Reviewerly/exHarmony huggingface dataset:
Are Bitcoins price predictable? Evidence from machine learning techniques using technical indicators

The uncertainties in future Bitcoin price make it difficult to accurately predict the price of Bitcoin. Accurately predicting the price for Bitcoin is therefore important for decision-making process of investors and market players in the cryptocurrency market. Using historical data from 01/01/2012 to 16/08/2019, machine learning techniques (Generalized linear model via penalized maximum likelihood, random forest, support vector regression with linear kernel, and stacking ensemble) were used to forecast the price of Bitcoin. The prediction models employed key and high dimensional technical indicators as the predictors. The performance of these techniques were evaluated using mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R-squared). The performance metrics revealed that the stacking ensemble model with two base learner (random forest and generalized linear model via penalized maximum likelihood) and support vector regression with linear kernel as meta-learner was the optimal model for forecasting Bitcoin price. The MAPE, RMSE, MAE, and R-squared values for the stacking ensemble model were 0.0191%, 15.5331 USD, 124.5508 USD, and 0.9967 respectively. These values show a high degree of reliability in predicting the price of Bitcoin using the stacking ensemble model. Accurately predicting the future price of Bitcoin will yield significant returns for investors and market players in the cryptocurrency market.
Risk analysis of selected buildings in Shahrekord based on HAZUS requirements using fuzzy logic

Earthquake is a natural and unpredictable phenomenon that causes a lot of human and financial losses in many seismic zones of world. Therefore, the prediction of seismic events is one of the most important in earthquake and structure literature. Moreover, planning for crisis prevention as well as taking appropriate decisions after the crisis in urban areas will significantly increase the impact of measures to reduce the seismic risk. One of the computational tools in the risk analysis is the fuzzy logic to predict the possible damages due to natural disasters. Precise scientific principles and many advantages of this tool in analyzing phenomena with probabilistic uncertainties have caused its application to be constantly expanded and developed. In the present paper, the use of fuzzy tools for classification of the structures in Shahrekord (IRAN) has been investigated and the seismic hazard of selected buildings in this city is examined as a sample of existing structures. The results are consistent with the experiences of the recent earthquakes in this country for the vulnerability of buildings in urban areas. Also, the results indicate that the proposed method has good adaptation and the parameters which have been used in the damage assessment, propose good reliable flexibility.
A general framework of particle swarm optimization

Particle swarm optimization (PSO) is an effective algorithm to solve the optimization problem in case that derivative of target function is inexistent or difficult to be determined. Because PSO has many parameters and variants, I propose a general framework of PSO called GPSO which aggregates important parameters and generalizes important variants so that researchers can customize PSO easily. Moreover, two main properties of PSO are exploration and exploitation. The exploration property aims to avoid premature converging so as to reach global optimal solution whereas the exploitation property aims to motivate PSO to converge as fast as possible. These two aspects are equally important. Therefore, GPSO also aims to balance the exploration and the exploitation. It is expected that GPSO supports users to tune parameters for not only solving premature problem but also fast convergence.
Cooperative principle in Facebook interaction among collegian

One of the main principles guiding people’s communication is the cooperative principle by American linguist Grice. The use of cooperative principle helps people to increase their flexibility and accuracy. The analysis of cooperative principle in a text or conversation shall be the learning for people to communicate well using language with each other. The paper aimed to find out whether the participants under study followed or violated the maxims proposed by Grice. Besides, it was to identify whether the cooperative principles become the participants’ orientation or mere redundant rules. The research employed a descriptive qualitative research of which the data were obtained from the conversation among six workmates in a Flary group discussion on Facebook page. The research founds that the conversation followed and violated the maxims by Grice. Maxim of quantity was the most frequently used by the participants (12.77%). Interestingly, it was also the most frequently violated (63.8%). Despite the violation occurred in the interactions, the conversation was smooth. When the speakers realized about the cooperative principles they had to obey, the conversation became more relaxed, effective, and efficient.
Visual Question Answering based on Local-Scene-Aware Referring Expression Generation

