Reviewerly/exHarmony huggingface dataset:
PEMBUATAN BOARD GAME UNITE AND FIGHT

cooperation is an important aspect that need to be taught as soon as possible, especially to teenager which is an important phase of individuality shaping. One of the example of such activity is outbound, but outbound is difficulties to be prepared, thus a convenient media to introduce the importance of cooperation is made in the form of board game with unique point in how to win the game. After that, board game with similar mechanic and companion app analyzed. The result then used to design the board game and companion app. The design of board game and companion app then made using adobe photoshop CS6 and unity. The board game made with cooperative play, action point allowance, variable phase order, programmed movement, dice rolling and grid movement mechanic. After made then the board game tested by doing blind test play with 10 people, after that each participant will be asked to fill the validation questionnaires, from their answers in the questionnaire inferred that the board game has positive result, which is to tell the participant that cooperation, communication and coordination is important, also the board game could attract their attention.
Numerical secondary terms in a Cohen–Lenstra conjecture on real quadratic fields

In 1984, Cohen and Lenstra made a number of conjectures regarding the class groups of quadratic fields. In particular, they predicted the proportion of real quadratic fields with class number divisible by an odd prime. We numerically investigate the difference between reality and these predictions. Using 4 million data points, we perform a curve fitting of the difference with a monomial term and demonstrate that there is reason to believe the term can be effectively approximated within the scope of our data set for odd primes less than 30. We use cross-validation to show that including our monomial term as a secondary term to the original conjecture reduces the overall error.
Enhancement of Geophysical Data for Kimberlite Exploration at Iron Mountain, Wyoming, USA

Composite geophysical data from the Iron Mountain Kimberlite District indicate enhancement of geophysical data may aid in kimberlite exploration by accentuating or revealing subtle anomalies.Surface total magnetic field intensity and conductivity data were processed jointly to assess the utility of their combination for rapid and reliable kimberlite detection.Several anomalies, interpreted to reflect kimberlite, were identified in both geophysical and enhanced data from the Iron Mountain area (Figure 1).Most aberrations generally occur on trend with known kimberlite blows and likely mark the subsurface extension or feeder dikes of these intrusions, or separate bodies emplaced along a common structural trend.
A Perspective of Conventional and Bio-inspired Optimization Techniques in Maximum Likelihood Parameter Estimation

Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and particle physics, and geographical satellite image classification, and so forth. Over the past decade, although many conventional numerical approximation approaches have been most successfully developed to solve the problems of maximum likelihood parameter estimation, bio-inspired optimization techniques have shown promising performance and gained an incredible recognition as an attractive solution to such problems. This review paper attempts to offer a comprehensive perspective of conventional and bio-inspired optimization techniques in maximum likelihood parameter estimation so as to highlight the challenges and key issues and encourage the researches for further progress.
Morphological, Resorption and Etch Feature Trends of Diamonds from Kimberlites within the Colorado-Wyoming State Line District, USA

Nearly 1700 carats of diaitKDnds were recovered during conpany bulk testing of kimberlite occurrences in the State Line District of the Colorado-Wyoming Kimberlite Province.Representative splits of parcels from each locality are being evaluated both physically and chemically along with mineral inclusions, but physical properties are emphasized in this study.Morphological and surface texture characteristics are being described in detail to generate data that may provide insight into the genesis and resorption history of this unique North American diamond suite.Parcels from seven occurrences (Aultman, Maxwell, Schaffer, Chicken Park, George Creek, Sloan 1 & 2, Sloan 5 & 6) (Fig. 1) are included in the evaluation (includes all stones in small parcels and several thousand stones overall).The classification system established by D.N. Robinson (1979) was utilized with modifications.
Kimberlite and lamproite comparative petrogeochemistry

