Reviewerly/exHarmony huggingface dataset:
Geology and ore genesis data of Elba manganese deposits, southern Eastern Desert, Egypt

The manganese deposits of Egypt are logged in many different localities in the Eastern Desert. Several manganese deposits were exploited by open cast mining excavation in the Red Sea coastal plain, particularly in the area covering from south-west of Halayeb Village to around the flood-plain of Wadi Elba north-east of Abu Ramad. Our study discussed the manganese deposits in twelve areas named as wadi Bashoya, Oshbia, N-Gabal Toyo, El-Hebal, Mateet, Blownay, Adeeb, Sarara, Sirmatai, Aqilahuq, Eikwan and N-wadi Ajway. There are two types of manganese deposits it can occur either as massive manganese ore type or mangneferous sandstone ore type. The area is situated at the Abu Ramad fault system which is the major belt of shearing within the NW -SE striking fault system. It forms part of Red Sea on south Eastern Desert in NW-SE direction with sub vertical dip. The deformation history attributed to Arc accretion tectonic of the Pan African Orogeny, also lies at the eastern part of North Hamizana Shear Zone. Binary diagram between (Co + Ni) wt. % versus (As + Cu + Mo + Pb + V + Zn) wt. % display the hydrothermal origin and supported by the MnO (wt. %) Fe2O3 (wt. %) and ppm (Cu + Co + Ni) 1000 triangle diagram and also by the Mn (wt. %) Fe (wt. %) and 10*(Ni + Co + Cu) wt. % triangle diagram. These deposits are characterized by low concentration of Cu, Ni and Co. The geochemical composition of manganese ores reflect formation by chemical precipitation from hydrothermal solution but occurrence of colloform texture, oolites in the mangneferous types denote to the redeposition by sedimentation processes.
Exploiting Images for Patent Search

Patent offices worldwide receive considerable numbers of patent documents that aim at describing and protecting innovative artifacts, processes, algorithms, and other inventions. These documents apart from the main text description may contain figures, drawings, and diagrams in an effort to better explain the patented object. Two main directions are presented in this chapter; concept-based and content-based patent retrieval. Concept-based search utilizes textual and visual information, fusing them in a classification late fusion stage. Conversely, content-based retrieval is based on the shape/content information from patent images and is therefore based on the visual descriptors that are extracted from binary images. Concepts are extracted using classification techniques, such as support vector machines and random forests. Adaptive hierarchical density histograms serve as binary image retrieval techniques that combine high efficiency and effectiveness, while being compact and therefore capable of dealing with large binary image databases. Given the vast number of images included in patent documents, it is highly significant for the patent experts to be able to examine them in their attempt to understand the patent contents and identify relevant inventions. Therefore, patent experts would benefit greatly from a tool that supports efficient patent image retrieval image retrieval patent and extends standard figure browsing and metadata-based retrieval by providing content-based search according to the query-by-example paradigm.
Designing an online-based questionnaire application for mobile devices

Abstract In a decision making and assessment, a lot of data is needed as a reference, the data needed can be obtained through filling out a questionnaire distributed to the community. With the rapid development of technology in the IT field, it is necessary to create a questionnaire application that is based online so that it can be accessed anywhere and anytime through the current mobile device. Online-based data storage needs are also important in a questionnaire, if all this time a questionnaire was made in paper form, in this paper we will discuss how to create an online questionnaire application. With the location of data storage also in online servers that can facilitate data processing. Application design is adapted to mobile devices that are currently owned by everyone. This paper aims to provide an effective and efficient solution for researchers, companies or individuals who want to use questionnaires as a method of data collection.
SimVecs: Similarity-Based Vectors for Utterance Representation in Conversational AI Systems

Conversational AI systems are gaining a lot of attention recently in both industrial and scientific domains, providing a natural way of interaction between customers and adaptive intelligent systems.A key requirement in these systems is the ability to efficiently parse user queries, understand the intent behind each query, and provide adequate responses to users.Therefore, many applications such as conversation bots and smart IoT devices has a natural language understanding (LU) service integrated within.One of the greatest challenges of language understanding services is efficient utterance (sentence) representation in vector space, which is an essential step for most ML tasks.In this paper, we propose a novel approach for generating vector space representations of conversational utterances using pair-wise similarity metrics.The proposed approach uses only a few corpora to tune the weights of the similarity metric without relying on external general purpose ontologies.Our experiments confirm that the generated vectors can improve the performance of LU services in unsupervised, semi-supervised and supervised learning tasks over state-ofthe-art prior works.
Blind Quality Index for Super Resolution Reconstructed Images Using First- and Second-Order Structural Degradation

