Returned Search Results Research Articles

Verifiable Keyword Search Encryption Scheme That Supports Revocation of Attributes

In recent years, searchable encryption technology and attribute encryption technology have been widely used in cloud storage environments, and attribute-based searchable encryption schemes can both achieve the retrieval of encrypted data and effectively solve the access control problem. Considering that existing attribute-based searchable encryption schemes for cloud storage only support keyword search and do not support attribute revocation, most of the schemes that support attribute revocation only consider the computational overhead of users and ignore the large amount of computational resources consumed by attribute authorization centers when updating keys. In addition, keyword search may lead to partial errors in the returned search results, leading to the wastage of computational and broadband resources. To solve these issues, this paper proposes an attribute-based searchable encryption scheme that supports attribute revocation and is verifiable. To realize fine-grained ciphertext search of encrypted data, support scenarios of dynamic changes of user attributes, and ensure that third-party servers perform the search process reliably and honestly while minimizing computation and storage costs, first, this paper implements attribute revocation with the attribute authorization center by creating a user revocation list and an attribute key revocation list. At the same time, the system updates the attribute key at the time of user search request, which effectively reduces the computational overhead. Second, a third-party auditor is introduced to ensure the correctness of the search results. Finally, the security of this paper is verified by theoretical analysis, and the efficiency and practicality of this paper are verified by comparing it to other schemes through simulation experiments.

A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata.

Putting real-time medical data processing applications into practice comes with some challenges such as scalability and performance. Processing medical images from different collaborators is an example of such applications, in which chest X-ray data are processed to extract knowledge. It is not easy to process data and get the required information in real time using central processing techniques when data get very large in size. In this paper, real-time data are filtered and forwarded to the right processing node by using the proposed topic-based hierarchical publish/subscribe messaging middleware in the distributed scalable network of collaborating computation nodes instead of classical approaches of centralized computation. This enables processing streaming medical data in near real time and makes a warning system possible. End users have the capability of filtering/searching. The returned search results can be images (COVID-19 or non-COVID-19) and their meta-data are gender and age. Here, COVID-19 is detected using a novel capsule network-based model from chest X-ray images. This middleware allows for a smaller search space as well as shorter times for obtaining search results.

Efficient Ranked Multi-Keyword Retrieval With Privacy Protection for Multiple Data Owners in Cloud Computing

With the significant rise in the demand for infinite storage resource and high-quality retrieval service, cloud computing is deemed to be a highly remarkable technology. For privacy concerns, several research works on ranked multi-keyword search over the encrypted cloud data under multiple data owner model have been developed. However, most of these schemes are vulnerable to keyword guessing attacks and equivalence test attack. Besides, they cannot accurately return the top - $k$ ranked search results from the encrypted data of different data owners to the data users. Obviously, these drawbacks may incur leakage of privacy-sensitive keyword information and the inaccuracy of returned search results. Therefore, in this article, we first propose a novel efficient ranked multi-keyword retrieval scheme with keyword privacy for multiple data owners, which empowers the cloud server to perform multi-keyword search over the cloud data and then, return the top - $k$ ranked search results to data users without leaking any keyword and trapdoor information. Additionally, we show through rigorous security analysis that our scheme is secure against the attacks launched by inside attackers and outside attackers. Finally, the performance evaluation indicates that our scheme has more satisfactory features than the existing ranked multi-keyword search schemes.

Examining Diabetes Management Apps Recommended From a Google Search: Content Analysis.

BackgroundThe availability of smartphone health apps empowers people to manage their own health. Currently, there are over 300,000 health apps available in the market targeting a variety of user needs from weight loss to management of chronic conditions, with diabetes being the most commonly targeted condition. To date, health apps largely fall outside government regulation, and there are no official guidelines to help clinicians and patients in app selection. Patients commonly resort to the internet for suggestions on which diabetes app to use.ObjectiveThe objective of this study was to investigate apps identified through a Google search and characterize these apps in terms of features that support diabetes management.MethodsWe performed a Google search for the “best diabetes apps 2017” and explored the first 4 search results. We identified and compiled a list of the apps recommended in the returned search results, which were Web articles. Information about each app was extracted from the papers and corresponding app store descriptions. We examined the apps for the following diabetes management features: medication management, blood glucose self-management, physical activity, diet and nutrition, and weight management.ResultsOverall, 26 apps were recommended in 4 papers. One app was listed in all 4 papers, and 3 apps appeared on 3 of the 4 lists. Apart from one paper, there were no explicit criteria to justify or explain the selection of apps. We found a wide variation in the type and the number of diabetes management features in the recommended apps. Five apps required payment to be used. Two-thirds of the apps had blood glucose management features, and less than half had medication management features. The most prevalent app features were nutrition or diet-related (19/24, 79%) and physical activity tracking (14/24, 58%).ConclusionsThe ambiguity of app selection and the wide variability in key features of the apps recommended for diabetes management may pose difficulties for patients when selecting the most appropriate app. It is critical to involve patients, clinicians, relevant professional bodies, and policy makers to define the key features an app should have for it to be classified as a “diabetes management” app. The lessons learned here may be extrapolated for the development and recommendation of apps for the management of other chronic conditions.

