Web Search Technology Research Articles

ROLE OF FINANCIAL TECHNOLOGY ON POVERTY ALLEVIATION IN ASIAN COUNTRIES: MEDIATING ROLE OF INSTITUTIONAL QUALITY

Although the rapid development of financial technology has been realized throughout the Asian countries, research studies seldom focus on the growing impact of financial technology on poverty alleviation. The research paper explores financial technology’s effect on poverty alleviation in the Asian countries. The dataset was collected from Asian countries between 2010 and 2021. A web search technology and frequent word analytics tool are employed to conceptualize variables, and after that, each selected region’s financial technology index is constructed. Given the variation in poverty across selected regions, a novel approach of vector quantile regression is used to quantify the effect of financial technology on poverty allocation. The suggested method’s parametric estimators are employed as “location” measurements of poverty. The findings of this research reveal that despite an uneven index of financial technology development across Asian economies, financial technology significantly impacts poverty alleviation in every selected Asian economy. Furthermore, financial technology strongly impacts poverty alleviation in developing economies than emerging economies of Asian countries. Considering the above findings, practitioners, governments and policy-makers need to foster advanced financial technology mechanisms, specifically in developing economies of the selected Asian economies.

User Behavior Simulation for Search Result Re-ranking

Result ranking is one of the major concerns for Web search technologies. Most existing methodologies rank search results in descending order of relevance. To model the interactions among search results, reinforcement learning (RL algorithms have been widely adopted for ranking tasks. However, the online training of RL methods is time and resource consuming at scale. As an alternative, learning ranking policies in the simulation environment is much more feasible and efficient. In this article, we propose two different simulation environments for the offline training of the RL ranking agent: the Context-aware Click Simulator (CCS) and the Fine-grained User Behavior Simulator with GAN (UserGAN). Based on the simulation environment, we also design a User Behavior Simulation for Reinforcement Learning (UBS4RL) re-ranking framework, which consists of three modules: a feature extractor for heterogeneous search results, a user simulator for collecting simulated user feedback, and a ranking agent for generation of optimized result lists. Extensive experiments on both simulated and practical Web search datasets show that (1) the proposed user simulators can capture and simulate fine-grained user behavior patterns by training on large-scale search logs, (2) the temporal information of user searching process is a strong signal for ranking evaluation, and (3) learning ranking policies from the simulation environment can effectively improve the search ranking performance.

Integration of “Offline + Online” Teaching Method of College English Based on Web Search Technology

The change of social demand for English application-oriented talents has affected the transformation of the way of cultivating talents in College English. The traditional training mode of indoctrination has exposed more and more disadvantages, and the demand of new teaching mode is imminent. This paper first analyzes the current situation of College English learning platform in China. Then, it discusses the design and implementation of “Online + offline” College English learning platform based on Web. The overall structure and function of the database are designed in detail. The teaching platform provides students with learning tools, learning resources, communication platform, testing and evaluation functions, and can evaluate students’ learning behavior, learning process and learning effect. This paper traces and collects a large amount of data left by learners in learning college English courses, and analyzes learners’ learning habits, learning progress and learning effect. Finally, according to the learning big data, this paper customized a reasonable personalized learning platform and improved the online teaching personalized service system.

Photo quality classification using deep learning

The detection of poor quality images for reasons such as focus, lighting, compression, and encoding is of great importance in the field of computer vision. The ability to quickly and automatically classify an image as poor quality creates opportunities for a multitude of applications such as digital cameras, phones, self-driving cars, and web search technologies. In this paper an end-to-end approach using Convolutional Neural Networks (CNN) is presented to classify images into six categories of bad lighting, Gaussian blur, motion blur, JPEG 2000, white-noise, and high quality reference images. A new dataset of images was produced and used to train and validate the model. Finally, the application of the developed model was evaluated using images from the German Traffic Sign Recognition Benchmark. The results show that the trained CNN can detect and correctly classify images into the aforementioned categories with high accuracy and the model can be easily re-calibrated for other applications with only a small sample of training images.

