Augmenting human memory using personal lifelogs

Memory is a key human facility to support life activities, including social interactions, life management and problem solving. Unfortunately, our memory is not perfect. Normal individuals will have occasional memory problems which can be frustrating, while those with memory impairments can often experience a greatly reduced quality of life. Augmenting memory has the potential to make normal individuals more effective, and those with significant memory problems to have a higher general quality of life. Current technologies are now making it possible to automatically capture and store daily life experiences over an extended period, potentially even over a lifetime. This type of data collection, often referred to as a personal life log (PLL), can include data such as continuously captured pictures or videos from a first person perspective, scanned copies of archival material such as books, electronic documents read or created, and emails and SMS messages sent and received, along with context data of time of capture and access and location via GPS sensors. PLLs offer the potential for memory augmentation. Existing work on PLLs has focused on the technologies of data capture and retrieval, but little work has been done to explore how these captured data and retrieval techniques can be applied to actual use by normal people in supporting their memory. In this paper, we explore the needs for augmenting human memory from normal people based on the psychology literature on mechanisms about memory problems, and discuss the possible functions that PLLs can provide to support these memory augmentation needs. Based on this, we also suggest guidelines for data for capture, retrieval needs and computer-based interface design. Finally we introduce our work-in-process prototype PLL search system in the iCLIPS project to give an example of augmenting human memory with PLLs and computer based interfaces

Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?