Gom cụm kết quả tìm kiếm video với hướng tiếp cận kết hợp đa đặc trưng

This paper aims to extend our previous researches on clustering web video search results, which reported in [1, 2, 3]. To search videos, users usually use online video search systems such as YouTube, Google Video. However, the returned search results of these systems may include many videos of different categories, and as a result, users find it difficult to locate video clips of interest. Therefore, clustering web video search results is necessary in order to improve the efficiency of searching. The main idea of paper based on analysing and combining the features extracted from video to find the set of appropriate features to improve the quality of video clusters.

Abstract A86: Specialty comparison of disparities-related research over forty years

Abstract Introduction: Research on health disparities in racial/ethnic minorities and the medically underserved is integral to the future of healthcare both globally and in the United States. This research is essential to understand and improve inequities in medicine, and to guarantee the delivery of first-class health care. While much research has investigated diversity in the medical field from the perspective of faculty, residents and medical students, little has been devoted to examining how much research is allotted to disparities in health-care with respect to racial/ethnic minorities and the medically underserved. To our knowledge, no investigation has compared specialties with regard disparity-related research. The purpose of this study was to compare allocation of research dedicated to the underserved across different specialties and time-periods. Methods: A PubMed data search for keywords of interest and control keywords was implemented for three time periods, 1969-1983, 1984-1998, and 1999-2013. Keywords of interest were, “racial,” “disparities,” “socioeconomic,” and “ethnic.” The keywords used as controls were, “diagnosis,” and “cell.” This was done for three journals from each of the major medical specialties: Internal Medicine (NEJM, Lancet, JAMA), Surgery (Annals of Surgery, British Journal of Surgery, Endoscopy), Pediatrics (Pediatrics, Archives of Pediatrics and Adolescent Medicine, Journal of Pediatrics), Obstetrics and Gynecology (Obstetrics and Gynecology, American Journal of Obstetrics and Gynecology, BJOG), in addition to journals from Oncology (CA-A Cancer Journal for Clinicians, JCO, JNCI) and Radiation Oncology (IJROBP, Radiotherapy and Oncology, Radiation Research). Journals were selected on the basis of relevance to a field, and Thomas Reuters ranking. All journals used in this analysis were established before 1985. For each search, the number of returned search results in addition to the total number of articles for that period was recorded to yield a percentage. Search entries utilized keywords exclusively, using Boolean operator filtering, to prevent overlap between search results. Statistical analysis was centered at time period 1969-1983 so as to determine whether changes occurred over time. Linear models were fitted between the specialties and the proportion of returned search results. Control keyword search results were included in the model for standardization. Additionally, the total for all three time periods was combined to generate a total disparity-related research percentage for each specialty. Results: All six specialties examined in this study experienced statistically significant increases in relevant search results over the time periods examined (p<0.05). Radiation Oncology and Surgery were observed to have the greatest changes over time showing changes statistically different than those observed in the other subspecialties. However, these were also the specialties with the smallest proportion of returned keywords in total over the three time periods examined: Radiation Oncology 0.23%, Surgery 0.29%, Internal Medicine 1.06%, Oncology 1.25%, Obstetrics and Gynecology 1.69%, Pediatrics 3.53%. Conclusion: Our study demonstrates that these 6 medical specialties are making great strides in the struggle to address and understand the unique disease processes and socioeconomic problems endemic to underserved populations. However, the distribution of research dedicated to this population is not equally distributed among medical specialties despite some advances. Greater attention and future research dedicated to this end should be made available-particularly to scientists and researchers in fields where such investigations are insufficient. Future studies should examine the root causes for the differences in the amount of disparity-related research among specialties. Citation Format: Awad Ahmed, Curtiland Deville. Specialty comparison of disparities-related research over forty years. [abstract]. In: Proceedings of the Seventh AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 9-12, 2014; San Antonio, TX. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2015;24(10 Suppl):Abstract nr A86.

