Reviewerly/exHarmony huggingface dataset:
N-grams for Image Classification and Retrieval

Content-based image retrieval (CBIR) and classification algorithms require features to be extracted from images. Global and low level image features such as color, texture, and shape fail to describe pattern variations within regions of an image. Bag of Visual Words approaches have emerged in recent years that extract features based on local pattern variations. These approaches typically outperform global feature methods in classification tasks. Recent studies have shown that Word N-Gram models common in text classification can be applied to images to achieve better classification performance than Bag of Visual Words methods as it results in more complete image representation. However, this adds to the dimensionality and computational cost. State of the art Deep learning models have been successful for image classification. However, huge training data required for these models is a big challenge. This book chapter reviews the literature on Bag of Visual Words and N-gram models for image classification and retrieval. It also discusses few cases where the N-gram models have outperformed or given comparable performance to the state of the art Deep Learning Models. The literature demonstrates that N-grams is a powerful and promising descriptor for image representation and is useful for various classification and retrieval applications.
The Being of the Word. Jubilee Collection Dedicated to the 60th Anniversary of Professor Zapryan Kozludzhov

The text presents “The being of the word. Jubilee collection dedicated to the 60th anniversary of professor Zapryan Kozludzhov”.
Context-aware adaptive personalised recommendation: a meta-hybrid

Recommenders take place on a wide scale of e-commerce systems, reducing the problem of information overload. The most common approach is to choose a recommender used by the system to make predictions. However, users vary from each other; thus, a one-fits-all approach seems to be sub-optimal. In this paper, we propose a meta-hybrid recommender that uses machine learning to predict an optimal algorithm. In this way, the best-performing recommender is used for each specific session and user. This selection depends on contextual and preferential information collected about the user. We use standard MovieLens and The Movie DB datasets for offline evaluation. We show that based on the proposed model, it is possible to predict which recommender will provide the most precise recommendations to a user. The theoretical performance of our meta-hybrid outperforms separate approaches by 20%-50% in normalised discounted gain and root mean square error metrics. However, it is hard to obtain the optimal performance based on widely-used standard information stored about users.
Information Security Assessment of Robots: Consideration on an implementation

Information security assessment and information security risk reduction are the keys for safety robots. Even on demonstrative experiments, risk assessment on information security is required for safety robots. In the case of demonstrative experiments, a series of robots are evaluated at various test fields, and those robots may have different type of optional functions which fit to the test fields. From the point of safety robots, each type robot shall be evaluated through risk assessment and reduce information risks so as to keep confidentiality, integrity and availability (CIA) of information assets within a robot system. This paper shows an example of the information risk analysis of the robot. Although the additional operational requirements successfully reduce the risks for the demonstrative experiment, additional information security functions may be required on commercial robots.
Troubleshooting Difficult Problems

Troubleshooting is a process of elimination, detective work to join up the dots, making connections between different aspects of the OS, hardware, apps, and other factors, and coming to conclusions based on technical knowledge and expertise. This book can give you the technical knowledge you need to diagnose and repair problems. What do you do, though, in terms of the actual detective work?
Assessing Impacts of Mining Activities on Land Use/Land Cover Change Using Remote Sensing and GIS Techniques: A Case Study in Campha City, Vietnam

Coal is one of the most mining commodities to date, especially to supply both national andinternational energy needs. Coal mining activities that are not well managed will have an impact on theoccurrence of environmental damage. The present study was undertaken to analyze the process of humaninducedlandscape transformation in the coal mines affected areas of Cam Pha, northeast Vietnam byinterpreting temporal remote sensing data and using Geographic Information System. This experimentrevealed that most of the study area was dominated by forest in all the time sequence period. The forestcover has decreased about 21.3%, meanwhile having nine fold increase in mining area from 1990 to 2020.The forest area lost during the study period was 7983.45 ha due to land cover conversion into mining area.The mining activities were also detrimental to the bare land and water body cover. The results of this studyare expected to be used to support government efforts and mining managers in post-mining coal activities.
Logging Data Completion Based on an MC-GAN-BiLSTM Model

