Reviewerly/exHarmony huggingface dataset:
An Effectual Video Indexing and Retrieval Model: A Comparative Study

Video indexing and retrieval are considered to be the most crucial factor that has to be concentrated in multimedia search engine based investigations. The analysis with multimedia content may enhance the accuracy of the indexing and retrieval system, which is performed with two different phases. Former is content-based, and the latter is context-based annotation. The previous annotation includes both higher level and lower level features, which is acquired from pixel intensity; similarly, the latter is based on semantic video details. Here, an extensive analysis is done with three different methods known as Fuzzy based SVM classifier, logit boost ensemble classifier, and Naive Bayes classifier, where feature extraction is concentrated on various factors. These diverse methods pretend to carry of retrieval of the video effectually with specific advantages and disadvantages. Here, numerical analysis is done with different factors like accuracy, sensitivity, specificity, F-measure, Recall, and so on. The total amount of frames considered for computation is also determined. The videos are attained from the available online dataset, and initial pre-processing is carried out to make the quality of image frames to be superior. The overall analysis shows the performance of classifiers with improved prediction accuracy for video retrieval.
Color deep learning profilometry for single-shot 3D shape measurement

Fringe projection profilometry (FPP) has been more widely applied in fields such as intelligent manufacturing and medical plastic surgery. Recovering the three-dimensional (3D) surface of an object from a single frame image has always been the pursued goal in FPP. The color fringe projection method is one of the most potential technologies to realize single-shot 3D imaging because of the multi-channel multiplexing. Inspired by the recent success of deep learning technologies for phase analysis, we propose a novel single-shot 3D shape measurement approach named color deep learning profilometry (CDLP). Through `learning' on extensive data sets, the properly trained neural network can gradually `predict' the crosstalk-free high-quality absolute phase corresponding to the depth information of the object directly from a color fringe image. Experimental results demonstrate that our method can obtain accurate phase information acquisition and robust phase unwrapping without any complex pre/post-processing.
Tourism and Insecurity in the World

The study assesses the role of forces of law and order in modulating the insecurity-tourism nexus in 163 countries for the period 2010 to 2015. Policy syndromes or insecurity dynamics include: violent crime, access to weapons, political instability and perception of criminality while the policy variables of forces of law and order are captured with “security officers & police” and “armed service personnel”. The empirical evidence is based on Negative Binomial regressions. The findings show that the policy variables can be effectively used to crowd-out the negative incidence of policy syndromes on tourist arrivals. The results are contingent on net effects (from conditional and unconditional effects), insecurity dynamics and thresholds. A threshold is an inflexion point at which the unfavorable unconditional effect from a policy syndrome of insecurity on tourist arrivals is completely neutralized by policy variables of forces of law and order. Policy implications are discussed.
Employing Space-Based Hyperspectral Imagery for Pipeline Leak Prevention, Detection and Compliance

Abstract We are now living in what has been described as the Experience Era, where lines between the digital and physical are increasingly blurred. As such, we are just beginning to see how customized access to space will improve asset stewardship in ways that are still evolving, as customization of on-orbit technology pushes the bounds of how we receive and process information. Specific to oil and gas operators, one technology being launched by microsatellite, hyperspectral imagery (HSI), is poised to enable unparalleled daily global pipeline leak prevention, detection and speciation, intrusion and change detection capabilities. This will replace conventional DOT pipeline patrol for compliance while contributing to our understanding of vapor emissions as regulated by the Environmental Protection Agency. This paper discusses both the evolving space marketplace and the state of the art for HSI, including current examples of hyperspectral findings regarding pipeline and terminal leaks. Successful deployment of HSI will drive a decrease in the number and magnitude of pipeline leaks using persistent, global, high-resolution data collection, rapid and reliable analysis, and immediate reporting of actionable information. For decades, satellite HSI technology has offered a promise of remote hydrocarbon detection and other features of interest. It is only now becoming scalable, accessible to, and cost-effective for the pipeline industry, and thus a reality for cost-effective pipeline stewardship.
Optimal Control for Acrobot with Two-link Manipulators

Abstract This paper presents a unified treatment of the optimal control of under-actuated two-link manipulators. Firstly, a nonlinear invertible transfer is introduced to simplify design of controller, then several optimal control laws are established by maximum principle. Controller for swing-up area is based on performance index, while controller for attractive area is based on LQR (linear quadric regulator). It is proved that optimal control is not singular, invariant sets are obtained by theoretical analysis. The strategy for Acrobat is to use optimal control law to pump manipulators into linearizable area, then switch control law to LQR, which drives system to up-straight position. Local and global stability were analyzed in detail under control strategy using LaSalle’s invariance principle and WCLF (non-smooth control Lyapunov function). Simulation and comparison with previous results show that the proposed approach here is valid and of advantages.
INVESTIGATING SPELLING ERRORS AMONG INDONESIAN EFL SECONDARY SCHOOL STUDENTS

