Miracle-dz/UniFAR-FTUs huggingface dataset:
Statement of Research Interests

In recent years, advances in technology have lead to the collection of large-scale patient data in the forms of Electronic Health Records (EHR). Analyzing EHR data is extremely important because it is the key to healthcare quality improvement, healthcare cost reduction, a better understanding of various types of diseases, and personalized medicine. However, learning from this data is very challenging because of their special nature: EHR data instances are traces of complex behaviors characterized by large number and types of temporal data (e.g. lab results, medications, text reports, etc.) sampled at irregular points, and represented by both structured and unstructured forms. Moreover, a majority of medical conditions are not explicitly annotated by the treating clinicians. This creates a need for additional annotation by clinical experts, which is a challenging task. The benefits of analyzing medical data and the practical challenges in effective mining of this data have created a number of research and funding opportunities. An appropriate analysis of medical data demands not only a knowledge of machine learning and information retrieval, but also knowledge and experience in working with medical data. Machine learning is concerned with the design of algorithms to automatically learn models from the data. Information retrieval is concerned with the extraction of useful information from different forms of data such as text, relational databases, and the World Wide Web. My expertise in machine learning and information retrieval, along with the experience I acquired during my post-doctoral research fellowship on working with medical data, places me in a relatively unique position to develop new research methods for analyzing this data. Below, I describe my • research background in machine learning and information retrieval and its relevancy to the biomedical research, • current contributions to biomedical informatics, and • future research directions in biomedical informatics.
Human Resource Management Practices and Employee Well-being in New Ventures: An Empirical Study Based on Organizational Justice and Supervisor-Subordinate Guanxi

With the development of positive psychology, more and more attention has been paid to the subjective feelings of employees in new ventures, and employee well-being has also been widely concerned by scholars. This study investigated the influence of human resource management practice on employee well-being in new ventures and verified the moderating effect of supervisor-subordinate guanxi and the mediating effect of organizational justice. With a sample of 376 employees, results from regression analysis indicate that (1) Human resource management practices in new ventures are positively correlated with employee well-being. (2) Human resource management practices in new enterprises are positively correlated with organizational justice. Organizational justice is positively correlated with employee well-being. Organizational justice has a mediating effect between human resource management practices of new ventures and employee well-being. (3) Supervisor-subordinate guanxi plays a moderating role between human resource management practices and organizational justice. And then theoretical implications and practical implications were discussed.
Alcohol Consumption Behavior among Undergraduate Students in Thailand: Development of a New Causal Relationship Model

Alcohol consumption among undergraduate students in Thailand is problematic. The aim of this study was to deepen our understanding of this problem by developing a causal relationship model for the alcohol consumption behavior of undergraduate students in Thailand, and to verify the model’s concordance with empirical data. Four latent variables were considered: alcohol consumption behavior, alcohol expectancy, drinking refusal self-efficacy, and health literacy. Participants included representative 1st – 5th year undergraduate students at the Thailand National Sports University, with 600 students being selected using stratified random sampling procedures. The descriptive statistics and the causal relationship model were analyzed using LISREL 8.80. The model developed was in good agreement with the empirical data (c2=228.66, df = 79, p > 0.05 , c2/df=2.894, SRMR =0.07, RMSEA =0.06, CFI =0.99, and RFI =0.98), with all computed indices passing the stipulated criteria. On the basis of the coefficients of determination in the structural equation model, alcohol expectancy, drinking refusal self-efficacy, and health literacy together accounted for 80% of the variance in the student’s alcohol consumption behavior. These theoretically based causal factors provide new directions for future intervention work aimed at modifying the alcohol consumption behaviors of undergraduate students at the Thailand National Sports University. This can be accomplished by developing activities that are suitable and contextually sensitive to their needs.
Crystal Structure and Electron Density of Tantalum Oxynitride, a Visible Light Responsive Photocatalyst

Tantalum oxynitride (TaON) is investigated by neutron and synchrotron diffraction analysis to clarify the structural and electronic features responsible for the visible-light activity of this material. In combination with density functional theory (DFT) calculations, the present measurements confirm the TaON photocatalyst to be monoclinic with space group P21/c (a = 4.94941(4) A, b = 5.01662(4) A, c = 5.16430(2) A, α = 90°, β = 99.6107(4)°, γ = 90°, V = 126.426(2) A3 at 298 K). Complete occupational ordering of the anions occurs in alternate layers normal to the [100] direction. Oxygen and nitrogen atoms in TaON are coordinated with three and four tantalum atoms, respectively. Bond valence sums for the oxygen and nitrogen atomic sites are calculated to be 2.1 and 3.2, respectively. Electron density analyses based on synchrotron data in combination with DFT calculations reveal covalent bonds between Ta and O atoms and between Ta and N atoms. The hybridization of anion 2p and Ta 5d orbitals attributed to th.
Phosphorus mineral evolution and prebiotic chemistry: from minerals to microbes

