Miracle-dz/UniFAR-FTUs huggingface dataset:
Perception, Knowledge, and Practice of ?Reflective Learning and Writing? Among Interns in Academy of Medical Sciences, Pariyaram

Background: Since we are moving towards competency based medical education, reflective practices are becoming relevant. Before introducing these new education systems, it is better to assess the perception, knowledge and practice of reflective learning among graduates. Hence the study was planned with following Objectives: 1. To understand the perception about reflective learning and writing among interns. 2. To assess the knowledge about reflective learning and writing among interns. 3. To evaluate the practice of reflective learning and writing among interns. Material and methods: A cross sectional study was designed. Study population included all the interns (72) worked in Academy of Medical Sciences during study period. A semi-structured interview format was used to collect data. Likert scale was used to score the answers under the three domains. Content analysis was also done for the narratives given by participants for open ended questions. Results: Majority of interns (95% and 98% respectively) have satisfactory perception score and knowledge score on reflective learning. But majority of interns (94%) do not have satisfactory practice of reflective writing. This is because they are not instructed to do it. Majority are willing to do it if they are guided. They think that it can be introduced into undergraduate curriculum and log book writing during internship. Conclusion: There will be acceptance to reflective learning and writing if it is introduced into curriculum.
Sensor Selection to Improve Estimates of Particulate Matter Concentration from a Low-Cost Network

Deployment of low-cost sensors in the field is increasingly popular. However, each sensor requires on-site calibration to increase the accuracy of the measurements. We established a laboratory method, the Average Slope Method, to select sensors with similar response so that a single, on-site calibration for one sensor can be used for all other sensors. The laboratory method was performed with aerosolized salt. Based on linear regression, we calculated slopes for 100 particulate matter (PM) sensors, and 50% of the PM sensors fell within ±14% of the average slope. We then compared our Average Slope Method with an Individual Slope Method and concluded that our first method balanced convenience and precision for our application. Laboratory selection was tested in the field, where we deployed 40 PM sensors inside a heavy-manufacturing site at spatially optimal locations and performed a field calibration to calculate a slope for three PM sensors with a reference instrument at one location. The average slope was applied to all PM sensors for mass concentration calculations. The calculated percent differences in the field were similar to the laboratory results. Therefore, we established a method that reduces the time and cost associated with calibration of low-cost sensors in the field.
Compensation for Domain Mismatch in Text-independent Speaker Recognition

Domain mismatch continues to be a major research challenge for speaker recognition in naturalistic audio streams. This study presents a new technique for domain mismatch compensation within a text-independent speaker recognition scenario. The proposed method is designed for the NIST speaker recognition evaluation 2016 (SRE16) task, where speakers from training, development and evaluation data belong to different sets of languages. An i-vector/PLDA speaker recognition system is adopted for this study. To address the mismatch problem, we propose to append auxiliary features to the i-vectors. These auxiliary features are adapted representations of the i-vectors to the speciﬁc in-domain data; therefore, the new feature vector has two parts: (1) i-vectors which represent speaker identity and (2) auxiliary features which are representations of i-vectors in the in-domain data feature space (and may not contain speaker identity information). This new concatenated feature vector (we call this a-vector ) is then post-processed with support vector discriminant analysis (SVDA) for further domain compensation. Evaluations based on the SRE16 conﬁrm the effectiveness of the proposed technique. In terms of minimum Cprimary cost, a-vector outperforms the i-vector consistently. Moreover, comparing to previous single systems introduced for SRE16, we achieved 8.5%-18% improvements in terms of equal error rate.
UNiCS: The Open Data Platform for Research and Innovation

The paper introduces UNiCS, the open platform for Research and Innovation data management. The UNiCS platform follows the Ontology-Based Data Access (OBDA) approach, which eases the access to a vast amount of heterogeneous data and offers the final users the possibility to formulate queries using terms from the knowledge domain they are experts in. In UNiCS, each query gets transformed in a set of optimised queries to different data sources. Moreover, the OBDA approach makes the semantics of the data explicit, thus offering an intuitive way to access, explore, visualise, analyse, and post-process them.
Multiple Mechanisms Inactivate the LIN-41 RNA-Binding Protein To Ensure a Robust Oocyte-to-Embryo Transition in Caenorhabditis elegans

