Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Framework of Planning Collaborative Learning Scenarios in Computer Science

The planning and implementation of collaborative learning is perceived by teachers as demanding and time-consuming. In addition to individual learners, groups must be taken into account – with their group dynamics, demands on group coordination, and group experiences. Our aim is to develop a framework for the instructional design of collaborative learning in computer science to support a systematic evidence-based implementation. In the article, we describe a work in progress framework, encompassing didactic analyses and decision fields, which are relevant for the planning phase. We provide recommendations for actions based on the results of empirical studies as well as a short teaching vignette to illustrate application in practice.
LIMU-BERT: Unleashing the Potential of Unlabeled Data for IMU Sensing Applications

Deep learning greatly empowers Inertial Measurement Unit (IMU) sensors for various mobile sensing applications, including human activity recognition, human-computer interaction, localization and tracking, and many more. Most existing works require substantial amounts of well-curated labeled data to train IMU-based sensing models, which incurs high annotation and training costs. Compared with labeled data, unlabeled IMU data are abundant and easily accessible. In this work, we present LIMU-BERT, a novel representation learning model that can make use of unlabeled IMU data and extract generalized rather than task-specific features. LIMU-BERT adopts the principle of self-supervised training of the natural language model BERT to effectively capture temporal relations and feature distributions in IMU sensor measurements. However, the original BERT is not adaptive to mobile IMU data. By meticulously observing the characteristics of IMU sensors, we propose a series of techniques and accordingly adapt LIMU-BERT to IMU sensing tasks. The designed models are lightweight and easily deployable on mobile devices. With the representations learned via LIMU-BERT, task-specific models trained with limited labeled samples can achieve superior performances. We extensively evaluate LIMU-BERT with four open datasets. The results show that the LIMU-BERT enhanced models significantly outperform existing approaches in two typical IMU sensing applications.
FedDL: Federated Learning via Dynamic Layer Sharing for Human Activity Recognition

Deep learning has been increasingly applied to improve human activity recognition (HAR) accuracy and reduce the human efforts of handcrafted feature extractions. Federated Learning (FL) is an emerging learning paradigm that enables the collaborative learning of a global model without exposing users' raw data. However, existing FL approaches yield unsatisfactory HAR performance as they fail to dynamically aggregate models according to the statistical diversity of users' data. In this paper, we propose FedDL, a novel federated learning system for HAR that can capture the underlying user relationships and apply them to learn personalized models for different users dynamically. Specifically, we design a dynamic layer sharing scheme that learns the similarity among users' model weights to form the sharing structure and merges models accordingly in an iterative, bottom-up layer-wise manner. FedDL merges local models based on the dynamic sharing scheme, significantly speeding up the convergence while maintaining high accuracy. We have implemented FedDL and evaluated using a new data set we collected using LiDAR and four public real-world datasets involving 178 users in total. The results show that FedDL outperforms several state-of-the-art FL paradigms in terms of model accuracy (by more than 15%), converging rate (by more than 70%), and communication overhead (about 30% reduction). Moreover, the testing results on the datasets of different scales show that FedDL has high scalability and hence can be deployed for large-scale real-world applications.
Experimental Scalability Study of Consortium Blockchains with BFT Consensus for IoT Automotive Use Case

Private or consortium blockchain networks have fewer verified participants and offer better throughput and transaction efficiency than public networks. However, as more and more blockchain consensuses are designed for private or consortium networks, their performances are often estimated without a practical use case implementation. In our use case, participants do not have to trust each other but still work together to build an ecosystem where users control their data and information. This paper analyzes the performance (transaction throughput, rejections, node participants) of Byzantine Fault Tolerant Consensus (BFT) using two blockchains: Hyperledger Sawtooth and Ethereum.
Morphy: Software Defined Charge Storage for the IoT

Recent innovations in energy harvesting promise extended operational life and reduced maintenance costs for the next generation of Internet of Things (IoT) platforms. However, energy management in these platforms remains problematic due to dynamism in energy supply and demand, inefficiency in storing and converting energy and a lack of per-task charge isolation. This paper tackles this problem by proposing a software defined charge storage module called Morphy, which combines a polymorphic capacitor array with intelligent power management software. Morphy delivers energy to application tasks in a flexible, efficient, and isolated manner. Morphy provides two software extensions to the Operating System scheduler: the energy semaphore blocks the execution of tasks until sufficient charge is available to safely run them, and the energy watchdog monitors and mitigates energy management bugs. We have realized a prototype of Morphy with the hardware form factor of a standard 9V (PP3) battery package and a software library that integrates with the FreeRTOS scheduler. Our evaluation shows that, in comparison to standard energy storage and management approaches, our prototype reaches an operational voltage more quickly, sustains operation longer in the case of power failure and effectively isolates charge storage for dedicated tasks with minimal compute, memory and energy overhead.
Observation of intensity flattened phase shifting enabled by unidirectional guided resonance

