Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review

In recent years, noncontact measurements of vital signs using cameras received a great amount of interest. However, some questions are unanswered: (i) Which vital sign is monitored using what type of camera? (ii) What is the performance and which factors affect it? (iii) Which health issues are addressed by camera-based techniques? Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, we conduct a systematic review of continuous camera-based vital sign monitoring using Scopus, PubMed, and the Association for Computing Machinery (ACM) databases. We consider articles that were published between January 2018 and April 2021 in the English language. We include five vital signs: heart rate (HR), respiratory rate (RR), blood pressure (BP), body skin temperature (BST), and oxygen saturation (SpO2). In total, we retrieve 905 articles and screened them regarding title, abstract, and full text. One hundred and four articles remained: 60, 20, 6, 2, and 1 of the articles focus on HR, RR, BP, BST, and SpO2, respectively, and 15 on multiple vital signs. HR and RR can be measured using red, green, and blue (RGB) and near-infrared (NIR) as well as far-infrared (FIR) cameras. So far, BP and SpO2 are monitored with RGB cameras only, whereas BST is derived from FIR cameras only. Under ideal conditions, the root mean squared error is around 2.60 bpm, 2.22 cpm, 6.91 mm Hg, 4.88 mm Hg, and 0.86 °C for HR, RR, systolic BP, diastolic BP, and BST, respectively. The estimated error for SpO2 is less than 1%, but it increases with movements of the subject and the camera-subject distance. Camera-based remote monitoring mainly explores intensive care, post-anaesthesia care, and sleep monitoring, but also explores special diseases such as heart failure. The monitored targets are newborn and pediatric patients, geriatric patients, athletes (e.g., exercising, cycling), and vehicle drivers. Camera-based techniques monitor HR, RR, and BST in static conditions within acceptable ranges for certain applications. The research gaps are large and heterogeneous populations, real-time scenarios, moving subjects, and accuracy of BP and SpO2 monitoring.
Advances in Simultaneous Localization and Mapping in Confined Underwater Environments Using Sonar and Optical Imaging.

Abstract : This thesis reports on the incorporation of surface information into a probabilistic simultaneous localization and mapping (SLAM) framework used on an autonomous underwater vehicle (AUV) designed for underwater inspection. AUVs operating in cluttered underwater environments, such as ship hulls or dams, are commonly equipped with Doppler based sensors, whichin addition to navigationprovide a sparse representation of the environment in the form of a three-dimensional (3D) point cloud. The goal of this thesis is to develop perceptual algorithms that take full advantage of these sparse observations for correcting navigational drift and building a model of the environment. In
UNSUPERVISED HARMONIOUS IMAGE COMPOSITION FOR DISASTER VICTIM DETECTION

Abstract. Deep detection networks trained with a large amount of annotated data achieve high accuracy in detecting various objects, such as pedestrians, cars, lanes, etc. These models have been deployed and used in many scenarios. A disaster victim detector is very useful when searching for victims who are partially buried by debris caused by earthquake or building collapse. However, considering that larger quantities of real images with buried victims are difficult to obtain for training, a deep detector model cannot give full play to its advantages. In this paper we generate realistic images for training a victim detector. We first randomly cut out human body parts from an open source human data set and paste them into the ruins background images. Then, we propose an unsupervised generative adversarial network (GAN) to harmonize the body parts to fit the style (illumination, texture and color characteristics) of the background. These generated images are finally used to fine-tune a detection network YOLOv5. We evaluate both the AP (average precision) for IoU (Intersection over Union) 0.5 and for IoU ∈ [0.5:0.05:0.95], which are denoted as AP@0:5 and AP@[. 5 : . 95], respectively. The best experimental results show that the YOLOv5l pre-trained on the COCO data set performs poorly on detecting victims, and the AP@[. 5 : . 95] is only 19.5%. The model that uses our composite images as fine-tuning data can effectively detect victims, and increases the AP@[. 5 : . 95] to 33.6%. The AP@0:5 increases from 32.4% to 53.4%. Our unsupervised harmonization method further improves the results by 2.1% and 6.1%, respectively.
Voice Assisted Navigation System for Visually Impaired

Vision is one of the most vital and important senses that human beings possess. Millions of people across the globe face issues related to eyesight and use spectacles to tackle this issue but the subcategory including those hundreds or thousands who can’t see at all, lead a difficult life. What they actually need are technologies and gadgets that can help them traverse with greater speed, comfort, and most importantly confidence. This project, “Voice Assisted Navigation System for Visually Impaired” aims to make life less problematic and easier for them. The project includes multiple modules which mainly help in obstacle detection, object identification using Machine Learning, GPS location gathering and sending trigger mail of coordinates and most importantly voice assistance. Keywords: Object detection, Machine Learning, GPS, Voice Assistance, Visually Impaired
Mixed Methods in Business, Management and Accounting Research: An Experimental Design in the Entrepreneurship Domain

