Miracle-dz/UniFAR-FTUs huggingface dataset:
Reshaping the Future

The coronavirus disease-2019 (COVID-19) pandemic posed unprecedented challenges for health care organizations, schools of nursing, and surrounding communities. Health care organizations encountered rapidly changing guidelines and shortages in the health care workforce and critical medical supplies. Schools of nursing were responding to abrupt suspensions of clinical placements, uncertainties about the transmission of the novel virus, and shortages of personal protective equipment. Despite these challenges, health care organizations and schools of nursing were laser-focused on the need to ensure a competent nursing workforce and timely pandemic response efforts. Thus, an innovative academic-practice collaboration between Yale New Haven Hospital and Quinnipiac University School of Nursing was developed to meet community vaccination and testing needs. The partnership was designed to provide undergraduate student nurses with valuable community health clinical hours during the spring 2021 semester while meeting the hospital's needs for supporting the COVID-19 vaccine clinics and testing sites. The positive outcomes of the collaboration and specific lessons learned are discussed.
The Application of Multimedia Electronic Teaching Plan in the Aerobics Teaching

As a pattern of computer-aided education,electronic teaching plan has become a more overwhelming instruction method in the university education gradually.And the application of the Multimedia Electronic Teaching Plan(METP)has also brought new teaching ideas to the instructi-on of physical education.So this article definitely expounds the importance,advantage and feasibi-ity of the METP in the aerobics teaching education basing on exploring the combination of the METP and the aerobics teaching,and desires to elevate the students' abilities of self study,observa-tion and self exercise etc.
Computer-based modeling of soft tissues for medical and gisapplications

The advances in medical imaging, beginning from the discovery of X-rays have nowadays opened new perspectives for improvements in the area of computer assisted surgical planning. Meanwhile, modern medical imaging techniques, such as Computer Tomography (CT) and Magnetic Resonance Imaging (MRI), are widely-used for diagnostic and require 3D models of human anatomy for visualization purposes. 3D body models provide the information on the geometrical disposition of different anatomical structures. In craniofacial surgery, there is a great demand for efficient computer assisted methods, which could enable flexible, accurate and robust simulations for the realistic prediction of postoperative appearance. The computer assisted surgical planning has many advantages in comparison with conventional planning systems. Once the virtual model of a patient is generated, various case scenarios of the surgical impact and their outcomes can be extensively studied. Better preparation, shorter operation time, lower costs are the immediate benefits. Since the improvement of the patientâ€™s aesthetics is one of the main aims in craniofacial surgery, the realistic prediction of the patientâ€™s postoperative appearance is an important feature of the surgical planning system, giving the surgeon unique feedback during the planning stage. The topic of soft tissue modeling has been the subject of intensive investigations of many research groups within the last few decades. Because of the complexity of soft tissue behavior and diverse types of application fields, a wide spectrum of approaches has been developed for their realistic simulation. This paper presents various approaches developed for simulation of human soft tissues that are being applied in the area of medical and GIS applications.
Computed tomography and clinical parameters predict intracerebral hemorrhage expansion

Abstract This study aimed to evaluate the association of imaging signs, and to establish a predictive model through selecting highly relevant imaging signs in combination with clinical parameters for hematoma expansion. Intracerebral Hemorrhage (ICH) patients who received 2 consecutive noncontrast computed tomography scans were examined and recruited through January 2014 to December 2020. Demographic information and clinical characteristics were collected. Two experienced radiologists reviewed baseline noncontrast computed tomography images to assess the imaging characteristics. Correlation analysis was analyzed with Pearson and Spearman correlation tests. The association between clinical and imaging predictors with hematoma expansion was evaluated in multivariate models. Receiver operating characteristic (ROC) curve analysis was adopted to evaluate predictive performance. A total of 232 ICH patients, with mean age of 59.73 years, and 31% of female were included, among which, 32 patients occurred with hematoma expansion. For sex, ICH density, low density in hematoma, the midline shift, and Glasgow Coma Scale score, liquid level, H-tra, edema Cor, H Volume, time from onset to examination, there were significant differences between the 2 groups. As for imaging signs, only blend sign showed a significant difference, that patients with blend sign had a higher incidence of ICH expansion. The logistic analysis found that radiation attenuation, liquid level, the midline shift, Glasgow Coma Scale score, history of ischemic stroke, and smoking could predict the occurrence of ICH expansion. In summary, the model combined radiological characteristics with clinical indicators showed considerable predictive performance. Further validation is needed to verify the findings and help transfer to clinical practice.
Physicality 2009 - towards a less-GUI interface Third International Workshop on Physicality

multi-disciplinary workshop is the third in its series aiming to explore the issues surrounding physicality. As digital technology increasingly invades the devices and products that surround us, interaction designers and product designers need to make sense of the subtle interactions between physical form and activity and the way these influence and are influenced by digital functionality and interaction. Our theme is "towards a less-GUI interface" inspired by the need to reduce the reliance on tiny screens through effective physical design. Such screens not only have an increasingly limited utility with an aging population but they are not always optimal.
Estratégia de Personalização Reativa na Apresentação dos Conteúdos de Cursos Baseados na Web Através de um Agente Assistente