The discovery of very high-grade diamond-bearing lamproites in 1979 in Australia [1] has stimulated the study on correlation between kimberlite and lamproite chemical compo¬ sition.For purpose of petrogeochemical comparison of lamprel¬ ites and kimberlites from the Siberian platform with lamproites of Western Australia 25 rock speciments (15 of lamproitic series and 10 of kimberlitic one) have been analysed in the Earth Crust Institute for content determination of 11 oxides (SiOg, TiOg, AlgOg, FegOg, FeO, MgO, CaO, NagQ, KgO, COg, HgO) and of 19 microelements (Ba, Li, Rb, Sr, Pb, Th, U, Zr, Nb, Y, La, Ce, Sc, V, Cr, Co, Ni, Zn, Sn).Rock samples of lamproitic series are represented by leucitites, leucitic lamproites and potassic minettes; kimberlite samples include basalloid and micaceous varieties.Has been made for the total sampling from 42 objects with 30 components in very of them (25 analyses on our data and 17 analyses of lamproites from Western Australia [1, table 31-343) Computer-aided factor analysis.The factor diagram for rockforming oxide and microelement contents (Fig. 1) well illustrates, on the one hand, the diffe¬ rence between kimberlites and lamproites and, on the other, between lamproites of the Siberian platform and lamproites of Western Australia: kimberlites contain more of MgO, COg and compatible elements (Cr, Ni, Co); lamproites of Western Austra¬ lia are enriched in TiOg and in a quite a number of incompatible elements (Sn, Pb, Rb, Zn, Th, Ce, La); lamproites from the Sibe¬ rian platform are characterized by higher AlgQg, NagO, U, V contents.In addition, the rocks of lamproitic series possess higher SiOg, KgO, Sr, Zn, Y, Ba contents compared to kimberli¬ tes, but lamproites and kimberlites of the Siberian platform are richer in FeO and CaO compared to lamproites of Western Australia FegOg, Li and Sc contents do not show distinct regu¬ larities in distribution between three rock groups.As diagram shows kimberlites of the Siberian platform and West Australian lamproites are located in the right half of the diagram (positive direction of the second factor) and lamproitic
Geochemistry of Indicator Minerals from Chinese Kimberlites and Lamproites

Over 900 grains of indicator minerals, including pyrope, chromite, ilmenite, LIMA and yimengite have been analyzed by EMP and proton microprobe.The bodies studied come from seven different provinces and include both diamondiferous and barren intrusives.The data show consistent patterns that can be used in exploration and evaluation of prospects within each area.Kimberlites and lamproites have been found only in the Sino-Korean craton and the Yangzi craton, while similar bodies in the Talimu craton (Xinjiang Autonomous Region) have proven to be lamprophyres.Most studied bodies in the Sino-Korean craton are Group I kimberlites; some contain economic deposits of diamonds, while others are barren.The Yangzi craton contains both lamproites and possible Group II kimberlites; some contain low grades of diamonds.Among the indicator minerals, pyrope and chromite are very common, while ilmenite is rare, being restricted to kimberlites in Shandong and Liaoning.LIMA and yimengite are found in Shandong kimberlites, especially Hongqi No. 27.
Fast Topology-Based Feature Tracking using a Directed Acyclic Graph

We present a method for tracking regions defined by Merge trees in time-dependent scalar fields. We build upon a recently published method that computes a directed acyclic graph (DAG) from local tracking information such as overlap and similarity, and tracks a single region by solving a shortest path problem in the DAG. However, the existing method is only able to track one selected region. Tracking all regions is not straightforward: the naïve version, tracking regions one by one, is very slow. We present a fast method here that tracks all regions at once. We showcase speedups of up to two orders of magnitude.
Influence of job dissatisfaction on recent raise of employee turnover intention in UAE banks: A mixed-methods approach