In the nematode Caenorhabditis elegans, the conserved LIN-41 RNA-binding protein is a translational repressor that coordinately controls oocyte growth and meiotic maturation. LIN-41 exerts these effects, at least in part, by preventing the premature activation of the cyclin-dependent kinase CDK-1. Here we investigate the mechanism by which LIN-41 is rapidly eliminated upon the onset of meiotic maturation. Elimination of LIN-41 requires the activities of CDK-1 and multiple SCF (Skp1, Cul1, and F-box protein)-type E3 ubiquitin ligase subunits, including the conserved substrate adaptor protein SEL-10/Fbw7/Cdc4, suggesting that LIN-41 is a target of ubiquitin-mediated protein degradation. Within the LIN-41 protein, two nonoverlapping regions, Deg-A and Deg-B, are individually necessary for LIN-41 degradation; both contain several potential phosphodegron sequences, and at least one of these sequences is required for LIN-41 degradation. Finally, Deg-A and Deg-B are sufficient, in combination, to mediate SEL-10-dependent degradation when transplanted into a different oocyte protein. Although LIN-41 is a potent inhibitor of protein translation and M phase entry, the failure to eliminate LIN-41 from early embryos does not result in the continued translational repression of LIN-41 oocyte messenger RNA targets. Based on these observations, we propose a model for the elimination of LIN-41 by the SEL-10 E3 ubiquitin ligase and suggest that LIN-41 is inactivated before it is degraded. Furthermore, we provide evidence that another RNA-binding protein, the GLD-1 tumor suppressor, is regulated similarly. Redundant mechanisms to extinguish translational repression by RNA-binding proteins may both control and provide robustness to irreversible developmental transitions, including meiotic maturation and the oocyte-to-embryo transition.
Performance Analysis of Symmetric Key Ciphers in Linear and Grid Based Sensor Networks

The linear and grid based Wireless Sensor Networks (WSN) are formed by applications where objects being monitored are either placed in linear or grid based form. E.g. monitoring oil, water or gas pipelines; perimeter surveillance; monitoring traffic level of city streets, goods warehouse monitoring. The security of data is a critical issue for all such applications and as the devices used for the monitoring purpose have several resource constraints (bandwidth, storage capacity, battery life); it is significant to have a lightweight security solution. Therefore, we consider symmetric key based solutions proposed in the literature as asymmetric based solutions require more computation, energy and storage of keys. We analyse the symmetric ciphers with respect to the performance parameters: RAM, ROM consumption and number of CPU cycles. We perform this simulation analysis in Contiki Cooja by considering an example scenario on two different motes namely: Sky and Z1. The aim of this analysis is to come up with the best suited symmetric key based cipher for the linear and grid based WSN.
Character-level Language Modeling with Gated Hierarchical Recurrent Neural Networks

Recurrent neural network (RNN)-based language models are widely used for speech recognition and translation applications. We propose a gated hierarchical recurrent neural network (GHRNN) and apply it to the character-level language modeling. GHRNN consists of multiple RNN units that operate with different time scales, and the frequency of operation at each unit is controlled by the learned gates from training data. In our model, GHRNN learns the hierarchical structure of character, sub-word, and word. Timing gates are included in the hierarchical connections to control the operating frequency of these units. The performance was measured for Penn Treebank and Wikitext-2 datasets. Experimental results showed lower bit per character (BPC) when compared to simply layered or skip-connected RNN models. Also, when a continuous cache model is added, the BPC of 1.192 is recorded, which is comparable to the state of the art result.
Wami Object Tracking Using L1Tracker Integrated with a Deep Detector

