Semantic Learning and Web Image Mining with Image Recognition and Classification

Image mining is more than just an extension of data mining to image domain. Web Image mining is a technique commonly used to extract knowledge directly from images on WWW. Since main targets of conventional Web mining are numerical and textual data, Web mining for image data is on demand. There are huge image data as well as text data on the Web. However, mining image data from the Web is paid less attention than mining text data, since treating semantics of images are much more difficult. This paper proposes a novel image recognition and image classification technique using a large number of images automatically gathered from the Web as learning images. For classification the system uses imagefeature- based search exploited in content-based image retrieval(CBIR), which do not restrict target images unlike conventional image recognition methods and support vector machine(SVM), which is one of the most efficient & widely used statistical method for generic image classification that fit to the learning tasks. By the experiments it is observed that the proposed system outperforms some existing search system

The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web Archives

The Archive Query Log (AQL) is a previously unused, comprehensive query log collected at the Internet Archive over the last 25 years. Its first version includes 356 million queries, 166 million search result pages, and 1.7 billion search results across 550 search providers. Although many query logs have been studied in the literature, the search providers that own them generally do not publish their logs to protect user privacy and vital business data. Of the few query logs publicly available, none combines size, scope, and diversity. The AQL is the first to do so, enabling research on new retrieval models and (diachronic) search engine analyses. Provided in a privacy-preserving manner, it promotes open research as well as more transparency and accountability in the search industry.Comment: SIGIR 2023 resource paper, 13 page

Semi-Supervised Speech Emotion Recognition with Ladder Networks

Speech emotion recognition (SER) systems find applications in various fields such as healthcare, education, and security and defense. A major drawback of these systems is their lack of generalization across different conditions. This problem can be solved by training models on large amounts of labeled data from the target domain, which is expensive and time-consuming. Another approach is to increase the generalization of the models. An effective way to achieve this goal is by regularizing the models through multitask learning (MTL), where auxiliary tasks are learned along with the primary task. These methods often require the use of labeled data which is computationally expensive to collect for emotion recognition (gender, speaker identity, age or other emotional descriptors). This study proposes the use of ladder networks for emotion recognition, which utilizes an unsupervised auxiliary task. The primary task is a regression problem to predict emotional attributes. The auxiliary task is the reconstruction of intermediate feature representations using a denoising autoencoder. This auxiliary task does not require labels so it is possible to train the framework in a semi-supervised fashion with abundant unlabeled data from the target domain. This study shows that the proposed approach creates a powerful framework for SER, achieving superior performance than fully supervised single-task learning (STL) and MTL baselines. The approach is implemented with several acoustic features, showing that ladder networks generalize significantly better in cross-corpus settings. Compared to the STL baselines, the proposed approach achieves relative gains in concordance correlation coefficient (CCC) between 3.0% and 3.5% for within corpus evaluations, and between 16.1% and 74.1% for cross corpus evaluations, highlighting the power of the architecture

Interactive Videos in IPTV using Hypervideo Links

[EN] One of the main advantages of Internet Protocol Television (IPTV) is the capability of full duplex communication between the Television (TV) provider and the clients which permits the interactivity of both. From this interactivity, TV providers can obtain extra information from clients. Moreover, clients can even take profit from this type of communication that allows them surfing between videos. However, this feature cannot be used in Digital Terrestrial Television (DTT). This paper shows the design and development of an interactive video for IPTV services that contains hypervideo links with additional information and content that is very useful for viewers. Furthermore, we detail the protocol used to maintain the service between the TV provider and the clients to maintain the service. Finally, the system is tested in a real scenario and the resulting measurements are provided in order to evaluate the network performance needed to offer an adequate service to each client because according to results shows, the use of hypervideo links does not imply an important increase in the ratio of packets per second.This work has been partially supported by the Postdoctoral Scholarship Contratos Postdoctorales UPV 2014 (PAID-10-14) of the Universitat Politècnica de València , by the Programa para la Formación de Personal Investigador (FPI-2015-S2-884) of the Universitat Politècnica de València .Jimenez, JM.; Lloret, J.; Abdullah, MT.; Sendra, S. (2017). Interactive Videos in IPTV using Hypervideo Links. Network Protocols and Algorithms. 9(3-4):77-93. https://doi.org/10.5296/npa.v9i3-4.12540S779393-