A Weakly Supervised Academic Search Model Based on Knowledge‐Enhanced Feature Representation

Internet of Things search has great potential applications with the rapid development of Internet of Things technology. Combining Internet of Things technology and academic search to build academic search framework based on Internet of Things is an effective solution to realize massive academic resource search. Recently, the academic big data has been characterized by a large number of types and spanning many fields. The traditional web search technology is no longer suitable for the search environment of academic big data. Thus, this paper designs academic search framework based on Internet of Things Technology. In order to alleviate the pressure of the cloud server processing massive academic big data, the edge server is introduced to clean and remove the redundancy of the data to form a clean data for further analysis and processing by the cloud server. Edge computing network effectively makes up for the deficiency of cloud computing in the conditions of distributed and high concurrent access, reduces long‐distance data transmission, and improves the quality of network user experience. For Academic Search, this paper proposes a novel weakly supervised academic search model based on knowledge‐enhanced feature representation. The proposed model can relieve high cost of acquisition of manually labeled data by obtaining a lot of pseudolabeled data and consider word‐level interactive matching and sentence‐level semantic matching for more accurate matching in the process of academic search. The experimental result on academic datasets demonstrate that the performance of the proposed model is much better than that of the existing methods.

An Integrated Search Framework for Leveraging the Knowledge-Based Web Ecosystem

The explosion of information constrains the judgement of search terms associated with Knowledge-Based Web Ecosystem (KBWE), making the retrieval of relevant information and its knowledge management challenging. The existing information retrieval (IR) tools and their fusion in a framework need attention, in which search results can effectively be managed. In this article, we demonstrate the effective use of information retrieval services by a variety of users and agents in various KBWE scenarios. An innovative Integrated Search Framework (ISF) is proposed, which utilises crawling strategies, web search technologies and traditional database search methods. Besides, ISF offers comprehensive, dynamic, personalized, and organization-oriented information retrieval services, ranging from the Internet, extranet, intranet, to personal desktop. In this empirical research, experiments are carried out demonstrating the improvements in the search process, as discerned in the conceptual ISF. The experimental results show improved precision compared with other popular search engines.

Analysis of iterative methods in PageRank computation

PageRank is one of the basic metric used in web search technology to rank the web pages. It uses power method to compute principal eigenvector of the web matrix of several billion nodes. PageRank method incorporates a parameter α called damping factor that plays a major role in PageRank computation. In this study, we have observed experimentally the efficiency of various iterative methods on hyperlink graph for different value of α. We conclude from experiment that Power method is effective and more competitive for the well condition problem i.e. small value of α. However, for α → 1 Power method becomes more complex, and other methods such as Aitken-Power, SOR, and Gauss-Seidel are more efficient than it in respect of CPU time as well as the number of iteration needed for convergence.

Survey on Intelligent Semantic Web Search Engines as Generic Search Engine

From the large database repositories globally the World Wide Web (WWW) allows the people to share the information (data). Billions of databases grown by the amount of information. We need to search the information will specialize tools known generically search engine. Many search engines available today for retrieving meaningful information is difficult. However to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper we present survey on the search engine generations and the role of search engines in intelligent web and semantic search technologies.

Anonymous Spatial Query on Non-Uniform Data

Location and local service is one of the hottest bunches of applications in recent years, due to the proliferation of Global Position System (GPS) and mobile web search technology. Spatial queries retrieving neighboring Point-Of-Interests (POI) require actual user locations for services. However, exposing the physical location of querier to service system may pose privacy threat to users, if malicious adversary has access to the system. To hinder the service system from obtaining the “true” location of querier, current obfuscation-based approach requires a trusted third party anonymizer. As for the data-encryption-based and cPIR-based approaches, they incur costly computation overheads. Although the secure hardware-aided PIR-based technique has been shown to be superior to formers, it did not consider the characteristics of data distribution of searching domain. To deal with the problem of non-uniform data distribution and efficient retrieval, we propose four schemes: MSQL, NSQL, MNSQL, MHBL, based on flexible multi-layer grids, non-empty lookup table and Hilbert space-filling curve for efficient storage and retrieval of POI data, so that improved performance of PIR-based techniques could be achieved. Numerical experiments demonstrate that the proposed techniques indeed deliver better efficiency under various criteria.

Triggers and Monitoring in Intelligent Personal Health Record

Although Web-based personal health records (PHRs) have been widely deployed, the existing ones have limited intelligence. Previously, we introduced expert system technology and Web search technology into the PHR domain and proposed the concept of an intelligent PHR (iPHR). iPHR provides personalized healthcare information to facilitate users' daily activities of living. The current iPHR is passive and follows the pull model of information distribution. This paper introduces triggers and monitoring into iPHR to make iPHR become active. Our idea is to let medical professionals pre-compile triggers and store them in iPHR's knowledge base. Each trigger corresponds to an abnormal event that may have potential medical impact. iPHR keeps collecting, processing, and analyzing the user's medical data from various sources such as wearable sensors. Whenever an abnormal event is detected from the user's medical data, the corresponding trigger fires and the related personalized healthcare information is pushed to the user using natural language generation technology, expert system technology, and Web search technology.

