Reviewerly/exHarmony huggingface dataset:
Vein topology, structures, and distribution during the prograde formation of an Archean gold stockwork

Abstract The Archean Cheechoo stockwork gold deposit is hosted by a felsic intrusion of tonalitic-granodioritic composition and crosscutting pegmatite dikes in the Eeyou Istchee James Bay area of Quebec, Canada (Archean Superior craton). The evolution of the stockwork is characterized herein using field relationships, vein density, and connectivity measurements on drill core and outcrop zones. The statistical distribution of gold is used to highlight mechanisms of stockwork emplacement and gold mineralization and remobilization. Two statistical populations of gold concentration are present. Population A is represented by gold grades below 1 g/t with a lognormal cumulative frequency. It is widespread in the hydrothermally altered (albite and quartz) and mineralized facies of the pluton. It is controlled by the development of quartz-feldspar-diopside veins as shown by the similar lognormal distribution of grades and vein density and by the correspondence of grades with network connectivity. Diopside and actinolite porphyroblasts in deformed veins within sodic and calcsilicate alteration zones are evidence for auriferous vein emplacement prior to the amphibolite facies peak of metamorphism. Population B (&amp;gt;1 g/t) is erratic and exhibits a strong nugget effect. It is present throughout the mineralized portion of the pluton and in pegmatites. This population is interpreted as the result of gold remobilization during prograde metamorphism and pegmatite emplacement following the metamorphic peak. The pegmatites are interpreted to have scavenged gold emplaced prior to peak metamorphism. These results show the isotropic behavior of the investigated stockwork during regional deformation and its development during the early stages of regional prograde metamorphism.
Users’ Sentiment Analysis of Shopping Websites Based on Online Reviews

t With the rapid development of online shopping, how to explore the value of online reviews, so as to give full play to their role in potential users’ purchasing decisions. Based on text mining and quantitative analysis, this paper studies the sentiment analysis of online reviews on B2C shopping website. The main attributes of commodity or service are extracted based on the order of word frequency in the online reviews. Text analysis method is used to judge the relationship between attributes of commodity or service and its emotional words. The fine-grained sentimental polarity and intensity of attributes are identified to analyze users’ concerns and preferences. The research shows that users pay more attention to the configuration and after-sales service of mobile, and have a positive sentimental orientation to most of attributes, especially unlocking function, hand feeling attribute and logistics service; and have a neutral sentimental orientation towards the attributes of battery and memory, and a negative sentimental orientation towards the membrane of mobile phone. The results can provide a reference for consumers to make purchasing decisions, for enterprises to improve product quality, and for shopping platform to optimize service.
Hybrid Quantum-Classical Dynamic Programming Algorithm

The Markov decision process has various applications in engineering, economics, operations research and artificial intelligence. Quantum computers provide a new way to tackle the computational problems in solving Markov decision process. We develop quantum circuits for dynamic programming algorithm to solve for the Markov decision process. The heuristic circuit construction method based on linear combinations of unitaries is demonstrated. The matrix decomposition and sampling are discussed. The computability advantage over classical Birkhoff-von Neumann method is proved. The connection to traditional dynamic programming algorithm is discussed in terms of functional equations. Our work suggests a new hybrid quantum-classical approach to dynamic programming algorithms.
Developing a Digital Healthcare Book based on the Blockchain Technology

Each Government is must ensure adequate health and social welfare for people. The healthcare book is a document historicizing medical information related to a person. It helps doctors to make consistent diagnoses. The major concern of doctors is to ensure the inalterability of information provided by the healthcare book. Based on the major benefits that Blockchain technology offers, we use it to design a digital healthcare book system by writing smart contract with Ethereum blockchain. It guarantees data integrity and provides secure information to doctors on various medical exams, patients’ pathologies and others. It is a low-cost and safe system in cases of emergency. In addition, an Ethereum based implementation is used to verify the feasibility of our proposed system.
Design and Development of Mobile Learning Platform Based on WeChat Mini Programs

