Miracle-dz/UniFAR-FTUs huggingface dataset:
In search of Insights from Community of Practice and Use of Telemedicine in Low income Countries: The case of Ethiopia

The use of ICT for exchange of medical records in provision of health care services appears to be a viable option for enhancing the capacity of low income developing countries struggling with shortage of qualified medical personnel. This study seeks to gain a preliminary assessment of whether health professionals who work in the health facilities covered by the Ethio-Indian Telemedicine program have adopted and used the Telemedicine technology to enhance health care services. Among other things, the study also aims to understand the extent to which health professionals in the study area consider Telemedicine as a viable option for delivery of health care services. Survey will be conducted to gather information from users in the study area. The study is primarily informed by literature on Community of Practice, Unified Theory of Acceptance and Use of Technology, and Case Study Research.
Predicting trusts among users of online communities: an epinions case study

Trust between a pair of users is an important piece of information for users in an online community (such as electronic commerce websites and product review websites) where users may rely on trust information to make decisions. In this paper, we address the problem of predicting whether a user trusts another user. Most prior work infers unknown trust ratings from known trust ratings. The effectiveness of this approach depends on the connectivity of the known web of trust and can be quite poor when the connectivity is very sparse which is often the case in an online community. In this paper, we therefore propose a classification approach to address the trust prediction problem. We develop a taxonomy to obtain an extensive set of relevant features derived from user attributes and user interactions in an online community. As a test case, we apply the approach to data collected from Epinions, a large product review community that supports various types of interactions as well as a web of trust that can be used for training and evaluation. Empirical results show that the trust among users can be effectively predicted using pre-trained classifiers.
Text Information and Spatial Abilities in Learning With Different Visualizations Formats

This research examined whether the informational advantage of an animation over a static picture (and over no visualizations as a control condition) can be compensated by presenting the information in the text that constitutes this informational advantage. In addition, it was investigated whether learners’ spatial abilities acted as a compensator in learning with a static picture compared to an animation. Moreover, the underlying cognitive processes were explored by eye tracking measures. Two hundred and one university students were randomly assigned to one to six conditions resulting from a 2 × 3 between-subjects design with text information (with vs. without dynamic information) and visualization format (no visualization vs. static picture vs. animation) as independent variables and spatial abilities as continuous factor. For learning outcomes, results revealed that, other than expected, text information did not moderate learning with the different visualization formats. However, learners receiving visualizations significantly outperformed learners in the control conditions, and learners receiving animations significantly outperformed learners receiving static pictures in a transfer test. An analysis of the eye tracking data revealed that this beneficial effect of animations over static pictures was mediated by a pupillometry measure that is supposed to reflect effortful cognitive processing. Spatial abilities acted as a compensator in learning with the two visualization formats: The advantage of animations was particularly evident for learners with low spatial abilities, but not for learners with high spatial abilities. These results indicate that the informational advantage of animations over static pictures cannot easily be compensated through text information, but by learners’ spatial abilities.
Modification of Bacterial Effector Proteins Inside Eukaryotic Host Cells

Pathogenic bacteria manipulate their hosts by delivering a number of virulence proteins -called effectors- directly into the plant or animal cells. Recent findings have shown that such effectors can suffer covalent modifications inside the eukaryotic cells. Here, we summarize the recent reports where effector modifications by the eukaryotic machinery have been described. We restrict our focus on proteins secreted by the type III or type IV systems, excluding other bacterial toxins. We describe the known examples of effectors whose enzymatic activity is triggered by interaction with plant and animal cell factors, including GTPases, E2-Ubiquitin conjugates, cyclophilin and thioredoxins. We focus on the structural interactions with these factors and their influence on effector function. We also review the described examples of host-mediated post-translational effector modifications which are required for proper subcellular location and function. These host-specific covalent modifications include phosphorylation, ubiquitination, SUMOylation, and lipidations such as prenylation, fatty acylation and phospholipid binding.
Security vulnerabilities of an remote password authentication scheme with smart card

The two-factor authentication scheme combined password with smart card is one of the most widely used methods. This paper analyzes the security performance of a latest proposed remote user authentication scheme and simulates the attacks it suffers: it's vulnerability to the password guessing attack, the server masquerade attack and lacks of password backward security. Based on the detailed security analysis, this paper would propose some useful suggestions for designing the secure scheme.
Exact and approximate epidemic models on networks: a new, improved closure relation

