Reviewerly/exHarmony huggingface dataset:
Evaluation of the Solar Radiation in a Seismic Zone

Abstract The solar radiation affects the life on Earth and it is important in meteorology, climatology, solar energy, agriculture, hydrology and seismology, too. The Sun warms the earth which reflects a part of the energy. An effect of tectonic stress increases the ground temperature that is radiated into the atmosphere. We study the possibility to use the variation of reflected energy in correlation with seismicity, radon concentration, CO2 emission, ULF radio waves, telluric currents, air ionization, temperature in borehole and acoustic noise. In this case, the solar radiation is a precursor factor. This research is important to study the effects of climate change. Analyzing the solar energy budget we find information about the atmosphere: aerosols, ozone, sulfur dioxide, carbon dioxide, nitrogen dioxide and pollution. Our multidisciplinary network (AeroSolSys) monitors Vrancea, a Romanian area characterized by deep earthquakes. For this study, we use two stations, Plostina and Vrancioaia, where we have a net radiometer and a pyranometer. The first sensor includes two pyranometers (up and low) and two pyrgeometers (up and low), a Pt-100 and a Thermistor. The spectral range covers both the Solar Radiation, 0.3 to 3 micrometers, and the Far Infrared Radiation, 4.5 to 42 micrometers. In this case, we measure and determine the temperature of sensor necessary for pyrgeometers, pyranometer short-wave radiation up and low EyU - EyL, pyrgeometers long-wave radiation up and low EgU - EgL, Albedo, net solar radiation NSR, net far infrared radiation NFIR, net total radiation NTR, sky temperature SkyT and, land surface temperature LST. In Vrancioaia, we have a pyranometer with range 0.3 – 2.8 micrometers and maxim 1600 W/m2. Two video cameras monitor the sky and the measurements are correlated with meteorological equipment installed in each location. We use information about solar activity from NOAA satellites and Copernicus CAMS. The humidity of ground is important in energy budget and it is estimated from meteorological data and telluric currents. The interval of analysis is 3 hours, between 2016.01.01 – 2018.05.31. It is difficult to notice the effect of tectonic stress since the daily and seasonal variations of solar radiation are high but global warming is obvious. Solar radiation monitoring helps us to understand other phenomena like radon emission or air ionization.
Mining Summary of Short Text with Centroid Similarity Distance

Text summarization aims at producing a concise summary that preserves key information. Many textual inputs are short and do not fit with the standard longer text-based techniques. Most of the existing short text summarization approaches rely on metadata information such as the authors or reply networks. However, not all raw textual data can provide such information. In this paper, we present our method to summarize short text using a centroid-based method with word embeddings. In particular, we consider the task when there is no metadata information other than the text itself. We show that the centroid embeddings approach can be applied to short text to capture semantically similar sentences for summarization. With further clustering strategy, we were able to identify relevant sub-topics that further improves the context diversity in the overall summary. The empirical evaluation demonstrates that our approach can outperform other methods on two annotated LREC track dataset.
An Efficient Implementation of the CRT Algorithm Based on an Interval-Index Characteristic and Minimum-Redundancy Residue Code

The Chinese remainder theorem (CRT), which appeared in ancient China, is widely used in many modern computer applications. This paper presents the CRT implementation by using the interval-index characteristic and minimum redundancy residue code. The proposed algorithm does not use large modulo addition operations and provides low computational complexity compared to conventional non-redundant RNS. The efficiency factors of using the minimally redundant RNS increase with the number [Formula: see text] of non-redundant moduli, asymptotically approaching the threshold [Formula: see text]. The new approach presented here will have a significant impact on many non-modular operations in RNS arithmetic, which currently use the CRT.
An improved saliency detection method based on hypercomplex Fourier transform

Abstract Saliency detection is a challenging direction in the field of computer vision. It can help us quickly obtain important information of image and video, and plays an important role in image compression, image segmentation, target recognition and other applications. Based on the Hypercomplex Fourier Transform (HFT) model, median filter and threshold segmentation are employed to obtain the image salient region, which solves the problem that the boundary is not clear and multiple targets cannot be separated, and achieves more accurate and stable detection effect.
A Fireworks-inspired Estimation of Distribution Algorithm

Abstract Estimation of distribution algorithm(EDA) is a popular evolutionary algorithm which is obtained widely attention. On the basis of EDA, combining with the idea of fireworks algorithm, we proposed a fireworks-inspired multi-model EDA(FMEDA). In FMEDA, multiple probabilistic models are used and generate different number of solutions by introducing the idea of firework explosion. Next, each model evolves by compared with its own best solution directly. The method of setting the limit of probability is taken to prevent from premature convergence. Applying this algorithm to the function optimization problem and the knapsack problem, the experimental result shows that the proposed algorithm has better performance than CGA and PBIL.
The Application of Category Theory to Epistemic and Poietic Processes