Traditional online learning relies heavily on computer equipment, which is inconvenient for moving and not suitable for fragmented and informal learning. To solve this problem, a mobile learning platform based on WeChat Mini Programs is proposed. Firstly, according to features of mobile learning, introduce the design background of the mobile learning platform. Then, the system architecture is designed after analyzing the functions and interaction requirements of the mobile learning platform, and there are five functional modules and an overall design process proposed. Finally, based on the functional modules and design process, use WeChat Mini Programs as the front end and the Cloud Base of WeChat as the back end to develop the mobile learning platform. Practice has proved that the mobile learning platform based on WeChat Mini Programs can realize mobile learning and exercising without installation on any devices, and solve the constraints of learners’ time and space.
Real-time Hand Gesture Recognition for Robotic Arm and Home Automation

Hand gestures are a symbolic and non-vocal language and are used by an individual to communicate. With computer vision, hand gestures can be detected and be used to talk with a capable computer, leading to the field of Human-Computer interconnection. The field of computer vision has been achieving cutting edge results with the advent of deep learning models. The work implements the Inception v3 architecture [1], which is a convolutional neural network. The model is retrained on our data set using Transfer learning, with which we reduce the requirements on computational resources, data and time. In this project, a hand gesture is performed in front of a web camera of a system. The gestures are predicted as one among six gestures with a corresponding probability. This project deals with the applications of the detected hand gestures in home automation and control of a robotic arm. Hand gestures are simple to perform, and it makes managing home effortless compared to manually intervening and providing instructions to a machine. In the home automation model, the gesture classification results from the system are transmitted to the microcontroller which switches on or off a home device. The robotic arm is a mechanical system which is used in manipulating the movement of lifting, moving, and placing the workpiece to lighten the work of man. It is equipped with servo motors and is controlled by our hand gestures to perform lifting and dropping of objects and rotation of the robotic arm.
A Reliable Hybrid Software Development Model: CRUP (Crystal Clear & RUP)

The turning point of software development dates back to 2001 when some specialists published the Agile Manifesto. After that, many companies are moving to Agile methods. However, it seems that in many cases, software teams and companies prefer to use a combination of Agile and plan-driven methods together to take advantage of both discipline and agility simultaneously. In this viewpoint, RUP as a traditional method combined with one or two Agile methods to solve the inefficiency of software development methods against new and changing customer requirements. However, combining such methods may lead to organizational overheads and challenges as well. In this study, having reviewed the most recent attempts to reach hybrid models, a new hybrid model will be presented based on RUP and Crystal Clear. Then, the new model employed in a Case Study. The results showed that this hybrid model has considerable capabilities in small and medium projects compared to its parents' methods.
Facial Expression Recognition Based on Improved VGGNet and Island Loss

In order to solve the problem of low recognition accuracy caused by the imbalance of the number of samples in facial expression data set, the small difference of class samples and the large difference within class. This paper proposes a face expression recognition algorithm combining improved VGGNet and Island Loss. This paper improves the VGGNet network by designing a new output module, which improves the feature extraction ability of the model and the model avoids the over-fitting problem. By improving Island Loss, the generalization ability of the model is not affected by hard samples. Through experiments, the recognition accuracy of the model on CK + and FER2013 data sets reaches 99.67% and 72.48% respectively, and the generalization ability is outstanding in practical application.
Comparative Research on Blockchain Consensus Algorithms Applied in the Internet of Things

The current Internet of Things system has security and privacy issues, insufficient resources and network transmission delays. The advantages of introducing blockchain technology are analyzed, and the blockchain architecture of the Internet of Things and the traditional Internet of Things are compared. This article mainly introduces the DPoS algorithm and the workload proof mechanism method. This paper uses the blockchain consensus algorithm of the Internet of Things to detect the Internet of Things, and establishes a potential DPoS algorithm model. The model is solved using the DPoS algorithm, and the blockchain consensus algorithm is evaluated, and the model is revised using historical data to improve the accuracy of the blockchain consensus algorithm evaluation. The experimental results of this paper show that the blockchain consensus algorithm improves the efficiency of the application of the Internet of Things by 53%, and reduces the false alarm rate and the false alarm rate. Finally, by comparing the blockchain consensus algorithm, the influence of the blockchain consensus algorithm on improving the application of the blockchain consensus algorithm in the Internet of Things is analyzed.
Aggregate Customer Products Reviews on machine learning techniques using ontology process