Visual question answering requires a deep understanding of both images and natural language. However, most methods mainly focus on visual concept; such as the relationships between various objects. The limited use of object categories combined with their relationships or simple question embedding is insufficient for representing complex scenes and explaining decisions. To address this limitation, we propose the use of text expressions generated for images, because such expressions have few structural constraints and can provide richer descriptions of images. The generated expressions can be incorporated with visual features and question embedding to obtain the question-relevant answer. A joint-embedding multi-head attention network is also proposed to model three different information modalities with co-attention. We quantitatively and qualitatively evaluated the proposed method on the VQA v2 dataset and compared it with state-of-the-art methods in terms of answer prediction. The quality of the generated expressions was also evaluated on the RefCOCO, RefCOCO+, and RefCOCOg datasets. Experimental results demonstrate the effectiveness of the proposed method and reveal that it outperformed all of the competing methods in terms of both quantitative and qualitative results.
Uncover quantumness in the crossover from BEC to quantum-correlated phase

Collective phenomena in the Tavis-Cummings model has been widely studied, focusing on the phase transition features. In many occasions, it has been used variational approaches that consider separated radiation-matters systems. In this paper, we examine the role of the quantum entanglement of an assembly of two-level emitters coupled to a single-mode cavity; this allows us to characterise the quantum correlated state for each regime. Statistical properties of the system, e.g., the first four statistical moments, show clearly the structure of the light and matter distributions. Even though the second order correlation function goes to one in some regimes, the statistical analysis evidence a sharp departure from coherent behaviour, contrarily to the common understanding.
Platonov’s Utopia as Freethinking

The article discusses the methods (formal-logical and dialectical), by the supperposition of which Andrei Platonov creates a unique style of narration in his only fully completed novel Chevengur . The purpose of the writer is to discover a way to build a utopian society, which after the Russian proletarian revolution of 1917 began to define planetary life as a theory and possible social action. A special role in the clarification of these methods belongs to the concept of the observer, or, as Platonov sometimes calls him, the overseer. In the 1920s, this idea developed intensively. On the one hand, the concept of observer shows the functioning of a certain knowledge of the world, and, on the other hand, through it, as if in a mirror, the problems posed by a person in the course of the quest for new solutions are reflected. The observer is the center of the universe, where all information is collected, and it is the observer who evaluates the internal and external freedom of events construction. The original goal of the Chevengur novel is to show the method of building a communist utopia, that is, a society that really has no place in the world. According to Platonov, only in this situation of absence, freethinking is possible. This paradox was formed due to the conviction that not only communism is the end of history, but that consciousness is precisely and only historical, and the process of history is exactly as it was presented by the creators of the communist idea Marx and Lenin. The communist idea of history implies transitions from one economic formation to another, the renewal of “united Earth.” As “the era of technical revolutions has come,” the freedom of thought is denied, and “careful thought about technology improvement” is only recognized. Therefore, utopia is a way of freethinking.
Pelatihan pembuatan media baca dan penerapan strategi lead reading bagi siswa kelas awal di Kabupaten Bantaeng

The purpose of the desired activity is to introduce ways of using and making reading media and the application of lead reading strategies for elementary school teachers in Kabupaten Bantaeng because most primary school teachers have difficulty in making media read and implementing appropriate strategies for students regarding reading. This is due to the lack of media availability and how to use it is not equipped. The method used in the service is the delivery of material in lectures and demonstrations, the practice of making teaching aids by the participants, and the practice of using the teaching aids by groups of participants. The product produced during service is read media.
예비유아교사의 글로벌 마인드 함양을 위한 교육모형 탐색연구

The global mind of pre-service early childhood education teachers is very important in terms of global citizenship education as well as children and their families who are global leaders of the future society. This study explores the constituent factors related to teacher personality for the concept of a comprehensive and broad global mind, and implements teacher personality education. In order to cultivate the global mindset of pre-service early childhood teachers, 28 questions were composed of 175 pre-service early childhood teachers in J region. As a result of the program execution, there was a statistically significant difference in the pre-post comparison and grade-by-grade comparison (p<.05). It was possible to see the process of change in the process of cultivating the global mind of pre-school teachers. Early childhood education teachers were able to perceive anxiety and fear about the rapidly changing future as a novelty of curiosity, and the differences between cultures were recognized as diverse rather than different. The avoidance tendency changed with an effort to the approach. This study finds the constituent factors of the comprehensive concept of the global mind in relation to the teacher's personality, which can be found meaningful in that it presents an educational model for cultivating the global mind of pre-school teachers.
OPTIMIZATION OF DYNAMIC ARCHIVES MANAGEMENT AT THE GENERAL SECTION OF THE SECRETARIAT KERINCI DISTRICT AREA

