Today, significant impacts of comments on the web affect people decisions while they are about to choose a product. Unfavorable effect of spam attacks in these reviews follows heavy damages for customers and organizations. The majority of methods so far classify reviews to spam and non-spam groups. Therefore, most researches are done on feature learning techniques to enhance the classification performance. From another point of view, presence of huge amount of features makes text classification overwhelming. Attention mechanism has lately been used to improve neural networks performance on sequence modeling. Instead of mining all existing features, attention can enables the model to concentrate on most important parts of the data. To these ends, we applied an attention based deep structure for detecting deceptive reviews. This model contributes distinguishing between truthful and fake reviews and benefits an attentional part to engineering better features. Our proposed model accuracy and scalability is comparable regard to the other common models.

Wireless traffic prediction is a fundamental enabler to proactive network optimisation in 5G and beyond. Forecasting extreme demand spikes and troughs is essential to avoiding outages and improving energy efficiency. However, current forecasting methods predominantly focus on overall forecast performance and/or do not offer probabilistic uncertainty quantification. Here, we design a feature embedding (FE) kernel for a Gaussian Process (GP) model to forecast traffic demand. The FE kernel enables us to trade-off overall forecast accuracy against peak-trough accuracy. Using real 4G base station data, we compare its performance against both conventional GPs, ARIMA models, as well as demonstrate the uncertainty quantification output. The advantage over neural network (e.g. CNN, LSTM) models is that the probabilistic forecast uncertainty can directly feed into decision processes in self-organizing-network (SON) modules.

Mutual calibration between color and depth cameras is a challenging topic in multi-modal data registration. In this paper, we are confronted with a "Bimodal Stereo" problem, which aims to solve camera pose from a pair of an uncalibrated color image and a depth map from different views automatically. To address this problem, an iterative Shape-from-Shading (SfS) based framework is proposed to estimate shape and pose simultaneously. In the pipeline, the estimated shape is refined by the shape prior from the given depth map under the estimated pose. Meanwhile, the estimated pose is improved by the registration of estimated shape and shape from given depth map. We also introduce a shading based refinement in the pipeline to address noisy depth map with holes. Extensive experiments showed that through our method, both the depth map, the recovered shape as well as its pose can be desirably refined and recovered.

Colon Cancer is one of the most common types of cancer. The treatment is planned to depend on the grade or stage of cancer. One of the preconditions for grading of colon cancer is to segment the glandular structures of tissues. Manual segmentation method is very time-consuming, and it leads to life risk for the patients. The principal objective of this project is to assist the pathologist to accurate detection of colon cancer. In this paper, the authors have proposed an algorithm for an automatic segmentation of glands in colon histology using local intensity and texture features. Here the dataset images are cropped into patches with different window sizes and taken the intensity of those patches, and also calculated texture-based features. Random forest classifier has been used to classify this patch into different labels. A multilevel random forest technique in a hierarchical way is proposed. This solution is fast, accurate and it is very much applicable in a clinical setup.

Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which can convey important contextual information for M3L. As such, they may have a compromised performance. In this paper, we propose a collaborative matrix factorization based solution called M3Lcmf. M3Lcmf first uses a heterogeneous network composed of nodes of bags, instances, and labels, to encode different types of relations via multiple relational data matrices. To preserve the intrinsic structure of the data matrices, M3Lcmf collaboratively factorizes them into low-rank matrices, explores the latent relationships between bags, instances, and labels, and selectively merges the data matrices. An aggregation scheme is further introduced to aggregate the instance-level labels into bag-level and to guide the factorization. An empirical study on benchmark datasets show that M3Lcmf outperforms other related competitive solutions both in the instance-level and bag-level prediction.

Operating with ignorance is an important concern of the Machine Learning research, especially when the objective is to discover knowledge from the imperfect data. Data mining (driven by appropriate knowledge discovery tools) is about processing available (observed, known and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples, which are not yet observed, known or understood. These tools traditionally take samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach and we suggest considering the things the other way around. What if the task would be as follows: how to learn a model based on our ignorance, i.e. by processing the shape of 'voids' within the available data space? Can we improve traditional classification by modeling also the ignorance? In this paper, we provide some algorithms for the discovery and visualizing of the ignorance zones in two-dimensional data spaces and design two ignorance-aware smart prototype selection techniques (incremental and adversarial) to improve the performance of the nearest neighbor classifiers. We present experiments with artificial and real datasets to test the concept of the usefulness of ignorance discovery in machine learning.

The FLAIRS poster track is designed to promote discussion of emerging ideas and work in order to encourage and help guide researchers — especially new researchers — who are able to present a full poster in the conference poster session and receive that critical work-shaping feedback that helps guide good work into great work. Abstracts of those posters appear here, which we hope to see fully developed into future FLAIRS papers..

Navigating a career constitutes one of life’s most enduring challenges, particularly within a unique organization like the US Navy. While the Navy has numerous resources for guidance, accessing and identifying key information sources across the many existing platforms can be challenging for sailors (e.g., determining the appropriate program or point of contact, developing an accurate understanding of the process, and even recognizing the need for planning itself). Focusing on intermediate goals, evaluations, education, certifications, and training is quite demanding, even before considering their cumulative long-term implications. These are on top of generic personal issues, such as financial difficulties and homesickness when at sea for prolonged periods. We present the preliminary construction of a conversational intelligent agent designed to provide a user-friendly, adaptive environment that recognizes user input pertinent to these issues and provides guidance to appropriate resources within the Navy. User input from “counseling sessions” is linked, using advanced natural language processing techniques, to our framework of Navy training and education standards, promotion protocols, and organizational structure, producing feedback on resources and recommendations sensitive to user history and stated career goals. The proposed innovative technology monitors sailors’ career progress, proactively triggering sessions before major career milestones or when performance drops below Navy expectations, by using a mixed-initiative design. System-triggered sessions involve positive feedback and informative dialogues (using existing Navy career guidance protocols). The intelligent agent also offers counseling for personal problems, triggering targeted dialogues designed to gather more information, offer tailored suggestions, and provide referrals to appropriate resources or to a human counselor when in-depth counseling is warranted. This software, currently in alpha testing, has the potential to serve as a centralized information hub, engaging and encouraging sailors to take ownership of their career paths in the most efficient way possible, benefiting both individuals and the Navy as a whole.

Hierarchical review aspect aggregation is an important challenge in review summarization. Currently, agglomerative clustering is widely used for hierarchical aspect aggregation. We identify an important but less studied issue in using agglomerative clustering for the aforementioned task. This paper proposes a novel approach to generate a multi-way hierarchy by adaptation of the multivariate concept. Furthermore, we propose a novel experimentation approach to evaluate the acceptability of the aspect relations obtained from the hierarchy generated.

We demonstrate the value of learning dense representations (embeddings) of collections of codes representing various domains ofmo medical information. These embeddings are learned jointly using sparse representations of diagnosis, procedures and prescriptions extracted from medical claims, in order to infer semantic relationships both within, as well as between domains. We show that learning effective embeddings allows for a rich representation of a patient's clinical state at a point in time, a mechanism for assigning robust clinical similarity between patients, and a data representation which is generally useful in modeling various health care related events, such as the next most likely event (i.e. diagnosis, procedure or prescription), or the likelihood of a specific event in the future (e.g. an emergency room visit). Three methods are showcased in this paper including: general embedding, task-specific embedding, and a combination of the two which we have deemed "super" embedding for the purpose of this paper.