes from a different but related distribution. Assuming the source and target domains data(e.g. images) come from a joint distribution but follow on different marginal distributions, the domain adaptation work aims to infer the joint distribution from the source and target domain to learn the domain invariant features. Therefore, in this study, I extend the existing state-of-the-art approach to solve the domain adaptation problem. In particular, I propose a new approach to infer the joint distribution of images from different distributions, namely Wasserstein Distance Guided Cross-Domain Learning (WDGCDL). WDGCDL applies the Wasserstein distance to estimate the divergence between the source and target distribution which provides good gradient property and promising generalisation bound. Moreover, to tackle the training difficulty of the proposed framework, I propose two different training schemes for stable training. Qualitative results show that this new approach is superior to the existing state-of-the-art methods in the standard domain adaptation benchmark.
Development E-Module of Customary Forest Kantuk Based on Android to Improve Self Regulated Learning of Biology Education Students

This study aims to: (1) produce customary forest ecology modules that are appropriate for environmental knowledge courses, (2) produce effective customary forest ecology modules to improve students' self regulated learning.Based on Research and Development, the stages of development of ADDIE, (Analysis), (Design), (Development), (Implementation) and (Evaluation).The study was conducted at Tanjungpura University Biology Education.Subject research 25 student at research classes and 29 student at experimental class.Data collection techniques used product feasibility assessment sheets, observation self regulated learning.Hypothesis testing in this study using U Mann Whitney test.The results of the study (1) e-module is suitable for Untan biology education students, (2) e-module effectively enhances of self regulated learning of Untan biology students.
A Study on Impacts of Service Failure Severity on Perceived Service Recovery Justice, Recovery Satisfaction and Repatronage Intention: Focus on Moderate Effect of Regulatory Focus

Almost companies try to get customer satisfaction, because customer satisfaction is important prerequisite for promoting repurchase, WoM delivery and maintaining a continuous relationship. The customer
The Development of Social Study Model through Online on Early Child Education Program at Muhammadiyah University Student

The aim of the study was to develop a learning model, so that it could become a guideline in learning Social Sciences for students of the Early Childhood Education Study Program.Renewal in the model developed innovates conventional learning models into modern learning.The method used in this study is Research and Development, with learning prototype products developed in the form of e-modules.The results of the study after being tested in the three educational institutions used as the place of research were obtained; (a) pretest 34.94% posttest 80.12%, difference of 45.18%,(b) pretest 29.26% posttest 79.26%, difference of 50%, (c) pretest 43.67%, posttest 86.57%, difference of 42.9%.The conclusion is that effectiveness and feasibility of the model are feasible and effective to be used in social studies learning.
Speaker Recognition and Gender Identification using Artificial Neural Network and Support Vector Machine

Identity of a person via voice is one of the most interesting techniques used for user identification. Accuracy of identification process depends on the number of feature vectors and the number of speakers. This paper aims to develop a system able to identify a person and gender from his speech. Recognition relies on English words as a recognition phrase. For speaker recognition speech features used are Mel Frequency Cepstral Coefficients (MFCCs), Energy, Pitch and Zero Crossing Rate while for gender recognition only MFCCs, Energy and pitch are used. Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used as classifiers.Experimental results demonstrated that the proposed system returns 97% accuracy rate for gender identification and 95% accuracy rate for speaker recognition.
V-Rod: Floor Interaction in VR

We present a novel cane-based device for interacting with floors in Virtual Reality (VR). We demonstrate its versatility and flexibility in several use-case scenarios like gaming and menu interaction. Initial feedback from users point towards better control in spatial tasks and increased comfort for tasks which require the users’ arms to be raised or extended for extended periods. By including a networked example, we are able to explore the asymmetrical aspect of VR interaction using the V-Rod. We demonstrate that the hardware and circuitry can deliver acceptable performance even for demanding applications. In addition, we propose that using a grounded, passive haptic device gives the user a better sense of balance, therefore decreasing risk of VR sickness. VR Balance is a game that intends to quantify the difference in comfort, intuitiveness and accuracy when using or not using a grounded passive haptic device.
Artificial Intelligence, Legal Change, and Separation of Powers

A number of prominent contemporary legal scholars have recently argued in favor of replacing at least some human legal decision making with Artificial Intelligence (AI), assuming that AI technology improves to a level these scholars deem appropriate. This article disagrees, particularly as regards Article III judges, for two main reasons. First, human judges must strike a delicate balance between stability and change; that is, between respect for precedent on the one hand, and adapting the law to unforeseen circumstances on the other, thus playing an important role in shaping the law that is not adequately considered in this literature, and that an AI judiciary may not be able to adequately replace. Second, the loss of human judges would likely lead to a loss or diminishment of the human legal community, such that fewer people would be paying attention to the law. This community of people with strong incentives to pay attention to the law is built around the Article III judiciary, and the diffusion of knowledge throughout this community may be a significant source of the judiciary's power to fulfill its role as a check on the other two branches. The potential benefits of an automated judiciary can be better achieved in other ways, and likely do not justify the risks. At the least, these concerns are not adequately addressed by those advocating for AI judges, and should be seriously considered in the context of any effort to automate parts of the judiciary.
Thickness measurement of transparent plate based on wavelength-tuning interferometry

