Reviewerly/exHarmony huggingface dataset:
Faster RBF Network Learning Utilizing Singular Regions

There are two ways to learn radial basis function (RBF) networks: one-stage and two-stage learnings. Recently a very powerful one-stage learning method called RBF-SSF has been proposed, which can stably find a series of excellent solutions, making good use of singular regions, and can monotonically decrease training error along with the increase of hidden units. RBF-SSF was built by applying the SSF (singularity stairs following) paradigm to RBF networks; the SSF paradigm was originally and successfully proposed for multilayer perceptrons. Although RBF-SSF has the strong capability to find excellent solutions, it required a lot of time mainly because it computes the Hessian. This paper proposes a faster version of RBF-SSF called RBF-SSF(pH) by introducing partial calculation of the Hessian. The experiments using two datasets showed RBF-SSF(pH) ran as fast as usual one-stage learning methods while keeping the excellent solution quality.
Machine Learning: Models And Algorithms

This textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, deep neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. We present statistical inference and graph theory in order to introduce probabilistic networks, such as generative models, random graphs, graphical models and complex networks. We discuss the modelling of sequential data and its application to sequence to sequence learning in natural language (NL). We introduce the Transformer model and discuss pre-training techniques in language understanding and language generation. Using stochastic control theory and dynamic programming, we introduce temporal difference (TD) learning and its extensions, discuss Deep BSDE, present reinforcement learning (RL) and deep RL, detailing various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and global search optimisation, and discuss the use of machine learning for solving some continuous and discrete optimisation problems. Finally, we apply supervised and reinforcement learning to the pricing and hedging of option prices.
Accelerating Template Matching for Efficient Object Tracking

Template matching is used to determine the degree of similarity between two images of the same size. Pearson’s Correlation Coefficient is applied, due to its property of invariance to brightness changes. This coefficient is computed for each image pixel, entailing a computationally intensive task. In order to accelerate this process, a dedicated co-processor was designed to implement this computation. To improve the search for the maximum correlation point between the image and the template, we used, in this work, Bacteria Foraging Optimization, one of the swarm intelligence strategies. The search process is run by an embedded general purpose processor. The work presented in this paper describes the implementation of the embedded system and compares the results obtained here to those previously obtained when using other swarm intelligent strategies.
Coverage and overlap of primary journals indexed by Library, Information Science and Technology Abstracts and Library and Information Science Abstracts

An analysis of primary documentary sources indexed by Library, Information Science and Technology Abstracts (LISTA) and Library and Information Science Abstracts (LISA) indicates that LISTA indexed 519 and LISA indexed 327 primary documentary sources. These sources mainly are scholarly or academic journals, magazines and trade journals. As proportion of total documentary sources, the share of scholarly journals indexed by LISA was more than LISTA. However, in absolute terms the journals indexed by LISTA were more than LISA. LISTA indexed scholarly journals from 37 countries and LISA indexed scholarly journals from 38 countries. LISA mainly emphasized indexing of scholarly periodicals as compared to LISTA, which also emphasized to index magazines. Of the total 646 scholarly titles indexed by LISTA and LISA, 131 titles were common to both databases. Maximum titles indexed by both the databases were published from USA and UK. Most of these journals were published by commercial publishers. Taylor and Francis Ltd contributed highest number of journals to both the databases.
Big data in health and disease: re-processing information for discovery and validation

A lot has been already said about the emerging role of big data in health and disease. Large scale data efforts are increasingly being undertaken in response to the advent of Personalized and Precision Medicine and in association with both the “omics revolution” and the Electronic Health Records centrality. big data have demonstrated that their complex characteristics bring both strength factors and bottlenecks to research problems widely identified, analyzed and reviewed across many sectors of medicine and public health. As the most significant feature of big data is “variety”, and this implies heterogeneity, our knowledge in complex disease contexts may substantially benefit from the fusion of different data types when a major role is assigned to harmonization and interoperability strategies. We discuss of an example, diabetes.
Guia interativo de boas práticas em Engenharia de Software

