Miracle-dz/UniFAR-FTUs huggingface dataset:
How to evaluate emotional experiences in television drama series: improving viewer evaluations using a combination of psychophysiological measurements and self-reports*

ABSTRACT This paper describes experiences with new methodological approaches utilised in early viewer evaluations of a public service television drama series. We would like to challenge self-reports as the only source of data in viewer evaluations of television drama series. Based on two viewer evaluation studies of one-hour drama series, both conducted in an applied research setting, we outline pros and cons of electroencephalography (EEG) and skin conductance (SC) measurements tested in combination with in-depth interviews and written self-reports. In conclusion, we suggest a research design consisting of skin conductance measurement with written self-reports as the preferred method for viewer evaluations of television drama series conducted in an applied research setting where time and money is an important constraint.
Positive expiratory pressure breathing speeds recovery of postexercise dyspnea in chronic obstructive pulmonary disease

OBJECTIVES Faster recovery of postexertional dyspnea might enable chronic obstructive pulmonary disease (COPD) patients to undertake more physical activity. The purpose of this study was to determine whether breathing with a positive expiratory load to reduce dynamic hyperinflation (DH) would hasten recovery. METHODS Thirteen male COPD patients (59 ± 7 years; Global Initiative for Obstructive Lung Disease Stages II and III) took part in a randomized cross-over trial in which they exercised by self-paced spot marching. Interventions at the end of exercise consisted of six breaths against either a 5-cm H2 O expiratory load (positive expiratory pressure [PEP]) or no load (Sham), with 3-hr rest between interventions. Recovery was followed for the next 10 min. Primary outcome measures were dyspnea during recovery and inspiratory capacity (IC), measured at rest, at the end of exercise and after the intervention; oxygen saturation, end-tidal CO2 , heart rate, and breathing frequency were also monitored. RESULTS Patients exercised for 5 min reaching a heart rate of 70% age-predicted maximum and developed dyspnea of 3-4 on the Modified Borg CR10 scale. Dyspnea recovered significantly faster after the PEP intervention in all patients, taking 2.8 ± 0.4 min to return to baseline compared with 5.1 ± 0.6 min for Sham (p < 0.01). IC declined at the end of exercise and was improved by PEP (+270 220-460 ml, median, interquartile range) more than Sham (+100, 40-160 ml). However, PEP was equally effective in reducing dyspnea in all patients irrespective of the degree of DH. Changes in oxygen saturation, end-tidal CO2 , heart rate, and breathing frequency were similar in PEP and Sham. CONCLUSIONS Positive expiratory pressure breathing is an effective means of reducing postexercise dyspnea and DH in COPD. The benefits were not limited to patients with high DH suggesting PEP may be used to speed recovery and increase the volume of exercise during pulmonary rehabilitation sessions and physical activity at home or work.
Fundamental Limits of Invisible Flow Fingerprinting

Network flow fingerprinting can be used to de-anonymize communications on anonymity systems such as Tor by linking the ingress and egress segments of anonymized connections. Assume Alice and Bob have access to the input and the output links of an anonymous network, respectively, and they wish to collaboratively reveal the connections between the input and the output links without being detected by Willie who protects the network. Alice generates a codebook where each codeword is a unique fingerprint indicating a sequence of inter-packet delays, and shares it only with Bob. To trace each flow, Alice selects a fingerprint and manipulates the packet timings of the flow to follow the packet timings suggested by the fingerprint, and Bob extracts the fingerprints from it after it passes through the network. We model the network as parallel <inline-formula> <tex-math notation="LaTeX">$M/M/1$ </tex-math></inline-formula> queues where each queue is shared by a flow <inline-formula> <tex-math notation="LaTeX">$f_{i}$ </tex-math></inline-formula> from Alice to Bob and other flows independent of <inline-formula> <tex-math notation="LaTeX">$f_{i}$ </tex-math></inline-formula>. Packet timings of the flows are governed by independent Poisson processes. Assuming all input flows have equal packet rates and that Bob observes only flows with fingerprints, we first present two scenarios: 1) Alice fingerprints all the flows and 2) Alice fingerprints a subset of the flows, unknown to Willie. Then, we extend the construction and analysis to the case of arbitrary flow rates and the case where Bob observes flows with and without fingerprints. For each scenario, we derive the number of flows that Alice and Bob can trace by fingerprinting.
The value of postpartum ultrasound for the diagnosis of retained products of conception: A systematic review