We propose an object tracking method for Wide Area Motion Imagery (WAMI) video sequences, which models the tracking as a regularization problem through sparse representation of aerial video content. The proposed object tracker, Ll Dpct, applies particle filter tracking, and unlike the existing methods, it integrates a deep-learning-based object detector into the regularization scheme to improve the tracking performance. In order to enhance robustness to occlusion and scale changes, Ll Dpct monitors the state propagation, the level of sparsity as well as the representation capability of the model and receives feedback from the detector to update the observation model of the particle filter. Ll Dpct incrementally updates the dictionary of the sparse representation that enables us to efficiently represent the appearance changes of the object arising from illumination changes and high motion. Numerical results obtained on commonly used VIVID and UAV123 datasets denote that Ll Dpct significantly improves the object tracking performance in terms of precision rate and success rate compared to the state-of-the-art trackers.
Oxygen Uptake, Heart Rate, and Lactate Responses for Continuous Forward Running and Stop-and-Go Running With and Without Directional Changes.

Bekraoui, N, Boussaidi, L, Cazorla, G, and Léger, L. Oxygen uptake, heart rate, and lactate responses for continuous forward running and stop-and-go running with and without directional changes. J Strength Cond Res XX(X): 000-000, 2018-The purpose of this study is to measure and quantify the differences for the gross oxygen uptake (V[Combining Dot Above]O2), heart rate (HR), and blood lactate ([LA]) responses to various modes of in-line running. Twelve healthy participants did an indirect continuous multistage field test to determine the maximal aerobic speed (MAS) before randomly performing in separate sessions 1 of the 3 running protocols at 3 intensities (60, 70, and 80% MAS) (a) in-line continuous running (IR), (b) fractioned running (FR) with stop and go every 20 m, and (c) 20-m shuttle run (SR). V[Combining Dot Above]O2 varied between 34.2 ± 2.5 and 54.6 ± 5.4 ml·kg·min, HR between 141 ± 14 and 184 ± 9 b·min, and [LA] between 4.2 ± 0.8 and 6.6 ± 0.9 mmol·L. In-line continuous running V[Combining Dot Above]O2 was lower than those of SR and FR at all intensities (p < 0.05). However, SR V[Combining Dot Above]O2 were higher than those of FR at 80% MAS only. Heart rate was similar in SR and FR but lower in IR at all intensities (p < 0.05). No significant differences were recorded in [LA]. Because only V[Combining Dot Above]O2 was affected by the introduction of a 180° angle directional change (SR vs. FR) at 80% MAS, the use of HR to assess such activities at such intensities will result in underestimating workloads. This study offers new insights into V[Combining Dot Above]O2, HR, and [LA] associated with accelerations while running with and without directional changes at submaximal speeds.
Polarization-Based Car Detection

Road scene understanding is a vital task for driving assistance systems. Robust vehicle detection is a precondition for diverse applications particularly for obstacle avoidance and secure navigation. Color images provide limited information about the physical properties of the object. This results in unstable vehicle detection caused mainly from road scene complexity (strong reflexions, noises and radiometric distortions). Instead, polarimetric images, characteristic of the light wave, can robustly describe important physical properties of the object (e.g., the surface geometric structure, material and roughness etc). This modality gives rich physical informations which could be complementary to classical color images features. In order to improve the robustness of the vehicle detection purpose, we propose in this paper a fusion model using polarization information and color image attributes. Our method is based on a feature selection procedure to get the most informative polarization feature and color-based ones. The proposed method, based on the Deformable Part based Models (DPM), has been evaluated on our self-collected database, showing good performances and encouraging results about the use of the polarimetric modality for road scenes analysis.
Two View Constraints on the Epipoles from Few Correspondences

In general it requires at least 7 point correspondences to compute the fundamental matrix between views. We use the cross ratio invariance between corresponding epipolar lines, stemming from epipolar line homography, to derive a simple formulation for the relationship between epipoles and corresponding points. We show how it can be used to reduce the number of required points for the epipolar geometry when some information about the epipoles is available and demonstrate this with a buddy search app.
Alzheimer'S Disease Diagnosis with FDG-PET Brain Images By Using Multi-Level Features

