Reviewerly/exHarmony huggingface dataset:
A comparison of three evolved controllers used for robotic navigation

This paper compares three evolved controllers including, an evolvable hardware controller, an artificial neural network and a lookup table. The comparison made between these controllers looks at relative evolutionary efficiency, controller performance and scalability. The controllers were evolved for three navigational behaviours including light following, obstacle avoidance, and the combined behaviours of light following while avoiding obstacles. Both monolithic and subsumption techniques were used to evolve the combined behaviours to evaluate scalability. It was found that all three evolved controllers performed the assigned tasks equally well. The evolutionary efficiency and scalability of the evolvable hardware and artificial neural network were similar, whereas the lookup table had an acceptable result but was subjective to scalability. The virtual-FPGA can be implemented in a fault tolerant system using a hybrid FGPAs with a hard-core processor for continuous evolution.
A Cloud-Based Privacy Preserving e-Healthcare System Using Whale Optimization

Internet is an integral part of our daily lives attributable to its increasing usage. During this model, users can impart their information and communicate with others through social communities. The e-healthcare community service considerably resolves the problems of individual patients that are remotely located, have uncomfortable medical conditions, or have caretaker responsibilities which are able to proscribe them from obtaining comfortable medical and emotional support. Participation in online social collaborations might not be straightforward because of cultural and language barriers. This paper proposes a privacy-preserving cooperative e-healthcare system that divides patients or caretakers into totally different groups. This technique permits patients or caretakers to talk with different patients with similar issues, perceive their feelings, and share several problems with their own. However throughout this method, personal and sensitive information can not be disclosed to anyone at any purpose of your time. The suggested model uses a special technique, called minmax whale optimization to cluster e-profiles based on their similarities. Finally, clustered profiles are encrypted by exploiting distributed hashing technique to preserve patients’ personal info. The results of planned framework are compared with famous privacy-preserving algorithms of clustering by using accepted similarity measures.
Correction to: Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations

The chapter was inadvertently published without the supplementary file. The supplementary file and its ESM Hint have been added to the chapter.
Fast Search of Massive High-Dimensional Vectors Similarity

It is common to perform similarity lookups of massive high-dimensional vectors in the fields of computer vision, natural language processing, big data and artificial intelligence. However, in actual business scenarios, it is not easy to calculate the results accurately, efficiently and quickly due to its large database. We use approximate nearest neighbor search based on product quantization and optimize the computer system to achieve fast lookups on the premise of making full use of memory.
Sparse Representation Label Fusion Method Combining Pixel Grayscale Weight for Brain MR Segmentation

Multi-atlas based segmentation (MAS) methods have demonstrated superior performance in the field of automatic image segmentation, and label fusion is an important part of MAS methods. In this paper, we propose a sparse representation label fusion (SRLF) method combining pixel grayscale weight. We adopt a strategy for solving sparse coefficients multiple times and introduce pixel grayscale weight information in the label fusion process. In order to verify the segmentation performance, we apply the proposed method to segment subcutaneous tissues in 3D brain MR images of the challenging publicly available IBSR datasets. The results show that our method effectively improves the defects of SRLF method and achieves higher segmentation accuracy. We also compared our methods with commonly used automatic segmentation tools and state-of-the-art methods, and the average Dice similarity coefficient (Dsc) of the subcutaneous tissues obtained by our method was significantly higher than that of the automatic segmentation tools and state-of-the-art methods.
L’Éducation sentimentale

Research Article| May 01 2020 L’Éducation sentimentale: Take 2 Ovidio Reyes Ovidio Reyes Ovidio Reyes’s latest publications are in Spillway and Salamander. Search for other works by this author on: This Site Google the minnesota review (2020) 2020 (94): 41. https://doi.org/10.1215/00265667-8128280 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Ovidio Reyes; L’Éducation sentimentale: Take 2. the minnesota review 1 May 2020; 2020 (94): 41. doi: https://doi.org/10.1215/00265667-8128280 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll Journalsthe minnesota review Search Advanced Search The text of this article is only available as a PDF. Copyright © 2020 Ovidio Reyes2020 Article PDF first page preview Close Modal You do not currently have access to this content.
Dynamic System Control Using Z-number Based Fuzzy Neural Network

Z-number based fuzzy neural network (ZFNN) for dynamic system control is proposed. Using the interpolative reasoning mechanism, the structure and design algorithms of the ZFNN are presented. The gradient algorithm is designed for adjusting the parameters of the ZFNN. The proposed ZFNN structure is tested for control of a dynamic system and the performance of the ZFNN based controller is compared with the performances of the other controllers. The results of the simulation show the efficiency of using the designed system in control of dynamic systems.
Assessing the stability of selected software components for reusability

