Miracle-dz/UniFAR-FTUs huggingface dataset:
A GRAPHICAL USER INTERFACE SIMULATOR FOR WIRELESS SENSOR NETWORKS LIFETIME ESTIMATION

The deployment of wireless sensor networks is highly dependent on the given application. In fact it depends on the environment characteristics, the available budget and the type and desired quality of service. Since making real tests is most of the times costly and time consuming, researchers and developers usually use simulation tools in order to simulate the behaviour of the network under particular conditions and in different scenarios. In order to make the available simulators suitable for the targeted application, users usually customize the source code by modifying it or adding other modules into it. This task is time consuming and complicated especially if the simulator is customized for the first time and if there is many dependencies between different units. This paper presents the performances and the features of a graphical user interface of an easy to use simulator for wireless sensor networks lifetime estimation.
Development of aboveground mangrove forests’ biomass dataset for Southeast Asia based on ALOS-PALSAR 25-m mosaic

Abstract. Southeast Asia (SEA) has the largest mangrove forest area in the world, which plays an important role in the global carbon cycle and is helping to mitigate climate change. In order to manage the mangrove forests in SEA, their total biomass needs to be determined. However, development of a biomass dataset based on field survey is time consuming. An aboveground biomass (AGB) dataset of mangrove forests was developed for SEA based on ALOS PALSAR 25-m mosaic. Specifically, ALOS-PALSAR 25-m images were first retrieved for SEA from the Kyoto and Carbon Initiative projects and then converted from a digital number to a normalized radar cross-section format in decibels. Samples of mangrove forests in SEA were collected as regions of interest from ALOS PALSAR data based on visual interpretation using Landsat data and Google Earth imagery. A rule-based classification method based on mangrove backscattering characteristics was then used to classify mangroves and nonmangroves in the region. Subsequently, an empirical model was adopted to estimate the AGB of the mangrove forests and an AGB dataset was developed. The results indicate that the spatial distribution of mangrove forests over SEA is 5.1 million hectares, and the estimated average AGB is 140.5 ± 136.1 Mg / ha.
An Efficient Enhanced Method for Indexing with Implementation in Spatial Database Student: WEI-YI LEE Advisor: Dr. SUH-YIN LEE Degree Program of Electrical Engineering Computer Science National Chiao Tung University

In this thesis we assess the efficiency issue when retrieving sets of objects from a very large spatial database. Thus enhanced performance will be empirically shown here through the new storing and indexing structure. The algorithm provides a condensed method to guide a spatial search and to enhance large data access operations by integrating hashing and R-Tree together. R-tree uses the bounding boxes to decide whether or not to search inside of a child node. In this way most of the nodes in the tree are proved during a search which makes R-trees become more suitable for database operations. We analyze current tree-based algorithms and verify that the new approach in the thesis improves the efficiency in the current architecture. To accomplish this, we use the bulk loading data with hashing into database together with experiments showing that the new algorithm supports spatial queries on spatial database efficiently.
Simple Equilibria in General Contests

We develop a general framework for studying contests, including the well-known models of Tullock(1980) and Lazear & Rosen (1981) as special cases. The contest outcome depends on playersâ€™ efforts and skills, the latter being subject to symmetric uncertainty. The model is tractable, because asymmetric equilibrium exists under general assumptions regarding production technologies and skill distributions. Using a link between our contest model and expected utility theory, we are able to derive new comparative statics results regarding how the size and composition of contests affect equilibrium effort, showing how standard results can be overturned. We also discuss the robustness ofour results to changes in the information structure and the implications of our findings for the optimal design of teams.
PLPF-A Profile-based Link Prediction Framework in a Time-evolving Graph

Connection logs are widely used to record connection behavior between sources and destinations, such as users and sites in the internet network, and are best represented as an evolving graph to illustrate the evolving connection behavior of users. Given a continuously recording connection log, when a new destination appears (i.e., connected by a few sources initially), one may want to know who else will then likely to connect it. In this work, we propose a framework using profiles of sources to predict the top-k possible links to the specific destination from k different sources which have never connected to it before, where a profile records the connection behavior of a source including the static and surprising features to represent his/her long-term and short-term interests. The concept behind our framework is that sources have similar connection behavior before are likely to have similar connection behavior after, hence sources having similar profiles as the first source who connects to the new destination are best candidates to connect the same destination later. In the experiment, we use a real dataset of the internet network to evaluate the performance of our method, and compare it to the state-of-the-art method driven by information flow [20]. It shows that our method has improvement in the effectiveness while comparing to the previous method, and our method has a consistent computation time cost rather than the increasing computation time cost using previous method while the whole connection log increases. Furthermore, we show that surprising features of connection behavior are much more useful when predicting new links in the internet network.
progress of epigenetic transgenerational phenotype

