Preparation and characterization of poly(vinyl alcohol)/gum tragacanth/cellulose nanocomposite film

The effects of gum tragacanth obtained from two species of Astragalus Gossypinus (GT-G) and A. Parrowianus (GT-P) at two levels of 10% and 30% combined with cellulose nanofibers (CNF; 5%) on the physico-mechanical and structural properties of polyvinyl alcohol (PVA) nanocomposite film were investigated in this study. The water solubility and water vapor permeability of the films decreased with increasing the content of both gums, especially in the film containing 30% GT-P. The highest values of the tensile strength (39.3 MPa) and elongation at break (445%) belonged to the treatment containing 10% GT-P (90/10P/0). The FTIR and DSC analyses confirmed good interactions between GT and PVA in the 90/10P/0 treatment. SEM images indicated the dense structure of this film as the optimum treatment. Although the presence of CNF in the films containing GT-G improved some properties, especially the Young modulus, it impaired all the functional properties of nanocomposite GT-P film.     

Receptance coupling for end mills

Identification of chatter free cutting conditions, the chatter stability lobes, requires a measurement of the frequency response function (FRF) of each tool mounted on the spindle. This paper presents a method of assembling known dynamics of the spindle–tool holder with an analytically modeled end mill using the receptance coupling technique. The classical receptance technique is enhanced by proposing a method of identifying the end mill–spindle/tool holder joint dynamics, which include both translational and rotational degrees of freedom. The method requires measurement of FRFs with impact tests applied on the spindle–tool holder assembly and blank calibration cylinders attached to the spindle. The spindle and tool holder characteristics are completely identified from the two experiments, and used for the mathematical prediction of FRF for end mills with arbitrary dimensions. The proposed method is experimentally proven and verified in cutting tests.     

Exploiting SSDs in operational multiversion databases

Multiversion databases store both current and historical data. Rows are typically annotated with timestamps representing the period when the row is/was valid. We develop novel techniques to reduce index maintenance in multiversion databases, so that indexes can be used effectively for analytical queries over current data without being a heavy burden on transaction throughput. To achieve this end, we re-design persistent index data structures in the storage hierarchy to employ an extra level of indirection. The indirection level is stored on solid-state disks that can support very fast random I/Os, so that traversing the extra level of indirection incurs a relatively small overhead. The extra level of indirection dramatically reduces the number of magnetic disk I/Os that are needed for index updates and localizes maintenance to indexes on updated attributes. Additionally, we batch insertions within the indirection layer in order to reduce physical disk I/Os for indexing new records. In this work, we further exploit SSDs by introducing novel DeltaBlock techniques for storing the recent changes to data on SSDs. Using our DeltaBlock, we propose an efficient method to periodically flush the recently changed data from SSDs to HDDs such that, on the one hand, we keep track of every change (or delta) for every record, and, on the other hand, we avoid redundantly storing the unchanged portion of updated records. By reducing the index maintenance overhead on transactions, we enable operational data stores to create more indexes to support queries. We have developed a prototype of our indirection proposal by extending the widely used generalized search tree open-source project, which is also employed in PostgreSQL. Our working implementation demonstrates that we can significantly reduce index maintenance and/or query processing cost by a factor of 3. For the insertion of new records, our novel batching technique can save up to 90 % of the insertion time. For updates, our prototype demonstrates that we can significantly reduce the database size by up to 80 % even with a modest space allocated for DeltaBlocks on SSDs.     

A GMM/HMM model for reconstruction of missing speech spectral components for continuous speech recognition

This paper presents a method for reconstructing unreliable spectral components of speech signals using the statistical distributions of the clean components. Our goal is to model the temporal patterns in speech signal and take advantage of correlations between speech features in both time and frequency domain simultaneously. In this approach, a hidden Markov model (HMM) is first trained on clean speech data to model the temporal patterns which appear in the sequences of the spectral components. Using this model and according to the probabilities of occurring noisy spectral component at each states, a probability distributions for noisy components are estimated. Then, by applying maximum a posteriori (MAP) estimation on the mentioned distributions, the final estimations of the unreliable spectral components are obtained. The proposed method is compared to a common missing feature method which is based on the probabilistic clustering of the feature vectors and also to a state of the art method based on sparse reconstruction. The experimental results exhibits significant improvement in recognition accuracy over a noise polluted Persian corpus.     