Abstract Phosphorus availability is considered a limiting factor in many scenarios for the origin of life. The concentration of P in environments of prebiotic interest will have been governed by the available mineral sources of P on the early Earth. A knowledge of early Earth P mineralogy and prevailing global and local environmental conditions is therefore needed to understand which scenarios for prebiotic chemistry are most plausible. Here, we review the plausible diversity of P-bearing phases at Earth's surface during the emergence of life. We consider phases that were delivered by meteorites (exogenous phases), as well as those that developed solely as a result of Earth system processes (endogenous phases). We take into account the known formation conditions of individual phases, as well as the observed temporal distributions of P-bearing minerals found at Earth's surface today. Our approach allows us to leverage what is known about changes in the Earth system in order to rule out the prebiotic relevance of many P-bearing phases. Meanwhile, we highlight a small number of phases that are of possible prebiotic relevance; specifically, exogenous schreibersite, merrillite, and apatite, and endogenous apatite, olivine, and glass. Prebiotic mineral-chemical scenarios can be formulated for each phase, with distinct requirements for the environmental and tectonic state of early Earth. We can therefore relate the plausibility of mineral-chemical scenarios to the nature of early Earth, bridging the fields of geoscience and prebiotic chemistry.
Learning Task Decomposition with Ordered Memory Policy Network

Many complex real-world tasks are composed of several levels of sub-tasks. Humans leverage these hierarchical structures to accelerate the learning process and achieve better generalization. In this work, we study the inductive bias and propose Ordered Memory Policy Network (OMPN) to discover subtask hierarchy by learning from demonstration. The discovered subtask hierarchy could be used to perform task decomposition, recovering the subtask boundaries in an unstruc-tured demonstration. Experiments on Craft and Dial demonstrate that our modelcan achieve higher task decomposition performance under both unsupervised and weakly supervised settings, comparing with strong baselines. OMPN can also bedirectly applied to partially observable environments and still achieve higher task decomposition performance. Our visualization further confirms that the subtask hierarchy can emerge in our model.
Exploring the Relationship between Problematic Internet Use and Well-Being among Adolescents: The Mediating Role of Resilience, Self-control, Negative Mood, and Loneliness

The increase in the massive use of the internet use in recent years, especially among youth, has led to an increase in the phenomenon called Problematic Internet Use (PIU). Problematic internet use is defined as an excessive and disproportionate use of the internet, which may result in psychological, social, academic, and professional difficulties in one's life. PIU relates to an individual’s inability to control internet use, which in turn leads to feelings of distress, anguish, and difficulties in daily activities. The problematic usage of the internet among adolescents has become an emerging mental health issue, and little is known about the nexus of relationships between problematic usage of the internet and the sense of well-being among youth. Thus, the current study aimed at examining the mediating role of four social-emotional variables in these relationships: resilience, self-control, negative mood, and loneliness. Data of 433 adolescents high school students (aged between 12 to 17, 219 [50.6%] boys and 214 [49.4%] girls) were analyzed using a Structural Equation Modeling (SEM). The analysis have revealed the following significant results: Among adolescents, low levels of resilience and high levels of negative mood (together), as well as low levels of self-control and high levels of negative mood (together) and low levels of resilience and high levels of loneliness, mediate these relationships. The findings indicate that high levels of PIU predict low levels of well-being. Girls were found to be higher in PIU, have more computer skills, and have more resilience than boys. The study results expose specific implications for intervention programs for adolescents in the context of PIU.
Face Mask Detection & Attendance System

- The Current COVID-19 pandemic has got the world to a halt and took everyone's attention, as it was a global pandemic the world knew about it. But some underdeveloped nations suffer from viral outbreaks every year. To protect ourselves from these viral outbreaks there is a need for a continuous supply of Sanitizers and face masks to maintain personal hygiene. To help such nations and give some contribution to the world, a novel idea of Face Mask Detection and Attendance System has been proposed in this paper. Also, the various techniques that can be used for facial recognition and object detection, like the HAAR cascade method, Machine learning Algorithms, Deep Learning, etc. are discussed.
BEHAVE: Behavior-Aware, Intelligent and Fair Resource Management for Heterogeneous Edge-IoT Systems