In the Indonesian context, students who learn English face some problems in spelling words. They often make spelling errors in essay writing. This study aims to explore and categorize spelling errors made by Indonesian secondary school students and the primary sources contributing the errors based on Error Analysis (EA) framework. The participants consisted of 38 students chosen from a secondary school in Lampung, Indonesia. The data were obtained from documentation of student’s written essays, and responses in the questionnaire analyzed based on Carney’s (1997) categories of spelling error and Richards’s (1980) types of error sources.  The findings showed that errors under categories of variant, slip, and the split was the most frequently found as the students were mistaken in choosing among the competing letters and sounds and also careless with the correct spelling. Besides, the major error sources were associated with the Indonesian (L1) interference when students transferred their mother tongue to the English (L2) spelling and the inconsistency between English spelling and sounds. The study implies that the learners’ better understanding of L1 and L2’s different systems of spelling as well as pronunciation rules will help them enhance their spelling performance. It is, therefore, suggested that the teachers give students more practice to drill and to pronounce the frequently misspelled words simultaneously to develop their spelling accuracy.
The Technology of Retail Central Bank Digital Currency

Central bank digital currencies (CBDCs) promise to provide cash-like safety and convenience for peer-to-peer payments. To do so, they must be resilient and accessible. They should also safeguard the user's privacy, while allowing for effective law enforcement. Different technical designs satisfy these attributes to varying degrees, depending on whether they feature intermediaries, a conventional or distributed infrastructure, account- or token-based access, and retail interlinkages across borders. We set out the underlying trade-offs and the related hierarchy of design choices.
Developing student worksheet based on iMindMap in animalia topic to train creative thinking ability

Creative thinking ability can be trained to students during learning process using suitable learning materials. The purpose of this study was to produce a valid and practical student worksheet based on iMindmap in Animalia topic to train students’ creative thinking ability. This R &amp; D referred to 4D model (define, design, develop, and disseminate) without disseminate phase. The validity of student worksheets was determined based on experts (material, education and media) assessment. Meanwhile, the practicality was evaluated based on teachers’ and students’ responses; and the readability of the worksheet decided based on Fry Graph. The data gained were analyzed descriptive-quantitatively. The research results revealed that the worksheet developed was declared as very valid (mode value: 4) and very practical based on practitioners' responses (mode value: 4) and students’ responses (the positive responses reached 90.45%). In addition, the readability test result was at level 10, which means that the worksheet was suitable for Grade 10. Hence, it can be concluded that the student worksheet based on iMindMap in Animalia topic can be used to train students’ creative thinking ability.
Commercial virtual reality headsets for developing augmented reality setups to track three-dimensional motion in real time

Abstract We designed and tested a 3D motion tracking system which displays acquired and derived data using augmented reality. This setup, based on a virtual reality headset and its controllers, is a relatively low-cost, high-precision, six degrees of freedom, high-speed, multiple-objects tracking device. This apparatus proves to be a very high-quality motion sensor which can be used in the physics laboratory for motion tracking. Stereo cameras may also be attached to the headset in order to achieve augmented reality, which can be a winning addition in order to effectively use these devices in the physics laboratory. In view of its characteristics, this system can be adopted in the study of motion both at high school and undergraduate physics laboratories.
PERANCANGAN SPACE MAKERDI PERPUSTAKAAN UNIVERSITAS SURABAYA SEBAGAI UPAYA PENINGKATAN FUNGSI FASILITAS DALAM PEMBELAJARAN DAN PENELITIAN

Abstract - Library as an academic support should have some complete, organize, and fuctional components than can support in learning, research, and community service. Makerspace will be added to Surabaya University library. Makerspace is a place for sharing tools, knowledge, and ideas. Library will not be a silent space anymore, but an open place to expression and develop creativity. The purpose of this research is to design makerspace table in Surabaya University for improving the facilities by using qualitative method through a book or internet and quantitative method by interview. The result of this research is a makerspace table with modern-futurictic concept and have many features to simplify the user, such a lighting, cutting mat, dustbin, stationery space, book shelves.
A method of locating the 3D centers of retroreflectors based on deep learning