a smart lecture recorder

Webcasting and recording of university lectures has become common practice. While much effort has been put into the development and improvement of formats and codecs, few computer scientist have studied how to improve the quality of the signal before it is digitized. A Lecture hall or a seminar room is not a professional recording studio. Good quality recordings require full-time technicians to setup and monitor the signals. Although often advertised, most current systems cannot yield professional quality recordings just by plugging a microphone into a sound card and starting the lecture. This paper describes a lecture broadcasting system that eases studioless voice recording by automatizing several tasks usually handled by professional audio technicians. The software described here measures the quality of the sound hardware used, monitors possible hardware malfunctions, prevents common user mistakes, and provides gain control and filter mechanisms

A Survey on Layer-Wise Security Attacks in IoT: Attacks, Countermeasures, and Open-Issues

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Security is a mandatory issue in any network, where sensitive data are transferred safely in the required direction. Wireless sensor networks (WSNs) are the networks formed in hostile areas for different applications. Whatever the application, the WSNs must gather a large amount of sensitive data and send them to an authorized body, generally a sink. WSN has integrated with Internet-of-Things (IoT) via internet access in sensor nodes along with internet-connected devices. The data gathered with IoT are enormous, which are eventually collected by WSN over the Internet. Due to several resource constraints, it is challenging to design a secure sensor network, and for a secure IoT it is essential to have a secure WSN. Most of the traditional security techniques do not work well for WSN. The merger of IoT and WSN has opened new challenges in designing a secure network. In this paper, we have discussed the challenges of creating a secure WSN. This research reviews the layer-wise security protocols for WSN and IoT in the literature. There are several issues and challenges for a secure WSN and IoT, which we have addressed in this research. This research pinpoints the new research opportunities in the security issues of both WSN and IoT. This survey climaxes in abstruse psychoanalysis of the network layer attacks. Finally, various attacks on the network using Cooja, a simulator of ContikiOS, are simulated.Peer reviewe

Building Representative Composite Items

International audienceThe problem of summarizing a large collection of homogeneous items has been addressed extensively in particular in the case of geo-tagged datasets (e.g. Flickr photos and tags). In our work, we study the problem of summarizing large collections of heterogeneous items. For example, a user planning to spend extended periods of time in a given city would be interested in seeing a map of that city with item summaries in different geographic areas, each containing a theater, a gym, a bakery, a few restaurants and a subway station. We propose to solve that problem by building representative Composite Items (CIs). To the best of our knowledge, this is the first work that addresses the problem of finding representative CIs for heterogeneous items. Our problem naturally arises when summarizing geo-tagged datasets but also in other datasets such as movie or music summarization. We formalize building representative CIs as an optimization problem and propose KFC, an extended fuzzy clustering algorithm to solve it. We show that KFC converges and run extensive experiments on a variety of real datasets that validate its effectiveness

Finding emotional-laden resources on the World Wide Web

Some content in multimedia resources can depict or evoke certain emotions in users. The aim of Emotional Information Retrieval (EmIR) and of our research is to identify knowledge about emotional-laden documents and to use these findings in a new kind of World Wide Web information service that allows users to search and browse by emotion. Our prototype, called Media EMOtion SEarch (MEMOSE), is largely based on the results of research regarding emotive music pieces, images and videos. In order to index both evoked and depicted emotions in these three media types and to make them searchable, we work with a controlled vocabulary, slide controls to adjust the emotions’ intensities, and broad folksonomies to identify and separate the correct resource-specific emotions. This separation of so-called power tags is based on a tag distribution which follows either an inverse power law (only one emotion was recognized) or an inverse-logistical shape (two or three emotions were recognized). Both distributions are well known in information science. MEMOSE consists of a tool for tagging basic emotions with the help of slide controls, a processing device to separate power tags, a retrieval component consisting of a search interface (for any topic in combination with one or more emotions) and a results screen. The latter shows two separately ranked lists of items for each media type (depicted and felt emotions), displaying thumbnails of resources, ranked by the mean values of intensity. In the evaluation of the MEMOSE prototype, study participants described our EmIR system as an enjoyable Web 2.0 service

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period