Edge computing is an emerging solution to support the future Internet of Things (IoT) applications that are delay-sensitive, processing-intensive or that require closer intelligence. Machine intelligence and data-driven approaches are envisioned to build future Edge-IoT systems that satisfy IoT devices’ demands for edge resources. However, significant challenges and technical barriers exist which complicate the resource management for such Edge-IoT systems. IoT devices running various applications can demonstrate a wide range of behaviors in the devices’ resource demand that are extremely difficult to manage. In addition, the management of multidimensional resources fairly and efficiently by the edge in such a setting is a challenging task. In this paper, we develop a novel data-driven resource management framework named BEHAVE that intelligently and fairly allocates edge resources to heterogeneous IoT devices with consideration of their behavior of resource demand (BRD). BEHAVE aims to holistically address the management technical barriers by: 1) building an efficient scheme for modeling and assessment of the BRD of IoT devices based on their resource requests and resource usage; 2) expanding a new Rational, Fair, and Truthful Resource Allocation (RFTA) model that binds the devices’ BRD and resource allocation to achieve fair allocation and encourage truthfulness in resource demand; and 3) developing an enhanced deep reinforcement learning (EDRL) scheme to achieve the RFTA goals. The evaluation results demonstrate BEHAVE's capability to analyze the IoT devices’ BRD and adjust its resource management policy accordingly.
Finger Vein Recognition and Intra-Subject Similarity Evaluation of Finger Veins using the CNN Triplet Loss

Finger vein recognition deals with the identification of subjects based on their venous pattern within the fingers. There is a lot of prior work using hand crafted features, but only little work using CNN based recognition systems. This article proposes a new approach using CNNs that utilizes the triplet loss function together with hard triplet online selection for finger vein recognition. The CNNs are used for three different use cases: (1) the classical recognition use case, where every finger of a subject is considered as a separate class, (2) an evaluation of the similarity of left and right hand fingers from the same subject and (3) an evaluation of the similarity of different fingers of the same subject. The results show that the proposed nets achieve superior results compared to prior work on finger vein recognition using the triplet loss function. Furtherly, we show that different fingers of the same subject, especially symmetric fingers (same finger type but from different hand), show enough similarities to perform recognition. The last statement contradicts the current understanding in the literature for finger vein biometry, in which it is assumed that different fingers of the same subject are unique identities.
The age of the palaeodunefield of the northern Murray Basin in South Australia: Preliminary results

A vast field of old desert dunes extends from northwestern Eyre Peninsula, across Yorke Peninsula and the northern Adelaide Plains into the Murray Basin and northwestern Victoria. There are patches of parabolic forms but the sand ridges under review are of seif, linear or longitudinal type. They trend NW–SE in the west and west–east in the east. Here, we record luminescence dates for three dune sites in the Waikerie district of the northwestern Murray Basin. They range from, respectively, 151–25.3 ka, 157–33.3 ka, and a basal age of 59.6 ka, with sand movement also indicated around 1906 and 1933 CE. Apart from the last named, no unconformities are discernible in the sampled sections. r 2007 Elsevier Ltd and INQUA. All rights reserved.
Volatility Derivatives

Volatility derivatives are a class of derivative securities where the payoff explicitly depends on some measure of the volatility of an underlying asset. Prominent examples of these derivatives include variance swaps and VIX futures and options. We provide an overview of the current market for these derivatives. We also survey the early literature on the subject. Finally, we provide relatively simple proofs of some fundamental results related to variance swaps and volatility swaps. 319 A nn u. R ev . F in an c. E co n. 2 00 9. 1: 31 933 9. D ow nl oa de d fr om w w w . a nn ua lr ev ie w s. or g A cc es s pr ov id ed b y C A SA I ns tit ut io n Id en tit y on 0 3/ 03 /2 1. F or p er so na l u se o nl y.
Two-dimensional concentration and temperature measurements in extended flames of industrial burners using PLIF

Concentration profiles of OH, O2 and NO as well as temperature fields in diffusion flames of a length of approx. 300 mm and 40 mm in diameter used for gas-phase synthesis of fused silica have been determined by Planar Laser Induced Fluorescence (PLIF). The measurements have been carried out using a tunable spectrally narrowed KrF laser, whose wavelengths could be switched pulse-to-pulse. The laser beam was shaped as a light sheet into the flame at a fixed position. The flame area under investigation was monitored by moving the burner mounted on a stepper motor. By adapted synchronization the laser induced fluorescence was continuously recorded over the height of the flame perpendicular to the laser light sheet with an intensified CCD camera (10 fps, 8 bit dynamic range, 768 x 576 pixels). By image processing the spatial offset between images was corrected and superposed images were averaged and analyzed. This method allows to investigate the flame by recording 2D-fluorescence images including an automatic correction of intensity inhomogeneities of the laser light sheet. Based on the excited radical or molecule the fluorescence images were used to determine concentration and temperature distributions to build up a 2D-map of the flame. The PLIF experiment was calibrated with precise determination of the temperature at one coordinate of the flame by Spontaneous Vibrational Raman Scattering (VRS) of N2. As a result temperatures up to 3200 K could be determined with an accuracy better than 3% and a spatial resolution better than 1 mm. Temperature variations in the flame at different gas flows of fuel and oxidizer could be monitored sensitively. Also, the influence of different carrier gases like N2, Ar and He on the temperature distribution was investigated. Fluctuations in gas flow caused by turbulence could be monitored as well.
Climate smart Agriculture Interventions in Selected Agricultural Growth Program-II Districts of Harari Region and Dire Dawa Administration: Double Cropping Practices

