snu-aidas/HVMap-dataset huggingface dataset:
An analysis of facial expression recognition under partial facial image occlusion

In this paper, an analysis of the effect of partial occlusion on facial expression recognition is investigated. The classification from partially occluded images in one of the six basic facial expressions is performed using a method based on Gabor wavelets texture information extraction, a supervised image decomposition method based on Discriminant Non-negative Matrix Factorization and a shape-based method that exploits the geometrical displacement of certain facial features. We demonstrate how partial occlusion affects the above mentioned methods in the classification of the six basic facial expressions, and indicate the way partial occlusion affects human observers when recognizing facial expressions. An attempt to specify which part of the face (left, right, lower or upper region) contains more discriminant information for each facial expression, is also made and conclusions regarding the pairs of facial expressions misclassifications that each type of occlusion introduces, are drawn. 2008 Published by Elsevier B.V.
Artificial intelligence applications in the intensive care unit.

OBJECTIVE
To review the history and current applications of artificial intelligence in the intensive care unit.


DATA SOURCES
The MEDLINE database, bibliographies of selected articles, and current texts on the subject.


STUDY SELECTION
The studies that were selected for review used artificial intelligence tools for a variety of intensive care applications, including direct patient care and retrospective database analysis.


DATA EXTRACTION
All literature relevant to the topic was reviewed.


DATA SYNTHESIS
Although some of the earliest artificial intelligence (AI) applications were medically oriented, AI has not been widely accepted in medicine. Despite this, patient demographic, clinical, and billing data are increasingly available in an electronic format and therefore susceptible to analysis by intelligent software. Individual AI tools are specifically suited to different tasks, such as waveform analysis or device control.


CONCLUSIONS
The intensive care environment is particularly suited to the implementation of AI tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new AI tools have become available in recent years that can function as intelligent assistants to clinicians, constantly monitoring electronic data streams for important trends, or adjusting the settings of bedside devices. The integration of these tools into the intensive care unit can be expected to reduce costs and improve patient outcomes.
Melody Generation for Pop Music via Word Representation of Musical Properties

Automatic melody generation for pop music has been a long-time aspiration for both AI researchers and musicians. However, learning to generate euphonious melody has turned out to be highly challenging due to a number of factors. Representation of multivariate property of notes has been one of the primary challenges. It is also difficult to remain in the permissible spectrum of musical variety, outside of which would be perceived as a plain random play without auditory pleasantness. Observing the conventional structure of pop music poses further challenges. In this paper, we propose to represent each note and its properties as a unique ‘word,’ thus lessening the prospect of misalignments between the properties, as well as reducing the complexity of learning. We also enforce regularization policies on the range of notes, thus encouraging the generated melody to stay close to what humans would find easy to follow. Furthermore, we generate melody conditioned on song part information, thus replicating the overall structure of a full song. Experimental results demonstrate that our model can generate auditorily pleasant songs that are more indistinguishable from human-written ones than previous models. 1
LiveNet: Improving features generalization for face liveness detection using convolution neural networks

Performance of face liveness detection algorithms in cross-database face liveness detection tests is one of the key issues in face-biometric based systems. Recently, Convolution Neural Networks (CNN) classifiers have shown remarkable performance in intra-database face liveness detection tests. However, a little effort has been made to improve the generalization capability of CNN classifiers for cross-database and unconstrained face liveness detection tests. In this paper, we propose an efficient strategy for training deep CNN classifiers for face liveness detection task. We utilize continuous data-randomization (like bootstrapping) in the form of small mini-batches during training CNN classifiers on small scale face antispoofing database. Experimental results revealed that the proposed approach reduces the training time by 18.39%, while significantly lowering the HTER by 8.28% and 14.14% in cross-database tests on CASIAFASD and Replay-Attack database respectively as compared to state-of-the-art approaches. Additionally, the proposed approach achieves satisfactory results on intra-database and cross-database face liveness detection tests, claiming a good generality over other state-of-the-art face anti-spoofing approaches. © 2018 Elsevier Ltd. All rights reserved.
Rough Set Theory in analyzing the attributes of combination values for the insurance market

