A Study On Information Retrieval Systems

A video is a key component of today's multimedia applications,  including Video Cassette Recording (VCR), Video-on-Demand (VoD), and virtual walkthrough. This happens supplementary with the fast amplification in video skill (Rynson W.H. Lau et al. 2000). Owing to innovation's progress in the  media, computerized TV, and data frameworks, an immense measure of video information is now exhaustively realistic (Walid G. Aref et al. 2003). The startling advancement in computerized video content has made entrée and moves the data in a tremendous video database a muddled and sensible issue (Chih-Wen Su et al. 2005). Therefore, the necessity for creating devices and frameworks that can effectively investigate the most needed video content, has evoked a great deal of interest among analysts. Sports video has been chosen as the prime application in this proposition since it has attracted viewers around the world

A Literature Study On Video Retrieval Approaches

A detailed survey has been carried out to identify the various research articles available in the literature in all the categories of video retrieval and to do the analysis of the major contributions and their advantages, following are the literature used for the assessment of the state-of-art work on video retrieval. Here, a large number of papershave been studied

Feature based dynamic intra-video indexing

A thesis submitted in partial fulfillment for the degree of Doctor of PhilosophyWith the advent of digital imagery and its wide spread application in all vistas of life, it has become an important component in the world of communication. Video content ranging from broadcast news, sports, personal videos, surveillance, movies and entertainment and similar domains is increasing exponentially in quantity and it is becoming a challenge to retrieve content of interest from the corpora. This has led to an increased interest amongst the researchers to investigate concepts of video structure analysis, feature extraction, content annotation, tagging, video indexing, querying and retrieval to fulfil the requirements. However, most of the previous work is confined within specific domain and constrained by the quality, processing and storage capabilities. This thesis presents a novel framework agglomerating the established approaches from feature extraction to browsing in one system of content based video retrieval. The proposed framework significantly fills the gap identified while satisfying the imposed constraints of processing, storage, quality and retrieval times. The output entails a framework, methodology and prototype application to allow the user to efficiently and effectively retrieved content of interest such as age, gender and activity by specifying the relevant query. Experiments have shown plausible results with an average precision and recall of 0.91 and 0.92 respectively for face detection using Haar wavelets based approach. Precision of age ranges from 0.82 to 0.91 and recall from 0.78 to 0.84. The recognition of gender gives better precision with males (0.89) compared to females while recall gives a higher value with females (0.92). Activity of the subject has been detected using Hough transform and classified using Hiddell Markov Model. A comprehensive dataset to support similar studies has also been developed as part of the research process. A Graphical User Interface (GUI) providing a friendly and intuitive interface has been integrated into the developed system to facilitate the retrieval process. The comparison results of the intraclass correlation coefficient (ICC) shows that the performance of the system closely resembles with that of the human annotator. The performance has been optimised for time and error rate

Machine Learning Models for Educational Platforms

Scaling up education online and onlife is presenting numerous key challenges, such as hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely. However, thanks to the wider availability of learning-related data and increasingly higher performance computing, Artificial Intelligence has the potential to turn such challenges into an unparalleled opportunity. One of its sub-fields, namely Machine Learning, is enabling machines to receive data and learn for themselves, without being programmed with rules. Bringing this intelligent support to education at large scale has a number of advantages, such as avoiding manual error-prone tasks and reducing the chance that learners do any misconduct. Planning, collecting, developing, and predicting become essential steps to make it concrete into real-world education. This thesis deals with the design, implementation, and evaluation of Machine Learning models in the context of online educational platforms deployed at large scale. Constructing and assessing the performance of intelligent models is a crucial step towards increasing reliability and convenience of such an educational medium. The contributions result in large data sets and high-performing models that capitalize on Natural Language Processing, Human Behavior Mining, and Machine Perception. The model decisions aim to support stakeholders over the instructional pipeline, specifically on content categorization, content recommendation, learners’ identity verification, and learners’ sentiment analysis. Past research in this field often relied on statistical processes hardly applicable at large scale. Through our studies, we explore opportunities and challenges introduced by Machine Learning for the above goals, a relevant and timely topic in literature. Supported by extensive experiments, our work reveals a clear opportunity in combining human and machine sensing for researchers interested in online education. Our findings illustrate the feasibility of designing and assessing Machine Learning models for categorization, recommendation, authentication, and sentiment prediction in this research area. Our results provide guidelines on model motivation, data collection, model design, and analysis techniques concerning the above applicative scenarios. Researchers can use our findings to improve data collection on educational platforms, to reduce bias in data and models, to increase model effectiveness, and to increase the reliability of their models, among others. We expect that this thesis can support the adoption of Machine Learning models in educational platforms even more, strengthening the role of data as a precious asset. The thesis outputs are publicly available at https://www.mirkomarras.com