Climate smart technology intervention in production and productivity enhancement of agriculture sector for small holder farmers’ livelihood improvement is indispensable options. Taking this in consideration the double cropping practice research activity was undertaken with objectives of evaluate the productivity and profitability of double cropping practice technology under farmers condition, build farmers’ knowledge and skill of different crop combination production and management practice and strengthen stakeholders linkages and collaboration among stakeholders. A total of fifteen (15) trial and follower farmers were selected and organized as form of Farmers Research Groups.
Analysis of jitter control using real time scheduling Analysis of jitter control using real time scheduling

Jitter is defined as variation in the delay of received packets. In other words, jitter can be termed as the varying part of delay. Jitter is the variation in the latency on a packet flow between two systems, when some packets take longer than expected from one system to the other. At the transmitting side, packets are sent in a continuous stream with the packets spaced equally apart in most cases. Due to network congestion, improper queuing, channel properties or configuration errors, this steady stream can become lumpy, or the delay between each packet can vary instead of remaining constant which results in jitter. Jitter reduction is a major problem in mission critical applications which requires stringent timing guarantees with jitter limitation of about 1µs. Extensive research is being carried out to reduce this jitter to a great extent. Various methods have been adopted to reduce jitter and one such method for reduction is usage of optimal scheduling algorithms for packet scheduling. In this project, I have reviewed some of the scheduling algorithms which have been used to reduce jitter and compared their results.
A Scalable Solution for Signaling Face Touches to Reduce the Spread of Surface-based Pathogens

Hand-to-Face transmission has been estimated to be a minority, yet non-negligible, vector of COVID-19 transmission and a major vector for multiple other pathogens. At the same time, as it cannot be effectively addressed with mainstream protection measures, such as wearing masks or tracing contacts, it remains largely untackled. To help address this issue, we have developed Saving Face - an app that alerts users when they are about to touch their faces, by analyzing the distortion patterns in the ultrasound signal emitted by their earphones. The system only relies on pre-existing hardware (a smartphone with generic earphones), which allows it to be rapidly scalable to billions of smartphone users worldwide. This paper describes the design, implementation and evaluation of the system, as well as the results of a user study testing the solution's accuracy, robustness, and user experience during various day-to-day activities (93.7% Sensitivity and 91.5% Precision, N=10). While this paper focuses on the system's application to detecting hand-to-face gestures, the technique can also be applicable to other types of gestures and gesture-based applications.
Resource-Guided Configuration Space Reduction for Deep Learning Models

Deep learning models, like traditional software systems, provide a large number of configuration options. A deep learning model can be configured with different hyperparameters and neural architectures. Recently, AutoML (Automated Machine Learning) has been widely adopted to automate model training by systematically exploring diverse configurations. However, current AutoML approaches do not take into consideration the computational constraints imposed by various resources such as available memory, computing power of devices, or execution time. The training with non-conforming configurations could lead to many failed AutoML trial jobs or inappropriate models, which cause significant resource waste and severely slow down development productivity. In this paper, we propose DnnSAT, a resource-guided AutoML approach for deep learning models to help existing AutoML tools efficiently reduce the configuration space ahead of time. DnnSAT can speed up the search process and achieve equal or even better model learning performance because it excludes trial jobs not satisfying the constraints and saves resources for more trials. We formulate the resource-guided configuration space reduction as a constraint satisfaction problem. DnnSAT includes a unified analytic cost model to construct common constraints with respect to the model weight size, number of floating-point operations, model inference time, and GPU memory consumption. It then utilizes an SMT solver to obtain the satisfiable configurations of hyperparameters and neural architectures. Our evaluation results demonstrate the effectiveness of DnnSAT in accelerating state-of-the-art AutoML methods (Hyperparameter Optimization and Neural Architecture Search) with an average speedup from 1.19X to 3.95X on public benchmarks. We believe that DnnSAT can make AutoML more practical in a real-world environment with constrained resources.
The presumed influence of election misinformation on others reduces our own satisfaction with democracy

Pervasive political misinformation threatens the integrity of American electoral democracy but not in the manner most commonly examined. We argue the presumed influence of misinformation (PIM) may be just as pernicious, and widespread, as any direct influence that political misinformation may have on voters. Our online survey of 2,474 respondents in the United States shows that greater attention to political news heightens PIM on others as opposed to oneself, especially among Democrats and Independents. In turn, PIM on others reduces satisfaction with American electoral democracy, eroding the “virtuous circle” between news and democracy, and possibly commitment to democracy in the long-term.