Recently, research that focuses on the rigorous understanding of the relation between simulation and/or exact models on graphs and approximate counterparts has gained lots of momentum. This includes revisiting the performance of classic pairwise models with closures at the level of pairs and/or triples as well as effective-degree-type models and those based on the probability generating function formalism. In this paper, for a fully connected graph and the simple $SIS$ (susceptible-infected-susceptible) epidemic model, a novel closure is introduced. This is done via using the equations for the moments of the distribution describing the number of infecteds at all times combined with the empirical observations that this is well described/approximated by a binomial distribution with time dependent parameters. This assumption allows us to express higher order moments in terms of lower order ones and this leads to a new closure. The significant feature of the new closure is that the difference of the exact system, given by the Kolmogorov equations, from the solution of the newly defined approximate system is of order $1/N^2$. This is in contrast with the $\mathcal{O}(1/N)$ difference corresponding to the approximate system obtained via the classic triple closure.
Towards MapReduce for Desktop Grid Computing

MapReduce is an emerging programming model for data-intense application proposed by Google, which has attracted a lot of attention recently. MapReduce borrows from functional programming, where programmer defines Map and Reduce tasks executed on large set of distributed data. In this paper we propose an implementation of the MapReduce programming model. We present the architecture of the prototype based on Bit Dew, a middleware for large scale data management on Desktop Grid. We describe the set of features which makes our approach suitable for large scale and loosely connected Internet Desktop Grid: massive fault tolerance, replica management, barriers-free execution, latency-hiding optimisation as well as distributed result checking. We also present performance evaluation of the prototype both against micro-benchmarks and real MapReduce application. The scalability test shows that we achieve linear speedup on the classical Word Count benchmark. Several scenarios involving lagger hosts and host crashes demonstrate that the prototype is able to cope with an experimental context similar to real-world Internet.
Self-Determination Theory and Children’s Singing Games In and Out of the Classroom: A Literature Review

In a bid to make formal music education experiences more relevant to children’s lives, teachers have sought to incorporate aspects of children’s informal learning practices into classroom settings. Singing games have a long history both in and out of school settings, and the recontextualization that occurs when brought into the music classroom typically leads to differences in issues such as player populations, modes of transmission, and pedagogical practices. This literature review examines the motivational impacts of these discrepancies, analyzing research on singing games through the lens of self-determination theory of achievement motivation. Self-determination theory posits that three basic psychological needs are essential for humans’ optional functioning—relatedness, competence, and autonomy. Each psychological need is defined, benefits are described, and distinctions are made between the ways that the need is met or thwarted in informal and formal settings during singing game play. Suggestions for music teachers are provided.
Revealing X-ray obscured quasars in SWIRE sources with extreme mid-IR/optical flux ratios

Recent works have suggested that selection criteria based on mid-IR properties, i.e. extreme colors and bright flux levels, can be used to reveal a population of dust-enshrouded, extremely-luminous quasars at z ~ 1–2. However, the X-ray spectral properties of these intriguing objects still remain largely unexplored. We have performed an X-ray study of a large sample of bright mid-IR (F_(24 μm) > 1.3 mJy) galaxies showing an extreme MIR/Optical flux ratio (F_(24 μm)/F_R > 2000) in order to confirm the presence of a luminous active nucleus in these very red objects. Sampling of a large area is required to pick up objects at the highest luminosities given their low surface density. Accordingly, we have applied our selection criteria to an area of ~6 deg^2 covered by XMM-Newton/Chandra observations within the ~50 deg^2 SWIRE survey, resulting in a final sample of 44 objects. The vast majority of the source redshifts, both spectroscopic and photometric, are in the range 0.7 ≾ z ≾ 2.5. The X-ray coverage of the sample is highly inhomogeneous (from snap-shot 5 ks Chandra observations to medium-deep XMM-Newton exposures of 70 ks) and, consequently, a sizable fraction of them (≈43%) remains undetected in the 0.5–10 keV band. Using spectral or hardness information we were able to estimate the value of the absorbing column density in 23 sources. 95% of them are consistent with being obscured by neutral gas with an intrinsic column density of NH ≥ 1022 cm^(−2). Remarkably, we also find that ~55% of these sources can be classified as type 2 quasars on the basis of their absorption properties and X-ray luminosity. Moreover, most of the X-ray undetected sources show extreme mid-IR colors, consistent with being luminous AGN-powered objects, suggesting they might host heavily obscured (possibly Compton-thick) quasars in X-rays. This demonstrates that our selection criteria applied to a wide area survey is very efficient in finding a large number of type 2 quasars at z ≳ 1. The existence of this class of very powerful, obscured quasars at high z could have important implications in the context of the formation and cosmological evolution of accreting supermassive black holes and their host galaxies.
Simultaneous determination of ethylene glycol and its major toxic metabolite, glycolic acid, in serum by gas chromatography.

