Miracle-dz/UniFAR-FTUs huggingface dataset:
A Genetic Methodology for Object Evolution

One of the major issues of object-oriented programming languages is lack of reclassification mechanisms. Reclassification allows an object to change its class at runtime. Object reclassification is desired in applications whose entities need to change dynamically at runtime. The previous approaches dealt with object reclassification in different ways. But none had approached the notion of real entities evolution, where objects can evolve while belonging to the same class. This had led to a large number of classes most of the times; making the design and the implementation of the intended system more complex. The desired evolution of objects should allow objects to evolve automatically at runtime, without changing their classes membership. In this work, we present a new approach for object evolution; inspired by some genetics concepts. Objects that belong to the same class can change their structure, functions and behaviors at run time automatically, while keeping their membership to the same class. A slight environmental influence on the proposed evolution process is introduced.
A Process-Based Analysis Of Object-Oriented System Analysis And Design

System Analysis and Design (SAND), is critical for any system development project. Most new systems are built using Object-Oriented System Analysis and Design (OOSAND). This paper critically examined and analyzed the OOSAND methodology to discover the underlying principles and rationales based on the inherent processes. There are a few past studies that had examined the factors influencing the processes but few had examined the processes themselves. This paper focuses on the SAND processes and examines the pragmatic issues concerning them. The significance of this research is that the knowledge gained in this exercise will provide systems analyst/programmers a better heuristics to migrate legacy systems to the new object-oriented system and enable higher analyst/programmer efficiency and effectiveness in conducting SAND.
Smart Class Using Li-Fi Technology

-----------------------------------------------------ABSTRACT----------------------------------------------------In modern era Smart classes is a new technology movement that is fast becoming an imperative for every progressive schools and colleges in world . It, is an in-the-classroom technology solution that has transformed teaching and learning, reaching out to millions of schools and colleges. Using Smart class teachers show the class a 2D/3D animation on a large screen. They can explain the fine points of the topic, zoom in to show the relevant visuals, freeze and annotate when and where they needs to emphasise along with engaging animations, colours, and sounds The teachers gain complete attention and interest of every child in the class. Every child gets a visual input on what, where, when and how anything happens and the concepts are well understood and internalised. In this technology all the network computers are connected to the server using the wired LAN technology. The physical transmission medium for a wired LAN involves cables, mainly twisted pair or fiber optics. But there are many disadvantages of using LANs like it is wired and therefore more difficult to setup drilling holes in wall, running cables in lofts etc, fitting sockets and redecorating hence the cost of installing the equipment is greater, more time consuming to set up, not as flexible, can only use where there is a socket, and need to be maintained by skilled technicians, and even a virus can spread more easily. So, to avoid all these disadvantages a Modern Technology can be used named Li-Fi (Light Fidelity) technology. Li-Fi can be thought of as a light-based Wi-Fi. That is, it uses light instead of radio waves to transmit information means replace the light by the energy efficient LED lightening which could enhance the capability and functionality by providing light in high speed data communication using Li-Fi modem. Li-Fi changes the intensity of the light in a certain way that a human eye can’t recognise it, but the detector at the other end of the link would convert this change in bit stream. --------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 25 June 2014 Date of Publication: 15 July 2014 ---------------------------------------------------------------------------------------------------------------------------------------
Energy aware dynamic virtual machine consolidation in cloud data centers

Cloud computing is a growing technology now-a-days, which provides various resources to perform complex tasks. These complex tasks can be performed with the help of datacenters. Data centers helps the incoming tasks by providing various resources like CPU, storage, network, bandwidth and memory, which has resulted in the increase of the total number of datacenters in the world. These data centers consume large volume of energy for performing the operations and which leads to high operation costs. Resources are the key cause for the power consumption in data centers along with the air and cooling systems. Energy consumption in data centers is comparative to the resource usage. Excessive amount of energy consumption by datacenters falls out in large power bills. There is a necessity to increase the energy efficiency of such data centers. We have proposed an Energy aware dynamic virtual machine consolidation (EADVMC) model which focuses on pm selection, vm selection, vm placement phases, which results in the reduced energy consumption and the Quality of service (QoS) to a considerable level.
Face Recognition in the Scrambled Domain Using MK-RDA and ANN

