July 29, 2019

3058 words 15 mins read

Paper Group ANR 31

Two-Stage Hybrid Day-Ahead Solar Forecasting. Highrisk Prediction from Electronic Medical Records via Deep Attention Networks. SRE: Semantic Rules Engine For the Industrial Internet-Of-Things Gateways. Automated Assessment of Facial Wrinkling: a case study on the effect of smoking. A Roadmap for the Development of the “SP Machine” for Artificial In …

Two-Stage Hybrid Day-Ahead Solar Forecasting


Title	Two-Stage Hybrid Day-Ahead Solar Forecasting
Authors	Mohana Alanazi, Mohsen Mahoor, Amin Khodaei
Abstract	Power supply from renewable resources is on a global rise where it is forecasted that renewable generation will surpass other types of generation in a foreseeable future. Increased generation from renewable resources, mainly solar and wind, exposes the power grid to more vulnerabilities, conceivably due to their variable generation, thus highlighting the importance of accurate forecasting methods. This paper proposes a two-stage day-ahead solar forecasting method that breaks down the forecasting into linear and nonlinear parts, determines subsequent forecasts, and accordingly, improves accuracy of the obtained results. To further reduce the error resulted from nonstationarity of the historical solar radiation data, a data processing approach, including pre-process and post-process levels, is integrated with the proposed method. Numerical simulations on three test days with different weather conditions exhibit the effectiveness of the proposed two-stage model.
Tasks
Published	2017-06-27
URL	http://arxiv.org/abs/1706.08699v1
PDF	http://arxiv.org/pdf/1706.08699v1.pdf
PWC	https://paperswithcode.com/paper/two-stage-hybrid-day-ahead-solar-forecasting
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Highrisk Prediction from Electronic Medical Records via Deep Attention Networks


Title	Highrisk Prediction from Electronic Medical Records via Deep Attention Networks
Authors	You Jin Kim, Yun-Geun Lee, Jeong Whun Kim, Jin Joo Park, Borim Ryu, Jung-Woo Ha
Abstract	Predicting highrisk vascular diseases is a significant issue in the medical domain. Most predicting methods predict the prognosis of patients from pathological and radiological measurements, which are expensive and require much time to be analyzed. Here we propose deep attention models that predict the onset of the high risky vascular disease from symbolic medical histories sequence of hypertension patients such as ICD-10 and pharmacy codes only, Medical History-based Prediction using Attention Network (MeHPAN). We demonstrate two types of attention models based on 1) bidirectional gated recurrent unit (R-MeHPAN) and 2) 1D convolutional multilayer model (C-MeHPAN). Two MeHPAN models are evaluated on approximately 50,000 hypertension patients with respect to precision, recall, f1-measure and area under the curve (AUC). Experimental results show that our MeHPAN methods outperform standard classification models. Comparing two MeHPANs, R-MeHPAN provides more better discriminative capability with respect to all metrics while C-MeHPAN presents much shorter training time with competitive accuracy.
Tasks	Deep Attention
Published	2017-11-30
URL	http://arxiv.org/abs/1712.00010v1
PDF	http://arxiv.org/pdf/1712.00010v1.pdf
PWC	https://paperswithcode.com/paper/highrisk-prediction-from-electronic-medical
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SRE: Semantic Rules Engine For the Industrial Internet-Of-Things Gateways


