Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic algorithms for adaptation to dynamic environments - a survey

Adaptation to dynamic environments is an important application of genetic algorithms (GAs). In management applications, such as job scheduling, flexibility to cope with changes of orders, due dates and malfunctions of machines is required by problem solvers. In experimental and simulation-based optimization, algorithms to cope with random fluctuations of fitness values are needed. Further, GAs are also applied to online and on-site optimization and adaptation to various problems. Recently, interest in GAs for dynamic environments has been growing, and a number of novel approaches have been proposed. This paper surveys the methods for applying the GAs to adaptation in dynamic environments. First, the frameworks of dynamic environment adaptation problems are categorized. Then GA techniques for these problems are overviewed. Benchmark problems are also reviewed. Finally, the authors touch on practical applications.
An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture: Quantum Multilayer Neural Network

This chapter proposes a quantum multi-layer neural network (QMLNN) architecture suitable for handwritten character recognition in real time, assisted by quantum backpropagation of errors calculated from the quantum-inspired fuzziness measure of network output states. It is composed of three second-order neighborhood-topologybased inter-connected layers of neurons represented by qubits known as input, hidden, and output layers. The QMLNN architecture is a feed forward network with standard quantum backpropagation algorithm for the adjustment of its weighted interconnection. QMLNN self-organizes the quantum fuzzy input image information by means of the quantum backpropagating errors at the intermediate and output layers of the architecture. The interconnection weights are described using rotation gates. After the network is stabilized, a quantum observation at the output layer destroys the superposition of quantum states in order to obtain true binary outputs. An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture: Quantum Multilayer Neural Network
Overlapping Community Change-Point Detection in an Evolving Network

Change-point detection is a task that looks for specific moments across which a network changes fundamentally. Change-point detection is one of the most important challenges for overlapping community evolution analysis, and its aim is to identify the moment, type, and degree of change of a specific dynamic event when an overlapping community is evolving. In contrast to overlapping community detection, change-point detection addresses the evolution of an overlapping community rather than a network topology. In this paper, we propose such a method by reformulating an overlapping community in the form of a one-dimensional stream constrained by gentle degree fluctuation and the heterogeneous size distribution of the overlapping communities. According to the number of interacting overlapping communities involved in a specific change event, overlapping community change-points are classified as unary or binary. Based on a signal processing framework and a decision function-based strategy, our proposed method finds the change-points for both unary and binary cases. The experimental results from a synthetic dataset show that our proposed approach can ensure higher accuracy and a lower false positive rate than the traditional two-stage approach.
ARTIFICIAL DEVELOPMENT AND EVOLUTION OF ARTIFICIAL NEURAL NETWORKS USING PARAMETRIC L-SYSTEMS WITH MEMORY

The Development of Neuro-Evolutives (NEAs) Algorithms, designed specifically for evolving Artificial Neural Networks (ANNs) architectures, has aroused the interest of many researchers in the last twenty-five years. This paper shows that a direction to be followed in the development of NEAs more biologically plausible is to evolve ANNs through artificial development models. Whereas the analogous biological process should be viewed as a process of organization carried out by genetic information encoded in DNA and when followed will generate the final shape of the organs, including the brain. This research develops a biologically inspired methodology for automatic design of ANNs. With this goal using an artificial development system based on a parametric Lindenmayer with memory (allowing to incorporate aspects of organization, geometry and repetition of patterns in obtaining the topologies of neural networks), integrated to a Genetic Algorithm (GA) which simulates artificial evolution, allowing generate architectures of ANNs direct and recurrent with optimal number of neurons and appropriate topology. The technique was tested on XOR problem and in Classification Problem, some advantages of the proposed methodology is that it increases the level of implicit parallelism of Genetic Algorithm (GA) and seems to be capable to generate satisfactory neural architectures, reducing the project cost and increasing the performance of the obtained ANN.
On the use of multicarrier direct sequence spread spectrum systems

The authors propose a multicarrier direct sequence spread spectrum system. By using this system, it is possible to get not only robustness against multipath fading, but to also achieve narrowband interference suppression; these advantages are obtained without the use of either an explicit RAKE structure or an interference suppression filter. The authors evaluate the performance of this system over both a frequency selective Rayleigh fading channel, and an additive white Gaussian noise channel in the presence of single tone interference. <<ETX>>
Outdoor Self-Localization Method Based on Clustering and RSSI

