Miracle-dz/UniFAR-FTUs huggingface dataset:
Do I Spit or Do I Pass?

This study explores privacy concerns perceived by people with respect to having their DNA tested by direct-to-consumer (DTC) genetic testing companies such as 23andMe and Ancestry.com. Data collected from 510 respondents indicate that those who have already obtained a DTC genetic test have significantly lower levels of privacy and security concerns than those who have not obtained a DTC genetic test. Qualitative data from respondents of both these groups show that the concerns are mostly similar. However, the factors perceived to alleviate privacy concerns are more varied and nuanced amongst those who have obtained a DTC genetic test. Our data suggest that privacy concerns or lack of concerns are based on complex and multiple considerations including data ownership, access control of data and regulatory authorities of social, political and legal systems. Respondents do not engage in a full cost/benefit analysis of having their DNA tested.
Literature Review of E-government Research: Content Analysis Based on Citespace

E-government has developed rapidly in just a few decades, but the literature on e-government research has exploded. What problems are these kinds of literature studying? What focus are scholars paying attention to this field? Do scholars from different disciplines pay attention to different focuses? Based on visual analysis software citespace, this paper answers these questions and analyzes the contents and concerns of e-government research from the perspective of disciplines through content analysis. This research found that there are mainly four disciplines that focus on the research of e-government. They are public administration, computer science, communication, and marketing. In addition, other disciplines such as business administration also pay attention to the research of e-government. These studies either focus on the government or citizens. Few studies pay attention to the penetration process of e-government. Moreover, the research on e-government combined with the cultural environment has not been deeply explored.
Cultivating the Community

A growing body of HCI research has sought to understand how online networks are utilized in the adoption and maintenance of disordered activities and behaviors associated with mental illness, including eating habits. However, individual-level influences over discrete online eating disorder (ED) communities are not yet well understood. This study reports results from a comprehensive network and content analysis (combining computational topic modeling and qualitative thematic analysis) of over 32,000 public tweets collected using popular ED-related hashtags during May 2020. Our findings indicate that this ED network in Twitter consists of multiple smaller ED communities where a majority of the nodes are exposed to unhealthy ED contents through retweeting certain influential central nodes. The emergence of novel linguistic indicators and trends (e.g., "#meanspo") also demonstrates the evolving nature of the ED network. This paper contextualizes ED influence in online communities through node-level participation and engagement, as well as relates emerging ED contents with established online behaviors, such as self-harassment.
Factors Influencing SMEs Sustainability: The Role of Islamic Crowdfunding

Small and Medium Enterprises (SMEs) contribute to generating the economy such as income and employment generation, widespread of product and services in city and rural areas. Almost 98.5 percent of business establishments in Malaysia are SME companies cut across all sizes and sectors. However, SMEs in Malaysia are unable to access external financing due to excessive, rigid procedures imposed by financial institutions and government agencies. To address and resolve this critical issue, with the advance of technology, entrepreneurs have become more independent and creative in raising capital through a process known as crowdfunding. This study intends to examine the role of Islamic crowdfunding towards SMEs sustainability in order to assist their financing need for business expansion or start-up in Malaysia. Furthermore, this study will empirically test the influence of strategic competitiveness and technology adoption on SMEs' sustainability. Primary data are collected using a self-administered questionnaire from a sample of 259 SMEs in Selangor. The data was computed using SmartPLS-3.2. 8 to examine the direct effect on SME's sustainability. The findings show that Islamic crowdfunding, technology adoption and strategic competitiveness influenced the SME's sustainability. Thus, this study disclosed that Islamic crowdfunding would also increase the capacity to boost SMEs' sustainability and development in Malaysia.
Modeling Influenza with a Forest Deep Neural Network Utilizing a Virtualized Clinical Semantic Network

CoViD-19 pandemic has shown that we have deep gaps in understanding this extremely infectious virus—not only both from a clinical diagnosis and treatment perspective—but also from a forecasting point of view, so that we are better prepared for the next onset of a similar pandemic, which, at this point, seems almost inevitable. In this paper, we present a novel approach towards modeling influenza, a closely related disease to CoViD-19, marrying clinical understanding with artificial intelligence, exploiting the Forest Deep Neural Network (fDNN) with accuracy rates in the 90% range.
Growing Process of Communities on Data Platforms: Case Analysis of a COVID-19 Dataset