The goal of this paper is to explore the potential for applying the notional framework of category theory to some aspects of human activity associated with acquiring knowledge (epistemic activity), as well as to activity of a sort resulting in the creation of artefacts of any kind (poietic activity). These kinds of activity are conceptualized as processes. The ideas and constitutive components of epistemic and poietic processes are introduced, and against this background possible applications of category theory to the analysis and monitoring of progress in the unfolding of such processes is considered.
Label generation system based on generative adversarial network for medical image

In recent years, the generation model has made great progress in the task of less label sample data. Aiming at the heavy task, high cost, time-consuming and laborious problems of medical image labeling, this paper proposes an image label generation model based on generative adversarial network (GAN). The generator consists of a convolution network and a long-term and short-term memory network. It generates a text description for the input image. At the same time, the discriminator consists of a convolution network, calculates the difference between the generated description and the real description, and transfers the gradient to complete the confrontation training. In this paper, the model is trained on the INbreast dataset, and the experiment show that the model achieves good results in the generation of medical image data labels.
Geology of Australian ore deposits

SummaryThe Australian Ore Deposits monograph, published recently by The Australasian Institute of Mining and Metallurgy, has its focus on descriptive summaries of ore deposit geology. These summaries are expected to be long lasting compilations with value well after mines are exhausted and closed. Along with the more ephemeral genetic ideas as to how deposits might have formed, the descriptive approach remains a major component of any exploration program and invaluable during mine geology. The monograph records the state of the Australian mineral industry in 2017 providing a basis for documenting changes and progress since the previous monograph in 1998 and earlier ones starting in 1953. It will also become a benchmark to measure progress in years ahead.
The User Experience Identity (UXi) Method

As soon as we interact with a digital product, we unconsciously perceive its appearance, its behavior, and its meaning. The story that we read from the nature of a product can be actively written using insights from psychology, neuroscience, and behavioral economics. This chapter illustrates how this can be achieved with the help of the UXi method. Three different mobile applications that have been designed with the help of this method serve as case studies. Based on a brand’s semantic map, the so-called empirical knowledge is first developed for the brand values. The empirical knowledge describes the memories that we associate with the corresponding value. From this, design elements (the so-called digital design codes) are derived. These encode their corresponding value on the basis of mental concepts. Finally, the guidelines that have been set up with the help of the UXi method will be presented as a tool to ensure that all digital products of a company tell the right story, trigger the values of a brand in every interaction, have a high-hedonic quality, and thus have great chances of success on the market.
Retrieving Relevant Passages Using N-grams for Open-Domain Question Answering

Question Answering is most likely one of the toughest tasks in the field of Natural Language Processing. It aims at directly returning accurate and short answers to questions asked by users in human language over a huge collection of documents or database. Recently, the continuously exponential rise of digital information has imposed the need for more direct access to relevant answers. Thus, question answering has been the subject of a widespread attention and has been extensively explored over the last few years. Retrieving passages remains a crucial but also a challenging task in question answering. Although there has been an abundance of work on this task, this latter still implies non-trivial endeavor. In this paper, we propose an ad-hoc passage retrieval approach for Question Answering using n-grams. This approach relies on a new measure of similarity between a passage and a question for the extraction and ranking of the different passages based on n-gram overlapping. More concretely, our measure is based on the dependency degree of n-gram words of the question in the passage. We validate our approach by the development of the “SysPex” system that automatically returns the most relevant passages to a given question.
An Improved Adaptive and Structured Sentence Embedding

Recently, attention mechanism has aroused great interest in various fields of Natural Language Processing (NLP). In this paper, we propose a new model for extracting an interpretable sentence embedding by introducing an "Adaptive self-attention". Instead of using a vector, we use a 2-D matrix to represent the embedding and each valid row of the matrix represents a part of sentence. In addition, a length hierarchy mechanism with a unique loss function is applied to adaptively adjust the number of the valid rows of the matrix, which can solve the problem of attention redundancy in short sentences and lack of attention in long sentences. We evaluate our model on text classification tasks: news categorization, review categorization and opinion classification. The results show that our model, compared with other sentence embedding methods, achieve significant improvement in terms of performance when there exists a large amount of data and the length of the data is evenly distributed.
Planning and management of a crime prevention strategy

