Obesity and chronic diet‐related diseases such as type 2 diabetes, hypertension, cardiovascular disease, cancers, and celiac are increasing worldwide. The increasing prevalence of these diseases has led nutritionists and food scientists to pay more attention to the relationship between diet and different disease risks. Among different foods, rice has received increasing attention because it is a major component of billions of peoples’ diets throughout the world. Rice is commonly consumed after polishing or whitening and the polished grain is known a high glycemic food because of its high starch content. In addition, the removal of the outer bran layer during rice milling results in a loss of nutrients, dietary fiber, and bioactive components. Therefore, many studies were performed to investigate the potential health benefits for the consumption of whole brown rice (BR) grain in comparison to the milled or white rice (WR). The objective of this work was to review the recent advances in research performed for purposes of evaluation of nutritional value and potential health benefits of the whole BR grain. Studies carried out for purposes of developing BR‐based food products are reviewed. BR safety and preservation treatments are also explored. In addition, economic and environmental benefits for the consumption of whole BR instead of the polished or WR are presented. Furthermore, challenges facing the commercialization of BR and future perspectives to promote its utilization as food are discussed.

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Storyline visualizations, which are useful in many applications, aim to illustrate the dynamic relationships between entities in a story. However, the growing complexity and scalability of stories pose great challenges for existing approaches. In this paper, we propose an efficient optimization approach to generating an aesthetically appealing storyline visualization, which effectively handles the hierarchical relationships between entities over time. The approach formulates the storyline layout as a novel hybrid optimization approach that combines discrete and continuous optimization. The discrete method generates an initial layout through the ordering and alignment of entities, and the continuous method optimizes the initial layout to produce the optimal one. The efficient approach makes real-time interactions (e.g., bundling and straightening) possible, thus enabling users to better understand and track how the story evolves. Experiments and case studies are conducted to demonstrate the effectiveness and usefulness of the optimization approach.

Abstract-Recent years have seen a large number of proposals affect personal security and mobility. Most recently, we have for anonymity mechanisms operating on the application layer. seen a remarkable upswing of privacy intrusions driven by Given that anonymity is no stronger than its weakest link, attempts to perform identity theft. It is evident that location such proposals are only meaningful if one can offer anonymity information may be used to better target victims of such guarantees on the communication layer as well. ANODRor ANonymous On Demand Routing -is one of the leading attacks, as well as attacks in the entire spectrum mentioned proposals to deal with this issue. In this paper, we propose a above. To limit the success of such attacks -without having novel technique to address the same problem, but at a lower to re-engineer our entire communication infrastructure -it cost. Our proposal, which we dub Discount-ANODR, is buit is important to develop techniques that implement sufficient around the same set of techniques as ANODR is. Our proposal is im prtantt deveop tehnique stattimlementesuf has the benefit of achieving substantially lower computation and levelsof privac, thoutandingsubstantialschanes of communication complexities at the cost of a slight reduction the network orthe computationalrequirements associatedwith of privacy guarantees. In particular, Discount-ANODR achieves performing routing. source anonymity and routing privacy. A route is "blindly gener-The motivation for this paper is to design a lightweight ated" by the intermediaries on the path between an anonymous source and an identified destination. Route requests in Discount-prceserving and routing pr otocol touace ANODR bear strong similarities to route requests in existing source anonymity and routing privacy. We define source source routing protocols, with the limitation that intermediaries anonymity as a property guaranteeing that an adversary can only know the destination of the request and the identity of not find evidence that a node is the originator of an observed the previous intermediary -but not whether the latter was the message or route request. Our definition of routing privacy originator of the request. The response to a route request protects * * a * -the compiled route by means of iterated symmetric encryption, crponds ro an proerty wo hdn tintitie of node on drawing on how messages are prepared before being submitted a path, from an adversary who may control one or more of to a typical synchronous mix network (or onion router). The these intermediaries. communication of data subsequently uses such "route onions" to Our approach is based on reactive source routing, where a channel the packet to the intended destination. We do not use route is obtained only when there is a demand to send a mesany key exchange, nor do we utilize public key operations at any time; consequently, we do not need to rely on any PKI, CRL or sage Reactive routing Is belleved to have less overhead than related constructions.proactive routing,...

Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks. In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory sequences for trajectory privacy-preserving, and discovers latent trajectory communities through clustering the uploaded trajectory sequences. Different with traditional trajectory privacy-preserving methods, we first partition original trajectory sequence into different segments. Then, the suitable locations and segments are selected to constitute obfuscated trajectory sequence. Specifically, we formulate the trajectory obfuscation problem to select an optimal trajectory sequence which has the smallest difference with original trajectory sequence. In order to prevent privacy leakage, we add Laplace noise and exponential noise to the outputs during the stages of location obfuscation matrix generation and trajectory sequence function generation, respectively. Through formal privacy analysis, we prove that DP-LTOD scheme can guarantee-differential private. Moreover, we develop a trajectory clustering algorithm to classify the trajectories into different kinds of clusters according to semantic distance and geographical distance. Extensive experiments on two real-world datasets illustrate that our DP-LTOD scheme can not only discover latent trajectory communities, but also protect user privacy from leaking.

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