Abstract Online customer reviews are the best advertisement and/or communication tools for product consistency. Consumers often prefer to do some research before agreeing to buy. Customer ratings are a significant source of knowledge for influencing consumer buying choices in the present. In this paper, we discussed SigmaCR’s aim to explore machine learning techniques and skills, natural language processing and information technology to create an intelligent device that analyses consumer product feedback via the Internet and provides comprehensive product summaries and consumer feelings. It would also be a very practical method for evaluating the power of many recent research projects, including ontology-based knowledge extraction and sentimental analysis.
Ergonomic and Usability Analysis of Platform for Communication of People with Limited Talk

The educational applications developed for smartphones inaugurate a new form of interaction, since the experience of use happens with the touch of the fingers on the screen, allowing a greater control on the interface. However, because it is a recent equipment, it still needs studies that lead to the adequate development of these applications, especially when these involve the internalization of knowledge beyond the operational level. Therefore, they involve conducting usability tests and analyzing the results. Previously, projects were carried out for certain segments of the population today, designers should think about the wide variety of existing consumers and their limitations. Thus, a design project must contain features that can encompass as many users as possible; what we call universal design. These types of concerns aim to improve the usability of the product and facilitate its use for the largest number of people, including left-handed people, elderly people, people with disabilities and/or people with special needs, always observing their daily lives. In this context, this research aimed to study the most appropriate way to present educational content on smartphones, starting from the methodological analyzes of Leventhal and Barnes (1998), regarding three applications: LetMeTalk, Aboard and Jade Autism. This work will not only focus on the ergonomic and usability analysis of platforms for alternative communication, but above all on the learning efficiency and skills of people with speech limitations, such as autistic individuals.
Classification of Personality based on Beauty Product Reviews Using the TF-IDF and Naïve Bayes (Case Study : Female Daily)

A person's personality is an important parameter to determine the character of each person and also as an assessment in various ways. In this day and age personality can not only be known from psychological tests, but also can be known in various ways. One way is through reviews presented in electronic media. In this study, a person's personality was classified into three Big Five personality groups, namely: Openness, Conscientiousness, and Extraversion using the Naive Bayes method and TF-IDF as Feature Extraction. The results of the classification that have been done get 81% accuracy with preproccessing scenarios using Stemming and Stopword, TF-IDF unigram, and BernoulliNB classifier type.
Conjugate Unscented Transform based Multiple Model Particle Filter

In this paper we develop the Multiple Model Particle Filter (MMPF) for nonlinear systems. The particle filter is used to estimate the conditional probability for the modes while the Conjugate Unscented Transform (CUT) based Kalman filter is used to estimate the dynamical state of the system. The resultant particle filter is a bank of nonlinear filters with the sequence of modes given by the samples. A tracking example is used to illustrate the working and efficacy of the MMPF with respect to the Interactive Multiple Model (IMM) filter. Comparison results are shown using the Extended Kalman filter and Conjugate Unscented Kalman filter for both the MMPF and IMM filters.
Emojitaliano: A Social and Crowdsourcing Experiment of the Creation of a Visual International Language

Inspired by the historical models of artificial and auxiliary languages, Emojitaliano is the result of a social and crowdsourcing experiment which was conducted by a group of seventeen translators, followers of the “Scritture brevi” blog, and led to the creation of an international language based on emojis. The experiment was carried out during 2016 on Twitter in the framework of the translation into emoji of Pinocchio, the famous Italian tale. Emojitaliano consists of 1) a repertoire of stable and coherent lexical correspondences between the emoji UNICODE set and the Italian language and 2) a set of predefined simplified rules agreed on during the translation process. Emojitaliano is stored in @Emojitalianobot, an online tool and digital environment for translation into emoji, running on Telegram, the popular instant messaging platform. It is the first open and free Emoji-Italian translation bot based on UNICODE descriptions, which contains a glossary with all the senses assigned by the translators to emojis during the translation process of the famous Italian novel. This paper presents the translation projects of Emojitaliano, the background and its lexicon and grammar and finally Emojitalianobot.
TTSD: A novel dataset for Turkish Text Summarization

