A Natural Language Query Interface for Searching Personal Information on Smartwatches

Currently, personal assistant systems, run on smartphones and use natural language interfaces. However, these systems rely mostly on the web for finding information. Mobile and wearable devices can collect an enormous amount of contextual personal data such as sleep and physical activities. These information objects and their applications are known as quantified-self, mobile health or personal informatics, and they can be used to provide a deeper insight into our behavior. To our knowledge, existing personal assistant systems do not support all types of quantified-self queries. In response to this, we have undertaken a user study to analyze a set of “textual questions/queries” that users have used to search their quantified-self or mobile health data. Through analyzing these questions, we have constructed a light-weight natural language based query interface - including a text parser algorithm and a user interface - to process the users’ queries that have been used for searching quantified-self information. This query interface has been designed to operate on small devices, i.e. smartwatches, as well as augmenting the personal assistant systems by allowing them to process end users’ natural language queries about their quantified-self data

Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data

This work introduces a set of scalable algorithms to identify patterns of human daily behaviors. These patterns are extracted from multivariate temporal data that have been collected from smartphones. We have exploited sensors that are available on these devices, and have identified frequent behavioral patterns with a temporal granularity, which has been inspired by the way individuals segment time into events. These patterns are helpful to both end-users and third parties who provide services based on this information. We have demonstrated our approach on two real-world datasets and showed that our pattern identification algorithms are scalable. This scalability makes analysis on resource constrained and small devices such as smartwatches feasible. Traditional data analysis systems are usually operated in a remote system outside the device. This is largely due to the lack of scalability originating from software and hardware restrictions of mobile/wearable devices. By analyzing the data on the device, the user has the control over the data, i.e. privacy, and the network costs will also be removed

Filtering activity and nutrient release by the keratose sponge sarcotragus spinosulus schmidt, 1862 (Porifera, demospongiae) at the laboratory scale

Sponges are an important constituent of filter-feeder benthic communities, characterized by high ecological plasticity and abundance. Free bacteria constitute an important quota of their diet, making them excellent candidates in aquaculture microbial bioremediation, where bacteria can be a serious problem. Although there are studies on this topic, certain promising species are still under investigation. Here we report applied microbiological research on the filtering activity of Sarcotragus spinosulus on two different concentrations of the pathogenic bacterium Vibrio para-haemolyticus in a laboratory experiment. To evaluate the effects of the filtration on the surrounding nutrient load, the release of ammonium, nitrate, and phosphate was also measured. The results obtained showed the efficient filtration capability of S. spinosulus as able to reduce the Vibrio load with a maximum retention efficiency of 99.72% and 99.35% at higher and lower Vibrio concentrations, respec-tively, and remarkable values of clearance rates (average maximum value 45.0 ± 4.1 mL h−1 g DW−1 ) at the highest Vibrio concentration tested. The nutrient release measured showed low values for each considered nutrient category at less than 1 mg L−1 for ammonium and phosphate and less than 5 mg L−1 for nitrate. The filtering activity and nutrient release by S. spinosulus suggest that this species represents a promising candidate in microbial bioremediation, showing an efficient capability in removing V. parahaemolyticus from seawater with a contribution to the nutrient load

Effect of initial configuration on network-based recommendation

In this paper, based on a weighted object network, we propose a recommendation algorithm, which is sensitive to the configuration of initial resource distribution. Even under the simplest case with binary resource, the current algorithm has remarkably higher accuracy than the widely applied global ranking method and collaborative filtering. Furthermore, we introduce a free parameter $\beta$ to regulate the initial configuration of resource. The numerical results indicate that decreasing the initial resource located on popular objects can further improve the algorithmic accuracy. More significantly, we argue that a better algorithm should simultaneously have higher accuracy and be more personal. According to a newly proposed measure about the degree of personalization, we demonstrate that a degree-dependent initial configuration can outperform the uniform case for both accuracy and personalization strength.Comment: 4 pages and 3 figure

Comparative Genomics Suggests a Taxonomic Revision of the Staphylococcus cohnii Species Complex

Staphylococcus cohnii (SC), a coagulase-negative bacterium, was first isolated in 1975 from human skin. Early phenotypic analyses led to the delineation of two subspecies (subsp.), Staphylococcus cohnii subsp. cohnii (SCC) and Staphylococcus cohnii subsp. urealyticus (SCU). SCC was considered to be specific to humans, whereas SCU apparently demonstrated a wider host range, from lower primates to humans. The type strains ATCC 29974 and ATCC 49330 have been designated for SCC and SCU, respectively. Comparative analysis of 66 complete genome sequences-including a novel SC isolate-revealed unexpected patterns within the SC complex, both in terms of genomic sequence identity and gene content, highlighting the presence of 3 phylogenetically distinct groups. Based on our observations, and on the current guidelines for taxonomic classification for bacterial species, we propose a revision of the SC species complex. We suggest that SCC and SCU should be regarded as two distinct species: SC and SU (Staphylococcus urealyticus), and that two distinct subspecies, SCC and SCB (SC subsp. barensis, represented by the novel strain isolated in Bari) should be recognized within SC. Furthermore, since large-scale comparative genomics studies recurrently suggest inconsistencies or conflicts in taxonomic assignments of bacterial species, we believe that the approach proposed here might be considered for more general application