[EN] Identifying deceptive online reviews is a challenging tasks for Natural Language Processing (NLP). Collecting corpora for the task is difficult, because normally it is not possible to know whether reviews are genuine. A common workaround involves collecting (supposedly) truthful reviews online and adding them to a set of deceptive reviews obtained through crowdsourcing services. Models trained this way are generally successful at discriminating between `genuine¿ online reviews and the crowdsourced deceptive reviews. It has been argued that the deceptive reviews obtained via crowdsourcing are very different from real fake reviews, but the claim has never been properly tested. In this paper, we compare (false) crowdsourced reviews with a set of `real¿ fake reviews published on line. We evaluate their degree of similarity and their usefulness in training models for the detection of untrustworthy reviews. We find that the deceptive reviews collected via crowdsourcing are significantly different from the fake reviews published online. In the case of the artificially produced deceptive texts, it turns out that their domain similarity with the targets affects the models¿ performance, much more than their untruthfulness. This suggests that the use of crowdsourced datasets for opinion spam detection may not result in models applicable to the real task of detecting deceptive reviews. As an alternative method to create large-size datasets for the fake reviews detection task, we propose methods based on the probabilistic annotation of unlabeled texts, relying on the use of meta-information generally available on the e-commerce sites. Such methods are independent from the content of the reviews and allow to train reliable models for the detection of fake reviews.Leticia Cagnina thanks CONICET for the continued financial support. This work was funded by MINECO/FEDER (Grant No. SomEMBED TIN2015-71147-C2-1-P). The work of Paolo Rosso was partially funded by the MISMIS-FAKEnHATE Spanish MICINN research project (PGC2018-096212-B-C31). Massimo Poesio was in part supported by the UK Economic and Social Research Council (Grant Number ES/M010236/1).Fornaciari, T.; Cagnina, L.; Rosso, P.; Poesio, M. (2020). Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?. Language Resources and Evaluation. 54(4):1019-1058. https://doi.org/10.1007/s10579-020-09486-5S10191058544Baeza-Yates, R. (2018). Bias on the web. Communications of the ACM, 61(6), 54–61.Banerjee, S., & Chua, A. Y. (2014). Applauses in hotel reviews: Genuine or deceptive? In: Science and Information Conference (SAI), 2014 (pp. 938–942). New York: IEEE.Bhargava, R., Baoni, A., & Sharma, Y. (2018). Composite sequential modeling for identifying fake reviews. Journal of Intelligent Systems,. https://doi.org/10.1515/jisys-2017-0501.Bickel, P. J., & Doksum, K. A. (2015). Mathematical statistics: Basic ideas and selected topics (2nd ed., Vol. 1). Boca Raton: Chapman and Hall/CRC Press.Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on computational learning theory (pp. 92–100). New York: ACM.Cagnina, L. C., & Rosso, P. (2017). Detecting deceptive opinions: Intra and cross-domain classification using an efficient representation. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 25(Suppl. 2), 151–174. https://doi.org/10.1142/S0218488517400165.Cardoso, E. F., Silva, R. M., & Almeida, T. A. (2018). Towards automatic filtering of fake reviews. Neurocomputing, 309, 106–116. https://doi.org/10.1016/j.neucom.2018.04.074.Carpenter, B. (2008). Multilevel bayesian models of categorical data annotation. Retrieved from http://lingpipe.files.wordpress.com/2008/11/carp-bayesian-multilevel-annotation.pdf.Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.Costa, P. T., & MacCrae, R. R. (1992). Revised NEO personality inventory (NEO PI-R) and NEO five-factor inventory (NEO FFI): Professional manual. Psychological Assessment Resources.Dawid, A. P., & Skene, A. M. (1979). Maximum likelihood estimation of observer error-rates using the EM algorithm. Applied Statistics, 28(1), 20–28.Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B (Methodological), 39(1), 1–38.Elkan, C., & Noto, K. (2008). Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 213–220). New York: ACM.Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., & Ghosh, R. (2013). Exploiting burstiness in reviews for review spammer detection. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (Vol. 13, pp. 175–184).Feng, S., Banerjee, R., & Choi, Y. (2012). Syntactic stylometry for deception detection. In: Proceedings of the 50th annual meeting of the association for computational linguistics (Vol. 2: Short Papers, pp. 171–175). Jeju Island: Association for Computational Linguistics.Forman, G. (2003). An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research, 3, 1289–1305.Fornaciari, T., & Poesio, M. (2013). Automatic deception detection in Italian court cases. Artificial intelligence and law, 21(3), 303–340. https://doi.org/10.1007/s10506-013-9140-4.Fornaciari, T., & Poesio, M. (2014). Identifying fake amazon reviews as learning from crowds. In: Proceedings of the 14th conference of the European chapter of the Association for Computational Linguistics (pp. 279–287). Gothenburg: Association for Computational Linguistics. Retrieved from http://www.aclweb.org/anthology/E14-1030.Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models., Analytical methods for social research Cambridge: Cambridge University Press.Graves, A., Jaitly, N., & Mohamed, A. R. (2013). Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE workshop on automatic speech recognition and understanding (ASRU) (pp. 273–278). New York: IEEE.Hernández-Castañeda, Á., & Calvo, H. (2017). Deceptive text detection using continuous semantic space models. Intelligent Data Analysis, 21(3), 679–695.Hernández Fusilier, D., Guzmán, R., Móntes y Gomez, M., & Rosso, P. (2013). Using pu-learning to detect deceptive opinion spam. In: Proc. of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 38–45).Hernández Fusilier, D., Montes-y Gómez, M., Rosso, P., & Cabrera, R. G. (2015). Detecting positive and negative deceptive opinions using pu-learning. Information Processing & Management, 51(4), 433–443.Hovy, D. (2016). The enemy in your own camp: How well can we detect statistically-generated fake reviews–an adversarial study. In: The 54th annual meeting of the association for computational linguistics (p 351).Jelinek, F., Lafferty, J. D., & Mercer, R. L. (1992). Basic methods of probabilistic context free grammars. Speech recognition and understanding (pp. 345–360). New York: Springer.Jindal, N., & Liu, B. (2008). Opinion spam and analysis. In: Proceedings of the 2008 international conference on web search and data mining (pp. 