Reviewerly/exHarmony huggingface dataset:
Simultaneous Local Adaptation for Different Local Properties

This paper proposes a method of adaptation for a problem holds local optima with different properties around them. We employed neighborhood based method into Adaptive DE:JADE with a mechanism of local adaptation simultaneously at different local optima. Experimental results revealed (i) local search is effective to multiple properties problem around different local optima, (ii) a mechanism of LJADE ables to locate and explore local optima adaptively, (iii) LJADE perform slower but more stabled exploration than JADE.
Transforming Transactional Marketing of Retailers Using Blockchain Approach

The commercial businesses like retailers of supermarkets have incorporated emerging technologies like targeted marketing, diversity marketing, transactional marketing, and various other strategies to generate greater business profit. Our proposed system aims to enhance the targeted marketing strategies by offering a reward based system using blockchain technology to maintain a blockchain of customers who can avail an offer proposed by the retailer. This system would be common to all the outlets of the retailer and a certain number of Value-coins (the cryptocurrency of the reward based system) would be issued to the customers on the purchase of every stipulated amount. Since the blockchain is a peer-to-peer digital cash system maintaining an immutable list of transaction records it can check the authenticity of each of the digital possessions (e.g. Value-Coins as in the case of this paper) using the distributed timestamp server technology. Thus, it makes it easier to realize the transactional marketing benefits using the blockchain approach.
Adaptive hybrid climate control in the manufacture of microelectronics

Abstract The article discusses the procedure for hybrid control of microclimate parameters in a clean room intended for the production of electronics, using a combination of technologies for recurrent estimation and fuzzy regulation. On the basis of the developed procedure, a smooth assessment of the current values of the controlled parameters and compensation control adequate to disturbing influences are provided. The developed technology is designed to provide an increase in the quality indicators of overcoming transient processes, and, consequently, to improve the efficiency of controlling the climate dynamic system during the implementation of the production process for manufacturing electronics with increased requirements for its operational characteristics. Modern information systems are characterized by the rapid development of the capabilities of the used hardware. At the same time, in order to achieve maximum results, the use of artificial intelligence technologies is inevitable, the most effective implementation of which requires adequate consideration of all available information about the controlled production process. In the formulation under consideration, the complexity of the problem is determined by the need to meet mutually conflicting requirements while ensuring specified values of climatic parameters. The specificity of the control problem is determined by the fact that a change (restoration) of the values of one controlled parameter should not lead to a loss of the set values of other climatic parameters. To achieve the described target effect, it is proposed to supplement the procedure of recurrent climate monitoring with a fuzzy logic mechanism when changing values of climatic parameters.
The Depth Estimation Method Based on Double-Cues Fusion for Light Field Images

As an importantLiu, Xinshi attribute of spatial information, depth information isFu, Dongmei indispensable in many applications such as object tracking. UnlikeWu, Chunhong the traditional imaging technology, which only records theSi, Ze position information of light propagation, the light field camera can record the position and direction information of light at the same time. By processing the light field image obtained from one shot, multiple images of different views and refocused images can be obtained, which provides parallax cue and ambiguity cue for depth estimation. Different from other researchers who only use a single cue in parallax or ambiguity cue, this paper adaptively fuses the two cues, constructs a unified combined calculation model, and achieves a double-cues fusion method for light field depth estimation. We have tested the algorithm on the public dataset of 4D light field benchmark, and achieved the top rank on the MSE metric.
Virtual Teacher Based Tool for Teaching Context-Free Grammars by Active Pedagogy and eMathTeacher Philosophy

We propose in this paper an electronic system for learning interactively context-free grammars through an Internet Web site. This system is based on active learning pedagogy and respects a philosophy called “eMathTeacher” proposed in [1]. Our system is implemented using modern Web technologies architecture, and can be accessed online with common Web Browser, the results show that the level of students to understand such difficult computer science discipline algorithms of discrete mathematics, while correcting their mistakes and guiding them to solve problems was enhanced markedly.
Remote sensing application to monitoring mine landuse changing in limestone/dolomite

Abstract This research was conducted to monitoring mine landuse Changing in Limestone/Dolomite at PT. Bakapindo in Jorong Durian, Kenagarian Kamang, Kamang Magek District, Agam Regency, West Sumatra Province. This research was carried out through visual interpretation of remote sensing data. The remote sensing data used is the Landsat imagery 8 Oli in 2018 which will be classified by the Supervised Classification method. Based on the results of the Landsat imagery 8 Oli processing with the observation years of 2013, 2016 and 2018 it was found that the mine area in 2013 was 12.53 hectares, in 2016 the area was 13.1678 hectares and in 2018 14,7586 hectares. Based on the results of the Landsat imagery processing in the observation years of 2013, 2016 and 2018, that for the mine area each year the observation changes. In the period of 2013 to 2016, the mining area increased by 0.6378 hectares and in 2016 to 2018 it increased by 1.0806 hectare.
Multiobjective Problem to Find Paths through Swarm Robotics