Facial look identity is a vital mission by means of human-interacting structures that goal to be aware of versions within the human’s emotional state. the principle challenge or the crucial part in surveillance society is the privacy-shielding era. because the rapid improvement in the internet international it turns into very essential to scramble the pics in the video or files for the duration of transmission. in this the biometric identity of photographs or faces from scrambled pictures plays a completely tough mission. Numbers of various technology are carried out to provide privateness for the duration of surveillance or during transmission of video however they're lack of essential traits, like reversibility or visible fine maintenance. in lots of scrambling methods the faces are covered by a few animation which may additionally or may not cover all faces or it receives hard to recover pics from this technique. Many guide method also are us used by which we will unscramble an photo but they are no longer powerful that a good deal. to overcome all this matters we proposed a novel approach- Many-Kernel Random Discriminate analysis (MK-RDA) to find out discriminative patterns from chaotic indicators. structures get better accuracy bring about best photos. To PIE and ORL datasets has getting above ninety% accuracy.
Sex and gender-related issues in biomedical science

“Gender” is a term commonly encountered in the media these days, but it seems that many people are not familiar with the exact difference between sex and gender. “Sex” refers to the biological classification of humans based on their reproductive systems and functions derived from chromosomal type or hormones, while “gender” denotes cultural attitudes and behaviors associated with stereotypical attitudes regarding a person’s sex that shape our conceptions of masculinity and femininity [1,2]. I was also unfamiliar with this terminological distinction and was introduced to the importance of sex and gender-related issues in science at a meeting of the Korean Federation of Women’s Science & Technology Associations in 2014. The Korean Federation of Women’s Science & Technology Associations introduced the concept of gendered innovations to Korea and has been making efforts to spread awareness of these issues in all fields of science, including engineering, architecture, and biomedical science. As part of this effort, the Center for Gendered Innovations in Science and Technology Research was launched in February 2016. In the biomedical field, the main sex and gender-related issues are the lack of gender-based analyses in clinical research and not using both sexes in animal experiments. Because doctors who treat patients know empirically that various diseases, such as irritable bowel syndrome, show a different clinical course in men and women, I readily accepted this concept and realized its importance. The problem of gender imbalance in clinical research has long been recognized; in fact, as early as 1993, the National Institutes of Health (NIH) enacted a policy ensuring that women and minorities are included in all human research and that an adequate number of participants are recruited to analyze gender differences in phase III clinical trials [3]. However, preclinical animal research is still mainly performed on male animals. Sometimes researchers do not perform sex-based analyses in experiments using both sexes, or even do not mention the sex of the experimental animals in the manuscript [4].
EFFECTIVENESS OF INTERNET PRODUCT MARKETING : A COGNITIVE MAPPING EVALUATION METHOD

In this research, Internet Marketing Mix, an offshoot of traditional 4Ps of marketing mix, is proposed as a road map for determining and realizing the marketing strategies of business administration. With the aim of identifying and evaluating the product, one of the internet marketing mix elements, Fuzzy Cognitive Mapping Method is used. Fuzzy Cognitive Mapping Method helps analyse the views and events that are carried on a subject with quantitative methods. This method can also be used to measure immeasurable or subjective fields. Therefore, many disciplines make use of this method. The usefulness of Fuzzy Cognitive Mapping Method in marketing science is seen through this study. Thus, this study is expected to be a guide to the research about internet marketing. Internet product consists of two main groups according to the study: digital product and services. Their procuring and consuming can be made on internet.
Application of Ensemble Models in Credit Scoring Models

Abstract Loan default is a serious problem in banking industries. Banking systems have strong processes in place for identification of customers with poor credit risk scores; however, most of the credit scoring models need to be constantly updated with newer variables and statistical techniques for improved accuracy. While totally eliminating default is almost impossible, loan risk teams, however, minimize the rate of default, thereby protecting banks from the adverse effects of loan default. Credit scoring models have used logistic regression and linear discriminant analysis for identification of potential defaulters. Newer and contemporary machine learning techniques have the ability to outperform classic old age techniques. This article aims to conduct empirical analysis on publically available bank loan dataset to study banking loan default using decision tree as the base learner and comparing it with ensemble tree learning techniques such as bagging, boosting, and random forests. The results of the empirical analysis suggest that the gradient boosting model outperforms the base decision tree learner, indicating that ensemble model works better than individual models. The study recommends that the risk team should adopt newer contemporary techniques to achieve better accuracy resulting in effective loan recovery strategies.
A Disturbance Observer Based Backstepping Controller Design for Missile Pitch Channel Systems

Abstract. A kind of disturbance observer method was adopted in backstepping controller design of pitch channel missile control system. Not only the outer disturbance but also the inner uncertainties of missile model can be estimated by two disturbance observer. Also the stability of the whole system was proved by constructing a Lyapunov function which is separate and not related with the stability of the observer. At last, detailed numerical simulation was done with a real simplified pitch channel system model to testify the effectiveness of the double observers backstepping control.
Manipulation dextre par des mains mécaniques avec prise en compte de la dynamique