We developed a gas-chromatographic procedure for the simultaneous determination of ethylene glycol (EG) and its major toxic metabolite, glycolic acid (GA), suitable for clinical use in instances of EG intoxication. After serum protein precipitation with acetonitrile (containing internal standard), the supernate is treated with 2,2-dimethoxypropane (containing dimethylformamide) to remove water, and the volume is then reduced by evaporation to <100 microL of dimethylformamide (but not to dryness). After trimethylsilyl derivatization, the resulting derivatives are analyzed by capillary column gas chromatography. Only 100 microL of serum is required and the entire determination, including calibrators and controls, takes <2 h. The method gives a linear response to at least 10 g/L EG and 5 g/L GA and has a limit of detection <10 mg/L. Intraassay CV is < or = 2.8% for EG (100 and 1000 mg/L) and GA (100 and 500 mg/L); between-day CV is < or = 6.5%. The absolute recovery from serum was 91% for EG and 77-82% for GA (200 and 2000 mg/L each). Relative to calibrators prepared bovine serum albumin (70 g/L), the recovery was 99-104% for EG (100 - 5000 mg/L) and 95-105% for GA (50 - 2500 mg/L). No clinically important interference was detected for >60 exogenous or endogenous compounds and drugs.
The Use of Prior Knowledge for Perceptual Inference Is Preserved in ASD

An amorphous collection of black and white patches (so-called Mooney images) can be perceived dramatically differently before versus after exposure to the natural source image. Prior experience causes the patches to (re)organize and fit together in a meaningful whole. Given recent hypotheses on a weaker role of priors in perception in individuals with autism spectrum disorders (ASD), we looked at improvements in recognition accuracy for Mooney images, before and after exposure to their source image, in typically developing (TD) individuals varying in ASD-like traits, and in a clinical group of adolescents with ASD (versus matched TD sample). We found typical prior-based performance improvements irrespective of ASD-like traits or ASD diagnosis, suggesting that the fast formation and application of specific priors is preserved in ASD. Together with earlier studies reporting intact use of other types of perceptual priors, these findings narrow down the candidate Bayesian accounts that are plausible for ASD.
A Classification of Configuration Spaces of Planar Robot Arms with Application to a Continuous Inverse Kinematics Problem

Using results on the topology of moduli space of polygons [Jaggi, 92; Kapovich and Millson, 94], it can be shown that for a planar robot arm with $n$ segments there are some values of the base-length, $z$, at which the configuration space of the constrained arm (arm with its end effector fixed) has two disconnected components, while at other values the constrained configuration space has one connected component. We first review some of these known results. Then the main design problem addressed in this paper is the construction of pairs of continuous inverse kinematics for arbitrary robot arms, with the property that the two inverse kinematics agree when the constrained configuration space has a single connected component, but they give distinct configurations (one in each connected component) when the configuration space of the constrained arm has two components. This design is made possible by a fundamental theoretical contribution in this paper -- a classification of configuration spaces of robot arms such that the type of path that the system (robot arm) takes through certain critical values of the forward kinematics function is completely determined by the class to which the configuration space of the arm belongs. This classification result makes the aforesaid design problem tractable, making it sufficient to design a pair of inverse kinematics for each class of configuration spaces (three of them in total). We discuss the motivation for this work, which comes from a more extensive problem of motion planning for the end effector of a robot arm requiring us to continuously sample one configuration from each connected component of the constrained configuration spaces. We demonstrate the low complexity of the presented algorithm through a Javascript + HTML5 based implementation available at this http URL
Transfer Learning on Heterogenous Feature Spaces via Spectral Transformation

Labeled examples are often expensive and time-consuming to obtain. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have (a) different feature spaces (e.g., image vs. text data), (b) different data distributions, and (c) different output spaces? This paper proposes a solution and discusses the conditions where this is possible and highly likely to produce better results. It works by first using spectral embedding to unify the different feature spaces of the target and source data sets, even when they have completely different feature spaces. The principle is to cast into an optimization objective that preserves the original structure of the data, while at the same time, maximizes the similarity between the two. Second, a judicious sample selection strategy is applied to select only those related source examples. At last, a Bayesian-based approach is applied to model the relationship between different output spaces. The three steps can bridge related heterogeneous sources in order to learn the target task. Among the 12 experiment data sets, for example, the images with wavelet-transformed-based features are used to predict another set of images whose features are constructed from color-histogram space. By using these extracted examples from heterogeneous sources, the models can reduce the error rate by as much as~50\%, compared with the methods using only the examples from the target task.
Modern Organic Precision E-Agriculture (MOPEA) Using Energy Efficient Wireless Sensor Network (WSN) Technology

