Miracle-dz/UniFAR-FTUs huggingface dataset:
An Ontological Structure for Gathering and Sharing Knowledge among Scientists through Experiment Modeling

This chapter presents a proposal for modeling / simulating experiments conducted by scientists working in common scientific problems, based on gathering and exploiting knowledge elements produced among them. The authors’ approach enables the adaptation of knowledge structures (bounded to scientific problems) and is based on recurrent refining processes that are fed by indicators, which come from collaboration among the scientists involved. This scheme captures a web-based infrastructure, which allows scientists to collaborate on synthesizing experiments online. The proposed model is approached as an ontology that contains scientific concepts and actions. This ontology is linked to the scientific problem and represents both the “common understanding” for such a problem and the way it could be managed by the group. This dynamic ontology will change its structure according to the collaboration acts among scientists. Frequent collaboration over certain elements of the experiment will make them prevail in time. Besides, this process has been defined in a way that provides a global understanding of the scientific treatment that could be applied on any scientific problem. Hence, the ontology represents a virtualization of the scientific experiment. This whole representation is aimed at providing the media for developing e-research among scientists that are working on common problems.
Learning with technology: theoretical foundations underpinning simulations in higher education

Embracing learning for the future through learning technologies requires a clearer understanding of the pedagogies that inform the simulated teaching and learning strategies used to facilitate student learning. Higher Education e-learning literature often groups educational games with simulation. However, educational simulation attributes are different from games or simulated games with very different aims and objectives within the learning context, which have implications for technology-based learning designs. In order to optimize the use of technologybased simulation this paper presents the theoretical foundations of educational simulation in a disciplinary context. Understanding the simulation pedagogy will assist academics to create technology-based simulated learning environments that highlight the inherent simulation attributes to enable and facilitate learning.
Reverse Logistics Network Design Using a Hybrid Genetic Algorithm and Simulated Annealing Methodology

Supply network coordination is an essential requirement in modern industry (Germain et al., 2007). According to Malone and Crowston, coordination is the concept of managing dependencies between activities (Malone and Crowston, 1994). Shared resources, producer/consumer relationships, simultaneous constraints, and tasks/subtasks can be identified as dependencies between activities in the supply network (Germain et al., 2007). Coordination is particularly problematic when considering supply networks, as aBStract
A Hybrid Classification Model for Diabetes Dataset Using Decision Tree

Abstract—Diabetes has become a modern day life style disease affecting millions of people around the world. Data mining is growing in relevance to solving real world problems and hence this can be applied to the diabetes problem as well. The study proposes to use the UCI repository dataset called PIMA Indians Diabetes dataset and decision tree algorithms like C4.5, J48 and FB Tree. The comparison study includes parameters like efficiency, accuracy and features or nodes selected. This hybrid model enables to accurately classify the diabetes dataset and help the people providing treatment as well as those suffering from the disease.
Structure of brevetoxin A (GB-1 toxin), the most potent toxin in the Florida red tide organism Gymnodinium breve (Ptychodiscus brevis).

bromide with NBS and debromination with zinc-copper couple, potassium iodide, and iodine in D M F (24%). 69i6 Again, C,, symmetry was evident from the N M R spectra. " The colorless crystalline substance (mp 27-29 "C) exhibits the following electronic spectrum: A, , cyclohexane 218 (c 8900) and 318 (3000). Photoelectron spectroscopic measurements on 1 will be reported elsewhere. Heating a CDC13 solution of 1 to 80 O C induces clean rearrangement to 1618 ( t l -7 h). The same isomerization can be achieved more rapicily by irradiation with a TLC UV lamp (A 254 nm). Accordingly, 1 finds [1,3] sigmatropic migration to be most accessible from its ground and excited states.
Study of Self-coordination Grid Computation Model Without Resource Management

A self-coordination grid computation model without resource management is proposed.The architecture,information management and recursive resource schedule algorithm of this model are presented.In this model,there hasn’t any managing node,and all the nodes have the same function,it uses the algorithm of evolutional eager scheduling to solve the dynamic of environment.Furthermore the preliminary performance evaluations are made,the simulation results show that this model can harvest the idle time of Internet connected computers to run very large distributed applications.
Shadow elimination for robust video surveillance

