Miracle-dz/UniFAR-FTUs huggingface dataset:
A Computable Phenotype Model for Classification of Men Who Have Sex With Men Within a Large Linked Database of Laboratory, Surveillance, and Administrative Healthcare Records

Background: Most public health datasets do not include sexual orientation measures, thereby limiting the availability of data to monitor health disparities, and evaluate tailored interventions. We therefore developed, validated, and applied a novel computable phenotype model to classify men who have sex with men (MSM) using multiple health datasets from British Columbia, Canada, 1990–2015. Methods: Three case surveillance databases, a public health laboratory database, and five administrative health databases were linked and deidentified (BC Hepatitis Testers Cohort), resulting in a retrospective cohort of 727,091 adult men. Known MSM status from the three disease case surveillance databases was used to develop a multivariable model for classifying MSM in the full cohort. Models were selected using “elastic-net” (GLMNet package) in R, and a final model optimized area under the receiver operating characteristics curve. We compared characteristics of known MSM, classified MSM, and classified heterosexual men. Findings: History of gonorrhea and syphilis diagnoses, HIV tests in the past year, history of visit to an identified gay and bisexual men's clinic, and residence in MSM-dense neighborhoods were all positively associated with being MSM. The selected model had sensitivity of 72%, specificity of 94%. Excluding those with known MSM status, a total of 85,521 men (12% of cohort) were classified as MSM. Interpretation: Computable phenotyping is a promising approach for classification of sexual minorities and investigation of health outcomes in the absence of routinely available self-report data.
HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media

We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets captured with the use of hardware (HW) synchronization, to the best of our knowledge, HUMAN4D is the first and only public resource that provides volumetric depth maps with high synchronization precision due to the use of intra- and inter-sensor HW-SYNC. Moreover, a spatio-temporally aligned scanned and rigged 3D character complements HUMAN4D to enable joint research on time-varying and high-quality dynamic meshes. We provide evaluation baselines by benchmarking HUMAN4D with state-of-the-art human pose estimation and 3D compression methods. We apply OpenPose and AlphaPose reaching 70.02% and 82.95% mAPPCKh-0.5 on single- and 68.48% and 73.94% mAPPCKh-0.5 on two-person 2D pose estimation, respectively. In 3D pose, a recent multi-view approach named Learnable Triangulation, achieves 80.26% mAPPCK3D-10cm. For 3D compression, we benchmark Draco, Corto and CWIPC open-source 3D codecs, respecting online encoding and steady bit-rates between 7–155 and 2–90 Mbps for mesh- and point-based volumetric video, respectively. Qualitative and quantitative visual comparison between mesh-based volumetric data reconstructed in different qualities and captured RGB, showcases the available options with respect to 4D representations. HUMAN4D is introduced to enable joint research on spatio-temporally aligned pose, volumetric, mRGBD and audio data cues. The dataset and its code are available online.
Simple But Effective Scale Estimation for Monocular Visual Odometry in Road Driving Scenarios

In large-scale environments, scale drift is a crucial problem of monocular visual simultaneous localization and mapping (SLAM). A common solution is to utilize the camera height, which can be obtained using the reconstructed 3D ground points (3DGPs) from two successive frames, as prior knowledge. Increasing the number of 3DGPs by using more proceeding frames can be a natural extension of this solution to estimate a more precise camera height. However, merely employing multiple frames based on conventional methods is hard to be directly applicable in a real-world scenario because the vehicle motion and inaccurate feature matching inevitably cause large uncertainty and noisy 3DGPs. In this study, we propose an elaborate method to collect confident 3DGPs from multiple frames for robust scale estimation. First, we gather 3DGP candidates that can be seen in more than a predefined number of frames. To verify the 3DGP candidates, we filter out the 3D points at the exterior of the road region obtained by the deep-learning-based road segmentation model. In addition, we formulate an optimization problem constrained by a simple but effective geometric assumption that the normal vector of the ground plane lies in the null space of a movement vector of the camera center, and provide a closed-form solution. ORB-SLAM with the proposed scale estimation method achieves the average translation error with 1.19% on the KITTI dataset, which outperforms the state-of-the-art conventional monocular visual SLAM methods in road driving scenarios.
Developing e-loyalty toward Online Retailer from e-WOM, Reputation, Information Quality and e-trust