The need to develop software of great quality with timely delivery and tested components gave birth to reuse. Component reusability entails the use (reuse) of existing artifacts to improve the quality and functionalities of software. Many researches have considered and justified common reusability factors such as customizability, portability, interface complexities, understandability/Documentability etc. but with limited works on stability as a factor. The need to experiment stability (in the context of volatility) as a factor for determining component reusability, is an attempt to lend our voice to the domain of component reusability. This study introduces and justifies stability, in the context of volatility of software component, as a factor that determines the reusability of software components. As part of the study's methodology, sixty-nine (69) software components were collected from third party, and data extracted from their features were used to compute the metric values of stability. The experimented conducted proved the stability status of the various component types considered.
Hand gesture recognition using discrete wavelet transform and hidden Markov models

Gesture recognition based on computer-vision is an important part of human-computer interaction. But it lacks in several points, that was image brightness, recognition time, and accuracy. Because of that goal of this research was to create a hand gesture recognition system that had good performances using discrete wavelet transform and hidden Markov models. The first process was pre-processing, which done by resizing the image to 128x128 pixels and then segmented the skin color. The second process was feature extraction using the discrete wavelet transform. The result was the feature value in the form of a feature vector from the image. The last process was gesture classification using hidden Markov models to calculate the highest probability of feature matrix which had obtained from the feature extraction process. The result of the system had 72% of accuracy using 150 training and 100 test data images that consist five gestures. The newness thing found in this experiment were the effect of acquisition and pre-processing. The accuracy had been escalated by 14% compared to Sebastien’s dataset at 58%. The increment effect propped by brightness and contrast value.
The Structuring of the Self Through Relational Patterns of Movement Using Data from the Microsoft Kinect 2 to Study Baby-Caregiver Interaction

In this paper we will illustrate the progress of a research that intends to use computational methods to analyze the complex interaction between a child and his caregiver, focusing on motor aspects. According to the theory of Ruella Frank, Gestalt psychotherapist, the child acquires motor patterns through the interaction with the caregiver that will be repeated over the course of life. Six fundamental movements have been identified and defined, which allow the child to develop self-awareness. The objective of the study is the creation of an observation tool of dyadic interaction through the automatic recognition of these movements whose purpose is the identification and differentiation of the peculiar vocabulary of movements that is established between parent and child. Thanks to the use of Kinect v2 and through the Kinect Studio program, we have trained a network to identify three of the six basic movements: “Reaching”, “Pushing” and “Pulling”. The results of this training show that the machine discriminates the gestures that have been defined and recognized through different features, although they visually share a part of the progression. The future perspectives of this study foresee to apply the methodology described in this paper to the other three gestures, widen the database and refine the tagging process, to obtain more precise results. The ultimate aim is to include the database in an application that we have proposed to develop, useful for the recognition of the peculiar dyadic baby-caregiver movements.
Sistem Informasi Geografis untuk Pemetaan Wifi.Id Corner dan Wifi Gratis di Kota Kupang Berbasis Web

WiFi is a well-known technology that utilizes electronic equipment to exchange data wirelessly (using radio waves) through a computer network, including high speed internet connections. In addition there are currently many internet services for the public provided by companies such as PT. Telkom which provides wifi.id corner and Diskominfo which provides free wifi for public to help people who need internet access for work purposes. In Kupang City Regency,have been many scattered public internet services such as wifi.id corner and free wifi spread, but will there are still many people who do not yet the location information of wifi.id corner and free wifi , so there is a sense of discomfort by people who really need these services. Therefore built a Geographic Information System (GIS) web-based for mapping wifi id corner locations and free wifi using the PHP programming language and MySQL database.This research uses the Waterfall Life Cycle Software Development (CLC) development method. The waterfall model has five stages, namely analysis, design, coding, testing and maintenance.The results of this study are Web-GIS applications that can provide information about Wifi Id Corner and Free Wifi in Kupang City as is of location maps, names, addresses, facilities and images wifi id corner locations and the selected free wifi locations.
IN-DEPTH SURVEY ON XOR GATE DESIGN