Variation in illumination conditions, caused by weather, season, time of day, etc., makes the task difficult when building surveillance systems of real world scenes. Especially, cast shadows produce troublesome appearance variations to accomplish monitoring with computer vision techniques, typically moving object tracking from a stationary viewpoint. To eliminate lighting effects robustly from image sequences as a preprocessing stage for robust video surveillance, we propose a framework based on the idea of intrinsic images. Unlike previous methods to derive intrinsic images, we derive time-varying reflectance images. As a result, we obtain illumination images that capture only lighting effects on the scene. Using these reflectance images and illumination images, we construct an illumination eigenspace to estimate directly the illumination images under the arbitrary lighting conditions of the same scene. By canceling out the lighting effects using this illumination image, robust video surveillance can be accomplished. We explain the theory of the framework with simulation results, and apply the framework to a real world monitoring data set to prove its effectiveness.
Phosphorylation of Purine (Phosphonomethoxy)alkyl Derivatives by Mitochondrial AMP Kinase (AK2 Type) from L1210 Cells

Substrate activity of purine (phosphonomethoxy)alkyl derivatives towards mitochondrial AMP kinase (AK2 type) from L1210 cells was studied. The native AMP kinase, purified nearly to homogeneity, is a monomer with molecular weight 26 kDa. The purified AMP kinase is specific for natural adenine nucleotides (AMP and dAMP) as phosphate acceptors but has a broad specificity to nucleoside 5'-triphosphates as phosphate donors. In addition to adenine acyclic nucleotide analogues, the enzyme is capable of phosphorylating also analogous derivatives containing 2,6-diaminopurine moiety. Kinetic data show that the substrate activity of these acyclic nucleoside phosphonates towards AK2 isoenzyme decreases in the order ( S )-HPMPA > ( R )-PMPA > PMEA > PMEDAP > ( S )-PMPDAP > ( R )-PMPDAP >> ( S )-PMPA. Acyclic nucleotide analogues do not exhibit any inhibitory activity towards AK2 isoenzyme.
Static & Dynamic Channel Assignment for Interference Analysis in Wireless Mesh Networks

This paper used to adapt the physical and protocol model which is used to analyze the interference characteristics in multihop wireless networks. The Physical model which is used as a reference model for Physical layer behavior which treat the interference as noise, but it is limited by its complexity. The Protocol model is simple but it produces an infeasible solution. The “Reality Check” a new mechanism which is introduced to produce a feasible solution for the protocol model and used to calculate the maximum interference range. In order to minimize the interference channel assignment is used in routers with multiradios, wireless mesh networks which uses the Multi-Radio conflict graph. The channel assignment scheme used is BFS-CA, Breadth First Search channel assignment, which is used to minimize the interference in wireless mesh networks. Key Words—Breadth First search channel assignment, Multi radio conflict graph model, Physical model, Protocol model, Reality check
Adaptive intra refresh for robust wireless multi-view video

ii Author’s Declaration . iii Author’s Contributions . iv Acknowledgement . viii Dedication . ix Table of Content . x List of Figures . xiv List of Tables . xvii List of Abbreviations . xviii Chapter
An Adaptive Fuzzy-Based Service-Oriented Approach with QoS Support for Vehicular Ad Hoc Networks

An attempt in implementing on-demand, QoS supported Vehicular Ad hoc Network (VANET) routing algorithms has taken new dimensions. Delivering information in time to achieve reliability across vehicles (nodes) is still being a challenge among the VANET research groups. An effort to develop a QoS adaptive routing schemes using soft computing techniques is made in this research work. SADVA – An adaptive fuzzy based QoS service oriented approach for VANET is presented in this paper. The performance of SADVA is compared with AODV (Perkins, Royer, & Das, 2003), GV-GRID (Li, & Yu, 2007), DSR (Johnson, Maltz, & Hu, 2004), FSR (Gerla, 2002), DYMO (Chakeres, & Perkins, 2006), REDEM (Prabhakar, Sivanandham, & Arunkumar, 2011b), and QARS (Prabhakar et al, 2011a). SADVA employs fuzzy logic system to determine the vehicle’s speed over an effective time period for different types of service in use between multiple VANET nodes to engage or cooperate in communication. This chapter focuses on designing and developing QoS aware routing protocol for multi-hop VANET. Metrics such as number of packets received per second, percentage of packet loss and time for route establishment are used to analyse the network situation. Simulation test runs are carried out using Two Ray Ground propagation model where vehicular traffic is generated according to a Poisson process. An Adaptive Fuzzy-Based Service-Oriented Approach with QoS Support for Vehicular Ad Hoc Networks
Research on an Improved Wireless Sensor Networks Effective Coverage Method

An improved wireless sensor networks effective coverage method based on differential evolution algorithm is proposed in this paper, which using quadratic interpolation method to improve the algorithm performance to further optimize the effective coverage of wireless sensor network nodes. Simulation results show that, compared to DE algorithm, the new algorithm can improve local search capabilities, reduce the computational cost of the algorithm, accelerate standards DE convergence, improve the accuracy of solution, and has some reference value for optimization application of wireless sensor network.
Data intensive ATLAS workflows in the Cloud

