Confined space reduced graphite oxide doped with sulfur as metal-free oxygen reduction catalyst

Reduced GO in confined space of silica gel nanopores doped with sulfur shows high catalytic activity for oxygen reduction reaction (ORR) in alkaline medium and exhibits a superior tolerance to the presence of methanol. Even though the partially reduced GO with hydroquinone structures is a good catalysts for ORR, it shows instability in KOH. The good performance of S-doped material is linked to the coexistence of sulfur and oxygen on the surface in equal atomic quantities and a unique porosity being the replica of the silica pores. The former leads to the positive charge on the carbon atoms, which are the reaction sites. Hydrophobicity of the surface and small pores enhance adsorption of O₂.     

Fast super-resolution algorithm using rotation-invariant ELBP classifier and hierarchical pattern matching

This paper proposes a fast super-resolution (SR) algorithm using content-adaptive two-dimensional (2D) finite impulse response (FIR) filters based on a rotation-invariant classifier. The proposed algorithm consists of a learning stage and an inference stage. In the learning stage, we cluster a sufficient number of low-resolution (LR) and high-resolution (HR) patch pairs into a specific number of groups using the rotation-invariant classifier, and choose a specific number of dominant clusters. Then, we compute the optimal 2D FIR filter(s) to synthesize a high-quality HR patch from an LR patch per cluster, and finally store the patch-adaptive 2D FIR filters in a dictionary. Also, we present a smart hierarchical addressing method for effective dictionary exploration in the inference stage. In the inference stage, the ELBP of each input LR patch is extracted in the same way as the learning stage, and the best matched FIR filter(s) to the input LR patch is found from the dictionary by the hierarchical addressing. Finally, we synthesize the HR patch by using the optimal 2D FIR filter. The experimental results show that the proposed algorithm produces better HR images than the existing SR methods, while providing fast running time.     

Unsupervised method for sentiment analysis in online texts

In recent years, the explosive growth of online media, such as blogs and social networking sites, has enabled individuals and organizations to write about their personal experiences and express opinions. Classifying these documents using a polarity metric is an arduous task. We propose a novel approach to predicting sentiment in online textual messages such as tweets and reviews, based on an unsupervised dependency parsing-based text classification method that leverages a variety of natural language processing techniques and sentiment features primarily derived from sentiment lexicons. These lexicons were created by means of a semiautomatic polarity expansion algorithm in order to improve accuracy in specific application domains. The results obtained for the Cornell Movie Review, Obama-McCain Debate and SemEval-2015 datasets confirm the competitive performance and the robustness of the system.     

Bounded memory protocols

It is well-known that the Dolev–Yao adversary is a powerful adversary. Besides acting as the network, intercepting, decomposing, composing and sending messages, he can remember as much information as he needs. That is, his memory is unbounded. We recently proposed a weaker Dolev–Yao like adversary, which also acts as the network, but whose memory is bounded. We showed that this Bounded Memory Dolev–Yao adversary, when given enough memory, can carry out many existing protocol anomalies. In particular, the known anomalies arise for bounded memory protocols, where although the total number of sessions is unbounded, there are only a bounded number of concurrent sessions and the honest participants of the protocol cannot remember an unbounded number of facts or an unbounded number of nonces at a time. This led us to the question of whether it is possible to infer an upper-bound on the memory required by the Dolev–Yao adversary to carry out an anomaly from the memory restrictions of the bounded protocol. This paper answers this question negatively (Theorem 8).     

Investigating possible kinetic limitations to MgB2 hydrogenation

An investigation is reported of possible kinetic limitations to MgB₂ hydrogenation. The role of H–H bond breaking, a necessary first step in the hydrogenation process, is assessed for bulk MgB₂, ball-milled MgB₂, as well as MgB₂ mixed with Pd, Fe and TiF₃ additives. The Pd and Fe additives in the MgB₂ material exist as dispersed metallic particles in the size range ~5–40 nm diameter. In contrast, TiF₃ reacts with MgB₂ to form Ti metal, elemental B and MgF₂, with the Ti and the MgF₂ phases proximate to each other and coating the MgB₂ particulates with a film of thickness ~3 nm. Sieverts studies of hydrogenation kinetics are reported and compared to the rate of H–H bond breaking as measured by H-D exchange studies. The results show that H–H bond dissociation does not limit the rate of hydrogenation of MgB₂ because H–H bond cleavage occurs rapidly compared to the initial MgB₂ hydrogenation. The results also show that surface diffusion of hydrogen atoms cannot be a limiting factor for MgB₂ hydrogenation. Instead, it is speculated that it is the intrinsic stability of the B–B extended hexagonal ring structure in MgB₂ that hinders the hydrogenation of this material. This supposition is supported by B K-edge x-ray absorption measurements of the materials, which showed spectroscopically that the B–B ring was intact in the material systems studied. The TiF₃/MgB₂ system was examined further theoretically with reaction thermodynamics and phase nucleation kinetic calculations to better understand the production of Ti metal when TiB₂ is thermodynamically favored. The results show that there exist physically reasonable ranges for which nucleation kinetics supersede thermodynamics in determining the reactive pathway for the TiF₃/MgB₂ system and perhaps for other additive systems as well.     