Analyzing the diversity of best practices in Software Engineering present in industry, the Software Engineering Research Group of the Federal Institute of Paraíba realized the need to organize them together in a web repository so that the access, identification and proper application in various software project scenarios is easier. The objective of work is to present the first functional version of this web environment, in order to enable the best practices in Software Engineering as researched by the group. This project has been developed using agile methodologies and is the result of a trio of complementary and collaborative projects from the research group: Repository of Practices in Management of Software Project, Repository of Practices in the Software Development Process, together with the project Reconfigurable Best Practices Environment in Software Engineering.
Clinical Pathways Inference from Decision Rules by Hybrid Stream Mining and Fuzzy Unordered Rule Induction Strategy in Diabetes Treatment

The article has been withdrawn at the request of the guest editor of the journal Current Bioinformatics CBIO: Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused. Bentham Science Disclaimer: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.
Hubungan Antara Penerapan Strategi Pembelajaran Dengan Aktivitas Belajar Siswa sma Negeri 1 Metro

The problem in this study is the application of learning strategies carried out by teachers in SMA N 1 Metro is good, but student learning activities are still classified as lacking. The formulation of the problem that the authors propose is: Is there a significant relationship between the applications of learning strategies to student learning activities. While the purpose of this study is to determine the level of closeness of the relationship between the application of learning strategies to student learning activities. To obtain the data of the author using the questionnaire, observation, documentation, and intervieu methods. Furthermore, using the data analysis method with the Product Moment formula can be obtained rXY results of 0.330 greater than the r table, both at the significance level of 1% and the significance level of 5%, which then obtained results for a 1% significance level of 0.325 and for the significance level 5 % of 0,250. The hypothesis that the authors propose is accepted, meaning there is a weak relationship between the applications of learning strategies to student learning activities.
Conditional Divergence Triangle for Joint Training of Generator, Energy-based and Inference Models

This paper proposes a conditional version of Divergence Triangle [1] as a framework to train generator, energy-based and inference models jointly with the information of labels, where the learning of the above three models are integrated perfectly in a unified probabilistic formulation. Experiments demonstrate that, within this one framework, we are able to complete the following tasks together, (1) control the fine-grained categories to generate realistic images, (2) obtain the meaningful representation of observed data in the low dimensions, and also (3) conduct label classification on unobserved data. Additionally, I also discuss a possible extension on Conditional Divergence Triangle model at the end of this paper for future work.
Reading Silently and Aloud

This chapter reviews the research in which oral and silent reading have been compared. The two modes of reading are compared on the following features: nature of the text, characteristics of the readers, eye movements, reading speed, and comprehension and memory. Oral reading took more time and required a larger number of fixations than did reading silently. Oral reading—reading aloud—is easily arranged and observed, but researchers and practitioners are rarely interested in oral reading for its own sake but most often as observable behavior that may provide information about the process of silent reading. Oral reading is slower than silent reading, possibly because it may require more regressions in order to remember the earlier text. Early researchers characterized good and poor readers in terms of their “perception accuracy,” but it is difficult to know what they meant by this archaic concept. They were saying that good readers are less bound by the text, whereas poor readers attend to small units.
Design Nonlinear Model Reference with Fuzzy Controller for Nonlinear SISO Second Order Systems

&lt;p&gt;Model reference controller is considering as one of the most useful controller to specific performance of systems where the desired output is produced for a given input. This system used the difference between the outputs of the plant and the desired model by comparing them to produce the signals of the control. This paper focus on design a model reference controller (MRC) combined with (type-1 and interval type-2) fuzzy control scheme for single input-single output (SISO) systems under uncertainty and external disturbance. The model reference controller is designed firstly without fuzzy scheme based on an optimal desired model and Lyapunov stability theory. Then a (type-1 and Interval type-2) fuzzy controller Takagi-Sugeno type is combine with the suggested MRC in order to enhance the performer of it, the common parts between the two fuzzy systems such as: fuzziﬁer, inference engine, fuzzy rule-base and defuzziﬁer are illustrated. In this paper the proposed controller is applied to controla (SISO) inverted pendulum sustem and the Matlab R2015 software is used to carry out two simulation cases for the overall controlled scheme. The obtained results for the two cases show that the proposed MRC with both fuzzy control schemes have acceptable performance, but it have better performance with the interval type-2 fuzzy scheme.&lt;/p&gt;
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs

