Arbitrary Keywords Research Articles

Blockchain-Based Employment Contract System Architecture Allowing Encrypted Keyword Searches

We introduce a blockchain-based online employment contract system to protect the rights and interests of both employees and employers. In the proposed model, an employer and a worker can interactively create a new electronic online contract, and the mutually signed contract is saved on a contract blockchain so that the contract becomes certifiable but cannot be forged by the contract signers. In particular, the blockchain in our system provides transactional privacy to protect sensitive personal information such as social identifier, contact information, income, and so forth, contained in the contract. Since a remote cloud server must provide not only secure storage, accessibility, and availability of all signed contracts, but also increased security in the server, we propose a new encrypted keyword search mechanism with enhanced search accuracy. Each contract is associated with encrypted keywords generated from the names of contractual parties and must remain confidential and anonymous even to the server. Although, the contracts must always be accessible by the contract signers, only the cloud server should be able to retrieve each user’s contract without decrypting the contract or identifying the contract signer. To meet these requirements, we propose a new encrypted keyword search mechanism based on Gentry’s homomorphic encryption technology; the server can find each user’s contract when two encrypted arbitrary keywords are homomorphic to each other. Since the keywords in the proposed system are based on person names or business names, they are easily predictable, and, thereby, many synonyms for a keyword can exist. Therefore, the proposed encrypted keyword search takes into account not only the keywords but also the ownership of each contract; in this way, the proposed search scheme is secure against a keyword guessing attack and provides strong search accuracy against the keyword synonyms. As a result, users can only access their own contracts, and the cloud server can exactly retrieve the requester’s contracts. Implementations for the proposed system and corresponding analysis on its security and simulated performance are provided.

Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?

The increasing amount of publicly available research data provides the opportunity to link and integrate data in order to create and prove novel hypotheses, to repeat experiments or to compare recent data to data collected at a different time or place. However, recent studies have shown that retrieving relevant data for data reuse is a time-consuming task in daily research practice. In this study, we explore what hampers dataset retrieval in biodiversity research, a field that produces a large amount of heterogeneous data. In particular, we focus on scholarly search interests and metadata, the primary source of data in a dataset retrieval system. We show that existing metadata currently poorly reflect information needs and therefore are the biggest obstacle in retrieving relevant data. Our findings indicate that for data seekers in the biodiversity domain environments, materials and chemicals, species, biological and chemical processes, locations, data parameters and data types are important information categories. These interests are well covered in metadata elements of domain-specific standards. However, instead of utilizing these standards, large data repositories tend to use metadata standards with domain-independent metadata fields that cover search interests only to some extent. A second problem are arbitrary keywords utilized in descriptive fields such as title, description or subject. Keywords support scholars in a full text search only if the provided terms syntactically match or their semantic relationship to terms used in a user query is known.

A deep HMM model for multiple keywords spotting in handwritten documents

In this paper, we propose a query by string word spotting system able to extract arbitrary keywords in handwritten documents, taking both segmentation and recognition decisions at the line level. The system relies on the combination of a HMM line model made of keyword and non-keyword (filler) models, with a deep neural network that estimates the state-dependent observation probabilities. Experiments are carried out on RIMES database, an unconstrained handwritten document database that is used for benchmarking different handwriting recognition tasks. The obtained results show the superiority of the proposed framework over the classical GMM–HMM and standard HMM hybrid architectures.

Dictionary of biomedical optics and biophotonics—A book review

VALERY Tuchin, a leading expert in biophotonics and biomedical optics, guest editor of the special issue on in vivo flow cytometry (1), coeditor of the Journal of Biophotonics (2), and editor of numerous textbooks recently published a dictionary on biomedical optics and biophotonics (3). Biophotonics can be described as the ‘‘development and application of optical techniques, particularly imaging, to the study of biological molecules, cells and tissue’’. One of the main benefits of using optical techniques which make up biophotonics is that they preserve the integrity of the biological cells being examined, according to Wikipedia (4), with an extensive explanation. So one may critically ask: Is it timely to publish a printed dictionary in the era of Wikipedia? The dictionary with approximately 2500 key terms and 576 pages is bolded throughout the text to direct the reader from definition to definition (similar to a hyperlink) and gives a rich understanding of the terminology in this quickly developing field. The key terms were selected by Professor Tuchin from the glossaries of his numerous text books on this topic and all definitions are precise and to the point. It contains all terms relevant for those working with biological material in combination with high-end optical technologies and is thereby a unique publication in the field. Even so, why have a 700g book on your shelf when all the information is also available on the web, in addition to rich information density, graphical explanations, and so on? But is the web information really better? I made, as an experimental scientist, the test. I selected arbitrary key words from the dictionary and looked them up in Wikipedia. And the answer is yes, you can also find the terms on the web but most explanations are extensive whereas those in the dictionary are crisply short and topically focused on biophotonics’ relationship. Not only that, but looking the terms up in the book is (astonishingly) faster than doing it via the internet. Still, for the electronic display aficionados the whole content of the dictionary is also available in electronic format so that you may also search and find key words on your tablet PC. Who should read it? In my view, Valery Tuchin’s dictionary is a must-have reference for all novices in the field. It is a rapid way to find definitions and connections with other fields and should be on the optical bench in any biophotonic laboratory.

Social image tagging using graph-based reinforcement on multi-type interrelated objects

Social image tagging is becoming increasingly popular with the development of social website, where images are annotated with arbitrary keywords called tags. Most of present image tagging approaches are mainly based on the visual similarity or mapping between visual feature and tags. However, in the social media environment, images are always associated with multi-type of object information (i.e., visual content, tags, and user contact information) which makes this task more challenging. In this paper, we propose to fuse multi-type of information to tag social image. Specifically, we model social image tagging as a “ranking and reinforcement” problem, and a novel graph-based reinforcement algorithm for interrelated multi-type objects is proposed. When a user issue a tagging request for a query image, a candidate tag set is derived and a set of friends of the query user is selected. Then a graph which contains three types of objects (i.e., visual features of the query image, candidate tags, and friend users) is constructed, and each type of objects are initially ranked based on their weight and intra-relation. Finally, candidate tags are re-ranked by our graph-based reinforcement algorithm which takes into consideration both inter-relation with visual features and friend users, and the top ranked tags are saved. Experiments on real-life dataset demonstrate that our algorithm significantly outperforms state-of-the-art algorithms.

Lexicon-free handwritten word spotting using character HMMs

For retrieving keywords from scanned handwritten documents, we present a word spotting system that is based on character Hidden Markov Models. In an efficient lexicon-free approach, arbitrary keywords can be spotted without pre-segmenting text lines into words. For a multi-writer scenario on the IAM off-line database as well as for two single writer scenarios on historical data sets, it is shown that the proposed learning-based system outperforms a standard template matching method.