--Agriculture and Environment are the most important areas on which human life depends. There is urgent need of use of different technologies to support these domains, for low cost and appropriate solutions to many problems. The wireless sensor network (WSN) is one of the most promising technologies to bring atomization and independence in these areas. Recently there has been drastic advancement in Wireless Sensor Networks due to the achievements in Micro and Nano-electro mechanical system (MEMS & NEMS) and low power electronic technologies. WSN technologies are viewed as an efficient tool for farmers in the world. WSN Technologies plays an important role in agriculture development, management, and climate change system in rural region. In this paper we have proposed MOPEA system architecture using energy efficient WSN technology and the importance of organic agriculture. This paper presents a survey on various energy saving mechanisms that are proposed for MAC protocols in WSN and some energy model have been analysed using QualNet 5.0. 2 simulator.
Development of Attitude through Critical Thinking

No doubt, across the world, advantages of critical thinking in education motivated educators focus on developing the critical thinking ability and traits among students more than ever before, yet ample evidence from literature has shown the lack of critical thinking skills among students at all level. The purpose of the study was to examine the effectiveness of two teaching strategies questioning and role playing the development of critical thinking attitude among elementary students. This article offers critical thinking as a prelude to developing attitude among students by using case study method and design based approach. Two groups each with 15 students were made using purposive and convenience sampling method. Evidence was collected in a usual classroom context in the form of pre and post questionnaire data. The focus was evaluated through the indirect evidence of its effects on students’ critical thinking attitude by using Paul's intellectual traits model. Findings suggest that critical thinking is a viable tool in influencing critical thinking attitudes of students in Pakistani context.
Implementation of Running Average Background Subtraction Algorithm in FPGA for Image Processing Applications

In this paper a new background subtraction algorithm was developed to detect moving objects from a stable system in which visual surveillance plays a major role. Initially it was implemented in MATLAB. Among all existing algorithms running average algorithm was choosen because of low computational complexity which is the major parameter of time in VLSI. The concept of the background subtraction is to subtract the current image with respect to the reference image and compare it with to the certain threshold values. We propose a new real time background subtraction algorithm which was implemented with verilog hdl in order to detect moving objects accurately. Our method involves three important modules background modelling; adaptive threshold estimation and finally fore ground extraction. Compared to all existing algorithms our method having low power consumption and low resource utilization. Here we have written the core processor Microblaze is designed in VHDL (VHSIC hardware description language), implemented using XILINX ISE 8.1 Design suite the algorithm is written in system C Language and tested in SPARTAN-3 FPGA kit by interfacing a test circuit with the PC using the RS232 cable. Area and the speed of the algorithm are also evaluated.
Identification of hBD-3 in respiratory tract and serum: the increase in pneumonia

Human β-defensin (hBD)-3, a 45 amino acid antimicrobial peptide, was originally isolated from human skin. hBD-3 mRNA has also been detected in the airways by RT-PCR. While hBD-3 may be involved in antimicrobial defences within the respiratory tract, the presence of hBD-3 peptide in the respiratory system has not yet been confirmed. The antimicrobial activity of the synthesised hBD-3 peptide was measured by a radial diffusion assay and a colony count assay. The present authors confirmed the presence of hBD-3 peptide in homogenates of human lung and serum using reverse-phase HPLC coupled with a highly sensitive RIA. The localisation of the hBD-3 peptide was investigated by immunohistochemistry. In addition, the serum concentrations of hBD-3 were measured by RIA. hBD-3 exhibited a strong antimicrobial activity, which was unaffected by increasing salt concentrations. Immunohistochemically, the current authors observed the expression of hBD-3 in bronchial and bronchiolar epithelial cells. The mean±sd serum concentration of hBD-3 in patients with bacterial pneumonia was 239.4±17.8 pg·mL−1 in the acute phase and, decreased to 159.3±20.1 pg·mL−1 after the completion of therapy. In conclusion, these findings will help elucidate the role of human β-defensin-3 in host immune responses and identify the pathophysiological significance of this molecule in respiratory infections.
Topological Order Planner for POMDPs

Over the past few years, point-based POMDP solvers scaled up to produce approximate solutions to mid-sized domains. However, to solve real world problems, solvers must exploit the structure of the domain. In this paper we focus on the topological structure of the problem, where the state space contains layers of states. We present here the Topological Order Planner (TOP) that utilizes the topological structure of the domain to compute belief space trajectories. TOP rapidly produces trajectories focused on the solveable regions of the belief space, thus reducing the number of redundant backups considerably. We demonstrate TOP to produce good quality policies faster than any other pointbased algorithm on domains with sufficient structure.