Content-based Access Research Articles

Retracted: Self-Sovereign Identity for Consented and Content-Based Access to Medical Records Using Blockchain

Retracted: Self-Sovereign Identity for Consented and Content-Based Access to Medical Records Using Blockchain

An AI-Generated Content-Based Access Control Strategy for WBANs in Healthcare Electronics Applications

An AI-Generated Content-Based Access Control Strategy for WBANs in Healthcare Electronics Applications

Self-Sovereign Identity for Consented and Content-Based Access to Medical Records Using Blockchain

Electronic health records (EHRs) and medical data are classified as personal data in every privacy law, meaning that any related service that includes processing such data must come with full security, confidentiality, privacy, and accountability. Solutions for health data management, as in storing it, sharing and processing it, are emerging quickly and were significantly boosted by the COVID-19 pandemic that created a need to move things online. EHRs make a crucial part of digital identity data, and the same digital identity trends—as in self-sovereign identity powered by decentralized ledger technologies like blockchain, are being researched or implemented in contexts managing digital interactions between health facilities, patients, and health professionals. In this paper, we propose a blockchain-based solution enabling secure exchange of EHRs between different parties powered by a self-sovereign identity (SSI) wallet and decentralized identifiers. We also make use of a consortium IPFS network for off-chain storage and attribute-based encryption (ABE) to ensure data confidentiality and integrity. Through our solution, we grant users full control over their medical data and enable them to securely share it in total confidentiality over secure communication channels between user wallets using encryption. We also use DIDs for better user privacy and limit any possible correlations or identification by using pairwise DIDs. Overall, combining this set of technologies guarantees secure exchange of EHRs, secure storage, and management along with by-design features inherited from the technological stack.

RTBAC: A Risk-Aware Topic-Based Access Control Model for Text Data with Paragraph-Level Authorization

Granting users precise access rights is one of the purposes of access control technologies. With the increasing requirements of fine-grained authorization, too strict or too loose access control models may cause many problems. In this paper, aiming at insufficient authorizations in text databases, we propose a risk-aware topic-based access control (RTBAC) model, which uses topics to represent the content relationships between users and data. The RTBAC model also uses risk technologies to grant users corresponding access rights based on their historical behaviours and their access requests. The RTBAC model is a fine-grained access control model, and the authorization of RTBAC can reach the paragraph level. Experimental results show that RTBAC is an efficient access control model and the access control granularity of the RTBAC model is more than 3 times that of the existing content-based access control models.

TBAC: A Fine-Grained Topic-Based Access Control Model for Text Data

Insufficient authorization and overauthorization are two main problems to be solved in access control systems. If the authorization is too strict, users might not be able to access data that should be accessible. If the authorization is too lax, users might obtain too many access rights, which may cause considerable risks. Finer-grained access control models are needed to solve these problems. In this paper, aiming at insufficient authorization in text databases, we propose the topic-based access control (TBAC) model and two implementation methods of the model (subject-to-object topic-based access control method PD-TBAC and object-to-subject topic-based access control method FD-TBAC). In the TBAC model, the access control decision for each user against each file is totally content-driven. We use the latent Dirichlet allocation (LDA) algorithm to extract topics from each paragraph in each file, and these topics are used to determine users’ access rights. Experimental results show that the access control granularity of TBAC is more than 4 times that of the existing content-based access control model.

On feature-based vocabulary selection mechanisms in late insertion: third person number agreement in the French future tense

We propose that feature bundles in syntactic computations coactivate subset vocabulary items in content-based access. Thus, in French, an inflectional node for the future tense bearing [Number: Plural] activates third-person singular -a underspecified for [Number: Ø] and third- person plural -ont specified for [Number: Plural]. These activations compete in externalization. [Number: Ø/Plural] defines a <-ont, -a> scale where plural-marked -ont carries more information than -a, allowing scalar inferencing in form selection. Given an activated Plural-marked -ont, -a (3ps.sg) is automatically interpreted as [–Plural] via scalar inferencing and becomes unsuitable for insertion. Thus, -ont (3ps.pl) must be selected when -a (3ps.sg) is eliminated. We tested our model assuming an interaction of cross-domain inferencing with morphological selection, using two experiments. In forced-paced reading and listening tasks, 19 native speaker subjects per task classified picture probes accompanying matching and mismatching subject-verb future tense agreement. Classification times for pictures semantically linked to the verb probed for an interaction between the processing of agreement morphology and the ongoing conceptual processing of the sentence. Classification times were modulated by the type of morphological mismatch. Singular verb form mangera (eat-fut.3ps.sg) in plural contexts slowed down picture classifications, whereas plural verb form mangeront (eat-fut.3ps.pl) in singular contexts did not. This specific interaction between purely formal agreement and conceptual-structure processing is unexplained by superset models of late insertion, or by interface relations, frequency, load of stored information, and phonological cohort activation. It suggests that domain-general principles of inference enrich domain-specific feature-based computations accessing vocabulary items through the coactivation of features.