In recent years, there have been growing expectations for the creation of new businesses and the improvement of the value of existing services by exchanging data in different fields. Data stored in-house within organizations have become a new source of innovation. While there is a high need for the value creation of data, determining the data value is not an easy task, as there is a wide range of factors to be considered, such as data pricing, acquisition cost, usage value, and update frequency. In this study, we observe communication, such as the sharing of know-hows in data exchange and analysis, and discuss the growing process of a community on the data platform. For the experiment, we focused on the data community in the COVID-19 disaster and used a unique dataset from the data platform Kaggle, which is the data analysis competition service. The results suggest that user actions differ in the discussion of the dataset and analysis. Moreover, providing topics, user participation, and activating actions in the early stages after the dataset is released are essential for forming a data community. We argue that the actions on the data analysis, such as comments and votes, are also crucial for fostering a common understanding of the data value.
Survival Analysis and Predictive Maintenance Models for non-sensored Assets in Facilities Management

Maintenance is a critical component of Facilities Management (FM), and with the proliferation of big data, Internet of Things (IoT), and Industry 4.0, predictive maintenance (PdM) has emerged as a critical maintenance technique. However, modern data-driven PdM tactics are based on sensor data, but there is no obvious way to imply PdM on older buildings that lack sensors. EQUANS is a company seeking recommendations for implying PdM in the management of a historic building. This paper demonstrates the potential of survival analysis with data-driven PdM using EQUANS’s non-sensored data, explicitly using the Kaplan-Meier method, parametric methods, Cox proportional hazard model, and accelerated failure time models. The boiler was chosen as the asset to focus on in this project, and the results indicated that the boiler’s survival might not be related to the frequency of service but the boiler’s age. The research findings propose a further step toward PdM for assets without sensors, and data collection and preventive maintenance can be improved.
CRISP-DM for Data Science: Strengths, Weaknesses and Potential Next Steps

This paper explores the strengths and weaknesses of CRISP-DM when used for data science projects. The paper then explores what key actions data science teams using CRISP-DM should consider that addresses CRISP-DM’s weaknesses. In brief, CRISP-DM, which is the most popular framework teams use to execute data science projects, provides an easy to understand description of the data science project workflow (i.e., the data science life cycle). However, CRISP-DM’s project phases miss some key aspects of the data science project life cycle. In addition, CRISP-DM’s task-focused approach fails to address how a team should prioritize tasks, and in general, collaborate and communicate. Hence, this paper also describes how CRISP-DM could be combined with a team coordination framework, such as Scrum or Data Driven Scrum, which is a newer collaboration framework developed to address the unique data science coordination challenges.
Multi-Feature Extraction with Ensemble Network for Tracing Chronic Retinal Disorders

The retina manifests a vital role in tracing chronic retinal disorders. It is located near the optic nerve that transforms the captured light into neural signals. The most prominent chronic eye diseases exhibit themselves in the retina. The analysis of a retina for detecting disease symptoms is quite challenging. Most of the prior methods developed using shallow and deep learning algorithms primarily emphasized single feature extraction for disease diagnosis. The underlying article has designed an ensemble network for extracting multiple retinal features using a single comprehensive platform. It includes a set of models that reflect feature-based needs to prevent intensity loss, micro-vessels overlap, and data redundancy. The proposed method has experimented with prominent benchmark datasets developed for vessels tree, optic disc/cup, and arteries/veins extraction. It is also compared with other methods and achieved promising results. Our platform is helpful for physicians to trace the variations in the retina of subjects facing chronic retinal disorders.
Addressing Load Imbalance in Bioinformatics and Biomedical Applications: Efficient Scheduling across Multiple GPUs

Computational bioinformatics and biomedical applications frequently contain heterogeneously sized units of work or tasks, for instance due to variability in the sizes of biological sequences and molecules. Variable-sized workloads lead to load imbalances in parallel implementations which detract from efficiency and performance. Many modern computing resources now have multiple graphics processing units(GPUs) per computer for acceleration. These multiple GPU resources need to be used efficiently through balancing of workloads across the GPUs. OpenMP is a portable directive-based parallel programming API used ubiquitously in bioscience applications to program CPUs; recently, the use of OpenMP directives for GPU acceleration has become possible. Here, motivated by experiences with imbalanced loads in GPU-accelerated bioinformatics applications, we address the load balancing problem using OpenMP task-to-GPU scheduling combined with OpenMP GPU offloading for multiply heterogeneous workloads – loads with both variable input sizes, and simultaneously, variable convergence rates for algorithms with a stochastic component – scheduled across multiple GPUs. We aim to develop strategies which are both easy to use and have lower overheads, and may be incorporated incrementally in existing programs which already make use of OpenMP for CPU-based threading in order to make use of multi-GPU computers. We test different combinations of input size variability and convergence rate variability, and characterize the effects of these different scenarios on the performance of scheduling strategies across multiple GPUs with OpenMP. We present several dynamic scheduling solutions for different parallel patterns, explore optimizations, and provide publicly available example computational kernels to make these strategies easy to use in programs. This work will enable application developers to efficiently and easily use multiple GPUs for imbalanced workloads found in bioinformatics and biomedical applications.
Estimating Forest Canopy Height With Multi-Spectral and Multi-Temporal Imagery Using Deep Learning