Intelligent Personal Health Record: Experience and Open Issues

Web-based personal health records (PHRs) are under massive deployment. To improve PHR's capability and usability, we previously proposed the concept of intelligent PHR (iPHR). By introducing and extending expert system technology and Web search technology into the PHR domain, iPHR can automatically provide users with personalized healthcare information to facilitate their daily activities of living. Our iPHR system currently provides three functions: guided search for disease information, recommendation of home nursing activities, and recommendation of home medical products. This paper discusses our experience with iPHR as well as the open issues, including both enhancements to the existing functions and potential new functions. We outline some preliminary solutions, whereas a main purpose of this paper is to stimulate future research work in the area of consumer health informatics.

STAIRS: Towards efficient full-text filtering and dissemination in DHT environments

Nowadays "live" content, such as weblog, wikipedia, and news, is ubiquitous in the Internet. Providing users with relevant content in a timely manner becomes a challenging problem. Differing from Web search technologies and RSS feeds/reader applications, this paper envisions a personalized full-text content filtering and dissemination system in a highly distributed environment such as a Distributed Hash Table (DHT) based Peer-to-Peer (P2P) Network. Users subscribe to their interested content by specifying input keywords and thresholds as filters. Then, content is disseminated to those users having interest in it. In the literature, full-text document publishing in DHTs has suffered for a long time from the high cost of forwarding a document to home nodes of all distinct terms. It is aggravated by the fact that a document contains a large number of distinct terms (typically tens or thousands of terms per document). In this paper, we propose a set of novel techniques to overcome such a high forwarding cost by carefully selecting a very small number of meaningful terms (or key features) among candidate terms inside each document. Next, to reduce the average hop count per forwarding, we further prune irrelevant documents during the forwarding path. Experiments based on two real query logs and two real data sets demonstrate the effectiveness of our solution.

Intelligent Semantic Web Search Engines: A Brief Survey

The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The amount of information grows billions of databases. We need to search the information will specialize tools known generically search engine. There are many of search engines available today, retrieving meaningful information is difficult. However to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper we present survey on the search engine generations and the role of search engines in intelligent web and semantic search technologies.

Intelligent Semantic Web Search Engines: A Brief Survey

The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The amount of information grows billions of databases. We need to search the information will specialize tools known generically search engine. There are many of search engines available today, retrieving meaningful information is difficult. However to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper we present survey on the search engine generations and the role of search engines in intelligent web and semantic search technologies .

P2pWeb: An open, decentralized infrastructure of Web servers for sharing ephemeral Web content

Nowadays, we are witnessing an increasing growth of Web 2.0 content such as micronews, blogs and RSS feeds. This trend exemplified by applications like Twitter and LiveJournal is starting to slow down not only by the limitations of existing services - proprietary and centralized, but also by the cumbersome process of discovering and tracking interesting content. This content is generally ephemeral and thus difficult to index by conventional Web search technology. This problem is exacerbated by the passive role adopted by Web content providers: it is surprising that Web servers publish information and expect that thousands of other servers (search engines, Web-based aggregators like GoogleNews, etc.) advertise their content to the world. In this work we propose p2pWeb, an open, decentralized infrastructure to enable Web servers to use their spare capacity to filter out, aggregate and disseminate Web content in a scalable and timely manner. p2pWeb is flexible enough to support a broad variety of services. The main property of p2pWeb is that all communication abstractions, including aggregation and multicast, are implemented hierarchically and using the HTTP protocol. Simulation results certify the viability of our approach.