RCBAC: A risk-aware content-based access control model for large-scale text data

Unstructured data (mostly text data) have become a vital part in the era of big data. Hence, it has become increasingly difficult to identify the internal relations among data and describing the access control object during the design of access control (especially fine-grained access control) policies. Furthermore, in recent years, security incidents have frequently occurred due to the leakage of secrets by insiders, in both enterprises and government agencies around the world. Due to dynamic user behavior, it is difficult to determine “curious accesses” and grant authority based on traditional static access control models. Therefore, we need a dynamic access control model that is content-driven and can be used to find curious users in daily practice. This paper proposes a risk-aware content-based access control model (RCBAC) which can be used to solve over-authorization problems and can grant file-level authority to users. Based on the relevance of the data content and the duties of each user, RCBAC can quantify the risk of both the access behavior and the access history; accordingly, each user's access ability can be adjusted dynamically. The experimental results show that the RCBAC model can separate curious users from normal users and limit the access ability of curious users.

Invited Paper] TRECVid Semantic Indexing of Video: A 6-Year Retrospective

Semantic indexing, or assigning semantic tags to video samples, is a key component for content-based access to video documents and collections. The Semantic Indexing task has been run at TRECVid from 2010 to 2015 with the support of NIST and the Quaero project. As with the previous High-Level Feature detection task which ran from 2002 to 2009, the semantic indexing task aims at evaluating methods and systems for detecting visual, auditory or multi-modal concepts in video shots. In addition to the main semantic indexing task, four secondary tasks were proposed namely the localization task, the concept pair task, the no annotation task, and the task. It attracted over 40 research teams during its running period. The task was conducted using a total of 1,400 hours of video data drawn from Internet Archive videos with Creative Commons licenses gathered by NIST. 200 hours of new test data was made available each year plus 200 more as development data in 2010. The number of target concepts to be detected started from 130 in 2010 and was extended to 346 in 2011. Both the increase in the volume of video data and in the number of target concepts favored the development of generic and scalable methods. Over 8 millions shots×concepts direct annotations plus over 20 millions indirect ones were produced by the participants and the Quaero project on a total of 800 hours of development data. Significant progress was accomplished during the period as this was accurately measured in the context of the progress task but also from some of the participants' contrast experiments. This paper describes the data, protocol and metrics used for the main and the secondary tasks, the results obtained and the main approaches used by participants.

DEVELOPMENT OF ONTOLOGY FOR INTELLIGENT SCIENTIFIC INTERNET RESOURCE DECISION-MAKING SUPPORT IN WEAKLY FORMALIZED DOMAINS

The paper presents the ontology of knowledge area "Decision-making support in weakly formalized domains". The questions connected with a context of intended use of the ontology are considered. This ontology is developed as conceptual base of an intelligent scientific Internet resource which contains the systematized information on this knowledge area, provides content-based access to the information, methods of its processing, and also to methods of the solution of typical tasks. The paper shows that the knowledge area described by the ontology is extension of the classical theory of decision making which focuses its researches substantially on the questions relating to forming, estimation and the choice of versions of the solution of problem situations, i.e. the final stages of decision making. In compliance with this fact the presented ontology describes along with concepts of the classical theory of decision making the methods and facilities obtained in allied branch of science which can be successfully applied in decision making process, including its initial stages. The ontology contains also the concepts allowing to describe the software tools and Internet resources created in the considered area as well as the activities performed in it.

Similarity Searching for the Big Data

Analysis of contemporary Big Data collections require an effective and efficient content-based access to data which is usually unstructured. This first implies a necessity to uncover descriptive knowledge of complex and heterogeneous objects to make them findable. Second, multimodal search structures are needed to efficiently execute complex similarity queries possibly in outsourced environments while preserving privacy. After explaining the impacts of Big Data on similarity searching and summarizing the state of the art in the search technology, four specific research objectives to tackle the challenges are outlined and discussed. It is believed that effective and efficient processing of raw data for object findability and developing hybrid similarity search structures for multi-modal and privacy-preserving searching are necessary to achieve a scalable similarity search technology able to operate on Big Data.