Super resolution (SR) image reconstruction has attracted increasing attention these years and many SR image reconstruction algorithms have been proposed for restoring a high-resolution image from one or multiple low-resolution images. However, how to objectively evaluate the quality of SR reconstructed images remains an open problem. Although a great number of image quality metrics have been proposed, they are quite limited to evaluate the quality of SR reconstructed images. Inspired by this, this paper presents a blind quality index for SR reconstructed images using first- and second-order structural degradation. First, the SR reconstructed image is decomposed into multi-order derivative magnitude maps, which are effective for first- and second-order structural representation. Then, log-energy based features are extracted on these multi-order derivative magnitude maps in the frequency domain. Finally, support vector regression is used to learn the quality model for SR reconstructed images. The results of extensive experiments that were conducted on one public database demonstrate the superior performance of the proposed method over the existing quality metrics. Moreover, the proposed method is less dependent on the number of training images and has low computational cost.
Tracker-Level Decision by Deep Reinforcement Learning for Robust Visual Tracking

In this paper, we formulate the multi-tracker tracking problem as a decision-making task and train an expert by the deep reinforcement learning (DRL) to select the best tracker. Specifically, the expert takes the response map of the tracker as input and outputs a scalar to indicate the reliability of the tracker. With the DRL, the expert can make full use of complementary information among base trackers. Furthermore, under the guidance of the deep expert, base trackers update themselves adaptively to capture the changes of object appearance and prevent corruption. The experimental results on public tracking benchmarks demonstrate that the proposed method outperforms the state-of-the-art methods.
Adaptive divergence for rapid adversarial optimization

Adversarial Optimization (AO) provides a reliable, practical way to match two implicitly defined distributions, one of which is usually represented by a sample of real data, and the other is defined by a generator. Typically, AO involves training of a high-capacity model on each step of the optimization. In this work, we consider computationally heavy generators, for which training of high-capacity models is associated with substantial computational costs. To address this problem, we introduce a novel family of divergences, which varies the capacity of the underlying model, and allows for a significant acceleration with respect to the number of samples drawn from the generator. We demonstrate the performance of the proposed divergences on several tasks, including tuning parameters of a physics simulator, namely, Pythia event generator.
Kaiwa Learning Effectiveness using Interview Method in Improving Japanese Speaking Comprehension

This study aims is to: (1) Determine LPK Matsushita Gobel Foundation Jakarta participants ability to speak Japanese before using interview method. (2) Determine Pakuan University participant’s ability to speak in Japanese before using interview method. (3) Knowing the differences of LPK Matsushita Gobel Foundation Jakarta participant’s ability to speak Japanese before and after using the interview method. (4) Determine whether the method can improve the LPK Matsushita Gobel Foundation Jakarta participant’s ability to speak Japanese or not. (5) Gain feedback from LPK Matsushita Gobel Foundation Jakarta participants towards this method. This research is a class action, using a quasi-experiment which focuses on one sample class. The research instrument consists pretest and post-test, questionnaires, and interviews. The result of those interviews are reported in front of the class into a series of stories. By this method of interview, not only the participants are able to have conversations with many people, but the participants were able to reiterated the interview results to the class and done in a way such as giving a speech.
Design and Implementation of Embedded Intelligent Control System Software Programming Based on Genetic Algorithms

Abstract Embedded system has been widely used in the fields of production, life and automatic control with its superior performance since it was developed, and it is developing towards a more convenient and intelligent direction. Because of the limited target machine resources of embedded system, it is impossible to build a huge and complex development environment on it, so its development environment and target operating environment are separated from each other. The application program is downloaded to the target machine for cross-debugging. After debugging, the application program is downloaded to the target machine for cross-debugging and debugging. The intelligent control is used to generate the change value of the controller parameters, so that the parameters of the controller are adaptive and robust. Through the modular design idea, the modules are relatively independent, which is convenient for software programming and debugging. In genetic algorithms, after coding to form the initial population, the task of genetic manipulation is to impose certain operations on the individual’s fitness assessment according to their degree of adaptation to the environment.
Assessing the Modelling Approach and Datasets Required for Fault Detection in Photovoltaic Systems