Background The goal of this review is to evaluate the value of ultrasound for detection of retained products of conception (RCOP) after delivery. Methods A systematic search was performed using ‘postpartum’, ‘retained placenta’, ‘retained products’ and ‘ultrasound’ resulting 82 publications, after screening titles and abstracts, 30 remained. Results On gray scale ultrasound, one must be focus on a thickened endometrial echo complex (EEC) with a cut off value of 10 mm and on an intracavitary mass. If these features are not visible, RPOC is rare. However, these findings are neither specific nor conclusive for RPOC and can even be seen in a normal postpartum uterus. Detection of hypervascularity in a thickened EEC or intracavitary mass with color Doppler ultrasound is very sensitive for RPOC but still not specific nor can it exclude RPOC. MRI seems best in differentiating RPOC, arteriovenous malformations and gestational trophoblastic disease. Conclusion There is no consensus on a standardised method for postpartum ultrasound. More research and standardization are necessary to differentiate of normal and pathological findings in the postpartum uterus.
Development and Psychometric Properties of The Delayed Childbearing Questionnaire (DCBQ-55)

The comprehensive assessment of delayed childbearing needs a valid and reliable instrument. Therefore, the aim of the present study was to develop an instrument to evaluate factors influencing delayed childbearing among women and to assess its psychometric properties. The current methodological study was performed in two phases of (i) qualitative instrument development, and (ii) quantitative psychometric assessment of the developed instrument. Face and content validity of the instrument was assessed by eligible women and a panel of experts. Construct validity was assessed using the exploratory factor analysis (EFA). For reliability, internal consistency reliability and intra-rater reliability analysis were used. The initial instrument developed from the qualitative phase consisted of 60 items, which were reduced to 55 items after the face and content validity processes. EFA (n = 300) using the Kaiser criteria (Eigenvalues > 1) and the scree plot led to a six-factor solution accounting for 61.24% of the observed variance. The Cronbach’s alpha coefficient, Spearman’s correlation, test–retest and intra-class correlation coefficients for the whole instrument were reported as 0.83, 0.86 and 0.81, respectively. The final instrument entitled the delayed childbearing questionnaire (DCBQ-55) included 50 items with six domains of ‘readiness for childbearing’, ‘stability in the partner relationship’, ‘awareness about the adverse outcomes of pregnancy in advanced maternal age’, ‘attitude toward delayed childbearing’, ‘family support’, and ‘social support’ on a five-point Likert scale. The DCBQ-55 as a simple, valid and reliable instrument can assess factors influencing delayed childbearing. It can be used by reproductive healthcare providers and policy makers to understand factors influencing delayed childbearing and devise appropriate strategies.
Global Weighted Average Pooling Bridges Pixel-level Localization and Image-level Classification

In this work, we first tackle the problem of simultaneous pixel-level localization and image-level classification with only image-level labels for fully convolutional network training. We investigate the global pooling method which plays a vital role in this task. Classical global max pooling and average pooling methods are hard to indicate the precise regions of objects. Therefore, we revisit the global weighted average pooling (GWAP) method for this task and propose the class-agnostic GWAP module and the class-specific GWAP module in this paper. We evaluate the classification and pixel-level localization ability on the ILSVRC benchmark dataset. Experimental results show that the proposed GWAP module can better capture the regions of the foreground objects. We further explore the knowledge transfer between the image classification task and the region-based object detection task. We propose a multi-task framework that combines our class-specific GWAP module with R-FCN. The framework is trained with few ground truth bounding boxes and large-scale image-level labels. We evaluate this framework on PASCAL VOC dataset. Experimental results show that this framework can use the data with only image-level labels to improve the generalization of the object detection model.
Research on Face Recognition Based on the Fusion of Convolution and Wavelet Neural Network

Intelligent artificial neural network (ANN) is an important method of reasoning and deduction of face recognition, which greatly improves the correctness and efficiency of intelligent decision making. The existed neural network algorithm for face recognition is difficult to take both self-learning and local reasoning into account, making the algorithm of face recognition highly complex and the processing delay too long. In order to solve the problem of high complexity and long delay, this paper analyzes the adaptability of the neural network theory and face recognition. Some newest algorithms are introduced, such as the backward feedback BP neural network, fine-tuning wavelet neural network, self -learning particle swarm neural network, and identity-preserving convolution neural network. A new method of face recognition based on the fusion of convolution and wavelet neural network is proposed, which optimizes the design of dynamic matching input layer, reduces the scale of large-scale data input, designs the implicit layer design of expansion with sharing and self-learning. It effectively achieve fusing self-learning and local reasoning. Besides it also improves the accuracy of face recognition by using the adaptive controllable feedback output layer.
Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs

Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. In this paper, a novel approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. First, the images of the road scene were converted to grayscale images, and then we filtered the grayscale images with simplified Gabor wavelets (SGW), where the parameters were optimized. The edges of the traffic signs were strengthened, which was helpful for the next stage of the process. Second, we extracted the region of interest using the maximally stable extremal regions algorithm and classified the superclass of traffic signs using the support vector machine (SVM). Finally, we used convolution neural networks with input by simplified Gabor feature maps, where the parameters were the same as the detection stage, to classify the traffic signs into their subclasses. The experimental results based on Chinese and German traffic sign databases showed that the proposed method obtained a comparable performance with the state-of-the-art method, and furthermore, the processing efficiency of the whole process of detection and classification was improved and met the real-time processing demands.
Modern Convex Optimization to Medical Image Analysis

Recently, diagnosis, therapy and monitoring of human diseases involve a variety of imaging modalities, such as magnetic resonance imaging(MRI), computed tomography(CT), Ultrasound(US) and Positron-emission tomography(PET) as well as a variety of modern optical techniques. Over the past two decade, it has been recognized that advanced image processing techniques provide valuable information to physicians for diagnosis, image guided therapy and surgery, and monitoring of the treated organ to the therapy. Many researchers and companies have invested significant efforts in the developments of advanced medical image analysis methods; especially in the two core studies of medical image segmentation and registration, segmentations of organs and lesions are used to quantify volumes and shapes used in diagnosis and monitoring treatment; registration of multimodality images of organs improves detection, diagnosis and staging of diseases as well as image-guided surgery and therapy, registration of images obtained from the same modality are used to monitor progression of therapy. These challenging clinical-motivated applications introduce novel and sophisticated mathematical problems which stimulate developments of advanced optimization and computing methods, especially convex optimization attaining optimum in a global sense, hence, bring an enormous spread of research topics for recent computational medical image analysis. Particularly, distinct from the usual image processing, most medical images have a big volume of acquired data, often in 3D or 4D (3D + t) along with great noises or incomplete image information, and form the challenging large-scale optimization problems; how to process such poor 'big data' of medical images efficiently and solve the corresponding optimization problems robustly are the key factors of modern medical image analysis.
Using JSON-LD to Compose Different IoT and Cloud Services

Internet of things and cloud computing are in the widespread use today, and often work together to accomplish complex business task and use cases. This paper propose the framework and its practical implementation to compose different things as services and cloud services. The ontology based approach and JSON-LD was used to semantically annotate both types of services, and enable the mechanism to semiautomatically compose these services. The use case and proof-ofconcept application that use the proposed theoretical approach is also described in this work. Keywords—interoperability; Internet of things; JSON-LD; cloud computing
Use and Effects of Patient Access to Medical Records in General Practice Through a Personal Health Record in the Netherlands: Protocol for a Mixed-Methods Study

Background In the Dutch health care system, general practitioners hold a central position. They store information from all health care providers who are involved with their patients in their electronic health records. Web-based access to the summary record in general practice through a personal health record (PHR) may increase patients’ insight into their medical conditions and help them to be involved in their care. Objective We describe the protocol that we will use to investigate the utilization of patients’ digital access to the summary of their medical records in general practice through a PHR and its effects on the involvement of patients in their care. Methods We will conduct a multilevel mixed-methods study in which the PHR and Web-based access to the summary record will be offered for 6 months to a random sample of 500 polypharmacy patients, 500 parents of children aged <4 years, and 500 adults who do not belong to the former two groups. At the patient level, a controlled before-after study will be conducted using surveys, and concurrently, qualitative data will be collected from focus group discussions, think-aloud observations, and semistructured interviews. At the general practice staff (GP staff) level, focus group discussions will be conducted at baseline and Q-methodology inquiries at the end of the study period. The primary outcomes at the patient level are barriers and facilitators for using the PHR and summary records and changes in taking an active role in decision making and care management and medication adherence. Outcomes at the GP staff level are attitudes before and opinions after the implementation of the intervention. Patient characteristics and changes in outcomes related to patient involvement during the study period will be compared between the users and nonusers of the intervention using chi-square tests and t tests. A thematic content analysis of the qualitative data will be performed, and the results will be used to interpret quantitative findings. Results Enrollment was completed in May 2017 and the possibility to view GP records through the PHR was implemented in December 2017. Data analysis is currently underway and the first results are expected to be submitted for publication in autumn 2019. Conclusions We expect that the findings of this study will be useful to health care providers and health care organizations that consider introducing the use of PHR and Web-based access to records and to those who have recently started using these. Trial Registration Netherlands Trial Registry NTR6395; http://www.trialregister. nl/trialreg/admin/rctview.asp? TC=6395 (Archived by WebCite at http://www.webcitation.org/71nc8jzwM) Registered Report Identifier RR1-10.2196/10193
Size-by-environment interactions: a neglected dimension of species’ responses to environmental variation