Two methods that used to measure the thickness of transparent plate were introduced in this paper. In conventional interferometry, multiple-surface interference fringes and the nonzero incidence angle complicated the calculation. In this study, the absolute thickness was obtained from the interferogram generated by two beams of coherent light reflected from the front surface and the rear surface of the transparent plate without reference surface. The thickness was obtained by analyzing light intensity of each point in the interferogram which varies sinusoidally with the wavelength in period method. While slope method figured out the mean thickness of whole surface with phase-shifting algorithm and least-square fitting algorithm. When the beam was perpendicular to the surface, the absolute thickness can be figured out with the refractive index n and &lambda;<sub>1</sub>, &lambda;<sub>2</sub> in first technique. The results of simulation experiments indicated that the error in first technique was with sub micrometer, however the error of slope method was only a few nanometers.
Cascading: Association Augmented Sequential Recommendation

Recently, recommendation according to sequential user behaviors has shown promising results in many application scenarios. Generally speaking, real-world sequential user behaviors usually reflect a hybrid of sequential influences and association relationships. However, most existing sequential recommendation methods mainly concentrate on sequential relationships while ignoring association relationships. In this paper, we propose a unified method that incorporates item association and sequential relationships for sequential recommendation. Specifically, we encode the item association as relations in item co-occurrence graph and mine it through graph embedding by GCNs. In the meanwhile, we model the sequential relationships through a widely used RNNs based sequential recommendation method named GRU4Rec. The two parts are connected into an end-to-end network with cascading style, which guarantees that representations for item associations and sequential relationships are learned simultaneously and make the learning process maintain low complexity. We perform extensive experiments on three widely used real-world datasets: TaoBao, Amazon Instant Video and Amazon Cell Phone and Accessories. Comprehensive results have shown that the proposed method outperforms several state-of-the-art methods. Furthermore, a qualitative analysis is also provided to encourage a better understanding about association relationships in sequential recommendation and illustrate the performance gain is exactly from item association.
Case-based method of training in the formation of universal competencies of students in educational organizations of the system of the Ministry of Internal Affairs of Russia

The article is devoted to the formation of universal competences of students in educational institutions of the system of the Ministry of Internal Affairs of Russia using the case-method of teaching. The authors have identified the main synonymous contradictions in the definition of the concept of “universal competence”. The main problems that lie in the choice of means and methods of development and the formation of universal competencies of students are highlighted. The subject of the study was determined the process of formation of universal competencies of students. The basic concepts of the competence approach are considered. It shows what universal competences should be formed in the framework of GEF3 ++ by a specialist in the specialty law enforcement. Disclosed the concept of educational technology, as well as the essence of the case-method of teaching. It schematically shows what the technological stages of the case-method of teaching consist of. An example of one of the mini-projects, which is aimed at the formation of universal competencies of students. Table 1 showed the stages of formation of universal competencies among students, and Table 2 showed the formation of universal competencies of students in a class on the basics of special ATS equipment.&#x0D; As a result of the study, it can be concluded that, in general, the case method allows to improve the educational process, involve students in the process of live communication, instill autonomy for searching, analyzing, extracting and evaluating information, which contributes to the process of developing universal competences of future police officers. In this paper, the following methods were applied: comparative analysis, case-method of teaching.
The Sounds of Music : Science of Musical Scales III -- Indian Classical

In the previous articles of this series, we have discussed the development of musical scales particularly that of the heptatonic scale which forms the basis of Western classical music today. In this last article, we take a look at the basic structure of scales used in Indian classical music and how different `raga's are generated through the simple process of scale shifting.
Offline handwritten mathematical symbol recognition utilising deep learning

This paper describes an approach for offline recognition of handwritten mathematical symbols. The process of symbol recognition in this paper includes symbol segmentation and accurate classification for over 300 classes. Many multidimensional mathematical symbols need both horizontal and vertical projection to be segmented. However, some symbols do not permit to be projected and stop segmentation, such as the root symbol. Besides, many mathematical symbols are structurally similar, specifically in handwritten such as 0 and null. There are more than 300 Mathematical symbols. Therefore, designing an accurate classifier for more than 300 classes is required. This paper initially addresses the issue regarding segmentation using Simple Linear Iterative Clustering (SLIC). Experimental results indicate that the accuracy of the designed kNN classifier is 84% for salient, 57% Histogram of Oriented Gradient (HOG), 53% for Linear Binary Pattern (LBP) and finally 43% for pixel intensity of raw image for 66 classes. 87 classes using modified LeNet represents 90% accuracy. Finally, for 101 classes, SqueezeNet ac
A Study on Prediction of Birth Weight for Infants using Transition Probability Matrix with respect to Haemoglobin Count

Rapid developments in stochastic models in recent years have resulted in significant changes in the way the world operates.This has an impact on educational needs, both in terms of the content and the delivery of educational services and there has been increasing pressure on decision -makers to acquire new technologies.This paper examines the mature and premature birth using Haemoglobin count during pregnancy time by Transition Probability Matrix.The maternal variables such as maternal weight, maternal height, haemoglobin count and birth weight of babies are considered as important factors in the study of demographic and public health related issues.The result shows that the birth weight mainly depends on Haemoglobin concentration.