XOR gate has a very important role in most of the cryptographic algorithms. Because of anticoincident property, the XOR gate can be used as a Cipher and the same architecture can be used for both encryption and decryption. In this paper, various transistor-level designs of XOR gates are analyzed based on transistor count, speed, power consumption, and signal level. This survey is done to find out the efficient XOR gate that can replace the traditional XOR gate used in cryptosystem over a decade so that the speed of encryption and decryption can be improved. Finally discussed the possible ways to design a novel XOR gate.
RETRACTED ARTICLE: Ontology for distributed architecture of pedagogical warehouse in the field of machine learning

Faced with the rapid development of information and communication means in the field of machine learning, a large mass of pedagogical resources is produced through numerous universities. The reuse of pedagogical objects produced locally is low and even nonexistent between universities. The main objectif of this manuscript is to pose the problematic of distributed warehouse model that allows the indexing of pedagogical objects based on the notion of ontology in order to meet the needs of learners or teachers who belong to different universities. To overcome the problem of the reuse of pedagogical objects (PO) that are low produced locally and even nonexistent between universities, we will represent and model distributed pedagogical warehouse which has a local architecture. Each pedagogical warehouse will be composed of two parts: classic database and a set of LOM descriptors (DeFalco 2018a, b) and semantic descriptors resulting after the indexing phase. In order to ensure the reuse of a PO, the semantic representation is based on the notion of ontology. We use a theme ontology of training which brings together the themes, notions and knowledge to be understood. After that, we will create distributed architecture of pedagogical meta warehouse model that we will use in our research, it is based on two types of structures: specific and generic, then we will explain how can learner access to the pedagogical resource which the system offers. The learner can also search for documents in the other associated universities taking into account access rights and this, according to the proposed distributed architecture. This approach will allow homogeneous interrogation of existing objects via a meta description of objects and provides by the same way advanced processing possibilities through the use of data warehouse functionality.
Quantitative Analysis Powered by Naïve Bayes Classifier Algorithm to Data-Related Publications Social-Scientific Network

Quantitative evaluation of a dataset can play an important role in pattern recognition of technical-scientific research involving behavior and dynamics in social networks. As an example, are the adaptive feature weighting approaches by naive Bayes text algorithm. This work aims to present an exploratory data analysis with a quantitative approach that involves pattern recognition using the Mendeley research network; to identify logics given the popularity of document access. To better analyze the results, the work was divided into four categories, each with three subcategories, that is, five, three, and two output classes. The name for these categories came up due to data collection, which also presented documents with open access, dismembering proceedings, and journals for two more categories. As a result, the performance for the test examples showed a lower error rate related to the subcategory two output classes in the criterion of popularity by using the naive Bayes algorithm in Mendeley.
Touch Position Detection on the Front of Face Using Passive High-Functional RFID Tag with Magnetic Sensor

We used passive, high-functional radiofrequency identification (RFID) tags with magnetic sensors to detect front of face touch positions without the requirement for a battery. We implemented a prototype system consisting of a goggle-type device equipped with passive high-functional RFID tags with magnetic sensor, a ring with permanent magnets, and touch detection software for machine-learning. We evaluated the classification accuracy of the six front of face touch positions and a ‘no-touch’ case. The discrimination rate when using the learning data was 83% but the real-time discrimination was only 65%. In future, we will aim to improve the accuracy, and define more touch points and gesture inputs.
Color Constancy Analysis Approach for Color Standardization on Malaria Thick and Thin Blood Smear Images

Malaria is an extensively prevalent blood infection, the most severe and widespread parasitic disease that stirring millions of people in the world. Currently, microscopy diagnosis still the most widely used method for malaria diagnosis. However, this procedure contains the probability of miscalculation of parasites due to human error. Computerized system by using image processing is recognized as a quick and easy ways to analyze a lot of blood samples. However, because of the non-standard preparation of the blood slides which producing color varieties in different slides will result on low quality images. Hence, it is difficult to identify the existence of malaria parasites as well as observing its morphological characteristics to recognize malaria parasites. Therefore, this paper aims to analyze the standardization performance between six types of color constancy algorithms namely, gray world (GW), white patch (WP), modified white patch (MWP), progressive hybrid (PH), shades of gray (SoG) and gray edge (GE) on both thick and thin blood smear malaria images of P. falciparum and P. vivax species. Six types of color constancy algorithms standardization performance are analysed by using quantitative measure namely, peak signal to noise ratio (PSNR), normalized absolute error (NAE), mean square error (MSE) and root mean square error (RMSE). Based on the qualitative and quantitative findings, the results show that SoG algorithm is the best color constancy as compared to others proposed color constancy. SoG algorithm has achieved the highest PSNR and lowest NAE, MSE and RMSE values, thus proved that the quality of malaria images have been improved.
Elementals Pollution Status of a University Dumpsite Soil in Ile-Ife, Nigeria