Statistics between 2011 and 2014 reveal that the ratio of UAE nationals in the banking sector has decreased from 35% to 34% despite growth in the sector. This study intends to focus on and identify the factors affecting the turnover among UAE nationals in the banking sector using qual-quant mixed methods approach. The estimated betas reveal that the cost of leaving a job, after service benefits, the financial benefits, the peer-to-peer and leader-employee relationship, and the career growth prospects significantly affect turnover intention. Further, financial benefits, after-service benefits are the most important variables affecting job satisfaction and organisational commitment. In addition, the qualitative analysis suggests that work overload and stress affect job satisfaction. The present paper enhances the rigour and validity of the data and findings by using a mixed methods approach. Findings of the paper call for a review of current managerial practices in the UAE banking sector.
Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin

In the absence of prior knowledge, ordinal embedding methods obtain new representation for items in a low-dimensional Euclidean space via a set of quadruple-wise comparisons. These ordinal comparisons often come from human annotators, and sufficient comparisons induce the success of classical approaches. However, collecting a large number of labeled data is known as a hard task, and most of the existing work pay little attention to the generalization ability with insufficient samples. Meanwhile, recent progress in large margin theory discloses that rather than just maximizing the minimum margin, both the margin mean and variance, which characterize the margin distribution, are more crucial to the overall generalization performance. To address the issue of insufficient training samples, we propose a margin distribution learning paradigm for ordinal embedding, entitled Distributional Margin based Ordinal Embedding (DMOE). Precisely, we first define the margin for ordinal embedding problem. Secondly, we formulate a concise objective function which avoids maximizing margin mean and minimizing margin variance directly but exhibits the similar effect. Moreover, an Augmented Lagrange Multiplier based algorithm is customized to seek the optimal solution of DMOE effectively. Experimental studies on both simulated and realworld datasets are provided to show the effectiveness of the proposed algorithm.
A novel chaos-based partial image encryption scheme using Lifting Wavelet Transform

Depending on applications and specific security requirements, digital images can either be fully or partially encrypted. Partial encryption is one of the methods that reduces computational and processing cost. To achieve partial encryption, chaotic maps in combination with different transforms, like the Wavelet Transform (WT), Discrete Cosine Transform (DCT) and Lifting Wavelet Transform (LWT) are often used. Due to higher efficiency and fast processing, LWT is preferred over wavelet transform. In this paper, a novel partial image encryption scheme based on LWT, Secure Hash Algorithm (SHA-512), Logistic-sine chaotic map, TD-ERCS chaotic map and Substitution Box (S-Box) is presented. In comparison to other schemes, the proposed encryption technique is computationally efficient, secure and sensitive to initial key conditions.
Preserving security using crisscross AES and FCFS scheduling in cloud computing

Cloud computing is a developing technology in distributed computing which provides pay service model as per user need and requirement. Cloud includes the collection of virtual machine which have both computational and storage facility. The objective of cloud computing is to provide effective process to hyper distributed resources. Nowadays, cloud system is developing and faces many challenges, two of them are scheduling process and other main challenge is security. Scheduling states about how scheduler adapts its scheduling strategy, according to the changing set of morals to control the order of work to be implemented by a computer system. In this research paper, a scheduling algorithm of collocate first come first server (FCFS) of supremacy elements is proposed where the system efficiency is improved using FCFS in parallel manner. FCFS is simple and fast. To address security problem, crisscross advance encryption standard (AES) is proposed by increasing the security in the cloud through the grid manner. AES uses an identical key for both encrypting and decrypting the text. Aggregate of this proposed work is to enhance the system efficiency and security by using the both crisscross AES and collocate FCFS of supremacy elements.
Which fuzzy logic operations are most appropriate for ontological semantics: Theoretical explanation of empirical observations

In several of their papers, Victor Raskin and coauthors proposed to use fuzzy techniques to make ontological semantics techniques more adequate in dealing with natural language. Specifically, they showed that the most adequate results appear when we use min as an “and”-operation and max as an “or”operation. It is interesting that in other applications of fuzzy techniques, such as intelligent control, other versions of fuzzy techniques are the most adequate. In this chapter, we explain why the above techniques are empirically the best in the semantics case.
Holographic displays: the next revolution in augmented-reality-guided surgery?

