Miracle-dz/UniFAR-FTUs huggingface dataset:
Research in Search Engine User Behavior Based on Log Analysis

User log analysis is important for both Web information retrieval technologies and commercial search engine algorithms. In order to better understand search behavior of Chinese Web search users, we presents an analysis of Sogou Search Engine query log consisting of approximately 50 million entries for search requests over a period of one month. The analysis includes search retrieval behavior in individual queries distribution, user request customs in the same session and whether using advanced search functions. Conclusions may help improve Web information retrieval algorithms and search performance evaluation methods.
Combining Self-organizing Feature Map with Support Vector Regression Based on Expert System

A new approach is proposed to model nonlinear dynamic systems by combining SOM(self-organizing feature map) with support vector regression (SVR) based on expert system. Thewhole system has a two-stage neural network architecture. In the first stage SOM is used as a clus-tering algorithm to partition the whole input space into several disjointed regions. A hierarchicalarchitecture is adopted in the partition to avoid the problem of predetermining the number of parti-tioned regions. Then, in the second stage, multiple SVR, also called SVR experts, that best fit eachpartitioned region by the combination of di? erent kernel function of SVR and promote the configura-tion and tuning of SVR. Finally, to apply this new approach to time-series prediction problems basedon the Mackey-Glass di? erential equation and Santa Fe data, the results show that SVR experts hase? ective improvement in the generalization performance in comparison with the single SVR model.
JPEG 2000 Still Image Data Compression

In the last few years technology advancement of digital processors and digital storage devices have resulted in dramatic reduction in the size and cost of digital memory and digital processing logic. The manipulation and analysis of digital pictorial information is referred to as digital image processing. Image data compression comes under the category of digital image processing. Image compression is the method of effectively coding digital images to reduce the number of bits required in representing an image. The purpose of doing so is to reduce the storage and transmission costs while maintaining good quality. There are two methods of compression, Lossy compression and Lossless compression. With the increasing use of multimedia technologies, Image compression requires higher performance as well as new features. To address this, need in the specific area of still image encoding, a new standard is used, the JPEG2000. It is interesting to note that JPEG2000 is being designed to address the requirements of a diversity of applications, e.g. internet, colour facsimile, printing, scanning, digital photography, remote sensing, mobile applications, medical imagery, digital library and E-commerce. We have implemented the basic architecture of the JPEG2000 using MATLAB. It can be used for both gray-scale and colour images.
Impact of Starting Position and Searching Mechanism on the Evolutionary Algorithm Convergence Rate

AbstractEvolutionary algorithms (EAs) have been used extensively to find globally optimal solutions for water distribution system (WDS) optimization problems. However, as these algorithms are being applied to increasingly complex systems, computational efficiency is becoming an issue, and hence approaches that enable near-optimal solutions to be identified within reasonable computational budgets have received increasing attention. One of these approaches is the initialization of EAs in a manner that accounts for domain knowledge of WDS design problems. Although the effectiveness of these initialization approaches has been studied previously, the impact of algorithm searching behavior on the speed with which near-optimal solutions can be found has not yet been examined. To this end, this study aims to investigate the relative impact of different algorithm initialization methods and searching mechanisms on the speed with which near-optimal solutions can be identified for large WDS optimization problems. Fit.
Computation and representation in the primate visual system

The purpose of vision is to find behaviorally relevant structure in the ever-flowing chaos of sensory input. In the primate, this goal is achieved by a hierarchy of cortical areas that extract increasingly complex forms of information from the light arriving at the retina. Despite success characterizing the early stages of this pathway – the retina, the lateral geniculate nucleus, and primary visual cortex (V1) – we have a poor understanding of how transformations in later stages yield selectivity for the complex shapes and objects that primates readily recognize. According to a classical, constructionist view, the later stages of the visual system assemble elementary inputs – like the oriented features encoded by V1 – into larger and more complex combinations, capturing the structural relationships that determine the visual world. But this approach has stumbled on the enigmatic second visual area, V2, whose neurons defy our intuitions about how to begin segmenting scenes and encoding the shapes of objects. In this thesis we develop a framework for the study of intermediate visual processing in the primate, focused on computation and representation in area V2. Rather than try to predict the responses of visual neurons to arbitrary inputs, we test hypotheses about their function by generating targeted experimental stimuli. The stimuli we use reflect the messy statistical reality of natural images, rather than intuitions about object construction. We identify novel responses properties in
Multi-Path Time Synchronization

