@article{kampe_reid_jones_colleen_sean_vogel_2019, title={Bringing the National Security Agency into the Classroom: Ethical Reflections on Academia-Intelligence Agency Partnerships}, volume={25}, ISSN={["1471-5546"]}, DOI={10.1007/s11948-017-9938-7}, abstractNote={Academia-intelligence agency collaborations are on the rise for a variety of reasons. These can take many forms, one of which is in the classroom, using students to stand in for intelligence analysts. Classrooms, however, are ethically complex spaces, with students considered vulnerable populations, and become even more complex when layering multiple goals, activities, tools, and stakeholders over those traditionally present. This does not necessarily mean one must shy away from academia-intelligence agency partnerships in classrooms, but that these must be conducted carefully and reflexively. This paper hopes to contribute to this conversation by describing one purposeful classroom encounter that occurred between a professor, students, and intelligence practitioners in the fall of 2015 at North Carolina State University: an experiment conducted as part of a graduate-level political science class that involved students working with a prototype analytic technology, a type of participatory sensing/self-tracking device, developed by the National Security Agency. This experiment opened up the following questions that this paper will explore: What social, ethical, and pedagogical considerations arise with the deployment of a prototype intelligence technology in the college classroom, and how can they be addressed? How can academia-intelligence agency collaboration in the classroom be conducted in ways that provide benefits to all parties, while minimizing disruptions and negative consequences? This paper will discuss the experimental findings in the context of ethical perspectives involved in values in design and participatory/self-tracking data practices, and discuss lessons learned for the ethics of future academia-intelligence agency partnerships in the classroom.}, number={3}, journal={SCIENCE AND ENGINEERING ETHICS}, author={Kampe, Christopher and Reid, Gwendolynne and Jones, Paul and Colleen, S. and Sean, S. and Vogel, Kathleen M.}, year={2019}, month={Jun}, pages={869–898} }
 @article{yang_he_xu_ni_jones_samatova_2018, title={An Intelligent and Hybrid Weighted Fuzzy Time Series Model Based on Empirical Mode Decomposition for Financial Markets Forecasting}, volume={10933}, ISBN={["978-3-319-95785-2"]}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85049883593&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-95786-9_8}, abstractNote={Given the potentially high impact of accurate financial market forecasting, there has been considerable research on time series analysis for financial markets. We present a new Intelligent Hybrid Weighted Fuzzy (IHWF) time series model to improve forecasting accuracy in financial markets, which are complex nonlinear time-sensitive systems, influenced by many factors. The IHWF model uniquely combines Empirical Mode Decomposition (EMD) with a novel weighted fuzzy time series method. The model is enhanced by an Adaptive Sine-Cosine Human Learning Optimization (ASCHLO) algorithm to help find optimal parameters that further improve forecasting performance. EMD is a time series processing technique to extract the possible modes of various kinds of institutional and individual investors and traders, embedded in a given time series. Subsequently, the proposed weighted fuzzy time series method with chronological order based frequency and Neighborhood Volatility Direction (NVD) is analyzed and integrated with ASCHLO to determine the effective universe discourse, intervals and weights. In order to evaluate the performance of proposed model, we evaluate actual trading data of Taiwan Capitalization Weighted Stock Index (TAIEX) from 1990 to 2004 and the findings are compared with other well-known forecasting models. The results show that the proposed method outperforms the listing models in terms of accuracy.}, journal={ADVANCES IN DATA MINING: APPLICATIONS AND THEORETICAL ASPECTS (ICDM 2018)}, author={Yang, Ruixin and He, Junyi and Xu, Mingyang and Ni, Haoqi and Jones, Paul and Samatova, Nagiza}, year={2018}, pages={104–118} }
 @article{xu_yang_jones_samatova_2018, title={Mining Aspect-Specific Opinions from Online Reviews Using a Latent Embedding Structured Topic Model}, volume={10762}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85055688176&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-77116-8_15}, abstractNote={Online reviews often contain user’s specific opinions on aspects (features) of items. These opinions are very useful to merchants and customers, but manually extracting them is time-consuming. Several topic models have been proposed to simultaneously extract item aspects and user’s opinions on the aspects, as well as to detect sentiment associated with the opinions. However, existing models tend to find poor aspect-opinion associations when limited examples of the required word co-occurrences are available in corpus. These models often also assign incorrect sentiment to words. In this paper, we propose a Latent embedding structured Opinion mining Topic model, called the LOT, which can simultaneously discover relevant aspect-level specific opinions from small or large numbers of reviews and to assign accurate sentiment to words. Experimental results for topic coherence, document sentiment classification, and a human evaluation all show that our proposed model achieves significant improvements over several state-of-the-art baselines.}, journal={COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, CICLING 2017, PT II}, author={Xu, Mingyang and Yang, Ruixin and Jones, Paul and Samatova, Nagiza F.}, year={2018}, pages={195–210} }
 @inproceedings{yang_xu_jones_samatova_2018, title={Real time utility-based recommendation for revenue optimization via an adaptive online Top-K high utility itemsets mining model}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85050186014&partnerID=MN8TOARS}, DOI={10.1109/fskd.2017.8393050}, abstractNote={Recommender Systems (RS) in e-commerce are typically used to suggest products to online shopping customers, and now play a key role in product marketing strategies for major online retailers, such as Walmart and Amazon. The main goal of such systems is to predict likely future customer desires and to trigger purchases through the timely provision of product recommendations. Therefore, RS have become indispensable tools for both customers and retailers. However, most existing RS recommend products from the point view of customers (i.e. likelihood of customer purchase) but ignore one of the most important business goals: the optimization of revenue. Consequently, there is an increasing need to learn utility patterns online and provide near real-time utility-based recommendations. To address these challenges, we first define the utility of recommendation sets and formulate the problem of real time utility-based recommendation. Next, we consider that online transaction streams are usually accompanied with flow fluctuation, and propose an Adaptive Online Top-K (RAOTK) high utility itemsets mining model to guide the utility-based recommendations. Additionally, three variants of this algorithm are described and we provide a structural comparison of the four algorithms with discussions on their advantages and limitations. Moreover, to make our model more personalized, we also take the buying power of customers into account and propose a simple but effective method to estimate the consumers' willingness to pay. Finally, extensive empirical results on real-world datasets show that the proposed model works effectively and outperforms several baselines.}, booktitle={ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery}, author={Yang, R. and Xu, M. and Jones, P. and Samatova, N.}, year={2018}, pages={1859–1866} }