Focusing on the passive localization problem of the wireless sensor network (WSN) outdoor, an outdoor self-localization algorithm based on clustering and RSSI is proposed, which can localize the passive multi-targets in the wireless sensor network area. It uses the RSSI location model to realize the self-localization of sensor nodes, and then classifies the passive multi-targets based on clustering. Finally, the poly-gonization centroid algorithm is used to calculate the final position of the target. Simulation results prove that the proposed outdoor self-localization algorithm has low complexity and deviation, which is suitable for the passive multi-target localization.
Essentials of Marketing Research

THE NATURE AND SCOPE OF MARKETING RESEARCH. A Decision-Making Perspective on Marketing Research. Marketing Research in Practice. The Marketing Research Process. Research Design and Implementation. DATA COLLECTION. Secondary and Standardized Sources of Marketing Data. Marketing Research on the Internet. Information Collection: Qualitative and Observational Methods. Information from Respondents: Issues in Data Collection. Attitude Measurement. Designing the Questionnaire. Sampling Fundamentals. DATA ANALYSIS. Fundamentals of Data Analysis. Hypothesis Testing. Correlation Analysis and Regression Analysis. APPLICATIONS. Presenting the Results. Applications of Marketing Research. APPENDIX. Table. Procedure. Glossary. Index.
Understanding interactive multidimensional projections

FADEL, S. G. Understanding interactive multidimensional projections. 2016. 88 f. Master dissertation (Master student Program in Computer Science and Computational Mathematics) – Instituto de Ciências Matemáticas e de Computação (ICMC/USP), São Carlos – SP. The large amount of available data on a diverse range of human activities provides many opportunities for understanding, improving and revealing unknown patterns in them. Powerful automatic methods for extracting this knowledge from data are already available from machine learning and data mining. They, however, rely on the expertise of analysts to improve their results when those are not satisfactory. In this context, interactive multidimensional projections are a useful tool for the analysis of multidimensional data by revealing their underlying structure while allowing the user to manipulate the results to provide further insight into this structure. This manipulation, however, has received little attention regarding their influence on the mappings, as they can change the final layout in unpredictable ways. This is the main motivation for this research: understanding the effects caused by changes in these mappings. We approach this problem from two perspectives. First, the user perspective, we designed and developed visualizations that help reduce the trial and error in this process by providing the right piece of information for performing manipulations. Furthermore, these visualizations help explain the changes in the map caused by such manipulations. Second, we defined the effectiveness of manipulation in quantitative terms, then developed an experimental framework for assessing manipulations in multidimensional projections under this view. This framework is based on improving mappings using known evaluation measures for these techniques. Using the improvement of measures as different types of manipulations, we perform a series of experiments on five datasets, five measures, and four techniques. Our experimental results show that there are possible types of manipulations that can happen effectively, with some techniques being more susceptible to manipulations than others. Key-words: information visualization, dimensionality reduction.
Implementação de cache no projeto ArchC

O projeto ArchC visa criar uma linguagem de descricao de arquiteturas, com o objetivo de se construir simuladores e toolchains de arquiteturas computacionais completas. O objetivo deste trabalho e dotar ArchC com capacidade para gerar simuladores de caches. Para tanto foi realizado um estudo detalhado das caches (tipos, organizacoes, configuracoes etc) e do funcionamento e do codigo do ArchC. O resultado foi a descricao de uma colecao de caches parametrizaveis que podem ser adicionadas `as arquiteturas descritas em ArchC. A implementacao das caches e modular, possuindo codigo isolado para a memoria de armazenamento da cache e politicas de operacao. A corretude da cache foi verificada utilizando uma sequ? encia de simulacoes de diversas configuracoes de cache e com comparacoes com o simulador dinero. A cache resultante apresentou um overhead, no tempo de simulacao, que varia entre 10% e 60%, quando comparada a um simulador sem cache. Abstract
Survey of content—based image retrieval

With the rapid development of information technology,content—based image retrieval is one the most popular technique in image retrieval today.This paper is based on summing up the development of image Retriveval,and some key issues on content—based image retrieval are introduced,the paper points out defects and dfficulties about existing image retrieval technology,and at the same time,the authors have presented the future research trends.
Motion-Onset Visual Evoked Potentials for Gaming: A pilot study

This paper details a pilot study for a motion onset Visual Evoked Potential (mVEP) based Brain Computer Interface (BCI) controlled game. mVEP is a type of VEP that uses visual responses from the dorsal pathway of the visual system allowing elegant visual stimuli to elicit different brain patterns depending on the motion and position of the stimuli. The study here was conducted to determine the most appropriate methods, parameters and EEG setup to use in order to extract reliable information when classifying responses on up to five different stimuli. Initial offline results show that 80% accuracy can achieved by averaging stimuli over 5 seconds when discriminating target versus non target. This was achieved by the use of simple averaging techniques and support vector machines. The initial results are encouraging, showing that mVEP may be used as a control system within a computer game. Details of the proposed games are also included.
A Study on Automatic Color Object Learning and Detection through Acoustic Instructions