In both plant and animal systems, size can determine whether an individual survives and grows under different environmental conditions. However, it is less clear whether and when size-dependent responses to the environment affect population dynamics. Size-by-environment interactions create pathways for environmental fluctuations to influence population dynamics by allowing for negative covariation between sizes within vital rates (e.g., small and large individuals have negatively covarying survival rates) and/or size-dependent variability in a vital rate (e.g., survival of large individuals varies less than small individuals through time). Whether these phenomena affect population dynamics depends on how they are mediated by elasticities (they must affect the sizes and vital rates that matter) and their projected impacts will depend on model functional form (the impact of reduced variance depends on the relationship between the environment and vital rate). We demonstrate these ideas with an analysis of fifteen species from five semiarid plant communities. We find that size-by-environment interactions are common but do not impact long-term population dynamics. Size-by-environment interactions may yet be important for other species. Our approach can be applied to species in other ecosystems to determine if and how size-by-environment interactions allow them to cope with, or exploit, fluctuating environments.
CT Perfusion in Patients with Lung Cancer: Squamous Cell Carcinoma and Adenocarcinoma Show a Different Blood Flow

Objectives To characterize tumour baseline blood flow (BF) in two lung cancer subtypes, adenocarcinoma (AC) and squamous cell carcinoma (SCC), also investigating those “borderline” cases whose perfusion value is closer to the group mean of the other histotype. Materials and Methods 26 patients (age range 36-81 years) with primary Non-Small Cell Lung Cancer (NSCLC), subdivided into 19 AC and 7 SCC, were enrolled in this study and underwent a CT perfusion, at diagnosis. BF values were computed according to the maximum-slope method and unreliable values (e.g., arising from artefacts or vessels) were automatically removed. The one-tail Welch's t-test (p-value <0.05) was employed for statistical assessment. Results At diagnosis, mean BF values (in [mL/min/100g]) of AC group [(83.5 ± 29.4)] are significantly greater than those of SCC subtype [(57.0 ± 27.2)] (p-value = 0.02). However, two central SCCs undergoing artefacts from vena cava and pulmonary artery have an artificially increased mean BF. Conclusions The different hemodynamic behaviour of AC and SCC should be considered as a biomarker supporting treatment planning to select the patients, mainly with AC, that would most benefit from antiangiogenic therapies. The significance of results was achieved by automatically detecting and excluding artefactual BF values.
Open Archive Toulouse Archive

Mainstream approaches to uncertainty modeling in relational databases are probabilistic. Still some researchers persist in proposing representations based on possibility theory. They are motivated by the ability of this latter setting for modeling epistemic uncertainty and by its qualitative nature. Interestingly enough, several possi-bilistic models have been proposed over time, and have been motivated by diﬀerent application needs ranging from database querying, to database design and to data cleaning. Thus, one may distinguish between four diﬀerent frameworks ordered here according to an increasing representation power: databases with (i) layered tuples; (ii) certainty-qualiﬁed attribute values; (iii) attribute values restricted by general possibility distributions; (iv) possibilistic c-tables. In each case, we discuss the role of the possibility-necessity duality, the limitations and the beneﬁt of the representation settings, and their suitability with respect to diﬀerent tasks.
A bridge over troubled water? A qualitative study of primary care patients’ experiences of a rehabilitation program

Purpose The aim of this study was to explore how patients on sick leave experienced and perceived the early collaboration work model (ECM) for rehabilitation. Patients and methods Data were collected via focus group discussions and individual interviews with 15 patients on sick leave, at nine primary health care centers. The systematic text condensation method described by Malterud was used for thematic analysis of meaning and content of data across cases. Results The participants perceived that sharing experiences with others in a similar situation was restorative. They described the importance of coordination in the program as well as the efforts of the rehabilitation coordinator, who helped with daily structure and support in the beginning as well as encouragement throughout the sick leave period. Some participants felt ashamed of being on sick leave and of being outside the community. Conclusion ECM with a rehabilitation coordinator who could share responsibility with the patient during the entire sick leave period was perceived to be of support during rehabilitation.
Artistic Instance-Aware Image Filtering by Convolutional Neural Networks

In the recent years, public use of artistic effects for editing and beautifying images has encouraged researchers to look for new approaches to this task. Most of the existing methods apply artistic effects to the whole image. Exploitation of neural network vision technologies like object detection and semantic segmentation could be a new viewpoint in this area. In this paper, we utilize an instance segmentation neural network to obtain a class mask for separately filtering the background and foreground of an image. We implement a top prior-mask selection to let us select an object class for filtering purpose. Different artistic effects are used in the filtering process to meet the requirements of a vast variety of users. Also, our method is flexible enough to allow the addition of new filters. We use pre-trained Mask R-CNN instance segmentation on the COCO dataset as the segmentation network. Experimental results on the use of different filters are performed. System’s output results show that this novel approach can create satisfying artistic images with fast operation and simple interface.