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Problem based learning

Merylin Monaro, Luciano Gamberini, Giuseppe Sartori
The detection of faked identities is a major problem in security. Current memory-detection techniques cannot be used as they require prior knowledge of the respondent's true identity. Here, we report a novel technique for detecting faked identities based on the use of unexpected questions that may be used to check the respondent identity without any prior autobiographical information. While truth-tellers respond automatically to unexpected questions, liars have to "build" and verify their responses. This lack of automaticity is reflected in the mouse movements used to record the responses as well as in the number of errors...
2017: PloS One
Yuanlong Li, Han Hu, Yonggang Wen, Jun Zhang
In this paper, we propose to detect the running applications in a server by classifying the observed power consumption series for the purpose of data center energy consumption monitoring and analysis. Time series classification problem has been extensively studied with various distance measurements developed; also recently the deep learning-based sequence models have been proved to be promising. In this paper, we propose a novel distance measurement and build a time series classification algorithm hybridizing nearest neighbor and long short term memory (LSTM) neural network...
May 24, 2017: IEEE Transactions on Cybernetics
Ziming Zhang, Yun Liu, Xi Chen, Yanjun Zhu, Ming-Ming Cheng, Venkatesh Saligrama, Philip H S Torr
We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING [1] but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially...
May 23, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Liansheng Zhuang, Zihan Zhou, Shenghua Gao, Jingwen Yin, Zhouchen Lin, Yi Ma
In the literature, most existing graph-based semi- supervised learning (SSL) methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the Low-Rank Representation (LRR), and propose a novel semi-supervised graph learning method called Semi-Supervised Low-Rank Representation (SSLRR)...
May 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Sandra González-Villà, Sergi Valverde, Mariano Cabezas, Deborah Pareto, Joan C Vilanova, Lluís Ramió-Torrentà, Àlex Rovira, Arnau Oliver, Xavier Lladó
In recent years, many automatic brain structure segmentation methods have been proposed. However, these methods are commonly tested with non-lesioned brains and the effect of lesions on their performance has not been evaluated. Here, we analyze the effect of multiple sclerosis (MS) lesions on three well-known automatic brain structure segmentation methods, namely, FreeSurfer, FIRST and multi-atlas fused by majority voting, which use learning-based, deformable and atlas-based strategies, respectively. To perform a quantitative analysis, 100 synthetic images of MS patients with a total of 2174 lesions are simulated on two public databases with available brain structure ground truth information (IBSR18 and MICCAI'12)...
2017: NeuroImage: Clinical
Léia G Gurgel, Mônica M C de Oliveira, Maria C R A Joly, Caroline T Reppold
Objective: The Computerized and Dynamic Writing Test (TIDE) is designed to examine the learning potential of adolescents in narrative writing. This was a validation study of the TIDE based on its internal structure. Learning potential is responsible for cognitive modifiabilty according to the Theory of Cognitive Structural Modifiability (CSM) developed by Feüerstein. Method: Included 304 participants between 10 and 17 years of age from schools in the South of Brazil. The data collection involved student groups that were divided according to age and school grade...
2017: Frontiers in Psychology
Sunghong Park, Dong-Gi Lee, Hyunjung Shin
BACKGROUND: Although drug discoveries can provide meaningful insights and significant enhancements in pharmaceutical field, the longevity and cost that it takes can be extensive where the success rate is low. In order to circumvent the problem, there has been increased interest in 'Drug Repositioning' where one searches for already approved drugs that have high potential of efficacy when applied to other diseases. To increase the success rate for drug repositioning, one considers stepwise screening and experiments based on biological reactions...
May 18, 2017: BMC Medical Informatics and Decision Making
Sunjoo Bang, Sangjoon Son, Hyunwoong Roh, Jihye Lee, Sungyun Bae, Kyungwon Lee, Changhyung Hong, Hyunjung Shin
BACKGROUND: The number of people with dementia is increasing along with people's ageing trend worldwide. Therefore, there are various researches to improve a dementia diagnosis process in the field of computer-aided diagnosis (CAD) technology. The most significant issue is that the evaluation processes by physician which is based on medical information for patients and questionnaire from their guardians are time consuming, subjective and prone to error. This problem can be solved by an overall data mining modeling, which subsidizes an intuitive decision of clinicians...
May 18, 2017: BMC Medical Informatics and Decision Making
Juan Camilo Ramírez, James A R Marshall
The evolutionary success of many organisms depends on their ability to make decisions based on estimates of the state of their environment (e.g., predation risk) from uncertain information. These decision problems have optimal solutions and individuals in nature are expected to evolve the behavioural mechanisms to make decisions as if using the optimal solutions. Bayesian inference is the optimal method to produce estimates from uncertain data, thus natural selection is expected to favour individuals with the behavioural mechanisms to make decisions as if they were computing Bayesian estimates in typically-experienced environments, although this does not necessarily imply that favoured decision-makers do perform Bayesian computations exactly...
May 20, 2017: Journal of Theoretical Biology
Kentaro Iwata, Asako Doi
No abstract text is available yet for this article.
May 16, 2017: International Journal of Medical Education
Zhen Yu, Ee-Leng Tan, Dong Ni, Jing Qin, Siping Chen, Shenli Li, Baiying Lei, Tianfu Wang
Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, considerable effort has been devoted to FFSP recognition using various hand-crafted features, but the recognition performance is still unsatisfactory due to the high intra-class variation of FFSPs and the high degree of visual similarity between FFSPs and other non-FFSPs...