This chapter considers the question of library crime as perceived by the practising police officer. Advice will be offered on ways in which the criminal threat in libraries can be significantly minimised by judicious security planning and investment. The emphasis throughout is on the concept of designing out crime. Library and information managers must face up to the contemporary reality of criminal activity in their organisations. Library and information managers, in facing up to the unpleasant truth of crime in their organisations, must take the question of security planning seriously. Criminals are people who are prepared to take chances, and the greater the likelihood of their being apprehended as a result of criminal activity, the less likely it is that he or she will take that risk. The responsibility of the library or information service manager is to try to achieve a balance between security and access, and this is not an easy objective to achieve.
Mineral Mapping of Drill Core Hyperspectral Data with Extreme Learning Machines

Hyperspectral scanners are increasingly being used in the mining industry as a non-destructive and non-invasive technique to efficiently map minerals in drill core samples. Hyperspectral data allows the characterization of different mineral assemblages, structural features and alteration patterns based on reflectance spectrum profiles. Traditional methods to analysis drill core hyperspectral data include the use of reference spectral libraries by visual analysis or a well established software. However, although these approaches produce good results, they are time-consuming and prone to errors. Therefore, in this paper, we take advantage of the latest and advanced machine learning techniques proposed in different scientific fields and explore the use of extreme learning machines (ELM) to map minerals in drill core hyperspectral data. This is a supervised technique that provides fast and automatic means to characterize hyperspectral data. To be able to implement this technique, a reference map was generated from the drill core hyperspectral data. The obtained results indicate that ELM can successfully map minerals in drill core hyperspectral data producing better quantitative and qualitative results than a typical RF classifier.
One-Pot Synthesis of Requirements Elicitation for Operational BI (OBI) System: in the Context of the Modern Business Environment

Background: Requirement elicitation is the first step for any project. The available BI requirement elicitation approaches are focused more towards: the top pyramid of the management, less focus on the business aspect of an organization, historical in nature, emphasis on data mining and data warehousing aspects, no clear separation between requirements, and lack of proper linkage between the requirements. The demand of BI shifts towards the operational front for last couple of years. The use of Operational BI is gaining more popularity among industry and business communities because of increased demand of real time BI. It provides a powerful analysis of both operational and business information in current time for all levels of the users in the organization.Objective: In the modern business environment, the business operates on networks that demands multi-level decision-making capabilities as compared to the traditional business approaches. Operational BI is one of the business information systems that support the modern business environment and provides timely decision-making information to all the users in the organization. The requirement elicitation methodology for Operational BI system is found open for research. A new approach for requirement elicitation for an Operational BI system is presented in this paper, which highly suits to the organizations in the modern business environment.Methods: A top down technique is employed in the proposed requirements methodology that focuses on the business context of an organization. The proposed requirement elicitation approach is highly suited for the organizations that operate in the modern business environment. This approach overcomes several limitations in the existing BI requirement approaches. A case study is presented to support the proposed requirement elicitation approach for OBI system.Conclusion: This approach has several advantages like fast development, clear definition, classification of various types of requirements and proper linkage between the requirements without any loss or missing of gathering requirements. Finally, it is to conclude that the proposed approach acts as a one-pot synthesis of requirements elicitation for Operational BI system.Keywords: Business Context, Business, Intelligence, Business Networks, Protocols, Modern Business, Environment, Operational Business, Intelligence Requirement, Elicitation, Requirement, Methodology
Detection System of Aceh Ethnic Music Types Based On Sound Using the Hubbard Stratonovich Transformation Method

Abstract Sound is a natural and efficient communication media to get an information. In everyday discoveries, someone is always looking for information with other people. Voice with information and data that someone wants to get through various media. Not only by talking we can get information, one of them is through by music. Not only as entertainment, music also have many benefits. At this time maybe not many people know what Indonesian traditional music is, especially Aceh music. Because in this modern era there are so many people who are starting to leave traditional Indonesian music, there are some people who do not know it, even do not want to know it at all for many reasons. One way it can be used to provide information about computer vision used in this study. This research was built using Borland Delphi 7.0 programming language. The sound recognition process is done by inputting the sound sample results and also the sound inputted will produce energy using the Hubbard Stratonovich Transformation method. The results showed that the system depends on the accuracy of the sound technique used, and also obtained an average of an average of around 60% to 90%. Using this system is expected to help release the types of music from Aceh.
Online dynamic graph drawing with inverse Markov analysis

