Classical machine learning models typically try to optimize the model based on the most discriminatory features of the data; however, they do not usually account for end user preferences. In certain applications, this can be a serious issue as models not aware of user preferences could become costly, untrustworthy, or privacy‐intrusive to use, thus becoming irrelevant and/or uninterpretable. Ideally, end users with domain knowledge could propose preferable features that the predictive model could then take into account. In this paper, we propose a generic modeling method that respects end user preferences via a relative ranking system to express multi‐criteria preferences and a regularization term in the model's objective function to incorporate the ranked preferences. In a more generic perspective, this method is able to plug user preferences into existing predictive models without creating completely new ones. We implement this method in the context of decision trees and are able to achieve a comparable classification accuracy while reducing the use of undesirable features.

Two-dimensional shallow water equations with uneven bottom and a Coriolis parameter f = f(0) + beta y, (beta not equal 0) in mass Lagrangian coordinates are studied in this paper. The equa-tions describing these flows are reduced to two Euler-Lagrange equations. The paper pro-vides a complete group classification of the equations and applications of Noether's theo-rem for constructing conservation laws. (C) 2020 Elsevier B.V. All rights reserved.

This paper deals with the one-dimensional Green–Naghdi equations describing the behavior of fluid flow over an uneven bottom topography depending on time. Using Matsuno’s approach, the corresponding equations are derived in Eulerian coordinates. Further study is performed in Lagrangian coordinates. This study allowed us to find the general form of the Lagrangian corresponding to the analyzed equations. Then, Noether’s theorem is used to derive conservation laws. As some of the tools in the application of Noether’s theorem are admitted generators, a complete group classification of the Green–Naghdi equations with respect to the bottom depending on time is performed. Using Noether’s theorem, the found Lagrangians, and the group classification, conservation laws of the one-dimensional Green–Naghdi equations with uneven bottom topography depending on time are obtained.

The two-dimensional shallow water equations with a particular bottom and the Coriolis's force $f=f_{0}+\Omega y$ are studied in this paper. The main goal of the paper is to describe all invariant solutions for which the reduced system is a system of ordinary differential equations. For solving the systems of ordinary differential equations we use the sixth-order Runge-Kutta method.

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.

We have developed the first, holistic, Adaptive Mesh Refinement (AMR) capable, in situ query framework to ensure the tractability of the current- and next-generation AMR simulation analysis on the path to extreme scale. We have addressed key issues pertaining to in situ analysis, memory hierarchy, generalized parallel indexing, index-compression, and real-time access pattern detection. We have published the following research papers in high profile peer reviewed computer science conferences: (a) Parallel In Situ Detection of Connected Components in Adaptive Mesh Refinement Data; (b) AMR-aware In Situ Indexing and Scalable Querying; (c) Pattern-driven dynamic data layout optimization; (d) In situ Storage Layout Optimization for AMR Spatio-temporal Read Accesses; (e) AMRZone: A Runtime AMR Data Sharing Framework for Scientific Applications; (f) Exploring Memory Hierarchy and Network Topology for Runtime AMR Data Sharing Across Scientific Applications.

We consider the problem of learning causal relationships in continuous additive noise models (ANM) from a machine learning perspective. Causal discovery from ANMs has primarily focused on testing for independence between the residuals and the true parent set of a variable. We posit that this unique association between residuals and the true parent set can be leveraged with kernel mean embedding to predict causal relationships in observational data. In particular, we propose a framework that finds useful patterns and constructs the causal graph by predicting the true parent set of each variable. We present an analysis of the patterns from kernel mean embeddings that explains their discriminative ability in predicting causal relationships. Finally, we perform simulations that demonstrate the effectiveness of our method.

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.

Sex differences in Alzheimer’s disease (AD) biology and progression are not yet fully characterized. The goal of this study is to examine the effect of sex on cognitive progression in subjects with high likelihood of mild cognitive impairment (MCI) due to Alzheimer’s and followed up to 10 years in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Cerebrospinal fluid total-tau and amyloid-beta (Aβ42) ratio values were used to sub-classify 559 MCI subjects (216 females, 343 males) as having “high” or “low” likelihood for MCI due to Alzheimer’s. Data were analyzed using mixed-effects models incorporating all follow-ups. The worsening from baseline in Alzheimer’s Disease Assessment Scale-Cognitive score (mean, SD) (9 ± 12) in subjects with high likelihood of MCI due to Alzheimer’s was markedly greater than that in subjects with low likelihood (1 ± 6, p < 0.0001). Among MCI due to AD subjects, the mean worsening in cognitive score was significantly greater in females (11.58 ± 14) than in males (6.87 ± 11, p = 0.006). Our findings highlight the need to further investigate these findings in other populations and develop sex specific timelines for Alzheimer’s disease progression.