Web-based education offers countless possibilities. As a result, it is conquering more and more followers. However, this universe of participants does not introduce homogeneity in what tells respect to his objective of teaching/learning, some of those differences can be observed, in the way the contents of the courses are inserted and organized and, mostly, how the students visualize the contents. This article proposes a strategy to personalize the visualization of the contents of the courses by the students, such strategy has as goal to adapt the presentation of a course plan as student's profile, modifying the way the contents will be introduced, guaranteeing a treatment personalized of the courses based on Web. This adaptation will be supervised through reactions, which will be activated by an Assisting Agent.
Al-Doped SnO2 as Transparent Heater: Influence of Deposition Time

The research on transparent heater (Thf) films rapidly increases due to their unique photoelectric properties, leading to new generation of optoelectronic device. Here, we report a simple method to fabricate transparent heater based on Al-doped SnO2 (ASO) thin films. ASO films with 5 wt% Al as dopant were synthesized with various deposition times, namely, 5, 10 and 15 minutes using ultrasonic spray pyrolysis method. The correlation of deposition time on their structural characteristic, optical, electrical and thermal properties has been investigated. X-ray diffraction studies found that all samples exhibit tetragonal structure with preferred orientation along (110) plane. Meanwhile, the UV-Vis transmittance indicated that the sample having good optical transparency in visible light spectrum with the average transmittance up to 89.7%. The sheet resistance of ASO thin films was found to decrease as the deposition time increases to 10 minutes. Furthermore, Al-doped SnO2 based transparent heater prepared with 10 minutes deposition time presents the excellent thermal temperature up to 76.3 °C at the applied voltage of 20 volt. The above findings reveal that Al-doped SnO2 can be used as an alternative compound to substitute higher cost indium tin oxide as transparent heater. Keywords: aluminium, composite, spray pyrolysis, SnO2, transparent heater
Monogenetic Forms of Parkinson’s Disease – Bridging the Gap Between Genetics and Biomarkers

The therapy of neurodegenerative diseases such as Parkinson’s disease (PD) is still limited to the treatment of symptoms and primarily aimed at compensating for dopaminergic hypofunction. Numerous disease-modifying therapies currently in the pipeline attempt to modify the underlying pathomechanisms. In recent decades, the results of molecular genetics and biomarker research have raised hopes of earlier diagnosis and new neuroprotective therapeutic approaches. As the disease-causing processes in monogenetic forms of PD are better understood than in sporadic PD, these disease subsets are likely to benefit first from disease-modifying therapies. Recent studies have suggested that disease-relevant changes found in genetically linked forms of PD (i.e., PARK-LRRK2, PARK-GBA) can also be reproduced in patients in whom no genetic cause can be found, i.e., those with sporadic PD. It can, therefore, be assumed that as soon as the first causal therapy for genetic forms of PD is approved, more patients with PD will undergo genetic testing and counseling. Regarding future neuroprotective trials in neurodegenerative diseases and objective parameters such as biomarkers with high sensitivity and specificity for the diagnosis and course of the disease are needed. These biomarkers will also serve to monitor treatment success in clinical trials. Promising examples in PD, such as alpha-synuclein species, lysosomal enzymes, markers of amyloid and tau pathology, and neurofilament light chain, are under investigation in blood and CSF. This paper provides an overview of the opportunities and current limitations of monogenetic diagnostic and biomarker research in PD and aims to build a bridge between current knowledge and association with PD genetics and biomarkers.
Implementing GDPR for mobile and ubiquitous computing

The General Data Protection Regulation (GDPR) presents directives to give data subjects control over their personal data. These directives impose data-collecting and processing organizations to take concrete actions for privacy preservation of users and non-users alike. Significant challenges arise when applying these directives to mobile and ubiquitous computing. Mobile and ubiquitous computing aim for computer use to be as transparent and seamless as possible. Inconspicuous devices continually sense their environment, often without the data subject's knowledge. This context significantly complicates the implementation of core GDPR directives, such as informing the user and collecting consent. In this paper, we challenge the mobile computing research community on how to address such issues in practical implementations that combine the philosophy of mobile and ubiquitous computing with often constraining privacy-regulations.
Design and implementation of offline shopping recommendation system based on intelligent technology

