Miracle-dz/UniFAR-FTUs huggingface dataset:
Dysphagia and poor oral health: significant risk factors of aspiration pneumonia in frail older people

Objective: To systematically review the risks for aspiration pneumonia in frail older people and the contribution of bad oral health among the risk factors. Design: Systematic literature review. Setting: Pubmed (Medline), Web of Science, Cochrane Library, EMBASE and CINAHL were searched for eligible studies, published in English in the period January 2000 to April 2009. Participants: Frail older people. Measurements: Only publications with regard to hospitalized, institutionalized, or frail homedwelling people of 60 years and older were eligible. Two authors independently assessed the publications for their methodological quality. Unadjusted and adjusted odds ratios and their corresponding 95% confidence intervals for respective risk factors related to aspiration pneumonia were extracted. The results were evaluated according to the levels of evidence of the Oxford Centre for Evidence-based Medicine. Results: A total of 21 publications fulfilled the quality criteria. Evidence level 2a (systematic review with homogeneity of cohort studies) was found for a positive relationship between aspiration pneumonia and age, male gender, lung diseases, dysphagia, and diabetes mellitus; 2b (individual cohort study) for severe dementia, angiotensin I-converting enzyme deletion/ deletion genotype, and bad oral health; 3a (systematic review with homogeneity of casecontrol studies) for malnutrition; 3b (individual case-control study) for Parkinson’s disease and the use of antipsychotic drugs, proton pump inhibitors, and angiotensin-converting enzyme inhibitors. The contribution of bad oral health among the risk factors seems limited. Conclusion: Thirteen significant risk factors were identified: age, male gender, lung diseases, dysphagia, diabetes mellitus, severe dementia, angiotensin I-converting enzyme deletion/ deletion genotype, bad oral health, malnutrition, Parkinson’s disease, and the use of antipsychotic drugs, proton pump inhibitors, and angiotensin-converting enzyme inhibitors. The contribution of bad oral health seems limited.
Novel aspects of pain management : opioids and beyond

Neurophysiology of Acute and Chronic Pain (A. Dickenson & A. Reeve). Animal Models of Pain (A. Cowan). Recent Advances in the Pharmacology of Opioids (M. Ossipov, et al.). Some New Insights into the Pharmacology of Nonsteroidal Anti--Inflammatory Drugs (K. McCormack). Peripherally Acting Analgesic Agents (H. Rang, et al.). Vanilloids as Analgsics (A. Dray). Neurokinin Antagonists (N. Rupniak & R. Hill). Excitatory Amino Acid Antagonists: Potential Analgesics for Persistent Pain (T. Coderre). Alpha--2 Adrenergic Agonists as Analgesics (T. Yaksh). Serotonin and Its Receptors in Pain Control (M. Hamon & S. Bourgoin). Purines: Potential for Development as Analgesic Agents (J. Sawynok & A. Poon). gamma--Aminobutyric Acid and Pain (N. Bowery & M. Malcagio). Cholinergic Agonists as Analgesics (E. Iwamoto). Dopaminergic Drugs as Analgesics (K. Franklin). Tricyclic and Other Antidepressants as Analgesics (A. Eschalier, et al.). Voltage--Gated Ion Channel Modulators (J. Hunter). Spinal Drug Interactions (J. Eisenach & G. Gebhart). Index.
Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework

End-to-end sequence generation is a popular technique for developing open domain dialogue systems, though they suffer from the safe response problem. Researchers have attempted to tackle this problem by incorporating generative models with the returns of retrieval systems. Recently, a skeleton-then-response framework has been shown promising results for this task. Nevertheless, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator are still challenging. This paper presents a novel framework in which the skeleton extraction is made by an interpretable matching model and the following skeleton-guided response generation is accomplished by a separately trained generator. Extensive experiments demonstrate the effectiveness of our model designs.
Attention Optimization for Abstractive Document Summarization

Attention plays a key role in the improvement of sequence-to-sequence-based document summarization models. To obtain a powerful attention helping with reproducing the most salient information and avoiding repetitions, we augment the vanilla attention model from both local and global aspects. We propose attention refinement unit paired with local variance loss to impose supervision on the attention model at each decoding step, and we also propose a global variance loss to optimize the attention distributions of all decoding steps from the global perspective. The performances on CNN/Daily Mail dataset verify the effectiveness of our methods.
Improving students' learning with a mobile augmented reality approach - the EduPARK game