Reliable monitoring for photovoltaic assets (PVs) is essential to ensuring uptake, long term performance, and maximum return on investment of renewable systems. To this end this paper investigates the input data and machine learning techniques required for day-behind predictions of PV generation, within the scope of conducting informed maintenance of these systems. Five years of PV generation data at hourly intervals were retrieved from four commercial building-mounted PV installations in the UK, as well as weather data retrieved from MIDAS. A support vector machine, random forest and artificial neural network were trained to predict PV power generation. Random forest performed best, achieving an average mean relative error of 2.7%. Irradiance, previous generation and solar position were found to be the most important variables. Overall, this work shows how low-cost data driven analysis of PV systems can be used to support the effective management of such assets.
Alternative Signatures based on Randomized Neural Network for Texture Classification

This paper describes two alternative methods for texture signature extraction based on a Randomized Neural Network (RNN), an artificial neural network with a single hidden layer architecture. The proposed signatures are promising ways to increase the discriminating capacity for texture recognition, while also keeping a fast feature building process. Experiments showed that the accuracy of texture classification using the new signatures is higher than other texture description methods in the literature.
‘Taking the Plunge’: The New Immersive Screens

Ariel Rogers addresses the contemporary experience of virtual reality technology and its long and volatile relationship to ideas of immersion. The multiplication and pervasion of screens has often been viewed as a break from previously dominant forms of screen engagement. Whereas viewers’ encounters with the twentieth-century cinema screen (conceived as singular and static) has typically been framed as an experience of centred space marked by fixity and transfixion, the experience of enclosure in multiple-screen environments has often been conceptualized via concepts of spatial fragmentation and information flow. Contemporary VR sets confound this distinction: not only are they ‘immersive’ and centring, they are also unanchored, breaking the tight identification of frame and screen that has dominated much of cinema’s history.
Creating a Simple Web App

In this chapter, we are going to be creating an extremely simple web application to use for demonstration purposes in subsequent chapters. The goal here is to get a full, end-to-end application up and running.
Detection of Potentially Students Drop Out of College in Case of Missing Value Using C4.5

The reputation of a university can be determined by the number of students drop out. This problem was experienced by many universities in Indonesia. It has been conducted by many researchers, however the data acquisition, attributes were not well explained. This study is aiming for giving projection related to the reasons behind students drop out by using machine learning technique. The challenging phase of preprocessing primary datasets are missing value, balanced class distribution, and a variety of data types. Two classes are applied: drop out and graduate students. By analyzing the problem of missing value data, it can reflect the basis of why students drop out or students who have the potential to drop out. According to the problem of balanced class distribution, Decision Tree algorithm is utilized, meanwhile for tackling the various of data types, we use C4.5. The result shows that 20 attributes using stratified sampling is the best of among all datasets and experimentations with an average AUC, accuracy, precision, and recall values of 0.98, 96.87, 98.75, and 97.84 respectively. It indicates that the proposed method is suitable for predicting students drop out with a balanced case of class distribution, despite having a missing data value problem.
Communicative Language Teaching (CLT) Approach Towards Speaking Ability

Communicative Language Teaching (CLT) is a way to deal with the educating of second and unknown dialects that emphasis on cooperation as both the methods and the last objective of learning a language. Targets are (1) to discover Effectiveness of CLT Approach in Improving Speaking Ability. (2) huge contrasts between students who are instructed by CLT and not. Quantitative research is a true design experiment. Design used was a pretest-posttest control group design. Population is eighth grader students in Junior High School 1 Tegal. Research uses cluster random sampling with two group design (experiment group and control group). Data was broke down utilizing a t-test, score of the t-test is 2.744. Score of t-table is at 5% level importance and on level of 62 is 1.670, t-test result is higher than t-table (2.744&gt;1.670). t-test hypothesis is rejected, and t-table hypothesis is accepted. CLT improve speaking capability.
Research on Investment Efficiency of Listed Companies Based on DEA Blockchain Concept

Now that society is in the second digital revolution, blockchain technology will be widely used in various fields. From the perspective of blockchain technology application, this paper takes the financial indicators of the input and output of the blockchain concept of China's A-share listed in 2015~2018 as a sample, and introduces the DEA method to analyze its investment efficiency. The results show that the introduction of blockchain technology is shorter and the investment efficiency is declining. Therefore, improving the cost management and controlling the cost of investment will help the investment efficiency of listed companies in the blockchain concept stocks.
Automatic diacritization of Tunisian dialect text using Recurrent Neural