219–230). New York: ACM.Karatzoglou, A., Meyer, D., & Hornik, K. (2006). Support vector machines in R. Journal of Statistical Software, 15(9), 1–28.Kim, S., Lee, S., Park, D., & Kang, J. (2017). Constructing and evaluating a novel crowdsourcing-based paraphrased opinion spam dataset. In: Proceedings of the 26th international conference on world wide web (pp. 827–836). Geneva: International World Wide Web Conferences Steering Committee.Li, F., Huang, M., Yang, Y., & Zhu, X. (2011). Learning to identify review spam. IJCAI Proceedings-International Joint Conference on Artificial Intelligence, 22(3), 2488–2493.Li, H., Chen, Z., Liu, B., Wei, X., & Shao, J. (2014a). Spotting fake reviews via collective positive-unlabeled learning. In: 2014 IEEE international conference on data mining (ICDM) (pp. 899–904). New York: IEEE.Li, H., Fei, G., Wang, S., Liu, B., Shao, W., Mukherjee, A., & Shao, J. (2017). Bimodal distribution and co-bursting in review spam detection. In: Proceedings of the 26th international conference on world wide web (pp. 1063–1072). Geneva: International World Wide Web Conferences Steering Committee.Li, H., Liu, B., Mukherjee, A., & Shao, J. (2014b). Spotting fake reviews using positive-unlabeled learning. Computación y Sistemas, 18(3), 467–475.Li, J., Ott, M., Cardie, C., & Hovy, E. H. (2014c). Towards a general rule for identifying deceptive opinion spam. In: ACL (Vol. 1, pp. 1566–1576).Lin, C. H., Hsu, P. Y., Cheng, M. S., Lei, H. T., & Hsu, M. C. (2017). Identifying deceptive review comments with rumor and lie theories. In: International conference in swarm intelligence (pp. 412–420). New York: Springer.Liu, B., Dai, Y., Li, X., Lee, W. S., & Yu, P. S. (2003). Building text classifiers using positive and unlabeled examples. In: Third IEEE international conference on data mining (pp. 179–186). New York: IEEE.Liu, B., Lee, W. S., Yu, P. S., & Li, X. (2002). Partially supervised classification of text documents. ICML, 2, 387–394.Martens, D., & Maalej, W. (2019). Towards understanding and detecting fake reviews in app stores. Empirical Software Engineering,. https://doi.org/10.1007/s10664-019-09706-9.Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:13013781.Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R. (2013a). Spotting opinion spammers using behavioral footprints. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 632–640) New York: ACM.Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N. S. (2013b). What yelp fake review filter might be doing? In: Proceedings of the seventh international AAAI conference on weblogs and social media.Negri, M., Bentivogli, L., Mehdad, Y., Giampiccolo, D., & Marchetti, A. (2011). Divide and conquer: Crowdsourcing the creation of cross-lingual textual entailment corpora. In: Proceedings of the conference on empirical methods in natural language processing (pp. 670–679). Stroudsburg: Association for Computational Linguistics.Ott, M., Cardie, C., & Hancock, J. T. (2013). Negative deceptive opinion spam. In: Proceedings of the 2013 conference of the North American chapter of the association for computational linguistics: human language technologies (pp. 497–501).Ott, M., Choi, Y., Cardie, C., & Hancock, J. (2011). Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual meeting of the association for computational linguistics: human language technologies (pp. 309–319). Portland, Oregon: Association for Computational Linguistics.Pennebaker, J. W., Francis, M. E., & Booth, R. J. (2001). Linguistic inquiry and word count (LIWC): LIWC2001. Mahwah: Lawrence Erlbaum Associates.Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532–1543).Raykar, V. C., Yu, S., Zhao, L. H., Valadez, G. H., Florin, C., Bogoni, L., et al. (2010). Learning from crowds. Journal of Machine Learning Research, 11, 1297–1322.Ren, Y., & Ji, D. (2017). Neural networks for deceptive opinion spam detection: An empirical study. Information Sciences, 385, 213–224.Rout, J. K., Dalmia, A., Choo, K. K. R., Bakshi, S., & Jena, S. K. (2017). Revisiting semi-supervised learning for online deceptive review detection. IEEE Access, 5(1), 1319–1327.Saini, M., & Sharan, A. (2017). Ensemble learning to find deceptive reviews using personality traits and reviews specific features. Journal of Digital Information Management, 12(2), 84–94.Salloum, W., Edwards, E., Ghaffarzadegan, S., Suendermann-Oeft, D., & Miller, M. (2017). Crowdsourced continuous improvement of medical speech recognition. In: The AAAI-17 workshop on crowdsourcing, deep learning, and artificial intelligence agents.Schmid, H. (1994). Probabilistic part-of-speech tagging using decision trees. In: Proceedings of international conference on new methods in language processing. Retrieved from http://www.ims.uni-stuttgart.de/ftp/pub/corpora/tree-tagger1.pdf.Shehnepoor, S., Salehi, M., Farahbakhsh, R., & Crespi, N. (2017). Netspam: A network-based spam detection framework for reviews in online social media. IEEE Transactions on Information Forensics and Security, 12(7), 1585–1595.Skeppstedt, M., Peldszus, A., & Stede, M. (2018). More or less controlled elicitation of argumentative text: Enlarging a microtext corpus via crowdsourcing. In: Proceedings of the 5th workshop on argument mining (pp. 155–163).Strapparava, C., & Mihalcea, R. (2009). The lie detector: Explorations in the automatic recognition of deceptive language. In: Proceedings of the 47th annual meeting of the association for computational linguistics and the 4th international joint conference on natural language processing.Streitfeld, D. (August $$25{{\rm th}}$$, 2012). The best book reviews money can buy. The New York Times.Whitehill, J., Wu, T., Bergsma, F., Movellan, J. R., & Ruvolo, P. L. (2009). Whose vote should count more: Optimal integration of labels from labelers of unknown expertise. Advances in neural information processing systems (pp. 2035–2043). Cambridge: MIT Press.Xie, S., Wang, G., Lin, S., & Yu, P. S. (2012). Review spam detection via temporal pattern discovery. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp 823–831). New York: ACM.Yang, Y., & Liu, X. (1999). A re-examination of text categorization methods. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’99 (pp. 42–49). New York: ACM.Zhang, W., Bu, C., Yoshida, T., & Zhang, S. (2016). Cospa: A co-training approach for spam review identification with support vector machine. Information, 7(1), 12.Zhang, W., Du, Y., Yoshida, T., & Wang, Q. (2018). DRI-RCNN: An approach to deceptive review identification using recurrent convolutional neural network. Information Processing & Management, 54(4), 576–592.Zhou, L., Shi, Y., & Zhang, D. (2008). A Statistical Language Modeling Approach to Online Deception Detection. IEEE Transactions on Knowledge and Data Engineering, 20(8), 1077–1081