The purpose of this paper is to present a survey study that analyzes mobile learning through students’ opinion regarding the use of mobile devices for learning, including their advantages and difficulties, as well as the educational value of a specific mobile learning strategy, reified in the EduPARK game, after an experience of exploring it in formal and non-formal educational contexts. ,This paper comprises a survey study where mobile learning is analyzed through the eyes of 244 students attending the second or third cycles of basic education. The acknowledged advantages and difficulties of the use of mobile devices for learning, as well as the educational value and usability of a specific mobile learning strategy, using the EduPARK app and game, were analyzed. ,Results revealed that most students owned a mobile device and were able to use them to learn. They had a positive perspective regarding mobile learning and valued the advantages of being easy to find up-to-date information, motivating for learning and easy to carry along. Difficulties are related to the use of mobile devices, such as requiring an internet connection, its slowness and prohibition of mobile devices in schools. The EduPARK game achieved an average educational value scale of 83.8 and an average system usability scale of 80.2, indicating its high educational value and usability for students. ,This paper presents empirical evidence regarding the effectiveness of the integration of mobile game-based augmented reality approaches in basic education to promote students’ learning. It also includes an example of excellent cross-subjects educational materials comprising a very useful tool for teachers and students to explore scientific knowledge by accessing appealing information on biological and historical references of a local urban park.
Evaluating and Improving Large-Scale Machine Learning Frameworks

Given the increasing popularity of Machine Learning, and the ever increasing need to solve larger and more complex learning challenges, it is unsurprising that numerous distributed learning strategies have been brought forward in recent years, along with many large scale Machine Learning frameworks. It is however unclear how well these strategies perform across different cluster and batch sizes, or what their hardware demands are, as there is little research in the public domain on this matter. Identifying the weaknesses and limitations of the parameter update strategies is, however, essential towards increasing the efficiency of large scale Machine Learning and making it commonplace. This thesis seeks to find the answers to these aforementioned issues, and provide evidence of the strategies’ limitations and the root causes behind them. To make the study possible, the thesis looks into particular implementations of the strategies within the TensorFlow and Caffe2 frameworks.
Do sleep and anesthesia share common multifractal EEG dynamics? Insights from adversarial domain adaptation

The brain displays scale-free and multifractal dynamics that change across different states of consciousness. Whether the multifractal properties of EEG data change in a similar way when shifting from a conscious to an unconscious state compared to shifting from wakefulness to sleep is still largely unknown. To address this we ask a slightly different question: How well can we use a classifier trained on sleep EEG multifractality data to correctly discriminate conscious and unconscious states. To this end, we used a Domain Adversarial Neural Network (DANN) framework geared towards discriminating neural signals recorded during conscious vs unconscious states (target domain), based on classification of brain signals recorded during wakefulness vs sleep (source domain). We compare results obtained with naïve transfer learning (no domain adaptation), with supervised and unsupervised domain adaptation. The input data consisted of multifractal parameters computed from EEG recordings. This paper reports two important findings: First, our analyses provide evidence for the feasibility of creating a DANN architecture that can learn to discriminate consciousness from anesthetic-induced unconsciousness via adversarial adaptation of sleep/wakefulness discrimination. Second, by exploring the topographies of the successful classification rates across the EEG array, we were able to identify functional similarities of EEG multifractality patterns across sleep and anesthesia.
ProSeqo: Projection Sequence Networks for On-Device Text Classification

We propose a novel on-device sequence model for text classification using recurrent projections. Our model ProSeqo uses dynamic recurrent projections without the need to store or look up any pre-trained embeddings. This results in fast and compact neural networks that can perform on-device inference for complex short and long text classification tasks. We conducted exhaustive evaluation on multiple text classification tasks. Results show that ProSeqo outperformed state-of-the-art neural and on-device approaches for short text classification tasks such as dialog act and intent prediction. To the best of our knowledge, ProSeqo is the first on-device long text classification neural model. It achieved comparable results to previous neural approaches for news article, answers and product categorization, while preserving small memory footprint and maintaining high accuracy.
A Survey on Information and Communication Technologies for Industry 4.0: State-of-the-Art, Taxonomies, Perspectives, and Challenges

A new industrial revolution is undergoing, based on a number of technological paradigms. The will to foster and guide this phenomenon has been summarized in the expression “Industry 4.0” (I4.0). Initiatives under this term share the vision that many key technologies underlying Cyber-Physical Systems and Big Data Analytics are converging to a new distributed, highly automated, and highly dynamic production network, and that this process needs regulatory and cultural advancements to effectively and timely develop. In this work, we focus on the technological aspect only, highlighting the unprecedented complexity of I4.0 emerging from the scientific literature. While previous works have focused on one or up to four related enablers, we consider ten technological enablers, including besides the most cited Big Data, Internet of Things, and Cloud Computing, also others more rarely considered as Fog and Mobile Computing, Artificial Intelligence, Human-Computer Interaction, Robotics, down to the often overlooked, very recent, or taken for granted Open-Source Software, Blockchain, and the Internet. For each we explore the main characteristics in relation to I4.0 and its interdependencies with other enablers. Finally we provide a detailed analysis of challenges in leveraging each of the enablers in I4.0, evidencing possible roadblocks to be overcome and pointing at possible future directions of research. Our goal is to provide a reference for the experts in some of the technological fields involved, for a reconnaissance of integration and hybridization possibilities with other fields in the endeavor of I4.0, as well as for the laymen, for a high-level grasp of the variety (and often deep history) of the scientific research backing I4.0.
E-service quality and actual use of e-banking: Explanation through the Technology Acceptance Model