: - Now days, there is increasing need of automated video analysis. The proposed system receives acoustic instruction as an input and analyzes the video frames and outputs the location of a moving object within the video frames. This can be viewed as segmenting an object of interest from a video sequence and keeping track of its direction, motion, shape, scale, and occlusion etc. and extract useful information using acoustic instructions. Its main task is to find and track a moving object or several objects in video sequences or image sequences using audio instructions. The objective is to use Automatic color object learning and detection through acoustic instructions (ACOLDAI) for Motion-based recognition of object which has wide range of real-time applications.
A Power Control Protocol for Ad Hoc Networks

Energy limitation has become a performance bottleneck for mobile ad hoc networks.IEEE 802.11 has been adopted as the current standard MAC protocol for ad hoc networks.However,it can't adjust power level dynamically,which decreases the performance of network in practical application.To reduce node nergy consumption,a power control MAC protocol based on 802.11 is proposed,which estimates the transmission power based on signal-to-noise ratio.Computer model is set up with OPNET.Simulation results indicate that compared to the IEEE 802.11 MAC protocol,the proposed protocol can decrease the power consumption greatly,and improve the energy utilization of mobile terminals while maintaining the throughput performance.It has some value to ad hoc network theory research and practical use.
Video Compression For Remote Vehicle Driving

In order to effectively drive a remote ground vehicle using video images, the operator must be provided with a natural, real-time video sequence and the imagery must be accurate and detailed enough so that the operator can make mobility and survivability decisions. Unfortunately, high data rate communication channels are often not feasible for this task. To accomplish remote driving using a low data rate channel, video compression techniques must be incorporated. This paper discusses the remote vehicle driving problem and describes several video compression algorithms that have been implemented on PIPE, a real-time pipelined image processing machine. The paper then discusses how these algorithms are evaluated on real-world remote driving tests. Finally, advanced techniques for video compression are proposed.
First Web Space Soft Tissue Release: A New Sequential Approach

Soft tissue release in the first web space plays an important role in addressing the muscle imbalance and correction of hallux valgus deformity. This procedure underwent significant modifications over the years since its original description. Therefore, the term “distal soft tissue release” replaced the eponym McBride or modified McBride procedure. It is more appropriately termed “first web space release. ” The current approach is through a dorsal incision in the web space. The structures released include the insertion of adductor tendon, the deep transverse metatarsal ligament, the lateral capsule, and the lateral sesamoid metatarsal ligament. This article describes a new approach to the soft tissue release. The incision is in the web fold of the first web space, allowing for better visualization, and there is a sequential assessment and release of the contracted structures.
NONPARAMETRIC BOOTSTRAP INTERVAL ESTIMATION OF SOFTWARE RELIABILITY AND ITS APPLICATION TO AN OPTIMAL RELEASE PROBLEM BASED ON A DISCRETIZED MODEL

We discuss a simulation-based interval estimation method of software reliability and cost-optimal software release time based on a nonparametric bootstrap method via a discretized software reliability growth model. In general, it is very difficult to derive probability distributions of model parameters, software reliability assessment measures, and optimal software release time analytically even if we assume an asymptotic theory assuming a large number of samples. The bootstrap method overcomes the problem above by obtaining the probability distributions of the parameter based on the simulation-based approach. This paper includes numerical examples for confirming that our bootstrap approach yields simulation-based probability distributions of model parameters, software reliability assessment measures, and cost-optimal software release time from actual software fault-count data.
Intertexts: Writings on Language, Utterance, and Context

Chapter 1 Introduction Part 2 Indexicality and Referential Practices Chapter 3 Extract from Referencial Practice: Language and Lived Space among the Maya (1990) Chapter 4 Metalanguage and Pragmatics of Deixis (1993) Part 5 Genre and Textuality Chapter 6 Authenticity and Ambivalence in the Text: A Colonial Maya Case (1986) Chapter 7 Discourse Genres in a Theory of Practice (1987) Chapter 8 Text and Texuality (1989) Part 9 Meaning in History Chapter 10 The Five Gourds of Memory (1993) Chapter 11 Copresence and Alterity in Maya Ritual Practice (1993) Chapter 12 Intertextuality of Space in Colonial Yucatan (1992) Chapter 13 Language and Discourse in Colonial Yucatan (1996)
A multi-objective optimization model for sustainable building design using genetic algorithms and fuzzy set theory