This study explored the concentrations of various elements in Obafemi Awolowo University dumpsite using Inductively Couple Plasma Optical Emission Spectroscopy. A total of fifty soil samples were collected each month for a duration of eight months. Each month, five soil samples each were randomly collected from different spot at 0-15 cm and 15-30 cm depths from each of the four equidistant circular points and at the centre of the dumpsite in well pre-treated sealed polythene bags. The values obtained revealed that Fe, Mn, Ti, Ba and Al were below the background values while Ni, Co, Zn, Cu, Be, Al, Se, Si, As, Zr and Y were above the background values. Geo-accumulation study indicated that the dumpsite was practically unpolluted with, Fe, Mn, Ni, Ti, Ba, Al, Si , U and Y; highly polluted with As and Cu; Co, Zn and Zr ranged from unpolluted to moderately polluted; Se ranged from unpolluted to highly polluted; while Be ranged from moderately polluted to highly polluted. Mean levels of the potentially toxic metals in the dumpsite and its vicinity were higher in the top soil than sub-soil; and metal concentrations in wet season were higher than dry season.
SilverCodes: Thin, Flexible, and Single-Line Connected Identifiers Inputted by Swiping with a Finger

This study investigates SilverCodes, thin and flexible identifiers that serve to constitute an input interface. SilverCodes are thin and flexible barcode-shaped identifiers. User input is achieved by swiping an identifier with a finger. SilverCodes are made from two sheets of paper pasted with conductive ink. Identifiers can thus be conveniently printed using an ordinary ink-jet printer. Multiple SilverCodes can be connected by a single-line wire to an external module that recognizes the identifier swiped by the user. Furthermore, SilverCodes can be stacked and identifiers can be recognized to an average accuracy of 95.3%.
Simulation of fire stations resources considering the downtime of machines: A case study

Considering the increasing growth of cities, population and urban fabric density, it seems necessary that emergency facilities and services such as fire stations are positioned optimally so that they can fulfill the demands well. The aim of this study is the optimization of equipment use in the fire stations, minimization the time to arrive at the incident through management of referral call to 125 Sari fire station center so that the referral call to the nearest fire station do not remain unanswered as much as possible and there will be no need to refer to another station. In this research, the resources required at Sari’s fire station were simulated using Enterprise Dynamic software. The input data of the simulation is based on the number and sequence of the time of people's phone calls. After collecting historical data from telephone calls using the function fitting method, the distribution function of available resources is calculated in Minitab software. In the following, the distribution functions of failure in the existing fire engines are calculated using the same method and the obtained information is simulated. The result indicates an improvement of 20% in relief time by adding one source in Sari fire station center.
Secure Dynamic Interactive Blood Bank based on Cognitive Computing

Cognitive based (Chatbot) blood bank provides the communication platform among the stakeholders of blood bank. In the past the blood recipient will have to contact the blood bank and the blood donors individually, which is a time consuming process.  To address this issue in this paper we propose a Secure Dynamic Interactive Blood Bank based on Cognitive Computing which can fulfill the blood request of the needy with less hardship. Hence the proposed work aims to overcome this problem by requesting the recipient to simply send a message to a chatbot.  The motivated individuals who are willing to donate blood can register their name by interacting with the chatbot. If the requested blood group is available at the blood bank / registered donor then the recipient will get contact details of the blood bank / registered donors available at that instant. Donor data will be maintained in Cloud database. The proposed system is a cognitive chatbot, which acts as a communication platform among the stakeholders such as blood bank, blood donor and the needy. This system is built using cognitive technology of Google; it makes conversations using chatbots very similar to human conversations, thereby making the proposed system more efficient compared to the existing ones.
Developing Mobile Water Operator Technology for Africa

The current status of drinking water networks in most African countries emphasizes the need for effective management methods for physical and financial operations. At the same time there are obstacles for implementing innovative technologies, as these bring operational and financial risks for water utilities, especially if it involves completely new platforms for the management of a utility’s infrastructure. Winning trust with your innovation cannot only be achieved by delivering a solid product. Involving local stakeholders is crucial for its success. Project scope changes during the development phase of your product introduce additional layers of complexity, something which is not uncommon due to unforeseen field conditions. The whole process of building up an idea to a proof of concept and testing its financial feasibility within a given time frame and in different environments is a challenge for many software companies operating in the water sector.