Canopy height is a vital indicator to asses carbon uptake and productivity of forests. However, precise measurements, such as from airborne or spaceborne 3D laser scanning (LiDAR), are expensive and usually cover only small areas. In this work, we propose a novel deep learning model that can generate detailed maps of tree canopy heights. In contrast to previous approaches that use a single image as input, we process multi-temporal data via a an adaptation of the popular U-Net architecture that is based on the EfficientNet and 3D convolution operators. To that end, our model receives multi-spectral Landsat satellite imagery as input and can predict continuous height maps. As labeled data, we resort to spatially sparse LiDAR data from ICESat-2. Thus, with such a model, one can produce dense canopy height maps given only multi-spectral Landsat data. Our experimental evaluation shows that our our model outperforms existing and improved single-temporal models. To test generalizability, we created a non-overlapping dataset to evaluate our approach and further tested the model performance on out-of-distribution data. The results show that our model can successfully learn drastic changes in distribution.
Accurate sex classification from neural responses to sexual stimuli

Sex differences in brain activity evoked by sexual stimuli remain elusive despite robust evidence for stronger enjoyment of and interest towards sexual stimuli in men than in women. To test whether visual sexual stimuli evoke different brain activity patterns in men and women, we measured haemodynamic brain activity induced by visual sexual stimuli in two experiments in 91 subjects (46 males). In one experiment, the subjects viewed sexual and non-sexual film clips and dynamic annotations for nudity in the clips was used to predict their hemodynamic activity. In the second experiment, the subjects viewed sexual and non-sexual pictures in an event-related design. Males showed stronger activation than females in the visual and prefrontal cortices and dorsal attention network in both experiments. Furthermore, using multivariate pattern classification we could accurately predict the sex of the subject on the basis of the brain activity elicited by the sexual stimuli. The classification generalized across the experiments indicating that the sex differences were consistent. Eye tracking data obtained from an independent sample of subjects (N = 110) showed that men looked longer than women at the chest area of the nude female actors in the film clips. These results indicate that visual sexual stimuli evoke discernible brain activity patterns in men and women which may reflect stronger attentional engagement with sexual stimuli in men than women.
Liquid-liquid phase separation of the chromosomal passenger complex drives parallel bundling of midzone microtubules

The spindle midzone is a dynamic structure that forms during anaphase, mediates chromosome segregation, and provides a signaling platform to position the cleavage furrow. The spindle midzone comprises two antiparallel bundles of microtubules (MTs) but the process of their formation is poorly understood. Here, we show that the Chromosomal Passenger Complex (CPC) undergoes liquid-liquid phase separation (LLPS) to generate parallel MT bundles in vitro when incubated with free tubulin and GTP. MT bundles emerge from CPC droplets with protruding minus-ends that then grow into long, tapered MT structures. During this growth, the CPC in condensates apparently reorganize to coat and bundle the resulting MT structures. CPC mutants attenuated for LLPS or MT binding prevented the generation of parallel MT bundles in vitro and reduced the number of MTs present at spindle midzones in HeLa cells. Our data uncovers a kinase-independent function of the CPC and provides models for how cells generate parallel-bundled MT structures that are important for the assembly of the mitotic spindle.
Estimating Pan evolutionary history from nucleotide site patterns

Introgression appears increasingly ubiquitous in the evolutionary history of various taxa, including humans. However, accurately estimating introgression is difficult, particularly when 1) there are many parameters, 2) multiple models fit the data well, and 3) parameters are not simultaneously estimated. Here, we use the software Legofit to investigate the evolutionary history of bonobos (Pan paniscus) and chimpanzees (P. troglodytes) using whole genome sequences. This approach 1) ignores within-population variation, reducing the number of parameters requiring estimation, 2) allows for model selection, and 3) simultaneously estimates all parameters. We tabulated site patterns from the autosomes of 71 bonobos and chimpanzees representing all five extant Pan lineages. We then compared previously proposed demographic models and estimated parameters using a deterministic approach. We further considered sex bias in Pan evolutionary history by analyzing the site patterns from the X chromosome. Introgression from bonobos into the ancestor of eastern and central chimpanzees and from western into eastern chimpanzees best explained the autosomal site patterns. This second event was substantial with an estimated 0.21 admixture proportion. Estimates of effective population size and most divergence dates are consistent with previous findings; however, we observe a deeper divergence within chimpanzees at 987 ka. Finally, we identify male-biased reproduction in Pan evolutionary history and suggest that western to eastern chimpanzee introgression was driven by western males mating with eastern females.
Can Time Series Data Mining Facilitate Wider Adoption of Mobile Health?

