Reviewerly/exHarmony huggingface dataset:
On Minerogenetic Series: The Third Discussion

Abstract The minerogenetic (or metallogenetic) series of mineral deposits (called minerogenetic series for short) is an academic idea (concept) of studying minerogenetic characteristics in mineral deposit geology. It uses the views of systematology and mobilism to study mineralizing processes and naturally occurring mineral deposit assemblages formed in various stages of geological development in specific tectonic environments. The minerogenetic series is also a natural classification of mineral deposits. There are five hierarchical orders in the classification, with the minerogenetic series as the basic order and the core. The first order includes three categories: minerogenetic series association, minerogenetic series type, and minerogenetic series group. The second is the minerogenetic series, which is decomposed into three orders, i.e. the third, fourth, and fifth orders. The third is the minerogenetic subseries, the fourth is mineral deposit models (types), and the fifth is mineral deposits. Minerogenetic series is comparable to a certain extent metallogenic systems both in the research content and in their own research content. The study of minerogenetic series has both great theoretical significance and practical value for mineral prospecting. However, the study is still in its initial stage and a further study remains to be conducted.
PEST Analysis Based on Fuzzy Decision Maps for the Ordering of Risk Factors in Territorial Planning of the Vinces Canton, Ecuador

The inadequate choice of the actions of growth and development in the Canton Vinces in the province of Los Ríos in Ecuador has increased the risk for the habitants in the face of natural phenomena. In the present work, a study is made of the environmental problems and risk situations that exist in the territory. In support of the decision-making process during territorial planning, a PEST analysis based on fuzzy decision maps is applied in order to obtain an ordering of the risk factors before the most unfavorable situations. The proposal identifies PEST factors and subfactors that allow forming a hierarchical structure of the model, compares the importance between the criteria to derive the vector of local weight using the eigenvalue approach, the influence of the model is analyzed through the cognitive map diffuse, the steady state matrix is obtained to then normalize the local weight vector obtaining as a result the normalization of the global weight.
Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation

This study proposes a lightweight solution to estimate affine parameters in affine motion compensation. Most of the current approaches start with an initial approximation based on the standard motion estimation, which only estimates the translation parameters. From there, iterative methods are used to find the best parameters, but they require a significant amount of time. The proposed method aims to speed up the process in two ways, first, skip evaluating affine prediction when it is likely to bring no encoding efficiency benefit, and second, by estimating better initial values for the iteration process. We use the optical flow between the reference picture and the current picture to estimate quickly the best encoding mode and get a better initial estimation. We achieve a reduction in encoding time over the reference of half when compared to the state of the art, with a loss in efficiency below 1%.
Methodology of probabilistic analysis of state dynamics of multi­dimensional semi­Markov dynamic systems

The problem of probabilistic analysis of a complex dynamic system, which in the process of functioning passes from one state to another at random times, is considered. The methodology for calculating the conditional probabilities of the system getting into a given state at a given time t, provided that at the initial time the system was in any of the possible states is proposed. The initial data for analysis are a set of experimentally obtained values of the duration of the system stay in each of the states before transition to another state. Approximation of the resulting histograms using the Erlang distribution gives a set of distribution densities of the duration of the system stay in possible states before transition to other states. At the same time, the choice of the proper Erlang distribution order provides an adequate description of the semi-Markov processes occurring in the system. The mathematical model that relates the obtained distribution densities to the functions determining the probabilistic dynamics of the system is proposed. The model describes a random process of system transitions from any possible initial state to any other state during a given time interval. Using the model, a system of integral equations for the desired functions describing the probabilistic transition process is obtained. To solve these equations, the Laplace transform is used. As a result of solving the system of integral equations, functions are obtained that specify the probability distribution of the system states at any time t. The same functions also describe the asymptotic probability distribution of states. An illustrative example of solving the problem for the case when the distribution densities of the lengths of the system stay in possible states are described by the second-order Erlang distributions is given. The solution procedure is described in detail for the most natural special case, when the initial state is H0
Mobile Real-Time Eye-Tracking for Gaze-Aware Security Surveillance Support Systems

The validity of two commercial-off-the-shelf eye trackers, including a fixed system and a mobile device, was compared in the context of a gaze-based assistant tool implemented to aid security surveillance. Participants performed a security surveillance simulation that required them to detect incidents across multiple video feeds. Aid from the Scantracker system—pinpointing neglected cameras while detecting and correcting attentional tunneling and vigilance decrease—was provided using gaze measures. Despite the lower amount of valid data for the mobile system, both devices computed similar measures of surveillance performance. Findings speak to the feasibility of using mobile robust-to-movement decision-support systems based on real-time gaze data to help improve incident detection in security surveillance environments.
INTEGRASI NAIVE BAYES DENGAN TEKNIK SAMPLING SMOTE UNTUK MENANGANI DATA TIDAK SEIMBANG