A generic construct based workload model for web search

Benchmarks are vital tools in the performance measurement, evaluation, and comparison of computer hardware and software systems. Standard benchmarks such as the TREC, TPC, SPEC, SAP, Oracle, Microsoft, IBM, Wisconsin, AS 3AP, OO1, OO7, XOO7 benchmarks have been used to assess the system performance. These benchmarks are domain-specific and domain-dependent in that they model typical applications and tie to a problem domain. Test results from these benchmarks are estimates of possible system performance for certain pre-determined problem types. When the user domain differs from the standard problem domain or when the application workload is divergent from the standard workload, they do not provide an accurate way to measure the system performance of the user problem domain. System performance of the actual problem domain in terms of data and transactions may vary significantly from the standard benchmarks. In this research, we address the issue of generalization and precision of benchmark workload model for web search technology. The current performance measurement and evaluation method suffers from the rough estimate of system performance which varies widely when the problem domain changes. The performance results provided by the vendors cannot be reproduced nor reused in the real users’ environment. Hence, in this research, we tackle the issue of domain boundness and workload boundness which represents the root of the problem of imprecise, ir-representative, and ir-reproducible performance results. We address the issue by presenting a domain-independent and workload-independent workload model benchmark method which is developed from the perspective of the user requirements and generic constructs. We present a user-driven workload model to develop a benchmark in a process of workload requirements representation, transformation, and generation via the common carrier of generic constructs. We aim to create a more generalized and precise evaluation method which derives test suites from the actual user domain and application setting. The workload model benchmark method comprises three main components. They are a high-level workload specification scheme, a translator of the scheme, and a set of generators to generate the test database and the test suite. They are based on the generic constructs. The specification scheme is used to formalize the workload requirements. The translator is used to transform the specification. The generator is used to produce the test database and the test workload. We determine the generic constructs via the analysis of search methods. The generic constructs form a page model, a query model, and a control model in the workload model development. The page model describes the web page structure. The query model defines the logics to query the web. The control model defines the control variables to set up the experiments. In this study, we have conducted ten baseline research experiments to validate the feasibility and validity of the benchmark method. An experimental prototype is built to execute these experiments. Experimental results demonstrate that the method based on generic constructs and driven by the perspective of user requirements is capable of modeling the standard benchmarks as well as more general benchmark requirements.

From randomized controlled trials to evidence grading schemes: current state of evidence‐based practice in social sciences

With the advance of web search and navigation technology, enormous amount of information, non-information, and misinformation may be obtained in milliseconds in response to questions about 'what works' in social sciences. Today, policy makers in non-medical public service arenas are under increasing pressure to make sound decisions based on scientific evidence. Some of these decisions are a matter of legal requirement. This paper shows how such movements are closely aligned with the evolution of organizations that develop and apply evidence standards and evidence grading schemes within the social science communities. The current state of evidence-based practice in social sciences is examined by reviewing the latest development of randomized trials and evidence grading schemes in the fields of education, criminal justice, and social welfare. Studies conducted under the auspices of the Campbell Collaboration and What Works Clearinghouse are used to illustrate ingredients of evidence grading schemes, graphic display of results of systematic reviews, and discrepancies of evidence derived from randomized trials and non-experimental trials. Furthermore, it is argued that the use of evidence on 'what works' depends on the potential users' awareness, understanding of the evidence, as well as their capacity and willingness to use it. Awareness and understanding depends on the world wide web and its augmentations, while capacity and willingness depends more on incentives to use good evidence and on political and ethical values. Implications for the future development of evidence grading organizations are discussed.

P2P Web Search Technology

Web搜索引擎已经成为人们从海量Web信息中快速找到所需信息的重要工具,随着Web数据量的爆炸性增长,传统的集中式搜索引擎已经越来越不能满足人们不断增长的信息获取需求.随着对等网络(peer-to-peer,简称P2P)技术的快速发展,人们提出了基于P2P的Web搜索技术并迅速成为研究热点.研究的目的是对现有的基于P2P的Web搜索技术进行总结,以期为进一步研究指明方向.首先分析了基于P2P的Web搜索面临的诸多挑战;然后重点总结分析了基于P2P的Web搜索的各项关键技术的研究现状,包括系统拓扑结构、数据存放策略、查询路由机制、索引切分策略、数据集选择、相关性排序、网页收集方法等;最后对已有的3个较有特色的基于P2P的Web搜索原型系统进行了介绍.;Web search engine has become a very important tool for finding information efficiently from the massive Web data. With the explosive growth of the Web data, traditional centralized search engines become harder to catch up with the growing step of people's information needs. With the rapid development of peer-to-peer (P2P) technology, the notion of P2P Web search has been proposed and quickly becomes a research focus. The goal of this paper is to give a brief summary of current P2P Web search technologies in order to facilitate future research. First, some main challenges for P2P Web search are presented. Then, key techniques for building a feasible and efficient P2P Web search engine are reviewed, including system topology, data placement, query routing, index partitioning, collection selection, relevance ranking and Web crawling. Finally, three recently proposed novel P2P Web search prototypes are introduced.