Purpose Laser-based measurement techniques offer various advantages over conventional measurement techniques, such as no-destructive, no-contact, fast and long measuring distance. In cooperative laser ranging systems, it’s crucial to extract center coordinates of retroreflectors to accomplish automatic measurement. To solve this problem, this paper aims to propose a novel method. Design/methodology/approach We propose a method using Mask RCNN (Region Convolutional Neural Network), with ResNet101 (Residual Network 101) and FPN (Feature Pyramid Network) as the backbone, to localize retroreflectors, realizing automatic recognition in different backgrounds. Compared with two other deep learning algorithms, experiments show that the recognition rate of Mask RCNN is better especially for small-scale targets. Based on this, an ellipse detection algorithm is introduced to obtain the ellipses of retroreflectors from recognized target areas. The center coordinates of retroreflectors in the camera coordinate system are obtained by using a mathematics method. Findings To verify the accuracy of this method, an experiment was carried out: the distance between two retroreflectors with a known distance of 1,000.109 mm was measured, with 2.596 mm root-mean-squar error, meeting the requirements of the coarse location of retroreflectors. Research limitations/implications The research limitations/implications are as follows: (i) As the data set only has 200 pictures, although we have used some data augmentation methods such as rotating, mirroring and cropping, there is still room for improvement in the generalization ability of detection. (ii) The ellipse detection algorithm needs to work in relatively dark conditions, as the retroreflector is made of stainless steel, which easily reflects light. Originality/value The originality/value of the article lies in being able to obtain center coordinates of multiple retroreflectors automatically even in a cluttered background; being able to recognize retroreflectors with different sizes, especially for small targets; meeting the recognition requirement of multiple targets in a large field of view and obtaining 3 D centers of targets by monocular model-based vision.
Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy

We study the global convergence and global optimality of actor-critic, one of the most popular families of reinforcement learning algorithms. While most existing works on actor-critic employ bi-level or two-timescale updates, we focus on the more practical single-timescale setting, where the actor and critic are updated simultaneously. Specifically, in each iteration, the critic update is obtained by applying the Bellman evaluation operator only once while the actor is updated in the policy gradient direction computed using the critic. Moreover, we consider two function approximation settings where both the actor and critic are represented by linear or deep neural networks. For both cases, we prove that the actor sequence converges to a globally optimal policy at a sublinear O(K−1/2) rate, where K is the number of iterations. To the best of our knowledge, we establish the rate of convergence and global optimality of single-timescale actor-critic with linear function approximation for the first time. Moreover, under the broader scope of policy optimization with nonlinear function approximation, we prove that actor-critic with deep neural network finds the globally optimal policy at a sublinear rate for the first time.
Right Code

These days, when you see the headline "How Safe Are Airplanes?" the text more likely refers to the spread of disease than the likelihood of a crash. With steady safety improvements over at least the last five decades, airplane crashes have become exceedingly uncommon, and they are rarely caused by flaws in the design of the airplane itself. The most common remaining causes are the usual suspects of human error, extreme weather events, and mechanical failure.
Optimasi Umur Pakai dan Efektivitas Boiler Akibat Korosi Menggunakan Algoritma Particle Swarm

One of the causes of boiler pipe breakage is corrosion. The prediction of the lifetime of the tube and superheater effectiveness on the boiler shall be calculated to determine and predict the failure time. The prediction of the lifetime of the tube is a prediction of the time of the failure of the superheater tube in the boiler so that the return can be performed. The effectiveness of the superheater in the boiler is a comparison of the heat transfer rate of the overheated tubes inside the boiler. The research method was research and development, a computational program using an optimization process. The variables used in the study were steam input temperature, metal tube temperature, steam outlet temperature, natural gas input temperature, natural gas mass flow rate, vapour mass flow rate, and natural gas outlet temperature. This study takes a case study of the superheater boiler from the company. The results of calculation of the life expectancy of the operating conditions shall be 11 years when the boiler data sheet is used and 14 years for using the actual boiler data. Superheater effectiveness in boilers with 56% is a data sheet on boilers and 56% of the actual data. Optimization of the particle swarm algorithm predicted a 29 year lifespan. The effectiveness of the superheater in the boiler is 86%.
A Study on the Orientation of Korean Grammar Books Toward Japanese Writing in the Early Modern Period : Focusing on Haneotong ( 『韓語通』) and Haneomunjeon ( 『韓語文典』)