Abstract All research strategies suffer from some weaknesses. Combining qualitative and quantitative approaches might offset some flaws. Recently a growing methodological trend arose from the acknowledgement that mixed methods may increase the value of several research projects. With this background, the paper analyses trends of mixed methods in the business, management and accounting area during the last twenty years. The analysis highlights domains where mixed methods are not frequently used, such as entrepreneurship, and presents ongoing research based on mixed methods on the Italian community enterprises, a set of collective entrepreneurial initiatives working for sustainable regeneration in their territories. These enterprises are elusive since they are neither easily identifiable nor extractable from databases. A fixed and sequential mixed method approach turns out to be effective for investigating these evolving enterprises. The work is relevant for novices to mixed methods research and provides meaningful insights to analyze a type of organisation that is very important in depleted contexts.
Characterization of Campylobacter jejuni in large-billed crows (Corvus macrorhynchos) in Tochigi prefecture, Japan

As free-living crows are a potential source of Campylobacter infections in broilers and cattle, we characterized Campylobacter spp. isolated from crows using multilocus sequence typing and antimicrobial susceptibility testing. We obtained 82 samples from 27 birds captured at seven different times using a trap set in Tochigi prefecture, Japan. Campylobacter jejuni was isolated from 55 (67.1%) of the 82 samples and classified into 29 sequence types, of which 21 were novel. Tetracycline and streptomycin resistance rates were 18.2% and 3.6%, respectively. These results show that most types of C. jejuni infecting crows differ from those isolated from humans, broilers, and cattle. Thus, the importance of free-living crows as reservoirs of Campylobacter infections in broilers and cattle may be limited.
Inertial Data-Based AI Approaches for ADL and Fall Recognition

The recognition of Activities of Daily Living (ADL) has been a widely debated topic, with applications in a vast range of fields. ADL recognition can be accomplished by processing data from wearable sensors, specially located at the lower trunk, which appears to be a suitable option in uncontrolled environments. Several authors have addressed ADL recognition using Artificial Intelligence (AI)-based algorithms, obtaining encouraging results. However, the number of ADL recognized by these algorithms is still limited, rarely focusing on transitional activities, and without addressing falls. Furthermore, the small amount of data used and the lack of information regarding validation processes are other drawbacks found in the literature. To overcome these drawbacks, a total of nine public and private datasets were merged in order to gather a large amount of data to improve the robustness of several ADL recognition algorithms. Furthermore, an AI-based framework was developed in this manuscript to perform a comparative analysis of several ADL Machine Learning (ML)-based classifiers. Feature selection algorithms were used to extract only the relevant features from the dataset’s lower trunk inertial data. For the recognition of 20 different ADL and falls, results have shown that the best performance was obtained with the K-NN classifier with the first 85 features ranked by Relief-F (98.22% accuracy). However, Ensemble Learning classifier with the first 65 features ranked by Principal Component Analysis (PCA) presented 96.53% overall accuracy while maintaining a lower classification time per window (0.039 ms), showing a higher potential for its usage in real-time scenarios in the future. Deep Learning algorithms were also tested. Despite its outcomes not being as good as in the prior procedure, their potential was also demonstrated (overall accuracy of 92.55% for Bidirectional Long Short-Term Memory (LSTM) Neural Network), indicating that they could be a valid option in the future.
Laser-induced fluorescence detection platform for point-of-care testing

Point-of-care testing (POCT) devices for continuous low-cost monitoring of critical patient parameters require miniaturized and integrated setups for performing quick high-sensitivity analyses, away from central clinical laboratories. This work presents a novel and promising laser-induced fluorescence platform for measurements in direct optical test formats that leads towards such powerful POCT devices based on fluorescence-labeled immunoassays. Ultimate sensitivity of thin film photodetectors, integrated with microfluidics, and a comprehensive optimization of all system components aim at low-level signal detection in the targeted biosensor application. The setup acquires fluorescence signals from the volume of a microfluidic channel. An innovative sandwiching process forms a flow channel in the microfluidic chips by embedding laser-cut double-sided adhesive tapes. The custom fit of amorphous silicon based photodiode arrays to the geometry of the flow channel enables miniaturization, fully adequate for POCT devices. A free-beam laser excitation with line focus provides excellent alignment stability, allows for easy and reliable swapping of the disposable microfluidic chips, and therewith greatly improves the ease of use of the resulting integrated device. As a proof-of-concept of this novel in-volume measurement approach, the limit of detection for the dye DY636-COOH in pure water as a model fluorophore is examined and found to be 26 nmol l−1.
Local and Social Dynamics in Innovation Ecosystems: The Case of the Video Game Industry in Montreal