FluoroDeoxyGlucose Positron Emission Tomography (FDG- PET) is an important and effective modality used for diagnosing Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI). In this paper, we develop a novel method by using single modality (FDG- PET) but multi-level features, which considers both region properties and connectivities between regions, to diagnose AD or MCI. First, post-processed FDG-PET images are segmented into 116 Regions of Interest according to Automated Anatomical Labeling atlas. Second, three levels of features are extracted. Then the 2nd-Level feature is decomposed into 3 different sets of features according to a proposed similarity-driven ranking method, which can not only reduce the feature dimension but also increase the classifier's diversity. Last, after feeding the 3 levels of features to different classifiers, the majority voting, is applied to make the prediction. Experiments on ADNI database show that the proposed method outperforms other FDG-PET-based classification algorithms.
A Knowledge Driven Structural Segmentation Approach for Play-Talk Classification During Autism Assessment

Automatically segmenting conversational audio into semantically relevant components has both computational and analytical signiﬁcance. In this paper, we segment play activities and conversational portions interspersed during clinically-administered interactions between a psychologist and a child with autism spectrum disorder (ASD). We show that various acoustic-prosodic and turn-taking features commonly used in the literature differ between these segments, and hence can possibly inﬂuence further inference tasks. We adopt a two-step approach for the segmentation problem by taking advantage of the structural relation between the two segments. First, we use a supervised machine learning algorithm to estimate class pos-teriors at frame-level. Next, we use an explicit-duration hidden Markov model (EDHMM) to align the states using the posteri-ors from the previous step. The durational distributions for both play and talk regions are learnt from training data and modeled using the EDHMM. Our results show that speech features can be used to successfully discriminate between play and talk activities, each providing important insights into the child’s condition.
Fast and Robust Disparity Estimation for Noisy Light Fields

Depth (disparity) estimation from a light field (a set of dense multiview images) has attracted much research interest recently. This paper is focused on how to handle noisy light field for disparity estimation’ because if left as it is the noise deteriorates the accuracy of estimated disparity maps. Several researchers have worked on this problem, e.g. by introducing disparity cues that are robust to noise. However, it is not easy to break the trade-off between the accuracy and computational speed. To tackle this trade-off, we have integrated a fast denoising scheme in a fast disparity estimation framework that works in the epipolar plane image (EPI) domain. Specifically, we found that a simple 1-D slanted filter is very effective for reducing noise while preserving the underlying structure in an EPI. Experimental results show that our method can achieve good accuracy with much less computational time compared to some state-of-the-art methods.
A Low-Dimensional Feature Transform for Keypoint Matching and Classification of Point Clouds without Normal Computation

Most feature descriptors need point normal information to be computed prior to computing the descriptor itself. We present a descriptor transform and apply it to the SHOT descriptor that allows to entirely omit the computation of normals. Further, our transform reduces the number of descriptor dimensions of SHOT by more than 90%, decreasing the computational requirements for feature matching and the memory footprint for feature storage. Despite the heavy reduction in the number of dimensions, our approach retains a high descriptiveness and the computational efficiency of SHOT. We evaluate the proposed transform on datasets for keypoint matching and 3D shape classification and show that it is competitive with state of the art descriptors.
Forward-Backward Attention Decoder

This paper investigates how forward and backward attentions can be integrated to improve the performance of attention-based sequence-to-sequence (seq2seq) speech recognition systems. In the proposed approach, speech is decoded from left to right as well as from right to left utilizing forward and backward attention vectors, and the best sentence hypothesis is searched for according to combined probabilities provided by the decoders of two directions. Our method takes advantage of two distinct and complementary ways of extracting information from the asymmetric time structure of speech. It also mitigates a drawback of attention-based models that they tend to output less reliable labels due to error accumulation when the utterance becomes longer. We also show the effectiveness of a multitask learning in which the forward decoder is jointly trained with back-ward decoding sharing a single encoder. The proposed forward-backward decoding improved word error rates (WERs) of word-level attention models by up to 12.7 % relative in speech recognition experiments using large-scale spontaneous speech corpora. They achieve much higher performances than a state-of-the-art hybrid DNN-HMM system while retaining the advantage of very low latency.
Using Prosodic and Lexical Information for Learning Utterance-level Behaviors in Psychotherapy