Abstract Phase-only light modulation is an important functionality for many optoelectronic applications. Although modulation efficiency can be significantly improved by using optical resonances, resonance detuning is always accompanied with dramatic intensity variation that is less ideal. Here, we propose a method to achieve intensity-flattened phase shifting by utilizing the unidirectional guided resonance (UGR) – a novel class of topologically enabled guided resonance that only radiates toward a single side. Consequently, the incident excites resonances and generates phase shifting, but it transmits to only one out-going port without other choice, which flattens the transmittance. Theory and simulation agree well and confirm our findings, in particular when nonradiative loss has been taken into account. By directly measuring the intensity and phase responses of UGR samples, a dip depth of 0.43 is observed with nonradiative Q around 2500. We further predict a dip depth of 0.13 can be achieved with a reasonable nonradiative Q around 8000 in state-of-art fabrication precision, which is sufficient and useful for the applications ranging from light projection, flat metalens optics, optical phased array, to light detection and ranging.
Speedometer reliability in regard to road traffic sustainability

Abstract The speedometer with radar head is a device displaying the instantaneous speed of vehicles in both the directions of the traffic lane. Interactive with the video, it collects and effectively interprets particular statistic data, such as the number of passed vehicles, classification of vehicles, exceeded speed, drivers´ behavior – speed change right before the measuring device, etc. The video is synchronized with the radar. In the areas where speedometer is installed, it is predicted that about 30% of the drivers slow down in front of the measuring device and about 60–90% of vehicles slow down after passing the device. The speedometer also serves as a light decelerator with respect to safe and sustainable traffic. The aim of the research was to carry out and subsequently to evaluate the three profile reviews executed on the selected road section under specific light and traffic conditions. After that, the evaluated data was compared with the real data gained by the respective reviews. The result of such comparison showed the measure of reliability and accuracy of the speedometer.
Improving Undergraduate Novice Programmer Comprehension through Case-Based Teaching with Roles of Variables to Provide Scaffolding

A role-based teaching approach was proposed in order to decrease the cognitive load placed by the case-based teaching method in the undergraduate novice programmer comprehension. The results are evaluated by using the SOLO (Structure of Observed Learning Outcomes) taxonomy. Data analysis suggested novice programmers with role-based teaching tended to experience better performances, including the SOLO level of program comprehension, program debugging scores, program explaining scores, except for programming language knowledge scores, compared with the classical case-based teaching method. Considering the SOLO category of program comprehension and performances, evidence that the roles of variables can provide scaffolding to understand case programs through combining its program structure with its related problem domain is discussed, and the SOLO categories for relational reasoning are proposed. Meanwhile, the roles of variables can assist the novice in learning programming language knowledge. These results indicate that combing case-based teaching with the role of variables is an effective way to improve novice program comprehension.
Stress of COVID-19, Anxiety, Economic Insecurity, and Mental Health Literacy: A Structural Equation Modeling Approach

The COVID-19 pandemic is currently a global health threat attributed to negatively affecting the mental health and well-being of people globally. The purpose of the current study is to examine the mediating roles of economic insecurity and mental health literacy in the relationship between stress about COVID-19 and anxiety. Results from the current study using a large sample of Chinese college students (N = 1,334) showed that stress of COVID-19 was positively associated with economic insecurity and anxiety while negatively associated with mental health literacy, which in turn was negatively associated with anxiety. These results elucidate our understanding of the role of mediators in stress about COVID-19 and anxiety. The findings are useful in terms of providing evidence for tailoring interventions and implementing preventative approaches to mitigate anxiety due to stress of COVID-19. Based on the present findings and within the context of COVID-19, the potential utility of promoting MHL to reduce the psychopathological consequences of COVID-19 is discussed.
CRISPR/Cas Technologies and Their Applications in Escherichia coli

The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) systems have revolutionized genome editing and greatly promoted the development of biotechnology. However, these systems unfortunately have not been developed and applied in bacteria as extensively as in eukaryotic organism. Here, the research progress on the most widely used CRISPR/Cas tools and their applications in Escherichia coli is summarized. Genome editing based on homologous recombination, non-homologous DNA end-joining, transposons, and base editors are discussed. Finally, the state of the art of transcriptional regulation using CRISPRi is briefly reviewed. This review provides a useful reference for the application of CRISPR/Cas systems in other bacterial species.
Food Insecurity and Bullying Victimization Among 170,618 Adolescents in 59 Countries

