Reviewerly/exHarmony huggingface dataset:
WORLD OF CRYPTO CURRENCY: A SURVEY

Crypto currency, an encoded, peer-to-peer network for encouraging digital barter, is a revolution created eight years back.Bitcoin, the first and most remarkable crypto currency, is making ready as a troublesome revolution to long standing and invariable budgetary installment structure that have been establish for a long time.While digital figure of money are not liable to substitute customary directive cash, they could replace the way Internet-associated international markets interface with one flipside, gathering high hindrances encompassing regularizing national capital standards and trade rates.Revolution launch at a fast rate, and the achievement of a given revolution is exclusively conducted by the market upon which it looks to progress.Cryptographic structure of money may alter computerized interchange showcases by making a free of charge streaming interchanging structure without expenses.AA-SWOT inspection of Bitcoin is betrayed, which lights up a part of the in progress occasions and growth that could impact even if Bitcoin build on to a move in monetary absolute models.A clearly distributed form of computerized money would authorize online segment to be sent exactly starting with one congregation then onto the next without exercising a economic foundation.Virtual Currencies and cryptographic money is another computerized expansion to the digital world and in addition worldwide budgetary framework that has not yet been completely regulated into the lawful structure of numerous countries over the globe.There are numerous lawful parts of Bitcoin and digital money all in all to consider.
Bitcoin in Indonesia: Hedging or Investment Instrument?

Uncertainties might compel many investors to hedge by buying globally traded assets, such as Bitcoin, which has also been used as a means of payment in several countries. Bitcoin does not originate from any centralized authority and cannot entirely be controlled; therefore, Bitcoin usage might potentially pose issues to the monetary authorities within a country. This paper analyzes the effect of both global and domestic uncertainty on Bitcoin's demand in Indonesia. Our result suggests that Bitcoin is used for hedging against uncertainties. The monetary policy implications of our results are also discussed.
Near-Optimal Glimpse Sequences for Training Hard Attention Neural Networks

Hard visual attention is a promising approach to reduce the computational burden of modern computer vision methodologies. Hard attention mechanisms are typically non-differentiable. They can be trained with reinforcement learning but the high-variance training this entails hinders more widespread application. We show how hard attention for image classification can be framed as a Bayesian optimal experimental design (BOED) problem. From this perspective, the optimal locations to attend to are those which provide the greatest expected reduction in the entropy of the classification distribution. We introduce methodology from the BOED literature to approximate this optimal behaviour, and use it to generate `near-optimal' sequences of attention locations. We then show how to use such sequences to partially supervise, and therefore speed up, the training of a hard attention mechanism. Although generating these sequences is computationally expensive, they can be reused by any other networks later trained on the same task.
Effective Subspace Indexing via Interpolation on Stiefel and Grassmann manifolds

We propose a novel local Subspace Indexing Model with Interpolation (SIM-I) for low-dimensional embedding of image datasets. Our SIM-I is constructed via two steps: in the first step we build a piece-wise linear affinity-aware subspace model under a given partition of the dataset; in the second step we interpolate between several adjacent linear subspace models constructed previously using the `center of mass calculation on Stiefel and Grassmann manifolds. The resulting subspace indexing model built by SIM-I is a globally non-linear low-dimensional embedding of the original data set. Furthermore, the interpolation step produces a `smoothed” version of the piece-wise linear embedding mapping constructed in the first step, and can be viewed as a regularization procedure. We provide experimental results validating the effectiveness of SIM-I, that improves PCA recovery for SIFT dataset and nearest-neighbor classification success rates for MNIST and CIFAR-10 datasets.
Detection of Removed Objects in 3D Meshes Using Up-to-Date Images for Mixed-Reality Applications

Precise knowledge of the real environment is a prerequisite for the integration of the real and virtual worlds in mixed-reality applications. However, real-time updating of a real environment model is a costly and difficult process; therefore, hybrid approaches have been developed: An updated world model can be inferred from an offline acquisition of the 3D world, which is then updated online using live image sequences under the condition of developing fast and robust change detection algorithms. Current algorithms are biased toward object insertion and often fail in object removal detection; in an environment where there is uniformity in the background—in color and intensity—the disappearances of foreground objects between the 3D scan of a scene and the capture of several new pictures of said scene are difficult to detect. The novelty of our approach is that we circumvent this issue by focusing on areas of least change in parts of the scene that should be occluded by the foreground. Through experimentation on realistic datasets, we show that this approach results in better detection and localization of removed objects. This technique can be paired with an insertion detection algorithm to provide a complete change detection framework.
Learning Gaussian Maps for Dense Object Detection