Verifiable Privacy-Preserving Multi-Keyword Text Search in the Cloud Supporting Similarity-Based Ranking

With the growing popularity of cloud computing, huge amount of documents are outsourced to the cloud for reduced management cost and ease of access. Although encryption helps protecting user data confidentiality, it leaves the well-functioning yet practically-efficient secure search functions over encrypted data a challenging problem. In this paper, we present a verifiable privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking to address this problem. To support multi-keyword search and search result ranking, we propose to build the search index based on term frequency- and the vector space model with cosine similarity measure to achieve higher search result accuracy. To improve the search efficiency, we propose a tree-based index structure and various adaptive methods for multi-dimensional (MD) algorithm so that the practical search efficiency is much better than that of linear search. To further enhance the search privacy, we propose two secure index schemes to meet the stringent privacy requirements under strong threat models, i.e., known ciphertext model and known background model. In addition, we devise a scheme upon the proposed index tree structure to enable authenticity check over the returned search results. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation.

Employing physical distance in ranking image-search results

In this paper, we enhance web image searching by exploiting the physical distance between an image on the web page and the surrounding text. The rationale is that the closer a term word to an image is, the more strong the relation between that word and image is. The physical distance is measured between the exact positions of a term and an image inside a webpage as it appears when it is completely rendered by a web browser. We show that incorporating physical distance in the ranking process indeed reduces the number of irrelevant images in the returned search results.

User feedback based metasearching using neural network

Metasearch engines are the web services that receive user queries and dispatch them to multiple crawler based search engines. After this, they collect the returned search results, reorder them and present the reordered list to the end user. To combine the results from different search engines, a metasearch engine may use different rank aggregation techniques to aggregate the various rankings of the search results to generate an overall ranking. If different rank aggregation techniques are used to collate search results, the results of metasearching for the same query may vary for the same set of participating search engines. In this paper, we discuss a metasearching technique that utilizes neural network based rank aggregation. Here, we formulate the rank aggregation problem as a function approximation problem. As the multilayer perceptrons are considered universal approximators, we use a multilayer perceptron for rank aggregation. We compare the performance of the neural network based method with four other methods namely rough set based method, modified rough set based method, Borda’s method and a Markov Chain based method (MC2) using three independent evaluators. Experimentally, we find that the neural network based method performs better than each of these four methods.

Natural language technology and query expansion: issues, state-of-the-art and perspectives

The availability of an abundance of knowledge sources has spurred a large amount of effort in the development and enhancement of Information Retrieval techniques. Users information needs are expressed in natural language and successful retrieval is very much dependent on the effective communication of the intended purpose. Natural language queries consist of multiple linguistic features which serve to represent the intended search goal. Linguistic characteristics that cause semantic ambiguity and misinterpretation of queries as well as additional factors such as the lack of familiarity with the search environment affect the users ability to accurately represent their information needs, coined by the concept intention gap. The latter directly affects the relevance of the returned search results which may not be to the users satisfaction and therefore is a major issue impacting the effectiveness of information retrieval systems. Central to our discussion is the identification of the significant constituents that characterize the query intent and their enrichment through the addition of meaningful terms, phrases or even latent representations, either manually or automatically to capture their intended meaning. Specifically, we discuss techniques to achieve the enrichment and in particular those utilizing the information gathered from statistical processing of term dependencies within a document corpus or from external knowledge sources such as ontologies. We lay down the anatomy of a generic linguistic based query expansion framework and propose its module-based decomposition, covering topical issues from query processing, information retrieval, computational linguistics and ontology engineering. For each of the modules we review state-of-the-art solutions in the literature categorized and analyzed under the light of the techniques used.

Exploring web neighbours in exploratory search

PurposeAccess to related information is a key requirement for exploratory search. The purpose of this research is to understand where related information may be found and how it may be explored by users.Design/methodology/approachEarlier research provides sufficient evidence that web graph neighborhoods of returned search results may contain documents related to users' intended search topic. However, in the literature, no interface mechanisms have been presented to enable exploration of these neighborhoods by users. Based on a modified web graph, this paper proposes tools and methods for displaying and exploring the graph neighborhood of any selected item in the search results list. Important issues that arise when implementing such an exploration model are discussed and utility of the proposed system is evaluated with user experiments.FindingsIn user experiments first, information related to intended search topic was often found in the web neighborhood of search results; second, exploring these graph neighborhoods with the proposed tools improved users' ability to reach the information they sought.Research limitations/implicationsThe test participants are computer science graduate students. Their skills may not be representative of the broad user population.Originality/valueThe lessons learned from this research point to a potentially fruitful direction for designing new search engine interfaces that support exploratory search.