This paper aims at a better understanding of the dynamics behind the concept of innovation ecosystems. We argue that interactions and interdependencies between the various actors of an ecosystem cannot be thought of as solely resulting from a complex arrangement of formal linkages. These dynamics cannot be disconnected from the local environment in which they are embedded, as this environment will be expected to provide a variety of resources that cannot be formally controlled by the actors of the system
Efficient Bayesian phase estimation via entropy-based sampling

Bayesian estimation approaches, which are capable of combining the information of experimental data from different likelihood functions to achieve high precisions, have been widely used in phase estimation via introducing a controllable auxiliary phase. Here, we present a Bayesian phase estimation (BPE) algorithm with an ingenious update rule of the auxiliary phase designed via entropy-based sampling. Unlike other adaptive BPE algorithms, the auxiliary phase in our algorithm is determined only once in a pre-estimation step. With simple statistical analysis on a small batch of data, an iteration rule for the auxiliary phase is pre-established and used in all afterward updates, instead of complex calculations in every update trails. During this pre-estimation process the most informative data can be selected, which guides one to perform the BPE with much less measurement times. As the measurement times for the same amount of Bayesian updates is significantly reduced, our algorithm via entropy-based sampling can work as efficient as other adaptive BPE algorithms and shares the advantages (such as wide dynamic range and perfect noise robustness) of non-adaptive BPE algorithms. Our algorithm is of promising applications in various practical quantum sensors such as atomic clocks and quantum magnetometers.
Resilience and sustainability strategies of Malaysian homestay program during COVID-19 pandemic

The COVID-19 outbreak in late 2019 has impacted all nations, societies, and industries globally due to the closure of international borders and strict travel restrictions. As one of the community-based tourism activities that have been viewed as a tool to stimulate rural community development, the Malaysian homestay program is no exception. This paper aims to explore the COVID-19 impacts, to examine how the homestay operators strived to be resilient and to understand the sustainability strategies adopted by them in reviving the industry post-COVID-19. By using a resilience-based framework, in-depth interviews with 10 homestay operators, one (1) state tourism official, and three (3) Homestay Chairman of the local homestay tourism association were conducted. Findings revealed that COVID-19 has affected the homestay operators' livelihoods, where they experienced a total loss of incomes due to booking cancellations and the complete halt of tourist arrivals. Operators began to re-visit their business models and mapped out the 'new norms of hospitality by leveraging technology and social media to promote their businesses online. ' All these innovative strategies need to be undertaken collectively and therefore leadership and the formation of strong human resources proved to be the best solutions. Diversifying and venturing into other businesses as second income generation provides a safety net in sustaining their livelihoods. It is suggested that the creation of new business models and pragmatic community tourism policies will essentially determine the homestay program's chances of survival, particularly during crisis or disaster, by transforming it into a much more sustainable form.
Hazard Perception Differences between Experienced and Less Experienced Drivers While Driving in Real World Hazards

Hazard perception is a key skill needed to drive a vehicle safely. Literature has shown that this skill improves with experience. Little is known regarding the time window in which novice drivers start exhibiting essential hazard perception skills as efficiently as their experienced counterparts. This research seeks to address this unknown through the use of a semi-naturalistic driving study employing eye-tracking technologies and examining the roadway eye-scanning pattern differences among less (1-2 years), intermediate (2-5 years) and highly (over 10 years) experienced drivers. A total of 15 participants, five in each group, were recruited for the study and asked to drive through two predetermined potentially hazardous scenarios. An observation time window, beginning at the first moment the potential hazard scenario came into view through the moment it had passed, was extracted from the recorded eye-movement videos. Eye fixation and gaze duration on the regions of interest were extracted using MAPPS software and analyzed. Although no significant difference was observed among the driver groups in terms of eye gaze or fixation duration, it was manifested from the study that the variance of eye gaze and fixation duration within group reduces considerably as the experience level increases.
Ambiente de edição de jogos digitais educacionais como fonte de dados para EDM

Os atuais sistemas de gerenciamento de aprendizagem digitais podem oferecer diversas funcionalidades para auxiliar professores no preparo e adequação de seus materiais pedagógicos para uso por seus alunos no ambiente de aprendizagem. Um recurso muito interessante que pode ser disponibilizado é o de atividades lúdicas e interativas organizadas em jogos digitais. No entanto, o desenvolvimento de jogos digitais é uma atividade complexa, o que dificulta seu uso pelos professores. Para facilitar esta tarefa, no presente trabalho apresentamos um ambiente de montagem de jogos digitais educacionais através de mecânicas modulares de jogos digitais tratando conteúdos pedagógicos como atividades virtuais interativas. Estas mecânicas oferecem não apenas um instrumento com que os professores podem “construir” jogos digitais personalizados com seus conteúdos temáticos, mas também uma ferramenta eficaz para a coleta de dados educacionais para analisar o processo de aprendizagem do aluno através de ferramentas como EDM (Educational Data Mining) e KDD (Knowledge Discovery in Databases).
Democratic aspects of learning and teaching, as perceived by preservice teachers face-to-face, blended, and virtual