Clock synchronization protocols are very widely used in IP-based networks. The Network Time Protocol (NTP) has been commonly deployed for many years, and the last few years have seen an increasingly rapid deployment of the Precision Time Protocol (PTP). As time- sensitive applications evolve, clock accuracy requirements are becoming increasingly stringent, requiring the time synchronization protocols to provide high accuracy. This memo describes a multi-path approach to PTP and NTP over IP networks, allowing the protocols to run concurrently over multiple communication paths between the master and slave clocks, without modifying these protocols. The multi-path approach can significantly contribute to clock accuracy, security and fault tolerance. The multi-path approach that is presented in this document enables backward compatibility with nodes that do not support the multi-path functionality.
Sparse Image and Signal Processing: Wavelets and Related Geometric Multiscale Analysis, Second Edition

This thoroughly updated new edition presents state of the art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Along with an up-to-the-minute description of required computation, it covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, in-painting, and compressed sensing. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. MATLAB and IDL code, available online at www.SparseSignalRecipes. info, accompany these methods and all applications.
Data Cleaning Utilizing Ontology Tool

Recent advancement in Internet Technologies has made web browsing increasingly easy and user friendly. From the traditional method of desktop web browsing and the birth of dial up modem connection, users nowadays are able to enjoy a fast and reliable web browsing via high speed wireless Internet connection and portable mobile devices. Browsing a web has become much easier with the state of the art search engines such as Google, which provide much functionalities which could make browsing easier such as improved crawler, easy to use search interface, web personlization, Web 3.0 support and integration and many more. In order to build a robust and reliable search engine, the developer needs to integrate all the data and present them in a meaningful format for user’s viewing convenience. Integrating these data is a tedious task as data usually occur in numerous format, and layout. Furthermore, web developers usually present the data content in various languages of their choice, which made the processing of these data increasingly difficult. There is also no standard convention to represent the data format and even a standardize rule to process this data has not been developed. To resolve this issue, researchers develop data extractor which could effectively extract data from web sources, tabulate them, and used it for further processing. However, not all data are correctly extracted, they may either missed certain valuable information or contain additional unnecessary information. In the case of unnecessary information, researchers use a cleaning method to remove them such that the data extracted are free of errors. Removing these data is important as unnecessary information may affect the accuracy of subsequent extractor tools, hence may eventually prevent the tool from performing its task efficiently. In this research proposal, we embark on a data cleaning tool to clean data using ontology tools. Experimental results show that our tool is highly efficient in data cleaning and is able to outperform existing state of the art tools.
Using Crowdsourcing to Provide QoS for Mobile Cloud Computing

Quality of cloud service (QoS) is one of the crucial factors for the success of cloud providers in mobile cloud computing. Context-awareness is a popular method for automatic awareness of the mobile environment and choosing the most suitable cloud provider. Lack of context information may harm the users’ confidence in the application rendering it useless. Thus, mobile devices need to be constantly aware of the environment and to test the performance of each cloud provider, which is inefficient and wastes energy. Crowdsourcing is a considerable technology to discover and select cloud services in order to provide intelligent, efficient, and stable discovering of services for mobile users based on group choice. This article introduces a crowdsourcing-based QoS supported mobile cloud service framework that fulfills mobile users’ satisfaction by sensing their context information and providing appropriate services to each of the users. Based on user's activity context, social context, service context, and device context, our framework dynamically adapts cloud service for the requests in different kinds of scenarios. The context-awareness based management approach efficiency achieves a reliable cloud service supported platform to supply the Quality of Service on mobile device.
The Psychological Foundations Of Culture

Thank you for reading the psychological foundations of culture. As you may know, people have search hundreds times for their favorite books like this the psychological foundations of culture, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some malicious virus inside their desktop computer. the psychological foundations of culture is available in our book collection an online access to it is set as public so you can download it instantly. Our digital library hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the the psychological foundations of culture is universally compatible with any devices to read.
Dynamic Reorganization of Location Databases Based on Clustering

How to effectively organize and store the profile of moving objects in a mobile environment, which can effectively lower the paging and update cost, is an important problem in location management. Combining data mining into a mobile environment is a challenging research task, which has broad prospective applications. In this paper, a solution is provided to optimize the placement of location databases from the aspect of data mining. First a new hierarchical clustering algorithm is presented to cluster the moving log, then use the clustering results to dynamically reunite location databases, thus the paging and update cost can be lowered effectively.
A Classification Framework on Opinion Mining for Effective Recommendation Systems