The purpose of this paper is to examine the characteristics and writing direction of Korean grammar books published in Japan in the early modern period, this will be done through a comparison of Haneotong(󰡔韓語通󰡕, 1909) and Haneomunjeon(󰡔韓語文典󰡕, 1909). This will be achieved by examining Korean󰠏related documents written by the Japanese after the publication of Haneoipmun(󰡔韓語入門󰡕) in 1880. This is done by comparing and analyzing the purpose of the compilation of the two texts (while noting the distinct character of the grammar books), in addition we look at the structure of the documents, and the diversity of Korean grammar descriptions which were all compared and analyzed. Unlike the authors of other workbooks and conversation books in the early modern period, the two researchers who had a high knowledge of Korean were the author of Haneotong(󰡔韓語通󰡕), Maema Kyosaku, and the author of Haneomunjeon(󰡔韓語文典󰡕, Takahashi Doru. The orientation of these grammar books was not seen as a description for practical purposes. Rather, the knowledge of the Korean language that was acquired by the two researchers was embodied in their writing qualities and can be viewed as a compilation centered on Korean grammar. Unlike other Korean language books of this period, these books were filled with insightful grammar descriptions. The linguistic characteristics of the Korean language were incorporated into the grammar books through the phonology and the history of the Korean language. Since these two scholars' writings have influenced Korean linguists after the post󰠏 Japanese colonial period, we emphasize that Korean linguists have been greatly helped in describing and organizing their grammar.
Deep Learning Methods for Semantic Matching, Image Retrieval and Camera Relocalization

Image matching is a central component in many computer vision applications. The field has progressed significantly with the advancement of deep learning models such as convolutional neural networks. The thesis makes several contributions in advancing the performance of existing CNN based approaches in closely related problem areas of image matching, namely semantic matching, image retrieval and image based localization. In this thesis, the problem of data and ground-truth labelling efficiency for training CNN models is studied in the context of semantic matching. A weakly supervised method is presented to address the problem of learning using small training datasets. The method first generates additional training samples using existing data and proposes a novel loss function based on cyclic consistency to regularize the training process. Results show that the proposed method can learn from weakly labelled data without pixel level correspondence information. In the next part of the thesis, we study the application of both global and local image matching to the problem of image retrieval. In the problem of particular landmark retrieval the thesis studies the role of contextual information in global query image representation which is generally ignored by existing approaches to remove noisy background information. An attention model is proposed that uses bottom-up saliency to modulate contextual information in intermediary CNN representations in a top-down manner. On the other hand, to address the challenges due to local variations in city-scale retrieval, the thesis proposes a geometric verification method using CNN based image matching. In addition, it proposes method for improving the accuracy and efficiency of the image matching method. Lastly, the thesis demonstrates methods utilizing the key concepts from image matching and image retrieval to address problems in the field of image based localization. In contrast to existing approaches the proposed method can be applied to novel scenes not seen during training and scales favourably with the size of the environment. In addition, a challenging indoor localization dataset is made publicly available to address limitation of existing datasets.
Human Germline Genome Modification and the Right to Science: A Comparative Study of National Laws and Policies Introduction

This is the Introduction to Andrea Boggio, Cesare P.R. Romano, Jessica Almqvist (eds.), Human Germline Genome Modification and the Right to Science: A Comparative Study of National Laws and Policies, Cambridge University Press, 2019. It is the version sent to the publisher, before editing. Please, cite to the published book. This chapter introduces the reader to the basics of human embryology and genetic editing, including CRISPR/Cas9 and similar genetic editing tools. Then, it explains the methodology of the book, how profiles states were chosen, what questions contributors were asked, and the general theoretical framework of the volume.
Microseismic Monitoring of Mines in Real Time with Ensemble Kalman Filter: A Canadian Case Study

Summary Rockbursts are extreme events resulting from complex interaction between mining activities and geology, and represent a big threat to mines. Throughout the past years, the use of passive seismic monitoring technics for mine safety and productivity have progressed significantly, aiming to understand and predict this hazard. In this work we propose an additional value to microseismic monitoring, translating the changes in rock mass stress as changes in velocity of seismic waves, and using these as a complementary variable for rockburst predictions. This work outlines a general framework for efficiently update velocity models combing Sequential Gaussian Simulation and Ensemble Kalman Filter techniques, facilitating real time monitoring of mines. Hoping to assist mine operators to better prevent rockburst in real time we present the performance of the method on a real 3D case of a deep mine in Canada.
An effective lossless dual-images based hiding method using block complexity and adopted thresholds

ABSTRACTDual-images based hiding method is one kind of the hiding scheme which uses two duplicated images to conceal the secret message to generate the stego-images. Only a legal person who owns two stego-images can extract the correct message from the image. This kind of schemes been paid a lot of attention recently because it has better image quality and embedding capacity and realizes the concept of secret sharing. Lu et al. (2017) proposed a reversible dual-image based hiding scheme that used a block folding strategy to reduce the distortion between the cover image and the stego-image. They divided the secret message into two parts and encoded them into smaller digits to enhance the image quality of the stego-image. But their scheme needs an extra pixel to record the section numbers that limit the capacity of the secret message. This paper considers the complexity of the pixel to analysis how many bits can be embedded in the pixel to solve the problem. Furthermore, the proposed scheme applies an adaptive thresholds strategy to decide the thresholds for controlling the image quality. Experimental results show that the proposed scheme indeed improves the hiding performance.