In this paper, we present a scheme to solve the problem to explore, cover and define paths in dangerous environments. The proposal is organized in three phases: exploration, mapping and path planning. Swarm robotics is used in the first phase along with a stigmergy approach for the communication between the agents. The explored cell will be stored in order to create a grid map. In the path planning phase, a multiobjective problem is used to minimize distance and danger, through a graph obtained by an adapted RRG, and a genetic algorithm called modified NSGA-II.
Automatic Black &amp;amp; White Images colorization using Convolutional neural network

In this paper, automatic black and white image colorization method has been proposed. The study is based on the best-known deep learning algorithm CNN (Convolutional neural network). The Model that developed taking the input in gray scale and predict the color of image based on the dataset that trained on it. The color space used in this work is Lab Color space the model takes the L channel as the input and the ab channels as the output. The Image Net dataset used and random selected image have been used to construct a mini dataset of images that contains 39,604 images splitted into 80% training and 20% testing. The proposed method has been tested and evaluated on samples images with Mean-squared error and peak signal to noise ratio and reached an average of MSE= 51.36 and PSNR= 31.
Ternary Convolutional Codes with Optimum Distance Spectrum

This letter presents ternary convolutional codes and their punctured codes with optimum distance spectrum.
A Re-Identification Strategy Using Machine Learning that Exploits Better Side Data

Data on people's daily activities are being collected as big data and then mined for corporate and public purposes. However, concern about privacy is the major obstacle to using such data. Although data anonymisation can mitigate the privacy risk, researchers have shown that people can often be re-identified by linking the anonymised data with other data (i.e. side data). Though re-identification has been improved by the use of machine learning, two problems remain: the unavailability of data and the inappropriateness of the side data. We have developed a re-identification strategy using machine learning in which anonymised data are linked to a different type of side data that are easy to obtain and for which the represented person is easy to identify. We tested our strategy on the re-identification of the owners of 78 anonymous Twitter accounts by using information on résumés as side data. We linked the Twitter posts and résumés by estimating the profiles of the account owners and matching them with those described in the résumés. We were able to link roughly 50% of the accounts to their owners when all tweets were used and roughly 20% of the accounts when only the latest 63 tweets were used. The proposed strategy would help people be better aware of the privacy risks of using personal data and hopefully lead to the implementation of improved protection measures.
Software Test Effort Estimation Based on Source Code Change History and Defect Information

Software test effort estimation has always been an important activity for meeting testing deadlines, relocating resources, and reducing testing cost. However, in practice, estimating test effort is still a major challenge because it is difficult to quantify and collect factors that affect accurate test effort. In this study, we propose a new test effort estimation model by analyzing the relationship between the number of defects collected through defect tracking tools and the source code changes collected through configuration management tools during the software development period. Experiments are performed to validate the proposed model using real industrial software project data. The results indicate that the proposed model achieves high estimation accuracy.
Safe Facial Multi-Attribute Feature Fusion Analysis Based on Embedded Multi-Task Learning

As the main branch of face analysis, facial multi-attribute analysis has a very broad application prospect in the fields of public security monitoring and human-computer interaction. Most deep learning methods have problems such as face alignment, complex network structure, and low computation speed. This paper analyses the potential correlation between facial multi-attributes, and proposes a Embedded Multi-Task Learning-based Safe Facial Multi-attribute Feature Fusion analysis model. Using Embedded Multi-Task Learning strategy and feature fusion method, a single neural network is used to compute the facial region bounding box and analysis multiple facial attributes. This method realizes rapid analysis of multi-attributes of unconstrained faces.
Deriving Logical Relations from Interpretations of Predicate Logic

This paper extends the results of Hermida's thesis about logical predicates to more general logical relations and a wider collection of types. The extension of type constructors from types to logical relations is derived from an interpretation of those constructors on a model of predicate logic. This is then further extended to n-ary relations by pullback. Hermida's theory shows how right adjoints in the category of fibrations are composed from a combination of Cartesian lifting and a local adjunction. This result is generalised to make it more applicable to left adjoints, and then shown to be stable under pullback, deriving an account of n-ary relations from standard predicate logic. A brief discussion of lifting monads to predicates includes the existence of an initial such lifting, generalising existing results.
Research and analysis of messages of users of social networks using BigData technology