Due to recent technological developments in banking, the use of e-banking is becoming increasingly widespread. This study explored the effect of e-service quality on actual use of e-banking services and explained it through the Technology Acceptance Model. A survey was conducted in Pakistan and 493 questionnaires were collected from e-banking users and the data was analyzed using AMOS 20 and SPSS 20. The results of the study indicate that e-service quality positively affects perceived usefulness, perceived ease of use and intention to use e-banking and perceived usefulness positively affects attitude towards using e-banking and behavioral intentions to use e-banking. Perceived ease of use affects attitudes towards e-banking and attitudes positively affect behavioral intentions to use e-banking and behavioral intentions further positively affect actual use of e-banking. The sequential mediation of attitude and intention has been confirmed between e-service quality and actual use of e-banking. This study has significance for the managers of banking industry and will enable them to influence the attitude of customers in favor of their services and also help them devise strategies to improve their profitability.
Differentiable Cloth Simulation for Inverse Problems

We propose a differentiable cloth simulator that can be embedded as a layer in deep neural networks. This approach provides an effective, robust framework for modeling cloth dynamics, self-collisions, and contacts. Due to the high dimensionality of the dynamical system in modeling cloth, traditional gradient computation for collision response can become impractical. To address this problem, we propose to compute the gradient directly using QR decomposition of a much smaller matrix. Experimental results indicate that our method can speed up backpropagation by two orders of magnitude. We demonstrate the presented approach on a number of inverse problems, including parameter estimation and motion control for cloth.
Technology Integration in Economic Learning: Is it effective?

The fast development of technology and information has a significant impact on all activities of human life. The industrial era 4.0 that is now happening, has fundamentally changed the way of thinking and activities and has a great influence on the world of education. The development of this era is characterized by the use of information and communication technology in all aspects of life, including in the learning process. Classrooms as a place to change, improve, and prepare students skills to compete in the community of industrial revolution 4.0 era. Therefore creative thinking, critical thinking and communication skills and the ability to use interactive and innovative media are important competencies in entering human life in this century. To achieve learning success and be able to compete in a global community, students must become experts and possess skills as communicators, creators, critical thinkers, and collaborators. One solution to overcome this problem is to use online learning media in online business learning at Vocational Schools. The purpose of this study is to find out the effectiveness of online learning media in online business learning in vocational schools, especially in improving the creativity of students. The method used in this study is the Experiment method using Factorial 2x2 design. The subjects in this study were students in class XI at Kuningan State Vocational High School. The results of the study show the enthusiasm of the students, this means that online learning media have a good influence on the learning process and enhance the creativity of students. Thus it can be concluded that online learning media are effective in improving students creative thinking skills. In addition, the results of the study also conclude that there are interactions between online learning media and online business interests towards students creative thinking abilities. Keywords—online media; interest in doing business; creative thinking ability
Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal

This paper describes a method of variable beam size inference for Recurrent Neural Network Grammar (rnng) by drawing inspiration from sequential Monte-Carlo methods such as particle filtering. The paper studies the relevance of such methods for speeding up the computations of direct generative parsing for rnng. But it also studies the potential cognitive interpretation of the underlying representations built by the search method (beam activity) through analysis of neuro-imaging signal.
Residual U-Net for Retinal Vessel Segmentation

In recent years, the influence of deep learning on retinal vessel segmentation has grown rapidly. Most of the available deep learning based methods use relatively shallow structures. However, due to the limited representative capacity, shallow networks will restrain deep learning models to segment both vessel and non-vessel pixels accurately. In this paper, we propose a residual U-Net for retinal vessel segmentation. Our network has several advantages. First, the network uses a new residual block structure. In the new structure, batch normalization layers are placed before the activation unit to achieve better performance and accelerate the convergence. Also, a dropout layer is utilized in the structure to alleviate over-fitting problems. Second, the depth of the network is increased by adding more residual blocks and strong dropouts which then allow the network to extract features better. Fundus images from the publicly available DRIVE and STARE datasets are used to evaluate the proposed network. Experimental result shows that the proposed modified residual U-Net has better performance than existing state-of-the-art algorithms.
Tracking Motivational Biases and Their Suppression in Time and Space