With more and more people choosing online shopping, the functions of large shopping malls in offline business districts have qui-etly changed. Large shopping malls no longer only provide goods, but provide more services. The ultimate goal of shopping mall recommendation system design is to recommend consumers to make appropriate choices at the right time. This paper describes how to realize the design of offline shopping recommendation system based on intelligent technology. This paper improves the traditional collaborative filtering algorithm based on users, and forms a set of trusted shopping recommendation service system based on users’ interests. The method forms user interest classification by analyzing user interest metadata, and generates data sets of different interest classes to help users solve the selection problems encountered in offline shopping. In this paper, the user’s registration information, the user’s behavior information in the mall, the similarity between users, the similarity between commodities, sales volume and so on are used reasonably. Through the combination of several algorithms, the problem of cold start is solved, and personalized recommendation to users is completed.
EarWalk: towards walking posture identification using earables

Stress on the knee --- caused by various factors including injuries, aging, and overweight --- is a major contributor to orthopedic disorders such as knee osteoarthritis (OA), a severe illness that can even lead to decreased ability to walk. One possible treatment for this problem is to have patients conduct gait modification, getting them to intentionally walk toe-in or toe-out, thereby reducing the stress on their knees. In this paper, we propose EarWalk, a novel solution that utilizes commodity wireless earables to provide constant and real-time feedback on the patients' gait modification. EarWalk leverages the built-in accelerometer in earables to sense and ultimately differentiate normal, toe-in, and toe-out gait postures due to the minute differences in their vibrations. As a proof-of-concept, we evaluate EarWalk with real-world data by inviting participants to walk while wearing a pair of earables, and demonstrate an average accuracy of over 95% in identifying the gait postures.
An optimal grasping point identification method based on deep learning and point cloud processing

With the development of the field of robotics and the increasingly convenient acquisition of point cloud data, point cloud is widely used in robots to complete many tasks more accurately because of its rich three-dimensional information. In this paper, we propose a method based on PointNet++ combining deep learning and point cloud processing to extract the grasping points of the objects. This method selects YCB dataset. The grasping point pairs in each perspective are sampled by point cloud feature point detection and scored by Force Closure together with Shape of the grasp polygon metrics. The input of PointNet++ is the single-perspective field point cloud after the searching algorithm based on grasping point pairs and the final output classification results are divided into two classes. Experimental results conducted based on Kinect2 and UR5 mechanical arm show that our method can achieve 85% accuracy in the two-class simulation experiment and 80.32% average success rate in the real grasping tasks, whose robustness is also verified in this paper.
Hybrid mobile vision for emerging applications

While mobile applications have greatly benefited from 2D computer vision algorithms such as object detection and classification, there is limited research on exploring 3D vision that is enabled by the increasing availability of depth cameras and LiDAR scanners on mobile devices. In this paper, we propose a hybrid mobile vision system that intelligently combines 2D and 3D vision for improving the performance of emerging applications such as augmented and mixed reality and volumetric content analytics. Our research is motivated by and explores the key observation of the crucial latency-accuracy tradeoff between 2D and 3D vision. We present a research agenda with two principles for enhancing mobile vision stack, complementing 3D vision with its 2D counterpart by leveraging their diverse resource/accuracy profiles and processing 3D data (e.g., point clouds) with 2D vision cues for mitigating the high computation and storage costs.
Vulnerability Sample Analysis Based on Dynamic Binary Platform

Based on the existing vulnerability exploitation detection technology, this paper analyzes the buffer overflow vulnerability exploitation process, and detects vulnerability triggering, protection mechanism bypassing and shellcode execution by dynamic binary instrumentation (DBI) monitoring program execution process; According to the analysis of vulnerability sample shellcode behavior, the method of recording instruction information and key API function information is designed based on instrumentation API function; A prototype platform of vulnerability sample transplantation and utilization based on dynamic binary is designed and implemented, and test analysis of 40 typical vulnerability samples is carried out to verify the effectiveness and feasibility of the system.
Concurrent Low-power Listening: A New Design Paradigm for Duty-cycling Communication