Dexterous manipulation is an essential task for an anthropomorphic robotic hand so that it can be considered in a humanoid or a human assistance robotics context. The research, in terms of prehensile manipulation frequently concern stable grasping of an object. The method shown here constitutes a solution in terms of dexterous manipulation, taking the movement dynamics into account. We present a strategy based on a geometrical approach, which concerns the linear and angular transformation of the object position initially grasped by a mechanic hand, and imposed by the latter. Object kinematics and dynamics are regarded; these aspects accompanied, with the mechanical limits of the hand are also considered. Mots clefs : tâche de manipulation, manipulation dextre, prehension, dynamique, main mecanique
A study on traffic flow prediction method using probe information in transportation system

With the remarkable development of Intelligent Transportation System in recent years, it is possible to easily collect traffic information and various information of the vehicle. Probe information provides more extensive traffic information in addition to the observation information. In this paper, we consider the traffic flow prediction method on urban road using probe information. Accurate and real-time traffic information is indispensable for the deployment of high-performance intelligent transportation systems. Traffic flow is complicated, but by deep learning that can acquire feature quantities automatically, it is possible to express the characteristics of the traffic flow without the prior knowledge such as the characteristics of the site, and it is expected to improve the prediction accuracy. Therefore, in this research, we consider a traffic flow prediction model using deep learning. Also, we compared it with other traffic flow prediction method.
Desarrollo de un Analizador en línea, de las concentraciones de Cobre en soluciones acuosas del proceso de Extracción por Solvente, mediante procesamiento de imágenes

espanolEste articulo presenta el desarrollo de un nuevo analizador, instrumento para medir en linea concentraciones de cobre en acuoso. Su principio de funcionamiento se basa en la aplicacion de tecnicas de procesamiento de imagenes y un sistema automatizado para la toma de muestras desde el proceso. En principio, este analizador ha sido desarrollado para el proceso de extraccion por solvente, que es una de las etapas del proceso hidrometalurgico para la obtencion de catodos de cobre. Los resultados logrados son alentadores pues el instrumento desarrollado presenta una precision aceptable para este tipo de instrumentos no convencionales, augurandose mediciones con mayor precision cuando se implemente el instrumento en su version industrial, con componentes de mayor calidad y robustez. Con este nuevo instrumento, las empresas mineras tendran una alternativa de medicion en linea de esta importante variable operacional, mejorando la operacion de la planta y el incremento en su eficiencia y recuperacion. EnglishThis article proposes a new analyzer, instrument to measure on line copper concentrations in aqueous. Its operating principle is based on technics of images processing and colorimetry, and an automated system for the taking of samples directly withdrawn from the process. In principle, this analyzer has been developed for the solvent extraction process, which is one of the stages of the hydrometallurgical process to obtain copper cathodes. The results of this development are promising due to the analyzer presents an acceptable precision for this type of non conventional instruments, remaining even a space for improvements when using components in its industrial version, of greater quality and robustness. With this new analyzer, the mining companies will have an alternative to measure on line this important operational variable, improving, with its application, the operation of the plant and increasing its efficiency and recovery.
ANALISIS USABILITAS SISTEM OPERASI WINDOWS 10 PADA PENGGUNA EXPERT DAN NOVICE

ABSTRAK sistem operasi Windows 10 merupakan sistem operasi komputer pribadi yang dikembangkan oleh Microsoft. Windows 10sebagai pembaruan dari sistem sebelumnya perluditinjau kekurangan dan kelebihannya berdasarkan pengalaman pengguna, sehinggadapat diberikan usulan perbaikan pada sistem operasi Windows 10. Berdasarkan permasalahan tersebut, perlu dilakukan penelitian untuk mengetahui tingkat usabilitas sistem operasi Windows 10. Penelitian ini mengukur tingkat usabilitas Windows 10 jika digunakan oleh pengguna mahir (expert) dibandingkan dengan pengguna pemula (novice). Metode yang digunakan adalah dengan usability testing dan kuisioner SUS (System Usability Scale). Usability testing digunakan dengan cara mengukur waktu yang dibutukan untuk menyelesaikan suatu tugas. Selain itu diliat pula jumlah kesalahan yang dilakukan saat melaksanakan suatu tugas yang diberikan. Kuisioner SUS digunakan sebagai penunjang data hasil pengukuran guna melihat tingkat kepuasan pengguna. Tugas yang dilakukan masing-masing responden adalah manajemen desktop, manajemen software aplikasi pada system operasi, manajemen browser sistem operasi Windows 10, penggunaan DOS sistem operasi Windows 10, manajemen windows explorer, manajemen devices sistem operasi Windows 10, dan manajemen power sistem operasi Windows 10. Sampel yang digunakan sebanyak 40 responden yang dibagi 2 kelompok responden expert dan novice. Hasil penelitian menunjukkan rata-rata waktu pengerjaan tugas responden expert sebesar 178,8 detik, jumlah kesalahan sebanyak 37 buah, dan total skor kuesioner SUS sebesar 70,25.Sedangkan rata-rata waktu pengerjaan tugas responden novice adalah 210,65 detik, jumlah kesalahan sebanyak 63 buah, dan total skor kuesioner SUS adalah sebesar 57,875 yang dapat dikatakantidasepenuhnya dapat menerima kehadiran sistem operasi Windows 10. Berdasarkan hasil analisis, perlu dilakukan perbaikan pada menu pengaturan yang sulit ditemukan, kemudian sering terjadinya update otomatis, juga perbaikan pada kompabilitas terhadap hardware dan software yang lama agar dapat dijalankan pada sistem operasi Windows 10
Medical Named Entity Recognition for Indonesian Language Using Word Representations