The epigenome undergoes a reprogramming process during gametogenesis and early embryogenesis. Therefore, it is believed that epigenetic information cannot be transmitted across generations. However, the occurrence of epigenetic transgenerational phenotype suggests that certain epigenetic marks may escape reprogramming. Although the existence of such a mode of inheritance has been controversial, there is increasing evidence that epigenetic memory does occur in mammals. Due to the reversibility of epigenetic modification, the epigenome is easily changed by a variety of environmental factors, such as chemicals, nutrition and behaviour. Therefore, it provides a potential mechanism for the transgenerational transmission of the impact of environmental factors. The purpose of this review is to introduce the concept of epigenetic transgenerational phenotype, to discuss the epigenetic reprogramming and the molecular mechanism of epigenetic transgenerational transmission, and to list some environmental factors that are associated with epigenetic transgenerational diseases.
A Study of Compliment Responses Among Male and Female Malay Undergraduates

To date, more studies on speech act, one of the branches of pragmatics, are widely researched. One particular speech act that has been increasingly studied is compliment responses Based on the research done on a small group, the findings shown that there were significant differences found in terms of responding to compliments among the male and female respondents. Due to that, the current study aimed to investigate the different types of compliment response strategies employed by 30 Malay undergraduates in one local university. The data which were in English language were analyzed using Herbert’s framework (1998) to determine the types of compliment response strategies. Role plays was employed as the main instrument for data collection method in this study. Findings showed that there were similarities and differences in the types of compliment response strategies employed by Malay male and female undergraduates. Although compliments paid to the people in the Eastern countries were normally denied (Chen, 1993), this research recorded different findings. Male participants had preferences for accepting compliments by praise upgrading themselves, while female participants had more preferences for rejecting compliments by using scaling-down strategies. Some of the compliment response strategies observed in this study were appreciation-token, comment-acceptance, and comment-history. Therefore, it can be concluded that Malay male and female undergraduates preferred different strategies in responding to compliments.
Edinburgh Research Explorer National trends in total cholesterol obscure heterogeneous changes in HDL and non-HDL cholesterol and total-to-HDL cholesterol ratio: an analysis of trends in Asian and Western countries

Background: Although high-density lipoprotein (HDL) and non-HDL cholesterol have opposite associations with coronary heart disease, multi-country reports of lipid trends only use total cholesterol (TC). Our aim was to compare trends in total, HDL and non-HDL cholesterol and the total-to-HDL cholesterol ratio in Asian and Western countries. Methods: We pooled 458 population-based studies with 82.1 million participants in 23 Asian and Western countries. We estimated changes in mean total, HDL and non-HDL cholesterol and mean total-to-HDL cholesterol ratio by country, sex and age group. Results: Since (cid:2) 1980, mean TC increased in Asian countries. In Japan and South Korea, the TC rise was due to rising HDL cholesterol, which increased by up to 0.17 mmol/L per decade in Japanese women; in China, it was due to rising non-HDL cholesterol. TC declined in Western countries, except in Polish men. The decline was largest in Finland and Norway, at (cid:2) 0.4 mmol/L per decade. The decline in TC in most Western countries was the net effect of an increase in HDL cholesterol and a decline in non-HDL cholesterol, with the HDL cholesterol increase largest in New Zealand and Switzerland. Mean total-to-HDL cholesterol ratio declined in Japan, South Korea and most Western countries, by as much as (cid:2) 0.7 per decade in Swiss men (equivalent to (cid:2) 26% decline in coronary heart disease risk per decade). The ratio increased in China. Conclusions: HDL cholesterol has risen and the total-to-HDL cholesterol ratio has declined in many Western countries, Japan and South Korea, with only a weak correlation with changes in TC or non-HDL cholesterol.
Study of Buffer-aided physical-layer network coding with ML and MMSE linear code designs for cooperative DS-CDMA systems

In this work, we propose buffer-aided physical-layer network coding (PLNC) techniques for cooperative direct-sequence code-division multiple access systems. In the proposed buffer-aided PLNC schemes, a relay pair selection algorithm is employed to obtain the relay pair and the packets in the buffer entries with the best performance and the associated link combinations used for data transmission. We also devise PLNC schemes based on the design of linear network coding matrices using maximum likelihood and minimum mean-square error criteria to generate the network coded symbols that are sent to the destination. Simulation results show that the proposed techniques significantly outperform prior art.
Searching for exotic long-lived particle states at the LHC using a deep neural network