Customer loyalty has been an important asset for an online retailer. Previous studies stated that e-trust is an important variable in building e-loyalty. Previous studies also show that e-loyalty and e-trust are influenced by reputation, information quality, and e-WOM. The purpose of this study is to empirically investigate the impacts of information quality, reputation, e-WOM on e-trust, and e-loyalty in Indonesian online retailer context. Data were collected to test the model from the millennial generation in the Indonesian as respondents, and empirical analyses were performed using SEM PLS. The results showed that e-trust is an important factor on e-loyalty, information quality has an impact on e-trust and e-loyalty, reputation also has impact on e-trust, and e-loyalty, information quality have a significant impact on e-trust and e-loyalty, and e-WOM only have a significant impact on e-loyalty.
Hybrid Precoding Based on Monopulse Ratio for Millimeter Wave Systems With Limited Feedback

Multiuser hybrid precoding in 5G NR mmWave communication system faces significant challenges such as establishing accurate directional radio links and maintaining links for mobile stations (MSs) moving in outdoor environments. A conventional solution relies on finite codebook-based beam sweeping for initial first-stage beam acquisition and subsequent second-stage beam tracking by sweeping adjacent beam pairs. However, such a conventional solution has inevitable residual AoA/AoD errors even after the best beam pair is established in the first stage and incurs nonnegligible overheads to sound adjacent beam pairs for maintaining the best beam pair in the second stage. To overcome these problems, a novel codebook-based two-stage solution that combines a novel beam tracking protocol with a low sounding overhead, a unique receiver structure employing a beam scheduler and a beam tester, and a fine accuracy residual AoA/AoD error estimation algorithm based on the monopulse ratio concept is proposed. The solution is unique because of the receiver structure for the residual AoA/AoD error estimation that exploits the cyclic prefix in OFDM systems, inspired by the monopulse ratio in radar systems. Moreover, it can be applied to MSs with a single RF chain. This solution, using the proposed receiver structure and algorithm, can establish a more accurate directional beam pair right after the initial beam sweeping in the first stage. For beam tracking in the second stage, it estimates the residual AoA/AoD errors of the current best beam pair rather than sweeping adjacent beam pairs, thereby reducing beam tracking overheads. Numerical evaluation and computer simulations show that the proposed solution offers more accurate beam acquisition (i.e., average array gain improvement of several dB) and costs considerably reduced beam sounding overheads compared to the conventional solution. Lastly, a ray-tracing tool is used to demonstrate that our solution is effective in practical channel parameters for outdoor environments.
Knowledge, Attitudes, and Behaviors on Utilizing Mobile Health Technology for TB in Indonesia: A Qualitative Pilot Study

Tuberculosis (TB) infections remain a global health burden with a high incidence rate in South-East Asia, including Indonesia. TB control strategy is founded on early case detection and complete treatment to minimize transmission and prevent the emergence of drug resistance. However, many patients face challenges to comply with daily medication, causing many to adhere inconsistently or stop prematurely. Technological solutions could enhance adherence to treatment and support national screening and follow-up policies. These include telephone video communication, enabling health professionals to watch patients take their medication, address patients' concerns, and provide advice and support. This manuscript describes the outcome of a qualitative pilot study, based on a series of focus group discussions to assess the knowledge, attitudes, and behaviors, on the potential utilization of mobile technology for health purposes with a particular focus on TB treatment follow-up. The findings illustrate that general knowledge of mobile health technologies, of their legal framework of operations, and of their exact potential within the healthcare system is incomplete or poor. The novel findings are as follows: (a) the willingness of participants to learn about these technologies, (b) the open and welcoming attitude toward receiving such information even within frontline community settings, and (c) the willingness to back a government-supported, healthcare-driven set of such initiatives. Potential implementation barriers have also been highlighted. This study is an important first step toward understanding the attitudes and behaviors on utilizing mobile health technology for TB in Indonesia.
Investigating Reflexive Responses to Explicit and Implicit Forms of Social Exclusion Using Immersive Virtual Environment Technology

Humans are strongly affected by social exclusion, a multifaceted and complex phenomenon of social life. However, individuals tend to respond differently depending on a multitude of individual and contextual factors. Firstly, with a view to increasing the ecological validity and experimental control of an exclusion manipulation in the laboratory setting, we made use of immersive virtual environment technology (IVET; an Oculus Rift Virtual Reality headset) to create a new exclusion paradigm. Secondly, given that a recent meta-analytic report on reflexive responses (i.e., affect and physiology) to manipulations of exclusion in the laboratory setting cites inconsistencies across findings (Blackhart et al., 2009), we focused on the form of exclusion manipulated to illustrate how this factor may help to explain the divergences in responses. We thus investigated how explicit and implicit forms of social exclusion may have a differential impact on self-reported affect, as well as on electrodermal and cardiovascular activity. Results from this laboratory study conducted with a varied sample of the local population made salient the affordances of IVET as a tool in exclusion research. They also helped to reconcile the conflicting findings in the literature relating to differences in the level of negative affect generated and shed light on physiological arousal in the wake of being excluded in different ways.
Virtualization of Virtual Field Trips: A Case Study from Higher Education in Environmental Engineering