Over the past decade there have been many efforts by the research community and government organizations to facilitate further adoption of mobile health and remote monitoring of patients. This involves remotely collecting data from patients and transferring data over the network (i.e., internet), so that the transferred data can be further processed and analyzed by the physicians. In this paper we propose a framework on efficient data collection, handling, transmission and processing of remote patient data into meaningful insights that could help physicians with diagnosis and monitoring health status of their patients.
From depression to suicidal discourse on Reddit

Different mental health conditions may lead to suicide, among which depression is one of the most common. One in six people affected by major depression dies by suicide, with suicide being the second leading cause of death among teenagers and young adults. Symptoms of suicidal risk often remain latent until the inevitable occurs, which highlights the significance of prevention when it comes to such conditions. Language encodes psychological aspects that reflect a s ubject’s p ersonality and state of mind and could be used as a proxy to identify shifts in a person’s mental health state. In this work we study how psychometric traits of language vary when a person moves from depression-related discourse to suicide-related discourse, analyzing posts from Reddit dedicated to these themes. For each user, we identify the changes in their psychometric profile when the discourse shifts, and try to pinpoint groups of users with the same behavior and psychometric evolution. Our findings highlight several psychometric features as the most involved in discourse shifting, setting the stage for easier identification of high-risk individuals from the way they express themselves through language.
A Conical Representation of Hydrogen Bond Geometry for Quantifying Bond Interactions

We present a new three dimensional representation of hydrogen bond donors and acceptors as spherical cones. The conical representation describes the range of bond lengths and bond angles at which a hydrogen bond can form. We hypothesized that three dimensional intersections of these cones can predict the formation of hydrogen bonds and potentially their contribution to protein-protein interactions. As a result, this representation enables a new technique for identifying similarities in bond formation and bond geometry.
Modified VSG Scheme for Secondary Frequency Regulation in Islanded Microgrid

The traditional control strategy of a microgrid in islanded mode mainly consisted of Droop control. To improve it Virtual Synchronous Generator (VSG) concept was developed. The objective of the method was to improve the frequency response of a system following a sudden disturbance. Though these traditional control strategies are successful in providing primary frequency response (PFR) and inertial response (IR), the frequency restoration process is overlooked. The proposed method introduces two PI controllers in the active power-frequency loop of VSG by which the system frequency returns attains the nominal value in a quick time after the disturbance. The process is called secondary frequency regulation (SFR) and the proposed controller is called modified VSG as it uses the same set of control models as used in VSG. This work focuses on the comparison of traditional and proposed controllers in providing the three responses namely IR, PFR, and SFR. The controller performance is validated using MATLAB Simulation.
A Comparative Study between ECG-based and PPG-based Heart Rate Monitors for Stress Detection

Recent advances in the field of wearable sensing has promoted the emergence of many health tracking devices, including heart rate monitors. Heart rate monitors are commonly either chest-based or wrist-based. Currently, it is unclear whether there is a substantial difference in the performance of these different heart rate monitors. To determine the difference in the performance, in this paper, we compare two chest-worn heart rate monitors and one wrist-worn heart rate monitor. Our initial results indicate that there is substantial difference between the devices - the root mean square error between devices can be above 10 beats per minute. However, even though there is difference in performance of different heart rate monitors, yet each of these devices are capable of detecting stress (using an machine learning model) with a F1-score of above 0.8. In this paper, we also introduce the idea of formally verifying the rules obtained from the machine learning classifier; such formal verification will enable improving the explainability and confidence of the outcome of the machine learning models.
Generative Adversarial Network Based Semi-supervised Learning for Epileptic Focus Localization

Accurate localization of the epileptic focus plays an important role in the success of epileptic surgery. Epileptic focus localization process helps the physicians to determine the epileptogenic source in the brain by classifying the intracranial Electroencephalogram (iEEG) recordings into focal and non-focal. Using machine learning to automatically localize the epileptic focus requires a large number of labeled EEG recordings to effectively tackle such challenging classification problem. However, obtaining that much of labeled EEG data is expensive and time consuming. In order to accurately automate the focus localization task while having limited number of labeled EEG recordings, we introduce a semi-supervised learning method based on convolutional generative adversarial network. The proposed method is able to accurately classify the EEG signals into focal and non-focal using small amount of labeled training data which helps in reducing the burden of EEG data annotations. Discriminative spatial features are automatically extracted and classified by convolutional layers from raw EEG data without any preprocessing rather than manual feature extraction in the previous work. High classification accuracy of 95.5% makes the proposed method the most efficient among the state of the art.