The absence of diacritical marks in the Arabic texts generally leads to morphological, syntactic and semantic ambiguities.This can be more blatant when one deals with under-resourced languages, such as the Tunisian dialect, which suffers from unavailability of basic tools and linguistic resources, like sufficient amount of corpora, multilingual dictionaries, morphological and syntactic analyzers.Thus, this language processing faces greater challenges due to the lack of these resources.The automatic diacritization of MSA text is one of the various complex problems that can be solved by deep neural networks today.Since the Tunisian dialect is an under-resourced language of MSA and as there are a lot of resemblance between both languages, we suggest to investigate a recurrent neural network (RNN) for this dialect diacritization problem.This model will be compared to our previous models models CRF and SMT (24) based on the same dialect corpus.We can experimentally show that our model can achieve better outcomes (DER of 10.72%), as compared to the two models CRF (DER of 20.25%) and SMT (DER of 33.15%).
Development of Web Based Courseware for Artificial Neural Networks

Artificial Neural Networks (ANN) are important data processing algorithms which are used for solving nonlinear problems. Through classical approaches, mathematical infrastructure and complex equations in ANN are difficult to understand. Interactive and multimedia-based courseware has the potential to overcome these difficulties. In this study, a web based educational courseware for ANN was developed to provide an effective and efficient learning environment so that the difficulties can be overcome. This interactive courseware was also enriched with animations and text-based course contents. In addition to this, the effects of ANN parameters’ changes were observed directly through graphical results. In this way, users can easily understand the fundamentals and working mechanism of ANN. Without using any commercial libraries, the courseware was developed with ASP.NET, an object-oriented programming language. The courseware supports file formats such as XML, TXT, and CSV so that it can co-operate with other software. “Balance and Scale” data set was used to evaluate the performance of the courseware. 0.9918 accuracy, 1 specificity and 1 sensitivity values were achieved. When this study is compared to previous studies, improvements in terms of visuality, understandability and interactivity can clearly be identified.
Supplier Impersonation Fraud Detection in Business-To-Business Transaction Networks Using Self-Organizing Maps

Supplier Impersonation Fraud (SIF) is a rising issue for Business to Business companies, as the use of remote and quick digital transactions has made the task of identifying fraudsters more difficult. In this paper, we propose data-driven fraud detection system whose goal is to provide an accurate estimation of transactions’ legitimacy by using the knowledge contained in the network of transactions created by the interaction of a company with its supplier. We consider the real dataset collected by SIS-ID for this work. We propose to use a graph-based approach to design an Anomaly Detection System (ADS) based on a Self-Organizing Map (SOM) allowing us to label a suspicious transaction as either legitimate or fraudulent based on its similarity with frequently occurring transactions. Experiments demonstrate that our approach identifies fraudulent transactions with high success in a real-life dataset.
Infrastructure and technology

Abstract The first chapter provided you an introduction to big data, the complexities associated with big data and the processing techniques for big data. This chapter will focus on technologies that are available or designed to process big data and the different architectures that can be adopted for processing vast amounts of data. This chapter provides you with an overview of many technologies. While no one technology is a deep dive or a focus area, we have attempted to provide concise overviews of all different technologies. We have referenced several whitepapers and Apache foundation page, apart from discussions with Hadoopteams at Cloudera and Hortonworks. The author thanks all those who provided time for the discussions. There are several improvements being made across all technologies, we will discuss them across chapters and this chapter focuses on fundamentals of Hadoop and NoSQL.
TA-CFNet: A New CFNet with Target Aware for Object Tracking

A new network constructed by the combination of Siamese network and correlation filter has achieved enormous popularity because Siamese networks obtain high accuracy and correlation filters provide amazing speed. How to tackle boundary effects problems brought by filters, to fuse CF layers with multi-layers of CNN is an essential problem of the new network. Most papers deal with the problem by simply adding cosine window to every image. However, if the target is too small for bounding box to include background information or if target is too big for bounding box to loss partial information. In this paper, a new CFNet with target aware (TA-CFNet) for object tracking is proposed. TA-CFNet intergrates current target position and feature weight map to form target likelihood matrix. This target likelihood matrix is used to optimize and update the correlation filter, so that the template object of the deep tracking network is framed as accurately as possible. Experimental results on OTB benchmarks for visual tracking demonstrate that our proposed method outperforms other trackers in deep learning.
Engineering Drawing Recognition Model with Convolutional Neural Network

We proposed a convolutional neural network architecture that achieves the new state of the art for classification and detection in the engineering drawings data sets. The main hallmark of this architecture is the algorithm has higher accuracy and faster rapidity for the recognition compared with the traditional algorithm. The data sets include three categories: the electrical engineering drawings, the mechanical engineering drawings and the text drawings. To meet the requirements of training pictures in experimental model, we adopted some data enhancement techniques to expand the data set, such as rotation transformation, random cutting and salt and pepper noise. By a carefully crafted design, we constructed a convolutional neural network with moderate depth while keeping the model classification accuracy of engineering drawings is more than 98%.