A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images. We explore novel machine learning approaches for answering visual-relational queries in web-extracted knowledge graphs. To this end, we have created ImageGraph, a KG with 1,330 relation types, 14,870 entities, and 829,931 images crawled from the web. With visual-relational KGs such as ImageGraph one can introduce novel probabilistic query types in which images are treated as first-class citizens. Both the prediction of relations between unseen images as well as multi-relational image retrieval can be expressed with specific families of visual-relational queries. We introduce novel combinations of convolutional networks and knowledge graph embedding methods to answer such queries. We also explore a zero-shot learning scenario where an image of an entirely new entity is linked with multiple relations to entities of an existing KG. The resulting multi-relational grounding of unseen entity images into a knowledge graph serves as a semantic entity representation. We conduct experiments to demonstrate that the proposed methods can answer these visual-relational queries efficiently and accurately.
Improving complex SMT strategies with learning

Abstract Satisfiability modulo theory (SMT) solving strategies are composed of various components and parameters that can dramatically affect the performance of an SMT solver. Each of these elements includes a huge amount of options that cannot be exploited without expert knowledge. In this work, we analyze separately the different strategy components of the Z3 theorem prover, which is one of the most important solvers of the SMT community. We propose some rules for modifying components, parameters, and structures of solving strategies. Using these rules inside different engines leads to an automated strategy learning process which does not require any end‐user expert knowledge to generate optimized strategies. Our algorithms and rules are validated by optimizing some solving strategies for some selected SMT logics. These strategies are then tested for solving some SMT library benchmarks issued from the SMT competitions. The strategies we automatically generated turn out to be very efficient.
A Web-Based Artwork Editing System Empowered by Neural Style Transfer

A technique called neural style transfer is an effective method for generating artistic images based on a deep learning technique. It can extract a mood of a specific painting and blends it with a different image. The original method, however, needs a high-performance computer to get an output image within a practical response time since the neural style transfer involves heavily-loaded processing. To solve the problem, we develop a web-based image editing system enabling users to readily access the function only by using a mobile device with a standard web browser and a network connection. The proposed system allows the users to easily generating a wide variety of artistic images like logos and image clips using the neural style transfer anywhere they have a connection to the Internet. We implement the system as a web application and conduct some experiments to verify the effectiveness of the system. We elaborate the implementation method, experimental results, and observations in this paper.
Take-Home Messages

Having introduced the sensors, a theoretical introduction to using them and investigating some case studies to show them in the use, it is over the reader now to take them out foraspin. These concluding remarks over some thoughts to consdier when using them for the first time as well as consideration for taking big ideas even further. Without meaningful data, assumptions can only be made. Worse still inaccurate information may be provided to athletes and coaches.
Reinforcement Learning Approach for Cooperative Control of Multi-Agent Systems

Reinforcement Learning (RL) systems are trial-and-error learners. This feature altogether with delayed reward, makes RL flexible, powerful and widely accepted. However, RL could not be suitable for control of critical systems where the learning of the control actions by trial and error is not an option. In the RL literature, the use of simulated experience generated by a model is called planning. In this paper, the planningByInstruction and planningByExploration techniques are introduced, implemented and compared to coordinate, a heterogeneous multi-agent architecture for distributed Large Scale Systems (LSS). This architecture was proposed by (Javalera 2016). The models used in this approach are part of a distributed architecture of agents. These models are used to simulate the behavior of the system when some coordinated actions are applied. This experience is learned by the so-called, LINKER agents, during an off-line training. An exploitation algorithm is used online, to coordinate and optimize the value of overlapping control variables of the agents in the distributed architecture in a cooperative way. This paper also presents a technique that offers a solution to the problem of the number of learning steps required to converge toward an optimal (or can be sub-optimal) policy for distributed control systems. An example is used to illustrate the proposed approach, showing exciting and promising results regarding the applicability to real systems.
Crowd Behavior Monitoring using Self-Adaptive Social Force Model