In this article, we explore a new design paradigm of duty-cycling mechanism that supports low-power devices to fully turn channel contention into transmission opportunities. To achieve this goal, we propose Concurrent Low-power Listening (CLPL) to enable contention-tolerant and concurrent media access control (MAC) for widely deployed low-power devices. The fundamental principle behind CLPL is that frequency modulated receiver can reliably demodulate the strongest signal even if cochannel interference and noise exist. By using CLPL, a sender inserts a series of tailor-made signals (namely, wake-up signal) between adjacent data frames to awaken appointed receiver, making it capable to receive the next data frame. According to system-defined maximum transmission power level, CLPL adopts an adaptive algorithm to adjust the transmission power of wake-up signals so that its signal strength is above receiver sensitivity and will not interfere with the other data frames in transit. By exploiting the spatial-temporal correlation, we further develop a light-weight wake-up signal detection method to enable a waiting sender to accurately identify the current channel condition. Then, it schedules the sender’s data frame transmissions by overlapping with those wake-up signals, without conflicting with existing data frame transmissions. We have implemented the prototype of CLPL and conducted extensive experiments on a real testbed. In comparison with the state-of-the-art low-power MAC schemes, such as ContikiMAC, A-MAC, BoX-MAC, and opportunistic scheme ORW, CLPL can improve the throughput by 2–6 times and halve the end-to-end transmission delay.
Probabilistic Programming Languages: Independent Choices and Deterministic Systems

Pearl [2000, p. 26] attributes to Laplace [1814] the idea of a probabilistic model as a deterministic system with stochastic inputs. Pearl defines causal models in terms of deterministic systems with stochastic inputs. In this paper, I show how deterministic systems with (independent) probabilistic inputs can also be seen as the basis of modern probabilistic programming languages. Probabilistic programs can be seen as consisting of independent choices (over which there are probability distributions) and deterministic programs that give the consequences of these choices. The work on developing such languages has gone in parallel with the development of causal models, and many of the foundations are remarkably similar. Most of the work in probabilistic programming languages has been in the context of specific languages. This paper abstracts the work on probabilistic programming languages from specific languages and explains some design choices in the design of these languages. Probabilistic programming languages have a rich history starting from the use of simulation languages such as Simula [Dahl and Nygaard 1966]. Simula was designed for discrete event simulations, and the built-in random number generator allowed for stochastic simulations. Modern probabilistic programming languages bring three extra features:
Design of Dual Frequency High Gain Antenna Based on Quasi-Continuous Metasurfaces

The acquisition and processing of EEG and sEMG is an important task in the field of biomechanics. In order to collect bioelectrical signals, new design method of high gain antenna has become a hot topic. This paper proposes a design method of dual-frequency high-gain antenna based on quasi-continuous metasurface. By adding metasurfaces, or adding slots to form quasi-continuous metasurfaces to replace the original patch radiating elements, the antenna performance is improved, and new ideas are provided for the realization of high-performance antennas. Based on the theoretical basis of the metasurface to increase the antenna gain, the quasi-continuous metasurface is loaded on the basis of the dual-frequency antenna, which improves the impedance matching of the dual-frequency antenna and at the same time increases the antenna gain. The proposed antenna design model is simulated and optimized, and the transmit antenna gain reaches 31.32dBi, and the receive antenna gain reaches 16.46dBi. The half-power beam width of the transmitting antenna is within 10°, and the half-power beam width of the receiving antenna is 36°, which fully meets the design requirements of the biomechanical signal acquisition antenna.
Distributed Beamforming Techniques for Flexible Communications Networks

In this paper, we implement and demonstrate a distributed communications beamforming system that provides increased range, data rate, and robustness to interference. By treating two distributed, locally-coherent meshes as antenna arrays, with ${n_{{m_A}}}$ and ${n_{{m_B}}}$ elements respectively, we develop an approach that produces an $n_{{m_A}}^2{n_{{m_B}}}$ performance improvement in signal-to-noise ratio over a traditional single-antenna to single-antenna link in the absence of interference. We further demonstrate that in the presence of interference, the signal-to-interference-plus-noise ratio improvement is significantly greater for a wide range of environments. We implement this system on a network of software-defined radios and demonstrate both simulated and experimental results both with and without interference.
Directional characteristics of THz outdoor channels - measurement and system performance implications

Next-generation wireless communication systems need to provide extremely high data rates that can enable upcoming applications. To obtain the large contiguous bandwidth required for providing such high data rates, usage of the spectrum above 100 GHz is envisioned. In order to determine the maximum data rate available in these bands, it is necessary to analyze the channels’ capacity. While previous capacity analyses for outdoor THz channels were based on models with simplifying assumptions, in the current paper we present the first capacity analysis using real-world measurement data for frequencies between 145 and 146 GHz collected from our recent double-directional urban channel measurements. The impact of various beamforming methods, including pure analog beamswitching and pure digital beamforming, on the achievable rates in both piconets and device-to-device settings are also investigated. These analyses are important to move towards the eventual realization of THz band communication.