We propose a generative adversarial neural network model for relation classification that attempts to emulate the way in which human analysts might process sentences. Our approach provides two unique benefits over existing capabilities: (1) we make predictions by finding and exploiting supportive rationales to improve interpretability (i.e. words or phrases extracted from a sentence that a person can reason upon), and (2) we allow predictions to be easily corrected by adjusting the rationales.Our model consists of three stages: Generator, Selector, and Encoder. The Generator identifies candidate text fragments; the Selector decides which fragments can be used as rationales depending on the goal; and finally, the Encoder performs relation reasoning on the rationales. While the Encoder is trained in a supervised manner to classify relations, the Generator and Selector are designed as unsupervised models to identify rationales without prior knowledge, although they can be semi-supervised through human annotations. We evaluate our model on data from SemEval 2010 that provides 19 relation-classes. Experiments demonstrate that our approach outperforms state-of-the-art models, and that our model is capable of extracting good rationales on its own as well as benefiting from labeled rationales if provided.

Rationale-based models provide a unique way to provide justifiable results for relation classification models by identifying rationales (key words and phrases that a person can use to justify the relation in the sentence) during the process. However, existing generative networks used to extract rationales come with a trade-off between extracting diversified rationales and achieving good classification results. In this paper, we propose a multilevel heuristic approach to regulate rationale extraction to avoid extracting monotonous rationales without compromising classification performance. In our model, rationale selection is regularized by a semi-supervised process and features from different levels: word, syntax, sentence, and corpus. We evaluate our approach on the SemEval 2010 dataset that includes 19 relation classes and the quality of extracted rationales with our manually-labeled rationales. Experiments show a significant improvement in classification performance and a 20% gain in rationale interpretability compared to state-of-the-art approaches.

Mining outliers in graph data is a rapidly growing area of research. Traditional methods focus either on static graphs, or restrict relationships to be pairwise. In this work we address both of these limitations directly, and propose the first approach for mining outliers in hyperedge streams. Hyperedges, which generalize edges, faithfully capture higher order relationships that naturally occur in complex systems. Our model annotates every incoming hyperedge with an outlier score, which is based on the incident vertices and the historical relationships among them. Additionally, we describe an approximation scheme that ensures our model is suitable for being run in streaming environments. Experimental results on several real-world datasets show our model effectively identifies outliers, and that our approximation provides speedups between 33–775x.

Alzheimer’s disease (AD) is a major public health threat; however, despite decades of research, the disease mechanisms are not completely understood, and there is a significant dearth of predictive biomarkers. The availability of systems biology approaches has opened new avenues for understanding disease mechanisms at a pathway level. However, to the best of our knowledge, no prior study has characterized the nature of pathway crosstalks in AD, or examined their utility as biomarkers for diagnosis or prognosis. In this paper, we build the first computational crosstalk model of AD incorporating genetics, antecedent knowledge, and biomarkers from a national study to create a generic pathway crosstalk reference map and to characterize the nature of genetic and protein pathway crosstalks in mild cognitive impairment (MCI) subjects. We perform initial studies of the utility of incorporating these crosstalks as biomarkers for assessing the risk of MCI progression to AD dementia. Our analysis identified Single Nucleotide Polymorphism-enriched pathways representing six of the seven Kyoto Encyclopedia of Genes and Genomes pathway categories. Integrating pathway crosstalks as a predictor improved the accuracy by 11.7% compared to standard clinical parameters and apolipoprotein E ε4 status alone. Our findings highlight the importance of moving beyond discrete biomarkers to studying interactions among complex biological pathways.

The success of sentence classification often depends on understanding both the syntactic and semantic properties of word-phrases. Recent progress on this task has been based on exploiting the grammatical structure of sentences but often this structure is difficult to parse and noisy. In this paper, we propose a structure-independent 'Gated Representation Alignment' (GRA) model that blends a phrase-focused Convolutional Neural Network (CNN) approach with sequence-oriented Recurrent Neural Network (RNN). Our novel alignment mechanism allows the RNN to selectively include phrase information in a word-by-word sentence representation, and to do this without awareness of the syntactic structure. An empirical evaluation of GRA shows higher prediction accuracy (up to 4.6%) of fine-grained sentiment ratings, when compared to other structure-independent baselines. We also show comparable results to several structure-dependent methods. Finally, we analyzed the effect of our alignment mechanism and found that this is critical to the effectiveness of the CNN-RNN hybrid.