Crowd can be defined as a large number of people gathered closely together. The larger size of crowd results number of behavior either in group or individually. To prevent or minimize the effects of the abnormal behavior, it has to be monitored continuously. Crowd behavior monitoring is an important task in public places to ensure public safety and avoid any unwanted incidents. It has become a popular research among computer vision communities nowadays due to its needs by the authorities. The current method used is Social Force Model (SFM), which can describe the behavior of a crowd based on the interaction forces between individuals. However, some limitations in the previous works caused by its parameters make it fail to correctly classify the crowd behavior into normal or abnormal. Hence, some modification has been introduced to SFM theory in order to provide significant interaction force; which absolutely portrayed the behavior of the crowd. This work aims to develop a crowd behavior monitoring system using Self-Adaptive SFM. This algorithm is jointly used with Horn-Schunck optical flow as a motion detector for the input video. Instead of using any segmentation methods, the motion of particles in each frame is captured by particle advection method. This is done by advected the particles using the underlying flow vectors of each particle. The obtained new locations for all the particles are necessary in estimating the interaction force of each particle. The combination of psychological and physical parameters in Self-Adaptive SFM makes it more realistic and mimicked the dynamic motion of people in a crowd. The estimated interaction forces of each particle represent the behavior of the crowd, whether it is normal or abnormal. The experimental evaluations on challenging datasets shows that the proposed method achieves the better detection result and outperforms the other methods, optical flow and SFM; with the average accuracy of about 94%.
Property Sales Data Processing Information System (SiPendar)

PT. Pratama Mega Konstruksindo is one of the companies engaged in Property, especially Housing. One of the fields that requires technological progress is one of them is the property sector, the rapid development in the property sector is currently urging property service companies to meet the demands of the wider community. Implementation of work related to housing sales. In managing the data, this company still uses a manual system, starting from the recording and calculation aspects so that its performance has not been effective. At PT Pratama Mega Konstruksindo this still manages data using Ms Excel. As well as down payment, cash payments and consumer data are recorded using Ms Excel. This can cause errors in recording transactions, data security that is not guaranteed confidentiality, ineffective employees at work because it requires more time to input and make sales reports and even loss of data. Therefore, PT. Pratama Mega Konstruksindo requires a system that can solve the problem. This data processing system is designed web-based using the PHP and MySql programming languages as data storage databases. With the existence of this website, it can help processing sales data more effectively and efficiently, reports can be printed in realtime and data security can be maintained
Dialog based Planning in Intelligent Tutoring System

A computer system that evaluates the students learning the process is so-called Intelligent Tutoring System (ITS). The important issues in constructing the ITS is the student cognitive level and the emotional status of them. There are many approaches were presented for ITS, but considering emotional intelligence of students still not existed on it. Today, the important factor for increasing the student’s performance is Emotional intelligence. This paper will introduce a new approach for modeling the dialog in tutoring system. The proposed approach depends on the hierarchical Planning technique. During this dialog, the system recognizes the student’s appraisal to construct a particular interpretation for the extrinsic events that are related to the student goal and at the same time overcoming their emotional reasoning. Accordingly, the dialog will try to maintain the emotional, motivational and cognitive student states, as well as enhancing the student’s emotional intelligence. In this approach, the type of personality of students is considered to select the suitable dialog, and according to the student emotional and motivational, the suitable regulation strategy is determined.