Antimicrobial resistance gene shuffling and a three-element mobilisation system in the monophasic Salmonella typhimurium strain ST1030

In this study we describe the genetic elements and the antimicrobial resistance units (RUs) harboured by the Salmonella Typhimurium monophasic variant 1,4,[5],12:i:- strain ST1030. Of the three identified RUs two were chromosomal, RU1 (IS26-blaTEM-1-IS26-strAB-sul2- IS26) and RU2 (IS26-tetR(B)-tetA(B)-ΔIS26), and one, RU3 (a sul3-associated class 1 integron with cassette array dfrA12-orfF-aadA2-cmlA1-aadA1), was embedded in a Tn21-derived element harboured by the conjugative I1 plasmid pST1030-1A. IS26 elements mediated the antimicrobial resistance gene (ARG) shuffling and this gave rise to pST1030-1A derivatives with different sets of ARGs. ST1030 also harboured two ColE1-like plasmids of which one, pST1030-2A, was mobilisable and the target of an intracellular translocation of the Tn21-derived element; the second (pST1030-3) was an orphan mob-associated oriT plasmid co-transferred with pST1030-1A and pST1030-2A. pST1030-2A and pST1030-3 also carried a parA gene and a type III restriction modification system, respectively. Overall analysis of our data reinforces the role played by IS26, Tn21-derived elements and non-conjugative plasmids in the spread of ARGs and supplies the first evidence, at least in Salmonella, for the identification of a natural isolate harbouring a three-element mobilisation system in the same cell

CSNL: A cost-sensitive non-linear decision tree algorithm

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification. The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes. The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes

Inducing safer oblique trees without costs

Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification. Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety. This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming

Towards persuasive social recommendation: knowledge model

[EN] The exponential growth of social networks makes fingerprint let by users on the Internet a great source of information, with data about their preferences, needs, goals, profile and social environment. These data are distributed across di↵erent sources of information (social networks, blogs, databases, etc.) that may contain inconsistencies and their accuracy is uncertain. Paradoxically, this unprecedented availability of heterogeneous data has meant that users have more information available than they actually are able to process and understand to extract useful knowledge from it. Therefore, new tools that help users in their decision-making processes within the network (e.g. which friends to contact with or which products to consume) are needed. In this paper, we show how we have used a graph-based model to extract and model data and transform it in valuable knowledge to develop a persuasive social recommendation system1.This work was partially supported by the project MINE-CO/FEDER TIN2012-365686-C03-01 of the Spanish government and by the Spanish Ministry of Education, Culture and Sports under the Program for R&D Valorisation and Joint Resources VLC/CAMPUS, as part of the Campus of International Excellence Program (Ref. SP20140788).Palanca Cámara, J.; Heras Barberá, SM.; Jorge Cano, J.; Julian Inglada, VJ. (2015). Towards persuasive social recommendation: knowledge model. ACM SIGAPP Applied Computing Review. 15(2):41-49. https://doi.org/10.1145/2815169.2815173S4149152Desel, J., Pernici, B., Weske, M. Mining Social Networks: Uncovering Interaction Patterns in Business Processes.Business Process Management, Berlin, vol. 3080, pp. 244--260 (2004)Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. on KDE 17(6) (2005) 734--749X. Zhou, Y. Xu, Y. Li, A. Josang, and C. Cox, "The state-of-the-art in personalized recommender systems for social networking,"Artificial Intelligence Review, vol. 37, no. 2, pp. 119--132, 2012.Ehrig M., "Ontology Alignment: Bridging the Semantic Gap,"Springer, 2007.Euzenat, J. and Shvaiko P., "Ontology matching,"Springer, Heidelberg (DE), 2007.Bleiholder, J., Naumann, F., "Data Fusion,"ACM Computing Surveys, 41(1):1--41, 2008.Halpin, H., Thomson, H., "Special Issue on Identify, Reference and the Web,"Int. Journal on Semantic Web and Information Systems, 4(2):1--72, 2008.I. Robinson, J. Webber, and E. Eifrem,Graph Databases.O'Reilly, 2013.M. Pazzani and D. Billsus,Content-Based Recommendation Systems, ser. LNCS. Springer-Verlag, 2007, vol. 4321, pp. 325--341.J. Schafer, D. Frankowski, J. Herlocker, and S. Sen,Collaborative Filtering Recommender Systems, ser. LNCS. Springer, 2007, v. 4321, pp. 291--324.R. Burke, "Hybrid Recommender Systems: Survey and Experiments,"User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331--370, 2002.C. Chesñevar, A. Maguitman, and M. González,Empowering Recommendation Technologies Through Argumentation.Springer, 2009, pp. 403--422.G. Linden, J. Hong, M. Stonebraker, and M. Guzdial:, "Recommendation Algorithms, Online Privacy and More,"Comm. of the ACM, vol. 52, no. 5, 2009.Khare, Rohit and Çelik, Tantek, "Microformats: a pragmatic path to the semantic web" in15th international conference on World Wide Web, ACM, 2006, pp. 865--866.R. Fogués, J. M. Such, A. Espinosa, and A. Garcia-Fornes. BFF: A tool for eliciting tie strength and user communities in social networking services.Information Systems Frontiers, 16(2), 225--237, 2014.S. Heras, V. Botti, and V. Julián. Argument-based agreements in agent societies.Neurocomputing, doi:10.1016/j.neucom.2011.02.022, 2011.S. Berkovsky, T. Kuflik, and F. Ricci. Mediation of user models for enhanced personalization in recommender systems. InUser Modeling and User-Adapted Interaction, 18(3), 245--286, 2008.I. Cantador, I. Konstas, and J. M. Jose. Categorising social tags to improve folksonomy-based recommendations.Web Semantics: Science, Services and Agents on the World Wide Web, 9(1), 1--15, 2011.I. Guy, N. Zwerdling, I. Ronen, D. Carmel, and E. Uziel. Social media recommendation based on people and tags. InProceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pp. 194--201, ACM, 2010.A. Tiroshi, S. Berkovsky, M. A. Kaafar, D. Vallet, and T. Kuflik. Graph-Based Recommendations: Make the Most Out of Social Data. InUser Modeling, Adaptation, and Personalization, pp. 447--458, Springer International Publishing, 2014.J. J. Pazos, A. Fernández, R. P. Díaz. Recommender Systems for the Social Web, Springer Berlin Heidelberg, 2012.M. Ueda, M. Takahata, and S. Nakajima. UserâĂŹs food preference extraction for personalized cooking recipe recommendation.Semantic Personalized Information Management: Retrieval and Recommendation, SPIM, pp. 98--105 2011.I. Mazzotta, F. De Rosis, and V. Carofiglio. Portia: A user-adapted persuasion system in the healthy-eating domain.Intelligent Systems, IEEE, 22(6), 42--51, 2007.A. Said, and A. Bellogín. You are what you eat! tracking health through recipe interactions. InProceedings of the 6th Workshop on Recommender Systems and the Social Web, RSWeb, 2014.J. Freyne, and S. Berkovsky. Intelligent food planning: personalized recipe recommendation. InProceedings of the 15th international conference on Intelligent user interfaces.pp. 321--324, ACM, 2010

Diffusion and persistence of multidrug resistant salmonella typhimurium strains phage type DT120 in Southern Italy

Sixty-two multidrug resistant Salmonella enterica serovar Typhimurium strains isolated from 255 clinical strains collected in Southern Italy in 2006–2008 were characterised for antimicrobial resistance genes, pulsotype, and phage type.Most strains (83.9%) were resistant to ampicillin, chloramphenicol, streptomycin, sulfamethoxazole, and tetracycline (resistance pattern ACSSuT) encoded in 88.5% by the PSE-1, floR, aadA2, sul1, and tet(G) gene cluster harboured by the Salmonella Genomic Island (SGI1). In 11.5% of strains, the resistance was encoded by the InH-like integron (OXA-30-aadA1) and the catA1, sul1, and tet(B) genes. STYMXB.0061 (75%) and DT120 (84.6%) were the prevalent pulsotype and phage type identified in these strains, respectively. Five other resistance patterns were also found either in single or in a low number of isolates with TEM, dfrA12, strAB, sul2, tet(A), and tet(B) encoding for the associated ampicillin, trimethoprim, streptomycin, sulfamethoxazole, and tetracycline resistances, respectively. The pandemic DT104 clone, resistance pattern ACSSuT encoded by SGI1, has largely been identified in Italy since 1992, while strains DT120, resistance pattern ACSSuT (encoded by SGI1), have never been previously reported in Italy. In Europe, clinical S. Typhimurium strains DT120 have mainly been reported from sporadic outbreaks linked to the consumption of pork products.However, none of these strains were STYMXB.0061 and the antimicrobial resistance was not linked to SGI1.Theprevalent identification and persistence ofDT120 isolates would suggest, in Southern Italy, a phage type shifting of the pandemic DT104 clone pulsotype STYMXB.0061.Additionally, these findings raise epidemiological concern about the potential diffusion of these emerging multidrug resistant (SGI linked) DT120 strains