Knowledge workers are frequently subject to information overload. As a result, when looking to make analytic judgements, they may only have time to search for evidence that already matches their existing viewpoint, leading to confirmation bias. New computer systems are needed that can help users overcome this and other cognitive biases. As an enabling step towards such systems, the research community has developed instrumentation software that captures data to help better understand sensemaking processes and workflows. However, existing instrumentation approaches are limited by the need to write operating system-specific (and often application-specific) code to 'see' what the user is doing inside different applications on their computer. This source code quickly becomes complex and brittle. Furthermore, this approach does not provide a holistic view of how the user is gleaning information from multiple applications at once. We propose an alternative approach to instrumentation based on automated analysis of desktop screenshots, and demonstrate this in the context of extraction of 'claims' from reports that users are writing, and association of these claims with 'evidence' obtained from web browsing. We evaluate our approach on a corpus of 121,000 screenshots obtained from a study of 150 participants carrying out a controlled analysis task. The topic of the task was previously unfamiliar to them (hence the need to search for evidence on the web). We report results from several variants of our approach using a human evaluation of extracted claim/evidence pairs, and find that a simple word matching metric (based on Jaccard similarity) can outperform more complex sentence similarity metrics. We also describe many of the difficulties inherent to screenshot analysis and our approaches to overcome them.

Detecting all communities in a single graph is a prevalent task in graph data analytics. However, many scientific applications naturally create data as an ensemble of graphs. For example, graph ensembles can be created from multiple: social networks at distinct points in time, biological networks created from independent experiments, and global climate networks created from unique climate models. In this work, we present a method for enumerating community subsets across an ensemble of graphs, with the ability to detect both persistent and discriminative subcommunities. Moreover, we support queries, consisting of user-specified vertices of interest and arbitrary ensemble slices, to produce output that is more relevant to the user while reducing output size and computation time. While related methods are designed around a single community definition, our method is designed around the idea that choosing an appropriate community definition often depends on the application at hand. Therefore, our goal is to provide a framework that can leverage the abundance of community detection methods available when discovering persistent and discriminative substructures.

Knowledge workers are being exposed to more information than ever before, as well as having to work in multi-tasking and collaborative environments. There is an increasing need for interfaces and algorithms to help automatically keep track of documents that are associated with both individual and team tasks. Previous approaches to the problem of automatically applying task labels to documents have been limited to small feature spaces or have not taken into account multi-user environments. Many different clues to potential task associations are available through user, task and document similarity metrics, as well as through temporal patterns in individual and team workflows. We present a network-fusion algorithm for automatic task-centric document curation, and show how this can guide a recent-work dashboard interface, which organizes user's documents and gathers feedback from them. Our approach efficiently computes representations of users, tasks and documents in a common vector space, and can easily take into account many different types of associations through the creation of edges in a multi-layer graph. We have demonstrated the effectiveness of this approach using labelled document corpora from three empirical studies with students and intelligence analysts. We have also shown how to leverage relationships between different entity types to increase classification accuracy by up to 20% over a simpler baseline, and with as little as 10% labelled data.

Financial forecasting is an extremely challenging task given the complex, nonlinear nature of financial market systems. To overcome this challenge, we present an intelligent weighted fuzzy time series model for financial forecasting, which uses a sine-cosine adaptive human learning optimization (SCHLO) algorithm to search for the optimal parameters for forecasting. New weighted operators that consider frequency based chronological order and stock volume are analyzed, and SCHLO is integrated to determine the effective intervals and weighting factors. Furthermore, a novel short-term trend repair operation is developed to complement the final forecasting process. Finally, the proposed model is applied to four world major trading markets: the Dow Jones Index (DJI), the German Stock Index (DAX), the Japanese Stock Index (NIKKEI), and Taiwan Stock Index (TAIEX). Experimental results show that our model is consistently more accurate than the state-of-the-art baseline methods. The easy implementation and effective forecasting performance suggest our proposed model could be a favorable market application prospect.