The existence of dynamic records must be managed in an orderly and orderly manner supported by the availability of complete, accurate and quality information so that it is useful for the creators, recipients and users. The research objective was to determine the optimization of dynamic archive management at the General Section of the Regional Secretariat of Kerinci Regency. This study uses a qualitative approach where data is obtained through free interviews. From the research, it is known that: Archive creation can be defined as the activity of recording activities or events in any form of media in accordance with the development of information and communication technology; The use and proper storage system in general greatly influences the role of administration in achieving organizational goals and has been implemented or implemented properly; A dynamic archive storage system is basically a method or method used by an organization in storing the resulting files so that these files can be stored and organized properly; Management of dynamic records can be carried out optimally if the understanding of the principles in archiving activities is mastered correctly and appropriately.
Super-Resolution Generative Adversarial Network with Modified Architecture for Single Image Super-Resolution

Recently, Single image Super-Resolution (SISR) has become an attractive research area in Image processing which generates a High-Resolution (HR) image by using Single Low-Resolution (LR) image. Deep learningbased SISR approaches have achieved better Super-Resolved (SR) results by using mean squared error (MSE) as an objective function that increases the quality of SR results over performance metrics like peak-signal-to-noise-ratio (PSNR) and structural similarity index (SSIM). Nevertheless, MSE based approaches lead to generate over smoothed images with less high-frequency texture information at larger upscaling factors. Recent experiments have proved that Generative Adversarial Networks (GAN) generates perceptually convincing SR images through efficient extraction of high-frequency information from single LR image. In this paper, we propose a GAN based approach for SISR with modified deep-residual network architecture. In our proposed technique, we introduce the bottle-neck convolutional (CN) layer in the network structure of the Generator. Adding bottle-neck layers improves the network performance through 1 x 1 convolutional layers which extract complex features from the input and also reduces the computational complexity compared to 3 x 3 convolution layers. We further improve the model performance by removing batch normalization layer from the entire generator to overcome the unpleasant artifacts and improves GPU usage while training.
Sistem Pakar Dalam Diagnosa Penyakit Kanker Rahim Menggunakan Metode Naïve Bayes dan Certainty Factor

The uterus is one of the reproductive organs, namely the mouth of the uterus which is very susceptible to cancer and very often women experience cancer due to a lack of health care. suffered by women in the uterus can be like cysts, cervical cancer, uterine cancer, vaginal cancer and others, cancer is also very difficult to cure so that patients eventually have to give up and face death. From this it is necessary to make a diagnosis from the beginning in order to minimize the number of deaths caused by cervical cancer which is faced by many women in the world, especially in Indonesia, in diagnosing this disease can use a computer system in collaboration with experts to produce a system called the system. experts as an effort to help solve problems that occur with uterine disease that is being experienced by some women. The results of this study will show the percentage level of disease experienced by patients by 88% with the help of implementing the naïve Bayes method and certainty factory
Research on Partner Selection in the Construction of Cloud Ecosystem

Partner selection is a key link for core enterprises to construct a cloud ecosystem which is directly related to the competitiveness and success of the cloud ecosystem. According to characteristics of the cloud ecosystem and the principle of partner selection, the evaluation index system of partner selection for core enterprises in cloud ecosystem is designed from two aspects of partner "capability factors" and "relationship factors". In view of the evaluation index system, this paper constructs a comprehensive evaluation model based on the combination of AHP and the entropy to determine the weight of the improved TOPSIS method, and applies it to the empirical analysis of partner selection for core enterprises in cloud ecosystem, and verifies its feasibility and effectiveness.
Perencanaan Pembuatan Aplikasi Mobile Revalcon Sampit Menggunakan Metode Net Present Value

This study examines the development plan of Revalcon's online motorcycle taxi service to simplify its services by creating a service application. The purpose of this research is to find out whether making this application can increase Revalcon's turnover, to find out that the author uses NPV (Net Present Value) as a tool for decision makers. The data collection process was carried out by interviewing Revalcon's management. The analysis results show that the final value of the test results is higher than the price of making the application. A review of the test results will be very useful for Revalcon's online motorcycle taxi in deciding on making service applications.
Hate Groups and the Use of Public Facilities

