Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Evaluation of Hadoop and Oracle Platform for Distributed Parallel Processing in Big Data Environments

Hadoop–a popular open-source implementation of MapReduce is widely used for the analysis of large datasets. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. In this paper we evaluate performance of Hadoop Platform and Oracle for Distributed Parallel Processing in large datasets. For evaluation, we implement a prototype of a virtual datacenter using distributed and parallel computing technology. The purpose of this paper is to reduce datacenter implementation cost using commodity hardware and provide high performance. Hadoop is installed on a commodity Linux cluster the distributed processing of large data sets across clusters of computers using distributed and parallel computing architecture. This paper also helps to explain about some new technology and framework which are open source; that can easily utilize those technologies for our complex data analysis which resembling structured, semi structured and non-structured data. Here we tried to demonstrate a performance comparison by executing some queries between distributed parallel computing system and traditional single computing system. For the simulation of the infrastructure Hadoop cluster has been used for distributed parallel processing and Oracle 11g is used for traditional single processing system. We prepare three virtual host for Hadoop cluster and a high-end hardware for Oracle 11g.
Configurable 3D-integrated focal-plane cellular sensor–processor array architecture

Mixed-signal cellular visual microprocessor architecture with digital processors is described. An Application Specific Integrated Circuit (ASIC) implementation is also demonstrated. The architecture is composed of a regular sensor readout circuit array, prepared for 3D face-to-face-type integration, and one or several cascaded array of mainly identical (single instruction multiple data, SIMD) processing elements. The individual array elements were derived from the same general Hardware Description Language (HDL) description and could be of different sizes, aspect ratio, and computing resources. Copyright © 2008 John Wiley & Sons, Ltd.
Function-defined shape node for VRML

This paper describes how the web-based visualisation can be greatly improved using the function-based shape modelling technique. We propose the function-defined VRML shape node, which allows the content creators to describe any complex models with relatively small functions compared to the large-size polygonal mesh based VRML nodes. The design, the implementation details, and the application examples of the proposed node are discussed. The software is available for downloading from the project website.
Mass Wasting and the Coriolis Effect on Asteroid Vesta

This work investigates the influence of the Coriolis force on mass motion related to the Rheasilvia impact basin on asteroid (4) Vesta's southern hemisphere. The giant basin is 500km in diameter, with a centre which nearly coincides with the rotation axis of Vesta. The Rheasilvia basin partially overlaps an earlier, similarly large impact basin, Veneneia. Mass motion within and in the vicinity of the Rheasilvia basin includes slumping and landslides, which, primarily due to their small linear extents, have not been noticeably affected by the Coriolis force. However, a series of ridges related to the basin exhibit significant curvature, which may record the effect of the Coriolis force on the mass motion which generated them. In this thesis 32 of these curved ridges, in three geologically distinct regions, were examined. The mass motion velocities from which the ridge curvatures may have resulted during the crater modification stage were investigated. Velocity profiles were derived by fitting inertial circles along the curved ridges and considering both the current and past rotation states of Vesta. An iterative, statistical approach was used, whereby the radii of inertial circles were obtained through repeated fitting to triplets of points across the ridges. The most frequently found radius for each central point was then used for velocity derivation at that point. The results of the velocity analysis are strongly supportive of a Coriolis force origin for the curved ridges. Derived velocities (29.6 ± 24.6 m/s) generally agree well with previously published predictions from numerical simulations of mass motion during the impact process. Topographical features such as local slope gradient and mass deposition regions on the curved ridges also independently agree with regions in which the calculated mass motion accelerates or decelerates. Sections of constant acceleration, deceleration and constant velocity are found, showing that mass motion is being governed by varying conditions of topography, regolith structure and friction. Estimates of material properties such as the effective viscosities (1.9-9.0·10⁶ Pa·s) and coefficients of friction (0.02-0.81) are derived from the velocity profile information in these sections. From measured accelerations of mass motions on the crater wall, it is also shown that the crater walls must have been locally steeper at the time of the mass motion. Together with these novel insights into the state and behaviour of material moving during the modification stage of Rheasilvia's formation, this work represents the first time that the Coriolis Effect on mass motions during crater formation has been shown to result in diagnostic features preserved until today.
Bulletin of Electrical Engineering and Informatics

Hamza Abu Owida, Hassan S. Migdadi, Omar Salah Mohamed Hemied, Nawaf Farhan Fankur Alshdaifat, Suhaila Farhan Ahmad Abuowaida, Rami S. Alkhawaldeh Department of Medical Engineering, Faculty of Engineering, Al-Ahliyya Amman University, Amman, Jordan Department of Electronics and Communications Engineering, Faculty of Engineering, Al-Ahliyya Amman University, Amman, Jordan Software Engineering Program, Department of Computer Science, Faculty of Computers and information Assuit University, Assuit, Egypt Department of Computer Science, Prince Hussein Bin Abdullah Faculty of Information Technology, Al al-Bayt University, Mafraq, Jordan Department of Computer Information Systems, Faculty of Information Technology and Systems, The University of Jordan, Aqaba, Jordan
Online Learning of Deeper Variable Ordering Heuristics for Constraint Optimisation Problems