Contexts and Contributions: Building the Distributed Library

This report updates and expands on A Survey of Digital Library Aggregation Services, originally commissioned by the DLF as an internal report in summer 2003, and released to the public later that year. It highlights major developments affecting the ecosystem of scholarly communications and digital libraries since the last survey and provides an analysis of OAI implementation demographics, based on a comparative review of repository registries and cross-archive search services. Secondly, it reviews the state-of-practice for a cohort of digital library aggregation services, grouping them in the context of the problem space to which they most closely adhere. Based in part on responses collected in fall 2005 from an online survey distributed to the original core services, the report investigates the purpose, function and challenges of next-generation aggregation services. On a case-by-case basis, the advances in each service are of interest in isolation from each other, but the report also attempts to situate these services in a larger context and to understand how they fit into a multi-dimensional and interdependent ecosystem supporting the worldwide community of scholars. Finally, the report summarizes the contributions of these services thus far and identifies obstacles requiring further attention to realize the goal of an open, distributed digital library system

Crowd-powered systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 217-237).Crowd-powered systems combine computation with human intelligence, drawn from large groups of people connecting and coordinating online. These hybrid systems enable applications and experiences that neither crowds nor computation could support alone. Unfortunately, crowd work is error-prone and slow, making it difficult to incorporate crowds as first-order building blocks in software systems. I introduce computational techniques that decompose complex tasks into simpler, verifiable steps to improve quality, and optimize work to return results in seconds. These techniques develop crowdsourcing as a platform so that it is reliable and responsive enough to be used in interactive systems. This thesis develops these ideas through a series of crowd-powered systems. The first, Soylent, is a word processor that uses paid micro-contributions to aid writing tasks such as text shortening and proofreading. Using Soylent is like having access to an entire editorial staff as you write. The second system, Adrenaline, is a camera that uses crowds to help amateur photographers capture the exact right moment for a photo. It finds the best smile and catches subjects in mid-air jumps, all in realtime. Moving beyond generic knowledge and paid crowds, I introduce techniques to motivate a social network that has specific expertise, and techniques to data mine crowd activity traces in support of a large number of uncommon user goals. These systems point to a future where social and crowd intelligence are central elements of interaction, software, and computation.by Michael Scott Bernstein.Ph.D