Field trips foster sustainable high-quality learning outcomes and are therefore one of the cornerstones of teaching in various disciplines. However, field trips are costly and less compatible with some teaching formats, such as distance learning. Using the example of a lecturer-guided field trip through a city quarter on the topic of infrastructure planning, the four-staged evolution of this lecturer-guided field trip via a location-based app guided field trip and via a virtual field trip to a virtualized virtual field trip (VVFT), in which all participants group-wise conduct the field trip at different locations using a 360-degree model and video conferencing, is described. A qualitative evaluation by means of guided interviews proves the usefulness of this learning activity, which is to be recommended for future advancements.
Guest Editorial Special Issue on Advances in Artificial Intelligence and Machine Learning for Networking

ARTIFICIAL Intelligence (AI) and Machine Learning (ML) approaches have emerged in the networking domain with great expectation. They can be broadly divided into AI/ML techniques for network engineering and management, network designs for AI/ML applications, and system concepts. AI/ML techniques for networking and management improve the way we address networking. They support efficient, rapid, and trustworthy engineering, operations, and management. As such, they meet the current interest in softwarization and network programmability that fuels the need for improved network automation in agile infrastructures, including edge and fog environments. Network design and optimization for AI/ML applications addresses the complementary topic of supporting AI/ML-based systems through novel networking techniques, including new architectures and algorithms. The third topic area is system implementation and open-source software development. This evolution draws particular attention to interdisciplinary approaches. Researchers in communication networks apply ML and AI concepts to optimize and automate network architecture, control, and management. Similarly, AI experts collaborate with networking researchers to optimize network support for architecture and design of data communication and processing for AI purposes. This special issue is a follow-up to the JSAC’s Special Issue on Artificial Intelligence and Machine Learning for Networking and Communications published in June 2019 [1]. It has been organized by the same core team of researchers.
Battery management system with testing protocols for kW-class vanadium redox flow batteries

A Battery Management System (BMS) for a kW-class vanadium redox flow battery (VRFB) was developed and is reported in this paper. This kind of BMSs is intrinsically different from those of solid-state batteries, due to the very different battery operating principle. Such BMS was built entirely in-house around a desktop computer provided with LabVIEW software which results in an expandable and flexible system capable of providing the battery with SCADA functions. It acquires and processes all data generated by the thermo-fluid and electric sensors, which are necessary to carry out the subsequent analyses. Moreover, this BMS also creates the feedback signals for controlling every operation, including experiments such as polarization curves plotting and efficiency computation. As regards polarization curves, this algorithm implements an experimental protocol that allows to obtain such performance in reproducible conditions and possibly comparable with those of similar stacks. The efficiency computation algorithm computes the energy absorbed (or delivered) by the battery while acquiring and controlling all signals so as to ensure safe conditions. The management of signal between stack and BMS includes the galvanic insulation of cell and stack voltages, consisting of multiple opto-isolators and a DC/DC transformer.
Fine-tuning Organizational Citizenship Behaviour and Knowledge Management Behaviour in the Higher Education

The drive to maintain the quality of education following the development of information technology requires adequate capability in knowledge management. Therefore, the higher education institutions demand the lecturers also supporting staff to acquire aligned behaviors in organizational citizenship and knowledge management. This research aims to measure organization citizenship behavior (OCB) and knowledge management behavior (KMB) of supporting staff in a higher education institution. The research was conducted at the Bina Nusantara University, especially supporting staff at Kemanggisan Campus Jakarta. Organizational citizenship behavior as the independent variable is reflected perfectly into five dimensions - altruism, civic virtue, consciousness, courtesy, and sportsmanship. Knowledge management behavior as the dependent variable is reflected perfectly into three dimensions: knowledge creation, knowledge sharing, and knowledge update. The measurement and structural model developed by using SmartPLS explain that OCB has a positive and significant impact on KMB. The results of statistical tests show that female supporting staffs more than five years of service has a higher score in OCB and KMB significantly.
Do Electronic Personal Health Records (E-PHR) Influence People Behavior to Manage Their Health?