Classification of data with unbalanced classes is a major problem in the field of machine learning and data mining. If working on unbalanced data, almost all classification algorithms will produce much higher accuracy for majority classes than minority classes. This research will implement the Synthetic Minority Over-sampling Technique (SMOTE) method to overcome unbalanced data on credit customer data in Rawamerta teacher cooperatives. The research methodology uses SEMMA with the stages of research Sample, Explore, Modify, Model, and Asses. The Sample Phase was conducted to choose the data of the Rawamerta Teachers Cooperative credit customers for 2015-2017 with a total of 878 data with the attributes used namely income, total deposits, loan amount, duration of installments, services, installments, and credit status. The Explore phase analyzes current classes which are categorized as majority classes because there are 813 data, while traffic classes can be categorized as minority classes because there are 65 data. The data shows an imbalance of data between the two classes. The Modify stages perform the 500% SMOTE process. The Model Stage classifies using Na�ve Bayes. Na�ve Bayes modeling with SMOTE produced 1131 successfully classified data correctly and 72 data were not classified correctly while without SMOTE resulted in 818 data was classified correctly and 60 data were not classified correctly.Keywords: Na�ve Bayes, SMOTE, unbalanced data
NCS based ultra low power optimized machine learning techniques for image classification

In recent years there has been an extensive development in the field of convolutional neural network-based image classification because of the human-like inference results obtained, but these massive networks are resource intensive and have high memory and computational requirements. Intel's Neural Compute Stick brings real time inference, prototyping and deployment of these DNNs to the network edge. In this paper we will discuss the development of a model for classification of book cover images into genres, and subsequently compiling the trained model for use with the Neural Compute Stick, so as to receive the optimized results in constrained environments thus ultimately leading to a system to judge a book by its cover which can be used even within a low power environment like a mobile device or Raspberry Pi, as the stick runs on power values as low as 1.2W.
LowResourceEval-2019: a shared task on morphological analysis for low-resource languages

The paper describes the results of the first shared task on morphological analysis for the languages of Russia, namely, Evenki, Karelian, Selkup, and Veps. For the languages in question, only small-sized corpora are available. The tasks include morphological analysis, word form generation and morpheme segmentation. Four teams participated in the shared task. Most of them use machine-learning approaches, outperforming the existing rule-based ones. The article describes the datasets prepared for the shared tasks and contains analysis of the participants' solutions. Language corpora having different formats were transformed into CONLL-U format. The universal format makes the datasets comparable to other language corpura and facilitates using them in other NLP tasks.
Augmented Reality Pengenalan Huruf dan Angka Arab Menggunakan Metode Marker Based Tracking Berbasis Android

Hijaiyah letters are Arabic letters that are in the Koran which are studied by Muslims. Number of Hijaiyah Letters which are generally known to total 30 letters. This study aims to provide an introduction hijaiyah letters, Arabic numerals and alphabets on android smartphone by applying Augmented Reality with the media guide as a marker emergence marker object so that learning becomes more attractive. From previous research, things that have been discussed are only introduces the application Augemented Reality hijaiyah and Alphabet letters using a media card with a distance of 30 cm recommendations and recommendations 600-900 corner. This application is built using the Unity 3D, Vuforia SDK with FAST Corner Detection algorithm and 3D objects created using Blender. This application uses the marker method used to determine the point of emergence of 3D objects. The results of validation testing concluded if all valid system. Testing algorithms with the point x1, y1, x2 and y2, condition of the camera to the marker on the distance is 50 cm at an angle of 450 and a distance of 50 cm - 60 cm at an angle of 900.
Possibilities of Popular (Recommendation) Online Bibliography in the Promotion of Reading