Based on Rough Set Theory, this research addresses the effect of attributes/features on the combination values of decisions that insurance companies make to satisfy customers’ needs. Attributes impact on combination values by yielding sets with fewer objects (such as one or two objects), which increases both the lower and upper approximations. It also increases the decision rules, and degrades the precision of decisions. Our approach redefines the value set of attributes through expert knowledge by reducing the independent data set and reclassifying it. This approach is based on an empirical study. The results demonstrate that the redefined combination values of attributes can contribute to the precision of decisions in insurance marketing. Following an empirical analysis, we use a hit test that incorporates 50 validated sample data into the decision rule so that the hit rate reaches 100%. The results of the empirical study indicate that the generated decision rules can cover all new data. Consequently, we believe that the effects of attributes on combination values can be fully applied in research into insurance marketing. 2005 Elsevier Ltd. All rights reserved.
Discrete Cross-Modal Hashing for Efficient Multimedia Retrieval

Hashing techniques have been widely adopted for cross-modal retrieval due to its low storage cost and fast query speed. Most existing cross-modal hashing methods aim to map heterogeneous data into the common low-dimensional hamming space and then threshold to obtain binary codes by relaxing the discrete constraint. However, this independent relaxation step also brings quantization errors, resulting in poor retrieval performances. Other cross-modal hashing methods try to directly optimize the challenging objective function with discrete binary constraints. Inspired by [1], we propose a novel supervised cross-modal hashing method called Discrete Cross-Modal Hashing (DCMH) to learn the discrete binary codes without relaxing them. DCMH is formulated through reconstructing the semantic similarity matrix and learning binary codes as ideal features for classification. Furthermore, DCMH alternately updates binary codes of each modality, and iteratively learns the discrete hashing codes bit by bit efficiently, which is quite promising for large-scale datasets. Extensive empirical results on three real-world datasets show that DCMH outperforms the baseline approaches significantly.
ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification

In this work, we introduce a new video representation for action classification that aggregates local convolutional features across the entire spatio-temporal extent of the video. We do so by integrating state-of-the-art two-stream networks [42] with learnable spatio-temporal feature aggregation [6]. The resulting architecture is end-to-end trainable for whole-video classification. We investigate different strategies for pooling across space and time and combining signals from the different streams. We find that: (i) it is important to pool jointly across space and time, but (ii) appearance and motion streams are best aggregated into their own separate representations. Finally, we show that our representation outperforms the two-stream base architecture by a large margin (13% relative) as well as outperforms other baselines with comparable base architectures on HMDB51, UCF101, and Charades video classification benchmarks.
Effects of lumbar stabilization exercise on functional disability and lumbar
lordosis angle in patients with chronic low back pain

[Purpose] This study examined the effects of lumbar stabilization exercises on the functional disability and lumbar lordosis angles in patients with chronic low back pain. [Subjects] The subjects were 30 patients with chronic low back pain divided into a lumbar stabilization exercise group (n = 15) and a conservative treatment group (n = 15). [Methods] The lumbar stabilization exercise and conservative treatment groups performed an exercise program and conservative physical treatment, respectively. Both programs were performed 3 times a week for 6 weeks. The degree of functional disability was assessed by the Oswestry disability index, and lumbar lordosis angles were measured by plain radiography. [Results] The Oswestry disability index decreased significantly in the both groups; however, it was significantly lower in the lumbar stabilization exercise group. The lumbar lordosis angle increased significantly in the lumbar stabilization exercise group after treatment and was also significantly greater than that in the conservative treatment group. [Conclusion] Lumbar stabilization exercise is more effective than conservative treatment for improving functional disability and lumbar lordosis angles.
A deep learning method for classifying mammographic breast density categories.

PURPOSE
Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow.


METHODS
In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier.


RESULTS
The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples. Using the pretrained model followed by a fine-tuning process with as few as 500 mammogram images led to an AUC of 0.9265. After removing the potentially inaccurately labeled images, AUC was increased to 0.9882 and 0.9857 for without and with the pretrained model, respectively, both significantly higher (P < 0.001) than when using the full imaging dataset.