VisInfo: a digital library system for time series research data based on exploratory search—a user-centered design approach

To this day, data-driven science is a widely accepted concept in the digital library (DL) context (Hey et al. in The fourth paradigm: data-intensive scientific discovery. Microsoft Research, 2009). In the same way, domain knowledge from information visualization, visual analytics, and exploratory search has found its way into the DL workflow. This trend is expected to continue, considering future DL challenges such as content-based access to new document types, visual search, and exploration for information landscapes, or big data in general. To cope with these challenges, DL actors need to collaborate with external specialists from different domains to complement each other and succeed in given tasks such as making research data publicly available. Through these interdisciplinary approaches, the DL ecosystem may contribute to applications focused on data-driven science and digital scholarship. In this work, we present VisInfo (2014) , a web-based digital library system (DLS) with the goal to provide visual access to time series research data. Based on an exploratory search (ES) concept (White and Roth in Synth Lect Inf Concepts Retr Serv 1(1):1---98, 2009), VisInfo at first provides a content-based overview visualization of large amounts of time series research data. Further, the system enables the user to define visual queries by example or by sketch. Finally, VisInfo presents visual-interactive capability for the exploration of search results. The development process of VisInfo was based on the user-centered design principle. Experts from computer science, a scientific digital library, usability engineering, and scientists from the earth, and environmental sciences were involved in an interdisciplinary approach. We report on comprehensive user studies in the requirement analysis phase based on paper prototyping, user interviews, screen casts, and user questionnaires. Heuristic evaluations and two usability testing rounds were applied during the system implementation and the deployment phase and certify measurable improvements for our DLS. Based on the lessons learned in VisInfo, we suggest a generalized project workflow that may be applied in related, prospective approaches.

A comprehensive review of significant researches on content based indexing and retrieval of visual information

Developments in multimedia technologies have paved way for the storage of huge collections of video documents on computer systems. It is essential to design tools for content-based access to the documents, so as to allow an efficient exploitation of these collections. Content based analysis provides a flexible and powerfulway to access video data when compared with the other traditional video analysis techniques. The area of content based video indexing and retrieval (CBVIR), focusing on automating the indexing, retrieval and management of video, has attracted extensive research in the last decade. CBVIR is a lively area of research with enduring acknowledgments from several domains. Herein a vital assessment of contemporary researches associated with the content-based indexing and retrieval of visual information. In this paper, we present an extensive review of significant researches on CBVIR. Concise description of content based video analysis along with the techniques associated with the content based video indexing and retrieval is presented.

Methodology of Multimedia and Visualization

ew visualization tools have been developed but geospatial data models and theories remain unchanged because they enable a user to determine a video's distinguishing content without investing long viewing times or requiring high network- transfer speeds. Recent developments in computer hardware and algorithm design have made possible content to integrate media indexing with computer visualization to achieve effective content-based access to video information. This paper starts addressing the theoretical aspect of a multimedia visualization and a consistent framework to express different types of data sources.

A geometrical distance measure for determining the similarity of musical harmony

In the last decade, digital repositories of music have undergone an enormous growth. Therefore, the availability of scalable and effective methods that provide content-based access to these repositories has become critically important. This study presents and tests a new geometric distance function that quantifies the harmonic distance between two pieces of music. Harmony is one of the most important aspects of music and we will show in this paper that harmonic similarity can significantly contribute to the retrieval of digital music. Yet, within the music information retrieval field, harmonic similarity measures have received far less attention compared to other similarity aspects. The distance function we present, the Tonal pitch step distance, is based on a cognitive model of tonality and captures the change of harmonic distance to the tonal center over time. This distance is compared to two other harmonic distance measures. We show that it can be efficiently used for retrieving similar chord sequences, and that it significantly outperforms a baseline string matching approach. Although the proposed method is not the best performing distance measure, it offers the best quality–runtime ratio. Furthermore, we demonstrate in a case study how our harmonic similarity measure can contribute to the musicological discussion of the melody and harmony in large-scale corpora.