Solvers for constraint optimisation problems exploit variable and value ordering heuristics. Numerous expert-designed heuristics exist, while recent research uses machine learning to learn novel heuristics. We introduce the concept of deep heuristics , a data-driven approach to learn extended versions of a given variable ordering heuristic. First, for a problem instance, an initial probing phase collects data, from which a deep heuristic function is learned. The learned heuristics can look ahead arbitrarily-many levels in the search tree instead of a ‘shallow’ localised lookup for classical heuristics. We demonstrate deep variable ordering heuristics based on the smallest, anti ﬁrst-fail, and maximum regret heuristics. The results show that deep heuristics solve 20% more problem instances while improving on overall runtime for the Open Stacks and Evilshop benchmark problems.
Automatic Segmentation of Coronary Lumen and External Elastic Membrane in Intravascular Ultrasound Images Using 8-layer U-Net

Liang Dong The Second A liated Hospital of Zhejiang University Wei Lu ArteryFlow Techology Co., Ltd. Jun Jiang Second A liated Hospital, School of Medicine, Zhejiang University Ya Zhao ArteryFlow Techology Co.,Ltd. Xiangfen Song ArteryFlow Techology Co., Ltd. Xiaochang Leng ArteryFlow Techology Co.,Ltd. Hang Zhao Artery ow Techology Co., Ltd. Jian’an Wang The Second A liated Hosiptal of Zhejiang University Changling Li The Second A liated Hospital of Zhejiang University Jianping Xiang (  ygnjd2016@gmail.com ) ArteryFlow Techology Co., Ltd.
IMPLIMENTATION ON DISTRIBUTED CO-OPERATIVE CACHING IN SOCIAL WIRELESS NETWORK (SWNET)

: This paper introduces co-operative caching policies for reducing electronic content provisioning cost in Social Wireless Networks (SWNET). SWNET are formed by mobile devices such as laptops, modern cell phones etc. sharing common electronic contents, data and actually gathering in public places like college campus, mall etc. Electronic object caching in such SWNET are shown to be able to minimize the content provisioning cost which mainly depend on service and pricing dependencies between various stakeholders including content provider(CP), network service provider, end consumer(EC). This paper introduces practical network service and pricing model which are used for creating two object caching strategies for minimizing provisioning cost in networks which are homogeneous and heterogeneous object demand. The paper develops analytical and simulation design for analyzing the proposed caching strategies in the presence of selfish user that deviates from networks-wide cost-optimal policies.
State-dependent representations of mixtures by the olfactory bulb

Sensory systems are often tasked to analyse complex signals from the environment, separating relevant from irrelevant parts. This process of decomposing signals is challenging when a mixture of signals does not equal the sum of its parts, leading to an unpredictable corruption of signal patterns. In olfaction, nonlinear summation is prevalent at various stages of sensory processing. Here, we investigate how the olfactory system deals with binary mixtures of odours under different brain states by two-photon imaging of olfactory bulb (OB) output neurons. Unlike previous studies using anaesthetised animals, we found that mixture summation is more linear in the early phase of evoked responses in awake, head-fixed mice performing an odour detection task, due to dampened responses. Despite smaller and more variable responses, decoding analyses indicated that the data from behaving mice was well discriminable. Curiously, the time course of decoding accuracy did not correlate strictly with the linearity of summation. Further, a comparison with naïve mice indicated that learning to accurately perform the mixture detection task is not accompanied by more linear mixture summation. Finally, using a simulation, we demonstrate that, while saturating sublinearity tends to degrade the discriminability, the extent of the impairment may depend on other factors, including pattern decorrelation. Altogether, our results demonstrate that the mixture representation in the primary olfactory area is state-dependent, but the analytical perception may not strictly correlate with linearity in summation.
Diatom-based reconstruction of trophic status changes recorded in varved sediments of Lake Żabińskie (northeastern Poland), AD 1888-2010