We present a tagging algorithm to identify displaced jets arising from the decays of new long-lived particle (LLP) states in data recorded by the CMS detector at the CERN LHC. The tagger is a multiclass classifier based on a deep neural network. Information from individual particles and secondary vertices within jets are refined through the use of convolutional networks, before being combined with high-level engineered variables via a dense network. The LLP decay length, cτ0, is used as an external parameter to the neural network, which allows for hypothesis testing over several orders of magnitude in cτ0. We define a method based on truth information from Monte Carlo simulation to reliably label jets originating from an LLP decay for supervised training. The training is performed by streaming ROOT trees containing O(100 M) jets directly into the TENSORFLOW queue and threading system. This custom workflow allows a flexible selection of input features and the asynchronous preprocessing of data, such as the resampling and shuffling of batches on the CPU, in parallel to training on the GPU. Domain adaptation is performed with control samples of pp collision data to ensure good agreement between data and Monte Carlo simulation. The tagger provides a rejection factor of 10 000 for jets from standard model processes while maintaining an LLP jet tagging efficiency of 30–80% for LLPs with 1mm ≤ cτ0 ≤ 10m. We describe the novel application of several machine learning techniques to LLP searches.
Face Detection using RGB Model

Skin tone detection had been performed by using RGB color system, it is an interested filled of working due to the great hazard facing the digital world at this time. Many of the RGB images are the most common area of that detection. In this paper, an experimental study of face detection algorithms based on “Skin Color” has been introduce using Matlab environment to detect the skin color of an image. Having a true color image is the input, then the resulted image is a skin image that are detected by the algorithm that is built for that purpose.
Nuclear Extracts of Mouse Cells Using The “Framed

We propose an improved earlier described “mirror” method [1] for detecting in cell 10 nuclear extracts mutations that arise in DNA during its replication due to misincorporation of 11 deoxyadenosine-5’-monophosphate (dAMP) opposite 7,8-dihydro-8-oxoguanine (8-oxoG). The 12 method is based on the synthesis of a complementary chain (“mirror”) by nuclear extracts of 13 different mice organs on a template containing 8-oxoG inside and dideoxycytidine residue (ddC) 14 at the 3’-end. The “mirror” was amplified by PCR using primers part of which was non15 complementary to the template. It allowed obtaining the “framed mirror” products. The 16 misincorporation of dAMP in “framed mirror” products forms an EcoRI restriction site. The 17 restriction analysis of double-stranded “framed mirror” products allows a quantification of the 18 mutation frequency in nuclear extracts. The data obtained showed that the mutagenic potential of 19 8-oxoG markedly varied in different organs of adult mice and embryos. 20
Evaluation of Trace Element Mobility in a Sandy Aquifer of the Athabasca Oil Sands, Alberta

The Athabasca oil sands deposit, located in Northeastern Alberta, is the largest oil reservoir in the world. Alberta’s oil sands industry extracts bitumen from these deposits and produces crude oil by additional processing. The produced tailings, a mixture of sand, silt, clay, water, residual bitumen, other hydrocarbons, and salts, are contained in impoundments that are generally constructed over low permeability glacial till and/or shale bedrock. However, in some cases, the glacial tills contain paleochannels that may provide migration pathways for process-affected (PA) water away from the tailings deposits. For example, Suncor’s South Tailings Pond (STP) is partially situated atop the Wood Creek Sand Channel (WCSC), a highly permeable Pleistocene glacial outwash channel (Holden et al., 2009; Tompkins, 2009). The location of the STP implies that during reclamation, removal/remediation of PA water and treatment of mature fine tailings using the wet landscape method may only partially restore this area as it does not address the effect on the underlying groundwater system (Tompkins, 2009). Therefore, a detailed understanding of trace metal mobility as a potential consequence of the PA water infiltration into the WCSC is essential in developing sustainable strategies to reclaim the STP.
Characterization of the C(p,p’)C Reaction (Ep=19.5–30 MeV) for Active Interrogation

J. Nattress, F. Sutanto, P.-W. Fang, Y. -Z. Chen, A. Cheng, K. -Y. Chu, T. -S. Duh, H. -Y. Tsai, M. -W. Lin3,∗ and I. Jovanovic Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI 48109 USA Oak Ridge National Laboratory, Oak Ridge, TN 37830 USA Institute of Nuclear Engineering and Science, National Tsing Hua University, Hsinchu 30013, Taiwan and Isotope Application Division, Institute of Nuclear Energy Research, Taoyuan City 32546, Taiwan (Dated: November 14, 2019)
Optimal Financing with Tokens

We develop a unifying model of the optimal issuance and design of tokens in the presence of frictions typical to the financing of entrepreneurial projects, such as the need to raise outside funds to finance platform development and the ensuing agency conflicts between platform developers and outsiders. Tokens possess utility features when they serve as the transaction medium on the platform. They possess security features when they distribute dividends. The paper shows how the optimal provision of utility and security features relates to financing needs, moral hazard, and platform characteristics and specifies the conditions under which initial coin offerings or security token offerings are optimal for platform financing.
Beyond Black-box Dictionary Learning for Waves