COLOR HISTOGRAM BASED MEDICAL IMAGE RETRIEVAL SYSTEM

This paper aims to focus on the feature extraction, selection and database creation of brain images for image retrieval which will aid for computer assisted diagnosis. The impact of content-based access to medical images is frequently reported but existing systems are designed for only a particular context of diagnosis. But, our concept of image retrieval in medical applications aims at a general structure for semantic content analysis that is suitable for numerous applications in case-based reasoning. By using the features, the database created for comparison. The color histogram is used to measure the similarity between the stored database image and the query image. The image which is more similar to the query image is retrieved as the resultant image. If the query

Introduction to the focused issue on music DLs

This focused issue on Music Digital Libraries of the International Journal of Digital Libraries aims at putting in tighter communication of related research areas that offer plenty of potential for combination andmutual inspiration, yet are only rarely combined froma research perspective:Music Information Retrieval (MIR) and Digital Libraries (DLs) technology. MIR is a research area that has a primary focus on content-based access and retrieval of music documents. Since the initial approaches based on the query-by-example paradigm, described also as query-by-hummingwhen themusical query was provided by the user directly by singing an excerpt of the melody, MIR research community enlarged its scope to include the recommendation (both of individual music documents and of sequences of songs organized in a playlist), genre classification, artist identification, and automatic tag generation and many other tasks. Other topics related to MIRaremore related tomusic processing, aimed at improved access to music content. To this end, research topics that are related to MIR include segmentation and structure analysis of music documents, both in symbolic and audio formats, track separation and transcription from audio, beat and tempo tracking, instrument identification.

ACTkit: A Framework for the Definition and Enforcement of Role, Content and Context-based Access Control Policies

This work describes a framework, called ACTkit, for the definition and enforcement of dynamic access control policies on (multi-tiered) information systems. ACTkit embodies a language for defining security policies built out of role-, context- and content-based access control rules and an access control module responsible for the policy enforcement. A model, which has been defined as an extension of Hierarchical RBAC to provide a precise semantics for the security policies, is also presented and discussed.

Content Based Image Retrieval for Medical Images Techniques and Storage Methods-Review Paper

In the medical field, images, and especially digital images, are produced in ever increasing quantities and used for diagnostics and therapy. Content based access to medical images for supporting clinical decision making has been proposed that would ease the management of clinical data and scenarios for the integration of content-based access methods into Picture Archiving and Communication Systems (PACS) have been created. This article gives an overview of available literature in the field of content based access to medical image data and on the technologies used in the field. Section 1 gives an introduction into generic content based image retrieval and the technologies used. Section 2 describes the basic algorithm used in the implemented systems. Section 3 describes various methods of implementing CBIR. New research directions are being defied that can prove to be useful. This article also identifies explanations to some of the outlined problems in the field as it looks like many propositions for systems are made from the medical domain and research prototypes are developed in computer science departments using medical datasets. Still, there are very few systems that seem to be used in clinical practice. Content-Based Image Retrieval (CBIR) has been a topic of research interest for nearly a decade. Approaches to date use image features for describing content. A survey of the literature shows that progress has been limited to prototype systems that make gross assumptions and approximations. The full text of the article is not available in the cache. Kindly refer the IJCA digital library at www.ijcaonline.org for the complete article. In case, you face problems while downloading the full-text, please send a mail to editor at editor@ijcaonline.org

A Content-Driven System Architecture for Tackling Automatic Cataloging of Animated Movie Databases

This article discusses content-based access to video information in large video databases and particularly, to retrieve animated movies. The authors examine temporal segmentation, and propose cut, fade and dissolve detection methods adapted to the constraints of this domain. Further, the authors discuss a fuzzy linguistic approach for deriving automatic symbolic/semantic content annotation in terms of color techniques and action content. The proposed content descriptions are then used with several data mining techniques SVM, k-means to automatically retrieve the animation genre and to classify animated movies according to some color techniques. The authors integrate all the previous techniques to constitute a prototype client-server architecture for a 3D virtual environment for interactive video retrieval.

Scenario-based query processing for video-surveillance archives

Automated video surveillance has emerged as a trendy application domain in recent years, and accessing the semantic content of surveillance video has become a challenging research area. The results of a considerable amount of research dealing with automated access to video surveillance have appeared in the literature; however, significant semantic gaps in event models and content-based access to surveillance video remain. In this paper, we propose a scenario-based query-processing system for video surveillance archives. In our system, a scenario is specified as a sequence of event predicates that can be enriched with object-based low-level features and directional predicates. We introduce an inverted tracking scheme, which effectively tracks the moving objects and enables view-based addressing of the scene. Our query-processing system also supports inverse querying and view-based querying, for after-the-fact activity analysis. We propose a specific surveillance query language to express the supported query types in a scenario-based manner. We also present a visual query-specification interface devised to facilitate the query-specification process. We have conducted performance experiments to show that our query-processing technique has a high expressive power and satisfactory retrieval accuracy in video surveillance.