Communication and re-use of chemical information in bioscience.

The current methods of publishing chemical information in bioscience articles are analysed. Using 3 papers as use-cases, it is shown that conventional methods using human procedures, including cut-and-paste are time-consuming and introduce errors. The meaning of chemical terms and the identity of compounds is often ambiguous. valuable experimental data such as spectra and computational results are almost always omitted. We describe an Open XML architecture at proof-of-concept which addresses these concerns. Compounds are identified through explicit connection tables or links to persistent Open resources such as PubChem. It is argued that if publishers adopt these tools and protocols, then the quality and quantity of chemical information available to bioscientists will increase and the authors, publishers and readers will find the process cost-effective.An article submitted to BiomedCentral Bioinformatics, created on request with their Publicon system. The transformed manuscript is archived as PDF. Although it has been through the publishers system this is purely automatic and the contents are those of a pre-refereed preprint. The formatting is provided by the system and tables and figures appear at the end. An accommpanying submission, http://www.dspace.cam.ac.uk/handle/1810/34580, describes the rationale and cultural aspects of publishing , abstracting and aggregating chemical information. BMC is an Open Access publisher and we emphasize that all content is re-usable under Creative Commons Licens

State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work

Towards a task aware operating system user interface

Tese de mestrado. Engenharia Informática. 2006. Faculdade de Engenharia. Universidade do Port