After the surge of hate speech by far-right groups in the late 2000s, several such groups sought to use local public facilities to promulgate hate speech chiefly against Koreans resident in Japan. This led local governments to confront the difficult question of how to strike a balance between freedom of assembly (and expression), on the one hand, and the interests of minority residents, on the other. In this chapter, it is argued that local governments can sometimes refuse hate groups use of their facilities to protect local minority residents and that the guidelines issued by some local governments are constitutional despite some notable defects. Many constitutional law scholars in Japan will certainly oppose this conclusion, but this chapter contends that their position cannot be supported, because it overcategorizes conflicting interests and abstracts highly complex factors. It must be recognized that there are exceptions to free speech protections in relation to certain categories of hate speech. Some cases on the use of public facilities by hate groups constitute an example of such exceptions, because certain uses will clearly harm minority residents and thereby contravene the public purpose of establishment and operation of those facilities.
Assessment of anthropogenic pollution of the topsoil in the city of Chernivtsi according to magnetic and geochemical studies

Summary We analyzed heavy metals content and magnetic susceptibility (χ) of urban soils sampled in Chernivtsi city (Ukraine). X vary from 10*10−8 to 1286*10−8 m3/kg with a median value of 66* 10−8 m3/kg. In 20% of samples χ exceeds the median value three times or more, indicating that the soil is contaminated with iron dust and magnetic particulate matter derived from anthropogenic aerosols. It was found that the priority pollutants of urban soils are Pb and Zn, the content of which exceeds the critical concentrations in the vast majority of samples. The maps were composed, showing spatial distribution of χ and Pollution Load Index (PLI), calculated by three metals (Zn, Pb, and Cu). For 51% of the samples, the PLI value indicates no contamination, for 35 % – moderate contamination level, 14 % – very strong soil contamination.
A Deep Learning Approach for Polite Dialogue Response Generation

Dialogue generation is the act of creating response sentences that are contextually relevant, meaning full and grammatically correct. A natural language sentence is characterized by different features including linguistic features and stylistic features. In a dialogue generation system, the generated response can be polite, rude or neutral. The main objective of this proposed system is to generate polite dialogue responses by using deep learning techniques. The system mainly contains two modules: the first module implements a politeness classifier and the second module implements a response generation system. Politeness classifier calculates the politeness score for the input sentence and by using this score the response is generated. For implementing the classifier the Stanford politeness corpus is used and for implementing dialogue generation model the Cornell movie dialogue corpus is used. The advantages of Bi-directional Long Short Term Memory (BLSTM) is used for building the system. Input to the system is a sentence which will be processed by the classifier and the output of the classifier and the sentence after vectorization is given to the dialogue generation module which will generate the final output. The main advantage of this system is that it is created by a deep learning model which will perform well in large dataset.
CHALLENGING THE CLOUD

Cloud computing has fulfilled the long-liven dream of proffering utility as a service. It has significantly contributed in transformations of Information Technology (IT) service provisioning and shaping the manner in which services are consumed and utilised. With the origin of cloud, the innovators and developers are no longer bothered of making any capital investments in hardware in order to deploy their services or gain expertise in operating them. Exclusively, the elasticity and potential offered by cloud is unprecedented in the history of IT. Conversely, the achievements through cloud computing though are beneficial but they rivet a large number of hard tasks owing to continuous tormenting by challenges. This paper comprehensively examines the challenges associated with the cloud computing. It presents a review of cloud paradigm and discusses the transition from different computing paradigms towards cloud. Conclusively, it highlights the challenges associated with cloud computing.
Relaxing Relationship Queries on Graph Data

In many domains we have witnessed the need to search a large entity-relation graph for direct and indirect relationships between a set of entities specified in a query. A search result, called a semantic association (SA), is typically a compact (e.g., diameter-constrained) connected subgraph containing all the query entities. For this problem of SA search, efficient algorithms exist but will return empty results if some query entities are distant in the graph. To reduce the occurrence of failing query and provide alternative results, we study the problem of query relaxation in the context of SA search. Simply relaxing the compactness constraint will sacrifice the compactness of an SA, and more importantly, may lead to performance issues and be impracticable. Instead, we focus on removing the smallest number of entities from the original failing query, to form a maximum successful sub-query which minimizes the loss of result quality caused by relaxation. We prove that verifying the success of a sub-query turns into finding an entity (called a certificate) that satisfies a distance-based condition about the query entities. To efficiently find a certificate of the success of a maximum sub-query, we propose a best-first search algorithm that leverages distance-based estimation to effectively prune the search space. We further improve its performance by adding two fine-grained heuristics: one based on degree and the other based on distance. Extensive experiments over popular RDF datasets demonstrate the efficiency of our algorithm, which is more scalable than baselines.