Background: Bullying victimization can impose long-lasting impacts on adolescent's current and subsequent well-being. Understanding the correlates of bullying victimization and how to prevent its occurrence is an urgent need. Food insecurity, an indicator of low socioeconomic status, may be related to bullying victimization. However, research on the association between food insecurity and bullying victimization is limited. Using a representative global sample, this study aimed to investigate the association between food insecurity and bullying victimization in adolescents and whether the association varied between country income levels, sexes, and age groups. Methods: Using cross-sectional, school-based data by 170,618 adolescents in 59 countries from the Global School-based Student Health Survey, multivariable logistic regression and meta-analysis were conducted to investigate the overall, country-stratified, sex-stratified, and age-stratified associations. Results: The prevalence of bullying victimization was 33.3%. In the total sample, adolescents with food insecurity showed significantly higher odds for bullying victimization than those without no food insecurity with a pooled odds ratio (OR) being 1.37 (1.28, 1.47). Further, the association was stronger in higher-income countries, females, and older adolescents. Conclusions: Bullying victimization is prevalent among global adolescents with food insecurity being a significant correlate. The identification of adolescents with perceptions of food insecurity and remedying this condition may be important to reduce the prevalence of bullying. This highlights the need to design and implement sex- and age-specific interventions focusing on remedying food insecurity and bullying victimization among in-school adolescents by taking country income levels into account.
Characterizing the Profile of Recreational Fishers Who Share Their Catches on Social Media

Social media may provide information for monitoring recreational fisheries, but several caveats prevent operationalization. Specifically, the fraction and profile of recreational fishers sharing their catches is not known. Our aim was to advance the monitoring capacities of recreational fishing using social media data. We collected data with onsite (face-to-face) survey and online (emails) questionnaires to characterize marine recreational fishers sharing catches on digital platforms (“sharers”) along with other demographic or fishing information. In the online survey we found that 38% of recreational fishers share their catches using digital platforms (including the private messaging platform WhatsApp), but such proportion dropped to 12% when considering only public or semi-public social media (Instagram was the most commonly used platform, followed by Facebook, YouTube, and Twitter). A similar pattern was found with the online questionnaire where sharers represented 37% of recreational fishers (including WhatsApp), while such proportion dropped to 21% when considering only public or semi-public social media. In general, sharers were more avid (24 and 35 yearly fishing trips for onsite and online survey, respectively) compared to non-sharers (18 and 31 yearly fishing trips). Sharers also spent more money on each fishing trip (on average 26 and 31 euro for onsite and online survey, respectively) than non-sharers (on average 21 and 28 euro for onsite and online survey, respectively), but they had similar chances of catching something. However, for fishers with catches, the harvest per unit effort of sharers was higher than that of non-sharers (0.4 and 0.5 kg/h with respect to 0.3 and 0.4 kg/h, for onsite and online survey, respectively). Moreover, recreational fishers that caught trophy, iconic, or emblematic species were more inclined to share their catches. This study represents an important advancement for integrating social media data into the monitoring of recreational fishing.
Navigation-Guided Transnasal Endoscopic Delineation of the Posterior Margin for Maxillary Sinus Cancers: A Preclinical Study