Due to environmental interference and operational errors, problems such as incomplete and random missing logging data have happened during the process of geophysical logging data collection. Since it is difficult to establish a geophysical model based on logging data and geological information, the data complementation effect of conventional methods is not very satisfactory. In this paper, we propose a GAN-LSTM model based on spatiotemporal sequence prediction. In the model, we adopt a generative adversarial network (GAN) as a network framework, and a long short-term memory (LSTM) neural network and a bi-directional long short-term memory (Bi-LSTM) as the basic modules. We use the LSTM instead of a fully-connected layer in the GAN to extract the potential information in the depth domain of the logging data. We complete the missing values of the logging data through an encoding-decoding structure that includes the Bi-LSTM. We use random missing values and consecutive missing values of logging data to simulate a data acquisition environment in the field and threshold control to simulate a processing environment in a laboratory for experiments. The experimental results show that the coefficient of determination (R2) of the GAN-LSTM model reaches 0.906 when 30% of random logging data are missing and 0.851 when 30% of consecutive logging data are missing. The effect of the model proposed in this paper is significantly higher than the commonly used random forest (RF), Sequence to Sequence (Seq2seq) and generative adversarial interpolation network (GAIN) models.
Fusion of Augmented Reality and High-Precision Map

In recent years, there has been a remarkable increase in the popularity of augmented reality. This is due to the fact that AR technology is being used in applications for smartphones, as well as in events and marketing in the industrial field. The market for services using AR technology in Japan is estimated to be 387.8 billion yen in 2020, and is expected to grow 47 times faster than in 2020 to 16 trillion yen by 2030. This AR technology uses media such as smartphones and tablets to superimpose and display location data that indicates where you are currently viewing the camera image in the real space, images that should not exist in the real space, and virtual objects. In each frame of the image, the position where the virtual object is superimposed has to be determined according to the position and posture of the camera at that time. This problem is called the geometric positioning problem in AR.
YOLOv3 based Reinforcement learning for mobile game playing policy

This paper proposes a reinforcement learning model that constructs a sequential behavioral decision policy for playing a game by extracting feature points in an environment in which a game image is given. In this paper, we propose a method of optimizing performance through state domain reduction, transfer learning, and multi-agent-based modeling to obtain the maximum score available for game environments that must continue their actions and have time limitations in decision making. These methods were implemented for the ‘Timberman’ game environment and experimented with learning performance by applying them as a player’s behavioral policy to evaluate the trained model.
Bagaimana Pengaturan Kepemilikan Cryptocurrency Oleh Perusahaan Berdasarkan Standar Akuntansi Keuangan?

&#x0D; This study investigates the application and recognition of accounting policies for ownership of Cryptocurrency assets by companies in Indonesia based on Financial Accounting Standards. This study uses a qualitative method with a scoping review approach to map the literature related to the concept of cryptocurrency and accounting arrangements for cryptocurrency ownership by companies. The scoping review carried out several stages, namely problem identification, identifying literature sources, literature selection, mapping and collecting literature, and discussion analysis. This study concludes that ownership of cryptocurrency by companies can be classified as assets based on IFRS and PSAK. The most relevant type of asset classification for Cryptocurrencies is inventory or intangible assets, depending on the purpose of ownership of the company. This research indicates that with the significant increase in the use of cryptocurrencies, standard-setting bodies need to set clear and specific accounting standards to help reduce uncertainty and provide relevant and useful guidance to both preparers and users of financial statements.
WAYS OF SEARCHING FOR A MODERN THEME IN THE THEATRICAL ART OF DAGESTAN

The article deals with the search for a modern theme and images in the drama and theater of the early 21st century both on the scale of the Russian theater and in the Dagestan drama. On specific examples from the practice of creative activity of the theaters of the republic for two decades of the XXI century, ways and means of solving the problem are identified.
PENERAPAN METODE DEMONSTRASI PADA MATA PELAJARAN TEKNOLOGI INFORMASI DAN KOMUNIKASI (TIK) MATERI MICROSOFT EXCEL DI SMP N 1 SALAM

&lt;em&gt;Many factors affect the success of the learning objectives. Teachers are one of the determining factors for the success of a quality learning process, where teachers not only teach knowledge but also train students' skills, attitudes and mentality. The factor causing the lack of activeness of students in participating in learning is monotonous learning so that students get bored quickly and feel bored. Learning objectives can be achieved through appropriate learning models and in accordance with the concepts of the subjects to be delivered. The purpose of this study is to (1). Describe the demonstration method; (2). Implementation of demonstration methods in ICT learning. The results of this study are: (1). The learning process is more focused on the material being studied; (2). Experiences and impressions as a result of learning are more inherent in students; (3). The attention of students can be more focused; (4). Students will be able to overcome a problem that occurs in connection with what has been demonstrated.&lt;/em&gt;
Artificial Intelligence Technologies in Building Resilient Machine Learning

Since the birth of artificial intelligence, theory and technology are increasingly mature, and the application field is also expanding. Some artificial intelligence technologies, such as deep learning, can help develop resilient machine learning to mitigate adversarial learning attacks. This paper uses a money lending case to explain the application of artificial intelligence in building resilient machine learning, and we use Generative Adversarial Network as an example. Moreover, we discuss the definition of resilient machine learning and give a review of adversarial machine learning attacks and threat actors.
Einstein’s Equation of motion for exterior test particles with spherical mass distribution having varied field, time and radial distance; Golden metric tensors approach.