Search engine marketing, inc: driving search traffic to your company's Web site

Search engine marketing, inc: driving search traffic to your company's Web site

Intelligent Systems: Methodology, Models, and Applications in Emerging Technologies

From year to year the number of investigations on intelligent systems grows rapidly. For example this year 245 papers from 45 countries were sent for the Second International IEEE Conference on Intelligent Systems (www.ieee-is.org; www.fnts-bg.org/is) and this is an increase of more than 50% by all indicators. The presented papers on intelligent systems were marked by big audiences and they provoked a significant interest that ultimately led to the formation of vivid discussions, exchange of ideas and locally provoked the creation of working groups for different applied projects. All this reflects the worldwide tendencies for the leading role of the research on intelligent systems theoretically and practically. The greater part of the presented research dealt with traditional for the intelligent systems problems like artificial intelligence, knowledge engineering, intelligent agents, neural and fuzzy networks, intelligent data processing, intelligent control and decision making systems, and also new interdisciplinary problems like ontology and semantics in Internet, fuzzy intuitionistic logic. The majority of papers from the European and American researchers are dedicated to the theory and the applications of the intelligent systems with machine learning, fuzzy inference or uncertainty. Another big group of papers focuses on the domain of building and integrating ontologies of applications with heterogeneous multiagent systems. A great number of papers on intelligent systems deals with fuzzy sets. The papers of many other researchers underscore the significance of the contemporary perception-oriented methods and also of different applications in the intelligent systems. On the first place this is valid for the paradigm of L. A. Zadeh 'computing with words'. The Guest Editors in the present specialized journal volume would like to introduce a wealth of research with an applied and theoretical character that possesses a common characteristic and it is the conference best papers complemented and updated by the new elaborations of the authors during the last half a year. A short description of the presented in the volume papers follows. In 'Combining Local and Global Access to Ontologies in a Multiagent System' <B>R. Brena and H. Ceballos (Mexico)</B> proposed an original way for operation with ontologies where a part of the ontology is processed by a client's component and the rest is transmitted to the other agents by an ontology agent. The inter-agent communication is improved in this way. In 'Fuzzy Querying of Evolutive Situations: Application to Driving Situations' <B>S. Ould Yahia and S. Loriette-Rougegrez (France)</B> present an approach to analysis of driving situations using multimedia images and fuzzy estimates that will improve the driver's security. In 'Rememberng What You Forget in an Online Shopping Context' <B>M. Halvey and M. Keane (Ireland)</B> presented their approach to constructing online system that predicts the items for future shopping sessions using a novel idea called Memory Zones. In 'Reinforcement Learning for Online Industrial Process Control' the authors <B>J. Govindhasamy et al. (Ireland)</B> use a synthesis of dynamic programming, reinforcement learning and backpropagation for a goal of modeling and controlling an industrial grinding process. The felicitous combination of methods contributes for a greater effectiveness of the applications compared to the existing controllers. In 'Dynamic Visualization of Information: From Database to Dataspace' the authors <B>C. St-Jacques and L. Paquin (Canada)</B> suggested a friendly online access to large multimedia databases. <B>W. Huang (UK)</B> redefines in 'Towards Context-Aware Knowledge Management in e-Enterprises' the concept of context in intelligent systems and proposes a set of meta-information elements for context description in a business environment. His approach is applicable in the E-business, in the Semantic Web and in the Semantic Grid. In 'Block-Based Change Detection in the Presence of Ambient Illuminaion Variations' <B>T. Alexandropoulos et al. (Greece)</B> use a statistic analysis, clustering and pattern recognition algorithms, etc. for the goal of noise extraction and the global illumination correction. In 'Combining Argumentation and Web Search Technology: Towards a Qualitative Approach for Ranking Results' <B>C. Chesñevar (Spain) and A. Maguitman (USA)</B> proposed a recommender system for improving the WEB search. Defeasible argumentation and decision support methods have been used in the system. In 'Modified Axiomatic Basis of Subjective Probability' <B>K. Tenekedjiev et al. (Bulgaria)</B> make a contribution to the axiomatic approach to subjective uncertainty by introducing a modified set of six axioms to subjective probabilities. In 'Fuzzy Rationality in Quantitative Decision Analysis' <B>N. Nikolova et al. (Bulgaria)</B> present a discussion on fuzzy rationality in the elicitation of subjective probabilities and utilities. The possibility to make this special issue was politely offered to the Guest Editors by Prof. Kaoru Hirota, Prof. Toshio Fukuda and we thank them for that. Due to the help of Kenta Uchino and also due to the new elaborations presented by explorers from Europe and America the appearance of this special issue became possible.