In this paper is dedicated to the World Cup held in the city of Samara from June 15 to July 15, 2018. As part of the work, a multithreaded collection in real time was organized, filtering and processing messages from users of the social network Twitter within the host city and its surroundings from May 15 to August 15, 2018. Then, a study was conducted of the texts of user messages on the subject of the popularity of topics and the construction of a “word cloud”. The second study was the construction of a diagram of the dynamics of the number of messages in different languages. As part of the work, modules for collecting, filtering and processing data using BigData technology were implemented.
A General Approach to Exploit Model Predictive Control for Guiding Automated Planning Search in Hybrid Domains

Automated planning techniques are increasingly exploited in real-world applications, thanks to their flexibility and robustness. Hybrid domains, those that require to reason both with discrete and continuous aspects, are particularly challenging to handle with existing planning approaches due to their complex dynamics. In this paper we present a general approach that allows to combine the strengths of automated planning and control systems to support reasoning in hybrid domains. In particular, we propose an architecture to integrate Model Predictive Control (MPC) techniques from the field of control systems into an automated planner, to guide the effective exploration of the search space.
A multi-objective cooperative coevolutionary approach for remote sensing image classification

A current problem that classification algorithms on high-resolution satellite images are addressing is the feature selection problem (FSP). The number of object's features on satellite images is often very large. However, not all these features contribute equally to the classification results. There are still many redundant, unrelated features. It is, therefore, necessary to select these features before performing the classification step. Besides, with the selected feature set, selecting a suitable classifier also plays a very important role. In this study, the authors solve this problem with a multi-objective co-operative co-evolutionary approach (MCCA). In the MCCA, we use two populations evolving together: one population helps to find the most important set of features (named Feature population) and the other helps to get the most appropriate classifier (named Classifier population). The performance of the MCCA is examined on three satellite image datasets. From experimental results, the proposed algorithm has shown the efficiency in improving classification accuracy as well as reducing the number of characteristics.
Analysis of Key Face Parts to Detect Emotional Expression Using a Neural Network Model

Psychological studies on recognition of facial expression reported that local parts of a face image such as eyes or mouth are important to detect the facial expression. On the other hand, artificial neural network technology has progressed greatly in facial recognition. However, the neural mechanism behind the recognition remains to be unknown. The purpose of this study is to extract important features from the neural network model after learning emotions from facial expression and to clarify which face parts are key to detect emotional expression. First, we trained a 2-layered neural network model with backpropagation for recognition of 7 kinds of emotional faces. Then, we found more weighted input pixels for the recognition by tracing and accumulating the synaptic weights linearly from the output layer toward the hidden layer. By this method, we extracted the 6 face-image filters for each emotional expression. Using the face-image filters divided into 10 parts, we designed a new analytical method to evaluate which facial part or parts are important for the discrimination of emotional expression. From this analysis, it can be concluded that key parts are very different for each emotion. For example, nose and mouth are effective for happy smile while strained cheek for sad face.
Qualification and Quantification on Viewpoint Biases in Large Scale Image Datasets for General Object Recognition

While recognizing an image taken from an arbitrary angle in a real environment, it may be challenging to obtain a good recognition result on occasion. That is, the recognition accuracy is affected by changing the viewpoint (object viewing direction). This phenomenon could come from the fact that people photograph target objects on well explainable aspect. It has been known empirically that the recognition rate varies depending on the viewpoint so far, but this topic remains underdeveloped. Therefore, in this research, we aim to clarify and visualize the existence of viewpoint biases in existing large object recognition image datasets. We also quantify the viewpoint biases by a definition of viewpoint bias index.
Textual Classification to Distributional Representation Using Cohesion Devices

Research on the information retrieval model using neural networks has been actively progressed to the retrieval of information and the classification of documents. Various algorithms have been applied to realize this research, and weights have been implemented to words and documents. As information technology accelerates, we might be necessary to understand the exact meaning of documents through the advanced method of obtaining and analyzing the values. Discussions on the frequency of words directly related to these studies have focused on English learning in social sciences or on pure language. In this paper, we propose a method that can more accurately analyze the meaning of sentences by reasonably increasing the frequency of important words in sentences. The essential words are based on cohesion devices used to make context. Several tests were conducted to verify the performance and the effect of improving the semantic analysis system, and we confirmed its result information in the sea.
The Creation of a Free Vocal SMS and Email Sender and Reader App with Chat Style Interface

The following topics are dealt with: learning (artificial intelligence); Internet of Things; neural nets; project management; computer aided instruction; data acquisition; wireless sensor networks; cloud computing; fuzzy set theory; Internet.
Modeling of Cognitive Evolution