Augmented Reality (AR) applied to surgical guidance is becoming increasingly important. The overlay of patient-specific 3D models onto the patient’s body surface provides access to information not normally available during surgery. Recent advances in tracking and display technologies open new opportunities to develop more efficient AR-guided surgery systems. To understand the current state of AR-guided surgery, we reviewed publications on systems which overlay virtual data onto patients. Our research shows that these systems have been used for numerous applications, e.g. to locate tumours or visualise the correct position of implants. However, many of these systems are technically complex and present poor ergonomics and limited accuracy. Novel holographic Head Mounted Displays (HMDs) which integrate tracking and registration technology (e.g. Microsoft HoloLens® or Magic Leap®) and Software Development Kits (SDKs) help to overcome these shortcomings: holographic HMDs have reduced technical complexity and improved ergonomics and the release of SDKs in recent years has allowed for a wide range of users to easily create AR applications. Their combined use can, therefore, facilitate the implementation of more sophisticated AR-guided surgery systems in the operating room. However, maximising surgical accuracy still remains a challenge for manufacturers and researchers.
A Robust Multilevel Speech Verification With Wavelet Decomposition for Inadequate Training Data Sets of Mobile Device Systems

With the development of speech signal processing, universality, easy collection and personal speech signal uniqueness, many researchers are attracted to the field of speech verification. Most of the current speech verifications are based on long training data sets in order to achieve good results, and there are no good verification schemes in case of inadequate training data sets. This paper proposes a novel architecture for speech verification using a multilevel method, which extracts feature parameters through a multiple wavelet transform for mobile phone voice. The experiments show that the multilevel wavelet authentication architecture improves performance in speech verification. The recognition rate of the mobile phone system is more robust and superior to other methods.
Deep Learning by Doing: The NVIDIA Deep Learning Institute and University Ambassador Program

Over the past two decades, High-Performance Computing (HPC) communities have developed many models for delivering education aiming to help students understand and harness the power of parallel and distributed computing. Most of these courses either lack a hands-on component or heavily focus on theoretical characterization behind complex algorithms. To bridge the gap between application and scientific theory, NVIDIA Deep Learning Institute (DLI) (this http URL) has designed an on-line education and training platform that helps students, developers, and engineers solve real-world problems in a wide range of domains using deep learning and accelerated computing. DLI's accelerated computing course content starts with the fundamentals of accelerating applications with CUDA and OpenACC in addition to other courses in training and deploying neural networks for deep learning. Advanced and domain-specific courses in deep learning are also available. The online platform enables students to use the latest AI frameworks, SDKs, and GPU-accelerated technologies on fully-configured GPU servers in the cloud so the focus is more on learning and less on environment setup. Students are offered project-based assessment and certification at the end of some courses. To support academics and university researchers teaching accelerated computing and deep learning, the DLI University Ambassador Program enables educators to teach free DLI courses to university students, faculty, and researchers.
Let's hear it for new ideas in 2019

Early Years EducatorVol. 20, No. 10 EditorialLet's hear it for new ideas in 2019Karen FauxKaren FauxSearch for more papers by this authorKaren FauxPublished Online:21 Dec 2018https://doi.org/10.12968/eyed.2019.20.10.5AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 2 February 2019Volume 20Issue 10ISSN (print): 1465-931XISSN (online): 2052-4617 Metrics History Published online 21 December 2018 Published in print 2 February 2019 Information© MA Education LimitedPDF download
FOURIER SERIES NONPARAMETRIC REGRESSION FOR THE MODELIZING IN THE TIDAL