With the recent trend of expressing opinions on the social media platforms like Twitter, Blogs, Reviews etc., a large amount of data is available for the analysis in the form of opinion mining. This analysis plays pivotal role in providing recommendation for ecommerce products, services and social networks, forecasting market movements and competition among businesses, etc. The authors present a literature review about the different techniques and applications of this field. The primary techniques can be classified into Data Mining methods, Natural Language Processing (NLP) and Machine learning algorithms. A classification framework is designed to depict the three levels of opinion mining –document level, Sentence Level and Aspect Level along with the methods involved in it. A system can be recommended on the basis of content based and collaborative filtering
Multiagents System applied on a Cyberbullying Model for a Social Network

Cyberbullying refers to repeated intimidation, over time, of a physical, verbal or psychological (including indirect and relational bullying) nature of a less powerful person by a more powerful person or group of persons through information and communication technologies, mediums such as mobile phone text messages, emails, phone calls, Internet chat rooms, instant messaging – and the latest trend – social networking websites such as Orkut, MySpace, Hi5, Facebook and video sharing sites like YouTube. Cyberbullying is a fast growing trend that experts believe is more harmful than typical schoolyard bullying. Nearly all of us can ABSTRACT
Designing Usable Speech Input for Virtual Environments

Speech is the primary mode of communication between humans, and something most people are able to use on a daily basis in order to interact with other people (Stedmon & Baber, 1999). For over 70 years the potential to interact with machines using speech input has been possible (Ullman, 1987), however it still remains an elusive concept without widespread use or acceptance. DOI: 10.4018/978-1-61350-516-8.ch008
Monitoring Open Access at a national level: French case study

After the launch of multiple plans for Open Science, there is now a need for an accurate method or tool to monitor the Open Science trends and in particular Open Access (OA) trends. We address this requirement with a methodology that we developed and tested for France, but that could be extended to other countries. Only open data and information available on the Web are used, leveraging as much as we can large-scale systems such as Unpaywall, HAL (the main open repository in France, part of the CNRS), ORCID and IDRef (referential for French Higher Education and Research). We used rule-based and machine learning techniques to enrich the metadata of the publications. We estimate that the overall OA rate for French affiliated publications ranges from 39% to 42% between 2013 and 2017. The trend is slightly up, except for the last year, but we gather evidence that shows this is a consequence of the moving nature of the OA status. Therefore these figures should be seen as a snapshot rather than definitive. For the last observed year (2017), we show that the OA rate varies according to the publication type, the publisher and the discipline (more than 60% in Mathematics while it is about 30% in Medical research which represents the largest share in the number of publications). We describe the main challenges of our method (detection of the publications with a French affiliation, metadata enrichment with machine learning, open access status) and evaluate the errors of each step. Most of the method is not country-specific and could be applied for another perimeter. Our implementation is open sourced on the repository https://github.com/dataesr/publications.
HİZMET KALİTESİNİN DAVRANIŞ TABANLI MÜŞTERİ İLİŞKİLERİ YÖNETİMİYLE İLİŞKİSİ: BİR BANKA UYGULAMASI

Gunumuz kuresel ve dinamik rekabet ortaminda musterilerle iliskileri gelistirmek isletmelerin rekabetci konumunu artirmada onemli bir faktordur. Finansal hizmetler sektorunde yer alan bankalarin, musteri sadakati yaratarak rekabet etmelerinde en etkili yol Musteri Iliskileri Yonetimi olarak adlandirilan musteriyle uzun donemli is iliskilerinin kurulmasidir. Bir baska acidan bakildiginda isletmelerde stratejik rekabet avantaji saglamada basvurulacak yollardan birinin hizmet kalitesinin arttirilmasi oldugu soylenebilir. Bu yuzden de bankalarin musteri memnuniyetini surekli olcerek memnuniyetsizligin ortaya ciktigi hizmetlerde iyilestirmelere gitmeleri kacinilmaz bir zorunluluk haline gelmektedir. Calisma, Istanbul ilinde faaliyet gosteren ozel bir banka subesinde gerceklestirilmis ve verileri SPSS programiyla analiz edilmistir. Her gecen gun yeni rakiplerin girdigi sektorde artan rekabet sartlarinda hazir bu banka subesinin hizmet kalitesinin musterilerin gozuyle degerlendirilmesi amaclanmistir. Banka musterilerinin sunulan hizmetlerden memnuniyetleri ile bankaya bagliliklarini arasindaki iliskiyi bir model yardimiyla betimlemek amaciyla tasarlanmistir. Bu amacla Istanbul'da ozel bir banka subesinin musterileri arasindan rassal olarak secilen 290 musteriye 40 ifadeyi iceren bir anket uygulanmistir. Analiz sonucunda bankalarin hizmet cevresinin ve guvenilirliginin kuruma bagliligi artiran onemli faktorler oldugu ortaya cikmistir.
La negación en español: análisis y tipología de patrones de negación