Golden metric tensors exterior to hypothetical distribution of mass whose field varies with time and radial distance have been used to construct the coefficient of affine connections that invariably was used to obtained the Einstein’s equations of motion for test particles of non-zero rest masses. The expression for the variation of time on a clock moving in this gravitational field was derived using the time equation of motion. The test particles in this field under the condition of pure polar motion have an inverse square dependence velocity which depends on radial distance. Our result indicates that despite using the golden metric tensor, the inverse square dependence of the velocity on radial distance has not been changed.
Aerial-Aquatic Locomotion in Nature

Water covers 363 million square km, or 72% of the earth’s surface. The vast majority of this water is saline (96%), frozen (2%) or groundwater (1%). The 10 $$^5$$ km $$^3$$ of surface freshwater (0.008%) is in turn concentrated almost entirely in three large great lake systems (Fig. 3.1), with a vanishing small amount of surface freshwater forming lakes and rivers.
Mobile Technologies and Solutions for Geological Exploration: Mobile Office GEO

Summary GEO Mobile Office is a unified digital platform that is a set of interconnected tools for operational management of geological exploration projects, and available for smartphones, personal computers, and tablets. The platform is designed to provide a unified environment for bringing together geographically distributed project teams, consisting of representatives of Gazprom Neft and their business partners. We see good prospects for using the GEO Mobile Office platform by any project teams that value transmission data speed, convenience, immersion, and security.
EFFECTIVE MANAGEMENT OF E-RESOURCES

The concept of 'library management' has been changing dramatically with the increase in the demand of academic inputs in education, policy planning, advocacy and day to day life. Shifting from 'print resource collection' to 'e-resource collection' no doubt makes the library more users friendly but on'the other hand it also emerges as a challenge before the library professionals to manage these resources due to its rapid adoption. E-resources management is one of the important components of library development. After automation and digitisation of library resources, there is a challenge for library professional to manage this tremendous increase ofc-resources. The voluminous e-resonrce can easily be managed through ERM system.
EXPLORING EVOLVING FAULTS WITH INTERACTIVE MULTI-TIME-SCALE VISUALIZATION OF PROTECTION RELAY EVENTS FROM A SUBSTATION

Distribution system operators have increasing interest in the detection of evolving faults to enable proactive planning and scheduled maintenance. Usually, the most accurate systems for the detection of specific phenomena of interest are dedicated monitoring systems for components like switchgear, breakers, transformers, or cables. Industry has been experimenting with the implementation of this kind of infrastructure at least for the most critical assets and locations. But building a large-scale monitoring system for entire assets is a demanding task and requires investments. In this paper, we explore whether events from medium voltage protection relays are suitable to indicate evolving faults in a distribution grid. We implemented an interactive visualization tool with an intuitive user interface using web technologies, enabling domain experts to explore patterns in time over multiple orders of magnitude, between milliseconds and months. The events were collected between May 2017 and September 2019 at a substation with 7 bays located in Noormarkku, Finland. Events were instances of 14 functions, grouped into 7 types. During the observation period, 232 faults (short circuits and earth faults) were registered in an experimental pilot system.
Into the Galant: Tartini, Telemann, Quantz, and Zuccari

The final ornamentation module deals with examples by Giuseppe Tartini, Georg Philipp Telemann, Johann Joachim Quantz, and Carlo Zuccari. Tartini’s treatise on ornamentation contains a clear and systematic approach to ornamentation, dealing with grace notes, trills, vibrato, turns, and mordents, and giving many examples of embellished cadences. Telemann’s <italic>Methodical Sonatas</italic> contain embellished slow movements in both French and Italian styles. They are categorized, and an exercise is devised for assimilating them. Quantz wrote one of the most comprehensive interval-based ornamentation tables of the entire Baroque period, relevant above all to the <italic>Style Galant</italic> and including note-by-note instructions for performance. Examples are given with these instructions reproduced. He also composed an Adagio with instructions for almost every note: these have been transcribed from the instructions for ease of study. Quantz also offers advice on cadenzas, of which two are reproduced. To conclude, a page of Carlo Zuccari’s 1760 <italic>True Method of Playing an Adagio</italic> is reproduced.
Stacked autoencoder for Arabic handwriting word recognition