Nowadays, Named Entity Recognition (NER) system is used in medical texts to obtain important medical information, like diseases, symptoms, and drugs. While most NER systems are applied to formal medical texts, informal ones like those from social media (also called semi-formal texts) are starting to get recognition as a gold mine for medical information. We propose a theoretical Named Entity Recognition (NER) model for semi-formal medical texts in our medical knowledge management system by comparing two kinds of word representations: cluster-based word representation and distributed representation.
Overview of the Research Status on Artificial Neural Networks

With the development of artificial intelligence, artificial neural network algorithm models that simulate the working pattern of human brain neurons have attracted more and more attention. The ability of artificial neural network on feature extraction and classification, has been widely used in image recognition, speech recognition, natural language processing, trend prediction and other fields that effectively assists or replaces the work which needs to be done by human image thinking. At the same time, artificial neural network is a machine learning system, and it will get better results with the progress of training. Based on the development history and research status of artificial neural networks, this paper analyzes and compares several neural network technology models and algorithms, and puts forward the analysis ideas and opinions.
Study on the use of vision and laser range sensors with graphical models for the slam problem

A strong requirement to deploy autonomous mobile robots is their capacity to localize themselves with a certain precision in relation to their environment. Localization exploits data gathered by sensors that either observe the inner states of the robot, like acceleration and speed, or the environment, like cameras and Light Detection And Ranging (LIDAR) sensors. The use of environment sensors has triggered the development of localization solutions that jointly estimate the robot position and the position of elements in the environment, referred to as Simultaneous Loealization and Mapping (SLAM) approaches. To handle the noise inherent of the data coming from the sensors, SLAM solutions are implemented in a probabilistic framework. First developments were based on Extended Kalman Filters, while a more recent developments use probabilistic graphical models to model the estimation problem and solve it through optimization. This thesis exploits the latter approach to develop two distinct techniques for autonomous ground vehicles: one using monocular vision, the other one using LIDAR. The lack of depth information in camera images has fostered the use of specific landmark parametrizations that isolate the unknown depth in one variable, concentrating its large uncertainty into a single parameter. One of these parametrizations, named Parallax Angle Parametrization, was originally introduced in the context of the Bundle Adjustment problem, that processes all the gathered data in a single global optimization step. We present how to exploit this parametrization in an incremental graph-based SLAM approach in which robot motion measures are also incorporated. LIDAR sensors can be used to build odometry-like solutions for localization by sequentially registering the point clouds acquired along a robot trajectory. We define a graphical model layer on top of a LIDAR odometry layer, that uses the Iterative Closest Points (ICP) algorithm as registration technique. Reference frames are defined along the robot trajectory, and ICP results are used to build a pose graph, used to solve an optimization problem that enables the correction of the robot trajectory and the environment map upon loop closures. After an introduction to the theory of graphical models applied to SLAM problem, the manuscript depicts these two approaches. Simulated and experimental results illustrate the developments throughout the manuscript, using classic and in-house datasets.
The need for ontologies: Bridging the barriers of terminology and data structure

This chapter describes the need for complex semantic models, i.e., ontologies of real-world categories, referents, and instances, to go beyond the barriers of terminology and data structures. Terms and local data structures are often tolerated in information technology because these are simpler, provide structures that humans can seemingly interpret easily and easily use for their databases and computer programs, and are locally salient. In particular, we focus on both the need for ontologies for data integration of databases, and the need for foundational ontologies to help address the issue of semantic interoperability. In other words, how do you span disparate domain ontologies, which themselves represent the real-world semantics of possibly multiple databases? We look at both sociocultural and geospatial domains and provide rationale for using foundational and domain ontologies for complex applications. We also describe the use of the Common Semantic Model (COSMO) ontology here, which is based on lexical-conceptual primitives originating in natural language, but we also allow room for alternative choices of foundational ontologies. The emphasis throughout this chapter is on database issues and the use of ontologies specifically for semantic data integration and system interoperability.