Nowadays, with the increase in the amount of data on the internet, it becomes extremely important to collect salient information from these data efficiently. Gathering and comprehending essential information from such tremendous data as soon as possible is a complex and challenging process for people. Many methods have been proposed to cope with this process. Automatic text summarization systems are one of these methods in term of the fact that it facilitates obtaining the required documents by eliminating the lack of resources and time. Most of these methods are evaluated in datasets which exist in the literature. For Turkish text summarization, it is not enough datasets. In this study, we presented a novel dataset (TTSD-Turkish Text Summarization Dataset) in order to be preferred both in extractive and in abstractive methods. It is evaluated with TextRank, Lexrank, LSA-based, Luhn and Random methods by using sumy package, is compared performances using ROUGE. It is obtained that the proposed dataset has successful results in each method.
Room03—the Echo of Poetry

With the progress of science and technology, virtual reality, human-computer interaction, network big data and other research fields and related products have entered people's lives in large quantities. At the same time, the relationship between people and space, network and data has also changed. People expect to realize the two-way flow of information in the different space between virtual and real through a more natural and harmonious interaction. This work will start from the most basic “3I” characteristics of virtual reality (immersion, interaction, imagination) and combine with the fourth “I” (Intelligence) characteristics, in order to realize the audience's experience process in multidimensional space such as vision, hearing and touch through immersive projection space (CAVE) and interactive sensor device. It tries to create a virtual reality space for the audience to empathize with and be understood by machines. When the audience approaches the work, the printer in the space prints words and phrases at random, and the audience makes the bouncing ball that projected on the wall make corresponding display feedback through the voice interaction of reading or dialogue. When audiences by changing move their arms, the bouncing spheres and countless particles would be deformed and displaced. The whole work intends to show the communication relationship between the individual people and machines. Machines are also trying to communicate and learn about human behavior.
Hyper-heuristics: Method of Differential Evolution and Bat Method for Selecting Classification Features

The paper proposes a hyper-heuristic GEBA algorithm for selecting significant classification features and evaluating the classification accuracy. The GEBA algorithm combines the bat echolocation algorithm and a modified version of the differential evolution algorithm. The experiments used the well-known k nearest neighbor classifier (KNN) and the decision tree-based classifier (CART). The effectiveness of the proposed GEBA algorithm was evaluated using a set of reference data sets from the UCI world library. The performance of the GEBA algorithm for solving the problem of selecting significant classification features was compared with the competing algorithms of bats, crows, and gray wolves, and the hybrid algorithm of particle swarm and gravity search. The proposed algorithm leads in the average and best values of the fitness function for most of the tested data sets and is also statistically significant compared to competing algorithms for the Wilcoxon T-test and the Friedman test with a confidence level of 0.95. The stability and accuracy of the GEBA algorithm are higher than that of competing algorithms on most reference datasets. A comparative analysis of the convergence rate of algorithms on a set of reference data sets for a given number of iterations showed that the GEBA algorithm converges faster to global optima than competing algorithms. The algorithm is planned to be used for solving traveling salesman problems, integer programming and minimax problems, medical image segmentation problems, and multidimensional optimization problems.
Object tracking with dynamic weighting Siamese network

Siamese-based models achieves outstanding performance in visual tracking. However, as the template is invariant during tracking, results in failing to capture the appearance changes over time, leading to incredible results. To tackle this problem, an object tracking method with dynamic weighting Siamese network has been proposed. First, we design a temporal attention module to obtain the time-sensitive features, which is learned by adaptively weighting the consecutive historical reference frames. Then, a spatial attention module is adopted to enhance the spatial information of the target, and promote the discriminative ability of the network. Finally, the Focal Loss is utilized to train the network offline, to improve the ability of handling the hard negative samples. We conducted the experiment on OTB2015 benchmark to evaluate the performance of the proposed method and other state-of-the-arts, comparison results demonstrate the good performance of our model.
Fuzzy Q-Learning Based Controller for Cost and Energy Efficient Load Balancing in Cloud Data Center

The goal of cloud controller is to focus on continuous delivery of services to user on demand basis followed by “pay-per-use” model. Due to the increasing demand of cloud services, energy consumption on data center is increasing rapidly which lead to high operational cost. The harmful emission from this energy intensive data center affects our environment badly and cause climate change significantly. So as an alternative we have focused on onsite green power generation to reduce the harmful effects of greenhouse gases. In this paper, we proposed a fuzzy Q-learning based self-learning controller to optimize the load for specific data center. The proposed method also helps to reduce uncertainty and solve the congestion issue efficiently through fuzzy linguistic behavior and membership function. In this proposal, fuzzy output parameter considered as reward value which is used to learn and update the state for each data centre.