Title	SRE: Semantic Rules Engine For the Industrial Internet-Of-Things Gateways
Authors	Charbel El Kaed, Imran Khan, Andre Van Den Berg, Hicham Hossayni, Christophe Saint-Marcel
Abstract	The Advent of the Internet-of-Things (IoT) paradigm has brought opportunities to solve many real-world problems. Energy management, for example, has attracted huge interest from academia, industries, governments and regulatory bodies. It involves collecting energy usage data, analyzing it, and optimizing the energy consumption by applying control strategies. However, in industrial environments, performing such optimization is not trivial. The changes in business rules, process control, and customer requirements make it much more challenging. In this paper, a Semantic Rules Engine (SRE) for industrial gateways is presented that allows implementing dynamic and flexible rule-based control strategies. It is simple, expressive, and allows managing rules on-the-fly without causing any service interruption. Additionally, it can handle semantic queries and provide results by inferring additional knowledge from previously defined concepts in ontologies. SRE has been validated and tested on different hardware platforms and in commercial products. Performance evaluations are also presented to validate its conformance to the customer requirements.
Tasks
Published	2017-10-26
URL	http://arxiv.org/abs/1710.09627v1
PDF	http://arxiv.org/pdf/1710.09627v1.pdf
PWC	https://paperswithcode.com/paper/sre-semantic-rules-engine-for-the-industrial
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Automated Assessment of Facial Wrinkling: a case study on the effect of smoking


Title	Automated Assessment of Facial Wrinkling: a case study on the effect of smoking
Authors	Omaima FathElrahman Osman, Remah Mutasim Ibrahim Elbashir, Imad Eldain Abbass, Connah Kendrick, Manu Goyal, Moi Hoon Yap
Abstract	Facial wrinkle is one of the most prominent biological changes that accompanying the natural aging process. However, there are some external factors contributing to premature wrinkles development, such as sun exposure and smoking. Clinical studies have shown that heavy smoking causes premature wrinkles development. However, there is no computerised system that can automatically assess the facial wrinkles on the whole face. This study investigates the effect of smoking on facial wrinkling using a social habit face dataset and an automated computerised computer vision algorithm. The wrinkles pattern represented in the intensity of 0-255 was first extracted using a modified Hybrid Hessian Filter. The face was divided into ten predefined regions, where the wrinkles in each region was extracted. Then the statistical analysis was performed to analyse which region is effected mainly by smoking. The result showed that the density of wrinkles for smokers in two regions around the mouth was significantly higher than the non-smokers, at p-value of 0.05. Other regions are inconclusive due to lack of large scale dataset. Finally, the wrinkle was visually compared between smoker and non-smoker faces by generating a generic 3D face model.
Tasks
Published	2017-08-06
URL	http://arxiv.org/abs/1708.01844v2
PDF	http://arxiv.org/pdf/1708.01844v2.pdf
PWC	https://paperswithcode.com/paper/automated-assessment-of-facial-wrinkling-a
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A Roadmap for the Development of the “SP Machine” for Artificial Intelligence


Title	A Roadmap for the Development of the “SP Machine” for Artificial Intelligence
Authors	J Gerard Wolff
Abstract	This paper describes a roadmap for the development of the “SP Machine”, based on the “SP Theory of Intelligence” and its realisation in the “SP Computer Model”. The SP Machine will be developed initially as a software virtual machine with high levels of parallel processing, hosted on a high-performance computer. The system should help users visualise knowledge structures and processing. Research is needed into how the system may discover low-level features in speech and in images. Strengths of the SP System in the processing of natural language may be augmented, in conjunction with the further development of the SP System’s strengths in unsupervised learning. Strengths of the SP System in pattern recognition may be developed for computer vision. Work is needed on the representation of numbers and the performance of arithmetic processes. A computer model is needed of “SP-Neural”, the version of the SP Theory expressed in terms of neurons and their inter-connections. The SP Machine has potential in many areas of application, several of which may be realised on short-to-medium timescales.
Tasks
Published	2017-06-28
URL	http://arxiv.org/abs/1707.00614v3
PDF	http://arxiv.org/pdf/1707.00614v3.pdf
PWC	https://paperswithcode.com/paper/a-roadmap-for-the-development-of-the-sp
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Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis


Title	Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis
Authors	Patrick Rodler, Wolfgang Schmid, Konstantin Schekotihin
Abstract	In this work we present strategies for (optimal) measurement selection in model-based sequential diagnosis. In particular, assuming a set of leading diagnoses being given, we show how queries (sets of measurements) can be computed and optimized along two dimensions: expected number of queries and cost per query. By means of a suitable decoupling of two optimizations and a clever search space reduction the computations are done without any inference engine calls. For the full search space, we give a method requiring only a polynomial number of inferences and guaranteeing query properties existing methods cannot provide. Evaluation results using real-world problems indicate that the new method computes (virtually) optimal queries instantly independently of the size and complexity of the considered diagnosis problems.
Tasks	Sequential Diagnosis
Published	2017-05-28
URL	http://arxiv.org/abs/1705.09879v1
PDF	http://arxiv.org/pdf/1705.09879v1.pdf
PWC	https://paperswithcode.com/paper/inexpensive-cost-optimized-measurement
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Clingo goes Linear Constraints over Reals and Integers


Title	Clingo goes Linear Constraints over Reals and Integers
Authors	Tomi Janhunen, Roland Kaminski, Max Ostrowski, Torsten Schaub, Sebastian Schellhorn, Philipp Wanko
Abstract	The recent series 5 of the ASP system clingo provides generic means to enhance basic Answer Set Programming (ASP) with theory reasoning capabilities. We instantiate this framework with different forms of linear constraints, discuss the respective implementations, and present techniques of how to use these constraints in a reactive context. More precisely, we introduce extensions to clingo with difference and linear constraints over integers and reals, respectively, and realize them in complementary ways. Finally, we empirically evaluate the resulting clingo derivatives clingo[dl] and clingo[lp] on common fragments and contrast them to related ASP systems. This paper is under consideration for acceptance in TPLP.
Tasks
Published	2017-07-13
URL	http://arxiv.org/abs/1707.04053v1
PDF	http://arxiv.org/pdf/1707.04053v1.pdf
PWC	https://paperswithcode.com/paper/clingo-goes-linear-constraints-over-reals-and
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Construction of Latent Descriptor Space and Inference Model of Hand-Object Interactions


Title	Construction of Latent Descriptor Space and Inference Model of Hand-Object Interactions
Authors	Tadashi Matsuo, Nobutaka Shimada
Abstract	Appearance-based generic object recognition is a challenging problem because all possible appearances of objects cannot be registered, especially as new objects are produced every day. Function of objects, however, has a comparatively small number of prototypes. Therefore, function-based classification of new objects could be a valuable tool for generic object recognition. Object functions are closely related to hand-object interactions during handling of a functional object; i.e., how the hand approaches the object, which parts of the object and contact the hand, and the shape of the hand during interaction. Hand-object interactions are helpful for modeling object functions. However, it is difficult to assign discrete labels to interactions because an object shape and grasping hand-postures intrinsically have continuous variations. To describe these interactions, we propose the interaction descriptor space which is acquired from unlabeled appearances of human hand-object interactions. By using interaction descriptors, we can numerically describe the relation between an object’s appearance and its possible interaction with the hand. The model infers the quantitative state of the interaction from the object image alone. It also identifies the parts of objects designed for hand interactions such as grips and handles. We demonstrate that the proposed method can unsupervisedly generate interaction descriptors that make clusters corresponding to interaction types. And also we demonstrate that the model can infer possible hand-object interactions.
Tasks	Object Recognition
Published	2017-09-12
URL	http://arxiv.org/abs/1709.03739v1
PDF	http://arxiv.org/pdf/1709.03739v1.pdf
PWC	https://paperswithcode.com/paper/construction-of-latent-descriptor-space-and
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Accurate Genomic Prediction Of Human Height


Title	Accurate Genomic Prediction Of Human Height
Authors	Louis Lello, Steven G. Avery, Laurent Tellier, Ana Vazquez, Gustavo de los Campos, Stephen D. H. Hsu
Abstract	We construct genomic predictors for heritable and extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, $\sim$40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate $\sim$0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction. The variance captured for height is comparable to the estimated SNP heritability from GCTA (GREML) analysis, and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for the SNPs used. Thus, our results resolve the common SNP portion of the “missing heritability” problem – i.e., the gap between prediction R-squared and SNP heritability. The $\sim$20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common SNPs. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier GWAS for out-of-sample validation of our results.
Tasks
Published	2017-09-19
URL	http://arxiv.org/abs/1709.06489v1
PDF	http://arxiv.org/pdf/1709.06489v1.pdf
PWC	https://paperswithcode.com/paper/accurate-genomic-prediction-of-human-height
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Fish School Search Algorithm for Constrained Optimization