This work discusses an optimization framework to embed dictionary learning frameworks with the wave equation as a strategy for incorporating prior scientific knowledge into a machine learning algorithm. We modify dictionary learning to study ultrasonic guided wave based defect detection for non-destructive structural health monitoring systems. Specifically, this work involves altering the popular K-SVD algorithm for dictionary learning by enforcing prior knowledge about the ultrasound guided wave problem through a physics-based regularization derived from the wave equation. We confer it the name “wave-informed K-SVD. ” Training a dictionary on data simulated from a fixed string added with noise using both K-SVD and wave-informed K-SVD, we show an improved physical consistency of columns of dictionary matrix with the known modal behavior of different onedimensional wave simulations is observed.
Lecturers’ and Students’ Perception toward the Importance of Development Multimedia-assisted Drama Appreciation Teaching Materials in College

The purpose of this research to describe lecturers’ and students’ perception toward the importance of development multimedia-assisted drama appreciation teaching materials in college. Teaching materials, all this time, it used to tend more theories and manually so that it could not give the experience contextually to students. Therefore, also through National Qualifications Framework (NQF) is stated the way students would have experience with find and learn by themselves. Besides, lecturers also are charged to be more creative and innovative, especially in design learning set, so as to be able to create interesting learn’s atmosphere. This is also same with the 4.0 revolution where the lecturers also have to be able design the learning that more contextual. The result of analysis requirment through questionnaire was obtained that it is necessary to develop multimedia-assisted drama appreciation teaching material.
Sparse Image Generation with Decoupled Generative Models

Learning to generate very sparse images with deep generative models has been a challenging problem due to issues such as mode collapse and sparse gradient signals. In this work, we propose a novel model combining a neural network generator with an explicit mixture model, which induces sparsity with a learnable Dirac delta mass at zero instead of using rectified linear units at the output. Our model decouples the sparsity level and the non-zero distribution of each pixel in the data while fitting both of them simultaneously by minimizing the model’s entropy. We demonstrate both theoretically and empirically that the model is able to learn a rich distribution of a sparse muon image dataset while maintaining desired physical properties such as isolation.
Mining of Closed Frequent Itemsets and Sequential Patterns in Data Streams Using Bit-Vector Based Method

Mining a data stream is an important data mining problem with broad applications, such as sensor network, stock analysis. It is a difficult problem because of some limitations in the data stream environment. In the first part of this paper, we propose New-Moment to mine closed frequent itemsets. New-Moment uses bit-vectors and a compact lexicographical tree to improve the performance of Moment algorithm. In the second part, we propose IncSPAM to mine sequential patterns with a new sliding window model. IncSPAM is based on SPAM and utilizes memory indexing technique to incrementally maintain sequential patterns in current sliding window. Experiments show that our approaches are efficient for mining patterns in a data stream.
Protecting privacy using HyperLogLog to process data from Location Based Social Networks

Location Based Social Networks (LBSN), such as Twitter or Instagram, provide a rich set of data. The primary motivation of users on these networks is to communicate and share information. However, since this data is publicly available on the internet, it can be used for applications beyond the LBSN service itself. With increasing network speeds and device performance, processing of LBSN data will soon be possible even in real time. It is about time to think of and prepare for the consequences.
IMPACT OF NUTRITION KNOWLEDGE ON THE NUTRITIONAL STATUS OF SCHOOL CHILDREN

School age is a strategic point of entry for improving attitudes and well being of children. It is a detrimental period of nutritional care. A correct dietary practice of an individual notably appears to enhance their living state as well as society's development. A situational analysis as part of the School Education Program Nutrition and Health education was extended among school children in the urban Mysore district. Around 28 girls and 45 boys aged between 8 to14 years were assessed for Nutritional knowledge practices using pretested standardized questionnaire. Finding revealed a 35 to 25% of girls and boys with poor knowledge, while 40 to 35% of girls and boys had average knowledge level about nutrition and health. Notably, girls showed greater nutrition knowledge level in comparison with boys. Anthropometric Evaluation revealed a difference of 2.0-3.3kgs in actual weight and respectively 10.26. 7cms of difference in actual height among Boys and girls as compared to NCHS data. Evidently, children were observed to be shorter and lighter for their age as compared to 50 th percentile of NCHS standard. A marked significance of 0.007and 0.003 correlations was noted between Nutrition Knowledge Aptitude Practice and Nutritional status among girls and boys. Relatively, Prevalence of anemia was observed among children. Thus, Research suggests that students should concentrate more towards healthy nutrition practices. It can be hypothesized that Nutrition education program conducted among children showed a positive effect on attitudes and nutrition behavior improvements.