CONCLUSIONS
Our study demonstrated high classification accuracies between two difficult to distinguish breast density categories that are routinely assessed by radiologists. We anticipate that our approach will help enhance current clinical assessment of breast density and better support consistent density notification to patients in breast cancer screening.
Plantar fasciopathy: revisiting the risk factors.

BACKGROUND
Plantar fasciopathy is the most common cause of acquired sub-calcaneal heel pain in adults. To-date, research of this condition has mainly focused on management rather than causal mechanisms. The aetiology of plantar fasciopathy is likely to be multifactorial, as both intrinsic and extrinsic risk factors have been reported. The purpose of this review is to critically reevaluate risk factors for plantar fasciopathy.


METHODS
A detailed literature review was undertaken using English language medical databases.


RESULTS
No clear consensus exists as to the relative strength of the risk factors reported.


CONCLUSIONS
To-date numerous studies have examined various intrinsic and extrinsic risk factors implicated in the aetiology of plantar fasciopathy. How these factors interact may provide useful data to establish an individuals' risk profile for plantar fasciopathy and their potential for response to treatment. Further research is indicated to rank the relative significance of these risk factors.
Why do Larks Perform Better at School than Owls ? The Mediating Effect of Conscientiousness

Article History: Received 26.06.2016 Received in revised form 25.10.2016 Accepted 26.10.2016 Available online 25.11.2016 Circadian preference refers to individuals’ preference for morning or evening activities. Its two dimensions (i.e., morningness and eveningness) are related to a number of academic outcomes. While morningness shows positive relations with academic achievement, eveningness shows negative relations. Further, morningness and eveningness show the same correlational pattern with conscientiousness (i.e., positive relations for morningness, negative relations for eveningness), which – in turn – predicts academic achievement. Therefore, the main aim of the present study was to investigate if the relation between circadian preference and academic achievement was mediated by conscientiousness. The sample comprised 422 students attending the 11th grade at a grammar school in Germany. Circadian preference (morningness and eveningness) and conscientiousness were assessed by self-report questionnaires; academic achievement was operationalized by school grades. Using confirmatory analyses and structural equation modelling, the results supported the assumption that conscientiousness mediates the relation between circadian preference and academic achievement. Implications for research into circadian preference and for education are discussed. © 2016 IOJES. All rights reserved
Loneliness, social contacts and Internet addiction: A cross-lagged panel study

This study aims to examine the causal priority in the observed empirical relationships between Internet addiction and other psychological problems. A cross-lagged panel survey of 361 college students in Hong Kong was conducted. Results show that excessive and unhealthy Internet use would increase feelings of loneliness over time. Although depression had a moderate and positive bivariate relationship with Internet addiction at each time point, such a relationship was not significant in the cross-lagged analyses. This study also found that online social contacts with friends and family were not an effective alternative for offline social interactions in reducing feelings of loneliness. Furthermore, while an increase in face-to-face contacts could help to reduce symptoms of Internet addiction, this effect may be neutralized by the increase in online social contacts as a result of excessive Internet use. Taken as a whole, findings from the study show a worrisome vicious cycle between loneliness and Internet addiction. 2013 Elsevier Ltd. All rights reserved.
Detecting Semantic Parts on Partially Occluded Objects

In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested on occluded images. The motivation is that there are infinite number of occlusion patterns in real world, which cannot be fully covered in the training data. So the models should be inherently robust and adaptive to occlusions instead of fitting / learning the occlusion patterns in the training data. Our approach detects semantic parts by accumulating the confidence of local visual cues. Specifically, the method uses a simple voting method, based on log-likelihood ratio tests and spatial constraints, to combine the evidence of local cues. These cues are called visual concepts, which are derived by clustering the internal states of deep networks. We evaluate our voting scheme on the VehicleSemanticPart dataset with dense part annotations. We randomly place two, three or four irrelevant objects onto the target object to generate testing images with various occlusions. Experiments show that our algorithm outperforms several competitors in semantic part detection when occlusions are present. This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. WANG ET.AL.: DETECTING SEMANTIC PARTS ON PARTIALLY OCCLUDED OBJECTS 1 Detecting Semantic Parts on Partially Occluded Objects Jianyu Wang*1 wjyouch@gmail.com Cihang Xie*2 cihangxie306@gmail.com Zhishuai Zhang*2 zhshuai.zhang@gmail.com Jun Zhu1 zhujun.sjtu@gmail.com Lingxi Xie( )2 198808xc@gmail.com Alan L. Yuille2 alan.l.yuille@gmail.com 1 Baidu Research (USA), Sunnyvale, CA 94089 USA 2 Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA . . . . * This work was done when the first author was a Ph.D. student at UCLA. The first three authors contributed equally to this work.
The individual and social dynamics of knowledge sharing: an exploratory study