Abstract We investigated diatom assemblages in surface sediments of 46 lakes in northern Poland and developed a diatom-based transfer function to infer epilimnetic total phosphorus (TP) concentrations. Multivariate ordination techniques (DCA, CCA) were used to identify major environmental gradients and to evaluate the effect of environmental parameters on the distribution of diatoms in the modern diatom dataset. The transfer function was developed using PLS, WA and WA-PLS models, and applied to a varved sediment core from Lake Żabińskie, AD 1888-2010. Annually-resolved quantitative reconstruction of TP concentrations shows that multidecadal changes in the TP level reflect the local settlement history, land-use changes and development of agriculture and tourism. The period of high trophic levels with maximum values of TP was documented until the late 1920s. In the 1930s-1970s period, TP generally decreased and eutraphentic flora was partly replaced by oligotraphentic and oligo-mesotraphentic diatom taxa. The reconstructed TP concentrations have started to increase from the 1970s. After the 1950s, strong short-term fluctuations of TP values were noted and explained by interactions between meteorological conditions, water column mixing and nutrient cycling in the lake.
Distributed Computation of a Spanning Tree in a Dynamic Graph by Mobile Agents

We describe a distributed algorithm for computing spanning trees in dynamic networks by using mobile agents. Each agent uses a random walk to move in the network and to construct a spanning subtree for the nodes visited for the first time. Spanning subtrees are merged when agents enter in an already discovered area of the network. In this case, we clone the agent having higher color to capture and to annex the adjacent subtree. We discuss in this paper this operation of agent cloning and show that it is useful to construct an efficient distributed algorithm for computing a spanning tree. The correction and the termination detection are also investigated. We give an implementation and a few experimental results which show the practical efficiency of the method.
Rethinking Our Global Coastal Investment Portfolio

Coastal gray infrastructure, including roads, bridges, and buildings, is critical for communities, and we invest significantly in it. We also invest in the restoration and maintenance of coastal green infrastructure such as wetlands and reefs, which provide many services and benefits to human communities. However, the relative scale of financial investments in different coastal sectors – that is, our global coastal investment portfolio – remains poorly understood. We identify some of the major sources of funding for coastal green and gray infrastructure globally, including international aid funds; US public and private funds for coastal conservation, restoration and management; and private (insurance) and public funds spent on rebuilding after coastal storms. For the 10-year period from 2004-2013, we identify a total of US $14 billion in funds for coastal conservation and green infrastructure and $198 billion in international aid funds for coastal grey infrastructure. Coastal storms during the same decade caused an estimated US $514 billion in damages globally, of which $214 billion were insured losses, while $3.5 billion in international aid funds were spent on relief and reconstruction after these storm events. We conclude that funds for green infrastructure make up a small fraction – roughly three per cent – of global financial investments along coastlines. We identify the major funders in different coastal spending categories and discuss ways to move toward a more balanced investment portfolio that could benefit biodiversity, prevent billions of dollars of storm damage, and help protect the lives and livelihoods of coastal residents. This research article is available in Journal of Ocean and Coastal Economics: https://cbe.miis. edu/joce/vol3/iss2/6
Literate programming

Moderator’s Introduction to Column 3 The mail prompted by Column 2 (Communications, December 1987, p. 1000) showed a broad array of opinions about what makes a good solution to a problem. Some readem found Jackson’s program too closely tied to the representation of the input data, while others thought that it did not take enough advantage of those same details. I was heartened to hear from readers who preferred my pseudo-code solution, even though it was sketch,y and incomplete. Som’e correspondence suggests that I need to correct a few misimpressions. First, the solutions that appear in this column are in no way certified to be “the best” or “perfec:t”; they are merely the product of their authors’ best attempts to produce literate programs. Second, I am not wed to the idea that what makes a program literate is the interleaving of code and nicely typeset comments; I would be delighted to hear from readers with oi her suggestions about how programs can be written literately-see address information at the end of this column. This column’s problem was posed by Mark Kahrs of Rutgers University.
Visual Image Reconstruction from Human Brain Activity using Linear Image Decoders plus Nonlinear Noise Suppression

—In recent years, substantial strides have been made in the field of visual image reconstruction, particularly in its capacity to generate high-quality visual representations from human brain activity. This advancement not only enables the recreation of visual content but also provides valuable insights into the intricate processes occurring within functional brain regions, contributing to a deeper understanding of brain function. However, linear decoding methods, which were once prominent in this field, have faced challenges due to their susceptibility to noise. As a result, they have gradually fallen out of favor. In response to this limitation, our study introduces a novel approach that combines linear mapping with nonlinear reconstruction techniques to recreate visual images perceived by research subjects based on their brain activity patterns. The primary challenge associated with linear mapping lies in its susceptibility to noise interference. To address this issue, we leverage a flexible denoised deep convolutional neural network, which surpasses the performance of traditional linear mapping. Our investigation encompasses three distinct linear mapping approaches, as well as the training of shallow and deep autoencoder denoised neural networks, including a pre-trained state-of-the-art denoised neural network. The outcome of our study reveals that the amalgamation of linear image decoding with nonlinear noise reduction significantly enhances the quality of reconstructed images from human brain activity. This suggests that our methodology holds promise for decoding intricate perceptual experiences directly from brain activity patterns. Moreover, our analysis highlights an interesting observation: during the encoder phases of the denoised autoencoder, color information emerges as the most crucial factor, while both color and texture information assume equal importance during the decoder phases. This observation underscores the distinct manner in which Alexnet neural networks represent feature information, setting the stage for further investigation and innovation in this domain.
Stock Market Manipulation Detection Using Continuous Wavelet Transform & Machine Learning Classification