Access to E-PHR information can help users to be more effective in managing their health, but there is not much to discussion in user's ability to successfully use E-PHR in managing health. This study aims to analyze the factors that influence user intentions to use E-PHR services to manage health. This research uses survey filling through questionnaires and data is processed and analyzed by using CB-SEM method. From this research, 553 respondents are Indonesian people who use E-PHR services available on mobile health applications. Factors that directly affect the attitude of E-PHR users are self-efficacy, perceived ease of use, perceived privacy & security, and subjective norm. Other factors that directly influence behavioral intention include perceived privacy & security, perceived severity, subjective norm and attitude.
Endocrine Requirements for Oocyte Maturation Following hCG, GnRH Agonist, and Kisspeptin During IVF Treatment

Objective The maturation of oocytes to acquire competence for fertilization is critical to the success of in vitro fertilization (IVF) treatment. It requires LH-like exposure, provided by either human chorionic gonadotropin (hCG), or gonadotropin releasing hormone agonist (GnRHa). More recently, the hypothalamic stimulator, kisspeptin, was used to mature oocytes. Herein, we examine the relationship between the endocrine changes following these agents and oocyte maturation. Design Retrospective cohort study. Methods Prospectively collected hormonal data from 499 research IVF cycles triggered with either hCG, GnRHa, or kisspeptin were evaluated. Results HCG-levels (121 iU/L) peaked at 24 h following hCG, whereas LH-levels peaked at ~4 h following GnRHa (140 iU/L), or kisspeptin (41 iU/L). HCG-levels were negatively associated with body-weight, whereas LH rises following GnRHa and kisspeptin were positively predicted by pre-trigger LH values. The odds of achieving the median mature oocyte yield for each trigger were increased by hCG/LH level. Progesterone rise during oocyte maturation occurred precipitously following each trigger and strongly predicted the number of mature oocytes retrieved. Progesterone rise was positively associated with the hCG-level following hCG trigger, but negatively with LH rise following all three triggers. The rise in progesterone per mature oocyte at 12 h was greater following GnRHa than following hCG or kisspeptin triggers. Conclusion The endocrine response during oocyte maturation significantly differed by each trigger. Counter-intuitively, progesterone rise during oocyte maturation was negatively associated with LH rise, even when accounting for the number of mature oocytes retrieved. These data expand our understanding of the endocrine changes during oocyte maturation and inform the design of future precision-triggering protocols.
Physico-chemical characterization of the water of Boufekrane River (Meknes, Morocco)

The Boufekrane River is an important stream which rises west of El Hajeb (Ain Maarouf), located at 740 m above sea level, and crosses the commune of Boufekrane and the city of Meknes. This river, which is used mainly by the population of the region for irrigation and swimming, receives the discharge of raw wastewater without prior treatment. Therefore, the assessment of the quality of its water is a priority. A physico-chemical characterization of the river water was carried out during the period of March 2017 - February 2018. The descriptive analysis of the results showed that the river water undergoes significant physico-chemical variations downstream wastewater discharges, with a slight self-purification during the course of the watercourse but without reaching the initial quality noted at the source. This analysis also defined the spatio-temporal variations. The study also revealed that the average values of the quality parameters (pH, nitrite, nitrate, dissolved oxygen, Iron, chlorides, turbidity, conductivity and sulfates) of the water at the source are respectively 7.63, 0.01 mg / l, 3.47 mg / l, 5.81 mg/l, 0.14 mg/l, 115.08 mg/l, 0.48 NTU, $726.33 \ \mu\mathrm{s}/\text{cm}$ and 21.88 mg/l. They are thus below the maximum limit values for drinking water which are respectively $6.5 < \text{pH} < 8.5, 5\ \hbox{`}\leq \mathrm{O}2\leq 8,0.5\ \text{mg}/\mathrm{l}$, 50 mg/l, 0.3 mg/l, 750 mg/l, 5 NTU, $2700\ \mu\mathrm{s}/\text{cm}$ and 400 mg/l.
Feasibility Study and Prototype Design of Freelancer Online Marketplace

The purpose of this research is to conduct feasibility study and design a prototype of freelancer online marketplace. This platform can help freelancers in Indonesia in finding project opportunities. Moreover, project owners who need people to work on their projects will be facilitated by this online marketplace. To see the feasibility we analyzed the current market situation and then design the freelancer online marketplace platform. Data are collected by distributing questionnaires and literature reviews have also been done to collect information regarding trend of online marketplace. We also use object-oriented method to analyze and design the freelancer online marketplace. Freelancer Online Marketplace provides business-driven service in collaborative way whereby it can be an opportunity for freelancers to find new job opportunities, increase their skills, and develop new experiences and portfolios. On the other Freelance Online Marketplace could be an effective option realizing or implementing an idea into a real finish product for Project Owners.