Object detection is a famous branch of research in computer vision, many state of the art object detection algorithms have been introduced in the recent past, but how good are those object detectors when it comes to dense object detection? In this paper we review common and highly accurate object detection methods on the scenes where numerous similar looking objects are placed in close proximity with each other. We also show that, multi-task learning of gaussian maps along with classification and bounding box regression gives us a significant boost in accuracy over the baseline. We introduce Gaussian Layer and Gaussian Decoder in the existing RetinaNet network for better accuracy in dense scenes, with the same computational cost as the RetinaNet. We show the gain of 6\% and 5\% in mAP with respect to baseline RetinaNet. Our method also achieves the state of the art accuracy on the SKU110K \cite{sku110k} dataset.
MAKING OF BOHEMIAN SKY DECORATION (FRINGE BOHEMIAN)

&lt;p&gt;For the student creativity program in this entrepreneurship class, we run a business program for hanging ornaments made of rope. We give this business the name of Making a Bohemian Ceiling Decoration (Fringe Bohemian). This Bohemian Ceiling Decoration is a craft made from rope. The rope used can be various as desired, such as rope, wool rope, plastic rope, etc. But we are here using the basic material of wool rope. This handicraft business aims to increase creativity and be innovative in entrepreneurship, especially for young entrepreneurs. The hanging decoration business from this rope is easy in the manufacturing process but it takes time and requires patience in making it and the materials needed to make this decoration easy to get. The form of our business is still in an individual stage only. We do not yet have official permission from the government, because this business is still new and is still trying to enter the business world. In the future, if this business can get extraordinary profits and have enough customers, then we will increase this business to be even bigger. Many factors need to be considered in order to advance this Bohemian Ceiling Decoration business. Important factors that must be considered in order to advance this business include choosing the right location, a good marketing strategy and determining affordable prices in the community. If all the factors above can be planned properly, it will be able to make this business grow well. For this reason, it is necessary to make a business plan as a reference in accelerating the business of making Bohemian Ceiling Decorations (Bohemian Fringe).&lt;/p&gt;
LibGuides: ACRL, IPEDS, and National Surveys: Circulation

Support guides and tutorials for PALNI libraries completing the ACRL, IPEDS, and other national surveys.
Services Composition in Multi-cloud Environments using the Skyline Service Algorithm

The rapid growth of cloud environments has led to the expansion of resources that offer a variety of services. The opertions of the services are usually very simple and may not satisfy the complex needs of the user, hence there is a need for a combination of these services that can fulfill the user's requirements. Most of the service composition methods in cloud environments assume that the involved services came from one cloud, and this is unrealistic because other clouds may provide more relevant services. The challenges in composition services distributed in multi-cloud environments include increased cost and a reduction in its speed due to the increasing number of services, providers, and clouds; so, in order to overcome these challenges, the number of providers and participating clouds must be reduced. This study used the Skyline service algorithm to compose services in multi-cloud environments, which examined all the clouds during the service composition process. The proposed method can provide an applicable composition service to the user with the lowest communication cost by considering the number of clouds and by using fewer providers. The Skyline algorithm involves two steps. In the first one, the best composition in a cloud environment is selected among all the possible providers by considering the number of providers and the communication time. In the second step, the Skyline algorithm is used to create all the possible compositions in a multi-cloud environment. Parameters such as fewer clouds and shorter communication times between the clouds are selected. The results show that the proposed method can find the composition with the least number of clouds, the lowest cost, and has the lowest calculation time. It can be said that the Skyline makes it possible to select a suitable composition of user-requested services in a multi-cloud environment.
Analyzing the Sentiments with Neural Network

In the modern era, ubiquitous means of communications are used.Thus, allowing various platforms where people can exchange their thoughts and opinions.This leads to the situation where the task of sentiment analysis comes into play which is the task of natural language processing.Many approaches such as Maximum Entropy, classifier of Naïve Bayes were used for the purpose of analyzing the sentiments of the users which helps the internet surfers to interpret whether a particular thing is liked or disliked by others.The paper proposes a system with backpropagation neural network (BPNN) which not only analyzes simple reviews but also the opinions that contain emoticons and negation.The neural network was implemented with various datasets and with several different activation functions such as ReLu (Rectified Linear Unit), sigmoid and hyperbolic tangent.The results depict that sigmoid activation function performed better than other.
Inovasi Pengabdian Pusat Kesehatan Matematika Sekolah (Puskesmas) dalam Pembelajaran Matematika Berbasis Alun-alun Kota