Security and Privacy Preservation in Mobile Advertising

Mobile advertising is emerging as a promising advertising strategy, which leverages prescriptive analytics, location-based distribution, and feedback-driven marketing to engage consumers with timely and targeted advertisements. In the current mobile advertising system, a third-party ad broker collects and manages advertisements for merchants who would like to promote their business to mobile users. Based on its large-scale database of user profiles, the ad broker can help the merchants to better reach out to customers with related interests and charges the merchants for ad dissemination services. Recently, mobile advertising technology has dominated the digital advertising industry and has become the main source of income for IT giants. However, there are many security and privacy challenges that may hinder the continuous success of the mobile advertising industry. First, there is a lack of advertising transparency in the current mobile advertising system. For example, mobile users are concerned about the reliability and trustworthiness of the ad dissemination process and advertising review system. Without proper countermeasures, mobile users can install ad-blocking software to filter out irrelevant or even misleading advertisements, which may lower the advertising investments from merchants. Second, as more strict privacy regulations (e.g. European General Data Privacy Regulations) take effect, it is critical to protect mobile users’ personal profiles from illegal sharing and exposure in the mobile advertising system. In this thesis, three security and privacy challenges for the mobile advertising system are identified and addressed with the designs, implementations, and evaluations of a blockchain-based architecture. First, we study the anonymous review system for the mobile advertising industry. When receiving advertisements from a specific merchant (e.g. a nearby restaurant), mobile users are more likely to browse the previous reviews about the merchant for quality-of-service assessments. However, current review systems are known for the lack of system transparency and are subject to many attacks, such as double reviews and deletions of negative reviews. We exploit the tamper-proof nature and the distributed consensus mechanism of the blockchain technology, to design a blockchain-based review system for mobile advertising, where review accumulations are transparent and verifiable to the public. To preserve user review privacy, we further design an anonymous review token generation scheme, where users are encouraged to leave reviews anonymously while still ensuring the review authenticity. We also explore the implementation challenges of the blockchain-based system on an Ethereum testing network and the experimental results demonstrate the application feasibility of the proposed anonymous review system. Second, we investigate the transparency issues for the targeted ad dissemination process. Specifically, we focus on a specific mobile advertising application: vehicular local advertising, where vehicular users send spatial-keyword queries to ad brokers to receive location-aware advertisements. To build a transparent advertising system, the ad brokers are required to provide mobile users with explanations on the ad dissemination process, e.g., why a specific ad is disseminated to a mobile user. However, such transparency explanations are often found incomplete and sometimes even misleading, which may lower the user trust on the advertising system if without proper countermeasures. Therefore, we design an advertising smart contract to efficiently realize a publicly verifiable spatial-keyword query scheme. Instead of directly implementing the spatial-keyword query scheme on the smart contract with prohibitive storage and computation cost, we exploit the on/off-chain computation models to trade the expensive on-chain cost for cheap off-chain cost. With two design strategies: digest-and-verify and divide-then-assemble, the on-chain cost for a single spatial keyword query is reduced to constant regardless of the scale of the spatial-keyword database. Extensive experiments are conducted to provide both on-chain and off-chain benchmarks with a verifiable computation framework. Third, we explore another critical requirement of the mobile advertising system: public accountability enforcement against advertising misconducts, if (1) mobile users receive irrelevant ads, or (2) advertising policies of merchants are not correctly computed in the ad dissemination process. This requires the design of a composite Succinct Non-interactive ARGument (SNARG) system, that can be tailored for different advertising transparency requirements and is efficient for the blockchain implementations. Moreover, pursuing public accountability should also achieve a strict privacy guarantee for the user profile. We also propose an accountability contract which can receive explanation requirements from both mobile users and merchants. To promote prompt on-chain responses, we design an incentive mechanism based on the pre-deposits of involved parties, i.e., ad brokers, mobile users, and merchants. If any advertising misconduct is identified, public accountability can be enforced by confiscating the pre-deposits of the misbehaving party. Comprehensive experiments and analyses are conducted to demonstrate the versatile functionalities and feasibility of the accountability contract. In summary, we have designed, implemented, and evaluated a blockchain-based architecture for security and privacy preservations in the mobile advertising. The designed architecture can not only enhance the transparency and accountability for the mobile advertising system, but has also achieved notably on-chain efficiency and privacy for real-world implementations. The results from the thesis may shed light on the future research and practice of a blockchain-based architecture for the privacy regulation compliance in the mobile advertising

A New Place to Work and Play: Play Labor and the Production of the New Worker-Subject at Hackathons

Since 2012, hundreds of companies have poured thousands of dollars into hackathons – finite events where creatives come together in small teams to design, build, and demo a new product of feature. The spectacle of the hackathon engages participants in a number of things: a transgressive ethos, disciplined play, and hacker’s literacies (Santo 2011). Based on my dissertation fieldwork at seven hackathons in three industries (journalism, healthcare, and e-government), I explore various types of play labor (Terranova 2000) based on the performances of eight teams. I show how teams creatively manage their peers’ affective and intellectual labor, and negotiate what appear to be industry-specific preferences for different technologies. In the process of competing for status and recognition, they engender distinct forms of play labor and making do. Hackathon participants directly embed resistance in their designs; some learn how to learn (Bateson 1972), giving them a strategic advantage over other classes of workers. They embody the characteristics of the new worker-subject required in the digital economy, as mutable, playful, and rapid

Special Libraries, Fall 1990

Volume 81, Issue 4https://scholarworks.sjsu.edu/sla_sl_1990/1003/thumbnail.jp