The report describes a new scientific area of investigation, namely, modeling of cognitive evolution. Cognitive evolution is the evolution of cognitive abilities of biological organisms. The important result of cognitive evolution is human thinking, which is used in the scientific cognition of nature. Modeling of cognitive evolution is the study of cognitive evolution by means of the mathematical and computer models of cognitive abilities of living organisms of different evolutionary levels. Underlying problems, backgrounds, and the sketch program for modeling of cognitive evolution are characterized. The initial models, which were developed in accordance with the sketch program, are briefly described. Possible applications of models of autonomous agents are discussed.
Potential Use of Bitcoin in B2C E-commerce

Bitcoin is a new digital currency with a very high visibility in media and research. Therefore, different aspects of potentials of Bitcoin are explored. In a prior investigation, several manifest indicators like transaction velocity have been identified as important influencing factors for the perceived use of digital currency. The focus of this paper is an empirical study, which examines factors of the potential use of Bitcoin in a B2C E-Commerce environment. More than 100 online merchants were interviewed in 2016. Based on a structural equation model (SEM), the results of the analysis show that the low transaction costs and acceptance are the main factors that influence the potential benefits of Bitcoin in B2C E-Commerce. The study also gives ideas for the relevance of further indicators.
Augmenting a Description Logic with Probability for Motion Patterns Within QSTR

Motion patterns are spatiotemporal.Talukdar, Upasana Human everyday spatiotemporal reasoning is predominantly qualitative; hence, aHazarika, Shyamanta M. qualitative abstraction of motion patterns holds promise. Reasoning over qualitative models of spatiotemporal change is referred to as qualitative spatiotemporal reasoning (QSTR). Recognition of patterns within a QSTR framework involves reasoning over a multitude of entities. These entities interacting in numerous ways often lead to hierarchies and ample uncertainties. Herein lies the motivation of using a formal basis for the representation of large and diverse knowledge. Description logics (DLs), a family of knowledge representation formalism providing object-oriented representation with formal semantics, are a natural choice. However, traditional DLs delineate a delicate approach to handle uncertain features and relationships. A paradigm that combines a DL formalization with probability theory is required. Bayesian Networks (BNs) are the most widely used models to represent knowledge about an uncertain domain. This paper combines a DL with BN and proposes a formal, explicit qualitative knowledge representation formalism for recognition of motion patterns within an uncertain domain.
Development of 3D Shape Measurement Device Using Feature Quantity Type Whole-Space Tabulation Method

A feature quantity type whole-space tabulation method (F-WSTM) was proposed by authors to make 3D shape measurement devices robust for vibrating. This method makes possible a camera calibration-free 3D shape measurement. Three phase information obtained with three projectors are used to obtain 3D coordinates without any camera parameters. That is, change of lens position does not cause the systematic error. In this method, focusing, zooming, pan and tilt are available anytime. In this paper, a prototype of a 3D shape measurement device using the F-WSTM was developed. The evaluation of the device was performed with an experiment of a shape measurement of a step object.
QoE Unfairness in Dynamic Adaptive Streaming over HTTP

Today, video streaming over the Internet is predominantly using Hypertext Transfer Protocol (HTTP) as its primary protocol over the Transmission Control Protocol (TCP). To allow heterogeneous devices on the heterogeneous networks to be able to play out the video stream, several HTTP adaptive streaming (HAS) technologies have been employed in numerous video services by adapting the video streaming to the network conditions. Dynamic Adaptive Streaming over HTTP (DASH) has been introduced as an interoperable solution. Since DASH clients may share one or more bottleneck links in a network, such as a home networks and other network links and request the video segments according to the buffer state, the bandwidth unfairness among clients and the underutilization of network bandwidth are inevitable. In this paper, we investigated some reasons why the Quality of Experience (QoE) unfairness occurs.
An Improved Evolutionary Hybrid Method for Cloud Provider Selection

Cloud Computing (CC) popularization unleashed the creation and competition of a large number of new companies providing CC services, the so called Cloud Providers (CPs). Thus, select the smallest CP set with the lowest price that are the most suitable to a given request (customer's needs, such as demanded resources and desired service quality) becomes a hard problem. Nonetheless, robust search methods like metaheuristics ones are used to solve it, for instance Evolutionary Algorithms (EAs). Performance Indicators (PIs) were used to quantify features of each CP used later to select them using a method. This paper proposes an improved hybrid method which uses in sequence: a deterministic, an EA and a local search method to find the most suitable possible CP set in the solution search space. This hybrid method was tested and has the highest assertiveness among all others methods developed to solve this problem, becoming a promising solution to a real application.