COMMUNITY SERVICE INNOVATION OF THE SCHOOL MATHEMATICS HEALTH CENTER (PUSKESMAS) IN LEARNING MATHEMATICS BASED ON THE TOWN SQUARE. The purpose of this study was to develop an innovation in the school’s mathematics health center in mathematics learning through city square-based mathematics learning. Descriptive method is used with subjects in early childhood and elementary school students who are in the location of the school mathematics health center. The instrument used by the researcher itself, data collection techniques using structured interviews, observation and documentation. The data analysis technique used triangulation, data reduction, data presentation and conclusion drawing. The results showed that the media presented at the Puskesmas were all new and they had not yet gotten them in their respective schools. The attitude of students who become happy, like, cheerful in learning mathematics and easy to understand mathematical concepts, and bring out the creativity of early age and elementary school students in learning mathematics. Student concentration also increases because the learning model used uses an individual approach, so that they can focus more on seeing the student learning process. Parents who accompany their children generally feel happy because their children enjoy learning mathematics. Puskesmas activities have an impact on early age and elementary school students to be happy learning mathematics, like learning with mathematics media, cheerful in following math tutoring and enjoy learning mathematics. This program is also highly supported by parents because it is very helpful in the learning process of their children and eases the family’s economic burden in education.
Libel Bullies, Defamation Victims, and Litigation Incentives

In the context of defamation law, we analyze a public figure’s incentives to bring negativevalue defamation suits in order to appear litigious, and how this affects her incentives to do wrong in the first place and the media’s incentives to expose this wrongdoing. In equilibrium, the public figure’s litigation incentives depend both on her own direct costs and benefits of doing so, and on journalists’ costs and benefits from litigation and publication. Furthermore, equilibrium wrongdoing and publication choices depend on an otherwise non-litigious public figure’s litigation payoffs. Potential effects of legal reform are briefly discussed.
LibGuides: OnRamps History: For Instructors: Home

A guide for OnRamps History Instructors on UT Austin Libraries access, information literacy and pedagogical resources.
The hybridized equilibrium and particle swarm optimization algorithms

Along with the development of nature inspired swarm-based algorithms, the individuals involved in updating their positions in swarms have changed from their average, to the historical best candidates, the best candidates and random selected candidates. The averages have been abandoned by now. However, only the individuals in particle swarm optimization (PSO) algorithm embraced the historical trajectories. In this paper, the newly proposed algorithm called the equilibrium optimization (EO) algorithm was improved with the hybridization of the PSO algorithm, the historical trajectories of individuals would play the role in updating the concentrations of individuals in EO swarms. Simulation experiments were carried and the final results verified the better performance of the improved hybridization.
Neuromarketing:os princípios do neurodesign e suas aplicações na comunicação da marca Nissin.

Neuromarketing, understood as the union of marketing with neuroscience, is a current topic and has been gaining a lot of space in areas such as marketing, economics, education and design. It is through it that it is possible to measure the conscious and unconscious mental states of the consumer, with the intention of facilitating the understanding of the consumer and stimulating him in the act of purchasing. Thus, the objective of the work is to understand how neurodesign, the union of neuromarketing with design together with color psychology, helps in understanding the consumer's unconscious thinking, as well as, these stimuli serve as motivation at the time of purchase.
Bases metodológicas para una práctica de campo integradora a través del uso de ExeLearning / Methodological bases for an integrative field practice through the use of ExeLearning

The formation of teachers has felt the necessity to advance toward the application of digital tools to achieve a better learning adapted to the modernity. However, the educational scenarios varied and it became necessary to strengthen an appropriate interaction among the educational contents, the educational technology and those implied in the teaching process - learning. The present work had objective to distinguish the methodological bases that allow integrating the software ExeLearning in the field practices that are carried out in the career of Degree in Education (Geography). It was used like design of qualitative investigation the investigation - action and as main result, the methodological bases are exposed to introduce the software with possibility of being extended to other similar computer tools and the design of the field practice in their three stages considering the use of the ExeLearning software. The investigation put in clear the formative potential of the field practice to integrate the development of abilities for the use of the technology in function of solving professional problems of the career, at the same time that it reflected the value of a pedagogic vision to favor the efficient use of the computer tools in the context study object.
Computer Vision and Machine Learning