The assimilation of democratic principles in learning and teaching engages learners in a challenging process that encourages innovation and initiative, cultivates their personal qualities, and allows them to acquire new methods, skills, and abilities. The aim of the present study was to examine whether and how democratic principles are reflected in learning and teaching in face-to-face, blended, and virtual learning, as perceived by preservice teachers in the final stages of their training. This is a quantitative study based on a sample of 360 preservice teachers studying at academic institutions in Israel. The findings show significant difference in the desired situation, as perceived by preservice teachers, between their schools and higher education institutions in relation to several principles, such as the learner’s choice of options and their exposure to digital libraries and information centers. The implications for practice or policy are that: (a) there are principles that are reflected more in higher education than in schools;(b) I found a significant difference between the two learning environments;(c) I found a significant difference in the desired condition between stages; and (d) I recommend encouraging the practice of applying democratic principles.
Stem Cell-Based Regenerative Therapy and Derived Products in COPD: A Systematic Review and Meta-Analysis

COPD is an incurable disorder, characterized by a progressive alveolar tissue destruction and defective mechanisms of repair and defense leading to emphysema. Currently, treatment for COPD is exclusively symptomatic; therefore, stem cell-based therapies represent a promising therapeutic approach to regenerate damaged structures of the respiratory system and restore lung function. The aim of this study was to provide a quantitative synthesis of the efficacy profile of stem cell-based regenerative therapies and derived products in COPD patients. A systematic review and meta-analysis was performed according to PRISMA-P. Data from 371 COPD patients were extracted from 11 studies. Active treatments elicited a strong tendency towards significance in FEV1 improvement (+71 mL 95% CI -2–145; p = 0.056) and significantly increased 6MWT (52 m 95% CI 18–87; p < 0.05) vs. baseline or control. Active treatments did not reduce the risk of hospitalization due to acute exacerbations (RR 0.77 95% CI 0.40–1.49; p > 0.05). This study suggests that stem cell-based regenerative therapies and derived products may be effective to treat COPD patients, but the current evidence comes from small clinical trials. Large and well-designed randomized controlled trials are needed to really quantify the beneficial impact of stem cell-based regenerative therapy and derived products in COPD.
Large-Area and Low-Cost Force/Tactile Capacitive Sensor for Soft Robotic Applications

This paper presents a novel design and development of a low-cost and multi-touch sensor based on capacitive variations. This new sensor is very flexible and easy to fabricate, making it an appropriate choice for soft robot applications. Materials (conductive ink, silicone, and control boards) used in this sensor are inexpensive and easily found in the market. The proposed sensor is made of a wafer of different layers, silicone layers with electrically conductive ink, and a pressure-sensitive conductive paper sheet. Previous approaches like e-skin can measure the contact point or pressure of conductive objects like the human body or finger, while the proposed design enables the sensor to detect the object’s contact point and the applied force without considering the material conductivity of the object. The sensor can detect five multi-touch points at the same time. A neural network architecture is used to calibrate the applied force with acceptable accuracy in the presence of noise, variation in gains, and non-linearity. The force measured in real time by a commercial precise force sensor (ATI) is mapped with the produced voltage obtained by changing the layers’ capacitance between two electrode layers. Finally, the soft robot gripper embedding the suggested tactile sensor is utilized to grasp an object with position and force feedback signals.
Pliegues de la ciudadanía. Notas analíticas para su revisión

La noción de ciudadanía se asienta sobre las ideas de membresía a una comunidad y como práctica deseable. En el presente trabajo abordo una problematización sobre la ciudadanía, sosteniendo que esta se constituye a partir de la exclusión y del conflicto. Lo cual nos otorga el puntapié para avanzar en la pregunta por el devenir del sujeto-ciudadano y las condiciones de (im)posibilidad desde las cuales emerge. Así, proponemos pensar una práctica identitaria de ciudadanía a partir de pliegues analíticos, compuestos en la presencia de una frontera antagónica como constitutiva a todo orden social; los intermedios entre lo público y lo privado y, por último, la posibilidad de emergencia de un nuevo orden social. Arribando a la conclusión de que estos pliegues ayudan a revisitar la ciudadanía para pensar su carácter precario y contingente; y que a su vez, tienen en cuenta los procesos de identificación de los sujetos.