The authors consider the possibilities of popular (recommendation) online bibliography in promoting young people’s reading. Basing on the analysis of the modern socio-cultural situation characterized by the rapid growth of digital interaction, the article raises the problem of fast and qualitative orientation in the increasing flow of information. The authors conclude that the real tool of information and bibliographic search for everyone can be online electronic bibliographic resources of libraries. There is insufficient information on the use of these resources by modern young people. There was conducted study in 2018 to obtain data on reader preferences and the bibliographic sources to which they mainly refer. Students of the Moscow City Pedagogical University attended it. The sample group consisted of 43 people. The authors assumed that modern young people are focused primarily on obtaining information on the Internet, while they have a need for competent assistance to find and select the right publications. Respondents got the questionnaire compiled of 13 questions. The processing of the survey results consisted of their qualitative and quantitative analysis. In accordance with the set tasks, the study was carried out in the following areas: analysis of sources of information about books and publications used by modern youth, study of inquiries to information resources of libraries, identification of satisfaction with the quality of bibliographic information. The results of the study showed that the respondents prefer to find complete, accessible and up-to-date information about books and publications, as well as the publications themselves, on the Internet. The most valuable and preferred is the bibliographic information provided in electronic form. At the same time, many respondents are not satisfied with their own search for the necessary and high-quality publications both in the libraries and on the Internet. The authors present the possibilities of network resources of popular (recommendation) bibliography as a navigator in fast and efficient search and selection of the necessary publications.
Faster horn diagnosis - a performance comparison of abductive reasoning algorithms

Abstract Abductive inference derives explanations for encountered anomalies and thus embodies a natural approach for diagnostic reasoning. Yet its computational complexity, which is inherent to the expressiveness of the underlying theory, remains a disadvantage. Even when restricting the representation to Horn formulae the problem is NP-complete. Hence, finding procedures that can efficiently solve abductive diagnosis problems is of particular interest from a research as well as practical point of view. In this paper, we aim at providing guidance on choosing an algorithm or tool when confronted with the issue of computing explanations in propositional logic-based abduction. Our focus lies on Horn representations, which provide a suitable language to describe most diagnostic scenarios. We illustrate abduction via two contrasting problem formulations: direct proof methods and conflict-driven techniques. While the former is based on determining logical consequences, the later searches for suitable refutations involving possible causes. To reveal runtime performance trends we conducted a case study, in which we compared publicly available general purpose tools, established Horn reasoning engines, as well as new variations of known methods as a means for abduction.
Perancangan Chatbot Menggunakan Dialogflow Natural Language Processing (Studi Kasus: Sistem Pemesanan pada Coffee Shop)

Some cases that are often experienced at a particular institution such as Micro Enterprise are often a staff / employee in providing information services and transactions that are carried out manually to customers related to these business activities. This cycle always repeats from one customer to another. The impact if there are conditions where the queue of customer that is quite crowded than the workload of staff/employees will be higher and the risk of error in transactions will be high too. The development of information technology in artificial intelligence on 4.0 industry era is moving forward. One of them is Machine Learning - Natural Language Processing (NLP) which is one of the sciences that focuses on how computers can understand the human language and response to it. Therefor in this research a chatbot system will be builtin providing information and conducting transaction with the customers. This chatbot will be develop using the Dialogflow tools provided by Google. This Chatbot that was build expected to be an alternative that can be implemented in various bussines to provide better service for customers
Data Curation in Academic Libraries as Part of the Digital Revolution

Purpose/Thesis: This paper outlines the role of data curation in the context of Research 2.0 and Research Data Management.Approach/Methods: The argument is based on a non-exhaustive review of the literature.Results and conclusions: Despite the relative vagueness and variety of definitions of data curation, academic libraries should engage in it.Research limitations: The study focused mainly on theoretical writings.Practical implications: The worldwide challenge associated with Research Data Management and data curation. Several countries and institutions have already answered the challenge, but the overall level of its recognition is low, and thus there is a need to raise awareness of its importance.Originality/Value: The premise of the argument is based on the assumption that views on data are changing.
Entanglement scaling in quantum advantage benchmarks

A contemporary technological milestone is to build a quantum device performing a computational task beyond the capability of any classical computer, an achievement known as quantum adversarial advantage. In what ways can the entanglement realized in such a demonstration be quantified? Inspired by the area law of tensor networks, we derive an upper bound for the minimum random circuit depth needed to generate the maximal bipartite entanglement correlations between all problem variables (qubits). This bound is lattice geometry dependent and makes explicit a nuance implicit in other proposals with physical consequence. The hardware itself should be able to support superlogarithmic ebits of entanglement across some poly($n$) number of qubit bipartitions; otherwise the quantum state itself will not possess volumetric entanglement scaling and full-lattice-range correlations. Hence, as we present a connection between quantum advantage protocols and quantum entanglement, the entanglement implicitly generated by such protocols can be tested separately to further ascertain the validity of any quantum advantage claim.
Synthesizing Data Using Variational Autoencoders for Handling Class Imbalanced Deep Learning