Action selection is not only based on acquired knowledge about action-outcome contingencies, but also by evolutionary "priors" such as motivational biases: Organisms show a tendency to invigorate responding when hoping for rewards, and to hold back when attempting to avoid punishments. While these biases are likely adaptive in many situations, they need to be inhibited when maladaptive. We probed the neural basis of overcoming these biases by measuring simultaneous EEG and fMRI. Successful detection and suppression of biases was associated with an increased synchronization in the alpha band 175-325 ms post-stimulus, which on a trial-by-trial basis was negatively correlated with BOLD signal in left MFG and right SMG. At a later time window around responses, there was a much stronger synchronization for executed vs. withheld actions in lower frequencies (peak in theta band), which was positively correlated on a trial-by-trial basis with BOLD signal in ACC/ SMA as well as bilateral motor cortex and operculum. Our work spatially locates oscillatory signatures of action selection and motivational conflict resolution.
Spam Detection using Sentiment Analysis of Text

: Nowadays, a big part of people rely on available content in social media in their decisions (e.g. reviews and feedback on a topic or product). The possibility that anybody can leave a review provides a golden opportunity for spammers to write spam reviews about products and services for different interests. Identifying these spammers and the spam content is a hot topic of research and although a considerable number of studies have been done recently toward this end, but so far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature type. In this study, we propose a novel framework, named NetSpam, which utilizes spam features for modeling review datasets as heterogeneous information networks to map spam detection procedure into a classification problem in such networks.
Supervised Polsar Image Classification by Combining Multiple Features

For polarimetric synthetic aperture radar (PolSAR) image classification, each pixel can be represented by multiple features from different perspectives, such as polarimetric feature (PF), texture feature (TF) and color feature (CF). Both multi-view canonical correlation analysis (MCCA) and multi-view spectral embedding (MSE) are two unsupervised multi-view subspace learning methods which search for different projection matrices for different features to combine multiple features in a common low-dimensional feature space. However, MCCA emphasizes the correlation of multiple features and MSE learns the complementarity of multiple features. To deeply learn the relation of multiple features, we incorporate MCCA with MSE based on the label information and a symmetric version of revised Wishart (SRW) distance for supervised PolSAR image feature extraction. Experimental results confirm that the proposed method can improve the classification performance.
Prevalence of Internal Parasites of Intensively Managed Pigs Located in Nsukka, South-East Nigeria

The prevalence of Gastrointestinal helminths in intensively managed pig farms located in Nsukka, SouthEast Nigeria was investigated in this study. Eighty-three (83) pigs of different ages and sexes from seven farms located in Nsukka LGA were randomly selected for the study. Fecal samples were collected from the rectum of selected animals and subjected to standard parasitological analysis. Of the 83 pigs examined, 20 animals (24.10%) were infected with Gastrointestinal (GI) parasite species. Three types of GI parasites were observed in infected animals and they include Hyostrongylus rubidus (18.07%), Ascaris suum (2.62%) and Trichuris suis (2.41%). There were no observed cases of mixed infections in infected animals. Infection was also found to be higher in male (31.58%) than in female pigs. Adult pigs (25.71%) were however more infected than younger pigs. In conclusion, there is need for better attention to be paid to improved sanitary conditions, improved management practices and better veterinary intervention with an effective anthelminthic programme in the study area. Also with regard to the observed tendency of the farmers to self-medicate their animals, constant surveillance and monitoring as well as public education campaigns is advocated to prevent the onset of resistance to available therapeutics.
Continuous Hand Gesture Spotting and Classification Using 3D Finger Joints Information

This paper presents a novel approach for continuous dynamic hand gesture recognition for RGB video input. Our approach contains two main modules. Firstly, in the gesture spotting module, the video sequence with continuous gestures are pre-segmented into isolated gestures. Secondly, the gesture classification module classifies the segmented gestures. In the gesture spotting module, the motion of the hand palm and finger movements are fed into Bidirectional Long Short-Term Memory (Bi-LSTM) network for gesture spotting purpose. In the gesture classification module, two 3D Convolution Neural Networks (3D_CNN) and one Long Short-Term Memory (LSTM) network are combined to efficiently utilize the combination of multiple data channels such as RGB, Optical Flow, and 3D key pose positions. The promising performance of our approach is obtained by experiments conducted on three publish datasets – Chalearn LAP ConGD dataset, 20BN-Jester, and NVIDIA Dynamic Hand gesture Dataset. Our approach achieves mean Jaccard Index of 0.5535, which outperforms the state-of-the-art methods on Chalearn LAP ConGD dataset.