This chapter introduces the main concepts of computer vision and machine learning. It describes important image preprocessing operators and image descriptors that are employed during the process of image classification. It provides the essential concepts in machine learning, including classification, ensemble learning, transfer learning, and feature learning. It also introduces the basics of convolutional neural networks.
Automating Guide selection process of the Department through Web Application

This is a website where guide selection was implemented. In this process every student has the access to view the profile of all teachers to register them as their project guides. First come First serve procedure is followed in this site. Selection of guides is based on the specialization of the Faculties of the Department. The main agenda is to assign faculties strong at particular areas to students’ projects based on that area. Faculty name has to be registered with all relevant details. The student register his/her details in the portal. The faculty register his/her details in the portal. The student checks for availability of the guide. If the guide is available then the student selects the guide and the guide slot availability is decremented by one. If the guide is not available the student has to select another guide from the portal. The list of selected students can be viewed by faculty. The guide selection detail will be displayed on the student portal. The complete details can be downloaded as a CSV file from the admin login. Technologies used for implementation are: HTML with bootstrap, Node Js/PhP , MySql database, Cloud Server: It is published through amazon web services.
PEMEROLEHAN BAHASA PADA ANAK USIA 2 TAHUN DILIHAT DARI ASPEK FONOLOGI

This article discusses language acquisition in children aged 2 years in terms of phonology. The purpose of this study was to analyze and describe language acquisition regarding aspects of vowel or consonant phonology in children aged 2 years. The research subject is a 2 year old child named RSH. This study used descriptive qualitative method. This technique uses data resulting from observations by inviting the child's direct communication, then the data is processed through appropriate exposure to the data in the field. The results obtained from this study show that the acquisition of RSH phonology, especially in vowels, has been mastered, but there are several vowels that are sometimes replaced, for example vowel [o] becomes [u], but so far, RSH has mastered the vowels [a], [i] , [u], [e], [o]. The first vowels he mastered were the vowels [a] and [u]. Likewise, the consonant sounds [b], [t], [h], [w], [y], [n], [m], [p], [j] have been well mastered. If the consonant sound is in the middle, the utterance has been obtained even though it is not correct, but if you find a consonant sound at the end of a word, RSH always interchanges it with a consonant sound [h].
MECHANISM FOR INCREASING THE ACTIVITY OF FUTURE COMPUTER SCIENCE TEACHERS

The system-forming concept of the learning process as a system is the goal of learning, the activity of the teacher, the activity of students and the result. The variable components of this process are the control tools. They include: the content of educational material, teaching methods, material means of learning, organizational forms of learning as a process and educational activities of students. They form a stable unity and integrity, subordinate to the General goals of education. The purpose of training performs a systemforming function of pedagogical activity, since the choice of content, methods, and means of education depends on its definition. The starting point for defining the goal and building a system of tasks. It serves as a specialist model based on the qualification characteristics.
Towards a reinforcement learning de novo genome assembler.

The use of reinforcement learning has proven to be very promising for solving complex activities without human supervision during their learning process. However, their successful applications are predominantly focused on fictional and entertainment problems - such as games. Based on the above, this work aims to shed light on the application of reinforcement learning to solve this relevant real-world problem, the genome assembly. By expanding the only approach found in the literature that addresses this problem, we carefully explored the aspects of intelligent agent learning, performed by the Q-learning algorithm, to understand its suitability to be applied in scenarios whose characteristics are more similar to those faced by real genome projects. The improvements proposed here include changing the previously proposed reward system and including state space exploration optimization strategies based on dynamic pruning and mutual collaboration with evolutionary computing. These investigations were tried on 23 new environments with larger inputs than those used previously. All these environments are freely available on the internet for the evolution of this research by the scientific community. The results suggest consistent performance progress using the proposed improvements, however, they also demonstrate the limitations of them, especially related to the high dimensionality of state and action spaces. We also present, later, the paths that can be traced to tackle genome assembly efficiently in real scenarios considering recent, successfully reinforcement learning applications - including deep reinforcement learning - from other domains dealing with high-dimensional inputs.
CitSciVirtual throughout May 2021

Citizen.Science.org have launched an exciting new online event for the Citizen Science community called CitSciVirtual which will run throughout May ...