This paper addresses the complex problem of learning from unbalanced datasets due to which traditional algorithms may perform poorly. Classification algorithms used for learning tend to favor the larger, less important classes in such problems. In this work, to handle unbalanced data problem, we synthesize data using variational autoencoders (VAE) on raw training samples and then, use various input sources (raw, combination of raw and synthetic) to train different models. We evaluate our method using multiple criteria on SVHN dataset which consists of complex images, and perform a comprehensive comparative analysis of popular CNN architectures when there is balanced and unbalanced data and determine which operates best in class imbalance problem. We found that data synthesis via VAE is reliable and robust, and can help to classify real data with higher accuracy than traditional (unbalanced) data. Our results demonstrate the strength of using VAE to solve the class imbalance problem.
Constant-Q Deep Coefficients for Playback Attack Detection

Under the framework of traditional power spectrum based feature extraction, in order to extract more discriminative information for playback attack detection, this paper proposes a feature by making use of deep neural network to describe the nonlinear relationship between power spectrum and discriminative information. Namely, constant-Q deep coefficients (CQDC). It relies on constant-Q transform, deep neural network and discrete cosine transform. In which, constant-Q transform is used to convert signal from the time domain into the frequency domain because it is a long-term transform that can provide more frequency detail, deep neural network is used to extract more discriminative information to discriminate playback speech from genuine speech and discrete cosine transform is used to decorrelate among the feature dimensions. ASVspoof 2017 corpus version 2.0 is used to evaluate the performance of CQDC. The experimental results show that CQDC outperforms the existing power spectrum obtained from constant-Q transform based features, and equal error can reduce from 19.18% to 51.56%. In addition, we found that discriminative information of CQDC hides in all frequency bins, which is different from commonly used features.
Real-Time Visual Tracking Base on SiamRPN with Generalized Intersection over Union

Deep learning-based tracking methods have great challenges to handle larger training data with aiming to be invariant to all sorts of appearance variations. In this paper, we incorporate a novel Generalized intersection over union (GIOU) as bounding box regression loss into Siamese framework based tracker, and propose a visual tracking method based on Siamese region proposal network (SiamRPN) with generalized intersection over union. We set out to bridge the gap between optimizing the commonly used bounding box regression loss and maximizing the Intersection over Union (IOU) metric value. Our target estimation component is trained to predict the overlap between the target object and an estimated bounding box. Moreover, it can relieve the case that non-overlapping bounding boxes in training phase. Experimental validations have shown that our tracker performs substantially improvement on the tracking benchmarks OTB100 and is effective to deformation, occlusion and other challenges in object tracking.
Image Encryption and Decryption using Chaotic System

The age of digital information uses images in fields like military and medical applications, but the security of those transacted images is still a question mark. To overcome this challenge, an efficient encryption system has to be developed which should accomplish confidentiality, integrity, security and it should also prevent the access of images by unauthorized users. Such an image encryption system has to be developed which provides enhanced security for images. This system uses two important techniques which is based on chaos theory namely: Confusion and Diffusion. Confusion part uses block scrambling and modified zigzag transformation while the Diffusion part uses 3D logistic map and key generation followed by additive cipher. This system also protects from statistical and differential attacks. The experimental results such as Entropy, Histogram analysis, Mean Absolute Error, Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI) proves that the security of the images has been preserved at a higher level and also prevents the unauthorized access to the sensitive information.
Modelling Tibetan Verbal Morphology

The Tibetan language, despite being spoken by 8 million people, is a lowresource language in NLP terms, and research to develop NLP tools and resources for the language has only just begun. In this paper, we focus on Tibetan verbal morphology — which is known to be quite irregular — and introduce a novel dataset for Tibetan verbal paradigms, comprising 1,433 lemmas with corresponding inflected forms. This enables the largest-scale NLP investigation to date on Tibetan morphological reinflection, wherein we compare the performance of several state-of-the-art models for morphological reinflection, and conduct an extensive error analysis. We show that 84% of errors are due to the irregularity of the Tibetan language.
Approaching Fall Classification Using the UP-Fall Detection Dataset: Analysis and Results from an International Competition

This chapter presents the results of the Challenge UP – Multimodal Fall Detection competition that was held during the 2019 International Joint Conference on Neural Networks (IJCNN 2019). This competition lies on the fall classification problem, and it aims to classify eleven human activities (i.e. five types of falls and six simple daily activities) using the joint information from different wearables, ambient sensors and video recordings, stored in a given dataset. After five months of competition, three winners and one honorific mention were awarded during the conference event. The machine learning model from the first place scored $$82.47\%$$ in $$F_1$$-score, outperforming the baseline of $$70.44\%$$. After analyzing the implementations from the participants, we summarized the insights and trends of fall classification.