Arabic handwritten recognition systems face several challenges such as the very diverse scripting styles, the presence of pseudo-words and the position-dependent shape of a character inside a given word, etc. These characteristics complicate the task of features extraction. Our proposed solution to this problem is a stacked autoencoder (SAE) unsupervised learning approach applied to resolve the unconstrained Arabic handwritten word recognition. Our strategy consists in using an unsupervised pre-training stage, i.e., SAE which will extract the features layer by layer, then, through fine-tuning, the global system will be used for classification tasks. By exploiting this, our system gets the advantage of applying a holistic approach, i.e., without word segmentation. In order to train our model, we have enhanced the NOUN v3 hybrid (i.e., offline and online) database that contains 9,600 handwritten Arabic words and 4,800 characters. However, this work is focusing on the offline recognition of Arabic word handwriting using a SAE-based architecture for images classification. Our experiment study shows that after a careful tuning of the main SAE parameters we got good results (98.03%).
Recognition of linear deformable objects using point cloud data for various objects mixedly placed

There are many deformable objects such as strings and paper, clothes surround people grows. It is difficult for a robot to manipulate deformable objects, because it needs to deal with various form. So, the demand that a robot manipulates deformable objects is growing. In our previous study, we have successfully operated a string with a one arm or two arms. However, in the previous research, the object placed in the work area of the manipulator is limited to one string, and it is not assumed to perform the knotting operation of the string under the environment in which some objects are mixed. In this research, A method has been proposed that recognizes an object from the point cloud data acquired by the depth camera and determines whether the point cloud is a string based on the variance of the point cloud on the surface.
First-Order Predicate Logics

The last chapter gives a short introduction to the study of first-order predicate logics built over weakly implicative logics. We follow a semantics-first approach in which we start from semantically defined predicate logics and then propose suitable Hilbert-style axiomatizations and prove corresponding completeness theorems by following non-trivial generalizations of Henkin’s proof of completeness of classical first-order logic. More precisely, to introduce a general semantics of predicate models we utilize the fact that any reduced model of a given weakly implicative logic is ordered and define the truth-value of a universal (resp. existentially) quantified formula as the infimum (resp. supremum) of the truth-values of its instances.Then, for any given class of reduced models of the logic in question, we define a corresponding consequence relation on predicate formulas. We focus on three particular meaningful logics: the predicate logic of all reduced models, the subdirectly irreducible ones, and the restriction of the latter to witnessed predicate models in which quantifiers are actually computed as minima and maxima. We propose axiomatic systems for these three logics and, under certain conditions, prove the corresponding completeness theorems.While the first completeness result is relatively straightforward and works in absolute generality, the other two require non-trivial modifications of Henkin’s proof by making use of a suitable disjunction connective which needs to be added as a requirement for the propositional logic. As a by-product, we also obtain, for certain logics, a form of Skolemization. The relatively modest assumptions on the propositional side allow for a wide generalization of previous approaches and help to illuminate the ‘essentially first-order’ steps in the classical Henkin’s proof.
Landmark Summaries

In this chapter, you will learn how to condense a data set down to one number (or two or a few numbers) that summarizes the data by expressing some of the most fundamental data characteristics. The methods most appropriate for a single list of numbers (ie, univariate data) include the following: One : The average, median, and mode are different ways of selecting a single number that closely describes all the numbers in a data set. Such a single-number summary is referred to as a typical value, center, or location . The average is best when total amounts are important to you (because it divides the total equally), and the average also works well when the histogram shows an approximately normal distribution. The median can work better when you have skewness or outliers (because it always chooses a value near the middle of the data), although the median might not work well when total amounts are important. The mode is the most common category (or midpoint of the tallest histogram bar with quantitative data) and is the best (and only) choice for nominal qualitative data. With ordinal qualitative data, either the median or the mode can be used, and all three of these summary methods are available with quantitative data. If some numbers in your data have more importance than others, you may use a weighted average to reflect this information. Two: A percentile summarizes information about ranks, characterizing the value attained by a given percentage of the data after they have been ordered from smallest to largest. There are many percentiles! For example, the median is the 50th percentile, and the quartiles are the 25th and 75th percentiles. Your company would be “at the 93rd percentile for revenues in your industry group” if your revenues are larger than those of about 93% of these companies. The box plot displays the five-number summary (smallest, 25th percentile, median, 75th percentile, largest), allowing you to focus on these essentials without the distractions of the additional details of a histogram. The cumulative distribution function displays all of the percentiles in full detail. Three: The standard deviation is an indication of how different the numbers in the data set are from their average. This concept is also referred to as diversity or variability and will be deferred to Chapter 5 .