May 17, 2017: IEEE Journal of Biomedical and Health Informatics
Yujin Zhu, Zhe Wang, Hongyuan Zha, Daqi Gao
Existing learning models for classification of imbalanced data sets can be grouped as either boundary-based or nonboundary-based depending on whether a decision hyperplane is used in the learning process. The focus of this paper is a new approach that leverages the advantage of both approaches. Specifically, our new model partitions the input space into three parts by creating two additional boundaries in the training process, and then makes the final decision based on a heuristic measurement between the test sample and a subset of selected training samples...
May 16, 2017: IEEE Transactions on Neural Networks and Learning Systems
Azalia Mirhoseini, Eva L Dyer, Ebrahim M Songhori, Richard Baraniuk, Farinaz Koushanfar
This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense data sets. Our framework exploits data structure to scalably factorize it into an ensemble of lower rank subspaces. The factorization creates sparse low-dimensional representations of the data, a property which is leveraged to devise effective mapping and scheduling of iterative learning algorithms on the distributed computing machines. We provide two APIs, one matrix-based and one graph-based, which facilitate automated adoption of the framework for performing several contemporary learning applications...
May 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
Andrzej Mizera, Jun Pang, Qixia Yuan
Probabilistic Boolean networks (PBNs) is a well-established computational framework for modelling biological systems. The steady-state dynamics of PBNs is of crucial importance in the study of such systems. However, for large PBNs, which often arise in systems biology, obtaining the steady-state distribution poses a significant challenge. In this paper, we revive the two-state Markov chain approach to solve this problem. This paper contributes in three aspects. First, we identify a problem of generating biased results with the approach and we propose a few heuristics to avoid such a pitfall...
May 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Hojjat Mousavi, Vishal Monga
Sparsity constrained single image super-resolution (SR) has been of much recent interest. A typical approach involves sparsely representing patches in a low-resolution (LR) input image via a dictionary of example LR patches, and then using the coefficients of this representation to generate the highresolution (HR) output via an analogous HR dictionary. However, most existing sparse representation methods for super resolution focus on the luminance channel information and do not capture interactions between color channels...
May 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Bing Su, Xiaoqing Ding, Changsong Liu, Hao Wang, Ying Wu
Feature space transformation (FST) techniques have been widely studied for dimensionality reduction in vector-based feature space. However, these techniques are inapplicable to sequence data because the features in the same sequence are not independent. In this paper, we propose a method called max-min inter-sequence distance analysis (MMSDA) to transform features in sequences into a low-dimensional subspace such that different sequence classes are holistically separated. To utilize the temporal dependencies, MMSDA first aligns features in sequences from the same class to an adapted number of temporal states and then constructs the sequence class separability based on the statistics of these ordered states...
May 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Wen Li, Zheng Xu, Dong Xu, Dengxin Dai, Luc Van Gool
Domain adaptation between diverse source and target domains is a challenging research problem, especially in the real-world visual recognition tasks where the images and videos consist of significant variations in viewpoints, illuminations, qualities, etc. In this paper, we propose a new approach for domain generalization and domain adaptation based on exemplar SVMs. Specifically, we decompose the source domain into many subdomains, each of which contains only one positive training sample and all negative samples...
May 16, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
M G Hart, C R Housden, J Suckling, R Tait, A Young, U Müller, V F J Newcombe, I Jalloh, B Pearson, J Cross, R A Trivedi, J D Pickard, B J Sahakian, P J Hutchinson
BACKGROUND/AIM: The safety of amateur and professional boxing is a contentious issue. We hypothesised that advanced magnetic resonance imaging and neuropsychological testing could provide evidence of acute and early brain injury in amateur boxers. METHODS: We recruited 30 participants from a university amateur boxing club in a prospective cohort study. Magnetic resonance imaging (MRI) and neuropsychological testing was performed at three time points: prior to starting training; within 48 h following a first major competition to detect acute brain injury; and one year follow-up...
2017: NeuroImage: Clinical
Mohammad Saleh Miri, Michael D Abràmoff, Young H Kwon, Milan Sonka, Mona K Garvin
Bruch's membrane opening-minimum rim width (BMO-MRW) is a recently proposed structural parameter which estimates the remaining nerve fiber bundles in the retina and is superior to other conventional structural parameters for diagnosing glaucoma. Measuring this structural parameter requires identification of BMO locations within spectral domain-optical coherence tomography (SD-OCT) volumes. While most automated approaches for segmentation of the BMO either segment the 2D projection of BMO points or identify BMO points in individual B-scans, in this work, we propose a machine-learning graph-based approach for true 3D segmentation of BMO from glaucomatous SD-OCT volumes...
May 6, 2017: Medical Image Analysis
Lin Cong, Qi Yan, Chenjing Sun, Yue Zhu, Guanjun Tu
PURPOSE: To assess the impact of problem and scripting-based learning (PSBL) on spine surgical trainees' learning outcomes. METHODS: 30 spine surgery postgraduate-year-1 residents (PGY-1s) from the First Hospital of China Medical University were randomly divided into two groups. The first group studied spine surgical skills and developed individual judgment under a conventional didactic model, whereas the PSBL group used PBL and Scripted model. A feedback questionnaire and the satisfaction of residents were evaluated by the first assistant surgeon immediately following each procedure...
May 19, 2017: European Spine Journal
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