Learning to extract domain-specific relations from complex sentences

Open Information Extraction (OIE) systems focus on identifying and extracting general relations from text. Most OIE systems utilize simple linguistic structure, such as part-of-speech or dependency features, to extract relations and arguments from a sentence. These approaches are simple and fast to implement, but suffer from two main drawbacks: i) they are less effective to handle complex sentences with multiple relations and shared arguments, and ii) they tend to extract overly-specific relations.This paper proposes an approach to Information Extraction called SemIE, which addresses both drawbacks. SemIE identifies significant relations from domain-specific text by utilizing a semantic structure that describes the domain of discourse. SemIE exploits the predicate-argument structure of a text, which is able to handle complex sentences. The semantics of the arguments are explicitly specified by mapping them to relevant concepts in the semantic structure.SemIE uses a semi-supervised learning approach to bootstrap training examples that cover all relations expressed in the semantic structure. SemIE inputs pairs of structured documents and uses a Greedy Mapping module to bootstrap a full set of training examples. The training examples are then used to learn the extraction and mapping rules.We evaluated the performance of SemIE by comparing it with OLLIE, a state-of-the-art OIE system. We tested SemIE and OLLIE on the task of extracting relations from text in the “movie” domain and found that on average, SemIE outperforms OLLIE. Furthermore, we also examined how the performance varies with sentence complexity and sentence length. The results prove the effectiveness of SemIE in handling complex sentences.     

Efficient and secure identity-based encryption scheme with equality test in cloud computing

Efficient searching on encrypted data outsourced to the cloud remains a research challenge. Identity-based encryption with equality test (IBEET) scheme has recently been identified as a viable solution, in which users can delegate a trapdoor to the server and the server then searches on user outsourced encrypted data to determine whether two different ciphertexts are encryptions of the same plaintext. Such schemes are, unfortunately, inefficient particularly for deployment on mobile devices (with limited power/battery life and computing capacity). In this paper, we propose an efficient IBEET scheme with bilinear pairing, which reduces the need for time-consuming HashToPoint function. We then prove the security of our scheme for one-way secure against chosen identity and chosen ciphertext attacks (OW–ID–CCA) in the random oracle model (ROM). The performance evaluation of our scheme demonstrates that in comparison to the scheme of Ma (2016), our scheme achieves a reduction of 36.7% and 39.24% in computation cost during the encryption phase and test phase, respectively, and that our scheme is suitable for (mobile) cloud deployment.     

A new LRBFCM-GBEM modeling algorithm for general solution of time fractional-order dual phase lag bioheat transfer problems in functionally graded tissues

The main objective of this article is to propose a new hybrid modeling algorithm based on combining local radial basis function collocation method (LRBFCM) and general boundary element method (GBEM) for solving time fractional-order dual phase lag bioheat transfer problems in functionally graded tissues. The LRBFCM was developed and implemented using an implicit time-stepping technique and Caputo time fractional derivative for solving the fractional-order governing equation without dual phase lags. Due to suitability of the GBEM for modeling of bioheat transfer in functionally graded tissues. Therefore, GBEM is applied for solving the dual phase lags governing equation without fractional-order derivative. The numerical results are depicted graphical forms to show the effects of functionally graded parameter, fractional-order parameter and anisotropy on the nonlinear temperature distribution. Also, these numerical results demonstrate the validity and accuracy of the proposed algorithm and technique.     

Application of ontology modularization to human-web interface design for knowledge sharing

Ontology has received considerable attention in the semantic technology community. This paper discusses the roles of ontology modularization in human-web interface design for knowledge sharing. It stresses that ontology can support human-web interaction as a key component of knowledge sharing on the Internet. The paper proposes an approach of ontology modularization to navigating and searching web portals for the purposes of knowledge sharing. A case study of human-web interface design using ontology modularization for a commercial software web portal is used to illustrate the application of the proposed approach.     

Improved macroblock level rate control for the spatially scalable extension of H.264/AVC

An improved rate control algorithm, designed for scalable video coders incorporating interlayer prediction, is proposed. Firstly, a Rate Distortion (RD) model for interlayer prediction involving the spatial enhancement layers is devised. An optimised Mean Absolute Difference (MAD) prediction model for the spatial enhancement layers that considers both the MAD from the spatial base layer in the same frame and the MAD from the corresponding macroblock in previous frames is also proposed. Simulation results show that the resulting algorithm produces accurate rate control with an average bit rate error of less than 0.26%. Compared with the JVT-W043 default rate control algorithm of the JSVM, the proposed algorithm improves the average PSNR by up to 0.53 dB or reduces the bit rate by an average of 10.95%. Furthermore, the proposed algorithm can be combined with the existing rate control scheme for H.264/AVC, resulting in further improvements.