What can possibly go wrong? Anticipatory work in space operations

This paper explores how different forms of anticipatory work contribute to reliability in high-risk space operations. It is based on ethnographic field work, participant observation and interviews supplemented with video recordings from a control room responsible for operating a microgravity greenhouse at the International Space Station (ISS). Drawing on examples from different stages of a biological experiment on the ISS, we demonstrate how engineers, researchers and technicians work to anticipate and proactively mitigate possible problems. Space research is expensive and risky. The experiments are planned over the course of many years by a globally distributed network of organizations. Owing to the inaccessibility of the ISS, every trivial detail that could possibly cause a problem is subject to scrutiny. We discuss what we label anticipatory work: practices constituted of an entanglement of cognitive, social and technical elements involved in anticipating and proactively mitigating everything that might go wrong. We show how the nature of anticipatory work changes between planning and the operational phases of an experiment. In the planning phase, operators inscribe their anticipation into technology and procedures. In the operational phase, we show how troubleshooting involves the ability to look ahead in the evolving temporal trajectory of the ISS operations and to juggle pre-planned fixes along these trajectories. A key objective of this paper is to illustrate how anticipation is shared between humans and different forms of technology. Moreover, it illustrates the importance of including considerations of temporality in safety and reliability research.     

Exploring and exploiting a historical corpus for Arabic

This paper presents a historical Arabic corpus named HAC. At this early embryonic stage of the project, we report about the design, the architecture and some of the experiments which we have conducted on HAC. The corpus, and accordingly the search results, will be represented using a primary XML exchange format. This will serve as an intermediate exchange tool within the project and will allow the user to process the results offline using some external tools. HAC is made up of Classical Arabic texts that cover 1600 years of language use; the Quranic text, Modern Standard Arabic texts, as well as a variety of monolingual Arabic dictionaries. The development of this historical corpus assists linguists and Arabic language learners to effectively explore, understand, and discover interesting knowledge hidden in millions of instances of language use. We used techniques from the field of natural language processing to process the data and a graph-based representation for the corpus. We provided researchers with an export facility to render further linguistic analysis possible.     

Cross level semantic similarity: an evaluation framework for universal measures of similarity

Semantic similarity has typically been measured across items of approximately similar sizes. As a result, similarity measures have largely ignored the fact that different types of linguistic item can potentially have similar or even identical meanings, and therefore are designed to compare only one type of linguistic item. Furthermore, nearly all current similarity benchmarks within NLP contain pairs of approximately the same size, such as word or sentence pairs, preventing the evaluation of methods that are capable of comparing different sized items. To address this, we introduce a new semantic evaluation called cross-level semantic similarity (CLSS), which measures the degree to which the meaning of a larger linguistic item, such as a paragraph, is captured by a smaller item, such as a sentence. Our pilot CLSS task was presented as part of SemEval-2014, which attracted 19 teams who submitted 38 systems. CLSS data contains a rich mixture of pairs, spanning from paragraphs to word senses to fully evaluate similarity measures that are capable of comparing items of any type. Furthermore, data sources were drawn from diverse corpora beyond just newswire, including domain-specific texts and social media. We describe the annotation process and its challenges, including a comparison with crowdsourcing, and identify the factors that make the dataset a rigorous assessment of a method’s quality. Furthermore, we examine in detail the systems participating in the SemEval task to identify the common factors associated with high performance and which aspects proved difficult to all systems. Our findings demonstrate that CLSS poses a significant challenge for similarity methods and provides clear directions for future work on universal similarity methods that can compare any pair of items.