Building facilities which provide shelters and facilitate various anthropogenic activities consume approximately 40% of the total energy in the world and are responsible for one third of global greenhouse gas emissions. Therefore, enhancing energy efficiency of buildings is one of the most pragmatic and effective measures for sustainable development in the building sector. As different configurations of building elements could affect the energy performance of buildings significantly, the early conceptual design stage is of paramount importance to the success of energy and carbon emissions reduction in buildings. In this paper, a multi-objective model is developed for the optimization of building design, with a special focus on the building envelope and indoor thermal environment settings. Genetic algorithm (GA) is applied as the optimization engine based on three design objectives, namely the economic aspect, energy performance and comfort of the end-users. The economic aspect of a design solution is appraised according to the life cycle costing, which discounts the costs incurred throughout the building life cycle into a present value and thus provides a common ground for comparison. As for the energy performance, a widely-adopted building energy simulation program, namely EnergyPlus is used for evaluating the energy consumption of a design solution. Through the fuzzy set theory, the uncertainty and vagueness regarding the end-users’ perception on comfort judgment towards the indoor thermal environment is captured and quantified. At the end, illustration of the model is presented via a case study to optimize fenestration configurations and indoor temperature setting of a small office building.
Location Inference and Recommendation in Social Networks

With the ubiquity of GPS-enabled smartphones, Location-Based Service (LBS) as a prominent product of social networks has become an essential part of our daily lives. People can easily socialize and share their check-in data (location, time, text, photo and etc.) via Location-Based Social Networks (LBSN). Through mining the check-in dataset, Point-Of-Interest(POI) recommendation systems assist users in exploring new attractive venues. The primary challenge regarding POI recommendation is to suggest a list of new interesting locations to the query user. Specifically, the excessive sparsity observed in user-location matrices is the main problem. The well known Collaborative Filtering (CF) methods are commonly used with other factors like geographical, social, context-oriented (e.g. text contents) and temporal effects to promote the effectiveness of the location recommendation systems. Despite the vital role of temporal influence, an insufficient amount of research has been devoted to considering the time factor in location recommendation. While we have an insufficient number of records regarding a user’s check-in at a particular location, predicting the time of the visit seems more problematic. We have dedicated part of our research to study various aspects of the temporal influence in the recommendation. Additionally, we use Twitter textual contents to extract a set of spatial phrases associated with each region. Such an act enriches the textual contents of the local POIs. This process enhances location recommendation systems, as it facilitates textual similarity among POI tags and the tweet history of the query user. In short, we aim to address the problems involved with both aspects of Location Inference and Recommendation in social networks. Our research in this thesis has four parts. Firstly, we define a problem which merely considers a single temporal aspect to enhance the performance of a location recommendation task. Subsequently, we develop a probabilistic model which detects a user’s temporal orientation based on visibility weights of POIs visited by her during weekday/weekend cycles. While this method is limited to a single temporal scale, the idea can be adapted to other time-related periodic cycles (e.g. daily home-work return trips). Secondly, we argue that the majority of existing methods merely concentrate on a limited number of temporal scales and neglect others. We propose a probabilistic generative model, named after Multi-aspect Time-related Influence (MATI) which employs the user’s check-in log to detect her temporal mobility pattern under various scales (e.g. minute, hour, day and so on). It then performs recommendation using multi-aspect temporal correlations between the query user and proposed locations. Thirdly, we further study the role of the time factor in recommendation models. We define a new problem to jointly model a pair of heterogeneous time-related
The Face of the City: Civic Portraiture and Civic Identity in Early Modern England

List of Figures Preface Introduction 1. The Formation of English Portrait Traditions 2. The Evidence: Patrons and Venues 3. Painters 4. Timing and Circumstances 5. Content and Meaning 6. Audience and Display 7. Conclusion Appendices A. Civic Portraits painted or acquired, 1500-1640 B. The Cost of Paintings, 1500-1640 Bibliography Index
School nurses’ health dialogues with pupils regarding food habits

The aim of this study was to describe the content of school nurses’ health dialogues with pupils regarding food habits. A qualitative content analysis of 24 recorded health dialogues resulted in five categories describing the content of the dialogues, regarding food habits. Current food habits covered food, mealtimes and food related to physical activity. Social context and food habits showed that social groups in the pupils’ surroundings affected their food habits. Society and food habits included external factors that affected food habits, such as school and laws. School nurses’ advice and support regarding food habits described how nurses offered advice, support and information. Pupils’ understanding of health in connection to food habits comprised the pupils’ knowledge, participation and willingness to change. The results showed a variety of interacting factors which affected the pupils’ food habits, upon which health-promotional work can be based.