In this paper, we present an approach for predicting utterance level behaviors in psychotherapy sessions using both speech and lexical features. We train long short term memory (LSTM) networks with an attention mechanism using words, both manually and automatically transcribed, and prosodic features, at the word level, to predict the annotated behaviors. We demonstrate that prosodic features provide discriminative information relevant to the behavior task and show that they improve prediction when fused with automatically derived lexical features. Additionally, we investigate the weights of the attention mechanism to determine words and prosodic patterns which are of importance to the behavior prediction task.
Automatic Question Detection from Acoustic and Phonetic Features Using Feature-wise Pre-training

This paper presents a novel question detection method from nat-ural speech using acoustic and phonetic features. The conventional methods based on Recurrent Neural Networks (RNNs) use only acoustic features. However, lexical cues are essential to identify some questions such as declarative questions. To this end we propose a new RNN-based question detection model which utilizes both acoustic and lexical information. Phonetic features which are suitable to describe interrogative cues are used as lexical information. Furthermore, we also propose a new training framework named feature-wise pre-training (FP) to combine the acoustic and phonetic features effectively. FP attempts to acquire interrogative cues in individual features instead of the combination of the features, which makes the model training more stable. The estimation models of the interroga-tives are then integrated and ﬁne-tuning is applied to obtain the uniﬁed comprehensive model. Experiments show that the proposed method offers better performance than the conventional benchmarks.
Multilingual Cross-domain Perspectives on Online Hate Speech

In this report, we present a study of eight corpora of online hate speech, by demonstrating the NLP techniques that we used to collect and analyze the jihadist, extremist, racist, and sexist content. Analysis of the multilingual corpora shows that the different contexts share certain characteristics in their hateful rhetoric. To expose the main features, we have focused on text classification, text profiling, keyword and collocation extraction, along with manual annotation and qualitative study.
Segmental Encoder-Decoder Models for Large Vocabulary Automatic Speech Recognition

It has been known for a long time that the classic Hidden-Markov-Model (HMM) derivation for speech recognition contains assumptions such as independence of observation vectors and weak duration modeling that are practical but unrealistic. When using the hybrid approach this is ampliﬁed by trying to ﬁt a discriminative model into a generative one. Hidden Conditional Random Fields (CRFs) and segmental models (e.g. Semi-Markov CRFs / Segmental CRFs) have been proposed as an alternative, but for a long time have failed to get traction until recently. In this paper we explore different length modeling approaches for segmental models, their relation to attention-based systems. Furthermore we show experimental results on a hand-writing recognition task and to the best of our knowledge the ﬁrst reported results on the Switchboard 300h speech recognition corpus using this approach.
Multi-channel Attention for End-to-End Speech Recognition

Recent end-to-end models for automatic speech recognition use sensory attention to integrate multiple input channels within a single neural network. However, these attention models are sensitive to the ordering of the channels used during training. This work proposes a sensory attention mechanism that is invariant to the channel ordering and only increases the overall parameter count by 0.09%. We demonstrate that even without re-training, our attention-equipped end-to-end model is able to deal with arbitrary numbers of input channels during inference. In comparison to a recent related model with sensory attention, our model when tested on the real noisy recordings from the multi-channel CHiME-4 dataset, achieves a relative character error rate (CER) improvement of 40.3% to 42.9%. In a two-channel conﬁguration experiment, the attention signal allows the lower signal-to-noise ratio (SNR) sensor to be identiﬁed with 97.7% accuracy.