Stock market manipulation detection is important for both investors and regulators. Being able to detect stock manipulation and preventing it gives investors the con dence in the market fairness and integrity. It also helps maintaining liquidity of the stocks and market e ciency. Implementing data mining algorithms in manipulation detection is a relatively recent technique but in the past few years there has been an increasing interest in it's applications in this domain. The bene t of monitoring manipulative trade behavior is that it can be implemented on live feed of stock data, which saves a lot of time in detecting stock
Blueprint for a microwave trapped ion quantum computer

Department of Physics and Astronomy, University of Sussex, Brighton BN1 9QH, U.K Google Inc., Santa Barbara, CA 93117, USA. Department of Physics and Astronomy Aarhus University, DK-8000 Aarhus C, Denmark. Center for Emergent Matter Science RIKEN, Wako-shi, Saitama 315-0198, Japan. Department Physik, Naturwissenschaftlich Technische Fakultät, Universität Siegen, 57068 Siegen, Germany. *Corresponding author. Email: w.k. hensinger@sussex.ac. uk
Predicción del rendimiento: Análisis de la escalabilidad de aplicaciones paralelas

Executing message-passing applications using an elevated number of resources is not a trivial task. Due to the complex interaction between the message-passing applications and the HPC system, depending on the system, many applications may suffer performance inefficiencies, when they scale to a large number of processes. This problem is particularly serious when the application is executed many times over a long period of time. With the purpose of avoiding these problems and making an efficient use of the system, as main contribution of this thesis, we propose the methodology P3S (Prediction of Parallel Program Scalability), which allows us to analyze and predict the strong scalability behavior for message-passing applications on a given system. The methodology strives to use a bounded analysis time, and a reduced set of resources to predict the application performance. The P3S methodology is based on analyzing the repetitive behavior of parallel message-passing applications. Such applications are composed of a set of phases, which are repeated through the whole application, independently of the number of application processes. The methodology is made up of three stages. A first characterization step, where the relevant phases of the parallel application are characterized, from the execution of a set of small-scale application signatures. A second stage of modeling of the application, where the application logical scalability model is generated for each relevant application phase, whereby to construct the scalable logical trace of the application. This trace will be used to predict the logical behavior of the application, as the number of the application processes increases. Finally, the last stage of the methodology consist of predicting the application performance for a specific number of processes. In order to do that, the scalable logical trace will be specified for the number of processes to predict the application performance, with the objective to predict the computational and communication time of each relevant phase for this number of processes, in order to obtain the performance prediction. The output of the P3S methodology will be the predicted curve of application speedup. Using this information, the users can select the most appropriate resources to execute his application on the target system, in order to use the system resources efficiently.
Computación y Sistemas, Vol. 22, No. 4, 2018

The estimation of the posterior distribution is the core problem in topic models, unfortunately it is intractable. There are approximation and sampling methods proposed to solve it. However, most of them do not have any clear theoretical guarantee of neither quality nor rate of convergence. Online Maximum a Posteriori Estimation (OPE) is another approach with concise guarantee on quality and convergence rate, in which we cast the estimation of the posterior distribution into a non-convex optimization problem. In this paper, we propose a more general and flexible version of OPE, namely Generalized Online Maximum a Posteriori Estimation (G-OPE), which not only enhances the flexibility of OPE in different real-world situations but also preserves key advantage theoretical characteristics of OPE when comparing to the state-of-the-art methods. We employ G-OPE as inference a document within large text corpora. The experimental and theoretical results show that our new approach performs better than OPE and other state-of-the-art methods.
Towards On the go matching of linked open data ontologies

Creating links between the schemas of published datasets is a key part of the Linked Open Data (LOD) paradigm. The ability to discover these links "on the go" requires for ontology matching techniques to achieve sufficient precision and recall within acceptable execution times. In this paper we add two methods to the AgreementMaker ontology matching system (but that could be used together with other systems). A first method consists of discovering a set of high-quality equivalence mappings based on concept similarity and of inferring subclass mappings from that set. A second method is based on the adoption of background terminology that serves as a mediator. Sets of synonyms and a hierarchy of concepts are taken into account, as well as subclass relationships involving external concepts that are imported into the ontologies being matched. Experimental results show that our approach has good performance and improves precision on most matching tasks when compared with a leading LOD approach.