Background The resection of advanced maxillary sinus cancers can be challenging due to the anatomical proximity to surrounding critical anatomical structures. Transnasal endoscopy can effectively aid the delineation of the posterior margin of resection. Implementation with 3D-rendered surgical navigation with virtual endoscopy (3D-SNVE) may represent a step forward. This study aimed to demonstrate and quantify the benefits of this technology. Material and Method Four maxillary tumor models with critical posterior extension were created in four artificial skulls (Sawbones®). Images were acquired with cone-beam computed tomography and the tumor and carotid were contoured. Eight head and neck surgeons were recruited for the simulations. Surgeons delineated the posterior margin of resection through a transnasal approach and avoided the carotid while establishing an adequate resection margin with respect to tumor extirpation. Three simulations were performed: 1) unguided: based on a pre-simulation study of cross-sectional imaging; 2) tumor-guided: guided by real-time tool tracking with 3D tumor and carotid rendering; 3) carotid-guided: tumor-guided with a 2-mm alert cloud surrounding the carotid. Distances of the planes from the carotid and tumor were classified as follows and the points of the plane were classified accordingly: “red”: through the carotid artery; “orange”: <2 mm from the carotid; “yellow”: >2 mm from the carotid and within the tumor or <5 mm from the tumor; “green”: >2 mm from the carotid and 5–10 mm from the tumor; and “blue”: >2 mm from the carotid and >10 mm from the tumor. The three techniques (unguided, tumor-guided, and carotid-guided) were compared. Results 3D-SNVE for the transnasal delineation of the posterior margin in maxillary tumor models significantly improved the rate of margin-negative clearance around the tumor and reduced damage to the carotid artery. “Green” cuts occurred in 52.4% in the unguided setting versus 62.1% and 64.9% in the tumor- and carotid-guided settings, respectively (p < 0.0001). “Red” cuts occurred 6.7% of the time in the unguided setting versus 0.9% and 1.0% in the tumor- and carotid-guided settings, respectively (p < 0.0001). Conclusions This preclinical study has demonstrated that 3D-SNVE provides a substantial improvement of the posterior margin delineation in terms of safety and oncological adequacy. Translation into the clinical setting, with a meticulous assessment of the oncological outcomes, will be the proposed next step.
Trends in Cognitive Function Among Chinese Elderly From 1998 to 2018: An Age-Period-Cohort Analysis

Objectives: To investigate the effects of age, period, and cohort (APC) on trends in cognitive function among the Chinese elderly, and to explore how gender gaps in cognitive function change with age, period, and cohort. Methods: This study used data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) from 1998 to 2018, and included 90,432 participants aged above 65 years old. The measurement of cognitive function was the score of the Mini-Mental State Examination (MMSE). Cross-classified random-effect models were used to investigate age, period, and cohort trends in cognitive function. Results: Mini-Mental State Examination scores decreased with age at an increasing rate. While the cohort effect was nearly stable, the period effect demonstrated a downward trend from 1998 to 2002 followed by a nearly flat line. Females were associated with lower MMSE scores than males. When age increased, the gender gaps in MMSE scores further increased. The period-based gender gaps in MMSE scores diverged throughout the 20 years, while the cohort-based gender disparities in MMSE scores converged with successive cohorts. Conclusions: Age, period, and cohort had different and independent effects on cognitive function among the Chinese elderly. The effect of age was stronger than that of period and cohort. Gender disparities in cognitive function increased with age and period, and decreased with successive cohorts.
Different Risks of Mortality and Longitudinal Transition Trajectories in New Potential Subtypes of the Preserved Ratio Impaired Spirometry: Evidence From the English Longitudinal Study of Aging

Background: Preserved ratio impaired spirometry (PRISm), characterized by the decreased forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) with a preserved FEV1/FVC ratio, is highly prevalent and heterogeneous. We aimed to identify the subtypes of PRISm and examine their differences in clinical characteristics, long-term mortality risks, and longitudinal transition trajectories. Methods: A total of 6,616 eligible subjects were included from the English longitudinal study of aging. Two subtypes of the PRISm were identified as mild PRISm (either of FEV1 and FVC <80% predicted value, FEV1/FVC ≥0.7) and severe PRISm (both FEV1 and FVC <80% predicted values, FEV1/FVC ≥0.7). Normal spirometry was defined as both FEV1 and FVC ≥80% predicted values and FEV1/FVC ≥0.7. Hazard ratios (HRs) and 95% CIs were calculated by the multiple Cox regression models. Longitudinal transition trajectories were described with repeated spirometry data. Results: At baseline, severe PRISm had increased respiratory symptoms, including higher percentages of phlegm, wheezing, dyspnea, chronic bronchitis, and emphysema than mild PRISm. After an average of 7.7 years of follow-up, severe PRISm significantly increased the risks of all-cause mortality (HR=1.91, 95%CI = 1.58–2.31), respiratory mortality (HR = 6.02, 95%CI = 2.83–12.84), and CVD mortality (HR = 2.11, 95%CI = 1.42–3.13) compared with the normal spirometry, but no significantly increased risks were found for mild PRISm. In the two longitudinal transitions, mild PRISm tended to transition toward normal spirometry (40.2 and 54.7%), but severe PRISm tended to maintain the status (42.4 and 30.4%) or transition toward Global Initiative for Chronic Obstructive Lung Disease (GOLD)2–4 (28.3 and 33.9%). Conclusion: Two subtypes of PRISm were identified. Severe PRISm had increased respiratory symptoms, higher mortality risks, and a higher probability of progressing to GOLD2–4 than mild PRISm. These findings provided new evidence for the stratified management of PRISm.