Purpose – This paper aims to examine how knowledge sharing behavior is influenced by three sets of dynamics: a rational calculus that weighs the costs and benefits of sharing; a dispositional preference that favors certain patterns of sharing outcomes; and a relational effect based on working relationships. Design/methodology/approach – Concepts from social exchange theory, social value orientation, and leader-member exchange theory are applied to analyze behavioral intentions to share knowledge. The study population consists of employees of a large pension fund in Canada. Participants answered a survey that used allocation games and situational vignettes to measure social value orientation, propensity to share knowledge, and perception of cost and benefit. Findings – The results suggest that personal preferences about the distribution of sharing outcomes, individual perceptions about costs and benefits, and structural relationship with knowledge recipients, all affect knowledge sharing behavior significantly. Notably, it was found that propensity to share knowledge is positively related to perceived benefit to the recipient, thus suggesting that evaluation of cost and benefit in social exchange is not limited to self-interest, but is also influenced by perceived recipient benefit. Moreover, it was found that the relationship with the sharing target (superior or colleague) also influenced sharing. Originality/value – Most studies emphasize the organizational benefits of knowledge sharing. This study examines knowledge sharing from the perspective of the individual who approaches knowledge sharing as a social exchange that involves perceptions of costs and benefits, preferences about sharing outcomes, and relationship with the sharing target. The study also introduces innovative methods to measure social value orientation and propensity to share knowledge.
A survey of communication/networking in Smart Grids

Smart Grid is designed to integrate advanced communication/networking technologies into electrical power grids to make them ‘‘smarter’’. Current situation is that most of the blackouts and voltage sags could be prevented if we have better and faster communication devices and technologies for the electrical grid. In order to make the current electrical power grid a Smart Grid, the design and implementation of a new communication infrastructure for the grid are two important fields of research. However, Smart Grid projects have only been proposed in recent years and only a few proposals for forwardlooking requirements and initial research work have been offered in this field. No any systematic reviews of communication/networking in Smart Grids have been conducted yet. Therefore, we conduct a systematic review of communication/networking technologies in Smart Grid in this paper, including communication/networking architecture, different communication technologies thatwould be employed into this architecture, quality of service (QoS), optimizing utilization of assets, control and management, etc. © 2011 Elsevier B.V. All rights reserved.
What influences literacy outcome in children with speech sound disorder?

PURPOSE
In this study, the authors evaluated literacy outcome in children with histories of speech sound disorder (SSD) who were characterized along 2 dimensions: broader language function and persistence of SSD. In previous studies, authors have demonstrated that each dimension relates to literacy but have not disentangled their effects. Methods Two groups of children (86 SSD and 37 controls) were recruited at ages 5-6 and were followed longitudinally. The authors report the literacy of children with SSD at ages 7-9, compared with controls and national norms, and relative to language skill and SSD persistence (both measured at age 5-6).


RESULTS
The SSD group demonstrated elevated rates of reading disability. Language skill but not SSD persistence predicted later literacy. However, SSD persistence was associated with phonological awareness impairments. Phonological awareness alone predicted literacy outcome less well than a model that also included syntax and nonverbal IQ.


CONCLUSIONS
Results support previous literature findings that SSD history predicts literacy difficulties and that the association is strongest for SSD + language impairment (LI). Magnitude of phonological impairment alone did not determine literacy outcome, as predicted by the core phonological deficit hypothesis. Instead, consistent with a multiple deficit approach, phonological deficits appeared to interact with other cognitive factors in literacy development.