Title	Fish School Search Algorithm for Constrained Optimization
Authors	Joao Batista Monteiro Filho, Isabela Maria Carneiro de Albuquerque, Fernando Buarque de Lima Neto
Abstract	In this work we investigate the effectiveness of the application of niching able swarm metaheuristic approaches in order to solve constrained optimization problems. Sub-swarms are used in order to allow the achievement of many feasible regions to be exploited in terms of fitness function. The niching approach employed was wFSS, a version of the Fish School Search algorithm devised specifically to deal with multi-modal search spaces. A base technique referred as wrFSS was conceived and three variations applying different constraint handling procedures were also proposed. Tests were performed in seven problems from CEC 2010 and a comparison with other approaches was carried out. Results show that the search strategy proposed is able to handle some heavily constrained problems and achieve results comparable to the state-of-the-art algorithms. However, we also observed that the local search operator present in wFSS and inherited by wrFSS makes the fitness convergence difficult when the feasible region presents some specific geometrical features.
Tasks
Published	2017-07-19
URL	http://arxiv.org/abs/1707.06169v1
PDF	http://arxiv.org/pdf/1707.06169v1.pdf
PWC	https://paperswithcode.com/paper/fish-school-search-algorithm-for-constrained
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Realization of Ontology Web Search Engine


Title	Realization of Ontology Web Search Engine
Authors	Olegs Verhodubs
Abstract	This paper describes the realization of the Ontology Web Search Engine. The Ontology Web Search Engine is realizable as independent project and as a part of other projects. The main purpose of this paper is to present the Ontology Web Search Engine realization details as the part of the Semantic Web Expert System and to present the results of the Ontology Web Search Engine functioning. It is expected that the Semantic Web Expert System will be able to process ontologies from the Web, generate rules from these ontologies and develop its knowledge base.
Tasks
Published	2017-02-22
URL	http://arxiv.org/abs/1702.06934v1
PDF	http://arxiv.org/pdf/1702.06934v1.pdf
PWC	https://paperswithcode.com/paper/realization-of-ontology-web-search-engine
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Lean From Thy Neighbor: Stochastic & Adversarial Bandits in a Network


Title	Lean From Thy Neighbor: Stochastic & Adversarial Bandits in a Network
Authors	L. Elisa Celis, Farnood Salehi
Abstract	An individual’s decisions are often guided by those of his or her peers, i.e., neighbors in a social network. Presumably, being privy to the experiences of others aids in learning and decision making, but how much advantage does an individual gain by observing her neighbors? Such problems make appearances in sociology and economics and, in this paper, we present a novel model to capture such decision-making processes and appeal to the classical multi-armed bandit framework to analyze it. Each individual, in addition to her own actions, can observe the actions and rewards obtained by her neighbors, and can use all of this information in order to minimize her own regret. We provide algorithms for this setting, both for stochastic and adversarial bandits, and show that their regret smoothly interpolates between the regret in the classical bandit setting and that of the full-information setting as a function of the neighbors’ exploration. In the stochastic setting the additional information must simply be incorporated into the usual estimation of the rewards, while in the adversarial setting this is attained by constructing a new unbiased estimator for the rewards and appropriately bounding the amount of additional information provided by the neighbors. These algorithms are optimal up to log factors; despite the fact that the agents act independently and selfishly, this implies that it is an approximate Nash equilibria for all agents to use our algorithms. Further, we show via empirical simulations that our algorithms, often significantly, outperform existing algorithms that one could apply to this setting.
Tasks	Decision Making
Published	2017-04-14
URL	http://arxiv.org/abs/1704.04470v1
PDF	http://arxiv.org/pdf/1704.04470v1.pdf
PWC	https://paperswithcode.com/paper/lean-from-thy-neighbor-stochastic-adversarial
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Parameters Optimization of Deep Learning Models using Particle Swarm Optimization