This contribution reports on the feasibility of executing data intensive workflows on Cloud infrastructures. In order to assess this, the metric ETC = Events/Time/Cost is formed, which quantifies the different workflow and infrastructure configurations that are tested against each other. In these tests ATLAS reconstruction Jobs are run, examining the effects of overcommitting (more parallel processes running than CPU cores available), scheduling (staggered execution) and scaling (number of cores). The desirability of commissioning storage in the Cloud is evaluated, in conjunction with a simple analytical model of the system, and correlated with questions about the network bandwidth, caches and what kind of storage to utilise. In the end a cost/benefit evaluation of different infrastructure configurations and workflows is undertaken, with the goal to find the maximum of the ETC value.
Balancing Method for High Dimensional Causal Inference

Causal inference has received great attention across different fields from economics, statistics, education, medicine, to machine learning. Within this area, inferring causal effects at individual level in observational studies has become an important task, especially in high dimensional settings. In this paper, we propose a framework for estimating Individualized Treatment Effects in high-dimensional non-experimental data. We provide both theoretical and empirical justifications, the latter by comparing our framework with current best-performing methods. Our proposed framework rivals the state-of-the-art methods in most settings and even outperforms them while being much simpler and easier to implement.
Efficient stream analysis and its application to big data processing

In 1968, 140 IT companies where solicited to build the ARPANET network. Most of them regarded the project as outlandish and only 12 bid, while today there are 3:4 billions Internet users. The advent of Internet in the 90s, the commercial outbreak of cloud computing in the 2000s and the growing use of connected mobile devices since 2009 pushed the telecommunication and IT world into a new stage of interconnectivity. Researchers and practitioners understood that the massive amount of available data entailed new opportunities as well as challenges. In particular, common data management techniques where not tailored to match this features and, soon enough, the term Big Data emerged to capture all of these characteristics. The aim of this book is to design novel algorithms in the data streaming model, or extend current solutions, to solve practical issues in Big Data applications.
Internet worms and global routing instabilities

We analyze the global BGP routing instabilities observed during the Code Red II and Nimda worm attacks in July and September 2001, respectively. Compelling analysis is shown on the correlation between the observed instabilities and the worm attacks. We analyze router failure modes that can be triggered by the abnormal traffic during the worm attack and how they can lead to global routing instability. Independent research has partially confirmed that such failure modes can and likely do occur in practice. Highly detailed large-scale simulations help close the loop, indicating that such failure modes do in fact trigger the kind of widespread BGP instabilities that were observed empirically.
A Preliminary Study on Serious Game for C Language Study of Beginners : freCman

This paper introduces a serious game called freCman developed for C programming language beginners. Since key words, syntax, and programming structure of C programming language are unfamiliar for them, they feel uneasy and have many difficulties to study. We developed three games such as shooting star C, finding hidden errors, unscrambling C codes through which C beginners can study C language easily. Also we developed CTS(Code to Speech) which speeches C source codes like English statements so that C beginners can be familiar with C key words and statements. To prove effectiveness of the freCman, some experiments have been conducted with C language beginners. Experiment results show that the freCman helps beginners studying C programming language much.
Shared-memory optimizations for virtual machines

Virtual machine environments are becoming more common due to the increased performance of commodity hardware and the emergence of cloud computing for large scale applications. As the use of virtual machines continues to grow, performance critical applications will require efficient mechanisms to achieve their tasks. We introduce Nahanni as a mechanism for shared memory communication in virtual machine environments. Nahanni allows virtualized applications, those running inside virtual machines, to communicate through shared memory for both data movement and synchronization when VMs are co-located on the same host machine. We describe the design, implementation, and evaluation of Nahanni as part of the QEMU/KVM virtualization platform. We have modified existing communication layers to measure the performance benefit of Nahanni. Through microbenchmarks and applications, we demonstrate that shared memory is a useful and efficient communication mechanism in virtualized environments. Further, we discuss how the design and implementation of Nahanni enables a new class of applications, ones that use structured data, to benefit from the use of shared memory.
Visualisation in speech corpora: maps and waves in the Glossa system

We present the Glossa web-based system for corpus search and results handling, focussing on two modes of visualisation implemented in the system. First, we describe the use of maps to show the geographical distribution of search results and its utility for exploring dialectal variation and discovering new isoglosses. Secondly, we present a functionality for speech visualisation, yielding dynamically generated representations of spectrograms, pitch and formants. The analyses are accompanied by the ability to replay selected parts of the waveform, as well as export and compare maximum, minimum and average values of the parameters for different selections. Among other things, this can be used to explore in more detail the set of spoken variants revealed by the geographical map view.
Algorithms for cross-lingual data interlinking