En este articulo se presentan los criterios aplicados para la anotacion del corpus SFU ReviewSP-NEGcon negacion y la tipologia linguistica correspondiente. Esta tipologia presenta la ventaja de ser facilmente expresable en terminos de un tagset para la anotacion de corpus, de presentar tipos claramente delimitados, evitando asi la ambiguedad en el proceso de anotacion, y de presentar una amplia cobertura, es decir, que ha servido para resolver todos los casos que han aparecido. El corpus contiene 400 comentarios y 198.551 palabras. Actualmente esta anotado en un 75% y, de un total de 6.331 oraciones revisadas, se han identificado 2.953 estructuras de negacion.
Robustly decoding multiple-line-structured light in temporal Fourier domain for fast and accurate three-dimensional reconstruction

Abstract. Compared with grayscale patterns in structured light illumination, line stripes, as one binary pattern, are attractive for being more conveniently decoded. However, traditional line scanning typically involves a single line shifting across objects to avoid spatial ambiguities possibly introduced by complex surface geometry of objects. We propose a multiple-line strategy for fast and accurate reconstructing of the three-dimensional surfaces of targets. First, we build a mathematical model for multiline scanning, and second, we derive phase computation from the model along time axis and resolve the issue of ambiguity naturally. Errors of the proposed method are theoretically analyzed, and it shows that the proposed method is immune to nonlinearity and is robust to uncertainty. Experimental results demonstrate that the proposed method is of advantages both in accuracy and time cost.
Technologies for discovery based learning

The digitization of learning content is rapidly changing the way that universities educate their students and creating opportunities for establishing lifelong relationships with former students. Prior efforts in e-learning focused on the production, capture and distribution of content. The next phase will focus on the generation of student learning experiences so that they can more easily organize, analyze, share, and discuss their insights, experiments, and results in a more effective manner. There are many solutions available for each of the above technologies, however there is currently a lack of easy-to-use and seamless automation that supports collective knowledge construction by the learners. We describe a prototype learning environment based on the automation of the analysis and indexing of digital content, the harvesting of pedagogical information from lectures and textbooks, and the personalized generation of learning experiences.
Google BigQuery Analytics

How to effectively use BigQuery, avoid common mistakes, and execute sophisticated queries against large datasetsGoogle BigQuery Analytics is the perfect guide for business and data analysts who want the latest tips on running complex queries and writing code to communicate with the BigQuery API. The book uses real-world examples to demonstrate current best practices and techniques, and also explains and demonstrates streaming ingestion, transformation via Hadoop in Google Compute engine, AppEngine datastore integration, and using GViz with Tableau to generate charts of query results. In addition to the mechanics of BigQuery, the book also covers the architecture of the underlying Dremel query engine, providing a thorough understanding that leads to better query results.Features a companion website that includes all code and data sets from the bookUses real-world examples to explain everything analysts need to know to effectively use BigQueryIncludes web application examples coded in Python
Ascending Disk: Theoretical and Numerical Study

In this work we analyzed a classical mechanical problem: a disc with a circular hole rotating and ascending on a inclined plane. The principal idea is to show the basic equations in both Lagrangian and Newtonian formalisms and to present numerical solutions. The equations are not solvable analytically, then we proceed to integrate numerically using the standardized Runge-Kutta method of order 4. We present several phase space showing the time evolution of the system. Additionally we presented an extension of the system considering a damping factor that decays exponentially in time.
A New Method for Collaborative Filtering Recommender Systems: The Case of Yahoo! Movies and TripAdvisor Datasets

Collaborative Filtering (CF) techniques, which attempt to predict what information will meet a user’s needs based on data coming from similar users, are becoming increasingly popular as ways to combat information overload. They use a single rating as input. However, the multi-criteria based CF presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects. This research proposes a new recommendation method using Adaptive Neuro Fuzzy Inference System (ANFIS) and Fuzzy Self-Organizing Map (SOM) for accuracy improvement of multi-criteria recommender systems. We also apply Principal Component Analysis (PCA) for dimensionality reduction and to address multi-collinearity induced from the interdependencies among criteria in multi-criteria CF datasets. Experimental results on Yahoo! Movies and TripAdvisor datasets demonstrated that the proposed method significantly improves recommendation accuracy of multi-criteria CF.