Title	Parameters Optimization of Deep Learning Models using Particle Swarm Optimization
Authors	Basheer Qolomany, Majdi Maabreh, Ala Al-Fuqaha, Ajay Gupta, Driss Benhaddou
Abstract	Deep learning has been successfully applied in several fields such as machine translation, manufacturing, and pattern recognition. However, successful application of deep learning depends upon appropriately setting its parameters to achieve high quality results. The number of hidden layers and the number of neurons in each layer of a deep machine learning network are two key parameters, which have main influence on the performance of the algorithm. Manual parameter setting and grid search approaches somewhat ease the users tasks in setting these important parameters. Nonetheless, these two techniques can be very time consuming. In this paper, we show that the Particle swarm optimization (PSO) technique holds great potential to optimize parameter settings and thus saves valuable computational resources during the tuning process of deep learning models. Specifically, we use a dataset collected from a Wi-Fi campus network to train deep learning models to predict the number of occupants and their locations. Our preliminary experiments indicate that PSO provides an efficient approach for tuning the optimal number of hidden layers and the number of neurons in each layer of the deep learning algorithm when compared to the grid search method. Our experiments illustrate that the exploration process of the landscape of configurations to find the optimal parameters is decreased by 77%-85%. In fact, the PSO yields even better accuracy results.
Tasks	Machine Translation
Published	2017-11-28
URL	http://arxiv.org/abs/1711.10354v1
PDF	http://arxiv.org/pdf/1711.10354v1.pdf
PWC	https://paperswithcode.com/paper/parameters-optimization-of-deep-learning
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Automatic Trimap Generation for Image Matting


Title	Automatic Trimap Generation for Image Matting
Authors	Vikas Gupta, Shanmuganathan Raman
Abstract	Image matting is a longstanding problem in computational photography. Although, it has been studied for more than two decades, yet there is a challenge of developing an automatic matting algorithm which does not require any human efforts. Most of the state-of-the-art matting algorithms require human intervention in the form of trimap or scribbles to generate the alpha matte form the input image. In this paper, we present a simple and efficient approach to automatically generate the trimap from the input image and make the whole matting process free from human-in-the-loop. We use learning based matting method to generate the matte from the automatically generated trimap. Experimental results demonstrate that our method produces good quality trimap which results into accurate matte estimation. We validate our results by replacing the automatically generated trimap by manually created trimap while using the same image matting algorithm.
Tasks	Image Matting
Published	2017-07-02
URL	http://arxiv.org/abs/1707.00333v2
PDF	http://arxiv.org/pdf/1707.00333v2.pdf
PWC	https://paperswithcode.com/paper/automatic-trimap-generation-for-image-matting
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Optimal Alarms for Vehicular Collision Detection


Title	Optimal Alarms for Vehicular Collision Detection
Authors	Michael Motro, Joydeep Ghosh, Chandra Bhat
Abstract	An important application of intelligent vehicles is advance detection of dangerous events such as collisions. This problem is framed as a problem of optimal alarm choice given predictive models for vehicle location and motion. Techniques for real-time collision detection are surveyed and grouped into three classes: random Monte Carlo sampling, faster deterministic approximations, and machine learning models trained by simulation. Theoretical guarantees on the performance of these collision detection techniques are provided where possible, and empirical analysis is provided for two example scenarios. Results validate Monte Carlo sampling as a robust solution despite its simplicity.
Tasks
Published	2017-08-16
URL	http://arxiv.org/abs/1708.04922v1
PDF	http://arxiv.org/pdf/1708.04922v1.pdf
PWC	https://paperswithcode.com/paper/optimal-alarms-for-vehicular-collision
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