Linked data technologies enable to publish and link structured data on the Web. Although RDF is not about text, many RDF data providers publish their data in their own language. Cross-lingual interlinking consists of discov- ering links between identical resources across data sets in different languages. In this report, we present a general framework for interlinking resources in different languages based on associating a specific representation to each re- source and computing a similarity between these representations. We describe and evaluate three methods using this approach: the two first methods are based on gathering virtual documents and translating them and the latter one represent them as bags of identifiers from a multilingual resource (BabelNet).
Interpretation and modification of TCP Vegas via optimization theory

TCP Vegas is analyzed and modified in the congestion avoidance phase by using optimization theory. The optimization approach is to maximize the aggregate utility of all sources subject to the capacity constraints of the network's resources. The congestion avoidance mechanism of sources and the active queue management in bottleneck routers can be interpreted as finding a solution of the optimization problem with constraints by using the first order method. In this method, we discuss several modifications of congestion avoidance in sources and queue management in routers with respect to 1) what the objective function of TCP Vegas should be, 2) how the active queue management should be designed in the framework of the optimization approach.
Research on Patients with Depression of Facial Expression Recognition based on Facereader6 Software

With the information society, the level of modern high-speed development, in order to adapt to constantly improve the economic standard of living, people are facing the pressure and thus the greater the incidence of depression showed a rising trend. The facial expression recognition based on the data of Facereader6 software, application SVM algorithm facial expression data in patients with depression feature extraction classification. Therefore, the law face expression feature extraction in patients with depression, according to the input different facial expressions to identify facial expressions in patients with depression. Population will suffer from depression in intervention and prevention have significant social role and practical significance.
The Budgeted Biomarker Discovery Problem

Researchers conduct association studies to discover biomarkers in order to gain new biological insight on complex diseases and phenotypes. Although most researchers have intuitions about what defines a biomarker and how to assess the results of an association study, there is neither a formal definition for what a biomarker is, nor objective goal for association studies. As a result, the literature is full of association studies with conflicting results – e.g., studies on the same phenotype that produce lists of biomarkers with little to no overlap. This thesis presents the “Budgeted Biomarker Discovery (BBD) problem”, which clearly defines (1) what a biomarker is, and (2) rewards for correctly identifying biomarkers and penalties for incorrectly identifying biomarkers. Furthermore, the BBD problem allows researchers to use a mixture of highand low-throughput technologies. In the context of discovering biomarkers from gene expression data, we show how future association studies can use both microarrays and qPCR data to objectively find the genes that are biomarkers in a cost efficient manner. We present several algorithms for solving the BBD problem, and show that good algorithms must make use of both microarrays and qPCR. Also, they must be able to adapt to the data as it is collected. For example, when solving a new BBD problem, we must begin by collecting microarrays because we do not yet know how many biomarkers we expect to identify, or which qPCR arrays would be most informative. Thus, we use the high-throughput microarrays to survey the problem, until we can identify which specific low-throughput qPCR arrays to use for focusing on those genes that are potentially biomarkers. To identify when this transition should occur, we present the problem of estimating the density of univariate statistics in highthroughput data, and we present our Fused Density Estimation (FDE) algorithm as
Rancang Bangun Aplikasi Data Mining untuk Memprediksi Hasil Belajar Siswa Sekolah Menengah Atas Berbasis Web dengan Algoritma K-NN (Studi Kasus: SMKN 2 Pekanbaru)

Curriculum 2013 (K-13) was first announced in 2014 which has been applied to number of schools. Preparation of this new curriculum by the government aimed at making education in Indonesia is not only focused on cognitive aspects or skills possessed, but also at students' interest and motivation. Unfortunately, behind the goal, there are issues occured in the school during the application of K-13. Those are input process and values conversion that takes relatively much time. The things are caused by the dissimilarity of the standards and the assessment scale between current curriculum with the previous one. Meanwhile, the academic system running in schools is still pretty conventional. Therefore, this research will construct an application which have capability to handle the things. Beside those additional features, this research is build an application in order to apply the data mining with k-NN algorithm to predict students learning outcomes based on certain subjects. Data source that used in this research were consisted into 500 data training that covered up all classes or labels. Testing methods which have been applied are black box testing and confusion matrix. There are 3 techniques of black box testing that applied in order to test the system functionality according to its input values. Those are equivalence class partitioning, boundary value analysis and decision table based testing. Meanwhile in confusion matrix, it has been done 3 times testing according by k value in k-NN algorithm. With k-5 acquired accurate rate 79.34%, k-10 with accurate rate 62.67%, then k-15 with accurate rate 64%. Thus, information that can conluded from those testing methods is the algorithm with k-5 is more accurate than any others.