The method of statistic used to estimate the estimation of sea water level is by nonparametric regression approaching of Fourier series. The rob flood caused by sea level rise in Semarang becomes a dissolved problem until today This results the need of modeling to predict and know how high sea level is.The fourier series have fluctuative data pattern because of its periodic character. This makes Fourier series as the appropriate approaching to modelize the sea tidal. Before modelizing the sea tidal with Fourier series approaching, It is previously necessary to find the optimal K value . Based on the determination of optimal K value, with GCV method, It is obtanied K equals 277. The result of average data of the Semarang sea tidal with reggression nonparametic method showed that R 2 is 95% and MSE = 4,42. The lowest tidalestimation resulted in Semarang is on March 2, 2016. Then the highest tidal estimation in Semarang Cityoccurred on August 31, 2016.
CNN-Based Deep Spatial Pyramid Match Kernel for Classification of Varying Size Images

This paper addresses the issues of handling varying size images in convolutional neural networks (CNNs). When images of different size are given as input to a CNN then it results in varying size set of activation maps at its convolution layer. We propose to explore two approaches to address varying size set of activation maps for the classification task. In the first approach, we explore deep spatial pyramid match kernel (DSPMK) to compute a matching score between two varying size sets of activation maps. We also propose to explore different pooling and normalization techniques for computing DSPMK. In the second approach, we propose to use spatial pyramid pooling (SPP) layer in CNN architectures to remove fixed-length constraint and to allow the original varying size image as input to train and fine-tune the CNN for different datasets. Experimental results show that proposed DSPMK-based SVM and SPP-layer based CNN frameworks achieve state-of-the-art results for scene image classification and fine-grained bird species classification tasks.
Implementation of Virtual Trial Room for Shopping Websites Using Image Processing

In today’s world, the use of e-commerce websites has increased a lot. Online shopping gives us more information and choice about different products under various categories which are readily available. The customer just has to choose between the different products, purchase them, and the product comes to the customer’s doorstep. Thus, many people like to buy many things online. Clothing is one of such categories that people can buy online. But for shopping clothes, this scenario is a little different. There is a major problem that people do not know how the clothes would actually look on them and so many people avoid buying clothes online. Sometimes the customers even send back the clothes they buy as it does not look good on them. This is why a virtual trial room has to be developed so that people do not have to wait to try on the clothes physically after it is delivered. They can try clothes virtually on the virtual trial room.
Two Step Method for Clustering Mixed Data untuk Menggerombolkan Toko Mainan Anak Digital

The development of digital trading system today, triggering the proliferation of shops that sell various needs in various marketplace. This is supported by the large number of internet users in Indonesia that facilitate the store with commercial-based digital to reach market share. One of the growing categories in a marketplace is the stores that sell toys. However, not all toy stores have a good reputation. Clustering based on store reputation indicators can be done to find out how the condition of toy stores in a marketplace. The store reputation indicators used are categorical and numerical scale variables. This study uses A Two-Step Method for Mixed Categorical and Numerical Data (TMCM), which is a clustering method that can cluster mixed numerical and categorical data that using a co-occurence concept. The result of this clustering found that the optimal number of cluster is five cluster based on the maximum value of Pseudo-F and the minimum value of ratio (R ).
A Signal Processing Approach to Reduce Motion Blur in Lcds by Deconvolution

An LCD, or Liquid Crystal Displays widely used as a screen that is used in many Televisions, Digital equipment screen, Mobile phones, cameras and tablets. LCD's are very flat and thin, which are actually composed of several layers. The sample-and-hold properties of the LCDs leads to motion blur. This problem can be eliminated by using a suitable signal processing approach using the Human Visual System (HVS) models. This method includes preprocessing the input data before it is fed to the display .In this paper, a new Total variation-based algorithm is introduced for image deconvolution by the assumptions of AWG (additive white Gaussian noise) and linear observations. The TV estimate is obtained by majorization-minimization approach, and by relying on convexity arguments .This method replaces a complex optimization problem by a sequence of simpler ones. The proposed algorithm either outperforms or equals when compared with the same kind of methods with similar computational complexity.