Multimodal Fusion for Video Search Reranking

Analysis on click-through data from a very large search engine log shows that users are usually interested in the top-ranked portion of returned search results. Therefore, it is crucial for search engines to achieve high accuracy on the top-ranked documents. While many methods exist for boosting video search performance, they either pay less attention to the above factor or encounter difficulties in practical applications. In this paper, we present a flexible and effective reranking method, called CR-Reranking, to improve the retrieval effectiveness. To offer high accuracy on the top-ranked results, CR-Reranking employs a cross-reference (CR) strategy to fuse multimodal cues. Specifically, multimodal features are first utilized separately to rerank the initial returned results at the cluster level, and then all the ranked clusters from different modalities are cooperatively used to infer the shots with high relevance. Experimental results show that the search quality, especially on the top-ranked results, is improved significantly.

Personalized information spaces: improved access to chemical digital libraries

Today digital libraries provide access to a vast, but largely unstructured, amount of document collections. Facing the ever increasing challenge of the information overload content providers have to focus on new ways in user-centered retrieval, not only providing tools for searching information, but tools for personalizing, managing, evaluating and working with the returned search results. Within the ViFaChem II research project the TIB and the L3S Research Center, Hannover, have developed an enhanced retrieval platform for chemical digital libraries. The prototypical interface processes full text and bibliographic metadata collections to create semantically enriched document collections with chemical metadata. Processing steps include text mining for chemical entities, reaction types and chemical structure reconstruction. However, the ViFaChem digital library does not only offer the classical document access via a text or chemical structure search, but also semantic search based on the faceted browsing paradigm. In particular it includes the Semantic GrowBag algorithm, which automatically creates facets for navigational access and query refinement using the relationship between documents and author keywords. Based on higher-order co-occurrences of keywords in documents these graphs hierarchically arrange all related topics dynamically with respect to the underlying content collection. Having different personal views on the document collection the user now can search and navigate through search results using individual retrieval strategies. Facets for chemical reactions, chemical entities or topics combined with an underlying ontology thus allow a semantic driven access to documents. Combined with Web 2.0 features the interface of ViFaChem II provides the user with a new experience in searching large document collections, where the user can navigate through query results based on his personalized knowledge space.

Personalised information spaces for chemical digital libraries

Today’s challenge for a chemist in information retrieval is no longer limited to the findand-search process for the right resources in a digital library, but to that of finding the right piece of information within a plethora of returned search results. Integrated search portals on the Internet are able to simultaneously search in multiple and heterogeneous document collections containing millions of documents. Information providers like digital libraries are challenged by the information overload resulting in the availability of more and more information.

Crystal structure of an alkaline serine protease from Nesterenkonia sp. defines a novel family of secreted bacterial proteases

Crystal structure of an alkaline serine protease from Nesterenkonia sp. defines a novel family of secreted bacterial proteases Na Yang, Jie Nan, Erik Brostromer, Rajni Hatti-Kaul, and Xiao-Dong Su* 1 Shenzhen Graduate School of Peking University, Shenzhen 518055, China 2 National Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing 100871, China 3 Department of Biotechnology, Center for Chemistry and Chemical Engineering, Lund University, S-221 00 Lund, Sweden

A co-training framework for searching XML documents

In this paper, we study the use of XML tagged keywords (or simply key-tags) to search an XML fragment in a collection of XML documents. We present techniques that are able to employ users’ evaluations as feedback and then to generate an adaptive ranked list of XML fragments as the search results. First, we extend the vector space model as a basis to search XML fragments. The model examines the relevance between the imposed key-tags and identified fragments in XML documents, and determines the ranked result as an output. Second, in order to deal with the diversified nature of XML documents, we present four XML Rankers (XRs), which have different strengths in terms of similarity, granularity, and ranking features. The XRs are specially tailored to diversified XML documents. We then evaluate the XML search effectiveness and quality for each tailored XR and propose a meta-XML ranker (MXR) comprising the four XRs. The MXR is trained via a machine learning training scheme, which we term the ranking support vector machine (RSVM) in a co-training framework (RSCF). The RSCF takes as input two sets of labelled fragments and feature vectors and then generates as output adaptive rankers in a learning process. We show empirically that, with only a small set of training XML fragments, the RSCF is able to improve after a few iterations in the learning process. Finally, we demonstrate that the RSCF-based MXR is able to bring out the strengths of the underlying XRs in order to adapt the users’ perspectives on the returned search results. By using a set of key-tag queries on a variety of XML documents, we show that the precision of the result of the RSCF-based MXR is effective.