In online dynamic graph drawing, constraints over nodes and node pairs help preserve a coherent mental map in a sequence of graphs. Defining the constraints is challenging due to the requirements of both preserving mental map and satisfying the visual aesthetics of a graph layout. Most existing algorithms basically depend on local changes but fail to do proper evaluations on the global propagation when setting constraints. To solve this problem, we introduce a heuristic model derived from PageRank which simulates the node movement as an inverse Markov process hence to give a global analysis of the layout's change, according to which different constraints can be set. These constraints, along with stress function, generate layouts maintaining spatial positions and shapes of relatively stable substructures between adjacent graphs. Experiments demonstrate that our method preserves both structure and position similarity to help users track graph changes visually.
Differences and similarities between reinforcement learning and the classical optimal control framework

Abstract In this contribution , we discuss Reinforcement Learning as an alternative way to solve optimal control problems. Especially for biomechanical models, well‐established classical techniques can become complex and time‐consuming, because biomechanical models have often much more actuators than degrees of freedom. Furthermore, the solution of such a technique is normally only applicable to this specific setting. This means, that a slightly change of the initial value of the model or the desired end position does make the computed solution useless. We give a short overview to Reinforcement Learning and apply it to an optimal control problem containing the above mentioned challenges. We use an algorithm, which updates the weights and biases of a neural network, which takes the role of a controller, using simulated trajectories of the model generated by the current neural network.
Real Time Detection and Tracking of a Model Car using Kalman Filter

This paper elucidates a procedure for real time detection and tracking of a moving model car in the sequence of image frames acquired by a static camera. The vehicle tracking is performed by exploiting the modelling behavior of Kalman filter. The raw image acquisition, background subtraction, detection of vehicle in image frames is performed using image processing techniques. The vehicle tracking is performed by employing the modelling efficiency of a traditional Kalman filter. The raw image acquisition, background subtraction, to detect vehicle in image frames is performed using image processing techniques. The vehicle parameter estimation is further performed using the Kalman filter. This system takes raw image of two dimensional (2D) drivable surface with car as input, and outputs the spatial coordinates of estimated car position. The system was implemented on a ‘VEEROBOT’ robotic car in the laboratory environment. Real time images were taken using a ‘Logitech’ webcam, and the relevant processing is carried out using ‘Raspberry pi 3’. The analysis of experimental results between the measured and estimated position of the car is presented in this paper.
Document Image Retrieval Based on Visual Saliency Maps

The following topics are dealt with: document image processing; feature extraction; convolutional neural nets; cameras; optical character recognition; text detection; image segmentation; handwritten character recognition; object detection; video signal processing.
Medical Imaging Importance in the Real World

In the medical image resolution, automatic segmentation is a challenging task, and it's still an unsolved problem for most medical applications due to the wide variety connected with image modalities, encoding parameters, and organic variability. In this chapter, a review and critique of medical image segmentation using clustering, compression, histogram, edge detection, parametric, variational model. and level set-based methods is presented. Modes of segmentation like manual, semi-automatic, interactive, and automatic are also discussed. To present current challenges, aim and motivation for doing fast, interactive and correct segmentation, the medical image modalities X-ray, CT, MRI, and PET are discussed in this chapter.
Software Development Practices and Frameworks Used in Spain and Costa Rica: A Survey and Comparative Analysis

Software development has been impacted by the arrival of agile frameworks, especially in the last two decades. The HELENA Project (Hybrid dEveLopmENt Approaches in software systems development) was developed to identify the use of these frameworks in relation to more traditional ones. As part of this project, a survey was carried out in 55 countries, including Spain and Costa Rica. This paper presents the comparison of the results of these two countries, particularly in relation to two topics: the degree of agility of the activities of the software development life cycle and what are the most used methods and frameworks in each country. The results show similarities in both topics for the two countries, such as the fact that the most agile-oriented activities are Implementation/Coding and Integration/Testing, or the widespread use of agile frameworks with Scrum in the first place, followed by Iterative Development and Kanban. There are, however, some differences, such as a greater presence in Spain of scaling agile frameworks.
Generation of Turning Walking Sensation by a Vestibular Display

This paper describes a new method to generate turning walking sensation by vestibular stimulation with initial (bias) inclination of the motion seat. Our vestibular display can move a seat in 3 degree-of-freedom: lifting, roll and pitch rotation. All of these motions are important for generating walking sensation. We investigated the intensity of turning sensation (i.e. sensation of self-direction changing), and the sensation of straight walking and left/right-turning walking with different conditions of initial roll angle. The result of the user study showed that our method could generate turning walking sensation at about 44 to 52 % and straight walking 57 % of real walking.